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Filtering by: Creator Liemohn, Michael W. Discipline Science

Data for Improvement of Plasma Sheet Neural Network Accuracy with Inclusion of Physical Information

Creator:

Swiger, Brian M., Liemohn, Michael W., and Ganushkina, Natalia Y.

Description:

We sampled the near-Earth plasma sheet using data from the NASA Time History of Events and Macroscale Interactions During Substorms mission. For the observations of the plasma sheet, we used corresponding interplanetary observations using the OMNI database. We used these data to develop a data-driven model that predicts plasma sheet electron flux from upstream solar wind variations. The model output data are included in this work, along with code for analyzing the model performance and producing figures used in the related publication. and Data files are included in hdf5 and Python pickle binary formats; scripts included are set up for use of Python 3 to access and process the pickle binary format data.

Keyword:

neural network, plasma sheet, solar wind, machine learning, keV electron flux, deep learning, and space weather

Citation to related publication:

Swiger, B. M., Liemohn, M. W., & Ganushkina, N. Y. (2020). Improvement of Plasma Sheet Neural Network Accuracy With Inclusion of Physical Information. Frontiers in Astronomy and Space Sciences, 7. https://doi.org/10.3389/fspas.2020.00042

Discipline:

Science and Engineering