Advancing MARFE detection in JET’s operational camera videos through Machine Learning techniques

Abstract

In order to prove the capability of operational cameras in nuclear fusion devices, the videos from the cameras at JET were used to detect the occurrence of MARFEs, an edge plasma phenomenon. Three techniques were tested in this work: two already reviewed in other publications and a new one based on intensity masks. Once these methods were validated, their output was used to develop several Machine Learning models to improve performance. A final Machine Learning model was devised using both data from the operational cameras and several signals and diagnostics from other instruments at JET. The outcomes achieved using all the methods presented were deemed satisfactory, leading to the final Machine Learning model exhibiting an impressive accuracy rate of 96.9%. Furthermore, the models allow for detection both in frame by frame (if only video data is used) and in 2 ms time steps should all diagnostics be used.