Prediction of Dynamic Plasmapause Location Using a Neural Network
Abstract As a common boundary layer that distinctly separates the regions of high-density plasmasphere and low-density plasmatrough, the plasmapause is essential to comprehend the dynamics and variability of the inner magnetosphere. Using the machine learning framework Pytorch and high-quality Van Allen Probes data set, we develop a neural network model to predict the global dynamic variation of the plasmapause location, along with the identification of 6537 plasmapause crossing events during the period from 2012 to 2017. To avoid the overfitting and optimize the model generalization, 5493 events during the period from September 2012 to December 2015 are adopted for division into the training set and validation set in terms of the 10-fold cross validation method, and the remaining 1044 events are used as the test set. The model parameterized by only AE or Kp index can reproduce the plasmapause locations similar to those modeled using all five considered solar wind and geomagnetic parameters. Model evaluation on the test set indicate that our neural network model is capable of predicting the plasmapause location with the lowest RMSE. Our model can also produce a smooth MLT variation of the plasmapause location with good accuracy, which can be incorporated into global radiation belt simulations and space weather forecasts under a variety of geomagnetic conditions. This article is protected by copyright. All rights reserved.
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