Weakly-Supervised Anomaly Detection in the Milky Way
Mariel Pettee Sowmya Thanvantri Benjamin Nachman David Shih Matthew R. Buckley Jack H. Collins
Large-scale astrophysics datasets 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 use Classification Without
Labels (CWoLa), a weakly-supervised anomaly detection method, to identify cold
stellar streams within the more than one billion Milky Way stars observed by
the Gaia satellite. CWoLa operates without the use of labeled streams or
knowledge of astrophysical principles. Instead, we train a classifier to
distinguish between mixed samples for which the proportions of signal and
background samples are unknown. 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.