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.

Abstract: Supersymmetry has often been invoked as the new physics that might reconcile the experimental muon magnetic anomaly, a(mu), with the theoretical prediction (basing the computation of the hadronic contribution on e(+)e(-) data). However, in the context of the constrained minimal supersymmetric standard model (CMSSM), the required supersymmetric contributions (which grow with decreasing supersymmetric masses) are in potential tension with a possibly large Higgs mass (which requires large stop masses). In the limit of very large m(h) supersymmetry gets decoupled, and the CMSSM must show the same discrepancy as the standard model with a(mu). But it is much less clear for which size of m(h) does the tension start to be unbearable. In this paper, we quantify this tension with the help of Bayesian techniques. We find that for m(h) >= 125 GeV the maximum level of discrepancy given the current data (similar to 3.2 sigma) is already achieved. Requiring less than 3 sigma discrepancy, implies m(h) less than or similar to 120 GeV. For a larger Higgs mass we should give up either the CMSSM model or the computation of a(mu) based on e(+)e(-); or accept living with such an inconsistency.

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.