Atmospheric retrievals (AR) characterize exoplanets by estimating atmospheric parameters from observed light spectra, typically by framing the task as a Bayesian inference problem. However, traditional approaches such as nested sampling are computationally expensive, thus sparking an interest in solutions based on machine learning (ML). In this ongoing work, we first explore flow matching posterior estimation (FMPE) as a new ML-based method for AR and find that, in our case, it is more accurate than neural posterior estimation (NPE), but less accurate than nested sampling. We then combine both FMPE and NPE with importance sampling, in which case both methods outperform nested sampling in terms of accuracy and simulation efficiency. Going forward, our analysis suggests that simulation-based inference with likelihood-based importance sampling provides a framework for accurate and efficient AR that may become a valuable tool not only for the analysis of observational data from existing telescopes, but also for the development of new missions and instruments.

翻译：大气反演（AR）通过从观测光谱中估计大气参数来表征系外行星，通常将该任务构建为贝叶斯推理问题。然而，嵌套采样等传统方法计算成本高昂，因此引发了基于机器学习（ML）解决方案的研究兴趣。在本项持续工作中，我们首先探索流匹配后验估计（FMPE）作为AR的一种新型ML方法，并发现，在我们的案例中，其精度优于神经后验估计（NPE），但不如嵌套采样。随后，我们将FMPE和NPE分别与重要性采样相结合，此时两种方法在精度和仿真效率方面均优于嵌套采样。展望未来，我们的分析表明，基于仿真的推理结合基于似然的重要性采样为准确高效的AR提供了一个框架，这不仅可能成为分析现有望远镜观测数据的宝贵工具，也可能推动新任务和仪器的研发。