Leptoquark Searches at TeV Scale Using Neural Networks at Hadron Collider
Ijaz Ahmed, Usman Ahmad, Jamil Muhammad, Saba Shafaq

TL;DR
This paper explores the use of machine learning techniques like BDTs, MLP, and likelihood methods to improve the detection sensitivity of leptoquarks at the LHC, achieving higher significance in various decay modes and mass ranges.
Contribution
It introduces a novel application of multiple machine learning algorithms to enhance leptoquark search sensitivity at the LHC, surpassing traditional cut-based methods.
Findings
Machine learning methods significantly improve signal-background discrimination.
Higher signal significance achieved for LQ masses up to 2.0 TeV.
Enhanced detection sensitivity at higher integrated luminosities.
Abstract
Several discrepancies in the decay of B-meson decay have drawn a lot of interest in the leptoquarks (LQ), making them an exciting discovery. The current research aims to discover the pair-production of leptoquarks that links strongly to the third generation of quarks and leptons at the center of mass energy =14 TeV, via proton-proton collisions at the Large Hadron Collider (LHC). Based on the lepton-quark coupling parameters and branching fractions, we separated our search into various benchmark points. The leading order (LO) signals and background processes are generated, while parton showering and hadronization is also performed to simulate the detector effects. The Boosted Decision Trees (BDTs), Multilayer Perceptron (MLP), and Likelihood (LH) methods are effective in improving signal-background discrimination compared to traditional cut-based analysis. The results indicate…
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Taxonomy
TopicsParticle Detector Development and Performance · Particle physics theoretical and experimental studies · Scientific Computing and Data Management
