PyATMOS: A Scalable Grid of Hypothetical Planetary Atmospheres

TL;DR

This paper introduces PyATMOS, a scalable cloud-based tool that efficiently simulates a wide range of hypothetical planetary atmospheres to aid in the assessment of exoplanet habitability.

Contribution

It presents a parallelised climate modelling framework that generates extensive datasets of simulated atmospheres, facilitating statistical analysis and future observational interpretation.

Findings

Simulated over 100,000 exoplanet atmospheres.

Publicly available dataset on NASA Exoplanet Archive.

Supports high-throughput habitability assessment.

Abstract

Cloud computing offers an opportunity to run compute-resource intensive climate models at scale by parallelising model runs such that datasets useful to the exoplanet community can be produced efficiently. To better understand the statistical distributions and properties of potentially habitable planetary atmospheres we implemented a parallelised climate modelling tool to scan a range of hypothetical atmospheres.Starting with a modern day Earth atmosphere, we iteratively and incrementally simulated a range of atmospheres to infer the landscape of the multi-parameter space, such as the abundances of biological mediated gases (\ce{O2}, \ce{CO2}, \ce{H2O}, \ce{CH4}, \ce{H2}, and \ce{N2}) that would yield `steady state' planetary atmospheres on Earth-like planets around solar-type stars. Our current datasets comprises of \numatmospheres simulated models of exoplanet atmospheres and is available publicly on the NASA Exoplanet Archive. Our scalable approach of analysing atmospheres could also help interpret future observations of planetary atmospheres by providing estimates of atmospheric gas fluxes and temperatures as a function of altitude. Such data could enable high-throughput first-order assessment of the potential habitability of exoplanetary surfaces and sepcan be a learning dataset for machine learning applications in the atmospheric and exoplanet science domain.