Weakly supervised anomaly detection in the Milky Way

Journal Article · 26 November 2023 · Monthly Notices of the Royal Astronomical Society

DOI: https://doi.org/10.1093/mnras/stad3663 · OSTI ID:2246972

Pettee, Mariel ; Thanvantri, Sowmya; Nachman, Benjamin; Shih, David ; Buckley, Matthew R. ; Collins, Jack H.

ABSTRACT Large-scale astrophysics data sets present an opportunity for new machine learning techniques to identify regions of interest that might otherwise be overlooked by traditional searches. To this end, we demonstrate how Classification Without Labels (CWoLa), a weakly supervised anomaly detection method, can help identify cold stellar streams within the more than one billion Milky Way stars observed by the Gaia satellite. CWoLa operates without the use of labelled streams or knowledge of astrophysical principles. Instead, it uses a classifier to distinguish between mixed samples for which the proportions of signal and background samples are unknown. As a proof of concept, we demonstrate that this computationally lightweight strategy is able to detect both simulated streams and the known stream GD-1 in data. Originally designed for high-energy collider physics, this technique may have broad applicability within astrophysics as well as other domains interested in identifying localized anomalies.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)
• Sponsoring Organization: USDOE; USDOE Office of Science (SC); USDOE Office of Science (SC), High Energy Physics (HEP)
• Grant/Contract Number: AC02-05CH11231; AC02-76SF00515
• OSTI ID: 2246972
• Journal Information: Monthly Notices of the Royal Astronomical Society, Journal Name: Monthly Notices of the Royal Astronomical Society Journal Issue: 3 Vol. 527; ISSN 0035-8711
• Publisher: Oxford University PressCopyright Statement
• Country of Publication: United Kingdom
• Language: English