# Automated classification of plasma regions using 3D particle energy distributions

## TL;DR

This paper presents a CNN-based method for rapid, accurate classification of plasma regions in the magnetosphere using 3D particle energy distributions, enabling efficient analysis of MMS satellite data.

## Contribution

The study introduces a novel CNN classifier trained on DIS spectrograms to identify four plasma regions with over 98% accuracy, facilitating large-scale plasma region analysis.

## Key findings

- Classifier accuracy exceeds 98%
- Enables fast, large-scale plasma region detection
- Identifies mixed plasma regions and bow shocks

## 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.