Search for $t\bar tt\bar tW$ Production at $\sqrt{s} = 13$ TeV Using a Modified Graph Neural Network at the LHC
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918108710567936 |
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| author | Ali, Syed Haider Ahmad, Ashfaq Saiel, Muhammad Shaukat, Nadeem |
| author_facet | Ali, Syed Haider Ahmad, Ashfaq Saiel, Muhammad Shaukat, Nadeem |
| contents | The simultaneous production of four top quarks in association with a ($W$) boson at $(\sqrt{s} = 13)$ TeV is an rare SM process with a next-to-leading-order (NLO) cross-section of $(6.6^{+2.4}_{-2.6} {ab})$\cite{saiel}. Identifying this process in the fully hadronic decay channel is particularly challenging due to overwhelming backgrounds from $t\bar{t}, t\bar{t}W, t\bar{t}Z$, and triple-top production processes. This study introduces a modified physics informed Neural Network, a hybrid graph neural network (GNN) enhancing event classification. The proposed model integrates Graph layers for particle-level features, a custom Multi Layer Perceptron(MLP) based global stream with a quantum circuit and cross-attention fusion to combine local and global representations. Physics-informed Loss function enforce jet multiplicity constraints, derived from event decay dynamics. Benchmarked against conventional methods, the GNN achieves a signal significance $(S/\sqrt{S+B})$ of $0.174$ and ROC-AUC of 0.974, surpassing BDT's significance of $0.148$ and ROC of $0.913$, while Xgboost achieves a significance of $0.149$ and ROC of $0.920$. The classification models are trained on Monte Carlo (MC) simulations, with events normalized using cross-section-based reweighting to reflect their expected contributions in a dataset corresponding to $350\;$fb$^{-1}$ of integrated luminosity. This enhanced approach offers a framework for precision event selection at the LHC, leveraging high dimensional statistical learning and physics informed inference to tackle fundamental HEP challenges, aligning with ML developments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_23723 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Search for $t\bar tt\bar tW$ Production at $\sqrt{s} = 13$ TeV Using a Modified Graph Neural Network at the LHC Ali, Syed Haider Ahmad, Ashfaq Saiel, Muhammad Shaukat, Nadeem High Energy Physics - Experiment High Energy Physics - Phenomenology The simultaneous production of four top quarks in association with a ($W$) boson at $(\sqrt{s} = 13)$ TeV is an rare SM process with a next-to-leading-order (NLO) cross-section of $(6.6^{+2.4}_{-2.6} {ab})$\cite{saiel}. Identifying this process in the fully hadronic decay channel is particularly challenging due to overwhelming backgrounds from $t\bar{t}, t\bar{t}W, t\bar{t}Z$, and triple-top production processes. This study introduces a modified physics informed Neural Network, a hybrid graph neural network (GNN) enhancing event classification. The proposed model integrates Graph layers for particle-level features, a custom Multi Layer Perceptron(MLP) based global stream with a quantum circuit and cross-attention fusion to combine local and global representations. Physics-informed Loss function enforce jet multiplicity constraints, derived from event decay dynamics. Benchmarked against conventional methods, the GNN achieves a signal significance $(S/\sqrt{S+B})$ of $0.174$ and ROC-AUC of 0.974, surpassing BDT's significance of $0.148$ and ROC of $0.913$, while Xgboost achieves a significance of $0.149$ and ROC of $0.920$. The classification models are trained on Monte Carlo (MC) simulations, with events normalized using cross-section-based reweighting to reflect their expected contributions in a dataset corresponding to $350\;$fb$^{-1}$ of integrated luminosity. This enhanced approach offers a framework for precision event selection at the LHC, leveraging high dimensional statistical learning and physics informed inference to tackle fundamental HEP challenges, aligning with ML developments. |
| title | Search for $t\bar tt\bar tW$ Production at $\sqrt{s} = 13$ TeV Using a Modified Graph Neural Network at the LHC |
| topic | High Energy Physics - Experiment High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2507.23723 |