Detection of Alfvén Eigenmodes on COMPASS with Generative Neural Networks. (16th November 2020)

• Record Type: Journal Article
• Authors: Škvára, Vít; Šmídl, Václav; Pevný, Tomáš; Seidl, Jakub; Havránek, Aleš; Tskhakaya, David
• Is Part Of: Fusion science and technology. Volume 76:Number 8(2020)
• Page Start: 962
• Page End: 971
• Publication Date: 2020-11-16
• Subjects: Tokamak -- generative models -- neural networks -- Alfvén eigenmodes; Fusion reactors -- Periodicals; Nuclear fusion -- Periodicals; Fusion reactors; Nuclear fusion; Periodicals; 621.48405
• DOI: 10.1080/15361055.2020.1820805
• Languages: English
• ISSNs: 1536-1055

Abstract

Abstract: Chirping Alfvén eigenmodes were observed at the COMPASS tokamak. They are believed to be driven by runaway electrons (REs), and as such, they provide a unique opportunity to study the physics of nonlinear interaction between REs and electromagnetic instabilities, including important topics of RE mitigation and losses. On COMPASS, they can be detected from spectrograms of certain magnetic probes. So far, their detection has required much manual effort since they occur rarely. We strive to automate this process using machine learning techniques based on generative neural networks. We present two different models that are trained using a smaller, manually labeled database and a larger unlabeled database from COMPASS experiments. In a number of experiments, we demonstrate that our approach is a viable option for automated detection of rare instabilities in tokamak plasma.