November 27, 2023 to December 1, 2023
Dual node
Europe/Paris timezone

Data-Driven Discovery: Machine Learning for the Detection and Characterization of X-ray Transients

Not scheduled
Dual node

Dual node

IAP (Paris) & CCA/Flatiron (New York) IAP 98bis Boulevard Arago 75014 Paris FRANCE CCA/Flatiron 5th Avenue New York (NY) USA
Poster New York Posters


Mr Steven Dillmann (University of Cambridge)


Recent serendipitous discoveries in X-ray astronomy such as extragalactic fast X-ray transients, Quasi-periodic eruptions, extroplanetary transits, and other rare short-duration phenomena in the X-ray sky highlight the importance of a systematic search for such events in X-ray archives. Variable-length time series data in form of X-ray eventfiles present a challenge for the identification of characteristic features of these time-domain anomalies with machine learning applications. Novel equal-length data representations of X-ray eventfiles capturing both time and energy information are introduced. We use these eventfile representations as features for an unsupervised X-ray transient detection pipeline involving principal component analysis or autoencoder feature learning followed by dimensionality reduction and clustering. The association of these clusters with previously identified transients produces a new set of X-ray transient candidates. Supervised regression and classification models are trained to characterize and predict the time-domain and spectral properties of X-ray eventfiles. We find 8956 X-ray transient candidates in the Chandra archive including a confirmed eclipsing low-mass X-ray binary system and a potential accretion-powered X-ray pulsar. The developed data science tools and catalog of X-ray transient candidates are made publicly available for the advancement of data-driven discoveries in the X-ray astronomy community.

Primary author

Mr Steven Dillmann (University of Cambridge)


Rafael Martínez-Galarza (Smithsonian Astrophysical Observatory) Dr Rosanne Di Stefano (Smithsonian Astrophysical Observatory) Dr Vinay Kashyap (Smithsonian Astrophysical Observatory)

Presentation materials