Rapid Parameter Estimation for Merging Massive Black Hole Binaries Using Continuous Normalizing Flows
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866929616355065856 |
|---|---|
| author | Liang, Bo Du, Minghui Wang, He Xu, Yuxiang Liu, Chang Wei, Xiaotong Xu, Peng Qiang, Li-e Luo, Ziren |
| author_facet | Liang, Bo Du, Minghui Wang, He Xu, Yuxiang Liu, Chang Wei, Xiaotong Xu, Peng Qiang, Li-e Luo, Ziren |
| contents | Detecting the coalescences of massive black hole binaries (MBHBs) is one of the primary targets for space-based gravitational wave observatories such as LISA, 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 | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_07125 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Rapid Parameter Estimation for Merging Massive Black Hole Binaries Using Continuous Normalizing Flows Liang, Bo Du, Minghui Wang, He Xu, Yuxiang Liu, Chang Wei, Xiaotong Xu, Peng Qiang, Li-e Luo, Ziren General Relativity and Quantum Cosmology Instrumentation and Methods for Astrophysics Data Analysis, Statistics and Probability Detecting the coalescences of massive black hole binaries (MBHBs) is one of the primary targets for space-based gravitational wave observatories such as LISA, 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. |
| title | Rapid Parameter Estimation for Merging Massive Black Hole Binaries Using Continuous Normalizing Flows |
| topic | General Relativity and Quantum Cosmology Instrumentation and Methods for Astrophysics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2407.07125 |