2026/04/18 by Yilan Qin, Chuanfei Dong, Hongyang Zhou +6 · 1 voice
Computer Science · Engineering · Physics and Astronomy · #Artificial neural network #Convolutional neural network #Exploration of Mars #Identification (biology) #Ionosphere and magnetosphere dynamics #Mars Exploration Program #Multilayer perceptron #Planetary Science and Exploration #Plasma #Solar wind #Space Satellite Systems and Control #astro-ph.EP #cs.LG #physics.plasm-ph #physics.space-ph
paper · pdf · open access · doi:10.48550/arxiv.2604.17131
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2026/04/18 · arxiv published 2026/04/18 · arxiv updated 2026/04/18 · openalex created_date 2026/04/22 · openalex updated_date 2026/07/28
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.