期刊
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
卷 503, 期 4, 页码 5263-5273出版社
OXFORD UNIV PRESS
DOI: 10.1093/mnras/stab744
关键词
methods: data analysis; methods: statistical; astronomical data bases: miscellaneous; catalogues; stars: general; galaxies: general
资金
- National Natural Science Foundation of China [11873066, U1731109]
- National Aeronautics and Space Administration
- National Development and Reform Commission
- Alfred P. Sloan Foundation
- US Department of Energy Office of Science
- Center for High-Performance Computing at the University of Utah
- Brazilian Participation Group
- Carnegie Institution for Science
- Carnegie Mellon University
- Chilean Participation Group
- French Participation Group
- Harvard-Smithsonian Center for Astrophysics
- Instituto de Astrofisica de Canarias
- Johns Hopkins University
- Kavli Institute for the Physics and Mathematics of the Universe (IPMU)/University of Tokyo
- Lawrence Berkeley National Laboratory
- Leibniz Institut fur Astrophysik Potsdam (AIP)
- Max-Planck-Institut fur Astronomie (MPIA Heidelberg)
- Max-PlanckInstitut fur Astrophysik (MPA Garching)
- Max-Planck-Institut fur Extraterrestrische Physik (MPE)
- National Astronomical Observatories of China
- New Mexico State University
- New York University
- University of Notre Dame
- Observatario Nacional/MCTI
- Ohio State University
- Pennsylvania State University
- Shanghai Astronomical Observatory
- United Kingdom Participation Group
- Universidad Nacional Autonoma de Mexico
- University of Arizona
- University of Colorado Boulder
- University of Oxford
- University of Portsmouth
- University of Utah
- University of Virginia
- University of Washington
- University of Wisconsin
- Vanderbilt University
- Yale University
The ESA's XMM-Newton created a new high-quality version of the XMM Newton serendipitous source catalogue, and used machine learning methods to classify X-ray sources from different bands, obtaining the best results. The distribution of stars, galaxies, and quasars in 2D parameter space is presented for further research of X-ray sources.
The ESA's X ray Multi mirror Mission (XMM-Newton) created a new high-quality version of the XMM Newton serendipitous source catalogue, 4XMM-DR9, which provides a wealth of information for observed sources. The 4XMM-DR9 catalogue is correlated with the Sloan Digital Sky Survey (SUSS) DR12 photometric data base and the AllWISE data base; we then get X-ray sources with information from the X-ray, optical, and/or infrared bands and obtain the XMM-WISE, XMM-SDSS, and XMM-WISE-SDSS samples. Based on the large spectroscopic surveys of SUSS and the Large Sky Area Multi-object Fiber Spectroscopic Telescope (LAMOST), we cross-match the XMM-WISE-SDSS sample with sources of known spectral classes, and obtain known samples of stars, galaxies, and quasars. The distribution of stars, galaxies, and quasars as well as all spectral classes of stars in 2D parameter space is presented. Various machine-learning methods are applied to different samples from different bands. The better classified results are retained. For the sample from the X-ray band, a rotation-forest classifier performs the best. For the sample from the X-ray and infrared bands, a random-forest algorithm outperforms all other methods. For the samples from the X-ray, optical, and/or infrared bands, the LogitBoost classifier shows its superiority. Thus, all X-ray sources in the 4XMM-DR9 catalogue with different input patterns are classified by their respective models that are created by these best methods. Their membership of and membership probabilities for individual X-ray sources are assigned. The classified result will be of great value for the further research of X-ray sources in greater detail.
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