Rare B and strange decays

Several deviations from the Standard Model predictions have been recently observed in the decays mediated by $b \rightarrow s l^+ l^-$ transitions. These could be pointing towards new vector-current contributions or could be explained by underestimated charm-loop effects. New results from an LHCb Run 1 $B^+\rightarrow K^+ \mu^+ \mu^-$ analysis that includes the decays via intermediate charm- resonances are discussed. Also, new results from the fully leptonic rare modes searches are presented. This includes the latest Run 1 and Run 2 $B^0_{(s)} \rightarrow \mu^+ \mu^-$ analysis from LHCb where the $B^0_{s} \rightarrow \mu^+ \mu^-$ candidates are used to determine the effective lifetime of the $B^0_{s} \rightarrow \mu^+ \mu^-$ decays - a pioneering result that in the future will solve the current ambiguity in the (pseudo-)scalar contributions

Performance of the LHCb High Level Trigger in 2012

The trigger system of the LHCb experiment is discussed in this paper and its performance is evaluated on a dataset recorded during the 2012 run of the LHC. The main purpose of the LHCb trigger system is to separate heavy flavour signals from the light quark background. The trigger reduces the roughly 11MHz of bunch-bunch crossings with inelastic collisions to a rate of 5kHz, which is written to storage.Comment: Proceedings for the 20th International Conference on Computing in High Energy and Nuclear Physics (CHEP

Stability and Sensitivity Measures for Solutions in Complex, Intelligent, Adaptive and Autonomous Systems

Simulation has become a pivotal tool for the design, analysis, and control of complex, intelligent, adaptive and autonomous systems and its components. However, due to the nature of these systems, traditional evaluation practices are often not sufficient. As the components follow adaptive rules, the cumulative events often exploit bifurcation enabling events, leading to clusters of solutions that do not follow the usual rules for standard distributed events. When using simulation for design, analysis, and control of such systems, the evaluation needs to be richer, applying bifurcation and cluster analysis to understand the distribution, applying factor analysis to understand the important factors for the necessary sensitivity analysis, and take not only point estimates for the solution and the sensitivity analysis into account, but contact a statistical stability analysis. The full exploitation of gaining numerical insights into the dynamic behavior and its deviations is needed. This paper introduces the pitfalls and recommends applicable methods and heuristics

Using Simulation Systems for Decision Support

This chapter describes the use of simulation systems for decision support in support of real operations, which is the most challenging application domain in the discipline of modeling and simulation. To this end, the systems must be integrated as services into the operational infrastructure. To support discovery, selection, and composition of services, they need to be annotated regarding technical, syntactic, semantic, pragmatic, dynamic, and conceptual categories. The systems themselves must be complete and validated. The data must be obtainable, preferably via common protocols shared with the operational infrastructure. Agents and automated forces must produce situation adequate behavior. If these requirements for simulation systems and their annotations are fulfilled, decision support simulation can contribute significantly to the situational awareness up to cognitive levels of the decision maker

Simulation-Based Optimization: Implications of Complex Adaptive Systems and Deep Uncertainty

Within the modeling and simulation community, simulation-based optimization has often been successfully used to improve productivity and business processes. However, the increased importance of using simulation to better understand complex adaptive systems and address operations research questions characterized by deep uncertainty, such as the need for policy support within socio-technical systems, leads to the necessity to revisit the way simulation can be applied in this new area. Similar observations can be made for complex adaptive systems that constantly change their behavior, which is reflected in a continually changing solution space. Deep uncertainty describes problems with inadequate or incomplete information about the system and the outcomes of interest. Complex adaptive systems under deep uncertainty must integrate the search for robust solutions by conducting exploratory modeling and analysis. This article visits both domains, shows what the new challenges are, and provides a framework to apply methods from operational research and complexity science to address them. With such extensions, simulation-based approaches will be able to support these new areas as well, although optimal solutions may no longer be obtainable. Instead, robust and sufficient solutions will become the objective of optimization processes