Towards the Objective Classification of Coronal Hole and Streamer Belt Solar Wind

We present a new solar wind origin classification scheme developed independently using unsupervised machine learning. The scheme deduces 3 types of solar wind; coronal hole wind, streamer belt wind, and 'unclassified' which does not fit into either of the previous two categories. The classification scheme is created using 6 non-evolving solar wind parameters (e.g., ion charge states and composition) measured during Ulysses' three fast latitude-scans. The scheme is subsequently applied to the whole of the Ulysses and ACE datasets. The scheme uses the oxygen charge state ratio, proton specific entropy, carbon charge state ratio, alpha-to-proton ratio, iron-to-oxygen ratio, and the mean iron charge state. Thus, the classification scheme is grounded in the properties of the solar source regions. Furthermore, the techniques used are selected specifically to reduce the introduction of subjective biases into the schemes. We demonstrate significant 'best case' disparities (~22% for Ulysses, ~8% for ACE) with the traditional "fast" and "slow" solar wind determined using speed thresholds.

Our results show how a data-driven approach to the classification of solar wind origins can yield results which differ to those obtained using other methods. As such, the results form an important part of the information required to validate how well current understanding of solar origins and the solar wind match with the data we have.