UNCERTAINTY INTERVALS FOR POLARIZED BEAM SCATTERING ASYMMETRY STATISTICS

In many scattering experiments, the quantity of most direct physical i nterest is a measure of the difference between two closely related sca ttering signals, each generated by a Poisson scattering process. This difference is often expressed in terms of an asymmetry statistic, that is, the difference normalized to the sum of the two signals, correcte d for an additive background contribution. Typically, a propagation of errors approach is used to compute confidence intervals for asymmetry . However, these confidence intervals are not reliable in general. In this work, generally accurate confidence intervals for asymmetry are o btained using a parametric bootstrap approach. Based on the observed d ata, data are simulated using a Monte Carlo resampling scheme. The res ampled data sets satisfy a constraint that ensures that background-cor rected count rates are not negative.