Reconstructing Equatorial Electron Flux Measurements from low-Earth-orbit: A Conjunction Based Framework

D.L. Stumbaugh, J. Bortnik, and S.G. Claudepierre 
Published in Space Weather

Summary:
We present a machine learning based model trained on a dataset that uses the global coherent nature of the radiation belts to reconstruct electron flux measurements near the geomagnetic equator. We establish conjunctions, or times, when the low-Earth-orbit (LEO) National Oceanic and Atmospheric Administration’s Polar Orbiting Environmental Satellites (POES) and the National Aeronautics and Space Administration’s near-equatorial Van Allen Probes are connected along the same magnetic field line and measuring the same electron population. Our conjunction dataset contains electron flux measurements, positional coordinates, and geomagnetic activity measurements. We use the conjunction dataset to train our machine learning model to reconstruct equatorial electron flux measurements. We show that the model performs well for data it was not trained on giving confidence that it can effectively predict fluxes. Our current work demonstrates that we can monitor in situ radiation belt fluxes using only relatively smaller and cost-effective satellites with a neural network model instead of the more traditional high-altitude satellites. The ability to predict radiation belt dynamics, and thus space weather, has become increasingly important for the broader society due to an increasing satellite infrastructure that is vulnerable to energetic electrons.