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dc.contributor.authorCao, Xin
dc.contributor.authorHalekas, Jasper
dc.contributor.authorHaaland, Stein Egil
dc.contributor.authorRuhunusiri, Suranga
dc.contributor.authorGlassmeier, Karl-Heinz
dc.date.accessioned2023-12-08T13:39:02Z
dc.date.available2023-12-08T13:39:02Z
dc.date.created2023-10-10T10:12:18Z
dc.date.issued2023
dc.identifier.issn2296-987X
dc.identifier.urihttps://hdl.handle.net/11250/3106662
dc.description.abstractIn 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.en_US
dc.language.isoengen_US
dc.publisherFrontiersen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleUsing machine learning to characterize solar wind driving of convection in the terrestrial magnetotail lobesen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.source.articlenumber1180410en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1
dc.identifier.doi10.3389/fspas.2023.1180410
dc.identifier.cristin2183199
dc.source.journalFrontiers in Astronomy and Space Sciencesen_US
dc.identifier.citationFrontiers in Astronomy and Space Sciences. 2023, 10, 1180410.en_US
dc.source.volume10en_US


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