Improving the direct determination of jV_tsj using deep learning

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.