# Inclusive Flavour Tagging Algorithm

## TL;DR

This paper introduces an inclusive flavour-tagging algorithm for neutral B mesons that leverages machine learning to improve performance in the challenging environment of the LHC, applicable to any proton-proton experiment.

## Contribution

It presents a new probabilistic, machine learning-based flavour-tagging algorithm that reduces dependence on lower-level identification, enhancing B meson tagging efficiency.

## Key findings

- Improved tagging performance in LHCb data.
- Reduced reliance on physics process information.
- Applicable to various proton-proton experiments.

## 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.