Search for diboson resonances in hadronic final states in 139 fb−1 of pp collisions at s√ = 13 TeV with the ATLAS detector

JHEP09(2019)091 Published for SISSA by Springer Received:June 21, 2019 Revised:July 24, 2019 Accepted:August 13, 2019 Published:September 12, 2019 Search for diboson resonances in hadronic final states in 139 fb−1of pp collisions at √s=13 TeV with the ATLAS detector The ATLAS collaboration E-mail: [email protected] Abstract: Narrow resonances decaying into WW,WZ or ZZ boson pairs are searched for in 139 fb−1of proton-proton collision data at a centre-of-mass energy of √s= 13 TeV recorded with the ATLAS detector at the Large Hadron Collider from 2015 to 2018. The diboson system is reconstructed using pairs of high transverse momentum, large-radius jets. These jets are built from a combination of calorimeterand tracker-inputs compatible with the hadronic decay of a boosted Wor Zboson, using jet mass and substructure properties. The search is performed for diboson resonances with masses greater than 1.3 TeV. No significant deviations from the background expectations are observed. Exclusion limits at the 95% confidence level are set on the production cross-section times branching ratio into dibosons for resonances in a range of theories beyond the Standard Model, with the highest excluded mass of a new gauge boson at 3.8 TeV in the context of mass-degenerate resonances that couple predominantly to gauge bosons. Keywords: Hadron-Hadron scattering (experiments) ArXiv ePrint: 1906.08589 Open Access, Copyright CERN, for the benefit of the ATLAS Collaboration. Article funded by SCOAP3. https://doi.org/10.1007/JHEP09(2019)091 JHEP09(2019)091 Contents 1 Introduction 1 2 ATLAS detector 2 3 Data 3 4 Simulation 3 4.1 Signal models 3 4.2 Simulated event samples 5 5 Reconstruction 6 5.1 Track-CaloClusters 6 5.2 Jet reconstruction 6 5.3 Leptons 8 6 Event selection 8 6.1 Vector-boson identification 9 6.2 Measurement of boson-tagging efficiency 10 6.3 Signal and background selection efficiency 12 7 Background parameterisation 13 8 Systematic uncertainties 16 9 Results 17 9.1 Background fit 17 9.2 Statistical analysis 17 10 Conclusion 18 The ATLAS collaboration 26 1 Introduction The discovery of new phenomena in high-energy proton-proton (pp) collisions is one of the main goals of the Large Hadron Collider (LHC). New heavy, TeV-scale, resonances of vector bosons V V (where Vrepresents a Wor a Zboson) are a possible signature of such new physics and are predicted in several extensions to the Standard Model (SM). These include extended gauge-symmetry models [1–3], Grand Unified theories [4–7], theories with warped extra dimensions [8–12], two-Higgs-doublet models [13], little-Higgs models [14], theories with new strong dynamics [15], including technicolour [16–18], and more generic composite Higgs models [19]. The data sample of 36.7 fb−1of pp collisions collected in 2015 and 2016 at the LHC at √s= 13 TeV offered improved sensitivity to heavy diboson resonances compared with earlier results. The ATLAS and CMS collaborations performed searches in the fully hadronic final states using this data [20–22] but no significant deviation – 1 – JHEP09(2019)091 from a smooth background consistent with the SM expectation was observed. Searches by ATLAS [23,24] and CMS [25,26] for semileptonic decay modes of the boson pair, as well as statistical combinations of various decay channels [27], on the same data also did not reveal any hint of new physics. This paper presents a search for narrow diboson resonances decaying into fully hadronic final states in 139 fb−1of pp collision data collected by the ATLAS experiment between 2015 and 2018. The Wand Zbosons produced in the decay of TeV-scale resonances are highly boosted, and are therefore reconstructed in ATLAS as a single large-radiusparameter jet. The signature of such heavy resonance decays is thus a resonant structure in the dijet invariant mass spectrum. Although the hadronic decays of vector bosons have the largest branching ratio (67% for Wand 70% for Zbosons), they suffer from background contamination from the production of multijet events. This background is larger by several orders of magnitude, and to suppress it, the characteristic jet substructure of W/Z boson decays is used. Contributions to the background from SM processes containing bosons, V+ jets, SM V V , tt and single top production, are significantly smaller. To improve the sensitivity of this search, new techniques are used. Novel inputs are used for jet finding, which improve the jet substructure resolution of ATLAS in highly boosted topologies [28]. To further benefit from these developments, a new approach for identifying boosted boson candidates is introduced. The identification of the boosted boson candidates is validated using the known SM V+ jets production. To avoid limitations caused by poor modelling or limited numbers of Monte Carlo (MC) generated background events, the observed background is characterised by a parametric function fit to the smoothly falling dijet invariant mass distribution. To assess the sensitivity of the search, to optimise the event selection and for comparison with the observed data, three specific benchmark models are used: a spin-0 radion [29] decaying into WW or ZZ; a spin-1 Heavy Vector Triplet (HVT) Model [30] that provides signals such as W0→WZ and Z0→WW; and a spin-2 graviton GKK →WW or ZZ, corresponding to Kaluza-Klein (KK) modes [8,9] of the Randall-Sundrum (RS) graviton [10–12]. These models assume production mechanisms either via gluon-gluon fusion or quark-antiquark annihilation. 2 ATLAS detector The ATLAS detector [31] surrounds nearly the entire solid angle around the ATLAS collision point. It has an approximately cylindrical geometry1and consists of an inner tracking detector surrounded by electromagnetic and hadronic calorimeters and a muon spectrometer. The tracking detector is placed within a 2 T axial magnetic field provided by a superconducting solenoid and measures charged-particle trajectories with silicon pixel and 1ATLAS 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 upward. Cylindrical coordinates (r, φ) are used in the transverse plane, φbeing the azimuthal angle around the beam pipe. The pseudorapidity is defined in terms of the polar angle θas η=−ln tan(θ/2).Angular distance is measured in units of ∆R=q(∆η)2+ (∆φ)2. – 2 – JHEP09(2019)091 silicon microstrip detectors that cover the pseudorapidity range |η|<2.5, and with a strawtube transition radiation tracker covering |η|<2.0. A new innermost pixel layer [32,33] inserted at a radius of 3.3 cm has been used since 2015. Electromagnetic and hadronic calorimeter systems provide energy measurements with high granularity. The electromagnetic calorimeter is a liquid-argon (LAr) sampling calorimeter with lead absorbers, spanning |η|<3.2 with barrel and endcap sections. The threelayer central hadronic calorimeter comprises scintillator tiles with steel absorbers and extends to |η|= 1.7. The hadronic endcap calorimeters measure particles in the region 1.5<|η|<3.2 using liquid argon with copper absorbers. The forward calorimeters cover 3.1<|η|<4.9, using LAr/copper modules for electromagnetic energy measurements and LAr/tungsten modules to measure hadronic energy. The muon spectrometer surrounds the calorimetry system and provides precision muon tracking and triggering. It includes three large superconducting air-core toroids providing a magnetic field for accurate momentum measurements in tracking drift chambers arranged in a barrel, covering |η|<1.0, and endcaps, extending to |η|= 2.7. Events are recorded in ATLAS if they satisfy a two-level trigger requirement [34]. The level-1 trigger detects jet and particle signatures in the calorimeter and muon systems with a fixed latency of 2.5 µs, and is designed to reduce the event rate to about 100 kHz. Jets are identified at level-1 with a sliding-window algorithm, searching for local maxima in square regions with size ∆η×∆φ= 0.8×0.8. The subsequent high-level trigger consists of software-based trigger filters that reduce the event rate to one kHz. 3 Data The search is performed using data collected by the ATLAS experiment from 2015 to 2018 from √s= 13 TeV LHC pp collisions. Events used in this search satisfied a single-jet trigger requiring at least one jet reconstructed at each trigger level. The final filter in the high-level trigger required a jet to satisfy a high transverse momentum (pT) threshold, pT≥360 GeV (2015), pT≥420 GeV (2016), pT≥440 GeV (2017 and 2018), reconstructed with the anti-ktalgorithm [35] and a large radius parameter (R= 1.0). Calorimeter-cell energy clusters calibrated to the hadronic scale utilising the local cell signal weighting method [36] were used as inputs. After requiring that the data were collected during stable beam conditions and the detector components relevant to the analysis were functional, the integrated luminosity was 3.2 fb−1in 2015, 33.0 fb−1in 2016, 44.3 fb−1in 2017 and 58.5 fb−1in 2018. 4 Simulation 4.1 Signal models MC simulation of signal events is used to optimise the sensitivity of the search and to interpret its results. Signals are simulated in three benchmark scenarios. In the first scenario, the gravitational fluctuations in the extra dimension of the Randall-Sundrum framework correspond to scalar fields, known as the radion, which are massless in the simplest scenario. A fundamental problem in the original Randall-Sundrum – 3 – JHEP09(2019)091 framework is that it lacks a mechanism to stabilise the radius of the compactified extra dimension, rc. One possible mechanism to achieve this is to introduce an additional bulk scalar radion, produced via gluon-gluon fusion, which has its interactions localised on the two ends of the extra dimension [37,38]. This causes the radion field to acquire a mass term, which is typically much smaller than the first KK excitation mass. The coupling of the radion field to SM fields scales inversely proportional to the model parameter ΛR=√g×k×e−kπrcqM3 5/k3where M5is the 5-dimensional Planck mass, which has been extensively studied in the literature [29,39,40,40], kthe curvature factor, and g is the 5-dimensional metric. The size of the extra dimension, defined as kπrc, is another parameter of the model. In this analysis, the curvature factor is set to kπrc= 35, and ΛR= 3 TeV is used. The couplings of the radion to fermions are proportional to the masses of the fermions, while the couplings are proportional to the square of the masses for bosons. For radion mass above ∼1 TeV, the dominant decay mode is into pairs of bosons. The decay width of the radion is approximately 10% of its pole mass, resulting in observable mass peaks with a width comparable to the experimental resolution (see section 6.3). The calculated production cross-section times branching ratio (σ×B) for a radion decaying into W W , with the Wdecaying hadronically, is 2.75 fb and 0.26 fb for radion masses of 2 TeV and 3 TeV, respectively. Corresponding values for a radion decaying into ZZ are 1.53 fb and 0.15 fb. The second scenario is based on two benchmark models, A and B, of the HVT phenomenological Lagrangian [30]. The Lagrangian introduces a new heavy vector triplet V0, where V0refers to W0and Z0, produced via quark-antiquark annihilation, whose members are degenerate in mass, and parameterises its couplings to SM fields in a generic manner, such that a large class of extensions to the SM can be described. Model A with the strength of the vector-boson interaction gV= 1 [30] describes scenarios where the new triplet field couples weakly to the SM fields and arises from an extension of the SM gauge group, with the heavy vector bosons having comparable branching ratios into fermions and gauge bosons. For W0and Z0masses of interest, the width of the new heavy bosons is approximately 2.5%, which results in observable mass peaks with a width dominated by the experimental resolution. The branching fraction of the new heavy boson W0(Z0) to each of the W Z and W H (W W and ZH) final states, where Hrepresents the Higgs boson, is approximately 2%. The calculated σ×B values for W0→WZ, with Wand Zbosons decaying hadronically, are 8.3 fb and 0.75 fb for W0masses of 2 TeV and 3 TeV, respectively. Corresponding values for Z0→WW are 3.8 fb and 0.34 fb. Model B with gV= 3 is representative of composite Higgs models, where the fermionic couplings to V0are suppressed. For the W0and Z0masses of interest, the branching fraction of the new heavy boson W0(Z0) to each of the WZ and WH (WW and ZH) final states is close to 50%. Resonance widths and experimental signatures are similar to those obtained for model A and the predicted σ×B values for W0→WZ, with hadronic Wand Zdecays, are 13 fb and 1.3 fb for W0masses of 2 TeV and 3 TeV, respectively. Corresponding values for Z0→WW are 6.0 fb and 0.55 fb. The third scenario considered is the bulk RS model [10] that extends the original RS model [8,41] with a warped extra dimension, by allowing the SM fields to propagate in – 4 – JHEP09(2019)091 the bulk of the extra dimension. This model is characterised by a dimensionless coupling constant κ/MPl ∼1, where κis the curvature of the warped extra dimension, and MPl is the reduced Planck mass. In this model, a Kaluza-Klein graviton, GKK, predominately produced via gluon-gluon fusion, decays into pairs of top quarks, pairs of Higgs bosons, WW and ZZ with significant branching fractions. The branching fraction of the GKK to WW (ZZ) ranges from 24% to 20% (12% to 10%) as the mass increases. The decay width of the GKK is approximately 6% of its pole mass, resulting in observable mass peaks with a width comparable to the experimental resolution, and σ× B for GKK →WW, with the Wdecaying hadronically, is 1.29 fb and 0.06 fb for GKK masses of 2 TeV and 3 TeV, respectively. Corresponding values for GKK →ZZ are 0.65 fb and 0.03 fb. 4.2 Simulated event samples For all MC samples, all hadronic decays were imposed at the generator level. MC samples for the radion, HVT, and RS models, were generated using MadGraph 2.2.2 [42] interfaced to Pythia 8.186 [43] for hadronisation using the leading-order (LO) NNPDF 2.3 parton distribution function (PDF) set [44] and the ATLAS A14 set of tuned parameters for the underlying event [45]. In all signal samples, the Wand Zbosons are primarily longitudinally polarised. The procedure to derive the optimal boson identification criteria (section 6.1) uses a dedicated sample of W0decaying only into W/Z bosons that in turn decay hadronically. Pythia 8.186 was used to generate this sample with the A14 set of tuned parameters for the underlying event and the NNPDF 2.3 LO PDF. The cross-section of the hard-scattering process was modified by applying an event-by-event weighting factor to broaden the width of the resonance and widen the pTdistribution of the electroweak bosons produced in its hadronic decays. Pythia 8.186 with the NNPDF 2.3 LO PDF set and the A14 set of tuned parameters was used to generate and shower multijet background events. Samples of W+jets and Z+jets events were generated with Sherpa 2.2.5 [46–49] interfaced with the NNPDF 3.0 next-to-next-to-leading-order (NNLO) PDF set [50]. A tt sample generated with PowhegBox v2 [51–53] with the NNPDF 3.0 next-to-leading-order (NLO) PDF [54], interfaced with Pythia 8.186 with the NNPDF 2.3 LO PDF and the A14 set of tuned parameters for parton showering is used for the V+jets study. EvtGen v1.2.0 [55] was used for properties of bottom and charm hadron decays, except for samples generated by Sherpa. For all MC samples, the final-state particles produced by the generators were propagated through a detailed detector simulation based on GEANT4 [56,57]. The mean number of pp interactions per bunch crossing, ‘pile-up’, was approximately 33 in the collision data being used for the analysis. The expected contribution from these minimum-bias pp interactions was accounted for by overlaying additional minimum-bias events generated with Pythia 8.186 using the ATLAS A3 [58] set of tuned parameter. The MC simulation events were weighted to match the distribution of the average number of interactions per bunch crossing observed in collision data. Simulated events were then reconstructed with the same algorithms as run on the collision data. – 5 – JHEP09(2019)091 5 Reconstruction The experimental signatures central to this analysis are hadronic jets. Since the decay products of TeV-scale resonances are highly boosted, their decay products become increasingly collimated and they are therefore reconstructed as a single large-radius jet. It is important that they can still be differentiated from multijet events where a jet is initiated by a single quark or gluon. This relies on both the energy and angular resolution of the detector used to reconstruct the jet. Although the analysis primarily relies on jets, reconstruction of lepton candidates is necessary to reject events that could bias the SM V+jets studies presented in section 6.2. 5.1 Track-CaloClusters In previous analyses, ATLAS has mainly focused on the use of calorimeter-based jet substructure, which exploits the exceptional energy resolution of the ATLAS calorimetry [36]. However, as the event becomes even more energetic, jets become so collimated that the calorimeter lacks the angular resolution to resolve the desired structure inside the jet. For boson jets with high transverse momentum, pT, only a handful of calorimeter-cell clusters are created, each with limited angular resolution, but excellent energy resolution. On the other hand, the tracking detector has excellent angular resolution and good reconstruction efficiency at very high energy [59], while its momentum resolution deteriorates. By combining information from the ATLAS calorimeter and tracking detectors, the precision of jet substructure techniques can be improved for a wide range of energies. This analysis uses a new unified object built from both the tracking and the calorimeter information, referred to as Track-CaloClusters (TCCs) [28]. This procedure is a type of particle flow, complementary to the energy subtraction algorithm described in the ATLAS particle flow paper [60] which improves the energy resolution of low-energy jets. The two algorithms are designed to improve the jet reconstruction performance in very different energy regimes, reflected in their distinct four-momentum construction and energy sharing procedures. Energy sharing in the TCC approach is based solely on a weighting scheme where only the relative track momenta are used to spatially redistribute the energy measured in the calorimeter. In practice, this means that the TCC algorithm uses the spatial coordinates of the tracker and the energy scale of the calorimeter. A more detailed description of TCCs can be found in ref. [28]. 5.2 Jet reconstruction This analysis uses anti-kt,R= 1.0 jets reconstructed from both the combined and the neutral TCCs. Combined TCCs are four-momenta created by combining the angular information of tracks with the energy information of the calorimeters. Neutral TCCs are calorimeter topo-clusters that could not be matched to any track, most likely representing energy deposits from neutral particles. The use of combined and neutral TCCs captures most of the hard-scatter energy and provides the best representation of the total energy flow in the event, as there are both charged and neutral contributions. The combined TCC component is robust against effects from pile-up since only tracks consistent with coming – 6 – JHEP09(2019)091 500 1000 1500 2500 [GeV] T 0 0.05 0.1 0.15 0.2 0.25 0.3 Fractional jet mass resolution LC Topo (m ) comb TCCs ATLAS Simulation qqqq→ R=1.0, WZ T anti k >200 GeV T jet |<2.0, p jet η| = 13 TeVs Generated jet p 2000 (a) 500 1000 1500 2500 [GeV] T 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Jet D2 resolution LC Topo TCCs ATLAS Simulation qqqq→ R=1.0, WZ T anti k >200 GeV T jet |<2.0, p jet η| = 13 TeVs Generated jet p 2000 (b) Figure 1. A comparison of (a) the fractional jet mass resolution for jets built from a linear combination of the calorimeter and track-only mass (LCTopo mcomb, solid line), and jets built using combined and neutral Track-CaloClusters objects (dashed lines) as a function of Monte Carlo generator-level jet pT. The fractional jet resolution of the D2variable (b) is compared between Track-CaloClusters and pure calorimeter jets. Only the two jets with the highest pTper event matched to a generated jet from a Wor Zboson are shown. from the primary vertex2are used. However, by including the neutral TCCs, these jets have a pile-up dependence similar to that of standard topo-cluster jets. Jets are therefore trimmed [61] to remove contributions from pile-up by removing any R= 0.2 subjet with less than 5% of the pTof the associated R= 1.0 jet. The clustering and trimming algorithms use the FastJet package [62]. The combination of pile-up suppression through track-to-primary-vertex matching and trimming makes these jets very robust against pileup [28]. A MC-based particle-level energy and mass calibration is applied to the jets, as described in ref. [63]. MC generator-level jets are built using the same algorithm and trimming procedure, with inputs of stable generator-level particles (cτ > 10 mm) excluding muons and neutrinos, and excluding particles from pile-up. These serve as the reference in figure 1. The energy and mass of MC generator-level jets also serves as reference to which reconstructed detector-level jets are corrected to in the above mentioned calibration procedure. Consequently, the mass of jets from Vbosons is not expected to match the pole mass of the bosons. Several jet properties can be used to discriminate hadronic decays of Wand Zbosons from background jets. Two providing strong discrimination are the jet mass and D2,3 where the latter is defined as a ratio of two-point to three-point energy correlation functions that are based on the energies of the jets’ constituents and their pairwise angular separations [64]. Signal jets are expected to peak at D2values below one, while jets from multijet background have significantly larger values. The radiation of a hard gluon can allow background jets to mimic a two-pronged structure and satisfy the tagging require2If more than one vertex is reconstructed, the one with the highest sum of p2 Tof the associated tracks is regarded as the primary vertex. 3The angular exponent β, defined in ref. [64], is set to unity. – 7 – JHEP09(2019)091 ments described above. Discrimination between boson jets and multijet background from such gluon-initiated jets can be attained by selecting on the charged hadron multiplicity, in form of the track multiplicity (ntrk) of the untrimmed R= 1.0 jet, considering tracks with pT>0.5 GeV consistent with coming from the primary vertex. Figure 1shows the striking improvement in D2resolutions4achieved with TCC jets. The mass resolution is superior to previously used jet mass variables (mcomb [65]) starting around a jet pTof 2 TeV. Below 1 TeV, the mass resolution in TCC jets is degraded. For identifying hadronically decaying Vbosons, the improvement in D2resolution far outweighs the slight degradation in mass resolution. 5.3 Leptons Electron identification is based on matching tracks to energy clusters in the electromagnetic calorimeter and calculating a likelihood based on several properties of the electron candidate. Electrons are required to have pT>25 GeV and |η|<2.5, and to satisfy the ‘medium’ identification criterion [66] and the ‘loose’ track-based isolation [66]. Muon identification relies on matching tracks in the inner detector to muon spectrometer tracks or track segments. Muons are required to have pT>25 GeV and |η|<2.5, and to satisfy the ‘loose’ selection criterion [67] and the ‘loose’ track isolation [67]. 6 Event selection To avoid contamination from non-collision backgrounds such as from calorimeter noise, beam halo, and cosmic rays, events containing an anti-ktjet built from calorimeter-cell clusters with R= 0.4 and pT>20 GeV failing to meet the loose quality criteria for consistency with production in pp collisions are rejected [68]. In addition, events with at least one lepton meeting the requirements defined in section 5.3 are rejected. There are no further requirements on leptons that are aligned with jets. Events are required to have at least two anti-kt,R= 1.0, jets originating from the primary vertex, one with pT>500 GeV and the second with pT>200 GeV. The leading (highest pT) and subleading of these jets must satisfy |η|<2.0 (to guarantee a good overlap with the tracking acceptance), have masses mJ>50 GeV, and their invariant mass, mJJ, must be larger than 1.3 TeV. The last requirement ensures that the triggers in use are fully efficient for the backgrounds and the benchmark signals. These selections are referred to as pre-selections. The pair of jets is then required to have a small separation in rapidity, |∆y12|<1.2. This requirement reduces the multijet background, which is mainly produced in t-channel processes with large rapidity differences, in contrast to signal events that are expected to be produced in s-channel processes with small rapidity differences. Additionally, to reject events with potentially badly reconstructed jets, a criterion is applied to the pTasymmetry, 4The resolution is defined as Rr= [Q84(Rr)−Q16(Rr)]/[2×Q50(Rr)] and Rd= 1/2hQ75(Rd)−Q25(Rd)i for the mass and D2, respectively, where Qxis the x% quantile boundary, meaning that Q50 is the median. The mass response is defined as Rr=mreco/mgen, while the residual of D2is Rd=D2,reco −D2,gen, where ‘gen’ and ‘reco’ refer to the generated and reconstructed properties of the jets. – 8 – JHEP09(2019)091 Figure 6. Four orthogonal regions used to build the fit control region for each signal region. A: |∆y12|>1.2 with both the jets boson-tagged, B: |∆y12|<1.2 with both the jets boson-tagged (this is the nominal signal region), C: |∆y12|>1.2 with the event not boson-tagged, D: |∆y12|<1.2 with the event not boson-tagged. Regions A and C are used to derive a per-event transfer factor from region D to the fit control region, which is representative of region B. A and C are also signal-depleted due to the |∆y12|>1.2 requirement. Events / 0.1 TeV 3− 10 2− 10 1− 10 1 10 2 10 3 10 4 10 Data Fit ATLAS s = 13 TeV, 139 fb-1 WZ CR /DOF = 3.9/5 2 χ [TeV] JJ m 1.5 2 2.5 3 3.5 4 4.5 5 Significance 2− 0 2 Figure 7. Comparison between fitted background shape and the mJJ spectra in an example WZ fit control region in data. The fitted background distribution is normalised to the data shown in the displayed mass range. The shaded bands represent the uncertainty in the background expectation calculated from the maximum-likelihood function. The lower panel shows the significance, defined as the z-value as described in ref. [72]. combined with bins that contain at least five events to compute the number of degrees of freedom (NDF). On average, the χ2/NDF is equal to unity with no cases for which the fit fails. Figure 7shows the fit result performed in an example WZ fit CR. Similar results are obtained for the other CRs, confirming the ability of the chosen background fit function (eq. (7.1)) to describe the expected background dijet mass spectra in the SRs. It is validated on both the data and the simulation that this parametric background description is valid up to 8.0 TeV, which is also the mass up to which the observed mJJ spectra are fit. The statistical uncertainty in the background expectation comes directly from the uncertainty in the fitted parameters of the background function, which assumes a smoothly falling mJJ distribution. Possible additional uncertainties due to the background model are – 15 – JHEP09(2019)091 assessed by considering signal-plus-background fits (also called spurious signal tests) of the chosen function to the fit control regions of data in which a signal contribution is expected to be negligible. The background is modelled with eq. (7.1) and the signal is modelled using resonance mass distributions from simulation. These procedures were estimated to introduce a bias smaller than 25% of the statistical uncertainty in the background estimate at any mass in the search region, and no additional uncertainty is assigned. 8 Systematic uncertainties The uncertainties affecting the background modelling are taken directly from the errors in the fit parameters of the background estimation procedure described in section 7. The systematic uncertainties in the expected signal yield and shapes arise from detector effects and MC modelling and are assessed and expressed in terms of nuisance parameters in the statistical analysis as described in section 9.2. The dominant sources of uncertainty in the signal modelling arise from uncertainties in the large-Rjet tagging efficiency and the jet pTcalibration. These two uncertainties, and the uncertainty in the fitted background, are also the only ones significantly affecting the statistical results. The uncertainty in the jet pTscale (JpTS) is evaluated using track-to-calorimeter double ratios between data and simulation [73]. The ratio of the calorimeter and track measures of jet pTis expected to be the same in data and simulation and any observed differences are assigned as baseline systematic uncertainties. Uncertainties obtained from this procedure assume no correlation between the two pTmeasures, while any residual correlation would modify them by a certain factor. An upper limit to the correlation between the two pTmeasures is found to be at the percent level by comparing the results of this double-ratio procedure between jets built from TCC inputs and jets built from calorimeter-only inputs. Additional uncertainties due to the track reconstruction efficiency, track impact parameter resolution, and track fake rate are taken into account. The size of the total JpTS uncertainty varies with jet pTand is between 2.5% and 5% for the full mass range. The impact of the jet pTresolution uncertainty is evaluated event-by-event by rerunning the analysis with an additional Gaussian smearing applied to the input jets’ pT to degrade the nominal resolution by the systematic uncertainty value. The systematic uncertainty in the width of the Gaussian distribution is an absolute 2% per jet, and is symmetrised. Uncertainty in the jet mass scale and resolution influences the observed jet mass, affecting the boson-tagging efficiency. Any uncertainty in the value of the boson-tagging discriminant D2or ntrk, would also affect the selection efficiency of the analysis. A scalefactor for the W/Z-tagging efficiency is derived as described in section 6.2. The changes to the overall yield is hence corrected by 0.85+0.23 −0.21 per event with the boson-tagging efficiency scale-factor, assuming full correlation between the two jets. The uncertainty in the scalefactor is assigned as a two-sided variation in the yield. Additional studies comparing jet properties in data and simulation confirm this uncertainty to be valid up to 7.0 TeV. – 16 – JHEP09(2019)091 The uncertainty in the combined 2015–2018 integrated luminosity is 1.7% [74], obtained using the LUCID-2 detector [75] for the primary luminosity measurements. The uncertainty from the trigger selection is found to be negligible, as the minimum requirement on the dijet invariant mass of 1.3 TeV guarantees that the trigger is fully efficient. Uncertainties in the behaviour of the PDFs at high Q2values can potentially have a large effect on the signal acceptance. This systematic uncertainty is estimated by taking the envelope formed by the largest deviations produced by the errors of three PDF sets, as set out by the PDF4LHC group [76]. A constant 1% uncertainty is applied in the case of the RS and radion models, and a pole-mass-dependent uncertainty ranging from 1%–12% is applied in the case of the HVT model. Systematic variations are used to cover uncertainties in the A14 tuned parameter values describing initial-state radiation, final-state radiation, and multi-parton interactions. The uncertainty in the signal acceptance is evaluated at the generator level, before boson-tagging requirements. Following the same procedure as for the PDFs, constant uncertainties of 3% (5%) are applied for the HVT (RS and radion) models. 9 Results 9.1 Background fit Figure 8shows the comparison of the dijet mass distributions of the selected events in the combined WW +WZ and WW +ZZ signal regions with the expected background distribution from the background-only fits to the data. The fitted background functions shown, labelled ‘Fit’, are evaluated in bins between 1.3 TeV and 8.0 TeV. No events are observed beyond 5.0 TeV. A total of 119 and 113 events are observed above 1.3 TeV in the WW +WZ and WW +ZZ signal regions, respectively. Due to the non-exclusive selections of the boson taggers, about 50% of events satisfying the W W selection also satisfy the ZZ selection. The highest mass event at 4.4 TeV is the same for both signal regions, and it is compatible with the background expectation in the high mass region. 9.2 Statistical analysis In the statistical analysis, the parameter of interest is the signal strength, which is defined as a scale-factor to the predicted signal normalisation of the model being tested. The analysis follows the frequentist approach with a test statistic based on the profile-likelihood ratio [77]. The test statistic extracts information about the signal strength from the binned maximum-likelihood fit of the signal-plus-background model to the data. The likelihood model is defined as, L=Y i Ppois(ni obs|ni exp)×G(α)×N(θ) where Ppois(ni obs|ni exp) is the Poisson probability to observe ni obs events if ni exp events are expected, G(α) are a series of Gaussian probability density functions modelling the systematic uncertainties, α, related to the shape of the signal, and N(θ) is a log-normal distribution for the nuisance parameters, θ, modelling the systematic uncertainty in the – 17 – JHEP09(2019)091 signal normalisation. The expected number of events is the bin-wise sum of those expected for the signal and background: nexp =nsig +nbg. The expected number of background events in dijet mass bin i,ni bg, is obtained by integrating dn/dxobtained from eq. (7.1) over that bin. Thus nbg is a function of the dijet background parameters p1,p2and p3. The expected number of signal events, nsig, is evaluated from MC simulation assuming the cross-section of the model under test multiplied by the signal strength, including the effects of the systematic uncertainties described in section 8. The significance of observed excesses over the background-only prediction is quantified using the local p0-value, defined as the probability of the background-only model to produce a signal-like fluctuation at least as large as that observed in the data. The most extreme p0 has a local significance of 1.8 standard deviations, and is found when testing the HVT W0→WW hypothesis at a resonance mass of 1.8 TeV. This is within the expected fluctuation of the background. Limits at 95% confidence level (CL) on the production cross-section times branching fraction to diboson final states for the benchmark signals are set with sampling distributions generated using pseudo-experiments. All systematic uncertainties are considered. The uncertainty in the W/Z-tagging efficiency is dominant at lower masses, while the uncertainty in the background modelling has largest impact at high masses. Uncertainties in the jet pTscale are at the percent level but are subordinate across the full mass range. The cross-section limits extracted for the different benchmark scenarios in the WW +WZ and WW +ZZ signal regions are shown in figure 9and table 2. Table 3presents the resonance mass ranges excluded at the 95% CL in the various signal regions and signal models considered in the search. 10 Conclusion A search for narrow heavy resonances decaying into dibosons in the all hadronic channel is performed using 139 fb−1of proton-proton collisions at √s= 13 TeV collected by the ATLAS experiment at the LHC from 2015 to 2018. The results of the search are shown for the WW +WZ,WW +ZZ channels, and are interpreted in terms of a radion model, two HVT benchmark models, and a bulk GKK model. The data are in agreement with the background expectations in all channels. Upper limits on the production cross-section times branching ratio to diboson final states for new resonances with masses greater than 1.3 TeV are set at the 95% CL. These results exclude at the 95% CL the production of WW +WZ from the HVT model A (model B) with gV= 1 (gV= 3) with masses in the range of 1.3 TeV–3.5 TeV (1.3 TeV–3.8 TeV). Production of a GKK in the bulk RS model with k/MPl = 1 is excluded in the range 1.3 TeV–1.8 TeV, at the 95% CL. Upper limits on the production cross section times branching ratio for a scalar-like radion are set with values of 5.72 fb and 1.86 fb at scalar masses of 2 TeV and 3 TeV, respectively. Acknowledgments 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. – 18 – JHEP09(2019)091 Events / 0.1 TeV 3− 10 2− 10 1− 10 1 10 2 10 3 10 4 10 Data Fit Fit + HVT model A m=2.0 TeV Fit + HVT model A m=3.5 TeV WZ or WW SR /DOF = 6.0/4 2 χ [TeV] JJ m 1.5 2 2.5 3 3.5 4 4.5 5 Significance 2− 0 2 ATLAS s = 13 TeV, 139 fb-1 (a) Events / 0.1 TeV 3− 10 2− 10 1− 10 1 10 2 10 3 10 4 10 Data Fit Fit + Bulk RS m=1.5 TeV Fit + Bulk RS m=2.6 TeV ZZ or WW SR /DOF = 3.1/3 2 χ [TeV] JJ m 1.5 2 2.5 3 3.5 4 4.5 5 Significance 2− 0 2 ATLAS s = 13 TeV, 139 fb-1 (b) Figure 8. Background-only fits to the dijet mass (mJJ) distributions in data after tagging in the combined (a) WW +W Z, and (b) WW +ZZ signal region. The shaded bands represent the uncertainty in the background expectation calculated from the maximum-likelihood function. The lower panels show the significance, defined as the z-value as described in ref. [68]. Selected theoretical signal distributions are overlaid on top of the background. m(V’) [TeV] 1.5 2 2.5 3 3.5 4 4.5 5 WW+WZ) [fb]→ B(V’ × V’) →(pp σ 2− 10 1− 10 1 10 2 10 3 10 4 10 ATLAS -1 = 13 TeV, 139 fbs qqqq→VV Observed 95% CL upper limit Expected 95% CL upper limit = 1 V HVT model A, g = 3 V HVT model B, g (a) ) [TeV] KK m(G 1.5 2 2.5 3 3.5 4 4.5 5 WW+ZZ) [fb]→ KK B(G×) KK G→(pp σ 2− 10 1− 10 1 10 2 10 3 10 4 10 ATLAS -1 = 13 TeV, 139 fbs qqqq→VV Observed 95% CL upper limit Expected 95% CL upper limit = 1 PI MBulk RS, k/ (b) Figure 9. Observed and expected limits at 95% CL on the cross-section times branching ratio for WW +WZ production as a function of (a) mV0, and for WW +ZZ production as a function of (b) the Bulk RS graviton mGKK . The predicted cross-section times branching ratio is shown (a) as dashed and solid lines for the HVT models A with gV= 1 and B with gV= 3, respectively, and (b) as a solid line for the bulk RS model with k/MPl = 1. Mass [TeV] Observed Limit [fb] Expected Limit [fb] Prediction [fb] 2.0 5.72 5.75 4.286 3.0 1.86 2.85 0.415 4.0 1.98 2.34 0.040 5.0 1.98 2.02 0.006 Table 2. Observed and expected limits at 95% CL on cross-section times branching ratio for WW +ZZ production for different radion masses mradion, as well as the predicted cross-section times branching ratio. – 19 – JHEP09(2019)091 Model Signal Region Excluded mass range [TeV] WW none Radion ZZ none WW +ZZ none WW 1.3–2.9 HVT model A, gV= 1 WZ 1.3–3.4 WW +WZ 1.3–3.5 WW 1.3–3.1 HVT model B, gV= 3 WZ 1.3–3.6 WW +WZ 1.3–3.8 WW 1.3–1.6 Bulk RS, k/MPl = 1 ZZ none WW +ZZ 1.3–1.8 Table 3. Observed excluded resonance masses (at 95% CL) in the individual and combined signal regions for the HVT, bulk RS and radion models. 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 MIZˇ S, 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 Sk lodowska-Curie Actions, European Union; Investissements d’ Avenir Labex and Idex, ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and GIF, Israel; CERCA Programme Generalitat de Catalunya, 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 (U.K.) and BNL (U.S.A.), the Tier-2 facilities worldwide and large non-WLCG resource providers. Major contributors of computing resources are listed in ref. [78]. – 20 – JHEP09(2019)091 Open Access. This article is distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited. References [1] G. Altarelli, B. Mele and M. Ruiz-Altaba, Searching for New Heavy Vector Bosons in p¯p Colliders,Z. Phys. C 45 (1989) 109 [Erratum ibid. C 47 (1990) 676] [INSPIRE]. [2] E. Eichten, I. Hinchliffe, K.D. Lane and C. Quigg, Super Collider Physics,Rev. Mod. Phys. 56 (1984) 579 [INSPIRE]. [3] C. Quigg, Gauge Theories of the Strong, Weak, and Electromagnetic Interactions, Princeton University Press, Princeton U.S.A. (2013). [4] J.C. Pati and A. Salam, Lepton Number as the Fourth “Color”,Phys. Rev. D 10 (1974) 275 [Erratum ibid. D 11 (1975) 703] [INSPIRE]. [5] H. Georgi and S.L. Glashow, Unity of All Elementary Particle Forces,Phys. Rev. Lett. 32 (1974) 438 [INSPIRE]. [6] H. Georgi, The State of the Art — Gauge Theories,AIP Conf. Proc. 23 (1975) 575 [INSPIRE]. [7] H. Fritzsch and P. Minkowski, Unified Interactions of Leptons and Hadrons,Annals Phys. 93 (1975) 193 [INSPIRE]. [8] L. Randall and R. Sundrum, A Large mass hierarchy from a small extra dimension,Phys. Rev. Lett. 83 (1999) 3370 [hep-ph/9905221] [INSPIRE]. [9] T. Han, J.D. Lykken and R.-J. Zhang, On Kaluza-Klein states from large extra dimensions, Phys. Rev. D 59 (1999) 105006 [hep-ph/9811350] [INSPIRE]. [10] K. Agashe, H. Davoudiasl, G. Perez and A. Soni, Warped Gravitons at the LHC and Beyond, Phys. Rev. D 76 (2007) 036006 [hep-ph/0701186] [INSPIRE]. [11] O. Antipin, D. Atwood and A. Soni, Search for RS gravitons via WLWLdecays,Phys. Lett. B 666 (2008) 155 [arXiv:0711.3175] [INSPIRE]. [12] O. Antipin and A. Soni, Towards establishing the spin of warped gravitons,JHEP 10 (2008) 018 [arXiv:0806.3427] [INSPIRE]. [13] G.C. Branco, P.M. Ferreira, L. Lavoura, M.N. Rebelo, M. Sher and J.P. Silva, Theory and phenomenology of two-Higgs-doublet models,Phys. Rept. 516 (2012) 1 [arXiv:1106.0034] [INSPIRE]. [14] M. Perelstein, Little Higgs models and their phenomenology,Prog. Part. Nucl. Phys. 58 (2007) 247 [hep-ph/0512128] [INSPIRE]. [15] F. Sannino, Technicolor and Beyond: Unification in Theory Space,J. Phys. Conf. Ser. 259 (2010) 012003 [arXiv:1010.3461] [INSPIRE]. [16] E. Eichten and K. Lane, Low-scale technicolor at the Tevatron and LHC,Phys. Lett. B 669 (2008) 235 [arXiv:0706.2339] [INSPIRE]. [17] S. Catterall, L. Del Debbio, J. Giedt and L. Keegan, MCRG Minimal Walking Technicolor, Phys. Rev. D 85 (2012) 094501 [arXiv:1108.3794] [INSPIRE]. [18] J.R. Andersen et al., Discovering Technicolor,Eur. Phys. J. Plus 126 (2011) 81 [arXiv:1104.1255] [INSPIRE]. – 21 – JHEP09(2019)091 [19] D. Barducci, A. Belyaev, S. Moretti, S. De Curtis and G.M. Pruna, LHC physics of extra gauge bosons in the 4DComposite Higgs Model,EPJ Web Conf. 60 (2013) 20049 [arXiv:1307.1782] [INSPIRE]. [20] ATLAS collaboration, Search for diboson resonances with boson-tagged jets in pp collisions at √s= 13 TeV with the ATLAS detector,Phys. Lett. B 777 (2018) 91 [arXiv:1708.04445] [INSPIRE]. [21] CMS collaboration, Search for massive resonances decaying into W W,WZ,ZZ,qW and qZ with dijet final states at √s= 13 TeV, Phys. Rev. D 97 (2018) 072006 [arXiv:1708.05379] [INSPIRE]. [22] CMS collaboration, A multi-dimensional search for new heavy resonances decaying to boosted WW, WZ, or ZZ boson pairs in the dijet final state at 13 TeV,arXiv:1906.05977 [INSPIRE]. [23] ATLAS collaboration, Search for WW/WZ resonance production in `νqq final states in pp collisions at √s= 13 TeV with the ATLAS detector,JHEP 03 (2018) 042 [arXiv:1710.07235] [INSPIRE]. [24] ATLAS collaboration, Searches for heavy ZZ and ZW resonances in the ``qq and ννqq final states in pp collisions at √s= 13 TeV with the ATLAS detector,JHEP 03 (2018) 009 [arXiv:1708.09638] [INSPIRE]. [25] CMS collaboration, Search for a heavy resonance decaying into a Z boson and a Z or W boson in 2`2qfinal states at √s= 13 TeV,JHEP 09 (2018) 101 [arXiv:1803.10093] [INSPIRE]. [26] CMS collaboration, Search for a heavy resonance decaying into a Z boson and a vector boson in the ννqq final state,JHEP 07 (2018) 075 [arXiv:1803.03838] [INSPIRE]. [27] ATLAS collaboration, Combination of searches for heavy resonances decaying into bosonic and leptonic final states using 36 fb−1of proton-proton collision data at √s= 13 TeV with the ATLAS detector,Phys. Rev. D 98 (2018) 052008 [arXiv:1808.02380] [INSPIRE]. [28] ATLAS collaboration, Improving jet substructure performance in ATLAS using Track-CaloClusters,ATL-PHYS-PUB-2017-015 (2017). [29] A. Oliveira, Gravity particles from Warped Extra Dimensions, predictions for LHC, arXiv:1404.0102 [INSPIRE]. [30] D. Pappadopulo, A. Thamm, R. Torre and A. Wulzer, Heavy Vector Triplets: Bridging Theory and Data,JHEP 09 (2014) 060 [arXiv:1402.4431] [INSPIRE]. [31] ATLAS collaboration, The ATLAS Experiment at the CERN Large Hadron Collider,2008 JINST 3S08003 [INSPIRE]. [32] ATLAS IBL collaboration, Production and Integration of the ATLAS Insertable B-Layer, 2018 JINST 13 T05008 [arXiv:1803.00844] [INSPIRE]. [33] ATLAS collaboration, ATLAS Insertable B-Layer Technical Design Report, CERN-LHCC-2010-013 (2010) [INSPIRE]. [34] ATLAS collaboration, Performance of the ATLAS Trigger System in 2015,Eur. Phys. J. C 77 (2017) 317 [arXiv:1611.09661] [INSPIRE]. [35] M. Cacciari, G.P. Salam and G. Soyez, The anti-ktjet clustering algorithm,JHEP 04 (2008) 063 [arXiv:0802.1189] [INSPIRE]. – 22 – JHEP09(2019)091 [36] ATLAS collaboration, Topological cell clustering in the ATLAS calorimeters and its performance in LHC Run 1,Eur. Phys. J. C 77 (2017) 490 [arXiv:1603.02934] [INSPIRE]. [37] W.D. Goldberger and M.B. Wise, Modulus stabilization with bulk fields,Phys. Rev. Lett. 83 (1999) 4922 [hep-ph/9907447] [INSPIRE]. [38] W.D. Goldberger and M.B. Wise, Phenomenology of a stabilized modulus,Phys. Lett. B 475 (2000) 275 [hep-ph/9911457] [INSPIRE]. [39] V. Barger and M. Ishida, Randall-Sundrum Reality at the LHC,Phys. Lett. B 709 (2012) 185 [arXiv:1110.6452] [INSPIRE]. [40] C. Cs´aki, J. Hubisz and S.J. Lee, Radion phenomenology in realistic warped space models, Phys. Rev. D 76 (2007) 125015 [arXiv:0705.3844] [INSPIRE]. [41] L. Randall and R. Sundrum, An Alternative to compactification,Phys. Rev. Lett. 83 (1999) 4690 [hep-th/9906064] [INSPIRE]. [42] J. Alwall et al., The automated computation of tree-level and next-to-leading order differential cross sections and their matching to parton shower simulations,JHEP 07 (2014) 079 [arXiv:1405.0301] [INSPIRE]. [43] T. Sj¨ostrand, S. Mrenna and P.Z. Skands, A Brief Introduction to PYTHIA 8.1,Comput. Phys. Commun. 178 (2008) 852 [arXiv:0710.3820] [INSPIRE]. [44] S. Carrazza, S. Forte and J. Rojo, Parton Distributions and Event Generators, in proceedings of the 43rd International Symposium on Multiparticle Dynamics (ISMD 2013) Chicago, Illinois, U.S.A., 15–20 September 2013, pp. 89–96 [arXiv:1311.5887] [INSPIRE]. [45] ATLAS collaboration, ATLAS Run 1 PYTHIA8 tunes,ATL-PHYS-PUB-2014-021 (2014). [46] T. Gleisberg et al., Event generation with SHERPA 1.1,JHEP 02 (2009) 007 [arXiv:0811.4622] [INSPIRE]. [47] S. H¨oche, F. Krauss, S. Schumann and F. Siegert, QCD matrix elements and truncated showers,JHEP 05 (2009) 053 [arXiv:0903.1219] [INSPIRE]. [48] T. Gleisberg and S. H¨oche, Comix, a new matrix element generator,JHEP 12 (2008) 039 [arXiv:0808.3674] [INSPIRE]. [49] S. Schumann and F. Krauss, A Parton shower algorithm based on Catani-Seymour dipole factorisation,JHEP 03 (2008) 038 [arXiv:0709.1027] [INSPIRE]. [50] H.-L. Lai et al., New parton distributions for collider physics,Phys. Rev. D 82 (2010) 074024 [arXiv:1007.2241] [INSPIRE]. [51] P. Nason, A New method for combining NLO QCD with shower Monte Carlo algorithms, JHEP 11 (2004) 040 [hep-ph/0409146] [INSPIRE]. [52] S. Frixione, P. Nason and C. Oleari, Matching NLO QCD computations with Parton Shower simulations: the POWHEG method,JHEP 11 (2007) 070 [arXiv:0709.2092] [INSPIRE]. [53] S. Alioli, P. Nason, C. Oleari and E. Re, A general framework for implementing NLO calculations in shower Monte Carlo programs: the POWHEG BOX,JHEP 06 (2010) 043 [arXiv:1002.2581] [INSPIRE]. [54] NNPDF collaboration, Parton distributions for the LHC Run II,JHEP 04 (2015) 040 [arXiv:1410.8849] [INSPIRE]. – 23 – JHEP09(2019)091 [55] D.J. Lange, The EvtGen particle decay simulation package,Nucl. Instrum. Meth. A 462 (2001) 152 [INSPIRE]. [56] GEANT4 collaboration, GEANT4: A Simulation toolkit,Nucl. Instrum. Meth. A 506 (2003) 250 [INSPIRE]. [57] ATLAS collaboration, The ATLAS Simulation Infrastructure,Eur. Phys. J. C 70 (2010) 823 [arXiv:1005.4568] [INSPIRE]. [58] ATLAS collaboration, A study of the PYTHIA 8 description of ATLAS minimum bias measurements with the Donnachie-Landshoff diffractive model,ATL-PHYS-PUB-2016-017 (2016). [59] ATLAS collaboration, Performance of the ATLAS Track Reconstruction Algorithms in Dense Environments in LHC Run 2,Eur. Phys. J. C 77 (2017) 673 [arXiv:1704.07983] [INSPIRE]. [60] ATLAS collaboration, Jet reconstruction and performance using particle flow with the ATLAS Detector,Eur. Phys. J. C 77 (2017) 466 [arXiv:1703.10485] [INSPIRE]. [61] D. Krohn, J. Thaler and L.-T. Wang, Jet Trimming,JHEP 02 (2010) 084 [arXiv:0912.1342] [INSPIRE]. [62] M. Cacciari, G.P. Salam and G. Soyez, FastJet User Manual,Eur. Phys. J. C 72 (2012) 1896 [arXiv:1111.6097] [INSPIRE]. [63] ATLAS collaboration, Identification of boosted, hadronically-decaying Wand Zbosons in √s= 13 TeV Monte Carlo Simulations for ATLAS,ATL-PHYS-PUB-2015-033 (2015). [64] A.J. Larkoski, G.P. Salam and J. Thaler, Energy Correlation Functions for Jet Substructure, JHEP 06 (2013) 108 [arXiv:1305.0007] [INSPIRE]. [65] ATLAS collaboration, Performance of top-quark and W-boson tagging with ATLAS in Run 2 of the LHC,Eur. Phys. J. C 79 (2019) 375 [arXiv:1808.07858] [INSPIRE]. [66] ATLAS collaboration, Electron and photon performance measurements with the ATLAS detector using the 2015–2017 LHC proton-proton collision data,arXiv:1908.00005 [INSPIRE]. [67] ATLAS collaboration, Muon reconstruction performance of the ATLAS detector in proton-proton collision data at √s= 13 TeV,Eur. Phys. J. C 76 (2016) 292 [arXiv:1603.05598] [INSPIRE]. [68] ATLAS collaboration, Selection of jets produced in 13 TeV proton-proton collisions with the ATLAS detector,ATLAS-CONF-2015-029 (2015) [INSPIRE]. [69] G. Punzi, Sensitivity of searches for new signals and its optimization,eConf C 030908 (2003) MODT002 [physics/0308063] [INSPIRE]. [70] J.M. Lindert et al., Precise predictions for V+jets dark matter backgrounds,Eur. Phys. J. C 77 (2017) 829 [arXiv:1705.04664] [INSPIRE]. [71] S.S. Wilks, The large-sample distribution of the likelihood ratio for testing composite hypotheses,Annals Math. Statist. 9(1938) 60 [INSPIRE]. [72] G. Choudalakis and D. Casadei, Plotting the Differences Between Data and Expectation,Eur. Phys. J. Plus 127 (2012) 25 [arXiv:1111.2062] [INSPIRE]. – 24 – JHEP09(2019)091 V. Konstantinides94, N. Konstantinidis94, B. Konya96, R. Kopeliansky65, S. Koperny83a, K. Korcyl84, K. Kordas162, G. Koren161, A. Korn94, I. Korolkov14, E.V. Korolkova149, N. Korotkova113, O. Kortner115, S. Kortner115, T. Kosek143, V.V. Kostyukhin24, A. Kotwal49, A. Koulouris10, A. Kourkoumeli-Charalampidi70a,70b, C. Kourkoumelis9, E. Kourlitis149, V. Kouskoura29, A.B. Kowalewska84, R. Kowalewski176, C. Kozakai163, W. Kozanecki145, A.S. Kozhin123, V.A. Kramarenko113, G. Kramberger91, D. Krasnopevtsev60a, M.W. Krasny136, A. Krasznahorkay36, D. Krauss115, J.A. Kremer83a, J. Kretzschmar90, P. Krieger167, F. Krieter114, A. Krishnan61b, K. Krizka18, K. Kroeninger47, H. Kroha115, J. Kroll141, J. Kroll137, J. Krstic16, U. Kruchonak79, H. Kr¨uger24, N. Krumnack78, M.C. Kruse49, J.A. Krzysiak84, T. Kubota104, S. Kuday4b, J.T. Kuechler46, S. Kuehn36, A. Kugel61a, T. Kuhl46, V. Kukhtin79, R. Kukla101, Y. Kulchitsky107,aj, S. Kuleshov147b, Y.P. Kulinich173, M. Kuna58, T. Kunigo85, A. Kupco141, T. Kupfer47, O. Kuprash52, H. Kurashige82, L.L. Kurchaninov168a, Y.A. Kurochkin107, A. Kurova112, M.G. Kurth15a,15d, E.S. Kuwertz36, M. Kuze165, A.K. Kvam148, J. Kvita130, T. Kwan103, A. La Rosa115, L. La Rotonda41b,41a, F. La Ruffa41b,41a, C. Lacasta174, F. Lacava72a,72b, D.P.J. Lack100, H. Lacker19, D. Lacour136, E. Ladygin79, R. Lafaye5, B. Laforge136, T. Lagouri33c, S. Lai53, S. Lammers65, W. Lampl7, C. Lampoudis162, E. Lan¸con29, 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. Lapertosa55b,55a, S. Laplace136, J.F. Laporte145, T. Lari68a, F. Lasagni Manghi23b,23a, M. Lassnig36, T.S. Lau63a, A. Laudrain132, A. Laurier34, M. Lavorgna69a,69b, M. Lazzaroni68a,68b, B. Le104, E. Le Guirriec101, M. LeBlanc7, T. LeCompte6, F. Ledroit-Guillon58, C.A. Lee29, G.R. Lee17, L. Lee59, S.C. Lee158, S.J. Lee34, B. Lefebvre168a, M. Lefebvre176, F. Legger114, C. Leggett18, K. Lehmann152, N. Lehmann182, G. Lehmann Miotto36, W.A. Leight46, A. Leisos162,w, M.A.L. Leite80d, C.E. Leitgeb114, R. Leitner143, D. Lellouch180,∗, K.J.C. Leney42, T. Lenz24, B. Lenzi36, R. Leone7, S. Leone71a, C. Leonidopoulos50, A. Leopold136, G. Lerner156, C. Leroy109, R. Les167, C.G. Lester32, M. Levchenko138, J. Levˆeque5, D. Levin105, L.J. Levinson180, D.J. Lewis21, B. Li15b, B. Li105, C-Q. Li60a, F. Li60c, H. Li60a, H. Li60b, J. Li60c, K. Li153, L. Li60c, M. Li15a, Q. Li15a,15d, Q.Y. Li60a, S. Li60d,60c, X. Li46, Y. Li46, Z. Li60b, Z. Liang15a, B. Liberti73a, A. Liblong167, K. Lie63c, S. Liem120, C.Y. Lin32, K. Lin106, T.H. Lin99, R.A. Linck65, J.H. Lindon21, A.L. Lionti54, E. Lipeles137, A. Lipniacka17, M. Lisovyi61b, T.M. Liss173,au, A. Lister175, A.M. Litke146, J.D. Little8, B. Liu78,ac, B.L Liu6, H.B. Liu29, H. Liu105, J.B. Liu60a, J.K.K. Liu135, K. Liu136, M. Liu60a, P. Liu18, Y. Liu15a,15d, Y.L. Liu105, Y.W. Liu60a, M. Livan70a,70b, A. Lleres58, J. Llorente Merino15a, S.L. Lloyd92, C.Y. Lo63b, F. Lo Sterzo42, E.M. Lobodzinska46, P. Loch7, S. Loffredo73a,73b, T. Lohse19, K. Lohwasser149, M. Lokajicek141, J.D. Long173, R.E. Long89, L. Longo36, K.A. Looper126, J.A. Lopez147b, I. Lopez Paz100, A. Lopez Solis149, J. Lorenz114, N. Lorenzo Martinez5, M. Losada22, P.J. L¨osel114, A. L¨osle52, X. Lou46, X. Lou15a, A. Lounis132, J. Love6, P.A. Love89, J.J. Lozano Bahilo174, M. Lu60a, Y.J. Lu64, H.J. Lubatti148, C. Luci72a,72b, A. Lucotte58, C. Luedtke52, F. Luehring65, I. Luise136, L. Luminari72a, B. Lund-Jensen154, M.S. Lutz102, D. Lynn29, R. Lysak141, E. Lytken96, F. Lyu15a, V. Lyubushkin79, T. Lyubushkina79, H. Ma29, L.L. Ma60b, Y. Ma60b, G. Maccarrone51, A. Macchiolo115, C.M. Macdonald149, J. Machado Miguens137, D. Madaffari174, R. Madar38, W.F. Mader48, N. Madysa48, J. Maeda82, K. Maekawa163, S. Maeland17, T. Maeno29, M. Maerker48, A.S. Maevskiy113, V. Magerl52, N. Magini78, D.J. Mahon39, C. Maidantchik80b, T. Maier114, A. Maio140a,140b,140d, O. Majersky28a, S. Majewski131, Y. Makida81, N. Makovec132, B. Malaescu136, Pa. Malecki84, V.P. Maleev138, F. Malek58, U. Mallik77, D. Malon6, C. Malone32, S. Maltezos10, S. Malyukov36, J. Mamuzic174, G. Mancini51, I. Mandi´c91, L. Manhaes de Andrade Filho80a, I.M. Maniatis162, J. Manjarres Ramos48, K.H. Mankinen96, A. Mann114, A. Manousos76, B. Mansoulie145, I. Manthos162, S. Manzoni120, A. Marantis162, – 31 – JHEP09(2019)091 G. Marceca30, L. Marchese135, G. Marchiori136, M. Marcisovsky141, C. Marcon96, C.A. Marin Tobon36, M. Marjanovic38, Z. Marshall18, M.U.F Martensson172, S. Marti-Garcia174, C.B. Martin126, T.A. Martin178, V.J. Martin50, B. Martin dit Latour17, L. Martinelli74a,74b, M. Martinez14,x, V.I. Martinez Outschoorn102, S. Martin-Haugh144, V.S. Martoiu27b, A.C. Martyniuk94, A. Marzin36, S.R. Maschek115, L. Masetti99, T. Mashimo163, R. Mashinistov110, J. Masik100, A.L. Maslennikov122b,122a, L.H. Mason104, L. Massa73a,73b, P. Massarotti69a,69b, P. Mastrandrea71a,71b, A. Mastroberardino41b,41a, T. Masubuchi163, A. Matic114, P. M¨attig24, J. Maurer27b, B. Maˇcek91, D.A. Maximov122b,122a, R. Mazini158, I. Maznas162, S.M. Mazza146, S.P. Mc Kee105, T.G. McCarthy115, L.I. McClymont94, W.P. McCormack18, E.F. McDonald104, J.A. Mcfayden36, M.A. McKay42, K.D. McLean176, S.J. McMahon144, P.C. McNamara104, C.J. McNicol178, R.A. McPherson176,ad, J.E. Mdhluli33c, Z.A. Meadows102, S. Meehan148, T. Megy52, S. Mehlhase114, A. Mehta90, T. Meideck58, B. Meirose43, D. Melini174, B.R. Mellado Garcia33c, J.D. Mellenthin53, M. Melo28a, F. Meloni46, A. Melzer24, S.B. Menary100, E.D. Mendes Gouveia140a,140e, L. Meng36, X.T. Meng105, S. Menke115, E. Meoni41b,41a, S. Mergelmeyer19, S.A.M. Merkt139, C. Merlassino20, P. Mermod54, L. Merola69a,69b, C. Meroni68a, O. Meshkov113,110, J.K.R. Meshreki151, A. Messina72a,72b, J. Metcalfe6, A.S. Mete171, C. Meyer65, J. Meyer160, J-P. Meyer145, H. Meyer Zu Theenhausen61a, F. Miano156, R.P. Middleton144, L. Mijovi´c50, G. Mikenberg180, M. Mikestikova141, M. Mikuˇz91, H. Mildner149, M. Milesi104, A. Milic167, D.A. Millar92, D.W. Miller37, A. Milov180, D.A. Milstead45a,45b, R.A. Mina153,p, A.A. Minaenko123, M. Mi˜nano Moya174, I.A. Minashvili159b, A.I. Mincer124, B. Mindur83a, M. Mineev79, Y. Minegishi163, Y. Ming181, L.M. Mir14, A. Mirto67a,67b, K.P. Mistry137, T. Mitani179, J. Mitrevski114, V.A. Mitsou174, M. Mittal60c, A. Miucci20, P.S. Miyagawa149, A. Mizukami81, J.U. Mj¨ornmark96, T. Mkrtchyan184, M. Mlynarikova143, T. Moa45a,45b, K. Mochizuki109, P. Mogg52, S. Mohapatra39, R. Moles-Valls24, M.C. Mondragon106, K. M¨onig46, J. Monk40, E. Monnier101, A. Montalbano152, J. Montejo Berlingen36, M. Montella94, F. Monticelli88, S. Monzani68a, N. Morange132, D. Moreno22, M. Moreno Ll´acer36, C. Moreno Martinez14, P. Morettini55b, M. Morgenstern120, S. Morgenstern48, D. Mori152, M. Morii59, M. Morinaga179, V. Morisbak134, A.K. Morley36, G. Mornacchi36, A.P. Morris94, L. Morvaj155, P. Moschovakos36, B. Moser120, M. Mosidze159b, T. Moskalets145, H.J. Moss149, J. Moss31,m, K. Motohashi165, E. Mountricha36, E.J.W. Moyse102, S. Muanza101, J. Mueller139, R.S.P. Mueller114, D. Muenstermann89, G.A. Mullier96, J.L. Munoz Martinez14, F.J. Munoz Sanchez100, P. Murin28b, W.J. Murray178,144, A. Murrone68a,68b, M. Muˇskinja18, C. Mwewa33a, A.G. Myagkov123,ao, J. Myers131, M. Myska142, B.P. Nachman18, O. Nackenhorst47, A.Nag Nag48, K. Nagai135, K. Nagano81, Y. Nagasaka62, M. Nagel52, E. Nagy101, A.M. Nairz36, Y. Nakahama117, K. Nakamura81, T. Nakamura163, I. Nakano127, H. Nanjo133, F. Napolitano61a, R.F. Naranjo Garcia46, R. Narayan42, D.I. Narrias Villar61a, I. Naryshkin138, T. Naumann46, G. Navarro22, H.A. Neal105,∗, P.Y. Nechaeva110, F. Nechansky46, T.J. Neep21, A. Negri70a,70b, M. Negrini23b, C. Nellist53, M.E. Nelson135, S. Nemecek141, P. Nemethy124, M. Nessi36,d, M.S. Neubauer173, M. Neumann182, P.R. Newman21, T.Y. Ng63c, Y.S. Ng19, Y.W.Y. Ng171, H.D.N. Nguyen101, T. Nguyen Manh109, E. Nibigira38, R.B. Nickerson135, 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. Nisati72a, N. Nishu60c, R. Nisius115, I. Nitsche47, T. Nitta179, T. Nobe163, Y. Noguchi85, I. Nomidis136, M.A. Nomura29, M. Nordberg36, N. Norjoharuddeen135, T. Novak91, O. Novgorodova48, R. Novotny142, L. Nozka130, K. Ntekas171, E. Nurse94, F.G. Oakham34,ax, H. Oberlack115, J. Ocariz136, A. Ochi82, I. Ochoa39, J.P. Ochoa-Ricoux147a, K. O’Connor26, S. Oda87, S. Odaka81, S. Oerdek53, A. Ogrodnik83a, A. Oh100, S.H. Oh49, C.C. Ohm154, H. Oide55b,55a, M.L. Ojeda167, H. Okawa169, Y. Okazaki85, Y. Okumura163, T. Okuyama81, – 32 – JHEP09(2019)091 A. Olariu27b, L.F. Oleiro Seabra140a, S.A. Olivares Pino147a, D. Oliveira Damazio29, J.L. Oliver1, M.J.R. Olsson171, A. Olszewski84, J. Olszowska84, D.C. O’Neil152, A. Onofre140a,140e, K. Onogi117, P.U.E. Onyisi11, H. Oppen134, M.J. Oreglia37, G.E. Orellana88, D. Orestano74a,74b, N. Orlando14, R.S. Orr167, V. O’Shea57, R. Ospanov60a, G. Otero y Garzon30, H. Otono87, M. Ouchrif35d, J. Ouellette29, F. Ould-Saada134, A. Ouraou145, Q. Ouyang15a, M. Owen57, R.E. Owen21, V.E. Ozcan12c, N. Ozturk8, J. Pacalt130, H.A. Pacey32, K. Pachal49, A. Pacheco Pages14, C. Padilla Aranda14, S. Pagan Griso18, M. Paganini183, G. Palacino65, S. Palazzo50, S. Palestini36, M. Palka83b, D. Pallin38, I. Panagoulias10, C.E. Pandini36, J.G. Panduro Vazquez93, P. Pani46, G. Panizzo66a,66c, L. Paolozzi54, C. Papadatos109, K. Papageorgiou9,h, A. Paramonov6, D. Paredes Hernandez63b, S.R. Paredes Saenz135, B. Parida166, T.H. Park167, A.J. Parker89, M.A. Parker32, F. Parodi55b,55a, E.W.P. Parrish121, J.A. Parsons39, U. Parzefall52, L. Pascual Dominguez136, V.R. Pascuzzi167, J.M.P. Pasner146, E. Pasqualucci72a, S. Passaggio55b, F. Pastore93, P. Pasuwan45a,45b, S. Pataraia99, J.R. Pater100, A. Pathak181, T. Pauly36, B. Pearson115, M. Pedersen134, L. Pedraza Diaz119, R. Pedro140a, T. Peiffer53, S.V. Peleganchuk122b,122a, O. Penc141, H. Peng60a, B.S. Peralva80a, M.M. Perego132, A.P. Pereira Peixoto140a, D.V. Perepelitsa29, F. Peri19, L. Perini68a,68b, H. Pernegger36, S. Perrella69a,69b, K. Peters46, R.F.Y. Peters100, B.A. Petersen36, T.C. Petersen40, E. Petit101, A. Petridis1, C. Petridou162, P. Petroff132, M. Petrov135, F. Petrucci74a,74b, M. Pettee183, N.E. Pettersson102, K. Petukhova143, A. Peyaud145, R. Pezoa147b, L. Pezzotti70a,70b, T. Pham104, F.H. Phillips106, P.W. Phillips144, M.W. Phipps173, G. Piacquadio155, E. Pianori18, A. Picazio102, R.H. Pickles100, R. Piegaia30, D. Pietreanu27b, J.E. Pilcher37, A.D. Pilkington100, M. Pinamonti73a,73b, J.L. Pinfold3, M. Pitt180, L. Pizzimento73a,73b, M.-A. Pleier29, V. Pleskot143, E. Plotnikova79, D. Pluth78, P. Podberezko122b,122a, R. Poettgen96, R. Poggi54, L. Poggioli132, I. Pogrebnyak106, D. Pohl24, I. Pokharel53, G. Polesello70a, A. Poley18, A. Policicchio72a,72b, R. Polifka143, A. Polini23b, C.S. Pollard46, V. Polychronakos29, D. Ponomarenko112, L. Pontecorvo36, S. Popa27a, G.A. Popeneciu27d, D.M. Portillo Quintero58, S. Pospisil142, K. Potamianos46, I.N. Potrap79, C.J. Potter32, H. Potti11, T. Poulsen96, J. Poveda36, T.D. Powell149, G. Pownall46, M.E. Pozo Astigarraga36, P. Pralavorio101, S. Prell78, D. Price100, M. Primavera67a, S. Prince103, M.L. Proffitt148, N. Proklova112, K. Prokofiev63c, F. Prokoshin79, S. Protopopescu29, J. Proudfoot6, M. Przybycien83a, D. Pudzha138, A. Puri173, P. Puzo132, J. Qian105, Y. Qin100, A. Quadt53, M. Queitsch-Maitland46, A. Qureshi1, P. Rados104, F. Ragusa68a,68b, G. Rahal97, J.A. Raine54, S. Rajagopalan29, A. Ramirez Morales92, K. Ran15a,15d, T. Rashid132, S. Raspopov5, M.G. Ratti68a,68b, D.M. Rauch46, F. Rauscher114, S. Rave99, B. Ravina149, I. Ravinovich180, J.H. Rawling100, M. Raymond36, A.L. Read134, N.P. Readioff58, M. Reale67a,67b, D.M. Rebuzzi70a,70b, A. Redelbach177, G. Redlinger29, K. Reeves43, L. Rehnisch19, J. Reichert137, D. Reikher161, A. Reiss99, A. Rej151, C. Rembser36, M. Renda27b, M. Rescigno72a, S. Resconi68a, E.D. Resseguie137, S. Rettie175, E. Reynolds21, O.L. Rezanova122b,122a, P. Reznicek143, E. Ricci75a,75b, R. Richter115, S. Richter46, E. Richter-Was83b, O. Ricken24, M. Ridel136, P. Rieck115, C.J. Riegel182, O. Rifki46, M. Rijssenbeek155, A. Rimoldi70a,70b, M. Rimoldi46, L. Rinaldi23b, G. Ripellino154, B. Risti´c89, E. Ritsch36, I. Riu14, J.C. Rivera Vergara176, F. Rizatdinova129, E. Rizvi92, C. Rizzi36, R.T. Roberts100, S.H. Robertson103,ad, M. Robin46, D. Robinson32, J.E.M. Robinson46, C.M. Robles Gajardo147b, A. Robson57, E. Rocco99, C. Roda71a,71b, S. Rodriguez Bosca174, A. Rodriguez Perez14, D. Rodriguez Rodriguez174, A.M. Rodr´ıguez Vera168b, S. Roe36, O. Røhne134, R. R¨ohrig115, C.P.A. Roland65, J. Roloff59, A. Romaniouk112, M. Romano23b,23a, N. Rompotis90, M. Ronzani124, L. Roos136, S. Rosati72a, K. Rosbach52, G. Rosin102, B.J. Rosser137, E. Rossi46, E. Rossi74a,74b, E. Rossi69a,69b, L.P. Rossi55b, L. Rossini68a,68b, R. Rosten14, M. Rotaru27b, J. Rothberg148, D. Rousseau132, G. Rovelli70a,70b, D. Roy33c, – 33 – JHEP09(2019)091 A. Rozanov101, Y. Rozen160, X. Ruan33c, F. Rubbo153, F. R¨uhr52, A. Ruiz-Martinez174, A. Rummler36, Z. Rurikova52, N.A. Rusakovich79, H.L. Russell103, L. Rustige38,47, J.P. Rutherfoord7, E.M. R¨uttinger46,j, Y.F. Ryabov138, M. Rybar39, G. Rybkin132, A. Ryzhov123, G.F. Rzehorz53, P. Sabatini53, G. Sabato120, S. Sacerdoti132, H.F-W. Sadrozinski146, R. Sadykov79, F. Safai Tehrani72a, B. Safarzadeh Samani156, P. Saha121, S. Saha103, M. Sahinsoy61a, A. Sahu182, M. Saimpert46, M. Saito163, T. Saito163, H. Sakamoto163, A. Sakharov124,an, D. Salamani54, G. Salamanna74a,74b, J.E. Salazar Loyola147b, P.H. Sales De Bruin172, A. Salnikov153, J. Salt174, D. Salvatore41b,41a, F. Salvatore156, A. Salvucci63a,63b,63c, A. Salzburger36, J. Samarati36, D. Sammel52, D. Sampsonidis162, D. Sampsonidou162, J. S´anchez174, A. Sanchez Pineda66a,66c, H. Sandaker134, C.O. Sander46, I.G. Sanderswood89, M. Sandhoff182, C. Sandoval22, D.P.C. Sankey144, M. Sannino55b,55a, Y. Sano117, A. Sansoni51, C. Santoni38, H. Santos140a,140b, S.N. Santpur18, A. Santra174, A. Sapronov79, J.G. Saraiva140a,140d, O. Sasaki81, K. Sato169, E. Sauvan5, P. Savard167,ax, N. Savic115, R. Sawada163, C. Sawyer144, L. Sawyer95,al, C. Sbarra23b, A. Sbrizzi23a, T. Scanlon94, J. Schaarschmidt148, P. Schacht115, B.M. Schachtner114, D. Schaefer37, L. Schaefer137, J. Schaeffer99, S. Schaepe36, U. Sch¨afer99, A.C. Schaffer132, D. Schaile114, R.D. Schamberger155, N. Scharmberg100, V.A. Schegelsky138, D. Scheirich143, F. Schenck19, M. Schernau171, C. Schiavi55b,55a, S. Schier146, L.K. Schildgen24, Z.M. Schillaci26, E.J. Schioppa36, M. Schioppa41b,41a, K.E. Schleicher52, S. Schlenker36, K.R. Schmidt-Sommerfeld115, K. Schmieden36, C. Schmitt99, S. Schmitt46, S. Schmitz99, J.C. Schmoeckel46, U. Schnoor52, L. Schoeffel145, A. Schoening61b, P.G. Scholer52, E. Schopf135, M. Schott99, J.F.P. Schouwenberg119, J. Schovancova36, S. Schramm54, F. Schroeder182, A. Schulte99, H-C. Schultz-Coulon61a, M. Schumacher52, B.A. Schumm146, Ph. Schune145, A. Schwartzman153, T.A. Schwarz105, Ph. Schwemling145, R. Schwienhorst106, A. Sciandra146, G. Sciolla26, M. Scodeggio46, M. Scornajenghi41b,41a, F. Scuri71a, F. Scutti104, L.M. Scyboz115, C.D. Sebastiani72a,72b, P. Seema19, S.C. Seidel118, A. Seiden146, T. Seiss37, J.M. Seixas80b, G. Sekhniaidze69a, K. Sekhon105, S.J. Sekula42, N. Semprini-Cesari23b,23a, S. Sen49, S. Senkin38, C. Serfon76, L. Serin132, L. Serkin66a,66b, M. Sessa60a, H. Severini128, F. Sforza170, A. Sfyrla54, E. Shabalina53, J.D. Shahinian146, N.W. Shaikh45a,45b, D. Shaked Renous180, L.Y. Shan15a, R. Shang173, J.T. Shank25, M. Shapiro18, A. Sharma135, A.S. Sharma1, P.B. Shatalov111, K. Shaw156, S.M. Shaw100, A. Shcherbakova138, Y. Shen128, N. Sherafati34, A.D. Sherman25, P. Sherwood94, L. Shi158,at, S. Shimizu81, C.O. Shimmin183, Y. Shimogama179, M. Shimojima116, I.P.J. Shipsey135, S. Shirabe87, M. Shiyakova79,aa, J. Shlomi180, A. Shmeleva110, M.J. Shochet37, S. Shojaii104, D.R. Shope128, S. Shrestha126, E.M. Shrif33c, E. Shulga180, P. Sicho141, A.M. Sickles173, P.E. Sidebo154, E. Sideras Haddad33c, O. Sidiropoulou36, A. Sidoti23b,23a, F. Siegert48, Dj. Sijacki16, M. Silva Jr.181, M.V. Silva Oliveira80a, S.B. Silverstein45a, S. Simion132, E. Simioni99, R. Simoniello99, S. Simsek12b, P. Sinervo167, V. Sinetckii113,110, N.B. Sinev131, M. Sioli23b,23a, I. Siral105, S.Yu. Sivoklokov113, J. Sj¨olin45a,45b, E. Skorda96, P. Skubic128, M. Slawinska84, K. Sliwa170, R. Slovak143, V. Smakhtin180, B.H. Smart144, J. Smiesko28a, N. Smirnov112, S.Yu. Smirnov112, Y. Smirnov112, L.N. Smirnova113,t, O. Smirnova96, J.W. Smith53, M. Smizanska89, K. Smolek142, A. Smykiewicz84, A.A. Snesarev110, H.L. Snoek120, I.M. Snyder131, S. Snyder29, R. Sobie176,ad, A.M. Soffa171, A. Soffer161, A. Søgaard50, F. Sohns53, C.A. Solans Sanchez36, E.Yu. Soldatov112, U. Soldevila174, A.A. Solodkov123, A. Soloshenko79, O.V. Solovyanov123, V. Solovyev138, P. Sommer149, H. Son170, W. Song144, W.Y. Song168b, A. Sopczak142, F. Sopkova28b, C.L. Sotiropoulou71a,71b, S. Sottocornola70a,70b, R. Soualah66a,66c,g, A.M. Soukharev122b,122a, D. South46, S. Spagnolo67a,67b, M. Spalla115, M. Spangenberg178, F. Span`o93, D. Sperlich52, T.M. Spieker61a, R. Spighi23b, G. Spigo36, M. Spina156, D.P. Spiteri57, M. Spousta143, A. Stabile68a,68b, B.L. Stamas121, R. Stamen61a, M. Stamenkovic120, – 34 – JHEP09(2019)091 E. Stanecka84, R.W. Stanek6, B. Stanislaus135, M.M. Stanitzki46, M. Stankaityte135, B. Stapf120, E.A. Starchenko123, G.H. Stark146, J. Stark58, S.H Stark40, P. Staroba141, P. Starovoitov61a, S. St¨arz103, R. Staszewski84, G. Stavropoulos44, M. Stegler46, P. Steinberg29, A.L. Steinhebel131, B. Stelzer152, H.J. Stelzer139, O. Stelzer-Chilton168a, H. Stenzel56, T.J. Stevenson156, G.A. Stewart36, M.C. Stockton36, G. Stoicea27b, M. Stolarski140a, P. Stolte53, S. Stonjek115, A. Straessner48, J. Strandberg154, S. Strandberg45a,45b, M. Strauss128, P. Strizenec28b, R. Str¨ohmer177, D.M. Strom131, R. Stroynowski42, A. Strubig50, S.A. Stucci29, B. Stugu17, J. Stupak128, N.A. Styles46, D. Su153, S. Suchek61a, V.V. Sulin110, M.J. Sullivan90, D.M.S. Sultan54, S. Sultansoy4c, T. Sumida85, S. Sun105, X. Sun3, K. Suruliz156, C.J.E. Suster157, M.R. Sutton156, S. Suzuki81, M. Svatos141, M. Swiatlowski37, S.P. Swift2, T. Swirski177, A. Sydorenko99, I. Sykora28a, M. Sykora143, T. Sykora143, D. Ta99, K. Tackmann46,y, J. Taenzer161, A. Taffard171, R. Tafirout168a, H. Takai29, R. Takashima86, K. Takeda82, T. Takeshita150, E.P. Takeva50, Y. Takubo81, M. Talby101, A.A. Talyshev122b,122a, N.M. Tamir161, J. Tanaka163, M. Tanaka165, R. Tanaka132, S. Tapia Araya173, S. Tapprogge99, A. Tarek Abouelfadl Mohamed136, S. Tarem160, G. Tarna27b,c, G.F. Tartarelli68a, P. Tas143, M. Tasevsky141, T. Tashiro85, E. Tassi41b,41a, A. Tavares Delgado140a,140b, Y. Tayalati35e, A.J. Taylor50, G.N. Taylor104, W. Taylor168b, A.S. Tee89, R. Teixeira De Lima153, P. Teixeira-Dias93, H. Ten Kate36, J.J. Teoh120, S. Terada81, K. Terashi163, J. Terron98, S. Terzo14, M. Testa51, R.J. Teuscher167,ad, S.J. Thais183, T. Theveneaux-Pelzer46, F. Thiele40, D.W. Thomas93, J.O. Thomas42, J.P. Thomas21, A.S. Thompson57, P.D. Thompson21, L.A. Thomsen183, E. Thomson137, Y. Tian39, R.E. Ticse Torres53, V.O. Tikhomirov110,ap, Yu.A. Tikhonov122b,122a, S. Timoshenko112, P. Tipton183, S. Tisserant101, K. Todome23b,23a, S. Todorova-Nova5, S. Todt48, J. Tojo87, S. Tok´ar28a, K. Tokushuku81, E. Tolley126, K.G. Tomiwa33c, M. Tomoto117, L. Tompkins153,p, K. Toms118, B. Tong59, P. Tornambe102, E. Torrence131, H. Torres48, E. Torr´o Pastor148, C. Tosciri135, J. Toth101,ab, D.R. Tovey149, A. Traeet17, C.J. Treado124, T. Trefzger177, F. Tresoldi156, A. Tricoli29, I.M. Trigger168a, S. Trincaz-Duvoid136, W. Trischuk167, B. Trocm´e58, A. Trofymov132, C. Troncon68a, M. Trovatelli176, F. Trovato156, L. Truong33b, M. Trzebinski84, A. Trzupek84, F. Tsai46, J.C-L. Tseng135, P.V. Tsiareshka107,aj, A. Tsirigotis162, N. Tsirintanis9, V. Tsiskaridze155, E.G. Tskhadadze159a, M. Tsopoulou162, I.I. Tsukerman111, V. Tsulaia18, S. Tsuno81, D. Tsybychev155, Y. Tu63b, A. Tudorache27b, V. Tudorache27b, T.T. Tulbure27a, A.N. Tuna59, S. Turchikhin79, D. Turgeman180, I. Turk Cakir4b,u, R.J. Turner21, R.T. Turra68a, P.M. Tuts39, S. Tzamarias162, E. Tzovara99, G. Ucchielli47, K. Uchida163, I. Ueda81, M. Ughetto45a,45b, F. Ukegawa169, G. Unal36, A. Undrus29, G. Unel171, F.C. Ungaro104, Y. Unno81, K. Uno163, J. Urban28b, P. Urquijo104, G. Usai8, J. Usui81, Z. Uysal12d, L. Vacavant101, V. Vacek142, B. Vachon103, K.O.H. Vadla134, A. Vaidya94, C. Valderanis114, E. Valdes Santurio45a,45b, M. Valente54, S. Valentinetti23b,23a, A. Valero174, L. Val´ery46, R.A. Vallance21, A. Vallier36, J.A. Valls Ferrer174, T.R. Van Daalen14, P. Van Gemmeren6, I. Van Vulpen120, M. Vanadia73a,73b, W. Vandelli36, A. Vaniachine166, D. Vannicola72a,72b, R. Vari72a, E.W. Varnes7, C. Varni55b,55a, T. Varol42, D. Varouchas132, K.E. Varvell157, M.E. Vasile27b, G.A. Vasquez176, J.G. Vasquez183, F. Vazeille38, D. Vazquez Furelos14, T. Vazquez Schroeder36, J. Veatch53, V. Vecchio74a,74b, M.J. Veen120, L.M. Veloce167, F. Veloso140a,140c, S. Veneziano72a, A. Ventura67a,67b, N. Venturi36, A. Verbytskyi115, V. Vercesi70a, M. Verducci74a,74b, C.M. Vergel Infante78, C. Vergis24, W. Verkerke120, A.T. Vermeulen120, J.C. Vermeulen120, M.C. Vetterli152,ax, N. Viaux Maira147b, M. Vicente Barreto Pinto54, T. Vickey149, O.E. Vickey Boeriu149, G.H.A. Viehhauser135, L. Vigani135, M. Villa23b,23a, M. Villaplana Perez68a,68b, E. Vilucchi51, M.G. Vincter34, V.B. Vinogradov79, A. Vishwakarma46, C. Vittori23b,23a, I. Vivarelli156, M. Vogel182, P. Vokac142, S.E. von Buddenbrock33c, E. Von Toerne24, V. Vorobel143, K. Vorobev112, M. Vos174, – 35 – JHEP09(2019)091 J.H. Vossebeld90, M. Vozak100, N. Vranjes16, M. Vranjes Milosavljevic16, V. Vrba142, M. Vreeswijk120, T. ˇ Sfiligoj91, R. Vuillermet36, I. Vukotic37, T. ˇ Zeniˇs28a, L. ˇ Zivkovi´c16, P. Wagner24, W. Wagner182, J. Wagner-Kuhr114, H. Wahlberg88, K. Wakamiya82, V.M. Walbrecht115, J. Walder89, R. Walker114, S.D. Walker93, W. Walkowiak151, V. Wallangen45a,45b, A.M. Wang59, C. Wang60b, F. Wang181, H. Wang18, H. Wang3, J. Wang157, J. Wang61b, P. Wang42, Q. Wang128, R.-J. Wang99, R. Wang60a, R. Wang6, S.M. Wang158, W.T. Wang60a, W. Wang15c,ae, W.X. Wang60a,ae, Y. Wang60a,am, Z. Wang60c, C. Wanotayaroj46, A. Warburton103, C.P. Ward32, D.R. Wardrope94, N. Warrack57, A. Washbrook50, A.T. Watson21, M.F. Watson21, G. Watts148, B.M. Waugh94, A.F. Webb11, S. Webb99, C. Weber183, M.S. Weber20, S.A. Weber34, S.M. Weber61a, A.R. Weidberg135, J. Weingarten47, M. Weirich99, C. Weiser52, P.S. Wells36, T. Wenaus29, T. Wengler36, S. Wenig36, N. Wermes24, M.D. Werner78, M. Wessels61a, T.D. Weston20, K. Whalen131, N.L. Whallon148, A.M. Wharton89, A.S. White105, A. White8, M.J. White1, D. Whiteson171, B.W. Whitmore89, F.J. Wickens144, W. Wiedenmann181, M. Wielers144, N. Wieseotte99, C. Wiglesworth40, L.A.M. Wiik-Fuchs52, F. Wilk100, H.G. Wilkens36, L.J. Wilkins93, H.H. Williams137, S. Williams32, C. Willis106, S. Willocq102, J.A. Wilson21, I. Wingerter-Seez5, E. Winkels156, F. Winklmeier131, O.J. Winston156, B.T. Winter52, M. Wittgen153, M. Wobisch95, A. Wolf99, T.M.H. Wolf120, R. Wolff101, R.W. W¨olker135, J. Wollrath52, M.W. Wolter84, H. Wolters140a,140c, V.W.S. Wong175, N.L. Woods146, S.D. Worm21, B.K. Wosiek84, K.W. Wo´zniak84, K. Wraight57, S.L. Wu181, X. Wu54, Y. Wu60a, T.R. Wyatt100, B.M. Wynne50, S. Xella40, Z. Xi105, L. Xia178, D. Xu15a, H. Xu60a,c, L. Xu29, T. Xu145, W. Xu105, Z. Xu60b, Z. Xu153, B. Yabsley157, S. Yacoob33a, K. Yajima133, D.P. Yallup94, D. Yamaguchi165, Y. Yamaguchi165, A. Yamamoto81, T. Yamanaka163, F. Yamane82, M. Yamatani163, T. Yamazaki163, Y. Yamazaki82, Z. Yan25, H.J. Yang60c,60d, H.T. Yang18, S. Yang77, X. Yang60b,58, Y. Yang163, W-M. Yao18, Y.C. Yap46, Y. Yasu81, E. Yatsenko60c,60d, J. Ye42, S. Ye29, I. Yeletskikh79, M.R. Yexley89, E. Yigitbasi25, K. Yorita179, K. Yoshihara137, C.J.S. Young36, C. Young153, J. Yu78, R. Yuan60b, X. Yue61a, S.P.Y. Yuen24, B. Zabinski84, G. Zacharis10, E. Zaffaroni54, J. Zahreddine136, A.M. Zaitsev123,ao, T. Zakareishvili159b, N. Zakharchuk34, S. Zambito59, D. Zanzi36, D.R. Zaripovas57, S.V. Zeißner47, C. Zeitnitz182, G. Zemaityte135, J.C. Zeng173, O. Zenin123, D. Zerwas132, M. Zgubiˇc135, D.F. Zhang15b, F. Zhang181, G. Zhang60a, G. Zhang15b, H. Zhang15c, J. Zhang6, L. Zhang15c, L. Zhang60a, M. Zhang173, R. Zhang60a, R. Zhang24, X. Zhang60b, Y. Zhang15a,15d, Z. Zhang63a, Z. Zhang132, P. Zhao49, Y. Zhao60b, Z. Zhao60a, A. Zhemchugov79, Z. Zheng105, D. Zhong173, B. Zhou105, C. Zhou181, M.S. Zhou15a,15d, M. Zhou155, N. Zhou60c, Y. Zhou7, C.G. Zhu60b, H.L. Zhu60a, H. Zhu15a, J. Zhu105, Y. Zhu60a, X. Zhuang15a, K. Zhukov110, V. Zhulanov122b,122a, D. Zieminska65, N.I. Zimine79, S. Zimmermann52, Z. Zinonos115, M. Ziolkowski151, G. Zobernig181, A. Zoccoli23b,23a, K. Zoch53, T.G. Zorbas149, R. Zou37, L. Zwalinski36 1Department of Physics, University of Adelaide, Adelaide; Australia 2Physics Department, SUNY Albany, Albany NY; United States of America 3Department of Physics, University of Alberta, Edmonton AB; Canada 4 (a)Department of Physics, Ankara University, Ankara;(b)Istanbul Aydin University, Istanbul;(c)Division of Physics, TOBB University of Economics and Technology, Ankara; Turkey 5LAPP, Universit´e Grenoble Alpes, Universit´e 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 – 36 – JHEP09(2019)091 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 F´ısica d’Altes Energies (IFAE), Barcelona Institute of Science and Technology, Barcelona; Spain 15 (a)Institute of High Energy Physics, Chinese Academy of Sciences, Beijing;(b)Physics Department, Tsinghua University, Beijing;(c)Department of Physics, Nanjing University, Nanjing;(d)University of Chinese Academy of Science (UCAS), Beijing; China 16 Institute of Physics, University of Belgrade, Belgrade; Serbia 17 Department for Physics and Technology, University of Bergen, Bergen; Norway 18 Physics Division, Lawrence Berkeley National Laboratory and University of California, Berkeley CA; United States of America 19 Institut f¨ur Physik, Humboldt Universit¨at 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 Facultad de Ciencias y Centro de Investigaci´ones, Universidad Antonio Nari˜no, Bogota; Colombia 23 (a)INFN Bologna and Universita’ di Bologna, Dipartimento di Fisica;(b)INFN Sezione di Bologna; Italy 24 Physikalisches Institut, Universit¨at 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 F´ısica, 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)Department of Mechanical Engineering Science, University of Johannesburg, Johannesburg;(c)School of Physics, University of the Witwatersrand, Johannesburg; South Africa 34 Department of Physics, Carleton University, Ottawa ON; Canada 35 (a)Facult´e des Sciences Ain Chock, R´eseau Universitaire de Physique des Hautes Energies - Universit´e Hassan II, Casablanca;(b)Facult´e des Sciences, Universit´e Ibn-Tofail, K´enitra;(c)Facult´e des Sciences Semlalia, Universit´e Cadi Ayyad, LPHEA-Marrakech;(d)Facult´e des Sciences, Universit´e Mohamed Premier and LPTPM, Oujda;(e)Facult´e des sciences, Universit´e Mohammed V, Rabat; Morocco 36 CERN, Geneva; Switzerland 37 Enrico Fermi Institute, University of Chicago, Chicago IL; United States of America 38 LPC, Universit´e 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, Universit`a della Calabria, Rende;(b)INFN Gruppo Collegato di Cosenza, Laboratori Nazionali di Frascati; Italy – 37 – JHEP09(2019)091 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 f¨ur Experimentelle Physik IV, Technische Universit¨at Dortmund, Dortmund; Germany 48 Institut f¨ur Kernund Teilchenphysik, Technische Universit¨at 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-Universit¨at Freiburg, Freiburg; Germany 53 II. Physikalisches Institut, Georg-August-Universit¨at G¨ottingen, G¨ottingen; Germany 54 D´epartement de Physique Nucl´eaire et Corpusculaire, Universit´e de Gen`eve, Gen`eve; Switzerland 55 (a)Dipartimento di Fisica, Universit`a di Genova, Genova;(b)INFN Sezione di Genova; Italy 56 II. Physikalisches Institut, Justus-Liebig-Universit¨at Giessen, Giessen; Germany 57 SUPA - School of Physics and Astronomy, University of Glasgow, Glasgow; United Kingdom 58 LPSC, Universit´e Grenoble Alpes, CNRS/IN2P3, Grenoble INP, Grenoble; France 59 Laboratory for Particle Physics and Cosmology, Harvard University, Cambridge MA; United States of America 60 (a)Department of Modern Physics and State Key Laboratory of Particle Detection and Electronics, University of Science and Technology of China, Hefei;(b)Institute of Frontier and Interdisciplinary Science and Key Laboratory of Particle Physics and Particle Irradiation (MOE), Shandong University, Qingdao;(c)School of Physics and Astronomy, Shanghai Jiao Tong University, KLPPAC-MoE, SKLPPC, Shanghai;(d)Tsung-Dao Lee Institute, Shanghai; China 61 (a)Kirchhoff-Institut f¨ur Physik, Ruprecht-Karls-Universit¨at Heidelberg, Heidelberg;(b)Physikalisches Institut, Ruprecht-Karls-Universit¨at Heidelberg, Heidelberg; Germany 62 Faculty of Applied Information Science, Hiroshima Institute of Technology, Hiroshima; Japan 63 (a)Department of Physics, Chinese University of Hong Kong, Shatin, N.T., Hong Kong;(b)Department of Physics, University of Hong Kong, Hong Kong;(c)Department of Physics and Institute for Advanced Study, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong; China 64 Department of Physics, National Tsing Hua University, Hsinchu; Taiwan 65 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, Universit`a di Udine, Udine; Italy 67 (a)INFN Sezione di Lecce;(b)Dipartimento di Matematica e Fisica, Universit`a del Salento, Lecce; Italy 68 (a)INFN Sezione di Milano;(b)Dipartimento di Fisica, Universit`a di Milano, Milano; Italy 69 (a)INFN Sezione di Napoli;(b)Dipartimento di Fisica, Universit`a di Napoli, Napoli; Italy 70 (a)INFN Sezione di Pavia;(b)Dipartimento di Fisica, Universit`a di Pavia, Pavia; Italy 71 (a)INFN Sezione di Pisa;(b)Dipartimento di Fisica E. Fermi, Universit`a di Pisa, Pisa; Italy 72 (a)INFN Sezione di Roma;(b)Dipartimento di Fisica, Sapienza Universit`a di Roma, Roma; Italy 73 (a)INFN Sezione di Roma Tor Vergata;(b)Dipartimento di Fisica, Universit`a di Roma Tor Vergata, Roma; Italy 74 (a)INFN Sezione di Roma Tre;(b)Dipartimento di Matematica e Fisica, Universit`a Roma Tre, Roma; Italy 75 (a)INFN-TIFPA;(b)Universit`a degli Studi di Trento, Trento; Italy 76 Institut f¨ur Astround Teilchenphysik, Leopold-Franzens-Universit¨at, 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 El´etrica, Universidade Federal de Juiz de Fora (UFJF), Juiz de – 38 – JHEP09(2019)091 Fora;(b)Universidade Federal do Rio De Janeiro COPPE/EE/IF, Rio de Janeiro;(c)Universidade Federal de S˜ao Jo˜ao del Rei (UFSJ), S˜ao Jo˜ao del Rei;(d)Instituto de F´ısica, Universidade de S˜ao Paulo, S˜ao 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 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 F´ısica 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, Joˇzef 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 Nucl´eaire et de Physique des Particules (IN2P3), Villeurbanne; France 98 Departamento de F´ısica Teorica C-15 and CIAFF, Universidad Aut´onoma de Madrid, Madrid; Spain 99 Institut f¨ur Physik, Universit¨at Mainz, Mainz; Germany 100 School of Physics and Astronomy, University of Manchester, Manchester; United Kingdom 101 CPPM, Aix-Marseille Universit´e, 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 Institute for Theoretical and Experimental Physics of the National Research Centre Kurchatov Institute, Moscow; Russia 112 National Research Nuclear University MEPhI, Moscow; Russia 113 D.V. Skobeltsyn Institute of Nuclear Physics, M.V. Lomonosov Moscow State University, Moscow; Russia 114 Fakult¨at f¨ur Physik, Ludwig-Maximilians-Universit¨at M¨unchen, M¨unchen; Germany 115 Max-Planck-Institut f¨ur Physik (Werner-Heisenberg-Institut), M¨unchen; Germany 116 Nagasaki Institute of Applied Science, Nagasaki; Japan 117 Graduate School of Science and Kobayashi-Maskawa Institute, Nagoya University, Nagoya; Japan 118 Department of Physics and Astronomy, University of New Mexico, Albuquerque NM; United States of America 119 Institute for Mathematics, Astrophysics and Particle Physics, Radboud University Nijmegen/Nikhef, Nijmegen; Netherlands – 39 – JHEP09(2019)091 120 Nikhef National Institute for Subatomic Physics and University of Amsterdam, Amsterdam; Netherlands 121 Department of Physics, Northern Illinois University, DeKalb IL; United States of America 122 (a)Budker Institute of Nuclear Physics and NSU, SB RAS, Novosibirsk;(b)Novosibirsk State University Novosibirsk; Russia 123 Institute for High Energy Physics of the National Research Centre Kurchatov Institute, Protvino; Russia 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 Faculty of Science, Okayama University, Okayama; Japan 128 Homer L. Dodge Department of Physics and Astronomy, University of Oklahoma, Norman OK; United States of America 129 Department of Physics, Oklahoma State University, Stillwater OK; United States of America 130 Palack´y University, RCPTM, Joint Laboratory of Optics, Olomouc; Czech Republic 131 Center for High Energy Physics, University of Oregon, Eugene OR; United States of America 132 LAL, Universit´e Paris-Sud, CNRS/IN2P3, Universit´e Paris-Saclay, Orsay; France 133 Graduate School of Science, Osaka University, Osaka; Japan 134 Department of Physics, University of Oslo, Oslo; Norway 135 Department of Physics, Oxford University, Oxford; United Kingdom 136 LPNHE, Sorbonne Universit´e, Paris Diderot Sorbonne Paris Cit´e, CNRS/IN2P3, Paris; France 137 Department of Physics, University of Pennsylvania, Philadelphia PA; United States of America 138 Konstantinov Nuclear Physics Institute of National Research Centre ”Kurchatov Institute”, PNPI, St. Petersburg; Russia 139 Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh PA; United States of America 140 (a)Laborat´orio de Instrumenta¸c˜ao e F´ısica Experimental de Part´ıculas - LIP;(b)Departamento de F´ısica, Faculdade de Ciˆencias, Universidade de Lisboa, Lisboa;(c)Departamento de F´ısica, Universidade de Coimbra, Coimbra;(d)Centro de F´ısica Nuclear da Universidade de Lisboa, Lisboa;(e)Departamento de F´ısica, Universidade do Minho, Braga;(f)Universidad de Granada, Granada (Spain);(g)Dep F´ısica and CEFITEC of Faculdade de Ciˆencias e Tecnologia, Universidade Nova de Lisboa, Caparica; Portugal 141 Institute of Physics of the Czech Academy of Sciences, Prague; Czech Republic 142 Czech Technical University in Prague, Prague; Czech Republic 143 Charles University, Faculty of Mathematics and Physics, Prague; Czech Republic 144 Particle Physics Department, Rutherford Appleton Laboratory, Didcot; United Kingdom 145 IRFU, CEA, Universit´e Paris-Saclay, Gif-sur-Yvette; France 146 Santa Cruz Institute for Particle Physics, University of California Santa Cruz, Santa Cruz CA; United States of America 147 (a)Departamento de F´ısica, Pontificia Universidad Cat´olica de Chile, Santiago;(b)Departamento de F´ısica, Universidad T´ecnica Federico Santa Mar´ıa, Valpara´ıso; Chile 148 Department of Physics, University of Washington, Seattle WA; United States of America 149 Department of Physics and Astronomy, University of Sheffield, Sheffield; United Kingdom 150 Department of Physics, Shinshu University, Nagano; Japan 151 Department Physik, Universit¨at Siegen, Siegen; Germany 152 Department of Physics, Simon Fraser University, Burnaby BC; Canada 153 SLAC National Accelerator Laboratory, Stanford CA; United States of America 154 Physics Department, Royal Institute of Technology, Stockholm; Sweden 155 Departments of Physics and Astronomy, Stony Brook University, Stony Brook NY; United States of America 156 Department of Physics and Astronomy, University of Sussex, Brighton; United Kingdom 157 School of Physics, University of Sydney, Sydney; Australia – 40 –