Minimum Fisher Tikhonov Regularization Adapted to Real-Time Tomography

Tomography inversion has been used routinely for processing outputs of plasma radiation diagnostics. Various tomographic algorithms have been developed, with those based on Tikhonov regularization being among the fastest while still providing reliable results. This paper presents a further speed optimization of the minimum Fisher Tikhonov regularization algorithm based on reducing iteration cycles used during the calculation. Ten to twentyfold speed gain is achieved compared to the original implementation. Robustness of the new method is demonstrated using both artificially generated data sets and real data from the soft X-ray diagnostics at the COMPASS tokamak. The advantage gained by the optimization is investigated in particular with respect to the possibility of real-time control of the plasma position; the option of impurity control is also discussed.