Accurate Machine Learning Atmospheric Retrieval via a Neural Network Surrogate Model for Radiative Transfer

Himes, Michael D., Harrington, Joseph, Cobb, Adam D. et al. (8 more authors) (2022) Accurate Machine Learning Atmospheric Retrieval via a Neural Network Surrogate Model for Radiative Transfer. The Planetary Science Journal. ISSN 2632-3338

Abstract

Metadata

Item Type: Article
Authors/Creators:
  • Himes, Michael D.
  • Harrington, Joseph
  • Cobb, Adam D.
  • Baydin, Atilim Gunes
  • Soboczenski, Frank ORCID logo https://orcid.org/0000-0001-8185-6094
  • O'Beirne, Molly D.
  • Zorzan, Simone
  • Wright, David C.
  • Scheffer, Zacchaeus
  • Domagal-Goldman, Shawn D.
  • Arney, Giada N.
Copyright, Publisher and Additional Information:

16 pages, 4 figures, submitted to PSJ 3/4/2020, revised 1/22/2021, accepted 2/4/2021, published 4/25/2022. Updated to match the published manuscript. Himes et al. 2022, PSJ, 3, 91

Keywords: astro-ph.IM,astro-ph.EP
Dates:
  • Published: 25 April 2022
  • Accepted: 4 February 2021
Institution: The University of York
Academic Units: The University of York > Faculty of Sciences (York) > Computer Science (York)
Depositing User: Pure (York)
Date Deposited: 25 Mar 2025 12:20
Last Modified: 25 Mar 2025 12:20
Published Version: https://doi.org/10.3847/PSJ/abe3fd
Status: Published
Refereed: Yes
Identification Number: 10.3847/PSJ/abe3fd
Open Archives Initiative ID (OAI ID):

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Filename: 2003.02430v4.pdf

Description: 2003.02430v4

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