Machine Learning Approaches to Top Quark Flavor-Changing Four-Fermion Interactions in Trilepton Signals at the LHC
Meisam Ghasemi Bostanabad, Mojtaba Mohammadi Najafabadi

TL;DR
This study employs machine learning to analyze top quark flavor-changing four-fermion interactions in trilepton signals at the LHC, setting new limits on interaction scales and interpreting results within a Z' model framework.
Contribution
It introduces machine learning techniques to improve detection sensitivity of top FCNC interactions and provides the first comprehensive EFT limits with realistic detector effects at high luminosity.
Findings
Limits of 5.5-5.7 TeV on interaction scale in t-tbar channel
Limits of 1.9-2.0 TeV in tW channel
Significant background reduction using ML classifiers
Abstract
We explore the top quark flavor-changing 4-Fermi interactions ( and ) with scalar, vector, and tensor structures using machine learning models to analyze tri-lepton processes at the LHC. The study is performed using and processes, where a top quark decays into . The analysis incorporates both reducible and irreducible backgrounds while accounting for realistic detector effects. The dominant backgrounds for these trilepton signatures arise from production, single top quark production in association with , and production (where ). These backgrounds are significantly reduced using machine learning-based classification models, which optimize event selection and improve signal sensitivity. For an integrated luminosity of 3000 fb at the LHC, we find that the expected confidence level (CL) limits on the…
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