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We conduct X-ray spectral fits on 184 likely counterparts to Fermi-LAT 3FGLunassociated sources. Characterization and classification of these sourcesallows for more complete population studies of the high-energy sky. Most ofthese X-ray spectra are well fit by an absorbed power law model, as expectedfor a population dominated by blazars and pulsars. A small subset of 7 X-raysources have spectra unlike the power law expected from a blazar or pulsar andmay be linked to coincident stars or background emission. We develop amultiwavelength machine learning classifier to categorize unassociated sourcesinto pulsars and blazars using gamma- and X-ray observations. Training a randomforest procedure with known pulsars and blazars, we achieve a cross-validatedclassification accuracy of 98.6%. Applying the random forest routine to theunassociated sources returned 126 likely blazar candidates (defined as $P_{bzr} > 90 \% $) and 5 likely pulsar candidates ($ P_{bzr} < 10 \% $). Ournew X-ray spectral analysis does not drastically alter the random forestclassifications of these sources compared to previous works, but it builds amore robust classification scheme and highlights the importance of X-rayspectral fitting. Our procedure can be further expanded with UV, visual, orradio spectral parameters or by measuring flux variability.

X-ray Spectra and Multiwavelength Machine Learning Classification for Likely Counterparts to Fermi 3FGL Unassociated Sources