Classification of a New X-ray Catalog of Likely Counterparts to 4FGL-DR4 Unassociated Gamma-ray Sources Using a Neural Network

Abstract

Our survey of the fourth $Fermi$ Large Area Telescope catalog (4FGL) unassociated gamma-ray source regions using the X-Ray Telescope (XRT) and Ultraviolet/Optical Telescope (UVOT) aboard the Neil Gehrels $Swift$ Observatory ($Swift$) provides new XRT and UVOT source detections and localizations to help identify potential low-energy counterparts to unassociated $Fermi$ gamma-ray sources. We present a catalog of 218 singlet and 70 multiplet $Swift$ X-ray sources detected within the positional uncertainty ellipses of 244 unassociated $Fermi$ gamma-ray sources from the 4FGL-DR4 catalog, 144 of which are not previously cataloged by Kerby et al. (2021b). For each X-ray source, we derive its X-ray flux and photon index, then use simultaneous UVOT observations with optical survey data to estimate its $V$-band magnitude. We use these parameters as inputs for a multi-layer perceptron (MLP) neural network classifier (NNC) trained to classify sources as blazars, pulsars, or ambiguous gamma-ray sources. For the 213 singlet sources with X-ray and optical data, we classify 173 as likely blazars ($P_{\mathrm{bzr}}>0.99$) and 6 as likely pulsars ($P_{\mathrm{bzr}}<0.01$), with 34 sources yielding ambiguous results. Including 70 multiplet X-ray sources, we increase the number of $P_{\mathrm{bzr}}>0.99$ to 227 and $P_{\mathrm{bzr}}<0.01$ to 16. For the subset of these classifications that have been previously studied, a large majority agree with prior classifications, supporting the validity of using this NNC to classify the unknown and newly detected gamma-ray sources.

19 Pages, 8 Figures, 9 Tables. Data presented in this submission is included in machine-readable format as 2 ancillary files. Accepted for publication in ApJ

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