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Exoplanet characterization using conditional invertible neural networks

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Context. The characterization of the interior of an exoplanet is an inverse problem. The solution requires statistical methods such as Bayesian inference. Current methods employ Markov chain Monte Carlo (MCMC) sampling to infer the posterior probability of the planetary structure parameters for a gi...

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Main Authors: Haldemann, Jonas (Author) , Ksoll, Victor F. (Author) , Walter, Daniel (Author) , Alibert, Yann (Author) , Klessen, Ralf S. (Author) , Benz, Willy (Author) , Köthe, Ullrich (Author) , Ardizzone, Lynton (Author) , Rother, Carsten (Author)
Format: Article (Journal)
Language:English
Published: 20 April 2023
In: Astronomy and astrophysics
Year: 2023, Volume: 672, Pages: 1-16
ISSN:1432-0746
DOI:10.1051/0004-6361/202243230
Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.1051/0004-6361/202243230
Verlag, kostenfrei, Volltext: https://www.aanda.org/articles/aa/abs/2023/04/aa43230-22/aa43230-22.html
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Author Notes:Jonas Haldemann, Victor Ksoll, Daniel Walter, Yann Alibert, Ralf S. Klessen, Willy Benz, Ullrich Koethe, Lynton Ardizzone, and Carsten Rother
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Exoplanet characterization using conditional invertible neural networks by Haldemann, Jonas (Author) , Ksoll, Victor F. (Author) , Walter, Daniel (Author) , Alibert, Yann (Author) , Klessen, Ralf S. (Author) , Benz, Willy (Author) , Köthe, Ullrich (Author) , Ardizzone, Lynton (Author) , Rother, Carsten (Author) ,


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