Automated classification of plasma regions using 3D particle energy distributions
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, magne...
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| creator | Olshevsky, Vyacheslav Khotyaintsev, Yuri V Lalti, Ahmad Divin, Andrey Delzanno, Gian Luca Anderzen, Sven Herman, Pawel Chien, Steven W D Avanov, Levon Dimmock, Andrew P Markidis, Stefano |
| description | 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. |
| doi_str_mv | 10.48550/arxiv.1908.05715 |
| format | Article |
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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. 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Data processing through the trained classifier is fast and efficient and thus can be used for classification for the whole MMS database.</description><subject>Artificial intelligence</subject><subject>Artificial neural networks</subject><subject>Automation</subject><subject>Classification</subject><subject>Cluster analysis</subject><subject>Clustering</subject><subject>Computer Science - Learning</subject><subject>Data processing</subject><subject>Energy distribution</subject><subject>Machine learning</subject><subject>Magnetopause</subject><subject>Magnetospheres</subject><subject>Particle energy</subject><subject>Physical properties</subject><subject>Physics - Space Physics</subject><subject>Plasma</subject><subject>Principal components analysis</subject><subject>Spectrometers</subject><subject>Vector quantization</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GOX</sourceid><recordid>eNotj8tqwzAQRUWh0JDmA7qqoGu70uhlL0P6SCFQCtkbSZaMgmO7kl2av6-TdHXhcGe4B6EHSnJeCEGedfwNPzktSZEToai4QQtgjGYFB7hDq5QOhBCQCoRgC_S1nsb-qEdXY9vqlIIPVo-h73Dv8TCTo8bRNTNIeEqhazB7wYOOY7Ctw65zsTnhOqQxBjOd79I9uvW6TW71n0u0f3vdb7bZ7vP9Y7PeZVoAyST1tlTSMU6M5J5bwYAXWimrSgOWagklBVc6OgNrLVWSytoYZklhpOdsiR6vby--1RDDUcdTdfauLt5z4-naGGL_Pbk0Vod-it28qQJQQjEBgrA_Q2Zbeg</recordid><startdate>20210921</startdate><enddate>20210921</enddate><creator>Olshevsky, Vyacheslav</creator><creator>Khotyaintsev, Yuri V</creator><creator>Lalti, Ahmad</creator><creator>Divin, Andrey</creator><creator>Delzanno, Gian Luca</creator><creator>Anderzen, Sven</creator><creator>Herman, Pawel</creator><creator>Chien, Steven W D</creator><creator>Avanov, Levon</creator><creator>Dimmock, Andrew P</creator><creator>Markidis, Stefano</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20210921</creationdate><title>Automated classification of plasma regions using 3D particle energy distributions</title><author>Olshevsky, Vyacheslav ; 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| subjects | Artificial intelligence Artificial neural networks Automation Classification Cluster analysis Clustering Computer Science - Learning Data processing Energy distribution Machine learning Magnetopause Magnetospheres Particle energy Physical properties Physics - Space Physics Plasma Principal components analysis Spectrometers Vector quantization |
| title | Automated classification of plasma regions using 3D particle energy distributions |
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