Abstract
Identifying the flavour of neutral B mesons production is one of the most important components needed in the study of time-dependent CP violation. The harsh environment of the Large Hadron Collider makes it particularly hard to succeed in this task. We present an inclusive flavour-tagging algorithm as an upgrade of the algorithms currently used by the LHCb experiment. Specifically, a probabilistic model which efficiently combines information from reconstructed vertices and tracks using machine learning is proposed. The algorithm does not use information about underlying physics process. It reduces the dependence on the performance of lower level identification capacities and thus increases the overall performance. The proposed inclusive flavour-tagging algorithm is applicable to tag the flavour of B mesons in any proton-proton experiment.
Highlights
B mesons contain either a b or ab quark, which defines their flavour
Same side (SS) taggers exploit light particles that evolve from the hadronisation process of the signal B meson like kaons, pions, and protons
The current version of the FT algorithm used by the LHCb [1, 2, 3], CMS [4], Atlas [5], CDF [6] and D0 [7] experiments tries to identify tracks and vertices produced on the opposite side (OS)/same side (SS) sides (SS tagging is done only by LHCb and CDF)
Summary
B mesons contain either a b or ab quark, which defines their flavour. The flavour-tagging (FT) algorithms determine the flavour of a reconstructed signal B meson candidate at the production point in proton-proton collisions. Same side (SS) taggers exploit light particles that evolve from the hadronisation process of the signal B meson like kaons, pions, and protons (see [2]). This paper describes a new approach to define the signal B flavour that exploits all available information in an event without using information about the underlying physics processes, like a tagging track (vertex) search. It uses an assumption similar to a naive Bayes model It assumes a strong independence of the tagging information available in the tracks and vertices. The usage of this formula, requires estimating probabilities P (sb · sp > 0|B, component) and P (sb · sp < 0|B, component) Note that these probabilities are established using different parameters of the signal B meson and a track/vertex, but not using their charges.
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