Search for Hidden-Sector Bosons in B-0 -> K*(0)mu(+)mu(-) Decays

A search is presented for hidden-sector bosons, χ, produced in the decay B0 → K ð892Þ0χ, with K ð892Þ0 → Kþπ− and χ → μþμ−. The search is performed using pp-collision data corresponding to 3.0 fb−1 collected with the LHCb detector. No significant signal is observed in the accessible mass range 214 ≤ mðχÞ ≤ 4350 MeV, and upper limits are placed on the branching fraction product B(B0 → K ð892Þ0χ) × Bðχ → μþμ−Þ as a function of the mass and lifetime of the χ boson. These limits are of the order of 10−9 for χ lifetimes less than 100 ps over most of the mðχÞ range, and place the most stringent constraints to date on many theories that predict the existence of additional low-mass bosons.

Title: 

Search for Hidden-Sector Bosons in B-0 -> K*(0)mu(+)mu(-) Decays
Authors: Aaij, R.
Adeva, B.
Adinolfi, M.
Keywords: Hidden-Sector Bosons
additional low-mass bosons
Issue Date: 2015
Publisher: Đại học Quốc gia Hà Nội
Citation: ISIKNOWLEDGE
Abstract: A search is presented for hidden-sector bosons, χ, produced in the decay B0 → K ð892Þ0χ, with K ð892Þ0 → Kþπ− and χ → μþμ−. The search is performed using pp-collision data corresponding to 3.0 fb−1 collected with the LHCb detector. No significant signal is observed in the accessible mass range 214 ≤ mðχÞ ≤ 4350 MeV, and upper limits are placed on the branching fraction product B(B0 → K ð892Þ0χ) × Bðχ → μþμ−Þ as a function of the mass and lifetime of the χ boson. These limits are of the order of 10−9 for χ lifetimes less than 100 ps over most of the mðχÞ range, and place the most stringent constraints to date on many theories that predict the existence of additional low-mass bosons.
Description: PHYSICAL REVIEW LETTERS Volume: 115 Issue: 16 Article Number: 161802 Published: OCT 16 2015 ; TNS05572
URI: http://repository.vnu.edu.vn/handle/VNU_123/27385
Appears in Collections:Bài báo của ĐHQGHN trong Web of Science

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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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