Optimizing the cut and count method in phenomenological studies

Abstract

We introduce an optimization technique to discriminate signal and background in any phenomenological study based on the cut and count-based method. The core ideas behind this algorithm is the introduction of a ranking scheme that can quantitatively assess the relative importance of various observables involved in a new physics process, and a more methodical way of choosing what cuts to impose. The technique is an iterative process that works with the help of the MadAnalysis5 interface. Working in the context of a simple BSM scenario with a heavy Higgs boson that decays to four leptons via ZZ, we demonstrate how automating the cut and count process in this specific way results in an enhanced discovery potential compared with the more traditional way of imposing cuts.