Kronberg, Elena A. https://orcid.org/0000-0001-7741-682X
Hannan, Tanveer https://orcid.org/0000-0001-9957-9531
Huthmacher, Jens https://orcid.org/0000-0001-7223-4103
Münzer, Marcus https://orcid.org/0000-0002-8712-4035
Peste, Florian https://orcid.org/0000-0002-7070-1078
Zhou, Ziyang https://orcid.org/0000-0003-0154-6948
Berrendorf, Max https://orcid.org/0000-0001-9724-4009
Faerman, Evgeniy https://orcid.org/0000-0001-8841-5128
Gastaldello, Fabio https://orcid.org/0000-0002-9112-0184
Ghizzardi, Simona https://orcid.org/0000-0003-0879-7328
Escoubet, Philippe https://orcid.org/0000-0003-4475-6769
Haaland, Stein https://orcid.org/0000-0002-1241-7570
Smirnov, Artem https://orcid.org/0000-0003-3689-4336
Sivadas, Nithin https://orcid.org/0000-0003-4278-0482
Allen, Robert C. https://orcid.org/0000-0003-2079-5683
Tiengo, Andrea https://orcid.org/0000-0002-6038-1090
Ilie, Raluca https://orcid.org/0000-0002-7305-2579
Funding for this research was provided by:
German Research Foundation (KR 4375/2-1)
NASA ∣ Earth Sciences Division (80NSSC17K0433)
Article Title: Prediction of Soft Proton Intensities in the Near-Earth Space Using Machine Learning
Journal Title: The Astrophysical Journal
Article Type: paper
Copyright Information: © 2021. The Author(s). Published by the American Astronomical Society.
Publication dates
Date Received: 2021-04-29
Date Accepted: 2021-08-02
Online publication date: 2021-11-02