Abstract: We study a supersymmetric version of the seesaw mechanism type III. The model consists of the minimal supersymmetric extension of the standard model particle content plus three copies of 24 superfields. The fermionic part of the SU(2) triplet contained in the 24 is responsible for the type-III seesaw, which is used to explain the observed neutrino masses and mixings. Complete copies of 24 are introduced to maintain gauge coupling unification. These additional states change the beta functions of the gauge couplings above the seesaw scale. Using minimal Supergravity boundary conditions, we calculate the resulting supersymmetric mass spectra at the electroweak scale using full 2-loop renormalization group equations. We show that the resulting spectrum can be quite different compared to the usual minimal Supergravity spectrum. We discuss how this might be used to obtain information on the seesaw scale from mass measurements. Constraints on the model space due to limits on lepton flavour violating decays are discussed. The main constraints come from the bounds on μ-> e gamma but there are also regions where the decay tau -> μgamma gives stronger constraints. We also calculate the regions allowed by the dark matter constraint. For the sake of completeness, we compare our results with those for the supersymmetric seesaw type II and, to some extent, with type I.

Abstract: We present a detailed analytical analysis of the propagation of Bogoliubov phonons on top of Bose-Einstein condensates with spatial and temporal steplike discontinuities in the speed of sound in the hydrodynamic limit. We focus on some features in the correlations patterns, in particular, of density-density correlations. The application to the study of the Hawking signal in the formation of acoustic black hole-like configurations is also discussed.

Abstract: Statistical inference of the fundamental parameters of supersymmetric theories is a challenging and active endeavor. Several sophisticated algorithms have been employed to this end. While Markov-Chain Monte Carlo (MCMC) and nested sampling techniques are geared towards Bayesian inference, they have also been used to estimate frequentist confidence intervals based on the profile likelihood ratio. We investigate the performance and appropriate configuration of MULTINEST, a nested sampling based algorithm, when used for profile likelihood-based analyses both on toy models and on the parameter space of the Constrained MSSM. We find that while the standard configuration previously used in the literarture is appropriate for an accurate reconstruction of the Bayesian posterior, the profile likelihood is poorly approximated. We identify a more appropriate MULTINEST configuration for profile likelihood analyses, which gives an excellent exploration of the profile likelihood (albeit at a larger computational cost), including the identification of the global maximum likelihood value. We conclude that with the appropriate configuration MULTINEST is a suitable tool for profile likelihood studies, indicating previous claims to the contrary are not well founded.