Robust impurity detection and tracking for tokamaks

TitleRobust impurity detection and tracking for tokamaks
Publication TypeJournal Article
Year of Publication2020
AuthorsC. Cowley, P. Fuller, Y. Andrew, L. James, L. Simons, M. Sertoli, T. Morgan, S. Brons, J. Scholten, J. Vernimmen, S. Silburn, A. Widdowson, I. Bykov, D. Durakov, P. Bryant, B. Harris
JournalPhysical Review E
Volume102
Issue4
Pagination043311
Date Published10/2020
Abstract

A robust impurity detection and tracking code, able to generate large sets of dust tracks from tokamak camera footage, is presented. This machine learning–based code is tested with cameras from the Joint European Torus, Doublet-III-D, and Magnum-PSI and is able to generate dust tracks with a 65–100% classification accuracy. Moreover, the number dust particles detected from a single camera shot can be up to the order of 1000. Several areas of improvement for the code are highlighted, such as generating more significant training data sets and accounting for selection biases. Although the code is tested with dust in single two-dimensional camera views, it could easily be applied to multiple-camera stereoscopic reconstruction or nondust impurities.

DOI10.1103/PhysRevE.102.043311
Division

PSI

Department

PMI

PID

5ea4950f4a1002643f2f0a19118d0466

Alternate TitlePhys. Rev. E

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