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Deep learning for plasma tomography using the bolometer system at JET
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
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Number of Authors: 11082017 (English)In: Fusion engineering and design, ISSN 0920-3796, E-ISSN 1873-7196, Vol. 114, p. 18-25Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier, 2017. Vol. 114, p. 18-25
Keywords [en]
Plasma diagnostics, Computed tomography, Neural networks, Deep learning
National Category
Subatomic Physics
Identifiers
URN: urn:nbn:se:uu:diva-399858DOI: 10.1016/j.fusengdes.2016.11.006ISI: 000393004700004OAI: oai:DiVA.org:uu-399858DiVA, id: diva2:1379536
Note

For complete list of authors see http://dx.doi.org/10.1016/j.fusengdes.2016.11.006

Available from: 2019-12-17 Created: 2019-12-17 Last updated: 2019-12-17Bibliographically approved

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Andersson Sundén, ErikBinda, FedericoCecconello, MarcoConroy, SeanDzysiuk, NataliiaEricsson, GöranEriksson, JacobHellesen, CarlHjalmarsson, AndersPossnert, GöranSjöstrand, HenrikSkiba, MateuszWeiszflog, Matthias

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Andersson Sundén, ErikBinda, FedericoCecconello, MarcoConroy, SeanDzysiuk, NataliiaEricsson, GöranEriksson, JacobHellesen, CarlHjalmarsson, AndersPossnert, GöranSjöstrand, HenrikSkiba, MateuszWeiszflog, Matthias
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Applied Nuclear Physics
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Subatomic Physics

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