Soft X-Ray Tomography for the Study of MHD Instability Mode and Impurity Transport

抄録

An advanced tomography method based on Bayesian probability theory is presented in this article. In the method, Gaussian Process (GP) prior is adopted as an effective approach to smoothness regularization which can be optimized based on the balance between model complexity and data constraint. In particular, to address the problem of varying smoothness in space, a non-stationary version of the GP has been developed and resolved via Bayesian hierarchical algorithm to implement locally adaptive smoothness regularization such that the accuracy of the reconstruction can be improve significantly. The Bayesian formulism allows the reliability of the reconstruction result to be examined by the confidence interval of a posterior probability. Through a wide range of applications, this tomography method is proved to be a robust tool for the study of magnetohydrodynamics (MHD) activity and impurity transport during HL-2A experimental campaigns.