Bayesian tomography and integrated data analysis in fusion diagnostics.

Area of Science:

• Plasma Physics
• Computational Physics
• Data Analysis

Background:

• Accurate reconstruction of plasma emission is crucial for understanding fusion device physics.
• Traditional tomography methods often struggle with non-uniform emission distributions.
• Bayesian inference provides a robust framework for handling uncertainty in complex systems.

Purpose of the Study:

• To introduce a novel Bayesian tomography method utilizing a non-stationary Gaussian process prior.
• To improve the accuracy and reliability of plasma emission reconstructions.
• To enable integrated analysis of heterogeneous diagnostics in fusion research.

Main Methods:

• Developed a Bayesian tomography framework incorporating a non-stationary Gaussian process prior.
• Utilized probabilistic methods to quantify solution uncertainty via posterior probability confidence intervals.
• Implemented a consistency check comparing predicted and measured data misfits against assumed errors.
• Applied the method to soft X-ray diagnostics data from the HL-2A tokamak.

Main Results:

• Significantly improved reconstruction accuracy by adapting to varying emission smoothness.
• Successfully resolved uncertainties in the final solution through posterior probability analysis.
• Demonstrated the method's capability for integrated analysis within a large-scale inference framework.
• Enabled exploration of equilibrium and Magnetohydrodynamic (MHD) instability modes in plasma.

Conclusions:

• The proposed Bayesian tomography method with a non-stationary Gaussian process prior offers superior accuracy for plasma emission reconstruction.
• This approach effectively handles uncertainty and improves the analysis of complex plasma physics.
• The method is suitable for integrated analysis of diverse diagnostic data in fusion research.