Abstract
Scientists have observed the occurrence of two distinctive subsets in Interplanetary Coronal Mass Ejections (ICMEs): magnetic clouds (MCs) and non-magnetic clouds (non-MCs). While we are aware of some of the distinctive features of MCs and non-MCs, we cannot draw a precise line between them. Features such as large magnetic field, low plasma-beta, low proton temperature, etc. suggest when an ICME event is also an MC event, however, this categorization is far from an automated process. In addition to being time-consuming, the results differ depending on the precision of definition. In this paper, we approach the MC and non-MC class distinction from a data analysis perspective and show a data-driven taxonomy of ICME events. We use a time series dataset from the Ulysses spacecraft combined with a list of labeled MC and non-MC events. The time series data are hierarchically clustered with Euclidean distance and Dynamic Time Warping algorithm, and we compare our MC and non-MC clusters with the results from classifications generated by domain experts.
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