Presentation #110.92 in the session “Stellar/Compact (Poster)”.
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 which, quite unlike the typical source, exhibits an unequaled number and variety of distinct variability patterns in X-rays. Many of these patterns contain large and unusual instabilities.
These variability patterns have been sorted (initially, by hand) into different classes based on count rate and color characteristics. In order to remove human decision-making from the pattern-recognition process, we employ unsupervised machine learning algorithms to find similarities in the X-ray behavior. We aim to identify any new patterns and to compare our findings to classes that have been previously identified in effort to further our understanding of the mechanisms governing accretion onto a black hole. We employ data obtained by the Neutron-Star Interior Composition ExploreR (NICER) on the International Space Station.