Reproducing Bayesian Posterior Distributions for Exoplanet Atmospheric Parameter Retrievals with a Machine Learning Surrogate Model

• Main Authors: Unlu, Eyup B, Forestano, Roy T, Matchev, Konstantin T, Matcheva, Katia
• Format: Journal Article
• Language: English
• Published: 16.10.2023
• Subjects: Computer Science - Learning; Physics - Data Analysis, Statistics and Probability; Physics - Earth and Planetary Astrophysics; Physics - Instrumentation and Methods for Astrophysics
• DOI: 10.48550/arxiv.2310.10521

We describe a machine-learning-based surrogate model for reproducing the Bayesian posterior distributions for exoplanet atmospheric parameters derived from transmission spectra of transiting planets with typical retrieval software such as TauRex. The model is trained on ground truth distributions for seven parameters: the planet radius, the atmospheric temperature, and the mixing ratios for five common absorbers: $H_2O$, $CH_4$, $NH_3$, $CO$ and $CO_2$. The model performance is enhanced by domain-inspired preprocessing of the features and the use of semi-supervised learning in order to leverage the large amount of unlabelled training data available. The model was among the winning solutions in the 2023 Ariel Machine Learning Data Challenge.