Treating model defects by fitting smoothly varying model parameters: Energy dependence in nuclear data evaluation

Abstract

The fitting of models to data is essential in nuclear data evaluation, as in many other fields of science. The models may be necessary for interpolation or extrapolation, but they are seldom perfect; there are model defects present which can result in severe biases and underestimated uncertainties.This work presents and investigates the idea to treat this problem by letting the model parameters vary smoothly with an input parameter. To be specific, the model parameters for neutron cross sections are allowed to vary with neutron energy. The parameter variation is limited by Gaussian processes, but the method should not be confused with adding a Gaussian process to the model.The performance of the method is studied using a large number of synthetic data sets, such that it is possible to quantitatively study the distribution of results compared to the underlying truth. There are imperfections in the results, but the method is seen to readily outperform fits without the energy-dependent parameters. In particular, the estimates of uncertainty and correlations are much better. Hence, the method seems to offer a promising route for future treatment of model defects, both for nuclear data and elsewhere.