Measurements of W+W−+ ≥ 1 jet production cross-sections in pp collisions at s√ = 13 TeV with the ATLAS detector

JHEP06(2021)003 Published for SISSA by Springer Received:March 19, 2021 Accepted:May 12, 2021 Published:June 1, 2021 Measurements of W+W−+≥1jet production cross-sections in pp collisions at √s= 13 TeV with the ATLAS detector The ATLAS collaboration E-mail: [email protected] Abstract: Fiducial and differential cross-section measurements of W+W−production in association with at least one hadronic jet are presented. These measurements are sensitive to the properties of electroweak-boson self-interactions and provide a test of perturbative quantum chromodynamics and the electroweak theory. The analysis is performed using proton-proton collision data collected at √s= 13 TeV with the ATLAS experiment, corresponding to an integrated luminosity of 139 fb−1. Events are selected with exactly one oppositely charged electron-muon pair and at least one hadronic jet with a transverse momentum of pT>30 GeV and a pseudorapidity of |η|<4.5. After subtracting the background contributions and correcting for detector effects, the jet-inclusive W+W−+≥1jet fiducial cross-section and W+W−+jets differential cross-sections with respect to several kinematic variables are measured. These measurements include leptonic quantities, such as the lepton transverse momenta and the transverse mass of the W+W−system, as well as jet-related observables such as the leading jet transverse momentum and the jet multiplicity. Limits on anomalous triple-gauge-boson couplings are obtained in a phase space where interference between the Standard Model amplitude and the anomalous amplitude is enhanced. Keywords: Hadron-Hadron scattering (experiments) ArXiv ePrint: 2103.10319 Open Access, Copyright CERN, for the benefit of the ATLAS Collaboration. Article funded by SCOAP3. https://doi.org/10.1007/JHEP06(2021)003 JHEP06(2021)003 Contents 1 Introduction 1 2 The ATLAS detector 4 3 Data and Monte Carlo samples 4 4 Event reconstruction and selection 6 5 Background estimate 8 5.1 Top-quark background 8 5.2 Drell-Yan background 10 5.3 Backgrounds with non-prompt or misidentified leptons 11 5.4 Other backgrounds 12 5.5 Selected WW candidate events 12 6 Fiducial and differential cross-section determination 15 7 Uncertainties 16 8 Results 19 8.1 Comparison with theoretical predictions 19 8.2 Effective field theory interpretation 21 9 Conclusion 27 A Measurement at high plead. lep. T28 Bt ¯ tbackground estimate 31 The ATLAS collaboration 42 1 Introduction The measurement of W-boson pair (WW) production cross-sections is an important test of the Standard Model (SM). WW production at hadron colliders is sensitive to the properties of electroweak-boson self-interactions and provides a test of perturbative quantum chromodynamics (QCD) and the electroweak (EW) theory. It also constitutes a large background in the measurement of Higgs boson production as well as in searches for physics beyond the SM. Inclusive and fiducial WW production cross-sections have been measured in proton-proton (pp) collisions at √s= 7 TeV [1,2], 8TeV [3–5] and 13TeV [6–8], as well – 1 – JHEP06(2021)003 as in e+e−collisions at LEP [9] and in p¯pcollisions at Tevatron [10–12]. However, to reduce backgrounds, the measurements of inclusive cross-sections typically require that the WW pair is produced without additional jet activity, or at most with one additional jet. The production of WW+jets has therefore not been studied in detail. This article presents results of measurements of fiducial and differential cross-sections for a WW pair produced in association with one or more jets. For the first time at the LHC, differential measurements are performed in a jet-inclusive phase space. This measurement complements previous results as the combination of measurements with and without jets improves the precision of the inclusive WW cross-section due to an anti-correlation of important systematic uncertainties, for example the jet energy scale uncertainty, as demonstrated in previous measurements from ATLAS [5] and CMS [8]. The analysis of one-jet topologies can also improve searches for anomalous triple-gaugeboson couplings (aTGCs), due to the increased interference between the SM amplitude and the anomalous amplitude [13]. The impact of the QWaTGC operator, as defined in ref. [14], increases rapidly with energy, making a measurement at the energies probed by the LHC important. However, at high centre-of-mass energy, the SM amplitude and the anomalous amplitude are dominated by different helicity configurations, so their interference is suppressed, which reduces the impact of the operator. The reduced sensitivity to the interference also poses a problem for the validity of the effective field theory interpretation, as contributions that are quadratic in the dimension-six amplitude, which are expected to be subdominant in the EFT expansion, become large. Requiring hard jets in addition to the diboson pair allows different helicity configurations and, thus, reduces the interference-suppression [13]. In pp collisions, two leading processes contribute to WW production: q¯q→WW in the tand s-channel, and loop-induced gluon-gluon fusion processes gg →WW. Beyond leading order in perturbation theory and in particular for WW+jets production, additional partonic initial states can contribute to both processes.1Representative diagrams for WW+jet production are shown in figure 1. In this analysis, the resonant gg →H→WW production is included in the signal definition and simulation, although the process is strongly suppressed via kinematic selection requirements. The measurement of WW →e±νµ∓νproduction cross-sections at √s= 13 TeV is performed, using pp collision data recorded by the ATLAS experiment in 2015–2018, corresponding to an integrated luminosity of 139fb−1. The number of events due to top-quark pair production (t¯ t), the largest background for this measurement, is reduced by rejecting events containing jets from b-hadron decays (b-jets). However, the t¯ tbackground is still sizeable due to the requirement that events contain at least one jet, and a data-driven method is required to reduce its contribution to systematic uncertainties in the measurement. This is achieved by simultaneously measuring the number of t¯ tevents and the efficiency of identifying b-jets in these events. The procedure reduces the impact of systematic uncertainties associated with the modelling of t¯ tevents and the b-tagging efficiency 1Even though different partonic initial states contribute to both processes, the notation gg →W W is used to identify the loop-induced gluon-gluon fusion channel while q¯q→WW describes the dominant production mode. – 2 – JHEP06(2021)003 W− W+ q′ q′′ qg W− W+ q q Z/γ∗ g W− W+ g g q q′ g Figure 1. Feynman diagrams for the production of a W+W−boson pair in association with a jet. calibration, and provides a precise and accurate estimate of the background up to partonic centre-of-mass energies of the order of 1 TeV and for up to five jets. The measurement is performed in a fiducial phase space close to the geometric and kinematic acceptance of the experimental analysis. The cross-section of WW production is measured differentially as a function of: •the transverse momentum2of the leading lepton, plead. lep. T, •the transverse momentum of the sub-leading lepton, psub-lead. lep. T, •the transverse momentum of the leading jet, plead. jet T, •the jet multiplicity, •the invariant mass of the lepton pair, meµ, •the transverse momentum of the lepton pair, pT,eµ, •the scalar sum of all jet transverse momenta, HT, •the scalar sum of all jet and lepton transverse momenta, ST, •the transverse mass of the dilepton system and the missing transverse momentum,3 mT,eµ, •the rapidity of the dilepton system, yeµ, •the azimuthal separation of the two leptons, ∆φ(e, µ), and •cos θ∗=|tanh(∆η(e, µ)/2)|, which is sensitive to the spin structure of the W-boson pair [15]. 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, φbeing the azimuthal angle around the z-axis. The rapidity is defined as y=1 2ln E+pz E−pzwhile the pseudorapidity is defined in terms of the polar angle θas η=−ln tan(θ/2). Angular distance is measured in units of ∆R≡p(∆η)2+ (∆φ)2. 3The transverse mass is defined as mT,eµ =p(ET,eµ +Emiss T)2−(~pT,eµ +~pmiss T)2, where ET,eµ = p|~pT,eµ|2+m2 eµ and Emiss Tis the magnitude of the missing transverse momentum. – 3 – JHEP06(2021)003 These observables comprehensively characterize W-boson kinematics and jet production in WW events. To facilitate an anomalous coupling interpretation that is less plagued by the aforementioned interference suppression, the differential cross sections as a function of meµ and ∆φ(e, µ)are also measured for plead. jet T>200 GeV, where the jet pTthreshold is chosen as a compromise between increased interference and good measurement precision. Additional measurements with plead. lep. T>200 GeV are presented in appendix A. 2 The ATLAS detector The ATLAS experiment [16] at the LHC [17] is a multipurpose particle detector with a forward-backward symmetric cylindrical geometry and a near 4πcoverage in solid angle. It consists of an inner tracking detector surrounded by a thin superconducting solenoid providing a 2 T axial magnetic field, electromagnetic and hadron calorimeters, and a muon spectrometer with three large superconducting toroidal magnets with eight coils each. The inner tracking detector covers the pseudorapidity range |η|<2.5. It consists of a high-granularity silicon pixel detector, including the insertable B-layer installed before Run 2 [18,19], followed by the silicon microstrip tracker. The silicon detectors are complemented by a transition radiation tracking detector, enabling extended track reconstruction within |η|<2.0. Lead/liquid-argon (LAr) sampling calorimeters provide electromagnetic (EM) energy measurements with high granularity. A steel/scintillator-tile hadron calorimeter covers the central pseudorapidity range (|η|<1.7). The endcap and forward regions are instrumented with copper/LAr and tungsten/LAr calorimeters for 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 2.0and 6.0Tm. across most of the detector. The muon spectrometer includes a system of precision tracking chambers and fast detectors for triggering. Events are selected using a two-level trigger system. The first-level trigger is implemented in hardware and uses a subset of the detector information to accept events at a rate of about 100 kHz. The level-1 trigger is followed by a software-based trigger that reduces the accepted event rate to 1 kHz on average depending on the data-taking conditions. 3 Data and Monte Carlo samples The analysis uses data collected in proton-proton collisions at a centre-of-mass energy of 13TeV from 2015 to 2018. After applying data quality criteria [20], the dataset corresponds to 139fb−1, with an uncertainty of 1.7% [21], obtained using the LUCID-2 detector [22] for the primary luminosity measurements. Monte Carlo (MC) simulated event samples are used to correct the signal yield for detector effects and to estimate background contributions. All samples were passed through a full simulation of the ATLAS detector [23], based on Geant4[24]. Table 1lists the configuration for the nominal MC simulation used in the analysis. – 4 – JHEP06(2021)003 Signal events were modelled using the Sherpa 2.2.2 [25] generator at next-to-leading order (NLO) accuracy in QCD for up to one additional parton, and leading-order (LO) accuracy for two to three additional parton emissions for q¯qinitial states. The matrix element calculation of gg →W W production, which includes off-shell effects and Higgs boson contributions, incorporates up to one additional parton emission at LO. It was matched and merged with the Sherpa parton shower based on Catani-Seymour dipole [26,27] using the MEPS@NLO prescription [28–31]. The virtual QCD corrections were provided by the OpenLoops library [32,33]. The NNPDF3.0NNLO set of parton distribution functions (PDF) was used [34], along with the dedicated set of tuned parton-shower parameters developed by the Sherpa authors. To assess the uncertainty in the matrix element calculation and the parton shower modelling, alternative events for q¯q→WW production were generated using the Powheg-Box v2 [35–38] generator at NLO accuracy in QCD. Events were interfaced to Pythia 8.186 [39] for the modelling of the parton shower, hadronization, and underlying event, with parameter values set according to the AZNLO set of tuned parameters [40]. The CT10nlo set PDF [41] was used for the hard-scattering processes, whereas the CTEQ6L1 PDF set [42] was used for the parton shower. The events were normalized to the next-tonext-to-leading order (NNLO) cross-section [43]. For the gg →WW initial state, which makes up only 5% of the signal, no alternative simulation is used. The production of t¯ tand single-top Wt events was modelled using the PowhegBox v2 [35–37,44] generator at NLO with the NNPDF3.0NLO [34] PDF. The events were interfaced to Pythia 8.230 [45] to model the parton shower, hadronization, and underlying event, with the A14 set of tuned parameters [46] and using the NNPDF2.3LO set of PDFs [47]. For t¯ tevent generation, the hdamp parameter4was set to 1.5mtop [48]. The diagram-removal scheme [49] was employed to handle the interference between the Wt and t¯ tproduction processes [48]. Alternative samples were generated to assess the uncertainties in the top-background modelling. The uncertainty due to initial-state radiation and higherorder QCD effects was estimated by simultaneous variations of the hdamp parameter and the renormalization and factorization scales, and by choosing the Var3c up/down variants of the A14 set of tuned parameters as described in ref. [50]. The impact of final-state radiation was evaluated with weights that account for the effect of varying the renormalisation scale for final-state parton-shower emissions up or down by a factor two. To assess the dependence on the t¯ t-Wt overlap removal scheme, the diagram-subtraction scheme [49] was employed as an alternative to the diagram-removal scheme. The uncertainty due to the parton shower and hadronization model was evaluated by comparing the nominal sample of events with an event sample generated by Powheg-Box v2 and interfaced to Herwig 7.04 [51,52], using the H7UE set of tuned parameters [52] and the MMHT2014LO PDF set [53]. To assess the uncertainty in the matching of NLO matrix elements to the parton shower, the nominal sample was compared with a sample generated by MadGraph5_aMC@NLO 2.6.2 [54] at NLO in QCD using the five-flavour scheme and the NNPDF2.3NLO PDF set. The events 4The hdamp parameter is a resummation damping factor and one of the parameters that control the matching of Powheg matrix elements to the parton shower and thus effectively regulates the high-pT radiation against which the t¯ tsystem recoils. – 5 – JHEP06(2021)003 were interfaced with Pythia 8, as for the nominal sample. The t¯ tsample was normalized to the cross-section prediction at NNLO QCD. in QCD including the resummation of nextto-next-to-leading logarithmic (NNLL) soft-gluon terms calculated using Top++2.0 [55– 61]. The inclusive cross-section for single-top Wt was corrected to the theory prediction calculated at NLO in QCD with NNLL soft-gluon corrections [62,63]. The background due to Z/γ∗+jets production was simulated with the Sherpa 2.2.1 generator using NLO-accurate matrix elements for up to two jets, and LO-accurate matrix elements for three and four jets calculated with the Comix [26] and OpenLoops libraries. They were matched with the Sherpa parton shower [27] using the MEPS@NLO prescription [28–31] and the set of tuned parameters developed by the Sherpa authors. The NNPDF3.0NNLO set of PDFs was used, and the samples were normalised to a NNLO prediction [64]. To assess the uncertainties in modelling the Z+jets process, an alternative sample was simulated using LO-accurate matrix elements with up to four final-state partons with MadGraph5_aMC@NLO 2.2.2, with the NNPDF2.3LO set of PDFs. Events were interfaced to Pythia 8.186 using the A14 set of tuned parameters. The overlap between matrix-element and parton-shower emissions was removed using the CKKW-L merging procedure [65,66]. The inclusive cross-section of both the nominal simulation and the alternative simulation was corrected to the theory prediction calculated at NNLO in QCD. The production of WZ,ZZ,V γ (with V=W, Z) and triboson (V V V , on-shell) final states was simulated with the Sherpa 2.2.2 and Sherpa 2.2.8 generators using OpenLoops at NLO QCD accuracy for up to one additional parton and LO accuracy for two to three additional parton emissions, matched and merged with the Sherpa parton shower. The V Z simulation includes V γ∗contributions for m(``)>4GeV. Samples were generated using the NNPDF3.0NNLO PDF set and normalized to the cross-section calculated by the event generator. Alternative samples for diboson backgrounds with WZ or ZZ production were generated in the same way as the nominal signal sample: the default Sherpa simulation was exchanged for Powheg +Pythia 8, using NLO-accurate matrix elements. The Powheg diboson cross-section was scaled to NNLO [67–70], while the cross-section calculated by Sherpa was found to be in good agreement with the NNLO value. Samples generated with Powheg-Box or MadGraph5_aMC@NLO used the EvtGen 1.2.0 or 1.6.0 program [71] to model the decay of bottom and charm hadrons. The effect of multiple interactions in the same and neighbouring bunch crossings (pile-up) was modelled by overlaying the hard-scattering event with simulated inelastic pp events generated with Pythia 8.186 using the NNPDF2.3LO set of PDFs and the A3 set of tuned parameters [72]. 4 Event reconstruction and selection Candidate WW events are selected by requiring exactly one isolated electron and one isolated muon with opposite charges. Events with two isolated leptons of the same flavour are not considered in the analysis due to the higher background from Drell-Yan events. Events were recorded by either single-electron or single-muon triggers [74,75]. The minimum pTthreshold varied during data-taking between 24GeV and 26 GeV for electrons, – 6 – JHEP06(2021)003 Process Generator Parton shower Matrix element O(αS)Normalization q¯q→WW Sherpa 2.2.2 Sherpa NLO (0–1 jet), LO (2–3 jets) Generator† gg →WW Sherpa 2.2.2 Sherpa LO (0–1 jet) Generator t¯ tPowheg-Box v2 Pythia 8 NLO NNLO+NNLL Wt Powheg-Box v2 Pythia 8 NLO NLO+NNLL Z+jets Sherpa 2.2.1 Sherpa NLO (0–2 jets), LO (3–4 jets) NNLO WZ,ZZ Sherpa 2.2.2 Sherpa NLO (0–1 jet), LO (2–3 jets) Generator† Wγ,Zγ Sherpa 2.2.8 Sherpa NLO (0–1 jet), LO (2–3 jets) Generator† VVV Sherpa 2.2.2 Sherpa NLO (0–1 jet), LO (2–3 jets) Generator† †: the cross-section calculated by Sherpa is found to be in good agreement with the NNLO result [67–70,73]. Table 1. Summary of the nominal Monte Carlo simulated samples used in the analysis. The gg →WW simulation includes Higgs boson contributions. The last two columns give the order in αSof the matrix element calculation and the overall cross-section normalization. All nominal MC samples use the NNPDF3.0 PDF set. The samples generated with Sherpa use the default set of tuned parton-shower parameters, while for the Powheg-Box samples the A14 set of tuned parameters and the NNPDF2.3LO PDF set are used for the parton shower. and between 20GeV and 26GeV for muons, both requiring ‘loose’ to ‘medium’ isolation criteria. Triggers with higher pTthresholds and looser isolation requirements are also used to increase the efficiency. The trigger selection efficiency is more than 99% for signal events fulfilling all other selection requirements, which are detailed below. Candidate events are required to have at least one vertex having at least two associated tracks with pT>400 MeV. The vertex with the highest Pp2 Tof the associated tracks is taken as the primary vertex. Electrons are reconstructed from energy deposits in the calorimeter that are matched to tracks [76]. Electron candidates are required to fulfil the ‘tight’ likelihood-based identification criteria as defined in ref. [76]. Furthermore, they are required to have ET>27 GeV and |η|<2.47, excluding the transition region between barrel and endcap regions, 1.37 <|η|<1.52. Muon candidates are reconstructed by combining a track in the inner detector (ID) with a track in the muon spectrometer [77]. Muons are required to have pT>27 GeV and |η|<2.5and to satisfy the Medium identification selection, as defined in ref. [77]. Leptons are required to be compatible with the primary vertex by imposing requirements on the impact parameters of associated tracks. The transverse impact parameter significance, d0/σd0is required to satisfy |d0/σd0|<5 (3) for electrons (muons). The longitudinal impact parameter z0must satisfy |z0·sin θ|<0.5 mm, where θis the polar angle of the track. Additionally, leptons are required to be isolated using information from the ID tracks and energy clusters in the calorimeters in a cone around the lepton. The Gradient working point is used for electrons [76], while for muons the Tight_FixedRad working point is used, which is similar to the Tight selection defined in ref. [78] but with altered criteria at muon pT>50 GeV in order to increase the background rejection. The electron or muon trigger object is required to match the respective reconstructed lepton. – 7 – JHEP06(2021)003 Jets are reconstructed using the anti-ktalgorithm [79] with a radius parameter of R= 0.4using particle-flow objects [80]. They are required to have pT>30 GeV and |η|<4.5. To suppress jets that originate from pile-up, a jet-vertex tagger [81] is applied to jets with pT<60 GeV and |η|<2.4. Jet energy scale and resolution are corrected with ηand pTdependent scale factors [82]. Jets with pT>20 GeV and |η|<2.5containing decay products of a b-hadron are identified using the DL1r b-tagging algorithm [83,84] at the 85% efficiency working point. The missing transverse momentum, with magnitude Emiss T, is computed as the negative of the vectorial sum of the transverse momenta of tracks associated with jets and muons, as well as tracks in the ID that are not associated with any other component. The pTof the electron track is replaced by the calibrated transverse momentum of the reconstructed electron [85]. In order to resolve the overlap between particles reconstructed as multiple physics objects in the detector, non-b-tagged jets are removed if they overlap, within ∆R < 0.2, with an electron, or with a muon if the jet has less than three associated tracks with pT>500 MeV and satisfies pµ T/pjet T>0.5, and the ratio of the muon pTto the sum of the track pTassociated with the jet is greater than 0.7. Electrons or muons overlapping within ∆R < 0.4with any jet, including b-tagged jets, after the former selection are removed. Events having at least one jet, but no b-tagged jets, are selected for the analysis. To reduce the Drell-Yan backgrounds, dominated by Z+jets events with Z→τ+τ−decays, the invariant mass of the electron-muon pair is required to be meµ >85 GeV. This requirement also reduces the contribution of resonant gg →H→WW production. Events with additional leptons with pT>10 GeV and satisfying Loose isolation and LooseLH (Loose) identification requirements for electrons (muons), are vetoed to reduce backgrounds due to WZ and ZZ production. Additionally, the subsets of events with high leading-jet transverse momentum, plead. jet T>200 GeV, are analysed in detail, to investigate the reduced interference-suppression in the aTGC interpretation. Table 2gives a summary of the lepton, jet and event selection requirements used to define the signal region. 5 Background estimate The top-quark background, from either t¯ tor single-top Wt production, comprises about 60% of the events passing the event selection and about 90% of the total background. Additional backgrounds considered are Z+jets production, events with non-prompt or misidentified leptons, diboson production (WZ,Wγ,ZZ, and Zγ), and triboson production. 5.1 Top-quark background An estimate of the t¯ tbackground is obtained using a data-driven technique, while the single-top Wt background is estimated using simulation and is found to contribute about 16% of the top-quark background. Following the procedure used in a measurement of the t¯ tcross-section [86], two control regions requiring exactly one and exactly two b-tagged jets are defined. All other selection criteria are the same as in the signal region. These regions are dominated by t¯ tevents and can be used to infer the number of t¯ tevents in – 8 – JHEP06(2021)003 Region Observed Predicted ±Error Purity t¯ tCR 1b 260 971 268000 ±19 000 87% t¯ tCR 2b 257 777 267000 ±21 000 96% Top enriched 7167 7000 ±1000 72% Same-sign VR 5095 5000±600 25% Drell-Yan VR 11 824 13 000 ±1600 74% V Z VR 14770 14 000 ±1900 94% V γ VR (OS) 2720 2670±240 63% V γ VR (SS) 2401 2250±240 76% Table 3. Summary of the observed and predicted events in the background control regions (CR) and validation regions (VR), and in the top-background enriched selection. The uncertainty in the prediction includes statistical and systematic effects, excluding theory uncertainties on the signal. The purity column gives the purity of the target process, relative to the total prediction. The t¯ t prediction in the two t¯ tcontrol regions is from simulation, while in the top-enriched region the data-driven estimate is used. Signal region plead. jet T>200 GeV Data 89239 5825 Total SM 91600 ±2500 5980 ±150 WW 28100 ±1200 31% 2480 ±60 42% Total bkg. 63500 ±1800 69% 3500 ±140 58% Top 55800 ±1500 61% 3030 ±110 51% Drell-Yan 2200 ±700 2% 66±9 1% Fake leptons 2700 ±1100 3% 140±70 2% WZ, ZZ, V γ 2800±500 3% 270±70 4% Table 4. Selected WW candidate events, together with the signal prediction and the background estimates. The uncertainties include statistical and systematic contributions, excluding theory uncertainties on the signal. The fractions in percent give the relative contribution to the total SM prediction. The individual uncertainties are correlated, and do not add up in quadrature to the total uncertainty. 6 Fiducial and differential cross-section determination The WW+jets cross-section is evaluated in the fiducial phase space of the WW →e±νµ∓ν decay channel as defined in table 5. In simulated events, electrons and muons are required to originate directly from the hard interaction and not from τ-lepton or hadron decays. The momenta of photons emitted in a cone of size ∆R=0.1 around the lepton direction that do not originate from hadron decays are added to the lepton momentum to form ‘dressed’ – 15 – JHEP06(2021)003 leptons. Stable final-state particles,5excluding prompt leptons and the associated photons, are clustered into particle-level jets using the anti-ktalgorithm with radius parameter R = 0.4. The missing transverse momentum is defined at particle level as the transverse component of the vectorial sum of the neutrino momenta. The nominal definition of the particle-level fiducial phase space does not include a veto on b-jets. Alternative results that include a veto on particle-level b-jets6with pT>20 GeV are provided in HEPData.7 The fiducial cross-section is obtained as follows: σfid =Nobs −Nbkg C×L , where Lis the integrated luminosity, Nobs is the observed number of events, Nbkg is the estimated number of background events and Caccounts for detector inefficiencies, resolution effects, and contributions from τ-lepton decays. Cis calculated as the number of simulated signal events passing the reconstruction-level event selection divided by the events in the fiducial phase space. Its numerical value is C= 0.747 ±0.061 and its uncertainty is dominated by uncertainties in jet energy scale, jet energy resolution, and pile-up modelling. The fraction of WW events passing the event selection but containing at least one lepton from τ-lepton decays is 9%. The differential cross-sections are determined using an iterative Bayesian unfolding method [93,94]. The unfolding procedure corrects for migrations between bins in the distributions during the reconstruction of the events, and applies fiducial as well as reconstruction efficiency corrections. The fiducial corrections take into account events that are reconstructed in the signal region, but originate from outside the fiducial region; the reconstruction efficiency corrects for events inside the fiducial region that are not reconstructed in the signal region due to detector inefficiencies. Tests with MC simulation demonstrate that the method is successful in retrieving the true distribution in the fiducial region from the reconstructed distribution in the signal region. To reduce bias due to the assumed true distribution, the method can be applied iteratively, at the cost of an increased statistical uncertainty. Two iterations are used to unfold the HT,ST, and plead. jet Tdistributions and the exclusive jet multiplicity, which are subject to large modelling uncertainties. For the remaining distributions, either the result is independent of the number of iterations, or the modelling uncertainty is not reduced and the statistical uncertainties increase. For these cases, only one unfolding iteration is performed. 7 Uncertainties Systematic uncertainties in the WW+jets cross-section measurements arise from experimental sources, the background determination, the procedures used to correct for detector effects, and theoretical uncertainties in the signal modelling. 5Particles are considered stable if their decay length cτ is greater than 1 cm. 6At particle level, b-jets are defined by ghost-association [92], wherein b-hadrons are included in the jet clustering as infinitely soft particles (ghosts). Jets with b-hadron ghosts among their constituents are b-jets. 7https://www.hepdata.net/record/100511. – 16 – JHEP06(2021)003 Fiducial selection requirements p` T>27 GeV |η`|<2.5 meµ >85 GeV pj T>30 GeV |yj|< 4.5 Table 5. Definition of the WW →eµ+jets fiducial phase space, where p` T(η`) refers to the transverse momentum (pseudorapidity) of charged leptons and pj T(yj) to the transverse momentum (rapidity) of jets. The dominant experimental systematic uncertainties arise in the calibration of the jet energy scale and resolution and the calibration of the b-tagging efficiency and mis-tag rates. Experimental uncertainties also encompass uncertainties in the calibration of lepton trigger, reconstruction, identification and isolation efficiencies, the calibration of the lepton momentum or energy scale and resolution, and the modelling of pile-up. All experimental uncertainties are evaluated by varying the respective calibrations, and propagating their effects through the analysis, affecting both the background estimates and the unfolding of detector effects. Systematic uncertainties in the estimate of fake leptons are derived by changing the selection used to estimate the weights, in order to change the composition of the sources of fake leptons. Additionally, the subtraction of the prompt-lepton sources in the control region is varied, and the statistical uncertainties of the weights are propagated. More details on the uncertainties affecting the fake-lepton estimate can be found in section 5. The estimate of the top background is affected by the statistical uncertainty of the number of events in the control region, and by uncertainties in the modelling of t¯ tand single-top Wt events, such as the uncertainty in the matrix element calculation, the parton shower modelling, the QCD scale choices, the initialand final-state radiation and the interference between t¯ tand single-top Wt events. These are evaluated by using the alternative simulations described in section 3and propagating the results through the top background estimate. The effect of the PDF uncertainty on the top background was evaluated, but found to be negligible. The uncertainty in minor backgrounds is estimated by varying their total cross-section within its uncertainty and by using alternative simulations, as described in section 5. The difference between nominal and alternative simulations covers PDF uncertainties, missing higher-order QCD corrections, and the parton shower model. The bias introduced by using distributions generated by the nominal signal simulation as a prior in the unfolding is estimated by reweighting these distributions at generator level with a smooth function such that, after including simulated detector effects, they closely resemble the background-subtracted data. This reweighted detector-level prediction is unfolded using the nominal unfolding set-up. The unfolding procedure is able to very accurately recover the generator-level distribution, so this uncertainty source is negligible. – 17 – JHEP06(2021)003 2 10 [GeV] lead. lep. T p 0 5 10 15 20 25 30 35 Relative Uncertainty [%] Total Uncertainty Jet Calibration Top Modelling Fake Lepton Backgr. Other Systematics Statistical Uncertainty ATLAS -1 = 13 TeV, 139 fbs j ν ± µν ± e→pp 2 10 [GeV] lead. jet T p 0 5 10 15 20 25 30 35 Relative Uncertainty [%] Total Uncertainty Jet Calibration Top Modelling Fake Lepton Backgr. Other Systematics Statistical Uncertainty ATLAS -1 = 13 TeV, 139 fbs j ν ± µν ± e→pp Figure 4. Relative size of uncertainties for the unfolded plead. lep. Tand plead. jet Tdistributions. “Jet Calibration” uncertainties encompass jet energy scale and resolution uncertainties while “Top Modelling” encompasses all t¯ tand single-top modelling uncertainties. “Fake Lepton Backgr.” is the uncertainty in the non-prompt-lepton estimate from the fake-factor method. “Other Systematics” includes modelling and total cross-section uncertainties in the remaining backgrounds, leptonrelated uncertainties as well as uncertainties due to pile-up reweighting and the signal modelling in the unfolding, while “Statistical Uncertainty” is the combined statistical uncertainty in the signal region, from control regions, and from MC simulations. Uncertainties in the unfolding procedure due to the theoretical modelling of the signal are evaluated by repeating the unfolding procedure with alternative signal simulations. The uncertainty due to missing higher-order QCD corrections is evaluated by varying the renormalization and factorization scales. The uncertainty due to the choice of generator for the hard interaction, the parton shower model and the underlying-event modelling is estimated using the alternative simulation of q¯q→WW production, from PowhegBox v2, interfaced to Pythia 8.186. For the uncertainty estimation, the alternative model is first reweighted to the nominal model, so that uncertainties due to disagreement in the predicted shape of distributions can be ignored, and only the difference in the prediction of the migration matrix and fiducial and efficiency corrections are taken into account. Statistical uncertainties are evaluated by creating pseudo data samples that are obtained by varying the data within their Poisson uncertainties in each bin and then propagating these varied samples through the unfolding. The statistical uncertainties of the background estimates, which include statistical uncertainties in MC predictions and due to the control regions used in estimating the top and fake-lepton backgrounds, are evaluated using the same method. If not stated otherwise, ‘statistical uncertainties’ refers to the combined statistical uncertainties from signal and control regions. Table 6gives a breakdown of the uncertainties in the fiducial cross-section measurements, and figure 4displays the uncertainties as a function of the unfolded plead. lep. Tand plead. jet Tdistributions. Jet-related uncertainties generally decrease with plead. jet Tand with correlated quantities such as plead. lep. T, while statistical uncertainties increase at high energy. This leads to a minimum of the total uncertainty for intermediate values of plead. jet T and plead. lep. T. – 18 – JHEP06(2021)003 Uncertainty source Relative effect Total uncertainty 10% Signal region statistical uncertainty 1.1% Data-driven background and MC statistics 1.2% Jet calibration 6.3% Top modelling 4.5% Fake-lepton background 4.3% Signal modelling 2.7% Other background 2.3% Flavour tagging 2.3% Luminosity 1.9% Other systematic uncertainties 0.6% Table 6. Breakdown of the uncertainties in the measured fiducial cross-section. “Jet calibration” uncertainties encompass jet energy scale and resolution uncertainties, “Top modelling” and “Signal modelling” are uncertainties in the theoretical modelling of the respective processes, “Fake-lepton background” is the uncertainty in the fake-lepton estimate while “Other background” is the uncertainty due to minor prompt-lepton backgrounds, “Flavour tagging” is all uncertainties in flavour tagging efficiency and mis-tag rate, and “Luminosity” is the uncertainty in the measurement of the integrated luminosity. All systematic uncertainties belonging to none of the above categories are included in “Other systematic uncertainties”. Statistical uncertainties arise in both the signal region and control region used for the data-driven top and fake-lepton estimates and also from backgrounds that are estimated using MC simulations. 8 Results The measured fiducial cross-section for W W +jets production, with WW →e±νµ∓ν, at √s= 13 TeV, for the phase space defined in table 5is σfid = 258 ±4 (stat.)±25 (syst.) fb, with a total uncertainty of 10%. In figure 5, the measured result is compared with various predictions for WW+jets production, and good agreement is found. Differential fiducial cross-sections are presented in figures 6to 8. Figure 9displays distributions in a phase space that additionally requires a jet with a transverse momentum of at least 200GeV. 8.1 Comparison with theoretical predictions The measurement is compared to the theory predictions listed in table 7. The measured fiducial cross-section is compatible with the prediction of 279 ±2(pdf) +20 −16 (scale)fb from MATRIX [32,33,43,73,95–99], which is accurate to NNLO (NLO) for q¯q→ WW (gg →WW) production, denoted nNNLO, but only NLO (LO) accurate for q¯q→WW (gg →WW) production in association with a jet. For this prediction, the NNPDF3.1NNLO parton distribution function is used, while renormalization and factorization scales are set to mW. In figure 5, the measured integrated fiducial cross-section is – 19 – JHEP06(2021)003 100 150 200 250 300 Integrated fiducial cross-section [fb] Data 25 (syst) fb± 4 (stat) ±258 MATRIX 2.0 nNNLO 18 (scale) fb± 2 (PDF) ±279 NLO EW⊗MATRIX 2.0 nNNLO 18 (scale) fb± 2 (PDF) ±278 Sherpa 2.2.2 (0-1j@NLO, 2-3j@LO)* 44 (scale) fb± 3 (PDF) ±277 MG5_aMC + Pythia8 FxFx (0-1j@NLO)* 16 (scale) fb± 3 (PDF) ±263 Powheg MiNLO + Pythia8 (0-1j@NLO)* 21 (scale) fb± 3 (PDF) ±254 WW→* + Sherpa & OpenLoops gg ATLAS -1 = 13 TeV, 139 fbs jν ± µν ± e→pp Data Stat. Unc. Tot. Unc. Predictions Figure 5. Comparison of the measured fiducial WW+jets cross-section with various theoretical predictions. Theoretical predictions are indicated as points with inner (outer) error bars denoting PDF (PDF+scale) uncertainties. The central value of the measured cross-section is indicated by a vertical line with the narrow band showing the statistical uncertainty and the wider band the total uncertainty including statistical and systematic uncertainties. The result is compared with a fixedorder parton-level prediction from MATRIX 2.0 that is accurate to NNLO (NLO) for q¯q→WW (gg →WW ) production, and a prediction that additionally accounts for EW corrections to WW + jet production, which have been calculated with Sherpa 2.2.2 + OpenLoops. It is also compared with predictions from Sherpa 2.2.2, MadGraph5_aMC@NLO +Pythia 8 with FxFx merging, and Powheg MiNLO +Pythia 8, which are all supplemented by a Sherpa 2.2.2 + OpenLoops gg →WW LO+PS prediction. also compared with a prediction that combines the QCD corrections from MATRIX with NLO EW corrections to WW+jets production that were generated with Sherpa 2.2.2 + OpenLoops [25,100–102]. Photon-induced contributions are included as an additive correction, while the EW correction to q¯q→WW is taken into account multiplicatively. The latter correction decreases the cross-section by 4%, while the former leads to an increase of 4%. The importance of both corrections increases with energy. The difference between an additive and multiplicative combination scheme for QCD and EW corrections is typically of the order 1% but can be as large as 10% in the highest HTand STbins. Also displayed in figure 5are the nominal q¯q→WW Sherpa 2.2.2 prediction, a prediction from MadGraph 2.3.3 using FxFx merging [103] and interfaced to Pythia 8.212, and a Powheg MiNLO [104] prediction interfaced to Pythia 8.244. All three calculations are NLO-accurate for WW production with one jet and use the NNPDF3.0 PDF set. The effects of scale uncertainties for all predictions are estimated by varying the factorization and renormalization scales of the hard process. The effects of scale uncertainties on the Sherpa 2.2.2 prediction are large in comparison with the other generators as this calculation includes leading-order matrix elements with up to three jets, which are strongly affected – 20 – JHEP06(2021)003 Process Generator Parton shower PDF Matrix element O(αS) q¯q→WW MATRIX 2.0 — NNPDF3.1 NNLO gg →WW MATRIX 2.0 — NNPDF3.1 NLO q¯q→WW Sherpa 2.2.2 Sherpa NNPDF3.0 NLO (0–1 jet), LO (2–3 jets) q¯q→WW Powheg MiNLO Pythia 8 NNPDF3.0 NLO (0–1 jet) q¯q→WW MadGraph 2.3.3 Pythia 8 NNPDF3.0 NLO (0–1 jet) gg →WW Sherpa 2.2.2 + OpenLoops Sherpa NNPDF3.0 LO (0–1 jet) Table 7. Summary of the theoretical predictions that are compared with the measured crosssections. The samples generated with Sherpa use the default parton-shower tune, while for those using Pythia 8 the A14 tune and the NNPDF2.3LO PDF set are used for the parton shower. by scale variations. Both predictions are supplemented by the Sherpa 2.2.2 + OpenLoops simulation of gg →W W , which is normalized to the total NLO QCD cross-section [99]. The measured distributions in figures 6–9are also compared with the predictions described above. Within uncertainties, all predictions give an excellent description of the observed data. For the nominal Sherpa 2.2.2 prediction, values of χ2divided by the number of degrees of freedom are below one, except for the meµ distribution measured for plead. jet T>200 GeV, for which the value is 1.4. Comparisons of the remaining predictions with the data yield similar χ2values, except for the jet multiplicity, HT, and STdistributions, where, for the highest multiplicities and energies, small discrepancies exist between data and predictions. 8.2 Effective field theory interpretation Many new-physics models that introduce new states at a high energy scale (Λ) can be described, at lower energy scales, by operators with mass dimensions larger than four in an effective field theory (EFT) framework. The higher-dimensional operators of the lowest order that can generate anomalous triple-gauge-boson couplings (aTGC) are of dimension six. The QWdimension-six operator, as defined in ref. [14], is of particular interest for an analysis of diboson production because it can only be measured in processes affected by modifications of the gauge-boson self-couplings. Its effect increases rapidly with the centreof-mass energy, making a measurement at the energies probed by the LHC important. However, the interference of the SM and anomalous amplitudes, and, thus, the observable consequences of the operator, decrease with increasing energy due to the different helicities of the dominant contributions to the two amplitudes [105,106]. As a consequence, the square of the anomalous dimension-six amplitude, which is quadratic in the ratio of the Wilson coefficient, cW, to Λ2, dominates, while, in general, the interference of dimensionsix operators with the SM is expected to be larger, as it is linear in cW/Λ2and, thus, less suppressed by Λ. The interference-suppression weakens the limits on cWthat can be achieved by a measurement of diboson production and also poses a problem for the validity of an interpretation in a dimension-six model, since other terms of order Λ−4, for example those due to dimension-eight operators, are neglected. Requiring a hard jet in addition to the diboson pair alters the relative contributions of different helicity configurations and reduces the suppression of the interference of SM and anomalous amplitudes [13]. – 21 – JHEP06(2021)003 2− 10 1− 10 1 10 2 10 [fb/GeV] lead. lep. T p/d σ d 1 10 2 10 3 10 4 10 [fb] σ ∆ Data and Stat. Uncertainty Total Uncertainty Sherpa 2.2.2 * MG5_aMC+Pythia8 FxFx * MiNLO+Pythia8 * NLO EW⊗MATRIX nNNLO WW→* plus Sherpa+OL gg ATLAS -1 = 13 TeV, 139 fbs j ν ± µν ± e→pp [GeV] lead. lep. T p 0.6 0.8 1 1.2 1.4 Prediction/Data 30 40 50 60 2 10 2 10×22 10×32 10×4.5≥ 2− 10 1− 10 1 10 2 10 [fb/GeV] sub-lead. lep. T p/d σ d 1 10 2 10 3 10 4 10 [fb] σ ∆ Data and Stat. Uncertainty Total Uncertainty Sherpa 2.2.2 * MG5_aMC+Pythia8 FxFx * MiNLO+Pythia8 * NLO EW⊗MATRIX nNNLO WW→* plus Sherpa+OL gg ATLAS -1 = 13 TeV, 139 fbs j ν ± µν ± e→pp [GeV] sub-lead. lep. T p 0.6 0.8 1 1.2 1.4 Prediction/Data 30 40 50 60 70 80 2 10 2 10×2≥ 1− 10 1 10 [fb/GeV] lead. jet T p/d σ d 10 2 10 3 10 [fb] σ ∆ Data and Stat. Uncertainty Total Uncertainty Sherpa 2.2.2 * MG5_aMC+Pythia8 FxFx * MiNLO+Pythia8 * NLO EW⊗MATRIX nNNLO WW→* plus Sherpa+OL gg ATLAS -1 = 13 TeV, 139 fbs j ν ± µν ± e→pp [GeV] lead. jet T p 0.6 0.8 1 1.2 1.4 Prediction/Data 40 50 60 2 10 2 10×22 10×32 10×4.5≥ 1− 10 1 10 [fb/GeV] T H/d σ d 10 2 10 3 10 [fb] σ ∆ Data and Stat. Uncertainty Total Uncertainty Sherpa 2.2.2 * MG5_aMC+Pythia8 FxFx * MiNLO+Pythia8 * NLO EW⊗MATRIX nNNLO WW→* plus Sherpa+OL gg ATLAS -1 = 13 TeV, 139 fbs j ν ± µν ± e→pp [GeV] T H 0.6 0.8 1 1.2 1.4 Prediction/Data 40 50 60 2 10 2 10×22 10×32 10×5≥ Figure 6. Measured fiducial cross-sections of W W+jets production for (from left to right and top to bottom): plead. lep. T,psub-lead. lep. T,plead. jet T, and HT. The last bin of each distribution is inclusive in the measured observable and the corresponding integrated cross-section is indicated by the righthand-side axis. The measured cross-section values are shown as points with error bars giving the statistical uncertainty and solid bands indicating the size of the total uncertainty. The results are compared with the NNLO prediction with extra NLO EW corrections and NLO corrections for gg →WW production (denoted MATRIX ⊗NLO EW) as well as NLO+PS predictions from Sherpa 2.2.2, MadGraph5_aMC@NLO +Pythia 8 with FxFx merging, and Powheg MiNLO +Pythia 8 for q¯qinitial states, combined with Sherpa +OpenLoops (LO+PS) for the gg initial state. The Sherpa 2.2.2 + OpenLoops prediction is normalized to the total cross-section calculated at NLO in QCD. Theoretical predictions are indicated as markers with vertical lines denoting PDF and scale uncertainties. – 22 – JHEP06(2021)003 1− 10 1 10 [fb/GeV] T S/d σ d 10 2 10 3 10 [fb] σ ∆ Data and Stat. Uncertainty Total Uncertainty Sherpa 2.2.2 * MG5_aMC+Pythia8 FxFx * MiNLO+Pythia8 * NLO EW⊗MATRIX nNNLO WW→* plus Sherpa+OL gg ATLAS -1 = 13 TeV, 139 fbs j ν ± µν ± e→pp [GeV] T S 0.6 0.8 1 1.2 1.4 Prediction/Data 2 10 2 10×9≥ 2 10×22 10×3 2− 10 1− 10 1 10 [fb/GeV] µT,e m/d σ d 1 10 2 10 3 10 [fb] σ ∆ Data and Stat. Uncertainty Total Uncertainty Sherpa 2.2.2 * MG5_aMC+Pythia8 FxFx * MiNLO+Pythia8 * NLO EW⊗MATRIX nNNLO WW→* plus Sherpa+OL gg ATLAS -1 = 13 TeV, 139 fbs j ν ± µν ± e→pp [GeV] µT,e m 0.6 0.8 1 1.2 1.4 Prediction/Data 2 10 2 10×8≥ 2 10×22 10×3 1− 10 1 10 [fb/GeV] µe m/d σ d 10 2 10 3 10 [fb] σ ∆ Data and Stat. Uncertainty Total Uncertainty Sherpa 2.2.2 * MG5_aMC+Pythia8 FxFx * MiNLO+Pythia8 * NLO EW⊗MATRIX nNNLO WW→* plus Sherpa+OL gg ATLAS -1 = 13 TeV, 139 fbs j ν ± µν ± e→pp [GeV] µe m 0.6 0.8 1 1.2 1.4 Prediction/Data 90 2 10 2 10×22 10×32 10×4≥ 1− 10 1 10 2 10 [fb/GeV] µT,e p/d σ d 10 2 10 3 10 4 10 [fb] σ ∆ Data and Stat. Uncertainty Total Uncertainty Sherpa 2.2.2 * MG5_aMC+Pythia8 FxFx * MiNLO+Pythia8 * NLO EW⊗MATRIX nNNLO WW→* plus Sherpa+OL gg ATLAS -1 = 13 TeV, 139 fbs j ν ± µν ± e→pp [GeV] µT,e p 0.6 0.8 1 1.2 1.4 Prediction/Data 20 30 40 50 60 2 10 2 10×22 10×3≥0 Figure 7. Measured fiducial cross-sections of WW+jets production for (from left to right and top to bottom): ST,mT,eµ,meµ, and pT,eµ. The last bin of each distribution is inclusive in the measured observable and the corresponding integrated cross-section is indicated by the right-handside axis. The measured cross-section values are shown as points with error bars giving the statistical uncertainty and solid bands indicating the size of the total uncertainty. The results are compared with the NNLO prediction with extra NLO EW corrections and NLO corrections for gg →WW production (denoted MATRIX ⊗NLO EW) as well as NLO+PS predictions from Sherpa 2.2.2, MadGraph5_aMC@NLO +Pythia 8 with FxFx merging, and Powheg MiNLO +Pythia 8 for q¯qinitial states, combined with Sherpa +OpenLoops (LO+PS) for the gg initial state. The Sherpa 2.2.2 + OpenLoops prediction is normalized to the total cross-section calculated at NLO in QCD. Theoretical predictions are indicated as markers with vertical lines denoting PDF and scale uncertainties. – 23 – JHEP06(2021)003 50 100 150 200 250 300 350 400 | [fb] µe φ ∆/d| σ d [fb] σ ∆ Data and Stat. Uncertainty Total Uncertainty Sherpa 2.2.2 * MG5_aMC+Pythia8 FxFx * MiNLO+Pythia8 * NLO EW⊗MATRIX nNNLO WW→* plus Sherpa+OL gg ATLAS -1 = 13 TeV, 139 fbs j ν ± µν ± e→pp 0 0.5 1 1.5 2 2.5 3 | µe φ ∆| 0.6 0.8 1 1.2 1.4 Prediction/Data 50 100 150 200 250 300 [fb] µe y/d σ d [fb] σ ∆ Data and Stat. Uncertainty Total Uncertainty Sherpa 2.2.2 * MG5_aMC+Pythia8 FxFx * MiNLO+Pythia8 * NLO EW⊗MATRIX nNNLO WW→* plus Sherpa+OL gg ATLAS -1 = 13 TeV, 139 fbs j ν ± µν ± e→pp 0 0.5 1 1.5 2 2.5 µe y 0.6 0.8 1 1.2 1.4 Prediction/Data 100 200 300 400 500 600 700 *) [fb] θ /dcos( σ d [fb] σ ∆ Data and Stat. Uncertainty Total Uncertainty Sherpa 2.2.2 * MG5_aMC+Pythia8 FxFx * MiNLO+Pythia8 * NLO EW⊗MATRIX nNNLO WW→* plus Sherpa+OL gg ATLAS -1 = 13 TeV, 139 fbs j ν ± µν ± e→pp 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 *) θ cos( 0.6 0.8 1 1.2 1.4 Prediction/Data 10 2 10 3 10 [fb] σ [fb] σ ∆ Data and Stat. Uncertainty Total Uncertainty Sherpa 2.2.2 * MG5_aMC+Pythia8 FxFx * MiNLO+Pythia8 * NLO EW⊗MATRIX nNNLO WW→* plus Sherpa+OL gg ATLAS -1 = 13 TeV, 139 fbs j ν ± µν ± e→pp 1 2 3 4 5≥ > 30 GeV) T pNumber of jets ( 0.5 1 1.5 Prediction/Data Figure 8. Measured fiducial cross-sections of W W+jets production for (from left to right and top to bottom): ∆φ(e, µ),yeµ,cos θ∗, and the exclusive jet multiplicity. The measured cross-section values are shown as points with error bars giving the statistical uncertainty and solid bands indicating the size of the total uncertainty. The results are compared with the NNLO prediction with extra NLO EW corrections and NLO corrections for gg →WW production (denoted MATRIX ⊗NLO EW) as well as NLO+PS predictions from Sherpa 2.2.2, MadGraph5_aMC@NLO +Pythia 8 with FxFx merging, and Powheg MiNLO +Pythia 8 for q¯qinitial states, combined with Sherpa +OpenLoops (LO+PS) for the gg initial state. The Sherpa 2.2.2 + OpenLoops prediction is normalized to the total cross-section calculated at NLO in QCD. Theoretical predictions are indicated as markers with vertical lines denoting PDF and scale uncertainties.The MATRIX prediction is not defined for more than two jet emissions. – 24 – JHEP06(2021)003 Bt¯ tbackground estimate Figure 12 shows the plead. lep. Tand jet multiplicity distributions in the two t¯ tcontrol regions, which require exactly one and exactly two b-jets, respectively. The b-jet correlation factor Cbfor the two distributions is shown in figure 13. Figure 14 shows the meµ distribution for plead. jet T>200 GeV in the two control regions and for the top-enriched selection, together with the b-jet correlation factor Cb. The excess of events predicted at high plead. lep. T, in comparison with data, is corrected for by the data-driven estimate, and no discrepancy is seen in the top-enriched selection, as shown in figure 2in the main body. The jet multiplicity is well modelled up to five selected jets. – 31 – JHEP06(2021)003 2 10 [GeV] lead. lep. T p 0.8 0.9 1 1.1 1.2 Data/SM 30 40 60 80 3 10×3 0 1000 2000 3000 4000 5000 Events / GeV Data (MC)tt Single top Wt Others syst.⊕Stat. ATLAS -1 = 13 TeV, 139 fbs -jetb CR 1 tt 1 2 3 4 5 > 30 GeV) T pNumber of jets ( 0.8 0.9 1 1.1 1.2 Data/SM 0 20 40 60 80 100 120 3 10× Events Data (MC)tt Single top Wt Others syst.⊕Stat. ATLAS -1 = 13 TeV, 139 fbs -jetb CR 1 tt 2 10 [GeV] lead. lep. T p 0.8 0.9 1 1.1 1.2 Data/SM 30 40 60 80 3 10×3 0 1000 2000 3000 4000 5000 Events / GeV Data (MC)tt Single top Wt Others syst.⊕Stat. ATLAS -1 = 13 TeV, 139 fbs -jetsb CR 2 tt 1 2 3 4 5 > 30 GeV) T pNumber of jets ( 0.8 0.9 1 1.1 1.2 Data/SM 0 20 40 60 80 100 120 140 3 10× Events Data (MC)tt Single top Wt Others syst.⊕Stat. ATLAS -1 = 13 TeV, 139 fbs -jetsb CR 2 tt Figure 12. Detector-level distributions of the plead. lep. T(left) and the jet multiplicity (right) in the t¯ tcontrol regions with one b-jet (left) and two b-jets (right). Data are shown together with the predictions for t¯ t, single-top Wt and other production processes from simulation. The last bin contains overflow events. The lower panels show the ratio of the data to the total prediction. The uncertainties shown include statistical and systematic uncertainties. – 32 – JHEP06(2021)003 2 10 3 10 [GeV] T Leading lepton p 0.95 0.96 0.97 0.98 0.99 1 1.01 1.02 1.03 b b-jet correlation C SimulationATLAS 1 2 3 4 5 Jet multiplicity (30 GeV) 0.75 0.8 0.85 0.9 0.95 1 1.05 b b-jet correlation C SimulationATLAS Figure 13. Distribution of the b-jet correlation factor Cbas a function of the plead. lep. T(left) and the jet multiplicity (right), as determined from the nominal t¯ tsimulation. The uncertainties shown include MC statistical and systematic uncertainties. – 33 – JHEP06(2021)003 2 10 3 10 [GeV] µe m 0.8 0.9 1 1.1 1.2 Data/SM 0 1000 2000 3000 4000 5000 6000 7000 Events / bin Data (MC)tt Single top Wt Others syst.⊕Stat. ATLAS -1 = 13 TeV, 139 fbs > 200 GeV lead. jet T p-jet, b CR 1 t t 2 10 3 10 [GeV] µe m 0.8 0.9 1 1.1 1.2 Data/SM 0 2 4 6 8 10 3 10× Events / bin Data (MC)tt Single top Wt Others syst.⊕Stat. ATLAS -1 = 13 TeV, 139 fbs > 200 GeV lead. jet T p-jets, b CR 2 t t 2 10 3 10 [GeV] µe m 0.8 1 1.2 Data/SM 0 5 10 15 20 25 30 Events / bin Data Top WW Fakes γWZ,ZZ,V Drell-Yan *syst.⊕Stat. *: without WW modelling uncertainties ATLAS -1 = 13 TeV, 139 fbs > 200 GeV lead. jet T pTop enriched, 2 10 3 10 [GeV] µe m 0.95 0.96 0.97 0.98 0.99 1 1.01 1.02 1.03 b b-jet correlation C SimulationATLAS Figure 14. Detector-level distributions of the dilepton invariant mass meµ for plead. jet T>200 GeV in the one b-tag and two b-tag control regions as well as the top-enriched region, together with the b-jet correlation factor Cb. The lower panels in the plots of detector-level distributions show the ratio of the data to the total prediction. 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Mondal150,S. Mondal140,K. Mönig46, E. Monnier101,A. Montalbano151,J. Montejo Berlingen36,M. Montella94,F. Monticelli88, N. Morange64,A.L. Moreira De Carvalho138a,M. Moreno Llácer172,C. Moreno Martinez14, P. Morettini55b,M. Morgenstern159,S. Morgenstern176,D. Mori151,M. Morii59,M. Morinaga177, V. Morisbak132,A.K. Morley36,A.P. Morris94,L. Morvaj36,P. Moschovakos36,B. Moser119, M. Mosidze158b,T. Moskalets52,P. Moskvitina118,J. Moss31,o,E.J.W. Moyse102,S. Muanza101, J. Mueller137,D. Muenstermann89,G.A. Mullier96, J.J. Mullin135,D.P. Mungo68a,68b, J.L. Munoz Martinez14,F.J. Munoz Sanchez100,M. Murin100,P. Murin28b,W.J. Murray176,142, A. Murrone68a,68b,J.M. Muse127,M. Muškinja18,C. Mwewa29,A.G. Myagkov122,af , A.A. Myers137,G. Myers65,J. Myers130,M. Myska140,B.P. Nachman18,O. Nackenhorst47, A.Nag Nag48,K. Nagai133,K. Nagano81,J.L. Nagle29,E. Nagy101,A.M. Nairz36, Y. Nakahama116,K. Nakamura81,H. Nanjo131,F. Napolitano61a,R.F. Naranjo Garcia46, R. Narayan42,I. Naryshkin136,M. Naseri34,T. Naumann46,G. Navarro22a, J. Navarro-Gonzalez172,P.Y. Nechaeva110,F. Nechansky46,T.J. Neep21,A. Negri70a,70b, M. Negrini23b,C. Nellist118,C. Nelson103,K. Nelson105,M.E. Nelson45a,45b,S. Nemecek139, M. Nessi36,g,M.S. Neubauer171,F. Neuhaus99, M. Neumann180,R. Newhouse173,P.R. Newman21, C.W. Ng137, Y.S. Ng19,Y.W.Y. Ng169,B. Ngair35f ,H.D.N. Nguyen101,T. Nguyen Manh109, E. Nibigira38,R.B. Nickerson133,R. Nicolaidou143,D.S. Nielsen40,J. Nielsen144,M. Niemeyer53, N. Nikiforou11,V. Nikolaenko122,af ,I. Nikolic-Audit134,K. Nikolopoulos21,P. Nilsson29, H.R. Nindhito54,A. Nisati72a,N. Nishu3,R. Nisius114,T. Nitta177,T. Nobe162,D.L. Noel32, Y. Noguchi85,I. Nomidis134, M.A. Nomura29,M.B. Norfolk148,R.R.B. Norisam94,J. Novak91, T. Novak46,O. Novgorodova48,L. Novotny140,R. Novotny117, L. Nozka129,K. Ntekas169, E. Nurse94,F.G. Oakham34,ak,J. Ocariz134,A. Ochi82,I. Ochoa138a,J.P. Ochoa-Ricoux145a, K. O’Connor26,S. Oda87,S. Odaka81,S. Oerdek53,A. Ogrodnik83a,A. Oh100,C.C. Ohm153, H. Oide163,R. Oishi162,M.L. Ojeda165,Y. Okazaki85, M.W. O’Keefe90,Y. Okumura162, A. Olariu27b,L.F. Oleiro Seabra138a,S.A. Olivares Pino145c,D. Oliveira Damazio29, D. Oliveira Goncalves80a,J.L. Oliver1,M.J.R. Olsson169,A. Olszewski84,J. Olszowska84, Ö.O. Öncel24,D.C. O’Neil151,A.P. O’neill133,A. Onofre138a,138e,P.U.E. Onyisi11, H. Oppen132, R.G. Oreamuno Madriz120,M.J. Oreglia37,G.E. Orellana88,D. Orestano74a,74b,N. Orlando14, R.S. Orr165,V. O’Shea57,R. Ospanov60a,G. Otero y Garzon30,H. Otono87,P.S. Ott61a, G.J. Ottino18,M. Ouchrif35e,J. Ouellette29,F. Ould-Saada132,A. Ouraou143,∗,Q. Ouyang15a, M. Owen57,R.E. Owen142,V.E. Ozcan12c,N. Ozturk8,J. Pacalt129,H.A. Pacey32,K. Pachal49, A. Pacheco Pages14,C. Padilla Aranda14,S. Pagan Griso18, G. Palacino65,S. Palazzo50, S. Palestini36,M. Palka83b,P. Palni83a,D.K. Panchal11,C.E. Pandini54,J.G. Panduro Vazquez93, – 48 – JHEP06(2021)003 P. Pani46,G. Panizzo66a,66c,L. Paolozzi54,C. Papadatos109,S. Parajuli42,A. Paramonov6, C. Paraskevopoulos10,D. Paredes Hernandez62b,S.R. Paredes Saenz133,B. Parida178, T.H. Park165,A.J. Parker31,M.A. Parker32,F. Parodi55b,55a,E.W. Parrish120,J.A. Parsons39, U. Parzefall52,L. Pascual Dominguez134,V.R. Pascuzzi18,J.M.P. Pasner144,F. Pasquali119, E. Pasqualucci72a,S. Passaggio55b,F. Pastore93,P. Pasuwan45a,45b,J.R. Pater100,A. Pathak179,k, J. Patton90,T. Pauly36,J. Pearkes152,M. Pedersen132,L. Pedraza Diaz118,R. Pedro138a, T. Peiffer53,S.V. Peleganchuk121b,121a,O. Penc139,C. Peng62b,H. Peng60a,M. Penzin164, B.S. Peralva80a,M.M. Perego64,A.P. Pereira Peixoto138a,L. Pereira Sanchez45a,45b, D.V. Perepelitsa29,E. Perez Codina166a,M. Perganti10,L. Perini68a,68b,H. Pernegger36, S. Perrella36,A. Perrevoort119,K. Peters46,R.F.Y. Peters100,B.A. Petersen36,T.C. Petersen40, E. Petit101,V. Petousis140,C. Petridou161, P. Petroff64,F. Petrucci74a,74b,M. Pettee181, N.E. Pettersson102,K. Petukhova141,A. Peyaud143,R. Pezoa145d,L. Pezzotti70a,70b, G. Pezzullo181,T. Pham104,P.W. Phillips142,M.W. Phipps171,G. Piacquadio154,E. Pianori18, F. Piazza68a,68b,A. Picazio102,R. Piegaia30, D. Pietreanu27b,J.E. Pilcher37,A.D. Pilkington100, M. Pinamonti66a,66c,J.L. Pinfold3, C. Pitman Donaldson94,D.A. Pizzi34,L. Pizzimento73a,73b, A. Pizzini119,M.-A. Pleier29, V. Plesanovs52,V. Pleskot141, E. Plotnikova79, P. Podberezko121b,121a,R. Poettgen96,R. Poggi54,L. Poggioli134,I. Pogrebnyak106,D. Pohl24, I. Pokharel53,G. Polesello70a,A. Poley151,166a,A. Policicchio72a,72b,R. Polifka141,A. Polini23b, C.S. Pollard46,Z.B. Pollock126,V. Polychronakos29,D. Ponomarenko111,L. Pontecorvo36, S. Popa27a,G.A. Popeneciu27d,L. Portales5,D.M. Portillo Quintero58,S. Pospisil140, P. Postolache27c,K. Potamianos133,I.N. Potrap79,C.J. Potter32,H. Potti11,T. Poulsen46, J. Poveda172,T.D. Powell148,G. Pownall46,M.E. Pozo Astigarraga36,A. Prades Ibanez172, P. Pralavorio101,M.M. Prapa44,S. Prell78,D. Price100,M. Primavera67a,M.A. Principe Martin98, M.L. Proffitt147,N. Proklova111,K. Prokofiev62c,F. Prokoshin79,S. Protopopescu29, J. Proudfoot6,M. Przybycien83a,D. Pudzha136, P. Puzo64,D. Pyatiizbyantseva111,J. Qian105, Y. Qin100,A. Quadt53,M. Queitsch-Maitland36,G. Rabanal Bolanos59,F. Ragusa68a,68b, G. Rahal97,J.A. Raine54,S. Rajagopalan29,K. Ran15a,15d,D.F. Rassloff61a,D.M. Rauch46, S. Rave99,B. Ravina57,I. Ravinovich178,M. Raymond36,A.L. Read132,N.P. Readioff148, M. Reale67a,67b,D.M. Rebuzzi70a,70b,G. Redlinger29,K. Reeves43,D. Reikher160, A. Reiss99, A. Rej150,C. Rembser36,A. Renardi46,M. Renda27b, M.B. Rendel114,A.G. Rennie57, S. Resconi68a,E.D. Resseguie18,S. Rettie94, B. Reynolds126,E. Reynolds21, M. Rezaei Estabragh180,O.L. Rezanova121b,121a,P. Reznicek141,E. Ricci75a,75b,R. Richter114, S. Richter46,E. Richter-Was83b,M. Ridel134,P. Rieck114,O. Rifki46, M. Rijssenbeek154, A. Rimoldi70a,70b,M. Rimoldi46,L. Rinaldi23b,T.T. Rinn171,M.P. Rinnagel113,G. Ripellino153, I. Riu14,P. Rivadeneira46,J.C. Rivera Vergara174,F. Rizatdinova128,E. Rizvi92,C. Rizzi54, S.H. Robertson103,aa,M. Robin46,D. Robinson32, C.M. Robles Gajardo145d, M. Robles Manzano99,A. Robson57,A. Rocchi73a,73b,C. Roda71a,71b,S. Rodriguez Bosca172, A. Rodriguez Rodriguez52,A.M. Rodríguez Vera166b, S. Roe36,J. Roggel180,O. Røhne132, R.A. Rojas145d,B. Roland52,C.P.A. Roland65,J. Roloff29,A. Romaniouk111,M. Romano23b,23a, N. Rompotis90,M. Ronzani124,L. Roos134,S. Rosati72a, G. Rosin102,B.J. Rosser135,E. Rossi165, E. Rossi5,E. Rossi69a,69b,L.P. Rossi55b,L. Rossini46,R. Rosten126,M. Rotaru27b,B. Rottler52, D. Rousseau64,D. Rousso32,G. Rovelli70a,70b,A. Roy11,A. Rozanov101,Y. Rozen159,X. Ruan33f , A.J. Ruby90,T.A. Ruggeri1,F. Rühr52,A. Ruiz-Martinez172,A. Rummler36,Z. Rurikova52, N.A. Rusakovich79,H.L. Russell36,L. Rustige38,J.P. Rutherfoord7,E.M. Rüttinger148, M. Rybar141,E.B. Rye132,A. Ryzhov122,J.A. Sabater Iglesias46,P. Sabatini172,L. Sabetta72a,72b, H.F-W. Sadrozinski144,R. Sadykov79,F. Safai Tehrani72a,B. Safarzadeh Samani155, M. Safdari152,P. Saha120,S. Saha103,M. Sahinsoy114,A. Sahu180,M. Saimpert36,M. Saito162, T. Saito162, D. Salamani54,G. Salamanna74a,74b,A. Salnikov152,J. Salt172,A. Salvador Salas14, – 49 – JHEP06(2021)003 D. Salvatore41b,41a,F. Salvatore155,A. Salzburger36,D. Sammel52,D. Sampsonidis161, D. Sampsonidou60d,60c,J. Sánchez172,A. Sanchez Pineda66a,36,66c,H. Sandaker132,C.O. Sander46, I.G. Sanderswood89,M. Sandhoff180,C. Sandoval22b,D.P.C. Sankey142,M. Sannino55b,55a, Y. Sano116,A. Sansoni51,C. Santoni38,H. Santos138a,138b,S.N. Santpur18,A. Santra178, K.A. Saoucha148,A. Sapronov79,J.G. Saraiva138a,138d,O. Sasaki81,K. Sato167, C. Sauer61b, F. Sauerburger52,E. Sauvan5,P. Savard165,ak,R. Sawada162,C. Sawyer142,L. Sawyer95, I. Sayago Galvan172,C. Sbarra23b,A. Sbrizzi66a,66c,T. Scanlon94,J. Schaarschmidt147, P. Schacht114,D. Schaefer37,L. Schaefer135,U. Schäfer99,A.C. Schaffer64,D. Schaile113, R.D. Schamberger154,E. Schanet113,C. Scharf19,N. Scharmberg100,V.A. Schegelsky136, D. Scheirich141,F. Schenck19,M. Schernau169,C. Schiavi55b,55a,L.K. Schildgen24, Z.M. Schillaci26,E.J. Schioppa67a,67b,M. Schioppa41b,41a,B. Schlag99,K.E. Schleicher52, S. Schlenker36,K. Schmieden99,C. Schmitt99,S. Schmitt46,L. Schoeffel143,A. Schoening61b, P.G. Scholer52,E. Schopf133,M. Schott99,J. Schovancova36,S. Schramm54,F. Schroeder180, A. Schulte99,H-C. Schultz-Coulon61a,M. Schumacher52,B.A. Schumm144,Ph. Schune143, A. Schwartzman152,T.A. Schwarz105,Ph. Schwemling143,R. Schwienhorst106,A. Sciandra144, G. Sciolla26,F. Scuri71a, F. Scutti104,C.D. Sebastiani90,K. Sedlaczek47,P. Seema19, S.C. Seidel117,A. Seiden144,B.D. Seidlitz29,T. Seiss37,C. Seitz46,J.M. Seixas80b, G. Sekhniaidze69a,S.J. Sekula42,L.P. Selem5,N. Semprini-Cesari23b,23a,S. Sen49,C. Serfon29, L. Serin64,L. Serkin66a,66b,M. Sessa60a,H. Severini127,S. Sevova152,F. Sforza55b,55a,A. Sfyrla54, E. Shabalina53,J.D. Shahinian135,N.W. Shaikh45a,45b,D. Shaked Renous178,L.Y. Shan15a, M. Shapiro18,A. Sharma36,A.S. Sharma1,S. Sharma46,P.B. Shatalov123,K. Shaw155, S.M. Shaw100, M. Shehade178, Y. Shen127,P. Sherwood94,L. Shi94,C.O. Shimmin181, Y. Shimogama177,M. Shimojima115,J.D. Shinner93,I.P.J. Shipsey133,S. Shirabe163, M. Shiyakova79,J. Shlomi178,M.J. Shochet37,J. Shojaii104,D.R. Shope153,S. Shrestha126, E.M. Shrif33f ,M.J. Shroff174,E. Shulga178,P. Sicho139,A.M. Sickles171,E. Sideras Haddad33f , O. Sidiropoulou36,A. Sidoti23b,23a,F. Siegert48,Dj. Sijacki16,M.V. Silva Oliveira36, S.B. Silverstein45a, S. Simion64,R. Simoniello36,S. Simsek12b,P. Sinervo165,V. Sinetckii112, S. Singh151,S. Sinha33f ,M. Sioli23b,23a,I. Siral130,S.Yu. Sivoklokov112,J. Sjölin45a,45b,A. Skaf53, E. Skorda96,P. Skubic127,M. Slawinska84,K. Sliwa168, V. Smakhtin178,B.H. Smart142, J. Smiesko141,S.Yu. Smirnov111,Y. Smirnov111,L.N. Smirnova112,s,O. Smirnova96,E.A. Smith37, H.A. Smith133,M. Smizanska89,K. Smolek140,A. Smykiewicz84,A.A. Snesarev110,H.L. Snoek119, I.M. Snyder130,S. Snyder29,R. Sobie174,aa,A. Soffer160,A. Søgaard50,F. Sohns53, C.A. Solans Sanchez36,E.Yu. Soldatov111,U. Soldevila172,A.A. Solodkov122,S. Solomon52, A. Soloshenko79,O.V. Solovyanov122,V. Solovyev136,P. Sommer148,H. Son168,A. Sonay14, W.Y. Song166b,A. Sopczak140, A.L. Sopio94,F. Sopkova28b,S. Sottocornola70a,70b, R. Soualah66a,66c,A.M. Soukharev121b,121a,Z. Soumaimi35f ,D. South46,S. Spagnolo67a,67b, M. Spalla114,M. Spangenberg176,F. Spanò93,D. Sperlich52,T.M. Spieker61a,G. Spigo36, M. Spina155,D.P. Spiteri57,M. Spousta141,A. Stabile68a,68b,B.L. Stamas120,R. Stamen61a, M. Stamenkovic119,A. Stampekis21,E. Stanecka84,B. Stanislaus133,M.M. Stanitzki46, M. Stankaityte133,B. Stapf46,E.A. Starchenko122,G.H. Stark144,J. Stark101, D.M. Starko166b, P. Staroba139,P. Starovoitov61a,S. Stärz103,R. Staszewski84,G. Stavropoulos44,P. Steinberg29, A.L. Steinhebel130,B. Stelzer151,166a,H.J. Stelzer137,O. Stelzer-Chilton166a,H. Stenzel56, T.J. Stevenson155,G.A. Stewart36,M.C. Stockton36,G. Stoicea27b,M. Stolarski138a, S. Stonjek114,A. Straessner48,J. Strandberg153,S. Strandberg45a,45b,M. Strauss127, T. Strebler101,P. Strizenec28b,R. Ströhmer175,D.M. Strom130,L.R. Strom46,R. Stroynowski42, A. Strubig45a,45b,S.A. Stucci29,B. Stugu17,J. Stupak127,N.A. Styles46,D. Su152,S. Su60a, W. Su60d,147,60c,X. Su60a, N.B. Suarez137,K. Sugizaki162,V.V. Sulin110,M.J. Sullivan90, D.M.S. Sultan54,S. Sultansoy4c,T. Sumida85,S. Sun105,S. Sun179,X. Sun100,C.J.E. Suster156, – 50 – JHEP06(2021)003 M.R. Sutton155,M. Svatos139,M. Swiatlowski166a, S.P. Swift2,T. Swirski175, A. Sydorenko99, I. Sykora28a,M. Sykora141,T. Sykora141,D. Ta99,K. Tackmann46,y,A. Taffard169, R. Tafirout166a,E. Tagiev122,R.H.M. Taibah134,R. Takashima86,K. Takeda82,T. Takeshita149, E.P. Takeva50,Y. Takubo81,M. Talby101,A.A. Talyshev121b,121a,K.C. Tam62b, N.M. Tamir160, J. Tanaka162,R. Tanaka64,S. Tapia Araya171,S. Tapprogge99,A. Tarek Abouelfadl Mohamed106, S. Tarem159,K. Tariq60b,G. Tarna27b,f,G.F. Tartarelli68a,P. Tas141,M. Tasevsky139, E. Tassi41b,41a,G. Tateno162,Y. Tayalati35f ,G.N. Taylor104,W. Taylor166b, H. Teagle90, A.S. Tee89,R. Teixeira De Lima152,P. Teixeira-Dias93, H. Ten Kate36,J.J. Teoh119, K. Terashi162,J. Terron98,S. Terzo14,M. Testa51,R.J. Teuscher165,aa,N. Themistokleous50, T. Theveneaux-Pelzer19, D.W. Thomas93,J.P. Thomas21,E.A. Thompson46,P.D. Thompson21, E. Thomson135,E.J. Thorpe92,Y. Tian53,V.O. Tikhomirov110,ag,Yu.A. Tikhonov121b,121a, S. Timoshenko111,P. Tipton181,S. Tisserant101,S.H. Tlou33f ,A. Tnourji38,K. Todome23b,23a, S. Todorova-Nova141, S. Todt48, M. Togawa81,J. Tojo87,S. Tokár28a,K. Tokushuku81, E. Tolley126,R. Tombs32,M. Tomoto81,116,L. Tompkins152,P. Tornambe102,E. Torrence130, H. Torres48,E. Torró Pastor172,M. Toscani30,C. Tosciri37,J. Toth101,z,D.R. Tovey148, A. Traeet17,C.J. Treado124,T. Trefzger175,A. Tricoli29,I.M. Trigger166a,S. Trincaz-Duvoid134, D.A. Trischuk173, W. Trischuk165,B. Trocmé58,A. Trofymov64,C. Troncon68a,F. Trovato155, L. Truong33c,M. Trzebinski84,A. Trzupek84,F. Tsai154,A. Tsiamis161, P.V. Tsiareshka107,ae, A. Tsirigotis161,w,V. Tsiskaridze154, E.G. Tskhadadze158a,M. Tsopoulou161,I.I. Tsukerman123, V. Tsulaia18,S. Tsuno81, O. Tsur159,D. Tsybychev154,Y. Tu62b,A. Tudorache27b, V. Tudorache27b,A.N. Tuna36,S. Turchikhin79,D. Turgeman178, I. Turk Cakir4b,u, R.J. Turner21, R. Turra68a,P.M. Tuts39, S. Tzamarias161,P. Tzanis10,E. Tzovara99, K. Uchida162, F. Ukegawa167,G. Unal36,M. Unal11,A. Undrus29,G. Unel169,F.C. Ungaro104,K. Uno162, J. Urban28b,P. Urquijo104,G. Usai8,R. Ushioda163,Z. Uysal12d,V. Vacek140,B. Vachon103, K.O.H. Vadla132,T. Vafeiadis36,C. Valderanis113,E. Valdes Santurio45a,45b,M. Valente166a, S. Valentinetti23b,23a,A. Valero172,L. Valéry46,R.A. Vallance21,A. Vallier36,J.A. Valls Ferrer172, T.R. Van Daalen14,P. Van Gemmeren6,S. Van Stroud94,I. Van Vulpen119,M. Vanadia73a,73b, W. Vandelli36,M. Vandenbroucke143,E.R. Vandewall128,D. Vannicola72a,72b,L. Vannoli55b,55a, R. Vari72a,E.W. Varnes7,C. Varni55b,55a,T. Varol157,D. Varouchas64,K.E. Varvell156, M.E. Vasile27b, L. Vaslin38,G.A. Vasquez174,F. Vazeille38,D. Vazquez Furelos14, T. Vazquez Schroeder36,J. Veatch53,V. Vecchio100,M.J. Veen119,L.M. Veloce165, F. Veloso138a,138c,S. Veneziano72a,A. Ventura67a,67b,A. Verbytskyi114,M. Verducci71a,71b, C. Vergis24,M. Verissimo De Araujo80b,W. Verkerke119,A.T. Vermeulen119,J.C. Vermeulen119, C. Vernieri152,P.J. Verschuuren93,M.L. Vesterbacka124,M.C. Vetterli151,ak,N. Viaux Maira145d, T. Vickey148,O.E. Vickey Boeriu148,G.H.A. Viehhauser133,L. Vigani61b,M. Villa23b,23a, M. Villaplana Perez172, E.M. Villhauer50,E. Vilucchi51,M.G. Vincter34,G.S. Virdee21, A. Vishwakarma50,C. Vittori23b,23a,I. Vivarelli155, V. Vladimirov176,M. Vogel180,P. Vokac140, J. Von Ahnen46,S.E. von Buddenbrock33f ,E. Von Toerne24,V. Vorobel141,K. Vorobev111, M. Vos172,J.H. Vossebeld90,M. Vozak100,N. Vranjes16,M. Vranjes Milosavljevic16, V. Vrba140,∗, M. Vreeswijk119,N.K. Vu101,R. Vuillermet36,I. Vukotic37,S. Wada167, C. Wagner102, P. Wagner24,W. Wagner180,S. Wahdan180,H. Wahlberg88,R. Wakasa167,M. Wakida116, V.M. Walbrecht114,J. Walder142,R. Walker113, S.D. Walker93,W. Walkowiak150, V. Wallangen45a,45b,A.M. Wang59,A.Z. Wang179,C. Wang60a,C. Wang60c,H. Wang18, J. Wang62a,P. Wang42,R.-J. Wang99,R. Wang59,R. Wang120,S.M. Wang157, S. Wang60b, T. Wang60a,W.T. Wang60a,W.X. Wang60a,X. Wang171,Y. Wang60a,Z. Wang105, C. Wanotayaroj36,A. Warburton103,C.P. Ward32,R.J. Ward21,N. Warrack57,A.T. Watson21, M.F. Watson21,G. Watts147,B.M. Waugh94,A.F. Webb11,C. Weber29,M.S. Weber20, S.A. Weber34,S.M. Weber61a, C. Wei60a,Y. Wei133,A.R. Weidberg133,J. Weingarten47, – 51 – JHEP06(2021)003 M. Weirich99,C. Weiser52,P.S. Wells36,T. Wenaus29,B. Wendland47,T. Wengler36,S. Wenig36, N. Wermes24,M. Wessels61a, T.D. Weston20,K. Whalen130,A.M. Wharton89,A.S. White59, A. White8,M.J. White1,D. Whiteson169,W. Wiedenmann179,C. Wiel48,M. Wielers142, N. Wieseotte99,C. Wiglesworth40,L.A.M. Wiik-Fuchs52,H.G. Wilkens36,L.J. Wilkins93, D.M. Williams39, H.H. Williams135,S. Williams32,S. Willocq102,P.J. Windischhofer133, I. Wingerter-Seez5,F. Winklmeier130,B.T. Winter52, M. Wittgen152,M. Wobisch95,A. Wolf99, R. Wölker133, J. Wollrath52,M.W. Wolter84,H. Wolters138a,138c,V.W.S. Wong173, A.F. Wongel46,N.L. Woods144,S.D. Worm46,B.K. Wosiek84,K.W. Woźniak84,K. Wraight57, J. Wu15a,15d,S.L. Wu179,X. Wu54,Y. Wu60a,Z. Wu143,J. Wuerzinger133,T.R. Wyatt100, B.M. Wynne50,S. Xella40, J. Xiang62c,X. Xiao105,X. Xie60a, I. Xiotidis155,D. Xu15a, H. Xu60a, H. Xu60a,L. Xu60a,R. Xu135,W. Xu105,Y. Xu15b,Z. Xu60b,Z. Xu152,B. Yabsley156, S. Yacoob33a,D.P. Yallup94,N. Yamaguchi87,Y. Yamaguchi163, M. Yamatani162, H. Yamauchi167, T. Yamazaki18,Y. Yamazaki82, J. Yan60c,Z. Yan25,H.J. Yang60c,60d,H.T. Yang18,S. Yang60a, T. Yang62c,X. Yang60a,X. Yang15a,Y. Yang162,Z. Yang105,60a,W-M. Yao18,Y.C. Yap46, H. Ye15c,J. Ye42,S. Ye29,I. Yeletskikh79,M.R. Yexley89,P. Yin39,K. Yorita177,K. Yoshihara78, C.J.S. Young36,C. Young152,R. Yuan60b,j,X. Yue61a,M. Zaazoua35f ,B. Zabinski84, G. Zacharis10,E. Zaffaroni54,J. Zahreddine101,A.M. Zaitsev122,af ,T. Zakareishvili158b, N. Zakharchuk34,S. Zambito36,D. Zanzi52,S.V. Zeißner47,C. Zeitnitz180,G. Zemaityte133, J.C. Zeng171,O. Zenin122,T. Ženiš28a,S. Zenz92,S. Zerradi35a,D. Zerwas64,M. Zgubič133, B. Zhang15c,D.F. Zhang15b,G. Zhang15b,J. Zhang6,K. Zhang15a,L. Zhang15c,M. Zhang171, R. Zhang179, S. Zhang105,X. Zhang60c,X. Zhang60b,Z. Zhang64,P. Zhao49,Y. Zhao144, Z. Zhao60a,A. Zhemchugov79,Z. Zheng105,D. Zhong171, B. Zhou105,C. Zhou179,H. Zhou7, M. Zhou154,N. Zhou60c, Y. Zhou7,C.G. Zhu60b,C. Zhu15a,15d,H.L. Zhu60a,H. Zhu15a,J. Zhu105, Y. Zhu60a,X. Zhuang15a,K. Zhukov110,V. Zhulanov121b,121a,D. Zieminska65,N.I. Zimine79, S. Zimmermann52,∗, Z. Zinonos114, M. Ziolkowski150,L. Živković16,A. Zoccoli23b,23a,K. Zoch53, T.G. Zorbas148,R. Zou37,W. Zou39 and 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, Univ. 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 – 52 – JHEP06(2021)003 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; 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)National Institute of Physics, University of the Philippines Diliman;(e)University of South Africa, Department of Physics, Pretoria;(f)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)Moroccan Foundation for Advanced Science Innovation and Research (MAScIR), Rabat;(e)LPMR, Faculté des Sciences, Université Mohamed Premier, Oujda;(f)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 – 53 – JHEP06(2021)003 48 Institut für Kernund Teilchenphysik, Technische Universität Dresden, Dresden; Germany 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, Key Laboratory for Particle Astrophysics and Cosmology (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 (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 63 Department of Physics, National Tsing Hua University, Hsinchu; Taiwan 64 IJCLab, Université Paris-Saclay, CNRS/IN2P3, 91405, Orsay; France 65 Department of Physics, Indiana University, Bloomington IN; United States of America 66 (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 67 (a)INFN Sezione di Lecce;(b)Dipartimento di Matematica e Fisica, Università del Salento, Lecce; Italy 68 (a)INFN Sezione di Milano;(b)Dipartimento di Fisica, Università di Milano, Milano; Italy 69 (a)INFN Sezione di Napoli;(b)Dipartimento di Fisica, Università di Napoli, Napoli; Italy 70 (a)INFN Sezione di Pavia;(b)Dipartimento di Fisica, Università di Pavia, Pavia; Italy 71 (a)INFN Sezione di Pisa;(b)Dipartimento di Fisica E. Fermi, Università di Pisa, Pisa; Italy 72 (a)INFN Sezione di Roma;(b)Dipartimento di Fisica, Sapienza Università di Roma, Roma; Italy 73 (a)INFN Sezione di Roma Tor Vergata;(b)Dipartimento di Fisica, Università di Roma Tor Vergata, Roma; Italy 74 (a)INFN Sezione di Roma Tre;(b)Dipartimento di Matematica e Fisica, Università Roma Tre, Roma; Italy 75 (a)INFN-TIFPA;(b)Università degli Studi di Trento, Trento; Italy 76 Institut für Astround Teilchenphysik, Leopold-Franzens-Universität, Innsbruck; Austria 77 University of Iowa, Iowa City IA; United States of America 78 Department of Physics and Astronomy, Iowa State University, Ames IA; United States of America 79 Joint Institute for Nuclear Research, Dubna; Russia 80 (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)Instituto de Física, Universidade de São Paulo, São Paulo; Brazil 81 KEK, High Energy Accelerator Research Organization, Tsukuba; Japan 82 Graduate School of Science, Kobe University, Kobe; Japan 83 (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 – 54 – JHEP06(2021)003 84 Institute of Nuclear Physics Polish Academy of Sciences, Krakow; Poland 85 Faculty of Science, Kyoto University, Kyoto; Japan 86 Kyoto University of Education, Kyoto; Japan 87 Research Center for Advanced Particle Physics and Department of Physics, Kyushu University, Fukuoka; Japan 88 Instituto de Física La Plata, Universidad Nacional de La Plata and CONICET, La Plata; Argentina 89 Physics Department, Lancaster University, Lancaster; United Kingdom 90 Oliver Lodge Laboratory, University of Liverpool, Liverpool; United Kingdom 91 Department of Experimental Particle Physics, Jožef Stefan Institute and Department of Physics, University of Ljubljana, Ljubljana; Slovenia 92 School of Physics and Astronomy, Queen Mary University of London, London; United Kingdom 93 Department of Physics, Royal Holloway University of London, Egham; United Kingdom 94 Department of Physics and Astronomy, University College London, London; United Kingdom 95 Louisiana Tech University, Ruston LA; United States of America 96 Fysiska institutionen, Lunds universitet, Lund; Sweden 97 Centre de Calcul de l’Institut National de Physique Nucléaire et de Physique des Particules (IN2P3), Villeurbanne; France 98 Departamento de Física Teorica C-15 and CIAFF, Universidad Autónoma de Madrid, Madrid; Spain 99 Institut für Physik, Universität Mainz, Mainz; Germany 100 School of Physics and Astronomy, University of Manchester, Manchester; United Kingdom 101 CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille; France 102 Department of Physics, University of Massachusetts, Amherst MA; United States of America 103 Department of Physics, McGill University, Montreal QC; Canada 104 School of Physics, University of Melbourne, Victoria; Australia 105 Department of Physics, University of Michigan, Ann Arbor MI; United States of America 106 Department of Physics and Astronomy, Michigan State University, East Lansing MI; United States of America 107 B.I. Stepanov Institute of Physics, National Academy of Sciences of Belarus, Minsk; Belarus 108 Research Institute for Nuclear Problems of Byelorussian State University, Minsk; Belarus 109 Group of Particle Physics, University of Montreal, Montreal QC; Canada 110 P.N. Lebedev Physical Institute of the Russian Academy of Sciences, Moscow; Russia 111 National Research Nuclear University MEPhI, Moscow; Russia 112 D.V. Skobeltsyn Institute of Nuclear Physics, M.V. Lomonosov Moscow State University, Moscow; Russia 113 Fakultät für Physik, Ludwig-Maximilians-Universität München, München; Germany 114 Max-Planck-Institut für Physik (Werner-Heisenberg-Institut), München; Germany 115 Nagasaki Institute of Applied Science, Nagasaki; Japan 116 Graduate School of Science and Kobayashi-Maskawa Institute, Nagoya University, Nagoya; Japan 117 Department of Physics and Astronomy, University of New Mexico, Albuquerque NM; United States of America 118 Institute for Mathematics, Astrophysics and Particle Physics, Radboud University/Nikhef, Nijmegen; Netherlands 119 Nikhef National Institute for Subatomic Physics and University of Amsterdam, Amsterdam; Netherlands 120 Department of Physics, Northern Illinois University, DeKalb IL; United States of America 121 (a)Budker Institute of Nuclear Physics and NSU, SB RAS, Novosibirsk;(b)Novosibirsk State University Novosibirsk; Russia 122 Institute for High Energy Physics of the National Research Centre Kurchatov Institute, Protvino; Russia 123 Institute for Theoretical and Experimental Physics named by A.I. Alikhanov of National Research Centre “Kurchatov Institute”, Moscow; Russia – 55 – JHEP06(2021)003 124 Department of Physics, New York University, New York NY; United States of America 125 Ochanomizu University, Otsuka, Bunkyo-ku, Tokyo; Japan 126 Ohio State University, Columbus OH; United States of America 127 Homer L. Dodge Department of Physics and Astronomy, University of Oklahoma, Norman OK; United States of America 128 Department of Physics, Oklahoma State University, Stillwater OK; United States of America 129 Palacký University, RCPTM, Joint Laboratory of Optics, Olomouc; Czech Republic 130 Institute for Fundamental Science, University of Oregon, Eugene, OR; United States of America 131 Graduate School of Science, Osaka University, Osaka; Japan 132 Department of Physics, University of Oslo, Oslo; Norway 133 Department of Physics, Oxford University, Oxford; United Kingdom 134 LPNHE, Sorbonne Université, Université de Paris, CNRS/IN2P3, Paris; France 135 Department of Physics, University of Pennsylvania, Philadelphia PA; United States of America 136 Konstantinov Nuclear Physics Institute of National Research Centre “Kurchatov Institute”, PNPI, St. Petersburg; Russia 137 Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh PA; United States of America 138 (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 139 Institute of Physics of the Czech Academy of Sciences, Prague; Czech Republic 140 Czech Technical University in Prague, Prague; Czech Republic 141 Charles University, Faculty of Mathematics and Physics, Prague; Czech Republic 142 Particle Physics Department, Rutherford Appleton Laboratory, Didcot; United Kingdom 143 IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette; France 144 Santa Cruz Institute for Particle Physics, University of California Santa Cruz, Santa Cruz CA; United States of America 145 (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 146 Universidade Federal de São João del Rei (UFSJ), São João del Rei; Brazil 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 – 56 –