Using machine learning to diagnose relativistic electron distributions in the Van Allen radiation belts
Using machine learning to diagnose relativistic electron distributions in the Van Allen radiation belts
复制标题
使用机器学习来诊断范艾伦辐射带中的相对论电子分布
DOI：
10.1093/rasti/rzad035
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Killey S
中科院分区：
文献类型：
--
作者：
Killey S
辐射带中相对论电子的行为很难诊断，因为它们的动力学是由同时发生的物理过程控制的，其中一些可能仍然未知。这些物理过程的特征难以在大量数据中识别;因此，开发了一种机器学习方法来对由不同机制驱动的高能电子分布进行分类。一系列无监督的机器学习工具已被应用于7年的货车艾伦探测器相对论电子质子望远镜数据，以识别六种不同类型的等离子体条件，每一种都具有明显形状的能量依赖俯仰角分布（PAD）。较低能量的PAD具有先前研究中预期的形状-蝴蝶，煎饼或平顶，提供了机器学习能够可靠地分类辐射带中相对论电子的证据。这一技术的进一步应用可应用于其他空间等离子体区域，以及来自帕克太阳探测器和太阳轨道器等内日光层飞行任务、行星磁层和JUICE使命的数据集。了解整个日光层的PAD使研究人员能够确定驱动俯仰角演变的物理机制，并研究它们的空间和时间依赖性以及物理特性。
The behaviour of relativistic electrons in the radiation belt is difficult to diagnose as their dynamics are controlled by simultaneous physical processes, some of which may be still unknown. Signatures of these physical processes are difficult to identify in large amounts of data; therefore, a machine learning approach is developed to classify energetic electron distributions which have been driven by different mechanisms. A series of unsupervised machine learning tools have been applied to 7 yrs of Van Allen ProbeRelativistic Electron-Proton Telescopedata to identify six different typical types of plasma conditions, each with a distinctly shaped energy-dependent pitch angle distribution (PAD). The PADs at lower energies have shapes as expected from previous studies – either butterfly, pancake, or flattop, providing evidence that machine learning has been able to reliably classify the relativistic electrons in the radiation belts. Further applications of this technique could be applied to other space plasma regions, and data sets from inner heliospheric missions such as Parker Solar Probe and Solar Orbiter, to planetary magnetospheres and the JUICE mission. Understanding PADs across the heliosphere enables researchers to determine the physical mechanisms that drive pitch angle evolution and investigate their spatial and temporal dependence and physical properties.