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  • Deep learning for plasma tomography using the bolometer system at JET
    Matos, F. A. ...
    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.
    Vir: Fusion engineering and design. - ISSN 0920-3796 (Vol. 114, 2017, str. 18-25)
    Vrsta gradiva - članek, sestavni del
    Leto - 2017
    Jezik - angleški
    COBISS.SI-ID - 31589927
    DOI

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