Pseudo-measurement simulations and bootstrap for the experimental cross-section covariances estimation with quality quantification

Abstract

The classical use of a generalized X2-distance to determine the evaluated cross section uncertainty requires the values of the experimental cross sections covariance matrix. The usual propagation error method to estimate the covariances is hardly usable and the lack of data prevents from using the direct empirical estimator. Thus we present an alternative which exploits a regression model of the experimental cross section to generate pseudo-measurements and thereby allows an estimation of experimental covariances. The problem of assessing the quality of the estimate still remains. In our approach, we propose to determine the estimation quality through the means of the bootstrap method. We show on numerical examples that the bootstrap allows to have an order of magnitude of the estimation quality through a matrix norm. All the results are illustrated with a toy model (where all quantities are known) and also with real cross-section data measurements.