Quantifying experimental edge plasma evolution via multidimensional adaptive Gaussian process regression

TL;DR

This paper introduces a multidimensional adaptive Gaussian process method for analyzing noisy, spatiotemporal edge plasma data in tokamaks, improving the quantification of dynamic plasma profiles and phenomena.

Contribution

The paper presents a novel adaptive Gaussian process framework that automatically manages noisy data and captures complex spatiotemporal plasma behaviors.

Findings

Effective handling of noisy measurements in plasma data

Enhanced quantification of edge plasma evolution

Application to Alcator C-Mod tokamak data

Abstract

The edge density and temperature of tokamak plasmas are strongly correlated with energy and particle confinement and their quantification is fundamental to understanding edge dynamics. These quantities exhibit behaviours ranging from sharp plasma gradients and fast transient phenomena (e.g. transitions between low and high confinement regimes) to nominal stationary phases. Analysis of experimental edge measurements therefore require robust fitting techniques to capture potentially stiff spatiotemporal evolution. Additionally, fusion plasma diagnostics inevitably involve measurement errors and data analysis requires a statistical framework to accurately quantify uncertainties. This paper outlines a generalized multidimensional adaptive Gaussian process routine capable of automatically handling noisy data and spatiotemporal correlations. We focus on the edge-pedestal region in order to underline advancements in quantifying time-dependent plasma profiles including transport barrier formation on the Alcator C-Mod tokamak.