“School of Particles And Accelerator”
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| Paper IPM / Particles And Accelerator / 18089 |
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| Abstract: | |||||
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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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