Abstract: We discuss aidnogenesis,(1) i.e. the generation of a dark matter asymmetry, via new sphaleron processes associated to an extra non-abelian gauge symmetry common to both the visible and the dark sectors. Such a theory can naturally produce an abundance of asymmetric dark matter which is of the same size as the lepton and baryon asymmetries, as suggested by the similar sizes of the observed baryonic and dark matter energy content, and provide a definite prediction for the mass of the dark matter particle. We discuss in detail a minimal realization in which the Standard Model is only extended by dark matter fermions which form “dark baryons” through an SU(3) interaction, and a (broken) horizontal symmetry that induces the new sphalerons. The dark matter mass is predicted to be similar to 6GeV, close to the region favored by DAMA and CoGeNT. Furthermore, a remnant of the horizontal symmetry should be broken at a lower scale and can also explain the Tevatron dimuon anomaly.

Abstract: We assess the coverage properties of confidence and credible intervals on the CMSSM parameter space inferred from a Bayesian posterior and the profile likelihood based on an ATLAS sensitivity study. In order to make those calculations feasible, we introduce a new method based on neural networks to approximate the mapping between CMSSM parameters and weak-scale particle masses. Our method reduces the computational effort needed to sample the CMSSM parameter space by a factor of similar to 10(4) with respect to conventional techniques. We find that both the Bayesian posterior and the profile likelihood intervals can significantly over-cover and identify the origin of this effect to physical boundaries in the parameter space. Finally, we point out that the effects intrinsic to the statistical procedure are conflated with simplifications to the likelihood functions from the experiments themselves.