A generalized bayesian inference method for constraining the interiors of super Earths and sub-Neptunes

TL;DR

This paper introduces a comprehensive Bayesian inference framework to constrain the interior structures of super Earths and sub-Neptunes, accounting for observational uncertainties and degeneracies in high-dimensional parameter spaces.

Contribution

It presents a novel probabilistic method combining MCMC and advanced structural models to analyze exoplanet interiors, including atmospheric effects and composition constraints.

Findings

Validated method against Neptune data.

Analyzed impact of observational and model uncertainties.

Provided probabilistic constraints on planetary interior parameters.

Abstract

We aim to present a generalized Bayesian inference method for constraining interiors of super Earths and sub-Neptunes. Our methodology succeeds in quantifying the degeneracy and correlation of structural parameters for high dimensional parameter spaces. Specifically, we identify what constraints can be placed on composition and thickness of core, mantle, ice, ocean, and atmospheric layers given observations of mass, radius, and bulk refractory abundance constraints (Fe, Mg, Si) from observations of the host star's photospheric composition. We employed a full probabilistic Bayesian inference analysis that formally accounts for observational and model uncertainties. Using a Markov chain Monte Carlo technique, we computed joint and marginal posterior probability distributions for all structural parameters of interest. We included state-of-the-art structural models based on self-consistent thermodynamics of core, mantle, high-pressure ice, and liquid water. Furthermore, we tested and compared two different atmospheric models that are tailored for modeling thick and thin atmospheres, respectively. First, we validate our method against Neptune. Second, we apply it to synthetic exoplanets of fixed mass and determine the effect on interior structure and composition when (1) radius, (2) atmospheric model, (3) data uncertainties, (4) semi-major axes, (5) atmospheric composition (i.e., a priori assumption of enriched envelopes versus pure H/He envelopes), and (6) prior distributions are varied. Our main conclusions are: [...]