Showing 1–50 of 788 results for author: Chen, L

Emergence of cooperation: A reputation-modulated reinforcement learning

Authors: Chenyang Zhao, Jiqiang Zhang, Li Chen, Yong Zou

Abstract: Reputation is widely recognized as a key mechanism for sustaining cooperation. However, most existing game-theoretic models treat reputation primarily as an external factor that modulates payoffs, interaction structures, or strategy update rules. In many social contexts, though, reputation operates primarily as information -- it shapes how individuals interpret their own experiences and assess the… ▽ More Reputation is widely recognized as a key mechanism for sustaining cooperation. However, most existing game-theoretic models treat reputation primarily as an external factor that modulates payoffs, interaction structures, or strategy update rules. In many social contexts, though, reputation operates primarily as information -- it shapes how individuals interpret their own experiences and assess the behavior of others. To bridge this gap, we propose a spatial prisoner's dilemma game grounded in the reinforcement learning paradigm, in which agents equipped with Q-learning integrate both individual and social information via a locally defined reputation metric to guide their decisions. Our results reveal that reputation-modulated learning significantly promotes the emergence of cooperative behavior, and we observe a discontinuous phase transition from full cooperation to full defection as the temptation increases. Cooperation spreads through the nucleation of cooperative clusters, whereas the disintegration of these clusters drives the system into an absorbing state of complete defection. Overall, this study demonstrates that reputation facilitates cooperation not only by providing direct incentives but also by reshaping the social information landscape that agents rely on for learning and adaptation. △ Less

Submitted 20 August, 2026; originally announced August 2026.

Interpretable AI predicts a 2026 summer dry anomaly in central China

Authors: Anran Wang, Wen Shi, Yong Luo, Jianbin Huang, Lijuan Chen, Junhu Zhao, Weixin Jin, Huihui Yuan

Abstract: Seasonal precipitation anomalies are largely regulated by atmospheric circulation, which dynamical models predict with greater reliability than precipitation itself. Here, we employ a deep learning model that translates dynamical circulation predictions into precipitation estimates. Predictions initialized from March to May consistently indicate a dry anomaly over central China in summer 2026. Ret… ▽ More Seasonal precipitation anomalies are largely regulated by atmospheric circulation, which dynamical models predict with greater reliability than precipitation itself. Here, we employ a deep learning model that translates dynamical circulation predictions into precipitation estimates. Predictions initialized from March to May consistently indicate a dry anomaly over central China in summer 2026. Retrospective evaluations revealed higher predictive skill in the analogue years, which also tended to feature central equatorial Pacific warming persisting from the preceding winter into summer. This warming favors an anomalous cyclonic circulation over the western North Pacific-South China Sea-South China region, which induces northerly winds and moisture divergence that jointly suppress rainfall over central China. Supporting this mechanism, layer-wise relevance propagation (LRP) independently identifies these northerly winds as the dominant driver of the prediction among all model inputs. Perturbation tests supported this attribution: removing LRP-identified features effectively eliminates the dry anomaly. Our framework thus provides physically interpretable explanations for AI-derived regional climate projections, facilitating evidence-based assessment before observational data become available. △ Less

Submitted 19 August, 2026; originally announced August 2026.

Sublime Transfer Printing of Three-Dimensional Nanostructure Ensembles

Authors: Lei Chen, Hao Wang, Wang Zhang, Fu Fan, Peng Liu, Xiaoxue Bi, John You En Chan, Cheng-Feng Pan, Bochang Wu, Zhengchao Liu, Rou Yun Teo, Hongtao Wang, Huigao Duan, Joel K. W. Yang

Abstract: High-resolution three-dimensional (3D) nanostructures for visible-light photon manipulation provide unique and bespoke capabilities in optics and photonics. However subwavelength nanofabrication and reliable ensemble manipulation of the 3D prints onto arbitrary substrates remain challenging. Here, we introduce sublime transfer strategy tailored for transfer printing ensembles of delicate 3D printe… ▽ More High-resolution three-dimensional (3D) nanostructures for visible-light photon manipulation provide unique and bespoke capabilities in optics and photonics. However subwavelength nanofabrication and reliable ensemble manipulation of the 3D prints onto arbitrary substrates remain challenging. Here, we introduce sublime transfer strategy tailored for transfer printing ensembles of delicate 3D printed nanostructures. This strategy enables conformal, damage-free integration of arrays of 3D structures on diverse substrates. Naphthalene acts as a transient stamp to encapsulate the structures during transfer and placement. We rely on the low sublimation temperature of naphthalene to release the structures reliably with nearly zero stress, preventing mechanical damage and positional misalignment. This approach is broadly applicable to integrate diverse nanostructures and photonic devices onto various substrates, and enabling inorganic architectures through ensemble uniform post-processing, including 2.5D photonic crystals on flexible PDMS, diffractive optical elements on curved lenses, spiral phase plates on CMOS chips, multilayer achromatic metalens on optical fiber facet, as well as 3D glass photonic crystals and optical topological resonators on anti-stiction quartz. △ Less

Submitted 17 August, 2026; originally announced August 2026.

A scalable chip-integrated single-photon source array based on 50 individually addressable neutral atoms

Authors: Ya-Dong Hu, Tian-Yang Zhang, Dong-Qi Ma, Yi-Chen Zhang, Liang Chen, Wen-Yi Zhu, Hong-Jie Fan, Yan-Lei Zhang, Zhu-Bo Wang, Gang Li, Xi-Feng Ren, Guang-Can Guo, Chang-Ling Zou

Abstract: Scalable arrays of identical single-photon sources are a central resource for photonic quantum information processing, quantum networks and quantum metrology. Neutral atoms provide intrinsically identical emitters that can be assembled and rearranged in optical tweezers, but a many-channel fiber interface to individually trapped atoms has remained a major technical challenge. Here we demonstrate a… ▽ More Scalable arrays of identical single-photon sources are a central resource for photonic quantum information processing, quantum networks and quantum metrology. Neutral atoms provide intrinsically identical emitters that can be assembled and rearranged in optical tweezers, but a many-channel fiber interface to individually trapped atoms has remained a major technical challenge. Here we demonstrate a chip-interfaced single-photon source array based on 50 individually addressable $^{87}\mathrm{Rb}$ atoms. A glass waveguide fan-out converts the \SI{5}{\micro m} pitch of the optical-tweezer array to the \SI{127}{\micro m} pitch of a commercial fiber array, mapping each atom to its own waveguide, fiber and single-photon detector. We resolve all 50 channels with an average nearest-neighbor cross-talk of $0.4\%$ and a uniform insertion loss of \SI{2.9}{dB}, and verify single-photon emission with $g^{(2)}(0)=0.29$, presently limited by detector dark counts and residual cooling-light scattering. Combining per-channel atom discrimination, rearrangement and reservoir replenishment, we prepare source subarrays of up to 24 atoms with a $93\%$ fill fraction. For small target numbers, atom loss is repaired from the reservoir at the detection-limited rate of \SI{118}{Hz}. We further fabricate a 784-channel waveguide chip, showing that the photonic interface can be extended well beyond the present number. This architecture establishes a fiber-native neutral-atom platform for larger arrays of identical single-photon sources. △ Less

Submitted 16 August, 2026; originally announced August 2026.

Experimental quantum telecloning across silicon photonic chips

Authors: Zicong Wen, Kai Wang, Bochi Wu, Leizhen Chen, Yan-Qing Lu, Shining Zhu, Xiao-Song Ma

Abstract: Telecloning -- the combination of quantum teleportation and cloning -- offers a powerful mechanism to disseminate unknown quantum states to multiple spatially separated recipients with optimal fidelity. Despite its conceptual importance for quantum networks, an experimental demonstration of symmetric qubit quantum telecloning remains elusive, particularly due to the challenges of generating multip… ▽ More Telecloning -- the combination of quantum teleportation and cloning -- offers a powerful mechanism to disseminate unknown quantum states to multiple spatially separated recipients with optimal fidelity. Despite its conceptual importance for quantum networks, an experimental demonstration of symmetric qubit quantum telecloning remains elusive, particularly due to the challenges of generating multipartite entangled resource states and implementing stable multi-photon interference across distributed nodes. Here, we realize the optimal 1 to 2 symmetric quantum telecloning using a scalable silicon photonic platform. We implement a six-photon protocol using two independent, fiber-linked photonic chips: one generating a heralded input state and the other preparing a four-photon entangled resource state. By performing an interchip Bell-state measurement, we successfully distribute the input state into two optimal clones at remote nodes. We observe an interchip cloning fidelity of 78.45 $\pm$ 1.39%, exceeding the classical limit of 2/3 by 8 standard deviations. Our results demonstrate the robust generation and manipulation of complex multi-photon states between integrated chips, providing a foundational building block for large-scale multi-party quantum networks. △ Less

Submitted 12 August, 2026; originally announced August 2026.

Homojunction-induced thermopower enhancement in polymer films

Authors: Zhen Xu, Hui Li, Guangzheng Zuo, Xiaojuan Dai, Jincheng Liao, Guofeng Cheng, Jian Song, Wenqing Zhang, Martijn Kemerink, Lidong Chen

Abstract: It has been more than twenty years since conductive polymers began to receive attention as an emerging thermoelectric material. However, the trade-off between electrical conductivity (σ) and thermopower (S) has proven to be a major challenge that has obstructed their use in actual devices. Here we report the discovery that the thermopower of the p- and n-type legs of organic thermogenerators can b… ▽ More It has been more than twenty years since conductive polymers began to receive attention as an emerging thermoelectric material. However, the trade-off between electrical conductivity (σ) and thermopower (S) has proven to be a major challenge that has obstructed their use in actual devices. Here we report the discovery that the thermopower of the p- and n-type legs of organic thermogenerators can be substantially enhanced, without significant deterioration of σ, by constructing an in-plane segmented structure consisting of a homojunction with different doping levels on either side. In such segmented layers, the S is abnormally higher than the average value of the constituent parts when applying a forward temperature gradient (heating the heavily doped counterpart), while it is lower upon a reverse temperature gradient. Typically, for a two-stage segmented film of p-type PDPP-Se, an abnormally large S of 210 uV K-1 and σ of 2.5*10^4 S m-1 are obtained, resulting in a large power factor (PF) of 1100 uW m-1 K-2 and a record ZT of 1.36 at room temperature. The enhanced thermopower is attributed to an additional voltage developed at the homojunction under heating as explained by kinetic Monte Carlo simulations. This finding provides a breakthrough approach to the modulation of thermoelectric transport properties of conductive polymers. △ Less

Submitted 7 August, 2026; originally announced August 2026.

Comments: 75 pages, 45 figures

Roadmap on UV-C photodetectors: materials, applications and industry perspectives

Authors: Fabien Massabuau, Drew Riley, Paul Meredith, Tilman Weiss, Damanpreet Kaur, Yuichi Oshima, Robert W. Martin, Eva Monroy, Le Chen, Hongwei Liang, Hong Yin, Keyun Gu, Meiyong Liao, Yaonan Hou, Fa Cao, Xiaosheng Fang, Ruiheng Li, Guoqiang Peng, Zhiwen Jin, Lijie Li, Nasim Zarrabi, Sebastian Wood, Jesper Skottfelt, Susan E. S. Spesyvtseva, Jonathan McKendry , et al. (22 additional authors not shown)

Abstract: UV-C photodetectors are poised to play an increasingly important role in future photonic technologies, driven by the rapid emergence of UV-C light sources and new wide bandgap semiconductors. These advances are enabling new levels of spectral selectivity, radiation hardness, sensitivity, and device integration, while opening opportunities across a broad range of applications. This roadmap provides… ▽ More UV-C photodetectors are poised to play an increasingly important role in future photonic technologies, driven by the rapid emergence of UV-C light sources and new wide bandgap semiconductors. These advances are enabling new levels of spectral selectivity, radiation hardness, sensitivity, and device integration, while opening opportunities across a broad range of applications. This roadmap provides a comprehensive overview of the current landscape of UV-C photodetection, spanning established and emerging material platforms (Ga2O3, AlGaN, BN, diamond, MgZnO, 2-dimensional materials, metal halide perovskites, micro-electromechanical systems), and their applications in metrology, astronomy, communications, environmental monitoring, fire detection, missile warning, gas sensing, and medical diagnostics. By identifying opportunities, bottlenecks, and future directions, this roadmap aims to support both newcomers and established researchers, with the aim of accelerating the translation of UV-C photodetectors into impactful technologies. △ Less

Submitted 7 August, 2026; originally announced August 2026.

Comments: 89 pages, 19 figures

Achieving 100$\,$MHz Instantaneous Bandwidth in a Broadband Rydberg Microwave Sensor

Authors: Yuhan Yan, Jinyin Wan, Xuejie Li, Xing Xia, Haojie Zhao, Binghong Yu, Jianliao Deng, L. Q. Chen, Huadong Cheng

Abstract: Rydberg atoms have attracted considerable attention in recent years as a novel platform for microwave sensing, owing to their unique physical merits: large transition dipole moments between Rydberg levels and broad frequency coverage. As a critical figure of merit for Rydberg microwave sensors, instantaneous bandwidth serves as a key benchmark for evaluating their viability in practical applicatio… ▽ More Rydberg atoms have attracted considerable attention in recent years as a novel platform for microwave sensing, owing to their unique physical merits: large transition dipole moments between Rydberg levels and broad frequency coverage. As a critical figure of merit for Rydberg microwave sensors, instantaneous bandwidth serves as a key benchmark for evaluating their viability in practical applications. Previous studies on instantaneous bandwidth remain limited to single-frequency operation, with typical demonstrated values of only tens of megahertz, a constraint that hampers the real-world deployment of this sensing technology. Here, we experimentally achieve an instantaneous bandwidth of over 100$\,$MHz across a broad frequency range of 2.7-20$\,$GHz and realize a sensitivity in the hundreds of nV$\,$cm$^{-1}\,$Hz$^{-1/2}$ range. The physical mechanism lies in the dressed-state coherence and the interference effect between different transition channels. Our work substantially broadens the instantaneous bandwidth of Rydberg microwave sensors and paves the way for their practical deployment in fields such as radar and wireless communications. △ Less

Submitted 28 July, 2026; originally announced July 2026.

Comments: 6 pages, 5 figures

Flow Reversal in Low-Prandtl-Number Convection via Lateral Confinement

Authors: Zhi-Han Wu, Long Chen, Yan-Wu Cao, Liang Xue, Ming-Zhu Ai, Juan-Cheng Yang, Ming-Jiu Ni

Abstract: A prevailing consensus holds that flow reversals of the large-scale circulation (LSC) are suppressed in low-Prandtl-number (Pr) fluids, as high thermal diffusivity rapidly dissipates the energy required to fuel the corner-vortex mechanisms. Here, we report Direct Numerical Simulations of liquid metal convection (Pr=0.029) revealing that strong lateral confinement defies this consensus, enabling su… ▽ More A prevailing consensus holds that flow reversals of the large-scale circulation (LSC) are suppressed in low-Prandtl-number (Pr) fluids, as high thermal diffusivity rapidly dissipates the energy required to fuel the corner-vortex mechanisms. Here, we report Direct Numerical Simulations of liquid metal convection (Pr=0.029) revealing that strong lateral confinement defies this consensus, enabling sustained LSC reversals. We show that confinement triggers a ``plume condensation" transition, reorganizing chaotic thermal plumes into highly coherent, quasi-linear structures. A thermal dissipation analysis demonstrates that this coherence drastically reduces heat loss during transport, allowing plumes to deliver sufficient buoyancy to corner vortices to drive reversals. We map a distinct ``island of reversal" in the parameter space, establishing lateral confinement as a control parameter capable of overcoming the stabilizing effects of high thermal diffusivity. △ Less

Submitted 24 July, 2026; originally announced July 2026.

Probing large mass-splitting inelastic Dark Matter with RES-NOVA

Authors: D. Alloni, G. Benato, P. Carniti, M. Cataldo, L. Chen, M. Clemenza, M. Consonni, G. Croci, I. Dafinei, F. A. Danevich, C. de Vecchi, D. Di Martino, R. Elleboro, N. Ferreiro Iachellini, F. Ferroni, F. Filippini, S. Ghislandi, A. Giachero, L. Gironi, P. Gorla, C. Gotti, D. L. Helis, D. V. Kasperovych, V. V. Kobychev, G. Marcucci , et al. (24 additional authors not shown)

Abstract: Probing inelastic dark matter at large mass splittings requires heavy target nuclei, an extended recoil-energy range, and the high-velocity tail of the dark-matter distribution. We exploit these features with the RES-NOVA prototype detector, featuring a PbWO4 cryogenic calorimeter, produced from archaeological Pb and operated at the deep-underground laboratory of Gran Sasso of INFN (Italy), analyz… ▽ More Probing inelastic dark matter at large mass splittings requires heavy target nuclei, an extended recoil-energy range, and the high-velocity tail of the dark-matter distribution. We exploit these features with the RES-NOVA prototype detector, featuring a PbWO4 cryogenic calorimeter, produced from archaeological Pb and operated at the deep-underground laboratory of Gran Sasso of INFN (Italy), analyzing a 32.4 g day exposure over 2.5 keV - 1 MeV under both the Standard Halo Model (SHM) and a Large Magellanic Cloud (LMC)-motivated velocity distribution. We extend direct-detection constraints beyond the 330 keV reach of established technologies (e.g. Xe-based TPCs), probing splittings up to 510 (780) keV in the SHM (LMC) benchmark, while future exposures will probe new regions of the parameter space. △ Less

Submitted 30 July, 2026; v1 submitted 20 July, 2026; originally announced July 2026.

Comments: reference update

Single-atom sensor for low-frequency electric field

Authors: Quan Yuan, Shuang-Qing Dai, Tai-Hao Cui, Pei-Dong Li, Yuan-Zhang Dong, Zhuo-Zhu Wu, Ji Li, Fei Zhou, Jian-Qi Zhang, Liang Chen, Mang Feng

Abstract: Precision measurement of low-frequency electric field (LFEF) signals with frequency from 30 kHz to 300 kHz is crucial for advancing both fundamental science and practical applications, owing to their unique frequency regime. For conventional electromagnetic antennas, the long wavelength (i.e., several kilometers) of the LFEF leads to a severe size constraint that efficient radiation becomes challe… ▽ More Precision measurement of low-frequency electric field (LFEF) signals with frequency from 30 kHz to 300 kHz is crucial for advancing both fundamental science and practical applications, owing to their unique frequency regime. For conventional electromagnetic antennas, the long wavelength (i.e., several kilometers) of the LFEF leads to a severe size constraint that efficient radiation becomes challenging to achieve when the antenna size is much smaller than the long wavelength of the LFEF signals, which in turn results in a reduction of measurement sensitivity and compromises antenna's performance. By exploiting the high intrinsic sensitivity of cold trapped ions to weak alternating electric signals via Coulomb interaction, we demonstrate a single-ion phonon laser sensor acted by an injection-locked 40Ca+ ion confined in a surface-electrode trap. Combining the beat frequency technique with the injection-locked phonon laser oscillation, we demonstrate a practical and efficient approach for simultaneous extraction of the frequency, phase, and amplitude from a single measurement, without the need for sideband cooling. This approach achieves precision detection for LFEF signals with the sensitivity of 404 uV/(m * Hz1/2) and the detection limit of 61.5 uV/m. Besides, this approach also shows remarkable robustness against noise. Our study helps realizing practical single-atom sensors in the low-frequency regime, opening avenues for applications in subsurface communication, precision metrology, mass spectrometry, and biomedical monitoring. △ Less

Submitted 20 July, 2026; originally announced July 2026.

Comments: 8 pages, 4 figures

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites

Authors: Fu Wang, Chi Yang, Qi-Feng Lu, Rui-Xia Liu, Xiao-Fei Yang, Xiao-Fang Liu, Bo Li, Lin Chen

Abstract: Multilayer cloud detection from active--passive observation is vital for numerical weather prediction. In this study, channel selections derived from threshold-based algorithms are embedded as feature-engineering priors into a 1D-CNN, and machine learning (ML) is used to learn latent physical relationships to simplify physical retrievals for operational deployment. The results show that the 1D-CNN… ▽ More Multilayer cloud detection from active--passive observation is vital for numerical weather prediction. In this study, channel selections derived from threshold-based algorithms are embedded as feature-engineering priors into a 1D-CNN, and machine learning (ML) is used to learn latent physical relationships to simplify physical retrievals for operational deployment. The results show that the 1D-CNN achieves a multilayer-cloud probability of detection ($\mathrm{POD}{\mathrm{mul}}$) of 0.620 and a false alarm rate ($\mathrm{FAR}{\mathrm{mul}}$) of 0.240, outperforming the conventional threshold algorithm ($\mathrm{POD}{\mathrm{mul}} = 0.558$, $\mathrm{FAR}{\mathrm{mul}} = 0.369$). These results demonstrate that prior physical knowledge derived from radiative transfer theory can serve as an effective feature-engineering prior. Further experiments show that ML-revealed physical mechanisms can also enhance traditional algorithms. Replacing AGRI channel 12 (C12, centered at $10.8~μ\mathrm{m}$) with channel 13 (C13, centered at $12.0~μ\mathrm{m}$) increased $\mathrm{POD}{\mathrm{mul}}$ from 0.558 to 0.609 without materially affecting $\mathrm{FAR}{\mathrm{mul}}$. However, for AHI, substituting the $11.2~μ\mathrm{m}$ channel with the $12.3~μ\mathrm{m}$ channel yielded negligible improvement. In addition to spectral response function (SRF) mismatches, a primary contributing factor is the channels' on-orbit radiometric stability. Hence, physics-informed machine-learning methods appear promising for advancing remote-sensing AI, while sensor-specific characteristics must be considered during operational transfer. △ Less

Submitted 7 July, 2026; originally announced July 2026.

The muon Moonshot: Moon subsurface tomography with upward-going muons

Authors: Zimo Hu, Leyun Gao, Zhengyun You, Qite Li, Qiang Li, Yuhong Yu, Liangwen Chen, Xueheng Zhang, Zhiyu Sun

Abstract: We propose a novel muon Moonshot concept for lunar subsurface tomography based on upward-going muons originated from the lunar regolith. Unlike the Earth, the Moon lacks an atmosphere, leaving a dense regolith below and a near-vacuum environment above. Consequently, while most downward-going hadrons are absorbed before decaying, upward-going hadrons escaping the regolith can decay in flight, produ… ▽ More We propose a novel muon Moonshot concept for lunar subsurface tomography based on upward-going muons originated from the lunar regolith. Unlike the Earth, the Moon lacks an atmosphere, leaving a dense regolith below and a near-vacuum environment above. Consequently, while most downward-going hadrons are absorbed before decaying, upward-going hadrons escaping the regolith can decay in flight, producing a significant source of lunar muons. These muons are detectable by instruments on the lunar surface or in near-lunar orbit. We perform Monte Carlo simulations to investigate their energy spectra, angular distributions, and integrated fluxes under various theoretical and detector configurations. The results indicate that the lunar muon flux is sensitive to detector altitude under a flat-terrain assumption, demonstrating its potential as a novel non-invasive probe of shallow subsurface voids. We also present case studies on the detection of underground cavities and water resources, with cavity-induced flux variations observable in less than two minutes and weaker water signals distinguishable after about 36 minutes of data collection with a $1~\mathrm{m^2}$ detector, and discuss potential implementations in future lunar missions. △ Less

Submitted 20 August, 2026; v1 submitted 11 July, 2026; originally announced July 2026.

Comments: 8 pages, 7 figures

Saturation-Aware Robust Trajectory Optimization for Reusable Launch Vehicles via Differentiable Physics

Authors: Liwei Chen, Tong Qin

Abstract: The high-angle-of-attack flip maneuver of reusable launch vehicles presents significant challenges for robust trajectory optimization due to the combined effects of highly nonlinear dynamics, aerodynamic uncertainties, and actuator saturation. This paper presents a differentiable physics framework for saturation-aware robust trajectory optimization. At its core, a Differentiable Particle Tube Cont… ▽ More The high-angle-of-attack flip maneuver of reusable launch vehicles presents significant challenges for robust trajectory optimization due to the combined effects of highly nonlinear dynamics, aerodynamic uncertainties, and actuator saturation. This paper presents a differentiable physics framework for saturation-aware robust trajectory optimization. At its core, a Differentiable Particle Tube Control (DPTC) scheme is developed to optimize uncertainty evolution through an ensemble-based distribution shaping strategy. State uncertainty is represented by a Lagrangian particle ensemble, while hard actuator projection operators are embedded directly into the computational graph, enabling the joint optimization of the nominal feedforward trajectory and a time-varying feedback policy via end-to-end backpropagation. The proposed framework is evaluated against an automatic differentiation-based Successive Convexification (AD-SCvx) baseline combined with a conventional covariance steering feedback strategy. Six-degree-of-freedom Monte Carlo simulations demonstrate that, although the baseline achieves nominal fuel-optimal solutions, its unconstrained feedback formulation becomes susceptible to actuator saturation under aerodynamic disturbances, leading to degraded closed-loop robustness. In contrast, the proposed DPTC framework proactively performs a constraint-aware performance trade-off by relaxing spatial tracking to preserve critical control authority. These results demonstrate that integrating differentiable physics with ensemble-based optimization provides an effective and practical framework for robust guidance in highly constrained aerospace flight systems. △ Less

Submitted 2 July, 2026; originally announced July 2026.

Low-latency FPGA-based electronic control system for fast preparation of defect-free atom arrays

Authors: Ya-Dong Hu, Dong-Qi Ma, Tian-Yang Zhang, Liang Chen, Yi-Chen Zhang, Xiao-Kang Zhong, Wen-Yi Zhu, Hong-Jie Fan, Qing-Xuan Jie, Yan-Lei Zhang, Gang Li, Xi-Feng Ren, Xu-Liang Zhang, Guang-Can Guo, Zhu-Bo Wang, Chang-Ling Zou

Abstract: The scalability of neutral atom quantum computing demands integrated electronic control systems with low latency, modular architecture, and real-time feedback capability. Here, we present an FPGA-based electronic control system that eliminates the PC from the feedback loop, integrating photon counting, real-time decision-making, and waveform generation within a unified PXIe architecture. The syste… ▽ More The scalability of neutral atom quantum computing demands integrated electronic control systems with low latency, modular architecture, and real-time feedback capability. Here, we present an FPGA-based electronic control system that eliminates the PC from the feedback loop, integrating photon counting, real-time decision-making, and waveform generation within a unified PXIe architecture. The system achieves a total feedback latency of $282\,\mathrm{μs}$ and is validated in practical experiments by assembling defect-free atom arrays from 24 stochastically loaded optical tweezers. A single-round rearrangement achieves a filling fraction of $\sim96\%$, while feedback-controlled iterative rearrangement over five rounds boosts the success probability for generating a 10-atom defect-free array from $65.7\%$ to $95.4\%$. This system establishes the electronic infrastructure necessary for mid-circuit measurement and real-time quantum error correction on neutral-atom platforms. △ Less

Submitted 9 July, 2026; originally announced July 2026.

Comments: 8 pages, 5 figures

Quantum Limits to Ground-State Cooling of Traveling Hypersound Phonons

Authors: Juntong Yang, Liang Chen, Xiaoyi Bao

Abstract: The steady final phonon occupation in waveguide optomechanical systems based on backward stimulated Brillouin-Mandelstam scattering has not been established in the strong-coupling regime. In this work, the displacement spectra of anti-Stokes optical modes and acoustic modes in tapered chalcogenide photonic crystal fiber are derived from the Lindblad (or Gorini-Kossakowski-Sudarshan-Lindblad) maste… ▽ More The steady final phonon occupation in waveguide optomechanical systems based on backward stimulated Brillouin-Mandelstam scattering has not been established in the strong-coupling regime. In this work, the displacement spectra of anti-Stokes optical modes and acoustic modes in tapered chalcogenide photonic crystal fiber are derived from the Lindblad (or Gorini-Kossakowski-Sudarshan-Lindblad) master equation. By analyzing the full spectral response, we indicate that the system can enter the strong-coupling regime through the emergence of normal-mode splitting and avoided crossings. Within a non-Hermitian framework, the threshold for strong coupling is identified, showing that it can be achieved at relatively low pump power even at room temperature. Furthermore, we derive a unified analytical expression for the final phonon occupation, revealing that quantum backaction and zero-point fluctuations impose additional fundamental limits that hinder the achievement of ground-state cooling. These results redefine the quantum limits of steady-state cooling in continuum optomechanics, motivating the search for new strategies to access the quantum ground-state of macroscopic phonons. △ Less

Submitted 2 July, 2026; originally announced July 2026.

Comments: 25 pages, 4 figures, submitted to Physical Review A

Compressive Spectrum Sensing via Spectral Multiplexing in Rydberg Atomic Receiver

Authors: Jun-Rong Chen, Yi-Ming Yin, Le-Bin Chen, Kai Wang, Bang Liu, Li-Hua Zhang, Hao Tian, Ming-Min Zhao, Bin-Bin Wei, Dong-Sheng Ding

Abstract: Rydberg-atomic receivers exhibit exceptional sensitivity yet are fundamentally constrained by the narrow instantaneous bandwidth, limiting their practical deployment in broadband scenarios. Prior approaches typically expand the bandwidth by physically broadening the atomic response, which usually requires auxiliary electromagnetic fields or stringent parameter tuning, thereby increasing overall sy… ▽ More Rydberg-atomic receivers exhibit exceptional sensitivity yet are fundamentally constrained by the narrow instantaneous bandwidth, limiting their practical deployment in broadband scenarios. Prior approaches typically expand the bandwidth by physically broadening the atomic response, which usually requires auxiliary electromagnetic fields or stringent parameter tuning, thereby increasing overall system complexity. Here, we propose a compressive spectral multiplexing framework implemented in a waveguide-coupled Rydberg atomic receiver using a frequency-modulated local oscillator (FMLO). The FMLO creates multiple parallel sensing channels that collectively constitute a physical compressive sensing matrix, generating multiple narrowband intermediate-frequency replicas of the input signal. Thus, a broadband microwave spectrum is projected onto a set of narrowband atomic responses. It is demonstrated that spectral information spanning a bandwidth of over 640 MHz can be effectively compressed into the intrinsic atomic bandwidth of 126 kHz, achieving a spectrum compression ratio exceeding 1000. Furthermore, these output replicas offer intrinsic measurement redundancy and facilitate signal-to-noise ratio enhancement. An approximate 10 dB gain is achieved in the required bit-energy-to-noise-power-density ratio for multi-channel communication via maximal-ratio combining. This approach requires no auxiliary fields or broadband electronics, providing a simple and scalable pathway for chip-scale quantum receivers, latency-critical sensing, and next-generation wireless communications. △ Less

Submitted 2 July, 2026; originally announced July 2026.

Improving Muon-Scattering Material Identification via Coarse Momentum Encoding and Unsupervised Domain Adaptation

Authors: Yuxin Bao, Zhao Zhang, Pei Yu, Liangwen Chen, Weibo He, Yu Zhang, Yuhong Yu, Xueheng Zhang, Lei Yang, Zhiyu Sun

Abstract: Cosmic-ray muon scattering has shown considerable potential for detecting nuclear materials and other dense contraband, but practical deployment remains challenging. A major difficulty arises from the coupling between material properties and muon momentum, since the broad natural momentum distribution influences the scattering angle and prevents unambiguous material identification. In this work, w… ▽ More Cosmic-ray muon scattering has shown considerable potential for detecting nuclear materials and other dense contraband, but practical deployment remains challenging. A major difficulty arises from the coupling between material properties and muon momentum, since the broad natural momentum distribution influences the scattering angle and prevents unambiguous material identification. In this work, we propose a Coarse Momentum-Aware Domain Adaptation (CMADA) method to enable precise identification of materials. Instead of relying on high-precision momentum measurements, the proposed framework adopts coarse momentum binning combined with unsupervised domain adaptation to learn transferable scattering representations. In addition, a precision review mode based on averaging repeated samplings was proposed to further enhances identification performance. The coarse momentum binning strategy improves same-domain identification accuracy from 62.15% without momentum information to 89.52% with 5-bin momentum information, and further to 93.37% (precision review mode). Furthermore, the proposed unsupervised domain adaptation framework improves the cross-domain identification accuracy from 71.71% for the source-only baseline to 89.00% without requiring target domain labels. △ Less

Submitted 29 June, 2026; originally announced June 2026.

Comments: 23 pages plus refs, 9 figures, 3 tables

Broadband Rydberg Atomic Microwave Sensing with 44.6$\,$MHz Instantaneous Bandwidth

Authors: Yuhan Yan, Xuejie Li, Jinyin Wan, Xing Xia, Haojie Zhao, Binghong Yu, Jianliao Deng, Huadong Cheng, L. Q. Chen

Abstract: Rydberg atoms have become a promising novel type of microwave sensor due to their excellent physical properties -- broad frequency coverage and large electric dipole moments. High sensitivity and broad instantaneous bandwidth are two indispensable requirements for deployable Rydberg microwave sensors. However, enabling broadband operation while retaining high sensitivity has been a longstanding ba… ▽ More Rydberg atoms have become a promising novel type of microwave sensor due to their excellent physical properties -- broad frequency coverage and large electric dipole moments. High sensitivity and broad instantaneous bandwidth are two indispensable requirements for deployable Rydberg microwave sensors. However, enabling broadband operation while retaining high sensitivity has been a longstanding barrier limiting their applications. We propose and experimentally demonstrate a Rydberg microwave sensor whose instantaneous bandwidth is significantly enhanced via an auxiliary microwave field. By finely modulating the Rydberg energy levels with this field, we broaden the bandwidth substantially while retaining the sensor's inherent high sensitivity. An instantaneous bandwidth of 44.6$\,$MHz ($\pm$22.3$\,$MHz) with a sensitivity of 225.7$\,$nV$\,$cm$^{-1}\,$Hz$^{-1/2}$ is realized in a thermal \(^{87}\)Rb vapor with the local microwave frequency of 16.03$\,$GHz. Our work delivers concurrent broad instantaneous bandwidth and high sensitivity for Rydberg microwave sensors, paving a technically viable path for their practical deployment in broadband microwave metrology, radar, and wireless communication. △ Less

Submitted 9 July, 2026; v1 submitted 24 June, 2026; originally announced June 2026.

Comments: 8 pages, 5 figures

FireDataForge: A Unified Framework for Multi-Source Wildfire Data Retrieval and Integration

Authors: Zeyu Xia, Lexie Chen, Ye Liu, Huilin Huang

Abstract: Wildfire research, modeling, and education require geospatial data from multiple sources that vary in formats, coordinate systems, spatial resolutions, and temporal cadences. This preprocessing burden limits reproducible reuse. We present FireDataForge, an open-source Python framework that automates retrieval and harmonization of 11 wildfire-related sources spanning fire behavior, weather, land co… ▽ More Wildfire research, modeling, and education require geospatial data from multiple sources that vary in formats, coordinate systems, spatial resolutions, and temporal cadences. This preprocessing burden limits reproducible reuse. We present FireDataForge, an open-source Python framework that automates retrieval and harmonization of 11 wildfire-related sources spanning fire behavior, weather, land cover, vegetation, elevation, built environment, wildland-urban interface, fire history, and satellite imagery. Given an MTBS Event ID, FireDataForge retrieves relevant datasets, aligns them to a common grid, and outputs analysis-ready NumPy arrays with embedded metadata. Batch processing of historical fires demonstrates support for fire behavior simulation, educational visualization, machine learning, and AI-assisted wildfire analysis. △ Less

Submitted 19 June, 2026; originally announced June 2026.

Comments: 5 pages, 2 figures. Accepted to the 2026 IEEE International Conference on Information Reuse and Integration for Data Science (IEEE IRI 2026)

Finite-Time Electrometry with a Quantum-Regime Single-Ion Phonon Laser

Authors: Pei-Dong Li, Yuan-Zhang Dong, Zhuo-Zhu Wu, Jia-Wei Wang, Ji Li, Jian-Qi Zhang, Zhi-Jiao Deng, Liang Chen, Mang Feng

Abstract: The phonon laser realized in a trapped ion, i.e., a self-sustained mechanical oscillator, has demonstrated the unique characteristics in practically detecting externally applied electric signals without the prerequisite of sideband cooling. Entering the quantum regime via sideband cooling is expected to further improve its sensing performance. Here we report the first experimental realization of a… ▽ More The phonon laser realized in a trapped ion, i.e., a self-sustained mechanical oscillator, has demonstrated the unique characteristics in practically detecting externally applied electric signals without the prerequisite of sideband cooling. Entering the quantum regime via sideband cooling is expected to further improve its sensing performance. Here we report the first experimental realization of a quantum-regime single-ion phonon laser ($\bar{n}<10$) using a trapped $^{40}\mathrm{Ca}^+$ ion and demonstrate electrometry based on its phase-space symmetry-breaking response to weak resonant electric fields. By tuning the phonon-laser parameters, we reveal that the sensing performance is fundamentally governed by the finite-time relaxation dynamics of the underlying open quantum system. We find that a slow Liouvillian relaxation, correlated with the finite experimental interaction window, effectively enhances the dynamic susceptibility while maintaining the structural robustness of the limit cycle. This regime, when applied to the detection of electric fields, produces a shot-noise-limited peak sensitivity of $14.15 \pm 0.77~μ\mathrm{V/m}/\sqrt{\mathrm{Hz}}$ and a minimum detectable field variation of $δE_{\mathrm{min}} \approx 1.83~μ\mathrm{V/m}$. Our results establish quantum phonon lasers as a practical platform for advanced sensing and highlight the central role of Liouvillian dynamics in non-equilibrium electrometry. △ Less

Submitted 18 June, 2026; originally announced June 2026.

Comments: 6 pages, 4 figures; plus Supplementary Materials

Carbon Layer Orientation and Closed-Pore Construction Achieving Ultra-Low Specific Surface Area Hard Carbon for High-Performance Na-ion Storage

Authors: Bowen Wang, Zihan Yang, Minghui Zhao, Wenjie Mai, Qing Xu, Huan Li, Liang Zhang, Chul Gyu Jhun, Le Chen, Wentao Zhang, Jingtai Zhao, Jinliang Li

Abstract: Addressing the critical trade-off between initial Coulombic efficiency (ICE) and reversible capacity in hard carbon anodes for Na-ion batteries (NIBs), we introduce a novel coupling strategy that combines carbon layer orientation reconstruction with closed-pore construction to produce hard carbon with an ultra-low specific surface area. We demonstrate that the nanographite domains within the hard… ▽ More Addressing the critical trade-off between initial Coulombic efficiency (ICE) and reversible capacity in hard carbon anodes for Na-ion batteries (NIBs), we introduce a novel coupling strategy that combines carbon layer orientation reconstruction with closed-pore construction to produce hard carbon with an ultra-low specific surface area. We demonstrate that the nanographite domains within the hard carbon precursor undergo entropy-driven orientation reconstruction through the synergistic regulation of heteroatom doping and medium-temperature carbonization. This process not only increases interlayer spacing and promotes structural disorder but also enables the formation of dense, closed pores and ultramicropores at domain boundaries via confined atomic migration, while simultaneously encapsulating surface open pores within internal closed ones. Due to this unique pore architecture, our hard carbon exhibits an ultra-low specific surface area of 1.89 m2 g-1 with a markedly higher proportion of closed pores. As a result, our hard carbon achieves a remarkable reversible capacity of 342.3 mAh g-1 at 20 mA g-1, with an exceptional ICE of 90.4% and a dominant plateau capacity of 262.3 mAh g-1 (76.6%) for NIBs. We believe this coupling strategy provides a new paradigm for the structural engineering of high-ICE anode materials in advanced NIBs. △ Less

Submitted 14 June, 2026; originally announced June 2026.

Comments: 24 pages, 7 figures

Acoustic propagation of a vortex beam in typical Arctic sound environments

Authors: Chengjun Wang, Liwei Chen, Tengjiao He, Lisheng Zhou, Zhixiong Gong

Abstract: This study investigates the propagation of acoustic vortex beams carrying orbital angular momentum (OAM) in the Arctic underwater environments including the half-channel and the double duct. We produce a vortex beam with a 126-element hexagonal transducer array and model the acoustic propagation based on the ray method. It is found that under the typical Arctic circumstances, vortex beams with hel… ▽ More This study investigates the propagation of acoustic vortex beams carrying orbital angular momentum (OAM) in the Arctic underwater environments including the half-channel and the double duct. We produce a vortex beam with a 126-element hexagonal transducer array and model the acoustic propagation based on the ray method. It is found that under the typical Arctic circumstances, vortex beams with helical phase structures exhibit two unique capabilities. First, in the near field, the divergent components of vortex beams traveling at steep grazing angles illuminate shadow zones without mechanical steering of the acoustic source, which cannot be obtained by point or coherent sources at the same configuration. Second, despite strong boundary interactions and sound-speed inhomogeneity, the phase singularities and OAM modal content remain remarkably robust and can be identified at long ranges to some extend. The ice cover induces a larger transmission loss compared to the pressure release boundary condition because of the acoustic absorption in the ice canopy modeled as an elastic layer. These results advance the understanding of structured acoustic wave propagation in complex polar environments and thus provide a theoretical basis for subglacial exploration and under-ice acoustic communication. △ Less

Submitted 13 June, 2026; originally announced June 2026.

Reinforcement Learning-Enabled Agent for Transmitter Optimization in Digital-Analog Radio-over-Fiber Fronthaul

Authors: Junhao Zhao, Huayuan Qin, Ouhan Huang, Zhongya Li, Chengxi Wang, Boyu Dong, Liangtao Chen, Xuyu Deng, An Yan, Penghao Luo, Renle Zheng, Yongzhu Hu, Aolong Sun, Yinjun Liu, Sizhe Xing, Nan Chi, Junwen Zhang

Abstract: Digital-analog radio-over-fiber (DA-RoF) has emerged as a promising fronthaul solution that combines the high spectral efficiency of analog transmission with the robustness of digital transmission. However, the performance of DA-RoF critically depends on several tightly coupled parameters, including the rounding factor (RF), scaling factor (SF), geometric shaping (GS) factor, and pre-equalization… ▽ More Digital-analog radio-over-fiber (DA-RoF) has emerged as a promising fronthaul solution that combines the high spectral efficiency of analog transmission with the robustness of digital transmission. However, the performance of DA-RoF critically depends on several tightly coupled parameters, including the rounding factor (RF), scaling factor (SF), geometric shaping (GS) factor, and pre-equalization taps coefficients, which jointly affect quantization noise, nonlinear distortion, and bandwidth-induced inter-symbol interference (ISI). Conventional grid search-based optimization is computationally prohibitive and impractical for optical communication. In this work, we propose a reinforcement-learning (RL)-enabled DA-RoF fronthaul agent architecture, capable of autonomously learning optimal transmitter parameters from end-to-end signal-to-noise ratio (SNR) feedback without a differentiable channel model. Experimental results demonstrate that the trained agent steadily improves SNR through sequential decision making and outperforms baseline, achieving ~2.7-dB SNR improvement for 1- to 4-order DA-RoF transmission, reaching final SNR of 35.8 dB, 42.9 dB, 53.8 dB, and 63.2 dB and supporting 1024-, 4096-, 16384-, 65536-quadrature amplitude modulation (QAM) format, respectively. These results validate that the proposed RL-enabled framework provides online, scalable, and hardware-efficient parameter optimization for DA-RoF fronthaul systems, paving the way toward high-order modulation format and intelligent next-generation radio access networks. △ Less

Submitted 3 June, 2026; originally announced June 2026.

Statistical study of energy dissipation in magnetic structures during turbulent reconnection in the Earth's magnetotail

Authors: Rachel Wang, Hantao Ji, Adam Robbins, Kendra Bergstedt, Narges Ahmadi, Robert Ergun, Li-Jen Chen, Jongsoo Yoo, Peiyun Shi, Yuka Doke

Abstract: Magnetic reconnection is a ubiquitous plasma phenomenon that plays a critical role in particle heating and energization. During reconnection, the topology of magnetic field rearranges, depositing energy into the surrounding plasma through bulk flow, thermal heating, or non-thermal particle acceleration. While the pathways of this transformation from magnetic energy into kinetic have been studied e… ▽ More Magnetic reconnection is a ubiquitous plasma phenomenon that plays a critical role in particle heating and energization. During reconnection, the topology of magnetic field rearranges, depositing energy into the surrounding plasma through bulk flow, thermal heating, or non-thermal particle acceleration. While the pathways of this transformation from magnetic energy into kinetic have been studied extensively in recent years through theoretical or case-by-case observations, comprehensive statistical studies remain limited. In this paper, we present a statistical investigation using data from the Magnetospheric Multiscale (MMS) mission, and detail the particle energization mechanisms in magnetic structures found near reconnecting regions in turbulent Earth's magnetotail. We find that electrons with motion perpendicular to the magnetic field dominate $\vec{j}\cdot\vec{E}$ dissipation. In contrast to the conventional picture of unidirectional energy transfer to particles by laminar two-dimensional (2D) reconnection, we find that energy exchange within magnetic structures during turbulent reconnection tends to be bidirectional with only a small positive bias from electromagnetic fields to particles. Specific electron energization mechanisms are quantified, including those due to parallel electric field, Fermi energization from curvature drift, betatron heating from magnetic field inhomogeneity, and polarization drift. △ Less

Submitted 27 May, 2026; originally announced May 2026.

Comments: 18 pages, 7 figures, accepted for publication in Journal of Geophysical Research - Space Physics

From Vintage Mythology to Topological Physics: Unveiling a Universal Structural Attractor in Alcoholic Beverage Aging

Authors: Xinyue Jiang, Heng Yang, Zhiyin Jiu, Youxi Luo, Lin Chen, Yuqun Xie

Abstract: Alcoholic beverage properties are increasingly understood through ethanol-water structural states rather than empirical labels such as alcohol content and vintage. Yet whether chronological vintage similarly reflects an intrinsic structural state remains unclear. Here, we apply persistent homology to map the topological evolution of self-assembled molecular aggregates in strong-aroma Baijiu aged 1… ▽ More Alcoholic beverage properties are increasingly understood through ethanol-water structural states rather than empirical labels such as alcohol content and vintage. Yet whether chronological vintage similarly reflects an intrinsic structural state remains unclear. Here, we apply persistent homology to map the topological evolution of self-assembled molecular aggregates in strong-aroma Baijiu aged 1-10 years. The resulting fingerprints reveal a three-stage maturation pathway: rapid scaffold consolidation (B0), population-level channel stabilization (B1), and non-monotonic cavity reorganization (B2). These coupled trajectories converge toward a mature topological state rather than passively tracking chronological age. We therefore propose a universal topological attractor, in which optimal aging is defined by a system's position in persistence space relative to a mature structural domain. This framework reframes beverage aging as navigation through structural state space, providing a physical basis for quality evaluation and accelerated maturation. △ Less

Submitted 25 May, 2026; originally announced May 2026.

Seeing Inside the Storm: Improving Nowcasting by Integrating Meteorological Drivers

Authors: Minghui Qiu, Jun Chen, Lin Chen, Weifeng Chen, Shuxin Zhong, Zhidan Liu, Yu Zhang, Kaishun Wu

Abstract: Most nowcasting systems, built on radar reflectivity, focus on current precipitation, ignoring the atmospheric precursors -- such as low-level convergence, turbulent eddies, and latent heating -- that offer a fleeting window to foresee storm birth. We introduce MeteoLogist, a physics-inspired radar intelligence framework that models the full life cycle of convection -- from its precursors to organ… ▽ More Most nowcasting systems, built on radar reflectivity, focus on current precipitation, ignoring the atmospheric precursors -- such as low-level convergence, turbulent eddies, and latent heating -- that offer a fleeting window to foresee storm birth. We introduce MeteoLogist, a physics-inspired radar intelligence framework that models the full life cycle of convection -- from its precursors to organized storm evolution. However, exploiting these precursors is non-trivial: they originate from multiple meteorological drivers -- thermodynamic, kinematic, and microphysical -- that evolve asynchronously (C1) and remain spatially fragmented (C2). To this end, MeteoLogist designs three tightly integrated components. The Physics-Tailored Encoders process radar echoes according to their intrinsic physical scales and semantics, forming thermodynamic, kinematic, and microphysical streams that capture distinct dynamical regimes. The Temporal-Phase Aligner addresses C1 by leveraging causal temporal attention to capture when and how different drivers interact and activate. The Cross-Field Spatial Aggregator addresses C2 through cross-regional fusion, aligning weak and scattered precursors across neighboring cells to expose upstream triggers and enforce spatial coherence. Evaluated on 3D-NEXRAD (2020--2022, US-wide), MeteoLogist boosts high-impact detection (CSI40) by +9.7% over strong baselines, and achieves a remarkable 37.67% gain during the storm-developing stage -- demonstrating true foresight in sensing storms before they appear. The code can be found in the supplementary material. △ Less

Submitted 22 May, 2026; originally announced May 2026.

Democratising Optical Orbital Angular Momentum: a Set of Cost-Effective Tools

Authors: Natasha Bierrum, Lyuxuan Chen, Ananya Kudaloor, Lok Kan Wan, Shupeng Yang, Yancen Hou, Xiwen Dong, Muskan Tuli, Richard Taylor, Petros Androvitsaneas, Carrie Weidner, Edmund Harbord

Abstract: Classical and quantum optical communication has gained popularity and momentum in recent years, with growing investment and innovation in quantum technologies. However, the main teaching method in the education of quantum mechanics include mathematically intensive derivations or abstract analogies for the complex systems. We propose a "poor man's" spatial light modulator experiment that is an enga… ▽ More Classical and quantum optical communication has gained popularity and momentum in recent years, with growing investment and innovation in quantum technologies. However, the main teaching method in the education of quantum mechanics include mathematically intensive derivations or abstract analogies for the complex systems. We propose a "poor man's" spatial light modulator experiment that is an engaging and interactive learning aid for teaching quantum mechanics and optical orbital angular momentum. Fork diffraction gratings were created on photographic slide film by outsourcing to an external company, and so the gratings were easy and cheap to produce. A simple setup with a fork diffraction grating and a laser pointer successfully produces vortex beams that possess orbital angular momentum, allowing for orbital angular momentum to be easily observed and investigated in a teaching environment. How the tools can be used effectively to enhance learning is discussed, either as a demonstration or as an investigative scientific learning environment activity. △ Less

Submitted 22 May, 2026; originally announced May 2026.

Comments: 17 pages, 8 figures, appendices containing 3 pages, 3 figures, and an ancillary file of a laboratory activity worksheet

Reinforcement Learning Assisted Quantum Simulation of Many-Body Excited States and Real-Time Dynamics

Authors: Jiaji Zhang, Lipeng Chen, Carlos L. Benavides-Riveros

Abstract: The computation of electronic excited states and real-time quantum dynamics of many-fermion systems is among the most promising applications of near-term quantum computing. In this work, we generalize the reinforcement learning contracted quantum eigensolver (RL-CQE), previously developed for ground-state problems, to electronic excited states and real-time quantum dynamics, in which a deep Q-netw… ▽ More The computation of electronic excited states and real-time quantum dynamics of many-fermion systems is among the most promising applications of near-term quantum computing. In this work, we generalize the reinforcement learning contracted quantum eigensolver (RL-CQE), previously developed for ground-state problems, to electronic excited states and real-time quantum dynamics, in which a deep Q-network agent adaptively selects the two-body operators at each iteration, yielding more compact ansätze and improved robustness with respect to critical hyperparameters. A key feature of the algorithm is a scalable state representation based on the ACSE residuals, whose dimension grows with the one-particle basis but remains independent of the number of targeted excited states. We also verify the equivalence of sign-free qubit operators in the excited-state setting, extending a result previously established for ground-state problems. Our RL-CQE for time evolution derives from a constant-scaling ansatz that represents the wave function with a fixed number of unitary transformations independent of simulation time $t$, enabled by the shared unitary structure of the purified ensemble treatment of excited states. Benchmarks on chemical systems demonstrate chemical accuracy with minimal operator counts across a range of bond lengths. △ Less

Submitted 18 May, 2026; originally announced May 2026.

Enhanced detection of electric field signals via squeezing-induced stochastic resonance

Authors: Ya-Qi Wei, Tai-Hao Cui, Quan Yuan, Pei-Dong Li, Yuan-Zhang Dong, Zhuo-Zhu Wu, Ji Li, Jia-Wei Wang, Fei Zhou, Ming-Xiao Li, Liang Chen, Zhu-Jun Zheng, Mang Feng

Abstract: Stochastic resonance (SR) could amplify weak electric-field signals in nonlinear systems by means of the externally injected noises. Here we propose and experimentally demonstrate a modified SR method, termed squeezing-induced SR, implemented in the system involving a trapped ion behaving as a Duffing oscillator. We find that squeezing the phase noise of the oscillator results in amplified fluctua… ▽ More Stochastic resonance (SR) could amplify weak electric-field signals in nonlinear systems by means of the externally injected noises. Here we propose and experimentally demonstrate a modified SR method, termed squeezing-induced SR, implemented in the system involving a trapped ion behaving as a Duffing oscillator. We find that squeezing the phase noise of the oscillator results in amplified fluctuation of the corresponding amplitude, which helps achieve the SR. Since no auxiliary noise source is needed, the squeezing-induced SR may enhance the signal-to-noise ratio by 4.28 $\pm$ 0.39 dB compared to the conventional noise-induced SR under identical conditions of the electric-field detection. This technique offers a promising approach for developing atomic ion sensors for detecting weak electric-field signals. △ Less

Submitted 18 May, 2026; originally announced May 2026.

Quantum circuits for the advection-diffusion equation with boundary conditions based on LCHS

Authors: Leyu Chen, Tiegang Liu, Liang Xu, and Kun Wang

Abstract: This paper proposes a systematic and explicit quantum circuit framework for solving advection-diffusion equations with boundary conditions, based on the Linear Combination of Hamiltonian Simulations (LCHS) method. By employing the Finite Volume Method (FVM) combined with various flux construction schemes, we elaborate the design of quantum circuits tailored explicitly for Robin boundary conditions… ▽ More This paper proposes a systematic and explicit quantum circuit framework for solving advection-diffusion equations with boundary conditions, based on the Linear Combination of Hamiltonian Simulations (LCHS) method. By employing the Finite Volume Method (FVM) combined with various flux construction schemes, we elaborate the design of quantum circuits tailored explicitly for Robin boundary conditions (including Dirichlet and Neumann boundary conditions as special cases) and periodic boundary conditions. In contrast to prior works on quantum simulation of advection-diffusion equations, we present a detailed error analysis for the linear combination of unitaries (LCU) induced by the constructed quantum circuits. A comprehensive gate complexity analysis demonstrates the quantum advantages over classical computing in high-dimensional scenarios. We simulate the proposed circuits on a fault-tolerant emulator, and numerical results validate the effectiveness of the proposed framework across homogeneous, inhomogeneous, and high-dimensional cases. The proposed framework is compatible with numerous spatial discretization methods and numerical schemes, extends naturally to other linear PDEs, and establishes a practical foundation for solving large-scale PDE problems on future fault-tolerant quantum computers. △ Less

Submitted 17 May, 2026; originally announced May 2026.

Comments: 47 pages,19 figures

MSC Class: 68Q09; 68Q12; 81P68; 65M06

Volumetric Optical Scattering Neural Networks

Authors: Xuhao Luo, Qiang Song, Weiwei Cai, Lei Chen, Enbo Yang, Hao Wang, Zhipei Sun, Yueqiang Hu, Joel K. W. Yang, Huigao Duan

Abstract: Optical neural networks offer a route to low-latency and energy-efficient inference by encoding computation in light propagation. However, most existing implementations rely on planar photonic circuits or discretely spaced diffractive layers, restricting volumetric integration and imposing stringent alignment requirements. Here we demonstrate a volumetric optical scattering neural network (OSNN) i… ▽ More Optical neural networks offer a route to low-latency and energy-efficient inference by encoding computation in light propagation. However, most existing implementations rely on planar photonic circuits or discretely spaced diffractive layers, restricting volumetric integration and imposing stringent alignment requirements. Here we demonstrate a volumetric optical scattering neural network (OSNN) in which densely packed weak scatterers form a three-dimensional, locally connected optical computing medium. In contrast to fully connected diffractive architectures, the OSNN uses near-field scattering interactions, described under the first-Born approximation, to compress optical interconnections into a monolithic volume. We implement this concept using resilient inverse design and two-photon nanolithography, yielding OSNN devices with a volume of ~$3.8*10^{-4}mm^{3}$ and a record-breaking neuron density of $1.0*10^{9}/mm^{3}$. Experimentally, the fabricated classifier achieves $94.8\%$ blind-test accuracy on MNIST, while the imager performs optical compressed imaging with a $1-μm$ effective resolution and average FSIM values of $0.93$ on Fashion-MNIST and $0.91$ on VesselMNIST3D. OSNN paves the way for ultra-dense, ultra-compact, and efficient optical computing, creating a universal platform for embedded optical intelligence and promising widespread application in AI fields ranging from autonomous driving to medical diagnosis. △ Less

Submitted 13 May, 2026; originally announced May 2026.

Exploring the Boundaries of Differentiable Radiation Transport and Detector Simulation

Authors: Jeffrey Krupa, Yiyang Zhao, Mihaly Novak, Max Aehle, Max Sagebaum, Long Chen, Nicolas Gauger, David Lange, Vassil Vassilev, Miaoyuan Liu, Lukas Heinrich, Michael Kagan

Abstract: We present an application of automatic differentiation for particle transport through matter using a Geant4-like radiation transport simulation with a full electromagnetic physics model. When differentiating this step-based transport, we observe exploding gradients driven by rare but extreme sensitivities at material boundaries, which propagate through subsequent transport and shower development.… ▽ More We present an application of automatic differentiation for particle transport through matter using a Geant4-like radiation transport simulation with a full electromagnetic physics model. When differentiating this step-based transport, we observe exploding gradients driven by rare but extreme sensitivities at material boundaries, which propagate through subsequent transport and shower development. To obtain usable derivatives for optimization, we introduce a targeted mitigation strategy that stops gradient propagation through boundary-crossing operations under identifiable unstable conditions while leaving the forward (primal) simulation unchanged. We demonstrate that this enables stable, optimization-ready gradients in a detector-design problem. △ Less

Submitted 3 June, 2026; v1 submitted 7 May, 2026; originally announced May 2026.

Analysis of Electromagnetic Scattering from Semiconductor Nanostructures by Solving Coupled Volume Integral and Two-fluid Hydrodynamic Equations

Authors: Doolos Aibek Uulu, Meruyert Khamitova, Rui Chen, Liang Chen, Ping Li, Hakan Bagci

Abstract: Semiconductor-based plasmonic nanostructures support localized surface plasmon modes in the infrared region. Unlike metallic nanostructures, they support both free electrons and holes, requiring a two-fluid hydrodynamic Drude equation (HDE) to accurately capture spatial dispersion effects and low-frequency acoustic plasmon modes that cannot be described by single-fluid models. In this work, a volu… ▽ More Semiconductor-based plasmonic nanostructures support localized surface plasmon modes in the infrared region. Unlike metallic nanostructures, they support both free electrons and holes, requiring a two-fluid hydrodynamic Drude equation (HDE) to accurately capture spatial dispersion effects and low-frequency acoustic plasmon modes that cannot be described by single-fluid models. In this work, a volume integral equation (VIE)-based solver is proposed for the analysis of electromagnetic scattering from semiconductor nanostructures. The proposed approach couples the VIE, formulated in terms of the electric flux density and the free-electron and hole polarization currents, with the two-fluid HDE. The coupled system is discretized using a tetrahedral mesh and solved efficiently using a two-level iterative solver. In contrast to finite-element-based methods, the proposed VIE-based approach does not require domain-wide meshing and inherently satisfies the radiation condition, thereby eliminating artificial absorbing boundaries. Numerical results for InSb-type semiconductor nanostructures demonstrate the accuracy and efficiency of the proposed VIE-based solver and its ability to capture unique optical phenomena, such as acoustic plasmon resonances and the blueshift of localized surface plasmon resonances, that cannot be described by the single-fluid HDE or classical Drude-based models. △ Less

Submitted 30 April, 2026; originally announced April 2026.

Wave-number-dependent closure condition for fluid moment equations

Authors: Yong Sun, Shijia Chen, Minqing He, Sizhong Wu, Rui Cheng, Jie Yang, Lei Yang, Zhiyu Sun, Liangwen Chen, Hua Zhang

Abstract: Fluid models offer crucial computational efficiency for plasma simulations, yet accurately capturing kinetic effects like Landau damping remains a fundamental challenge. While conventional closures (e.g., Hammett-Perkins and Hunana) are widely used, their fidelity relative to exact kinetic response degrades significantly depending on the perturbation wave number. Here, we propose a novel wave-numb… ▽ More Fluid models offer crucial computational efficiency for plasma simulations, yet accurately capturing kinetic effects like Landau damping remains a fundamental challenge. While conventional closures (e.g., Hammett-Perkins and Hunana) are widely used, their fidelity relative to exact kinetic response degrades significantly depending on the perturbation wave number. Here, we propose a novel wave-number-dependent closure condition for the three-moment fluid equations that explicitly preserves the primary dispersion relation. By mapping Padé approximant coefficients directly to the kinetic roots of the collisionless Vlasov-Poisson system, we derive an analytical closure that rigorously embeds exact kinetic scaling across all spatial scales. We further demonstrate that this framework readily extends to collisional plasmas via the BGK model. This deterministic approach precisely captures the long-term macroscopic evolution of fluid moments and field energy, offering a rigorous foundation for high-fidelity fluid modeling. △ Less

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

All-Optical High-Resolution Real-Time Temperature Estimation Method Based on Fiber-Optic Interferometry

Authors: Jingwen Yang, Long Chen, Haoliang Yu, Xiaofeng Jin, Jianxiang Miao, Jia Kong

Abstract: High-resolution temperature monitoring is essential for many engineering and scientific applications, but conventional sensors are limited by insufficient resolution and susceptibility to electromagnetic interference. Fiber-optic interferometers provide high sensitivity and intrinsic electromagnetic immunity; however, their practical performance is hindered by nonlinear temperature-intensity respo… ▽ More High-resolution temperature monitoring is essential for many engineering and scientific applications, but conventional sensors are limited by insufficient resolution and susceptibility to electromagnetic interference. Fiber-optic interferometers provide high sensitivity and intrinsic electromagnetic immunity; however, their practical performance is hindered by nonlinear temperature-intensity responses, phase ambiguity, and environmental disturbances. Here, we develop an extended Kalman filter (EKF)-based approach that incorporates system non-linearity and noise statistics to enable robust real-time temperature estimation from interferometric signals. In numerical simulations, our EKF-based method reduces the estimation error to 2.21e-5 K, while experiments achieve a resolution of 8.34e-5 K under strong disturbances, corresponding to a threefold improvement over conventional intensity-based inversion method and an order-of-magnitude enhancement compared with traditional based measurement. These results demonstrate a compact and robust strategy for high-resolution, real-time, all-optical temperature sensing with strong immunity to electromagnetic interference. △ Less

Submitted 25 April, 2026; originally announced April 2026.

Comments: 6 pages, 5 figures

Fully multiplexed photonic tensor computing

Authors: Aolong Sun, Junhao Zhao, Fangchen Hu, Sizhe Xing, Yuqin Yuan, Jialin He, Yongzhu Hu, Xuyu Deng, Yinjun Liu, Ouhan Huang, Baiheng Zhao, Hancheng Liu, Tian Dong, Jingkai Zhou, Haoyang Sun, Liang Chen, Chao Shen, Feng Bao, Ziwei Li, Jianyang Shi, Wei Chu, Bowei Dong, Nan Chi, Junwen Zhang

Abstract: Tensor operations dominate modern computational workloads, yet their further acceleration demands hardware platforms with greater parallelism. Although photonic computing provides a compelling route for parallel processing, fully exploiting all native multiplexing dimensions of optical fields is impeded by the challenges in routing and programming light in all dimensions simultaneously. Here we in… ▽ More Tensor operations dominate modern computational workloads, yet their further acceleration demands hardware platforms with greater parallelism. Although photonic computing provides a compelling route for parallel processing, fully exploiting all native multiplexing dimensions of optical fields is impeded by the challenges in routing and programming light in all dimensions simultaneously. Here we introduce FieldCore, a fully multiplexed photonic tensor core that jointly harnesses wavelength, radio-frequency, guided-mode, time and space dimensions, thereby enabling parallelism to scale multiplicatively within a single optical field. Enabled by inverse-designed silicon photonics, FieldCore preserves a uniform programmed computation across all multiplexed channels in parallel. Experimentally, we validate and benchmark its performance from ultra-high-baudrate arithmetic operations to high-fidelity image convolution and parallel handwritten-digit recognition. We further use FieldCore to unlock applications that naturally require high-dimensional data processing, such as high-dimensional hyperspectral classification and massively parallel mechanical fault diagnosis. Our FieldCore supports an estimated aggregate compute throughput of 69.12 tera operations per second (TOPS) and accommodates up to 1,800 parallel input streams within a single core, establishing a scalable paradigm for fully multiplexed photonic tensor computing and AI inference. △ Less

Submitted 24 April, 2026; originally announced April 2026.

AI-Driven Performance-to-Design Generation and Optimization of Marine Propellers

Authors: Leah Chen, Keni Chih-Hua Wu, Boon Tat Chia, Xiuqing Xing, Jian Cheng Wong

Abstract: AI is increasingly used to accelerate engineering design by improving decision-making and shortening iteration cycles. Application to marine propeller design, however, remains challenging due to scarce training data and the lack of widely available pretrained models. We address this gap with a physics-based data generation pipeline and a generative-AI framework for direct performance-to-design gen… ▽ More AI is increasingly used to accelerate engineering design by improving decision-making and shortening iteration cycles. Application to marine propeller design, however, remains challenging due to scarce training data and the lack of widely available pretrained models. We address this gap with a physics-based data generation pipeline and a generative-AI framework for direct performance-to-design generation tailored to marine propellers. First, we build a database of over 20,000 four- and five-bladed propeller geometries, each accompanied by simulated open-water performance curves. On top of this dataset, we develop a three-module design framework: (1) A Conditional Generation Model that proposes candidate geometries conditioned on design specifications such as target thrust, power, and diameter. (2) A Performance Prediction Model, implemented as a neural-network surrogate, that predicts thrust, torque, and efficiency in milliseconds, enabling rapid evaluation of generated designs. (3) A design refinement stage that applies evolutionary optimization to enforce practical constraints such as required thrust under power limits and bounds on blade-area ratio and thickness. Experimental results over a range of operating conditions show that the framework can generate hydrodynamically plausible propeller designs that match prescribed performance targets while substantially reducing design-iteration time relative to the traditional expert-guided refinement. Latent diffusion-based generator produces more diverse designs under the same conditions than the conditional variational autoencoder, suggesting a stronger capacity for design-space exploration with diffusion models. By coupling physics-based data synthesis with modular AI models, the proposed approach streamlines the propeller design cycle and reduces reliance on expensive high-fidelity simulations to final validation stages. △ Less

Submitted 24 April, 2026; originally announced April 2026.

Comments: Accepted at OMAE 2026

A neural operator framework for data-driven discovery of stability and receptivity in physical systems

Authors: Chengyun Wang, Liwei Chen, Nils Thuerey

Abstract: Understanding how complex systems respond to perturbations, such as whether they will remain stable or what their most sensitive patterns are, is a fundamental challenge across science and engineering. Traditional stability and receptivity (resolvent) analyses are powerful but rely on known equations and linearization, limiting their use in nonlinear or poorly modeled systems. Here, we introduce a… ▽ More Understanding how complex systems respond to perturbations, such as whether they will remain stable or what their most sensitive patterns are, is a fundamental challenge across science and engineering. Traditional stability and receptivity (resolvent) analyses are powerful but rely on known equations and linearization, limiting their use in nonlinear or poorly modeled systems. Here, we introduce a data-driven framework that automatically identifies stability properties and optimal forcing responses from observation data alone, without requiring governing equations. By training a neural network as a dynamics emulator and using automatic differentiation to extract its Jacobian, we can compute eigenmodes and resolvent modes directly from data. We demonstrate the method on both canonical chaotic models and high-dimensional fluid flows, successfully identifying dominant instability modes and input-output structures even in strongly nonlinear regimes. By leveraging a neural network-based emulator, we readily obtain a nonlinear representation of system dynamics while additionally retrieving intricate dynamical patterns that were previously difficult to resolve. This equation-free methodology establishes a broadly applicable tool for analyzing complex, high-dimensional datasets, with immediate relevance to grand challenges in fields such as climate science, neuroscience, and fluid engineering. △ Less

Submitted 4 August, 2026; v1 submitted 21 April, 2026; originally announced April 2026.

System Size Dependence of Collisionless Reconnection Rate

Authors: Yi-Min Huang, Naoki Bessho, Li-Jen Chen, Judith T. Karpen, Amitava Bhattacharjee

Abstract: It is a widely accepted paradigm that collisionless magnetic reconnection proceeds at a universal fast rate of $\sim0.1$ when normalized to a properly defined reconnecting magnetic field and Alfvén speed, effectively independent of the macroscopic system size. This conclusion, derived primarily from kinetic simulations of classical Harris current sheets with kinetic-scale thickness, stands in cont… ▽ More It is a widely accepted paradigm that collisionless magnetic reconnection proceeds at a universal fast rate of $\sim0.1$ when normalized to a properly defined reconnecting magnetic field and Alfvén speed, effectively independent of the macroscopic system size. This conclusion, derived primarily from kinetic simulations of classical Harris current sheets with kinetic-scale thickness, stands in contrast to results from forced reconnection and island coalescence, where the rate significantly depends on the system size. Here, we reconcile this disparity by performing a rigorous scaling study using both particle-in-cell and Hall magnetohydrodynamic simulations. We demonstrate that when the global magnetic configuration is self-consistently preserved by scaling the initial current sheet thickness proportionally with the system size, the ``universal'' fast rate disappears. Instead, the reconnection rate decreases as the system size increases. These results indicate that dependence on macroscopic scales is not peculiar to specific geometries but is a fundamental property of collisionless reconnection, effectively unifying the Harris sheet with other configurations exhibiting size-dependence. △ Less

Submitted 20 April, 2026; originally announced April 2026.

Simultaneous TRACERS and THEMIS Observations of Reversed Cusp Ion Dispersions and Dual-Lobe Reconnection

Authors: M. Øieroset, S. A. Fuselier, J. B. Bonnell, R. A. Roglans, J. S. Halekas, R. J. Strangeway, T. D. Phan, R. G. Gomez, S. M. Petrinec, K. J. Trattner, S. R. Shaver, K. A. Goodrich, S. A. Henderson, S. L. Soni, V. Angelopoulos, B. L. Burkholder, H. Cao, L-J. Chen, H. K. Connor, D. M. Miles, A. Moore, J. Ng, Y. Shen

Abstract: We present observations from two consecutive TRACERS-2 orbits through the northern low-altitude cusp. During the first crossing, TRACERS-2 observed reversed cusp ion dispersion and sunward convection, consistent with magnetopause reconnection tailward of the cusp during this northward IMF interval. Simultaneous THEMIS-D observations at the equatorial magnetopause show heated magnetosheath plasma c… ▽ More We present observations from two consecutive TRACERS-2 orbits through the northern low-altitude cusp. During the first crossing, TRACERS-2 observed reversed cusp ion dispersion and sunward convection, consistent with magnetopause reconnection tailward of the cusp during this northward IMF interval. Simultaneous THEMIS-D observations at the equatorial magnetopause show heated magnetosheath plasma captured on closed field lines, with similar particle spectra as in in the low-altitude cusp, indicating that reconnection indeed occurred tailward of the cusp and in both hemispheres. When TRACERS-2 traversed the northern cusp again, 95 minutes later, the IMF was dominated by a negative BX component. Despite the different IMF conditions, TRACERS-2 recorded nearly the same cusp signatures as before, i.e., reversed ion dispersion and sunward convection. The observations indicate that tailward-of-cusp reconnection can occur for both northward and BX-dominated IMF and that these distinct IMF geometries can produce remarkably similar plasma and field signatures in the low-altitude cusp. △ Less

Submitted 7 July, 2026; v1 submitted 15 April, 2026; originally announced April 2026.

Journal ref: Geophysical Research Letters, 53, e2026GL123404

Learning step-level dynamic soaring in shear flow

Authors: Lunbing Chen, Jixin Lu, Yufei Yin, Jinpeng Huang, Yang Xiang, Hong Liu

Abstract: Dynamic soaring enables sustained flight by extracting energy from wind shear, yet it is commonly understood as a cycle-level maneuver that assumes stable flow conditions. In realistic unsteady environments, however, such assumptions are often violated, raising the question of whether explicit cycle-level planning is necessary. Here, we show that dynamic soaring can emerge from step-level, state-f… ▽ More Dynamic soaring enables sustained flight by extracting energy from wind shear, yet it is commonly understood as a cycle-level maneuver that assumes stable flow conditions. In realistic unsteady environments, however, such assumptions are often violated, raising the question of whether explicit cycle-level planning is necessary. Here, we show that dynamic soaring can emerge from step-level, state-feedback control using only local sensing, without explicit trajectory planning. Using deep reinforcement learning as a tool, we obtain policies that achieve robust omnidirectional navigation across diverse shear-flow conditions. The learned behavior organizes into a structured control law that coordinates turning and vertical motion, giving rise to a two-phase strategy governed by a trade-off between energy extraction and directional progress. The resulting policy generalizes across varying conditions and reproduces key features observed in biological flight and optimal-control solutions. These findings identify a feedback-based control structure underlying dynamic soaring, demonstrating that efficient energy-harvesting flight can emerge from local interactions with the flow without explicit planning, and providing insights for biological flight and autonomous systems in complex, flow-coupled environments. △ Less

Submitted 14 April, 2026; originally announced April 2026.

MMS Insights into CME Driven Sub-Alfvénic Solar Wind at 1 AU

Authors: Harsha Gurram, Li-Jen Chen, Matthew R. Argall, Subash Adhikari, Lynn B. Wilson, Jason R. Shuster, Victoria D. Wilder

Abstract: We report the properties of electron distributions and turbulence during a Coronal Mass Ejection (CME) in April 2023 observed by Magnetospheric Multiscale (MMS). The CME exhibits a clear sheath and magnetic cloud (MC), and within the MC, the solar wind becomes sub-Alfvénic for two hours. We investigate plasma and turbulence properties of the sub-Alfvénic CME wind and compare them with those in the… ▽ More We report the properties of electron distributions and turbulence during a Coronal Mass Ejection (CME) in April 2023 observed by Magnetospheric Multiscale (MMS). The CME exhibits a clear sheath and magnetic cloud (MC), and within the MC, the solar wind becomes sub-Alfvénic for two hours. We investigate plasma and turbulence properties of the sub-Alfvénic CME wind and compare them with those in the super-Alfvénic solar wind in the MC and CME sheath. Electrons within the sub-Alfvénic MC show significantly higher temperatures than those in the CME sheath and the super-Alfvénic MC, with their one-dimensional distributions revealing super-thermal tail and a depletion in electron populations between 15-50 eV. Within the CME sheath, isolated regions of electron heating are observed, where parallel energy flux is enhanced up to ~1 keV. Magnetic field fluctuations within the sub-Alfvénic MC interval exhibit negligible cross helicity and steeper-than-Kolmogorov scaling in the inertial range, with no clear spectral break. These fluctuations also show reduced intermittency at ion and sub-ion scales, emerging intermittency at electron scales, and weak magnetic compressibility. Together, these observations point to the presence of weak magnetohydrodynamic (MHD) turbulence within the sub-Alfvénic MC, resembling conditions commonly observed in planetary magnetospheres such as Jupiter's. △ Less

Submitted 13 April, 2026; originally announced April 2026.

Comments: 10 pages, 5 figures, 1 table

Ultrafast decoupling of the pseudogap from superconductivity in a pressurized cuprate

Authors: Yanghao Meng, Wenjin Mao, Liucheng Chen, Elbert E. M. Chia, Yifeng Yang, Jianlin Luo, Lin Zhao, Xingjiang Zhou, Xiaohui Yu, Xinbo Wang

Abstract: The relationship between the pseudogap and superconductivity remains a central puzzle in the physics of cuprates. Hydrostatic pressure provides a clean tuning parameter free from chemical disorder, yet probing the microscopic energy scales of these phases under compression has remained experimentally challenging. Here, we utilize ultrafast optical spectroscopy to construct the high-pressure phase… ▽ More The relationship between the pseudogap and superconductivity remains a central puzzle in the physics of cuprates. Hydrostatic pressure provides a clean tuning parameter free from chemical disorder, yet probing the microscopic energy scales of these phases under compression has remained experimentally challenging. Here, we utilize ultrafast optical spectroscopy to construct the high-pressure phase diagram of the underdoped cuprate Bi$_2$Sr$_2$CaCu$_2$O$_{8+δ}$ up to 37 GPa. Our results reveal a striking dichotomy within the pseudogap state: while the onset temperature $T^*$ rises monotonically with pressure, the energy gap $Δ_{\mathrm{PG}}$ is continuously suppressed. In contrast, the critical temperature $T_{\mathrm{c}}$ and the superconducting gap $Δ_{\mathrm{SC}}$ trace a correlated dome-like trajectory, demonstrating that superconductivity evolves independently from the pseudogap. Furthermore, an abrupt collapse of the gap ratio $2Δ_{\mathrm{SC}}/k_{\mathrm{B}}T_{\mathrm{c}}$ near 8 GPa marks a pressure-driven dimensional crossover, quenching two-dimensional phase fluctuations to stabilize global three-dimensional coherence. Upon reaching 37 GPa, the superconducting condensate is completely quenched into an insulating-like state. By resolving the extended phase evolution, our findings disentangle the pseudogap and superconducting orders, establishing a rigorous experimental basis for the pairing mechanism of high-temperature superconductivity. △ Less

Submitted 11 April, 2026; originally announced April 2026.

Comments: 29 pages, 10 figures

Characterize localization length of disordered lattices via critical coupling effect

Authors: Fuhao Ji, Xiangqi Huang, Luxing Chen, Yuxiang Tian, Wenjing Li, Yinying Peng, Yuge Qiu, Lu Zhang, Liwei Zhang, Mingfang Yi, Peilong Hong

Abstract: Light localization by scattering is a fundamental mechanism driving phase transitions of wave transport in disordered systems. Characterizing the localization length in scattering systems is crucial yet challenging. In this Letter, we demonstrate a spatially matched coupling scheme using wavefront shaping to resolve the intrinsic localization length in two-dimensional disordered lattices. By tailo… ▽ More Light localization by scattering is a fundamental mechanism driving phase transitions of wave transport in disordered systems. Characterizing the localization length in scattering systems is crucial yet challenging. In this Letter, we demonstrate a spatially matched coupling scheme using wavefront shaping to resolve the intrinsic localization length in two-dimensional disordered lattices. By tailoring the incident wavefront, our method facilitates efficient coupling of light to the minimum localized mode. We apply this approach to measure two different self-assembled lattices, and report the first observation of the critical coupling effect, which allows for the direct determination of the characteristic size of minimum localized mode. Our results reveal that for a fixed lattice periodicity, increasing the air-hole diameter significantly reduces this intrinsic localization length. This far-field metrology offers a robust framework for probing wave localization in complex media, which should be useful in various applications such as random lasing and nonlinear optics △ Less

Submitted 4 April, 2026; originally announced April 2026.

Comments: 5 pages, 5 figures

MSC Class: 78-05

Alloying Controlled Tuning of Interfacial Spin Orbit Interaction and Magnetic Damping in Crystalline FeCo Alloys

Authors: Hongrui Lao, Matthias Kronseder, Zhe Yuan, Thomas Narr, Thomas N. G. Meier, Nadine Mundigl, Christian H. Back, Lin Chen

Abstract: The discovery of intrinsic spin orbit fields in noncentrosymmetric ferromagnets has attracted considerable interest for both fundamental studies and technological applications. However, once such materials are synthesized, the strength of the spin orbit fields is difficult to tune because it is primarily a bulk property. Here, we demonstrate that the interfacial spin orbit interaction (SOI) in sin… ▽ More The discovery of intrinsic spin orbit fields in noncentrosymmetric ferromagnets has attracted considerable interest for both fundamental studies and technological applications. However, once such materials are synthesized, the strength of the spin orbit fields is difficult to tune because it is primarily a bulk property. Here, we demonstrate that the interfacial spin orbit interaction (SOI) in single crystalline FeCo thin films grown on GaAs(001) can be continuously tuned via alloying. Using spin orbit ferromagnetic resonance, we find that the Lande g factor, the Gilbert damping (alpha), and the interfacial spin orbit fields exhibit a common nonmonotonic dependence on Co concentration. A pronounced minimum occurs near x ~ 0.2 where an ultra low damping alpha ~ 0.0015 is achieved. Furthermore, we observe linear scaling between alpha and (g-2)^2, establishing a direct correlation between interfacial SOI and magnetic relaxation. These results identify alloying as an effective knob to engineer interfacial SOI and damping in single crystalline ferromagnet semiconductor heterostructures. △ Less

Submitted 28 March, 2026; originally announced March 2026.

Current-tunable room temperature ferromagnetism and current-driven phase transitions

Authors: Jianping Guo, Peng Rao, Xinhao Huang, Tailai Xu, Yuxuan Guo, Jian Shao, Cheng Sun, Anton Orekhov, Thomas N. G. Meier, Johannes Knolle, Christian H. Back, Lin Chen

Abstract: It is generally assumed that the application of a charge-current in ferromagnetic metals suppresses their ferromagnetic order through trivial Joule heating. Here, we demonstrate that a charge current can instead enhance magnetic ordering. Using a WTe2/Fe3Ge2Te (FGT) stack as a model system, we show that a charge current flowing in WTe2 controls the ferromagnetic properties and magnetic phase trans… ▽ More It is generally assumed that the application of a charge-current in ferromagnetic metals suppresses their ferromagnetic order through trivial Joule heating. Here, we demonstrate that a charge current can instead enhance magnetic ordering. Using a WTe2/Fe3Ge2Te (FGT) stack as a model system, we show that a charge current flowing in WTe2 controls the ferromagnetic properties and magnetic phase transition of the adjacent FGT via a current-induced effective magnetic-field arising from orbital magnetization. Remarkably, the charge current drives a substantial enhancement of the Curie temperature, boosting it well above room temperature. Furthermore, we show that the charge-current enables controlled tuning of the phase transitions in FGT, which confirms the scaling behaviour of a ferromagnet-paramagnet phase transition. This work provides a pathway for integrating two-dimensional ferromagnets into spintronic functionalities at technologically relevant temperatures and for exploring novel current-driven phenomena in ferromagnetic systems. △ Less

Submitted 28 March, 2026; originally announced March 2026.

On the Codesign of Scientific Experiments and Industrial Systems

Authors: Tommaso Dorigo, Pietro Vischia, Shahzaib Abbas, Tosin Adewumi, Lama Alkhaled, Lorenzo Arsini, Muhammad Awais, Maxim Borisyak, András Bóta, Florian Bury, Sascha Caron, James Carzon, Long Chen, Prakash C. Chhipa, Paul Christakopoulos, Jacopo De Piccoli, Andrea De Vita, Zlatan Dimitrov, Michele Doro, Luigi Favaro, Francesco Ferranti, Santiago Folgueras, Rihab Gargouri, Nicolas R. Gauger, Andrea Giammanco , et al. (62 additional authors not shown)

Abstract: The optimization of large experiments in fundamental science, such as detectors for subnuclear physics at particle colliders, shares with the optimization of complex systems for industrial or societal applications the common issue of addressing the inter-relation between parameters describing the hardware used in data production and parameters used to analyse those data. While in many cases this c… ▽ More The optimization of large experiments in fundamental science, such as detectors for subnuclear physics at particle colliders, shares with the optimization of complex systems for industrial or societal applications the common issue of addressing the inter-relation between parameters describing the hardware used in data production and parameters used to analyse those data. While in many cases this coupling can be ignored -- when the problem can be successfully factored into simpler sub-tasks and the latter addressed serially -- there are situations in which that approach fails to converge to the absolute maximum of expected performance, as it results in a mis-alignment of the optimized hardware and software solutions. In this work we consider a few use cases of interest in fundamental science collected primarily from particle physics and related areas, and a pot-pourri of industrial and societal applications where the matter is similarly of relevance. We discuss the emergence of strong hardware-software coupling in some of those systems, as well as co-design procedures that may be deployed to identify the global maximum of their relevant utility functions. We observe how numerous opportunities exist to advance methods and tools for hardware-software co-design optimization, bridging fundamental science and industry through application- and challenge-driven projects, and shaping the future of scientific experiments and industrial systems. △ Less

Submitted 27 March, 2026; originally announced March 2026.

Comments: 91 pages, 58 figures

Ab Initio Simulation of Femtosecond Time-Resolved Multi-Pulse Spectroscopies applied to the Heptazine$\cdots$H$_2$O Complex

Authors: Sebastian V. Pios, Maxim F. Gelin, Wolfgang Domcke, Lipeng Chen

Abstract: In multi-dimensional time-resolved spectroscopic experiments, multiple (more than two) short laser pulses with variable pulse delay times are employed for the time-resolved exploration of the photoinduced dynamics of molecular chromophores. In the present work, the quasi-classical doorway-window (DW) methodology recently developed for transient absorption pump-probe (PP) spectroscopy [M. F. Gelin… ▽ More In multi-dimensional time-resolved spectroscopic experiments, multiple (more than two) short laser pulses with variable pulse delay times are employed for the time-resolved exploration of the photoinduced dynamics of molecular chromophores. In the present work, the quasi-classical doorway-window (DW) methodology recently developed for transient absorption pump-probe (PP) spectroscopy [M. F. Gelin et al., J. Chem. Theory Comput. 2021, 17, 2394] has been generalized to multi-pulse spectroscopies. Pump-push-probe (PPP) spectroscopy (involving three laser pulses) and pump-induced two-dimensional (P-2D) spectroscopy (involving five laser pulses) are considered as specific examples. The quasi-classical DW approximation results in conceptually simple and computationally efficient simulation protocols which are suitable for implementation with $ab$ $initio$ on-the-fly electronic-structure calculations. Simulations of PPP and P-2D spectra performed for the hydrogen-bonded heptazine$\cdots$H$_2$O complex illustrate that pump-stimulated experiments provide much richer information on the ultrafast radiationless relaxation dynamics of the excited electronic states of the heptazine$\cdots$H$_2$O complex than conventional PP and 2D experiments. △ Less

Submitted 23 March, 2026; originally announced March 2026.

Data-driven ensemble prediction of the global ocean

Authors: Qiusheng Huang, Xiaohui Zhong, Anboyu Guo, Ziyi Peng, Lei Chen, Hao Li

Abstract: Data-driven models have advanced deterministic ocean forecasting, but extending machine learning to probabilistic global ocean prediction remains an open challenge. Here we introduce FuXi-ONS, the first machine-learning ensemble forecasting system for the global ocean, providing 5-day forecasts on a global 1° grid up to 365 days for sea-surface temperature, sea-surface height, subsurface temperatu… ▽ More Data-driven models have advanced deterministic ocean forecasting, but extending machine learning to probabilistic global ocean prediction remains an open challenge. Here we introduce FuXi-ONS, the first machine-learning ensemble forecasting system for the global ocean, providing 5-day forecasts on a global 1° grid up to 365 days for sea-surface temperature, sea-surface height, subsurface temperature, salinity and ocean currents. Rather than relying on repeated integration of computationally expensive numerical models, FuXi-ONS learns physically structured perturbations and incorporates an atmospheric encoding module to stabilize long-range forecasts. Evaluated against GLORYS12 reanalysis, FuXi-ONS improves both ensemble-mean skill and probabilistic forecast quality relative to deterministic and noise-perturbed baselines, and shows competitive performance against established seasonal forecast references for SST and Niño3.4 variability, while running orders of magnitude faster than conventional ensemble systems. These results provide a strong example of machine learning advancing a core problem in ocean science, and establish a practical path toward efficient probabilistic ocean forecasting and climate risk assessment. △ Less

Submitted 19 March, 2026; originally announced March 2026.