Olshevsky, V., Khotyaintsev, Y. V., Lalti, A., Divin, A., Delzanno, G. L., Anderzén, S., Herman, P., Chien, S. , Avanov, L. , Dimmock, A. , and S. Markidis. Automated classification of plasma regions using 3D particle energy distributions. Journal of Geophysical Research: Space Physics, 126, e2021JA029620, 2021. doi:10.1029/2021JA029620
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.