Using Machine Learning to Diagnose Relativistic Electron Distributions in the Van Allen Radiation Belts

Shannon Killey*, Jonathan Rae, Suman Chakraborty, Andy Smith, Sarah Bentley, Mayur R. Bakrania, R. Wainwright, Clare Watt, Jasmine Sandhu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

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 Probe Relativistic Electron-Proton Telescope data 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.
Original languageEnglish
Article numberrzad035
Pages (from-to)548-561
Number of pages14
JournalRoyal Astronomical Society Techniques and Instruments
Volume2
Issue number1
Early online date16 Aug 2023
DOIs
Publication statusPublished - 2023

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