A new inversion method for the spectroscopic analysis of image data.

摘要

Spectroscopic modeling and analysis of plasma conditions in inertial confinement fusion implosion cores has been recently extended to extract electron temperature and density spatial-distributions. The inversion method presented here is based on a multi-objective approach to data analysis driven by a Pareto genetic algorithm (PGA) followed up by a non-linear least-squares minimization refining step. The method can determine the implosion core spatial structure from several combinations of experimental datasets, including the space-integrated line spectrum, and x-ray narrow-band image intensity profiles on the image plane and/or narrow-band emissivity profiles in the object space. This method combines the best characteristics of both PGA search and optimization and non-linear least-squares minimization. Furthermore, this new method removes the need of geometrical inversion which requires working only with optically thin images. Thus, optically thin and thick can simultaneously be used on the analysis.