A Machine-learning-based Model of the Three-dimensional Ion Flux in the Earth’s Northern Cusp.

Magnetic reconnection on the dayside magnetopause is considered the primary mechanism for transporting mass, momentum, and energy from the solar wind into the terrestrial magnetosphere. Several studies have demonstrated that the spatiotemporal dynamics of the dayside magnetic reconnection can be inferred remotely from the analysis of the time-energy dispersion of ions in the Earth’s cusps. Despite the immense number of in-situ cusp measurements acquired by numerous space-based instruments, it is still challenging to determine the overall cusp behavior owing to the intermittency of the measurement acquisition. To overcome this issue, this work implements a regression model of the three-dimensional (3-D) ion flux in the Earth’s Northern cusp based on artificial neural networks (ANN) and numerous of measurements of the cusp under varying solar wind conditions. For training process, we have used solar wind parameters and geomagnetic indices obtained from the OmniWeb database as input, and in-situ ion flux measurements acquired by the CIS/CODIF/HIA instruments on board ESA’s multi-spacecraft Cluster mission during the period from 2001 to 2009 for supervised output. The model allows the reconstruction of the time-dependent, 3-D ion flux distribution within the cusp region, which serves to determine the boundaries of the high-altitude cusp, analyze its structural response to time-dependent solar wind conditions, and investigate the relationship between the cusp and dayside magnetic reconnection. In addition, we will show how an explainability technique (DeepSHAP) on the ANN-based model can be used to investigate the physical relationship (importance) of each of the inputs with the reconstructed ion flux structure.