Goldstini Can Give the Higgs a Boost

Supersymmetric collider phenomenology depends crucially on whether the lightest observable-sector supersymmetric particle (LOSP) decays, and if so, what the LOSP decay products are. For instance, in SUSY models where the gravitino is lighter than the LOSP, the LOSP decays to its superpartner and a longitudinal gravitino via supercurrent couplings. In this paper, we show that LOSP decays can be substantially modified when there are multiple sectors that break supersymmetry, where in addition to the gravitino there are light uneaten goldstini. As a particularly striking example, a bino-like LOSP can have a near 100% branching fraction to a higgs boson and an uneaten goldstino, even if the LOSP has negligible higgsino fraction. This occurs because the uneaten goldstino is unconstrained by the supercurrent, allowing additional operators to mediate LOSP decay. These operators can be enhanced in the presence of an R symmetry, leading to copious boosted higgs production in SUSY cascade decays.Comment: 30 pages, 12 figures; v2: title change, clarifications added, version to appear in JHE

Twenty Years of SUGRA

A brief review is given of the developments of mSUGRA and its extensions since the formulation of these models in 1982. Future directions and prospects are also discussed.Comment: Invited talk at the International Conference BEYOND-2003, Schloss Ringberg, Germany, June 10-14, 2003; 21 pages, Late

The Cosmological Constant

This is a review of the physics and cosmology of the cosmological constant. Focusing on recent developments, I present a pedagogical overview of cosmology in the presence of a cosmological constant, observational constraints on its magnitude, and the physics of a small (and potentially nonzero) vacuum energy.Comment: 50 pages. Submitted to Living Reviews in Relativity (http://www.livingreviews.org/), December 199

Flow modelling of quasi-Newtonian fluids in two-scale fibrous fabrics: Advanced simulations

Permeability is the fundamental macroscopic material property needed to quantify the flow in a fibrous medium viewed as a porous medium. Composite processing models require the permeability as input data to predict flow patterns and pressure fields. In a previous work, the expressions of macroscopic permeability were derived in a double-scale porosity medium for both Newtonian and generalized Newtonian (shear-thinning) resins. In the linear case, only a microscopic calculation on a representative volume is required, implying as many microscopic calculations as there are representative microscopic volumes in the whole fibrous structure. In the non-linear case, and even when the porous microstructure can be described by a unique representative volume, a large number of microscopic calculations must be carried out as the microscale resin viscosity depends on the macroscopic velocity, which in turn depends on the permeability that results from a microscopic calculation. An original and efficient offline-online procedure was proposed for the solution of non-linear flow problems related to generalized Newtonian fluids in porous media. In this paper, this procedure is generalized to quasi-Newtonian fluids in order to evaluate the effect of extensional viscosity on the resulting upscaled permeability. This work constitutes a natural step forward in the definition of equivalent saturated permeabilities for linear and non-linear fluids

A Manifold Learning Approach for Integrated Computational Materials Engineering

Image-based simulation is becoming an appealing technique to homogenize properties of real microstructures of heterogeneous materials. However fast computation techniques are needed to take decisions in a limited time-scale. Techniques based on standard computational homogenization are seriously compromised by the real-time constraint. The combination of model reduction techniques and high performance computing contribute to alleviate such a constraint but the amount of computation remains excessive in many cases. In this paper we consider an alternative route that makes use of techniques traditionally considered for machine learning purposes in order to extract the manifold in which data and fields can be interpolated accurately and in real-time and with minimum amount of online computation. Locallly Linear Embedding is considered in this work for the real-time thermal homogenization of heterogeneous microstructures