• Prediction of Soft Proton Intensities in the Near-Earth Space Using Machine Learning 

      Kronberg, Elena A.; Hannan, Tanveer; Huthmacher, Jens; Münzer, Marcus; Peste, Florian; Zhou, Ziyang; Berrendorf, Max; Faerman, Evgeniy; Gastaldello, Fabio; Ghizzardi, Simona; Escoubet, Philippe; Haaland, Stein; Smirnov, Artem; Sivadas, Nithin; Allen, Robert C.; Tiengo, Andrea; Ilie, Raluca (Journal article; Peer reviewed, 2021)
      The spatial distribution of energetic protons contributes to the understanding of magnetospheric dynamics. Based upon 17 yr of the Cluster/RAPID observations, we have derived machine-learning-based models to predict the ...