Gaussian process tomography (GPT) is a method used for obtaining real-time tomographic reconstructions of the plasma emissivity profile in tokamaks, given some model for the underlying physical processes involved. GPT can also be used, thanks to Bayesian formalism, to perform model selection, i.e., comparing different models and choosing the one with maximum evidence. However, the computations involved in this particular step may become slow for data with high dimensionality, especially when comparing the evidence for many different models. Using measurements collected by the Soft X-Ray (SXR) diagnostic in the ASDEX Upgrade tokamak, we train a convolutional neural network to map SXR tomographic projections to the corresponding GPT model whose evidence is highest. We then compare the network's results, and the time required to calculate them, with those obtained through analytical Bayesian formalism. In addition, we use the network's classifications to produce tomographic reconstructions of the plasma emissivity profile.

中文翻译：

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ASDEX 升级托卡马克中高斯过程软 X 射线断层扫描模型选择的深度学习

高斯过程断层扫描 (GPT) 是一种用于获取托卡马克中等离子体发射率分布的实时断层扫描重建的方法，为所涉及的潜在物理过程提供一些模型。由于贝叶斯形式主义，GPT 也可用于执行模型选择，即比较不同模型并选择具有最大证据的模型。但是，对于高维数据，此特定步骤中涉及的计算可能会变慢，尤其是在比较许多不同模型的证据时。使用 ASDEX 升级托卡马克中软 X 射线 (SXR) 诊断收集的测量值，我们训练卷积神经网络将 SXR 断层扫描投影映射到证据最高的相应 GPT 模型。然后我们比较网络的结果，以及计算它们所需的时间，与通过分析贝叶斯形式主义获得的那些。此外，我们使用网络的分类来生成等离子体发射率剖面的断层扫描重建。