In the last decade or so, estimating uncertainties associated with nuclear data has become an almost mandatory step in any new nuclear data evaluation. The mathematics needed to infer such estimates look deceptively simple, masking the hidden complexities due to imprecise and contradictory experimental data and natural limitations of simplified physics models. Through examples of evaluated covariance matrices for the soon-to-be-released U.S. ENDF/B-VIII.0 library, e.g., cross sections, spectrum, multiplicity, this paper discusses some uncertainty quantification methodologies in use today, their strengths, their pitfalls, and alternative approaches that have proved to be highly successful in other fields. The important issue of how to interpret and use the covariance matrices coming out of the evaluated nuclear data libraries is discussed.
Soft-constrained interval predictor models and epistemic reliability intervals: A new tool for uncertainty quantification with limited experimental data
