Neural networks: from image recognition to tokamak plasma tomography. (June 2019)

• Record Type: Journal Article
• Authors: Jardin, Axel; Bielecki, Jakub; Mazon, Didier; Dankowski, Jan; Król, Krzysztof; Peysson, Yves; Scholz, Marek
• Is Part Of: Laser and particle beams. Volume 37:Number 2(2019)
• Page Start: 171
• Page End: 175
• Publication Date: 2019-06
• Subjects: Neural networks, -- soft X rays, -- tokamaks, -- impurities, -- tomography; Laser beams -- Periodicals; Particle beams -- Periodicals; 535.5
• DOI: 10.1017/S0263034619000296
• Languages: English
• ISSNs: 0263-0346

Abstract

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.