Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908503246897152 |
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| author | Liang, Bo Liu, Chang Song, Hanlin Lyu, Zhenwei Du, Minghui Xu, Peng Luo, Ziren He, Sensen Gu, Haohao Zhao, Tianyu Xu, Manjia Liang Yuxiang Qiang, Li-e Sun, Mingming Qian, Wei-Liang |
| author_facet | Liang, Bo Liu, Chang Song, Hanlin Lyu, Zhenwei Du, Minghui Xu, Peng Luo, Ziren He, Sensen Gu, Haohao Zhao, Tianyu Xu, Manjia Liang Yuxiang Qiang, Li-e Sun, Mingming Qian, Wei-Liang |
| contents | The detection of gravitational waves from extreme-mass-ratio inspirals (EMRIs) in space-borne antennas like Taiji and LISA promises deep insights into strong-field gravity and black hole physics. However, the complex, highly degenerate, and non-convex likelihood landscapes characteristic of EMRI parameter spaces pose severe challenges for conventional Markov chain Monte Carlo (MCMC) methods. Under realistic instrumental noise and broad priors, these methods demand impractical computational costs but are prone to becoming trapped in local maxima, leading to biased and unreliable parameter estimates. To address this, we introduce Flow-Matching Markov Chain Monte Carlo (FM-MCMC), a novel Bayesian framework that integrates continuous normalizing flows (CNFs) with parallel tempering MCMC (PTMCMC). By generating high-likelihood regions via CNFs and refining them through PTMCMC, FM-MCMC enables robust exploration of the nontrivial parameter spaces, while achieving orders-of-magnitude improvement in computational efficiency and, more importantly, ensuring statistically reliable and unbiased inference. By enabling real-time, unbiased parameter inference, FM-MCMC could unlock the full scientific potential of EMRI observations, and would serve as a scalable pipeline for precision gravitational-wave astronomy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_00348 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Liang, Bo Liu, Chang Song, Hanlin Lyu, Zhenwei Du, Minghui Xu, Peng Luo, Ziren He, Sensen Gu, Haohao Zhao, Tianyu Xu, Manjia Liang Yuxiang Qiang, Li-e Sun, Mingming Qian, Wei-Liang General Relativity and Quantum Cosmology Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics Space Physics The detection of gravitational waves from extreme-mass-ratio inspirals (EMRIs) in space-borne antennas like Taiji and LISA promises deep insights into strong-field gravity and black hole physics. However, the complex, highly degenerate, and non-convex likelihood landscapes characteristic of EMRI parameter spaces pose severe challenges for conventional Markov chain Monte Carlo (MCMC) methods. Under realistic instrumental noise and broad priors, these methods demand impractical computational costs but are prone to becoming trapped in local maxima, leading to biased and unreliable parameter estimates. To address this, we introduce Flow-Matching Markov Chain Monte Carlo (FM-MCMC), a novel Bayesian framework that integrates continuous normalizing flows (CNFs) with parallel tempering MCMC (PTMCMC). By generating high-likelihood regions via CNFs and refining them through PTMCMC, FM-MCMC enables robust exploration of the nontrivial parameter spaces, while achieving orders-of-magnitude improvement in computational efficiency and, more importantly, ensuring statistically reliable and unbiased inference. By enabling real-time, unbiased parameter inference, FM-MCMC could unlock the full scientific potential of EMRI observations, and would serve as a scalable pipeline for precision gravitational-wave astronomy. |
| title | Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion |
| topic | General Relativity and Quantum Cosmology Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics Space Physics |
| url | https://arxiv.org/abs/2508.00348 |