Speaker
Description
The black hole binary GRS 1915+105, a typical microquasar, is known for its unique X-ray variability, which can reveal critical insights into black hole physics. Belloni et al. (2000) indicated that it is possible to classify its remarkable variability into twelve classes. This classification provides a foundation for exploring the physical phenomena underlying these variations, which can significantly impact our understanding of the accretion disk around the black hole and the formation of the jet. To analyze the long-term evolution of GRS 1915+105, it is necessary to classify the entire Rossi X-ray Timing Explorer (RXTE) data set with high accuracy over its 16-yr lifetime. We aim to develop machine learning classification models that capture time-varying patterns and identify unknown classes. We also aim to investigate how transitions between the classes occur. Using the empirical classification of Belloni et al. (2000) as labeling data, we employed deep learning to classify the variability patterns of RXTE X-ray light curves. The preliminary model achieved classification accuracy of over 97% and an average AUC of over 99%. Using the classification models, we can determine how long each pattern persists, how the system transitions between them, and whether other variability patterns exist. Ultimately, GRS 1915+105 will help shed light on what distinguishes the microquasar from other black hole binaries and on the conditions necessary for jets to form.