Weakly-Supervised Anomaly Detection in the Milky Way
Mariel Pettee (Lawrence Berkeley National Laboratory / Flatiron Institute Center for Computational Astrophysics)
Abstract: Classification Without Labels (CWoLa) is a weakly-supervised anomaly detection technique that leverages neural networks to identify cold stellar streams within the more than one billion Milky Way stars observed by the Gaia satellite. The CWoLa methodology operates without the use of labeled 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. 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.
HEP - phenomenologyHEP - theorymathematical physics
Audience: researchers in the topic
Series comments: Description: Weekly research seminar of the NHETC at Rutgers University
Livestream link is available on the webpage.
| Organizers: | Christina Pettola*, Vivek Saxena, Nicolas Fernandez Gonzalez, Erica DiPaola* |
| *contact for this listing |