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Using machine learning to characterize solar wind driving of convection in the terrestrial magnetotail lobes
Journal article   Open access   Peer reviewed

Using machine learning to characterize solar wind driving of convection in the terrestrial magnetotail lobes

Xin Cao, Jasper S. Halekas, Stein Haaland, Suranga Ruhunusiri and Karl-Heinz Glassmeier
Frontiers in astronomy and space sciences, Vol.10, 1180410
08/14/2023
DOI: 10.3389/fspas.2023.1180410
url
https://doi.org/10.3389/fspas.2023.1180410View
Published (Version of record) Open Access

Abstract

In order to quantitatively investigate the mechanism of how magnetospheric convection is driven in the region of magnetotail lobes on a global scale, we analyzed data from the ARTEMIS spacecraft in the deep tail and data from the Cluster spacecraft in the near and mid-tail regions. Our previous work revealed that, in the lobes near the Moon’s orbit, the convection can be estimated by using ARTEMIS measurements of lunar ions’ velocity. Based on that, in this paper, we applied machine learning models to these measurements to determine which upstream solar wind parameters significantly drive the lobe convection in magnetotail regions, to help us understand the mechanism that controls the dynamics of the tail lobes. The results demonstrate that the correlations between the predicted and measured convection velocities for the machine learning models (>0.75) are superior to those of the multiple linear regression model (∼0.23–0.43) in the testing dataset. The systematic analysis shows that the IMF and magnetospheric activity play an important role in influencing plasma convection in the global magnetotail lobes.

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