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Plasma image classification using cosine similarity constrained convolutional neural network

Falato, Michael J. and Wolfe, Bradley T. and Natan, Tali M. and Zhang, Xinhua and Marshall, Ryan S. and Zhou, Yi and Bellan, Paul M. and Wang, Zhehui (2022) Plasma image classification using cosine similarity constrained convolutional neural network. Journal of Plasma Physics, 88 (6). Art. No. 895880603. ISSN 0022-3778. doi:10.1017/s0022377822000940.

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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 not only investigating the underlying physics of the experiments but also 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, labelled data set which includes three classes corresponding to varying levels of kink instability.

Item Type:Article
Related URLs:
URLURL TypeDescription
Falato, Michael J.0000-0002-4510-7325
Natan, Tali M.0000-0002-7703-6701
Marshall, Ryan S.0000-0003-0429-3923
Bellan, Paul M.0000-0002-0886-8782
Wang, Zhehui0000-0001-7826-4063
Additional Information:M.J.F. was supported in part by a US DoE Science Undergraduate Laboratory Internships (SULI) award. This work was also supported in part by the US Department of Energy through the Los Alamos National Laboratory (ICF program). Los Alamos National Laboratory is operated by Triad National Security, LLC, for the National Nuclear Security Administration of US Department of Energy (Contract No. 89233218CNA000001).
Funding AgencyGrant Number
Department of Energy (DOE)89233218CNA000001
Issue or Number:6
Record Number:CaltechAUTHORS:20230123-451828800.33
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:118899
Deposited By: Research Services Depository
Deposited On:17 Feb 2023 02:59
Last Modified:17 Feb 2023 02:59

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