Machine-learning Inferences of the Interior Structure of Rocky Exoplanets from Bulk Observational Constraints

Abstract

Abstract Characterizing the interiors of rocky exoplanets is important to understand planetary populations and further investigate planetary habitability. New observable constraints and inference techniques have been explored for this purpose. In this work, we design and train mixture density networks (MDNs) to predict the interior properties of rocky exoplanets with large compositional diversity. In addition to measurements of mass and radius, bulk refractory elemental abundance ratios and the static Love number k 2 are used to constrain the interior of rocky exoplanets. It is found that the MDNs are able to infer the interior properties of rocky exoplanets from the available measurements of exoplanets. Compared with powerful inversion methods based on Bayesian inference, the trained MDNs provide a more rapid characterization of planetary interiors for each individual planet. The MDN model offers a convenient and practical tool for probabilistic inferences of planetary interiors.