Identification of Plasma Waves at Saturn Using Convolutional Neural Networks.

Area of Science:

• Space Physics
• Planetary Science
• Artificial Intelligence

Background:

• The Cassini mission at Saturn generated extensive data, necessitating efficient analysis methods.
• Investigating plasma waves and instabilities in Saturn's magnetosphere is of significant scientific interest.
• Manual analysis of large spacecraft datasets for specific phenomena like plasma waves is becoming infeasible.

Purpose of the Study:

• To demonstrate the feasibility of using artificial neural networks (ANNs) for identifying plasma waves in planetary environments.
• To develop and validate a machine learning model for detecting low-frequency plasma waves in Saturn's upstream region.

Main Methods:

• A convolutional neural network (CNN) was trained to identify plasma waves.
• Images were constructed from Cassini magnetometer time series data for training.
• Network architecture was systematically varied during training and validation.

Main Results:

• The trained CNN achieved a high accuracy of 94% ± 2% in identifying upstream plasma waves.
• The model demonstrated effective identification of low-frequency plasma waves.
• The study confirmed the capability of ANNs in analyzing complex space plasma data.

Conclusions:

• Artificial neural networks, specifically CNNs, are a feasible and accurate tool for identifying plasma waves in Saturn's magnetosphere.
• This approach can be extended to analyze spacecraft data from other planetary and lunar plasma environments.
• ANNs offer a powerful solution for efficiently processing large volumes of space science data.