Objectively Determining States of the Solar Wind Using Machine Learning

Abstract

Abstract Conclusively determining the states of the solar wind will aid in tracing the origins of those states to the Sun, and in the process help to find the wind’s origin and acceleration mechanism(s). Prior studies have characterized the various states of the wind, making lists that are only partially based on objective criteria; different approaches obtain substantially different results. To uncover the unbiased states of the solar wind, we use “k-means clustering”—an unsupervised machine learning method—including constructed multipoint variables. The method allows exploration of different descriptive state variables and numbers of fundamental states (clusters). We show that the clusters reveal structures similar to those found by more ad hoc means, including coronal hole wind, interplanetary coronal mass ejections, “slow wind” (better: noncoronal hole flow), “pseudostreamers,” and stream interaction regions, but with differences that should be useful in refining our previous ideas. These results demonstrate the viability of the approach and warrant further study to understand the origin of remaining discrepancies. Complexity in k-means characterization of the wind may ultimately point to complexity at the source; studies closer to the Sun with Parker Solar Probe will help. We confirm the utility of a set of variables that can serve as a proxy for composition measurements. This proxy permits studies at high time resolution and where composition is not available. This and our recently developed unsupervised multivariate clustering technique are expected to be beneficial in the automated identification of structures and events in a variety of studies.