Rapid search for massive black hole binary coalescences using deep learning
文献类型：期刊论文
| 作者 | Ruan, Wen-Hong; Wang, He3,4; Liu, Chang; Guo, Zong-Kuan
|
| 刊名 | PHYSICS LETTERS B
 |
| 出版日期 | 2023 |
| 卷号 | 841页码:137904 |
| ISSN号 | 0370-2693 |
| DOI | 10.1016/j.physletb.2023.137904 |
| 英文摘要 | The coalescences of massive black hole binaries are one of the main targets of space-based gravitational wave observatories. Such gravitational wave sources are expected to be accompanied by electromagnetic emissions. Low latency detection of the massive black hole mergers provides a start point for a global-fit analysis to explore the large parameter space of signals simultaneously being present in the data but at great computational cost. To alleviate this issue, we present a deep learning method for rapidly searching for signals of massive black hole binaries in gravitational wave data. Our model is capable of processing a year of data, simulated from the LISA data challenge, in only several seconds, while identifying all coalescences of massive black hole binaries with no false alarms. We further demonstrate that the model shows robust resistance to a wide range of generalization cases, including various waveform families and updated instrumental configurations. This method offers an effective approach that combines advances in artificial intelligence to open a new pathway for space-based gravitational wave observations.(c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons .org /licenses /by/4.0/). Funded by SCOAP3. |
| 学科主题 | Astronomy & Astrophysics ; Physics |
| 语种 | 英语 |
| 源URL | [http://ir.itp.ac.cn/handle/311006/28066]  |
| 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.Univ Chinese Acad Sci, Hangzhou Inst Adv Study, Sch Fundamental Phys & Math Sci, Hangzhou 310024, Peoples R China 2.Univ Chinese Acad Sci, Sch Phys Sci, 19 Yuquan Rd, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Theoret Phys, CAS Key Lab Theoret Phys, Beijing 100190, Peoples R China 4.Univ Chinese Acad Sci, Int Ctr Theoret Phys Asia Pacific, Beijing 100190, Peoples R China 5.Univ Chinese Acad Sci, Taiji Lab Gravitat Wave Universe, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ruan, Wen-Hong,Wang, He,Liu, Chang,et al. Rapid search for massive black hole binary coalescences using deep learning[J]. PHYSICS LETTERS B,2023,841:137904. |
| APA | Ruan, Wen-Hong,Wang, He,Liu, Chang,&Guo, Zong-Kuan.(2023).Rapid search for massive black hole binary coalescences using deep learning.PHYSICS LETTERS B,841,137904. |
| MLA | Ruan, Wen-Hong,et al."Rapid search for massive black hole binary coalescences using deep learning".PHYSICS LETTERS B 841(2023):137904. |
入库方式： OAI收割
来源：理论物理研究所
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