Zou et al. 2020 « Space Environment Modeling Group

Zou Z., Y. Y. Shprits, B. Ni, N. A. Aseev, P. Zuo, F. Wei, (2020), An artificial neural network model of electron fluxes in the Earth’s central plasma sheet: a THEMIS survey, Astrophysics And Space Science, 365, 100, doi:10.1007/s10509-020-03819-0

Abstract

The Earth’s central plasma sheet plays an important role in mass and energy transport in the whole magnetosphere. Here, we first present a new approach, i.e., an Artificial Neural Network (ANN) model, to investigate the electron number fluxes in the central plasma sheet. With the time series of 8 solar wind/geomagnetic indices and spatial locations as inputs, the model has been trained, validated, and tested with three isolated groups of measurements from Time History of Events and Macroscale Interaction during the Substorm (THEMIS) – A/D/E spacecraft from April 1, 2007 to December 30, 2015. The plasma sheet electron flux is shown to be accurately reproduced by the ANN model with a total correlation coefficient (R) above ∼0.91 and a root-mean-square-error (RMSE) less than 0.36 between the data and model target in a spatial region from radial distance 7 RE to 12 RE (where RE is the Earth’s radius) at the nightside of between 18 MLT through 24 MLT and up to 0.6 MLT (Magnetic Local Time) for energies at 0.06 – 293 keV. Global and spectral distributions of reproduced values can also capture the dawn-dusk asymmetry and the dependence on radial distances of plasma sheet electron fluxes. Our developed artificial neural network (ANN) therefore has a good capability in statistically reproducing the plasma sheet electron fluxes for a variety of substorm activities, and can be readily adopted for building up the boundary conditions for physics-based simulation efforts that model the dynamics of the radiation belt electrons and other parts of the terrestrial magnetosphere.

Authors (sorted by name)

Aseev Ni Shprits Wei Zou Zuo

Journal / Conference

Astrophysics And Space Science

Bibtex

@ARTICLE{10.1007/s10509-020-03819-0,
       author = {{Zou}, Zhengyang and {Shprits}, Yuri Y. and {Ni}, Binbin and {Aseev}, Nikita A. and {Zuo}, Pingbing and {Wei}, Fengsi},
        title = "{An artificial neural network model of electron fluxes in the Earth's central plasma sheet: a THEMIS survey}",
      journal = {Astrophysics and Space Science },
     keywords = {Plasma sheet electron number fluxes, Artificial Neural Network model, Global distributions, Energy spectrum},
         year = 2020,
        month = jun,
       volume = {365},
       number = {6},
          eid = {100},
        pages = {100},
          doi = {10.1007/s10509-020-03819-0},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2020Ap&SS.365..100Z},
      abstract = {The Earth’s central plasma sheet plays an important role in mass and energy transport in the whole magnetosphere. Here, we first present a new approach, i.e., an Artificial Neural Network (ANN) model, to investigate the electron number fluxes in the central plasma sheet. With the time series of 8 solar wind/geomagnetic indices and spatial locations as inputs, the model has been trained, validated, and tested with three isolated groups of measurements from Time History of Events and Macroscale Interaction during the Substorm (THEMIS) – A/D/E spacecraft from April 1, 2007 to December 30, 2015. The plasma sheet electron flux is shown to be accurately reproduced by the ANN model with a total correlation coefficient (R) above ∼0.91 and a root-mean-square-error (RMSE) less than 0.36 between the data and model target in a spatial region from radial distance 7 RE to 12 RE (where RE is the Earth’s radius) at the nightside of between 18 MLT through 24 MLT and up to 0.6 MLT (Magnetic Local Time) for energies at 0.06 – 293 keV. Global and spectral distributions of reproduced values can also capture the dawn-dusk asymmetry and the dependence on radial distances of plasma sheet electron fluxes. Our developed artificial neural network (ANN) therefore has a good capability in statistically reproducing the plasma sheet electron fluxes for a variety of substorm activities, and can be readily adopted for building up the boundary conditions for physics-based simulation efforts that model the dynamics of the radiation belt electrons and other parts of the terrestrial magnetosphere.}
}