Parameter Inference for Coalescing Massive Black Hole Binaries Using Deep Learning
文献类型：期刊论文
| 作者 | Ruan, Wenhong1; Wang, He2,3; Liu, Chang1; Guo, Zongkuan1,4
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| 刊名 | UNIVERSE
 |
| 出版日期 | 2023 |
| 卷号 | 9期号:9页码:407 |
| DOI | 10.3390/universe9090407 |
| 英文摘要 | In the 2030s, a new era of gravitational wave (GW) observations will dawn as multiple space-based GW detectors, such as the Laser Interferometer Space Antenna, Taiji, and TianQin, will open the millihertz window for GW astronomy. These detectors are poised to detect a multitude of GW signals emitted by different sources. It is a challenging task for GW data analysis to recover the parameters of these sources at a low computational cost. Generally, the matched filtering approach entails exploring an extensive parameter space for all resolvable sources, incurring a substantial cost owing to the generation of GW waveform templates. To alleviate the challenge, we make an attempt to perform parameter inference for coalescing massive black hole binaries (MBHBs) using deep learning. The model trained in this work has the capability to produce 50,000 posterior samples for the redshifted total mass, mass ratio, coalescence time, and luminosity distance of an MBHB in about twenty seconds. Our model can serve as an effective data pre-processing tool, reducing the volume of parameter space by more than four orders of magnitude for MBHB signals with a signal-to-noise ratio larger than 100. Moreover, the model exhibits robustness when handling input data that contain multiple MBHB signals. |
| 学科主题 | Astronomy & Astrophysics ; Physics |
| 语种 | 英语 |
| 源URL | [http://ir.itp.ac.cn/handle/311006/28044]  |
| 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, 19A Yuquan Rd, Beijing 100049, Peoples R China 3.Univ Chinese Acad Sci, Int Ctr Theoret Phys Asia Pacific, Beijing 100049, Peoples R China 4.Univ Chinese Acad Sci, Taiji Lab Gravitat Wave Universe, Beijing 100049, Peoples R China 5.Chinese Acad Sci, Inst Theoret Phys, CAS Key Lab Theoret Phys, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ruan, Wenhong,Wang, He,Liu, Chang,et al. Parameter Inference for Coalescing Massive Black Hole Binaries Using Deep Learning[J]. UNIVERSE,2023,9(9):407. |
| APA | Ruan, Wenhong,Wang, He,Liu, Chang,&Guo, Zongkuan.(2023).Parameter Inference for Coalescing Massive Black Hole Binaries Using Deep Learning.UNIVERSE,9(9),407. |
| MLA | Ruan, Wenhong,et al."Parameter Inference for Coalescing Massive Black Hole Binaries Using Deep Learning".UNIVERSE 9.9(2023):407. |
入库方式： OAI收割
来源：理论物理研究所
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