Automated classification of plasma regions using 3D particle energy distributions
This work provides an automated tool for space physics researchers to analyze large datasets from the MMS mission, though it is incremental as it applies existing methods to new data.
The authors tackled the problem of classifying plasma regions in the dayside magnetosphere by training a convolutional neural network on ion spectrograms, achieving an accuracy of >98% and enabling efficient detection of mixed regions like the bow shock.
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.