A Multiwavelength Machine-learning Approach to Classifying X-Ray Sources in the Fields of Unidentified 4FGL-DR4 Sources

Abstract

Abstract A large fraction of Fermi-Large Area Telescope (LAT) sources in the fourth Fermi-LAT 14 yr catalog (4FGL) still remain unidentified (unIDed). We continued to improve our machine-learning pipeline and used it to classify 1206 X-ray sources with signal-to-noise ratios >3 located within the extent of 73 unIDed 4FGL sources with Chandra X-ray Observatory observations included in the Chandra Source Catalog 2.0. Recent improvements to our pipeline include astrometric corrections, probabilistic cross-matching to lower-frequency counterparts, and a more realistic oversampling method. X-ray sources are classified into eight broad predetermined astrophysical classes defined in the updated training data set, which we also release. We present details of the machine-learning classification, describe the pipeline improvements, and perform an additional spectral and variability analysis for brighter sources. The classifications give 103 plausible X-ray counterparts to 42 GeV sources. We identify 2 GeV sources as isolated neutron star candidates, 16 as active galactic nucleus candidates, seven as sources associated with star-forming regions, and eight as ambiguous cases. For the remaining 40 unIDed 4FGL sources, we could not identify any plausible counterpart in X-rays, or they are too close to the Galactic Center. Finally, we outline the observational strategies and further improvements in the pipeline that can lead to more accurate classifications.