Evidence Cross-Validation and Bayesian Inference of MAST Plasma Equilibria

TL;DR

This paper applies Bayesian analysis to infer plasma current profiles and flux-surface geometry in MAST tokamak discharges, introducing a new diagnostic outlier removal method that improves the accuracy of equilibrium reconstructions.

Contribution

It presents a novel Bayesian inference framework for MAST plasma equilibria, including a diagnostic outlier detection technique, enhancing the reliability of current profile and flux-surface geometry estimations.

Findings

Accurate plasma current profiles inferred with Bayesian methods.

Effective outlier detection improves equilibrium reconstruction.

Predicted Shafranov shift consistent with other diagnostics.

Abstract

In this paper, current profiles for plasma discharges on the Mega-Ampere Spherical Tokamak (MAST) are directly calculated from pickup coil, flux loop and Motional-Stark Effect (MSE) observations via methods based in the statistical theory of Bayesian analysis. By representing toroidal plasma current as a series of axisymmetric current beams with rectangular cross-section and inferring the current for each one of these beams, flux-surface geometry and q-profiles are subsequently calculated by elementary application of Biot-Savart's law. The use of this plasma model in the context of Bayesian analysis was pioneered by Svensson and Werner on the Joint-European Tokamak (JET) [J. Svensson and A. Werner. Current tomography for axisymmetric plasmas. {\em Plasma Physics and Controlled Fusion}, 50(8):085002, 2008]. In this framework, linear forward models are used to generate diagnostic predictions, and the probability distribution for the currents in the collection of plasma beams was subsequently calculated directly via application of Bayes' formula. In this work, we introduce a new diagnostic technique to identify and remove outlier observations associated with diagnostics falling out of calibration or suffering from an unidentified malfunction. These modifications enable good agreement between Bayesian inference of the last closed flux-surface (LCFS) with other corroborating data, such as such as that from force balance considerations using EFIT++ [L. Appel et al., Proc. 33rd EPS Conf., Rome, Italy, 2006]. In addition, this analysis also yields errors on the plasma current profile and flux-surface geometry, as well as directly predicting the Shafranov shift of the plasma core.