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Atmospheric retrievals (AR) of exoplanets typically rely on a combination ofa Bayesian inference technique and a forward simulator to estimate atmosphericproperties from an observed spectrum. A key component in simulating spectra isthe pressure-temperature (PT) profile, which describes the thermal structure ofthe atmosphere. Current AR pipelines commonly use ad hoc fitting functions herethat limit the retrieved PT profiles to simple approximations, but still use arelatively large number of parameters. In this work, we introduce aconceptually new, data-driven parameterization scheme for physically consistentPT profiles that does not require explicit assumptions about the functionalform of the PT profiles and uses fewer parameters than existing methods. Ourapproach consists of a latent variable model (based on a neural network) thatlearns a distribution over functions (PT profiles). Each profile is representedby a low-dimensional vector that can be used to condition a decoder networkthat maps $P$ to $T$. When training and evaluating our method on two publiclyavailable datasets of self-consistent PT profiles, we find that our methodachieves, on average, better fit quality than existing baseline methods,despite using fewer parameters. In an AR based on existing literature, ourmodel (using two parameters) produces a tighter, more accurate posterior forthe PT profile than the five-parameter polynomial baseline, while also speedingup the retrieval by more than a factor of three. By providing parametric accessto physically consistent PT profiles, and by reducing the number of parametersrequired to describe a PT profile (thereby reducing computational cost orfreeing resources for additional parameters of interest), our method can helpimprove AR and thus our understanding of exoplanet atmospheres and theirhabitability.

Parameterizing pressure-temperature profiles of exoplanet atmospheres with neural networks