New graph-neural-network flavor tagger for Belle II and measurement of $\sin2\phi_1$ in $B^0 \to J\psi K_S^0$ decays

Abstract 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, $S = (0.724 \pm 0.035 \pm 0.009)$ and $C = (-0.035 \pm 0.026 \pm 0.029)$.
Tags
Working group Time Dependent CP Violation
Principal authors @  Thibaud Humair, Yo Sato, Petros Stavroulakis, Oskar Tittel, Radek Zlebcik
Reference Phys.Rev.D 110, 012001 (2024), DOI: 10.1103/PhysRevD.110.012001
Document BELLE2-PUB-PH-2023-012
Links arXiv:2402.17260, Inspire, PRD
Bibtex 
@article{Belle-II:2024lwr,
    author = "Adachi, I. and others",
    collaboration = "Belle-II",
    title = "{New graph-neural-network flavor tagger for Belle II and measurement of sin 2{\ensuremath{\phi}}1 in B0{\textrightarrow}J/{\ensuremath{\psi}}KS0 decays}",
    eprint = "2402.17260",
    archivePrefix = "arXiv",
    primaryClass = "hep-ex",
    reportNumber = "Belle II Preprint 2024-006, KEK Preprint 2023-53",
    doi = "10.1103/PhysRevD.110.012001",
    journal = "Phys. Rev. D",
    volume = "110",
    number = "1",
    pages = "012001",
    year = "2024"
}