Speaker
Description
The precise determination of the initial flavour of neutral B mesons is crucial for time-dependent measurements of CP violation and mixing parameters, where it directly constrains the ultimate physics sensitivity. Leveraging the unprecedented data set of Run 3 and capitalizing on modern algorithmic advances, the LHCb collaboration has undertaken a comprehensive redesign of its flavour tagging (FT) algorithms. This new system extensively exploits state-of-the-art machine learning techniques, including advanced deep neural network architectures, to optimally combine information from diverse tagging particles. This contribution presents the novel FT strategy, detailing the machine learning frameworks and showcasing the resulting performance gains that pave the way for next-generation precision measurements in heavy-flavour physics.
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