Abstract The JET baseline scenario is being developed to achieve high fusion performance and sustained fusion power. However, with higher plasma current and higher input power, an increase in pulse disruptivity is being observed. Although there is a wide range of possible disruption causes, the present disruptions seem to be closely related to radiative phenomena such as impurity accumulation, core radiation, and radiative collapse. In this work, we focus on bolometer tomography to reconstruct the plasma radiation profile, and on top of it, we apply anomaly detection to identify the radiation patterns that precede major disruptions. The approach makes extensive use of machine learning. First, we train a surrogate model for plasma tomography based on matrix multiplication, which provides a fast method to compute the plasma radiation profiles across the full extent of any given pulse. Then, we train a variational autoencoder to reproduce the radiation profiles by encoding them into a latent distribution and subsequently decoding them. As an anomaly detector, the variational autoencoder struggles to reproduce unusual behaviors that include not only the actual disruptions but their precursors as well. These precursors are identified based on an analysis of the anomaly score across all baseline pulses in two recent campaigns at JET.

中文翻译：

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基于等离子断层扫描的中断前体分析的深度学习

摘要 正在开发 JET 基线情景以实现高聚变性能和持续的聚变功率。然而，随着等离子体电流和输入功率的增加，人们观察到脉冲破坏性增加。尽管存在多种可能的中断原因，但目前的中断似乎与杂质积累、核心辐射和辐射坍塌等辐射现象密切相关。在这项工作中，我们专注于辐射热计断层扫描来重建等离子体辐射剖面，在此之上，我们应用异常检测来识别重大破坏之前的辐射模式。该方法广泛使用机器学习。首先，我们训练了一个基于矩阵乘法的等离子断层扫描的替代模型，它提供了一种快速方法来计算任何给定脉冲的整个范围内的等离子体辐射分布。然后，我们训练一个变分自动编码器，通过将它们编码成潜在分布并随后对其进行解码来重现辐射剖面。作为异常检测器，变分自编码器努力重现异常行为，其中不仅包括实际中断，还包括它们的前兆。这些前兆是根据对 JET 最近两次活动中所有基线脉冲的异常分数的分析来确定的。变分自编码器努力重现不寻常的行为，不仅包括实际的中断，还包括它们的前兆。这些前兆是根据对 JET 最近两次活动中所有基线脉冲的异常分数的分析来确定的。变分自编码器努力重现不寻常的行为，不仅包括实际的中断，还包括它们的前兆。这些前兆是根据对 JET 最近两次活动中所有基线脉冲的异常分数的分析来确定的。