EXO−ATMOS: A Scalable Grid of Hypothetical Planetary Atmospheres

Abstract

The NASA Frontier Development Laboratory (FDL) is an annual science accelerator that focuses on applying machine learning and large-scale computing to challenges in space science and exploration. During the 2018 FDL program, we implemented a cloud-based strategy to better understand the statistical distributions of habitable planets and life in the universe and lay out an avenue to characterize the potential role of biological regulation of planetary atmospheres. We simulated a range of atmospheres to infer the landscape of the multi-parameter space, such as the abundances of biological mediated gases that would yield stable (non-runaway) planetary atmospheres on Earth-like planets around solar-type stars. The dataset of planetary atmospheres we have generated can be used for training machine learning models to bootstrap the ATMOS code. It is an open-source dataset available for the community to understand distributions of habitability parameters such as surface temperatures and free energy available to life on different classes of atmosphere bearing planets. Our scalable tool, once coupled to a generalized ecosystem model, could help derive estimates of the biological mediated atmospheric gas fluxes and help constrain the type and the extent of exobiology on exoplanets based on the remotely detected atmospheric compositions.