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Physics Physics

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Sunday 10 May

Fluid Dynamics

Optimal non-linear mechanisms for laminar-turbulent transition of a shock-induced separated shear layer

Flavio Savarino, Denis Sipp and Georgios Rigas

Laminar-turbulent transition in shock wave-boundary-layer interactions (SWBLI) remains a major challenge for hypersonic vehicle design, with implications for drag, heat transfer, and structural loads. Linear optimal perturbation analyses can identify candidate instabilities, but the full route to breakdown in SWBLI requires nonlinear optimisation. Here, we characterise the optimal transition pathway in a globally stable yet convectively unstable Mach 2.15 oblique SWBLI using a nonlinear input-output optimisation framework based on the space-time spectral Navier-Stokes formulation of Poulain et al. (Comput. Fluids, 2024). The nonlinear frequency-domain approach captures mean-flow distortion, resolves triadic energy transfers, and extracts intrinsic nonlinear stresses that activate additional instability mechanisms. We identify a four-stage pathway: (1) optimal forcing of oblique first Mack mode waves at moderate frequencies; (2) nonlinear self-interaction of counter-propagating Mack waves, generating streamwise Gortler-like vortices in the reattachment region where streamline curvature peaks; (3) lift-up of streamwise velocity streaks by these vortices; and (4) subharmonic sinuous secondary instability leading to streak breakdown. Optimisation across forcing amplitudes from infinitesimal to transitional levels yields quasi-invariant optimal forcing structures, showing that exciting the oblique first Mack mode alone can trigger the turbulent cascade. Parametric studies over frequency-wavenumber space and forcing configurations confirm this preferential pathway. By resolving nonlinear energy transfers with a finite number of harmonics, this work provides a tractable framework for transition prediction and control strategy development in high-speed separated flows, bridging linear stability theory and fully turbulent simulation.
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History and Philosophy of Physics · Cross-listed from quant-ph

Classical Limit: Dissipation of Spekkens' Generalised Contextuality under Decoherence

Enrico Bozzetto and Jonte R. Hance

When does a decohering quantum system become classical? Generalised noncontextuality gives one criterion, for scenarios rich enough in preparations: a system is classical once its statistics admit an underlying model in which operationally indistinguishable procedures are physically identical. Where it crosses depends not on the state and channel alone but on which preparations, transformations and measurements the experiment offers, and if every measurement is allowed there is no crossing. For any pairwise unbiased bases in any dimension, given preparations holding their eigenstates and surrounding the maximally mixed state, the question reduces to a finite linear program; for a qudit of odd prime dimension $d$ restricted to the stabiliser subtheory, with one depolarised magic state, we solve it in closed form. With preparations and measurements alone the qutrit strange state is classical at zero noise; with the Clifford gates added, each channel represented once, it crosses at $3/5$; and with a random gate represented, as Spekkens' treatment asks, by the matching mixture of stochastic maps, at $3/4$. A Clifford orbit of assignments supports such a representation only if it has $d^2$ members, leaving, in every odd prime dimension, only Gross's discrete Wigner function: the crossing sits where it stops being negative.
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Atomic Physics · Cross-listed from quant-ph

Single-atom trapping in the evanescent field of an integrated photonic resonator

Yair Margalit, Omri Davidson, Oded Zemer et al. (+16 more)

Strong atom-photon interactions on scalable photonic platforms hold significant potential for both atomic and photonic quantum information platforms. In particular, trapping of a single atom on a planar photonic integrated resonator at the subwavelength distances required for strong coupling to the guided modes has remained an outstanding challenge. Here we demonstrate efficient trapping of a single ultracold rubidium atom within the evanescent field of an integrated silicon-nitride microring resonator, at distances of 150-200 nm from the chip surface. Efficient, single-stroke loading process is achieved using an evanescent-field mechanism related to Sisyphus cooling, in which a single scattering event dissipates the atom's kinetic energy and transfers it into a near-surface trap. We observe logarithmic scaling of trapping durations spanning from sub-millisecond timescales up to 1 second, without continuous cooling. The trapped atom couples efficiently to the resonator, enabling on-chip photon collection, photon antibunching, and Purcell-enhanced spontaneous emission with single-atom cooperativity exceeding unity. Our results establish the potential of CMOS-compatible chip-based atom-photon interfaces for scalable quantum photonic circuits.
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All authors Yair Margalit, Omri Davidson, Oded Zemer, Yoad Michael, Orel Bechler, Dror Liran, Noam Gross, Doron Azoury, Jeremy Raskop, Yaakov Yudkin, Gabriel Guendelman, Moshe Katzman, Michael Nagli, Yair Antman, Nadav Kandel, Geva Arwas, Idit Peer, Ofer Firstenberg, Barak Dayan

Computational Physics · Cross-listed from cs.LG

HS-FNO: History-Space Fourier Neural Operator for Non-Markovian Partial Differential Equations

Lennon J. Shikhman

Neural operators provide fast surrogate models for time-dependent partial differential equations, but their standard autoregressive use usually assumes that the instantaneous field $u(t,\cdot)$ is a complete state. This assumption fails for delay equations, distributed-memory systems, and other non-Markovian dynamics: two trajectories may agree at time $t$ and nevertheless have different futures because their histories differ. We introduce the History-Space Fourier Neural Operator (HS-FNO), a neural operator for delay and memory-driven PDEs formulated on the lifted state $u_t(\theta,x)=u(t+\theta,x)$, $\theta\in[-\tau,0]$. The key computational step is to decompose one history-state update into a learned predictor for the newly exposed future slice and an exact shift-append transport for the portion of the history window already known from the previous state. This avoids learning deterministic history coordinates, reduces the learned output dimension, and enforces the natural discrete history update. We test HS-FNO on five benchmark families covering delayed reaction–diffusion, spatial epidemiology, nonlocal neural-field dynamics, delayed waves, and distributed-memory closures. Across ten random seeds, HS-FNO attains the lowest aggregate one-step, history-space, and rollout errors among the principal baselines. The largest gain occurs in autoregressive prediction, where aggregate rollout error decreases from $0.241$, $0.188$, and $0.185$ for current-state, lag-stack, and unconstrained history-to-history operators, respectively, to $0.094$. The same model uses fewer parameters than unconstrained history prediction. These results indicate that enforcing the discrete shift structure of history-state evolution is an effective inductive bias for non-Markovian PDE surrogate modeling.
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Physics Education

Using Consumer Cameras to Observe Scintillation Light from Radiation

Yuzuka Sasaki, Yuuki Wada and Kazuo S. Tanaka

For a long time, the cloud chamber was the only educational tool available for measuring radiation. In recent years, simple radiation detectors combining scintillators with silicon photomultipliers have become increasingly common for these purposes. However, students are not able to see the scintillation light, the core process of radiation measurements with scintillators. Therefore, we explored the possibility of detecting scintillation light using two general-purpose cameras. In addition, we examined how differences in the spatial distribution relate to radiation types and energies. Scintillation light were able to be measured by a general-use camera, and their spatial distribution indicates radiation energy. This method could be utilized as an accessible imaging setup to compare radiation properties in a classroom.
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Computational Physics · Cross-listed from math.NA

A boundary integral method for wave scattering in a heterogeneous medium with a moving obstacle

Raaghav Ramani

We propose a time-domain boundary integral method to model linear wave propagation with refractive, focusing, and Doppler effects arising from medium heterogeneities and moving obstacles. In contrast to existing techniques, our method avoids volumetric discretization and yields a formulation posed only on the boundary of the obstacle. We combine two classical ingredients: a geometric–optics parametrix to capture leading-order behavior at propagating wavefronts, and a ray-based characterization of the distorted causal geometry. The former provides a framework for defining layer potentials and deriving the associated boundary integral equations, while the latter enables a pure boundary-only formulation. Taken together, these ingredients extend existing numerical techniques for the homogeneous, fixed-boundary case to the heterogeneous, moving-boundary setting, with appropriate modifications to capture the discrete causal structure arising from the intersection of distorted light cones with the worldsheet of the moving boundary. Numerical experiments demonstrate the ability of the method to resolve Doppler effects from moving obstacles, including a rotating turbine configuration, with stable performance up to Mach 0.9. In heterogeneous media, the method captures strong refractive effects from spherical inclusions: wave propagation wrapping around a gas bubble in water, and defocusing from a hot fireball rising through a stratified atmosphere.
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Chemical Physics

Enabling Structure-Only Initialization and Out-of-Distribution Generalization in GNN-based Molecular Dynamics Simulators

S. A. Shteingolts, Salman N. Salman and Dan Mendels

Machine learning-based simulators offer the potential to model the dynamics of complex systems more efficiently than classical approaches, while retaining differentiability, a key property for materials design. Graph neural network (GNN)-based simulators have shown strong performance across a range of physical domains, including molecular dynamics. However, their reliance on temporal context for accurate prediction limits their use in inverse design settings, where simulations must be initialized from a single static configuration. Moreover, inverse design requires robust out-of-distribution (OOD) generalization, as candidate structures typically lie outside the training domain. Here, we address both challenges by introducing two complementary strategies that enable stable and accurate structure-only initialization of GNN-based simulations. To directly target OOD generalization, we propose an inference-time physics-based optimization framework that constrains model predictions to remain physically consistent during rollout. In addition, we introduce a differentiable, GNN-based barostat that enables accurate tracking of system dimensions and pressure, critical for capturing macroscopic responses and supporting OOD generalization. We evaluate these approaches in the context of uniaxial compression of disordered elastic networks spanning a broad range of geometries, Poisson ratios, and microscopic behaviors. We find that, together, these methods substantially improve rollout stability and enable reliable OOD generalization, including regimes with distinct, more complex dynamics than those in the training data. These results show that, when properly initialized and constrained, GNN-based simulators can serve as efficient and generalizable tools for materials discovery and structural optimization, advancing their use in materials, molecular, and dynamical system design.
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Applied Physics · Cross-listed from cond-mat.mes-hall

Coherence, long-range transport and nuclear polarization in a driven-dissipative dark exciton condensate

Amit Jash, Maheswar Swar, Uri Shimon et al. (+2 more)

We report direct evidence for macroscopic coherence in a condensate of dark dipolar excitons in coupled quantum wells and show that its formation follows a non-equilibrium, driven-dissipative mechanism. The condensation transition is governed by gain-loss competition, in which the exceptionally long lifetime of dark excitons enables their dominance in mode selection. Condensate formation is revealed by photoluminescence darkening, changes in radiative recombination channels, and the emergence of long-range hydrodynamic transport manifested by propagation of density (sound) modes over millimeter-scale distances. The buildup of dark exciton density induces dynamic nuclear polarization, which closes the dark-bright exciton gap, \Delta, via the Overhauser field. This leads to nuclear spin polarization across the entire mesa, far beyond the optically excited region, and produces pronounced hysteresis behavior. At \Delta ~ 0 the gap is locked and the condensate loss are minimal, resulting in a second threshold manifested as a photoluminescence blueshift. Coherence is revealed through interference between incident and boundary-reflected exciton currents, producing spatial modulation of the photoluminescence from the radiative reservoir and enabling extraction of the condensate coherence length. These results establish dark excitons as a platform for coherent quantum fluids in a driven-dissipative, strongly interacting regime with electrical tunability, bridging the physics of polariton condensates and matter-like excitonic systems.
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All authors Amit Jash, Maheswar Swar, Uri Shimon, Vladimir Umansky, Israel Bar-Joseph

Optics

Accuracy assessment of scalar wave propagation methods for diffractive optics design: from thin elements to thick binary grating

Nicolas Barré

We present a systematic accuracy assessment of the thin-element approximation (TEA), the beam propagation method (BPM), and the wave propagation method (WPM) for binary diffractive gratings, using the rigorous Fourier modal method (FMM) as a reference. Random binary gratings are generated over a range of spatial frequency cutoffs and thicknesses, and the transmitted field overlap between each scalar method and the reference is measured. The results are summarized as accuracy maps in the spatial frequency-thickness parameter space, revealing the domain of validity of each method and providing practical guidelines for the choice of forward model in diffractive optics inverse design pipelines.
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Physics and Society · Cross-listed from cs.AI

Strategic commitments shape collective cybersecurity under AI inequality

Adeela Bashir, Zia Ush Shamszaman, Zhao Song et al. (+2 more)

The growing integration of AI into cybersecurity is reshaping the balance between attackers and defenders. When access to advanced AI-enabled defence tools is uneven, resource-limited defenders may be unable to adopt effective protection, creating persistent system vulnerabilities. We study the impact of differential AI access using an evolutionary game-theoretic model in a finite population. We first show that when high-capability defence is costly, the population is driven toward low-cost, weak-defence behaviour, sustaining attacks and weakening long-run security. To address this problem, we introduce differential access to AI defence tools by allowing defenders to choose between low- and high-capability protection based on their resources. We then examine the role of a small group of committed defenders who always adopt strong defence and influence others through social learning. Although commitment increases the prevalence of strong defence, it alone cannot stabilise secure outcomes due to high defence costs. We therefore incorporate a targeted subsidy to remove the cost disadvantage from committed defenders. Our analysis shows that subsidised commitment significantly increases strong defence adoption, suppresses successful attacks, and improves overall system resilience. Simulations across a broad parameter space confirm that subsidies consistently outperform commitment alone. In addition, social-welfare analysis shows improved defender outcomes while keeping attacker gains low. These findings suggest that targeted support for key defenders can be an effective mechanism for stabilising cybersecurity in AI-driven environments and provide a theoretical bridge between cybersecurity policy, AI governance, and strategic allocation of defensive AI capabilities.
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All authors Adeela Bashir, Zia Ush Shamszaman, Zhao Song, Matjaz Perc, The Anh Han

Computational Physics · Cross-listed from nlin.PS

Classification of Chimera States via Fourier Analysis and Unsupervised Learning

Rommel Tchinda Djeudjo, Riccardo Muolo, Thierry Njougouo et al. (+1 more)

Chimera states are among the most intriguing phenomena in nonlinear dynamics, characterized by the coexistence of coherent and incoherent behavior in systems of coupled identical oscillators. Many methods have been proposed to detect chimera states and to distinguish their different types. However, such methods often suffer from important limitations that prevent sufficiently precise classification. In this work, we overcome the issue by considering a method based on Fourier analysis to determine key signal characteristics such as amplitude, phase, and frequency, jointly with an unsupervised clustering step acting on normalized total variations, measures of local spatial changes of the above-mentioned dynamical features. The proposed method allows us to identify regions in parameter space returning chimera states, but also to further distinguish between the different types. The method is applied to a network of Rayleigh oscillators, which has been shown to exhibit a rich variety of dynamical patterns.
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All authors Rommel Tchinda Djeudjo, Riccardo Muolo, Thierry Njougouo, Timoteo Carletti

Chemical Physics

Systematic Fine-Tuning of MACE Interatomic Potentials for Catalysis

Nima Karimitari, Jacob Clary, Derek Vigil-Fowler et al. (+3 more)

Once trained, machine-learned interatomic potentials (MLIPs) provide a fast and accurate way to study catalytic reaction pathways, but their performance strongly depends on the training set. Here, we compare nine MLIPs trained with different data sets and strategies, including from-scratch (FS) training and fine-tuning (FT) of large foundation models. The models are evaluated on reaction energies, $E_{r}$, and reaction energy barriers, $E_{a}$, for 141 reactions, including CO$_2$ reduction to C$_2$ and C$_3$ products, propane dehydrogenation, hydrogen intercalation on Pd, and out-of-distribution oxygen evolution reaction (OER) on metal oxides. FS models trained with 5%–10% perturbed high-energy configurations from molecular dynamics or contour exploration reduce the error by more than twofold compared with models trained only on relaxation trajectories. In contrast, FT MLIPs are less sensitive to sampling and transfer well to out-of-distribution reactions. An MLIP fine-tuned on metallic catalysts achieves a 0.30 eV MAE for OER on iridium oxide polymorphs, outperforming out-of-the-box MACE-MH-1 by 0.08 eV and the best FS model by 0.14 eV. A model fine-tuned to O and OH adsorption on metal oxides gives a 0.19 eV reaction-barrier MAE for out-of-distribution CO$_2$RR on Cu, comparable to an FS model trained on in-distribution C–C bond-breaking reactions. Finally, a large MLIP fine-tuned on 49,860 configurations gives the best overall performance across metallic and metal-oxide catalysts and was used to screen a large left-out set of bimetallic alloys, achieving a 0.15 eV MAE for $E_{r}$, even for adsorbates on unseen Miller-index surfaces such as (532). This work identifies the training configurations needed for accurate FS and FT MLIPs for catalytic reaction modeling.
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All authors Nima Karimitari, Jacob Clary, Derek Vigil-Fowler, Ravishankar Sundararaman, Gábor Csányi, Christopher Sutton

Optics

Substrate-engineered tunable bound states in the continuum and directional radiation in dielectric metasurfaces

Hao Song, Yanming Sun, Jian Li et al. (+2 more)

Tunable bound states in the continuum (BICs) in metasurfaces offer powerful opportunities to control light-matter interactions, yet the role of out-of-plane symmetry breaking remains poorly understood. Here, we reveal a mechanism that enables tunable high-Q BICs and directional radiation through out-of-plane symmetry breaking in all-dielectric metasurfaces. A substrate-free metasurface composed of periodically arranged multilayer cylinders that support overlapping magnetic dipole and electric quadrupole resonances, yielding electric mirror and symmetry-protected BIC responses at 1550 nm. Introducing multilayer substrates breaks out-of-plane symmetry and excites guided modes. When the guided-mode wavelength matches that of the BIC and coupling to the substrate is suppressed, the BIC wavelength remains nearly invariant, while the Q factor increases with layer number. In contrast, spectral detuning and enhanced coupling lead to pronounced blueshifts and rapid Q degradation. The interplay between guided-mode matching and coupling strength thus governs whether a BIC remains robust or becomes tunable. These findings establish a general framework for BIC engineering via out-of-plane symmetry breaking and provide a versatile platform for tunable metasurfaces with potential applications in integrated optics.
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All authors Hao Song, Yanming Sun, Jian Li, Wanlin Wang, Ming Chun Tang

Applied Physics

From Angle of Repose to Heap Morphology: Full-Field Calibration of DEM for Granular Powders

Olivier Gaboriault, Antonella Succar, Cléo Delêtre et al. (+4 more)

The calibration of discrete element method (DEM) models is commonly performed by tuning model parameters to match an experimental measurements, most commonly the angle of repose (AOR). Although widely used, AOR-based calibration metrics do not adequately characterize the full heap morphology, particularly when dealing with cohesive granular materials. As a result, AOR-based calibrations often leads to non-unique parameter sets. In this work, we propose a DEM calibration procedure based on full-field image analysis of static powder heaps rather than scalar AOR measurements. The method compares an average experimental heap profile (AEHP), obtained from repeated GranuHeap experiments, with an average numerical heap profile (ANHP) generated from DEM simulations. This comparison is performed using pixel-wise grayscale intensity values of both average heap profiles. Two metal powders commonly used in additive manufacturing, Ti6Al4V and Al6061, are used to evaluate the proposed methodology. This work highlights the limitations of traditional AOR-based approaches and demonstrates that full-field heap morphology offers a more reliable framework for DEM calibration.
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All authors Olivier Gaboriault, Antonella Succar, Cléo Delêtre, Anatolie Timercan, Roger Pelletier, David Melancon, Bruno Blais

Optics

Noise-Resilient Imaging through Coherence Filtering

Pranay Mohta, Keval Moliya, Aniket Nag et al. (+2 more)

Noise is a significant challenge in imaging. Conventional intensity-based techniques mitigate noise through various filtering methods, but they often require prior knowledge of noise characteristics and struggle, especially under low-light conditions and with spatially structured noise. Quantum distillation provides enhanced noise rejection; however, its applicability is limited as it requires specialised illumination and substantial modifications to existing imaging setups. In this article, we introduce a coherence-based image distillation approach that separates object from noise by leveraging the difference in their temporal coherence properties. We implement this through our interferometric protocol, which enables imaging based on spatial coherence while simultaneously filtering out noise via temporal coherence. This overcomes the limitations of both intensity-based and quantum distillation methods. We experimentally demonstrate noise resilience by successfully recovering feature-rich objects, such as QR codes and grayscale wheels, obscured by spatially uniform and structured noise 20 times as intense as the object. We further show that our method remains effective for fields with substantial spectral overlap, outperforming spectral filtering in regimes where the latter provides little noise suppression. This approach provides a robust framework for noise-resilient imaging with applications in optical communication, fluorescence microscopy, and biological imaging at both high and low light levels.
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All authors Pranay Mohta, Keval Moliya, Aniket Nag, Shaurya Aarav, Anand Kumar Jha

Atomic Physics · Cross-listed from cs.LG

Teaching Molecular Dynamics to a Non-Autoregressive Ionic Transport Predictor

Jiyeon Kim, Byungju Lee and Won-Yong Shin

Unlike most static material properties widely studied in the machine learning literature, ionic transport properties are inherently dynamic, making their fast and accurate prediction from static atomic structures challenging. The current standard approach, molecular dynamics (MD) simulations, suffers from prohibitively high computational cost. Recent autoregressive learning-based MD acceleration methods requiring sequential inference remain slow and prone to error accumulation; in contrast, existing non-autoregressive material property prediction models are less accurate because they fail to exploit dynamics. Moreover, existing methods typically benefit from datasets either with or without atomic trajectories, but not both. To overcome these limitations, we propose a non-autoregressive learning framework based on auxiliary modality learning, which treats atomic trajectories as an auxiliary modality during training but does not require them at inference. This enables the predictor to learn dynamics without sequential inference while benefiting from both types of datasets. As a result, our framework achieves over 200 times speedup compared to autoregressive models on the dataset with atomic trajectories while substantially reducing prediction error relative to non-autoregressive benchmarks across both types of datasets. Our code is available at https://github.com/jykim-git/MD.
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Computational Physics

Constitutive Priors for Inverse Design

Jinkyo Han and Bahador Bahmani

With recent advances in material synthesis and additive manufacturing, material systems can be designed to achieve prescribed mechanical responses. An important class of such problems is the inverse design of elastic networks that attain a target configuration under loading, with applications in robotics, aerospace, and shape-morphing structures. This work presents a framework that formulates inverse design directly within the learned family of constitutive behaviors. Given a collection of stress-strain responses, we construct a constitutive prior, defined as a low-dimensional latent representation of admissible material laws learned directly from these responses. Spatially varying latent variables are then optimized subject to the governing equilibrium equations so that the deformed network matches a target configuration. The constitutive prior is represented using an energy-based, partially input-convex neural network that enforces the constitutive constraints by construction. To improve robustness in the resulting nonconvex optimization problem, the framework combines homotopy-continuation-based optimization with correspondence-free point cloud matching, allowing the target and optimized geometries to have different discretizations. The proposed approach is demonstrated on several inverse design problems for nonlinear elastic networks, and quantitative comparisons with alternative optimization strategies show improved robustness and optimization performance.
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Applied Physics · Cross-listed from cs.LG

Semi-Supervised Neural Super-Resolution for Mesh-Based Simulations

Jiyeon Kim, Youngjoon Hong and Won-Yong Shin

Mesh-based simulations provide high-fidelity solutions to partial differential equations (PDEs), but achieving such accuracy typically requires fine meshes, leading to substantial computational overhead. Super-resolution techniques aim to mitigate this cost by reconstructing high-resolution (HR), high-fidelity solutions from low-cost, low-resolution (LR) counterparts. However, training neural networks for super-resolution often demands large amounts of expensive HR supervision data. To address this challenge, we propose SuperMeshNet, an HR data-efficient super-resolution framework for mesh-based simulations aided by message passing neural networks (MPNNs). At its core, SuperMeshNet introduces complementary learning, a semi-supervised approach that effectively leverages both 1) a small amount of paired LR-HR data and 2) abundant unpaired LR data via two jointly trained, complementary MPNN-based models. Additionally, our model is enriched by inductive biases, which are empirically shown to further improve super-resolution performance. Extensive experiments demonstrate that SuperMeshNet requires 90% less HR data to achieve even lower root mean square error (RMSE) than that of the fully supervised benchmark without the inductive biases. The source code and datasets are available at https://github.com/jykim-git/SuperMeshNet.git.
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Medical Physics

Moving MRI: Imaging a moving body with a moving magnet

Jingting Yao, Artan Kaso, Nikhil Patel et al. (+4 more)

Current magnetic resonance imaging (MRI) requires the subject to remain stationary to limit motion artifacts and avoid unwanted field-induced brain stimulation. However, imaging during large-scale motion could enable studies in which motion itself is central. One example is the study of brain networks involved in vestibular function, which senses head motion. Here, we demonstrate Moving MRI (mMRI), a system that enables imaging during large-scale motion by moving the subject and scanner together to minimize relative motion. We implemented a proof-of-concept platform using a compact, cryogen-free superconducting magnet mounted on a pneumatically actuated tilt mechanism that moves the magnet, gradients, and RF coil as a unit during scanning. Phantom and in vivo rat brain scans were acquired during repetitive tilting. We characterized artifacts arising from tilt-induced field shifts and residual subject-scanner motion, and partially reduced these effects. mMRI enables imaging during large-scale movement and may broaden access to naturalistic vestibular paradigms while providing a foundation for future human systems.
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All authors Jingting Yao, Artan Kaso, Nikhil Patel, Yin-Ching Iris Chen, Andre van der Kouwe, Daniel M. Merfeld, Jerome L. Ackerman

Optics

Resolution Estimation of a Digital Holographic Microscope Using Neural Network Analysis of Reconstructed Images

A. G. Fedorov

This paper presents a method for estimating the resolution of a digital holographic microscope using neural network analysis of reconstructed images. The spectral bandwidth of the source ($\Delta \lambda$) is used as a controlled image degradation parameter. Numerical simulations were performed within inline Gabor holography. A dataset of reconstructed images was generated for several test objects over a $\Delta \lambda$ range from 0.05 to 20 nm. The model predicts $\Delta \lambda$ from reconstructed images with high precision. The predictions are consistent with standard resolution metrics, including FWHM, MTF, and the USAF resolution criterion. The generalization analysis shows that the model is sensitive to the type of degradation. It captures interferometric distortions and responds selectively to the underlying physical mechanism. The proposed approach enables resolution estimation without explicit modeling of all degradation factors and can be applied to compact holographic systems.
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Saturday 9 May

Atmospheric and Oceanic Physics

Visibility nowcasting in South Korea: a machine learning approach to class imbalance and distribution shift

Bong Gyun Shin, Chan Sik Lee and Hyesun Suh

Atmospheric visibility is a critical variable for transportation safety and air quality management, however, accurate prediction remains challenging due to the complex interactions between meteorological conditions and air pollutants, as well as the rarity of low-visibility events. This study introduces a machine learning framework to nowcast visibility in six major South Korean cities. To handle the imbalance in the 2018-2020 training data, we applied the Synthetic Minority Over-sampling Technique with Nominal and Continuous (SMOTENC) and Conditional Tabular Generative Adversarial Network (CTGAN). An ensemble approach combining machine learning and deep learning models was then used and evaluated on a 2021 test dataset. The results revealed a marked decline in predictive performance in the test set compared to the cross-validation phase. This degradation was attributed to a distributional shift between training and testing periods, which was quantitatively confirmed by measuring the Wasserstein distance of the most influential feature identified by SHAP analysis. In general, this study presents a methodology that aims to simultaneously address the dual challenges of data imbalance and temporal distributional shifts, and emphasizes the necessity of accounting for evolving external environmental factors when implementing nowcasting models on time-series data.
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Applied Physics

Warhead Verification with Neutron Beams and Electric Cryptography

Nolan Kowitt, Michael Moore, Kevin Sanchez et al. (+2 more)

Future arms control treaties may need to reliably verify warheads for dismantlement as part of the treaty verification process without exposing carefully guarded weapons information. Neutron Resonance Transmission Analysis has been proposed as a strategy to verify the authenticity of nuclear warheads and warhead components slated for dismantlement in an arms control verification exercise. Most actinides have resonant neutron absorption lines in the eV region, so by measuring the energy resolved transmission of neutrons and observing the resulting isotope-specific absorption lines, the authenticity of a nuclear device can be verified. To ensure acceptance of this technique by treaty partners, the measurement should minimize information learned about the warhead, including both geometric and isotopic features. Past implementations of transmission analysis acquired and compared neutron time-of-flight spectra. We have developed an analog electric cryptographic measurement proof of concept system where only counts in specific resonance energy windows are observed. The system uses discrete analog components, producing a complete data acquisition and analysis circuit. By limiting the design to easily verifiable parts, the entire apparatus is transparent to authentication and certification. The information security provided by this analog measurement technique may make it the verification basis of future ambitious arms control treaties that explicitly stipulate the dismantlement of nuclear weapons.
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All authors Nolan Kowitt, Michael Moore, Kevin Sanchez, Mital Zalavadia, Areg Danagoulian

Medical Physics

Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data

Jingtai Song, Qinsheng Zhu, Xiaodong Xing et al. (+4 more)

Quantifying hemodynamics in the curved segments of the intracranial internal carotid artery is a core challenge in diagnosing vascular stenosis. Conventional full-field imaging, such as 4D Flow MRI, is costly and difficult to widely promote. Meanwhile, reconstructing full-field fluid information from easily accessible and non-invasive sparse measurement data (such as transcranial Doppler ultrasound/computed tomography angiography) is essentially a highly challenging ill-posed inverse problem. To overcome the severe optimization difficulties and generalization failures of conventional physics-informed neural networks (PINNs) in highly tortuous geometries, we propose a dual-correction physics-informed neural network (DCP-INN) framework taking into account a causal decoupling strategy. The proposed DCP-INN model utilizes a diamond-shaped main network to capture low-frequency trends in physical evolution, and employs a parallel wide-deep correction network to compensate for high-frequency residuals resulting from complex geometric shapes. Furthermore, the framework introduces a high-order physical loss function based on Taylor expansion to enhance local continuity under extremely sparse data constraints. To validate the proposed method, we performed computational evaluations on realistic vascular geometries with significant tortuosity. The results demonstrate that the method effectively mitigates optimization challenges and significantly reduces flow field reconstruction error. This study not only achieves physically credible and robust flow field reconstruction in complex morphologies but also provides a highly promising algorithmic foundation for building low-cost, high-resolution personalized cardiovascular digital twins in future.
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All authors Jingtai Song, Qinsheng Zhu, Xiaodong Xing, Yufeng Tang, Zhiyun Zhang, Xianwen Zhang, Hao Wu

Instrumentation and Detectors

The ICESPICE demonstrator for particle/$\gamma$-$e^{-}$ coincidence experiments at Florida State University

A.L. Conley, M. Spieker, R. Aggarwal et al. (+17 more)

The Internal Conversion Electron SPectrometer In Coincidence Experiments (ICESPICE) demonstrator has been developed at Florida State University to enable particle/gamma-electron coincidence measurements in low-energy nuclear structure studies. ICESPICE is based on the mini-orange spectrometer concept and features a modular design using commercially available permanent magnets arranged in toroidal configurations to transport internal conversion electrons to room-temperature PIPS detectors while suppressing background from undesired particles. The system was optimized through SolidWorks modeling, COMSOL magnetic field simulations, and Geant4 particle tracking to maximize the magnetic transmission probability for electrons around 1 MeV. Commissioning tests using a calibrated 207Bi source demonstrated the performance of multiple spectrometer-detector configurations. Coincidence measurements between CeBr3 detectors from the CeBrA array and PIPS detectors revealed clear gamma-electron correlations. The first in-beam particle-electron measurements using ICESPICE were performed with the Super-Enge Split-Pole Spectrograph (SE-SPS) in the 208Pb(d,t)207Pb reaction. Prompt coincidences between tritons detected with the SE-SPS and electrons detected with ICESPICE were observed. The presented results show that ICESPICE is a promising ancillary detector system for in-beam internal conversion electron spectroscopy at the FSU SE-SPS.
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All authors A.L. Conley, M. Spieker, R. Aggarwal, L.T. Baby, J. Davis, J. Esparza, I. Hay, B. Kelly, T. Kirk, M.I. Khawaja, R. Mahajan, S.T. Marley, M. Mestayer, A.B. Morelock, A. Peters, A.M. Ring, J. Sheridan, V. Sitaraman, T. Stuck, I. Wiedenhöver

General Physics

A Structural Link Between the Bohm Quantum Potential and the Scalar Mode of Aharonov-Bohm Electrodynamics in a Bosonic Schrödinger Model

R. Pullano and G. Modanese

We discuss a formal and physical connection between the Bohm quantum potential and the scalar mode of the Aharonov-Bohm extension of electrodynamics. The analysis is motivated by the effective non-relativistic bosonic model recently proposed by Minotti and Modanese, in which the electromagnetic field is coupled to a conserved current while the field equations contain an additional source term. In the Madelung representation $\psi=R\exp(i\theta/\hbar)$, the Bohm quantum potential $ Q_B=-\frac{\hbar^2}{2m}\frac{\nabla^2 R}{R} $ is determined by the relative curvature $\nabla^2R/R$ of the amplitude profile $R$. In the same bosonic model, the scalar electromagnetic mode $S=\partial_\mu A^\mu$ is sourced by the extra-current $I=\partial_\mu j^\mu$, which contains the density-weighted electromagnetic combination $\nabla\cdot(R^2\mathbf A)$. Thus $Q_B$ does not act as a direct source of $S$; rather, the two quantities probe different differential aspects of the same amplitude profile: $Q_B$ is sensitive to the relative curvature of $R$, whereas the source of $S$ is sensitive to its density and gradient content through $R^2$ and $\nabla R$. We show that, once boundary and normalization data are fixed, this observation may be written as a mediated functional dependence of $S$ on $Q_B$ through $R$. We also clarify the physical status of $Q_B$: although it is state-dependent and should not be interpreted as an autonomous external potential, its density-weighted integral gives the amplitude-gradient energy, equivalently a Fisher-information contribution. This makes $Q_B$ a compact diagnostic of quantum pressure, rigidity, and inhomogeneity of a bosonic condensate. The resulting link with $S$ is therefore best understood as a structural relation between the order-parameter amplitude profile of the condensate and the scalar sector of the extended electromagnetic theory.
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Optics

Raman suppression in nanophotonics enabled by multimode spectral filtering

Yunxiang Song, Jinsheng Lu, Xinrui Zhu et al. (+7 more)

Miniaturized photonic cavities generating nonlinear optical states of light are central to telecommunications and metrology applications. The emergence of such states is primarily underpinned by the ubiquitous Kerr nonlinearity that is present in all media. However, stimulated Raman scattering (SRS), an additional process inherent to many materials, has been shown to critically hinder the states' formation, imposing fundamental constraints on the choice of photonic platforms. Here, we introduce a novel strategy for the suppression of SRS in nanophotonic devices, adaptable to diverse Raman spectral responses. This is achieved by controlling the coupling and loss among multiple transverse spatial modes of the system, tailored across ultrabroad spectral bandwidths. Specifically, we combine nanometrically-corrugated Bragg gratings and tapered waveguides that, together enable co-directional multimode coupling and mode-selective filtering. We use lithium niobate as an exemplary Raman-active material to realize the concept, and we demonstrate the robust generation of two distinct Kerr nonlinear states (corresponding to coherent optical frequency combs) using the fabricated devices. The simplicity and generality of the concept suggest wide applicability to classical and quantum light generation on many technologically-relevant platforms nominally plagued by SRS (e.g., silicon and diamond photonics). More broadly, our multimode spectral shaping and filtering concept opens a path forward for highly-structured, wavelength-specific losses in nanophotonic waveguides and cavities, with potential applications in ultrafast and nonlinear integrated photonics.
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All authors Yunxiang Song, Jinsheng Lu, Xinrui Zhu, Danxian Liu, Zongda Li, Pawan Ratra, Norman Lippok, Miro Erkintalo, Federico Capasso, Marko Loncar

Plasma Physics

MPEX AI Digital Twins

Gary Staebler, Rhea Barnett, Mark Cianciosa et al. (+10 more)

Our vision for the MPEX AI Digital Twins project is to supply experimental and physics model simulation data to train Artificial Intelligence (AI) models for data processing, analysis, operational control, PMI and materials simulation to maximize the scientific output of the MPEX device. Ultimately, an AI digital twin of MPEX material assessment metrics for tested and synthetic material types with simulated PMI will be trained by the AI Modeling Teams on the experimental and physics simulation data submitted to the American Science Cloud by this project
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All authors Gary Staebler, Rhea Barnett, Mark Cianciosa, Rinkle Juneja, Atul Kumar, Wouter Tierens, Minglei Yang, Cory Hauck, Richard Archibald, Pablo Seleson, Sam Reeve, Ben Dudson, Vasily Geyko

Plasma Physics

Probing In-Solid Proton Energy Distributions in Laser-Driven Fusion via Nuclear Activation Diagnostics

Hiroki Matsubara, Ryunosuke Takizawa, Yuga Karaki et al. (+25 more)

The energy distribution of energetic protons inside a solid target is a key quantity governing nuclear reaction yields and energy deposition in high-intensity laser-driven fusion, including nonthermal proton–boron (p–B) schemes and proton fast ignition. Yet it has remained inaccessible to conventional particle diagnostics, which detect only ions escaping the target and are perturbed by intense plasma electromagnetic fields. Here we establish a quantitative diagnostic that uses nuclear activation reactions occurring within the target itself as an internal probe of the in-solid proton energy distribution. Applied to laser-driven p–B fusion experiments on the kJ-class laser, the method reconstructs an exponential-equivalent in-solid proton energy distribution from the absolute yields of $^{11}\mathrm{C}$ and $^{7}\mathrm{Be}$ produced via $\mathrm{^{11}B(p,n)^{11}C}$ and $\mathrm{^{10}B(p,\alpha)^{7}Be}$, and yields the absolute number of $\mathrm{^{11}B(p,2\alpha)^{4}He}$ reactions through a side-channel analysis with propagated cross-section uncertainties. This work opens a quantitative window onto the in-solid proton dynamics that drive nuclear reactions in laser-driven fusion experiments.
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All authors Hiroki Matsubara, Ryunosuke Takizawa, Yuga Karaki, Ryuya Yamada, Tomoyuki Johzaki, Rinya Akematsu, Ryo Omura, Kai Kimura, Fuka Nikaido, Toshiharu Yasui, Takumi Minami, Law King Fai Farley, Akifumi Yogo, Yuki Abe, Yasuhiro Kuramitsu, Yuji Fukuda, Takehito Hayakawa, Masato Kanasaki, Koichi Honda, Kohei Yamanoi, Keisuke Takahashi, Koji Tsubakimoto, Yu Yamamoto, Hideyuki Maruta, Atsushi Sunahara, Seita Iizuka, Shuji Nakamura, Shinsuke Fujioka

Instrumentation and Detectors · Cross-listed from hep-ph

Ortho-Positronium Three-Photon Decays: Physics Constraints and a Closed-Form Energy Method for Annihilation Vertex Reconstruction

L. Raczyński, W. Krzemień, A. Coussat et al. (+5 more)

We examine the physical foundations of orthopositronium three-photon decay in the context of annihilation vertex reconstruction, focusing on how energy-momentum conservation constrains the space of physically admissible solutions. Finally, we provide a closed-form analytical derivation of an energy-based vertex reconstruction algorithm.
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All authors L. Raczyński, W. Krzemień, A. Coussat, M. Bała, B.C. Hiesmayr, K. Klimaszewski, M. Obara, R. Y. Shopa

Fluid Dynamics · Cross-listed from cond-mat.soft

Lubrication-Induced Newtonianization Enables Passive Transport of Non-Newtonian materials

Arvind Arun Dev, Paszkal Papp, Thomas M. Hermans et al. (+1 more)

Non Newtonian flows are typically governed by intrinsic bulk rheology, which imposes strong constraints on transport through confined geometries. Here, we show that stable boundary lubrication can fundamentally alter this behavior by localizing shear within a thin, low-viscosity interfacial layer. As a result, the nonlinear rheological response of a broad class of complex materials, including yield-stress, shear-dependent, and thixotropic materials, is strongly suppressed during flow. Using analytical solutions of Stokes flow and numerical simulations, we demonstrate that lubrication-induced shear localization leads to an apparent Newtonianization of transport, in which the macroscopic flow response becomes primarily controlled by the lubricating layer and geometric confinement rather than the intrinsic material properties. In this regime, materials that would otherwise require large pressure gradients can be transported at substantially lower driving forces. Notably, this boundary-dominated transport enables gravity-driven passive flow with orders-of-magnitude enhancement in throughput compared to rigid-wall conduits. These results establish lubrication as a powerful mechanism for tuning and simplifying complex fluid transport, with implications for biological systems, soft and jammed materials, and energy-efficient fluids.
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All authors Arvind Arun Dev, Paszkal Papp, Thomas M. Hermans, Bernard Doudin