Testing hadronic-model predictions of depth of maximum of air-shower profiles and ground-particle signals using hybrid data of the Pierre Auger Observatory

We test the predictions of hadronic interaction models regarding the depth of maximum of air-shower profiles, Xmax , and ground-particle signals in water-Cherenkov detectors at 1000 m from the shower core, Sð1000Þ, using the data from the fluorescence and surface detectors of the Pierre Auger Observatory. The test consists of fitting the measured two-dimensional (Sð1000Þ, Xmax ) distributions using templates for simulated air showers produced with hadronic interaction models E pos-LHC , QGSJ et-II -04, SIBYLL 2.3d and leaving the scales of predicted Xmax and the signals from hadronic component at ground as free-fit parameters. The method relies on the assumption that the mass composition remains the same at all zenith angles, while the longitudinal shower development and attenuation of ground signal depend on the mass composition in a correlated way. The analysis was applied to 2239 events detected by both the fluorescence and surface detectors of the Pierre Auger Observatory with energies between 10 18.5 eV to 10 19.0 eV and zenith angles below 60°. We found, that within the assumptions of the method, the best description of the data is achieved if the predictions of the hadronic interaction models are shifted to deeper Xmax values and larger hadronic signals at all zenith angles. Given the magnitude of the shifts and the data sample size, the statistical significance of the improvement of data description using the modifications considered in the paper is larger than 5σ even for any linear combination of experimental systematic uncertainties

Identification of beauty and charm quark jets at LHCb

Identification of jets originating from beauty and charm quarks is important for measuring Standard Model processes and for searching for new physics. The performance of algorithms developed to select b- and c-quark jets is measured using data recorded by LHCb from proton-proton collisions at √s = 7 TeV in 2011 and at √s = 8 TeV in 2012. The efficiency for identifying a b(c) jet is about 65%(25%) with a probability for misidentifying a light-parton jet of 0.3% for jets with transverse momentum p(T) > 20GeV and pseudorapidity 2.2 < η < 4.2. The dependence of the performance on the p(T) and η of the jet is also measured

Identification of beauty and charm quark jets at LHCb

Identification of jets originating from beauty and charm quarks is important for measuring Standard Model processes and for searching for new physics. The performance of algorithms developed to select b- and c-quark jets is measured using data recorded by LHCb from proton-proton collisions at root s = 7TeV in 2011 and at root s = 8TeV in 2012. The efficiency for identifying a b (c) jet is about 65%(25%) with a probability for misidentifying a light-parton jet of 0.3% for jets with transverse momentum pT > 20GeV and pseudorapidity 2 : 2 < eta < 4.2. The dependence of the performance on the pT and eta of the jet is also measured

Observation of the B0→ρ0ρ0 decay from an amplitude analysis of B0→(π+π−)(π+π−) decays

Proton-proton collision data recorded in 2011 and 2012 by the LHCb experiment, corresponding to an integrated luminosity of 3.0 fb(-1), are analysed to search for the charmless B-0 -> rho(0)rho(0) decay. More than 600 B-0 -> (pi(+)pi(-))(pi(+)pi(-)) signal decays are selected and used to perform an amplitude, analysis, under the assumption of no CP violation in the decay, from which the B-0 -> rho(0)rho(0) decay is observed for the first time with 7.1 standard deviations significance. The fraction of B-0 -> rho(0)rho(0) decays yielding a longitudinally polarised final state is measured to be f(L) = 0.745(-0.058)(+0.048)(stat) +/- 0.034(syst). The B-0 -> rho(0)rho(0) branching fraction, using the B-0 -> phi K*(892)(0) decay as reference, is also reported as B(B-0 -> rho(0)rho(0)) = (0.94 +/- 0.17(stat) +/- 0.09(syst) +/- 0.06(BF)) x 10(-6). (C) 2015 CERN for the benefit of the LHCb Collaboration. Published by Elsevier B.V. This is an open access article under the CC BY licens

Observation of the decay (B)over-bar(s)(0) -> psi(2S)K+pi(-)

The decay (B) over bar (0)(s) -> psi(2S)K+pi(-) is observed using a data set corresponding to an integrated luminosity of 3.0 fb(-1) collected by the LHCb experiment in pp collisions at centre-of-mass energies of 7 and 8 TeV. The branching fraction relative to the B-0 -> psi(2S)K+pi(-) decay mode is measured to be B((B) over bar (0)(s) -> psi(2S)K+pi(-))/B(B-0 -> psi(2S)K+pi(-)) = 5.38 +/- 0.36 (stat) +/- 0.22 (syst) +/- 0.31 (f(s)/f(d)) %, where f(s)/f(d) indicates the uncertainty due to the ratio of probabilities for a b quark to hadronise into a B-s(0) or B-0 meson. Using an amplitude analysis, the fraction of decays proceeding via an intermediate K*(892)(0) meson is measured to be 0.645 +/- 0.049 (stat) +/- 0.049 (syst) and its longitudinal polarisation fraction is 0.524 +/- 0.056 (stat) +/- 0.029 (syst). The relative branching fraction for this component is determined to be B((B) over bar (0)(s) -> psi(2S)K*(892)(0))/B(B-0 -> psi(2S)K*(892)(0)) = 5.58 +/- 0.57 (stat) +/- 0.40 (syst) +/- 0.32 (f(s)/f(d)) %. In addition, the mass splitting between the B-s(0) and B-0 mesons is measured as M(B-s(0)) - M(B-0) = 87.45 +/- 0.44 (stat) +/- 0.09 (syst) MeV/c(2). (C) 2015 CERN for the benefit of the LHCb Collaboration. Published by Elsevier B.V. This is an open access article under the CC BY licens

B flavour tagging using charm decays at the LHCb experiment

An algorithm is described for tagging the flavour content at production of neutral B mesons in the LHCb experiment. The algorithm exploits the correlation of the flavour of a B meson with the charge of a reconstructed secondary charm hadron from the decay of the other b hadron produced in the proton-proton collision. Charm hadron candidates are identified in a number of fully or partially reconstructed Cabibbo-favoured decay modes. The algorithm is calibrated on the self-tagged decay modes B+ -> J/psi K+ and B-0 -> J/psi K*(0) using 3.0fb(-1) of data collected by the LHCb experiment at pp centre-of-mass energies of 7TeV and 8TeV. Its tagging power on these samples of B -> J/psi X decays is (0.30 +/- 0.01 +/- 0.01) %

Enhanced interpretation of newborn screening results without analyte cutoff values

A collaboration among 157 newborn screening programs in 47 countries has lead to the creation of a database of 705,333 discrete analyte concentrations from 11,462 cases affected with 57 metabolic disorders, and from 631 heterozygotes for 12 conditions. This evidence was first applied to establish disease ranges for amino acids and acylcarnitines, and clinically validate 114 cutoff target ranges. Objective: To improve quality and performance with an evidence-based approach, multivariate pattern recognition software has been developed to aid in the interpretation of complex analyte profiles. The software generates tools that convert multiple clinically significant results into a single numerical score based on overlap between normal and disease ranges, penetration within the disease range, differences between specific conditions, and weighted correction factors. Design: Eighty-five on-line tools target either a single condition or the differential diagnosis between two or more conditions. Scores are expressed as a numerical value and as the percentile rank among all cases with the condition chosen as primary target, and are compared to interpretation guidelines. Tools are updated automatically after any new data submission (2009- 2011: 5.2 new cases added per day on average). Main outcome measures: Retrospective evaluation of past cases suggest that these tools could have avoided at least half of 277 false positive outcomes caused by carrier status for fatty acid oxidation disorders, and could have prevented 88% of false negative events caused by cutoff 7 values set inappropriately. In Minnesota, their prospective application has been a major contributing factor to the sustained achievement of a false positive rate below 0.1% and a positive predictive value above 60%. Conclusions: Application of this computational approach to raw data could make cutoff values for single analytes effectively obsolete. This paradigm is not limited to newborn screening and is applicable to the interpretation of diverse multi-analyte profiles utilized in laboratory medicine. Abstract wor