论文标题
通过无监督的机器学习发现的带宽和频率之间的相关性
Correlation between bandwidth and frequency of plasmaspheric hiss uncovered with unsupervised machine learning
论文作者
论文摘要
先前对等离子体HIS的统计研究研究了磁层中各个点的磁场功率光谱的平均形状。但是，这种方法并未考虑以下事实：在给定的L壳和磁性局部时间存在非常不同的光谱形状。将数据平均在一起意味着光谱形状的重要特征会丢失。在本文中，我们使用一种无监督的机器学习技术来对等离子球嘶嘶声进行分类。与以前的研究相反，这项技术使我们能够识别具有“相似”形状的功率谱并研究其空间分布，而无需平均较大的光谱形状。我们表明，HIS频率和带宽之间存在强大的负相关性，这表明观察到的模式与原位波的生长一致。
Previous statistical studies of plasmaspheric hiss investigated the averaged shape of the magnetic field power spectra at various points in the magnetosphere. However, this approach does not consider the fact that very diverse spectral shapes exist at a given L-shell and magnetic local time. Averaging the data together means that important features of the spectral shapes are lost. In this paper, we use an unsupervised machine learning technique to categorize plasmaspheric hiss. In contrast to the previous studies, this technique allows us to identify power spectra that have "similar" shapes and study their spatial distribution without averaging together vastly different spectral shapes. We show that strong negative correlations exist between the hiss frequency and bandwidth, which suggests that the observed patterns are consistent with in situ wave growth.
