Deep learning for Gaussian process soft x-ray tomography model selection in the ASDEX Upgrade tokamak

Abstract

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.