arXiv subjects

Dongyue Li

Publications and source records attributed to Dongyue Li.

At least 19 recordsLinked to original sources

arXiv2026

Extreme Multiwavelength Variability in the Recurrent Intermediate-type Changing-look Seyfert Galaxy SDSS J234728.73+242745.7

We report the discovery of the Seyfert galaxy J234728.73+242745.7 as a recurrent intermediate-type CLAGN with extreme optical/UV variability. Optical spectra spanning nearly three decades reveal Type 1.0$\rightarrow$1.5$\rightarrow$1.2 transitions, accompanied by the disappearance and reappearance of broad He II $\lambda4686$ and a broad-line Balmer decrement evolving from 4.5 to 7.2 and then 2.4. Monitoring reveals coordinated variability from X-rays to the MIR. SED decomposition yields a host-corrected g-band high-to-low-state flux ratio of $5.4\pm0.4$, exceeding the Swift/XRT ratio of $2.7\pm0.8$ across the changing-look transition. This discrepancy may arise from reduced dust extinction, as correcting for the low-state continuum reddening ($A_V^{\rm cont}=0.66\pm0.23$ mag) brings the optical/UV ratios close to the X-ray value, although the correction is model-dependent and intrinsically weaker X-ray variability may also contribute. However, X-ray spectra show no compelling evidence for intrinsic gas absorption or its variability ($N_{\rm H}^{\rm intr}\lesssim5\times10^{20}$ cm$^{-2}$). In contrast, EP/WXT monitoring during the recent high state reveals order-of-magnitude X-ray fluctuations on monthly-to-sub-year timescales, exceeding contemporaneous optical variations. Meanwhile, the radio flux density remains stable at $\sim2$ mJy across all ASKAP observations since mid-2020. Together with optical/UV and MIR variability and the broad He II transition, these results favor intrinsic accretion-state changes as the primary driver, while allowing a secondary contribution from variable obscuration. The source displays a loop-like MIR color--magnitude evolution, likely driven by the delayed response of an extended torus. This recurrent CLAGN offers a laboratory for probing the dynamic response of the broad-line region and circumnuclear dust to accretion-state transitions.

astro-ph.HE↗

arXiv2026

RPSD: Retrieval-Privileged On-Policy Self-Distillation with Optimal Transport

Sequential recommendation predicts a user's next interaction from historical behavior. Recent LLM-based methods recast this task as generative recommendation and increasingly adopt reinforcement learning such as GRPO for post-training. However, these methods suffer from two limitations, namely credit-blind optimization, where a sequence-level reward is shared across all generated tokens, and collaborative-blind supervision, where each training signal is derived from a single user and ignores behavioral patterns shared across similar users. To address both issues, we propose Retrieval-Privileged on-policy Self-Distillation (RPSD). For credit-blind optimization, we introduce on-policy self-distillation (OPSD), in which a teacher with privileged knowledge re-evaluates the student's on-policy response, and the token-level confidence difference between the student and teacher reshapes the shared sequence-level advantage into token-specific credit. However, the privileged knowledge in vanilla OPSD is still derived from a single user, leaving collaborative-blind supervision unresolved. We therefore ground the teacher in collaborative behavioral evidence from similar users. Specifically, we use temporally aware optimal transport (OT) to compare interaction trajectories across users by jointly considering item similarity and temporal structure, and further apply diversity-aware selection to retain complementary trajectories as privileged evidence. In this way, on-policy self-distillation provides fine-grained token-level credit, while OT enriches the teacher with structured cross-user evidence. To the best of our knowledge, this is the first work to bring on-policy self-distillation to sequential recommendation and to use cross-user behavior as its privileged signal. Extensive experiments across multiple real-world datasets demonstrate that RPSD achieves state-of-the-art performance.

cs.AI↗

arXiv2026

EP241021a as An Off-nuclear Month-duration Transient in a Dwarf Starburst Galaxy

EP241021a is a peculiar fast X-ray transient exhibiting a long X-ray plateau and an optical rebrightening, both challenging the traditional jet afterglow model. Whether its nature is a stellar explosion or a tidal disruption event (TDE) remains debated. Here we present late-time HST/WFC3 and Gemini-N/GMOS observations of its host galaxy at $z=0.748$. After $\sim300$ days post-trigger, the transient emission had faded below detection, revealing a dwarf disk galaxy with an effective radius $R_e=1.7$ kpc, a stellar mass $M_\star\sim3\times10^8M_\odot$, and a high star formation rate of $\sim5\ M_\odot\ {\rm yr}^{-1}$. Combined with previous very long baseline interferometry measurements, the transient can be located in the galactic disk with a projected physical offset of $0.95\pm0.13$ kpc from the galaxy center. Host-subtracted optical light curves show that the rebrightening ceased at $\sim47$ days, followed by a sudden drop with a power-law index steeper than $-3.3$, coincident with a similar drop in the X-ray light curve. This synchronous drop in both bands is consistent with jet shut-off, and the required month-long jet activity suggests a persistently accreting compact object fed by a TDE. Among the TDE scenarios, the off-nuclear location and the starburst host galaxy slightly favor the post-supernova micro-TDE scenario in a binary system, in which a natal kick drives the newborn compact object into a tidal disruption of its companion. Nevertheless, an off-nuclear intermediate-mass black hole TDE or a stellar-mass black hole TDE induced by dynamical scattering in a dense stellar cluster cannot yet be excluded.

astro-ph.HE↗

arXiv2026

EP J062132.0+623837: A Multi-wavelength Study of a Changing-look Active Galactic Nucleus Discovered by Einstein Probe

EP J062132.0+623837 is a new X-ray source identified in 14-day binned survey data from the Wide-field X-ray Telescope aboard the Einstein Probe (EP). Subsequent X-ray observations with the Follow-up X-ray Telescope (FXT) on board EP localized the source to the central region of the galaxy 2MASX J06213207+6238390 at $z=0.084$. Here, we present a comprehensive analysis of the multi-wavelength data for this object. During the FXT and Swift/XRT follow-up, the source remained X-ray active, with observed luminosities of $(3-9)\times10^{43}$ erg s$^{-1}$, and its X-ray spectra were well described by power-law models with photon indices of $Γ\sim1.5$. The optical spectrum of the host galaxy clearly reveals a broad-line active galactic nucleus (AGN) exhibiting significantly enhanced broad Balmer emission lines compared to an archival spectrum obtained four years earlier, thereby marking a changing-look transition from Type 1.8 to Type 1.5. Furthermore, long-term optical and mid-infrared light curves indicate that the nucleus underwent dramatic brightening around 2010 and has since shown substantial variability. Given the spatial coincidence and multi-wavelength behavior, we identify EP J062132.0+623837 as a changing-look AGN, initially detected by EP and subsequently confirmed to exhibit large-amplitude variability across multiple bands. This case study demonstrates the great potential of wide-field time-domain X-ray surveys for discovering changing-look AGNs, paving a new avenue for their systematic identification as a powerful complement to ongoing optical surveys.

astro-ph.GA↗

arXiv2026

SurgCast: Action-Conditioned Future Skeletons for Controllable Surgical World Models

Surgical world models must turn prospective robot actions into visually faithful, structurally consistent instrument motion. We introduce SurgCast, which predicts future instrument skeletons from current structure, robot state, and future actions, then uses the forecast to control video generation. Dual-path injection combines skeleton geometry with derived, instrument-specific motion descriptors through geometric residuals and conditioning modulation. Both paths complement native action conditioning and are retained in a four-step causal student. On SutureBot and SRTH-Porcine-Cholei, the teacher and student improve all seven reported quality and instrument-consistency metrics over their respective direct-action baselines; teacher FVD decreases by 37.6% and 67.3%. Ablations show that future actions improve skeleton prediction and that combining both injection paths performs best across all six metrics under predicted skeletons. These results support using predicted articulated structure and its motion semantics as a control interface for surgical video prediction. Project videos and examples are available at https://surgcast.netlify.app/.

cs.CV↗

arXiv2026

Quantifying the Stability of Multi-Step Reasoning via Error Amplification

We consider the stability of multi-step reasoning processes, which have extensive applications in language models, including chain-of-thought and algorithmic reasoning. While longer sequences of reasoning can improve a model's generation capability at test time, the errors due to intermediate reasoning steps can accumulate in autoregressive generation, and thus grow substantially at the end. In this paper, we ask: What are the key factors determining the stability of multi-step reasoning? First, we show an inference error bound governed by the product of spectral norms of the Jacobians taken through the input space across generation steps. This product can be viewed as an error amplification factor, which could scale exponentially with the number of reasoning steps, serving as a quantitative measure of reasoning stability. Second, we analyze this measure in transformer models trained to predict simple tasks like linear and quadratic functions. We theoretically prove that the transformer model converges to a solution where the stability measure decays, thus yielding nearly zero inference loss over (arbitrarily) long steps. Finally, the stability analysis leads to several algorithmic implications for controlling the stability, through (i) chain-of-thought length compression that reduces the sensitivity of each step, and (ii) quantization-aware training that regularizes the input Jacobian norms. We validate the proposed algorithms by fine-tuning language models on graph-algorithmic reasoning tasks and symbolic state-tracking tasks. Across seven evaluations, our algorithms improve over baseline comparisons by 3.5% on average, and by 8.2% for longer-length inputs. Ablation analysis validates that the stability measure is drastically reduced by 3-8$\times$, confirming the regularization effect on the spectral norms of the (input space) Jacobians.

cs.LG↗

arXiv2026

MoPLEx: Estimating Plackett-Luce Mixture Models for Multi-Objective Alignment

We study learning a mixture of $k$ Plackett-Luce models from multi-way ranking responses from annotators that may represent heterogeneous underlying preferences. This problem has many applications in AI alignment and preference optimization. Prior work has studied mixtures of Bradley-Terry models from pairwise comparisons. However, estimating a mixture of multi-way ranking models can become theoretically unidentifiable when $k$ exceeds $m/2$, where $m$ is the ranking length. We design an efficient algorithm to address this issue by first augmenting the rankings to a larger size (e.g., generating comparisons from a base model), followed by a gradient-based estimation to reduce inference cost (in the input embedding space). With this procedure in mind, we then fit a mixture of Plackett-Luce (PL) models via an expectation-maximization-style iteration, or MoPLEx in short. We conduct extensive experiments to verify this algorithm. First, we find that the gradient-based approximation estimates true probabilities with less than 5% error on models with up to 34 billion parameters. Second, MoPLEx improves clustering and ranking accuracy by an average of 43.7% and 15.2% over baselines using a single PL model or a mixture of Bradley-Terry models, on UltraFeedback and PERSONA datasets. These results demonstrate the effectiveness of MoPLEx for tackling multi-way rankings following heterogeneous preferences through measuring alignment via gradients.

cs.LG↗

arXiv2026

TopoAgent: A Structure-Aware Perception-to-Reasoning Framework for Diagram-to-Graph Topology Extraction with Large Vision-Language Models

Diagram-to-graph topology extraction aims to extract a graph of entities and their connections from a structural diagram. This task remains challenging for current vision-language models because it requires both fine-grained perceptual grounding and topology-aware reasoning with global consistency. We present TopoBench-180, a human-verified benchmark for diagram-to-graph topology extraction, and TopoAgent, a structure-aware perception-to-reasoning framework for reliable topology extraction using large vision-language models. TopoBench-180 contains 180 structural diagrams spanning Web-style and Network-style categories, paired with canonical graph annotations. TopoAgent progressively extracts the target graph by combining grounded perception, global structural priors, canonical node inventory construction, node-centric local-to-global relation reasoning, and topological consistency enforcement. Experiments on TopoBench-180 show that TopoAgent outperforms strong vision-language model baselines and recent visual reasoning frameworks, especially on edge extraction. More broadly, this work fills an important gap in multimodal structured understanding by establishing a benchmark and framework for diagram-to-graph topology extraction. The benchmark and associated resources will be publicly released at https://huggingface.co/datasets/WayneGuo0011/TopoBench-180.

cs.CV↗

arXiv2026

A superflare of BP Tau simultaneously caught by EP X-ray and TESS optical observations

Multiwavelength observations of stellar flares trace the activity of different components of the stars' outer atmosphere, providing insight into their interactions. In the present paper, we report a superflare from BP Tau, simultaneously observed with the Wide-field X-ray Telescope (WXT) on board the Einstein Probe (EP) satellite and TESS. While we attribute the X-ray flux increase to a magnetically powered flare, the optical light curve likely results from the superposition of the flare and an accretion burst. The X-ray flare has a mean flux of $(1.5^{+0.3}_{-0.4})\times10^{-11}$ erg cm$^{-2}$ s$^{-1}$ in the WXT energy band (0.5-4.0 keV), with e-folding times of $1.7\pm1.0$ ks and $14\pm5$ ks for the rise and decay phase, respectively. The corresponding time-integrated flare energy is $(1.0\pm 0.2)\times 10^{36}$ erg. The optical flare has an e-folding time of $0.33\pm0.04$ ks for the rise phase, but the data do not constrain the decay timescale. Assuming a decay phase equal to the rise phase, the resulting optical flare energy is $(2.8\pm0.4)\times10^{34}$ erg in the TESS band ($\sim6,000$-$\sim10,000$ Å), corresponding to a bolometric energy of $(1.9\pm0.3)\times10^{35}$ erg (assuming a blackbody at 11000 K). The Follow-up X-ray Telescope (FXT) on EP triggered an observation $\sim1.5$ day after the flare, with a flux of $(4.6^{+0.2}_{-0.5})\times10^{-13}$ erg cm$^{-2}$ s$^{-1}$ (0.5-10.0 keV), indicating that BP Tau had returned to quiescence. This work demonstrates the potential of jointly analyzing EP and TESS data for superflares. WXT is expected to detect $\sim800$ superflares per year, with FXT capable of slewing to the flaring star within $\sim3$-5 minutes. The large field of view of both missions offers us the opportunity to study multiwavelength variability during energetic flares.

astro-ph.SR↗

arXiv2026

CrossScope: A Role-Asymmetric World Model for Joint Dual-Scope Surgical Video Prediction

Visual world models typically learn future dynamics from a single observation stream, limiting their ability to model cooperative systems with multiple independently moving observers. We investigate this challenge in Mother--Child endoscopic retrograde cholangiopancreatography (ERCP), where two flexible scopes provide complementary yet role-dependent views without a calibrated stereo relationship. Unlike conventional multi-view fusion that assumes symmetric information exchange, we formulate \textbf{role-asymmetric dual-scope future prediction}, where cross-view evidence is selectively transferred according to the prediction target and its underlying spatial requirements. We propose \textbf{CrossScope}, a dual-stream surgical world model that preserves view-specific experts while enabling target-specific evidence routing through geometry-guided residual interactions. CrossScope learns two complementary communication directions: geometric motion cues from the Mother view guide Child-view future dynamics, while pose-aligned Child appearance supports Mother-view prediction only when valid spatial correspondence is established. This design allows each scope to contribute task-relevant evidence without compromising its view-specific representation. To evaluate this problem, we establish a paired dual-scope benchmark comprising synchronized phantom and real-world ERCP episodes, with evaluations assessing visual fidelity, structural preservation, target localization, and motion consistency. Experiments demonstrate that CrossScope consistently outperforms strong surgical video generation baselines, validating the importance of role-aware evidence routing for multi-observer visual world modeling.

cs.CV↗

arXiv2026

Efficiently Learning Branching Networks for Multitask Algorithmic Reasoning

Algorithmic reasoning -- the ability to perform step-by-step logical inference -- is a synthetic benchmark for evaluating multi-step reasoning abilities, designed for graph neural networks and also for transformer models. Prior work has evaluated reasoning for executing a single algorithmic task, whereas a more desirable objective is to perform multiple algorithmic reasoning tasks simultaneously. We start by noting that this is inherently difficult due to differences arising from the execution traces of the algorithms (such as depth- vs. breadth-first search), which cause interference when they are trained together. In this paper, we introduce {branching neural networks}, a new architecture for multitask algorithmic reasoning. The main idea is to search for a recursive tree-structured partition of $n$ algorithmic tasks into a $k$-ary tree (divided into $L$ layers). Naive search requires $O(k^{nL})$ complexity; we develop an algorithm that reduces this to $O(nL)$ by solving a convex relaxation at each layer to approximate an optimal partition. Our approach clusters these tasks using gradient-based affinity and can be used on top of any base model. We validate our approach on algorithmic reasoning benchmarks and their extensions with text descriptions. We show that gradient-based affinity scores help estimate true performance with less than 5% error, measured across eight different architectures with up to 34 billion parameters. On the CLRS benchmark, our approach outperforms existing graph neural networks by 3.7% and baselines by 1.2%, while reducing runtime by 48% and memory usage by 26%. The learned branching structure shows a hierarchical clustering of related algorithms. On three text-based graph reasoning benchmarks, our approach improves over baseline methods by 3.2%. Finally, we validate our approach for overlapping community detection.

cs.LG↗

arXiv2026

X-rays breaking out of pre-explosion ejecta mark a supernova's first light

Massive stars die as core-collapse supernovae, whose optical light emerges days after the implosion. Theory predicts that the initial collapse-driven shock, upon breaking through the star and dense circumstellar medium, emits a brief thermal flash of soft X-rays and ultraviolet. Yet these elusive first signals have remained largely undetected, owing to limited wide-field soft X-ray monitoring. Here we report the discovery of a soft X-ray flash, EP260321a, followed days later by a broad-lined supernova from an envelope-stripped progenitor. Its X-ray spectrum, best modeled with blackbody, establishes it as the long-sought archetypal shock breakout. The burst's duration and energetics place the breakout at a radius of 300 solar radii, tracing a dense surrounding shell and revealing abrupt mass ejection within the final month before collapse.

astro-ph.HE↗

arXiv2026

M-EPDet: Real-Time Real-Bogus Classification and Transient Candidate Judgement for the EP-WXT Pipeline via Multi-Modal Data

The Wide-field X-ray Telescope (WXT) onboard the Einstein Probe (EP) produces a large post-detection candidate stream in which genuine astrophysical sources coexist with instrumental artifacts and Cosmic Ray events. We present M-EPDet, a three-step post-detection framework for real-time candidate vetting in EP-WXT lobster-eye Micro-pore Optics (MPO) data. The framework combines a ResNet-based Arm filter, a dual-branch temporal-spectral Cosmic Ray filter, and a background-aware Bayesian Blocks module for single-exposure variability screening. Using on-orbit EP-WXT observations, we report decoupled metrics for the cascading system. M-EPDet achieves a Real-Bogus Recall of 98.31\% ($98.53\% \times 99.78\%$) for genuine astrophysical sources, together with rejection rates of 92.99\% for instrumental artifacts and 98.18\% for Cosmic Ray events. In the final step, the Bayesian Blocks module flags 0.75\% of the post-filtration observations, corresponding to a 99.25\% reduction in candidate volume. The system is deployed in the EP-WXT pipeline as a lightweight real-time service, reducing the manual-inspection burden in candidate vetting.

astro-ph.IM↗

arXiv2026

EP250827b/SN 2025wkm: An X-ray Flash-Supernova Powered by a Central Engine and Circumstellar Interaction

We present the discovery of EP250827b/SN 2025wkm, an X-ray Flash (XRF) discovered by the Einstein Probe (EP), accompanied by a broad-line Type Ic supernova (SN Ic-BL) at $z = 0.1194$. EP250827b possesses a prompt X-ray luminosity of $\sim 10^{45} \, \rm{erg \, s^{-1}}$, lasts over 1000 seconds, and has a peak energy $E_{\rm{p}} < 1.5$ keV at 90\% confidence. SN 2025wkm possesses a double-peaked optical light curve (LC), though its bolometric luminosity plateaus after its initial peak for $\sim 20$ days, consistent with a central engine injecting additional energy into the explosion. Its spectrum transitions from a blue to red continuum with clear blueshifted broad absorption features consistent with a SN Ic-BL classification. We do not detect any transient radio emission and rule out the existence of an on-axis, energetic jet $\gtrsim 10^{50}~$erg assuming a typical LGRB circumburst constant density ($n \approx 10^{-3}$--$10^{-1}~{\rm cm}^{-3}$) and microphysical parameters ($ε_{\rm e} = 0.1$ and $ε_{\rm B} = 0.01$). In the model we invoke, the collapse gives rise to a long-lived magnetar, potentially surrounded by an accretion disk. Magnetically--driven winds from the magnetar and the disk mix together and break out with a velocity $\sim 0.35c$ and interact with an extended circumstellar medium with radius $\sim 10^{13}$ cm, generating X-ray breakout emission through non-thermal free-free processes. The disk outflows and magnetar winds power blackbody photospheric emission as they cool adiabatically and thermalize, producing the first SN peak. The spin-down luminosity of the magnetar and radioactive decay of $^{56}$Ni powers the late-time emission. We end by discussing the landscape of XRF-SNe within the context of EP's recent discoveries.

astro-ph.HE↗

arXiv2026

WinQ: Accelerating Quantization-Aware Training of Language Models Around Saddle Points

Quantization-aware training (QAT) is widely adopted to quantize language models by training full-precision weights using gradients from the quantized model. The main bottleneck is its slow convergence and early performance plateau, particularly below 4-bit-widths. While this problem has been observed in prior work, its precise cause remains unclear. In this paper, we analyze the convergence of QAT by estimating the spectrum of the loss-surface Hessians. We find that the weights converge to flat regions around saddle points, where a large fraction of the Hessian eigenvalues are both positive and negative. During training, an increasing fraction of Hessian eigenvalues concentrates around zero, whose magnitude decreases. At lower bit-widths, the magnitude of eigenvalues in the Hessian spectrum is significantly smaller. To mitigate these issues, we propose an algorithm called WinQ to accelerate QAT, which involves: (1) periodically resetting weights to the linear interpolation of full-precision and quantized weights, reducing the distance to the quantization grid and increasing eigenvalue magnitude, and (2) computing gradients of noise-injected weights to regularize the Hessian. Extensive experiments show that WinQ accelerates QAT by up to 4 times across various quantization methods and models. Under the same training cost, WinQ improves state-of-the-art sub-4-bit quantization by up to 8.8%. These results are consistent across 16 settings with different language models, quantization methods, and bit widths.

cs.LG↗

arXiv2026

Not All Pixels Are Equal: Confidence-Guided Attention for Feature Matching

Semi-dense feature matching methods have been significantly advanced by leveraging attention mechanisms to extract discriminative descriptors. However, most existing approaches treat all pixels equally during attention computations, which can potentially introduce noise and redundancy from irrelevant regions. To address this issue, we propose a confidence-guided attention that adaptively prunes attention weights for each pixel based on precomputed matching confidence maps. These maps are generated by evaluating the mutual similarity between feature pairs extracted from the backbone, where high confidence indicates a high potential for matching. Then the attention is refined through two steps: (1) a confidence-guided bias is introduced to adaptively adjust the attention distributions for each query pixel, avoiding irrelevant interactions between non-overlap pixels; (2) the corresponding confidence map is additionally employed to rescale value features during feature aggregation, attenuating the influence of uncertain regions. Moreover, a classification loss is introduced to encourage the backbone's features to discriminate between matchable and non-matchable regions. Extensive experiments on three benchmarks demonstrate that the proposal outperforms existing state-of-the-art methods.

cs.CV↗

arXiv2026

VLingNav: Embodied Navigation with Adaptive Reasoning and Visual-Assisted Linguistic Memory

VLA models have shown promising potential in embodied navigation by unifying perception and planning while inheriting the strong generalization abilities of large VLMs. However, most existing VLA models rely on reactive mappings directly from observations to actions, lacking the explicit reasoning capabilities and persistent memory required for complex, long-horizon navigation tasks. To address these challenges, we propose VLingNav, a VLA model for embodied navigation grounded in linguistic-driven cognition. First, inspired by the dual-process theory of human cognition, we introduce an adaptive chain-of-thought mechanism, which dynamically triggers explicit reasoning only when necessary, enabling the agent to fluidly switch between fast, intuitive execution and slow, deliberate planning. Second, to handle long-horizon spatial dependencies, we develop a visual-assisted linguistic memory module that constructs a persistent, cross-modal semantic memory, enabling the agent to recall past observations to prevent repetitive exploration and infer movement trends for dynamic environments. For the training recipe, we construct Nav-AdaCoT-2.9M, the largest embodied navigation dataset with reasoning annotations to date, enriched with adaptive CoT annotations that induce a reasoning paradigm capable of adjusting both when to think and what to think about. Moreover, we incorporate an online expert-guided reinforcement learning stage, enabling the model to surpass pure imitation learning and to acquire more robust, self-explored navigation behaviors. Extensive experiments demonstrate that VLingNav achieves state-of-the-art performance across a wide range of embodied navigation benchmarks. Notably, VLingNav transfers to real-world robotic platforms in a zero-shot manner, executing various navigation tasks and demonstrating strong cross-domain and cross-task generalization.

cs.RO↗

arXiv2026

Einstein Probe Discovery of an X-ray Flare from K-type Star PM J23221-0301

Stellar flares are an intense stellar activity that can significantly impact the atmospheric composition of the surrounding planets and even the possible existence of life. During such events, the radiative energy of the star is primarily concentrated in the optical and X-ray bands, with the X-ray flux potentially increasing by tens or even hundreds of times. Einstein Probe (EP) detected a new X-ray transient EP J2322.1-0301 on 27 September 2024. Its spatial localization shows a high positional coincidence with the nearby high proper motion K-type star PM J23221-0301. Follow-up X-ray observations confirmed the flux enhancement of the source, while optical spectroscopic monitoring revealed time-variable features, particularly the disappearance of the H-alpha emission line. This X-ray flare is consistent with a characteristic fast-rise-exponential-decay (FRED) light curve, with a rise timescale of 1.4 ks, a decay timescale of 5.7 ks, and a total duration of about 7.1 ks. The peak luminosity in the 0.5-4.0 keV energy band reached about 1.3 x 10^31 erg s^-1, with a total energy release of about 9.1 x 10^34 erg, consistent with the empirical energy correlations observed in magnetic-reconnection-driven stellar flares, as inferred from the multitemperature plasma structure and H-alpha-X-ray energy correlation. This discovery underscores EP's capability in understanding stellar magnetic activity via observing stellar transients.

astro-ph.HE↗