Weakly-Supervised Anomaly Detection in the Milky Way
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866917863250460672 |
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| author | Pettee, Mariel Thanvantri, Sowmya Nachman, Benjamin Shih, David Buckley, Matthew R. Collins, Jack H. |
| author_facet | Pettee, Mariel Thanvantri, Sowmya Nachman, Benjamin Shih, David Buckley, Matthew R. Collins, Jack H. |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2305_03761 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Weakly-Supervised Anomaly Detection in the Milky Way Pettee, Mariel Thanvantri, Sowmya Nachman, Benjamin Shih, David Buckley, Matthew R. Collins, Jack H. Astrophysics of Galaxies Machine Learning High Energy Physics - Phenomenology Data Analysis, Statistics and Probability 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. |
| title | Weakly-Supervised Anomaly Detection in the Milky Way |
| topic | Astrophysics of Galaxies Machine Learning High Energy Physics - Phenomenology Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2305.03761 |