Search for single vector-like $B$ quark production in hadronic final states at the LHC
Bingfang Yang, Zejun Li, Xinglong Jia, Stefano Moretti, Liangliang, Shang

TL;DR
This study explores the detection prospects of a vector-like B quark at the LHC using advanced machine learning techniques to enhance signal identification and background rejection, especially at high masses.
Contribution
It introduces a combined cut-and-count and XGBoost method for improved VLB detection, demonstrating enhanced sensitivity over traditional approaches in high-mass regions.
Findings
Significant background reduction with higher signal retention.
Enhanced discovery potential for VLB masses above 1500 GeV.
Exclusion and discovery reach up to 4750 GeV for certain coupling scenarios.
Abstract
In this paper, we study the discovery potential of a Vector-Like quark (VLB) via the process at the Large Hadron Collider (LHC) with TeV. In the framework of a simplified model, we perform a scan over its parameter space and test its viability following a Monte Carlo analysis developed to include all production and decay dynamics. We use cut-and-count combined with Extreme Gradient Boosting (XGBoost) methods to classify the signal and background events in order to improve the efficiency of signal identification and background rejection. We find that this approach can reduce background events significantly while the signal retention rate is much higher than that of traditional methods, thereby improving the VLB discovery potential. We then calculate the exclusion and discovery capabilities for VLBs and find that the…
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Taxonomy
TopicsParticle physics theoretical and experimental studies · High-Energy Particle Collisions Research · Distributed and Parallel Computing Systems
