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.

中文翻译：

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使用生成神经网络在 COMPASS 上检测阿尔芬本征模式

摘要 在罗盘托卡马克上观察到啁啾阿尔芬本征模。它们被认为是由失控电子 (RE) 驱动的，因此，它们为研究 RE 与电磁不稳定性之间的非线性相互作用的物理学提供了独特的机会，包括 RE 缓解和损耗的重要主题。在 COMPASS 上，可以从某些磁探头的光谱图中检测到它们。到目前为止，由于它们很少发生，因此它们的检测需要大量的手动工作。我们努力使用基于生成神经网络的机器学习技术来自动化这个过程。我们展示了两种不同的模型，它们使用来自 COMPASS 实验的较小的手动标记数据库和较大的未标记数据库进行训练。在多次实验中，