Observation of the decay Bc + → J/ψK +K-π+

The decay Bc + → J/ψK+K -π+ is observed for the first time, using proton-proton collisions collected with the LHCb detector corresponding to an integrated luminosity of 3 fb-1. A signal yield of 78 ± 14 decays is reported with a significance of 6.2 standard deviations. The ratio of the branching fraction of Bc + → J/ψK +K-π+ decays to that of Bc + → J/ψπ+ decays is measured to be 0:53 ± 0:10 ± 0:05, where the first uncertainty is statistical and the second is systematic. Copyright CERN

Title:

Observation of the decay Bc + → J/ψK +K-π+
Authors: R., Aaij
B., Adeva
M., Adinolfi
Keywords: B physics
Hadron-Hadron scattering
Flavor physics
Branching fraction
Issue Date: 2013
Publisher: H. : ĐHQGHN
Abstract: The decay Bc + → J/ψK+K -π+ is observed for the first time, using proton-proton collisions collected with the LHCb detector corresponding to an integrated luminosity of 3 fb-1. A signal yield of 78 ± 14 decays is reported with a significance of 6.2 standard deviations. The ratio of the branching fraction of Bc + → J/ψK +K-π+ decays to that of Bc + → J/ψπ+ decays is measured to be 0:53 ± 0:10 ± 0:05, where the first uncertainty is statistical and the second is systematic. Copyright CERN
Description: Journal of High Energy Physics 2013(11)
URI: http://repository.vnu.edu.vn/handle/VNU_123/27722
Appears in Collections:Bài báo của ĐHQGHN trong Scopus

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This study tested the use of machine learning techniques for the estimation of above-ground biomass (AGB) of Sonneratia caseolaris in a coastal area of Hai Phong city, Vietnam. We employed a GIS database and multi-layer perceptron neural networks (MLPNN) to build and verify an AGB model, drawing upon data from a survey of 1508 mangrove trees in 18 sampling plots and ALOS-2 PALSAR imagery. We assessed the model’s performance using root-mean-square error, mean absolute error, coefficient of determination (R2), and leave-one-out cross-validation. We also compared the model’s usability with four machine learning techniques: support vector regression, radial basis function neural networks, Gaussian process, and random forest. The MLPNN model performed well and outperformed the machine learning techniques. The MLPNN model-estimated AGB ranged between 2.78 and 298.95 Mg ha−1 (average = 55.8 Mg ha−1); below-ground biomass ranged between 4.06 and 436.47 Mg ha−1 (average = 81.47 M...
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