  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.
