Deep learning for plasma tomography using the bolometer system at JET

- Plasma tomography is able to reconstruct the plasma profile from radiation measurements along several lines of sight.
- The reconstruction can be performed with neural networks, but previous work focused on learning a parametric model.
- Deep learning can be used to reconstruct the full 2D plasma profile with the same resolution as existing tomograms.
- We introduce a deep neural network to generate an image from 1D projection data based on a series of up-convolutions.
- After training on JET data, the network provides accurate reconstructions with an average pixel error as low as 2%.

Deep learning is having a profound impact in many fields, especially those that involve some form of image processing. Deep neural networks excel in turning an input image into a set of high-level features. On the other hand, tomography deals with the inverse problem of recreating an image from a number of projections. In plasma diagnostics, tomography aims at reconstructing the cross-section of the plasma from radiation measurements. This reconstruction can be computed with neural networks. However, previous attempts have focused on learning a parametric model of the plasma profile. In this work, we use a deep neural network to produce a full, pixel-by-pixel reconstruction of the plasma profile. For this purpose, we use the overview bolometer system at JET, and we introduce an up-convolutional network that has been trained and tested on a large set of sample tomograms. We show that this network is able to reproduce existing reconstructions with a high level of accuracy, as measured by several metrics.