Exploring the Long-Term Evolution of GRS 1915+105

TL;DR

This study analyzes the long-term evolution of GRS 1915+105 using 16 years of RXTE data, employing machine learning for state classification and revealing significant changes in state distribution over time.

Contribution

It introduces a comprehensive machine learning approach to classify states and analyze their evolution in GRS 1915+105 over sixteen years.

Findings

States' temporal distribution changed significantly over 16 years

Machine learning effectively classifies different states

Physical interpretations link states to chaotic and stochastic processes

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.