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Rapid characterization of exoplanet interiors with Mixture Density Networks

Baumeister, Philipp and Tosi, Nicola (2022) Rapid characterization of exoplanet interiors with Mixture Density Networks. 10th Joint Workshop on High Pressure, Planetary, and Plasma Physics (HP4), 2022-09-28 - 2022-09-30, Brüssel, Belgien.

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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.

Item URL in elib:https://elib.dlr.de/191280/
Document Type:Conference or Workshop Item (Speech)
Title:Rapid characterization of exoplanet interiors with Mixture Density Networks
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Baumeister, PhilippUNSPECIFIEDhttps://orcid.org/0000-0001-9284-0143UNSPECIFIED
Tosi, NicolaUNSPECIFIEDhttps://orcid.org/0000-0002-4912-2848UNSPECIFIED
Date:30 September 2022
Refereed publication:No
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Keywords:exoplanet, machine learning, interior structure, Love numbers
Event Title:10th Joint Workshop on High Pressure, Planetary, and Plasma Physics (HP4)
Event Location:Brüssel, Belgien
Event Type:international Conference
Event Start Date:28 September 2022
Event End Date:30 September 2022
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space Exploration
DLR - Research area:Raumfahrt
DLR - Program:R EW - Space Exploration
DLR - Research theme (Project):R - Exploration of the Solar System
Location: Berlin-Adlershof
Institutes and Institutions:Institute of Planetary Research > Planetary Physics
Institute of Planetary Research > Extrasolar Planets and Atmospheres
Deposited By: Baumeister, Philipp
Deposited On:30 Nov 2022 14:02
Last Modified:24 Apr 2024 20:52

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