Rapid characterization of exoplanet interiors with Mixture Density Networks

Abstract

Characterizing the interior structure of exoplanets is an essential part in understanding the diversity of observed exoplanets, their formation processes and their evolution. As the interior of an exoplanet is inaccessible to observations, an inverse problem must be solved, where numerical structure models need to conform to observed parameters such as mass and radius. Since the relative proportions of iron, silicates, water ice, and volatile elements are not known, this is a highly degenerate problem, where even with accurate radius and mass measurements many different solutions for the internal structure can be found. In practice, this means that a large number of interior structures need to be calculated, making the characterization of exoplanets time consuming and computationally expensive. We present here a new machine-learning-based approach to the interior characterization of observed exoplanets using Mixture Density Networks that improves upon our previous work (Baumeister et al. 2020). This improved model, trained on a large database of synthetic interior structures, can make a complete probabilistic inference about possible planetary interior structures within a fraction of a second, without the need for extensive modeling of each exoplanet's interior. Building on our earlier work, we can demonstrate how the model, trained on different sets of (potentially) observable parameters including the received irradiation at the planet's orbit and the fluid Love number, can help to further constrain the interior of a large number of exoplanets.