Inferring Atmospheric Properties of Exoplanets with Flow Matching and Neural Importance Sampling
This work addresses a domain-specific problem for astronomers by providing incremental improvements in simulation-based inference for exoplanet characterization.
The paper tackles the computational expense of atmospheric retrievals for exoplanets by exploring flow matching posterior estimation and combining it with neural posterior estimation and importance sampling, finding that these methods outperform traditional nested sampling in accuracy and efficiency.
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.