Nonlinear bayesian tomography of ion temperature and velocity for Doppler coherence imaging spectroscopy in RT-1
This work provides a robust diagnostic tool for plasma physics, specifically for magnetospheric plasma studies, representing a novel method for a known bottleneck in tomography.
The authors tackled the problem of reconstructing ion temperature and velocity distributions in plasmas using Coherence Imaging Spectroscopy by developing a novel Bayesian tomography approach with nonlinear Gaussian Process Tomography. Their method, validated on simulations and experimental data from the RT-1 device, enabled robust reconstruction in high-temperature and velocity regions and revealed detailed spatial structures.
We present a novel Bayesian tomography approach for Coherence Imaging Spectroscopy (CIS) that simultaneously reconstructs ion temperature and velocity distributions in plasmas. Utilizing nonlinear Gaussian Process Tomography (GPT) with the Laplace approximation, we model prior distributions of log-emissivity, temperature, and velocity as Gaussian processes. This framework rigorously incorporates nonlinear effects and temperature dependencies often neglected in conventional CIS tomography, enabling robust reconstruction even in the region of high temperature and velocity. By applying a log-Gaussian process, we also address issues like velocity divergence in low-emissivity regions. Validated with phantom simulations and experimental data from the RT-1 device, our method reveals detailed spatial structures of ion temperature and toroidal ion flow characteristic of magnetospheric plasma. This work significantly broadens the scope of CIS tomography, offering a robust tool for plasma diagnostics and facilitating integration with complementary measurement techniques.