  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.
