Abstract: 

Since the first discoveries of extrasolar planets in the 1990s, more than 4000 exoplanets have been discovered to date, and the number is growing rapidly with new dedicated space and ground-based surveys. Based on radius measurements via transit observations and mass estimations via radial velocity measurements, the inner structure of planets can be modeled numerically. This characterization is crucial for our understanding of the diversity of the observed planets, their formation processes, and the question whether or not they can support life. However, even with accurate radius and mass measurements, many different solutions for the internal structure can be found, since the relative proportions of iron, silicates, water ice, and volatile elements are not known.

In this talk, I will present a novel machine-learning approach to infer planetary interiors based on observational data, and how we can use this to identify potentially observable parameters that can better constrain the range of possible interior structures.

Machine learning can avoid the need for extensive interior modeling for each individual exoplanet by learning from large sets of precalculated data generated with suitable forward models, and thus capable of rapidly determining the range of physically meaningful interiors of observed exoplanets.

Join Zoom Meeting: https://aarhusuniversity.zoom.us/j/62576434552