Published for SISSA by Springer Received: May 20, 2019 Revised: August 6, 2019 Accepted: August 28, 2019 Published: October 9, 2019 Measurement of ZZ production in the ``νν final state JHEP10(2019)127 with the ATLAS detector in pp collisions at √ s = 13 TeV The ATLAS collaboration E-mail: atlas.publications@cern.ch Abstract: This paper presents a measurement of ZZ production with the ATLAS de- tector at the Large Hadron Collider. The measurement is carried out in the final state with two charged leptons and two neutrinos, using data collected during 2015 and 2016 √ in pp collisions at s = 13 TeV, corresponding to an integrated luminosity of 36.1 fb−1 . The integrated cross-sections in the total and fiducial phase spaces are measured with an uncertainty of 7% and compared with Standard Model predictions, and differential mea- surements in the fiducial phase space are reported. No significant deviations from the Standard Model predictions are observed, and stringent constraints are placed on anoma- lous couplings corresponding to neutral triple gauge-boson interactions. Keywords: Hadron-Hadron scattering (experiments) ArXiv ePrint: 1905.07163 Open Access, Copyright CERN, https://doi.org/10.1007/JHEP10(2019)127 for the benefit of the ATLAS Collaboration. 3 Article funded by SCOAP . Contents 1 Introduction 1 2 ATLAS detector 4 3 Data and simulation 5 4 Selection of ``νν events 7 JHEP10(2019)127 5 Total and fiducial phase spaces 10 6 Background estimation 12 7 Systematic uncertainties 15 8 Integrated cross-section results 16 9 Differential cross-section results 19 10 Search for aTGCs 20 11 Conclusion 24 The ATLAS collaboration 31 1 Introduction In the Standard Model (SM), the production of gauge boson pairs has a profound connec- tion with the non-Abelian nature of the electroweak (EW) theory and with the spontaneous breaking of the EW gauge symmetry. In addition, a broad range of new phenomena be- yond the SM (BSM) are predicted to reveal themselves through diboson production. The study of diboson production probes a cornerstone of the EW theory and possible BSM physics scenarios, and it constitutes a salient component of the physics programme at the Large Hadron Collider (LHC). Among all the diboson processes, the production of two on-shell Z bosons has the smallest cross-section, but is nevertheless quite competitive for measurements and searches, because of its generally good signal-to-background ratio for the fully leptonic decay channels. For instance, the ZZ process is a leading channel to search for anomalous neutral triple-gauge-boson couplings (aTGCs) [1] and to study the off-shell production of the Higgs boson [2, 3]. Figure 1 shows representative Feynman diagrams for ZZ production at the LHC. The dominant process is t-channel production with a quark and anti-quark initial state, –1– q̄ Z g q q̄ Z g Z q Z q Z q Z (a) (b) (c) JHEP10(2019)127 g Z g Z q̄ Z aTGC H∗ g Z g Z q Z (d) (e) (f ) Figure 1. Representative Feynman diagrams for ZZ production at the LHC: (a) lowest-order t-channel qqZZ production; (b) production of ZZ plus one parton through the q q̄ initial state; (c) production of ZZ plus one parton through the qg initial state; (d) ggZZ production with a fermion loop; (e) ggZZ production involving an exchange of a virtual Higgs boson; (f) s-channel production with aTGCs. hereafter denoted by the qqZZ process. Higher-order QCD corrections to the qqZZ process are found to be sizeable [4], and two tree-level diagrams concerning production of two Z bosons and one outgoing parton are shown. The gluon fusion process (ggZZ) includes two sub-processes, one with a fermion loop and the other involving a virtual Higgs boson. Although the ggZZ process only appears at O(αS2 ), it nevertheless has a non-negligible contribution of O(10%) to the total ZZ production rate due to the large gluon flux at the LHC. The s-channel production is forbidden at the lowest order; however, the neutral TGCs can still acquire small values of O(10−4 ) in the SM, due to the correction with a fermion loop [5]. The observation of aTGCs with larger values would hint at the existence of new physics. Measurements of ZZ production at the LHC have been carried out in two decay final states, one with four charged leptons (4`) and the other with two charged leptons and two neutrinos (``νν). Using LHC Run-1 and Run-2 data, multiple results [6–12] have been reported by the ATLAS and CMS experiments. The most precise results to date have been obtained from the 4` channel using 13 TeV data [8, 12], where the integrated production cross-section has been measured to a precision of 5% and the upper bound on neutral aTGC parameters has been reduced to 10−3 . The improved experimental precision –2– has stimulated theoretical calculations with a greater accuracy, and the next-to-next-to- leading-order (NNLO) QCD [4, 13, 14, 74, 75] and next-to-leading-order (NLO) EW [15, 16] predictions have become available for the qqZZ process. This paper presents a measurement of ZZ production using 36.1 fb−1 of data collected √ with the ATLAS detector in pp collisions at s = 13 TeV. This analysis is performed in the ``νν (` = e or µ) final state, which has a larger branching fraction but suffers from higher background contamination in comparison with the 4` channel. To ensure a good signal-to- background ratio, the experimental selection requires one Z boson boosted against the other in the transverse plane, which results in a pair of high-pT isolated leptons and significant miss missing transverse momentum (ET ). The ``νν channel thus offers higher data statistics JHEP10(2019)127 than the 4` channel for events with high-pT Z bosons, and offers competitive precision for integrated and differential measurements, as well as good sensitivity to aTGCs. The dominant background arises from W Z production where the Z boson decays into a pair of charged leptons. About 60% of the W Z events which contribute to the ``νν final state have the W boson decaying leptonically (W → `ν or W → τ ν → ` + 3ν, ` = e or µ), where the final-state lepton escapes detection. The remaining 40% W Z contribution is related to the W → τ ν decay with subsequent hadronic decays of the τ -lepton. Another important background comes from the processes that genuinely produce the ``νν final state but contain a lepton pair not originating directly from a Z-boson decay. This background, referred to as the non-resonant-`` background, includes W W , top-quark (tt and W t), and Z → τ τ production. The production of a Z boson in association with jet(s) (Z + jets) also constitutes a potentially large background source. The Z + jets events with large miss “fake” ET arise from heavy-flavour hadron decays in the accompanying jet(s), from jet mismeasurements in certain regions of the detector, and from the measurement resolution itself, owing to the additional pp collisions in the same or neighbouring proton bunch crossings (pile-up). The ZZ → 4` process yields a small contribution when one lepton pair misses detection, and it is considered as a background in this measurement. Finally, minor background contributions are expected from three-boson production (V V V with V = W or Z) and production of tt accompanied by one or two vector bosons (tt̄V ). The integrated cross-section of ZZ production is measured in a fiducial phase space fid and then extrapolated to a total phase space. The determination of the fiducial (σZZ→``νν ) tot and total (σZZ ) cross-sections is obtained as shown in eq. (1.1): obs obs fid NZZ tot NZZ σZZ→``νν = , σZZ = , (1.1) L × CZZ L × CZZ × AZZ × B where CZZ stands for an overall efficiency correction factor, AZZ is the fiducial acceptance, obs and B is the branching fraction of the ZZ → ``νν (` = e, µ) decay. The signal yield NZZ miss is determined through a fit to the observed ET spectrum, which leads to improved sensitivity compared with a simple event-counting method. The AZZ (CZZ ) factor is exp,fid exp,tot exp,det exp,fid exp,det exp,fid exp,tot calculated as NZZ /NZZ (NZZ /NZZ ), where NZZ , NZZ , and NZZ correspond to the expected signal yields for the ZZ → ``νν final state after the detector- level selection, in the fiducial region, and in the total phase space, respectively. The definitions of the total and fiducial phase spaces are elaborated in section 5. The simulated –3– events arising from the ZZ → τ τ νν decays with the subsequent τ → `νν decays of both τ -leptons are considered as signal events at detector level but excluded in the fiducial measurements. Throughout this paper, “Z → ``” denotes the decays of a Z boson or a virtual photon into a charged-lepton pair. Furthermore, differential cross-sections are reported in the fiducial region for eight kinematic variables, which are sensitive to effects from higher-order corrections and possible BSM physics. These variables include the transverse momentum of the leading lepton (p`1 jet1 `` ZZ T ), the leading jet (pT ), the dilepton system (pT ), and the ZZ system (pT ), the transverse mass of the ZZ system (mZZ 1 T ), the absolute rapidity of the dilepton system (|y`` |), the azimuthal angle difference between the two leptons (∆φ`` ), and the number of JHEP10(2019)127 jets (Njets ). Since no significant deviations from the SM are observed, upper limits are placed on the aTGC parameters [1], which typically manifest themselves as a signal excess √ growing rapidly as the partonic centre-of-mass energy ( ŝ) increases. In this analysis, aTGCs are searched for using the p`` T spectrum in the fiducial region, motivated by the fact `` √ that pT is correlated with ŝ and has a good experimental resolution. 2 ATLAS detector The ATLAS detector [17–19] is a large multi-purpose detector with a forward-backward symmetric cylindrical geometry and nearly 4π coverage in solid angle.2 It consists of an inner tracking detector surrounded by a thin superconducting solenoid, electromagnetic and hadronic calorimeters, and a muon spectrometer incorporating three large superconducting toroidal magnets each having eight coils assembled radially and symmetrically around the beam axis. The inner-detector system (ID) is immersed in a 2 T axial magnetic field and provides charged-particle tracking in the range |η| < 2.5. A high-granularity silicon pixel detector covers the vertex region and usually provides four measurements per track. The pixel detector is followed by a silicon microstrip tracker which usually provides four measure- ment points per track. These silicon detectors are complemented by a transition radiation tracker, which enables radially extended track reconstruction and improved momentum measurements up to |η| = 2.0. The transition radiation tracker also provides electron identification information based on the fraction of hits (typically 30 hits in total) above a high-energy threshold designed for optimal electron-pion separation. The calorimeter system covers the pseudorapidity range |η| < 4.9. Within the region |η| < 3.2, electromagnetic calorimetry is provided by barrel and endcap lead/liquid-argon (LAr) sampling calorimeters, with an additional thin LAr presampler covering |η| < 1.8, v" 2 2 u r # u  2 r  2 1 ZZ 2 `` 2 miss `` ~ Tmiss . mT =t m Z + p T + m Z + ET − p~T + E 2 ATLAS 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, φ being the azimuthal angle around the z-axis. The pseudorapidity is defined in terms of the polar angle θ as η = − ln tan(θ/2). –4– to correct for energy loss in material upstream of the calorimeters. Hadronic calorimetry is provided by a steel/scintillating-tile calorimeter, segmented into three barrel structures within |η| < 1.7, and two copper/LAr hadronic endcap calorimeters. The solid angle coverage is completed with forward copper/LAr and tungsten/LAr calorimeter modules optimised for electromagnetic and hadronic measurements, respectively. The muon spectrometer (MS) comprises separate trigger and high-precision tracking chambers measuring the deflection of muons in a magnetic field generated by supercon- ducting air-core toroids. The field integral of the toroids ranges between 2.0 and 6.0 T·m across most of the detector. A set of precision chambers covers the region |η| < 2.7 with three layers of monitored drift tubes, complemented by cathode strip chambers in the first JHEP10(2019)127 measurement layer of the forward region, where the background is highest. The muon trigger system covers the range |η| < 2.4 with resistive-plate chambers in the barrel, and thin-gap chambers in the endcap regions. A two-level trigger system [20] is used to select events for offline analysis. The first-level trigger is implemented in hardware and uses a subset of the detector information. This is followed by the software-based high-level trigger, reducing the event rate to about 1 kHz. 3 Data and simulation This measurement utilises data collected by the ATLAS detector during the 2015 and 2016 data-taking periods. The data were recorded with a combination of single-lepton triggers, picking up events containing either an isolated lepton above a low-pT threshold or a high- pT lepton without any isolation requirement. The lower pT threshold for the isolated electron (muon) trigger ranges from 24 (20) to 26 GeV depending on the instantaneous luminosity. The higher pT threshold is 50 (60) GeV for the electron (muon) case over all the data-taking periods. Signal events satisfying the event selection described in section 4 are expected to have an overall trigger efficiency of 98%. Monte Carlo event simulation was deployed to model the signal and various background processes (summarised in table 1). In the determination of integrated cross-sections, the miss AZZ and CZZ factors as well as the ET shape for the ZZ signal process were obtained from simulation. The background contributions were either predicted by simulation or estimated in data with the assistance of simulation. The qqZZ process was modelled with Powheg-Box v2 [21–24] interfaced to Pyth- ia8.186 [25] for modelling of the parton showering, hadronisation and underlying event (UEPS). The NLO matrix-element (ME) calculation set both the factorisation (µF ) and renormalisation (µR ) scales to the invariant mass of the ZZ system (mZZ ), and used the NLO CT10 [26] parton distribution function (PDF). The UEPS algorithm used a set of tuned parameters called the AZNLO tune [27]. The production cross-sections as a function of mZZ were corrected to NNLO QCD and NLO EW accuracies in the total phase space. The QCD K-factors were derived using the MATRIX program [13], which computes the NNLO cross-section using the same QCD scales and the NNLO CT10 PDF [28]. The EW correction was applied using K-factors provided by the authors of ref. [15]. The QCD correction is about +10% for the entire mZZ spectrum, while the EW correction –5– Process Generator Simulation accuracy Cross-section accuracy Powheg-Box v2 + Pythia8.186 NLO QCD NNLO QCD + NLO EW qqZZ Sherpa2.2.2 NLO QCD 0-1p, LO QCD 2-3p gg2vv3.1.6 + Pythia8.186 LO QCD NLO QCD ggZZ Sherpa2.1.1 LO QCD 0-1p qqZZ (aTGCs) Sherpa2.1.1 NLO QCD 0-1p, LO QCD 2-3p Powheg-Box v2 + Pythia8.186 WZ NLO QCD Powheg-Box v2 + Herwig++ WW Powheg-Box v2 + Pythia8.186 NLO QCD qqZZ → 4` Powheg-Box v2 + Pythia8.186 NLO QCD NNLO QCD + NLO EW ggZZ → 4` gg2vv3.1.6 + Pythia8.186 LO QCD NLO QCD JHEP10(2019)127 Z + jets Sherpa2.2.1 NLO QCD 0-2p, LO QCD 3-5p NNLO QCD tt Powheg-Box v2 + Pythia6.428 NLO QCD NNLO QCD Wt Powheg-Box v2 + Pythia6.428 NLO QCD NNLO QCD VVV Sherpa2.1.1 NLO QCD tt̄V MadGraph5 aMC@NLO + Pythia8.186 LO QCD NLO QCD Table 1. Summary of Monte Carlo event simulation tools with their theoretical accuracy for each process, where “p” stands for parton(s). For the first two signal processes and the W Z process, the first (second) row describes the baseline (alternative) simulation. The theoretical accuracy of the normalisation used for the total production cross-section of each process is shown in the last column. is about −4% at low mZZ but has a larger impact at high mZZ , which cancels out the positive QCD correction for mZZ around 500 GeV. An alternative sample was generated with Sherpa2.2.2 [29] using the NNLO NNPDF3.0 PDF [30] and the same choice of QCD scales. The Sherpa generator and its associated UEPS algorithm has NLO QCD accuracy for inclusive observables and extended QCD precision for events with one or more outgoing partons (NLO for up to one parton, LO for two and three partons). The ggZZ events were simulated with the LO gg2vv3.1.6 [31, 32] generator using the NNLO CT10 PDF, and then interfaced to Pythia8.186 using the A14 tune [33]. The production cross-section was corrected to NLO QCD accuracy using a K-factor of 1.7 reported in ref. [34]. An alternative modelling was provided by Sherpa2.1.1 [35] with the NLO CT10 PDF, which extended the LO QCD calculation to events with one parton. Both generators used mZZ /2 for the QCD scales, and they incorporated both the fermion-loop and the Higgs processes, together with the interference between the two. To study the effects of aTGCs, an additional sample for the SM qqZZ process was generated at NLO in QCD using Sherpa2.1.1 with the NLO CT10 PDF. The simulated sample was interfaced to a parton-level program [1] following the procedures detailed in ref. [36], and then event-by-event weights reflecting the relative change in the cross-sections due to any aTGCs were computed. A parameterisation of aTGC contributions as a function of any kinematic variable can be derived with this information. This procedure was adopted in the previous ZZ measurements [6–8]. Production of ZZ → 4` events was modelled in the same way as the signal events. The diboson background processes W Z and W W were generated with Powheg-Box v2 using the NLO ME calculation and the NLO CT10 PDF, and then interfaced to Pythia8.186 –6– with the AZNLO tune. An alternative W Z sample was produced with Powheg-Box v2 interfaced to Herwig++ [37], for the study of UEPS uncertainties. The interference be- tween the W W and ZZ processes in the ``νν final state was found to be negligible [16] and was therefore not considered in this analysis. Both the tt and W t events were simulated at NLO in QCD with Powheg-Box v2 [38, 39] and interfaced to Pythia6.428 [40], and the production cross-sections were corrected to NNLO QCD precision [41, 42]. Sherpa2.2.1 with the NNLO NNPDF3.0 PDF was used to model the Z + jets process. The production cross-section for the Z + jets process was calculated with NNLO QCD precision, while the simulation has NLO QCD precision for events with zero, one and two partons, and provided a LO QCD description for events with three to five partons. The rare V V V background, JHEP10(2019)127 consisting of W W W , W W Z, W ZZ and ZZZ processes, was modelled with Sherpa2.1.1 with NLO QCD precision. MadGraph5 aMC@NLO [43] interfaced to Pythia8.186 was used to generate the tt̄V background events that account for ttW , ttZ and ttW W produc- tion processes. The tt̄V process was calculated at LO QCD accuracy, and its production cross-section was corrected to NLO QCD precision [43]. Generated events were then processed through the ATLAS detector simulation [44] based on GEANT4 [45] to emulate the response of the detector to the final-state particles. Pile-up was simulated with Pythia8.186 using the A2 tune [46] and overlaid on simulated events to mimic the real collision environment. The distribution of the average number of interactions per bunch crossing in the simulation was weighted to reflect that in data. Simulated events were processed with the same reconstruction algorithms as for the data. Furthermore, the lepton momentum scale and resolution, and the lepton reconstruction, identification, isolation and trigger efficiencies in the simulation were corrected to match those measured in data. 4 Selection of ``νν events This analysis selects a detector signature with a pair of high-pT isolated electrons (ee) or miss muons (µµ) and large ET . The ee and µµ channels are combined to obtain the final results. The event selection strategy was optimised to cope with the large background contamination. The selection requirements lead to a highly boosted Z boson back-to-back with the missing transverse momentum vector (E ~ Tmiss ). Backgrounds are further reduced by removing events with extra leptons or any jets containing b-hadrons (“b-jets”). Therefore, a precise understanding of the overall reconstruction and selection of leptons, jets, and miss ET is required in this measurement. Events are first required to have a collision vertex associated with at least two tracks each with pT > 0.4 GeV. The vertex with the highest scalar p2T sum of the associated tracks is referred to as the primary vertex. Electrons are reconstructed from energy deposits in the EM calorimeter matched to a track reconstructed in the ID. The electron identification imposes selections on the number of hits in the ID and requirements on a likelihood discriminant, built from variables related to EM calorimeter shower shapes, track-cluster matching, track quality, and transition radi- ation [47]. Electrons must satisfy the “medium” identification criterion [47], which is about –7– 90% efficient for electrons with pT ≈ 40 GeV. Candidate electrons must have pT > 7 GeV and pseudorapidity |η| < 2.47. Muons are reconstructed by combining all the hits associ- ated with a pair of matched tracks reconstructed in the ID and MS, taking into account the energy loss in the calorimeter. Muons are identified by requiring a sufficient number of ID and MS hits, and good consistency between the ID and MS track measurements as well as good combined fit quality [48], and they must satisfy the “medium” identification criterion [48], which has an overall efficiency of 96%. Candidate muons are required to have pT > 7 GeV and |η| < 2.5. To further suppress misidentified lepton and cosmic-ray background contributions, the absolute value of the longitudinal impact parameter of lep- tons with respect to the primary vertex must be smaller than 0.5 mm, and the transverse JHEP10(2019)127 impact parameter divided by its error must be less than 5 (3) for electrons (muons). In addition, the “loose” isolation criteria defined in refs. [47, 48] are applied. The isolation selection imposes requirements on both the track-based and calorimeter-based isolation variables, and varies as a function of pT to maintain a uniform efficiency above 98% for prompt leptons. Jets are reconstructed with the anti-kt algorithm [49] with radius parameter R = 0.4, using as input positive-energy topological clusters in the calorimeters [50–52]. The jet energy scale is calibrated using simulation and further corrected with in situ methods [51]. Candidate jets must have pT > 20 GeV and |η| < 4.5. Additional requirements using the track and vertex information inside a jet [53] are applied for jets with pT < 60 GeV and |η| < 2.5 to suppress pile-up contributions. Candidate b-jets (pT > 20 GeV and |η| < 2.5) are identified with an algorithm providing 85% signal efficiency and a rejection factor of 33 against light-flavour jets [54]. Leptons and jets may be close to each other or overlapping, even after implementing the full set of object selections. The appearance of such overlapping objects may lead to ambiguities in the event selection and in the energy measurement of the physics objects. A common procedure in the ATLAS experiment, as detailed in ref. [55], is applied to resolve the ambiguities. This requirement helps to suppress the occurrence of two “problematic” scenarios, one with energy measurement of electrons biased due to nearby jets, and the other with a jet producing non-prompt muons through meson decays in flight. The E ~ Tmiss vector is computed as the negative of the vector sum of transverse momenta of all the leptons and jets, as well as the tracks originating from the primary vertex but not associated with any of the leptons or jets (“soft-term”) [56]. The soft-term is computed in miss a way minimising the impact of pile-up in the ET reconstruction. Candidate events are preselected by requiring exactly two selected electrons or muons with opposite charges and pT > 20 GeV. The leading lepton is further required to have pT > 30 GeV, well above the threshold of the single-lepton triggers. To suppress the W Z background, events containing any additional lepton satisfying the “loose” rather than “medium” identification requirement, in addition to the other requirements, are rejected. The dilepton invariant mass (m`` ) is required to be in the range between 76 and 106 GeV, which largely reduces the contamination from the non-resonant-`` background. Figure 2 miss shows the observed and expected ET spectra after imposing the above requirements (“preselection”). The fractional experimental uncertainties in the expected spectra increase –8– Events / 30 GeV Events / 30 GeV 10 10 10 10 Data WZ Data WZ 10 9 ATLAS Z( → ee) + jets ZZ → 4l 10 9 ATLAS Z( → µµ) + jets ZZ → 4l -1 10 8 s = 13 TeV, 36.1 fb Non-resonant-ll Other bgds. 10 8 s = 13 TeV, 36.1 fb-1 Non-resonant-ll Other bgds. ee ZZ → llν ν Uncertainty ZZ → llν ν Uncertainty 107 107 µµ 6 6 10 10 5 5 10 10 104 104 3 3 10 10 102 102 10 10 1 1 10−1 10−1 Data / Pred. Data / Pred. 1.4 1.4 1.2 1.2 1 1 0.8 0.8 0.6 0.6 JHEP10(2019)127 0 100 200 300 400 500 0 100 200 300 400 500 Emiss T [GeV] Emiss T [GeV] miss Figure 2. The ET distributions after the preselection for the ee (left) and µµ (right) channels. The expectation is derived from simulation and the hashed band accounts for the statistical and experimental uncertainties. The experimental uncertainties are described in section 7. The last bin in the distributions contains the overflow events. The arrows indicate that the differences between the data and the expectation in some bins exceed the y-axis scope of the bottom plots. The “other” background corresponds to the V V V and tt̄V processes. miss as a function of ET in the region dominated by the Z + jets process, as a result of the miss asymmetric migration effects along the steeply falling ET distribution and the large miss jet-related uncertainty for Z + jets events at high ET . The top-quark processes with miss miss miss genuine ET dominate the high ET region. For ET around 200 GeV, top-quark events generally contain less jet activity than Z +jets events: this leads to correspondingly smaller experimental uncertainties. The experimental uncertainties are elaborated in section 7. The data sample after the preselection is dominated by the Z + jets and non-resonant-`` miss processes. To suppress these backgrounds, a further selection based on ET and event topology is applied. miss Candidate events are required to have ET > 110 GeV and VT /ST > 0.65, where VT is the magnitude of the vector sum of transverse momenta of selected leptons and jets, and ST is the scalar pT sum of the corresponding objects. The variable VT /ST was found to be miss less sensitive to jet experimental uncertainties than similar variables such as ET /ST . To further reduce the impact of jet energy scale uncertainties, the calculation of VT and ST uses “hard jets” which are required to have pT > 25 GeV for the central region (|η| < 2.4) miss and pT > 40 GeV for the forward region (2.4 < |η| < 4.5). The ET cut suppresses the Z + jets contamination by many orders of magnitude, and the residual Z + jets events, miss which have large fake ET , are further suppressed by the VT /ST requirement. As the miss consequence of the combined ET and VT /ST requirement, the Z + jets process only constitutes a small fraction of the total background after the full selection. Additional selection criteria based on angular variables are imposed to ensure the desired detector signature, which helps to further reject the Z + jets and non-resonant- `` background events. The azimuthal angle difference between the dilepton system and E pT`` , E ~ Tmiss , ∆φ(~ ~ Tmiss ), must be larger than 2.2 radians, and the selected leptons must be q close to each other, with the distance ∆R`` = (∆φ`` )2 + (∆η`` )2 < 1.9. Finally, events –9– Step Selection criteria Two leptons Two opposite-sign leptons, leading (subleading) pT > 30 (20) GeV Jets pT > 20 GeV, |η| < 4.5, and ∆R > 0.4 relative to the leptons Third-lepton veto No additional lepton with pT > 7 GeV m`` 76 < m`` < 106 GeV Hard jets pT > 25 GeV for |η| < 2.4, pT > 40 GeV for 2.4 < |η| < 4.5 miss miss ET and VT /ST ET > 110 GeV and VT /ST > 0.65 ∆R`` ∆R`` < 1.9 JHEP10(2019)127 pT`` , E ∆φ(~ ~ Tmiss ) ∆φ(~ `` ~ miss pT , ET ) > 2.2 radians b-jet veto N (b-jets) = 0 with b-jet pT > 20 GeV and |η| < 2.5 Table 2. Event selection criteria for the ``νν signature. Born-level leptons (ee or µµ) Total phase space 66 < m`` , mνν < 116 GeV Dressed leptons (e or µ): pT > 7 GeV, |η| < 2.5 Jets: pT > 20 GeV, |η| < 4.5 Reject leptons if overlapping with a jet within ∆R < 0.4 Fiducial phase space Two leptons with leading (subleading) pT > 30 (20) GeV 76 < m`` < 106 GeV miss ET > 90 GeV and VT /ST > 0.65 `` ~ miss ∆φ(~pT , ET ) > 2.2 radians and ∆R`` < 1.9 Table 3. Definitions of the total and fiducial phase spaces for the ZZ → ``νν signal. containing one or more b-jets are vetoed to further suppress the tt and W t backgrounds. The full event selection is summarised in table 2. Figure 3 gives the observed and simulated pT`` , E spectra for VT /ST , ∆R`` , ∆φ(~ ~ Tmiss ), and the number of b-jets, where each plot is made with the implementation of all the cuts prior to the cut on that variable, according to the cut sequence in table 2. 5 Total and fiducial phase spaces The definitions of the total and fiducial phase spaces are summarised in table 3. The total phase space is defined as in ref. [8] for the ZZ → 4` measurement, requiring 66 < m`` , mνν < 116 GeV (` = e or µ), where the leptons and neutrinos originate from the Z-boson decays. The four-momenta of the leptons are defined at Born level, i.e. before any QED final-state radiation. The fiducial phase space is defined with a set of criteria very close to that of the detector-level event selection (table 2). This strategy helps to reduce the amount of phase- – 10 – Events / 0.1 Events / 0.2 107 ATLAS Data Non-resonant-ll 5 ATLAS Data Non-resonant-ll 10 6 ZZ → llνν ZZ → 4l ZZ → llνν ZZ → 4l 10 s = 13 TeV, 36.1fb-1 s = 13 TeV, 36.1fb-1 WZ Other bgds. WZ Other bgds. 4 5 ee+µµ Z( → ll) + jets Uncertainty 10 Z( → ll) + jets Uncertainty 10 ee+µµ 4 3 10 10 3 10 102 102 10 10 1 1 −1 10 10−1 Data / Pred. Data / Pred. 1.5 1.5 1 1 JHEP10(2019)127 0.5 0.5 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.5 1 1.5 2 2.5 3 3.5 4 VT/ST ∆R ll Events Events / 0.4 10 5 ATLAS Data Non-resonant-ll 10 5 ATLAS Data Non-resonant-ll ZZ → llνν ZZ → 4l s = 13 TeV, 36.1fb-1 ZZ → llνν ZZ → 4l s = 13 TeV, 36.1fb-1 WZ Other bgds. WZ Other bgds. 104 Z( → ll) + jets Uncertainty 104 Z( → ll) + jets Uncertainty ee+µµ ee+µµ 3 3 10 10 102 102 10 10 1 1 −1 10 10−1 Data / Pred. Data / Pred. 1.5 1.5 1 1 0.5 0.5 2 2.2 2.4 2.6 2.8 → 3 3.2 0 1 2 3 4 → ∆φ(p ll , Emiss T ) [rad] Number of b-jets T Figure 3. The VT /ST , ∆R`` , ∆φ(~ pT`` , E ~ Tmiss ), and the number of b-jets distributions for the combination of ee and µµ channels with the implementation of all the cuts in table 2 prior to the cut on that variable. The expectation is derived from simulation and the hashed band accounts for the statistical and experimental uncertainties. The first bin in the distribution of ∆φ(~ pT`` , E ~ Tmiss ) (bottom left) contains the underflow events. The arrow in the VT /ST distribution indicates that the difference between the data and the expectation exceeds the y-axis scope of the bottom plot. The “other” background corresponds to the V V V and tt̄V processes. space extrapolation in the fiducial measurements and therefore minimises the theoretical uncertainties of the results. The criteria are applied to “particle-level” physics objects, which are reconstructed from stable final-state particles, prior to their interactions with the detector. For electrons and muons, QED final-state radiation is partly recovered by adding to the lepton four-momentum the four-momenta of surrounding photons not origi- nating from hadrons within an angular distance ∆R < 0.1 (dressed leptons). Particle-level jets are built with the anti-kt algorithm with radius parameter R = 0.4, using all final- state particles as the input (excluding muons and neutrinos). As shown in table 3, the selection requirements for the fiducial phase space closely follow those in table 2. The E~ Tmiss vector is defined as the sum of transverse momenta of the two neutrinos from the miss Z-boson decays. This measurement requires large ET , which has a detector resolution of around 10 GeV [56] in the phase space considered here. To accommodate the majority of – 11 – ZZ qqZZ ggZZ ee µµ ee µµ ee µµ 220 ± 15 229 ± 15 Signal yield (± 2 stat ±7 exp (± 2 stat ±7 exp 194 ± 12 202 ± 12 25 ± 15 26 ± 16 ± 13 theory ) ± 13 theory ) CZZ — (54.7 ± 1.7)% (56.6 ± 1.7)% (53.1 ± 1.8)% (55.5 ± 2.2)% exp,fid σZZ→``νν 22.4 ± 1.3 fb 18.8 ± 1.0 fb 2.6 ± 0.8 fb AZZ — (5.3 ± 0.1)% (5.3 ± 0.3)% exp,tot σZZ 15.7 ± 0.7 pb 13.9 ± 0.4 pb 1.8 ± 0.6 pb Table 4. Predictions for the signal yields at detector level, for the CZZ and AZZ coefficients defined JHEP10(2019)127 in eq. (1.1), and for the cross-sections in the fiducial and total phase spaces. The first column gives the corresponding predictions for the total ZZ process, combined from those shown separately for the qqZZ and ggZZ sub-processes. The errors include both the statistical and systematic uncertainties (see section 7). The statistical, experimental, and theoretical uncertainties are also shown separately for the combined signal yields. miss the events selected at detector level, the ET threshold is therefore lowered to 90 GeV in the fiducial region. The efficiency of the b-jet veto is found to be 98% in the fiducial region and consistent between the Powheg+Pythia8 and Sherpa generators. No requirement is made on the number of b-jets in the fiducial selection. Table 4 gives the expected signal yields at detector level, the AZZ and CZZ factors, and the predicted cross-sections. The qqZZ and ggZZ processes have similar final-state kinematic distributions and their AZZ and CZZ factors are similar. The corresponding factors for the total ZZ process are averaged from that for the two sub-processes, weighted by the respective cross-sections. The cross-section predictions for the total phase space are corrected for the branching fraction of the ZZ → ``νν decays, 1.35% with a negligible uncertainty, obtained from refs. [57, 58]. The expected fiducial and total cross-sections, exp,fid exp,tot σZZ→``νν and σZZ , are calculated from simulation, including the higher-order corrections detailed in section 3. The total uncertainties in these predictions are also provided in table 4, and the procedures used to derive these uncertainties are described in section 7. 6 Background estimation After the event selection, the overall signal-to-background ratio is about 1.7. The W Z and non-resonant-`` backgrounds account for 72% and 21% of the total background contribu- tion, respectively, and are estimated from control regions in data. The Z + jets background is largely suppressed, yielding a relative contribution of only 4%, and is estimated from data. Finally, the small remaining contributions from other processes, amounting in total to 3% of the total background, are estimated from simulation. The various background estimates and their uncertainties are described below. To estimate the dominant resonant background from W Z production, a control region enriched in W Z events, with a purity of 90%, is defined using the preselection criteria, ex- cept that a third lepton with pT > 20 GeV and satisfying the medium identification criteria is required. Several further selections such as VT /ST > 0.3, b-jets veto, and mW T > 60 GeV, – 12 – where mW T is constructed from the third lepton’s transverse momentum and the ET ~ miss vector,3 are applied to suppress non-W Z contributions. A normalisation factor (fW Z ) is calculated in the control region as the number of observed events in data, subtracting the non-W Z contributions estimated from simulation, divided by the predicted W Z yield. The factor fW Z is found to be 1.26 ± 0.04 (stat), which is consistent with a recent W Z measure- ment [59], performed within a broader fiducial phase space and using a recent calculation of the W Z total cross-section at NNLO in QCD [60, 76]. The statistical uncertainty of the data in the control region leads to a 3% uncertainty in the W Z estimate in the signal region. The systematic uncertainty is evaluated for the ratio of the W Z predictions in the signal and control regions. The experimental uncertainty in this ratio is 3.5%, and the JHEP10(2019)127 theoretical uncertainty is 3.3%, calculated as the sum in quadrature of the PDF, scale, and UEPS uncertainties. The non-W Z contribution in the control region is less than 10%. The uncertainty related to the subtraction of the non-W Z contribution, estimated by imposing cross-section uncertainties for all the relevant processes, is found to be about 2%. The total uncertainty in the W Z estimate is about 6%. The kinematic distributions are estimated from simulation, with both the experimental and theoretical uncertainties considered. To estimate the non-resonant-`` background, including W W , top-quark (tt and W t), and Z → τ τ production, a control region dominated by the non-resonant-`` processes (with a purity above 95%) is defined with all the event selection criteria in table 2, except that the final state is required to contain an opposite-sign eµ pair. The non-resonant-`` con- tribution in the ee (µµ) channel is calculated as one half of the observed data yield after subtracting the contribution from the other background processes in the control region, and then corrected for the difference in the lepton reconstruction and identification effi- ciencies between selecting an eµ pair and an ee (µµ) pair. The lepton efficiency correction is derived as the square root of the ratio of the numbers of µµ and ee events in data after the preselection. The choice of deriving the correction after preselection minimises the resulting statistical uncertainty. The total uncertainty in the non-resonant-`` estimate is about 16%, including the statistical uncertainty of the data in the control region (14%) and the method bias estimated from simulation (7%). The kinematic distributions for the non- resonant-`` background estimate in the signal region are predicted with simulation, and the assigned systematic uncertainty covers the experimental uncertainty in the simulated shape as well as the difference between data and simulation in the control region. Figure 4 gives two examples of comparing data and predictions in the W Z and non- resonant-`` background control regions. The left-hand figure is the mW T distribution in the W Z control region, where the normalisation factor fW Z is applied to the W Z simulation and good agreement between the observed and predicted shapes is found. The right- miss hand figure is the ET distribution in the non-resonant-`` control region, where the W W and top-quark (tt and W t) production processes are dominant. Both the statistical and experimental uncertainties are included in the hashed bands in these figures. A data-driven method is used to estimate the Z + jets background. This method defines three independent regions (labelled as B, C and D) which are enriched in Z + q 3 W ` miss ` ~ miss mT = 2pT ET [1 − cos ∆φ(~ pT , E T )]. – 13 – Events / 20 GeV Events / 10 GeV ATLAS Data WW 104 ATLAS Data WZ Top quarks Top quarks 104 s = 13 TeV, 36.1 fb-1 s = 13 TeV, 36.1 fb-1 WZ Z( → ll) + jets Other bgds. 10 3 WW Other bgds. WZ CR ZZ Uncertainty Non-resonant-ll CR ZZ Uncertainty 3 10 102 102 10 1 10 −1 10 1 10−2 Data / Pred. Data / Pred. 1.5 1.5 1 1 0.5 0.5 JHEP10(2019)127 60 80 100 120 140 160 180 200 100 120 140 160 180 200 220 mW T [GeV] Emiss T [GeV] Figure 4. Distributions in the control regions (CR), of mW T for the W Z CR (left) and of ET miss for the non-resonant-`` CR (right). The data are compared with the predictions from simulation, where the W Z contribution is scaled by the normalisation factor of 1.26 described in the text. The last bin in the distributions contains the overflow events. The hashed bands include both the statistical and experimental uncertainties. The “other” background corresponds to the V V V and tt̄V processes. jets events and are not overlapping with the signal region (labelled as A). The data yields after subtracting the non-Z contributions in these regions (nB , nC and nD ) are used to predict the Z + jets contribution in the signal region (nA ), calculated as nC × nB /nD . The main assumption of the method is that nA /nC = nB /nD . The control region definitions are optimised to ensure that this assumption is valid within uncertainties evaluated from simulation. The control regions are defined using the preselection requirements plus the miss b-jets veto. A further requirement of ET > 30 GeV and VT /ST > 0.2 is imposed to miss miss remove the low-ET phase space which is far away from the signal region. The ET and VT /ST variables are expected to have a small correlation with the topological variables, so the various requirements to define the control regions are grouped together, such that the correlations between regions are minimised. Specifically, two Boolean variables are defined miss as, α = “ET > 110 GeV and VT /ST > 0.65” and β = “∆φ(~ pT`` , E ~ Tmiss ) > 2.2 radians and ∆R`` < 1.9”. The four regions are then defined as follows: • Region A: α = TRUE and β = TRUE • Region B: α = FALSE and β = TRUE • Region C: α = TRUE and β = FALSE • Region D: α = FALSE and β = FALSE Regions B and D are dominated by the Z + jets process (with a purity greater than 95%), while its relative contribution in region C is only 70% because the tt contribution in this phase space region remains large. The derived Z + jets contribution is corrected for the closure factor (nA /nC × nD /nB ) estimated from simulation. This factor is found to be – 14 – 0.9 and has a relative uncertainty of 48%, consisting of the statistical (40%), experimental (22%), and methodology uncertainties (15%). The experimental uncertainty in the closure factor is dominated by jet energy scale and resolution. The methodology uncertainty miss covers the variations obtained by changing the ET and VT /ST thresholds in the low- miss ET removal requirement by 40%. The Z +jets estimation is also subject to the statistical uncertainty of the data (5%) and the subtraction of non-Z contributions in the control regions (5%). The non-Z subtraction uncertainty is driven by the modelling uncertainty miss for the main non-Z process in region C (tt production), which is about 10–20% for ET above 100 GeV [61]. The total uncertainty on the Z + jets estimate is about 50%. The kinematic distributions for the Z + jets background in the signal region are derived from JHEP10(2019)127 the data in region C, together with a systematic uncertainty assigned in a way similar to that described above for the non-resonant-`` background. The ZZ → 4`, V V V and tt̄V (V ) backgrounds are estimated from simulation, and their contributions have a total uncertainty of 10-20%, including both the theoretical cross- section [8, 62, 63] and the experimental uncertainties. 7 Systematic uncertainties The measurement results and predictions are subject to theoretical and experimental un- certainties, as well as uncertainties related to the background estimation. The background uncertainties are explained in section 6. The statistical uncertainties of the simulated sam- ples for both the signal and background processes are also taken into account wherever applicable. The systematic uncertainty sources for the signal process are detailed below. The theoretical uncertainties for the dominant qqZZ signal sub-process are estimated with the Powheg+Pythia8 generator, since only the total cross-section has been cal- culated to NNLO QCD and NLO EW accuracies. The theoretical uncertainties originate from the PDF choice, the missing higher-order QCD calculation, and the UEPS modelling. The PDF uncertainty is calculated as the 68% confidence-level eigenvector uncertainty [26] of the nominal PDF used in the simulation. The uncertainty due to the QCD calcula- tion, also referred to as the “scale” uncertainty, covers the variations of predictions from changing the QCD renormalisation and factorisation scales. The QCD scales are varied independently by factors ranging from one half to two, which in total yields seven dif- ferent scale choices including the nominal one. The UEPS uncertainty is taken as the difference in the predictions between the Herwig++ and the default showering programs. The fractional theoretical uncertainty in AZZ for the qqZZ process is about 1.8%, while the overall uncertainties in the cross-section predictions in the total and fiducial phase spaces are about 3% and 5%, respectively. The Sherpa generator is used to cross-check the nominal predictions, and the AZZ factors from Powheg and Sherpa are consistent with each other within the uncertainty. The CZZ predictions from the two generators are found to be consistent within the statistical uncertainty of 1%, and in this measurement, the theoretical uncertainty in CZZ is neglected. The understanding of the p`` T spectrum in the fiducial phase space is crucial for the study of aTGCs, and the predictions from the two generators differ by up to 10% for – 15 – p`` T around 300 GeV, which is slightly above the theoretical uncertainty of the Powheg prediction. The Powheg prediction with the K-factors applied has better precision in terms of the EW calculation, while the Sherpa generator is expected to give a better description of ZZ production with extra QCD radiation. Finally, an uncertainty is applied to the p`` T prediction, as the sum in quadrature of the theoretical uncertainty estimated with Powheg and the difference between Powheg and Sherpa, which is about 5% for p`` `` T around 150 GeV and increases to about 11% for pT above 250 GeV. A constant 30% uncertainty is assigned to the total ggZZ cross-section prediction, which covers the uncertainties concerning the NLO K-factor [34] and the potential missing higher-order contributions [64]. The AZZ predictions for the ggZZ process from the gg2vv JHEP10(2019)127 and Sherpa generators are found to be consistent, and the AZZ uncertainty is estimated with Sherpa and found to be 4.6%. The theoretical uncertainty in CZZ is neglected for the ggZZ process. The major experimental uncertainties originate from the luminosity uncertainty, the momentum scale and resolution of leptons and jets, and the lepton reconstruction and selection efficiencies [47, 48, 51, 65]. Smaller experimental uncertainties are also considered, which include uncertainties due to the trigger selection efficiency, the b-jet identification miss efficiency, the calculation of the ET soft-term, and the variation of the average number of interactions per bunch crossing (hereafter referred to as pile-up uncertainty). The pile- up uncertainty covers the uncertainty on the ratio between the predicted and measured inelastic cross-section in the fiducial volume defined by MX > 13 GeV where MX is the mass of the hadronic system [66]. Overall, the total experimental uncertainty on CZZ is 3.1%, dominated by the jet and lepton components. The uncertainty in the combined 2015+2016 integrated luminosity is 2.1%. It is derived, following a methodology similar to that detailed in ref. [67], and using the LUCID-2 detector for the baseline luminosity measurements [68], from calibration of the luminosity scale using x–y beam-separation scans. The fractional uncertainties in AZZ and CZZ are summarised in table 5. In this analysis, the theoretical uncertainties are treated as uncorrelated between the qqZZ and ggZZ processes, while the experimental uncertainties are considered as fully correlated across the relevant processes and final-state channels. 8 Integrated cross-section results Table 6 lists separately for the ee and µµ channels the observed data yields and the ex- pectations for the signal and background contributions after the final selection. Figure 5 miss shows for the combined ee and µµ channels the observed and expected ET distributions, which are in good agreement. fid tot The integrated fiducial and total cross-sections (σZZ→``νν and σZZ ) are determined miss by binned maximum-likelihood fits to the ET distributions. As shown in figure 5, the miss miss signal-to-background ratio increases as ET becomes larger. The use of ET improves the precision of the measured fiducial cross-section relatively by 5% compared with the case where no kinematic information is used. – 16 – AZZ CZZ qqZZ ggZZ ee µµ Stat. 1.0% 1.1% Stat. 0.6% 0.6% Electron 2.0% — PDF 0.8% 3.5% Muon — 1.9% Scale 1.4% 2.0% Jet 2.0% 2.0% UEPS 0.1% 2.0% Soft 0.9% 1.1% Total 1.9% 4.6% Total 3.1% 3.1% JHEP10(2019)127 Table 5. Fractional uncertainties for AZZ and CZZ , with the contributions from the various sources, theoretical only for AZZ and experimental only for CZZ . The uncertainties in AZZ for the qqZZ and ggZZ sub-processes are given in different columns. The uncertainties in CZZ for the ee and µµ channels of the inclusive ZZ process are given in separate columns. The total uncertainties in miss AZZ and CZZ are given in the last rows, respectively. The “Soft” term includes the ET soft-term and the pile-up uncertainties. ee µµ Data 371 416 Signal qqZZ 194 ± 3± 12 202 ± 3± 12 ggZZ 25.1 ± 0.3 ± 7.7 26.4 ± 0.3 ± 8.1 Backgrounds WZ 92.9 ± 3.0 ± 4.8 100.7 ± 3.2 ± 5.2 Non-resonant-`` 25.5 ± 3.4 ± 1.8 31.5 ± 4.2 ± 2.2 Z + jets 4.7 ± 0.2 ± 2.3 5.9 ± 0.3 ± 2.8 ZZ → 4` 3.8 ± 0.2 ± 0.3 4.2 ± 0.2 ± 0.3 Others 0.87 ± 0.03 ± 0.17 0.87 ± 0.03 ± 0.17 Background expected 128 ± 5± 6 143 ± 5± 6 Total expected 347 ± 5± 15 372 ± 6± 16 Table 6. Observed data yields and expected signal and background contributions, shown separately for the ee and µµ channels. The errors shown for the expected yields correspond to the statistical and systematic contributions in that order. The expected background and signal+background yields are shown in the last two rows, where the uncertainties are computed as the sum in quadrature of those from the individual processes. miss The expected yield in each channel i and in each ET bin j is given by: ij fid i ij ij tot Nexp = σZZ→``νν × L × CZZ × fZZ + Nbkg = σZZ × B × L × AiZZ × CZZ i ij × fZZ ij + Nbkg , ij where L is the integrated luminosity, Nbkg the expected background yield, B the branching ij fraction for the ZZ → ``νν decay (` = e or µ), and fZZ is the fraction of signal events – 17 – Events / 30 GeV 5 ATLAS Data Z( → ll) + jets 10 -1 ZZ → llνν ZZ → 4l s = 13 TeV, 36.1fb WZ Other bgds. 104 ee+µµ Non-resonant-ll Uncertainty 3 10 102 10 1 −1 10 10−2 JHEP10(2019)127 Data / Pred. 1.4 1.2 1 0.8 0.6 150 200 250 300 350 400 450 500 miss ET [GeV] miss Figure 5. Observed and expected ET distributions after the final selection for the combined ee + µµ channel before the fit procedure. The error bars on the data points correspond to the data statistical uncertainties, and the hashed band for the prediction includes both the statistical uncer- tainties of the simulation and the systematic uncertainties. The “other” background corresponds to the V V V and tt̄V processes. in bin j with respect to the total distribution. The number of events follows a Poisson distribution in each bin, and the systematic uncertainties are treated as Gaussian nuisance parameters, θk , in the fit. For each source of systematic uncertainty, k, a single nuisance parameter is used for all the processes and channels where this uncertainty matters. The statistical uncertainty due to the limited size of simulated samples is treated as uncorrelated among bins and channels. The binned likelihood function is built over all bins as follows: ij ij YY Y L(σ, θ~ ) = Pois(Nobs |Nexp (σ, θ~ )) × Gaus(θk ), i j k ij where Nobs is the observed data yield in each bin. Table 7 summarises the main sources of uncertainty in the measured combined fidu- cial cross-section, where individual sources of a similar nature are grouped together. The statistical and total systematic uncertainties in the measurement are of similar sizes. Ta- ble 8 shows the measured fiducial cross-sections, separately for each channel and for their combination, together with the breakdown of their uncertainties. The ee and µµ channel cross-sections are compatible within their respective statistical uncertainties. The mea- sured combined fiducial cross-section has a total uncertainty of 7%, which is significantly better than the previous measurement [7], and comparable in size to that obtained in the ZZ → 4` channel [8, 12]. Table 8 also shows the combined measured total cross-section, as well as the predictions for the cross-sections, as taken from table 4. The combined – 18 – Lumi. Electron Muon Jet Total Data stat. Total syst. 2.2% 1.2% 1.1% 2.1% 7.0% 5.5% 4.3% WZ Non-resonant-`` Z + jets Sim. stat. 1.6% 1.6% 0.4% 0.7% Table 7. Relative contributions to the measured combined fiducial cross-section from the main sources of uncertainty after the fit procedure. The total uncertainty includes the data statistical and systematic components. For the systematic uncertainty, the individual sources of a similar nature are grouped together for simplicity. “Sim. stat.” indicates the uncertainty source corresponding to the limited size of the simulation samples for the signal and background processes. JHEP10(2019)127 Measured Predicted ee 12.2 ± 1.0 (stat) ± 0.5 (syst) ± 0.3 (lumi) 11.2 ± 0.6 fid σZZ→``νν [fb] µµ 13.3 ± 1.0 (stat) ± 0.5 (syst) ± 0.3 (lumi) 11.2 ± 0.6 ee + µµ 25.4 ± 1.4 (stat) ± 0.9 (syst) ± 0.5 (lumi) 22.4 ± 1.3 tot σZZ [pb] Total 17.8 ± 1.0 (stat) ± 0.7 (syst) ± 0.4 (lumi) 15.7 ± 0.7 Table 8. Measured and predicted integrated cross-sections in the fiducial and total phase spaces, together with the breakdown of their uncertainties. The luminosity uncertainty is quoted separately from the other systematic uncertainties. The measurements are also shown separately for the ee and µµ channels in the case of the fiducial cross-section. measurement is about 13% higher than the prediction, which is not significant given the size of the measurement and prediction uncertainties. 9 Differential cross-section results Differential cross-sections are measured in the fiducial phase space by counting data events observed in each bin of the observables of interest, after subtracting the expected back- ground contribution, and correcting for the detector effects with the unfolding procedure, chosen here to be the iterative Bayesian unfolding method of ref. [69]. The unfolding pro- cess takes into account fiducial corrections (correcting for events outside the fiducial phase space but passing the detector-level selections), bin-to-bin migrations due to detector res- olution, and detector inefficiencies. An optimal number of two iterations is used for this analysis, as a balance between the size of the statistical uncertainty in the measurement and residual biases from the method. The residual bias is in almost all bins below 1%, as estimated by comparing the results obtained using different prior distributions (constant, expected, observed) in the unfolding process. The experimental uncertainties for the measurement results are evaluated by varying the response matrices for the unfolding according to the ±1σ effects of each uncertainty source, and by comparing the resulting unfolded results with the nominal one. The back- ground uncertainties are considered at the stage of the background subtraction. The sta- tistical uncertainty of the data is estimated by repeating the unfolding procedure with 2000 – 19 – p`` T range [GeV] 50–110 110–130 130–150 150–170 170–200 200–250 250–350 350–1000 Measured σ (fb) 9.3 6.6 3.6 2.1 2.5 2.0 1.1 0.4 Total unc. 17.7% 13.6% 15.2% 18.6% 18.6% 17.6% 24.9% 40.5% Stat. unc. 14.7% 11.1% 14.0% 17.7% 16.0% 16.9% 23.4% 39.4% Syst. unc. 7.0% 4.5% 5.0% 4.3% 3.9% 4.6% 4.6% 5.5% Bkg. unc. 6.9% 6.4% 3.2% 3.7% 8.6% 2.1% 7.1% 7.6% Sim. stat. 1.2% 0.7% 0.7% 0.8% 0.9% 0.9% 1.1% 2.0% Electron 0.7% 0.8% 0.9% 1.7% 1.3% 1.6% 2.1% 3.2% Muon 1.0% 1.3% 1.0% 1.1% 1.2% 1.4% 2.0% 1.7% JHEP10(2019)127 Jet 5.4% 2.9% 3.8% 3.0% 2.3% 2.1% 2.7% 2.5% Soft 3.6% 2.2% 2.0% 0.8% 1.3% 2.7% 0.3% 1.7% Luminosity 2.1% 2.1% 2.1% 2.1% 2.1% 2.1% 2.1% 2.1% Table 9. Measured cross-sections and breakdown of uncertainties (%) for the unfolded p`` T distribu- tion in the fiducial region. The top part of the table gives separately the three main contributions to the total uncertainty, arising respectively from data statistics (labelled Stat.), background subtrac- tion (labelled Bkg.), and other systematic uncertainties (labelled Syst.). The bottom part of the table shows a more detailed breakdown of the third contribution (Syst.). The “Soft” term includes miss the ET soft-term and the pile-up uncertainties. sets of pseudo-data and then taking the root mean square of the deviations of the resulting spectra from the data spectrum. The response matrices are also subject to the statistical uncertainty of the simulated samples, which is estimated using a similar approach. Figures 6 and 7 present the measured differential cross-sections for the eight observables of interest defined in section 1. The binning for each variable is chosen to minimise the bin-to-bin migrations while preserving a sufficient number of events per bin. The Njets spectrum is measured only for hard jets as defined in section 4, and the pjet1 T distribution is obtained in the fiducial phase space of events containing at least one hard jet. The predictions from Powheg+gg2vv and Sherpa are also shown in figures 6 and 7, and are found to be in agreement with the measurements within uncertainties. The electroweak production of ZZ associated with two jets is not taken into account in the predictions due to its negligible contribution. The differential measurements are largely dominated by the statistical uncertainty on the data, but the systematic uncertainties contribute significantly in certain regions of phase space. As an example, the uncertainties from the various sources for the differential measurement of the p``T distribution are listed in table 9. Comparing with the Run-1 results of ref. [7], this measurement is obtained from a larger dataset with highly improved accuracy and for a wider range of observables. 10 Search for aTGCs The search for aTGCs is carried out using the unfolded p``T distribution of figure 6 in the fiducial phase space. The contribution due to aTGCs is introduced using an effective vertex function approach [1]. It includes two coupling parameters that violate charge-parity (CP) – 20 – ∆σ/∆mTZZ [ fb / GeV ] ∆σ/∆pZZ [ fb / GeV ] Data 102 Data ATLAS ATLAS Total uncertainty Total uncertainty s = 13 TeV, 36.1 fb-1 POWHEG + GG2VV (K-factor) s = 13 TeV, 36.1 fb-1 POWHEG + GG2VV (K-factor) 1 10 SHERPA (qq+gg) SHERPA (qq+gg) 1 T 10−1 10−1 10−2 10−2 10−3 10−4 10−3 1.4 Pred. / Data Pred. / Data 1.2 1.5 1 1 0.8 0.5 0.6 JHEP10(2019)127 200 300 400 500 103 0 50 100 150 103 mZZ pZZ [GeV] T [GeV] T ∆σ/∆pl1 [ fb / GeV ] Data ∆σ/∆p [ fb / GeV ] ATLAS Data ATLAS 10 10 Total uncertainty Total uncertainty s = 13 TeV, 36.1 fb-1 s = 13 TeV, 36.1 fb-1 POWHEG + GG2VV (K-factor) POWHEG + GG2VV (K-factor) 1 SHERPA (qq+gg) SHERPA (qq+gg) 1 T T ll 10−1 10−1 10−2 10−2 10−3 10−3 1.4 Pred. / Data Pred. / Data 1.5 1.2 1 1 0.8 0.5 0.6 50 100 150 200 250 300 350 1000 50 100 150 200 pll [GeV] pl1 [GeV] T T Figure 6. The measured differential cross-sections for (top left), (top right), (bot- mZZ T pZZ T p`` T `1 tom left), and pT (bottom right) in the fiducial region. The hashed bands in light grey give the total uncertainty. Also shown are the predictions as obtained from Sherpa (solid line) and Powheg+gg2vv (dashed line) with higher-order K-factors, as described in section 3. The last bin in the four distributions is shown using a different x-axis scale for better visualisation. symmetry, f4γ and f4Z , as well as two CP-conserving ones, f5γ and f5Z . The sensitivity range of this search is found to be within the unitarity bounds, so no form factors [1] are applied to the coupling parameters in this analysis. Furthermore, the coupling parameters are used to extract information about the dimension-eight operators of the effective field theory [70]. The relative change in the SM production cross-section in each p`` T bin is parameterised as a quadratic function depending on the coupling parameters. The parameterisation for the qqZZ process is derived from simulation as described in section 3, while the impact of the aTGCs is neglected for the ggZZ process. Since the sensitivity to possible aTGCs lies in the high-p`` `` T region, only the bins with pT > 150 GeV are considered in the search. Figure 8 compares the measured p`` T spectrum in this region of phase space to the SM prediction alone and to the SM prediction augmented with aTGCs corresponding to differ- ent values of the coupling parameters described above. The highest-p`` T bins are required to contain a minimum of ten observed events at the detector level, which arises from the fact that the limits on aTGCs derived from the unfolded distribution rely on a Gaussian – 21 – ∆σ [ fb / rad ] ∆σ [ fb ] 14 Data Data ATLAS 14 ATLAS Total uncertainty Total uncertainty 12 s = 13 TeV, 36.1 fb-1 POWHEG + GG2VV (K-factor) 12 s = 13 TeV, 36.1 fb-1 POWHEG + GG2VV (K-factor) 10 SHERPA (qq+gg) SHERPA (qq+gg) 10 8 8 6 6 4 4 2 2 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 1.4 Pred. / Data Pred. / Data 1.5 1.2 1 1 0.8 0.6 0.5 0.4 JHEP10(2019)127 0 0.5 1 1.5 2 2.5 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 |y | ∆φ /π [rad] ll ll ∆σ [ fb ] [ fb / GeV ] 103 ATLAS Data Data ATLAS Total uncertainty Total uncertainty s = 13 TeV, 36.1 fb-1 1 s = 13 TeV, 36.1 fb-1 POWHEG + GG2VV (K-factor) POWHEG + GG2VV (K-factor) 2 10 SHERPA (qq+gg) SHERPA (qq+gg) jet1 T ∆σ/∆p 10 10−1 1 10−2 10−1 Pred. / Data 1.5 Pred. / Data 1.5 1 1 0.5 0.5 0 1 2 3-10 25 30 35 40 45 50 55 60 102 jet1 Njets p [GeV] T Figure 7. The measured differential cross-sections for |y`` | (top left), ∆φ`` (top right), Njets (bottom left), and pjet1 T (bottom right) in the fiducial region. The hashed bands in light grey give the total uncertainty. Also shown are the predictions as obtained from Sherpa (solid line) and Powheg+gg2vv (dashed line) with higher-order K-factors, as described in section 3. The last bin in the distributions of Njets and pjet1 T is shown using a different x-axis scale for better visualisation. approximation for the statistical fluctuations from the data. The total uncertainty in the measured cross-section in the last bin is about 40%, while the corresponding SM prediction has an uncertainty of 15%. The sensitivity to aTGCs is thus still limited by the statistical uncertainty of the data. Since the observation is consistent with the SM prediction, 95% confidence intervals (CIs) for the coupling parameters are derived. The test statistic is based on a profile log- likelihood ratio [71], and the likelihood function is described by a multivariate Gaussian distribution with the following form:   Y 1 1 T −1 L= q × exp − (~x − µ ~ ) C (~x − µ~) × Gaus(θs ), 5 2 (2π) |C| s where ~x represents the measured p`` ~ the expected one, and C is the expected T spectrum, µ covariance matrix in the differential measurement, with the term |C| standing for the deter- minant of the covariance matrix. Both ~x and µ ~ have five elements, corresponding to the five – 22 – ∆σ [ fb / GeV ] ∆σ [ fb / GeV ] ATLAS Data ATLAS Data 1 s=13 TeV, 36.1 fb-1 SM ZZ 1 s=13 TeV, 36.1 fb-1 SM ZZ γ fZ4=1.5×10-3 f4=1.5×10-3 γ 10−1 fZ4=1.0×10-3 10−1 f4=1.0×10-3 γ fZ4=0.5×10-3 f4=0.5×10-3 Theo. Uncer. Theo. Uncer. 10−2 10−2 10−3 10−3 10−4 10−4 200 300 400 500 600 1000 200 300 400 500 600 1000 JHEP10(2019)127 pll [GeV] pll [GeV] T T ∆σ [ fb / GeV ] ∆σ [ fb / GeV ] ATLAS Data ATLAS Data 1 s=13 TeV, 36.1 fb-1 SM ZZ 1 s=13 TeV, 36.1 fb-1 SM ZZ γ fZ5=1.5×10-3 f5=1.5×10-3 γ 10−1 fZ5=1.0×10-3 10−1 f5=1.0×10-3 γ fZ5=0.5×10-3 f5=0.5×10-3 Theo. Uncer. Theo. Uncer. 10−2 10−2 10−3 10−3 10−4 10−4 200 300 400 500 600 1000 200 300 400 500 600 1000 pll [GeV] pll [GeV] T T Figure 8. Unfolded measured distribution of p`` `` T for the bins with pT > 150 GeV. The data are compared with the SM prediction from Powheg+gg2vv with higher-order K-factors to which are added possible aTGC contributions for different values of the strength of the coupling parameters defined in the text. The results are shown separately for f4Z (top left), f4γ (top right), f5Z (bottom left), and f5γ (bottom right). The uncertainty band represents the theoretical uncertainties in the SM predictions. p`` T bins used in this search. The expectation µ ~ depends on the coupling parameters and a set of Gaussian nuisance parameters, θs , which encapsulate the theoretical uncertainties in the prediction. The expected covariance matrix C is first derived by unfolding the predicted SM spectrum at detector level and then modified to account for the fact that the prediction µ ~ , varied in the statistical test, differs from the SM prediction µ~0 in the fiducial region in the case of non-zero coupling parameters. The relative difference between µ ~ and µ~0 can be expressed using a vector k, with k = µ /µ0 for bin-i. Each matrix element C ij is the sum of ~ i i i ij a statistical uncertainty component (Cstat ) and systematic uncertainty components arising ij ij from SM ZZ process (CZZ-syst ) and background contribution (Cbkg-syst ); the dependence p of C on µ ~ is implemented such that Cstat and CZZ−syst scale with k × k j and k i × k j , ij ij i respectively. The 95% CIs are derived by scanning the parameter space and using a frequentist method with the CLs formalism [72]. In the first step, the one-dimensional CI for each aTGC parameter is derived with all other parameters set to zero, and the results are shown in table 10. These intervals are found to be more stringent than those obtained – 23 – f4γ f4Z f5γ f5Z Expected [×10−3 ] [−1.3, 1.3] [−1.1, 1.1] [−1.3, 1.3] [−1.1, 1.1] −3 Observed [×10 ] [−1.2, 1.2] [−1.0, 1.0] [−1.2, 1.2] [−1.0, 1.0] Table 10. One-dimensional 95% confidence intervals for the aTGC parameters described in the text. The limits on each parameter are derived with the other parameters set to zero. from the ZZ → 4` channel based on the same ATLAS data sample [8]. In the second step, two-dimensional CIs for each pair of coupling parameters are derived with the other JHEP10(2019)127 two parameters set to zero. Figure 9 presents the two-dimensional CI contours for the six possible pairs of aTGC coupling parameters. Finally, in the context of effective field theories, a one-dimensional 95% CI can be placed on the CP-even dimension-eight operator CB̄W /Λ4 [70]. This is obtained by extrapolating the results of table 10 following the conversion formula in ref. [70]. A 95% CI of [−4.0, 4.0] in units of TeV−4 is set for CB̄W /Λ4 , which is improved by 30% with respect to the ATLAS results in the ZZ → 4` channel [8]. 11 Conclusion This paper presents a measurement of ZZ production using the ``νν final state, based on √ 36.1 fb−1 of data collected by the ATLAS detector at the LHC in pp collisions at s = 13 TeV. The fiducial cross-section of the combined ee and µµ channels is measured to fid be σZZ→``νν = 25.4 ± 1.4 (stat) ± 0.9 (syst) ± 0.5 (lumi) fb, in agreement with the SM prediction of 22.4 ± 1.3 fb. The integrated cross-sections in the total and fiducial phase spaces are measured with a total uncertainty of 7%, which is significantly better than the previous measurement using the 8 TeV data. The measured cross-sections are slightly larger than the SM predictions, but the difference is not significant considering the measurement and prediction uncertainties. Differential cross-sections are reported for eight different kinematic variables in the fiducial phase space, and no significant deviation from the expectations is found. The measured p`` T spectrum in the fiducial phase space is used to set limits on aTGCs, and the obtained 95% confidence intervals for the aTGC parameters are more stringent than those derived from the 4` channel. 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. We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Aus- tralia; 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 – 24 – ×10−3 ×10−3 5 5 fZ4 fZ5 Observed Observed ATLAS ATLAS 4 -1 Expected 4 -1 Expected s = 13TeV, 36.1 fb Expected ± 1 σ s = 13TeV, 36.1 fb Expected ± 1 σ 3 Expected ± 2 σ SM Pred. 3 Expected ± 2 σ SM Pred. 2 2 1 1 0 0 −1 −1 −2 −2 −3 ×10−3 −3 ×10−3 −4 −3 −2 −1 0 1 2 3 4 −4 −3 −2 −1 0 1 2 3 4 γ γ JHEP10(2019)127 f4 f4 ×10−3 ×10−3 5 5 f5 fZ5 γ Observed Observed ATLAS ATLAS 4 -1 Expected 4 -1 Expected s = 13TeV, 36.1 fb Expected ± 1 σ s = 13TeV, 36.1 fb Expected ± 1σ 3 Expected ± 2 σ SM Pred. 3 Expected ± 2 σ SM Pred. 2 2 1 1 0 0 −1 −1 −2 −2 −3 ×10−3 −3 ×10−3 −4 −3 −2 −1 0 1 2 3 4 −4 −3 −2 −1 0 1 2 3 4 γ f4 fZ4 ×10−3 ×10−3 5 5 f5 fZ5 γ Observed ATLAS ATLAS Observed 4 -1 Expected 4 Expected s = 13TeV, 36.1 fb Expected ± 1 σ s = 13TeV, 36.1 fb-1 Expected ± 1 σ 3 Expected ± 2 σ SM Pred. 3 Expected ± 2 σ SM Pred. 2 2 1 1 0 0 −1 −1 −2 −2 −3 ×10−3 −3 ×10−3 −4 −3 −2 −1 0 1 2 3 4 −4 −3 −2 −1 0 1 2 3 4 γ fZ4 f5 Figure 9. Expected and observed regions excluded at 95% confidence level as a function of two aTGC parameters. Confidence intervals for each pair of coupling parameters are derived with the other two parameters set to zero. SAR, China; ISF and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia and NRC KI, Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members – 25 – have received support from BCKDF, CANARIE, CRC and Compute Canada, Canada; COST, ERC, ERDF, Horizon 2020, and Marie Sklodowska-Curie Actions, European Union; Investissements d’ Avenir Labex and Idex, ANR, France; DFG and AvH Foundation, Ger- many; 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 JHEP10(2019)127 (U.S.A.), the Tier-2 facilities worldwide and large non-WLCG resource providers. Ma- jor contributors of computing resources are listed in ref. 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Flierl114 , L.F. Flores137 , L.R. Flores Castillo63a , F.M. Follega75a,75b , N. Fomin17 , G.T. Forcolin75a,75b , A. Formica145 , F.A. Förster14 , A.C. Forti100 , A.G. Foster21 , D. Fournier132 , H. Fox89 , S. Fracchia149 , P. Francavilla71a,71b , M. Franchini23b,23a , S. Franchino61a , D. Francis36 , L. Franconi20 , M. Franklin59 , M. Frate171 , A.N. Fray92 , B. Freund109 , W.S. Freund80b , E.M. Freundlich47 , D.C. Frizzell128 , D. Froidevaux36 , J.A. Frost135 , C. Fukunaga164 , E. Fullana Torregrosa174 , E. Fumagalli55b,55a , T. Fusayasu116 , J. Fuster174 , A. Gabrielli23b,23a , A. Gabrielli18 , G.P. Gach83a , S. Gadatsch54 , P. Gadow115 , G. Gagliardi55b,55a , L.G. Gagnon109 , C. Galea27b , B. Galhardo140a,140c , E.J. Gallas135 , B.J. Gallop144 , P. Gallus142 , G. Galster40 , R. Gamboa Goni92 , K.K. Gan126 , S. Ganguly180 , J. Gao60a , Y. Gao90 , Y.S. Gao31,l , C. Garcı́a174 , JHEP10(2019)127 J.E. Garcı́a Navarro174 , J.A. Garcı́a Pascual15a , C. Garcia-Argos52 , M. Garcia-Sciveres18 , R.W. Gardner37 , N. Garelli153 , S. Gargiulo52 , V. Garonne134 , A. Gaudiello55b,55a , G. Gaudio70a , I.L. Gavrilenko110 , A. Gavrilyuk111 , C. Gay175 , G. Gaycken24 , E.N. Gazis10 , A.A. Geanta27b , C.N.P. Gee144 , J. Geisen53 , M. Geisen99 , M.P. Geisler61a , C. Gemme55b , M.H. Genest58 , C. Geng105 , S. Gentile72a,72b , S. George93 , T. Geralis44 , D. Gerbaudo14 , L.O. Gerlach53 , G. Gessner47 , S. Ghasemi151 , M. Ghasemi Bostanabad176 , M. Ghneimat24 , A. Ghosh77 , B. Giacobbe23b , S. Giagu72a,72b , N. Giangiacomi23b,23a , P. Giannetti71a , A. Giannini69a,69b , S.M. Gibson93 , M. Gignac146 , D. Gillberg34 , G. Gilles182 , D.M. Gingrich3,av , M.P. Giordani66a,66c , F.M. Giorgi23b , P.F. Giraud145 , G. Giugliarelli66a,66c , D. Giugni68a , F. Giuli73a,73b , M. Giulini61b , S. Gkaitatzis162 , I. Gkialas9,h , E.L. Gkougkousis14 , P. Gkountoumis10 , L.K. Gladilin113 , C. Glasman98 , J. Glatzer14 , P.C.F. Glaysher46 , A. Glazov46 , M. Goblirsch-Kolb26 , S. Goldfarb104 , T. Golling54 , D. Golubkov123 , A. Gomes140a,140b , R. Goncalves Gama53 , R. Gonçalo140a,140b , G. Gonella52 , L. Gonella21 , A. Gongadze79 , F. Gonnella21 , J.L. Gonski59 , S. González de la Hoz174 , S. Gonzalez-Sevilla54 , G.R. Gonzalvo Rodriguez174 , L. Goossens36 , P.A. Gorbounov111 , H.A. Gordon29 , B. Gorini36 , E. Gorini67a,67b , A. Gorišek91 , A.T. Goshaw49 , C. Gössling47 , M.I. Gostkin79 , C.A. Gottardo24 , C.R. Goudet132 , M. Gouighri35b , D. Goujdami35c , A.G. Goussiou148 , N. Govender33b,a , C. Goy5 , E. Gozani160 , I. Grabowska-Bold83a , P.O.J. Gradin172 , E.C. Graham90 , J. Gramling171 , E. Gramstad134 , S. Grancagnolo19 , M. Grandi156 , V. Gratchev138 , P.M. Gravila27f , F.G. Gravili67a,67b , C. Gray57 , H.M. Gray18 , C. Grefe24 , K. Gregersen96 , I.M. Gregor46 , P. Grenier153 , K. Grevtsov46 , N.A. Grieser128 , J. Griffiths8 , A.A. Grillo146 , K. Grimm31,k , S. Grinstein14,x , J.-F. Grivaz132 , S. Groh99 , E. Gross180 , J. Grosse-Knetter53 , Z.J. Grout94 , C. Grud105 , A. Grummer118 , L. Guan105 , W. Guan181 , J. Guenther36 , A. Guerguichon132 , F. Guescini168a , D. Guest171 , R. Gugel52 , B. Gui126 , T. Guillemin5 , S. Guindon36 , U. Gul57 , J. Guo60c , W. Guo105 , Y. Guo60a,r , Z. Guo101 , R. Gupta46 , S. Gurbuz12c , G. Gustavino128 , P. Gutierrez128 , C. Gutschow94 , C. Guyot145 , M.P. Guzik83a , C. Gwenlan135 , C.B. Gwilliam90 , A. Haas124 , C. Haber18 , H.K. Hadavand8 , N. Haddad35e , A. Hadef60a , S. Hageböck36 , M. Hagihara169 , M. Haleem177 , J. Haley129 , G. Halladjian106 , G.D. Hallewell101 , K. Hamacher182 , P. Hamal130 , K. Hamano176 , H. Hamdaoui35e , G.N. Hamity149 , K. Han60a,aj , L. Han60a , S. Han15a,15d , K. Hanagaki81,u , M. Hance146 , D.M. Handl114 , B. Haney137 , R. Hankache136 , P. Hanke61a , E. Hansen96 , J.B. Hansen40 , J.D. Hansen40 , M.C. Hansen24 , P.H. Hansen40 , E.C. Hanson100 , K. Hara169 , A.S. Hard181 , T. Harenberg182 , S. Harkusha107 , P.F. Harrison178 , N.M. Hartmann114 , Y. Hasegawa150 , A. Hasib50 , S. Hassani145 , S. Haug20 , R. Hauser106 , L. Hauswald48 , L.B. Havener39 , M. Havranek142 , C.M. Hawkes21 , R.J. Hawkings36 , D. Hayden106 , C. Hayes155 , R.L. Hayes175 , C.P. Hays135 , J.M. Hays92 , H.S. Hayward90 , S.J. Haywood144 , F. He60a , M.P. Heath50 , V. Hedberg96 , L. Heelan8 , S. Heer24 , K.K. Heidegger52 , J. Heilman34 , S. Heim46 , T. Heim18 , B. Heinemann46,aq , J.J. Heinrich131 , L. Heinrich36 , C. Heinz56 , J. Hejbal141 , – 34 – L. Helary61b , A. Held175 , S. Hellesund134 , C.M. Helling146 , S. Hellman45a,45b , C. Helsens36 , R.C.W. Henderson89 , Y. Heng181 , S. Henkelmann175 , A.M. Henriques Correia36 , G.H. Herbert19 , H. Herde26 , V. Herget177 , Y. Hernández Jiménez33c , H. Herr99 , M.G. Herrmann114 , T. Herrmann48 , G. Herten52 , R. Hertenberger114 , L. Hervas36 , T.C. Herwig137 , G.G. Hesketh94 , N.P. Hessey168a , A. Higashida163 , S. Higashino81 , E. Higón-Rodriguez174 , K. Hildebrand37 , E. Hill176 , J.C. Hill32 , K.K. Hill29 , K.H. Hiller46 , S.J. Hillier21 , M. Hils48 , I. Hinchliffe18 , F. Hinterkeuser24 , M. Hirose133 , S. Hirose52 , D. Hirschbuehl182 , B. Hiti91 , O. Hladik141 , D.R. Hlaluku33c , X. Hoad50 , J. Hobbs155 , N. Hod180 , M.C. Hodgkinson149 , A. Hoecker36 , F. Hoenig114 , D. Hohn52 , D. Hohov132 , T.R. Holmes37 , M. Holzbock114 , L.B.A.H Hommels32 , S. Honda169 , T. Honda81 , T.M. Hong139 , A. Hönle115 , B.H. Hooberman173 , W.H. Hopkins6 , Y. Horii117 , P. Horn48 , A.J. Horton152 , L.A. Horyn37 , J-Y. Hostachy58 , A. Hostiuc148 , S. Hou158 , JHEP10(2019)127 A. Hoummada35a , J. Howarth100 , J. Hoya88 , M. Hrabovsky130 , J. Hrdinka76 , I. Hristova19 , J. Hrivnac132 , A. Hrynevich108 , T. Hryn’ova5 , P.J. Hsu64 , S.-C. Hsu148 , Q. Hu29 , S. Hu60c , Y. Huang15a , Z. Hubacek142 , F. Hubaut101 , M. Huebner24 , F. Huegging24 , T.B. Huffman135 , M. Huhtinen36 , R.F.H. Hunter34 , P. Huo155 , A.M. Hupe34 , N. Huseynov79,ae , J. Huston106 , J. Huth59 , R. Hyneman105 , S. Hyrych28a , G. Iacobucci54 , G. Iakovidis29 , I. Ibragimov151 , L. Iconomidou-Fayard132 , Z. Idrissi35e , P.I. Iengo36 , R. Ignazzi40 , O. Igonkina120,z , R. Iguchi163 , T. Iizawa54 , Y. Ikegami81 , M. Ikeno81 , D. Iliadis162 , N. Ilic119 , F. Iltzsche48 , G. Introzzi70a,70b , M. Iodice74a , K. Iordanidou39 , V. Ippolito72a,72b , M.F. Isacson172 , N. Ishijima133 , M. Ishino163 , M. Ishitsuka165 , W. Islam129 , C. Issever135 , S. Istin160 , F. Ito169 , J.M. Iturbe Ponce63a , R. Iuppa75a,75b , A. Ivina180 , H. Iwasaki81 , J.M. Izen43 , V. Izzo69a , P. Jacka141 , P. Jackson1 , R.M. Jacobs24 , V. Jain2 , G. Jäkel182 , K.B. Jakobi99 , K. Jakobs52 , S. Jakobsen76 , T. Jakoubek141 , J. Jamieson57 , D.O. Jamin129 , R. Jansky54 , J. Janssen24 , M. Janus53 , P.A. Janus83a , G. Jarlskog96 , N. Javadov79,ae , T. Javůrek36 , M. Javurkova52 , F. Jeanneau145 , L. Jeanty131 , J. Jejelava159a,af , A. Jelinskas178 , P. Jenni52,b , J. Jeong46 , N. Jeong46 , S. Jézéquel5 , H. Ji181 , J. Jia155 , H. Jiang78 , Y. Jiang60a , Z. Jiang153,p , S. Jiggins52 , F.A. Jimenez Morales38 , J. Jimenez Pena174 , S. Jin15c , A. Jinaru27b , O. Jinnouchi165 , H. Jivan33c , P. Johansson149 , K.A. Johns7 , C.A. Johnson65 , K. Jon-And45a,45b , R.W.L. Jones89 , S.D. Jones156 , S. Jones7 , T.J. Jones90 , J. Jongmanns61a , P.M. Jorge140a,140b , J. Jovicevic168a , X. Ju18 , J.J. Junggeburth115 , A. Juste Rozas14,x , A. Kaczmarska84 , M. Kado132 , H. Kagan126 , M. Kagan153 , T. Kaji179 , E. Kajomovitz160 , C.W. Kalderon96 , A. Kaluza99 , A. Kamenshchikov123 , L. Kanjir91 , Y. Kano163 , V.A. Kantserov112 , J. Kanzaki81 , L.S. Kaplan181 , D. Kar33c , M.J. Kareem168b , E. Karentzos10 , S.N. Karpov79 , Z.M. Karpova79 , V. Kartvelishvili89 , A.N. Karyukhin123 , L. Kashif181 , R.D. Kass126 , A. Kastanas45a,45b , Y. Kataoka163 , C. Kato60d,60c , J. Katzy46 , K. Kawade82 , K. Kawagoe87 , T. Kawaguchi117 , T. Kawamoto163 , G. Kawamura53 , E.F. Kay176 , V.F. Kazanin122b,122a , R. Keeler176 , R. Kehoe42 , J.S. Keller34 , E. Kellermann96 , J.J. Kempster21 , J. Kendrick21 , O. Kepka141 , S. Kersten182 , B.P. Kerševan91 , S. Ketabchi Haghighat167 , R.A. Keyes103 , M. Khader173 , F. Khalil-Zada13 , A. Khanov129 , A.G. Kharlamov122b,122a , T. Kharlamova122b,122a , E.E. Khoda175 , A. Khodinov166 , T.J. Khoo54 , E. Khramov79 , J. Khubua159b , S. Kido82 , M. Kiehn54 , C.R. Kilby93 , Y.K. Kim37 , N. Kimura66a,66c , O.M. Kind19 , B.T. King90,∗ , D. Kirchmeier48 , J. Kirk144 , A.E. Kiryunin115 , T. Kishimoto163 , V. Kitali46 , O. Kivernyk5 , E. Kladiva28b,∗ , T. Klapdor-Kleingrothaus52 , M.H. Klein105 , M. Klein90 , U. Klein90 , K. Kleinknecht99 , P. Klimek121 , A. Klimentov29 , T. Klingl24 , T. Klioutchnikova36 , F.F. Klitzner114 , P. Kluit120 , S. Kluth115 , E. Kneringer76 , E.B.F.G. Knoops101 , A. Knue52 , D. Kobayashi87 , T. Kobayashi163 , M. Kobel48 , M. Kocian153 , P. Kodys143 , P.T. Koenig24 , T. Koffas34 , N.M. Köhler115 , T. Koi153 , M. Kolb61b , I. Koletsou5 , T. Kondo81 , N. Kondrashova60c , K. Köneke52 , A.C. König119 , T. Kono125 , R. Konoplich124,am , V. Konstantinides94 , N. Konstantinidis94 , B. Konya96 , R. Kopeliansky65 , S. Koperny83a , – 35 – 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 , A. Krishnan61b , K. Krizka18 , K. Kroeninger47 , H. Kroha115 , J. Kroll141 , J. Kroll137 , J. Krstic16 , U. Kruchonak79 , H. Krüger24 , N. Krumnack78 , M.C. Kruse49 , T. Kubota104 , S. Kuday4b , J.T. Kuechler46 , S. Kuehn36 , A. Kugel61a , T. Kuhl46 , V. Kukhtin79 , R. Kukla101 , Y. Kulchitsky107,ai , S. Kuleshov147b , Y.P. Kulinich173 , M. Kuna58 , T. Kunigo85 , A. Kupco141 , T. Kupfer47 , O. Kuprash52 , H. Kurashige82 , L.L. Kurchaninov168a , Y.A. Kurochkin107 , JHEP10(2019)127 A. Kurova112 , M.G. Kurth15a,15d , E.S. Kuwertz36 , M. Kuze165 , A.K. Kvam148 , J. Kvita130 , T. Kwan103 , A. La Rosa115 , J.L. La Rosa Navarro80d , 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 , E. Lançon29 , U. Landgraf52 , M.P.J. Landon92 , M.C. Lanfermann54 , V.S. Lang46 , J.C. Lange53 , R.J. Langenberg36 , A.J. Lankford171 , F. Lanni29 , K. Lantzsch24 , A. Lanza70a , A. 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 , O. Le Dortz136 , 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,v , M.A.L. Leite80d , 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. Levêque5 , D. Levin105 , L.J. Levinson180 , D.J. Lewis21 , B. Li15b , B. Li105 , C-Q. Li60a,al , 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. 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,as , A. Lister175 , A.M. Litke146 , J.D. Little8 , B. Liu78 , 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. Lösel114 , A. Lösle52 , X. Lou46 , X. Lou15a , A. Lounis132 , J. Love6 , P.A. Love89 , J.J. Lozano Bahilo174 , H. Lu63a , 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 Miguens137,140b , 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. Mandić91 , 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 , G. Marceca30 , L. Marchese135 , G. Marchiori136 , M. Marcisovsky141 , C. Marcon96 , – 36 – C.A. Marin Tobon36 , M. Marjanovic38 , F. Marroquim80b , Z. Marshall18 , M.U.F Martensson172 , S. Marti-Garcia174 , C.B. Martin126 , T.A. Martin178 , V.J. Martin50 , B. Martin dit Latour17 , M. Martinez14,x , V.I. Martinez Outschoorn102 , S. Martin-Haugh144 , V.S. Martoiu27b , A.C. Martyniuk94 , A. Marzin36 , 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. Mättig24 , J. Maurer27b , B. Maček91 , S.J. Maxfield90 , 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,ac , J.E. Mdhluli33c , Z.A. Meadows102 , S. Meehan148 , T. Megy52 , S. Mehlhase114 , A. Mehta90 , T. Meideck58 , B. Meirose43 , D. Melini174 , JHEP10(2019)127 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 , 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. Mijović50 , G. Mikenberg180 , M. Mikestikova141 , M. Mikuž91 , 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. Miñano 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. Mjörnmark96 , T. Mkrtchyan184 , M. Mlynarikova143 , T. Moa45a,45b , K. Mochizuki109 , P. Mogg52 , S. Mohapatra39 , R. Moles-Valls24 , M.C. Mondragon106 , K. Mönig46 , J. Monk40 , E. Monnier101 , A. Montalbano152 , J. Montejo Berlingen36 , M. Montella94 , F. Monticelli88 , S. Monzani68a , N. Morange132 , D. Moreno22 , M. Moreno Llácer36 , P. Morettini55b , M. Morgenstern120 , S. Morgenstern48 , D. Mori152 , M. Morii59 , M. Morinaga179 , V. Morisbak134 , A.K. Morley36 , G. Mornacchi36 , A.P. Morris94 , L. Morvaj155 , P. Moschovakos10 , B. Moser120 , M. Mosidze159b , H.J. Moss149 , J. Moss31,m , K. Motohashi165 , E. Mountricha36 , E.J.W. Moyse102 , S. Muanza101 , F. Mueller115 , 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. Muškinja18 , C. Mwewa33a , A.G. Myagkov123,an , 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. Narayan11 , 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 , S. Nektarijevic119 , 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,an , 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 , M. Nomachi133 , I. Nomidis136 , M.A. Nomura29 , M. Nordberg36 , N. Norjoharuddeen135 , T. Novak91 , O. Novgorodova48 , R. Novotny142 , L. Nozka130 , K. Ntekas171 , E. Nurse94 , F. Nuti104 , F.G. Oakham34,av , 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 , A. Olariu27b , L.F. Oleiro Seabra140a , S.A. Olivares Pino147a , – 37 – 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 , Y. Oren161 , D. Orestano74a,74b , N. Orlando14 , R.S. Orr167 , B. Osculati55b,55a,∗ , V. O’Shea57 , R. Ospanov60a , G. Otero y Garzon30 , H. Otono87 , M. Ouchrif35d , 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 , JHEP10(2019)127 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,140b , S.V. Peleganchuk122b,122a , O. Penc141 , C. Peng15a , H. Peng60a , B.S. Peralva80a , M.M. Perego132 , A.P. Pereira Peixoto140a,140e , D.V. Perepelitsa29 , F. Peri19 , L. Perini68a,68b , H. Pernegger36 , S. Perrella69a,69b , V.D. Peshekhonov79,∗ , K. Peters46 , R.F.Y. Peters100 , B.A. Petersen36 , T.C. Petersen40 , E. Petit58 , A. Petridis1 , C. Petridou162 , P. Petroff132 , M. Petrov135 , F. Petrucci74a,74b , M. Pettee183 , N.E. Pettersson102 , K. Petukhova143 , A. Peyaud145 , R. Pezoa147b , 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. Polifka36 , A. Polini23b , C.S. Pollard46 , V. Polychronakos29 , D. Ponomarenko112 , L. Pontecorvo36 , S. Popa27a , G.A. Popeneciu27d , D.M. Portillo Quintero136 , 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. Prokoshin147b , S. Protopopescu29 , J. Proudfoot6 , M. Przybycien83a , 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 , R.G. Reed33c , K. Reeves43 , L. Rehnisch19 , J. Reichert137 , D. Reikher161 , A. Reiss99 , A. Rej151 , C. Rembser36 , H. Ren15a , 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. Rimoldi20 , L. Rinaldi23b , G. Ripellino154 , B. Ristić89 , E. Ritsch36 , I. Riu14 , J.C. Rivera Vergara147a , F. Rizatdinova129 , E. Rizvi92 , C. Rizzi36 , R.T. Roberts100 , S.H. Robertson103,ac , M. Robin46 , D. Robinson32 , J.E.M. Robinson46 , A. Robson57 , E. Rocco99 , C. Roda71a,71b , Y. Rodina101 , S. Rodriguez Bosca174 , A. Rodriguez Perez14 , D. Rodriguez Rodriguez174 , A.M. Rodrı́guez Vera168b , S. Roe36 , O. Røhne134 , R. Röhrig115 , C.P.A. Roland65 , J. Roloff59 , A. Romaniouk112 , M. Romano23b,23a , N. Rompotis90 , M. Ronzani124 , L. Roos136 , S. Rosati72a , K. Rosbach52 , N-A. Rosien53 , G. Rosin102 , B.J. Rosser137 , E. Rossi46 , E. Rossi74a,74b , E. Rossi69a,69b , L.P. Rossi55b , L. Rossini68a,68b , J.H.N. Rosten32 , R. Rosten14 , M. Rotaru27b , J. Rothberg148 , D. Rousseau132 , D. Roy33c , A. Rozanov101 , Y. Rozen160 , – 38 – X. Ruan33c , F. Rubbo153 , F. Rühr52 , A. Ruiz-Martinez174 , A. Rummler36 , Z. Rurikova52 , N.A. Rusakovich79 , H.L. Russell103 , L. Rustige38,47 , J.P. Rutherfoord7 , E.M. Rüttinger46,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,am , D. Salamani54 , G. Salamanna74a,74b , J.E. Salazar Loyola147b , P.H. Sales De Bruin172 , D. Salihagic115,∗ , A. Salnikov153 , J. Salt174 , D. Salvatore41b,41a , F. Salvatore156 , A. Salvucci63a,63b,63c , A. Salzburger36 , J. Samarati36 , D. Sammel52 , D. Sampsonidis162 , D. Sampsonidou162 , J. Sánchez174 , A. Sanchez Pineda66a,66c , H. Sandaker134 , C.O. Sander46 , M. Sandhoff182 , C. Sandoval22 , D.P.C. Sankey144 , M. Sannino55b,55a , Y. Sano117 , A. Sansoni51 , C. Santoni38 , H. Santos140a,140b , S.N. Santpur18 , JHEP10(2019)127 A. Santra174 , A. Sapronov79 , J.G. Saraiva140a,140d , O. Sasaki81 , K. Sato169 , E. Sauvan5 , P. Savard167,av , N. Savic115 , R. Sawada163 , C. Sawyer144 , L. Sawyer95,ak , C. Sbarra23b , A. Sbrizzi23a , T. Scanlon94 , J. Schaarschmidt148 , P. Schacht115 , B.M. Schachtner114 , D. Schaefer37 , L. Schaefer137 , J. Schaeffer99 , S. Schaepe36 , U. Schäfer99 , 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 , 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. Sciandra24 , G. Sciolla26 , 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. 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Sobie176,ac , A.M. Soffa171 , A. Soffer161 , A. Søgaard50 , F. Sohns53 , G. Sokhrannyi91 , 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. Spanò93 , D. Sperlich19 , T.M. Spieker61a , R. Spighi23b , G. Spigo36 , L.A. Spiller104 , M. Spina156 , D.P. Spiteri57 , M. Spousta143 , A. Stabile68a,68b , B.L. Stamas121 , R. Stamen61a , M. Stamenkovic120 , S. Stamm19 , E. Stanecka84 , R.W. Stanek6 , B. Stanislaus135 , – 39 – M.M. Stanitzki46 , M. Stankaityte135 , B. Stapf120 , E.A. Starchenko123 , G.H. Stark146 , J. Stark58 , S.H Stark40 , P. Staroba141 , P. Starovoitov61a , S. Stärz103 , R. Staszewski84 , G. Stavropoulos44 , M. Stegler46 , P. Steinberg29 , B. Stelzer152 , H.J. Stelzer36 , 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. Ströhmer177 , D.M. Strom131 , R. Stroynowski42 , A. Strubig50 , S.A. Stucci29 , B. Stugu17 , J. Stupak128 , N.A. Styles46 , D. Su153 , S. Suchek61a , Y. Sugaya133 , 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 , A. Sydorenko99 , I. Sykora28a , M. Sykora143 , T. Sykora143 , D. Ta99 , K. Tackmann46,y , J. Taenzer161 , A. Taffard171 , R. Tafirout168a , E. Tahirovic92 , H. Takai29 , R. Takashima86 , JHEP10(2019)127 K. Takeda82 , T. Takeshita150 , E.P. Takeva50 , Y. Takubo81 , M. Talby101 , A.A. Talyshev122b,122a , N.M. Tamir161 , J. Tanaka163 , M. Tanaka165 , R. Tanaka132 , B.B. Tannenwald126 , 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 , P.T.E. 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,ac , 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,ao , Yu.A. Tikhonov122b,122a , S. Timoshenko112 , P. Tipton183 , S. Tisserant101 , K. Todome23b,23a , S. Todorova-Nova5 , S. Todt48 , J. Tojo87 , S. Tokár28a , K. Tokushuku81 , E. Tolley126 , K.G. Tomiwa33c , M. Tomoto117 , L. Tompkins153,p , K. Toms118 , B. Tong59 , P. Tornambe102 , E. Torrence131 , H. Torres48 , E. Torró Pastor148 , C. Tosciri135 , J. Toth101,ab , D.R. Tovey149 , C.J. Treado124 , T. Trefzger177 , F. Tresoldi156 , A. Tricoli29 , I.M. Trigger168a , S. Trincaz-Duvoid136 , W. Trischuk167 , B. Trocmé58 , A. Trofymov132 , C. Troncon68a , M. Trovatelli176 , F. Trovato156 , L. Truong33b , M. Trzebinski84 , A. Trzupek84 , F. Tsai46 , J.C-L. Tseng135 , P.V. Tsiareshka107,ai , 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,t , R.J. Turner21 , R.T. Turra68a , P.M. Tuts39 , S. Tzamarias162 , E. Tzovara99 , G. Ucchielli47 , 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 , 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. Valéry46 , 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 , 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 , 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,av , N. Viaux Maira147b , M. Vicente Barreto Pinto54 , I. 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Zwalinski36 1 Department of Physics, University of Adelaide, Adelaide, Australia 2 Physics Department, SUNY Albany, Albany NY, United States of America 3 Department of Physics, University of Alberta, Edmonton AB, Canada 4 (a) (b) Department of Physics, Ankara University, Ankara; Istanbul Aydin University, Istanbul; (c) Division of Physics, TOBB University of Economics and Technology, Ankara, Turkey 5 LAPP, Université Grenoble Alpes, Université Savoie Mont Blanc, CNRS/IN2P3, Annecy, France 6 High Energy Physics Division, Argonne National Laboratory, Argonne IL, United States of America 7 Department of Physics, University of Arizona, Tucson AZ, United States of America 8 Department of Physics, University of Texas at Arlington, Arlington TX, United States of America 9 Physics 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 – 41 – 12 (a) (b) Bahcesehir University, Faculty of Engineering and Natural Sciences, Istanbul; Istanbul Bilgi (c) University, Faculty of Engineering and Natural Sciences, Istanbul; Department of Physics, (d) Bogazici University, Istanbul; Department of Physics Engineering, Gaziantep University, Gaziantep, Turkey 13 Institute of Physics, Azerbaijan Academy of Sciences, Baku, Azerbaijan 14 Institut de Fı́sica d’Altes Energies (IFAE), Barcelona Institute of Science and Technology, Barcelona, Spain 15 (a) (b) Institute of High Energy Physics, Chinese Academy of Sciences, Beijing; Physics Department, (c) (d) Tsinghua University, Beijing; Department of Physics, Nanjing University, Nanjing; 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 JHEP10(2019)127 18 Physics Division, Lawrence Berkeley National Laboratory and University of California, Berkeley CA, United States of America 19 Institut für Physik, Humboldt Universität zu Berlin, Berlin, Germany 20 Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics, University of Bern, Bern, Switzerland 21 School of Physics and Astronomy, University of Birmingham, Birmingham, United Kingdom 22 Facultad de Ciencias y Centro de Investigaciónes, Universidad Antonio Nariño, Bogota, Colombia 23 (a) (b) INFN Bologna and Universita’ di Bologna, Dipartimento di Fisica; INFN Sezione di Bologna, Italy 24 Physikalisches Institut, Universität Bonn, Bonn, Germany 25 Department of Physics, Boston University, Boston MA, United States of America 26 Department of Physics, Brandeis University, Waltham MA, United States of America 27 (a) (b) Transilvania University of Brasov, Brasov; Horia Hulubei National Institute of Physics and (c) Nuclear Engineering, Bucharest; Department of Physics, Alexandru Ioan Cuza University of Iasi, (d) Iasi; National Institute for Research and Development of Isotopic and Molecular Technologies, (e) (f ) Physics Department, Cluj-Napoca; University Politehnica Bucharest, Bucharest; 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 Fı́sica, 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) (b) Department of Physics, University of Cape Town, Cape Town; Department of Mechanical (c) Engineering Science, University of Johannesburg, Johannesburg; School of Physics, University of the Witwatersrand, Johannesburg, South Africa 34 Department of Physics, Carleton University, Ottawa ON, Canada 35 (a) Faculté des Sciences Ain Chock, Réseau Universitaire de Physique des Hautes Energies — (b) Université Hassan II, Casablanca; Faculté des Sciences, Université Ibn-Tofail, Kénitra; (c) (d) Faculté des Sciences Semlalia, Université Cadi Ayyad, LPHEA-Marrakech; Faculté des (e) Sciences, Université Mohamed Premier and LPTPM, Oujda; Faculté des sciences, Université Mohammed V, Rabat, Morocco 36 CERN, Geneva, Switzerland 37 Enrico Fermi Institute, University of Chicago, Chicago IL, United States of America 38 LPC, Université Clermont Auvergne, CNRS/IN2P3, Clermont-Ferrand, France 39 Nevis Laboratory, Columbia University, Irvington NY, United States of America 40 Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark 41 (a) (b) Dipartimento di Fisica, Università della Calabria, Rende; INFN Gruppo Collegato di Cosenza, Laboratori Nazionali di Frascati, Italy – 42 – 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) (b) Department of Physics, Stockholm University; Oskar Klein Centre, Stockholm, Sweden 46 Deutsches Elektronen-Synchrotron DESY, Hamburg and Zeuthen, Germany 47 Lehrstuhl für Experimentelle Physik IV, Technische Universität Dortmund, Dortmund, Germany 48 Institut für Kern und Teilchenphysik, Technische Universität Dresden, Dresden, Germany 49 Department of Physics, Duke University, Durham NC, United States of America 50 SUPA — School of Physics and Astronomy, University of Edinburgh, Edinburgh, United Kingdom 51 INFN e Laboratori Nazionali di Frascati, Frascati, Italy 52 Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany 53 II. Physikalisches Institut, Georg-August-Universität Göttingen, Göttingen, Germany JHEP10(2019)127 54 Département de Physique Nucléaire et Corpusculaire, Université de Genève, Genève, Switzerland 55 (a) (b) Dipartimento di Fisica, Università di Genova, Genova; INFN Sezione di Genova, Italy 56 II. Physikalisches Institut, Justus-Liebig-Universität Giessen, Giessen, Germany 57 SUPA — School of Physics and Astronomy, University of Glasgow, Glasgow, United Kingdom 58 LPSC, Université Grenoble Alpes, CNRS/IN2P3, Grenoble INP, Grenoble, France 59 Laboratory for Particle Physics and Cosmology, Harvard University, Cambridge MA, United States of America 60 (a) Department of Modern Physics and State Key Laboratory of Particle Detection and Electronics, (b) University of Science and Technology of China, Hefei; Institute of Frontier and Interdisciplinary Science and Key Laboratory of Particle Physics and Particle Irradiation (MOE), Shandong (c) University, Qingdao; School of Physics and Astronomy, Shanghai Jiao Tong University, (d) KLPPAC-MoE, SKLPPC, Shanghai; Tsung-Dao Lee Institute, Shanghai, China 61 (a) Kirchhoff-Institut für Physik, Ruprecht-Karls-Universität Heidelberg, Heidelberg; (b) Physikalisches Institut, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany 62 Faculty of Applied Information Science, Hiroshima Institute of Technology, Hiroshima, Japan 63 (a) Department of Physics, Chinese University of Hong Kong, Shatin, N.T., Hong Kong; (b) (c) Department of Physics, University of Hong Kong, Hong Kong; 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) (b) (c) INFN Gruppo Collegato di Udine, Sezione di Trieste, Udine; ICTP, Trieste; Dipartimento Politecnico di Ingegneria e Architettura, Università di Udine, Udine, Italy 67 (a) (b) INFN Sezione di Lecce; Dipartimento di Matematica e Fisica, Università del Salento, Lecce, Italy 68 (a) (b) INFN Sezione di Milano; Dipartimento di Fisica, Università di Milano, Milano, Italy 69 (a) (b) INFN Sezione di Napoli; Dipartimento di Fisica, Università di Napoli, Napoli, Italy 70 (a) (b) INFN Sezione di Pavia; Dipartimento di Fisica, Università di Pavia, Pavia, Italy 71 (a) (b) INFN Sezione di Pisa; Dipartimento di Fisica E. Fermi, Università di Pisa, Pisa, Italy 72 (a) (b) INFN Sezione di Roma; Dipartimento di Fisica, Sapienza Università di Roma, Roma, Italy 73 (a) (b) INFN Sezione di Roma Tor Vergata; Dipartimento di Fisica, Università di Roma Tor Vergata, Roma, Italy 74 (a) (b) INFN Sezione di Roma Tre; Dipartimento di Matematica e Fisica, Università Roma Tre, Roma, Italy 75 (a) (b) INFN-TIFPA; Università degli Studi di Trento, Trento, Italy 76 Institut für Astro und Teilchenphysik, Leopold-Franzens-Universität, Innsbruck, Austria 77 University of Iowa, Iowa City IA, United States of America 78 Department of Physics and Astronomy, Iowa State University, Ames IA, United States of America 79 Joint Institute for Nuclear Research, Dubna, Russia – 43 – 80 (a) Departamento de Engenharia Elétrica, Universidade Federal de Juiz de Fora (UFJF), Juiz de (b) (c) Fora; Universidade Federal do Rio De Janeiro COPPE/EE/IF, Rio de Janeiro; Universidade (d) Federal de São João del Rei (UFSJ), São João del Rei; Instituto de Fı́sica, Universidade de São Paulo, São Paulo, Brazil 81 KEK, High Energy Accelerator Research Organization, Tsukuba, Japan 82 Graduate School of Science, Kobe University, Kobe, Japan 83 (a) AGH University of Science and Technology, Faculty of Physics and Applied Computer Science, (b) Krakow; 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, JHEP10(2019)127 Fukuoka, Japan 88 Instituto de Fı́sica La Plata, Universidad Nacional de La Plata and CONICET, La Plata, Argentina 89 Physics Department, Lancaster University, Lancaster, United Kingdom 90 Oliver Lodge Laboratory, University of Liverpool, Liverpool, United Kingdom 91 Department of Experimental Particle Physics, Jožef Stefan Institute and Department of Physics, University of Ljubljana, Ljubljana, Slovenia 92 School of Physics and Astronomy, Queen Mary University of London, London, United Kingdom 93 Department of Physics, Royal Holloway University of London, Egham, United Kingdom 94 Department of Physics and Astronomy, University College London, London, United Kingdom 95 Louisiana Tech University, Ruston LA, United States of America 96 Fysiska institutionen, Lunds universitet, Lund, Sweden 97 Centre de Calcul de l’Institut National de Physique Nucléaire et de Physique des Particules (IN2P3), Villeurbanne, France 98 Departamento de Fı́sica Teorica C-15 and CIAFF, Universidad Autónoma de Madrid, Madrid, Spain 99 Institut für Physik, Universität Mainz, Mainz, Germany 100 School of Physics and Astronomy, University of Manchester, Manchester, United Kingdom 101 CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille, France 102 Department of Physics, University of Massachusetts, Amherst MA, United States of America 103 Department of Physics, McGill University, Montreal QC, Canada 104 School of Physics, University of Melbourne, Victoria, Australia 105 Department of Physics, University of Michigan, Ann Arbor MI, United States of America 106 Department of Physics and Astronomy, Michigan State University, East Lansing MI, United States of America 107 B.I. Stepanov Institute of Physics, National Academy of Sciences of Belarus, Minsk, Belarus 108 Research Institute for Nuclear Problems of Byelorussian State University, Minsk, Belarus 109 Group of Particle Physics, University of Montreal, Montreal QC, Canada 110 P.N. Lebedev Physical Institute of the Russian Academy of Sciences, Moscow, Russia 111 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 Fakultät für Physik, Ludwig-Maximilians-Universität München, München, Germany 115 Max-Planck-Institut für Physik (Werner-Heisenberg-Institut), München, Germany 116 Nagasaki Institute of Applied Science, Nagasaki, Japan 117 Graduate School of Science and Kobayashi-Maskawa Institute, Nagoya University, Nagoya, Japan 118 Department of Physics and Astronomy, University of New Mexico, Albuquerque NM, United States of America 119 Institute for Mathematics, Astrophysics and Particle Physics, Radboud University Nijmegen/Nikhef, Nijmegen, Netherlands – 44 – 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) (b) Budker Institute of Nuclear Physics and NSU, SB RAS, Novosibirsk; 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, JHEP10(2019)127 United States of America 129 Department of Physics, Oklahoma State University, Stillwater OK, United States of America 130 Palacký University, RCPTM, Joint Laboratory of Optics, Olomouc, Czech Republic 131 Center for High Energy Physics, University of Oregon, Eugene OR, United States of America 132 LAL, Université Paris-Sud, CNRS/IN2P3, Université 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 Université, Paris Diderot Sorbonne Paris Cité, 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) (b) Laboratório de Instrumentação e Fı́sica Experimental de Partı́culas — LIP; Departamento de (c) Fı́sica, Faculdade de Ciências, Universidade de Lisboa, Lisboa; Departamento de Fı́sica, (d) Universidade de Coimbra, Coimbra; Centro de Fı́sica Nuclear da Universidade de Lisboa, Lisboa; (e) (f ) Departamento de Fı́sica, Universidade do Minho, Braga; Universidad de Granada, Granada (g) (Spain); Dep Fı́sica and CEFITEC of Faculdade de Ciências 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, Université 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) (b) Departamento de Fı́sica, Pontificia Universidad Católica de Chile, Santiago; Departamento de Fı́sica, Universidad Técnica Federico Santa Marı́a, Valparaı́so, 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, Universität 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 – 45 – 158 Institute of Physics, Academia Sinica, Taipei, Taiwan 159 (a) (b) E. Andronikashvili Institute of Physics, Iv. Javakhishvili Tbilisi State University, Tbilisi; High Energy Physics Institute, Tbilisi State University, Tbilisi, Georgia 160 Department of Physics, Technion, Israel Institute of Technology, Haifa, Israel 161 Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University, Tel Aviv, Israel 162 Department of Physics, Aristotle University of Thessaloniki, Thessaloniki, Greece 163 International Center for Elementary Particle Physics and Department of Physics, University of Tokyo, Tokyo, Japan 164 Graduate School of Science and Technology, Tokyo Metropolitan University, Tokyo, Japan 165 Department of Physics, Tokyo Institute of Technology, Tokyo, Japan 166 Tomsk State University, Tomsk, Russia JHEP10(2019)127 167 Department of Physics, University of Toronto, Toronto ON, Canada 168 (a) (b) TRIUMF, Vancouver BC; Department of Physics and Astronomy, York University, Toronto ON, Canada 169 Division of Physics and Tomonaga Center for the History of the Universe, Faculty of Pure and Applied Sciences, University of Tsukuba, Tsukuba, Japan 170 Department of Physics and Astronomy, Tufts University, Medford MA, United States of America 171 Department of Physics and Astronomy, University of California Irvine, Irvine CA, United States of America 172 Department of Physics and Astronomy, University of Uppsala, Uppsala, Sweden 173 Department of Physics, University of Illinois, Urbana IL, United States of America 174 Instituto de Fı́sica Corpuscular (IFIC), Centro Mixto Universidad de Valencia — CSIC, Valencia, Spain 175 Department of Physics, University of British Columbia, Vancouver BC, Canada 176 Department of Physics and Astronomy, University of Victoria, Victoria BC, Canada 177 Fakultät für Physik und Astronomie, Julius-Maximilians-Universität Würzburg, Würzburg, Germany 178 Department of Physics, University of Warwick, Coventry, United Kingdom 179 Waseda University, Tokyo, Japan 180 Department of Particle Physics, Weizmann Institute of Science, Rehovot, Israel 181 Department of Physics, University of Wisconsin, Madison WI, United States of America 182 Fakultät für Mathematik und Naturwissenschaften, Fachgruppe Physik, Bergische Universität Wuppertal, Wuppertal, Germany 183 Department of Physics, Yale University, New Haven CT, United States of America 184 Yerevan Physics Institute, Yerevan, Armenia a Also at Centre for High Performance Computing, CSIR Campus, Rosebank, Cape Town, South Africa b Also at CERN, Geneva, Switzerland c Also at CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille, France d Also at Département de Physique Nucléaire et Corpusculaire, Université de Genève, Genève, Switzerland e Also at Departament de Fisica de la Universitat Autonoma de Barcelona, Barcelona, Spain f Also at Departamento de Fı́sica, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal g Also at Department of Applied Physics and Astronomy, University of Sharjah, Sharjah, United Arab Emirates h Also at Department of Financial and Management Engineering, University of the Aegean, Chios, Greece i Also at Department of Physics and Astronomy, University of Louisville, Louisville, KY, United States of America – 46 – j Also at Department of Physics and Astronomy, University of Sheffield, Sheffield, United Kingdom k Also at Department of Physics, California State University, East Bay, United States of America l Also at Department of Physics, California State University, Fresno, United States of America m Also at Department of Physics, California State University, Sacramento, United States of America n Also at Department of Physics, King’s College London, London, United Kingdom o Also at Department of Physics, St. Petersburg State Polytechnical University, St. Petersburg, Russia p Also at Department of Physics, Stanford University, Stanford CA, United States of America q Also at Department of Physics, University of Fribourg, Fribourg, Switzerland r Also at Department of Physics, University of Michigan, Ann Arbor MI, United States of America s Also at Faculty of Physics, M.V. Lomonosov Moscow State University, Moscow, Russia t Also at Giresun University, Faculty of Engineering, Giresun, Turkey u Also at Graduate School of Science, Osaka University, Osaka, Japan JHEP10(2019)127 v Also at Hellenic Open University, Patras, Greece w Also at Horia Hulubei National Institute of Physics and Nuclear Engineering, Bucharest, Romania x Also at Institucio Catalana de Recerca i Estudis Avancats, ICREA, Barcelona, Spain y Also at Institut für Experimentalphysik, Universität Hamburg, Hamburg, Germany z Also at Institute for Mathematics, Astrophysics and Particle Physics, Radboud University Nijmegen/Nikhef, Nijmegen, Netherlands aa Also at Institute for Nuclear Research and Nuclear Energy (INRNE) of the Bulgarian Academy of Sciences, Sofia, Bulgaria ab Also at Institute for Particle and Nuclear Physics, Wigner Research Centre for Physics, Budapest, Hungary ac Also at Institute of Particle Physics (IPP), Canada ad Also at Institute of Physics, Academia Sinica, Taipei, Taiwan ae Also at Institute of Physics, Azerbaijan Academy of Sciences, Baku, Azerbaijan af Also at Institute of Theoretical Physics, Ilia State University, Tbilisi, Georgia ag Also at Instituto de Fisica Teorica, IFT-UAM/CSIC, Madrid, Spain ah Also at Istanbul University, Department of Physics, Istanbul, Turkey ai Also at Joint Institute for Nuclear Research, Dubna, Russia aj Also at LAL, Université Paris-Sud, CNRS/IN2P3, Université Paris-Saclay, Orsay, France ak Also at Louisiana Tech University, Ruston LA, United States of America al Also at LPNHE, Sorbonne Université, Paris Diderot Sorbonne Paris Cité, CNRS/IN2P3, Paris, France am Also at Manhattan College, New York NY, United States of America an Also at Moscow Institute of Physics and Technology State University, Dolgoprudny, Russia ao Also at National Research Nuclear University MEPhI, Moscow, Russia ap Also at Physics Department, An-Najah National University, Nablus, Palestine aq Also at Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany ar Also at School of Physics, Sun Yat-sen University, Guangzhou, China as Also at The City College of New York, New York NY, United States of America at Also at The Collaborative Innovation Center of Quantum Matter (CICQM), Beijing, China au Also at Tomsk State University, Tomsk, and Moscow Institute of Physics and Technology State University, Dolgoprudny, Russia av Also at TRIUMF, Vancouver BC, Canada aw Also at Universita di Napoli Parthenope, Napoli, Italy ∗ Deceased – 47 –