MSSM Forecast for the LHC

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.

42 pages: added figures and references

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• Identification of Dark Matter particles with LHC and direct detection data
• The fine-tuning cost of the likelihood in SUSY models
• The health of SUSY after the Higgs discovery and the XENON100 data
• A Coverage Study of the CMSSM Based on ATLAS Sensitivity Using Fast Neural Networks Techniques
• Efficient reconstruction of CMSSM parameters from LHC data - A case study
• Tuning supersymmetric models at the LHC: A comparative analysis at two-loop level
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• Global fits of the Minimal Universal Extra Dimensions scenario
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• Fine Tuning in the Constrained Exceptional Supersymmetric Standard Model
• Naturalness of the relaxion mechanism
• Naturalness of MSSM dark matter
• Multi-lepton signatures at LHC from sneutrino dark matter
• Is the CNMSSM more credible than the CMSSM?
• Fixing the EW scale in supersymmetric models after the Higgs discovery
• Bayesian analysis and naturalness of (Next-to-)Minimal Supersymmetric Models
• Impact of nucleon matrix element uncertainties on the interpretation of direct and indirect dark matter search results
• Naturalness in low-scale SUSY models and "non-linear" MSSM
• Naturalness made easy: two-loop naturalness bounds on minimal SM extensions
• Bayesian naturalness, simplicity, and testability applied to the $B-L$ MSSM GUT
• Histogram comparison as a powerful tool for the search of new physics at LHC. Application to CMSSM
• Quantifying the tension between the Higgs mass and (g-2)_mu in the CMSSM
• SUSY naturalness without prejudice
• LHC and dark matter phenomenology of the NUGHM
• Revealing mass-degenerate states in Higgs boson signals