Graphical neural net flavour tagging and measurement of sin 2beta at Belle II

Sumitted to PubDB: 2024-04-04

Category: Talk, Visibility: Public

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Authors	Petros Stavroulakis
Date	March 24, 2024
Belle II Number	BELLE2-TALK-CONF-2024-044
Abstract	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 B0 → J/ψ K0S decays to measure the mixing-induced and direct CP violation parameters, S=(0.724 ± 0.035 ± 0.014) and C=(−0.035 ± 0.026 ± 0.013).
Conference	Moriond EW 2024 - Electroweak Interactions & Unified Theories