arXiv subjects

Xiaohan Chen

Publications and source records attributed to Xiaohan Chen.

At least 19 recordsLinked to original sources

arXiv2026

Differential Reddening and Extinction Law Analyses of Galactic Open Clusters

Extinction significantly affects open cluster parameters and their use in studies of Galactic structure, yet homogeneous large sample measurements of open cluster extinction properties remain limited. Using Gaia-era open cluster member samples combined with multi-band photometry and stellar parameters, we derive color excesses of member stars and provide the homogeneous characterization of the mean reddening, differential reddening, and color excess ratio (CER) at the cluster scale. Differential reddening increases systematically with mean reddening, with highly reddened clusters near the Galactic plane showing stronger extinction variations. Star-by-star reddening corrections narrow color--magnitude diagram (CMD) sequences in 369 of 435 clusters (85%) with reliable CMD-width measurements, and cluster color excess maps reveal small-scale extinction structures. The median CER is compatible with the standard diffuse interstellar medium extinction curve, while the broad CER distribution and its large-scale variations across the Galactic disk likely reflect differences in the dominant dust environments sampled along different Galactic sight lines.

astro-ph.GA↗

arXiv2026

Stellar Density Classification and Regression for CSST Multi-color Imaging Using Deep Learning

The Chinese Space Station Survey Telescope (CSST) aims to map the universe across an unprecedented dynamic range of stellar densities, spanning from extragalactic voids to the crowded Galactic center (e.g. a few stars and galaxies in the voids and $>10^5$ stars per detector in Galactic center). However, processing such heterogeneous data with a general source extraction pipeline introduces significant systematic uncertainties, standard algorithms exhibit poor accuracy in crowded fields and suffer from increased astrometric uncertainty in void regions. To mitigate these systematics, we propose a hierarchical, two-stage deep learning model for adaptive data reduction. The first stage ('classification') employs a ResNet-34 model to classify images into six discrete density categories, achieving $98.83\%$ in global accuracy. This classification acts as a critical decision gate, ensuring high calibration accuracy in the crowded fields. In the second stage ('regression'), a ResNet-50 regression model predicts the bright stars ($<23.5$ mag) in the field, which is essential for astrometric calibration, achieving a mean absolute error (MAE) of 0.0824 dex. By decoupling density characterization from source extraction, our model ensures that photometric and astrometric algorithms are optimally matched to the stellar density environment, thereby enhancing the fidelity and homogeneity of CSST as well as future large sky survey data products.

astro-ph.IM↗

arXiv2026

SN 2023fyq: direct detection of a Type Ibn supernova progenitor and its multi-wavelength environmental constraints

Context. Type Ibn supernovae (SNe) are characterized by narrow helium emission lines arising from ejecta-circumstellar medium interaction, yet their progenitors remain debated, with both massive Wolf-Rayet stars and low-mass helium stars in binaries proposed. Aims. We aim to directly identify the progenitor of the Type Ibn SN 2023fyq and to characterize its environment in order to constrain the progenitor's nature and evolutionary channel. Methods. We search for the SN progenitor based on pre-explosion and late-time HST and JWST images and derive its properties by fitting the spectral energy distribution. We investigate the SN environment by probing the stars, dust, ionized gas and molecular gas with a multi-wavelength dataset including HST and JWST imaging, VLT/MUSE integral-field-unit spectroscopy and ALMA CO (2--1) radio interferometry. Results. We discover a pre-explosion source at the SN position, which is consistent with a hot ($T>$15000 K) and luminous (log($L$/$L_\odot$) $\gtrsim$ 5.5) SN progenitor and a possible host star cluster. The progenitor is confirmed to have disappeared after explosion. Analysis of the SN environment implies that the progenitor likely has an age of log($t$/yr) = 7.1--7.2. These phenomena disfavor a very massive single-star progenitor and instead support a binary scenario involving a low-mass helium star and a compact object; the observed progenitor emission likely arises from binary interaction that began at least $\sim$12 yr before the explosion. Conclusions. SN 2023fyq is the first Type Ibn SN with a directly detected progenitor and a possible host star cluster. It adds to the diversity of Type Ibn SNe in terms of their progenitor channels and mass-loss mechanisms.

astro-ph.SR↗

arXiv2026

Not All Who Wander Are Lost: Early Excess Demographics in the Volume-limited ZTF DR2 SN Ia Sample

Early-time flux excesses in Type Ia supernovae (SNe~Ia) offer a unique insight into their progenitor systems and explosion mechanisms. Although individual early-excess events and larger searches have been reported, demographic studies remain limited by sample size. We present a systematic search for early-time excess emission in a volume-limited sample ($z<0.06$) of SNe~Ia based on the Zwicky Transient Facility Data Release 2 (ZTF DR2). Using ZTF $g$- and $r$-band light curves, we identify candidates showing early-excesses shortly after the explosion time, and we apply conservative coverage and quality requirements to build reliable ``excess'' and ``no-excess'' bump and no-bump catalogs. From an initial sample of 1547 SNe~Ia, our final catalogs contain 42 early-excess and 110 no-excess events. We compare the two populations using SN and host environment parameters from ZTF DR2 and quantify the differences using two-sample statistical tests. We find the strongest differences are in SN light-curve properties: early-excess events have larger SALT2 stretch $x_1$ ($7.91\sigma$) and larger $r$-band secondary-maximum flux $\mathcal{F}_{r_2}$ ($6.25\sigma$), while differences in SALT2 color $c$ are weak ($0.57\sigma$). Early-excess events also favor bluer $(g-z)_{\rm local}$ ($3.41\sigma$) and lower $\log_{10} (M_*/M_\odot)_{\rm local}$ ($2.73\sigma$). Our results connect early excesses with SNe~Ia diversity, and motivate further analyses of upcoming larger samples.

astro-ph.HE↗

arXiv2026

A statistical study of the environmental age of core-collapse supernovae based on VLT/MUSE integral-field-unit spectroscopy

We aim to understand the progenitor channels of CCSNe via a statistical study of the ages of their environments. We compiled a large and minimally biased sample of 128 CCSNe discovered by untargeted wide-field transient surveys and with archival VLT/MUSE integral-field-unit spectroscopy. We measured the local H{\alpha} luminosity within a 300-pc aperture centered on the SN explosion site as an empirical proxy for the environmental age. We find that the mean local H$\alpha$ luminosities are ordered as II(P) $\approx$ IIb $\lesssim$ Ib $<$ Ic. The differences among Types~II(P), IIb and Ib are very small, if any. Type~Ic SNe are located in clearly younger environments than the other types. Our result suggests that Type Ic SNe have much younger and more massive progenitors than the other CCSN types and they likely originate from a distinct progenitor channel. The distinction between Types II(P), IIb and Ib SNe is insensitive to progenitor mass and mainly due to the different binary separation; in contrast, Type Ic SNe predominantly require much higher-mass progenitors accompanied by close companions with large mass ratios and/or much stronger stellar wind that depends sensitively on progenitor mass.

astro-ph.SR↗

arXiv2026

MovieTeller: Tool-augmented Movie Synopsis with ID Consistent Progressive Abstraction

With the explosive growth of digital entertainment, automated video summarization has become indispensable for applications such as content indexing, personalized recommendation, and efficient media archiving. Automatic synopsis generation for long-form videos, such as movies and TV series, presents a significant challenge for existing Vision-Language Models (VLMs). While proficient at single-image captioning, these general-purpose models often exhibit critical failures in long-duration contexts, primarily a lack of ID-consistent character identification and a fractured narrative coherence. To overcome these limitations, we propose MovieTeller, a novel framework for generating movie synopses via tool-augmented progressive abstraction. Our core contribution is a training-free, tool-augmented, fact-grounded generation process. Instead of requiring costly model fine-tuning, our framework directly leverages off-the-shelf models in a plug-and-play manner. We first invoke a specialized face recognition model as an external "tool" to establish Factual Groundings--precise character identities and their corresponding bounding boxes. These groundings are then injected into the prompt to steer the VLM's reasoning, ensuring the generated scene descriptions are anchored to verifiable facts. Furthermore, our progressive abstraction pipeline decomposes the summarization of a full-length movie into a multi-stage process, effectively mitigating the context length limitations of current VLMs. Experiments demonstrate that our approach yields significant improvements in factual accuracy, character consistency, and overall narrative coherence compared to end-to-end baselines.

cs.CV↗

arXiv2026

SN 2024abfl: A Low-Luminosity Type IIP Supernova in NGC 2146 from a Low-Mass Red Supergiant Progenitor

Type IIP supernovae (SNe IIP) exhibit a significant diversity in their explosion properties, yet the physical mechanisms driving this diversity remain unknown. In this work, we present photometric and spectroscopic observations of SN 2024abfl, a SN IIP in NGC 2146 with a directly detected red supergiant (RSG) progenitor. We find it has a low plateau luminosity ($M_V \sim -15$ mag) and a relatively long plateau length ($\sim 126.5$ days). By fitting a semi-analytical model, we estimated a $^{56}$Ni mass of $\sim 0.009 M_\odot$, an initial kinetic energy of $\sim 0.42$ foe, an initial thermal energy of $\sim 0.03$ foe and an ejecta mass of $\sim 8.3 M_\odot$. The spectral evolution of SN 2024abfl is similar to those of other SNe IIP, except for much lower ejecta velocities at similar epochs. At later epochs, we find a relatively high-velocity H$\alpha$ absorption feature at $\sim -4000$ km s$^{-1}$, possibly due to a fast-moving plume of matter in the inner ejecta, and two emission features at $\pm 2000$ km s$^{-1}$, possibly caused by CSM interaction. We estimate the progenitor mass to be $\le 15 M_\odot$ based on nebular spectra. We conclude that SN 2024abfl is a low-luminosity SN IIP originating from a low-mass RSG progenitor.

astro-ph.HE↗

arXiv2026

Direct Detection of Type II-P Supernova Progenitors with the $\textit{Euclid}$ and CSST Surveys

Identifying and characterizing supernova (SN) progenitor stars remains a central yet difficult goal in SN research, limited by archival images lacking sufficient depth or spatial resolution and circumstellar dust biasing intrinsic parameter estimates. This field will be revolutionized by $\textit{Euclid}$ and the upcoming Chinese Space-station Survey Telescope (CSST), which conduct deep, wide-field, high-resolution and multi-band imaging surveys. We evaluate their detection capability by comparing model magnitudes of RSG progenitors with detection limits, finding their optical and near-infrared filters highly effective. Monte-Carlo simulations predict that completed $\textit{Euclid}$ and CSST surveys will enable $\lesssim$13 (or 24) progenitor detections per year within the mass range of 8--16 (or 8--25)\,$M_\odot$, an order of magnitude higher than the current detection rate of $\sim$1 per year (primarily based on HST). With the circumstellar dust, the emerging spectral energy distribution (SED) of the SN progenitor is mainly affected by the optical depth and is almost independent of dust temperature in their survey filters. Mock tests demonstrate that the progenitor mass and dust optical depth can be derived simultaneously by fitting the observed SED over 11 survey filters while fixing dust temperature to a typical value. $\textit{Euclid}$ and CSST will significantly enlarge the sample of direct progenitor detections with accurate mass measurements, crucial for resolving the long-standing RSG problem.

astro-ph.SR↗

arXiv2025

$M^3-Verse$: A "Spot the Difference" Challenge for Large Multimodal Models

Modern Large Multimodal Models (LMMs) have demonstrated extraordinary ability in static image and single-state spatial-temporal understanding. However, their capacity to comprehend the dynamic changes of objects within a shared spatial context between two distinct video observations, remains largely unexplored. This ability to reason about transformations within a consistent environment is particularly crucial for advancements in the field of spatial intelligence. In this paper, we introduce $M^3-Verse$, a Multi-Modal, Multi-State, Multi-Dimensional benchmark, to formally evaluate this capability. It is built upon paired videos that provide multi-perspective observations of an indoor scene before and after a state change. The benchmark contains a total of 270 scenes and 2,932 questions, which are categorized into over 50 subtasks that probe 4 core capabilities. We evaluate 16 state-of-the-art LMMs and observe their limitations in tracking state transitions. To address these challenges, we further propose a simple yet effective baseline that achieves significant performance improvements in multi-state perception. $M^3-Verse$ thus provides a challenging new testbed to catalyze the development of next-generation models with a more holistic understanding of our dynamic visual world. You can get the construction pipeline from https://github.com/Wal-K-aWay/M3-Verse_pipeline and full benchmark data from https://www.modelscope.cn/datasets/WalKaWay/M3-Verse.

cs.CV↗

arXiv2025

PreResQ-R1: Response-Preference Disentangled Ranking-and-Scoring Reinforcement Optimization for Robust Visual Quality Assessment

Visual Quality Assessment (QA) seeks to predict human perceptual judgments of visual fidelity. While recent multimodal large language models (MLLMs) show promise in reasoning about image and video quality, existing approaches mainly rely on supervised fine-tuning or rank-only objectives, resulting in shallow reasoning, poor score calibration, and limited cross-domain generalization. We propose PreResQ-R1, a Preference-Response Disentangled Reinforcement Learning framework that unifies absolute score regression and relative ranking consistency within a single reasoning-driven optimization scheme. Unlike prior QA methods, PreResQ-R1 introduces a dual-branch reward formulation that separately models intra-sample response coherence and inter-sample preference alignment, optimized via Group Relative Policy Optimization (GRPO). This design encourages fine-grained, stable, and interpretable chain-of-thought reasoning about perceptual quality. To extend beyond static imagery, we further design a global-temporal and local-spatial data flow strategy for Video Quality Assessment. Remarkably, with reinforcement fine-tuning on only 6K images and 28K videos, PreResQ-R1 achieves state-of-the-art results across 10 IQA and 5 VQA benchmarks under both SRCC and PLCC metrics, surpassing by margins of 5.30% and textbf2.15% in IQA task, respectively. Beyond quantitative gains, it produces human-aligned reasoning traces that reveal the perceptual cues underlying quality judgments.

cs.CV↗

arXiv2025

Slitless Spectroscopy Source Detection Using YOLO Deep Neural Network

Slitless spectroscopy eliminates the need for slits, allowing light to pass directly through a prism or grism to generate a spectral dispersion image that encompasses all celestial objects within a specified area. This technique enables highly efficient spectral acquisition. However, when processing CSST slitless spectroscopy data, the unique design of its focal plane introduces a challenge: photometric and slitless spectroscopic images do not have a one-to-one correspondence. As a result, it becomes essential to first identify and count the sources in the slitless spectroscopic images before extracting spectra. To address this challenge, we employed the You Only Look Once (YOLO) object detection algorithm to develop a model for detecting targets in slitless spectroscopy images. This model was trained on 1,560 simulated CSST slitless spectroscopic images. These simulations were generated from the CSST Cycle 6 and Cycle 9 main survey data products, representing the Galactic and nearby galaxy regions and the high galactic latitude regions, respectively. On the validation set, the model achieved a precision of 88.6% and recall of 90.4% for spectral lines, and 87.0% and 80.8% for zeroth-order images. In testing, it maintained a detection rate >80% for targets brighter than 21 mag (medium-density regions) and 20 mag (low-density regions) in the Galactic and nearby galaxies regions, and >70% for targets brighter than 18 mag in high galactic latitude regions.

astro-ph.IM↗

arXiv2025

Artificial Satellite Trails Detection Using U-Net Deep Neural Network and Line Segment Detector Algorithm

With the rapid increase in the number of artificial satellites, astronomical imaging is experiencing growing interference. When these satellites reflect sunlight, they produce streak-like artifacts in photometry images. Such satellite trails can introduce false sources and cause significant photometric errors. As a result, accurately identifying the positions of satellite trails in observational data has become essential. In this work, we propose a satellite trail detection model that combines the U-Net deep neural network for image segmentation with the Line Segment Detector (LSD) algorithm. The model is trained on 375 simulated images of satellite trails, generated using data from the Mini-SiTian Array. Experimental results show that for trails with a signal-to-noise ratio (SNR) greater than 3, the detection rate exceeds 99. Additionally, when applied to real observational data from the Mini-SiTian Array, the model achieves a recall of 79.57 and a precision of 74.56.

cs.CV↗

arXiv2025

SaraCoder: Orchestrating Semantic and Structural Cues for Resource-Optimized Repository-Level Code Completion

Despite Retrieval-Augmented Generation improving code completion, traditional retrieval methods struggle with information redundancy and a lack of diversity within limited context windows. To solve this, we propose a resource-optimized retrieval augmentation method, SaraCoder. It maximizes information diversity and representativeness in a limited context window, significantly boosting the accuracy and reliability of repository-level code completion. Its core Hierarchical Feature Optimization module systematically refines candidates by distilling deep semantic relationships, pruning exact duplicates, assessing structural similarity with a novel graph-based metric that weighs edits by their topological importance, and reranking results to maximize both relevance and diversity. Furthermore, an External-Aware Identifier Disambiguator module accurately resolves cross-file symbol ambiguity via dependency analysis. Extensive experiments on the challenging CrossCodeEval and RepoEval-Updated benchmarks demonstrate that SaraCoder outperforms existing baselines across multiple programming languages and models. Our work proves that systematically refining retrieval results across multiple dimensions provides a new paradigm for building more accurate and resource-optimized repository-level code completion systems.

cs.SE↗

arXiv2025

Distances of Supernova Remnants Associated with Neutron Stars in the Galaxy

Accurate distance measurements to supernova remnants (SNRs) are essential for determining their physical parameters, such as size, age, explosion energy, and for constraining the properties of associated neutron stars (NSs). We present an extinction--distance method that combines precise Gaia DR3 photometry, parallax, and stellar parameters from the SHBoost catalog to homogeneously construct extinction--distance profiles for 44 NS-associated Galactic SNRs. Applying a statistical model, we identify clear extinction jumps along each sightline, corresponding to probable SNR distances. We classify the results into three reliability levels (A, B, and C), primarily based on comparisons with previously reported kinematic distances, supplemented by independent estimates from other methods. Our results show that the majority of reliable distances (17 Level A and 8 Level B) are located within 5 kpc, predominantly in the Local Arm. This study presents an independent and effective method for determining distances to SNRs, particularly for those with small angular sizes or located in the second and third Galactic quadrants. Although the current method is limited to within 5 kpc due to the precision constraints of Gaia parallax and photometry, the upcoming Gaia DR4 release, combined with complementary infrared data, will extend its applicability to more distant and heavily obscured SNRs, and help resolve kinematic distance ambiguities.

astro-ph.GA↗

arXiv2025

A Comprehensive Study on the Use of Word Embedding Models in Software Engineering Domain

Word embedding (WE) techniques are advanced textual semantic representation models oriented from the natural language processing (NLP) area. Inspired by their effectiveness in facilitating various NLP tasks, more and more researchers attempt to adopt these WE models for their software engineering (SE) tasks, of which semantic representation of software artifacts such as bug reports and code snippets is the basis for further model building. However, existing studies are generally isolated from each other without comprehensive comparison and discussion. This not only makes the best practice of such cross-discipline technique adoption buried in scattered papers, but also makes us kind of blind to current progress in the semantic representation of SE artifacts. To this end, we decided to perform a comprehensive study on the use of WE models in the SE domain. 181 primary studies published in mainstream software engineering venues are collected for analysis. Several research questions related to the SE applications, the training strategy of WE models, the comparison with traditional semantic representation methods, etc., are answered. With the answers, we get a systematical view of the current practice of using WE for the SE domain, and figure out the challenges and actions in adopting or developing practical semantic representation approaches for the SE artifacts used in a series of SE tasks.

cs.SE↗

arXiv2024

Chasing Better Deep Image Priors between Over- and Under-parameterization

Deep Neural Networks (DNNs) are well-known to act as over-parameterized deep image priors (DIP) that regularize various image inverse problems. Meanwhile, researchers also proposed extremely compact, under-parameterized image priors (e.g., deep decoder) that are strikingly competent for image restoration too, despite a loss of accuracy. These two extremes push us to think whether there exists a better solution in the middle: between over- and under-parameterized image priors, can one identify "intermediate" parameterized image priors that achieve better trade-offs between performance, efficiency, and even preserving strong transferability? Drawing inspirations from the lottery ticket hypothesis (LTH), we conjecture and study a novel "lottery image prior" (LIP) by exploiting DNN inherent sparsity, stated as: given an over-parameterized DNN-based image prior, it will contain a sparse subnetwork that can be trained in isolation, to match the original DNN's performance when being applied as a prior to various image inverse problems. Our results validate the superiority of LIPs: we can successfully locate the LIP subnetworks from over-parameterized DIPs at substantial sparsity ranges. Those LIP subnetworks significantly outperform deep decoders under comparably compact model sizes (by often fully preserving the effectiveness of their over-parameterized counterparts), and they also possess high transferability across different images as well as restoration task types. Besides, we also extend LIP to compressive sensing image reconstruction, where a pre-trained GAN generator is used as the prior (in contrast to untrained DIP or deep decoder), and confirm its validity in this setting too. To our best knowledge, this is the first time that LTH is demonstrated to be relevant in the context of inverse problems or image priors.

cs.CV↗

arXiv2024

Experimental practical quantum tokens with transaction time advantage

Quantum money is the first invention in quantum information science, promising advantages over classical money by simultaneously achieving unforgeability, user privacy, and instant validation. However, standard quantum money relies on quantum memories and long-distance quantum communication, which are technologically extremely challenging. Quantum "S-money" tokens eliminate these technological requirements while preserving unforgeability, user privacy, and instant validation. Here, we report the first full experimental demonstration of quantum S-tokens, proven secure despite errors, losses and experimental imperfections. The heralded single-photon source with a high system efficiency of 88.24% protects against arbitrary multi-photon attacks arising from losses in the quantum token generation. Following short-range quantum communication, the token is stored, transacted, and verified using classical bits. We demonstrate a transaction time advantage over intra-city 2.77 km and inter-city 60.54 km optical fibre networks, compared with optimal classical cross-checking schemes. Our implementation demonstrates the practicality of quantum S-tokens for applications requiring high security, privacy and minimal transaction times, like financial trading and network control. It is also the first demonstration of a quantitative quantum time advantage in relativistic cryptography, showing the enhanced cryptographic power of simultaneously considering quantum and relativistic physics.

quant-ph↗

arXiv2024

Expressive Power of Graph Neural Networks for (Mixed-Integer) Quadratic Programs

Quadratic programming (QP) is the most widely applied category of problems in nonlinear programming. Many applications require real-time/fast solutions, though not necessarily with high precision. Existing methods either involve matrix decomposition or use the preconditioned conjugate gradient method. For relatively large instances, these methods cannot achieve the real-time requirement unless there is an effective preconditioner. Recently, graph neural networks (GNNs) opened new possibilities for QP. Some promising empirical studies of applying GNNs for QP tasks show that GNNs can capture key characteristics of an optimization instance and provide adaptive guidance accordingly to crucial configurations during the solving process, or directly provide an approximate solution. However, the theoretical understanding of GNNs in this context remains limited. Specifically, it is unclear what GNNs can and cannot achieve for QP tasks in theory. This work addresses this gap in the context of linearly constrained QP tasks. In the continuous setting, we prove that message-passing GNNs can universally represent fundamental properties of convex quadratic programs, including feasibility, optimal objective values, and optimal solutions. In the more challenging mixed-integer setting, while GNNs are not universal approximators, we identify a subclass of QP problems that GNNs can reliably represent.

cs.LG↗