arXiv subjects

Shirui Wei

Publications and source records attributed to Shirui Wei.

5 recordsLinked to original sources

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.

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arXiv2026

A Value-added Physical Properties Catalog for Low-redshift Galaxies from DESI Legacy Imaging Surveys DR10

Galaxy physical properties-such as star formation rate (SFR), stellar mass, and gas-phase metallicity-are essential for population studies and evolutionary analyses. Deriving these quantities for billions of galaxies in modern imaging surveys presents significant challenges due to limited spectroscopy and the computational costs associated with traditional spectral energy distribution fitting. As a result, many galaxies in large photometric surveys still lack homogeneous property estimates. This study introduces a multimodal deep learning model that integrates optical imaging with photometric catalog features to estimate SFR, stellar mass, and oxygen abundance in low-redshift galaxies. The model incorporates a ResNet-based convolutional neural network to extract spatial information from multiband images and a multilayer perceptron that processes catalog-level photometric features, leveraging complementary constraints from morphology, surface brightness, and broadband colors. Trained on reference measurements from the MPA-JHU DR8 catalog, the model is optimized for efficient large-scale estimation. When applied to the DESI Legacy Imaging Surveys (LS) DR10, the model generates a value-added catalog containing physical property estimates for approximately 547 million galaxies with redshifts z <= 0.5. Validation through comparisons with independent catalogs and exploration of key scaling relations demonstrates that while the derived properties are not intended for precision measurements of individual objects, they effectively capture the dominant astrophysical trends necessary for ensemble studies. This catalog represents the first homogeneous set of photometry-based SFR, stellar mass, and metallicity estimates for DESI LS DR10, providing a vital resource for statistical studies of galaxies in the local Universe and facilitating comparisons with current and future spectroscopic surveys.

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arXiv2026

Deep learning-based astronomical multimodal data fusion: A comprehensive review

With the rapid advancements in observational technologies and the widespread implementation of large-scale sky surveys, diverse electromagnetic wave data (e.g., optical and infrared) and non-electromagnetic wave data (e.g., gravitational waves) have become increasingly accessible. Astronomy has thus entered an unprecedented era of data abundance and complexity. Astronomers have long relied on unimodal data analysis to perceive the universe, but these efforts often provide only limited insights when confronted with the current massive and heterogeneous astronomical data. In this context, multimodal data fusion (MDF), as an emerging method, provides new opportunities to enhance the value of astronomical data and deepening the understanding of the universe by integrating information from different modalities. Recent progress in artificial intelligence (AI), particularly in deep learning (DL), has greatly accelerated the development of multimodal research in astronomy. Therefore, a timely review of this field is essential. This paper begins by discussing the motivation and necessity of astronomical MDF, followed by an overview of astronomical data sources and major data modalities. It then introduces representative DL models commonly used in astronomical multimodal studies, the general fusion process as well as various fusion strategies, emphasizing their characteristics, applicability, advantages, and limitations. Subsequently, the paper surveys existing astronomical multimodal studies and datasets. Finally, the discussion section synthesizes key findings, identifies potential challenges, and suggests promising directions for future research. By offering a structured overview and critical analysis, this review aims to inspire and guide researchers engaged in DL-based MDF in astronomy.

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arXiv2025

A Catalogue of Mid-infrared Variable Sources from unTimely

The WISE and NEOWISE missions have provided the only mid-infrared all-sky time-domain data, opening a unique observational window for variability studies. Yet, a comprehensive and systematic catalog of mid-infrared variable sources has remained unavailable. In this work, we construct the first large-scale mid-infrared variability catalog based on the unTimely coadded photometry, covering tens of millions of sources. By employing a Bayesian Gaussian mixture model with a Dirichlet process, we identified 8,256,042 variable sources in the W1 band and 7,147,661 in the W2 band, significantly expanding the landscape of known mid-infrared variables. In addition to robust variability metrics, our analysis highlights rare and extreme outliers through dedicated outlier-detection algorithms, enabling the discovery of unusual classes of objects such as eruptive young stellar objects, highly variable active galactic nuclei, and other rare transients. This unprecedented dataset provides a new foundation for time-domain astronomy in the mid-infrared, offering complementary insights to optical and near-infrared surveys, and opening the door to systematic investigations of stellar evolution, accretion processes, and dust-enshrouded astrophysical environments on a Galactic and extragalactic scale.

astro-ph.IM↗

arXiv2025

Photometric redshift estimation for emission line galaxies of DESI Legacy Imaging Surveys by CNN-MLP

Emission Line Galaxies (ELGs) are crucial for cosmological studies, particularly in understanding the large-scale structure of the Universe and the role of dark energy. ELGs form an essential component of the target catalogue for the Dark Energy Spectroscopic Instrument (DESI), a major astronomical survey. However, the accurate selection of ELGs for such surveys is challenging due to the inherent uncertainties in determining their redshifts with photometric data. In order to improve the accuracy of photometric redshift estimation for ELGs, we propose a novel approach CNN-MLP that combines Convolutional Neural Networks (CNNs) with Multilayer Perceptrons (MLPs). This approach integrates both images and photometric data derived from the DESI Legacy Imaging Surveys Data Release 10. By leveraging the complementary strengths of CNNs (for image data processing) and MLPs (for photometric feature integration), the CNN-MLP model achieves a $\sigma_{\mathrm{NMAD}}$ (normalised median absolute deviation) of 0.0140 and an outlier fraction of 2.57%. Compared to other models, CNN-MLP demonstrates a significant improvement in the accuracy of ELG photometric redshift estimation, which directly benefits the target selection process for DESI. In addition, we explore the photometric redshifts of different galaxy types (Starforming, Starburst, AGN, Broadline). Furthermore, this approach will contribute to more reliable photometric redshift estimation in ongoing and future large-scale sky surveys (e.g. LSST, CSST, Euclid), enhancing the overall efficiency of cosmological research and galaxy surveys.

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