Learning to detect continuous gravitational waves: an open data-analysis competition
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914226392530944 |
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| author | Tenorio, Rodrigo Williams, Michael J. Bayley, Joseph Messenger, Christopher Demkin, Maggie Reade, Walter Koda, Jun Yamakawa, Yoichi Yamaguchi, Taiki Abe, Kenshin Achard, Chris Bukhari, Habib S. T. Shugaev, Maxim V. Sokolov, Gleb Yoshihara, Hiroshi Debout, Vincent Goulet, Sebastien Tastet, Jean-Loup Timiryasov, Inar Ruchayskiy, Oleg Kanonik, Dzianis Seferbekov, Selim Saito, Shohei Sato, Ryotaro Segawa, Shinsaku Zhyvalkouski, Artsem Uchida, Yusuke Yokoi, Shingo Sayed, Anjum Yamashita, Isamu Xing, Rui-Qi Wang, Ziyue |
| author_facet | Tenorio, Rodrigo Williams, Michael J. Bayley, Joseph Messenger, Christopher Demkin, Maggie Reade, Walter Koda, Jun Yamakawa, Yoichi Yamaguchi, Taiki Abe, Kenshin Achard, Chris Bukhari, Habib S. T. Shugaev, Maxim V. Sokolov, Gleb Yoshihara, Hiroshi Debout, Vincent Goulet, Sebastien Tastet, Jean-Loup Timiryasov, Inar Ruchayskiy, Oleg Kanonik, Dzianis Seferbekov, Selim Saito, Shohei Sato, Ryotaro Segawa, Shinsaku Zhyvalkouski, Artsem Uchida, Yusuke Yokoi, Shingo Sayed, Anjum Yamashita, Isamu Xing, Rui-Qi Wang, Ziyue |
| contents | We report results of a public data-analysis challenge, hosted on the open data-science platform Kaggle, to detect simulated continuous gravitational-wave signals (CWs). These are weak signals from rapidly spinning neutron stars that remain undetected despite extensive searches. The competition dataset consisted of a population of CW signals using both simulated and real LIGO detector data matching the conditions of actual CW searches. The competition attracted more than 1,000 participants to develop realistic CW search algorithms. We describe the top 10 approaches and discuss their applicability as a pre-processing step compared to standard CW-search approaches. For the competition's dataset, we find that top approaches can reduce the computing cost by 1 to 3 orders of magnitude at a false-dismissal probability comparable to standard CW searches. Additionally, the competition drove the development of new GPU-accelerated detection pipelines, which facilitated their adoption in other areas of gravitational-wave data analysis. We release the associated dataset, which constitutes the first open standardized benchmark for CW detection, to enable reproducible method comparisons and to encourage further developments toward the first detection of these elusive signals. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_06445 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning to detect continuous gravitational waves: an open data-analysis competition Tenorio, Rodrigo Williams, Michael J. Bayley, Joseph Messenger, Christopher Demkin, Maggie Reade, Walter Koda, Jun Yamakawa, Yoichi Yamaguchi, Taiki Abe, Kenshin Achard, Chris Bukhari, Habib S. T. Shugaev, Maxim V. Sokolov, Gleb Yoshihara, Hiroshi Debout, Vincent Goulet, Sebastien Tastet, Jean-Loup Timiryasov, Inar Ruchayskiy, Oleg Kanonik, Dzianis Seferbekov, Selim Saito, Shohei Sato, Ryotaro Segawa, Shinsaku Zhyvalkouski, Artsem Uchida, Yusuke Yokoi, Shingo Sayed, Anjum Yamashita, Isamu Xing, Rui-Qi Wang, Ziyue General Relativity and Quantum Cosmology High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics Data Analysis, Statistics and Probability Physics and Society We report results of a public data-analysis challenge, hosted on the open data-science platform Kaggle, to detect simulated continuous gravitational-wave signals (CWs). These are weak signals from rapidly spinning neutron stars that remain undetected despite extensive searches. The competition dataset consisted of a population of CW signals using both simulated and real LIGO detector data matching the conditions of actual CW searches. The competition attracted more than 1,000 participants to develop realistic CW search algorithms. We describe the top 10 approaches and discuss their applicability as a pre-processing step compared to standard CW-search approaches. For the competition's dataset, we find that top approaches can reduce the computing cost by 1 to 3 orders of magnitude at a false-dismissal probability comparable to standard CW searches. Additionally, the competition drove the development of new GPU-accelerated detection pipelines, which facilitated their adoption in other areas of gravitational-wave data analysis. We release the associated dataset, which constitutes the first open standardized benchmark for CW detection, to enable reproducible method comparisons and to encourage further developments toward the first detection of these elusive signals. |
| title | Learning to detect continuous gravitational waves: an open data-analysis competition |
| topic | General Relativity and Quantum Cosmology High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics Data Analysis, Statistics and Probability Physics and Society |
| url | https://arxiv.org/abs/2509.06445 |