Deep learning for plasma tomography using the bolometer system at JET

TL;DR

This paper presents a deep neural network approach for pixel-by-pixel plasma tomography reconstruction from bolometer data at JET, achieving high accuracy in reproducing existing plasma cross-section images.

Contribution

It introduces an up-convolutional neural network for full image reconstruction of plasma profiles, moving beyond parametric models used previously.

Findings

High accuracy in plasma image reconstruction

Effective neural network architecture for tomography

Validated on large dataset of sample tomograms

Abstract

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.