$B$-flavor tagging at Belle II

TL;DR

This paper introduces new flavor tagging algorithms at Belle II for identifying the quark flavor of bottom mesons, crucial for studying quark-flavor mixing and CP violation, with validated performance on real data.

Contribution

Development and validation of novel flavor tagging algorithms, including deep learning methods, for bottom mesons at Belle II, achieving high effective tagging efficiency.

Findings

Effective tagging efficiency of 30.0% for category-based algorithms.

Effective tagging efficiency of 28.8% for deep-learning algorithms.

Validation performed using 62.8 fb$^{-1}$ of data from Belle II.

Abstract

We report on new flavor tagging algorithms developed to determine the quark-flavor content of bottom ($B$) mesons at Belle II. The algorithms provide essential inputs for measurements of quark-flavor mixing and charge-parity violation. We validate and evaluate the performance of the algorithms using hadronic $B$ decays with flavor-specific final states reconstructed in a data set corresponding to an integrated luminosity of $62.8$ fb$^{-1}$, collected at the $\Upsilon$(4$S$) resonance with the Belle II detector at the SuperKEKB collider. We measure the total effective tagging efficiency to be $\varepsilon_{\rm eff} = \big(30.0 \pm 1.2(\text{stat}) \pm 0.4(\text{syst})\big)\%$ for a category-based algorithm and $\varepsilon_{\rm eff} = \big(28.8 \pm 1.2(\text{stat}) \pm 0.4(\text{syst})\big)\%$ for a deep-learning-based algorithm.