Automated Classification of Plasma Regions at Mars Using Machine Learning

TL;DR

This paper presents a CNN-based machine learning method for accurately classifying plasma regions around Mars using ion energy spectra, aiding planetary science research.

Contribution

It introduces a CNN classifier that effectively distinguishes plasma regions at Mars, improving automation over previous methods.

Findings

CNN reliably distinguishes plasma regions; MLP struggles with separation.

The approach enables large-scale plasma classification at Mars.

Applicable to future planetary missions.

Abstract

The plasma environment around Mars is highly variable because it is strongly influenced by the solar wind. Accurate identification of plasma regions around Mars is important for the community studying solar wind-Mars interactions, region-specific plasma processes, and atmospheric escape. In this study, we develop a machine-learning-based classifier to automatically identify three key plasma regions--solar wind, magnetosheath, and induced magnetosphere--using only ion omnidirectional energy spectra measured by the MAVEN Solar Wind Ion Analyzer (SWIA). Two neural network architectures are evaluated: a multilayer perceptron (MLP) and a convolutional neural network (CNN) that incorporates short temporal sequences. Our results show that the CNN can reliably distinguish the three plasma regions, whereas the MLP struggles to separate the solar wind and magnetosheath. Therefore, the CNN-based approach provides an efficient and accurate framework for large-scale plasma region identification at Mars and can be readily applied to future planetary missions.