AUTHOR=Swiger Brian M. , Liemohn Michael W. , Ganushkina Natalia Y. TITLE=Improvement of Plasma Sheet Neural Network Accuracy With Inclusion of Physical Information JOURNAL=Frontiers in Astronomy and Space Sciences VOLUME=7 YEAR=2020 URL=https://www.frontiersin.org/journals/astronomy-and-space-sciences/articles/10.3389/fspas.2020.00042 DOI=10.3389/fspas.2020.00042 ISSN=2296-987X ABSTRACT=

The near-Earth plasma sheet is the source for electrons in the inner magnetosphere. The coupling between the solar wind and the near-Earth plasma sheet is dominated by non-linear processes, making any relationship difficult to infer. We report on the development of a neural network to capture the non-linear behavior between solar wind variations and the response of energetic electron flux in the plasma sheet. To train the neural network algorithm, we developed a data set with inputs from solar wind monitoring spacecraft. The targets come from three probes of the Time History of Events and Macroscale Interactions during Substorms mission as the spacecraft traversed the plasma sheet from years 2008–2019. Preliminary findings during the development of the neural network model show that tuning input parameters based on previously known physical properties is conducive to improving model performance.