Automated classification of plasma regions using 3D particle energy distributions

Abstract

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.

Accepted to JGR: Space Physics

References in corpus (1)

• Automatic classification of plasma regions in near-Earth space with supervised machine learning: application to Magnetospheric Multi Scale 2016-2019 observations

Cited by in corpus (4)

• A database of MMS bow shock crossings compiled using machine learning
• AI in Space for Scientific Missions: Strategies for Minimizing Neural-Network Model Upload
• Evaluating Four FPGA-accelerated Space Use Cases based on Neural Network Algorithms for On-board Inference
• Occurrence of Non-Stationarity at Earth's Quasi-Perpendicular Bow Shock