Estimating Orbital Parameters of Direct Imaging Exoplanet Using Neural Network

TL;DR

This paper introduces a novel flow-matching MCMC algorithm that significantly accelerates the estimation of exoplanet orbital parameters, combining deep generative models with traditional sampling for improved efficiency and accuracy.

Contribution

The paper presents a new FM-MCMC method that integrates flow matching posterior estimation with MCMC, achieving faster and more accurate orbital parameter inference.

Findings

77.8 times faster than PTMCMC

365.4 times faster than nested sampling

Achieved highest average log-likelihood

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.