PHYSICAL REVIEW D 112, 052003 (2025)
PHYSICAL REVIEW D 112, 052003 (2025)
Jeewon Heo ,1 Woojin Jang ,1 Jason S. H. Lee ,1,* Youn Jung Roh ,1 Ian James Watson ,1,† and Seungjin Yang 2
1Department of Physics, University of Seoul, Seoul 02504, Republic of Korea
2Department of Physics, Kyung Hee University, Seoul 02453, Republic of Korea
Abstract: An s-jet tagging approach to determine the Cabibbo-Kobayashi-Maskawa matrix component jVtsj directly in the dileptonic final state events of the top pair production in proton-proton collisions has been
previously studied by measuring the branching fraction of the decay of one of the top quarks by t → sW.
The main challenge is improving the discrimination performance between strange jets from top decays and
other jets. This study proposes novel jet discriminators, called DiSaJa, using a Transformer-based deep
learning method. The first model, DiSaJa-H, utilizes multidomain inputs (jets, leptons, and missing
transverse momentum). An additional model, DiSaJa-L, further improves the setup by using lower-level jet
constituent information, rather than the high-level clustered information. DiSaJa-L is a novel model that
combines low-level jet constituent analysis with event classification using multidomain inputs. The model
performance is evaluated via a CMS-like LHC Run 2 fast simulation by comparing various statistical test
results to those from a Transformer-based jet classifier which considers only the individual jets. This study
shows that the DiSaJa models have significant performance gains over the individual jet classifier, and we
show the potential of the measurement during Run 3 of the LHC and the High-Luminosity LHC.
