Optics
Spin-Split Dispersion of Leaky Surface plasmons in Inversion- Symmetric System
Sujit Rajak, Nishkarsh Kumar, Dheeraj Yadav et al. (+6 more)
Spin-dependent dispersion and Rashba effect are manifestations of universal
spin orbit interaction associated with the breaking of the spatial inversion
symmetry in condensed matter and in optical systems. In sharp contrast to this, we report a
spin-split dispersion effect of leaky surface plasmons in an inversion-symmetric one dimensional plasmonic grating system. In our system, the signature of
spin-momentum locking and the resulting
spin-polarization dependent splitting of dispersion of the surface plasmons are observed through the leakage radiation detected in a
Fourier (momentum) domain optical arrangement. The setup enables single-shot recording of the full polarization-resolved dispersion (frequency vs transverse momentum (k)) of the leaky surface plasmons. Momentum domain polarization analysis identified a transverse momentum (k) dependent linear birefringence-linear dichroism effect (referred to as the
geometric LB-LD effect) responsible for the observed
spin-split dispersion. This unconventional SOI effect is reminiscent of the recently reported LB-LD effect resulting in
giant chirality in centrosymmetric
crystal, albeit with
geometric origin. It is demonstrated that the interplay of the geometrical polarization transformation in focused polarized light and subsequent interaction of the structured field polarization with the plasmonic grating leads to the
evolution of strong geometrical phase gradient or
spin(circular polarization)-dependent transverse momentum of light resulting in
spin-split dispersion. Our study offers a new paradigm of
spin-based dispersion engineering and
spin-enabled nano-optical functionalities in simple symmetric metasurfaces using
geometric LB-LD effect.
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All authors Sujit Rajak, Nishkarsh Kumar, Dheeraj Yadav, Suman Mandal, Jeeban K. Nayak, Ayan Banerjee, Subhasish Dutta Gupta, Olivier Martin, Nirmalya Ghosh
Medical Physics
Ultra-High Dose-Rates, the FLASH Effect, and Hydrogen Peroxide Yields: Do Experiments and Simulations Really Disagree?
Marc Benjamin Hahn
Radiation chemistry of model systems irradiated with ultra-high dose-rates (UHDR) is key to obtain a mechanistic understanding of the sparing of healthy tissue, which is called the FLASH effect. It is envisioned to be used for efficient treatment of cancer by FLASH radiotherapy. However, it seems that even the most simple model systems, water irradiated with varying dose-rates (DR), pose a challenge. This became evident, as differences within measured and predicted hydrogen peroxide (H2O2) yields (g-values) for exposure of
liquid samples to conventional DR and UHDR were reported. Many of the recently reported values contradict older experiments and current Monte-Carlo simulations(MCS). In the present work, we aim to identify possible reasons of these discrepancies and propose ways to overcome this issue. Hereby a short review of recent and classical literature concerning experimental and simulational studies is performed. The studies cover different radiation sources, from gamma rays, high-energy electrons, heavy particles (
protons and ions) with low and high linear energy transfer (LET), and samples of hypoxic & oxygenated water, with cosolutes such as bovine-serum albumine (BSA). Results are for additional experimental parameters, such as solvent, sample container and analysis methods used to determine the respective g-values of H2O2. Similarly the parameter of the MCS by the step-by-step approach, or the independent-reaction time (IRT) method are discussed. Here, UHDR induced modification of the radical-radical interaction and dynamics, not governed by
diffusion processes, may cause problems. Approaches to test these different models are highlighted to allow progress: by making the step from a purely descriptive discourse of the effects observed, towards testable models, which should clarify the reasons of how and why such a disagreement came to light in the first place.
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Applied Physics
Electromagnetic Investigation of Crosstalk in Bent Microstrip Lines with Partial and Apertured Shielding: Simulations and Measurements
Mohammad Eskandari and Mojtaba Joodaki
This paper presents an electromagnetic investigation of the crosstalk between two bent microstrip lines (MLs) separated by a perforated planar shield. As an extension of our previous study, the effects of various discontinuities in either the MLs or the shield along the coupling path are analyzed through numerical simulations and validated by measurements. The underlying electromagnetic mechanisms are also discussed. Furthermore,
multimodal wave theory in a rectangular waveguide is applied to predict crosstalk behavior when the shield contains an aperture. This study aims to conceptually elucidate complex crosstalk phenomena that are difficult to model using
circuit theory, and successful predictions of crosstalk behavior are presented for different problem cases.
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Computational Physics · Cross-listed from cond-mat.str-el
Enhancing Neural Network Backflow
Kieran Loehr and Bryan K. Clark
Accurately describing the ground state of strongly correlated systems is essential for understanding their emergent properties. Neural Network Backflow (NNBF) is a powerful variational ansatz that enhances mean-field wave functions by introducing configuration-dependent modifications to single-particle orbitals. Although NNBF is theoretically universal in the limit of large networks, we find that practical gains saturate with increasing network size. Instead, significant improvements can be achieved by using a multi-determinant ansatz. We explore efficient ways to generate these multi-determinant expansions without increasing the number of variational parameters. In particular, we study single-step Lanczos and
symmetry projection techniques, benchmarking their performance against
diffusion Monte Carlo and NNBF applied to alternative mean fields. Benchmarking on a doped periodic square Hubbard model near optimal doping, we find that a Lanczos step,
diffusion Monte Carlo, and projection onto a
symmetry sector all give similar improvements achieving state-of-the-art energies at minimal cost. By further optimizing the projected symmetrized states directly, we gain significantly in energy. Using this technique we report the lowest variational energies for this
Hamiltonian on $4\times 16$ and $4 \times 8$
lattices as well as accurate variance extrapolated energies. We also show the
evolution of
spin, charge, and pair correlation functions as the quality of the variational ansatz improves.
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Medical Physics · Cross-listed from cs.CV
PF-DAformer: Proximal Femur Segmentation via Domain Adaptive Transformer for Dual-Center QCT
Rochak Dhakal, Chen Zhao, Zixin Shi et al. (+6 more)
Quantitative computed
tomography (QCT) plays a crucial role in assessing bone strength and fracture risk by enabling volumetric analysis of bone density distribution in the proximal femur. However, deploying automated
segmentation models in practice remains difficult because deep networks trained on one
dataset often fail when applied to another. This failure stems from domain shift, where scanners, reconstruction settings, and patient demographics vary across institutions, leading to unstable predictions and unreliable quantitative metrics. Overcoming this barrier is essential for multi-center osteoporosis research and for ensuring that radiomics and structural finite element analysis results remain reproducible across sites. In this work, we developed a domain-adaptive
transformer segmentation framework tailored for multi-institutional QCT. Our model is trained and validated on one of the largest hip fracture related research cohorts to date, comprising 1,024 QCT images scans from Tulane University and 384 scans from Rochester, Minnesota for proximal femur
segmentation. To address domain shift, we integrate two complementary strategies within a 3D TransUNet backbone:
adversarial alignment via Gradient Reversal Layer (GRL), which discourages the network from encoding site-specific cues, and statistical
alignment via Maximum Mean Discrepancy (MMD), which explicitly reduces distributional mismatches between institutions. This dual mechanism balances invariance and fine-grained
alignment, enabling scanner-agnostic feature learning while preserving anatomical detail.
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All authors Rochak Dhakal, Chen Zhao, Zixin Shi, Joyce H. Keyak, Tadashi S. Kaneko, Kuan-Jui Su, Hui Shen, Hong-Wen Deng, Weihua Zhou
Physics and Society
Analyzing Swing States in Presidential Elections: The Case of Wisconsin
Michelle Zuo
This paper quantitatively analyzes county-level voting patterns in Wisconsin's presidential elections from 2000 to 2024. As a pivotal swing state, Wisconsin has alternated between Democratic and Republican candidates since 2012. Using data from the Wisconsin Elections Commission, we examine vote totals across 72 counties and seven election cycles. Pearson correlations measure similarity in county voting trajectories, while choropleth maps visualize spatial shifts. Results show strong clustering of vote changes: Democratic and Republican gains between 2016 and 2020 were concentrated in southeastern urban and suburban counties, with rural areas showing little change. Correlations reveal a north-south divide, as southern counties exhibit similar trends and northern ones diverge. These findings highlight spatial heterogeneity in electoral dynamics and the decisive role of urban mobilization in statewide outcomes.
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Atomic Physics · Cross-listed from cond-mat.quant-gas
Single-fluid model for rotating annular supersolids and its experimental implications
Niccolò Preti, Nicolò Antolini, Charles Drevon et al. (+6 more)
The famous two-fluid model of finite-temperature superfluids has been recently extended to describe the mixed classical-superfluid dynamics of the newly discovered supersolid phase of matter. We show that for rigidly rotating supersolids one can derive a more appropriate single-fluid model, in which the seemingly classical and superfluid contributions to the motion emerge from a spatially varying phase of the global wavefunction. That allows to design experimental protocols to excite and detect the peculiar rotation dynamics of annular supersolids, including partially quantized supercurrents, in which each atom brings less than $\hbar$ unit of angular momentum. Our results are valid for a more general class of density-modulated superfluids.
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All authors Niccolò Preti, Nicolò Antolini, Charles Drevon, Pietro Lombardi, Andrea Fioretti, Carlo Gabbanini, Giovanni Ferioli, Giovanni Modugno, Giulio Biagioni
Chemical Physics
Orbital Optimization and Neural-Network-Assisted Configuration Interaction Calculations of Rydberg States
Gianluca Levi, Max Kroesbergen, Louis Thirion et al. (+5 more)
Rydberg excited states of molecules pose a challenge for electronic structure calculations because of their highly diffuse electron distribution. Even large and elaborate atomic basis sets tend to underrepresent the long-range tail, overly confining the Rydberg state. An approach is presented here where the molecular orbitals are variationally optimized for the excited state using a plane wave basis set in a Hartree-Fock calculation, followed by a configuration interaction calculation. The use of excited state optimized orbitals greatly enhances the convergence of the many-body calculation, as illustrated by a full configuration interaction calculation of the $2s$ Rydberg state of H$_2$. A neural-network-based selective configuration interaction approach is then applied to calculations of $3s$ and $3p$ states of H$_2$O and NH$_3$. The obtained values of excitation energy are in close agreement with experimental measurements as well as previous many-body calculations where sufficiently diffuse atomic basis sets were used. Calculations using atomic basis sets lacking extra diffuse functions, such as aug-cc-pVTZ, give significantly higher estimates due to confinement of the Rydberg states.
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All authors Gianluca Levi, Max Kroesbergen, Louis Thirion, Yorick L. A. Schmerwitz, Elvar Ö. Jónsson, Pavlo Bilous, Philipp Hansmann, Hannes Jónsson
Plasma Physics
Spectral Deconvolution without the Deconvolution: Extracting Temperature from X-ray Thomson Scattering Spectra without the Source-and-Instrument Function
Thomas Gawne, Alina Kononov, Andrew Baczewski et al. (+7 more)
X-ray Thomson scattering (XRTS) probes the dynamic structure factor of the system, but the measured spectrum is broadened by the combined source-and-instrument function (SIF) of the setup. In order to extract properties such as temperature from an XRTS spectrum, the broadening by the SIF needs to be removed. Recent work [Dornheim et al. Nature Commun. 13, 7911 (2022)] has suggested that the SIF may be deconvolved using the two-sided Laplace transform. However, the extracted information can depend strongly on the shape of the input SIF, and the SIF is in practice challenging to measure accurately. Here, we propose an alternative approach: we demonstrate that considering ratios of Laplace-transformed XRTS spectra collected at different scattering angles is equivalent to performing the deconvolution, but without the need for explicit knowledge of the SIF. From these ratios, it is possible to directly extract the temperature from the scattering spectra, when the system is in thermal
equilibrium. We find the method to be generally robust to spectral noise and physical differences between the spectrometers, and we explore situations in which the method breaks down. Furthermore, the fact that consistent temperatures can be extracted for systems in thermal
equilibrium indicates that non-
equilibrium effects could be identified by inconsistent temperatures of a few eV between the ratios of three or more scattering angles.
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All authors Thomas Gawne, Alina Kononov, Andrew Baczewski, Hannah Bellenbaum, Maximilian P Böhme, Zhandos Moldabekov, Thomas R Preston, Sebastian Schwalbe, Jan Vorberger, Tobias Dornheim
Instrumentation and Detectors
Characterization of the H2M Monolithic CMOS Sensor
Rafael Ballabriga, Eric Buschmann, Michael Campbell et al. (+29 more)
The H2M (Hybrid-to-Monolithic) is a monolithic pixel sensor manufactured in a modified \SI{65}{\nano\meter}~CMOS imaging process with a small collection electrode. Its design addresses the challenges of porting an existing hybrid pixel detector architecture into a monolithic chip, using a digital-on-top design methodology, and developing a compact digital
cell library. Each square pixel integrates an analog front-end and digital pulse processing with an 8-bit counter within a \SI{35}{\micro\meter}~pitch. This contribution presents the performance of H2M based on laboratory and test beam measurements, including a comparison with analog front-end simulations in terms of gain and noise. A particular emphasis is placed on backside thinning in order to reduce material budget, down to a total chip thickness of \SI{21}{\micro\meter} for which no degradation in MIP detection performance is observed. For all investigated samples, a MIP detection efficiency above \SI{99}{%} is achieved below a threshold of approximately 205 electrons. At this threshold, the fake-hit rate corresponds to a
matrix occupancy of fewer than one pixel per the \SI{500}{\nano\second}~frame. Measurements reveal a non-uniform in-pixel response, attributed to the formation of local potential wells in regions with low electric field. A simulation flow combining technology computer-aided design, Monte Carlo, and
circuit simulations is used to investigate and describe this behavior, and is applied to develop mitigation strategies for future chip submissions with similar features.
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All authors Rafael Ballabriga, Eric Buschmann, Michael Campbell, Raimon Casanova Mohr, Dominik Dannheim, Jona Dilg, Ana Dorda, Ono Feyens, Finn King, Philipp Gadow, Ingrid-Maria Gregor, Karsten Hansen, Yajun He, Lennart Huth, Iraklis Kremastiotis, Stephan Lachnit, Corentin Lemoine, Stefano Maffessanti, Larissa Mendes, Younes Otarid, Christian Reckleben, Sébastien Rettie, Manuel Alejandro del Rio Viera, Sara Ruiz Daza, Judith Schlaadt, Adriana Simancas, Walter Snoeys, Simon Spannagel, Tomas Vanat, Anastasiia Velyka, Gianpiero Vignola, Håkan Wennlöf
Atomic Physics
Wavefront Curvature and Transverse Atomic Motion in Time-Resolved Atom Interferometry: Impact and Mitigation
Noam Mouelle, Jeremiah Mitchell, Valerie Gibson et al. (+1 more)
Time-resolved atom interferometry, as employed in applications such as gravitational wave detection and searches for ultra-light dark matter, requires precise control over systematic effects. In this work, we investigate phase noise arising from shot-to-shot fluctuations in the atoms' transverse motion in the presence of the wavefront
curvature of the interferometer beam, and analyse its dependence on the
laser-beam geometry in long-baseline, large-momentum-transfer atom interferometers. We use a semi-classical framework to derive analytical expressions for the effective phase perturbation in position-averaged measurements and validate them using Monte Carlo simulations. Applied to 100-m and 1-km atom gradiometers representative of next-generation experiments, the model shows that configurations maximizing pulse efficiency also amplify
curvature-induced phase noise, requiring micron-level control of the atom cloud's centre-of-mass position and sub-micron-per-second control of its centre-of-mass velocity to achieve sub-$10^{-5}$ rad phase stability. Alternative beam geometries can suppress this noise by up to two orders of magnitude, but at the cost of reduced pulse efficiency. To address this limitation, we propose a mitigation strategy based on position-resolved phase-shift readout, which empirically learns and corrects the wavefront-induced bias from measurable quantities such as the phase-shift gradient and final cloud position. This approach restores high-sensitivity operation in the maximum-pulse-efficiency configuration without detailed beam characterisation, providing a practical route towards next-generation, time-resolved atom interferometers operating at the $10^{-5}$ rad noise level.
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All authors Noam Mouelle, Jeremiah Mitchell, Valerie Gibson, Ulrich Schneider
Fluid Dynamics
Reducing base drag on road vehicles using pulsed jets optimized by hybrid genetic algorithms
Isaac Robledo, Juan Alfaro, Víctor Duro et al. (+3 more)
Aerodynamic drag on flat-backed vehicles like vans and trucks is dominated by a low-pressure wake, whose control is critical for reducing fuel consumption. This paper presents an experimental study at $Re_W\approx 78,300$ on active flow control using four pulsed jets at the rear edges of a bluff body model. A hybrid genetic
algorithm, combining a global search with a local gradient-based optimizer, was used to determine the best-performing jet actuation parameters in an experiment-in-the-loop setup. The cost function was designed to achieve a net energy saving by simultaneously minimizing aerodynamic drag and penalizing the actuation's energy consumption. The optimization campaign successfully identified a control strategy that yields a drag reduction of approximately 8.8%. The best-performing control law features a strong, low-frequency actuation from the bottom jet, which targets the main
vortex shedding, while the top and lateral jets address higher-frequency, less energetic phenomena. Particle Image Velocimetry analysis reveals a significant upward shift and stabilization of the wake, leading to substantial pressure recovery on the model's lower base. Ultimately, this work demonstrates that a model-free optimization approach can successfully identify non-intuitive, multi-faceted actuation strategies that yield significant and energetically efficient drag reduction.
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All authors Isaac Robledo, Juan Alfaro, Víctor Duro, Alberto Solera-Rico, Rodrigo Castellanos, Carlos Sanmiguel Vila
Fluid Dynamics
Stabilizing Rayleigh-Benard convection with reinforcement learning trained on a reduced-order model
Qiwei Chen and C. Ricardo Constante-Amores
Rayleigh-Benard convection (RBC) is a canonical system for buoyancy-driven
turbulence and heat transport, central to geophysical and industrial flows. Developing efficient control strategies remains challenging at high Rayleigh numbers, where fully resolved simulations are computationally expensive. We use a control framework that couples data-driven
manifold dynamics (DManD) with reinforcement learning (RL) to suppress convective heat transfer. We find a coordinate transformation to a low-dimensional system using POD and autoencoders, and then learn an
evolution equation for this low-dimensional state using neural ODEs. The reduced model reproduces key system features while enabling rapid policy training. Policies trained in the DManD environment and deployed in DNS achieve a 16-23 % reduction in the Nusselt number for both single- and dual-boundary actuation. Physically, the learned strategy modulates near-wall heat flux to stabilize and thicken the thermal boundary layer, weaken plume ejection, and damp the wall-driven instabilities that seed convective bursts. Crucially, the controller drives the flow toward a quasi-steady state characterized by suppressed temporal fluctuations and spatially steady heat-flux patterns. This work establishes DManD-RL as a physically interpretable, scalable approach for
turbulence control in high-dimensional flows.
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Computational Physics · Cross-listed from cond-mat.mtrl-sci
Temperature dependent ferroelectricity in strained KTaO3 with machine learned force field
Yu Zhu, Luigi Ranalli, Taikang Chen et al. (+2 more)
Ferroelectric materials are a class of dielectrics that exhibit spontaneous polarization which can be reversed under an external electric field. The emergence of ferroelectric order in incipient ferroelectrics is a topic of considerable interest from both fundamental and applied perspectives. Among the various strategies explored, strain engineering has been proven to be a powerful method for tuning ferroelectric polarization in materials. In the case of KTaO3, first principles calculations have suggested that strain can drive a ferroelectric phase transition. In this study, we investigate the impact of in-plane uniaxial and biaxial strain, ranging from 0% to 1%, on pristine KTaO3 to explore its potential for ferroelectricity induction via inversion
symmetry breaking. By integrating density functional theory calculations with the
stochastic self-consistent harmonic approximation assisted by on the fly machine learned force field, we obtain accurate structural information and dynamical properties under varying strain conditions while incorporating higher-order anharmonic effects. Employing the Berry phase method, we obtained the ferroelectric polarization of the strained structures over the entire temperature range up to 300 K. Our findings provide valuable insights into the role of strain in stabilizing ferroelectricity in KTaO3, offering guidance for future experimental and theoretical studies on strain-engineered ferroelectric materials.
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All authors Yu Zhu, Luigi Ranalli, Taikang Chen, Wei Ren, Cesare Franchini
Physics Education · Cross-listed from astro-ph.IM
Stellar Spectroscopy Using Diffraction Grating, CMOS Monochrome Sensor and Reflecting Telescopes
Abhinav Roy and Niti Singh
We present the design and testing of a compact
stellar spectrometer developed for undergraduate and outreach applications. The spectrometer module, comprising an inexpensive 600 lines/mm diffraction grating, a CMOS monochrome sensor, and a 3D-printed mount, can be constructed at modest cost and integrated with reflecting
telescopes commonly available at educational institutions. Calibration was performed using a helium emission source in the laboratory and Vega as a spectrophotometric standard, supported by a custom Python-based data-reduction pipeline for wavelength calibration and spectral stacking. The spectrometer successfully recorded usable spectra of bright
stars including Vega, Sirius, Procyon, Capella, and Betelgeuse, covering spectral types A through M. The results demonstrate that meaningful
stellar spectroscopy can be achieved by institutions with existing
telescope infrastructure, providing a practical framework for student-led astronomical instrumentation projects.
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Computational Physics · Cross-listed from hep-lat
Generative sampling with physics-informed kernels
Friederike Ihssen, Renzo Kapust and Jan M. Pawlowski
We construct a generative network for Monte-Carlo sampling in
lattice field theories and beyond, for which the learning of layerwise propagation is done and optimised independently on each layer. The architecture uses physics-informed renormalisation group flows that provide access to the layerwise propagation step from one layer to the next in terms of a simple first order partial differential equation for the respective renormalisation group kernel through a given layer. Thus, it transforms the generative task into that of solving once the set of independent and linear differential equations for the kernels of the transformation. As these equations are analytically known, the kernels can be refined iteratively. This allows us to structurally tackle out-of-domain problems generally encountered in generative models and opens the path to further optimisation. We illustrate the practical feasibility of the architecture within simulations in scalar field theories.
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Atmospheric and Oceanic Physics
Fire Behavior Monitoring using MeteoSat Third Generation, FCI-FireDyn algorithm: Rate Of Spread and Burnt Area Dynamics for large fire event
Ronan Paugam, Akli Benali, Julia Harvie et al. (+3 more)
This study presents FCI-FireDyn, a new
algorithm developed to monitor wildfire dynamics using the Flexible Combined Imager (FCI) onboard the Meteosat Third Generation satellite. Leveraging the high temporal resolution of FCI (10-minute full-disk observations), the
algorithm derives fire arrival time maps, rate of spread (ROS), and Burn Area (BA)
evolution at sub-kilometer spatial resolution and 2-minute temporal intervals. The method combines threshold-based MWIR detection, spatio-temporal interpolation to reconstruct fire front progression and ROS fields at 175 m resolution. FCI-FireDyn was tested on three major fire events in Southern Europe (Portugal, Greece, and France) from the 2024 2025 seasons. The retrieved BA and Fire Growth Rate show good agreement with reference
datasets from EFFIS, Copernicus EMS, and PT-FireSprd, with total final BA deviations below 20%. The
algorithm captures distinct propagation phases, including acceleration episodes that precede FRP peaks, highlighting a potential for NRT fire behavior monitoring. Despite limitations due to FCI spatial resolution, results demonstrate that it provides sufficient spatio-temporal coverage to estimate front-scale fire dynamics. FCI-FireDyn thus represents a proof of concept for deriving high-frequency fire behavior metrics from geostationary observations to support operational and modeling applications.
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All authors Ronan Paugam, Akli Benali, Julia Harvie, Andrea Meraner, Niels Andela, Weidong Xu
Medical Physics
Zoeppritz equations: from seismology to medical exploration
Harry G. Saavedra and Ramiro Moro
More than a century ago, Karl Bernhard Zoeppritz derived the equations that determine the reflected and transmitted coefficients at a planar interface for an incident seismic wave. The coefficients so obtained are a function of the elastic parameters of the media on each side of the interface and the angle of incidence. Approximations of the equations have been proposed and used in geophysical exploration, however, full use of the equations and their generalization to multiple layers could offer richer information about the properties of the media and be helpful in medical diagnosis via ultrasound. In this work, we investigate how to extract information from the angle-dependent reflection coefficients, including critical angles and the wave distortion at the interface between two and three media. It is shown that it is possible to separate the effect of density from speed of sound mismatch by measuring amplitudes as a function of angle of incidence (AVA). And examining the critical angle and waveform distortion of the reflected waves can reveal the thickness of an intermediate layer, even with subwavelength resolution. These studies could be integrated into medical imaging and also into the training of artificial intelligence systems that assist in diagnosis. In particular, they could help prevent cerebrovascular accidents by early detection of the formation and hardening of plaque in the arteries that irrigate the
brain.
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Optics
Quantifying Resolution in Pink Beam Dark Field X-ray Microscopy: Experiments and Simulations
Michela La Bella, Henning Friis Poulsen, Steffen Staeck et al. (+4 more)
Pink-beam Dark-Field X-ray Microscopy (pDFXM) is a powerful emerging technique for time-resolved studies of microstructure and strain
evolution in bulk crystalline materials. In this work, we systematically assess the performance of pDFXM relative to monochromatic DFXM when using a compound refractive lens (CRL) as the objective. Analytical expressions for the spatial and angular resolution are derived and compared with numerical simulations based on geometrical optics and experimental data. The pink-beam configuration provides an increased diffraction intensity depending on the deformation state of the sample, accompanied by a general tenfold degradation in angular resolution along the rocking and longitudinal directions. This trade-off is disadvantageous for axial strain mapping, but can be advantageous in cases where integrated intensities are needed. For a perfect
crystal under parallel illumination with a pink beam, our results show that chromatic aberration is absent, whereas under condensed illumination it becomes significant. The aberration is shown to depend strongly on the local distortion of the
crystal. Weak-beam imaging conditions, such as those required for resolving dislocations, are shown to remain feasible under pink-beam operation and may even provide an improved signal-to-noise ratio. The higher incident flux, enhanced by nearly two orders of magnitude, is quantified in terms of beam heating effects, and implications for optimized scanning protocols are discussed.
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All authors Michela La Bella, Henning Friis Poulsen, Steffen Staeck, Nils Axel Henningsson, Merve Pinar Kabukcuoglu, Carsten Detlefs, Can Yildirim
Biological Physics · Cross-listed from cond-mat.soft
Enzyme Active Bath Affects Protein Condensation
Kevin Ching, Anthony Estrada, Nicholas M Rubayiza et al. (+3 more)
We investigate how an active bath of enzymes influences the
liquid-
liquid phase separation (LLPS) of a non-interacting condensing
protein. The enzyme we choose to use as the active driver is urease, an enzyme that has been shown by several groups to exhibit enhanced
diffusion in the presence of its substrate. The non-interacting LLPS
protein is ubiquilin-2, a
protein that condenses with increasing temperature and salt. Using a microfluidic device with semipermeable membranes, we create a chemostatic environment to maintain the substrate content to feed the enzymatic bath and remove the products of the chemical reaction. Thus, we isolate the physical enhanced fluctuations from the chemical changes of the enzyme activity. We also compare the results to controls without activity or in the presence of the products of the reaction. We find that the active bath is able to enhance droplet size, density, and concentration, implying that more ubiquilin-2 is in condensed form. This result is consistent with an interpretation that the active bath acts as an effective temperature. Simulations provide an underlying interpretation for our experimental results. Together, these findings provide the first demonstration that physical enzymatic activity can act as an effective temperature to modify LLPS behavior, with implications for intracellular organization in the enzymatically active cellular environment.
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All authors Kevin Ching, Anthony Estrada, Nicholas M Rubayiza, Ligesh Theeyancheri, Jennifer M. Schwarz, Jennifer L Ross
Geophysics · Cross-listed from cs.CV
Towards Reliable Sea Ice Drift Estimation in the Arctic Deep Learning Optical Flow on RADARSAT-2
Daniela Martin and Joseph Gallego
Accurate estimation of sea ice drift is critical for Arctic
navigation,
climate research, and operational
forecasting. While optical flow, a computer
vision technique for estimating pixel wise motion between consecutive images, has advanced rapidly in computer
vision, its applicability to geophysical problems and to satellite SAR imagery remains underexplored. Classical optical flow methods rely on mathematical models and strong assumptions about motion, which limit their accuracy in complex scenarios. Recent deep learning based approaches have substantially improved performance and are now the standard in computer
vision, motivating their application to sea ice drift estimation. We present the first large scale
benchmark of 48 deep learning optical flow models on RADARSAT 2 ScanSAR sea ice imagery, evaluated with endpoint error (EPE) and Fl all metrics against GNSS tracked buoys. Several models achieve sub kilometer accuracy (EPE 6 to 8 pixels, 300 to 400 m), a small error relative to the spatial scales of sea ice motion and typical
navigation requirements in the Arctic. Our results demonstrate that the models are capable of capturing consistent regional drift patterns and that recent deep learning based optical flow methods, which have substantially improved motion estimation accuracy compared to classical methods, can be effectively transferred to polar remote sensing. Optical flow produces spatially continuous drift fields, providing motion estimates for every image pixel rather than at sparse buoy locations, offering new opportunities for
navigation and
climate modeling.
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Chemical Physics
Knowledge Distillation of Noisy Force Labels for Improved Coarse-Grained Force Fields
Feranmi V. Olowookere, Sakib Matin, Aleksandra Pachalieva et al. (+2 more)
Molecular dynamics simulations are an integral tool for studying the atomistic behavior of materials under diverse conditions. However, they can be computationally demanding in wall-clock time, especially for large systems, which limits the time and length scales accessible. Coarse-grained (CG) models reduce computational expense by grouping atoms into simplified representations commonly called beads, but sacrifice atomic detail and introduce mapping noise, complicating the training of machine-learned surrogates. Moreover, because CG models inherently include entropic contributions, they cannot be fit directly to all-atom energies, leaving instantaneous, noisy forces as the only state-specific quantities available for training. Here, we apply a knowledge
distillation framework by first training an initial CG neural network potential (the teacher) solely on AA-to-CG mapped forces to denoise those labels, then distill its force and energy predictions to train refined CG models (the student) in both single- and ensemble-training setups while exploring different force and energy target combinations. We validate this framework on a complex molecular fluid, a deep eutectic solvent, by evaluating two-, three-, and many-body properties and compare the CG and all-atom results. Our findings demonstrate that training a student model on ensemble teacher-predicted forces and per-bead energies improves the quality and stability of CG force fields.
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All authors Feranmi V. Olowookere, Sakib Matin, Aleksandra Pachalieva, Nicholas Lubbers, Emily Shinkle
Plasma Physics
Nonlocal Model for Electron Heat Flux and Self-generated Magnetic Field
Xinyu Zhu, Wenqiang Yuan, Yusen Wang et al. (+4 more)
Coupling of electron heat conduction and
magnetic field takes significant effects in inertial confinement fusion (ICF). As the nonlocal models for electron heat conduction have been developed for modeling kinetic effects on heat flux in hydrodynamic scale, modeling kinetic effects on
magnetic field are still restricted to flux limiters instead of nonlocal corrections. We propose a new nonlocal model which can recover the kinetic effects for heat conduction and
magnetic field in hydrodynamic scale simultaneously. We clarify the necessity of self-consistently considering the electric field corrections in nonlocal models to get reasonable physical quantities. Using the new nonlocal model, the nonlocal corrections of transport coefficients in magnetized
plasma and the
magnetic field generation without density gradients are systematically studied. We find nonlocal effects significantly change the
magnetic field distribution in
laser ablation, which potentially influences the hydrodynamic instabilities in ICF.
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All authors Xinyu Zhu, Wenqiang Yuan, Yusen Wang, Zhipeng Zhang, Xianxu Jin, Zhonghai Zhao, Bin Qiao
Optics
Protected Ion Beam Fabrication of Two-Dimensional Transition Metal Dichalcogenides based Photonic Devices
Lekshmi Eswaramoorthy, Parul Sharma, Brijesh Kumar et al. (+4 more)
Two-dimensional (2D) transition metal dichalcogenides are pivotal for next-generation photonic devices due to their exceptional optical properties and strong light-matter interactions. However, their atomic thinness renders them susceptible to damage during nanoscale fabrication. Focused ion beam technology, while offering precise defect engineering for tailoring optoelectronic properties, often induces collateral damage far beyond the target region, compromising device performance. This study addresses the critical challenge of preserving the intrinsic optical characteristics of 2D TMDCs during FIB patterning. We demonstrate that conventional dielectric encapsulation fails to protect 2D TMDCs from gallium ion-induced damage, leading to persistent defects and quenched optical responses in patterned microstructures. In contrast, polymeric encapsulation with PMMA (polymethyl methacrylate) effectively mitigates damage by acting as a sacrificial layer that absorbs ion impact, thereby preserving the optical properties of the underlying TMDC. Furthermore, we leverage XeF2-assisted Ga ion beam direct patterning, which significantly reduces collateral damage, minimizes Ga ion implantation, and enables precise anisotropic material removal, yielding ultra-smooth sidewalls critical for high-quality photonic resonators. This combined approach of PMMA encapsulation and XeF2-assisted FIB patterning offers a robust, cost-effective, and scalable single-step fabrication route for integrating 2D TMDCs into high-performance photonic devices, thereby maintaining their intrinsic optical functionality essential for advancing
quantum technologies and compact optical
circuits.
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All authors Lekshmi Eswaramoorthy, Parul Sharma, Brijesh Kumar, Abhay Anand, Anuj Kumar Singh, Sudha Mokkapati, Anshuman Kumar
Applied Physics
Tailored heat treatments to characterise the fracture resistance of critical weld regions in hydrogen transmission pipelines
D.R. Chalfoun, J. Parker, M. Gagliano et al. (+1 more)
A new protocol is presented to directly characterise the toughness of microstructural regions present within the weld heat-affected zone (HAZ), the most vulnerable location governing the structural integrity of hydrogen transport pipelines. Heat treatments are tailored to obtain bulk specimens that replicate predominantly ferritic-bainitic, bainitic, and martensitic microstructures present in the HAZ. These are applied to a range of pipeline steels to investigate the role of manufacturing era (vintage versus modern), chemical composition, and grade. The heat treatments successfully reproduce the hardness levels and microstructures observed in the HAZ of existing natural gas pipelines. Subsequently, fracture experiments are conducted in air and pure H2 at 100 bar, revealing a reduced fracture resistance and higher hydrogen embrittlement susceptibility of the HAZ microstructures, with initiation toughness values as low as 32 MPa$\sqrt{\text{m}}$. The findings emphasise the need to adequately consider the influence of microstructure and hard, brittle zones within the HAZ.
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All authors D.R. Chalfoun, J. Parker, M. Gagliano, E. Martínez-Pañeda
Applied Physics · Cross-listed from quant-ph
Diamond quantum sensing at record high pressure up to 240 GPa
Qingtao Hao, Ze-Xu He, Na Zuo et al. (+10 more)
Quantum sensing utilizing nitrogen-vacancy (NV) centers in diamond has emerged as a transformative technology for probing
magnetic phase transition1-4, evidencing Meissner effect of
superconductors1,5-9, and visualizing stress distribution3,9 under extreme conditions. Recent development in NV configurations and hydrostatic environments have raised the operational pressures of NV centers to 140 GPa2,6,10,11, but substantial challenges remain in extending sensing capabilities into multi-megabar range, critical for research in hydrogen-rich
superconductors like La-Sc-H ($T_{\text{c}}$ of 271-298 K at 195-266 GPa)12 and
evolution of minerals near Earth's core13. Here we report the fabrication of shallow NV centers through ion implantation followed by high-pressure and high-temperature (HPHT) annealing, leading to increased density, improved coherence, and mitigated internal stresses, a pre-requisite for reducing their degradation under compression. This NV magnetometry enable breakthrough of pressure capabilities exceeding 240 GPa, constrained by structural integrity of the 50 um diamond anvils, suggesting that the untapped pressure limit may enable further advancements with smaller cutlets or more robust diamonds. We present compelling evidence of the Meissner effect and trapped flux at record-high pressure of 180 GPa for superconducting transition in elemental titanium (Ti) as
benchmark, establishing a solid foundation for high-pressure magnetometry in exploring complex
quantum phenomena at previously unreachable pressures.
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All authors Qingtao Hao, Ze-Xu He, Na Zuo, Yang Chen, Xiangzhuo Xing, Xiaoran Zhang, Xinyu Zhuang, Zhixiang Shi, Xin Chen, Jian-Gang Guo, Gang-Qin Liu, Xiaobing Liu, Yanming Ma
Applied Physics
Large-scale programmable phononic integrated circuits
Xin-Biao Xu, Yu Zeng, Jia-Qi Wang et al. (+11 more)
Electronic and photonic chips revolutionized information technology through massive integration of functional elements, yet
phonons as fundamental information carriers in solids remain underestimated. Here, we demonstrate large-scale programmable phononic integrated
circuits (PnICs) for complex signal processing. We developed a comprehensive library of gigahertz-frequency phononic building blocks that control acoustic wave propagation, polarization, and dispersion. Combining these elements, we demonstrate an ultra-compact 1$\times$128 on-chip acoustic power splitter with unprecedented integration density of 3,000/cm$^2$, a 21-port acoustic frequency demultiplexer with 3.8~MHz resolution, and a four-channel reconfigurable frequency synthesizer. This work establishes scalable phononic integration as the third pillar of information processing alongside electronics and photonics, enabling hybrid chips that combine all three domains for advanced signal processing and
quantum information applications.
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All authors Xin-Biao Xu, Yu Zeng, Jia-Qi Wang, Zheng-Hui Tian, Ji-Zhe Zhang, Yuan-Hao Yang, Zheng-Xu Zhu, Jia-Hua Zou, Liantao Xiao, Weiting Wang, Bao-Zhen Wang, Guang-Can Guo, Luyan Sun, Chang-Ling Zou
Chemical Physics
Ring-polymer instanton theory for tunneling between asymmetric wells
Marit R. Fiechter, Gabriel Laude and Jeremy O. Richardson
Instanton theory has arisen as a practical tool for calculating tunneling splittings in molecular systems. Unfortunately, the original formulation of instanton theory fundamentally breaks down when trying to calculate the level splitting in asymmetric double wells, as there is no imaginary-time periodic orbit connecting the two non-degenerate minima. We have therefore developed a new formulation of instanton theory based on a projected flux correlation function that is applicable to these asymmetric systems. Comparison with exact
quantum-mechanical results in one- and two-dimensional models demonstrates that it has a reasonably high accuracy, similar to that reported for instanton theory in the symmetric case. The theory is then applied to study tunneling between non-degenerate minima in the biomolecule $\alpha$-fenchol, for which we find good agreement with experiment. Finally, we use the connection to instanton rate theory, which is also derived from flux correlation functions, to discuss the often misunderstood relationship between tunneling splittings and reaction rate constants.
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Computational Physics · Cross-listed from math.NA
Accelerated decomposition of bistochastic kernel matrices by low rank approximation
Chris Vales and Dimitrios Giannakis
We develop an accelerated
algorithm for the approximate
eigenvalue decomposition of symmetrically normalized kernel
matrices, focusing on a bistochastic normalization. Our approach constructs a low rank approximation of the original kernel
matrix by the pivoted partial Cholesky
algorithm, and uses it to compute an approximate decomposition of its normalization without requiring the formation of the full kernel
matrix. The cost of the proposed
algorithm depends linearly on the size of the employed training
dataset and quadratically on the rank of the low rank approximation, offering a significant cost reduction compared to the naive approach. We derive trace norm error bounds for the approximation of two classes of normalized kernel
matrices. We apply the proposed
algorithm to the kernel based extraction of spatiotemporal patterns from chaotic Kuramoto-Sivashinsky dynamics, using a distributed GPU implementation that can scale to large
datasets.
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Physics and Society
Proxemics and Permeability of the Pedestrian Group
Saleh Albeaik, Faisal Alsallum and Mohamad Alrished
People tend to walk in groups, and interactions with those groups have a significant impact on crowd behavior and pedestrian traffic dynamics. Social norms can be seen as unwritten rules regulating people interactions in social settings. This article studies people interactions with groups and the emergence of group proxemics. Group zones, zone occupancy counts and people clearance from the group are studied using naturalistic data. Analysis indicate potential presence of three different zones in addition to the public zone. People tend to remain in the public zone and only progressively get closer to groups, and those closer approaches happen in a low frequency and for brief periods of time.
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