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Solar Wind Classifications at Mars using Machine Learning Techniques
Preprint   Open access

Solar Wind Classifications at Mars using Machine Learning Techniques

Catherine E Regan, Silvia Ferro, Austin M Smith, Alvin J. G Angeles, Nicholas A Gross, Farzad Kamalabadi, Marco Velli and Jasper S Halekas
ArXiv.org
Cornell University
04/09/2026
DOI: 10.48550/arxiv.2604.08710
url
https://doi.org/10.48550/arxiv.2604.08710View
Preprint (Author's original) This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

Understanding solar wind variability throughout the heliosphere is essential for fundamental space physics and future exploration of the Moon and Mars. The Mars Atmosphere and Volatile EvolutioN (MAVEN) spacecraft has provided upstream solar wind measurements at Mars spanning Solar Cycles 24 and 25, enabling a statistical investigation of solar wind regimes at this heliocentric distance. In this work, we apply an unsupervised machine-learning framework combining Principal Component Analysis and K-Means clustering to a normalized, multi-dimensional solar wind dataset to identify recurrent solar wind regimes in a physically interpretable, data-driven manner. The resulting classification reveals distinct slow, fast, intermediate, and compressed solar wind regimes whose relative occurrence and temporal organization are strongly modulated by solar activity. This manuscript is part of the Heliophysics Summer School Machine Learning Special Collection.
 Physics - Space Physics

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