Year: 2024
Type: preprint
Abstract: Extreme-mass-ratio inspiral (EMRI) signals pose significant challenges in gravitational wave (GW) astronomy owing to their low-frequency nature and highly complex waveforms, which occupy a high-dimensional parameter space with numerous variables. Given their extended inspiral timescales and low signal-to-noise ratios, EMRI signals warrant prolonged observation periods. Parameter estimation becomes particularly challenging due to non-local parameter degeneracies, arising from multiple local maxima, as well as flat regions and ridges inherent in the likelihood function. These factors lead to exceptionally high time complexity for parameter analysis while employing traditional matched filtering and random sampling methods. To address these challenges, the present study applies machine learning to Bayesian posterior estimation of EMRI signals, leveraging the recently developed flow matching technique based on ODE neural networks. Our approach demonstrates computational efficiency several orders of magnitude faster than the traditional Markov Chain Monte Carlo (MCMC) methods, while preserving the unbiasedness of parameter estimation. We show that machine learning technology has the potential to efficiently handle the vast parameter space, involving up to seventeen parameters, associated with EMRI signals. Furthermore, to our knowledge, this is the first instance of applying machine learning, specifically the Continuous Normalizing Flows (CNFs), to EMRI signal analysis. Our findings highlight the promising potential of machine learning in EMRI waveform analysis, offering new perspectives for the advancement of space-based GW detection and GW astronomy.
Source: arXiv (Cornell University)
Authors: Liang, Bo, Guo, Hong, Zhao, Tianyu, wang, He, Evangelinelis, Herik, Xu, Yuxiang, liu, Chang, Liang, Manjia, Wei, Xiaotong, Yuan, Yong, Xu, Peng, Du, Minghui, Qian, Wei-Liang, Luo, Ziren
Institutions: Universidade de São Paulo, ARC Centre of Excellence for Gravitational Wave Discovery, International Centre for Theoretical Physics Asia-Pacific, University of Chinese Academy of Sciences, Universidade Federal Fluminense, Chinese Academy of Sciences, National Space Science Center, Institute of Mechanics
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Funders: Fundação de Amparo à Pesquisa do Estado de São Paulo, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Chinese Academy of Sciences, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, National Key Research and Development Program of China
OpenAlex ID: W4403663466