A 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
Category: Proceeding, Visibility: Public
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| Authors | Petros Stavroulakis |
|---|---|
| Date | May 8, 2024 |
| Belle II Number | BELLE2-CONF-PROC-2024-009 |
| 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. We evaluate its performance using $B$ decays to flavor-specific hadronic final states reconstructed in a 362 $\mathrm{fb}^{-1}$ sample of electron-positron collisions at the $\Upsilon(4S)$ resonance recorded 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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