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

Our survey of the fourth $\mathit{Fermi}$ 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 $\mathit{Swift}$ Observatory ($\mathit{Swift}$) provides new XRT and UVOT source detections and localizations to help identify potential low-energy counterparts to unassociated $\mathit{Fermi}$ gamma-ray sources. We present a catalog of 218 singlet and 70 multiplet $\mathit{Swift}$ X-ray sources detected within the positional uncertainty ellipses of 244 unassociated $\mathit{Fermi}$ 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.