Bayesian Parameter Estimation for Data Integration in ICF Experiments

Bayesian parameter estimation is a powerful tool for the interpretation of experimental data and discriminating between models. It is particularly powerful when applied to data integration, the task of simultaneously integrating multiple disparate diagnostic data sets to constrain a model. This technique is demonstrated on data obtained from MagLIF experiments where imaging, spectroscopic, x-ray, and neutron data are all used to simultaneously constrain the set of parameters that best describe the observables. Our algorithm also gives confidence intervals and correlations directly from the analysis, as well as the ability to estimate the value of information for each of the diagnostic inputs.