  Gaussian process tomography (GPT) is a method used for obtaining real-time
tomographic reconstructions of the plasma emissivity profile in a tokamak,
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 ASDEX Upgrade Soft X-ray (SXR)
diagnostic, we train a convolutional neural network (CNN) 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, whose quality we evaluate by
comparing their projection into measurement space with the existing
measurements themselves.
