Search for Vector-Like Singlet Top ($T$) Quark in a Future Muon-Proton ($\mu p$) Collider at $\sqrt{s} = 5.29, 6.48,$ and $9.16$ TeV using Advanced Machine Learning Architectures
Haroon Sagheer, M. Tayyab Javaid, Mudassar Hussain, M. Danial Farooq, Ijaz Ahmed, Jamil Muhammad

TL;DR
This study assesses the discovery potential of vector-like top quarks at future muon-proton colliders using advanced machine learning techniques, demonstrating significant sensitivity across various energies and decay channels.
Contribution
It introduces a comprehensive analysis employing multivariate classifiers to optimize detection of vector-like top quarks at future muon-proton colliders, exploring multiple decay channels and energy configurations.
Findings
9.16 TeV collider can discover top quarks up to 4 TeV with 100 fb$^{-1}$.
Hadronic channels outperform at intermediate masses, leptonic channels are more robust at higher masses.
High luminosity enables probing of new physics with significant sensitivity to coupling strengths.
Abstract
In this work, we explore the discovery potential of Vector-Like Singlet Top quarks () at a future collider with center-of-mass energies of 5.29, 6.48, and 9.16 TeV, providing a unique environment to probe beyond Standard Model limits. We analyze the decay mode in both fully hadronic () and leptonic () final states, offering a multi-channel assessment of -quark sensitivity across a mass range of 2 to 5 TeV. Our methodology employs multivariate classifiers such as Boosted Decision Trees (BDTs) and Multi-Layer Perceptrons (MLP) to optimize signal-to-background discrimination in complex final states. The results demonstrate that the 9.16 TeV benchmark acts as a definitive discovery machine; even with 100 fb of data, the statistical significance exceeds up to 4 TeV masses. We identify a crossover effect where hadronic channels provide…
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Taxonomy
TopicsParticle physics theoretical and experimental studies · Computational Physics and Python Applications · Quantum Chromodynamics and Particle Interactions
