A Machine Learning approach for classification of accretion states of Black hole binaries

TL;DR

This paper introduces a machine learning-based method to classify accretion states of black hole X-ray binaries using multi-mission observational data, outperforming traditional classification techniques.

Contribution

It is the first application of machine learning algorithms to classify accretion states of black hole binaries across multiple observational datasets.

Findings

Clustering algorithms effectively group similar accretion states.

K-Means clustering yields more reliable classifications than Hierarchical clustering.

Machine learning classification surpasses standard methods in accuracy.

Abstract

In this paper, we employ Machine Learning algorithms on multi-mission observations for the classification of accretion states of outbursting Black hole X-ray binaries for the first time. Archival data from RXTE, Swift, MAXI and AstroSat observatories are used to generate the hardness intensity diagrams (HIDs) for outbursts of the sources XTE J1859+226 (1999 outburst), GX 339-4 (2002, 2004, 2007 and 2010 outbursts), IGR J17091-3624 (2016 outburst), and MAXI J1535-571 (2017 outburst). Based on variation of X-ray flux, hardness ratios, presence of various types of Quasi-periodic Oscillations (QPOs), photon indices and disk temperature, we apply clustering algorithms like K-Means clustering and Hierarchical clustering to classify the accretion states (clusters) of each outburst. As multiple parameters are involved in the classification process, we show that clustering algorithms club together the observations of similar characteristics more efficiently than the `standard' method of classification. We also infer that K-Means clustering provides more reliable results than Hierarchical clustering. We demonstrate the importance of the classification based on machine learning by comparing it with results from `standard' classification.