Histogram comparison as a powerful tool for the search of new physics at LHC. Application to CMSSM

TL;DR

This paper introduces a rigorous histogram comparison method that effectively incorporates uncertainties, enhancing the search for new physics at the LHC, demonstrated through a Bayesian analysis of the CMSSM parameter space.

Contribution

It presents a novel, statistically rigorous approach for comparing experimental and theoretical histograms, improving the detection of new physics signals at the LHC.

Findings

Effective identification of the true CMSSM parameters

High efficiency in distinguishing supersymmetric models

Robustness against statistical and systematic uncertainties

Abstract

We propose a rigorous and effective way to compare experimental and theoretical histograms, incorporating the different sources of statistical and systematic uncertainties. This is a useful tool to extract as much information as possible from the comparison between experimental data with theoretical simulations, optimizing the chances of identifying New Physics at the LHC. We illustrate this by showing how a search in the CMSSM parameter space, using Bayesian techniques, can effectively find the correct values of the CMSSM parameters by comparing histograms of events with multijets + missing transverse momentum displayed in the effective-mass variable. The procedure is in fact very efficient to identify the true supersymmetric model, in the case supersymmetry is really there and accessible to the LHC.