A central question in exoplanetary research is the composition and internal structure of exoplanets. However, the observable parameters of an exoplanet are scarce and generally limited to the planet’s mass and radius and the properties of its host star. This means that it is not possible to fully constrain the internal structure parameters of the planet, such as the iron core mass fraction and the thickness of a potential water layer or gaseous atmosphere, from these observations: The problem is intrinsically degenerate. Instead, Bayesian inference is used to obtain a probability distribution of the internal structure parameters.
We have developed a Bayesian inference model that uses a neural network based full grid scheme instead of the traditionally used Markov chain Monte Carlo approach. We already used this model to characterise the internal structure of various exoplanets observed by the CHEOPS mission of the European Space Agency, e.g. TOI-178 (Leleu et al. 2021) or TOI-561 (Lacedelli et al. 2022). Here, we present our updated model and an overview and re-analysis of all the planetary systems that we have characterised so far, with the aim of studying the properties of these systems on a population level.