Model Testing in Particle Physics

One of the principle goals in modern particle physics research is to discover new physics processes which cannot be described by the Standard Model (SM). Many theoretical models provide predictions for observables at the LHC and other particle physics experiments which deviate from that expected in the SM. In order to test the validity of a given model, a rigorous statistical analysis is performed in order to determine the level of consistency of the data with that particular model. A common problem is that often the parameters of the model being tested are a priori unknown. In these cases some extra uncertainty must be assigned to the parameters of interest due to lack of exact knowledge of the model being used. A new method is presented here which demonstrates how one can assign appropriate systematic uncertainties from model choice assumptions.