Nonlinear Bayesian Doppler tomography for simultaneous reconstruction of flow and temperature

Area of Science:

• Plasma physics
• Spectroscopic diagnostics
• Bayesian inference

Background:

• Doppler spectral imaging is crucial for understanding plasma behavior.
• Conventional spectral tomography faces challenges with velocity reconstruction in low-emissivity regions.
• Accurate measurement of ion temperature and flow velocity is essential for plasma confinement and stability.

Purpose of the Study:

• To present a nonlinear Bayesian tomographic framework for enhanced Doppler spectral imaging.
• To enable simultaneous reconstruction of emissivity, ion temperature, and flow velocity.
• To overcome limitations of conventional methods in low-signal and high-variation plasma regimes.

Main Methods:

• Nonlinear Gaussian process tomography with Laplace approximation.
• Full Doppler forward model integration.
• Log-Gaussian process prior for velocity reconstruction stabilization.

Main Results:

• Successful verification using synthetic phantom data.
• Application to Coherence Imaging Spectroscopy (CIS) measurements in the RT-1 device.
• Resolved spatial structures of ion temperature and toroidal ion flow in magnetospheric plasma.

Conclusions:

• The framework provides a robust Bayesian approach for Doppler spectral tomography.
• It extends Coherence Imaging Spectroscopy (CIS) capabilities to stronger flows and temperature variations.
• The method can be integrated with other spectroscopic diagnostics for comprehensive plasma analysis.