Modeling Ring Current Proton Fluxes Using Artificial Neural Network and Van Allen Probe Measurements

Terrestrial ring current dynamics are a critical part of the near-space environment, in that they directly drive geomagnetic field variations that control particle drifts, and define geomagnetic storms. The present study aims to specify a global and time-varying distribution of ring current proton using geomagnetic indices and solar wind parameters with their history as input. We train an artificial neural network (ANN) model to reproduce proton fluxes measured by the Radiation Belt Storm Probes Ion Composition Experiment instrument onboard Van Allen Probes. By choosing optimal feature parameters and their history length, the model results show a high correlation and a small error between model specifications and satellite measurements. The modeled results well capture energy-dependent proton dynamics in association with geomagnetic storms, including inward radial diffusion, acceleration and decay. Our ANN model produces proton fluxes with their corresponding 3D spatiotemporal variations, capturing the latitudinal distribution and local time asymmetry that are consistent with observations and that can further inform theory.

Automated summary

This study uses geomagnetic indices and solar wind parameters to train an artificial neural network model, which can provide a global and time-varying distribution of the ring current proton. The model results show a high correlation and small error with satellite measurements, accurately capturing the energy-dependent proton dynamics during geomagnetic storms. The model results also capture spatiotemporal variations in proton fluxes, including latitudinal distribution and local time asymmetry, which are consistent with observations and can inform further theoretical development.