Machine Learning Solar Wind Driving Magnetospheric Convection in Tail Lobes
This work addresses the mechanisms controlling magnetospheric dynamics in space physics, but it is incremental as it applies existing machine learning methods to new data in this domain.
The study tackled the problem of understanding what upstream factors drive magnetospheric convection in the magnetotail lobes by analyzing spacecraft data with machine learning models, resulting in correlations between predicted and test convection velocities exceeding 0.75, significantly better than multiple linear regression models at 0.23-0.43.
To quantitatively study the driving mechanisms of magnetospheric convection in the magnetotail lobes on a global scale, we utilize data from the ARTEMIS spacecraft in the deep tail and the Cluster spacecraft in the near tail. Previous work demonstrated that, in the lobes near the Moon, we can estimate the convection by utilizing ARTEMIS measurements of lunar ions velocity. In this paper, we analyze these datasets with machine learning models to determine what upstream factors drive the lobe convection in different magnetotail regions and thereby understand the mechanisms that control the dynamics of the tail lobes. Our results show that the correlations between the predicted and test convection velocities for the machine learning models (>0.75) are much better than those of the multiple linear regression model (~ 0.23 - 0.43). The systematic analysis reveals that the IMF and magnetospheric activity play an important role in influencing plasma convection in the global magnetotail lobes.