Automated classification of Chandra X-ray point sources using machine learning methods

A large number of unidentified sources found by astronomical surveys and other observations necessitate the use of an automated classification technique based on machine learning methods. The aim of this paper is to find a suitable automated classifier to identify the point X-ray sources in the Chandra Source Catalogue (CSC) 2.0 in the categories of active galactic nuclei (AGN), X-ray emitting stars, young stellar objects (YSOs), high-mass X-ray binaries (HMXBs), low-mass X-ray binaries (LMXBs), ultra luminous X-ray sources (ULXs), cataclysmic variables (CVs), and pulsars. The catalogue consists of 3, 17, 000 sources, out of which we select 2,77,069 point sources based on the quality flags available in CSC 2.0. In order to identify unknown sources of CSC 2.0, we use multi-wavelength features, such as magnitudes in optical/UV bands from Gaia-EDR3, SDSS and GALEX, and magnitudes in IR bands from 2MASS, WISE and MIPS-Spitzer, in addition to X-ray features (flux and variability) from CSC 2.0. We find the Light Gradient Boosted Machine, an advanced decision tree-based machine learning classification algorithm, suitable for our purpose and achieve 93% precision, 93% recall score and 0.91 Mathew's Correlation coefficient score.