Anatomy and Phenomenology of FCNC and CPV Effects in SUSY Theories

We perform an extensive study of FCNC and CP Violation within Supersymmetric (SUSY) theories with particular emphasis put on processes governed by b->s transitions and of their correlations with processes governed by b->d transitions, s->d transitions, $D^0-bar D^0$ oscillations, lepton flavour violating decays, electric dipole moments and (g-2)_mu. We first perform a comprehensive model-independent analysis of Delta F=2 observables and we emphasize the usefulness of the R_b-gamma plane in exhibiting transparently various tensions in the present UT analyses. Secondly, we consider a number of SUSY models: the general MSSM, a flavour blind MSSM, the MSSM with Minimal Flavour Violation as well as SUSY flavour models based on abelian and non-abelian flavour symmetries that show representative flavour structures in the soft SUSY breaking terms. We show how the characteristic patterns of correlations among the considered flavour observables allow to distinguish between these different SUSY scenarios. Of particular importance are the correlations between the CP asymmetry S_psi phi and B_s->mu^+mu^-, between the anomalies in S_phi K_S and S_psi phi, between S_phi K_S and d_e, between S_psi phi and (g-2)_mu and also those involving lepton flavour violating decays. In our analysis, the presence of right-handed currents and of the double Higgs penguin contributions to B_s mixing plays a very important role. We propose a DNA-Flavour Test of NP models including Supersymmetry, the Littlest Higgs model with T-parity and the Randall-Sundrum model with custodial protection, with the aim of showing a tool to distinguish between these NP scenarios, once additional data on flavour changing processes become available.

Optimizing Nuclear Reaction Analysis (NRA) using Bayesian Experimental Design

Nuclear Reaction Analysis with ${}^{3}$He holds the promise to measure Deuterium depth profiles up to large depths. However, the extraction of the depth profile from the measured data is an ill-posed inversion problem. Here we demonstrate how Bayesian Experimental Design can be used to optimize the number of measurements as well as the measurement energies to maximize the information gain. Comparison of the inversion properties of the optimized design with standard settings reveals huge possible gains. Application of the posterior sampling method allows to optimize the experimental settings interactively during the measurement process.