Modeling the Dynamic Variability of Sub‐Relativistic Outer Radiation Belt Electron Fluxes Using Machine Learning
Abstract We present a set of neural network models that reproduce the dynamics of electron fluxes in the range of 50 keV ∼1 MeV in the outer radiation belt. The Outer Radiation belt Electron Neural net model for Medium energy electrons uses only solar wind conditions and geomagnetic indices as input...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-08-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2022SW003079 |
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| _version_ | 1841536518532890624 |
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| author | Donglai Ma Xiangning Chu Jacob Bortnik Seth G. Claudepierre W. Kent Tobiska Alfredo Cruz S. Dave Bouwer Joseph F. Fennell J. Bernard Blake |
| author_facet | Donglai Ma Xiangning Chu Jacob Bortnik Seth G. Claudepierre W. Kent Tobiska Alfredo Cruz S. Dave Bouwer Joseph F. Fennell J. Bernard Blake |
| author_sort | Donglai Ma |
| collection | DOAJ |
| description | Abstract We present a set of neural network models that reproduce the dynamics of electron fluxes in the range of 50 keV ∼1 MeV in the outer radiation belt. The Outer Radiation belt Electron Neural net model for Medium energy electrons uses only solar wind conditions and geomagnetic indices as input. The models are trained on electron flux data from the Magnetic Electron Ion Spectrometer instrument onboard Van Allen Probes, and they can reproduce the dynamic variations of electron fluxes in different energy channels. The model results show high coefficient of determination (R2 ∼ 0.78–0.92) on the test data set, an out‐of‐sample 30‐day period from 25 February to 25 March in 2017, when a geomagnetic storm took place, as well as an out‐of‐sample one year period after March 2018. In addition, the models are able to capture electron dynamics such as intensifications, decays, dropouts, and the Magnetic Local Time dependence of the lower energy (∼<100 keV) electron fluxes during storms. The models have reliable prediction capability and can be used for a wide range of space weather applications. The general framework of building our model is not limited to radiation belt fluxes and could be used to build machine learning models for a variety of other plasma parameters in the Earth's magnetosphere. |
| format | Article |
| id | doaj-art-d00e873392754c7185b7f49ba9b92c8d |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2022-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-d00e873392754c7185b7f49ba9b92c8d2025-01-14T16:27:07ZengWileySpace Weather1542-73902022-08-01208n/an/a10.1029/2022SW003079Modeling the Dynamic Variability of Sub‐Relativistic Outer Radiation Belt Electron Fluxes Using Machine LearningDonglai Ma0Xiangning Chu1Jacob Bortnik2Seth G. Claudepierre3W. Kent Tobiska4Alfredo Cruz5S. Dave Bouwer6Joseph F. Fennell7J. Bernard Blake8Department of Atmospheric and Oceanic Sciences University of California Los Angeles CA USALaboratory for Atmospheric and Space Physics University of Colorado Boulder Boulder CO USADepartment of Atmospheric and Oceanic Sciences University of California Los Angeles CA USADepartment of Atmospheric and Oceanic Sciences University of California Los Angeles CA USASpace Environment Technologies Pacific Palisades CA USASpace Environment Technologies Pacific Palisades CA USASpace Environment Technologies Pacific Palisades CA USASpace Sciences Department The Aerospace Corporation El Segundo CA USASpace Sciences Department The Aerospace Corporation El Segundo CA USAAbstract We present a set of neural network models that reproduce the dynamics of electron fluxes in the range of 50 keV ∼1 MeV in the outer radiation belt. The Outer Radiation belt Electron Neural net model for Medium energy electrons uses only solar wind conditions and geomagnetic indices as input. The models are trained on electron flux data from the Magnetic Electron Ion Spectrometer instrument onboard Van Allen Probes, and they can reproduce the dynamic variations of electron fluxes in different energy channels. The model results show high coefficient of determination (R2 ∼ 0.78–0.92) on the test data set, an out‐of‐sample 30‐day period from 25 February to 25 March in 2017, when a geomagnetic storm took place, as well as an out‐of‐sample one year period after March 2018. In addition, the models are able to capture electron dynamics such as intensifications, decays, dropouts, and the Magnetic Local Time dependence of the lower energy (∼<100 keV) electron fluxes during storms. The models have reliable prediction capability and can be used for a wide range of space weather applications. The general framework of building our model is not limited to radiation belt fluxes and could be used to build machine learning models for a variety of other plasma parameters in the Earth's magnetosphere.https://doi.org/10.1029/2022SW003079machine learningradiation beltselectron flux |
| spellingShingle | Donglai Ma Xiangning Chu Jacob Bortnik Seth G. Claudepierre W. Kent Tobiska Alfredo Cruz S. Dave Bouwer Joseph F. Fennell J. Bernard Blake Modeling the Dynamic Variability of Sub‐Relativistic Outer Radiation Belt Electron Fluxes Using Machine Learning Space Weather machine learning radiation belts electron flux |
| title | Modeling the Dynamic Variability of Sub‐Relativistic Outer Radiation Belt Electron Fluxes Using Machine Learning |
| title_full | Modeling the Dynamic Variability of Sub‐Relativistic Outer Radiation Belt Electron Fluxes Using Machine Learning |
| title_fullStr | Modeling the Dynamic Variability of Sub‐Relativistic Outer Radiation Belt Electron Fluxes Using Machine Learning |
| title_full_unstemmed | Modeling the Dynamic Variability of Sub‐Relativistic Outer Radiation Belt Electron Fluxes Using Machine Learning |
| title_short | Modeling the Dynamic Variability of Sub‐Relativistic Outer Radiation Belt Electron Fluxes Using Machine Learning |
| title_sort | modeling the dynamic variability of sub relativistic outer radiation belt electron fluxes using machine learning |
| topic | machine learning radiation belts electron flux |
| url | https://doi.org/10.1029/2022SW003079 |
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