Inversion methods for fast-ion velocity-space tomography in fusion plasmas

Abstract

Velocity-space tomography has been used to infer 2D fast-ion velocity distribution functions.Here we compare the performance of five different inversion methods: Truncated singular value decomposition, maximum entropy, minimum Fisher information and zeroth-and first-order Tikhonov regularization.The inversion methods are applied on fast-ion D α [1,2] measurements taken just before and just after a sawtooth crash in the ASDEX Upgrade tokamak as well as on synthetic measurements from different test distributions.We find that the methods regularizing by penalizing steep gradients perform best.Furthermore, most of the methods agree that passing as well as trapped ions are ejected from the plasma center and that this ejection depletes the passing ion population more strongly than the trapped ion population.We assess the uncertainty of the calculated inversions taking into account photon noise, uncertainties in the forward model as well as uncertainties introduced by the regularization.