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Physics

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Thursday 9 July

General Relativity and Quantum Cosmology

Acceleration in 3D Einstein-Gauss-Bonnet Gravity

Cendikiawan Suryaatmadja, Dong-han Yeom and Robert Mann

We present a new class of exact solutions in Einstein-Gauss-Bonnet gravity in 2+1 dimensions that generalize the C-metric. This set of metrics equals the C-metric multiplied by a factor which, along with the massless scalar field, depends on a single variable whose value governs the structure of the spacetime. As in Einstein gravity there are three classes of metrics, but within each class we find six distinct subclasses of solutions. After discussing their basic structure, we concentrate only on one subclass that is locally AdS. In the zero-coupling limit, this subclass of solutions not only remains well defined and recovers the C-metric but also encompasses two previously unknown representations of the AdS spacetime. Furthermore, we establish the existence of a domain wall and delineate its energy conditions. We also find new classes of solutions of non-constant curvature, whose interpretation remains to be understood.
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Earth and Planetary Astrophysics

A Retrieval Framework for Observationally Constraining the Parameters of Circumplanetary Disks

Aster Taylor and Fred Adams

As they form, giant planets are surrounded by disks of gas and dust sourced from the background circumstellar disk. Although there have been few detections to date, upcoming instruments are likely to discover many more of these systems in the coming decades. Accurate spectral modeling will enable these observations to constrain the properties of these forming systems. Towards this end, we have constructed a semianalytic model for the structure and radiative signatures of geometrically thick circumplanetary disks and their planet hosts. Fitting these radiative signatures to synthetic observations of a two-dimensional disk model then quantifies the parameter constraints that can be derived (subject to model assumptions). This machinery provides estimates of the values and uncertainties in system parameters, and some combinations of parameters have significantly smaller uncertainties than others. This model is then used to fit observations of real protoplanets, with good results. The derived parameters provide useful context about the local extinction, formation history, and initial entropy of these objects.
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Materials Science

Interfacial chirality-induced magnetic-field-free switching with high energy efficiency in all-vdW heterostructures

Kai-Xuan Zhang, Suik Cheon, Seungbok Lee et al. (+12 more)

Chirality, a central concept across many scientific disciplines, continues to inspire the discovery of novel physical phenomena. In condensed matter physics, structural chirality - defined by the absence of mirror plane symmetries - has primarily been explored in bulk materials. However, new chiral phenomena can emerge uniquely at the interface, distinct from their bulk counterparts, when a chiral material forms a heterostructure. Here, we demonstrate that all van-der-Waals (vdW) heterostructure composed of the chiral Co1/3TaS2 and the achiral vdW ferromagnet Fe3GeTe2 exhibits two distinct and unconventional spin-orbit torques originating from the interfacial chirality. These torques enable magnetic-field-free switching of perpendicular magnetization with ultralow current density ~ 10^6 A/cm^2 and minimal power dissipation < 10^15 W/m^3. Moreover, by replacing Fe3GeTe2 with a similar vdW ferromagnet, Fe3GaTe2, but of higher Curie temperature, we achieved the magnetic-field-free switching at room temperature in the Fe3GaTe2/Co1/3TaS2 vdW heterostructure. Our findings establish interfacial chirality as a powerful new handle for spintronic control, opening a new pathway to explore chirality-induced phenomena beyond the bulk symmetry constraints - and paving the way toward highly efficient, low-power spintronic devices based on all-vdW heterostructures.
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All authors Kai-Xuan Zhang, Suik Cheon, Seungbok Lee, Joonyoung Choi, Jihoon Keum, Hyuncheol Kim, Yeochan An, Woonghee Cho, Suhan Son, Jingyuan Cui, Pyeongjae Park, Younjung Jo, Jun Sung Kim, Hyun-Woo Lee, Je-Geun Park

Instrumentation and Methods for Astrophysics

On-Orbit Calibration of Danuri/PolCam. II. Image Artifact Correction

Kilho Baek, Sungsoo S. Kim, Minsup Jeong et al. (+1 more)

Danuri, South Korea's first lunar orbiter, was launched on 2022 August 5, and has completed its two-year nominal mission phase. The wide-angle Polarimetric Camera (PolCam) onboard Danuri is the first instrument to conduct global polarimetric observations from lunar orbit. This paper presents the image artifact correction pipeline for PolCam's on-orbit data. It encompasses the primary steps of the radiometric calibration, consisting of dark current removal, smear correction, and flat-fielding, except for inter-channel relative response and the final radiance calculation. Notably, PolCam's raw data exhibit severe smear artifacts induced by the frame-transfer CCD architecture, which degrade both radiometric fidelity and the accuracy of polarimetric measurements. These smear artifacts have been mitigated through a rigorous correction algorithm, restoring data quality to a level sufficient for scientific analysis and facilitating the precise derivation of the degree of linear polarization. These steps serve as an essential prerequisite for conducting subsequent absolute radiance and photometric calibrations. Finally, we present representative examples of polarimetric measurements to validate the calibration performance. Although the current calibration focuses on restoring data quality for qualitative scientific analysis, these results demonstrate the expected inverse linear relationship between intensity and polarization.
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All authors Kilho Baek, Sungsoo S. Kim, Minsup Jeong, Young-Jun Choi

Superconductivity

Observation of giant nonvolatile magneto-thermal switching in superconductor-ferromagnet hybrids

Yui Sakamoto, Fuyuki Ando, Keigo Ito et al. (+6 more)

Magneto-thermal switch is a crucial thermal component which enables heat transfer control by the application of an external magnetic field. Recently, a nonvolatile behavior in magneto-thermal conductivity at zero magnetic field was observed in type-II and phase-separated superconductors owing to magnetic flux pinning nature, leading to an energy-efficient thermal control technology. However, the nonvolatile magneto-thermal switching ratio has been much lower than the volatile one in conventional materials. Here, we demonstrate a giant nonvolatile magneto-thermal switching in ferromagnetic Fe-superconducting Pb hybrids. The dispersion of pure Fe particles realizes increased electron and decreased phonon contributions in the thermal conductivity, which enhances the magneto-thermal switching ratio at the superconducting-to-normal conducting phase transition. Furthermore, in concert with trapped magnetic flux by supercurrent, ferromagnetic moment of Fe breaks the superconductivity of Pb matrix at zero magnetic field, enabling a significantly large nonvolatility even with a slight amount of Fe inclusions. Consequently, the nonvolatile magneto-thermal switching ratio reaches 719% in maximum at the Fe ratio of 8.7 vol%, which is more than twice the previous record value observed in Pb-Sn composites and the volatile one in pure Pb. This work broadens the exploration space and strategy for giant nonvolatile magneto-thermal switching materials.
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All authors Yui Sakamoto, Fuyuki Ando, Keigo Ito, Hossein Sepehri-Amin, Takamasa Hirai, Yuto Watanabe, Poonam Rani, Yoshikazu Mizuguchi, Ken-ichi Uchida

Chemical Physics

Reaction-network reasoning with frontier models for experimentally confirmed catalyst-selectivity hypotheses

Sutanay Choudhury, Anwesha Banerjee, Udishnu Sanyal et al. (+7 more)

Catalysts are essential for sustainable chemical manufacturing, yet discovering novel architectures remains a bottleneck dominated by trial-and-error experimentation and computationally intensive screening. In complex reactions such as electrochemical carbon dioxide reduction, product selectivity is governed by dynamic interfacial, electrolyte, and potential factors as well as kinetic pathway competition. Conventional descriptor-based machine learning and computational potentials struggle to resolve these mechanistic branch points, primarily relying on static ground-state descriptors or bulk structural correlations rather than end-to-end topological pathway analysis. Here, we show that frontier language models, when strictly constrained to reason over explicit reaction networks, can discover novel catalysts by identifying the physical levers that govern pathway competition. We developed a human-AI co-thinking framework that enforces network invariance to extract testable hypotheses from complex chemical graphs. Applied to CO2 electroreduction, the framework identified ketene desorption and hydroxide capture as the acetate-forming pathway, and predicted a distinct adsorbed CO and CH2 coupling route to ketene. By isolating actionable control levers, specifically local alkalinity, controlled iron incorporation, and restricted interfacial proton-donor accessibility, the framework guided the prospective synthesis of a copper-iron oxide catalyst demonstrating a threefold increase in acetate selectivity over matched Cu-rich baselines. This mechanism-guided reasoning architecture shifts the computational paradigm from retrospective statistical prediction to forward-looking hypothesis generation, providing a broadly applicable blueprint for mechanism-guided materials discovery.
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All authors Sutanay Choudhury, Anwesha Banerjee, Udishnu Sanyal, Jorin Dawidowicz, Chiezugolum Ijeoma Odilinye, Jesun Firoz, Liney Arnadottir, Simone Raugei, Johannes Lercher, Arnab Dutta

Quantum Physics

Optimizing LZSM protocol for high-fidelity gates in open-system fluxonium

Santiago Ferreyra, Valent{\i}n Reparaz, Maria Jose Sanchez et al. (+2 more)

Quantum gates based on resonant Rabi oscillations are inherently slow for small-frequency qubits. They are also prone to errors due to counter-rotating terms. However, when the anharmonicity is sufficiently high, as in the fluxonium architecture, alternative manipulation protocols can outperform standard resonant driving. In this work, we implement fast, high-fidelity quantum gates based on a one-period Landau-Zener-Stückelberg-Majorana (LZSM) driving protocol. We derive analytical expressions that simplify the exploration of the parameter space while accounting for the multi-level structure of the circuit. Furthermore, we analyze the role of leakage, discussing strategies to mitigate it and identifying regimes in which it becomes the dominant source of error. Finally, to evaluate the impact of dissipation on gate fidelity, we develop a robust formalism suitable for analyzing the open-system performance of quantum gates in the strong driving regime.
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All authors Santiago Ferreyra, Valent{\i}n Reparaz, Maria Jose Sanchez, Leandro Tosi, Daniel Dominguez

Quantum Physics · Cross-listed from cs.IT

Exact exponents for smoothing the max-relative entropy and quantum information decoupling in von Neumann algebras

Zhiwen Lin, Hongsen Qiu and Xinyu Zhang

We establish the exact exponent for smoothing the max-relative entropy on arbitrary von Neumann algebras. The proof first develops the required large-deviation and smoothing estimates in the semifinite setting and then passes to general von Neumann algebras through Haagerup-Junge-Xu reduction. We next study catalytic quantum information decoupling when the reference system is described by an arbitrary von Neumann algebra. A key ingredient is an operator layer-cake formula valid on arbitrary von Neumann algebras. This formula leads to a convex-split estimate for normal states with a general von Neumann algebraic reference system. Combining this estimate with the smoothing exponent result, we obtain lower and upper bounds on the catalytic-decoupling reliability function. These bounds coincide below the corresponding critical rate, yielding the exact decoupling reliability function in that regime.
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Wednesday 8 July

Physics and Society

Cognitive Cells: A Compositional Framework for Populations of Small Language Models

Silvan Ferreira

Recent work on large language models and agentic systems raises a basic question that current practice leaves open: how should artificial cognition be decomposed, measured, and composed? We propose studying multi-agent systems from a fixed unit we call a cognitive cell: a small, frozen language model with bounded memory and a message interface. The methodological commitment, the fixed-cell principle, is to hold this unit constant and vary only the population size, the communication topology, the message bandwidth, and the coordination protocol, so that collective behavior becomes a measurable property of a known device rather than an artifact of per-study engineering. We characterize a single cell by a compact datasheet of measurable parameters, and we ask when replicating and connecting cells improves performance: first we measure how one cell behaves alone, then we replicate it and test when voting, communication, and topology help. Instantiating the framework with small frozen models (1.5 and 3 billion parameters), we report a first round of measurements. Adding cells helps only when their errors are not too correlated. A simple correct/incorrect voting model is a useful but conservative null: real open-ended voting can exceed it, because errors are dispersed across many wrong answers rather than concentrated on one. Popular interactive protocols, namely debate, a shared blackboard, and chain revision, do not beat a matched-cost voting baseline in our setting. Finally, a cell's ability to relay several facts, itself a datasheet quantity, predicts whether a population can solve tasks whose evidence exceeds any single cell's memory. We present these as initial measurements within a broader program on scalable artificial cognition, in which multi-agent architectures appear as the special case of cells autonomous enough to be treated as agents.
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Physics and Society

Minimum-Distortion Wealth Taxation, I: Information-Theoretic versus Transport-Geometric Optimality on the Proportional Class

Anders G Frøseth

We characterise minimum-distortion wealth taxation under two contrasting normative criteria within a Fokker-Planck framework on log-wealth: the JKO free-energy gap, an information-theoretic measure aligned with the Mirrleesian decision-distortion tradition, and the squared 2-Wasserstein distance from the no-tax distribution at horizon $T$, a transport-geometric measure aligned with the Saez-Zucman distributional-compression tradition. Restricting to the neutrality-preserving (C1)-(C3) schedule class of a companion paper (Froseth 2026), both optima admit closed forms in the two-dimensional design plane parametrised by the corporate-dividend retention $k = (1-\tau_c)(1-\tau_d)$ and the proportional wealth-tax rate $\tau_w$. The JKO optimum partitions the regime axis into three phases as a function of the dimensionless ratio $\rho = \Sigma_0 m_0/\sigma^2$, with $m_0 = \mu - \sigma^2/2$ the geometric mean log-return: a pure wealth-tax phase at low $\rho$, a mixed-instrument phase at intermediate $\rho$, and a pure flow-tax phase at high $\rho$. The $W_2$ optimum, by contrast, is degenerate in this calibration: it pins to the pure flow-tax corner across the whole regime axis. The criterion contrast admits an economically meaningful reading via a bluntness index $B(m_0) = b/(a m_0)$ that measures the wealth-tax channel's mean-displacement-per-revenue overshoot relative to the flow-tax channel; JKO weights $B$ linearly, $W_2$ weights it quadratically, and the two normative traditions correspond to this difference in weighting. Norwegian-flavoured calibrations sit inside the JKO mixed-instrument phase under stock-heavy portfolio volatility but move into the pure flow-tax phase under the realised effective volatility of typical real-estate-heavy households.
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High Energy Astrophysical Phenomena

A Diagnostic Inverse-PINN Study of Identifiability in Reduced Black-Hole Spin Inference

Stella Menziltsidou

Black-hole spin is a fundamental parameter in relativistic astrophysics, influencing accretion efficiency, jet launching, and perturbative modes. This work investigates a hybrid physicsinformed inverse framework for reduced black-hole spin inference. Spin recovery is formulated as an inverse problem constrained by a reduced scalar angular Teukolsky-like equation. A Physics-Informed Neural Network approximates the angular mode function, while the spin parameter is treated as a trainable physical quantity linked to the residual of the governing differential operator. The framework is evaluated under controlled synthetic and noisecontaminated angular-mode configurations across multiple reference spin values. The main result is diagnostic rather than confirmatory. The PINN reproduces angular-mode profiles qualitatively; however, the inferred spin values cluster near the upper part of the allowed interval rather than accurately recovering the full range of reference spins. This reveals a weak-identifiability regime in which accurate angular-profile reconstruction and low residual loss do not necessarily imply accurate spin recovery. The results highlight the diagnostic value of physics-informed constraints for reduced black-hole spin-inference experiments, while also showing the need for further identifiability analysis, loss reweighting, joint eigenvalue inference, full Kerr perturbation modeling, realistic uncertainty models, and validation against numerical solvers.
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Physics and Society

CityReal: Human-Aligned Urban Behavior and City Dynamics Simulation with Large-Scale LLM Agents

Nicolas Bougie, Xiaotong Ye and Narimasa Watanabe

Large-scale urban simulation plays a pivotal role in social science, traffic safety, and transportation policy. Recent work has shown that large language models, when prompted as agents, can generate lifelike daily routines at city scale. Yet these methods typically rely on few-shot prompting, causing agents to reproduce the LLM's behavioral priors rather than the target population. We introduce CityReal, a modular framework for human-aligned urban simulation. CityReal models agents as intention-driven decision makers that pursue coherent mobility and activity plans rather than isolated step-by-step choices. They adapt over time by learning habits and preferences based on experience and constraints. To improve population-level realism, we learn textual adapters for behavior modules that align agent decisions with observed population statistics. Experiments show that CityReal improves alignment with real-world human behavior at both micro and macro levels. Scaling to tens of thousands of agents, it supports analysis of crowd density, place popularity, mobility flows, and well-being under different urban scenarios, offering a scalable testbed for urban simulation and forecasting.
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General Physics

Reliable Weyl Diagnostics for an Inhomogeneous Universe in Numerical Relativity

Hassan Ugail

The electric and magnetic parts of the Weyl tensor provide a geometric diagnostic for describing the local gravitational structure of an inhomogeneous universe, and they allow us to distinguish tidal, gravitomagnetic, radiative, and silent regimes. However, when these diagnostics are computed from numerical relativity data, their interpretation depends on how well the data satisfy the Einstein constraint equations. A small constraint error can create a spurious magnetic signal, especially near a silent regime, and it may therefore be mistaken for genuine radiative structure. In this work, we treat the Hamiltonian and momentum constraint residuals as reliability gates for the Weyl diagnostics. Rather than imposing a fixed tolerance, we measure how a constraint error affects the corresponding Weyl classification, and we then calibrate the tolerance required to achieve a chosen accuracy. Our noise-induced synthetic tests show that the Hamiltonian residual determines the reliability of the electric Weyl diagnostic, whereas the momentum residual determines the reliability of the magnetic Weyl diagnostic. Furthermore, a real numerical relativity initial-data slice confirms that no universal tolerance is adequate. Thus, the calibration measures the slice-level sensitivity of the diagnostics rather than the truncation error of an evolution.
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Quantum Physics

Number Fluctuations and Entanglement-Spectrum Participation in Monitored Free Fermions

Enso O. Torres Alegre

On finite system sizes, monitored one-dimensional free-fermion chains display a broad crossover in entanglement scaling as the measurement rate increases, from sub-extensive behavior at weak monitoring toward an area law at strong monitoring. Analytical field theory and recent large-scale simulations indicate that this apparent change is not a finite-rate transition in the thermodynamic limit. I test, using trajectory-resolved correlation-matrix simulations (chains up to $L=96$ and up to 128 Born-rule trajectories per parameter point), whether two quantities built from the single-particle entanglement spectrum are useful finite-size diagnostics: the bipartite particle-number fluctuation $F_A=\sum_k\nu_k(1-\nu_k)$ and a participation-style effective number of entangling modes, $\mathcal{M}_A=\exp[-\sum_k w_k\ln w_k]$, with $w_k\propto\nu_k(1-\nu_k)$. Both quantities track the crossover. However, for trajectory- and time-averaged steady-state values, $\mathcal{M}$ is nearly a deterministic function of $F$: a pooled curve explains 99.7% of its variance across all rates, so $\mathcal{M}$ carries little independent information. Its main advantage is statistical: its trajectory-to-trajectory coefficient of variation is up to a factor of $\sim2$ smaller than that of the entropy or $F$ under strong monitoring. Subsystem-scaling comparisons at $L=48$–$96$ also show that a pure logarithmic law is not statistically preferred under weak monitoring; a small residual quasi-extensive component and a drifting logarithmic coefficient are instead consistent with a crossover rather than a critical phase. Thus, $F$ remains the natural experimentally motivated companion to the entropy, whereas $\mathcal{M}$ is best viewed as a variance-reduced numerical summary of essentially the same information.
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Quantum Physics

A Hybrid Classical-Quantum Approach for Multi-Constrained Location Optimization Problem

Jorge Saavedra-Benavides, J. Alejandro Montanez-Barrera, Alberto Maldonado-Romo et al. (+1 more)

The Maximal Covering Location Problem (MCLP) is an NP-hard Combinatorial Optimization Problem (COP) that aims to determine the optimal facility placements that maximize total coverage. It is characterized by both equality and inequality constraints, which ensure correct coverage but significantly increase the complexity of exploring the solution space as instance size grows. Hybrid quantum-classical approaches might offer a promising alternative to classical optimization methods by enabling the exploration of complex energy landscapes through quantum superposition and probabilistic sampling. In this work, the MCLP is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) model, where constraint embedding plays a critical role in solution quality. In particular, Unbalanced Penalization (UP) is employed as an alternative to the Slack Variables (SV) for handling inequality constraints without increasing the number of variables. This study focuses on QAOA and one of its variants, the WS-QAOA, which leverages a biased initial state derived from a continuous relaxation of the problem. Additionally, a linear ramp (LR) parameter schedule is incorporated to reduce optimization complexity. The performance of these techniques is evaluated both individually and in combination, as a function of circuit depth $p$ and problem size. Results show that the combined approach of UP, LR, and WS-QAOA consistently improves solution quality and feasibility metrics, while maintaining robust performance as the problem size increases, highlighting its potential within hybrid quantum-classical optimization frameworks.
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All authors Jorge Saavedra-Benavides, J. Alejandro Montanez-Barrera, Alberto Maldonado-Romo, Daniel Sierra-Sosa

Quantum Physics

Intelligence-Guided Adaptive Purification for DDoS-Resilient Quantum Networks: A CUDA-Q based Study

Santanu Ganguly

Quantum-repeater networks require adaptive control policies that balance entanglement generation rate, end-to-end fidelity, purification overhead, and memory-induced latency. This tradeoff becomes more complex when the classical control plane is degraded by cyber anomalies or denial-of-service traffic. We develop a CUDA-Q/SeQUeNCe co-simulation workflow for studying adaptive entanglement purification in heterogeneous linear repeater chains. CUDA-Q noisy quantum kernels are used to estimate primitive entanglement purification and swapping behavior, while SeQUeNCe provides an event-layer model for stochastic link generation, waiting-time-dependent memory decay, purification failure, and end-to-end swapping. Under stationary conditions, we compare no purification, local threshold purification, mean-field predictive purification, fixed purification, and a resource-penalized risk-aware predictive policy. In an 8-node chain, the resource-penalized risk-aware controller increases above-target delivery probability relative to fixed purification while reducing latency and purification overhead. We then couple the quantum-network controller to anomaly scores derived from the CSE-CIC-IDS2018 benign-to-SSDP intrusion-detection trace. During the attack period, the attack-unaware controller maintains high raw delivery, but its above-target entanglement delivery falls to 0.098+/-0.007; the IDS-aware resource-adaptive controller switches to more purification-heavy masks and increases above-target delivery to 0.344+/-0.011, closely matching the oracle-aware value of approximately 0.335. These results demonstrate that cyber-state awareness can improve useful quantum-network outcomes by trading raw throughput for fidelity-qualified entanglement delivery.
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Quantum Physics

QR-SPPS: Quantum-Native Retail Shock Propagation and Policy Stress Simulator

Sumit Tapas Chongder

Classical supply chain risk models treat node failures as statistically independent events, systematically underestimating correlated cascade failures across multi-tier supplier networks. We present QR-SPPS (Quantum-Native Retail Shock Propagation and Policy Stress Simulator), a quantum-native framework for retail supply chain risk analysis implemented on the Qiskit ecosystem using OpenFermion-based Ising Hamiltonian encoding. A 40-node, four-tier supply network is mapped to a 40-qubit Hamiltonian with ZZ coupling terms representing correlated supplier dependencies. A hardware-efficient Variational Quantum Eigensolver (VQE) computes the stress ground state, revealing entangled cascade failures that differ substantially from classical Monte Carlo predictions. We further introduce the application of ADAPT-VQE gradient screening for counterfactual policy evaluation, enabling real-time ranking of six crisis interventions without repeated variational optimization. Finally, Density-of-States Quantum Phase Estimation (DOS-QPE) reconstructs the eigenspectrum through Trotter evolution and estimates Boltzmann-weighted catastrophe probabilities as a function of market-volatility temperature, providing a quantum-native tail-risk metric compatible with Value-at-Risk analysis. The framework demonstrates scalable quantum algorithms for correlated supply chain stress propagation, policy optimization, and systemic risk quantification while highlighting the exponential computational barriers faced by classical simulation at industrial-scale problem sizes.
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Geophysics

Observability of Finite-Depth Double-Diffusive Exchange from Sparse Temperature-Salinity Measurements

Sriram P. Kalathoor

Double-diffusive interfaces can support exchange histories that are spatially organized but only sparsely observed. A resolved three-dimensional calculation contains the route by which scalar gradients broaden, remain compact, connect with remote parts of a finite-depth layer, or organize horizontally, whereas field products usually provide profiles, repeated casts, autonomous-float records, or hydrographic sections. We ask which route-relevant features of finite-depth double-diffusive exchange remain observable after that measurement reduction. Four controlled finite-depth exchange histories are treated as known truth fields and sampled with vertical-profile, profile-bundle, vertical-coarsening, Argo-style, and section-like observing formats. The resulting observables are compared with selected Ice-Tethered Profiler, Argo, and CCHDO/GO-SHIP products to place the synthetic measurements in realistic observing contexts. Isolated profiles are weak route identifiers: at the final comparison time, strict case accuracy remains below 0.48, route-family accuracy is about 0.53–0.56 in leave-one-out profile classification, and 22 of 36 profiles are closer to a different-route profile than to a same-route profile under the current feature set. Profile bundles are substantially more informative. In the bundle-enumeration framework, the one-profile late-time route-family baseline is 0.694, while two-, three-, and four-profile bundles reach route-family accuracies of 0.917, 0.961, and 0.994. Coarse vertical sampling can preserve broad route separation while distorting local interface-width estimates, and section-like sampling adds horizontal-scale information only when station spacing resolves the relevant mode. The useful observing unit for hidden finite-depth double-diffusive exchange is therefore an ensemble or section, not an isolated cast.
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Quantum Physics

EPIC-CIM: Training Convolutional Neural Networks on a Coherent Ising Machine via Equilibrium Propagation

Xingrui Yin, Shenwei Kang, Haoqi He et al. (+7 more)

Quantum convolutional neural networks, due to the involvement of quantum measurements and discrete quantum state evolution, face inherent training challenges associated with non-differentiable operations and discrete optimization dynamics, which make conventional gradient-based learning difficult to apply effectively. In this context, energy-based learning provides a promising alternative by reformulating network training as an energy minimization process without explicit gradient backpropagation.In this framework, input data are processed through convolutional operations, followed by quantum sampling to generate intermediate binary representations, while the output layer also relies on quantum sampling to produce final predictions. The overall network energy is composed of convolutional feature matching terms, linear coupling terms at the output layer, and global output constraint terms, enabling both parameter updates and feature evolution to be described through physically interpretable energy dynamics. Furthermore, under the equilibrium propagation mechanism, the energy difference between the free phase and the weakly clamped phase is exploited to drive parameter updates without explicit gradient computation, thereby enabling stable and consistent learning in non-differentiable and discrete spaces. While remaining consistent with classical convolutional learning theory, the proposed framework enhances interpretability and observability through quantum energy modeling, offering a unified physical perspective for efficient QCNN training and the integration of quantum computing with artificial intelligence.
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All authors Xingrui Yin, Shenwei Kang, Haoqi He, Yan Xiao, Hongdong Zhu, Hai Wei, Yin Ma, Qi Gao, Xiaochun Cao, Kai Wen

Cosmology and Nongalactic Astrophysics

Remembering George Smoot

John Berliner, Tom Broadhurst, Martin Bucher et al. (+30 more)

George Smoot (1945-2025) was a major figure in observational cosmology, particularly through his role in the precision measurement of the CMB dipole and the discovery of the primordial CMB temperature anisotropy as part of the COBE team. This collection of 33 essays from collaborators, colleagues, students, and friends offers varied insights into the work, career, and life of George Smoot.
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All authors John Berliner, Tom Broadhurst, Martin Bucher, Robert Cahn, Jorge Cervantes-Cota, Steven Chu, Nóra Csiszár, Razieh Emami, Leo Fung, Ken Ganga, Alison Goldwyn, Bill Green, Paula Keeney, Dhiraj Kumar Hazra, Albert Wai Kit Lau, Sean Li, Eric Linder, Charley Lineweaver, Tao Liu, Omar López-Cruz, Philip Lubin, Kam-Biu Luk, John Mather, Saul Perlmutter, Federico Piazza, Graciela Tiburcio Pintos, Barbara Rittner, Uroš Seljak, Mehdi Shafiee, Tony Spadafora, Henry Tye, Ann Whetstone, Alex Zhavoronkov

Materials Science · Cross-listed from cs.AI

PHITSBench: an execution-scored benchmark for AI-assisted PHITS radiation-transport input generation using natural language

Xianglin Ji and Svetlana V. Boriskina

We introduce PHITSBench, an execution-scored benchmark for the Monte Carlo Particle and Heavy Ion Transport code System (PHITS). PHITSBench comprises 282 transport-scorable tasks spanning three common workflow categories: parameter editing (Edit), syntax repair (Repair ), and complete simulation generation from natural-language descriptions (Reproduce). Each task is evaluated using a Composite Metric Score that combines execution success with agreement between generated and reference transport observables. Using PHITSBench, we evaluate five GPT-5.4-based configurations ranging from zero-shot prompting to knowledge-augmented and agentic workflows. Without domain-specific knowledge, the model performs well on editing and repair tasks (95% and 70% success, respectively) but fails to generate correct simulations from scratch (0% success on the Reproduce track). A structured, machine-readable PHITS knowledge catalog, supplied alongside the user manual, raises single-shot Reproduce-task success to 57%. Agentic execution provides a further improvement to 66-73%, but at increased computational cost. Failure analysis shows that the remaining errors are dominated by incorrect selection and configuration of physical observables rather than syntax generation. These results suggest that future progress in AI-assisted radiation-transport modeling will depend as much on machine-readable knowledge bases, curated domain-training datasets, and execution-grounded evaluation environments as on advances in foundation models themselves.
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General Relativity and Quantum Cosmology

Novel Kerr-Hernquist Black Hole: Quasibound State, Scalar Cloud, Bomb, Superradiant Scattering

David Senjaya

We present a novel rotating black hole solution surrounded by a Hernquist dark matter halo, obtained by applying the Newman–Janis algorithm to the exact Schwarzschild–Hernquist spacetime. The resulting Kerr–Hernquist geometry provides an axisymmetric background for investigating scalar-field dynamics in realistic dark matter environments. Using the analytical asymptotic matching method, we derive the quasibound-state spectrum, identify the conditions for scalar cloud formation and the black hole bomb instability, and obtain an analytic expression for the superradiant scattering amplification factor. We show that the halo preserves the hydrogen-like structure of the quasibound-state spectrum while introducing corrections governed by the combination $\rho_0 r_0^3$. Increasing the halo density and scale radius enhances the scalar-field binding energy, lowers the critical field mass for scalar cloud formation, suppresses the growth rate of the superradiant instability for co-rotating modes ($m_\ell>0$), and accelerates the decay of counter-rotating modes ($m_\ell<0$). Furthermore, the dark matter halo reduces both the magnitude and frequency range of superradiant amplification, thereby weakening energy extraction from the black hole. These results demonstrate that the Kerr–Hernquist geometry provides a unified framework for studying quasibound states, scalar clouds, black hole bombs, and superradiant scattering, while revealing how a Hernquist dark matter halo leaves observable imprints on the spectrum and stability of rotating black holes.
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Quantum Physics

Procrustes Tomography – reconstructing noisy quantum channels made easy

Josey Stevens, Reece Robertson and Sebastian Deffner

What is the most expensive part of quantum device characterization? Clearly, the answer is quantum process tomography. However, especially for noisy intermediate-scale quantum (NISQ) computers, a comprehensive understanding of the noisy quantum dynamics is essential in interpreting the computational output. In this work, we introduce an efficient method – Procrustes tomography – that outperforms established methods in a number of aspects. After a pedagogical and constructive introduction, we demonstrate the utility of the method for representative examples of noisy quantum channels.
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Atomic Physics

Theoretical ab initio Evolution of Satellite Intensity near Threshold for Cu K-shell transitions

Daniel Pinheiro, Gonçalo Baptista, César Godinho et al. (+9 more)

In this work, we have investigated the evolution of satellite intensity near the ionization threshold for Cu K-shell transitions through theoretical methods. Employing standard state-of-the-art ab initio methods, we have calculated all Cu K-shell transitions and simulated the full K$\alpha_1$ and K$\alpha_2$ spectrum where all transition parameters, as well as shake probabilities were determined theoretically. Through these calculations we show that standard state-of-the-art ab initio methods achieve good agreement with experiment and enable us to simulate the intensity evolution near ionization thresholds within a good margin of error. Below-threshold satellite intensity was found to originate from resonant 1s$\rightarrow$3d and 1s$\rightarrow$4p excitations in Cu(I) and Cu(II) oxide phases respectively, which were included in our simulations.
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All authors Daniel Pinheiro, Gonçalo Baptista, César Godinho, André Fernandes, Jorge Machado, Pedro Amaro, Nancy Paul, Martino Trassinelli, Miguel Avillez, Paul Indelicato, José Paulo Santos, Mauro Guerra

Soft Condensed Matter

A Generalized Mechanical Model for the Cycle Rank Dependence of Stretch at Break in Phantom Chain Star Polymer Networks

Yuichi Masubuchi, Takato Ishida and Takashi Uneyama

A simple mechanical model was recently proposed to explain the universality of stretch at break ({\lambda}_b) as a function of cycle-rank density ({\xi}) in phantom-chain network simulations [J Non-Newtonian Fluid Mech., 349, 105620 (2026)]. Here, that model is reformulated as a series of the bottleneck strand and the surrounding network, yielding {\lambda}_b-1=({\lambda}_bs-1)[1+{\nu}_h/(1+c{\xi})]. In this formula, {\lambda}_bsis the stretch at break of the bottleneck strand, {\nu}_h is the number of stiff units in series along the rupture path, and c is a geometric constant for the parallel redundancy of the medium. Since c and {\nu}_hare difficult to separate over the examined range of {\xi}, c is fixed, and {\lambda}_bs and {\nu}_hare treated as fitting parameters. The formula is applied to phantom-chain simulations of networks with various conditions. In all cases, it reasonably captures the data, and the two parameters represent network characteristics.
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Accelerator Physics

TeV Electron Beams from Plasma Acceleration via Regenerative Cascading

Chaojie Zhang and Chan Joshi

Plasma accelerators sustain gradients orders of magnitude higher than conventional radiofrequency machines, but most proposed paths to TeV energies still require tens of stages, each demanding sub-micrometer alignment, femtosecond synchronization, and precise matching of the accelerating trailing bunch. Here we introduce plasma wakefield acceleration via regenerative cascading, in which each stage self-injects a fresh trailing electron bunch and the accelerated trailing bunch becomes the driver for the next stage. This approach has several advantages: energy multiplication instead of addition; automatic alignment, synchronization, and matching of the trailing bunch to the wake; and trailing bunch brightness reset in each stage. Particle-in-cell simulations show the generation of a 1.1 TeV electron beam with ~0.3% rms energy spread and 0.12 nC charge from a two-stage, sub-kilometer plasma accelerator driven by a 45 GeV, 100 nC beam. The low energy spread is achieved via dynamic beam loading in the evolving wake of the post-depletion driver that acts as a built-in energy dechirper.
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Quantum Physics

A Quantum Reservoir Architecture for Chaotic Forecasting and a Test of Whether Its High Dimension Helps

Tushar Pandey

Quantum reservoir computing uses a fixed quantum circuit as a feature generator and trains only a simple linear readout on top of it. This makes it cheap to train and free of the optimisation problems that affect many quantum machine-learning models. A natural worry is that the very large feature space the circuit produces might inflate apparent performance without adding anything real. This paper provides two things. First, it gives a complete, reproducible recipe for one such reservoir applied to forecasting chaotic systems, including how data is fed in, how the circuit is built, and how the readout is trained. Second, it gives a way to tell whether the reservoir's high dimension is actually doing useful work. We grow the size of the prediction problem and the size of the quantum reservoir together, so that extra capacity cannot be the explanation for any improvement, and we track a single stability number that measures how well behaved the readout fit is. On two chaotic test systems, a spatiotemporal chain and a shallow-water fluid model, the quantum reservoir keeps a flat, stable error as both sizes grow, while a matched classical reservoir does not. We report where the classical baseline is in fact stronger, so the comparison is honest. The result is a clean specification plus a diagnostic that other groups can apply to any reservoir whose features have a known scale.
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Computational Physics

A Puck-informed mode-resolved phase-field fatigue framework for unidirectional composites

Aamir Dean

Fatigue fracture in unidirectional fibre-reinforced composites is strongly mode dependent: transverse and off-axis cycling is governed by matrix and inter-fibre mechanisms, whereas fibre-aligned cycling activates a longitudinal channel with a higher fracture-energy scale and a different crack topology. Single-damage-variable models can fit global stiffness loss but cannot identify the active mechanism. This work proposes a Puck-informed, mode-resolved phase-field fatigue framework with separate channels for fibre-dominated and matrix/inter-fibre fatigue. Each channel has its own fatigue history, threshold, and resistance-degradation law. Fatigue does not directly degrade elastic stiffness; it lowers the fracture resistance of the active channel, while the corresponding phase field controls stiffness loss and crack-path evolution. The formulation is implemented in Abaqus/Standard using a compact UMAT-UEL architecture with one orthotropic mechanical routine and two scalar phase-field layers. Using one fixed IM7/8552 material and fatigue card, the model is verified through one-element tests, parameter sweeps, and centred-notch and open-hole tension cases at 0, 45, and 90 degrees under monotonic and cyclic loading. Without orientation- or geometry-specific tuning, the framework reproduces transverse matrix/inter-fibre cracking at 90 degrees, off-axis cracking at 45 degrees, and longitudinal matrix splitting with delayed fibre activation at 0 degrees. The fatigue lives follow the expected ordering: 45- and 90-degree cases fail within about 1,000 cycles, while 0-degree cases run out to 200,000 cycles without fibre cracking. Additional load, hole-size, mesh, length-scale, and cycle-block studies confirm consistent crack modes and converged trends. The study is a numerical verification and cross-geometry consistency assessment, not a calibrated experimental life-prediction claim.
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Atmospheric and Oceanic Physics

Orthogonality of coastal trapped waves

Jörn Callies

Coastal trapped wave modes are shown to be orthogonal in the sense that they make independent contributions to the energy of the wave field. The hydrostatic Boussinesq dynamics on an $f$-plane, linearized around a state of rest, are formulated as a (generalized) Schrödinger equation, which exposes the Hermitian structure of the wave operator that implies the orthogonality of eigenmodes. This formulation, which becomes particularly simple in weak form, is parlayed into a finite-element discretization that preserves the symmetries of the original problem and therefore the energy conservation and orthogonality of modes. The geostrophic momentum approximation, under which the orthogonality of modes has been recognized previously, is reprised and discussed in the present context to emphasize that low-frequency coastal trapped waves are edge waves. Their dynamics are governed by boundary potential-vorticity anomalies, both Bretherton-type contributions familiar from the quasi-geostrophic equations and lateral contributions that are important for steep slopes and coastal walls.
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Astrophysics of Galaxies

There is no single density: star-forming regions and galaxies hold more dense ionized gas than long assumed

J. Eduardo Méndez-Delgado, Christophe Morisset, William J. Henney et al. (+29 more)

Ionized gas fills star-forming regions and galaxies, and nearly everything we know about its temperature, pressure, mass, and composition is inferred from its emission lines [1-3]. The electron density is needed for all of these, yet a longstanding puzzle has resisted explanation: different density-sensitive lines, applied to the same gas, return values that disagree by up to two orders of magnitude. This is usually attributed either to each line tracing a physically distinct ionization zone or to imperfect atomic data [4-7]. Here we show that the disagreement is neither a flaw in the atomic data nor an ionization-stratification effect, but something more fundamental. Each diagnostic is tuned to a particular density, and when a nebula contains gas across a wide range of densities, as real nebulae do, each line reports the part of that range it is most sensitive to. The diagnostics do not measure a representative average density; they respond to different parts of a broad density distribution. This resolves the discrepancy with a simple relation between the density each line returns and the density it is most sensitive to, a relation that holds from individual H II regions to whole galaxies, near and far, and reveals that ionized nebulae contain far more dense gas than any one diagnostic implies. A nebula has no single electron density to measure, but a broad density distribution, and the masses, pressures, abundances and energetics built on the single-density assumption must be reconsidered, from nearby star-forming regions to galaxies across cosmic time.
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All authors J. Eduardo Méndez-Delgado, Christophe Morisset, William J. Henney, Niv Drory, Oleg V. Egorov, Sebastián F. Sánchez, Juna A. Kollmeier, Kathryn Kreckel, Guillermo Blanc, Grażyna Stasińska, Evelyn J. Johnston, Héctor J. Ibarra-Medel, Alfredo J. Mejía-Narváez, César Esteban, Jorge García-Rojas, Amrita Singh, Ivan Yu. Katkov, Evan D. Skillman, Rogelio Orozco-Duarte, Igor A. Zinchenko, Alejandra Z. Lugo-Aranda, Aida Wofford, Simon C. O. Glover, Evgeniya Egorova, Rodolfo de J. Zermeño, Lesly C. Castañeda-Carlos, Fu-Heng Liang, Natascha Sattler, Irene Cruz-González, Joel R. Brownstein, Thomas Hilder, Donald P. Schneider