Search for Young Stellar Objects within 4XMM-DR13 Using CatBoost and SPE

Abstract Classifying and summarizing large data sets from different sky survey projects is essential for various subsequent scientific research. By combining data from 4XMM-DR13, Sloan Digital Sky Survey (SDSS) DR18, and CatWISE, we formed an XMM-WISE-SDSS sample that included information in the X-ray, optical, and infrared bands. By cross matching this sample with data sets from known spectral classifications from SDSS and LAMOST, we obtained a training data set containing stars, galaxies, quasars, and young stellar objects (YSOs). Two machine learning methods, CatBoost and Self-Paced Ensemble (SPE), were used to train and construct machine learning models through training sets to classify the XMM-WISE-SDSS sample. Notably, the SPE classifier showed excellent performance in YSO classification, identifying 1102 YSO candidates from 160,545 sources, including 258 known YSOs. Then we further verify whether these candidates are YSOs by the spectra in LAMOST and the identification in the SIMBAD and VizieR databases. Finally there are 412 unidentified YSO candidates. The discovery of these new YSOs is an important addition to existing YSO samples and will deepen our understanding of star formation and evolution. Moreover we provided a classification catalog for the whole XMM-WISE-SDSS sample.

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Publication Details

Journal
The Astronomical Journal
Published
2024-10-17
DOI
https://doi.org/10.3847/1538-3881/ad781c
Primary Topic
Stellar, planetary, and galactic studies
Type
article
Field-Weighted Citation Impact
0.00

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article

Search for Young Stellar Objects within 4XMM-DR13 Using CatBoost and SPE

Changhua Li, Xiangyao Ma, Zihan Kang, Ji Li et al.
The Astronomical Journal
Stellar, planetary, and galactic studies
article

Search for Young Stellar Objects within 4XMM-DR13 Using CatBoost and SPE

Changhua Li, Xiangyao Ma, Zihan Kang, Ji Li, Jingyi Zhang, Yanxia Zhang
article en

Abstract

Abstract Classifying and summarizing large data sets from different sky survey projects is essential for various subsequent scientific research. By combining data from 4XMM-DR13, Sloan Digital Sky Survey (SDSS) DR18, and CatWISE, we formed an XMM-WISE-SDSS sample that included information in the X-ray, optical, and infrared bands. By cross matching this sample with data sets from known spectral classifications from SDSS and LAMOST, we obtained a training data set containing stars, galaxies, quasars, and young stellar objects (YSOs). Two machine learning methods, CatBoost and Self-Paced Ensemble (SPE), were used to train and construct machine learning models through training sets to classify the XMM-WISE-SDSS sample. Notably, the SPE classifier showed excellent performance in YSO classification, identifying 1102 YSO candidates from 160,545 sources, including 258 known YSOs. Then we further verify whether these candidates are YSOs by the spectra in LAMOST and the identification in the SIMBAD and VizieR databases. Finally there are 412 unidentified YSO candidates. The discovery of these new YSOs is an important addition to existing YSO samples and will deepen our understanding of star formation and evolution. Moreover we provided a classification catalog for the whole XMM-WISE-SDSS sample.

The Astronomical JournalVol. 168(5)
Chinese Academy of Sciences (CN), National Astronomical Observatories (CN), Hebei Normal University (CN)
Smithsonian Institution, U.S. Department of Energy, National Aeronautics and Space Administration, Alfred P. Sloan Foundation, Yale University, Carnegie Institution of Washington, Vanderbilt University, Ohio State University, California Institute of Technology, University of Utah, University of Washington, Johns Hopkins University, Carnegie Mellon University, University of Notre Dame, University of Portsmouth, New Mexico State University, York University, University of Oxford, National Natural Science Foundation of China, Chinese Academy of Sciences, Ministério da Ciência, Tecnologia e Inovação, Universidad Nacional Autónoma de México, National Development and Reform Commission, Max-Planck-Institut für Astrophysik, Max-Planck-Institut für Astronomie, Instituto de Astrofísica de Canarias, Office of Science, Jet Propulsion Laboratory, University of California, Los Angeles, University of Colorado Boulder, Smithsonian Astrophysical Observatory, Leibniz-Gemeinschaft, National Astronomical Observatories, Chinese Academy of Sciences, Lawrence Berkeley National Laboratory
Openalex Percentile: Top 87%
Stellar, planetary, and galactic studies
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