Instrumentation and Detectors
DNN-based Signal Processing for Liquid Argon Time Projection Chambers
Avinay Bhat, Mun Jung Jung, Gray Putnam et al. (+1 more)
We investigate a deep learning-based signal processing for
liquid argon time projection chambers (LArTPCs), a leading detector technology in
neutrino physics. Identifying regions of interest (ROIs) in LArTPCs is challenging due to signal cancellation from bipolar responses and various detector effects observed in real data. We approach ROI identification as an image
segmentation task, and employ a U-ResNet architecture. The network is trained on samples that incorporate detector geometry information and include a range of detector variations. Our approach significantly outperforms traditional methods while maintaining robustness across diverse detector conditions. This method has been adopted for signal processing in the Short-Baseline
Neutrino program and provides a valuable foundation for future experiments such as the Deep Underground
Neutrino Experiment.
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All authors Avinay Bhat, Mun Jung Jung, Gray Putnam, Haiwang Yu
Fluid Dynamics · Cross-listed from cs.LG
A data free neural operator enabling fast inference of 2D and 3D Navier Stokes equations
Junho Choi, Teng-Yuan Chang, Namjung Kim et al. (+1 more)
Ensemble simulations of high-dimensional flow models (e.g., Navier Stokes type PDEs) are computationally prohibitive for real time applications. Neural operators enable fast inference but are limited by costly data requirements and poor generalization to 3D flows. We present a data-free operator network for the Navier Stokes equations that eliminates the need for paired solution data and enables robust, real time inference for large ensemble
forecasting. The physics-grounded architecture takes initial and boundary conditions as well as forcing functions, yielding solutions robust to high variability and perturbations. Across 2D
benchmarks and 3D test cases, the method surpasses prior neural operators in accuracy and, for ensembles, achieves greater efficiency than conventional numerical solvers. Notably, it delivers accurate solutions of the three dimensional Navier Stokes equations, a regime not previously demonstrated for data free neural operators. By uniting a numerically grounded architecture with the scalability of machine learning, this approach establishes a practical pathway toward data free, high fidelity PDE surrogates for end to end scientific simulation and prediction.
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All authors Junho Choi, Teng-Yuan Chang, Namjung Kim, Youngjoon Hong
Applied Physics
A Continuum Macro-Model for Bistable Periodic Auxetic Surfaces
Emmanuel Sansusthy Tardio, Tian Chen and Theocharis Baxevanis
A macro-constitutive model for the deformation response of periodic rotating bistable auxetic surfaces is developed. Focus is placed on isotropic surfaces made of bistable hexagonal
cells composed of six triangular units with two stable
equilibrium states. Adopting a variational formulation, the effective stress-strain response is derived from a free energy function expressed in terms of the invariants of the logarithmic strain. To address the mathematical ill-posedness and numerical artifacts–such as mesh sensitivity–arising from the double-well nature of the free energy, two regularization approaches are introduced: (i) a gradient-enhanced first invariant of the logarithmic strain, and (ii) an artificial material rate dependency. Although neither regularization guarantees solution uniqueness, the former mitigates mesh sensitivity, while the latter improves the convergence behavior of the nonlinear numerical scheme by promoting smooth temporal
evolution of transition localization and enabling the system to overcome snap-backs induced by local non-proportional loading near transition fronts. The model is implemented using membrane/shell structural elements and plane stress continuum ones within the ABAQUS finite element suite. Numerical simulations demonstrate the efficacy of the proposed formulation and its implementation.
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History and Philosophy of Physics
Two Shades of Quark Color: Parallel Canons across the Cold War Divide
Vitaly Pronskikh
The introduction of the color
quantum number is conventionally narrated as a linear progression from the
quark-model statistics paradox to
quantum chromodynamics (QCD). This paper challenges that teleology by arguing that "color" emerged as two conceptually distinct constructs during the Cold War. The first, originating with Han and Nambu and culminating in QCD, conceived of color as a local gauge charge, the source of a fundamental force mediated by
gluons. The second, developed at the Joint Institute for
Nuclear Research (JINR) in Dubna, treated color as a hidden, three-valued label–a statistical and structural property within a composite, S-
matrix-inflected
hadron model. We trace these parallel narratives, linking the Dubna approach to a holist epistemology that prioritizes observable amplitudes and global constraints, and the QCD approach to a reductionist program grounded in micro-dynamics. A case study of Fermilab's E-36 experimental chain (1970–78) shows how an observables-first design-tuned to S-
matrix and Regge constraints on forward elastic scattering–performed robustly within its natural domain but was ultimately discontinued amid declining theoretical interest and involvement. The subsequent hegemony of QCD retroactively projected its gauge-theoretic conception of color onto history, erasing this epistemic diversity. We conclude that the marginalization of Dubna's structural color was not merely a political outcome of the Cold War but a result of deep ontological and philosophical divergences, advocating for a domain-sensitive pluralism in the historiography of particle physics.
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Optics
Mid-infrared continua via spectral broadening and difference frequency generation in a nanophotonic lithium niobate waveguide
Markus Ludwig, Furkan Ayhan, Thibault Voumard et al. (+5 more)
Periodically poled thin film lithium niobate waveguides provide simultaneous access to efficient second and third order nonlinear processes, enabling broadband generation of coherent
laser light. Here, we demonstrate the generation of a broadband mid-infrared continuum in a nanophotonic lithium niobate waveguide pumped by a telecom-wavelength femtosecond source. Specifically, our dual-stage design includes both third-order nonlinear spectral broadening followed by a dedicated periodically poled waveguide section performing efficient broadband intrapulse difference frequency generation. Driven by sub-100 fs pulses with approximately 200 pJ pulse energy, the generated mid-infrared light covers wavelengths from 3200 to 4800 nm. Cascaded harmonic generation also extends the spectrum into the visible and ultraviolet domains, resulting in an overall spectral bandwidth ranging from 350 to 4800 nm.
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All authors Markus Ludwig, Furkan Ayhan, Thibault Voumard, Weichen Fan, Mahmoud A. Gaafar, Victor Brasch, Luis G. Villanueva, Tobias Herr
Medical Physics
Predicting Wrist Osteoporosis from excised human finger bones using spatially offset Raman spectroscopy, A Cadaveric Study
Mohammad Hosseini, Sadia Afrin, Anthony Yosick et al. (+3 more)
Osteoporosis and osteopenia remain vastly underdiagnosed. Current clinical screening relies almost exclusively on dual-energy X-ray absorptiometry (DXA), which measures bone mineral density (BMD) but fails to capture the compositional changes that lead to BMD loss. We investigated whether Spatially Offset Raman Spectroscopy (SORS) applied to excised finger bones can assess subsurface biochemical markers capable of diagnosing osteoporosis and osteopenia and predicting wrist DXA T-scores. Raman spectra were acquired ex vivo on the mid-shaft of the proximal phalanx of the second digit from 25 female cadavers spanning the three T-score categories (n=8 normal, n=6 osteopenic, and n=11 osteoporotic) at spatial offsets of 0, 3, and 6 mm from a
laser irradiation spot. After normalizing spectra to the PO43- peak, group-averaged spectra of the three categories, measured at 3-mm offset, showed clear differences in the CO32-, Amide III, CH2, and Amide I bands. Quantitatively, four out of five mineral-to-
matrix ratios differed significantly (p < 0.05) between normal and osteopenic bone, and between osteopenic and osteoporotic bone, and all five ratios showed significant differences between normal and osteoporotic bone. In contrast, the 0-mm offset suffered diminished contrast, and the 6-mm offset did not enhance discrimination between different groups, compared with the 3-mm offset. A leave-one-out, partial-least-squares regression model built from the 3-mm spectra predicted distal radius DXA T-score with a Pearson correlation of r = 0.85 and a root-mean-square error of cross-validation of 1 T-score units, correctly classifying 92% of specimens.
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All authors Mohammad Hosseini, Sadia Afrin, Anthony Yosick, Emma Schenker, Hani Awad, Andrew J. Berger
Physics Education
Analyzing New Planetary Systems at School: Applications of Newton's Law of Universal Gravitation and Kepler's Third Law
Rubén Montecinos, Carla Hernández, Irma Fuentes-Morales et al. (+4 more)
As scientific knowledge expands, science education may not always keep pace with the latest advancements in astrophysics. A solid scientific education is crucial for preparing students for 21st-century challenges. However, science education often focuses narrowly on specific content, neglecting frontier scientific research. To address this, a teaching sequence was developed in Chile using real
exoplanet data from the Open
Exoplanet Catalog and NASA's Eye on
Exoplanets webpage. This integrates cutting-edge astrophysical concepts into classroom discussions. Analyzing this data
prompts students to discuss how Newton's law of universal gravitation and Kepler's third law apply to current research on extrasolar systems. This sequence deepens understanding of these principles within modern astrophysics, enriching science education. Such activities spark new research questions akin to those debated in scientific circles, enhancing insights into planetary formation.
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All authors Rubén Montecinos, Carla Hernández, Irma Fuentes-Morales, Fernanda Alarcón, Ignacia Benito, Luciano Laroze, Sebastián Pérez
Medical Physics
Low-Dose CT Imaging Using a Regularization-Enhanced Efficient Diffusion Probabilistic Model
Qiang Li, Mojtaba Safari, Shansong Wang et al. (+4 more)
Low-dose computed
tomography (LDCT) reduces patient radiation exposure but introduces substantial noise that degrades image quality and hinders diagnostic accuracy. Existing denoising approaches often require many
diffusion steps, limiting real-time applicability. We propose a Regularization-Enhanced Efficient
Diffusion Probabilistic Model (RE-EDPM), a rapid and high-fidelity LDCT denoising framework that integrates a residual shifting mechanism to align low-dose and full-dose distributions and performs only four reverse
diffusion steps using a Swin-based U-Net backbone. A composite loss combining pixel reconstruction, perceptual similarity (LPIPS), and total variation (TV) regularization effectively suppresses spatially varying noise while preserving anatomical structures. RE-EDPM was evaluated on a public LDCT
benchmark across dose levels and anatomical sites. On 10 percent dose chest and 25 percent dose abdominal scans, it achieved SSIM = 0.879 (0.068), PSNR = 31.60 (2.52) dB, VIFp = 0.366 (0.121) for chest, and SSIM = 0.971 (0.000), PSNR = 36.69 (2.54) dB, VIFp = 0.510 (0.007) for abdomen. Visual and statistical analyses, including ablation and Wilcoxon signed-rank tests (p < 0.05), confirm significant contributions from residual shifting and regularization terms. RE-EDPM processes two 512x512 slices in about 0.25 s on modern GPUs, supporting near real-time clinical use. The proposed framework achieves an optimal balance between noise suppression and anatomical fidelity, offering an efficient solution for LDCT restoration and broader medical image enhancement tasks.
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All authors Qiang Li, Mojtaba Safari, Shansong Wang, Huiqiao Xie, Jie Ding, Tonghe Wang, Xiaofeng Yang
Optics
Exceptional Points and Lasing Thresholds: When Lower-Q Modes Win
Julius Kullig, Qi Zhong, Jan Wiersig et al. (+1 more)
One of the most fundamental questions in
laser physics is the following: Which mode of an optical cavity will reach the lasing threshold first when gain is applied? Intuitively, the answer appears straightforward: When a particular mode is both temporally well confined (i.e., exhibits the highest quality factor) and experiences initially the largest increase of the modal gain, it is naturally expected to lase first. However, in this work, we demonstrate that this intuition can fail in surprising ways. Specifically, we show that in the presence of non-Hermitian degeneracies, known as exceptional points, the expected mode hierarchy can be dramatically altered. These spectral singularities can give rise to counterintuitive mode switching, where a mode with a lower quality factor and initially smaller increase of modal gain reaches the lasing threshold ahead of a more favorable competitor. Remarkably, this effect can occur even under spatially uniform pumping, underscoring the subtle and profound influence of non-Hermitian physics on lasing dynamics.
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All authors Julius Kullig, Qi Zhong, Jan Wiersig, Ramy El-Ganainy
Optics
Hyperspectral Reconstruction using Discrete LED-Structured Illumination
John C. Howell, Pieter H. Neethling and Tjaart P. J. Kruger
We consider the use of digital signal processing to reconstruct continuous reflectance spectra using a small finite set of randomly illuminated light emitting diodes (LEDs). We simulate the use of LEDs having identical spectral distance and Gaussian bandwidth whose illumination overlaps its nearest neighbors. An object, whose reflectance spectrum is to be determined, is illuminated by a series of random spectral patterns consisting of randomly chosen LEDs with random intensity. We quantify the information within the illumination patterns using the singular value decomposition (SVD) and reconstruct reflectance spectra, specifically hemoglobin and several green vegetation spectra using the pseudoinverse of the SVD for a given amount of noise. We show that for sparse plant spectra, it is possible to reconstruct the continuous green vegetation spectra with RMSE less than 1% with as few as 25 LEDs. Our study demonstrates that reconstructing sparse reflectance spectra based on random structured illumination can enable low-cost LED-based cameras to perform equally well as expensive cameras, especially for dedicated applications.
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Computational Physics · Cross-listed from cs.LG
A Physics-informed Multi-resolution Neural Operator
Sumanta Roy, Bahador Bahmani, Ioannis G. Kevrekidis et al. (+1 more)
The predictive accuracy of operator learning frameworks depends on the quality and quantity of available training data (input-output function pairs), often requiring substantial amounts of high-fidelity data, which can be challenging to obtain in some real-world engineering applications. These
datasets may be unevenly discretized from one realization to another, with the grid resolution varying across samples. In this study, we introduce a physics-informed operator learning approach by extending the Resolution Independent Neural Operator (RINO) framework to a fully data-free setup, addressing both challenges simultaneously. Here, the arbitrarily (but sufficiently finely) discretized input functions are projected onto a latent
embedding space (i.e., a vector space of finite dimensions), using pre-trained basis functions. The operator associated with the underlying partial differential equations (PDEs) is then approximated by a simple multi-layer perceptron (MLP), which takes as input a latent code along with spatiotemporal coordinates to produce the solution in the physical space. The PDEs are enforced via a finite difference solver in the physical space. The validation and performance of the proposed method are benchmarked on several numerical examples with multi-resolution data, where input functions are sampled at varying resolutions, including both coarse and fine discretizations.
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All authors Sumanta Roy, Bahador Bahmani, Ioannis G. Kevrekidis, Michael D. Shields
Applied Physics · Cross-listed from cond-mat.mtrl-sci
Stress in chromium thin films deposited by DC magnetron sputtering on grounded cupper and stainless-steel substrate holders
M.D. Medina, H.I. Giron, K. Paucar et al. (+2 more)
Chromium thin films deposited on silicon substrates by DC magnetron sputtering were systematically investigated as a function of film thickness, using a DC power of 50 W and a post-deposition annealing temperature of 200 C. Two types of grounded substrate holders, copper and stainless steel, were employed to assess substrate-dependent effects. The intrinsic stress, determined by the wafer
curvature method, decreases with increasing film thickness but increases with the annealing temperature. It is observed that for thinner as-deposited chromium films, the stress showed a pronounced irreversible increase when measured immediately after deposition and after several days of aging. Films deposited on copper holders consistently exhibited higher stress values than those grown on stainless steel holders. These observations suggest that the intrinsic stress in as-deposited films is linked to the growth mechanism, while the stress increase after annealing may be related to thermally active
diffusion and structural relaxation. The higher stress in films grown on copper substrate holder can likely be associated with enhanced ion bombardment due to the higher electrical conductivity of copper.
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All authors M.D. Medina, H.I. Giron, K. Paucar, A. Talledo, B.R. Pujada
Optics · Cross-listed from quant-ph
Spectrally Robust Photon-Pair Generation in Topological Waveguide Arrays
A. Zecchetto, J.-R. Coudevylle, M. Morassi et al. (+4 more)
Harnessing topological effects offers a route to protect
quantum states of light from imperfections, potentially enabling more robust platforms for
quantum information processing. This capability is particularly relevant for active photonic
circuits that generate
quantum light directly on-chip. However, the impact of
topology on the spectral properties of parametric processes, crucial for scalable
quantum photonic
circuits, remains largely unexplored. Here, we address this question by investigating
photon-pair generation via spontaneous parametric down-conversion (SPDC) in nonlinear waveguide arrays, combining experiment and theory. A systematic comparison of uniform, trivial, and topological Su-Schrieffer-Heeger (SSH) arrays reveals that only the topological configuration preserves a stable SPDC resonance spectrum under coupling disorder, with fluctuations in the resonance wavelength reduced by more than an order of magnitude. An analytical model captures the origin of this robustness in the band-structure properties of the interacting modes. Numerical analysis further shows that each SSH array can operate as a robust
photon-pair source, enabling stable interference between multiple such sources and the generation of high-dimensional entangled states with high fidelity. These results establish nonlinear waveguide arrays as a promising platform for harnessing the interplay of
topology and nonlinearity in
quantum photonic
circuits.
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All authors A. Zecchetto, J.-R. Coudevylle, M. Morassi, A. Lemaître, M.I. Amanti, S. Ducci, F. Baboux
Geophysics
Exploration of Machine Learning Methods to Seismic Event Discrimination in the Pacific Northwest
Akash Kharita, Marine Denolle, Alexander R Hutko et al. (+2 more)
Accurately separating tectonic, anthropogenic, and geomorphologic seismic sources is essential for Pacific Northwest (PNW) monitoring but remains difficult as networks densify and signals overlap. Prior work largely treats binary discrimination and seldom compares classic ML (feature-engineered) and deep learning (end-to-end) approaches under a common, multi-class setting with operational constraints. We evaluate methods and features for four-way source discrimination - earthquakes, explosions, surface events, and noise - and identify models that are both accurate and deployable. Using ~200k three-component waveforms from >70k events in an AI-curated PNW
dataset, we test random-forest classifiers on TSFEL, physics-informed, and scattering features, and CNNs that ingest time series (1D) or spectrograms (2D); we
benchmark on a balanced common test set, a 10k event network
dataset, and out-of-domain data (global surface events; near-field blasts). CNNs taking spectrograms lead with accuracy performance over 92% for within-domain (as a short-and-fat CNN SeismicCNN 2D) and out-of-domain (as a long and skinny CNN QuakeXNet 2D), versus 89% for the best random forest; performance remains strong at low SNR and longer distances, and generalizes to independent network and global
datasets. QuakeXNet-2D is lightweight (~70k parameters; ~1.2 MB), implemented into seisbench, scans a full day of 100 Hz, three-component data in ~9 s on commodity hardware, with released checkpoints. These results show spectrogram-based CNNs provide state-of-the-art accuracy, efficiency, and robustness for real-time PNW operations and transferable surface-event monitoring.
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All authors Akash Kharita, Marine Denolle, Alexander R Hutko, J. Renate Hartog, Stephen D. Malone
Optics
Addressing modulational instability in anti-resonant hollow-core fibers for pulse compression
Michael Hemsworth, TJ Hammond, Arthur K. Mills et al. (+1 more)
When pulses propagate in gas-filled anti-resonant hollow-core fibers (AR-HCFs) modulational instability (MI) can lead to pulse break-up and loss of coherence. In pulse broadening and compression schemes, MI is a parasitic effect that induces significant shot-to-shot fluctuations of the peak power of compressed pulses and increases rapidly over a narrow range of input pulse energies. In this work we use experimental studies and supporting numerical simulations to compare two AR-HCFs that are chosen to enhance or suppress MI. We demonstrate that judicious selection of the wall thickness of the anti-resonant elements (AREs) can drastically reduce the MI gain, thereby increasing the limit of pulse energy scaling of stable ultrafast pulse compression.
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All authors Michael Hemsworth, TJ Hammond, Arthur K. Mills, David J. Jones
Optics
A global inverse-problem approach to quantitative photo-switching optoacoustic mesoscopy
Yan Liu, Jonathan Chuah, Michael Unser et al. (+1 more)
In this paper, we propose a global framework that includes a detailed model of the photo-switching and acoustic processes for photo-switching optoacoustic mesoscopy, based on the underlying physics. We efficiently implement two forward models as
matrix-free linear operators and join them as one forward operator. Then, we reconstruct the concentration maps directly from the temporal series of acoustic signals through the resolution of one combined inverse problem. For robustness against noise and clean unmixing results, we adopt a hybrid regularization technique composed of the $l_1$ and total-variation regularizers applied to two different spaces. We use a proximal-gradient
algorithm to solve the minimization problem. Our numerical results show that our regularized one-step approach is the most robust in terms of noise and experimental setup. It consistently achieves higher-quality images, as compared to two-step or unregularized methods.
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All authors Yan Liu, Jonathan Chuah, Michael Unser, Jonathan Dong
Computational Physics · Cross-listed from astro-ph.HE
Hydrodynamic Simulations of Tidal Disruption Encores
Ian P.A. Johnson, Taeho Ryu and Rosalba Perna
We present hydrodynamic simulations with the moving-mesh code AREPO of
Tidal Disruption Encores (TDEEs) in
nuclear star clusters (NSCs). TDEEs arise when a
stellar-mass black hole (sBH) disrupts a
star within the NSC, producing debris that is unbound from the sBH but remains gravitationally bound to the central massive black hole (MBH), leading to a delayed secondary
flare. We find that the morphology and thermodynamics of the fallback material depend sensitively on the disruption geometry, MBH mass, and sBH-MBH separation. We identify two distinct morphological outcomes: ring encores, where debris circularize into a
torus, and direct encores, where streams plunge toward the MBH, with encore luminosities peaking at times corresponding to the freefall timescale and one orbital period, respectively. Across all simulated cases, we find these events exhibit luminosities of $10^{40}-10^{42}$ erg/s with lightcurves characteristic of their morphology. Our work greatly improves the predictions of TDEE lightcurves and empowers observations to probe into NSC dynamics and sBH population while providing possible explanations for anomalous TDE-like
flares.
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Data Analysis and Statistics · Cross-listed from nucl-ex
Robust and Generalizable Background Subtraction on Images of Calorimeter Jets using Unsupervised Generative Learning
Yeonju Go, Dmitrii Torbunov, Yi Huang et al. (+8 more)
Accurate separation of signal from background is one of the main challenges for precision measurements across high-energy and
nuclear physics. Conventional supervised learning methods are insufficient here because the required paired signal and background examples are impossible to acquire in real experiments. Here, we introduce an unsupervised unpaired image-to-image translation neural network that learns to separate the signal and background from the input experimental data using cycle-consistency principles. We demonstrate the efficacy of this approach using images composed of simulated calorimeter data from the sPHENIX experiment, where physics signals (jets) are immersed in the extremely dense and fluctuating heavy-ion collision environment. Our method outperforms conventional subtraction
algorithms in fidelity and overcomes the limitations of supervised methods. Furthermore, we evaluated the model's robustness in an out-of-distribution test scenario designed to emulate modified jets as in real experimental data. The model, trained on a simpler
dataset, maintained its high fidelity on a more realistic, highly modified jet signal. This work represents the first use of unsupervised unpaired generative models for full detector jet background subtraction and offers a path for novel applications in real experimental data, enabling high-precision analyses across a wide range of imaging-based experiments.
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All authors Yeonju Go, Dmitrii Torbunov, Yi Huang, Shuhang Li, Timothy Rinn, Haiwang Yu, Brett Viren, Meifeng Lin, Yihui Ren, Dennis Perepelitsa, Jin Huang
Plasma Physics
Quantum Kinetic Modeling of KEEN waves in a Warm-Dense Regime
F. Alejandro Padilla-Gomez, Sining Gong, Michael S. Murillo et al. (+2 more)
We report a fully kinetic,
quantum study of Kinetic Electrostatic Electron Nonlinear (KEEN) waves, showing that
quantum diffraction systematically erodes the classical trapping mechanism, narrow harmonic locking to the fundamental, and hasten post-drive decay. Electrons are evolved with a second-order Strang-split 1D1V Wigner-Poisson solver that couples conservative semi-Lagrangian WENO advection to an analytic
Fourier space update for the non-local Wigner term, while ions remain classical. Short, frequency-tuned ponderomotive pulses drive KEEN formation in a uniform Maxwellian
plasma; as the dimensionless
quantum parameter H rises from the classical limit to values relevant to warm-dense matter, doped
semiconductors, and 2D electron systems, the drive threshold increases, higher harmonics are damped, trapped electron
vortices diffuse, and the subplasma electrostatic energy relaxes to a lower stationary level, as confirmed by continuous wavelet analysis. These microscopic changes carry macroscopic weight. Ignition-scale capsules now compress matter to regimes where the electron de Broglie wavelength rivals the Debye length, making classical kinetic descriptions insufficient. By extending KEEN physics into this
quantum domain, our results offer a potential diagnostic of nonequilibrium electron dynamics for next-generation inertial-confinement designs and high-energy-density platforms, indicating that predictive fusion modeling may benefit from the integration of kinetic fidelity with
quantum effects.
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All authors F. Alejandro Padilla-Gomez, Sining Gong, Michael S. Murillo, F. R. Graziani, Andrew J. Christlieb
Physics and Society
Non-Markovian Collective Motion from Self-Regulated Perceptual Dynamics
Jyotiranjan Beuria
Collective motion in active matter is usually modelled through instantaneous local
alignment, where each
agent updates its heading from the current configuration of its neighbours. Many biological and engineered
agents, however, possess internal regulatory variables that evolve more slowly than
alignment itself and can store information about past
alignment states. We introduce a minimal two-timescale model in which each
agent carries a fast perceptual register and a slow regulatory variable. The fast register encodes the instantaneous tendency to align with neighbouring headings, while the slow variable integrates recent
alignment and feeds back into subsequent
alignment decisions. The internal dynamics are formulated using a GKSL-derived Bloch representation, used only as a positivity-preserving effective description of bounded two-state variables; no microscopic
quantum dynamics is assumed. The model reduces to Vicsek-type
alignment in the fast-relaxation, weak-feedback limit, but shows distinct behaviour when slow feedback is active. Simulations reveal slow-fast relaxation, feedback-induced hysteresis, finite
memory-dependent loop area, and non-monotonic coordination between collective order and regulatory tone. These results show how effective non-Markovian collective motion can emerge from local internal feedback.
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Fluid Dynamics
Refinement of a Poroelastic Model for Zero Porosity: Finite Element Implementation and Investigation of Fluid Mechanics in the Perivascular Space
Mohammad Jannesari, Beatrice Ghitti, Bruce J. Gluckman et al. (+1 more)
In conventional formulations of poroelasticity, when the porosity approaches zero or vanishes in some parts of the poroelastic domain, if only temporarily, the governing equations degenerate to those for the solid phase thereby inhibiting a suitable determination of the fluid velocity field. To address this challenge, we reformulated a poroelastic model based on mixture theory to accommodate scenarios with zero porosity. We verified our model using the method of manufactured solutions and demonstrated its ability to handle extreme conditions in a sample test problem. As an application of our framework, we investigated peristaltic flow in the perivascular space of a penetrating arteriole in
brain. Our analysis revealed that some literature-suggested parameters can drive the model to predict extreme non-physiological conditions. We further demonstrated that these extreme conditions can be somewhat mitigated by accounting for the deformation of the surrounding
brain tissue.
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All authors Mohammad Jannesari, Beatrice Ghitti, Bruce J. Gluckman, Francesco Costanzo
Atomic Physics · Cross-listed from cond-mat.quant-gas
Coupling-induced universal dynamics in bilayer two-dimensional Bose gases
En Chang, Vijay Pal Singh, Abel Beregi et al. (+4 more)
The emergence of order in many-body systems and the associated self-similar dynamics governed by dynamical scaling laws is a hallmark of universality far from
equilibrium. Measuring and classifying such nontrivial behavior for novel
symmetry classes remains challenging. Here, we realize a well-controlled interlayer coupling quench in a tunable
bilayer two-dimensional Bose gas, driving the system to an ordered phase. We observe robust self-similar dynamics and a universal critical exponent consistent with
diffusion-like coarsening, driven by
vortex and antivortex annihilation induced by the interlayer coupling. Our results extend the understanding of universal dynamics in many-body systems and provide a robust foundation for quantitative tests of nonequilibrium effective field theories.
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All authors En Chang, Vijay Pal Singh, Abel Beregi, Erik Rydow, Ludwig Mathey, Christopher J. Foot, Shinichi Sunami
Optics
Coherent all-optical tuning of large-area phase-gradient metasurface
Zhiping He, Xu Fang and Juejun Hu
Tunable active metasurfaces have become a major research focus in recent years. Among tuning mechanisms, all-optical coherent control stands out because it requires no material or
geometric change, enabling ultrafast, low-energy, interference-based modulation of amplitude, phase, and polarization in ultrathin devices. However, when applied to phase-gradient metasurfaces, coherent control has been limited to small apertures effectively confined to a single Fresnel zone, leading to large divergence and degraded beam quality. Here we propose and numerically validate a scalable method that enables large-area coherent control. The key idea is to use coherent illumination to tune the phase gradient within each Fresnel zone while a direct search
algorithm optimizes zone-by-zone parameters to meet system-level targets. Using this principle, we demonstrate continuous tuning of a large-area metasurface for continuous beam-steering without per-meta-atom phase actuation. The same framework applies broadly to continuously tunable phase-gradient optics, including varifocal metalenses, parfocal zoom metalenses, tunable axicons, and related dynamic focusing elements.
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Plasma Physics
Design of Backscatter Tailored Optical Fibers for distributed magnetic field sensing using Fiber Optic Pulsed Polarimetry
Roger J Smith
Fiber optic pulsed polarimetry is a
LIDAR-like fiber sensing technique that uses a backscatter enhanced single mode backscatter-tailored optical fiber(BTOF) to measure the distributed B fields on all
Magnetic Fusion Energy devices. The BTOF has a series of wavelength resonant reflection fiber Bragg gratings written uniformly along its length. The fiber's Verdet constant determines the strength of the Faraday effect which effectuates the measurement of local B along the fiber placed intimately next to or within a magnetized
plasma volume. A robust measurement of the field distribution along the fiber is obtained at high rep rates, 5 MHz, high spatial resolution(1-10cm), high B field accuracy(<1%) and temporal response (ns). Multipathing in the BTOF produces 3rd order reflections that contaminate the
LIDAR signal.
Algorithms are given for calculating the level of contamination for uniform and flat reflection designs, in particular and any reflection series in general. The contamination is bracketed giving confidence in designing and implementing a BTOF. Applications include
magnetic fusion devices, rail guns, high temperature superconducting magnets and magnetized target fusion research.
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Fluid Dynamics
High-Speed Imagery Analysis of Droplet Impact on Van der Waals and Non-Van der Waals Soft-Textured Oil-Infused Surfaces
Shubham S. Ganar, Deepak J. and Arindam Das
This study investigates the impact of surface functionalization, oil coating, and oil absorption on droplet impact behavior on textured polydimethylsiloxane(PDMS) substrates. The textured surfaces were fabricated with square micro-posts having spacings of 5 and 20 microns. The PDMS samples were functionalized with octadecyltrichlorosilane (OTS) to improve water repellency. Following, the surfaces were either coated with or allowed to absorb two different lubricants, silicone oil (SO-5cSt) and hexadecane. We performed detailed wetting measurements on both untreated and OTS-functionalized substrates. These measurements provided useful insights into how water and lubricants were retained and distributed under static conditions. High-speed imaging was used to capture droplet impact across a range of Weber numbers. On SO-5cSt-absorbed substrates, droplets consistently showed complete rebound at all Weber numbers, regardless of post spacing. This robust rebound was attributed to the oil's ability to fill the gaps between the posts through capillary action, while also forming a stable lubricating layer above the texture. This thin oil film reduced friction between the droplet and the surface, enabling the droplet to retain sufficient energy for complete rebound. In contrast, hexadecane-absorbed substrates displayed different dynamics. At low Weber numbers, only partial rebound was observed, while at intermediate values, droplets rebounded completely. However, droplets no longer rebounded at higher Weber numbers and remained deposited. Repeated droplet impacts further demonstrated that hexadecane-infused surfaces gradually lost oil from the textured gaps, resulting in a decline in rebound performance over time. This effect was not observed with SO-5cSt, underscoring the importance of lubricant affinity and stability.
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Computational Physics · Cross-listed from cs.LG
Training Deep Physics-Informed Kolmogorov-Arnold Networks
Spyros Rigas, Fotios Anagnostopoulos, Michalis Papachristou et al. (+1 more)
Since their introduction, Kolmogorov-Arnold Networks (KANs) have been successfully applied across several domains, with physics-informed machine learning (PIML) emerging as one of the areas where they have thrived. In the PIML setting, Chebyshev-based physics-informed KANs (cPIKANs) have become the standard due to their computational efficiency. However, like their multilayer perceptron-based counterparts, cPIKANs face significant challenges when scaled to depth, leading to training instabilities that limit their applicability to several PDE problems. To address this, we propose a basis-agnostic, Glorot-like initialization scheme that preserves activation variance and yields substantial improvements in stability and accuracy over the default initialization of cPIKANs. Inspired by the PirateNet architecture, we further introduce Residual-Gated Adaptive KANs (RGA KANs), designed to mitigate divergence in deep cPIKANs where initialization alone is not sufficient. Through empirical tests and information bottleneck analysis, we show that RGA KANs successfully traverse all training phases, unlike baseline cPIKANs, which stagnate in the
diffusion phase in specific PDE settings. Evaluations on nine standard forward PDE
benchmarks under a fixed training pipeline with adaptive components demonstrate that RGA KANs consistently outperform parameter-matched cPIKANs and PirateNets - often by several orders of magnitude - while remaining stable in settings where the others diverge.
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All authors Spyros Rigas, Fotios Anagnostopoulos, Michalis Papachristou, Georgios Alexandridis
Computational Physics · Cross-listed from stat.AP
A Physics-Informed Variational Inference Framework for Identifying Attributions of Extreme Stress Events in Low-Grain Polycrystals
Yinling Zhang, Samuel D. Dunham, Curt A. Bronkhorst et al. (+1 more)
Polycrystalline metal failure often begins with stress concentration at grain boundaries. Identifying which microstructural features trigger these events is important but challenging because these extreme damage events are rare and the failure mechanisms involve multiple complex processes across scales. Most existing inference methods focus on average behavior rather than rare events, whereas standard sample-based methods are computationally expensive for high-dimensional complex systems. In this paper, we develop a new variational inference framework that integrates a recently developed computationally efficient physics-informed statistical model with extreme value statistics to significantly facilitate the identification of material failure attributions. First, we reformulate the objective to emphasize observed exceedances by incorporating extreme-value theory into the likelihood, thereby highlighting tail behavior. Second, we constrain inference via a physics-informed statistical model that characterizes microstructure-stress relationships, which uniquely provides physically consistent predictions for these rare events. Third, mixture models in a reduced latent space are developed to capture the non-Gaussian characteristics of microstructural features, allowing the identification of multiple underlying mechanisms. In both controlled and realistic experimental tests for the bicrystal configuration, the framework achieves reliable extreme-event prediction and reveals the microstructural features associated with material failure, providing physical insights for material design with uncertainty quantification.
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All authors Yinling Zhang, Samuel D. Dunham, Curt A. Bronkhorst, Nan Chen
Chemical Physics
Prospects towards Paired Electrolysis at Industrial Currents
Lu Xia, Kaiqi Zhao, Sunil Kadam et al. (+3 more)
Paired electrolysis at industrial current densities offers an energy-efficient and sustainable alternative to thermocatalytic chemical synthesis by leveraging anodic and cathodic valorization. However, its industrial feasibility remains constrained by system integration, including reactor assembly, asymmetric electron transfer kinetics, membrane selection, mass transport limitations, and techno-economic bottlenecks. Addressing these challenges requires an engineering-driven approach that integrates reactor architecture, electrode-electrolyte interactions, reaction pairing, and process optimization. Here, we discuss scale-specific electrochemical reactor assembly strategies, transitioning from half-
cell research to full-scale stack validation. We develop reaction pairing frameworks that align electrocatalyst design with electrochemical kinetics, enhancing efficiency and selectivity under industrial operating conditions. We also establish application-dependent key performance indicators (KPIs) and
benchmark propylene oxidation coupled with hydrogen
evolution reaction (HER) or oxygen reduction reaction (ORR) against existing industrial routes to evaluate process viability. Finally, we propose hybrid integration models that embed paired electrolysis into existing industrial workflows, overcoming adoption barriers.
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All authors Lu Xia, Kaiqi Zhao, Sunil Kadam, M. Dolores Blanco-González, María D. Hernández Alonso, F. Pelayo García de Arquer
Instrumentation and Detectors
Filling the Gap: Atom Probe Tomography of Porous Structures Enabled by Site Specific SEMGlu Curing
Lukas Worch, James O. Douglas, Kavin Arunasalam et al. (+3 more)
Porous microstructures, while central to many functional materials, remain difficult to characterize quantitatively by atom probe
tomography (APT). Although several strategies have been proposed over the past decade, most remain constrained by significant practical or technical limitations. Here, we introduce an in situ pore filling approach that integrates seamlessly into conventional specimen preparation workflows. The method employs a
vacuum compatible resin that is rapidly cured by the electron beam during standard ion beam based preparation, eliminating the need for additional instrumentation or extensive sample handling. We demonstrate the effectiveness of this approach using a porous SnSe + MXene electrode, a material system otherwise difficult to analyze via APT characterisation. This method offers a robust, accessible solution for extending APT analysis to porous materials
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All authors Lukas Worch, James O. Douglas, Kavin Arunasalam, Baptiste Gault, Valeria Nicolosi, Michele Shelly Conroy
Fluid Dynamics
On the choking mechanism in supersonic ejectors: a one-dimensional analysis of Reynolds-Averaged Navier Stokes simulations
Jan Van den Berghe, Miguel A. Mendez and Yann Bartosiewicz
Ejectors are passive devices used in refrigeration, propulsion, and process industries to compress a secondary stream without moving parts. The engineering modeling of choking in these devices remains an open question, with two mechanisms-Fabri and compound choking-proposed in the literature. This work develops a unified one-dimensional framework that implements both mechanisms and compares them with axisymmetric Reynolds-Averaged Navier Stokes (RANS) data processed by cross-sectional averaging. The compound formulation incorporates wall and inter-stream friction and a local pressure-equalization procedure that enables stable integration through the sonic point, together with a normal shock reconstruction. The Fabri formulation is assessed by imposing the dividing streamline extracted from RANS, isolating the sonic condition while avoiding additional modeling assumptions. The calibrated compound model predicts on-design secondary mass flow typically within 2 % with respect to the RANS simulations, rising to 5 % for a strongly under-expanded primary jet due to the equal-pressure constraint. The Fabri analysis attains less than 1 % error in on-design entrainment but exhibits high sensitivity to the dividing streamline and closure, which limits predictive use beyond on-design. Overall, the results show that Fabri and compound mechanisms can coexist within the same device and operating map, each capturing distinct aspects of the physics and offering complementary modeling value. Nevertheless, compound choking emerges as the more general mechanism governing flow rate blockage, as evidenced by choked flows with a subsonic secondary stream.
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