Identification of fast solar wind flows and CMEs in the in situ data using Self-Organizing Maps and clustering techniques

The identification and characterization of the coronal mass ejections (CMEs) and fast solar wind flows in the in situ data are important for understanding dynamics of these phenomena and consequently for space weather forecasting. In this study, we apply Self-Organizing Maps (SOMs) and clustering techniques to analyze in situ solar wind observations. SOMs (Kohonen, T, 1982) [1] an unsupervised learning technique, is employed to project high-dimensional interplanetary plasma parameters such as velocity, density, temperature, and magnetic field onto a lower-dimensional representation, preserving the topological structure of the data. Clustering algorithms, such as k-means, are then applied to the SOM output to distinguish between ICME events, fast and slow solar wind flows.
Our approach is validated using a few months long interval of the ACE and Wind in situ observations, with labeled CME intervals from Richardson and Cane [2] as a benchmark. This combination of SOMs and clustering provides a framework for automated identification of interplanetary plasma structures, important for space weather studies but also for operational services. 

[1] T. Kohonen, ‘Self-organized formation of topologically correct feature maps’, Biol. Cybern., vol. 43, no. 1, pp. 59–69, Jan. 1982, doi: 10.1007/BF00337288
[2] Richardson, Ian; Cane, Hilary, 2024, "Near-Earth Interplanetary Coronal Mass Ejections Since January 1996"https://doi.org/10.7910/DVN/C2MHTH