论文标题
通过划分N-T空间，MMS轨道上的自动区域识别
Automatic Region Identification over the MMS Orbit by Partitioning n-T space
论文作者
论文摘要
空间等离子体数据分析和任务操作通过磁层不同区域之间的等离子体数据的分类以及它们之间边界区域的识别。没有计算机自动化，这意味着将大量数据分类到手工挑选区域。使用创建的手工标记数据来支持快速等离子体仪器的校准，该任务以99.9％的精度为MMS任务自动化。该方法将数量密度和离子温度平面分为每个区域的子平台，使用称为支持向量机器的机器学习技术在子平台之间拟合边界。本文介绍的这种方法是新颖的，因为它提供了统计自动化能力和解释性，从而产生了对任务如何执行的科学见解。
Space plasma data analysis and mission operations are aided by the categorization of plasma data between different regions of the magnetosphere and identification of the boundary regions between them. Without computerized automation this means sorting large amounts of data to hand-pick regions. Using hand-labeled data created to support calibration of the Fast Plasma Instrument, this task was automated for the MMS mission with 99.9% accuracy. The method partitions the number density and ion temperature plane into sub-planes for each region, fitting boundaries between the sub-planes using a machine learning technique known as the support vector machine. This method presented in this paper is novel because it offers both statistical automation power and interpretability that yields scientific insight into how the task is performed.
