arXiv subjects

Mudassar Hussain

Publications and source records attributed to Mudassar Hussain.

4 recordsLinked to original sources

arXiv2026

Search for Vector-Like Singlet Top ($T$) Quark in a Future Muon-Proton ($μp$) Collider at $\sqrt{s} = 5.29, 6.48,$ and $9.16$ TeV using Advanced Machine Learning Architectures

In this work, we explore the discovery potential of Vector-Like Singlet Top quarks ($T$) at a future $μp$ 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 $T \to Wb$ decay mode in both fully hadronic ($bjj$) and leptonic ($blν$) final states, offering a multi-channel assessment of $T$-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$^{-1}$ of data, the statistical significance exceeds $5σ$ up to 4 TeV masses. We identify a crossover effect where hadronic channels provide superior reach at intermediate masses due to higher branching ratios, while leptonic channels offer robustness at 5 TeV where purity limits detection. Incorporating a 20\% systematic uncertainty via Asimov significance ($Z_A$), we quantify the transition from fluctuation-dominated to systematic-dominated regimes at high luminosities. At 3000 fb$^{-1}$, regions with $g^{*} \in [0.20, 0.50]$ and $m_T$ up to 4 TeV are discoverable via the hadronic channel with MLP, and regions with $g^{*} \in [0.10, 0.50]$ and $m_T$ up to 5 TeV are accessible through the leptonic channel with BDT, highlighting the collider's potential to probe new physics beyond the Standard Model.

hep-ph↗

arXiv2026

Probing Vector-Like Quarks at a future Muon-Proton Collider

This study investigates the discovery potential of a singly produced vector-like top quark ($T$) at a future muon-proton collider with center-of-mass energies of 5.29, 6.48, and 9.16~TeV using a model-independent effective Lagrangian consistent with CKM and electroweak constraints. The $T$ quark predominantly decays into $Wb$, with production cross sections peaking at 9.16~TeV and decreasing above 3~TeV due to parton distribution functions (PDFs) and phase-space suppression. Sensitivity is enhanced through optimized kinematic selections, with the hadronic channel providing higher event rates due to the larger hadronic branching fraction of the $W$ boson, while the leptonic channel offers a cleaner background environment. At an integrated luminosity of 3000~fb$^{-1}$, a 3~TeV $T$ quark can be observed with statistical significances of $21.86σ$ and $3.75σ$ in the hadronic and leptonic channels, respectively. A machine-learning analysis employing a Boosted Decision Tree (BDT) and a Multi-Layer Perceptron (MLP) is performed at 9.16~TeV for $m_T = 3000$~GeV using $S/B$ and $S/\sqrt{S+B}$ as performance metrics. The MLP consistently outperforms the BDT, achieving a hadronic purity gain of approximately 2.62 while maintaining stable performance across all luminosities. These results demonstrate that a future muon-proton collider can probe vector-like $T$ quark masses up to approximately 3.5~TeV, significantly extending the search for physics beyond the Standard Model.

hep-ph↗

arXiv2026

Impact of Colliding Beams Helicity on the Production of Leptoquarks and Collider Experimental Parameters

Vector Leptoquarks (VLQs) have emerged as primary candidates for resolving discrepancies in the Standard Model, specifically within $B$-meson decay channels and the anomalous magnetic moment of the muon. This work presents a rigorous evaluation of VLQ pair production across $e^{-}e^{+}$ collision modes at future linear colliders with center-of-mass energies ranging from 14~TeV to 100~TeV. Our analysis demonstrates that longitudinal beam polarization is a transformative tool for enhancing signal sensitivity. We find that $e^{-}e^{+}$ annihilation consistently yields superior cross-sections compared to photon fusion processes across a mass range of 500--3000~GeV. By optimizing beam helicity to specific configurations, such as $P_{e^{-}} = -0.8$ and $P_{e^{+}} = +0.6$, the production cross-section can be maximized to 120~fb at $\sqrt{s} = 3$~TeV. We further establish that the Left-Right Asymmetry ($A_{LR}$) serves as a robust discriminator for the chiral structure of new physics, peaking at 0.16 under full polarization. Additionally, we show that effective luminosity can be enhanced to 95\% of the total luminosity, while high polarization degrees significantly suppress relative uncertainties in the effective polarization. These results provide a quantitative roadmap for optimizing discovery potential and minimizing systematic errors in future high-energy physics experiments.

hep-ph↗

arXiv2026

Unraveling Dirac Magnetic Monopoles with Muon Beams at TeV Energies Using Machine Learning

The focus of this paper is the production of magnetic monopoles Drell-Yan and the Photon-Fusion mechanisms to generate velocity-dependent scalar, fermionic, and vector monopoles of spin angular momentum $0,\frac{1}{2},1$ respectively at a future muon collider. A computational study compares the monopole pair-production cross-sections for both methods at various center-of-mass energies ($\sqrt{s}$) with different magnetic dipole moments. The comparison of kinematic distributions of monopoles at the generator and reconstructed level is demonstrated for both DY and PF mechanisms. We demonstrate the observability of magnetic monopoles against the most relevant Standard Model background using multivariate analysis techniques. Specifically, we apply three different classifiers based on neural networks, e.g., Boosted Decision Trees, Multilayer Perceptrons, and Likelihood methods, to evaluate their effectiveness. Our results highlight the efficiency and robustness of these approaches in distinguishing magnetic monopole signals from background noise.

hep-ph↗