PyATMOS: A Scalable Grid of Hypothetical Planetary Atmospheres

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 (O2, CO2, H2O, CH4, H2, and N2) that would yield ‘steady state’ planetary atmospheres on Earth-like planets around solar-type stars

Our current datasets comprises of atmospheres 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.

Aditya Chopra, Aaron C Bell, William Fawcett, Rodd Talebi, Daniel Angerhausen, Atılım Güneş Baydin, Anamaria Berea, Nathalie A. Cabrol, Christopher Kempes, Massimo Mascaro

Comments: 9 pages, 6 figures
Subjects: Earth and Planetary Astrophysics (astro-ph.EP); Instrumentation and Methods for Astrophysics (astro-ph.IM)
Cite as: arXiv:2308.10624 [astro-ph.EP] (or arXiv:2308.10624v1 [astro-ph.EP] for this version)
Submission history
From: Aditya Chopra
[v1] Mon, 21 Aug 2023 10:49:10 UTC (557 KB)
https://arxiv.org/abs/2308.10624
Astrobiology, Astrochemistry,