|
|
Binosi, D., & Papavassiliou, J. (2011). Gauge invariant Ansatz for a special three-gluon vertex. J. High Energy Phys., 03(3), 121–23pp.
Abstract: We construct a general Ansatz for the three-particle vertex describing the interaction of one background and two quantum gluons, by simultaneously solving the Ward and Slavnov-Taylor identities it satisfies. This vertex is known to be essential for the gauge-invariant truncation of the Schwinger-Dyson equations of QCD, based on the pinch technique and the background field method. A key step in this construction is the formal derivation of a set of crucial constraints (shown to be valid to all orders), relating the various form factors of the ghost Green's functions appearing in the aforementioned Slavnov-Taylor identity. When inserted into the Schwinger-Dyson equation for the gluon propagator, this vertex gives rise to a number of highly non-trivial cancellations, which are absolutely indispensable for the self-consistency of the entire approach.
|
|
|
|
Barenboim, G., & Rasero, J. (2011). Baryogenesis from a right-handed neutrino condensate. J. High Energy Phys., 03(3), 097–15pp.
Abstract: We show that the baryon asymmetry of the Universe can be generated by a strongly coupled right handed neutrino condensate which also drives inflation. The resulting model has only a small number of parameters, which completely determine not only the baryon asymmetry of the Universe and the mass of the right handed neutrino but also the inflationary phase. This feature allows us to make predictions that will be tested by current and planned experiments. As compared to the usual approach our dynamical framework is both economical and predictive.
|
|
|
|
Choi, K. Y., Lopez-Fogliani, D. E., Muñoz, C., & Ruiz de Austri, R. (2010). Gamma-ray detection from gravitino dark matter decay in the μnu SSM. J. Cosmol. Astropart. Phys., 03(3), 028–14pp.
Abstract: The μnu SSM provides a solution to the mu-problem of the MSSM and explains the origin of neutrino masses by simply using right-handed neutrino superfields. Given that R-parity is broken in this model, the gravitino is a natural candidate for dark matter since its lifetime becomes much longer than the age of the Universe. We consider the implications of gravitino dark matter in the μnu SSM, analyzing in particular the prospects for detecting gamma rays from decaying gravitinos. If the gravitino explains the whole dark matter component, a gravitino mass larger than 20 GeV is disfavored by the isotropic diffuse photon background measurements. On the other hand, a gravitino with a mass range between 0.1 – 20 GeV gives rise to a signal that might be observed by the FERMI satellite. In this way important regions of the parameter space of the μnu SSM can be checked.
|
|
|
|
Barenboim, G., Fernandez-Martinez, E., Mena, O., & Verde, L. (2010). The dark side of curvature. J. Cosmol. Astropart. Phys., 03(3), 008–17pp.
Abstract: Geometrical tests such as the combination of the Hubble parameter H(z) and the angular diameter distance d(A)(z) can, in principle, break the degeneracy between the dark energy equation of state parameter w(z), and the spatial curvature Omega(k) in a direct, model-independent way. In practice, constraints on these quantities achievable from realistic experiments, such as those to be provided by Baryon Acoustic Oscillation (BAO) galaxy surveys in combination with CMB data, can resolve the cosmic confusion between the dark energy equation of state parameter and curvature only statistically and within a parameterized model for w(z). Combining measurements of both H(z) and d(A)(z) up to sufficiently high redshifts z similar to 2 and employing a parameterization of the redshift evolution of the dark energy equation of state are the keys to resolve the w(z) – Omega(k) degeneracy.
|
|
|
|
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.
|
|
