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Accepted paper at Space Weather Journal

Medium Energy Electron Flux in Earth's Outer Radiation Belt (MERLIN): A Machine Learning Model



A. Smirnov, M. Berrendorf, Y. Shprits, E. A. Kronberg, H. J. Allison, N. A. Aseev, I. S. Zhelavskaya, S. K. Morley, G. D. Reeves, M. R. Carver, F. Effenberger


The radiation belts of the Earth, filled with energetic electrons, comprise complex and dynamic systems that pose a significant threat to a variety of satellite systems. While various models of the relativistic electron flux have been developed for geostationary orbit (GEO), the behaviour of the medium energy (120-600 keV) electrons below GEO remains poorly quantified. In this paper we present a Medium Energy electRon flux In Earth's outer radiatioN belt (MERLIN) model based on the Light Gradient Boosting (LightGBM) machine learning algorithm. The MERLIN model takes as input the satellite position, a combination of geomagnetic indices and solar wind parameters including the time history of velocity, and does not use persistence. MERLIN is trained and validated on ≥15 years of the GPS electron flux data, and tested on more than 1.5 years of measurements. 10-fold cross validation (CV) yields that the model predicts the MEO radiation environment well, both in terms of dynamics and amplitudes of flux. Evaluation on the test set yields high correlation between the predicted and observed electron flux (0.8) and low values of absolute error. The MERLIN model can have wide Space Weather applications, providing information for the scientific community in the form of radiation belts reconstructions, as well as industry for satellite mission design, nowcast of the MEO environment and surface charging analysis. preprint at essoar