Estimating Orbital Parameters of Direct Imaging Exoplanet Using Neural Network

• URL: http://arxiv.org/abs/2510.17459v2
• Date: Fri, 07 Nov 2025 13:42:16 GMT
• Title: Estimating Orbital Parameters of Direct Imaging Exoplanet Using Neural Network
• Authors: Bo Liang, Hanlin Song, Chang Liu, Tianyu Zhao, Yuxiang Xu, Zihao Xiao, Manjia Liang, Minghui Du, Wei-Liang Qian, Li-e Qiang, Peng Xu, Ziren Luo,
• Abstract summary: We propose a new flow-matching chain Monte Carlo (FM-MCMC) algorithm for estimating the orbital parameters of exoplanetary systems.<n>Compared to traditional methods that rely on random sampling within the Bayesian framework, our approach first constrains the prior range of physical parameters.<n>Our methodology establishes a versatile paradigm for synergizing deep generative models with traditional sampling.
• Score: 7.8014094634387305
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: In this work, we propose a new flow-matching Markov chain Monte Carlo (FM-MCMC) algorithm for estimating the orbital parameters of exoplanetary systems, especially for those only one exoplanet is involved. Compared to traditional methods that rely on random sampling within the Bayesian framework, our approach first leverages flow matching posterior estimation (FMPE) to efficiently constrain the prior range of physical parameters, and then employs MCMC to accurately infer the posterior distribution. For example, in the orbital parameter inference of beta Pictoris b, our model achieved a substantial speed-up while maintaining comparable accuracy-running 77.8 times faster than Parallel Tempered MCMC (PTMCMC) and 365.4 times faster than nested sampling. Moreover, our FM-MCMC method also attained the highest average log-likelihood among all approaches, demonstrating its superior sampling efficiency and accuracy. This highlights the scalability and efficiency of our approach, making it well-suited for processing the massive datasets expected from future exoplanet surveys. Beyond astrophysics, our methodology establishes a versatile paradigm for synergizing deep generative models with traditional sampling, which can be adopted to tackle complex inference problems in other fields, such as cosmology, biomedical imaging, and particle physics.

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