Data-Driven Classification of Coronal Hole and Streamer Belt Solar Wind
Data-Driven Classification of Coronal Hole and Streamer Belt Solar Wind
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DOI：
10.1007/s11207-020-01609-z
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发表时间：
2020-03
期刊：
影响因子：
2.8
通讯作者：
T. Bloch;C. Watt;M. Owens;Leland McInnes;A. Macneil
中科院分区：
文献类型：
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作者：
T. Bloch;C. Watt;M. Owens;Leland McInnes;A. Macneil
我们提出了两个新的太阳风起源分类方案，使用无监督机器学习独立开发。第一个方案的目的是将太阳风分为三种类型：冕洞风、飘带风和不属于前两类的“未分类”。第二个方案独立地从数据中得出三个集群;冕洞和飘带风，以及一个不同的未分类集群。分类方案是使用非演变的太阳风参数，如离子电荷状态和成分，在threeUlyssfast纬度扫描测量。该方案随后被应用到尤利西斯和高级成分资源管理器（ACE）数据集。第一种方案是基于氧荷态比和质子比熵。第二个使用这些数据，以及碳电荷状态比，α-质子比，铁-氧比和平均铁电荷状态。因此，分类方案以太阳源区域的属性为基础。此外，所使用的技术是专门选择的，以减少引入主观偏见的计划。我们证明了显着的最佳情况下的差异（最小108%，最大1022%）与传统的快速和慢速太阳风使用速度阈值确定。通过比较黄道内（ACE）和黄道外（ESTA）资料的结果，我们发现冕洞风结构的形态差异。我们的研究结果表明，数据驱动的太阳风起源分类方法可以产生不同于使用其他方法获得的结果。因此，这些结果构成了验证当前对太阳起源和太阳风的理解与我们现有数据匹配程度所需信息的重要组成部分。
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 threeUlyssesfast latitude scans. The schemes are subsequently applied to theUlyssesand theAdvanced 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.