Robust impurity detection and tracking for tokamaks

C. Cowley, P. Fuller, Y. Andrew, L. James, L. Simons, M. Sertoli, S. Silburn, A. Widdowson, Contributors Jet Contributors, I. Bykov, D. Rudakov, T. Morgan, S. Brons, J. Scholten, J. Vernimmen, P. Bryant, B. Harris

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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.

Original languageEnglish (US)
Article number043311
JournalPhysical Review E
Issue number4
StatePublished - Oct 2020
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Condensed Matter Physics
  • Statistical and Nonlinear Physics
  • Statistics and Probability


Dive into the research topics of 'Robust impurity detection and tracking for tokamaks'. Together they form a unique fingerprint.

Cite this