INARA: A Bayesian Deep Learning Framework for Exoplanet Atmospheric Retrieval

Abstract

Determining an exoplanet's atmospheric properties from an observed spectrum (atmospheric retrieval) is a time-consuming and compute-intensive inverse modeling technique. They require complex algorithms that generate many atmospheric models and compare their simulated spectra to the observational data to find the most probable values and associated uncertainties for each model parameter. Retrieval may be the first method to find extraterrestrial life by remotely detecting biosignatures, atmospheric species indicative of biological activity. The work presented here is a result of the NASA Frontier Development Lab Astrobiology Team II. We present an ML-based retrieval framework called Intelligent exoplaNet Atmospheric RetrievAl (INARA) that consists of a Bayesian deep learning model for retrieval and a data set of 3,000,000 synthetic rocky exoplanetary spectra generated using approximately 2,000 high-end VMs and instances of the NASA Planetary Spectrum Generator (PSG). The generated dataset encompasses spectra based on a given planetary system model, where we consider F-, G-, K-, and M-type main sequence stars. Observations are simulated using an instrument model of the Large UltraViolet/Optical/InfraRed Surveyor (LUVOIR). Our work represents the first ML retrieval framework for rocky, terrestrial exoplanets and the first synthetic data set of terrestrial spectra generated at this scale.