Single Gaussian Process Method for Arbitrary Tokamak Regimes with a Statistical Analysis

TL;DR

This paper introduces a novel Gaussian Process Regression approach using change-point detection and Student's t-distribution to improve profile fitting in tokamak regimes, demonstrating superior performance through synthetic data analysis.

Contribution

It presents a new GPR method combining change-point detection and Student's t-distribution, optimized for varying tokamak regimes, with comprehensive statistical validation.

Findings

Full Bayesian approach with change-point method performs best.

Student's t-distribution handles outliers effectively.

Method outperforms traditional fitting techniques.

Abstract

Gaussian Process Regression (GPR) is a Bayesian method for inferring profiles based on input data. The technique is increasing in popularity in the fusion community due to its many advantages over traditional fitting techniques including intrinsic uncertainty quantification and robustness to over-fitting. This work investigates the use of a new method, the change-point method, for handling the varying length scales found in different tokamak regimes. The use of the Student's t-distribution for the Bayesian likelihood probability is also investigated and shown to be advantageous in providing good fits in profiles with many outliers. To compare different methods, synthetic data generated from analytic profiles is used to create a database enabling a quantitative statistical comparison of which methods perform the best. Using a full Bayesian approach with the change-point method, Mat\'ern kernel for the prior probability, and Student's t-distribution for the likelihood is shown to give the best results.