Nonlinear Bayesian Doppler Tomography for Simultaneous Reconstruction of Flow and Temperature

TL;DR

This paper introduces a nonlinear Bayesian Doppler tomography method that simultaneously reconstructs flow, temperature, and emissivity from spectral data, improving stability and applicability in plasma diagnostics.

Contribution

It develops a Bayesian framework with Gaussian process priors and a Laplace approximation for stable, simultaneous reconstruction of multiple plasma parameters from spectral measurements.

Findings

Successfully verified with synthetic data

Applied to RT-1 device measurements

Resolved ion temperature and flow structures

Abstract

We present a nonlinear Bayesian tomographic framework for Doppler spectral imaging that enables simultaneous reconstruction of emissivity, ion temperature, and flow velocity from line-integrated spectra. The method employs nonlinear Gaussian process tomography (GPT) with a Laplace approximation while retaining the full Doppler forward model. A log-Gaussian process prior stabilizes the velocity reconstruction in low-emissivity regions where Doppler information becomes weak, preventing the unphysical divergence of velocity estimates commonly encountered in conventional spectral tomography. The reconstruction method is verified using synthetic phantom data and applied to coherence imaging spectroscopy (CIS) measurements in the RT-1 device, resolving spatial structures of ion temperature and toroidal ion flow characteristic of magnetospheric plasma in the RT-1 device. The framework extends existing CIS tomography to regimes with strong flows and large temperature variations and provides a general Bayesian approach for Doppler spectral tomography that can be integrated with complementary spectroscopic diagnostics.