Showing 1–50 of 2,307 results for author: Chen, Y

On-chip Bragg peak extraction from MHz frame rate X-ray detectors using a cellular automaton architecture

Authors: S. Fowler, S. Strempfer, D. Beniwal, M. Hammer, H. Shi, S. Gnanasekaran, N. Contini, T. Guruswamy, Y. Chen, L. Rota, D. Doering, A. Dragone, T. Zhou, M. Cherukara, K. Yoshii, A. Miceli

Abstract: High-frame rate pixel detectors can produce data volumes that exceed available off-chip bandwidth, yet in many applications only a sparse subset of each frame carries relevant information. Spatially localized events, including diffraction peaks in crystallography, particle hits in tracking detectors, fluorescence spots in biological imaging, and other applications, all require that clusters of abo… ▽ More High-frame rate pixel detectors can produce data volumes that exceed available off-chip bandwidth, yet in many applications only a sparse subset of each frame carries relevant information. Spatially localized events, including diffraction peaks in crystallography, particle hits in tracking detectors, fluorescence spots in biological imaging, and other applications, all require that clusters of above-threshold pixels be identified and extracted from an otherwise featureless background. Conventionally this is performed in software algorithms such as connected-component labeling on full frames, but at MHz frame rates the resulting throughput becomes prohibitive. We present a lightweight hardware architecture that performs peak localization and patch extraction directly in the sensor silicon, transmitting only small pixel patches rather than complete frames. FPGA-based testing on an AMD Alveo V80 validated the synthesizability and timing closure of the peak-finding module in real hardware. The design replaces global connected-component labeling with a cellular automaton that uses purely local, fixed-iteration neighborhood operations, eliminating the label storage and equivalence-resolution logic that make conventional approaches impractical on-chip. We validate the architecture on X-ray Bragg peak detection for far-field high-energy diffraction microscopy and show that every peak found by a software reference is recovered, with equivalent downstream reconstructions. The architecture sustains several-hundred-kHz frame rates in 130nm CMOS and exceeds 1MHz in 28nm. △ Less

Submitted 19 August, 2026; originally announced August 2026.

Comments: 18 pages, 6 figures, 5 tables

Acoustic toroidal vortices with programmable links and knots

Authors: Shuai Liu, Xiang-Yuan Xu, Hao Ge, Yijie Shen, Yan-Feng Chen, Ming-Hui Lu

Abstract: Toroidal vortices are three-dimensional torus-shaped wave structures characterized by phase circulation around a closed vortex line. Their toroidal geometry provides a natural foundation for constructing linked and knotted wave structures. Here we experimentally synthesize scalar acoustic toroidal vortices using a programmable circular phased array. Full spatiotemporal measurements directly resolv… ▽ More Toroidal vortices are three-dimensional torus-shaped wave structures characterized by phase circulation around a closed vortex line. Their toroidal geometry provides a natural foundation for constructing linked and knotted wave structures. Here we experimentally synthesize scalar acoustic toroidal vortices using a programmable circular phased array. Full spatiotemporal measurements directly resolve the toroidal envelope, the closed phase-singularity ring, the associated poloidal phase winding, and the free-space evolution of the wave packet. By introducing an independently controlled phase winding along the toroidal cycle, we realize scalar acoustic hopfions and directly reconstruct their three-dimensional equiphase fibers from the measured complex pressure field. Varying the poloidal and toroidal winding numbers controls the phase-fiber geometry, linking, and connectivity, yielding a Hopf link, a multicomponent torus link, and a trefoil knot. These results provide direct experimental access to the geometry, propagation dynamics, and phase-fiber topology of scalar toroidal wave fields, establishing a reconfigurable acoustic platform for linked and knotted wave structures. △ Less

Submitted 15 August, 2026; originally announced August 2026.

AlgoPlasma: Open Algorithms for Plasma Modeling

Authors: Yinjian Zhao, Zhongping Zhao, Zhe Liu, Baisheng Wang, Zilong Peng, Xin Luo, Lihuan Xie, Xi Chen, Zhijun Zhou, Kunpeng Zhong, Yingjie Chen, Changzheng Hu

Abstract: AlgoPlasma is an open-source library in which core numerical algorithms for plasma modeling are implemented as modular, well-documented, and independently testable components. Rather than offering a complete simulation code, it allows researchers to select, adapt, and assemble the required components into application-specific workflows. The current release is centered on particle-based simulation,… ▽ More AlgoPlasma is an open-source library in which core numerical algorithms for plasma modeling are implemented as modular, well-documented, and independently testable components. Rather than offering a complete simulation code, it allows researchers to select, adapt, and assemble the required components into application-specific workflows. The current release is centered on particle-based simulation, while AlgoPlasma is designed to encompass a broader range of approaches to plasma modeling. It provides components for particle initialization and advancement, particle--grid coupling, field solution, collision modeling, parallel data exchange, input/output, and selected fluid updates. Documentation links mathematical formulations to source implementations, interfaces, and usage, while verification and validation cases evaluate numerical accuracy and physical behavior. AlgoPlasma thus establishes a shared algorithmic foundation for plasma modeling, transforming repeatedly reimplemented numerical methods into open, reusable, tested, and explainable components for research, verification, education, and collaborative development. △ Less

Submitted 15 August, 2026; originally announced August 2026.

Time-resolved correlation engineering in DLCZ Raman photon sources

Authors: Jiun-Shiuan Shiu, Chang-Wei Lin, Chi-Ming Yang, Ite A. Yu, Yong-Fan Chen

Abstract: Memory-assisted quantum networks require photon sources with controllable temporal and correlation properties. The Duan-Lukin-Cirac-Zoller (DLCZ) protocol provides a platform based on spontaneous Raman scattering in atomic ensembles, but a unified predictive theory connecting control parameters to correlations under realistic propagation and noise conditions remains lacking. Here we present a prop… ▽ More Memory-assisted quantum networks require photon sources with controllable temporal and correlation properties. The Duan-Lukin-Cirac-Zoller (DLCZ) protocol provides a platform based on spontaneous Raman scattering in atomic ensembles, but a unified predictive theory connecting control parameters to correlations under realistic propagation and noise conditions remains lacking. Here we present a propagation-inclusive open-system quantum theory that retains write-induced population redistribution while combining Heisenberg-Langevin dynamics with Maxwell-Schrödinger propagation. We experimentally validate its key predictions. The theory predicts time-dependent Stokes generation, spin-wave evolution, retrieved anti-Stokes wavepackets, and time-resolved cross-correlations. Experiments confirm robust correlations under retrieval tuning and enhanced correlations for shorter write pulses, consistent with the different scaling of correlated coincidences and accidental backgrounds with the mean spin-wave excitation number. Classically controlled retrieval enables temporal gating and slicing of the anti-Stokes wavepacket, establishing a quantitative framework for correlation engineering in memory-compatible DLCZ photon sources. △ Less

Submitted 13 August, 2026; originally announced August 2026.

Comments: 17 pages, 10 figures

Geometric phase-space nonseparability triggers giant optical shifts

Authors: Kaiqi Zhu, Yonglei Liu, Yao Zhao, Zhongyi Hu, Jiahui Shen, Yimeng Zhu, Lin Liu, Yangjian Cai, Fei Wang, Sergey A. Ponomarenko, Yahong Chen

Abstract: Nonseparability among multiple degrees of freedom has enabled fundamental advances in structured light and related applications. Here we unveil a previously overlooked form of nonseparability in phase space, which we term geometric phase-space nonseparability. The latter arises solely from the wavefront curvature of a conventional wave packet, such as a fundamental Gaussian beam. This phase-space… ▽ More Nonseparability among multiple degrees of freedom has enabled fundamental advances in structured light and related applications. Here we unveil a previously overlooked form of nonseparability in phase space, which we term geometric phase-space nonseparability. The latter arises solely from the wavefront curvature of a conventional wave packet, such as a fundamental Gaussian beam. This phase-space structure manifests as a position-dependent transverse-momentum distribution across the beam profile leading to the giant spatial and angular beam shifts upon reflection at a planar interface that we predict analytically and observe experimentally. Remarkably, the curvature-induced phase-space correlation remains robust against spatial-coherence degradation, allowing the giant shifts to persist even in the nearly incoherent regime. Our results establish wavefront curvature as a general mechanism for engineering beam shifts across optical, acoustic, and matter-wave systems. △ Less

Submitted 9 August, 2026; originally announced August 2026.

Comments: 6 pages, 5 figures

Bell nonlocality with directly generated telecom-band spin-photon entanglement

Authors: Dong-Yu Huang, Jian Wang, Xiao-Long Zhou, Ze-Min Shen, Si-Jian He, Qi-Yang Huang, Yi-Jia Liu, Yu-Shu Chen, Quan Jiang, Chuan-Feng Li, Guang-Can Guo

Abstract: Quantum nonlocality, typically revealed through entanglement distribution across quantum networks, is a cornerstone of quantum information science. Long-distance distribution of entanglement requires the information carrier, i.e. flying photons, to operate in the minimum-loss telecom band of optical fiber. While extensive efforts have been devoted to the direct generation of entanglement between C… ▽ More Quantum nonlocality, typically revealed through entanglement distribution across quantum networks, is a cornerstone of quantum information science. Long-distance distribution of entanglement requires the information carrier, i.e. flying photons, to operate in the minimum-loss telecom band of optical fiber. While extensive efforts have been devoted to the direct generation of entanglement between C-band telecom photons and various stationary spins, the verification of quantum nonlocality remains an outstanding challenge. Here, utilizing a dipole transition in rubidium atoms with a wavelength of 1530 nm and a cavity-assisted protocol, we achieve resonant excitation and direct emission of C-band telecom photons from a single atom, generating spin-photon entanglement with a measured Bell state fidelity exceeding 91.4%. We then verify Bell nonlocality by observing a Bell inequality violation of 2.455(77) > 2 using this high-quality entangled pair. These results extend the wavelength of a single-atom quantum emitter to the telecom C-band, achieving sufficiently high-fidelity spin-photon entanglement to finally verify Bell nonlocality. This work thereby provides a promising building block for a large-scale atom-based quantum network capable of distributed quantum metrology and long-distance quantum communication. △ Less

Submitted 8 August, 2026; originally announced August 2026.

Comments: 13 pages, 4+5 figures, 1 table

Vendor-Agnostic Joint Relaxometry and Myelin Water Fraction Mapping with B1 and Motion Correction

Authors: Unay Dorken Gallastegi, Shohei Fujita, Yohan Jun, Antoine Delattre-Klauser, Gian Franco Piredda, Tom Hilbert, Cemre Ariyurek, Eugene Milshteyn, Shizhuo Li, Yuting Chen, Xingwang Yong, Kwok-Shing Chan, Qiang Liu, Seonghwan Yee, Yogesh Rathi, Maxim Zaitsev, Jon-Fredrik Nielsen, Onur Afacan, Camilo Jaimes, Patricia Ellen Grant, Borjan Gagoski, Berkin Bilgic

Abstract: Obtaining consistent quantitative maps of myelin content and relaxation times across different sites and vendors is essential for advancing our understanding of brain development. Herein, we present a harmonized, vendor-agnostic magnetic resonance acquisition method designed for joint T1, T2, and myelin water fraction mapping, along with a method for rapid B1+ and B1- field estimation. We used our… ▽ More Obtaining consistent quantitative maps of myelin content and relaxation times across different sites and vendors is essential for advancing our understanding of brain development. Herein, we present a harmonized, vendor-agnostic magnetic resonance acquisition method designed for joint T1, T2, and myelin water fraction mapping, along with a method for rapid B1+ and B1- field estimation. We used our dictionary-based fitting and multi-compartment modeling for joint mapping of T1, T2 and myelin water fraction. Self-navigation-based retrospective motion correction was integrated with subspace reconstruction to track and correct rigid head motion during scanning, operating without the need for external hardware. Simulations, phantom and in vivo experiments confirmed the sensitivity and accuracy of the method, particularly for short T2 values corresponding to myelin, and demonstrated consistent performance across multiple scanner types. Coupled with the harmonized calibration scan, the proposed package offers a practical tool for multi-site, multi-vendor neuroimaging studies in both adult and pediatric populations. △ Less

Submitted 7 August, 2026; originally announced August 2026.

Comments: 49 pages, 8 figures, supplementary material included. Submitted to Imaging Neuroscience

Linear and nonlinear benchmark of gyrokinetic simulation of energetic particle driven toroidal Alfven eigenmodes in ITPA TAE benchmark case

Authors: Youjun Hu, Yang Chen, Lei Ye, Zhiyong Qiu, Youwen Sun

Abstract: A new gyrokinetic code, TEK, was benchmarked in simulating energetic particle (EP) driven toroidal Alfven eigenmodes (TAEs) in the simple tokamak configuration chosen by the ITPA-EP group for code benchmarking purpose. Linear benchmark has been well established by other codes, whereas nonlinear benchmark for this case is lacking. This paper presents, besides the linear benchmark, nonlinear results… ▽ More A new gyrokinetic code, TEK, was benchmarked in simulating energetic particle (EP) driven toroidal Alfven eigenmodes (TAEs) in the simple tokamak configuration chosen by the ITPA-EP group for code benchmarking purpose. Linear benchmark has been well established by other codes, whereas nonlinear benchmark for this case is lacking. This paper presents, besides the linear benchmark, nonlinear results for both single-n and multiple-n simulations (n is the toroidal mode number). The nonlinear results are in good agreement with an analytical theory on zonal field beat-driven by Alfven eigenmodes, partially verifying correctness of the nonlinear simulations. The saturation level and the resulting EP transport are examined. This provides data for future inter-code nonlinear benchmarking. In TEK, all species (electrons, thermal ions, EPs) are treated on the same footing using the gyrokinetic model (with electrons in the zero Larmor radius limit). The electromagnetic cancellation problem is mitigated by using the mixed-variable pullback method. Numerical details related to electromagnetic gyrokinetic simulation are discussed. △ Less

Submitted 6 August, 2026; originally announced August 2026.

Estimating the sensitivity of the IceCube Upgrade to probe the interior of the Earth using atmospheric neutrino oscillations

Authors: The IceCube Collaboration, R. Abbasi, M. Ackermann, J. Adams, S. K. Agarwalla, J. A. Aguilar, M. Ahlers, J. M. Alameddine, S. Ali, N. M. Amin, K. Andeen, C. Arg{ü}elles, S. Athanasiadou, S. N. Axani, R. Babu, X. Bai, A. Balagopal V., S. W. Barwick, V. Basu, R. Bay, J. J. Beatty, J. Becker Tjus, P. Behrens, J. Beise, C. Bellenghi , et al. (399 additional authors not shown)

Abstract: The IceCube Upgrade is a densely instrumented central region of the IceCube Neutrino Observatory, deployed during the 2025-26 polar season. It will reduce the detector's energy threshold and improve overall reconstruction capabilities for multi-GeV atmospheric neutrinos, which in turn enhance their sensitivity to Earth matter effects as they traverse through the deep Earth. In this study, we descr… ▽ More The IceCube Upgrade is a densely instrumented central region of the IceCube Neutrino Observatory, deployed during the 2025-26 polar season. It will reduce the detector's energy threshold and improve overall reconstruction capabilities for multi-GeV atmospheric neutrinos, which in turn enhance their sensitivity to Earth matter effects as they traverse through the deep Earth. In this study, we describe the potential of the IceCube Upgrade to observe Earth matter effects on atmospheric neutrinos and estimate the detector's sensitivity to probe key features of the Preliminary Reference Earth Model by utilizing these observations. We highlight the IceCube Upgrade's capability to estimate the mass of the Earth and verify the non-homogeneous distribution of matter density within the Earth. We also estimate the IceCube Upgrade sensitivity to measure the correlated densities of the Earth layers while incorporating constraints from the mass and moment of inertia of the Earth. Neutrino-based results would be independent and complementary to the seismic and gravitational measurements. △ Less

Submitted 6 August, 2026; originally announced August 2026.

Comments: 20 pages, 13 figures, 2 tables, and 1 appendix

TIDE: A Physically Diverse 3D Turbulence Benchmark Dataset for Advancing Scientific Machine Learning

Authors: Yilong Dai, Yiming Sun, Yiheng Chen, Shengyu Chen, Peyman Givi, Xiaowei Jia, Runlong Yu

Abstract: Turbulence is a central testbed for machine learning on physical dynamics because its governing laws are known exactly. However, most existing studies remain in 2D, while 3D turbulence has fundamentally different physics and is far more costly to simulate. Existing 3D resources also typically provide only one realization per configuration, making it difficult to distinguish learning the dynamics f… ▽ More Turbulence is a central testbed for machine learning on physical dynamics because its governing laws are known exactly. However, most existing studies remain in 2D, while 3D turbulence has fundamentally different physics and is far more costly to simulate. Existing 3D resources also typically provide only one realization per configuration, making it difficult to distinguish learning the dynamics from fitting the statistics of a single flow. In this paper, we introduce TIDE (Turbulent Incompressible DNS Ensembles), a 256^3 DNS corpus and benchmark for 3D incompressible turbulence, with 15 configurations on eight controlled axes, independent ensembles, pressure fields, and equation-level verification. The benchmark includes five tasks, standardized learned baselines, controlled generalization splits, and physical-fidelity metrics alongside pointwise error. Across the main forecasting configurations, current learned models barely outperform persistence and still make about twice the error of a spectral solver given the true equations. Moreover, lower pointwise error can coincide with severely distorted small-scale dynamics, showing that accuracy alone does not ensure physical fidelity. Generalization results further show that most regime shifts reflect limited training coverage, whereas forced-to-decay transfer exposes a missing conditioning variable: operators trained under forcing continue to predict driven evolution when the external drive is removed. Closing these accuracy, fidelity, and conditioning gaps is the central open problem made measurable by TIDE. △ Less

Submitted 4 August, 2026; originally announced August 2026.

Comments: 20 pages, 13 figures, 28 tables. Dataset: https://huggingface.co/datasets/ydai17/TIDE ; Code: https://github.com/Dyloong1/TIDE-dataset-benchmark ; DOI: https://doi.org/10.5281/zenodo.21589489

MSC Class: 76F65; 68T07 ACM Class: I.2.6; J.2; I.6.3

MWF-MIMOSA for efficient simultaneous relaxometry and myelin water fraction mapping

Authors: Yuting Chen, Yohan Jun, Hyeong-Geol Shin, Shizhuo Li, Shohei Fujita, Xingwang Yong, Jiye Kim, Jongho Lee, Gian Franco Piredda, Tom Hilbert, Aneri Bhatt, Susie Y. Huang, Huafeng Liu, Huihui Ye, Shahin Nasr, Borjan Gagoski, Kwok-Shing Chan, Berkin Bilgic

Abstract: Quantitative magnetic resonance imaging (qMRI) provides improved sensitivity and specificity to tissue composition and pathological alterations compared with conventional contrast-weighted imaging. Among various qMRI biomarkers, myelin water imaging is of particular interest because myelin plays a central role in brain function and its alteration is closely associated with many neurological diseas… ▽ More Quantitative magnetic resonance imaging (qMRI) provides improved sensitivity and specificity to tissue composition and pathological alterations compared with conventional contrast-weighted imaging. Among various qMRI biomarkers, myelin water imaging is of particular interest because myelin plays a central role in brain function and its alteration is closely associated with many neurological diseases. However, conventional myelin water fraction (MWF) mapping techniques are often limited by long scan times, low spatial resolution, reduced signal-to-noise ratio (SNR), and high specific absorption rate (SAR). Here, we propose MWF-MIMOSA for efficient simultaneous T1, T2, T2* mapping, magnetic susceptibility source separation, and MWF estimation. To achieve this, multi-contrast and multi-slice zero-shot self-supervised learning (MZS-SSL) was used to jointly reconstruct whole-brain complex-valued images. To improve computational efficiency of the parameter estimation step, a multilayer perceptron (MLP) was trained within the GACELLE GPU-accelerated parameter estimation framework to circumvent the computationally intensive Bloch simulation process, resulting in a >100-fold computational speed-up in MWF estimation. Numerical simulations were performed to evaluate the accuracy and precision of MWF-MIMOSA, and in-vivo results further demonstrated its robustness. Comparison with existing myelin water imaging methods showed that MWF-MIMOSA is highly correlated with established approaches, while providing complementary quantitative parameter maps at higher spatial resolution and with shorter scan times. Notably, simultaneous multi-parametric mapping was achieved in 5 min at 1 mm isotropic resolution, and in 10 min at 0.7 mm isotropic resolution. These results demonstrate the potential of MWF-MIMOSA for fast, high-resolution simultaneous relaxometry and myelin water imaging. △ Less

Submitted 30 July, 2026; originally announced July 2026.

Comments: 43 pages, 16 figures

MSC Class: 94A08; 92C55; 68T07

Multi-mode fiber enabled multi-wavelength optical trapping and dynamic manipulation

Authors: Yikun Shen, Chuangye Zhang, Yuquan Zhang, Jiahui Pan, Siwei Chen, Hongyang Xu, Yixuan Chen, Qi Jin, Xiaocong Yuan, Changjun Min

Abstract: Optical fiber tweezers offer distinct advantages for long-distance manipulation, compact integration, and minimally invasive operation in biological environments. However, most optical fiber tweezers rely on single-mode fibers (SMFs), which are constrained by limited optical mode diversity and reduced control flexibility. Although multi-mode fibers (MMFs) support a wider spectrum of propagation mo… ▽ More Optical fiber tweezers offer distinct advantages for long-distance manipulation, compact integration, and minimally invasive operation in biological environments. However, most optical fiber tweezers rely on single-mode fibers (SMFs), which are constrained by limited optical mode diversity and reduced control flexibility. Although multi-mode fibers (MMFs) support a wider spectrum of propagation modes, their inherent mixed guided modes with low coherence become a long-standing limitation for the design of focused trapping configurations. To address these limitations, we propose and experimentally validate a fully MMF-based optical tweezer system integrated with a micro-lens structure fabricated on the fiber facet, enabling stable optical trapping across multiple wavelengths and dynamic manipulation of trapped cells. Employing 532 nm continuous-wave and 800 nm femtosecond lasers, we demonstrate that both light sources can generate tightly focused optical spots through the micro-lens with a high numerical aperture (NA>0.7), achieving robust trapping and axial dynamic manipulation of cells. Compared with conventional SMF-based tweezers, this approach leverages the broadband and multi-mode properties of MMFs, allows for wavelength-flexible and dynamically adjustable trapping of cells, and paves the way for lab-on-fiber biophotonic platforms with potential applications such as interventional manipulation, cell sorting, and cellular fluorescence analysis. △ Less

Submitted 30 July, 2026; originally announced July 2026.

DoTime: A Synthetic Benchmark Generator for Interventional and Counterfactual Time Series

Authors: Dennis Thumm, Billy Tim Anthony, Ying Chen

Abstract: Most benchmarks for causal inference over time series are observational, small, or domain-specific, leaving interventional and counterfactual estimation under-served exactly where it matters most, such as in healthcare, policy evaluation, and climate science. We introduce \textbf{DoTime}, an open, scalable, and theoretically grounded generator of multivariate temporal structural causal models (TSC… ▽ More Most benchmarks for causal inference over time series are observational, small, or domain-specific, leaving interventional and counterfactual estimation under-served exactly where it matters most, such as in healthcare, policy evaluation, and climate science. We introduce \textbf{DoTime}, an open, scalable, and theoretically grounded generator of multivariate temporal structural causal models (TSCMs) with interventions, released as the \code{dotime} PyPI package together with four frozen evaluation suites. Beyond existing work, it adds capabilities absent from prior generators: continuous-time intervention \emph{windows}, counterfactual sampling modes with a positivity guard, regime-switching SCMs as a strict generalization of interrupted time series, non-stationary dynamics by construction with switching SCM parameters, and deterministic ramp and sinusoidal intervention profiles that place trends and structural breaks \emph{inside} the evaluation window. Moreover, it demonstrates the suitability of the generator as a prior for a causal foundation model reference implementation. The released suites span a training-scale snapshot of $100{,}000$ trajectories and eight named identification structures, each with exact ground truth: paired interventional trajectories from the same SCM throughout, and shared-noise counterfactuals in the continuous-time suite. We ship reference baseline implementations with an evaluation harness, and pose a falsifiable claim: interventional training buys a measurable direction-accuracy advantage over an observational model of identical capacity. It is tested across three training seeds per arm. Under structure-matched evaluation on held-out episodes, the interventional prior-fitted network's (PFN) gap is positive in every structure, trajectory length, and seed tested. △ Less

Submitted 29 July, 2026; originally announced July 2026.

Generation of high-fluence and high-intensity hard x-ray attosecond pulses at European XFEL

Authors: Ichiro Inoue, Ulrike Boesenberg, Rustam Rysov, Takahiro Sato, Ichika Harima, Chenzhi Xu, Jia Liu, Thomas M. Linker, Zain Abhari, Andrei Benediktovitch, Uwe Bergmann, Ye Chen, Lu Cao, Winfried Decking, Gianluca Geloni, Marc Guetg, Trey Guest, Aliaksei Halavanau, Jörg Hallmann, Takashi Kimura, Naresh Kujala, Aliaksandr Leonau, Shan Liu, Tianyun Long, Johannes Möller , et al. (19 additional authors not shown)

Abstract: By combining hard x-ray attosecond pulses from the European XFEL with total-reflection focusing x-ray optics, we generated nanofocused hard x-ray attosecond pulses with intensities and fluences comparable to the highest values attained in the hard x-ray regime. A peak intensity on the order of 10$^{20}$ W/cm$^2$ is confirmed through the observation of saturation in amplified spontaneous emission f… ▽ More By combining hard x-ray attosecond pulses from the European XFEL with total-reflection focusing x-ray optics, we generated nanofocused hard x-ray attosecond pulses with intensities and fluences comparable to the highest values attained in the hard x-ray regime. A peak intensity on the order of 10$^{20}$ W/cm$^2$ is confirmed through the observation of saturation in amplified spontaneous emission from copper atoms. These x-ray pulses enable new scientific opportunities, including the exploration of higher-order nonlinear light--matter interactions, damage-free structure determination, and coherent control of atoms and molecules. △ Less

Submitted 31 July, 2026; v1 submitted 29 July, 2026; originally announced July 2026.

Levitated nano-trampoline resonators for magnetic field sensing

Authors: Xianfeng Chen, Nirmala Raj, Matthew R. Chua, Yi Fan Chen, Chenyue Gu, Minxing Xu, Young-Wook Cho, Syed M. Assad, Lu Ding, Ping Koy Lam

Abstract: Levitated systems and high-$Q$ membrane nanomechanical resonators have achieved exceptional sensitivity in precision sensing, but functionalizing such resonators for practical applications without degrading their low dissipation remains challenging. Here, we combine diamagnetic levitation with a high-$Q$ nanomechanical resonator to realize a high-precision magnetometer for sensing weak oscillating… ▽ More Levitated systems and high-$Q$ membrane nanomechanical resonators have achieved exceptional sensitivity in precision sensing, but functionalizing such resonators for practical applications without degrading their low dissipation remains challenging. Here, we combine diamagnetic levitation with a high-$Q$ nanomechanical resonator to realize a high-precision magnetometer for sensing weak oscillating magnetic fields. A macroscopic diamagnetically levitated graphite plate acts as a free-floating proof mass that couples strongly to magnetic fields, converting them into mechanical motion that is resonantly amplified by a low-dissipation nano-trampoline resonator. Operating at room temperature and without magnetic shielding, we achieve a peak magnetic-field sensitivity of $4.5\, \mathrm{pT}/\sqrt{\mathrm{Hz}}$ using a resonator with a mechanical quality factor of $Q=6\times10^{6}$ at $443\, \mathrm{kHz}$. The system sensitivity is limited by thermomechanical noise. With further improvements in mechanical $Q$, this hybrid levitated platform offers a pathway toward femtotesla-level AC magnetic-field sensing, establishing diamagnetically levitated nanomechanical resonators as a new class of high-sensitivity magnetometers at room temperature. △ Less

Submitted 24 July, 2026; originally announced July 2026.

Comments: 16 pages, 13 figures

Diffusion-guided optimization for full waveform inversion

Authors: Yiran Shen, Yangkang Chen, Björn Engquist

Abstract: We present a diffusion-guided full waveform inversion (FWI) study in which pretrained diffusion generative models are used as learned regularizers inside a PDE-constrained seismic inversion loop. We compare three training-free guidance strategies: Manifold-Preserving Guided Diffusion (MPGD), SDEdit-based initialization, and Split Gibbs Diffusion Sampling (SGDS), which alternates between FWI likeli… ▽ More We present a diffusion-guided full waveform inversion (FWI) study in which pretrained diffusion generative models are used as learned regularizers inside a PDE-constrained seismic inversion loop. We compare three training-free guidance strategies: Manifold-Preserving Guided Diffusion (MPGD), SDEdit-based initialization, and Split Gibbs Diffusion Sampling (SGDS), which alternates between FWI likelihood updates and diffusion-prior denoising. The proposed workflow keeps wave-equation modeling in the inversion loop and uses a geological prior to stabilize model components that are weakly constrained by the seismic data. Controlled GeoFWI experiments, benchmark-scale Marmousi and Overthrust tests, a difficult Sigsbee2A salt test, and noise-degradation studies show that SGDS improves reconstruction quality relative to conventional L2 and total-variation regularized FWI in clean and moderately noisy synthetic settings. Overall, these experiments demonstrate that diffusion-guided optimization can serve as a practical learned regularization strategy for synthetic FWI benchmarks while preserving the wave-equation modeling loop. △ Less

Submitted 24 July, 2026; originally announced July 2026.

Comments: 32 pages, 16 figures. Source code: https://github.com/shenyiran91/SGDS-FWI

Raman-Assisted Multiband Nonlinear Frequency-Conversion Network in a High-Q LTOI Microdisk

Authors: Zhifan Fang, Yuxuan He, Zhangning Pan, Xianfeng Chen, Yuping Chen

Abstract: On-chip nonlinear frequency conversion offers a key route to broadband coherent light sources, but spanning telecom, visible, and ultraviolet wavelengths within a single resonator remains challenging. Lithium tantalate-on-insulator (LTOI), which has recently emerged as a promising material platform for integrated photonics, combines strong Raman activity, a large second-order nonlinearity, broad o… ▽ More On-chip nonlinear frequency conversion offers a key route to broadband coherent light sources, but spanning telecom, visible, and ultraviolet wavelengths within a single resonator remains challenging. Lithium tantalate-on-insulator (LTOI), which has recently emerged as a promising material platform for integrated photonics, combines strong Raman activity, a large second-order nonlinearity, broad optical transparency and high resistance to photorefractive damage, thereby attracting increasing attention for on-chip nonlinear frequency conversion. Here, we experimentally demonstrate a Raman-assisted multiband frequency-conversion network in a high-Q LTOI microdisk with a loaded quality factor of 2.48x10^6. The resonant pumping produced high-purity single-mode Raman lasing with a 3.14 mW threshold, 32.44% slope efficiency, and an excellent side-mode suppression ratio (SMSR) of 29.5 dB. Under a nearby pump condition, we also observe multiple Stokes components together with an anti-Stokes line on the short-wavelength side of the pump. The resulting multiple intracavity Stokes fields subsequently acted as frequency seeds for cascaded chi^(2) processes, producing near-infrared and visible signals and extending the emission to 312.6 nm in the ultraviolet. These findings establish the cooperative action of Raman gain and second-order nonlinearity across widely separated spectral bands within a single microcavity. The device therefore offers a route toward integrated multiband light sources and a platform for studying coupled nonlinear dynamics. △ Less

Submitted 21 July, 2026; originally announced July 2026.

Comments: 11 pages, 5 figures

Localized crystallization of Ce:YIG thin films on Si using CO2 laser annealing for integrated nonreciprocal photonic device applications

Authors: Xinran Ji, Junxian Wang, Tianchi Zhang, Xuan Zhao, Di Wu, Zixuan Wei, Yizhi Chen, Jialong Wang, Lei Bi

Abstract: Laser annealing (LA) technique has emerged as an effective method for localized crystallization of magneto-optical (MO) garnet thin films on semiconductor substrates. However, no studies have explored the crystallization and magneto-optical (MO) properties of cerium-substituted yttrium iron garnet (Ce:YIG, Ce1Y2Fe5O12) thin films for integrated photonic device applications using LA technique. In t… ▽ More Laser annealing (LA) technique has emerged as an effective method for localized crystallization of magneto-optical (MO) garnet thin films on semiconductor substrates. However, no studies have explored the crystallization and magneto-optical (MO) properties of cerium-substituted yttrium iron garnet (Ce:YIG, Ce1Y2Fe5O12) thin films for integrated photonic device applications using LA technique. In this study, we provide a comprehensive investigation into the laser annealing of Ce:YIG films deposited on SiO2 substrates and silicon nitride photonic waveguides for integrated nonreciprocal photonic device applications. Garnet phase was successfully observed in films grown on SiO2 substrates, and SiN waveguides with laser annealing of sputtered Ce:YIG films on top of a laser annealed Y3Fe5O12 seed layer. The magneto-optical (MO) properties of Ce:YIG films on oxidized Si substrates were found to be comparable to those prepared by rapid thermal annealing (RTA). A Mach-Zehnder Interferometer (MZI) type optical isolator based on Ce:YIG film on SiN was fabricated, exhibiting a saturation Faraday rotation of -2317.7 deg/cm and propagation loss of 188.2 dB/cm. Isolation ratio of 27.1 dB and insertion loss of 10.1 dB were achieved at 1552.7 nm wavelength. △ Less

Submitted 20 July, 2026; originally announced July 2026.

Final assessment of radioactive impurities in the JUNO detector

Authors: Thomas Adam, Fengpeng An, Costas Andreopoulos, Giuseppe Andronico, Nikolay Anfimov, Vito Antonelli, Tatiana Antoshkina, João Pedro Athayde Marcondes de André, Didier Auguste, Nikita Balashov, Andrea Barresi, Davide Basilico, Eric Baussan, Marco Beretta, Antonio Bergnoli, Nikita Bessonov, Daniel Bick, Lukas Bieger, Svetlana Biktemerova, Thilo Birkenfeld, Simon Blyth, Manuel Böhles, Anastasia Bolshakova, Mathieu Bongrand, Matteo Borghesi , et al. (549 additional authors not shown)

Abstract: The Jiangmen Underground Neutrino Observatory (JUNO) collaboration has completed the construction of the 20,000-ton liquid scintillator detector and the associated muon veto detector system. To meet the physics objectives, the materials used in the detector must exhibit low radioactive contamination. The single-event rate in the fiducial volume (R $<$ 17.2 m) of the scintillator is required to be… ▽ More The Jiangmen Underground Neutrino Observatory (JUNO) collaboration has completed the construction of the 20,000-ton liquid scintillator detector and the associated muon veto detector system. To meet the physics objectives, the materials used in the detector must exhibit low radioactive contamination. The single-event rate in the fiducial volume (R $<$ 17.2 m) of the scintillator is required to be approximately 7 Hz for energies above 0.7 MeV, resulting in an accidental coincidence background of about 1 event per day for reactor neutrino physics analyses. Since the beginning of the construction phase, we have screened the natural radioactivity content of thousands of materials, to select those that meet the design background budget. The radioactive impurity concentrations of the materials ultimately used in the JUNO detector are summarized in this paper. The construction of the entire detector and the subsequent filling of the liquid scintillator were completed in August 2025. From the initial data, the total count rate of natural radioactivity within the detector's fiducial volume has met the requirements and is sufficient to support the reactor antineutrino analysis. △ Less

Submitted 19 July, 2026; originally announced July 2026.

Operation and performance of ProtoDUNE Dual Phase liquid argon time projection chamber

Authors: DUNE Collaboration, S. Abbaslu, F. Abd Alrahman, A. Abed Abud, R. Acciarri, L. P. Accorsi, M. A. Acero, M. R. Adames, G. Adamov, M. Adamowski, K. Adhikari, C. Adriano, K. Agudelo-Jaramillo, F. Akbar, F. Alemanno, N. S. Alex, L. Aliaga Soplin, A. Alqaisi, M. Alrashed, A. Alton, R. Alvarez, T. Alves, A. Aman, H. Amar, R. Amarinei , et al. (1341 additional authors not shown)

Abstract: ProtoDUNE-DP was the largest ever built Liquid Argon Time Projection Chamber (LArTPC) operating in Dual-Phase (DP) mode, with a liquid target and charge read-out placed in the gas. It had an active volume of $6\times6\times6$\,m$^3$ corresponding to an active mass of 300\,t (total LAr mass of 720\,t), constructed at the CERN Neutrino Platform and took data from 2019 to 2020 with cosmic muons. In P… ▽ More ProtoDUNE-DP was the largest ever built Liquid Argon Time Projection Chamber (LArTPC) operating in Dual-Phase (DP) mode, with a liquid target and charge read-out placed in the gas. It had an active volume of $6\times6\times6$\,m$^3$ corresponding to an active mass of 300\,t (total LAr mass of 720\,t), constructed at the CERN Neutrino Platform and took data from 2019 to 2020 with cosmic muons. In ProtoDUNE-DP the electric drift field is oriented in the vertical direction, causing the electrons to drift vertically towards the anode at the top. The ionization charge is then extracted into the gaseous argon above the liquid surface, amplified by Townsend avalanches, and collected by the charge readout planes. The detector experienced significant technical problems affecting the long-term operation of the Charge Readout Planes, formed by the Large Electron Multipliers, but other critical segments demonstrated required performance including the delivery of -300 kV to the TPC cathode, verification of replaceable charge read-out electronics, and operation of the photon detection system. ProtoDUNE-DP experience resulted in improved designs of the Vertical Drift LArTPC. △ Less

Submitted 21 July, 2026; v1 submitted 17 July, 2026; originally announced July 2026.

Comments: 103 pages, 66 figures

Report number: FERMILAB-PUB-26-0466-LBNF

High-rate continuous-variable quantum key distribution coexisting with Tb/s coherent classical transmission in hollow-core fiber

Authors: Xitao Ji, Siyu Chen, Peng Li, Mingming Zhang, Yilun Chen, Jun Gao, Rui Lin, Bacco Davide, Siqi Yan, Ming Tang

Abstract: Quantum key distribution (QKD) can provide secret keys with security rooted in quantum mechanics, but operation alongside high-capacity classical traffic remains limited by the excess-noise budget of weak quantum states in conventional solid-core fiber. Here, we combine ultralow-loss anti-resonant hollow-core fiber with residual-carrier-assisted discrete-modulation continuous-variable QKD (DM-CV-Q… ▽ More Quantum key distribution (QKD) can provide secret keys with security rooted in quantum mechanics, but operation alongside high-capacity classical traffic remains limited by the excess-noise budget of weak quantum states in conventional solid-core fiber. Here, we combine ultralow-loss anti-resonant hollow-core fiber with residual-carrier-assisted discrete-modulation continuous-variable QKD (DM-CV-QKD) to address both propagation-induced coexistence noise and low-SNR phase recovery. Over a 24.3-km hollow-core link with 3.3-dB end-to-end loss, a dual-polarization 15-Gbaud DM-CV-QKD channel achieves an average asymptotic secret-key rate (SKR) of 153.22 Mb/s and a finite-size SKR of 149.99 Mb/s, while 39 coherent wavelength-division-multiplexed channels deliver an aggregate data rate of 7.6 Tb/s and a net data rate of 7.2 Tb/s. The system can even sustain a positive SKR under a high classical launch power of up to 15 dBm, without an optical bandpass filter (BPF). Finite-size analysis against collective attacks further yields a projected positive secret-key rate at a 100-km-equivalent condition. These results show that an anti-resonant hollow-core fiber, combined with carrier-assisted phase recovery, can greatly extend the operating regime of shared-fiber quantum-secured coherent links, pointing to a promising approach for integrating high-rate CV-QKD with high-capacity optical networks. △ Less

Submitted 16 July, 2026; originally announced July 2026.

Criticality and reduced dynamical resilience in PM2.5 pollution systems

Authors: Yuan Chen, Yongwen Zhang, Xu Li, Dean Chen, Jingfang Fan, Yosef Ashkenazy, Deliang Chen, Shlomo Havlin

Abstract: Concentration-based metrics underpin air-quality assessment, while dynamical persistence and recovery describe how rapidly high-PM2.5 episodes dissipate and how strongly they retain memory. Here we introduce a finite-memory multiplicative reversion (FMMR) process that links the lognormal concentration backbone of PM2.5 variability with event recurrence, temporal memory, variance amplification and… ▽ More Concentration-based metrics underpin air-quality assessment, while dynamical persistence and recovery describe how rapidly high-PM2.5 episodes dissipate and how strongly they retain memory. Here we introduce a finite-memory multiplicative reversion (FMMR) process that links the lognormal concentration backbone of PM2.5 variability with event recurrence, temporal memory, variance amplification and local dynamical resilience. Across station observations and reanalysis data, elevated PM2.5 regimes show a coherent set of critical signatures: stronger memory, rising autocorrelation, broader upper tails, amplified variance, reduced resilience and more clustered exceedance events. Together, these co-occurring signals reveal dynamical criticality in PM2.5 pollution systems, with critical slowing down expressed as a loss of restoring capacity under high-pollution conditions. A gridded comparison across populated and emission-influenced regions further shows that areas with similar PM2.5 burden can differ in recovery capacity, while eastern China has shifted toward higher resilience during recent air-quality improvements and India and West Africa occupy lower-resilience states. By identifying where pollution burden and recovery capacity diverge, these findings establish dynamical persistence and resilience as complementary dimensions of PM2.5 risk and provide a quantitative basis for resilience-oriented air-quality assessment. △ Less

Submitted 16 July, 2026; originally announced July 2026.

Comments: 33 pages, 4 figures

$\texttt{iNORG}$: An open-source quantum impurity solver package based on the natural orbitals renormalization group

Authors: Jia-Ming Wang, Yi-Heng Tian, Yin Chen, Ru Zheng, Rong-Qiang He, Zhong-Yi Lu

Abstract: In the context of dynamical mean-field theory (DMFT) calculations for strongly correlated electron systems, quantum impurity solvers play a central computational role in treating correlated lattice models and realistic materials. Consequently, developing efficient and robust quantum impurity solvers remains a key challenge. In this paper, we present an open-source quantum impurity solver package b… ▽ More In the context of dynamical mean-field theory (DMFT) calculations for strongly correlated electron systems, quantum impurity solvers play a central computational role in treating correlated lattice models and realistic materials. Consequently, developing efficient and robust quantum impurity solvers remains a key challenge. In this paper, we present an open-source quantum impurity solver package based on the natural orbitals renormalization group (NORG) method, dubbed $\texttt{iNORG}$. This software delivers high accuracy with reduced computational cost by optimizing the bath representation using natural orbitals and incorporating advanced features such as efficient Hilbert space selection and efficient algorithms for computing Green's functions. We first introduce the basic principle of the NORG method and then discuss the implementation details. The software framework, major features, and installation procedure for $\texttt{iNORG}$ are explained as well. Finally, several simple examples are presented to demonstrate the usage of $\texttt{iNORG}$. △ Less

Submitted 15 July, 2026; originally announced July 2026.

Comments: 27 pages, 6 figures

Dispersion-Guided Physics-Aware Deep Inverse Operator for Surface Wave Mode Separation

Authors: Yang Cui, Sujith Swaminadhan, Yangkang Chen, Christian Schiffer, Myrto Papadopoulou

Abstract: Surface-wave (SW) dispersion analysis is widely used in near-surface geophysics and seismology to determine shear-wave velocity structures by measuring SW geometric dispersion in seismic data. Among the available approaches, multichannel analysis of surface waves (MASW) and two-station methods are commonly employed to extract dispersion information for SW inversion. However, the coexistence of fun… ▽ More Surface-wave (SW) dispersion analysis is widely used in near-surface geophysics and seismology to determine shear-wave velocity structures by measuring SW geometric dispersion in seismic data. Among the available approaches, multichannel analysis of surface waves (MASW) and two-station methods are commonly employed to extract dispersion information for SW inversion. However, the coexistence of fundamental and higher modes in seismic data poses challenges for these methods, particularly for two-station analysis. To separate the different mode components, we propose a physics-aware unsupervised deep-learning framework. The method acts as a deep inverse operator that directly separates fundamental- and higher-mode components in the time-space domain using an adaptive Gaussian mask constructed in the frequency-phase-velocity (f-v) domain. Physical constraints are incorporated into the loss function by maximizing energy concentration within the target mask while suppressing leakage outside it. Through backpropagation, the network learns the inverse mapping from physical constraints in the f-v domain to wavefield separation in the time-space domain without requiring labeled training data. Numerical experiments on both synthetic and field data show that the framework provides a robust and automated solution for SW mode separation, facilitating more reliable dispersion-curve picking and improving the accuracy of subsequent SW inversion. △ Less

Submitted 14 July, 2026; originally announced July 2026.

Full-Path Nonlinear Modeling of Microwave Power Transmission Through Ionospheric Plasma for Space Solar Power Station

Authors: Pengan Guo, Lei Chang, Yuhan Chen, Ya Gao, Longshuai Ye, Jikai Sun, Huaiqing Zhang, Jian Li

Abstract: Space Solar Power Station (SSPS) concepts rely on gigawatt-class microwave beams to carry orbital solar energy through the ionosphere, where the beam and the plasma form a coupled nonlinear system: the field heats electrons, the heating alters the collision frequency and plasma density, and the modified medium in turn reshapes the field. To our knowledge, this work is the first study to quantify t… ▽ More Space Solar Power Station (SSPS) concepts rely on gigawatt-class microwave beams to carry orbital solar energy through the ionosphere, where the beam and the plasma form a coupled nonlinear system: the field heats electrons, the heating alters the collision frequency and plasma density, and the modified medium in turn reshapes the field. To our knowledge, this work is the first study to quantify this two-way interaction between microwave power transmission and the ionospheric plasma environment through full-path nonlinear modeling. The 340 km path from 400 km to 60 km altitude is reconstructed by 34 cascaded two-dimensional axisymmetric finite-element full-wave segments with complex-field transfer, using International Reference Ionosphere (IRI) electron-density and NRLMSISE-00 neutral-atmosphere inputs. A Shallow Neural Network (SNN) surrogate replaces the implicit electron energy balance with an explicit closure that maps altitude and local field magnitude to electron temperature and effective collision frequency, enabling stable nonlinear iteration. For 1 GW beams at 2.45 GHz and 5.8 GHz, the volume-integrated Ohmic deposition is 29.4 kW and 5.11 kW, respectively -- fractional losses of order $10^{-5}$ -- and the ratio between the two bands follows the $ω^{-2}$ scaling of collisional absorption. The deposition concentrates near 95 km altitude, where the product of electron density and collision frequency peaks, whereas the electron-temperature perturbation (up to 3815 K) maximizes in the F region, where cooling is weakest; ponderomotive density depletion remains below 0.02\%. The ionosphere is therefore effectively transparent to the SSPS power budget but not to the beam phase: localized heating and refractive perturbation accumulate phase-front distortion relevant to phased-array beam control, rectenna phase compensation, and environmental assessment. △ Less

Submitted 13 July, 2026; originally announced July 2026.

Comments: 27 pages, 13 figures

Leveraging Raman response in X-cut thin-film lithium tantalate for ultrabroadband combs and polychromatic visible light

Authors: Xin Wang, Mingkun Xiao, Min Sun, Ronghong Gao, Yuqi Chen, Zhengshun Lei, Xun Zhang, Wenfeng Zhou, Jintian Lin, Yikai Su, Xingchen Ji, Yong Zhang

Abstract: X-cut thin-film lithium tantalate (TFLT) offers a unique combination of third nonlinearity, electro-optic effects, and a high optical damage threshold. However, its strong Raman response has historically hindered broadband Kerr comb generation. Here, we leverage this inherent Raman response by engineering coupling-defined dissipation. This allows us to reconfigure the relative thresholds of Raman… ▽ More X-cut thin-film lithium tantalate (TFLT) offers a unique combination of third nonlinearity, electro-optic effects, and a high optical damage threshold. However, its strong Raman response has historically hindered broadband Kerr comb generation. Here, we leverage this inherent Raman response by engineering coupling-defined dissipation. This allows us to reconfigure the relative thresholds of Raman and Kerr processes without modifying the intrinsic microresonator dispersion. Through this coupling-engineered threshold control, we can deliberately access distinct comb states, ranging from pure Kerr combs to Raman-Kerr synergistic broadband combs. We demonstrate a Kerr comb spanning 450 nm and a Raman-Kerr comb spanning 650 nm, representing the broadest combs reported to date on X-cut TFLT platforms. Moreover, in strongly coupled devices, we show that a single near-infrared pump can generate visible emission across multiple bands (from violet to red) via cascaded second sum-frequency processes. Our work demonstrates that a strong Raman response can be transformed from a parasitic competitor into an enabling mechanism for achieving broader comb spectra and generating polychromatic visible light. This work establishes X-cut TFLT as a powerful monolithic platform for nonlinear light sources, electro-optic functions, and complex photonic systems. △ Less

Submitted 13 July, 2026; originally announced July 2026.

Quality control and quality assurance evaluation of ALFE2, a large-dynamic-range front-end ASIC de-veloped for the ATLAS Liquid Argon Calorimeter high-luminosity LHC upgrade

Authors: E. Buschmann, G. Carini, G. Chatzianastasiou, H. Chen, Y. Chen, M. Dabrowski, G. Deptuch, L. Duflot, M. Feo, J. Kierstead, T. Liu, H. Ma, D. Matakias, N. Morange, M. Oliveira, S. Rescia, E. Rossi, S. Tang, M. Tamari, H. Xu

Abstract: ALFE2 is a front-end ASIC developed for the ATLAS Liquid Argon (LAr) Calorimeter upgrade during the High-Luminosity Large Hadron Collider (HL-LHC) phase. ALFE2 comprises four preamplifier/shaper channels, each providing two distinct gain outputs to cover a 16-bit dynamic range. A robotic system has been developed for the automatic quality control test of ALFE2, and over 10% of the 80,000 chips hav… ▽ More ALFE2 is a front-end ASIC developed for the ATLAS Liquid Argon (LAr) Calorimeter upgrade during the High-Luminosity Large Hadron Collider (HL-LHC) phase. ALFE2 comprises four preamplifier/shaper channels, each providing two distinct gain outputs to cover a 16-bit dynamic range. A robotic system has been developed for the automatic quality control test of ALFE2, and over 10% of the 80,000 chips have been evaluated by September 2025. The evaluation has allowed us to establish grading criteria. Using these criteria, a yield of over 85% was achieved in the evaluation tests, and these criteria are now being applied to the ongoing full-production QC. Irradiation tests were also performed for the quality assurance of ALFE2. No significant performance degradation was observed during the total-ionizing-dose (TID) test. Based on the single-event effect (SEE) test results, an error rate of fewer than 4.6 single-event upsets (SEUs) per day is extrapolated for the entire ATLAS LAr Calorimeter during HL-LHC operation. △ Less

Submitted 17 August, 2026; v1 submitted 11 July, 2026; originally announced July 2026.

Comments: 16 pages, 14 figures

Program-Synthesis-Driven Autodesign of Universal Unitary Operators

Authors: Yifei Zhang, Dong Chen, Fan Wang, Wenrui Zhang, Yan Chen, Dingding Han, Jianmin Yuan, Xiangjin Kong, Yu-Gang Ma

Abstract: We demonstrate that AI-driven program synthesis can autonomously discover fundamental strategies for decomposing unitary matrices in photonic networks. By extending DreamCoder to complex-valued linear algebra, the system generates decomposition programs achieving the minimal $N(N-1)/2$ Mach-Zehnder interferometers, distinct from both Reck and Clements architectures. Learned programs encode dimensi… ▽ More We demonstrate that AI-driven program synthesis can autonomously discover fundamental strategies for decomposing unitary matrices in photonic networks. By extending DreamCoder to complex-valued linear algebra, the system generates decomposition programs achieving the minimal $N(N-1)/2$ Mach-Zehnder interferometers, distinct from both Reck and Clements architectures. Learned programs encode dimension-agnostic invariants: strategies discovered for $5 \times 5$ matrices generalize to higher dimensions such as $64 \times 64$. The discovered programs encode interpretable, dimension-agnostic construction rules. These rules generalize across matrix sizes without retraining, demonstrating that autonomous program synthesis can serve as a scalable paradigm for algorithm discovery and the automated design of universal unitary operators. Beyond universal decompositions, the system automatically exploits matrix structure to reduce the interferometer count below the universal theoretical bound. For instance, for Householder matrices, it discovers a dimension-independent rule that requires only $2N-3$ MZIs. This achieves linear, rather than quadratic, scaling and generalizes to arbitrary $N$ without retraining. For matrices obtained from the singular value decomposition of sparse matrices, reductions generally increase with sparsity, reaching up to 38% fewer MZIs than the universal theoretical bound $N(N-1)/2$ at 95% sparsity. These MZI reductions translate directly into practical hardware benefits for scalable photonic implementations. Taken together, the system functions as a single unified engine that discovers both universal decomposition rules and matrix-specific optimizations, without being provided with the structural or analytical properties of the input matrices. △ Less

Submitted 11 July, 2026; originally announced July 2026.

Knowledge-Constrained Shape Optimization with a Mixture-of-Experts Neural Operator for High-Confidence Design

Authors: Wenhao Fan, Yuanwei Bin, Jianghan Gu, Wenfa Luo, Jiao Xiang, Yuntian Chen, Shiyi Chen

Abstract: Engineering shape optimization faces challenges in both expert-dependent problem setup and surrogate-model reliability. In practical aerodynamic design, optimization settings such as editable regions, deformation ranges, and design-preservation constraints are typically specified manually by experienced engineers, while surrogate-based optimization may become unreliable for heterogeneous geometry… ▽ More Engineering shape optimization faces challenges in both expert-dependent problem setup and surrogate-model reliability. In practical aerodynamic design, optimization settings such as editable regions, deformation ranges, and design-preservation constraints are typically specified manually by experienced engineers, while surrogate-based optimization may become unreliable for heterogeneous geometry databases and out-of-distribution designs. To address these challenges, we propose a knowledge-constrained shape-optimization framework that translates knowledge-based constraints and user intent into quantifiable parameters of DFFD-based deformation operators, enabling engineering-aware and controllable constrained optimization. We further develop a Mixture-of-Experts Neural Operator (MoE-NO) to improve drag prediction and trend consistency over heterogeneous aerodynamic datasets. Based on the MoE-NO encoder and Mahalanobis distance, an uncertainty-estimation strategy is introduced to detect out-of-distribution geometries and selectively trigger physics-solver feedback for local sample enrichment. Experiments on in-house MPV, SUV, and Sedan datasets show that MoE-NO achieves a test-set MAPE of $1.16\%$ and a trend-prediction accuracy of $94.34\%$, outperforming the best baseline results of $1.52\%$ and $90.34\%$, respectively. Vehicle shape-optimization experiments further yield CFD-validated drag coefficient reductions of approximately $4\%$ to $10\%$. △ Less

Submitted 6 July, 2026; originally announced July 2026.

Implicit discretization schemes for full-kinetic ion and drift-kinetic electron simulations

Authors: Zilong Li, Yang Chen, Haotian Chen, Lei Ye, Zhe Gao, Wei Chen

Abstract: We present a new electromagnetic plasma simulation model with full-kinetic ions and drift-kinetic electrons. This model (termed as FIDES) solves the electric field using the implicit perpendicular Ohm's law and a novel implicit parallel Ampere's law, where the latter requires an implicit scheme for the parallel electric field in advancing the electron weights. To suppress unphysical high-frequency… ▽ More We present a new electromagnetic plasma simulation model with full-kinetic ions and drift-kinetic electrons. This model (termed as FIDES) solves the electric field using the implicit perpendicular Ohm's law and a novel implicit parallel Ampere's law, where the latter requires an implicit scheme for the parallel electric field in advancing the electron weights. To suppress unphysical high-frequency instabilities, ion weights are advanced using an implicit scheme for perpendicular electric fields. Simulations of perpendicular and parallel waves validate the model's capability in handling high-frequency physics. Low-frequency wave simulations demonstrate that the implicit parallel Ampere's law can mitigate the cancellation problem more effectively than the conventional schemes using the parallel Ohm's law. To reduce the numerical damping from implicit time-stepping, we develop a second-order scheme for particle pushing. Meanwhile, an integrated strategy combining the first- and second-order schemes is employed to suppress odd-even decoupling while maintaining the accuracy of the second-order formulation. △ Less

Submitted 9 July, 2026; originally announced July 2026.

Scalar-Wave Dispersion in Vectorial Photonic Crystals via Site-Adapted p Orbitals

Authors: Yan-Long Chen, Kin Hung Fung, C. T. Chan, Qinghua Guo

Abstract: Electromagnetic waves are intrinsically vectorial and require description via polarization, unlike scalar fields such as acoustic pressure or electronic wavefunctions. In three dimensions, the transversality constraint further prevents any globally smooth transverse-polarization frame at the $Γ$ point, which would apparently rule out a simple scalar band structure for three-dimensional (3D) photon… ▽ More Electromagnetic waves are intrinsically vectorial and require description via polarization, unlike scalar fields such as acoustic pressure or electronic wavefunctions. In three dimensions, the transversality constraint further prevents any globally smooth transverse-polarization frame at the $Γ$ point, which would apparently rule out a simple scalar band structure for three-dimensional (3D) photonic crystals. We show here that site-adapted $p$-orbitals can realize scalar-wave dispersion: the induced band representation is isomorphic to the scalar elementary band representation up to a one-dimensional character twist, so the symmetry-enforced degeneracies and compatibility relations are the same. We demonstrate this mechanism experimentally in 3D photonic meta-crystals, where the local $p$-orbital axes adapt from site to site according to symmetry. In contrast to a fixed-polarization reduction (e.g., in 2D), our construction preserves site-polarization textures while simultaneously supporting a scalar network with one amplitude per site. Thus, it offers a pathway from vectorial photonic degrees of freedom to scalar band engineering, keeping polarization as an active design knob. △ Less

Submitted 8 July, 2026; originally announced July 2026.

Comments: 7 pages, 4 figures

From Data Completeness to Data Sufficiency: A Task-Driven Imaging Framework for Intraoperative CBCT under Quality-Time-Dose Trade-offs

Authors: Yi Jia, Rongjun Ge, Yang Chen, Yan Xi, Wenjun Xia

Abstract: Mobile C-arm cone-beam computed tomography (CBCT) has been widely used for real-time intraoperative 3D imaging. However, current practice often mechanically applies the fan-beam CT criterion of "180° plus fan angle" in pursuit of "data completeness" in reconstruction. This review argues that, under the single circular trajectory of three-dimensional cone-beam geometry, complete data are mathematic… ▽ More Mobile C-arm cone-beam computed tomography (CBCT) has been widely used for real-time intraoperative 3D imaging. However, current practice often mechanically applies the fan-beam CT criterion of "180° plus fan angle" in pursuit of "data completeness" in reconstruction. This review argues that, under the single circular trajectory of three-dimensional cone-beam geometry, complete data are mathematically unattainable; moreover, blindly increasing sampling may exacerbate the trade-off among intraoperative image quality (Q), imaging time (T), and radiation dose (D). Against this background, this review reframes the evaluation of intraoperative CBCT around "data sufficiency" rather than "data completeness." This perspective moves beyond the excessive pursuit of absolute mathematical and analytic accuracy, and instead emphasizes task-specific minimum image-quality thresholds required for clinical decision-making. By synthesizing evidence from multiple clinical scenarios, this review suggests that approximation errors can be acceptable when clinical decision-making requirements are satisfied, thereby achieving a Q-T-D balance. △ Less

Submitted 8 July, 2026; originally announced July 2026.

Thermodynamic Limits on Reliable Signaling by Biochemical Traveling Waves

Authors: Shengyao Luo, Yuping Chen, Yuansheng Cao

Abstract: Biochemical traveling waves transmit signals across cells and tissues, but the thermodynamic cost of reliable propagation remains unclear. We develop a stochastic thermodynamic framework for reaction--diffusion systems with stable traveling waves and show that diffusion of the wave position is bounded by the dissipation specifically associated with propagation. The bound follows by projecting nois… ▽ More Biochemical traveling waves transmit signals across cells and tissues, but the thermodynamic cost of reliable propagation remains unclear. We develop a stochastic thermodynamic framework for reaction--diffusion systems with stable traveling waves and show that diffusion of the wave position is bounded by the dissipation specifically associated with propagation. The bound follows by projecting noisy field dynamics onto the adjoint translational mode, which maps the wave position to an effective biased random walk. Its tightness is controlled by the non-self-adjoint part of the linearized dynamics, with finite wave speed and antisymmetric reaction dynamics generically producing deviations from equality. For excitable trigger waves in a FitzHugh--Nagumo model, we show that the slow inhibitor dominates the propagation cost, yielding a trade-off among wave speed, inhibitor amplitude, and dissipation. We test these predictions in stochastic simulations of a microscopic Belousov--Zhabotinsky reaction--diffusion system and find consistent signatures in mitotic trigger-wave experiments in \textit{Xenopus} egg extracts. The same relation further imposes an annihilation-limited bound on the reliable signaling rate of wave trains. △ Less

Submitted 8 July, 2026; originally announced July 2026.

Comments: 7 pages, 5 figures in the maintext; 16 pages, 8 figures in the supporting information

Thermodynamic phase transitions in lattice spin systems with severe kinetic constraints: Numerical simulation results

Authors: Ruifeng Liu, Jianwen Zhou, Yejia Chen, Jiahang Chen, Hai-Jun Zhou

Abstract: The Fredrickson-Andersen model with hyperparameter $K=1$ is a severely constrained kinetic lattice spin system, such that any site is temporarily blocked from changing its packing state (empty or occupied) if there is one or more occupied nearest neighbors. Starting from a completely random initial configuration with a fraction $ρ$ of sites being occupied, some of the sites may be permanently froz… ▽ More The Fredrickson-Andersen model with hyperparameter $K=1$ is a severely constrained kinetic lattice spin system, such that any site is temporarily blocked from changing its packing state (empty or occupied) if there is one or more occupied nearest neighbors. Starting from a completely random initial configuration with a fraction $ρ$ of sites being occupied, some of the sites may be permanently frozen to their initial state under this severe kinetic constraint. The remaining sites can switch states at least occasionally, and they form the unfrozen subsystem associated with the given initial configuration. In the present work we investigate thermodynamic phase transitions in such unfrozen subsystems of the two-dimensional square lattice and the three-dimensional cubic lattice by extensive numerical simulations. We demonstrate that the giant connected component of the unfrozen subsystem collapses at certain critical value $ρ_{c}$ of initial packing density, with $ρ_c = 0.2475$ for the square lattice and $ρ_c = 0.2809$ for the cubic lattice. This phase transition belongs to the same universality class of the conventional site percolation. We also observe that the ground states (densest packing configurations) experience a continuous crystal-to-glass phase transition at the critical value $ρ^* = 0.1423$ of initial packing density for the cubic lattice. For the two-dimensional square lattice we argue that long-range crystalline order is destroyed in the ground states as long as the initial packing density $ρ$ is positive. △ Less

Submitted 7 July, 2026; originally announced July 2026.

Comments: 38 pages, 10 figures, under review at Physical Review E

Journal ref: Physical Review E 114, 024112 (2026)

Exact flat bands in a 3D photonic crystal

Authors: Kin Hung Fung, Yan-Long Chen, C. T. Chan, Qinghua Guo

Abstract: Photonic flat bands are hard to engineer because Maxwell's equations are vectorial: transversality obstructs the localized scalar-like bases that generate destructive-interference flat bands in tight-binding models. We show that a three-dimensional metallic network of dipolar cavities joined by waveguide channels--a fully vectorial photonic crystal belonging to space group No. 224--hosts an exact… ▽ More Photonic flat bands are hard to engineer because Maxwell's equations are vectorial: transversality obstructs the localized scalar-like bases that generate destructive-interference flat bands in tight-binding models. We show that a three-dimensional metallic network of dipolar cavities joined by waveguide channels--a fully vectorial photonic crystal belonging to space group No. 224--hosts an exact scalar sector, carrying exact flat bands. The twelve-band vector problem contains one self-adaptive radial dipole axis per site whose projection is exactly the scalar four-band Hamiltonian of the same network. A microwave-scale coupled-dipole calculation confirms this scalar-vectorial duality. The result is a symmetry-based design rule for scalar-like flat bands in reciprocal vector media. △ Less

Submitted 7 July, 2026; originally announced July 2026.

Comments: 6 pages, 3 figures

Broadband microwave time-frequency analysis via stabilized period-one oscillation and recirculating frequency shifting with shared fiber loop

Authors: Xianxin Zhang, Chi Jiang, Taixia Shi, Yang Chen

Abstract: To address the need for time-frequency analysis (TFA) of broadband microwave signals and to overcome the reliance of existing schemes on large-bandwidth swept microwave sources, we propose a broadband microwave signal TFA approach based on stabilized period-one (P1) oscillation and recirculating frequency shifting (RFS). By employing a feedback loop, a stable swept optical signal is generated from… ▽ More To address the need for time-frequency analysis (TFA) of broadband microwave signals and to overcome the reliance of existing schemes on large-bandwidth swept microwave sources, we propose a broadband microwave signal TFA approach based on stabilized period-one (P1) oscillation and recirculating frequency shifting (RFS). By employing a feedback loop, a stable swept optical signal is generated from the stabilized P1 oscillation of a semiconductor laser, and its sweep bandwidth is further extended by an RFS loop to achieve multi-fold bandwidth expansion. For system compactness, the feedback loop and RFS loop share a common long fiber. The combined operation produces a broadband swept optical signal, which, through stimulated Brillouin scattering-based frequency-to-time mapping, enables TFA and frequency measurement of broadband microwave signals. Experimental results demonstrate an instantaneous analysis bandwidth of up to 57 GHz, a frequency resolution of 60 MHz, and a maximum mean absolute frequency measurement error of 38.16 MHz. △ Less

Submitted 7 July, 2026; originally announced July 2026.

Comments: 11 pages, 5 figures

Trigger system for the Payload for Ultrahigh Energy Observations (PUEO) balloon-borne neutrino detector

Authors: Q. Abarr, J. Alfaro, P. Allison, J. Alvarez-Muñiz, T. Anderson, H. Barnett, A. Basharina-Freshville, J. J. Beatty, L. Beaufore, D. Z. Besson, M. Betts, R. Bose, D. Braun, B. Chamanbahar, P. Chen, Y. Chen, J. M. Clem, T. Coakley, A. Connolly, K. Couberly, L. Cremonesi, A. Cummings, P. Dasgupta, C. Deaconu, J. Flaherty , et al. (45 additional authors not shown)

Abstract: The Payload for Ultrahigh Energy Observations (PUEO) is a NASA balloon-borne instrument for the detection of ultra-high energy (UHE) neutrinos with energies above $10^{17.5}~\textrm{eV}$ via either the Askaryan effect or geomagnetic emissions from an upward-going air shower. The main instrument trigger system for PUEO is a fully digital supersample rate beamformer based on 24 Xilinx Radio Frequenc… ▽ More The Payload for Ultrahigh Energy Observations (PUEO) is a NASA balloon-borne instrument for the detection of ultra-high energy (UHE) neutrinos with energies above $10^{17.5}~\textrm{eV}$ via either the Askaryan effect or geomagnetic emissions from an upward-going air shower. The main instrument trigger system for PUEO is a fully digital supersample rate beamformer based on 24 Xilinx Radio Frequency System-on-a-Chip (RFSoC) digitizers sampling 192 channels operating at $3~\textrm{GSa/s}$ and a system clock frequency of $375~\textrm{MHz}$. The trigger implements frequency band conditioning, dynamic radio-frequency interference (RFI) rejection, and matched filtering, with significant emphasis on optimization to reduce both the power and resource usage while maintaining sensitivity. The system implements 48 total synthetic antenna beams with up to 8 antennas each, covering a $\sim25^\circ$ range in zenith and $\sim60^\circ$ range in azimuth. Preflight testing demonstrated a trigger performance of a minimum signal-to-noise ratio (SNR) of $\sim1.5$ using simulated signals while consuming between $5-7~\textrm{W}$ in the trigger logic. △ Less

Submitted 7 July, 2026; v1 submitted 6 July, 2026; originally announced July 2026.

Comments: 10 pages, 11 figures, submitted to IEEE Transactions on Nuclear Science

On the electromagnetic effects of collisionless trapped-electron modes

Authors: Yao Yao, Haotian Chen, Yang Chen, Jiquan Li, Xuru Duan

Abstract: We present a linear gyrokinetic theory for the electromagnetic collisionless trapped-electron mode (CTEM). It is found that the weak electromagnetic effects of CTEMs originate from the particle dynamics. Theoretical analysis reveals that the kinetic and fluid-like components of the trapped-electron parallel current cancel at leading order. The ion parallel current is also negligible due to the wea… ▽ More We present a linear gyrokinetic theory for the electromagnetic collisionless trapped-electron mode (CTEM). It is found that the weak electromagnetic effects of CTEMs originate from the particle dynamics. Theoretical analysis reveals that the kinetic and fluid-like components of the trapped-electron parallel current cancel at leading order. The ion parallel current is also negligible due to the weak ion transit resonance. Consequently, the perturbed parallel current in the electromagnetic CTEM is dominated by passing electrons. We demonstrate that these characteristics of particle dynamics decouple the CTEM from the shear Alfvén wave branch, rendering the electromagnetic effects subdominant. Both eigenmode analyses and gyrokinetic simulations validate these findings. △ Less

Submitted 6 July, 2026; originally announced July 2026.

Comments: 17 pages, 6 figures, submitted to Nuclear Fusion

High-Energy Neutrino Tomography of the Earth's Interior with IceCube

Authors: The IceCube Collaboration, R. Abbasi, M. Ackermann, J. Adams, J. A. Aguilar, M. Ahlers, J. M. Alameddine, S. Ali, N. M. Amin, K. Andeen, C. Argüelles, S. Athanasiadou, S. N. Axani, R. Babu, X. Bai, A. Balagopal V., S. W. Barwick, V. Basu, R. Bay, J. J. Beatty, J. Becker Tjus, P. Behrens, J. Beise, C. Bellenghi, S. Benkel , et al. (395 additional authors not shown)

Abstract: The Earth's interior reflects its geological evolution, from accretion to present-day dynamics. Its structure drives the geodynamo in the outer core, generating the magnetic field that shields the surface from charged cosmic radiation. The primary observables of the Earth's interior are its radial density distribution and derived quantities such as its mass and moment of inertia. These have tradit… ▽ More The Earth's interior reflects its geological evolution, from accretion to present-day dynamics. Its structure drives the geodynamo in the outer core, generating the magnetic field that shields the surface from charged cosmic radiation. The primary observables of the Earth's interior are its radial density distribution and derived quantities such as its mass and moment of inertia. These have traditionally been inferred from gravity and seismic wave propagation, which probe the macroscopic response of matter to gravitational and elastic forces. Here we instead constrain the Earth's density profile using high-energy neutrinos observed by the IceCube Neutrino Observatory at the South Pole. We analyze 10.7 years of predominantly muon-neutrino data spanning 500 GeV--100 TeV, including atmospheric neutrinos produced by cosmic-ray interactions in the Earth's atmosphere and the diffuse astrophysical neutrino flux. Neutrino attenuation depends on both the traversed column density and neutrino energy. By measuring the zenith- and energy-dependent flux suppression, we infer the Earth's radial density profile by fitting a concentric uniform-density shell model that incorporates neutrino fluxes, interaction cross sections, detector response, and glacial-ice systematic uncertainties. From the resulting density posteriors, we derive the Earth's mass and polar moment of inertia as measured by neutrinos. These are the most precise weak-interaction measurements of these quantities to date and are consistent with the Preliminary Reference Earth Model and independent gravitational determinations. Our results demonstrate that neutrinos provide a novel probe of planetary interiors via a distinct physical interaction, complementing gravity and seismology. With improved detectors and precision, neutrinos will further contribute to a multifaceted understanding of the Earth's structure. △ Less

Submitted 7 July, 2026; v1 submitted 2 July, 2026; originally announced July 2026.

WavePID: Low-energy flavor identification using single-PMT time series in IceCube

Authors: The IceCube Collaboration, R. Abbasi, M. Ackermann, J. Adams, J. A. Aguilar, M. Ahlers, J. M. Alameddine, S. Ali, N. M. Amin, K. Andeen, C. Argüelles, S. Athanasiadou, S. N. Axani, R. Babu, X. Bai, A. Balagopal V., S. W. Barwick, V. Basu, R. Bay, J. J. Beatty, J. Becker Tjus, P. Behrens, J. Beise, C. Bellenghi, S. Benkel , et al. (395 additional authors not shown)

Abstract: The IceCube Neutrino Observatory, a cubic-kilometer detector at the South Pole, identifies neutrino flavor through event morphology. Sparse photon detection makes this classification particularly challenging in the 5--100~GeV regime, the energy range relevant for oscillation measurements and searches for physics beyond the Standard Model. We introduce WavePID, a template-based log-likelihood-ratio… ▽ More The IceCube Neutrino Observatory, a cubic-kilometer detector at the South Pole, identifies neutrino flavor through event morphology. Sparse photon detection makes this classification particularly challenging in the 5--100~GeV regime, the energy range relevant for oscillation measurements and searches for physics beyond the Standard Model. We introduce WavePID, a template-based log-likelihood-ratio classifier that exploits nanosecond-scale timing on individual detector modules through three observables: the distance to the reconstructed vertex, the early-charge fraction, and the module-to-module time difference. Evaluated on a cascade-enriched sample selected by a state-of-the-art graph neural network, WavePID improves both cascade purity and classification performance over the neural network alone. This demonstrates that per-module pulse timing carries flavor-identification information complementary to morphology-based classifiers, opening a new physics-motivated observable for low-energy neutrino reconstruction. Geant4 simulations associate this signal with differences in Cherenkov emission geometry between muon tracks and electromagnetic showers. These results motivate exploiting nanosecond-scale pulse timing in future low-energy classifiers and in detector designs with improved per-module timing in next-generation neutrino telescopes. △ Less

Submitted 20 August, 2026; v1 submitted 2 July, 2026; originally announced July 2026.

Comments: 14 pages with 7 figures; plus 3 pages supplemental material, submitted to Journal of Instrumentation

Ai2-Kit: Streamlining AI-Accelerated Ab Initio Workflows for Complex Chemical Systems

Authors: Sheng Bi, Wei-Hong Xu, Yong-Bin Zhuang, Jia-Xin Zhu, Jiang-Peng Qiu, Yu-Hang Tang, Xiang-Long Du, Qi You, Yun-Pei Liu, Fu-Qiang Gong, Yu-Xin Guo, Yi-Ze Wang, Cheng-Xuan Wang, Zi-Heng Gong, Zi-Qiang Chen, Chang Liu, Siyuan Han, Jian Gu, Jia-Xin Li, Yi-Ming Chen, Lin Huang, Si-Jie Chen, Bo-Ying Huang, Jie-Zhen Xia, Fan-Jie Xu , et al. (25 additional authors not shown)

Abstract: Molecular simulations of complex chemical systems, such as catalysis, electrochemistry, and energy storage, often need to capture the interplay of effects such as electronic structure, finite-temperature fluctuations, and electric-field response. Such complexity is difficult to address with traditional ab initio calculations, which are limited by the time and length scales they can reach. AI-accel… ▽ More Molecular simulations of complex chemical systems, such as catalysis, electrochemistry, and energy storage, often need to capture the interplay of effects such as electronic structure, finite-temperature fluctuations, and electric-field response. Such complexity is difficult to address with traditional ab initio calculations, which are limited by the time and length scales they can reach. AI-accelerated ab initio (AI2) methods use machine learning potentials trained on first-principles data to replace expensive electronic-structure calculations, extending ab initio accuracy to these regimes, but their routine application requires reliable workflows that connect first-principles calculations, model training, molecular dynamics, enhanced sampling, trajectory analysis, and HPC orchestration. Here we present ai2-kit, a software toolkit for developing accessible, reproducible, and extensible AI2 workflows. ai2-kit provides high-semantic-density command-line interfaces and Python APIs for structure and dataset conversion, batch task generation, active-learning screening, job orchestration, and workflow recovery. We demonstrate ai2-kit in four representative applications: active-learning-based machine learning potential construction, free-energy perturbation for redox and acid-base processes, electrochemical machine learning potentials for electrified interfaces, and spectroscopies from machine learning molecular dynamics. ai2-kit also provides AI-agent skills that help users adapt these use cases into customized workflows for their own chemical systems and computational software stacks. Together, ai2-kit helps turn AI2 methods from bespoke computational protocols into reusable and extensible workflows for complex chemical systems, from model construction to property prediction. △ Less

Submitted 14 July, 2026; v1 submitted 1 July, 2026; originally announced July 2026.

Comments: 24 pages, 11 figures. Submitted to Chemical Science

Reconfigurable wavelength-encoded stochastic illumination for active hyperspectral imaging

Authors: Yi-Jing Chen, Bao-Lei Liu, Ze-Yuan Dong, Zhi-Hao Zhao, Yi-Ying Zhang, Chun-Min Yu, Zhi-Hua Xu, Yuan-Jin Yu, Zhao-Hua Yang

Abstract: Traditional hyperspectral imaging (HSI) relies on sequential scanning with complex and bulky hardware, inherently limiting its temporal resolution while increasing system complexity and cost. Computational HSI offers cost-effective alternatives with simplified hardware. However, most existing computational methods rely on fixed spectral encoding units, which lack adaptability for different spectra… ▽ More Traditional hyperspectral imaging (HSI) relies on sequential scanning with complex and bulky hardware, inherently limiting its temporal resolution while increasing system complexity and cost. Computational HSI offers cost-effective alternatives with simplified hardware. However, most existing computational methods rely on fixed spectral encoding units, which lack adaptability for different spectral tasks. Here, we present a reconfigurable optical stochastic encoding (ROSE) framework with programmable illumination, which can be adaptively optimized for different spectral tasks, for high-throughput, compressive HSI. By leveraging an array of monochromatic light-emitting diodes (LEDs), we synthesize stochastic spectral patterns that enable compressive acquisition using a standard monochrome camera. The proposed framework allows dynamic reconfiguration of illumination patterns, making it adaptable to diverse imaging requirements. We experimentally validate the proposed method and achieve HSI with a spatial resolution of 2048 by 1536, reconstructing 60 spectral bands across the spectral range of 400-700 nm. Furthermore, we introduce an automatic optimization strategy to search for optimal illuminations tailored to specific tasks, improving both reconstruction accuracy and task-oriented performance. We demonstrate the effectiveness of our approach in applications including anti-counterfeiting inspection and oral imaging, and further validate its compatibility with standard microscope and endoscope systems. The developed ROSE illumination module could serve as a universal, plug-and-play add-on for conventional cameras and existing optical systems, providing a cost-effective pathway to upgrade them into high-performance, task-adaptive HSI systems. △ Less

Submitted 30 June, 2026; originally announced June 2026.

Comments: 18 pages, 7 figures

From Materials Database to Materials Bank: Assetizing Data for AI Driven Materials Innovation

Authors: Chenyao Ma, Di Zhang, Weibo Gong, Wei Du, Rui Su, Yuhang Chen, Kan Xu, Huan Gu, Limin Li, Piao Ma, Zhenghao Li, Hao Li

Abstract: Driven by high-throughput experimentation, computational modeling, and artificial intelligence (AI), materials data has expanded at an unprecedented rate. Conventional materials databases function only as passive repositories, archiving raw experimental records indiscriminately including both successful and failed data, without systematic value filtering or asset management. This creates a critica… ▽ More Driven by high-throughput experimentation, computational modeling, and artificial intelligence (AI), materials data has expanded at an unprecedented rate. Conventional materials databases function only as passive repositories, archiving raw experimental records indiscriminately including both successful and failed data, without systematic value filtering or asset management. This creates a critical gap between massive data accumulation and actionable innovation, hindering the identification of high-potential materials and industrial translation. To address this bottleneck, we propose an industrialization-oriented Materials Bank, a dedicated valuefiltering and assetization layer that operates beyond traditional databases. It does not merely curate high-quality data but systematically elevates qualified candidates into standardized, upgradable materials assets via a multi-dimensional BankCard framework covering scientific validity, synthesis feasibility, application readiness, and industrial value. By unifying databases, AI models, automated experimentation, and multi-criteria assessment into a cohesive closed-loop ecosystem, the Materials Bank establishes a clear trajectory from data to knowledge, candidate, asset, and product. It serves not as an enhanced database or screening tool, but as a decision infrastructure bridging academic discovery and industrial demand, offering a scalable paradigm to accelerate AI-driven materials innovation and deliver tangible real-world impact. △ Less

Submitted 25 July, 2026; v1 submitted 30 June, 2026; originally announced June 2026.

Journal ref: AI for Materials 2026, 1(1), 9

Joint discovery of governing partial differential equations from multi-source datasets by competitive optimization

Authors: Hao Xu, Siyu Lou, Yuntian Chen, Dongxiao Zhang

Abstract: Discovering governing equations directly from observational data is a key step towards interpretable scientific machine learning. Current data-driven approaches typically operate on a single dataset, inherently limiting their performance when faced with restricted observations. In practice, multiple datasets are often available for the same physical system, distinguished only by distinct initial c… ▽ More Discovering governing equations directly from observational data is a key step towards interpretable scientific machine learning. Current data-driven approaches typically operate on a single dataset, inherently limiting their performance when faced with restricted observations. In practice, multiple datasets are often available for the same physical system, distinguished only by distinct initial conditions or boundary configurations. Here, we present a competitive optimization framework designed to discover shared partial differential equations (PDEs) from multi-source datasets, termed MCO-PDE. The framework first trains independent neural surrogates for each data source, and then employs a soft-competitive weighting mechanism to dynamically assess dataset credibility and aggregate a consensus global coefficient. Integrated with a genetic algorithm for structural search, this approach simultaneously identifies the functional forms and parameters of the governing laws. We demonstrate that fusing as few as 50 observations per dataset across seven cases recovers canonical equations with high accuracy. The framework inherently handles two- and three-dimensional domains characterized by irregular boundaries and heterogeneous coefficients, and successfully extracts physically meaningful laws from real-world wave-tank experiments. Overall, this work establishes a promising route for automated scientific discovery via heterogeneous data fusion. △ Less

Submitted 29 June, 2026; originally announced June 2026.

Comb-enabled spectral-domain image transport through perturbation-prone multimode fibers

Authors: Maohan Li, Zijian Wang, Bowen Sun, Zhuoren Wan, Xiangze Ma, Xiuxiu Zhang, Yuan Chen, Mei Yang, Qi Wen, Zhaoyang Wen, Ming Yan, Heping Zeng

Abstract: Multimode fibers (MMFs) offer a compact platform for imaging, sensing, and information transport, but their practical deployment is hindered by sensitivity to fiber perturbations, which alter modal coupling and invalidate conventional speckle-based calibrations. Here, we demonstrate perturbation-resilient image transport through MMFs by combining image-to-spectrum encoding with dual-comb spectrosc… ▽ More Multimode fibers (MMFs) offer a compact platform for imaging, sensing, and information transport, but their practical deployment is hindered by sensitivity to fiber perturbations, which alter modal coupling and invalidate conventional speckle-based calibrations. Here, we demonstrate perturbation-resilient image transport through MMFs by combining image-to-spectrum encoding with dual-comb spectroscopy. Two-dimensional images are converted into comb-line-resolved spectral signatures before fiber transmission, allowing spatial information to be carried in the spectral domain rather than in the output speckle field. After propagation, dual-comb heterodyne detection maps the encoded spectrum into the radio-frequency domain, enabling massively parallel spectral readout with a single photodetector. Neural-network-assisted compressive reconstruction further enables high-fidelity imaging from sparse, noisy, and spectrally aliased measurements. Our approach achieves Pearson correlation coefficients exceeding 0.9 under strong fiber perturbations and supports frame rates up to 2.5 MHz, allowing the observation of transient switching dynamics in a digital micromirror device. These results establish a powerful tool for robust, real-time image transport through flexible MMFs, with potential applications in remote sensing and fiber-based optical instrumentation. △ Less

Submitted 29 June, 2026; originally announced June 2026.

Comments: 20 pages,4 figures

Gaussian Quantum Metrology with Realistic Linear Sensors

Authors: Jacques Ding, James W. Gardner, Tuvia Gefen, Yanbei Chen

Abstract: Quantum sensing promises enhanced precision, but the usual quantum Cramer Rao bound can be too optimistic for realistic linear sensors, where squeezing, filtering, and loss reshape quantum noise. We derive the tight Holevo Cramer Rao bound and show that realistic degradation yields a hierarchy with the usual bound and homodyne readout. This hierarchy already exists in gravitational-wave detectors.… ▽ More Quantum sensing promises enhanced precision, but the usual quantum Cramer Rao bound can be too optimistic for realistic linear sensors, where squeezing, filtering, and loss reshape quantum noise. We derive the tight Holevo Cramer Rao bound and show that realistic degradation yields a hierarchy with the usual bound and homodyne readout. This hierarchy already exists in gravitational-wave detectors. We propose a hardware-efficient readout that reaches the Holevo bound without extra signal loss, increasing compact-binary detection rates by up to 25% over the present LIGO homodyne readout. △ Less

Submitted 28 June, 2026; originally announced June 2026.

Comments: 7 pages, 3 figures

Report number: LIGO-P2600266

In-flight calibration of the Wide-field X-ray Telescope on board the Einstein Probe

Authors: Huaqing Cheng, Hai-Wu Pan, Yuan Liu, Jingwei Hu, Haonan Yang, Donghua Zhao, Zhixing Ling, Yifan Chen, Xiaojin Sun, Longhui Li, Ge Jin, Wenxin Wang, Xue Yang, He-Yang Liu, Chen Zhang, Shuang-Nan Zhang, Weimin Yuan

Abstract: By utilizing novel lobster-eye optics, the Wide-field X-ray Telescope (WXT) onboard the Einstein Probe (EP) satellite achieves an unprecedented combination of a large instantaneous field-of-view (FoV) and high sensitivity for monitoring the dynamic X-ray sky. In this paper, we present the in-orbit calibration results of the WXT during its first two and a half years of operations. By conducting obs… ▽ More By utilizing novel lobster-eye optics, the Wide-field X-ray Telescope (WXT) onboard the Einstein Probe (EP) satellite achieves an unprecedented combination of a large instantaneous field-of-view (FoV) and high sensitivity for monitoring the dynamic X-ray sky. In this paper, we present the in-orbit calibration results of the WXT during its first two and a half years of operations. By conducting observations of standard celestial sources--including the Crab Nebula, Scorpius X-1, and Cassiopeia A--we systematically characterized key instrumental properties. Our analysis demonstrates that the in-orbit performance of the WXT agrees with prelaunch ground calibrations well. The spatial resolution, denoted by the full width at half maximum (FWHM) of the focal spot, typically ranges from $3'$ to $6'$ across $\sim$90% of the FoV, with a median of $\sim 4.3'$. The post-calibration source positioning accuracy achieves $1.3'$ (at the 90% confidence level). The in-orbit effective area is consistent with model predictions and ground measurements, exhibiting an overall systematic uncertainty of $\lesssim 10\%$ (90% C.L.) in the 0.5-4 keV band. While the vast majority of the detectors remain highly stable, a noticeable long-term degradation at the low-energy end ($\sim30\%$-$40\%$, 0.4-0.6 keV) is observed in a few specific modules. Furthermore, spectral evaluations using Cas A confirm the stability of the energy scale and spectral resolution of the focal-plane Complementary Metal-Oxide Semiconductor (CMOS) detectors. All derived calibration products have been incorporated into the WXT calibration database (CALDB). These results comprehensively verify the instrumental capabilities of the WXT, providing a solid foundation for the reliable analysis of scientific observations. △ Less

Submitted 26 June, 2026; originally announced June 2026.

Comments: 13 pages, 13 figures, 3 tables. Submitted to Astronomy & Astrophysics

An ultralow-loss integrated photonic platform for discrete-variable quantum information processing

Authors: Ruiyang Chen, Zeying Zhong, Sanli Huang, Sicheng Zeng, Zhenyuan Shang, Yue Hu, Zhen Chen, Yuan Chen, Shuyi Li, Xue Bai, Yi-Han Luo, Junqiu Liu

Abstract: Photonic integrated circuits offer a scalable and robust route toward quantum information technologies by consolidating photon sources and linear optical networks onto compact, wafer-manufacturable chips. Although silicon photonics has enabled diverse discrete-variable quantum breakthroughs -- spanning multiphoton entanglement, quantum networking, and photonic qubit fusion for quantum computing --… ▽ More Photonic integrated circuits offer a scalable and robust route toward quantum information technologies by consolidating photon sources and linear optical networks onto compact, wafer-manufacturable chips. Although silicon photonics has enabled diverse discrete-variable quantum breakthroughs -- spanning multiphoton entanglement, quantum networking, and photonic qubit fusion for quantum computing -- scaling these platforms beyond proof-of-principle demonstrations remains severely constrained by a critical system-level bottleneck. Optical loss compounds rapidly across photon generation, routing, and state analysis, causing multiphoton generation probabilities to plummet exponentially as circuit depth and complexity grow. Here we overcome this rate-loss barrier by demonstrating a monolithic, ultralow-loss silicon nitride (Si$_3$N$_4$) integrated photonic platform engineered for high-performance discrete-variable quantum information processing. Our architecture seamlessly integrates narrowband photon-pair sources with low-loss qubit-fusion circuits and reconfigurable state-analysis interferometers. The on-chip sources prepare Einstein-Podolsky-Rosen (EPR) states with a fidelity of 0.9875(3) and exhibit near-unity photon indistinguishability, yielding a heralded Hong-Ou-Mandel interference visibility of 0.990(6). By executing on-chip fusion of two EPR states, we synthesize and characterize four-photon Greenberger-Horne-Zeilinger states with a record fidelity of 0.943(8) and a fourfold count rate of 27 Hz -- more than two orders of magnitude higher than previous silicon-photonic implementations. Combined with standard CMOS-compatible fabrication on 150-mm-diameter wafers, these results establish ultralow-loss Si$_3$N$_4$ integrated photonics as a definitive, manufacturable platform for deployable, large-scale quantum information processors. △ Less

Submitted 20 July, 2026; v1 submitted 25 June, 2026; originally announced June 2026.

An Iterative Dual-Channel Neural Quantum State Algorithm for Selected Configuration Interaction

Authors: Jen-Yu Chang, Yi-Chun Chang, Yu-Jui Lin, Ming-Chun Yang, Hsiu-Chi Tsai, Tai-Yue Li, Nan Yow Chen, Tsung-Wei Huang, En-Jui Kuo

Abstract: Accurately solving the electronic Schrödinger equation for strongly correlated systems remains a central challenge in quantum chemistry, where the exponential growth of configuration space limits the applicability of exact methods. Selected Configuration Interaction (SCI) algorithms address this challenge by adaptively constructing compact determinantal expansions, yet their efficiency depends cri… ▽ More Accurately solving the electronic Schrödinger equation for strongly correlated systems remains a central challenge in quantum chemistry, where the exponential growth of configuration space limits the applicability of exact methods. Selected Configuration Interaction (SCI) algorithms address this challenge by adaptively constructing compact determinantal expansions, yet their efficiency depends critically on the quality of the sampling strategy used to identify chemically important configurations. Here we introduce the Handover Iterative Neural Quantum State (HI-NQS) algorithm, which embeds a classically trained autoregressive Transformer neural quantum state within the iterative sample--diagonalize--update framework of Sample-Based Quantum Diagonalization. A dual-channel Transformer architecture with explicit spin-up/spin-down cross-attention encodes fermionic spin structure as an architectural inductive bias, enabling expressive and physically informed wavefunction representations. After each subspace diagonalization, the resulting eigenvector is distilled back into the network through a factorized spin-marginal teacher signal, establishing a closed feedback loop between generative sampling and exact diagonalization. Benchmarks across a range of small molecules and a systematic nitrogen active-space series demonstrate that HI-NQS achieves chemical accuracy on all systems tested, with determinant-count scaling substantially more favorable than conventional CIPSI-based SCI for all but the smallest active spaces. All calculations are performed on GPU hardware without quantum computing resources, establishing HI-NQS as an efficient and scalable purely classical approach to the selected configuration interaction problem. △ Less

Submitted 25 June, 2026; originally announced June 2026.

Epitaxial Strain Activates Altermagnetic Spin-Splitting Torques in RuO2(100)

Authors: Qi Jia, Seung Gyo Jeong, Seungjun Lee, Denis Tonini, Anand Santhosh, Yifei Yang, Xiangrui Li, Brahmdutta Dixit, Shuang Liang, Yu-Chia Chen, Tony Low, Bharat Jalan, Jian-Ping Wang

Abstract: The altermagnetic nature of rutile RuO2 remains under active debate: bulk measurements indicate a nearly nonmagnetic ground state, whereas thin-film studies have reported symmetry-dependent transport signatures consistent with altermagnetism. Here, we provide experimental evidence that altermagnetic spin splitting in RuO2 is a strain-stabilized emergent state rather than an intrinsic bulk property… ▽ More The altermagnetic nature of rutile RuO2 remains under active debate: bulk measurements indicate a nearly nonmagnetic ground state, whereas thin-film studies have reported symmetry-dependent transport signatures consistent with altermagnetism. Here, we provide experimental evidence that altermagnetic spin splitting in RuO2 is a strain-stabilized emergent state rather than an intrinsic bulk property. Angular-resolved spin-torque measurements reveal a symmetry-selected spin Hall response characteristic of altermagnetic spin splitting, which is strongest in the strained regime but progressively suppressed as the lattice relaxes toward the bulk limit. Complementary magnetic measurements further reveal enhanced coercivity and exchange-bias behavior exclusively in strained films, indicating the emergence of a strain-stabilized magnetic state. First-principles calculations reproduce the strain-dependent evolution of the Neel order and spin-split electronic structure, supporting the experimental observations. Together, these results establish altermagnetic spin splitting in RuO2 as a strain-stabilized emergent state and provide a unified explanation for the long-standing discrepancy between bulk and thin-film observations. △ Less

Submitted 24 June, 2026; originally announced June 2026.