Computational Physics
Floquet-Engineered Odd-Parity Altermagnetic Higher-Order Topology in a Two-Dimensional Antiferromagnet Cr$_2$CH$_2$
Xiaorong Zou, Hyeon Suk Shin, Baibiao Huang et al. (+5 more)
Periodic driving provides a platform to dynamically tailor
quantum states of matter, yet its impact on
symmetry-protected topological phases remains incompletely understood. Here, we demonstrate that periodic driving enables the realization of an odd-parity altermagnetic (AM) higher-order topological
insulator (HOTI) phase in the Cr$_2$CH$_2$ monolayer. In
equilibrium, Cr$_2$CH$_2$ is a 2D
antiferromagnetic (AFM) HOTI protected by $\mathcal C_3$ rotational
symmetry, characterized by a
symmetry indicator $\chi^{(3)}$ = $\{-2,1\}$ and robust corner states. Under circularly polarized light (CPL), the system develops a f-wave altermagnetic state governed by the
symmetry $[C_{2}||\overline{3}_{001}]$ with odd-parity
spin splitting. Despite substantial Floquet-induced band renormalization, the $\mathcal C_3$-protected corner states remain intact over a broad range of driving strengths, highlighting the altermagnetic higher-order
topology under Floquet driving. As the light intensity increases, the system gradually evolves into an altermagnetic semimetallic state. These results establish a direct connection between magnetism and
topology in a periodically driven AFM system, offering a route toward the control of coupled
spin and topological transport.
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All authors Xiaorong Zou, Hyeon Suk Shin, Baibiao Huang, Yanmei Zang, Ying Dai, Chengwang Niu, Chang-Jong Kang, Chang Woo Myung
Data Analysis and Statistics · Cross-listed from cond-mat.dis-nn
Activation Functions, Statistics and Learning of Higher-Order Interactions in Restricted Boltzmann Machines
Giovanni di Sarra and Yasser Roudi
The great success of neural networks primarily arises from the presence of the large number of weight parameters combined with nonlinearities in the input-output relationship of single
neurons. In this work, we study the relationship between the statistical properties of the weights and the nonlinearity of the hidden unit in Restricted Boltzmann Machines (RBMs) on the one side, and the distribution induced on binary visible units. We do this for four commonly used activation functions: Linear, Step, ReLU, and Exponential, and make qualitative predictions about the ability of these models to learn distributions with strong higher order interactions over the visible nodes. We show that in general, in an ensemble of RBMs with Gaussian weights, these distributions are rare and hard to learn, except when the hidden unit activation function is an Exponential.
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Chemical Physics
Importance of nuclear quantum effects on the structure of supercooled water around its liquid–liquid critical point
Michael Beerbaum, Julian Heske, Jure Gujt et al. (+1 more)
Supercooled water is expected to exhibit a
liquid–
liquid phase transition between low- and high-density
liquid states, possibly terminating in a
liquid–
liquid critical point in the experimentally difficult no man's land. Because the hydrogen atoms are light,
nuclear quantum effects (NQE) may alter the structural signatures used to identify this transition. Here, we compare classical molecular dynamics and path-integral molecular dynamics simulations of a flexible q-TIP4P/F-like water model in the deeply supercooled regime. The classical simulations show a pronounced density change at 180 K between 180 and 220 MPa, whereas the path-integral simulations exhibit a smoother pressure dependence. Radial distribution functions and bond-order parameters show that NQE broaden pair correlations, reduce the tetrahedral order of the first hydration shell, and slightly increase the Steinhardt $Q_6$ parameter. These results demonstrate that NQE modify both low- and high-density
liquid structures and therefore need to be included when interpreting structural signatures of the
liquid–
liquid transition in supercooled water.
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All authors Michael Beerbaum, Julian Heske, Jure Gujt, Thomas D. Kühne
Physics Education
Who Gets to Do Physics? Occupational Stereotypes in AI-Generated Problem Sets
Bilas Paul
As AI-generated problem sets gain traction in introductory physics courses, their technical correctness is well established - but the social assumptions embedded in their framing have gone largely unexamined. This study analyzes 600 introductory physics problems generated by four AI systems - Grok~4, GPT-5.2, Claude Sonnet 4.6, and Gemini 3 Flash - across structured
prompts involving occupations (CEO, Physicist, High School Teacher, Nurse, Construction Worker, and Migrant Worker). Problems were coded on five dimensions: hazard presence, hazard type, agency role, cognitive role, and object ownership. While the physics content is technically sound across all platforms, our analysis reveals systematic occupational stratification in narrative framing. Hazardous scenarios were concentrated in Migrant Worker and Construction Worker problems, with exposure-related hazards (electrocution, burns, radiation, heat or chemical exposure) especially concentrated in Migrant Worker problems. Passive-accident framing - the persona as the recipient of an injury - appeared in one in eight Migrant Worker problems and never appeared for the Physicist, Teacher, or CEO. Possessive ownership
language was reserved almost exclusively for the CEO. These patterns suggest that AI-generated physics problems can introduce surface-level diversity while reproducing occupational hierarchies in who acts, who owns, and who is placed at risk. We discuss implications for physics teaching and offer simple screening strategies for instructors using AI-generated problems.
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Computational Physics · Cross-listed from eess.IV
An evaluation framework for sparse 4D (3D + time) imaging reconstruction via bootstrapped cross-validation
Yuhe Zhang, Zisheng Yao, Zhe Hu et al. (+2 more)
Four-dimensional (4D; 3D + time) microscopic imaging has emerged as a powerful technique for investigating dynamic phenomena in complex systems, enabling direct visualization of structural
evolution in space and time. However, when pushing the limits of spatiotemporal resolution, most time-resolved imaging techniques yield inherently sparse 4D
datasets. While deep learning-based reconstruction methods have shown promise in reconstructing 4D from sparse spatiotemporal measurements, a practical approach for evaluating their performance in the absence of a 4D reference has, to the best of our knowledge, been lacking. Here, we present a bootstrapped cross-validation framework that estimates reconstruction performance by quantifying correlations between reconstructions generated from independently sampled subsets of the acquired data, as inspired by the 3D validation strategy in cryo-electron microscopy, where reconstructions from split
datasets are compared to assess resolutions. This enables both qualitative and quantitative assessment in the absence of ground truth. We investigate two representative scenarios with sparse and ultra-sparse X-ray
datasets and validate this approach using 4D-ONIX, a 4D deep-learning reconstruction method, on simulated water droplet collision experiments. The proposed approach provides a reference-free framework for performance estimation and support for better-informed experimental strategies across a wide range of ultrafast imaging applications.
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All authors Yuhe Zhang, Zisheng Yao, Zhe Hu, Tobias Ritschel, Pablo Villanueva-Perez
Optics · Cross-listed from stat.ML
Information Processing Capacity of Stationary Physical Systems: Theory, Data-efficient Estimation Methods, and Photonic Demonstration
Rahul Uma Ramachandran and Serge Massar
Physical computing systems provide a promising route toward hardware-native machine learning, but their computational capabilities remain difficult to characterize in a principled, task-independent, and data-efficient way. We extend the Information Processing Capacity (IPC) framework to stationary physical computing systems and establish several fundamental results: individual capacities are bounded between zero and one, their sum over a complete basis is bounded by the number of readouts, and noise strictly reduces this bound. We address the finite-sample estimation of IPC and derive the asymptotic form of the systematic positive bias affecting naive
estimators. Building on these results, we introduce data-efficient estimation methods based on Richardson extrapolation and Sobol quasi-random sampling. We validate the framework experimentally using a photonic computing system based on picosecond
laser pulses propagating through a nonlinear optical fibre. By varying the
laser power and fibre length, we observe systematic shifts of the IPC distribution toward higher-order nonlinear capacities induced by the Kerr effect. Finally, we demonstrate that the total IPC strongly correlates with performance on
benchmark machine-learning tasks and provides a reliable estimate of the effective dimensionality of the system. These results establish IPC as a practical bridge between the intrinsic dynamics of physical computing systems and their machine-learning performance.
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Chemical Physics · Cross-listed from cond-mat.mtrl-sci
Atomistic Modeling of Chemical Disorder in Materials: Bridging Conventional Methods and AI-Assisted Approaches
Jiayu Peng and Peichen Zhong
Chemical disorder, originating from the mixed occupation of crystallographic sites by multiple elements, is widespread in
alloys, ceramics, and compositionally complex materials, where short- and long-range orderings strongly influence properties. A central obstacle is the representation gap between experiments and simulations: experiments often report disorder as partial occupancies and ensemble-averaged behaviors, whereas atomistic simulations and AI workflows usually require fully specified configurations. Tackling this gap requires computational methods that convert averaged disorder descriptions into representative configurational ensembles while balancing cost, bias, and fidelity. This challenge has become more urgent in AI-driven computational discovery, where ignoring disorder may cause AI workflows to misrank stability, misjudge novelty, and misdirect experiments with too-idealized representations. This Review highlights how conventional and AI-driven methods can bridge this representation gap. We assess the strengths and limitations of approaches spanning mean-field theories, cluster expansion, quasi-random approximations, Monte Carlo, and emerging schemes powered by universal interatomic potentials and generative models. We further highlight how AI can accelerate various computational schemes by lowering the cost of microstate evaluation, configurational exploration, and atomistic-to-thermodynamic closure. We also emphasize how AI can enable disorder-native capabilities, including workflow triage, ordering-sensitive and alchemical representations, generative models of disordered structures and distributions, and kinetics-aware disorder prediction. Together, this framework outlines a practical roadmap toward disorder-native AI, which can transform chemical disorder from a representational obstacle into a controllable variable for realistic AI-accelerated materials discovery.
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Geophysics
Seismic Depth Imaging of the 2024 Noto Earthquake (M7.6) Rupture Area
Hamzeh Mohammadigheymasi and Jin-Oh Park
On January 1, 2024, a moment magnitude (Mw) 7.6 earthquake struck the Noto Peninsula, Japan, causing intense ground shaking and triggering a tsunami along the Japan Sea coast. Preliminary analysis by the Japan Meteorological Agency (JMA) identified a reverse-fault rupture consistent with a northwest-southeast compressional stress regime. Aftershock distribution analysis (JMA, 2024) revealed that the causative fault extended approximately 150 km from the western Noto Peninsula to the northeastern offshore area, aligning with the inferred tsunami source region. While the rupture mechanism and impacts have been studied, high-resolution seismic imaging of the shallow crustal structure within the rupture zone remains limited. To address this gap, the Atmosphere and Ocean Research Institute (AORI) at the University of Tokyo conducted a multichannel seismic (MCS) reflection survey aboard the R/V Hakuho-Maru in March 2024, collecting high-quality MCS data along 14 profiles (approx. 45 km each). The data were processed using an advanced depth imaging workflow incorporating grid-based
tomography refined by automated continuity attributes to enhance reflection coherency. Structural attributes (dip and continuity) were extracted from migrated sections and used for automated horizon picking via seismic pencil construction. The P-wave velocity model was iteratively refined using grid-based
tomography to optimize horizon
alignment and minimize residual moveout (RMO) in migrated common image gathers. The resulting 2D seismic sections and 3D visualizations provide the first high-resolution images of the shallow rupture zone associated with the 2024 Noto earthquake. This
dataset offers a critical foundation for ongoing research into fault geometry, rupture dynamics, and the broader seismotectonic framework of the region.
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Atomic Physics
Achieving High Filling of an Optical Lattice by Light-Assisted Redistribution of Atoms
Lauren Weiss, Evan Yamaguchi, Claire Pritts et al. (+2 more)
Scalable arrays of individual atoms provide an ideal starting point for
quantum information and simulation experiments. However, their preparation is often limited by light-assisted collisions (LACs), which typically result in parity-projected filling fractions of $f \approx 0.5$. In this work we demonstrate a light-assisted redistribution process in the
Quantum Matter Synthesizer that overcomes this constraint by stochastically moving atoms from multiply occupied
lattice sites to neighboring vacant sites. Using a blue-detuned optical pumping beam during degenerate Raman sideband cooling, we achieve single-atom filling fractions of $70-80\%$. We find that over 50$\%$ of the atoms involved in radiative collisions are retained in the
lattice. The redistribution process involves many LACs over an extended time as atoms diffuse to empty sites. Our demonstration offers a scalable and efficient pathway toward unity-filled atom arrays without the need for complex rearrangement protocols, with broad applicability to
quantum simulation, precision measurements, and
quantum information control.
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All authors Lauren Weiss, Evan Yamaguchi, Claire Pritts, Tadej Mežnaršič, Cheng Chin
Plasma Physics · Cross-listed from astro-ph.HE
Radiative PIC simulations of relativistic pair plasma: multiple interacting current sheets and turbulent evolution
Fulvia Pucci, Elena Amato, Dario Borgogno et al. (+5 more)
Two-dimensional relativistic particle-in-
cell (PIC) simulations of radiative
magnetic reconnection in pair
plasmas with multiple interacting current sheets are carried out to mimic the dynamics in high-energy astrophysical environments, such as particle acceleration regions in
pulsar wind nebulae and relativistic
outflows, where the
magnetic field is expected to reverse polarity multiple times. Initially, due to reconnection within each isolated sheet, particles are accelerated and synchrotron emission beyond the burn-off limit is confirmed, even if the particle distribution function shows steep slopes. After this phase, plasmoids lead to cross-sheet interactions and merging, with new current sheets formed. In this regime a Kolmogorov-like spectrum for the
magnetic energy develops over a couple of decades, followed by a dissipation range starting around 5~$d_e$ (electron inertial lengths), showing that multi-sheet reconnection evolves nonlinearly into well-developed
turbulence. This phase provides secondary acceleration and further cooling by synchrotron emission, with intermittent radiative bursts. We show that high energy accelerated particles by the primary current sheets are further energized during the turbulent phase, while the distribution of the most energetic particles remains steep.
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All authors Fulvia Pucci, Elena Amato, Dario Borgogno, Niccolo' Bucciantini, Maria Elena Innocenti, Kevin M Shoeffler, Marco Tavani, Valerio Vittorini
Optics
Tunable cylindrical vector beam generation via low-cost printed binary holograms
Emilio E. Ramos-Torres, Beatriz Morales Cruzado, Benjamin Perez-Garcia et al. (+1 more)
We report a low-cost method for generating cylindrical vector beams using binary holograms printed on acetate sheets and a modified Michelson interferometer incorporating a cylindrical-lens mode converter. By simply exchanging the hologram the device produces a variety of CVBs with tunable spatial-polarisation nonseparability. The transverse polarisation distributions reconstructed via Stokes polarimetry show spatial-polarisation features consistent with numerical simulations. The degree of nonseparability is further quantified using the vector quality factor (concurrence), demonstrating values in good agreement with theoretical expectations across the generated states. The use of wave-plate retarders enables continuous tuning from scalar to fully vector beams. The simplicity, robustness, and low cost of the proposed system make it an attractive alternative to programmable modulators for compact optical platforms and teaching laboratories.
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All authors Emilio E. Ramos-Torres, Beatriz Morales Cruzado, Benjamin Perez-Garcia, Carmelo Rosales-Guzmán
Fluid Dynamics · Cross-listed from cs.LG
The impact of observation density on Bayesian inversion of latent dynamics in shock-dominated flows
Bipin Tiwari, Muhammad Abid and Omer San
Inferring unknown initial states in shock-dominated compressible flows from sparse and noisy measurements is a challenging ill-posed inverse problem due to nonlinear wave interactions and limited sensing. In this work, we develop a non-intrusive reduced-order modeling framework for efficient
Bayesian initial-state inversion with uncertainty quantification. The framework combines a convolutional autoencoder with a learned latent-space forward operator. The autoencoder compresses high-dimensional flow fields into a compact nonlinear latent representation, while the forward operator predicts final-time latent states from encoded initial conditions. This AE-ROM surrogate enables rapid forward evaluations and is embedded within a No-U-Turn Sampler (NUTS) for posterior exploration. The framework is demonstrated using 500 high-fidelity Sod shock tube simulations generated through Latin hypercube sampling and solved using a fifth-order WENO scheme. The inverse problem seeks to recover unknown left and right density and pressure states from sparse noisy observations of final-time density and pressure fields. Results show that the AE-ROM accurately reconstructs key shock-tube structures, including the rarefaction wave, contact discontinuity, and shock front. A latent dimension of 32 provides an effective balance between reconstruction accuracy and reduced-space compactness, while 250 training simulations are sufficient for accurate reconstruction. Increasing observation density significantly contracts posterior uncertainty, reducing the mean posterior standard deviation by approximately 78% for density and 76% for pressure. Overall, the proposed framework provides a computationally efficient and uncertainty-aware approach for inverse analysis of shock-dominated flows, with potential extensions to multidimensional compressible-flow and digital-twin applications.
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Plasma Physics
Kinetic theory of the Thermal Farley-Buneman Instability in the E-region ionosphere
Yakov S. Dimant and Meers M. Oppenheim
This paper develops a fully kinetic linear theory of the thermal Farley-Buneman instability (TFBI) in the E-region ionosphere with unmagnetized ions. The TFBI combines spatially uniform E-region
plasma instabilities, such as the Farley-Buneman instability (FBI), ion thermal instability (ITI), and electron thermal instability (ETI). Similar collision-dominated
plasma processes can also occur in the
solar and
stellar chromospheres, as well as in other planetary atmospheres. For the first time in the theory of the FBI-related processes, the kinetic description of ions includes the driving electric field, resulting in automatic inclusion of the ITI. This analytic theory has produced a comprehensive linear wave dispersion relation. It is remarkable that, similarly to the oversimplified earlier ion-kinetic studies, this much more general kinetic dispersion relation involves only elementary functions and the standard
plasma dispersion function (albeit of several different arguments). This new theory is limited to
plasma waves with the frequencies of the order, or larger than, the ion-neutral collision frequency. This inherently kinetic frequency range is of importance for accurate interpretation of radar signals scattered from relatively high E-region altitudes, but at altitudes where ions are unmagnetized (mostly, below 110 km).
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Fluid Dynamics · Cross-listed from astro-ph.HE
Magnetohydrodynamics Simulations
E. A. Huerta
Magnetohydrodynamics (MHD) couples the Navier–Stokes and Maxwell equations into a nonlinear system of partial differential equations governing
stellar interiors, astrophysical jets, fusion
plasmas, and space weather. Numerical advances, including finite-volume Godunov schemes, constrained-transport
algorithms, high-order spectral-element and discontinuous-Galerkin discretisations, and adaptive mesh refinement, have made MHD a predictive tool for
solar eruptions, tokamak confinement, and magnetised
turbulence. A fundamental barrier nevertheless remains. In three-dimensional MHD
turbulence, the degrees of freedom required to resolve all active scales grow as $\mathcal{O}(\mathrm{Re}^{9/4})$ or faster, where $\mathrm{Re}$ is the Reynolds number. Direct numerical simulation is therefore intractable at astrophysical and fusion-relevant parameters, particularly when the Lundquist number $S$ exceeds $10^{10}$ and both viscous and resistive dissipation ranges must be resolved. Kinetic closures, radiation transport, and uncertainty quantification further increase the cost. This chapter examines how AI may help bridge this gap. We review physics-informed neural networks,
Fourier neural operators and physics-informed neural operators, which learn solution operators across families of MHD problems; and hybrid operator-
diffusion frameworks that combine deterministic surrogates with score-based generative models to recover broadband turbulent spectra. These developments are set within the wider landscape of exascale high-order solvers, GPU acceleration, task-based parallelism, data-driven sub-grid closures, and prospective
quantum algorithms for implicit linear systems in resistive MHD. The central claim is that physics-informed AI, integrated with conventional solvers and trained on leadership-scale simulations, offers a credible route to regimes beyond the reach of classical discretisation alone.
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Chemical Physics · Cross-listed from cs.LG
Generative Pseudo-Force Fields for Molecular Generation
Stefaan Simon Pierre Hessmann, Khaled Kahouli, Stefan Gugler et al. (+4 more)
Generating stable molecular conformations typically forces a tradeoff between the physical realism of energy-based relaxation and the sampling efficiency of data-driven generative models. While machine learning force fields (MLFFs) can sample stable conformations by relaxing molecular geometries according to physical forces, they require costly ab-initio training data. Conversely,
diffusion models (DMs) learn from
equilibrium data alone but are dependent on noise schedules and time-step conditioning. In this work, we propose generative pseudo-force fields (GPFFs) to bridge these paradigms by training an MLFF on a quadratic pseudo-potential energy surface relative to reference
equilibrium structures. Because no ab-initio calculations are required for the perturbed geometries, non-
equilibrium training data can be generated on the fly by perturbing the
equilibria with Gaussian noise. We show that GPFFs constitute a time-step-agnostic variant of variance exploding DMs: the score comes from the predicted pseudo-forces but because force magnitudes implicitly encode the noise level, no time-step conditioning is needed. Our GPFF can hence be used as a drop-in replacement in standard
diffusion sampling (ancestral, Heun) but also facilitates more efficient, adaptive variants and an MLFF inspired direct denoising scheme. Our proposed sampling
algorithms support arbitrary structural priors and
geometric constraints. On QM9, GPFF has 100 % validity at 256 neural function evaluations (NFE) and over 50 % at just 6 NFE, outperforming
diffusion baselines across all samplers. Combined with custom priors, we showcase the fast and accurate generation process of our method in a molecular editor for a drug design setting, where a molecule is generated in real time.
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All authors Stefaan Simon Pierre Hessmann, Khaled Kahouli, Stefan Gugler, Michael Plainer, Frank Noé, Klaus-Robert Müller, Niklas Wolf Andreas Gebauer
Chemical Physics
Cavity-modified exciton-exciton annihilation in disordered molecular systems
I. Sokolovskii, B. S. Humphries, J. Blumberger et al. (+1 more)
Recent experiments have shown contradictory effects of strong light-matter coupling on
exciton-
exciton annihilation (EEA) in organic molecular systems. In this work, we perform numerical simulations of polariton dynamics and reveal the role of strong coupling in changing the EEA rate. The results of our simulations suggest that strong coupling allows to partially overcome disorder in systems with poor
exciton mobility via delocalisation of
excitons owing to the interaction with the common cavity mode. This leads to an enhanced connectivity between
excitons and, consequently, to an increase in the EEA rate. Conversely, in systems with high
exciton mobility, in which disorder has a much smaller effect on excitation energy transfer,
excitons can interact strongly even without coupling to the cavity
photons at the
exciton densities at which EEA typically occurs. In this case, the EEA rate can be even lower than in bare molecules due to the existence of a competing decay channel associated with
photon leakage through the cavity mirrors. We also find that in the weak coupling regime, the EEA rate appears to be suppressed due to this decay channel regardless of the
exciton transport properties. Our simulations resolve the experimental controversy on the effect of strong coupling on EEA and provide guidance for minimising the EEA rate towards a more feasible realisation of Bose-Einstein condensation of polaritons.
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All authors I. Sokolovskii, B. S. Humphries, J. Blumberger, G. Groenhof
Plasma Physics
Laser-driven Ion and Neutron Sources from Medium Repetition Ultrashort PW Laser
X. Jiao, C. Jeon, G. Tiwari et al. (+9 more)
We report the first experiment investigating ion acceleration and
neutron generation irradiating thin plastic targets (CH2) and deuterated plastic targets (CD2) of thickness ranging from 30nm to 160nm using the 4PW (0.1 Hz)
laser at CoReLS in South Korea. Thin wedge-shaped filters exploiting differing stopping ranges were designed to distinguish carbon 6+ ions from deuterons in shots with CD2 targets. The maximum energies of all ion species from both CH2 and CD2 targets were found to increase linearly with the
laser intensity. The highest observed energy of each ion species scales as q (charge)^2/mass, which is more similar to the scaling expected for ponderomotive acceleration than to the scaling expected for TNSA. The maximum ion energies were also found to increase with target thickness. Utilizing the secondary interactions of the deuteron beam, we created a fast
neutron source via deuteron breakup reactions on a copper converter. The
neutron spectrum follows an exponential distribution with energy up to 15MeV. A
neutron yield of 2x10^7n/J was observed from thicker targets, comparable to TNSA-regime
laser-driven
neutron sources and within one order of magnitude of the highest yields reported using Break-out-afterburner (BOA) acceleration with beryllium converters.
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All authors X. Jiao, C. Jeon, G. Tiwari, S. G. Lee, O. Labun, I. W. Choi, L. A. Labun, Mara Klebonas, C. Hojbota, D. D. Phan, C. H. Nam, B. M. Hegelich
Plasma Physics
Laser-Wakefield-Driven Photonuclear and Laser-Driven DD Fusion Neutron Sources for Fast Neutron Capture: A Start-to-End Simulation Study
Ou Z. Labun, D. D. Phan, L. Labun et al. (+6 more)
Laser-driven
neutron sources offer ultrashort pulse durations and extreme peak fluxes inaccessible to conventional facilities, enabling novel time-of-flight(TOF) spectroscopy and
nuclear astrophysics measurements. We present the first complete start-to-end simulation comparison of deuterium-deuterium (DD) bulk fusion and
laser wakefield acceleration-driven photonuclear
neutron sources, evaluated for fast
neutron capture relevant to the r-process. The simulation chain couples particle-in-
cell modeling of the
laser-
plasma interaction, Geant4 Monte Carlo
neutron transport with shielding and background characterization, and a NON-SMOKER-based event generator for multi-
neutron capture on Au197 and Rh103. We derive scaling laws for
neutron yield, pulse duration, and peak flux from 1J terawatt to 250J petawatt-class systems, including DD bulk fusion scaling laws specific to the short-pulse regime where volumetric ion heating via
plasma expansion timescales governs yield. Under realistic experimental conditions, DD fusion produces quasi-monoenergetic 2.45MeV
neutrons with less than 20ps pulses, 100 micro source size, and peak fluxes exceeding 10^22 cm^(-2) s^(-1), ideal for high-resolution TOF spectroscopy. LWFA sources generate broader spectra with larger than 50ps pulses, but high repetition rates (up to 100 Hz) yield a 36x advantage in cumulative capture events for short-lived isomers. We conclude these sources are complementary: DD fusion maximizes per-shot peak brightness, while LWFA provides high-throughput accumulation for systematic studies. These results establish design criteria for the first direct
laser-driven rapid
neutron capture experiments at facilities including PHELIX, ELI-NP, and TW-class systems such as UT3.
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All authors Ou Z. Labun, D. D. Phan, L. Labun, M. L. Klebonas, Calin Hojbota, Philip Franke, Sam Yoffe, Rahul Kumar, B. M. Hegelich
Plasma Physics
Magnetic Prandtl number dependence of plasmoid-mediated reconnection
Vinay Kumar and Axel Brandenburg
We investigate the dependence of the plasmoid-mediated
magnetic reconnection rate on the
magnetic Prandtl number using two-dimensional magnetohydrodynamic simulations of two coalescing
magnetic islands. For Lundquist numbers below the onset of the plasmoid instability, the reconnection rate follows the expected Sweet-Parker scaling and decreases with increasing
magnetic Prandtl number. However, once the current sheet becomes plasmoid unstable, the dependence on the
magnetic Prandtl number weakens considerably. In the fully plasmoid-mediated regime, we find reconnection rates that remain nearly independent of the
magnetic Prandtl number over the explored parameter range. We show that the largest reconnection rates are associated with strongly non-linear phases involving plasmoid interactions and mergers. We further compare our results with simulations of the boundary-driven Taylor problem, where previous studies reported a stronger
magnetic Prandtl number dependence, and provide a possible explanation for the differing scalings obtained in the two setups. These results may have implications for reconnection-mediated decay in magnetically dominated
turbulence and related astrophysical systems.
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Instrumentation and Detectors
NESSA: a compact 14 MeV D-T neutron source facility at Uppsala University
Sandipan Dawn, Elias Arnqvist, Göran Ericsson et al. (+6 more)
The NESSA (
Neutron Source in Uppsala) facility hosts a compact 14 MeV deuterium-tritium sealed tube
neutron generator at the Ångström Laboratory, Uppsala University. The generator, housed in a bunker inside the FREIA
hall, reaches a maximum yield of $4.7\times10^{8}$ n/s. This paper describes the facility: the generator, the bunker and its shielding, the detector systems, and the Monte Carlo models used to characterize the
neutron field. We also report the first commissioning measurements: yield calibration with $^{93}$Nb activation foils, fission chamber response at two positions, and simulated air and structural activation. Initial indium foil activations and single event effect (SEE) tests on silicon devices are also presented. The facility will be used for
nuclear data measurements,
neutron detector response studies, moderation and thermalization experiments, irradiation testing of electronics as well as for training and education.
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All authors Sandipan Dawn, Elias Arnqvist, Göran Ericsson, Linus Hägg, Stefan Jarl-Holm, Mattias Lantz, Andreas Lindner, Erik Andersson Sundén, Stephan Pomp
Physics and Society
Candidacy and Trigger: A Two-Phase Empirical Model of Hierarchical Collapse
Kristian Sestak
We test a dynamic ODE model of hierarchical asymmetry on a panel of 260 countries over 1960-2023, drawing on World Bank, Penn World Table, V-Dem and World Inequality Database sources. In cross-section the model holds partially: trade openness and bottom-of-distribution health suppress within-country asymmetry. The annual time-
evolution equation fails, with out-of-sample R^2 at or below zero across five functional forms. The same state vector, augmented with
market, debt and trajectory features, is much more successful as a discriminator: a four-layer leave-one-collapse-out classifier separates 29 historical collapses from 60 stable controls at a nested cross-validated AUC of 0.91. The signal splits into a chronic risk profile visible a decade before the event and an acute inflection three to five years before. Three independent tests reject the endogenous-drift reading of collapse. What remains is a candidacy-and-trigger picture in which structural variables identify the high-risk countries while collapse timing is set by shocks outside the modelled system. A separate strand documents a lagged co-movement between global fertility and global asymmetry on a single n=63 aggregate series; taken alone this would suggest a selection-pool channel. The same pattern is then tested within countries, within demographic strata, inside a two-way fixed-effects panel and through a migration-mediated cross-country interaction model, and the directional reading fails in each. The aggregate co-movement is a compositional effect rather than a causal channel. A global event-study on 7,316 peer-event observations confirms regional spillover in asymmetry and a novel post-collapse degradation of bottom-of-distribution health in regional neighbours. A pre-registered forward-look produces a top-20 / bottom-20 ranking to be evaluated over 2026-2036.
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Optics · Cross-listed from eess.SP
VCSEL-based PAM-4 transmission system emulator: A data-driven deep learning perspective
Stavros Deligiannidis, Nikos Argyris, Stefanos Dris et al. (+4 more)
We demonstrate a data-driven framework for emulating high-speed VCSEL-based 4-level Pulse Amplitude Modulation (PAM-4) optical interconnects using bidirectional Long Short-Term
Memory (Bi-LSTM) networks. Unlike conventional rate-equation models, which are computationally intensive and often require difficult parameter tuning, our approach utilizes experimental waveforms to learn the end-to-end system dynamics. By employing transfer learning and weight interpolation, we extend the model to new operating regimes with a 20-fold reduction in computation time compared to independent training, while maintaining normalized mean squared error below 0.04. This emulator provides a rapid, accurate tool for the design and optimization of short-reach optical links.
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All authors Stavros Deligiannidis, Nikos Argyris, Stefanos Dris, Dimitris Kalavrouziotis, Paraskevas Bakopoulos, Charis Mesaritakis, Adonis Bogris
Optics · Cross-listed from quant-ph
Detecting nonclassicality in randomly-displaced copies of a squeezed state
Mehmet Emre Tasgin
We address a fundamental question: Can one determine whether a received signal is squeezed when each copy arrives with a different displacement/amplitude? We introduce an interaction
Hamiltonian that converts quadrature squeezing into number squeezing. Using this conversion, we test whether the copies satisfy $g^{(2)}(0)<1$. The
Hamiltonian itself does not create nonclassicality; it only transfers it from quadrature squeezing to number squeezing. This allows us to identify squeezing even when individual copies have random displacements.
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Optics
From order to chaos in a chip-scale Kerr parametric oscillator
Luca O. Trinchão, Juan Diego Mazo-Vásquez, Miguel Nienstedt et al. (+13 more)
Integrated photonics has enabled a wide class of chip-scale light sources and
quantum technologies. Within this field, microresonator-based degenerate optical parametric oscillators (DOPOs) have gained prominence. Above a critical power threshold, these systems undergo spontaneous
symmetry breaking to settle into one of two stable, {\pi}-phase-shifted states – a mechanism successfully used for
quantum random number generation and photonic Ising machines. Here, we show that DOPOs based on the Kerr nonlinearity host a significantly broader range of nonlinear dynamics than previously explored. Using a silicon nitride microring resonator, we experimentally identify
Hopf bifurcations that trigger a transition from stationary operation to self-sustained oscillations at MHz frequencies. By adjusting pump detunings and powers, we achieve turnkey control over these oscillatory regimes, navigating the system between stable binary states and periodic limit cycles. Furthermore, we report the experimental observation of period-doubling bifurcations, which numerical simulations reveal as the precursor to a cascading instability culminating in chaos at elevated pump powers. Our results establish a framework for controlling nonlinear instabilities in chip-scale parametric oscillators, with applications in programmable photonic hardware and dynamical optical computing.
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All authors Luca O. Trinchão, Juan Diego Mazo-Vásquez, Miguel Nienstedt, Luiz Peres, Julius T. Gohsrich, Eduardo S. Gonçalves, Alekhya Ghosh, Arghadeep Pal, Laís Fujii dos Santos, Paulo F. Jarschel, Thiago P. Mayer Alegre, Nathalia B. Tomazio, Flore K. Kunst, Pascal Del'Haye, Lewis Hill, Gustavo S. Wiederhecker
Atomic Physics · Cross-listed from cond-mat.quant-gas
Can machine learning for quantum-gas experiments be explainable?
I. B. Spielman amd J. P. Zwolak
Virtually all aspects of many-body atomic physics are challenging: experiments are technically demanding,
datasets have become enormous, and the
memory and CPU requirements for classical simulation of generic
quantum systems often scale exponentially with system size. Machine learning (ML) methods are already assisting in each of these areas and are poised to become transformative. Here, we focus on two specific applications of ML to cold-atom-based
quantum simulators. These devices generally generate data in the form of images; we first showcase denoising of raw images and then identify solitonic waves in Bose-Einstein condensates. In both of these examples, we comment on the interplay between performance, model complexity, and interpretability.
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Optics
Spatiotemporal representation of a two-vortex reconnection as a single rotating vortex
Jordan M. Adams
Reconnections and rotations of lines are dual descriptions of the same saddle-shaped spacetime surface. We show that a reconnection between two line occurring over time is a single line that rotates over space progression. Both rotating lines and reconnections possess the same saddle shape sheet geometry in four-dimensional space-time, with different orientations. Cyclic precessing lines occurring over time are arrays of reconnections occurring spatially. We show that a
magnetic reconnection occurring over time can be seen as a single continuous line vector potential rotating spatially, where the full
evolution traces a saddle shape surface. Finally, we show that a single tilted spatiotemporal optical
vortex precesses with spatial progression, and as a result can be seen as two
vortices reconnecting. Given the unique spatiotemporal
evolution, we also analyzed the relativistic angular momentum of these electromagnetic fields.
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Plasma Physics
Bright, directional electron emission from nanowire coated targets under petawatt, femtosecond irradiation
Ameya Parab, Jian Fuh Ong, Stefania Ionescu et al. (+13 more)
Interactions of relativistically intense
laser pulses with structured targets have long been explored for controlling energy absorption and particle acceleration. However, at upcoming multi-petawatt
laser facilities, the survivability of such nanostructures under realistic temporal contrast conditions remains a key concern. We report an experimental and simulation study of nanowire targets irradiated by the ELI-NP 1-PW
laser without a
plasma mirror. At the built in, readily available contrast of $10^{-10}$, the nanowires survive the
laser pre-pulse and produce a robust enhancement in relativistic electron flux, energy, and directional emission compared to flat targets indicating that at better contrasts they can show similar enhancement at the 10 PW level. These results establish nanowire targets as resilient and reliable tools for relativistic electron manipulation at state of the art facilities.
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All authors Ameya Parab, Jian Fuh Ong, Stefania Ionescu, Sagar Dam, Sk Rakeeb, Hideaki Habara, Y.Keita, Rudrajyoti Palit, Daniel Popa, Deepak Sangwan, Klaus Spohr, Lucian Tudor, Adrian Vatcu, Prashant Kumar Singh, Kazuo A. Tanaka, G.Ravindra Kumar
Atomic Physics
Switching Rydberg interactions by three orders of magnitude using a terahertz field
Karen Wadenpfuhl, Aaron Reinhard, Oliver Hughes et al. (+3 more)
Atom-based
quantum computing exploits the ability to enhance atom-atom interactions by employing
laser excitation to higher-excited Rydberg states. Additional fields that drive transitions between Rydberg states can offer independent control of these atom-atom interactions. However, as microwave (mw) fields only provide access to states with similar principal
quantum number $n$, their ability to switch the interactions' strength is limited. Here, we use a pulsed terahertz field to rapidly switch the strength of interactions between Rydberg atoms by three orders of magnitude. We demonstrate interaction switching using
photon storage, where the terahertz field induces an interaction induced dephasing of the stored
photon. This ability to switch interactions offers advantages for single-
qubit readout, state-detection schemes,
quantum annealing, and Rydberg
quantum optics.
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All authors Karen Wadenpfuhl, Aaron Reinhard, Oliver Hughes, Lucy Downes, Kevin Weatherill, C. Stuart Adams
Atomic Physics · Cross-listed from quant-ph
Universal Jaynes-Cummings Control of an Oscillator
Jordan Huang, Ethan Kasaba, Thomas J. DiNapoli et al. (+2 more)
The Jaynes-Cummings (JC) interaction-the coherent exchange of excitations between a two-level system and a harmonic oscillator-is one of the fundamental interactions of
quantum optics, realized across platforms such as cavity
quantum electrodynamics, trapped ions, mechanical resonators, and superconducting
circuits. Although JC interactions and
qubit rotations form a universal gate set for oscillator control, practical implementations have not been demonstrated. Here we develop and experimentally demonstrate universal JC-based oscillator control by compiling arbitrary
unitary gates into sequences of JC interactions and
qubit rotations. In our experiment, the oscillator is realized using a mode of a high quality factor microwave cavity and the ancilla
qubit using a superconducting transmon
circuit, with the JC interaction implemented by a sideband interaction enabled by the Josephson nonlinearity. The native gates are constructed to be closed below a chosen cutoff
photon number, encoding a qudit with suppressed leakage errors, while ancilla relaxation errors are detectable. We further find that the dispersive shift serves as a compilation resource that reduces
circuit depths. We demonstrate universal qudit control and implement a single-qutrit gate set with a mean post-selected process fidelity of 96.3%, as well as ququart and ququint shift gates. These results establish Jaynes-Cummings control as a practical route to universal oscillator control, enabling programmable bosonic processors across a variety of
quantum platforms.
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All authors Jordan Huang, Ethan Kasaba, Thomas J. DiNapoli, Tanay Roy, Srivatsan Chakram
Atomic Physics · Cross-listed from cond-mat.mtrl-sci
Electronic mechanism of sub-100-fs demagnetization induced by a femtosecond light pulse
Konrad J. Kapcia, Victor Tkachenko, Flavio Capotondi et al. (+4 more)
A quantitative understanding of the processes that trigger light-induced demagnetization on ultrashort timescales is crucial for achieving an ultrafast, radiation-controlled
magnetic response in materials. This milestone is essential for developing next-generation
magnetic storage devices and ultrafast
magnetic switches. In this theoretical study, we investigated demagnetization triggered in a single
magnetic domain by light pulses ranging from a few to a few tens of femtoseconds in duration, with
photon energies spanning the optical and X-ray regimes, under strongly non-
equilibrium conditions. We predicted a loss of magnetization in the sub-100-fs range in all cases, primarily due to the excitation of the electronic system and the subsequent redistribution of electrons within the magneto-sensitive band. The considered timescales were too short for
phonon-mediated processes or inter-site Heisenberg exchange processes to contribute significantly. These findings pave the way for highly accurate, radiation-driven magnetization control in
magnetic materials at sub-100-femtosecond timescales with potential practical applications.
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All authors Konrad J. Kapcia, Victor Tkachenko, Flavio Capotondi, Alexander Lichtenstein, Serguei Molodtsov, Przemysław Piekarz, Beata Ziaja