Paper ID: 2410.12424 • Published Oct 16, 2024
Nonlinear bayesian tomography of ion temperature and velocity for Doppler coherence imaging spectroscopy in RT-1
Kenji Ueda, Masaki. Nishiura
TL;DR
Get AI-generated summaries with premium
Get AI-generated summaries with premium
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.