Showing 1–50 of 205 results for author: Hu, S

Dynamics of a microroller under confinement

Authors: Han Gao, Nan Xie, Zaiyi Shen, Xiaoping Hu, Shiyuan Hu, Ye Xu

Abstract: Rotating particles can translate when placed near a surface, forming microrollers with a wide range of biomedical and microfluidic applications. In this work, we investigate the dynamics of microrollers in confined microchannels with different geometries by combining experiments, numerical simulations, and scaling analysis. In constricted channels, we find that the translational velocity of a micr… ▽ More Rotating particles can translate when placed near a surface, forming microrollers with a wide range of biomedical and microfluidic applications. In this work, we investigate the dynamics of microrollers in confined microchannels with different geometries by combining experiments, numerical simulations, and scaling analysis. In constricted channels, we find that the translational velocity of a microroller decreases as it approaches the constricted region. In both rectangular and cylindrical channels, velocity reversal occurs as the characteristic channel width decreases. Using the force-free condition for free translation, we develop a systematic scaling framework that can be generalized to different channel geometries. The scaling analysis yields functional dependences of the translational velocity on the degree of confinement, which agree well with both experiments and simulations. Importantly, we demonstrate that the viscous stress generated by the far-field rotlet flow governs the observed velocity reduction and reversal, while the translational resistance resulting from the near-field shear flow suppresses translation under tight confinement. The distinct roles of these flow components revealed by our analysis may provide practical guidance for controlling microroller dynamics in confined fluid environments. △ Less

Submitted 15 July, 2026; originally announced July 2026.

Magnetic field generation by the Rayleigh-Taylor instability in laser-driven planar plastic targets

Authors: L Gao, PM Nilson, IV Igumenschev, SX Hu, JR Davies, C Stoeckl, MG Haines, DH Froula, R Betti, DD Meyerhofer

Abstract: Magnetic fields generated by the Rayleigh-Taylor instability were measured in laser-accelerated planar foils using ultrafast proton radiography. Thin plastic foils were irradiated with $\sim$4-kJ, 2.5-ns laser pulses focused to an intensity of $\sim$10$^{14}$ W$/$cm$^{2}$ on the OMEGA EP Laser System. Target modulations were seeded by laser nonuniformities and amplified during target acceleration… ▽ More Magnetic fields generated by the Rayleigh-Taylor instability were measured in laser-accelerated planar foils using ultrafast proton radiography. Thin plastic foils were irradiated with $\sim$4-kJ, 2.5-ns laser pulses focused to an intensity of $\sim$10$^{14}$ W$/$cm$^{2}$ on the OMEGA EP Laser System. Target modulations were seeded by laser nonuniformities and amplified during target acceleration by the Rayleigh-Taylor instability. The experimental data show the hydrodynamic evolution of the target and MG-level magnetic fields generated in the broken foil. The experimental data are in good agreement with predictions from 2-D magnetohydrodynamic simulations. △ Less

Submitted 7 July, 2026; originally announced July 2026.

Journal ref: Phys. Rev. Lett. 109, 115001 (2012)

Semi-analytical model for the rising sheet generated by droplet-pair impact

Authors: Shushan Hu, Liwu Fan, Nan Hu

Abstract: When two low-Ohnesorge-number drops impact a dry substrate simultaneously, their spreading lamellae collide and lift a free-standing vertical sheet. The sheet grows by inertial feeding from the spreading drops and is pulled back by capillary retraction at its rim. We develop a semi-analytical model for this rising sheet by extending the single-drop impact description of~\citet{Gordillo2019} to the… ▽ More When two low-Ohnesorge-number drops impact a dry substrate simultaneously, their spreading lamellae collide and lift a free-standing vertical sheet. The sheet grows by inertial feeding from the spreading drops and is pulled back by capillary retraction at its rim. We develop a semi-analytical model for this rising sheet by extending the single-drop impact description of~\citet{Gordillo2019} to the two-drop geometry. The thin-film flow in the sheet is coupled at its base to the colliding lamellae and at its apex to a capillary-retarded rim. The sheet interior is then solved along ballistic characteristics in two stages: a lamella-fed stage, for which the velocity and thickness fields can be obtained in closed form, and a post-lamella stage, for which the inlet conditions are taken from simulations. The resulting framework gives the three-dimensional velocity and thickness fields and therefore the full sheet shape. On the centreline, the apex height and local thickness are obtained explicitly, showing that the different Weber-number exponents reported in the literature arise from a crossover rather than from a single universal scaling law. At sufficiently large Weber number, the apex pinches off. A linear Rayleigh--Plateau analysis, using the time-dependent jet diameter and deceleration predicted by the model, then bounds the maximum attainable height and closes the description of the pinch-off regime. △ Less

Submitted 2 July, 2026; originally announced July 2026.

Constrained Optimization Algorithms for Orbital Optimization in Quantum Chemistry

Authors: Junzhe Zhang, Shuoyi Hu, Bing Gu

Abstract: We present a modular constrained-orbital-optimization framework for quantum chemistry. The formulation separates the correlated electronic-structure solver from the orbital optimizer: the solver supplies one- and two-particle reduced density matrices, while the molecular orbitals are updated on the orthonormality-constrained Stiefel manifold with an implicit steepest-descent algorithm. Because the… ▽ More We present a modular constrained-orbital-optimization framework for quantum chemistry. The formulation separates the correlated electronic-structure solver from the orbital optimizer: the solver supplies one- and two-particle reduced density matrices, while the molecular orbitals are updated on the orthonormality-constrained Stiefel manifold with an implicit steepest-descent algorithm. Because the orbital optimizer only requires reduced density matrices, MP2, CASCI, and DMRG can be treated within the same interface. For CASCI solvers, the approach is closely related to optimal-orbital full configuration interaction and CASSCF\cite{helgaker_MulticonfigurationalSelfConsistentField_2000a}, but uses a solver-independent constrained-optimization update rather than CAS-specific orbital-rotation equations. When conventional CASSCF orbital-rotation iterations converge to higher-energy local solutions, CO-CAS can recover lower-energy stationary solutions. We also introduce a modified direct inversion in the iterative subspace procedure to accelerate macro-iteration convergence and a dynamical-weighting scheme to improve state-averaged excited-state calculations. Applications to LiF, H$_2$O, and pyrazine show that orbital optimization lowers energies relative to fixed-orbital MP2, CASCI, and DMRG references while improving convergence and potential-energy-curve smoothness. △ Less

Submitted 16 June, 2026; originally announced June 2026.

Hyperon-Nucleon Spectrometer

Authors: Xiaozhi Bai, Xu Cao, Zhe Cao, Jinhui Chen, Kai Chen, Qibo Chen, Shi Chen, Xin Chen, Yuquan Chen, Zhenyu Chen, Jianping Dai, Heng-Tong Ding, Dongshuo Du, Shuxian Du, Limin Duan, Zhe Duan, Anhui Feng, Jie Feng, Yicheng Feng, Jinlin Fu, Xiaofeng Fu, Chaosong Gao, Liang Ge, Wenwen Ge, Lisheng Geng , et al. (215 additional authors not shown)

Abstract: Chirality lies at the heart of low-energy QCD, governing the symmetry structure that shapes hadron masses and strong interaction dynamics. Among the most compelling open questions tied to chiral dynamics and spontaneous chiral symmetry breaking is the longstanding $Λ$ polarization puzzle, in which $Λ$ hyperons produced in unpolarized hadronic collisions exhibit a surprisingly large transverse pola… ▽ More Chirality lies at the heart of low-energy QCD, governing the symmetry structure that shapes hadron masses and strong interaction dynamics. Among the most compelling open questions tied to chiral dynamics and spontaneous chiral symmetry breaking is the longstanding $Λ$ polarization puzzle, in which $Λ$ hyperons produced in unpolarized hadronic collisions exhibit a surprisingly large transverse polarization that remains theoretically unexplained. This whitepaper presents the proposal for the Hyperon-Nucleon Spectrometer (H-NS) at the High-Intensity heavy-ion Accelerator Facility (HIAF). Leveraging the high energy and high intensity of HIAF's proton and heavy-ion beams, the H-NS experiment will perform systematic studies of hyperon polarization phenomena and their underlying mechanisms in proton-proton ($pp$), proton-nucleus ($pA$), and nucleus-nucleus ($AA$) collisions in the fixed target mode. A wide-range beam energy scan, including proton beams from 3 GeV up to 9.3 GeV (HIAF) and up to 32 GeV (upgraded HIAF), will be conducted to examine the dependence of polarization on collision energy. The spectrometer is designed with specialized detectors capable of high-precision reconstruction of final-state baryon polarizations. Among its many interesting and important measurements, H-NS will simultaneously measure hyperon and proton spin observables to explore the polarization mechanism in hadronic interactions and the spin structure of baryons. Furthermore, the use of $pA$ and $AA$ collisions will enable detailed investigations of cold and hot nuclear matter effects on spin polarization. Its physics program and detector development will significantly benefit the future Electron-ion Collider in China. △ Less

Submitted 4 June, 2026; originally announced June 2026.

Comments: 69 pages, Hyperon-Nucleon Spectrometer (H-NS) white paper

Coherent Molecular Deceleration via Vibrational Bichromatic Force

Authors: Meng-Yi Yu, Ya-Nan Lv, Cun-Feng Cheng, Shui-Ming Hu

Abstract: We propose a scheme for direct laser deceleration of molecules based on a vibrational transition-mediated bichromatic force (VBCF). By precisely engineering mid-infrared optical fields, we establish coherent absorption-stimulated emission cycles while exploiting the long lifetime of vibrational excited states to suppress spontaneous decay and decoherence, rendering the deceleration process effecti… ▽ More We propose a scheme for direct laser deceleration of molecules based on a vibrational transition-mediated bichromatic force (VBCF). By precisely engineering mid-infrared optical fields, we establish coherent absorption-stimulated emission cycles while exploiting the long lifetime of vibrational excited states to suppress spontaneous decay and decoherence, rendering the deceleration process effectively non-dissipative. Unlike schemes based on electronic transitions, our approach completely circumvents the restrictive Franck-Condon factors. Using the fundamental vibrational transition of $^{13}$CO$_2$ as a test case, we achieve a deceleration of $1.45\times 10^5$~m/s$^2$ with negligible population loss over the full interaction time. This VBCF framework provides a general route to cold molecules applicable to any species with an allowed fundamental vibrational transition, opening broad prospects in cold chemistry and quantum metrology. △ Less

Submitted 6 July, 2026; v1 submitted 19 May, 2026; originally announced May 2026.

Comments: 16 pages, 6 figures, 1 table

Quantum effects in plasmas

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

Abstract: 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… ▽ 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. △ Less

Submitted 4 April, 2026; originally announced April 2026.

Engineering-Oriented Symbolic Regression: LLMs as Physics Agents for Discovery of Simulation-Ready Constitutive Laws

Authors: Yue Wu, Tianhao Su, Mingchuan Zhao, Shunbo Hu, Deng Pan

Abstract: The discovery of constitutive laws for complex materials has historically faced a dichotomy between high-fidelity data-driven approaches, which demand prohibitive full-field experimental data, and traditional engineering fitting, which often yields numerically unstable models outside calibration regimes. In this work, we propose an Engineering-Oriented Symbolic Regression (EO-SR) framework that br… ▽ More The discovery of constitutive laws for complex materials has historically faced a dichotomy between high-fidelity data-driven approaches, which demand prohibitive full-field experimental data, and traditional engineering fitting, which often yields numerically unstable models outside calibration regimes. In this work, we propose an Engineering-Oriented Symbolic Regression (EO-SR) framework that bridges this gap by leveraging Large Language Models (LLMs) as "Physics-Informed Agents." Unlike unconstrained symbolic regression, our framework utilizes an LLM Agent to zero-shot synthesize executable physical constraints -- specifically thermodynamic consistency and frame indifference -- transforming the search process from mathematical curve-fitting into a physics-governed discovery engine. We validate this approach on the hyperelastic modeling of rubber-like materials using standard Treloar datasets. The framework autonomously identifies a novel hybrid constitutive law that combines a Mooney-Rivlin linear base with a rational locking term. This discovered model not only achieves high predictive accuracy across multi-axial deformation modes (including zero-shot prediction of pure shear) but also guarantees unconditional convexity. Finite element validation demonstrates that while industry-standard models (e.g., Ogden N=3) fail due to numerical singularities under severe transverse compression, the EO-SR-discovered model maintains robust convergence. This study establishes a generalized, low-barrier pathway for discovering simulation-ready constitutive closures that satisfy both data accuracy and rigorous physical laws. △ Less

Submitted 11 February, 2026; originally announced March 2026.

Hybrid Longitudinal-Transverse Propagating Electric Fields in Photonic Crystal Waveguides

Authors: Yanrong Zhang, Hooman Barati Sedeh, Christopher S. Whittington, Natalia M. Litchinitser, Shuren Hu, Sharon M. Weiss

Abstract: In a uniform, source-free, and unbounded medium, Maxwell's equations require electromagnetic waves to be purely transverse. However, when a beam of light is tightly focused or strongly confined, a longitudinal field component can emerge. Strong longitudinal fields enable many novel phenomena and applications, including single molecule detection, near-field imaging, and high-resolution photolithogr… ▽ More In a uniform, source-free, and unbounded medium, Maxwell's equations require electromagnetic waves to be purely transverse. However, when a beam of light is tightly focused or strongly confined, a longitudinal field component can emerge. Strong longitudinal fields enable many novel phenomena and applications, including single molecule detection, near-field imaging, and high-resolution photolithography. Although the behavior of the longitudinal electric (LE) field component of the electromagnetic field in ordinary waveguides is well established, judicious nanostructuring offers unprecedented control over its strength as well as spatial and spectral distribution. Here, we demonstrate a full-vectorial theory and experimental results showing that for specially designed waveguides, such as one-dimensional antislot photonic crystal (PhC) waveguides, the LE field can hybridize with the transverse electric (TE) field in the waveguide and can be subsequently decomposed into independent polarizations through far field imaging. When the in-plane mirror symmetry of a PhC unit cell is broken, coupling between LE and TE modes produces two hybrid LE-TE modes and opens a new photonic bandgap. The LE-TE composition of the hybrid modes and the width of the resulting bandgap can be tuned by changing the rotation angle of the antislot within the unit cell. We show that a 45-degree antislot orientation with respect to the propagation direction yields hybrid modes with the largest LE field contribution and the widest geometry-induced bandgap. Such engineered PhC waveguides enable new on-chip photonic functionalities, including in-plane angle-invariant dipole coupling in quantum systems, higher-order polarization-division multiplexing, and enhanced control of light flow. △ Less

Submitted 4 March, 2026; originally announced March 2026.

Development of Low-Noise Two-stage dc-SQUID for TES Detector Readout

Authors: Nan Li, Mengjie Song, Sixiao Hu, Wentao Wu, Songqing Liu, Tangchong Kuang, Yudong Gu, Xiangxiang Ren, Xufang Li, He Gao, Zhengwei Li, Congzhan Liu

Abstract: Direct-current superconducting quantum interference devices (dc-SQUIDs) are one of the most sensitive magnetic detectors. These sensors are extensively used in the readout of superconducting transition edge sensors (TESs), which are used for the detection of weak signals. A cosmic microwave background (CMB) polarization telescope operating in 22-48 GHz is currently under developing. The TESs calor… ▽ More Direct-current superconducting quantum interference devices (dc-SQUIDs) are one of the most sensitive magnetic detectors. These sensors are extensively used in the readout of superconducting transition edge sensors (TESs), which are used for the detection of weak signals. A cosmic microwave background (CMB) polarization telescope operating in 22-48 GHz is currently under developing. The TESs calorimeter of the telescope will be readout by a time-division multiplexer (TDM) SQUID readout system. We develop a two-stage dc-SQUID amplifier circuit, comprising an input-stage SQUID with 4 SQUID cells and a series SQUID array (SSA) with 100 SQUID cells. This configuration has been shown to achieve extremely high signal gain while effectively controlling system noise. We assess the system noise at $300$ $mK$ in an adiabatic demagnetisation refrigerator (ADR). The the measured magnetic flux noise of the two-stage SQUID circuit system is approximately $0.3$ $μΦ_{0}/\sqrt{Hz}$ at $10$ $kHz$. The current noise equivalent to the input coil of input SQUID is about $2.4$ $pA/\sqrt{Hz}$. This result meets the low-noise readout requirements of the CMB TES and other applications with TES detectors. △ Less

Submitted 28 January, 2026; v1 submitted 27 January, 2026; originally announced January 2026.

Comments: 10 pages, 8 figures

Dynamical Origin of (469219) Kamo`oalewa of Tianwen-2 Mission from the Main-Belt: $ν_6$ Secular Resonance, Flora Family or 3:1 Resonance with Jupiter

Authors: Yandong Wang, Shoucun Hu, Jianghui Ji, Jiajun Ying

Abstract: China's Tianwen-2 mission, launched on 29 May 2025, targets the near-Earth object (469219) Kamo`oalewa, an Earth quasi-satellite trapped in a 1:1 mean-motion resonance with our planet. Determining the origin of Kamo`oalewa is central to understanding the formation pathways and dynamical evolution of Earth's quasi-satellite population. Here we show a strong possibility of main-belt origin for Kamo`… ▽ More China's Tianwen-2 mission, launched on 29 May 2025, targets the near-Earth object (469219) Kamo`oalewa, an Earth quasi-satellite trapped in a 1:1 mean-motion resonance with our planet. Determining the origin of Kamo`oalewa is central to understanding the formation pathways and dynamical evolution of Earth's quasi-satellite population. Here we show a strong possibility of main-belt origin for Kamo`oalewa using long-term dynamical simulations. We examine three candidate source regions: the $ν_6$ secular resonance ($ν_6$), the 3:1 mean-motion resonance with Jupiter (3:1J MMR), and the Flora family. A total of 42,825 test particles were integrated over 100 Myr. We find that asteroids from all three regions can be transported onto Kamo`oalewa-like orbits, albeit with markedly different efficiencies. Particles originating near the $ν_6$ show the highest transfer probability (3.31%), followed by the Flora family (2.54%) and the 3:1J MMR (0.39%). We further identify representative dynamical pathways linking these source regions to Earth quasi-satellite orbits. The Tianwen-2 spacecraft is expected to rendezvous with Kamo`oalewa in 2026, performing close-proximity operations and returning samples. The mission will provide decisive observational constraints on the asteroid's composition and physical properties, offering a critical test of its proposed origin. △ Less

Submitted 19 January, 2026; originally announced January 2026.

Comments: 13 pages, 3 figures, accepted for publication in Research in Astronomy and Astrophysics

Journal ref: Res. Astron. Astrophys., Volume 26, Number 4, article id. 041001 (2026)

Model-Driven GPR Inversion Network With Surrogate Forward Solver

Authors: Huilin Zhou, Xin Liu, Kexiang Wang, Shufan Hu

Abstract: Data-driven deep learning is considered a promising solution for ground-penetrating radar (GPR) full-waveform inversion (FWI), while its generalization ability is limited due to the heavy reliance on abundant labeled samples. In contrast, Deep unfolding network (DUN) usually exhibits better generalization by integrating model-driven and data-driven approaches, yet its application to GPR FWI remain… ▽ More Data-driven deep learning is considered a promising solution for ground-penetrating radar (GPR) full-waveform inversion (FWI), while its generalization ability is limited due to the heavy reliance on abundant labeled samples. In contrast, Deep unfolding network (DUN) usually exhibits better generalization by integrating model-driven and data-driven approaches, yet its application to GPR FWI remains challenging due to the high computational cost associated with forward simulations. In this paper, we integrate a deep learning-based (DL-based) forward solver within an unfolding framework to form a fully neural-network-based architecture, UA-Net, for GPR FWI. The forward solver rapidly predicts B-scans given permittivity and conductivity models and enables automatic differentiation to compute gradients for inversion. In the inversion stage, an optimization process based on the Alternating Direction Method of Multipliers (ADMM) is unfolded into a multi-stage network with three interconnected modules: data fitting, regularization, and multiplier update. Specifically, the regularization module is trained end-to-end for adaptive learning of sparse target features. Experimental results demonstrate that UA-Net outperforms classical FWI and data-driven methods in reconstruction accuracy. Moreover, by employing transfer learning to fine-tune the network, UA-Net can be effectively applied to field data and produce reliable results. △ Less

Submitted 15 January, 2026; originally announced January 2026.

Comments: 13 pages, 13 figures. Preprint

Nonlinear spin-Wave Doppler effect for flexible tuning of magnonic frequencies

Authors: Jinchen Hou, Shaojie Hu, Long You

Abstract: We theoretically propose a nonlinear spin-wave Doppler effect, in which the time-dependent motion of a magnetic energy boundary acts as an active frequency modulator, directly converting boundary-induced phase dynamics into instantaneous spectral synthesis for propagating spin-wave modes. In contrast to the conventional linear Doppler effect governed by constant relative velocity, this mechanism e… ▽ More We theoretically propose a nonlinear spin-wave Doppler effect, in which the time-dependent motion of a magnetic energy boundary acts as an active frequency modulator, directly converting boundary-induced phase dynamics into instantaneous spectral synthesis for propagating spin-wave modes. In contrast to the conventional linear Doppler effect governed by constant relative velocity, this mechanism enables dynamic phase-to-frequency transduction, generating high-order harmonics, magnonic frequency combs, and coherent chirped sidebands, without requiring nonlinear magnon-magnon coupling or multi-magnon scattering. Micromagnetic simulations on voltage-controlled anisotropy boundaries in ferroelectric/ferromagnetic (FE/FM) heterostructures demonstrate that the comb spacing and spectral topology are determined solely by boundary kinematics, confirming direct Doppler phase coupling between boundary motion and spin-wave propagation. These results establish moving magnetic-energy boundaries as a new class of on-chip spectral synthesizers and define a coherent and energy-efficient framework for flexible tuning of magnonic frequencies, fundamentally distinct from traditional passive scattering or nonlinear multi-magnon mechanisms. △ Less

Submitted 5 January, 2026; originally announced January 2026.

Comments: 19pages, 4 figures

Journal ref: Physical Review B 113, L180411 (2026)

Bayesian full waveform inversion with learned prior using deep convolutional autoencoder

Authors: Shuhua Hu, Mrinal K Sen, Zeyu Zhao, Abdelrahman Elmeliegy, Shuo Zhang

Abstract: Full waveform inversion (FWI) can be expressed in a Bayesian framework, where the associated uncertainties are captured by the posterior probability distribution (PPD). In practice, solving Bayesian FWI with sampling-based methods such as Markov chain Monte Carlo (MCMC) is computationally demanding because of the extremely high dimensionality of the model space. To alleviate this difficulty, we de… ▽ More Full waveform inversion (FWI) can be expressed in a Bayesian framework, where the associated uncertainties are captured by the posterior probability distribution (PPD). In practice, solving Bayesian FWI with sampling-based methods such as Markov chain Monte Carlo (MCMC) is computationally demanding because of the extremely high dimensionality of the model space. To alleviate this difficulty, we develop a deep convolutional autoencoder (CAE) that serves as a learned prior for the inversion. The CAE compresses detailed subsurface velocity models into a low-dimensional latent representation, achieving more effective and geologically consistent model reduction than conventional dimension reduction approaches. The inversion procedure employs an adaptive gradient-based MCMC algorithm enhanced by automatic differentiation-based FWI to compute gradients efficiently in the latent space. In addition, we implement a transfer learning strategy through online fine-tuning during inversion, enabling the framework to adapt to velocity structures not represented in the original training set. Numerical experiments with synthetic data show that the method can reconstruct velocity models and assess uncertainty with improved efficiency compared to traditional MCMC methods. △ Less

Submitted 4 November, 2025; originally announced November 2025.

Comments: 16 pages, 19 figures, 2 tables

Spatial and temporal study of the post-compressed high-power laser pulses for coherent extreme ultraviolet source development

Authors: Cong Zhou, Haina Wu, Chaoneng Wu, Yitong Zhao, Chen Wang, Jiayue Liu, Zige Qiu, Wei Zhang, Yapei Peng, Mingyuan Shi, Shuyuan Hu, Xiaoliang Liu, Sizhong Wu, Jie Yang, Cangtao Zhou, Lu Li

Abstract: We compared the performance of two post-compression techniques, a gas-filled hollow-core fiber (HCF) and a multi-pass cell (MPC), using a high-power ytterbium-doped fiber laser. The HCF produced 27 fs pulses from 230 fs inputs at >50% efficiency, whereas the MPC achieved 34 fs pulses with significantly higher efficiency (>88%). Both results aligned well with numerical simulations. Crucially, spati… ▽ More We compared the performance of two post-compression techniques, a gas-filled hollow-core fiber (HCF) and a multi-pass cell (MPC), using a high-power ytterbium-doped fiber laser. The HCF produced 27 fs pulses from 230 fs inputs at >50% efficiency, whereas the MPC achieved 34 fs pulses with significantly higher efficiency (>88%). Both results aligned well with numerical simulations. Crucially, spatial wavefront analysis revealed that the HCF acts as a modal filter, improving beam quality, whereas the MPC introduces aberrations through cumulative mirror errors. Furthermore, we characterize the photon flux of high harmonic generation driven by the post-compressed pulses from the HCF and MPC. These finding highlights that post-compression technique based on self-phase modulation is efficient for the intensity boosting of femtosecond laser system, providing opportunities for generating high quality extreme ultraviolet (XUV) sources. In addition, further improvement of spatial wavefront quality is suggested using the HCF as a single compressor or output component of the cascade compressor. △ Less

Submitted 10 December, 2025; v1 submitted 30 October, 2025; originally announced October 2025.

Comments: The content is incomplete and requires revision. It has been decided to withdraw this article

Confinement reduces surface accumulation of swimming bacteria

Authors: Da Wei, Shiyuan Hu, Tangmiao Tang, Yaochen Yang, Fanlong Meng, Yi Peng

Abstract: Many swimming bacteria naturally inhabit confined environments, yet how confinement influences their swimming behaviors remains unclear. Here, we combine experiments, continuum modeling and particle-based simulations to investigate near-surface bacterial swimming in dilute suspensions under varying confinement. Confinement reduces near-surface accumulation and facilitates bacterial escape. These e… ▽ More Many swimming bacteria naturally inhabit confined environments, yet how confinement influences their swimming behaviors remains unclear. Here, we combine experiments, continuum modeling and particle-based simulations to investigate near-surface bacterial swimming in dilute suspensions under varying confinement. Confinement reduces near-surface accumulation and facilitates bacterial escape. These effects are quantitatively captured by models incorporating the force quadrupole, a higher-order hydrodynamic singularity, that generates a rotational flow reorienting bacteria away from surfaces. Under strong confinement, bacterial trajectories straighten due to the balancing torques exerted by opposing surfaces. These findings highlight the role of hydrodynamic quadrupole interactions in near-surface bacterial motility, with implications for microbial ecology, infection control, and industrial applications. △ Less

Submitted 10 October, 2025; v1 submitted 9 October, 2025; originally announced October 2025.

BigBang-Proton Technical Report: Next-Word-Prediction is Scientific Multitask Learner

Authors: Hengkui Wu, Liujiang Liu, Jihua He, Qihao Wang, Keke Zhao, Shuyang Hu, Renle Fu, Dahao Liang, Lingyu Zeng, Bruce Liu, Yuan Liu, Jin Zhan, Jiaqiang Niu, Xinglong Jia, Yaqin Hu, Wenjun Ji, Panpan Chi, Ken Chen, Hengyuan Wu, Yingsi Xin, Yongfeng Zhu, Yuexin Wang, Manqi Ruan, Ningtao Bian, Xiaohua Wu , et al. (1 additional authors not shown)

Abstract: We introduce BigBang-Proton, a unified sequence-based architecture for auto-regressive language modeling pretrained on cross-scale, cross-structure, cross-discipline real-world scientific tasks to construct a scientific multi-task learner. BigBang-Proton incorporates three fundamental innovations compared to mainstream general-purpose LLMs: Theory-Experiment Learning paradigm aligns large-scale nu… ▽ More We introduce BigBang-Proton, a unified sequence-based architecture for auto-regressive language modeling pretrained on cross-scale, cross-structure, cross-discipline real-world scientific tasks to construct a scientific multi-task learner. BigBang-Proton incorporates three fundamental innovations compared to mainstream general-purpose LLMs: Theory-Experiment Learning paradigm aligns large-scale numerical experimental data with theoretical text corpora; Binary Patch Encoding replaces byte pair encoding(BPE) tokenization; Monte Carlo Attention substitutes traditional transformer architectures. Through next-word-prediction pretraining on cross-discipline scientific datasets of real-world problems mixed with general textual corpus, followed by fine-tuning and inference on downstream tasks, BigBang-Proton demonstrates 100\% accuracy in up to 50-digit arithmetic addition operations, performance on par with leading specialized models in particle physics jet tagging, matching MAE of specialized models in inter-atomic potential simulation, performance comparable to traditional spatiotemporal models in water quality prediction, and benchmark-exceeding performance in genome modeling. These results prove that language-guided scientific computing can match or exceed the performance of task-specific scientific models while maintaining multitask learning capabilities. We further hypothesize to scale the pretraining to the universe scale as a fundamental step toward developing material world foundational model. △ Less

Submitted 30 September, 2025; originally announced October 2025.

Comments: 93 pages, 39 figures

MSC Class: 68T05; 68T50; 00A69; 94A99 ACM Class: I.2.6; I.2.7; J.2; I.6.3; K.4.1

Emergent Slow Thinking in LLMs as Inverse Tree Freezing

Authors: Sihan Hu, Xiansheng Cai, Yuan Huang, Zhiyuan Yao, Linfeng Zhang, Pan Zhang, Youjin Deng, Kun Chen

Abstract: Reinforcement learning with verifiable rewards (RLVR) enables large language models to acquire slow, multi-step reasoning from sparse final-answer signals. We provide a statistical-physics picture of this emergence. We show that an autoregressive model's finite capacity forces it to compress its exponentially large prefix space into a Markov network of predictive states, on which slow thinking unf… ▽ More Reinforcement learning with verifiable rewards (RLVR) enables large language models to acquire slow, multi-step reasoning from sparse final-answer signals. We provide a statistical-physics picture of this emergence. We show that an autoregressive model's finite capacity forces it to compress its exponentially large prefix space into a Markov network of predictive states, on which slow thinking unfolds as a random walk -- the Concept Network (CoNet) picture. Within CoNet, RLVR dynamics are governed by two mechanisms: merging of compatible paths and frustrated competition among incompatible ones. Together they drive the network through nucleation, growth, and freezing into multi-input, single-output directed inverse trees. The picture reproduces the training dynamics of a 1.5-billion-parameter LLM and yields three predictions: reasoning chains lengthen as a geometric necessity of sparse topology; SFT induces catastrophic forgetting through bridge-node rupture; and frustration drives policy collapse. Building on the structural timing inherent in inverse-tree freezing, we propose Annealed-RLVR -- a brief SFT intervention at the moment of maximum frustration. It outperforms standard RLVR on both in- and out-of-distribution benchmarks, with the largest gains at high sampling budgets where standard RLVR collapses. The same SFT applied after the trees freeze instead triggers catastrophic forgetting, isolating timing as the active ingredient. △ Less

Submitted 6 May, 2026; v1 submitted 28 September, 2025; originally announced September 2025.

Comments: 34 pages, 17 figures, 1 table

Anomalous Transport of Elongated Particles in Oscillatory Vortical Flows

Authors: Shiyuan Hu, Xiuyuan Yang, Nan Luo, Jun Zhang, Xingkun Man

Abstract: We investigate the transport dynamics of elongated particles in cellular vortical flows that undergo spatial oscillations over time. Experimental flow visualizations reveal mixed flow fields with chaotic and elliptic regions coexisting. Surprisingly, the particle transport rate does not increase monotonically with particle length, even though longer particles are expected to explore neighboring vo… ▽ More We investigate the transport dynamics of elongated particles in cellular vortical flows that undergo spatial oscillations over time. Experimental flow visualizations reveal mixed flow fields with chaotic and elliptic regions coexisting. Surprisingly, the particle transport rate does not increase monotonically with particle length, even though longer particles are expected to explore neighboring vortices more easily. Numerical simulations in a much larger system produce similar transport anomalies, characterized by subdiffusion due to frequent long-time trapping in vortices at certain lengths, but normal diffusion at others. At moderate oscillation frequencies, these long-time trapping events occur within the chaotic region; at high frequencies, they occur in the elliptic regions, but only for particles whose lengths match these regions. In the latter case, subdiffusion is robust against random noise. Our results reveal new mechanisms for controlling particle diffusion in fluid flows. △ Less

Submitted 13 August, 2025; originally announced August 2025.

Magnetic-free terahertz nonreciprocity via temporal dissipative barriers

Authors: Mingyu Tong, Yuze Hu, Siyang Hu, Hongsheng Chen, Tian Jiang, Yihao Yang

Abstract: Terahertz (THz) nonreciprocal devices are essential for advancing future fundamental science, wireless communications, imaging, and sensing. Current THz nonreciprocal devices mostly rely on magnetic materials, which, however, suffer from large volume, operation under an external magnetic field, and low-temperature environment, rendering them poorly compatible with miniaturized developments. Here,w… ▽ More Terahertz (THz) nonreciprocal devices are essential for advancing future fundamental science, wireless communications, imaging, and sensing. Current THz nonreciprocal devices mostly rely on magnetic materials, which, however, suffer from large volume, operation under an external magnetic field, and low-temperature environment, rendering them poorly compatible with miniaturized developments. Here,we propose an unconventional method for achieving THz nonreciprocity free from magnetic materials. The scheme relies on a temporal dissipative barrier, a transient loss variation generated by photoexcited carriers, and the nonreciprocity arises from the distinct coupling behavior for different polarizations with the barrier. The isolation efficiency correlates with the temporal barrier width, resonant mode detuning, and the working frequency, and has been significantly enhanced by introducing a dark mode. We experimentally confirm our method in a THz optically active metasurface with wave-flow isolation exceeding 20 dB across a bandwidth greater than 0.4 THz. Theoretical predictions indicate peak isolation surpassing 60 dB, with experimental results achieving over 30 dB at 0.7 THz. Our approach unlocks the potential of miniaturized, integrated, magnetic-free THz nonreciprocal devices for various applications. △ Less

Submitted 7 August, 2025; originally announced August 2025.

Passive cell body plays active roles in microalgal swimming via nonreciprocal interactions

Authors: Xiaoping Hu, Zhaorong Liu, Da Wei, Shiyuan Hu

Abstract: The cell body of flagellated microalgae is commonly considered to act merely as a passive load during swimming, and a larger body size would simply reduce the speed. In this work, we use numerical simulations based on a boundary element method to investigate the effect of body-flagella hydrodynamic interactions (HIs) on the swimming performance of the biflagellate, \textit{C. reinhardtii}. We find… ▽ More The cell body of flagellated microalgae is commonly considered to act merely as a passive load during swimming, and a larger body size would simply reduce the speed. In this work, we use numerical simulations based on a boundary element method to investigate the effect of body-flagella hydrodynamic interactions (HIs) on the swimming performance of the biflagellate, \textit{C. reinhardtii}. We find that body-flagella HIs significantly enhance the swimming speed and efficiency. As the body size increases, the competition between the enhanced HIs and the increased viscous drag leads to an optimal body size for swimming. Based on the simplified three-sphere model, we further demonstrate that the enhancement by body-flagella HIs arises from an effective non-reciprocity: the body affects the flagella more strongly during the power stroke, while the flagella affect the body more strongly during the recovery stroke. Our results have implications for both microalgal swimming and laboratory designs of biohybrid microrobots. △ Less

Submitted 27 January, 2026; v1 submitted 25 July, 2025; originally announced July 2025.

Learning-at-Criticality in Large Language Models for Quantum Field Theory and Beyond

Authors: Xiansheng Cai, Sihan Hu, Tao Wang, Yuan Huang, Pan Zhang, Youjin Deng, Kun Chen

Abstract: Fundamental physics often confronts complex symbolic problems with few guiding exemplars or established principles. While artificial intelligence (AI) offers promise, its typical need for vast datasets to learn from hinders its use in these information-scarce frontiers. We introduce learning at criticality (LaC), a reinforcement learning (RL) scheme that tunes Large Language Models (LLMs) to a sha… ▽ More Fundamental physics often confronts complex symbolic problems with few guiding exemplars or established principles. While artificial intelligence (AI) offers promise, its typical need for vast datasets to learn from hinders its use in these information-scarce frontiers. We introduce learning at criticality (LaC), a reinforcement learning (RL) scheme that tunes Large Language Models (LLMs) to a sharp learning transition, addressing this information scarcity. At this transition, LLMs achieve peak generalization from minimal data, exemplified by 7-digit base-7 addition -- a test of nontrivial arithmetic reasoning. To elucidate this peak, we analyze a minimal concept-network model (CoNet) designed to capture the essence of how LLMs might link tokens. Trained on a single exemplar, this model also undergoes a sharp learning transition. This transition exhibits hallmarks of a second-order phase transition, notably power-law distributed solution path lengths. At this critical point, the system maximizes a ``critical thinking pattern" crucial for generalization, enabled by the underlying scale-free exploration. This suggests LLMs reach peak performance by operating at criticality, where such explorative dynamics enable the extraction of underlying operational rules. We demonstrate LaC in quantum field theory: an 8B-parameter LLM, tuned to its critical point by LaC using a few exemplars of symbolic Matsubara sums, solves unseen, higher-order problems, significantly outperforming far larger models. LaC thus leverages critical phenomena, a physical principle, to empower AI for complex, data-sparse challenges in fundamental physics. △ Less

Submitted 5 November, 2025; v1 submitted 4 June, 2025; originally announced June 2025.

Scaling Physical Reasoning with the PHYSICS Dataset

Authors: Shenghe Zheng, Qianjia Cheng, Junchi Yao, Mengsong Wu, Haonan He, Ning Ding, Yu Cheng, Shuyue Hu, Lei Bai, Dongzhan Zhou, Ganqu Cui, Peng Ye

Abstract: Large Language Models (LLMs) have achieved remarkable progress on advanced reasoning tasks such as mathematics and coding competitions. Meanwhile, physics, despite being both reasoning-intensive and essential to real-world understanding, received limited academic and industrial attention. This paper introduces PHYSICS, a dataset containing 16,568 high-quality physics problems spanning subjects and… ▽ More Large Language Models (LLMs) have achieved remarkable progress on advanced reasoning tasks such as mathematics and coding competitions. Meanwhile, physics, despite being both reasoning-intensive and essential to real-world understanding, received limited academic and industrial attention. This paper introduces PHYSICS, a dataset containing 16,568 high-quality physics problems spanning subjects and difficulty levels, to facilitate this issue. Specifically, PHYSICS is curated with exercises from over 100 textbooks through a carefully designed pipeline for quality control. It covers five major physics domains: Mechanics, Electromagnetism, Thermodynamics, Optics, and Modern Physics. It also spans a wide range of difficulty levels, from high school to graduate-level physics courses. To utilize the data for improving and evaluating the model's physical reasoning capabilities, we split the dataset into training and test sets, and provide reasoning paths generated by powerful reasoning models for the training data to facilitate model training. In addition, for the evaluation part, we find that existing evaluation frameworks exhibit biases in aspects such as units, simplification, and precision in physics domain. To balance efficiency and accuracy, we introduce a Rule+Model evaluation framework tailored to physics problems. Our evaluations on current state-of-the-art open-source and proprietary models highlight the limitations of current models in handling physics-related tasks. We hope that our dataset and evaluation methodology will jointly advance the development of LLMs in the field of physics. The code and data can be found at: https://github.com/Zhengsh123/PHYSICS. △ Less

Submitted 17 October, 2025; v1 submitted 21 May, 2025; originally announced June 2025.

Comments: Accepted to the NeurIPS Datasets and Benchmarks Track

The Geography of Transportation Cybersecurity: Visitor Flows, Industry Clusters, and Spatial Dynamics

Authors: Yuhao Wang, Kailai Wang, Songhua Hu, Yunpeng, Zhang, Gino Lim, Pengyu Zhu

Abstract: The rapid evolution of the transportation cybersecurity ecosystem, encompassing cybersecurity, automotive, and transportation and logistics sectors, will lead to the formation of distinct spatial clusters and visitor flow patterns across the US. This study examines the spatiotemporal dynamics of visitor flows, analyzing how socioeconomic factors shape industry clustering and workforce distribution… ▽ More The rapid evolution of the transportation cybersecurity ecosystem, encompassing cybersecurity, automotive, and transportation and logistics sectors, will lead to the formation of distinct spatial clusters and visitor flow patterns across the US. This study examines the spatiotemporal dynamics of visitor flows, analyzing how socioeconomic factors shape industry clustering and workforce distribution within these evolving sectors. To model and predict visitor flow patterns, we develop a BiTransGCN framework, integrating an attention-based Transformer architecture with a Graph Convolutional Network backbone. By integrating AI-enabled forecasting techniques with spatial analysis, this study improves our ability to track, interpret, and anticipate changes in industry clustering and mobility trends, thereby supporting strategic planning for a secure and resilient transportation network. It offers a data-driven foundation for economic planning, workforce development, and targeted investments in the transportation cybersecurity ecosystem. △ Less

Submitted 12 May, 2025; originally announced May 2025.

Machine Learning Assisted Long-Range Wireless Power Transfer

Authors: Likai Wang, Yuqian Wang, Shengyu Hu, Yunhui Li, Hong Chen, Ce Wang, Zhiwei Guo

Abstract: Near-field magnetic resonance wireless power transfer (WPT) technology has garnered significant attention due to its broad application prospects in medical implants, electric vehicles, and robotics. Addressing the challenges faced by traditional WPT systems in frequency optimization and sensitivity to environmental disturbances, this study innovatively applies the gradient descent optimization alg… ▽ More Near-field magnetic resonance wireless power transfer (WPT) technology has garnered significant attention due to its broad application prospects in medical implants, electric vehicles, and robotics. Addressing the challenges faced by traditional WPT systems in frequency optimization and sensitivity to environmental disturbances, this study innovatively applies the gradient descent optimization algorithm to enhance a system with topological characteristics. Experimental results demonstrate that the machine learning-optimized Su-Schrieffer-Heeger (SSH)-like chain exhibits exceptional performance in transfer efficiency and system robustness. This achievement integrates non-Hermitian physics, topological physics, and machine learning, opening up new avenues and showcasing immense potential for the development of high-performance near-field wave functional devices. △ Less

Submitted 12 May, 2025; originally announced May 2025.

Non-Hermitian exceptional physics in RP^2 hyperbolic media

Authors: Shengyu Hu, Zhiwei Guo, Wenwei Liu, Shuqi Chen, Hong Chen

Abstract: Conventional momentum space provides an orientable base space of a torus for topological classifications based on band theory. Here, we introduce a non-orientable momentum space isomorphic to the real projective plane RP^2 within the low-symmetry media. We show that the local band fluidity can be characterized by an expanded dihedral group with non-Abelian properties, while the global band fluidit… ▽ More Conventional momentum space provides an orientable base space of a torus for topological classifications based on band theory. Here, we introduce a non-orientable momentum space isomorphic to the real projective plane RP^2 within the low-symmetry media. We show that the local band fluidity can be characterized by an expanded dihedral group with non-Abelian properties, while the global band fluidity offers a versatile platform to explore the evolution of non-Hermitian exceptional manifolds, including order-1, higher-order, hybrid exceptional manifolds, diabolic points and even bound states in the continuum. Furthermore, the non-orientable momentum space can pave the way for exploring the emergence of phenomena for exceptional manifolds. △ Less

Submitted 8 May, 2025; originally announced May 2025.

Roadmap for warm dense matter physics

Authors: Jan Vorberger, Frank Graziani, David Riley, Andrew D. Baczewski, Isabelle Baraffe, Mandy Bethkenhagen, Simon Blouin, Maximilian P. Böhme, Michael Bonitz, Michael Bussmann, Alexis Casner, Witold Cayzac, Peter Celliers, Gilles Chabrier, Nicolas Chamel, Dave Chapman, Mohan Chen, Jean Clérouin, Gilbert Collins, Federica Coppari, Tilo Döppner, Tobias Dornheim, Luke B. Fletcher, Dirk O. Gericke, Siegfried Glenzer , et al. (49 additional authors not shown)

Abstract: This roadmap presents the state-of-the-art, current challenges and near future developments anticipated in the thriving field of warm dense matter physics. Originating from strongly coupled plasma physics, high pressure physics and high energy density science, the warm dense matter physics community has recently taken a giant leap forward. This is due to spectacular developments in laser technolog… ▽ More This roadmap presents the state-of-the-art, current challenges and near future developments anticipated in the thriving field of warm dense matter physics. Originating from strongly coupled plasma physics, high pressure physics and high energy density science, the warm dense matter physics community has recently taken a giant leap forward. This is due to spectacular developments in laser technology, diagnostic capabilities, and computer simulation techniques. Only in the last decade has it become possible to perform accurate enough simulations \& experiments to truly verify theoretical results as well as to reliably design experiments based on predictions. Consequently, this roadmap discusses recent developments and contemporary challenges that are faced by theoretical methods, and experimental techniques needed to create and diagnose warm dense matter. A large part of this roadmap is dedicated to specific warm dense matter systems and applications in astrophysics, inertial confinement fusion and novel material synthesis. △ Less

Submitted 13 February, 2026; v1 submitted 5 May, 2025; originally announced May 2025.

Thermal noise induced probability switching in magnetic tunnel junction based on spin-circuit simulation

Authors: Shaojie Hu, Fupeng Gao, Tengwei Huang, Zhizhong Wang, Hui Li, Dawei Wang

Abstract: The probability switching characteristics in spin transfer torque magnetic tunnel junctions (STT-MTJs) are simulated by considering thermal noise using a spin-circuit module. Thermal noise significantly affects the probability switching for pulse durations exceeding 10 ns, while no probability switching properties are observed for pulses shorter than 1 ns due to the precessional switching. For pul… ▽ More The probability switching characteristics in spin transfer torque magnetic tunnel junctions (STT-MTJs) are simulated by considering thermal noise using a spin-circuit module. Thermal noise significantly affects the probability switching for pulse durations exceeding 10 ns, while no probability switching properties are observed for pulses shorter than 1 ns due to the precessional switching. For pulse durations between 1 ns and 10 ns, the occurrence of mixed probability and abrupt switching suggests that thermal noise partially influences the switching properties. These results demonstrate the effectiveness of our simulation model in capturing the MTJ properties under the influence of thermal noise. The spin-circuit module used in this study lays the groundwork for future circuit system designs utilizing MTJ devices, such as true random number generators and neural network computing. △ Less

Submitted 1 May, 2025; originally announced May 2025.

Comments: 11 pages 4 figures

A Navier-Stokes-Peridynamics hybrid algorithm for the coupling of compressible flows and fracturing materials

Authors: Mingshuo Han, Shiwei Hu, Tianbai Xiao, Yonghao Zhang

Abstract: Modeling and simulation of fluid-structure interactions are crucial to the success of aerospace engineering. This work addresses a novel hybrid algorithm that models the close coupling between compressible flows and deformable materials using a mesoscopic approach. Specifically, the high-speed flows are described by the gas-kinetic scheme, which is a robust Navier-Stokes alternative solver built o… ▽ More Modeling and simulation of fluid-structure interactions are crucial to the success of aerospace engineering. This work addresses a novel hybrid algorithm that models the close coupling between compressible flows and deformable materials using a mesoscopic approach. Specifically, the high-speed flows are described by the gas-kinetic scheme, which is a robust Navier-Stokes alternative solver built on the molecular kinetic theory. The deformation, damage, and fracture of materials are depicted using the bond-based peridynamics, which serves as coarse-grained molecular dynamics to construct non-local extensions of classical continuum mechanics. The evolution of fluids and materials are closely coupled using the ghost-cell immersed boundary method. Within each time step, the solutions of flow and solid fields are updated simultaneously, and physics-driven boundary conditions are exchanged for each other via ghost cells. Extensive numerical experiments, including crack propagation in a pre-cracked plate, subsonic flow around the NACA0012 airfoil, supersonic flow around the circular cylinder, and shock wave impacting on the elastic panel, are performed to validate the algorithm. The simulation results demonstrate the unique advantages of current hybrid algorithm in solving fracture propagation induced by high-speed flows. △ Less

Submitted 15 April, 2025; originally announced April 2025.

Comments: 25 pages, 17 figures, 3 tables

New beyond-Voigt line-shape profile recommended for the HITRAN database

Authors: P. Wcisło, N. Stolarczyk, M. Słowiński, H. Jóźwiak, D. Lisak, R. Ciuryło, A. Cygan, F. Schreier, C. D. Boone, A. Castrillo, L. Gianfrani, Y. Tan, S-M. Hu, E. Adkins, J. T. Hodges, H. Tran, H. N. Ngo, J. -M. Hartmann, S. Beguier, A. Campargue, R. J. Hargreaves, L. S. Rothman, I. E. Gordon

Abstract: Parameters associated with the collisional perturbation of spectral lines are essential for modeling the absorption of electromagnetic radiation in gas media. The HITRAN molecular spectroscopic database provides these parameters, although originally they were associated only with the Voigt profile parameterization. However, in the HITRAN2016 and HITRAN2020 editions, Voigt, speed-dependent Voigt an… ▽ More Parameters associated with the collisional perturbation of spectral lines are essential for modeling the absorption of electromagnetic radiation in gas media. The HITRAN molecular spectroscopic database provides these parameters, although originally they were associated only with the Voigt profile parameterization. However, in the HITRAN2016 and HITRAN2020 editions, Voigt, speed-dependent Voigt and Hartmann-Tran (HT) profiles have been incorporated, thanks to the new relational structure of the database. The HT profile was introduced in HITRAN in 2016 as a recommended profile for the most accurate spectral interpretations and modeling. It was parameterized with a four-temperature-range temperature dependence. Since then, however, some features of the HT profile have been revealed that are problematic from a practical perspective. These are: the singular behavior of the temperature dependencies of the velocity-changing parameters when the shift parameter crosses zero and the difficulty in evaluating the former for mixtures. In this article, we summarize efforts to eliminate the above-mentioned problems that led us to recommend using the quadratic speed-dependent hard-collision (qSDHC) profile with double-power-law (DPL) temperature dependencies. We refer to this profile as a modified Hartmann-Tran (mHT) profile. The computational cost of evaluating it is the same as for the HT profile. We give a detailed description of the mHT profile (also including line mixing) and discuss the representation of its parameters, together with their DPL temperature parametrization adopted in the HITRAN database. We discuss an efficient algorithm for evaluating this profile and provide corresponding computer codes in several programming languages: Fortran, Python, MATLAB, Wolfram Mathematica, and LabVIEW. We also discuss the associated update of the HITRAN Application Programming Interface (HAPI). △ Less

Submitted 27 March, 2025; originally announced March 2025.

Crystal nucleation rates in one-component Yukawa systems

Authors: B. Arnold, J. Daligault, D. Saumon, Antoine Bédard, S. X. Hu

Abstract: Nucleation in the supercooled Yukawa system is relevant for addressing current challenges in understanding a range of crystallizing systems including white dwarf (WD) stars. We use both brute force and seeded molecular dynamics simulations to study homogeneous nucleation of crystals from supercooled Yukawa liquids. With our improved approach to seeded simulations, we obtain quantitative prediction… ▽ More Nucleation in the supercooled Yukawa system is relevant for addressing current challenges in understanding a range of crystallizing systems including white dwarf (WD) stars. We use both brute force and seeded molecular dynamics simulations to study homogeneous nucleation of crystals from supercooled Yukawa liquids. With our improved approach to seeded simulations, we obtain quantitative predictions of the crystal nucleation rate and cluster size distributions as a function of temperature and screening length. These quantitative results show trends towards fast nucleation with short-ranged potentials. They also indicate that for temperatures $T > 0.9T_m$, where $T_m$ is the melt temperature, classical homogeneous nucleation is too slow to initiate crystallization but transient clusters of around 100 particles should be common. We apply these general results to a typical WD model and obtain a delay of approximately 0.6 Gyr in the onset of crystallization that may be observable. △ Less

Submitted 7 March, 2025; originally announced March 2025.

Comments: 17 pages, 14 figures

Report number: LA-UR-24-30554

Journal ref: Phys. Rev. E 111, 025206, Published 21 February, 2025

Mid-infrared laser chaos lidar

Authors: Kai-Li Lin, Peng-Lei Wang, Yi-Bo Peng, Shiyu Hu, Chunfang Cao, Cheng-Ting Lee, Qian Gong, Fan-Yi Lin, Wenxiang Huang, Cheng Wang

Abstract: Chaos lidars detect targets through the cross-correlation between the back-scattered chaos signal from the target and the local reference one. Chaos lidars have excellent anti-jamming and anti-interference capabilities, owing to the random nature of chaotic oscillations. However, most chaos lidars operate in the near-infrared spectral regime, where the atmospheric attenuation is significant. Here… ▽ More Chaos lidars detect targets through the cross-correlation between the back-scattered chaos signal from the target and the local reference one. Chaos lidars have excellent anti-jamming and anti-interference capabilities, owing to the random nature of chaotic oscillations. However, most chaos lidars operate in the near-infrared spectral regime, where the atmospheric attenuation is significant. Here we show a mid-infrared chaos lidar, which is suitable for long-reach ranging and imaging applications within the low-loss transmission window of the atmosphere. The proof-of-concept mid-infrared chaos lidar utilizes an interband cascade laser with optical feedback as the laser chaos source. Experimental results reveal that the chaos lidar achieves an accuracy better than 0.9 cm and a precision better than 0.3 cm for ranging distances up to 300 cm. In addition, it is found that a minimum signal-to-noise ratio of only 1 dB is required to sustain both sub-cm accuracy and sub-cm precision. This work paves the way for developing remote chaos lidar systems in the mid-infrared spectral regime. △ Less

Submitted 6 March, 2025; originally announced March 2025.

Phase evolution of strong-field ionization

Authors: Lynda R Hutcheson, Maximilian Hartmann, Gergana D Borisova, Paul Birk, Shuyuan Hu, Christian Ott, Thomas Pfeifer, Hugo W van der Hart, Andrew C Brown

Abstract: We investigate the time-dependent evolution of the dipole phase shift induced by strong-field ionization (SFI) using attosecond transient absorption spectroscopy (ATAS) for time-delays where the pump-probe pulses overlap. We study measured and calculated time-dependent ATA spectra of the ionic 4d-5p transition in xenon, and present the time-dependent line shape parameters in the complex plane. We… ▽ More We investigate the time-dependent evolution of the dipole phase shift induced by strong-field ionization (SFI) using attosecond transient absorption spectroscopy (ATAS) for time-delays where the pump-probe pulses overlap. We study measured and calculated time-dependent ATA spectra of the ionic 4d-5p transition in xenon, and present the time-dependent line shape parameters in the complex plane. We attribute the complex, attosecond-scale dynamics to the contribution of three distinct processes: accumulation of ionization, transient population, and reversible population of excited states arising from polarization of the ground state. △ Less

Submitted 26 February, 2025; originally announced February 2025.

Comments: 6 pages, 6 figures. All data in the manuscript and the minimal model can be found in the GitHub repository, see https://github.com/lhutcheson/dipole_response.git

Evaluating The Performance of Using Large Language Models to Automate Summarization of CT Simulation Orders in Radiation Oncology

Authors: Meiyun Cao, Shaw Hu, Jason Sharp, Edward Clouser, Jason Holmes, Linda L. Lam, Xiaoning Ding, Diego Santos Toesca, Wendy S. Lindholm, Samir H. Patel, Sujay A. Vora, Peilong Wang, Wei Liu

Abstract: Purpose: This study aims to use a large language model (LLM) to automate the generation of summaries from the CT simulation orders and evaluate its performance. Materials and Methods: A total of 607 CT simulation orders for patients were collected from the Aria database at our institution. A locally hosted Llama 3.1 405B model, accessed via the Application Programming Interface (API) service, wa… ▽ More Purpose: This study aims to use a large language model (LLM) to automate the generation of summaries from the CT simulation orders and evaluate its performance. Materials and Methods: A total of 607 CT simulation orders for patients were collected from the Aria database at our institution. A locally hosted Llama 3.1 405B model, accessed via the Application Programming Interface (API) service, was used to extract keywords from the CT simulation orders and generate summaries. The downloaded CT simulation orders were categorized into seven groups based on treatment modalities and disease sites. For each group, a customized instruction prompt was developed collaboratively with therapists to guide the Llama 3.1 405B model in generating summaries. The ground truth for the corresponding summaries was manually derived by carefully reviewing each CT simulation order and subsequently verified by therapists. The accuracy of the LLM-generated summaries was evaluated by therapists using the verified ground truth as a reference. Results: About 98% of the LLM-generated summaries aligned with the manually generated ground truth in terms of accuracy. Our evaluations showed an improved consistency in format and enhanced readability of the LLM-generated summaries compared to the corresponding therapists-generated summaries. This automated approach demonstrated a consistent performance across all groups, regardless of modality or disease site. Conclusions: This study demonstrated the high precision and consistency of the Llama 3.1 405B model in extracting keywords and summarizing CT simulation orders, suggesting that LLMs have great potential to help with this task, reduce the workload of therapists and improve workflow efficiency. △ Less

Submitted 27 January, 2025; originally announced January 2025.

Thermal Induced Structural Competitiveness and Metastability of Body-centered Cubic Iron under Non-Equilibrium Conditions

Authors: Shuai Zhang, Aliza Panjwani, Penghao Xiao, Maitrayee Ghosh, Tadashi Ogitsu, Yuan Ping, S. X. Hu

Abstract: The structure and stability of iron near melting at multi-megabar pressures are of significant interest in high pressure physics and earth and planetary sciences. While the body-centered cubic (BCC) phase is generally recognized as unstable at lower temperatures, its stability relative to the hexagonal close-packed (HCP) phase at high temperatures (approximately 0.5 eV) in the Earth's inner core (… ▽ More The structure and stability of iron near melting at multi-megabar pressures are of significant interest in high pressure physics and earth and planetary sciences. While the body-centered cubic (BCC) phase is generally recognized as unstable at lower temperatures, its stability relative to the hexagonal close-packed (HCP) phase at high temperatures (approximately 0.5 eV) in the Earth's inner core (IC) remains a topic of ongoing theoretical and experimental debate. Our ab initio calculations show a significant drop in energy, the emergence of a plateau and a local minimum in the potential energy surface, and stabilization of all phonon modes at elevated electron temperatures (>1-1.5 eV). These effects increase the competition among the BCC, HCP, and the face-centered cubic (FCC) phases and lead to the metastability of the BCC structure. Furthermore, the thermodynamic stability of BCC iron is enhanced by its substantial lattice vibration entropy. This thermally induced structural competitiveness and metastability under non-equilibrium conditions provide a clear theoretical framework for understanding iron phase relations and solidification processes, both experimentally and in the IC. △ Less

Submitted 31 December, 2024; originally announced January 2025.

Comments: 5 pages, 4 figures

Journal ref: Phys. Rev. B 111, 224106 (2025)

Real-Time Analysis of Nanoscale Dynamics in Membrane Protein Insertion via Single-Molecule Imaging

Authors: C. Yang, D. Ma, S. Hu, M. Li, Y. Lu

Abstract: Membrane proteins often need to be inserted into or attached on the cell membrane to perform their functions. Understanding their transmembrane topology and conformational dynamics during insertion is crucial for elucidating their roles. However, it remains challenging to monitor nanoscale changes in insertion depth of individual proteins in membranes. Here, we introduce two single molecule imagin… ▽ More Membrane proteins often need to be inserted into or attached on the cell membrane to perform their functions. Understanding their transmembrane topology and conformational dynamics during insertion is crucial for elucidating their roles. However, it remains challenging to monitor nanoscale changes in insertion depth of individual proteins in membranes. Here, we introduce two single molecule imaging methods, SIFA and LipoFRET, designed for in vitro observation of the nanoscale architecture of membrane proteins within membranes. These methods have demonstrated their efficacy in studying biomolecules interacting with bio-membranes with sub-nanometer precision. △ Less

Submitted 27 December, 2024; originally announced December 2024.

Comments: 20 pages, 2 figures

Journal ref: Biophysics Reports, 2024, 10(6): 369-376

Single-molecule Surface-Induced Fluorescence Attenuation Based on Reduced Graphene Oxide

Authors: Q. Fan, C. Yang, S. Hu, C. Xu, M. Li, Y. Lu

Abstract: Single-molecule surface-induced fluorescence attenuation (smSIFA) is a precise method for studying the vertical movement of biological macromolecules using two-dimensional material acceptors. Unlike other methods, smSIFA is not influenced by the planar motion of membranes or proteins. However, the detection range and accuracy of vertical movement are dependent on the properties of these two-dimens… ▽ More Single-molecule surface-induced fluorescence attenuation (smSIFA) is a precise method for studying the vertical movement of biological macromolecules using two-dimensional material acceptors. Unlike other methods, smSIFA is not influenced by the planar motion of membranes or proteins. However, the detection range and accuracy of vertical movement are dependent on the properties of these two-dimensional materials. Recently, smSIFA utilizing graphene oxide and graphene has significantly advanced the study of biomacromolecules, although the detection range is restricted by their inherent quenching distances. Modifying these distances necessitates the replacement of the medium material, which presents challenges in material selection and preparation. Consequently, there is a pressing need to develop controllable materials for smSIFA applications. In this study, we enhance the smSIFA technique using graphene oxide as the medium acceptor through thermal reduction. By adjusting the reduction temperature, we prepare reduced graphene oxides at varying degrees of reduction, thus fine-tuning the quenching distances. The adjustment of these distances is measured using fluorescently labeled DNA. This modified smSIFA approach, employing reduced graphene oxide, is then applied to observe conformational changes in the Holliday junction, demonstrating the enhanced detection capabilities of reduced graphene oxide. △ Less

Submitted 27 December, 2024; originally announced December 2024.

Comments: 13 pages, 4 figures, 2 tables

Journal ref: Acta Physica Sinica, 2023, 72(14): 147801

Effects of Thom disk on alleviating ground effects of a wall-mounted rotating cylinder

Authors: Bao-Yuan Zhao, Kai Zhang, Dai Zhou, Shiliang Hu, Hanfeng Wang

Abstract: This study investigates the effects of Thom disks on alleviating ground effects by wall-mounted rotating cylinders, also known as Flettner rotors, which utilize wind energy for ship propulsion. Through three-dimensional direct numerical simulations, our findings reveal that introducing a secondary Thom disk near the ground significantly reduces the three-dimensional flow pattern induced by the gro… ▽ More This study investigates the effects of Thom disks on alleviating ground effects by wall-mounted rotating cylinders, also known as Flettner rotors, which utilize wind energy for ship propulsion. Through three-dimensional direct numerical simulations, our findings reveal that introducing a secondary Thom disk near the ground significantly reduces the three-dimensional flow pattern induced by the ground, leading to a more uniform pressure distribution along the rotor's surface. We also explore how the vertical placement of a secondary Thom disk influences wake dynamics and aerodynamic forces. Optimal placement of the secondary disk is found at the ground, which maximizes the lift-to-drag ratio. △ Less

Submitted 16 December, 2024; originally announced December 2024.

Scaling Particle Collision Data Analysis

Authors: Hengkui Wu, Panpan Chi, Yongfeng Zhu, Liujiang Liu, Shuyang Hu, Yuexin Wang, Chen Zhou, Qihao Wang, Yingsi Xin, Bruce Liu, Dahao Liang, Xinglong Jia, Manqi Ruan

Abstract: For decades, researchers have developed task-specific models to address scientific challenges across diverse disciplines. Recently, large language models (LLMs) have shown enormous capabilities in handling general tasks; however, these models encounter difficulties in addressing real-world scientific problems, particularly in domains involving large-scale numerical data analysis, such as experimen… ▽ More For decades, researchers have developed task-specific models to address scientific challenges across diverse disciplines. Recently, large language models (LLMs) have shown enormous capabilities in handling general tasks; however, these models encounter difficulties in addressing real-world scientific problems, particularly in domains involving large-scale numerical data analysis, such as experimental high energy physics. This limitation is primarily due to BPE tokenization's inefficacy with numerical data. In this paper, we propose a task-agnostic architecture, BBT-Neutron, which employs a binary tokenization method to facilitate pretraining on a mixture of textual and large-scale numerical experimental data. We demonstrate the application of BBT-Neutron to Jet Origin Identification (JoI), a critical categorization challenge in high-energy physics that distinguishes jets originating from various quarks or gluons. Our results indicate that BBT-Neutron achieves comparable performance to state-of-the-art task-specific JoI models. Furthermore, we examine the scaling behavior of BBT-Neutron's performance with increasing data volume, suggesting the potential for BBT-Neutron to serve as a foundational model for particle physics data analysis, with possible extensions to a broad spectrum of scientific computing applications for Big Science experiments, industrial manufacturing and spacial computing. The project code is available at https://github.com/supersymmetry-technologies/bbt-neutron. △ Less

Submitted 9 December, 2024; v1 submitted 28 November, 2024; originally announced December 2024.

100 years of plastic -- using the past to guide the future

Authors: Chao Liu, Roland Geyer, Shanying Hu

Abstract: Robust and credible material flow data are required to support the ongoing efforts to reconcile the economic and social benefits of plastics with their human and environmental health impacts. This study presents a global, but regionalized, life cycle material flow analysis (MFA) of all plastic polymers and applications for the period 1950-2020. It also illustrates how this dataset can be used to g… ▽ More Robust and credible material flow data are required to support the ongoing efforts to reconcile the economic and social benefits of plastics with their human and environmental health impacts. This study presents a global, but regionalized, life cycle material flow analysis (MFA) of all plastic polymers and applications for the period 1950-2020. It also illustrates how this dataset can be used to generate possible scenarios for the next 30 years. The historical account documents how the relentless growth of plastic production and use has consistently outpaced waste management systems worldwide and currently generates on the order of 60 Mt of mismanaged plastic waste annually. The scenarios show that robust interventions are needed to avoid annual plastic waste mismanagement from doubling by 2050. △ Less

Submitted 20 November, 2024; originally announced November 2024.

Intertwined effects of elastic deformation and damage on vortex pinning and Jc degradation in polycrystalline superconductors

Authors: Qing-Yu Wang, Shuai Hu, You-He Zhou, Cun Xue

Abstract: The damage and the critical current density (Jc) degradation of polycrystalline superconductors induced by strain dramatically influence their performance in applications. Unfortunately, the state-of-the-art experimental techniques are unable to detect the damage of internal polycrystalline structures and the microscopic superconductivity in the presence of strain. We propose a groundbreaking mult… ▽ More The damage and the critical current density (Jc) degradation of polycrystalline superconductors induced by strain dramatically influence their performance in applications. Unfortunately, the state-of-the-art experimental techniques are unable to detect the damage of internal polycrystalline structures and the microscopic superconductivity in the presence of strain. We propose a groundbreaking multi-scale theoretical framework aimed at revealing the underlying physical mechanisms of the reversible and irreversible Jc degradation induced by the strain through tackling the complex intertwined effects of elastic deformation and damage on the superconductivity of grain boundaries and the associated vortex pinning. The results are well validated by experimental measurements. Utilizing the benchmarked physical model, we demonstrate that the damage evolutions of polycrystalline superconductors in the presence of strain can be approximately estimated by means of the electromagnetic experiments on Jc. Furthermore, we also discuss the characteristics of damage and Jc degradation of polycrystalline superconductors subjected to biaxial mechanical loads. The findings will pave the way to investigate the tunable vortex pinning and Jc of superconductors by strain, and to develop a brand new electromagnetic method to manifest the damage of polycrystalline superconductors. △ Less

Submitted 27 November, 2024; v1 submitted 18 November, 2024; originally announced November 2024.

Comments: 13 pages, 4figures

Post-selection shifts the transition frequency of helium in an atomic beam

Authors: Jin-Lu Wen, Jia-Dong Tang, Ya-Nan Lv, Yu R. Sun, Chang-Ling Zou, Jun-Feng Dong, Shui-Ming Hu

Abstract: Post-selecting output states in measurements can effectively amplify weak signals and improve precision. However, post-selection effects may also introduce unintended biases in precision measurements. Here, we investigate the influence of post-selection in the precision spectroscopy of the $2^3S - 2^3P$ transition of helium ($^4$He) using an atomic beam. We directly observe that post-selection bas… ▽ More Post-selecting output states in measurements can effectively amplify weak signals and improve precision. However, post-selection effects may also introduce unintended biases in precision measurements. Here, we investigate the influence of post-selection in the precision spectroscopy of the $2^3S - 2^3P$ transition of helium ($^4$He) using an atomic beam. We directly observe that post-selection based on atomic positions causes a shift in the measured transition frequency, amounting to approximately -55 kHz. After accounting for this post-selection shift, we obtain a corrected frequency of $276,764,094,712.45 \pm 0.86$ kHz for the $2^3S_1 - 2^3P_0$ transition. Combining this result with existing data for $^3$He, we derive a new value for the difference in squared nuclear charge radii, $δr^2 [r_{h}^{2} - r_α^{2}] = 1.0733 \pm 0.0021$ fm$^2$. This value shows a $2.8σ$ deviation from measurements of muonic helium ions, potentially pointing to new physics that challenges lepton universality in quantum electrodynamics. △ Less

Submitted 15 November, 2024; originally announced November 2024.

Comments: 14 pages including appendix

Molecular Dynamics and Machine Learning Unlock Possibilities in Beauty Design -- A Perspective

Authors: Yuzhi Xu, Haowei Ni, Qinhui Gao, Chia-Hua Chang, Yanran Huo, Fanyu Zhao, Shiyu Hu, Wei Xia, Yike Zhang, Radu Grovu, Min He, John. Z. H. Zhang, Yuanqing Wang

Abstract: Computational molecular design -- the endeavor to design molecules, with various missions, aided by machine learning and molecular dynamics approaches, has been widely applied to create valuable new molecular entities, from small molecule therapeutics to protein biologics. In the small data regime, physics-based approaches model the interaction between the molecule being designed and proteins of k… ▽ More Computational molecular design -- the endeavor to design molecules, with various missions, aided by machine learning and molecular dynamics approaches, has been widely applied to create valuable new molecular entities, from small molecule therapeutics to protein biologics. In the small data regime, physics-based approaches model the interaction between the molecule being designed and proteins of key physiological functions, providing structural insights into the mechanism. When abundant data has been collected, a quantitative structure-activity relationship (QSAR) can be more directly constructed from experimental data, from which machine learning can distill key insights to guide the design of the next round of experiment design. Machine learning methodologies can also facilitate physical modeling, from improving the accuracy of force fields and extending them to unseen chemical spaces, to more directly enhancing the sampling on the conformational spaces. We argue that these techniques are mature enough to be applied to not just extend the longevity of life, but the beauty it manifests. In this perspective, we review the current frontiers in the research \& development of skin care products, as well as the statistical and physical toolbox applicable to addressing the challenges in this industry. Feasible interdisciplinary research projects are proposed to harness the power of machine learning tools to design innovative, effective, and inexpensive skin care products. △ Less

Submitted 28 October, 2024; v1 submitted 8 October, 2024; originally announced October 2024.

Optimizing attosecond pulse generation in solids by modulating electronic dynamics with monochromatic laser field

Authors: Xinyuan Zhang, Shiqi Hu, Mengxue Guan, Sheng Meng

Abstract: A practical approach is proposed for efficiently generating ultrashort attosecond pulses (APs) from realistic solid-state materials, aiming to optimize pulse width effectively. By adjusting the photon energy while maintaining a constant peak electric field, this strategy modulates the peak vector potential and laser field period, thereby controlling the high harmonic cutoff energy and the time-dom… ▽ More A practical approach is proposed for efficiently generating ultrashort attosecond pulses (APs) from realistic solid-state materials, aiming to optimize pulse width effectively. By adjusting the photon energy while maintaining a constant peak electric field, this strategy modulates the peak vector potential and laser field period, thereby controlling the high harmonic cutoff energy and the time-domain emission characteristics of the harmonics. The field-driven electronic dynamics lead to a non-monotonic variation in both the intensity and duration of the generated attosecond pulses. The light field frequency can be adjusted to yield the optimal pulse. Beyond the primary demonstration with hexagonal boron nitride as a prototypical material, significant pulse width optimization has been achieved across a range of different materials. This straightforward and versatile strategy shows promise for application in solid-state materials, offering new pathways to promote high harmonic performance. △ Less

Submitted 23 February, 2025; v1 submitted 22 October, 2024; originally announced October 2024.

Comments: 10 pages, 9 figures

Journal ref: Phys. Rev. A 111, 023521 (2025)

Distinguishing Backward Volume Magnetostatic Spin Wave Vectors via the Spin Wave Doppler Effect

Authors: Xuhui Su, Dawei Wang, Shaojie Hu

Abstract: Spin waves (SWs) and their quanta, magnons, are essential to achieving low-power information transmission in future spintronic devices. Backward volume magnetostatic spin waves (BVMSWs) exhibit a unique dispersion relationship: one frequency corresponding to two distinct wave vectors. At low wave numbers, dipole-dipole interactions dominate, resulting in negative group velocities, whereas at high… ▽ More Spin waves (SWs) and their quanta, magnons, are essential to achieving low-power information transmission in future spintronic devices. Backward volume magnetostatic spin waves (BVMSWs) exhibit a unique dispersion relationship: one frequency corresponding to two distinct wave vectors. At low wave numbers, dipole-dipole interactions dominate, resulting in negative group velocities, whereas at high wave numbers, exchange interactions prevail, producing positive group velocities. This dual behavior complicates wave vector identification and obscures intrinsic spin-wave interactions. In this study, we propose an approach based on the spin wave Doppler effect to effectively distinguish different wave vectors. At low wave numbers, the inverse Doppler effect occurs due to antiparallel phase and group velocities, while at high wave numbers, a normal Doppler effect emerges from parallel velocities. This method not only clarifies the underlying spin-wave interactions but also help mitigate serious interference issues in the design of spin logic circuits. △ Less

Submitted 3 May, 2025; v1 submitted 17 September, 2024; originally announced September 2024.

Comments: 14 pages, 6 figures

Study of the decay and production properties of $D_{s1}(2536)$ and $D_{s2}^*(2573)$

Authors: M. Ablikim, M. N. Achasov, P. Adlarson, O. Afedulidis, X. C. Ai, R. Aliberti, A. Amoroso, Q. An, Y. Bai, O. Bakina, I. Balossino, Y. Ban, H. -R. Bao, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann , et al. (645 additional authors not shown)

Abstract: The $e^+e^-\rightarrow D_s^+D_{s1}(2536)^-$ and $e^+e^-\rightarrow D_s^+D^*_{s2}(2573)^-$ processes are studied using data samples collected with the BESIII detector at center-of-mass energies from 4.530 to 4.946~GeV. The absolute branching fractions of $D_{s1}(2536)^- \rightarrow \bar{D}^{*0}K^-$ and $D_{s2}^*(2573)^- \rightarrow \bar{D}^0K^-$ are measured for the first time to be… ▽ More The $e^+e^-\rightarrow D_s^+D_{s1}(2536)^-$ and $e^+e^-\rightarrow D_s^+D^*_{s2}(2573)^-$ processes are studied using data samples collected with the BESIII detector at center-of-mass energies from 4.530 to 4.946~GeV. The absolute branching fractions of $D_{s1}(2536)^- \rightarrow \bar{D}^{*0}K^-$ and $D_{s2}^*(2573)^- \rightarrow \bar{D}^0K^-$ are measured for the first time to be $(35.9\pm 4.8\pm 3.5)\%$ and $(37.4\pm 3.1\pm 4.6)\%$, respectively. The measurements are in tension with predictions based on the assumption that the $D_{s1}(2536)$ and $D_{s2}^*(2573)$ are dominated by a bare $c\bar{s}$ component. The $e^+e^-\rightarrow D_s^+D_{s1}(2536)^-$ and $e^+e^-\rightarrow D_s^+D^*_{s2}(2573)^-$ cross sections are measured, and a resonant structure at around 4.6~GeV with a width of 50~MeV is observed for the first time with a statistical significance of $15σ$ in the $e^+e^-\rightarrow D_s^+D^*_{s2}(2573)^-$ process. It could be the $Y(4626)$ found by the Belle collaboration in the $D_s^+D_{s1}(2536)^{-}$ final state, since they have similar masses and widths. There is also evidence for a structure at around 4.75~GeV in both processes. △ Less

Submitted 10 July, 2024; originally announced July 2024.

Beyond a binary theorizing of prosociality

Authors: Chen Shen, Zhixue He, Hao Guo, Shuyue Hu, Jun Tanimoto, Lei Shi, Petter Holme

Abstract: A stylized experiment, the public goods game, has taught us the peculiar reproducible fact that humans tend to contribute more to shared resources than expected from economically rational assumptions. There have been two competing explanations for this phenomenon: either contributing to the public good is an innate human trait (the prosocial preference hypothesis) or a transitory effect while lear… ▽ More A stylized experiment, the public goods game, has taught us the peculiar reproducible fact that humans tend to contribute more to shared resources than expected from economically rational assumptions. There have been two competing explanations for this phenomenon: either contributing to the public good is an innate human trait (the prosocial preference hypothesis) or a transitory effect while learning the game (the confused learner hypothesis). We use large-scale experimental data from a novel experimental design to distinguish between these two hypotheses. By monitoring the effects of zealots (persistently cooperating bots) and varying the participants' awareness of them, we find a considerably more complex scenario than previously reported. People indeed have a prosocial bias, but not to the degree that they always forego taking action to increase their profit. While our findings end the simplistic theorizing of prosociality in the public goods game, an observed positive, cooperative response to zealots has actionable policy implications. △ Less

Submitted 6 June, 2024; originally announced June 2024.

Report number: 121 (49) e2412195121

Journal ref: Proc. Natl. Acad. Sci. U.S.A. November 27, 2024

Prediction of Energy Resolution in the JUNO Experiment

Authors: JUNO Collaboration, Angel Abusleme, Thomas Adam, Kai Adamowicz, Shakeel Ahmad, Rizwan Ahmed, Sebastiano Aiello, Fengpeng An, Qi An, Giuseppe Andronico, Nikolay Anfimov, Vito Antonelli, Tatiana Antoshkina, João Pedro Athayde Marcondes de André, Didier Auguste, Weidong Bai, Nikita Balashov, Wander Baldini, Andrea Barresi, Davide Basilico, Eric Baussan, Marco Bellato, Marco Beretta, Antonio Bergnoli, Daniel Bick , et al. (629 additional authors not shown)

Abstract: This paper presents an energy resolution study of the JUNO experiment, incorporating the latest knowledge acquired during the detector construction phase. The determination of neutrino mass ordering in JUNO requires an exceptional energy resolution better than 3\% at 1~MeV. To achieve this ambitious goal, significant efforts have been undertaken in the design and production of the key components o… ▽ More This paper presents an energy resolution study of the JUNO experiment, incorporating the latest knowledge acquired during the detector construction phase. The determination of neutrino mass ordering in JUNO requires an exceptional energy resolution better than 3\% at 1~MeV. To achieve this ambitious goal, significant efforts have been undertaken in the design and production of the key components of the JUNO detector. Various factors affecting the detection of inverse beta decay signals have an impact on the energy resolution, extending beyond the statistical fluctuations of the detected number of photons, such as the properties of the liquid scintillator, performance of photomultiplier tubes, and the energy reconstruction algorithm. To account for these effects, a full JUNO simulation and reconstruction approach is employed. This enables the modeling of all relevant effects and the evaluation of associated inputs to accurately estimate the energy resolution. The results of study reveal an energy resolution of 2.95\% at 1~MeV. Furthermore, this study assesses the contribution of major effects to the overall energy resolution budget. This analysis serves as a reference for interpreting future measurements of energy resolution during JUNO data collection. Moreover, it provides a guideline for comprehending the energy resolution characteristics of liquid scintillator-based detectors. △ Less

Submitted 9 January, 2025; v1 submitted 28 May, 2024; originally announced May 2024.

Journal ref: Chinese Phys. C 49 013003 (2025)

Data quality control system and long-term performance monitor of the LHAASO-KM2A

Authors: Zhen Cao, F. Aharonian, Axikegu, Y. X. Bai, Y. W. Bao, D. Bastieri, X. J. Bi, Y. J. Bi, W. Bian, A. V. Bukevich, Q. Cao, W. Y. Cao, Zhe Cao, J. Chang, J. F. Chang, A. M. Chen, E. S. Chen, H. X. Chen, Liang Chen, Lin Chen, Long Chen, M. J. Chen, M. L. Chen, Q. H. Chen, S. Chen , et al. (263 additional authors not shown)

Abstract: The KM2A is the largest sub-array of the Large High Altitude Air Shower Observatory (LHAASO). It consists of 5216 electromagnetic particle detectors (EDs) and 1188 muon detectors (MDs). The data recorded by the EDs and MDs are used to reconstruct primary information of cosmic ray and gamma-ray showers. This information is used for physical analysis in gamma-ray astronomy and cosmic ray physics. To… ▽ More The KM2A is the largest sub-array of the Large High Altitude Air Shower Observatory (LHAASO). It consists of 5216 electromagnetic particle detectors (EDs) and 1188 muon detectors (MDs). The data recorded by the EDs and MDs are used to reconstruct primary information of cosmic ray and gamma-ray showers. This information is used for physical analysis in gamma-ray astronomy and cosmic ray physics. To ensure the reliability of the LHAASO-KM2A data, a three-level quality control system has been established. It is used to monitor the status of detector units, stability of reconstructed parameters and the performance of the array based on observations of the Crab Nebula and Moon shadow. This paper will introduce the control system and its application on the LHAASO-KM2A data collected from August 2021 to July 2023. During this period, the pointing and angular resolution of the array were stable. From the observations of the Moon shadow and Crab Nebula, the results achieved using the two methods are consistent with each other. According to the observation of the Crab Nebula at energies from 25 TeV to 100 TeV, the time averaged pointing errors are estimated to be $-0.003^{\circ} \pm 0.005^{\circ}$ and $0.001^{\circ} \pm 0.006^{\circ}$ in the R.A. and Dec directions, respectively. △ Less

Submitted 13 June, 2024; v1 submitted 20 May, 2024; originally announced May 2024.

Comments: 15 pages, 9 figures

First principles simulations of dense hydrogen

Authors: Michael Bonitz, Jan Vorberger, Mandy Bethkenhagen, Maximilian Böhme, David Ceperley, Alexey Filinov, Thomas Gawne, Frank Graziani, Gianluca Gregori, Paul Hamann, Stephanie Hansen, Markus Holzmann, S. X. Hu, Hanno Kählert, Valentin Karasiev, Uwe Kleinschmidt, Linda Kordts, Christopher Makait, Burkhard Militzer, Zhandos Moldabekov, Carlo Pierleoni, Martin Preising, Kushal Ramakrishna, Ronald Redmer, Sebastian Schwalbe , et al. (2 additional authors not shown)

Abstract: Accurate knowledge of the properties of hydrogen at high compression is crucial for astrophysics (e.g. planetary and stellar interiors, brown dwarfs, atmosphere of compact stars) and laboratory experiments, including inertial confinement fusion. There exists experimental data for the equation of state, conductivity, and Thomson scattering spectra. However, the analysis of the measurements at extre… ▽ More Accurate knowledge of the properties of hydrogen at high compression is crucial for astrophysics (e.g. planetary and stellar interiors, brown dwarfs, atmosphere of compact stars) and laboratory experiments, including inertial confinement fusion. There exists experimental data for the equation of state, conductivity, and Thomson scattering spectra. However, the analysis of the measurements at extreme pressures and temperatures typically involves additional model assumptions, which makes it difficult to assess the accuracy of the experimental data. rigorously. On the other hand, theory and modeling have produced extensive collections of data. They originate from a very large variety of models and simulations including path integral Monte Carlo (PIMC) simulations, density functional theory (DFT), chemical models, machine-learned models, and combinations thereof. At the same time, each of these methods has fundamental limitations (fermion sign problem in PIMC, approximate exchange-correlation functionals of DFT, inconsistent interaction energy contributions in chemical models, etc.), so for some parameter ranges accurate predictions are difficult. Recently, a number of breakthroughs in first principle PIMC and DFT simulations were achieved which are discussed in this review. Here we use these results to benchmark different simulation methods. We present an update of the hydrogen phase diagram at high pressures, the expected phase transitions, and thermodynamic properties including the equation of state and momentum distribution. Furthermore, we discuss available dynamic results for warm dense hydrogen, including the conductivity, dynamic structure factor, plasmon dispersion, imaginary-time structure, and density response functions. We conclude by outlining strategies to combine different simulations to achieve accurate theoretical predictions. △ Less

Submitted 17 May, 2024; originally announced May 2024.