PHYSICAL REVIEW D 99, 052009 (2019) Search for four-top-quark production in the single-lepton and opposite-sign pffiffi dilepton final states in pp collisions at s = 13 TeV with the ATLAS detector M. Aaboud et al.* (ATLAS Collaboration) (Received 7 November 2018; published 26 March 2019) A search for four-top-quark production, tt̄tt̄, is presented. It is based on proton-proton collision data pffiffiffi with a center-of-mass energy s ¼ 13 TeV collected by the ATLAS detector at the Large Hadron Collider during the years 2015 and 2016, corresponding to an integrated luminosity of 36.1 fb−1 . Data are analyzed in both the single-lepton and opposite-sign dilepton channels, characterized by the presence of one or two isolated electrons or muons with high-transverse momentum and multiple jets. A data- driven method is used to estimate the dominant background from top-quark pair production in association with jets. No significant excess above the Standard Model expectation is observed. The result is combined with the previous same-sign dilepton and multilepton searches carried out by the ATLAS Collaboration and an observed (expected) upper limit of 5.3 (2.1) times the four-top-quark Standard Model cross section is obtained at 95% confidence level. Additionally, an upper limit on the anomalous four-top-quark production cross section is set in the context of an effective field theory model. DOI: 10.1103/PhysRevD.99.052009 I. INTRODUCTION function (PDF) uncertainties of the order of 30% and 6%, respectively [16,17]. Previous searchespfor ffiffiffi four-top-quark With a mass close to the scale of electroweak symmetry production using LHC Run 2 data at s ¼ 13 TeV were breaking, the top quark, besides having a large coupling performed by both the ATLAS [18–20] and CMS [21–24] to the Standard Model (SM) Higgs boson [1,2], is Collaborations. Among them, the most sensitive one is a predicted to have large couplings to new particles CMS search [24] obtaining an observed (expected) hypothesized in many models beyond the Standard 95% confidence level (C.L.) upper limit of 4.5 (2.3) times Model (BSM) [3–5]. Possible new phenomena may the SM expectation. Searches for anomalous tt̄tt̄ produc- enhance the cross sections over SM predictions for tion via an EFT model were recently performed by the various processes involving top quarks, and in particular ATLAS Collaboration [19,20], which set an observed for the production of four top quarks [6–15]. This paper (expected) upper limit of 16 fb (31 fb) on the production focuses on a search for four-top-quark (tt̄tt̄) production cross section at 95% C.L. via the SM processes in proton-proton (pp) collisions at The four-top-quark production events can give rise to the Large Hadron Collider (LHC) and the results are different final states depending on the hadronic or interpreted in the context of an effective field theory semileptonic decay mode of each of the top quarks. The (EFT) approach where the BSM contribution is repre- four-top-quark decay topology considered in this search sented via a four-top-quarks contact interaction [10]. corresponds to either single-lepton events with one isolated The SM four-top-quark production cross section (σ tSM t̄tt̄ ) in pffiffiffi charged lepton (electron or muon)1 or dilepton events with pp collisions at a center-of-mass energy s ¼ 13 TeV is two opposite-sign charged leptons (electrons or muons). predicted to be σ tSMt̄tt̄ ¼ 9.2 fb at next-to-leading-order The event topology also features high jet multiplicity and (NLO) accuracy in QCD, with scale and parton distribution high multiplicity of jets containing b-hadrons. Signal events are characterized by high scalar sum of the jet * transverse momenta (HhadT ), which provides good discrimi- Full author list given at the end of the article. nation against the dominant background, i.e., top-quark Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to The τ-leptons are not reconstructed; however, isolated elec- 1 the author(s) and the published article’s title, journal citation, tron and muon by-products of the leptonically decaying τ-leptons and DOI. Funded by SCOAP3. are considered in the analysis. 2470-0010=2019=99(5)=052009(36) 052009-1 © 2019 CERN, for the ATLAS Collaboration M. AABOUD et al. PHYS. REV. D 99, 052009 (2019) pair production in association with jets (tt̄ þ jets). Given tiles or liquid argon as the active medium and with steel, that the four-top-quark production events are expected to copper or tungsten as the absorber material. An extensive contain hadronically decaying top-quark candidates with muon spectrometer with an air-core toroid magnet system collimated or partially collimated topologies, the analysis surrounds the calorimeters. It includes three layers of makes use of “mass-tagged reclustered large-R (RCLR) high-precision tracking chambers, which provide cover- jets” [25,26], which will be described in Sec. III. age in the range jηj < 2.7. The field integral of the toroid Selected events in each of the two channels are classified magnets ranges from 2.0 to 6.0 Tm across most of the into several categories according to the number of jets, detector. A two-level trigger system [30], the first level b-tagged jets and mass-tagged RCLR jets. A data-driven using custom hardware and followed by a software-based method is developed to estimate the dominant tt̄ þ jets level, is used to reduce the event rate to a maximum of background. The Monte Carlo (MC) simulation is used in around one kHz for offline storage. order to estimate correction factors and evaluate the systematic uncertainties of the data-driven estimate. III. OBJECT AND EVENT SELECTION The paper is organized as follows: the ATLAS detector is described in Sec. II. Section III summarizes the selection pffiffiEvents ffi are selected from pp collisions with criteria applied to events and reconstructed objects. The s ¼ 13 TeV recorded by the ATLAS detector in 2015 simulation-based signal and background modeling, and 2016. Only events for which all relevant subsystems together with the data-driven estimation of nonprompt were operational are considered. The data set corresponds and fake lepton backgrounds are discussed in Sec. IV. to an integrated luminosity of 36.1  0.8 fb−1 [31]. The Section V is devoted to the search strategy and classifica- event reconstruction is affected by multiple inelastic pp tion of event topologies, while the tt̄ þ jets background collisions in a single bunch crossing and by collisions estimation technique using data is described in Sec. VI. The in neighboring bunch crossings, referred to as “pileup.” systematic uncertainties are summarized in Sec. VII. The number of interactions per bunch crossing in this data Section VIII presents the results and the combination with set ranges from about 8 to 45 interactions. Events are the same-sign dilepton and multilepton final-states search required to have at least one reconstructed vertex with two [20] carried out by ATLAS. or more associated tracks with transverse momentum pT > 0.4 GeV. If multiple vertices are reconstructed, the vertex with the largest sum of the squares of the transverse II. ATLAS DETECTOR momenta of associated tracks is taken as the primary The ATLAS detector [27] at the LHC is a multipurpose vertex [32]. particle detector with a forward-backward symmetric Events in both the single-lepton and dilepton channels cylindrical geometry and nearly 4π coverage in solid were recorded using single-lepton triggers. Events were angle.2 It consists of an inner tracking detector (ID), selected using triggers with either low pT thresholds and a electromagnetic and hadronic calorimeters, and a muon lepton-isolation requirement, or with higher thresholds but spectrometer. The inner detector, including the insertable with a looser identification criterion and without any B-layer [28,29], provides charged-particle tracking from isolation requirement. The lowest pT threshold used for silicon pixel and microstrip detectors in the pseudora- muons is 20 (26) GeV in 2015 (2016), while the higher pT pidity region jηj < 2.5, surrounded by a transition radi- threshold is 50 GeV in both years. For electrons, triggers ation tracker that enhances electron identification in the with a pT threshold of 24 (26) GeV in 2015 (2016) and region jηj < 2.0. The ID is surrounded by a thin super- isolation requirements are used along with triggers with a conducting solenoid providing an axial 2 T magnetic 60 GeV threshold and no isolation requirement, and with a field, and by a fine-granularity lead/liquid-argon electro- 120 (140) GeV threshold with looser identification criteria. magnetic calorimeter covering jηj < 3.2, which provides Electron candidates are reconstructed [33,34] from an energy measurements of electromagnetic showers. isolated electromagnetic calorimeter energy deposit, Hadron calorimetry is also based on the sampling matched to a track in the ID, within the fiducial region technique and covers jηj < 4.9, with either scintillator of jηcluster j < 2.47, where ηcluster is the pseudorapidity of the calorimeter energy deposit associated with the electron 2 ATLAS uses a right-handed coordinate system with its origin candidate. Candidates within the transition region between at the nominal interaction point (IP) in the center of the detector. the barrel and end cap electromagnetic calorimeters, The positive x axis is defined by the direction from the IP to the 1.37 < jηcluster j < 1.52, are excluded. The electron candi- center of the LHC ring, with the positive y axis pointing dates are required to have pT > 30 GeV and to satisfy upwards, while the beam direction defines the z axis. Cylindrical “tight” likelihood-based identification criteria [34] based coordinates (r, ϕ) are used in the transverse plane, ϕ being the azimuthal angle around the z axis. The pseudorapidity η is on calorimeter, tracking and combined variables that defined in terms of the polar angle θ by η ¼ − ln tanðθ=2Þ. provide good separation between electrons and jets. Unlesspstated otherwise, ffi the angular distance is defined as ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Muon candidates are reconstructed [35] by combining ΔR ≡ ðΔηÞ2 þ ðΔϕÞ2 . tracks reconstructed in both the ID and the muon 052009-2 SEARCH FOR FOUR-TOP-QUARK PRODUCTION IN THE … PHYS. REV. D 99, 052009 (2019) spectrometer. Candidates are required to pass the selection is within ΔRy ¼ 0.4 of an electron, the electron “medium” identification criteria [35] and to have is discarded. The overlap removal procedure between the pT > 30 GeV and jηj < 2.5. To reduce the contribution remaining jet candidates and muon candidates is designed from nonprompt leptons (e.g., from semileptonic b- or to remove those muons that are likely to have arisen in c-hadron decays), photon conversions and hadrons, lepton the decay chain of hadrons and to retain the overlapping candidates are also required to be isolated. The lepton jet instead. Jets and muons may also appear in close isolation is estimated using the scalar sum P oftrk all tracks proximity. For example, a muon with high-pT brems- excluding the lepton candidate itself (I R ¼ pT ) within a strahlung radiation inside the calorimeter may be recon- cone defined by ΔR < Rcut along the direction of the lepton. structed as a jet. In such cases, the jet should be removed The value of Rcut is the smaller of rmin and 10 GeV=plT , and the muon retained. Such jets are characterized by where rmin is set to 0.2 (0.3) for electron (muon) candidates, having very few matching inner-detector tracks. Selected and plT is the lepton pT . All lepton candidates are required to muons that satisfy ΔRðμ; jetÞ < 0.04 þ 10 GeV=pμT are satisfy I R =plT < 0.06. Finally, lepton tracks must match the rejected if the jet has at least three tracks originating from primary vertex of the event: the longitudinal impact param- the primary vertex; otherwise the jet is removed and the eter z0 is required to satisfy jz0 sin θj < 0.5 mm, where θ is muon is kept. the polar angle of the track. The transverse impact parameter The selected and calibrated small-R jets with significance jd0 j=σðd0 Þ must be less than 5 for electrons and pT > 25 GeV and passing both the requirement on the 3 for muons, where d0 is the transverse impact parameter and JVT and the overlap removal with leptons are used as inputs σðd0 Þ is its uncertainty. for further jet reclustering [25] using the anti-kt algorithm Jet candidates are reconstructed from three-dimensional with a radius parameter of R ¼ 1.0. These reclustered topological energy clusters [36] in the calorimeter using the large-R jets are referred to as RCLR jets. The calibration anti-kt jet algorithm [37–39] with a radius parameter of 0.4, corrections and uncertainties in the RCLR jets are automati- and these are referred to as “small-R jets”. Each topological cally inherited from the small-R jets [26]. In order to further cluster is calibrated to the electromagnetic energy scale suppress contributions from pileup and other soft radiation, prior to jet reconstruction [40]. The reconstructed jets are the RCLR jets are trimmed [49] by removing all small-R jets then calibrated to the particle level by the application of a within a reclustered jet that have pT below 5% of the pT jet energy scale derived from simulation [41]. After energy of the reclustered jet. Due to the pileup suppression and calibration, jets are required to satisfy the pT > 25 GeV pT > 25 GeV requirements made on the small-R jets, the and jηj < 2.5 selection. Quality criteria are imposed to probability for a small-R jet to be removed from the identify jets arising from noncollision sources or detector corresponding reclustered jet by the trimming requirement noise and any event containing such a jet is removed [42]. is less than 1%. The resulting RCLR jets are used to identify Finally, to reduce the effect of pileup, an additional hadronically decaying top-quark candidates. RCLR jets that requirement is made on the jet vertex tagger (JVT) have pT > 200 GeV, jηj < 2.0, mass4 larger than 100 GeV discriminant [43] for jets with pT < 60 GeV and jηj < 2.4. and at least one constituent small-R jet are referred to as Jets are tagged as containing a b-hadron via a multi- “mass-tagged RCLR jets.” variate b-tagging algorithm [44,45]. For each jet, a value The missing transverse momentum in the event, whose for the multivariate b-tagging discriminant is calculated, magnitude will be denoted in the following by Emiss T , is and the jet is considered b-tagged if this value is above a defined as the negative vector sum of the pT of recon- given threshold. The threshold used in this search corre- structed and calibrated objects in the event, where only sponds to an average 77% efficiency to tag a jet containing primary objects enter the sum (e.g., RCLR jets are not a b-hadron, with a light-jet rejection factor of ∼134 and a used). This sum includes a term to account for energy from charm-jet rejection factor of ∼6, as determined for jets with low-momentum particles in the event that are not associated pT > 20 GeV and jηj < 2.5 in simulated tt̄ events [46–48]. with any of the selected objects, which is calculated from To avoid assigning a single detector response to more inner detector tracks matched to the reconstructed primary than one reconstructed object, a sequential overlap-removal vertex in the event [50]. procedure is adopted. Electron candidates that lie within Events of interest are required to have at least one ΔR ¼ 0.01 of a muon candidate are removed to suppress reconstructed lepton that matches, within ΔR < 0.15, the contributions from bremsstrahlung. To prevent double- lepton with the same flavor reconstructed by the trigger counting of electron energy deposits ffi as jets, the closest pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi algorithm. Events in the opposite-sign dilepton channel jet within ΔRy ¼ ðΔyÞ2 þ ðΔϕÞ2 ¼ 0.2 of a selected are retained if they contain exactly two opposite-sign electron is removed.3 If the nearest jet surviving that charged leptons (electrons or muons) and at least four jets satisfying the quality and kinematic criteria discussed The rapidity is defined as y ¼ 12 ln Eþp 3 z E−pz , where E is 4 the energy and pz is the longitudinal component of the The reclustered jet mass is computed from the sum of the momentum along the beam pipe. four-momenta of the associated small-R jets [26]. 052009-3 M. AABOUD et al. PHYS. REV. D 99, 052009 (2019) TABLE I. Summary of preselection requirements for the single- of hadronic objects (jets, hadrons) as leptons or the lepton and dilepton channels. Here mW T is the transverse mass of presence of a nonprompt electron or muon. These events the lepton and the Emiss T vector, and mll denotes the dilepton are referred to as the “fake and nonprompt lepton” back- invariant mass in the ee and μμ channels. ground in the remainder of this paper. MC simulation samples are used to model the expected Preselection requirements distributions of the signal and most of the background Requirement Single-lepton Dilepton processes. The fake and nonprompt lepton background in Trigger Single-lepton triggers the single-lepton channel is estimated with a fully data- Leptons 1 isolated 2 isolated, opposite-sign driven method. The tt̄ þ jets background, which is dom- Jets ≥ 5 jets ≥ 4 jets inant in regions with very high jet and b-jet multiplicities, b-tagged jets ≥ 2 b-tagged jets is estimated via a dedicated data-driven method, with Other Emiss T > 20 GeV mll > 50 GeV some correction factors taken from the MC simulation, as ET þ m W miss T > 60 GeV jm ll − 91 GeVj > 8 GeV described in Sec. VI. The MC samples were processed either through the full ATLAS detector simulation [51] above, of which at least two must be b-tagged. In both the based on GEANT4 [52], or through a faster simulation ee and μμ channels, the dilepton invariant mass (mll ) making use of parametrized showers in the calorimeters must be above 50 GeV and outside the Z mass window [53]. To model the effects of pileup, events from mini- 83–99 GeV. Events not in the opposite-sign dilepton mum-bias interactions were generated using the PYTHIA channel may enter the single-lepton channel if they 8.186 [54] event generator and overlaid on the simulated contain exactly one lepton and at least five jets, of which hard-scatter events according to the luminosity profile of at least two are b-tagged. The above selection criteria the recorded data. All simulated samples were processed imply that events containing two leptons with the same through the same reconstruction algorithms and analysis charge, or three or more leptons of any charge are chain as the data. In the simulation, the top-quark mass excluded from the selection. This is done in order to was assumed to be mtop ¼ 172.5 GeV. The heavy-flavor maintain orthogonality with the complementary search decays were modeled using the EVTGEN 1.2.0 [55] with same-sign dilepton and multilepton final states [20] program, except for processes modeled using the carried out by ATLAS, as these results are combined with SHERPA generator [56]. the results presented here (Sec. VIII). Additional require- ments are made to suppress the background from multijet A. Signal modeling production in the single-lepton channel. Requirements are Simulated events for the main signal process, i.e., the made on Emiss T as well as on the transverse mass of the four-top-quark production with SM kinematics, were combined lepton and Emiss system5 (mW T ): ET > 20 GeV miss T generated at leading order (LO) with the MADGRAPH5_ and ET þ mT > 60 GeV. miss W aMC@NLO 2.2.2 [17] generator and the NNPDF2.3 LO The above requirements are referred to as “preselection” PDF set [57], interfaced to PYTHIA 8.186 using the A14 and are summarized in Table I. Events satisfying either the set of tuned parameters [58], which will be denoted in the single-electron or single-muon selections are combined and following by A14 tune. The SM tt̄tt̄ sample is normalized treated as a “single-lepton” analysis channel, and events to a cross section of 9.2 fb, computed at NLO in satisfying any of the opposite-sign lepton selections (ee, μμ QCD [17]. or eμ) are combined and treated as a “dilepton” analysis This search also probes a BSM model with kinematic channel. characteristics similar to those of the SM tt̄tt̄ events: the tt̄tt̄ IV. SIGNAL AND BACKGROUND MODELING production via an effective field theory involving a four- fermion contact interaction [10]. The EFT tt̄tt̄ sample was After the event preselection, the main background generated at LO with the MADGRAPH5_aMC@NLO 2.2.2 processes arise from the SM production of tt̄ þ jets and generator and the NNPDF2.3 LO PDF set, interfaced to single top-quarks, as well as W- or Z-boson production in PYTHIA 8.186 with the A14 tune. It is normalized assuming association with jets. Small contributions arise from the jC4t j=Λ2 ¼ 4π TeV−2 , where C4t denotes the coupling associated production of a vector boson V (V ¼ W, Z) or a constant and Λ the energy scale of new physics, which Higgs boson and a tt̄ pair (tt̄ þ V and tt̄ þ H) and from yields a cross section of 926.3 fb computed using diboson (WW, WZ, ZZ) production. Multijet events MADGRAPH5_aMC@NLO. Details of this BSM scenario contribute to the selected sample via the misidentification can be found in Refs. [19,20]. pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 5 mWT ¼ 2plT Emiss l T ð1 − cos ΔϕÞ, where pT is the transverse B. Background modeling momentum of the lepton and Δϕ is the azimuthal angle separation between the lepton and the direction of the missing The dominant tt̄ þ jets background estimation relies transverse momentum. on the data-driven technique described in Sec. VI. The 052009-4 SEARCH FOR FOUR-TOP-QUARK PRODUCTION IN THE … PHYS. REV. D 99, 052009 (2019) validation of this technique and the extraction of the Samples of tt̄ þ V (with V ¼ W or Z, including corresponding correction factors were performed with nonresonant Z=γ  contributions) were generated with simulated MC tt̄ þ jets events, generated with POWHEG- MADGRAPH5_aMC@NLO 2.3.2, using NLO in QCD BOX v2 [59–62], which provides NLO accuracy in QCD matrix elements and the NNPDF3.0NLO [87] PDF set. for the tt̄ process and uses the CT10 PDF set [63]. Showering was performed using PYTHIA 8.210 and the A14 Showering was performed using PYTHIA 6.428 [64] with tune. The tt̄ þ V events are normalized to their NLO cross the CTEQ6L PDF set [65] and the PERUGIA2012 tune section [17]. A sample of tt̄ þ H events was generated [66]. The hard-process factorization scale μF and renorm- using MADGRAPH5_aMC@NLO 2.3.2 generator and the alization scale μR were qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi set to the default POWHEG value: NNPDF3.0NLO PDF set. Showering was performed using μ¼ m2top þ p2T;top , where pT;top is the transverse momen- PYTHIA 8.210 and the A14 tune. Inclusive decays of the Higgs boson are assumed in the generation of the tt̄ þ H tum of the top quark in the tt̄ center-of-mass reference sample, which is normalized to the corresponding cross frame. The POWHEG model resummation damping param- section calculated at NLO [88,89]. Rare backgrounds, such eter, hdamp , which controls the matching of matrix elements as tt̄ þ WW and triple-top-quark production (tt̄ þ t, to parton showers and regulates the high-pT parton radi- tt̄ þ tW), were generated at LO with MADGRAPH5_ ation, was set to mtop [67]. The sample is normalized to the aMC@NLO 2.2.2 with no additional partons and interfaced theoretical cross-section value for the inclusive tt̄ process of with PYTHIA 8.186. They are normalized using cross 832þ40 −46 pb obtained with TOP++ [68], calculated at next-to- sections computed at NLO in QCD [17,90]. next-to-leading order (NNLO) in QCD, and including resummation of next-to-next-to-leading logarithmic soft C. Estimation of nonprompt gluon terms [69–73]. and fake lepton backgrounds Samples of W=Z þ jets events were generated with the SHERPA 2.2 [56] generator. The matrix element calculation In the single-lepton channel, the background from was performed with up to two partons at NLO in QCD and events with a fake or nonprompt lepton is estimated up to four partons at LO using matrix elements from COMIX from data using a “matrix method” technique [91,92]. [74] and OPENLOOPS [75]. The matrix element calculation Events are selected using looser isolation or identification was merged with the SHERPA [76] parton shower (PS) using requirements for the lepton and are then weighted the ME þ PS@NLO prescription [77]. The PDF set used according to the efficiencies for both prompt and back- for the matrix element calculation is NNPDF3.0nnlo with ground (fake and nonprompt) leptons to pass the tighter a dedicated PS tuning developed by the SHERPA authors. default selection. These efficiencies are measured in data The W þ jets and Z þ jets samples are normalized to their using dedicated control regions. The contribution from inclusive production cross section estimates at NNLO in events with a fake or nonprompt lepton is found to be QCD, calculated with FEWZ [78,79]. consistent with zero in regions defined by the presence of Samples of single-top-quark backgrounds, correspond- two or more mass-tagged RCLR jets, as well as in the ing to the Wt and s-channel production mechanisms, were regions requiring the presence of at least one mass-tagged generated with POWHEG-BOX v1 [80] at NLO accuracy RCLR jet and at least four b-tagged jets. The contribution using the CT10 PDF set. Overlaps between the tt̄ and Wt is at most 6% in the rest of the signal regions (described final states were removed using the “diagram removal” in Sec. V). scheme [81]. Samples of t-channel single-top-quark events In the dilepton channel, the majority (90%) of events were generated using the POWHEG-BOX v1 [82,83] NLO containing one prompt lepton and one background lepton, generator that uses the four-flavor scheme. The fixed four- arising from either a heavy-flavor hadron decay, photon flavor PDF set CT10f4 [63] was used for the matrix conversion, jet misidentification or light-meson decay, element calculations. Showering was performed using originate from the single-lepton tt̄ þ jets background. PYTHIA 6.428 with the PERUGIA2012 tune. The single- This contribution is included in the estimation via the top-quark samples are normalized to the approximate data-driven technique described in Sec. VI, while the NNLO cross sections [84–86]. small fraction of fake and nonprompt leptons events Diboson processes with one of the bosons decaying arising from W þ jets and tt̄ þ V events is estimated hadronically and the other leptonically were simulated from MC simulation.6 The total contribution is found to using the SHERPA 2.1.1 generator. They were calculated be less than 8% of the total background in the signal for up to one (ZZ) or zero (WW, WZ) additional partons at regions. NLO, and up to three additional partons at LO, using the same procedure as for W=Z þ jets. The CT10 PDF set was used together with a dedicated PS tuning of the SHERPA 6 No data-driven estimation was attempted due to the statistical fragmentation model. All diboson samples are normalized overlap with the same-sign dilepton and multilepton final-states to their NLO cross sections provided by SHERPA. search [20]. 052009-5 M. AABOUD et al. PHYS. REV. D 99, 052009 (2019) V. SEARCH STRATEGY Twelve regions in the single-lepton channel and eight regions in the dilepton channel with the largest signal-to- Signal events from SM four-top-quark production in the background ratios (up to 5.7% in the single-lepton channel single lepton (opposite-sign dilepton) decay channel are and 7.0% in the dilepton channel), assuming SM tt̄tt̄ characterized by the presence of one charged lepton (two production cross section and kinematics, are referred to opposite-sign charged leptons), missing transverse momen- as signal regions. These regions are included in the tum from the escaping neutrino(s) and a high number of simultaneous fit to extract the signal cross section and high-pT jets. At LO the single-lepton (opposite-sign have high jet multiplicities (≥ 9j and ≥ 7j for single-lepton dilepton) decay will potentially have an event topology and dilepton respectively) and high b-tagged jet multiplic- with ten (eight) jets, when each parton from a top-quark ities (≥ 3b). Since events from the main tt̄ þ jets back- decay gives rise to a separate jet: six (four) jets are light-jets ground are characterized by at most one hadronically and four are b-quark jets. However, the topology of a decaying top quark in the single-lepton channel and no reconstructed event could differ due to the limited detector hadronically decaying top quarks in the dilepton channel, acceptance, the b-tagging efficiency, and the possible the signal regions are split into 0, 1 and ≥ 2J in the single- presence of jets arising either from additional radiation lepton case, and into 0 and ≥ 1J in the dilepton case. and multiple parton interactions (MPI) or from collimated Twelve validation regions in the single-lepton channel partons not resolved as separate objects. Events are and four validation regions in the dilepton channel are classified in several regions to optimize the sensitivity of defined. These regions do not overlap with the signal the search, to perform a data-driven estimate of the tt̄ þ jets region selections and feature low expected signal-to- background (described in Sec. VI) and to validate the background ratios (less than 1%). They are not included background prediction. in the fit nor used to extract information from the data. Preselected events in each of the two channels are These regions are designed primarily to validate the data- classified according to their event topology, defined by the driven estimate of the tt̄ þ jets background (introduced in number of jets with pT > 25 GeV and the number of Sec. VI) and to confirm the validity of the assumption that b-jets. Several regions are split according to the mass- the tt̄ þ jets data-driven estimate can be extrapolated to tagged RCLR jet multiplicity in addition to the jet and the signal regions. The validation regions in the single- b-tagged jet multiplicities. In the following, a region with lepton channel contain exactly seven or exactly eight jets m jets (j), of which n are b-tagged jets (b) and from which of which three or at least four are b-tagged. In the p separate mass-tagged RCLR jets (J) are reconstructed is dilepton channel, the validation regions have exactly six referred to as “mj, nb, pJ.” When no mass-tagged RCLR jets of which three or at least four are b-tagged. In each jet multiplicity is specified, no selection on these objects is of the two channels these validation regions are split performed. according to the mass-tagged RCLR jet multiplicity in the The following regions are defined to be orthogonal using same way as the corresponding signal regions. the classification described above: 20 “signal regions,” 16 With the goal of estimating the tt̄ þ jets background in “validation regions,” 18 “source regions” and 2 “efficiency the signal regions, data events with lower jet and/or b-jet extraction regions,” as shown in Fig. 1. (a) (b) FIG. 1. Schematic view of the different analysis regions in (a) the single-lepton and (b) the dilepton channels. The three axes represent the jet multiplicity, the b-tagged jet multiplicity and the mass-tagged RCLR jet multiplicity. The efficiency extraction region in each channel is defined inclusively in the mass-tagged RCLR jet multiplicity. 052009-6 SEARCH FOR FOUR-TOP-QUARK PRODUCTION IN THE … PHYS. REV. D 99, 052009 (2019) 0.7 ATLAS Simulation ATLAS Simulation 0.6 s = 13 TeV, Single lepton, ≥ 5 j, ≥ 2 b s = 13 TeV, Dilepton, ≥ 4 j, ≥ 2 b 0.6 Total background Total background 0.5 0.5 t ttt (SM) t ttt (SM) Fraction / bin Fraction / bin t ttt via CI tttt via CI 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0 0 5 6 7 8 9 10 11 12 13 14 15 4 5 6 7 8 9 10 11 12 13 14 Number of jets Number of jets (a) (b) 1 0.9 ATLAS Simulation ATLAS Simulation s = 13 TeV, Single lepton, ≥ 5 j, ≥ 2 b s = 13 TeV, Dilepton, ≥ 4 j, ≥ 2 b 0.8 Total background 0.8 Total background 0.7 t ttt (SM) tttt (SM) Fraction / bin Fraction / bin 0.6 t ttt via CI 0.6 tttt via CI 0.5 0.4 0.4 0.3 0.2 0.2 0.1 0 0 2 3 4 5 6 7 2 3 4 5 6 7 Number of b-jets Number of b-jets (c) (d) FIG. 2. (a), (b) The jet multiplicity and (c), (d) the b-jet multiplicity distributions after preselection for the total predicted background with the tt̄ þ jets background estimated via MC simulation (shaded histogram) and the signal scenarios considered in this search in the single-lepton (a), (c) and the dilepton (b), (d) channels. The signals shown correspond to four-top-quark production with SM kinematics (solid) and tt̄tt̄ production involving a four-fermion contact interaction (dashed). The distributions are normalized to unit area. The last bin in each distribution contains the overflow. multiplicities are used in the data-driven method single-lepton and dilepton channels. The distributions described in Sec. VI. The 18 source regions are built shown are for the total predicted background, with the using events with high jet multiplicity: 7, 8, 9, ≥ 10 for tt̄ þ jets background estimated via MC simulation, and for the single-lepton channel and 6, 7, ≥ 8 for the dilepton the considered four-top-quark signal scenarios. Figures 3(a) channel, out of which exactly 2 jets are b-tagged. They and 3(b) show the same distributions but for the mass- are used to build pseudodata event samples in the signal tagged RCLR jet multiplicity. and validation regions with same jet multiplicities but Figures 3(c) and 3(d) compare the expected shapes of higher number of b-tagged jets. Efficiency extraction the scalar sum of the jet transverse momenta, considering regions are characterized by lower jet multiplicities: five all selected jets (H had T ), between the different four- or six jets for the single-lepton channel and four or five top-quark signal scenarios and the total predicted back- for the dilepton channel, out of which 2, 3 or ≥ 4 are ground. Given the different kinematic features, the H hadT b-tagged. They are used to extract the b-tagging proba- distribution provides a suitable discrimination between bilities, since they provide a sample depleted of signal events from the signal hypotheses and the background, and dominated by tt̄ þ jets. Neither the efficiency extrac- and is used as the main discriminating variable in each tion regions nor the source regions are included in the of the regions. The signal-to-background discrimination final fit to data. is therefore provided by the combination of the Figure 2 shows the expected shapes of the jet and event categorization and the Hhad T distribution in each b-jet multiplicity distributions after preselection in the category. 052009-7 M. AABOUD et al. PHYS. REV. D 99, 052009 (2019) 1 ATLAS Simulation 1 ATLAS Simulation s = 13 TeV, Single lepton, ≥ 5 j, ≥ 2 b s = 13 TeV, Dilepton, ≥ 4 j, ≥ 2 b 0.8 Total background Total background 0.8 t ttt (SM) tttt (SM) Fraction / bin Fraction / bin 0.6 t ttt via CI t ttt via CI 0.6 0.4 0.4 0.2 0.2 0 0 0 1 2 3 4 0 1 2 3 4 Number of mass-tagged jets (J) Number of mass-tagged jets (J) (a) (b) 0.6 0.5 ATLAS Simulation ATLAS Simulation s = 13 TeV, Single lepton, ≥ 5 j, ≥ 2 b s = 13 TeV, Dilepton, ≥ 4 j, ≥ 2 b 0.5 0.4 Total background Total background t ttt (SM) 0.4 tttt (SM) Fraction / bin Fraction / bin 0.3 tttt via CI tt tt via CI 0.3 0.2 0.2 0.1 0.1 0 0 0 500 1000 1500 2000 2500 3000 0 500 1000 1500 2000 2500 3000 Hhad T [GeV] H had T [GeV] (c) (d) FIG. 3. (a), (b) The mass-tagged RCLR jet multiplicity distributions and (c), (d) the H had T distributions after preselection for the total predicted background with the tt̄ þ jets background estimated via MC simulation (shaded histogram) and signals for the single-lepton (a), (c) and the dilepton (b), (d) channels. The signals shown correspond to four-top-quark production with SM kinematics (solid) and tt̄tt̄ production involving a four-fermion contact interaction (dashed). The distributions are normalized to unit area. The last bin in each distribution contains the overflow. VI. tt̄ + jets BACKGROUND ESTIMATION by TRFtt̄. The method assumes that the probability of USING DATA: THE TRFtt̄ METHOD b-tagging an additional7 jet in a tt̄ þ jets event, where the additional jets can include c- and b-jets, is essentially The MC simulation-based approach at NLO accuracy independent of the number of additional jets. With this in QCD for the prediction of the inclusive tt̄ background is assumption, the tagging probability, as a function of the not expected to model well the very high jet and b-jet kinematic properties of the jet, can be estimated in lower multiplicity regions exploited in this search. Given the lack jet-multiplicity events and then applied to data events with of multileg calculations, the MC simulation-based approach the same jet multiplicity as signal-region events, but lower relies on the description of such large multiplicities through b-tagged jet multiplicity, where the signal contamination is the parton-shower formalism with consequently large negligible. These b-tagging probabilities are measured and uncertainties. Therefore, a data-driven method is used to applied as a function of some of the jet and event properties. estimate the dominant background from tt̄ þ jets in regions Simulation-based corrections are then applied in order to with very high jet and b-jet multiplicities. This method correct for the fact that the assumptions stated above may provides a more accurate prediction of this background than a purely simulation-based approach and avoids the need to not be completely valid. Systematic uncertainties in these estimate modeling uncertainties (documented in Sec. VII) corrections are propagated through the final estimate. by extrapolation from kinematic regimes with different numbers of jets and b-tagged jets. The estimate is based on a method introduced in 7 Additional refers to all jets in addition to the bðb̄Þ-jets Ref. [93] and is referred to as “tag rate function for originating from the tt̄ decay. This includes the jets possibly tt̄ þ jets events,” which will be denoted in the following originating from hadronically decaying W bosons. 052009-8 SEARCH FOR FOUR-TOP-QUARK PRODUCTION IN THE … PHYS. REV. D 99, 052009 (2019) 0.08 0.06 ATLAS ε ≥b2b 0.04 s = 13 TeV, 36.1 fb-1 Single Lepton 0.02 Dilepton 0.08 0.06 ε b≥3b 0.04 0.02 0 50 100 150 200 250 5 10 15 20 p jet [GeV] jet,jet Δ R min ×N jet T FIG. 4. Values of the per-jet b-tagging probability for tt̄ þ jets events as a function of the jet transverse momentum (pjet T ) and the minimum ΔR between the considered jet and the other jets in the event, scaled by the jet multiplicity (ΔRjet;jet min × N jet ), as measured in data requiring the presence of at least two b-jets (ε≥2b b ) and least three b-jets (ε≥3b b ). b-tagging probabilities are shown separately for single-lepton and dilepton events. The vertical error bars indicate the statistical uncertainties, while the shaded (hatched) areas indicate the combined statistical and systematic uncertainties. In the dilepton case, a constant b-tagging probability is assumed as a function of ΔRjet;jet min × N jet for ε≥3b b . The per-jet b-tagging probabilities εb are measured in Figure 4 shows the measured values of the b-tagging the efficiency extraction regions (described in Sec. V), probability in the single-lepton and dilepton channels. It after subtracting the contribution from all non-tt̄ processes can be seen how the ε≥3b b are systematically lower than the modeled with MC simulation, amounting to 8%–14% ε≥2b b in the case of the single-lepton channel, while they of the total background, depending on the channel and on are systematically higher in the case of the dilepton the signal region considered. In order to take into account channel. This effect is due to the presence of hadronically the correlation of εb with the b-tagged jet multiplicities, decaying W bosons only in the single-lepton channel, two sets of probabilities ε≥2b b and ε≥3b b are extracted which can give rise only to light-jets or c-jets. In the separately for each of the two analysis channels. The dilepton case, when ε≥3b b is computed in the dominant measurement of ε≥2b b (ε≥3b b ) is done from events with ≥ 2 four jet multiplicity, this leaves only one jet where this (≥ 3) b-tagged jets. The two (three) b-tagged jets with the b-tagging probability can be sampled, and this jet is likely highest values of the multivariate b-tagging discriminant to be a b-jet or c-jet, neglecting the mistag probability and in the event are excluded from the computation. All considering the relative contributions of tt̄ þ single and probabilities εb are measured both as a function of jet pT double c=b through gluon splitting. This is not the case in and as a function of the quantity ΔRjet;jet jet min × N : the the single-lepton channel, where, instead, three tagged minimum distance in the η-ϕ plane between the given jets out of five can easily be the consequence of tagging jet and all the other jets in the event, multiplied by the jet a c-jet from the W boson, hence reducing the probability multiplicity8 N jet , chosen in order to take into account the of tagging an additional jet. In the dilepton case, the correlation between the b-tagging probability and the dependence on ΔRjet;jet min × N jet for the ≥ 3b selection was presence of nearby jets (see Ref. [93]). found to be compatible with a constant value within statistics. These b-tagging probabilities are then used to build 8 “pseudodata samples” in validation and signal regions: this Assuming a uniform random distribution of jets across the jet;jet is done by applying the information derived from the η-ϕ plane, ΔRmin is inversely proportional to N jet . Variables parametrizing the b-tagging probability should be chosen to be measured εb to the data in the source regions containing the mostly independent of N jet , to allow the extrapolation of the same number of jets and mass-tagged RCLR jets, account- b-tagging probabilities from low to high multiplicity regions. ing for the fact that this starting sample contains two 052009-9 M. AABOUD et al. PHYS. REV. D 99, 052009 (2019) 10000 ATLAS Single Lepton Data t t t t (SM) t t + jets (data-driven) s = 13 TeV, 36.1 fb-1 Prefit t t + H/V Non-t t Uncertainty 8000 7 j, 3 b, 0 J 8 j, 3 b, 0 J 7 j, ≥ 4 b, 0 J 8 j, ≥ 4 b, 0 J Events / bin 6000 400 1000 1500 4000 300 1000 500 200 2000 500 100 0 Data / Pred. 1.25 1 0.75 0.5 0 500 1000 0 500 1000 0 500 1000 0 500 1000 1500 H had T [GeV] H had T [GeV] H had T [GeV] H had T [GeV] (a) 2500 ATLAS Single Lepton Data t t t t (SM) t t + jets (data-driven) s = 13 TeV, 36.1 fb-1 Prefit t t + H/V Non-t t Uncertainty 2000 7 j, 3 b, 1 J 8 j, 3 b, 1 J 7 j, ≥ 4 b, 1 J 8 j, ≥ 4 b, 1 J Events / bin 1500 300 150 600 1000 200 400 100 500 100 50 200 0 Data / Pred. 1.25 1 0.75 0.5 0 500 1000 1500 0 500 1000 1500 0 500 1000 1500 0 500 1000 1500 2000 H had T [GeV] H had T [GeV] H had T [GeV] H had T [GeV] (b) ATLAS Single Lepton Data t t t t (SM) t t + jets (data-driven) 250 s = 13 TeV, 36.1 fb-1 Prefit t t + H/V Non-t t Uncertainty 200 7 j, 3 b, ≥ 2 J 8 j, 3 b, ≥ 2 J 7 j, ≥ 4 b, ≥ 2 J 8 j, ≥ 4 b, ≥ 2 J Events / bin 150 30 150 30 100 100 20 20 50 50 10 10 0 Data / Pred. 1.25 1 0.75 0.5 0 1000 2000 0 1000 2000 0 1000 2000 0 1000 2000 3000 H had T [GeV] H had T [GeV] H had T [GeV] H had T [GeV] (c) FIG. 5. Comparison between data and prediction of the Hhad T distributions in the single-lepton validation regions prior to the combined fit to data in the single-lepton and dilepton channels. The tt̄ þ jets background is estimated with the data-driven method. The tt̄ þ V and tt̄ þ H processes are denoted tt̄ þ H=V. Contributions from W=Z þ jets, single-top, diboson and multijet backgrounds are combined into a single background source referred to as “Non-tt̄”. The hashed area represents the combined statistical and systematic uncertainties of the prediction. The last bin in all figures contains the overflow. The lower panel shows the ratio between the data and the total prediction, including the SM tt̄tt̄ signal. An arrow indicates that the point is off scale. b-tagged jets [93]. The small non-tt̄ þ jets background corresponding probability. For the estimate in the 3b contribution is subtracted, analogously to the procedure categories, the procedure above is applied using only described in Ref. [94]. In this way, jets that were not b-tagging probabilities extracted from events in the ≥ 2b b-tagged in the original data sample can be promoted to region (ε≥2b b ). For the estimate in the ≥ 4b categories, a b-tagged jets in a given pseudodata events sample, with a two-step procedure is applied: the estimates in the corre- weight determined by εb, which accounts for the sponding 3b categories are used as the starting point to 052009-10 SEARCH FOR FOUR-TOP-QUARK PRODUCTION IN THE … PHYS. REV. D 99, 052009 (2019) 600 ATLAS Dilepton Data t t t t (SM) t t + jets (data-driven) s = 13 TeV, 36.1 fb-1 Prefit t t + H/V Non-t t Uncertainty 500 6 j, 3 b, 0 J 6 j, ≥ 4 b, 0 J 6 j, 3 b, ≥ 1 J 6 j, ≥ 4 b, ≥ 1 J Events / bin 400 60 300 40 10 200 40 20 5 100 20 0 Data / Pred. 1.25 1 0.75 0.5 0 500 1000 0 500 1000 0 500 1000 1500 2000 0 500 1000 1500 2000 2500 H had T [GeV] H had T [GeV] H had T [GeV] H had T [GeV] FIG. 6. Comparison between data and prediction of the Hhad T distributions in the dilepton validation regions prior to the combined fit to data in the single-lepton and dilepton channels. The tt̄ þ jets background is estimated with the data-driven method. The tt̄ þ V and tt̄ þ H processes are denoted tt̄ þ H=V. Contributions from W=Z þ jets, single-top, diboson and multijet backgrounds are combined into a single background source referred to as “Non-tt̄”. The hashed area represents the combined statistical and systematic uncertainties of the prediction. The last bin in all figures contains the overflow. The lower panel shows the ratio between the data and the total prediction, including the SM tt̄tt̄ signal. An arrow indicates that the point is off scale. apply again the same procedure, now using b-tagging systematic uncertainties, two sources of statistical probabilities extracted from events in the ≥ 3b region uncertainties are considered. The first is the statistical (ε≥3b b ). uncertainty affecting the purely data-driven estimate, due The last step of the method relies on the MC simulation to the limited numbers of data events in the source to correct the estimate in each of the considered bins and to regions. The second source comes from the MC correc- assign a set of systematic uncertainties. In order to achieve tion factor, given the limited number of simulated events this, all the steps described above are applied to MC both in the source regions and in the signal and validation simulated tt̄ þ jets events: the b-tagging probability εb is regions. extracted from simulated events in the efficiency extraction Validation regions are designed primarily to validate regions and is then used to reweight simulated events in the the TRFtt̄ data-driven estimate of the dominant tt̄ þ jets source regions, obtaining an estimate in the signal and background and confirm the validity of the assumption that validation regions. The resulting estimate in bin i of H had the estimate can be extrapolated to the signal regions. T distributions, BTRF tt̄ ;MC , is then compared with the predic- Comparisons of the HhadT distributions between data and the i tion from simulated events selected in the signal and total SM prediction (including the SM four-top-quark signal) in the validation regions prior to the fit to data validation regions, BMC i , extracting a correction factor TRFtt̄ ;MC are presented in Fig. 5 for the single-lepton channel and in for each considered bin, defined as Ci ¼ BMC i =Bi . Fig. 6 for the dilepton channel. The tt̄ þ jets background is The correction is then applied bin-by-bin to the purely data- estimated with the data-driven method, including the MC driven estimate, BTRF i tt̄ ;Data to obtain a corrected estimate in correction factors and the systematic uncertainties. Data TRFtt̄ each bin, Bi ¼ Ci × BTRF i tt̄ ;Data . These corrections agree well with the SM expectation within the uncertain- reweight Bi TRFtt̄ ;Data by less than 20% on average, varying ties, validating the overall data-driven procedure and the in magnitude region by region, and are primarily aimed to assumptions made. account for effects such as the dependence of the b-tagging probability on other jet or event properties than the ones VII. SYSTEMATIC UNCERTAINTIES used in the parametrization. Several sources of systematic uncertainties that can A full set of systematic uncertainties is then derived for affect the normalization of signal and background and the the estimate BTRF i tt̄ by repeating the described procedure shape of the Hhad distributions are considered. The T on MC simulated events with systematic variations systematic uncertainties of the data-driven estimate for applied. For each considered source of systematic uncer- the tt̄ þ jets background are propagated as described in tainty affecting the tt̄ þ jets MC prediction (see Sec. VII), Sec. VI. For each considered source of systematic a new set of correction factors C0i is derived. In this ratio, uncertainty affecting the tt̄ þ jets MC prediction, a new systematic variations ΔBi partially cancel out since C0i ¼ set of correction factors C0i is derived, by coherently TRFtt̄ ;MC ðB þ ΔBÞMC i =ðB þ ΔBÞi ≃ Ci × ½1 þ ðΔBi =Bi ÞMC − replacing the nominal MC prediction with the systematic ðΔBi =Bi ÞTRFtt̄ ;MC . The cancellation is exact for some variation in all regions. The usage of this data-driven uncertainties, e.g., overall normalization. Besides the technique to estimate the tt̄ þ jets background, as 052009-11 M. AABOUD et al. PHYS. REV. D 99, 052009 (2019) opposed to a purely simulation-based approach, allows to underlying event affecting the reconstruction of the missing reduce significantly the uncertainty on its prediction in transverse momentum are also taken into account. the high jet and b-tagged jet multiplicity topologies exploited by this search. B. Modeling uncertainties As mentioned in Sec. VI, common normalization A. Experimental uncertainties uncertainties for tt̄ þ jets that equally affect BMC and i The uncertainty in the combined 2015+2016 integrated BiTRFtt̄ ;MC have no impact on their ratios Ci , and conse- luminosity affecting the overall normalisation of all proc- quently on the total TRFtt̄ prediction. Instead, uncertainties esses estimated from the simulation is 2.1%. It is derived, in the tt̄ þ jets heavy-flavor content or kinematics can have following a methodology similar to that detailed in residual systematic effects on the TRFtt̄ prediction. Ref. [31], and using the LUCID-2 detector for the baseline Therefore, no uncertainty is assigned to the inclusive tt̄ luminosity measurements [95], from calibration of the production cross section in the search, while variations of luminosity scale using x-y beam-separation scans. This the relative fractions of tt̄ events with additional jets systematic uncertainty is applied to all processes modeled originating from b- and c- quarks, as well as comparisons using MC simulations. of tt̄ þ jets kinematics with alternative predictions, are Uncertainties associated with jets primarily arise from the considered as systematic uncertainties related to the theory jet energy scale. The jet energy scale (JES) and its uncertainty modeling of the tt̄ þ jets process, as described below. A are derived by combining information from test-beam data, categorization of tt̄ þ jets events is performed for the LHC collision data and simulation [41]. The JES uncertainty purpose of assigning systematic uncertainties associated is split into 21 uncorrelated sources, which have different with the modeling of heavy-flavor production in different dependencies on jet pT and η. In particular, three uncertain- topologies [96]. Events are categorized depending on the ties account for differences in the jet response and simulated flavor content of additional particle jets and labeled either jet composition of light-quark, b-quark, and gluon-initiated tt̄þ ≥ 1b or tt̄þ ≥ 1c, while the remaining events are jets. The flavor response uncertainties are derived by labeled as tt̄ þ light-jets events, including those with no comparing the average jet response for each jet flavor using additional jets. PYTHIA and HERWIG++. The flavor composition uncertainty Detailed comparisons of tt̄þ ≥ 1b production between is assumed to be a 50% quark and 50% gluon composition the nominal NLO POWHEG-BOX v2 + PYTHIA 6.428 tt̄ with a conservative 100% uncertainty. Uncertainties in the jet inclusive MC sample and an NLO prediction based on mass scale, the jet energy resolution and the efficiency to pass SHERPA + OPENLOOPS [56,75] (referred to as SHERPAOL) the JVT requirement are also considered. have shown that the cross sections agree within 50% [97]. The efficiency of the b-tagging algorithm is measured for Therefore, a normalization uncertainty of 50% is applied to each jet flavor using control samples in data and in the tt̄þ ≥ 1b component of the tt̄ þ jets background simulation. From these measurements, correction factors obtained from the POWHEG-BOX v2 + PYTHIA 6.428 MC simulation. In the absence of an NLO prediction are derived to match the tagging rates in the simulation for the tt̄þ ≥ 1c background, a 50% systematic uncer- [44,47,48]. Uncertainties in these corrections include a total tainty is also applied to the tt̄þ ≥ 1c component, and the of six independent sources affecting b-jets and four inde- uncertainties in the tt̄þ ≥ 1b and tt̄þ ≥ 1c background pendent sources affecting c-jets. Each uncertainty has a normalizations are taken as uncorrelated. The overall different dependence on jet pT . Seventeen uncertainties are normalization of all systematic uncertainties in the considered for the light-jet tagging, which depend on the tt̄ þ jets prediction, except these explicit uncertainties in jet pT and η. These systematic uncertainties are taken as the tt̄þ ≥ 1c and tt̄þ ≥ 1b normalizations, is fixed to the uncorrelated between b-jets, c-jets, and light-flavor jets. An nominal one and only migrations across categories and additional uncertainty is included due to the extrapolation distortions to the shape of the kinematic distributions are of these corrections to jets with pT beyond the kinematic considered. reach of the data calibration samples used (pT > 300 GeV To provide a comparison with a different parton-shower for b- and c-jets and pT > 750 GeV for light-jets) and is model, an alternative tt̄ sample was generated using the taken to be correlated among the three jet flavors. same POWHEG model setup as for the nominal sample Uncertainties associated with leptons arise from the described in Sec. IV, except the PS, hadronization, trigger, reconstruction, identification, and isolation efficien- underlying-event (UE) and MPI are simulated using cies, as well as the lepton momentum scale and resolution. HERWIG++ (version 2.7.1) [98] with the UEEE5 tune These are measured in data using leptons in Z → lþ l− and [99] and the corresponding CTEQ6L1 PDF set. To assess pffiffiffi J=ψ → lþ l− events at s ¼ 13 TeV [34,35]. the systematic uncertainties related to the use of different All uncertainties in energy scales and resolutions are models for the hard-scattering generation, while maintain- propagated to the missing transverse momentum. Additional ing the same PS model, a sample using MADGRAPH5_ small uncertainties associated with the modeling of the aMC@NLO [17] interfaced to HERWIG++ 2.7.1 was 052009-12 SEARCH FOR FOUR-TOP-QUARK PRODUCTION IN THE … PHYS. REV. D 99, 052009 (2019) generated. The effects of initial- and final-state radiation average of the theoretical uncertainties in t-, Wt- and (ISR/FSR) are explored using two alternative POWHEG- s-channel production [84–86]. BOX v2 + PYTHIA 6.428 samples, one with hdamp set to Uncertainties in the diboson background normalization 2 × mtop , the renormalization and factorization scales set to include 5% from the NLO cross sections [106], as well as half the nominal value and using the PERUGIA2012 high- an additional 24% normalization uncertainty added in variation UE tune, giving more radiation, and one with the quadrature for each additional inclusive jet multiplicity PERUGIA2012 low-variation UE tune, hdamp ¼ mtop and bin: this assumes that two of the jets originate from the the renormalization and factorization scales set to twice the W=Z decays, as in WW=WZ → lνjj. Recent comparisons nominal value, giving less radiation [100]. The μR and μF between data and SHERPA 2.1.1 for WZð→ lνllÞþ ≥ 4 scale variations and the hdamp variations are kept corre- jets show agreement within the experimental uncertainty lated, since the two proposed variations cover the full set of of approximately 40% [107], which further justifies the uncertainties obtained by changing the scales and the above uncertainties. Uncertainties in the tt̄ þ V and tt̄ þ H resummation damping parameter independently. normalizations are 15% and þ10 −13 %, respectively, from Previous studies have seen that NNLO calculations the uncertainties in their respective NLO cross sections provide better agreement with data than NLO calculation, [88,89,108,109]. particularly for the top-quark pT distribution [101]. Hence, For the determination of the SM tt̄tt̄ production signal an uncertainty in the modeling of the top-quark pT strength, no uncertainty is assigned to the theoretical cross distribution is evaluated by taking the full difference section for this process. In the extraction of the exclusion between applying and not applying the reweighting to limits on tt̄tt̄ production via BSM models, the SM tt̄tt̄ match the predictions at NNLO accuracy in QCD process is considered as background and a conservative [102,103] of the top-quark pT distribution. This uncer- 50% normalization uncertainty is assigned to the total tt̄tt̄ tainty only affects the tt̄ þ light-jets and tt̄þ ≥ 1c events, cross section, taking into account the uncertainties in both for which NNLO predictions have been derived in its production cross section and possible acceptance and literature. shape variations [16,17]. In the case of tt̄þ ≥ 1b events, an uncertainty is assigned Uncertainties in the data-driven fake or nonprompt by comparing the NLO prediction in the four-flavor scheme lepton background estimate include contributions from of tt̄þ ≥ 1b including parton shower [97] based on the limited sample size in data, particularly at high jet and SHERPAOL with the nominal NLO POWHEG-BOX v2 + b-tag multiplicities, from the uncertainty in the real and PYTHIA 6.428 inclusive tt̄ MC sample with a five-flavor fake efficiencies extracted from data in dedicated control scheme, by means of a generator-level reweighting, as regions (e.g., selected with a requirement on either the detailed in Ref. [96]. This reweighting is performed maximum Emiss T or mW T ), as well as from the extrapolation separately for each of the tt̄þ ≥ 1b subcategories in such from these control regions to the analysis regions, as a way that their inter-normalization and the shape of the detailed in Ref. [92]. Based on comparisons between data relevant kinematic distributions are at NLO accuracy, while and the total prediction in these control regions, the preserving the nominal tt̄þ ≥ 1b cross section in POWHEG- normalization uncertainties assumed for this background BOX v2 + PYTHIA 6.428. Additional uncertainties are are 50% (100%) for events with a central (forward) assessed for those contributions of tt̄þ ≥ 1b background electron, and 50% for muons, taken to be uncorrelated which are not part of the NLO prediction, namely from MPI across regions with different mass-tagged RCLR jet or FSR from top-quark decay products. They are assessed multiplicities and between electron and muon channels. via the alternative radiation samples described above. No explicit shape uncertainty is assigned due to the Uncertainties affecting the modeling of the W=Z þ jets associated large statistical uncertainties. These uncertain- background include 5% scale uncertainty from their ties are uncorrelated between bins in the final discrimi- respective normalizations to the theoretical NNLO cross nant distribution and effectively cover possible shape sections [104]. An additional 24% normalization uncer- uncertainties. tainty is added in quadrature for each additional inclusive jet-multiplicity bin, based on a comparison among different VIII. RESULTS algorithms for merging LO matrix elements and parton showers [105]. Therefore, normalization uncertainties of Following the statistical method presented below, four- 54% and 59% are assigned for events with exactly five jets top-quark production signals are searched for by perform- and at least six jets, respectively. These normalization ing a binned profile likelihood fit to the Hhad T distribution uncertainties are taken as correlated (uncorrelated) across simultaneously in the 12 signal regions in the single-lepton jet multiplicities within signal regions (efficiency extraction channel and 8 signal regions in the dilepton channel, using regions). Uncertainties affecting the modeling of the single- a total of 20 final-state topologies. The single-lepton and top-quark background include an uncertainty of þ5% and dilepton channels are combined in order to gain sensitivity −4% in the total cross section estimated as a weighted to different four-top-quark production signals. 052009-13 M. AABOUD et al. PHYS. REV. D 99, 052009 (2019) A. Statistical interpretation all bins considered in each search (namely, all H had T bins in For each search, the Hhad the 20 signal regions defined in Fig. 1). The likelihood T distributions across all regions considered are jointly analyzed to test for the presence of a function depends on the signal-strength parameter μ, a signal predicted by the benchmark scenarios. The statistical multiplicative factor that scales the number of expected interpretation uses a binned likelihood function Lðμ; θÞ signal events, and θ, a set of nuisance parameters (NPs) that constructed as a product of Poisson probability terms over encode the effect of systematic uncertainties on the signal ATLAS Single Lepton Data t t t t (SM) t t + jets (data-driven) 900 s = 13 TeV, 36.1 fb-1 Postfit t t + H/V Non-t t Uncertainty 800 700 9 j, 3 b, 0 J ≥ 10 j, 3 b, 0 J 9 j, ≥ 4 b, 0 J ≥ 10 j, ≥ 4 b, 0 J Events / bin 600 150 80 500 200 60 400 100 300 40 200 100 50 20 100 0 Data / Pred. 1.25 1 0.75 0.5 0 500 1000 0 500 1000 0 500 1000 0 500 1000 1500 H had T [GeV] H had T [GeV] H had T [GeV] H had T [GeV] (a) ATLAS Single Lepton Data t t t t (SM) t t + jets (data-driven) 350 s = 13 TeV, 36.1 fb-1 Postfit t t + H/V Non-t t Uncertainty 300 9 j, 3 b, 1 J ≥ 10 j, 3 b, 1 J 9 j, ≥ 4 b, 1 J ≥ 10 j, ≥ 4 b, 1 J Events / bin 250 200 150 80 150 60 40 100 100 40 50 20 50 20 0 Data / Pred. 1.25 1 0.75 0.5 0 500 1000 1500 0 500 1000 1500 0 500 1000 1500 0 500 1000 1500 2000 H had T [GeV] H had T [GeV] H had T [GeV] H had T [GeV] (b) ATLAS Single Lepton Data t t t t (SM) t t + jets (data-driven) 120 s = 13 TeV, 36.1 fb-1 Postfit t t + H/V Non-t t Uncertainty 100 9 j, 3 b, ≥ 2 J ≥ 10 j, 3 b, ≥ 2 J 9 j, ≥ 4 b, ≥ 2 J ≥ 10 j, ≥ 4 b, ≥ 2 J Events / bin 80 30 80 40 60 60 20 30 40 20 40 10 20 20 10 0 Data / Pred. 1.25 1 0.75 0.5 0 1000 2000 0 1000 2000 0 1000 2000 0 1000 2000 3000 H had T [GeV] H had T [GeV] H had T [GeV] H had T [GeV] (c) FIG. 7. Comparison between data and prediction of the Hhad T distributions in the single-lepton signal regions after the combined fit to data in both the single-lepton and dilepton channels. The tt̄ þ jets background is estimated with the data-driven method. The tt̄ þ V and tt̄ þ H processes are denoted tt̄ þ H=V. Contributions from W=Z þ jets, single-top, diboson and multijet backgrounds are combined into a single background source referred to as “Non-tt̄”. The hashed area represents the combined statistical and systematic uncertainties of the prediction. The last bin in all figures contains the overflow. The lower panel shows the ratio between the data and the total prediction, including the SM tt̄tt̄ signal scaled by the best-fit signal strength. An arrow indicates that the point is off scale. 052009-14 SEARCH FOR FOUR-TOP-QUARK PRODUCTION IN THE … PHYS. REV. D 99, 052009 (2019) ATLAS Dilepton Data t t t t (SM) t t + jets (data-driven) 120 s = 13 TeV, 36.1 fb-1 Postfit t t + H/V Non-t t Uncertainty 100 7 j, 3 b, 0 J 7 j, ≥ 4 b, 0 J ≥ 8 j, 3 b, 0 J ≥ 8 j, ≥ 4 b, 0 J Events / bin 80 40 10 20 60 30 15 40 20 5 10 20 5 10 0 Data / Pred. 1.25 1 0.75 0.5 0 500 1000 0 500 1000 0 500 1000 0 500 1000 1500 H had T [GeV] H had T [GeV] H had T [GeV] H had T [GeV] (a) 45 ATLAS Dilepton Data t t t t (SM) t t + jets (data-driven) 40 s = 13 TeV, 36.1 fb-1 Postfit t t + H/V Non-t t Uncertainty 35 30 7 j, 3 b, ≥ 1 J 7 j, ≥ 4 b, ≥ 1 J ≥ 8 j, 3 b, ≥ 1 J ≥ 8 j, ≥ 4 b, ≥ 1 J Events / bin 25 20 10 30 8 15 6 20 10 5 4 10 2 5 0 Data / Pred. 1.25 1 0.75 0.5 0 500 1000 1500 2000 0 500 1000 1500 2000 0 500 1000 1500 2000 0 500 1000 1500 2000 2500 H had T [GeV] H had T [GeV] H had T [GeV] H had T [GeV] (b) FIG. 8. Comparison between data and prediction of the H had T distributions in the dilepton signal regions after the combined fit to data in both the single-lepton and dilepton channels. The tt̄ þ jets background is estimated with the data-driven method. The tt̄ þ V and tt̄ þ H processes are denoted tt̄ þ H=V. Contributions from W=Z þ jets, single-top, diboson and multijet backgrounds are combined into a single background source referred to as “Non-tt̄”. The hashed area represents the combined statistical and systematic uncertainties of the prediction. The last bin in all figures contains the overflow. The lower panel shows the ratio between the data and the total prediction, including the SM tt̄tt̄ signal scaled by the best-fit signal strength. An arrow indicates that the point is off scale. and background expectations, which are implemented in the ROOFIT package [110,111]. In the absence of any the likelihood function as Gaussian, log-normal or Poisson significant excess above the background expectation, constraints. Individual sources of systematic uncertainty upper limits on the signal production cross section for are considered to be uncorrelated. Correlations of a given each of the signal scenarios considered in Sec. IVA are systematic uncertainty are maintained across processes derived by using qμ and the CLs method [112,113]. For a and channels. The statistical uncertainty of the prediction, given signal scenario, values of the production cross which incorporates the statistical uncertainty of the MC section (parametrized by μ) yielding CLs < 0.05, where events and of the data-driven fake and nonprompt lepton CLs is computed using the asymptotic approximation estimate, is included in the likelihood in the form of [114], are excluded at > 95% C:L: additional nuisance parameters, one for each of the included bins. B. Comparison between data and prediction The test statistic qμ is defined as the profile likelihood in signal regions after the fit to data ˆ ratio: qμ ¼ −2 lnðLðμ; θ̂μ Þ=Lðμ̂; θ̂ÞÞ, where μ̂ and θ̂ are the A binned likelihood fit to the data is performed in the values of the parameters that maximize the likelihood 12 signal regions in the single-lepton channel and 8 signal function (with the constraint 0 ≤ μ̂ ≤ μ), and θ̂ˆ are the μ regions in the dilepton channel, leading to good agree- values of the NPs that maximize the likelihood function for ment between data and postfit estimates. Comparisons of a given value of μ. The test statistic qμ is implemented in the H had T distributions between data and the total SM 052009-15 M. AABOUD et al. PHYS. REV. D 99, 052009 (2019) TABLE II. Breakdown of the contributions to the uncertainties Table II shows the postfit impact of the largest sources of on μ. The quoted uncertainties Δμ are obtained by repeating the systematic uncertainty on the signal strength μ after the fit with certain sets of nuisance parameters fixed to their postfit simultaneous fit to data in the single-lepton and dilepton values, and subtracting in quadrature the resulting total uncer- channels. The leading sources of systematic uncertainty tainty of μ from the uncertainty from the full fit. The total vary depending on the analysis region considered. The statistical uncertainty is evaluated by fixing all nuisance param- eters in the fit. The line “background-model statistical uncer- largest contributions are due to the uncertainty associated tainty” refers to the statistical uncertainties of the MC event with the choice of tt̄ þ jets parton shower and hadroniza- samples and in the data-driven determination of the tt̄ þ jets and tion model and that of the tt̄ þ jets NLO generator, as well the nonprompt and fake-lepton background components. These as large statistical uncertainties associated with the back- uncertainties are evaluated after the fit described in Sec. VIII. ground prediction. Uncertainty source Δμ C. Limits on four-top-quark production tt̄ þ jets modeling þ1.2 −0.96 in the single-lepton and dilepton channel Background-model statistical uncertainty þ0.91 −0.85 Jet energy scale and resolution, jet mass þ0.38 −0.16 No significant excess of events above the SM back- Other background modeling þ0.26 −0.20 ground prediction, excluding the SM tt̄tt̄ production, is b-tagging efficiency and mistag rates þ0.33 −0.10 found. In the case of tt̄tt̄ production with SM kinematics, JVT, pileup modeling þ0.18 −0.073 an observed (expected) 95% C.L. upper limit on the tt̄ þ H=V modeling þ0.053 −0.055 production cross section of 47 fb (33 fb) is obtained, Luminosity þ0.050 −0.026 corresponding to an upper limit on σðtt̄tt̄Þ relative to the Total systematic uncertainty þ1.6 −1.4 SM prediction of 5.1 (3.6). The SM fitted signal strength μ, Total statistical uncertainty þ1.1 −1.0 after combination of the single-lepton and dilepton chan- nels, is measured to be 1.7þ1.9 −1.7 . Total uncertainty þ1.9 −1.7 The search is used to set limits on BSM four-top- quark production via an EFT model (see Sec. IV). For setting limits on this BSM model, the SM tt̄tt̄ process is considered as a background. In the case of tt̄tt̄ production prediction (including the SM tt̄tt̄ signal) in the signal via an EFT model with a four-top-quark contact interaction, regions, after the combined fit to data in the signal-plus- an observed (expected) 95% C.L. upper limit on the background hypothesis in the two channels, are presented production cross section of 21 fb (22 fb) is obtained. in Fig. 7 for the single-lepton channel and in Fig. 8 for the The cross-section limit for the contact interaction case dilepton channel. Good agreement of the extrapolated fit is lower than in the SM because the contact interaction results is observed as well in the validation regions, which tends to result in final-state objects with slightly larger are presented in the Appendix. momenta (see e.g., Fig. 3). The upper limit on the ATLAS s = 13 TeV, 36.1 fb-1 ATLAS s = 13 TeV, 36.1 fb-1 t t t t (SM) tot. t t t t (SM) stat. Single lep. / OS dilep. tot ( stat syst ) +1.9 +1.1 +1.6 Single lep. / OS dilep. 1.7 −1.7 ( −1.0 −1.4 ) SS dilep. / trilep. +1.8 +1.6 +0.9 SS dilep. / trilep. 4.4 −1.6 ( −1.4 −0.8 ) Expected ± 1σ Expected ± 2σ +1.3 +0.9 +0.9 Combined Combined 3.1 ( ) Observed −1.2 −0.8 −0.9 Expected (μ=1) 0 2 4 6 8 10 −2 0 2 4 6 8 10 12 14 16 95% CL limit on μ = σ t t t t / σ t t t t SM Best-fit μ = σ t t t t / σ t t t t SM (a) (b) FIG. 9. (a) Summary of the 95% C.L. upper limits on σðtt̄tt̄Þ relative to the SM prediction in the individual channels and for the combination. The observed limits (solid black lines) are shown together with the expected limits in the background-only hypothesis (dashed black lines) and in the SM signal-plus-background hypothesis case (dashed red lines). One- and two-standard-deviation uncertainty bands around the expected limits in the background-only hypothesis are also shown. (b) Summary of the signal-strength measurements in the individual channels and for the combination. The statistical uncertainties are evaluated from a fit to the data performed with all the nuisance parameters associated with systematic uncertainties fixed to their postfit values from the nominal fit. 052009-16 SEARCH FOR FOUR-TOP-QUARK PRODUCTION IN THE … PHYS. REV. D 99, 052009 (2019) production cross section can be translated into an observed uncertainty in μ is mainly statistical, while the systematic (expected) limit on the free parameter of the model uncertainties dominate the sensitivity of the search in the jC4t j=Λ2 < 1.9 TeV−2 (1.9 TeV−2 ). single lepton/OS dilepton channel. The probability that the results of the two searches are compatible is assessed by D. Combination with the same-sign dilepton comparing the maximum-likelihood values for a fit per- and multilepton final-state search formed after decorrelating the signal-strength parameters in the two channels and for the nominal combined fit with a The ATLAS Collaboration has carried out a search common signal-strength parameter. The probability of −1 of pp collisions at pffiffiffi new physics using 36.1 fb for obtaining a discrepancy between the two signal-strength s ¼ 13 TeV in the same-sign dilepton and multilepton parameters equal to or larger than the one obtained is found final states (referred to as “SS dilepton/trilepton” channel) to be 31%. [20]. In order to improve the sensitivity to final states Limits are also set for BSM tt̄tt̄ production via an EFT containing four top quarks, the results of the search in model with a four-top-quark contact interaction. In this single-lepton events or dilepton events with two opposite- benchmark scenario, the SM tt̄tt̄ sample is included as a sign charged leptons reported in Sec. VIII C (referred to as background process. A combined observed (expected) “single lepton/OS dilepton” channel) are combined with 95% C.L. upper limit on the production cross section of the results from the complementary SS dilepton/trilepton 21 fb (15 fb) is obtained, which translates into an observed channel. (expected) limit on the free parameter of the model of In the combination, all the experimental systematic jC4t j=Λ2 < 1.9 TeV−2 (1.6 TeV−2 ). uncertainties (described in Sec. VII A) are treated as fully correlated between the two channels, while all the back- IX. SUMMARY ground modeling systematic uncertainties described in Sec. VII B are kept uncorrelated with those in the SS A search for four-top-quark production in the single- dilepton/trilepton channel. This choice is motivated by the lepton and opposite-sign dilepton channels is presented. −1 different nature of most of the background contributions in pffiffiffi consists of 36.1 fb of proton- The analyzed data sample the two channels, the different importance of the common proton collisions at s ¼ 13 TeV collected with the background processes and the different techniques used for ATLAS detector at the Large Hadron Collider during the data-driven estimates. 2015 and 2016. In order to improve the sensitivity of the The expected sensitivity to the SM tt̄tt̄ production from search, events are categorized according to their jet, the combination of the two searches, expressed in terms of b-tagged jet and mass-tagged reclustered large-R jet signal significance relative to the background-only predic- multiplicities. No significant excess of events above the tion, is 1.0 standard deviation, while the observed value is SM background expectation is found. For the four-top- 2.8 standard deviations. The excess is driven by the SS quark Standard Model production, an observed (expected) dilepton/trilepton channel, where the observed (expected) 95% C.L. upper limit on the production cross section of SM tt̄tt̄ signal significance amounts to 3.0 (0.8) standard 47 fb (33 fb), corresponding to 5.1 (3.6) times the SM deviations, to be compared with the 1.0 (0.6) standard prediction, is obtained. The result is combined with the deviation found in the single lepton/OS dilepton search. same-sign dilepton and multilepton final-states search The kinematic properties of the SS dilepton/trilepton events carried out by ATLAS [20] and an observed (expected) were compared with the expectations from the BSM tt̄tt̄ upper limit of 49 fb (19 fb), corresponding to 5.3 (2.1) production benchmark models studied therein, and found to times the SM prediction is obtained at 95% C.L. agree poorly with all of them, in particular for the b-tagged Additionally, in the case of four-top-quark production jet multiplicity. via an EFT model with a four-top-quark contact inter- Assuming no signal, an observed (expected) 95% C.L. action, a combined observed (expected) 95% C.L. upper upper limit on the SM four-top-quark production limit on the production cross section of 21 fb (15 fb) is cross section of 49 fb (19 fb) is obtained. It corresponds obtained. to an upper limit on σðtt̄tt̄Þ relative to the SM prediction of 5.3 (2.1). In the signal-plus-background hypothesis, ACKNOWLEDGMENTS the best-fit value of the SM cross section is found to be We thank CERN for the very successful operation of σ tSM t̄tt̄ ¼ 28.5þ12 −11 fb, to be compared to the theoretical pre- the LHC, as well as the support staff from our institutions diction of 9.2þ2.9−2.4 (scale) 0.5 (PDF) fb [17]. Figure 9(a) without whom ATLAS could not be operated efficiently. shows the expected and observed upper limits on σ tSM t̄tt̄ for We acknowledge the support of ANPCyT, Argentina; the two searches separately and for the combined search, YerPhI, Armenia; ARC, Australia; BMWFW and FWF, while Fig. 9(b) shows a summary of the signal-strength Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and measurements for each of the two searches and their FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; combination. In the SS dilepton/trilepton channel the CONICYT, Chile; CAS, MOST and NSFC, China; 052009-17 M. AABOUD et al. PHYS. REV. D 99, 052009 (2019) COLCIENCIAS, Colombia; MSMT CR, MPO CR and from all WLCG partners is acknowledged gratefully, in VSC CR, Czech Republic; DNRF and DNSRC, particular from CERN, the ATLAS Tier-1 facilities at Denmark; IN2P3-CNRS, CEA-DRF/IRFU, France; TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), SRNSFG, Georgia; BMBF, HGF, and MPG, Germany; CC-IN2P3 (France), KIT/GridKA (Germany), INFN- GSRT, Greece; RGC, Hong Kong SAR, China; ISF and CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, ASGC (Taiwan), RAL (UK) and BNL (USA), the Tier- Japan; CNRST, Morocco; NWO, Netherlands; RCN, 2 facilities worldwide and large non-WLCG resource Norway; MNiSW and NCN, Poland; FCT, Portugal; providers. Major contributors of computing resources are MNE/IFA, Romania; MES of Russia and NRC KI, listed in Ref. [115]. Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg APPENDIX: COMPARISON BETWEEN DATA Foundation, Sweden; SERI, SNSF and Cantons of AND PREDICTION OF THE Hhad T Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, DISTRIBUTIONS IN VALIDATION REGIONS Turkey; STFC, United Kingdom; DOE and NSF, United AFTER THE FIT TO DATA States of America. In addition, individual groups and members have received support from BCKDF, Figures 10 and 11 show comparisons of the Hhad T CANARIE, CRC and Compute Canada, Canada; distributions in the validation regions between the data COST, ERC, ERDF, Horizon 2020, and Marie and the postfit prediction in the dilepton and in the single- Skłodowska-Curie Actions, European Union; lepton channels, respectively. The postfit prediction is Investissements d’ Avenir Labex and Idex, ANR, obtained from the fit to data presented in Sec. VIII in France; DFG and AvH Foundation, Germany; the 20 signal regions and propagated to the validation Herakleitos, Thales and Aristeia programmes co-financed regions, which are not included in the fit nor used to extract by EU-ESF and the Greek NSRF, Greece; BSF-NSF and information from the data. The good level of agreement GIF, Israel; CERCA Programme Generalitat de found between data and prediction in these regions is Catalunya, Spain; The Royal Society and Leverhulme therefore an indication of the validity of the extrapolation of Trust, United Kingdom. The crucial computing support the results of the fit between different regions. 600 ATLAS Dilepton Data t t t t (SM) t t + jets (data-driven) s = 13 TeV, 36.1 fb-1 Postfit t t + H/V Non-t t Uncertainty 500 6 j, 3 b, 0 J 6 j, ≥ 4 b, 0 J 6 j, 3 b, ≥ 1 J 6 j, ≥ 4 b, ≥ 1 J 400 Events / bin 60 300 40 10 40 200 20 5 100 20 0 Data / Pred. 1.25 1 0.75 0.5 0 500 1000 0 500 1000 0 500 1000 1500 2000 0 500 1000 1500 2000 2500 H had T [GeV] H had T [GeV] H had T [GeV] H had T [GeV] FIG. 10. Comparison between data and prediction of the Hhad T distributions in the dilepton validation regions after the combined fit to data in the single-lepton and dilepton channels. The tt̄ þ jets background is estimated with the data-driven method. The tt̄ þ V and tt̄ þ H processes are denoted tt̄ þ H=V. Contributions from W=Z þ jets, single-top, diboson and multijet backgrounds are combined into a single background source referred to as “Non-tt̄”. The hashed area represents the combined statistical and systematic uncertainties of the prediction. The last bin in all figures contains the overflow. The lower panel shows the ratio between the data and the total prediction, including the SM tt̄tt̄ signal scaled by the best-fit signal strength. An arrow indicates that the point is off scale. 052009-18 SEARCH FOR FOUR-TOP-QUARK PRODUCTION IN THE … PHYS. REV. D 99, 052009 (2019) 10000 ATLAS Single Lepton Data t t t t (SM) t t + jets (data-driven) s = 13 TeV, 36.1 fb-1 Postfit t t + H/V Non-t t Uncertainty 8000 7 j, 3 b, 0 J 8 j, 3 b, 0 J 7 j, ≥ 4 b, 0 J 8 j, ≥ 4 b, 0 J Events / bin 6000 400 1500 1000 4000 300 1000 500 200 2000 500 100 0 Data / Pred. 1.25 1 0.75 0.5 0 500 1000 0 500 1000 0 500 1000 0 500 1000 1500 H had T [GeV] H had T [GeV] H had T [GeV] H had T [GeV] (a) 2500 ATLAS Single Lepton Data t t t t (SM) t t + jets (data-driven) s = 13 TeV, 36.1 fb-1 Postfit t t + H/V Non-t t Uncertainty 2000 7 j, 3 b, 1 J 8 j, 3 b, 1 J 7 j, ≥ 4 b, 1 J 8 j, ≥ 4 b, 1 J Events / bin 1500 800 300 150 600 1000 200 100 400 500 100 50 200 0 Data / Pred. 1.25 1 0.75 0.5 0 500 1000 1500 0 500 1000 1500 0 500 1000 1500 0 500 1000 1500 2000 H had T [GeV] H had T [GeV] H had T [GeV] H had T [GeV] (b) ATLAS Single Lepton Data t t t t (SM) t t + jets (data-driven) 250 s = 13 TeV, 36.1 fb-1 Postfit t t + H/V Non-t t Uncertainty 200 7 j, 3 b, ≥ 2 J 8 j , 3 b, ≥ 2 J 7 j, ≥ 4 b, ≥ 2 J 8 j, ≥ 4 b, ≥ 2 J Events / bin 150 150 30 30 100 100 20 20 50 50 10 10 0 Data / Pred. 1.25 1 0.75 0.5 0 1000 2000 0 1000 2000 0 1000 2000 0 1000 2000 3000 H had T [GeV] H had T [GeV] H had T [GeV] H had T [GeV] (c) FIG. 11. 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Zwalinski35 (ATLAS Collaboration) 1 Department of Physics, University of Adelaide, Adelaide, Australia 2 Physics Department, SUNY Albany, Albany, New York, USA 3 Department of Physics, University of Alberta, Edmonton, Alberta, Canada 4a Department of Physics, Ankara University, Ankara, Turkey 4b Istanbul Aydin University, Istanbul, Turkey 4c 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, Illinois, USA 7 Department of Physics, University of Arizona, Tucson, Arizona, USA 8 Department of Physics, University of Texas at Arlington, Arlington, Texas, USA 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, Texas, USA 12a Bahcesehir University, Faculty of Engineering and Natural Sciences, Istanbul, Turkey 12b Istanbul Bilgi University, Faculty of Engineering and Natural Sciences, Istanbul, Turkey 12c Department of Physics, Bogazici University, Istanbul, Turkey 12d Department of Physics Engineering, Gaziantep University, Gaziantep, Turkey 13 Institute of Physics, Azerbaijan Academy of Sciences, Baku, Azerbaijan 14 Institut de Física d’Altes Energies (IFAE), Barcelona Institute of Science and Technology, Barcelona, Spain 15a Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China 15b Physics Department, Tsinghua University, Beijing, China 15c Department of Physics, Nanjing University, Nanjing, China 15d 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, California, USA 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 23a Dipartimento di Fisica e Astronomia, Università di Bologna, Bologna, Italy 23b INFN Sezione di Bologna, Bologna, Italy 24 Physikalisches Institut, Universität Bonn, Bonn, Germany 25 Department of Physics, Boston University, Boston, Massachusetts, USA 26 Department of Physics, Brandeis University, Waltham, Massachusetts, USA 27a Transilvania University of Brasov, Brasov, Romania 27b Horia Hulubei National Institute of Physics and Nuclear Engineering, Bucharest, Romania 27c Department of Physics, Alexandru Ioan Cuza University of Iasi, Iasi, Romania 27d National Institute for Research and Development of Isotopic and Molecular Technologies, Physics Department, Cluj-Napoca, Romania 27e University Politehnica Bucharest, Bucharest, Romania 27f West University in Timisoara, Timisoara, Romania 28a Faculty of Mathematics, Physics and Informatics, Comenius University, Bratislava, Slovak Republic 28b Department of Subnuclear Physics, Institute of Experimental Physics of the Slovak Academy of Sciences, Kosice, Slovak Republic 29 Physics Department, Brookhaven National Laboratory, Upton, New York, USA 30 Departamento de Física, Universidad de Buenos Aires, Buenos Aires, Argentina 31 Cavendish Laboratory, University of Cambridge, Cambridge, United Kingdom 32a Department of Physics, University of Cape Town, Cape Town, South Africa 052009-31 M. AABOUD et al. PHYS. REV. D 99, 052009 (2019) 32b Department of Mechanical Engineering Science, University of Johannesburg, Johannesburg, South Africa 32c School of Physics, University of the Witwatersrand, Johannesburg, South Africa 33 Department of Physics, Carleton University, Ottawa, Ontario, Canada 34a Faculté des Sciences Ain Chock, Réseau Universitaire de Physique des Hautes Energies - Université Hassan II, Casablanca, Morocco 34b Centre National de l’Energie des Sciences Techniques Nucleaires (CNESTEN), Rabat, Morocco 34c Faculté des Sciences Semlalia, Université Cadi Ayyad, LPHEA-Marrakech, Morocco 34d Faculté des Sciences, Université Mohamed Premier and LPTPM, Oujda, Morocco 34e Faculté des sciences, Université Mohammed V, Rabat, Morocco 35 CERN, Geneva, Switzerland 36 Enrico Fermi Institute, University of Chicago, Chicago, Illinois, USA 37 LPC, Université Clermont Auvergne, CNRS/IN2P3, Clermont-Ferrand, France 38 Nevis Laboratory, Columbia University, Irvington, New York, USA 39 Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark 40a Dipartimento di Fisica, Università della Calabria, Rende, Italy 40b INFN Gruppo Collegato di Cosenza, Laboratori Nazionali di Frascati, Italy 41 Physics Department, Southern Methodist University, Dallas, Texas, USA 42 Physics Department, University of Texas at Dallas, Richardson, Texas, USA 43a Department of Physics, Stockholm University, Sweden 43b Oskar Klein Centre, Stockholm, Sweden 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, North Carolina, USA 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 53a Dipartimento di Fisica, Università di Genova, Genova, Italy 53b INFN Sezione di Genova, 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 56 LPSC, Université Grenoble Alpes, CNRS/IN2P3, Grenoble INP, Grenoble, France 57 Laboratory for Particle Physics and Cosmology, Harvard University, Cambridge, Massachusetts, USA 58a Department of Modern Physics and State Key Laboratory of Particle Detection and Electronics, University of Science and Technology of China, Hefei, China 58b Institute of Frontier and Interdisciplinary Science and Key Laboratory of Particle Physics and Particle Irradiation (MOE), Shandong University, Qingdao, China 58c School of Physics and Astronomy, Shanghai Jiao Tong University, KLPPAC-MoE, SKLPPC, Shanghai, China 58d Tsung-Dao Lee Institute, Shanghai, China 59a Kirchhoff-Institut für Physik, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany 59b Physikalisches Institut, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany 60 Faculty of Applied Information Science, Hiroshima Institute of Technology, Hiroshima, Japan 61a Department of Physics, Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China 61b Department of Physics, University of Hong Kong, Hong Kong, China 61c 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, Indiana, USA 64a INFN Gruppo Collegato di Udine, Sezione di Trieste, Udine, Italy 64b ICTP, Trieste, Italy 64c Dipartimento di Chimica, Fisica e Ambiente, Università di Udine, Udine, Italy 65a INFN Sezione di Lecce, Lecce, Italy 65b Dipartimento di Matematica e Fisica, Università del Salento, Lecce, Italy 66a INFN Sezione di Milano, Milano, Italy 66b Dipartimento di Fisica, Università di Milano, Milano, Italy 67a INFN Sezione di Napoli, Napoli, Italy 052009-32 SEARCH FOR FOUR-TOP-QUARK PRODUCTION IN THE … PHYS. REV. D 99, 052009 (2019) 67b Dipartimento di Fisica, Università di Napoli, Napoli, Italy 68a INFN Sezione di Pavia, Pavia, Italy 68b Dipartimento di Fisica, Università di Pavia, Pavia, Italy 69a INFN Sezione di Pisa, Pisa, Italy 69b Dipartimento di Fisica E. Fermi, Università di Pisa, Pisa, Italy 70a INFN Sezione di Roma, Roma, Italy 70b Dipartimento di Fisica, Sapienza Università di Roma, Roma, Italy 71a INFN Sezione di Roma Tor Vergata, Roma, Italy 71b Dipartimento di Fisica, Università di Roma Tor Vergata, Roma, Italy 72a INFN Sezione di Roma Tre, Roma, Italy 72b Dipartimento di Matematica e Fisica, Università Roma Tre, Roma, Italy 73a INFN-TIFPA, Trento, Italy 73b Università degli Studi di Trento, Trento, Italy 74 Institut für Astro- und Teilchenphysik, Leopold-Franzens-Universität, Innsbruck, Austria 75 University of Iowa, Iowa City, Iowa, USA 76 Department of Physics and Astronomy, Iowa State University, Ames, Iowa, USA 77 Joint Institute for Nuclear Research, Dubna, Russia 78a Departamento de Engenharia Elétrica, Universidade Federal de Juiz de Fora (UFJF), Juiz de Fora, Brazil 78b Universidade Federal do Rio De Janeiro COPPE/EE/IF, Rio de Janeiro, Brazil 78c Universidade Federal de São João del Rei (UFSJ), São João del Rei, Brazil 78d Instituto de Física, Universidade de São Paulo, São Paulo, Brazil 79 KEK, High Energy Accelerator Research Organization, Tsukuba, Japan 80 Graduate School of Science, Kobe University, Kobe, Japan 81a AGH University of Science and Technology, Faculty of Physics and Applied Computer Science, Krakow, Poland 81b 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 Física 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, 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, Louisiana, USA 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 Física 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, Massachusetts, USA 101 Department of Physics, McGill University, Montreal, Québec, Canada 102 School of Physics, University of Melbourne, Victoria, Australia 103 Department of Physics, University of Michigan, Ann Arbor, Michigan, USA 104 Department of Physics and Astronomy, Michigan State University, East Lansing, Michigan, USA 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, Québec, 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 052009-33 M. AABOUD et al. PHYS. REV. D 99, 052009 (2019) 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, New Mexico, USA 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, Illinois, USA 120a Budker Institute of Nuclear Physics and NSU, SB RAS, Novosibirsk, Russia 120b Novosibirsk State University Novosibirsk, Russia 121 Institute for High Energy Physics of the National Research Centre Kurchatov Institute, Protvino, Russia 122 Department of Physics, New York University, New York, New York, USA 123 The Ohio State University, Columbus, Ohio, USA 124 Faculty of Science, Okayama University, Okayama, Japan 125 Homer L. Dodge Department of Physics and Astronomy, University of Oklahoma, Norman, Oklahoma, USA 126 Department of Physics, Oklahoma State University, Stillwater, Oklahoma, USA 127 Palacký University, RCPTM, Joint Laboratory of Optics, Olomouc, Czech Republic 128 Center for High Energy Physics, University of Oregon, Eugene, Oregon, USA 129 LAL, Université Paris-Sud, CNRS/IN2P3, Université Paris-Saclay, Orsay, France 130 Graduate School of Science, Osaka University, Osaka, Japan 131 Department of Physics, University of Oslo, Oslo, Norway 132 Department of Physics, Oxford University, Oxford, United Kingdom 133 LPNHE, Sorbonne Université, Paris Diderot Sorbonne Paris Cité, CNRS/IN2P3, Paris, France 134 Department of Physics, University of Pennsylvania, Philadelphia, Pennsylvania, USA 135 Konstantinov Nuclear Physics Institute of National Research Centre “Kurchatov Institute”, PNPI, St. Petersburg, Russia 136 Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, Pennsylvania, USA 137a Laboratório de Instrumentação e Física Experimental de Partículas - LIP, Portugal 137b Departamento de Física, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal 137c Departamento de Física, Universidade de Coimbra, Coimbra, Portugal 137d Centro de Física Nuclear da Universidade de Lisboa, Lisboa, Portugal 137e Departamento de Física, Universidade do Minho, Braga, Portugal 137f Departamento de Física Teorica y del Cosmos, Universidad de Granada, Granada (Spain), Spain 137g Dep Física and CEFITEC of Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal 138 Institute of Physics, Academy of Sciences of the Czech Republic, Prague, Czech Republic 139 Czech Technical University in Prague, Prague, Czech Republic 140 Charles University, Faculty of Mathematics and Physics, Prague, Czech Republic 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, California, USA 144a Departamento de Física, Pontificia Universidad Católica de Chile, Santiago, Chile 144b Departamento de Física, Universidad Técnica Federico Santa María, Valparaíso, Chile 145 Department of Physics, University of Washington, Seattle, Washington, USA 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, British Columbia, Canada 150 SLAC National Accelerator Laboratory, Stanford, California, USA 151 Physics Department, Royal Institute of Technology, Stockholm, Sweden 152 Departments of Physics and Astronomy, Stony Brook University, Stony Brook, New York, USA 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 156a E. Andronikashvili Institute of Physics, Iv. Javakhishvili Tbilisi State University, Tbilisi, Georgia 052009-34 SEARCH FOR FOUR-TOP-QUARK PRODUCTION IN THE … PHYS. REV. D 99, 052009 (2019) 156b 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 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, Ontario, Canada 165a TRIUMF, Vancouver, British Columbia, Canada 165b Department of Physics and Astronomy, York University, Toronto, Ontario, 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, Massachusetts, USA 168 Department of Physics and Astronomy, University of California Irvine, Irvine, California, USA 169 Department of Physics and Astronomy, University of Uppsala, Uppsala, Sweden 170 Department of Physics, University of Illinois, Urbana, Illinois, USA 171 Instituto de Física Corpuscular (IFIC), Centro Mixto Universidad de Valencia - CSIC, Valencia, Spain 172 Department of Physics, University of British Columbia, Vancouver, British Columbia, Canada 173 Department of Physics and Astronomy, University of Victoria, Victoria, British Columbia, 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, Wisconsin, USA 179 Fakultät für Mathematik und Naturwissenschaften, Fachgruppe Physik, Bergische Universität Wuppertal, Wuppertal, Germany 180 Department of Physics, Yale University, New Haven, Connecticut, USA 181 Yerevan Physics Institute, Yerevan, Armenia a Deceased. b Also at Department of Physics, King’s College London, London, United Kingdom. c Also at Istanbul University, Department of Physics, Istanbul, Turkey. d Also at Instituto de Física Teórica de la Universidad Autónoma de Madrid, Spain. e Also at Institute of Physics, Azerbaijan Academy of Sciences, Baku, Azerbaijan. f Also at TRIUMF, Vancouver, British Columbia, Canada. g Also at Department of Physics and Astronomy, University of Louisville, Louisville, Kentucky, USA. h Also at Department of Physics, California State University, Fresno, California, USA. i Also at Department of Physics, University of Fribourg, Fribourg, Switzerland. j Also at Departament de Fisica de la Universitat Autonoma de Barcelona, Barcelona, Spain. k Also at Tomsk State University, Tomsk, and Moscow Institute of Physics and Technology State University, Dolgoprudny, Russia. l Also at The Collaborative Innovation Center of Quantum Matter (CICQM), Beijing, China. m Also at Departamento de Física, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal. n Also at Universita di Napoli Parthenope, Napoli, Italy. o Also at Institute of Particle Physics (IPP), Victoria, BC, Canada. p Also at II. Physikalisches Institut, Georg-August-Universität Göttingen, Göttingen, Germany. q Also at Dipartimento di Fisica E. Fermi, Università di Pisa, Pisa, Italy. r Also at Horia Hulubei National Institute of Physics and Nuclear Engineering, Bucharest, Romania. s Also at CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille, France. t Also at Department of Physics, St. Petersburg State Polytechnical University, St. Petersburg, Russia. u Also at Borough of Manhattan Community College, City University of New York, New York, USA. v Also at Department of Financial and Management Engineering, University of the Aegean, Chios, Greece. w Also at Centre for High Performance Computing, CSIR Campus, Rosebank, Cape Town, South Africa. x Also at Louisiana Tech University, Ruston, Louisiana, USA. y Also at California State University, East Bay, California, USA. z Also at Institucio Catalana de Recerca i Estudis Avancats, ICREA, Barcelona, Spain. aa Also at Department of Physics, University of Michigan, Ann Arbor, Michigan, USA. bb Also at LAL, Université Paris-Sud, CNRS/IN2P3, Université Paris-Saclay, Orsay, France. cc Also at Graduate School of Science, Osaka University, Osaka, Japan. 052009-35 M. AABOUD et al. PHYS. REV. D 99, 052009 (2019) dd Also at Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany. ee Also at Institute for Mathematics, Astrophysics and Particle Physics, Radboud University Nijmegen/Nikhef, Nijmegen, Netherlands. ff Also at Near East University, Nicosia, North Cyprus, Mersin, Turkey. gg Also at Institute of Theoretical Physics, Ilia State University, Tbilisi, Georgia. hh Also at CERN, Geneva, Switzerland. ii Also at Department of Physics, Stanford University, Stanford, California, USA. jj Also at Manhattan College, New York, New York, USA. kk Also at Hellenic Open University, Patras, Greece. ll Also at LPNHE, Sorbonne Université, Paris Diderot Sorbonne Paris Cité, CNRS/IN2P3, Paris, France. mm Also at The City College of New York, New York, New York, USA. nn Also at Departamento de Física Teorica y del Cosmos, Universidad de Granada, Granada (Spain), Spain. oo Also at Department of Physics, California State University, Sacramento, California, USA. pp Also at Moscow Institute of Physics and Technology State University, Dolgoprudny, Russia. qq Also at Département de Physique Nucléaire et Corpusculaire, Université de Genève, Genève, Switzerland. rr Also at Department of Physics and Astronomy, University of Sheffield, Sheffield, United Kingdom. ss Also at School of Physics, Sun Yat-sen University, Guangzhou, China. tt Also at Department of Applied Physics and Astronomy, University of Sharjah, Sharjah, United Arab Emirates. uu Also at Institut für Experimentalphysik, Universität Hamburg, Hamburg, Germany. vv Also at National Research Nuclear University MEPhI, Moscow, Russia. ww Also at Institute for Particle and Nuclear Physics, Wigner Research Centre for Physics, Budapest, Hungary. xx Also at Giresun University, Faculty of Engineering, Giresun, Turkey. yy Also at Institute of Physics, Academia Sinica, Taipei, Taiwan. 052009-36