Nonlinear Gaussian process tomography with imposed non-negativity constraints on physical quantities for plasma diagnostics
This work addresses a specific issue in plasma diagnostics by imposing physical constraints, representing an incremental improvement over prior tomographic methods.
The authors tackled the problem of non-physical negative values in plasma tomography by proposing nonlinear Gaussian process tomography with non-negativity constraints, which outperformed existing methods like standard GPT and MFI in reconstruction accuracy on the RT-1 device.
We propose a novel tomographic method, nonlinear Gaussian process tomography (nonlinear GPT) that employs the Laplace approximation to ensure the non-negative physical quantity, such as the emissivity of plasma optical diagnostics. This new method implements a logarithmic Gaussian process (log-GP) to model plasma distribution more naturally, thereby expanding the limitations of standard GPT, which are restricted to linear problems and may yield non-physical negative values. The effectiveness of the proposed log-GP tomography is demonstrated through a case study using the Ring Trap 1 (RT-1) device, where log-GPT outperforms existing methods, standard GPT, and the Minimum Fisher Information (MFI) methods in terms of reconstruction accuracy. The result highlights the effectiveness of nonlinear GPT for imposing physical constraints in applications to an inverse problem.