Back to Search Start Over

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

Authors :
Gebhard, Timothy D.
Wildberger, Jonas
Dax, Maximilian
Angerhausen, Daniel
Quanz, Sascha P.
Schölkopf, Bernhard
Gebhard, Timothy D.
Wildberger, Jonas
Dax, Maximilian
Angerhausen, Daniel
Quanz, Sascha P.
Schölkopf, Bernhard
Publication Year :
2023

Abstract

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.<br />Comment: Accepted at the "AI to Accelerate Science and Engineering (AI2ASE)" workshop at AAAI 2024

Details

Database :
OAIster
Publication Type :
Electronic Resource
Accession number :
edsoai.on1430703998
Document Type :
Electronic Resource