Using a priori knowledge for developing bolometric tomography in toroidal devices

Area of Science:

• Plasma physics
• Tomographic imaging
• Image reconstruction

Background:

• Tomographic imaging of magnetically confined plasmas often suffers from missing observational data.
• Prior information from modeled plasma profiles can mitigate these data gaps.
• Existing regularization techniques require enhancement for adaptive image generation.

Purpose of the Study:

• To develop an improved method for tomographic image reconstruction in toroidal plasmas.
• To address challenges posed by missing observational data using prior information.
• To enhance the accuracy of plasma radiation structure visualization.

Main Methods:

• Extended Tikhonov regularization with Euclidean distance for image reconstruction.
• Developed a model fitting procedure for adaptive reference image generation.
• Applied the new method to three-dimensional imaging-bolometer tomography in the Large Helical Device.

Main Results:

• The enhanced Tikhonov regularization effectively reduced artifacts in reconstructed plasma images.
• Adaptive generation of reference images improved the quality of tomography.
• The method demonstrated utility in a challenging, ill-conditioned tomography scenario.

Conclusions:

• The novel approach offers a valuable countermeasure against missing data in plasma tomography.
• Improved image quality facilitates better recognition of plasma radiation structures.
• This method enhances the diagnostic capabilities for magnetically confined fusion plasmas.