LHCb

Abstract:

The calibration and performance of the opposite-side flavour tagging algorithms used for the measurements of time-dependent asymmetries at the LHCb experiment are described. The algorithms have been developed using simulated events and optimized and calibrated with B⁺→J/ψK⁺, B⁰→J/ψK^*0 and B⁰→D^*-μ⁺νμ decay modes with 0. 37 fb^-1 of data collected in pp collisions at s = 7 TeV during the 2011 physics run. The opposite-side tagging power is determined in the B⁺→J/ψK⁺ channel to be (2. 10±0. 08±0. 24) %, where the first uncertainty is statistical and the second is systematic. © 2012 CERN for the benefit of the LHCb collaboration.

Abstract:

The first measurements of the coherence factor RKS0Kπ and the average strong-phase difference δDKS0Kπ in D0→KS0K ∓π± decays are reported. These parameters can be used to improve the determination of the unitary triangle angle γ in B-→D∼K- decays, where D∼ is either a D0 or a D̄0 meson decaying to the same final state, and also in studies of charm mixing. The measurements of the coherence factor and strong-phase difference are made using quantum-correlated, fully reconstructed D0D̄0 pairs produced in e+e- collisions at the ψ(3770) resonance. The measured values are RKS0Kπ=0. 73±0.08 and δDKS0Kπ=(8.3±15.2)° for an unrestricted kinematic region and RK*K=1.00±0.16 and δDK *K=(26.5±15.8)° for a region where the combined KS0π± invariant mass is within 100MeV/c2 of the K *(892)± mass. These results indicate a significant level of coherence in the decay. In addition, isobar models are presented for the two decays, which show the dominance of the K *(892)± resonance. The branching ratio B(D0→KS0K+π-)/B(D0→KS0K -π+) is determined to be 0.592±0.044(stat) ±0.018(syst), which is more precise than previous measurements. © 2012 American Physical Society.

Abstract:

The use of Global Positioning Systems (GPS) and Geographical Information Systems (GIS) in disease surveys and reporting is becoming increasingly routine, enabling a better understanding of spatial epidemiology and the improvement of surveillance and control strategies. In turn, the greater availability of spatially referenced epidemiological data is driving the rapid expansion of disease mapping and spatial modeling methods, which are becoming increasingly detailed and sophisticated, with rigorous handling of uncertainties. This expansion has, however, not been matched by advancements in the development of spatial datasets of human population distribution that accompany disease maps or spatial models.Where risks are heterogeneous across population groups or space or dependent on transmission between individuals, spatial data on human population distributions and demographic structures are required to estimate infectious disease risks, burdens, and dynamics. The disease impact in terms of morbidity, mortality, and speed of spread varies substantially with demographic profiles, so that identifying the most exposed or affected populations becomes a key aspect of planning and targeting interventions. Subnational breakdowns of population counts by age and sex are routinely collected during national censuses and maintained in finer detail within microcensus data. Moreover, demographic and health surveys continue to collect representative and contemporary samples from clusters of communities in low-income countries where census data may be less detailed and not collected regularly. Together, these freely available datasets form a rich resource for quantifying and understanding the spatial variations in the sizes and distributions of those most at risk of disease in low income regions, yet at present, they remain unconnected data scattered across national statistical offices and websites.In this paper we discuss the deficiencies of existing spatial population datasets and their limitations on epidemiological analyses. We review sources of detailed, contemporary, freely available and relevant spatial demographic data focusing on low income regions where such data are often sparse and highlight the value of incorporating these through a set of examples of their application in disease studies. Moreover, the importance of acknowledging, measuring, and accounting for uncertainty in spatial demographic datasets is outlined. Finally, a strategy for building an open-access database of spatial demographic data that is tailored to epidemiological applications is put forward. © 2012 Tatem et al.; licensee BioMed Central Ltd.