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Deep neural networks for plasma tomography with applications to JET and COMPASS
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: 12512019 (English)In: Journal of Instrumentation, ISSN 1748-0221, E-ISSN 1748-0221, Vol. 14, article id C09011Article in journal (Refereed) Published
Abstract [en]

Convolutional neural networks (CNNs) have found applications in many image processing tasks, such as feature extraction, image classification, and object recognition. It has also been shown that the inverse of CNNs, so-called deconvolutional neural networks, can be used for inverse problems such as plasma tomography. In essence, plasma tomography consists in reconstructing the 2D plasma profile on a poloidal cross-section of a fusion device, based on line-integrated measurements from multiple radiation detectors. Since the reconstruction process is computationally intensive, a deconvolutional neural network trained to produce the same results will yield a significant computational speedup, at the expense of a small error which can be assessed using different metrics. In this work, we discuss the design principles behind such networks, including the use of multiple layers, how they can be stacked, and how their dimensions can be tuned according to the number of detectors and the desired tomographic resolution for a given fusion device. We describe the application of such networks at JET and COMPASS, where at JET we use the bolometer system, and at COMPASS we use the soft X-ray diagnostic based on photodiode arrays.

Place, publisher, year, edition, pages
2019. Vol. 14, article id C09011
Keywords [en]
Computerized Tomography (CT) and Computed Radiography (CR), Plasma diagnostics - interferometry, spectroscopy and imaging
National Category
Fusion, Plasma and Space Physics
Identifiers
URN: urn:nbn:se:uu:diva-399021DOI: 10.1088/1748-0221/14/09/C09011ISI: 000486989800011OAI: oai:DiVA.org:uu-399021DiVA, id: diva2:1377411
Conference
3rd European Conference on Plasma Diagnostics (ECPD), MAY 06-10, 2019, Lisbon, PORTUGAL
Funder
EU, Horizon 2020, 633053
Note

For complete list of authors see http://dx.doi.org/10.1088/1748-0221/14/09/C09011

Available from: 2019-12-11 Created: 2019-12-11 Last updated: 2019-12-11Bibliographically approved

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Andersson Sundén, ErikBinda, FedericoCecconello, MarcoConroy, SeanEricsson, GöranEriksson, JacobHellesen, CarlHjalmarsson, AndersPossnert, GöranPrimetzhofer, DanielSahlberg, ArneSjöstrand, HenrikSkiba, MateuszWeiszflog, Matthias

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Andersson Sundén, ErikBinda, FedericoCecconello, MarcoConroy, SeanEricsson, GöranEriksson, JacobHellesen, CarlHjalmarsson, AndersPossnert, GöranPrimetzhofer, DanielSahlberg, ArneSjöstrand, HenrikSkiba, MateuszWeiszflog, Matthias
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Applied Nuclear Physics
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Journal of Instrumentation
Fusion, Plasma and Space Physics

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