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.

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

References in corpus (6)

• Rotation-invariant convolutional neural networks for galaxy morphology prediction
• A Parallax Distance to the Microquasar GRS 1915+105 and a Revised Estimate of its Black Hole Mass
• Power-Colours: Simple X-ray Binary Variability Comparison
• On the limit cycle instability in magnetized accretion discs
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Cited by in corpus (9)

• A Morphological Classification Model to Identify Unresolved PanSTARRS1 Sources: Application in the ZTF Real-Time Pipeline
• Determination of chaotic behaviour in time series generated by charged particle motion around magnetized Schwarzschild black holes
• A variable corona for GRS 1915+105
• Phase-resolved spectroscopy of a quasi-periodic oscillation in the black hole X-ray binary GRS 1915+105 with NICER and NuSTAR
• A Machine Learning approach for classification of accretion states of Black hole binaries
• A Machine Learning Approach For Classifying Low-mass X-ray Binaries Based On Their Compact Object Nature
• A non-linear mathematical model for the X-ray variability classes of the microquasar GRS 1915+105 -- I: quiescent, spiking states and QPOs
• Light curve fingerprints: an automated approach to the extraction of X-ray variability patterns with feature aggregation -- an example application to GRS 1915+105
• Recurrent Instability in LMXB Accretion Disks: How Strange is GRS 1915+105?