Using Dimensionality Reduction and Clustering Techniques to Classify Space Plasma Regimes
Using Dimensionality Reduction and Clustering Techniques to Classify Space Plasma Regimes
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DOI：
10.3389/fspas.2020.593516
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发表时间：
2020-09
期刊：
影响因子：
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通讯作者：
M. Bakrania;I. J. Rae;A. Walsh;D. Verscharen;Andy W. Smith
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作者：
M. Bakrania;I. J. Rae;A. Walsh;D. Verscharen;Andy W. Smith
无碰撞空间等离子体环境的典型特征是不同的粒子群。虽然它们的速度分布函数的矩有助于区分不同的等离子体状态，但分布函数本身提供了关于等离子体状态的更全面的信息，特别是在分布函数包括非热效应时。然而，与矩不同，分布函数不容易用少量参数来表征，这使得它们的分类更难以实现。为了进行这种分类，我们建议通过应用降维和聚类方法来区分不同的等离子体区域的电子分布在俯仰角和能量空间。我们利用四个独立的算法来实现我们的血浆分类：自动编码器，主成分分析，均值漂移，凝聚聚类。我们测试我们的分类算法，应用我们的计划，在地球的磁尾测量的等离子体电子和电流实验仪器的数据。传统上，人们认为地球的磁尾被分成三个不同的区域（等离子体片、等离子体片边界层和叶），主要由它们的等离子体特性来定义。从ECLAT数据库与相关的分类的基础上的等离子体参数，我们确定了八个不同的组的分布，这是依赖于显着更复杂的等离子体和场动力学。通过比较的平均分布，以及等离子体和磁场参数为每个区域，我们涉及到不同的等离子体片人口的几个群体，其余的我们归因于等离子体片边界层和叶。我们发现每个分类区域与ECLAT结果之间存在明显区别。对空间等离子体环境中不同区域的自动分类为确定近地空间中控制粒子群的物理过程提供了一个有用的工具。这些工具是独立于模型的，提供可重复的结果，而不需要设置任意的阈值、限制或专家判断。类似的方法可以在航天器上使用，以减少分布的维数，以便在未来的飞行任务中优化数据收集和下行链路资源。
Collisionless space plasma environments are typically characterized by distinct particle populations. Although moments of their velocity distribution functions help in distinguishing different plasma regimes, the distribution functions themselves provide more comprehensive information about the plasma state, especially at times when the distribution function includes non-thermal effects. Unlike moments, however, distribution functions are not easily characterized by a small number of parameters, making their classification more difficult to achieve. In order to perform this classification, we propose to distinguish between the different plasma regions by applying dimensionality reduction and clustering methods to electron distributions in pitch angle and energy space. We utilize four separate algorithms to achieve our plasma classifications: autoencoders, principal component analysis, mean shift, and agglomerative clustering. We test our classification algorithms by applying our scheme to data from the Cluster-Plasma Electron and Current Experiment instrument measured in the Earth’s magnetotail. Traditionally, it is thought that the Earth’s magnetotail is split into three different regions (the plasma sheet, the plasma sheet boundary layer, and the lobes), that are primarily defined by their plasma characteristics. Starting with the ECLAT database with associated classifications based on the plasma parameters, we identify eight distinct groups of distributions, that are dependent upon significantly more complex plasma and field dynamics. By comparing the average distributions as well as the plasma and magnetic field parameters for each region, we relate several of the groups to different plasma sheet populations, and the rest we attribute to the plasma sheet boundary layer and the lobes. We find clear distinctions between each of our classified regions and the ECLAT results. The automated classification of different regions in space plasma environments provides a useful tool to identify the physical processes governing particle populations in near-Earth space. These tools are model independent, providing reproducible results without requiring the placement of arbitrary thresholds, limits or expert judgment. Similar methods could be used onboard spacecraft to reduce the dimensionality of distributions in order to optimize data collection and downlink resources in future missions.