A Machine Learning Approach For Classifying Low-mass X-ray Binaries Based On Their Compact Object Nature

TL;DR

This paper demonstrates that a machine learning model, specifically a random forest classifier, can effectively classify low-mass X-ray binaries as hosting either a black hole or a neutron star based on their X-ray energy spectra, achieving around 87% accuracy.

Contribution

The study introduces a machine learning approach using random forests to classify LMXBs by their compact object, improving classification speed and robustness over traditional observational methods.

Findings

Achieved 87% average accuracy in classifying LMXBs

Successfully predicted classes for ambiguous sources

Potential for integration into data pipelines for future missions

Abstract

Low Mass X-ray binaries (LMXBs) are binary systems where one of the components is either a black hole or a neutron star and the other is a less massive star. It is challenging to unambiguously determine whether a LMXB hosts a black hole or a neutron star. In the last few decades, multiple observational works have tried, with different levels of success, to address this problem. In this paper, we explore the use of machine learning to tackle this observational challenge. We train a random forest classifier to identify the type of compact object using the energy spectrum in the energy range 5-25 keV obtained from the Rossi X-ray Timing Explorer archive. We report an average accuracy of 87+/-13 in classifying the spectra of LMXB sources. We further use the trained model for predicting the classes for LMXB systems with unknown or ambiguous classification. With the ever-increasing volume of astronomical data in the X-ray domain from present and upcoming missions (e.g., SWIFT, XMM-Newton, XARM, ATHENA, NICER), such methods can be extremely useful for faster and robust classification of X-ray sources and can also be deployed as part of the data reduction pipeline.