Toward 3D Retrieval of Exoplanet Atmospheres: Assessing Thermochemical Equilibrium Estimation Methods

Abstract Characterizing exoplanetary atmospheres via Bayesian retrievals requires assuming some chemistry model, such as thermochemical equilibrium or parameterized abundances. The higher-resolution data offered by upcoming telescopes enable more complex models within retrieval frameworks. Yet many codes that model processes like photochemistry and vertical transport are computationally expensive, directly incorporating them into a 1D can result in prohibitively long execution times. Additionally, phase-curve observations with motivate 2D 3D models, further exacerbating the lengthy runtime for frameworks models. Here we compare approximation methods based on their speed accuracy respect to Gibbs energy-minimization code. We find that, while all offer orders-of-magnitude reductions computational cost, neural network surrogate perform accurately than other approaches considered, achieving median absolute dex error of <0.03 phase space considered. While our results study suggests higher-dimensional could be incorporated this modeling approach.