Space-based gravitational wave signal detection and extraction with deep neural network
文献类型：期刊论文
| 作者 | Zhao, Tianyu1,2; Lyu, Ruoxi3; Wang, He4,5,6; Cao, Zhoujian1,7; Ren, Zhixiang2 |
| 刊名 | COMMUNICATIONS PHYSICS
 |
| 出版日期 | 2023 |
| 卷号 | 6期号:1页码:212 |
| ISSN号 | 2399-3650 |
| DOI | 10.1038/s42005-023-01334-6 |
| 英文摘要 | 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. Gravitational wave (GW) astronomy has opened a new window of opportunity for our understanding of the Universe, but GW data processing is notoriously complicated due to high noise. Here the authors present a proof-of-concept data analysis scheme based on neural networks for GW signals detection of data from future space-based observatories. |
| 学科主题 | Physics |
| 语种 | 英语 |
| 源URL | [http://ir.itp.ac.cn/handle/311006/28076]  |
| 专题 | 理论物理研究所_理论物理所1978-2010年知识产出 |
| 作者单位 | 1.Beijing Normal Univ, Dept Astron, Beijing 100875, Peoples R China 2.Beijing Normal Univ, Inst Frontiers Astron & Astrophys, Beijing 102206, Peoples R China 3.Peng Cheng Lab, Shenzhen 518055, Peoples R China 4.Univ Auckland, Dept Stat, Auckland 1142, New Zealand 5.Univ Chinese Acad Sci UCAS, Int Ctr Theoret Phys Asia Pacific, Beijing 100190, Peoples R China 6.UCAS, Taiji Lab Gravitat Wave Universe, Beijing 100049, Peoples R China 7.Chinese Acad Sci, Inst Theoret Phys, CAS Key Lab Theoret Phys, Beijing 100190, Peoples R China 8.UCAS, Hangzhou Inst Adv Study, Sch Fundamental Phys & Math Sci, Hangzhou 310024, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhao, Tianyu,Lyu, Ruoxi,Wang, He,et al. Space-based gravitational wave signal detection and extraction with deep neural network[J]. COMMUNICATIONS PHYSICS,2023,6(1):212. |
| APA | Zhao, Tianyu,Lyu, Ruoxi,Wang, He,Cao, Zhoujian,&Ren, Zhixiang.(2023).Space-based gravitational wave signal detection and extraction with deep neural network.COMMUNICATIONS PHYSICS,6(1),212. |
| MLA | Zhao, Tianyu,et al."Space-based gravitational wave signal detection and extraction with deep neural network".COMMUNICATIONS PHYSICS 6.1(2023):212. |
入库方式： OAI收割
来源：理论物理研究所
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