Speaker
Description
One of the main goals of the LHCb experiment is to study charge-parity violation by looking at the decays of the large variety of beauty mesons created in pp collisions at LHC. Such studies are particularly challenging in the presence of B−B oscillations as the B meson flavour at production time might be different from the flavour at its decay time. Flavour Tagging algorithms exploit the correlations between the B meson production flavour and features of the global event to tag the candidate as B or B. Together with the tagging decision, the probability of a wrong tagging decision must be provided which is estimated through the application of Machine Learning algorithms. In Run 3 of the LHC, the LHCb experiment operates at the average non-empty bunch crossing rate of 30 MHz with an upgraded detector and a solely software-based trigger. The purpose of this contribution is to introduce the strategy and developments of the Flavour Tagging algorithms for Run 3.
