An optimization method for ITER radial x-ray camera line-of-sight design basing on Bayesian uncertainty analysis

Abstract

This paper presents a novel uncertainty optimization algorithm for the design of line-of-sight systems used in tomographic inversion. By extending Gaussian process tomography from discrete pixel space to continuous function space through Bayesian inference, we introduce an uncertainty function and analyze its typical distributions. We develop an algorithm to minimize the uncertainty, which is then applied to optimize the line-of-sight configuration of the internal camera in the ITER project. Uncertainty analysis and phantom testing are conducted to validate the effectiveness of the proposed algorithm. The results demonstrate improved accuracy and stability in tomographic reconstructions. This study contributes to the advancement of line-of-sight design for tomographic inversion, offering a practical solution for optimizing diagnostic systems in complex experimental settings.