Rapid eccentric spin-aligned binary black hole waveform generation based on deep learning
文献类型：期刊论文
| 作者 | Shi, Ruijun1,2; Zhou, Yue3; Zhao TY(赵天宇)4; Wang, Zun1,2; Ren, Zhixiang3; Cao, Zhoujian1,2,5 |
| 刊名 | PHYSICAL REVIEW D
 |
| 出版日期 | 2025-02-07 |
| 卷号 | 111期号:4页码:13 |
| ISSN号 | 2470-0010 |
| DOI | 10.1103/PhysRevD.111.044016 |
| 通讯作者 | Ren, Zhixiang(renzhx@pcl.ac.cn) ; Cao, Zhoujian(zjcao@bnu.edu.cn) |
| 英文摘要 | Accurate waveform templates of binary black holes (BBHs) with eccentric orbits are essential for the detection and precise parameter estimation of gravitational waves (GWs). While SEOBNRE produces accurate time-domain waveforms for eccentric BBH systems, its generation speed remains a critical bottleneck in analyzing such systems. Accelerating template generation is crucial to data analysis improvement and valuable information extraction from observational data. We present SEOBNRE_AIq5e2, an innovative artificial intelligence-based surrogate model that was crafted to accelerate waveform generation for eccentric, spin-aligned BBH systems. SEOBNRE_AIq5e2 incorporates an advanced adaptive resampling technique during training, enabling the generation of eccentric BBH waveforms with mass ratios up to 5, eccentricities below 0.2, and spins chi Z up to 0.6. It achieves an impressive generation speed of 4.3 ms per waveform with a mean mismatch of 1.02 x 10-3. With the exceptional accuracy and rapid performance, SEOBNRE_AIq5e2 emerges as a promising waveform template for future analysis of eccentric gravitational wave data. |
| 分类号 | 二类/Q1 |
| WOS关键词 | DYNAMICAL FORMATION ; ADVANCED LIGO ; MERGERS ; 1ST ; SIGNATURES ; SEARCH |
| 资助项目 | National Key Research and Development Program of China[2021YFC2203001] ; NSFC[11920101003] ; NSFC[12021003] ; Interdisciplinary Research Funds of Beijing Normal University ; CAS Project for Young Scientists in Basic Research Grant[YSBR-006] ; Peng Cheng Laboratory ; Peng Cheng Cloud-Brain |
| WOS研究方向 | Astronomy & Astrophysics ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:001470364800007 |
| 资助机构 | National Key Research and Development Program of China ; NSFC ; Interdisciplinary Research Funds of Beijing Normal University ; CAS Project for Young Scientists in Basic Research Grant ; Peng Cheng Laboratory ; Peng Cheng Cloud-Brain |
| 其他责任者 | Ren, Zhixiang，Cao, Zhoujian |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/101046]  |
| 专题 | 力学研究所_国家微重力实验室 |
| 作者单位 | 1.Beijing Normal Univ, Inst Frontiers Astron & Astrophys, Beijing 102206, Peoples R China; 2.Beijing Normal Univ, Sch Phys & Astron, Beijing 100875, Peoples R China; 3.Peng Cheng Lab, Shenzhen 518055, Peoples R China; 4.Chinese Acad Sci, Inst Mech, Ctr Gravitat Wave Expt, Natl Micrograv Lab, Beijing 100190, Peoples R China; 5.UCAS, Hangzhou Inst Adv Study, Sch Fundamental Phys & Math Sci, Hangzhou 310024, Peoples R China |
| 推荐引用方式 GB/T 7714 | Shi, Ruijun,Zhou, Yue,Zhao TY,et al. Rapid eccentric spin-aligned binary black hole waveform generation based on deep learning[J]. PHYSICAL REVIEW D,2025,111(4):13. |
| APA | Shi, Ruijun,Zhou, Yue,赵天宇,Wang, Zun,Ren, Zhixiang,&Cao, Zhoujian.(2025).Rapid eccentric spin-aligned binary black hole waveform generation based on deep learning.PHYSICAL REVIEW D,111(4),13. |
| MLA | Shi, Ruijun,et al."Rapid eccentric spin-aligned binary black hole waveform generation based on deep learning".PHYSICAL REVIEW D 111.4(2025):13. |
入库方式： OAI收割
来源：力学研究所
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