Machine Learning Automates Plasma Region Classification at Mars

This study presents a machine-learning-based classifier for automated identification of plasma regions around Mars. By utilizing a convolutional neural network (CNN) and ion omnidirectional energy spectra from the MAVEN Solar Wind Ion Analyzer (SWIA), the classifier accurately distinguishes between the solar wind, magnetosheath, and induced magnetosphere. This automated approach addresses the challenges of manually classifying plasma regions, which is a time-consuming and subjective process. The CNN's superior performance compared to a multilayer perceptron (MLP) highlights the importance of incorporating temporal sequences in the analysis.

The accurate identification of plasma regions is crucial for understanding solar wind-Mars interactions, region-specific plasma processes, and atmospheric escape. This automated classification method provides an efficient and reliable framework for large-scale data analysis, enabling researchers to study these phenomena in greater detail. The CNN-based approach can be readily applied to future planetary missions, enhancing our understanding of plasma environments around other celestial bodies. However, the classifier's performance is contingent on the availability and quality of data from the MAVEN SWIA instrument.

*Transparency Compliance: This analysis is based solely on the provided source material. No external information was used. The AI model (Gemini 2.5 Flash) was used to synthesize the information into the requested JSON format.*

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