2009/11/30 by Maria Eugenia Cabrera, J. Alberto Casas, Alberto Casas +1 · 1 citation
Computer Science · Physics and Astronomy · Social Sciences · #Bayesian probability #Computational Physics and Python Applications #Dark matter #Higgs boson #International Science and Diplomacy #Large Hadron Collider #Limit (mathematics) #Logarithm #Minimal Supersymmetric Standard Model #Observable #Parameter space #Particle physics theoretical and experimental studies #hep-ph
paper · pdf · doi:10.1007/jhep05(2010)043
published as JHEP 1005:043,2010 · 42 pages: added figures and references
openalex publication_date 2010/05/01 · arxiv created 2010/12/10 · arxiv updated 2010/12/13 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
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 MZ 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 μ>0) is well inside the LHC scope. We also perform a Bayesian comparison of the positive- and negative-μ possibilities.