Integrated Data Analysis of Plasma Electron Density Profile Tomography for HL-3 with Gaussian Process Regression
This work addresses plasma diagnostics for fusion energy research in tokamaks, representing an incremental improvement in data integration methods.
The researchers tackled plasma electron density profile tomography in the HL-3 tokamak by proposing an integrated data analysis model using Gaussian Process Regression, which combined line-integral and point measurements to achieve an average relative error of 3.60×10⁻⁴ in reconstructed profiles.
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.