Space-based gravitational wave signal detection and extraction with deep neural network
Abstract Space-based gravitational wave (GW) detectors will be able to observe signals from sources that are otherwise nearly impossible from current ground-based detection. Consequently, the well established signal detection method, matched filtering, will require a complex template bank, leading t...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-08-01
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| Series: | Communications Physics |
| Online Access: | https://doi.org/10.1038/s42005-023-01334-6 |
| _version_ | 1797559182641070080 |
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| author | Tianyu Zhao Ruoxi Lyu He Wang Zhoujian Cao Zhixiang Ren |
| author_facet | Tianyu Zhao Ruoxi Lyu He Wang Zhoujian Cao Zhixiang Ren |
| author_sort | Tianyu Zhao |
| collection | DOAJ |
| description | Abstract Space-based gravitational wave (GW) detectors will be able to observe signals from sources that are otherwise nearly impossible from current ground-based detection. Consequently, the well established signal detection method, matched filtering, will require a complex template bank, leading to a computational cost that is too expensive in practice. Here, we develop a high-accuracy GW signal detection and extraction method for all space-based GW sources. As a proof of concept, we show that a science-driven and uniform multi-stage self-attention-based deep neural network can identify synthetic signals that are submerged in Gaussian noise. Our method exhibits a detection rate exceeding 99% in identifying signals from various sources, with the signal-to-noise ratio at 50, at a false alarm rate of 1%. while obtaining at least 95% similarity compared with target signals. We further demonstrate the interpretability and strong generalization behavior for several extended scenarios. |
| first_indexed | 2024-03-10T17:41:47Z |
| format | Article |
| id | doaj.art-743e494077be44419b694b2212bb5da9 |
| institution | Directory Open Access Journal |
| issn | 2399-3650 |
| language | English |
| last_indexed | 2024-03-10T17:41:47Z |
| publishDate | 2023-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Physics |
| spelling | doaj.art-743e494077be44419b694b2212bb5da92023-11-20T09:39:47ZengNature PortfolioCommunications Physics2399-36502023-08-016111210.1038/s42005-023-01334-6Space-based gravitational wave signal detection and extraction with deep neural networkTianyu Zhao0Ruoxi Lyu1He Wang2Zhoujian Cao3Zhixiang Ren4Department of Astronomy, Beijing Normal UniversityDepartment of Statistics, University of AucklandInternational Centre for Theoretical Physics Asia-Pacific, University of Chinese Academy of Sciences (UCAS)Department of Astronomy, Beijing Normal UniversityPeng Cheng LaboratoryAbstract Space-based gravitational wave (GW) detectors will be able to observe signals from sources that are otherwise nearly impossible from current ground-based detection. Consequently, the well established signal detection method, matched filtering, will require a complex template bank, leading to a computational cost that is too expensive in practice. Here, we develop a high-accuracy GW signal detection and extraction method for all space-based GW sources. As a proof of concept, we show that a science-driven and uniform multi-stage self-attention-based deep neural network can identify synthetic signals that are submerged in Gaussian noise. Our method exhibits a detection rate exceeding 99% in identifying signals from various sources, with the signal-to-noise ratio at 50, at a false alarm rate of 1%. while obtaining at least 95% similarity compared with target signals. We further demonstrate the interpretability and strong generalization behavior for several extended scenarios.https://doi.org/10.1038/s42005-023-01334-6 |
| spellingShingle | Tianyu Zhao Ruoxi Lyu He Wang Zhoujian Cao Zhixiang Ren Space-based gravitational wave signal detection and extraction with deep neural network Communications Physics |
| title | Space-based gravitational wave signal detection and extraction with deep neural network |
| title_full | Space-based gravitational wave signal detection and extraction with deep neural network |
| title_fullStr | Space-based gravitational wave signal detection and extraction with deep neural network |
| title_full_unstemmed | Space-based gravitational wave signal detection and extraction with deep neural network |
| title_short | Space-based gravitational wave signal detection and extraction with deep neural network |
| title_sort | space based gravitational wave signal detection and extraction with deep neural network |
| url | https://doi.org/10.1038/s42005-023-01334-6 |
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