Energetic Electron Flux Predictions in the Near‐Earth Plasma Sheet From Solar Wind Driving
Abstract Suprathermal electrons in the near‐Earth plasma sheet are important for inner magnetosphere considerations. They are the source population for outer radiation belt electrons and they pose risks to geosynchronous satellites through their contribution to surface charging. We use empirical mod...
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| Format: | Article |
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Wiley
2022-11-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2022SW003150 |
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| author | B. M. Swiger M. W. Liemohn N. Y. Ganushkina S. V. Dubyagin |
| author_facet | B. M. Swiger M. W. Liemohn N. Y. Ganushkina S. V. Dubyagin |
| author_sort | B. M. Swiger |
| collection | DOAJ |
| description | Abstract Suprathermal electrons in the near‐Earth plasma sheet are important for inner magnetosphere considerations. They are the source population for outer radiation belt electrons and they pose risks to geosynchronous satellites through their contribution to surface charging. We use empirical modeling to address relationships between solar driving parameters and plasma sheet electron flux. Using Time History of Events and Macroscale Interactions during Substorms, OMNI, and Flare Irradiance Spectral Model Version 2 data, we develop a neural network model to predict differential electron flux from 0.08 to 93 keV in the plasma sheet, at distances from 6 to 12 RE. Driving parameters include solar wind (SW) density and speed, interplanetary magnetic field (IMF) BZ and BY, solar extreme ultraviolet flux, IMF BZ ultra‐low frequency (ULF) wave power, SW‐magnetosphere coupling functions Pα1 and NXCF, and the 4‐hr time history of these parameters. Our model predicts overall plasma sheet electron flux variations with correlation coefficients of between 0.59 and 0.77, and median symmetric accuracy in the 41%–140% range (depending on energy). We find that short time‐scale electron flux variations are not reproduced using short time‐scale inputs. Using a recently published technique to extract information from neural networks, we determine the most important drivers impacting model prediction are VSW, VBS, and IMF BZ. SW‐magnetosphere coupling functions that include IMF clock angle, IMF BZ ULF wave power, and IMF BY have little impact in our model of electron flux in the near‐Earth plasma sheet. The new model, built directly on differential flux, outperforms an existing model that derives fluxes from plasma moments, with the performance improvement increasing with increasing energy. |
| format | Article |
| id | doaj-art-05ee0f35b34249109e0a4d72c4003c1d |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2022-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-05ee0f35b34249109e0a4d72c4003c1d2025-01-14T16:35:33ZengWileySpace Weather1542-73902022-11-012011n/an/a10.1029/2022SW003150Energetic Electron Flux Predictions in the Near‐Earth Plasma Sheet From Solar Wind DrivingB. M. Swiger0M. W. Liemohn1N. Y. Ganushkina2S. V. Dubyagin3Climate and Space Sciences and Engineering University of Michigan Ann Arbor MI USAClimate and Space Sciences and Engineering University of Michigan Ann Arbor MI USAClimate and Space Sciences and Engineering University of Michigan Ann Arbor MI USAFinnish Meteorological Institute Helsinki FinlandAbstract Suprathermal electrons in the near‐Earth plasma sheet are important for inner magnetosphere considerations. They are the source population for outer radiation belt electrons and they pose risks to geosynchronous satellites through their contribution to surface charging. We use empirical modeling to address relationships between solar driving parameters and plasma sheet electron flux. Using Time History of Events and Macroscale Interactions during Substorms, OMNI, and Flare Irradiance Spectral Model Version 2 data, we develop a neural network model to predict differential electron flux from 0.08 to 93 keV in the plasma sheet, at distances from 6 to 12 RE. Driving parameters include solar wind (SW) density and speed, interplanetary magnetic field (IMF) BZ and BY, solar extreme ultraviolet flux, IMF BZ ultra‐low frequency (ULF) wave power, SW‐magnetosphere coupling functions Pα1 and NXCF, and the 4‐hr time history of these parameters. Our model predicts overall plasma sheet electron flux variations with correlation coefficients of between 0.59 and 0.77, and median symmetric accuracy in the 41%–140% range (depending on energy). We find that short time‐scale electron flux variations are not reproduced using short time‐scale inputs. Using a recently published technique to extract information from neural networks, we determine the most important drivers impacting model prediction are VSW, VBS, and IMF BZ. SW‐magnetosphere coupling functions that include IMF clock angle, IMF BZ ULF wave power, and IMF BY have little impact in our model of electron flux in the near‐Earth plasma sheet. The new model, built directly on differential flux, outperforms an existing model that derives fluxes from plasma moments, with the performance improvement increasing with increasing energy.https://doi.org/10.1029/2022SW003150plasma sheetsolar windneural networkelectron fluxmachine learning |
| spellingShingle | B. M. Swiger M. W. Liemohn N. Y. Ganushkina S. V. Dubyagin Energetic Electron Flux Predictions in the Near‐Earth Plasma Sheet From Solar Wind Driving Space Weather plasma sheet solar wind neural network electron flux machine learning |
| title | Energetic Electron Flux Predictions in the Near‐Earth Plasma Sheet From Solar Wind Driving |
| title_full | Energetic Electron Flux Predictions in the Near‐Earth Plasma Sheet From Solar Wind Driving |
| title_fullStr | Energetic Electron Flux Predictions in the Near‐Earth Plasma Sheet From Solar Wind Driving |
| title_full_unstemmed | Energetic Electron Flux Predictions in the Near‐Earth Plasma Sheet From Solar Wind Driving |
| title_short | Energetic Electron Flux Predictions in the Near‐Earth Plasma Sheet From Solar Wind Driving |
| title_sort | energetic electron flux predictions in the near earth plasma sheet from solar wind driving |
| topic | plasma sheet solar wind neural network electron flux machine learning |
| url | https://doi.org/10.1029/2022SW003150 |
| work_keys_str_mv | AT bmswiger energeticelectronfluxpredictionsinthenearearthplasmasheetfromsolarwinddriving AT mwliemohn energeticelectronfluxpredictionsinthenearearthplasmasheetfromsolarwinddriving AT nyganushkina energeticelectronfluxpredictionsinthenearearthplasmasheetfromsolarwinddriving AT svdubyagin energeticelectronfluxpredictionsinthenearearthplasmasheetfromsolarwinddriving |