Exploring the Long-Term Evolution of GRS 1915+105

Abstract

Among the population of known galactic black hole X-ray binaries, GRS 1915+105 stands out in multiple ways. It has been in continuous outburst since 1992, and has shown a wide range of different states that can be distinguished by their timing and spectral properties. These states, also observed in IGR J17091-3624, have in the past been linked to accretion dynamics. Here, we present the first comprehensive study into the long-term evolution of GRS 1915+105, using the entire data set observed with RXTE over its sixteen-year lifetime. We develop a set of descriptive features allowing for automatic separation of states, and show that supervised machine learning in the form of logistic regression and random forests can be used to efficiently classify the entire data set. For the first time, we explore the duty cycle and time evolution of states over the entire sixteen-year time span, and find that the temporal distribution of states has significantly changed over the span of the observations. We connect the machine classification with physical interpretations of the phenomenology in terms of chaotic and stochastic processes.

arXiv comment15 pages, 12 figures; published in MNRAS; associated code and data at https://github.com/dhuppenkothen/BlackHoleML ; we welcome comments

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• Using the bispectrum to probe radio X-ray correlations in GRS 1915+105
• A non-linear mathematical model for the X-ray variability classes of the microquasar GRS 1915+105 -- I: quiescent, spiking states and QPOs
• Time domain methods for X-ray and gamma-ray astronomy
• QPOML: A Machine Learning Approach to Detect and Characterize Quasi-Periodic Oscillations in X-ray Binaries
• Recurrent Instability in LMXB Accretion Disks: How Strange is GRS 1915+105?
• Light curve fingerprints: an automated approach to the extraction of X-ray variability patterns with feature aggregation -- an example application to GRS 1915+105