Inferring Atmospheric Properties of Exoplanets with Flow Matching and Neural Importance Sampling
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.
Timothy D. Gebhard, Jonas Wildberger, Maximilian Dax, Daniel Angerhausen, Sascha P. Quanz, Bernhard Schölkopf
Comments: Accepted at the “AI to Accelerate Science and Engineering (AI2ASE)” workshop at AAAI 2024
Subjects: Instrumentation and Methods for Astrophysics (astro-ph.IM); Earth and Planetary Astrophysics (astro-ph.EP); Machine Learning (cs.LG)
Cite as: arXiv:2312.08295 [astro-ph.IM] (or arXiv:2312.08295v1 [astro-ph.IM] for this version)
Submission history
From: Timothy D. Gebhard
[v1] Wed, 13 Dec 2023 17:12:03 UTC (3,782 KB)
https://arxiv.org/abs/2312.08295
Astrobiology
