Bonn 2020 – wissenschaftliches Programm

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EP: Fachverband Extraterrestrische Physik

EP 13: Exoplanets and Astrobiology

EP 13.5: Vortrag

Freitag, 3. April 2020, 11:15–11:30, H-HS VIII

Machine learning inference of the interior structure of low-mass exoplanets — •Philipp Baumeister1, Sebastiano Padovan2, Nicola Tosi1,2, Gregoire Montavon3, Nadine Nettelmann2, Jasmine MacKenzie1, and Mareike Godolt11Centre of Astronomy and Astrophysics, Technische Universität Berlin — 2Institute of Planetary Research, German Aerospace Center (DLR), Berlin — 3Institute of Software Engineering and Theoretical Computer Science, Technische Universität Berlin

We explore the application of machine learning based on mixture density neural networks (MDNs) to the interior characterization of low-mass exoplanets up to 25 Earth masses constrained by mass, radius, and fluid Love number k2. With a dataset of 900 000 synthetic planets, consisting of an iron-rich core, a silicate mantle, a high-pressure ice shell, and a gaseous H/He envelope, we train a MDN using planetary mass and radius as inputs to the network. We show that the MDN is able to infer the distribution of possible thicknesses of each planetary layer from mass and radius of the planet. This approach obviates the time-consuming task of calculating such distributions with a dedicated set of forward models for each individual planet. The fluid Love number k2 bears constraints on the mass distribution in the planets’ interior and will be measured for an increasing number of exoplanets in the future. Adding k2 as an input to the MDN significantly decreases the degeneracy of possible interior structures.

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