Back to Search
Start Over
New graph-neural-network flavor tagger for Belle II and measurement of $\sin2\phi_1$ in $B^0 \to J/\psi K^0_\text{S}$ decays
- Source :
- Phys. Rev. D 110, 012001, 2024
- Publication Year :
- 2024
-
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 $C = (-0.035 \pm 0.026 \pm 0.029)$.<br />Comment: 15 pages, 9 figures
- Subjects :
- High Energy Physics - Experiment
Subjects
Details
- Database :
- arXiv
- Journal :
- Phys. Rev. D 110, 012001, 2024
- Publication Type :
- Report
- Accession number :
- edsarx.2402.17260
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1103/PhysRevD.110.012001