29 January 2024 to 2 February 2024
CERN
Europe/Zurich timezone

Deep learning for the rare top decay t→sW at the LHC

31 Jan 2024, 15:40
5m
61/1-201 - Pas perdus - Not a meeting room - (CERN)

61/1-201 - Pas perdus - Not a meeting room -

CERN

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Poster 2 ML for analysis : event classification, statistical analysis and inference, including anomaly detection Poster Session

Speaker

Jeewon Heo (University of Seoul, Department of Physics (KR))

Description

The Cabibbo Kobayashi Maskawa (CKM) matrix describes the flavor-changing quark interactions. Vts is the matrix element that describes the coupling between the top and strange quark, has not been directly measured. A direct measurement of |Vts| can be performed by identifying the strange jets from top decays. The strange jet tagging problem is challenging due to the similarity of strange jets and light jets. When tagging the strange jet decaying from top quarks, both the jet properties and topology of the event can be considered. For this task, we employ a deep-learning model, SAJA (Self-Attention for Jet Assignment), based on the self-attention mechanism that can utilize all event topology and jet properties. The SAJA model finds the jets decaying from t→sW in the Dileptonic top pair production using the whole event information.

Authors

Ian James Watson (University of Seoul) Inkyu Park (University of Seoul, Department of Physics (KR)) Jason Lee (University of Seoul (KR)) Jeewon Heo (University of Seoul, Department of Physics (KR)) Seungjin Yang (Kyung Hee University (KR)) Woojin Jang (University of Seoul, Department of Physics (KR)) Youn Jung Roh (University of Seoul, Department of Physics (KR))

Presentation materials