Kavli Affiliate: T. Higuchi
| First 5 Authors: Belle II Collaboration, I. Adachi, L. Aggarwal, H. Ahmed, H. Aihara
| Summary:
We present GFlaT, a new algorithm that uses a graph-neural-network to
determine the flavor of neutral $B$ mesons produced in $Upsilon(4S)$ decays.
It improves previous algorithms by using the information from all charged
final-state particles and the relations between them. We evaluate its
performance using $B$ decays to flavor-specific hadronic final states
reconstructed in a 362 $text{fb}^{-1}$ sample of electron-positron collisions
collected at the $Upsilon(4S)$ resonance with the Belle II detector at the
SuperKEKB collider. We achieve an effective tagging efficiency of $(37.40 pm
0.43 pm 0.36) %$, where the first uncertainty is statistical and the second
systematic, which is $18%$ better than the previous Belle II algorithm.
Demonstrating the algorithm, we use $B^{0}to J/psi K^0_text{S}$ decays to
measure the mixing-induced and direct $CP$ violation parameters, $S = (0.724
pm 0.035 pm 0.014)$ and $C = (-0.035 pm 0.026 pm 0.013)$.
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