Identification of Plasma Waves at Saturn Using Convolutional Neural Networks

View Researcher's Other Codes

Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).

Please contact us in case of a broken link from here

Authors Suranga Ruhunusiri
Journal/Conference Name IEEE Transactions on Plasma Science
Paper Category
Paper Abstract Cassini has recently completed its 13-year mission at Saturn leaving a vast data set. A large interest among the scientific community is to investigate plasma waves and instabilities at Saturn. It is no longer feasible to manually search through Cassini's vast data set to identify all such waves of interest. Thus, the feasibility of using artificial neural networks (ANNs) to identify plasma waves at Saturn is demonstrated using Cassini data. A convolutional neural network (CNN) was trained to identify low-frequency plasma waves that occur in the upstream region of Saturn using images constructed from the Cassini magnetometer time series data. By systematically varying the network architecture during training and validation, a CNN was obtained that can identify upstream waves with an accuracy of 94% ± 2%. The CNN's high accuracy for wave identification demonstrates that it is, in fact, feasible to use ANNs to identify plasma waves at Saturn and by extension in other planetary and lunar plasma environments using spacecraft data.
Date of publication 2018
Code Programming Language Matlab
Comment

Copyright Researcher 2021