# Deep learning for plasma tomography using the bolometer system at JET

arXiv: https://arxiv.org/abs/1701.00322 · HTML: https://mlsanity.com/paper/1701.00322/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jan 2017.

## Built on by (most cited first, 2 of 2)

- [Machine learning methods for Schlieren imaging of a plasma channel in tenuous atomic vapor](https://mlsanity.com/paper/2205.12731/) (2022)
- [Machine Learning for Detection of 3D Features using sparse X-ray data](https://mlsanity.com/paper/2206.02564/) (2022)

## Abstract (arXiv)

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.