Physics and Society · Cross-listed from cs.DL
Bridging the Language Gap in Scholarly Data I: Enhancing Author Disambiguation Algorithms for Chinese Names
Mingrong She, Liuhuaying Yang, Ana Maria Jaramillo et al. (+1 more)
Disambiguating scholars with identical names is essential for accurate authorship assignment and robust large-scale scientometric research. Existing methods are often designed for Latin-script metadata and perform poorly on Chinese names. In international publications, Chinese names typically appear as Romanized Pinyin, which is highly ambiguous as it can map to multiple distinct characters. Chinese characters, in contrast, reduce but do not eliminate this ambiguity, and are rarely available in international records. To address both challenges, we propose a rule-based disambiguation framework that integrates co-authorship networks, citation networks, author affiliations, and content similarity. We apply this framework to 65,241 physics papers from the China National Knowledge Infrastructure (CNKI), spanning over 70 years of data. On a human annotated sample of 80 name pairs, our method achieves F1-scores of 0.88 for Pinyin names and 0.89 for character-based names, outperforming two baseline approaches, with improvements driven primarily by higher recall. The comparable performance across both writing systems shows that our approach is script-agnostic, enabling reliable large-scale scientometric analyses.
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All authors Mingrong She, Liuhuaying Yang, Ana Maria Jaramillo, Lisette Espín-Noboa
Optics
Characterize localization length of disordered lattices via critical coupling effect
Fuhao Ji, Xiangqi Huang, Luxing Chen et al. (+8 more)
Light localization by scattering is a fundamental mechanism driving phase transitions of wave transport in disordered systems. Characterizing the localization length in scattering systems is crucial yet challenging. In this Letter, we demonstrate a spatially matched coupling scheme using wavefront shaping to resolve the intrinsic localization length in two-dimensional disordered
lattices. By tailoring the incident wavefront, our method facilitates efficient coupling of light to the minimum localized mode. We apply this approach to measure two different self-assembled
lattices, and report the first observation of the critical coupling effect, which allows for the direct determination of the characteristic size of minimum localized mode. Our results reveal that for a fixed
lattice periodicity, increasing the air-hole diameter significantly reduces this intrinsic localization length. This far-field metrology offers a robust framework for probing wave localization in complex media, which should be useful in various applications such as random lasing and nonlinear optics
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All authors Fuhao Ji, Xiangqi Huang, Luxing Chen, Yuxiang Tian, Wenjing Li, Yinying Peng, Yuge Qiu, Lu Zhang, Liwei Zhang, Mingfang Yi, Peilong Hong
Plasma Physics
M. Bonitz, H. Kählert, D. Krimans et al. (+11 more)
The year 2025 had been designated by UNESCO as the International Year of
Quantum Science and Technology. 125 years ago Max Planck's discovery of radiation quanta started the
quantum era and 100 years ago
quantum mechanics was discovered by Schroedinger, Heisenberg, Bohr, Pauli, Dirac, Born,
Fermi and many others. By now,
quantum mechanics is the theoretical foundation of most fields of physics and chemistry, and it is the basis for modern nanotechnology. How about
plasma physics? How important are
quantum effects in
plasmas? In what experiments
quantum effects are observed and where do they govern the behavior of
plasmas? How can these effects be treated theoretically and via computer simulations? Starting with a brief historical overview we discuss the broad parameter range that is characteristic for
plasmas and outline where
quantum effects are relevant. This is the case primarily for warm dense matter and inertial fusion
plasmas. We provide an overview on the theoretical
quantum methods that are available for these dense
plasmas and how their respective advantages can be combined in order to achieve predictive capability. The key is a downfolding approach that is based on first principles simulations.
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All authors M. Bonitz, H. Kählert, D. Krimans, C. Makait, P. Hamann, J. Vorberger, Zh. Moldabekov, S. X. Hu, V.V. Karasiev, D. Kraus, H. Kersten, J.-P. Joost, P. Ludwig, T. Dornheim
Computational Physics · Cross-listed from cs.CV
Shower-Aware Dual-Stream Voxel Networks for Structural Defect Detection in Cosmic-Ray Muon Tomography
Parthiv Dasgupta, Sambhav Agarwal, Palash Dutta et al. (+2 more)
We present SA-DSVN, a 3D convolutional architecture for voxel-level
segmentation of structural defects in reinforced concrete using
cosmic-ray
muon tomography. Unlike conventional reconstruction methods (POCA, MLSD) that rely solely on
muon scattering angles, our approach jointly processes scattering kinematics (9 channels) and secondary electromagnetic shower multiplicities (40 channels) through independent encoder streams fused via cross-
attention. Training data were generated using Vega, a cloud-native Geant4 simulation framework, producing 4.5 million
muon events across 900 volumes containing four defect types - honeycombing, shear fracture, corrosion voids, and delamination - embedded within a dense 7x7 rebar cage. A five-variant ablation study demonstrates that the shower multiplicity stream alone accounts for the majority of discriminative power, raising defect-mean Dice from 0.535 (scattering only) to 0.685 (shower only). On 60 independently simulated validation volumes, the model achieves 96.3% voxel accuracy, per-defect Dice scores of 0.59-0.81, and 100% volume-level detection sensitivity at 10 ms inference per volume. These results establish secondary shower multiplicity as a previously unexploited but highly effective feature for learned
muon tomographic reconstruction.
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All authors Parthiv Dasgupta, Sambhav Agarwal, Palash Dutta, Raja Karmakar, Sudeshna Goswami
Computational Physics
Integrating Gaussian Random Functions with Genetic Algorithms for the Optimization of Functionally Graded Lattice Structures
Piyush Agrawal and Manish Agrawal
The properties of
lattice-based structures can be enhanced by varying their
geometric parameters in a graded manner, and the gradation can be tailored to extremize a particular objective. In this manuscript, we propose a non-gradient-based optimization framework to find the tailor-made graded profiles for
lattice-based structures. The key challenge addressed in the work is to ensure the graded nature/smoothness of the underlying structure in a non-gradient-based optimization scheme. As we demonstrate in the manuscript, the conventional implementation of the genetic
algorithm provides structures with abrupt changes, leading to issues such as stress concentration. In this work, we propose a Gaussian random function (GRF)/Gaussian process regression (GPR) integrated genetic
algorithm to obtain an optimal graded
lattice profile for a given objective. The integration of the GRF/GPR along with a projection operator ensures the smoothness of the designs at each stage of the optimization. We present several numerical examples to demonstrate that the proposed framework provides smoother designs that are less susceptible to stress concentration, while ensuring satisfaction of the underlying objective.
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Accelerator Physics · Cross-listed from cond-mat.supr-con
Analytical evaluation of surface barrier and resistance in iron-based superconducting multilayers for Superconducting Radio-Frequency applications
Carlos Redondo Herrero and Akira Miyazaki
New superconducting materials, particularly iron-based
superconductors (IBS), have recently attracted
attention for their potential applications in particle detectors and accelerators. This paper discusses the application of these materials in multilayer structures for
radio-frequency resonators used to accelerate charged particles, with the aim of improving performance compared to bulk niobium. These materials are compared with previously studied multilayers composed of conventional
superconductors in terms of the maximum
magnetic field they can withstand, their surface resistance, and their power loss per unit surface area. Finally, perspectives and future applications aimed at increasing operating temperatures are discussed.
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Computational Physics · Cross-listed from cond-mat.soft
Advanced Modelling Methodologies for Anisotropic Magnetic Colloids
Jorge L. C. Domingos
Anisotropic
magnetic colloids with permanent dipole moments exhibit rich field-responsive behavior arising from the interplay between particle geometry, dipolar interactions, and external driving. Modeling these systems remains challenging due to the long-range nature of dipolar forces,
geometric anisotropy, dipole–particle misalignment, and the complexity of implementing anisotropic steric interactions. This review discusses particle-based numerical strategies to model such systems, including single-site, multi-bead, shifted-dipole, and multicore representations. We analyze how different levels of description capture key physical mechanisms, from steric constraints and directional binding to internal
magnetic structure and nonequilibrium dynamics. Particular emphasis is placed on dipole–particle misalignment as a control parameter that strongly affects interaction landscapes and self-assembly pathways. We also highlight recent machine learning approaches as emerging tools to construct effective interaction potentials and accelerate simulations. By comparing the main methodologies and their limitations, this review outlines current challenges and perspectives toward more predictive and efficient modeling of anisotropic
magnetic colloids.
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Physics and Society · Cross-listed from cs.GT
Asymmetric reformulation of draw rules in chess and its implications for game theory: Repetition as loss for White
Chong Qi
Repetition-based draw rules in deterministic games like chess ensure termination but introduce strategic artifacts, allowing players to enforce draws independent of positional value. We propose an asymmetric modification: threefold repetition results in a loss for White if it is responsible for initiating it. This rule directly targets the persistent first-move advantage and removes low-effort draw strategies available to White. The new rule is expected to reduce draw rates, re-balance first-move advantage, and promote exploration in both human and artificial play. We outline a computational framework with existing and newly designed neural-network chess engines for the empirical validation of the proposal and analyze it from the perspectives of game theory and graph dynamics.
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Atomic Physics
Spectral-Domain Coherent Control of Broadband Raman Coupling in Atom Interferometry
Sheng-Zhe Wang, Wei-Chen Jia, Yue Xin et al. (+3 more)
The performance of atom interferometers is commonly limited by the finite spectral acceptance of atomic beam splitters and mirrors, which restricts efficient coupling to atoms with large Doppler shifts and reduces the usable atomic flux. Here, we demonstrate spectral-domain coherent control of Raman coupling by engineering its effective two-
photon spectrum. By synthesizing multiple frequency components, the Raman interaction simultaneously addresses a broad range of atomic velocities, effectively overcoming the conventional transit-time-limited linewidth. Implemented in a continuous atomic-beam Mach-Zehnder interferometer, where the transverse Doppler broadening is 17 times larger than the intrinsic Raman linewidth, this approach enhances the fringe contrast from 5.9(2)% to 15.1(2)%, indicating a substantial increase in effective atomic participation. Our results establish spectral-domain coherent control as a general strategy for achieving spectrally robust atom interferometry and open new opportunities for
quantum sensing in systems with strong inhomogeneous broadening.
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All authors Sheng-Zhe Wang, Wei-Chen Jia, Yue Xin, Qian-Lan Cai, Yingpeng Zhao, Yan-Ying Feng
Biological Physics · Cross-listed from nlin.AO
Overcoming unfairness via repeated interactions in mini-ultimatum game
Prosanta Mandal, Arunava Patra and Sagar Chakraborty
Repeated interactions are ubiquitous and known to promote social behaviour. While research often focuses on cooperation in the Prisoner's Dilemma, experimental evidence suggests repeated interactions also foster
fairness. This study addresses a gap in the literature by theoretically modelling the
evolution of
fairness within a repeated mini-ultimatum game. Specifically, we construct a repeated-game framework where offerers and accepters interact using reactive strategies. We then investigate whether fair reactive strategy pairs are resilient against unfair mutants in a two-species population. By analyzing short-term evolutionary stability via the concept of two-species evolutionary stable strategy, we identify a critical effective game length: below this value,
fairness is promoted by offerers and accepters who comply with their partner's past actions. Above this critical value,
fairness is maintained by `complier' offerers and fair accepters. We also show that specific reactive strategies effectively facilitate the emergence and sustenance of
fairness in long-term mutation-selection dynamics. To this end, we develop a two-population
stochastic dynamics model – a generalization of classical adaptive dynamics – that accounts for finite population sizes and non-local mutants in the reactive strategy space.
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Atmospheric and Oceanic Physics
On the instability of some upward propagating, exact, nonlinear mountain waves
Christian Puntini
Using the short-wavelength instability method, we investigate the linear instability of an exact solution describing upward-propagating mountain waves, derived in A.~Constantin, J. Phys. A: Math. Theor. (2023), for a dry and adiabatic compressible flow. Within this approach, the stability problem reduces to the analysis of a system of ordinary differential equations with periodic coefficients along the fluid trajectories, which we study by means of Floquet theory. We show that the Floquet multipliers are of the form $\{1,\mu,\mu^{-1}\}$, the multiplier $1$ being associated with the conservation of potential vorticity, so that stability is decided by a single discriminant, which coincides with that of a Hill equation. For neutral stratification the flow is unstable whenever the wave steepness exceeds the critical value $\frac{1}{3}$, while a weakly stable stratification lowers this threshold to $\frac{1}{3}-\frac{4}{9}\nu^2+O(\nu^4)$, $\nu$ being the ratio between the Brunt–Väisälä frequency and the orbital frequency of the air parcels. The instability is a subharmonic parametric resonance. Since the solution is given in Lagrangian coordinates, these results identify an unstable region in the upper part of the laminar layer, beneath the tropopause, where the wave is steepest.
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Atomic Physics · Cross-listed from quant-ph
Interaction of twisted light with free twisted atoms
I. Pavlov, A. Chaikovskaia and D. Karlovets
We investigate absorption and scattering of structured light by atoms, treating the
photon and the atomic center of mass as spatially localized wave packets. We show that
vortex photons can transfer orbital angular momentum (OAM) to the atomic center of mass with near-perfect efficiency in head-on collisions when the impact parameter $b$ is smaller than the atomic transverse coherence length $\sigma$, which ranges from nanometers to sub-micrometer scales. Larger offsets result in a shifted mean OAM and a finite variance, both controlled by the ratio $b/\sigma$. The wave-packet nature of light enables electronic transitions that violate standard selection rules, albeit with a clear hierarchy where the dipole transition dominates. For femtosecond pulses, the finite spatial coherence of the
photon leads to measurable shaping of the resonant absorption lines. We demonstrate a transverse
recoil of the atom in a vicinity of the photonic
vortex, dubbed "the superkick", and its dual effect - "the selfkick" - when an initially twisted atomic packet experiences
recoil upon absorbing a gaussian
photon. These phenomena are within reach of experimental capabilities using structured light in combination with cold atomic beams and ions in Penning traps, providing a route to the controlled generation and manipulation of non-gaussian atomic packets.
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Atmospheric and Oceanic Physics
Optimizing Objective Model Calibration Approaches using Single Column Models
Pappu Paul and Cristian Proistosescu
Sub-grid scale parameterizations in atmospheric models involve numerous uncertain parameters that must be tuned to align simulations with observations. Here, we propose a framework for assessing objective tuning frameworks using the Single Column Atmosphere Model (SCAM), which retains key physical parameterizations of general circulation models (GCMs) while greatly reducing computational cost. We conduct a perfect-model experiment where we run SCAM with a known "true" parameter set to generate synthetic observations that mimic Atmospheric Radiation Measurement (ARM) Intensive Observation Periods. Perturbed parameter ensembles are constructed by varying microphysics, convection, and aerosol parameters, and cloud-radiation fields are evaluated over the Southern Great Plains. We find that point estimates find solutions that greatly reduce model-observation misfit without recovering the true parameter values. In contrast, a
Bayesian framework using a Gaussian Process emulator with Markov Chain Monte Carlo sampling yields tighter constraints on some parameters and more consistent recovery across experiments and variables. The perfect model framework allows to assess which observables yield most information, which parameters are recoverable given a certain set of observations, and what is the minimum observational record needed. Although this study focuses on a single location with synthetic observations, such experiments provide a controlled setting to evaluate and identify robust calibration frameworks, which can then be extended to multiple locations and real observations with greater confidence.
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Computational Physics · Cross-listed from quant-ph
Moving Detector Quantum Walk with Random Relocation
Md Aquib Molla and Sanchari Goswami
We study a discrete-time
quantum walk in presence of a detector at $x_D$ initially. The detector here is repeatedly removed after a span of $t_R$, the removal time, and reinserted at random locations. Two relocation rules are considered here: In Model~1, the detector is reinserted at any site beyond $x_D$, while in Model~2, reinsertion is done within a restricted window around the position of the detector at that time. Both variants behave like Semi Infinite Walk (SIW) for large $t_R$, where the detector behaves effectively as a fixed boundary. However, in the rapid-relocation regime, i.e., when $t_R$ is small, the behaviours are different. Model~1 permits greater spreading due to unrestricted reinsertion, which is different from Model~2. The time
evolution of occupation probability ratio of our walker to that of an infinite walker at $x_D$, i.e., $f(x_D,t)/f_\infty(x_D,t)$, initially show the feature of a SIW upto $t=t_R$, then show some oscillatory behaviour and finally reach a saturation value for both the models. The ratio enhancing under certain conditions of $x_D$ and $t_R$, is a purely
quantum mechanical effect. The saturation ratio shows a crossover behavior below and above a removal time $t_R^*$. At sites $x \neq x_D$ the occupation probablity ratios at a certain time reveals that for small $t_R$, the behaviours of the two models are drastically different from each other, as well as from Semi Infinite Walk (SIW), Quenched
Quantum Walk (QQW) and Moving Detector
Quantum Walk (MDQW). The correlation ratios of the two models with that of Infinite Walk (IW) show interesting time dependence for sites to the left or right of the initial detector position $x_D$.
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Optics · Cross-listed from cs.CV
Physics-Informed Untrained Learning for RGB-Guided Superresolution Single-Pixel Hyperspectral Imaging
Hao Zhang, Bilige Xu, Lichen Wei et al. (+2 more)
Single-pixel imaging (SPI) offers a cost-effective route to hyperspectral acquisition but struggles to recover high-fidelity spatial and spectral details under extremely low sampling rates, a severely ill-posed inverse problem. While deep learning has shown potential, existing data-driven methods demand large-scale pretraining
datasets that are often impractical in hyperspectral imaging. To overcome this limitation, we propose an end-to-end physics-informed framework that leverages untrained neural networks and RGB guidance for joint hyperspectral reconstruction and super-resolution without any external training data. The framework comprises three physically grounded stages: (1) a Regularized Least-Squares method with RGB-derived Grayscale Priors (LS-RGP) that initializes the solution by exploiting cross-modal structural correlations; (2) an Untrained Hyperspectral Recovery Network (UHRNet) that refines the reconstruction through measurement consistency and hybrid regularization; and (3) a
Transformer-based Untrained Super-Resolution Network (USRNet) that upsamples the spatial resolution via cross-modal
attention, transferring high-frequency details from the RGB guide. Extensive experiments on
benchmark datasets demonstrate that our approach significantly surpasses state-of-the-art
algorithms in both reconstruction accuracy and spectral fidelity. Moreover, a proof-of-concept experiment using a physical single-pixel imaging system validates the framework's practical applicability, successfully reconstructing a 144-band hyperspectral data cube at a mere 6.25% sampling rate. The proposed method thus provides a robust, data-efficient solution for computational hyperspectral imaging.
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All authors Hao Zhang, Bilige Xu, Lichen Wei, Xu Ma, Wenyi Ren
Plasma Physics
Enhanced electron injection for efficient proton acceleration and neutron production in femtosecond laser-driven nano-structured targets
Yingzi Dai, Chengyu Qin, Hui Zhang et al. (+22 more)
Micro- or nano-structured targets are advantageous in enhancing and manipulating
laser-
proton acceleration, due to the increased absorption of
laser energy and onset of direct
laser acceleration for high-energy electrons. Here, we experimentally demonstrate that nano-wire-array printed on a flat substrate is an efficient nano-injector of relativistic electrons that leads to a significant boost of
laser-driven
proton acceleration and
neutron production beyond normal geometry. By employing an ultra-intense (2*1021 W/cm2) femtosecond
laser pulse to irradiate nano-wire-array targets,
protons with cut-off energies of 62.8 MeV are generated, and notably, the energy conversion efficiency from
laser to
protons reaches up to 9% - 3.5 times higher than that of flat foils. After bombarding a beryllium converter, 1.1*1010
neutrons are produced. Full 3D particle-in-
cell simulations have reproduced experimental results and reveal interference mechanisms between the nano-wires and substrate, leading to continuous pumping of electrons from the substrate and standing-wave enhanced re-injection from the wire tip. This efficient injection finally results in the large sheath field and thus high yield of energetic
protons and
neutrons. Dependence on the wire length and scaling with
laser amplitude are further discussed. These results suggest that 3D-printed structures are promising in developing compact
laser-driven high-flux
proton and
neutron sources for numerous applications.
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All authors Yingzi Dai, Chengyu Qin, Hui Zhang, Guoqiang Zhang, Changbo Fu, Xiangai Deng, Dirui Xu, Shuai Xu, Xuesong Geng, Jing Wang, Bowen Zhang, Yunwei Cui, Xiaojing Guo, Weifu Yin, Yanqi Liu, Xingyan Liu, Cheng Wang, Zongxin Zhang, Bingnan Shi, Lianghong Yu, Xiaoyan Liang, Yuxin Leng, Baifei Shen, Liangliang Ji, Ruxin Li
Plasma Physics
Relaxed magnetohydrodynamics with cross-field flow
Arash Tavassoli, Stuart R. Hudson, Zhisong Qu et al. (+1 more)
The phase-space Lagrangian model of Dewar et al. (Phys.
Plasmas 27, 062507, 2020) provides a framework for incorporating cross-field flow into relaxed
equilibria while retaining ideal magnetohydrodynamics force balance. Here, we characterize the steady-state solution space and identify a solvability condition that couples the prescribed constrained flow to the geometry through the metric tensor. Using this condition, we construct
equilibria in slab, cylindrical, and toroidal geometries. In toroidal geometry, the cross-field flow strongly correlates with
magnetic-island structure: varying the rotation frequency modifies the dominant
Fourier harmonic of the radial component of the
magnetic field and can drive a transition from a primary (m = 1) island to secondary (m = 2) islands. In slab and cylindrical geometries, flow parameters weakly affect island width but strongly modify
equilibrium profiles.
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All authors Arash Tavassoli, Stuart R. Hudson, Zhisong Qu, Matthew Hole
Biological Physics
Thermal fluctuations set fundamental limits on ion channel function
Jose M. Betancourt and Benjamin B. Machta
Voltage-gated ion channels are essential for propagating signals in
neurons. Each channel senses the local membrane potential created by nearby ions. Fluctuations in these ions introduce two fundamental noise sources: (i) shot noise, from the discreteness of ionic charge, and (ii) Johnson-Nyquist noise, from long-wavelength thermal fluctuations of the electric field. We show that, for an individual channel, shot noise dominates and sets an intrinsic limit to voltage sensing. On the $10$ $\mu$s timescales relevant to channel gating, this limit corresponds to an accuracy of about $10$ mV – close to measured channel sensitivities. When signals from many channels are aggregated, Johnson-Nyquist noise eventually overtakes shot noise and bounds the total information that can be sensed from the environment. This transition occurs at an ion channel density of $< 1$ channel/$\mu$m$^2$ for slow signals and around $10^2-10^4$ channels/$\mu$m$^2$ for signals with $10$ $\mu$s timescales, both of which are within the range of experimentally-measured densities for somas and axon initial segments, respectively. These results provide design principles for single-channel architecture and collective sensing and suggest that neuronal computation is ultimately constrained by thermal fluctuations.
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