Plasma current tomography for HL-2A based on Bayesian inference

An accurate plasma current profile has irreplaceable value for the steady-state operation of the plasma. In this study, plasma current tomography based on Bayesian inference is applied to an HL-2A device and used to reconstruct the plasma current profile. Two different Bayesian probability priors are tried, namely the Conditional AutoRegressive (CAR) prior and the Advanced Squared Exponential (ASE) kernel prior. Compared to the CAR prior, the ASE kernel prior adopts non-stationary hyperparameters and introduces the current profile of the reference discharge into the hyperparameters, which can make the shape of the current profile more flexible in space. The results indicate that the ASE prior couples more information, reduces the probability of unreasonable solutions, and achieves higher reconstruction accuracy.