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

@article{id2782,
  author = {Kronberg, Elena A. and Hannan, Tanveer and Huthmacher, Jens and M\"unzer, Marcus and Peste, Florian and Zhou, Ziyang and Berrendorf, Max and Faerman, Evgeniy and Gastaldello, Fabio and Ghizzardi, Simona and Escoubet, Philippe and Haaland, Stein and Smirnov, Artem and Sivadas, Nithin and Allen, Robert C. and Tiengo, Andrea and Ilie, Raluca},
  doi = {10.3847/1538-4357/ac1b30},
  eid = {76},
  journal = {{\textbackslash}apj},
  language = {en},
  number = {1},
  pages = {76},
  title = {Prediction of Soft Proton Intensities in the Near-Earth Space Using Machine Learning},
  volume = {921},
  year = {2021},
}

%O Journal Article
%A Kronberg, Elena A.
%A Hannan, Tanveer
%A Huthmacher, Jens
%A Münzer, Marcus
%A Peste, Florian
%A Zhou, Ziyang
%A Berrendorf, Max
%A Faerman, Evgeniy
%A Gastaldello, Fabio
%A Ghizzardi, Simona
%A Escoubet, Philippe
%A Haaland, Stein
%A Smirnov, Artem
%A Sivadas, Nithin
%A Allen, Robert C.
%A Tiengo, Andrea
%A Ilie, Raluca
%R 10.3847/1538-4357/ac1b30
%1 76
%J \apj
%G en
%N 1
%P 76
%T Prediction of Soft Proton Intensities in the Near-Earth Space Using Machine Learning
%V 921
%D 2021