Finding viable Models in SUSY Parameter Spaces with Signal Specific Discovery Potential

TL;DR

This paper introduces an efficient framework using an improved MCMC method to identify viable SUSY models with specific experimental signatures, aiding targeted searches at the LHC considering recent experimental constraints.

Contribution

The paper presents a novel MCMC-based approach combined with clustering to find and classify SUSY models with desired phenomenology, optimizing experimental search strategies.

Findings

Probed over 105,000 models to identify viable CMSSM scenarios.

Selected 10 benchmark points with diverse phenomenological features.

Demonstrated the method's application to LHC tau-lepton signatures.

Abstract

Recent results from ATLAS gives a Higgs mass of 125.5 GeV, further constrain already highly constrained supersymmetric models such as pMSSM or CMSSM/mSUGRA. Finding potentially discoverable and non-excluded regions of model parameter space is becoming increasingly difficult. Several groups have invested large effort in studying the consequences of Higgs mass bounds, upper limits on rare $B$-meson decays, and limits on relic dark matter density on constrained models, aiming at predicting superpartner masses, and establishing likelihood of SUSY models compared to that of the Standard Model vis-\'a-vis experimental data. In this paper a framework for efficient search for discoverable, non-excluded regions of different SUSY spaces giving specific experimental signature of interest is presented. The method employs an improved Markov Chain Monte Carlo (MCMC) scheme exploiting an iteratively updated likelihood function to guide search for viable models. Existing experimental and theoretical bounds as well as the LHC discovery potential are taken into account. This includes recent bounds on relic dark matter density, the Higgs sector and rare $B$-mesons decays. A clustering algorithm is applied to classify selected models according to expected phenomenology enabling automated choice of experimental benchmarks and regions to be used for optimizing searches. The aim is to provide experimentalist with a viable tool helping to target experimental signatures to search for, once a class of models of interest is established. As an example a search for viable CMSSM models with $\tau$-lepton signatures observable with the 2012 LHC data set is presented. In the search 105209 unique models were probed. From these, ten reference benchmark points covering different ranges of phenomenological observables at the LHC were selected.