Abstract

The production of a top quark in association with a Z boson is investigated. The proton–proton collision data collected by the ATLAS experiment at the LHC in 2015 and 2016 at a centre-of-mass energy of s=13TeV are used, corresponding to an integrated luminosity of 36.1fb−1. Events containing three identified leptons (electrons and/or muons) and two jets, one of which is identified as a b-quark jet are selected. The major backgrounds are diboson, tt¯ and Z+jets production. A neural network is used to improve the background rejection and extract the signal. The resulting significance is 4.2σ in the data and the expected significance is 5.4σ. The measured cross-section for tZq production is 600±170(stat.)±140(syst.)fb.

Highlights

  • At hadron colliders, the top quark is typically produced in ttpairs through the strong interaction or as a single top or antitop quark through the electroweak interaction

  • Studies in the diboson validation regions (VR) indicated that the number of events predicted by the Sherpa Monte Carlo (MC) samples is lower than the number observed

  • The likelihood is maximised on the neural network (NN) output distribution in the signal region

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Summary

Introduction

The top quark is typically produced in ttpairs through the strong interaction or as a single top or antitop quark through the electroweak interaction. The associated t W production was first observed in 8 TeV proton–proton collisions at the Large Hadron Collider (LHC) [6,7]. These single-top-quark channels allow a direct determination of the dominant t W b vertex and of the magnitude of the CKM matrix element |Vtb| [8] using their measured cross-sections. This channel probes two SM couplings in a single process, whereas the similar final state tt Z only probes the t Z coupling. T Z q, where the Z boson decays into electrons or muons and the W boson from the top quark decays leptonically

ATLAS detector
Data and simulation samples
Object reconstruction
Background estimation
Multivariate analysis
Systematic uncertainties
Results
10. Conclusion
Methods
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