Unsupervised Machine Learning for Classifying Variability in X-ray Light Curves: Case Study on GRS 1915+105

Anand T.S., Shree Nandini; Sunitha, N.R.; Aftab, Nafisa

Please use this identifier to cite or link to this item: http://hdl.handle.net/2289/8610

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DC Field	Value	Language
dc.contributor.author	Anand T.S., Shree Nandini	-
dc.contributor.author	Sunitha, N.R.	-
dc.contributor.author	Aftab, Nafisa	-
dc.date.accessioned	2026-01-12T11:18:52Z	-
dc.date.available	2026-01-12T11:18:52Z	-
dc.date.issued	2025-12-30	-
dc.identifier.citation	IEEE International Conference for Women in Innovation, Technology and Entrepreneurship (ICWITE), 1-6 - January 2025	en_US
dc.identifier.uri	http://hdl.handle.net/2289/8610	-
dc.description	Restricted Access.	en_US
dc.description.abstract	X-ray astronomy provides a unique window into high energy astrophysical processes, with light curves serving as key tools for probing the temporal variability around compact objects such as black holes and neutron stars. Traditionally, analyzing such variability requires manual inspection and expert defined features, which become increasingly infeasible with the growing volume of data from modern X-ray missions. In this work, we explore the potential of unsupervised Machine learning for classifying X-ray light curve morphologies. We demonstrate our approach using simulated light curves inspired by the variability classes of the micro quasar GRS 1915+105, a well-studied black hole X-ray binary known for its rich and diverse temporal behavior. By selecting representative patterns from the GRS 1915+105 variability zoo, we train and evaluate clustering algorithms on their ability to group light curves. Our results show that unsupervised learning methods can successfully distinguish between different morphological classes without prior labels, highlighting the promise of Machine Learning in automating and scaling, the time domain analysis in X-ray astronomy.	en_US
dc.language.iso	en	en_US
dc.publisher	IEEE	en_US
dc.relation.uri	https://doi.org/10.1109/icwite64848.2025.11306946	en_US
dc.rights	2025 IEEE	en_US
dc.title	Unsupervised Machine Learning for Classifying Variability in X-ray Light Curves: Case Study on GRS 1915+105	en_US
dc.type	Article	en_US
Appears in Collections:	Research Papers (A&A)

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