Trotta, R., Johannesson, G., Moskalenko, I. V., Porter, T. A., Ruiz de Austri, R., & Strong, A. W. (2011). Constraints on Cosmic-Ray Propagation Models from a Global Bayesian Analysis. Astrophys. J., 729(2), 106–16pp.
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.
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Bertone, G., Kong, K. C., Ruiz de Austri, R., & Trotta, R. (2011). Global fits of the minimal universal extra dimensions scenario. Phys. Rev. D, 83(3), 036008–15pp.
Abstract: In theories with universal extra dimensions (UED), the gamma(1) particle, first excited state of the hypercharge gauge boson, provides an excellent dark matter (DM) candidate. Here, we use a modified version of the SUPERBAYES code to perform a Bayesian analysis of the minimal UED scenario, in order to assess its detectability at accelerators and with DM experiments. We derive, in particular, the most probable range of mass and scattering cross sections off nucleons, keeping into account cosmological and electroweak precision constraints. The consequences for the detectability of the gamma(1) with direct and indirect experiments are dramatic. The spin-independent cross section probability distribution peaks at similar to 10(-11) pb, i.e. below the sensitivity of ton-scale experiments. The spin-dependent cross section drives the predicted neutrino flux from the center of the Sun below the reach of present and upcoming experiments. The only strategy that remains open appears to be direct detection with ton-scale experiments sensitive to spin-dependent cross sections. On the other hand, the LHC with 1 fb(-1) of data should be able to probe the current best-fit UED parameters.
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Roszkowski, L., Ruiz de Austri, R., Trotta, R., Tsai, Y. L. S., & Varley, T. A. (2011). Global fits of the nonuniversal Higgs model. Phys. Rev. D, 83(1), 015014–19pp.
Abstract: We carry out global fits to the nonuniversal Higgs Model (NUHM), applying all relevant present-day constraints. We present global probability maps for the NUHM parameters and observables (including collider signatures, direct, and indirect detection quantities), both in terms of posterior probabilities and in terms of profile likelihood maps. We identify regions of the parameter space where the neutralino dark matter in the model is either binolike, or else higgsinolike with mass close to 1 TeV and a spin-independent scattering cross section similar to 10(-9)-10(-8) pb. We trace the occurrence of the higgsinolike region to be a consequence of a mild focusing effect in the running of one of the Higgs masses, the existence of which in the NUHM we identify in our analysis. Although the usual binolike neutralino is more prominent, higgsinolike dark matter cannot be excluded, however its significance strongly depends on the prior and statistics used to assess it. We note that, despite experimental constraints often favoring different regions of parameter space to the constrained minimal supersymmetric standard model, most observational consequences appear fairly similar, which will make it challenging to distinguish the two models experimentally.
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Pato, M., Baudis, L., Bertone, G., Ruiz de Austri, R., Strigari, L. E., & Trotta, R. (2011). Complementarity of dark matter direct detection targets. Phys. Rev. D, 83(8), 083505–11pp.
Abstract: We investigate the reconstruction capabilities of the dark matter mass and spin-independent cross section from future ton-scale direct detection experiments using germanium, xenon, or argon as targets. Adopting realistic values for the exposure, energy threshold, and resolution of dark matter experiments which will come online within 5 to 10 years, the degree of complementarity between different targets is quantified. We investigate how the uncertainty in the astrophysical parameters controlling the local dark matter density and velocity distribution affects the reconstruction. For a 50 GeV WIMP, astrophysical uncertainties degrade the accuracy in the mass reconstruction by up to a factor of similar to 4 for xenon and germanium, compared to the case when astrophysical quantities are fixed. However, the combination of argon, germanium, and xenon data increases the constraining power by a factor of similar to 2 compared to germanium or xenon alone. We show that future direct detection experiments can achieve self-calibration of some astrophysical parameters, and they will be able to constrain the WIMP mass with only very weak external astrophysical constraints.
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Bridges, M., Cranmer, K., Feroz, F., Hobson, M., Ruiz de Austri, R., & Trotta, R. (2011). A coverage study of the CMSSM based on ATLAS sensitivity using fast neural networks techniques. J. High Energy Phys., 03(3), 012–23pp.
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.
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