MSSM Forecast for the LHC

TL;DR

This paper forecasts the MSSM parameter space for the LHC using an improved Bayesian analysis, revealing that the low-energy region is statistically favored and that the likelihood of discovery depends on specific experimental constraints.

Contribution

It introduces a comprehensive Bayesian approach to forecast MSSM parameters, accounting for all experimental constraints without ad hoc fine-tuning penalties.

Findings

Low-energy MSSM region is statistically favored.

Results are stable across different priors and parameter limits.

The likelihood of LHC discovery varies with different experimental constraints.

Abstract

We perform a forecast of the MSSM with universal soft terms (CMSSM) for the LHC, based on an improved Bayesian analysis. We do not incorporate ad hoc measures of the fine-tuning to penalize unnatural possibilities: such penalization arises from the Bayesian analysis itself when the experimental value of $M_Z$ is considered. This allows to scan the whole parameter space, allowing arbitrarily large soft terms. Still the low-energy region is statistically favoured (even before including dark matter or g-2 constraints). Contrary to other studies, the results are almost unaffected by changing the upper limits taken for the soft terms. The results are also remarkable stable when using flat or logarithmic priors, a fact that arises from the larger statistical weight of the low-energy region in both cases. Then we incorporate all the important experimental constrains to the analysis, obtaining a map of the probability density of the MSSM parameter space, i.e. the forecast of the MSSM. Since not all the experimental information is equally robust, we perform separate analyses depending on the group of observables used. When only the most robust ones are used, the favoured region of the parameter space contains a significant portion outside the LHC reach. This effect gets reinforced if the Higgs mass is not close to its present experimental limit and persits when dark matter constraints are included. Only when the g-2 constraint (based on $e^+e^-$ data) is considered, the preferred region (for $\mu>0$) is well inside the LHC scope. We also perform a Bayesian comparison of the positive- and negative-$\mu$ possibilities.