Machine Learning Approaches to Top Quark Flavor-Changing Four-Fermion Interactions in Trilepton Signals at the LHC
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908408400052224 |
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| author | Bostanabad, Meisam Ghasemi Najafabadi, Mojtaba Mohammadi |
| author_facet | Bostanabad, Meisam Ghasemi Najafabadi, Mojtaba Mohammadi |
| contents | 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 $Λ\leq 5.5$ TeV for $tuee$ and $Λ\leq 5.7$ TeV for $tcee$ in the $t\bar{t}$ channel, and $Λ\leq 1.9$ TeV ($tuee$) and $Λ\leq 2.0$ 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_18667 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning Approaches to Top Quark Flavor-Changing Four-Fermion Interactions in Trilepton Signals at the LHC Bostanabad, Meisam Ghasemi Najafabadi, Mojtaba Mohammadi High Energy Physics - Phenomenology 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 $Λ\leq 5.5$ TeV for $tuee$ and $Λ\leq 5.7$ TeV for $tcee$ in the $t\bar{t}$ channel, and $Λ\leq 1.9$ TeV ($tuee$) and $Λ\leq 2.0$ 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. |
| title | Machine Learning Approaches to Top Quark Flavor-Changing Four-Fermion Interactions in Trilepton Signals at the LHC |
| topic | High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2502.18667 |