Bayesian Study and Naturalness in MSSM Forecast for the LHC

TL;DR

This paper uses Bayesian methods to forecast the CMSSM parameter space for the LHC, revealing stable preferred regions and the impact of fine-tuning considerations based on current experimental data.

Contribution

It introduces an improved Bayesian analysis for the CMSSM, accounting for theoretical and experimental knowledge, and highlights the naturalness and fine-tuning issues in the model.

Findings

Preferred regions of CMSSM parameter space identified

Fine-tuning penalization arises from Bayesian analysis

Results stable across different priors

Abstract

We perform a forecast of the CMSSM for the LHC based in an improved Bayesian analysis taking into account the present theoretical and experimental wisdom about the model. In this way we obtain a map of the preferred regions of the CMSSM parameter space and show that fine-tuning penalization arises from the Bayesian analysis itself when the experimental value of Mz is considered. The results are remarkable stable when using different priors