Massive Multi‐Mission Statistical Study and Analytical Modeling of the Earth's Magnetopause: 1. A Gradient Boosting Based Automatic Detection of Near‐Earth Regions

@article{Nguyen2021MassiveMS,  title={Massive Multi‐Mission Statistical Study and Analytical Modeling of the Earth's Magnetopause: 1. A Gradient Boosting Based Automatic Detection of Near‐Earth Regions},  author={Gautier Nguyen and N.Aunai and B. Michotte de Welle and A.Jeandet and B.Lavraud and D.Fontaine},  journal={Journal of Geophysical Research: Space Physics},  year={2021},  volume={127},  url={https://api.semanticscholar.org/CorpusID:245248549}}
  • G. NguyenN.AunaiD.Fontaine
  • Published inJournal of Geophysical…14 December 2021
  • Physics, Environmental Science
  • Journal of Geophysical Research: Space Physics
We present an automatic classification method of the three near‐Earth regions, the magnetosphere, the magnetosheath and the solar wind from their in situ data measurement by multiple spacecraft. Based on gradient boosting classifier, this very simple and very fast method outperforms the detection routines based on manually set thresholds. The method is used to identify 15,062 magnetopause crossings and 17,227 bow shock crossings in the data of 11 different spacecraft of the THEMIS, ARTEMIS… 

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Massive Multi‐Mission Statistical Study and Analytical Modeling of the Earth's Magnetopause: 3. An Asymmetric Non Indented Magnetopause Analytical Model

In a companion statistical study, we showed that the expression of the magnetopause surface as a power law of an elliptic function of the zenith angle θ holds at lunar distances, that the flaring of…

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[1] The present empirical models describing a location and shape of the magnetopause and bow shock are based on a statistical evaluation of magnetopause and bow shock crossings. The crossings are…

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We present results from a new three‐dimensional empirical magnetopause model based on 15,089 magnetopause crossings from 23 spacecraft. To construct the model, we introduce a Support Vector…

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This study applies the fully convolutional neural network (FCN) deep machine-leaning algorithm to the recent Magnetospheric Multi Scale mission data in order to classify 10 key plasma regions in near-Earth space for the period 2016-2019, indicating that such method can be successfully applied to any in situ spacecraft plasma database.

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[1] A new three-dimensional asymmetric magnetopause model has been developed for corrected GSM coordinates and parameterized by the solar wind dynamic and magnetic pressures (Pd + Pm), the…

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