elib
DLR-Header
DLR-Logo -> http://www.dlr.de
DLR Portal Home | Impressum | Datenschutz | Kontakt | English
Schriftgröße: [-] Text [+]

Rapid characterization of exoplanet interiors with Mixture Density Networks

Baumeister, Philipp und 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.

Dieses Archiv kann nicht den Volltext zur Verfügung stellen.

Kurzfassung

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.

elib-URL des Eintrags:https://elib.dlr.de/191280/
Dokumentart:Konferenzbeitrag (Vortrag)
Titel:Rapid characterization of exoplanet interiors with Mixture Density Networks
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Baumeister, PhilippPhilipp.Baumeister (at) dlr.dehttps://orcid.org/0000-0001-9284-0143NICHT SPEZIFIZIERT
Tosi, Nicolanicola.tosi (at) dlr.dehttps://orcid.org/0000-0002-4912-2848NICHT SPEZIFIZIERT
Datum:30 September 2022
Referierte Publikation:Nein
Open Access:Nein
Gold Open Access:Nein
In SCOPUS:Nein
In ISI Web of Science:Nein
Status:veröffentlicht
Stichwörter:exoplanet, machine learning, interior structure, Love numbers
Veranstaltungstitel:10th Joint Workshop on High Pressure, Planetary, and Plasma Physics (HP4)
Veranstaltungsort:Brüssel, Belgien
Veranstaltungsart:internationale Konferenz
Veranstaltungsbeginn:28 September 2022
Veranstaltungsende:30 September 2022
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Erforschung des Weltraums
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R EW - Erforschung des Weltraums
DLR - Teilgebiet (Projekt, Vorhaben):R - Exploration des Sonnensystems
Standort: Berlin-Adlershof
Institute & Einrichtungen:Institut für Planetenforschung > Planetenphysik
Institut für Planetenforschung > Extrasolare Planeten und Atmosphären
Hinterlegt von: Baumeister, Philipp
Hinterlegt am:30 Nov 2022 14:02
Letzte Änderung:24 Apr 2024 20:52

Nur für Mitarbeiter des Archivs: Kontrollseite des Eintrags

Blättern
Suchen
Hilfe & Kontakt
Informationen
electronic library verwendet EPrints 3.3.12
Gestaltung Webseite und Datenbank: Copyright © Deutsches Zentrum für Luft- und Raumfahrt (DLR). Alle Rechte vorbehalten.