Speaker
Description
The Earth’s radiation belts host electron populations that frequently deviate from thermodynamic equilibrium, exhibiting pronounced suprathermal tails and anisotropies driven by wave–particle interactions. Accurately representing these non-Maxwellian features is essential for understanding acceleration processes and improving predictive capabilities. In this work, we introduce an adaptive hybrid distribution modeling framework for radiation belt electrons. The method automatically identifies the optimal functional representation of the observed electron flux among Maxwellian, kappa, bi-Maxwellian, bi-kappa, and hybrid Maxwellian–kappa distributions. Model selection is performed algorithmically based on goodness-of-fit metrics, enabling a data-driven characterization of the underlying plasma regime. The selected distribution parameters are then used to reconstruct the electron flux density and to provide physically interpretable quantities such as density, characteristic temperature, and suprathermal index. Building upon this representation, we implement a neural network forecasting model that leverages the fitted distribution parameters to predict electron flux variability. This adaptive framework provides a unified, physically grounded approach to modeling and forecasting radiation belt electron populations, capturing both near-equilibrium and strongly non-thermal regimes.