Unknown Unknowns in Inertial Confinement Fusion

Uncertainty quantification is a key part of physics; scientific researchers attempt to model both statistical and systematic uncertainties in their data as best as possible, often using a Bayesian framework. However it is well known that most statistical claims should be taken contextually; even if certain models are excluded at a very high degree of confidence, researchers are typically aware there may be systematics that were not accounted for, and thus typically will require confirmation from multiple independent sources, diagnostics, or experiments, before any novel results are truly accepted. In this work we compare two methods in the literature that seek to attempt to quantify these `unknown unknowns' - in particular attempting to produce realistic thick tails in the posterior of parameter estimation problems, that account for the possible existence of very large unknown effects. We apply these methods to the field of inertial confinement fusion to produce thick-tailed probability distributions for predictions of the outcome of future experiments.