Automated Classification of Plasma Regions Using 3D Particle Energy DistributionsShow others and affiliations
2021 (English)In: Journal of Geophysical Research - Space Physics, ISSN 2169-9380, E-ISSN 2169-9402, Vol. 126, no 10, article id e2021JA029620Article in journal (Refereed) Published
Abstract [en]
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 Magnetospheric Multiscale Mission (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.
Place, publisher, year, edition, pages
American Geophysical Union (AGU) , 2021. Vol. 126, no 10, article id e2021JA029620
Keywords [en]
MMS, machine learning, bow shock
National Category
Fusion, Plasma and Space Physics
Identifiers
URN: urn:nbn:se:kth:diva-304839DOI: 10.1029/2021JA029620ISI: 000711498900007Scopus ID: 2-s2.0-85118179129OAI: oai:DiVA.org:kth-304839DiVA, id: diva2:1613307
Note
QC 20211122
2021-11-222021-11-222022-06-25Bibliographically approved