arXiv subjects

Rui Xue

Publications and source records attributed to Rui Xue.

At least 37 records · Page 2Linked to original sources

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↗

arXiv2025

Time-Dependent Leptohadronic Modeling of Markarian 421

Due to its proximity, Markarian 421 is one of the most extensively studied jetted active galactic nuclei. Its spectral energy distribution and light curve are widely studied, serving as primary means for understanding jet radiation mechanisms. Numerous intriguing observational results have been discovered, some of which, such as the hard X-ray excess, and the associated variability between X-ray and very-high-energy (VHE) emissions, challenge the commonly adopted one-zone leptonic model. In this work, by establishing a time-dependent leptohadronic model, we explore whether the hard X-ray excess and the associated variability between X-ray and VHE emissions could be interpreted by emission from hadronic interactions. Our modeling finds that for the hard X-ray excess found in 2013, both of the secondary emissions from photohadronic and hadronuclear interactions could be a possible explanation for the hard X-ray excess without introducing a super-Eddington jet power. The emission from the photohadronic interactions contributes only to the hard X-ray band, while the hadronuclear interactions also predict VHE emissions associated with the hard X-rays. While for the hard X-ray excess found in 2016, only the secondary emissions from photohadronic interactions provide an interpretation at the cost of introducing a super-Eddington jet power. For the associated variability between X-ray and VHE emissions in 2017, we find that hadronic interactions fail to provide a possible interpretation.

astro-ph.HE↗

arXiv2025

A characteristic optical variability time scale in jetted active galactic nuclei: a large gamma-ray emission sample

The variability mechanisms from jetted AGNs are still under debate. Here the damped random walk (DRW) model, implemented through Gaussian Processe (GPs), is used to fit the $ZTF$ long-term optical light curves of 1684 $γ$-ray emission jetted AGNs. This analysis yields one of the largest samples with characteristic optical variability timescales for jetted AGNs. A single DRW model from GPs can fit the optical light curve of most jetted AGNs well/potentially well, while there are still some jetted AGNs whose light curve can not be fitted well by a single DRW model. After the jet power, proxied by gamma-ray luminosity, is introduced as a new parameter, new relationships among intrinsic variability time scales, black hole mass and jet power are discovered for efficient accretion AGNs ($τ^{\rm in} \propto M_{\rm BH}^{0.29^{+0.06}_{-0.06}}P_{\rm jet}^{-0.3^{+0.03}_{-0.03}}$ with scatter of approximately 0.09~dex) and for inefficient accretion AGNs ($τ^{\rm in} \propto M_{\rm BH}^{0.06^{+0.07}_{-0.07}}P_{\rm jet}^{0.37^{+0.11}_{-0.11}}$ with scatter of approximately 0.14~dex), respectively. Our results support that the optical variability of jetted AGNs with efficient accretion may originate within the standard accretion disk at UV emitting radii similar to non-jetted AGNs, and is directly related to the acceleration of shock in the jet and then enhanced through the beaming effect in beamed AGNs. For the jetted AGNs with inefficient accretion, the intrinsic timescale is consistent with the escape timescale of electrons.

astro-ph.GA↗

arXiv2025

Diverse Magnetic Phase Diagram and Anomalous Hall Effect in Antiferromagetic LuMn$_6$Sn$_6$

The interactions between conduction electrons and magnetism can significantly enhance the Hall signal, a phenomenon known as the anomalous Hall effect (AHE). While the AHE is generally not expected in antiferromagnets, a large AHE is observed in certain antiferromagnets with noncollinear spin textures and nonvanishing Berry curvature. In this work, we present a rich temperature and magnetic phase diagram with eight distinct magnetic phases for the antiferromagnetic kagome compound LuMn$_6$Sn$_6$. The Hall effect analysis in LuMn$_6$Sn$_6$ reveals both intriguing physical phenomena and methodological challenges. In the coplanar canted antiferromagnetic phase, we observe an AHE, which likely originates from the intrinsic effects. At low temperatures, upon entering the ferromagnetic phase, the AHE sharply increases and exceeds the conventional limits expected from intrinsic mechanisms. We also demonstrate the limitations of standard experimental methods in extracting the topological contribution to the Hall effect data. We show the importance of considering magnetoresistance anisotropy when estimating the anomalous and topological Hall effects. These shortcomings in current approaches in partitioning the Hall response necessitate new tools to interpret transport results in complex magnetic materials such as LuMn$_6$Sn$_6$.

cond-mat.str-el↗

arXiv2025

H$^3$GNNs: Harmonizing Heterophily and Homophily in GNNs via Joint Structural Node Encoding and Self-Supervised Learning

Graph Neural Networks (GNNs) struggle to balance heterophily and homophily in representation learning, a challenge further amplified in self-supervised settings. We propose H$^3$GNNs, an end-to-end self-supervised learning framework that harmonizes both structural properties through two key innovations: (i) Joint Structural Node Encoding. We embed nodes into a unified space combining linear and non-linear feature projections with K-hop structural representations via a Weighted Graph Convolution Network(WGCN). A cross-attention mechanism enhances awareness and adaptability to heterophily and homophily. (ii) Self-Supervised Learning Using Teacher-Student Predictive Architectures with Node-Difficulty Driven Dynamic Masking Strategies. We use a teacher-student model, the student sees the masked input graph and predicts node features inferred by the teacher that sees the full input graph in the joint encoding space. To enhance learning difficulty, we introduce two novel node-predictive-difficulty-based masking strategies. Experiments on seven benchmarks (four heterophily datasets and three homophily datasets) confirm the effectiveness and efficiency of H$^3$GNNs across diverse graph types. Our H$^3$GNNs achieves overall state-of-the-art performance on the four heterophily datasets, while retaining on-par performance to previous state-of-the-art methods on the three homophily datasets.

cs.LG↗

arXiv2024

Revisiting the flaring activity in early 2015 of BL Lacertae object S5 0716+714

In this work, we analyzed multi-wavelength data of the BL Lac object S5 0716+714 to investigate its emission mechanisms during a flaring state observed in early 2015. We examined the temporal behavior and broadband spectral energy distributions (SEDs) during the flare. The size of the $γ$-ray emission region was estimated based on the variability timescale. To explore the multi-wavelength properties of S5 0716+714, we employed three one-zone models: the SSC model, the SSC plus EC model, and the SSC plus pp interactions model, to reproduce the SEDs. Our findings indicate that while the SSC model can describe the SEDs, it requires an extreme Doppler factor. In contrast, the SSC plus EC model successfully fits the SEDs under the assumption of weak external photon fields but requires a high Doppler factor. Additionally, the SSC plus interactions model also reproduces the SEDs, with $γ$-ray emission originating from $π^{0}$ decay. However, this model leads to a jet power that exceeds the Eddington luminosity, which remains plausible due to the flaring state or the presence of a highly collimated jet.

astro-ph.HE↗

arXiv2024

Detection of an Orphan X-ray Flare from a Blazar Candidate EP240709a with Einstein Probe

Blazars are often observed to flare across multiple wavelengths. Orphan flares from blazars have been only detected a few times, providing an opportunity to understand the structure of the jet in the accreting system. We report a remarkable orphan X-ray flare from a blazar candidate EP240709a, detected by Einstein Probe (EP) in July 2024. The multi-band spectral properties and variability support EP240709a as a high-energy peaked BL Lacertae-type object. The flux in 0.5-10 keV increases by at least 28 times to the value of low state in 2020, with non-detection of remarkable flaring in other bands during the same period. EP240709a exhibits the harder-when-brighter tendency in the X-ray band during the orphan flare, while its infrared-optical spectra are featureless. We employ one-zone and two-zone leptonic synchrotron self-Compton models to perform the spectral energy distribution fitting. Detecting this rare orphan flare shows the potential of EP in discovering peculiar activities from AGN in high-cadence X-ray sky surveys.

astro-ph.HE↗

arXiv2024

Efficient End-to-end Language Model Fine-tuning on Graphs

Learning from Text-Attributed Graphs (TAGs) has attracted significant attention due to its wide range of real-world applications. The rapid evolution of language models (LMs) has revolutionized the way we process textual data, which indicates a strong potential to replace shallow text embedding generally used in Graph Neural Networks (GNNs). However, we find that existing LM approaches that exploit text information in graphs suffer from inferior computation and data efficiency. In this study, we introduce LEADING, a novel and efficient approach for end-to-end fine-tuning of language models on TAGs. To enhance data efficiency, LEADING efficiently transfers rich knowledge from LMs to downstream graph learning tasks with limited labeled data by employing end-to-end training of LMs and GNNs in a semi-supervised learning setting. To address associated computation efficiency issues, it introduces two techniques: neighbor decoupling targeting LMs and implicit graph modeling targeting GNNs, respectively. Our proposed approach demonstrates superior performance, achieving state-of-the-art (SOTA) results on the ogbn-arxiv leaderboard, while maintaining computation cost and memory overhead comparable to graph-less fine-tuning of LMs. Through comprehensive experiments, we showcase its superior computation and data efficiency, presenting a promising solution for various LMs and graph learning tasks on TAGs.

cs.LG↗

arXiv2024

Haste Makes Waste: A Simple Approach for Scaling Graph Neural Networks

Graph neural networks (GNNs) have demonstrated remarkable success in graph representation learning, and various sampling approaches have been proposed to scale GNNs to applications with large-scale graphs. A class of promising GNN training algorithms take advantage of historical embeddings to reduce the computation and memory cost while maintaining the model expressiveness of GNNs. However, they incur significant computation bias due to the stale feature history. In this paper, we provide a comprehensive analysis of their staleness and inferior performance on large-scale problems. Motivated by our discoveries, we propose a simple yet highly effective training algorithm (REST) to effectively reduce feature staleness, which leads to significantly improved performance and convergence across varying batch sizes. The proposed algorithm seamlessly integrates with existing solutions, boasting easy implementation, while comprehensive experiments underscore its superior performance and efficiency on large-scale benchmarks. Specifically, our improvements to state-of-the-art historical embedding methods result in a 2.7% and 3.6% performance enhancement on the ogbn-papers100M and ogbn-products dataset respectively, accompanied by notably accelerated convergence.

cs.LG↗

arXiv2024

Testing Isomorphism of Graphs in Polynomial Time

Given a graph $G$, the graph $[G]$ obtained by adding, for each pair of vertices of $G$, a unique vertex adjacent to both vertices is called the binding graph of $G$. In this work, we show that the class of binding graphs is graph-isomorphism complete and that the stable partitions of binding graphs by the Weisfeiler-Lehman (WL) algorithm produce automorphism partitions. To test the isomorphism of two graphs $G$ and $H$, one computes the stable graph of the binding graph $[G\uplus H]$ for the disjoint union graph $G\uplus H$. The automorphism partition reveals the isomorphism of $G$ and $H$. Because the WL algorithm is a polynomial-time procedure, the claim can be made that the graph-isomorphism problem is in complexity class $\mathtt{P}$.

math.CO↗

arXiv2024

A Minimal Stochastic Variability Model of Blazars in Turbulent Cascade

In this paper, we propose a novel minimal physical model to elucidate the long-term stochastic variability of blazars. The model is built on the realistic background of magnetized plasma jets dissipating energy through a turbulent cascade process that transfers energy to small-scale structures with highly anisotropic radiation. The model demonstrates the ability to spontaneously generate variability features consistent with observations of blazars under uniformly random fluctuations in the underlying physical parameters. This indicates that the model possesses self-similarity across multiple time scales, providing a natural explanation for the universal power spectral density (PSD) structure observed in different types of blazars. Moreover, the model exhibits that when the cascade process produces a relatively flat blob energy distribution, the spectral index of the model-simulated PSD in the high-frequency regime will be steeper than that predicted by the Damped Random Walk (DRW) model, which is in agreement with recent observations of active galactic nucleus (AGN) variability, providing a plausible theoretical explanation. The model is also able to reproduce the observed fractional variability amplitude (FVA) characteristics of blazars, and suggests that the specific particle acceleration and radiative cooling processes within the blob may not be the key factor shaping the long-term stochastic variability. This minimal model provides a new physical perspective for understanding the long-term stochastic variability of blazars.

astro-ph.HE↗

arXiv2024

Hadronuclear interactions in AGN jets as the origin of the diffuse high-energy neutrino background

The origin of diffuse high-energy neutrinos from TeV to PeV energies detected by IceCube Observatory remains a mystery. In our previous work, we have shown that hadronuclear (p-p) interactions in AGN jets could be important and generate detectable very-high-energy emissions. Here, we further explore these interactions in the AGN jets based on their luminosity function. The diffuse neutrino flux and corresponding $γ$-ray flux have been calculated and compared with observational data. In our modeling, two beaming patterns are considered separately. To make sure that the corresponding $γ$-ray flux does not overshoot the diffuse $γ$-ray background, we find that if the neutrino production region in jet is opaque to $γ$ rays, p-p interactions in AGN jets with a small viewing angle (the blazar case) are able to interpret the PeV neutrino background. Similarly, AGN jets with a large viewing angle (the radio galaxy case) may interpret the TeV neutrino background. While, if the neutrino production region is transparent to $γ$ rays, only blazars have the potential to interpret the DNB around PeV band. Some caveats are also discussed.

astro-ph.HE↗

arXiv2024

Constraining the Physical Parameters of Blazars Using the Seed Factor Approach

The discovery that blazars dominate the extra-galactic γ-ray sky is a triumph in the Fermi era. However, the exact location of γ-ray emission region still remains in debate. Low-synchrotron-peaked blazars (LSPs) are estimated to produce high-energy radiation through the external Compton process, thus their emission regions are closely related to the external photon fields. We employed the seed factor approach proposed by Georganopoulos et al. It directly matches the observed seed factor of each LSP with the characteristic seed factors of external photon fields to locate the γ-ray emission region. A sample of 1138 LSPs with peak frequencies and peak luminosities was adopted to plot a histogram distribution of observed seed factors. We also collected some spectral energy distributions (SEDs) of historical flare states to investigate the variation of γ-ray emission region. Those SEDs were fitted by both quadratic and cubic functions using the Markov-chain Monte Carlo method. Furthermore, we derived some physical parameters of blazars and compared them with the constraint of internal γγ-absorption. We find that dusty torus dominates the soft photon fields of LSPs and most γ-ray emission regions of LSPs are located at 1-10 pc. The soft photon fields could also transition from dusty torus to broad line region and cosmic microwave background in different flare states. Our results suggest that the cubic function is better than the quadratic function to fit the SEDs.

astro-ph.HE↗

arXiv2024

Awakening of A Blazar at Redshift 2.7 Temporally Coincident with Arrival of Cospatial Neutrino Event IceCube-201221A

We report on multiwavelength studies of a blazar NVSS J171822+423948, which is identified as the low-energy counterpart of 4FGL J1718.5+4237, the unique $γ$-ray source known to be cospatial with the IceCube neutrino event IC-201221A. After a 12-year long quiescent period undetected by Fermi-LAT, $γ$-ray activities with a tenfold flux increase emerge soon (a few tens of days) after arrival of the neutrino. Associated optical flares in the ZTF $g$, $r$, and $i$ bands are observed together with elevated WISE infrared fluxes. Synchronized variations suggest that both the $γ$-ray emission and the neutrino event are connected to the blazar. Furthermore, the optical spectrum reveals emission lines at a redshift $z$ = 2.68 $\pm$ 0.01. Thus, it is the first candidate for a neutrino-emitting blazar at the redshift above 2. Discussions of theoretical constraints of neutrino production and comparisons with other candidates are presented.

astro-ph.HE↗

arXiv2024

Linear-Time Graph Neural Networks for Scalable Recommendations

In an era of information explosion, recommender systems are vital tools to deliver personalized recommendations for users. The key of recommender systems is to forecast users' future behaviors based on previous user-item interactions. Due to their strong expressive power of capturing high-order connectivities in user-item interaction data, recent years have witnessed a rising interest in leveraging Graph Neural Networks (GNNs) to boost the prediction performance of recommender systems. Nonetheless, classic Matrix Factorization (MF) and Deep Neural Network (DNN) approaches still play an important role in real-world large-scale recommender systems due to their scalability advantages. Despite the existence of GNN-acceleration solutions, it remains an open question whether GNN-based recommender systems can scale as efficiently as classic MF and DNN methods. In this paper, we propose a Linear-Time Graph Neural Network (LTGNN) to scale up GNN-based recommender systems to achieve comparable scalability as classic MF approaches while maintaining GNNs' powerful expressiveness for superior prediction accuracy. Extensive experiments and ablation studies are presented to validate the effectiveness and scalability of the proposed algorithm. Our implementation based on PyTorch is available.

cs.IR↗

arXiv2024

The physical properties of Fermi-4LAC low-synchrotron-peaked BL Lac objects

Previous studies on the fitting of spectral energy distributions (SEDs) often apply the external-Compton process to interpret the high-energy peak of low-synchrotron-peaked (LSP) BL Lac objects (LBLs), despite the lack of strong broad emission lines observed for LBLs. In this work, we collect quasi-simultaneous multi-wavelength data of 15 LBLs from the Fermi fourth LAT AGN catalog (4LAC). We propose an analytical method to assess the necessity of external photon fields in the framework of one-zone scenario. Following derived analytical results, we fit the SEDs of these LBLs with the conventional one-zone leptonic model and study their jet physical properties. Our main results can be summarized as follows. (1)We find that most LBLs cannot be fitted by the one-zone synchrotron self-Compton (SSC) model. This indicates that external photons play a crucial role in the high-energy emission of LBLs, therefore we suggest that LBLs are masquerading BL Lacs. (2) We suggest that the $γ$-ray emitting regions of LBLs are located outside the broad-line region and within the dusty torus. (3) By extending the analytical method to all types of LSPs in Fermi-4LAC (using historical data), we find that the high-energy peaks of some flat spectrum radio quasars and blazar candidates of unknown types can be attributed to the SSC emission, implying that the importance of external photons could be minor. We suggest that the variability timescale may help distinguish the origin of the high-energy peak.

astro-ph.HE↗