Comparison of deconvolution techniques for 1D neutron emission profile reconstruction using ITER Radial Neutron Camera synthetic data and JET neutron camera measurements

Abstract The primary purpose of the ITER Radial Neutron Camera (RNC) is the real-time control of plasma burn. It requires neutron emissivity profile reconstruction with an accuracy better than 10% and a time resolution of 10 ms. Algorithms based on the Tikhonov Regularization, Minimum Fisher Information, Maximum Entropy and Maximum Likelihood methods were compared for the 1D deconvolution of the neutron emissivity profile from RNC measurements. The reconstruction performance was evaluated using the baseline RNC architecture and two ITER DT 15 MA scenarios of inductive operation. The reconstruction was carried out assuming constant neutron emissivity on the magnetic flux surfaces: in this case, the neutron profile can be represented as a normalized poloidal magnetic flux function. The number of the used flux surfaces was about twice the number of lines of sight in the RNC. All methods (except Maximum Entropy) achieved a reconstruction accuracy better than 10%. The two Tikhonov Regularization algorithms provide in general a good reconstruction, with the second-order derivative regularization matrix giving a better accuracy than 10% in a wider range of the normalized poloidal magnetic flux (Ψ) but a higher standard deviation than the first-order derivative regularization matrix. At the same time, the Minimum Fisher Information proved to be the most stable method. The performance of these two best methods was validated with actual experimental data using the JET neutron camera measurements collected in the second deuterium-tritium campaign.