Cancelling out systematic uncertainties

We present a method to minimize, or even cancel out, the nuisance parameters affecting a measurement. Our approach is general and can be applied to any experiment or observation. We compare it with the bayesian technique used to deal with nuisance parameters: marginalization, and show how the method compares and improves by avoiding biases. We illustrate the method with several examples taken from the astrophysics and cosmology world: baryonic acoustic oscillations, cosmic clocks, Supernova Type Ia luminosity distance, neutrino oscillations and dark matter detection. By applying the method we recover some known results but also find some interesting new ones. For baryonic acoustic oscillati…