Neural Network Imputation of the Pitch‐Angle‐Resolved Medium‐Energy Electron Flux Data in LEO

Abstract Pitch‐angle‐resolved electron flux data are crucial for investigating variations in electron flux within the magnetosphere and the underlying mechanisms, such as radial diffusion and wave‐particle interactions. The Medium‐Energy Electron Detector (MEED) onboard the Fengyun‐3E (FY‐3E) satellite in Low Earth Orbit (LEO) provides 18‐directional local pitch angle observations, enabling studies of medium‐energy electrons in LEO. However, the MEED cannot cover the full local pitch angle range continuously from 0° to 180° due to limitations of the satellite's three‐axis stabilized attitude control system. At mid‐latitudes, its pitch angle coverage spans approximately 100° (40°–140°), with improved coverage at low and high latitudes. To extend MEED's coverage toward full global pitch angle observations, we propose a data imputation method using the MEED data and machine learning technology. We have trained eight imputation models based on Multi‐Layer Perceptron (MLP) using electron flux data near 90° local pitch angle and satellite orbital data to generate missing data near 0° and 180° with 280–600 keV energies. For unseen data in test sets, the correlation coefficient r between the models' reconstructions and observations is at least 0.915, with a maximum Root Mean Square Error (RMSE) of 0.110 on the logarithmic scale, which indicates that the models can provide reasonably reliable estimates for missing values. Besides, these imputation models may support future near‐real‐time electron flux imputation when operational data streams become available. This work helps expand the mid‐latitude pitch angle coverage of the FY‐3E MEED, improving data usability, and aiding the development of electron flux prediction models in LEO.