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.

翻译：本研究提出了一种新的流匹配马尔可夫链蒙特卡洛（FM-MCMC）算法，用于估计系外行星系统的轨道参数，尤其适用于仅包含单一行星的系统。相较于传统贝叶斯框架下依赖随机采样的方法，我们的方法首先利用流匹配后验估计（FMPE）高效约束物理参数的先验范围，随后采用MCMC精确推断后验分布。例如，在β绘架座b的轨道参数推断中，我们的模型在保持相当精度的同时实现了显著加速——运行速度比并行回火MCMC（PTMCMC）快77.8倍，比嵌套采样快365.4倍。此外，我们的FM-MCMC方法在所有方法中获得了最高的平均对数似然值，证明了其卓越的采样效率与精度。这凸显了我们方法的可扩展性与高效性，使其特别适用于处理未来系外行星巡天预期产生的大规模数据集。除天体物理学外，我们的方法论建立了深度生成模型与传统采样技术协同的通用范式，可应用于解决宇宙学、生物医学成像及粒子物理等其他领域的复杂推断问题。