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Paper IPM / Particles / 18089

School of Particles and Accelerator

Title:

Machine Learning Approaches to Top Quark Flavor-Changing Four-Fermion Interactions in Trilepton Signals at the LHC

Author(s):

1.	Meisam Ghasemi Bostanabad
2.	Mojtaba Mohammadi Najafabadi

Status:

Submitted

Journal:

Year:

2025

Supported by:

IPM

Abstract:

We explore the top quark flavor-changing 4-Fermi interactions (

$tuee$ and

$tcee$ ) with scalar, vector, and tensor structures using machine learning models to analyze tri-lepton processes at the LHC. The study is performed using

$t\bar{t}$ and

$tW$ processes, where a top quark decays into

$u/c+e^{+}+e^{-}$ . The analysis incorporates both reducible and irreducible backgrounds while accounting for realistic detector effects. The dominant backgrounds for these trilepton signatures arise from

$t\bar{t}$ production, single top quark production in association with

$V$ , and

$VV$ production (where

$V = W, Z$ ). 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

$^{-1}$ at the LHC, we find that the expected

$95\%$ confidence level (CL) limits on the scale of 4-Fermi FCNC interactions reach

$\Lambda \leq 5.5$ TeV for

$tuee$ and

$\Lambda \leq 5.7$ TeV for

$tcee$ in the

$t\bar{t}$ channel, and

$\Lambda \leq 1.9$ TeV (

$tuee$ ) and

$\Lambda \leq 2.0$ TeV (

$tcee$ ) in the

$tW$ channel. We also provide an interpretation of our EFT analysis in the context of a specific

$Z'$ model, illustrating how the derived constraints translate into bounds on the parameter space of a heavy neutral gauge boson mediating flavor-changing interactions.

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