# Plasma Image Classification Using Cosine Similarity Constrained CNN Source: [Plasma Image Classification Using Cosine Similarity Constrained CNN]() Collection index: [llms.txt]() Data updated: 2025-10-25T04:14:18.236Z Authors: Michael J. Falato, Bradley T. Wolfe, Tali M. Natan, Xinhua Zhang, Ryan S. Marshall, Yi Zhou, Paul M. Bellan, Zhehui Wang Published: 2022-05-10T00:56:17.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()