Clustering Wind data at 1 AU to contextualize magnetic reconnection in the solar wind

TL;DR

This study uses unsupervised machine learning techniques to classify solar wind data at 1 AU, revealing the conditions under which magnetic reconnection events occur, and providing a new framework for understanding solar wind structures.

Contribution

It introduces the application of self-organizing maps and K-Means clustering to solar wind data, offering a novel way to interpret the conditions associated with magnetic reconnection events.

Findings

Reconnection events are mainly associated with slow solar wind.

Five distinct clusters of solar wind were identified, including transient-related clusters.

Most reconnection events occur in the slow solar wind cluster.

Abstract

Context. Magnetic reconnection events are frequently observed in the solar wind. Understanding the patterns and structures within the solar wind is crucial to put observed magnetic reconnection events into context, since their occurrence rate and properties are likely influenced by solar wind conditions. Aims. We employed unsupervised learning techniques such as self-organizing maps (SOM) and K-Means to cluster and interpret solar wind data at 1 AU for an improved understanding of the conditions that lead to magnetic reconnection in the solar wind. Methods. We collected magnetic field data and proton density, proton temperature, and solar wind speed measurements taken by the Wind spacecraft. After preprocessing the data, we trained a SOM to visualize the high-dimensional data in a lower-dimensional space and applied K-Means clustering to identify distinct clusters within the solar wind data. Results. Our analysis revealed that the reconnection events are distributed across five different clusters: a) slow solar wind, b) compressed slow wind, c) highly Alfv\'enic wind, d) compressed fast wind, and e) ejecta. Compressed slow and fast wind and ejecta are clusters associated with solar wind transients such as stream interaction regions and interplanetary coronal mass ejections. The majority of the reconnection events are associated with the slow solar wind, followed by the highly Alfv\'enic wind, compressed slow wind, and compressed fast wind, and a small fraction of the reconnection events are associated with ejecta. Conclusions. Unsupervised learning approaches with SOM and K-Means lead to physically interpretable solar wind clusters based on their transients and allow for the contextualization of magnetic reconnection exhausts' occurrence in the solar wind.