A search for the leptonic charge asymmetry ($A_\text{c}^{\ell}$) of top-quark$-$antiquark pair production in association with a $W$ boson ($t\bar{t}W$) is presented. The search is performed using final states with exactly three charged light leptons (electrons or muons) and is based on $\sqrt{s} = 13$ TeV proton$-$proton collision data collected with the ATLAS detector at the Large Hadron Collider at CERN during the years 2015$-$2018, corresponding to an integrated luminosity of 139 fb$^{-1}$. A profile-likelihood fit to the event yields in multiple regions corresponding to positive and negative differences between the pseudorapidities of the charged leptons from top-quark and top-antiquark decays is used to extract the charge asymmetry. At reconstruction level, the asymmetry is found to be $-0.123 \pm 0.136$ (stat.) $\pm \, 0.051$ (syst.). An unfolding procedure is applied to convert the result at reconstruction level into a charge-asymmetry value in a fiducial volume at particle level with the result of $-0.112 \pm 0.170$ (stat.) $\pm \, 0.054$ (syst.). The Standard Model expectations for these two observables are calculated using Monte Carlo simulations with next-to-leading-order plus parton shower precision in quantum chromodynamics and including next-to-leading-order electroweak corrections. They are $-0.084 \, ^{+0.005}_{-0.003}$ (scale) $\pm\, 0.006$ (MC stat.) and $-0.063 \, ^{+0.007}_{-0.004}$ (scale) $\pm\, 0.004$ (MC stat.) respectively, and in agreement with the measurements.
Measured values of the leptonic charge asymmetry ($A_c^{\ell}$) in ttW production in the three lepton channel. Results are given at reconstruction level and at particle level. Expected values are obtained using the Sherpa MC generator.
Definition of the fiducial phase space at particle level with the light lepton candidates $(\ell=e,\mu)$, jets ($j$) and invariant mass of the opposite sign same flavour lepton pair ($m_{OSSF}^{ll}$).
Correlation matrix between the Normalisation Factors and the Nuisance Parameters (NP) in the fit using using both statistical and systematic uncertainties to data in all analysis regions.
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