Modeling the Dynamic Variability of Sub-Relativistic Outer Radiation Belt Electron Fluxes Using Machine Learning

We present a set of neural network models that reproduce the dynamics of electron fluxes in the range of 50 keV similar to 1 MeV in the outer radiation belt. The Outer Radiation belt Electron Neural net model for Medium energy electrons uses only solar wind conditions and geomagnetic indices as input. The models are trained on electron flux data from the Magnetic Electron Ion Spectrometer instrument onboard Van Allen Probes, and they can reproduce the dynamic variations of electron fluxes in different energy channels. The model results show high coefficient of determination (R-2 similar to 0.78-0.92) on the test data set, an out-of-sample 30-day period from 25 February to 25 March in 2017, when a geomagnetic storm took place, as well as an out-of-sample one year period after March 2018. In addition, the models are able to capture electron dynamics such as intensifications, decays, dropouts, and the Magnetic Local Time dependence of the lower energy (similar to<100 keV) electron fluxes during storms. The models have reliable prediction capability and can be used for a wide range of space weather applications. The general framework of building our model is not limited to radiation belt fluxes and could be used to build machine learning models for a variety of other plasma parameters in the Earth's magnetosphere.

Automated summary

In this study, a set of neural network models are proposed to accurately reproduce the dynamics of electron fluxes in the outer radiation belt. These models have reliable prediction capability and can be applied to a wide range of space weather applications.