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 (ML) 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 approximate to 317 000 sources, out of which we select 277 069 point sources based on the quality flags available in CSC 2.0. In order to identify unknown sources of CSC 2.0, we use MW features, such as magnitudes in optical/ultraviolet bands from Gaia-EDR3, Sloan Digital Sky Survey, and GALEX, and magnitudes in infrared 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 ML classification algorithm, suitable for our purpose and achieve 93 per cent precision, 93 per cent recall score, and 0.91 Mathew's Correlation coefficient score. With the trained classifier, we identified 54 770 (14 066) sources with more than 3 sigma (4 sigma) confidence, out of which there are 32 600 (8574) AGNs, 16,148 (5,166) stars, 5,184 (208) YSOs, 439 (46) HMXBs, 197 (71) LMXBs, 50 (0) ULXs, 89 (1) CVs, and 63 (0) pulsars. This method can also be useful for identifying sources of other catalogues reliably.