Modeling Radiation Belt Electrons With Information Theory Informed Neural Networks - Simon Wing; Drew L. Turner; Aleksandr Y. Ukhorskiy; Jay R. Johnson; Thomas Sotirelis; Romina Nikoukar; Giuseppe Romeo (Space Weather)
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 parameters (SYM‐H and AL), and L*. The model outputs the radiation belt electron phase space density (PSD). The model is operational from L* = 3 to 6.5. The model is constructed with neural networks assisted by information theory. Information theory is used to select the most effective and relevant solar wind and magnetospheric input parameters plus their lag times based on their information transfer to the PSD. Based on the test set, the model prediction efficiency (PE) increases with increasing L*, ranging from −0.043 at L* = 3 to 0.76 at L* = 6.5. The model PE is near 0 at L* = 3–4 because at this L* range, the solar wind and magnetospheric parameters transfer little information to the PSD. Using solar wind observations at L1 and magnetospheric index (AL and SYM‐H) models solely driven by solar wind, the radiation belt model can be used to forecast PSD 30–60 min ahead. This baseline model can potentially complement a class of empirical models that input data from low earth orbit (LEO).