Abstract:
In this paper, we employ Machine Learning algorithms on multimission 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 disc 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.
