arXiv subjects

Xin Wu

Publications and source records attributed to Xin Wu.

At least 55 records · Page 3Linked to original sources

arXiv2025

The enhanced X-ray Timing and Polarimetry mission -- eXTP for launch in 2030

In this paper we present the current status of the enhanced X-ray Timing and Polarimetry mission, which has been fully approved for launch in 2030. eXTP is a space science mission designed to study fundamental physics under extreme conditions of matter density, gravity, and magnetism. The mission aims at determining the equation of state of matter at supra-nuclear density, measuring the effects of quantum electro-dynamics, and understanding the dynamics of matter in strong-field gravity. In addition to investigating fundamental physics, the eXTP mission is poised to become a leading observatory for time-domain and multi-messenger astronomy in the 2030's, as well as providing observations of unprecedented quality on a variety of galactic and extragalactic objects. After briefly introducing the history and a summary of the scientific objectives of the eXTP mission, this paper presents a comprehensive overview of: 1) the cutting-edge technology, technical specifications, and anticipated performance of the mission's scientific instruments; 2) the full mission profile, encompassing spacecraft design, operational capabilities, and ground segment infrastructure.

astro-ph.HE↗

arXiv2025

Gamma-Ray Burst Polarimetry with the POLAR and POLAR-2 missions

Gamma-Ray Bursts are among the most powerful and violent events in the Universe. Despite over half a century of observations of these transient sources, many open questions remain about their nature and the physical emission mechanisms at play. Polarization measurements of the GRB prompt gamma-ray emission have long been theorized to be able to answer most of these questions. Early polarization measurements did not allow to draw clear conclusions because of limited significance. With the aim of better characterizing the polarization of these prompt emissions, a compact Gamma-Ray polarimeter called POLAR has been sent to space as part of the Tiangong-2 Chinese space lab for 6 months of operations starting September 2016. The instrument detected 55 GRBs as well as several pulsars. Time-integrated polarization analysis of the 14 brightest detected GRBs has shown that the prompt emission is lowly polarized or fully unpolarized. However, time-resolved analysis depicted strong hints of an evolving polarization angle within single pulses, washing out the polarization degree in time-integrated analyses. Energy-resolved polarization analysis has shown no constraining results due to limited statistics. Hence, a more sensitive $γ$-ray polarimeter is required to perform detailed energy and time-resolved polarization analysis of the prompt gamma-ray emission of GRBs. Based on the success of the POLAR mission, a larger-scale instrument, approved for launch to the China Space Station (CSS) in 2027, is currently being developed by a Swiss, Chinese, Polish, and German collaboration. Thanks to its large sensitivity in the 20-800~keV range, POLAR-2 will produce polarization measurements of at least 50 GRBs per year with a precision equal to or higher than the best results published by POLAR, allowing for good quality time and energy resolved analysis.

astro-ph.HE↗

arXiv2025

Rescattering effects on spin-interference for $ρ^{0}$ photoproduction in heavy-ion collisions

Recent measurements by various experiments in ultra-peripheral collisions have observed spin-interference in $ρ^{0}$ photoproduction, marking a breakthrough in Fermi-scale quantum interference experiments. Building on this, STAR extended the measurement to hadronic heavy-ion collisions, where significant rescattering effects on $ρ^{0}$ mesons were expected. In this study, we investigate how these rescattering effects influence the measurement of spin-interference. By embedding $ρ^{0}$ mesons produced via photoproduction, modeled by the Vector Meson Dominance model, into the Ultrarelativistic Quantum Molecular Dynamics framework, we estimate the impact on the $\cos2ϕ$ and $\cos4ϕ$ modulations, where $ϕ$ is the angle between $ρ^{0}$ and one of the daughters' ($π^{\pm}$) transverse momentum. The results indicate a significant suppression of the $\cos2ϕ$ modulation, while the $\cos4ϕ$ modulation remains largely unaffected, which provides insight for understanding the difference due to rescattering effects between experimental measurements and theoretical predictions for $ρ^{0}$ photoproduction in heavy-ion collisions.

nucl-th↗

arXiv2025

Chaos of charged particles in quadrupole magnetic fields under Schwarzschild backgrounds

A four-vector potential of an external test electromagnetic field in a Schwarzschild background is described in terms of a combination of dipole and quadrupole magnetic fields. This combination is an interior solution of the source-free Maxwell equations. Such external test magnetic fields cause the dynamics of charged particles around the black hole to be nonintegrable, and are mainly responsible for chaotic dynamics of charged particles. In addition to the external magnetic fields, some circumstances should be required for the onset of chaos. The effect of the magnetic fields on chaos is shown clearly through an explicit symplectic integrator and a fast Lyapunov indicator. The inclusion of the quadrupole magnetic fields easily induces chaos, compared with that of the dipole magnetic fields. This result is because the Lorentz forces from the quadrupole magnetic fields are larger than those from the dipole magnetic fields. In addition, the Lorentz forces act as attractive forces, which are helpful to bring the occurrence of chaos in the nonintegrable case.

gr-qc↗

arXiv2025

Influence of the residual magnetic field on the azimuthal distribution of final-state particles in photon-nuclear processes

In relativistic heavy-ion collisions, charged particles are accelerated to nearly the speed of light, and their external electromagnetic fields can be effectively approximated as quasi-real photons. These photons interact with another nucleus via photon-nuclear interactions, producing vector mesons. These vector mesons possess extremely low transverse momentum (pT ~ 0.1 GeV/c), distinguishing them from particles produced via hadronic interactions. STAR and ALICE have observed J/psi, rho0 and other vector mesons with very low pT, which are well described by photoproduction models. This unique characteristic of having extremely low transverse momentum allows them to serve as a novel experimental probe. Recent STAR results show that the equivalent photons in photoproduction processes are fully linearly polarized, affecting the azimuthal distribution of final-state particles like rho0 -> pi+ pi-. Since the polarization links to the initial collision geometry, the rho0 azimuthal modulation can probe nuclear structure. However, the post-collision magnetic field may deflect these particles, distorting the azimuthal distribution and complicating structure measurements. We simulated the distribution of residual magnetic fields over time under different collision conditions using UrQMD for Au+Au collisions at sqrt(sNN)=200 GeV and calculated their effects on the azimuthal modulation ( ) of photoproduced rho0. Our results show that in peripheral collisions, the field significantly alters the for photoproduced rho0 with pT ~ 0.1 GeV/c. This provides key insights for future nuclear structure studies via photoproduction in peripheral collisions.

hep-ph↗

arXiv2025

Design and Performance of a Universal SiPM Readout System for X- and Gamma-Ray Missions

The advent of both multi-messenger and time-domain astrophysics over the last decade has seen a large interest in the development of small-scale, cheap, and robust gamma-ray detectors. This has been further encouraged by the availability of CubeSat platforms. Of particular interest are detectors capable of producing spectral and localization measurements of X and gamma-ray transients to allow for accurate follow-up measurements at different wavelengths. A vast number of the instruments developed for such purposes in the last years use a combination of scintillators and Silicon Photomultipliers (SiPMs) for photon detection. Here, we present the design, performance, and space qualification of a readout system capable of reading out 64 SiPM channels. This low-power and low-cost system was originally designed for the POLAR-2 mission, a large scale gamma-ray polarimeter. However, its flexible design makes it equally suitable for use on various CubeSat missions. The system was found to perform well when reading out both plastic and high Z scintillators using a total of 1.8~W. The space qualified design furthermore relies on commercial off-the-shelf components, thereby also removing most international export issues. In this paper, we will present the overall design, the performance of the electronics, its performance when reading out various scintillators and the successful space-qualification of this design.

astro-ph.IM↗

arXiv2025

Energy Reconstruction of Non-fiducial Electron-Positron Events in the DAMPE Experiment Using Convolutional Neural Networks

The Dark Matter Particle Explorer (DAMPE) is a space-based Cosmic-Ray (CR) observatory with the aim, among others, to study Cosmic-Ray Electrons (CREs) up to 10 TeV. Due to the low CRE rate at multi-TeV energies, we aim to increasing the acceptance by selecting events outside the fiducial volume. The complex topology of non-fiducial events requires the development of a novel energy reconstruction method. We propose the usage of Convolutional Neural Networks for a regression task to recover an accurate estimation of the initial energy.

astro-ph.IM↗

arXiv2025

V2I-Calib++: A Multi-terminal Spatial Calibration Approach in Urban Intersections for Collaborative Perception

Urban intersections, dense with pedestrian and vehicular traffic and compounded by GPS signal obstructions from high-rise buildings, are among the most challenging areas in urban traffic systems. Traditional single-vehicle intelligence systems often perform poorly in such environments due to a lack of global traffic flow information and the ability to respond to unexpected events. Vehicle-to-Everything (V2X) technology, through real-time communication between vehicles (V2V) and vehicles to infrastructure (V2I), offers a robust solution. However, practical applications still face numerous challenges. Calibration among heterogeneous vehicle and infrastructure endpoints in multi-end LiDAR systems is crucial for ensuring the accuracy and consistency of perception system data. Most existing multi-end calibration methods rely on initial calibration values provided by positioning systems, but the instability of GPS signals due to high buildings in urban canyons poses severe challenges to these methods. To address this issue, this paper proposes a novel multi-end LiDAR system calibration method that does not require positioning priors to determine initial external parameters and meets real-time requirements. Our method introduces an innovative multi-end perception object association technique, utilizing a new Overall Distance metric (oDist) to measure the spatial association between perception objects, and effectively combines global consistency search algorithms with optimal transport theory. By this means, we can extract co-observed targets from object association results for further external parameter computation and optimization. Extensive comparative and ablation experiments conducted on the simulated dataset V2X-Sim and the real dataset DAIR-V2X confirm the effectiveness and efficiency of our method. The code for this method can be accessed at: https://github.com/MassimoQu/v2i-calib.

cs.RO↗

arXiv2025

SU(3) flavor symmetry analysis of hyperon non-leptonic two body decays

This paper present a systematic study of hyperon non-leptonic two-body decays induced by light quark transitions, particularly the $s \rightarrow u\bar{u}d$ process, within the framework of SU(3) flavor symmetry. The effective weak Hamiltonian is decomposed into irreducible SU(3) representations, including the 27-plet and octet components, and applied to analyze decays of octet and decuplet baryons and charmed baryons. Both the irreducible representation amplitude (IRA) approach and the topological diagrammatic analysis (TDA) are employed to construct decay amplitudes and constrain the parameter space. SU(3) symmetry-breaking effects arising from the strange quark mass are incorporated systematically. A global fit to current experimental data allows us to extract form factors and predict branching ratios and asymmetry parameters for several decay channels, including $Λ^0 \rightarrow pπ^-$, $Σ^+ \rightarrow pπ^0$, and $Ω^- \rightarrow Ξ^0π^-$. Our results demonstrate the predictive power of SU(3) flavor symmetry while highlighting significant symmetry-breaking effects, especially in amplitudes related to the 27-plet. Notably, the $Σ^+ \rightarrow pπ^0$ decay channel exhibits a deviation exceeding $1σ$ from experimental measurements, suggesting the possible presence of new decay mechanisms or contributions beyond the Standard Model. This work provides a systematic framework for future tests of the Standard Model and the search for new physics in hyperon decays.

hep-ph↗

arXiv2025

VersaTune: An Efficient Data Composition Framework for Training Multi-Capability LLMs

As demonstrated by the proprietary Large Language Models (LLMs) such as GPT and Claude series, LLMs have the potential to achieve remarkable proficiency across a wide range of domains, including law, medicine, finance, science, code, etc., all within a single model. These capabilities are further augmented during the Supervised Fine-Tuning (SFT) phase. Despite their potential, existing work mainly focuses on domain-specific enhancements during fine-tuning, the challenge of which lies in catastrophic forgetting of knowledge across other domains. In this study, we introduce **VersaTune**, a novel data composition framework designed for enhancing LLMs' overall multi-domain capabilities during training. We begin with detecting the distribution of domain-specific knowledge within the base model, followed by the training data composition that aligns with the model's existing knowledge distribution. During the subsequent training process, domain weights are dynamically adjusted based on their learnable potential and forgetting degree. Experimental results indicate that VersaTune is effective in multi-domain fostering, with an improvement of 35.21\% in the overall multi-ability performances compared to uniform domain weights. Furthermore, we find that Qwen-2.5-32B + VersaTune even surpasses frontier models, including GPT-4o, Claude3.5-Sonnet and DeepSeek-V3 by 0.86\%, 4.76\% and 4.60\%. Additionally, in scenarios where flexible expansion of a specific domain is required, VersaTune reduces the performance degradation in other domains by 38.77\%, while preserving the training efficacy of the target domain.

cs.CL↗

arXiv2025

DialogueReason: Rule-Based RL Sparks Dialogue Reasoning in LLMs

We propose DialogueReason, a reasoning paradigm that uncovers the lost roles in monologue-style reasoning models, aiming to boost diversity and coherency of the reasoning process. Recent advances in RL-based large reasoning models have led to impressive long CoT capabilities and high performance on math and science benchmarks. However, these reasoning models rely mainly on monologue-style reasoning, which often limits reasoning diversity and coherency, frequently recycling fixed strategies or exhibiting unnecessary shifts in attention. Our work consists of an analysis of monologue reasoning patterns and the development of a dialogue-based reasoning approach. We first introduce the Compound-QA task, which concatenates multiple problems into a single prompt to assess both diversity and coherency of reasoning. Our analysis shows that Compound-QA exposes weaknesses in monologue reasoning, evidenced by both quantitative metrics and qualitative reasoning traces. Building on the analysis, we propose a dialogue-based reasoning, named DialogueReason, structured around agents, environment, and interactions. Using PPO with rule-based rewards, we train open-source LLMs (Qwen-QWQ and Qwen-Base) to adopt dialogue reasoning. We evaluate trained models on MATH, AIME, and GPQA datasets, showing that the dialogue reasoning model outperforms monologue models under more complex compound questions. Additionally, we discuss how dialogue-based reasoning helps enhance interpretability, facilitate more intuitive human interaction, and inspire advances in multi-agent system design.

cs.AI↗

arXiv2025

Probing the Collision Geometry via Two-Photon Processes in Heavy-Ion Collisions

The initial collision geometry, including the reaction plane, is crucial for interpreting collective phenomena in relativistic heavy-ion collisions, yet it remains experimentally inaccessible through conventional measurements. Recent studies propose utilizing photon-induced processes as a direct probe, leveraging the complete linear polarization of emitted photons whose orientation strongly correlates with the collision geometry. In this work, we employ a QED-based approach to systematically investigate dilepton production via two-photon processes in heavy-ion collisions at RHIC and LHC energies and detector acceptances. Our calculations reveal that dilepton emission exhibits significant sensitivity to the initial collision geometry through both the azimuthal angles of their emission (defined by the relative momentum vector of the two leptons) and the overall momentum orientation of the dilepton pairs. These findings highlight the potential of two-photon-generated dileptons as a novel, polarization-driven probe to quantify the initial collision geometry and reduce uncertainties in characterizing quark-gluon plasma properties.

hep-ph↗

arXiv2025

Interface phonon modes governing the ideal limit of thermal transport across diamond/cubic boron nitride interfaces

Understanding the ideal limit of interfacial thermal conductance (ITC) across semiconductor heterointerfaces is crucial for optimizing heat dissipation in practical applications. By employing a highly accurate and efficient machine-learned potential trained herein, we perform extensive non-equilibrium molecular dynamics simulations to investigate the ITC of diamond/cubic boron nitride ($c$BN) interfaces. The ideal diamond/$c$BN interface exhibits an unprecedented ITC of 11.0 $\pm$ 0.1 GW m$^{-2}$ K$^{-1}$, setting a new upper bound for heterostructure interfaces. This exceptional conductance originates from extended phonon modes due to acoustic matching and localized C-atom modes that propagate through B-C bonds. However, atomic diffusion across the ideal interface creates mixing layers that disrupt these characteristic phonon modes, substantially suppressing the thermal transport from its ideal limit. Our findings reveal how interface phonon modes govern thermal transport across diamond/$c$BN interfaces, providing insights for thermal management in semiconductor devices.

cond-mat.mtrl-sci↗

arXiv2025

Benchmarking semi-empirical quantum chemical methods on liquid water

Stimulated by the renewed interest and recent developments in semi-empirical quantum chemical (SQC) methods for noncovalent interactions, we examine the properties of liquid water at ambient conditions by means of molecular dynamics (MD) simulations, both with the conventional NDDO-type (neglect of diatomic differential overlap) methods, e.g. AM1 and PM6, and with DFTB-type (density-functional tight-binding) methods, e.g. DFTB2 and GFN-xTB. Besides the original parameter sets, some specifically reparametrized SQC methods (denoted as AM1-W, PM6-fm, and DFTB2-iBi) targeting various smaller water systems ranging from molecular clusters to bulk are considered as well. The quality of these different SQC methods for describing liquid water properties at ambient conditions are assessed by comparison to well-established experimental data and also to BLYP-D3 density functional theory-based ab initio MD simulations. Our analyses reveal that static and dynamics properties of bulk water are poorly described by all considered SQC methods with the original parameters, regardless of the underlying theoretical models, with most of the methods suffering from too weak hydrogen bonds and hence predicting a far too fluid water with highly distorted hydrogen bond kinetics. On the other hand, the reparametrized force-matchcd PM6-fm method is shown to be able to quantitatively reproduce the static and dynamic features of liquid water, and thus can be used as a computationally efficient alternative to electronic structure-based MD simulations for liquid water that requires extended length and time scales. DFTB2-iBi predicts a slightly overstructured water with reduced fluidity, whereas AM1-W gives an amorphous ice-like structure for water at ambient conditions.

physics.comp-ph↗

arXiv2025

Probing the ideal limit of interfacial thermal conductance in two-dimensional van der Waals heterostructures

Probing the ideal limit of interfacial thermal conductance (ITC) in two-dimensional (2D) heterointerfaces is of paramount importance for assessing heat dissipation in 2D-based nanoelectronics. Using graphene/hexagonal boron nitride (Gr/$h$-BN), a structurally isomorphous heterostructure with minimal mass contrast, as a prototype, we develop an accurate yet highly efficient machine-learned potential (MLP) model, which drives nonequilibrium molecular dynamics (NEMD) simulations on a realistically large system with over 300,000 atoms, enabling us to report the ideal limit range of ITC for 2D heterostructures at room temperature. We further unveil an intriguing stacking-sequence-dependent ITC hierarchy in the Gr/$h$-BN heterostructure, which can be connected to moiré patterns and is likely universal in van der Waals layered materials. The underlying atomic-level mechanisms can be succinctly summarized as energy-favorable stacking sequences facilitating out-of-plane phonon energy transmission. This work demonstrates that MLP-driven MD simulations can serve as a new paradigm for probing and understanding thermal transport mechanisms in 2D heterostructures and other layered materials.

physics.comp-ph↗

arXiv2025

Probing Gluon Shadowing in Heavy Nuclei through Bayesian Reweighting of J/$ψ$ Photoproduction in Ultra-Peripheral Collisions

The gluon distribution in nuclei plays a pivotal role in understanding quantum chromodynamics (QCD) under extreme nuclear environments, yet remains poorly constrained compared to quark distributions. Coherent \jpsi photoproduction in ultra-peripheral heavy-ion collisions ($γ+ A \rightarrow \mathrm{J}/ψ+ A$) provides a unique solution to this challenge, serving as a sensitive probe of nuclear gluon densities. In this study, we perform Bayesian reweighting on the EPPS21 and nCTEQ15 sets of nuclear parton distribution functions (nPDF) by incorporating coherent \jpsi photoproduction measurements from both RHIC and LHC. The Bayesian-reweighted gluon modification factors $\mathrm{R_g^{A}}(x, Q^2 = 2.4\ \mathrm{GeV}^2)$ reveal pronounced nuclear shadowing in the Pb nuclei, with $\mathrm{R_g^{\mathrm{Pb}}} \approx 0.60$ at $x = 10^{-4}$, while simultaneously achieving a great reduction of the uncertainties in the density of the gluon across the critical Bjorken-$x$ range $10^{-5} < x < 10^{-3}$ compared to initial predictions of the nPDF. This work establishes coherent \jpsi photoproduction as a precision tool for gluon nPDF extraction, overcoming traditional deep-inelastic scattering limitations through perturbative QCD-calibrated probes. The constrained nPDFs demonstrate improved consistency with the experimental data across collider energies, particularly in the shadowing-dominated regime.

hep-ph↗

arXiv2025

DULRTC-RME: A Deep Unrolled Low-rank Tensor Completion Network for Radio Map Estimation

Radio maps enrich radio propagation and spectrum occupancy information, which provides fundamental support for the operation and optimization of wireless communication systems. Traditional radio maps are mainly achieved by extensive manual channel measurements, which is time-consuming and inefficient. To reduce the complexity of channel measurements, radio map estimation (RME) through novel artificial intelligence techniques has emerged to attain higher resolution radio maps from sparse measurements or few observations. However, black box problems and strong dependency on training data make learning-based methods less explainable, while model-based methods offer strong theoretical grounding but perform inferior to the learning-based methods. In this paper, we develop a deep unrolled low-rank tensor completion network (DULRTC-RME) for radio map estimation, which integrates theoretical interpretability and learning ability by unrolling the tedious low-rank tensor completion optimization into a deep network. It is the first time that algorithm unrolling technology has been used in the RME field. Experimental results demonstrate that DULRTC-RME outperforms existing RME methods.

eess.SP↗

arXiv2025

DistrEE: Distributed Early Exit of Deep Neural Network Inference on Edge Devices

Distributed DNN inference is becoming increasingly important as the demand for intelligent services at the network edge grows. By leveraging the power of distributed computing, edge devices can perform complicated and resource-hungry inference tasks previously only possible on powerful servers, enabling new applications in areas such as autonomous vehicles, industrial automation, and smart homes. However, it is challenging to achieve accurate and efficient distributed edge inference due to the fluctuating nature of the actual resources of the devices and the processing difficulty of the input data. In this work, we propose DistrEE, a distributed DNN inference framework that can exit model inference early to meet specific quality of service requirements. In particular, the framework firstly integrates model early exit and distributed inference for multi-node collaborative inferencing scenarios. Furthermore, it designs an early exit policy to control when the model inference terminates. Extensive simulation results demonstrate that DistrEE can efficiently realize efficient collaborative inference, achieving an effective trade-off between inference latency and accuracy.

cs.DC↗