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arXiv 1908.05715physics.space-phcs.LGeess.IV
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Automated classification of plasma regions using 3D particle energy distributions

Swedish Institute of Space Physics（瑞典空间物理研究所）；St. Petersburg State University（圣彼得堡国立大学）；Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）；KTH Royal Institute of Technology（皇家理工学院）；NASA Goddard Space Flight Center（美国国家航空航天局戈达德太空飞行中心）；Main Astronomical Observatory（主天文台）；Uppsala University（乌普萨拉大学）
Vyacheslav Olshevsky, Yuri V. Khotyaintsev, Ahmad Lalti, Andrey Divin, Gian Luca Delzanno, Sven Anderzen, Pawel Herman, Steven W. D. Chien, Levon Avanov, Andrew… 展开作者

Vyacheslav Olshevsky, Yuri V. Khotyaintsev, Ahmad Lalti, Andrey Divin, Gian Luca Delzanno, Sven Anderzen, Pawel Herman, Steven W. D. Chien, Levon Avanov, Andrew P. Dimmock, Stefano Markidis

备注：
Accepted to JGR: Space Physics
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英文摘要：

We investigate the properties of the ion sky maps produced by the Dual Ion Spectrometers (DIS) from the Fast Plasma Investigation (FPI). We have trained a convolutional neural network classifier to predict four regions crossed by the MMS on the dayside magnetosphere: solar wind, ion foreshock, magnetosheath, and magnetopause using solely DIS spectrograms. The accuracy of the classifier is >98%. We use the classifier to detect mixed plasma regions, in particular to find the bow shock regions. A similar approach can be used to identify the magnetopause crossings and reveal regions prone to magnetic reconnection. Data processing through the trained classifier is fast and efficient and thus can be used for classification for the whole MMS database.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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