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Neural networks: from image recognition to tokamak plasma tomography

Published online by Cambridge University Press:  17 April 2019

Axel Jardin*
Affiliation:
Institute of Nuclear Physics Polish Academy of Sciences (IFJ PAN), PL-31-342, Krakow, Poland
Jakub Bielecki
Affiliation:
Institute of Nuclear Physics Polish Academy of Sciences (IFJ PAN), PL-31-342, Krakow, Poland
Didier Mazon
Affiliation:
CEA, IRFM F-13108 Saint Paul-lez-Durance, France
Jan Dankowski
Affiliation:
Institute of Nuclear Physics Polish Academy of Sciences (IFJ PAN), PL-31-342, Krakow, Poland
Krzysztof Król
Affiliation:
Institute of Nuclear Physics Polish Academy of Sciences (IFJ PAN), PL-31-342, Krakow, Poland
Yves Peysson
Affiliation:
CEA, IRFM F-13108 Saint Paul-lez-Durance, France
Marek Scholz
Affiliation:
Institute of Nuclear Physics Polish Academy of Sciences (IFJ PAN), PL-31-342, Krakow, Poland
*
Author for correspondence: Axel Jardin, Institute of Nuclear Physics Polish Academy of Sciences (IFJ PAN), PL-31-342, Krakow, Poland. E-mail: axel.jardin@ifj.edu.pl

Abstract

In this paper, the possibility of using neural networks for fast tomographic reconstructions of tokamak plasma soft X-ray (SXR) emissivity is investigated. Indeed, the radiative cooling of heavy impurities like tungsten could be detrimental for the plasma core performances of ITER, thus developing robust and fast SXR diagnostic tools is a crucial issue to monitor the impurities and to mitigate in real-time their central accumulation. As preliminary work, a database of emissivity phantoms with associated synthetic measurements is used to train the neural network to solve the inversion problem. The inversion method, training process, and first tomographic reconstructions are presented with the perspectives about our future work.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2019 

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