English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
  Local TagsRelease HistoryDetailsSummary

Released
Conference Paper

Using machine learning to diagnose relativistic electron distributions in the Van Allen radiation belts

Authors

Killey,  Shannon
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Rae,  I. Jonathan
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Chakraborty,  Suman
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Smith,  Andy W.
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Bentley,  Sarah N.
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Bakrania,  Mayur R.
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Wainwright,  Rosemary
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Watt,  Clare E. J.
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Sandhu,  Jasmine K.
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

External Ressource
No external resources are shared
Fulltext (public)
There are no public fulltexts stored in GFZpublic
Supplementary Material (public)
There is no public supplementary material available
Citation

Killey, S., Rae, I. J., Chakraborty, S., Smith, A. W., Bentley, S. N., Bakrania, M. R., Wainwright, R., Watt, C. E. J., Sandhu, J. K. (2023): Using machine learning to diagnose relativistic electron distributions in the Van Allen radiation belts, XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) (Berlin 2023).
https://doi.org/10.57757/IUGG23-1732


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5017886
Abstract
The behaviour of relativistic electrons in the radiation belt is difficult to diagnose as their dynamics are controlled by simultaneous physics 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 compilation of unsupervised machine learning tools has been applied to 7-years of Van Allen Probe Relativistic Electron Proton Telescope data to identify 5 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 both denoise REPT data, and classify the relativistic electrons in the radiation belts. By understanding pitch angle distributions of relativistic electrons, we determine the physical mechanisms that drive pitch angle evolution and investigate their spatial and temporal dependence and physical properties. Further applications of this technique could be applied to other space plasma regions, and datasets from inner heliospheric missions such as Parker Solar Probe and Solar Orbiter, to planetary magnetospheres and the JUICE mission.