arXiv subjects

Dongwei Fan

Publications and source records attributed to Dongwei Fan.

At least 19 recordsLinked to original sources

arXiv2026

GSED: The Galactic Stellar Extinction Database

Reliable extinction correction is essential for nearly all astrophysical studies within the Galaxy. We present the Galactic Stellar Extinction Database (GSED, https://nadc.china-vo.org/data/gsed/), a homogenised database that unifies six representative 3D extinction datasets under a common $E(B-V)$ and parallax-distance baseline. A six-layer multilayer perceptron is designed to correct the systematic differences in both extinction and distance across the heterogeneous input catalogues. Applying the trained models yields a catalogue of over 1.9 billion homogenised entries, which is built into a publicly accessible, real-time query service: a user supplies a coordinate and a search radius, the system retrieves the data, fits the distance--extinction relation, returns $E(B-V)$ together with $E(G_{\rm BP}-G_{\rm RP})$ and $A_V$, and allows the raw catalogue and the fitted curve to be downloaded. By delivering extinction as raw stellar measurements rather than voxelised map products and retaining the capacity to incorporate future datasets, GSED provides a flexible, traceable, and extensible new tool for Galactic extinction correction and dust-structure studies.

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arXiv2026

A Gaia-linked High-purity QSO Candidate Catalog in Selected Fields with Extinction-binned Calibration and Spectrum-informed Training

We present an extinction-calibrated, Gaia-source-level QSO candidate catalog for selected fields, designed as a high-purity input catalog for fiber-spectroscopic follow-up rather than as an all-sky QSO census. The deployed selector uses Gaia astrometry and photometry, optical/infrared catalog features, and E(B-V)-binned threshold calibration; spectra are used only during training via a source-grouped spectrum-teacher model. The sample definition is layered: a four-field core domain ladder provides the main validation baseline, four application/stress-test fields probe portability, and COSMOS is treated separately as an Extreme Deep boundary case. At the recommended conservative operating point, calibrated to a validation-set purity of 0.98, the P3 spectrum-informed catalog selector achieves a measured test-set purity of 0.9809 and a spectroscopic-label completeness of 0.8869 within the frozen Gaia-linked benchmark, whereas the Gaia official QSO probability yields a spectroscopic-label completeness of 0.4493 under the same threshold protocol. The evaluation protocol excludes downstream validation/test Gaia source IDs from teacher fitting and checkpoint selection, and uses teacher probabilities only for downstream training rows. Relative to the earlier P2 teacher, P3 yields a modest mean completeness gain across five seeds, with a small decrease in purity and a small increase in false positives; the gain is most evident in higher-extinction and faint-source diagnostics. The released product is a catalog and empirical selection-function data product with source identifiers, field-layer assignments, input-coverage flags, calibrated scores, threshold flags, validation metadata, and provenance/QC fields. In COSMOS, the Gaia-linked parent set is much shallower than COSMOS2020; the robust 39-object subset is interpreted as a purity-oriented priority list rather than a completeness measurement.

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arXiv2026

Homogenization of the Stetson Photometry with the BEST Database

As one of the most widely recognized high-quality standard stars, the Stetson standards have been extensively used as a photometric reference for calibrating other surveys. In this work, we present an independent validation and re-calibration of the Stetson standard star photometry using the BEST database. Based on typically 30,000-70,000 calibration stars per band, we find that the original Stetson photometry achieves field-to-field zero-point precisions of approximately 10--40\,mmag in the $UBVRI$-band. In addition, significant spatially dependent magnitude offsets are detected within individual Stetson fields for all bands, with magnitudes exceeding 1\%, probably caused by the calibration errors in the Stetson photometry. After correcting those systematic errors, the agreement between the Stetson and BEST photometry is improved to $\sim$5\,mmag for individual fields for $BVRI$-band. The re-calibrated photometry is further validated using the SCR standards, yielding agreement better than 10\,mmag for individual stars in the $BVRI$ bands and confirming zero-point precisions of 2--4\,mmag in the $BVI$ band. The precisions is further confirmed by checks using Gaia DR3 broad band colors. These results highlight the power of the BEST database for improving photometric calibration and suggest that, if feasible, it be incorporated into the calibration process of future releases of the Stetson standard catalog.

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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.

astro-ph.IM↗

arXiv2026

A Large and Precise All-Sky Photometric Standard Star Dataset Across More Than 200 Passbands

High-precision photometric standard stars play a key role in enabling accurate photometric calibration and advancing various fields of astronomy. However, due to limitations in calibration methods and the limited availability and underuse of high-precision reference data, existing photometric standard stars may suffer from insufficient numbers, systematic errors exceeding 10 milli-magnitude (mmag), limited photometric band coverage, or incomplete sky coverage, among other issues. To overcome these limitations, we have constructed the largest (over 200 million stars, 1000 times the widely recognized Landolt standards in the same magnitude range), most precise (better than 10 mmag), and most comprehensive (over 200 bands, nearly 40 times the coverage of traditional standards) all-sky standard stars. Based on standards, we have calibrated multiple survey datasets to mmag precision, and subsequently developed a complete sky distribution of stars for the Pan-STARRS system. This database, the BEst STars Database (BEST), is expected to pave the way for achieving mmag-level - or even higher - photometric precision in large-scale surveys, and to play a central role in shaping a high-precision astronomical measurement framework.

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arXiv2025

Mock Observations for the CSST Mission: Main Surveys -- the Mock Catalogue

The Chinese Space Station Survey Telescope (CSST) is a flagship space mission, designed to carry out a large-area sky survey to explore the nature of dark matter and dark energy in the Universe. The onboard multi-band imaging and slitless spectroscopic modules will enable us to obtain photometric data for billions of galaxies and stars, as well as hundreds of millions of spectroscopic measurements, advancing various scientific analyses such as galaxy clustering and weak gravitational lensing. To support the image simulations for the main survey of the CSST mission, we present a mock catalogue of stars and galaxies. For stars, the mock catalogue is generated using either Galaxia or TRILEGAL, both of which provide a range of stellar properties to meet the requirements of CSST image simulations. For galaxies, we built a mock light-cone up to redshift z~3.5 from the cosmological Nbody simulation and populated the mock galaxy catalogue from the dark mater haloes using a semi-analytical galaxy formation model. We then performed a full-sky ray-tracing simulation of weak gravitational lensing to obtain lensing shear at the position of each galaxy in the light-cone. To support both multi-band imaging and slitless spectroscopic simulations, we computed the spectral energy distribution (SED) for each galaxy based on its star formation history using a supervised deep-learning model and determined the magnitudes in each band using the CSST throughputs. Finally, the properties of our mock galaxies include positions, redshifts, stellar masses, shapes, sizes, SEDs, lensing shears and magnifications. We have validated our mock catalogue against observational data and theoretical models, with results showing good overall agreement. The catalogue provides a flexible dataset for the development of CSST image processing and can support a wide range of cosmological analyses within the CSST mission.

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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.

astro-ph.IM↗

arXiv2025

FALCO: a Foundation model of Astronomical Light Curves for time dOmain astronomy

Time-domain surveys have advanced astronomical research by revealing diverse variable phenomena, from stellar flares to transient events. The scale and complexity of survey data, along with the demand for rapid classification, present significant challenges for analysis. While machine learning offers solutions, most existing models are tailored to single tasks, struggle to generalize, and depend heavily on large, accurately labeled datasets. We introduce FALCO, a foundation model for astronomical light curve analysis in time-domain astronomy. This work presents the initial version of FALCO trained via self-supervised learning on unlabeled Kepler light curves using a Transformer-based architecture. The model has been evaluated on three distinct tasks and demonstrates strong generalization: achieving 95 percent accuracy in stellar variability classification across eight classes, an overall RMSE of 0.1305 dex in surface gravity estimation (notably improved to below 0.08 dex when log g is less than 1, and approximately 0.02 dex near log g equals 3), and 87 percent precision in flare identification. These results highlight the model's versatility and ability to learn generalizable representations from light curves, enabling straightforward adaptation to diverse tasks. We further analyze the impact of model scaling and sequence length, finding performance improves with larger models and longer input sequences. We also apply FALCO to derive surface gravity (log g) measurements for 179,732 Kepler stars from their light curves.

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arXiv2025

A Highly Efficient Cross-matching Scheme using Learned Index Structure

Spatial data fusion is a bottleneck when it meets the scale of 10 billion records. Cross-matching celestial catalogs is just one example of this. To challenge this, we present a framework that enables efficient cross-matching using Learned Index Structures. Our approach involves a data transformation method to map multi-dimensional data into easily learnable distributions, coupled with a novel search algorithm that leverages the advantages of model pairs, significantly enhancing the efficiency of nearest-neighbor search. In this study, we utilized celestial catalog data derived from astronomical surveys to construct the index and evaluated the speed of the cross-matching process. Using the HEALPix segmentation scheme, we built an independent model object for each tile and developed an end-to-end pipeline to construct a framework with semantic guarantees for record retrieval in query and range search. Our results show that the proposed method improves cross-matching speed by more than four times compared to KD-trees for a radius range between 1 milli-arcseconds and 100 arcseconds.

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arXiv2025

Identification of a dwarf galaxy stream in Gaia, and its possible association with the VPOS structure

Low surface density streams are important tracers to study the formation and evolution of the Milky Way. Using the accurate astrometric measurements from Gaia mission, we discover a low surface density stream in the north hemisphere, with length of $\sim110$ degree and width of $1.23$ kpc. The vertical velocity dispersion perpendicular to the stream is around $22.4$ km s$^{-1}$. The spectral data of member candidate stars from LAMOST and DESI shows a large metallicity range from $-1.8$ to $-0.7$. Based on those properties we claim that the stream is originated from a dwarf galaxy. The median metallicity of $\mathrm{[Fe/H]}=-1.3$ indicates a massive dwarf galaxy origination with stellar mass around $2.0\times10^7M_\odot$, which is comparable with the Fornax dwarf galaxy and smaller than LMC/SMC and Sagittarius. We also find the globular cluster Pyxis is highly associated with the stream in the phase space $E-L_Z$ and metallicity. The massive progenitor also suggests that many dwarf galaxies, including massive ones, have been disrupted during their evolution orbiting the Milky Way and left with very low surface density structures. This is important to understand the {\it missing satellites} problem. The orbit information of the stream shows tight association between its progenitor and the Vast POlar Structure (VPOS), which indicates that the satellites fell into the Milky Way in groups, which brought many globular clusters into the Milky Way.

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arXiv2024

Einstein Probe discovery of EP240408a: a peculiar X-ray transient with an intermediate timescale

We report the discovery of a peculiar X-ray transient, EP240408a, by Einstein Probe (EP) and follow-up studies made with EP, Swift, NICER, GROND, ATCA and other ground-based multi-wavelength telescopes. The new transient was first detected with Wide-field X-ray Telescope (WXT) on board EP on April 8th, 2024, manifested in an intense yet brief X-ray flare lasting for 12 seconds. The flare reached a peak flux of 3.9x10^(-9) erg/cm2/s in 0.5-4 keV, about 300 times brighter than the underlying X-ray emission detected throughout the observation. Rapid and more precise follow-up observations by EP/FXT, Swift and NICER confirmed the finding of this new transient. Its X-ray spectrum is non-thermal in 0.5-10 keV, with a power-law photon index varying within 1.8-2.5. The X-ray light curve shows a plateau lasting for about 4 days, followed by a steep decay till becoming undetectable about 10 days after the initial detection. Based on its temporal property and constraints from previous EP observations, an unusual timescale in the range of 7-23 days is found for EP240408a, which is intermediate between the commonly found fast and long-term transients. No counterparts have been found in optical and near-infrared, with the earliest observation at 17 hours after the initial X-ray detection, suggestive of intrinsically weak emission in these bands. We demonstrate that the remarkable properties of EP240408a are inconsistent with any of the transient types known so far, by comparison with, in particular, jetted tidal disruption events, gamma-ray bursts, X-ray binaries and fast blue optical transients. The nature of EP240408a thus remains an enigma. We suggest that EP240408a may represent a new type of transients with intermediate timescales of the order of about 10 days. The detection and follow-ups of more of such objects are essential for revealing their origin.

astro-ph.HE↗

arXiv2024

Data-driven stellar intrinsic colors and dust reddenings for spectro-photometric data: From the blue-edge method to a machine-learning approach

Intrinsic colors (ICs) of stars are essential for the studies on both stellar physics and dust reddening. In this work, we developed an XGBoost model to predict the ICs with the atmospheric parameters $T_{\rm eff}$, ${\rm log}\,g$, and $\rm [M/H]$. The model was trained and tested for three colors at Gaia and 2MASS bands with 1,040,446 low-reddening sources. The atmospheric parameters were determined by the Gaia DR3 GSP-phot module and were validated by comparing with APOGEE and LAMOST. We further confirmed that the biases in GSP-phot parameters, especially for $\rm [M/H]$, do not present a significant impact on the IC prediction. The generalization error of the model estimated by the test set is 0.014 mag for $(G_{\rm BP}\,{-}\,G_{\rm RP})_0$, 0.050 mag for $(G_{\rm BP}\,{-}\,K_{\rm S})_0$, and 0.040 mag for $(J\,{-}\,K_{\rm S})_0$. The model was applied to a sample containing 5,714,528 reddened stars with stellar parameters from Andrae et al. (2023) to calculate ICs and reddenings. The high consistency in the comparison of $E(J\,{-}\,K_{\rm S})$ between our results and literature values further validates the accuracy of the XGBoost model. The variation of $E(G_{\rm BP}\,{-}\,K_{\rm S})/E(G_{\rm BP}\,{-}\,G_{\rm RP})$, a representation of the extinction law, with Galactic longitude is found on large scales. This work preliminarily presents the feasibility and the accuracy of the machine-learning approach for IC and dust reddening calculation, whose products could be widely applied to spectro-photometric data. The data sets and trained model can be accessed via \url{https://doi.org/10.5281/zenodo.12787594}. The models for more bands will be completed in the following works.

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arXiv2023

Astronomical Knowledge Entity Extraction in Astrophysics Journal Articles via Large Language Models

Astronomical knowledge entities, such as celestial object identifiers, are crucial for literature retrieval and knowledge graph construction, and other research and applications in the field of astronomy. Traditional methods of extracting knowledge entities from texts face challenges like high manual effort, poor generalization, and costly maintenance. Consequently, there is a pressing need for improved methods to efficiently extract them. This study explores the potential of pre-trained Large Language Models (LLMs) to perform astronomical knowledge entity extraction (KEE) task from astrophysical journal articles using prompts. We propose a prompting strategy called Prompt-KEE, which includes five prompt elements, and design eight combination prompts based on them. Celestial object identifier and telescope name, two most typical astronomical knowledge entities, are selected to be experimental object. And we introduce four currently representative LLMs, namely Llama-2-70B, GPT-3.5, GPT-4, and Claude 2. To accommodate their token limitations, we construct two datasets: the full texts and paragraph collections of 30 articles. Leveraging the eight prompts, we test on full texts with GPT-4 and Claude 2, on paragraph collections with all LLMs. The experimental results demonstrated that pre-trained LLMs have the significant potential to perform KEE tasks in astrophysics journal articles, but there are differences in their performance. Furthermore, we analyze some important factors that influence the performance of LLMs in entity extraction and provide insights for future KEE tasks in astrophysical articles using LLMs.

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arXiv2023

The first comprehensive Milky Way stellar mock catalogue for the Chinese Space Station Telescope Survey Camera

The Chinese Space Station Telescope (CSST) is a cutting-edge two-meter astronomical space telescope currently under construction. Its primary Survey Camera (SC) is designed to conduct large-area imaging sky surveys using a sophisticated seven-band photometric system. The resulting data will provide unprecedented data for studying the structure and stellar populations of the Milky Way. To support the CSST development and scientific projects related to its survey data, we generate the first comprehensive Milky Way stellar mock catalogue for the CSST SC photometric system using the TRILEGAL stellar population synthesis tool. The catalogue includes approximately 12.6 billion stars, covering a wide range of stellar parameters, photometry, astrometry, and kinematics, with magnitude reaching down to $g\,=\,27.5$ mag in the AB magnitude system. The catalogue represents our benchmark understanding of the stellar populations in the Milky Way, enabling a direct comparison with the future CSST survey data. Particularly, it sheds light on faint stars that are hidden from current sky surveys. Our crowding limit analysis based on this catalogue provides compelling evidence for the extension of the CSST Optical Survey (OS) to cover low Galactic latitude regions. The strategic extension of the CSST-OS coverage, combined with this comprehensive mock catalogue, will enable transformative science with the CSST.

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arXiv2023

CSST WL preparation I: forecast the impact from non-Gaussian covariances and requirements on systematics-control

The precise estimation of the statistical errors and accurate removal of the systematical errors are the two major challenges for the stage IV cosmic shear surveys. We explore their impact for the China Space-Station Telescope (CSST) with survey area $\sim17,500\deg^2$ up to redshift $\sim4$. We consider statistical error contributed from Gaussian covariance, connected non-Gaussian covariance and super-sample covariance. We find the non-Gaussian covariances, which is dominated by the super-sample covariance, can largely reduce the signal-to-noise of the two-point statistics for CSST, leading to a $\sim1/3$ loss in the figure-of-merit for the matter clustering properties ($\sigma_8-\Omega_m$ plane) and $1/6$ in the dark energy equation-of-state ($w_0-w_a$ plane). We further put requirements of systematics-mitigation on: intrinsic alignment of galaxies, baryonic feedback, shear multiplicative bias, and bias in the redshift distribution, for an unbiased cosmology. The $10^{-2}$ to $10^{-3}$ level requirements emphasize strong needs in related studies, to support future model selections and the associated priors for the nuisance parameters.

astro-ph.CO↗

arXiv2022

Photometric redshift estimation of galaxies in the DESI Legacy Imaging Surveys

The accurate estimation of photometric redshifts plays a crucial role in accomplishing science objectives of the large survey projects. The template-fitting and machine learning are the two main types of methods applied currently. Based on the training set obtained by cross-correlating the DESI Legacy Imaging Surveys DR9 galaxy catalogue and SDSS DR16 galaxy catalogue, the two kinds of methods are used and optimized, such as EAZY for template-fitting approach and CATBOOST for machine learning. Then the created models are tested by the cross-matched samples of the DESI Legacy Imaging SurveysDR9 galaxy catalogue with LAMOST DR7, GAMA DR3 and WiggleZ galaxy catalogues. Moreover three machine learning methods (CATBOOST, Multi-Layer Perceptron and Random Forest) are compared, CATBOOST shows its superiority for our case. By feature selection and optimization of model parameters, CATBOOST can obtain higher accuracy with optical and infrared photometric information, the best performance ($MSE=0.0032$, $\sigma_{NMAD}=0.0156$ and $O=0.88$ per cent) with $g \le 24.0$, $r \le 23.4$ and $z \le 22.5$ is achieved. But EAZY can provide more accurate photometric redshift estimation for high redshift galaxies, especially beyond the redhisft range of training sample. Finally, we finish the redshift estimation of all DESI DR9 galaxies with CATBOOST and EAZY, which will contribute to the further study of galaxies and their properties.

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arXiv2021

Photometric Redshift Estimation of BASS DR3 Quasars by Machine Learning

Correlating BASS DR3 catalogue with ALLWISE database, the data from optical and infrared information are obtained. The quasars from SDSS are taken as training and test samples while those from LAMOST are considered as external test sample. We propose two schemes to construct the redshift estimation models with XGBoost, CatBoost and Random forest. One scheme (namely one-step model) is to predict photometric redshifts directly based on the optimal models created by these three algorithms; the other scheme (namely two-step model) is to firstly classify the data into low- and high- redshift datasets, and then predict photometric redshifts of these two datasets separately. For one-step model, the performance of these three algorithms on photometric redshift estimation is compared with different training samples, and CatBoost is superior to XGBoost and Random forest. For two-step model, the performance of these three algorithms on the classification of low- and high-redshift subsamples are compared, and CatBoost still shows the best performance. Therefore CatBoost is regard as the core algorithm of classification and regression in two-step model. By contrast with one-step model, two-step model is optimal when predicting photometric redshift of quasars, especially for high redshift quasars. Finally the two models are applied to predict photometric redshifts of all quasar candidates of BASS DR3. The number of high redshift quasar candidates is 3938 (redshift $\ge 3.5$) and 121 (redshift $\ge 4.5$) by two-step model. The predicted result will be helpful for quasar research and follow up observation of high redshift quasars.

astro-ph.IM↗

arXiv2021

Identification of BASS DR3 Sources as Stars, Galaxies and Quasars by XGBoost

The Beijing-Arizona Sky Survey (BASS) Data Release 3 (DR3) catalogue was released in 2019, which contains the data from all BASS and the Mosaic z-band Legacy Survey (MzLS) observations during 2015 January and 2019 March, about 200 million sources. We cross-match BASS DR3 with spectral databases from the Sloan Digital Sky Survey (SDSS) and the Large Sky Area Multi-object Fiber Spectroscopic Telescope (LAMOST) to obtain the spectroscopic classes of known samples. Then, the samples are cross-matched with ALLWISE database. Based on optical and infrared information of the samples, we use the XGBoost algorithm to construct different classifiers, including binary classification and multiclass classification. The accuracy of these classifiers with the best input pattern is larger than 90.0 per cent. Finally, all selected sources in the BASS DR3 catalogue are classified by these classifiers. The classification label and probabilities for individual sources are assigned by different classifiers. When the predicted results by binary classification are the same as multiclass classification with optical and infrared information, the number of star, galaxy and quasar candidates is separately 12 375 838 (P_S>0.95), 18 606 073 (P_G>0.95) and 798 928 (P_Q>0.95). For these sources without infrared information, the predicted results can be as a reference. Those candidates may be taken as input catalogue of LAMOST, DESI or other projects for follow up observation. The classified result will be of great help and reference for future research of the BASS DR3 sources.

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