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Atmospheric retrievals (AR) characterize exoplanets by estimating atmosphericparameters from observed light spectra, typically by framing the task as aBayesian inference problem. However, traditional approaches such as nestedsampling are computationally expensive, thus sparking an interest in solutionsbased on machine learning (ML). In this ongoing work, we first explore flowmatching posterior estimation (FMPE) as a new ML-based method for AR and findthat, 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 withimportance sampling, in which case both methods outperform nested sampling interms of accuracy and simulation efficiency. Going forward, our analysissuggests that simulation-based inference with likelihood-based importancesampling provides a framework for accurate and efficient AR that may become avaluable tool not only for the analysis of observational data from existingtelescopes, but also for the development of new missions and instruments.

Inferring Atmospheric Properties of Exoplanets with Flow Matching and Neural Importance Sampling