Medical Physics
Event-sparse stack denoising for 4D-STEM applications
Gregory Nordahl, Rebekka Klemmt and Espen Drath Bøjesen
We introduce a denoising method for four-dimensional scanning transmission electron microscopy (4D-STEM) that relies on processing local, scan position-independent electron event-sparse data stacks, called event-sparse stack denoising. This method adds an extra time dimension during data collection by recording multiple electron event-sparse diffraction patterns. The resulting
datasets are effectively five-dimensional, referred to as locally time-resolved STEM (LTR-STEM). Diffraction data stacks at each scan position are processed using one of two sparsity denoising pipelines: 1) the density-based spatial clustering of applications with noise (DBSCAN)
algorithm followed by multi-step persistence thresholding, or 2) sparse principal component analysis (sparse PCA), followed by single-step thresholding. Both methods perform well for diffraction data denoising, as shown by simulated peak signal-to-noise ratio (PSNR) curves, denoised experimental data for virtual imaging, and application-specific denoising for defect detection. PSNR analysis indicates that sparsity-denoised 4D-STEM data reaches the same PSNR as raw data at approximately 16% of the exposure time, demonstrating comparable image quality with a lower dose. In defect detection, a 4.1x increase in sensitivity to relative radial disk shift is observed in the denoised data. Moreover, the LTR-STEM technique may be used to inspect material degradation by tracking changes in diffraction disk intensity, allowing for critical dose estimation and exposure-selective imaging.
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Fluid Dynamics · Cross-listed from cond-mat.soft
Friction modifies the quasistatic mechanical response of a confined, poroelastic medium
Térence Desclaux, Callum Cuttle, Chris W. MacMinn et al. (+1 more)
The mechanical response of elastic porous media confined within rigid geometries is central to a wide range of industrial, geological, and biomedical systems. However, current models for these problems typically overlook the role of wall friction, and particularly its interaction with confinement. Here, we develop a theoretical framework to describe the interplay between the mechanics of the medium and Coulomb friction at the confining walls for slow, quasistatic deformations in response to two canonical uniaxial forcings: piston-driven loading (i.e., an imposed effective stress at the top boundary) and fluid-driven loading (i.e., an imposed fluid pressure at the top boundary) followed by unloading. We find that, during compression, the stress field evolves according to a quasistatic advection-
diffusion equation, extending classical poroelasticity results. The magnitude of friction is controlled by a single dimensionless number proportional to the friction coefficient and the aspect ratio of the confining geometry. During decompression, a portion of the solid
matrix remains stuck due to friction, leading to hysteresis and to the propagation of a slip front. In piston-driven loading, the frictional stress is directly coupled to the solid effective stress, leading to exponential damping of the loading and striking changes to the displacement field. However, this coupling limits the energy dissipated by friction. In fluid-driven loading, the pressure gradient locally adds energy, decoupling elastic energy storage and frictional energy dissipation. The displacement remains qualitatively unchanged but is quantitatively reduced due to large energy dissipation. In both cases, friction can have a substantial impact on the apparent mechanical properties of the medium.
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All authors Térence Desclaux, Callum Cuttle, Chris W. MacMinn, Olivier Liot
Applied Physics · Cross-listed from quant-ph
Sensitivity threshold defines the optimal spin subset for ensemble quantum sensing
Suwan I. Kang, Minhyeok Kim, Sanghyo Park et al. (+3 more)
Inhomogeneous broadening and spatial gradients in control fields inevitably produce large variations in characteristic sensitivity across
spin ensembles. We derive an analytic expression for the sensitivity of an inhomogeneous ensemble and introduce a sensitivity threshold that identifies the optimal subset of
spins. For both pulsed and continuous-wave magnetometry, the optimal ensembles deliver up to an eightfold improvement over conventional schemes relying on nominally uniform regions of the ensemble. We demonstrate phase-only digital holography to implement the optimal ensembles and show that the measured illumination non-uniformity limits the sensitivity by 14%. This approach enables optimal
quantum sensing in optically scattering environments.
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All authors Suwan I. Kang, Minhyeok Kim, Sanghyo Park, Heonsik Lee, Keunyoung Lee, Donggyu Kim
Atmospheric and Oceanic Physics
Convective and non-convective nature of Balearic meteotsunamis:a 50-year historical review
Agustí Jansà, Diego S. Carrió, Camilo Melo et al. (+3 more)
Meteotsunamis, locally known as rissaga in the Balearic Islands, are significant sea-level oscillations induced by atmospheric disturbances, with amplitudes and frequencies comparable to seismic tsunamis. The port of Ciutadella (Menorca), is a recognized hotspot for meteotsunami occurrence. This study presents a 50-year analysis of 191 meteotsunami events recorded in Ciutadella between 1975 and 2025, focusing on the distinction between the convective and non-convective nature of the pressure disturbances that triggered the meteotsunami event and their implications for predictability and risk assessment. Events were classified using historical records, in-situ barographic and sea-level observations, satellite imagery, and lightning data. Results show that non-convective meteotsunamis are typically triggered by internal
gravity waves, producing relatively regular and moderate pressure fluctuations. In contrast, convective events are associated with abrupt pressure jumps and tend to produce the most extreme sea-level oscillations, exceeding 3-4 meters in some cases, such as the catastrophic events of June 1984 and June 2006.
Forecasting skill varied by event type: A full-realistic high-resolution ocean-atmosphere modelling system (BRIFS) performed better for convective cases, whereas a targeted reduced-physics method (TRAM) was more accurate for non-convective scenarios. This study provides the most extensive meteotsunami
dataset for the Balearic Islands to date and offers a novel framework for understanding the contrasting dynamics and characteristics of both types of events. The results support the development of improved early-warning systems and more effective coastal risk mitigation strategies in vulnerable regions.
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All authors Agustí Jansà, Diego S. Carrió, Camilo Melo, Romualdo Romero, Baptiste Mourre, Víctor Homar
Computational Physics
Ultra-Fast Muon Transport via Histogram Sampling on GPUs
Luis Felipe P. Cattelan, Shah Rukh Qasim, Patrick H. Owen et al. (+1 more)
We present a GPU-accelerated method for
muon transport based on histogram sampling that delivers orders of magnitude faster performance than CPU-based Geant4 simulation. Our method employs precomputed histograms of momentum loss and scattering, derived from detailed Geant4 simulations, to statistically reproduce all the non-decaying physics processes during
muon traversal through matter. Implemented as a CUDA kernel, the parallel
algorithm enables the concurrent simulation of tens of thousands of particles on a single GPU whilst taking into account a complex geometry and a
magnetic field force integrated using a fourth-order Runge-Kutta method. Validation against Geant4 in both simple and realistic detector geometries shows that the approach preserves key physical features while achieving speedups of several orders of magnitude, even compared to CPU-based simulations on a large CPU farm with over a thousand cores. This work highlights the significant potential of GPU-based implementations for particle transport, with applicability extending to
neutrino propagation and future implementations including discrete processes such as particle decay.
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All authors Luis Felipe P. Cattelan, Shah Rukh Qasim, Patrick H. Owen, Nicola Serra
Atomic Physics
Rapid all-optical loading of trapped ions using a miniaturised atom source
Lorenzo Versini, Tim F. Wohlers-Reichel, Catherine E. J. Challoner et al. (+8 more)
We characterise an efficient optically-heated neutral atom source for ion trapping. We observe loading rates of up to $24(3)\,\mathrm{s}^{-1}$ with heating powers below $85\,\mathrm{mW}$, and demonstrate loading of a single ion in under $30\,\mathrm{s}$ with $41.4(4)\,\mathrm{mW}$ of optical power in a room-temperature ion trap system with an ionisation probability of $1.50(5)\times10^{-5}$. We calibrate a thermal model for the source's internal temperature by imaging the fluorescence of a collimated flux of neutral calcium that effuses from the oven at various optical heating powers. We show that the thermal performance of this oven is mainly limited by radiative losses. We explore the effect of second-stage photo-ionisation
laser power on the loading rate, and identify a path beyond the loading rates reported in this study. We predict that this source is also well-suited to a wide range of metals used in ion-trapping.
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All authors Lorenzo Versini, Tim F. Wohlers-Reichel, Catherine E. J. Challoner, Thomas Hinde, Arjun D. Rao, William J. Hughes, Peter Drmota, Thomas H. Doherty, Laurent J. Stephenson, Jacob A. Blackmore, Joseph F. Goodwin
Classical Physics
Classical Dirac particle: Mass and Spin invariance and radiation reaction
Martin Rivas
According to the atomic principle an elementary particle has no excited states and under any interaction, if it is not annihilated, its internal structure cannot be modified. The intrinsic properties are the mass $m$ and the absolute value of the
spin in the center of mass frame $S=\hbar/2$. We analyze the closed system made of a single Dirac particle and an external electromagnetic field. The Poincaré invariance of the dynamics implies that the energy, linear momentum and angular momentum of the whole system must be conserved. The Dirac particle has two distinguished points, the center of charge ${\bf r}$ and the center of mass ${\bf q}$. When interacting, the energy expended by the field is the work done by the external Lorentz force along the center of charge trajectory. The variation of the mechanical energy of the particle is the work done by the external Lorentz force along the center of mass trajectory. If these two works are different, the excess of energy must be transformed into radiation, returning that energy to the field. The accelerated Dirac particle radiates. Accelerated spinless particles do not radiate. We analyze the
spin dynamics of the Dirac particle under an external electromagnetic field. The requirement that the absolute value of the
spin for the center of mass observer cannot be modified by the interaction, give rise to a twofold feature: a modification of the dynamical equation of the acceleration of the CM, which includes a new reaction force along the linear momentum that suggests a linear momentum emission in the opposite direction, and also an external reaction torque, that can be interpreted as the radiation of angular momentum in the orthogonal direction to the zitter plane.
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Space Physics
Identification and Characterization of the Topside Bulge of the Venusian Ionosphere
Satyandra M. Sharma, Varun Sheel and Martin Pätzold
Venus, in the absence of an intrinsic
magnetic field, undergoes a direct interaction between its ionosphere and the
solar wind. Previous missions, including Mariner, Venera, and the Pioneer Venus Orbiter (PVO), reported a recurring localized increase in electron density, often termed a "bulge," at altitudes between 160 and 200 km. This study investigates this topside bulge using over 200 dayside electron density profiles derived from the Venus
Radio Science experiment (VeRa) onboard the Venus Express (VEX). We employ an automated, gradient-based classification
algorithm to provide a quantitative and reproducible method for identifying and categorizing the bulge morphology into three types. Type 1 profiles exhibit a distinct secondary peak above the main V2 layer. Type 2 profiles display a shoulder-like feature near the bulge altitude. Type 3 bulges are not visually apparent but can be identified through residuals obtained after subtracting a Chapman layer fit to the V2 peak. The bulge is detected in over 80% of the analyzed profiles, with a higher occurrence during periods of low
solar activity and at lower
solar zenith angles (SZA). Type 1 morphologies are only observed at low latitudes (within $\pm 40^\circ$). The peak altitude of the bulge negatively correlates with SZA, suggesting that thermospheric cooling toward the terminator significantly influences the bulge altitude. The occurrence patterns and morphological characteristics indicate that the bulge is likely influenced by external drivers, such as
solar wind interaction, rather than being solely a result of local photochemical processes.
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Optics
Pockels effect induced strong Kerr nonlinearity in a lithium niobate waveguide
Haoran Li, Fei Huang, Jingyan Guo et al. (+9 more)
The utilization of Kerr nonlinearity in lithium niobate has been extensively investigated over the years. Nevertheless, the practical implementation of Kerr nonlinearity in waveguides has been constrained by the material's inherently low third-order nonlinear coefficients. Here, we present a significant advancement by demonstrating Pockels effect-induced strong Kerr nonlinearity in a periodically poled thin-film lithium niobate waveguide. Both effective four-wave mixing (FWM) and cascaded effective FWM processes are experimentally observed. The induced FWM process achieves a remarkable maximum output power of -8.5 dBm, spanning a wavelength spectrum of over 116.8 nm. Analysis reveals that the induced effective Kerr nonlinearity exhibits a substantial effective nonlinear refractive index as $2.9\times 10^{-15} m^{2}W^{-1}$, corresponding to an effective nonlinear refractive index enhancement factor of $1.6\times 10^{4}$ relative to the intrinsic value. Moreover, a wavelength-converting experiment demonstrates a flat optic-to-optic response over a broadband radiofrequency spectrum, confirming that signal integrity is well preserved after on-chip effective FWM conversion. Therefore, the demonstrated efficient and broadband Pockels effect induced effective Kerr nonlinearity paves the way for novel applications in diverse fields, including spectroscopy, parametric amplification,
quantum correlation studies, and wavelength conversion technologies.
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All authors Haoran Li, Fei Huang, Jingyan Guo, He Gao, Hanwen Li, Zhile Wu, Xinmin Yao, Zhengyuan Bao, Huan Li, Yaocheng Shi, Zejie Yu, Daoxin Dai
Optics
An Extended Mixed Quantum/Classical Approach for Quantitative Calculation of Complex Refractive Index
Ian F. Mochida, Tetsuyuki Takayama, Shoichi Yamaguchi et al. (+1 more)
The mixed
quantum/classical approach of Skinner and co-workers has been widely used to calculate the line shapes of the infrared spectra of water (H2O), but less
attention has been paid to the use of this approach in quantitatively calculating spectral intensity, thereby limiting direct comparisons of calculated and experimental spectra. Here, we extend this theoretical framework to facilitate direct computation of the full complex refractive index of water, replacing the normalized ordinate used in previous studies. Our results for the OH stretching region of H2O capture both the shapes and intensities of the experimental spectra. They reveal that inclusion of the local field effect is crucial to the accurate reproduction of spectral intensity. This extended approach enables new areas of analysis of the bulk, thin-film, and cluster spectra of water.
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All authors Ian F. Mochida, Tetsuyuki Takayama, Shoichi Yamaguchi, Tetsuya Hama
Geophysics
Coherent Source Subsampling: A Data-Driven Strategy for Restoring Causal-Acausal Symmetry in Ambient Seismic Wavefield Correlations
Sanket Narayan Bajad, Pushkar Bharadwaj and Pawan Bharadwaj
Ambient noise
tomography relies on the assumption that the seismic wavefield is equipartitioned. In practice, ambient noise sources are spatially and temporally heterogeneous, producing biased estimates of the Green's function between stations. We introduce a data-driven method, Coherent Source Subsampling (CSS), which selects and averages only cross-correlation time windows associated with excitation of sources in the stationary zone. By restricting the ensemble average to these windows, CSS mitigates the effects of nonuniform source distribution and restores causal-acausal
symmetry in the retrieved interstation response. Applications to regional ambient-noise
datasets show that CSS stabilizes surface-wave dispersion measurements even when source statistics violate the assumptions of standard seismic interferometry. For the central California
dataset, CSS-derived group-velocity tomograms consistently image a high-velocity block between the Rinconada and San Andreas faults across multiple periods. In comparison, the full-ensemble (linear) average does not capture this block, which is well established. Our approach is particularly useful for short-duration passive surveys.
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Computational Physics · Cross-listed from cond-mat.mtrl-sci
Engineering Multifunctional Response in Monolayer Fe3O4 via Zr Adsorption: From Half-Metallicity to Enhanced Piezoelectricity
Sikander Azam, Qaiser Rafiq, Rajwali Khan et al. (+1 more)
Two-dimensional (2D)
magnetic oxides are increasingly studied for their multifunctional potential in fields like spintronics, optoelectronics, and energy conversion. In this research, we conduct a detailed first-principles study of pure monolayer Fe3O4 and its modification through Zr adsorption at two sites: on top of an Fe atom and at the bridge between Fe atoms. Using
spin-polarized density functional theory with the GGA plus U method, we examine how adsorption affects structure, electronic,
magnetic, optical, elastic, and piezoelectric properties. The original monolayer shows half-
metallicity, strong
spin polarization, and a moderate in-plane piezoelectric effect. Zr adsorption causes local
lattice distortions and orbital hybridization, resulting in intermediate electronic states, a reduced bandgap, and increased optical absorption in both
spin channels. Notably, Zr at the bridge site greatly enhances dielectric response, optical conductivity, and piezoelectric coefficients, tripling e11 compared to the pristine layer. Elastic constants indicate mechanical softening after functionalization, and energy loss spectra display shifts in plasmon resonance. These findings suggest Zr adsorption offers a controllable, non-destructive way to tune
spin, charge, and
lattice interactions in Fe4O4 monolayers, connecting
magnetic, optical, and piezoelectric functionalities within a single 2D material platform.
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All authors Sikander Azam, Qaiser Rafiq, Rajwali Khan, Hamdy Khamees Thabet
Geophysics
A Sampling Strategy Benchmark for Machine-Learning-Based Seismic Liquefaction Prediction
Jilei Hu, Fenglin He, Lianming Huang et al. (+1 more)
Sampling strategy including sampling methods and training set configurations (training set sample size, train-test split ratio, and class distribution) significantly affects machine-learning (ML) model performance in seismic liquefaction prediction. However, existing ML applications in seismic liquefaction prediction remain fragmented: sampling strategies vary widely across studies without a unified
benchmark. Moreover, these studies generally optimize the sample set configuration independently, ignoring the interaction among training set configurations. To address these limitations, this study establishes a
benchmark that systematically evaluates sampling methods, training set sample sizes, train-test split ratios, class distributions, and training set configurations coupling on seven mainstream ML models performance, and further improves the predictive accuracy of seismic liquefaction-using a database of 250 historical liquefaction events, evaluated by Acc and F1. The results show that ordered systematic sampling yields the best performance across all models. The optimal model can be trained when the training set sample size is 200, the train-test split ratio is 80:20, and the class distribution range is 1-1.5. Among them, the train-test split ratio most significantly influenced performance, followed by the class distribution, with the training set sample size having the least effect. Furthermore, the Random Forest model achieves the highest performance, while the K-Nearest Neighbor model performs the weakest. Importantly, this study systematically identifies and verifies for the first time that there will be an interaction effect among training set configurations, rather than a simple additive effect. This study provides a
benchmark for scholars to select the optimal sampling method and training set configurations to obtain high accuracy in ML-based liquefaction prediction.
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All authors Jilei Hu, Fenglin He, Lianming Huang, Qianfeng Wang
Fluid Dynamics · Cross-listed from nucl-th
Stationary Couette-type flows in relativistic fluids
Lorenzo Gavassino, Patrick Niekamp, Sören Schlichting et al. (+1 more)
We investigate a class of stationary, planar-symmetric solutions of relativistic hydrodynamics, in which a dissipative fluid is confined between two parallel plates that move relative to each other and/or are maintained at different temperatures. We find that neglecting the heat flux leads to qualitatively incorrect flow profiles, even in systems with temperature-independent viscosity. This arises from the fact that, in special relativity, the heat flux itself contributes to the momentum density (the so-called "inertia of heat"). This effect is most evident in the Landau frame, where the fluid removes the excess energy generated by viscous heating by streaming across the boundaries. The analysis is further extended to the limit of vanishing chemical potential.
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All authors Lorenzo Gavassino, Patrick Niekamp, Sören Schlichting, Gabriel S Denicol
Atmospheric and Oceanic Physics
Impact of Background Conditions on the Structure and Propagation of the Boreal Summer Quasi-Biweekly Oscillation
Shubhrangshu Biswas, Jai Sukhatme and Bishakhdatta Gayen
We examine the westward-propagating quasi-biweekly oscillation (QBWO) during boreal summer, with a focus on how background moisture and winds shape its structure and propagation. In dry regions, convection lags the circulation by nearly a quarter cycle, whereas in very moist regions it becomes nearly in-phase and extends across the QBWO gyre. As the background moistens, moisture anomalies increasingly align with the QBWO circulation. Despite differences in environmental moisture and wind conditions, several structural features remain robust: outgoing longwave radiation and moisture anomalies stay collocated, moisture and pressure-velocity anomalies remain vertically upright, and the filtered winds retain a first-baroclinic mode structure. A vorticity budget shows that, although the planetary vorticity-gradient term is important, both planetary stretching and horizontal advection are needed to explain the vorticity tendency- and their relative importance shifts with the moisture regime. In dry and moderately moist regions with easterly mean flow, mean winds primarily advect vorticity anomalies. In contrast, in very moist regions with westerly flow, anomalous winds instead advect the background vorticity. An analogous transition occurs in the moisture budget: in dry and moderately moist environments, zonal mean flow advection dominates, but in very moist regions, strong background moisture gradients allow eddy advection of the mean moisture field to become the leading term. In the moist regime, vertical advection, precipitation, and evaporation also contribute substantially to the moisture tendency. Overall, the QBWO behaves like a mean-flow-driven linear mode in dry and moderately moist regions with easterly background winds, but shifts toward a regime dominated by eddy advection of background vorticity and moisture in very moist regions characterized by westerly flow.
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Space Physics
Generation of proton beams at switchback boundary-like rotational discontinuities in the solar wind
Rong Lin, Fabio Bacchini, Jiansen He et al. (+2 more)
Alfvénic rotational discontinuities (RDs) are abundant in the inner heliosphere and can be used to model the boundary of switchbacks, i.e. Alfvénic
magnetic kinks. To investigate the effects of RDs on
proton kinetics, we model a pair of switchback-boundary-like RDs with a hybrid Particle-In-
Cell (PIC) approach in a 2D system. We find that, at one of the boundary RDs, a significant population of
protons remains trapped over long times, creating a secondary beam-like component with temperature anisotropy $T_\perp/T_\|\gtrsim4$ in the
proton velocity distribution function that excites ion cyclotron waves within the downstream portion of the transition layer. Further analysis suggests that the static electric field in the vicinity of the RD is the key factor in trapping the
protons. This work indicates that switchback boundaries could represent a viable environment for the creation of
proton beams in the heliosphere; it also highlights the need to investigate RD sub-structures, especially the embedded current systems of interplanetary RDs. Finally, this paper underscores the importance of high-resolution observations of the
solar wind velocity distributions around RDs.
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All authors Rong Lin, Fabio Bacchini, Jiansen He, Luca Pezzini, Jingyu Peng
Applied Physics · Cross-listed from cond-mat.mtrl-sci
Electric-Field-Controlled Altermagnetic Transition for Neuromorphic Computing
Zhiyuan Duan, Peixin Qin, Chengyan Zhong et al. (+12 more)
Altermagnets represent a novel
magnetic phase with transformative potential for ultrafast spintronics, yet efficient control of their
magnetic states remains challenging. We demonstrate an ultra-low-power electric-field control of altermagnetism in MnTe through strain-mediated coupling in MnTe/PMN-PT
heterostructures with negligible Joule heating. Application of +6 kV/cm electric fields induces piezoelectric strain in PMN-PT, modulating the Néel temperature from 310 to 328 K. As a result, around the
magnetic phase transition, the altermagnetic
spin splitting of MnTe is reversibly switched "on" and "off" by the electric fields. Meanwhile, the piezoelectric strain generates
lattice distortions and
magnetic structure changes in MnTe, enabling up to 9.7% resistance modulation around the
magnetic phase transition temperature. Leveraging this effect, we implement programmable resistance states in a Hopfield neuromorphic network, achieving 100% pattern recognition accuracy at <=40% noise levels. This approach establishes the electric-field control as a low-power strategy for altermagnetic manipulation while demonstrating the viability of altermagnetic materials for energy-efficient neuromorphic computing beyond conventional charge-based architectures.
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All authors Zhiyuan Duan, Peixin Qin, Chengyan Zhong, Shaoxuan Zhang, Li Liu, Guojian Zhao, Xiaoning Wang, Hongyu Chen, Ziang Meng, Jingyu Li, Sixu Jiang, Xiaoyang Tan, Qiong Wu, Yu Liu, Zhiqi Liu
Atomic and Molecular Clusters · Cross-listed from cond-mat.mes-hall
Excitation energies and UV-Vis absorption spectra from INDO/s+ML
Ezekiel Oyeniyi and Omololu Akin-Ojo
The semi-empirical INDO/s method is popular for studies of excitation energies and absorption of molecules due to its low computational requirement, making it possible to make predictions for large systems. However, its accuracy is generally low, particularly, when compared with the typical accuracy of other methods such as time-dependent density functional theory (TDDFT). Here, we present machine learning (ML) models that correct the INDO/s results with negligible increases in the amount of computing resources needed. While INDO/s excitations energies have an average error of about 1.1 eV relative to TDDFT energies, the added ML corrections reduce the error to 0.2 eV. Furthermore, this combination of INDO/s and ML produces UV-Vis absorption spectra that are in good agreement with the TDDFT predictions.
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Classical Physics · Cross-listed from cs.LG
Fitting magnetization data using continued fraction of straight lines
Vijay Prakash S
Magnetization of a
ferromagnetic substance in response to an externally applied
magnetic field increases with the strength of the field. This is because at the microscopic level,
magnetic moments in certain regions or domains of the substance increasingly align with the applied field, while the amount of misaligned domains decreases. The
alignment of such
magnetic domains with an applied
magnetic field forms the physical basis for the nonlinearity of magnetization. In this paper, the nonlinear function is approximated as a combination of continued fraction of straight lines. The resulting fit is used to interpret the nonlinear behavior in both growing and shrinking
magnetic domains. The continued fraction of straight lines used here is an algebraic expression which can be used to estimate parameters using nonlinear regression.
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Physics and Society · Cross-listed from cs.GT
The $k$-flip Ising game
Aleksandr Kovalenko and Andrey Leonidov
The game-theoretic view on the classical Ising model - the noisy binary choice Ising game, - is a well-known conceptual framework that, on the one hand, highlights similar patterns between social or artificial
agent-based systems and ensembles of interacting physical
spins and, on the other hand, helps to stress important differences between the ones. The study considers one of such differences - the possibility of simultaneous decision making of several
agents that is typical for game theoretic interaction. To do this we analyse a partially parallel discrete time dynamics of an Ising-type system of $N$ interacting binary units on a complete graph in which at each time steps $k$ arbitrarily chosen units can change their states is analysed. The particular problem under study is a $k$ - dependence of the decay of a metastable configuration into a stable one. The analysis is based on an explicit analytical calculation, for arbitrary noise, of the transition
matrix characterising the $k$ - flip
evolution as well as the first two moments of the distribution of the fraction of players choosing one of the two available strategies. First two moments of the first hitting time distribution for sample trajectories corresponding to transition from a metastable and unstable states to a stable one are considered. A nontrivial dependence of these moments on $k$ for the decay of a metastable state is discussed. A presence of the minima at certain $k_{\rm min}$ is attributed to a competition between $k$-dependent
diffusion and restoring forces.
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Medical Physics
KRAS G12D protein screening for pancreatic cancer clinical trials using an AlGaN/GaN high electron mobility transistor biosensor
Sheng-Ting Hung, Cheng Yan Lee, Chen-Yu Lien et al. (+7 more)
Clinical trials screening KRAS G12D
protein for 30 pancreatic ductal adenocarcinoma (PDAC) patients and 30 healthy donors were conducted utilizing an AlGaN/GaN high electron mobility transistor (HEMT) biosensor. All resistance change ratios of PDAC patients are higher than the standard deviation above the mean resistance change ratio obtained from all healthy donors. The results demonstrate the effectiveness of the HEMT biosensor and reveal its potential for early detection of pancreatic cancer with KRAS G12D
protein screening.
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All authors Sheng-Ting Hung, Cheng Yan Lee, Chen-Yu Lien, Cheng-Hsuan Chan, Ya-Han Yang, Quark Yungsung Chen, Kuang-Hung Cheng, Kung-Kai Kuo, Li-Wei Tu, Ching-Wen Chang
Optics · Cross-listed from quant-ph
Generation of mechanical cat-like states via optomagnomechanics
Hao-Tian Li, Hong-Bin Wang, Zi-Xu Lu et al. (+1 more)
We propose an optomagnomechanical approach for preparing a cat-like superposition state of mechanical motion. Our protocol consists of two steps and is based on the magnomechanical system where the magnetostrictively induced displacement further couples to an optical cavity mode via radiation pressure. We first prepare a squeezed mechanical state by driving the magnomechanical system with two microwave pulses. We then switch off the microwave drives and send a weak red-detuned optical pulse to the optical cavity to weakly activate the optomechanical anti-Stokes scattering. We show that $k$
phonons can be subtracted from the prepared squeezed thermal state, conditioned on the detection of $k$ anti-Stokes
photons from the cavity output field, which prepares the mechanical motion in a cat-like state. The work provides a new avenue for preparing mechanical superposition states by combining opto- and magnomechanics and may find applications in the study of macroscopic
quantum states and the test of collapse theories.
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All authors Hao-Tian Li, Hong-Bin Wang, Zi-Xu Lu, Jie Li
Applied Physics · Cross-listed from quant-ph
Optomagnonic generation of entangled travelling fields with different polarizations
Zi-Xu Lu, Huai-Bing Zhu, Xuan Zuo et al. (+1 more)
The optomagnonic coupling between
magnons and optical
photons is an essential component for building remote
quantum networks based on magnonics. Here we show that such a coupling, manifested as the
magnon-induced Brillouin light scattering, can be exploited to entangle two propagating optical fields. The protocol employs two pairs of the whispering gallery modes coupled to the same
magnon mode in a YIG sphere. In each pair a strong pump field is applied to activate either Stokes or anti-Stokes scattering. Due to the
magnon mode involving in the two scattering processes and as a mediation, Stokes and anti-Stokes
photons of different polarizations get entangled. The
entanglement can be extracted by filtering the travelling output fields centered at the Stokes and anti-Stokes sidebands. Optimal conditions are identified under which strong output
entanglement can be achieved.
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All authors Zi-Xu Lu, Huai-Bing Zhu, Xuan Zuo, Jie Li
Biological Physics · Cross-listed from q-bio.MN
Tracking large chemical reaction networks and rare events by neural networks
Jiayu Weng, Xinyi Zhu, Jing Liu et al. (+3 more)
Chemical reaction networks are widely used to model
stochastic dynamics in biophysical processes. Their exact probabilistic description via the chemical master equation becomes intractable for high-dimensional systems due to the exponential growth of the state space. This difficulty is further exacerbated when metastable transitions and rare events dominate the dynamics. Here, we present an advanced neural-network approach that effectively tackles large reaction networks and rare-event dynamics within a unified framework. Building on autoregressive representations of the full joint probability distribution, we achieve efficient training through second-order optimization methods, including natural gradient descent and the time-dependent variational principle. We leverage enhanced-sampling strategies that enable accurate resolution of low-probability regions governing rare transitions. We demonstrate the method on a multistable genetic toggle switch and a mitogen-activated
protein kinase (MAPK) cascade consisting of 16 species and 35 reactions with dense coupling. We further quantify rare events in spatially extended Schlögl models in both one- and two-dimensional
lattices, reaching system sizes with extremely large state spaces up to $3\times10^{15}$. These results highlight the potential of neural modeling as a flexible and scalable framework for studying high-dimensional chemical reaction networks and rare events.
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All authors Jiayu Weng, Xinyi Zhu, Jing Liu, Linyuan Lü, Pan Zhang, Ying Tang
Biological Physics
Motifs in self-organising cells
Ying Chen Lim, Rakesh Das, Tetsuya Hiraiwa et al. (+1 more)
In complex systems, groups of interacting objects may form prevalent and persistent spatiotemporal patterns, which we refer to as motifs. These motifs can exhibit features that reveal how individual objects interact with one another. Simultaneously, the motifs can also interact, causing new coarse-grained properties to emerge in the system. In this paper, we found motifs in a simulated system of Dynamically Self-Organising
cells. We also found that quantifying these motifs with a set of physically interpretable structural and dynamic features efficiently captures the interaction dynamics of the motifs' underlying
cells. Using these motif features, we revealed packing strain and defects in large compact aggregates, semi-periodicity in motif ensembles, and phase space classes with unsupervised machine learning. Additionally, we trained neural networks to infer the critical hidden microscopic interaction parameters within each motif from coarse-grained motif features extracted from snapshots of the system. Furthermore, we uncovered emergent features that can predict the movement of
cell collectives by hierarchically coarse-graining smaller motifs into larger ones (e.g. motif clusters). We speculate that this concept of motif hierarchies may be applied broadly to many-body interacting systems that are otherwise too complex to understand.
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All authors Ying Chen Lim, Rakesh Das, Tetsuya Hiraiwa, N. Duane Loh
Atomic Physics · Cross-listed from quant-ph
Tunable discrete quasi-time crystal from a single drive
Xu Feng, Shuo Liu, Shu Chen et al. (+1 more)
The search for exotic temporal orders in
quantum matter, such as discrete quasi-time
crystals (DQTCs), has become an important theme in nonequilibrium physics. However, realizing these phases has so far required complex protocols, such as drives with multiple incommensurate frequencies. Here, we present a significantly simpler mechanism: the emergence of DQTCs in a dissipative collective
spin system subjected to only a single periodic drive. Remarkably, the characteristic frequencies of this novel phase are not fixed but can be continuously tuned by varying the strength of the drive. Even more strikingly, this tunability is punctuated by Arnold tongues, within which the response main frequency locks to rational fractions of the drive. Our model further provides a unified framework that also encompasses stationary, discrete time
crystals and chaotic phases. This discovery simplifies the requirements for generating complex temporal orders and opens a viable route towards the experimental control and manipulation of quasi-time crystalline matter.
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All authors Xu Feng, Shuo Liu, Shu Chen, Shi-Xin Zhang
Physics and Society
Techno-Economic Assessment of Wind-Powered Green Hydrogen Production for US Industrial Decarbonization
Bibish Chaulagain and Sanjeev Khanna
This study evaluates the techno-economic feasibility of supplying industrial thermal loads with green hydrogen produced via water electrolysis using two pathways off-grid systems powered by co-located wind turbines and battery energy storage (BESS), and on-grid systems that procure electricity directly from the wind farm power node and operate electrolysers in response to real-time locational marginal prices (LMPs).The optimization results show that off-grid wind-to-hydrogen configurations in high-resource regions can achieve levelized costs of hydrogen (LCOH) on the order of \$7/kg, driven by high wind capacity factors and optimized BESS sizing that ensures operational continuity .Similarly in, on-grid, price-responsive operation achieves LCOH values of \$0.5/kg, reflecting sensitivity to electricity
market volatility. Overall, the results suggest that Midwest wind-rich regions can support competitive green hydrogen production for industrial heat, with grid-connected electrolysers remaining attractive in locations with frequent low LMP periods. This dual-path analysis provides a transparent framework for industrial hydrogen deployment and highlights practical transition strategies for decarbonizing U.S. manufacturing.
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General Physics
Geometric Origin of Lepton Anomalous Magnetic Moments: A Dimensionless Framework from Primitive Triangle Families
Percy Quispe Hancco, Artemio N. Condori Mamani, Ceferino Quispe Hancco et al. (+2 more)
We present a phenomenological
geometric framework deriving the anomalous
magnetic moments of
leptons from a single dimensionless constant V0 = 0.658944. This value emerges as a
geometric attractor identified from exactly 18 primitive triangle families, whose completeness is supported by Diophantine constraints and extensive computational searches. The methodology connects three classical mathematical frameworks: De Moivre s
theorem (1707), Chebyshev
polynomials (1854), and results on the finiteness of integral points. Extended searches expanding the parameter space by a factor of 15 yield no new families, confirming saturation. The constant V0 connects to the Koide formula through Delta = 2/3 - V0 and approximates cos(13*pi/48) to 0.06 percent, suggesting links to cyclotomic fields. Using only dimensionless quantities, we obtain the electron anomaly ae with precision 0.15 ppb, the
muon anomaly a_mu with 17 ppb, and the tau anomaly a_tau with 3.4 ppm. The framework is phenomenological and does not claim a derivation from
quantum field theory, but its mathematical constraints yield testable predictions for future precision measurements.
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All authors Percy Quispe Hancco, Artemio N. Condori Mamani, Ceferino Quispe Hancco, Aldo H. Zanabria Galvez, Hugo Quispe Hancco
Fluid Dynamics · Cross-listed from cs.LG
A Kernel-based Resource-efficient Neural Surrogate for Multi-fidelity Prediction of Aerodynamic Field
Apurba Sarker, Reza T. Batley, Darshan Sarojini et al. (+1 more)
Surrogate models provide fast alternatives to costly aerodynamic simulations and are extremely useful in design and optimization applications. This study proposes the use of a recent kernel-based neural surrogate, KHRONOS. In this work, we blend sparse high-fidelity (HF) data with low-fidelity (LF) information to predict aerodynamic fields under varying constraints in computational resources. Unlike traditional approaches, KHRONOS is built upon variational principles, interpolation theory, and tensor decomposition. These elements provide a mathematical basis for heavy
pruning compared to dense neural networks. Using the AirfRANS
dataset as a high-fidelity
benchmark and NeuralFoil to generate low-fidelity counterparts, this work compares the performance of KHRONOS with three contemporary model architectures: a multilayer perceptron (MLP), a graph neural network (GNN), and a physics-informed neural network (PINN). We consider varying levels of high-fidelity data availability (0%, 10%, and 30%) and increasingly complex geometry parameterizations. These are used to predict the surface pressure coefficient distribution over the airfoil. Results indicate that, whilst all models eventually achieve comparable predictive accuracy, KHRONOS excels in resource-constrained conditions. In this domain, KHRONOS consistently requires orders of magnitude fewer trainable parameters and delivers much faster training and inference than contemporary dense neural networks at comparable accuracy. These findings highlight the potential of KHRONOS and similar architectures to balance accuracy and efficiency in multi-fidelity aerodynamic field prediction.
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All authors Apurba Sarker, Reza T. Batley, Darshan Sarojini, Sourav Saha
Data Analysis and Statistics · Cross-listed from astro-ph.IM
Enhancing evidence estimation through informed probability density approximation
El Mehdi Zahraoui, Patricio Maturana-Russel, Avi Vajpeyi et al. (+3 more)
We introduce the Morph approximation, a class of product approximations of probability densities that selects low-order disjoint parameter blocks by maximizing the sum of their total correlations. We use the posterior approximation via Morph as the importance distribution in optimal bridge sampling. We denote this procedure by MorphZ, which serves as a post-processing
estimator of the marginal likelihood. The MorphZ
estimator requires only posterior samples, and is fully agnostic regarding the choice of sampler. We evaluate MorphZ's performance across statistical
benchmarks,
pulsar timing array (PTA) models, compact binary coalescence (CBC) gravitational-wave (GW) simulations and the GW150914 event. Across these applications, spanning low to high dimensionalities, MorphZ yields accurate evidence estimates at substantially reduced computational cost relative to standard approaches. We have found that when these approaches fail to provide accurate estimates, MorphZ has proven to either resolve the estimation failure or significantly improve the results. Its bridge sampling relative error diagnostic provides conservative uncertainty estimates. Because MorphZ operates directly on posterior draws, it complements exploration-oriented samplers by enabling fast and reliable evidence estimation, while it can be seamlessly integrated into existing inference workflows.
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All authors El Mehdi Zahraoui, Patricio Maturana-Russel, Avi Vajpeyi, Willem van Straten, Renate Meyer, Sergei Gulyaev