Finding radio transients with anomaly detection and active learning based on volunteer classifications
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| author | Andersson, Alex Lintott, Chris Fender, Rob Lochner, Michelle Woudt, Patrick Eijnden, Jakob van den van der Horst, Alexander Horesh, Assaf Saikia, Payaswini Sivakoff, Gregory R. Tremou, Lilia Vaccari, Mattia |
| author_facet | Andersson, Alex Lintott, Chris Fender, Rob Lochner, Michelle Woudt, Patrick Eijnden, Jakob van den van der Horst, Alexander Horesh, Assaf Saikia, Payaswini Sivakoff, Gregory R. Tremou, Lilia Vaccari, Mattia |
| contents | In this work we explore the applicability of unsupervised machine learning algorithms to finding radio transients. Facilities such as the Square Kilometre Array (SKA) will provide huge volumes of data in which to detect rare transients; the challenge for astronomers is how to find them. We demonstrate the effectiveness of anomaly detection algorithms using 1.3 GHz light curves from the SKA precursor MeerKAT. We make use of three sets of descriptive parameters ('feature sets') as applied to two anomaly detection techniques in the Astronomaly package and analyse our performance by comparison with citizen science labels on the same dataset. Using transients found by volunteers as our ground truth, we demonstrate that anomaly detection techniques can recall over half of the radio transients in the 10 per cent of the data with the highest anomaly scores. We find that the choice of anomaly detection algorithm makes a minor difference, but that feature set choice is crucial, especially when considering available resources for human inspection and/or follow-up. Active learning, where human labels are given for just 2 per cent of the data, improves recall by up to 20 percentage points, depending on the combination of features and model used. The best performing results produce a factor of 5 times fewer sources requiring vetting by experts. This is the first effort to apply anomaly detection techniques to finding radio transients and shows great promise for application to other datasets, and as a real-time transient detection system for upcoming large surveys. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_01034 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Finding radio transients with anomaly detection and active learning based on volunteer classifications Andersson, Alex Lintott, Chris Fender, Rob Lochner, Michelle Woudt, Patrick Eijnden, Jakob van den van der Horst, Alexander Horesh, Assaf Saikia, Payaswini Sivakoff, Gregory R. Tremou, Lilia Vaccari, Mattia Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena In this work we explore the applicability of unsupervised machine learning algorithms to finding radio transients. Facilities such as the Square Kilometre Array (SKA) will provide huge volumes of data in which to detect rare transients; the challenge for astronomers is how to find them. We demonstrate the effectiveness of anomaly detection algorithms using 1.3 GHz light curves from the SKA precursor MeerKAT. We make use of three sets of descriptive parameters ('feature sets') as applied to two anomaly detection techniques in the Astronomaly package and analyse our performance by comparison with citizen science labels on the same dataset. Using transients found by volunteers as our ground truth, we demonstrate that anomaly detection techniques can recall over half of the radio transients in the 10 per cent of the data with the highest anomaly scores. We find that the choice of anomaly detection algorithm makes a minor difference, but that feature set choice is crucial, especially when considering available resources for human inspection and/or follow-up. Active learning, where human labels are given for just 2 per cent of the data, improves recall by up to 20 percentage points, depending on the combination of features and model used. The best performing results produce a factor of 5 times fewer sources requiring vetting by experts. This is the first effort to apply anomaly detection techniques to finding radio transients and shows great promise for application to other datasets, and as a real-time transient detection system for upcoming large surveys. |
| title | Finding radio transients with anomaly detection and active learning based on volunteer classifications |
| topic | Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena |
| url | https://arxiv.org/abs/2410.01034 |