1,175,116 research outputs found

    Study of CP violation in Dalitz-plot analyses of B0 --> K+K-KS, B+ --> K+K-K+, and B+ --> KSKSK+

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    We perform amplitude analyses of the decays B0K+KKS0B^0 \to K^+K^-K^0_S, B+K+KK+B^+ \rightarrow K^+K^-K^+, and B+KS0KS0K+B^+ \to K^0_S K^0_S K^+, and measure CP-violating parameters and partial branching fractions. The results are based on a data sample of approximately 470×106470\times 10^6 BBˉB\bar{B} decays, collected with the BABAR detector at the PEP-II asymmetric-energy BB factory at the SLAC National Accelerator Laboratory. For B+K+KK+B^+ \to K^+K^-K^+, we find a direct CP asymmetry in B+ϕ(1020)K+B^+ \to \phi(1020)K^+ of ACP=(12.8±4.4±1.3)A_{CP}= (12.8\pm 4.4 \pm 1.3)%, which differs from zero by 2.8σ2.8 \sigma. For B0K+KKS0B^0 \to K^+K^-K^0_S, we measure the CP-violating phase βeff(ϕ(1020)KS0)=(21±6±2)\beta_{\rm eff} (\phi(1020)K^0_S) = (21\pm 6 \pm 2)^\circ. For B+KS0KS0K+B^+ \to K^0_S K^0_S K^+, we measure an overall direct CP asymmetry of ACP=(45+4±2)A_{CP} = (4 ^{+4}_{-5} \pm 2)%. We also perform an angular-moment analysis of the three channels, and determine that the fX(1500)f_X(1500) state can be described well by the sum of the resonances f0(1500)f_0(1500), f2(1525)f_2^{\prime}(1525), and f0(1710)f_0(1710).Comment: 35 pages, 68 postscript figures. v3 - minor modifications to agree with published versio

    Measurement of the Bottom-Strange Meson Mixing Phase in the Full CDF Data Set

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    We report a measurement of the bottom-strange meson mixing phase \beta_s using the time evolution of B0_s -> J/\psi (->\mu+\mu-) \phi (-> K+ K-) decays in which the quark-flavor content of the bottom-strange meson is identified at production. This measurement uses the full data set of proton-antiproton collisions at sqrt(s)= 1.96 TeV collected by the Collider Detector experiment at the Fermilab Tevatron, corresponding to 9.6 fb-1 of integrated luminosity. We report confidence regions in the two-dimensional space of \beta_s and the B0_s decay-width difference \Delta\Gamma_s, and measure \beta_s in [-\pi/2, -1.51] U [-0.06, 0.30] U [1.26, \pi/2] at the 68% confidence level, in agreement with the standard model expectation. Assuming the standard model value of \beta_s, we also determine \Delta\Gamma_s = 0.068 +- 0.026 (stat) +- 0.009 (syst) ps-1 and the mean B0_s lifetime, \tau_s = 1.528 +- 0.019 (stat) +- 0.009 (syst) ps, which are consistent and competitive with determinations by other experiments.Comment: 8 pages, 2 figures, Phys. Rev. Lett 109, 171802 (2012

    Book review: The politics of language education: Individuals and institutions.

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    This article reviews the book: “The politics of language education: Individuals and institutions”, edited by J.C. Alderson

    Book review : International entrepreneurship in family businesses

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    J.C. Casillas, F.J. Acedo and A.M. Moreno (2007) International Entrepreneurship in Family Businesses, Edward Elgar, Cheltenham, United Kingdom, ISBN 978 1 845428792<br /

    Shrinking a large dataset to identify variables associated with increased risk of Plasmodium falciparum infection in Western Kenya

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    Large datasets are often not amenable to analysis using traditional single-step approaches. Here, our general objective was to apply imputation techniques, principal component analysis (PCA), elastic net and generalized linear models to a large dataset in a systematic approach to extract the most meaningful predictors for a health outcome. We extracted predictors for Plasmodium falciparum infection, from a large covariate dataset while facing limited numbers of observations, using data from the People, Animals, and their Zoonoses (PAZ) project to demonstrate these techniques: data collected from 415 homesteads in western Kenya, contained over 1500 variables that describe the health, environment, and social factors of the humans, livestock, and the homesteads in which they reside. The wide, sparse dataset was simplified to 42 predictors of P. falciparum malaria infection and wealth rankings were produced for all homesteads. The 42 predictors make biological sense and are supported by previous studies. This systematic data-mining approach we used would make many large datasets more manageable and informative for decision-making processes and health policy prioritization

    To: Mrs. J.C. McKinley & J.C., Jr.

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