A Comparative Study of Machine-learning Methods for X-Ray Binary Classification
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.
- Publication:
-
The Astrophysical Journal
- Pub Date:
- July 2022
- DOI:
- 10.3847/1538-4357/ac6184
- arXiv:
- arXiv:2204.00346
- Bibcode:
- 2022ApJ...933..116D
- Keywords:
-
- X-ray binary stars;
- Black holes;
- Pulsars;
- Neutron stars;
- Astrostatistics;
- Bayesian statistics;
- 1811;
- 162;
- 1306;
- 1108;
- 1882;
- 1900;
- Astrophysics - High Energy Astrophysical Phenomena;
- Astrophysics - Instrumentation and Methods for Astrophysics
- E-Print:
- 24 pages, 17 figures, Accepted for publication in ApJ