A machine learning classifier for fast radio burst detection at the VLBA
Time domain radio astronomy observing campaigns frequently generate large volumes of data. Our goal is to develop automated methods that can identify events of interest buried within the larger data stream. The V-FASTR fast transient system was designed to detect rare fast radio bursts within data c...
| Main Authors: | , , , , , , , , |
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| Format: | Journal Article |
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University of Chicago Press
2016
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| Online Access: | http://hdl.handle.net/20.500.11937/28711 |
| _version_ | 1848752609915043840 |
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| author | Wagstaff, K. Tang, B. Thompson, D. Khudikyan, S. Wyngaard, J. Deller, A. Palaniswamy, D. Tingay, Steven Wayth, Randall |
| author_facet | Wagstaff, K. Tang, B. Thompson, D. Khudikyan, S. Wyngaard, J. Deller, A. Palaniswamy, D. Tingay, Steven Wayth, Randall |
| author_sort | Wagstaff, K. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Time domain radio astronomy observing campaigns frequently generate large volumes of data. Our goal is to develop automated methods that can identify events of interest buried within the larger data stream. The V-FASTR fast transient system was designed to detect rare fast radio bursts within data collected by the Very Long Baseline Array. The resulting event candidates constitute a significant burden in terms of subsequent human reviewing time. We have trained and deployed a machine learning classifier that marks each candidate detection as a pulse from a known pulsar, an artifact due to radio frequency interference, or a potential new discovery. The classifier maintains high reliability by restricting its predictions to those with at least 90% confidence. We have also implemented several efficiency and usability improvements to the V-FASTR web-based candidate review system. Overall, we found that time spent reviewing decreased and the fraction of interesting candidates increased. The classifier now classifies (and therefore filters) 80%–90% of the candidates, with an accuracy greater than 98%, leaving only the 10%–20% most promising candidates to be reviewed by humans. |
| first_indexed | 2025-11-14T08:11:21Z |
| format | Journal Article |
| id | curtin-20.500.11937-28711 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:11:21Z |
| publishDate | 2016 |
| publisher | University of Chicago Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-287112019-02-19T05:35:34Z A machine learning classifier for fast radio burst detection at the VLBA Wagstaff, K. Tang, B. Thompson, D. Khudikyan, S. Wyngaard, J. Deller, A. Palaniswamy, D. Tingay, Steven Wayth, Randall Time domain radio astronomy observing campaigns frequently generate large volumes of data. Our goal is to develop automated methods that can identify events of interest buried within the larger data stream. The V-FASTR fast transient system was designed to detect rare fast radio bursts within data collected by the Very Long Baseline Array. The resulting event candidates constitute a significant burden in terms of subsequent human reviewing time. We have trained and deployed a machine learning classifier that marks each candidate detection as a pulse from a known pulsar, an artifact due to radio frequency interference, or a potential new discovery. The classifier maintains high reliability by restricting its predictions to those with at least 90% confidence. We have also implemented several efficiency and usability improvements to the V-FASTR web-based candidate review system. Overall, we found that time spent reviewing decreased and the fraction of interesting candidates increased. The classifier now classifies (and therefore filters) 80%–90% of the candidates, with an accuracy greater than 98%, leaving only the 10%–20% most promising candidates to be reviewed by humans. 2016 Journal Article http://hdl.handle.net/20.500.11937/28711 10.1088/1538-3873/128/966/084503 University of Chicago Press fulltext |
| spellingShingle | Wagstaff, K. Tang, B. Thompson, D. Khudikyan, S. Wyngaard, J. Deller, A. Palaniswamy, D. Tingay, Steven Wayth, Randall A machine learning classifier for fast radio burst detection at the VLBA |
| title | A machine learning classifier for fast radio burst detection at the VLBA |
| title_full | A machine learning classifier for fast radio burst detection at the VLBA |
| title_fullStr | A machine learning classifier for fast radio burst detection at the VLBA |
| title_full_unstemmed | A machine learning classifier for fast radio burst detection at the VLBA |
| title_short | A machine learning classifier for fast radio burst detection at the VLBA |
| title_sort | machine learning classifier for fast radio burst detection at the vlba |
| url | http://hdl.handle.net/20.500.11937/28711 |