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

Using Deep Learning neural networks to predict the interior composition of exoplanets

Baumeister, Philipp and Padovan, Sebastiano and Tosi, Nicola and Montavon, Grégoire (2018) Using Deep Learning neural networks to predict the interior composition of exoplanets. PLATO Theory Workshop 2018, 3-5 Dec 2018, Cambridge, UK.

[img] PDF
1MB

Abstract

One of the main goals in exoplanetary science is the interior characterization of observed exoplanets. A common approach to characterize the interior of a known exoplanet is the use of numerical models to compute an interior structure which complies with the measured mass and radius of the planet (Sotin et al. 2017, Seager et al. 2007). With only these two observables, possible solutions tend to be highly degenerate, with multiple, qualitatively different interior compositions that can match the observations equally well. Other potential observables include the Love number k2 (bearing information on the mass concentration in the interior of the planet), and the elemental abundances of the host star, which may be representative of those of the planet. We explore the application of a deep learning neural network to the interior characterization of exoplanets. We employ a simple 1D structure model to construct a large training set of sub-Neptunian exoplanets up to 20 Earth-masses. A model planet consists of five layers: an iron-rich core, a lower and upper silicate mantle, a water ice layer, and a gaseous H/He envelope. The size of each layer is constrained by prescribed mass fractions. Using a feedforward neural network trained on a large dataset of such modelled planets, we show that we can reasonably well predict the original model input parameters (core, mantle, ice layer and atmosphere mass fractions) from just mass, radius and the fluid Love number k2.

Item URL in elib:https://elib.dlr.de/125014/
Document Type:Conference or Workshop Item (Poster)
Title:Using Deep Learning neural networks to predict the interior composition of exoplanets
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Baumeister, Philippphilipp.baumeister (at) dlr.dehttps://orcid.org/0000-0001-9284-0143
Padovan, SebastianoSebastiano.Padovan (at) dlr.dehttps://orcid.org/0000-0002-8652-3704
Tosi, Nicolanicola.tosi (at) dlr.dehttps://orcid.org/0000-0002-4912-2848
Montavon, GrégoireInstitut für Softwaretechnik und Theoretische Informatik, Technische Universität BerlinUNSPECIFIED
Date:3 December 2018
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:exoplanets, interior structure, machine learning, neural networks
Event Title:PLATO Theory Workshop 2018
Event Location:Cambridge, UK
Event Type:Workshop
Event Dates:3-5 Dec 2018
Organizer:University of Cambridge (Institute of Astronomy)
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space Science and Exploration
DLR - Research area:Raumfahrt
DLR - Program:R EW - Erforschung des Weltraums
DLR - Research theme (Project):R - Vorhaben Exploration des Sonnensystems
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:14 Dec 2018 08:32
Last Modified:31 Jul 2019 20:22

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.