arXiv subjects

Rui Xue

Publications and source records attributed to Rui Xue.

At least 19 recordsLinked to original sources

arXiv2026

Rapid Variability and Broadband Spectral Modeling in the Flaring Activity of BL Lacertae

We report a multi-wavelength study of two flaring episodes of the blazar BL Lacertae during MJD 60500-60800 (9 July 2024 - 5 May 2025). The source reached a daily-averaged $\gamma$-ray flux of $(1.03 \pm 0.05) \times 10^{-5} \, \mathrm{ph \, cm^{-2} \, s^{-1}}$ ($E > 100$ MeV) on MJD 60588 (5 October 2024). Using orbit-binned data from the Large Area Telescope (LAT) onboard the \textit{Fermi Gamma-ray Space Telescope}, we identify a minimum flux halving timescale of $\tau = 1.33 \pm 0.29$ hr. This constrains the upper limit on the $\gamma$-ray emitting region size to $R \le 2.0 \times 10^{15}$ cm, as well as its distance from the central supermassive black hole to $R_\mathrm{H} \le 5.9 \times 10^{16}$ cm, assuming a Doppler factor of $\delta = 14.8$ derived from the spectral energy distribution (SED) modeling. We find tentative evidence for sub-minute $\gamma$-ray variability with a minimum doubling time of $0.7 \pm 0.2$ min ($p$-value = 0.03). This may originate from an extremely compact region with a size of $R \le 1.8 \times 10^{13}$ cm, suggesting that the emission arises from magnetohydrodynamic substructures, such as plasmoids within a magnetic reconnection zone. Spectral analysis reveals a significant ``softer-when-brighter'' trend ($r = 0.96, p = 4.5 \times 10^{-4}$) during the minute-scale flare peaks, indicating a complex interplay between particle acceleration and radiative cooling. The SED is reproduced using a one-zone leptonic model, in which synchrotron self-Compton (SSC) and external Compton (EC) scattering effectively account for the high-energy emissions. The reduced magnetic field strengths and hard electron injection spectral indices observed during the flaring states suggest enhanced particle acceleration efficiency, possibly associated with relativistic magnetic reconnection.

astro-ph.HE↗

arXiv2026

ReCoG: Reciprocal Co-Evolution for Multimodal Graph Learning

Multimodal graph learning requires jointly training over graph structure and heterogeneous node attributes, yet existing methods largely decouple these processes: prior multimodal graph neural networks (GNNs) focus on aligning modalities in a shared embedding space while operating on fixed or weakly adapted graph structures, and graph structure learning approaches infer topology from unimodal node representations without accounting for multimodal interactions. This separation fundamentally limits the ability of GNNs to capture semantically meaningful relationships in multimodal settings, where observed edges are often noisy, incomplete, or misaligned with underlying semantics. We propose ReCoG (Reciprocal Co-Evolution for Multimodal Graph Learning), a new learning paradigm that tightly couples graph structure learning and multimodal representation learning through end-to-end reciprocal interaction. Concretely, ReCoG integrates (i) a multimodal graph refiner that infers and corrects edges using cross-modal semantic evidence, and (ii) a coupled cross-modal message passing mechanism that performs joint intra- and inter-modality propagation over the refined graph. This unified design yields greater expressiveness than decoupled or two-stage formulations and allows dynamic interaction between topology and representation learning. Across diverse benchmarks for node classification and link prediction, ReCoG consistently outperforms strong multimodal graph structure learning baselines, including graph foundation models. Our results demonstrate that reciprocal co-evolution of structure and semantics is important for effective multimodal graph learning, challenging the prevailing separation between topology and representation learning.

cs.LG↗

arXiv2026

Jet Power, Bulk Lorentz Factor, Black Hole Spin, and Magnetic Field of Accretion Disk in Jetted Active Galactic Nuclei: A Large Gamma-Ray Emission Sample

We present a catalog of physical parameters for powerful jet-accretion disk-black hole systems in one of the largest samples of gamma-ray emitting jetted active galactic nuclei (AGNs), including jet kinetic and radiative powers, jet radiative efficiencies, bulk Lorentz factors, black hole spins, accretion-disk magnetic fields and Compton dominance. Comparing jet kinetic power estimators for blazars, values derived from spectral energy distribution (SED) fitting tend to exceed those estimated via cavity power and other scaling relations. For radiatively efficient AGNs, most sources are inferred to possess high spins; for radiatively inefficient AGNs, many potentially have high spins, though some may differ. This indicates that black hole spin does not effectively distinguish radiatively efficient from inefficient jetted AGNs. Our results suggest accretion-disk magnetic field strength as a key discriminator, proposing a tentative dividing value of $\approx 10^{3.9}$ Gauss between radiatively efficient and inefficient populations. Jet power and bulk Lorentz factor exhibit significant correlations with black hole mass in radiatively efficient AGNs, while weak-to-moderate correlations are observed in radiatively inefficient AGNs within narrow accretion-rate bins. Our analysis reveals that jet power correlates with both disk luminosity and magnetic field strength. Furthermore, correlations linking Eddington ratio and Compton dominance with jet properties are consistent with the jet-accretion connection. Finally, jet radiative power and bulk Lorentz factor show a potential dependence on black hole spin. These results are consistent with the scenario in which jets are powered and accelerated by energy extraction from rapidly spinning black holes via accretion-disk magnetic fields.

astro-ph.HE↗

arXiv2026

Circular polarization effects induced by photon-axion mixing in astrophysical environments

Axions and axion-like particles (ALPs) are compelling candidates for dark matter and new physics beyond the Standard Model. Photon-axion mixing in external magnetic fields modifies the photon energy spectrum and linear polarization state, and also induces circular polarization signals. Compared to spectral and linear polarization methods, circular polarization benefits from lower astrophysical background contamination, providing an independent probe for axion searches. In this work, we study the circular polarization induced by photon-axion mixing within the chiral basis framework. By analytically solving the evolution equations under the single-domain approximation, we derive an expression for the circular polarization degree $P_C$, applicable in the resonant, strong coupling, and weak coupling regimes. Within single-domain magnetic field models, we compare the energy-dependent circular polarization in four astrophysical environments (AGN jets, intracluster medium, intergalactic medium, and Galactic magnetic fields). We find that the X-ray to MeV band represents the most sensitive observational window. Using the blazar S4 0954+65 as a case study, phase accumulation in random magnetic domains causes the circular polarization degree to fluctuate with redshift and exhibit pronounced energy structures. Using the optical circular polarization upper limit $P_C < 0.184\%$ (measured in the z-SDSS band) from this source, we statistically constrain $g_{a\gamma\gamma} \lesssim 3\times 10^{-11}\,\mathrm{GeV}^{-1}$ (95\% confidence level) for $m_a \sim 10^{-16}$--$10^{-10}\ \mathrm{eV}$, with the strongest constraint reaching $g_{a\gamma\gamma}\sim6\times10^{-12}\,\mathrm{GeV}^{-1}$ near $m_a \sim 10^{-14}\,\mathrm{eV}$. These results establish circular polarization as a complementary axion probe.

astro-ph.HE↗

arXiv2026

Locating the Production Sites of High-Energy Neutrinos in Blazar Jets

The production sites of high-energy neutrinos in blazar jets remain poorly constrained. In this work, we investigate the physical conditions required for efficient neutrino production by using radio-constrained jet properties to evaluate the radial evolution of the external-to-magnetic energy density ratio (Compton dominance $Y$). We identify $Y \gg 1$ as the key physical condition for efficient neutrino production, as it simultaneously enhances photohadronic interactions and suppresses synchrotron radiation from secondary pairs, thereby avoiding an excess of hard X-ray emission. We find that such large values of $Y$ are most readily achieved near or within the broad-line region. This large-$Y$ condition is generally incompatible with reproducing the observed broadband spectral energy distribution within a single emission region, naturally indicating that the neutrino-emitting region is physically distinct from the dominant electromagnetic emission zone. We further show that such a scenario can be realized either if the jet completes its acceleration within sub-parsec scales or if the bulk Lorentz factor is intrinsically large, both of which appear uncommon based on current observations. These results offer a physically motivated framework for identifying neutrino production sites, provide a natural explanation for the rarity of blazar--neutrino associations, and underscore the importance of constraining jet property at sub-parsec scales in the search for neutrino-emitting blazars.

astro-ph.HE↗

arXiv2026

On the maximum neutrino flux of blazars in the one-zone leptohadronic model

The origin of extragalactic high-energy neutrinos remains a major mystery in astrophysics, with blazars as leading candidate sources. The widely adopted one-zone leptohadronic jet model, however, faces severe challenges from stringent X-ray observational constraints. In this work, we present an analytical approach that derives the maximum neutrino flux as a function of the observed X-ray flux and the corresponding physical parameters attainable within the one-zone leptohadronic framework. Applying this approach to a sample of neutrino candidate blazars, we further perform numerical modeling and find agreement between analytical and numerical results. Both approaches consistently show that the model-predicted neutrino fluxes do not significantly exceed those obtained in previous one-zone studies and remain below the flux levels inferred from IceCube observations, suggesting that the one-zone scenario alone is unlikely to fully account for high-energy neutrino-blazar associations. This highlights the importance of considering multi-zone models or alternative production sites (e.g., jet base, hot corona) to better explain high-energy neutrino origins in blazars.

astro-ph.HE↗

arXiv2026

Quasi-Simultaneous Broadband Spectral Energy Distributions of a Sample of Fermi Blazars -- I. Correlation Results

Blazars' non-thermal emission shows rapid variability across all wavelengths, so spectral energy distributions (SEDs) built from quasi-simultaneous data are crucial for revealing the jets physical properties. In this work, we construct quasi-simultaneous broadband SEDs for 93 Fermi blazars (56 FSRQs, 35 BL Lacs, and 2 blazar candidates of uncertain type), fit both peaks with cubic functions to allow for potential asymmetries, and examine correlations among key parameters. Our main results are summarized as follows: (1) We find that synchrotron peak frequency and curvature are only weakly related, suggesting that charged particles are accelerated by mixed acceleration mechanism. (2) The blazar sequence is confirmed in the observer's frame through negative correlations of both the bolometic luminosity $\log L_{\rm bol}$ and the Compton dominance $\log Y$ with the synchrotron peak frequency $\log \nu_{\rm syn}^{\rm peak}$. After correcting for Doppler boosting, a weak positive correlation emerges between $\log L_{\rm bol}$ and $\log \nu_{\rm syn}^{\rm peak}$. FSRQs and BL Lacs exhibit distinct correlation patterns within the blazar sequence, indicating differences in cooling mechanisms. (3) Using variability time lags between 0.1-1 GeV and 1-300 GeV light curves, we estimate lower limits of Doppler factors for 4 sources, providing a jet-speed diagnostic anchored directly to the $\gamma$-ray emission zone.

astro-ph.HE↗

arXiv2026

Imprints of gravitational waves from magnetar spindown in GRB X-ray afterglows

Given that newborn magnetars are considered potential central engines of gamma-ray bursts (GRBs), there is strong motivation to identify gravitational wave (GW) signatures within GRB samples. If the X-ray afterglow of a GRB is powered by a magnetar, and the initial spindown of the magnetar is dominated by the GW radiation induced by $r$-mode instability or magnetic-field-induced deformation, the decay of the X-ray flux would record the information of the GW radiation. We find that GRB 130603B potentially represents a rare and precious case where the spindown of the central magnetar is dominated in-turn by $r$-mode and magnetic distortion-induced GW radiation. By fitting the X-ray light curve of GRB 130603B in this model, we obtain the initial spin period of magnetar $\sim 5.3\times 10^{-4}$ s, the effective dipole magnetic field strength $\sim 5.2\times 10^{14}$ G, the ellipticity of the magnetar $\sim 1.3\times 10^{-4}$, and the amplitude of $r$-mode oscillation $\sim3.3\times 10^{-2}$. It may serve as a reliable approach for investigating neutron star physics by comparing the parameters estimated using the method presented in this manuscript with those obtained from future GW observations.

astro-ph.HE↗

arXiv2026

Anisotropic Gyromagnetic Ratio and Orthogonal Einstein-de Haas Effect

We theoretically demonstrate an orthogonal Einstein-de Haas effect, where the rotation of ferromagnetic materials is caused by the change of magnetization in the direction orthogonal to the rotation axis. This amounts to an anisotropic gyromagnetic ratio. To reveal its microscopic origin, we treat the spin-orbit coupling as a perturbation, integrate out the electronic degree of freedom, and show that in collinear ferromagnets the phonon angular momentum admits a dipolar structure in the spin-order space due to the constraint of the spin group symmetry. The spin-flipping and spin-conserving parts of the spin-orbit coupling contribute differently to such a dipolar structure. All these features are exemplified in a lattice electron-phonon model with ferromagnetic order and $C_{1h}$ point group symmetry. Our work lays the ground for revealing the connection between phonon angular momentum and general spin-order configurations.

cond-mat.mes-hall↗

arXiv2025

Training Report of TeleChat3-MoE

TeleChat3-MoE is the latest series of TeleChat large language models, featuring a Mixture-of-Experts (MoE) architecture with parameter counts ranging from 105 billion to over one trillion,trained end-to-end on Ascend NPU cluster. This technical report mainly presents the underlying training infrastructure that enables reliable and efficient scaling to frontier model sizes. We detail systematic methodologies for operator-level and end-to-end numerical accuracy verification, ensuring consistency across hardware platforms and distributed parallelism strategies. Furthermore, we introduce a suite of performance optimizations, including interleaved pipeline scheduling, attention-aware data scheduling for long-sequence training,hierarchical and overlapped communication for expert parallelism, and DVM-based operator fusion. A systematic parallelization framework, leveraging analytical estimation and integer linear programming, is also proposed to optimize multi-dimensional parallelism configurations. Additionally, we present methodological approaches to cluster-level optimizations, addressing host- and device-bound bottlenecks during large-scale training tasks. These infrastructure advancements yield significant throughput improvements and near-linear scaling on clusters comprising thousands of devices, providing a robust foundation for large-scale language model development on hardware ecosystems.

cs.CL↗

arXiv2025

E2E-GRec: An End-to-End Joint Training Framework for Graph Neural Networks and Recommender Systems

Graph Neural Networks (GNNs) have emerged as powerful tools for modeling graph-structured data and have been widely used in recommender systems, such as for capturing complex user-item and item-item relations. However, most industrial deployments adopt a two-stage pipeline: GNNs are first pre-trained offline to generate node embeddings, which are then used as static features for downstream recommender systems. This decoupled paradigm leads to two key limitations: (1) high computational overhead, since large-scale GNN inference must be repeatedly executed to refresh embeddings; and (2) lack of joint optimization, as the gradient from the recommender system cannot directly influence the GNN learning process, causing the GNN to be suboptimally informative for the recommendation task. In this paper, we propose E2E-GRec, a novel end-to-end training framework that unifies GNN training with the recommender system. Our framework is characterized by three key components: (i) efficient subgraph sampling from a large-scale cross-domain heterogeneous graph to ensure training scalability and efficiency; (ii) a Graph Feature Auto-Encoder (GFAE) serving as an auxiliary self-supervised task to guide the GNN to learn structurally meaningful embeddings; and (iii) a two-level feature fusion mechanism combined with Gradnorm-based dynamic loss balancing, which stabilizes graph-aware multi-task end-to-end training. Extensive offline evaluations, online A/B tests (e.g., a +0.133% relative improvement in stay duration, a 0.3171% reduction in the average number of videos a user skips) on large-scale production data, together with theoretical analysis, demonstrate that E2E-GRec consistently surpasses traditional approaches, yielding significant gains across multiple recommendation metrics.

cs.LG↗

arXiv2025

CroTad: A Contrastive Reinforcement Learning Framework for Online Trajectory Anomaly Detection

Detecting trajectory anomalies is a vital task in modern Intelligent Transportation Systems (ITS), enabling the identification of unsafe, inefficient, or irregular travel behaviours. While deep learning has emerged as the dominant approach, several key challenges remain unresolved. First, sub-trajectory anomaly detection, capable of pinpointing the precise segments where anomalies occur, remains underexplored compared to whole-trajectory analysis. Second, many existing methods depend on carefully tuned thresholds, limiting their adaptability in real-world applications. Moreover, the irregular sampling of trajectory data and the presence of noise in training sets further degrade model performance, making it difficult to learn reliable representations of normal routes. To address these challenges, we propose a contrastive reinforcement learning framework for online trajectory anomaly detection, CroTad. Our method is threshold-free and robust to noisy, irregularly sampled data. By incorporating contrastive learning, CroTad learns to extract diverse normal travel patterns for different itineraries and effectively distinguish anomalous behaviours at both sub-trajectory and point levels. The detection module leverages deep reinforcement learning to perform online, real-time anomaly scoring, enabling timely and fine-grained identification of abnormal segments. Extensive experiments on two real-world datasets demonstrate the effectiveness and robustness of our framework across various evaluation scenarios.

cs.LG↗

arXiv2025

VISAGNN: Versatile Staleness-Aware Efficient Training on Large-Scale Graphs

Graph Neural Networks (GNNs) have shown exceptional success in graph representation learning and a wide range of real-world applications. However, scaling deeper GNNs poses challenges due to the neighbor explosion problem when training on large-scale graphs. To mitigate this, a promising class of GNN training algorithms utilizes historical embeddings to reduce computation and memory costs while preserving the expressiveness of the model. These methods leverage historical embeddings for out-of-batch nodes, effectively approximating full-batch training without losing any neighbor information-a limitation found in traditional sampling methods. However, the staleness of these historical embeddings often introduces significant bias, acting as a bottleneck that can adversely affect model performance. In this paper, we propose a novel VersatIle Staleness-Aware GNN, named VISAGNN, which dynamically and adaptively incorporates staleness criteria into the large-scale GNN training process. By embedding staleness into the message passing mechanism, loss function, and historical embeddings during training, our approach enables the model to adaptively mitigate the negative effects of stale embeddings, thereby reducing estimation errors and enhancing downstream accuracy. Comprehensive experiments demonstrate the effectiveness of our method in overcoming the staleness issue of existing historical embedding techniques, showcasing its superior performance and efficiency on large-scale benchmarks, along with significantly faster convergence.

cs.LG↗

arXiv2025

The averaged broadband spectral energy distribution study of Fermi bright BL Lac objects

The physics-determined broadband spectral energy distributions (SEDs) of blazars have been widely used to study the property during their flaring/outburst states, while the non-flaring state takes up most of their lifetime and the general property of blazars has been barely discussed. In this work, for the first time, we used the archival data and employed the physics-determined SED processing method to form approximately average-state SEDs for 513 \textit{Fermi} bright BL Lacs. In general, we found that the magnetic field ($B$) is weaker than those obtained for flaring/outburst state by nearly one order of magnitude, and the dissipation region size ($R$) is larger than those obtained for flaring/outburst state, suggesting that the dissipation region could be more extend and less magnetized. A correlation between the synchrotron-self Compton (SSC) peak frequency ($\log \nu_{\rm ssc}$) against the synchrotron peak frequency ($\log \nu_{\rm sy}$) suggest that the inverse Compton scattering of HBLs suffer a significant Klein-Nishina (KN) suppression, we quantified the condition of KN suppression by determining the critical synchrotron peak frequency ($\nu_{\rm sy}^{\rm c}$) and found 359 out of 513 sources in our sample suffer KN suppression. Furthermore, our analysis of the relationship between synchrotron curvature ($1/b_{\rm sy}$) and $\log \nu_{\rm sy}$ indicates that the energy-dependent probability acceleration (EDPA) mechanism may dominate the particle acceleration in BL Lac jets.

astro-ph.HE↗

arXiv2025

Two-sided-loop jet originates from the filament internal reconnection

Magnetic reconnection driving two-sided-loop jet is typically associated with interactions between an emerging bipole and the overlying horizontal magnetic field, or between filaments from separate magnetic systems. Leveraging high temporal and spatial resolution observations from ground-based and space-borne instruments, we have identified a two-sided-loop jet originating from magnetic reconnection between threads within a single filament. Our observations show that as two initially crossing filamentary threads within the filament converge, reconnection takes place at their intersection. In the Doppler images, distinct redshift and blueshift signals are observed at the locations where the filament threads intersected. This process generates a two-sided-loop jet with outflow speeds of \speed{22.2} and \speed{62.5}. Following reconnection, the original crossing threads transform into two parallel threads that subsequently separate at speeds of \speed{2.8} and \speed{8.3}. This observation offers a new perspective on the mechanisms responsible for jet formation.

astro-ph.SR↗

arXiv2025

On the origin of a possible hard VHE spectrum from M87 discovered by LHAASO

Recent LHAASO observations hint at potential spectral hardening around 20 TeV in M87's very high energy (VHE) emission, suggesting a possible new radiation component. In this work, we construct averaged multiwavelength SEDs by combining data from Chandra and Swift-UVOT/XRT covering the same period as the LHAASO detection to investigate the origin of this feature. We test several radiation mechanisms, including the pp interaction, proton synchrotron emission, photomeson process and two-zone leptonic model. We find that only the pion decay gamma rays in pp interactions can interpret this feature in the framework of the one-zone model. With analytical analysis, we prove that proton synchrotron emission cannot generate a hard spectrum above 0.17~TeV. For photomeson model, it requires an emission zone compressed near the Schwarzschild radius of the central supermassive black hole, incompatible with broadband optical-GeV spectral constraints. In addition, the two-zone leptonic model also emerges as a viable alternative.

astro-ph.HE↗

arXiv2025

Technical Report of TeleChat2, TeleChat2.5 and T1

We introduce the latest series of TeleChat models: \textbf{TeleChat2}, \textbf{TeleChat2.5}, and \textbf{T1}, offering a significant upgrade over their predecessor, TeleChat. Despite minimal changes to the model architecture, the new series achieves substantial performance gains through enhanced training strategies in both pre-training and post-training stages. The series begins with \textbf{TeleChat2}, which undergoes pretraining on 10 trillion high-quality and diverse tokens. This is followed by Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) to further enhance its capabilities. \textbf{TeleChat2.5} and \textbf{T1} expand the pipeline by incorporating a continual pretraining phase with domain-specific datasets, combined with reinforcement learning (RL) to improve performance in code generation and mathematical reasoning tasks. The \textbf{T1} variant is designed for complex reasoning, supporting long Chain-of-Thought (CoT) reasoning and demonstrating substantial improvements in mathematics and coding. In contrast, \textbf{TeleChat2.5} prioritizes speed, delivering rapid inference. Both flagship models of \textbf{T1} and \textbf{TeleChat2.5} are dense Transformer-based architectures with 115B parameters, showcasing significant advancements in reasoning and general task performance compared to the original TeleChat. Notably, \textbf{T1-115B} outperform proprietary models such as OpenAI's o1-mini and GPT-4o. We publicly release \textbf{TeleChat2}, \textbf{TeleChat2.5} and \textbf{T1}, including post-trained versions with 35B and 115B parameters, to empower developers and researchers with state-of-the-art language models tailored for diverse applications.

cs.CL↗

arXiv2025

Generative artificial intelligence and hybrid models to accelerate LES in reactive flows: Application to hydrogen/methane combustion

With increasing emphasis on carbon neutrality, accurate and efficient combustion prediction has become essential for the design and optimization of new generation combustion systems. This study established a computational framework by combining large eddy simulation (LES) with a generative machine learning approach which integrates modal decomposition and neural network, enabling fast prediction of hydrogen-methane combustion. A canonical jet-in-hot-coflow burner was selected as the benchmark configuration. LES was performed using eddy dissipation concept model in conjunction with a 17-species and 58-step skeletal mechanism. Reasonable agreement between LES results and experimental data was obtained for temperature and species mass fraction, confirming the accuracy of the present LES results. Flow characteristics and flame structures were analyzed, providing a reference for choosing parameters in prediction. Proper orthogonal decomposition (POD) was used to extract dominant flow features, and a hybrid autoregressive model, which combines modal decomposition with a deep learning (POD-DL) was constructed to forecast the temporal evolution of the combustion field. Comparison between the predicted results and LES data, including instantaneous contours, radial distributions, histogram and relative root mean square error, demonstrated a reasonable agreement. The main complexity lies in capturing the chaotic and fine-scale structures inherent to turbulent combustion. To the authors' knowledge, this is the first application of such a hybrid generative model to reactive flow prediction, representing an important step toward using data-driven surrogates to accelerate CFD simulations in combustion research. The proposed approach achieves speed-up ratios of 121 and 845 relative to LES for two tested cases. The implementation will be integrated into the upcoming release of the ModelFLOWs-app.

physics.flu-dyn↗