Automated Classification of Plasma Regions at Mars Using Machine Learning
For the Mars plasma community, this provides an efficient tool for large-scale region identification, applicable to future missions.
This study develops a CNN-based classifier that uses MAVEN SWIA ion energy spectra to automatically identify three plasma regions at Mars (solar wind, magnetosheath, induced magnetosphere), achieving reliable separation where an MLP fails.
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.