Phys. Rev. D 110, 012001 (2024)

SCIE

New graph-neural-network flavor tagger for Belle II and measurement of sin 2ϕ1 in B 0 → J/ψK0 S decays

Belle II Collaboration (I. Adachi, ..., N. Anh Ky, ...)

We present GFlaT, a new algorithm that uses a graph-neural-network to determine the flavor of neutral B mesons produced in Υ(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 fb−1 sample of electron-positron collisions collected at the Υ(4S) resonance with the Belle II detector at the SuperKEKB collider. We achieve an effective tagging efficiency of (37.40 ± 0.43 ± 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 → J/ψK 0 S decays to measure the mixing-induced and direct CP violation parameters, S = (0.724 ± 0.035 ± 0.009) and C = (−0.035 ± 0.026 ± 0.029).

DOI: https://doi.org/10.1103/PhysRevD.110.012001

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