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

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.

20 pages, 27 figures. For associated projection code to view the interactive 3D plot, see https://github.com/bjricketts/grs1915-auto-encoder

References in corpus (8)

• A Parallax Distance to the Microquasar GRS 1915+105 and a Revised Estimate of its Black Hole Mass
• Surveying the reach and maturity of machine learning and artificial intelligence in astronomy
• A NICER View of a Highly-Absorbed Flare in GRS 1915+105
• Disk-Wind Connection During the Heartbeats of GRS 1915+105
• Exploring the Long-Term Evolution of GRS 1915+105
• The Novel Obscured State of Stellar-mass Black Hole GRS 1915+105
• A timing view of the heartbeat state of 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

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