Plasma Image Classification Using Cosine Similarity Constrained CNN

TL;DR

This paper introduces a deep learning approach utilizing cosine similarity for plasma image classification, achieving high accuracy in distinguishing stability states and instability levels in laboratory plasma jet images.

Contribution

It proposes a novel use of cosine similarity as a feature selection and loss function in CNN training for plasma image classification, with a simple vector comparison algorithm.

Findings

93% accuracy in binary classification of plasma stability

92% accuracy in five-class classification of kink instability levels

Effective use of cosine similarity in deep learning for plasma image analysis

Abstract

Plasma jets are widely investigated both in the laboratory and in nature. Astrophysical objects such as black holes, active galactic nuclei, and young stellar objects commonly emit plasma jets in various forms. With the availability of data from plasma jet experiments resembling astrophysical plasma jets, classification of such data would potentially aid in investigating not only the underlying physics of the experiments but the study of astrophysical jets. In this work we use deep learning to process all of the laboratory plasma images from the Caltech Spheromak Experiment spanning two decades. We found that cosine similarity can aid in feature selection, classify images through comparison of feature vector direction, and be used as a loss function for the training of AlexNet for plasma image classification. We also develop a simple vector direction comparison algorithm for binary and multi-class classification. Using our algorithm we demonstrate 93% accurate binary classification to distinguish unstable columns from stable columns and 92% accurate five-way classification of a small, labeled data set which includes three classes corresponding to varying levels of kink instability.