Using Bayesian Analysis and Gaussian Processes to Infer Electron Temperature and Density Profiles on the MAST Experiment

TL;DR

This paper introduces a Bayesian framework employing Gaussian processes and advanced quadrature techniques to accurately infer electron temperature and density profiles in the MAST experiment, integrating multiple diagnostic data sources.

Contribution

The paper presents a novel Bayesian inference method that combines Gaussian process priors and Gauss-Laguerre quadratures for profile estimation in fusion plasma diagnostics.

Findings

Accurate electron temperature and density profiles inferred from combined TS and interferometry data.

The method provides a mollification length-scale for profiles, enhancing smoothness and physical plausibility.

Comparison shows improved profile estimation over standard analysis methods.

Abstract

A unified, Bayesian inference of midplane electron temperature and density profiles using both Thompson scattering (TS) and interferometric data is presented. Beyond the Bayesian nature of the analysis, novel features of the inference are the use of a Gaussian process prior to infer a mollification length-scale of inferred profiles and the use of Gauss-Laguerre quadratures to directly calculate the depolarisation term associated with the TS forward model. Results are presented from an application of the method to data from the high resolution TS system on the Mega-Ampere Spherical Tokamak, along with a comparison to profiles coming from the standard analysis carried out on that system.