arXiv Daily Digest - 2026-04-24

Execution of quantum algorithms on large-scale quantum computers will require extremely low logical error rates, which necessitates the development of scalable decoding architectures. Local decoders are promising candidates for this task, as they avoid the communication and data processing bottlenecks inherent in global decoding strategies. Cellular automaton (CA) decoders represent a distinct class of local decoders, offering a path toward the low-latency, real-time decoding required for practical applications. In this work, we present SCALA (Signaling CA with Local Attraction), a novel non-hierarchical cellular automaton decoder for quantum repetition and toric codes. By evaluating SCALA alongside the hierarchical CA decoder proposed by Harrington, we provide a direct comparison between non-hierarchical and renormalization-group-style local decoding strategies. We characterize SCALA across three key metrics: Performance, scalability, and robustness. Our results show that SCALA achieves a code-capacity threshold of approximately $p_c\approx 7.5\%$ and provides strong sub-threshold scaling of about $p_L\propto p^{d/4}$ on the toric code. In terms of scalability, our non-hierarchical design ensures that the local computational resources remain independent of system size, yielding a modular local architecture suitable for hardware implementation. Finally, SCALA demonstrates strong robustness to qubit measurement errors and noise within the decoder itself, a critical advantage for real-time decoding on noisy hardware. Our results establish SCALA as a high-performance, scalable, and robust local decoder for scalable quantum error correction.

Parametrically driven oscillators provide a natural platform for neuromorphic computation, where nonlinear mode coupling and intrinsic dynamics enable both memory and high-dimensional transformation. Here, we investigate a two-mode system exhibiting 2:1 parametric resonance and demonstrate its operation as a reservoir computer across distinct dynamical regimes, including sub-threshold, parametric resonance, and frequency-comb states. By encoding input signals into the drive amplitude and sampling the resulting temporal and spectral responses, we perform one step-ahead prediction of benchmark chaotic systems, including Mackey-Glass, Rossler, and Lorenz dynamics. We find that optimal computational performance is achieved within the parametric resonance regime, where nonlinear interactions are activated while temporal coherence is preserved. In contrast, although frequency-comb states introduce increased spectral dimensionality, their performance is not consistently good across their existence band and also degrades in the chaotic comb regime due to loss of phase coherence. Mapping prediction error over parameter space reveals a direct correspondence between computational capability and the underlying bifurcation structure, with low-error regions aligned with the parametric resonance boundary. We further show that the input modulation, the detuning from the frequency matching condition, damping ratio, and input data rate systematically control the accessible dynamical regimes and thereby the computational performance. These results establish parametric resonance as a robust operating regime for oscillator-based reservoir computing and provide design principles for tuning physical systems toward optimal neuromorphic functionality.

We present the RLL-realization of extended orthosymplectic quantum supergroups for any parity sequence, with R-matrices evaluated in the earlier work arxiv:2408.16720. Our isomorphism is compatible with the internal structure of generalized doubles. We also relate different sign conventions through 2-cocycle twists. Furthermore, we establish a factorization of the reduced R-matrix within the RLL-realization.

Plants solve complex problems without centralized control, relying instead on growth-driven dynamics to sense, navigate, and optimize resource acquisition. This review presents a unified physical framework for understanding plant behavior through three complementary principles: distributed physical computation, embodied mechanical intelligence, and functional stochasticity. Tropic responses and circumnutations are interpreted as spatio-temporal dynamical systems in which information is encoded in biochemical and mechanical fields, integrated over space and time, and translated into differential growth. Mechanical interactions couple morphology to environmental constraints, enabling computation through material properties. Stochastic fluctuations, from molecular to organismal scales, act as functional resources that enhance sensing, exploration, and collective organization. Together, these processes position plants as a model system for decentralized computation in active matter, where behavior and structure emerge from the interplay of growth, transport, mechanics, and noise.

Recent advances in manufacturing photonic integrated devices enable efficient coupling between high-Q microresonators in both linear and nonlinear regimes, creating a tunable, complex, hybridized optical system. Considering two coupled microresonators with normal and anomalous dispersion and equal free spectral range (FSR), we theoretically predict a novel nonlinear phenomenon: fully coherent hybridization of dissipative Kerr solitons (DKS) and propose a realistic integrated photonic design for its experimental observation. Using the Lugiato-Lefever equations in the supermode basis, we show that the emergent picture of inter-resonator DKS interactions can be understood as the formation of coherent structures in both supermodes generated by an unusual four-wave mixing process. The found hybridized DKS states can exhibit a broad, flat spectral profile near the pumped mode and remarkable oscillatory features in the spectral wings, promising broad applications in the generation and control of optical Kerr frequency combs.

Black-hole accretion systems exhibit a characteristic coexistence of activities: broad-band X-ray variability, hot coronae, wide-angle winds, and both steady and discrete jets. This coexistence suggests a persistently time-dependent magnetic background in which noisy fluctuations and explosive release are both essential. In this paper, we connect them all to intermittent magnetic reconnection and propose a Synchronized Spin Model (SSM) in which multiple local dynamos in a rotating accretion flow are represented as interacting macro-spins. Their synchronization, partial synchronization, excursion, and reversal define a compact set of collective variables that organize both timing statistics and large-scale morphology. In this picture, multiscale magnetic reconnection sustains coronal heating, flares, intermittent outflows, and discrete jet activity, while the same synchronization dynamics produce amplitude modulation and demodulation, providing a route to $1/f$-like variability, rms--flux/Taylor-like scaling, and approximately log-normal statistics of the demodulated envelope. We further argue that, although the continuous flux distribution in black-hole systems is more naturally discussed in multiplicative or log-normal terms, broader event-catalog statistics remain useful for describing suitably defined burst hierarchies, particularly by analogy with solar and stellar flare systems. The hard/soft cycle of X-ray binaries is then interpreted as motion through magnetic state space.

Semi-discrete (differential-difference) matrix Lax representations (Lax pairs) play an essential role in the theory of integrable differential-difference equations. Fix a (1+1)-dimensional evolutionary differential-difference (semi-discrete) equation and consider matrix Lax representations (MLRs) of this equation. Two MLRs are said to be gauge equivalent if one of them can be obtained from the other by applying a (local) matrix gauge transformation. Gauge transformations (GTs) form an infinite-dimensional group, which acts on the set of MLRs of a given equation. Two MLRs are gauge equivalent iff they belong to the same orbit of this action. When one tries to establish integrability (in the sense of soliton theory) for a given equation, one is interested in MLRs which depend on a parameter (usually called the spectral parameter) such that the parameter cannot be removed by any GT. We introduce and study explicit invariants with respect to the action of GTs on the set of MLRs for a given (1+1)-dimensional evolutionary differential-difference equation with any number of components. Using these invariants, we obtain the following results: - Consider a MLR with a parameter $λ$. If at least one of the invariants computed for this MLR depends nontrivially on $λ$, then the parameter cannot be removed by any GT. - When we have two different MLRs for a given equation, we present necessary conditions for these two MLRs to be gauge equivalent. Our results on semi-discrete MLRs of differential-difference equations are inspired by results of S$.$Yu. Sakovich and M. Marvan on (continuous) zero-curvature representations of partial differential equations. A comparison with some of the results of S$.$Yu. Sakovich and M. Marvan is presented.

We present a scalable, data-driven simulation framework for large-scale heating, ventilation, and air conditioning (HVAC) systems that couples physics-informed neural ordinary differential equations (PINODEs) with differential-algebraic equation (DAE) solvers. At the component level, we learn heat-exchanger dynamics using an implicit PINODE formulation that predicts conserved quantities (refrigerant mass $M_r$ and internal energy $E_\text{hx}$) as outputs, enabling physics-informed training via automatic differentiation of mass/energy balances. Stable long-horizon prediction is achieved through gradient-stabilized latent evolution with gated architectures and layer normalization. At the system level, we integrate learned components with DAE solvers (IDA and DASSL) that explicitly enforce junction constraints (pressure equilibrium and mass-flow consistency), and we use Bayesian optimization to tune solver parameters for accuracy--efficiency trade-offs. To reduce residual system-level bias, we introduce a lightweight corrector network trained on short trajectory segments. Across dual-compressor and scaled network studies, the proposed approach attains multi-fold speedups over high-fidelity simulation while keeping errors low (MAPE below a few percent) and scales to systems with up to 16 compressor-condenser pairs.

The wave is considered a paradigm in dance and connects bodily expression with nature. Although wave concepts such as propagation and phase have proven to be powerful tools for dance analysis, many aspects of bodily expression, including partner dance, have been investigated using numerical approaches and neural networks. Complementarily, compact analytical models have been especially successful for describing human motion, particularly gait. Here, we leverage wave-physics concepts to provide a comprehensive wave-based and oscillatory analytical characterization of expressive motion in partner dance. We apply this framework to Bachata Sensual, a dance style in which the wave is the leitmotif. We analyse three dance couples (Phase I) performing five movement sequences and one composite. The sequences exhibit multiple wave phenomena, from time-dependent interference to the generation-like emergence of harmonics. Within this wave-physics perspective, the formalism can be viewed as a choreographic motion notation. As an illustrative acoustic analogy, harmonic components extracted under boundary conditions can be mapped to audible frequencies, forming musical dyads. Within certain limits and not rigidly constrained by body morphology, modal response can be tuned to underpin fluid motion, adapting across musical timescales and movement patterns. Overall, this wave-physics notation highlights connections between partner-dance expressivity and harmonic nature.

Quantum networks are a keystone of the quantum internet. However, existing implementations remain largely confined to static point-to-point links due to the absence of a switching paradigm capable of dynamically routing fragile quantum entanglement without introducing decoherence. Here, we propose the Universal Quantum Switch, a foundational building block allowing on-demand, non-blocking, and encoding-agnostic routing of quantum information, as well as seamless modality conversion between disparate quantum platforms. We develop a prototype in thin-film lithium niobate and experimentally demonstrate robust switching with $\le 4\%$ decoherence via thermo-optic modulation and high-speed electro-optic switching of arbitrary entangled states at 1 MHz. Moreover, we show that our platform can support reconfiguration speeds up to 1 GHz. To our knowledge, this work represents the first demonstration of multi-node dynamic entanglement distribution at these speeds. Complementing these experimental results, we project the architecture's scalability, showing dimension-independent decoherence, and provide a scalable, interoperable building block for heterogeneous quantum network fabrics.

One of the main challenges in numerically solving partial differential equations is finding a discretisation for the computational domain that balances the accurate representation of the underlying field with computational efficiency. Meshless methods approximate differential operators based on the values of the field in computational nodes, offering a natural approach to adaptivity. The density of computational nodes can either be increased to enhance accuracy or decreased to reduce the number of numerical operations, depending on the properties of the intermediate solution. In this paper, we utilise an adaptive discretisation approach for the numerical simulation of natural convection in non-Newtonian fluid flow. The shear-thinning behaviour is interesting both due to its numerous occurrences in nature, blood being a prime example, and due to its properties, as the decreasing viscosity with increasing shear rate results in sharper flow structures. We focus on the de Vahl Davis test case, a natural convection driven flow in a differentially heated rectangular cavity. The thin boundary layer flow along the vertical boundaries makes this an ideal test case for refinement. We demonstrate that adaptively refining the node density enhances computational efficiency and examine how the parameters for adaptive refinement affect the solution.

Generative artificial intelligence (genAI) is rapidly reshaping how knowledge and culture are produced and consumed. Yet generative models are vulnerable to model collapse: when trained on data generated by earlier versions of themselves, their outputs can lose diversity and accuracy. This creates a social dilemma, because delegating tasks to genAI can be individually beneficial in the short term even as widespread adoption degrades future model performance. Here we develop a parsimonious model of behavior in collaborative interactions in which individuals can either exert human effort, rely on genAI, or refrain from work altogether. The welfare consequences of genAI are organized by a simple two-dimensional taxonomy: the strength of the incentive to perform the task without AI, and the severity of model collapse. Within this framework, the introduction of genAI -- while initially beneficial at the individual level -- will reduce social welfare for the most important types of tasks. In addition, habit formation around genAI use can couple otherwise separate domains, so that adoption in low-stakes tasks spills over into high-value tasks and amplifies welfare losses. Together, these results identify a general pathway by which, in the absence of intervention, individually rational adoption of genAI will assuredly and profoundly reduce collective welfare.

The hierarchical organization of the brain is a fundamental structural principle, while brain criticality is a leading hypothesis for its collective dynamics. However, the connection between structure and signatures of criticality remains an open question. Here, we address this issue by applying phenomenological renormalization group approaches to large-scale neuronal spiking activity from the mouse visual cortex and hippocampus. We find that signatures of criticality are not uniform, but instead vary systematically along the known anatomical hierarchy in both brain systems. Strikingly, the direction along this gradient is inconsistent across different criticality exponents, revealing a nontrivial, measure-dependent organization: exponents based on static properties point to a gradient in one direction, while the exponent based on dynamic properties points in the opposite direction. Moreover, the signatures across the visual system are strongly modulated by the engagement in a visual task. We show that the correlations among criticality markers of different brain regions during active engagement are sufficient to reconstruct the anatomical hierarchy from the dynamics. Scaling exponents closely follow a theoretically predicted scaling relation among them, and covary with the hierarchical position. Our findings provide a direct link between the collective dynamics of neurons and the macroscopic architecture of the brain.

In response to the urban heat island effects and building energy demands in Singapore, this study proposes an agentic AI-enabled reasoning framework that integrates large language models (LLMs) with lightweight physics-based models. Through prompt customization, the LLMs interpret urban design tasks, extract relevant policies, and activate appropriate physics-based models for evaluation, forming a closed-loop reasoning-action process. These lightweight physics-based models leverage core thermal and airflow principles, streamlining conventional models to reduce computational time while predicting microclimate variables, such as building surface temperature, ground radiant heat, and airflow conditions, thereby enabling the estimation of thermal comfort indices, e.g., physiological equivalent temperature (PET), and building energy usage. This framework allows users to explore a variety of climate-resilient building surface strategies, e.g., green façades and cool paint applications, that improve thermal comfort while reducing wall heat gain and energy demand. By combining the autonomous reasoning capacity of LLMs with the rapid quantitative evaluation of lightweight physics-based models, the proposed system demonstrates potential for cross-disciplinary applications in sustainable urban design, indoor-outdoor environmental integration, and climate adaptation planning. The source code and data used in this study are available at: https://github.com/PgUpDn/urban-cooling-agent.

The room-temperature altermagnet \mathrm{KV_2Se_2O} possesses nearly orthogonal flat Fermi surfaces, which in an idealized $d$-wave limit enable complete spin-channel separation and a theoretical charge-to-spin conversion efficiency (CSE) of 100%. Realistic samples, however, host residual elliptical Fermi pockets that enhance charge conductivity while suppressing spin conductivity, drastically reducing the CSE. Here we show that in-plane equibiaxial tensile strain systematically eliminates these parasitic pockets, restoring the flat-band geometry. Our first-principles calculations reveal that the CSE increases monotonically with strain, reaching a record value of approximately 96% at 4% strain. An effective tight-binding model fitted to the computed band structure accurately captures the evolution of the Fermi surface and confirms that the suppression of the pockets -- governed by reduced next-nearest-neighbor hoppings -- is the dominant mechanism for the strain-enhanced CSE. We further identify an unconventional out-of-plane spin current that emerges under tilted electric fields and achieves a CSE of nearly 55% at optimal orientations, offering a promising pathway for field-free perpendicular magnetization switching. Our work establishes strain engineering as a practical route to approach the ultimate conversion limit in altermagnets and provides a design principle for high-efficiency spintronic devices.

Physics-informed neural networks (PINNs) have garnered significant interest for their potential in solving partial differential equations (PDEs) that govern a wide range of physical phenomena. By incorporating physical laws into the learning process, PINN models have demonstrated the ability to learn physical outcomes reasonably well. However, current PINN approaches struggle to predict or solve new PDEs effectively when there is a lack of training examples, indicating they do not generalize well to unseen problem instances. In this paper, we present a transferable learning approach for PINNs premised on a fast Pseudoinverse PINN framework (Pi-PINN). Pi-PINN learns a transferable physics-informed representation in a shared embedding space and enables rapid solving of both known and unknown PDE instances via closed-form head adaptation using a least-squares-optimal pseudoinverse under PDE constraints. We further investigate the synergies between data-driven multi-task learning loss and physics-informed loss, providing insights into the design of more performant PINNs. We demonstrate the effectiveness of Pi-PINN on various PDE problems, including Poisson's equation, Helmholtz equation, and Burgers' equation, achieving fast and accurate physics-informed solutions without requiring any data for unseen instances. Pi-PINN can produce predictions 100-1000 times faster than a typical PINN, while producing predictions with 10-100 times lower relative error than a typical data-driven model even with only two training samples. Overall, our findings highlight the potential of transferable representations with closed-form head adaptation to enhance the efficiency and generalization of PINNs across PDE families and scientific and engineering applications.

Optical imaging and spectroscopy solutions, such as near-infrared spectroscopy (NIRS) and diffuse optical tomography (DOT), have the potential to provide compact, bedside monitoring of the placenta in the clinic, thanks to recent advancements in miniaturisation and wireless wearability. This would provide neonatologist with continuous assessment of the pregnancy status in real-time, as well as tools to possibly predict delivery outcomes. We present here OptoCENTAL, a standardized platform based on multiple optical phantoms, from digital, through solid to liquid, for a comprehensive bench-testing, characterisation and validation of any photonics solution and instrumentation that aims at in vivo, clinical monitoring of the human placenta. Results: Exemplary applications of the OptoCENTAL platform on different types of optical systems, from wearable, continuous-wave devices to broadband and time-domain NIRS systems, demonstrate the flexibility of its procedures to be implemented with any setup, allowing users to compare performances across different solutions. The results also show the capability of OptoCENTAL to provide quantitative assessment of the major features required by any photonic solution for providing effective and efficient monitoring of the placenta, including basic instrument performances, quantification of monitoring accuracy, as well as depth sensitivity. OptoCENTAL represent the first-of-a-kind effort in standardising bench-testing and validation of optical imaging and spectroscopy methods in the framework of placental clinical applications, further advancing the translation of such modalities into the hospitals, as well as towards future certification and commercialisation of such technologies.

Light detection and ranging (LiDAR) is widely used in autonomous systems and industrial metrology; however, the simultaneous acquisition of three-dimensional (3D) structure and broadband spectral information remains challenging, as conventional hyperspectral LiDAR relies on wavelength-scanning or spectrometer-based detection that limits speed. Here, we demonstrate a hyperspectral 3D ghost imaging LiDAR that eliminates these bottlenecks. By combining a stochastic broadband laser with single-pixel detection, and integrating spatiotemporal encoding with spectral ghost imaging in a time-of-flight framework, the system enables pulse-resolved recovery of spatial and spectral information. Consequently, we achieve a line-scanning rate of 60.5 MHz (point rate 1.8 GHz) and a ranging precision of 0.02 mm within a 10 μs integration time. Each voxel contains a 1.4 nm resolution spectrum over 1100-1250 nm, enabling simultaneous 3D imaging and chemical identification. This approach provides a route to high-speed hyperspectral LiDAR for environmental monitoring, precision agriculture, and industrial inspection.

Monolayers of transition metal dichalcogenides (TMDCs), known for their strong excitonic states with high binding energies in the visible spectrum at room temperature, offer great potential for polariton-driven devices. While polariton guided modes in bulk TMDCs have been reported the real space experimental observation of 2D exciton-polariton guided modes in a monolayer remains challenging due to various mode cut-off conditions that arise as the TMDC layer becomes thinner, including cut-off frequency, mode confinement and boundary conditions. Here using scanning near-field optical microscopy (s-SNOM), we directly visualized the real-space propagation of these guided modes for the first time in an angstrom-thick, suspended monolayer of WSe2. Through numerical simulations we have also validated that the guided mode can only exist in a monolayer WSe2 when symmetric cladding conditions are closely applied. By tuning the excitation laser energy and analysing the guided mode distribution, we observed a pronounced back-bending dispersion around the A exciton, indicating strong light-matter interactions, and confirmed the existence of the fundamental TE0 exciton polariton (EP) propagation mode. The unique dispersion characteristics of these modes were further validated through theoretical modelling of the mode in free-standing monolayer WSe2. Our findings provide crucial experimental evidence of guided mode EPs in atomically thin TMDCs, opening new possibilities for nanoscale photonic applications.

Continuous monitoring of physiological signals is essential for the early detection of health problems. A measurement system that ensures high sensitivity, accuracy, and user comfort is needed. In this study, we designed and optimized a flexible piezoresistive yarn (FPY) sensor to achieve a high sensitivity and wide working range for detecting physiological signals. The representative sensor design was constructed by applying an FPY bonding pattern, utilizing tightly arranged triangular patterns and using minimal FPY. The prototype sensor operates in two measurement modes, strain and pressure, and was evaluated for measuring neck motion, finger bending, respiratory signals, and arterial blood pressure (ABP) waveforms. A qualitative evaluation, performed by comparing the characteristics of the measurement results of each physiological signal with those from related studies, indicates a high similarity in its morphological characteristics. Then, a quantitative evaluation through baseline drift analysis demonstrates that the FPY sensor displays high measurement stability. The ABP waveform measurement shows the most stable baseline, with a mean absolute error (MAE) of $0.0051 \pm 0.0029$ in terms of baseline drift, using normalized values from 0 to 1. Based on our results, the prototype sensor can be used as an innovative solution for physiological signal monitoring and can be further enhanced for personalized healthcare and sports applications.

Bacterial colonies composed of elongated cells form active nematic fluids that spontaneously self-organise into ordered domains of aligned cells and exhibit self-generated chaotic flows powered by cell growth. While their dynamics have attracted significant attention, the role of initial conditions remains largely unexplored due to a lack of precise patterning methods. Here, we harness the precision of capillary assembly to pattern Bacillus subtilis endospores into arrays with controlled positions and orientations at single-cell resolution. Upon germination and growth of cell chains, we quantify the dynamics and morphologies of the resulting bacterial films. While orthogonally seeded spores lead to chaotic dynamics, seeding them with parallel orientations yields films with high nematic order across millimetres, which subsequently synchronously buckle upon further growth. Our observations are captured by numerical simulations and a model that describes the buckling dynamics starting from the mechanical properties of individual filaments. By programming local cell orientation with single-cell precision, we finally harness nematic alignment to create macroscopic bacterial films with local optical anisotropy, via structural colouration and light polarisation. Our findings demonstrate that initial conditions play a key role and offer exciting opportunities to control the spatio-temporal organization of bacterial assemblies towards addressing open biological questions and realizing living materials with tailored properties.

Accurate characterisation of margins in excised breast cancer tumours is critical to the success of surgical interventions, yet margin status is typically confirmed post-operatively using histopathology. Here we present a new approach to intraoperative margin assessment based on microwave single pixel imaging, demonstrating tissue phantom hydration mapping across large areas (~10 cm x 10 cm) at ~1 mm resolution. By leveraging the photo-induced change in microwave transparency of a silicon modulator placed under the sample, we map the microwave reflectivity and identify positive margins with deeply sub-wavelength resolution. We test the discriminatory capabilities of our approach using gelatine-based tumour phantoms with variations in water density representative of the margin and cancerous tissues of a resected tumour. We demonstrate the capability to identify, locate and quantify inadequate margins up to the typically targeted minimum thickness of 2 mm. Furthermore, using numerical modelling, we show that our approach is expected to be resilient to patient-specific tissue differences. Our technique has potential for future deployment as a real-time intraoperative tissue margin analysis tool.

Autocatalytic processes underlie diverse systems in which replication is triggered at interfaces, including heterogeneous catalysis on solid substrates, enzyme activity at membranes, viral infections, biofilm growth, and spatially structured ecosystems. In a typical scenario, particles move in a bulk medium and interact with surface regions, where they may either disappear or reproduce through branching, splitting or fission. Here, we develop a general theoretical framework to understand such surface-mediated autocatalytic processes. We show that the interplay between loss and replication at surfaces gives rise to rich population dynamics. For this purpose, we derive a renewal-type nonlinear integral equation for the generating function of the population size, providing access to its full probability distribution and statistical moments. We further establish an equivalent description in terms of a Fokker-Planck equation with nonlinear Robin-type boundary conditions that encode surface reactions. Our results identify distinct dynamical regimes and universal scaling laws, and provide a unified framework to predict when surface activity promotes extinction or explosive growth. These findings offer quantitative insight into catalytic efficiency, metabolic regulation, and population persistence in spatially heterogeneous environments.

The spin angular momentum (SAM) of light has become a cornerstone of numerous photonic applications, including optical communication and chiral photonics. Because SAM is inherently associated with circularly polarized light (CPL), the ability to modulate CPL in a controlled and efficient manner is essential not only for advancing fundamental studies of light-matter interactions but also for enabling next-generation photonic technologies. However, such modulation is commonly realized by structurally chiral systems, which inherently limits the feasibility of dynamic tuning. Here, we demonstrate that one-dimensional plasmonic crystals (1D PlCs), despite their structural symmetry, can serve as a platform for controllable CPL generation. By employing an electron beam in scanning transmission electron microscopy (STEM), we coherently excite transition radiation and emission from 1D PlC modes. Their interference produces energy- and momentum- (emission angle-) resolved CPL, which clearly reveals its dispersion and spatial dependence at the nanoscale, providing direct guidance for its manipulation and offering insights into the design of plasmonic devices including the phase information. Furthermore, interference with surface plasmon polariton scattering at the structural boundary enables the efficiency modulation of CPL generation via the excitation position along the terrace.

Identifying critical nodes in complex networks is a fundamental task in graph mining. Yet, methods addressing an all-or-nothing coverage mechanics in a bipartite dependency network, a graph with two types of nodes where edges represent dependency relationships across the two groups only, remain largely unexplored. We formalize the CriticalSet problem: given an arbitrary bipartite graph modeling dependencies of items on contributors, identify the set of k contributors whose removal isolates the largest number of items. We prove that this problem is NP-hard and requires maximizing a supermodular set function, for which standard forward greedy algorithms provide no approximation guarantees. Consequently, we model CriticalSet as a coalitional game, deriving a closed-form centrality, ShapleyCov, based on the Shapley value. This measure can be interpreted as the expected number of items isolated by a contributor's departure. Leveraging these insights, we propose MinCov, a linear-time iterative peeling algorithm that explicitly accounts for connection redundancy, prioritizing contributors who uniquely support many items. Extensive experiments on synthetic and large-scale real datasets, including a Wikipedia graph with over 250 million edges, reveal that MinCov and ShapleyCov significantly outperform traditional baselines. Notably, MinCov achieves near-optimal performance, within 0.02 AUC of a Stochastic Hill Climbing metaheuristic, while remaining several orders of magnitude faster.

In this study, we take a systematic look at the unrealised part of public transport networks (PTNs) with functional connections. We consider their complement graphs and study their structure. The complement graph $\bar G$ of an unweighted graph $G$ is a straightforward concept, yielding a graph on the same set of nodes, and an edge exists in $\bar G$ if and only if it is not present in $G$. In contrast, a weighted complement graph cannot be uniquely determined. However, if we consider PTNs with travel times as edge weights, there are physical constraints on the possible weight ranges. We propose a method to construct weighted complement graphs of operational PTN graph representations based on the geographical distances between nodes (representing stops) and assign weights to edges based on distance, combined with network-specific distributions of effective velocities and waiting times. We observe that the most central nodes in the weighted complement graph do not correspond to the least central nodes in the original network but are, remarkably, those in the geographical centre of the network that lack topological connectedness. Testing against null models on a dataset of 31 metro networks worldwide confirms that this is a fundamentally spatial effect.

Interferometry provides highly sensitive access to optical phase and is central to much of modern metrology and phase imaging methods. Conventional implementations, however, often face trade-offs between mechanical stability and experimental or computational complexity. Here, we present a general framework for designing custom interferometers within a single optical beam by exploiting structured light. This approach yields compact, robust common-path configurations that bypass the need for complex post-processing and can easily be integrated into existing setups. We demonstrate the versatility of this concept by designing a range of interferometers, each tailored by the structured mode, and implement them through active and passive modal conversion optics, proving its adaptability to different experimental requirements. To showcase the practical utility of our framework, we apply it to quantitative phase imaging over a variety of physical samples, showing excellent agreement with atomic force microscopy benchmarks. Furthermore, we emphasise the flexibility of our structured light interferometers by mapping phase objects to a choice of either amplitude or polarisation, the latter providing a direct route toward real-time phase-retrieval. This cost-effective approach offers a practical, high-throughput solution for phase-sensitive metrology across fields such as fundamental physics, biology, and material science.

A thin-sheet (TS) volume integral equation (VIE) formulation incorporating generalized sheet transition conditions (GSTCs) is presented for the simulation of three-dimensional (3D) bianisotropic metasurfaces. The metasurface is represented as an equivalent TS, with its constitutive tensors derived from the GSTC susceptibility tensors. Invoking the TS approximation, the governing VIEs are reduced to surface integral equations (SIEs), in which tangential and normal flux density components are treated as distinct sets of unknowns and discretized using Rao-Wilton-Glisson and pulse basis functions, respectively. In contrast to conventional GSTC approaches based on conventional SIEs, which represent only tangential fields, the proposed framework rigorously enforces the bianisotropic GSTCs, including normal field interactions, while retaining the flux-based VIE character of the formulation. Numerical examples demonstrate the accuracy and robustness of the proposed TS-VIE-GSTC solver for polarization rotation, perfect reflection, multi-directional attenuation, and oblique phase-shift transformation.

Theory was formulated for scattering by a coated chiral sphere of a plane wave of arbitrary polarization state with amplitude modulated by a Gaussian pulse. The spherical core and the concentric shell of the sphere were composed of two different homogeneous materials, both isotropic chiral. Calculations of energy efficiencies for extinction, total scattering, and absorption were carried out for the shell material with experimentally determined constitutive parameters, the core being vacuous. All three energy efficiencies depend on the relative thickness of the shell and the circular polarization state of the carrier plane wave.

Exciton-polaritons provide a great platform for developing ultrafast all-optical logic gates for quantum and optical chips. However, progress toward practical polariton logic remains limited due to incomplete logical functionality on a single device. Herein, we present a single-device perovskite polariton platform enabling reconfigurable, ultrafast logic gates with functional completeness. The device consists of an optically trapped perovskite microwire, generating well-controlled non-equilibrium polariton condensation states for multiple logic operation channels. By tailoring the power of signal and gate beams, the same device is programmed to execute three basic Boolean functions (AND,OR,and NOT) and a high-order XOR function with a high on/off ratio of 21 dB, and a fast response time 6.7 ps. The reconfigurability arises from the selective activation of different nonlinear responses of polariton condensates, including amplification, seeding state transitions, and nonlinear interaction. These results provide valuable insights for advancing exciton-polariton logic gates.

Neural network potentials (NNPs) are rapidly changing the landscape of state-of-the-art molecular dynamics (MD) simulations. To make full use of this development, the community needs flexible, easy-to-use interfaces firmly integrated with existing methodologies. To address this, we here present an interface for hybrid machine learning/molecular mechanics (ML/MM) simulations implemented in the widely used MD code GROMACS. The interface enables NNPs trained in the PyTorch framework to contribute energies and forces during MD simulations, either for selected subsets or entire molecular systems. By defining a flexible set of model inputs and outputs, the interface is agnostic to specific NNP architectures and can accommodate a wide range of descriptor-based and message-passing models. In particular, the design integrates NNP inference seamlessly into the extensive GROMACS molecular simulation ecosystem, providing users with the capability to straightforwardly combine NNPs with existing advanced sampling and free energy workflows. We demonstrate the capabilities of the interface using several representative applications, including enhanced sampling of peptide torsional free energy landscapes, absolute solvation free energy calculations, and protein--ligand simulations. We also run performance benchmarks on water boxes for several different NNP architectures. Our interface is available in recent GROMACS releases, and we believe it will provide a practical foundation for incorporating machine learning potentials into production MD simulations of biomolecular systems.

Urban expansion fronts display a robust local roughness exponent together with strongly dispersed growth and nonuniversal dynamic exponents. We show that this coexistence can arise from a disorder-controlled crossover in projected-front growth. Introducing a minimal Eden model, in which geographic constraints act as quenched dilution and coalescence as quenched local acceleration, we demonstrate that the resulting front evolves through a long preasymptotic regime controlled by ordinary two-dimensional percolation before crossing over to asymptotic KPZ growth. In this regime, the local roughness remains close to $1/2$, while the large-scale exponents vary broadly with disorder and acceleration. These results provide a minimal explanation of urban-front roughening and suggest a more general mechanism for stochastic growth in heterogeneous media.

Engineering structures are increasingly designed using numerical optimisation. However, traditional optimisation methods can be challenging with multiple objectives and many parameters. In machine learning, stable training of artificial neural networks with millions or billions of parameters is achieved using automatic differentiation frameworks such as JAX and Pytorch. Because these frameworks provide accelerated numerical linear algebra with automatic gradient tracking, they also enable differentiable implementations of numerical methods to be built. This facilitates faster gradient-based optimisation of geometry and materials, as well as solution of inverse problems. We demonstrate JAX-BEM, a differentiable Boundary Element Method (BEM) solver, showing that it matches the error of existing BEM codes for a benchmark problem and enables gradient-based geometry optimisation. Although the demonstrated examples are for acoustic simulations, the concept could be readily extended to electromagnetic waves.

Hopfions, as three-dimensional topologically nontrivial structures described by poloidal and toroidal winding numbers, hold promise as robust information carriers in spintronics, functional materials, and optical communications. Although they have been experimentally realized in various physical systems, such realizations have been restricted to low orders, with the winding numbers lacking tunability. Here, using optical fields as our platform, we outline how to make tunable hopfions in any order with any winding number. We use tailored superpositions of Laguerre-Gaussian modes in free-space as our construction, achieving effective control for arbitrary-order poloidal and toroidal winding numbers, which we demonstrate up to orders 5 and 3, respectively, for a new state-of-the-art. The resulting torus-knot structures are visualized experimentally via polarization filaments, confirming the designed topological textures. Our work reports an exotic optical topologies observed in free space, provides a systematic route hopfions of any order, with implications for topological photonics, optical communications, and analogies in magnetic and condensed-matter systems.

Chip-scale semiconductor laser frequency combs offer remarkable prospects for compact and power-efficient optical sensors. For the laser to be suitable for typical comb applications, its degree of coherence must first be assessed from a microwave self-mixing signal. Unfortunately, such measurements require scarcely available high-speed photodetectors with multi-GHz bandwidths and radio-frequency electronics. However, in this work, we demonstrate a simplified approach to comb coherence assessment for interband cascade lasers based on a relationship between easily-accessible MHz-frequency (baseband) noise and the multi-GHz-frequency intermode beat note. The downconversion of microwave noise to near-DC frequencies is found to originate intrinsically from the laser, which simultaneously acts as a frequency mixer due to electrical nonlinearities and a phase-to-amplitude noise converter due to the linewidth enhancement factor. Correlation between the electrical signals is explored in both frequency and time domains. Since this phenomenon is potentially universal in semiconductor lasers, it creates a new opportunity for frequency comb characterization, which may be particularly valuable in wavelength regions where fast photodetectors have limited availability.

Temporal metamaterials, created by modulating the refractive index in time, offer powerful means of controlling wave propagation but still lack a systematic design methodology. Here, we develop an analytic inverse-design framework rooted in space-time duality and the established theory of one-dimensional spatial inverse scattering. By prescribing reflection (backward-wave) and transmission (forward-wave) responses in rational-function form, we obtain closed-form refractive-index modulations that are guaranteed to be physically admissible. This approach avoids iterative optimization and provides direct analytic control of the modulation. We illustrate the method with syntheses of mathematical operators, such as derivatives and integrals, as well as Chebyshev- and Butterworth-type filters, and validate the results through finite-difference time-domain simulations. Our findings establish a general route to temporal media with tailored functional and spectral responses, enabling applications in wave-based information processing, programmable filtering, and amplification schemes inspired by photonic time crystals.

We present a Monte Carlo study of the fractal geometry of clusters formed by discrete-time simple random walks (sRW) of $L^2$ steps on a periodic square $L\times L$ lattice. We verify with high precision that the asymptotic behavior of the cluster mass follows $M/L^2 \simeq (\ln L)^{-1} [\fracπ{2}+b (\ln L)^{-2}]$, with $b\approx -(π/2)^{-2}$, demonstrating marginal ``logarithmic fractals". We further determine the fractal dimension of the hull to be $d_{\rm hull}=1.333\,29(14)=4/3$, in excellent agreement with the prediction of Schramm-Loewner evolution ($\rm SLE_{8/3}$) for the Brownian frontier universality class. More importantly, we analyze the chemical distance $S$ spanning the cluster and obtain strong evidence that it asymptotically scales as $S\sim L(\ln L)^{1/4}$, lying exactly on the theoretical upper bound for the chemical distance for level-set percolation clusters on the two-dimensional Gaussian free field. Our numerical results show that the sRW cluster exhibits a conformally invariant external frontier and contains highly efficient asymptotically linear connective paths.

Theory and empirical research on management teams' influence on firm performance have witnessed continuous development, and by now incorporate numerous details. Classic, experiment-based studies examining social systems collect vast amount of data, but often times investigate only the first one or two modes of the distribution of measured variables, and experience difficulty in analyzing the effect of context. For example, in functional diversity research, management teams are described by measures incorporating complex distributions of capabilities of individual managers and teams of managers. To investigate the effect of hidden distributions, and the effect of functional diversity composition on team communication and performance, we developed an agent-based model, and conducted a series of simulation experiments. Modeling results show that depending on the context, such as communication scheme among interacting agents, or their functional composition, intrapersonal functional diversity (IFD), and dominant function diversity (DFD) might enhance or reduce performance and communication among agents. Furthermore, simulation results also suggest that a third measure is required alongside IFD and DFD capturing the aggregate expertise of the team to comprehensively account for empirical findings.

Inverse-designed nanophotonic media are a promising platform for compact optical neural networks, but training them end to end is expensive because each adjoint iteration couples the full-wave solver to the dataset minibatch, so the number of electromagnetic simulations scales with both the network depth and the batch size. We introduce a two-stage surrogate workflow that decouples task learning from electromagnetic realization. In the first stage, the trainable optical block is represented as a passive complex matrix with bounded singular values and the classification task is solved directly in matrix space at negligible cost. In the second stage, the selected target operator is transferred to a fabrication-aware freeform device through an adjoint problem driven by a Frobenius-norm transmission residual and a reflection penalty, which removes the minibatch dependence from the full-wave loop and yields a smoother loss landscape than intensity-domain cross-entropy. We further introduce a banded-router architecture composed with a fixed evanescent-coupling region, which exploits the bandwidth-additive property of matrix products to realize dense effective operators within a design region roughly half as long as a fully local router would require. The framework is validated on three tasks. On MedMNIST, the realized all-optical classifier reproduces the surrogate accuracy within $0.6$ percentage points after only 20 adjoint epochs. On RSSCN7, the banded router plus evanescent stage improves test accuracy by more than 15 percentage points over a linear readout baseline. A Yin-Yang task confirms that the same framework supports nonlinear decision boundaries. These results indicate that surrogate-guided inverse design is a practical route to training compact photonic processors with simulation budgets orders of magnitude smaller than direct geometry-to-task pipelines.

Market instability has been extensively studied using mathematical approaches to characterize complex trading dynamics and detect structural change points. This study explores the potential for early warning of market instability by applying the Dynamical Network Marker (DNM) theory to order placement and execution data from the Tokyo Stock Exchange. DNM theory identifies indicators associated with critical slowing down -- a precursor to critical transitions -- in high-dimensional systems of many interacting elements. In this study, market participants are identified using virtual server IDs from the trading system, and multivariate time series representing their trading activities are constructed. This framework treats each participant as an interacting element, thereby enabling the application of DNM theory to the resulting time series. The results suggest that early warning signals of large price movements can be detected on a daily time scale. These findings highlight the potential to develop practical DNM-based early-warning systems for large price movements by further refining forecasting horizons and integrating multiple time series capturing different aspects of trading behavior.

Whether the flash state in electrically driven solids involves non-equilibrium defect production or is accounted for by Joule heating alone has been debated since 2010. Using positron annihilation spectroscopy on copper, we observe a fully reversible, electrically switchable vacancy population: the DBS S-parameter rises above baseline whenever applied current exceeds a critical density and returns on current removal. Positron lifetime spectroscopy independently confirms open-volume defect formation and reveals a void to cluster relaxation hierarchy. The current-induced vacancy concentration exceeds the thermal-equilibrium value at 352C by > 106x, is present only while current is applied, and vanishes within minutes. The nucleation rate scales steeply with the applied current, connecting the minute-scale kinetics resolved here to the sub-second flash events observed in ceramic sintering. These results demonstrate current-induced Frenkel-pair production in a metal and identify a defect-mediated, non-equilibrium contribution to the flash state.

Toroidal vortices in fluid and gas dynamics underpin a broad spectrum of scientific and technological fields, from elementary particle physics to condensed matter systems, and have recently garnered significant attention in optics because of their inherent topological stability. Here we report the experimental observation of toroidal vortices in stochastic optical wavefields with partial coherence, termed coherence toroidal vortices, which eliminates deterministic topological signatures in conventional optical degrees of freedom while unveiling statistically hidden correlation topologies. These underlying topologies-including both fundamental and higher-order hopfionic textures-emerge exclusively in second-order field correlations and are accessible only through statistical measurements. We further examine the impact of chaotic channels on the stability of these statistically veiled correlation topologies, demonstrating that their topological invariants remain robust under realistic environmental perturbations. These findings are experimentally validated and offer novel insights into the potential of toroidal light vortices serving as controllable channels for directional energy and information transfer within complex media.

Additive manufacturing and welding processes are highly sensitive to heat dissipation, where improper thermal management leads to residual stresses, distortions, and cracking. Existing heat transfer models, such as Rosenthal's solutions, fail to handle finite 3D geometries, cooling effects, or transient behavior, limiting their accuracy. We overcome these limitations by developing an analytical framework that incorporates cooling boundary conditions mimicking Newton's Law of Cooling. Using two different and proven-equivalent approaches, Laplace transform and Fourier series, we derive closed-form solutions for transient and steady-state temperature profiles under various heat sources, including Gaussian, ellipsoidal, double-ellipsoidal, and time-dependent on/off switch sources. We compare our analytical solutions to numerical implementations, demonstrating strong agreement while providing deeper physical insight. This approach significantly reduces computational cost and experimental requirements, making it a scalable tool for optimizing thermal predictions and mitigating residual stresses in metal-based manufacturing. Additionally, our framework enables the generation of synthetic datasets for machine learning models to predict heat distribution efficiently.

The pursuit of compact, programmable light sources with high coherence and spectral purity hinges on establishing a precise set of phase relationships in light-matter interactions. Here, we demonstrate that the quadratic dispersion of freely propagating electron wavepacket serves as a programmable quantum medium. Prepared in a coherent momentum-state ladder via a single laser interaction, the electron subsequently undergoes deterministic phase evolution during free propagation-an intrinsic process that compiles its quantum state into two distinct emission channels. This mechanism, quantified by a quantum bunching factor, enables: (i) Talbot-resonant bunching, where the electron density self-structures into sub-cycle combs with tunable harmonic selectivity, and (ii) coherent phase transfer of the programmed quadratic phase to light, generating nonclassical photon states such as multi-component Schrodinger cat states via measurement-conditioned interaction. This quadratic-phase programming establishes a versatile platform for on-demand quantum state synthesis, bridging beam engineering with electron wavefunction shaping for compact quantum light sources, coherent radiation control, and scalable quantum information processing.

We demonstrate, for the first time to our knowledge, ultra-low-noise supercontinuum (SC) generation in normal-dispersion fluoride fibres pumped by femtosecond (fs) pulses. We have investigated two elliptical-core polarisation-maintaining (PM) ZBLAN fibres with core dimensions 6.7$\times$2.7 $μ$m and 8.9$\times$4.1 $μ$m, experimentally measured to have normal dispersion up to 3.77 $μ$m and 3.25 $μ$m, respectively; the smaller-core fibre yields ultra-low-noise SC spanning 1.537-2.196 $μ$m with a minimum relative-intensity noise (RIN) of 0.22% at 1.7 $μ$m, and the larger-core fibre yields 1.507-2.250 $μ$m with 0.36% at 2.0 $μ$m. To aid the generation of low-noise SC, we developed an all-PM thulium chirped-pulse amplifier delivering 58 fs pulses at 1.85 $μ$m, 210 mW average power at 40 MHz, with 0.41% RIN, seeded by a part of an ultra-low-noise SC using a 1.55 $μ$m fs laser and an all-normal-dispersion (ANDi) silica fibre for precise seed control. These results establish a robust, alignment-free pathway to extend ultra-low-noise ANDi-fibre SC towards the mid-infrared using PM fluoride fibres.

Toward the goal of using Scientific Machine Learning (SciML) emulators to improve the numerical representation of aerosol processes in global atmospheric models, we explore the emulation of aerosol microphysics processes under cloud-free conditions in the 4-mode Modal Aerosol Module (MAM4) within the Energy Exascale Earth System Model version 2 (E3SMv2). To develop an in-depth understanding of the challenges and opportunities in applying SciML to aerosol processes, we begin with a simple feedforward neural network architecture that has been used in earlier studies, but we systematically examine key emulator design choices, including architecture complexity and variable normalization, while closely monitoring training convergence behavior. Our results show that optimization convergence, scaling strategy, and network complexity strongly influence emulation accuracy. When effective scaling is applied and convergence is achieved, the relatively simple architecture, used together with a moderate network size, can reproduce key features of the microphysics-induced aerosol concentration changes with promising accuracy. These findings provide practical clues for the next stages of emulator development; they also provide general insights that are likely applicable to the emulation of other aerosol processes, as well as other atmospheric physics involving multi-scale variability.

Patterned-transducer thermoreflectance enhances sensitivity to low-thermal-conductivity materials by suppressing lateral heat spreading in the metal transducer, but its wider use is limited by the cost of repeated high-fidelity forward evaluations in iterative fitting. Here, we develop a transfer-learning-enhanced POD-FNN surrogate for rapid phase prediction in patterned-transducer thermoreflectance, using patterned FDTR as a representative case. A validated COMSOL model is first constructed, and proper orthogonal decomposition is applied directly to the phase signals to build a compact reduced-order representation. A feedforward neural network is then trained to predict the POD coefficients from thermophysical and geometric parameters. Within the original parameter domain, the surrogate achieves mean and median RMSE values of 0.19 and 0.17 degrees, with a maximum RMSE below 0.47 degrees, while reducing the average prediction time per signal from 5.39 s to 0.01 s (about 534x). In inverse analysis, the fitting time for a representative case is reduced from about 18950 s to about 65 s with comparable accuracy. The framework is further applied to measured Al/SiO2 samples, yielding stable silica thermal conductivities of 1.44 +/- 0.088, 1.43 +/- 0.093, and 1.50 +/- 0.079 W/(m K) for conventional FDTR and patterned FDTR with pattern radii of 5.3 and 3.25 um, respectively. Transfer learning further improves performance in expanded parameter domains, with the TL-FR strategy giving the best overall results. Reducing the additional target-domain dataset from 6000 to 1000 samples also lowers the high-fidelity data-generation time from about 34179 s to about 5885 s. The proposed framework provides an accurate and efficient route for repeated forward evaluation, rapid inverse fitting, and cost-effective model updating in patterned thermoreflectance workflows.

Transcranial focused ultrasound (tFUS) offers noninvasive access to deep brain circuits but remains limited by skull-induced phase aberration, acoustic impedance mismatch, and poor volumetric control of intracranial pressure fields. Conventional phased-array and planar holographic strategies compensate aberrations electronically or computationally, yet do not resolve geometric and coupling inconsistencies imposed by subject-specific cranial morphology. We introduce personalized skull-conforming acoustic holograms that physically encode individualized wavefront corrections into a conformal acoustic interface. Within a subject-specific volumetric holography (SSVH) framework, cranial geometry and therapeutic constraints are embedded into a physics-based optimization pipeline for holographic phase synthesis. The resulting lens is integrated with a skull- and skin-conforming coupling layer that enhances impedance continuity, reduces reflection losses, and stabilizes spatial alignment, enabling simultaneous aberration mitigation and efficient transcranial transmission. Numerical simulations across multiple subjects and targets demonstrate consistent volumetric focusing and reliable target coverage while maintaining pressure fields within safety limits. Experimental validation using an ex vivo human skull confirms accurate fabrication, effective acoustic coupling, and faithful reconstruction of designed three-dimensional acoustic fields. By unifying wavefront engineering with anatomical conformity, this work establishes skull-conforming acoustic holography as a scalable strategy for high-fidelity, anatomically adaptive transcranial ultrasound targeting.

The transition of materials and devices to nanometer, atomic, and quantum scales makes thermal characterization increasingly challenging, driving the need for advanced nanoscale thermometry. Fluorescence nanothermometry has emerged as a powerful approach, enabling remote, spatially resolved temperature measurements with sub-micrometer-to-nanometer precision across applications in nanoelectronics, microfluidics, and biological systems. In these systems, temperature is inferred from variations in fluorescence observables, including spectral position, intensity, linewidth, and excited-state dynamics. This review provides a comprehensive and critical overview of fluorescence nanothermometry, covering fundamental mechanisms, material platforms, recent advances, and emerging applications. It further presents a critical evaluation of key challenges and discusses emerging strategies and future research directions toward achieving robust, real-time thermometry. It is anticipated that this review will stimulate further advances in material platforms and system design, accelerating the development of accurate, scalable, and application-ready nanoscale thermometers.

The Müller boundary integral equation for penetrable electromagnetic scattering is conventionally discretized using divergence-conforming basis functions, a restriction inherited from the PMCHWT framework. This paper demonstrates that this constraint can be bypassed. The double-gradient operator in the Müller formulation acts on the kernel difference $\varphi_a - \varphi_i$, so that the $\mathcal{O}(R^{-3})$ hypersingularity cancels identically, reducing the operators to weakly singular $\mathcal{O}(R^{-1})$ kernels. Exploiting this cancellation, we develop a nodal, high-order Galerkin formulation using $\mathrm{P}_2$ isoparametric shape functions on curved manifolds. The surface vector field is constructed via a metric-weighted orthonormal tangent frame. The singular integrals are evaluated by Sauter--Schwab quadrature, and a Morton-ordered Block Jacobi preconditioner is introduced. By capturing the dominant near-field interactions within geometrically clustered diagonal blocks, it yields robust, superlinear GMRES convergence under extreme material and geometric parameters. Validation against semi-analytical EBCM references confirms high-order spatial accuracy and optical-theorem satisfaction to high precision.

Data assimilation (DA) improves prediction of chaotic systems by combining model forecasts with sparse, noisy observations. Many DA methods are inherently probabilistic, but accurate probabilistic DA is often computationally expensive because it requires repeated high-resolution (HR) forecasts and large ensembles. In this study, we develop DiffSRDA, a probabilistic spatiotemporal super-resolution data assimilation framework based on denoising diffusion models, and evaluate it on an idealized barotropic ocean jet instability testbed. DiffSRDA is trained offline to generate short HR analysis windows conditioned on (i) a time series of low-resolution (LR) forecast frames and (ii) sparse HR observations. Repeated reverse diffusion sampling then produces an ensemble of HR analyses, providing both point estimates and uncertainty information. Despite relying only on low-cost LR forecasts, DiffSRDA achieves reconstruction quality close to that of an Ensemble Kalman Filter (EnKF) driven by HR forecasts, while improving over deterministic CNN-based SRDA baselines. The sampled ensemble also yields physically meaningful uncertainty patterns, with spread concentrated in dynamically active regions similarly to EnKF. A key practical result is that accurate base DiffSRDA cycling does not require long reverse chains: most of the full-chain accuracy is retained with only a few reverse steps, making diffusion-based SRDA practical for repeated cycling. Finally, by exploiting the score-based structure of diffusion sampling, we demonstrate training-free observation-consistency guidance for deployment-time sensor-layout shifts, enabling improved use of changed observation configurations without retraining. Overall, diffusion models provide a practical, uncertainty-aware, and computationally efficient approach for spatiotemporal SRDA in chaotic fluid flows.

The Smith Hat tile is the first known aperiodic monotile, having been discovered in 2023. The simple structure, constructed using only 8 kites, is unique and well motivated for analysis within percolation theory. The primary goal of this paper is to discover the critical threshold $p_c$ in both site and bond Bernoulli structures using Monte Carlo simulation for the Smith hat tile(1,$\sqrt3$). Our findings are site and bond values of $p_c^s = 0.822725 \pm 0.000044$ and $p_c^b = 0.798161 \pm 0.000044$ for edge percolation and $0.544247 \pm 0.000101$ for site percolation on the dual graph.

We present a general and efficient approach to compute phase-resolved multidimensional spectra of anharmonic molecular polaritons, based on a semiclassical evolution of the molecular Hamiltonian and cavity field in the large-$\mathcal{N}$ limit of many molecules coupled to a confined photonic mode. By systematically expanding the response in both amplitudes and phases of the input fields, our method enables a transparent and computationally simple construction of phase-cycled two-dimensional single- and double-quantum polariton spectra from the underlying nonlinear signal components. Here, phase cycling acts as an analogue of phase matching with oblique pulses, allowing for the isolation of the contributing nonlinear pathways in Liouville space. We specialize to vibrational polaritons and benchmark the method through direct comparison with experimentally measured single-quantum spectra, providing an explanation for the longstanding puzzle of the polariton bleach effect observed at short waiting times. Further, we show how the imprint of various types of anharmonicities on the double-excitation manifold can be directly probed and analyzed through double-quantum coherence spectroscopy. Taken together, our results establish a practical and powerful framework for the modeling and interpretation of nonlinear spectroscopic experiments on strongly coupled light-matter platforms and for guiding the design of cavity-enhanced molecular platforms.

A major unresolved question in Neuroscience is: What is the origin of the observed scale-invariant correlations in neural activity? Many researchers support the ``criticality hypothesis,'' which proposes that the brain operates near a critical point, optimizing various information processing functions. We argue that such a critical point may not exist. Rather, the coupling between neurons and slowly varying resources (acting as ``memory''), may instead generate a robust phase of neural activity with such scale-invariant correlations. This ``memory-induced'' long-range order (MILRO) phase is then stable to perturbations, unlike a critical point. We suggest that this MILRO phase could provide a more natural and consistent explanation of the existing experimental data than the criticality hypothesis.

Point defects in solid-state materials are now routinely simulated using large supercell structures, requiring efficient quantum mechanical solutions. Data-driven and machine learning (ML) models trained on computational data can enable rapid defect property predictions and high-throughput screening. In this article, we provide an overview of prominent efforts to accelerate defect simulations using these approaches. We begin by discussing the motivations for data-driven techniques in defect modeling, and describe efforts over the past decade to use descriptor-based models for rapid screening of defect properties -- most notably in oxides. In particular, we discuss case studies where surrogate models and interatomic potentials were trained on density functional theory (DFT) data, leading to predictions with quantum-mechanical accuracies at a fraction of the cost. In addition to geometry relaxation and formation energy predictions, these interatomic potentials are capable of predicting phonon modes and vibrational free energies to yield defect energetics at finite temperatures -- representing a key frontier for computational defect research. We finish with a discussion on how to connect these approaches and their outputs with experimental data, and provide an outlook on this burgeoning sub-field.

The Madden-Julian Oscillation (MJO) is a planetary-scale convective system characterized by large-scale envelopes of enhanced and suppressed convection that contain numerous mesoscale convective systems (MCSs). While MCSs are widely recognized as the fundamental convective elements embedded within the MJO, their relationship with the MJO is intrinsically two-way: the MJO modulates the large-scale dynamical and thermodynamic environment that organizes MCS activity, while the collective upscale impacts of MCSs feed back onto the MJO through the transport of momentum and heat. However, the nature of this bidirectional interaction remains insufficiently quantified from an observational perspective. In this study, we use satellite-based MJO indices together with a long-term, objectively tracked MCS dataset to investigate the two-way feedback mechanisms between the MJO and MCSs. By compositing MCS activity across different MJO phases and analyzing their environmental conditions, we quantify how the evolving MJO circulation regulates MCS frequency, intensity, and organization. At the same time, we diagnose the aggregate influence of MCS populations on the large-scale MJO circulation through their associated momentum and thermodynamic anomalies. Our results reveal a robust two-way coupling between the MJO and MCSs. Enhanced MCS activity preferentially occurs in specific MJO phases associated with favorable moisture, instability, and vertical shear, indicating strong MJO control on MCS organization. Conversely, periods of enhanced MCS activity are associated with coherent large-scale circulation anomalies consistent with upscale transport of momentum and moisture that reinforce the MJO convective envelope and support its eastward propagation. This feedback suggests that MCSs are not merely passive responses to the MJO environment, but actively contribute to its maintenance and evolution.

Mesoscale convective systems MCSs play a central role in tropical rainfall and are closely linked to extreme precipitation and large scale variability. However, a quantitative understanding of their environmental controls remains incomplete. In this study, we construct an observational MCS dataset by applying an objective tracking algorithm to satellite and reanalysis data, and examine the climatology of tropical MCSs. We further use a Random Forest model to quantify environmental controls at the monthly scale. The results show pronounced spatial and seasonal variability in tropical MCS activity, closely tied to large scale circulation and moisture availability. Environmental predictors explain up to about 50\% of the variance in monthly MCS frequency and associated precipitation. Moisture convergence atmospheric instability and column integrated water vapor emerge as the leading controlling factors. Partial dependence analyses reveal clear nonlinear interactions among key predictors. The relative importance of environmental controls also varies with region and season, with thermodynamic factors dominating in some regimes and dynamic factors such as vertical wind shear playing a larger role in others. Overall, this study provides an observationally constrained quantification of environmental controls on tropical MCSs and offers new insight into their variability and potential response to climate variability and change.

Altermagnetism, a recently identified third class of collinear magnetism with spin-split bands and vanishing net magnetization, has emerged in hexagonal \alphaMnTe{} and is regarded as a promising platform for ultrafast, stray-field-free spintronics and for optical readout of spin order at telecommunication wavelengths. Whether the macroscopic symmetry-breaking signatures reported in MnTe, a spontaneous Hall effect and a tiny ``gossamer'' remanent moment, reflect the ideal altermagnetic order or are activated by defects remains an open question. Here we report giant spontaneous Kerr rotations of up to $\pm 1500\microrad$ in \alphaMnTe{} single crystals at the telecommunication wavelength of $1550\,\mathrm{nm}$, onsetting precisely at the Néel temperature $\TN = 307\,\mathrm{K}$. In contrast, a stoichiometric insulating \alphaMnTe{} thin film shows no detectable signal. The bulk--film contrast identifies carrier self-doping, rather than the ideal altermagnetic order, as the source of macroscopic magneto-optical response, establishing telecom-wavelength Kerr imaging as a practical readout for altermagnetic spintronics.

Accurate modeling of ion-molecule reaction networks is essential for understanding the chemical evolution of planetary ionospheres, particularly for giant planets where proton-transfer chains drive atmospheric composition. However, predicting reaction rates in these ultracold environments remains a challenge due to the non-trivial interplay between vibrational dynamics and quantum tunneling. In this work we present a chaos-diagnostic framework that integrates multireference electronic structure theory, Adiabatic Gauge Potentials (AGP), and Random Matrix Theory (RMT) to characterize the microscopic dynamics of proton transport. Using the formation of H+3 and the proton-bound cluster H+5 as representative model systems relevant to Jovian atmospheres, we demonstrate that the transition state acts as a dynamical bottleneck where quantum chaos is notably suppressed, effectively enhancing tunneling probabilities. We introduce a fragility index based on the AGP slope to quantify how specific vibrational modes reintroduce chaos and suppress reactivity. This diagnostic approach offers a generalizable, data-driven metric for identifying vibrationally gated pathways in complex astrochemical networks, providing a theoretical basis for refining kinetic models of planetary and interstellar plasmas

Optical vortex knots have been realized in monochromatic laser beams, but monochromatic fields are stationary and their topology is frozen. Here we show that knotted spatiotemporal vortices, whose phase singularities form closed loops in spacetime, undergo topology changing reconnections with free space propagation. When null lines of different vector components unlink, the electric spin, magnetic spin, linear momentum, and electromagnetic helicity densities, each built from a specific pair of field components, twist to exactly compensate the change in linking number. This compensation is enforced by the argument principle where the total for each component pair, combining mutual phase twist, geometric linking, and open-line threading, vanishes identically and remains exactly zero through all reconnection events.

We investigate the effects of coupling a quantum-magnetic cavity field to molecules. Our high-precision auxiliary-field quantum Monte Carlo calculations capture the effect of the cavity field in the presence of electron correlations, and their interplay and competition. In H$_2$, we find that a strong enough cavity coupling makes the original bound ground state metastable, along with inverting the singlet-triplet gap. In ring molecules (e.g., H$_n$), the magnetic cavity coupling stabilizes symmetric geometries. As a consequence, open-shell rings such as H$_4$, H$_8$, or C$_4$H$_4$, which would undergo Jahn-Teller distortions outside of the cavity, obtain exotic spin or ring-current polarized, antiaromatic ground states. These effects are enhanced by increasing the molecule concentration inside the cavity. Our results suggest cavity quantum electrodynamics beyond the long-wavelength approximation as a promising avenue for cavity-altered chemistry.

An enormous amount of R&D effort has resulted in many new resonant anomaly detection methods being proposed in recent years. However, the vast majority of previous R&D studies have suffered from two limitations: they have focused on a very small set of simulated signal benchmark models; and they have either used small sets of carefully crafted high-level jet substructure observables, which can be highly performant but are prone to model dependence, or the full collider event phase space, which is more agnostic but suffers from reduced sensitivity. In this work, we address both limitations: we formulate a number of new simulated signal benchmarks, which we make publicly available in a format fully compatible with the LHCO R&D benchmark; and we explore a high-level, yet highly agnostic, observable set consisting of Energy Flow Polynomials in addition to the usual subjettiness variables. We evaluate this "kitchen sink" observable set for both an idealized anomaly detector and the CWoLa hunting task, along with three baseline observable sets (the Baseline LHC Olympics set, subjettiness observables, and Energy Flow Polynomials). We find that our kitchen sink approach is the most sensitive to a broad range of signal types. Furthermore, we show that an attribute bagging variant, in which each ensemble member is trained on a random subset of substructure observables, yields comparable anomaly detection performance while significantly reducing training cost.

We study exploration properties of a random walk on a network. For a fully connected network we find that the problem can be mapped to the well known coupon collector problem, thus allowing us to estimate form of $P(S,t)$: the distribution of number of distinct nodes $S$ visited by the random walk upto time $t$. From a practical point of view, however, both the fully connected network and hops taking place after fixed intervals are an idealization. We solve this problem by introducing the formalism of continuous time random walks wherein the random walk spends a random amount of time a node before hopping to its neighboring node. The formalism allows us to study the large deviation limit of $P(S,t)$ under very mild conditions that the distribution of waiting times $ψ(τ)$ exhibits analyticity at small times. Furthermore, we find that at small times, the properties of $P(S,t)$ are largely independent of the network topology, and are governed solely by the waiting time characteristics.

The discovery of higher-order topological insulator (HOTI) has established a new paradigm for understanding symmetry-constrained boundary electronic states. Here, based on first-principles calculations, we demonstrate the emergence of HOTI phase in organic lattices of two-dimensional azulenoid-kekulene-type carbon allotropes, namely AKC-[3,3] and AKC-[6,0]. Enabled by the $C_6$ rotational symmetry, the nontrivial bulk topology is confirmed through the topological invariant and fractionally quantized corner charge, giving $\{[M^{(I)}_{2}],[K^{(3)}_{2}]\}$ = $\{0,2\}$ and $Q_{\mathrm{corner}} = e/3$, respectively, accompanied by the emergence of exotic corner states in nanoflakes. Notably, the structural modifications are explored, revealing that in the derived structure PAK-[6,0], whose corner-localized states are preserved, highlighting the robustness of the higher-order topological phase. These findings highlight azulenoid-kekulene-based carbon allotropes as a promising platform to explore the interplay between structural design, crystalline symmetry, and higher-order topological boundary responses in two dimensional carbon systems.

We develop a thermodynamic theory of algorithmic catalysis within the watts-per-intelligence framework, identifying reusable computational structures that reduce irreversible operations for a task class while satisfying bounded restoration and structural selectivity constraints. We prove that any class-specific speed-up is upper-bounded by the algorithmic mutual information between the substrate and the class descriptor, and that installing this information incurs a minimum thermodynamic cost via Landauer erasure. Combining these results yields a coupling theorem that lower-bounds the deployment horizon required for a catalyst to be energetically favourable. The framework is illustrated on an affine SAT class and situates contemporary learned systems within a unified information-thermodynamic constraint on intelligent computation.

Recent work on the Loeb Scale has provided astronomy a structured framework for assessing anomalous interstellar objects, including a quantitative mapping of a classification ranking, its evolution with the addition of data, and a broader observational strategy for firming its verdict. What remains unclear is the epistemic and methodological meaning of the threshold built into that framework. Here we argue that the central philosophical issue is no longer whether astronomy can define such a threshold, but how a threshold already in place should regulate scientific inquiry under uncertainty. We suggest that candidate technosignature status, such as Level 4 on the Loeb Scale, should be understood as an intermediate epistemic status: stronger than permissive openness, weaker than confirmation, yet sufficient to justify methodological escalation. The argument proceeds in three steps. First, it reconstructs the recent philosophical debate through the work of Lomas, Lane, and Cowie. Second, it turns to historical cases discussed by Kaplan (2026) to show that important discoveries are often delayed not only by weak evidence, but also by paradigms, prestige, and institutional filtering. Third, it interprets candidate status as a form of structured scientific commitment under uncertainty, one that justifies intensified observation, broader hypothesis management, and more deliberate allocation of attention and resources without licensing belief in artificial origin. The paper concludes by arguing that AI should not be the arbitrator in deducing an extraterrestrial origin, but can support the detection, comparison, and prioritization of anomalies once a candidate status has been formally recognized.

The recent detections of thioformyl cyanide (HCSCN) and propynethial (HCSCCH) in TMC-1 provide critical insights into the interstellar sulfur inventory, yet their sequestration and survivability on dust grain mantles remain poorly constrained. Here, we present a computational study of the site-specific adsorption of HCSCN and HCSCCH on amorphous solid water (ASW), modelled via water clusters (H2O)n, n = 6-16, at the wB97X-D/def2-TZVP level of theory, corroborated by QTAIM topological analyses and TD-DFT vertical excitations. Our results reveal a highly heterogeneous binding environment, with desorption energies spanning 1500 to 4900 K. Strongly bound cavity sites induce significant Stark shifts in the C=S stretching modes. Crucially, while the ice matrix exerts a negligible solvatochromic shift on UV transition wavelengths, deeply bound CN-cavity configurations exhibit a pronounced hyperchromic enhancement of the oscillator strength. Implementing these site-specific parameters into the UCLCHEM gas-grain code demonstrates that these species undergo a gradual thermal desorption profile rather than a singular sublimation event. Furthermore, the hyperchromic effect establishes a Survival Paradox: while deeply trapped populations are thermodynamically shielded against thermal desorption, they simultaneously possess enhanced UV absorption cross-sections, rendering them vulnerable to photodissociation by the interstellar radiation field prior to sublimation.

Humans and modern vision models can reach similar classification accuracy while making systematically different kinds of mistakes - differing not in how often they err, but in who gets mistaken for whom, and in which direction. We show that these directional confusions reveal distinct inductive biases that are invisible to accuracy alone. Using matched human and deep vision model responses on a natural-image categorization task under 12 perturbation types, we quantify asymmetry in confusion matrices and link it to generalization geometry through a Rate-Distortion (RD) framework, summarized by three geometric signatures (slope (beta), curvature (kappa)) and efficiency (AUC). We find that humans exhibit broad but weak asymmetries, whereas deep vision models show sparser, stronger directional collapses. Robustness training reduces global asymmetry but fails to recover the human-like breadth-strength profile of graded similarity. Mechanistic simulations further show that different asymmetry organizations shift the RD frontier in opposite directions, even when matched for performance. Together, these results position directional confusions and RD geometry as compact, interpretable signatures of inductive bias under distribution shift.

Neural networks exhibit a remarkable degree of representational convergence across diverse architectures, training objectives, and even data modalities. This convergence is predictive of alignment with brain representation. A recent hypothesis suggests this arises from learning the underlying structure in the environment in similar ways. However, it is unclear how individual stimuli elicit convergent representations across networks. An image can be perceived in multiple ways and expressed differently using words. Here, we introduce a methodology based on the Generalized Procrustes Algorithm to measure intra-modal representational convergence at the single-stimulus level. We applied this to vision models with distinct training objectives, selecting stimuli based on their degree of alignment (intra-modal dispersion). Crucially, we found that this intra-modal dispersion strongly modulates alignment between vision and language models (cross-modal convergence). Specifically, stimuli with low intra-modal dispersion (high agreement among vision models) elicited significantly higher cross-modal alignment than those with high dispersion, by up to a factor of two (e.g., in pairings of DINOv2 with language models). This effect was robust to stimulus selection criteria and generalized across different pairings of vision and language models. Measuring convergence at the single-stimulus level provides a path toward understanding the sources of convergence and divergence across modalities, and between neural networks and human neural representations.

Protein--ligand docking is widely used in structure-based discovery, but routine studies often fail at the workflow level rather than at the scoring level. Receptor cleaning, ligand preparation, file conversion, box definition, run organization, and downstream parsing are frequently handled by fragmented scripts, which reduces reproducibility, obscures provenance, and complicates comparative analysis across targets, ligands, and docking settings. We present ProDock, an open-source Python toolkit for reproducible protein--ligand docking and postprocessing. ProDock organizes application-oriented docking into four connected layers: receptor and ligand preprocessing, provenance-aware docking execution, postprocessing of poses and interaction fingerprints, and SQLite-backed storage for later querying. The package supports inputs ranging from PDB identifiers and local receptor files to \texttt{SMILES} strings and prepared ligand directories, and integrates receptor preparation, ligand preparation, reference-ligand-based box generation, campaign serialization, batch docking, pose crawling, score extraction, interaction profiling, and database insertion within a consistent project-local workflow. By representing studies as explicit many-to-many campaigns linking multiple receptors, ligands, and docking backends, ProDock converts fragmented engine-specific outputs into structured analytical results that are easier to compare, reuse, and audit. ProDock is implemented in Python and released under an open-source license at https://github.com/Medicine-Artificial-Intelligence/ProDock. Documentation is available at https://prodock.readthedocs.io/en/latest.

Diffusion-based generative models have reformed generative AI, and have enabled new capabilities in the science domain, for example, generating 3D structures of molecules. Due to the intrinsic problem structure of certain tasks, there is often a symmetry in the system, which identifies objects that can be converted by a group action as equivalent, hence the target distribution is essentially defined on the quotient space with respect to the group. In this work, we establish a formal framework for diffusion modeling on a general quotient space, and apply it to molecular structure generation which follows the special Euclidean group $\text{SE}(3)$ symmetry. The framework reduces the necessity of learning the component corresponding to the group action, hence simplifies learning difficulty over conventional group-equivariant diffusion models, and the sampler guarantees recovering the target distribution, while heuristic alignment strategies lack proper samplers. The arguments are empirically validated on structure generation for small molecules and proteins, indicating that the principled quotient-space diffusion model provides a new framework that outperforms previous symmetry treatments.

Neuroscientists and computer vision researchers use model-brain alignment benchmarks to compare artificial and biological vision systems. These benchmarks rank models according to alignment measures such as the similarity of representational geometry or the predictability of neural responses from model activations. However, recent works have identified a number of problems with these rankings, among them their lack of discriminative power and robustness, raising the conceptual question of what it means for a model to be brain-aligned. Here we introduce alignment patterns -- characteristic functional relationship profiles of each brain region to all others -- and propose that models should reproduce these patterns to qualify as brain-aligned. First, we apply a standard benchmarking pipeline to a broad spectrum of vision models of the BOLD Moments video fMRI dataset across visual regions of interest (ROIs). We find diverse models appear equivalent in their brain alignment, reflecting the lack of discriminative power of conventional alignment benchmarking pipelines. In contrast, alignment pattern analysis (APA) is a second-order structural consistency test: a model aligned to a given ROI should reproduce that ROI's characteristic cross-region alignment profile. Applying APA, we find that, while these patterns are highly stable across brains of different subjects, even top-ranked models often fail to capture them. Finally, we argue for a clearer distinction between the criteria a model must meet to serve as a tool versus as a computational model for human visual cortex. Conventional alignment measures may be sufficient for identifying neurally predictive models, but claims about computational or algorithmic similarity may require a stronger basis of evidence, including the reproducibility of relational alignment patterns.

Mutational signatures describe the pattern of mutations over the different mutation types. Each mutation type is determined by a base substitution and the flanking nucleotides to the left and right of that base substitution. Due to the widespread interest in mutational signatures, several efforts have been devoted to the development of methods for robust and stable signature estimation. Here, we combine various extensions of the standard framework to estimate mutational signatures. These extensions include (a) incorporating opportunities to the analysis, (b) allowing for extended sequence contexts, (c) using the Negative Binomial model, and (d) parametrizing the signatures. We show that the combination of these four extensions gives very robust and reliable mutational signatures. In particular, we highlight the importance of including mutational opportunities and parametrizing the signatures when the mutation types describe an extended sequence context with two or three flanking nucleotides to each side of the base substitution.

Spatial transcriptomics (ST) enables spatially resolved gene profiling but remains expensive and low-throughput, limiting large-cohort studies and routine clinical use. Predicting spatial gene expression from routine hematoxylin and eosin (H&E) slides is a promising alternative, yet under realistic leave-one-slide-out evaluation, existing models often suffer from slide-level appearance shifts and regression-driven over-smoothing that suppress biologically meaningful variation. CHRep is a two-phase framework for robust histology-to-expression prediction. In the training phase, CHRep learns a structure-aware representation by jointly optimizing correlation-aware regression, symmetric image-expression alignment, and coordinate-induced spatial topology regularization. In the inference phase, cross-slide robustness is improved without backbone fine-tuning through a lightweight calibration module trained on the training slides, which combines a non-parametric estimate from a training gallery with a magnitude-regularized correction module. Unlike prior embedding-alignment or retrieval-based transfer methods that rely on a single prediction route, CHRep couples topology-preserving representation learning with post-hoc calibration, enabling stable neighborhood retrieval and controlled bias correction under slide-level shifts. Across the three cohorts, CHRep consistently improves gene-wise correlation under leave-one-slide-out evaluation, with the largest gains observed on Alex+10x. Relative to HAGE, the Pearson correlation coefficient on all considered genes [PCC(ACG)] increases by 4.0% on cSCC and 9.8% on HER2+. Relative to mclSTExp, PCC(ACG) further improves by 39.5% on Alex+10x, together with 9.7% and 9.0% reductions in mean squared error (MSE) and mean absolute error (MAE), respectively.

\textbf{Background:} Regulatory frameworks for AI in healthcare, including the EU AI Act and FDA guidance on AI/ML-based medical devices, require clinical decision support to demonstrate not only accuracy but auditability. Existing formal languages for clinical logic validate syntactic and structural correctness but not whether decision rules use epistemologically appropriate evidence. \textbf{Methods:} Drawing on design-by-contract principles, we introduce meta-predicates -- predicates about predicates -- for asserting epistemological constraints on clinical decision rules expressed in a DSL. An epistemological type system classifies annotations along four dimensions: purpose, knowledge domain, scale, and method of acquisition. Meta-predicates assert which evidence types are permissible in any given rule. The framework is instantiated in AnFiSA, an open-source platform for genetic variant curation, and demonstrated using the Brigham Genomics Medicine protocol on 5.6 million variants from the Genome in a Bottle benchmark. \textbf{Results:} Decision trees used in variant interpretation can be reformulated as unate cascades, enabling per-variant audit trails that identify which rule classified each variant and why. Meta-predicate validation catches epistemological errors before deployment, whether rules are human-written or AI-generated. The approach complements post-hoc methods such as LIME and SHAP: where explanation reveals what evidence was used after the fact, meta-predicates constrain what evidence may be used before deployment, while preserving human readability. \textbf{Conclusions:} Meta-predicate validation is a step toward demonstrating not only that decisions are accurate but that they rest on appropriate evidence in ways that can be independently audited. While demonstrated in genomics, the approach generalises to any domain requiring auditable decision logic.

We study semisupervised mean estimation with a small labeled sample, a large unlabeled sample, and a black-box prediction model whose output may be miscalibrated. A standard approach in this setting is augmented inverse-probability weighting (AIPW) [Robins et al., 1994], which protects against prediction-model misspecification but can be inefficient when the prediction score is poorly aligned with the outcome scale. We introduce Calibrated Prediction-Powered Inference, which post-hoc calibrates the prediction score on the labeled sample before using it for semisupervised estimation. This simple step requires no retraining and can improve the original score both as a predictor of the outcome and as a regression adjustment for semisupervised inference. We study both linear and isotonic calibration. For isotonic calibration, we establish first-order optimality guarantees: isotonic post-processing can improve predictive accuracy and estimator efficiency relative to the original score and simpler post-processing rules, while no further post-processing of the fitted isotonic score yields additional first-order gains. For linear calibration, we show first-order equivalence to PPI++. We also clarify the relationship among existing estimators, showing that the original PPI estimator is a special case of AIPW and can be inefficient when the prediction model is accurate, while PPI++ is AIPW with empirical efficiency maximization [Rubin et al., 2008]. In simulations and real-data experiments, our calibrated estimators often outperform PPI and are competitive with, or outperform, AIPW and PPI++. We provide an accompanying Python package, ppi_aipw, at https://larsvanderlaan.github.io/ppi-aipw/.

Micro-actions are subtle, localized movements lasting 1-3 seconds such as scratching one's head or tapping fingers. Such subtle actions are essential for social communication, ubiquitously used in natural interactions, and thus critical for fine-grained video understanding, yet remain poorly understood by current computer vision systems. We identify a fundamental challenge: micro-actions exhibit diverse spatio-temporal characteristics where some are defined by spatial configurations while others manifest through temporal dynamics. Existing methods that commit to a single spatio-temporal decomposition cannot accommodate this diversity. We propose a dual-path network that processes anatomically-grounded spatial entities through parallel Spatial-Temporal (ST) and Temporal-Spatial (TS) pathways. The ST path captures spatial configurations before modeling temporal dynamics, while the TS path inverts this order to prioritize temporal dynamics. Rather than fixed fusion, we introduce entity-level adaptive routing where each body part learns its optimal processing preference, complemented by Mutual Action Consistency (MAC) loss that enforces cross-path coherence. Extensive experiments demonstrate competitive performance on MA-52 dataset and state-of-the-art results on iMiGUE dataset. Our work reveals that architectural adaptation to the inherent complexity of micro-actions is essential for advancing fine-grained video understanding.

Pancreatic tumor segmentation in contrast-enhanced computed tomography (CT) is clinically important yet technically challenging: lesions are often small, heterogeneous, and easily confused with surrounding soft tissue, and models that perform well on one cohort frequently degrade under cohort shift. Our goal is to improve cross-cohort generalization while keeping the model architecture simple, efficient, and practical for 3D CT segmentation. We introduce PanGuide3D, a cohort-robust architecture with a shared 3D encoder, a pancreas decoder that predicts a probabilistic pancreas map, and a tumor decoder that is explicitly conditioned on this pancreas probability at multiple scales via differentiable soft gating. To capture long-range context under distribution shift, we further add a lightweight Transformer bottleneck in the U-Net bottleneck representation. We evaluate cohort transfer by training on the PanTS (Pancreatic Tumor Segmentation) cohort and testing both in-cohort (PanTS) and out-of-cohort on MSD (Medical Segmentation Decathlon) Task07 Pancreas, using matched preprocessing and training protocols across strong baselines. We collect voxel-level segmentation metrics, patient-level tumor detection, subgroup analyses by tumor size and anatomical location, volume-conditioned performance analyses, and calibration measurements to assess reliability. Across the evaluated models, PanGuide3D achieves the best overall tumor performance and shows improved cross-cohort generalization, particularly for small tumors and challenging anatomical locations, while reducing anatomically implausible false positives. These findings support probabilistic anatomical conditioning as a practical strategy for improving cross-cohort robustness in an end-to-end model and suggest potential utility for contouring support, treatment planning, and multi-institutional studies.

A comprehensive analysis of viral mutations is essential for understanding viral evolution, disease epidemiology, diagnosis, drug resistance, etc. However, challenges remain in capturing complex mutation patterns and supporting diverse viral families with varying genome architectures. To address these needs, we present VARIANT, an web server for mutational analysis of RNA viral genomes and associated viral products across both single- and multi-segment virus genomes. The server takes as input a viral reference genome, a reference protein sequence, and/or multiple sequence alignment, and automatically provides full annotation of mutation types, including standard categories such as point mutations (missense, silent, and nonsense), insertions, deletions, or frameshift events in both coding and non-coding regions. In addition, VARIANT detects three biologically significant mutation patterns that are overlooked by conventional software/packages: ``row mutations'' (consecutive substitutions within a window of 3 nts), ``hot mutations'' (two non-consecutive substitutions within a window of 3 nts), and potential programmed ribosomal frameshifting (PRF) regions. The server currently contains automatic analysis of major viral pathogens, including SARS-CoV-2, HIV-1, Influenza H3N2, Ebola virus, and Chikungunya virus. It also allows users to analyze customized viruses. Users can track VARIANT analysis progress in real time, visualize mutation distributions, and download structured results in ZIP format. VARIANT also incorporates dual graph topology analysis to classify frameshifting element structures from dot-bracket notation input. This feature enables systematic comparison of RNA secondary structure motifs across viral families by mapping structures to a comprehensive library of dual graph topologies. The web server is freely available at https://variant.up.railway.app.

Low-rank adaptation (LoRA) has emerged as the de facto standard for parameter-efficient fine-tuning (PEFT) of foundation models, enabling the adaptation of billion-parameter networks with minimal computational and memory overhead. Despite its empirical success and rapid proliferation of variants, it remains elusive which architectural choices, optimization techniques, and deployment constraints should guide practical method selection. This overview revisits LoRA through the lens of signal processing (SP), bridging modern adapter designs with classical low-rank modeling tools and inverse problems, as well as highlighting how SP principles can inform principled advances of fine-tuning approaches. Rather than providing a comprehensive enumeration and empirical comparisons of LoRA variants, emphasis is placed on the technical mechanisms underpinning these approaches to justify their effectiveness. These advances are categorized into three complementary axes: architectural design, efficient optimization, and pertinent applications. The first axis builds on singular value decomposition (SVD)-based factorization, rank-augmentation constructions, and cross-layer tensorization, while the second axis deals with initialization, alternating solvers, gauge-invariant optimization, and parameterization-aware methods. Beyond fine-tuning, emerging applications of LoRA are accounted across the entire lifecycle of large models, ranging from pre- and post-training to serving/deployment. Finally, open research directions are outlined at the confluence of SP and deep learning to catalyze a bidirectional frontier: classical SP tools provide a principled vocabulary for designing principled PEFT methods, while the unique challenges facing modern deep learning, especially the overwhelming scale and prohibitive overhead, also offer new research lines benefiting the SP community in return.

Electric arc welding (EAW) exhibits strongly non stationary and temporally evolving behavior, making reliable assessment of arc stability difficult using conventional frame based approaches. In this study, arc dynamics are modeled as a sequence of latent operational regimes within a probabilistic state-space framework. The welding current signal is transformed into a time-frequency domain using Short-Time Fourier Transform (STFT), and a set of physically meaningful spectral descriptors, including energy, entropy, and centroid, is extracted to construct the observation sequence. A Hidden Markov Model (HMM) is employed to capture temporal dependencies and estimate the evolution of arc states. The analysis reveals three dominant regimes, transient, stable, and extinction, with a clear monotonic increase in spectral energy and a corresponding decrease in entropy, indicating reduced variability under stable conditions. Despite partial overlap in the feature space, the inferred state sequence exhibits strong temporal coherence, supported by high state persistence and low transition rates. These findings highlight the limitations of static classification and emphasize the importance of temporal modeling. The proposed framework provides an interpretable and physically consistent representation of arc behavior, enabling more realistic monitoring and analysis of stability dynamics in welding processes.

Denoising of periodic signals and accurate waveform estimation are core tasks across many signal processing domains, including speech, music, medical diagnostics, radio, and sonar. Although deep learning methods have recently shown performance improvements over classical approaches, they require substantial computational resources and are usually trained separately for each signal observation. This study proposes a computationally efficient method based on DCNN and Re-sampling, termed R-DCNN, designed for operation under strict power and resource constraints. The approach targets signals with varying fundamental frequencies and requires only a single observation for training. It generalizes to additional signals via a lightweight resampling step that aligns time scales in signals with different frequencies to re-use the same network weights. Despite its low computational complexity, R-DCNN achieves performance comparable to state-of-the-art classical methods, such as autoregressive (AR)-based techniques, as well as conventional DCNNs trained individually for each observation. This combination of efficiency and performance makes the proposed method particularly well suited for deployment in resource-constrained environments without sacrificing denoising or estimation accuracy.

In many applications of state estimation, the process noise is colored; this case is addressed by applying the standard Kalman filter (KF) to dynamics that are augmented with the coloring dynamics. The present paper considers the case where the coloring dynamics are unknown, which renders the estimates obtained from the standard approach suboptimal. To address this problem, the present paper proposes an adaptive technique based on the principle that, if the measurement noise is white, then the innovations sequence is white if and only if the process noise is white. Leveraging this fact, an Innovations-Whitening Adaptive Kalman Filter (IWAKF) is developed, which learns the process-noise coloring online. By embedding an unknown coloring filter in a state-augmentation framework, IWAKF adapts its parameters by minimizing the empirical autocorrelation of the innovations, thereby driving them toward whiteness and restoring near-optimality without prior knowledge of the coloring dynamics.

PRIMEX (prime-based graph encoding and extraction) is a recently proposed framework for scalable distributed fusion. In PRIMEX, the information pedigree of state estimates or probability density functions is encoded using the information codes, enabling lightweight arithmetic for redundancy removal and data integration. Building on PRIMEX and its memoryless fusion strategy based on a least-squares approximation, in this paper we present two efficient distributed tracking algorithms: a consensus-based PRIMEX method that fuses information from all neighbors, and a greedy gossip-based PRIMEX method that fuses with the most informative neighbor. To further increase communication efficiency, we incorporate an event-triggered mechanism, in which transmission decisions are driven by information novelty measured using differences between the information codes. The proposed methods are evaluated and compared with covariance intersection and centralized fusion in a distributed single target tracking scenario. Simulation results show that PRIMEX-based methods remain competitive in tracking accuracy while improving communication efficiency.

Efficient beam alignment is fundamental to high-throughput and reliable connectivity in Vehicle-to-Everything (V2X) systems. However, conventional beam management in dynamic vehicular topologies incurs prohibitive alignment overhead and struggles to maintain robust links under rapid mobility. To overcome these challenges, this paper proposes a distributed multimodal graph beam alignment (GBA) framework. The core innovation lies in leveraging onboard multimodal sensing data to predict implicit feedback while employing graph neural networks to coordinate multi-user alignment, thereby jointly enhancing scalability and drastically reducing overhead. The architecture adopts a dual-network design with GBA-RSU and GBA-Vehicle units, optimized through a hybrid strategy of centralized learning and federated learning (FL) to balance global performance with local privacy. Furthermore, a dedicated data augmentation (DA) scheme is introduced to address multimodal data imbalance issues in vehicular networks. Negative augmentation applies dominant modality dropout to bolster robustness, while positive augmentation generates underrepresented samples to mitigate label imbalance. Numerical results demonstrate that GBA maintains a competitive sum rate on par with high-resolution codebook-based feedback yet reduces beam alignment overhead by over 90\% and scales efficiently in mobile scenarios. Notably, integrating DA enables GBA to consistently outperform state-of-the-art FL-based alignment benchmarks, with particularly pronounced gains under severe label and modality imbalance, establishing a practical solution for V2X beam management.

High-fidelity control of superconducting qubits requires carefully shaped microwave pulses that account for multiple error channels. In this work, we present a pedagogical introduction to pulse-shaping techniques for transmon qubits, aiming to provide a unified, accessible framework that integrates physical intuition for pulse design, analytical understanding of gate-level descriptions, and practical considerations of hardware. This article further aims to serve as a guide for students and early researchers entering superconducting quantum computing. We begin by examining simple pulse envelopes and their spectral properties, highlighting how finite bandwidth leads to leakage outside the computational subspace. These observations motivate the introduction of the derivative removal by adiabatic gate (DRAG) technique, which uses a quadrature component proportional to the pulse's time derivative to suppress off-resonant excitations. We analyze the single-qubit case using the Magnus expansion, which provides a clear understanding of the order-by-order introduction of error channels. We discuss the practical hardware realities of control pulse generation, focusing on arbitrary waveform generators (AWG), local oscillators (LO), and IQ mixing. Common imperfections are discussed in terms of their impact on the effective pulse shape and qubit Hamiltonian. Finally, we extend the discussion to two-qubit operations, focusing on the cross-resonance gate and the emergence of effective interactions.

Deep learning-based channel estimation has been recognized as a promising technique for sixth-generation wireless systems. However, most existing approaches rely solely on least-squares estimates obtained from demodulation reference signals, which fail to explicitly exploit channel time-frequency correlation parameters. Inspired by the independent channel parameter estimation enabled by semi-static reference signals in modern wireless systems, this letter presents a parameter-aware deep learning-based channel estimation framework termed HyperCEUNet. Specifically, the proposed hypernetwork generates an adaptive front-end convolutional layer based on estimated channel parameters, serving as a pre-filtering stage before the UNet-based estimator. In addition, the Wiener-filtered channel estimates are adopted to provide a correlation-aware initialization for data resources. Simulation results demonstrate that our proposed HyperCEUNet effectively improves channel estimation accuracy compared with its conventional counterparts.

With the evolution of multiple-input multiple-output (MIMO) technology toward extremely large (XL) MIMO systems comprising hundreds of, or more, antennas, this work investigates scalable and fronthaul-efficient reception design for the uplink of cell-free (CF) XL-MIMO systems. In such systems, the uplink signals transmitted by mobile user equipments (UEs) are jointly decoded at a central processing unit (CPU) connected to distributed access points (APs) via finite-capacity fronthaul links. We address the joint optimization of linear transform matrices, used by the APs to reduce the signal dimension and fronthaul load, and fronthaul compression strategies to maximize the uplink sumrate. A fractional programming (FP)-based iterative algorithm is first developed, followed by a reduced-complexity variant, termed accelerated FP (A-FP), along with its decentralized implementation whose fronthaul overhead remains independent of the number of AP antennas. Numerical results show that the proposed A-FP scheme significantly reduces computational complexity compared to FP implemented with general-purpose solvers, while substantially outperforming scalable baseline schemes that rely solely on local channel state information.

Precise positioning is a key enabler for emerging 5G applications, from autonomous transport to industrial automation. Yet the open physical layer (PL) leaves standard positioning reference signals (PRSs) vulnerable to manipulation. This work addresses the security of downlink observed time difference of arrival positioning (DL-OTDOA) through three contributions. First, we introduce VeriLoc, an open-source system-level simulator designed for realistic channel modeling and PL threat injection. Second, we propose three novel security techniques to enhance resilience and threat detection: encrypted PRS to prevent adversarial waveform synthesis, angular-based source authentication (ABSA), and a cross-layer downlink-uplink handshaking protocol to detect attacks that cannot be mitigated by encryption. Third, utilizing VeriLoc, we evaluate the proposed techniques alongside position tracking and a PRS authentication scheme, which extends the original hash-based message authentication code (HMAC) scheme design to support digital signatures. Simulation results demonstrate that while encryption, authentication schemes, and tracking robustly counter selective PRS spoofing and jamming, the proposed spatial and cross-layer mechanisms are essential for detecting meaconing, collectively maintaining attack detection rates in excess of 90% while keeping false alarm rates minimal.

Approximate Message Passing (AMP) is a general framework for iterative algorithms, originally developed for compressed sensing and later extended to a wide range of high-dimensional inference problems. Although recent work has advanced matrix AMP, complex AMP, and AMP for non-separable functions independently, a unified state evolution theory for complex AMP with non-separable denoisers has been lacking. This article fills that gap by establishing state evolution in the setting of complex, non-separable denoising functions. The proposed approach constructs an augmented real-valued system that lifts the problem to a higher-dimensional space, then recovers the complex domain through a many-to-one canonical transformation. Under this construction, the Onsager correction naturally involves Wirtinger derivatives, and the resulting state evolution reduces to scalar complex recursions despite the non-separable structure of the denoisers. The framework extends to the matrix-valued setting, accommodating multiple feature vectors simultaneously. This generalization enables AMP to exploit joint structural constraints, such as simultaneous group and element sparsity, in complex-valued recovery problems. The complex sparse group least absolute shrinkage and selection operator (LASSO) serves as a key instantiation, motivated by preamble detection in Orthogonal Time-Frequency Space (OTFS)-based unsourced random access. Numerical experiments confirm that state evolution accurately predicts performance and show that complex non-separable denoising can produce significant gains over separable and real-valued alternatives.

The FR3 band has emerged as the major focus of 6G wireless research. FR3 cellular operation presents the challenge of extreme bandwidth combined with physically large antenna arrays. In this regime, conventional phase-shift beamforming entails a loss of coherence (beam-squint), and has to be replaced by true time delay beamforming (TTD). It happens that TTD is mathematically equivalent to taking the Radon transform of the space/time measurements. We exploit fifty years of research in the application of the Radon transform to computer tomography and to seismic exploration to elucidate the workings of TTD. We use the Radon transform combined with semblance detection and Radon slowness filtering to remove far-field signals from the measured space/time signals from a linear array, leaving only near-field signals. In turn we partition the array into sub-arrays. For each sub-array we estimate, via the semblance Radon transform, the angles-of-arrival of the near-field signals. We then use triangulation to estimate the coordinates of the near-field sources. Finally we integrate the original space/time data along hyperbolic trajectories to extract the individual near-field signal envelopes.

Federated learning (FL) enables collaborative model training without sharing raw data; however, the presence of noisy labels across distributed clients can severely degrade the learning performance. In this paper, we propose FedSIR, a multi-stage framework for robust FL under noisy labels. Different from existing approaches that mainly rely on designing noise-tolerant loss functions or exploiting loss dynamics during training, our method leverages the spectral structure of client feature representations to identify and mitigate label noise. Our framework consists of three key components. First, we identify clean and noisy clients by analyzing the spectral consistency of class-wise feature subspaces with minimal communication overhead. Second, clean clients provide spectral references that enable noisy clients to relabel potentially corrupted samples using both dominant class directions and residual subspaces. Third, we employ a noise-aware training strategy that integrates logit-adjusted loss, knowledge distillation, and distance-aware aggregation to further stabilize federated optimization. Extensive experiments on standard FL benchmarks demonstrate that FedSIR consistently outperforms state-of-the-art methods for FL with noisy labels. The code is available at https://github.com/sinagh72/FedSIR.

Direct satellite uplink is severely constrained by limited link budgets, which hinder the exploitation of wideband resources, and ultimately limit the throughout. This paper presents a pilot-less coded modulation scheme based on sparse superposition coding (SSC) to enable efficient wideband usage in coverage-limited scenarios. This scheme leverages the structured Zadoff-Chu quasi-orthogonal (ZC-QO) dictionary to support scalable transmission. To address decoding complexity, the SSC transmitted signal embeds root index information via indicator sequences, allowing the receiver to restrict the decoding search space. In addition, a multi-codeword transmission framework with repetition and stop-feedback is developed, enabling reliable communication and better resource utilization. Simulation results show that the proposed scheme achieves throughput gains compared to a more conventional narrow-band multi-dimensional constellation-based approach.

Channel knowledge map (CKM) is a promising technique to achieve environment-aware wireless communication and sensing. Constructing the complete CKM based on channel knowledge observations at sparse locations is a fundamental problem for CKM-enabled wireless networks. However, most existing works on CKM construction only consider the special type of CKM, i.e., the channel gain map (CGM), which only records the channel gain value for each location. In this paper, we consider the channel spatial correlation map (SCM) construction, which signifies the location-specific spatial correlation matrix for multi-antenna systems. Unlike CGM construction, constructing SCM poses significant challenges due to its extremely high-dimensional structure. To address this issue, we first decompose the high-dimensional SCM into lower-dimensional path gain map (PGM) and path angle map (PAM). Then we propose a deep learning model termed E-SRResNet for constructing high-quality SCM from sparse samples, which incorporates multi-head attention (MHA) mechanisms and multi-scale feature fusion (MSFF) to accurately model both local and global spatial relationships of channel parameters and complex nonlinear mappings. Furthermore, we preprocess the dataset to provide priors including line-of-sight (LoS) map, binary building map and base station (BS) map for the model to reconstruct SCM more accurately. Simulations conducted on the CKMImageNet dataset demonstrate that the proposed E-SRResNet achieves significant performance improvements over baseline methods. Moreover, the cosine similarity between the constructed SCM and the ground truth exceeds 0.8 in most regions, validating the effectiveness of the proposed construction method.

Integrated Sensing and Communication (ISAC) systems require efficient beamforming architectures to jointly support communication and sensing functionalities. To reduce hardware overhead, Hybrid Beamforming (HBF) has been widely studied and shown to achieve performance close to fully digital beamforming under practical hardware constraints. As a promising evolution, Reconfigurable Antenna (RA) technologies have recently emerged to further enhance beamforming Degrees of Freedom (DoFs) by dynamically reconfiguring antenna Electromagnetic(EM) characteristics, yet their integration into ISAC systems remains largely unexplored. In this paper, we investigate an RA-assisted ISAC system and develop a decoupled Triple-Hybrid Beamforming (Tri-HBF) framework that alternatively optimizes digital, analog, and EM beamformers to maximize the communication rate and sensing Signal-to-Clutter-plus-NoiseRatio (SCNR). For both Single-user Single-target (SUST) and Multiple-user Multiple-target (MUMT) scenarios, we first transform the original fractional objectives into fraction-free ones via methods tailored to their respective structures. The resulting problems are then solved via alternating optimization over different variable blocks. Closed-form updates are derived for all variables except the EM beamforming subproblem in the MUMT scenario. To further reduce the complexity introduced by Semidefinite Relaxation (SDR) in EM beamforming, we propose a low-complexity iterative approach across antennas with closed-form updates. Simulation results demonstrate that the proposed scheme significantly outperforms benchmark designs with conventional omnidirectional and directional antennas, achievingalmost 100% improvement in spectrum efficiency and 62.5% reduction in antenna overhead, thereby unveiling the

We introduce a graph-signal generalisation of Sample Entropy, denoted SampEn$_{G}$, to quantify irregularity of graph signals on a continuous state space, complementing existing methods on symbolic dynamics. Our approach replaces the temporal delay embedding of classical SampEn with a multi-hop graph-based embedding: for each node, we aggregate patterns from local walk-weighted neighbourhood averages computed via powers of the graph shift operator. We show empirically that SampEn$_{G}$ reduces to classical 1D SampEn on directed path graphs, and validate its nonlinear sensitivity using the logistic map. Experiments on directed Erdős--Rényi graph signals further characterise its behaviour with connectivity and pattern length $m$, with practical runtimes on the order of thousands of nodes. We expect SampEn$_{G}$ to open up new ways to analyse graph signals, generalising SampEn and the concept of conditional entropy to extending nonlinear analysis to a wide variety of network data.

Timely information delivery in low-altitude networks is critical for many time-sensitive applications, such as unmanned aerial vehicle (UAV) navigation, inspection, and surveillance. The key challenge lies in balancing three competing factors: stringent data freshness requirements, UAV onboard energy consumption, and interference with terrestrial services. Addressing this challenge requires not only efficient power and channel allocation strategies but also effective communication timing over the entire operation horizon. In this work, we propose a model predictive communication (MPComm) framework, enabled by advanced channel sensing techniques, in which the channel conditions that the UAV will experience are largely predictable. Within this framework, we formulate a constrained bi-objective optimization problem to achieve a desired trade-off between energy consumption and terrestrial channel occupation, subject to a strict timeliness constraint. We solve this problem using Pareto analysis and show that the original non-convex, mixed-integer problem can be decomposed into a two-layer structure: the outer layer determines the optimal communication timing, while the inner layer determines the optimal power and channel allocation for each communication interval. An efficient algorithm for the inner problem is developed using non-convex analysis, with asymptotic optimality guarantees, while the outer problem is solved optimally via a simple graph search, with edges characterized by inner solutions. The proposed approach applies to a broad class of problem variants, including objective transformations and single-objective specializations. Numerical results demonstrate the efficiency of the proposed solution, achieving up to a six-fold reduction in terrestrial channel occupation and a 6dB energy saving compared to benchmark schemes.

Reconfigurable Intelligent Surfaces (RIS) have emerged as a key enabler for programmable wireless environments in future Beyond-5G (B5G) and 6G networks. In the meantime, Integrated Sensing and Communication (ISAC) and Physical-Layer Security (PLS) are becoming essential functionalities for next-generation wireless systems, particularly in safety and mission-critical applications. However, jointly optimizing RIS-assisted systems to support communication, sensing, and security introduces complex and often conflicting design trade-offs. In this work, a multi-objective optimization framework for RIS-assisted networks is proposed, aiming to jointly analyze communication performance, sensing accuracy, and security-related channel properties in a unified system perspective. The proposed model jointly considers RIS deployment location, orientation, surface size, and an ISAC configuration weight that controls the allocation of RIS reflection gain between communication and sensing tasks. Simulation results reveal inherent trade-offs among communication reliability, sensing accuracy, and security performance. The proposed framework provides valuable insights into the interplay between communication, sensing, and security, and enables the design of efficient RIS deployment and configuration strategies for secure ISAC-enabled 6G wireless networks.

Physical layer (PHY) steganography conceals secrets by making subtle modifications to transmitted radio waveforms, which can be applied to establish covert communication systems. Given the widespread deployment of Wi-Fi infrastructures, hiding secrets within Wi-Fi transmissions exhibits significant covertness and has attracted increasing research attention. Recent advances in Wi-Fi steganography have focused on embedding secrets within channel state information (CSI) by applying artificial finite impulse response (FIR) filters to outgoing signals. These methods can emulate natural wireless propagation effects, thereby evading detection by eavesdroppers. However, existing CSI-based approaches suffer from two critical limitations: vulnerability to environmental variations and limited steganographic capacity. This work presents a Wi-Fi steganography system that mitigates these constraints. Specifically, we introduce a CSI division mechanism to decouple artificial CSI components from natural wireless channel responses. In essence, secrets are embedded within the quotient of two consecutive CSI measurements. Furthermore, we propose an encoder-decoder neural network framework that automatically learns optimal strategies for FIR filter generation and secret recovery, substantially enhancing steganographic capacity. We implemented a prototype using commercial off-the-shelf hardware, including a software-defined radio (SDR) transmitter and two receiver platforms: ANTSDR and ESP32. Experimental evaluations demonstrate that the system achieves robust performance under dynamic environmental conditions while significantly improving steganographic capacity.

Extremely large-scale multiple-input multiple-output (XL-MIMO) is a key enabling technology for sixth-generation (6G) communication systems. Nevertheless, the increase in array aperture and signal bandwidth brings new challenges to wideband channel estimation in XL-MIMO systems. Motivated by recent advances in deep generative modeling, we propose a diffusion model-based method for near-field wideband channel estimation in XL-MIMO systems. We first analyze the statistical correlation of wideband channel and show that near-field wideband channel exhibits both spatial non-stationarity and beam split effects. Based on these observations, the channel estimation problem is formulated as a Bayesian posterior inference task, in which a diffusion model is employed to learn the prior distribution of the channel. To further enhance the representation of complex spatial-frequency channel structures, we design a denoising network with a multi-scale attention mechanism. In particular, the network extracts multi-scale spatial-frequency features via parallel convolutional branches with different receptive fields, and combines feature attention and spatial attention modules to adaptively emphasize critical channel features. This design enables more accurate modeling of near-field wideband channel distributions and consequently improves channel estimation performance. Experimental results demonstrate that the proposed method exhibits superior robustness to existing baseline schemes for XL-MIMO wideband channel estimation under different experimental settings.

The sixth generation (6G) communication networks are expected to provide high data rates, ultra-reliable communication, and massive connectivity, especially in challenging environments such as dense urban areas and disaster-affected regions. However, traditional terrestrial-only networks face significant challenges in these scenarios, including signal blockages from high-rise buildings, traffic congestion, and dynamic user distributions. To address these limitations, we propose the adaptive multi-UAV deployment (AMUD) framework within satellite air-ground integrated networks (SAGINs). The AMUD framework dynamically deploys amplify-and-forward multiple unmanned aerial vehicle relay (UAVr) in with low Earth orbit (LEO) satellites to improve coverage, alleviate congestion, and ensure reliable communication in non-line-of-sight and high-demand conditions. We formulate an optimization problem that aims to jointly maximize the energy efficiency of the total network and the total capacity while ensuring the fairness of the total capacity and satisfying the users' requirements. The simulation results demonstrate that AMUD improves the total capacity of the network, improves the total energy efficiency, and increases the fairness of the capacity compared to traditional LEO satellite and ground base station (LEO-GBS) only systems.

We investigate higher-rank transmissions for multi-connected Extended Reality (XR) devices enabled through tethering group (TGr), in which a nearby tethering User Equipment (UE) cooperates with an XR UE via a short-range tethering link (TL). In contrast to prior studies that are limited to rank-1 transmission and ideal tethering assumptions, we analyze TGr performance under higher-rank point-to-multipoint (PTM) transmission and realistic TL delays. Conventional single Outer Loop Link Adaptation (OLLA) offset results in inaccurate throughput prediction across ranks, leading to suboptimal rank selection. To address this limitation, we propose a multi-offset Outer Loop Link Adaptation (MO-OLLA) framework that introduces rank-dependent signal-to-interference-plus-noise ratio (SINR) correction to improve Link Adaptation (LA) accuracy. Furthermore, a Wireless Fidelity (WiFi) based delay model is incorporated to characterize the impact of practical TL constraints including limited bandwidth and achievable throughput on XR capacity and cellular resource utilization, providing the first such analysis for higher-rank multi-connected XR device. System-level simulations demonstrate that MO-OLLA provides up to 20% performance improvement over conventional OLLA for multi-connected XR UEs. Moreover, TGrs effectively exploit higher-rank transmission, achieving XR capacity gains of 180-200% over single-link XR UEs under ideal TL conditions. Critically, the gains of the TGr remain at 165-180% under realistic high-throughput TLs relative to single-link XR UEs, confirming the practical viability of TGr based cooperation for XR capacity enhancements within existing cellular resources.

In recent years, WiFi sensing has been recognized as a promising technology to bring respiratory monitoring into everyday homes, thanks to its contactless nature and ubiquitous availability. However, existing WiFi-based respiratory monitoring systems still fall short of deployment-oriented performance: they suffer from restrained hardware scalability, limited accuracy, and are highly sensitive to user location. To overcome these limitations and push WiFi sensing towards clinically meaningful precision, we propose RespirFi, a novel system that robustly delivers high-fidelity respiratory waveforms with WiFi Channel State Information (CSI), thereby enabling accurate estimation of key physiological biomarkers. At the core of RespirFi is a theoretical human reflection model, through which we perform an in-depth characterization of how CSI variations are shaped by both subcarrier frequency and spatial user location. Guided by these insights, we develop a location-robust waveform construction method that adaptively selects high quality subcarriers and aligns their waveform trends, ensuring accurate waveform recovery. Furthermore, we propose a breathing phase identification method that leverages inter-subcarrier CSI differences to reliably distinguish inhalation from exhalation. We implement RespirFi over commodity WiFi devices, and extensive experiments demonstrate that it outperforms state-of-the-art approaches across a wide range of clinically relevant respiratory metrics.

In frequency division duplex massive multiple-input multiple-output systems, downlink channel state information must be fed back within a limited uplink budget. While transform coding with Karhunen-Loeve transform and reverse water-filling is rate-distortion optimal for Gaussian channels, its performance is limited by basis mismatch between the user and base station. We analyze this mismatch and propose a practical architecture separating long-term basis feedback from short-term coefficient quantization. Using a random vector quantization, we derive a closed-form end-to-end mean square error expression. This allows us to characterize the optimal rate split and identify a phase transition threshold for basis updates. Simulations on correlated Gaussian and COST2100 channels demonstrate near-optimal performance, robustness to update overhead, and significant complexity reduction compared to deep-learning-based autoencoders.

This article introduces the HYMN (HYbrid Multi-technology Navigation) dataset: a multi-system, and time synchronized dataset for localization research based on opportunistic signals collected in an indoor-outdoor scenario. HYMN comprises measurement data collected in an industrial hall setting for five different positioning systems including Ultra-Wideband (UWB), Bluetooth Low Energy (BLE), WiFi, 5G, and Global Navigation Satellite System (GNSS). Unlike existing datasets that focus on single technologies or purely indoor/outdoor scenarios, HYMN combines five positioning technologies with explicit coverage of indoor-outdoor transitions, enabling multi-sensor fusion research for seamless localization. Each instance of data is identified through a unique measurement id and it represents time-stamped observations relevant for each system respectively along with the ground truth information. HYMN is designed to support a wide range of localization tasks including multi-sensor fingerprinting, cross-technology fusion, and seamless indoor-outdoor positioning. The synchronized measurements from GNSS and other terrestrial systems enable researchers to investigate how heterogeneous signals complement each other to overcome individual technology limitations such as GNSS degradation in covered areas or terrestrial system variability in dynamic environments.

This paper provides a sparse signal recovery algorithm, DU-PSISTA (Deep Unfolded-Periodic Sketched Iterative Shrinkage-Thresholding Algorithm), which aims to balance computational efficiency and accuracy for recovering high-dimensional sparse signals, and a convergence analysis under sufficient conditions. DU-PSISTA introduces a random matrix projection known as sketching to reduce the dimensionality of gradient computations and periodically alternates between the standard ISTA and the sketched variant. This hybrid structure enables flexible control over the trade-off between accuracy and computational complexity through a pre-configurable period parameter. The algorithm includes many parameters to be tuned such as step sizes and thresholding factors so that we incorporate deep unfolding that optimizes the parameters through data-driven training, enabling the algorithm to adaptively improve convergence speed and performance. We show that the proposed method achieves a linear-type contraction to a neighborhood of the true sparse signal with properly selected parameters. The analysis provides an interpretation for the effectiveness of the hybrid structure to improve recovery accuracy. Numerical experiments confirm that our method achieves comparable recovery performance to conventional deep unfolded ISTA while reducing computational complexity, especially when the period parameter and sketch size are properly selected. The results are also consistent with the theoretical insights.

We present principles of algebraic diversity (AD), a group-theoretic approach to signal processing exploiting signal symmetry to extract more information per observation, complementing classical methods that use temporal and spatial diversity. The transformations under which a signal's statistics are invariant form a matched group; this group determines the natural transform for analysis, and averaging an estimator over the group action reduces variance without requiring additional snapshots. The viewpoint is broadened in five directions beyond the single-observation measurement of a companion paper. Rank promotion admits AD on scalar data streams and identifies the law of large numbers as the trivial-group case of a $(G, L)$ continuum combining sample-count with group-orbit averaging. An eigentensor hierarchy handles signals with nested symmetry. A blind group-matching methodology identifies the matched group from data via a polynomial-time generalized eigenvalue problem on the unitary Lie algebra, placing the DFT, DCT, and Karhunen--Loève transforms as distinguished points on a transform manifold. A cost-symmetry matching principle then extends AD from measurement to blind and adaptive signal processing generally; blind equalization is the lead detailed example, with the Constant Modulus Algorithm's residual phase ambiguity predicted analytically and matched within $1.6^\circ$ on 3GPP TDL multipath channels, and other blind problems in signal processing are mapped into the framework. Four theorems formalize a structural capacity $κ$, the Rényi-2 analog of Shannon and von Neumann's Rényi-1 entropies, quantifying how a signal's information is organized rather than how much information it contains. AD complements prior algebraic approaches including invariant estimation, minimax robust estimation, algebraic signal processing, and compressed sensing.

Optical wireless communication (OWC) has emerged as a promising candidate for future high-capacity indoor wireless networks, driven by its large unregulated spectrum, high spatial reuse, and ability to support multi-gigabit data rates. However, OWC systems are highly sensitive to user mobility, as link performance depends strongly on the spatial alignment between transmitter and receiver. Accurate modelling of user position and device orientation is therefore essential for reliable channel estimation and system evaluation. To that effect, this paper proposes a hybrid Gauss--Markov and long short-term memory (GM--LSTM) mobility model for indoor OWC environments. The Gauss--Markov component captures the temporal correlation of user motion, while the LSTM learns residual behaviour to model non-linear movement patterns and orientation dynamics. The proposed model jointly predicts user position and device orientation, enabling improved representation of mobility in OWC channels. Performance is evaluated using prediction accuracy and per-user data rate evolution. Results show that the proposed hybrid GM--LSTM model outperforms conventional Random Waypoint and Gauss--Markov models, providing more accurate mobility prediction and more stable communication performance in dynamic indoor environments.

We compare three secrecy-coding schemes for the degraded wiretap binary symmetric channel (BSC) in the finite-blocklength regime: (i) polar wiretap coset codes, (ii) PAC codes used as wiretap coset codes, and (iii) the invertible-extractor (IE) framework of Bellare-Tessaro. Our comparison is empirical and uses a common semantic-secrecy metric (distinguishing advantage). For polar coset codes, we compute Eve's polarized bit-channel capacities (via the Tal-Vardy construction) to obtain explicit finite-length upper bounds on mutual-information leakage, yielding strong secrecy bounds. For PAC coset codes, we prove that Eve's synthesized bit-channels are equivalent to those of polar codes (up to a permutation), so the same leakage bounds apply; we then convert these strong-secrecy bounds into semantic-secrecy guarantees for symmetric wiretap channels. For the IE scheme, we use the closed-form semantic-secrecy bounds given in the reference work. Finally, we report finite-length results that jointly characterize (a) semantic-secrecy guarantees against Eve and (b) frame-error-rate performance at Bob, illustrating that PAC codes can significantly improve reliability without changing the secrecy bounds inherited from polar coding. Moreover, under the finite-length bounds considered in this work, polar/PAC secrecy codes provide tighter security guarantees than the invertible-extractor framework.

This paper provides novel insights into channel and subspace codes in nonadaptive channel sensing with a single RF chain. Observing that this problem naturally maps to a noncoherent decoding problem, we show that the sensing performance of the maximum likelihood (ML) angle estimator, which does not require knowledge of the typically unknown channel coefficient, is governed by two key terms: the minimum subspace distance and beam gain of the used beamformers. We derive an exact expression for the subspace distance of binary linear channel codes mapped to BPSK, which illuminates the relationship between subspace and Hamming distance, used to design subspace and channel codes, respectively. Our result also reveals why good Hamming distance alone is insufficient for sensing, and shows that well-known families of channel codes such as Reed-Muller codes, yield zero subspace distance and thereby poor sensing performance when used naively without proper codebook pruning. Finally, we introduce so-called beamspace subspace codes based on sparse antenna selection patterns (Golomb rulers), which we show provide near-optimal subspace distance. We demonstrate that this property of judiciously designed sparse arrays can be leveraged together with beamforming gain via convolutional beamspaces, enabling hardware- and sample-efficient channel sensing with theoretical guarantees in large-scale multiantenna communications.

Ultra-low-power (ULP) Internet of Things (IoT) applications demand communication architectures with minimal energy consumption. Noise Modulation (NoiseMod) addresses this by encoding data through the statistical variance of a noise-like signal, eliminating the need for a coherent carrier. To bridge the gap between theoretical potential and practical deployment, this paper benchmarks NoiseMod against standard modulations like BPSK and NC-FSK. We analytically derive the optimal detection threshold and Bit Error Rate (BER) for AWGN and Rayleigh fading channels. Our results show that non-coherent NoiseMod suffers a catastrophic error floor in fading environments, making architectural additions like channel state information (CSI) estimation and 2-antenna selection diversity desirable. Using an ADC-aware energy model, we reveal that NoiseMod's oversampling severely bottlenecks capacity and imposes an 8 dB SNR penalty compared to NC-FSK for a $10^{-3}$ BER in AWGN. Despite its oscillator-free design drastically reducing baseline circuit power, these limitations establish a critical energy crossover distance, which decreases with frequency. Below this distance, NoiseMod offers superior energy efficiency; beyond it, the radiated power needed to overcome its SNR penalty makes coherent schemes like BPSK vastly superior.

We study spectral properties of quantum many-body Hamiltonians through a subsystem-based framework. Given a Hamiltonian of the form $H = \sum_{X \subseteq Λ} Φ(X)$ acting on a tensor product Hilbert space, we associate to each subset $S \subseteq Λ$ a subsystem Hamiltonian $H_S$ and its spectrum $\mathcal{S}(S) = σ(H_S)$. This produces a family of spectra indexed by subsystems, allowing spectral data to be organized according to interaction structure. We show that subsystem Hamiltonians admit local approximations: $H_S$ can be approximated by operators supported on finite neighborhoods with an error bounded by $\|H_S - H_{S,r}\| \le |S| e^{-μr} \|Φ\|_μ$. As a consequence, subsystem spectra are stable under truncation in the sense that $d_H(\mathcal{S}(S), σ(H_{S,r})) \le |S| e^{-μr} \|Φ\|_μ.$ We then prove that for disjoint subsets $S_1, S_2 \subseteq Λ$, the subsystem spectrum is approximately additive: $d_H\big(\mathcal{S}(S_1 \cup S_2), \mathcal{S}(S_1) + \mathcal{S}(S_2)\big) \le (|S_1| + |S_2|) e^{-μD} \|Φ\|_μ,$ where $D = d(S_1, S_2)$. In the finite-range case, this relation becomes exact. The results show that spectral properties reflect the locality of interactions not only at the level of operators, but also at the level of spectra. The framework provides a way to study many-body systems in which interaction geometry directly shapes spectral behavior.

Belief propagation has recently emerged as a powerful framework for evaluating tensor networks in higher dimensions, combining computational efficiency with provable analytical guarantees. In this work, we develop the first end-to-end theory of tensor network belief propagation for a class of projected entangled pair states satisfying \emph{strong injectivity}. We show that when the injectivity parameter exceeds a constant threshold, BP fixed points can be found efficiently, and a cluster-corrected BP algorithm computes physical quantities to $1/\mathrm{poly}(N)$ error in $\mathrm{poly}(N)$ time for an $N$ qubit system. We identify a striking phenomenon we term \emph{algorithmic locality}: local perturbations of the tensor network affect the BP fixed point with an influence decaying rapidly with distance. As a result, updates to the fixed point after a local perturbation can be carried out using only local recomputation. Moreover, through the cluster expansion, this locality extends to observables, implying that local expectation values can be approximated from local data with controlled accuracy. Our results provide the first rigorous guarantee for the effectiveness of tensor-network belief propagation on a wide class of many-body states, bridging a gap between widely used numerical practice and provable algorithmic performance.

Quantum resources such as entanglement form the backbone of quantum technologies and their efficient generation is a central objective of modern quantum platforms. Independently, quantum batteries have emerged as nanoscale devices that utilize collective quantum effects to store energy with a charging advantage over classical strategies. Here, we show that these two pursuits can co-exist: protocols for fast generation of resourceful quantum states can simultaneously charge a quantum battery with a collective advantage, and conversely, a quantum battery protocol with a charging advantage can produce resource-rich states. Using this connection, we propose an integrated hardware protocol on superconducting circuits in which each experimental run can interchangeably accomplish either quantum battery charging, or quantum sensing through generation of metrologically useful states. Our results establish that quantum resources and stored energy are distinct yet co-producable quantities, opening the door to modular quantum architectures that dynamically switch between sensing and energy-storage functions, thereby producing additional functionalities without extra hardware cost.

Peaked quantum circuits, whose output distribution is sharply concentrated on a single bitstring, have emerged as a promising candidate for verifiable quantum advantage, as the correctness of the quantum output can be checked by simply comparing against the known peak. Recent work by Gharibyan et al. arXiv:2510.25838 claimed heuristic quantum advantage using peaked circuits executed on Quantinuum's 56-qubit H2 processor. These peaked circuits concentrate their output on a single hidden bitstring by training a shallow simulable circuit variationally and inserting an obfuscated permutation to increase the depth to a level that makes classical simulation intractable, with estimated runtimes of years for the largest instances. We show that these circuits can be efficiently simulated classically. We describe a method that efficiently performs a full tensor network contraction, allowing near-exact sampling and extraction of the peaked bitstring. The method exploits the mirrored structure of the circuit and iteratively cancels both halves into a Matrix Product Operator (MPO), and avoids the obfuscated permutation by greedily reducing the MPO bond dimension through a process we call unswapping. The method can fully contract and extract the peak of the largest circuit in approximately one hour on a single GPU, around half the time it took to run on the quantum hardware.

We investigate the limits of quantum error correction (QEC) in neutral-atom processors approaching high-fidelity gates and fast cycle times. We show that shorter QEC cycles amplify platform-specific errors, notably Rydberg excitation hopping, and hinder decay of residual Rydberg population, leading to non-Markovian correlated errors that degrade logical performance. To address this, we introduce loss biasing, where spurious Rydberg excitations are rapidly converted into atom loss via mid-circuit ionization, transforming errors into erasure-like noise and suppressing their propagation. Loss biasing restores the fault-tolerant logical error scaling for intra-cycle Pauli errors; furthermore, we argue that when supported with loss-aware decoding, it can achieve the optimal scaling of erasures while enabling shorter QEC cycles with reduced hardware overhead. We outline an implementation using fast autoionization in alkaline-earth(-like) atoms, establishing loss biasing as a practical route toward fault-tolerant quantum computing with sub-millisecond QEC cycles.

Solid-state spin defects hold great promise as building blocks for various quantum technologies. Embedding spin centers in $p$-$n$ diodes under reverse bias has proved to be a powerful strategy to narrow the optical linewidth and increase spin coherence, while also enabling control of the photoluminescence wavelength via Stark shift. Given the multitude of parameters influencing spin centers in diodes (e.g., doping densities and profiles, temperature, bias voltage, spin center position), a question that has not yet been answered is: which set of these design parameters maximizes spin center coherence? In this work, we address this question by developing a scaled gradient descent optimization algorithm that minimizes the optical linewidth of spin centers by combining the numerical solution of a diode's Poisson equation with calculated charge noise from the non-depleted regions. Our optimization is performed for both single- and multiple-parameter cases for divacancies in SiC $p$-$i$-$n$ diodes, including reverse-bias voltage, doping density and profile, and diode total length. Importantly, the optimization is subject to realistic physical constraints, such as small operating bias voltages, avoidance of the dielectric breakdown regime and physical thresholds for doping density. Additionally, due to the leakage current at reverse bias voltages, we develop a new formalism to investigate its influence on coherence. We show that the corresponding noise can be mitigated by implanting spin defects away from the diode's surfaces. Our work provides guidance on experimentally relevant diodes for hosting spin centers with the narrowest optical linewidths and longest coherence times.

Deep reinforcement learning (RL) for quantum circuit optimization faces three fundamental bottlenecks: replay buffers that ignore the reliability of temporal-difference (TD) targets, curriculum-based architecture search that triggers a full quantum-classical evaluation at every environment step, and the routine discard of noiseless trajectories when retraining under hardware noise. We address all three by treating the replay buffer as a primary algorithmic lever for quantum optimization. We introduce ReaPER$+$, an annealed replay rule that transitions from TD error-driven prioritization early in training to reliability-aware sampling as value estimates mature, achieving $4-32\times$ gains in sample efficiency over fixed PER, ReaPER, and uniform replay while consistently discovering more compact circuits across quantum compilation and QAS benchmarks; validation on LunarLander-v3 confirms the principle is domain-agnostic. Furthermore we eliminate the quantum-classical evaluation bottleneck in curriculum RL by introducing OptCRLQAS which amortizes expensive evaluations over multiple architectural edits, cutting wall-clock time per episode by up to $67.5\%$ on a 12-qubit optimization problem without degrading solution quality. Finally we introduce a lightweight replay-buffer transfer scheme that warm-starts noisy-setting learning by reusing noiseless trajectories, without network-weight transfer or $ε$-greedy pretraining. This reduces steps to chemical accuracy by up to $85-90\%$ and final energy error by up to $90\%$ over from-scratch baselines on 6-, 8-, and 12-qubit molecular tasks. Together, these results establish that experience storage, sampling, and transfer are decisive levers for scalable, noise-robust quantum circuit optimization.

Systematic errors in the parameter estimation (PE) of gravitational wave (GW) mergers can arise from various sources, including waveform systematics, noise mischaracterization, data analysis artifacts, and other unknown factors. In this study, we analyze selected events from the first three observing runs of the LIGO-Virgo-KAGRA (LVK) collaboration. We choose events that have been flagged in various studies as potentially affected by systematic errors. Here, we reanalyze these events using a couple of parametric models developed in previous work that incorporate uncertainties in both the phase and amplitude of the GW waveform. In this data-driven approach, we apply sufficiently broad priors on the uncertainty parameters to account for potential systematic errors. Our findings show that the proposed method effectively reduces systematic errors, even those arising from data artifacts, such as glitches occurring near a signal and the deglitching process in GW frame files. Similarly, inconsistent results from different waveform models become much more consistent in our framework. One noteworthy event we examine is GW191109\_010717, which is particularly interesting due to its anti-aligned spin properties. We report that, within our framework, the event still exhibits anti-aligned spin characteristics, but the inference results become consistent across raw and deglitched frame files, as well as across the waveform models used for this event (IMRPhenomXPHM, IMRPhenomXO4a, and NRSur7dq4). A similar trend is observed for the event GW200129\_065458, which previously yielded a high, but inconsistent precession parameter among different waveform models. In contrast, we observe a non-zero and consistent value of $χ_{p}=0.60^{+0.31}_{-0.33}, 0.58^{+0.30}_{-0.29}$ and $0.56^{+0.31}_{-0.28}$ for the IMRPhenomXPHM, IMRPhenomXO4a, and NRSur7dq4 waveform models, respectively.

New sources of charge-parity (CP) violation beyond those described in the Standard Model (SM) are required to explain the observed matter--antimatter asymmetry of the Universe. The Standard Model Effective Field Theory (SMEFT) provides a framework to introduce additional electroweak sources of CP-odd physics in a model-independent manner. However, these CP-violating signatures are mostly degenerate to CP-even SMEFT operators in polarisation-blind observables, distinguishable only in the SM-New Physics (NP) interference using the azimuthal decay angle. Using Quantum Tomography techniques, we present a new approach to constraining these NP effects. Reconstructing the spin density matrix (SDM) of a diboson system, we go beyond `interference resurrection' to exploit the full signature of the Beyond-SM physics, including the pure quadratic NP terms. We show that this approach provides superior simultaneous sensitivity to characteristic features of CP-even and CP-odd contributions, including effects not fully captured by traditional angular observables.

Bosonic quantum error correction enables hardware-efficient protection of quantum information by encoding logical qubits in harmonic oscillators. Bosonic grid states, such as Gottesman-Kitaev-Preskill (GKP) states, are particularly promising due to their potential to correct small displacements and boson loss. However, their generation remains challenging, typically relying on probabilistic protocols or auxiliary qubit systems. Here, we propose deterministic protocols for generating bosonic grid states using programmable nonlinear bosonic circuits composed solely of squeezing, displacement, and Kerr operations. We show that aiming to enforce GKP symmetries in the output of these circuits yields states with competitive performance with respect to current realizations, but whose quality saturates with increasing circuit depth due to imperfect symmetry restoration. Instead, we find that these bosonic circuits naturally give rise to a distinct class of states, that we label as phased-comb states, which are unitarily related to standard grid states but feature an intrinsic phase structure. We demonstrate that these states define a scalable bosonic quantum error-correcting code with near-optimal performance under boson loss comparable to that of approximate GKP states. We further analyze their logical operations and show how to implement a universal gate set for them. Our results establish programmable nonlinear bosonic circuits as a viable route towards the generation of scalable bosonic quantum error-correcting states beyond standard GKP encodings.

We quantize a classically stable system of a harmonic oscillator polynomially coupled to a ghost with negative kinetic energy. We prove that due to an integral of motion with a positive discrete spectrum: i) the Hamiltonian has a pure point spectrum unbounded in both directions, ii) the evolution is manifestly unitary, iii) the vacuum is well-defined, iv) expectation values for squares of canonical variables are bounded. Numerical solutions of the Schrödinger equation confirm these results. We argue that the discrete spectrum of the integral of motion enforces stability for extended interactions.

The Dicke model (DM) serves as a paradigm for understanding collective light-matter interactions. We introduce the chiral Dicke model, a generalization where an atomic ensemble couples to a two-mode cavity via chiral interactions. Unlike the standard DM, the chiral DM is endowed with an inherent continuous $U(1)$ symmetry associated with angular momentum conservation. The ground-state phase diagram and the associated quantum phase transitions are charted out, revealing a $U(1)$-broken superradiant phase that spans a broad parameter space. We demonstrate that the spectrum of quantum fluctuations is highly tunable in both the symmetric and broken phases. Strikingly, our calculations reveal that the system exhibits `multiversality', where distinct universality classes govern the transition between the same two phases. In particular, along a special line in parameter space, the dynamical critical exponent for the normal-superradiant phase transition changes from $zν=1$ to $zν=1/2$. Our work establishes the chiral Dicke model as a powerful platform to realize novel quantum phases and multiversal critical phenomena in light-matter coupled systems.

Page and Wootters (1983) showed how time and dynamics can emerge in a stationary system containing a clock. Albrecht (1995) later showed, for discrete time, that within this framework any dynamical evolution can be obtained simply by choosing a different clock. Marletto and Vedral (2017) claimed that this ambiguity disappears assuming that the clock and the rest of the world do not interact. I show that their proof relies on an incorrect mathematical assumption. Also, eliminating the ambiguity completely would obstruct spacetime symmetries. Whereas the original clock ambiguity concerns all possible histories of a discrete-time system evolving under arbitrary Hamiltonians, but not the Hamiltonians themselves, I prove a stronger version for continuous and discrete unbounded time: the ambiguity extends to both histories and Hamiltonians, including noninteracting ones. Only the dimension of the Hilbert space remains. One might hope to dismiss the ambiguity as merely perspectival, but I show that this would predict incorrect correlations between outcomes and their records, making even knowledge impossible. Purely relational approaches therefore face both the stronger and the original clock ambiguity problems. The ambiguity is removed by taking into account the physical meaning of the operators.

Exact quantum codes detecting a prescribed set of Pauli errors are approached through algebraic constructions--stabilizer, codeword-stabilized, permutation-invariant, topological, and related families. Geometrically, exact Pauli detection is governed by joint higher-rank numerical ranges of these Pauli operators, whose structure for rank $\geq 2$ is largely uncharted. From this viewpoint, we show that such codes often form connected continuous families rather than collections of disjoint solution regions. These families are characterized by a single scalar derived from the Knill-Laflamme conditions: denoted $λ^*$, it is the Euclidean norm of the signature vector of Pauli expectation values on the maximally mixed code state, and provides a one-parameter summary of the code's joint Pauli variance profile. Within these continuous landscapes, stabilizer codes occupy only discrete, measure-zero subsets of the attainable $λ^*$-spectrum, exposing a largely unexplored continuum of genuinely nonadditive exact codes. We establish this picture by analyzing the geometry of higher-rank operator compressions, and extend it to symmetry-restricted settings where cyclic and permutation symmetries are imposed on both the error model and the code projector. Small-system cases reveal interval, singleton, and empty regimes through eigenvalue interlacing and symmetry-sector decompositions; larger systems are treated numerically via Stiefel-manifold optimization and symmetry-adapted parameterizations. In every unrestricted and symmetry-compatible case analyzed, the attainable $λ^*$-spectrum forms a single closed interval whenever nonempty--although a general proof remains open. These results place stabilizer, symmetric, and nonadditive code families within a unified higher-rank variance framework, suggesting a continuous geometric perspective on the landscape of exact quantum codes.

This thesis is concerned with rigorous security analyses of practical Quantum Key Distribution (QKD) protocols, using a variety of modern proof techniques. The main results are as follows. First, we establish a security proof for variable-length QKD protocols against IID collective attacks, and extend this result to coherent attacks using the postselection technique. In doing so, we resolve a long-standing flaw in the application of the postselection technique to QKD, thereby placing it on a rigorous mathematical footing. Second, we develop a method to bound phase error rates in entropic uncertainty relation-based and phase error rate-based proofs, using only the observed statistics of the protocol, even when detectors are imperfect and only approximately characterized. This removes a key assumption of identical detector behaviour and enables these techniques to be applied in realistic settings. Third, we present a very general security analysis based on the marginal-constrained entropy accumulation theorem. The resulting framework can be readily adapted to practical imperfections and side channels, and is suitable for certification efforts. Finally, we show that the security of QKD protocols under realistic authentication assumptions can be reduced to the standard idealized setting, where authentication is assumed to behave honestly, with only minor protocol modifications. A distinctive feature of this thesis is its unified presentation of several major QKD security proof frameworks using consistent protocol descriptions and notation. Consequently, this thesis is intended not only as a collection of new technical results, but also as a useful reference for understanding rigorous security analysis in quantum key distribution.

The partial oracles framework is a quantum search algorithm that has the potential to exceed the quadratic speedup of Grover's algorithm, up to a theoretical maximum of an exponential speedup. Until now, however, the framework has lacked an explicit method for constructing the operator that represents the search iteration. In this paper, we provide the missing construction, for the special case of an oracle function definable using only in-place operations (that is, where the calculated result of the oracle function can be read just from the qubits in the search index). The restriction to in-place operations means that the current work does not yet exhibit quantum advantage: oracle functions constructed using only in-place operations are always classically reversible. To demonstrate quantum advantage, it will be necessary to extend this construction method to include out-of-place operations (part II). As part of the construction of the search iteration operator, we define a new type of transform, the reciprocal transform, which is applied to the oracle function. We show that the reciprocal transform obeys a chain rule, which makes it possible to break down complex transforms into simple steps. To illustrate the practical application of this search method, we apply the reciprocal transform to elementary operations from the SHA-256 hash algorithm: addition modulo $2^n$, the $Maj(a, b, c)$ function, the $Ch(a, b, c)$ function, and the bit shift functions. We also introduce the QFrame python library, which is used to automate the construction of quantum circuits that represent reciprocal transforms.

We present a fast, memory-efficient, unitarity-preserving numerical method beyond the rotating-wave approximation for the closed Tavis-Cummings model in which a multilevel spin system interacts with a cavity mode. This model can describe the interaction of an ensemble of spins with a cavity mode in which the spin frequency and other parameters are time-dependent. The method exploits the fact that, while the Tavis-Cummings model is not tri-diagonal, it can be brought into tri-diagonal form by a change of basis that can be implemented purely by re-indexing (permuting basis elements), which is a fast operation. By truncating the Fock basis of the cavity mode, the computational complexity of the method is linear in the total dimension of the coupled system, both in time and memory. The method can be employed to simulate any closed quantum system whose Hamiltonian terms can be brought into tri-diagonal form.

This article opens new window to obtain accelerating scaling attractors without any need of dark energy. We study cosmological dynamics in a two-fluid system where pressureless dark matter (DM) undergoes adiabatic particle creation and exchanges energy with a barotropic fluid. Considering six widely used interaction prescriptions, we formulate the corresponding autonomous systems in a compact phase space and perform a unified dynamical analysis. We find that accelerating scaling attractors, namely late-time states where both fluids coexist with fixed energy fractions, arise only when the interaction is controlled by the DM density and energy flows from DM to the second fluid. Such attractors appear in the global and local DM-based interactions, and in the global mixed case, but are entirely absent when the interaction depends on the second fluid or on local mixed terms, which instead drive the universe to a DM-dominated accelerating phase. These results clarify the unique conditions under which matter creation can mimic dark-energy-like behaviour without introducing a dark-energy component.

High-quality photonic Bell state measurements (BSMs) enable scalable universal quantum computing and long distance quantum communication. However, when implemented with linear optics, BSMs are fundamentally probabilistic, introducing substantial hardware overheads and limiting noise tolerance in photonic quantum computing architectures. Nonlinear interactions at the single-photon level can overcome these limitations by enabling near-deterministic photon-photon gates. Here, we demonstrate a passive photon-sorting circuit based on the induced nonlinearity arising from photon scattering in a solid-state quantum emitter. The scattering is implemented in a directional waveguide-emitter coupling interface and embedded on-chip into a linear optical circuit, through which we demonstrate sorting of one- and two-photon components with a success probability of 62%. We find that the current system can enable BSMs with a 57% post-selected success probability without ancillary photons, exceeding the linear-optical limit of 50%, and can be readily improved to >65% with design optimisations.

In this work, we systematically investigate the quantum-information diagnostics of cosmological perturbations with a nontrivial sound speed, utilizing a normalized open two-mode squeezed-state framework. Rather than introducing new observables, our analysis focuses on how a modified sound speed dynamically reshapes the Schrödinger evolution of the squeezing parameters ($r_k$ and $φ_k$). We demonstrate how these dynamical changes are inherited by the reduced density matrix of the observable sector. By employing a sound-speed-resonance parametrization, we derive and evaluate the purity, von Neumann entropy, Rényi entropies, and logarithmic negativity. To overcome the intrinsic multiscale stiffness of the post-inflationary equations, we introduce a bounded variable $x = \tanh r_k$ as a partial regularization, which enables reliable numerical simulations exclusively within the inflationary regime. Our numerical results reveal that a nontrivial sound speed significantly suppresses the purity of the reduced state, indicating enhanced effective mixedness. Simultaneously, it strongly amplifies and modulates both the entropic and entanglement diagnostics. More precisely, a nontrivial sound speed postpones the onset of classicality by modulating the decoherence process. Ultimately, we show that a nontrivial sound speed leaves distinct and identifiable quantum-information signatures within the entanglement structure of the early universe.

Gravitational path-integral over $\mathbb{R}\times S^3$ complex metrics with fluctuations is studied in 4D for Einstein-Hilbert gravity in Lorentzian signature, with the aim to investigate the IR properties of complex saddles for various boundary choices. General covariance doesn't allow arbitrary boundary choices for the background and fluctuations. In the ADM-decomposition, while imposing ``no-boundary'' condition at the initial boundary, two scenarios are considered for the final boundary: Dirichlet and fixed extrinsic curvature. Universe undergoes transition from a Euclidean to Lorentzian phase in either scenario, where the dominant saddle in Euclidean phase correspond to a Euclidean metric (imaginary time), while the Lorentzian phase has two complex metrics as dominant saddles which superimpose. One-loop corrected lapse action is computed using Hurwitz-Zeta regularization. UV-divergences canceled by suitable counter terms lead to a renormalized lapse action. One-loop renormalized Hartle-Hawking wave-function is computed using the Picard-Lefschetz and WKB methods, where the contributions coming from the metric-fluctuations show secularly growing infrared divergences as the Universe expands. This is compared with the situation in pure Lorentzian dS, corresponding to a Universe transitioning from an initial state of vanishing conjugate momenta to final state of fixed extrinsic curvature, thereby giving real saddles. Picard-Lefschetz methods alone are not sufficient to overcome the technical hurdles in the one-loop computation, which needs to be supplemented by an $iε$-prescription, achieved via slight complexification of the cosmological constant $Λ$. The UV renormalized one-loop dS wavefunction has the same leading IR divergence as for the Hartle-Hawking no-boundary Universe. Interestingly for all boundary choices considered, the saddles remain KSW-allowed.

Quantum technologies in the terahertz (THz) require a coherent interface between addressable qubits and THz quantum channels -- a capacity that so far, remains largely underdeveloped. Here, we propose and demonstrate the generation of steady-state entanglement between polar quantum emitters, mediated by THz photons. We exploit strong visible-light driving of the emitters to create Rabi-split dressed eigenstates whose energy separation can be optically tuned into the THz regime. The polar nature of the emitters activates THz transitions within these eigenstates, allowing them to couple to a THz photonic mode that induces collective dissipative dynamics. A coherent driving and control of these effective THz emitters is achieved by using a sideband optical drive with detuning close to the THz transition frequency. The resulting interplay of collective dissipation and driving activates a mechanism to generate steady-state entanglement with high values of the concurrence ($C>0.9$), attainable under experimentally feasible parameters. Crucially, both coherent manipulation and quantum state tomography are implemented entirely through optical means, avoiding direct THz control and detection. This establishes a hybrid visible-THz quantum interface in which a THz channel mediates qubit-qubit entanglement (a key operational requirement for THz quantum technologies) while remaining optically accessible.

Modular quantum computing architectures require error correction schemes that remain effective in the presence of noisy inter-processor operations. As such, minimizing the number of such operations on logical circuits partitioned across quantum processors is a primary objective of distributed quantum computing. In this work, we develop basic techniques for qubit allocation using an exemplar color code family and explore generalizations to other color codes. In particular, we show that a 10% reduction in processor-nonlocal gates is achievable in a setting where syndrome extraction occurs after every logical gate, as in today's devices, and that this scales to significantly greater advantages in the multi-qubit case. We also explore methods of achieving universal gate sets efficiently in this distributed logical setting and evaluate the trade-offs of multiple approaches such as magic state distillation, code switching, and a new method based on logical swaps. Finally, we discuss some considerations for an allocation algorithm for these architectures to perform scalably and connect it to existing work on quantum circuit partitions.

We show that detecting steady-state Coulomb-mediated reduction in the thermal variance of the differential motional mode of two nanospheres can bound the Continuous Spontaneous Localization (CSL) parameter ($λ_{\text{CSL}}$). For realistic experimental parameters, the resulting bounds are comparable to those obtained from X-ray emission experiments and surpass those set by bulk-heating ones. Unlike these latter experiments, our bounds are robust against plausible coloured-noise extensions of collapse models. In the short-time regime, we find that a weak Coulomb-induced entanglement-based test between two charged nanospheres initialized in ground state can provide constraints on $λ_{\text{CSL}}$ comparable to limits set by early X-ray experiments.

We describe the design, implementation, and nine-month operational experience of the software management stack for Lagrange, an IQM Spark five-qubit superconducting quantum computer jointly acquired by LINKS Foundation, Politecnico di Torino and the Italian National Institute of Metrological Research (INRiM), and managed by LINKS. Lagrange is, to our knowledge, the first quantum computer in Italy that is fully operational and accessible to students and researchers from multiple institutions under formal service agreements, and to the general public under commercial agreements. When installed in mid-2025, the IQM Spark hardware was delivered by the vendor with authentication only: no billing, project management or fair usage enforcement were provided. We developed a modular middleware layer that filled that gap without modifying any vendor client software, by intercepting API calls through a proxy that enforces project-based budgets, reservation-aware authorisation, and per-user fairness policies. The middleware adopts a plugin architecture that cleanly separates vendor-specific logic from site-specific policies, enabling reuse across different quantum hardware backends and deployment contexts. Since entering production in September 2025, the system has processed over 240,000 quantum jobs totalling more than 1 week of QPU execution time, with greater than 98% uptime. Notably, students at Politecnico di Torino regularly use the machine during both lectures and formal examinations -- a practice we believe to be unique in Europe. We report on the system architecture, the operational lessons learned, and the infrastructure choices that made this deployment possible.

The creation of high-quality cluster states in superconducting microwave circuits is a relevant ingredient in continuous-variable quantum computing. Although large-scale cluster states have been established in optical systems, dissipation prevents their direct applicability to the microwave realm. Recent improvements in superconducting parametric circuits, in particular Josephson parametric amplifiers (JPA) and traveling wave parametric amplifiers (TWPA), have permitted substantial progress in producing entangled states using microwave photons. In this paper, we examine experimentally and theoretically the effects of numerous parametric pump tones on the degree of two-mode squeezing in a quantum circuit and apply it to the JPA. We find that additional pumps diminish the initial two-mode correlations achieved with a single pump by redistributing it among a larger network of modes and by introducing entanglement with additional idler frequencies. Taking into account the actual heterodyne measurement conditions, the experimental results are consistent with theoretical expectations.

We introduce a family of phenomenological cosmological models featuring an interacting dark sector modulated by a sparseness scale parameter, in order to describe the late-time accelerated expansion of the universe. The sparseness scale, inspired by well-established saturation mechanisms in ecology and biology, is introduced in the interaction as a half-saturation constant that bounds the energy exchange between dark matter and dark energy, controls the dynamical behaviour of the physical variables and can prevent the phantom crossing. We consider three nonlinear interacting models, where two of them recover the linear interacting scenarios when the sparsity parameter vanishes. We examine the phase-space of the cosmological field equations by using the Hubble normalization approach. We determine the stationary points and their stability properties in order to reconstruct the asymptotics behaviour of the field equations. Such an analysis allows us to demonstrate the effects of the sparseness scale on the background dynamics. We test the interacting models with observational data. Specifically, we employ Supernovae catalogues, cosmic chronometers, Baryon Acoustic Oscillation measurements from DESI DR2, and redshift-space distortion measurements of the growth of large-scale structure through the $f$ and $fσ_8$ observables. The Bayesian analysis suggests that, for two of the three models, a vanishing sparsity parameter is disfavoured at more than the 95\% confidence interval, providing observational support for a nonzero sparseness scale in the dark sector interaction.

The 21-cm signal, one of the most promising probes of the high-redshift Universe, has traditionally been modelled without accounting for the effects of active galactic nuclei (AGN) in the pre-JWST era, primarily due to the lack of observational evidence for AGNs at z > 6. However, following the discovery of several AGNs at redshifts as high as z ~ 10 by JWST, it has become imperative to incorporate the impact of these early AGNs when predicting the 21-cm signal. Supposing that these AGNs are seeded by primordial black holes (PBHs), we study their impact with a semi-numerical model setup. Specifically, we extended the explicitly photon-conserving reionization framework, SCRIPT, including essential cosmic dawn physics and PBH contributions. This enables us to compute both the global signal and the power spectrum of the 21-cm line over the redshift range z ~ 30 - 5 within a self-consistent framework. Building on this setup, we then investigate the impact of different PBH mass functions (obeying current observational constraints) on the resulting signal. The X-ray heating from PBHs can substantially make the depth of the global 21-cm signal shallower and suppress the expected power amplitude during cosmic dawn. We also find that the choice of mass function plays a crucial role in shaping the 21-cm signal, and can, in fact, lead to significantly different predictions.

We have considered the problem of the influence of inhomogeneity of gravitational field on transport effects predicted by the field theory describing massless Dirac fermions in the Maxwell and dark matter background. As a model of dark sector one takes into account dark photon model, where the hidden sector is described by the auxiliary U(1)-gauge field coupled to the visible sector. Elaborating the model we restrict our considerations to the case when Weyl type conformal transformation slightly differs from the Minkowski spacetime. This assumption simplifies the calculations and enables us not to use complicated methods of the quantum field theory in the curved background. The resulting currents stemming both from visible and dark sectors are proportional to the adequate beta functions appearing in the elaborated systems. For charge-less dark sector we predict corrections to the scale conductivities in both sectors: linear in α in the dark sector and quadratic in the visible one.

The optical spectrum of a quantum system is jointly determined by the properties of the emitter and the driving field. All-optical spectral control can hence be a promising method to engineer the properties of single photon emitters for quantum technological applications. It was proposed that driving a two-level system with a periodic sequence of optical pi-pulses during the excited state lifetime shifts the emission and absorption maximum to an arbitrarily detuned pulse carrier frequency, enabling the mitigation of spectral diffusion in noisy emitters. In this article, we report on the first experimental observation of this effect. We implement the protocol on a solid-state emitter and reduce its inhomogeneously broadened optical linewidth close to the lifetime limit. By detuning the excitation laser, we are able to concentrate approximately half of the absorption to a freely selectable target frequency. Our approach is solely based on properties of coherently evolving quantum systems, rendering it applicable to a wide range of individual and ensembles of quantum emitters.

We present a new software package for efficient quantum circuit generation, designed to achieve optimal runtime performance. Despite being in an early stage of development, our implementation demonstrates significant advantages over existing tools. Using the quantum Fourier transform (QFT) as a benchmark, we show that our backend can generate circuits for systems with up to 2000 qubits faster than widely used frameworks such as Qiskit and Q#. This improvement is particularly relevant for applications where classical preprocessing time, including circuit generation, must be minimized to not diminish any potential quantum advantage - for example, in combinatorial optimization tasks. Additionally, our software provides high-level primitives for bit- and integer-level manipulations, offering a simplified interface for integration with high-level quantum programming languages.

The recent detection of a stochastic gravitational wave background by pulsar timing arrays has opened a new window in understanding supermassive black hole binaries and in probing the universe at the early time. Recently, pulsar timing array (PTA) collaborations have been further paving the way to probe anisotropies in the stochastic gravitational wave background. This study investigates dipole-type statistical anisotropy in the primordial power spectrum within a phenomenological framework. We demonstrate that the primordial dipole induces both dipolar and quadrupolar anisotropies in the energy density spectrum of scalar-induced gravitational waves (SIGWs), without generating extra polarization modes. Based on this anisotropic spectrum, we derive the corresponding PTA overlap reduction functions (ORFs), which exhibit frequency dependence, with the anisotropies enhanced on small scales. Furthermore, owing to the non-uniform distribution of millisecond pulsars over the sky in current PTA dataset, the ORFs exhibit a morphology that explicitly depends on the preferred direction of the anisotropy. However, our bayesian analysis of the NANOGrav 15-year dataset still yields no significant evidence for a preferred direction and a weak upper limit on anisotropy amplitude $(g\lesssim0.5)$. This result arises because the observational frequency band lies below the spectral peak, where our models predict suppressed anisotropic contributions. This limitation highlights the potential of future PTA observations. Specifically, datasets with broader frequency coverage are expected to tighten constraints on dipole-type anisotropy.

We establish a relation between the exponential decay rates of quantum Markov semigroups with respect to different inner products. More precisely, it was conjectured by Fagnola, Poletti, Sasso and Umanità that for a Gaussian quantum Markov semigroup, the exponential decay rate with respect to the KMS inner product is bounded below by the exponential decay rate for the GNS inner product. We show that this is indeed the case and not limited to Gaussian quantum Markov semigroups, but holds for quantum Markov semigroups with a faithful normal invariant state on arbitrary von Neumann algebras. Additionally, the KMS inner product can be replaced by a whole class of inner products induced by operator monotone functions.

This paper presents a systematic study of the chaotic dynamics of charged test particles around purely magnetically charged black holes immersed in a uniform external magnetic field within the framework of Einstein-ModMax theory. By constructing an explicit symplectic integrator, we obtain high-precision numerical solutions of the equations of motion. Combining the observational constraints from the Event Horizon Telescope (EHT) shadow images, we further restrict the parameter ranges of the model. We apply Shannon entropy and MIPP (mutual information for particle pairs) as effective indicators to identify the chaotic behavior of charged test particles in the spacetime of this black hole. Numerical results indicate that these indicators can clearly distinguish between regular and chaotic motion of orbits in strong gravitational field systems. Further analysis reveals that, compared to the key conserved quantities that determine the global dynamical behavior of the system -- energy $E$ and angular momentum $L$, the sensitivity of the system parameters $e^{-ν}$ and $Q_{m}$ to transitions in the orbital dynamical states is significantly reduced. This study provides a new perspective for a deeper understanding of the characterization and evolution mechanisms of chaotic dynamics in strong gravitational fields.

We investigate the shadow, timelike geodesic structure, radiation properties of thin accretion disks, and optical appearance of a static spherically symmetric regular black hole, constructed based on the Dehnen-type density profile. Using observational data from M87* and Sgr A*, we constrain the model parameter $a$ at both $1σ$ and $2σ$ confidence levels. Based on the Page--Thorne model, we calculate the local radiative flux, redshift factor distribution, and the radiation flux received by a distant observer, systematically examining the effects of the parameter $a$ and the viewing angle on the black hole image. The results show that larger $a$ will enlarge the effective radiation area of the accretion disk and significantly enhance the asymmetry and Doppler boosting effects of the direct and secondary images at large viewing angles.

Systematic control errors remain a primary obstacle to realizing high-fidelity single-qubit gates. We introduce composite pulse sequences that implement X and Hadamard gates while simultaneously compensating amplitude (Rabi-frequency), detuning (frequency), and duration errors. Our construction uses two complementary strategies: (i) derivative-based cancellation of error terms in the full unitary (not just the transition probability), formulated via the Cayley-Klein parametrization, and (ii) direct minimization of the average gate infidelity over prescribed error ranges. We derive symmetric five-pulse solutions with closed-form phases that cancel all first-order terms (including the mixed derivative), and numerically optimize longer sequences -- up to 15 pulses -- to achieve higher-order suppression. We also show that standard ``universal'' five-pulse sequences (U5a/U5b) emerge as simple phase-shifted instances of our symmetric solutions, yielding broad robustness to both detuning and amplitude errors. Finally, we construct variable-area sequences for $R_x(π/2)$, which, up to virtual Z rotations, benchmark the Hadamard gate. Across all families we observe the expected trade-off between sequence length and robustness window, with substantial boosts in fidelity over large error domains.

We propose a semiclassical framework for solving open quantum dynamics in driven-dissipative spin systems. Our method consists of generalized spin-wave approximations tailored to describing quantum trajectories unravelled from the master equation, and generically applies to regimes beyond the reach of conventional spin-wave theories, including short-range interactions and local quantum jumps, enabling the efficient simulation of large-scale interacting spins. We illustrate the versatility of our framework by studying a variable-range driven-dissipative Ising model on a 2D lattice. When the dissipation acts along the drive axis, we find a continuous phase transition breaking the $\mathbb{Z}_2$ symmetry, and demonstrate that the interaction range, when tuned from fully-connected to nearest-neighbour, profoundly alters the universality class of the criticality. With the dissipation along the interaction axis, we show the emergence of a first-order transition. Demonstrated with both state-diffusion and quantum-jump types of trajectory dynamics, our framework provides a powerful toolbox for the efficient semiclassical description of non-equilibrium dynamics and many-body phases in spin systems.

We construct mode-selective effective models describing the interaction of the quantum plasmon-polariton field supported by a finite dielectric medium and one or several quantum emitters. The construction of the effective model is based on the decomposition of the field into bright modes relevant to the interaction with the emitters and dark modes, which do not interact with the emitters. We show that the quantum plasmon-polariton field can be represented equivalently by a double-continuum spectrum or by a single hybrid continuum spectrum for each emitter. The system of the electromagnetic field coupled to a finite medium is composed of two families of continuum modes, each of them with an infinite degeneracy. The two families are deformations of the free electromagnetic field and the free medium, induced by the interaction between them, as described by the Lippmann-Schwinger equations. We show that if there are $N$ emitters interacting with this plasmon-polariton field, the effective interaction involves a much smaller set of bosonic continuum modes: the interacting part of the continuum can be described by $N$ non-degenerate one-dimensional continua, one for each emitter. The representation of the interaction in terms of a single hybrid continuum spectrum coincides with the one within the macroscopic Langevin model with bulk medium. This coincidence is explained by an exact compensation of two terms, one in the coupling term of the Hamiltonian and the other one in a Green tensor identity.

The paper develops a model to understand the effective quantum geometry of a black hole horizon and the emission of Hawking spectrum in 2+1 dimensions. We argue that one may view the black hole horizon as formed out of quantised lengths of elementary quanta of value $8π\ell_{P}\, n$, where $n\in \mathbb{N}$, and $\ell_{P}$ is the Planck length. To an observer near the black hole horizon, the entropy (or length of horizon cross-section) is related to the black hole energy. Hence, one may develop a formulation of length ensemble (similar to the area canonical ensemble of Krasnov) from which the black body spectrum may be obtained directly. To this local observer, the temperature of the Hawking spectrum is modified due to the Tolman factor.

Fermion condensate inflation, where inflation emerges from four-fermion interactions induced by spacetime torsion, removes the need for additional scalar fields beyond the Standard Model. In this framework, the fermion field can be decomposed into two distinguished sectors, each giving rise to bound states. After integrating out fermions, the bound fields play the roles of the inflaton and the auxiliary fields, resembling hybrid inflation with a waterfall mechanism. The inclusion of an axial chemical potential naturally introduces a mechanism to end inflation and trigger instant preheating. During the waterfall phase, the effective potential of the fermion condensate supports the formation of non-topological solitons such as Q-balls, which act as seeds of primordial black holes. This model is intrinsically connected to Chern-Simons gravity, which implies a parity-violating universe. Consequently, both the primordial black hole (PBH) dark-matter abundance and parity-violation signatures could provide observational tests of the model.

We study the quadrupolar part of the gravitational waveform $h_{ij}$ (encoded in the helicity-($-2)$ radiative quadrupole moment $U_2 = \frac{1}{2!} \bar m^{i} \bar m^{j } U_{i j} \in\frac{R}{4G} \bar m^{i} \bar m^{j } h_{i j}\equiv W $) emitted during the scattering of two masses. Working within the Multipolar Post-Minkowskian (MPM) formalism, we compute the time-domain value of $U_2$ at the third-and-a-half post-Newtonian (3.5PN) accuracy by using the 3.5PN radiation-reacted quasi-Keplerian representation of the hyperbolic motion. We then explicitly evaluate the {\it frequency-domain} value of $U_2$ up to the 2-loop level, i.e. $ O(G^4)$ contributions to $h_{ij}(ω, θ,φ)$, corresponding to $O(G^3)$ contributions to $\hat U_2(ω, θ,φ)$. The nonlinear memory contribution to the waveform in the center-of-mass frame is computed too, and checked against the soft-limit of the waveform. The 1-loop truncation of our 3.5PN frequency-domain MPM waveform is found to agree with corresponding existing Effective Field Theory (EFT) results when subtracting the dipolar part of the Veneziano-Vilkovisky supertranslation connecting the MPM and EFT Bondi-Metzner-Sachs (BMS) frames.

We characterize nonequilibrium phases in long-range dissipative spin systems through the statistical properties of quantum jump trajectories. While the average dynamics governed by the Lindblad master equation provides access to steady-state expectation values of order parameters, the quantum trajectory framework reveals features encoded in the spatial and temporal correlations of detection events. Focusing on a model exhibiting a paramagnetic-to-ferromagnetic phase transition, we investigate the full counting statistics of quantum jumps using a tilted Lindbladian approach. We combine this with cluster mean-field and cumulant expansion techniques, which allow us to capture, respectively, the short- and long-range structure of jump correlations. In addition, we study the waiting-time distributions of detection events. We show that quantum jump correlations display clear signatures of the underlying phases and reveal distinct dynamical features across the transition. Our results highlight the potential of trajectory-resolved observables as probes of collective behavior in open quantum many-body systems and provide new insights into the role of long-range interactions in shaping nonequilibrium dynamics.

The generalized second laws of quantum thermodynamics are usually formulated in terms of Rényi divergences and the associated family of generalized free energies. In catalytic thermal transformations, this framework typically certifies the existence of a suitable catalyst but does not make the catalytic contribution explicit in the resulting system-level inequalities. Here we develop a complementary formulation based on non-additive divergences, whose pseudo-additive structure yields a family of generalized free energies with an explicit catalyst-dependent correction term. For uncorrelated catalytic thermal transformations, we show that this leads to non-additive second-law relations that make the catalytic contribution explicit and provide nontrivial constraints on admissible catalysts when the catalyst is returned only approximately. We also analyze correlated catalytic thermal transformations and show, through explicit finite-dimensional examples, that reduced-state data are generally insufficient to characterize thermodynamic accessibility: the thermo-majorization behavior of the joint transformation can change while the system and catalyst marginals remain fixed, and even states with identical marginals and the same mutual information can exhibit different thermo-majorization accessibility. Our results show that non-additivity can be thermodynamically informative in uncorrelated catalysis, whereas correlated catalysis generally requires a genuinely joint-state-sensitive description beyond reduced-state monotones.

We investigate the viability of Tsallis holographic dark energy (THDE) models, focusing on the role of the infrared (IR) cutoff in the growth of cosmic structures. Considering two commonly used choices of the cutoff, the particle horizon and the future event horizon, we analyze the evolution of linear matter perturbations and compute the growth factor, growth rate, and the observable $fσ_8(z)$. These predictions are compared with observational data from redshift-space distortion measurements. We find that the growth history is highly sensitive to the choice of IR cutoff. Models based on the future event horizon are consistent with observational data and can provide a fit comparable to, or slightly better than, the $Λ$CDM model for suitable values of the Tsallis parameter $δ$. In contrast, models constructed using the particle horizon generally fail to reproduce the observed growth of structure. These results demonstrate that the viability of THDE models depends crucially on the choice of IR cutoff and highlight the importance of structure formation as a stringent test of generalized holographic dark energy scenarios.

Photonic quantum computing provides a promising route toward quantum computation by naturally supporting the measurement-based quantum computation (MBQC) model. In MBQC, programs are executed through measurements on a pre-generated graph state, whose construction largely depends on probabilistic fusion operations. However, fusion operations in PQC are vulnerable to two major error sources: fusion failure and fusion erasure. As a result, MBQC compilation must account for both error mechanisms to generate reliable and efficient photonic executions. Prior state-of-the-art MBQC compilation, represented by OneAdapt, is designed for all-photonic architectures and mainly focuses on handling fusion failures. Nevertheless, it does not explicitly model fusion erasures induced by photon loss, which can be substantially more damaging than fusion failures. To mitigate fusion erasure errors, we introduce a new MBQC compilation scheme built upon the spin qubit quantum memory. We propose tree-encoded fusion, an encoding strategy that suppresses erasure errors during graph-state generation. We further incorporate this scheme into a compiler framework with algorithms that reduce the execution overhead of quantum programs. We evaluate the proposed framework using a realistic PQC simulator on six representative quantum algorithm benchmarks across multiple program scales. The results show that tree-encoded fusion achieves better robustness than alternative fusion-encoding strategies, and that our compiler provides exponential improvement over OneAdapt. In addition, we validate the feasibility of our approach through a proof-of-concept demonstration on real PQC hardware.

Fault-tolerant quantum computing increasingly demands rigorous, circuit-level evaluation of diverse quantum error correction (QEC) protocols and efficient prototyping of new ones. Such evaluation requires both the physical circuit and its Detector Error Model (DEM) to simulate end-to-end logical error rates. However, DEM construction today is performed by manual annotation, a tedious and error-prone process that effectively limits evaluation to simple memory experiments. We present LightStim, a framework that automates DEM construction concurrently with circuit compilation by maintaining a Pauli tableau augmented with measurement records, with no protocol-specific input required. We benchmark LightStim across protocols from memory experiments to end-to-end distillation circuits; cross-validation against public implementations confirms exact detector and observable counts and consistent logical error rates. LightStim additionally accelerates the exploration of new protocols, which we demonstrate through a novel heterogeneous cross-code lattice surgery design between surface and punctured quantum Reed-Muller codes. These capabilities together make LightStim a unified infrastructure for systematic QEC protocol evaluation and exploration.

We investigate the reduced dynamics of two identical superconducting qubits capacitively coupled through a finite-length transmission line. Starting from circuit quantization, we derive a circuit Hamiltonian that naturally separates the line modes into even- and odd-parity sectors coupled to collective qubit operators. Depending on the hierarchy between the qubit frequency $ω_q$, the mode spacing $ω_{TL}$, and the coupling scale $ω_g$, the line acts either as a structured reservoir or as a discrete few-mode coupler. In the long-line continuum limit, each sector is described by a Drude--Lorentz spectral density and the dynamics is solved with the hierarchical equations of motion. Using the Breuer--Laine--Piilo measure, we identify the parameter region in which the reduced dynamics exhibits non-Markovian relaxation. In the short-line limit, the continuum description breaks down and the dynamics becomes respectively multimode or single-mode. This establishes a unified cQED picture of the dynamical regimes of finite-length transmission lines in superconducting-circuit architectures.

Closed-loop superconducting-qubit calibration has matured into DAG-orchestrated protocol chains, yet published frameworks treat the bath via a Markovian master equation or a phenomenological likelihood, absorbing bath structure into fit residuals instead of reporting it as a diagnostic. We integrate a QuTiP 5.x hierarchical-equations-of-motion (HEOM) solver driven by a Tier-1 1/f Burkard bath into a multi-protocol calibration DAG (Rabi -> {Ramsey || T1}) and benchmark it against sesolve and mesolve on a frozen platform in a pulse-level simulator (no hardware validation). The Ramsey channel carries the headline: the Markovian fit is censored by its exponential-family numerical ceiling, while HEOM recovers a physical revival envelope whose primary T2* separates from the Markovian reference by at least 13x at 95% independent-bootstrap confidence within the HEOM-feasible budget; the point-estimate ratio reaches >=28x on the 50-point primary-t1 grid and ~72x on the 30-point biexp-family tau_aw pivot at L=5. Rabi contrast falls 2.17% below mesolve on a noise-limited 30-point grid; the paired-bootstrap CI crosses zero, so this channel corroborates rather than independently establishes the non-Markovian signature. T1 decay shape matches across backends (beta=1.000), yet HEOM's initial occupation drops from 1.000 to 0.879 -- a bath-dressed contamination stable under a 16-point densification. The DAG adds 9.62 us average per-protocol scheduling overhead, no meaningful latency penalty at protocol granularity. HEOM-in-loop thereby changes what calibration reports: bath structure appears as a quantifiable residual rather than a hidden confound.

We construct asymptotically AdS$_5$ black brane solutions in a theory of gravity with an infinite series of curvature corrections. The action is based on an $O(d,d)$ symmetric ansatz which has been argued to describe the classical NSNS sector of string theories. We find that, for this general class of theories, the singularity behind the horizon is not resolved by the curvature corrections. The approach to the singularity is however generically modified, being characterized by different Kasner exponents. We also show that, in the presence of a non-trivial dilaton, a slight generalization of these types of curvature corrections can generate dynamically a negative cosmological constant in the region of small coupling. This provides a mechanism through which asymptotic freedom could emerge in the hypothetical string dual of QCD.

This study investigates the non-singular bounce within the framework of Myrzakulov-type $f(R,T) = R + αT + βT^2$ gravity by adopting a Chaplygin gas equation of state. We employ two methodologies: a reconstruction scheme via a symmetric scale factor ansatz (Model I) and an autonomous dynamical system analysis (Model II). Our results indicate that the quadratic trace parameter $β$ acts as a primary physical driver; specifically, for $β< 0$, the matter-geometry coupling generates sufficient geometric repulsion to effectively violate the Null Energy Condition (NEC) at high densities without the requirement of exotic matter fields. A numerical scan of the $(β, ρ_0)$ parameter space indicates a critical density threshold required to initiate the bounce, below which the Universe follows a singular General Relativity trajectory. The models are shown to be physically viable, with the effective equation of state asymptotically approaching a de Sitter attractor ($w_{\text{eff}} \to -1$) and the squared speed of sound remaining within the stability and causality bounds ($0 \le c_s^2 \le 1$). This study shows that the $f(R,T)$ framework provides a stable, classically geometric alternative to the Big Bang singularity, consistent with both early-universe requirements and late-time accelerated expansion.

High-precision optical phase stabilization in quantum networks is fundamentally constrained by the strict photon-flux and duty-cycle limits required to avoid disturbing fragile quantum states. This challenge becomes especially critical when coordinating multiple independent light sources for multi-step quantum protocols. Here, we develop an integrated phase-stabilization framework that incorporates a Bayesian phase estimator to optimally extract information from sparse single-photon detection events. This approach outperforms conventional maximum-likelihood estimation and achieves the shot-noise limit under minimal photon flux. The framework enables real-time correction of combined phase noise from both nodal lasers and transmission fibers, facilitating a two-step excitation protocol for heralded entanglement generation between separate trapped-ion nodes via single-photon interference. Operating with a detected photon rate of approximately 1 MHz and a duty cycle less than or equal to 6.5%, the system maintains interferometric visibility greater than 97% over fiber links of 10 km and 100 km. This phase control yields deterministic ion-ion entanglement with parity contrast exceeding 85% at both distances, enabling device-independent quantum key distribution. Moreover, the resulting memory-memory entanglement at 10 km survives beyond the average time required to establish it -- a fundamental requirement for quantum repeaters. This work establishes a robust and scalable foundation for practical long-distance quantum networks.

We show in this article the improvements which can be obtained in the description of the luminescence properties of Pr3+ doped materials by using an extension of the Judd-Ofelt theory in order to relax some strong selection rules and approximations of the standard formalism and to better account for the influence of the 4f5d excited electronic configuration. The demonstration is made by re-examining the case of Pr3+:YAG, a well known luminescent and laser crystal with a very low energy 4f5d absorption band. Our extension thus provides a better agreement between calculated and measured absorption intensities, especially for the hypersensitive 3 H4 $\rightarrow$ 3 P2 transition. A comparison is made with the results obtained in the case of Pr3+:ZBLAN, a laser fluoride glass with much higher 4f5d absorption levels. Our investigation also gives the opportunity, in the case of Pr3+:YAG, to provide more complete and more reliable absorption and emission data than reported in the past literature and to exploit these data to better address the question of laser operation at various emission wavelengths. It is thus demonstrated that laser operation should be possible with improved laser performance at 488 nm, 616 nm and 744 nm, as it was already achieved in the past, but also at 566 nm and 931 nm by using appropriate laser cavities and laser mirrors.

We consider two-dimensional harmonic oscillator in the complex Bargmann-Fock-Segal representation with $T^*{\mathbb R}^{2}={\mathbb C}^2$ as classical phase space. We show that the eigenfunctions $ψ_n$ of the quantum Hamiltonian correspond to complex radial coordinates in the reduced phase space ${\mathbb C}^2/{\mathbb Z}_n\subset{\mathbb C}^2$. They describe ${\mathbb Z}_n$-invariant motion of particle along a circle $S^1$ in lens space $S^3/{\mathbb Z}_n\subset{\mathbb C}^2/{\mathbb Z}_n$, where ${\mathbb Z}_n$ is the cyclic group of rotation by an angle $2π/n$ on the circle $S^1$, $n=1,2,...\,$. Thus the general solution of the Schrödinger equation carries information about an infinite number of admissible classical states $ψ_n$ that can be mapped to other states after lifting into the quantum bundle. We show that in the Kepler/hydrogen atom problem there is a similar correspondence between classical and quantum states.

Several approximation procedures, such as the full or partial rotating-wave, time-averaging, and geometric-arithmetic approximations, have been proposed to derive Gorini-Kossakowski-Sudarshan-Lindblad (GKSL) generators from the Born-Markov quantum master equation (e.g., the Redfield equation). Establishing rigorous error bounds for these approximations is of fundamental and practical importance. However, existing bounds face two major limitations: they are highly specific to individual methods, and, more critically, they diverge in the long-time limit, ensuring the accuracy of the derived GKSL generator only in short-time regimes. In this Letter, we resolve both issues by deriving a unified, rigorous error bound for a general class of approximation methods -- termed temporal coarse graining -- that encompasses all aforementioned schemes. Crucially, our error bound is time-uniform. This guarantees that GKSL generators obtained via temporal coarse graining remain accurate for arbitrarily long times, provided the dissipation timescale is significantly longer than the bath correlation timescale.

We investigate optimized vertical ion-shuttling protocols for trapped-ion applications across a range of ion-trap experiments, including three-dimensional gradient-measurement sensors, on-chip ion fluorescence collection and imaging, improved laser accessibility, and quantum information processing. In this work, we focus on minimizing motional energy gain during ion transport. Our findings indicate that anomalous heating becomes the dominant limiting factor only for shuttling durations exceeding $500~μ\mathrm{s}$, whereas the final motional excitation is strongly dependent on the selected transport protocol. Using a recently measured heating rate of $(3.1 \pm 0.35)$ quanta/ms at an ion--surface separation of $134 \pm 1.5~μ\mathrm{m}$, we demonstrate that the motional excitation can be restricted to fewer than eight quanta when the ion is vertically displaced by $86~μ\mathrm{m}$ from its initial position. These results enable adiabatic shuttling within $0.5~\mathrm{ms}$, thereby meeting the operational requirements for high-fidelity quantum sensing and coherent control.

We prove an exact quantum conservation law for a harmonic oscillator coupled to a ghost degree of freedom: a second classical conserved quantity lifts to a quantum operator that commutes with the Hamiltonian with no hbar corrections, yielding a rigorous, state-independent upper bound on the mean squared phase-space radius for all time and every quantum state with finite initial second moments. The proof uses only canonical commutation relations and the Leibniz rule; it requires no confining potential, no spectral assumptions, and no perturbative expansion. The interaction studied here is bounded and vanishes at large separations, the generic situation in effective field theory, yet this suffices to guarantee quantum stability in the sense of bounded second moments. Three independent numerical frameworks (Heisenberg picture, Schrodinger picture, and Fock-space diagonalization) confirm wavepacket confinement below the analytic bound, a real energy spectrum, and Poisson level statistics numerically consistent with an integrable structure. The absence of a confining potential means the proof is silent on spectral discreteness and the existence of a ground state; those questions, addressed for polynomial confining interactions in concurrent work, remain open for the interaction class studied here and represent the sharpest targets for future work. Ghost quantum instability is therefore not an inevitable consequence of a wrong-sign kinetic term but depends critically on the interaction structure.

Magnetic quantum sensors based on trapped ions utilize properties of quantum mechanics which have optimized precision and beat current limits in sensor technology. Trapped ions are highly sensitive in a large span of signal ranging from DC or static B-field to the radiofrequency range in 100s of MHz and can attain the sensitivity in the range of pT to sub pT . They are tuneable to frequencies of interest and can be used as a lock-in frequency detector. This modelling and simulation based study presents an innovative design of Surface Paul Traps, enabling the use of trapped ions as ultra-sensitive sensors for magnetic field detection and precise measurement of magnetic field gradients at a sub-millimeter spatial resolution. The novel design features multiple trapping regions, allowing for the mapping of magnetic fields across various ion-trapping zones. The study demonstrates groundbreaking advancements in ion manipulation and confinement through innovative chip architecture.

We present pygridsynth, an open-source Python library for ancilla-free approximate Clifford+$T$ synthesis that runs in $O(\log(1/ε))$ for precision $ε$. For $n=1, 2$ qubits, the library builds upon established efficient and high-precision synthesis routines, such as nearly optimal $Z$-rotation synthesis and magnitude approximation. For $n\ge 3$ qubits, we introduce a partial-decomposition technique that generalizes the magnitude approximation, reducing constant factors in the $T$-count as $(\frac{21}{8}\cdot 4^n - \frac{9}{2}\cdot 2^n + 9)\log_2(1/ε) + o(\log(1/ε))$. The package also exposes a mixed-synthesis workflow that approximates target unitary channels by probabilistic mixtures of Clifford+$T$ circuits, for which we empirically find that the synthesis error is reduced from $ε$ to $ε^2/(2n)$. Taken together, these features make pygridsynth a Python-native platform for high-precision Clifford$+T$ synthesis and for benchmarking unitary and mixed synthesis strategies on multi-qubit instances.

We study the gravitational collapse of a non-interacting mix of perfect fluid and a spatially homogeneous scalar field within a Chiellini-integrable framework. We choose an extended Higgs-type self-interaction potential and reduce the Klein-Gordon equation into a generalized damped Milne-Pinney class of differential equation. We derive a closed-form analytical solution for the scalar field, the scale factor and explore the collapsing branch of the same. We find that it exhibits an asymptotic collapse in which the proper volume decreases monotonically but never reaches zero at finite time. We analyze the energy conditions for the constituent elements of the collapsing sphere. While the scalar field remains canonical in nature, we find that the perfect fluid can violated the Null Energy Condition. We also study the formation of apparent horizon condition and find multiple possibilities depending on the parameter space : either no trapped surface or the formation of multiple apparent horizons. We match the interior homogeneous solution to a generalized Vaidya exterior via the Israel-Darmois junction conditions, yielding the corresponding boundary mass function, ensuring a smooth collapse scenario.

In quantum estimation for a $d$-parameter family of density operators on a finite-dimensional Hilbert space $\mathcal{H}$, an estimator is specified by a pair $\left(M,\hatθ\right)$, where $M$ is a POVM with a finite outcome set $Ω$ and $\hatθ:Ω\to\mathbb{R}^{d}$ is a classical estimator map. Since the number of outcomes $\left|Ω\right|$ is a priori unbounded, the space of admissible POVMs is vast, which makes the search for optimal estimators difficult. In this paper, for the minimization of the weighted trace of the mean squared error among locally unbiased estimators, we prove that it suffices to consider POVMs with at most $\left({\rm dim}\,\mathcal{H}\right)^{2}+d(d+1)/2-1$ outcomes, and that an optimal measurement can be chosen to be rank-one. For the minimization of the average weighted trace of the mean squared error in Bayesian estimation, we show that it suffices to consider POVMs with at most $\left( {\rm dim}\, \mathcal{H}\right)^{2}$outcomes, and again an optimal POVM can be taken to be rank-one. Furthermore, when the model admits a real sufficient subalgebra, we show that the $\left( {\rm dim}\, \mathcal{H} \right)^{2}$ term in the above support-size bounds can be reduced in both the locally unbiased and Bayesian settings. These bounds substantially reduce the search space for optimal measurements and justify restricting numerical optimization to rank-one POVMs with finitely many outcomes.

The quantum Mpemba effect (ME) in Markovian systems is conventionally explained by a smaller overlap between the initial state and the slowest decay mode (SDM). Such state, initially farther away from equilibrium or steady state, relaxes faster than closer ones, resulting to a crossing of their trajectories. This picture, by neglecting the transient dynamics, holds in the long-time limit. Here we experimentally observe multiple trajectory crossings (multi-ME) in the relaxation dynamics of a trapped ion. Such novel dynamics takes place in a unusual scenario where the initial state instead has a larger overlap with the SDM. We develop a theoretical framework based on relaxation speed to understand the multi-ME. We show that the initial relaxation speed is governed by the fastest decay mode, which together with the SDM overlap gives a phase diagram that reveals both the occurrence and the types of quantum ME observed in our experiment. Our study goes beyond the simple picture based on the long-time limit, tracks continuously the quantum ME dynamics, and establishes a comprehensive framework to describe the transient quantum relaxation.

There has been a recent revival in understanding the spontaneous scalarization phenomenon in scalar-tensor gravity as a phase transition. Using the tools of the Landau theory, we now know that first-order transitions where scalarization occurs in a discontinuous manner is more prominent than what had been considered in the literature, and this might lead to novel observation channels. However, the examples so far have been restricted to specific quadratic scalar coupling terms and spherically symmetric stars. Here we explore the phase transition structure of scalarization for more general couplings, considering linear as well as quadratic terms in the conformal scaling factor of the theory. Moreover, we also investigate the effect of rotation on the scalarization phase transition. Both of these considerations are natural choices since the coupling in a scalar-tensor theory can appear at all orders, and astrophysical neutron stars commonly have angular momentum. The introduction of linear coupling leads to a complex solution space which is harder to explore. However, we demonstrate that the Landau model of scalarization enables us to systematically find the branches of scalarized solutions that are commonly overlooked in numerical searches, providing a novel tool. On the other hand, the main effect of stellar rotation is shifting the stellar masses at which the phase transition occurs to higher values, but the qualitative picture remains similar to what happens under spherical symmetry.

We revisit the quantization of the order parameter, which we refer to as third quantization, from the perspective of the commutation relation between the phase operator of the order parameter and the particle-number operator. We show that this macroscopic commutation relation does not constitute an independent fundamental postulate added to quantum mechanics, but instead emerges naturally from second quantization in the thermodynamic limit for both bosonic and fermionic many-body systems. In this sense, both Bose-Einstein condensates (BECs) and Bardeen-Cooper-Schrieffer (BCS) states can be understood as macroscopic quantum states described by bosonic coherent states: in BEC, bosons condense into a single coherent mode with a well-defined phase, while in BCS systems, collective excitations of Cooper pairs can also acquire an effectively bosonic coherent description. On this basis, we propose a new macroscopic interpretation of the BCS-BEC crossover. To characterize this crossover, we model a conventional superconductor as an assembly of macroscopically separated superconducting segments. As the intra-segment coupling increases, the system evolves from a BCS-like regime toward a BEC-like regime, in which the segments collectively behave as macroscopic coherent states. Inter-segment tunneling then locks their phases, establishes global phase coherence, and gives rise to a bulk Bose-Einstein condensate. The phase diagram of the BCS-BEC crossover can thus be understood as a manifestation of a macroscopic quantum process governed by the coherent-state dynamics of the order parameter. Our results provide a unified perspective on BEC, BCS superconductivity, and the BCS-BEC crossover within the framework of third quantization.

As quantum hardware scales toward fault tolerant operation, the demand for correct quantum error correction (QEC) circuits far outpaces manual design capacity. AI agents offer a promising path to automating this synthesis, yet no benchmark exists to measure their progress on the specialized task of generating QEC circuits. We introduce StabilizerBench, a benchmark suite of 192 stabilizer codes spanning 12 families, 4-196 qubits, and distances 2-21, organized into three tasks of increasing difficulty: state preparation circuit generation, circuit optimization under semantic constraints, and fault tolerant circuit synthesis. Although motivated by QEC, stabilizer circuits exercise core competencies required for general quantum programming, including gate decomposition, qubit routing, and semantic preserving transformations, while admitting efficient verification via the Gottesman Knill theorem, enabling the benchmark to scale to large codes without the exponential cost of full unitary comparison. We define a unified generator weighted scoring system with two tiers: a capability score measuring breadth of success and a quality score capturing circuit merit. We also introduce continuous fault tolerance and optimization metrics that grade error resilience and circuit improvements beyond binary pass or fail. Following the design of classical benchmarks such as SWE-bench, StabilizerBench specifies inputs, verification oracles, and scoring but leaves prompts and agent strategies open. We evaluate three frontier AI agents and find the benchmark discriminates across models and tasks with substantial headroom for improvement.

A random access code (RAC) encodes an $L$-bit string into a $k$-bit $(L>k)$ message from which any designated source bit can be recovered with high probability. Its quantum counterpart, a quantum random access code (QRAC), replaces the $k$-bit message with $k$ qubits. While upper bounds on the decoding success probability have long been studied in both classical and quantum settings, explicit constructions of optimal codes are known only in special cases, even for classical RACs. In this paper, we develop a constructive framework for classical $(L,k)$-RACs under both average- and worst-case criteria. We show that optimal code design reduces to selecting $2^k$ points in $\{0,1\}^L$ and $[0,1]^L$ for the average- and worst-case criteria, respectively, so as to minimize a distance-like objective. This characterization yields explicit constructions for general $(L,k)$. For $k=L-1$, we further obtain closed-form optimal encoders and decoders for both criteria, and show that the resulting classical $(L,L-1)$-RACs attain the corresponding proved upper bounds. We also show that these optimal classical codes induce $(L,L-1)$-QRACs that attain a conjectured upper bound on the decoding success probability. Numerical optimization suggests little difference between RACs and QRACs in the average-case setting, but a potentially large classical-quantum gap in the worst-case nonasymptotic regime.

Quantum state tomography (QST) scales exponentially in both measurement and computational cost, making full reconstruction impractical for multi-qubit systems. Existing approaches attempt to reduce this complexity, but do not explicitly restrict the operator space based on physically relevant correlations. We develop a structured QST framework in which the density matrix is reconstructed using a restricted set of observables in a Gibbs representation. The Structured Gibbs Quantum State Tomography (SG-QST) is built by progressively including local, nearest-neighbor, and global correlations. Benchmarking on three, four, and five-qubit. GHZ states shows that comparable fidelity can be achieved with significantly fewer parameters by restricting the operator space to physically relevant observables. These results demonstrate that physically motivated operator-space restriction enables efficient and interpretable quantum state reconstruction.

There has been intensive research on increasing the utility and performance of Parameterized Quantum Circuits (PQCs) in the past couple of years. Owing to this research, there are now several inductive biases available to a quantum algorithms researchers to design a good circuit for their chosen task. In this paper, we focus on the problem of finding performant initial parameters for a given PQC. Different from previous research that focuses on finding the right \emph{distribution}, we focus on finding the \emph{hyperparameters} for any given distribution. To that end we introduce an evolutionary-search based algorithm that finds optimal hyperparameter given a PQC and quantum task. Our empirical results indicate that our algorithm consistently leads to selection of performant initial parameters tuned specifically to the ansatz and the quantum task leading to faster convergence and performance. More importantly, our algorithm does not \emph{negatively} affect the barren plateau phenomenon. In other words, the initial parameters suggested by algorithm do not worsen the gradient variance scaling for a given initializing distribution.

Superradiant instability of ultralight bosons can produce clouds around rotating black holes, whose continuous gravitational wave (GW) emission is a promising observational target. Precise predictions of the signal frequency and its evolution are essential for detecting such continuous GWs. For axions, self-interactions can populate multiple superradiant modes via nonlinear couplings, and GW emission can occur through various channels. To calculate the frequency shifts of GWs emitted through these channels, we employ relativistic perturbation theory based on a bilinear form. We apply this framework to self-interaction effects for the first time, and also revisit the treatment of the self-gravity contribution. Our results provide a simple and unified framework for calculating frequency shifts, including cases in which multiple modes are excited, and are relevant for next-generation GW observations.

We study the late-time evolution of an anisotropic Bianchi-I universe with radiation in the framework of asymptotically safe gravity. We first discuss the radiation-dominated universe for the perfect fluid with the equation of state $p=ρ/3$, and find that the classical evolution involves logarithmic terms, which lead to a slow approach toward isotropy. The quantum effects introduce subleading corrections that soften the anisotropy in the intermediate stage. Next we discuss the universe with magnetic fields. For a vanishing classical cosmological constant, we find that the universe in general evolves toward a Kasner-type regime with persistent anisotropy while the expansion rate is enhanced by quantum effects, leading to a faster decay of the magnetic field. In contrast, for a nonzero classical cosmological constant, the late-time dynamics are dominated by the cosmological constant, and the universe asymptotically approaches an isotropic de Sitter phase with exponential decay of both anisotropies and the magnetic field. Finally, we employ Hodge duality to demonstrate that these cosmological findings apply equally to environments dominated by electric fields.

Fast qubit reset is essential for qubit reuse in the noisy intermediate-scale quantum computing era, yet it conflicts with the weak decoherence required for high-fidelity computation. We solve the time-optimal reset problem for a frequency-tunable qubit coupled to a structural environment under realistic spectral and control constraints. The optimal strategy consists of a switch--restore--switch sequence, where the qubit is moved from a low-decoherence computational configuration to a high-decoherence restoring configuration and then returned for reuse. For superconducting qubits in four representative environments, this strategy reduces the reset time from typically $\gtrsim\SI{100}{\nano\second}$ to $\SI{20}{\nano\second}$, about $40\%$ of a typical two-qubit gate time, while achieving a reset precision of $10^{-5}$. Our results identify environmental spectral structure as a practical resource for rapid, high-fidelity qubit reset and provide a design principle for qubit reuse on qubit-limited processors.

We obtain exact expressions for various factors involved in the Hadamard form of the retarded Green function for the (Bardeen-Press-)Teukolsky equation on Schwarzschild spacetime. We use these to improve on previous results for the calculation of this Green function. We work in a spacetime $\mathcal{M}_2\times\mathbb{S}^2$ conformal to Schwarzschild, in which the metric takes a direct product form. This allows us to derive a separable form for the direct (i.e., singular) part of the Hadamard form of the retarded Green function. The angular factor in this quantity is calculated explicitly. This shows an interesting interplay between geodesics of $\mathbb{S}^2$, spin-weighted spherical harmonics, and Euler angles. The $\mathcal{M}_2$ factor equates to a spin-dependent factor that satisfies a transport equation along geodesics, times the square root of the van Vleck determinant. Both terms are calculated in an exact form for constant radius orbits (which includes the cases of circular timelike geodesics and static worldlines of Schwarzschild spacetime). This separable form also allows us to obtain the multipolar $\ell$-modes of the direct part for electromagnetic and gravitational field perturbations. We then use these $\ell$-modes to calculate, in the gravitational case, the retarded Green function minus its direct part: this is a better representation in practise of the retarded Green function for points near coincidence.

In continuously monitored quantum systems, the feedback protocol of García-Pintos, Liu, and Gorshkov reshapes the arrow of time: a Hamiltonian $H_{\mathrm{meas}} = r A / τ$ applied with gain $X$ tilts the distribution of measurement trajectories, with $X < -2$ producing statistically time-reversed outcomes. Why this specific Hamiltonian achieves reversal, and how the mechanism relates to score-based diffusion models in machine learning, has remained unexplained. We compute the functional derivative of the log path probability of the quantum trajectory distribution directly in density-matrix space. Combining Girsanov's theorem applied to the measurement record, Fréchet differentiation on the Banach space of trace-class operators, and Kähler geometry on the pure-state projective manifold, we prove that $δ\log P_F / δρ= r A / τ= H_{\mathrm{meas}}$. The García-Pintos feedback Hamiltonian is the score function of the quantum trajectory distribution -- exactly the object Anderson's reverse-time diffusion theorem requires for trajectory reversal. The identification extends to multi-qubit systems with independent measurement channels, where the score is a sum of local operators. Two consequences follow. First, the feedback gain $X$ generates a continuous one-parameter family of path measures (for feedback-active Hamiltonians with $[H, A] \neq 0$), with $X = -2$ recovering the backward process in leading-order linearization -- a structure absent from classical diffusion, where reversal is binary. Second, the score identification enables machine learning (ML) score estimation methods -- denoising score matching, sliced score matching -- to replace the analytic formula when its idealizations (unit efficiency, zero delay, Gaussian noise) fail in real experiments.

We study the dynamics of $N$ photons in a Fock state, initially located inside one cavity, and coupled by an optical fiber to a second cavity. The entanglement of the photons is monitored by projective measurements, repeated with a fixed time step. This approach is applied to the formation of a photonic N00N state. We calculate the probability of the transition of $N$ photons from the left to the right cavity and the probability of the return of $N$ photons to the left cavity under repeated projective measurements. The entanglement is analyzed for the N00N state by its fidelity and its phase sensitivity, while for the entanglement between the states in the two cavities the entanglement entropy is calculated. In addition, we study the monitored evolution of photons in a single cavity, which are coupled to a single qubit, using the Jaynes-Cummings model. Photon entanglement is analyzed in terms of the entanglement entropy. In all these cases we find that entanglement is sensitive to the details of monitoring protocol, which can be used to control photon entanglement for specific applications.

We study the magnetic-field dependence of the interactions between two alkaline-earth(-like) Rydberg atoms, ${}^{88}$Sr and ${}^{174}$Yb. Considering the pair of Rydberg states $|ns,{}^3S_1,m_J\rangle$ and $|(n+1)s,{}^3S_1,m_J\rangle$, we show that the effective Hamiltonian takes the form of an {\it XXZ}-type quantum spin model, as in the alkali-atom case [M. Kunimi and T. Tomita, Phys. Rev. A {\bf 112}, L051301 (2025)]. We find that the behavior of the anisotropy parameter for ${}^{174}$Yb at zero magnetic field is significantly different from that for other atomic species. This behavior originates from the strong spin-orbit coupling in ${}^{174}$Yb. We systematically calculate the interaction parameters of the {\it XXZ} model in the presence of a magnetic field and show that they can be tuned by the field. As applications to quantum many-body problems, we investigate one-dimensional systems in the large-anisotropy regime and show that the folded {\it XXZ} model can be realized in ${}^{174}$Yb systems without fine-tuning of the field. We also investigate two-dimensional square-lattice systems and show that a supersolid phase can emerge in the ground state at the mean-field level.

This work presents a non-equilibrium framework for thermodynamicized black holes, inspired by the entropy-functional interpretation of emergent gravity and by residue-based methods in black hole thermodynamics. The main idea is to unify three components: an entropy functional principle for selecting physical on-shell backgrounds, a Euclidean and contour-based description of the horizon temperature through simple pole singularities, and a topological residue classification of multi-horizon black hole configurations. On this basis, the paper introduces a quasi-stationary non-equilibrium partition functional in which irreversible entropy production appears as an additional contribution to the singular action. The formalism reproduces the standard equilibrium relations in the adiabatic limit, while also extending them to dynamically driven black-hole systems with matter, charge, and rotational fluxes. The framework is then applied to Kerr Newman type black holes in constant curvature f(R) gravity, where the equilibrium entropy remains weighted by the derivative of f at the background curvature, while non-equilibrium corrections arise from flux-induced deformations of the effective thermodynamic action. The analysis further shows that the outer and inner horizons carry opposite topological orientations, so the non-extremal Kerr Newman family stays in the topological class W = 0 unless a horizon bifurcation or merger changes the singularity structure. Finally, several function plots are provided to illustrate the behavior of equilibrium and non-equilibrium free energy, the Kerr Newman temperature curve, and the entropy production law.

In this paper, we investigate the cosmic expansion scenarios within the framework of $f(Q,T)$ gravity by using the affine equation of state (EoS) parameter. Specifically, we consider the linear form $f(Q,T)=Q+βT$, where $β$ is a free model parameter. We use Bayesian statistical methods, specifically the $χ^2$ minimization technique to constrain the model parameters using Cosmic Chronometer (CC), Pantheon+SH0ES and DESI BAO data. We further analyze the characteristics of the derived cosmological model. A comprehensive study of energy density, pressure, equation of state parameter and cosmographic parameters are carried out to understand the evolution of the Universe in this model. The determination of present age of the universe for this model is within $1σ$ with Planck results.

We introduce a qubit- and gate-efficient higher-order unconstrained binary optimization (HUBO) encoding for graph partitioning problems requiring label-count minimization. This widely applicable class of problems includes minimum graph coloring, minimum $k$-cut, and community detection. To the best of our knowledge, this is the first work to address the optimization versions of these problems in a quantum setting, rather than only their decision counterparts. Our construction encodes each $k$-valued vertex variable using $\lceil \log_2 k \rceil$ bits and employs a novel lexicographic penalty system that implicitly minimizes partition count without requiring dedicated indicator variables. We derive provably sufficient conditions on all penalty coefficients, including those arising from Rosenberg quadratization, guaranteeing feasibility and optimality of the lowest-energy solution. Analogous conditions are derived for a one-hot encoding to enable controlled comparison. We also show that our encoding reduces two-qubit gate count per QAOA layer from $Θ(|V||k|^2 + |E||k|)$ for the one-hot encoding to $Θ(|E| \cdot |k| \lceil\log_2 |k|\rceil)$. Benchmarking on a quantum annealer demonstrates that our logarithmic encoding significantly improves solution quality and time-to-solution for minimum graph coloring relative to one-hot encoding, with greater advantage as problem size increases.

The global topology of spacetime, though invisible to local curvature measurements, leaves signatures on the correlation functions of quantum fields. We study these signatures using an Unruh-DeWitt particle detector operating in four-dimensional Minkowski spacetime with two spatial directions periodically identified, yielding a spatial topology $\mathbb{R}\times T^2$. We compute detector transition rates for three trajectories: uniform inertial motion, uniform proper acceleration directed along one of the compact axes, and uniform proper acceleration along the non-compact axis. Our results show how a local quantum measurement can reveal features of the large-scale spatial topology.

Estimating local observables in Gibbs states is a central problem in quantum simulation. While this task is BQP-complete at asymptotically low temperatures, the possibility of quantum advantage at constant temperature remains open. The Sachdev-Ye-Kitaev (SYK) model is a natural candidate: at any constant temperature, its Gibbs states have polynomial quantum circuit complexity and are not described by Gaussian states. Rigorous analyses of the SYK model are difficult due to the failure of known techniques using random matrix theory, cluster expansions, and rigorous formulations of the quantum path integral and replica trick. Despite this, we give a rigorous proof of a quasipolynomial-time classical algorithm that estimates SYK local thermal expectations at sufficiently high constant temperature. Our result introduces a new Wick-pair cluster expansion that we expect to be broadly useful for disordered quantum many-body systems.

We compute the abundance of cosmological gravitational waves produced during the evolution of an ultralight vector (spin-1) dark matter field. A homogeneous background vector field breaks spatial isotropy, requiring a Bianchi I geometry and inducing a mixing between the scalar, vector, and tensor perturbation sectors. We derive the perturbation equations in this background and show that, as a consequence of this mixing, scalar perturbations act as a source of tensor modes, generating a stochastic GW background. The production and cosmological evolution of these gravitational waves are implemented in a modified version of CLASS, from which we obtain the present-day spectrum.

We present a modified gravity framework where the vacuum is modeled as a Higgs-type scalar field $χ$ undergoing spontaneous symmetry breaking. By introducing a coupling $Q^ν= αT \nabla^νχ$, we formalize a displacement principle where baryonic matter acts as an impurity in the vacuum substrate. This interaction leads to a restorative buoyancy force that modifies the geodesic equation and violates the Weak Equivalence Principle. We show that this mechanism naturally recovers the Schwarzschild metric in the vacuum limit while providing a Yukawa-corrected Newtonian potential in the presence of matter. This correction offers a dynamical explanation for flat galactic rotation curves and a tracking mechanism for the cosmological constant, potentially resolving the coincidence and fine-tuning problems without the need of dark sectors.

While massive black hole binaries (MBHBs) merge at gravitational-wave frequencies above the pulsar timing array (PTA) sensitivity band, we show that they leave orphaned low-frequency contributions in the PTA pulsar term. Due to the light-propagation time between each pulsar in the array and Earth, the pulsar term acts as a time-delayed probe of a chirping merger with a specific frequency response determined by the direction of origin and intrinsic properties of the MBHB. We provide a detailed consideration of how such a multiband signal would manifest in a full PTA, demonstrate an approach to stack these orphaned pulsar terms across the array, and discuss prospects for an archival, multiband search in conjunction with MBHB mergers observed in astrometric data or spaceborne interferometers like LISA.

We investigate how laser-driven, cooperative dipole-dipole interactions in weakly trapped atomic arrays give rise to self-organized configurations. Starting from an analytically tractable two-emitter system, we identify the possible steady-state spatial arrangements accessible to the atoms. We then extend this analysis to larger ensembles in both linear and ring geometries. In linear chains, we demonstrate the emergence of topologically nontrivial dimerized configurations across a range of initial interatomic spacings. In ring geometries, we find that the system undergoes self-organized contraction and expansion, enabling access to length scales below those set by the trapping lattice. Our results demonstrate that collective light-matter interactions in free space can spontaneously generate modified ordered geometries, even when the emitters are initially separated by distances larger than their transition wavelength.

Pulsar timing arrays record gravitational waves from supermassive black hole binaries at two spacetime points: an Earth term, measured when the wave passes the Earth, and a pulsar term, measured when the wave passed each pulsar at an earlier epoch. We show that a future $μ$Hz-band detection of a nearby massive binary by a mission such as $μ$Ares would turn PTA pulsar terms into targeted probes of binary evolution. In analogy with supernova light echoes, each pulsar term acts as a gravity echo: a dated snapshot of the binary at an earlier stage of its inspiral. Together, the $μ$Hz Earth-term measurement and the nHz pulsar-term echoes provide a temporal baseline that neither detector could access alone. For a fiducial equal-mass binary with total mass $10^9\,M_\odot$ at 80~Mpc, we find a combined pulsar timing array echo signal-to-noise ratio of 33, with up to 24 pulsars individually resolving the signal among pulsars with 50-year baselines. The angular dependence of the single-pulsar echo sensitivity alone enables independent sky localization of the source to $\sim$10--100~deg$^2$, and the resolved pulsar-term frequencies directly measure the binary inspiral rate hundreds to thousands of years ago. With sufficient pulsar distance precision, a small set of anchor pulsars could additionally phase-connect the array and trace the post-Newtonian evolution coherently over kpc baselines. The source population required for gravity echoes is drawn from the same massive-end census responsible for the observed nanoHertz stochastic background.

We present an invariant relational path-integral quantization framework for general-relativistic gauge field theories based on the Dressing Field Method. The construction implements an automatic anomaly-cancellation mechanism that encompasses Bardeen-Wess-Zumino counterterms. The resulting framework unifies invariant schemes across contexts ranging from electroweak theory to cosmology, and is amenable to lattice implementations, key to high-precision tests in both domains.

Relativistic effects are sensitive to subtle changes in dark energy. These effects grow on very large scales and at high redshifts, which will be the reach of upcoming surveys. We investigate these effects in both the linear and the angular galaxy power spectra in a late-time universe dominated by cold dark matter and k-essence, focusing on three core models (dilaton, tachyon, and DBI scalar fields) and contrasting their predictions with those of the concordance model. By enforcing identical present-day cosmological parameters, we isolate the imprints of k-essence dynamics and perturbations on very large scales. We found that relativistic corrections dominate on very large scales and grow with redshift, but are largely insensitive to k-essence microphysics in Fourier space, leading to strong degeneracies among the models. However, in the angular power spectrum, where line-of-sight integrals are naturally included, relativistic effects are significantly amplified, yielding better sensitivity to clustering k-essence. In particular, the tachyon exhibits clear deviations across multipoles and redshifts, with distinct imprints in the Doppler and the combined (velocity and gravitational) potentials contributions. Furthermore, our results show that neglecting relativistic corrections can lead to systematic misestimation of deviations of k-essence from the cosmological constant. Our results show the relativistic angular galaxy power spectrum as a more consistent and robust probe of ultra-large-scale physics. These findings underscore the need for full relativistic modelling in next-generation surveys that are targeting horizon-scale modes, where the imprint of non-standard dark energy is most pronounced.

By monitoring the times of arrival of radio pulses from millisecond pulsars, Pulsar Timing Arrays (PTAs) serve as unique gravitational wave (GW) laboratories in the nanohertz band. To date, the primary astrophysical sources of GWs targeted in this frequency range have been inspiraling supermassive black hole binaries (SMBHBs) on circular and eccentric orbits. In this work, we demonstrate that, thanks to the so-called pulsar term in the timing residual waveform of GW signals, PTAs can probe individual SMBHBs that merged before timing observations began. We refer to the latter as \emph{zombie binaries}. Using SMBHB population models consistent with current PTA constraints, we find that while the probability of detecting such systems in existing PTA datasets remains low, the Square Kilometer Array observatory is expected to achieve sufficient sensitivity to have a few zombie binaries with a signal-to-noise ratio exceeding 3 in its data. Although their confident identification might be challenging, this new class of PTA sources opens a novel window for studying the most massive SMBHBs in our local universe.

Gravitational wave (GW) detector LISA will observe near-coalescence extreme mass ratio inspirals (EMRIs), which typically form in galactic central accretion disks. Gas torques on EMRI will alter its GW-driven inspiral trajectory from the vacuum expectation, leading to potentially LISA-observable GW dephasing ($Δψ_{\rm gas}$). Most studies compute $Δψ_{\rm gas}$ for a thin, laminar disk, with negligible flow turbulence, where the disk exerts a fairly well-understood linear torque ($T_{\rm lin}$). However, these disks must be turbulent due to magneto-rotational instability in the inner regions. Hence, we present a proof-of-concept general, agnostic prescription for the turbulent torque ($T_{\rm turb}$) acting on an EMRI by modeling it as a Gaussian distribution around $T_{\rm lin}$, based on recent advances from a global hydrodynamical (HD) study. We compute $Δψ_{\rm gas}$ for the ``golden'' circular EMRI with total source mass $M=10^6~{\rm M}_\odot$ and mass ratio $q=5\times10^{-5}$ in its final four-year evolution at redshift $z=0.276$ and signal-to-noise ratio (SNR) $=50$ by varying Eddington ratio ${\rm f}_{\rm Edd}$, turbulence normalization $C$ ($=~360$ in the aforementioned HD study), disk aspect ratio $h_0$, and turbo-viscous coefficient $α$ in a reasonable parameters space. We find that for ${\rm f}_{\rm Edd}\gtrsim0.3$, $C\gtrsim300$, $h_0\gtrsim0.03$, and $α\gtrsim0.1$, gas-induced dephasings are unobservable if only considering $T_{\rm lin}$ but could become detectable ($Δψ_{\rm gas}>8/$SNR) if EMRIs exhibit chaotic migration due to turbulent gas flow. Hence, this work motivates running MHD simulations of accretion disks with embedded LISA EMRIs in the early in-spiral phase over long enough timescales to understand the evolution of their orbital elements and the imprint of the turbulent environment on their gravitational waveforms.

We explore the application of the Variational Quantum Eigensolver (VQE) to investigate the ground state properties, particularly the entanglement entropy, of the Transverse Field Ising Model (TFIM) in one, two, and three dimensions, considering systems of up to 27 spins. By benchmarking VQE results against exact diagonalization and analyzing the entanglement properties across different system sizes and geometries, we assess the algorithm's effectiveness in capturing critical phenomena. Using results of TFIM, we also investigate how VQE's expressivity and optimization influence the simulation of highly entangled quantum states. We employ different ansätze: the hardware-efficient EfficientSU2 from Qiskit, the physics-inspired Hamiltonian Variational ansätz (HVA) and HVA with symmetry breaking, and benchmark their performance using energy variance, entanglement entropy, spin correlations, and magnetization. We further discuss the implications for scaling these methods to larger quantum systems.

We present a covariant and gauge-invariant formulation of the theory of radial adiabatic linear perturbations of self-gravitating, non-dissipative imperfect fluids within the theory of general relativity. By codifying the thermodynamical properties of the source into an equation of state and an ansatz on anisotropic pressure that involves both matter and kinematic variables, we obtain a set of equations that is directly applicable to a wide variety of thermodynamic theories for matter fields. As examples, we evaluate and compare the predictions of the Eckart theory, the Bemfica-Disconzi-Noronha-Kovtun theory, and the Truncated Israel-Stewart theory on the properties and evolution of radial adiabatic perturbations of stellar compact objects modeled by classical equilibrium solutions. Introducing a new solution of the Einstein field equations, and imposing causality, we propose an upper bound for the maximum compactness of dynamically stable stars with non-trivial radial and tangential pressures.

This work investigates the emergent thermalization regimes in a chaotic Tavis-Cummings (TC) model and their implications in quantum spectroscopy. While the TC model is a cornerstone of cavity quantum electrodynamics, traditional treatments often overlook many-body effects that arise in the thermodynamic limit. We utilize the Eigenstate Thermalization Hypothesis to demonstrate that a non-integrable excitonic Hamiltonian within the material manifold drives local thermalization. By tuning the polariton splitting $g$, we observe two dynamical regimes: a thermalizing regime at low interactions driven by quantum chaos and ergodicity, and a non-thermalizing regime at high interactions where strong coupling suppresses ergodicity. We further show that these regimes have direct implications on output photon statistics, specifically influencing the correlation times $τ_c$ of the cavity population and the second-order correlation function $g^{(2)}(t+τ)$. We propose that entangled-biphoton spectroscopy serves as an ideal experimental platform to probe these effects and to allow the characterization of the underlying many-body exciton-coupling disorder $σ$ through coincidence measurements of the output. Taken together, these results exploit a naturally occurring many-body phenomenon to bridge theoretical predictions with experimental observables.

In this work, we show that Berry phase estimation admits a natural and universal adiabatic error-cancellation mechanism, making it a promising candidate for practical quantum computing before full fault tolerance. Combining finite-runtime evolutions under $\pm H$ along the loop cancels the leading $O(T^{-1})$ phase error exactly, and Richardson extrapolation further reduces the residual error to an oscillatory term with endpoint-controlled coefficient $O(\|\dot H(0)\|^2Δ(0)^{-4}T^{-2})$. Beyond this deterministic cancellation, we establish that, for suitable smooth runtime distributions, runtime randomization suppresses the remaining oscillatory contribution to $O(T^{-M})$ for any fixed $M$, leading to a randomized Hadamard-test algorithm for Berry phase estimation over the full range $[0,2π)$ with improved runtime scaling under standard sample complexity.

Recent LIGO-Virgo-KAGRA (LVK) analyses have revealed complex structure in the binary black hole (BBH) population, including distinct features in the primary mass spectrum and nontrivial spin-mass correlations. However, the phenomenological models used to capture these features often lack analytic transparency, making it difficult to isolate robust physical laws from modeling artifacts. To address this, symbolic regression is applied to the posterior inference products of the GWTC-4 catalog, discovering compact, closed-form analytic expressions for four key population relationships: (i) the merger-rate evolution with redshift; (ii) the mass-ratio dependence of the effective-spin distribution; (iii) the redshift evolution of the effective-spin distribution; and (iv) the conditional mass-ratio distributions associated with the 10 solar mass and 35 solar mass primary mass peaks. This framework successfully compresses both rigid and highly flexible models into differentiable phenomenological laws, dynamically recovering a consistent low-redshift merger-rate slope without assuming an a priori power-law form. The exact analytic derivatives provided by symbolic regression show that the mass ratio--effective spin and redshift--effective spin correlations are robustly driven by broadening of the posterior widths rather than shifts in the mean. Furthermore, qualitatively distinct functional forms for the mass-ratio distributions conditioned on the 10 solar mass and 35 solar mass primary mass peaks are identified. These closed-form expressions enable exact analytic gradient diagnostics and compact surrogate summaries, particularly for flexible numerical posteriors that are not otherwise available in low-dimensional analytic form. They also facilitate rapid downstream calculations for rate forecasting, formation channel comparison, and stochastic background estimation.

Building on recent work introducing the idea of Quasilocal Probability in curved spacetime, we develop its observational implications for black hole ringdown in detail. We show that horizon-induced probability flux leads to an effective non-Hermitian dynamics producing three distinctive signatures, which are correlated multi-mode deviations, weak amplitude dependence and a mismatch between waveform damping and energy accounting. These effects arise from a single boundary-flux mechanism and therefore exhibit a constrained, low-dimensional structure not expected in generic modified gravity scenarios. We demonstrate that while individual deviations may be mimicked, their combined pattern provides a robust discriminator of quasilocal probability. We further argue that upcoming gravitational wave observations can probe these signatures at meaningful precision. We also establish that black hole ringdown is a novel arena to test whether quantum mechanical Hermiticity is really a fundamental property or an emergent symmetry in quantum gravity.

Ringdown signals from perturbed black holes (BHs) offer a clean window into BH spacetime, strong-field gravity, and fundamental physics. Presently the quasi-normal modes of stellar-mass BH ringdowns have been successfully extracted in the ground-based gravitational wave (GW) observations. Looking ahead, the future space-borne observatories will listen to the ringdowns from massive BH binary coalescences more loudly and resolve multiple modes to unprecedented precision, which calls for efficient approaches to mitigate the sharply increasing computational burden. We develop a practical ringdown analysis pipeline for space-borne detectors by implementing FIREFLY, a novel acceleration algorithm validated in ground-based detectors, and for the first time demonstrate its compatibility and effectiveness with the time-delay interferometry (TDI) observables. With high fidelity, we achieve a $\sim 200$-fold speedup for a simulated ringdown signal including six modes, providing a viable and scalable route for multi-mode ringdown analysis in the space context. This new approach has sound statistical interpretation and is extensible to other GW sources in band.

Quantum resources are increasingly integrated into high-performance computing (HPC) and cloud environments, but quantum high-performance computing (QHPC) software stacks remain isolated, often proprietary, full-stack solutions lacking common interfaces across runtime, resource management, orchestration, and execution layers. This paper analyzes nine production QHPC stacks and identifies common design patterns and emerging requirements, covering deployment models, application interaction patterns, SDK support, and readiness for fault-tolerant operation. The survey exposes consistent needs in runtime abstraction, resource management, interconnect semantics, and observability. Based on these findings, we propose the open quantum-HPC software ecosystem ( openQSE) reference architecture as a first step toward unifying the state-of-the-practice. openQSE defines a set of layer boundaries that allow different implementations to interoperate while preserving deployment flexibility, and is structured to support both current noisy intermediate-scale quantum (NISQ) workloads and future fault-tolerant quantum computing (FTQC) systems without changes to upper-layer application interfaces.

Practical quantum advantage is expected to depend on fault-tolerant quantum computing, although the architectural overhead needed to support fault tolerance is still extremely high. Prior FTQC designs generally emphasize either fast logical-qubit accessibility at the cost of significant qubit overhead, or high logical-qubit density at the cost of added workload latency. We propose an architecture that balances these competing objectives by placing surface-code patches around an ancilla-centric region, which yields nearly uniform ancilla access for all data qubits. Building on this design, we introduce a new workload-driven placement method that uses the $T$-gate profile of an application to determine an effective floorplan. We further provide a reconfigurable optimization for reducing the latency of $Y$-gate measurements on a per-workload basis. To improve flexibility, we also study concurrent execution of multiple programs on the same architecture. Numerical evaluation indicates that our approach keeps cycles per instruction near the optimal regime while reducing the number of required data tiles by up to $\sim21\%$, and achieves up to $\sim90\%$ efficiency when running 10 programs concurrently.

Nitrogen-vacancy (NV) centers in diamond are of great interest for nano- and macro-scale magnetic field sensing. Most sensing protocols rely on conventional optical readout, which is limited by photon shot noise. The recently developed photoelectrical (PE) readout of the NV center electron spin state promises to overcome these limitations. However, the noise of the PE readout and its influence on readout efficiency have not been thoroughly studied. In this work, we perform magnetic field sensing and estimate the sensitivity using optical and PE readout with a single and an ensemble of NV centers in diamond. We investigate the electronic noise associated with the photoelectric detection and estimate the readout efficiency, using Gaussian statistics. Our quantitative analysis shows that the Johnson-Nyquist noise-limited photoelectric magnetic field sensitivity could outperform optical measurements by an order of magnitude. This work is an essential step towards the development of on-chip magnetometers using photoelectrical detection in diamond.

In this paper, we investigate the quasinormal modes of two covariant effective black holes characterized by the quantum parameter $ζ$, charge $Q$, and cosmological constant $Λ$, under the scalar perturbation. By employing the pseudo-spectral method, we numerically calculate the quasinormal frequencies and analyze the influence of $ζ$ on the spectra with respect to $Q$. Our results demonstrate that while the quantum parameter $ζ$ significantly modifies the quasinormal frequency spectrum, the non-monotonic behavior and overtone outbursts persist. Notably, the impact of quantum gravity on the overtone outbursts is not merely limited to enhancement or suppression; instead, it introduces additional spectral features. Furthermore, a comprehensive analysis of the full quasinormal mode spectrum reveals rich interactions between complex and purely imaginary modes, including damping-rate crossings and merging-splitting behavior. These phenomena typically accompany overtone outbursts in near-extremal regimes, suggesting a potential connection between mode interactions and overtone outbursts. This work emphasizes the necessity of analyzing the full quasinormal frequency spectrum rather than focussing solely on fundamental modes, and provides novel insights into its underlying spectral structures.

We find that heavy quark transport beyond leading logarithm at weak coupling is intrinsically non-Gaussian: the longitudinal momentum transfer distribution has asymmetric exponential tails that are crucial for equilibration dynamics. We show this by computing the leading-order momentum transfer kernel for relativistic heavy quarks in weakly coupled non-Abelian plasmas, matching perturbative momentum transfer on the thermal scale to hard-thermal-loop-resummed soft physics. This is the same structure previously found in strongly coupled holographic plasmas, showing that it is not peculiar to weak or strong coupling, conformality, or supersymmetry. We therefore expect that this is a robust feature that physical quark-gluon plasma should also exhibit.

The chiral magnetic effect consists in the induction of the electric current along the direction of the magnetic field. The corresponding transport coefficient $b_0$, known as the chiral magnetic conductivity, is proportional to the chiral imbalance in the medium. In many systems, such as quark-gluon plasma, $b_0$ is time-dependent. This paper studies the effect of the time variation of $b_0$ on the particle spectra and energy loss produced through the chiral Cherenkov and associated processes in Abelian and non-Abelian systems. The rates of all processes are derived in the ultra-relativistic approximation. The results are applied to the relativistic heavy-ion collisions utilizing a specific model describing the relaxation of the initial $P$-odd domain within the quark-gluon plasma. The corresponding energy loss is computed. The results suggest strong polarization of jets in quark-gluon plasma.

Neutrino experiments are often limited by low statistics, sizable systematic uncertainties, and coarse observable binning, which can hinder discrimination among competing beyond-the-Standard-Model (BSM) explanations of anomalous signals. In particular, analyses based primarily on total event-rate differences are vulnerable to source-normalization uncertainties and to degeneracies among models that induce similar inclusive yields. Using stopped-pion coherent elastic neutrino-nucleus scattering (CE$ν$NS) as a benchmark environment, we study how much model-discrimination power can be obtained from correlations in baseline, recoil energy, and timing that are less sensitive to the total rate. As benchmark BSM scenarios, we consider a $3+1$ sterile-neutrino framework and neutral-current non-standard neutrino interactions (NSI). We show with a likelihood-based analysis that these scenarios can be distinguished in nontrivial regions of parameter space once multidimensional shape information is retained. We further demonstrate with convolutional neural networks that substantial discrimination remains possible even after the total event rate is explicitly removed from the input, indicating that the relevant information is genuinely encoded in the shape of the CE$ν$NS distribution. Finally, through multi-class classification within the sterile parameter space, we show that in favorable regions the same observables can support approximate localization of the underlying sterile-neutrino benchmark point. Our results highlight the complementary roles of conventional and machine-learning-based inference in moving neutrino new-physics searches from anomaly detection to physics interpretation.

While spin polarization from synchrotron radiation is well established, the polarization of orbital angular momentum (OAM) in such radiative processes remains elusive. We study radiation and polarization of relativistic electrons in a uniform magnetic field, focusing on OAM polarization radiation for vortex electrons which carry intrinsic OAM. The results illustrate that transition rates are asymmetric in the low-photon-energy regime, favoring OAM decrease, analogous to the spin-flip asymmetry in the Sokolov-Ternov effect. Under these conditions, synchrotron radiation can polarize the OAM. The characteristic relaxation time and stationary-state OAM distribution are obtained analytically. The polarization of spin about \(\mathcal{P}_{\text{spin}}\) reaches \(92.38\%\), while that of \(\mathcal{P}_{\text{OAM}}\) can even approach almost unity for a large OAM; however, their polarization behaviors are different. For typical storage ring parameters, the OAM polarization time is orders of magnitude shorter than the spin polarization time. Thus, synchrotron radiation offers a mechanism for controlling vortex electron beams which carry OAM for high-energy accelerator applications.

We have succeeded in operating a transition-edge sensor (TES) spectrometer and evaluating its performance at the SPring-8 synchrotron X-ray light source. The TES spectrometer consists of a 240 pixel National Institute of Standards and Technology (NIST) TES system, and 220 pixels are operated simultaneously with an energy resolution of $4$~eV at 6~keV at a rate of about 1~c/s/pixel. The tolerance for high count rates is evaluated in terms of energy resolution and live time fraction, leading to an empirical compromise of about 2 x 10^3 c/s/all pixels with an energy resolution of 5 eV at 6 keV. By utilizing the TES's wide-band spectroscopic capability, simultaneous multi-element analysis is demonstrated for a standard sample. We conducted X-ray absorption near-edge structure (XANES) analysis in fluorescence mode using the TES spectrometer. The excellent energy resolution of the TES enabled us to detect weak fluorescence lines from dilute samples and trace elements that have previously been difficult to resolve due to the nearly overlapping emission lines of other dominant elements. The neighboring lines of As K alpha and Pb L alpha2 of the standard sample were clearly resolved and the XANES of Pb L alpha2 was obtained. Moreover, the X-ray spectrum from the small amount of Fe in aerosols was distinguished from the spectrum of a blank target, which helps us to understand the targets and the environment. These results are the first important step for the application of high resolution TES-based spectroscopy at hard X-ray synchrotron facilities.

True leptonium states ($l^+ l^-$) are compact pure QED systems, first theoretically predicted eight decades ago. Although considerable efforts have been devoted to their search, only positronium has been experimentally confirmed shortly after its theoretical prediction. By contrast, dimuonium ($μ^+ μ^-$) and tauonium ($τ^+ τ^-$) remain unobserved to date, partly due to their low production yields. In this work, we find that a significant number of ortho-leptonium states can be generated through the triphoton interaction process in ultraperipheral heavy-ion collisions (UPCs). In this process, two photons are emitted from one beam, while the third photon originates from the other beam. This unique interaction mechanism thus provides a distinctive opportunity to pinpoint dimuonium and tauonium. Moreover, within the three-body interaction mechanism, we find that the experimental data for $J/ψ$ production and dimuon production in ultraperipheral Pb+Pb collisions at the Large Hadron Collider (LHC) can be well reproduced.

The photon hologram of a one-particle density matrix of a photon gas is derived including the case where the energy of a probe photon is above the electron-positron pair creation threshold. The explicit expressions for the holograms of a photon gas with one-particle density matrix in the form of a single Gaussian and of coherent and incoherent lattices of Gaussians are obtained. The conditions for resonant cones of coherent scattering by coherent and incoherent lattices are found. These conditions turn out to be different. The explicit expression for the dielectric susceptibility tensor of a photon gas and of a single photon prepared in arbitrary quantum states are derived on the probe photon mass-shell. It is established that a photon gas and a single photon behave in coherent photon scattering as a medium with linear and circular birefringences that is transparent below the electron-positron creation threshold and is absorbing otherwise. It is shown that, for the probe photon energies of order $1$ GeV and higher, the energies of target photons of order $1$ eV and higher, and the photon gas density such that the classical intensity parameter is of order unity, the hologram of the photon gas can be measured with existing experimental facilities.

We quantise integrable point-particle systems with opposite-sign kinetic terms and nontrivial interactions. Using methods from separability theory, we show that previously determined classical stability conditions also imply discrete separated eigenvalue spectra. The resulting energy spectrum is unbounded above and below but not necessarily dense. We establish sufficient conditions for (i) exactly one accumulation point, or (ii) none at all. This dispels the widespread notion that ghostly quantum systems must have a continuous or dense energy spectrum.

We present the first implementation of AI agents into the design and optimization of detectors in high-energy physics experiments via a bilevel optimization framework that vertically integrates detector geometry, front-end digitization, and high-level reconstruction algorithm parameters in differentiable full simulations. Using the example of a dual-readout, segmented crystal EM calorimeter with a baseline resolution of $3\%/\sqrt{E}$, we investigate the capabilities and value propositions of AI agents in the identification and reduction of key detector parameters and in the nonlinear traversal of a given detector design's full parameter space. We find that LLM-based reasoning models today, without being given additional experiment-specific context, are able to effectively execute complex workflows and proactively suggest generic but relevant avenues for further study or improvement. Here, we demonstrate an AI agent's ability to use the workflow to simultaneously optimize a representative subset of vertically integrated detector parameters: crystal granularity and length, number of ADC bits and sampling rate, and center-of-gravity hit-clustering radius. We find that effective integration of agents into the complex workflows of frontier areas of research not only significantly reduces labor and compute, but opens up efficient avenues for computational validation of first-principles design choices. While the ability to make autonomous leaps of physics-motivated judgment or insight is not demonstrated in this work, this study defines the current frontier of experimental design methods in high-energy physics.

Wilson coefficients in dimension-six effective field theory are constrained in a combined fit to several ATLAS measurements. These inputs probe Higgs-boson processes across multiple production and decay modes, di-Higgs signatures in the $b\bar{b}γγ$ and $b\bar{b}ττ$ final states, $WW$ and $WZ$ diboson signatures, electroweak $Zjj$ final states, high-mass Drell-Yan interactions, and top-antitop events in both resolved and boosted topologies. Precision electroweak observables from LEP, SLD, and ATLAS are also included. A total of 48 parameters, including individual Wilson coefficients in the Warsaw basis and linear combinations of Wilson coefficients, are constrained simultaneously. Constraints on two-Higgs-doublet models and heavy-vector-boson models are also obtained by matching a relevant sub-set of the results with their parameters. This combined fit provides the most comprehensive effective field theory interpretation of experimental data by the ATLAS Collaboration to date. No significant deviations from the Standard Model are observed.

Dark matter search strategies have started advancing towards the neutrino fog. In this regard, compact objects such as neutron stars have already demonstrated their ability in probing such low DM-nucleon cross-sections from dark matter induced effects. In the optically thin limit, effect of dark matter self-interaction becomes relevant and may assist the capture and thermalization of dark matter inside stars, imparting observable changes on neutron star temperatures. The resulting radiation although weak can be potentially detected by the James Webb Space Telescope and upcoming Thirty Meter Telescope and the European Extremely Large Telescope. Observation of cold neutron stars accompanied by advancements in direct detection probes would provide stringent constraints or a smoking-gun signature for dark matter self-interactions. The potential detection of a neutron star with surface temperatures $\sim (1000 - 1200)$ K in the optically thin limit can push the bounds on asymmetric dark matter self-interaction cross-section to approximately two orders of magnitude more stringent than the bullet cluster.

We investigate dilepton production from an isospin-asymmetric hot and dense medium in order to explore the role of isospin imbalance in electromagnetic spectral properties. We focus in particular on modifications of the dilepton production rate associated with the onset of pion condensation, which can occur in the presence of a finite isospin chemical potential. We employ the Nambu--Jona-Lasinio model with isoscalar--vector interaction. We examine the phase structure in the $T-μ_I$ plane and estimate the vector current correlator--resummed dilepton rate for an effective quark chemical potential. We find that the interplay between isospin asymmetry, pion condensation, and vector interactions leads to nontrivial modifications of the dilepton yield. In particular, we observe two key features of the pion condensed phase: an enhancement at lower invariant mass and a prominent plateau-like structure which also help clearly identify the pion condensed phase from a chirally broken/restored phase. These results highlight the potential sensitivity of dilepton observables to pion-condensed phase of QCD matter, with possible implications for future low-energy heavy-ion collision experiments as well as isospin-rich environments such as neutron star matter.

Cryogenic applications in high-energy physics (HEP) demand reliable, low-power CMOS electronics capable of operating at liquid nitrogen temperatures (77 K). The open-source SkyWater 130nm (SKY130) CMOS process has previously been shown to operate at temperatures as low as 4 K making it a promising candidate for HEP applications. In this work, we characterize and model SKY130 low-threshold voltage transistors at 77 K, which is a temperature commonly used in modeling applications for liquid argon detectors. DC characteristic measurements were performed at both room temperature and liquid nitrogen temperature. We created a cryogenic modeling approach to produce a SPICE-compatible, isothermal BSIM4-based model for select transistor sizes at 77 K. The resulting model agrees with data at 77 K with an average error on the order of 20% (relative RMS) and shows no dependence on drain voltage. Due to the open-source nature of SKY130, we have made our models publicly available on Github. We hope this work will continue the trend for democratizing circuit design at cryogenic temperatures in high-energy physics by enabling open access to accurate cryogenic CMOS device models at 77 K.

In this study, we calculate the $γZ$-exchange contribution to elastic $ep$ scattering at large momentum transfer within perturbative QCD. We present analytical expressions for the $γZ$-exchange contributions to the amplitudes. We also estimate the asymptotic behaviors of the amplitude contributions and of the physical quantity $A_{\text{PV}}$ at high momentum transfer. These asymptotic behaviors determine the subtraction order in the dispersion relations (DRs) satisfied by the amplitudes. We find that the DR usually used in the literature for the axial-vector part of the amplitude is not valid at high $Q^2$ and should be modified to a once-subtracted form. Within the present pQCD framework and the adopted proton distribution amplitudes, these high-energy properties also provide nontrivial constraints on low-energy DR assumptions.

The internal low-energy symmetry of the massless Lorentz-invariant Dirac Hamiltonian in $2+1$ dimensions is known to be $O(2N)$, where $N$ is the number of two-component Dirac fermions. Here we point out that there exists an analogous internal symmetry of the single-particle quadratic-band-touching Hamiltonian in two spatial dimensions, and it is the unitary symplectic group, $USp(2N)$. All fermionic bilinears belong to one of the three small irreducible representations of this group. The interacting theory that respects the $USp(2N)$ symmetry and the spatial rotations is constructed and found to allow two independent interaction terms. When these interactions are infrared-relevant the symplectic symmetry either remains preserved or becomes spontaneously broken to $USp(N) \times USp(N)$. The symmetry in the lattices such as honeycomb to infinite order in the dispersion's expansion in powers of local momentum is given by the overlap of the symplectic and the orthogonal groups. We show that this overlap is $O(2N) \bigcap USp(2N) = U(N)$.

The radioisotope $^{125}\text{Xe}$ is a short-lived ($T_{1/2}\sim16.9 h$) activation product of the neutron calibrations performed in the LUX-ZEPLIN experiment. Subsequently, $^{125}$Xe decays primarily ($>99\%$) via electron capture, but positron emission has been confirmed by direct measurement to at least the 243 keV level of $^{125}\text{I}$. An additional decay to the 188keV level is expected from triple-coincident measurements of the annihilation and relaxation $γ$ rays, but has not been directly confirmed. By utilizing multiple-scatter event analysis and the pre-activation data to constrain backgrounds, this work reports positron emission with a statistical significance of 5.5$σ$. This corresponds to a total branching ratio of $0.29\pm0.08_{\text{stat.}}\pm0.04_{\text{sys.}}$ %, and is the first constraint to the individual branching levels of $^{125}\text{I}$.

We study a minimal framework that naturally yields viable Dark Matter, a strong first-order electroweak phase transition and low-scale resonant leptogenesis. Augmenting the Standard Model with three heavy Majorana neutrinos, we study the corresponding neutrino-extended Standard Model Effective Field Theory, including operators upto mass-dimension six. The pure Higgs operator provides the dominant enhancement required for a strong first-order electroweak phase transition, while the remaining operators yield subleading effects consistent with electroweak precision constraints. The signal for the stochastic gravitational-wave background is dominated by sound waves in the plasma, with magnetohydrodynamic turbulence providing a subleading contribution. Low-scale resonant leptogenesis is realized through tiny mass splittings among quasi-degenerate heavy neutrinos, dynamically generated in the symmetric phase by the combined effect of one-loop RG-induced corrections and finite-temperature contributions. Solving the Boltzmann equations, we show that the observed baryon asymmetry of the Universe can be reproduced while remaining consistent with neutrino oscillation data and charged-lepton-flavor-violation constraints. One of the heavy neutrinos is stabilized by a discrete symmetry thereby acting as a fermionic dark matter candidate. Its interactions with the Standard Model arise from dimension-five and dimension-six effective operators, leading to viable annihilation, elastic scattering, and indirect detection phenomenology compatible with current experimental bounds. The dark matter sector remains decoupled from the dynamics of the electroweak phase transition and leptogenesis, allowing all three phenomena to be consistently realized within a unified effective field theory framework.

We propose a reconstruction of the full $(x, ξ, t)$ dependence of unpolarized isovector proton generalized parton distributions (GPDs) $H^{u-d}$ and $E^{u-d}$ from lattice QCD data in the pseudo-distribution formalism. For the first time, we extract double distributions (DDs) directly from lattice data, enforcing therefore an important property of GPDs linked to Lorentz symmetry. We use the flexible framework of multidimensional Gaussian process regression to regularize the inverse problem and present an assessment of the impact of model dependence on the systematic uncertainty. Our lattice ensemble corresponds to a pion mass $m_π= 358$~MeV and a lattice spacing $a = 0.094$~fm. We use larger hadron momenta, up to 2.7~GeV, and kinematic coverage compared to our previous computations and extract additional skewness-dependent moments of the GPD.

We compute the holographic complexity of conformal quantum fields in rigid global de Sitter spacetime (dS$_{d}$) using the volume and action prescriptions. First we consider AdS$_{d+1}$ spacetime in global dS$_{d}$ foliations, and compute the complexity of the CFT supported on the global dS$_{d}$ conformal boundary. Next, we consider CFT supported on a global dS$_d$ (UV) brane embedded in AdS$_{d+1}$ spacetime, and compute the holographic complexity in this brane set up. We compare and contrast the results in the two cases, as well as with related results in the literature obtained in alternative holographic set ups involving patches of de Sitter spacetime covered by static coordinates or conformal (Poincaré) coordinates.

We investigate the strong decay properties of light unflavored and strange mesons within a relativistic quark-pair-creation (QPC) framework, and compare the results with those obtained in the conventional non-relativistic QPC model. Our analysis shows that, within the present theoretical and experimental uncertainties, the relativistic QPC model yields predictions for strong decay widths of comparable overall quality to those of the non-relativistic QPC model. This indicates that the non-relativistic QPC approach remains adequate for estimating decay widths in most practical applications. Nevertheless, owing to the inclusion of Lorentz boosts and Wigner rotations, the relativistic QPC model exhibits a stronger suppression of decay amplitudes in the high-energy region. This feature may be useful in studies based on unquenched quark models, where the relativistic QPC coupling could lead to more controlled meson-loop effects and mass shifts.

We investigate quantum entanglement and Bell-inequality violation in top-antitop pair production at future lepton colliders such as the International Linear Collider (ILC) and multi-TeV muon colliders. Within the Standard Model (SM), the process proceeds through $s$-channel $γ$ and $Z$ exchange and exhibits characteristic spin-correlation patterns that encode a non-trivial amount of entanglement. We then examine how these features are modified in several well-motivated extensions of the SM:(i) a neutral scalar mediator that couples to charged leptons and top quarks via Yukawa interactions and contributes as an additional $s$-channel exchange; (ii) the minimal gauged $U(1)_{B-L}$ model, which introduces a new neutral gauge boson $Z'$ coupling vectorially to SM fermions; and (iii) a Randall-Sundrum scenario, in which the exchange of massive Kaluza-Klein gravitons arising from a warped extra dimension induces additional spin-dependent interactions. For all cases, we evaluate quantum-information observables, including the entanglement marker, the concurrence, and the maximal Clauser-Horne-Shimony-Holt parameter, and study their dependence on the center-of-mass energy, scattering angle, and model parameters. We find that, relative to the SM expectation, the entanglement is typically reduced in the scalar-mediator scenario, while sizable deviations can arise in the $U(1)_{B-L}$ and Randall-Sundrum cases for phenomenologically relevant regions of parameter space. These results demonstrate the potential of quantum-information observables as sensitive probes of new neutral interactions and extra-dimensional dynamics in future lepton colliders.

We report results from a 13-liter purified liquid argon test stand at Wellesley College. The system includes a single-pass liquid-phase purification column, a double-gridded purity monitor to assess the electron lifetime, and a slow control and data acquisition system. Initial measurements demonstrate an O$_2$-equivalent impurity concentration of 0.25 ppb, corresponding to an electron lifetime of 1.2 ms at a drift field of 500 V/cm. This test stand supports ongoing detector R&D on charge and light readout technologies for future large-scale liquid argon time projection chambers, such as Q-Pix and other cold electronics systems, as part of a facility at Wellesley College for fundamental studies of LArTPC readouts.

A new excited charm-strange meson is observed through an amplitude analysis of the full phase space of $B^0\to D^+ D^- K^+ π^-$ decays. The analysis is based on a proton-proton collision data sample collected by the \lhcb experiment at a center-of-mass energy $\sqrt{s} = 13\,\text{TeV}$, corresponding to an integrated luminosity of $5.4\text{fb}^{-1}$. The statistical significance of the new state exceeds $10$ standard deviations. Its Breit--Wigner mass and width are measured to be $m_0 = {2933}^{+6}_{-5}(\text{stat})^{+4}_{-3}(\text{syst}) \,\text{MeV} $ and $Γ_0 = {72}^{+18}_{-12}(\text{stat})^{+\phantom{0}7}_{-10}(\text{syst}) \,\text{MeV} $, respectively, and its spin-parity quantum numbers are determined to be $J^P = 1^+$. This new meson, denoted as $D_{s1}(2933)^+$, is a candidate for a $D_s(2P^{(\prime)}_{1})^+$ state.

We present a dynamical solution to the dark matter-baryon coincidence problem based on the neutron portal operator connecting the visible and dark sector asymmetries. This framework is motivated by the possibility that a strongly supercooled dark confinement phase transition accounts for the nano-Hz stochastic gravitational wave signal observed by pulsar timing arrays, while also generating the dark matter and baryon asymmetry in the Universe. We show that the GeV-scale mass of asymmetric dark matter can be naturally correlated with the (multi-)TeV scale cut-off for the neutron portal through its ultraviolet completion. The dark sector is governed by an approximate fixed point and confines once the heavy portal states are integrated out, dynamically generating a scale of $\mathcal{O} ({\rm GeV})$. We analyze both tree and loop-level ultraviolet completions and demonstrate how the resulting confinement scale is linked to the effective neutron portal scale. We also discuss cosmological constraints and experimental prospects in beam dump searches and colliders for probing the neutron portal.

Ultra high energy cosmic rays have been see coming from the direction of the local cosmic void. We use this fact to argue that at least some of these these cosmic rays are relatively light magnetic monopoles and that their relative fraction above 1020 eV can be found from full sky observations.

Boundary conformal field theory (BCFT) provides a universal framework for critical phenomena in the presence of boundaries. We determine BCFT data for the normal and ordinary boundary universality classes of the $1+1$-dimensional boundaries of the $2+1$-dimensional $O(2)$ and $O(3)$ Wilson-Fisher fixed points, realized microscopically by a bilayer Heisenberg model on the fuzzy sphere. Using the fuzzy-sphere state-operator correspondence, we obtain boundary operator spectra, identify low-lying boundary primary operators, extract operator-product-expansion (OPE) data, and estimate the boundary central charges for both boundary conditions. For the normal boundary condition, the universal amplitudes $a_σ$ and $b_t$ extracted from one- and two-point functions agree quantitatively with Monte Carlo benchmarks where available. For both $N=2$ and $N=3$, we find a positive extraordinary-log exponent $α$, providing independent microscopic evidence for extraordinary-log boundary criticality. Our results extend fuzzy-sphere BCFT spectroscopy beyond the Ising universality class to continuous $O(N)$ symmetry.

Jet modification in heavy-ion collisions provides microscopic access to the properties of the quark-gluon plasma. However, conventional approaches based on traditional global observables, such as \(R_{AA}\), capture limited information about the complex dynamics of parton-medium interactions during hard scatterings. In this work, we apply sequential machine learning architectures to the jet declustering history tree, achieving improved classification performance compared with static models that learn only from a single stage of the jet evolution. We find that models trained on different medium implementations exhibit meaningful performance modification under cross-domain validation, indicating that machine learning is sensitive to implementation-specific features that traditional observables may not resolve. These results suggest new opportunities for using machine learning as an analysis tool to overcome some of the limitations of traditional jet-modification studies.

Astrophysics plays a pivotal role in the quest for axions and axion-like particles, offering guidance to experimental efforts and enabling the investigation of axion properties that cannot be probed otherwise. In this context, the extreme conditions in the interiors of compact stellar objects -- such as core-collapse supernovae, neutron stars, and binary neutron star mergers -- significantly enhance axion production, providing unparalleled sensitivity to extremely feeble couplings to Standard Model particles. In this context, the techniques of multimessenger astrophysics deepens the understanding of powerful transient events, maximizing the capabilities of current instruments to identify possible signatures of axion emission.

In Run 3 of the LHC, the instantaneous luminosity at the LHCb interaction point has been increased by a factor of five, from $4\times 10^{32}\rm{cm}^{-2}\rm{s}^{-1}$ to $2\times 10^{33}\rm{cm}^{-2}\rm{s}^{-1}$. Several hardware interventions, including a complete overhaul of the readout electronics, have been carried out on the muon detector. The muon identification algorithms in the software trigger were improved with the aim of ensuring Run 2 performance under a higher particle rate. The operation and calibration of the upgraded muon detector are presented. The muon detection efficiency and muon identification performance are evaluated on data calibration samples collected during the year 2024. A muon identification efficiency above 90\% with sub-percent hadron misidentification probability is achieved by exploiting the pattern of hits in the muon detector.

Anomaly detection in High Energy Physics requires identifying rare signals against overwhelming backgrounds, without prior knowledge of the signal. We present the first application of masked-token prediction, a technique from Large Language Models, to this problem. A lightweight encoder architecture trained solely on background events captures the structure of Standard Model (SM) physics; at inference, sequences deviating from this learned structure are flagged as anomalous. We evaluate the approach on searches for four-top-quark production and supersymmetric gluino pair production, both featuring top-rich final states with substantial missing transverse energy, covering SM and beyond the Standard Model (BSM) scenarios. Strong performance on the four-top signature, which closely resembles background, demonstrates the method's sensitivity to subtle deviations. We further show that the tokenization strategy significantly impacts performance: deep-learned tokenization via vector-quantized variational autoencoders (VQ-VAE) outperforms look-up table tokenization. Comparison with established anomaly detection baselines confirms robustness. These results highlight the potential of token-based collider data representations combined with transformer architectures for new-physics discovery. Once trained on SM background, the model transfers across different BSM searches, enabling scalable, model-independent anomaly detection at reduced computational cost.

The chiral algebra bootstrap (CAB) is a novel bootstrap program for form factors in quantum-integrable self-dual gauge theories, some of which in turn are helicity amplitudes in the corresponding gauge theories. The singularities that recursively generate a given (loop-level) form factor are holomorphic collinear splitting functions, equivalently celestial chiral algebra OPEs, of the self-dual theory. In this note, we apply the chiral algebra bootstrap to the simple example of self-dual 4d $\mathscr{N}=4$ super Yang-Mills (SDSYM). We use a combination of twistor space input, Koszul duality, supersymmetry, and associativity to obtain the all-loop holomorphic collinear splitting functions for SDSYM. We also use associativity to provide a simple proof of the conjecture that there are no double-poles in the loop-level OPEs for this theory. We conclude by computing several form factors, including both a reproduction of several known results and novel form factors up to two loops involving insertions of powers of the anti-self-dual field strength. These form factors compute a supersymmetric version of Higgs amplitudes in the self-dual sector. Detailed sample computations are provided to familiarize the reader with the CAB method.

We present a dual gravity interpretation of the complex reparametrization mode $ψ(u)$ that governs the soft dynamics of double-scaled SYK in the presence of a time-dependent Maldacena-Qi coupling. We find that the dual gravity system takes the form of 2+1-dimensional Einstein-de Sitter gravity with an energy distribution localized on a dS$_2$ slice within dS$_3$. The effective SYK equations of motion take the form of the Israel junction conditions across the dS$_2$ slice. We study the 1D effective action of the SYK soft mode and show that it coincides with the effective action derived from 3D Einstein-de Sitter gravity with conformal boundary conditions on $\mathscr{I}^\pm$. The boundary conditions split $\mathscr{I}^\pm$ into two hyperbolic $k=-1$ slices, and the holographic screen is placed at the intersection. We adapt the Gibbons-Hawking calculation of the Schwarzschild-de Sitter entropy to the case with $k=-1$ boundary conditions and find that it reproduces the semiclassical DSSYK entropy. The boundary-to-boundary Green functions in 3D de Sitter are equal to the square of DSSYK two-point functions. We give an alternative holographic interpretation of our results in terms of 3D AdS gravity with two time directions.

A search for dark matter produced in association with a dark Higgs boson decaying into a bottom quark-antiquark pair has been performed using proton-proton collision data at a center-of-mass energy of 13 TeV. The search uses data collected with the CMS detector at the CERN LHC during the 2016$-$2018 data-taking period, corresponding to an integrated luminosity of 138 fb$^{-1}$. The results are interpreted in terms of a theoretical model of dark matter production that, together with a spin-1 gauge boson mediator, predicts the existence of a Higgs-boson-like particle in the dark sector (i.e., a dark Higgs boson). This search focuses on an experimental signature with large missing transverse momentum from dark matter production and a resonant structure in the invariant mass of the bottom quark-antiquark pair from the dark Higgs boson decay. Upper limits at 95% confidence level on the signal strength for dark Higgs boson mass hypotheses below 160 GeV are set. Values of the mediator mass up to 4.5 (2.5) TeV are excluded at 95% confidence level for a dark Higgs boson mass of 50 (150) GeV. This represents the most stringent limits set to date for the dark Higgs boson masses considered in this study.

In this paper, we have studied the impact of two different formalisms of quantum decoherence in determining the sensitivities of the two future long-baseline experiments DUNE and P2SO. In Formalism-A, we will assume that the decoherence matrix is defined in a matter mass eigenstate basis which is the basis that diagonalizes the Hamiltonian for neutrinos in matter, with a constant matter density. In Formalism-B, we will define the decoherence matrix in the vacuum mass eigenstate basis and then rotate it to matter mass basis via an unitary transformation. By using different values of the decoherence parameter $Γ$, we will show how these two formalisms differ at the probability level and then we will demonstrate how the sensitivities can differ at the $χ^2$ level. Our results show that if the values of $Γ$ is small, then these two formalisms yield same probability in vacuum. However, if the values of $Γ$ is large or if there is strong matter effect, then these two formalisms yield very different results.

We conjecture, and show in a plethora of examples, that the sphere partition function of 3d $\mathcal{N}=4$ Chern-Simons-matter theories equals a sum of twisted traces on tensor products of Verma modules over the quantization of the moduli spaces of vacua. This extends a conjecture of Gaiotto-Okazaki to Chern-Simons-matter theories. We also show that the partition function of every Abelian gauge theory with higher charges has such twisted trace decomposition, and uncover new Abelian dualities between theories with and without Chern-Simons couplings.

This paper presents a comprehensive study of the reconstruction of sub-GeV neutrino charged-current interactions within a Liquid Argon Time Projection Chamber (LArTPC). We demonstrate that traditional charge-based calorimetry is fundamentally limited at sub-GeV scales by significant recombination fluctuations and missing hadronic energy. We show that energy reconstruction using energy deposited as scintillation light (L) partially benefits from the previously reported self-compensating light effect. At neutrino energies above 400 MeV, the light-only reconstruction still outperforms charge-only methods that can separate EM and hadronic objects. The performance of the two remains comparable below 300 MeV. Using the energy-deposit information from both detector signals, we demonstrate a 70% efficiency in separating electron neutrinos and antineutrinos. By using a proximity-based algorithm coupled with a geometric lepton-exclusion cone, we also demonstrate the ability to isolate neutron-induced energy depositions from background. This enables an improvement of sub-GeV direction reconstruction by about 20 degrees for antineutrinos. This study provides new insights into how to enhance the physics reach of future LArTPC atmospheric neutrino analyses.

The determination of leptonic CP violation is a primary goal of future long-baseline neutrino experiments such as DUNE and T2HK. The extraction of $δ_{\mathrm{CP}}$ relies on the near-to-far extrapolation and on the assumed knowledge of the cross-section ratios $σ_{ν_e}/σ_{ν_μ}$ and $σ_{\barν_e}/σ_{\barν_μ}$, which are typically inferred under theoretical assumptions such as lepton universality and depend on nuclear modeling. In this work, we quantify how much of the sensitivity of DUNE and T2HK arises from these assumptions by performing a model-agnostic, data-driven estimation of systematic uncertainties in $ν_e$ and $\barν_e$ cross sections. We find that adopting such an agnostic approach can substantially degrade the CP-violation sensitivity, reducing it by nearly $3σ$ at maximal CP violation for DUNE, and $4σ$ for T2HK. We then assess the impact of the proposed $ν$SCOPE experiment, which, through a combination of neutrino tagging and the Narrow-Band Off-Axis technique, can provide percent-level measurements of $σ_{ν_μ}$ and $σ_{\barν_μ}$ and constrain the ratio $σ_{ν_e}/σ_{ν_μ}$ at the $\sim 2\%$ level. We show that including prospective $ν$SCOPE measurements largely restores the lost sensitivity, highlighting that precise external cross-section measurements may be essential for a fully robust determination of $δ_{\mathrm{CP}}$ and for breaking its degeneracy with nuclear mis-modeling or possible new physics affecting neutrino detection.

We present SubTropica, a Mathematica package that performs symbolic integration of multi-polylogarithmic integrals using recent advances in tropical geometry. It focuses on the class of linearly-reducible Euler integrals, such as Feynman integrals, and expands them using a tropical subtraction scheme. The engine behind it is HyperIntica, a native Mathematica package for hyperlogarithm integration that can be used independently. This paper documents both packages and illustrates their usage on examples from across different physics applications. Additionally, we introduce an AI-driven library of Feynman integrals, which catalogs diagrams discussed in the literature and serves as a database for computed results. Its online version is available at: https://subtropi.ca and features a graphical user interface for diagram input and retrieval of records.

Upcoming long-baseline neutrino oscillation experiments such as DUNE aim to achieve unprecedented precision, but their physics reach is ultimately constrained by systematic uncertainties in neutrino flux predictions and neutrino-nucleus cross sections. These limitations are especially critical for new-physics searches in neutrino oscillations at the near detector, including non-unitarity and sterile neutrinos, where the signal manifests as small distortions in the energy spectrum and is therefore highly sensitive to spectral uncertainties. The PRISM (Precision Reaction Independent Spectrum Measurement) technique offers a robust strategy to mitigate these effects by exploiting measurements at multiple off-axis angles, effectively providing a data-driven handle to reduce systematics. In this work, we demonstrate that PRISM can significantly reduce the impact of large systematic uncertainties, restoring sensitivity to non-unitarity and sterile neutrino scenarios in the electron and muon sectors to a level comparable to that obtained with small spectral uncertainties. We also include the results for the $τ$ sector with PRISM; however, in this case, since the majority of the flux measured at off-axis angles lies below the $τ$ production threshold, we find the improvement to be marginal. As part of this work, we have obtained neutrino and antineutrino fluxes for different off-axis angles with higher statistics than those provided by the DUNE collaboration. We make available these fluxes as auxiliary material to this manuscript.

We revisit near-threshold $J/ψ$ photoproduction on the nucleon within the OPE sum-rule framework combined with vector-meson dominance and dispersion relations, using modern NNLO gluon distributions (ABMP16, MSHT20, CT18, NNPDF4.0). Two complementary pathologies are identified. The moment-based cross-section reconstruction fails near threshold: the small-$x$ singularity of modern PDFs distorts the Mellin moment hierarchy and drives the threshold exponent to $a\simeq 17$-$20$, compared to $a\simeq 1$-$2$ for the 1999 scaling parametrization. The direct convolution approach avoids this artefact and describes the threshold data (GlueX, Cornell) for all four PDF sets, but overshoots HERA measurements at $W\gtrsim 90$~GeV by a factor $7$-$12$ -- an intrinsic feature of leading-twist convolution with any small-$x$-singular PDF, already noted in the 1999 analysis. A minimal eikonal unitarization of the amplitude, with an energy-dependent saturation scale fitted to HERA data, reconciles the convolution with the full $W$-range measurements while leaving the threshold description unchanged. Near threshold the dispersive real part dominates the cross section, anchored by the OPE subtraction constant $M_{ψN}(0)\simeq 36$-$39$~GeV$^{-2}$.

We develop a resurgent approach to the problem of unique continuation of mock theta functions across their natural boundary. The starting point is the representation of the associated Mordell-Appell integrals as Laplace transforms of resurgent functions, which serve as the primary analytic objects. By rotating the Laplace contour by $π$, i.e. onto the Stokes line, one obtains, in all known cases, the mock-modular relations between the Mordell-Appell integrals and the corresponding unary series in $\hat q=e^{-πi τ}$ and $\hat q_1=e^{-πi (-1/τ)}$. We then prove that these relations admit a unique solution on the $q$-side, expressed in terms of $q=e^{πi τ}$ and $q_1=e^{πi (-1/τ)}$, with coefficients determined by the corresponding Mordell-Appell integrals. This yields a canonical continuation across the natural boundary, given by a resurgent extension of the classical principle of permanence of relations, and singles out a distinguished family of mock theta functions in each group. We present a complete analysis for the order 3 and 5 cases (mf3 and mf5). The method extends naturally to higher orders; a general theory will appear in a separate paper.

In quadratic gravity, with a positive Weyl squared coefficient, the extra spin-2 sector is shown to correspond to a dual inverted harmonic oscillator, instead of a ghost. Using the Wightman spectrum condition, we prove that the associated Källén--Lehmann spectral density vanishes, reflecting the absence of a normalizable ground state and the spacelike nature of the propagator pole. This uniquely fixes the propagator to a principal value form as a theorem, not a prescription. The optical theorem is satisfied, the dual IHO spin-2 is not an asymptotic state, and gives only virtual contributions at all loop orders. As a result, unitarity is preserved consistently with renormalizability.

The multiplicity-dependent suppression of $Υ(n\mathrm{S})$ states measured by CMS in $pp$ at $\sqrt s=7\,$TeV \cite{CMS2020}, and of $ψ(2S)\big/J/ψ$ measured by LHCb at $\sqrt s=13\,$TeV \cite{LHCb2024}, is subjected to four multi-differential tests: \emph{cone isolation}, \emph{azimuthal sectors}, \emph{transverse sphericity}, and \emph{prompt vs. non-prompt}. Cone and sphericity close a \emph{scissors constraint}: the local reading of the Comover Interaction Model is in tension with the cone data, its global reading with the sphericity data. The non-prompt flatness forces the mechanism to act at early proper times. None of the considered hadronic or string-based frameworks -- CIM local or global, PYTHIA 8 MPI \cite{Sjostrand2015}, rope hadronisation \cite{Bierlich2015}, CGC \cite{Ma2015}, Trainor TCM \cite{Trainor2008} -- naturally satisfies the four constraints simultaneously in its published form. The surviving class is an early, globally correlated medium consistent with partonic degrees of freedom, co-occurring with the ALICE strangeness enhancement \cite{ALICE_SE}, the long-range ridge \cite{CMS_ridge}, and below the threshold of the partonic baryon-meson $v_2$ \cite{ALICE_v2}, in a density window compatible with the Campanini \& Ferri equation of state \cite{Campanini2011}.

Hydrogen atom tunneling likely plays a substantial role in the gas-phase chemistry of astrochemical environments. To determine the potential effect that it has on the chemical modeling of aromatic molecules, we screened the kida.uva.2024 network, and our own expanded network to find reactions which could be significantly accelerated by hydrogen atom tunneling in the ISM. In total, 64 reactions were identified. The hydrogen abstraction reactions from H$_{2}$ to four key interstellar radicals (C$_{2}$H, OH, CN, and NH$_{2}$) were studied further using newly calculated potential energy surfaces and RRKM analyses to determine rate coefficients for a temperature of 10 K and a density of 2 $\times$ 10$^{4}$ cm$^{-3}$. Despite having low rate coefficients of 1.66 $\times$ 10$^{-15}$, 8.17 $\times$ 10$^{-16}$ and 3.15 $\times$ 10$^{-16}$ $\mathrm{cm^{3}\,s^{-1}}$ the C$_{2}$H, OH, and CN reactions are competitive in the ISM, due to large overall rates caused by the high abundance of molecular hydrogen. The calculated value for the NH$_{2}$ reaction, however, was much smaller and found to be inefficient at ISM conditions. The possible effects of all other considered reactions were studied with simulations using calculated collision limit rate coefficients. Upper and lower bounds were then placed on modeled aromatic abundances using the most significant reactions. Due to the dependence of calculated aromatic abundances on reactions involving c-C$_{6}$H$_{5}^{+}$ and the recent questions surrounding its reactivity, we also explored the abundance variations caused by reactions leading to or involving c-C$_{6}$H$_{5}^{+}$.

The Dyson Minds 2025 Workshop, held at the Center for Brains, Minds & Machines at MIT and organized by Penn State, MIT, and The Ultraintelligence Foundation, brought together researchers in astrophysics, engineering, artificial intelligence, computer science, and philosophy to examine "Dyson Minds" -- large-scale post-biological intelligences powered by energy harvested from supermassive black holes (SMBHs). Building on the ideas of F. J. Dyson (1960, 1966) and I. J. Good (1966), participants explored the physical, engineering, behavioral, and observational consequences of civilizations embodied as machinery operating near the universe's most powerful energy sources. The workshop aimed to develop new observational strategies capable of detecting signatures of such systems. Despite the highly cross-disciplinary scope, discussions centered on how a Dyson Mind might be constructed, how it might behave, and how those factors would shape strategies for the search for extraterrestrial intelligence. Key themes included the thermodynamic, mechanical, and stability limits of Dyson swarms; the trade-offs between power availability and communication latency in distributed minds; and how observability changes depending on whether Dyson Minds act as coherent entities or as loosely coordinated collectives. Across these topics, the consensus was that details of architecture and behavior strongly influence observational signatures. A major recommendation was to apply anomaly-detection methods to archival datasets, including those from WISE, JWST, and the Event Horizon Telescope, to identify unusual sources potentially overlooked by standard reduction pipelines. By integrating insights from multiple disciplines, the meeting advanced concrete, observation-focused strategies for future technosignature searches around SMBHs.

We use GRAVITY near-infrared (NIR) flare astrometry to test whether Sagittarius A* could be a solitonic boson star. We consider five spherically symmetric solitonic boson-star models with different effective radii, together with the Schwarzschild black hole. Treating the flares as hot spots on circular equatorial orbits, we analyze their centroid motions and images in these spacetimes and use them for parameter fitting. We perform the fitting using both $χ^2$ analysis and Markov Chain Monte Carlo (MCMC) methods, which yield consistent results: the inferred masses of boson-star models are systematically larger than the established value of $4.3\times10^6M_\odot$. Notably, more diffusive boson stars exhibit imaging properties closer to those of a black hole, leading to mass estimates that are correspondingly closer to the established value. Overall, our results place stringent constraints on solitonic boson star interpretations of Sagittarius A*, although do not completely rule them out.

We have searched for the 3.55 keV line from sterile neutrino decay using 3.1 megaseconds of RXTE cluster data. A 2.5$σ$ excess of emission over a thermal model is found over the energy span of the 3.55 keV line in the combined spectra of the eight clusters that individually have an excess. The residuals are added to increase the signal to noise ratio of the excess, which is then modeled with a Gaussian to simulate the instrumental spectral response. We find a significant correlation (r = 0.76) for a line centered at 3.6 keV with a model flux of 3.07 x 10$^{-5}$ ph cm$^{-2}$ s$^{-1}$. Mixing angle for detected clusters ranges from 0.35 to 6.2 x 10$^{-10}$. The decay rate inferred from the line flux is strongly correlated (r = 0.87) with cluster temperature, which is due to hotter, more massive clusters having a larger amount of dark matter. Approximately half of the decay line total flux comes from the Coma cluster. We fit the Coma cluster spectrum with two different three-component models. The first includes a Gaussian fixed at 3.55 keV to model soft emission. The second three-component model uses a second thermal component to model soft emission. The model fit parameters indicate that the second thermal component is modeling high-energy residuals rather than low ones, where the Gaussian is. Though our line fluxes exceed most reported detections and upper limits, they do not overproduce the dark matter. We conclude that some fraction of the marginally detected excess could be attributed to the decay line since low-temperature thermal emission and systematics fail to model it completely.

(Abridged) We aim to investigate the inner regions of large and massive disks orbiting T Tauri stars, thought to be progenitors of systems with wide-orbit planets and possible cases of halted pebble drift. We analyze the MIRI spectra of three disks from the MINDS program: V1094 Sco, DL Tau, and IM Lup. The spectra reveal a striking diversity. V1094 Sco and DL Tau exhibit the highest C$_2$H$_2$/H$_2$O flux ratio in the MINDS sample of T Tauri disks. In V1094 Sco, even cold C$_4$H$_2$ is seen. In contrast, the IM Lup spectrum is dominated by O-bearing species. No one-to-one correspondence is found between the gas in the outer disk, as traced by the C$_2$H/C$^{18}$O flux ratio, and that of the inner disk as traced by the C$_2$H$_2$/H$_2$O flux ratio. To explain these results, we propose a scenario based on a toy model of halted pebble drift. We show that a volatile C/O ratio close to unity and low C and O abundances in inner disks arise only if: (1) ~95$\%$ of the icy grains are blocked in the outer disk, (2) the outer disk is chemically evolved, and (3) the gas in the outer disk has had time to reach the inner disk. DL Tau and perhaps V1094 Sco would be the rare examples for which all these conditions are met. Therefore, a high C$_2$H$_2$/H$_2$O flux ratio in pebble-rich disks would have a different origin than proposed for very-low mass stars, for which fast drift of O-rich pebbles would eventually leave a C-rich inner disk. We also show for the first time that the disks with high C$_2$H$_2$/H$_2$O flux ratio exhibit a prominent silica dust component, a result found in four disks published so far (V1094 Sco, DL Tau, CY Tau, DoAr 33). We propose that the reformation of dust at the sublimation front of silicates in a gas with super-solar (but below unity) C/O ratio leads to a silica stoichiometry (SiO$_2$). In turn, silica is a promising diagnostic of the C/O ratio in the inner disks.

Constraining primordial non-Gaussianity via its scale-dependent imprint on galaxy clustering requires knowledge of the bias parameter $b_φ$, which is exactly degenerate with $f^{\rm{loc}}_{\rm{NL}}$ at leading order. To break this degeneracy, current analyses adopt the relation $\left(b_φ = 2δ_c\left(b_1 - 1\right)\right)$ based on the assumption of a universal mass function. This relation is known to break down for physically motivated galaxy selections, introducing systematic errors in the inferred $f^{\rm{loc}}_{\rm{NL}}$ that scale directly with the assumed $b_φ$ prior. We present a framework to construct physically motivated, observation-conditioned priors on $b_φ$ by marginalizing over galaxy formation uncertainties. We use the CAMELS-SAM simulation suite, augmented by separate Universe simulations, to measure galaxy formation observables, like the stellar mass function (SMF) and the stellar-to-halo mass relationship (SHMR), and $b_φ$ across a range of galaxy formation parameters. From these measurements, we construct a distribution of $b_φ$ conditioned on observations, and we select our galaxy sample to resemble the DESI Emission Line Galaxy (ELG) sample. Conditioning on the SMF or SHMR decreases $σ_{b_φ}$ from $0.69$ to $0.08$ and $0.02$ respectively -- reductions of $88\%$ and $97\%$ -- with consistent results when conditioning on the observed data directly. Despite substantial shifts in the galaxy formation posteriors driven by known SC-SAM discrepancies at high halo masses, the resulting $b_φ$ distributions remain mutually consistent across all observables. The SMF and SHMR are found to carry sufficient constraining power to reduce the galaxy formation uncertainty in $b_φ$ relevant for $f^{\rm{loc}}_{\rm{NL}}$ inference with next-generation spectroscopic surveys

We model the multi-band lightcurves of 80 SNe Ic-BL, including 11 associated with lGRBs, using a magnetar engine model with $^{56}$Ni decay. We find that the data are all consistent with a magnetar central engine, and such a model yields high-quality fits across the sample. The medians with $1σ$ regions of the key parameters are $P_{\rm{i}}\sim2.04^{+1.84}_{-0.96}\,{\rm{ms}}$, $B_{\rm{p}}\sim3.96^{+3.28}_{-1.40}\times10^{15}\,{\rm{G}}$, $M_{\rm{ej}}\sim2.30^{+1.48}_{-1.02}\,M_\odot$, and $M_{\rm{Ni}}\sim0.18^{+0.14}_{-0.09}\,M_\odot$, with strong and statistically significant correlations observed for both $M_{\rm{ej}}-P_{\rm{i}}$ (anti-correlation) and $M_{\rm{Ni}}-M_{\rm{ej}}$ (correlation). Comparing the SN Ic-BL samples with and without lGRB association using fitting parameters, we find no significant difference between them, although the GRB-associated sample is slightly brighter, possibly due to an observational bias. Relative to ordinary SNe Ic, SNe Ic-BL have similar $^{56}$Ni and ejecta masses, suggesting comparable pre-SN progenitor properties, with differences possibly arising from the presence of a magnetar engine. In comparison with other possible magnetar-powered SESNe, including SLSNe Ic and FBOTs, we confirm a strong universal $M_{\rm{ej}}-P_{\rm{i}}$ correlation, indicating a common origin. SNe Ic-BL and SLSNe Ic have similar ejecta mass distributions, typically $M_{\rm ej}\gtrsim0.5\,M_\odot$, while FBOTs mostly lie below this value. Differences between SNe Ic-BL and SLSNe Ic may arise from magnetar properties, with SN Ic-BL magnetars rotating faster and having stronger fields. Moreover, the $P_{\rm{i}}-B_{\rm{p}}$ distribution of lGRB magnetars largely overlaps with that of SN Ic-BL magnetars. In connection with binary simulation results, we propose a unified physical classification and progenitor framework for magnetar-powered and ordinary SESNe.

Cosmic rays (CRs) drive ionization and influence gas dynamics in molecular clouds (MCs), potentially impacting the resulting star formation outcomes. Although previous simulations of individual star formation have included methods for cosmic ray transport (CRT), none have been large enough to resolve the stellar initial mass function (IMF). We conduct numerical simulations following the collapse of a $20000 M_{\odot}$ MC and the subsequent star formation including CRT, both with and without CRs accelerated by winds from the young massive stars, and compare against a non-CRT simulation. We show that after the first massive stars form, the cavity produced by feedback is more pronounced in the CRT simulations because the external CRs are able to propagate inwards and compress the gas into higher density structures. This increases the subsequent star formation in the cloud; by the end of the simulation, the SFE in the CRT simulation including stellar wind CRs is 43 \% higher than the non-CRT simulation. The IMF is also top heavy in comparison, with a slope above 1 $M_{\odot}$ that is shallower by $\sim 20$ \%. These effects are also present in the simulation without wind-accelerated CRs, but they are not as pronounced; the SFE is only 16 \% higher than the non-CRT simulation, and the IMF high-mass slope is shallower by $\sim 10$ \%. These results may explain some of the observed top-heavy IMFs, which typically occur in high-CR environments such as the galactic center.

We investigated the high-mass star formation activity in a subregion of the Sagittarius E star-forming complex, centered at (l,b) = (358.69 deg, 0.03 deg), where infrared and radio sources trace a prominent U-shaped structure that has not been identified in previous studies. We used radio continuum data from the Global View on Star Formation (GLOSTAR) survey, which is a wide-band radio (4-8 GHz) survey of the Milky Way that combines data from the Karl G. Jansky Very Large Array and the Effelsberg 100 m telescope. Using BLOBCAT source extraction software, we identified 49 compact radio sources. Based on multiwavelength associations and spectral index estimates, we identified GLOSTAR counterparts to 27 previously confirmed HII regions, detected radio emission from 3 WISE "radio-quiet" candidates, and report 5 new HII region candidates. The derived physical properties indicate that most are relatively evolved HII regions. We find around 50 cold dust clumps, predominantly toward the south and southeast. Mid-infrared flux-ratio maps ([4.5]/[3.6]) show localized shock enhancements along the arc and adjacent clumps, and 15 clumps exhibit SiO emission with broad components indicative of shocks. Together with CO data, the SiO velocity components delineate a continuous (>100 km/s) velocity bridge that links the far dust-lane inflow to the central molecular zone (CMZ) stream. The largest concentration of clumps and compact HII regions lies at this interface. These combined diagnostics favor a scenario in which bar-driven cloud-cloud collision at the far dust-lane-CMZ interface compressed the gas and triggered the observed high-mass star formation.

The Seyfert 1 galaxy NGC 3783 hosts a multiphase warm absorber (WA) that has been extensively studied in the X-ray band. High-resolution spectra from 2000-2001 revealed a complex outflow with multiple ionization and velocity components. Two decades later, new XMM-Newton and XRISM observations allow us to investigate the long-term evolution of these outflows. We perform joint spectral modeling of the XMM-Newton/RGS and XRISM/Resolve time-averaged spectra using the pion photoionization code within SPEX. We derive the ionization parameter, column density, turbulent velocity, and outflow velocity for each absorption component, and investigate their thermal stability and Absorption Measure Distribution (AMD) to characterize the physical and dynamical properties of the WA in NGC 3783 in 2024. We compare these results with the 2000-2001 epoch to assess long-term variability, stability, and possible changes in the absorber population. We identify eight WA components spanning log $ξ=$ 1.08-3.38 and outflow velocities of 480-1230 km s$^{-1}$. The ranges of column densities and turbulent velocities remain broadly consistent with the WAs from 2000-2001, but the earlier data contained more low-ionization, high-velocity components. The total column density in 2024 is 1.5 times larger than in 2000-2001, requiring replenishment by fresh material. The dominant Unresolved Transition Array (UTA) absorber (Comp. B3) has increased its column density by a factor of three while maintaining a similar ionization parameter. The WAs in NGC 3783 have undergone significant structural and dynamical evolution over the past 24 years.

We report the discovery of DESI-HVS1, a hypervelocity star (HVS) candidate identified from DESI DR1 spectroscopy and Gaia DR3 astrometry. DESI-HVS1 is an old, low-mass, metal-poor F-type star with a mass of $0.8\,M_\odot$, an age of $\sim14.1$~Gyr, and $\mathrm{[Fe/H]}=-1.6$. It is located at a heliocentric distance of $3.77^{+0.39}_{-0.36}$~kpc and has a Galactocentric total velocity of $523^{+46}_{-47}\,\mathrm{km\,s^{-1}}$, marginally exceeding the local escape speed, corresponding to an unbound probability of $P_{\rm ub} \sim 50\%$. Backward orbit integrations show that DESI-HVS1 had a closest approach to the Galactic Centre (GC) of $0.40^{+0.23}_{-0.11}\,\mathrm{kpc}$, with a velocity of $682^{+22}_{-35}\,\mathrm{km\,s^{-1}}$ and a flight time of $12.89^{+0.92}_{-0.74}\,\mathrm{Myr}$. The reconstructed orbit exhibits a clear perigalactic turning point and only a single crossing of the Galactic midplane ($P_{\rm cross} > 0.95$). These properties suggest that DESI-HVS1 is most naturally explained by the Hills mechanism, although alternative scenarios cannot be entirely ruled out. Its discovery provides the first strong evidence for an old, low-mass HVS candidate consistent with a GC origin, indicating that the apparent dominance of young, massive GC-origin HVSs is likely a consequence of observational selection effects.

The distribution of galaxies, halo abundance, and peculiar velocities are influenced by non-linear gravitational interactions, making the study of non-linear evolution crucial for accurate cosmological predictions. We explore these aspects using N-body simulations. Theoretical models of the halo mass function (HMF) can be formulated without referencing a cosmological model or input power spectrum. HMF obtained from N-body simulations show systematic deviations of 5-20\% from theoretical predictions. The physical origin of deviations may result from cosmology, the power spectrum, or both. We examine HMF deviations from universality for scale-free power spectra with an Einstein-de Sitter cosmology. We demonstrate that the mass function exhibits an explicit dependence on the slope of the input power spectrum. We find that an effective index of the $Λ$CDM model can correspond to the HMF from scale-free cosmologies as a first approximation. Furthermore, structure formation has led to deviations from homogeneity and isotropy on scales up to at least $100$ Mpc/h, expected to affect measurements of $H_0$. We revisit this issue of the concordance model. We find a correlation between errors in $H_0$ estimates and the density around the observer. Further, our mock observations reveal that deviations of up to 5\% can occur in Milky Way-sized halos. While this finding alone does not fully resolve the Hubble tension, it may account for part of it. It is essential to understand the limitations of N-body simulations to avoid misinterpreting data. We show that the missing power at small scales introduces errors in the root-mean-square fluctuations and in the simulated mass function. Our analytical calculation indicates that mode coupling between small and large scales depends on resolving collapsed halos. Therefore, accurate mode coupling estimates require sufficient halos in the simulation.

We present multi-wavelength study of the $γ$/X-ray transient EP250416a (also designated GRB 250416C), triggered by the Einstein Probe (EP) Wide-field X-ray Telescope and also by SVOM and Konus-Wind. Observations spanning the gamma-ray, X-ray, and optical bands facilitated detailed analysis of the burst's prompt emission, afterglow evolution, and physical origin. EP250416a exhibits a burst duration of 30 s in X-ray and 17.7 s in gamma-rays, with joint spectral fitting of 0.5-5000 keV data gives $E\rm_{peak}=342_{-232}^{+90}$ keV. Optical spectroscopy of the afterglow, acquired with the Gemini Multi-Object Spectrograph (GMOS) on Gemini South, yielded a redshift of $z=0.963$. Accounting for the measured redshift, the isotropic energies are $E\rm_{X,iso}=2.7_{-0.5}^{+0.9}\times10^{50}$ erg and $E\rm_{γ,iso}=7.34_{-2.1}^{+5.1}\times10^{51}$ erg, aligning with the Amati relation for long GRBs. The fluence ratio $\rm S(25-50~keV)/S(50-100~keV)=0.78_{-0.15}^{+0.1}$ classifies EP250416a as an X-ray rich (XRR) GRB. The X-ray afterglow shows an initial shallow decay ($α\approx -0.5$) transitioning to a canonical decay phase ($α\approx -1$), with a very late jet break at $t\sim 1.5\times 10^6$ s, corresponding to a jet half-opening angle of $θ_j=10.6_{-1.8}^{+1.9}$ degrees. EP250416a is optically dark, as it shows only a faint $r$-band detection ($r=24.16$ mag) from Gemini South-GMOS and a low optical-to-X-ray spectral index $β_{\rm OX} = 0.3$. This may be attributed to significant host-galaxy extinction, with a required $A_V^{\text{host}}=5.5\ \text{mag}$ derived from the extinction curve model.

We present the first high-resolution spectroscopy of the black hole high-mass X-ray binary Cygnus X-1 with XRISM, including orbital-phase-resolved analyses and tentative evidence of short-term variability in the Fe-K band on second timescales. Using data from the Performance Verification phase in April 2024, we analyzed spectral variability across orbital phases with the Resolve microcalorimeter and the Xtend CCD imager. The unprecedented resolution of Resolve reveals variability in highly ionized Fe absorption lines. The absorption features show orbital-phase-dependent variability in column density, ionization state, and blueshifted velocity, suggesting structural variations in the focused stellar wind along the line of sight. We also find indications of subtle broadening of the neutral Fe emission profile. In addition, intensity-sorted spectroscopy during dip phases suggests possible variability on timescales of a few seconds in the absorption features, consistent with cooler, denser and lower-ionized gas clumps. Although the statistical significance is limited, these results hint that the stellar wind and the X-rays from the accretion disk around the black hole may interact on timescales as short as a few seconds. These XRISM results constrain wind-fed accretion in Cyg X-1 and highlight Resolve's capability to probe plasma environments in high-mass X-ray binaries.

We demonstrate that discrete self-similarity (DSS), originally discovered in the collapse of a massless scalar field in an asymptotically flat system, survives in primordial black hole (PBH) formation within an expanding cosmological background. Using fully relativistic numerical simulations of massless scalar-field collapse in an Friedmann-Lemaître-Robertson-Walker universe, we resolve the critical regime down to $|p-p_c|\sim 10^{-8}$, where $p$ and $p_c$ respectively are a parameter of the family of initial data and its threshold value, and find clear log-periodic oscillations in the PBH mass scaling relation. The detailed structure of these oscillations differs from that previously reported in the asymptotically flat case, exhibiting a more pronounced asymmetry between peaks and troughs. Analyzing two distinct families of initial data (Gaussian and piecewise rational curvature profiles), we find critical exponents and DSS periods that differ slightly but are broadly consistent within uncertainties. The presence of DSS implies characteristic log-periodic modulations in the PBH mass spectrum, with potential consequences for PBH abundances and the spectrum of induced gravitational waves.

JWST has revealed a population of super-luminous early galaxies with a volume density in excess of most expectations. The spectra reveal diverse properties: while some reveal strong emission lines characteristic of galaxies in the midst of strong bursts, others show weak emission lines that could reflect old stellar populations, large escape fractions, or post-burst star formation histories. Through the JWST Cycle 4 large program SPURS, we have obtained ultra-deep (29 hr) rest-frame UV spectroscopy of a z=9.3 super-luminous ($M_{\rm UV}=-21.66$) galaxy with large assembled stellar mass (1.6$\times$10$^9$ $M_\odot$) and extremely weak emission lines (H$β$ EW $\approx25$~Å). The strong stellar wind features and rest-optical line ratios suggest the galaxy is already significantly enriched, with a metallicity of 0.4--0.7~Z$_\odot$. The interstellar absorption lines reveal outflows ($v\simeq -161$~km~s$^{-1}$) with a large neutral gas covering fraction, suggesting that the weak emission lines are not due to large escape fractions. The combination of the Balmer break, weak emission lines, and stellar wind features constrains the star formation history, indicating a recent burst of star formation lasting 10--20 Myr followed by a downturn over the last 10~Myr. The observations suggest that $z\gtrsim 9$ weak emission line galaxies such as this source can be explained by stochastic star formation, provided that the downturns in star formation are recent (i.e., <10 Myr prior to observation). The ultra-deep grating spectrum enables the IGM damping wing to be characterized, decoupling the effects of local absorption. The smooth Ly$α$ break indicates that this source, one of the most massive galaxies known at z>9, is likely situated in a small ionized bubble ($0.29_{-0.09}^{+0.11}$~pMpc), as is common at large neutral hydrogen fractions ($\bar{x}_{\rm HI}=0.81_{-0.21}^{+0.14}$).

We present the first high-resolution X-ray spectral analysis of Cygnus X-1 using XRISM. The observation was carried out from April 7 to 10, 2024, covering the orbital phase range 0.65--0.17 during its low/hard state. Taking advantage of the exceptional energy resolution of the Resolve instrument, we examined highly ionized iron absorption lines and characterized the ionization states, column densities, and line-of-sight velocities of the absorbing plasma. Spectral analysis revealed an ionization parameter of approximately 3, column densities of a few times 10^21 cm^-2, and a blueshifted velocity of approximately 100 km s^-1. The observation was divided into two phases: before and after orbital phase phi_orb = 0.9, corresponding to non-dipping and dipping intervals. While only weak absorption features were present before phi_orb = 0.9, strong absorption by He-like and H-like Fe appeared during the dipping phase. We measured equivalent widths of 2.3 eV, 0.4 eV, and 1.2 eV for He-like Fe K-alpha, and H-like Ly-alpha1 and Ly-alpha2, respectively, demonstrating the capability of XRISM Resolve to securely detect narrow absorption features of only a few eV. These measurements trace the motion of the absorbing material and offer insight into the kinematics and spatial distribution of the wind in the vicinity of the black hole. These findings enhance our understanding of wind-fed accretion in Cygnus X-1 and highlight the importance of continued high-resolution X-ray observations to further constrain the physical properties of winds and accretion flows in high-mass X-ray binaries.

We present ultra-deep ($\simeq 20-30$ hours), rest-frame UV spectroscopy with NIRSpec/JWST of two UV-bright galaxies at $z\sim 8.7$, CEERS-1019 and CEERS-1025 ($Z_{\rm neb} \simeq 0.1 Z_{\odot}$). The spectra reveal exceptionally strong P-Cygni profiles in wind lines (NV $λ$1240 and CIV $λ$1550) and significant broad and strong HeII $λ$1640 emission ($\rm EW\simeq 2-4$ A). We compare the observations with synthetic stellar population models at $Z_{\star} \simeq 0.1 Z_{\odot}$, both including and excluding very massive stars (VMS). Models including VMS provide a markedly improved fit to the data relative to non-VMS models ($Δ$AIC and $Δ$BIC $> 70$), which fail to reproduce the observed strengths of the wind features. A comparison with empirical spectra of VMS-dominated systems in the local Universe further supports this interpretation. The best-fit VMS models imply extremely young ages of the stellar populations ($\simeq 1.5-2.0$Myr) and high ionizing photon production efficiencies ($\log ξ_{\rm ion} [\rm Hz erg^{-1}] \gtrsim 25.8$), exceeding those inferred from non-VMS models by $\sim 0.1-0.2$ dex. These results provide evidence for an overabundance of VMS at high-$z$ with an IMF extending well beyond $100 M_{\odot}$, and highlight their potential role in shaping the rest-frame UV spectra, chemical enrichment, and ionizing output of galaxies in the early Universe.

We present a new implementation of the SFUMATO code, called SFUMATO#, for solving self-gravitational radiation hydrodynamics problems using adaptive mesh refinement (AMR) with the CUDA/HIP programming frameworks. The code incorporates a multigrid solver for self-gravity, radiation transfer with M1 closure and reduced speed of light approximation, non-equilibrium chemistry, thermal evolution, and sink particle schemes. We develop new non-equilibrium chemistry and thermal solvers based on a linearized implicit method, whose accuracy is validated through a series of test problems by comparison with solutions obtained using the Newton-Raphson method. By incorporating the heat capacity of dust grains, the dust temperature can be evolved without iterative energy-balance calculations. From the perspective of computational cost, we demonstrate that adopting an increased pseudo dust heat capacity accelerates the chemistry solver while preserving accuracy, even when the value is increased by up to three orders of magnitude relative to the realistic value. In addition, we perform a suite of test problems to confirm the validity of the other components of our implementation. The code supports multi-GPU execution via MPI-based parallelization. We measure the strong-scaling performance of the hydrodynamics and self-gravity solvers on both uniform and AMR grids, as well as the overall code performance using a giant molecular cloud simulation. We find that the computational cost of the self-gravity solver increases with the number of MPI processes, indicating that efficient parallel performance is achieved only when the number of devices is chosen such that the cost of the self-gravity solver remains comparable to that of the other components.

We investigate electron--positron pair creation through the Schwinger process in newborn magnetars with millisecond spin periods and surface dipole fields close to or above the QED critical field, $B_{\rm Q} = 4.414\times10^{13}\,\mathrm{G}$. In the unscreened field scenario, we derive the analytical global pair creation flux and recast it into a compact form with accurate analytic approximations. For a fiducial model with $B_{\rm p} = 10^{14}\,\mathrm{G}$ and $P_0 = 1\,\mathrm{ms}$, the Schwinger channel exceeds the classical Goldreich--Julian particle supply by many orders of magnitude and becomes the dominant source of charges at the earliest stage of the magnetar. The associated discharge removes about $90\%$ of the initial rotational energy within 30 ms, suppresses the gravitational-wave loss channel, and implies that the observable millisecond phase is extremely short in this unscreened scenario. The rapid energy release over such a short timescale may also provide a viable power source for astrophysical transients. Extending the same fiducial model to $10^4\,\mathrm{yr}$ gives spin periods of order seconds, linking newborn millisecond magnetars to the mature magnetar population.

Most Active Galactic Nuclei (AGN) are Radio Quiet, with radio emission that may arise from star-formation activity, AGN-driven winds, weak jets, and coronal activity. Disentangling these mechanisms is challenging and requires detailed multi-wavelength investigation, but it is crucial for quantifying AGN feedback in galaxy evolution. We present a detailed radio investigation of 21 X-ray selected AGN in the Supermassive Black Hole Winds in X-Rays (SUBWAYS) sample (log Lbol = 44.9-46.3 erg/s, z=0.1-0.5), selected to systematically search for Ultra-Fast Outflows (UFOs). UFOs are detected in 30% of the targets, making the sample particularly well-suited for investigating the role and signatures of multi-scale outflows at different frequencies. We build the radio SED of the sources complementing our proprietary data, collected with the JVLA at 1.5 and 6 GHz, with images from LoTSS and other publicly available radio surveys between 150 and 1400 MHz. We investigate the role and occurrence of the aforementioned mechanisms, with particular interest in outflows and their possible relation with UFOs. We combined information on spectral indices, luminosities, and morphologies of the radio emission with properties derived in other wavebands, such as Star Formation Rate, X-ray luminosity, Eddington ratio or the UFO kinetic luminosity. All the sources are detected and are mostly consistent with RQ AGN. For 80% of the sources the data suggest the presence of an outflow (wind or weak jet). Interestingly, our results indicate that AGN with UFOs tend to have larger radio extension and a steep radio spectrum consistent with outflows. Moreover, the radio emission of the 6 UFO hosts is consistent with predictions from wind-driven shock models, possibly indicating a direct connection between the two phases. Alternatively, this may reflect physical conditions favouring the rise of both phenomena.

The estimation of stellar atmospheric parameters for large-scale samples, particularly metal-poor stars, is a cornerstone of Galactic archaeology. In this work, we optimized a photometric filter design tailored to measuring stellar metallicities for very metal-poor stars with [Fe/H]$< -1$.The optimal configurations consist of a central wavelength $λ_{\rm c}$ = 3960 Angstrom with a bandwidth $Δλ$ = 80 Angstrom for giant stars, and $λ_{\rm c} $= 3920 Angstrom with $Δλ$ = 80 Angstrom for dwarf stars. By applying these optimized filters to synthetic photometry derived from Gaia XP spectra, we inferred metallicities for both populations. Both internal and external validations demonstrate high precision across a wide metallicity range: 0.18-0.19 dex for $-2 \le \rm [Fe/H] \le -1$, 0.23-0.33 dex for $-3 \le \rm [Fe/H] \le -2$, and approximately 0.39 dex for the most metal-poor regime, successfully extending down to $\rm [Fe/H] \approx -4$ for giant stars, $\rm [Fe/H] \approx -3.3$ for dwarf stars. Finally, we present a catalog of approximately 14.5 million metal-poor stars with robust $\rm [Fe/H]$ measurements, along with more than ten thousand red giant ultra metal-poor candidates with $\rm [Fe/H] < -4.0$, providing a valuable resource for exploring the early formation and chemical evolution of the Milky Way.

We investigate dense-matter equations of state (EOSs) within a Bayesian framework, with particular emphasis on whether recent small-radius compact-star candidates can be accommodated in a twin-star scenario. For the hadronic sector, we adopt a meta-modeling EOS constrained by the NICER mass--radius measurements of PSR J0030$+$0451, PSR J0437$-$4715, PSR J0614$-$3329, and the massive pulsar PSR J0740$+$6620. The hadronic inference indicates that PSR J0614$-$3329 favors a somewhat softer EOS than the other two \(\sim1.4\,M_\odot\) pulsars, while the \(\sim2\,M_\odot\) constraint prevents the EOS from becoming too soft. We then introduce a strong first-order phase transition through a constant-speed-of-sound quark-matter segment. Using HESS J1731$-$347 and XTE J1814$-$338 to constrain the phase-transition parameters, we find a preferred transition density of \(n_\mathrm{t}\sim2.7\text{--}2.8\,n_0\), a sizable energy-density jump of \(600\text{--}700\) MeV, and a relatively large post-transition sound speed of \(c_s^2/c^2\sim0.85\). Such a phase transition generates a disconnected hybrid branch with radii of about \(6\text{--}7\) km at masses around \(1.2\text{--}1.4\,M_\odot\), and strongly suppresses the dimensionless tidal deformability relative to the purely hadronic branch. This pronounced change in tidal deformability is a characteristic signature of the twin-star mechanism and may provide an important observational tool for identifying phase transitions in neutron-star matter in future multimessenger measurements. These results show that small-radius compact stars can provide direct constraints on both the strength of a first-order phase transition and the stiffness of the post-transition phase in dense matter.

Calcium-aluminum-rich inclusions (CAIs) in carbonaceous chondritic meteorites are the oldest relics in the solar system. Notably, their radiogenic age feature a brief (100 kyr) condensation episode. In contrast, the reservoirs of the short-lived isotopes in CAIs, presumably supernovae or asymptotic giant stars, pollutes star-forming regions in giant molecular cloud complexes (GMC) over much longer (Myr) duration. Through a series of numerical simulations, we show here the possibility that, within an extended region (2$\sim$3 AU), nearly all ``pre-solar'' CAI-loaded grains in the infall clouds were sublimated and re-condensed during the early ($ \lesssim 10^5$ yr) infall and formation of class-0 disks. We adopt a set of initial conditions from a previous hydrodynamic simulation of the collapse of GMC and the formation of young stellar clusters. We analyze the evolution of the disk's thermal distribution and dynamical structure resulting from the interaction between circumstellar disks and infalling gas. Our follow-up simulations, with much higher resolution, show significant and rapid changes in the disk orientation and morphology due to the dynamic infall of external streamers. Warps and global spiral density waves commonly appear. They lead to intense dissipation which heats the gas to sufficiently high temperature to sublimate prior-generation CAIs. This solid-to-gas phase transition is followed by subsequent cooling and re-condensation. The CAI contained in the meteorites today could be the relics of the last episode of major infall onto class 0 disks.

We investigate whether the observed radio-infrared-$γ$-ray correlation in star-forming galaxies is a geometric effect rather than a signature of local cosmic-ray (CR) calorimetry. Using the GALPROP framework, we generate synthetic observations for external viewers from a grid of 3D Milky Way models with varied CR source, gas, interstellar radiation, and magnetic field distributions, all normalised to reproduce local CR data. We find that a tight, quasi-linear correlation arises naturally from line-of-sight integration through the extended, radially-structured disc, even when local calorimetry is absent. The correlation's properties depend strongly on viewing geometry, preserving its form under moderate inclination but breaking down in edge-on views where galactic components are stratified. We conclude that the correlation is primarily an emergent property of geometric projection, not local physics. This implies that its scatter is likely not random noise but a diagnostic of underlying galactic structure and viewing angle.

Models predict that chemical enrichment and gas redistribution should proceed rapidly once star formation begins, yet direct observational constraints at the earliest cosmic epochs have been scarce. Here we present evidence that metal-enriched gas in multiple ionic phases was already present around galaxies before the midpoint of cosmic reionization. Using JWST/NIRSpec rest-frame ultraviolet spectroscopy of three galaxies at redshifts $z=7.2-9.3$, we detect blueshifted metal absorption in all three systems; across the sample, the detected transitions span neutral, low-ionization, and high-ionization species, including O I, Si II, C II, Si IV, and C IV. These absorption features show velocity offsets of order $|Δv| \sim 50$--$250\,\mathrm{km\,s^{-1}}$, predominantly blueshifted relative to the systemic redshifts of the host galaxies derived from nebular emission lines. This ionic coexistence within a broadly shared velocity structure, together with the observed equivalent-width ratios, is consistent with outflowing or otherwise kinematically disturbed galaxy-associated gas, similar to that seen at lower redshift. The observations therefore indicate that metal-enriched gas associated with galaxies was already kinematically disturbed at very early times, requiring rapid metal production in the early generations of stars. These results show that key conditions for baryon cycling were established in at least a subset of luminous galaxies within the first several hundred million years of cosmic time, well before the completion of reionization.

Various processes can induce long-lived overdense rings and arcs in protoplanetary and AGN accretion discs, such as the accumulation of gas at the outer edge of the dead zone, or the infall of material. Using the local approximation of dynamical friction, we investigate the orbital evolution of a low-mass highly-eccentric point-mass accretor (perturber) embedded in an isothermal disc hosting a density ring. We specifically consider the regime in which the eccentricity exceeds four times the disc aspect ratio. For prograde perturbers, orbits that cross the ring progressively circularize while their semi-major axes converge toward the ring radius. As a result, perturbers accumulate, forming a population ring superimposed on the gaseous ring. The ring therefore acts as a migration trap for these eccentric orbits. We also find that prograde orbits tangent to the ring, either at apocentre or pericentre, remain tangential throughout their evolution; perturbers confined to these trajectories experience the highest accretion rates. In contrast, retrograde perturbers always migrate inward. Once the semi-major axis becomes smaller than the ring radius, the eccentricity grows, but not enough for the orbit to intersect the ring again. We also discuss how feedback effects, such as jet launching and thermal torques, could modify the effective forces acting on the perturbers.

The internal structure of dark matter halos on sub-galactic scales remains a key open question, particularly in the context of the core-cusp problem. Ultra-faint dwarf galaxies (UFDs), owing to their extreme dark matter dominance, provide a promising laboratory to probe these density profiles through stellar tracers. In this work, we assess the capability of the Chinese Space Station Telescope (CSST) to detect and characterize wide binary stars in the nearby UFD Segue 1, using mock observations. We generate mock binary populations based on our existing $N$-body simulations and incorporate realistic CSST observational conditions, including the expected deep-field limiting magnitude ($g \sim 27.5$ mag) and a photometric completeness of approximately $90\%$. The two-point correlation function (2PCF) of stellar pairs is used as a statistical tool to recover the binary fraction under these assumptions. We find that CSST can robustly detect wide binaries at the $3σ$ level for binary fractions as low as $f_b \gtrsim 0.01$, provided a stellar sample size of $N_{\mathrm{star}} \gtrsim 2300$. However, distinguishing between cusped and cored dark matter profiles is significantly more demanding, requiring $N_{\mathrm{star}} \gtrsim 6000$ and $f_b \gtrsim 0.1$ within $\sim 40\mathrm{kpc}$.

In core-collapse supernovae (CCSNe), deleptonization normally favors $ν_e$ over $\barν_e$ emission. However, lepton-number emission self-sustained asymmetry (LESA) can make the energy-integrated emitted lepton-number flux negative along some directions. We derive a simple diagnostic for this transition and test it in 33 independent 3D CCSN simulations: 25 Princeton/Fornax models ($8.1$--$100\,M_\odot$) and 8 Garching models, including non-, slow-, and fast-rotating $15\,M_\odot$ cases. Of 23 non-black-hole-forming Princeton models, 22 cross the threshold, with median onset $t_c=225\,\mathrm{ms}$, IQR $162$--$264\,\mathrm{ms}$, and cross-model scatter $\mathrm{CV}=18.6\%$. Full-sky flux-sign searches show that the threshold identifies the anti-LESA-pole transition, distinguishing the global LESA-driven crossing from early localized turbulent crossings. The fast-rotating Garching $15\,M_\odot$ model, where rapid rotation suppresses the LESA dipole, is correctly classified as a non-crosser without using any rotation parameter. Both black-hole-forming Princeton models cross near $250\,\mathrm{ms}$ post-bounce and remain above threshold for $1807$ and $2463\,\mathrm{ms}$ before collapse. Thus, in the next nearby CCSN, the emitted $\barν_e$ energy flux may exceed the $ν_e$ flux along some lines of sight. Such directions may also correlate with sustained fast flavor instability, although testing this requires local phase-space distributions or dedicated linear stability analysis. The relevant quantity here is the energy-integrated emitted flux field, i.e. a luminosity difference per steradian, not a neutrino number flux.

We report on the spectroscopic confirmation of overdense regions of massive quiescent galaxies (QGs) in the early Universe with JWST/NIRSpec. Based on data from the DeepDive NIRSpec program and archival data from the Dawn JWST Archive, we confirm three QGs in the vicinity of Jekyll & Hyde, a pair of massive QG and a dusty star-forming galaxy, at $z=3.71$ and two QGs around SXDS-27434 at $z=4.01$. According to the analysis of galaxy number density with photometric redshifts, Jekyll & Hyde (SXDS-27434) are in an overdense region, where the number density of galaxies is three times higher than the average in the COSMOS (SXDS) field. SED fitting suggests that most of the QGs follow similar star formation histories and have consistent formation and quenching epochs. The same trend is observed in other proto-clusters hosting QGs that were already identified by ground-based telescopes, indicating that the large-scale environment plays an important role in the formation of QGs. In addition, JWST spectra reveal a broad H$α$ emission line from SXDS-27434 and faint emission lines from other three QGs, which are identified as AGN-driven based on their emission line ratios. The overdensity is also reproduced by the Illustris TNG300 simulation at $z=3.71$, in which the member QGs also have similar quenching epochs. These results suggest that large-scale structure may enhance merger activity and/or gas accretion and trigger AGN feedback, which simultaneously drives galaxy quenching in the overdensity.

The James Webb Space Telescope (JWST) has revealed a puzzling population of massive black holes in the first billion years, many of which are over-massive compared to their hosts (obese black holes), and reside in metal-poor hosts, posing a challenge for theoretical models at these early epochs. In this work, we compare the observational properties of astrophysically-seeded black holes using the DELPHI semi-analytic model and cosmologically-seeded primordial black holes (PBHs) using the PHANES analytic model. We explore the growth of light ($\sim 100 M_\odot$) and heavy ($\sim 10^{3-5}M_\odot$) seeds through mergers and accretion (both Eddington-limited and at super-Eddington rates) in the astrophysical scenario; PBHs (seeded between $10^{0.5-6}M_\odot$) only grow through accretion at sub-Eddington rates. Comparing to observables at $z \sim 5-10$, the only model that can be ruled out is the one where we allow Eddington-limited accretion onto light seeds. The observed high values of the black hole mass-stellar mass relation ($0.3-1$) can be reproduced by both PBHs and heavy seeds accreting at super-Eddington rates. However, only the PBH and Eddington-limited heavy seeding models can simultaneously reproduce the observed black hole masses (${\rm M_{bh}}$), stellar masses ($M_*$), and extremely low host metallicities ($Z \leq 0.01 Z_\odot$) inferred at $z \sim 7-10$. Crucially, we find PBHs show decrease in the black hole mass-stellar mass ratio with increasing halo mass at all redshifts, contrary to any astrophysical black hole model. Selecting systems at $z \sim 7$ with ${\rm M_{bh}}/M_* > 0.1$ and bolometric luminosities $\sim 10^{44-46} {\rm erg~s^{-1}}$ that show a negative black hole to stellar mass ratio and reside in $10^{9-11}M_\odot$ halos offer a promising clustering-based discriminant of PBH seeding models.

We use the 3D fully kinetic simulation to study different turbulence modes and turbulence anisotropy of relativistic turbulence in magnetically dominated collisionless plasmas. We extend the method developed by Cho & Lazarian (2002) for decomposing non-relativistic magnetohydrodynamic (MHD) turbulence into Alfvén, fast, and slow modes to the regime of collisionless plasmas. We find that Alfvén and slow modes are anisotropic, following the Goldreich & Sridhar (1995) scaling, while fast modes are isotropic. We observe a larger kinetic energy fraction of fast modes compared to that in the non-relativistic MHD turbulence, suggesting a stronger coupling of Alfvén and fast modes in relativistic magnetized turbulence in collisionless plasmas. We further examine the dynamic alignment and find a weaker scale dependence of the alignment angle than previously proposed. The dominant thermal fluctuations in the kinetic range can cause flattening of the turbulent velocity structure function and weakening of the turbulence anisotropy and dynamic alignment near the kinetic scales.

GD-1 is among the longest, coldest stellar streams in the Milky Way, making it an ideal target for probing dark matter substructure through dynamical heating. We present a catalog of 608 spectroscopically confirmed GD-1 members from the first three years of Dark Energy Spectroscopic Instrument (DESI) observations. This constitutes the largest homogeneous spectroscopic sample of GD-1, doubling the number of members previously available only through heterogeneous compilations combining multiple surveys with different systematics. Using these data, we derive updated stream tracks in sky position, proper motion, and radial velocity that extend over $100^\circ$ of the stream. We apply a Gaussian mixture model to decompose the stream into a dynamically cold thin component ($σ_V = 2.49\pm 0.28$ km s$^{-1}$, width $= 0.23\pm0.01^\circ$) and a kinematically hot cocoon ($σ_V = 6.13\pm0.75$ km s$^{-1}$, width $= 2.18\pm0.17^\circ$). The cocoon contains $\sim30\%$ of members and its velocity dispersion is consistent with $\sim11$ Gyr of heating by cold dark matter subhalos. We also detect a large proper motion dispersion ($41.36\pm4.98$ km s$^{-1}$) along the stream direction in the cocoon component. This feature indicates a significant line-of-sight distance spread in the cocoon, and its origin will be further explored in a forthcoming paper. These measurements demonstrate the power of DESI spectroscopy for characterizing the multi-component phase-space structure of stellar streams and constraining small-scale dark matter substructure.

We investigate the connection between supermassive black holes (SMBHs), their host galaxies, and large-scale dark-matter halos using broad-line X-ray AGN from the XMM--XXL and Stripe\,82X surveys, together with galaxies from VIPERS and SDSS/Stripe\,82. Building on the homogeneous host-galaxy catalogue presented in Paper~I, we test whether AGN with a given black-hole mass, $M_{\rm BH}$, inhabit different large-scale environments from non-AGN galaxies with similar host properties. We first examine the empirical $M_{\rm BH}$--$M_{\star}$ relation of the AGN sample. We find a shallow trend with substantial scatter, likely driven by flux-limited selection effects and uncertainties in virial black-hole mass estimates. The ratio $M_{\rm BH}/M_{\star}$ decreases with increasing stellar mass, and AGN lying above and below the empirical relation show different median host properties, consistent with non-synchronous SMBH and stellar growth. We then divide the AGN into two black-hole mass bins, $8.0 \le \log(M_{\rm BH}/M_\odot) < 8.5$ and $8.5 \le \log(M_{\rm BH}/M_\odot) < 9.0$, and construct galaxy control samples matched in $M_{\star}$, SFR, and sSFR using a multivariate nearest-neighbour method. From AGN--galaxy cross-correlation functions, we infer the characteristic halo masses of AGN and matched galaxies. In the lower-$M_{\rm BH}$ bin, AGN occupy halos statistically indistinguishable from those of their controls. In the higher-$M_{\rm BH}$ bin, we find a mild indication that AGN may reside in somewhat more massive halos, with a difference of about 0.4 dex, although still consistent within the uncertainties. If confirmed with larger samples, this would suggest that halo-scale processes become important mainly at the highest $M_{\rm BH}$.

Radio continuum observations are widely used to study cosmic ray (CR) electron populations and transport processes in star-forming galaxies, but their interpretation relies on several simplifying assumptions. Here, we revisit three common assumptions: that some vertical radio profiles can be explained by CR advection alone, that radio spectra directly trace the galaxy-wide CR electron spectrum, and that bremsstrahlung and Coulomb losses are negligible for radio-emitting electrons. We model radio emission using time-dependent CR electron evolution in a magnetohydrodynamical simulation of an isolated Milky Way-mass galaxy. CR electron spectra are evolved self-consistently along Lagrangian tracer particles with the CREST framework, including injection at supernova remnants, advection with the gas, and spatially and temporally varying radiative losses. We compare these results to commonly adopted steady-state models. We find that advection-only transport in self-consistently driven galactic winds fails to reproduce the extended vertical radio intensity profiles observed in edge-on galaxies, despite reproducing the observed steepening of spectral indices with height. This is because slowly accelerating winds keep electrons in strong cooling environments for too long. Matching observed radio haloes with advection alone requires unrealistically high midplane wind velocities, implying that additional transport or re-acceleration processes are required. Although galaxy-integrated CR electron spectra at radio-emitting energies are similar across models, the resulting synchrotron spectra differ systematically because radio emission is biased toward young electrons in dense, strongly magnetised regions. Finally, we show that bremsstrahlung and Coulomb losses significantly shape radio spectra even when their loss rate is subdominant and therefore cannot be neglected.

We present high-sensitivity Very Long Baseline Interferometry (VLBI) observations of four ultraluminous X-ray sources (ULXs): Holmberg II X-1, IC 342 X-1, NGC 6946 X-1, and NGC 925 X-1. No compact emission was detected on milliarcsecond scales, with rms noise levels reaching approximately 5--20 $μ$Jy. The corresponding $5σ$ flux density upper limits reach $\sim 26\,μ\mathrm{Jy}$, implying radio luminosity limits $L_{\rm R} \lesssim 2 \times 10^{33}\,\mathrm{erg\,s^{-1}}$. This disfavors any persistently bright hard-state-like compact core at our sensitivity level. The previously reported VLBI core in Holmberg II X-1 exhibits significant long-term variability, broadly consistent with an overall decline over the past decades. This behavior is consistent with emission from optically-thin ejecta undergoing adiabatic expansion. The VLBI non-detections may reflect intrinsically weak/intermittent compact emission, and/or low--surface--brightness structure that is resolved out by VLBI, and/or absorption/propagation effects such as free--free absorption in dense, ionized winds.

When a radio jet is partially optically thick in the launching region, its apparent compact core may display frequency-dependent positional shifts. High-precision astrometric measurements of core shifts enable astronomers to pinpoint the jet's origin and place tight constraints on the magnetic field. BL Lacertae, the archetypal BL Lac object, hosts a highly variable and well-collimated jet. To independently constrain its innermost core shifts, we conducted very long baseline interferometric (VLBI) observations at 8.4, 12.4, 15.2, 23.6, and 43.2 GHz. By exploiting a nearby (13.3 arcmin) steep-spectrum calibrator (NVSS J220340+420839) through inverse phase-referencing VLBI astrometry, we detect nearly unbiased two-dimensional core shift measurements with state-of-the-art precisions of 5-30 $μ$as, which are significant at $>3σ$ confidence. The core shift between 8.4 and 43.2 GHz reaches 250 $μ$as. The apparent core shifts scale with frequency as $ν^{-1/k_r}$, implying the existence of an optically thick region in the upstream of jet. The derived core-shift index, $k_r\!=\!1.18^{+0.59}_{-0.34}$, is consistent, within uncertainties, with the canonical $k_r\!=\!1$ expected under energy equipartition between the jet particle and magnetic field energy densities, while allowing for modest deviations given that BL Lacertae was captured in a flaring state.

Spectroscopic surveys such as the Dark Energy Spectroscopic Instrument (DESI) and Euclid are mapping the spatial distribution of millions of galaxies, with Emission Line Galaxies (ELGs) serving as the dominant tracer in the redshift range $0.8<z<1.6$. Standard approaches for extracting cosmological information from galaxy clustering, however, typically discard highly constraining measurements from the nonlinear regime. We apply SHAMe-SF - a modification of Subhalo Abundance Matching tailored for star-forming galaxy samples - to analyse the three-dimensional clustering of DESI ELGs from the One-Percent data release, extending their cosmological analysis deep into the nonlinear regime. We validate our pipeline using two mock ELG samples drawn from the state-of-the-art cosmological hydrodynamical simulation MillenniumTNG, demonstrating that our model yields unbiased constraints on $σ_8$ and $Ω_{\rm m}h^2$ down to scales of $0.3~h^{-1}$Mpc on both samples. We find that including scales below $0.8~h^{-1}$Mpc is critical for mitigating projection effects and obtaining unbiased constraints on $σ_8$. Applied to the DESI One-Percent measurements, our analysis yields $\sim6$% constraints on $σ_8 = 0.81^{+0.05}_{-0.06}$ and $Ω_{\rm m}h^2=0.146^{+0.009}_{-0.009}$. Remarkably, the accuracy of these constraints is similar to that obtained from the combined full-shape analysis of all DESI DR1 tracers, yet using only 1% of the survey volume. A naive extrapolation of our results from the One-Percent to the full survey area suggests that the complete survey could deliver roughly an order-of-magnitude improvement in precision - a prospect that, while subject to significant practical challenges, illustrates the cosmological potential encoded in the nonlinear regime.