Showing 1–50 of 214 results for author: Xie, H

State-Space Model-Enabled Reinforcement Learning for Magnetic Configuration Controlon EXL-50U

Authors: Pei Guo, Zhengyuan Chen, Jianguo Chen, Xuanhe Wang, Guoyang Shi, Siqi Ding, Yapeng Zhang, Lei Xing, Yong Liu, Xiang Gu, Tiantian Sun, Xiuchun Lun, Jia Li, Zhengxiong Wang, Huasheng Xie, Hanyue Zhao, Yuejiang Shi, Xianming Song, Tianyuan Liu, EXL-50U Team

Abstract: Accurate feedback control of the plasma current ($I_p$) and centroid position $(R_c,Z_c)$ is essential for the stable operation of spherical torus (ST) plasmas. Conventional proportional-integral-derivative (PID) controllers require extensive manual tuning and struggle with the fast, strongly coupled dynamics that arise as plasma performance improves. Reinforcement learning (RL) has recently emerg… ▽ More Accurate feedback control of the plasma current ($I_p$) and centroid position $(R_c,Z_c)$ is essential for the stable operation of spherical torus (ST) plasmas. Conventional proportional-integral-derivative (PID) controllers require extensive manual tuning and struggle with the fast, strongly coupled dynamics that arise as plasma performance improves. Reinforcement learning (RL) has recently emerged as a promising alternative to such complex magnetic control problems, yet its practical deployment on ST devices remains challenging. This paper presents a practical RL controller for the EXL-50U ST, trained within a rigid RZIP state-space model (SSM) that enables efficient offline policy learning. A lightweight plasma position reconstructor is developed to estimate $(R_c,Z_c)$ from magnetic probe signals within the real-time control cycle. The trained policy is seamlessly deployed on the EXL-50U plasma control system, achieving stable regulation of $I_p$ and $(R_c,Z_c)$ and sustaining discharges up to 650 ms under RL control. These results demonstrate the feasibility and practical potential of model-informed RL for magnetic configuration control in ST devices, offering a promising direction beyond conventional PID-based schemes. △ Less

Submitted 21 August, 2026; originally announced August 2026.

Sub-Second Collisionless Gyrokinetic Eigenvalue Solutions via Orbit-Invariant Decomposition

Authors: Anrui Luo, Jingyi Yu, Huasheng Xie, Jian Bao

Abstract: Fast analysis of microscopic drift-wave instabilities based on linear gyrokinetic simulation is desirable for modeling anomalous transport in fusion device. In this work, we present an orbit-invariant decomposition method for solving collisionless gyrokinetic eigenvalue problems. By discretizing velocity space along orbit invariants using particle energy and magnetic moment, the full eigenvalue ma… ▽ More Fast analysis of microscopic drift-wave instabilities based on linear gyrokinetic simulation is desirable for modeling anomalous transport in fusion device. In this work, we present an orbit-invariant decomposition method for solving collisionless gyrokinetic eigenvalue problems. By discretizing velocity space along orbit invariants using particle energy and magnetic moment, the full eigenvalue matrix is separated into independent orbit blocks that couple with each other through the field equation, greatly reducing both matrix dimension and computational cost without sacrificing physics. Based on this method, we extend the MGK code [Phys.\ Plasmas 24, 072106 (2017)] with both CPU and GPU implementations, supporting collisionless electrostatic linear simulations in $s$--$α$ and Miller equilibrium model with kinetic. For kinetic ion temperature gradient (ITG) and trapped electron mode (TEM) eigenvalue problems, the solver reduces single-solution times to the 0.01--0.1s range---more than three orders of magnitude faster than CGYRO on the same hardware---enabling efficient large-scale parameter scans. The eigenfrequencies and mode structures are verified by comparing with CGYRO results. The method is generally adapt to to all collisionless gyrokinetic eigenvalue formulations and can be extended to fully electromagnetic simulations. △ Less

Submitted 18 August, 2026; originally announced August 2026.

Comments: 13 pages, 6 figures

BORAY-3D: A ray tracing code for three-dimensional magnetized plasma configurations

Authors: Yuxuan Wang, Huasheng Xie

Abstract: Ray tracing codes are useful tools for studying electromagnetic wave propagation and absorption using the geometrical-optics approximation. Existing codes commonly provide either broad radio-frequency coverage in axisymmetric equilibria or three-dimensional capability specialized for electron-cyclotron (EC) applications. BORAY-3D integrates three desirable features in a single version. First, it h… ▽ More Ray tracing codes are useful tools for studying electromagnetic wave propagation and absorption using the geometrical-optics approximation. Existing codes commonly provide either broad radio-frequency coverage in axisymmetric equilibria or three-dimensional capability specialized for electron-cyclotron (EC) applications. BORAY-3D integrates three desirable features in a single version. First, it has a broad frequency range of validity regime from ion-cyclotron, helicon and lower-hybrid waves to EC waves and emission. Second, it provides a unified treatment of arbitrary two- and three-dimensional magnetic-plasma configurations, including both closed and open field-line regions. Third, it incorporates fully relativistic Maxwellian EC absorption. The code extends the axisymmetric BORAY formulation by solving the ray equations in cylindrical coordinates $(r,φ,z)$ while allowing the toroidal mode number $n_φ$ to vary. Magnetic-field, density and temperature data are directly described in $(r,φ,z)$ coordinates without the restriction of flux functions, so that numerical equilibria and analytic field models can be handled in the same form. The non-relativistic hot-plasma model inherited from BORAY is used for lower-hybrid, ion-cyclotron and helicon absorption, whereas the relativistic model is coupled to reciprocal radiative transfer for electron cyclotron emission (ECE). Practical applications include 13.56 MHz helicon and 50 MHz fast waves, a 3.7 GHz lower-hybrid wave, and 115--220 GHz EC emission. BORAY-3D has been systematically benchmarked against GENRAY for tokamak toroidal-field ripple, Raytrax and TRAVIS for W7-X, as well as public HSX heating and W7-X ECE results. △ Less

Submitted 6 August, 2026; originally announced August 2026.

Comments: 12 pages, 7 figures

Parameter Scan of Multi-Fluid Equilibria in Rotating p-11B Plasmas: Effects on Fusion Power and Bremsstrahlung Losses

Authors: Xingyu Li, Huasheng Xie, Lai Wei, Zhengxiong Wang

Abstract: We present VEQ-MF, a fast spectral parameter-scan framework for two-dimensional axisymmetric multi-fluid equilibria with prescribed species-dependent toroidal rotation. The solver couples generalized Boltzmann density responses, quasineutral electrostatic polarization, and a generalized Grad--Shafranov equation, extending reduced-parameter Grad-Shafranov and VEQ formulations to multi-species rotat… ▽ More We present VEQ-MF, a fast spectral parameter-scan framework for two-dimensional axisymmetric multi-fluid equilibria with prescribed species-dependent toroidal rotation. The solver couples generalized Boltzmann density responses, quasineutral electrostatic polarization, and a generalized Grad--Shafranov equation, extending reduced-parameter Grad-Shafranov and VEQ formulations to multi-species rotating equilibria. Rotating $p\text{-}^{11}\text{B}$ spherical-tokamak configurations are used as a demanding test case. Independent scans of the proton and boron rotation frequencies are performed in EHL-2 and EHL-3B geometries. The computed fields are then post-processed to obtain fusion power from a drift-Maxwellian reaction-rate coefficient and bremsstrahlung power from an analytical radiation model. Three in-range EHL-3B finite-difference benchmarks give global stored-energy, bremsstrahlung-power, and fusion-power differences of $1.7$--$3.4\%$, while a representative convergence check shows sub-percent sensitivity to increasing the spectral-parameter number and negligible sensitivity to Gaussian-grid refinement. The core equilibrium solve remains fast for repeated scans, with representative nonzero EHL-3B cases requiring $0.032$--$0.050$~s per point in MATLAB, excluding post-processing, interpolation, plotting, and file export. The scans identify two competing multi-fluid effects. Under iso-rotation, outward boron accumulation increases the volume-integrated $n_e^2$, so the fusion-to-bremsstrahlung power ratio $\mathcal{R}_{\mathrm{fb}}$ decreases with increasing rotation. Species-dependent toroidal rotation weakens centrifugal polarization and lowers bremsstrahlung power, while the relative toroidal flow in the larger EHL-3B geometry raises the drift-Maxwellian reaction-rate coefficient and thereby modifies fusion power. △ Less

Submitted 15 July, 2026; originally announced July 2026.

Comments: 29 pages, 21 figures

First reduced model for integrated computations of helicon wave heating and current drive in magnetic fusion plasmas

Authors: Zi-Chen Kan, Lei Chang, Zhen-Yu Wang, Hua-Sheng Xie, Ping-Wei Zheng, Lai Wei, Qi-Bin Luan, Xue-Mei Zhai, Zhao-Qing Hu, Zheng-Xiong Wang, Matthew Hole, Zhi-Song Qu

Abstract: Fast predictive modelling of radio-frequency heating and current drive is important for integrated tokamak scenario design, yet kinetic calculations of helicon-wave absorption remain too computationally expensive for large-scale parameter scans. We present a reduced model for helicon-wave heating and current drive that retains the dominant parallel electron Landau-damping channel. The wave respons… ▽ More Fast predictive modelling of radio-frequency heating and current drive is important for integrated tokamak scenario design, yet kinetic calculations of helicon-wave absorption remain too computationally expensive for large-scale parameter scans. We present a reduced model for helicon-wave heating and current drive that retains the dominant parallel electron Landau-damping channel. The wave response is evaluated on the cold-plasma dispersion root, and a single-Landau-pole correction is introduced to obtain compact expressions for the local damping rate and current-drive efficiency. The model is benchmarked against the Chiu-Chan heating model using approximately 1.6 million samples covering representative conditions of EAST, HL-3, DIII-D and KSTAR. The reduction error is found to be governed primarily by the electron Landau parameter and electron beta. Within an identified sub-lower-hybrid-frequency validity window, results from different devices collapse onto a common error curve, which enables an empirical correction that is further tested using ITER-like and BEST-like extrapolation cases. Near and above the lower-hybrid frequency, the agreement deteriorates rapidly owing to changes in the cold-dispersion root structure and the breakdown of the single-branch WKB description. When coupled to a reduced current-drive source, the corrected heating model gives a median deviation of 10.8 percent from the Landau-channel Ehst-Karney reference and reproduces published CFETR current-density profiles. The resulting model provides a computationally efficient reduced closure for helicon-wave heating and current-drive calculations, together with physically interpretable limits on its range of validity. △ Less

Submitted 14 July, 2026; originally announced July 2026.

VSC: A Zero-Dimensional Fusion Design Platform for Multiple Magnetic Configurations

Authors: Zekun Wang, Huasheng Xie, Feng Zhang, Jian Bao, Ming Yang

Abstract: The VeloAlpha System Code (VSC) is a computational framework for zero-dimensional fusion power-balance studies across five magnetic-confinement configurations: tokamaks, magnetic mirrors, field-reversed configurations (FRCs), dipoles, and stellarators. A common power-balance formulation connects fusion production, charged-particle deposition, radiation, transport loss, external heating, and fusion… ▽ More The VeloAlpha System Code (VSC) is a computational framework for zero-dimensional fusion power-balance studies across five magnetic-confinement configurations: tokamaks, magnetic mirrors, field-reversed configurations (FRCs), dipoles, and stellarators. A common power-balance formulation connects fusion production, charged-particle deposition, radiation, transport loss, external heating, and fusion gain, while each configuration retains its own geometry, profile weights, confinement model, and operating constraints. The same solver interface supports both single-point calculations and two-dimensional plasma operating contour (POPCON) scans, producing fusion and heating powers, gain, radiation and transport losses, geometry quantities, and configuration-specific validity indicators. VSC therefore makes it possible to study how assumptions about density, temperature, magnetic field, confinement, and geometry shape the accessible operating space of different fusion concepts within one traceable framework. By combining reduced-order physics models with a unified computational platform, VSC enables rapid assessment and comparative analysis of candidate fusion reactor concepts during the early design stage. △ Less

Submitted 13 July, 2026; originally announced July 2026.

Comments: 33 pages, 11 figures

μ-MOPA Architecture for Photonic Integrated Solid State Laser

Authors: Yu Guo, Yubo Wang, Haoqi Zhao, Fengyan Yang, Guangcanlan Yang, Hao Xie, Hong X. Tang

Abstract: Diode-pumped solid-state (DPSS) lasers play a central role in modern photonics owing to their exceptional efficiency and ability to extend spectral coverage beyond the reach of semiconductor diodes. These attributes have enabled breakthroughs in precision metrology, quantum optics, and coherent communications. However, bringing the proven advantages of DPSS gain media such as Nd:YAG onto an integr… ▽ More Diode-pumped solid-state (DPSS) lasers play a central role in modern photonics owing to their exceptional efficiency and ability to extend spectral coverage beyond the reach of semiconductor diodes. These attributes have enabled breakthroughs in precision metrology, quantum optics, and coherent communications. However, bringing the proven advantages of DPSS gain media such as Nd:YAG onto an integrated photonic platform has remained difficult, largely due to inefficient pump utilization and limited power-scaling in chip-scale implementations. Here, we demonstrate the first photonic-integrated Nd:YAG laser-amplifier system that overcomes these challenges with a micro-chip based master-oscillator-power-amplifier (μ-MOPA) architecture. The seed laser, employing a double-resonant microring resonator, could reach a threshold as low as 2.9 μW. The single-pass waveguide amplifier, when optimized separately, provides up to 46.6 dB small-signal gain. Combining the low-threshold seed with cascaded waveguide amplifiers, the integrated μ-MOPA delivers more than 12 dBm of amplified continuous-wave output power. These results establish Nd:YAG waveguide integration as a practical route to compact and high-performance solid-state light sources. △ Less

Submitted 18 June, 2026; originally announced June 2026.

Plasma Instabilities in Arbitrary Distributions: Comparison between ALPS and BO

Authors: Xudong Guo, Huasheng Xie, Kristopher G. Klein, D. Verscharen, Chen Shi, Jinsong Zhao

Abstract: Determining accurate wave dispersion relations is a central problem in plasma physics. Recent advances have enabled the numerical computation of linear dispersion relation in plasmas with arbitrary particle velocity distribution functions (VDFs), using two distinct solvers, BO and ALPS. Their reliability and mutual consistency, however, have not been systematically tested for a broad range of VDFs… ▽ More Determining accurate wave dispersion relations is a central problem in plasma physics. Recent advances have enabled the numerical computation of linear dispersion relation in plasmas with arbitrary particle velocity distribution functions (VDFs), using two distinct solvers, BO and ALPS. Their reliability and mutual consistency, however, have not been systematically tested for a broad range of VDFs. Here we compare the dispersion relations obtained from BO and ALPS for several representative distributions. We find that the two solvers give consistent unstable modes for kappa distributions with large values of $κ$, as well as for ring-beam, shell, and proton core-beam distributions. BO, however, becomes unreliable for kappa distributions with $κ< 4$. For an observationally derived VDF, the two solvers give similar real frequencies for the unstable waves but substantially different growth rates. This difference is mainly caused by the imperfect fitting of the input distribution required by BO. Despite this limitation, BO has a clear computational advantage because it can obtain all roots in a single run. Considering the complementary strengths of the two solvers, their combined use can provide a more reliable and effective framework for investigating instabilities in non-Maxwellian plasma environments. △ Less

Submitted 12 June, 2026; originally announced June 2026.

Comments: 11 pages, 9 figures

VEQ: a fast parametric Grad--Shafranov solver for fixed-boundary tokamak equilibria with flexible source profiles

Authors: Ruohan Zhang, Huasheng Xie, Yueyan Li, Weiqi Meng, Feng Wang, Zhengxiong Wang

Abstract: Veloce EQuilibrium (VEQ) is a compact parametric framework for tokamak modeling workflows that repeatedly query continuous fixed-boundary equilibria at low latency. The VEQPy implementation evaluated here is an axisymmetric fixed-boundary Grad-Shafranov solver whose main solve enforces a variationally induced projected residual. Its active unknowns are MXH-type flux-surface harmonics and shifted-C… ▽ More Veloce EQuilibrium (VEQ) is a compact parametric framework for tokamak modeling workflows that repeatedly query continuous fixed-boundary equilibria at low latency. The VEQPy implementation evaluated here is an axisymmetric fixed-boundary Grad-Shafranov solver whose main solve enforces a variationally induced projected residual. Its active unknowns are MXH-type flux-surface harmonics and shifted-Chebyshev coefficients for radial profile and source closures. Six input routes accept pressure-gradient, toroidal-field-function, poloidal-flux-gradient, enclosed toroidal current, current-density and safety-factor information through route-specific closures, while all routes map to the same finite-dimensional residual operator. Controlled tests show route consistency for smooth, mutually compatible inputs generated from a common reference equilibrium. For Pareto-selected reduced configurations in three G-EQDSK cases, the most accurate selected rows correspond to a D-shaped case (9 active parameters, minor-radius-normalized shape error 1.4e-3, solve-only median 1.6 ms), an H-mode case (65, 1.1e-3, 19 ms), and an X-point case treated as a smoothed fixed-boundary representation of a diverted boundary (94, 1.9e-3, 15 ms). Sampled pointwise strong-form Grad-Shafranov diagnostics show that enriching the active representation mainly improves interior force balance, whereas the global RMS and maximum values for the H-mode and X-point cases remain dominated by near-boundary contributions. In an isolated one-dimensional transport-geometry coupling test against the target geometry read from G-EQDSK, the temperature-profile response remains below about one percent. These results support using VEQ for repeated equilibrium-geometry queries, provided that pointwise diagnostics are retained to screen cases requiring boundary refinement, local correction or higher-fidelity equilibrium solves. △ Less

Submitted 10 June, 2026; originally announced June 2026.

The Pseudospectral Method for the Dirac Equation with Confining Potential

Authors: Dengshan Liu, Huihui Xie, Pengxiang Du, Jian Li, Tomoya Naito

Abstract: We observe that solving the Dirac equation for confined potentials using the generalized pseudospectral (GPS) method leads to deteriorating convergence of energy eigenvalues and highly oscillatory in wave functions as the confinement radius decreases. It is found that this issue stems from the first-order differentiation formulation employed in GPS method. Motivated by this insight, we adopt the k… ▽ More We observe that solving the Dirac equation for confined potentials using the generalized pseudospectral (GPS) method leads to deteriorating convergence of energy eigenvalues and highly oscillatory in wave functions as the confinement radius decreases. It is found that this issue stems from the first-order differentiation formulation employed in GPS method. Motivated by this insight, we adopt the kinetically balanced generalized pseudospectral method, which incorporates the kinetically-balanced condition into the GPS method. Numerical results demonstrate that the mono-kinetically-balanced generalized pseu dospectral (MKB-GPS) method yields converged energy eigenvalues and generates smooth, continuous wave functions. This is the first application of the MKB-GPS method to confined potentials, and its effectiveness is validated for small confinement radii. △ Less

Submitted 24 May, 2026; originally announced May 2026.

Comments: 14 pages, 10 figures

Report number: RIKEN-iTHEMS-Report-26

Forecasting Return Time of Extreme Precipitation by Large Deviation Theory

Authors: Haotian Xie, Haoxian Liu, Jingfang Fan, Ying Tang

Abstract: Forecasting extreme precipitation is essential yet challenging due to its rarity and complexity. We develop a large deviation framework to estimate the return times of extreme precipitation events. We first find that the Landau distribution, originally introduced in plasma physics, accurately captures extreme precipitation at approximately 93% of global locations, outperforming conventional extrem… ▽ More Forecasting extreme precipitation is essential yet challenging due to its rarity and complexity. We develop a large deviation framework to estimate the return times of extreme precipitation events. We first find that the Landau distribution, originally introduced in plasma physics, accurately captures extreme precipitation at approximately 93% of global locations, outperforming conventional extreme value distributions with 76% matched locations under the same accuracy criterion. Enriching rare event samples by the fitted Landau distribution, we obtain more accurate estimates of large deviation rate functions and return times, enabling forecasts beyond historically observed precipitation intensities. Mapping historical return times to future projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6), we show that return time curves under different emission scenarios collapse onto a unified relation, revealing a sharply increased lifetime exposure to extreme precipitation for 21st-century birth cohorts under most future emission scenarios. △ Less

Submitted 12 April, 2026; originally announced April 2026.

Predictions of charge density distributions for nuclei with $Z \geq 8$

Authors: Yun Dong Wang, Tian Shuai Shang, Hui Hui Xie, Peng Xiang Du, Jian Li, Haozhao Liang

Abstract: A deep neural network (DNN) has been developed to accurately predict nuclear charge density distributions for nuclei with proton numbers $Z \geq 8$. By incorporating essential nuclear structure features, the model achieves a significant improvement in predictive accuracy over conventional methods. The charge density distributions are analyzed using a Fourier-Bessel (FB) series expansion, and the D… ▽ More A deep neural network (DNN) has been developed to accurately predict nuclear charge density distributions for nuclei with proton numbers $Z \geq 8$. By incorporating essential nuclear structure features, the model achieves a significant improvement in predictive accuracy over conventional methods. The charge density distributions are analyzed using a Fourier-Bessel (FB) series expansion, and the DNN is trained on a comprehensive dataset derived from relativistic continuum Hartree-Bogoliubov (RCHB) theory calculations. The model demonstrates exceptional performance, with root-mean-square deviations of 0.0123 fm and 0.0198 fm for charge radii on the training and validation sets, respectively, remarkably surpassing the precision of the original RCHB calculations. Beyond advancing nuclear physics research, this high-precision model provides critical data for applications in atomic physics, nuclear astrophysics, and related fields. △ Less

Submitted 6 April, 2026; originally announced April 2026.

Comments: 56 pages, 4 tables, 3 figures

Journal ref: NUCL SCI TECH 37, 93 (2026)

Spectral convergence of sum-of-Gaussians tensor neural networks for many-electron Schrödinger equation

Authors: Teng Wu, Qi Zhou, Huangjie Zheng, Hehu Xie, Zhenli Xu

Abstract: We present an improved version of the sum-of-Gaussians tensor neural network (SOG-TNN) architecture for solving many-electron Schrödinger equation for one-dimensional soft-Coulomb systems. Model reduction techniques are introduced to reduce the number of tensor-factorized bases under the SOG approximation of the kernel. The Slater determinant ansatz is employed so that the anti-symmetric property… ▽ More We present an improved version of the sum-of-Gaussians tensor neural network (SOG-TNN) architecture for solving many-electron Schrödinger equation for one-dimensional soft-Coulomb systems. Model reduction techniques are introduced to reduce the number of tensor-factorized bases under the SOG approximation of the kernel. The Slater determinant ansatz is employed so that the anti-symmetric property of the wave function can be strictly preserved. Numerical results show that the SOG-TNN achieves high accuracy with remarkably small basis sizes. Robust spectral convergence with respect to the basis size is also observed, consistently characterized by a mixed algebraic-exponential model for the error decay. These findings validate that the SOG-TNN architecture provides an ultra-efficient and low-rank representation of complex multi-electron wave functions, shedding light on high-fidelity quantum calculations in larger-scale many-electron systems. △ Less

Submitted 24 March, 2026; originally announced March 2026.

Comments: 9 pages, 3 figures

Finite-nuclear-size effect for hydrogenlike ions under high external pressure

Authors: Dengshan Liu, Huihui Xie, Pengxiang Du, Tianshuai Shang, Jian Li, Jiguang Li, Tomoya Naito

Abstract: The influence of pressure on finite-nuclear-size corrections to atomic energy levels and electron-capture decay rate is investigated in confined hydrogenlike ions. The ions are modeled inside an impenetrable spherical cavity, with a Gaussian distribution used to represent the nuclear charge distribution. For each confinement radius used to simulate external pressure, the energies and wave function… ▽ More The influence of pressure on finite-nuclear-size corrections to atomic energy levels and electron-capture decay rate is investigated in confined hydrogenlike ions. The ions are modeled inside an impenetrable spherical cavity, with a Gaussian distribution used to represent the nuclear charge distribution. For each confinement radius used to simulate external pressure, the energies and wave functions of the lowest-lying bound states are determined by numerically solving the Dirac equation via the kinetically balanced generalized pseudospectral method. In contrast to unconfined ions, both the FNS corrections and electron-capture decay rates increase markedly under pressure and exhibit parallel trends with increasing confinement. Pressure also removes level degeneracies and alters the relative magnitudes of FNS corrections across different bound states. Moreover, the nuclear charge radius is found to significantly affect the pressure-enhanced electron-capture decay rate. △ Less

Submitted 24 March, 2026; originally announced March 2026.

Report number: RIKEN-iTHEMS-Report-26

Journal ref: Commun. Theor. Phys. 78 (2026) 075301 (7pp)

Interface Engineered Moiré Graphene Superlattices: Breaking the Auger Carrier Multiplication Limit for Infrared Single-Photon Detection

Authors: Sichao Du, Ning Li, Zhufeng Pan, Munir Ali, Hengrui Zhang, Duokai Chang, Yuehang Zhang, Qiang Wen, Shuo Zhang, Hao Wu, Yunlei Sun, Qiuting Wang, Hao Xie, Chaohao Chen, Zhenyi Ni, Qiangbing Guo, Duo Xiao, Wen-Yan Yin

Abstract: Hot electrons undergo Auger scattering during their relaxation process has a multiplication effect,which can generate more electrons above the Fermi level, thus improving the efficiency of photoelectric signal conversion.However,the photo-current gain brought by the Auger carrier multiplication is generally limited with a value less than 5,due to the rapid recombination of photo-generated charge-c… ▽ More Hot electrons undergo Auger scattering during their relaxation process has a multiplication effect,which can generate more electrons above the Fermi level, thus improving the efficiency of photoelectric signal conversion.However,the photo-current gain brought by the Auger carrier multiplication is generally limited with a value less than 5,due to the rapid recombination of photo-generated charge-carriers and the inherently low light absorption of two-dimensional materials.Herein,by twisting graphene to an interlayer angle of 10_o,we report a layer-dependent electronic correlations leading to an efficient carrier multiplication gain of 10³.This is primarily offered by the additional localized density-of-states at interface of the bi-layer 10_o,moire graphene,and the enhanced interlayer coupling of electron waves in a five-layer moire graphene superlattice structure.Therefore,we can harvest the hot electrons during their energy relaxation through a thermalized optical phonon bottleneck effect.It is this effect that promotes the accumulated hot electrons to achieve a maximum Auger scattering rate ~ 10¹⁰*ps^-1*cm^-2.Furthermore,the ballistic transport of these hot electrons and Schottky barrier from a 90 nm thick silicon-on-insulator (SOI) silicon effectively block the thermal noise,thus leading to a highly sensitive near-infrared detection characteristic.At a low incident light power of ~ 10^-13 W/cm²,the resulting signal-to-noise ratio is more than 100 dB.The strengthened electromagnetic interaction from highly thermalized optical phonon in stacked moire graphene is utilized in this work.The hot electron multiplication suggests the applicability of Van der Waals moire superlattice architecture for harvesting charge carriers,thus paving the pathway to design infrared single-photon avalanche detectors. △ Less

Submitted 10 March, 2026; originally announced March 2026.

Comments: 15pages,4figures

CBCT-Based Synthetic CT Generation Using Conditional Flow Matching Model

Authors: Junbo Peng, Huiqiao Xie, Tonghe Wang, Xiangyang Tang, Xiaofeng Yang

Abstract: Daily or weekly cone-beam computed tomography (CBCT) is employed in image-guided radiotherapy (IGRT) for precise patient alignment. However, its clinical utility in quantitative tasks is hindered by severe artifacts and inaccurate Hounsfeld unit (HU). It is essential to enhance CBCT image quality to a level comparable with that of conventional CT scans. This study proposed a conditional flow match… ▽ More Daily or weekly cone-beam computed tomography (CBCT) is employed in image-guided radiotherapy (IGRT) for precise patient alignment. However, its clinical utility in quantitative tasks is hindered by severe artifacts and inaccurate Hounsfeld unit (HU). It is essential to enhance CBCT image quality to a level comparable with that of conventional CT scans. This study proposed a conditional flow matching model that gradually transforms a sample from normal distribution to the corresponding CT sample conditioned on the input CBCT image. The proposed model was trained using CBCT and deformed planning CT (dpCT) image pairs in a supervised learning scheme. The feasibility of the conditional flow matching model was verified using studies of brain, head-and-neck (HN), and lung patients. The quantitative performance was evaluated using three metrics, including mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and normalized cross-correlation (NCC). The proposed flow matching model was also compared to other flow matching and diffusion-based generative models for sCT generation. The proposed flow matching model effectively reduced multiple types of artifacts on CBCT images in all the studies. In the study of brain patient, the MAE, PSNR, and NCC of the sCT were improved to 26.02 HU, 32.35 dB, and 0.99, respectively, from 40.63 HU, 27.87 dB, and 0.98 on the CBCT images. In the study of HN patient, the metrics were improved to 33.17 HU, 28.68 dB, 0.98 from 38.99 HU, 27.00 dB, 0.98. In the lung patient study, the metrics were 25.09 HU, 32.81 dB, 0.99 and 32.90 HU, 30.48 dB, 0.98 for sCT and CBCT, respectively. The proposed conditional flow matching model effectively synthesizes high-quality CT-like images from CBCT, achieving accurate HU representation and artifact reduction. This enables more reliable organ segmentation and dose calculation in CBCT-guided online ART workflows. △ Less

Submitted 5 March, 2026; originally announced March 2026.

Energization of Proton via Beam-Driven Ion Bernstein Waves in p11B Plasmas

Authors: Yangchun Liu, Hairong Huang, Dong Wu, Tianxing Hu, Huasheng Xie, Bing Liu, Zhengmao Sheng, Jiaqi Dong, Yueng-Kay Martin Peng

Abstract: Energizing background ions plays a pivotal role in all forms of thermal nuclear fusion, as it can increase the fusion reaction rate without affecting the overall mechanical equilibrium. This is particularly critical for p11B fusion due to its exceptionally high operating temperature and substantial energy losses from bremsstrahlung radiation. Here, we report a nonlinear mechanism that efficiently… ▽ More Energizing background ions plays a pivotal role in all forms of thermal nuclear fusion, as it can increase the fusion reaction rate without affecting the overall mechanical equilibrium. This is particularly critical for p11B fusion due to its exceptionally high operating temperature and substantial energy losses from bremsstrahlung radiation. Here, we report a nonlinear mechanism that efficiently transfers the energy of injected heating beams to background protons in p11B mixed plasmas, via fully kinetic Particle-In-Cell (PIC) simulations. When a proton neutral beam is injected into p11B plasmas, it triggers the excitation of ion Bernstein waves (IBWs) at harmonics of the proton cyclotron frequency. In the initial linear stage, the energy channels to background electrons and protons might be comparable, consistent with theoretical model for the energy transfer. However, in the latter nonlinear stage, the dominant channel transfers to background protons, generating a non-Maxwellian population of energetic protons. This transition is driven by a nonlinear spectral cascade of IBWs toward lower frequencies and longer wavelengths, which strengthens wave proton coupling while suppressing wave electron coupling. △ Less

Submitted 3 March, 2026; originally announced March 2026.

AgentCAT: An LLM Agent for Extracting and Analyzing Catalytic Reaction Data from Chemical Engineering Literature

Authors: Wei Yang, Zihao Liu, Tao Tan, Xiao Hu, Hong Xie, Lulu Li Xin Li, Jianyu Han, Defu Lian, Mao Ye

Abstract: This paper presents a large language model (LLM) agent named AgentCAT, which extracts and analyzes catalytic reaction data from chemical engineering papers, %and supports natural language based interactive analysis of the extracted data. AgentCAT serves as an alternative to overcome the long-standing data bottleneck in chemical engineering field, and its natural language based interactive data ana… ▽ More This paper presents a large language model (LLM) agent named AgentCAT, which extracts and analyzes catalytic reaction data from chemical engineering papers, %and supports natural language based interactive analysis of the extracted data. AgentCAT serves as an alternative to overcome the long-standing data bottleneck in chemical engineering field, and its natural language based interactive data analysis functionality is friendly to the community. AgentCAT also presents a formal abstraction and challenge analysis of the catalytic reaction data extraction task in an artificial intelligence-friendly manner. This abstraction would help the artificial intelligence community understand this problem and in turn would attract more attention to address it. Technically, the complex catalytic process leads to complicated dependency structure in catalytic reaction data with respect to elementary reaction steps, molecular behaviors, measurement evidence, etc. This dependency structure makes it challenging to guarantee the correctness and completeness of data extraction, as well as representing them for analysis. AgentCAT addresses this challenge and it makes four folds of technical contributions: (1) a schema-governed extraction pipeline with progressive schema evolution, enabling robust data extraction from chemical engineering papers; (2) a dependency-aware reaction-network knowledge graph that links catalysts/active sites, synthesis-derived descriptors, mechanistic claims with evidence, and macroscopic outcomes, preserving process coupling and traceability; (3) a general querying module that supports natural-language exploration and visualization over the constructed graph for cross-paper analysis; (4) an evaluation on $\sim$800 peer-reviewed chemical engineering publications demonstrating the effectiveness of AgentCAT. △ Less

Submitted 9 February, 2026; originally announced February 2026.

Investigation of Toroidal Rotation Effects on Spherical Torus Equilibria using the Fast Spectral Solver VEQ-R

Authors: Xingyu Li, Huasheng Xie, Lai Wei, Zhengxiong Wang

Abstract: Standard reduced models often fail to adequately describe the complex geometric response of tokamak plasmas to strong toroidal rotation. In this work, we present VEQ-R, a computationally efficient spectral solver designed to calculate fixed-boundary equilibria with arbitrary toroidal flow. In contrast to computationally intensive grid-based codes, our model employs a 12-parameter shifted Chebyshev… ▽ More Standard reduced models often fail to adequately describe the complex geometric response of tokamak plasmas to strong toroidal rotation. In this work, we present VEQ-R, a computationally efficient spectral solver designed to calculate fixed-boundary equilibria with arbitrary toroidal flow. In contrast to computationally intensive grid-based codes, our model employs a 12-parameter shifted Chebyshev spectral expansion to explicitly resolve radial variations in high-order shaping profiles--such as dynamic elongation and triangularity. This capability allows the solver to accurately capture differential flux surface distortions (non-rigid effects) even in challenging sonic regimes ($M \sim 1.0$). By synergizing this compact variational formulation with a novel ``Matrix-Kernel'' acceleration technique, we transform the problem into pre-computed algebraic matrix operations. This approach achieves convergence in approximately 5 ms, maintaining exceptional geometric fidelity compared to high-resolution benchmarks while balancing speed and accuracy. Our analysis reveals that rotation-induced flux compression leads to a monotonic decrease in the core safety factor $q_0$, pushing it dangerously close to unity--a structural deformation mechanism effectively captured by this approximate yet robust solver. △ Less

Submitted 11 February, 2026; originally announced February 2026.

Comments: 17 pages, 10 figures

Development of a Reduced Multi-Fluid Equilibrium Model and Its Application to Proton-Boron Spherical Tokamaks

Authors: Huasheng Xie, Xingyu Li, Jiaqi Dong, Zhiwei Ma, Yunfeng Liang, Yuejiang Shi, Wenjun Liu, Yueng-Kay Martin Peng, Lai Wei, Zhengxiong Wang, Hanyue Zhao

Abstract: Proton-Boron fusion requires extreme ion temperatures and robust confinement, making Spherical Tokamaks (ST) with high-power neutral beam injection primary candidates. In these devices, strong toroidal rotation and the large mass disparity between protons and boron ions drive complex multi-fluid effects - specifically centrifugal species separation and electrostatic polarization - that standard si… ▽ More Proton-Boron fusion requires extreme ion temperatures and robust confinement, making Spherical Tokamaks (ST) with high-power neutral beam injection primary candidates. In these devices, strong toroidal rotation and the large mass disparity between protons and boron ions drive complex multi-fluid effects - specifically centrifugal species separation and electrostatic polarization - that standard single-fluid magnetohydrodynamic (MHD) models fail to capture. While comprehensive multi-fluid models are often numerically stiff, we develop a reduced model balancing physical fidelity with computational robustness. By retaining dominant toroidal rotation and self-consistent potential while neglecting poloidal inertia and pressure anisotropy, the model couples a generalized Grad-Shafranov equation with species-specific Bernoulli relations and a quasi-neutrality constraint. The model is applied to two representative p-B ST configurations: the experimental EHL-2 and reactor-scale EHL-3B. Simulation results demonstrate that equilibrium modifications are governed by the ion Mach number ($M$). In the low-rotation regime ($M < 0.5$), multi-fluid effects are weak and solutions approach the single-fluid limit. However, at $M > 2$, strong centrifugal forces drive significant boron accumulation at the low-field side (LFS) and generate an internal electrostatic potential on the order of 10 kV. These findings confirm the necessity of multi-fluid modeling for accurate p-$^{11}$B reactor design and establish a theoretical foundation for future investigations into stability, transport, and free-boundary dynamics. △ Less

Submitted 9 February, 2026; originally announced February 2026.

Comments: 11 pages, 7 figures

Journal ref: Plasma Phys. Control. Fusion 68 (2026) 075038

Physics-Informed Chebyshev Polynomial Neural Operator for Parametric Partial Differential Equations

Authors: Biao Chen, Jing Wang, Hairun Xie, Qineng Wang, Shuai Zhang, Yifan Xia, Jifa Zhang

Abstract: Neural operators have emerged as powerful deep learning frameworks for approximating solution operators of parameterized partial differential equations (PDE). However, current methods predominantly rely on multilayer perceptrons (MLPs) for mapping inputs to solutions, which impairs training robustness in physics-informed settings due to inherent spectral biases and fixed activation functions. To o… ▽ More Neural operators have emerged as powerful deep learning frameworks for approximating solution operators of parameterized partial differential equations (PDE). However, current methods predominantly rely on multilayer perceptrons (MLPs) for mapping inputs to solutions, which impairs training robustness in physics-informed settings due to inherent spectral biases and fixed activation functions. To overcome the architectural limitations, we introduce the Physics-Informed Chebyshev Polynomial Neural Operator (CPNO), a novel mesh-free framework that leverages a basis transformation to replace unstable monomial expansions with the numerically stable Chebyshev spectral basis. By integrating parameter dependent modulation mechanism to main net, CPNO constructs PDE solutions in a near-optimal functional space, decoupling the model from MLP-specific constraints and enhancing multi-scale representation. Theoretical analysis demonstrates the Chebyshev basis's near-minimax uniform approximation properties and superior conditioning, with Lebesgue constants growing logarithmically with degree, thereby mitigating spectral bias and ensuring stable gradient flow during optimization. Numerical experiments on benchmark parameterized PDEs show that CPNO achieves superior accuracy, faster convergence, and enhanced robustness to hyperparameters. The experiment of transonic airfoil flow has demonstrated the capability of CPNO in characterizing complex geometric problems. △ Less

Submitted 2 February, 2026; originally announced February 2026.

Comments: 28pages

A Novel Numerical Algorithms Optimization Method with Machine Learning Frameworks: Application on Real-time Plasmas Equilibrium Reconstruction in EXL-50U Spherical Torus

Authors: G. H. Zheng, S. F. Liu, X. Gu, Y. P. Zhang, J. Li, Y. Liu, X. C. Lun, L. Xing, J. G. Chen, Z. Y. Chen, Y. Yu, D. Guo, Z. Y. Yang, H. S. Xie, X. M. Song, Y. J. Shi, EXL-50U Team

Abstract: This work proposes for the first time a novel optimization method for numerical algorithms, which takes advantages of machine learning frameworks PyTorch and TensorRT, leveraging their modularity, low development threshold, and automatic tuning characteristics to achieve a real-time plasmas reconstruction algorithm called PTEFIT as an application in tokamak-based controlled fusion that combines pe… ▽ More This work proposes for the first time a novel optimization method for numerical algorithms, which takes advantages of machine learning frameworks PyTorch and TensorRT, leveraging their modularity, low development threshold, and automatic tuning characteristics to achieve a real-time plasmas reconstruction algorithm called PTEFIT as an application in tokamak-based controlled fusion that combines performance, flexibility, and usability. The algorithm has been deployed and routinely operated on the EXL-50U spherical tokamak, with an average inference time of only 0.268ms per time slice at $129\times 129$ resolution, and has successfully driven feedback control of the maximum radial position of plasmas and isoflux control. We believe that its design philosophy has sufficient potential to accelerate development and optimization in GPU parallel computing, and is expected to be extended to other numerical algorithms. △ Less

Submitted 18 January, 2026; originally announced January 2026.

A wafer-scale ultrasensitive programmable chiroptical sensor

Authors: Haoyu Xie, Jichao Fan, Zarif Ahmad Razin Bhuiyan, Saqlain Raza, Mohammad Mohammadi, Cheng Guo, Yunshan Wang, Jun Liu, Weilu Gao

Abstract: Chiroptical enantioselective sensing is gaining traction across various applications. However, intrinsic molecular chiroptical responses are weak, and existing amplification approaches add synthesis, manufacturing, or operational complexity that limits sensitivity, scalability, and dynamic control. Here, we present a fundamentally new sensing paradigm merging adsorption-driven chirality induction… ▽ More Chiroptical enantioselective sensing is gaining traction across various applications. However, intrinsic molecular chiroptical responses are weak, and existing amplification approaches add synthesis, manufacturing, or operational complexity that limits sensitivity, scalability, and dynamic control. Here, we present a fundamentally new sensing paradigm merging adsorption-driven chirality induction with wafer-scale optical transduction in a programmable heterostructure containing twisted aligned carbon nanotubes (CNTs) and phase change materials (PCMs). Chiral molecules adsorb onto CNTs to form chiroptically active composites that are macroscopically assembled by alignment and rotational stacking, yielding large ultraviolet circular dichroism (CD). We resolve molecule concentration and handedness in a single device without lithography, hotspot delivery, or differential protocols, achieving sub-$μ$M sensitivity for CD-silent glucose and chiral amino acids enabled by $>10^5\,\mathrm{M^{-1}}$ adsorption constants. We validate adsorption using molecular dynamics simulations, reproduce experimental results using chiral transfer matrix simulations, and realize sensor programmability by tuning the PCM layer. This platform enables cost-effective in-situ enantiomer monitoring in aqueous environments. △ Less

Submitted 16 January, 2026; originally announced January 2026.

Comments: 40 pages, 15 figures

What Is the Minimum Number of Parameters Required to Represent Solutions of the Grad-Shafranov Equation?

Authors: Huasheng Xie, Yueyan Li

Abstract: Fast and accurate solutions of the Grad--Shafranov (GS) equation are essential for equilibrium analysis, integrated modeling, and surrogate model construction in magnetic confinement fusion. In this work, we address a fundamental question: what is the minimum number of free parameters required to accurately represent numerical solutions of the GS equation under fixed-boundary conditions? We demons… ▽ More Fast and accurate solutions of the Grad--Shafranov (GS) equation are essential for equilibrium analysis, integrated modeling, and surrogate model construction in magnetic confinement fusion. In this work, we address a fundamental question: what is the minimum number of free parameters required to accurately represent numerical solutions of the GS equation under fixed-boundary conditions? We demonstrate that, for most practical applications, GS equilibria can be represented using only 2--5 free parameters while maintaining relative errors below 5\%. For higher-accuracy requirements, we introduce a unified spectral representation based on the Miller extended harmonic (MXH) expansion in the poloidal direction combined with shifted Chebyshev (Cheb) polynomials in the radial direction. This MXH--Cheb basis exhibits rapid convergence for two-dimensional GS equilibria. For configurations where three geometric moments (shift, elongation, and triangularity) are specified at the last closed flux surface (LCFS), relative errors on the order of $10^{-2}$--$10^{-3}$ can be achieved using as few as 13--20 parameters. In more general cases, including up--down asymmetric equilibria, X-point configurations, and stiff pressure and current profiles (e.g., H-mode pedestals), accuracies beyond this level can be obtained with fewer than 100 parameters. The resulting equilibrium configurations and profile functions are fully analytical, with smooth derivatives of all orders. These results provide a systematic foundation for developing high-fidelity, ultra-fast GS solvers and enable efficient reduced-order and AI-based surrogate modeling of tokamak equilibria. △ Less

Submitted 6 January, 2026; originally announced January 2026.

Comments: 13 pages, 5 figures

Journal ref: Plasma Phys. Control. Fusion 68 (2026) 085021

Non-Inductive Current Start-Up Using Multi-Harmonic Electron Cyclotron Wave and Current Ramp-Up Through Combined Electron Cyclotron Wave and Ohmic Heating in EXL-50U Spherical Torus

Authors: Xinchen Jiang, Yuejiang Shi, Yueng-Kay Martin Peng, Shaodong Song, Wenjun Liu, Xianming Song, Xiang Gu, Ji Qi, Dong Guo, Debabrata Banerjee, Lili Dong, Zhenxing Wang, Chunyan Li, Junquan Lin, Pingwei Zheng, Haojie MA, Huasheng Xie, Jiaqi Dong, Qingwei Yang, Yunfeng Liang, Baoshan Yuan, Xianmei Zhang, Minsheng Liu, EXL-50U team

Abstract: The non-inductive current start-up by multi-harmonic electron cyclotron wave has been systematically investigated in the EXL-50U spherical torus. Significant enhancements of the driven current with increasing number of resonance layers have been demonstrated by variation of the number of harmonic resonance layers of the ECW through adjustment of the magnetic field or plasma cross section. The crit… ▽ More The non-inductive current start-up by multi-harmonic electron cyclotron wave has been systematically investigated in the EXL-50U spherical torus. Significant enhancements of the driven current with increasing number of resonance layers have been demonstrated by variation of the number of harmonic resonance layers of the ECW through adjustment of the magnetic field or plasma cross section. The critical role of multi-harmonic ECW in enhancing the driven current has been experimentally verified for the first time. To explain the related experimental observations, a physical mechanism involving multi-harmonic heating, multiple reflections, and multi-pass absorption - leading to the generation of high-energy electrons via X-mode wave or electron Bernstein wave has been proposed. The current drive capacity of the first harmonic extraordinary mode of the ECW has also been experimentally confirmed for the first time. After the application of Ohmic heating during the current ramp-up phase, the current drive efficiency of ECW is further enhanced. Leveraging the synergistic effect between ECW and Ohmic heating, EXL-50U achieved a plasma current of 1 MA, with the non-inductively driven current fraction reaching 70%. △ Less

Submitted 21 December, 2025; originally announced December 2025.

BO-PBK: A comprehensive solver for dispersion relations of obliquely propagating waves in magnetized multi-species plasma with anisotropic loss-cone drift product-bi-kappa distribution

Authors: Wei Bai, Huasheng Xie

Abstract: We present BO-PBK (BO-Product-Bi-Kappa), a new solver for kinetic dispersion relations of obliquely propagating waves in magnetized plasmas with complex velocity distributions. It reformulates the linearized Vlasov-Maxwell system into a compact eigenvalue problem, enabling direct computation of multiple wave branches and unstable modes without iterative initial-value searches. Key innovations incl… ▽ More We present BO-PBK (BO-Product-Bi-Kappa), a new solver for kinetic dispersion relations of obliquely propagating waves in magnetized plasmas with complex velocity distributions. It reformulates the linearized Vlasov-Maxwell system into a compact eigenvalue problem, enabling direct computation of multiple wave branches and unstable modes without iterative initial-value searches. Key innovations include a unified framework supporting product-bi-kappa, kappa-Maxwellian, bi-Maxwellian, and hybrid distributions with multi-component and loss-cone features; a concise rational-form eigenvalue formulation; and a 2--3 times reduction in matrix dimensions compared to the BO-KM solver, with improved efficiency at larger kappa indices. Benchmark tests confirm accurate reproduction of standard kinetic results and efficient resolution of waves and instabilities. BO-PBK thus provides a computationally efficient tool for wave and stability analysis in space and laboratory plasmas. △ Less

Submitted 7 December, 2025; originally announced December 2025.

Physics-informed Neural Operator Learning for Nonlinear Grad-Shafranov Equation

Authors: Siqi Ding, Zitong Zhang, Guoyang Shi, Xingyu Li, Xiang Gu, Yanan Xu, Huasheng Xie, Hanyue Zhao, Yuejiang Shi, Tianyuan Liu

Abstract: As artificial intelligence emerges as a transformative enabler for fusion energy commercialization, fast and accurate solvers become increasingly critical. In magnetic confinement nuclear fusion, rapid and accurate solution of the Grad-Shafranov equation (GSE) is essential for real-time plasma control and analysis. Traditional numerical solvers achieve high precision but are computationally prohib… ▽ More As artificial intelligence emerges as a transformative enabler for fusion energy commercialization, fast and accurate solvers become increasingly critical. In magnetic confinement nuclear fusion, rapid and accurate solution of the Grad-Shafranov equation (GSE) is essential for real-time plasma control and analysis. Traditional numerical solvers achieve high precision but are computationally prohibitive, while data-driven surrogates infer quickly but fail to enforce physical laws and generalize poorly beyond training distributions. To address this challenge, we present a Physics-Informed Neural Operator (PINO) that directly learns the GSE solution operator, mapping shape parameters of last closed flux surface to equilibrium solutions for realistic nonlinear current profiles. Comprehensive benchmarking of five neural architectures identifies the novel Transformer-KAN (Kolmogorov-Arnold Network) Neural Operator (TKNO) as achieving highest accuracy (0.25% mean L2 relative error) under supervised training (only data-driven). However, all data-driven models exhibit large physics residuals, indicating poor physical consistency. Our unsupervised training can reduce the residuals by nearly four orders of magnitude through embedding physics-based loss terms without labeled data. Critically, semi-supervised learning--integrating sparse labeled data (100 interior points) with physics constraints--achieves optimal balance: 0.48% interpolation error and the most robust extrapolation performance (4.76% error, 8.9x degradation factor vs 39.8x for supervised models). Accelerated by TensorRT optimization, our models enable millisecond-level inference, establishing PINO as a promising pathway for next-generation fusion control systems. △ Less

Submitted 5 December, 2025; v1 submitted 24 November, 2025; originally announced November 2025.

Comments: 42 pages, 17 figures, 8 tables,

A robust method for calculating plasma waves absorption in magnetized plasmas and its implementation in the BORAY ray-tracing code

Authors: Wanying Yu, Huasheng Xie, Aohua Mao, Haojie Ma, Zhengxiong Wang

Abstract: This paper presents a robust numerical method for calculating the total absorption rate of electromagnetic waves in magnetized plasmas, capable of determining the absorption ratio among different plasma components. The method adopts Ronnmark's expressions for the plasma dispersion function, $Z(ζ)$, and the dielectric tensor, K, to overcome the convergence issues and computational inefficiency of t… ▽ More This paper presents a robust numerical method for calculating the total absorption rate of electromagnetic waves in magnetized plasmas, capable of determining the absorption ratio among different plasma components. The method adopts Ronnmark's expressions for the plasma dispersion function, $Z(ζ)$, and the dielectric tensor, K, to overcome the convergence issues and computational inefficiency of the traditional Bessel function summation approach for evaluating the hot plasma dispersion relation, D, particularly at large $k_\perp$. It has been implemented and validated across multiple frequency regimes, including ion cyclotron (ICRF), lower hybrid (LHRF), and electron cyclotron (ECRF) ranges of frequencies, with results benchmarked against conventional $Z(ζ)$ and Bessel function expansions. Integrated into the BORAY ray-tracing code, the method uses expressions derived from the anti-Hermitian part of the dielectric tensor to compute absorption ratios. This work extends the ray-tracing framework, providing a more reliable tool for wave heating simulations. △ Less

Submitted 16 November, 2025; originally announced November 2025.

Comments: 18 pages, 8 figures

Low-Dose CT Imaging Using a Regularization-Enhanced Efficient Diffusion Probabilistic Model

Authors: Qiang Li, Mojtaba Safari, Shansong Wang, Huiqiao Xie, Jie Ding, Tonghe Wang, Xiaofeng Yang

Abstract: Low-dose computed tomography (LDCT) reduces patient radiation exposure but introduces substantial noise that degrades image quality and hinders diagnostic accuracy. Existing denoising approaches often require many diffusion steps, limiting real-time applicability. We propose a Regularization-Enhanced Efficient Diffusion Probabilistic Model (RE-EDPM), a rapid and high-fidelity LDCT denoising framew… ▽ More Low-dose computed tomography (LDCT) reduces patient radiation exposure but introduces substantial noise that degrades image quality and hinders diagnostic accuracy. Existing denoising approaches often require many diffusion steps, limiting real-time applicability. We propose a Regularization-Enhanced Efficient Diffusion Probabilistic Model (RE-EDPM), a rapid and high-fidelity LDCT denoising framework that integrates a residual shifting mechanism to align low-dose and full-dose distributions and performs only four reverse diffusion steps using a Swin-based U-Net backbone. A composite loss combining pixel reconstruction, perceptual similarity (LPIPS), and total variation (TV) regularization effectively suppresses spatially varying noise while preserving anatomical structures. RE-EDPM was evaluated on a public LDCT benchmark across dose levels and anatomical sites. On 10 percent dose chest and 25 percent dose abdominal scans, it achieved SSIM = 0.879 (0.068), PSNR = 31.60 (2.52) dB, VIFp = 0.366 (0.121) for chest, and SSIM = 0.971 (0.000), PSNR = 36.69 (2.54) dB, VIFp = 0.510 (0.007) for abdomen. Visual and statistical analyses, including ablation and Wilcoxon signed-rank tests (p < 0.05), confirm significant contributions from residual shifting and regularization terms. RE-EDPM processes two 512x512 slices in about 0.25 s on modern GPUs, supporting near real-time clinical use. The proposed framework achieves an optimal balance between noise suppression and anatomical fidelity, offering an efficient solution for LDCT restoration and broader medical image enhancement tasks. △ Less

Submitted 27 October, 2025; originally announced October 2025.

A finite-element Delta-Sternheimer approach for computing accurate all-electron RPA correlation energies of polyatomic molecules

Authors: Hao Peng, Haochen Liu, Chuhao Li, Hehu Xie, Xinguo Ren

Abstract: Attaining a reliable complete basis set (CBS) limit remains a significant challenge in ab initio correlated electronic-structure calculations. Building on our previous work for atoms and diatomic molecules, we present a finite-element (FE) Delta Sternheimer approach for numerically accurate random phase approximation (RPA) calculations applicable to general molecules. This approach seamlessly inte… ▽ More Attaining a reliable complete basis set (CBS) limit remains a significant challenge in ab initio correlated electronic-structure calculations. Building on our previous work for atoms and diatomic molecules, we present a finite-element (FE) Delta Sternheimer approach for numerically accurate random phase approximation (RPA) calculations applicable to general molecules. This approach seamlessly integrates atomic orbital basis sets with FE grids, enabling an arbitrary precision representation of first order wavefunctions. As a result, the density response function and RPA correlation energies can be computed with fully controlled numerical precision. The Delta Sternheimer approach thus provides direct access to RPA correlation energies at the CBS limit, eliminating reliance on conventional extrapolation schemes. We apply this approach to two problems: The energy hierarchy of 20 water-dimer configurations and the atomization energies of 50 molecules from the G2 set. For the water dimer, we examine the basis set dependence of the isomer energy ordering. For the G2 set, we investigate the residual numerical uncertainty in the conventional extrapolated CBS limit, both with and without correction for basis-set superposition error (BSSE). △ Less

Submitted 27 March, 2026; v1 submitted 17 October, 2025; originally announced October 2025.

A Suspended 4H-Silicon Carbide Membrane Platform for Defect Integration into Quantum Devices

Authors: Amberly H. Xie, Aaron M. Day, Jonathan R. Dietz, Chang Jin, Chaoshen Zhang, Eliana Mann, Zhujing Xu, Marko Loncar, Evelyn L. Hu

Abstract: 4H-silicon carbide is a promising platform for solid-state quantum technology due to its commercial availability as a wide bandgap semiconductor and ability to host numerous spin-active color centers. Integrating color centers into suspended nanodevices enhances defect control and readout--key advances needed to fully harness their potential. However, challenges in developing robust fabrication pr… ▽ More 4H-silicon carbide is a promising platform for solid-state quantum technology due to its commercial availability as a wide bandgap semiconductor and ability to host numerous spin-active color centers. Integrating color centers into suspended nanodevices enhances defect control and readout--key advances needed to fully harness their potential. However, challenges in developing robust fabrication processes for 4H-SiC thin films--due to the material's chemical and mechanical stability--limit their implementation in quantum applications. Here, we report on a new fabrication approach that first synthesizes suspended thin films from a monolithic platform, then patterns devices. With this technique, we fabricate and characterize structures tailored for defect integration, demonstrating 1D photonic crystal cavities, with and without waveguide interfaces, and lithium niobate on 4H-SiC acoustic cavities. This approach allows for greater fabrication flexibility--supporting high temperature annealing and heterogeneous material platform compatibility--providing a versatile platform for scalable fabrication of 4H-SiC devices for quantum technologies. △ Less

Submitted 14 August, 2025; originally announced August 2025.

Sum-of-Gaussians tensor neural networks for high-dimensional Schrödinger equation

Authors: Qi Zhou, Teng Wu, Jianghao Liu, Qingyuan Sun, Hehu Xie, Zhenli Xu

Abstract: We propose an accurate, efficient, and low-memory sum-of-Gaussians tensor neural network (SOG-TNN) algorithm for solving the high-dimensional Schrödinger equation. The SOG-TNN utilizes a low-rank tensor product representation of the solution to overcome the curse of dimensionality associated with high-dimensional integration. To handle the Coulomb interaction, we introduce an SOG decomposition to… ▽ More We propose an accurate, efficient, and low-memory sum-of-Gaussians tensor neural network (SOG-TNN) algorithm for solving the high-dimensional Schrödinger equation. The SOG-TNN utilizes a low-rank tensor product representation of the solution to overcome the curse of dimensionality associated with high-dimensional integration. To handle the Coulomb interaction, we introduce an SOG decomposition to approximate the interaction kernel such that it is dimensionally separable, leading to a tensor representation with rapid convergence. We further develop a range-splitting scheme that partitions the Gaussian terms into short-, long-, and mid-range components. They are treated with the asymptotic expansion, the low-rank Chebyshev expansion, and the model reduction with singular-value decomposition, respectively, significantly reducing the number of two-dimensional integrals in computing electron-electron interactions. The SOG decomposition well resolves the computational challenge due to the singularity of the Coulomb interaction, leading to an efficient algorithm for the high-dimensional problem under the TNN framework. Numerical results demonstrate the outstanding performance of the new method, revealing that the SOG-TNN is a promising way for accurately tackling quantum systems. △ Less

Submitted 4 March, 2026; v1 submitted 14 August, 2025; originally announced August 2025.

Comments: 31 pages, 8 figures

MSC Class: 35Q40; 65D40; 65N25; 68W25; 68W40

Deep Variational Free Energy Calculation of Hydrogen Hugoniot

Authors: Zihang Li, Hao Xie, Xinyang Dong, Lei Wang

Abstract: We develop a deep variational free energy framework to compute the equation of state of hydrogen in the warm dense matter region. This method parameterizes the variational density matrix of hydrogen nuclei and electrons at finite temperature using three deep generative models: a normalizing flow model for the Boltzmann distribution of the classical nuclei, an autoregressive transformer for the dis… ▽ More We develop a deep variational free energy framework to compute the equation of state of hydrogen in the warm dense matter region. This method parameterizes the variational density matrix of hydrogen nuclei and electrons at finite temperature using three deep generative models: a normalizing flow model for the Boltzmann distribution of the classical nuclei, an autoregressive transformer for the distribution of electrons in excited states, and a permutational equivariant flow model for the unitary backflow transformation of electron coordinates in Hartree-Fock states. By jointly optimizing the three neural networks to minimize the variational free energy, we obtain the equation of state and related thermodynamic properties of dense hydrogen for the temperature range where electrons occupy excited states. We compare our results with other theoretical and experimental results on the deuterium Hugoniot curve, aiming to resolve existing discrepancies. Our results bridge the gap between the results obtained by path-integral Monte Carlo calculations at high temperature and ground-state electronic methods at low temperature, thus providing a valuable benchmark for hydrogen in the warm dense matter region. △ Less

Submitted 22 December, 2025; v1 submitted 24 July, 2025; originally announced July 2025.

Comments: 8+18 pages, 4+12 figures, for source code and raw data, see https://github.com/fermiflow/Hugoniot, https://github.com/ZihangL/hqc, https://huggingface.co/datasets/Kelvin2025q/hugoniot

LFR-PINO: A Layered Fourier Reduced Physics-Informed Neural Operator for Parametric PDEs

Authors: Jing Wang, Biao Chen, Hairun Xie, Rui Wang, Yifan Xia, Jifa Zhang, Hui Xu

Abstract: Physics-informed neural operators have emerged as a powerful paradigm for solving parametric partial differential equations (PDEs), particularly in the aerospace field, enabling the learning of solution operators that generalize across parameter spaces. However, existing methods either suffer from limited expressiveness due to fixed basis/coefficient designs, or face computational challenges due t… ▽ More Physics-informed neural operators have emerged as a powerful paradigm for solving parametric partial differential equations (PDEs), particularly in the aerospace field, enabling the learning of solution operators that generalize across parameter spaces. However, existing methods either suffer from limited expressiveness due to fixed basis/coefficient designs, or face computational challenges due to the high dimensionality of the parameter-to-weight mapping space. We present LFR-PINO, a novel physics-informed neural operator that introduces two key innovations: (1) a layered hypernetwork architecture that enables specialized parameter generation for each network layer, and (2) a frequency-domain reduction strategy that significantly reduces parameter count while preserving essential spectral features. This design enables efficient learning of a universal PDE solver through pre-training, capable of directly handling new equations while allowing optional fine-tuning for enhanced precision. The effectiveness of this approach is demonstrated through comprehensive experiments on four representative PDE problems, where LFR-PINO achieves 22.8%-68.7% error reduction compared to state-of-the-art baselines. Notably, frequency-domain reduction strategy reduces memory usage by 28.6%-69.3% compared to Hyper-PINNs while maintaining solution accuracy, striking an optimal balance between computational efficiency and solution fidelity. △ Less

Submitted 21 June, 2025; originally announced June 2025.

Comments: 28 pages, 17 figures

A compressible Reynolds-averaged mixing model considering turbulent entropy and heat flux

Authors: Hansong Xie, Tengfei Luo, Yaomin Zhao, Yousheng Zhang, Jianchun Wang

Abstract: In typical nature and engineering scenarios, such as supernova explosion and inertial confinement fusion, mixing flows induced by hydrodynamics interfacial instabilities are essentially compressible. Despite their significance, accurate predictive tools for these compressible flows remain scarce. For engineering applications, the Reynolds-averaged Navier-Stokes (RANS) simulation stands out as the… ▽ More In typical nature and engineering scenarios, such as supernova explosion and inertial confinement fusion, mixing flows induced by hydrodynamics interfacial instabilities are essentially compressible. Despite their significance, accurate predictive tools for these compressible flows remain scarce. For engineering applications, the Reynolds-averaged Navier-Stokes (RANS) simulation stands out as the most practical approach due to its outstanding computational efficiency. However, the majority of RANS mixing studies reported have concentrated on incompressible scenarios, with quite limited attention given to compressible cases. Moreover, most of the existing RANS mixing models demonstrate significantly inaccurate predictions for compressible mixing flow. This study develops a novel compressible RANS mixing model by incorporating physical compressibility corrections into the incompressible K-L-y mixing transition model recently proposed by Xie et al. (J. Fluid Mech., 1002, A31, 2025). Specifically, taking the density-stratified Rayleigh-Taylor mixing flows as representative compressible cases, we firstly analyze the limitations of the existing model for compressible flows, based on high-fidelity data and local instability criteria. Subsequently, the equation of state for a perfect gas and the thermodynamic Gibbs relation are employed to derive comprehensive compressibility corrections. The crucial turbulent entropy and heat flux are integrated into the closure of the key turbulent mass flux term of the turbulent kinetic energy equation. These corrections enable the model to accurately depict compressible mixing flows. Systematic validations confirm the efficacy of the proposed modeling scheme. This study offers a promising strategy for modeling compressible mixing flows, paving the way for more accurate predictions in complex scenarios. △ Less

Submitted 19 June, 2025; originally announced June 2025.

MXene triggers high toughness, high strength and low hysteresis hydrogels for printed artificial tissue

Authors: Chendong Zhao, Yaxing Li, Qinglong He, Shangpeng Qin, Huiqi Xie, Chuanfang Zhang

Abstract: Substituting load-bearing tissues requires hydrogels with rapid processability, excellent mechanical strength and fatigue resistance. Conventional homogeneously polymerized hydrogels with short-chains/excessive branching exhibit low strength/toughness, being inadequate for artificial tissues. Here we introduce the heterogeneous polymerization-accelerated reaction kinetics on the Ti3C2Tx MXene micr… ▽ More Substituting load-bearing tissues requires hydrogels with rapid processability, excellent mechanical strength and fatigue resistance. Conventional homogeneously polymerized hydrogels with short-chains/excessive branching exhibit low strength/toughness, being inadequate for artificial tissues. Here we introduce the heterogeneous polymerization-accelerated reaction kinetics on the Ti3C2Tx MXene microreactor and sluggish kinetics beyond-to rapidly produce hydrogels within minutes. This allows the hyperbranched domains embedded within a highly entangled matrix, leading to excellent strength (2.4 MPa)/toughness (75.2 kJ m-2) and low hysteresis (2.9%) in hydrogels superior to the rest ones. The rapid liquid-to-solid transition triggered by MXene suggests the great possibility of 3D printed robust hydrogels toward artificial tissue. Importantly, these printed hydrogels-based artificial ligaments have demonstrated impressive load-bearing capacity, wear resistance, and suturability compared to commercial analogs. △ Less

Submitted 15 June, 2025; originally announced June 2025.

Bremsstrahlung radiation power in non-Maxwellian plasmas

Authors: Chaotong Yang, Kai Li, Huasheng Xie

Abstract: In plasmas, bremsstrahlung includes electron-ion (e-i) bremsstrahlung and electron-electron (e-e) bremsstrahlung. Bremsstrahlung radiation power loss is one of the most significant losses in fusion plasmas, which is more pronounced in higher temperature fusion. The factors that affect bremsstrahlung power include the mean electron energy and the electron velocity distribution shape. In this study,… ▽ More In plasmas, bremsstrahlung includes electron-ion (e-i) bremsstrahlung and electron-electron (e-e) bremsstrahlung. Bremsstrahlung radiation power loss is one of the most significant losses in fusion plasmas, which is more pronounced in higher temperature fusion. The factors that affect bremsstrahlung power include the mean electron energy and the electron velocity distribution shape. In this study, we systematically study the influence of the electron velocity distribution shape on the bremsstrahlung power with fixed total electron energy. It was found that the existing electron velocity distribution shapes have little effect on the bremsstrahlung power. In addition, by analyzing the bounds of bremsstrahlung power, we have provided the theoretical upper and lower bounds of e-i radiation. Our analysis reveals that the e-i bremsstrahlung power depends critically on the degree of energy distribution concentration. Specifically, in non-relativistic regimes, concentrated energy distributions enhance the radiation power, whereas in high-temperature relativistic regimes, such concentration suppresses it. This discrepancy arises from the distinct contributions of high-energy electron populations to radiation power across different energy regimes. For e-e bremsstrahlung, a similar dependence on energy concentration is observed. Furthermore, e-e radiation power exhibits additional sensitivity to the anisotropy of the electron velocity distribution function. These rules could provide a basis for reducing bremsstrahlung power losses in fusion plasmas. △ Less

Submitted 23 April, 2025; originally announced April 2025.

Comments: 13 pages, 21 figures

Carbon-Nanotube/$β$-Ga$_2$O$_3$ Heterojunction PIN Diodes

Authors: Hunter D. Ellis, Botong Li, Haoyu Xie, Jichao Fan, Imteaz Rahaman, Weilu Gao, Kai Fu

Abstract: $β$-Ga$_2$O$_3… ▽ More $β$-Ga$_2$O$_3$ is gaining attention as a promising semiconductor for next-generation high-power, high-efficiency, and high-temperature electronic devices, thanks to its exceptional material properties. However, challenges such as the lack of viable p-type doping have hindered its full potential, particularly in the development of ambipolar devices. This work introduces a novel heterojunction diode (HD) that combines p-type carbon nanotubes (CNTs) with i/n-type $β$-Ga$_2$O$_3$ to overcome these limitations. For the first time, a CNT/$β$-Ga$_2$O$_3$ hetero-p-n-junction diode is fabricated. Compared to a traditional Schottky barrier diode (SBD) with the same $β$-Ga$_2$O$_3$ epilayer, the CNT/$β$-Ga$_2$O$_3$ HD demonstrates significant improvements, including a higher rectifying ratio ($1.2 \times 10^{11}$), a larger turn-on voltage (1.96 V), a drastically reduced leakage current at temperatures up to 300 °C, and a 26.7% increase in breakdown voltage. Notably, the CNT/$β$-Ga$_2$O$_3$ HD exhibits a low ideality factor of 1.02, signifying an ideal interface between the materials. These results underline the potential of CNT/$β$-Ga$_2$O$_3$ heterojunctions for electronic applications, offering a promising solution to current limitations in $β$-Ga$_2$O$_3$-based devices. △ Less

Submitted 27 March, 2025; originally announced March 2025.

Comments: 17 pages, 5 figures

EFIT-mini: An Embedded, Multi-task Neural Network-driven Equilibrium Inversion Algorithm

Authors: Guohui Zheng, Songfen Liu, Huasheng Xie, Hanyue Zhao, Yapeng Zhang, Xiang Gu, Zhengyuan Chen, Tiantian Sun, Yanan Xu, Jia Li, Dong Guo, Renyi Tao, Youjun Hu, Zongyu Yang

Abstract: Equilibrium reconstruction, which infers internal magnetic fields, plasmas current, and pressure distributions in tokamaks using diagnostic and coil current data, is crucial for controlled magnetic confinement nuclear fusion research. However, traditional numerical methods often fall short of real-time control needs due to time-consuming computations or iteration convergence issues. This paper int… ▽ More Equilibrium reconstruction, which infers internal magnetic fields, plasmas current, and pressure distributions in tokamaks using diagnostic and coil current data, is crucial for controlled magnetic confinement nuclear fusion research. However, traditional numerical methods often fall short of real-time control needs due to time-consuming computations or iteration convergence issues. This paper introduces EFIT-mini, a novel algorithm blending machine learning with numerical simulation. It employs a multi-task neural network to replace complex steps in numerical equilibrium inversion, such as magnetic surface boundary identification, combining the strengths of both approaches while mitigating their individual drawbacks. The neural network processes coil currents and magnetic measurements to directly output plasmas parameters, including polynomial coefficients for $p'$ and $ff'$, providing high-precision initial values for subsequent Picard iterations. Compared to existing AI-driven methods, EFIT-mini incorporates more physical priors (e.g., least squares constraints) to enhance inversion accuracy. Validated on EXL-50U tokamak discharge data, EFIT-mini achieves over 98% overlap in the last closed flux surface area with traditional methods. Besides, EFIT-mini's neural network and full algorithm compute single time slices in just 0.11ms and 0.36ms at 129$\times$129 resolution, respectively, representing a three-order-of-magnitude speedup. This innovative approach leverages machine learning's speed and numerical algorithms' explainability, offering a robust solution for real-time plasmas shape control and potential extension to kinetic equilibrium reconstruction. Its efficiency and versatility position EFIT-mini as a promising tool for tokamak real-time monitoring and control, as well as for providing key inputs to other real-time inversion algorithms. △ Less

Submitted 25 March, 2025; originally announced March 2025.

Developing a Linear Fluid Plasma Model with Accurate Kinetic Bernstein Waves: A First Step

Authors: Huasheng Xie

Abstract: Kinetic models provide highly accurate descriptions of plasma waves but involve complex integrals that are computationally expensive to solve. To facilitate a fluid-like treatment of the system, we propose rational approximations for both the plasma dispersion function in the parallel integral and the Bessel function in the perpendicular integral, ensuring that the system remains rational with res… ▽ More Kinetic models provide highly accurate descriptions of plasma waves but involve complex integrals that are computationally expensive to solve. To facilitate a fluid-like treatment of the system, we propose rational approximations for both the plasma dispersion function in the parallel integral and the Bessel function in the perpendicular integral, ensuring that the system remains rational with respect to all three variables: wave frequency $ω$, parallel wavevector $k_\parallel$, and perpendicular wavevector $k_\perp$. By accurately approximating the Bessel function over a wide range of Larmor radius $ρ_{cs}$ values, from $k_\perpρ_{cs} \to 0$ to $k_\perpρ_{cs} \to \infty$, we present an initial attempt to incorporate kinetic Bernstein waves into a fluid model. As an application, we employ this model to analyze { electromagnetic plasma} wave propagation conditions (i.e., accessibility) by solving for the complex perpendicular wavevector $k_\perp$ using a matrix eigenvalue method with given input parameters. This work may contribute to studies of electron cyclotron resonance heating (ECRH) and ion cyclotron resonance frequency (ICRF) heating in magnetized confinement plasmas. △ Less

Submitted 12 August, 2025; v1 submitted 10 February, 2025; originally announced February 2025.

Comments: 7 pages, 4 figures

Journal ref: Phys. Plasmas 32, 082110 (2025)

Overview of EXL-50 Research Progress and Future Plan

Authors: Yuejiang Shi, Yumin Wang, Bing Liu, Xianming Song, Shaodong Song, Xinchen Jiang, Dong Guo, Di Luo, Xiang Gu, Tiantian Sun, Xianli Huang, Zhi Li, Lili Dong, Xueyun Wang, Gang Yin, Mingyuan Wang, Wenjun Liu, Hanyue Zhao, Huasheng Xie, Yong, Liu, Dongkai Qi, Bo Xing, Jiangbo Ding, Chao Wu , et al. (15 additional authors not shown)

Abstract: XuanLong-50 (EXL-50) is the first medium-size spherical torus (ST) in China, with the toroidal field at major radius at 50 cm around 0.5T. CS-free and non-inductive current drive via electron cyclotron resonance heating (ECRH) was the main physics research issue for EXL-50. Discharges with plasma currents of 50 kA - 180 kA were routinely obtained in EXL-50, with the current flattop sustained for u… ▽ More XuanLong-50 (EXL-50) is the first medium-size spherical torus (ST) in China, with the toroidal field at major radius at 50 cm around 0.5T. CS-free and non-inductive current drive via electron cyclotron resonance heating (ECRH) was the main physics research issue for EXL-50. Discharges with plasma currents of 50 kA - 180 kA were routinely obtained in EXL-50, with the current flattop sustained for up to or beyond 2 s. The current drive effectiveness on EXL-50 was as high as 1 A/W for low-density discharges using 28GHz ECRH alone for heating power less than 200 kW. The plasma current reached Ip>80 kA for high-density (5*10e18m-2) discharges with 150 kW 28GHz ECRH. Higher performance discharge (Ip of about 120 kA and core density of about 1*10e19m-3) was achieved with 150 kW 50GHz ECRH. The plasma current in EXL-50 was mainly carried by the energetic electrons.Multi-fluid equilibrium model has been successfully applied to reconstruct the magnetic flux surface and the measured plasma parameters of the EXL-50 equilibrium. The physics mechanisms for the solenoid-free ECRH current drive and the energetic electrons has also been investigated. Preliminary experimental results show that 100 kW of lower hybrid current drive (LHCD) waves can drive 20 kA of plasma current. Several boron injection systems were installed and tested in EXL-50, including B2H6 gas puffing, boron powder injection, boron pellet injection. The research plan of EXL-50U, which is the upgrade machine of EXL-50, is also presented. △ Less

Submitted 7 February, 2025; originally announced February 2025.

Report number: Vol.65 (2025) 092004

Journal ref: Nuclear Fusion 2025

A Denser Hydrogen Inferred from First-Principles Simulations Challenges Jupiter's Interior Models

Authors: Cesare Cozza, Kousuke Nakano, Saburo Howard, Hao Xie, Ravit Helled, Guglielmo Mazzola

Abstract: First-principle modeling of dense hydrogen is crucial in materials and planetary sciences. Despite its apparent simplicity, predicting the ionic and electronic structure of hydrogen is a formidable challenge, and it is connected with the insulator-to-metal transition, a century-old problem in condensed matter. Accurate simulations of liquid hydrogen are also essential for modeling gas giant planet… ▽ More First-principle modeling of dense hydrogen is crucial in materials and planetary sciences. Despite its apparent simplicity, predicting the ionic and electronic structure of hydrogen is a formidable challenge, and it is connected with the insulator-to-metal transition, a century-old problem in condensed matter. Accurate simulations of liquid hydrogen are also essential for modeling gas giant planets. Here we perform an exhaustive study of the equation of state of hydrogen using Density Functional Theory and quantum Monte Carlo simulations. We find that the pressure predicted by Density Functional Theory may vary qualitatively when using different functionals. The predictive power of first-principle simulations is restored by validating each functional against higher-level wavefunction theories, represented by computationally intensive variational and diffusion Monte Carlo calculations. Our simulations provide evidence that hydrogen is denser at planetary conditions, compared to currently used equations of state. For Jupiter, this implies a lower bulk metallicity (i.e., a smaller mass of heavy elements). Our results further amplify the inconsistency between Jupiter's atmospheric metallicity measured by the Galileo probe and the envelope metallicity inferred from interior models. △ Less

Submitted 28 July, 2025; v1 submitted 22 January, 2025; originally announced January 2025.

Comments: We add Huguniot calculation with SCAN+vv10, benchmarks with more functionals, and provide additional tests

Journal ref: Phys. Rev. Research 8, 013089 (2026)

Accurate and thermodynamically consistent hydrogen equation of state for planetary modeling with flow matching

Authors: Hao Xie, Saburo Howard, Guglielmo Mazzola

Abstract: Accurate determination of the equation of state of dense hydrogen is essential for understanding gas giants. Currently, there is still no consensus on methods for calculating its entropy, which play a fundamental role and can result in qualitatively different predictions for Jupiter's interior. Here, we investigate various aspects of entropy calculation for dense hydrogen based on ab initio molecu… ▽ More Accurate determination of the equation of state of dense hydrogen is essential for understanding gas giants. Currently, there is still no consensus on methods for calculating its entropy, which play a fundamental role and can result in qualitatively different predictions for Jupiter's interior. Here, we investigate various aspects of entropy calculation for dense hydrogen based on ab initio molecular dynamics simulations. Specifically, we employ the recently developed flow matching method to validate the accuracy of the traditional thermodynamic integration approach. We then clearly identify pitfalls in previous attempts and propose a reliable framework for constructing the hydrogen equation of state, which is accurate and thermodynamically consistent across a wide range of temperature and pressure conditions. This allows us to conclusively address the long-standing discrepancies in Jupiter's adiabat among earlier studies, demonstrating the potential of our approach for providing reliable equations of state of diverse materials. △ Less

Submitted 9 August, 2025; v1 submitted 17 January, 2025; originally announced January 2025.

Comments: 7+7 pages, 4+8 figures

Journal ref: Phys. Rev. Research 7, L032028 (2025)

Deep variational free energy prediction of dense hydrogen solid at 1200K

Authors: Xinyang Dong, Hao Xie, Yixiao Chen, Wenshuo Liang, Linfeng Zhang, Lei Wang, Han Wang

Abstract: We perform deep variational free energy calculations to investigate the dense hydrogen system at 1200 K and high pressures. In this computational framework, neural networks are used to model the free energy through the proton Boltzmann distribution and the electron wavefunction. By directly minimizing the free energy, our results reveal the emergence of a crystalline order associated with the cent… ▽ More We perform deep variational free energy calculations to investigate the dense hydrogen system at 1200 K and high pressures. In this computational framework, neural networks are used to model the free energy through the proton Boltzmann distribution and the electron wavefunction. By directly minimizing the free energy, our results reveal the emergence of a crystalline order associated with the center of mass of hydrogen molecules at approximately 180 GPa. This transition from atomic liquid to a molecular solid is marked by discontinuities in both the pressure and thermal entropy. Additionally, we discuss the broader implications and limitations of these findings in the context of recent studies of dense hydrogen under similar conditions. △ Less

Submitted 29 June, 2025; v1 submitted 16 January, 2025; originally announced January 2025.

Journal ref: Phys. Rev. B 111, 214118 (2025)

Efficient Framework for Solving Plasma Waves with Arbitrary Distributions

Authors: Huasheng Xie

Abstract: Plasma, which constitutes 99\% of the visible matter in the universe, is characterized by a wide range of waves and instabilities that play a pivotal role in space physics, astrophysics, laser-plasma interactions, fusion research, and laboratory experiments. The linear physics of these phenomena is described by kinetic dispersion relations (KDR). However, solving KDRs for arbitrary velocity distri… ▽ More Plasma, which constitutes 99\% of the visible matter in the universe, is characterized by a wide range of waves and instabilities that play a pivotal role in space physics, astrophysics, laser-plasma interactions, fusion research, and laboratory experiments. The linear physics of these phenomena is described by kinetic dispersion relations (KDR). However, solving KDRs for arbitrary velocity distributions remains a significant challenge, particularly for non-Maxwellian distributions frequently observed in various plasma environments. This work introduces a novel, efficient, and unified numerical framework to address this challenge. The proposed method rapidly and accurately yields all significant solutions of KDRs for nearly arbitrary velocity distributions, supporting both unstable and damped modes across all frequencies and wavevectors. The approach expands plasma species' velocity distribution functions using a series of carefully chosen orthogonal basis functions and employs a highly accurate rational approximation to transform the problem into an equivalent matrix eigenvalue problem, eliminating the need for initial guesses. The efficiency and versatility of this framework are demonstrated, enabling simplified studies of plasma waves with arbitrary distributions. This advancement paves the way for uncovering new physics in natural plasma environments, such as spacecraft observations in space plasmas, and applications like wave heating in fusion research. △ Less

Submitted 11 January, 2025; originally announced January 2025.

Comments: 6 pages, 5 figures, with 22 pages of supplementary material

Journal ref: Phys. Plasmas 32, 060702 (2025)

Spatiotemporal Gaussian Optimization for 4D Cone Beam CT Reconstruction from Sparse Projections

Authors: Yabo Fu, Hao Zhang, Weixing Cai, Huiqiao Xie, Licheng Kuo, Laura Cervino, Jean Moran, Xiang Li, Tianfang Li

Abstract: In image-guided radiotherapy (IGRT), four-dimensional cone-beam computed tomography (4D-CBCT) is critical for assessing tumor motion during a patients breathing cycle prior to beam delivery. However, generating 4D-CBCT images with sufficient quality requires significantly more projection images than a standard 3D-CBCT scan, leading to extended scanning times and increased imaging dose to the patie… ▽ More In image-guided radiotherapy (IGRT), four-dimensional cone-beam computed tomography (4D-CBCT) is critical for assessing tumor motion during a patients breathing cycle prior to beam delivery. However, generating 4D-CBCT images with sufficient quality requires significantly more projection images than a standard 3D-CBCT scan, leading to extended scanning times and increased imaging dose to the patient. To address these limitations, there is a strong demand for methods capable of reconstructing high-quality 4D-CBCT images from a 1-minute 3D-CBCT acquisition. The challenge lies in the sparse sampling of projections, which introduces severe streaking artifacts and compromises image quality. This paper introduces a novel framework leveraging spatiotemporal Gaussian representation for 4D-CBCT reconstruction from sparse projections, achieving a balance between streak artifact reduction, dynamic motion preservation, and fine detail restoration. Each Gaussian is characterized by its 3D position, covariance, rotation, and density. Two-dimensional X-ray projection images can be rendered from the Gaussian point cloud representation via X-ray rasterization. The properties of each Gaussian were optimized by minimizing the discrepancy between the measured projections and the rendered X-ray projections. A Gaussian deformation network is jointly optimized to deform these Gaussian properties to obtain a 4D Gaussian representation for dynamic CBCT scene modeling. The final 4D-CBCT images are reconstructed by voxelizing the 4D Gaussians, achieving a high-quality representation that preserves both motion dynamics and spatial detail. The code and reconstruction results can be found at https://github.com/fuyabo/4DGS_for_4DCBCT/tree/main △ Less

Submitted 7 January, 2025; originally announced January 2025.

Comments: 11 pages, 10 figures

Competing Hexagonal and Square Lattices on a Spherical Surface

Authors: Han Xie, Wenyu Liu, Zhenyue Lu, Jeff Z. Y. Chen, Yao Li

Abstract: The structural properties of packed soft-core particles provide a platform to understand the cross-pollinated physical concepts in solid-state- and soft-matter physics. Confined on spherical surface, the traditional differential geometry also dictates the overall defect properties in otherwise regular crystal lattices. Using molecular dynamics simulation of the Hertzian model as a tool, we report… ▽ More The structural properties of packed soft-core particles provide a platform to understand the cross-pollinated physical concepts in solid-state- and soft-matter physics. Confined on spherical surface, the traditional differential geometry also dictates the overall defect properties in otherwise regular crystal lattices. Using molecular dynamics simulation of the Hertzian model as a tool, we report here the emergence of new types of disclination patterns: domain and counter-domain defects, when hexagonal and square patterns coexist. A new angle is presented to understand the incompatibility between tiling lattice shapes and the available spherical areal shapes, which is common in nature -- from molecular systems in biology to backbone construction in architectures. △ Less

Submitted 2 January, 2025; originally announced January 2025.

Comments: Authors' version of the article submitted to Nano Letters and accepted

A Generalizable 3D Diffusion Framework for Low-Dose and Few-View Cardiac SPECT

Authors: Huidong Xie, Weijie Gan, Wei Ji, Xiongchao Chen, Alaa Alashi, Stephanie L. Thorn, Bo Zhou, Qiong Liu, Menghua Xia, Xueqi Guo, Yi-Hwa Liu, Hongyu An, Ulugbek S. Kamilov, Ge Wang, Albert J. Sinusas, Chi Liu

Abstract: Myocardial perfusion imaging using SPECT is widely utilized to diagnose coronary artery diseases, but image quality can be negatively affected in low-dose and few-view acquisition settings. Although various deep learning methods have been introduced to improve image quality from low-dose or few-view SPECT data, previous approaches often fail to generalize across different acquisition settings, lim… ▽ More Myocardial perfusion imaging using SPECT is widely utilized to diagnose coronary artery diseases, but image quality can be negatively affected in low-dose and few-view acquisition settings. Although various deep learning methods have been introduced to improve image quality from low-dose or few-view SPECT data, previous approaches often fail to generalize across different acquisition settings, limiting their applicability in reality. This work introduced DiffSPECT-3D, a diffusion framework for 3D cardiac SPECT imaging that effectively adapts to different acquisition settings without requiring further network re-training or fine-tuning. Using both image and projection data, a consistency strategy is proposed to ensure that diffusion sampling at each step aligns with the low-dose/few-view projection measurements, the image data, and the scanner geometry, thus enabling generalization to different low-dose/few-view settings. Incorporating anatomical spatial information from CT and total variation constraint, we proposed a 2.5D conditional strategy to allow the DiffSPECT-3D to observe 3D contextual information from the entire image volume, addressing the 3D memory issues in diffusion model. We extensively evaluated the proposed method on 1,325 clinical 99mTc tetrofosmin stress/rest studies from 795 patients. Each study was reconstructed into 5 different low-count and 5 different few-view levels for model evaluations, ranging from 1% to 50% and from 1 view to 9 view, respectively. Validated against cardiac catheterization results and diagnostic comments from nuclear cardiologists, the presented results show the potential to achieve low-dose and few-view SPECT imaging without compromising clinical performance. Additionally, DiffSPECT-3D could be directly applied to full-dose SPECT images to further improve image quality, especially in a low-dose stress-first cardiac SPECT imaging protocol. △ Less

Submitted 21 December, 2024; originally announced December 2024.

Comments: 13 pages, 6 figures, 2 tables. Paper under review. Oral presentation at IEEE MIC 2024

Introduction to Fusion Ignition Principles: Zeroth Order Factors of Fusion Energy Research

Authors: Huasheng Xie

Abstract: The physical goal of fusion energy research is to confine fusion fuel in a certain way so that the energy released from fusion exceeds the energy consumed to sustain the fusion process, thereby achieving economically viable energy production. Based on fundamental physics, this book focuses on the physics of the core region in fusion energy reactors, discussing the relevant limiting factors and par… ▽ More The physical goal of fusion energy research is to confine fusion fuel in a certain way so that the energy released from fusion exceeds the energy consumed to sustain the fusion process, thereby achieving economically viable energy production. Based on fundamental physics, this book focuses on the physics of the core region in fusion energy reactors, discussing the relevant limiting factors and parameter ranges. By examining the zeroth-order quantity system of the fundamental principles of fusion ignition, we review the current progress and challenges in fusion energy research. These challenges encompass various aspects such as physics, engineering, materials, and economics, providing better insights for the future development of fusion as an energy source. [This English version is primarily a translation generated using ChatGPT based on the original Chinese book, and can be cited as "Huasheng Xie, Introduction to Fusion Ignition Principles, USTC press, Hefei, 2023." For any inaccuracies or ambiguities, please refer to the original Chinese version for clarification.] △ Less

Submitted 23 October, 2024; originally announced October 2024.

Comments: 183 pages

Acceleration of positive muons by a radio-frequency cavity

Authors: S. Aritome, K. Futatsukawa, H. Hara, K. Hayasaka, Y. Ibaraki, T. Ichikawa, T. Iijima, H. Iinuma, Y. Ikedo, Y. Imai, K. Inami, K. Ishida, S. Kamal, S. Kamioka, N. Kawamura, M. Kimura, A. Koda, S. Koji, K. Kojima, A. Kondo, Y. Kondo, M. Kuzuba, R. Matsushita, T. Mibe, Y. Miyamoto , et al. (30 additional authors not shown)

Abstract: Acceleration of positive muons from thermal energy to $100~$keV has been demonstrated. Thermal muons were generated by resonant multi-photon ionization of muonium atoms emitted from a sheet of laser-ablated aerogel. The thermal muons were first electrostatically accelerated to $5.7~$keV, followed by further acceleration to 100 keV using a radio-frequency quadrupole. The transverse normalized emitt… ▽ More Acceleration of positive muons from thermal energy to $100~$keV has been demonstrated. Thermal muons were generated by resonant multi-photon ionization of muonium atoms emitted from a sheet of laser-ablated aerogel. The thermal muons were first electrostatically accelerated to $5.7~$keV, followed by further acceleration to 100 keV using a radio-frequency quadrupole. The transverse normalized emittance of the accelerated muons in the horizontal and vertical planes were $0.85 \pm 0.25 ~\rm{(stat.)}~^{+0.22}_{-0.13} ~\rm{(syst.)}~π~$mm$\cdot$mrad and $0.32\pm 0.03~\rm{(stat.)} ^{+0.05}_{-0.02} ~\rm{(syst.)}~π~$mm$\cdot$mrad, respectively. The measured emittance values demonstrated phase space reduction by a factor of $2.0\times 10^2$ (horizontal) and $4.1\times 10^2$ (vertical) allowing good acceleration efficiency. These results pave the way to realize the first-ever muon accelerator for a variety of applications in particle physics, material science, and other fields. △ Less

Submitted 17 June, 2025; v1 submitted 15 October, 2024; originally announced October 2024.

Comments: version accepted for publication in PRL

Journal ref: Phys. Rev. Lett. 134, 245001 (2025)