A new algorithm for identifying the flavour of $B_s^0$ mesons at LHCb

TL;DR

This paper introduces a novel neural network-based algorithm for determining the initial flavour of $B_s^0$ mesons at LHCb, improving tagging power by about 50% over previous methods.

Contribution

The paper presents a new flavour tagging algorithm utilizing two neural networks, calibrated with LHCb data, significantly enhancing tagging efficiency for $B_s^0$ mesons.

Findings

Tagging power of 1.80% in $B_s^0 o D_s^- au^+$ decays.

Calibration methods include resolving $B_s^0$-$ar{B}_s^0$ oscillations.

Improved tagging performance by approximately 50%.

Abstract

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.