elib
DLR-Header
DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

Machine learning inference of the interior structure of low-mass exoplanet

Baumeister, Philipp and Padovan, Sebastiano and Tosi, Nicola and Montavon, Grégoire and Nettelmann, Nadine and MacKenzie, Jasmine and Godolt, Mareike (2020) Machine learning inference of the interior structure of low-mass exoplanet. The Astrophysical Journal, 889 (1). American Astronomical Society. doi: 10.3847/1538-4357/ab5d32. ISSN 0004-637X.

[img] PDF - Published version
1MB

Official URL: https://iopscience.iop.org/article/10.3847/1538-4357/ab5d32

Abstract

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, k 2. We create a data set 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, to train a MDN using planetary mass and radius as inputs to the network. For this layered structure, 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. While gas-rich planets may be characterized by compositional gradients rather than distinct layers, the method presented here can be easily extended to any interior structure model. The fluid Love number k 2 bears constraints on the mass distribution in the planets' interiors and will be measured for an increasing number of exoplanets in the future. Adding k 2 as an input to the MDN significantly decreases the degeneracy of the possible interior structures. In an open repository, we provide the trained MDN to be used through a Python Notebook.

Item URL in elib:https://elib.dlr.de/136726/
Document Type:Article
Title:Machine learning inference of the interior structure of low-mass exoplanet
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Baumeister, PhilippPhilipp.Baumeister (at) dlr.dehttps://orcid.org/0000-0001-9284-0143
Padovan, SebastianoSebastiano.Padovan (at) dlr.deUNSPECIFIED
Tosi, Nicolanicola.tosi (at) dlr.deUNSPECIFIED
Montavon, GrégoireInstitut für Softwaretechnik und Theoretische Informatik, Technische Universität BerlinUNSPECIFIED
Nettelmann, NadineNadine.Nettelmann (at) dlr.deUNSPECIFIED
MacKenzie, JasmineTechnische Universität Berlin, Zentrum für Astronomie und Astrophysik, Hardenbergstraße 36, 10623 BerlinUNSPECIFIED
Godolt, MareikeMareike.Godolt (at) dlr.deUNSPECIFIED
Date:2020
Journal or Publication Title:The Astrophysical Journal
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:889
DOI :10.3847/1538-4357/ab5d32
Publisher:American Astronomical Society
ISSN:0004-637X
Status:Published
Keywords:Exoplanet structure; Exoplanets; Neural networks; Planetary interior; Computational methods; Planetary theory
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: Tosi, Nicola
Deposited On:27 Oct 2020 07:45
Last Modified:01 Feb 2021 03:00

Repository Staff Only: item control page

Browse
Search
Help & Contact
Information
electronic library is running on EPrints 3.3.12
Copyright © 2008-2017 German Aerospace Center (DLR). All rights reserved.