Rapid parameter estimation for merging massive black hole binaries using continuous normalizing flows
Detecting the coalescences of massive black hole binaries (MBHBs) is one of the primary targets for space-based gravitational wave observatories such as laser interferometer space antenna, Taiji, and Tianqin. The fast and accurate parameter estimation of merging MBHBs is of great significance for th...
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IOP Publishing
2024-01-01
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| Series: | Machine Learning: Science and Technology |
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| Online Access: | https://doi.org/10.1088/2632-2153/ad8da9 |
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| author | Bo Liang Minghui Du He Wang Yuxiang Xu Chang Liu Xiaotong Wei Peng Xu Li-e Qiang Ziren Luo |
| author_facet | Bo Liang Minghui Du He Wang Yuxiang Xu Chang Liu Xiaotong Wei Peng Xu Li-e Qiang Ziren Luo |
| author_sort | Bo Liang |
| collection | DOAJ |
| description | Detecting the coalescences of massive black hole binaries (MBHBs) is one of the primary targets for space-based gravitational wave observatories such as laser interferometer space antenna, Taiji, and Tianqin. The fast and accurate parameter estimation of merging MBHBs is of great significance for the global fitting of all resolvable sources, as well as the astrophysical interpretation of gravitational wave signals. However, such analyses usually entail significant computational costs. To address these challenges, inspired by the latest progress in generative models, we explore the application of continuous normalizing flows (CNFs) on the parameter estimation of MBHBs. Specifically, we employ linear interpolation and trig interpolation methods to construct transport paths for training CNFs. Additionally, we creatively introduce a parameter transformation method based on the symmetry in the detector’s response function. This transformation is integrated within CNFs, allowing us to train the model using a simplified dataset, and then perform parameter estimation on more general data, hence also acting as a crucial factor in improving the training speed. In conclusion, for the first time, within a comprehensive and reasonable parameter range, we have achieved a complete and unbiased 11-dimensional rapid inference for MBHBs in the presence of astrophysical confusion noise using CNFs. In the experiments based on simulated data, our model produces posterior distributions comparable to those obtained by nested sampling. |
| format | Article |
| id | doaj-art-deff55d350cd436786009dc46ea69182 |
| institution | OA Journals |
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| language | English |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
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| series | Machine Learning: Science and Technology |
| spelling | doaj-art-deff55d350cd436786009dc46ea691822025-08-20T02:14:35ZengIOP PublishingMachine Learning: Science and Technology2632-21532024-01-015404504010.1088/2632-2153/ad8da9Rapid parameter estimation for merging massive black hole binaries using continuous normalizing flowsBo Liang0https://orcid.org/0009-0002-4350-1852Minghui Du1https://orcid.org/0000-0003-2155-3280He Wang2https://orcid.org/0000-0002-1353-391XYuxiang Xu3Chang Liu4Xiaotong Wei5https://orcid.org/0009-0009-8800-5626Peng Xu6Li-e Qiang7https://orcid.org/0000-0002-7186-0994Ziren Luo8https://orcid.org/0000-0002-9533-8025Center for Gravitational Wave Experiment, National Microgravity Laboratory, Institute of Mechanics , Chinese Academy of Sciences, Beijing 100190, People’s Republic of China; Key Laboratory of Gravitational Wave Precision Measurement of Zhejiang Province, Hangzhou Institute for Advanced Study , UCAS, Hangzhou 310024, People’s Republic of China; Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences , Shanghai 201800, People’s Republic of China; Taiji Laboratory for Gravitational Wave Universe (Beijing/Hangzhou), University of Chinese Academy of Sciences (UCAS) , Beijing 100049, People’s Republic of ChinaCenter for Gravitational Wave Experiment, National Microgravity Laboratory, Institute of Mechanics , Chinese Academy of Sciences, Beijing 100190, People’s Republic of ChinaTaiji Laboratory for Gravitational Wave Universe (Beijing/Hangzhou), University of Chinese Academy of Sciences (UCAS) , Beijing 100049, People’s Republic of China; International Centre for Theoretical Physics Asia-Pacific (ICTP-AP), University of Chinese Academy of Sciences (UCAS) , Beijing 100049, People’s Republic of ChinaCenter for Gravitational Wave Experiment, National Microgravity Laboratory, Institute of Mechanics , Chinese Academy of Sciences, Beijing 100190, People’s Republic of China; Key Laboratory of Gravitational Wave Precision Measurement of Zhejiang Province, Hangzhou Institute for Advanced Study , UCAS, Hangzhou 310024, People’s Republic of China; Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences , Shanghai 201800, People’s Republic of China; Taiji Laboratory for Gravitational Wave Universe (Beijing/Hangzhou), University of Chinese Academy of Sciences (UCAS) , Beijing 100049, People’s Republic of ChinaNational Space Science Center, Chinese Academy of Sciences , Beijing 100190, People’s Republic of ChinaCenter for Gravitational Wave Experiment, National Microgravity Laboratory, Institute of Mechanics , Chinese Academy of Sciences, Beijing 100190, People’s Republic of ChinaCenter for Gravitational Wave Experiment, National Microgravity Laboratory, Institute of Mechanics , Chinese Academy of Sciences, Beijing 100190, People’s Republic of China; Key Laboratory of Gravitational Wave Precision Measurement of Zhejiang Province, Hangzhou Institute for Advanced Study , UCAS, Hangzhou 310024, People’s Republic of China; Taiji Laboratory for Gravitational Wave Universe (Beijing/Hangzhou), University of Chinese Academy of Sciences (UCAS) , Beijing 100049, People’s Republic of China; Lanzhou Center of Theoretical Physics, Lanzhou University , Lanzhou 730000, People’s Republic of ChinaNational Space Science Center, Chinese Academy of Sciences , Beijing 100190, People’s Republic of ChinaCenter for Gravitational Wave Experiment, National Microgravity Laboratory, Institute of Mechanics , Chinese Academy of Sciences, Beijing 100190, People’s Republic of China; Key Laboratory of Gravitational Wave Precision Measurement of Zhejiang Province, Hangzhou Institute for Advanced Study , UCAS, Hangzhou 310024, People’s Republic of China; Taiji Laboratory for Gravitational Wave Universe (Beijing/Hangzhou), University of Chinese Academy of Sciences (UCAS) , Beijing 100049, People’s Republic of China; International Centre for Theoretical Physics Asia-Pacific (ICTP-AP), University of Chinese Academy of Sciences (UCAS) , Beijing 100049, People’s Republic of ChinaDetecting the coalescences of massive black hole binaries (MBHBs) is one of the primary targets for space-based gravitational wave observatories such as laser interferometer space antenna, Taiji, and Tianqin. The fast and accurate parameter estimation of merging MBHBs is of great significance for the global fitting of all resolvable sources, as well as the astrophysical interpretation of gravitational wave signals. However, such analyses usually entail significant computational costs. To address these challenges, inspired by the latest progress in generative models, we explore the application of continuous normalizing flows (CNFs) on the parameter estimation of MBHBs. Specifically, we employ linear interpolation and trig interpolation methods to construct transport paths for training CNFs. Additionally, we creatively introduce a parameter transformation method based on the symmetry in the detector’s response function. This transformation is integrated within CNFs, allowing us to train the model using a simplified dataset, and then perform parameter estimation on more general data, hence also acting as a crucial factor in improving the training speed. In conclusion, for the first time, within a comprehensive and reasonable parameter range, we have achieved a complete and unbiased 11-dimensional rapid inference for MBHBs in the presence of astrophysical confusion noise using CNFs. In the experiments based on simulated data, our model produces posterior distributions comparable to those obtained by nested sampling.https://doi.org/10.1088/2632-2153/ad8da9gravitational wavemassive black hole binariescontinuous normalizing flowsflow matching |
| spellingShingle | Bo Liang Minghui Du He Wang Yuxiang Xu Chang Liu Xiaotong Wei Peng Xu Li-e Qiang Ziren Luo Rapid parameter estimation for merging massive black hole binaries using continuous normalizing flows Machine Learning: Science and Technology gravitational wave massive black hole binaries continuous normalizing flows flow matching |
| title | Rapid parameter estimation for merging massive black hole binaries using continuous normalizing flows |
| title_full | Rapid parameter estimation for merging massive black hole binaries using continuous normalizing flows |
| title_fullStr | Rapid parameter estimation for merging massive black hole binaries using continuous normalizing flows |
| title_full_unstemmed | Rapid parameter estimation for merging massive black hole binaries using continuous normalizing flows |
| title_short | Rapid parameter estimation for merging massive black hole binaries using continuous normalizing flows |
| title_sort | rapid parameter estimation for merging massive black hole binaries using continuous normalizing flows |
| topic | gravitational wave massive black hole binaries continuous normalizing flows flow matching |
| url | https://doi.org/10.1088/2632-2153/ad8da9 |
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