Physics and Society
Neural Networks Measure Peace Levels from News Data similar to Peace Indices
Pablo Lara-Martínez, Bibiana Obregón-Quintana, Larry S. Liebovitch et al. (+2 more)
Traditional methods for assessing national peace levels typically rely on socio-economic indicators or conflict incidence, often overlooking the nuanced signals embedded in public discourse. This study presents a novel computational framework to quantify peace levels by analyzing the structural and stylistic features of news text, rather than solely its content. Using the News on the Web (NOW) corpus comprising articles from 20 countries, we evaluate the efficacy of advanced word
embeddings managed via ChromaDB compared to standard Doc2Vec models. We propose a 1D Convolutional Neural Network (CNN) architecture for classification and regression tasks, contrasting its performance against a k-Nearest Neighbors (k-NN) baseline. Our results demonstrate that the Neural Network significantly outperforms the k-NN model in classification metrics and, crucially, preserves the numerical relationship of peace rankings, exhibiting a strong correlation with the Positive Peace Index (PPI) even for out-of-sample countries. These findings suggest that the how of communication - the latent linguistic structures - serves as a robust, emergent indicator of societal stability. This research offers a non-invasive, scalable tool for real-time monitoring of social and societal dynamics and peacebuilding efforts.
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All authors Pablo Lara-Martínez, Bibiana Obregón-Quintana, Larry S. Liebovitch, Peter T. Coleman, Lev Guzmán-Vargas
Data Analysis and Statistics
Emilio Pisanty, Nicola Mayer, Andrés Ordóñez et al. (+2 more)
We develop a family of
chiral measures to quantify the chirality of a distribution and assign it a handedness. Our measures are built using the tensorial moments of the distribution, which naturally encode its spatial character, not only via its angular shape consistently with existing multipolar-moment approaches, but also its radial dependence. We combine these tensorial moments into a rotationally-invariant pseudoscalar using a newly-defined cross product and triple product for arbitrary symmetric tensors. We analyze these measures for a variety of toy-model distributions, providing intuition for the geometry and guiding the choice of
chiral measure optimal for a given distribution. We also apply our measures to a physically-motivated example coming from photoionization in polychromatic
chiral light. Our work provides a robust, flexible, intuitive, highly geometrical, and physically-driven framework for understanding and quantifying the chirality of a wide variety of distributions, together with an open-source software package that makes this toolbox readily applicable for the analysis of numerical or experimental data.
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All authors Emilio Pisanty, Nicola Mayer, Andrés Ordóñez, Alexander Löhr, Margarita Khokhlova
Medical Physics · Cross-listed from eess.IV
Beyond Benchmarks: A Framework for Post Deployment Validation of CT Lung Nodule Detection AI
Daniel Soliman
Background: Artificial intelligence (AI) assisted lung nodule detection systems are increasingly deployed in clinical settings without site-specific validation. Performance reported under
benchmark conditions may not reflect real-world behavior when acquisition parameters differ from training data. Purpose: To propose and demonstrate a physics-guided framework for evaluating the sensitivity of a deployed lung nodule detection model to systematic variation in CT acquisition parameters. Methods: Twenty-one cases from the publicly available LIDC-IDRI
dataset were evaluated using a MONAI RetinaNet model pretrained on LUNA16 (fold 0, no fine-tuning). Five imaging conditions were tested: baseline, 25% dose reduction, 50% dose reduction, 3 mm slice thickness, and 5 mm slice thickness. Dose reduction was simulated via image-domain Gaussian noise; slice thickness via moving average along the z-axis. Detection sensitivity was computed at a confidence threshold of 0.5 with a 15 mm matching criterion. Results: Baseline sensitivity was 45.2% (57/126 consensus nodules). Dose reduction produced slight degradation: 41.3% at 25% dose and 42.1% at 50% dose. The 5 mm slice thickness condition produced a marked drop to 26.2% - a 19 percentage point reduction representing a 42% relative decrease from baseline. This finding was consistent across confidence thresholds from 0.1 to 0.9. Per-case analysis revealed heterogeneous performance including two cases with complete detection failure at baseline. Conclusion: Slice thickness represents a more fundamental constraint on AI detection performance than image noise under the conditions tested. The proposed framework is reproducible, requires no proprietary scanner data, and is designed to serve as the basis for ongoing post-deployment QA in resource-constrained environment.
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Physics and Society
Topology as a Language for Emergent Organization in Complex Systems: Multiscale Structure, Higher-Order Interactions, and Early Warning Signals
Mark M. Bailey
Complex systems are difficult to study not only because they are nonlinear, multiscale, and often nonstationary, but because their scientifically relevant organization is often invisible at the level of individual components, pairwise interactions, or low-order summary statistics. This review argues that
topology has become valuable in complex-systems science because it provides a mathematical
language for representing emergent organization when relevant structure is distributed, relational, and robust across scale. We synthesize work on persistent homology, Mapper, simplicial complexes, hypergraphs, and related operators, while distinguishing invariant-based topological methods from broader
topology-inspired representations. We show how persistence formalizes multiscale stability, how higher-order models preserve collective interactions erased by pairwise graphs, and how topological approaches complement rather than replace statistics, graph theory, and geometry. We review applications in nonlinear dynamics, neuroscience, finance, ecology, materials science, and anomaly detection, emphasizing a common logic:
topology turns reorganizing structure into measurable signals for regime shifts, state transitions, and early warning. Across domains, these methods are most effective when the scientific target is organizational rather than scalar, when threshold ambiguity is intrinsic to the problem, and when
topology functions as a structural diagnostic or feature extractor within a broader analytic pipeline. We conclude by identifying key limitations, including representation dependence, inferential challenges, interpretability, computational scaling, and the narrowness of one-parameter workflows, and by outlining a research agenda linking
topology more closely to dynamics, causality, streaming decision support,
topology-aware AI, and socio-technical resilience.
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Chemical Physics
KANEL: Kolmogorov-Arnold Network Ensemble Learning Enables Early Hit Enrichment in High-Throughput Virtual Screening
Pavel Koptev, Nikita Krainov, Konstantin Malkov et al. (+1 more)
Machine learning models of chemical bioactivity are increasingly used for prioritizing a small number of compounds in virtual screening libraries for experimental follow-up. In these applications, assessing model accuracy by early hit enrichment such as Positive Predicted Value (PPV) calculated for top N hits (PPV@N) is more appropriate and actionable than traditional global metrics such as AUC. We present KANEL, an ensemble workflow that combines interpretable Kolmogorov-Arnold Networks (KANs) with XGBoost, random forest, and multilayer perceptron models trained on complementary molecular representations (LillyMol descriptors, RDKit-derived descriptors, and Morgan fingerprints).
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All authors Pavel Koptev, Nikita Krainov, Konstantin Malkov, Alexander Tropsha
Chemical Physics
Implementation of the multigrid Gaussian-Plane-Wave algorithm with GPU acceleration in PySCF
Rui Li, Xing Zhang, Qiming Sun et al. (+3 more)
We introduce a GPU-accelerated multigrid Gaussian-Plane-Wave density fitting (FFTDF) approach for efficient Fock builds and
nuclear gradient evaluations within Kohn-Sham density functional theory, as implemented in the GPU4PySCF module of PySCF. Our CUDA kernels employ a grid-based parallelization strategy for contracting Gaussian basis function pairs and achieve up to 80% of the FP64 peak performance on NVIDIA GPUs, with no loss of efficiency for high angular momentum (up to f-shell) functions.
Benchmark calculations on molecules and solids with up to 1536 atoms and 20480 basis functions show up to 25x speedup on an H100 GPU relative to the CPU implementation on a 28-core shared
memory node. For a 256-water cluster, the ground-state energy and
nuclear gradients can be computed in ~30 seconds on a single H100 GPU. This implementation serves as an open-source foundation for many applications, such as ab initio molecular dynamics and high-throughput calculations.
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All authors Rui Li, Xing Zhang, Qiming Sun, Yuanheng Wang, Junjie Yang, Garnet Kin-Lic Chan
Applied Physics · Cross-listed from math-ph
The Dynamic Doppler Spectrum Induced by Nonlinear Sensor Motion: Relativistic Kinematics and 4D Frenet-Serret Spacetime Geometry
Bryce M. Barclay and Alex Mahalov
Fundamental to the analysis of nonlinear relativistic motion is the precise characterization of the induced dynamic Doppler effects. In this work, we analyze the electromagnetic signals observed by non-inertial receivers using two frameworks to describe the relativistic motion. We first consider observer paths described by higher-order kinematic 4 vectors: relativistic acceleration and jolt. The dynamic Doppler effects of relativistic acceleration and jolt are exponential spectral broadening and exponential amplitude growth or decay. We derive compact expressions for the spectrum transformation resulting from relativistic acceleration and jolt. The jolt induces nonlinear skewed chirps in observed signals. Next we consider observer paths described by the 4D Frenet-Serret frame and the
curvature and torsion of the observer path. We obtain descriptions of the amplitude and phase fluctuations of the signal in terms of the
geometric parameters of
curvature and torsion. Concise, interpretable descriptions of non-inertial dynamic Doppler effects provide a useful diagnostic and predictive tool for engineering applications including radar, sensing, and communications systems.
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Atmospheric and Oceanic Physics
Increasing trends in the severity of Australian fire weather conditions over the past century
Soubhik Biswas, Andrew Dowdy and Savin Chand
Understanding how weather and
climate influence fire risk is important for many purposes, including
climate adaptation planning and decision-making in sectors such as emergency management, finance, health and infrastructure (e.g., for energy and water availability). In this study, bias-corrected 20CRv2c reanalysis data are used to investigate the climatology and long-term trends of weather conditions associated with landscape fires in Australia. The McArthur Forest Fire Danger Index (FFDI) is used here as a broad-scale representation of weather conditions known to influence fire behaviour based on wind speed, humidity, temperature and rainfall measures. In particular, using this reanalysis
dataset allows analysis over a longer time period than previous studies, from 1876 to 2011. Another novel aspect is that trends are examined using several different approaches, including a method to help account for the influence of interannual drivers of
climate variability not previously used for fire weather analysis. Results show increases in mean and extreme seasonal FFDI values throughout Australia in general, with all statistically significant trends being positive in sign for individual
climate zones. Humidity and temperature trends, attributable to human-caused
climate change, are shown to be the main cause of the increase in dangerous weather conditions for fires. These findings build on previous studies, with the novel data and methods used adding confidence to the overall understanding of fire risk factors in a changing
climate.
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Chemical Physics
Permeation of hydrogen across graphdiyne: molecular dynamics vs. quantum simulations and role of membrane motion
Mateo Rodríguez, José Campos-Martínez and Marta I. Hernández
Previous research based on electronic structure calculations and molecular dynamics (MD) simulations have demonstrated that graphdiyne (GDY) is a very suitable two-dimensional membrane for the separation of small molecules in a gas mixture of different species. However,
quantum effects may play a role in the dynamics of these permeation processes when light molecules are the ones involved in the crossing of the GDY subnanometric pores. In this work we report rigorous
quantum-mechanical calculations together with equivalent MD simulations of the transport of H2 molecules through a static GDY membrane, as a case study for the validity of the application to these problems of classical dynamics. The force fields employed are based on an improved Lennard-Jones formulation, with parameters optimized by means of accurate ab initio calculations. It is found that, although
quantum effects are still significant at the temperatures of interest (between 250 and 350 K), MD simulations are able to reasonably reproduce the dependence of the
quantum permeances with the temperature. Moreover, MD permeances computed with
quantum corrections through Feynman-Hibbs effective potentials provide a lower bound to
quantum permeances, while the pure classical counterpart gives an upper bound, thus leading to a well delimited range of confidence of the permeation results. Furthermore, within MD simulations it is possible to incorporate the thermal motion of the GDY layer and in this situation it is observed an enhancement of the permeances with respect to the fixed membrane case, due to a significant reduction of the permeation barriers when the GDY atoms are allowed to vibrate. It seems apparent therefore, that modeling the membrane motion is crucial to provide reliable simulations of the gas transport features.
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Optics
A Terahertz Bandpass Filter Using a Capacitive Transition Circuit and a Spoof Surface Plasmon Polariton Waveguide
Mohsen Haghighat and Levi Smith
This paper presents a novel terahertz (THz) bandpass filter (BPF) based on a spoof surface plasmon polariton (SSPP) waveguide with a center frequency of 1 THz and a 3 dB bandwidth of 0.3 THz. The proposed BPF comprises cascaded high-pass and low-pass elements. The high-pass element is a capacitive gap in the SSPP transition
circuit, and the low-pass element is the SSPP waveguide itself. We find that the measurement results, including cut-off frequencies, align well with the theoretical predictions and simulations. To the authors' knowledge, the proposed SSPP BPF is the first of its kind.
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Chemical Physics · Cross-listed from cond-mat.mtrl-sci
Concerted Electron-Ion Transport by Polyacrylonitrile Elucidated with Reactive Deep Learning Potentials
Rajni Chahal-Crockett, Michael D. Toomey, Logan T. Kearney et al. (+4 more)
Charge transport in
polymers, such as polyacrylonitrile (PAN), is crucial for electronics and energy storage. For instance, PAN can transport cations e.g., Li+, by facilitating dynamic cation-nitrile coordination in batteries. However, little is known regarding the underlying role of complex reactive
polymer configurations. Herein, we develop a deep-learning potential, trained on ab initio energies and forces of nonequilibrium reactive PAN configurations, to unravel the kinetics of PAN cyclization initiated by a nucleophile (OH- dissociated from LiOH) attacking the terminal nitrile carbon. We find, based on the reaction free-energetics, rates, and charge analysis, that the nucleophile attack producing the first ring is the rate-limiting step, which subsequently triggers Li+-coupled electron transfer along the PAN backbone, causing ~10,000 times faster sequential ring-formation of the remaining nitriles. PAN's extended configurations, where dipolar and H-bonding interactions are minimal, enable such rapid kinetics. By validating our computational findings with IR and NMR experiments, we establish a pathway for designing reactive
polymers with enhanced charge transport for energy applications.
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All authors Rajni Chahal-Crockett, Michael D. Toomey, Logan T. Kearney, Yawei Gao, Joshua T. Damron, Amit K. Naskar, Santanu Roy
Atomic Physics
Absence of Far-Detuned Attractive Optical Traps for Alkali Rydberg Atoms
Gabriel E. Patenotte, Youngshin Kim, Samuel Gebretsadkan et al. (+1 more)
Neutral-atom
quantum simulation is susceptible to
entanglement between the atom's internal electronic state and its center-of-mass position. In many alkali Rydberg platforms, the '
spin-motion coupling' is exacerbated by the free expansion required to avoid ponderomotive anti-trapping from optical fields. A recent proposal (arXiv:2505.01071) claims sufficiently excited Rydberg states could be trapped in a monochromatic, far-detuned, circularly polarized optical field by harnessing a large vector polarizability. We disprove the proposal through analytic calculation and measurement of the vector polarizability of the $54S$, $54P$, and $53D$ orbitals of Cesium. Regarding the optical angular frequency $\omega$, we analytically derive that the scalar, vector, and tensor polarizabilities scale as $\omega^{-2}$, $\omega^{-3}$, and $\omega^{-4}$, as opposed to the proposed scaling of $\omega^{-2}$, $\omega^{-1}$, and $\omega^{-2}$. We refine the sum-over-states expression for vector and tensor polarizability to be numerically stable and predict negligible vector and tensor polarizabilities far detuned from resonances, in agreement with our measurements. However, we find vector polarizability can enhance a recent proposal for near-detuned attractive trapping. Furthermore, we evaluate the breakdown of the electric-dipole approximation and derive no effect stronger than ponderomotive repulsion. We conclude that an attractive, monochromatic, far-detuned optical trap for alkali Rydberg states is not possible, regardless of the beam geometry.
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All authors Gabriel E. Patenotte, Youngshin Kim, Samuel Gebretsadkan, Kang-Kuen Ni
Physics and Society
Mobility shapes heat exposure inequalities in cities
Marc Duran-Sala, Mattia Mazzoli, Martin Hendrick et al. (+1 more)
Segregation has long been recognized as a driver of environmental inequalities, with disadvantaged groups often living in neighborhoods where heat-related risks are highest. Yet, it remains unclear how daily mobility patterns, embedded within heterogeneous urban heat fields, shape heat exposure inequalities across sociodemographic groups. Using mobile phone records of daily mobility flows and urban temperature fields across 23 Spanish cities, we develop a network-based framework to quantify how different sociodemographic groups experience heat through their daily movements. We further apply the framework to tract-level commuting networks across 30 major US cities as an external validation, yielding qualitatively comparable patterns. We find systematic income-related inequalities, with low-income groups consistently experiencing higher exposure than high-income groups, while age-related disparities are smaller in magnitude. These inequalities intensify during commuting trips, indicating that routine mobility amplifies spatial heat gradients more than non-routine movements. Finally, we show that parsimonious population-based mobility models with group-agnostic mobility rules reproduce an important component of the observed exposure disparities, suggesting that these inequalities emerge from the interplay between the unequal spatial organization of daily activities across sociodemographic groups and urban heat gradients. Our findings provide a generalizable framework to characterize inequalities in mobility-based heat exposure across cities and inform
climate-resilient urban planning and public health strategies under intensifying
climate-related risks.
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All authors Marc Duran-Sala, Mattia Mazzoli, Martin Hendrick, Gabriele Manoli
Instrumentation and Detectors
PySiPMGUI: A Universal Python-Based Software for Photodetector I-V Quality Assurance: From Underground Dark Matter Searches to Astroparticle Cherenkov Cameras
Tanay Dey, Suraj Shaw, Ritabrata Banerjee et al. (+2 more)
Silicon photomultipliers (SiPMs) are currently the most prevalent
photon detection technology in modern experiments in high-energy physics, astroparticle physics,
neutrino physics, and dark matter searches. The high detection efficiency for
photons, excellent timing resolution, small size, and
magnetic field independence make them ideal for precision measurements in low-light conditions. However, key parameters like breakdown voltage, gain, and dark count rate show a strong dependence on the bias voltage and temperature, requiring a systematic characterization. In this work, we present an open-source graphical user interface (GUI) for automated SiPM characterization, leveraging standard laboratory instrument communication protocols (PyVISA). The tool provides a free, open, and platform-independent solution for detector R&D and large-scale SiPM characterization, and is available at this GitHuB repository [1]. The analysis procedure is validated against the manufacturer's datasheet for the SensL MicroFC-60035-SMT, yielding results in good agreement for both breakdown voltage and dark count rate.
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All authors Tanay Dey, Suraj Shaw, Ritabrata Banerjee, Pratik Majumdar, Satyaki Bhattacharya
Atomic Physics
Dynamics of electromagnetically induced water molecule fragmentation
Anton V. Bibikov, Sergei N. Yudin, Maria M. Popova et al. (+2 more)
The development of intense high-energy radiation sources and the improvement of techniques for detecting charged fragments have made possible experiments on multiple ionization of a molecule with registration of the momentum and charge of dissociation products in coincidence. This technique allows to determine (`fix') a molecular geometry at the time of fragmentation and called fixed-in-space molecule. In this work, the dynamics the water molecule dissociation fragments resulting from interaction with intense X-ray radiation has been studied. The charge distribution of oxygen ions was calculated, Newton diagrams were constructed for fragments -
protons and the oxygen ion - for various charge states of the latter, and the released kinetic energy was evaluated. Calculations were performed using the [1]code for parameters close to [2]. The predictions for the different pulse parameters are done.
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All authors Anton V. Bibikov, Sergei N. Yudin, Maria M. Popova, Alexei N. Grum-Grzhimailo, Elena V. Gryzlova
Chemical Physics
Autotuning T-PaiNN: Enabling Data-Efficient GNN Interatomic Potential Development via Classical-to-Quantum Transfer Learning
Vivienne Pelletier, Vedant Bhat, Daniel J. Rivera et al. (+2 more)
Machine-learned interatomic potentials (MLIPs), particularly graph neural network (GNN)-based models, offer a promising route to achieving near-density functional theory (DFT) accuracy at significantly reduced computational cost. However, their practical deployment is often limited by the large volumes of expensive
quantum mechanical training data required. In this work, we introduce a transfer learning framework, Transfer-PaiNN (T-PaiNN), that substantially improves the data efficiency of GNN-MLIPs by leveraging inexpensive classical force field data. The approach consists of pretraining a PaiNN MLIP architecture on large-scale
datasets generated from classical molecular simulations, followed by fine-tuning (dubbed autotuning) using a comparatively small DFT
dataset. We demonstrate the effectiveness of autotuning T-PaiNN on both gas-phase molecular systems (QM9
dataset) and condensed-phase
liquid water. Across all cases, T-PaiNN significantly outperforms models trained solely on DFT data, achieving order-of-magnitude reductions in mean absolute error while accelerating training convergence. For example, using the QM9 data set, error reductions of up to 25 times are observed in low-data regimes, while
liquid water simulations show improved predictions of energies, forces, and experimentally relevant properties such as density and
diffusion. These gains arise from the model's ability to learn general features of the potential energy surface from extensive classical sampling, which are subsequently refined to
quantum accuracy. Overall, this work establishes transfer learning from classical force fields as a practical and computationally efficient strategy for developing high-accuracy, data-efficient GNN interatomic potentials, enabling broader application of MLIPs to complex chemical systems.
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All authors Vivienne Pelletier, Vedant Bhat, Daniel J. Rivera, Steven A. Wilson, Christopher L. Muhich
Geophysics
Scalable Bayesian full waveform inversion via dual augmented Lagrangian SVGD
Kamal Aghazade, Ali Siahkoohi and Ali Gholami
Full waveform inversion is an ill-posed inverse problem whose solution non-uniqueness – i.e., arising from band-limited, finite-aperture, noisy data – calls for uncertainty quantification to avoid overconfident geological interpretations.
Bayesian inference addresses this need by characterizing the solution as a posterior distribution rather than a single point estimate. Sampling from this distribution, however, remains computationally challenging: Markov chain Monte Carlo and non-amortized variational inference require repeated wave equation solves, while amortized variational inference approaches that avoid repeated solves rely on training data that are inherently scarce in geoscience and face unresolved generalization challenges in high dimensions. To address these limitations, we integrate Stein variational gradient descent with the alternating direction method of multipliers under a dual augmented Lagrangian formulation. By fixing the wave operator at a background model that is updated between frequency batches, it need only be factorized once per particle per frequency, eliminating per-iteration refactorization and reducing the total cost to that of a handful of deterministic inversions while inheriting the favorable conditioning of extended-space formulations. Applied to the Marmousi~II model, the proposed method provides well-calibrated uncertainty estimates and achieves inversion quality comparable to that of the standard augmented Lagrangian SVGD at a fraction of the computational cost.
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Optics
Ultra-Short flying-focus
Jérôme Touguet, Igor A. Andriyash, Lucas Rovige et al. (+1 more)
Achromatic flying-focus enables programmable control of intensity peak velocity, with applications in ultrafast optics. However, spatiotemporal coupling inherently elongates ultrashort pulses by introducing frequency-dependent focusing and arrival-time dispersion. We present a theoretical model identifying this pulse-lengthening effect and propose a radially-dependent spectral chirp to compensate for chromatic timing mismatches. Numerical simulations confirm that this approach preserves both pulse duration and programmed flying-focus velocity over extended focal regions. Additionally, dispersive media such as
plasmas can naturally mitigate elongation. These results extend achromatic flying-focus techniques to ultrashort pulses, enabling new opportunities in
laser–
plasma interactions and high-field nonlinear optics.
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All authors Jérôme Touguet, Igor A. Andriyash, Lucas Rovige, Cédric Thaury
Physics and Society
Dynamics of voting strategies and public good funding
Jonathan Engle and Bryce Morsky
We model an electorate voting on the funding of a public good in a two-party system in an evolutionary game theory framework. Voters adopt one of four strategies: Consensus-makers, Gridlockers, Party 1 Zealots, and Party 2 Zealots, which they may change via imitation. The public good benefits both individuals locally and those in neighbouring regions due to spillover effects. A system of differential equations governs the spatial movement of individuals and shifts in their voting strategies. Local social interactions drive strategy
evolution, while migration occurs toward areas of higher utility, which is a function of both social and economic factors. Our results reveal bistability and significant spatial variations. Locally, populations converge to a politically gridlocked state or a mix of consensus-makers and zealots, determining public good provisioning. We find that public good spillovers generate a free-rider effect and poorly funded regions become spatially tied to, and dependent upon, well-funded ones.
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Fluid Dynamics
Detonation propagation in weakly confined gases
Youssef K. Wahba, XiaoCheng Mi, Charles B. Kiyanda et al. (+1 more)
This study investigates the propagation of detonations along a layered configuration where a reactive gas is weakly confined by a hotter inert layer. CFD simulations are performed using a single-step, non-Arrhenius reaction model designed to suppress cellular instabilities, enabling formulation of a theoretical framework directly compared with simulation results. The simulations reach a quasi-steady state, revealing distinct flowfield regimes that depend on the acoustic-impedance ratio and relative layer thicknesses, with some detonations exhibiting velocity deficits while others propagate above the ideal Chapman-Jouguet (CJ) speed. Analytical models are developed to interpret these regimes. When a precursor shock is observed in the inert layer, the detonation is overdriven; this is modeled using shock-polar analysis and velocity estimates based on the approach of Mitrofanov (Acta Astronaut. 3:995-1004, 1976). An analytical criterion for precursor shock onset is proposed. In underdriven scenarios, the detonation front exhibits positive
curvature, analyzed using a
geometric construction wherein the relationship between wave speed and front
curvature is evaluated a priori. A simplified characteristic-based model captures the decay of the shock wave in the inert layer, after which shock-polar analysis determines the resulting wave interaction. Predictions from these models are assembled into a phase map delineating regions of overdriven and underdriven behavior, along with corresponding shock interactions, in the space of acoustic impedance and area ratios. This map is compared directly with CFD results. The combined numerical-theoretical framework clarifies transition mechanisms governing layered detonations and provides insights into detonation dynamics relevant to rotating detonation engines in which the detonation is bounded by hotter combustion products from a previous cycle.
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All authors Youssef K. Wahba, XiaoCheng Mi, Charles B. Kiyanda, Andrew J. Higgins
Biological Physics · Cross-listed from cs.AI
When Is Collective Intelligence a Lottery? Multi-Agent Scaling Laws for Memetic Drift in LLMs
Hidenori Tanaka
Multi-
agent systems powered by large
language models (LLMs) are increasingly deployed in settings that shape consequential decisions, both directly and indirectly. Yet it remains unclear whether their outcomes reflect collective
reasoning, systematic bias, or mere chance. Recent work has sharpened this question with naming games, showing that even when no individual
agent favors any label a priori, populations rapidly break
symmetry and reach consensus. Here, we reveal the mechanism by introducing a minimal model, Quantized Simplex Gossip (QSG), and trace the microscopic origin of this agreement to mutual in-context learning. In QSG,
agents maintain internal belief states but learn from one another's sampled outputs, so one
agent's arbitrary choice becomes the next
agent's evidence and can compound toward agreement. By analogy with neutral
evolution, we call this sampling-driven regime memetic drift. QSG predicts a crossover from a drift-dominated regime, where consensus is effectively a lottery, to a selection regime, where weak biases are amplified and shape the outcome. We derive scaling laws for drift-induced polarization as a function of population size, communication bandwidth, in-context adaptation rate, and
agents' internal uncertainty, and we validate them in both QSG simulations and naming-game experiments with LLM populations. Together, these results provide a framework for studying the collective mechanisms of social representation formation in multi-
agent systems.
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Instrumentation and Detectors · Cross-listed from astro-ph.IM
Global detector network to search for high-frequency gravitational waves (GravNet): conceptual design
Dorian Amaral, Diego Blas, Yuliia Borysenkova et al. (+30 more)
We propose GravNet (Global detector network to search for high-frequency gravitational waves), a novel experimental scheme enabling the search for gravitational waves in the MHz to GHz frequency range. Such high-frequency gravitational waves could arise from a variety of phenomena connected to some of the most pressing and fundamental questions in modern cosmology. The GravNet concept is based on synchronous measurements of signals from multiple experimental measurement devices operating at geographically separated locations. While gravitational-wave-induced signatures may be present in the signal of a single detector, distinguishing them from instrumental or environmental noise is highly challenging. By analyzing correlations between signals from several distant detectors, the detection significance is substantially enhanced, while simultaneously enabling studies of the nature and origin of the gravitational-wave signal. In this work, we discuss the GravNet concept specifically in the context of cavities operated in strong
magnetic fields, as these currently represent the most technically mature and experimentally advanced realization of the scheme. As part of this proposal, a first demonstration experiment using a non-superconducting cavity has been performed, providing the basis for the data-analysis strategies discussed in this work. Finally, we outline the prospects and future development of GravNet as a global network for high-frequency gravitational-wave searches.
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All authors Dorian Amaral, Diego Blas, Yuliia Borysenkova, Dmitry Budker, Alessandro D'Elia, Giorgio Dho, Alejandro Díaz-Morcillo, Daniele Di Gioacchino, Sebastian Ellis, Claudio Gatti, Benito Gimeno, Jordan Gué, Stefan Horodenski, Saarik Kalia, Younggeun Kim, Tom Krokotsch, Tomas Kvietkauskas, Adrián Lambíes-Asensio, Carlo Ligi, Giovanni Maccarrone, Giovanni Mazzitelli, Juan Monzó-Cabrera, José R. Navarro-Madrid, José Reina-Valero, Alessio Rettaroli, Kristof Schmieden, Tim Schneemann, Matthias Schott, Simone Tocci, Sandro Tomassini, Oleg Tretiak, Luca Visinelli, Changhao Xu
Fluid Dynamics · Cross-listed from cs.LG
Learning Mesh-Free Discrete Differential Operators with Self-Supervised Graph Neural Networks
Lucas Gerken Starepravo, Georgios Fourtakas, Steven Lind et al. (+3 more)
Mesh-free numerical methods provide flexible discretisations for complex geometries; however, classical meshless discrete differential operators typically trade low computational cost for limited accuracy or high accuracy for substantial per-stencil computation. We introduce a parametrised framework for learning mesh-free discrete differential operators using a graph neural network trained via
polynomial moment constraints derived from truncated Taylor expansions. The model maps local stencils relative positions directly to discrete operator weights. The current work demonstrates that neural networks can learn classical
polynomial consistency while retaining robustness to irregular neighbourhood geometry. The learned operators depend only on local geometry, are resolution-agnostic, and can be reused across particle configurations and governing equations. We evaluate the framework using standard numerical analysis diagnostics, showing improved accuracy over Smoothed Particle Hydrodynamics, and a favourable accuracy-cost trade-off relative to a representative high-order consistent mesh-free method in the moderate-accuracy regime. Applicability is demonstrated by solving the weakly compressible Navier-Stokes equations using the learned operators.
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All authors Lucas Gerken Starepravo, Georgios Fourtakas, Steven Lind, Ajay B. Harish, Tianning Tang, Jack R. C. King
Chemical Physics · Cross-listed from cs.LG
How unconstrained machine-learning models learn physical symmetries
Michelangelo Domina, Joseph William Abbott, Paolo Pegolo et al. (+2 more)
The requirement of generating predictions that exactly fulfill the fundamental
symmetry of the corresponding physical quantities has profoundly shaped the development of machine-learning models for physical simulations. In many cases, models are built using constrained mathematical forms that ensure that
symmetries are enforced exactly. However, unconstrained models that do not obey rotational
symmetries are often found to have competitive performance, and to be able to learn to a high level of accuracy an approximate equivariant behavior with a simple data augmentation strategy. In this paper, we introduce rigorous metrics to measure the
symmetry content of the learned representations in such models, and assess the accuracy by which the outputs fulfill the equivariant condition. We apply these metrics to two unconstrained,
transformer-based models operating on decorated point clouds (a graph neural network for atomistic simulations and a PointNet-style architecture for particle physics) to investigate how
symmetry information is processed across architectural layers and is learned during training. Based on these insights, we establish a rigorous framework for diagnosing spectral failure modes in ML models. Enabled by this analysis, we demonstrate that one can achieve superior stability and accuracy by strategically injecting the minimum required inductive biases, preserving the high expressivity and scalability of unconstrained architectures while guaranteeing physical fidelity.
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All authors Michelangelo Domina, Joseph William Abbott, Paolo Pegolo, Filippo Bigi, Michele Ceriotti
Instrumentation and Detectors
On peculiarities of the annealing process for highly transparent silica-based aerogel tiles manufactured in Novosibirsk
A.Yu. Barnyakov, A.F. Danilyuk, A.A. Kattsin et al. (+3 more)
A collaboration between the Boreskov Institute of Catalysis and the Budker Institute of
Nuclear Physics (BINP) has been producing silica aerogel blocks for Cherenkov detectors since 1986. Novosibirsk-manufactured aerogel is used in several experiments: KEDR and SND (BINP, Russia), LHCb (CERN, Switzerland), AMS-02 (ISS), and CLAS12 RICH (Jefferson Lab, USA). This work describes key advancements in the production technology for large-scale aerogel radiators used in Ring-Imaging CHerenkov (RICH) detectors. Annealing is one of the major stages of the highly transparent aerogel production in Novosibirsk. Its procedure was investigated in detail and optimized to improve the yield of aerogel tiles useful for the RICH detectors. The optical and mechanical parameters of the largest silica aerogel samples produced in Novosibirsk using the new annealing procedure are presented.
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All authors A.Yu. Barnyakov, A.F. Danilyuk, A.A. Kattsin, E.A. Kravchenko, A.A. Pochtar, A.Yu. Predein
Chemical Physics
Electronic properties of the Radium-monochalcogenides RaX (X = O,S,Se) and RaO+/- ions
Mateo Londoño and Jesús Pérez-Ríos
We present a theoretical investigation on the electronic structure and properties of radium monochalcogenides, with chalcogens O, S, and Se, as well as the ionic species RaO +/-. Our approach combines fully relativistic and partially relativistic
quantum-chemistry methods. Electronic properties are obtained using the exact two-component
Hamiltonian-based coupled-cluster approach with single, double, and perturbative triple excitations [CCSD(T)+ X2C], while potential energy curves are computed using an internally contracted multireference configuration interaction method, including relativistic effects through small-core pseudopotentials and Pauli-Breit operator diagonalization (MRCI+Q+ECP+SO). The dimers exhibit very large permanent dipole moments and sizable dipolar polarizabilities, while the Franck-Condon factors among the lowest electronic states are highly non-diagonal. These features are discussed in terms of the divalent character of the chemical bonding in the neutral species.
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Chemical Physics
Orientation Reconstruction of Proteins using Coulomb Explosions
Tomas André, Alfredo Bellisario, Nicusor Timneanu et al. (+1 more)
We solve the orientation recovery of a tumbling
protein in the gas phase from single-event measurements of the spatial positions of its ions after an X-ray
laser induced explosion. We simulate diffracted X-ray signal and ion dynamics under experimental conditions and compare our method to conventional orientation recovery in single-particle imaging with X-ray free-electron
lasers using only diffraction data. We reconstruct 3D diffraction intensities using orientations recovered from the ion signatures and retrieve the electron density with established phase-
retrieval algorithms. We test our orientation recovery procedure on 56
proteins ranging from 14 to 52 kDa (1800 to 6500 atoms), achieving roughly an angular error of around 5{\deg}. The resulting 3D electron-density reconstructions are compared to ground-truth volumes simulated at the same nominal resolution, and achieve the resolution at the edge of the detector in conditions similar to current single-particle imaging setups. We investigate the reconstruction quality and demonstrate that ion data can be used for reliable orientation recovery of particles in single-particle imaging, achieving orientation on par or better than currently used recovery techniques. This work shows the potential of ion detection for retrieving additional information from the sample fragmentation, and boost single particle imaging with X-ray
lasers in the cases where the diffraction signal is a limiting factor.
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All authors Tomas André, Alfredo Bellisario, Nicusor Timneanu, Carl Caleman
Chemical Physics · Cross-listed from cond-mat.other
Capturing thermal effects beyond the zero-temperature approximation using the uniform electron gas
Brianna Aguilar-Solis, Brittany P. Harding and Aurora Pribram-Jones
Density functional theory at finite temperatures often relies on the zero-temperature approximation, which uses a ground-state exchange-correlation functional with thermalized densities. This approach, however, neglects the explicit temperature dependence of the exchange-correlation free energy – a key factor in regimes such as warm dense matter, where both electronic and thermal effects are significant. In this work, we introduce the
entropy-corrected zero-temperature approach, in which the exchange-correlation
entropy is extracted using the generalized thermal adiabatic connection formula to construct a thermal correction to the standard zero-temperature approximation. Using a uniform electron gas parametrization, we compare this approach to the finite-temperature adiabatic connection and demonstrate that it performs best at lower densities. This provides a useful complement to zero-temperature density functional approximations, which generally perform better at moderate-to-large densities. We further identify a density-dependent intersection between the adiabatic connection curves, revealing a dependence on the ground state correlation energy and correlation potential. Additionally, extension of the
entropy corrected approach applied as a local density approximation–like temperature correction to the zero temperature approximation is discussed.
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Physics and Society
Cascading Failures and Critical Infrastructures in Future Renewable European Power Systems
Maurizio Titz, Franz Kaiser, Johannes Kruse et al. (+8 more)
The world's power systems are undergoing a rapid transformation, shifting away from carbon-intensive power generation to renewable sources. As a result, electricity is being transported over ever longer distances, while the intrinsic system inertia provided by thermal power plants decreases. Together, these developments raise the probability of cascading line failures and reduce the stability of the system after a system split. In this article, we assess the risk of cascading failures and system splits in the European power grid for different carbon reduction scenarios. We analyze the most likely and most dangerous splits, and identify critical transmission infrastructures and we discuss potential countermeasures that can address the problem of cascades. Our results show that while the risks of splits causing power failures rises with decarbonization, it can be mitigated cost efficiently.
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All authors Maurizio Titz, Franz Kaiser, Johannes Kruse, Philipp C. Böttcher, Jan Lange, Martha Frysztacki, Dominic Hewes, Michael Orlishausen, Mark Thiele, Tom Brown, Dirk Witthaut
Plasma Physics
Study of Low-Frequency Core-Edge Coupling in a Tokamak: I. Experimental Observation in KSTAR
Wonjun Lee, Andreas Bierwage, Seungmin Bong et al. (+6 more)
Double-peaked fishbone events across multiple KSTAR discharges are investigated. The normalized beta $\beta_{\mathrm{N}}$ and the edge safety factor $q_{\mathrm{95}}$ under which the fishbones appear vary depending on the presence and form of external
magnetic perturbations. The fishbone strength is closely related to $\beta_{\mathrm{N}}$ and $q_{\mathrm{95}}$: as $\beta_{\mathrm{N}}$ increases and $q_{\mathrm{95}}$ decreases, the fishbone strength increases. Measured fishbone-relevant signals are decomposed into amplitude envelope and phase components in the temporal domain, which are analyzed separately. In terms of the amplitude envelope component, the edge electron temperature fluctuation $\tilde T_\mathrm{e}^{\mathrm{Edge}}$ becomes more correlated with the poloidal
magnetic fluctuation $\dot{B}_\mathrm{\theta}$ compared to the core electron temperature fluctuation $\tilde T_\mathrm{e}^{\mathrm{Core}}$ as fishbone strength increases. In terms of the phase component, the phase of $\tilde T_\mathrm{e}^{\mathrm{Edge}}$ precedes the phase of $\tilde T_\mathrm{e}^{\mathrm{Core}}$ except in the case of very weak fishbones where the phase relations are inconclusive due to weak fishbone activity at the edge
plasma, which is comparable to background fluctuations. The investigation suggests the possibility that the edge activity is not a mere side effect of the core activity, but could play an active role.
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All authors Wonjun Lee, Andreas Bierwage, Seungmin Bong, Jaewook Kim, K.D. Lee, J.G. Bak, G.J. Choi, C. Sung, Y.-c. Ghim