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Title: New graph-neural-network flavor tagger for Belle II and measurement of sin 2ϕ1 in B0→J/ψKS0 decays
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. 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 ( 4 S ) 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 B 0 J / ψ K S 0 decays to measure the mixing-induced and direct C P violation parameters, S = ( 0.724 ± 0.035 ± 0.009 ) and C = ( 0.035 ± 0.026 ± 0.029 ) . Published by the American Physical Society2024  more » « less
Award ID(s):
2209481
PAR ID:
10613035
Author(s) / Creator(s):
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Corporate Creator(s):
Publisher / Repository:
American Physical Society
Date Published:
Journal Name:
Physical Review D
Volume:
110
Issue:
1
ISSN:
2470-0010
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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