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首页> 外文期刊>Journal of Geophysical Research, A. Space Physics: JGR >Classification of SolarWindWith Machine Learning
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Classification of SolarWindWith Machine Learning

机译：SolarWindWith机器学习分类

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摘要

We present a four-category classification algorithm for the solar wind, based on Gaussian Process. The four categories are the ones previously adopted in Xu and Borovsky (2015): ejecta, coronal hole origin plasma, streamer belt origin plasma, and sector reversal origin plasma. The algorithm is trained and tested on a labeled portion of the OMNI data set. It uses seven inputs: the solar wind speed V_(sw),the temperature standard deviation σ_T , the sunspot number R, the F_(10.7) index, the Alfven speed vA, the proton specific entropy S_p, and the proton temperature T_p compared to a velocity-dependent expected temperature. The output of the Gaussian Process classifier is a four-element vector containing the probabilities that an event (one reading from the hourly averaged OMNI database) belongs to each category. The probabilistic nature of the prediction allows for a more informative and flexible interpretation of the results, for instance, being able to classify events as "undecided." The new method has a median accuracy larger than 90% for all categories, even using a small set of data for training. The Receiver Operating Characteristic curve and the reliability diagram also demonstrate the excellent quality of this new method. Finally, we use the algorithm to classify a large portion of the OMNI data set, and we present for the first time transition probabilities between different solar wind categories. Such probabilities represent the "climatological" statistics that determine the solar wind baseline.
机译：我们提出一个four-category分类太阳风的算法,基于高斯的过程。以前采用徐和Borovsky (2015):喷出物,日冕洞起源等离子体、拖缆带起源等离子体和部门逆转起源等离子体。该算法训练和测试标签全方位的数据集的一部分。输入:太阳风速度V_ (sw)温度标准差σ_T,太阳黑子数R f(10.7)指数,阿尔芬速度弗吉尼亚州,质子特定熵S_p,质子T_p而温度数值预测的温度。高斯过程分类器的输出是一个研制出包含概率向量这一个事件(一个小时的阅读平均OMNI数据库)属于每个类别。允许的概率特性预测更丰富和灵活解释的结果,例如,能够将事件作为“犹豫不决”。新方法具有中等精度大于90%对所有类别,甚至通过一组小的数据进行训练。特性曲线和可靠性图也展示了优良的品质新方法。分类的很大一部分泛光灯的数据集,我们第一次转变之间的概率不同的太阳风类别。“气候”统计数据确定太阳风基线。

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