Measurement of fiducial and differential W+W− production cross-sections at √ s = 13 TeV with the ATLAS detector

Eur. Phys. J. C (2019) 79:884 https://doi.org/10.1140/epjc/s10052-019-7371-6 Regular Article - Experimental Physics Measurement of fiducial and differential W+W−production cross-sections at √s=13 TeV with the ATLAS detector ATLAS Collaboration CERN, 1211 Geneva 23, Switzerland Received: 13 May 2019 / Accepted: 5 October 2019 / Published online: 29 October 2019 © CERN for the benefit of the ATLAS collaboration 2019 Abstract Ameasurementoffiducialanddifferential crosssections for W+W−production in proton–proton collisions at √s=13 TeV with the ATLAS experiment at the Large Hadron Collider using data corresponding to an integrated luminosity of 36.1fb −1is presented. Events with one electron and one muon are selected, corresponding to the decay of the diboson system as WW →e±νμ∓ν. To suppress topquark background, events containing jets with a transverse momentum exceeding 35 GeV are not included in the measurement phase space. The fiducial cross-section, six differential distributions and the cross-section as a function of the jet-veto transverse momentum threshold are measured and compared with several theoretical predictions. Constraints on anomalous electroweak gauge boson self-interactions are also presented in the framework of a dimension-six effective field theory. Contents 1 Introduction ..................... 1 2 ATLAS detector ................... 2 3 Data and simulated event samples .......... 3 4 Event reconstruction and selection .......... 4 4.1 Trigger ...................... 4 4.2 Leptons ..................... 4 4.3 Jets ........................ 5 4.4 Missing transverse momentum ......... 5 4.5 Signal region definition ............. 5 5 Background estimation ................ 6 5.1 Background from top-quark production .... 6 5.2 Background from Drell–Yan production .... 7 5.3 Background from W+jets production ...... 7 5.4 Background from multi-boson production ... 9 5.5 WW candidate events and estimated background yields .................. 9 6 Fiducial cross-section determination .........10 7 Systematic uncertainties ...............10 e-mail: [email protected] 8 Theoretical predictions ................12 9 Results ........................13 9.1 Cross-section measurements and comparisons with theoretical predictions ...........13 9.2 Limits on anomalous gauge couplings .....14 10 Conclusion ......................17 References ........................17 1 Introduction The measurement of the production of W-boson pairs through interactions of quarks and gluons probes the electroweak (EW) gauge structure of the Standard Model (SM) and allows further tests of the strong interaction between quarks and gluons. The WW production process is also important as it constitutes large irreducible backgrounds in searches for physics beyond the SM and to H→WW∗production. Its large production cross-section combined with the large sample of proton–proton (pp) collision data delivered by the Large Hadron Collider (LHC), enables this process to be studied differentially with a better statistical precision than was possible in previous measurements. The first measurements of WW production were carried out at the LEP electron–positron collider [1]. At the Tevatron this process was measured in proton–antiproton collisions by the CDF [2,3] and DØ [4] Collaborations. In pp collisions at the LHC, WW production cross-sections were determined for centre-of-mass energies of √s=7 TeV and √s=8TeVbytheATLAS[5,6] and CMS [7,8] Collaborations. In addition, a dedicated measurement of the WW +1jet final state was carried out by the ATLAS Collaboration [9] at √s=8TeV.At√s=13 TeV, the total cross-section for WW production was measured by the ATLAS Collaboration [10], albeit only for the small 2015 data sample, which did not allow any differential studies. The cross-section measurements at √s=7 and √s= 8 TeV revealed discrepancies between data and theory that have since been addressed through the inclusion of higher123 884 Page 2 of 34 Eur. Phys. J. C (2019) 79 :884 order corrections in perturbative quantum chromodynamics (QCD)[11–16].Thishasremediedthemismatchbetweenthe total measured and predicted cross-sections, but some discrepancies in the differential distributions persist. The highenergy behaviour of the WW cross-section and the angular distributions of the WW decay products could be affected by new physical phenomena at higher partonic centre-ofmass energies, such as EW doublet or triplet scalars [17,18] or degenerate and non-degenerate top-quark superpartners (stops) in supersymmetry (SUSY) scenarios [19,20]. These specific models can be constrained by their contribution to dimension-six operators in an effective Lagrangian at tree level [17]. At lower partonic centre-of-mass energies, WW production can also be used to provide complementary constraints on compressed EW SUSY scenarios with low stop masses [21]. TheWWsignaliscomposedoftwoleadingsub-processes: q¯q→WW production1(in the tand s-channels) and gluon–gluon fusion production (both non-resonant gg → WW and resonant gg →H→WW). Figure 1shows representative sub-processes. To allow for a proper treatment and inclusion of the interference, which is especially relevant in the tails of kinematic distributions, the resonant production is kept as part of the signal. The fiducial phase space is defined to be orthogonal to the H→WW measurements by the ATLAS Collaboration [22,23] using a requirement on the dilepton invariant mass. Therefore the Higgs boson contribution included in the signal definition is dominated by off-shell production and interference effects. The production of two Wbosons from the decay of top–antitop quark pairs is not considered part of the signal. The different sub-processes for WW production are known theoretically at different orders in the strong coupling constant αs.Theq¯q→WW production cross-section is known to O(α2 s), next-to-next-to-leading order (NNLO) [11,15]. Recently, also a NNLO prediction matched to a parton shower has become available [15,24,25]. The nonresonant gg →WW production cross-section is known to O(α3 s), next-to-leading order (NLO) [26], and its interference with the resonant gg →WW production cross-section is known to O(α2 s). This paper presents a measurement of the fiducial crosssection for WW production at √s=13 TeV using pp collision data recorded in 2015 and 2016 by the ATLAS experiment,correspondingtoanintegratedluminosityof 36.1fb−1. The WW →e±νμ∓νdecay channel is studied (denoted in the following by WW →eμ). The measurement is performed in a phase space close to the geometric and kinematic acceptance of the experimental analysis. This includes a veto on the presence of jets with transverse momenta (pT) above 1The notation q¯q→WW is used to include both the q¯qand qg initial states for WW production. a series of thresholds, with a pT=35 GeV threshold used as a baseline. Measuring the fiducial cross-section as a function of the jet veto pTthreshold provides an indirect measure of the jet pTspectrum in WW events, without removing the jet veto that is necessary for background suppression. Sixdifferentialdistributionsinvolvingkinematicvariables of the final-state charged leptons are measured in the baseline phase space. Three of them characterize the energy of the process: the transverse momentum of the leading lepton plead  T, the invariant mass of the dilepton system meμand the transverse momentum of the dilepton system peμ T. Three further distributions probe angular correlations and the spin state of the WW system. These are the rapidity of the dilepton system |yeμ|, the difference in azimuthal angle between the decay leptons φeμ, and |cos θ∗|defined as: |cos θ∗|= tanh ηeμ 2 , where ηeμis the difference between the pseudorapidities of the leptons.2This variable is longitudinally boost-invariant and sensitive to the spin structure of the produced diparticle pairs as discussed in Ref. [27]. The unfolded plead  Tdistribution is used to set limits on anomalous triple-gauge-boson couplings, since this distribution was identified as the most sensitive to the effect of these couplings. 2 ATLAS detector The ATLAS detector [28] at the LHC is a multipurpose particle detector with a forward–backward symmetric cylindrical geometry and nearly 4πcoverage in solid angle. It consists of inner tracking devices surrounded by a superconducting solenoid, electromagnetic (EM) and hadronic calorimeters, and a muon spectrometer. The inner detector (ID) provides charged-particle tracking in the pseudorapidity region |η|<2.5 and vertex reconstruction. It comprises a silicon pixel detector, a silicon microstrip tracker, and a straw-tube transitionradiationtracker. The ID is placed inside a solenoid that produces a 2T axial magnetic field. Lead/liquid-argon (LAr) sampling calorimeters provide EM energy measurements with high granularity. A steel/scintillator-tile hadronic calorimeter covers the central pseudorapidity range |η|< 1.7. The endcap and forward regions are instrumented with LAr calorimeters for both the EM and hadronic energy mea2ATLAS uses a right-handed coordinate system with its origin at the nominal interaction point in the centre of the detector and the z-axis coinciding with the axis of the beam pipe. The x-axis points from the interaction point to the centre of the LHC ring, and the y-axis points upward. The pseudorapidity is defined in terms of the polar angle θ as η=−ln tan(θ/2),andφis the azimuthal angle around the beam pipe relative to the x-axis. The angular distance is defined as R= (η)2+(φ)2. Transverse energy is computed as ET=E·sin θ. 123 Eur. Phys. J. C (2019) 79 :884 Page 3 of 34 884 ¯q q q W W ¯q q W W Z/γ∗ g g W W g g W W H Fig. 1 Feynman diagrams for SM WW production at tree level (from left to right): q¯qinitial-state t-channel, q¯qinitial-state s-channel, gg initial-state non-resonant and gg initial-state resonant production. The s-channel production contains the WWZ and WWγtriple-gaugecouplingvertices.Thegluon–gluonfusionprocessesaremediatedeither by a quark loop (gg →WW) or the resonant production of a Higgs boson with subsequent decay into WW (gg →H→WW) surements up to |η|=4.9. The muon spectrometer (MS) is operated in a magnetic field provided by air-core superconducting toroids and includes tracking chambers for precise muon momentum measurements up to |η|=2.7 and trigger chambers covering the range |η|<2.4. A two-level trigger system [29] selects the events used in the analysis. The first level is implemented in custom electronics, while the second trigger level is a flexible softwarebased system. 3 Data and simulated event samples The data were collected at a centre-of-mass energy of 13 TeV during 2015 and 2016, and correspond to an integrated luminosity of 36.1fb−1. Only high-quality data with all detectors in normal operating conditions are analysed. The average number of interactions per bunch crossing was estimated to be μ=24. Simulated event samples are used for most of the background estimates, for the correction of the signal yield due to detector effects, and for comparison with the measured cross-sections. The WW signal was modelled using the NLO perturbative QCD PowhegBox v2 event generator [30–34]forq¯qinitial states. The gg →WW contribution was generated using the Sherpa 2.1.1+OpenLoops framework [35,36] at leading order (LO) with up to one additional parton and includes non-resonantandresonantHiggsbosonproduction and interference terms. The Sherpa 2.1.1+OpenLoops framework also allows these contributions to be generated and studied separately. In both cases, the CT10 [37] parton distribution functions (PDF) were used. PowhegBox was interfaced to Pythia 8.210 [38] for the modelling of parton showers and hadronization as well as underlying-event simulation, using the AZNLO [39] set of tuned parameters (‘tune’) and the CTEQ6L1 [40]PDFset.Sherpa used its own parton shower, fragmentation and underlying-event model. Alternative signal samples for the quark-induced production were generated using PowhegBox interfaced to Herwig++ 2.7.1 [41] with the UEEE5 tune [42], and using the Sherpa 2.2.2 generator with its own model for parton showering, hadronization and the underlying event. The Sherpa 2.2.2 prediction was obtained at NLO with up to one additional parton emission and up to three at LO and employs the NNPDF3.0 [43] PDF set. The WW signal predictions were normalized to the NNLO cross-section [11]; the gg →WW process was normalized to its inclusive NLO cross-section [26]. The background processes considered are: top-quark pair production (t¯ t), associated production of a top quark with aWboson (Wt), single vector-boson production (Wor Z, in association with jets), multijet production, other diboson production (WZ,ZZ,Wγand Zγ) and triboson production (WWW,WWZ,WZZand ZZZ),where Zstandsfor Z/γ ∗. For the generation of t¯ tand Wt processes at NLO, PowhegBox v2 [44] and PowhegBox v1 [30] respectively were used with the CT10 PDF set. For the parton shower, hadronization and underlying event, simulated events were interfaced to Pythia 8.186 for t¯ tand Pythia 6.425 [45] for single-top production, using the A14 tune [46] and the Perugia 2012 [47] tune, respectively. The top-quark masswassetto172.5GeV. Inthet¯ tsample,thehdamp parameter that regulates the high-pTemission, against which the t¯ tsystem recoils, was set to 1.5 times the top-quark mass following studies reported in Ref. [48]. Alternative samples were generated with different settings to assess the uncertainty in modelling top-quark events. To estimate uncertainties in additional QCD radiation in top-quark processes, a pair of samples was produced with the alternative sets of A14 (t¯ t) or Perugia 2012 (Wt) parameters for higher and lower radiation, as well as with different renormalization and factorization scales which were both varied either by a factor of 2 or 0.5. For the higher-radiation samples, the value of the hdamp parameter was doubled. Two alternative Monte Carlo (MC) programs were used to estimate the impact of the choice of hard-scatter generator and hadronization algorithm in top-quark events; for each of these samples one of the two components was replaced by an alternative choice. The alter123 884 Page 4 of 34 Eur. Phys. J. C (2019) 79 :884 native choices are MadGraph5_aMC@NLO 2.3 [49]for the hard-scatter generator and Herwig 7[50](Herwig++ 2.7.1) for the hadronization algorithm in t¯ t(Wt) events. In addition, the modelling of the overlap at NLO between Wt and t¯ tdiagrams [51] was studied. The effect was assessed by generating Wt events with different schemes for overlap removal using the PowhegBox event generator interfaced to Pythia 6.425 for the simulation of parton showering and non-perturbative effects. The top-quark events were normalized using the NNLO+next-to-next-to-leading-logarithm (NNLL) QCD cross-section [52]fort¯ t, and the NLO+NNLL cross-section [53]forWt production. The Z+jets process (with Z→ee/μμ/ττ) was modelled using Sherpa 2.2.1 [54] with the NNPDF3.0 PDF set. This process was calculated with up to two additional partons at NLOandup tofour additionalpartonsatLO.The W+jets and alternative Z+jets events were produced with the PowhegBox generator at NLO accuracy using the CT10 PDF set, interfaced to Pythia 8.186 for parton showering, hadronization and the underlying event. As in the WW samples, the AZNLO tune was used for the underlying event together with the CTEQ6L1 PDF set. The Z+jets and W+jets events were normalized using their respective NNLO cross-section calculations [55]. Thebackgroundfrom dibosonproductionprocesses(WZ, ZZ,Wγand Zγ)was simulatedusing the Sherpa 2.2.2generator with the NNPDF 3.0 PDF set. The samples include up to one additional parton emission at NLO and up to three at LO. Alternative samples for WZ and ZZ processes were produced using the same PowhegBox+Pythia 8set-upas the q¯q-initiated WW signal samples discussed above. The background from triboson production was modelled using the Sherpa+OpenLoops generator with the CT10 PDF set, calculated at NLO for inclusive production and including up to two hard parton emissions at LO. The WZ,ZZ and triboson samples produced with Sherpa were normalized to the cross-section calculated by Sherpa, with hard parton emissions at NLO or LO as discussed, and thus already capturing some of the NNLO effects. The WZand ZZbackgrounds simulated with PowhegBox were normalized to their NNLO cross-sections [56–60]. EvtGen 1.2.0 [61] was used for the properties of the bottom and charm hadron decays after hadronization in all samples generated with PowhegBox and MadGraph5_aMC@NLO. Additional interactions in the same or nearby bunch crossings (pile-up) were simulated using Pythia 8.186 using the A2 tune [62] and the MSTW2008LO PDF [63] set and were overlaid on the simulated signal and background events. All simulated event samples were produced using the ATLAS simulation infrastructure [64], using the full Geant 4[65] simulation of the ATLAS detector. Simulated events were then reconstructed with the same software as used for the data and were corrected with data-driven correction factorstoaccountfordifferences in lepton and jet reconstruction and identification between data and simulation. These corrections are of the order of 1–3%. 4 Event reconstruction and selection The WW event candidates are selected by requiring each eventto contain exactlyone electron and exactlyonemuonof opposite charge, each passing the selections described below. Events with a same-flavour lepton pair are not used because they have a larger background from the Drell–Yan process. Candidate events are required to have at least one vertex with at least two associated tracks with pT>400 MeV. The vertex with the highest p2 Tof the associated tracks is considered to be the primary vertex. 4.1 Trigger Candidate events were recorded by either a single-muon or a single-electron trigger that imposed a minimum lepton transverse momentum threshold that varied during datataking.The pTthresholdoftheleptons required by triggers in 2015 was 24 GeV for electrons and 20 GeV for muons, both satisfying loose isolation requirements. Due to the higher instantaneous luminosity in 2016 the trigger threshold was increased to 26 GeV for both the electrons and the muons, and more restrictive isolation for both the leptons as well as more restrictive identification requirements for electrons wereapplied.Additionally,single-leptontriggerswithhigher pTthresholds but with no isolation or with loosened identification criteria were used to increase the efficiency. The trigger efficiency for events satisfying the full selection criteria described below is about 99% and is determined using a simulated signal sample that is corrected to reflect the data efficiencies with corrections measured using Z→ee [66] and Z→μμ [67] events. These data-driven corrections are of the order of 2% with permille level uncertainties. 4.2 Leptons Electron candidates are reconstructed from the combination of a cluster of energy deposits in the EM calorimeter and a track in the ID [66]. Candidate electrons must satisfy the TightLH quality definitiondescribed inRef. [66].Signalelectrons are required to have ET>27 GeV and the pseudorapidity of electrons is required to be |η|<2.47, excluding the transition region between the barrel and endcaps in the LAr calorimeter (1.37 <|η|<1.52). In addition, a requirement is added to reject electrons that potentially stem from photon conversions to reduce the Wγbackground [66]. This uses a simple classification based on the candidate electron’s 123 Eur. Phys. J. C (2019) 79 :884 Page 5 of 34 884 E/pand pT, the presence of a hit in the pixel detector, and the secondary-vertex information, to determine whether the electron could also be considered as a photon candidate and rejected. Muon candidates are reconstructed by combining a track in the ID with a track in the MS [67]. The Medium quality criterion, as defined in Ref. [67], is applied to the combined tracks. Signal muons are required to have pT>27 GeV and |η|<2.5. Leptons are required to originate from the primary vertex. The longitudinal impact parameter of each lepton track, calculated relative to the primary vertex and multiplied by sinθ of the track, is required to be smaller than 0.5 mm. Furthermore, the significance of the transverse impact parameter, defined by the transverse impact parameter (d0) of a lepton track relative to the beam line, divided by its estimated uncertainty (σd0), is required to satisfy |d0/σd0|<3.0(5.0) for muons (electrons). Leptons are also required to be isolated using information from ID tracks and energy clusters in the calorimeters in a cone around the lepton. The expected isolation efficiency is at least 90% (99%) at a pTof 25 (60) GeV using the Gradient working point defined in Refs. [66,67]. 4.3 Jets Jetcandidatesarereconstructedwithinthecalorimeteracceptance using the anti-ktjet clustering algorithm [68]usingthe FastJet code [69] with a radius parameter of R=0.4, which combines clusters of topologically connected calorimeter cells [70,71]. The jet energy is calibrated by applying a pTand η-dependent correction derived from MC simulation with additional corrections based on data [72]. As part of the jet energy calibration a pile-up correction based on the concept of jet area is applied to the jet candidates [73]. Jets are required to have a pseudorapidity |η|<4.5. The jet-vertex-tagger (JVT) technique [74]isusedtoseparate hard-scatter jets from pile-up jets within the acceptance of the tracking detector by requiring a significant fraction of the jets’ summed track pTto come from tracks associated with the primary vertex. For jets with 2.5<|η|<4.5, a forward-JVT selection is applied to suppress pile-up jets [75]. Candidate jets are discarded if they are within a cone of size R=0.2 around an electron candidate, or if they have fewer than three associated tracks and are within a cone of size R=0.2 around a muon candidate. However, if a jet with three or more associated tracks is within a cone of size R=0.4 around a muon candidate, or any jet is within a region 0.2<R<0.4 around an electron candidate, the corresponding electron or muon candidate is discarded. Within the ID acceptance, jets originating from the fragmentation of b-hadrons (b-jets) are identified using a multivariate algorithm (MV2c10 BDT) [76,77]. The chosen operating point has an efficiency of 85% for selecting jets containing b-hadrons, as estimated from a sample of simulated t¯ tevents and validated with data [77]. 4.4 Missing transverse momentum The missing transverse momentum 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 pT of the electron track is replaced by the calibrated transverse momentum of the reconstructed electron [78]. This definition has been updated for Run 2 data-taking conditions [79], and denoted by  Emiss,track Twith its absolute value denoted by Emiss,track T. The tracks are required to be associated with the primary vertex and to satisfy the selection criteria described in Ref. [79]. The Emiss,track Ttakes advantage of the excellent vertex resolution of the ATLAS detector and gives a missing transverse momentum estimate that is robust in the presence of pile-up, but it neglects the contribution of neutral particles, which do not form tracks in the ID. The pseudorapidity coverage of Emiss,track Tis also limited to the tracking volume of |η|<2.5, which is smaller than the calorimeter coverage of |η|<4.9. For events without any reconstructed jets, the Emiss,track Tprovides a small improvement of the Emiss Tresolution compared with the standard reconstruction algorithms [79]. 4.5 Signal region definition The signal region (SR), in which the measurement is performed, is defined as follows. To reduce the background from other diboson processes, events are required to have no additional electrons or muons with pT>10 GeV fulfilling loosened selection criteria. For this looser selection, the GradientLoose isolation requirement [66,67] is used for both the electrons and the muons, which has an expected isolation efficiency of at least 95% (99%) at a pTof 25 (60) GeV. Moreover, a less stringent MediumLH requirement [66]is applied for electron identification. To suppress the background contribution from top-quark production, events are required to have no jets with pT >35 GeV and |η|<4.5, and no b-jets with pT>20 GeV and |η|<2.5. The jet pTrequirement is optimized to minimize the total systematic uncertainty in the measurement. The additional b-jet veto requirement allows the background from top-quark production to be suppressed by a factor of three, while keeping 97% of the WW signal events. For the remaining top-quark background events that pass all selectioncriteria,the b-jetsaremainlyproducedoutsidetheaccep123 884 Page 6 of 34 Eur. Phys. J . C (2019) 79 :884 Table 1 Summary of lepton, jet, and event selection criteria for WW candidate events. In the table stands for eor μ. The definitions of lepton identification and isolation are detailed in Refs. [66]and[67] Selection requirement Selection value p T>27 GeV η|ηe|<2.47 (excluding 1.37 <|ηe|< 1.52), |ημ|<2.5 Lepton identification TightLH (electron), Medium (muon) Lepton isolation Gradient working point Number of additional leptons (pT>10 GeV) 0 Number of jets (pT>35 GeV, |η|<4.5) 0 Number of b-tagged jets (pT>20 GeV, |η|<2.5) 0 Emiss,track T>20 GeV peμ T>30 GeV meμ>55 GeV tance of the detector (pT<20 GeV or |η|>2.5), according to MC simulation. In addition, the requirements of Emiss,track T>20 GeV and peμ T>30 GeV suppress the Drell–Yan background contributions. A further requirement on the invariant mass of the lepton pair (meμ>55 GeV) reduces the H→WW∗contribution to a level below 1% of the expected signal. This last requirement is inverted compared with the one used in the recent measurement of H→WW∗production at 13TeVbyATLAS[23],makingthe twomeasurements statistically independent. Otherwise, both measurements use similar selections for events in the 0-jet category, although with lower lepton pTrequirements in the H→WW∗analysis. Thelepton,jet,andeventselectioncriteriaaresummarized in Table 1. 5 Background estimation AfterapplyingallselectionrequirementsdescribedinSect.4, the dominant background is from top-quark production. This includes t¯ tand W-associated single-top production, which both yield two real leptons in the final state. The non-prompt lepton background originates from leptonic decays of heavy quarks, hadrons misidentified as leptons, and electrons from photon conversions. Such leptonlikeobjectsarecollectivelyreferredtoas fakeleptons.Events withfakeleptonsaremainlydueto theproductionof W+jets, sand t-channel single-top production, both with leptonic W-boson decay and a jet misidentified as a lepton, or from multijet production with two jets misidentified as leptons. Other processes can contribute as well, but are negligible in the signal region. Since most of these events – more than 98% – correspond to W+jets production, this background is referred to as W+jets background in the following. Drell–Yan production of τ-leptons (Z→ττ) can also give rise to the eμfinal state. Other diboson (WZ,ZZ,Wγ and Zγ) and triboson (VVV, where V=W,Z) production processes constitute a smaller background contribution. A summary table comparing the number of observed candidate events in data to the respective numbers of predicted signal and background events in the signal region can be found in Sect. 5.5. 5.1 Background from top-quark production Background from top-quark production is estimated using a partly data-driven method [6,80], in which the top-quark contribution is extrapolated from a control region (top CR) to the signal region. The top CR is selected by applying the WW signalselection exceptfor the b-jet and jet-vetorequirements. To reduce the WW signal contamination in this control region, an additional requirement on the scalar sum of the transverse momenta of leptons and jets, HT>200 GeV, is applied. The remaining non-top-quark contribution estimated by MC simulation is subtracted and the resulting number of top-quark events, Ntop CR, is corrected for the HTcut efficiency, HT, using top-quark MC samples. With the efficiency for top-quark events to satisfy the jet-veto requirement, jet-veto, the top-quark background contribution in the signal region can be calculated as: Ntop SR =Ntop CR HT×jet-veto . The jet-veto efficiency, which mainly quantifies the fraction of top events with jets below the jet-veto and b-jet-veto pT thresholds, is calculated from simulation, with an extra correction factor [6,80]: jet-veto =MC jet-veto ×Data single-jet-veto MC single-jet-veto njets(1) where single-jet-veto is defined as the fraction of top-quark events that contain no jets other than the b-tagged jet, and MC jet-veto extrapolates the top-quark MC prediction from the top CR (without HTrequirement) to the signal region. The single-jet-veto is determined both in data and simulation using eventswithtwoleptons,thesamerequirementson Emiss,track T, peμ Tand meμas for the signal selection, and at least one btagged jet. The small contributions to this region of the signal and other background contributions, mainly W+jets production, are subtracted before the calculation of Data single-jet-veto. The ratio Data single-jet-veto/MC single-jet-veto then corrects for differences in the veto efficiency for a single jet between data and 123 Eur. Phys. J. C (2019) 79 :884 Page 7 of 34 884 simulation. It is found to be consistent with one. The exponent njetsrepresents the average number of jets in the top CR and is measured to be approximately 2.5 in both data and top-quark background simulation. It is varied by ±1.0 as part of the uncertainty in the method to conservatively cover njetsvariations in different control regions as well as variations due to detector uncertainties and modelling, with a small impact (1.8%) on the total uncertainty in the top-quark background estimate. The top-quark background estimate includes detector uncertainties in addition to the uncertainties in the method. Modellinguncertaintiesare determined using alternativeMC samplesandincludethemodellingofthepartonshower,extra QCD radiation and the effect of the choice of generators. Interference effects between Wt and t¯ tare also considered. These modelling uncertainties are estimated by comparing the results from different MC samples described in Sect. 3. The cross-section uncertainty is taken to be 6% for t¯ t[52,81– 86]and10%for Wtproduction[53,87].Thetotaluncertainty in the top-quark background estimate in the signal region is about 12% using this partly data-driven approach, making use of cancellations of systematic uncertainties in the ratio MC jet-veto/(MC single-jet-veto)njetsin Eq. (1). It is dominated by the b-tagging and modelling uncertainties. The contribution of the t¯ tand Wt background to the total expected yield in the signal region is about 25% (17% t¯ tand 8% Wt). The differential top-quark background contribution and its uncertainties are evaluated by applying the same procedure in each bin of the measured observables. As an example, Fig. 2shows the relevant quantities used in this partly datadriven method, as a function of the transverse momentum of the leading (highest pT) lepton. The systematic uncertainties in Ntop SR are significantly reduced due to the systematics cancellations compared with the uncertainty bands from Fig. 2. The decrease of MC jet-veto at high leading lepton pTis due to an increaseinthetypical pTofextrajetswhichrecoilagainstthe leptons,nearingthejet-veto pTthreshold,andhencereducing the probability to still pass the jet veto. Since the efficiency ratio, Data single-jet-veto/MC single-jet-veto, is found to be independent of any kinematic variable, the single value of 0.98 ±0.05 is used for all differential distributions. This is shown as a dashed line in the lower right panel of Fig. 2. 5.2 Background from Drell–Yan production The estimate of the Drell–Yan background process is based on MC simulation, with a 5% theoretical cross-section uncertainty [88]. A validation region dominated by Drell–Yan events is defined with the same selections as for the signal region, but with the eμinvariant mass required to be 45 GeV <meμ<80 GeV and with the events failing either the peμ T-ortheEmiss,track T-requirement to make the sample orthogonal to that in the signal region. Good agreement between the data and the simulation is observed in this region. The shape uncertainty is evaluated by using an alternativeMCeventgenerator,asdetailedinSect. 3,and includes uncertaintiesduetothemodellingoftheacceptance.Thetotal uncertainty in the Drell–Yan background is 11% and the contribution of this background in the signal region is found to be 4%. 5.3 Background from W+jets production The yield of W+jets is estimated by comparing in data the number of events with leptons satisfying either of two alternative sets of selection requirements, together with the WW signal selection criteria, following the same procedure as that described in Ref. [6]. The loose lepton selection criteria are defined such that the signal sample is a subset of the loose lepton sample. For electrons, the loose selection corresponds to the MediumLH quality definition [66] and no isolation requirements are imposed. For muons, the loose selection is the same as for signal muons, except that the isolation requirement is omitted. The tight selection criteria are the same as those used for the signal selection. With the introduction of real-lepton and fake-lepton efficiencies, a system of four equations can be solved to estimate the number of W+jets events. Here, the number of events that have exactly one loose muon (electron) and one tight electron (muon), two loose leptons or two tight leptons, are used. The real-lepton (fake-lepton) efficiency used in these equations is defined as the probability for prompt (fake) leptons selected with the loose criteria to satisfy the tight selection criteria. The efficiencies for real electrons (muons) are determined using MC simulation, with data-to-MC correction factors [66,67] applied. The efficiencies for fake electrons (muons) are measured using a multijet data sample, in a control region with exactly one loose electron (muon) and between one and three jets. Events in this control region are also required to have low Emiss,track Tand low transverse mass3mT, to fulfil angular requirements between Emiss,track Tand the jets in the event, and to have no b-tagged jets. Real-lepton contributions to the control region are estimated using MC simulation and are subtracted. Both the realand fake-lepton efficiencies are derived as functions of pTand ηof the lepton. This is sufficient to describe the most important correlations with the differential distributions studied. Moreover, as the loose lepton selection in the W+jets background estimate at low lepton-pT (pT<50 GeV for muons or pT<60 GeV for electrons) 3The transverse mass is defined as: mT= 2p TEmiss,track T1−cos φ(, Emiss,track T). 123 884 Page 8 of 34 Eur. Phys. J . C (2019) 79 :884 Fig. 2 Inputs to the partly data-driven method for the top background estimate as a function of the pTof the leading lepton: (upper left) events selected in data and in simulation in the top CR, with a requirement of HT>200 GeV applied, (upper right) the HTcut efficiency HT,(lower left) the MC-based jet-veto efficiency MC jet-veto, and (lower right) the efficiency ratio Data single-jet-veto/MC single-jet-veto. The latter is constant within uncertainties, and therefore replaced by the inclusive efficiency ratio (dashed line). In all figures, statistical and systematic uncertainties are displayed as hatched bands is typically looser than in the trigger selection, the efficiencies are provided separately for low-pTelectrons or muons that satisfy or fail to satisfy the trigger selection requirements. The fake-lepton efficiency for the non-triggered leptons is estimated using events recorded with triggers that have lower muon-pT, only MediumLH electron quality and no lepton isolation requirements, but only record a fraction of the events satisfying these criteria. The uncertainty in the W+jets background is directly related to the uncertainties in the realand fake-lepton efficiencies. For real-lepton efficiencies, these take into account uncertainties in electron and muon reconstruction and isolation correction factors. Uncertainties in the fake lepton efficiencies include variations in the control region definition, as well as normalization and shape uncertainties in the subtracted contributions from other processes in the control region. The control region variations are designed to cover the uncertainty in the flavour composition of the jets faking leptons, and include variations of the mTrequirement and the number of b-tagged jets. The total uncertainty in the W+jets yield is 90% and is dominated by the uncertainty in the fake and real electron efficiencies, because of the greater contribution of electron fakes to the W+jets background. The W+jets background amounts to 3% of the expected yield in the signal region. Thedifferential W+jets distributions necessary for the differential cross-section measurements are also determined in a fully data-driven way, by evaluating the same system of linear equations [6] in each bin of the differential distributions. The predicted contributions to the backgrounds from W+jets are validated using a data control sample in which the two selected leptons are required to have the same electric charge (same-sign) and satisfy all the other selection requirements.Figure3showsthepseudorapiditydifference between the leptons and the transverse momentum of the sub-leading 123 Eur. Phys. J. C (2019) 79 :884 Page 9 of 34 884 Fig. 3 Distributions of the pseudorapidity difference between the leptons (left) and the transverse momentum of the sub-leading lepton (right) for the same-sign validation region. The uncertainties shown include statistical and systematic uncertainties lepton for this same-sign control sample. The predictions and the data agree well. 5.4 Background from multi-boson production The estimate of the diboson background from WZ,ZZ,Wγ and Zγprocesses is based on MC simulation. These processes contribute about 3% to the total number of events. The uncertainty in the cross-section for these diboson processes is taken as 10% [89,90] and variations in the shape and the acceptance are considered for WZ and ZZ production by using alternative MC generators, as detailed in Sect. 3. The Vγbackground simulation is validated in data using the events passing the same selection as for the signal region, exceptinvertingtheelectronidentificationcriteriaandrequiring the reconstructed electron track to have no hit in the innermost layer of the pixel detector. The WZ background simulation is validated in data using events that allow for the presence of a third loosely isolated lepton with pT>10 GeV andrequire thesame-flavourleptonpairtobe ofopposite sign and with invariant mass of 80 GeV <mee/μμ <100 GeV, while otherwise passing the signal region selection. Good agreement between the data and the simulation is found in both regions. Thebackgroundfromtribosonproduction(WWW,WWZ, WZZand ZZZ) is less than 0.1% and is evaluated using MC simulation. The cross-section uncertainty is taken as 30% [89]. 5.5 WW candidate events and estimated background yields After applying all the selection requirements, 12 659 events are observed in data, with a contribution of 65% from WW Table 2 Number of events observed in data, compared with the numbers of predicted signal and background events in the signal region. The systematic uncertainties, described in Sect. 7, do not include the uncertainty in the integrated luminosity. The uncertainties in the total background and in the sum of signal and background are the sums in quadrature of the uncertainties in the various background and signal sources Number of events Statistical uncertainty Systematic uncertainty Top-quark 3120 ±50 ±370 Drell–Yan 431 ±13 ±44 W+jets 310 ±60 ±280 WZ 290 ±11 ±33 ZZ 16 ±1±2 Vγ66 ±11 ±10 Triboson 8 ±1±3 Total background 4240 ±80 ±470 Signal (WW)7690 ±30 ±220 Total signal+background 11,930 ±90 ±520 Data 12,659 – – production,whichisestimatedusingsimulation(seeSect. 3). A summary of the data, signal, and background yields is shown in Table 2. Kinematic distributions comparing the selected data with the signal and backgrounds in the signal region are shown in Fig. 4. Fair agreement between data and expectations is observed for the overall normalization and the shapes of various kinematic distributions. Small underpredictions in the peak region of the leading lepton pTdistribution, the low meμregion and a small downward trend in the ratio of the data to expectations in the φeμdistribution have also been observed in the previous ATLAS measurement at 123 884 Page 16 of 34 Eur. Phys. J. C (2019) 79 :884 Fig. 8 Measured fiducial cross-sections of WW →eμproduction for two of the six observables: φeμand |cosθ∗|. The measured crosssection 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, and with NLO+PS predictions from PowhegBox+Pythia 8, PowhegBox+Herwig++ and Sherpa 2.2.2 for q¯qinitial states, combined with Sherpa+OpenLoops (LO+PS) for the gg initial states. All three q¯q NLO+PS predictions are normalized to the NNLO theoretical prediction for the total cross-section, with the gg LO+PS contribution normalized to NLO. Theoretical predictions are indicated as markers with hatched bands denoting PDF+scale uncertainties Templates of the plead  Tdistribution representing the pure SM contribution, the aTGC contribution, and the interference between the SM and aTGC contributions at LO are prepared at generator level using MadGraph5_aMC@NLO version 2.6.3.2 [110], interfaced to Pythia 8.212 with the A14 tune for parton showering and hadronization. The relative size of the SM cross-section modification increases with plead  Tso that the last measured bin is most sensitive to the aTGC effects. To ensure a good agreement of the MadGraph5_aMC@NLO prediction with the baseline SM prediction, a bin-wise correction, determined as the ratio of the pure SM contributions from PowhegBox+Pythia 8 (normalized to the NNLO cross-section) and MadGraph5_aMC@NLO, is applied. It is verified that the pure SM assumption used in the unfolding procedure introduces no bias to the extraction of limits from the unfolded cross-section. A reweighting procedure implemented in the MadGraph5_aMC@NLO [111] generator is used to obtain multiple signal predictions that include aTGCs of a magnitude corresponding to the upper limits set by the Run 1 analysis [6]. The simulation is interfaced to Herwig 6.5 [112] and passed through the ATLAS detectorsimulation.Neitherthereconstructionefficiencyand the fiducial corrections nor the bin-to-bin migrations are significantly different. The measured plead  Tcross-section and the MadGraph5_aMC@NLO prediction, interfaced to Pythia 8, as described above, are used to construct a likelihood function, in which statistical and systematic measurement uncertainties are modelled by a multivariate Gaussian distribution.Systematicuncertaintiesin thetheoryprediction areconsideredasnuisanceparameters,eachconstrainedwith a Gaussian distribution. Since electroweak radiative effects are already partially taken into account in the parton shower of the MadGraph5_aMC@NLO prediction, the effect of applying NLO EW corrections to the plead  Tdistribution in addition is considered as a further systematic uncertainty. Frequentist confidence intervals for the EFT coefficients are computed from values of a profile likelihood ratio test statistic [113]. Observed and expected 95% CL intervals for the EFT coefficients are summarized in Table 6.Due to the higher centre-of-mass energy, the limits reported here are more restrictive than those previously published by the ATLAS and CMS Collaborations in the WW final state [6,8]. Comparedtoresultsfrom inclusive WZproduction[114]and electroweak Wand Zboson production in association with two jets [115], both at √s=13 TeV, the limits on cB/2 123 Eur. Phys. J. C (2019) 79 :884 Page 17 of 34 884 Table 6 The expected and observed 95% CL intervals for the anomalous coupling parameters of the EFT model [109]. There is a change in convention relative to Ref. [6] that changes the sign on some of these parameters Parameter Observed 95% CL [TeV−2] Expected 95% CL [TeV−2] cWWW/2[−3.4, 3.3] [−3.0, 3.0] cW/2[−7.4, 4.1] [−6.4, 5.1] cB/2[−21, 18] [−18, 17] c˜ WWW/2[−1.6, 1.6] [−1.5, 1.5] c˜ W/2[−76, 76] [−91, 91] from this analysis are the most stringent (by about a factor 2), while those on cWWW/2and cW/2are weaker by factors of about 1.6 – 4. Limits on the CP-odd operators O˜ WWW and O˜ Ware not provided by the other two measurements. The sensitivity to dimension-six operators mostly stems fromtheir directeffectonthe WW cross-section asafunction of plead  T, except for the cWcoefficient where both the direct contribution and the interference between the SM and terms containing EFT operators contribute equally. 10 Conclusion The cross-section for the production of W+W−pairs in pp collisions at √s=13 TeV (with subsequent decays into WW →eνeμνμ) is measured in a fiducial phase space that excludes the presence of jets with transverse momentum above 35 GeV. The measurement is performed with data recorded by the ATLAS experiment at the LHC in 2015 and 2016, which correspond to an integrated luminosity of 36.1fb −1. The measured fiducial cross-section is σfid =(379.1±5.0 (stat) ±25.4 (syst) ±8.0(lumi) )fb, and is found to be consistent with theoretical predictions, including NNLO QCD and NLO EW corrections. The fiducial cross-section is also measured as a function of the transversemomentumthresholdforthejetveto.Differentialcrosssections are measured as a function of kinematic and angular variables of the final-state charged leptons and are compared with several predictions from perturbative QCD calculations. Data and theory show fair agreement for all differential distributions. The distribution of the transverse momentum of the leading lepton is used to investigate anomalous triplegauge-boson coupling parameters. No evidence for anomalous WWZ and WWγcouplings is found, hence limits on their magnitudes are set. These limits are more restrictive than those derived at √s=8TeV. 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, CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF, and MPG, Germany; GSRT, Greece; RGC, 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, Russian 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, CRC and Compute Canada, Canada; COST, ERC, ERDF, Horizon 2020, and Marie Skłodowska-Curie Actions, European Union; Investissements d’ Avenir Labex and Idex, ANR, France; DFG and AvH Foundation, Germany;Herakleitos,ThalesandAristeiaprogrammesco-financedbyEUESF and the Greek NSRF, Greece; BSF-NSF and GIF, Israel; CERCA Programme Generalitat de Catalunya, Spain; 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), NL-T1 (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. [116]. Data Availability Statement This manuscript has no associated data or the data will not be deposited. [Authors’ comment: All ATLAS scientific output is published in journals, and preliminary results are made available in Conference Notes. All are openly available,without restriction on use by external parties beyond copyright law and the standard conditions agreed by CERN. Data associated with journal publications are also made available: tables and data from plots (e.g. cross section values, likelihood profiles, selection efficiencies, cross section limits, ...) are stored in appropriate repositories such as HEPDATA (http:// hepdata.cedar.ac.uk/). ATLAS also strives to make additional material related to the paper available that allows a reinterpretation of the data in the context of new theoretical models. 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Kuze165,A.K.Kvam 148, J. Kvita130, T. Kwan103,A.LaRosa 115, J.L.LaRosaNavarro 80d, L. La Rotonda41a,41b,F.LaRuffa 41b,41a, C. Lacasta174, F. Lacava72a,72b, D.P.J.Lack 100, H. Lacker19, D. Lacour136, E. Ladygin79, R. Lafaye5, B. Laforge136, T. Lagouri33c, S. Lai53, S. Lammers65, W. Lampl7, E. Lançon29, U. Landgraf52, M. P. J. Landon92, M. C. Lanfermann54, V. S. Lang46, J. C. Lange53, R. J. Langenberg36, A. J. Lankford171, F. Lanni29, K. Lantzsch24, A. Lanza70a, A. Lapertosa55a,55b, S. Laplace136, J. F. Laporte145,T.Lari 68a, F. Lasagni Manghi23a,23b, M. Lassnig36,T.S.Lau 63a, A. Laudrain132, A. Laurier34, M. Lavorgna69a,69b, M. Lazzaroni68a,68b,B.Le 104,E.LeGuirriec 101, M. LeBlanc7, T. LeCompte6, F. Ledroit-Guillon58,C.A.Lee 29,G.R.Lee 17,L.Lee 59, S.C.Lee 158, S.J.Lee 34, B. Lefebvre168a, M. Lefebvre176, F. Legger114, C. Leggett18, K. Lehmann152, N. Lehmann182, G. Lehmann Miotto36, W. A. Leight46,A.Leisos 162,w, M. A. L. Leite80d, R. Leitner143, D. Lellouch180,*, K.J.C.Leney 42, T. Lenz24, B. Lenzi36, R. Leone7, S. Leone71a, C. Leonidopoulos50, A. Leopold136, G. Lerner156,C.Leroy 109,R.Les 167,C.G.Lester 32, M. Levchenko138, J. Levêque5, D. Levin105, L.J.Levinson 180,D.J.Lewis 21,B.Li 15b,B.Li 105,C.-Q.Li 60a,F.Li 60c,H.Li 60a,H.Li 60b,J.Li 60c, K. Li153,L.Li 60c,M.Li 15a,Q.Li 15a,15d,Q.Y.Li 60a,S.Li 60d,60c,X.Li 46,Y.Li 46,Z.Li 60b, Z. Liang15a, B. Liberti73a, A. Liblong167,K.Lie 63c,S.Liem 120,C.Y.Lin 32,K.Lin 106,T.H.Lin 99, R. A. Linck65, J. H. Lindon21, A. L. Lionti54, 123 Eur. Phys. J. C (2019) 79 :884 Page 25 of 34 884 E. Lipeles137, A. Lipniacka17, M. Lisovyi61b,T.M.Liss 173,au, A. Lister175, A. M. Litke146, J. D. Little8,B.Liu 78,ac, B. L. Liu6,H.B.Liu 29,H.Liu 105, J.B.Liu 60a,J.K.K.Liu 135,K.Liu 136,M.Liu 60a,P.Liu 18,Y.Liu 15a,15d, Y. L. Liu105,Y.W.Liu 60a,M.Livan 70a,70b,A.Lleres 58, J. Llorente Merino15a,S.L.Lloyd 92,C.Y.Lo 63b,F.LoSterzo 42, E. M. Lobodzinska46, P. Loch7, S. Loffredo73a,73b, T. Lohse19, K. Lohwasser149, M. Lokajicek141, J. D. 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Minaenko123, M. Miñano Moya174, I. A. Minashvili159b, A. I. Mincer124, B. Mindur83a, M. Mineev79, Y. Minegishi163,Y.Ming 181,L.M.Mir 14, A. Mirto67a,67b, K. P. Mistry137, T. Mitani179, J. Mitrevski114, V.A.Mitsou 174, M. Mittal60c, A. Miucci20, P. S. Miyagawa149, A. Mizukami81, J.U.Mjörnmark 96, T. Mkrtchyan184, M. Mlynarikova143,T.Moa 45a,45b, K. Mochizuki109, P. Mogg52, S. Mohapatra39, R. Moles-Valls24, M. C. Mondragon106, K. Mönig46, J. Monk40, E. Monnier101, A. Montalbano152, J. Montejo Berlingen36, M. Montella94, F. Monticelli88, S. Monzani68a, N. Morange132, D. Moreno22, M. Moreno Llácer36, P. Morettini55b, M. Morgenstern120, S. Morgenstern48,D.Mori 152,M.Morii 59, M. Morinaga179, V. Morisbak134,A.K.Morley 36, G. Mornacchi36, A. P. Morris94,L.Morvaj 155, P. Moschovakos10, B. Moser120, M. Mosidze159b, T. Moskalets145,H.J.Moss 149,J.Moss 31,n, K. Motohashi165, E. Mountricha36, E.J.W.Moyse 102, S. Muanza101, F. Mueller115, J. Mueller139, R.S.P.Mueller 114, D. Muenstermann89, G. A. Mullier96, J. L. Munoz Martinez14, F. J. Munoz Sanchez100,P.Murin 28b, W.J.Murray 144,178, A. Murrone68a,68b, M. Muškinja18,C.Mwewa 33a, A. G. Myagkov123,ao, J. Myers131, M. Myska142, B. P. Nachman18, O. Nackenhorst47,A.NagNag 48, K. Nagai135, K. Nagano81, Y. Nagasaka62, M. Nagel52, E. Nagy101,A.M.Nairz 36, Y. Nakahama117, K. Nakamura81, T. Nakamura163, I. Nakano127, H. Nanjo133, F. Napolitano61a, R. F. Naranjo Garcia46, R. Narayan11, D. I. Narrias Villar61a, I. Naryshkin138, T. Naumann46, G. Navarro22, H. A. Neal105,*, P. Y. Nechaeva110, F. Nechansky46, T. J. Neep21,A.Negri 70a,70b,M.Negrini 23b, S. Nektarijevic119, C. Nellist53, M.E.Nelson 135, S. Nemecek141, P. Nemethy124, M. Nessi36,e, M. S. Neubauer173, M. Neumann182, P. R. Newman21,T.Y.Ng 63c, Y. S. Ng19,Y.W.Y.Ng 171, H. D. N. Nguyen101, T. Nguyen Manh109, E. Nibigira38,R.B.Nickerson 135, R. Nicolaidou145, D. S. Nielsen40, J. Nielsen146, N. Nikiforou11, V. Nikolaenko123,ao, I. Nikolic-Audit136, K. Nikolopoulos21, P. Nilsson29, H. R. Nindhito54, Y. Ninomiya81,A.Nisati 72a,N.Nishu 60c, R. Nisius115, I. Nitsche47, T. Nitta179, T. Nobe163, Y. Noguchi85, M. Nomachi133, I. Nomidis136, M.A.Nomura 29, M. Nordberg36, N. Norjoharuddeen135,T.Novak 91, O. Novgorodova48, R. Novotny142, L. Nozka130, K. Ntekas171, E. Nurse94,F.Nuti 104, F. G. Oakham34,ax, H. Oberlack115, J. Ocariz136, A. Ochi82, I. Ochoa39, J. P. Ochoa-Ricoux147a, K. O’Connor26,S.Oda 87, S. Odaka81, S. Oerdek53, A. Ogrodnik83a,A.Oh 100,S.H.Oh 49,C.C.Ohm 154,H.Oide 55a,55b, M. L. Ojeda167,H.Okawa 169, Y. Okazaki85, 123 884 Page 32 of 34 Eur. Phys. J. C (2019) 79 :884 140 (a)Laboratório de Instrumentação e Física Experimental de Partículas-LIP, Lisbon, Portugal; (b)Departamento de Física, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal; (c)Departamento de Física, Universidade de Coimbra, Coimbra, Portugal; (d)Centro de Física Nuclear da Universidade de Lisboa, Lisbon, Portugal; (e)Departamento de Física, Universidade do Minho, Braga, Portugal; (f)Universidad de Granada, Granada, Spain; (g)Dep Física and CEFITEC of Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal; (h)Av. Rovisco Pais, 1, 1049-001 Lisbon, Portugal 141 Institute of Physics of the Czech Academy of Sciences, Prague, Czech Republic 142 Czech Technical University in Prague, Prague, Czech Republic 143 Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic 144 Particle Physics Department, Rutherford Appleton Laboratory, Didcot, UK 145 IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France 146 Santa Cruz Institute for Particle Physics, University of California Santa Cruz, Santa Cruz, CA, USA 147 (a)Departamento de Física, Pontificia Universidad Católica de Chile, Santiago, Chile; (b)Departamento de Física, Universidad Técnica Federico Santa María, Valparaíso, Chile 148 Department of Physics, University of Washington, Seattle, WA, USA 149 Department of Physics and Astronomy, University of Sheffield, Sheffield, UK 150 Department of Physics, Shinshu University, Nagano, Japan 151 Department Physik, Universität Siegen, Siegen, Germany 152 Department of Physics, Simon Fraser University, Burnaby, BC, Canada 153 SLAC National Accelerator Laboratory, Stanford, CA, USA 154 Physics Department, Royal Institute of Technology, Stockholm, Sweden 155 Departments of Physics and Astronomy, Stony Brook University, Stony Brook, NY, USA 156 Department of Physics and Astronomy, University of Sussex, Brighton, UK 157 School of Physics, University of Sydney, Sydney, Australia 158 Institute of Physics, Academia Sinica, Taipei, Taiwan 159 (a)E. Andronikashvili Institute of Physics, Iv. Javakhishvili Tbilisi State University, Tbilisi, Georgia; (b)High Energy Physics Institute, Tbilisi State University, Tbilisi, Georgia 160 Department of Physics, Technion, Israel Institute of Technology, Haifa, Israel 161 Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University, Tel Aviv, Israel 162 Department of Physics, Aristotle University of Thessaloniki, Thessaloniki, Greece 163 International Center for Elementary Particle Physics and Department of Physics, University of Tokyo, Tokyo, Japan 164 Graduate School of Science and Technology, Tokyo Metropolitan University, Tokyo, Japan 165 Department of Physics, Tokyo Institute of Technology, Tokyo, Japan 166 Tomsk State University, Tomsk, Russia 167 Department of Physics, University of Toronto, Toronto, ON, Canada 168 (a)TRIUMF, Vancouver, BC, Canada; (b)Department of Physics and Astronomy, York University, Toronto, ON, Canada 169 Division of Physics and Tomonaga Center for the History of the Universe, Faculty of Pure and Applied Sciences, University of Tsukuba, Tsukuba, Japan 170 Department of Physics and Astronomy, Tufts University, Medford, MA, USA 171 Department of Physics and Astronomy, University of California Irvine, Irvine, CA, USA 172 Department of Physics and Astronomy, University of Uppsala, Uppsala, Sweden 173 Department of Physics, University of Illinois, Urbana, IL, USA 174 Instituto de Física Corpuscular (IFIC), Centro Mixto Universidad de Valencia - CSIC, Valencia, Spain 175 Department of Physics, University of British Columbia, Vancouver, BC, Canada 176 Department of Physics and Astronomy, University of Victoria, Victoria, BC, Canada 177 Fakultät für Physik und Astronomie, Julius-Maximilians-Universität Würzburg, Würzburg, Germany 178 Department of Physics, University of Warwick, Coventry, UK 179 Waseda University, Tokyo, Japan 180 Department of Particle Physics, Weizmann Institute of Science, Rehovot, Israel 181 Department of Physics, University of Wisconsin, Madison, WI, USA 182 Fakultät für Mathematik und Naturwissenschaften, Fachgruppe Physik, Bergische Universität Wuppertal, Wuppertal, Germany 183 Department of Physics, Yale University, New Haven, CT, USA 123 Eur. Phys. J. C (2019) 79 :884 Page 33 of 34 884 184 Yerevan Physics Institute, Yerevan, Armenia aAlso at Borough of Manhattan Community College, City University of New York, New York, NY, USA bAlso at Centre for High Performance Computing, CSIR Campus, Rosebank, Cape Town, South Africa 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, Geneva, Switzerland fAlso at Departament de Fisica de la Universitat Autonoma de Barcelona, Barcelona, Spain gAlso at Departamento de Física, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal hAlso at Department of Applied Physics and Astronomy, University of Sharjah, Sharjah, United Arab Emirates iAlso at Department of Financial and Management Engineering, University of the Aegean, Chios, Greece jAlso at Department of Physics and Astronomy, University of Louisville, Louisville, KY, USA kAlso at Department of Physics and Astronomy, University of Sheffield, Sheffield, UK lAlso at Department of Physics, California State University, East Bay, USA mAlso at Department of Physics, California State University, Fresno, USA nAlso at Department of Physics, California State University, Sacramento, USA oAlso at Department of Physics, King’s College London, London, UK pAlso at Department of Physics, St. Petersburg State Polytechnical University, St. Petersburg, Russia qAlso at Department of Physics, Stanford University, Stanford, CA, USA rAlso at Department of Physics, University of Fribourg, Fribourg, Switzerland sAlso at Department of Physics, University of Michigan, Ann Arbor, MI, USA tAlso at Faculty of Physics, M.V. Lomonosov Moscow State University, Moscow, Russia uAlso at Giresun University, Faculty of Engineering, Giresu, Turkey vAlso at Graduate School of Science, Osaka University, Osaka, Japan wAlso at Hellenic Open University, Patras, Greece xAlso at Institucio Catalana de Recerca i Estudis Avancats, ICREA, Barcelona, Spain yAlso at Institut für Experimentalphysik, Universität Hamburg, Hamburg, Germany zAlso at Institute for Mathematics, Astrophysics and Particle Physics, Radboud University Nijmegen/Nikhef, Nijmegen, The Netherlands aa Also at Institute for Nuclear Research and Nuclear Energy (INRNE) of the Bulgarian Academy of Sciences, Sofia, Bulgaria ab Also at Institute for Particle and Nuclear Physics, Wigner Research Centre for Physics, Budapest, Hungary ac Also at Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China ad Also at Institute of Particle Physics (IPP), Vancouver, Canada ae Also at Institute of Physics, Academia Sinica, Taipei, Taiwan af Also at Institute of Physics, Azerbaijan Academy of Sciences, Baku, Azerbaijan ag Also at Institute of Theoretical Physics, Ilia State University, Tbilisi, Georgia ah Also at Instituto de Fisica Teorica, IFT-UAM/CSIC, Madrid, Spain ai Also at Istanbul University, Dept. of Physics, Istanbul, Turkey aj Also at Joint Institute for Nuclear Research, Dubna, Russia ak Also at LAL, Université Paris-Sud, CNRS/IN2P3, Université Paris-Saclay, Orsay, France al Also at Louisiana Tech University, Ruston, LA, USA am Also at LPNHE, Sorbonne Université, Université de Paris, CNRS/IN2P3, Paris, France an Also at Manhattan College, New York, NY, USA ao Also at Moscow Institute of Physics and Technology State University, Dolgoprudny, Russia ap Also at National Research Nuclear University MEPhI, Moscow, Russia aq Also at Physics Department, An-Najah National University, Nablus, Palestine ar Also at Physics Department, University of South Africa, Pretoria, South Africa as Also at Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany at Also at School of Physics, Sun Yat-sen University, Guangzhou, China au Also at The City College of New York, New York, NY, USA av Also at The Collaborative Innovation Center of Quantum Matter (CICQM), Beijing, China 123 884 Page 34 of 34 Eur. Phys. J. C (2019) 79 :884 aw Also at Tomsk State University, Tomsk, and Moscow Institute of Physics and Technology State University, Dolgoprudny, Russia ax Also at TRIUMF, Vancouver, BC, Canada ay Also at Universita di Napoli Parthenope, Naples, Italy ∗Deceased 123