Combining Experiments with Systematic Errors

Abstract

We consider fits to two or more datasets for which results from the sa me experiment share a common systematic uncertainty in addition to their individ ual statistical errors. This is important in extracting the maximum information from a set of similar bu t different experiments (or the same experiment under different conditions) an alysing similar but different datasets, as happens at the LHC and other particle colliders. There are two techniques in use: using the full matrix and using extra paramneters, and we show, for a completely general fit, that for an addit ive uncertainty they are in principle equivalent even though in practice the det ails differ. For a multiplicative error the matrix fit is equivalent to the extra parameter fit if the factor is applied to the data points but not if it is applied to the function: the former leads to biassed estimates and the latter avoids them.