Measurement of the t(t)over-bar production cross-section in the lepton plus jets channel at root s=13 TeV with the ATLAS experiment

Physics Letters B 810 (2020) 135797 Contents lists available at ScienceDirect Physics Letters B www.elsevier.com/locate/physletb Measurement of the t¯ tproduction cross-section in the lepton+jets channel at √s=13 TeV with the ATLAS experiment .The ATLAS Collaboration a r t i c l e i n f o a b s t r a c t Article history: Received 24 June 2020 Received in revised form 4 September 2020 Accepted 17 September 2020 Available online 22 September 2020 Editor: M. Doser The top anti-top quark production cross-section is measured in the lepton+jets channel using proton– proton collision data at a centre-of-mass energy of √s=13 TeV collected with the ATLAS detector at the LHC. The dataset corresponds to an integrated luminosity of 139 fb−1. Events with exactly one charged lepton and four or more jets in the final state, with at least one jet containing b-hadrons, are used to determine the t¯ tproduction cross-section through a profile-likelihood fit. The inclusive cross-section is measured to be σinc =830 ±0.4(stat.)±36 (syst.) ±14 (lumi.) pb with a relative uncertainty of 4.6%. The result is consistent with theoretical calculations at next-to-next-to-leading order in perturbative QCD. The fiducial t¯ tcross-section within the experimental acceptance is also measured. ©2020 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Funded by SCOAP3. 1. Introduction The top quark is the heaviest elementary particle in the Standard Model (SM), with a mass mtclose to the electroweak symmetry breaking scale [1,2]. Studies of top-quark production and decays provide a precise probe of the SM as well as its extensions [3]. At the CERN Large Hadron Collider (LHC), top quarks are primarily produced in quark–antiquark pairs (t¯ t) and form an important background in many searches for physics beyond the SM. Thus, a precise measurement of the t¯ tcross-section, and comparison with theoretical predictions of high precision, are a critical part of the LHC physics programme. A theoretical calculation of the t¯ tcross-section, σt¯ t, is available at next-to-next-to-leading order (NNLO) in quantum chromodynamics (QCD). It includes the resummation of the next-to-nextto-leading logarithmic (NNLL) soft-gluon terms [4–9] and predicts σt¯ t=832+20 −29 (scale) ±35 (PDF +αS)pb in proton–proton (pp) collisions at a centre-of-mass energy of 13 TeV, as calculated by the Top++ (v2.0) program [10], using the MSTW2008 NNLO PDF set [11,12]as the central PDF set and assuming mt=172.5GeV. The scale uncertainty was determined from the envelope of predictions with the QCD renormalisation and factorisation scales varied independently up or down by a factor of two. The combined uncertainty due to the parton distribution functions (PDFs) and the strong coupling constant, αS, was calculated following the PDF4LHC prescription [13]with the MSTW2008 NNLO, CT10 NNLO [14,15] and NNPDF2.3 5fFFN NNLO [16]PDF sets. E-mail address: atlas .publications @cern .ch. Measurements of inclusive σt¯ tat 7, 8 and 13 TeV were performed by both the ATLAS [17–19] and CMS [20–24] collaborations. All measurements are consistent with NNLO+NNLL QCD predictions. Additionally, the CMS Collaboration performed a measurement of σt¯ tat √s=5.02 TeV [25]. At √s=13 TeV, the ATLAS Collaboration used a data sample of 36.1 fb−1and events with an opposite-charge electron–muon pair in the final state to obtain σt¯ t=826.4 ±3.6(stat.) ±11.5(syst.) ±15.7(lumi.) ± 1.9(beam)pb [26], giving a total relative uncertainty of 2.4%. This Letter documents measurements of the t¯ tcross-sections in the full phase space (inclusive) and in a phase space defined to be close to the experimental measurement range (fiducial) at √s=13 TeV, using the full pp dataset collected during 2015–2018. It targets the lepton+jets t¯ tdecay mode, where one Wboson originating from the top quark decays leptonically and the other Wboson decays hadronically, i.e. t¯ t→W+W−b¯ b→νq¯ qb¯ b, producing a final state with one high-momentum electron or muon and four jets, two of which are b-quark-initiated jets.1A small contribution from t¯ tevents with both Wbosons decaying leptonically producing the same final state due to one lepton being out of acceptance is treated as signal. A profile-likelihood fit to data in three nonoverlapping regions is employed to perform the measurement. The study presented in this letter probes a final state that is complementary to the one explored in Ref. [26] and is sensitive to different t¯ tmodelling uncertainties, e.g. uncertainties related to quark jets, the understanding of which is mandatory for a large 1Events involving W→τν decays with a subsequent decay of the τ-lepton into eνeντor μνμντare included in the signal. https://doi.org/10.1016/j.physletb.2020.135797 0370-2693/©2020 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Funded by SCOAP3. The ATLAS Collaboration Physics Letters B 810 (2020) 135797 number of top-quark precision measurements and searches beyond the SM. 2. ATLAS detector ATLAS [27–29]is a multipurpose particle detector designed with a forward–backward symmetric cylindrical geometry and nearly full 4πcoverage in solid angle.2It consists of an inner tracking detector surrounded by a thin superconducting solenoid providing a 2Taxial magnetic field, electromagnetic and hadronic calorimeters, and a muon spectrometer. The inner tracking detector covers the pseudorapidity range |η| <2.5 and is composed of silicon pixel, silicon microstrip, and transition radiation tracking (TRT) detectors. Lead/liquid-argon (LAr) sampling calorimeters provide electromagnetic (EM) energy measurements with high granularity. Hadronic calorimetry is provided by the steel/scintillator-tile calorimeter covering the central pseudorapidity range (|η| <1.7). The endcap and forward regions are instrumented with LAr calorimeters for both the EM and hadronic energy measurements up to |η| =4.9. The muon spectrometer surrounds the calorimeters and is based on three large air-core toroidal superconducting magnets with eight coils each. The field integral of the toroids ranges between 2.0 and 6.0 Tm across most of the detector. The muon spectrometer includes a system of precision tracking chambers and fast detectors for triggering. A twolevel trigger system is used to select events. The first-level trigger is implemented in hardware and uses a subset of the detector information to keep the accepted event rate below 100 kHz [30]. This is followed by a software-based trigger that reduces the accepted event rate to 1kHz on average. 3. Data and simulation samples The analysis is performed using the full Run 2 LHC pp collision data sample at √s=13 TeV recorded by the ATLAS detector, corresponding to an integrated luminosity of 139 fb−1after data quality requirements [31]are imposed. Events are required to pass a single-electron or single-muon trigger with thresholds that were progressively raised during the data collection period to account for the increase of instantaneous luminosity. Monte Carlo (MC) simulations are used to optimise the analysis and to evaluate acceptances, efficiencies and uncertainties in t¯ t signal and all backgrounds except for the multijet background that is estimated using a data-driven technique. The effect of multiple interactions in the same and neighbouring bunch crossings (pileup) was modelled by overlaying the original hard-scattering event with simulated inelastic pp events generated by Pythia 8.186 [32] using the NNPDF2.3 LO set of PDFs [16] and parameter values set according to the A3 tune [33]. The production of t¯ tevents was modelled using the nextto-leading-order (NLO) matrix element (ME) implemented in the HVQ program [34,35]from the Powheg-Box v2 [36–38] generator with the NNPDF3.0 NLO [39]PDF and the hdamp parameter set to 1.5 mt[40].3The t¯ tsample is normalised to the NNLO+NNLL crosssection. The single-top-quark t-channel, s-channel and tW associated production processes were also modelled at NLO in QCD using 2ATLAS uses a right-handed coordinate system with its origin at the nominal interaction point (IP) in the centre of the detector and the z-axis along the beam pipe. The x-axis points from the IP to the centre of the LHC ring, and the y-axis points upwards. Cylindrical coordinates (r, φ) are used in the transverse plane, φ being the azimuthal angle around the z-axis. The pseudorapidity is defined in terms of the polar angle θas η=− lntan(θ/2). Angular distance is measured in units of R ≡(η)2+(φ)2. 3The hdamp parameter controls the transverse momentum, pT, of the first additional emission beyond the leading-order Feynman diagram in the parton shower and therefore regulates the high-pTemission against which the t¯ tsystem recoils. Powheg-Box v2. For all top-quark processes, Pythia 8.230 [41], using the A14 tune [42] and the NNPDF2.3 LO PDF set, was interfaced to Powheg-Box v2 to simulate the parton shower and hadronisation. The diagram removal scheme [43]was employed in the tW simulation to handle the interference with t¯ tproduction [40]. The V+jets (V=W, Z) backgrounds were simulated with the Sherpa v2.2.1 [44] generator using NLO-accurate MEs for up to two jets, and MEs accurate to leading order (LO) for up to four jets calculated with the Comix [45] and OpenLoops [46,47]libraries. They were matched with the Sherpa parton shower [48] using the MEPS@NLO prescription [49–52] and the tune developed by the Sherpa authors. Diboson production was generated using Sherpa v2.2.2 with MEs computed at NLO accuracy in QCD for up to one additional parton and at LO accuracy for up to three additional partons. The NNPDF3.0 NNLO PDF set [39]was used for the V+jets and diboson samples. The productions of t¯ tH and t¯ tV events were modelled at NLO using the Powheg-Box v2 and MadGraph5_aMC@NLO v2.3.3 [53] generators, respectively, with the NNPDF3.0 NLO PDF set. Pythia 8.230 with the A14 tune and the NNPDF2.3 LO PDF was used to simulate the parton showers. All simulated background samples are normalised to their cross-sections, computed to the highest order available in perturbation theory. The top-quark mass is set to mt=172.5GeVin all simulated samples. The EvtGen v1.6.0 program [54]was used to simulate the decay of bottom and charm hadrons for all event generators except Sherpa. The nominal t¯ tsignal and background samples were processed through the ATLAS simulation software [55] based on GEANT4 [56]. Some of the alternative t¯ tsamples used to evaluate systematic uncertainties were processed through a fast detector simulation making use of parameterised showers in the calorimeters [57]. Corrections are applied to the simulated events so that the selection efficiencies, energy scales and resolutions of particle candidates match those determined from data control samples. 4. Object selection The following sections describe the detectorand particle-level objects used in the inclusive and fiducial cross-section measurements. 4.1. Detector-level objects Electron candidates are reconstructed from energy clusters in the EM calorimeter that match a reconstructed track. Electrons are identified with a likelihood method [58], and are required to meet the tight identification criterion based on shower shapes in the EM calorimeter, track quality and detection of transition radiation produced in the TRT. Electrons are required to have a calorimeter cluster satisfying |ηclust| <2.47. Additionally, electrons in the transition region between barrel and endcap calorimeters with 1.37 <|ηclust| <1.52 are excluded. The electron candidates have to pass pTand η-dependent isolation requirements based on the track and calorimeter activity around them. Muons are reconstructed using information from both the inner detector and the muon spectrometer. Muon candidates are required to have |η| <2.5, to pass medium quality requirements [59] and fulfil isolation criteria based on the calorimeter and tracking information: the calorimeter cluster energy within a cone of size of R =0.2 around the muon track divided by the muon pTmust be smaller than 0.15 and the ratio of the summed transverse momenta of additional tracks within a cone of R =0.3to the muon pTmust be smaller than 0.04. Selected electrons (muons) must have a transverse impact parameter significance |d0/σd0| <5 (3)and a longitudinal impact parameter |z0sinθ| <0.5mm relative to the event’s primary vertex [60]. 2 The ATLAS Collaboration Physics Letters B 810 (2020) 135797 Table 1 Expected event yields including all uncertainties after the event selection compared to data in the three signal regions. The t¯ tX category contains t¯ tV and t¯ tH contributions. SR1 SR2 SR3 t¯ t3630000±210 000 990000±90000 980 000±100000 W+jets 350000±160000 24000±10 000 17 000±9000 Single top 255000±31000 52 000±7000 37000±8000 Z+jets & diboson 80000±40000 8000 ±4000 5800 ±3000 t¯ tX 15600±2100 2110 ±290 7200±1000 Multijet 210000±80000 28000±10 000 22 000±8000 Total prediction 4540000±310 000 1110000±100 000 1 070000±100000 Data 4540886 1 100 558 1103317 Jets are formed from clusters of topologically connected calorimeter cells [61]using the anti-ktjet algorithm [62]with the radius parameter R =0.4implemented in FastJet [63], and are calibrated to particle level as described in Ref. [64]. To suppress jets originating from pile-up collisions, cuts on the Jet Vertex Tagger (JVT) [65] discriminant are applied for jets with pTbelow 120 GeV. Jets containing b-hadrons are identified (b-tagged) via a multivariate algorithm, MV2c10, combining observables sensitive to lifetimes, production mechanisms, and decay properties of bhadrons [66]. A working point with an average efficiency of 60% for b-quark-initiated jets in t¯ tevents and rejection factors against light-quark/gluon-initiated jets and c-quark-initiated jets of 1200 and 55, respectively, is used [67–69]. The missing transverse momentum with magnitude, Emiss T, is defined as the negative vector sum of the transverse momenta of the reconstructed and calibrated physics objects (electrons, photons, hadronically decaying τ-leptons, jets and muons) and a soft term built from all tracks that are associated with the primary vertex, but not with these objects, is included [70,71]. 4.2. Particle-level objects Particle-level objects are defined in simulated events by using only stable particles, i.e. particles with a mean lifetime greater than 30 ps. The fiducial phase space used for the σt¯ tmeasurement is defined using a set of requirements applied to particle-level objects analogous to those used in the selection of the detector-level objects. Leptons are defined as electrons or muons originating from Wdecays, including those from intermediate τ-leptons. The fourmomentum of each charged lepton is summed with the fourmomenta of all radiated photons within a cone of size R =0.1 about its direction, excluding photons from hadron decays, to account for bremsstrahlung. Leptons are required to have pT> 25 GeV and |η| <2.5. Jets are defined using the anti-ktalgorithm with a radius parameter of R =0.4. All stable particles are considered for jet clustering, except for the electrons, muons, and photons used in the lepton definitions. Jets are required to have pT>25 GeV and |η| <2.5 and are identified as b-jets via ghost matching to weakly decaying b-hadrons [62]. The fiducial region is defined by requiring exactly one electron or muon, and at least four jets, one or exactly two of which must be identified as b-jets. Possible double-counting of objects reconstructed at detectoror particle-levels satisfying multiple object definitions is resolved using the same algorithms as in Ref. [72]. 5. Analysis strategy 5.1. Event selection Selected events are required to have exactly one reconstructed electron or muon with pT>25 GeV for the 2015 data-taking period, pT>27 GeV for the 2016 data-taking period and pT> 28 GeV for the 2017 and 2018 data-taking periods, to account for different single-lepton trigger thresholds. Events must have at least four reconstructed jets with pT>25 GeV and |η| <2.5with one or exactly two of the reconstructed jets being b-tagged. To suppress the contribution of the multijet background, events in the electron+jets channel are required to have Emiss T>30 GeV and mT(W) >30 GeV, while in the muon+jets channel, due to a smaller contribution of this background, a looser criterion Emiss T+mT(W) > 60 GeV is applied.4The measurement of the t¯ tcross-section is performed by splitting the selected sample into three non-overlapping signal regions according to the number of jets and b-tagged jets. The region with the highest background fraction (SR1) is selected by requiring ≥4 jets and exactly 1 b-tagged jet. The SR2 (SR3) region has exactly 4 (≥5) jets, exactly two of which must be btagged. The SR1 and SR2 regions have different sensitivities to the background and b-jet modelling while the SR3 provides information about modelling of extra radiation in t¯ tevents. The number of background events meeting the selection criteria is estimated using MC simulations for all processes with the exception of a small contribution from multijet events with a nonprompt or misidentified lepton arising from photon conversions, heavy-flavour hadrons decaying leptonically, and jets misidentified as leptons. A data-driven matrix method [72]based on the measurement of lepton selection efficiencies using different identification and isolation criteria is used to estimate this background. Expected and observed event yields are shown in Table 1and are in excellent agreement. The expected yields include all uncertainties described in Section 6. 5.2. Observables used in the fit The t¯ tcross-section is extracted from a simultaneous profilelikelihood fit of data distributions to the sum of signal and background distributions in the three regions. Each region exploits a different fit variable. In SR1, the aplanarity (A) is used, as was done in previous t¯ tcross-section measurements [73,74]. It is defined entirely with jet information as A =3 2λ3, where λ3is the smallest eigenvalue of the sphericity tensor, Sαβ[75,76].5In SR2, the minimum lepton–jet mass, mmin j, calculated as the minimum invariant mass over all lepton–jet pairs, is exploited. In SR3, a system likely originating from a hadronically decaying top quark is constructed. It consists of a b-tagged jet and two other jets, corresponding to the permutation with the highest pTfor the vector 4mT(W) =2p TEmiss T(1−cosφ), where p Tis the transverse momentum of the charged lepton and φis the opening azimuthal angle between the charged lepton and missing transverse momenta. 5The Sαβ=ipα ipβ i i|pi|2, where pirepresents the three-momentum of jet i; α, β∈ x, y, zand the sum runs over all jets. 3 The ATLAS Collaboration Physics Letters B 810 (2020) 135797 sum of four momenta of the three constituent jets. The average angular distance between the three constituent jets, Ravg bjj , is computed and used in the fit. The choice of variables is driven by their ability to separate t¯ tsignal from the backgrounds, the reduced sensitivity to jet-related experimental and t¯ tmodelling uncertainties achieved by exploiting ratios of jet momenta (A) or angular information (Ravg bjj ), and good agreement between the prediction and data. There is no single variable that satisfies these requirements in all three regions. 6. Systematic uncertainties Several sources of systematic uncertainties affect the fiducial and inclusive t¯ tcross-section measurements by changing the estimated signal and background rates and the shapes of the distributions used in the fit. All uncertainties are treated as correlated between signal regions, unless explicitly specified otherwise. They can be classified into experimental and modelling uncertainties in the t¯ tsignal and in backgrounds. 6.1. Experimental uncertainties The uncertainty in the combined 2015–2018 integrated luminosity (Lint) is 1.7% [77], obtained using the LUCID-2 detector [78] for the primary luminosity measurements. Reconstruction, identification, isolation and trigger performance for electrons and muons differ between data and MC simulations. Scale factors are applied to simulated events to correct for the differences. These scale factors, as well as the lepton momentum scale and resolution, are assessed using Z→+−events in simulation and data with methods similar to those described in Refs. [58,59]. The associated systematic uncertainties are propagated to the distributions used in the fit. Their combined effects on the cross-section measurement are referred to as “Muon reconstruction” and “Electron reconstruction” in Table 3. The jet energy scale (JES) is calibrated using a combination of test beam data, simulation and in situ techniques [64]. Its uncertainty is decomposed into a set of 29 uncorrelated components, with contributions from pile-up, jet flavour composition, singleparticle response, and effects of jets not contained within the calorimeter. The uncertainty of the jet energy resolution (JER) is represented by eight components accounting for jet-pTand ηdependent differences between simulation and data [79]. The uncertainty in the efficiency to pass the JVT requirement for pile-up suppression is also considered [65]. The combined effect on the cross-section measurement of jet-related uncertainties is referred to as “Jet reconstruction” in Table 3. The uncertainties in the b-tagging calibration are determined separately for b-jets, c-jets and light-flavour-jets [66,68,69]using an 85-component breakdown (45 for b-jets, 20 for c-jets and 20 for light-flavour jets). They depend on pTfor band c-jets, and on pTand ηfor light-flavour jets, and they account for differences between data and simulation. The impact of these uncertainties on the cross-section measurement is referred to as “Flavour tagging” in Table 3. The uncertainty in Emiss Tdue to a possible miscalibration of its soft-track component is derived from data–simulation comparisons of the pTbalance between the hard and the soft Emiss Tcomponents [70]. To account for the difference in pile-up distributions between the simulation and data, the pile-up profile in the simulation is corrected to match the one in data. The uncertainty associated with the correction factor is applied. The combined impact of the Emiss Tand pile-up uncertainties is referred to as “Emiss T + pile-up” in Table 3. 6.2. Signal modelling The uncertainty due to missing higher-order QCD corrections in the ME computation is estimated by independently varying the renormalisation (μR) and factorisation (μF) scales by factors of 2.0 and 0.5 with respect to the central value. Additionally, uncertainties in the amounts of initialand final-state radiation (FSR) from the parton shower are assessed by, respectively varying the corresponding parameter of the A14 parton shower tune (Var3c) [42] and by varying by factors of 2.0 and 0.5 the scale μFSR R. All four variations are taken to be uncorrelated between the signal regions but fully correlated across bins in each region. The combined impact of all scale uncertainties is referred to as “t¯ tscale variations” in Table 3. An uncertainty due to the choice of the hdamp parameter value is determined by comparing the nominal t¯ tsample with the one produced with the same settings but with the hdamp parameter set to 3mtand is symmetrised. The level of agreement between data and prediction for the lepton pTand the leading jet pTimproves if the top-quark pTdistribution in the nominal t¯ tsimulation is corrected to match the top-quark pTcalculated at NNLO in QCD with NLO electroweak corrections [80]. In this analysis, the full difference between the nominal and the reweighted simulated t¯ tsample is taken as a systematic uncertainty and symmetrised. This approach is preferable to applying a correction to the nominal simulation because for some variables the level of agreement between data and prediction deteriorates after applying the correction. To avoid double counting, modelling uncertainties, which are evaluated using alternative samples, are derived as the difference between the nominal and alternative samples, both reweighted to the top-quark pTtheory prediction. Uncertainties due to the choice of parton shower and hadronisation model are estimated by comparing the nominal sample from Powheg-Box interfaced to Pythia with an alternative sample generated with the same Powheg-Box set-up but interfaced to Herwig 7.0.4 [81,82]with angle-ordered parton shower model, the H7UE tune [81] and the MMHT2014LO PDF set [83]. Further details about the sample settings can be found in Ref. [84]. The difference between the two models is split into three components. The first component represents the total t¯ tacceptance in the three regions (“Shower model incl. acceptance” in Fig. 3). The second component is sensitive to the t¯ tyield difference in the individual signal regions (“Shower migration parameter” in Fig. 3). The last component is responsible for the shape effect on the fitted distributions. It is represented by three nuisance parameters (NPs), one per region (referred to as “Shower model shape” followed by a region name in Fig. 3), to ensure that shape effects are uncorrelated between the regions since different variables are used in the fit. All three components are symmetrised. The combined impact of all uncertainties due to the choice of parton shower and hadronisation model is referred to as “t¯ tshower/hadronisation” in Table 3. The PDF4LHC15 meta-PDFs are used to estimate the systematic effects, including impact on the acceptance, due to uncertainties in the PDF, following the updated PDF4LHC15 prescription [85]. A set of 30 Hessian eigenvectors corresponding to independent PDF variations is included in the fit. The central values of the NNPDF3.0 PDF used to simulate the nominal t¯ tsample and the PDF4LHC15 set are found to be consistent. 6.3. Background modelling Uncertainties in the multijet background estimation include a 50% uncertainty in the normalisation to cover differences between the data and the matrix method prediction in various control regions enriched in multijet background events [72] and an uncertainty from the choice of parameterisation of the efficiencies for 4 The ATLAS Collaboration Physics Letters B 810 (2020) 135797 real and misidentified leptons. These uncertainties are treated as uncorrelated between all regions and between electron+jets and muon+jets events due to different composition of the multijet background in these regions and different choice of efficiency parameterisation in the electron+jets and muon+jets channels. The impact of the multijet background estimation uncertainty on the measurement is referred to as “Multijet background” in Table 3. The tW contribution is the largest among the three single-topquark production channels. A normalisation uncertainty of 5.4% is applied to the single-top-quark background, corresponding to the theoretical uncertainty of the tW cross-section [86]. Similarly to the t¯ tmodelling uncertainties, the effects of the μRand μFvariations in the ME, the variations of parameters related to initialand final-state radiation in the parton shower and the impact of the parton shower choice are evaluated for the single-top-quark background. An additional uncertainty arising from the method used to handle interference between tW and t¯ tproduction is determined by comparing the tW simulated sample that uses the diagram-subtraction method [87]with the nominal one based on the diagram-removal technique. Several uncertainties affect the modelling of the W+jets background. Variations of μRand μFare used to derive the W+jets normalisation uncertainties in each region. They amount to about 45% and are treated as uncorrelated between the regions selected with 1-b-tag (SR1) and 2-b-tag (SR2 and SR3) requirements. The effects on the shape of the distributions arising from the μRand μFvariations, from the choice of ME to parton-shower CKKW matching scale [51,88] and from the scale used for the resummation of soft-gluon emission in the nominal sample are also included. A normalisation uncertainty of 50% is applied to the combined Z+jets and diboson background based on the studies of the μRand μFvariations for the W+jets process. A normalisation uncertainty of 13.3% is applied [89]to the t¯ tX contribution, based on the theoretical cross-section uncertainties for the t¯ tV and t¯ tH processes. For the backgrounds, the systematic uncertainties due to the PDF choice are found to be negligible. The combined effect on the measured cross-section of all MC simulation background modelling uncertainties is referred to as “MC background modelling” in Table 3. 7. Extraction of the t ¯ tcross-section Events fulfilling the criteria described in Section 5are used to perform measurements of the fiducial and inclusive t¯ tcrosssections from a profile-likelihood fit to data. The fit uses the distributions of variables described in Section 5.2 in three signal regions, and the systematic uncertainties (see Section 6) are included in the fit as NPs. Statistical uncertainties in each bin due to the limited size of the simulated samples are taken into account by dedicated nuisance parameters using the Barlow-Beeston technique [90] and their effect on the measurement is referred to as “Simulation stat. uncertainty” in Table 3. The cross-section for producing t¯ tevents in the fiducial region, σfid, is defined as σfid =νfid/Lint, where νfid is the number of t¯ tevents in the fiducial volume determined by the fit. The inclusive cross-section, σinc, is related to the fiducial one via σfid =Afid ×σinc, where Afid =Nfid/Ntot is the fiducial acceptance with Nfid (Ntot) being the number of t¯ tevents obtained from a simulated signal sample after (before) applying the particlelevel selection. For the σfid measurement, all samples of simulated events used to evaluate the t¯ tmodelling uncertainties are scaled to the same fiducial acceptance, defined in Section 4.2. The fiducial acceptance is evaluated using the nominal t¯ tsample reweighted to match the top-quark pTtheoretical calculation to be consistent with the treatment of the alternative t¯ tsamples. Such scaling Table 2 Fiducial acceptances for different t¯ tmodels, with the variations relative to the nominal model, after applying the particle-level event selection. The uncertainty in the acceptance due to each systematic variation (Aalt fid) is computed with respect to the acceptance obtained from the nominal t¯ tsample reweighted to the NNLO theory prediction of the top-quark pTgiven in the second row (Anom fid ). The PDF uncertainty is a sum in quadrature of uncertainties from 30 independent PDF variations in the PDF4LHC15 prescription. The last row shows the total relative uncertainty in the nominal acceptance. Generator set-up Afid [%] Aalt fid−Anom fid Anom fid [%] Powheg+Pythia nominal 13.50 0.00 Powheg+Pythia top-quark pTreweighted 13.40 −0.75 μFSR R×2 13.58 1.29 μFSR R×0.5 13.18 −1.64 μR×2 13.37 −0.25 μR×0.5 13.45 0.38 μF×2 13.38 −0.15 μF×0.5 13.43 0.17 Var3cUp 13.46 0.41 Var3cDown 13.35 −0.38 hdamp ×2 13.57 1.21 Powheg+Herwig 13.44 0.31 PDF4LHC15 variations 0.47 Total +1.9 −2.2 ensures that in each signal region the remaining normalisation uncertainties from t¯ tmodelling correspond to the uncertainties in the correction factor C=Nreco/Nfid, where Nreco is the number of selected events in a given region. The scaled distributions enter the fit to measure σfid, thus reducing the impact of t¯ tmodelling uncertainties by reducing the normalisation effects. For the σinc extraction, the t¯ tmodelling uncertainties include the uncertainties corresponding to the extrapolation of each systematic uncertainty component to the full phase space. The acceptance Afid for different systematic variations of the t¯ tmodel is shown in Table 2. The PDF uncertainty is calculated following the PDF4LHC15 prescription as a sum in quadrature of uncertainties from 30 independent PDF variations. The relative acceptance uncertainty in the propagation of the fiducial cross-section to the full phase space for the nominal t¯ tmodel is +1.9 −2.2%. 8. Results The t¯ tfiducial cross-section is found to be σfid =110.7±0.05 (stat.) +4.5 −4.3(syst.) ±1.9(lumi.)pb =110.7±4.8pb. Here, the luminosity uncertainty is obtained by repeating the fit, fixing the corresponding nuisance parameter, and subtracting in quadrature the resulting uncertainty from the total uncertainty of the nominal fit. The systematic uncertainty is determined by subtracting in quadrature the statistical uncertainty, obtained from a fit where all NPs are fixed to the values determined by the fit (post-fit), and the luminosity uncertainty, from the total uncertainty. Fig. 1displays the post-fit distributions of the observables used in the fit in each region. Fig. 2shows preand post-fit distributions of one kinematic variable per region, which is not included in the fit, demonstrating that the level of agreement between the prediction and the data improves after the fit. The HTdistribution shows a difference between prediction and data, which is covered by the uncertainties both before and after the fit. This feature has no effect on the variables used in the fit or on the result. The effect of the residual disagreement in the distribution of the fourth largest jet pTin SR2, which is not fully covered by the post-fit uncertainty 5 The ATLAS Collaboration Physics Letters B 810 (2020) 135797 Fig. 1. Post-fit distributions of t¯ tsignal and backgrounds compared with data for the observables used in the fiducial cross-section fit. The hatched bands represent combined statistical and systematic uncertainties, after propagating the constraints and correlations obtained from the fit to data. All background categories except single top and W+jets are combined in one category called Other bkg. The first and last bins contain underflow and overflow events, respectively. band, is tested as follows. Pseudo-data are created by reweighting the detector-level prediction for events passing the selection to match the corresponding distribution in data in SR2, and the t¯ t cross-section is extracted. No significant impact on the measured cross-section is observed. Using the measured fiducial cross-section and the acceptance with its uncertainty from Table 2, and assuming that the uncertainties of the Afid are not correlated with those obtained in the fit, the t¯ tcross-section extrapolated to the full phase space is σext inc =820 ±0.4(stat.)±37 (syst.) ±14 (lumi.) pb =820 ±40 pb. The t¯ tcross-section in the full phase space, referred to as inclusive cross-section, measured in the dedicated fit is σinc =830 ±0.4(stat.)±36 (syst.) ±14 (lumi.) pb =830 ±38 pb. The two results are compatible within the uncertainties and are in agreement with the theoretical NNLO + NNLL prediction for the top-quark mass of 172.5 GeV. The difference between the central values arises from the different assumptions related to the t¯ tmodelling uncertainties. For the inclusive measurement, the alternative models are assumed to have the same σt¯ tin the full phase space, while for the fiducial measurement they are assumed to have the same cross-section after applying the fiducial selection. This results in different normalisation components of the signal modelling uncertainties, leading to different impacts of these uncertainties on the measured cross-section for the same post-fit values of the corresponding nuisance parameters. The dependence of the measured inclusive t¯ tcross-section on mtis determined by repeating the fit to data after replacing the nominal input t¯ tdistributions by those from the samples generated with the same set-up as the nominal but with mt=171, 172, 173 and 174 GeV, assuming that the t¯ tmodelling uncertainties are independent of mt. The dependence is found to be 1/σinc ×dσinc/dmt=−1.7%/GeV. Fig. 3presents the ranking of the effects of different systematic uncertainties on the inclusive measurement. The impact of each NP, θ, is computed by comparing the nominal best-fit value of σinc with the result of the fit when fixing the considered nuisance parameter to its best-fit value, ˆ θ, shifted by its pre-fit (post-fit) uncertainties ±θ(±ˆ θ). The ranking plot shows that the uncertainty in σinc is dominated by the difference in the t¯ tinclusive acceptance and the migration parameter between the nominal and the alternative parton shower and hadronisation model. The NP corresponding to the migration parameter is constrained, indicating that the normalisation effects of the alternative model vary significantly between the three regions. In SR1 (SR3), the alternative model predicts 1.4% (2.3%) larger yield while in SR2 it predicts 7.1% smaller yield than in the nominal t¯ tsimulation. These variations are much larger than the data uncertainty and allow the data to constrain this uncertainty. To check that this choice for the parameterisation of the parton shower systematic uncertainty does not affect the result, an alternative parameterisation is implemented with three normalisation and three shape NPs uncorrelated between three signal regions. No change in the central value or total uncertainty is observed, while the parameters show similar level of constraints and pulls as in the baseline fit. Other significant contributions to the uncertainty arise from the modelling of finalstate radiation in SR1 and the top-quark pTmodel. As expected, the latter is pulled towards the NNLO prediction, which is approximated here by a one-dimensional top-quark pTreweighting. The uncertainty in the integrated luminosity is the highest-ranked experimental uncertainty. A breakdown of the contributions from different categories of systematic uncertainties is presented in Table 3. The largest uncertainties, in both the fiducial and inclusive cross-section measurements, arise from the shower and hadronisation modelling and the scale variations. The source of the largest experimental uncertainty is the jet reconstruction category which includes uncertainties from jet identification, calibration, resolution and the JVT requirement. Several tests were performed to check the stability of the result. To examine the disagreement between data and prediction observed in jet pTspectra as illustrated in Fig. 2, the impact of changing the minimum jet pTrequirement was studied by repeating the analysis while selecting events with a minimum jet pTof 30 GeV and 35 GeV instead of 25 GeV. In both cases, the measured cross-section changed by less than 2% and did not show a trend depending on the jet pTcut. 6 The ATLAS Collaboration Physics Letters B 810 (2020) 135797 Fig. 2. Pre-fit (top) and post-fit (bottom) distributions of the scalar sum of jet transverse momenta in the event (HT) in SR1 (left), the fourth largest jet pTin SR2 (middle) and the lepton pTin SR3 (right) for the fiducial cross-section measurement. The hatched bands represent combined statistical and systematic uncertainties. The first and last bins contain underflow and overflow events, respectively. The approach to performing ME to parton shower matching differs between NLO generators and, in general, can be a source of uncertainty. However, it is not straightforward to separate the effect of the algorithmic difference in the implementation of such matching from other effects when replacing one ME generator by an alternative one, matched to the same parton shower. This may involve changes in the parameters of the parton shower that can lead to a much larger effect than the targeted one. For this reason, the effect of the generator choice is not included in the fit model. However, its impact on the result is checked by comparing two alternative t¯ tsamples generated with Powheg-Box v2 and MadGraph5_aMC@NLO, both interfaced to Herwig 7.1.3 [91]. A symmetrised difference between these two samples is applied as an additional systematic uncertainty, correlated between regions. No significant impact on the central value or the uncertainty is observed for either the inclusive or the fiducial measurements. The stability of the result with respect to the choice of correlation scheme for the initialand final-state radiation uncertainties, and for the μRand μFscale variations, was studied. In the alternative scheme, the uncertainties were treated as fully correlated across the signal regions. No effect on either the measured crosssections or the uncertainties was observed. 9. Conclusion Measurements of the inclusive and fiducial t¯ tproduction crosssections are performed in the lepton+jets channel using proton– proton collision data at √s=13 TeV recorded by the ATLAS detector at the LHC during 2015–2018, corresponding to an integrated luminosity of 139 fb−1. The analysis is performed in three regions requiring different jet multiplicities and different numbers of b-tagged jets. The t¯ tproduction cross-section and its uncertainty are extracted from a profile-likelihood fit to data of the distributions of discriminating variables in these three regions, assuming mt=172.5GeV. The fiducial cross-section is measured with a precision of 4.3% to be σfid =110.7 ±4.8pb=110.7 ± 0.05 (stat.)+4.5 −4.3(syst.) ±1.9(lumi.)pb, and the inclusive crosssection is measured with a precision of 4.6% to be σinc =830 ± 38 pb =830 ±0.4(stat.)±36 (syst.) ±14 (lumi.) pb. The inclusive result is in agreement with the theoretical NNLO + NNLL 7 The ATLAS Collaboration Physics Letters B 810 (2020) 135797 Fig. 3. Ranking plot showing the effect of the ten most important systematic uncertainties on the measured cross-section, normalised to the predicted value, in the inclusive fit to data. The impact of each NP, σinc/σpred. inc , is computed by comparing the nominal best-fit value of σinc/σpred inc with the result of the fit when fixing the considered nuisance parameter to its best-fit value, ˆ θ, shifted by its pre-fit and post-fit uncertainties ±θ(±ˆ θ). The empty boxes show the pre-fit impact while the filled boxes show the post-fit impact of each nuisance parameter on the result. The black dots represent the post-fit value (pull) of each NP where the pre-fit value is subtracted, while the black line represents the post-fit uncertainty normalised to the pre-fit uncertainty. The “JES (pile-up subtraction)” is one of the 29 components of the JES uncertainty, the “FSR model SR1” is the FSR scale uncertainty in SR1 and the “PDF4LHC NP4” is one of the 30 independent PDF variations. Other components are described in Section 6. Table 3 Impact of different categories of systematic uncertainties and data statistics on the fiducial and inclusive measurements. The quoted values are obtained by repeating the fit, fixing a set of nuisance parameters of the sources corresponding to the considered category, and subtracting in quadrature the resulting uncertainty from the total uncertainty of the nominal fit presented in the last line. The total uncertainty is different from the sum in quadrature of the different components due to correlations between nuisance parameters built by the fit. The categories are defined in Section 6. Category σfid σfid [%] σinc σinc [%] Signal modelling t¯ tshower/hadronisation ±2.8±2.9 t¯ tscale variations ±1.4±2.0 Top pTNNLO reweighting ±0.4±1.1 t¯ th damp ±1.5±1.4 t¯ tPDF ±1.4±1.5 Background modelling MC background modelling ±1.8±2.0 Multijet background ±0.8±0.6 Detector modelling Jet reconstruction ±2.5±2.6 Luminosity ±1.7±1.7 Flavour tagging ±1.2±1.3 Emiss T+pile-up ±0.3±0.3 Muon reconstruction ±0.6±0.5 Electron reconstruction ±0.7±0.6 Simulation stat. uncertainty ±0.6±0.7 Total systematic uncertainty ±4.3±4.6 Data statistical uncertainty ±0.05 ±0.05 Total uncertainty ±4.3±4.6 QCD calculation as well as with the ATLAS measurement in the electron–muon channel and with CMS measurements. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements We thank CERN for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS and CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF and MPG, Germany; GSRT, Greece; RGC and Hong Kong SAR, China; ISF and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia and NRC KI, Russia Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, Canarie, Compute Canada and CRC, Canada; ERC, ERDF, Horizon 2020, Marie Skłodowska-Curie Actions and COST, European Union; Investissements d’Avenir Labex, Investissements d’Avenir Idex and ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and GIF, Israel; CERCA Programme Generalitat de Catalunya and PROMETEO Programme Generalitat Valenciana, Spain; Göran Gustafssons Stiftelse, Sweden; The Royal Society and Leverhulme Trust, United Kingdom. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NLT1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (UK) and BNL (USA), the Tier-2 facilities worldwide and large non-WLCG resource providers. Major contributors of computing resources are listed in Ref. [92]. References [1] ATLAS Collaboration, Measurement of the top quark mass in the t¯ t→lepton + jets channel from √s=8TeV ATLAS data and combination with previous results, Eur. Phys. J. C 79 (2019) 290, arXiv:1810 .01772 [hep -ex]. [2] CMS Collaboration, Measurement of the top quark mass using proton–proton data at √s=7and 8 TeV, Phys. Rev. D 93 (2016) 072004, arXiv:1509 .04044 [hep -ex]. [3] A. Buckley, et al., Constraining top quark effective theory in the LHC Run II era, J. High Energy Phys. 04 (2016) 15, arXiv:1512 .03360 [hep -ph]. [4] M. Cacciari, M. Czakon, M. Mangano, A. Mitov, P. Nason, Top-pair production at hadron colliders with next-to-next-to-leading logarithmic soft-gluon resummation, Phys. Lett. B 710 (2012) 612, arXiv:1111.5869 [hep -ph]. [5] P. Bärnreuther, M. Czakon, A. Mitov, Percent-level-precision physics at the Tevatron: next-to-next-to-leading order QCD corrections to q¯ q→t¯ t+X, Phys. Rev. Lett. 109 (2012) 132001, arXiv:1204 .5201 [hep -ph]. [6] M. Czakon, A. Mitov, NNLO corrections to top-pair production at hadron colliders: the all-fermionic scattering channels, J. High Energy Phys. 12 (2012) 054, arXiv:1207.0236 [hep -ph]. 8 The ATLAS Collaboration Physics Letters B 810 (2020) 135797 [7] M. Czakon, A. Mitov, NNLO corrections to top pair production at hadron colliders: the quark-gluon reaction, J. High Energy Phys. 01 (2013) 080, arXiv: 1210 .6832 [hep -ph]. [8] M. Czakon, P. Fiedler, A. Mitov, Total top-quark pair-production cross section at hadron colliders through O(α4 S), Phys. Rev. Lett. 110 (2013) 252004, arXiv: 1303 .6254 [hep -ph]. [9] S. Catani, et al., Top-quark pair hadroproduction at next-to-next-to-leading order in QCD, Phys. Rev. D 99 (2019) 051501, arXiv:1901.04005 [hep -ph]. [10] M. Czakon, A. Mitov, Top++: a program for the calculation of the top-pair cross-section at hadron colliders, Comput. Phys. Commun. 185 (2014) 2930, arXiv:1112 .5675 [hep -ph]. [11] A.D. Martin, W.J. Stirling, R.S. Thorne, G. Watt, Parton distributions for the LHC, Eur. Phys. J. C 63 (2009) 189, arXiv:0901.0002 [hep -ph]. [12] A.D. Martin, W.J. Stirling, R.S. Thorne, G. Watt, Uncertainties on αSin global PDF analyses and implications for predicted hadronic cross sections, Eur. Phys. J. C 64 (2009) 653, arXiv:0905 .3531 [hep -ph]. [13] M. Botje, et al., The PDF4LHC working group interim recommendations, arXiv: 1101.0538 [hep -ph], 2011. [14] H.-L. Lai, et al., New parton distributions for collider physics, Phys. Rev. D 82 (2010) 074024, arXiv:1007.2241 [hep -ph]. [15] J. Gao, et al., CT10 next-to-next-to-leading order global analysis of QCD, Phys. Rev. D 89 (2014) 033009, arXiv:1302 .6246 [hep -ph]. [16] R.D. Ball, et al., Parton distributions with LHC data, Nucl. Phys. B 867 (2013) 244, arXiv:1207.1303 [hep -ph]. [17] ATLAS Collaboration, Measurement of the t¯ tproduction cross-section using eμ events with b-tagged jets in pp collisions at √s=7and 8 TeV with the ATLAS detector, Eur. Phys. J. C 74 (2014) 3109, arXiv:1406 .5375 [hep -ex], Addendum: Eur. Phys. J. C 76 (2016) 642. [18] ATLAS Collaboration, Measurement of the t¯ tproduction cross section in the τ+jets final state in pp collisions at √s=8TeV using the ATLAS detector, Phys. Rev. D 95 (2017) 072003, arXiv:1702 .08839 [hep -ex]. [19] ATLAS Collaboration, Measurement of the t¯ tproduction cross-section and lepton differential distributions in eμdilepton events from pp collisions at √s= 13 TeV with the ATLAS detector, Eur. Phys. J. C 80 (2020) 528, arXiv:1910 .08819 [hep -ex]. [20] CMS Collaboration, Measurement of the t¯ tproduction cross section in the eμ channel in proton–proton collisions at √s=7and 8 TeV, J. High Energy Phys. 08 (2016) 029, arXiv:1603 .02303 [hep -ex]. [21] CMS Collaboration, Measurements of the t¯ tproduction cross section in lepton+jets final states in pp collisions at 8 TeV and ratio of 8 to 7 TeV cross sections, Eur. Phys. J. C 77 (2017) 15, arXiv:1602 .09024 [hep -ex]. [22] CMS Collaboration, Measurement of the t¯ tproduction cross section using events in the eμfinal state in pp collisions at √s=13 TeV, Eur. Phys. J. C 77 (2017) 172, arXiv:1611.04040 [hep -ex]. [23] CMS Collaboration, Measurement of the t¯ tproduction cross section using events with one lepton and at least one jet in pp collisions at √s=13 TeV, J. High Energy Phys. 09 (2017) 051, arXiv:1701.06228 [hep -ex]. [24] CMS Collaboration, Measurement of the t¯ tproduction cross section, the top quark mass, and the strong coupling constant using dilepton events in pp collisions at √s=13 TeV, Eur. Phys. J. C 79 (2019) 368, arXiv:1812 .10505 [hep -ex]. [25] CMS Collaboration, Measurement of the inclusive t¯ tcross section in pp collisions at √s=5.02 TeV using final states with at least one charged lepton, J. High Energy Phys. 03 (2018) 115, arXiv:1711.03143 [hep -ex]. [26] ATLAS Collaboration, Measurement of the t¯ tproduction cross-section and lepton differential distributions in eμdilepton events from pp collisions at √s= 13 TeV with the ATLAS detector, arXiv:1910 .08819 [hep -ex], 2019. [27] ATLAS Collaboration, The ATLAS experiment at the CERN Large Hadron Collider, J. Instrum. 3 (2008) S08003. [28] B. Abbott, et al., Production and integration of the ATLAS Insertable B-Layer, J. Instrum. 13 (2018) T05008, arXiv:1803 .00844 [physics .ins -det]. [29] ATLAS Collaboration, ATLAS Insertable B-Layer technical design report addendum, CERN-LHCC-2012-009, Addendum to CERN-LHCC-2010-013, ATLAS-TDR019, https://cds .cern .ch /record /1451888, 2012. [30] ATLAS Collaboration, Performance of the ATLAS trigger system in 2015, Eur. Phys. J. C 77 (2017) 317, arXiv:1611.09661 [hep -ex]. [31] ATLAS Collaboration, ATLAS data quality operations and performance for 2015–2018 data-taking, J. Instrum. 15 (2020) P04003, arXiv:1911.04632 [physics .ins -det]. [32] T. Sjöstrand, S. Mrenna, P. Skands, A brief introduction to PYTHIA 8.1, Comput. Phys. Commun. 178 (2008) 852, arXiv:0710 .3820 [hep -ph]. [33] ATLAS Collaboration, The Pythia 8 A3 tune description of ATLAS minimum bias and inelastic measurements incorporating the Donnachie–Landshoff diffractive model, ATL-PHYS-PUB-2016-017, https://cds .cern .ch /record /2206965, 2016. [34] S. Frixione, P. Nason, G. Ridolfi, A positive-weight next-to-leading-order Monte Carlo for heavy flavour hadroproduction, J. High Energy Phys. 09 (2007) 126, arXiv:0707.3088 [hep -ph]. [35] S. Frixione, P. Nason, G. Ridolfi, The POWHEG-hvq manual version 1.0, arXiv: 0707.3081 [hep -ph], 2007. [36] P. Nason, A new method for combining NLO QCD with shower Monte Carlo algorithms, J. High Energy Phys. 11 (2004) 040, arXiv:hep -ph /0409146. [37] S. Frixione, P. Nason, C. Oleari, Matching NLO QCD computations with parton shower simulations: the POWHEG method, J. High Energy Phys. 11 (2007) 070, arXiv:0709 .2092 [hep -ph]. [38] S. Alioli, P. Nason, C. Oleari, E. Re, A general framework for implementing NLO calculations in shower Monte Carlo programs: the POWHEG BOX, J. High Energy Phys. 06 (2010) 043, arXiv:1002 .2581 [hep -ph]. [39] R.D. Ball, et al., Parton distributions for the LHC Run II, J. High Energy Phys. 04 (2015) 040, arXiv:1410 .8849 [hep -ph]. [40] ATLAS Collaboration, Studies on top-quark Monte Carlo modelling for Top2016, ATL-PHYS-PUB-2016-020, https://cds .cern .ch /record /2216168, 2016. [41] T. Sjöstrand, et al., An introduction to PYTHIA 8.2, Comput. Phys. Commun. 191 (2015) 159, arXiv:1410 .3012 [hep -ph]. [42] ATLAS Collaboration, ATLAS Pythia 8 tunes to 7 TeV data, ATL-PHYS-PUB-2014021, https://cds .cern .ch /record /1966419, 2014. [43] S. Frixione, E. Laenen, P. Motylinski, C.D. White, B.R. Webber, Single-top hadroproduction in association with a W boson, J. High Energy Phys. 07 (2008) 029, arXiv:0805 .3067 [hep -ph]. [44] E. Bothmann, et al., Event generation with Sherpa 2.2, SciPost Phys. 7 (2019) 034, arXiv:1905 .09127 [hep -ph]. [45] T. Gleisberg, S. Höche, Comix, a new matrix element generator, J. High Energy Phys. 12 (2008) 039, arXiv:0808 .3674 [hep -ph]. [46] F. Cascioli, P. Maierhöfer, S. Pozzorini, Scattering amplitudes with open loops, Phys. Rev. Lett. 108 (2012) 111601, arXiv:1111.5206 [hep -ph]. [47] A. Denner, S. Dittmaier, L. Hofer, Collier: a fortran-based complex one-loop library in extended regularizations, Comput. Phys. Commun. 212 (2017) 220, arXiv:1604 .06792 [hep -ph]. [48] S. Schumann, F. Krauss, A parton shower algorithm based on Catani–Seymour dipole factorisation, J. High Energy Phys. 03 (2008) 038, arXiv:0709 .1027 [hep - ph]. [49] S. Höche, F. Krauss, M. Schönherr, F. Siegert, A critical appraisal of NLO +PS matching methods, J. High Energy Phys. 09 (2012) 049, arXiv:1111.1220 [hep - ph]. [50] S. Höche, F. Krauss, M. Schönherr, F. Siegert, QCD matrix elements +parton showers. The NLO case, J. High Energy Phys. 04 (2013) 027, arXiv:1207.5030 [hep -ph]. [51] S. Catani, F. Krauss, B.R. Webber, R. Kuhn, QCD matrix elements +parton showers, J. High Energy Phys. 11 (2001) 063, arXiv:hep -ph /0109231 [hep -ph]. [52] S. Höche, F. Krauss, S. Schumann, F. Siegert, QCD matrix elements and truncated showers, J. High Energy Phys. 05 (2009) 053, arXiv:0903 .1219 [hep -ph]. [53] J. Alwall, et al., The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations, J. High Energy Phys. 07 (2014) 079, arXiv:1405 .0301 [hep -ph]. [54] D.J. Lange, The EvtGen particle decay simulation package, Nucl. Instrum. Methods Phys. Res., Sect. A 462 (2001) 152. [55] ATLAS Collaboration, The ATLAS simulation infrastructure, Eur. Phys. J. C 70 (2010) 823, arXiv:1005 .4568 [physics .ins -det]. [56] S. Agostinelli, et al., GEANT4 – a simulation toolkit, Nucl. Instrum. Methods Phys. Res., Sect. A 506 (2003) 250. [57] ATLAS Collaboration, Fast simulation for ATLAS: Atlfast-II and ISF, ATL-S0FTPR0C-2012-065, http://cds .cern .ch /record /1458503, 2012. [58] ATLAS Collaboration, Electron and photon performance measurements with the ATLAS detector using the 2015–2017 LHC proton-proton collision data, J. Instrum. 14 (2019) P12006, arXiv:1908 .00005 [hep -ex]. [59] ATLAS Collaboration, Muon reconstruction performance of the ATLAS detector in proton-proton collision data at √s=13 TeV, Eur. Phys. J. C 76 (2016) 292, arXiv:1603 .05598 [hep -ex]. [60] ATLAS Collaboration, Vertex reconstruction performance of the ATLAS detector at √s=13 TeV, ATL-PHYS-PUB-2015-026, https://cds .cern .ch /record /2037717, 2015. [61] ATLAS Collaboration, Topological cell clustering in the ATLAS calorimeters and its performance in LHC Run 1, Eur. Phys. J. C 77 (2017) 490, arXiv:1603 .02934 [hep -ex]. [62] M. Cacciari, G.P. Salam, G. Soyez, The anti-ktjet clustering algorithm, J. High Energy Phys. 04 (2008) 063, arXiv:0802 .1189 [hep -ph]. [63] M. Cacciari, G.P. Salam, G. Soyez, FastJet user manual, Eur. Phys. J. C 72 (2012) 1896, arXiv:1111.6097 [hep -ph]. [64] ATLAS Collaboration, Jet energy scale measurements and their systematic uncertainties in proton–proton collisions at √s=13 TeV with the ATLAS detector, Phys. Rev. D 96 (2017) 072002, arXiv:1703 .09665 [hep -ex]. [65] ATLAS Collaboration, Performance of pile-up mitigation techniques for jets in pp collisions at √s=8TeV using the ATLAS detector, Eur. Phys. J. C 76 (2016) 581, arXiv:1510 .03823 [hep -ex]. [66] ATLAS Collaboration, ATLAS b-jet identification performance and efficiency measurement with t¯ tevents in pp collisions at √s=13 TeV, Eur. Phys. J. C 79 (2019) 970, arXiv:1907.05120 [hep -ex]. [67] ATLAS Collaboration, Measurements of b-jet tagging efficiency with the ATLAS detector using t¯ tevents at √s=13 TeV, J. High Energy Phys. 08 (2018) 089, arXiv:1805 .01845 [hep -ex]. [68] ATLAS Collaboration, Measurement of b-tagging efficiency of c-jets in t¯ tevents using a likelihood approach with the ATLAS detector, ATLAS-CONF-2018-001, https://cds .cern .ch /record /2306649, 2018. 9 The ATLAS Collaboration Physics Letters B 810 (2020) 135797 C. Nellist119, C. Nelson104, M.E. Nelson45a,45b, S. Nemecek140, M. Nessi36,e, M.S. Neubauer172, F. Neuhaus100, M. Neumann181, R. Newhouse174, P.R. Newman 21, C.W. Ng138, Y.S. Ng 19, Y.W.Y. Ng 170, B. Ngair35e, H.D.N. Nguyen102, T. Nguyen Manh110, E. Nibigira38, R.B. Nickerson134, R. Nicolaidou 144, D.S. Nielsen40, J. Nielsen 145, M. Niemeyer 53, N. Nikiforou11, V. Nikolaenko123,ag, I. Nikolic-Audit 135, K. Nikolopoulos21, P. Nilsson29, H.R. Nindhito54, A. Nisati73a, N. Nishu 60c, R. Nisius115, I. Nitsche47, T. Nitta 178, T. Nobe 162, D.L. Noel32, Y. Noguchi86, I. Nomidis135, M.A. Nomura29, M. Nordberg36, J. Novak92, T. Novak 92, O. Novgorodova48, R. Novotny 141, L. Nozka130, K. Ntekas170, E. Nurse95, F.G. Oakham34,al, H. Oberlack115, J. Ocariz135, A. Ochi83, I. Ochoa39, J.P. Ochoa-Ricoux146a, K. O’Connor26, S. Oda 88, S. Odaka82, S. Oerdek53, A. Ogrodnik84a, A. Oh101, C.C. Ohm153, H. Oide 164, M.L. Ojeda 166, H. Okawa168, Y. Okazaki86, M.W. O’Keefe91, Y. Okumura162, A. Olariu27b, L.F. Oleiro Seabra 139a, S.A. Olivares Pino146a, D. Oliveira Damazio 29, J.L. Oliver 1, M.J.R. Olsson 170, A. Olszewski85, J. Olszowska85, Ö.O. Öncel24, D.C. O’Neil151, A.P. O’neill134, A. Onofre139a,139e, P.U.E. Onyisi11, H. Oppen 133, R.G. Oreamuno Madriz121, M.J. Oreglia37, G.E. Orellana89, D. Orestano75a,75b, N. Orlando14, R.S. Orr166, V. O’Shea57, R. Ospanov60a, G. Otero y Garzon30, H. Otono88, P.S. Ott 61a, G.J. Ottino18, M. Ouchrif35d, J. Ouellette29, F. Ould-Saada 133, A. Ouraou144, Q. Ouyang15a, M. Owen57, R.E. Owen143, V.E. Ozcan12c, N. Ozturk8, J. Pacalt130, H.A. Pacey32, K. Pachal49, A. Pacheco Pages14, C. Padilla Aranda14, S. Pagan Griso18, G. Palacino66, S. Palazzo50, S. Palestini36, M. Palka84b, P. Palni 84a, C.E. Pandini54, J.G. Panduro Vazquez 94, P. Pani 46, G. Panizzo67a,67c, L. Paolozzi54, C. Papadatos110, K. Papageorgiou9,g, S. Parajuli 42, A. Paramonov6, C. Paraskevopoulos10, D. Paredes Hernandez63b, S.R. Paredes Saenz134, B. Parida179, T.H. Park 166, A.J. Parker31, M.A. Parker32, F. Parodi 55b,55a, E.W. Parrish121, J.A. Parsons39, U. Parzefall 52, L. Pascual Dominguez135, V.R. Pascuzzi18, J.M.P. Pasner145, F. Pasquali 120, E. Pasqualucci73a, S. Passaggio55b, F. Pastore 94, P. Pasuwan 45a,45b, S. Pataraia 100, J.R. Pater101, A. Pathak180,i, J. Patton91, T. Pauly 36, J. Pearkes152, B. Pearson115, M. Pedersen 133, L. Pedraza Diaz119, R. Pedro139a, T. Peiffer53, S.V. Peleganchuk122b,122a, O. Penc140, H. Peng60a, B.S. Peralva 81a, M.M. Perego65, A.P. Pereira Peixoto139a, L. Pereira Sanchez45a,45b, D.V. Perepelitsa29, E. Perez Codina 167a, F. Peri 19, L. Perini69a,69b, H. Pernegger36, S. Perrella36, A. Perrevoort 120, K. Peters46, R.F.Y. Peters101, B.A. Petersen36, T.C. Petersen 40, E. Petit102, V. Petousis141, A. Petridis1, C. Petridou161, P. Petroff 65, F. Petrucci75a,75b, M. Pettee182, N.E. Pettersson103, K. Petukhova 142, A. Peyaud144, R. Pezoa146d, L. Pezzotti71a,71b, T. Pham 105, P.W. Phillips 143, M.W. Phipps172, G. Piacquadio154, E. Pianori18, A. Picazio103, R.H. Pickles101, R. Piegaia30, D. Pietreanu27b, J.E. Pilcher37, A.D. Pilkington101, M. Pinamonti67a,67c, J.L. Pinfold3, C. Pitman Donaldson95, M. Pitt 160, L. Pizzimento74a,74b, A. Pizzini120, M.-A. Pleier 29, V. Plesanovs52, V. Pleskot142, E. Plotnikova80, P. Podberezko122b,122a, R. Poettgen97, R. Poggi54, L. Poggioli135, I. Pogrebnyak107, D. Pohl24, I. Pokharel53, G. Polesello71a, A. Poley151,167a, A. Policicchio73a,73b, R. Polifka142, A. Polini23b, C.S. Pollard46, V. Polychronakos29, D. Ponomarenko112, L. Pontecorvo36, S. Popa27a, G.A. Popeneciu27d, L. Portales5, D.M. Portillo Quintero58, S. Pospisil141, K. Potamianos46, I.N. Potrap80, C.J. Potter32, H. Potti11, T. Poulsen 97, J. Poveda 173, T.D. Powell 148, G. Pownall46, M.E. Pozo Astigarraga36, A. Prades Ibanez173, P. Pralavorio 102, S. Prell79, D. Price101, M. Primavera68a, M.L. Proffitt147, N. Proklova112, K. Prokofiev63c, F. Prokoshin80, S. Protopopescu29, J. Proudfoot6, M. Przybycien84a, D. Pudzha137, A. Puri172, P. Puzo 65, D. Pyatiizbyantseva 112, J. Qian106, Y. Qin 101, A. Quadt53, M. Queitsch-Maitland36, M. Racko28a, F. Ragusa 69a,69b, G. Rahal98, J.A. Raine 54, S. Rajagopalan29, A. Ramirez Morales93, K. Ran 15a,15d, D.M. Rauch46, F. Rauscher114, S. Rave100, B. Ravina57, I. Ravinovich179, J.H. Rawling101, M. Raymond36, A.L. Read133, N.P. Readioff148, M. Reale68a,68b, D.M. Rebuzzi71a,71b, G. Redlinger29, K. Reeves 43, J. Reichert136, D. Reikher160, A. Reiss100, A. Rej150, C. Rembser36, A. Renardi46, M. Renda27b, M.B. Rendel115, A.G. Rennie57, S. Resconi69a, E.D. Resseguie18, S. Rettie95, B. Reynolds127, E. Reynolds21, O.L. Rezanova122b,122a, P. Reznicek 142, E. Ricci76a,76b, R. Richter115, S. Richter46, E. Richter-Was 84b, M. Ridel135, P. Rieck115, O. Rifki46, M. Rijssenbeek154, A. Rimoldi71a,71b, M. Rimoldi 46, L. Rinaldi23b, T.T. Rinn 172, G. Ripellino153, I. Riu 14, P. Rivadeneira46, J.C. Rivera Vergara175, F. Rizatdinova 129, E. Rizvi93, C. Rizzi 36, S.H. Robertson104,ab, M. Robin46, D. Robinson32, C.M. Robles Gajardo146d, M. Robles Manzano100, A. Robson57, A. Rocchi74a,74b, E. Rocco100, C. Roda72a,72b, S. Rodriguez Bosca173, A.M. Rodríguez Vera167b, S. Roe36, J. Roggel181, O. Røhne133, R. Röhrig 115, R.A. Rojas146d, B. Roland52, C.P.A. Roland 66, J. Roloff29, A. Romaniouk112, M. Romano23b,23a, N. Rompotis91, M. Ronzani125, 16 The ATLAS Collaboration Physics Letters B 810 (2020) 135797 L. Roos135, S. Rosati73a, G. Rosin103, B.J. Rosser136, E. Rossi46, E. Rossi75a,75b, E. Rossi70a,70b, L.P. Rossi55b, L. Rossini46, R. Rosten14, M. Rotaru27b, B. Rottler52, D. Rousseau65, G. Rovelli71a,71b, A. Roy11, D. Roy33e, A. Rozanov102, Y. Rozen 159, X. Ruan33e, T.A. Ruggeri1, F. Rühr 52, A. Ruiz-Martinez173, A. Rummler36, Z. Rurikova52, N.A. Rusakovich80, H.L. Russell104, L. Rustige38,47, J.P. Rutherfoord7, E.M. Rüttinger148, M. Rybar 142, G. Rybkin65, E.B. Rye133, A. Ryzhov123, J.A. Sabater Iglesias46, P. Sabatini 53, L. Sabetta73a,73b, S. Sacerdoti65, H.F-W. Sadrozinski145, R. Sadykov 80, F. Safai Tehrani73a, B. Safarzadeh Samani155, M. Safdari152, P. Saha 121, S. Saha104, M. Sahinsoy115, A. Sahu181, M. Saimpert36, M. Saito162, T. Saito162, H. Sakamoto162, D. Salamani 54, G. Salamanna75a,75b, A. Salnikov152, J. Salt 173, A. Salvador Salas14, D. Salvatore41b,41a, F. Salvatore 155, A. Salvucci63a,63b,63c, A. Salzburger36, J. Samarati36, D. Sammel52, D. Sampsonidis161, D. Sampsonidou161, J. Sánchez173, A. Sanchez Pineda67a,36,67c, H. Sandaker133, C.O. Sander46, I.G. Sanderswood90, M. Sandhoff181, C. Sandoval 22b, D.P.C. Sankey143, M. Sannino 55b,55a, Y. Sano 117, A. Sansoni51, C. Santoni38, H. Santos139a,139b, S.N. Santpur18, A. Santra173, K.A. Saoucha148, A. Sapronov80, J.G. Saraiva 139a,139d, O. Sasaki 82, K. Sato168, F. Sauerburger52, E. Sauvan5, P. Savard 166,al, R. Sawada162, C. Sawyer143, L. Sawyer96,af , I. Sayago Galvan173, C. Sbarra23b, A. Sbrizzi67a,67c, T. Scanlon 95, J. Schaarschmidt147, P. Schacht 115, D. Schaefer37, L. Schaefer136, S. Schaepe36, U. Schäfer 100, A.C. Schaffer65, D. Schaile114, R.D. Schamberger154, E. Schanet114, C. Scharf19, N. Scharmberg101, V.A. Schegelsky137, D. Scheirich142, F. Schenck19, M. Schernau170, C. Schiavi55b,55a, L.K. Schildgen24, Z.M. Schillaci26, E.J. Schioppa68a,68b, M. Schioppa41b,41a, K.E. Schleicher52, S. Schlenker36, K.R. Schmidt-Sommerfeld115, K. Schmieden36, C. Schmitt100, S. Schmitt46, L. Schoeffel144, A. Schoening61b, P.G. Scholer 52, E. Schopf134, M. Schott100, J.F.P. Schouwenberg119, J. Schovancova36, S. Schramm54, F. Schroeder 181, A. Schulte100, H-C. Schultz-Coulon61a, M. Schumacher52, B.A. Schumm145, Ph. Schune144, A. Schwartzman152, T.A. Schwarz 106, Ph. Schwemling 144, R. Schwienhorst107, A. Sciandra145, G. Sciolla26, M. Scornajenghi41b,41a, F. Scuri 72a, F. Scutti105, L.M. Scyboz 115, C.D. Sebastiani91, P. Seema19, S.C. Seidel 118, A. Seiden145, B.D. Seidlitz 29, T. Seiss37, C. Seitz 46, J.M. Seixas81b, G. Sekhniaidze 70a, S.J. Sekula 42, N. Semprini-Cesari23b,23a, S. Sen49, C. Serfon29, L. Serin65, L. Serkin67a,67b, M. Sessa60a, H. Severini128, S. Sevova152, F. Sforza 55b,55a, A. Sfyrla54, E. Shabalina53, J.D. Shahinian 145, N.W. Shaikh45a,45b, D. Shaked Renous179, L.Y. Shan15a, M. Shapiro18, A. Sharma134, A.S. Sharma1, P.B. Shatalov124, K. Shaw 155, S.M. Shaw101, M. Shehade179, Y. Shen128, A.D. Sherman25, P. Sherwood95, L. Shi95, C.O. Shimmin 182, Y. Shimogama178, M. Shimojima116, I.P.J. Shipsey134, S. Shirabe164, M. Shiyakova80,z, J. Shlomi 179, A. Shmeleva111, M.J. Shochet37, J. Shojaii 105, D.R. Shope153, S. Shrestha127, E.M. Shrif33e, M.J. Shroff175, E. Shulga179, P. Sicho 140, A.M. Sickles172, E. Sideras Haddad33e, O. Sidiropoulou36, A. Sidoti23b,23a, F. Siegert48, Dj. Sijacki16, M.Jr. Silva180, M.V. Silva Oliveira36, S.B. Silverstein45a, S. Simion 65, R. Simoniello100, C.J. Simpson-allsop21, S. Simsek12b, P. Sinervo166, V. Sinetckii113, S. Singh 151, M. Sioli23b,23a, I. Siral131, S.Yu. Sivoklokov113, J. Sjölin 45a,45b, A. Skaf53, E. Skorda97, P. Skubic128, M. Slawinska85, K. Sliwa169, R. Slovak142, V. Smakhtin 179, B.H. Smart143, J. Smiesko28b, N. Smirnov112, S.Yu. Smirnov112, Y. Smirnov 112, L.N. Smirnova113,r, O. Smirnova97, E.A. Smith37, H.A. Smith134, M. Smizanska 90, K. Smolek141, A. Smykiewicz85, A.A. Snesarev111, H.L. Snoek120, I.M. Snyder 131, S. Snyder 29, R. Sobie175,ab, A. Soffer160, A. Søgaard50, F. Sohns53, C.A. Solans Sanchez36, E.Yu. Soldatov112, U. Soldevila173, A.A. Solodkov123, A. Soloshenko80, O.V. Solovyanov123, V. Solovyev137, P. Sommer148, H. Son169, A. Sonay14, W. Song 143, W.Y. Song 167b, A. Sopczak141, A.L. Sopio95, F. Sopkova 28b, S. Sottocornola 71a,71b, R. Soualah67a,67c, A.M. Soukharev122b,122a, D. South46, S. Spagnolo 68a,68b, M. Spalla115, M. Spangenberg177, F. Spanò 94, D. Sperlich52, T.M. Spieker61a, G. Spigo36, M. Spina155, D.P. Spiteri57, M. Spousta142, A. Stabile69a,69b, B.L. Stamas121, R. Stamen61a, M. Stamenkovic120, A. Stampekis21, E. Stanecka85, B. Stanislaus134, M.M. Stanitzki46, M. Stankaityte134, B. Stapf120, E.A. Starchenko123, G.H. Stark145, J. Stark58, P. Staroba 140, P. Starovoitov 61a, S. Stärz104, R. Staszewski85, G. Stavropoulos44, M. Stegler46, P. Steinberg29, A.L. Steinhebel131, B. Stelzer151,167a, H.J. Stelzer138, O. Stelzer-Chilton167a, H. Stenzel 56, T.J. Stevenson155, G.A. Stewart36, M.C. Stockton36, G. Stoicea27b, M. Stolarski139a, S. Stonjek115, A. Straessner 48, J. Strandberg153, S. Strandberg45a,45b, M. Strauss128, T. Strebler 102, P. Strizenec28b, R. Ströhmer176, D.M. Strom131, R. Stroynowski42, A. Strubig50, S.A. Stucci29, B. Stugu17, J. Stupak128, N.A. Styles46, D. Su152, W. Su 60c,147, X. Su60a, V.V. Sulin 111, M.J. Sullivan91, D.M.S. Sultan54, S. Sultansoy4c, T. Sumida86, S. Sun 106, X. Sun101, C.J.E. Suster156, 17 The ATLAS Collaboration Physics Letters B 810 (2020) 135797 M.R. Sutton155, S. Suzuki 82, M. Svatos140, M. Swiatlowski 167a, S.P. Swift 2, T. Swirski176, A. Sydorenko100, I. Sykora 28a, M. Sykora142, T. Sykora 142, D. Ta100, K. Tackmann46,x, J. Taenzer160, A. Taffard170, R. Tafirout167a, E. Tagiev123, R. Takashima87, K. Takeda 83, T. Takeshita149, E.P. Takeva50, Y. Takubo 82, M. Talby102, A.A. Talyshev122b,122a, K.C. Tam 63b, N.M. Tamir 160, J. Tanaka 162, R. Tanaka65, S. Tapia Araya172, S. Tapprogge100, A. Tarek Abouelfadl Mohamed107, S. Tarem159, K. Tariq 60b, G. Tarna27b,d, G.F. Tartarelli69a, P. Tas 142, M. Tasevsky140, E. Tassi41b,41a, A. Tavares Delgado139a, Y. Tayalati 35e, A.J. Taylor50, G.N. Taylor105, W. Taylor 167b, H. Teagle 91, A.S. Tee90, R. Teixeira De Lima152, P. Teixeira-Dias94, H. Ten Kate36, J.J. Teoh120, K. Terashi162, J. Terron99, S. Terzo14, M. Testa51, R.J. Teuscher166,ab, S.J. Thais 182, N. Themistokleous50, T. Theveneaux-Pelzer46, F. Thiele40, D.W. Thomas 94, J.O. Thomas42, J.P. Thomas21, E.A. Thompson46, P.D. Thompson21, E. Thomson136, E.J. Thorpe93, R.E. Ticse Torres53, V.O. Tikhomirov111,ah, Yu.A. Tikhonov122b,122a, S. Timoshenko112, P. Tipton 182, S. Tisserant102, K. Todome23b,23a, S. Todorova-Nova142, S. Todt48, J. Tojo88, S. Tokár 28a, K. Tokushuku 82, E. Tolley127, R. Tombs32, K.G. Tomiwa33e, M. Tomoto117, L. Tompkins152, P. Tornambe103, E. Torrence131, H. Torres48, E. Torró Pastor173, C. Tosciri 134, J. Toth102,aa, D.R. Tovey148, A. Traeet17, C.J. Treado125, T. Trefzger176, F. Tresoldi 155, A. Tricoli29, I.M. Trigger167a, S. Trincaz-Duvoid135, D.A. Trischuk174, W. Trischuk 166, B. Trocmé 58, A. Trofymov65, C. Troncon69a, F. Trovato 155, L. Truong33c, M. Trzebinski85, A. Trzupek85, F. Tsai 46, J.C-L. Tseng 134, P.V. Tsiareshka 108,ae, A. Tsirigotis161,u, V. Tsiskaridze 154, E.G. Tskhadadze158a, M. Tsopoulou161, I.I. Tsukerman124, V. Tsulaia18, S. Tsuno 82, D. Tsybychev154, Y. Tu 63b, A. Tudorache27b, V. Tudorache27b, T.T. Tulbure 27a, A.N. Tuna59, S. Turchikhin80, D. Turgeman179, I. Turk Cakir4b,s, R.J. Turner21, R. Turra69a, P.M. Tuts 39, S. Tzamarias161, E. Tzovara100, K. Uchida162, F. Ukegawa 168, G. Unal36, M. Unal11, A. Undrus29, G. Unel170, F.C. Ungaro 105, Y. Unno82, K. Uno162, J. Urban28b, P. Urquijo 105, G. Usai8, Z. Uysal12d, V. Vacek 141, B. Vachon104, K.O.H. Vadla 133, T. Vafeiadis36, A. Vaidya95, C. Valderanis114, E. Valdes Santurio45a,45b, M. Valente54, S. Valentinetti23b,23a, A. Valero173, L. Valéry46, R.A. Vallance21, A. Vallier36, J.A. Valls Ferrer173, T.R. Van Daalen 14, P. Van Gemmeren6, S. Van Stroud95, I. Van Vulpen120, M. Vanadia 74a,74b, W. Vandelli36, M. Vandenbroucke144, E.R. Vandewall 129, A. Vaniachine165, D. Vannicola 73a,73b, R. Vari73a, E.W. Varnes 7, C. Varni55b,55a, T. Varol 157, D. Varouchas65, K.E. Varvell156, M.E. Vasile27b, G.A. Vasquez 175, F. Vazeille38, D. Vazquez Furelos14, T. Vazquez Schroeder36, J. Veatch53, V. Vecchio101, M.J. Veen 120, L.M. Veloce166, F. Veloso 139a,139c, S. Veneziano73a, A. Ventura68a,68b, A. Verbytskyi115, V. Vercesi71a, M. Verducci72a,72b, C.M. Vergel Infante79, C. Vergis24, W. Verkerke120, A.T. Vermeulen120, J.C. Vermeulen 120, C. Vernieri152, P.J. Verschuuren 94, M.C. Vetterli151,al, N. Viaux Maira146d, T. Vickey 148, O.E. Vickey Boeriu148, G.H.A. Viehhauser134, L. Vigani61b, M. Villa 23b,23a, M. Villaplana Perez3, E.M. Villhauer50, E. Vilucchi51, M.G. Vincter34, G.S. Virdee21, A. Vishwakarma50, C. Vittori23b,23a, I. Vivarelli155, M. Vogel181, P. Vokac 141, S.E. von Buddenbrock 33e, E. Von Toerne24, V. Vorobel142, K. Vorobev112, M. Vos173, J.H. Vossebeld 91, M. Vozak101, N. Vranjes16, M. Vranjes Milosavljevic16, V. Vrba141, M. Vreeswijk120, N.K. Vu102, R. Vuillermet36, I. Vukotic37, S. Wada168, P. Wagner 24, W. Wagner 181, J. Wagner-Kuhr114, S. Wahdan181, H. Wahlberg89, R. Wakasa 168, V.M. Walbrecht115, J. Walder 143, R. Walker114, S.D. Walker94, W. Walkowiak150, V. Wallangen45a,45b, A.M. Wang59, A.Z. Wang180, C. Wang60a, C. Wang 60c, F. Wang 180, H. Wang18, H. Wang3, J. Wang63a, P. Wang 42, Q. Wang128, R.-J. Wang 100, R. Wang60a, R. Wang6, S.M. Wang 157, W.T. Wang 60a, W. Wang 15c, W.X. Wang 60a, Y. Wang 60a, Z. Wang 106, C. Wanotayaroj46, A. Warburton104, C.P. Ward32, R.J. Ward21, N. Warrack57, A.T. Watson21, M.F. Watson21, G. Watts147, B.M. Waugh95, A.F. Webb11, C. Weber29, M.S. Weber 20, S.A. Weber34, S.M. Weber61a, A.R. Weidberg134, J. Weingarten47, M. Weirich100, C. Weiser52, P.S. Wells 36, T. Wenaus 29, B. Wendland47, T. Wengler 36, S. Wenig36, N. Wermes 24, M. Wessels 61a, T.D. Weston20, K. Whalen131, A.M. Wharton90, A.S. White106, A. White8, M.J. White1, D. Whiteson170, B.W. Whitmore90, W. Wiedenmann 180, C. Wiel48, M. Wielers 143, N. Wieseotte100, C. Wiglesworth40, L.A.M. Wiik-Fuchs52, H.G. Wilkens36, L.J. Wilkins 94, H.H. Williams 136, S. Williams32, S. Willocq 103, P.J. Windischhofer134, I. Wingerter-Seez5, E. Winkels155, F. Winklmeier 131, B.T. Winter52, M. Wittgen152, M. Wobisch96, A. Wolf100, R. Wölker134, J. Wollrath52, M.W. Wolter85, H. Wolters139a,139c, V.W.S. Wong174, N.L. Woods 145, S.D. Worm 46, B.K. Wosiek85, K.W. Wo´zniak85, K. Wraight57, S.L. Wu180, X. Wu54, Y. Wu 60a, J. Wuerzinger134, T.R. Wyatt 101, B.M. Wynne50, S. Xella40, L. Xia177, J. Xiang 63c, X. Xiao106, X. Xie60a, I. Xiotidis155, D. Xu15a, H. Xu60a, H. Xu60a, L. Xu29, 18 The ATLAS Collaboration Physics Letters B 810 (2020) 135797 T. Xu 144, W. Xu 106, Z. Xu60b, Z. Xu152, B. Yabsley156, S. Yacoob33a, D.P. Yallup95, N. Yamaguchi88, Y. Yamaguchi 164, A. Yamamoto82, M. Yamatani162, T. Yamazaki 162, Y. Yamazaki 83, J. Yan60c, Z. Yan25, H.J. Yang60c,60d, H.T. Yang 18, S. Yang60a, T. Yang 63c, X. Yang60b,58, Y. Yang 162, Z. Yang60a, W-M. Yao18, Y.C. Yap 46, E. Yatsenko60c, H. Ye15c, J. Ye42, S. Ye29, I. Yeletskikh80, M.R. Yexley90, E. Yigitbasi 25, P. Yin 39, K. Yorita178, K. Yoshihara79, C.J.S. Young36, C. Young152, J. Yu 79, R. Yuan 60b,h, X. Yue61a, M. Zaazoua35e, B. Zabinski 85, G. Zacharis10, E. Zaffaroni54, J. Zahreddine135, A.M. Zaitsev123,ag, T. Zakareishvili158b, N. Zakharchuk34, S. Zambito36, D. Zanzi 36, S.V. Zeißner47, C. Zeitnitz 181, G. Zemaityte134, J.C. Zeng172, O. Zenin 123, T. Ženiš 28a, D. Zerwas65, M. Zgubiˇ c134, B. Zhang15c, D.F. Zhang15b, G. Zhang 15b, J. Zhang6, Kaili. Zhang15a, L. Zhang 15c, L. Zhang60a, M. Zhang172, R. Zhang180, S. Zhang 106, X. Zhang60c, X. Zhang60b, Y. Zhang 15a,15d, Z. Zhang 63a, Z. Zhang65, P. Zhao 49, Z. Zhao60a, A. Zhemchugov80, Z. Zheng 106, D. Zhong172, B. Zhou106, C. Zhou 180, H. Zhou7, M.S. Zhou15a,15d, M. Zhou 154, N. Zhou60c, Y. Zhou 7, C.G. Zhu 60b, C. Zhu 15a,15d, H.L. Zhu60a, H. Zhu 15a, J. Zhu106, Y. Zhu 60a, X. Zhuang15a, K. Zhukov 111, V. Zhulanov122b,122a, D. Zieminska66, N.I. Zimine80, S. Zimmermann52, Z. Zinonos 115, M. Ziolkowski150, L. Živkovi´ c16, G. Zobernig180, A. Zoccoli 23b,23a, K. Zoch53, T.G. Zorbas 148, R. Zou37, L. Zwalinski36 1Department of Physics, University of Adelaide, Adelaide; Australia 2Physics Department, SUNY Albany, Albany NY; United States of America 3Department of Physics, University of Alberta, Edmonton AB; Canada 4(a)Department of Physics, Ankara University, Ankara; (b)Istanbul Aydin University, Application and Research Center for Advanced Studies, Istanbul; (c)Division of Physics, TOBB University of Economics and Technology, Ankara; Turkey 5LAPP, Université Grenoble Alpes, Université Savoie Mont Blanc, CNRS/IN2P3, Annecy; France 6High Energy Physics Division, Argonne National Laboratory, Argonne IL; United States of America 7Department of Physics, University of Arizona, Tucson AZ; United States of America 8Department of Physics, University of Texas at Arlington, Arlington TX; United States of America 9Physics Department, National and Kapodistrian University of Athens, Athens; Greece 10 Physics Department, National Technical University of Athens, Zografou; Greece 11 Department of Physics, University of Texas at Austin, Austin TX; United States of America 12 (a)Bahcesehir University, Faculty of Engineering and Natural Sciences, Istanbul; (b)Istanbul Bilgi University, Faculty of Engineering and Natural Sciences, Istanbul; (c)Department of Physics, Bogazici University, Istanbul; (d)Department of Physics Engineering, Gaziantep University, Gaziantep; Turkey 13 Institute of Physics, Azerbaijan Academy of Sciences, Baku; Azerbaijan 14 Institut de Física d’Altes Energies (IFAE), Barcelona Institute of Science and Technology, Barcelona; Spain 15 (a)Institute of High Energy Physics, Chinese Academy of Sciences, Beijing; (b)Physics Department, Tsinghua University, Beijing; (c)Department of Physics, Nanjing University, Nanjing; (d)University of Chinese Academy of Science (UCAS), Beijing; China 16 Institute of Physics, University of Belgrade, Belgrade; Serbia 17 Department for Physics and Technology, University of Bergen, Bergen; Norway 18 Physics Division, Lawrence Berkeley National Laboratory and University of California, Berkeley CA; United States of America 19 Institut für Physik, Humboldt Universität zu Berlin, Berlin; Germany 20 Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics, University of Bern, Bern; Switzerland 21 School of Physics and Astronomy, University of Birmingham, Birmingham; United Kingdom 22 (a)Facultad de Ciencias y Centro de Investigaciónes, Universidad Antonio Nariño, Bogotá; (b)Departamento de Física, Universidad Nacional de Colombia, Bogotá; Colombia 23 (a)INFN Bologna and Universita’ di Bologna, Dipartimento di Fisica; (b)INFN Sezione di Bologna; Italy 24 Physikalisches Institut, Universität Bonn, Bonn; Germany 25 Department of Physics, Boston University, Boston MA; United States of America 26 Department of Physics, Brandeis University, Waltham MA; United States of America 27 (a)Transilvania University of Brasov, Brasov; (b)Horia Hulubei National Institute of Physics and Nuclear Engineering, Bucharest; (c)Department of Physics, Alexandru Ioan Cuza University of Iasi, Iasi; (d)National Institute for Research and Development of Isotopic and Molecular Technologies, Physics Department, Cluj-Napoca; (e)University Politehnica Bucharest, Bucharest; (f)West University in Timisoara, Timisoara; Romania 28 (a)Faculty of Mathematics, Physics and Informatics, Comenius University, Bratislava; (b)Department of Subnuclear Physics, Institute of Experimental Physics of the Slovak Academy of Sciences, Kosice; Slovak Republic 29 Physics Department, Brookhaven National Laboratory, Upton NY; United States of America 30 Departamento de Física, Universidad de Buenos Aires, Buenos Aires; Argentina 31 California State University, CA; United States of America 32 Cavendish Laboratory, University of Cambridge, Cambridge; United Kingdom 33 (a)Department of Physics, University of Cape Town, Cape Town; (b)iThemba Labs, Western Cape; (c)Department of Mechanical Engineering Science, University of Johannesburg, Johannesburg; (d)University of South Africa, Department of Physics, Pretoria; (e)School of Physics, University of the Witwatersrand, Johannesburg; South Africa 34 Department of Physics, Carleton University, Ottawa ON; Canada 35 (a)Faculté des Sciences Ain Chock, Réseau Universitaire de Physique des Hautes Energies – Université Hassan II, Casablanca; (b)Faculté des Sciences, Université Ibn-Tofail, Kénitra; (c)Faculté des Sciences Semlalia, Université Cadi Ayyad, LPHEA, Marrakech; (d)Faculté des Sciences, Université Mohamed Premier and LPTPM, Oujda; (e)Faculté des sciences, Université Mohammed V, Rabat; Morocco 36 CERN, Geneva; Switzerland 37 Enrico Fermi Institute, University of Chicago, Chicago IL; United States of America 38 LPC, Université Clermont Auvergne, CNRS/IN2P3, Clermont-Ferrand; France 39 Nevis Laboratory, Columbia University, Irvington NY; United States of America 40 Niels Bohr Institute, University of Copenhagen, Copenhagen; Denmark 41 (a)Dipartimento di Fisica, Università della Calabria, Rende; (b)INFN Gruppo Collegato di Cosenza, Laboratori Nazionali di Frascati; Italy 42 Physics Department, Southern Methodist University, Dallas TX; United States of America 43 Physics Department, University of Texas at Dallas, Richardson TX; United States of America 44 National Centre for Scientific Research “Demokritos”, Agia Paraskevi; Greece 45 (a)Department of Physics, Stockholm University; (b)Oskar Klein Centre, Stockholm; Sweden 46 Deutsches Elektronen-Synchrotron DESY, Hamburg and Zeuthen; Germany 47 Lehrstuhl für Experimentelle Physik IV, Technische Universität Dortmund, Dortmund; Germany 48 Institut für Kernund Teilchenphysik, Technische Universität Dresden, Dresden; Germany 19 The ATLAS Collaboration Physics Letters B 810 (2020) 135797 49 Department of Physics, Duke University, Durham NC; United States of America 50 SUPA – School of Physics and Astronomy, University of Edinburgh, Edinburgh; United Kingdom 51 INFN e Laboratori Nazionali di Frascati, Frascati; Italy 52 Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg; Germany 53 II. Physikalisches Institut, Georg-August-Universität Göttingen, Göttingen; Germany 54 Département de Physique Nucléaire et Corpusculaire, Université de Genève, Genève; Switzerland 55 (a)Dipartimento di Fisica, Università di Genova, Genova; (b)INFN Sezione di Genova; Italy 56 II. Physikalisches Institut, Justus-Liebig-Universität Giessen, Giessen; Germany 57 SUPA – School of Physics and Astronomy, University of Glasgow, Glasgow; United Kingdom 58 LPSC, Université Grenoble Alpes, CNRS/IN2P3, Grenoble INP, Grenoble; France 59 Laboratory for Particle Physics and Cosmology, Harvard University, Cambridge MA; United States of America 60 (a)Department of Modern Physics and State Key Laboratory of Particle Detection and Electronics, University of Science and Technology of China, Hefei; (b)Institute of Frontier and Interdisciplinary Science and Key Laboratory of Particle Physics and Particle Irradiation (MOE), Shandong University, Qingdao; (c)School of Physics and Astronomy, Shanghai Jiao Tong University, KLPPAC-MoE, SKLPPC, Shanghai; (d)Tsung-Dao Lee Institute, Shanghai; China 61 (a)Kirchhoff-Institut für Physik, Ruprecht-Karls-Universität Heidelberg, Heidelberg; (b)Physikalisches Institut, Ruprecht-Karls-Universität Heidelberg, Heidelberg; Germany 62 Faculty of Applied Information Science, Hiroshima Institute of Technology, Hiroshima; Japan 63 (a)Department of Physics, Chinese University of Hong Kong, Shatin, N.T., Hong Kong; (b)Department of Physics, University of Hong Kong, Hong Kong; (c)Department of Physics and Institute for Advanced Study, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong; China 64 Department of Physics, National Tsing Hua University, Hsinchu; Taiwan 65 IJCLab, Université Paris-Saclay, CNRS/IN2P3, 91405, Orsay; France 66 Department of Physics, Indiana University, Bloomington IN; United States of America 67 (a)INFN Gruppo Collegato di Udine, Sezione di Trieste, Udine; (b)ICTP, Trieste; (c)Dipartimento Politecnico di Ingegneria e Architettura, Università di Udine, Udine; Italy 68 (a)INFN Sezione di Lecce; (b)Dipartimento di Matematica e Fisica, Università del Salento, Lecce; Italy 69 (a)INFN Sezione di Milano; (b)Dipartimento di Fisica, Università di Milano, Milano; Italy 70 (a)INFN Sezione di Napoli; (b)Dipartimento di Fisica, Università di Napoli, Napoli; Italy 71 (a)INFN Sezione di Pavia; (b)Dipartimento di Fisica, Università di Pavia, Pavia; Italy 72 (a)INFN Sezione di Pisa; (b)Dipartimento di Fisica E. Fermi, Università di Pisa, Pisa; Italy 73 (a)INFN Sezione di Roma; (b)Dipartimento di Fisica, Sapienza Università di Roma, Roma; Italy 74 (a)INFN Sezione di Roma Tor Vergata; (b)Dipartimento di Fisica, Università di Roma Tor Vergata, Roma; Italy 75 (a)INFN Sezione di Roma Tre; (b)Dipartimento di Matematica e Fisica, Università Roma Tre, Roma; Italy 76 (a)INFN-TIFPA; (b)Università degli Studi di Trento, Trento; Italy 77 Institut für Astround Teilchenphysik, Leopold-Franzens-Universität, Innsbruck; Austria 78 University of Iowa, Iowa City IA; United States of America 79 Department of Physics and Astronomy, Iowa State University, Ames IA; United States of America 80 Joint Institute for Nuclear Research, Dubna; Russia 81 (a)Departamento de Engenharia Elétrica, Universidade Federal de Juiz de Fora (UFJF), Juiz de Fora; (b)Universidade Federal do Rio De Janeiro COPPE/EE/IF, Rio de Janeiro; (c)Universidade Federal de São João del Rei (UFSJ), São João del Rei; (d)Instituto de Física, Universidade de São Paulo, São Paulo; Brazil 82 KEK, High Energy Accelerator Research Organization, Tsukuba; Japan 83 Graduate School of Science, Kobe University, Kobe; Japan 84 (a)AGH University of Science and Technology, Faculty of Physics and Applied Computer Science, Krakow; (b)Marian Smoluchowski Institute of Physics, Jagiellonian University, Krakow; Poland 85 Institute of Nuclear Physics Polish Academy of Sciences, Krakow; Poland 86 Faculty of Science, Kyoto University, Kyoto; Japan 87 Kyoto University of Education, Kyoto; Japan 88 Research Center for Advanced Particle Physics and Department of Physics, Kyushu University, Fukuoka ; Japan 89 Instituto de Física La Plata, Universidad Nacional de La Plata and CONICET, La Plata; Argentina 90 Physics Department, Lancaster University, Lancaster; United Kingdom 91 Oliver Lodge Laboratory, University of Liverpool, Liverpool; United Kingdom 92 Department of Experimental Particle Physics, Jožef Stefan Institute and Department of Physics, University of Ljubljana, Ljubljana; Slovenia 93 School of Physics and Astronomy, Queen Mary University of London, London; United Kingdom 94 Department of Physics, Royal Holloway University of London, Egham; United Kingdom 95 Department of Physics and Astronomy, University College London, London; United Kingdom 96 Louisiana Tech University, Ruston LA; United States of America 97 Fysiska institutionen, Lunds universitet, Lund; Sweden 98 Centre de Calcul de l’Institut National de Physique Nucléaire et de Physique des Particules (IN2P3), Villeurbanne; France 99 Departamento de Física Teorica C-15 and CIAFF, Universidad Autónoma de Madrid, Madrid; Spain 100 Institut für Physik, Universität Mainz, Mainz; Germany 101 School of Physics and Astronomy, University of Manchester, Manchester; United Kingdom 102 CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille; France 103 Department of Physics, University of Massachusetts, Amherst MA; United States of America 104 Department of Physics, McGill University, Montreal QC; Canada 105 School of Physics, University of Melbourne, Victoria; Australia 106 Department of Physics, University of Michigan, Ann Arbor MI; United States of America 107 Department of Physics and Astronomy, Michigan State University, East Lansing MI; United States of America 108 B.I. Stepanov Institute of Physics, National Academy of Sciences of Belarus, Minsk; Belarus 109 Research Institute for Nuclear Problems of Byelorussian State University, Minsk; Belarus 110 Group of Particle Physics, University of Montreal, Montreal QC; Canada 111 P.N. Lebedev Physical Institute of the Russian Academy of Sciences, Moscow; Russia 112 National Research Nuclear University MEPhI, Moscow; Russia 113 D.V. Skobeltsyn Institute of Nuclear Physics, M.V. Lomonosov Moscow State University, Moscow; Russia 114 Fakultät für Physik, Ludwig-Maximilians-Universität München, München; Germany 115 Max-Planck-Institut für Physik (Werner-Heisenberg-Institut), München; Germany 116 Nagasaki Institute of Applied Science, Nagasaki; Japan 117 Graduate School of Science and Kobayashi-Maskawa Institute, Nagoya University, Nagoya; Japan 118 Department of Physics and Astronomy, University of New Mexico, Albuquerque NM; United States of America 119 Institute for Mathematics, Astrophysics and Particle Physics, Radboud University Nijmegen/Nikhef, Nijmegen; Netherlands 120 Nikhef National Institute for Subatomic Physics and University of Amsterdam, Amsterdam; Netherlands 121 Department of Physics, Northern Illinois University, DeKalb IL; United States of America 122 (a)Budker Institute of Nuclear Physics and NSU, SB RAS, Novosibirsk; (b)Novosibirsk State University Novosibirsk; Russia 123 Institute for High Energy Physics of the National Research Centre Kurchatov Institute, Protvino; Russia 20 The ATLAS Collaboration Physics Letters B 810 (2020) 135797 124 Institute for Theoretical and Experimental Physics named by A.I. Alikhanov of National Research Centre “Kurchatov Institute”, Moscow; Russia 125 Department of Physics, New York University, New York NY; United States of America 126 Ochanomizu University, Otsuka, Bunkyo-ku, Tokyo; Japan 127 Ohio State University, Columbus OH; United States of America 128 Homer L. Dodge Department of Physics and Astronomy, University of Oklahoma, Norman OK; United States of America 129 Department of Physics, Oklahoma State University, Stillwater OK; United States of America 130 Palacký University, RCPTM, Joint Laboratory of Optics, Olomouc; Czech Republic 131 Institute for Fundamental Science, University of Oregon, Eugene, OR; United States of America 132 Graduate School of Science, Osaka University, Osaka; Japan 133 Department of Physics, University of Oslo, Oslo; Norway 134 Department of Physics, Oxford University, Oxford; United Kingdom 135 LPNHE, Sorbonne Université, Université de Paris, CNRS/IN2P3, Paris; France 136 Department of Physics, University of Pennsylvania, Philadelphia PA; United States of America 137 Konstantinov Nuclear Physics Institute of National Research Centre “Kurchatov Institute”, PNPI, St. Petersburg; Russia 138 Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh PA; United States of America 139 (a)Laboratório de Instrumentação e Física Experimental de Partículas – LIP, Lisboa; (b)Departamento de Física, Faculdade de Ciências, Universidade de Lisboa, Lisboa; (c)Departamento de Física, Universidade de Coimbra, Coimbra; (d)Centro de Física Nuclear da Universidade de Lisboa, Lisboa; (e)Departamento de Física, Universidade do Minho, Braga; (f)Departamento de Física Teórica y del Cosmos, Universidad de Granada, Granada (Spain); (g)Dep Física and CEFITEC of Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica; (h)Instituto Superior Técnico, Universidade de Lisboa, Lisboa; Portugal 140 Institute of Physics of the Czech Academy of Sciences, Prague; Czech Republic 141 Czech Technical University in Prague, Prague; Czech Republic 142 Charles University, Faculty of Mathematics and Physics, Prague; Czech Republic 143 Particle Physics Department, Rutherford Appleton Laboratory, Didcot; United Kingdom 144 IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette; France 145 Santa Cruz Institute for Particle Physics, University of California Santa Cruz, Santa Cruz CA; United States of America 146 (a)Departamento de Física, Pontificia Universidad Católica de Chile, Santiago; (b)Universidad Andres Bello, Department of Physics, Santiago; (c)Instituto de Alta Investigación, Universidad de Tarapacá; (d)Departamento de Física, Universidad Técnica Federico Santa María, Valparaíso; Chile 147 Department of Physics, University of Washington, Seattle WA; United States of America 148 Department of Physics and Astronomy, University of Sheffield, Sheffield; United Kingdom 149 Department of Physics, Shinshu University, Nagano; Japan 150 Department Physik, Universität Siegen, Siegen; Germany 151 Department of Physics, Simon Fraser University, Burnaby BC; Canada 152 SLAC National Accelerator Laboratory, Stanford CA; United States of America 153 Physics Department, Royal Institute of Technology, Stockholm; Sweden 154 Departments of Physics and Astronomy, Stony Brook University, Stony Brook NY; United States of America 155 Department of Physics and Astronomy, University of Sussex, Brighton; United Kingdom 156 School of Physics, University of Sydney, Sydney; Australia 157 Institute of Physics, Academia Sinica, Taipei; Taiwan 158 (a)E. Andronikashvili Institute of Physics, Iv. Javakhishvili Tbilisi State University, Tbilisi; (b)High Energy Physics Institute, Tbilisi State University, Tbilisi; Georgia 159 Department of Physics, Technion, Israel Institute of Technology, Haifa; Israel 160 Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University, Tel Aviv; Israel 161 Department of Physics, Aristotle University of Thessaloniki, Thessaloniki; Greece 162 International Center for Elementary Particle Physics and Department of Physics, University of Tokyo, Tokyo; Japan 163 Graduate School of Science and Technology, Tokyo Metropolitan University, Tokyo; Japan 164 Department of Physics, Tokyo Institute of Technology, Tokyo; Japan 165 Tomsk State University, Tomsk; Russia 166 Department of Physics, University of Toronto, Toronto ON; Canada 167 (a)TRIUMF, Vancouver BC; (b)Department of Physics and Astronomy, York University, Toronto ON; Canada 168 Division of Physics and Tomonaga Center for the History of the Universe, Faculty of Pure and Applied Sciences, University of Tsukuba, Tsukuba; Japan 169 Department of Physics and Astronomy, Tufts University, Medford MA; United States of America 170 Department of Physics and Astronomy, University of California Irvine, Irvine CA; United States of America 171 Department of Physics and Astronomy, University of Uppsala, Uppsala; Sweden 172 Department of Physics, University of Illinois, Urbana IL; United States of America 173 Instituto de Física Corpuscular (IFIC), Centro Mixto Universidad de Valencia – CSIC, Valencia; Spain 174 Department of Physics, University of British Columbia, Vancouver BC; Canada 175 Department of Physics and Astronomy, University of Victoria, Victoria BC; Canada 176 Fakultät für Physik und Astronomie, Julius-Maximilians-Universität Würzburg, Würzburg; Germany 177 Department of Physics, University of Warwick, Coventry; United Kingdom 178 Waseda University, Tokyo; Japan 179 Department of Particle Physics, Weizmann Institute of Science, Rehovot; Israel 180 Department of Physics, University of Wisconsin, Madison WI; United States of America 181 Fakultät für Mathematik und Naturwissenschaften, Fachgruppe Physik, Bergische Universität Wuppertal, Wuppertal; Germany 182 Department of Physics, Yale University, New Haven CT; United States of America aAlso at Borough of Manhattan Community College, City University of New York, New York NY; United States of America. bAlso at Centro Studi e Ricerche Enrico Fermi; Italy. cAlso at CERN, Geneva; Switzerland. dAlso at CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille; France. eAlso at Département de Physique Nucléaire et Corpusculaire, Université de Genève, Genève; Switzerland. fAlso at Departament de Fisica de la Universitat Autonoma de Barcelona, Barcelona; Spain. gAlso at Department of Financial and Management Engineering, University of the Aegean, Chios; Greece. hAlso at Department of Physics and Astronomy, Michigan State University, East Lansing MI; United States of America. iAlso at Department of Physics and Astronomy, University of Louisville, Louisville, KY; United States of America. jAlso at Department of Physics, Ben Gurion University of the Negev, Beer Sheva; Israel. kAlso at Department of Physics, California State University, East Bay; United States of America. lAlso at Department of Physics, California State University, Fresno; United States of America. mAlso at Department of Physics, California State University, Sacramento; United States of America. nAlso at Department of Physics, King’s College London, London; United Kingdom. 21 The ATLAS Collaboration Physics Letters B 810 (2020) 135797 oAlso at Department of Physics, St. Petersburg State Polytechnical University, St. Petersburg; Russia. pAlso at Department of Physics, University of Fribourg, Fribourg; Switzerland. qAlso at Dipartimento di Matematica, Informatica e Fisica, Università di Udine, Udine; Italy. rAlso at Faculty of Physics, M.V. Lomonosov Moscow State University, Moscow; Russia. sAlso at Giresun University, Faculty of Engineering, Giresun; Turkey. tAlso at Graduate School of Science, Osaka University, Osaka; Japan. uAlso at Hellenic Open University, Patras; Greece. vAlso at IJCLab, Université Paris-Saclay, CNRS/IN2P3, 91405, Orsay; France. wAlso at Institucio Catalana de Recerca i Estudis Avancats, ICREA, Barcelona; Spain. xAlso at Institut für Experimentalphysik, Universität Hamburg, Hamburg; Germany. yAlso at Institute for Mathematics, Astrophysics and Particle Physics, Radboud University Nijmegen/Nikhef, Nijmegen; Netherlands. zAlso at Institute for Nuclear Research and Nuclear Energy (INRNE) of the Bulgarian Academy of Sciences, Sofia; Bulgaria. aa Also at Institute for Particle and Nuclear Physics, Wigner Research Centre for Physics, Budapest; Hungary. ab Also at Institute of Particle Physics (IPP), Vancouver; Canada. ac Also at Institute of Physics, Azerbaijan Academy of Sciences, Baku; Azerbaijan. ad Also at Instituto de Fisica Teorica, IFT-UAM/CSIC, Madrid; Spain. ae Also at Joint Institute for Nuclear Research, Dubna; Russia. af Also at Louisiana Tech University, Ruston LA; United States of America. ag Also at Moscow Institute of Physics and Technology State University, Dolgoprudny; Russia. ah Also at National Research Nuclear University MEPhI, Moscow; Russia. ai Also at Physics Department, An-Najah National University, Nablus; Palestine. aj Also at Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg; Germany. ak Also at The City College of New York, New York NY; United States of America. al Also at TRIUMF, Vancouver BC; Canada. am Also at Universita di Napoli Parthenope, Napoli; Italy. an Also at University of Chinese Academy of Sciences (UCAS), Beijing; China. ∗Deceased. 22