  Over the past decade, the study of extrasolar planets has evolved rapidly
from plain detection and identification to comprehensive categorization and
characterization of exoplanet systems and their atmospheres. Atmospheric
retrieval, the inverse modeling technique used to determine an exoplanetary
atmosphere's temperature structure and composition from an observed spectrum,
is both time-consuming and compute-intensive, requiring complex algorithms that
compare thousands to millions of atmospheric models to the observational data
to find the most probable values and associated uncertainties for each model
parameter. For rocky, terrestrial planets, the retrieved atmospheric
composition can give insight into the surface fluxes of gaseous species
necessary to maintain the stability of that atmosphere, which may in turn
provide insight into the geological and/or biological processes active on the
planet. These atmospheres contain many molecules, some of them biosignatures,
spectral fingerprints indicative of biological activity, which will become
observable with the next generation of telescopes. Runtimes of traditional
retrieval models scale with the number of model parameters, so as more
molecular species are considered, runtimes can become prohibitively long.
Recent advances in machine learning (ML) and computer vision offer new ways to
reduce the time to perform a retrieval by orders of magnitude, given a
sufficient data set to train with. Here 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 the NASA
Planetary Spectrum Generator. Our work represents the first ML retrieval model
for rocky, terrestrial exoplanets and the first synthetic data set of
terrestrial spectra generated at this scale.
