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ExoMDN: Rapid characterization of exoplanet interior structures with mixture density networks

Baumeister, Philipp and Tosi, Nicola (2023) ExoMDN: Rapid characterization of exoplanet interior structures with mixture density networks. Astronomy & Astrophysics, 676, A106. EDP Sciences. doi: 10.1051/0004-6361/202346216. ISSN 0004-6361.

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Official URL: https://www.aanda.org/articles/aa/full_html/2023/08/aa46216-23/aa46216-23.html


Characterizing the interior structure of exoplanets is essential for understanding their diversity, formation, and evolution. As the interior of exoplanets is inaccessible to observations, an inverse problem must be solved, where numerical structure models need to conform to observable parameters such as mass and radius. This is a highly degenerate problem whose solution often relies on computationally-expensive and time-consuming inference methods such as Markov Chain Monte Carlo. We present ExoMDN, a machine-learning model for the interior characterization of exoplanets based on Mixture Density Networks (MDN). The model is trained on a large dataset of more than 5.6 million synthetic planets below 25 Earth masses consisting of an iron core, a silicate mantle, a water and high-pressure ice layer, and a H/He atmosphere. We employ log-ratio transformations to convert the interior structure data into a form that the MDN can easily handle. Given mass, radius, and equilibrium temperature, we show that ExoMDN can deliver a full posterior distribution of mass fractions and thicknesses of each planetary layer in under a second on a standard Intel i5 CPU. Observational uncertainties can be easily accounted for through repeated predictions from within the uncertainties. We use ExoMDN to characterize the interior of 22 confirmed exoplanets with mass and radius uncertainties below 10% and 5% respectively, including the well studied GJ 1214 b, GJ 486 b, and the TRAPPIST-1 planets. We discuss the inclusion of the fluid Love number k2 as an additional (potential) observable, showing how it can significantly reduce the degeneracy of interior structures. Utilizing the fast predictions of ExoMDN, we show that measuring k2 with an accuracy of 10% can constrain the thickness of core and mantle of an Earth analog to around 13% of the true values.

Item URL in elib:https://elib.dlr.de/195685/
Document Type:Article
Additional Information:Bisher nur online erschienen.
Title:ExoMDN: Rapid characterization of exoplanet interior structures with mixture density networks
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Baumeister, PhilippUNSPECIFIEDhttps://orcid.org/0000-0001-9284-0143143112608
Tosi, NicolaUNSPECIFIEDhttps://orcid.org/0000-0002-4912-2848UNSPECIFIED
Date:14 June 2023
Journal or Publication Title:Astronomy & Astrophysics
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
Page Range:A106
Publisher:EDP Sciences
Keywords:planet interior, exoplanets, planet composition, machine learning, neural networks, interior characterization, mixture density networks, exomdn
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Robotics
DLR - Research area:Raumfahrt
DLR - Program:R RO - Robotics
DLR - Research theme (Project):R - Planetary Exploration
Location: Berlin-Adlershof
Institutes and Institutions:Institute of Planetary Research > Planetary Physics
Deposited By: Baumeister, Philipp
Deposited On:27 Jun 2023 13:25
Last Modified:27 Sep 2023 14:46

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