A Comparative Study of Machine-learning Methods for X-Ray Binary Classification

Abstract

Abstract X-ray binaries (XRBs) consist of a compact object that accretes material from an orbiting secondary star. The most secure method we have for determining if the compact object is a black hole is to determine its mass: This is limited to bright objects and requires substantial time-intensive spectroscopic monitoring. With new X-ray sources being discovered with different X-ray observatories, developing efficient, robust means to classify compact objects becomes increasingly important. We compare three machine-learning classification methods (Bayesian Gaussian Processes (BGPs), K-Nearest Neighbors (KNN), Support Vector Machines) for determining whether the compact objects are neutron stars or black holes (BHs) in XRB systems. Each machine-learning method uses spatial patterns that exist between systems of the same type in 3D color–color–intensity diagrams. We used lightcurves extracted using 6 yr of data with MAXI/GSC for 44 representative sources. We find that all three methods are highly accurate in distinguishing pulsing from nonpulsing neutron stars (NPNS) with 95% of NPNS and 100% of pulsars accurately predicted. All three methods have high accuracy in distinguishing BHs from pulsars (92%) but continue to confuse BHs with a subclass of NPNS, called bursters, with KNN doing the best at only 50% accuracy for predicting BHs. The precision of all three methods is high, providing equivalent results over 5–10 independent runs. In future work, we will suggest a fourth dimension be incorporated to mitigate the confusion of BHs with bursters. This work paves the way toward more robust methods to efficiently distinguish BHs, NPNS, and pulsars.