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Identifying X-ray binary (XRB) candidates in nearby galaxies requiresdistinguishing them from possible contaminants including foreground stars andbackground active galactic nuclei. This work investigates the use of supervisedmachine learning algorithms to identify high-probability X-ray binarycandidates. Using a catalogue of 943 Chandra X-ray sources in the Andromedagalaxy, we trained and tested several classification algorithms using the X-rayproperties of 163 sources with previously known types. Amongst the algorithmstested, we find that random forest classifiers give the best performance andwork better in a binary classification (XRB/non-XRB) context compared to theuse of multiple classes. Evaluating our method by comparing withclassifications from visible-light and hard X-ray observations as part of thePanchromatic Hubble Andromeda Treasury, we find compatibility at the 90% level,although we caution that the number of sources in common is rather small. Theestimated probability that an object is an X-ray binary agrees well between therandom forest binary and multiclass approaches and we find that theclassifications with the highest confidence are in the X-ray binary class. Themost discriminating X-ray bands for classification are the 1.7-2.8, 0.5-1.0,2.0-4.0, and 2.0-7.0 keV photon flux ratios. Of the 780 unclassified sources inthe Andromeda catalogue, we identify 16 new high-probability X-ray binarycandidates and tabulate their properties for follow-up.

Identifying New X-ray Binary Candidates in M31 using Random Forest Classification