Inclusive Flavour Tagging Algorithm
This work addresses a critical component for studying time-dependent CP violation in particle physics experiments, offering an incremental upgrade to existing algorithms used by the LHCb experiment.
The paper tackles the challenge of identifying the flavor of neutral B mesons in the harsh Large Hadron Collider environment by proposing an inclusive flavor-tagging algorithm that uses a probabilistic model with machine learning to combine vertex and track information, resulting in increased overall performance without relying on underlying physics processes.
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.