Journal
FUSION SCIENCE AND TECHNOLOGY
Volume 76, Issue 8, Pages 962-971Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/15361055.2020.1820805
Keywords
Tokamak; generative models; neural networks; Alfvé n eigenmodes
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Funding
- Czech Technical University in Prague [SGS18/188/OHK4/3T/14]
- GACR [18-21409S]
- project COMPASS-U: Tokamak for Cutting-Edge Fusion Research [CZ.02.1.01/0.0/0.0/16_019/0000768]
- European structural and investment funds
- NVIDIA Corporation
- Euratom research and training programme 2014-2018 [633053]
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Chirping Alfven 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.
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