The impact of a 126 GeV Higgs on the neutralino mass

We highlight the differences of the dark matter sector between the constrained minimal supersymmetric SM (CMSSM) and the next-to-minimal supersymmetric SM (NMSSM) including the 126 GeV Higgs boson using GUT scale parameters. In the dark matter sector the two models are quite orthogonal: in the CMSSM the WIMP is largely a bino and requires large masses from the LHC constraints. In the NMSSM the WIMP has a large singlino component and is therefore independent of the LHC SUSY mass limits. The light NMSSM neutralino mass range is of interest for the hints concerning light WIMPs in the Fermi data. Such low mass WIMPs cannot be explained in the CMSSM. Furthermore, prospects for discovery of XENON1T and LHC at 14 TeV are given.Comment: 18 pages, 5 figures, this version is accepted by PLB after modifications including additional figure

Distributed multilevel optimization for complex structures

Optimization problems concerning complex structures with many design variables may entail an unacceptable computational cost. This problem can be reduced considerably with a multilevel approach: A structure consisting of several components is optimized as a whole (global) as well as on the component level. In this paper, an optimization method is discussed with applications in the assessment of the impact of new design considerations in the development of a structure. A strategy based on fully stressed design is applied for optimization problems in linear statics. A global model is used to calculate the interactions (e.g., loads) for each of the components. These components are then optimized using the prescribed interactions, followed by a new global calculation to update the interactions. Mixed discrete and continuous design variables as well as different design configurations are possible. An application of this strategy is presented in the form of the full optimization of a vertical tail plane center box of a generic large passenger aircraft. In linear dynamics, the parametrization of the component interactions is problematic due to the frequency dependence. Hence, a modified method is presented in which the speed of component mode synthesis is used to avoid this parametrization. This method is applied to a simple test case that originates from noise control. \u

Can we discover a light singlet-like NMSSM Higgs boson at the LHC?

In the next-to minimal supersymmetric standard model (NMSSM) one additional singlet-like Higgs boson with small couplings to standard model (SM) particles is introduced. Although the mass can be well below the discovered 125 GeV Higgs boson mass its small couplings may make a discovery at the LHC difficult. We use a novel scanning technique to efficiently scan the whole parameter space and determine the range of cross sections and branching ratios for the light singlet-like Higgs boson below 125 GeV. This allows to determine the perspectives for the future discovery potential at the LHC. Specific LHC benchmark points are selected representing the salient NMSSM features.Comment: 22 pages, 5 figures, this version is accepted by PLB after minor modification

A Hybrid Design Optimization Method using Enriched Craig-Bampton Approach

A hybrid design optimization method is presented which combines a number of techniques such as Component Mode Synthesis (CMS), Design of Computer Experiments and Neural Networks for surrogate modeling with Genetic Algorithms and Sequential Quadratic Programming for optimization. In the method, the FE analysis is decomposed and reduced by a well-known CMS technique called the Craig-Bampton method. Since the optimization method requires CMS calculations of the updated model at each of its iterations due to the changes in the design variables, one can either reuse the reduction basis of the initial components or compute new reduction basis for the condensation of the system matrices. The first option usually leads to inaccurate results and the last one increases the omputation time. In the method, instead of using one of these options, the Enriched Craig-Bampton method, proposed by Masson et al., is employed for efficient optimization. New basis for the modified components are generated by extending the corresponding initial reduction basis with a set of static residual vectors which are calculated using prior knowledge of the initial component designs. Thus, time consuming complete component analyzes are prevented. A theoretical test problem is used for the demonstration of the method

On a novel approach for optimizing composite materials panel using surrogate models

This paper describes an optimization procedure to design thermoplastic composite panels under axial compressive load conditions. Minimum weight is the goal. The panel design is subject to buckling constraints. The presence of the bending-twisting coupling and of particular boundary conditions does not allow an analytical solution for the critical buckling load. Surrogate models are used to approximate the buckling response of the plate in a fast and reliable way. Therefore, two surrogate models are compared to study their effectiveness in composite optimization. The first one is a linear approximation based on the buckling constitutive equation. The second consists in the application of the Kriging surrogate. Constraints given from practical blending rules are also introduced in the optimization. Discrete values of ply thicknesses is a requirement. An ad-hoc discrete optimization strategy is developed, which enables to handle discrete variables