Explainable deep learning for the analysis of MHD spectrograms in nuclear fusion. Issue 1 (30th December 2021)

• Record Type: Journal Article
• Authors: Ferreira, Diogo R; Martins, Tiago A; Rodrigues, Paulo
• Other Names: collab.
• Is Part Of: Machine learning: science and technology. Volume 3:Issue 1(2022)
• Publication Date: 2021-12-30
• Subjects: interpretable machine learning -- deep learning -- convolutional neural networks -- class activation mapping; 006.31
• DOI: 10.1088/2632-2153/ac44aa
• Languages: English
• ISSNs: 2632-2153

Abstract

Abstract: In the nuclear fusion community, there are many specialized techniques to analyze the data coming from a variety of diagnostics. One of such techniques is the use of spectrograms to analyze the magnetohydrodynamic (MHD) behavior of fusion plasmas. Physicists look at the spectrogram to identify the oscillation modes of the plasma, and to study instabilities that may lead to plasma disruptions. One of the major causes of disruptions occurs when an oscillation mode interacts with the wall, stops rotating, and becomes a locked mode. In this work, we use deep learning to predict the occurrence of locked modes from MHD spectrograms. In particular, we use a convolutional neural network with class activation mapping to pinpoint the exact behavior that the model thinks is responsible for the locked mode. Surprisingly, we find that, in general, the model explanation agrees quite well with the physical interpretation of the behavior observed in the spectrogram.