The detection, extraction and parameter estimation of extreme-mass-ratio inspirals with deep learning
文献类型：期刊论文
| 作者 | Yun,Qianyun8,9,10; Han,WenBiao6,7,9,10; Guo,YiYang3,4,5; Wang,He2; Du MH(杜明辉)1 |
| 刊名 | SCIENCE CHINA-PHYSICS MECHANICS & ASTRONOMY
 |
| 出版日期 | 2025 |
| 卷号 | 68期号:1页码:11 |
| 关键词 | gravitational wave EMRIs deep-learing |
| ISSN号 | 1674-7348 |
| DOI | 10.1007/s11433-024-2500-x |
| 通讯作者 | Han, Wen-Biao(wbhan@shao.ac.cn) |
| 英文摘要 | One of the primary goals of space-borne gravitational wave detectors is to detect and analyze extreme-mass-ratio inspirals (EMRIs). This task is particularly challenging because EMRI signals are complex, lengthy, and faint. In this work, we introduce a 2-layer convolutional neural network (CNN) approach to detect EMRI signals for space-borne detectors, achieving a true positive rate (TPR) of 96.9% at a 1% false positive rate (FPR) for signal-to-noise ratio (SNR) from 50 to 100. Especially, the key intrinsic parameters of EMRIs such as the mass, spin of the supermassive black hole (SMBH) and the initial eccentricity of the orbit can also be inferred directly by employing a neural network. The mass and spin of the SMBH can be determined at 99% and 92% respectively. This will greatly reduce the parameter spaces and computing cost for the following Bayesian parameter estimation. Our model also has a low dependency on the accuracy of the waveform model. This study underscores the potential of deep learning methods in EMRI data analysis, enabling the rapid detection of EMRI signals and efficient parameter estimation. |
| 分类号 | 一类 |
| WOS关键词 | GENERAL-RELATIVITY ; TAIJI PROGRAM ; SPACE ; PHYSICS |
| 资助项目 | National Key R&D Program of China[2021YFC2203002] ; National Natural Science Foundation of China[12173071] ; National Natural Science Foundation of China[12473075] |
| WOS研究方向 | Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:001360206000005 |
| 资助机构 | National Key R&D Program of China ; National Natural Science Foundation of China |
| 其他责任者 | Han, Wen-Biao |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/97498]  |
| 专题 | 力学研究所_国家微重力实验室 |
| 作者单位 | 1.Chinese Acad Sci, Inst Mech, Ctr Gravitat Wave Expt, Natl Micrograv Lab, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci UCAS, Int Ctr Theoret Phys Asia Pacific ICTP, AP, Beijing 100049, Peoples R China; 3.Lanzhou Univ, Res Ctr Gravitat, Lanzhou 730000, Peoples R China; 4.Lanzhou Univ, Inst Theoret Phys, Lanzhou 730000, Peoples R China; 5.Lanzhou Univ, Lanzhou Ctr Theoret Phys, Key Lab Theoret Phys Gansu Prov, Key Lab Quantum Theory & Applicat MoE, Lanzhou 730000, Peoples R China; 6.Univ Chinese Acad Sci, Taiji Lab Gravitat Wave Universe Beijing Hangzhou, Beijing 100049, Peoples R China; 7.Univ Chinese Acad Sci, Sch Astron & Space Sci, Beijing 100049, Peoples R China; 8.Shanghai Jiao Tong Univ, Sch Phys & Astron, Shanghai 200240, Peoples R China; 9.Chinese Acad Sci, Shanghai Astron Observ, Shanghai 200030, Peoples R China; 10.Univ Chinese Acad Sci, Hangzhou Inst Adv Study, Hangzhou 310124, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Yun,Qianyun,Han,WenBiao,Guo,YiYang,et al. The detection, extraction and parameter estimation of extreme-mass-ratio inspirals with deep learning[J]. SCIENCE CHINA-PHYSICS MECHANICS & ASTRONOMY,2025,68(1):11. |
| APA | Yun,Qianyun,Han,WenBiao,Guo,YiYang,Wang,He,&杜明辉.(2025).The detection, extraction and parameter estimation of extreme-mass-ratio inspirals with deep learning.SCIENCE CHINA-PHYSICS MECHANICS & ASTRONOMY,68(1),11. |
| MLA | Yun,Qianyun,et al."The detection, extraction and parameter estimation of extreme-mass-ratio inspirals with deep learning".SCIENCE CHINA-PHYSICS MECHANICS & ASTRONOMY 68.1(2025):11. |
入库方式： OAI收割
来源：力学研究所
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