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 μ> 0) is well inside the LHC scope. We also perform a Bayesian comparison of the positive- and negative-mu possibilities.

Abstract: We present global fits of an effective field theory description of real, and complex scalar dark matter candidates. We simultaneously take into account all possible dimension 6 operators consisting of dark matter bilinears and gauge invariant combinations of quark and gluon fields. We derive constraints on the free model parameters for both the real (five parameters) and complex (seven) scalar dark matter models obtained by combining Planck data on the cosmic microwave background, direct detection limits from LUX, and indirect detection limits from the Fermi Large Area Telescope. We find that for real scalars indirect dark matter searches disfavour a dark matter particle mass below 100 GeV. For the complex scalar dark matter particle current data have a limited impact due to the presence of operators that lead to p-wave annihilation, and also do not contribute to the spin-independent scattering cross-section. Although current data are not informative enough to strongly constrain the theory parameter space, we demonstrate the power of our formalism to reconstruct the theoretical parameters compatible with an actual dark matter detection, by assuming that the excess of gamma rays observed by the Fermi Large Area Telescope towards the Galactic centre is entirely due to dark matter annihilations. Please note that the excess can very well be due to astrophysical sources such as millisecond pulsars. We find that scalar dark matter interacting via effective field theory operators can in principle explain the Galactic centre excess, but that such interpretation is in strong tension with the non-detection of gamma rays from dwarf galaxies in the real scalar case. In the complex scalar case there is enough freedom to relieve the tension.

Abstract: Research in many areas of modern physics such as, e. g., indirect searches for dark matter and particle acceleration in supernova remnant shocks rely heavily on studies of cosmic rays (CRs) and associated diffuse emissions (radio, microwave, X-rays, gamma-rays). While very detailed numerical models of CR propagation exist, a quantitative statistical analysis of such models has been so far hampered by the large computational effort that those models require. Although statistical analyses have been carried out before using semi-analytical models (where the computation is much faster), the evaluation of the results obtained from such models is difficult, as they necessarily suffer from many simplifying assumptions. The main objective of this paper is to present a working method for a full Bayesian parameter estimation for a numerical CR propagation model. For this study, we use the GALPROP code, the most advanced of its kind, which uses astrophysical information, and nuclear and particle data as inputs to self-consistently predict CRs, gamma-rays, synchrotron, and other observables. We demonstrate that a full Bayesian analysis is possible using nested sampling and Markov Chain Monte Carlo methods (implemented in the SuperBayeS code) despite the heavy computational demands of a numerical propagation code. The best-fit values of parameters found in this analysis are in agreement with previous, significantly simpler, studies also based on GALPROP.