Published for SISSA by Springer Received: November 13, 2018 Revised: March 14, 2019 Accepted: March 26, 2019 Published: April 12, 2019 JHEP04(2019)092 Search for Higgs boson pair production in the √ bb̄W W ∗ decay mode at s = 13 TeV with the ATLAS detector The ATLAS collaboration E-mail: atlas.publications@cern.ch Abstract: A search for Higgs boson pair production in the bb̄W W ∗ decay mode is per- formed in the bb̄`νqq final state using 36.1 fb−1 of proton-proton collision data at a centre- of-mass energy of 13 TeV recorded with the ATLAS detector at the Large Hadron Collider. No evidence of events beyond the background expectation is found. Upper limits on the non-resonant pp → HH production cross section of 10 pb and on the resonant production cross section as a function of the HH invariant mass are obtained. Resonant production limits are set for scalar and spin-2 graviton hypotheses in the mass range 500 to 3000 GeV. Keywords: Hadron-Hadron scattering (experiments) ArXiv ePrint: 1811.04671 Open Access, Copyright CERN, https://doi.org/10.1007/JHEP04(2019)092 for the benefit of the ATLAS Collaboration. Article funded by SCOAP3 . Contents 1 Introduction 1 2 Data and simulation samples 3 3 Object reconstruction 5 4 Resolved analysis 7 JHEP04(2019)092 4.1 Resolved analysis: event selection 7 4.2 Resolved analysis: background determination 8 4.3 Resolved analysis: systematic uncertainties 11 5 Boosted analysis 14 5.1 Boosted analysis: event selection 14 5.2 Boosted analysis: background determination 15 5.3 Boosted analysis: systematic uncertainties 16 6 Results 18 6.1 Resolved analysis 18 6.2 Boosted analysis 24 6.3 Summary 26 7 Conclusion 27 The ATLAS collaboration 33 1 Introduction The Higgs boson (H) is an essential part of the Standard Model (SM) and it has a crucial role in the electroweak symmetry breaking (EWSB) mechanism [1–6]. In this mechanism, an SU(2) doublet scalar field is subject to a potential energy term whose shape allows the doublet field to acquire a vacuum expectation value that breaks the SU(2) symmetry and produces the Higgs boson and its potential energy term. This potential is the last piece of the SM Lagrangian which is yet to be directly tested. The shape of the Higgs boson potential in the SM can be expressed as a function of the Fermi coupling constant GF and the Higgs boson mass mH . A direct phenomenological prediction of the SM due to the potential is the interaction of the Higgs boson with itself at tree level (self-interaction), which can be probed by studying di-Higgs boson produc- tion in proton-proton collisions, as illustrated in figure 1(a). The self-interaction diagram together with the quark-loop contributions, primarily via the top-Higgs Yukawa coupling, –1– Figure 1. Leading-order Feynman diagrams for non-resonant production of Higgs boson pairs in the Standard Model through (a) the Higgs boson self-coupling and (b) the Higgs-fermion Yukawa JHEP04(2019)092 interaction. The H ∗ refers to the off-shell Higgs boson mediator. figure 1(b), are the leading-order Feynman diagrams for Higgs boson pair production. The SM cross section for pp → HH is extremely small, e.g. 33.4 fb at 13 TeV [7]. Physics beyond the SM can manifest in the increased production with respect to the SM predictions of the non-resonant HH final state or in the resonant production of particles that decay into a pair of SM Higgs bosons. The analysis presented here is potentially sensitive to cases where the decaying particle is a scalar, as in the MSSM [8] and 2HDM models [9], or a spin-2 graviton, as in Randall-Sundrum models [10]. The signals under study are non-resonant HH production with event kinematics predicted by the SM and resonant HH production with event kinematics consistent with the decays of heavy spin-0 or spin-2 resonances. Previous searches for pp → HH production were performed by the ATLAS and CMS √ collaborations in Run 1 of the LHC at s = 8 TeV. Decay modes with 4b [11, 12], bb̄τ + τ − [13, 14], γγbb̄ [15, 16] and γγW W ∗ [13] in the final state were studied. Furthermore, ATLAS also published a combination of all of the explored channels [13]. √ Results at s = 13 TeV were published by the ATLAS Collaboration in the 4b [17], bb̄τ + τ − [18, 19], bb̄γγ [20] and W W γγ [21] decay mode and by CMS in the 4b [22] , bb̄τ + τ − [23], bb̄γγ [24] and in the bb̄W W ∗ channel using the dileptonic W W ∗ decay mode [25]. Given the low expected yield for SM HH non-resonant production, it is of great importance to understand the sensitivity for the observation of the Higgs boson pair production in all possible decay channels, including bb̄W W ∗ , which will improve projections for future high-luminosity and high-energy colliders. This paper reports results of a search for Higgs boson pair production where one Higgs boson decays via H → bb̄, and the other decays via H → W W ∗ . The H → W W ∗ branching fraction is the second largest after H → bb̄, so the bb̄W W ∗ final state can be sensitive to HH production if the signal can be well separated from the dominant tt̄ background. The W W ∗ system decays into `νqq, where ` is either an electron or a muon, and the small contamination from leptonic τ decays is not explicitly vetoed in the analysis. Figure 2 shows a schematic diagram of resonant production of the Higgs boson pair with the subsequent decays H → W W ∗ and H → bb̄. Two complementary techniques are used to reconstruct the Higgs boson candidate that decays into two b quarks. Both techniques use the anti-kt jet algorithm [26] but –2– Figure 2. Schematic diagram of resonant Higgs boson pair production with the subsequent Higgs JHEP04(2019)092 and W -boson decays. with different radius parameters. The first technique employs jets with radius parameter R = 0.4 and it is used when each b quark from the H → bb̄ decay can be reconstructed as a distinct b jet. The second technique is used when this is not possible, due to the large boost of the b-quark pair. In this case the Higgs boson candidate is identified as a single anti-kt jet with radius parameter R = 1.0. The analysis using the first technique is referred to as the “resolved” analysis and that using the second technique is referred to as the “boosted” analysis. In both analyses, the jets from the hadronically decaying W boson are reconstructed as anti-kt jets with radius parameter R = 0.4. The resonant HH search is performed using both the resolved and the boosted analysis methods. The resolved analysis is performed between 500 and 3000 GeV, while the boosted analysis between 800 and 3000 GeV. The resolved analysis is divided into three selections, one targeting low mass values, a second designed for high mass values and a specific analysis for the 500 GeV mass value. Because the three resolved, and the boosted analyses do not select orthogonal samples, they are not combined statistically. However, results from all these different techniques are presented to illustrate their sensitivity reach. For the non-resonant search a dedicated selection of the resolved analysis is used. The dominant background in the bb̄W W ∗ final state is tt̄ production, with smaller con- tributions from W bosons produced in association with jets (W +jets) and multijet events in which a jet is misidentified as a lepton. The analysis defines one signal region for each signal hypothesis and, in order to avoid biases in the analysis selection, the analysis proce- dures and the event selection are optimised without reference to data in the signal regions. 2 Data and simulation samples The ATLAS detector [27] is a general-purpose particle detector at the Large Hadron Col- lider optimised to discover and measure a broad range of physics processes. It consists of an inner tracking detector surrounded by a thin superconducting solenoid, electromagnetic and hadronic calorimeters, and a muon spectrometer incorporating three large supercon- ducting toroid magnets.1 1 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 –3– The dataset used in this analysis corresponds to an integrated luminosity of 36.1 fb −1 (3.2 fb−1 from 2015 and 32.9 fb−1 from 2016) recorded by single-electron or single-muon triggers. The single-lepton trigger efficiency ranges from 75% to 90% (75% to 80%) for electrons (muons) depending on the signal mass, for selected lepton candidates above pT thresholds defined in section 4.1. Samples of simulated signal and background events were used to design the event selection and estimate the signal acceptance and the background yields from various SM processes. When searching for a new resonance (denoted by X in the following), specific sim- ulation models must be employed. Therefore, the spin-0 states were treated as narrow heavy neutral Higgs bosons, while the spin-2 states were modelled as Randall-Sundrum JHEP04(2019)092 (RS) gravitons [28, 29]. The parameters used in the RS graviton simulation were: c = k/M̄Pl equal to 1.0 or 2.0, where k is the curvature of the warped extra dimension and M̄Pl = 2.4 × 1018 GeV is the effective four-dimensional Planck scale. The graviton signal samples were generated at leading order (LO) with Madgraph5 aMC@NLO [30] using the NNPDF2.3 [31] LO parton distribution function (PDF) set, and Pythia 8.186 [32] to model the parton showers and hadronisation process with a set of tuned underlying- event parameters called the A14 tune [33]. Only the c = 2.0 samples were fully simulated, while the c = 1.0 samples were obtained by reweighting them using the Monte Carlo (MC) generator-level mHH distribution. Scalar signal samples were generated at next-to-leading order (NLO) with Mad- graph5 aMC@NLO interfaced to Herwig++ [34] using the CT10 PDF set [35] and the UE-EE-5-CTEQ6L1 tune. The simulation produced the Higgs boson pair through gluon-gluon fusion using an effective field theory approach to take into account the finite value of the top-quark mass mt [36]. Events were first generated with an effective La- grangian in the infinite top-quark mass approximation, and then reweighted with form factors that take into account the finite mass of the top quark. The non-resonant signal samples were simulated with Madgraph5 aMC@NLO + Herwig++ using the CT10 PDF set; and the same approach for the inclusion of finite mt effects was used [37]. In addition, scale factors dependent on the HH invariant mass mHH at generator level were applied to match the MC mHH distribution with an NLO calculation that computes exact finite mt contributions [38]. All signal samples were generated with 100% of Higgs boson pairs decaying into bb̄W W ∗ , and the samples were then normalised assuming B(H → W W ∗ ) = 0.22 and B(H → bb̄) = 0.57 [7]. Sherpa v2.2 [39] with the NNPDF 3.0 [40] PDF set was used as the baseline generator for the (W → `ν)/(Z → ``)+jets background. The W/Z+jets samples were normalised using the FEWZ [41] inclusive cross section with NNLO accuracy. The diboson processes (W W , W Z and ZZ) were generated at NLO with Sherpa v2.1.1 [39] with the CT10 [35] PDF set and normalised using the Sherpa cross-section prediction. The tt̄ background samples were generated with Powheg-Box v2 [42] using the CT10 PDF set. Powheg-Box v2 was interfaced to Pythia 6.428 [43] for parton showers, using of the LHC ring, and the y-axis points upwards. Cylindrical coordinates (r, φ) are used in the transverse plane, φ being the azimuthal angle around the z-axis. The pseudorapidity is defined p in terms of the polar angle θ as η = − ln tan(θ/2). The angular distance is measured in units of ∆R ≡ (∆η)2 + (∆φ)2 . –4– the Perugia2012 [44] tune with the CTEQ6L1 [45] set of PDFs for the underlying-event description. EvtGen v1.2.0 [46] was used to simulate the bottom- and charm-hadron decays. The mass of the top quark was set to mt = 172.5 GeV. At least one top quark in the tt̄ event was required to decay into a final state with a lepton. For the tt̄ sample the parameter Hdamp, used to regulate the high-pT gluon emission in Powheg, was set to mt , giving good modelling of the high-pT region [47]. The interference between the tt̄ background and the signal is extremely small due to the small width of the Higgs boson (ΓH ∼4 MeV) and it has been neglected in this analysis. The tt̄ cross section is calculated to next-to-next-to-leading order in QCD including resummation of soft gluon contributions at next-to-next-to-leading-logarithm (NNLL) accuracy using Top++ 2.0 [48]. JHEP04(2019)092 Single-top-quark events in the W -top, s, and t channels were generated using Powheg- Box v1 [49, 50]. The overall normalisation of single-top-quark production in each channel was rescaled according to its approximate NNLO cross section [51–53]. The effect of multiple pp interactions in the same and neighbouring bunch crossings (pile-up) was included by overlaying minimum-bias collisions, simulated with Pythia 8.186, on each generated signal and background event. The interval between proton bunches was 25 ns in all of the data analysed. The number of overlaid collisions was such that the distribution of the number of interactions per pp bunch crossing in the simulation matches that observed in the data: on average 14 interactions per bunch crossing in 2015 and 23.5 interactions per bunch crossing in 2016. The generated samples were processed through a Geant4-based detector simulation [54, 55] with the standard ATLAS reconstruction software used for collision data. 3 Object reconstruction In the present work an “object” is defined to be a reconstructed jet, electron, or muon. Electrons are required to pass the “TightLH” selection as described in refs. [56, 57], have pT > 27 GeV and be within |η| < 2.47, excluding the transition region between the barrel and endcaps in the LAr calorimeter (1.37 < |η| < 1.52). In addition, the electron is required to be isolated. In order to calculate the isolation variable, the pT of the tracks in a cone of ∆R around the lepton track is summed ( pT ), where ∆R = min(10 GeV/peT , 0.2) and peT P pT /peT (isolation variable) is required P is the electron transverse momentum. The ratio to be less than 0.06. Muons are reconstructed as described in ref. [58] and required to pass the “Medium” identification criterion and have |η| < 2.5. The muon isolation variables are similar to the electron isolation variables with the only difference being that the maximum cone size is ∆R = 0.3 rather than 0.2. Jets are reconstructed using the anti-kt algorithm [26] with a radius parameter of 0.4, and are required to have pT > 20 GeV and |η| < 2.5. Suppression of jets likely to have originated from pile-up interactions is achieved using a boosted decision tree in an algorithm that has an efficiency of 90% for jets with pT < 50 GeV and |η| < 2.5 [59]. The jet-flavour tagging algorithm [60] is used to select signal events and to suppress multijet, W +jets, Z+jets and diboson backgrounds. The jets containing b hadrons are called b jets in this –5– work. The jet-flavour tagging algorithm parameters were chosen such that the b-tagging efficiency is 85% for jets with pT of at least 20 GeV as determined in simulated inclusive tt̄ events [60] . At this efficiency, for jets with a pT distribution similar to that originating from jets in tt̄ events, the charm-quark component is suppressed by a factor of 3.1 while the light-quark component is suppressed by a factor of 34. Jets that are not tagged as b jets are collectively referred to as “light-quark jets”. Large-R jets are reconstructed using the anti-kt algorithm with a radius parameter of 1.0 and are trimmed to reduce pile-up contributions to the jet, as described in ref. [61]. The jet mass (mJ ) resolution is improved at high momentum using tracking in addition to calorimeter information [62]. This leads to a smaller mass resolution and better estimate JHEP04(2019)092 of the median mass value than obtained using only calorimeter energy clusters. The energy and mass scales of the trimmed jets are then calibrated using pT - and η-dependent calibra- tion factors derived from simulation [63]. Large-R jets are required to have pT > 250 GeV, mJ > 30 GeV and |η| < 2.0. The identification of large-R jets consistent with boosted Higgs boson decays uses jets built from tracks reconstructed from the ATLAS Inner Detec- tor (referred to as track-jets) to identify the b jets within the large-R jets. The track-jets are built with the anti-kt algorithm with R = 0.2 [64]. They are required to have pT > 10 GeV, |η| < 2.5, and are matched to the large-R jets with a ghost-association algorithm [65]. The small radius parameter of the track-jets enables two nearby b hadrons to be identified when their ∆R separation is less than 0.4, which is beneficial when reconstructing high-pT Higgs boson candidates. The b-tagging requirements of the boosted analyses use working points that lead to an efficiency of 77% for b jets with pT > 20 GeV when evaluated in a sample of simulated tt̄ events. At this efficiency, for jets with a pT distribution similar to that originating from jets in tt̄ events, the charm-quark component is suppressed by a factor of 12 (7.1) for the R = 0.4 jets (track-jets), while the light-quark component is suppressed by a factor of 380 for the jets with R = 0.4 and 120 for the track-jets. The calorimeter-based missing transverse momentum with magnitude ET miss is calcu- lated as the negative vectorial sum of the transverse momenta of all calibrated selected objects, such as electrons and jets, and is corrected to take into account the transverse mo- mentum of muons. Tracks with ptrack T > 500 MeV, compatible with the primary vertex but not matched to any reconstructed object, are included in the reconstruction to take into account the soft-radiation component that does not get clustered into any hard object [66]. To avoid double-counting, overlapping objects are removed from the analysis accord- ing to the following procedure. Muons sharing their track with an electron are removed if they are calorimeter-tagged.2 Otherwise, the electron is removed. Jets overlapping with electrons within an angular distance ∆R = 0.2 are removed. Jets overlapping with muons within ∆R = 0.2 and having less than three tracks or carrying less than 50% of the muon pT are removed. Electrons overlapping with remaining jets within ∆R = min(0.4, 0.04 + 10 GeV/peT ) are removed. Muons overlapping with remaining jets within ∆R = min(0.4, 0.04 + 10 GeV/pµT ) are removed. 2 Muons are identified by matching an Inner Detector reconstructed track with a track in the Muon Spectrometer or by matching an energy deposit, compatible with a minimum ionising particle, in the outer layers of the Tile Calorimeter (calorimeter-tagged muons). –6– 4 Resolved analysis 4.1 Resolved analysis: event selection At lowest order in QCD the final-state particles consist of one charged lepton, one neu- trino, and jets of colourless hadrons from four quarks, two being b quarks. Therefore, the corresponding detector signature is one charged lepton (e/µ), large ET miss , and four or more jets. Two of these jets are b-tagged jets from the Higgs boson decay, and two jets are not b-tagged jets from the hadronic W boson decay. The data used in the analysis were recorded by several single-electron or single-muon triggers in 2015 and 2016. In 2015, the electron (muon) trigger required a pT > 24 (20) GeV JHEP04(2019)092 electron (muon) candidate. Because of a higher instantaneous luminosity, in 2016 the electron trigger required a pT > 26 GeV electron candidate, while muons were triggered using a pT threshold of 24 GeV at the beginning of data taking, and 26 GeV for the rest of the year. In both 2015 and 2016, a threshold of pT > 27 GeV was applied offline on the selected lepton candidate. The analysis selects events that contain at least one reconstructed electron or muon matching a trigger-lepton candidate. In order to ensure that the leptons originate from the interaction point, requirements on the transverse (d0 ) and longitudinal (z0 ) impact parameters of the leptons relative to the primary vertex are imposed. In particular, defining σd0 as the uncertainty in the measured d0 and θ as the angle of the track relative to the beam axis, the requirements |d0 |/σd0 < 2 and |z0 sin θ| < 0.5 mm are applied. The requirement on |d0 |/σd0 is relaxed to define control regions in order to estimate the multijet background. The highest pT lepton is then retained as the analysis lepton. Events are required to have exactly two b-tagged jets, which form the Higgs boson candidate. Since events are accepted if they contain two or more light-quark jets, in events with more than two light-quark jets, the three leading jets are considered, and the pair with the lowest ∆R between them is selected as the W boson candidate. From MC simulation it was found that, when the light quarks from the W boson are matched to reconstructed jets by requiring that the ∆R between the jet and the quark is less than 0.3, this procedure yields the correct jet assignment in 70% of the cases. The event kinematics of the H → W W ∗ → `νqq topology can be fully reconstructed. Among all four-momenta of the final-state particles, only the component of the neutrino momentum along the beam axis, referred to as longitudinal momentum (pz ) in the following, is unknown while its transverse momentum is assumed to be the ET miss . The longitudinal momentum of the neutrino is computed by solving a quadratic equation in pz , employing the four-momenta of the lepton and the hadronic W boson, the ET miss , and the m = 125 GeV H ∗ constraint on the W W system. No W -boson mass constraint is applied to either the hadronic or the leptonic W boson decay, allowing either W boson to be off-shell. Whenever two real solutions are obtained, the ν candidate with the smallest ∆R relative to the lepton direction is retained. Studies performed by matching the ν candidate with the MC generator-level neutrino show that this procedure finds the correct solution for the neutrino pz in 60% (75%) of cases for a resonant signal of mass 700 (3000) GeV. If two complex solutions are found, only the real part of the solutions is retained. With the neutrino –7– Definition of the HH → bb̄W W ∗ kinematic variables pT of the bb̄ system pbTb̄ ∗ pT of the W W ∗ system pW T W ∆R of the W W ∗ system ∆RW W ∗ W W ∗ system mass mW W ∗ bb̄ system mass mbb̄ Di-Higgs boson system invariant mass mHH Table 1. Selection variables used to identify the HH → bb̄W W ∗ decay chain in the resolved JHEP04(2019)092 analysis. The mW W ∗ variable is exactly equal to mH if a real solution for the neutrino pz is found. It is larger otherwise. longitudinal momentum computed, the di-Higgs invariant mass can be fully reconstructed and employed to discriminate against backgrounds. Kinematic selections are used to suppress the tt̄ background relative to the signal. The tt̄ events are typically characterised by two b jets and two W bosons such that the ∆R separation between the b jets is large, and similarly the ∆R separation between the W bosons is also large. In contrast, in particular when the invariant mass of the heavy resonance is large, the signal is characterised by two b jets and two W bosons which are closer in ∆R in signal events with respect to the tt̄ background events. Moreover, for the signal the two b jets have an invariant mass equal to mH , while this is not the case for the tt̄ background, where a much broader distribution is expected. The symbols of the kinematic variables that discriminate between signal and background are listed in table 1. The selection requirements on the kinematic variables defining the signal region were chosen to maximise the expected sensitivity to various signals. The optimisation was per- formed for a spin-0 signal considering resonance masses (mX ) from 500 GeV to 3000 GeV in steps of 100 GeV. The same selection was used for the spin-2 signal models while SM Higgs pair production was used to optimise the non-resonant analysis. Below 500 GeV the top-quark background increases significantly, and hence rapidly reduces sensitivity. The selection criteria define four sets of requirements, referred as non-res, m500, low- mass and high-mass in the following. They are shown in table 2. The non-res and m500 se- lections are exclusively used for non-resonant signal and resonant signal with mass 500 GeV respectively. The low-mass selection is used for signal masses from 600 to 1300 GeV, while the high-mass selection is used for signals with masses between 1400 and 3000 GeV. In addition, requirements are placed on the reconstructed di-Higgs invariant mass mHH as a function of the signal resonance mass mX , as shown in table 3. The resolution of the reconstructed mHH ranges from 6% at 500 GeV to 10% at 3000 GeV. 4.2 Resolved analysis: background determination In this analysis the presence of a signal is indicated by an excess of events over the SM prediction for the background yield in the signal regions, so it is of great importance to properly estimate the amount of background in those regions. The dominant background –8– Variable non-res m500 low-mass high-mass miss ET [GeV] > 25 > 25 > 25 > 25 mW W ∗ [GeV] < 130 < 130 < 130 none pbTb̄ [GeV] > 300 > 210 > 210 > 350 pW W ∗ [GeV] > 250 > 150 > 250 > 250 T ∆RW W ∗ none none none < 1.5 mbb̄ [GeV] 105–135 105–135 105–135 105–135 Table 2. Criteria for non-resonant, m500, low-mass and high-mass selections in the resolved JHEP04(2019)092 analysis. mX [GeV] 500 600 700 750 800 mHH window [GeV] 480–530 560–640 625–775 660–840 695–905 mX [GeV] 900 1000 1100 1200 1300 mHH window [GeV] 760–967 840–1160 925–1275 1010–1390 1095–1505 mX [GeV] 1400 1500 1600 1800 2000 mHH window [GeV] 1250–1550 1340–1660 1430–1770 1750–2020 1910–2170 mX [GeV] 2250 2500 2750 3000 mHH window [GeV] 2040–2460 2330–2740 2570–2950 2760–3210 Table 3. Window requirements on mHH as a function of the resonance mass mX in the resolved analysis. is the tt̄ process. Dedicated control regions are used to normalise and validate the estimate of this background. The tt̄ normalisation is performed using three data control regions, one for the non-res, a second for the m500 and low-mass, and a third for the high-mass selection. These control regions are obtained by selecting events outside the mbb̄ window [100, 140] GeV and applying only the ET miss , m bb̄ W W ∗ (where applicable) and pT requirements shown in table 2 for the respective selections. In all regions, the event yields of W/Z+jets, single-top-quark and diboson events are modelled using simulated events and normalised to the expected SM cross sections. The multijet component of the background originates from events where either a jet is incorrectly identified as a lepton, or a non-prompt lepton is produced in heavy-flavour decays, or from photon conversions. It is characterised by low ET miss and high |d |/σ 0 d0 values of the lepton. The multijet background makes a significant contamination in the top control regions. Therefore, this background is estimated in top-background control region and signal region using a data-driven two-dimensional sideband method, labelled the ABCD method, that uses three additional regions denoted in the following by B, C and D. The region of interest, signal or control region, is indicated by A. –9– Process non-res m500 and low-mass high-mass tt̄ 110 ± 6 532 ± 13 8570 ± 50 Multijet 33 ± 4 250 ± 30 1540 ± 250 W +jets 29 ± 1 125 ± 3 2259 ± 8 Single top 20 ± 2 76 ± 4 1780 ± 20 Dibosons 2.2 ± 0.4 8.3 ± 0.8 171 ± 4 Z+jets 6.7 ± 0.2 27.1 ± 0.8 404 ± 2 Background sum 201 ± 8 1015 ± 34 14720 ± 260 JHEP04(2019)092 Data 206 1069 14862 Table 4. Data and estimated background yields in the non-res, m500 and low-mass, and high-mass top-background control regions of the resolved analysis. The uncertainty shown for the multijet background is due to the number of data events in the C region (as defined in the text). For all other backgrounds the uncertainties are due to the finite MC sample sizes. The B, C and D regions are defined in the following way: miss < 25 GeV and |d |/σ • region B: ET 0 d0 < 2.0, miss > 25 GeV and |d |/σ • region C: ET 0 d0 > 2.0, and miss < 25 GeV and |d |/σ • region D: ET 0 d0 > 2.0, while NA ,NB ,NC and ND indicate the number of events in the A,B,C and D regions, respectively. In the absence of correlations between the ET miss and |d |/σ 0 d0 variables, the relation NA = NC NB /ND holds, while in practice a correlation among variables results in a correction factor F to be applied to the computed ratio NAcorrected = F NC NB /ND . The correction factor F is estimated from data at an early stage of the analysis selection once a veto on the signal candidates is applied by inverting the requirement on the mbb̄ variable. It is computed using the relation F = NA ND /(NC NB ). Systematic uncertainties in F are described in section 4.3. In order to reduce statistical uncertainties in the computation, the shape of the mbb̄ distribution is derived at an earlier stage of the selection sequence, after applying the mW W ∗ < 130 GeV and pbTb̄ > 210 GeV requirements for the non-res, m500 and low-mass analyses and the pbTb̄ > 350 GeV and pW W ∗ > 250 GeV requirements T for the high-mass analysis. It was verified that subsequent requirements do not affect the mbb̄ shape, which can therefore be used at the end of the selection sequence. Table 4 summarises the numbers of observed and estimated events in the three top- quark control regions. The event yields in the control regions are used as input to the statistical analysis. Major contamination in the tt̄ control regions comes from multijet and W +jets backgrounds; as a result the tt̄ purity ranges from 52% to 58%. The modelling of the background was checked at all selection stages and, in general, shows good agreement with data. Figure 3 shows the mT distribution of the leptonic W – 10 – Events/20 GeV Events/20 GeV 160 ATLAS Data 1400 ATLAS Data s = 13 TeV, 36.1 fb-1 Other s = 13 TeV, 36.1 fb-1 Other 140 Multijet 1200 Multijet HH → bbWW* → bblνqq HH → bbWW* → bblνqq W+jets W+jets 120 non-resonant, top CR 1000 m500/low-mass, top CR tt tt 100 MC Stat + Syst Unc. MC Stat + Syst Unc. 800 80 600 60 400 40 20 200 0 0 Data-Bkg Data-Bkg 1 MC Stat Unc. 1 MC Stat Unc. Bkg Bkg 0 0 −1 −1 0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100 JHEP04(2019)092 mT [GeV] mT [GeV] Events/20 GeV 5000 ATLAS Data s = 13 TeV, 36.1 fb-1 Other HH → bbWW* → bblνqq Multijet 4000 W+jets high-mass, top CR tt 3000 MC Stat + Syst Unc. 2000 1000 0 Data-Bkg 0.4 MC Stat Unc. Bkg 0 −0.4 0 20 40 60 80 100 120 140 160 180 200 mT [GeV] Figure 3. The mT distribution in the three top-background control regions for the non-res, low- mass, and the high-mass selections of the resolved analyses. The signal contamination is negligible, and hence not shown. The lower panel shows the fractional difference between the data and the total expected background with the corresponding statistical and total uncertainty. boson candidate in the three top control regions. The mT variable is defined as: q mT = 2p`T ET miss · (1 − cos∆φ) , where ∆φ is the azimuthal angle between p`T and ET miss . The multijet background populates the low values of the mT distribution, so any mis-modelling of the multijet background would be clearly visible in the mT distribution. Figures 4 and 5 show the mbb̄ distributions at the selection stage where all requirements, including the mHH cut, are applied except the one on mbb̄ itself. The expected background is in agreement with the data over the entire distribution, and close to the signal region in particular. All simulated backgrounds are normalised according to their theoretical cross-sections, except tt̄, which is normalised in the top CRs. 4.3 Resolved analysis: systematic uncertainties The main systematic uncertainties in the background estimate arise from the potential mis-modelling of background components. For tt̄ background, MC simulation is used to derive the acceptances in all analysis regions, while the normalisation is taken from the top – 11 – 45 Events/20 GeV Events/20 GeV ATLAS Data 45 ATLAS Data 40 s = 13 TeV, 36.1 fb-1 HH SM x 300 s = 13 TeV, 36.1 fb-1 Rescaled Signal 40 Other Other 35 HH → bbWW* → bblνqq HH → bbWW* → bblνqq non-resonant Multijet 35 m500, mX = 500 GeV Multijet 30 W+jets 30 W+jets tt tt 25 MC Stat + Syst Unc. 25 MC Stat + Syst Unc. 20 20 15 15 10 10 5 5 0 0 Data-Bkg Data-Bkg 1 MC Stat Unc. 1 MC Stat Unc. Bkg Bkg 0 0 −1 −1 0 50 100 150 200 250 300 350 400 0 50 100 150 200 250 300 350 400 mbb [GeV] mbb [GeV] JHEP04(2019)092 Figure 4. The mbb̄ distribution in the resolved analysis for the non-res and m500 selections at the end of the selection sequence, before applying the mbb̄ requirement. The signals shown are from SM non-resonant HH production scaled up by a factor of 300 (left) and from a scalar resonance with mass 500 GeV scaled to the expected upper-limit cross section reported in section 6 (right). The lower panel shows the fractional difference between data and the total expected background with the corresponding statistical and total uncertainty. 22 Events/20 GeV Events/20 GeV 22 ATLAS Data 20 ATLAS Data s = 13 TeV, 36.1 fb-1 Rescaled Signal s = 13 TeV, 36.1 fb-1 Rescaled Signal 20 Other 18 Other 18 HH → bbWW* → bblνqq HH → bbWW* → bblνqq Multijet 16 Multijet low-mass, mX = 1000 GeV high-mass, m = 2000 GeV 16 W+jets X W+jets 14 14 tt tt MC Stat + Syst Unc. 12 MC Stat + Syst Unc. 12 10 10 8 8 6 6 4 4 2 2 0 0 Data-Bkg Data-Bkg 1 MC Stat Unc. 1 MC Stat Unc. Bkg Bkg 0 0 −1 −1 0 50 100 150 200 250 300 350 400 0 50 100 150 200 250 300 350 400 mbb [GeV] mbb [GeV] Figure 5. The mbb̄ distribution in the resolved analysis for the low-mass and high-mass selections at the end of the selection sequence, before applying the mbb̄ requirement. The signals shown are from scalar resonances with mass 1000 GeV (left) and 2000 GeV (right) scaled to the expected upper- limit cross section reported in section 6. The lower panel shows the fractional difference between data and the total expected background with the corresponding statistical and total uncertainty. control region and applied in the signal regions. Therefore, the acceptance ratio between signal and control regions is affected by theoretical uncertainties in the simulated tt̄ sample. These uncertainties are estimated by considering five sources: the matrix element generator used for the tt̄ simulation and the matching scheme used to match the NLO matrix ele- ment with the parton shower, the parton shower modelling, the initial-state (Initial State Radiation, ISR) and final-state (Final State Radiation, FSR) gluon emission modelling, the dependence on the choice of the PDF set and the dependence on the renormalisation and factorisation scales. Matrix element generator and matching systematic uncertainties are computed by comparing samples generated by aMC@NLO [30] and Powheg, both interfaced with Herwig++ for showering and fragmentation. Parton shower systematic – 12 – Source non-res (%) m500 and low-mass (%) high-mass (%) Matrix element 7 0.5 4 Parton shower 4 16 10 ISR/FSR 15 5 8 PDF 5 3 6 Scale 3 2 4 Total 18 17 15 JHEP04(2019)092 Table 5. Percentage uncertainties from tt̄ modelling on the tt̄ background contributions in all signal regions of the resolved analysis. Source non-res (%) m500 and low-mass (%) high-mass (%) SR CR SR CR SR CR Modelling/Parton Shower 40 40 40 40 20 20 PDF 30 7 40 10 30 20 Scale 20 30 20 30 30 30 Table 6. Theoretical percentage uncertainties on the predicted W/Z+jets event yield in the top control regions and the signal regions for all selections. uncertainties are computed by comparing samples generated using Powheg+Pythia6 and Powheg+Herwig++. Initial-state and final-state radiation systematic uncertainties are computed by varying the generator parameters from their nominal values to increase or decrease the amount of radiation. The PDF uncertainties are computed using the eigenvectors of the CT10 PDF set. Uncertainties due to missing higher-order corrections, labelled scale uncertainties, are computed by independently scaling the renormalisation and factorisation scales in aMC@NLO+Herwig++ by a factor of two, while keeping the renor- malisation/factorisation scaling ratio between 1/2 and 2. These systematic uncertainties are summarised in table 5. Uncertainties in the modelling of W +jets background are computed in each signal region (SR) and top control region (CR). Three sources of uncertainty are considered: scale variation, PDF set variation and generator modelling uncertainties. Scale uncertainties are computed by scaling the nominal renormalisation and factorisation scales by a factor of two. PDF uncertainties are computed using the NNPDF [40] error set, while generator modelling uncertainties are obtained by comparing the nominal Sherpa-generated sample with a sample generated with Alpgen [67] and showered with Pythia6 [43]. The values obtained in each region are summarised in table 6. For the data-driven multijet background, three sources of uncertainty are identified. The non-closure correction term F is computed using data at an early stage of the selection sequence, where contamination by the signal can be considered negligible. Its difference from the value obtained using a simulated multijet event sample is 40% and is assigned as an – 13 – uncertainty in the multijet estimation. The F value can be affected by the analysis selection requirements. A systematic uncertainty (extrapolation uncertainty) is added by comparing the maximum variation among the F values evaluated after each selection requirement. Finally, the uncertainty due to the dependence of the F value on lepton flavour (flavour uncertainty) is computed as the maximum difference between the nominal F value and the F value calculated for electrons and muons separately. The extrapolation (flavour) uncertainty is found to be 16% (9%) for the non-res selection, 32% (9%) for the m500 and low-mass resonant selections, and 45% (6%) for the high-mass resonant selection. Single-top-quark production is one of the smaller backgrounds in this analysis. Theo- retical cross-section uncertainties vary from 5% for associated W t production to 4% for s- JHEP04(2019)092 and t-channel single-top production. The largest of these is conservatively assigned to all single-top production modes. Further modelling systematic uncertainties are calculated by employing the difference between the nominal sample using the Diagram Removal scheme described in ref. [68] and a sample using the Diagram Subtraction scheme for the dominant single-top production mode, W t. The uncertainties are 50%, for the non-res, m500 and low-mass analyses, and 80% for the high-mass analysis. Systematic uncertainties in the signal acceptance are computed by varying the renor- malisation and factorisation scales with a variation of up to a factor of two, and using the same procedure as for the tt̄ background. PDF uncertainties are computed using PDF4LHC15 30 [69] PDF sets, which include the envelope of three PDF sets, namely CT14, MMHT14, NNPDF3.0. The resulting uncertainties are less than 1.1% for the scale and less than 1.3% for the PDFs. Parton shower uncertainties are computed by comparing the Herwig++ showering with that of Pythia8, and this results in less than 2% uncertainty. The detector-related systematic uncertainties affect both the background estimate and the signal yield. In this analysis the largest of these uncertainties are related to the jet en- ergy scale (JES), jet energy resolution (JER), b-tagging efficiencies and mis-tagging rates. √ The JES uncertainties for the small-R jets are derived from s = 13 TeV data and sim- ulations [70], while the JER uncertainties are extrapolated from 8 TeV data using MC simulations [71]. The uncertainty due to b-tagging is evaluated following the procedure described in ref. [60]. The uncertainties associated with lepton reconstruction and energy measurements have a negligible impact on the final results. All lepton and jet measure- ment uncertainties are propagated to the calculation of ET miss , and additional uncertainties are included in the scale and resolution of the soft term. The overall impact of the ET miss soft-term uncertainties is also small. Finally, the uncertainty in the combined integrated luminosity is 3.2% [72]. 5 Boosted analysis 5.1 Boosted analysis: event selection As in the resolved analysis, data used in the boosted analysis were recorded by single-lepton triggers, and only events that contain at least one reconstructed electron or muon matching the trigger lepton candidate are analysed. Requirements on pT , |d0 |/σd0 and |z0 sin θ| of the lepton tracks are also the same as in the resolved analysis. – 14 – Events are required to have at least one large-R jet with an angular distance ∆R > 1.0 from the reconstructed lepton. The highest-pT large-R jet is identified as the H → bb̄ can- didate. The large-R jet mass is required to be between 30 GeV and 300 GeV. In order to reconstruct the H → W W ∗ system, events with at least two small-R jets with an angular distance ∆R > 1.4 from the H → bb̄ candidate are selected. The hadronically and lepton- ically decaying W bosons are then reconstructed following the same algorithm as in the re- solved analysis. In order to reduce the tt̄ background, events are rejected if they contain any small-R jet passing the b-tagging requirement on the small-R jet as described in section 3. Signal regions (SR) are defined with at least two associated track jets within the large-R jet and requiring that the two highest-pT track jets pass the b-tagging requirement on track JHEP04(2019)092 jets as described in section 3. The large-R jet mass must be between 90 GeV and 140 GeV. An additional requirement of ET miss > 50 GeV is imposed to reject multijet backgrounds. For narrow-width scalar signals, the selection efficiency ranges between 3% and 0.6% for masses from 1000 GeV to 3000 GeV. Similarly, for graviton signals with c=1.0 (c=2.0), the selection efficiency ranges between 3% (3%) and 0.4% (0.8%) for masses from 1000 GeV to 3000 GeV. In order to assess the modelling of the dominant tt̄ background, a validation region (VR) is defined outside the large-R jet signal region mass window and labelled top VR. Any event with a large-R jet mass mLarge-R jet < 90 GeV or mLarge-R jet > 140 GeV falls in the top VR. By construction, the top VR is orthogonal to the SR. 5.2 Boosted analysis: background determination In the boosted analysis the presence of a signal is indicated by an excess of events above the SM prediction of the background mHH distribution at the end of the event selection. Similarly to the resolved analysis, the tt̄ process is the dominant background. Therefore, a dedicated validation region is used to check its modelling as defined in section 5.1. The event yields from tt̄, W/Z+jets, single-top-quark and diboson processes in the signal region and the top VR are modelled using simulation and normalised to the expected SM cross section described in section 2. The multijet component of the background is estimated using the data-driven method as in the resolved analysis. In the boosted analysis a higher requirement on ET miss (E miss > T 50 GeV) is applied, while the cut on |d0 |/σd0 is the same. For the boosted analysis, the correlation between |d0 |/σd0 and ET miss is estimated in multiple MC background samples and also in data, and it is found to be negligible. Hence, the multijet yield in region A can be estimated using the relation NA = NC NB /ND . The multijet estimation is performed separately for the muon and the electron channel. The NB /ND ratio is calculated inclusively in the large-R jet mass distribution. The mHH distribution of the multijet background is estimated by subtracting the prompt-lepton MC backgrounds from the data in the 1-tag region, where the 1-tag region is defined as the region where all selections are applied except that the large-R jet is required to have only one track jet tagged as a b jet. The modelling of the background is checked in the top VR. Table 7 reports the numbers of observed and predicted background events in the top VR, showing good agreement between the two. In order to check the validity of the multijet background determination, the mT distribution is shown in figure 6. This variable is particularly sensitive to the – 15 – Process Events tt̄ 1000 ± 21 W +jets 570 ± 10 Multijet 380 ± 20 Single top 160 ± 7 Dibosons 40 ± 3 Z+jets 56 ± 2 Background sum 2206 ± 31 JHEP04(2019)092 Data 2179 Table 7. Predicted and observed event yields in the top VR for the boosted analysis. The uncer- tainty shown for the multijet background is due to the number of data events in the C region. For all other backgrounds the uncertainties are due to the finite MC sample sizes. Events/20 GeV Events/10 GeV 1000 ATLAS Data ATLAS Data s = 13 TeV, 36.1 fb-1 Other 500 s = 13 TeV, 36.1 fb-1 Rescaled Signal Multijet Other 800 HH → bbWW* → bblνqq HH → bbWW* → bblνqq W+jets Multijet Boosted, top VR 400 Boosted, mX = 2000 GeV tt W+jets 600 MC Stat + Syst Unc. tt 300 MC Stat + Syst Unc. 400 200 200 100 0 0 Data-Bkg Data-Bkg MC Stat Unc. MC Stat Unc. 0.5 1 Bkg Bkg 0 0 −0.5 −1 0 50 100 150 200 250 300 50 100 150 200 250 300 mT [GeV] mLarge-R jet [GeV] Figure 6. The mT distribution (left) in the top VR, and inclusive mLarge-R jet distribution (right) after applying all selections. The signal distribution is negligible in the left plot, while in the right plot it has been scaled to the expected upper-limit cross section reported in section 6. The lower panel shows the fractional difference between data and the total expected background with the corresponding statistical and total uncertainty. multijet background contamination. Additionally, the mLarge-R jet variable used to define the signal region and the top VR is shown in the same figure. The data and predicted background agree well, which builds confidence in the estimated efficiency of the mLarge-R jet requirement for signal and background. 5.3 Boosted analysis: systematic uncertainties The evaluation of detector modelling uncertainties in the boosted analysis follows the same approach as in the resolved analysis. The significant additions to those described in section 4.3 are the uncertainties related to the large-R jets. The large-R jet energy resolution and scale, and jet mass resolution and scale uncertainties are derived in situ from 8 TeV pp collision data, taking into account MC simulation extrapolations for the – 16 – Source Uncertainty (%) Matrix element 7.1 Parton shower 7.8 ISR/FSR 8.4 PDF 1.9 Scale 5.0 Total 14.5 JHEP04(2019)092 Table 8. Uncertainties from different sources in the predicted yield of the tt̄ background in the signal region of the boosted analysis. different detector and beam conditions present in 8 and 13 TeV data-taking periods [73]. The uncertainty in the b-tagging efficiency for track jets is evaluated with the same method used for resolved calorimeter jets. The impact of these uncertainties on the final fit are shown in table 13. All SM backgrounds, except multijet, are modelled using MC simulation. Therefore, predicted yields in both the signal and the top validation regions are affected by theoret- ical uncertainties. These uncertainties are computed following the same procedure as in the resolved analysis for tt̄, W/Z+jets, single-top-quark and diboson backgrounds. For the tt̄ background in the signal region, the uncertainties are summarised in table 8. The uncertainties on single top quark production range from 20% for ISR/FSR to 70%, stem- ming from the difference between the diagram removal and diagram subtraction schemes. Uncertainties in the modelling of W/Z+jets background range from 10% stemming from PDF uncertainties to 45% stemming from scale uncertainties. Diboson processes have a negligible impact on the total background. For the normalisation of the multijet background predicted in region A (See sec- tion 5.2), several sources of uncertainty are considered. The uncertainties in the normalisa- tion of tt̄ and W/Z+jets in regions B, C and D contribute a systematic uncertainty of 25% and 30% respectively. The relative difference between the large-R jet mass acceptance in the 1-tag region C and in the 2-tag region C accounts for 15%. The propagation of the sta- tistical uncertainty in the multijet yield in region C and the uncertainty in the NB /ND ratio contribute about 23%. The propagation of detector modelling systematic uncertainties, in- cluding the modelling uncertainty of the |d0 |/σd0 requirement and of the MC backgrounds with prompt leptons subtracted from data in regions B, D and C, contribute about 45%. As an additional check on the prediction of the multijet yield with the ABCD method, a condi- tional background-only likelihood fit of the large-R jet mass distribution is performed in the VR. The difference between the multijet yield estimated with this method and the ABCD prediction is assigned as an uncertainty. This error accounts for 23% of the total uncer- tainty in the multijet estimation. All different sources of uncertainty are treated as indepen- dent and added in quadrature for the final uncertainty of 80% in the multijet normalisation. – 17 – For the simulated backgrounds, the systematic uncertainty in the mHH distribution shape is determined by comparing the nominal MC sample with the corresponding alter- native (variation) MC samples described in section 4.3. The shape systematic uncertainty is determined by fitting a first-order polynomial to the ratio of the variation mHH dis- tribution to the nominal mHH distribution, while keeping the same normalisation. For the data-driven multijet background, the uncertainty in the mHH distribution shape is determined by comparing the shapes in the 2-tag and 1-tag C regions. Theoretical systematic uncertainties in the signal acceptance are computed following the same algorithm as the resolved analysis. The resulting uncertainties are less than 0.5% JHEP04(2019)092 for uncertainties due to missing higher-order corrections (labelled scale), less than 0.5% for those due to PDFs, and approximately 2% (5%) in the lower (higher) mass range for those due to the parton shower. 6 Results Resolved and boosted analyses have non-trivial event overlap. In fact, a set of energy deposits in the calorimeter can be reconstructed both as two jets of ∆R = 0.4 and one Large-R jet with ∆R = 1.0. Due to this difficulty the two analyses are not statistically combined. The results from each analysis for the entire explored mass range are presented here. For the non-resonant signal search, only the resolved analysis is used. For the resonance search, the sensitivity of the analyses vary as a function of the resonance mass. This dependence is different for the narrow scalar search and the RS graviton search. In the following, section 6.1 describes the resolved analysis and provides results of the non-resonant signal search and of the resonant signal search for the m500, the low-mass and the high-mass selections. Section 6.2 provides results for the resonant signal search in the boosted selection for both the narrow scalar and the RS graviton signal models. Section 6.3 summarises the final results, both for the non-resonant case and for the resonant case. In the resonant case, for each mass point, the result of the analysis having the best sensitivity is presented. 6.1 Resolved analysis The resolved analysis is described in detail in section 4. The event selection is described in section 4.1 and summarised in table 2. For each selected event, the invariant mass of the HH system (mHH ) is reconstructed and its distribution is shown in figure 7 for the non- res and the m500 analyses, and in figure 8 for the low-mass and the high-mass analyses. Data are generally in good agreement with the expected background predictions within the total uncertainty. The signal mHH distribution is shown in the figure for the non- resonant, the scalar resonance, and the two graviton hypotheses with c = 1.0 and c = 2.0. Because the scalar-resonance samples are simulated in the narrow-width approximation, the reconstructed resonance width is exclusively due to the detector resolution. The same holds for graviton samples with c = 1.0, while c = 2.0 graviton samples have a significant intrinsic width that leads to a loss of sensitivity. – 18 – Events/175 GeV Events/50 GeV 24 Data ATLAS Data ATLAS 22 HH SM x 150 140 Rescaled GKK * (c=1.0) s = 13 TeV, 36.1 fb-1 s = 13 TeV, 36.1 fb-1 20 Other Rescaled GKK * (c=2.0) HH → bbWW* → bblνqq 120 HH → bbWW* → bblνqq 18 Multijet Rescaled Scalar non-resonant m500, mX = 500 GeV Other 16 W+jets 100 tt Multijet 14 W+jets MC Stat + Syst Unc. 80 12 tt 10 60 MC Stat + Syst Unc. 8 6 40 4 20 2 0 0 Data-Bkg Data-Bkg 2 MC Stat Unc. 1 MC Stat Unc. 1 Bkg Bkg JHEP04(2019)092 0 0 −1 −2 −1 0 200 400 600 800 1000 1200 1400 1600 1800 2000 0 200 400 600 800 1000 1200 mHH [GeV] mHH [GeV] Figure 7. mHH distributions for non-resonant and m500 selections in the resolved analysis. For each selection the corresponding signal hypothesis, non-resonant, scalar resonance, and graviton with c = 1.0 and c = 2.0, is shown. For scalar and graviton signals, resonances with mass 500 GeV are shown. The lower panel shows the fractional difference between data and the total expected background with the corresponding statistical and total uncertainty. The non-resonant signal is multiplied by a factor of 150 with respect to the expected SM cross section. The scalar signal is multiplied by a factor of 5, the graviton c = 1.0 by a factor of 5 and the graviton c = 2.0 by a factor of 1 with respect to the expected upper-limit cross section reported in section 6. Events/215 GeV Events/230 GeV 200 ATLAS Data ATLAS Data 600 Rescaled GKK * (c=1.0) Rescaled GKK * (c=1.0) 180 s = 13 TeV, 36.1 fb-1 s = 13 TeV, 36.1 fb-1 Rescaled GKK * (c=2.0) Rescaled GKK * (c=2.0) HH → bbWW* → bblνqq 500 HH → bbWW* → bblνqq 160 Rescaled Scalar Rescaled Scalar low-mass, mX = 1000 GeV Other high-mass, m = 2000 GeV Other X 140 Multijet 400 Multijet 120 W+jets W+jets tt tt 100 300 MC Stat + Syst Unc. MC Stat + Syst Unc. 80 60 200 40 100 20 0 0 Data-Bkg Data-Bkg 1 MC Stat Unc. 2 MC Stat Unc. 1 Bkg Bkg 0 0 −1 −1 −2 0 200 400 600 800 1000 1200 1400 1600 1800 2000 0 500 1000 1500 2000 2500 3000 mHH [GeV] mHH [GeV] Figure 8. mHH distributions in the resolved analysis selections. For each selection the corre- sponding signal hypothesis, scalar resonance, and graviton with c = 1.0 and c = 2.0, and mass 1000 (2000) GeV for the low-mass (high-mass) analysis, are shown. The lower panel shows the fractional difference between data and the total expected background with the corresponding statistical and total uncertainty. In the plot on the left the scalar signal is multiplied by a factor of 8, the graviton c = 1.0 by a factor of 10 and the graviton c = 2.0 by a factor of 2 with respect to the expected upper-limit cross section reported in section 6; for the plot on the right the multiplying factors are 20 for the scalar signal, 10 for the graviton c = 1.0 signal and 5 for the graviton c = 2.0 signal. – 19 – The mHH distribution is sampled with resonance-mass-dependent mHH requirements as reported in table 3. The numbers of events in the signal and control regions (the tt̄ control region and the C region of the multijet estimation procedure) are simultaneously fit using a maximum-likelihood approach. The fit includes six contributions: signal, W +jets, Z+jets, tt̄, single-top-quark production, diboson and multijet. The tt̄ and multijet normalisations are free to float, the C region of the ABCD method being directly used in the fit, while the diboson, W +jets and Z+jets backgrounds are constrained to the expected SM cross sections within their uncertainties. The fit is performed after combining the electron and muon channels. Statistical uncertainties due to the limited sample sizes of the simulated background processes are JHEP04(2019)092 taken into account in the fit by means of nuisance parameters, which are parameterised by Poisson priors. Systematic uncertainties are taken into account as nuisance parameters with Gaussian constraints. For each source of systematic uncertainty, the correlations across bins and between different kinematic regions, as well as those between signal and background, are taken into account. Table 9 shows the post-fit number of predicted backgrounds, observed data, and the signal events normalised to the expected upper limit cross sections. Expected event yields vary across mass because of varying selections. For instance, the requirement on pbTb̄ is higher in non-res selection than in low-mass selection. Similarly, even within low-mass or high-mass selection, the requirement on mHH vary across mass. No significant excess over the expectation is observed and the results are used to eval- uate an upper limit at the 95% confidence level (CL) on the production cross section times the branching fraction for the signal hypotheses under consideration. The exclusion lim- its are calculated with a modified frequentist method [74], also known as CLs , and the profile-likelihood test statistic [75]. None of the considered systematic uncertainties is sig- nificantly constrained or pulled in the likelihood fit. In the non-resonant signal hypothesis the observed (expected) upper limit on the σ(pp → HH) × B(HH → bb̄W W ∗ ) at 95% CL is: σ(pp → HH) · B(HH → bb̄W W ∗ ) < 2.5 2.5+1.0  −0.7 pb. The branching fraction B(HH → bb̄W W ∗ ) = 2 × B(H → bb̄) × B(H → W W ∗ ) = 0.248 is used to obtain the following observed (expected) limit on the HH production cross section at 95% CL: σ(pp → HH) < 10 10+4  −3 pb, which corresponds to 300 (300+100 −80 ) times the SM predicted cross section. Including only the statistical uncertainty, the expected upper limit for the non-resonant production is 190 times the SM prediction. This result, when compared with other HH decay channels, is not competitive. This is mainly due to the similarity of the reconstructed mHH spectrum between the non-resonant SM signal and the tt̄ background that makes the separation between the two processes difficult. Figure 9 shows the expected and observed limit curves for the production cross section of a scalar S and graviton G∗KK particle. The graviton case is studied for the two values of the model parameter c described previously. Different selections are used in different resonance mass ranges without attempting to statistically combine them. The switch from – 20 – Resonant analysis mX [GeV] S G∗KK (c = 1.0) G∗KK (c = 2.0) Total Bkg. Data 500 18 ± 5 20 ± 5 18 ± 5 19 ± 6 26 600 13 ± 2 15 ± 2 13 ± 2 17 ± 6 16 700 16 ± 2 17 ± 2 16 ± 2 25 ± 8 22 750 20 ± 2 22 ± 2 20 ± 2 22 ± 9 27 800 18.4 ± 1.5 19.7 ± 1.6 18.2 ± 1.5 20 ± 8 28 900 16.3 ± 1.6 17.0 ± 1.7 16.1 ± 1.6 20 ± 7 23 JHEP04(2019)092 1000 12.0 ± 1.3 12.3 ± 1.4 11.9 ± 1.3 14 ± 5 11 1100 9.6 ± 1.2 9.8 ± 1.2 9.5 ± 1.1 8±3 8 1200 8.1 ± 0.9 8.2 ± 0.9 8.1 ± 0.9 6±3 5 1300 5.1 ± 0.7 5.1 ± 0.7 6.2 ± 0.8 3.5 ± 1.8 1 1400 4.3 ± 0.3 4.1 ± 0.3 4.0 ± 0.3 1.1 ± 0.2 0 1500 3.5 ± 0.3 3.5 ± 0.3 3.5 ± 0.3 1.1 ± 0.2 0 1600 3.1 ± 0.3 3.1 ± 0.3 3.2 ± 0.3 0.4 ± 0.3 1 1800 14.1 ± 1.8 14 ± 2 14 ± 2 17 ± 5 21 2000 8.7 ± 1.0 8.9 ± 1.0 8.8 ± 1.0 8±3 9 2250 7.9 ± 1.1 8.2 ± 1.2 8.2 ± 1.2 6±2 7 2500 5.5 ± 0.8 5.6 ± 0.8 5.6 ± 0.8 3.3 ± 1.4 3 2750 5.7 ± 1.0 6.1 ± 1.1 6.0 ± 1.1 3.1 ± 1.3 3 3000 4.3 ± 0.7 4.6 ± 0.7 4.5 ± 0.7 2.1 ± 1.0 1 Non-resonant analysis Rescaled SM signal Total Bkg. Data 17 ± 2 21 ± 8 22 Table 9. Data event yields, and post-fit signal and background event yields in the final signal region for the non-resonant analysis and the resonant analysis in the 500–3000 GeV mass range. The errors shown are the MC statistical and systematic uncertainties described in section 4.3. The yields are shown for three signal models: a scalar (S) and two Randall-Sundrum gravitons with c = 1.0 and c = 2.0 (G∗KK ). Signal event yields are normalised to the expected upper-limit cross section. one selection to another is performed based on the best expected limit for that resonance mass. The outcome of this procedure is that the m500 selection is used to set limits on resonances of mass of 500 GeV, the low-mass selection is used up to masses of 1600 GeV, while the high-mass selection is used in the mass range 1600–3000 GeV. Overall, the resolved analysis is most sensitive for a mass value of 1300 GeV with an expected upper limit of 0.35 pb on σ(pp → HH). At this mass the observed exclusion limit is 0.2 pb. In both the non-resonant and resonant cases, the impact of the systematic – 21 – 95% CL Limit on σ(pp → S→ HH) [pb] 95% CL Limit on σ(pp → G*KK→ HH) [pb] Observed ATLAS Observed (c=1.0) ATLAS 103 Expected s= 13 TeV, 36.1 fb-1 103 Expected (c=1.0) s= 13 TeV, 36.1 fb-1 Expected ±1σ Expected ±1σ (c=1.0) Expected ±2σ Expected ±2σ (c=1.0) Expected Stats Only Observed (c=2.0) 102 102 Expected (c=2.0) low-mass high-mass low-mass high-mass 10 10 1 1 JHEP04(2019)092 10−1 10−1 500 1000 1500 2000 2500 3000 500 1000 1500 2000 2500 3000 m S [GeV] m G* [GeV] KK Figure 9. Expected and observed upper limit at 95% CL on the cross section of resonant pair production for the resolved analysis in the heavy scalar boson S model (left) and the spin-2 graviton model in two c parameter hypotheses (right). The left plot also shows the expected limit without including the systematic errors in order to show their impact. The impact of systematic errors is similar for the graviton models. uncertainties is observed to be large. In order to quantify the impact of the systematic un- certainties, a fit is performed where the estimated signal yield, normalised to an arbitrary cross-section value, is multiplied by a scaling factor αsig , which is treated as the parameter of interest in the fit. The fit is performed using pseudo-data and the contribution to the uncertainty in αsig from several sources is determined. The contribution of the statistical uncertainty to the total uncertainty in αsig , shown in table 10, is decomposed into signal region statistics, top CR statistics and multijet CR statistics. The contribution of the systematic uncertainties to the total uncertainty is decomposed into the dominant compo- nents and shown in table 11. The dominant systematic uncertainties vary across the mass range, but some of the most relevant ones are due to tt̄ modelling, b-tagging systematic uncertainties, and those related to jet measurements. – 22 – Statistical source Resolved analysis Non-Res (%) 500 GeV (%) 1000 GeV (%) 2000 GeV (%) Signal region +60/–40 +60/–60 +70/–60 +80/–70 Top control region +40/–30 +28/–30 +20/–12 +13/–13 Multijet control region +40/–30 +24/–26 +30/–30 +30/–30 Total statistical +80/–60 +70/–70 +80/–70 +90/–80 JHEP04(2019)092 Table 10. Statistical contribution (in percentage) to the total error in the scaling factor αsig for the non-resonant signal and three scalar-signal mass hypotheses, 500 GeV, 1000 GeV and 2000 GeV, in the resolved analysis. The values are extracted by calculating the difference in quadrature between the total statistical error and the error obtained after setting constant the normalisation factor of the background that dominates the region of interest. Systematic source Resolved analysis Non-Res (%) 500 GeV (%) 1000 GeV (%) 2000 GeV (%) tt̄ modelling ISR/FSR +30/–20 +10/–5 +7 / –4 +2/–2 Multijet uncertainty +10/–10 +20/–10 +20 / –20 +30/–30 tt̄ Matrix Element +10/–10 — — — W +jets modelling PDF +4/–7 +10/–10 +2 / –6 +7/–5 W +jets modelling scale +9/–10 +9/–4 +9 / –2 +20/–10 W +jets modelling gen. +10/–8 +10/–10 +9 / –1 +9/–9 tt̄ modelling PS +3/–2 +30/–20 +20 / –20 +2/–2 b tagging +30/–20 +11/–5 +7 / –6 +30/–30 JES/JER +13/–20 +20/–20 +50 / –50 +10/–6 miss soft term res. ET +20/–20 +8/–1 +9 / –7 +7/–7 Pile-up reweighting +3/–10 +5/–3 +9 / –10 +6/–6 Total systematic +60/–80 +70/–70 +60/–70 +40/–60 Table 11. Systematic contributions (in percentage) to the total error in the scaling factor αsig for the non-resonant signal and three scalar-signal mass hypotheses, 500 GeV, 1000 GeV and 2000 GeV, in the resolved analysis. The first column quotes the source of the systematic uncertainty. The 00 −00 symbol indicates that the specified source is negligible. The contribution is obtained by calculating the difference in quadrature between the total error in αsig and that obtained by setting constant the nuisance parameter(s) relative to the contribution(s) under study. – 23 – mX [ GeV ] S G∗KK (c = 1.0) G∗KK (c = 2.0) Total Bkg. Data 2000 28 ± 0.5 36.4 ± 0.8 43.0 ± 0.7 1255 ± 27 1107 Table 12. Data event yields, and post-fit signal and background event yields in the final signal region for the boosted analysis and the scalar S and graviton (c = 1.0 and c = 2.0) G∗KK particle hypotheses. The errors shown are the MC statistical and systematic uncertainties described in section 5.3. For illustration a signal mass point of 2000 GeV is reported in the table. The signal samples are normalised to the expected upper limit cross sections. 6.2 Boosted analysis JHEP04(2019)092 The boosted analysis applies the selection criteria described in section 5.1. After applying the large-R jet mass requirement 90 < mLarge-R jet < 140 GeV, the mHH distribution is reconstructed and its shape is fit to data using MC signal and background templates. The distribution is fit using 17 bins, with almost uniform width except at low and high mHH , where the bin width is modified in order to have a MC statistical uncertainty smaller than 20%. All backgrounds, except multijet, are simulated using MC generators and normalised using the cross section of the simulated process. The multijet background is estimated using the ABCD method, and its normalisation obtained from this method is kept fixed in the fit. The bias due to possible signal contamination in the ABCD regions was studied and found to have negligible effect on the result. The integral of the mHH distribution for the boosted analysis is shown in table 12. Systematic uncertainties affecting the mHH shape are parameterised as linear func- tions of mHH , and the function parameters are treated as nuisance parameters in the fit. Statistical uncertainties due to the limited sample sizes of the simulated background pro- cesses are taken into account in the fit by means of further nuisance parameters, which are parameterised by Poisson priors. The systematic uncertainties included in the fit are described in section 5.3. The contribution of the systematic uncertainties to the total uncertainty is decomposed into the dominant components and summarised in table 13. The most relevant systematic uncertainties are due to the limited size of the MC samples, the tt̄ modelling and the b-tagging systematic uncertainties. Figure 10 shows the mHH distribution for data and the background components for the boosted analysis. Data are generally in good agreement with the background expectations within the quoted systematic errors. The signal mHH distribution is shown in the figure for the scalar resonance, and the two graviton hypotheses with c = 1.0 and c = 2.0. Figure 11 shows the observed and the expected upper limit on the production cross section of the scalar S and the two graviton (c = 1.0 and c = 2.0) G∗KK particles. – 24 – Uncertainty source Boosted analysis 1500 GeV [%] 2000 GeV [%] 2500 GeV [%] 3000 GeV [%] Data statistics +50/–52 +59/–61 +64/–66 +70/–72 Total systematic +87/–85 +81/–79 +76/–75 +71/–69 MC statistics +42/–48 +42/–50 +39/–48 +39/–49 tt̄ modelling +29/–31 +36/–38 +40/–45 +32/–39 Multijet uncertainty +11/–14 +19/–23 +16/–20 +11/–16 JHEP04(2019)092 W +jets modelling +27/–30 +8/–12 +11/–10 +11/–10 Single-top modelling +22/–26 +5/–6 +4/–5 +5/–5 b tagging +31/–19 +36/–22 +36/–17 +34/–14 JES/JER +14/–14 +6/–6 +14/–11 +7/–9 Large-R jet +29/–10 +27/–8 +27/–7 +29/–8 Table 13. Statistical and systematic contributions (in percentage) to the total error in the scaling factor αsig in the boosted analysis for four mass hypotheses: 1500 GeV, 2000 GeV, 2500 GeV and 3000 GeV. The first column quotes the source of the uncertainty. The contribution is obtained by calculating the difference in quadrature between the total error in αsig and that obtained by setting constant the nuisance parameter(s) relative to the contribution(s) under study. Events/100 GeV ATLAS Data 300 -1 Rescaled GKK * (c=1.0) s = 13 TeV, 36.1 fb Rescaled GKK * (c=2.0) HH → bbWW* → bblνqq 250 Rescaled Scalar Boosted, mX = 2000 GeV Other 200 Multijet W+jets tt 150 MC Stat + Syst Unc. 100 50 0 Data-Bkg 0.5 MC Stat Unc. Bkg 0 −0.5 0 500 1000 1500 2000 2500 3000 mHH [GeV] Figure 10. mHH distributions after the global likelihood fit for the boosted analysis. The lower panel shows the fractional difference between data and the total expected background with the corresponding statistical and total uncertainty. The signals shown correspond to resonances of mass 2000 GeV. The scalar signal is multiplied by a factor of 4, and both graviton signal samples by a factor of 20 with respect to the expected upper-limit cross section reported in section 6. – 25 – 95% CL Limit on σ(pp → S→ HH) [pb] 95% CL Limit on σ(pp → G*KK→ HH) [pb] Observed ATLAS Observed (c=1.0) ATLAS 103 Expected s= 13 TeV, 36.1 fb-1 103 Expected (c=1.0) s= 13 TeV, 36.1 fb-1 Expected ±1σ Expected ±1σ (c=1.0) Expected ±2σ Expected ±2σ (c=1.0) Expected Stats Only Observed (c=2.0) 102 102 Expected (c=2.0) 10 10 1 1 JHEP04(2019)092 10−1 10−1 500 1000 1500 2000 2500 3000 500 1000 1500 2000 2500 3000 m S [GeV] m G* [GeV] KK Figure 11. Expected and observed upper limits at 95% CL on the cross section of resonant pair production for the heavy scalar boson S model (left) and for spin-2 graviton model in two c parameter hypotheses (right) in the boosted analysis. The left plot also shows the expected limits without including the systematic errors in order to show their impact. The impact of systematic errors is similar for the graviton models. 6.3 Summary Results of the two analyses are summarised in figure 12 (left) for the scalar interpretation, and figure 12 (right) for the RS graviton interpretation. The sensitivity of the boosted analysis is higher than the resolved analysis (the expected limit being lower) for masses larger than 1300 GeV in the scalar interpretation, and for masses larger than 800 GeV in the RS graviton interpretation. For masses lower than these values, the limits of the resolved analysis are presented in the figure, otherwise the boosted-analysis limits are shown. In addition, the expected limits of both analyses are shown near the mass values where the switch between the two limit curves occurs. Finally, the observed upper limits on the production cross sections range from 5.6 pb for mX = 500 GeV to 0.51 pb for mX = 3000 GeV for the scalar signal model. For graviton signals in the same mass interval, they range from 21 pb (18 pb) to 0.28 pb (0.31 pb) for a RS graviton model with c = 2.0 (1.0). No boosted analysis was performed for the non-resonant SM signal model. For the non-resonant signal hypothesis the observed (expected) upper limit on the σ(pp → HH) × B(HH → bb̄W W ∗ ) at 95% CL is: σ(pp → HH) · B(HH → bb̄W W ∗ ) < 2.5 2.5+1.0  −0.7 pb, which corresponds to 300 (300+100 −80 ) times the SM predicted cross section. – 26 – 95% CL Limit on σ(pp → S→ HH) [pb] 95% CL Limit on σ(pp → G*KK → HH) [pb] Observed ATLAS Observed (c=1.0) ATLAS 103 Expected Resolved s= 13 TeV, 36.1 fb-1 103 Expected resolved (c=1.0) Expected boosted (c=1.0) s= 13 TeV, 36.1 fb-1 Expected Boosted Expected ±1σ (c=1.0) Expected ±1σ Expected ±2σ (c=1.0) Observed (c=2.0) Expected ±2σ 102 102 Expected resolved (c=2.0) Expected boosted (c=2.0) Resolved Boosted Resolved Boosted 10 10 1 1 10−1 10−1 JHEP04(2019)092 500 1000 1500 2000 2500 3000 500 1000 1500 2000 2500 3000 m S [GeV] m G* [GeV] KK Figure 12. Expected and observed upper limits at 95% CL on the cross-section of the resonant scalar signal model (left) and the resonant graviton signal model for the c = 1.0 and c = 2.0 hypotheses (right). The observed limits of the scalar and graviton signal models are switched at a mass of 1300 GeV and 800 GeV respectively. The expected limits of both analyses are shown in a region around the switching points. The switching point is chosen at the mass value where the boosted analysis becomes more sensitive than the resolved analysis. 7 Conclusion A search for resonant and non-resonant Higgs boson pair production in the bb̄W W ∗ decay mode is performed in the bb̄`νqq final state using pp collision data corresponding to an √ integrated luminosity of 36.1 fb−1 , collected at s = 13 TeV by the ATLAS detector at the Large Hadron Collider. No evidence of an excess of events over the background expectation is found. Limits are set on resonant production as a function of the resonance mass for a scalar resonance and for spin-2 gravitons in the mass range 500 to 3000 GeV. An upper limit is set on the cross section of non-resonant pair production σ(pp → HH) · B(HH → bb̄W W ∗ ) < 2.5 pb at 95% CL corresponding to 300 times the predicted SM cross section. Given the result of this work, in order to bring relevant sensitivity improvement to the HH non-resonant SM searches in this channel at the LHC and at future colliders, more advanced analysis techniques, development of new methods for the normalisation of the tt̄ background, and a more refined estimation of the multijet background, need to be deployed. 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; – 27 – SRNSFG, Georgia; BMBF, HGF, and MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia and NRC KI, Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, CANARIE, CRC and Compute Canada, Canada; COST, ERC, ERDF, Horizon 2020, and Marie Sklodowska-Curie Actions, European Union; JHEP04(2019)092 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 (U.S.A.), the Tier-2 facilities worldwide and large non-WLCG resource providers. Ma- jor contributors of computing resources are listed in ref. [76]. Open Access. This article is distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited. References [1] F. Englert and R. Brout, Broken symmetry and the mass of gauge vector mesons, Phys. Rev. Lett. 13 (1964) 321 [INSPIRE]. [2] P.W. Higgs, Broken symmetries, massless particles and gauge fields, Phys. Lett. 12 (1964) 132 [INSPIRE]. [3] P.W. Higgs, Broken symmetries and the masses of gauge bosons, Phys. Rev. Lett. 13 (1964) 508 [INSPIRE]. [4] G.S. Guralnik, C.R. Hagen and T.W.B. Kibble, Global conservation laws and massless particles, Phys. Rev. Lett. 13 (1964) 585 [INSPIRE]. [5] P.W. Higgs, Spontaneous symmetry breakdown without massless bosons, Phys. Rev. 145 (1966) 1156 [INSPIRE]. [6] T.W.B. Kibble, Symmetry breaking in non-Abelian gauge theories, Phys. Rev. 155 (1967) 1554 [INSPIRE]. [7] LHC Higgs Cross Section Working Group collaboration, Handbook of LHC Higgs cross sections: 4. Deciphering the nature of the Higgs sector, arXiv:1610.07922 [INSPIRE]. [8] H.E. Haber and G.L. Kane, The search for supersymmetry: probing physics beyond the Standard Model, Phys. Rept. 117 (1985) 75 [INSPIRE]. – 28 – [9] G.C. Branco, P.M. Ferreira, L. Lavoura, M.N. Rebelo, M. Sher and J.P. Silva, Theory and phenomenology of two-Higgs-doublet models, Phys. Rept. 516 (2012) 1 [arXiv:1106.0034] [INSPIRE]. [10] L. Randall and R. Sundrum, A large mass hierarchy from a small extra dimension, Phys. Rev. Lett. 83 (1999) 3370 [hep-ph/9905221] [INSPIRE]. [11] ATLAS collaboration, Search for Higgs boson pair production in the bb̄bb̄ final state from pp √ collisions at s = 8 TeV with the ATLAS detector, Eur. Phys. J. C 75 (2015) 412 [arXiv:1506.00285] [INSPIRE]. [12] CMS collaboration, Search for resonant pair production of Higgs bosons decaying to two bottom quark-antiquark pairs in proton-proton collisions at 8 TeV, Phys. Lett. B 749 (2015) JHEP04(2019)092 560 [arXiv:1503.04114] [INSPIRE]. [13] ATLAS collaboration, Searches for Higgs boson pair production in the hh → bbτ τ , γγW W ∗ , γγbb, bbbb channels with the ATLAS detector, Phys. Rev. D 92 (2015) 092004 [arXiv:1509.04670] [INSPIRE]. [14] CMS collaboration, Searches for a heavy scalar boson H decaying to a pair of 125 GeV Higgs bosons hh or for a heavy pseudoscalar boson A decaying to Zh, in the final states with h → τ τ , Phys. Lett. B 755 (2016) 217 [arXiv:1510.01181] [INSPIRE]. [15] CMS collaboration, Search for two Higgs bosons in final states containing two photons and two bottom quarks in proton-proton collisions at 8 TeV, Phys. Rev. D 94 (2016) 052012 [arXiv:1603.06896] [INSPIRE]. [16] ATLAS collaboration, Search for Higgs boson pair production in the γγbb̄ final state using √ pp collision data at s = 8 TeV from the ATLAS detector, Phys. Rev. Lett. 114 (2015) 081802 [arXiv:1406.5053] [INSPIRE]. [17] ATLAS collaboration, Search for pair production of Higgs bosons in the bb̄bb̄ final state using √ proton-proton collisions at s = 13 TeV with the ATLAS detector, JHEP 01 (2019) 030 [arXiv:1804.06174] [INSPIRE]. [18] ATLAS collaboration, Search for resonant and non-resonant Higgs boson pair production in √ the bb̄τ + τ − decay channel in pp collisions at s = 13 TeV with the ATLAS detector, Phys. Rev. Lett. 121 (2018) 191801 [Erratum ibid. 122 (2019) 089901] [arXiv:1808.00336] [INSPIRE]. [19] CMS collaboration, Search for heavy resonances decaying into two Higgs bosons or into a Higgs boson and a W or Z boson in proton-proton collisions at 13 TeV, JHEP 01 (2019) 051 [arXiv:1808.01365] [INSPIRE]. [20] ATLAS collaboration, Search for Higgs boson pair production in the γγbb̄ final state with 13 TeV pp collision data collected by the ATLAS experiment, JHEP 11 (2018) 040 [arXiv:1807.04873] [INSPIRE]. [21] ATLAS collaboration, Search for Higgs boson pair production in the γγW W ∗ channel using √ pp collision data recorded at s = 13 TeV with the ATLAS detector, Eur. Phys. J. C 78 (2018) 1007 [arXiv:1807.08567] [INSPIRE]. [22] CMS collaboration, Search for resonant pair production of Higgs bosons decaying to bottom quark-antiquark pairs in proton-proton collisions at 13 TeV, JHEP 08 (2018) 152 [arXiv:1806.03548] [INSPIRE]. [23] CMS collaboration, Search for Higgs boson pair production in events with two bottom quarks √ and two tau leptons in proton-proton collisions at s = 13 TeV, Phys. Lett. B 778 (2018) 101 [arXiv:1707.02909] [INSPIRE]. – 29 – [24] CMS collaboration, Search for Higgs boson pair production in the γγbb̄ final state in pp √ collisions at s = 13 TeV, Phys. Lett. B 788 (2019) 7 [arXiv:1806.00408] [INSPIRE]. [25] CMS collaboration, Search for resonant and nonresonant Higgs boson pair production in the √ bb̄`ν`ν final state in proton-proton collisions at s = 13 TeV, JHEP 01 (2018) 054 [arXiv:1708.04188] [INSPIRE]. [26] M. Cacciari, G.P. Salam and G. Soyez, The anti-kt jet clustering algorithm, JHEP 04 (2008) 063 [arXiv:0802.1189] [INSPIRE]. [27] ATLAS collaboration, The ATLAS experiment at the CERN Large Hadron Collider, 2008 JINST 3 S08003 [INSPIRE]. JHEP04(2019)092 [28] K. Agashe, H. Davoudiasl, G. Perez and A. Soni, Warped gravitons at the LHC and beyond, Phys. Rev. D 76 (2007) 036006 [hep-ph/0701186] [INSPIRE]. [29] A.L. Fitzpatrick, J. Kaplan, L. Randall and L.-T. Wang, Searching for the Kaluza-Klein graviton in bulk RS models, JHEP 09 (2007) 013 [hep-ph/0701150] [INSPIRE]. [30] J. Alwall et al., The automated computation of tree-level and next-to-leading order differential cross sections and their matching to parton shower simulations, JHEP 07 (2014) 079 [arXiv:1405.0301] [INSPIRE]. [31] R.D. Ball et al., Parton distributions with LHC data, Nucl. Phys. B 867 (2013) 244 [arXiv:1207.1303] [INSPIRE]. [32] T. Sjöstrand, S. Mrenna and P.Z. Skands, A brief introduction to PYTHIA 8.1, Comput. Phys. Commun. 178 (2008) 852 [arXiv:0710.3820] [INSPIRE]. [33] ATLAS collaboration, ATLAS run 1 PYTHIA8 tunes, ATL-PHYS-PUB-2014-021, CERN, Geneva, Switzerland (2014). [34] M. Bahr et al., HERWIG++ physics and manual, Eur. Phys. J. C 58 (2008) 639 [arXiv:0803.0883] [INSPIRE]. [35] H.-L. Lai et al., New parton distributions for collider physics, Phys. Rev. D 82 (2010) 074024 [arXiv:1007.2241] [INSPIRE]. [36] B. Hespel, D. Lopez-Val and E. Vryonidou, Higgs pair production via gluon fusion in the two-Higgs-doublet model, JHEP 09 (2014) 124 [arXiv:1407.0281] [INSPIRE]. [37] R. Frederix et al., Higgs pair production at the LHC with NLO and parton-shower effects, Phys. Lett. B 732 (2014) 142 [arXiv:1401.7340] [INSPIRE]. [38] S. Borowka et al., Higgs boson pair production in gluon fusion at next-to-leading order with full top-quark mass dependence, Phys. Rev. Lett. 117 (2016) 012001 [Erratum ibid. 117 (2016) 079901] [arXiv:1604.06447] [INSPIRE]. [39] T. Gleisberg et al., Event generation with SHERPA 1.1, JHEP 02 (2009) 007 [arXiv:0811.4622] [INSPIRE]. [40] NNPDF collaboration, Parton distributions for the LHC run II, JHEP 04 (2015) 040 [arXiv:1410.8849] [INSPIRE]. [41] R. Gavin, Y. Li, F. Petriello and S. Quackenbush, FEWZ 2.0: a code for hadronic Z production at next-to-next-to-leading order, Comput. Phys. Commun. 182 (2011) 2388 [arXiv:1011.3540] [INSPIRE]. [42] S. Frixione, P. Nason and C. Oleari, Matching NLO QCD computations with parton shower simulations: the POWHEG method, JHEP 11 (2007) 070 [arXiv:0709.2092] [INSPIRE]. – 30 – [43] T. Sjöstrand, S. Mrenna and P.Z. Skands, PYTHIA 6.4 physics and manual, JHEP 05 (2006) 026 [hep-ph/0603175] [INSPIRE]. [44] P.Z. Skands, Tuning Monte Carlo generators: the Perugia tunes, Phys. Rev. D 82 (2010) 074018 [arXiv:1005.3457] [INSPIRE]. [45] J. Pumplin, D.R. Stump, J. Huston, H.L. Lai, P.M. Nadolsky and W.K. Tung, New generation of parton distributions with uncertainties from global QCD analysis, JHEP 07 (2002) 012 [hep-ph/0201195] [INSPIRE]. [46] D.J. Lange, The EvtGen particle decay simulation package, Nucl. Instrum. Meth. A 462 (2001) 152 [INSPIRE]. JHEP04(2019)092 [47] ATLAS collaboration, Comparison of Monte Carlo generator predictions for gap fraction and jet multiplicity observables in top-antitop events, ATL-PHYS-PUB-2014-005, CERN, Geneva, Switzerland (2014). [48] M. Czakon and A. Mitov, Top++: a program for the calculation of the top-pair cross-section at hadron colliders, Comput. Phys. Commun. 185 (2014) 2930 [arXiv:1112.5675] [INSPIRE]. [49] S. Alioli, P. Nason, C. Oleari and E. Re, NLO single-top production matched with shower in POWHEG: s- and t-channel contributions, JHEP 09 (2009) 111 [Erratum ibid. 02 (2010) 011] [arXiv:0907.4076] [INSPIRE]. [50] E. Re, Single-top W t-channel production matched with parton showers using the POWHEG method, Eur. Phys. J. C 71 (2011) 1547 [arXiv:1009.2450] [INSPIRE]. [51] N. Kidonakis, Next-to-next-to-leading-order collinear and soft gluon corrections for t-channel single top quark production, Phys. Rev. D 83 (2011) 091503 [arXiv:1103.2792] [INSPIRE]. [52] N. Kidonakis, Two-loop soft anomalous dimensions for single top quark associated production with a W − or H − , Phys. Rev. D 82 (2010) 054018 [arXiv:1005.4451] [INSPIRE]. [53] N. Kidonakis, NNLL resummation for s-channel single top quark production, Phys. Rev. D 81 (2010) 054028 [arXiv:1001.5034] [INSPIRE]. [54] GEANT4 collaboration, GEANT4: a simulation toolkit, Nucl. Instrum. Meth. A 506 (2003) 250 [INSPIRE]. [55] ATLAS collaboration, The ATLAS simulation infrastructure, Eur. Phys. J. C 70 (2010) 823 [arXiv:1005.4568] [INSPIRE]. [56] ATLAS collaboration, Electron efficiency measurements with the ATLAS detector using 2012 LHC proton-proton collision data, Eur. Phys. J. C 77 (2017) 195 [arXiv:1612.01456] [INSPIRE]. [57] ATLAS collaboration, Electron efficiency measurements with the ATLAS detector using the 2015 LHC proton-proton collision data, ATLAS-CONF-2016-024, CERN, Geneva, Switzerland (2016). [58] ATLAS collaboration, Muon reconstruction performance of the ATLAS detector in √ proton-proton collision data at s = 13 TeV, Eur. Phys. J. C 76 (2016) 292 [arXiv:1603.05598] [INSPIRE]. [59] ATLAS collaboration, Tagging and suppression of pileup jets with the ATLAS detector, ATLAS-CONF-2014-018, CERN, Geneva, Switzerland (2014). [60] ATLAS collaboration, Measurements of b-jet tagging efficiency with the ATLAS detector √ using tt̄ events at s = 13 TeV, JHEP 08 (2018) 089 [arXiv:1805.01845] [INSPIRE]. – 31 – [61] D. Krohn, J. Thaler and L.-T. Wang, Jet trimming, JHEP 02 (2010) 084 [arXiv:0912.1342] [INSPIRE]. [62] ATLAS collaboration, Jet mass reconstruction with the ATLAS detector in early run 2 data, ATLAS-CONF-2016-035, CERN, Geneva, Switzerland (2016). [63] ATLAS collaboration, Performance of jet substructure techniques for large-R jets in √ proton-proton collisions at s = 7 TeV using the ATLAS detector, JHEP 09 (2013) 076 [arXiv:1306.4945] [INSPIRE]. [64] ATLAS collaboration, Boosted Higgs (→ bb̄) boson identification with the ATLAS detector at √ s = 13 TeV, ATLAS-CONF-2016-039, CERN, Geneva, Switzerland (2016). JHEP04(2019)092 [65] M. Cacciari, G.P. Salam and G. Soyez, The catchment area of jets, JHEP 04 (2008) 005 [arXiv:0802.1188] [INSPIRE]. [66] ATLAS collaboration, Expected performance of missing transverse momentum √ reconstruction for the ATLAS detector at s = 13 TeV, ATL-PHYS-PUB-2015-023, CERN, Geneva, Switzerland (2015). [67] M.L. Mangano, M. Moretti, F. Piccinini, R. Pittau and A.D. Polosa, ALPGEN, a generator for hard multiparton processes in hadronic collisions, JHEP 07 (2003) 001 [hep-ph/0206293] [INSPIRE]. [68] S. Frixione, E. Laenen, P. Motylinski, B.R. Webber and C.D. White, Single-top hadroproduction in association with a W boson, JHEP 07 (2008) 029 [arXiv:0805.3067] [INSPIRE]. [69] J. Butterworth et al., PDF4LHC recommendations for LHC run II, J. Phys. G 43 (2016) 023001 [arXiv:1510.03865] [INSPIRE]. [70] ATLAS collaboration, Jet energy scale measurements and their systematic uncertainties in √ proton-proton collisions at s = 13 TeV with the ATLAS detector, Phys. Rev. D 96 (2017) 072002 [arXiv:1703.09665] [INSPIRE]. [71] ATLAS collaboration, Jet calibration and systematic uncertainties for jets reconstructed in √ the ATLAS detector at s = 13 TeV, ATL-PHYS-PUB-2015-015, CERN, Geneva, Switzerland (2015). √ [72] ATLAS collaboration, Luminosity determination in pp collisions at s = 8 TeV using the ATLAS detector at the LHC, Eur. Phys. J. C 76 (2016) 653 [arXiv:1608.03953] [INSPIRE]. [73] ATLAS collaboration, Identification of boosted, hadronically-decaying W and Z bosons in √ s = 13 TeV Monte Carlo simulations for ATLAS, ATL-PHYS-PUB-2015-033, CERN, Geneva, Switzerland (2015). [74] A.L. Read, Presentation of search results: the CLs technique, J. Phys. G 28 (2002) 2693 [INSPIRE]. [75] G. Cowan, K. Cranmer, E. Gross and O. Vitells, Asymptotic formulae for likelihood-based tests of new physics, Eur. Phys. J. C 71 (2011) 1554 [Erratum ibid. C 73 (2013) 2501] [arXiv:1007.1727] [INSPIRE]. [76] ATLAS collaboration, ATLAS computing acknowledgements, ATL-GEN-PUB-2016-002, CERN, Geneva, Switzerland (2016). – 32 – The ATLAS collaboration M. Aaboud34d , G. Aad99 , B. Abbott124 , O. Abdinov13,∗ , B. Abeloos128 , D.K. Abhayasinghe91 , S.H. Abidi164 , O.S. AbouZeid39 , N.L. Abraham153 , H. Abramowicz158 , H. Abreu157 , Y. Abulaiti6 , B.S. Acharya64a,64b,o , S. Adachi160 , L. Adam97 , L. Adamczyk81a , J. Adelman119 , M. Adersberger112 , A. Adiguzel12c,ag , T. Adye141 , A.A. Affolder143 , Y. Afik157 , C. Agheorghiesei27c , J.A. Aguilar-Saavedra136f,136a , F. Ahmadov77,ae , G. Aielli71a,71b , S. Akatsuka83 , T.P.A. Åkesson94 , E. Akilli52 , A.V. Akimov108 , G.L. Alberghi23b,23a , J. Albert173 , P. Albicocco49 , M.J. Alconada Verzini86 , S. Alderweireldt117 , M. Aleksa35 , I.N. Aleksandrov77 , C. Alexa27b , D. Alexandre19 , T. Alexopoulos10 , M. Alhroob124 , B. Ali138 , G. Alimonti66a , JHEP04(2019)092 J. Alison36 , S.P. Alkire145 , C. Allaire128 , B.M.M. Allbrooke153 , B.W. Allen127 , P.P. Allport21 , A. Aloisio67a,67b , A. Alonso39 , F. Alonso86 , C. Alpigiani145 , A.A. Alshehri55 , M.I. Alstaty99 , B. Alvarez Gonzalez35 , D. Álvarez Piqueras171 , M.G. Alviggi67a,67b , B.T. Amadio18 , Y. Amaral Coutinho78b , A. Ambler101 , L. Ambroz131 , C. Amelung26 , D. Amidei103 , S.P. Amor Dos Santos136a,136c , S. Amoroso44 , C.S. Amrouche52 , F. An76 , C. Anastopoulos146 , L.S. Ancu52 , N. Andari142 , T. Andeen11 , C.F. Anders59b , J.K. Anders20 , K.J. Anderson36 , A. Andreazza66a,66b , V. Andrei59a , C.R. Anelli173 , S. Angelidakis37 , I. Angelozzi118 , A. Angerami38 , A.V. Anisenkov120b,120a , A. Annovi69a , C. Antel59a , M.T. Anthony146 , M. Antonelli49 , D.J.A. Antrim168 , F. Anulli70a , M. Aoki79 , J.A. Aparisi Pozo171 , L. Aperio Bella35 , G. Arabidze104 , J.P. Araque136a , V. Araujo Ferraz78b , R. Araujo Pereira78b , A.T.H. Arce47 , R.E. Ardell91 , F.A. Arduh86 , J-F. Arguin107 , S. Argyropoulos75 , J.-H. Arling44 , A.J. Armbruster35 , L.J. Armitage90 , A Armstrong168 , O. Arnaez164 , H. Arnold118 , M. Arratia31 , O. Arslan24 , A. Artamonov109,∗ , G. Artoni131 , S. Artz97 , S. Asai160 , N. Asbah57 , E.M. Asimakopoulou169 , L. Asquith153 , K. Assamagan29 , R. Astalos28a , R.J. Atkin32a , M. Atkinson170 , N.B. Atlay148 , K. Augsten138 , G. Avolio35 , R. Avramidou58a , M.K. Ayoub15a , A.M. Azoulay165b , G. Azuelos107,at , A.E. Baas59a , M.J. Baca21 , H. Bachacou142 , K. Bachas65a,65b , M. Backes131 , P. Bagnaia70a,70b , M. Bahmani82 , H. Bahrasemani149 , A.J. Bailey171 , J.T. Baines141 , M. Bajic39 , C. Bakalis10 , O.K. Baker180 , P.J. Bakker118 , D. Bakshi Gupta8 , S. Balaji154 , E.M. Baldin120b,120a , P. Balek177 , F. Balli142 , W.K. Balunas133 , J. Balz97 , E. Banas82 , A. Bandyopadhyay24 , S. Banerjee178,k , A.A.E. Bannoura179 , L. Barak158 , W.M. Barbe37 , E.L. Barberio102 , D. Barberis53b,53a , M. Barbero99 , T. Barillari113 , M-S. Barisits35 , J. Barkeloo127 , T. Barklow150 , R. Barnea157 , S.L. Barnes58c , B.M. Barnett141 , R.M. Barnett18 , Z. Barnovska-Blenessy58a , A. Baroncelli72a , G. Barone29 , A.J. Barr131 , L. Barranco Navarro171 , F. Barreiro96 , J. Barreiro Guimarães da Costa15a , R. Bartoldus150 , A.E. Barton87 , P. Bartos28a , A. Basalaev134 , A. Bassalat128 , R.L. Bates55 , S.J. Batista164 , S. Batlamous34e , J.R. Batley31 , M. Battaglia143 , M. Bauce70a,70b , F. Bauer142 , K.T. Bauer168 , H.S. Bawa150,m , J.B. Beacham122 , T. Beau132 , P.H. Beauchemin167 , P. Bechtle24 , H.C. Beck51 , H.P. Beck20,r , K. Becker50 , M. Becker97 , C. Becot44 , A. Beddall12d , A.J. Beddall12a , V.A. Bednyakov77 , M. Bedognetti118 , C.P. Bee152 , T.A. Beermann74 , M. Begalli78b , M. Begel29 , A. Behera152 , J.K. Behr44 , A.S. Bell92 , G. Bella158 , L. Bellagamba23b , A. Bellerive33 , M. Bellomo157 , P. Bellos9 , K. Belotskiy110 , N.L. Belyaev110 , O. Benary158,∗ , D. Benchekroun34a , M. Bender112 , N. Benekos10 , Y. Benhammou158 , E. Benhar Noccioli180 , J. Benitez75 , D.P. Benjamin6 , M. Benoit52 , J.R. Bensinger26 , S. Bentvelsen118 , L. Beresford131 , M. Beretta49 , D. Berge44 , E. Bergeaas Kuutmann169 , N. Berger5 , L.J. Bergsten26 , J. Beringer18 , S. Berlendis7 , N.R. Bernard100 , G. Bernardi132 , C. Bernius150 , F.U. Bernlochner24 , T. Berry91 , P. Berta97 , C. Bertella15a , G. Bertoli43a,43b , I.A. Bertram87 , G.J. Besjes39 , O. Bessidskaia Bylund179 , M. Bessner44 , N. Besson142 , A. Bethani98 , S. Bethke113 , A. Betti24 , A.J. Bevan90 , J. Beyer113 , R. Bi135 , R.M. Bianchi135 , O. Biebel112 , D. Biedermann19 , R. Bielski35 , K. Bierwagen97 , – 33 – N.V. Biesuz69a,69b , M. Biglietti72a , T.R.V. Billoud107 , M. Bindi51 , A. Bingul12d , C. Bini70a,70b , S. Biondi23b,23a , M. Birman177 , T. Bisanz51 , J.P. Biswal158 , C. Bittrich46 , D.M. Bjergaard47 , J.E. Black150 , K.M. Black25 , T. Blazek28a , I. Bloch44 , C. Blocker26 , A. Blue55 , U. Blumenschein90 , Dr. Blunier144a , G.J. Bobbink118 , V.S. Bobrovnikov120b,120a , S.S. Bocchetta94 , A. Bocci47 , D. Boerner179 , D. Bogavac112 , A.G. Bogdanchikov120b,120a , C. Bohm43a , V. Boisvert91 , P. Bokan169 , T. Bold81a , A.S. Boldyrev111 , A.E. Bolz59b , M. Bomben132 , M. Bona90 , J.S. Bonilla127 , M. Boonekamp142 , H.M. Borecka-Bielska88 , A. Borisov140 , G. Borissov87 , J. Bortfeldt35 , D. Bortoletto131 , V. Bortolotto71a,71b , D. Boscherini23b , M. Bosman14 , J.D. Bossio Sola30 , K. Bouaouda34a , J. Boudreau135 , E.V. Bouhova-Thacker87 , D. Boumediene37 , C. Bourdarios128 , S.K. Boutle55 , A. Boveia122 , J. Boyd35 , D. Boye32b , I.R. Boyko77 , A.J. Bozson91 , J. Bracinik21 , N. Brahimi99 , A. Brandt8 , G. Brandt179 , O. Brandt59a , F. Braren44 , U. Bratzler161 , JHEP04(2019)092 B. Brau100 , J.E. Brau127 , W.D. Breaden Madden55 , K. Brendlinger44 , L. Brenner44 , R. Brenner169 , S. Bressler177 , B. Brickwedde97 , D.L. Briglin21 , D. Britton55 , D. Britzger113 , I. Brock24 , R. Brock104 , G. Brooijmans38 , T. Brooks91 , W.K. Brooks144b , E. Brost119 , J.H Broughton21 , P.A. Bruckman de Renstrom82 , D. Bruncko28b , A. Bruni23b , G. Bruni23b , L.S. Bruni118 , S. Bruno71a,71b , B.H. Brunt31 , M. Bruschi23b , N. Bruscino135 , P. Bryant36 , L. Bryngemark94 , T. Buanes17 , Q. Buat35 , P. Buchholz148 , A.G. Buckley55 , I.A. Budagov77 , M.K. Bugge130 , F. Bührer50 , O. Bulekov110 , D. Bullock8 , T.J. Burch119 , S. Burdin88 , C.D. Burgard118 , A.M. Burger5 , B. Burghgrave119 , K. Burka82 , S. Burke141 , I. Burmeister45 , J.T.P. Burr131 , V. Büscher97 , E. Buschmann51 , P. Bussey55 , J.M. Butler25 , C.M. Buttar55 , J.M. Butterworth92 , P. Butti35 , W. Buttinger35 , A. Buzatu155 , A.R. Buzykaev120b,120a , G. Cabras23b,23a , S. Cabrera Urbán171 , D. Caforio138 , H. Cai170 , V.M.M. Cairo2 , O. Cakir4a , N. Calace52 , P. Calafiura18 , A. Calandri99 , G. Calderini132 , P. Calfayan63 , G. Callea55 , L.P. Caloba78b , S. Calvente Lopez96 , D. Calvet37 , S. Calvet37 , T.P. Calvet152 , M. Calvetti69a,69b , R. Camacho Toro132 , S. Camarda35 , D. Camarero Munoz96 , P. Camarri71a,71b , D. Cameron130 , R. Caminal Armadans100 , C. Camincher35 , S. Campana35 , M. Campanelli92 , A. Camplani39 , A. Campoverde148 , V. Canale67a,67b , M. Cano Bret58c , J. Cantero125 , T. Cao158 , Y. Cao170 , M.D.M. Capeans Garrido35 , I. Caprini27b , M. Caprini27b , M. Capua40b,40a , R.M. Carbone38 , R. Cardarelli71a , F.C. Cardillo146 , I. Carli139 , T. Carli35 , G. Carlino67a , B.T. Carlson135 , L. Carminati66a,66b , R.M.D. Carney43a,43b , S. Caron117 , E. Carquin144b , S. Carrá66a,66b , J.W.S. Carter164 , D. Casadei32b , M.P. Casado14,g , A.F. Casha164 , D.W. Casper168 , R. Castelijn118 , F.L. Castillo171 , V. Castillo Gimenez171 , N.F. Castro136a,136e , A. Catinaccio35 , J.R. Catmore130 , A. Cattai35 , J. Caudron24 , V. Cavaliere29 , E. Cavallaro14 , D. Cavalli66a , M. Cavalli-Sforza14 , V. Cavasinni69a,69b , E. Celebi12b , F. Ceradini72a,72b , L. Cerda Alberich171 , A.S. Cerqueira78a , A. Cerri153 , L. Cerrito71a,71b , F. Cerutti18 , A. Cervelli23b,23a , S.A. Cetin12b , A. Chafaq34a , D Chakraborty119 , S.K. Chan57 , W.S. Chan118 , J.D. Chapman31 , B. Chargeishvili156b , D.G. Charlton21 , C.C. Chau33 , C.A. Chavez Barajas153 , S. Che122 , A. Chegwidden104 , S. Chekanov6 , S.V. Chekulaev165a , G.A. Chelkov77,as , M.A. Chelstowska35 , C. Chen58a , C.H. Chen76 , H. Chen29 , J. Chen58a , J. Chen38 , S. Chen133 , S.J. Chen15c , X. Chen15b,ar , Y. Chen80 , Y-H. Chen44 , H.C. Cheng61a , H.J. Cheng15d , A. Cheplakov77 , E. Cheremushkina140 , R. Cherkaoui El Moursli34e , E. Cheu7 , K. Cheung62 , T.J.A. Chevalérias142 , L. Chevalier142 , V. Chiarella49 , G. Chiarelli69a , G. Chiodini65a , A.S. Chisholm35,21 , A. Chitan27b , I. Chiu160 , Y.H. Chiu173 , M.V. Chizhov77 , K. Choi63 , A.R. Chomont128 , S. Chouridou159 , Y.S. Chow118 , V. Christodoulou92 , M.C. Chu61a , J. Chudoba137 , A.J. Chuinard101 , J.J. Chwastowski82 , L. Chytka126 , D. Cinca45 , V. Cindro89 , I.A. Cioară24 , A. Ciocio18 , F. Cirotto67a,67b , Z.H. Citron177 , M. Citterio66a , A. Clark52 , M.R. Clark38 , P.J. Clark48 , C. Clement43a,43b , Y. Coadou99 , M. Cobal64a,64c , A. Coccaro53b,53a , J. Cochran76 , H. Cohen158 , A.E.C. Coimbra177 , L. Colasurdo117 , B. Cole38 , A.P. Colijn118 , J. Collot56 , – 34 – P. Conde Muiño136a,136b , E. Coniavitis50 , S.H. Connell32b , I.A. Connelly98 , S. Constantinescu27b , F. Conventi67a,au , A.M. Cooper-Sarkar131 , F. Cormier172 , K.J.R. Cormier164 , L.D. Corpe92 , M. Corradi70a,70b , E.E. Corrigan94 , F. Corriveau101,ac , A. Cortes-Gonzalez35 , M.J. Costa171 , F. Costanza5 , D. Costanzo146 , G. Cottin31 , G. Cowan91 , B.E. Cox98 , J. Crane98 , K. Cranmer121 , S.J. Crawley55 , R.A. Creager133 , G. Cree33 , S. Crépé-Renaudin56 , F. Crescioli132 , M. Cristinziani24 , V. Croft121 , G. Crosetti40b,40a , A. Cueto96 , T. Cuhadar Donszelmann146 , A.R. Cukierman150 , S. Czekierda82 , P. Czodrowski35 , M.J. Da Cunha Sargedas De Sousa58b , C. Da Via98 , W. Dabrowski81a , T. Dado28a,x , S. Dahbi34e , T. Dai103 , F. Dallaire107 , C. Dallapiccola100 , M. Dam39 , G. D’amen23b,23a , J. Damp97 , J.R. Dandoy133 , M.F. Daneri30 , N.P. Dang178,k , N.D Dann98 , M. Danninger172 , V. Dao35 , G. Darbo53b , S. Darmora8 , O. Dartsi5 , A. Dattagupta127 , T. Daubney44 , S. D’Auria66a,66b , W. Davey24 , C. David44 , T. Davidek139 , JHEP04(2019)092 D.R. Davis47 , E. Dawe102 , I. Dawson146 , K. De8 , R. De Asmundis67a , A. De Benedetti124 , M. De Beurs118 , S. De Castro23b,23a , S. De Cecco70a,70b , N. De Groot117 , P. de Jong118 , H. De la Torre104 , F. De Lorenzi76 , A. De Maria69a,69b , D. De Pedis70a , A. De Salvo70a , U. De Sanctis71a,71b , M. De Santis71a,71b , A. De Santo153 , K. De Vasconcelos Corga99 , J.B. De Vivie De Regie128 , C. Debenedetti143 , D.V. Dedovich77 , N. Dehghanian3 , M. Del Gaudio40b,40a , J. Del Peso96 , Y. Delabat Diaz44 , D. Delgove128 , F. Deliot142 , C.M. Delitzsch7 , M. Della Pietra67a,67b , D. Della Volpe52 , A. Dell’Acqua35 , L. Dell’Asta25 , M. Delmastro5 , C. Delporte128 , P.A. Delsart56 , D.A. DeMarco164 , S. Demers180 , M. Demichev77 , S.P. Denisov140 , D. Denysiuk118 , L. D’Eramo132 , D. Derendarz82 , J.E. Derkaoui34d , F. Derue132 , P. Dervan88 , K. Desch24 , C. Deterre44 , K. Dette164 , M.R. Devesa30 , P.O. Deviveiros35 , A. Dewhurst141 , S. Dhaliwal26 , F.A. Di Bello52 , A. Di Ciaccio71a,71b , L. Di Ciaccio5 , W.K. Di Clemente133 , C. Di Donato67a,67b , A. Di Girolamo35 , G. Di Gregorio69a,69b , B. Di Micco72a,72b , R. Di Nardo100 , K.F. Di Petrillo57 , R. Di Sipio164 , D. Di Valentino33 , C. Diaconu99 , M. Diamond164 , F.A. Dias39 , T. Dias Do Vale136a , M.A. Diaz144a , J. Dickinson18 , E.B. Diehl103 , J. Dietrich19 , S. Dı́ez Cornell44 , A. Dimitrievska18 , J. Dingfelder24 , F. Dittus35 , F. Djama99 , T. Djobava156b , J.I. Djuvsland59a , M.A.B. Do Vale78c , M. Dobre27b , D. Dodsworth26 , C. Doglioni94 , J. Dolejsi139 , Z. Dolezal139 , M. Donadelli78d , J. Donini37 , A. D’onofrio90 , M. D’Onofrio88 , J. Dopke141 , A. Doria67a , M.T. Dova86 , A.T. Doyle55 , E. Drechsler51 , E. Dreyer149 , T. Dreyer51 , Y. Du58b , F. Dubinin108 , M. Dubovsky28a , A. Dubreuil52 , E. Duchovni177 , G. Duckeck112 , A. Ducourthial132 , O.A. Ducu107,w , D. Duda113 , A. Dudarev35 , A.C. Dudder97 , E.M. Duffield18 , L. Duflot128 , M. Dührssen35 , C. Dülsen179 , M. Dumancic177 , A.E. Dumitriu27b,e , A.K. Duncan55 , M. Dunford59a , A. Duperrin99 , H. Duran Yildiz4a , M. Düren54 , A. Durglishvili156b , D. Duschinger46 , B. Dutta44 , D. Duvnjak1 , M. Dyndal44 , S. Dysch98 , B.S. Dziedzic82 , C. Eckardt44 , K.M. Ecker113 , R.C. Edgar103 , T. Eifert35 , G. Eigen17 , K. Einsweiler18 , T. Ekelof169 , M. El Kacimi34c , R. El Kosseifi99 , V. Ellajosyula99 , M. Ellert169 , F. Ellinghaus179 , A.A. Elliot90 , N. Ellis35 , J. Elmsheuser29 , M. Elsing35 , D. Emeliyanov141 , A. Emerman38 , Y. Enari160 , J.S. Ennis175 , M.B. Epland47 , J. Erdmann45 , A. Ereditato20 , S. Errede170 , M. Escalier128 , C. Escobar171 , O. Estrada Pastor171 , A.I. Etienvre142 , E. Etzion158 , H. Evans63 , A. Ezhilov134 , M. Ezzi34e , F. Fabbri55 , L. Fabbri23b,23a , V. Fabiani117 , G. Facini92 , R.M. Faisca Rodrigues Pereira136a , R.M. Fakhrutdinov140 , S. Falciano70a , P.J. Falke5 , S. Falke5 , J. Faltova139 , Y. Fang15a , M. Fanti66a,66b , A. Farbin8 , A. Farilla72a , E.M. Farina68a,68b , T. Farooque104 , S. Farrell18 , S.M. Farrington175 , P. Farthouat35 , F. Fassi34e , P. Fassnacht35 , D. Fassouliotis9 , M. Faucci Giannelli48 , A. Favareto53b,53a , W.J. Fawcett31 , L. Fayard128 , O.L. Fedin134,p , W. Fedorko172 , M. Feickert41 , S. Feigl130 , L. Feligioni99 , C. Feng58b , E.J. Feng35 , M. Feng47 , M.J. Fenton55 , A.B. Fenyuk140 , L. Feremenga8 , J. Ferrando44 , A. Ferrari169 , P. Ferrari118 , R. Ferrari68a , D.E. Ferreira de Lima59b , A. Ferrer171 , D. Ferrere52 , C. Ferretti103 , F. Fiedler97 , A. Filipčič89 , F. Filthaut117 , K.D. Finelli25 , M.C.N. Fiolhais136a,136c,a , L. Fiorini171 , – 35 – C. Fischer14 , W.C. Fisher104 , N. Flaschel44 , I. Fleck148 , P. Fleischmann103 , R.R.M. Fletcher133 , T. Flick179 , B.M. Flierl112 , L.M. Flores133 , L.R. Flores Castillo61a , F.M. Follega73a,73b , N. Fomin17 , G.T. Forcolin73a,73b , A. Formica142 , F.A. Förster14 , A.C. Forti98 , A.G. Foster21 , D. Fournier128 , H. Fox87 , S. Fracchia146 , P. Francavilla69a,69b , M. Franchini23b,23a , S. Franchino59a , D. Francis35 , L. Franconi143 , M. Franklin57 , M. Frate168 , M. Fraternali68a,68b , A.N. Fray90 , D. Freeborn92 , B. Freund107 , W.S. Freund78b , E.M. Freundlich45 , D.C. Frizzell124 , D. Froidevaux35 , J.A. Frost131 , C. Fukunaga161 , E. Fullana Torregrosa171 , T. Fusayasu114 , J. Fuster171 , O. Gabizon157 , A. Gabrielli23b,23a , A. Gabrielli18 , G.P. Gach81a , S. Gadatsch52 , P. Gadow113 , G. Gagliardi53b,53a , L.G. Gagnon107 , C. Galea27b , B. Galhardo136a,136c , E.J. Gallas131 , B.J. Gallop141 , P. Gallus138 , G. Galster39 , R. Gamboa Goni90 , K.K. Gan122 , S. Ganguly177 , J. Gao58a , Y. Gao88 , Y.S. Gao150,m , C. Garcı́a171 , J.E. Garcı́a Navarro171 , JHEP04(2019)092 J.A. Garcı́a Pascual15a , M. Garcia-Sciveres18 , R.W. Gardner36 , N. Garelli150 , S. Gargiulo50 , V. Garonne130 , K. Gasnikova44 , A. Gaudiello53b,53a , G. Gaudio68a , I.L. Gavrilenko108 , A. Gavrilyuk109 , C. Gay172 , G. Gaycken24 , E.N. Gazis10 , C.N.P. Gee141 , J. Geisen51 , M. Geisen97 , M.P. Geisler59a , C. Gemme53b , M.H. Genest56 , C. Geng103 , S. Gentile70a,70b , S. George91 , D. Gerbaudo14 , G. Gessner45 , S. Ghasemi148 , M. Ghasemi Bostanabad173 , M. Ghneimat24 , B. Giacobbe23b , S. Giagu70a,70b , N. Giangiacomi23b,23a , P. Giannetti69a , A. Giannini67a,67b , S.M. Gibson91 , M. Gignac143 , D. Gillberg33 , G. Gilles179 , D.M. Gingrich3,at , M.P. Giordani64a,64c , F.M. Giorgi23b , P.F. Giraud142 , P. Giromini57 , G. Giugliarelli64a,64c , D. Giugni66a , F. Giuli131 , M. Giulini59b , S. Gkaitatzis159 , I. Gkialas9,j , E.L. Gkougkousis14 , P. Gkountoumis10 , L.K. Gladilin111 , C. Glasman96 , J. Glatzer14 , P.C.F. Glaysher44 , A. Glazov44 , M. Goblirsch-Kolb26 , J. Godlewski82 , S. Goldfarb102 , T. Golling52 , D. Golubkov140 , A. Gomes136a,136b,136d , R. Goncalves Gama51 , R. Gonçalo136a , G. Gonella50 , L. Gonella21 , A. Gongadze77 , F. Gonnella21 , J.L. Gonski57 , S. González de la Hoz171 , S. Gonzalez-Sevilla52 , L. Goossens35 , P.A. Gorbounov109 , H.A. Gordon29 , B. Gorini35 , E. Gorini65a,65b , A. Gorišek89 , A.T. Goshaw47 , C. Gössling45 , M.I. Gostkin77 , C.A. Gottardo24 , C.R. Goudet128 , D. Goujdami34c , A.G. Goussiou145 , N. Govender32b,c , C. Goy5 , E. Gozani157 , I. Grabowska-Bold81a , P.O.J. Gradin169 , E.C. Graham88 , J. Gramling168 , E. Gramstad130 , S. Grancagnolo19 , V. Gratchev134 , P.M. Gravila27f , F.G. Gravili65a,65b , C. Gray55 , H.M. Gray18 , Z.D. Greenwood93,aj , C. Grefe24 , K. Gregersen94 , I.M. Gregor44 , P. Grenier150 , K. Grevtsov44 , N.A. Grieser124 , J. Griffiths8 , A.A. Grillo143 , K. Grimm150,b , S. Grinstein14,y , Ph. Gris37 , J.-F. Grivaz128 , S. Groh97 , E. Gross177 , J. Grosse-Knetter51 , G.C. Grossi93 , Z.J. Grout92 , C. Grud103 , A. Grummer116 , L. Guan103 , W. Guan178 , J. Guenther35 , A. Guerguichon128 , F. Guescini165a , D. Guest168 , R. Gugel50 , B. Gui122 , T. Guillemin5 , S. Guindon35 , U. Gul55 , C. Gumpert35 , J. Guo58c , W. Guo103 , Y. Guo58a,s , Z. Guo99 , R. Gupta44 , S. Gurbuz12c , G. Gustavino124 , B.J. Gutelman157 , P. Gutierrez124 , C. Gutschow92 , C. Guyot142 , M.P. Guzik81a , C. Gwenlan131 , C.B. Gwilliam88 , A. Haas121 , C. Haber18 , H.K. Hadavand8 , N. Haddad34e , A. Hadef58a , S. Hageböck24 , M. Hagihara166 , H. Hakobyan181,∗ , M. Haleem174 , J. Haley125 , G. Halladjian104 , G.D. Hallewell99 , K. Hamacher179 , P. Hamal126 , K. Hamano173 , A. Hamilton32a , G.N. Hamity146 , K. Han58a,ai , L. Han58a , S. Han15d , K. Hanagaki79,u , M. Hance143 , D.M. Handl112 , B. Haney133 , R. Hankache132 , P. Hanke59a , E. Hansen94 , J.B. Hansen39 , J.D. Hansen39 , M.C. Hansen24 , P.H. Hansen39 , K. Hara166 , A.S. Hard178 , T. Harenberg179 , S. Harkusha105 , P.F. Harrison175 , N.M. Hartmann112 , Y. Hasegawa147 , A. Hasib48 , S. Hassani142 , S. Haug20 , R. Hauser104 , L. Hauswald46 , L.B. Havener38 , M. Havranek138 , C.M. Hawkes21 , R.J. Hawkings35 , D. Hayden104 , C. Hayes152 , C.P. Hays131 , J.M. Hays90 , H.S. Hayward88 , S.J. Haywood141 , M.P. Heath48 , V. Hedberg94 , L. Heelan8 , S. Heer24 , K.K. Heidegger50 , J. Heilman33 , S. Heim44 , T. Heim18 , B. Heinemann44,ao , J.J. Heinrich112 , L. Heinrich121 , C. Heinz54 , J. Hejbal137 , L. Helary35 , A. Held172 , S. Hellesund130 , C.M. Helling143 , – 36 – S. Hellman43a,43b , C. Helsens35 , R.C.W. Henderson87 , Y. Heng178 , S. Henkelmann172 , A.M. Henriques Correia35 , G.H. Herbert19 , H. Herde26 , V. Herget174 , Y. Hernández Jiménez32c , H. Herr97 , M.G. Herrmann112 , T. Herrmann46 , G. Herten50 , R. Hertenberger112 , L. Hervas35 , T.C. Herwig133 , G.G. Hesketh92 , N.P. Hessey165a , A. Higashida160 , S. Higashino79 , E. Higón-Rodriguez171 , K. Hildebrand36 , E. Hill173 , J.C. Hill31 , K.K. Hill29 , K.H. Hiller44 , S.J. Hillier21 , M. Hils46 , I. Hinchliffe18 , F. Hinterkeuser24 , M. Hirose129 , D. Hirschbuehl179 , B. Hiti89 , O. Hladik137 , D.R. Hlaluku32c , X. Hoad48 , J. Hobbs152 , N. Hod165a , M.C. Hodgkinson146 , A. Hoecker35 , M.R. Hoeferkamp116 , F. Hoenig112 , D. Hohn50 , D. Hohov128 , T.R. Holmes36 , M. Holzbock112 , M. Homann45 , B.H. Hommels31 , S. Honda166 , T. Honda79 , T.M. Hong135 , A. Hönle113 , B.H. Hooberman170 , W.H. Hopkins127 , Y. Horii115 , P. Horn46 , A.J. Horton149 , L.A. Horyn36 , J-Y. Hostachy56 , A. Hostiuc145 , S. Hou155 , A. Hoummada34a , JHEP04(2019)092 J. Howarth98 , J. Hoya86 , M. Hrabovsky126 , I. Hristova19 , J. Hrivnac128 , A. Hrynevich106 , T. Hryn’ova5 , P.J. Hsu62 , S.-C. Hsu145 , Q. Hu29 , S. Hu58c , Y. Huang15a , Z. Hubacek138 , F. Hubaut99 , M. Huebner24 , F. Huegging24 , T.B. Huffman131 , M. Huhtinen35 , R.F.H. Hunter33 , P. Huo152 , A.M. Hupe33 , N. Huseynov77,ae , J. Huston104 , J. Huth57 , R. Hyneman103 , G. Iacobucci52 , G. Iakovidis29 , I. Ibragimov148 , L. Iconomidou-Fayard128 , Z. Idrissi34e , P. Iengo35 , R. Ignazzi39 , O. Igonkina118,aa , R. Iguchi160 , T. Iizawa52 , Y. Ikegami79 , M. Ikeno79 , D. Iliadis159 , N. Ilic150 , F. Iltzsche46 , G. Introzzi68a,68b , M. Iodice72a , K. Iordanidou38 , V. Ippolito70a,70b , M.F. Isacson169 , N. Ishijima129 , M. Ishino160 , M. Ishitsuka162 , W. Islam125 , C. Issever131 , S. Istin157 , F. Ito166 , J.M. Iturbe Ponce61a , R. Iuppa73a,73b , A. Ivina177 , H. Iwasaki79 , J.M. Izen42 , V. Izzo67a , P. Jacka137 , P. Jackson1 , R.M. Jacobs24 , V. Jain2 , G. Jäkel179 , K.B. Jakobi97 , K. Jakobs50 , S. Jakobsen74 , T. Jakoubek137 , D.O. Jamin125 , R. Jansky52 , J. Janssen24 , M. Janus51 , P.A. Janus81a , G. Jarlskog94 , N. Javadov77,ae , T. Javůrek35 , M. Javurkova50 , F. Jeanneau142 , L. Jeanty18 , J. Jejelava156a,af , A. Jelinskas175 , P. Jenni50,d , J. Jeong44 , N. Jeong44 , S. Jézéquel5 , H. Ji178 , J. Jia152 , H. Jiang76 , Y. Jiang58a , Z. Jiang150,q , S. Jiggins50 , F.A. Jimenez Morales37 , J. Jimenez Pena171 , S. Jin15c , A. Jinaru27b , O. Jinnouchi162 , H. Jivan32c , P. Johansson146 , K.A. Johns7 , C.A. Johnson63 , W.J. Johnson145 , K. Jon-And43a,43b , R.W.L. Jones87 , S.D. Jones153 , S. Jones7 , T.J. Jones88 , J. Jongmanns59a , P.M. Jorge136a,136b , J. Jovicevic165a , X. Ju18 , J.J. Junggeburth113 , A. Juste Rozas14,y , A. Kaczmarska82 , M. Kado128 , H. Kagan122 , M. Kagan150 , T. Kaji176 , E. Kajomovitz157 , C.W. Kalderon94 , A. Kaluza97 , S. Kama41 , A. Kamenshchikov140 , L. Kanjir89 , Y. Kano160 , V.A. Kantserov110 , J. Kanzaki79 , L.S. Kaplan178 , D. Kar32c , M.J. Kareem165b , E. Karentzos10 , S.N. Karpov77 , Z.M. Karpova77 , V. Kartvelishvili87 , A.N. Karyukhin140 , L. Kashif178 , R.D. Kass122 , A. Kastanas43a,43b , Y. Kataoka160 , C. Kato58d,58c , J. Katzy44 , K. Kawade80 , K. Kawagoe85 , T. Kawamoto160 , G. Kawamura51 , E.F. Kay88 , V.F. Kazanin120b,120a , R. Keeler173 , R. Kehoe41 , J.S. Keller33 , E. Kellermann94 , J.J. Kempster21 , J. Kendrick21 , O. Kepka137 , S. Kersten179 , B.P. Kerševan89 , S. Ketabchi Haghighat164 , R.A. Keyes101 , M. Khader170 , F. Khalil-Zada13 , A. Khanov125 , A.G. Kharlamov120b,120a , T. Kharlamova120b,120a , E.E. Khoda172 , A. Khodinov163 , T.J. Khoo52 , E. Khramov77 , J. Khubua156b , S. Kido80 , M. Kiehn52 , C.R. Kilby91 , Y.K. Kim36 , N. Kimura64a,64c , O.M. Kind19 , B.T. King88 , D. Kirchmeier46 , J. Kirk141 , A.E. Kiryunin113 , T. Kishimoto160 , D. Kisielewska81a , V. Kitali44 , O. Kivernyk5 , E. Kladiva28b,∗ , T. Klapdor-Kleingrothaus50 , M.H. Klein103 , M. Klein88 , U. Klein88 , K. Kleinknecht97 , P. Klimek119 , A. Klimentov29 , T. Klingl24 , T. Klioutchnikova35 , F.F. Klitzner112 , P. Kluit118 , S. Kluth113 , E. Kneringer74 , E.B.F.G. Knoops99 , A. Knue50 , A. Kobayashi160 , D. Kobayashi85 , T. Kobayashi160 , M. Kobel46 , M. Kocian150 , P. Kodys139 , P.T. Koenig24 , T. Koffas33 , E. Koffeman118 , N.M. Köhler113 , T. Koi150 , M. Kolb59b , I. Koletsou5 , T. Kondo79 , N. Kondrashova58c , K. Köneke50 , A.C. König117 , T. Kono79 , R. Konoplich121,al , V. Konstantinides92 , N. Konstantinidis92 , B. Konya94 , R. Kopeliansky63 , S. Koperny81a , – 37 – K. Korcyl82 , K. Kordas159 , G. Koren158 , A. Korn92 , I. Korolkov14 , E.V. Korolkova146 , N. Korotkova111 , O. Kortner113 , S. Kortner113 , T. Kosek139 , V.V. Kostyukhin24 , A. Kotwal47 , A. Koulouris10 , A. Kourkoumeli-Charalampidi68a,68b , C. Kourkoumelis9 , E. Kourlitis146 , V. Kouskoura29 , A.B. Kowalewska82 , R. Kowalewski173 , T.Z. Kowalski81a , C. Kozakai160 , W. Kozanecki142 , A.S. Kozhin140 , V.A. Kramarenko111 , G. Kramberger89 , D. Krasnopevtsev58a , M.W. Krasny132 , A. Krasznahorkay35 , D. Krauss113 , J.A. Kremer81a , J. Kretzschmar88 , P. Krieger164 , K. Krizka18 , K. Kroeninger45 , H. Kroha113 , J. Kroll137 , J. Kroll133 , J. Krstic16 , U. Kruchonak77 , H. Krüger24 , N. Krumnack76 , M.C. Kruse47 , T. Kubota102 , S. Kuday4b , J.T. Kuechler179 , S. Kuehn35 , A. Kugel59a , T. Kuhl44 , V. Kukhtin77 , R. Kukla99 , Y. Kulchitsky105,ah , S. Kuleshov144b , Y.P. Kulinich170 , M. Kuna56 , T. Kunigo83 , A. Kupco137 , T. Kupfer45 , O. Kuprash158 , H. Kurashige80 , L.L. Kurchaninov165a , Y.A. Kurochkin105 , JHEP04(2019)092 A. Kurova110 , M.G. Kurth15d , E.S. Kuwertz35 , M. Kuze162 , J. Kvita126 , T. Kwan101 , A. La Rosa113 , J.L. La Rosa Navarro78d , L. La Rotonda40b,40a , F. La Ruffa40b,40a , C. Lacasta171 , F. Lacava70a,70b , J. Lacey44 , D.P.J. Lack98 , H. Lacker19 , D. Lacour132 , E. Ladygin77 , R. Lafaye5 , B. Laforge132 , T. Lagouri32c , S. Lai51 , S. Lammers63 , W. Lampl7 , E. Lançon29 , U. Landgraf50 , M.P.J. Landon90 , M.C. Lanfermann52 , V.S. Lang44 , J.C. Lange51 , R.J. Langenberg35 , A.J. Lankford168 , F. Lanni29 , K. Lantzsch24 , A. Lanza68a , A. Lapertosa53b,53a , S. Laplace132 , J.F. Laporte142 , T. Lari66a , F. Lasagni Manghi23b,23a , M. Lassnig35 , T.S. Lau61a , A. Laudrain128 , M. Lavorgna67a,67b , M. Lazzaroni66a,66b , B. Le102 , O. Le Dortz132 , E. Le Guirriec99 , E.P. Le Quilleuc142 , M. LeBlanc7 , T. LeCompte6 , F. Ledroit-Guillon56 , C.A. Lee29 , G.R. Lee144a , L. Lee57 , S.C. Lee155 , B. Lefebvre101 , M. Lefebvre173 , F. Legger112 , C. Leggett18 , K. Lehmann149 , N. Lehmann179 , G. Lehmann Miotto35 , W.A. Leight44 , A. Leisos159,v , M.A.L. Leite78d , R. Leitner139 , D. Lellouch177 , K.J.C. Leney92 , T. Lenz24 , B. Lenzi35 , R. Leone7 , S. Leone69a , C. Leonidopoulos48 , G. Lerner153 , C. Leroy107 , R. Les164 , A.A.J. Lesage142 , C.G. Lester31 , M. Levchenko134 , J. Levêque5 , D. Levin103 , L.J. Levinson177 , D. Lewis90 , B. Li15b , B. Li103 , C-Q. Li58a,ak , H. Li58a , H. Li58b , L. Li58c , M. Li15a , Q. Li15d , Q.Y. Li58a , S. Li58d,58c , X. Li58c , Y. Li148 , Z. Liang15a , B. Liberti71a , A. Liblong164 , K. Lie61c , S. Liem118 , A. Limosani154 , C.Y. Lin31 , K. Lin104 , T.H. Lin97 , R.A. Linck63 , J.H. Lindon21 , B.E. Lindquist152 , A.L. Lionti52 , E. Lipeles133 , A. Lipniacka17 , M. Lisovyi59b , T.M. Liss170,aq , A. Lister172 , A.M. Litke143 , J.D. Little8 , B. Liu76 , B.L Liu6 , H.B. Liu29 , H. Liu103 , J.B. Liu58a , J.K.K. Liu131 , K. Liu132 , M. Liu58a , P. Liu18 , Y. Liu15a , Y.L. Liu58a , Y.W. Liu58a , M. Livan68a,68b , A. Lleres56 , J. Llorente Merino15a , S.L. Lloyd90 , C.Y. Lo61b , F. Lo Sterzo41 , E.M. Lobodzinska44 , P. Loch7 , T. Lohse19 , K. Lohwasser146 , M. Lokajicek137 , J.D. Long170 , R.E. Long87 , L. Longo65a,65b , K.A. Looper122 , J.A. Lopez144b , I. Lopez Paz98 , A. Lopez Solis146 , J. Lorenz112 , N. Lorenzo Martinez5 , M. Losada22 , P.J. Lösel112 , A. Lösle50 , X. Lou44 , X. Lou15a , A. Lounis128 , J. Love6 , P.A. Love87 , J.J. Lozano Bahilo171 , H. Lu61a , M. Lu58a , Y.J. Lu62 , H.J. Lubatti145 , C. Luci70a,70b , A. Lucotte56 , C. Luedtke50 , F. Luehring63 , I. Luise132 , L. Luminari70a , B. Lund-Jensen151 , M.S. Lutz100 , P.M. Luzi132 , D. Lynn29 , R. Lysak137 , E. Lytken94 , F. Lyu15a , V. Lyubushkin77 , T. Lyubushkina77 , H. Ma29 , L.L. Ma58b , Y. Ma58b , G. Maccarrone49 , A. Macchiolo113 , C.M. Macdonald146 , J. Machado Miguens133,136b , D. Madaffari171 , R. Madar37 , W.F. Mader46 , A. Madsen44 , N. Madysa46 , J. Maeda80 , K. Maekawa160 , S. Maeland17 , T. Maeno29 , M. Maerker46 , A.S. Maevskiy111 , V. Magerl50 , D.J. Mahon38 , C. Maidantchik78b , T. Maier112 , A. Maio136a,136b,136d , O. Majersky28a , S. Majewski127 , Y. Makida79 , N. Makovec128 , B. Malaescu132 , Pa. Malecki82 , V.P. Maleev134 , F. Malek56 , U. Mallik75 , D. Malon6 , C. Malone31 , S. Maltezos10 , S. Malyukov35 , J. Mamuzic171 , G. Mancini49 , I. Mandić89 , J. Maneira136a , L. Manhaes de Andrade Filho78a , J. Manjarres Ramos46 , K.H. Mankinen94 , A. Mann112 , A. Manousos74 , B. Mansoulie142 , J.D. Mansour15a , S. Manzoni66a,66b , A. Marantis159 , G. Marceca30 , L. March52 , L. Marchese131 , G. Marchiori132 , M. Marcisovsky137 , C. Marcon94 , – 38 – C.A. Marin Tobon35 , M. Marjanovic37 , F. Marroquim78b , Z. Marshall18 , M.U.F Martensson169 , S. Marti-Garcia171 , C.B. Martin122 , T.A. Martin175 , V.J. Martin48 , B. Martin dit Latour17 , M. Martinez14,y , V.I. Martinez Outschoorn100 , S. Martin-Haugh141 , V.S. Martoiu27b , A.C. Martyniuk92 , A. Marzin35 , L. Masetti97 , T. Mashimo160 , R. Mashinistov108 , J. Masik98 , A.L. Maslennikov120b,120a , L.H. Mason102 , L. Massa71a,71b , P. Massarotti67a,67b , P. Mastrandrea152 , A. Mastroberardino40b,40a , T. Masubuchi160 , P. Mättig179 , J. Maurer27b , B. Maček89 , S.J. Maxfield88 , D.A. Maximov120b,120a , R. Mazini155 , I. Maznas159 , S.M. Mazza143 , G. Mc Goldrick164 , S.P. Mc Kee103 , A. McCarn41 , T.G. McCarthy113 , L.I. McClymont92 , W.P. McCormack18 , E.F. McDonald102 , J.A. Mcfayden35 , G. Mchedlidze51 , M.A. McKay41 , K.D. McLean173 , S.J. McMahon141 , P.C. McNamara102 , C.J. McNicol175 , R.A. McPherson173,ac , J.E. Mdhluli32c , Z.A. Meadows100 , S. Meehan145 , T.M. Megy50 , S. Mehlhase112 , A. Mehta88 , JHEP04(2019)092 T. Meideck56 , B. Meirose42 , D. Melini171,h , B.R. Mellado Garcia32c , J.D. Mellenthin51 , M. Melo28a , F. Meloni44 , A. Melzer24 , S.B. Menary98 , E.D. Mendes Gouveia136a , L. Meng88 , X.T. Meng103 , S. Menke113 , E. Meoni40b,40a , S. Mergelmeyer19 , S.A.M. Merkt135 , C. Merlassino20 , P. Mermod52 , L. Merola67a,67b , C. Meroni66a , F.S. Merritt36 , A. Messina70a,70b , J. Metcalfe6 , A.S. Mete168 , C. Meyer133 , J. Meyer157 , J-P. Meyer142 , H. Meyer Zu Theenhausen59a , F. Miano153 , R.P. Middleton141 , L. Mijović48 , G. Mikenberg177 , M. Mikestikova137 , M. Mikuž89 , M. Milesi102 , A. Milic164 , D.A. Millar90 , D.W. Miller36 , A. Milov177 , D.A. Milstead43a,43b , R.A. Mina150,q , A.A. Minaenko140 , M. Miñano Moya171 , I.A. Minashvili156b , A.I. Mincer121 , B. Mindur81a , M. Mineev77 , Y. Minegishi160 , Y. Ming178 , L.M. Mir14 , A. Mirto65a,65b , K.P. Mistry133 , T. Mitani176 , J. Mitrevski112 , V.A. Mitsou171 , M. Mittal58c , A. Miucci20 , P.S. Miyagawa146 , A. Mizukami79 , J.U. Mjörnmark94 , T. Mkrtchyan181 , M. Mlynarikova139 , T. Moa43a,43b , K. Mochizuki107 , P. Mogg50 , S. Mohapatra38 , S. Molander43a,43b , R. Moles-Valls24 , M.C. Mondragon104 , K. Mönig44 , J. Monk39 , E. Monnier99 , A. Montalbano149 , J. Montejo Berlingen35 , F. Monticelli86 , S. Monzani66a , N. Morange128 , D. Moreno22 , M. Moreno Llácer35 , P. Morettini53b , M. Morgenstern118 , S. Morgenstern46 , D. Mori149 , M. Morii57 , M. Morinaga176 , V. Morisbak130 , A.K. Morley35 , G. Mornacchi35 , A.P. Morris92 , J.D. Morris90 , L. Morvaj152 , P. Moschovakos10 , M. Mosidze156b , H.J. Moss146 , J. Moss150,n , K. Motohashi162 , R. Mount150 , E. Mountricha35 , E.J.W. Moyse100 , S. Muanza99 , F. Mueller113 , J. Mueller135 , R.S.P. Mueller112 , D. Muenstermann87 , G.A. Mullier94 , F.J. Munoz Sanchez98 , P. Murin28b , W.J. Murray175,141 , A. Murrone66a,66b , M. Muškinja89 , C. Mwewa32a , A.G. Myagkov140,am , J. Myers127 , M. Myska138 , B.P. Nachman18 , O. Nackenhorst45 , K. Nagai131 , K. Nagano79 , Y. Nagasaka60 , M. Nagel50 , E. Nagy99 , A.M. 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Pacheco Rodriguez142 , C. Padilla Aranda14 , S. Pagan Griso18 , M. Paganini180 , G. Palacino63 , S. Palazzo48 , S. Palestini35 , M. Palka81b , D. Pallin37 , I. Panagoulias10 , C.E. Pandini35 , J.G. Panduro Vazquez91 , P. Pani35 , G. Panizzo64a,64c , L. Paolozzi52 , T.D. Papadopoulou10 , K. Papageorgiou9,j , A. Paramonov6 , D. Paredes Hernandez61b , S.R. Paredes Saenz131 , B. Parida163 , T.H. Park33 , A.J. Parker87 , K.A. Parker44 , M.A. Parker31 , F. Parodi53b,53a , J.A. Parsons38 , U. Parzefall50 , V.R. Pascuzzi164 , JHEP04(2019)092 J.M.P. Pasner143 , E. Pasqualucci70a , S. Passaggio53b , F. Pastore91 , P. Pasuwan43a,43b , S. Pataraia97 , J.R. Pater98 , A. Pathak178,k , T. Pauly35 , B. Pearson113 , M. Pedersen130 , L. Pedraza Diaz117 , R. Pedro136a,136b , S.V. Peleganchuk120b,120a , O. Penc137 , C. Peng15d , H. Peng58a , B.S. Peralva78a , M.M. Perego128 , A.P. Pereira Peixoto136a , D.V. Perepelitsa29 , F. Peri19 , L. Perini66a,66b , H. Pernegger35 , S. Perrella67a,67b , V.D. 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Tovey146 , C.J. Treado121 , T. Trefzger174 , F. Tresoldi153 , A. Tricoli29 , I.M. Trigger165a , S. Trincaz-Duvoid132 , M.F. Tripiana14 , W. Trischuk164 , B. Trocmé56 , A. Trofymov128 , C. Troncon66a , M. Trovatelli173 , F. Trovato153 , L. Truong32b , M. Trzebinski82 , A. Trzupek82 , F. Tsai44 , J.C-L. Tseng131 , P.V. Tsiareshka105,ah , A. Tsirigotis159 , N. Tsirintanis9 , V. Tsiskaridze152 , E.G. Tskhadadze156a , I.I. Tsukerman109 , V. Tsulaia18 , S. Tsuno79 , D. Tsybychev152,163 , Y. Tu61b , A. Tudorache27b , V. Tudorache27b , T.T. Tulbure27a , A.N. Tuna57 , S. Turchikhin77 , D. Turgeman177 , I. Turk Cakir4b,t , R. Turra66a , P.M. Tuts38 , E. Tzovara97 , G. Ucchielli45 , I. Ueda79 , M. Ughetto43a,43b , F. Ukegawa166 , G. Unal35 , A. Undrus29 , G. Unel168 , F.C. Ungaro102 , Y. Unno79 , K. Uno160 , J. Urban28b , P. Urquijo102 , P. Urrejola97 , G. Usai8 , J. Usui79 , L. Vacavant99 , V. Vacek138 , B. Vachon101 , K.O.H. Vadla130 , A. Vaidya92 , C. Valderanis112 , E. Valdes Santurio43a,43b , M. Valente52 , S. Valentinetti23b,23a , A. Valero171 , L. Valéry44 , R.A. Vallance21 , A. Vallier5 , J.A. Valls Ferrer171 , T.R. Van Daalen14 , H. Van der Graaf118 , P. Van Gemmeren6 , I. Van Vulpen118 , M. Vanadia71a,71b , W. Vandelli35 , A. Vaniachine163 , P. Vankov118 , R. Vari70a , E.W. Varnes7 , C. Varni53b,53a , T. Varol41 , D. Varouchas128 , K.E. Varvell154 , G.A. Vasquez144b , J.G. Vasquez180 , F. Vazeille37 , D. Vazquez Furelos14 , T. Vazquez Schroeder35 , J. Veatch51 , V. Vecchio72a,72b , L.M. Veloce164 , F. Veloso136a,136c , S. Veneziano70a , A. Ventura65a,65b , N. Venturi35 , V. Vercesi68a , M. Verducci72a,72b , C.M. Vergel Infante76 , C. Vergis24 , W. Verkerke118 , A.T. Vermeulen118 , J.C. Vermeulen118 , M.C. Vetterli149,at , N. Viaux Maira144b , M. Vicente Barreto Pinto52 , I. Vichou170,∗ , T. Vickey146 , O.E. Vickey Boeriu146 , G.H.A. Viehhauser131 , S. Viel18 , L. Vigani131 , M. Villa23b,23a , M. Villaplana Perez66a,66b , E. Vilucchi49 , M.G. Vincter33 , V.B. Vinogradov77 , A. Vishwakarma44 , C. Vittori23b,23a , I. Vivarelli153 , S. Vlachos10 , M. Vogel179 , P. Vokac138 , G. Volpi14 , S.E. von Buddenbrock32c , E. Von Toerne24 , V. Vorobel139 , K. Vorobev110 , M. Vos171 , J.H. Vossebeld88 , N. Vranjes16 , M. Vranjes Milosavljevic16 , V. Vrba138 , M. Vreeswijk118 , T. Šfiligoj89 , R. Vuillermet35 , I. Vukotic36 , T. Ženiš28a , L. Živković16 , P. Wagner24 , W. Wagner179 , J. Wagner-Kuhr112 , H. Wahlberg86 , S. Wahrmund46 , K. Wakamiya80 , – 42 – V.M. Walbrecht113 , J. Walder87 , R. Walker112 , S.D. Walker91 , W. Walkowiak148 , V. Wallangen43a,43b , A.M. Wang57 , C. Wang58b , F. Wang178 , H. Wang18 , H. Wang3 , J. Wang154 , J. Wang59b , P. Wang41 , Q. Wang124 , R.-J. Wang132 , R. Wang58a , R. Wang6 , S.M. Wang155 , W.T. Wang58a , W. Wang15c,ad , W.X. Wang58a,ad , Y. Wang58a,ak , Z. Wang58c , C. Wanotayaroj44 , A. Warburton101 , C.P. Ward31 , D.R. Wardrope92 , A. Washbrook48 , P.M. Watkins21 , A.T. Watson21 , M.F. Watson21 , G. Watts145 , S. Watts98 , B.M. Waugh92 , A.F. Webb11 , S. Webb97 , C. Weber180 , M.S. Weber20 , S.A. Weber33 , S.M. Weber59a , A.R. Weidberg131 , J. Weingarten45 , M. Weirich97 , C. Weiser50 , P.S. Wells35 , T. Wenaus29 , T. Wengler35 , S. Wenig35 , N. Wermes24 , M.D. Werner76 , P. Werner35 , M. Wessels59a , T.D. Weston20 , K. Whalen127 , N.L. Whallon145 , A.M. Wharton87 , A.S. White103 , A. White8 , M.J. White1 , R. White144b , D. Whiteson168 , B.W. Whitmore87 , F.J. Wickens141 , W. Wiedenmann178 , M. Wielers141 , JHEP04(2019)092 C. Wiglesworth39 , L.A.M. Wiik-Fuchs50 , F. Wilk98 , H.G. Wilkens35 , L.J. Wilkins91 , H.H. Williams133 , S. Williams31 , C. Willis104 , S. Willocq100 , J.A. Wilson21 , I. Wingerter-Seez5 , E. Winkels153 , F. Winklmeier127 , O.J. Winston153 , B.T. Winter50 , M. Wittgen150 , M. Wobisch93 , A. Wolf97 , T.M.H. Wolf118 , R. Wolff99 , M.W. Wolter82 , H. Wolters136a,136c , V.W.S. Wong172 , N.L. Woods143 , S.D. Worm21 , B.K. Wosiek82 , K.W. Woźniak82 , K. Wraight55 , M. Wu36 , S.L. Wu178 , X. Wu52 , Y. Wu58a , T.R. Wyatt98 , B.M. Wynne48 , S. Xella39 , Z. Xi103 , L. Xia175 , D. Xu15a , H. Xu58a,e , L. Xu29 , T. Xu142 , W. Xu103 , Z. Xu150 , B. Yabsley154 , S. Yacoob32a , K. Yajima129 , D.P. Yallup92 , D. Yamaguchi162 , Y. Yamaguchi162 , A. Yamamoto79 , T. Yamanaka160 , F. Yamane80 , M. Yamatani160 , T. Yamazaki160 , Y. Yamazaki80 , Z. Yan25 , H.J. Yang58c,58d , H.T. Yang18 , S. Yang75 , Y. Yang160 , Z. Yang17 , W-M. Yao18 , Y.C. Yap44 , Y. Yasu79 , E. Yatsenko58c,58d , J. Ye41 , S. Ye29 , I. Yeletskikh77 , E. Yigitbasi25 , E. Yildirim97 , K. Yorita176 , K. Yoshihara133 , C.J.S. Young35 , C. Young150 , J. Yu8 , J. Yu76 , X. Yue59a , S.P.Y. Yuen24 , B. Zabinski82 , G. Zacharis10 , E. Zaffaroni52 , R. Zaidan14 , A.M. Zaitsev140,am , T. Zakareishvili156b , N. Zakharchuk33 , J. Zalieckas17 , S. Zambito57 , D. Zanzi35 , D.R. Zaripovas55 , S.V. Zeißner45 , C. Zeitnitz179 , G. Zemaityte131 , J.C. Zeng170 , Q. Zeng150 , O. Zenin140 , D. Zerwas128 , M. Zgubič131 , D.F. Zhang58b , D. Zhang103 , F. Zhang178 , G. Zhang58a , G. Zhang15b , H. Zhang15c , J. Zhang6 , L. Zhang15c , L. Zhang58a , M. Zhang170 , P. Zhang15c , R. Zhang58a , R. Zhang24 , X. Zhang58b , Y. Zhang15d , Z. Zhang128 , P. Zhao47 , Y. Zhao58b,128,ai , Z. Zhao58a , A. Zhemchugov77 , Z. Zheng103 , D. Zhong170 , B. Zhou103 , C. Zhou178 , L. Zhou41 , M.S. Zhou15d , M. Zhou152 , N. Zhou58c , Y. Zhou7 , C.G. Zhu58b , H.L. Zhu58a , H. Zhu15a , J. Zhu103 , Y. Zhu58a , X. Zhuang15a , K. Zhukov108 , V. Zhulanov120b,120a , A. Zibell174 , D. Zieminska63 , N.I. Zimine77 , S. Zimmermann50 , Z. Zinonos113 , M. Ziolkowski148 , G. Zobernig178 , A. Zoccoli23b,23a , K. Zoch51 , T.G. Zorbas146 , R. Zou36 , M. Zur Nedden19 and L. Zwalinski35 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) Department of Physics, Ankara University, Ankara;(b) 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 12 (a) Bahcesehir University, Faculty of Engineering and Natural Sciences, Istanbul; (b) Istanbul Bilgi University, Faculty of Engineering and Natural Sciences, Istanbul;(c) Department of Physics, – 43 – Bogazici University, Istanbul;(d) Department of Physics Engineering, Gaziantep University, Gaziantep; Turkey 13 Institute of Physics, Azerbaijan Academy of Sciences, Baku; Azerbaijan 14 Institut de Fı́sica d’Altes Energies (IFAE), Barcelona Institute of Science and Technology, Barcelona; Spain 15 (a) Institute of High Energy Physics, Chinese Academy of Sciences, Beijing;(b) Physics Department, Tsinghua University, Beijing;(c) Department of Physics, Nanjing University, Nanjing;(d) University of Chinese Academy of Science (UCAS), Beijing; China 16 Institute of Physics, University of Belgrade, Belgrade; Serbia 17 Department for Physics and Technology, University of Bergen, Bergen; Norway 18 Physics Division, Lawrence Berkeley National Laboratory and University of California, Berkeley CA; United States of America JHEP04(2019)092 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 Centro de Investigaciónes, Universidad Antonio Nariño, Bogota; Colombia 23 (a) Dipartimento di Fisica e Astronomia, Università di Bologna, Bologna; (b) INFN Sezione di Bologna; Italy 24 Physikalisches Institut, Universität Bonn, Bonn; Germany 25 Department of Physics, Boston University, Boston MA; United States of America 26 Department of Physics, Brandeis University, Waltham MA; United States of America 27 (a) Transilvania University of Brasov, Brasov;(b) Horia Hulubei National Institute of Physics and Nuclear Engineering, Bucharest;(c) Department of Physics, Alexandru Ioan Cuza University of Iasi, Iasi;(d) National Institute for Research and Development of Isotopic and Molecular Technologies, Physics Department, Cluj-Napoca;(e) University Politehnica Bucharest, Bucharest;(f ) West University in Timisoara, Timisoara; Romania 28 (a) Faculty of Mathematics, Physics and Informatics, Comenius University, Bratislava;(b) Department of Subnuclear Physics, Institute of Experimental Physics of the Slovak Academy of Sciences, Kosice; Slovak Republic 29 Physics Department, Brookhaven National Laboratory, Upton NY; United States of America 30 Departamento de Fı́sica, Universidad de Buenos Aires, Buenos Aires; Argentina 31 Cavendish Laboratory, University of Cambridge, Cambridge; United Kingdom 32 (a) Department of Physics, University of Cape Town, Cape Town;(b) Department of Mechanical Engineering Science, University of Johannesburg, Johannesburg;(c) School of Physics, University of the Witwatersrand, Johannesburg; South Africa 33 Department of Physics, Carleton University, Ottawa ON; Canada 34 (a) Faculté des Sciences Ain Chock, Réseau Universitaire de Physique des Hautes Energies — Université Hassan II, Casablanca;(b) Centre National de l’Energie des Sciences Techniques Nucleaires (CNESTEN), Rabat;(c) Faculté des Sciences Semlalia, Université Cadi Ayyad, LPHEA-Marrakech;(d) Faculté des Sciences, Université Mohamed Premier and LPTPM, Oujda;(e) Faculté des sciences, Université Mohammed V, Rabat; Morocco 35 CERN, Geneva; Switzerland 36 Enrico Fermi Institute, University of Chicago, Chicago IL; United States of America 37 LPC, Université Clermont Auvergne, CNRS/IN2P3, Clermont-Ferrand; France 38 Nevis Laboratory, Columbia University, Irvington NY; United States of America 39 Niels Bohr Institute, University of Copenhagen, Copenhagen; Denmark 40 (a) Dipartimento di Fisica, Università della Calabria, Rende;(b) INFN Gruppo Collegato di Cosenza, Laboratori Nazionali di Frascati; Italy 41 Physics Department, Southern Methodist University, Dallas TX; United States of America 42 Physics Department, University of Texas at Dallas, Richardson TX; United States of America 43 (a) Department of Physics, Stockholm University;(b) Oskar Klein Centre, Stockholm; Sweden – 44 – 44 Deutsches Elektronen-Synchrotron DESY, Hamburg and Zeuthen; Germany 45 Lehrstuhl für Experimentelle Physik IV, Technische Universität Dortmund, Dortmund; Germany 46 Institut für Kern- und Teilchenphysik, Technische Universität Dresden, Dresden; Germany 47 Department of Physics, Duke University, Durham NC; United States of America 48 SUPA — School of Physics and Astronomy, University of Edinburgh, Edinburgh; United Kingdom 49 INFN e Laboratori Nazionali di Frascati, Frascati; Italy 50 Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg; Germany 51 II. Physikalisches Institut, Georg-August-Universität Göttingen, Göttingen; Germany 52 Département de Physique Nucléaire et Corpusculaire, Université de Genève, Genève; Switzerland 53 (a) Dipartimento di Fisica, Università di Genova, Genova;(b) INFN Sezione di Genova; Italy 54 II. Physikalisches Institut, Justus-Liebig-Universität Giessen, Giessen; Germany 55 SUPA — School of Physics and Astronomy, University of Glasgow, Glasgow; United Kingdom JHEP04(2019)092 56 LPSC, Université Grenoble Alpes, CNRS/IN2P3, Grenoble INP, Grenoble; France 57 Laboratory for Particle Physics and Cosmology, Harvard University, Cambridge MA; United States of America 58 (a) Department of Modern Physics and State Key Laboratory of Particle Detection and Electronics, University of Science and Technology of China, Hefei;(b) Institute of Frontier and Interdisciplinary Science and Key Laboratory of Particle Physics and Particle Irradiation (MOE), Shandong University, Qingdao;(c) School of Physics and Astronomy, Shanghai Jiao Tong University, KLPPAC-MoE, SKLPPC, Shanghai;(d) Tsung-Dao Lee Institute, Shanghai; China 59 (a) Kirchhoff-Institut für Physik, Ruprecht-Karls-Universität Heidelberg, Heidelberg; (b) Physikalisches Institut, Ruprecht-Karls-Universität Heidelberg, Heidelberg; Germany 60 Faculty of Applied Information Science, Hiroshima Institute of Technology, Hiroshima; Japan 61 (a) Department of Physics, Chinese University of Hong Kong, Shatin, N.T., Hong Kong;(b) Department of Physics, University of Hong Kong, Hong Kong;(c) Department of Physics and Institute for Advanced Study, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong; China 62 Department of Physics, National Tsing Hua University, Hsinchu; Taiwan 63 Department of Physics, Indiana University, Bloomington IN; United States of America 64 (a) INFN Gruppo Collegato di Udine, Sezione di Trieste, Udine;(b) ICTP, Trieste;(c) Dipartimento di Chimica, Fisica e Ambiente, Università di Udine, Udine; Italy 65 (a) INFN Sezione di Lecce;(b) Dipartimento di Matematica e Fisica, Università del Salento, Lecce; Italy 66 (a) INFN Sezione di Milano;(b) Dipartimento di Fisica, Università di Milano, Milano; Italy 67 (a) INFN Sezione di Napoli;(b) Dipartimento di Fisica, Università di Napoli, Napoli; Italy 68 (a) INFN Sezione di Pavia;(b) Dipartimento di Fisica, Università di Pavia, Pavia; Italy 69 (a) INFN Sezione di Pisa;(b) Dipartimento di Fisica E. Fermi, Università di Pisa, Pisa; Italy 70 (a) INFN Sezione di Roma;(b) Dipartimento di Fisica, Sapienza Università di Roma, Roma; Italy 71 (a) INFN Sezione di Roma Tor Vergata;(b) Dipartimento di Fisica, Università di Roma Tor Vergata, Roma; Italy 72 (a) INFN Sezione di Roma Tre;(b) Dipartimento di Matematica e Fisica, Università Roma Tre, Roma; Italy 73 (a) INFN-TIFPA;(b) Università degli Studi di Trento, Trento; Italy 74 Institut für Astro- und Teilchenphysik, Leopold-Franzens-Universität, Innsbruck; Austria 75 University of Iowa, Iowa City IA; United States of America 76 Department of Physics and Astronomy, Iowa State University, Ames IA; United States of America 77 Joint Institute for Nuclear Research, Dubna; Russia 78 (a) Departamento de Engenharia Elétrica, Universidade Federal de Juiz de Fora (UFJF), Juiz de Fora;(b) Universidade Federal do Rio De Janeiro COPPE/EE/IF, Rio de Janeiro; (c) Universidade Federal de São João del Rei (UFSJ), São João del Rei;(d) Instituto de Fı́sica, Universidade de São Paulo, São Paulo; Brazil 79 KEK, High Energy Accelerator Research Organization, Tsukuba; Japan – 45 – 80 Graduate School of Science, Kobe University, Kobe; Japan 81 (a) AGH University of Science and Technology, Faculty of Physics and Applied Computer Science, Krakow;(b) Marian Smoluchowski Institute of Physics, Jagiellonian University, Krakow; Poland 82 Institute of Nuclear Physics Polish Academy of Sciences, Krakow; Poland 83 Faculty of Science, Kyoto University, Kyoto; Japan 84 Kyoto University of Education, Kyoto; Japan 85 Research Center for Advanced Particle Physics and Department of Physics, Kyushu University, Fukuoka; Japan 86 Instituto de Fı́sica La Plata, Universidad Nacional de La Plata and CONICET, La Plata; Argentina 87 Physics Department, Lancaster University, Lancaster; United Kingdom 88 Oliver Lodge Laboratory, University of Liverpool, Liverpool; United Kingdom 89 Department of Experimental Particle Physics, Jožef Stefan Institute and Department of Physics, JHEP04(2019)092 University of Ljubljana, Ljubljana; Slovenia 90 School of Physics and Astronomy, Queen Mary University of London, London; United Kingdom 91 Department of Physics, Royal Holloway University of London, Egham; United Kingdom 92 Department of Physics and Astronomy, University College London, London; United Kingdom 93 Louisiana Tech University, Ruston LA; United States of America 94 Fysiska institutionen, Lunds universitet, Lund; Sweden 95 Centre de Calcul de l’Institut National de Physique Nucléaire et de Physique des Particules (IN2P3), Villeurbanne; France 96 Departamento de Fı́sica Teorica C-15 and CIAFF, Universidad Autónoma de Madrid, Madrid; Spain 97 Institut für Physik, Universität Mainz, Mainz; Germany 98 School of Physics and Astronomy, University of Manchester, Manchester; United Kingdom 99 CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille; France 100 Department of Physics, University of Massachusetts, Amherst MA; United States of America 101 Department of Physics, McGill University, Montreal QC; Canada 102 School of Physics, University of Melbourne, Victoria; Australia 103 Department of Physics, University of Michigan, Ann Arbor MI; United States of America 104 Department of Physics and Astronomy, Michigan State University, East Lansing MI; United States of America 105 B.I. Stepanov Institute of Physics, National Academy of Sciences of Belarus, Minsk; Belarus 106 Research Institute for Nuclear Problems of Byelorussian State University, Minsk; Belarus 107 Group of Particle Physics, University of Montreal, Montreal QC; Canada 108 P.N. Lebedev Physical Institute of the Russian Academy of Sciences, Moscow; Russia 109 Institute for Theoretical and Experimental Physics (ITEP), Moscow; Russia 110 National Research Nuclear University MEPhI, Moscow; Russia 111 D.V. Skobeltsyn Institute of Nuclear Physics, M.V. Lomonosov Moscow State University, Moscow; Russia 112 Fakultät für Physik, Ludwig-Maximilians-Universität München, München; Germany 113 Max-Planck-Institut für Physik (Werner-Heisenberg-Institut), München; Germany 114 Nagasaki Institute of Applied Science, Nagasaki; Japan 115 Graduate School of Science and Kobayashi-Maskawa Institute, Nagoya University, Nagoya; Japan 116 Department of Physics and Astronomy, University of New Mexico, Albuquerque NM; United States of America 117 Institute for Mathematics, Astrophysics and Particle Physics, Radboud University Nijmegen/Nikhef, Nijmegen; Netherlands 118 Nikhef National Institute for Subatomic Physics and University of Amsterdam, Amsterdam; Netherlands 119 Department of Physics, Northern Illinois University, DeKalb IL; United States of America 120 (a) Budker Institute of Nuclear Physics, SB RAS, Novosibirsk;(b) Novosibirsk State University Novosibirsk; Russia – 46 – 121 Department of Physics, New York University, New York NY; United States of America 122 Ohio State University, Columbus OH; United States of America 123 Faculty of Science, Okayama University, Okayama; Japan 124 Homer L. Dodge Department of Physics and Astronomy, University of Oklahoma, Norman OK; United States of America 125 Department of Physics, Oklahoma State University, Stillwater OK; United States of America 126 Palacký University, RCPTM, Joint Laboratory of Optics, Olomouc; Czech Republic 127 Center for High Energy Physics, University of Oregon, Eugene OR; United States of America 128 LAL, Université Paris-Sud, CNRS/IN2P3, Université Paris-Saclay, Orsay; France 129 Graduate School of Science, Osaka University, Osaka; Japan 130 Department of Physics, University of Oslo, Oslo; Norway 131 Department of Physics, Oxford University, Oxford; United Kingdom JHEP04(2019)092 132 LPNHE, Sorbonne Université, Paris Diderot Sorbonne Paris Cité, CNRS/IN2P3, Paris; France 133 Department of Physics, University of Pennsylvania, Philadelphia PA; United States of America 134 Konstantinov Nuclear Physics Institute of National Research Centre “Kurchatov Institute”, PNPI, St. Petersburg; Russia 135 Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh PA; United States of America 136 (a) Laboratório de Instrumentação e Fı́sica Experimental de Partı́culas — LIP; (b) Departamento de Fı́sica, Faculdade de Ciências, Universidade de Lisboa, Lisboa; (c) Departamento de Fı́sica, Universidade de Coimbra, Coimbra;(d) Centro de Fı́sica Nuclear da Universidade de Lisboa, Lisboa;(e) Departamento de Fı́sica, Universidade do Minho, Braga;(f ) Departamento de Fı́sica Teorica y del Cosmos, Universidad de Granada, Granada (Spain); (g) Dep Fı́sica and CEFITEC of Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica; Portugal 137 Institute of Physics, Academy of Sciences of the Czech Republic, Prague; Czech Republic 138 Czech Technical University in Prague, Prague; Czech Republic 139 Charles University, Faculty of Mathematics and Physics, Prague; Czech Republic 140 State Research Center Institute for High Energy Physics, NRC KI, Protvino; Russia 141 Particle Physics Department, Rutherford Appleton Laboratory, Didcot; United Kingdom 142 IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette; France 143 Santa Cruz Institute for Particle Physics, University of California Santa Cruz, Santa Cruz CA; United States of America 144 (a) Departamento de Fı́sica, Pontificia Universidad Católica de Chile, Santiago; (b) Departamento de Fı́sica, Universidad Técnica Federico Santa Marı́a, Valparaı́so; Chile 145 Department of Physics, University of Washington, Seattle WA; United States of America 146 Department of Physics and Astronomy, University of Sheffield, Sheffield; United Kingdom 147 Department of Physics, Shinshu University, Nagano; Japan 148 Department Physik, Universität Siegen, Siegen; Germany 149 Department of Physics, Simon Fraser University, Burnaby BC; Canada 150 SLAC National Accelerator Laboratory, Stanford CA; United States of America 151 Physics Department, Royal Institute of Technology, Stockholm; Sweden 152 Departments of Physics and Astronomy, Stony Brook University, Stony Brook NY; United States of America 153 Department of Physics and Astronomy, University of Sussex, Brighton; United Kingdom 154 School of Physics, University of Sydney, Sydney; Australia 155 Institute of Physics, Academia Sinica, Taipei; Taiwan 156 (a) E. Andronikashvili Institute of Physics, Iv. Javakhishvili Tbilisi State University, Tbilisi; (b) High Energy Physics Institute, Tbilisi State University, Tbilisi; Georgia 157 Department of Physics, Technion, Israel Institute of Technology, Haifa; Israel 158 Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University, Tel Aviv; Israel 159 Department of Physics, Aristotle University of Thessaloniki, Thessaloniki; Greece – 47 – 160 International Center for Elementary Particle Physics and Department of Physics, University of Tokyo, Tokyo; Japan 161 Graduate School of Science and Technology, Tokyo Metropolitan University, Tokyo; Japan 162 Department of Physics, Tokyo Institute of Technology, Tokyo; Japan 163 Tomsk State University, Tomsk; Russia 164 Department of Physics, University of Toronto, Toronto ON; Canada 165 (a) TRIUMF, Vancouver BC;(b) Department of Physics and Astronomy, York University, Toronto ON; Canada 166 Division of Physics and Tomonaga Center for the History of the Universe, Faculty of Pure and Applied Sciences, University of Tsukuba, Tsukuba; Japan 167 Department of Physics and Astronomy, Tufts University, Medford MA; United States of America 168 Department of Physics and Astronomy, University of California Irvine, Irvine CA; United States of JHEP04(2019)092 America 169 Department of Physics and Astronomy, University of Uppsala, Uppsala; Sweden 170 Department of Physics, University of Illinois, Urbana IL; United States of America 171 Instituto de Fı́sica Corpuscular (IFIC), Centro Mixto Universidad de Valencia — CSIC, Valencia; Spain 172 Department of Physics, University of British Columbia, Vancouver BC; Canada 173 Department of Physics and Astronomy, University of Victoria, Victoria BC; Canada 174 Fakultät für Physik und Astronomie, Julius-Maximilians-Universität Würzburg, Würzburg; Germany 175 Department of Physics, University of Warwick, Coventry; United Kingdom 176 Waseda University, Tokyo; Japan 177 Department of Particle Physics, Weizmann Institute of Science, Rehovot; Israel 178 Department of Physics, University of Wisconsin, Madison WI; United States of America 179 Fakultät für Mathematik und Naturwissenschaften, Fachgruppe Physik, Bergische Universität Wuppertal, Wuppertal; Germany 180 Department of Physics, Yale University, New Haven CT; United States of America 181 Yerevan Physics Institute, Yerevan; Armenia a Also at Borough of Manhattan Community College, City University of New York, NY; United States of America b Also at California State University, East Bay; United States of America c Also at Centre for High Performance Computing, CSIR Campus, Rosebank, Cape Town; South Africa d Also at CERN, Geneva; Switzerland e Also at CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille; France f Also at Département de Physique Nucléaire et Corpusculaire, Université de Genève, Genève; Switzerland g Also at Departament de Fisica de la Universitat Autonoma de Barcelona, Barcelona; Spain h Also at Departamento de Fı́sica Teorica y del Cosmos, Universidad de Granada, Granada (Spain); Spain i Also at Department of Applied Physics and Astronomy, University of Sharjah, Sharjah; United Arab Emirates j Also at Department of Financial and Management Engineering, University of the Aegean, Chios; Greece k Also at Department of Physics and Astronomy, University of Louisville, Louisville, KY; United States of America l Also at Department of Physics and Astronomy, University of Sheffield, Sheffield; United Kingdom m Also at Department of Physics, California State University, Fresno CA; United States of America n Also at Department of Physics, California State University, Sacramento CA; United States of America – 48 – o Also at Department of Physics, King’s College London, London; United Kingdom p Also at Department of Physics, St. Petersburg State Polytechnical University, St. Petersburg; Russia q Also at Department of Physics, Stanford University; United States of America r Also at Department of Physics, University of Fribourg, Fribourg; Switzerland s Also at Department of Physics, University of Michigan, Ann Arbor MI; United States of America t Also at Giresun University, Faculty of Engineering, Giresun; Turkey u Also at Graduate School of Science, Osaka University, Osaka; Japan 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 II. Physikalisches Institut, Georg-August-Universität Göttingen, Göttingen; Germany y Also at Institucio Catalana de Recerca i Estudis Avancats, ICREA, Barcelona; Spain JHEP04(2019)092 z Also at Institut für Experimentalphysik, Universität Hamburg, Hamburg; Germany aa Also at Institute for Mathematics, Astrophysics and Particle Physics, Radboud University Nijmegen/Nikhef, Nijmegen; Netherlands 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 Istanbul University, Dept. of Physics, Istanbul; Turkey ah Also at Joint Institute for Nuclear Research, Dubna; Russia ai Also at LAL, Université Paris-Sud, CNRS/IN2P3, Université Paris-Saclay, Orsay; France aj Also at Louisiana Tech University, Ruston LA; United States of America ak Also at LPNHE, Sorbonne Université, Paris Diderot Sorbonne Paris Cité, CNRS/IN2P3, Paris; France al Also at Manhattan College, New York NY; United States of America am Also at Moscow Institute of Physics and Technology State University, Dolgoprudny; Russia an Also at National Research Nuclear University MEPhI, Moscow; Russia ao Also at Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg; Germany ap Also at School of Physics, Sun Yat-sen University, Guangzhou; China aq Also at The City College of New York, New York NY; United States of America ar Also at The Collaborative Innovation Center of Quantum Matter (CICQM), Beijing; China as Also at Tomsk State University, Tomsk, and Moscow Institute of Physics and Technology State University, Dolgoprudny; Russia at Also at TRIUMF, Vancouver BC; Canada au Also at Universita di Napoli Parthenope, Napoli; Italy ∗ Deceased – 49 –