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To quantitatively study the driving mechanisms of magnetospheric convectionin the magnetotail lobes on a global scale, we utilize data from the ARTEMISspacecraft in the deep tail and the Cluster spacecraft in the near tail.Previous work demonstrated that, in the lobes near the Moon, we can estimatethe convection by utilizing ARTEMIS measurements of lunar ions velocity. Inthis paper, we analyze these datasets with machine learning models to determinewhat upstream factors drive the lobe convection in different magnetotailregions and thereby understand the mechanisms that control the dynamics of thetail lobes. Our results show that the correlations between the predicted andtest convection velocities for the machine learning models (>0.75) are muchbetter than those of the multiple linear regression model (~ 0.23 - 0.43). Thesystematic analysis reveals that the IMF and magnetospheric activity play animportant role in influencing plasma convection in the global magnetotaillobes.

Machine Learning Solar Wind Driving Magnetospheric Convection in Tail Lobes