Sifting data in the real world

In the real world, experimental data are rarely, if ever, distributed as a normal (Gaussian) distribution. As an example, a large set of data—such as the cross sections for particle scattering as a function of energy contained in the archives of the Particle Data Group[1]—is a compendium of all published data, and hence, unscreened. Inspection of similar data sets quickly shows that, for many reasons, these data sets have many outliers—points well beyond what is expected from a normal distribution—thus ruling out the use of conventional χ techniques. This note suggests an adaptive algorithm that allows a phenomenologist to apply to the data sample a sieve whose mesh is coarse enough to let the background fall through, but fine enough to retain the preponderance of the signal, thus sifting the data. A prescription is given for finding a robust estimate of the best-fit model parameters in the presence of a noisy background, together with a robust estimate of the model parameter errors, as well as a determination of the goodness-of-fit of the data to the theoretical hypothesis. Extensive computer simulations are carried out to test the algorithm for both its accuracy and stability under varying background conditions.