Integrated Data Analysis of Plasma Electron Density Profile Tomography for HL-3 with Gaussian Process Regression

TL;DR

This paper presents an integrated data analysis model using Gaussian Process Regression for reconstructing plasma electron density profiles in the HL-3 tokamak, effectively combining multiple measurement types for accurate 2D tomography.

Contribution

The paper introduces a novel Gaussian Process Regression-based model that integrates line-integral and point measurements with magnetic equilibrium data for improved plasma profile reconstruction.

Findings

Reconstruction error as low as 3.60*10^(-4)

Model effectively combines diverse measurement data

Sensitivity tests confirm robustness of the method

Abstract

An integrated data analysis model based on Gaussian Process Regression is proposed for plasma electron density profile tomography in the HL-3 tokamak. The model combines line-integral measurements from the far-infrared laser interferometer with point measurements obtained via the frequency-modulated continuous wave reflectometry. By employing Gaussian Process Regression, the model effectively incorporates point measurements into 2D profile reconstructions, while coordinate mapping integrates magnetic equilibrium information. The average relative error of the reconstructed profile obtained by the integrated data analysis model with normalized magnetic flux is as low as 3.60*10^(-4). Additionally, sensitivity tests were conducted on the grid resolution, the standard deviation of diagnostic data, and noise levels, providing a robust foundation for the real application to experimental data.