We present two new solar wind origin classification schemes developed independently using unsupervised machine learning. The first scheme aims to classify solar wind into three types: coronal-hole wind, streamer-belt wind, and ‘unclassified’ which does not fit into either of the previous two categories. The second scheme independently derives three clusters from the data; the coronal-hole and streamer-belt winds, and a differing unclassified cluster. The classification schemes are created using non-evolving solar wind parameters, such as ion charge states and composition, measured during the three Ulysses fast latitude scans. The schemes are subsequently applied to the Ulysses and the Advanced Compositional Explorer (ACE) datasets. The first scheme is based on oxygen charge state ratio and proton specific entropy. The second uses these data, as well as the carbon charge state ratio, the alpha-to-proton ratio, the iron-to-oxygen ratio, and the mean iron charge state. Thus, the classification schemes are 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 (minimum ≈8%, maximum ≈22%) with the traditional fast and slow solar wind determined using speed thresholds. By comparing the results between the in- (ACE) and out-of-ecliptic ( Ulysses ) data, we find morphological differences in the structure of coronal-hole wind. Our results show how a data-driven approach to the classification of solar wind origins can yield results which differ from 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.

中文翻译：

---

日冕孔和拖缆带太阳风的数据驱动分类

我们提出了两种使用无监督机器学习独立开发的新太阳风起源分类方案。第一种方案旨在将太阳风分为三种类型：冕洞风、流光带风和不属于前两类中的任何一类的“未分类”。第二种方案从数据中独立推导出三个簇；日冕洞和流光带风，以及不同的未分类星团。分类方案是使用非演化的太阳风参数创建的，例如在三个 Ulysses 快速纬度扫描期间测量的离子电荷状态和成分。这些方案随后应用于 Ulysses 和 Advanced Compositional Explorer (ACE) 数据集。第一种方案基于氧电荷态比和质子比熵。第二个使用这些数据，以及碳电荷态比、α-质子比、铁-氧比和平均铁电荷态。因此，分类方案基于太阳能源区域的特性。此外，所使用的技术经过专门选择，以减少将主观偏见引入方案中。我们展示了与使用速度阈值确定的传统快速和慢速太阳风的显着最佳情况差异（最小 ≈8%，最大 ≈22%）。通过比较黄道内（ACE）和黄道外（尤利西斯）数据之间的结果，我们发现日冕孔风结构的形态差异。我们的结果表明，对太阳风起源进行分类的数据驱动方法如何产生与使用其他方法获得的结果不同的结果。因此，