Mapping the X-ray variability of GRS1915+105 with machine learning

TL;DR

This study uses unsupervised machine learning, specifically auto-encoders, to classify and analyze the complex X-ray variability patterns of the black hole binary GRS 1915+105, revealing natural groupings aligned with traditional classifications.

Contribution

Introduces an auto-encoder based clustering approach to classify X-ray variability patterns in GRS 1915+105, reducing human bias and uncovering underlying behavioral components.

Findings

Auto-encoder groups similar observations effectively

Classifications align with Belloni et al's system

Reveals three distinct behavioral components

Abstract

Black hole X-ray binary systems (BHBs) contain a close companion star accreting onto a stellar-mass black hole. A typical BHB undergoes transient outbursts during which it exhibits a sequence of long-lived spectral states, each of which is relatively stable. GRS 1915+105 is a unique BHB that exhibits an unequaled number and variety of distinct variability patterns in X-rays. Many of these patterns contain unusual behaviour not seen in other sources. These variability patterns have been sorted into different classes based on count rate and color characteristics by Belloni et al (2000). In order to remove human decision-making from the pattern-recognition process, we employ an unsupervised machine learning algorithm called an auto-encoder to learn what classifications are naturally distinct by allowing the algorithm to cluster observations. We focus on observations taken by the Rossi X-ray Timing Explorer's Proportional Counter Array. We find that the auto-encoder closely groups observations together that are classified as similar under the Belloni et al (2000) system, but that there is reasonable grounds for defining each class as made up of components from 3 groups of distinct behaviour.