Plasma parameter profile inference from limited data utilizing second-order derivative priors and physic-based constraints.

A Bayesian framework has been used to improve the quality of inferred plasma parameter profiles. An integrated data analysis allows for coherent combinations of different diagnostics, and Gaussian process regression provides a reliable regularization process and systematic uncertainty estimation. In this paper, we propose a new profile inference framework that utilizes our prior knowledge about plasma physics, along with integrated data analysis and a Gaussian process. In order to facilitate the use of the Markov chain Monte Carlo sampling, we use a Gaussian process to define quantities corresponding to the second derivatives of the profiles. We validate the analysis technique by using a synthetic one-dimensional plasma, in which the transport properties are known and demonstrate that the proposed analysis technique can infer plasma parameter profiles from line-integrated measurements only. Furthermore, we can even infer unknown parameters in our physics models when our physics knowledge on the system is incomplete. This analysis framework is applicable to laboratory plasmas and provides a means to investigate plasma parameters, to which standard diagnostics are not directly sensitive. [ABSTRACT FROM AUTHOR]