Development of a Bayesian method for the analysis of inertial confinement fusion experiments on the NIF

TL;DR

This paper introduces a Bayesian inference method tailored for inertial confinement fusion experiments, enabling comprehensive analysis of experimental parameters and physical models, demonstrated on NIF data to improve understanding of microphysics.

Contribution

The paper presents a portable Bayesian inference approach that incorporates all relevant experimental parameters and prior knowledge, improving analysis of ICF experiments over traditional methods.

Findings

Variations in target dimensions significantly affect inference results.

Ignoring prior knowledge can lead to unreasonable model adjustments.

The method effectively quantifies prior errors in microphysics models.

Abstract

The complex nature of inertial confinement fusion (ICF) experiments results in a very large number of experimental parameters that are only known with limited reliability. These parameters, combined with the myriad physical models that govern target evolution, make the reliable extraction of physics from experimental campaigns very difficult. We develop an inference method that allows all important experimental parameters, and previous knowledge, to be taken into account when investigating underlying microphysics models. The result is framed as a modified $\chi^{2}$ analysis which is easy to implement in existing analyses, and quite portable. We present a first application to a recent convergent ablator experiment performed at the NIF, and investigate the effect of variations in all physical dimensions of the target (very difficult to do using other methods). We show that for well characterised targets in which dimensions vary at the 0.5% level there is little effect, but 3% variations change the results of inferences dramatically. Our Bayesian method allows particular inference results to be associated with prior errors in microphysics models; in our example, tuning the carbon opacity to match experimental data (i.e., ignoring prior knowledge) is equivalent to an assumed prior error of 400% in the tabop opacity tables. This large error is unreasonable, underlining the importance of including prior knowledge in the analysis of these experiments.