Modeling Radiation Belt Electrons With Information Theory Informed Neural Networks

Modeling Radiation Belt Electrons With Information Theory Informed Neural Networks

Abstract An empirical model of radiation belt relativistic electrons (μ = 560–875 MeV G−1 and I = 0.088–0.14 RE G0.5) with average energy ∼1.3 MeV is developed. The model inputs solar wind parameters (velocity, density, interplanetary magnetic field (IMF) |B|, Bz, and By), magnetospheric state param...

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Bibliographic Details
Main Authors: Simon Wing, Drew L. Turner, Aleksandr Y. Ukhorskiy, Jay R. Johnson, Thomas Sotirelis, Romina Nikoukar, Giuseppe Romeo
Format: Article
Language:English
Published: Wiley 2022-08-01
Series:Space Weather
Subjects:
radiation belt
relativistic electrons
solar wind drivers
machine learning
information theory
empirical model
Online Access:https://doi.org/10.1029/2022SW003090
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https://doi.org/10.1029/2022SW003090

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