Exoplanetary Atmospheric Retrieval via Bayesian Machine Learning

Abstract

Atmospheric retrieval, the inverse modeling technique whereby atmospheric properties are inferred from observations, is computationally expensive and time consuming. Recently, machine learning (ML) approaches to atmospheric retrieval have been shown to provide results consistent with traditional approaches in just seconds to minutes. We introduce plan-net, the first ensemble of Bayesian neural networks for atmospheric retrieval. Our novel likelihood function captures parameter correlations, improving uncertainty estimations over standard likelihood functions common in ML. We replicate the results of Marquez-Neila et al. (2018), and we demonstrate plan-net's improvement in accuracy over their random forest regression tree when applied to their synthetic data set of hot Jupiter WFC3 transmission spectra. We apply a trained plan-net ensemble to the transmission spectrum of WASP-12b and find results generally consistent with the literature. We also apply plan-net to our data set of over 3 million synthetic terrestrial exoplanet spectra generated using the NASA Planetary Spectrum Generator.