A new graph-neural-network flavor tagger for Belle~II and measurement of $\sin 2 ϕ_{1}$ in $B^{0} \to J / ψ K_{S}^{0}$ decays

Sumitted to PubDB: 2024-05-08

Category: Proceeding, Visibility: Public

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Authors	Petros Stavroulakis
Date	2024-05-08
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 $Υ (4 S)$ decays. We evaluate its performance using $B$ decays to flavor-specific hadronic final states reconstructed in a 362 ${fb}^{- 1}$ sample of electron-positron collisions at the $Υ (4 S)$ 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 $B^{0} \to J / ψ K_{S}^{0}$ decays to measure the mixing-induced and direct $C P$ 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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