A new algorithm for identifying the flavour of B0s mesons at LHCb

Journal article

A new algorithm for the determination of the initial flavour of $B_s^0$ mesons is presented. The algorithm is based on two neural networks and exploits the $b$ hadron production mechanism at a hadron collider. The first network is trained to select charged kaons produced in association with the $B_s^0$ meson. The second network combines the kaon charges to assign the $B_s^0$ flavour and estimates the probability of a wrong assignment. The algorithm is calibrated using data corresponding to an integrated luminosity of 3 fb$^{-1}$ collected by the LHCb experiment in proton-proton collisions at 7 and 8 TeV centre-of-mass energies. The calibration is performed in two ways: by resolving the $B_s^0$-$\bar{B}_s^0$ flavour oscillations in $B_s^0 \to D_s^- \pi^+$ decays, and by analysing flavour-specific $B_{s 2}^{*}(5840)^0 \to B^+ K^-$ decays. The tagging power measured in $B_s^0 \to D_s^- \pi^+$ decays is found to be $(1.80 \pm 0.19({\rm stat}) \pm 0.18({\rm syst}))$\%, which is an improvement of about 50\% compared to a similar algorithm previously used in the LHCb experiment.

Authors: Cheung, S; Evans, T; Gandini, P; Hadavizadeh, T; Harnew, N

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Institute of Physics
• Journal: Journal of Instrumentation
• Volume: 11
• Issue: 05
• Pages: P05010
• Publication date: 2016-05-01
• Acceptance date: 2016-04-14
• DOI: 10.1088/1748-0221/11/05/P05010
• ISSN: 1748-0221
• Keywords: Analysis and statistical methods; Particle identification methods; Pattern recognition, cluster finding, calibration and fitting methods