Using multi-spectral imaging and advanced data analysis to improve the tokamak fusion concept.

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I studied for both my undergraduate and master degrees in Physics at the University of Padua, in Italy. My interest in nuclear fusion started during my bachelor thesis research on developing a convolutional neural network for the analysis of infrared imaging system data on the SPIDER experiment. It then continued during my masters degree through elective courses and it culminated in my masters thesis on the study of parametric decay instabilities during Electron Cyclotron Resonance (ECR) heating on the NORTH tokamak at the Technical University of Denmark. My PhD project, supervised by Prof. Bruce Lipschultz and Prof. Kieran Gibson at the University of York and Dr. James Harrison at CCFE, involves the study of the divertor region of the MAST-U tokamak through the new Multi Wavelength-Imaging (MWI) diagnostic. This diagnostic allows researchers to acquire eleven video movies corresponding to 2D brightness images of the divertor region, each filtered for a different wavelength. The profiles have different dependencies on the plasma parameters and by putting them together with the data from the other diagnostics, these parameters can be reconstructed. The MWI data will be integrated in the Bayesian framework currently being used to determine the divertor state, thereby improving its capability. Handling the large exhaust heat loads corresponding to that of a reactor is one of the main problems on the way to commercial fusion tokamaks providing a net power source. The magnetic topology of the divertor (where the exhaust power is carried towards surfaces) in MAST-U is flexible and the MWI diagnostic will help in determining the most appropriate divertor configuration to optimize the dissipation of power and thus reduce peak power loads.