Advancing Space-Based Gravitational Wave Astronomy: Rapid Parameter Estimation via Normalizing Flows
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911779868639232 |
|---|---|
| author | Du, Minghui Liang, Bo Wang, He Xu, Peng Luo, Ziren Wu, Yueliang |
| author_facet | Du, Minghui Liang, Bo Wang, He Xu, Peng Luo, Ziren Wu, Yueliang |
| contents | Gravitational wave (GW) astronomy is witnessing a transformative shift from terrestrial to space-based detection, with missions like Taiji at the forefront. While the transition brings unprecedented opportunities for exploring massive black hole binaries (MBHBs), it also imposes complex challenges in data analysis, particularly in parameter estimation amidst confusion noise. Addressing this gap, we utilize scalable normalizing flow models to achieve rapid and accurate inference within the Taiji environment. Innovatively, our approach simplifies the data's complexity, employs a transformation mapping to overcome the year-period time-dependent response function, and unveils additional multimodality in the arrival time parameter. Our method estimates MBHBs several orders of magnitude faster than conventional techniques, maintaining high accuracy even in complex backgrounds. These findings significantly enhance the efficiency of GW data analysis, paving the way for rapid detection and alerting systems and enriching our ability to explore the universe through space-based GW observation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_05510 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Advancing Space-Based Gravitational Wave Astronomy: Rapid Parameter Estimation via Normalizing Flows Du, Minghui Liang, Bo Wang, He Xu, Peng Luo, Ziren Wu, Yueliang Instrumentation and Methods for Astrophysics General Relativity and Quantum Cosmology Data Analysis, Statistics and Probability Gravitational wave (GW) astronomy is witnessing a transformative shift from terrestrial to space-based detection, with missions like Taiji at the forefront. While the transition brings unprecedented opportunities for exploring massive black hole binaries (MBHBs), it also imposes complex challenges in data analysis, particularly in parameter estimation amidst confusion noise. Addressing this gap, we utilize scalable normalizing flow models to achieve rapid and accurate inference within the Taiji environment. Innovatively, our approach simplifies the data's complexity, employs a transformation mapping to overcome the year-period time-dependent response function, and unveils additional multimodality in the arrival time parameter. Our method estimates MBHBs several orders of magnitude faster than conventional techniques, maintaining high accuracy even in complex backgrounds. These findings significantly enhance the efficiency of GW data analysis, paving the way for rapid detection and alerting systems and enriching our ability to explore the universe through space-based GW observation. |
| title | Advancing Space-Based Gravitational Wave Astronomy: Rapid Parameter Estimation via Normalizing Flows |
| topic | Instrumentation and Methods for Astrophysics General Relativity and Quantum Cosmology Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2308.05510 |