4.7 Article

Classification of 4XMM-DR9 sources by machine learning

期刊

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

  1. National Natural Science Foundation of China [11873066, U1731109]
  2. National Aeronautics and Space Administration
  3. National Development and Reform Commission
  4. Alfred P. Sloan Foundation
  5. US Department of Energy Office of Science
  6. Center for High-Performance Computing at the University of Utah
  7. Brazilian Participation Group
  8. Carnegie Institution for Science
  9. Carnegie Mellon University
  10. Chilean Participation Group
  11. French Participation Group
  12. Harvard-Smithsonian Center for Astrophysics
  13. Instituto de Astrofisica de Canarias
  14. Johns Hopkins University
  15. Kavli Institute for the Physics and Mathematics of the Universe (IPMU)/University of Tokyo
  16. Lawrence Berkeley National Laboratory
  17. Leibniz Institut fur Astrophysik Potsdam (AIP)
  18. Max-Planck-Institut fur Astronomie (MPIA Heidelberg)
  19. Max-PlanckInstitut fur Astrophysik (MPA Garching)
  20. Max-Planck-Institut fur Extraterrestrische Physik (MPE)
  21. National Astronomical Observatories of China
  22. New Mexico State University
  23. New York University
  24. University of Notre Dame
  25. Observatario Nacional/MCTI
  26. Ohio State University
  27. Pennsylvania State University
  28. Shanghai Astronomical Observatory
  29. United Kingdom Participation Group
  30. Universidad Nacional Autonoma de Mexico
  31. University of Arizona
  32. University of Colorado Boulder
  33. University of Oxford
  34. University of Portsmouth
  35. University of Utah
  36. University of Virginia
  37. University of Washington
  38. University of Wisconsin
  39. Vanderbilt University
  40. Yale University

向作者/读者索取更多资源

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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