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Search for single vector-like $B$ quark production in hadronic final states at the LHC

Search for single vector-like $B$ quark production in hadronic final states at the LHC
Search for single vector-like $B$ quark production in hadronic final states at the LHC
In this paper, we study the discovery potential of a Vector-Like $B$ quark (VLB) via the process $pp \to B(\to bZ)j\to b(Z \to \nu_l\bar{\nu_l})j$ at the Large Hadron Collider (LHC) with $\sqrt{s}=14$ 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 advantages of the cut-and-count plus XGBoost method especially lie in the high-mass region, i.e., $m_B > 1500 \text{ GeV}$. We finally obtain the following LHC results in terms of the coupling and chiral structure of a singlet heavy VLB interactions: (i) for $g^{\ast}$=0.2 and $R_L=0$ with 3000 fb$^{-1}$, the $B$ quark mass can be be excluded (discovered) up to 3000 GeV (2500 GeV); (ii) for $g^{\ast}$=0.2 and $R_L=0.5$ with 3000 fb$^{-1}$, the exclusion (discovery) region can reach up to 4750 GeV (4250 GeV).
hep-ph
Yang, Bingfang
4c259e23-165a-4199-a7b0-80ae7c9deb47
Li, Zejun
54ab7a9c-a700-47ca-942e-d3484e061098
Jia, Xinglong
02877223-60ed-4a0b-a341-6ada056792cb
Moretti, Stefano
b57cf0f0-4bc3-4e02-96e3-071255366614
Shang, Liangliang
b48abb04-3629-4a0c-9549-367783e50a3b
Yang, Bingfang
4c259e23-165a-4199-a7b0-80ae7c9deb47
Li, Zejun
54ab7a9c-a700-47ca-942e-d3484e061098
Jia, Xinglong
02877223-60ed-4a0b-a341-6ada056792cb
Moretti, Stefano
b57cf0f0-4bc3-4e02-96e3-071255366614
Shang, Liangliang
b48abb04-3629-4a0c-9549-367783e50a3b

[Unknown type: UNSPECIFIED]

Record type: UNSPECIFIED

Abstract

In this paper, we study the discovery potential of a Vector-Like $B$ quark (VLB) via the process $pp \to B(\to bZ)j\to b(Z \to \nu_l\bar{\nu_l})j$ at the Large Hadron Collider (LHC) with $\sqrt{s}=14$ 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 advantages of the cut-and-count plus XGBoost method especially lie in the high-mass region, i.e., $m_B > 1500 \text{ GeV}$. We finally obtain the following LHC results in terms of the coupling and chiral structure of a singlet heavy VLB interactions: (i) for $g^{\ast}$=0.2 and $R_L=0$ with 3000 fb$^{-1}$, the $B$ quark mass can be be excluded (discovered) up to 3000 GeV (2500 GeV); (ii) for $g^{\ast}$=0.2 and $R_L=0.5$ with 3000 fb$^{-1}$, the exclusion (discovery) region can reach up to 4750 GeV (4250 GeV).

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2405.13452v2 - Author's Original
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More information

Accepted/In Press date: 22 May 2024
Additional Information: 26 pages, 7 figures, 5 tables
Keywords: hep-ph

Identifiers

Local EPrints ID: 491468
URI: http://eprints.soton.ac.uk/id/eprint/491468
PURE UUID: d626933e-f84f-4150-8e01-1f09e4d3eca0
ORCID for Stefano Moretti: ORCID iD orcid.org/0000-0002-8601-7246

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Date deposited: 24 Jun 2024 17:09
Last modified: 25 Jun 2024 01:38

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Contributors

Author: Bingfang Yang
Author: Zejun Li
Author: Xinglong Jia
Author: Stefano Moretti ORCID iD
Author: Liangliang Shang

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