Identification of Plasma Waves at Saturn Using Convolutional Neural Networks.

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.