Published for SISSA by Springer Received: November 30, 2018 Revised: March 11, 2019 Accepted: March 28, 2019 Published: April 4, 2019 Measurements of inclusive and differential fiducial JHEP04(2019)046 cross-sections of tt̄ production with additional heavy-flavour jets in proton-proton collisions at √ s = 13 TeV with the ATLAS detector The ATLAS collaboration E-mail: atlas.publications@cern.ch Abstract: This paper presents measurements of tt̄ production in association with addi- tional b-jets in pp collisions at the LHC at a centre-of-mass energy of 13 TeV. The data were recorded with the ATLAS detector and correspond to an integrated luminosity of 36.1 fb−1 . Fiducial cross-section measurements are performed in the dilepton and lepton-plus-jets tt̄ decay channels. Results are presented at particle level in the form of inclusive cross-sections of tt̄ final states with three and four b-jets as well as differential cross-sections as a function of global event properties and properties of b-jet pairs. The measured inclusive fiducial cross-sections generally exceed the tt̄bb̄ predictions from various next-to-leading-order ma- trix element calculations matched to a parton shower but are compatible within the total uncertainties. The experimental uncertainties are smaller than the uncertainties in the predictions. Comparisons of state-of-the-art theoretical predictions with the differential measurements are shown and good agreement with data is found for most of them. Keywords: Hadron-Hadron scattering (experiments), Heavy quark production ArXiv ePrint: 1811.12113 Open Access, Copyright CERN, https://doi.org/10.1007/JHEP04(2019)046 for the benefit of the ATLAS Collaboration. Article funded by SCOAP3 . Contents 1 Introduction 2 2 ATLAS detector 4 3 Monte Carlo simulation 5 JHEP04(2019)046 4 Object reconstruction and identification 9 4.1 Detector-level object reconstruction 9 4.2 Particle-level object definitions 10 5 Event selection and definition of the fiducial phase space 11 5.1 Data event selection 11 5.2 Fiducial phase-space definition 11 6 Background estimation 12 6.1 Background from single-top, Z/γ ∗ + jets and W + jets events 12 6.2 Background from non-prompt and fake leptons 13 6.3 Data and prediction comparison of baseline selection 16 7 Extraction of the fiducial cross-sections 16 7.1 Data-driven correction factors for flavour composition of additional jets in tt̄ events 16 7.2 Unfolding 20 8 Systematic uncertainties 21 8.1 Experimental uncertainties 21 8.2 Modelling systematic uncertainties 23 8.3 Uncertainty in tt̄c and tt̄l background 23 8.4 Uncertainty in non-tt̄ background estimation 24 8.5 Propagation of uncertainties 24 9 Inclusive and differential fiducial cross-section results 25 10 Summary 30 The ATLAS collaboration 49 –1– b g t g t b b H g b g t t (a) (b) (c) Figure 1. Example Feynman diagrams of processes leading to a tt̄bb̄ final state, including (a) QCD tt̄bb̄ production, (b) tt̄H(H → bb̄), and (c) tt̄Z(Z → bb̄). JHEP04(2019)046 1 Introduction Measurements of the production cross-section of top-antitop quark pairs (tt̄) with additional jets provide important tests of quantum chromodynamics (QCD) predictions. Among these, the process of tt̄ produced in association with jets originating from b-quarks (b-jets) is particularly important to measure, as there are many uncertainties in the calculation of the process. For example, calculating the amplitude for the process shown in figure 1a is a challenge due to the non-negligible mass of the b-quark. It is therefore important to compare the predictions with both inclusive and differential experimental cross-section measurements of tt̄ production with additional b-jets. State-of-the-art QCD calculations give predictions for the tt̄ production cross-section with up to two additional massless par- tons at next-to-leading order (NLO) in perturbation theory matched to a parton shower [1], and the QCD production of tt̄bb̄ is calculated at NLO matched to a parton shower [2–5]. Moreover, since the discovery of the Higgs boson [6, 7], the determination of the Higgs coupling to the heaviest elementary particle, the top quark, is a crucial test of the Standard Model (SM). Direct measurements of the top-quark Yukawa coupling are performed in events where a Higgs boson is produced in association with a top-quark pair (tt̄H) [8, 9]. The Higgs branching ratios are dominated by the H → bb̄ decay [10, 11], and therefore the tt̄H process can be measured with the best statistical precision using events where the Higgs boson decays in this manner, leading to a tt̄bb̄ final state as shown in figure 1b. However, this channel suffers from a large background from QCD tt̄bb̄ production indicated in figure 1a [12, 13]. Measurements of tt̄H(H → bb̄) would benefit from a better understanding of the QCD production of tt̄bb̄ as predicted by the SM and, in particular, improved Monte Carlo (MC) modelling. The measurements presented in this paper were chosen in order to provide data needed to improve the QCD MC modelling of the tt̄bb̄ process. The differential observables are particularly interesting as they are sensitive to the relative contribution of events from tt̄-associated Higgs production (tt̄H) with H → bb̄ decays to QCD-produced tt̄bb̄ events in various phase space regions. Even though the aim is to improve the modelling of QCD production of additional b-jets in tt̄ events, this analysis measures their production without separating the different production channels such as tt̄H or tt̄ in association with a vector boson (tt̄V ), for example the tt̄Z process shown in figure 1c. –2– In this paper, measurements of fiducial cross-sections are presented using data recorded by the ATLAS detector during 2015 and 2016 in proton-proton (pp) collisions at a centre- √ of-mass energy s = 13 TeV, corresponding to a total integrated luminosity of 36.1 fb−1 . In addition, differential measurements at this centre-of-mass energy are presented as a function of various observables. Previous measurements of tt̄ production with additional √ heavy-flavour jets have been reported by ATLAS at s = 7 TeV [14] and both CMS and √ ATLAS at s = 8 TeV [15–17]. CMS has also reported a measurement of the inclusive √ tt̄bb̄ cross-section using 2.3 fb−1 at s = 13 TeV [18]. Since the top quark decays into a b-quark and W boson nearly 100% of the time, tt̄ JHEP04(2019)046 events are typically classified according to how the two W bosons decay. In this analysis, two channels are considered: the eµ channel, in which both W bosons decay leptonically, one into a muon and muon neutrino and the other into an electron and electron neutrino, and the lepton-plus-jets channel (lepton + jets), in which one W boson decays into an isolated charged lepton (an electron or muon) and corresponding neutrino and the other W boson decays into a pair of quarks. Electrons and muons produced either directly in the decay of the W boson or via an intermediate τ -lepton are included in both channels. The decay of a top-quark pair results in two b-quarks and therefore a final state which includes the production of two additional b-quarks may contain up to four b-jets. The inclusive fiducial cross-sections are presented for events with at least three b-jets and for events with at least four b-jets. The differential cross-sections are presented for events with at least three b-jets in the eµ channel and with at least four b-jets in the lepton + jets channel. The results are obtained as a function of the transverse momentum (pT )1 of each of the b-jets, the scalar sum of the pT of the lepton(s) and jets in the events (HT ) and of only jets in the events (HThad ) and as a function of the b-jet multiplicity (Nb-jets ). This analysis does not attempt to identify the origin of the b-jets, i.e. it does not distinguish between additional b-jets and b-jets that come from the top-quark decays. This is to avoid using simulation-based information to attribute b-jets to a particular production process, which would lead to significant modelling uncertainties. Instead, differential cross- sections are measured as a function of kinematic distributions of pairs of b-jets. The reported distributions could be used to distinguish the contribution of specific production mechanisms: the pair made from the two b-jets closest in angular distance is expected to be formed by b-jets from gluon splitting and the pair made from the two highest-pT b-jets is expected to be dominated by top-pair production. For each of these pairs, the distributions are measured for the angular separation between the b-jets (∆R(b, b)), the invariant mass (mbb ) and transverse momentum (pT,bb ). It should be noted that for events with at least three b-jets, it is likely that one of the two closest b-jets originates from the top quark. Hence the simple picture that the two closest b-jets are usually from gluon splitting may not apply. 1 ATLAS uses a right-handed coordinate system with its origin at the nominal interaction point (IP) in the centre of the detector and the z-axis along the beam pipe. The x-axis points from the IP to the centre of the LHC ring, and the y-axis points upward. Cylindrical coordinates (r, φ) are used in the transverse plane, φ being the azimuthal angle around the z-axis. The pseudorapidity is defined in terms of the p θ as η = − ln tan(θ/2). The angular separation between two points in η and φ is defined as polar angle ∆R = (∆η)2 + (∆φ)2 . –3– However, ∆R, mbb and pT,bb are used for reconstruction of the final state in analyses with multiple b-jets and therefore probing the modelling of these observables is important. The cross-sections are obtained by subtracting the estimated number of non-tt̄ back- ground events from the data distributions. At detector level, jets are identified as containing b-hadrons (“b-tagging”) by a multivariate algorithm [19]. The tt̄ background resulting from additional light-flavour and charm-quark jets wrongly identified as b-jets is evaluated using a template fit, in which the templates are constructed from the output discriminant of the b-tagging algorithm. The background-subtracted distributions are corrected for acceptance and detector effects using an unfolding technique that includes corrections for the tt̄-related backgrounds. JHEP04(2019)046 This paper is laid out as follows. The experimental set-up for the collected data is de- scribed in section 2. Details of the simulation used in this analysis are provided in section 3. The reconstruction and identification of leptons and jets, the b-tagging of jets at detector level, and the definitions of objects at particle level are described in section 4. The selection of reconstructed events and the definition of the fiducial phase space are given in section 5. Estimation of the background from non-tt̄ processes is described in section 6. The method to estimate the tt̄ background with additional jets misidentified as b-jets and the unfolding procedure to correct the data to particle level for fiducial cross-section measurements are explained in section 7. Sources of systematic uncertainties and their propagation to the measured cross-sections are described in section 8. The measured inclusive and normalised differential fiducial cross-sections and the comparison with various theoretical predictions are presented in section 9. Finally, the results are summarised in section 10. 2 ATLAS detector The ATLAS detector [20] at the LHC covers nearly the entire solid angle around the colli- sion point. It consists of an inner-tracking detector surrounded by a thin superconducting solenoid, electromagnetic and hadronic calorimeters, and a muon spectrometer incorporat- ing three large superconducting toroidal magnets. The inner detector (ID) system is immersed in a 2 T axial magnetic field and provides charged-particle tracking in the pseudorapidity range |η| < 2.5. The ID is composed of silicon detectors and the transition radiation tracker. The high-granularity silicon pixel detector covers the interaction region and is followed by the silicon microstrip tracker. The innermost silicon pixel layer, added to the inner detector before the start of Run-2 data taking [21, 22], improves the identification of b-jets. The tracking capabilities of the silicon detectors are augmented by the transition radiation tracker, which is located at a larger radius and enables track reconstruction up to |η| = 2.0. It also provides signals used to separate electrons from pions. The calorimeter system covers the range |η| < 4.9. Within the region |η| < 3.2, electromagnetic calorimetry is provided by barrel and endcap high-granularity lead/liquid- argon (LAr) electromagnetic calorimeters, with an additional thin LAr presampler covering |η| < 1.8 to correct for energy loss in material upstream of the calorimeters. Hadronic calorimetry is provided by the steel/scintillating-tile calorimeter, segmented into three –4– barrel structures within |η| < 1.7, and two copper/LAr hadronic endcap calorimeters. The solid angle coverage is completed with forward copper/LAr and tungsten/LAr calorimeter modules optimised for electromagnetic and hadronic measurements, respectively. The muon spectrometer (MS) comprises separate trigger and high-precision tracking chambers measuring the deflection of muons in a magnetic field generated by the supercon- ducting air-core toroids. The field integral of the toroids ranges between 2.0 and 6.0 T m across most of the detector. A set of precision chambers covers the region |η| < 2.7 with three layers of drift tubes, complemented by cathode strip chambers in the forward region, where the background is highest. The muon trigger system covers the range |η| < 2.4 with resistive plate chambers in the barrel, and thin gap chambers in the endcap regions. JHEP04(2019)046 A two-level trigger system is used for event selection [23, 24]. The first trigger level is implemented in hardware and uses a subset of detector information to reduce the event rate to a design value of at most 100 kHz. This is followed by a software-based trigger that reduces the event rate to about 1 kHz. 3 Monte Carlo simulation Monte Carlo simulations are used in three ways in this analysis: to estimate the signal and background composition of the selected data samples, to determine correction factors for detector and acceptance effects for unfolding, and finally to estimate systematic uncer- tainties. In addition, theoretical predictions are compared with the unfolded data. The computer codes used to generate the samples and how they were configured are described in the following. The signal MC samples used in the analysis are listed in table 1. The nominal tt̄ sample was generated using the Powheg-Box generator (version 2, r3026) [25–28] at next-to-leading-order (NLO) in αs with the NNPDF3.0NLO set of par- ton distribution functions (PDF) in the matrix element calculation. The parton shower, fragmentation, and the underlying event were simulated using Pythia 8.210 [29] with the NNPDF2.3LO PDF sets [30, 31] and the corresponding A14 set of tuned parameters [32]. The hdamp parameter, which controls the pT of the hardest additional parton emission beyond the Born configuration, was set to 1.5mt [33], where mt denotes the top-quark mass. The Powheg hardness criterion used in the matching (POWHEG:pTdef) is set to 2 following q a study in ref. [33]. The renormalisation and factorisation scales were set to µ = m2t + p2T,t , where pT,t is the transverse momentum of the top quark. Additional jets, including b-jets, were generated by the hardest additional parton emission and from parton showering. This sample is called Powheg+Pythia 8 in the following. Processes involving the production of a W, Z or Higgs boson in addition to a tt̄ pair were simulated using the MadGraph5 aMC@NLO generator [34, 35] at NLO in αs in the matrix element calculation. The parton shower, fragmentation and underlying event were simulated using Pythia 8 with the A14 parton shower tune. A dynamic renormal- isation and factorisation scale set toqHT /2 was used, where HT is defined as the scalar sum of the transverse mass, mT = m2 + p2T , of all partons in the partonic final state. The NNPDF3.0NLO PDF set was used in the matrix element calculation while the NNPDF2.3LO PDF set was used in the parton shower. In the case of tt̄H, the Higgs –5– Generator sample Process Matching Tune Use Powheg-Box v2 + tt̄ NLO Powheg A14 nom. Pythia 8.210 hdamp = 1.5mt MadGraph5 aMC@NLO + tt̄ + V /H NLO MC@NLO A14 nom. Pythia 8.210 Powheg-Box v2 + tt̄ NLO Powheg A14Var3cDown syst. Pythia 8.210 RadLo hdamp = 1.5mt Powheg-Box v2 + tt̄ NLO Powheg A14Var3cUp syst. Pythia 8.210 RadHi hdamp = 3.0mt Powheg-Box v2 + tt̄ NLO Powheg H7UE syst. Herwig 7.01 hdamp = 1.5mt JHEP04(2019)046 Sherpa 2.2.1 tt̄ tt̄ +0,1 parton at NLO MePs@Nlo Sherpa syst. +2,3,4 partons at LO MadGraph5 aMC@NLO + tt̄ NLO MC@NLO A14 comp. Pythia 8.210 Sherpa 2.2.1 tt̄bb̄ (4FS) tt̄bb̄ NLO MC@NLO Sherpa comp. PowHel + tt̄bb̄ NLO Powheg A14 comp. Pythia 8.210 (5FS) hdamp = HT /2 PowHel + tt̄bb̄ NLO Powheg A14 comp. Pythia 8.210 (4FS) hdamp = HT /2 Powheg-Box v2 + tt̄bb̄ NLO Powheg A14 comp. Pythia 8.210 tt̄bb̄ (4FS) hdamp = HT /2 Table 1. Summary of the MC sample set-ups used for modelling the signal processes (tt̄ + tt̄V + tt̄H) for the data analysis and for comparisons with the measured cross-sections and differential distributions. All samples used the NNPDF3.0NLO PDF set with the exception of the two Sherpa samples, which used NNPDF3.0NNLO. The different blocks indicate from top to bottom the samples used as nominal MC (nom.), systematic variations (syst.) and for comparison only (comp.). For details see section 3. boson mass was set to 125 GeV and all possible Higgs decay modes were allowed, with the branching fractions calculated with HDECAY [36, 37]. The tt̄W and tt̄Z samples are normalised to cross-sections calculated to NLO in αs with MadGraph5 aMC@NLO. The tt̄H sample is normalised to a cross-section calculated to NLO accuracy in QCD, including NLO electroweak corrections [36]. Alternative tt̄ samples were generated to assess the uncertainties due to a particular choice of QCD MC model for the production of the additional b-jets and to compare with unfolded data, as listed in table 1. In order to investigate the effects of initial- and final-state radiation, two samples were generated using Powheg+Pythia 8 with the renormalisation and factorisation scales varied by a factor of 2 (0.5) and using low-radiation (high-radiation) variations of the A14 tune and an hdamp value of 1.5mt (3.0mt ), corresponding to less (more) parton shower radiation [33]. These samples are called Powheg+Pythia 8 (RadLo) and Powheg+Pythia 8 (RadHi) in the following. To estimate the effect of the choice of parton shower and hadronisation algorithms, a MC sample was generated by interfacing Powheg with Herwig 7 [38, 39] (v7.01) using the H7UE set of tuned parameters [39]. –6– In order to estimate the effects of QCD scales, and matching and merging algorithms used in the NLO tt̄ matrix element calculation and the parton shower to predict additional b-jets, events were generated with the Sherpa 2.2.1 generator [40], which models the zero and one additional-parton process at NLO accuracy and up to four additional partons at LO accuracy, using the MePs@Nlo prescription [41]. Additional b-quarks were treated as massless and the NNPDF3.0NNLO PDF set was used. The calculation uses its own parton shower tune. This sample is referred to as Sherpa 2.2 tt̄. In addition to the tt̄ samples described above, a tt̄ sample was generated using the MadGraph5 aMC@NLO [34] (v2.3.3) generator, interfaced to Pythia 8.210 and is referred to as MadGraph5 aMC@NLO+Pythia 8 hereafter. As with the nominal JHEP04(2019)046 Powheg+Pythia 8 tt̄ sample, the NNPDF3.0NLO PDF set was used in the matrix element calculation and the NNPDF2.3LO PDF set was used in the parton shower. This sample is used to calculate the fraction of tt̄ +V /H events in tt̄ events and to compare with the data. The A14 set of tuned parameters was used for Pythia. The tt̄ samples are normalised to a cross-section of σtt̄ = 832+46 −51 pb as calculated with the Top++2.0 program to next-to-next-to-leading order (NNLO) in perturbative QCD, including soft-gluon resummation to next-to-next-to-leading-log (NNLL) order (see ref. [42] and references therein), and assuming mt = 172.5 GeV. The uncertainty in the theoretical cross-section comes from independent variations of the factorisation and renormalisation scales and variations in the PDF and αS , following the PDF4LHC prescription with the MSTW 2008 NNLO, CT10 NNLO and NNPDF2.3 5f FFN PDF sets (see ref. [43] and references therein, and refs. [44–46]). Four more predictions were calculated only for comparisons with data and are all based on tt̄bb̄ matrix element calculations. These predictions all use the same renormalisation and factorisation scale definitions as the study presented in ref. [36]. The renormalisation scale, Q 1/4 µR , is set to µR = i=t,t̄,b,b̄ ETi , where ETi refers to the transverse energy of the parton i in the partonic final state, and the factorisation scale, µF , is set to HT /2 which is defined as 1 X µF = HT /2 = ET,i , 2 i=t,t̄,b,b̄,j where j refers to the additional QCD-radiated partons at NLO. Three of the four predictions are based on the Powheg method, and use the Pythia 8 parton shower with the same parton shower tune and the same matching settings as the nominal Powheg+Pythia 8 sample, with the exception of the hdamp parameter, which is set to the same value as the factorisation scale, i.e. HT /2. In the tt̄bb̄ matrix element calculations with massive b-quarks, the b-quark mass is set to mb = 4.75 GeV. The set-up of the four dedicated samples are described below. A sample of tt̄bb̄ events was generated using Sherpa+OpenLoops [2]. The tt̄bb̄ matrix elements were calculated with massive b-quarks at NLO, using the Comix [47] and Open- Loops [48] matrix element generators, and merged with the Sherpa parton shower, tuned by the authors [49]. The four-flavour NNLO NNPDF3.0 PDF set was used. The resumma- tion scale, µQ , was set to the same value as µF . This sample is referred to as Sherpa 2.2 tt̄bb̄ (4FS). A sample of tt̄bb̄ events was generated using the PowHel generator [3], where the –7– matrix elements were calculated at NLO assuming massless b-quarks and using the five- flavour NLO NNPDF3.0 PDF set. Events were required to have the invariant mass, mbb , of the bb̄ system to be larger than 9.5 GeV and the pT of the b-quark larger than 4.75 GeV as described in ref. [36]. These events were matched to the Pythia 8 parton shower using the Powheg method. This sample is referred to as PowHel+Pythia 8 tt̄bb̄ (5FS). A sample of tt̄bb̄ events using the PowHel generator where the matrix elements were calculated at NLO with massive b-quarks and using the four-flavour NLO NNPDF3.0 PDF set [4]. Events were matched to the Pythia 8 parton shower using the Powheg method. This sample is referred to as PowHel+Pythia 8 tt̄bb̄ (4FS). A sample of tt̄bb̄ events using the Powheg generator where tt̄bb̄ matrix elements were JHEP04(2019)046 calculated at NLO with massive b-quarks and using the four-flavour NLO NNPDF3.0 PDF set [5]. Events were matched to the Pythia 8 parton shower using the Powheg method. This sample is referred to as Powheg+Pythia 8 tt̄bb̄ (4FS) to distinguish it from the nominal Powheg+Pythia 8 sample mentioned above. For all samples involving top quarks, mt was set to 172.5 GeV and the EvtGen v1.2.0 program [50] was used for properties of the bottom and charm hadron decays except for the Sherpa samples. To preserve the spin correlation information, top quarks were decayed following the method of ref. [51] which is implemented in Powheg-Box and by MadSpin [52] in the MadGraph5 aMC@NLO+Pythia 8 samples. Sherpa performs its own calculation for spin correlation. Both of the PowHel+Pythia 8 tt̄bb̄ samples used Pythia to decay the top quarks, with a top-quark decay width of 1.33 GeV, and hence these predictions do not include tt̄ spin correlations. The production of single top-quarks in the tW - and s-channels was simulated using the Powheg-Box (v2, r2819) NLO generator with the CT10 PDF set in the matrix element calculations. Electroweak t-channel single-top-quark events were generated using the Powheg-Box (v1, r2556) generator. This generator uses the four-flavour scheme for the NLO matrix elements calculation together with the fixed four-flavour PDF set CT10f4. For all top processes, top-quark spin correlations are preserved (in the case of the t-channel, top quarks were decayed using MadSpin). The interference between tt̄ and tW production is accounted for using the diagram-removal scheme [53]. The parton shower, fragmentation, and the underlying event were simulated using Pythia 6.428 [54] with the CTEQ6L1 PDF sets and the Perugia 2012 tune (P2012) [55, 56]. The single-top MC samples for the t- and s-channels are normalised to cross-sections from NLO predictions [57, 58], while the tW - channel MC sample is normalised to approximate NNLO [59]. Events containing W or Z bosons with associated jets were simulated using the Sherpa 2.2.1 generator. Matrix elements were calculated for up to two partons at NLO and up to four partons at leading order (LO) using the Comix and OpenLoops matrix element generators and merged with the Sherpa parton shower using the MePs@Nlo prescription. The NNPDF3.0NNLO PDF set was used in conjunction with parton shower tuning developed by the Sherpa authors. The W/Z+jets events are normalised to NNLO cross-sections, computed using Fewz [60] with the MSTW 2008 NNLO PDF set. Diboson processes were simulated using the Sherpa 2.1.1 generator. Matrix elements were calculated using the Comix and OpenLoops matrix element generators and merged –8– with the Sherpa parton shower using the MePs@Nlo prescription. In the case of both bosons decaying leptonically, matrix elements contain all diagrams with four electroweak vertices and were calculated for up to one (four charged leptons or two charged leptons and two neutrinos) or zero partons (three charged leptons and one neutrino) at NLO, and up to three partons at LO. In the cases where one of the bosons decays hadronically and the other leptonically, matrix elements were calculated with up to one (ZZ) or zero (W W, W Z) additional partons at NLO and up to three additional partons at LO. The CT10 PDF set was used in conjunction with parton shower tuning developed by the Sherpa authors. In all MC simulation samples, the effect of multiple pp interactions per bunch crossing (pile-up) was modelled by adding multiple minimum-bias events simulated with Pythia 8.186 [29], JHEP04(2019)046 the A2 set of tuned parameters [61] and the MSTW2008LO set of PDFs [62]. The MC simulation samples are re-weighted to reproduce the distribution of the mean number of interactions per bunch crossing observed in the data. 4 Object reconstruction and identification 4.1 Detector-level object reconstruction A description of the main reconstruction and identification criteria applied for electrons, muons, jets and b-jets is given below. Electrons are reconstructed [63] by matching ID tracks to clusters in the electromag- netic calorimeter. Electrons must satisfy the tight identification criterion, based on a likelihood discriminant combining observables related to the shower shape in the calorime- ter and to the track matching the electromagnetic cluster, and are required to be isolated in both the ID and the EM calorimeter using the pT -dependent isolation working point. Electrons are required to have pT > 25 GeV and |ηcluster | < 2.47. Electrons that fall in the transition region between the barrel and endcap calorimeters (1.37 < |ηcluster | < 1.52) are poorly measured and are therefore not considered in this analysis. Muon candidates are reconstructed [64] by matching ID tracks to tracks in the muon spectrometer. Track reconstruction is performed independently in the ID and MS before a combined track is formed with a global re-fit to hits in the ID and MS. Muon candidates are required to have pT > 25 GeV and |η| < 2.5, must satisfy the medium identification criteria and are required to be isolated using the pT -dependent isolation working point. Electron and muon tracks are required to be associated with the primary vertex. This association requires the electron (muon) track to have |d0 |/σd0 < 5 (3) and |∆z0 sin θ| < 0.5 mm, where d0 and z0 are the transverse and longitudinal impact parameters of the electron (muon) track, respectively, σd0 is the uncertainty in the measurement of d0 , and θ is the angle of the track relative to the axis parallel to the beamline. Reconstruction, identification and isolation efficiencies of electrons (muons) are cor- rected in simulation to match those observed in data using Z → e+ e− (µ+ µ− ) events, and the position and width of the observed Z boson peak is used to calibrate the electron (muon) energy (momentum) scale and resolution. The anti-kt algorithm [65] with a radius parameter of R = 0.4 is used to reconstruct jets with a four-momentum recombination scheme, using energy deposits in topological clusters –9– in the calorimeter as inputs [66]. Jets are calibrated using a series of simulation-based corrections and in situ techniques [67]. Calibrated jets are required to have pT > 25 GeV and |η| < 2.5 so that data from the ID is available for determining whether they contain b-hadrons. Jets with pT < 60 GeV and |η| < 2.4 are required to be identified as originating from the primary vertex using a jet-vertex tagger (JVT) algorithm [68]. Jets containing b-hadrons are identified exploiting the lifetimes of b-hadrons and their masses. A multivariate algorithm, MV2c10, that combines track and secondary-vertex information is used to distinguish b-jets from other jets [69]. Four working points are defined by different b-tagging discriminant output thresholds corresponding to efficiencies JHEP04(2019)046 of 85%, 77%, 70% and 60% in simulated tt̄ events for b-jets with pT > 20 GeV and rejection factors ranging from 3–35 for c-jets and 30–1500 for light-flavour jets [19, 69]. After selecting electrons, muons and jets as defined above, several criteria are applied to ensure that objects do not overlap. If a selected electron and muon share a track then the electron is rejected. If an electron is within ∆R = 0.2 of one or more jets then the closest jet to the electron is removed. If there are remaining jets within ∆R = 0.4 of an electron then the electron is removed. When a jet is within ∆R = 0.4 of a muon, it is removed if it has fewer than three tracks, otherwise the muon is removed. 4.2 Particle-level object definitions Particle-level objects are selected in simulated events using definitions that closely match the detector-level objects defined in section 4.1. Particle-level objects are defined using stable particles having a proper lifetime greater than 30 ps. This analysis considers electrons and muons that do not come from hadron decays for the fiducial definition.2 In order to take into account final-state photon radiation, the four-momentum of each lepton is modified by adding to it the four-momenta of all photons, not originating from a hadron, that are located within a ∆R = 0.1 cone around the lepton. Electrons and muons are required to have pT > 25 GeV and |η| < 2.5. Jets are clustered using the anti-kt algorithm with a radius parameter of 0.4. All stable particles are included except those identified as electrons and muons, and the photons added to them, using the definition above and neutrinos not from hadron decays. These jets do not include particles from pile-up events but do include those from the underlying event. The decay products of hadronically decaying τ -leptons are therefore included. Jets are required to have pT > 25 GeV and |η| < 2.5. Jets are identified as b-jets by requiring that at least one b-hadron with pT > 5 GeV is matched to the jet by ghost association [70]. Here, the ghost-association procedure includes b-hadrons in the jet clustering after scaling their pT to a negligible value. A similar procedure is followed to define c-jets, with the b-jet definition taking precedence, i.e. a jet containing one b-hadron and one c-hadron is defined as a b-jet. Jets that do not contain either a b-hadron or a c-hadron are considered to be light-flavour jets. 2 Electrons and muons from τ decays are thus included. – 10 – Electrons and muons that meet the selection criteria defined above are required to be separated from selected jets by ∆R(lepton, jet) > 0.4. This ensures compatibility with the detector-level selection defined in section 4.1. 5 Event selection and definition of the fiducial phase space 5.1 Data event selection The data analysed were collected by the ATLAS detector in 2015 and 2016 during stable √ pp collisions at s = 13 TeV while all components of the ATLAS detector were fully operational. The total integrated luminosity recorded in this period is 36.1 fb−1 . JHEP04(2019)046 In order to ensure events originate from pp collisions, events are required to have at least one primary vertex with at least two tracks. The primary vertex is defined as the P 2 vertex with the highest pT of tracks assigned to it. Single-electron or single-muon triggers are used to select the events. They require a pT of at least 20 (26) GeV for muons and 24 (26) GeV for electrons for the 2015 (2016) data set and also include requirements on the lepton quality and isolation. These triggers are complemented by others with higher pT requirements but loosened isolation requirements to ensure maximum efficiencies at higher lepton pT . In the eµ channel, events are required to have exactly one electron and one muon of pT > 27 GeV and with opposite electric charge. At least one of the two leptons must be matched in flavour and angle to a trigger object. In the lepton + jets channel, exactly one selected lepton of pT > 27 GeV is required and must be matched to the trigger object that triggered the event. In the eµ channel, at least two jets are required and at least two of these must be b-tagged at the 77% efficiency b-tagging working point for the baseline selection. The measurement of the fiducial cross-section with one (two) additional b-jets requires at least three (at least four) jets to be b-tagged. For the measurement of the b-jet multiplicity distribution, at least two jets are required and at least two of them must be b-tagged. All other differential cross-section measurements in the eµ channel require at least three jets and at least three of these must be b-tagged. In the lepton+jets channel, at least five jets are required and at least two of these must be b-tagged for the baseline selection. For the measurement of the fiducial cross-section with one (two) additional b-jets, five (six) jets are required, of which at least three (at least four) must be b-tagged. For the measurement of the differential cross-sections, at least six jets, at least four of which are b-tagged, are required. In this channel, b-jets are identified using the tighter 60% efficiency b-tagging working point to better suppress c-jets from W − → c̄s or W + → cs̄ decays. 5.2 Fiducial phase-space definition The phase space in which the fiducial cross-section is measured is defined using particle- level objects with kinematic requirements similar to those placed on reconstructed objects in the event selection. The definitions of the fiducial phase spaces used for the cross- sections measurements are given below. The data are corrected to particle level using – 11 – slightly different definitions of the fiducial phase space depending on the top-pair decay channel and on the observable. In the eµ channel, fiducial cross-sections are determined by requiring exactly one elec- tron and one muon with opposite-sign charge at particle level and at least three (at least four) b-jet(s) for the fiducial cross-section with one (two) additional b-jets. The normalised differential cross-sections are measured in the fiducial volume containing the leptons and at least two b-jets for the distribution differential in number of b-jets and at least three b-jets for all other differential measurements. In the lepton + jets channel, the fiducial phase space for the measurement of the integrated cross-section with one (two) additional b-jet(s) is defined as containing exactly JHEP04(2019)046 one particle-level electron or muon and five (six) jets, at least three (four) of which are b-jets. Differential cross-sections are measured in a fiducial volume containing at least six jets and where at least four of them are required to be b-jets. 6 Background estimation The baseline selection with at least two b-tagged jets results in a sample with only small backgrounds from processes other than tt̄ production. As mentioned before, events with additional b-jets produced in tt̄V or tt̄H production are treated as signal. The estimation of tt̄ production in association with additional light-flavour jets or c-jets is described in section 7.1 and is performed simultaneously with the extraction of fiducial cross-sections. The remaining background events are classified into two types: those with prompt leptons from single top, W or Z decays (including those produced via leptonic τ decays), which are discussed in section 6.1, and those where at least one of the reconstructed lepton candidates is non-prompt or “fake” (NP & fake lep.), i.e. a non-prompt lepton from the decay of a b- or c-hadron, an electron from a photon conversion, hadronic jet activity misidentified as an electron, or a muon produced from an in-flight decay of a pion or kaon. This is estimated using a combined data-driven and simulation-based approach in the eµ channel, and a data-driven approach in the lepton + jets channel, both of which are described in section 6.2. 6.1 Background from single-top, Z/γ ∗ + jets and W + jets events The background from single top-quark production is estimated from the MC simulation predictions in both the eµ and lepton + jets channels. This background contributes 3% of the event yields in both channels, with slightly smaller contributions in the four b-jets selections. In the eµ channel, a very small number of events from Drell-Yan production and ∗ Z/γ (→ τ τ )+jets fulfil the selection criteria. This background is estimated from MC simulation scaled to the data with separate scale factors for the two-b-tagged jets and three- b-tagged jets cases. The scale factors are derived from data events that have a reconstructed mass of the dilepton system corresponding to the Z boson mass and that fulfil the standard selection except that the lepton flavour is ee or µµ. The fraction of background events from Z/γ ∗ (→ τ τ )+jets is below two per mill for all b-tagged jet multiplicities. A small number – 12 – of Z/γ ∗ +jets events, where the Z/γ ∗ is decaying into any lepton flavour pair, can enter in the lepton + jets channel and is estimated from MC simulation. In the lepton + jets channel, a small background from W + jets remains after the event selection; however, this contribution is below 2% in events that have at least three b-tagged jets. This background is estimated directly from MC simulation. 6.2 Background from non-prompt and fake leptons In the eµ channel, the normalisation of this background is estimated from data using events in which the electron and muon have the same-sign electric charge. The method is described JHEP04(2019)046 in ref. [71]. Known sources of same-sign prompt leptons are subtracted from the data and the non-prompt and fake background is extracted by scaling the remaining data events by a transfer factor determined from MC simulation. This transfer factor is defined as the ratio of predicted opposite-sign to predicted same-sign non-prompt and fake leptons. In the lepton + jets channel, the background from non-prompt and fake leptons is estimated using the matrix method [72]. A sample enriched in non-prompt and fake leptons is obtained by removing the isolation and impact parameter requirements on the lepton selections defined in section 4. The efficiency for these leptons, hereafter referred to as loose leptons, to meet the identification criteria defined in section 4.1 is then measured separately for prompt and fake leptons.3 For both electrons and muons the efficiency for a prompt loose lepton to pass the identification criteria defined in section 4.1 is measured using a sample of Z boson decays. The efficiency for fake loose leptons to pass the identification criteria is measured using events that have low missing transverse momentum for electrons and high lepton impact-parameter significance for muons. These efficiencies allow the number of fake leptons selected in the signal region to be estimated. 3 Here fake leptons also include non-prompt leptons. – 13 – Events Events 109 ATLAS Data Data tt 108 ATLAS tt 108 s = 13 TeV, 36.1 fb-1 ttH s = 13 TeV, 36.1 fb-1 ttH eµ channel 107 ≥ 2b @77% pre-fit ttV 107 lepton+jets channel ≥5j ≥2b @60% pre-fit ttV Single top Single top 106 Z/γ *+jets 106 W+jets Z+jets Diboson 105 Diboson 105 NP & fake lep. NP & fake lep. Syst. 104 Syst. 104 103 103 102 102 JHEP04(2019)046 10 10 1 1 Data/Pred. Data/Pred. 1.4 1.2 1.2 1.1 1 1 0.8 0.9 0.6 0.8 2 3 ≥4 2 3 4 ≥5 N b-jets N b-jets (a) (b) Figure 2. Comparison of the data distributions with predictions for the number of b-tagged jets, in events with at least 2 b-tagged jets, in the (a) eµ and (b) lepton + jets channels. The systematic uncertainty band, shown in grey, includes all uncertainties from experimental sources. Events / GeV Events / GeV 50 ATLAS Data 400 Data tt ATLAS tt s = 13 TeV, 36.1 fb-1 350 s = 13 TeV, 36.1 fb-1 ttH ttH eµ channel lepton+jets channel ttV 40 ≥ 3b @77% pre-fit ttV ≥5j ≥3b @60% pre-fit Single top Single top 300 W+jets Z/γ *+jets Z+jets 30 Diboson 250 Diboson NP & fake lep. NP & fake lep. 200 Syst. Syst. 20 150 100 10 50 0 0 Data/Pred. Data/Pred. 1.4 1.4 1.2 1.2 1 1 0.8 0.8 0.6 0.6 50 100 150 200 250 300 50 100 150 200 250 300 b b p 1 [GeV] p 1 [GeV] T T (a) (b) Figure 3. Comparison of the data distributions with predictions for the leading b-tagged jet pT , in events with at least 3 b-tagged jets, in the (a) eµ and (b) lepton + jets channels. The systematic uncertainty band, shown in grey, includes all uncertainties from experimental sources. Events that fall outside of the range of the x-axis are not included in the plot. – 14 – Process 2b ≥ 3b ≥ 4b Signal (tt̄ + tt̄H + tt̄V ) 74 400 ± 2 900 3 200 ± 310 210 ± 29 tt̄ 74 200 ± 2 900 3 100 ± 310 190 ± 29 tt̄H 45.3 ± 6.6 36.5 ± 7.0 9.4 ± 3.3 tt̄V 190 ± 16 33.5 ± 6.7 4.4 ± 2.2 Background 3 150 ± 810 140 ± 53 9.2 ± 5.6 Single top 2 460 ± 540 96 ± 32 4.1 ± 2.5 JHEP04(2019)046 NP and fake lep. 600 ± 600 43 ± 43 5.1 ± 5.1 Z/γ ∗ +jets 53 ± 13 1.3 ± 0.3 0.07 ± 0.02 Diboson 38 ± 20 1.0 ± 1.1 < 0.01 Expected 77 600 ± 3 000 3 320 ± 320 216 ± 30 Observed 76 425 3 809 267 Table 2. Predicted and observed eµ channel event yields in 2b, ≥ 3b and ≥ 4b selections. The quoted errors are symmetrised and indicate total statistical and systematic uncertainties in predic- tions due to experimental sources. Process ≥ 5j, ≥ 2b ≥ 5j, ≥ 3b ≥ 5j, = 3b ≥ 6j, ≥ 4b Signal (tt̄ + tt̄H + tt̄V ) 429 000 ± 42 000 23 700 ± 2 200 22 300 ± 2 100 1 130 ± 110 tt̄ 426 000 ± 42 000 23 000 ± 2 200 21 700 ± 2 100 1 030 ± 110 tt̄H 1 250 ± 58 437 ± 23 351 ± 18 68.3 ± 5.8 tt̄V 2 020 ± 110 250 ± 16 215 ± 14 28.3 ± 2.8 Background 39 500 ± 7 900 2 230 ± 470 2 110 ± 450 87 ± 23 Single top 16 400 ± 2 000 856 ± 99 803 ± 94 35.7 ± 6.5 NP and fake lep. 11 000 ± 5 500 740 ± 380 710 ± 360 32 ± 21 W +jets 8 600 ± 5 300 440 ± 270 410 ± 260 11.0 ± 6.9 ∗ Z/γ +jets 2 960 ± 480 164 ± 26 155 ± 26 5.9 ± 1.5 Diboson 529 ± 80 34.0 ± 5.6 32.0 ± 5.5 1.79 ± 0.58 Expected 469 000 ± 42 000 26 000 ± 2 300 24 400 ± 2 200 1 220 ± 110 Observed 469 793 28 167 26 389 1 316 Table 3. Predicted and observed lepton + jets event yields in the ≥ 5j ≥ 2b, ≥ 5j ≥ 3b, ≥ 5j = 3b, and ≥ 6j ≥ 4b selections. The quoted uncertainties are symmetrised and indicate total statistical and systematic uncertainties in predictions due to experimental sources. – 15 – 6.3 Data and prediction comparison of baseline selection The overall number of events fulfilling the baseline selection is well described by the pre- diction in both channels, as seen in tables 2 and 3 and figure 2, where b and j denote a b-jet and a jet of any flavour, respectively. However, the number of events with more than two b-tagged jets is slightly underestimated, as shown in figures 2 and 3. Therefore, data-driven scale factors are derived to correct the predictions of additional c-jets or light jets in the tt̄ MC simulation, as described in the next section. 7 Extraction of the fiducial cross-sections JHEP04(2019)046 Fiducial cross-sections in the phase spaces defined in section 5.2 for the different observables are extracted from detector-level distributions obtained after the event selections described in section 5.1 and subtracting the number of background events produced by the non-tt̄ processes described in section 6. After the subtraction of non-tt̄ background, the data suffer from backgrounds from tt̄ events with additional light-flavour jets (tt̄l) or c-jets (tt̄c) that are misidentified as b-jets by the b-tagging algorithm. The correction factors for these backgrounds are measured in data, as presented in section 7.1. The data are then unfolded using the corrected MC simulation as described in section 7.2. 7.1 Data-driven correction factors for flavour composition of additional jets in tt̄ events The measurement of tt̄ + b-jets production is dependent on the determination of the back- ground from other tt̄ processes. For example, according to simulation studies in the eµ channel, only about 50% of the events selected at detector level with at least three b-tagged jets at the 77% efficiency working point and within the fiducial phase space of the analysis, also have at least three b-jets at particle level. The other events contain at least one c-jet or light-flavour jet which is misidentified as a b-jet. The cross-section of tt̄ with additional jet production has been measured with 10% (16%) uncertainty for events with two (three) additional jets [73]. However, these measurements did not determine the flavours of the additional jets. Due to the lack of precise measurements of these processes, template fits to data are performed to extract the tt̄b signal yields and estimate the tt̄c and tt̄l backgrounds as described in the following. The templates are constructed from tt̄, tt̄H and tt̄V MC simulated samples, as the signal includes the contributions from tt̄V and tt̄H. The events in the eµ channel are selected within an analysis region consisting of at least three b-tagged jets at the 77% b-tagging working point as specified in section 5.1. This avoids extrapolation of the background shapes determined outside the selected region into the analysis region. The fit in the lepton + jets channel is performed on a sample with at least five jets, at least two of which are b-tagged with a b-tagging efficiency of 60%. While this means that the MC simulation is needed to extrapolate the results of the fit into the signal regions, it allows the tt̄l background to be extracted in what is effectively a control region. The lepton + jets channel suffers from an additional background due to W + → cs̄ or corresponding W − decays in the inclusive tt̄ process, where the c-jet is misidentified as – 16 – Category eµ lepton + jets tt̄b ≥3 b-jets ≥3 b-jets tt̄c < 3 b-jets and ≥ 1 c-jet < 3 b-jets and ≥ 2 c-jets tt̄l events that do not meet above criteria events that do not meet above criteria Table 4. Event categorisation (for the definition of the MC templates) based on the particle-level selections of b-jets, c-jets and light-flavour jets. a b-jet. In order to separate this background from tt̄+c-jets events, events containing only JHEP04(2019)046 one particle-level c-jet are attributed to this background and grouped into a tt̄l class, while those with two particle-level c-jets are placed into a tt̄c class, as summarised in table 4. In this sample, 85% of the events with exactly one particle-level c-jet are found to contain W → cs̄(c̄s) decays, according to tt̄ MC simulation. Templates are created for events in the different categories described in table 4 using the b-tagging discriminant value of the jet with the third-highest b-tagging discriminant in the eµ channel, and the two jets with the third- and fourth-highest b-tagging discriminant values in the lepton + jets channel. The discriminant values are divided into five b-tagging discriminant bins such that each bin corresponds to a certain range of b-tagging efficiencies defined by the working points. The bins range from 1 to 5, corresponding to efficiencies of 100%–85%, 85%–77%, 77%– 70%, 70%–60%, and < 60% respectively. In the eµ channel, one-dimensional templates with three bins are formed corresponding to b-tagging efficiencies between 77% and 0% for the jet with the third highest b-tagging discriminant value. In the lepton + jets channel, two-dimensional templates are created using the b-tagging discriminant values of the two jets with the third- and fourth-highest b-tagging discriminant values, corresponding to b-tagging efficiencies between 100% and 0% for the two jets. In both channels, one template is created from the sum of all backgrounds described in section 6 and three templates are created from tt̄, tt̄V and tt̄H MC simulations, to account for tt̄b, tt̄c and tt̄l events, as detailed in table 4. These templates are then fitted to the data using a binned maximum-likelihood fit, with a Poisson likelihood n Y e−νk (~α) νk (~ α ) xk L(~ α|x1 , . . . , xn ) = , xk ! k where xk is the number of events in bin k of the data template and νk (~ α) is the expected number of events, and depends upon a number of free parameters, α ~. In the eµ channel, two free parameters are used, such that the expected number of events in bin k is   νk (αb , αcl ) = αb Ntkt̄b + αcl Ntkt̄c + Ntkt̄l + Nnon-t k t̄ , where Ntkt̄b , Ntkt̄c , Ntkt̄l and Nnon-t k t̄ are the numbers of events in bin k of the tt̄b, tt̄c, tt̄l and non-tt̄ background templates, respectively. The scale factors obtained from the fit are αb = 1.37 ± 0.06 and αcl = 1.05 ± 0.04, where the quoted uncertainties are statistical only. – 17 – Events Events 2500 Data ATLAS 107 ATLAS ttc+ttl αcl = 1.05 ± 0.04 s = 13 TeV, 36.1 fb-1 s = 13 TeV, 36.1 fb-1 Data eµ channel ttb αb = 1.37 ± 0.06 lepton+jets channel 2000 6 ≥5j ≥2b @60% ttl αl = 0.962 ± 0.003 ≥ 3b @77% Non-tt 10 Pre-fit ttc αc = 1.59 ± 0.06 5 ttb αb = 1.11± 0.02 1500 10 Non-tt Pre-fit 1000 104 500 103 102 JHEP04(2019)046 0 Pred./Data Pred./Data 1.4 Pre-fit Post-fit 1.2 Pre-fit Post-fit 1.2 1 1 0.8 0.8 0.6 3rd jet 1 2 3 4 5 2 3 4 5 3 4 5 4 5 5 3 4 5 4th jet 1 1 1 1 1 2 2 2 2 3 3 3 4 4 5 b -tag discriminant bin b -tag discriminant bin (a) (b) Figure 4. The b-tagging distribution of the third-highest b-tagging discriminant-ranked jet for the (a) eµ channel, and of the third and fourth b-tagging discriminant-ranked jet for the (b) lepton+jets channel. For clarity, the two-dimensional lepton + jets templates have been flattened into one dimension. The ratios of total predictions before and after the fit to the data are shown in the lower panel. The vertical bar in each ratio represents only the statistical uncertainty, and the grey bands represent the total error including systematic uncertainties from experimental sources. The extracted scale factors αb , αc , αl , αcl are given considering only statistical uncertainties. Figure 4a shows the distributions of the templates before and after scaling the templates by these scale factors. In the lepton + jets channel, three free parameters, αb , αc and αl , are used in the maximum-likelihood fit, such that the expected number of events in bin k is νk (αb , αc , αl ) = αb Ntkt̄b + αc Ntkt̄c + αl Ntkt̄l + Nnon-t k t̄ . (7.1) The best-fit values of the free parameters are αb = 1.11 ± 0.02, αc = 1.59 ± 0.06 and αl = 0.962 ± 0.003 where the quoted uncertainties are statistical only. Including systematic uncertainties, the values of αb extracted in the eµ and lepton + jets channels are found to be compatible at a level better than 1.5 standard deviations. Some of the dominant common systematic uncertainties have small correlations between the two channels, while the uncertainty in αb due to the modelling of the tt̄c template in the eµ channel, as discussed in section 8.3 is uncorrelated between the two channels. Taking only this uncertainty as uncorrelated, the values of αb extracted from the two channels are found be compatible at a level better than 1.7 standard deviations. Figure 4b shows the distribution of the b- tagging discriminant before and after the fit. For clarity, the two-dimensional lepton + jets templates are flattened into a single dimension. Figures 5 and 6 show the comparison of data and predictions for the b-tagged jet multiplicity and the leading b-tagged jet pT in the eµ and lepton + jets channels after the tt̄b signal, and the tt̄c and tt̄l backgrounds, are – 18 – Events Events 109 ATLAS Data Data tt 108 ATLAS tt 108 s = 13 TeV, 36.1 fb-1 ttH s = 13 TeV, 36.1 fb-1 ttH eµ channel 107 ≥ 2b @77% post-fit ttV 107 lepton+jets channel ≥5j ≥2b ttV Single top Single top 6 W+jets 106 Z/γ *+jets 10 Z+jets Diboson 105 Diboson 105 NP & fake lep. NP & fake lep. Syst. 104 Syst. 104 103 103 102 102 JHEP04(2019)046 10 10 1 1 Data/Pred. Data/Pred. 1.4 1.2 1.2 1.1 1 1 0.8 0.9 0.6 0.8 2 3 ≥4 2 3 4 ≥5 N b-jets N b-jets (a) (b) Figure 5. Comparison of the data distributions with predictions, after applying scale factors, for the number of b-tagged jets, in events with at least 2 b-tagged jets, in the (a) eµ and (b) lepton + jets channels. The systematic uncertainty band, shown in grey, includes all uncertainties from experimental sources. Events / GeV Events / GeV 50 ATLAS Data 400 ATLAS Data tt tt s = 13 TeV, 36.1 fb-1 s = 13 TeV, 36.1 fb-1 ttH eµ channel ttH 350 lepton+jets channel ttV 40 ≥ 3b @77% post-fit ttV ≥5j ≥3b Single top Single top 300 W+jets Z/γ *+jets Z+jets 30 Diboson 250 Diboson NP & fake lep. NP & fake lep. Syst. Syst. 200 20 150 100 10 50 0 0 Data/Pred. Data/Pred. 1.4 1.4 1.2 1.2 1 1 0.8 0.8 0.6 0.6 50 100 150 200 250 300 50 100 150 200 250 300 b b p 1 [GeV] p 1 [GeV] T T (a) (b) Figure 6. Comparison of the data distributions with predictions for the leading b-tagged jet pT , after applying scale factors, in events with at least 3 b-tagged jets, in the (a) eµ and (b) lepton + jets channels. The systematic uncertainty band, shown in grey, includes all uncertainties from experimental sources. Events that fall outside of the range of the x-axis are not included in the plot. – 19 – scaled by the extracted scale factors. The data are described much better by the prediction after the scaling is applied. 7.2 Unfolding The measured distributions at detector level are unfolded to the particle level. The unfold- ing procedure corrects for resolution effects and for detector efficiencies and acceptances. j First, the number of non-tt̄ background events in bin j (Nnon-t t̄-bkg ), described in section 6, j is subtracted from the data distribution at the detector level in bin j (Ndata ). This re- tains a mixture of signal and tt̄-related backgrounds, the latter coming from mis-tagged JHEP04(2019)046 events as described in section 7.1. A series of corrections are then applied, with all correc- tions derived from simulated tt̄, tt̄H and tt̄V events. Following the subtraction of non-tt̄ background, the data are first corrected for mis-tagged events by applying a correction αb Ntjt̄b,reco ftjt̄b = , αb Ntjt̄b,reco + B j where αb is defined in the previous section, Ntjt̄b,reco is the number of detector-level tt̄b events predicted by MC simulation, and B j is the number of detector-level tt̄c and tt̄l events in bin j, after being scaled by the fit parameters, αcl or αc and αl , defined in the previous section. In the eµ channel,   B j = αcl Ntjt̄c,reco + Ntjt̄l,reco , and in the lepton + jets channel, B j = αc Ntjt̄c,reco + αl Ntjt̄l,reco , where Ntjt̄c,reco and Ntjt̄l,reco are the numbers of reconstructed tt̄c and tt̄l events in bin j, j as predicted by MC simulation, respectively. Next, an acceptance correction, faccept , is applied, which corrects for the fiducial acceptance and is defined as the probability of a tt̄b event passing the detector-level selection in a given bin j (Ntjt̄b,reco ) to also fall within the fiducial particle-level phase space (Ntjt̄b,reco∧part ). It is estimated as j Ntjt̄b,reco∧part faccept = . Ntjt̄b,reco The detector-level objects are required to be matched within ∆R = 0.4 to the corresponding particle-level objects. This requirement leads to a better correspondence between the particle and detector levels and improves the unfolding performance. The matching factor j fmatching is defined as j Ntjt̄b,reco∧part∧matched fmatching = , Ntjt̄b,reco∧part where Ntjt̄b,reco∧part∧matched is the subset of reconstructed events falling in the particle-level fiducial volume which are matched to the corresponding particle-level objects. – 20 – The remaining part of the unfolding procedure consists of effectively inverting the migration matrix M to correct for the resolution effects and subsequently correcting for detector inefficiencies. An iterative Bayesian unfolding technique [74], as implemented in the RooUnfold software package [75], is used. The matrix, M, represents the probability for a particle-level event in bin i to be reconstructed in bin j. The chosen binning is optimised for each distribution to have a migration matrix with a large fraction of events on the diagonal and a sufficient number of events in each bin. The Bayesian unfolding technique performs the effective matrix inversion, M−1 ij , iteratively. Four iterations are used for all measured distributions. i corrects for the reconstruction efficiency and is defined as Finally, the factor feff JHEP04(2019)046 i Ntit̄b,part∧reco∧matched feff = , Ntit̄b,part where Ntit̄b,part is the number of tt̄b events passing the particle-level selection in bin i and Ntit̄b,part∧reco∧matched is the number of tt̄b events at particle level in bin i that also pass the detector-level selection, containing matched objects. The unfolding procedure for an observable X at particle level can be summarised by the following expression i dσ fid Nunfold 1 X j j j j j i = i = i i M−1 ij fmatching faccept ftt̄b (Ndata − Nnon-tt̄-bkg ) , dX L ∆X L ∆X feff j where ∆X i is the bin width, Nunfoldi is the number of events in bin i of the unfolded distribution and L is the integrated luminosity. In this paper, the integrated fiducial cross- section σ fid is obtained from P i dσ fid Z fid Nunfold σ = dX = dX L and is used as a normalisation factor such that results are presented in terms of a relative differential cross-section as 1/σ fid · dσ fid /dX i . 8 Systematic uncertainties In this section, the statistical and systematic uncertainties considered in this analysis are described. Experimental sources of uncertainty are described in section 8.1, sources of uncertainty due to tt̄ modelling are described in section 8.2 and uncertainties due to the treatment of the tt̄ (tt̄c and tt̄l) and non-tt̄ background processes are described in sec- tions 8.3 and 8.4, respectively. The method used to propagate the effects of systematics uncertainties to the final results are described in section 8.5. The impact of these uncertain- ties on the fiducial and differential cross-section measurements are discussed in section 9. 8.1 Experimental uncertainties The uncertainty in the combined 2015+2016 integrated luminosity is 2.1%. It is derived, following a methodology similar to that detailed in ref. [76], and using the LUCID-2 detector – 21 – for the baseline luminosity measurements [77], from a calibration of the luminosity scale using x–y beam-separation scans. The uncertainty in the pile-up reweighting of the reconstructed events in the MC sim- ulation is estimated by comparing the distribution of the number of primary vertices in the MC simulation with the one in data as a function of the instantaneous luminosity. Differences between these distributions are adjusted by scaling the mean number of pp in- teractions per bunch crossing in the MC simulation and the ±1σ uncertainties are assigned to these scaling factors. The pile-up weights are recalculated after varying the scale factors within their uncertainties. As discussed in section 4, scale factors to correct differences seen in the lepton re- JHEP04(2019)046 construction, identification and trigger efficiency between the data and MC simulation are derived using a tag-and-probe technique in Z → e+ e− and Z → µ+ µ− events [63, 64, 78]. The electron (muon) momentum scale and resolution are determined using the measure- ment of the position and width of the Z boson peak in Z → e+ e− (µ+ µ− ) events [63, 64, 78]. The lepton uncertainties considered in this analysis are considerably smaller than the jet and flavour-tagging uncertainties. The JVT is calibrated using Z (→ µµ) + jet events where the jet balances the pT of the Z boson. Scale factors binned in jet pT are applied to each event in order to correct for small differences in the JVT efficiency between the data and MC simulation. The scale factors are 0.963 ± 0.006 for jets with 20 < pT < 30 GeV, getting closer to one with smaller uncertainties as the jet pT increases. The uncertainty in the efficiency to pass the JVT requirement is evaluated by varying the scale factors within their uncertainties [79]. Jets are calibrated using a series of simulation-based corrections and in situ tech- niques [67]. The uncertainties due to the jet energy scale (JES) are estimated using a combination of simulations, test-beam data and in situ measurements. Contributions from the jet-flavour composition, η-intercalibration, leakage of the hadron showers beyond the extent of the hadronic calorimeters (punch-through), single-particle response, calorimeter response to different jet flavours, and pile-up are taken into account, resulting in 21 orthog- onal uncertainty components. The total uncertainty due to the JES is one of the dominant uncertainties in this analysis. The jet energy resolution (JER) is measured using both data and simulation. First, the “true” resolution is measured by comparing the particle and reconstructed jet pT in MC simulation as a function of the jet pT and η. Second, an in situ measurement of the JER is made using the bisector method in dijet events [80]. The resolution in data and MC simulation are compared and the energies of jets in the MC simulation are smeared to match the resolution observed in data. The uncertainties in the JER stem from uncertainties in both the modelling and the data-driven method. Differences in the b-tagging and c-jet mis-tag efficiencies between the data and MC simulation are corrected using scale factors derived from dilepton tt̄ events and lepton+jets tt̄ events, respectively. A negative-tag method is used to calibrate mis-tagged light-flavour (u, d, s) jets [81]. The scale factors are measured for different b-tagging working points and as a function of jet kinematics, namely the jet pT for the b-tagging efficiency and c-jet mis-tag scale factors, and the jet pT and η for the light-flavour jet mis-tag scale factors. – 22 – The c-jet and light-jet mis-tag scale factors are known to a precision of 6–22% [82] and 15– 75% [81], respectively. The associated flavour-tagging uncertainties, split into eigenvector components, are computed by varying the scale factors within their uncertainties. In total, there are 30 components related to the b-tagging efficiencies and 15 (80) components related to the mis-tag rates of c-jets (light-flavour jets). Due to the large number of b-tagged jets in each event used in this analysis, the total uncertainty due to b-tagging is one of the dominant uncertainties in this analysis. 8.2 Modelling systematic uncertainties Uncertainties due to the choice of tt̄ MC generator are evaluated by unfolding alternative JHEP04(2019)046 tt̄ samples, described in section 3 and presented in table 1, with the nominal unfolding set- up. Uncertainties related to the choice of matrix element generator (labelled “generator” uncertainty) are evaluated using the Sherpa 2.2 tt̄ sample. This generator comes with its own parton shower and hadronisation model; hence these are included in the variation. Uncertainties due to the choice of parton shower and hadronisation model are evaluated using the Powheg+Herwig 7 sample, in which only the parton shower and hadronisation model is varied relative to the nominal Powheg+Pythia 8 sample. Additionally, two MC samples are used to evaluate an uncertainty in the modelling of initial- and final-state radiation, namely the RadHi and RadLo samples described in section 3. The uncertainty due to the choice of PDF is evaluated following the PDF4LHC pre- scription [83] using event weights that are available in the nominal Powheg+Pythia 8 sample. The uncertainty in the tt̄H cross-section is evaluated by scaling the tt̄H compo- nent of the prediction by factors of zero and two, with the nominal values being taken from theoretical predictions. A factor of two is chosen as this is the current 95% confidence-level upper limit on the tt̄H → bb̄ signal strength as measured by ATLAS [12]. The uncertainty in the tt̄V cross-section is evaluated by varying the tt̄V component of the prediction up and down by 30% to cover the measured uncertainty in this process [84]. 8.3 Uncertainty in tt̄c and tt̄l background Since the tt̄c and tt̄l backgrounds in the eµ channel are determined within a single fit, the uncertainty in this result is determined by changing the sample composition. This is achieved by loosening the b-tagging requirement on the jet with the third-highest b-tagging discriminant value, such that it is tagged at the 85% b-tagging efficiency working point or not required to be b-tagged at all. This results in the templates having more bins and allows the likelihood to be modified such that three free parameters are used in the fit. The number of expected events is then given by eq. (7.1). With these looser selections the values of αc vary by about 40% and this is used as a systematic uncertainty in the tt̄c template. The validity of this uncertainty is checked by investigating the variations in the values of the tt̄c scale factors after fitting to pseudo-data from alternative MC samples and it is found to cover the uncertainties in the tt̄c template modelling. The values of αl remain consistent within the statistical uncertainty in fits with looser selections. After propagating the uncertainty in the tt̄c template through the nominal fit set-up, by varying the input tt̄c template by ±40% before performing the fit, the value of αb is found to change by ±11%, – 23 – while the value of αcl changes by ±7%. When evaluating systematic uncertainties related to the choice of tt̄ model in the eµ channel, double counting of these uncertainties with uncertainties associated with the difference of tt̄b, tt̄c and tt̄l fractions in the alternative MC samples is avoided by repeating the flavour-composition fits for each systematic model. In the lepton + jets channel uncertainties in the flavour composition are taken directly from the samples used to evaluate systematic uncertainties in the modelling, as described in section 8.2. 8.4 Uncertainty in non-tt̄ background estimation The uncertainty in the single-top background is evaluated by comparing the nominal single- JHEP04(2019)046 top tW sample (with overlap with tt̄ removed via the diagram-removal scheme) with an alternative sample generated using the diagram-subtraction scheme [53]. Potential effects of QCD radiation on the single-top background are estimated using MC simulation predictions where the renormalisation and factorisation scales were varied by factors of 0.5 and 2. The uncertainty in the inclusive single-top cross-section [59] is taken to be +5%−4% . The uncertainty attributed to the W + jets background normalisation is evaluated by varying the renormalisation and factorisation scales in the MC simulation prediction by a factor of two up and down. Furthermore, the uncertainty due to PDFs is estimated by using a set of 100 different PDF eigenvectors recommended in ref. [83]. An additional uncertainty of 30% is assumed for the normalisation of the W +heavy-flavour jets cross-section, based on MC simulation comparisons performed in the context of ref. [12]. The uncertainty in the non-prompt or fake lepton background is obtained by varying the estimate of this background by a factor of ±50% (±100%) in the lepton + jets (eµ) channel. No shape uncertainty is applied, as this background is small in both channels. The uncertainty in the Drell-Yan background normalisation is evaluated by varying the estimate of this background by ±25%. It accounts for the impact of the reconstructed- mass resolution of the Z boson in the Z → ee and Z → µµ events, for the background contribution of the tt̄ events in the Z + jets selection, and for differences in the scale factors obtained from each of the individual Z → ee and Z → µµ decay channels relative to the nominal scale factor obtained from the combined Z → ee and Z → µµ sample. 8.5 Propagation of uncertainties Pseudo-experiments based on 10 000 histogram replicas are performed to evaluate statis- tical uncertainties for each distribution considered. Each entry for every event is given a random weight drawn from a Poisson distribution with a mean of one. Each of these histograms is then unfolded using the unfolding procedure described in section 7.2. The standard deviation of each bin across all unfolded histogram replicas is then taken as the statistical uncertainty in that bin. This procedure is similar to simply obtaining pseudo- experiments by directly Poisson-fluctuating the measured data distributions, but has the added advantage that correlations between bins of different distributions are conserved. This procedure is extended to include all experimental systematic uncertainties. For each systematic uncertainty effect considered, the relative variation due to that uncertainty is obtained at the detector level, using the nominal MC sample. Rather than unfolding – 24 – each shifted histogram individually, each Poisson-fluctuated data distribution is smeared by all experimental systematic uncertainties simultaneously. For each pseudo-experiment, and for each uncertainty considered, the size of the shift applied is obtained randomly from a Gaussian distribution with a mean of zero and width equal to the relative shift at detector level in each bin due to that uncertainty, producing a new detector-level distribution. The same procedure that is followed for the statistical uncertainty alone is then followed to get the sum of the statistical and experimental systematic uncertainty. When evaluating the systematic uncertainties in this way, the data-driven correction factors are not extracted for each individual pseudo-experiment and instead the values obtained in section 7.1 are used. In the case of tt̄ modelling systematic uncertainties, detector-level distributions from JHEP04(2019)046 alternative MC samples are unfolded using the unfolding procedure described in section 7.2, with the unfolding corrections derived from the nominal Powheg+Pythia 8 sample. The unfolded distributions are compared with the particle-level distribution from the alternative sample and the relative difference in each bin is taken as the systematic uncertainty. 9 Inclusive and differential fiducial cross-section results The unfolded results are presented in this section as inclusive fiducial cross-sections and as normalised differential fiducial cross-sections as a function of the b-jet multiplicity, global event properties and kinematic variables. Table 5 lists the measured fiducial cross-sections for tt̄ production in association with additional at least one and at least two b-jets and table 6 lists the contributions to the uncertainty in these cross-sections. The most pre- cise cross-section measurements are for the ≥ 3b phase space in the eµ channel, which has an uncertainty of 13%, and the ≥ 6j, ≥ 4b phase space in the lepton + jets chan- nel, which has an uncertainty of 17%. The uncertainties are dominated by systematic uncertainties, which are mainly caused by the uncertainties due to tt̄ modelling and the uncertainties related to b-tagging and the jet energy scale. In the eµ channel, the uncer- tainty due to the tt̄c fit variations is also significant. This measurement is more precise than the uncertainties in the theoretical predictions of the inclusive cross-section for this process, which are 20%–30% [36]. The results are summarised in figure 7 after subtracting the MadGraph5 aMC@NLO+Pythia 8 predicted values of tt̄H and tt̄V cross-sections from the measured fiducial tt̄bb̄ cross-section, and compared with tt̄bb̄ predictions from Sherpa 2.2 tt̄bb̄, Powheg+Pythia 8 and PowHel+Pythia 8 tt̄bb̄. This procedure of tt̄H and tt̄V subtraction is also employed for all following figures showing the normalised differential distributions. Figure 8 shows the normalised fiducial cross-section as a function of the b-jet mul- tiplicity compared with predictions from various MC generator set-ups. A quantitative assessment of the level of agreement between data and the various predictions is performed by calculating a χ2 for each prediction. The χ2 is defined as χ2 = Sb−1 T V −1 Sb−1 , where V −1 is the inverse of the covariance matrix V , calculated for each variable including all statistical and systematic uncertainties and Sb−1 is a vector of the differences between – 25 – eµ [fb] lepton + jets [fb] ≥ 3b ≥ 4b ≥ 5j, ≥ 3b ≥ 6j, ≥ 4b 181 27 2450 359 Measured ± 5 (stat) ± 3 (stat) ± 40 (stat) ± 11 (stat) ± 24 (syst) ± 7 (syst) ± 690 (syst) ± 61 (syst) tt̄X(X = H, V ) MC 4 2 80 28 Measured − tt̄X 177 25 2370 331 Sherpa 2.2 tt̄bb̄ (4FS) 103 ± 30 17.3 ± 4.2 1600 ± 530 270 ± 70 JHEP04(2019)046 Powheg+Pythia 8 tt̄bb̄ (4FS) 104 16.5 1520 260 PowHel+Pythia 8 tt̄bb̄ (5FS) 152 18.7 1360 290 PowHel+Pythia 8 tt̄bb̄ (4FS) 105 18.2 1690 300 Table 5. Measured and predicted fiducial cross-section results for additional b-jet production in the eµ and the lepton + jets decay channels. ATLAS lepton+jets ( 3b) 1 s = 13 TeV, 36.1 fb lepton+jets ( 4b) Data - ttX(X = H, V) e ( 3b) Stat. uncert. Total uncert. Sherpa 2.2 ttbb (4FS) Powheg+Pythia8 ttbb (4FS) e ( 4b) PowHel+Pythia8 ttbb (5FS) PowHel+Pythia8 ttbb (4FS) 1 2 3 4 0.5 1.0 1.5 10 10 10 10 fid [fb] Pred./(Data - ttX) Figure 7. The measured fiducial cross-sections, with tt̄H and tt̄V contributions subtracted from data, compared with tt̄bb̄ predictions obtained using Sherpa 2.2 tt̄bb̄ with uncertainties obtained by varying the renormalisation and factorisation scales by factors of 0.5 and 2.0 and including PDF uncertainties. Comparisons with the central values of the predictions of Powheg+Pythia 8 and PowHel+Pythia 8 tt̄bb̄ are also made. No uncertainties are included in the subtraction of the tt̄H or tt̄V predictions. – 26 – Source Fiducial cross-section phase space eµ lepton + jets ≥ 3b ≥ 4b ≥ 5j, ≥ 3b ≥ 6j, ≥ 4b unc. [%] unc. [%] unc. [%] unc. [%] Data statistics 2.7 9.0 1.7 3.0 Luminosity 2.1 2.1 2.3 2.3 Jet 2.6 4.3 3.6 7.2 b-tagging 4.5 5.2 17 8.6 JHEP04(2019)046 Lepton 0.9 0.8 0.8 0.9 Pile-up 2.1 3.5 1.6 1.3 tt̄c fit variation 5.9 11 — — Non-tt̄ bkg 0.8 2.0 1.7 1.8 Detector+background total syst. 8.5 14 18 12 Parton shower 9.0 6.5 12 6.3 Generator 0.2 18 16 8.7 ISR/FSR 4.0 3.9 6.2 2.9 PDF 0.6 0.4 0.3 0.1 tt̄V /tt̄H 0.7 1.4 2.2 0.3 MC sample statistics 1.8 5.3 1.2 4.3 tt̄ modelling total syst. 10 20 21 12 Total syst. 13 24 28 17 Total 13 26 28 17 Table 6. Main systematic uncertainties in percentage for particle-level measurement of inclusive cross-sections in ≥ 3 b and ≥ 4 b phase space. the measured and predicted cross-sections being tested. The resulting value of the χ2 calculation is converted into a p-value using the number of degrees of freedom for each variable, which is the number of bins minus one in the case of the normalised differential cross-sections to reflect the normalisation constraint. As normalised distributions are used, one element of Sb−1 is discarded in the calculation along with the corresponding row and column of the covariance matrix. The resulting χ2 does not depend on the element of Sb−1 or the row and column of the covariance matrix that is discarded. The resulting χ2 values are shown in table 7, where the second column is for the normalised b-jets multiplicity distribution with Nb-jets ≥ 2 and the last column is for the normalised b-jets multiplicity distribution with Nb-jets ≥ 3. All MC predictions that calculate the top-quark pair production matrix element at NLO, but rely on the parton shower for high jet multiplicities, predict too few events with three or four b-jets. This – 27 – suggests that the b-jet production by the parton shower is not optimal in these set-ups. The situation does not improve significantly when the renormalisation and factorisation scales in the matrix element calculation and in the parton shower are changed by factors of 0.5 and 2, as shown in the middle ratio panel of figure 8. Sherpa 2.2 tt̄, which models one additional-parton process at NLO accuracy and up to four additional partons at LO accuracy, is the only one of the presented generators that describes the b-jet production well over the full phase space. Predictions that include additional massive b-quarks in the matrix element calculation (Sherpa 2.2 tt̄bb̄ (4FS), PowHel+Pythia 8 tt̄bb̄ (4FS), Powheg+Pythia 8 tt̄bb̄ (4FS)) do not provide top-pair production without additional b-jets and cannot be compared with JHEP04(2019)046 the region with less than three b-jets. Table 7 therefore also includes χ2 values where the total additional b-jet production has been adjusted through the normalisation to Nb-jets ≥ 3. The relative rate of one, two and more than two additional b-jets is described well by all predictions. It is also interesting to note that parton shower generators predict the relative rate of one and two additional b-jets well once the total additional b-jet production has also been adjusted through the normalisation to Nb-jets ≥ 3. The comparison of the predictions from various MC generators with the data are made after subtracting the simulation-estimated contributions of tt̄V and tt̄H production from the data. The third ratio panel of figure 8 shows the ratio of predictions of normalised differ- ential cross-sections from MadGraph5 aMC@NLO+Pythia 8 including (numerator) and not including (denominator) the contributions from the tt̄V and tt̄H processes. The impact of including these processes in the prediction increases with b-jet multiplicity, resulting in a change of about 10% relative to the QCD tt̄ prediction alone in the inclusive four-b-jet bin. Observables sensitive to the details of the QCD modelling of additional b-jet production are studied in events with at least three b-jets in the eµ channel and in events with at least four b-jets in the lepton + jets channel. While the sample with at least four b-jets has high signal purity, leading to smaller dependence on the MC models, the eµ channel benefits from an order of magnitude larger size of the sample containing at least three b-jets. Distributions for HT and HThad are shown in figures 9 and 10. Assessments of the level of agreement between data and the various MC predictions are presented in table 8. The data are well described by all MC models in both channels within uncertainties of 10%– 30%, except for MadGraph5 aMC@NLO+Pythia 8, which shows poor agreement in the lepton + jets channel. Major contributions of systematics uncertainties in the measurement from various sources are illustrated in figure 11. Parton shower modelling is the dominant uncertainty in most regions of HThad . Similar uncertainties are found in the measurement of HT , where the low HT region has relatively larger uncertainties due to QCD radiation scale variations because of softer jets contributing to this region. The pT distributions of the pT -ordered b-jets are shown in figure 12 and figure 13 for events with ≥ 3 b-jets in the eµ channel and ≥ 4 b-jets in the lepton + jets channel, respectively, with quantitative assessments of the level of data-MC agreement shown in table 9. Most MC predictions describe the data well, except PowHel+Pythia 8 tt̄bb̄ (5FS) for the leading and third-highest pT b-jets in events with ≥ 3 b-jets in the eµ channel. As the b-jets from the top-quark decays have a tendency to be harder than the b-jets – 28 – b -jet 10 ATLAS d σ tt eµ channel σ tt d N s=13 TeV, 36.1 fb-1 ≥ 2 b -jets 1 Data - t t X (X = H ,V ) 1 Powheg+Pythia8 MG5_aMC@NLO+Pythia8 Powheg+Herwig7 Sherpa 2.2 t t −1 10 Syst. Stat. 10−2 10−3 JHEP04(2019)046 Data-t t X 1.5 MC 1 0.5 Data-t t X 1.5 Powheg+Pythia8 (RadHi) Powheg+Pythia8 (RadLo) MC 1 0.5 Powheg+Pythia8 σ t t dσ QCD 1.1 dσ t t tt 1 σ QCD 0.9 MG5_aMC@NLO+Pythia8 tt 2 3 ≥4 N b-jets Figure 8. The relative differential cross-section as a function of the b-jet multiplicity in events with at least two b-jets in the eµ channel compared with various MC generators. The tt̄H and tt̄V contributions are subtracted from data. Three ratio panels are shown, the first two of which show the ratios of various predictions to data. The third panel shows the ratio of predictions of normalised differential cross-sections from MadGraph5 aMC@NLO+Pythia 8 including (numerator) and not including (denominator) the contributions from tt̄V and tt̄H production. Uncertainty bands represent the statistical and total systematic uncertainties as described in section 8. from additional b-quark production via gluon splitting, the leading and sub-leading b-jet distributions have relatively higher probability to contain the b-jets from the top-quark decays, while the third and the fourth b-jet distributions contain mainly jets from gluon splitting. The measurement uncertainties are between 10% and 25% depending on the pT of the jet and the top-quark decay channel. Statistical uncertainties are dominant in only the highest pT bins. The uncertainties are dominated by systematic uncertainties in the jet-energy scale and the b-tagging algorithm. Figures 14 and 15 show the distribution of the mass, the angular distance ∆R and pT of the b1 b2 system built from the two highest-pT b-jets. The pT of the b1 b2 system is measured with a precision of 10%–15% over the full range in the eµ channel and with an uncertainty of 20%–25% in the lepton + jets channel. It is well described by the different MC predictions, which vary significantly less than the experimental uncertainty. The distributions of the ∆R between the two b-jets and the invariant mass of the b1 b2 pair are measured with slightly higher uncertainties and also show little variation between the different predictions. Good agreement between the data and the models is confirmed by the p-values listed in table 10. – 29 – Generators Nb-jets : [2, 3, ≥ 4b] Nb-jets : [3, ≥ 4b] χ2 / NDF p-value χ2 / NDF p-value eµ channel Powheg+Pythia 8 18.1 /2 < 0.01 < 0.01 / 1 1.0 MadGraph5 aMC@NLO+Pythia 8 14.1 /2 < 0.01 0.05 / 1 0.83 Sherpa 2.2 tt̄ 0.85 /2 0.65 0.06 / 1 0.80 Sherpa 2.2 tt̄bb̄ (4FS) — — 0.37 / 1 0.54 PowHel+Pythia 8 tt̄bb̄ (5FS) — — 0.33 / 1 0.56 JHEP04(2019)046 PowHel+Pythia 8 tt̄bb̄ (4FS) — — 0.76 / 1 0.38 Powheg+Herwig 7 39.4 /2 < 0.01 0.26 / 1 0.61 Powheg+Pythia 8 tt̄bb̄ (4FS) — — 0.28 / 1 0.60 Powheg+Pythia 8 (RadHi) 9.2 /2 0.01 0.08 / 1 0.77 Powheg+Pythia 8 (RadLo) 27.0 /2 < 0.01 0.01 / 1 0.92 Table 7. Values of χ2 per degree of freedom and p-values between the unfolded normalised cross- section and the predictions for b-jet multiplicity measurements in the eµ channel. The number of degrees of freedom is equal to the number of bins minus one. Calculations are performed after subtracting estimated contributions from tt̄H and tt̄V from the data. In the two right columns, data and predictions are normalised to cross-section for Nb-jets ≥ 3 before calculating χ2 per degree of freedom and p-values. Figures 16 and 17 show the same observables but reconstructed from the pair of two closest b-jets in the event, i.e. those with the smallest ∆R, denoted by m∆min bb , p∆min T,bb , and ∆R ∆min (b, b). The experimental uncertainties are similar to those using the b-jet pair with the highest pT . However, the model variations are larger and PowHel+Pythia 8 tt̄bb̄ (5FS) does not describe the data with ≥ 3b-jets in the eµ channel well. 10 Summary Measurements of inclusive and normalised differential cross-sections of pairs of top-quarks in association with heavy-flavour jets in 13 TeV pp collisions are presented using a data sample of 36.1 fb−1 collected by the ATLAS detector at the LHC. The results are shown in both the eµ and lepton + jets channels within fiducial phase spaces. The background coming from tt̄ production in association with additional light-flavour and charm-quark jets is evaluated using a fit to a binned b-tagging discriminant. The data after background subtraction are unfolded to particle level to correct for detector and acceptance effects. The fiducial cross-sections are measured for ≥ 3b and ≥ 4b phase spaces in the eµ channel, and for ≥ 5j, ≥ 3b and ≥ 6j, ≥ 4b phase spaces in the lepton + jets channel. The two cross- section measurements with the smallest uncertainties, 13% and 17%, are those for ≥ 3b in the eµ channel and ≥ 6j, ≥ 4b in the lepton + jets channel, respectively. The measured cross-sections, after subtracting estimated contributions from tt̄H and tt̄V , are compared – 30 – σ t t b dH [GeV ] σ t t b dH had [GeV ] -1 -1 10−2 ATLAS eµ channel 10−2 ATLAS eµ channel s=13 TeV, 36.1 fb-1 ≥ 3 b -jets s=13 TeV, 36.1 fb-1 ≥ 3 b -jets 1 d σ ttb d σ ttb T T 10−3 10−3 1 Data - t t X (X = H ,V ) Data - t t X (X = H ,V ) 10−4 Powheg+Pythia8 Powheg+Pythia8 MG5_aMC@NLO+Pythia8 MG5_aMC@NLO+Pythia8 Powheg+Herwig7 10−4 Powheg+Herwig7 Syst. Syst. −5 10 Stat. Stat. Data-t t X Data-t t X Data-t t X Data-t t X 1.5 1.5 MC MC 1 1 0.5 0.5 JHEP04(2019)046 Powheg+Pythia8 (RadLo) 1.5 Powheg+Pythia8 (RadHi) Powheg+Pythia8 (RadLo) 1.5 Powheg+Pythia8 (RadHi) MC MC 1 1 0.5 Powheg+Pythia8 t t b b (4FS) Powheg+Pythia8 0.5 Powheg+Pythia8 t t b b (4FS) Powheg+Pythia8 σ t t b dσ QCD Data-t t X σ t t b dσ QCD Data-t t X 1.5 Sherpa 2.2 t t Sherpa 2.2 t t b b (4FS) 1.5 Sherpa 2.2 t t PowHel+Pythia8 t t b b (4FS) MC MC Sherpa 2.2 t t b b (4FS) 1 1 0.5 PowHel+Pythia8 t t b b (4FS) PowHel+Pythia8 t t b b (5FS) 0.5 PowHel+Pythia8 t t b b (5FS) dσ t t b dσ t t b ttb ttb 1.1 1.1 1 1 σ QCD σ QCD ttb ttb 0.9 MG5_aMC@NLO+Pythia8 0.9 MG5_aMC@NLO+Pythia8 200 300 400 1000 2000 200 300 400 1000 2000 H T [GeV] H had T [GeV] (a) (b) Figure 9. Relative differential cross-sections as a function of (a) HT , (b) HThad in events with at least three b-jets in the eµ channel compared with various MC generators. The tt̄H and tt̄V contributions are subtracted from data. Four ratio panels are shown, the first three of which show the ratios of various predictions to data. The last panel shows the ratio of predictions of normalised differential cross-sections from MadGraph5 aMC@NLO+Pythia 8 including (numerator) and not including (denominator) the contributions from tt̄V and tt̄H production. Uncertainty bands represent the statistical and total systematic uncertainties as described in section 8. Events with HT (HThad ) values outside the axis range are not included in the plot. with various tt̄bb̄ predictions and are found to be higher than predicted but compatible within the uncertainties. The normalised fiducial differential cross-sections are presented as a function of several relevant kinematic variables and global event properties. In general, the different observ- ables are measured with a precision of 10% in most of the phase space, rising to 30% at the edge of the phase space for some of the observables. The observables are well described by most MC predictions in both channels. However, it is worth noting that in all the predic- tions where additional b-jets are dominantly produced by the parton shower, they predict too few events with more b-jets than those produced in top decays. Only Sherpa 2.2 tt̄ describes the full b-jet multiplicity spectrum, and in events with ≥ 3 b-jets it yields the best agreement with data in most of the observables. PowHel+Pythia 8 tt̄bb̄ (5FS) shows poor agreement in some of the observables in events with ≥ 3 b-jets in the eµ chan- nel. The differential kinematic distributions are equally well described by predictions that have additional b-jet production that is generated by the parton shower calculation and by predictions with additional b-quarks in the matrix element. – 31 – σ t t bb dH [GeV ] σ t t bb dH had [GeV ] -1 -1 10−2 ATLAS lepton+jets channel 10−2 ATLAS lepton+jets channel s=13 TeV, 36.1 fb-1 ≥ 6j, ≥ 4b s=13 TeV, 36.1 fb-1 ≥ 6j, ≥ 4b 1 d σ t t bb d σ t t bb T Data - t t X (X = H,V ) Data - t t X (X = H,V ) T Powheg+Pythia8 Powheg+Pythia8 10−3 MG5_aMC@NLO+Pythia8 10−3 MG5_aMC@NLO+Pythia8 Powheg+Herwig7 Powheg+Herwig7 1 Syst. Syst. Stat. Stat. 10−4 10−4 1.4 1.4 Data-t t X Data-t t X 1.2 1.2 MC MC 1 1 JHEP04(2019)046 0.8 0.8 0.6 0.6 1.4 1.4 Data-t t X Data-t t X Powheg+Pythia8 (RadHi) Powheg+Pythia8 (RadLo) Powheg+Pythia8 (RadHi) Powheg+Pythia8 (RadLo) 1.2 1.2 MC MC 1 1 0.8 0.8 0.6 Powheg+Pythia8 t t b b (4FS) Powheg+Pythia8 0.6 Powheg+Pythia8 t t b b (4FS) Powheg+Pythia8 1.4 1.4 σ t t bb dσ QCD Data-t t X σ t t bb dσ QCD Data-t t X Sherpa 2.2 t t Sherpa 2.2 t t b b (4FS) Sherpa 2.2 t t Sherpa 2.2 t t b b (4FS) 1.2 1.2 MC MC 1 1 0.8 0.8 0.6 PowHel+Pythia8 t t b b (4FS) PowHel+Pythia8 t t b b (5FS) 0.6 PowHel+Pythia8 t t b b (4FS) PowHel+Pythia8 t t b b (5FS) dσ t t bb dσ t t bb t t bb t t bb 1.1 1.1 1 1 σ QCD σ QCD t t bb t t bb 0.9 MG5_aMC@NLO+Pythia8 0.9 MG5_aMC@NLO+Pythia8 3×102 103 2×103 2×102 103 2×103 H T [GeV] H had T [GeV] (a) (b) Figure 10. Relative differential cross-sections as a function of (a) HT , (b) HThad in events with at least four b-jets in the lepton+jets channel compared with various MC generators. The tt̄H and tt̄V contributions are subtracted from data. Four ratio panels are shown, the first three of which show the ratios of various predictions to data. The last panel shows the ratio of predictions of normalised differential cross-sections from MadGraph5 aMC@NLO+Pythia 8 including (numerator) and not including (denominator) the contributions from tt̄V and tt̄H production. Uncertainty bands represent the statistical and total systematic uncertainties as described in section 8. Events with HT (HThad ) values outside the axis range are not included in the plot. Relative uncertainties Relative uncertainties Parton shower ATLAS 0.7 Parton shower ATLAS 0.8 Generator s = 13 TeV, 36.1 fb -1 Generator s = 13 TeV, 36.1 fb -1 QCD radiation 0.6 QCD radiation 0.6 JES/JER eµ channel 0.5 JES/JER lepton+jets channel b -tagging ≥ 3 b -jets b -tagging ≥ 6j, ≥ 4b t t c fit variation 0.4 Statistical Unc. 0.4 Statistical Unc. Total uncertainty Total uncertainty 0.3 0.2 0.2 0.1 0 0 −0.2 −0.1 −0.2 −0.4 200 300 400 1000 2000 2×102 103 2×103 H had T [GeV] H had T [GeV] (a) (b) Figure 11. Relative systematic uncertainties from various theoretical and experimental sources for HThad variable measured in the (a) eµ and (b) lepton + jets channels. – 32 – [GeV-1] 1 d σ t t b [GeV-1] ATLAS eµ channel 10−1 ATLAS eµ channel s=13 TeV, 36.1 fb-1 ≥ 3 b -jets s=13 TeV, 36.1 fb-1 ≥ 3 b -jets 10−2 d σ ttb dp 1 dp 2 T b T b 10−2 Data - t t X (X = H ,V ) Powheg+Pythia8 σ ttb σ ttb 1 MG5_aMC@NLO+Pythia8 10−3 Data - t t X (X = H ,V ) 10−3 Powheg+Herwig7 Powheg+Pythia8 Syst. MG5_aMC@NLO+Pythia8 Stat. Powheg+Herwig7 Syst. 10−4 10−4 Stat. Data-t t X Data-t t X Data-t t X Data-t t X 1.5 1.5 MC MC 1 1 0.5 0.5 1.5 Powheg+Pythia8 (RadHi) Powheg+Pythia8 (RadLo) 1.5 Powheg+Pythia8 (RadHi) Powheg+Pythia8 (RadLo) MC MC JHEP04(2019)046 1 1 0.5 Powheg+Pythia8 t t b b (4FS) Powheg+Pythia8 0.5 Powheg+Pythia8 t t b b (4FS) Powheg+Pythia8 σ t t b dσ QCD Data-t t X σ t t b dσ QCD Data-t t X 1.5 Sherpa 2.2 t t PowHel+Pythia8 t t b b (4FS) 1.5 Sherpa 2.2 t t MC MC Sherpa 2.2 t t b b (4FS) Sherpa 2.2 t t b b (4FS) 1 1 0.5 PowHel+Pythia8 t t b b (5FS) 0.5 PowHel+Pythia8 t t b b (4FS) PowHel+Pythia8 t t b b (5FS) dσ t t b dσ t t b ttb ttb 1.1 1.1 1 1 σ QCD σ QCD ttb ttb 0.9 MG5_aMC@NLO+Pythia8 0.9 MG5_aMC@NLO+Pythia8 30 4050 102 2×102 103 30 4050 102 2×102 103 b1 b p [GeV] p 2 [GeV] T T (a) (b) [GeV-1] ATLAS eµ channel 10−1 s=13 TeV, 36.1 fb-1 ≥ 3 b -jets d σ ttb dp 3 T b Data - t t X (X = H ,V ) 10−2 Powheg+Pythia8 σ ttb 1 MG5_aMC@NLO+Pythia8 Powheg+Herwig7 10−3 Syst. Stat. 10−4 Data-t t X Data-t t X 1.5 MC 1 0.5 Powheg+Pythia8 (RadHi) Powheg+Pythia8 (RadLo) 1.5 MC 1 0.5 Powheg+Pythia8 t t b b (4FS) Powheg+Pythia8 σ t t b dσ QCD Data-t t X Sherpa 2.2 t t PowHel+Pythia8 t t b b (4FS) 1.5 MC 1 0.5 Sherpa 2.2 t t b b (4FS) PowHel+Pythia8 t t b b (5FS) dσ t t b ttb 1.1 1 σ QCD ttb 0.9 MG5_aMC@NLO+Pythia8 30 4050 102 2×102 103 b p 3 [GeV] T (c) Figure 12. Relative differential cross-sections as a function of b-jets pT for pT -ordered b-jets in events with at least three b-jets in the eµ channel compared with various MC generators. The tt̄H and tt̄V contributions are subtracted from data. (a) leading b-jet pT , (b) sub-leading b-jet pT , (c) third-leading b-jet pT . Four ratio panels are shown, the first three of which show the ratios of various predictions to data. The last panel shows the ratio of predictions of normalised differential cross-sections from MadGraph5 aMC@NLO+Pythia 8 including (numerator) and not including (denominator) the contributions from tt̄V and tt̄H production. Uncertainty bands represent the statistical and total systematic uncertainties as described in section 8. Events with b-jets pT values outside the axis range are not included in the plot. – 33 – [GeV-1] [GeV-1] ATLAS lepton+jets channel ATLAS lepton+jets channel s=13 TeV, 36.1 fb-1 ≥ 6j, ≥ 4b s=13 TeV, 36.1 fb-1 ≥ 6j, ≥ 4b d σ t t bb d σ t t bb 10−2 dp 1 dp 2 10−2 T T b Data - t t X (X = H,V ) Data - t t X (X = H,V ) b Powheg+Pythia8 Powheg+Pythia8 MG5_aMC@NLO+Pythia8 MG5_aMC@NLO+Pythia8 σ t t bb σ t t bb Powheg+Herwig7 Powheg+Herwig7 1 1 Syst. Syst. 10−3 Stat. 10−3 Stat. 10−4 10−4 1.4 1.4 Data-t t X Data-t t X 1.2 1.2 MC MC 1 1 0.8 0.8 0.6 0.6 Data-t t X Data-t t X Powheg+Pythia8 (RadHi) Powheg+Pythia8 (RadLo) Powheg+Pythia8 (RadHi) Powheg+Pythia8 (RadLo) 1.5 1.5 MC MC JHEP04(2019)046 1 1 0.5 Powheg+Pythia8 t t b b (4FS) Powheg+Pythia8 0.5 Powheg+Pythia8 t t b b (4FS) Powheg+Pythia8 σ t t bb dσ QCD Data-t t X σ t t bb dσ QCD Data-t t X Sherpa 2.2 t t Sherpa 2.2 t t b b (4FS) Sherpa 2.2 t t Sherpa 2.2 t t b b (4FS) 1.5 1.5 MC MC 1 1 0.5 PowHel+Pythia8 t t b b (4FS) PowHel+Pythia8 t t b b (5FS) 0.5 PowHel+Pythia8 t t b b (4FS) PowHel+Pythia8 t t b b (5FS) dσ t t bb dσ t t bb t t bb t t bb 1.1 1.1 1 1 σ QCD σ QCD t t bb t t bb 0.9 MG5_aMC@NLO+Pythia8 0.9 MG5_aMC@NLO+Pythia8 40 102 2×102 103 2×103 30 40 102 2×102 103 b1 b p [GeV] p 2 [GeV] T T (a) (b) 1 d σ t t bb [GeV-1] [GeV-1] 10−1 ATLAS lepton+jets channel ATLAS lepton+jets channel s=13 TeV, 36.1 fb-1 ≥ 6j, ≥ 4b s=13 TeV, 36.1 fb-1 ≥ 6j, ≥ 4b 10−1 d σ t t bb dp 3 dp 4 T T Data - t t X (X = H,V ) Data - t t X (X = H,V ) b b −2 10 Powheg+Pythia8 Powheg+Pythia8 MG5_aMC@NLO+Pythia8 MG5_aMC@NLO+Pythia8 σ t t bb σ t t bb Powheg+Herwig7 Powheg+Herwig7 10−2 1 Syst. Syst. Stat. Stat. 10−3 10−3 10−4 1.4 1.4 Data-t t X Data-t t X 1.2 1.2 MC MC 1 1 0.8 0.8 0.6 0.6 Data-t t X Data-t t X Powheg+Pythia8 (RadHi) Powheg+Pythia8 (RadLo) Powheg+Pythia8 (RadHi) Powheg+Pythia8 (RadLo) 1.5 1.5 MC MC 1 1 0.5 Powheg+Pythia8 t t b b (4FS) Powheg+Pythia8 0.5 Powheg+Pythia8 t t b b (4FS) Powheg+Pythia8 σ t t bb dσ QCD Data-t t X σ t t bb dσ QCD Data-t t X Sherpa 2.2 t t Sherpa 2.2 t t b b (4FS) Sherpa 2.2 t t Sherpa 2.2 t t b b (4FS) 1.5 1.5 MC MC 1 1 0.5 PowHel+Pythia8 t t b b (4FS) PowHel+Pythia8 t t b b (5FS) 0.5 PowHel+Pythia8 t t b b (4FS) PowHel+Pythia8 t t b b (5FS) dσ t t bb dσ t t bb t t bb t t bb 1.1 1.1 1 1 σ QCD σ QCD t t bb t t bb 0.9 MG5_aMC@NLO+Pythia8 0.9 MG5_aMC@NLO+Pythia8 30 40 5060 102 2×102 30 40 5060 102 2×102 3×102 b3 b p [GeV] p 4 [GeV] T T (c) (d) Figure 13. Relative differential cross-sections as a function of b-jets pT for pT -ordered b-jets in events with at least four b-jets in the lepton + jets channel compared with various MC generators. The tt̄H and tt̄V contributions are subtracted from data. (a) leading b-jet pT , (b) sub-leading b-jet pT , (c) third-leading b-jet pT , (d) fourth-leading b-jet pT . Four ratio panels are shown, the first three of which show the ratios of various predictions to data. The last panel shows the ratio of predictions of normalised differential cross-sections from MadGraph5 aMC@NLO+Pythia 8 including (numerator) and not including (denominator) the contributions from tt̄V and tt̄H pro- duction. Uncertainty bands represent the statistical and total systematic uncertainties as described in section 8. Events with b-jets pT values outside the axis range are not included in the plot. – 34 – [GeV-1] [GeV-1] 10−2 ATLAS eµ channel 10−2 ATLAS eµ channel s=13 TeV, 36.1 fb-1 ≥ 3 b -jets s=13 TeV, 36.1 fb-1 ≥ 3 b -jets 1 2 1 2 d σ ttb bb T,b b d σ ttb σ t t b dm σ t t b dp 10−3 1 1 Data - t t X (X = H ,V ) Data - t t X (X = H ,V ) 10−3 Powheg+Pythia8 Powheg+Pythia8 MG5_aMC@NLO+Pythia8 MG5_aMC@NLO+Pythia8 Powheg+Herwig7 Powheg+Herwig7 Syst. 10−4 Syst. Stat. Stat. Data-t t X Data-t t X Data-t t X Data-t t X 1.5 1.2 MC MC 1 1 0.5 0.8 1.5 Powheg+Pythia8 (RadHi) Powheg+Pythia8 (RadLo) 1.2 Powheg+Pythia8 (RadHi) Powheg+Pythia8 (RadLo) JHEP04(2019)046 MC MC 1 1 0.5 Powheg+Pythia8 t t b b (4FS) Powheg+Pythia8 0.8 Powheg+Pythia8 t t b b (4FS) Powheg+Pythia8 σ t t b dσ QCD Data-t t X σ t t b dσ QCD Data-t t X 1.5 Sherpa 2.2 t t Sherpa 2.2 t t b b (4FS) 1.2 Sherpa 2.2 t t PowHel+Pythia8 t t b b (4FS) MC MC 1 1 0.5 PowHel+Pythia8 t t b b (4FS) PowHel+Pythia8 t t b b (5FS) 0.8 Sherpa 2.2 t t b b (4FS) PowHel+Pythia8 t t b b (5FS) dσ t t b dσ t t b ttb ttb 1.1 1.1 1 1 σ QCD σ QCD ttb ttb 0.9 MG5_aMC@NLO+Pythia8 0.9 MG5_aMC@NLO+Pythia8 3040 102 2×102 103 2×103 10 102 103 mb b [GeV] p [GeV] 1 2 T,b 1b 2 (a) (b) σ t t b d ∆R (b ,b ) 1 2 ATLAS eµ channel d σ ttb s=13 TeV, 36.1 fb-1 ≥ 3 b -jets 1 Data - t t X (X = H ,V ) Powheg+Pythia8 1 MG5_aMC@NLO+Pythia8 Powheg+Herwig7 Syst. Stat. 10−1 Data-t t X Data-t t X 1.2 MC 1 0.8 Powheg+Pythia8 (RadHi) Powheg+Pythia8 (RadLo) 1.2 MC 1 0.8 Powheg+Pythia8 t t b b (4FS) Powheg+Pythia8 σ t t b dσ QCD Data-t t X Sherpa 2.2 t t Sherpa 2.2 t t b b (4FS) 1.2 MC 1 0.8 PowHel+Pythia8 t t b b (4FS) PowHel+Pythia8 t t b b (5FS) dσ t t b ttb 1.1 1 σ QCD ttb 0.9 MG5_aMC@NLO+Pythia8 4×10−1 1 2 3 4 5 6 78 ∆R b b 1 2 (c) Figure 14. Relative differential cross-sections as a function of (a) mb1 b2 , (b) pT,b1 b2 , and (c) ∆Rb1 ,b2 of two highest-pT b-jets in events with at least three b-jets in the eµ channel compared with various MC generators. The tt̄H and tt̄V contributions are subtracted from data. Four ratio panels are shown, the first three of which show the ratios of various predictions to data. The last panel shows the ratio of predictions of normalised differential cross-sections from Mad- Graph5 aMC@NLO+Pythia 8 including (numerator) and not including (denominator) the con- tributions from tt̄V and tt̄H production. Uncertainty bands represent the statistical and total systematic uncertainties as described in section 8. Events with observable values outside the axis range are not included in the plot. – 35 – [GeV-1] 1 d σ t t bb [GeV-1] ATLAS lepton+jets channel ATLAS lepton+jets channel 10−2 s=13 TeV, 36.1 fb-1 ≥ 6j, ≥ 4b s=13 TeV, 36.1 fb-1 ≥ 6j, ≥ 4b 10−2 T, b 1b 2 d σ t t bb 1 2 bb σ t t bb dm σ t t bb dp 10−3 10−3 1 Data - t t X (X = H,V ) Data - t t X (X = H,V ) Powheg+Pythia8 Powheg+Pythia8 MG5_aMC@NLO+Pythia8 MG5_aMC@NLO+Pythia8 Powheg+Herwig7 Powheg+Herwig7 10−4 Syst. Syst. Stat. 10−4 Stat. 1.4 1.4 Data-t t X Data-t t X 1.2 1.2 MC MC 1 1 0.8 0.8 0.6 0.6 Data-t t X Data-t t X Powheg+Pythia8 (RadHi) Powheg+Pythia8 (RadLo) Powheg+Pythia8 (RadHi) Powheg+Pythia8 (RadLo) 1.5 1.5 MC MC JHEP04(2019)046 1 1 0.5 Powheg+Pythia8 t t b b (4FS) Powheg+Pythia8 0.5 Powheg+Pythia8 t t b b (4FS) Powheg+Pythia8 σ t t bb dσ QCD Data-t t X σ t t bb dσ QCD Data-t t X Sherpa 2.2 t t Sherpa 2.2 t t b b (4FS) Sherpa 2.2 t t Sherpa 2.2 t t b b (4FS) 1.5 1.5 MC MC 1 1 0.5 PowHel+Pythia8 t t b b (4FS) PowHel+Pythia8 t t b b (5FS) 0.5 PowHel+Pythia8 t t b b (4FS) PowHel+Pythia8 t t b b (5FS) dσ t t bb dσ t t bb t t bb t t bb 1.1 1.1 1 1 σ QCD σ QCD t t bb t t bb 0.9 MG5_aMC@NLO+Pythia8 0.9 MG5_aMC@NLO+Pythia8 102 103 10−1 1 10 102 103 m b b [GeV] p [GeV] 1 2 T, b 1b 2 (a) (b) 1 2 d σ t t bb bb 1.2 ATLAS lepton+jets channel σ t t bb d∆R s=13 TeV, 36.1 fb-1 ≥ 6j, ≥ 4b 1 Data - t t X (X = H,V ) 1 Powheg+Pythia8 0.8 MG5_aMC@NLO+Pythia8 Powheg+Herwig7 0.6 Syst. Stat. 0.4 0.2 1.4 Data-t t X 1.2 MC 1 0.8 0.6 Data-t t X Powheg+Pythia8 (RadHi) Powheg+Pythia8 (RadLo) 1.5 MC 1 0.5 Powheg+Pythia8 t t b b (4FS) Powheg+Pythia8 σ t t bb dσ QCD Data-t t X Sherpa 2.2 t t Sherpa 2.2 t t b b (4FS) 1.5 MC 1 0.5 PowHel+Pythia8 t t b b (4FS) PowHel+Pythia8 t t b b (5FS) dσ t t bb t t bb 1.1 1 σ QCD t t bb 0.9 MG5_aMC@NLO+Pythia8 1 2 3 4 5 ∆R b b 1 2 (c) Figure 15. Relative differential cross-sections as a function of (a) mb1 b2 , (b) pT,b1 b2 , and (c) ∆Rb1 ,b2 of the two highest-pT b-jets in events with at least four b-jets in the lepton + jets channel compared with various MC generators. The tt̄H and tt̄V contributions are subtracted from data. Four ratio panels are shown, the first three of which show the ratios of various predictions to data. The last panel shows the ratio of predictions of normalised differential cross-sections from MadGraph5 aMC@NLO+Pythia 8 including (numerator) and not including (denominator) the contributions from tt̄V and tt̄H production. Uncertainty bands represent the statistical and total systematic uncertainties as described in section 8. Events with observable values outside the axis range are not included in the plot. – 36 – HT HThad χ2 / NDF p-value χ2 / NDF p-value Generator JHEP04(2019)046 eµ channel, ≥ 3 b-jets Powheg+Pythia 8 0.95 / 4 0.92 2.68 / 3 0.44 MadGraph5 aMC@NLO+Pythia 8 3.71 / 4 0.45 3.72 / 3 0.29 Sherpa 2.2 tt̄ 0.58 / 4 0.97 2.26 / 3 0.52 Sherpa 2.2 tt̄bb̄ (4FS) 0.35 / 4 0.99 0.40 / 3 0.94 PowHel+Pythia 8 tt̄bb̄ (5FS) 4.88 / 4 0.30 1.85 / 3 0.60 PowHel+Pythia 8 tt̄bb̄ (4FS) 1.39 / 4 0.85 3.33 / 3 0.32 Powheg+Herwig 7 0.26 / 4 0.99 2.28 / 3 0.52 Powheg+Pythia 8 tt̄bb̄ (4FS) 0.63 / 4 0.96 3.93 / 3 0.27 Powheg+Pythia 8 (RadHi) 4.09 / 4 0.39 6.43 / 3 0.09 Powheg+Pythia 8 (RadLo) 0.14 / 4 1.0 1.06 / 3 0.79 lepton+jets channel, ≥ 6 jets, ≥ 4 b-jets Powheg+Pythia 8 0.60 / 4 0.96 1.41 / 4 0.84 MadGraph5 aMC@NLO+Pythia 8 9.88 / 4 0.04 17.6 / 4 < 0.01 Sherpa 2.2 tt̄ 0.72 / 4 0.95 1.38 / 4 0.85 Sherpa 2.2 tt̄bb̄ (4FS) 1.09 / 4 0.90 2.58 / 4 0.63 PowHel+Pythia 8 tt̄bb̄ (5FS) 0.81 / 4 0.94 1.40 / 4 0.84 PowHel+Pythia 8 tt̄bb̄ (4FS) 1.38 / 4 0.85 2.38 / 4 0.67 Powheg+Herwig 7 4.27 / 4 0.37 7.00 / 4 0.14 Powheg+Pythia 8 tt̄bb̄ (4FS) 0.72 / 4 0.95 1.71 / 4 0.79 Powheg+Pythia 8 (RadHi) 0.94 / 4 0.92 0.96 / 4 0.92 Powheg+Pythia 8 (RadLo) 1.15 / 4 0.89 2.57 / 4 0.63 Table 8. Values of χ2 per degree of freedom and p-values between the unfolded normalised cross- sections and the various predictions for the HT and HThad measurements in the eµ and lepton + jets channels. The number of degrees of freedom is equal to the number of bins in the measured distribution minus one. – 37 – pbT1 pbT2 pbT3 pbT4 χ2 / NDF p-value χ2 / NDF p-value χ2 / NDF p-value χ2 / NDF p-value Generator eµ channel, ≥ 3 b-jets Powheg+Pythia 8 2.09 /4 0.72 0.50 / 3 0.92 0.09 / 2 0.95 — — MadGraph5 aMC@NLO+Pythia 8 2.62 /4 0.62 0.27 / 3 0.97 0.33 / 2 0.85 — — Sherpa 2.2 tt̄ 0.98 /4 0.91 0.67 / 3 0.88 0.02 / 2 0.99 — — Sherpa 2.2 tt̄bb̄ (4FS) 3.52 /4 0.47 0.68 / 3 0.88 0.21 / 2 0.90 — — PowHel+Pythia 8 tt̄bb̄ (5FS) 10.9 /4 0.03 2.58 / 3 0.46 3.91 / 2 0.14 — — PowHel+Pythia 8 tt̄bb̄ (4FS) 6.21 /4 0.18 1.96 / 3 0.58 1.30 / 2 0.52 — — Powheg+Herwig 7 1.16 /4 0.89 1.02 / 3 0.80 0.02 / 2 0.99 — — Powheg+Pythia 8 tt̄bb̄ (4FS) 2.62 /4 0.62 0.53 / 3 0.91 0.46 / 2 0.80 — — Powheg+Pythia 8 (RadHi) 2.71 /4 0.61 0.56 / 3 0.91 0.26 / 2 0.88 — — Powheg+Pythia 8 (RadLo) 1.93 /4 0.75 0.64 / 3 0.89 0.05 / 2 0.97 — — lepton+jets channel, ≥ 6 jets, ≥ 4 b-jets – 38 – Powheg+Pythia 8 2.09 /4 0.72 2.98 / 3 0.40 1.42 / 3 0.70 0.20 / 2 0.90 MadGraph5 aMC@NLO+Pythia 8 5.20 /4 0.27 5.31 / 3 0.15 1.87 / 3 0.60 0.08 / 2 0.96 Sherpa 2.2 tt̄ 2.01 /4 0.73 2.46 / 3 0.48 1.75 / 3 0.63 0.15 / 2 0.93 Sherpa 2.2 tt̄bb̄ (4FS) 2.04 /4 0.73 2.82 / 3 0.42 1.23 / 3 0.75 0.52 / 2 0.77 PowHel+Pythia 8 tt̄bb̄ (5FS) 2.07 /4 0.72 3.65 / 3 0.30 1.73 / 3 0.63 0.85 / 2 0.65 PowHel+Pythia 8 tt̄bb̄ (4FS) 2.52 /4 0.64 2.37 / 3 0.50 2.41 / 3 0.49 0.18 / 2 0.91 Powheg+Herwig 7 2.58 /4 0.63 3.50 / 3 0.32 1.30 / 3 0.73 0.26 / 2 0.88 Powheg+Pythia 8 tt̄bb̄ (4FS) 1.76 /4 0.78 2.02 / 3 0.57 1.83 / 3 0.61 0.84 / 2 0.66 Powheg+Pythia 8 (RadHi) 1.50 /4 0.83 2.39 / 3 0.50 1.74 / 3 0.63 0.37 / 2 0.83 Powheg+Pythia 8 (RadLo) 2.17 /4 0.70 3.75 / 3 0.29 1.42 / 3 0.70 0.17 / 2 0.92 Table 9. Values of χ2 per degree of freedom and p-values between the unfolded normalised cross-sections and the various predictions for the three (four) leading b-jet pT measurements in the eµ (lepton + jets) channel. The number of degrees of freedom is equal to the number of bins in the measured distribution minus one. JHEP04(2019)046 mb1 b2 pT,b1 b2 ∆Rb1 b2 χ2 / NDF p-value χ2 / NDF p-value χ2 / NDF p-value JHEP04(2019)046 Generator eµ channel, ≥ 3 b-jets Powheg+Pythia 8 1.55 / 4 0.82 1.74 / 3 0.63 0.70 / 4 0.95 MadGraph5 aMC@NLO+Pythia 8 1.73 / 4 0.79 1.08 / 3 0.78 3.73 / 4 0.44 Sherpa 2.2 tt̄ 0.25 / 4 0.99 0.64 / 3 0.89 0.99 / 4 0.91 Sherpa 2.2 tt̄bb̄ (4FS) 2.88 / 4 0.58 0.76 / 3 0.86 2.88 / 4 0.58 PowHel+Pythia 8 tt̄bb̄ (5FS) 3.74 / 4 0.44 4.75 / 3 0.19 4.70 / 4 0.32 PowHel+Pythia 8 tt̄bb̄ (4FS) 1.35 / 4 0.85 2.90 / 3 0.41 0.86 / 4 0.93 Powheg+Herwig 7 0.48 / 4 0.98 0.42 / 3 0.94 0.97 / 4 0.91 Powheg+Pythia 8 tt̄bb̄ (4FS) 1.89 / 4 0.76 0.79 / 3 0.85 0.68 / 4 0.95 Powheg+Pythia 8 (RadHi) 3.77 / 4 0.44 3.49 / 3 0.32 0.50 / 4 0.97 Powheg+Pythia 8 (RadLo) 1.04 / 4 0.90 0.95 / 3 0.81 1.01 / 4 0.91 lepton+jets channel, ≥ 6 jets, ≥ 4 b-jets Powheg+Pythia 8 1.82 / 5 0.87 1.66 / 5 0.89 2.48 / 6 0.87 MadGraph5 aMC@NLO+Pythia 8 4.11 / 5 0.53 4.63 / 5 0.46 2.90 / 6 0.82 Sherpa 2.2 tt̄ 2.84 / 5 0.72 1.79 / 5 0.88 3.40 / 6 0.76 Sherpa 2.2 tt̄bb̄ (4FS) 2.40 / 5 0.79 1.76 / 5 0.88 3.37 / 6 0.76 PowHel+Pythia 8 tt̄bb̄ (5FS) 2.39 / 5 0.79 1.85 / 5 0.87 2.94 / 6 0.82 PowHel+Pythia 8 tt̄bb̄ (4FS) 3.71 / 5 0.59 2.49 / 5 0.78 4.79 / 6 0.57 Powheg+Herwig 7 2.46 / 5 0.78 2.60 / 5 0.76 2.80 / 6 0.83 Powheg+Pythia 8 tt̄bb̄ (4FS) 1.88 / 5 0.87 1.51 / 5 0.91 2.79 / 6 0.83 Powheg+Pythia 8 (RadHi) 1.68 / 5 0.89 1.67 / 5 0.89 2.72 / 6 0.84 Powheg+Pythia 8 (RadLo) 1.89 / 5 0.86 2.35 / 5 0.80 2.63 / 6 0.85 Table 10. Values of χ2 per degree of freedom and p-values between the unfolded normalised cross- sections and the various predictions for the mass, pT and ∆R of the leading two b-jets in the eµ and lepton + jets channels. The number of degrees of freedom is equal to the number of bins in the measured distribution minus one. – 39 – m∆min bb p∆min T,bb ∆R∆min bb χ2 / NDF p-value χ2 / NDF p-value χ2 / NDF p-value Generator eµ channel, ≥ 3 b-jets Powheg+Pythia 8 1.37 / 4 0.85 0.42 / 4 0.98 0.78 / 3 0.86 MadGraph5 aMC@NLO+Pythia 8 3.67 / 4 0.45 2.50 / 4 0.65 1.22 / 3 0.75 Sherpa 2.2 tt̄ 0.17 / 4 1.0 0.06 / 4 1.0 0.99 / 3 0.80 Sherpa 2.2 tt̄bb̄ (4FS) 1.36 / 4 0.85 0.52 / 4 0.97 0.21 / 3 0.98 PowHel+Pythia 8 tt̄bb̄ (5FS) 0.18 / 4 1.0 12.7 / 4 0.01 27.9 / 3 < 0.01 PowHel+Pythia 8 tt̄bb̄ (4FS) 4.29 / 4 0.37 2.36 / 4 0.67 0.81 / 3 0.85 JHEP04(2019)046 Powheg+Herwig 7 0.87 / 4 0.93 0.06 / 4 1.0 0.95 / 3 0.81 Powheg+Pythia 8 tt̄bb̄ (4FS) 1.12 / 4 0.89 1.00 / 4 0.91 0.30 / 3 0.96 Powheg+Pythia 8 (RadHi) 1.94 / 4 0.75 1.31 / 4 0.86 0.51 / 3 0.92 Powheg+Pythia 8 (RadLo) 0.99 / 4 0.91 0.28 / 4 0.99 0.86 / 3 0.84 lepton+jets channel, ≥ 6 jets, ≥ 4 b-jets Powheg+Pythia 8 0.86 / 4 0.93 0.99 / 4 0.91 3.22 / 5 0.67 MadGraph5 aMC@NLO+Pythia 8 1.01 / 4 0.91 4.33 / 4 0.36 3.19 / 5 0.67 Sherpa 2.2 tt̄ 0.66 / 4 0.96 1.21 / 4 0.88 4.98 / 5 0.42 Sherpa 2.2 tt̄bb̄ (4FS) 1.44 / 4 0.84 0.89 / 4 0.93 4.07 / 5 0.54 PowHel+Pythia 8 tt̄bb̄ (5FS) 1.08 / 4 0.90 1.61 / 4 0.81 3.14 / 5 0.68 PowHel+Pythia 8 tt̄bb̄ (4FS) 1.93 / 4 0.75 0.30 / 4 1.0 5.43 / 5 0.37 Powheg+Herwig 7 1.32 / 4 0.86 1.47 / 4 0.83 4.53 / 5 0.48 Powheg+Pythia 8 tt̄bb̄ (4FS) 1.05 / 4 0.90 0.82 / 4 0.94 3.87 / 5 0.57 Powheg+Pythia 8 (RadHi) 1.51 / 4 0.83 0.95 / 4 0.92 2.98 / 5 0.70 Powheg+Pythia 8 (RadLo) 0.77 / 4 0.94 1.51 / 4 0.83 3.25 / 5 0.66 Table 11. Values of χ2 per degree of freedom and p-values between the unfolded normalised cross- sections and the various predictions for the mass, pT and ∆R of the closest two b-jets in the eµ and lepton + jets channels. The number of degrees of freedom is equal to the number of bins in the measured distribution minus one. Acknowledgments We thank CERN for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Aus- tralia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS, CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF, and MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia and NRC KI, Russian Federation; JINR; – 40 – σ t t b d m∆min [GeV ] σ t t b d p ∆min [GeV ] -1 -1 ATLAS eµ channel 10−2 ATLAS eµ channel s=13 TeV, 36.1 fb-1 ≥ 3 b -jets s=13 TeV, 36.1 fb-1 ≥ 3 b -jets 10−2 T, bb 1 d σ ttb d σ ttb bb 10−3 1 10−3 Data - t t X (X = H ,V ) Data - t t X (X = H ,V ) Powheg+Pythia8 Powheg+Pythia8 MG5_aMC@NLO+Pythia8 MG5_aMC@NLO+Pythia8 Powheg+Herwig7 Powheg+Herwig7 10−4 Syst. Stat. 10−4 Syst. Stat. Data-t t X Data-t t X Data-t t X Data-t t X 1.5 1.5 MC MC 1 1 0.5 0.5 1.5 Powheg+Pythia8 Powheg+Pythia8 (RadLo) 1.5 Powheg+Pythia8 (RadHi) Powheg+Pythia8 (RadLo) MC MC JHEP04(2019)046 1 1 0.5 Powheg+Pythia8 t t b b (4FS) Powheg+Pythia8 (RadHi) 0.5 Powheg+Pythia8 t t b b (4FS) Powheg+Pythia8 σ t t b dσ QCD Data-t t X σ t t b dσ QCD Data-t t X 1.5 Sherpa 2.2 t t PowHel+Pythia8 t t b b (4FS) 1.5 Sherpa 2.2 t t PowHel+Pythia8 t t b b (4FS) MC MC 1 1 0.5 Sherpa 2.2 t t b b (4FS) PowHel+Pythia8 t t b b (5FS) 0.5 Sherpa 2.2 t t b b (4FS) PowHel+Pythia8 t t b b (5FS) dσ t t b dσ t t b ttb ttb 1.1 1.1 1 1 σ QCD σ QCD ttb ttb 0.9 MG5_aMC@NLO+Pythia8 0.9 MG5_aMC@NLO+Pythia8 20 3040 102 2×102 103 20 3040 102 2×102 103 m ∆bbmin [GeV] p ∆min [GeV] T,bb (a) (b) σ t t b d ∆R ∆min d σ ttb bb ATLAS eµ channel s=13 TeV, 36.1 fb-1 ≥ 3 b -jets 1 1 Data - t t X (X = H ,V ) Powheg+Pythia8 MG5_aMC@NLO+Pythia8 Powheg+Herwig7 10−1 Syst. Stat. Data-t t X Data-t t X 1.2 MC 1 0.8 Powheg+Pythia8 (RadHi) Powheg+Pythia8 (RadLo) 1.2 MC 1 0.8 Powheg+Pythia8 t t b b (4FS) Powheg+Pythia8 σ t t b dσ QCD Data-t t X Sherpa 2.2 t t 1.2 MC Sherpa 2.2 t t b b (4FS) 1 0.8 PowHel+Pythia8 t t b b (4FS) PowHel+Pythia8 t t b b (5FS) dσ t t b ttb 1.1 1 σ QCD ttb 0.9 MG5_aMC@NLO+Pythia8 0.5 1 1.5 2 2.5 3 ∆R ∆bbmin (c) Figure 16. Relative differential cross-sections as a function of (a) m∆min bb , (b) p∆min T,bb and (c) ∆min ∆Rbb of two closest b-jets in ∆R in events with at least three b-jets in the eµ channel com- pared with various MC generators. The tt̄H and tt̄V contributions are subtracted from data. Four ratio panels are shown, the first three of which show the ratios of various predictions to data. The last panel shows the ratio of predictions of normalised differential cross-sections from MadGraph5 aMC@NLO+Pythia 8 including (numerator) and not including (denominator) the contributions from tt̄V and tt̄H production. Uncertainty bands represent the statistical and total systematic uncertainties as described in section 8. Events with observable values outside the axis range are not included in the plot. – 41 – 10−1 σ t t bb dm ∆min [GeV ] σ t t bb dp ∆min [GeV ] -1 -1 ATLAS lepton+jets channel ATLAS lepton+jets channel s=13 TeV, 36.1 fb-1 ≥ 6j, ≥ 4b s=13 TeV, 36.1 fb-1 ≥ 6j, ≥ 4b −2 10 1 d σ t t bb T, bb 1 d σ t t bb 10−2 bb Data - t t X (X = H,V ) Data - t t X (X = H,V ) 10−3 Powheg+Pythia8 10−3 Powheg+Pythia8 MG5_aMC@NLO+Pythia8 MG5_aMC@NLO+Pythia8 Powheg+Herwig7 Powheg+Herwig7 Syst. Syst. 10−4 Stat. 10−4 Stat. 1.4 1.4 Data-t t X Data-t t X 1.2 1.2 MC MC 1 1 0.8 0.8 0.6 0.6 Data-t t X Data-t t X Powheg+Pythia8 (RadHi) Powheg+Pythia8 (RadLo) Powheg+Pythia8 (RadHi) Powheg+Pythia8 (RadLo) 1.5 1.5 MC MC JHEP04(2019)046 1 1 0.5 Powheg+Pythia8 t t b b (4FS) Powheg+Pythia8 0.5 Powheg+Pythia8 t t b b (4FS) Powheg+Pythia8 σ t t bb dσ QCD Data-t t X σ t t bb dσ QCD Data-t t X Sherpa 2.2 t t Sherpa 2.2 t t b b (4FS) Sherpa 2.2 t t Sherpa 2.2 t t b b (4FS) 1.5 1.5 MC MC 1 1 0.5 PowHel+Pythia8 t t b b (4FS) PowHel+Pythia8 t t b b (5FS) 0.5 PowHel+Pythia8 t t b b (4FS) PowHel+Pythia8 t t b b (5FS) dσ t t bb dσ t t bb t t bb t t bb 1.1 1.1 1 1 σ QCD σ QCD t t bb t t bb 0.9 MG5_aMC@NLO+Pythia8 0.9 MG5_aMC@NLO+Pythia8 102 103 102 103 m ∆bbmin [GeV] p ∆min [GeV] T, bb (a) (b) 2.2 σ t t bb d∆R ∆min d σ t t bb 2 ATLAS bb lepton+jets channel -1 ≥ 6j, ≥ 4b 1.8 s=13 TeV, 36.1 fb 1.6 Data - t t X (X = H,V ) 1 1.4 Powheg+Pythia8 MG5_aMC@NLO+Pythia8 1.2 Powheg+Herwig7 1 Syst. Stat. 0.8 0.6 0.4 0.2 0 Data-t t X 1.5 MC 1 0.5 Data-t t X Powheg+Pythia8 (RadHi) Powheg+Pythia8 (RadLo) 1.5 MC 1 0.5 Powheg+Pythia8 t t b b (4FS) Powheg+Pythia8 σ t t bb dσ QCD Data-t t X Sherpa 2.2 t t Sherpa 2.2 t t b b (4FS) 1.5 MC 1 0.5 PowHel+Pythia8 t t b b (4FS) PowHel+Pythia8 t t b b (5FS) dσ t t bb t t bb 1.1 1 σ QCD t t bb 0.9 MG5_aMC@NLO+Pythia8 0.5 1 1.5 2 2.5 3 ∆R ∆bbmin (c) Figure 17. Relative differential cross-sections as a function of (a) m∆minbb , (b) p∆min T,bb and (c) ∆min ∆Rbb of two closest b-jets in ∆R in events with at least four b-jets in the lepton + jets chan- nel compared with various MC generators. The tt̄H and tt̄V contributions are subtracted from data. Four ratio panels are shown: the first three show the ratios of various predictions to data. The last panel shows the ratio of predictions of normalised differential cross-sections from Mad- Graph5 aMC@NLO+Pythia 8 including (numerator) and not including (denominator) the con- tributions from tt̄V and tt̄H production. Uncertainty bands represent the statistical and total systematic uncertainties as described in section 8. Events with observable values outside the axis range are not included in the plot. – 42 – MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, CANARIE, CRC and Compute Canada, Canada; COST, ERC, ERDF, Horizon 2020, and Marie Sklodowska-Curie Actions, European Union; Investissements d’ Avenir Labex and Idex, ANR, France; DFG and AvH Foundation, Ger- many; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and GIF, Israel; CERCA Programme Generalitat de Catalunya, Spain; The Royal Society and Leverhulme Trust, United Kingdom. JHEP04(2019)046 The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (U.K.) and BNL (U.S.A.), the Tier-2 facilities worldwide and large non-WLCG resource providers. Ma- jor contributors of computing resources are listed in ref. [85]. Open Access. This article is distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited. References [1] S. Höche et al., Next-to-leading order QCD predictions for top-quark pair production with up to two jets merged with a parton shower, Phys. Lett. B 748 (2015) 74 [arXiv:1402.6293] [INSPIRE]. [2] F. Cascioli, P. Maierhöfer, N. Moretti, S. Pozzorini and F. Siegert, NLO matching for tt̄bb̄ production with massive b-quarks, Phys. Lett. B 734 (2014) 210 [arXiv:1309.5912] [INSPIRE]. [3] M.V. Garzelli, A. Kardos and Z. Trócsányi, Hadroproduction of tt̄bb̄ final states at LHC: predictions at NLO accuracy matched with parton shower, JHEP 03 (2015) 083 [arXiv:1408.0266] [INSPIRE]. [4] G. Bevilacqua, M.V. Garzelli and A. Kardos, tt̄bb̄ hadroproduction with massive bottom quarks with PowHel, arXiv:1709.06915 [INSPIRE]. [5] T. Ježo, J.M. Lindert, N. Moretti and S. Pozzorini, New NLOPS predictions for tt̄ + b-jet production at the LHC, Eur. Phys. J. C 78 (2018) 502 [arXiv:1802.00426] [INSPIRE]. [6] ATLAS collaboration, Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC, Phys. Lett. B 716 (2012) 1 [arXiv:1207.7214] [INSPIRE]. [7] CMS collaboration, Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC, Phys. Lett. B 716 (2012) 30 [arXiv:1207.7235] [INSPIRE]. [8] ATLAS collaboration, Observation of Higgs boson production in association with a top quark pair at the LHC with the ATLAS detector, Phys. Lett. B 784 (2018) 173 [arXiv:1806.00425] [INSPIRE]. – 43 – [9] CMS collaboration, Observation of tt̄H production, Phys. Rev. Lett. 120 (2018) 231801 [arXiv:1804.02610] [INSPIRE]. [10] CMS collaboration, Observation of Higgs boson decay to bottom quarks, Phys. Rev. Lett. 121 (2018) 121801 [arXiv:1808.08242] [INSPIRE]. [11] ATLAS collaboration, Observation of H → bb̄ decays and V H production with the ATLAS detector, Phys. Lett. B 786 (2018) 59 [arXiv:1808.08238] [INSPIRE]. [12] ATLAS collaboration, Search for the Standard Model Higgs boson produced in association √ with top quarks and decaying into a bb̄ pair in pp collisions at s = 13 TeV with the ATLAS detector, Phys. Rev. D 97 (2018) 072016 [arXiv:1712.08895] [INSPIRE]. JHEP04(2019)046 [13] CMS collaboration, Search for tt̄H production in the H → bb̄ decay channel with leptonic tt̄ √ decays in proton-proton collisions at s = 13 TeV, JHEP 03 (2019) 026 [arXiv:1804.03682] [INSPIRE]. [14] ATLAS collaboration, Study of heavy-flavor quarks produced in association with top-quark √ pairs at s = 7 TeV using the ATLAS detector, Phys. Rev. D 89 (2014) 072012 [arXiv:1304.6386] [INSPIRE]. [15] ATLAS collaboration, Measurements of fiducial cross-sections for tt̄ production with one or √ two additional b-jets in pp collisions at s = 8 TeV using the ATLAS detector, Eur. Phys. J. C 76 (2016) 11 [arXiv:1508.06868] [INSPIRE]. [16] CMS collaboration, Measurement of tt̄ production with additional jet activity, including b √ quark jets, in the dilepton decay channel using pp collisions at s = 8 TeV, Eur. Phys. J. C 76 (2016) 379 [arXiv:1510.03072] [INSPIRE]. [17] CMS collaboration, Measurement of the cross section ratio σtt̄bb̄ /σtt̄jj in pp collisions at √ s = 8 TeV, Phys. Lett. B 746 (2015) 132 [arXiv:1411.5621] [INSPIRE]. [18] CMS collaboration, Measurements of tt̄ cross sections in association with b jets and inclusive √ jets and their ratio using dilepton final states in pp collisions at s = 13 TeV, Phys. Lett. B 776 (2018) 355 [arXiv:1705.10141] [INSPIRE]. [19] ATLAS collaboration, Measurements of b-jet tagging efficiency with the ATLAS detector √ using tt̄ events at s = 13 TeV, JHEP 08 (2018) 089 [arXiv:1805.01845] [INSPIRE]. [20] ATLAS collaboration, The ATLAS experiment at the CERN Large Hadron Collider, 2008 JINST 3 S08003 [INSPIRE]. [21] ATLAS collaboration, ATLAS insertable B-layer technical design report, CERN-LHCC-2010-013, CERN, Geneva, Switzerland (2010). [22] ATLAS IBL collaboration, Production and integration of the ATLAS insertable B-layer, 2018 JINST 13 T05008 [arXiv:1803.00844] [INSPIRE]. [23] ATLAS collaboration, Performance of the ATLAS trigger system in 2010, Eur. Phys. J. C 72 (2012) 1849 [arXiv:1110.1530] [INSPIRE]. [24] ATLAS collaboration, Performance of the ATLAS trigger system in 2015, Eur. Phys. J. C 77 (2017) 317 [arXiv:1611.09661] [INSPIRE]. [25] P. Nason, A new method for combining NLO QCD with shower Monte Carlo algorithms, JHEP 11 (2004) 040 [hep-ph/0409146] [INSPIRE]. [26] S. Frixione, P. Nason and C. Oleari, Matching NLO QCD computations with parton shower simulations: the POWHEG method, JHEP 11 (2007) 070 [arXiv:0709.2092] [INSPIRE]. – 44 – [27] S. Alioli, P. Nason, C. Oleari and E. Re, A general framework for implementing NLO calculations in shower Monte Carlo programs: the POWHEG BOX, JHEP 06 (2010) 043 [arXiv:1002.2581] [INSPIRE]. [28] S. Frixione, P. Nason and G. Ridolfi, A positive-weight next-to-leading-order Monte Carlo for heavy flavour hadroproduction, JHEP 09 (2007) 126 [arXiv:0707.3088] [INSPIRE]. [29] T. Sjöstrand et al., An introduction to PYTHIA 8.2, Comput. Phys. Commun. 191 (2015) 159 [arXiv:1410.3012] [INSPIRE]. [30] R.D. Ball et al., Parton distributions with LHC data, Nucl. Phys. B 867 (2013) 244 [arXiv:1207.1303] [INSPIRE]. JHEP04(2019)046 [31] NNPDF collaboration, Parton distributions for the LHC run II, JHEP 04 (2015) 040 [arXiv:1410.8849] [INSPIRE]. [32] ATLAS collaboration, ATLAS run 1 PYTHIA8 tunes, ATL-PHYS-PUB-2014-021, CERN, Geneva, Switzerland (2014). [33] ATLAS collaboration, Studies on top-quark Monte Carlo modelling for Top2016, ATL-PHYS-PUB-2016-020, CERN, Geneva, Switzerland (2016). [34] J. Alwall et al., The automated computation of tree-level and next-to-leading order differential cross sections and their matching to parton shower simulations, JHEP 07 (2014) 079 [arXiv:1405.0301] [INSPIRE]. √ [35] ATLAS collaboration, Modelling of the tt̄H and tt̄V (V = W, Z) processes for s = 13 TeV ATLAS analyses, ATL-PHYS-PUB-2016-005, CERN, Geneva, Switzerland (2016). [36] LHC Higgs Cross Section Working Group collaboration, Handbook of LHC Higgs cross sections: 4. Deciphering the nature of the Higgs sector, arXiv:1610.07922 [INSPIRE]. [37] A. Djouadi, J. Kalinowski and M. Spira, HDECAY: a program for Higgs boson decays in the Standard Model and its supersymmetric extension, Comput. Phys. Commun. 108 (1998) 56 [hep-ph/9704448] [INSPIRE]. [38] M. Bahr et al., HERWIG++ physics and manual, Eur. Phys. J. C 58 (2008) 639 [arXiv:0803.0883] [INSPIRE]. [39] J. Bellm et al., HERWIG 7.0/HERWIG++ 3.0 release note, Eur. Phys. J. C 76 (2016) 196 [arXiv:1512.01178] [INSPIRE]. [40] T. Gleisberg et al., Event generation with SHERPA 1.1, JHEP 02 (2009) 007 [arXiv:0811.4622] [INSPIRE]. [41] S. Höche, F. Krauss, M. Schönherr and F. Siegert, QCD matrix elements + parton showers: the NLO case, JHEP 04 (2013) 027 [arXiv:1207.5030] [INSPIRE]. [42] M. Czakon and A. Mitov, Top++: a program for the calculation of the top-pair cross-section at hadron colliders, Comput. Phys. Commun. 185 (2014) 2930 [arXiv:1112.5675] [INSPIRE]. [43] M. Botje et al., The PDF4LHC working group interim recommendations, arXiv:1101.0538 [INSPIRE]. [44] A.D. Martin, W.J. Stirling, R.S. Thorne and G. Watt, Uncertainties on αs in global PDF analyses and implications for predicted hadronic cross sections, Eur. Phys. J. C 64 (2009) 653 [arXiv:0905.3531] [INSPIRE]. [45] J. Gao et al., CT10 next-to-next-to-leading order global analysis of QCD, Phys. Rev. D 89 (2014) 033009 [arXiv:1302.6246] [INSPIRE]. – 45 – [46] R.D. Ball et al., Parton distributions with LHC data, Nucl. Phys. B 867 (2013) 244 [arXiv:1207.1303] [INSPIRE]. [47] T. Gleisberg and S. Höche, Comix, a new matrix element generator, JHEP 12 (2008) 039 [arXiv:0808.3674] [INSPIRE]. [48] F. Cascioli, P. Maierhofer and S. Pozzorini, Scattering amplitudes with open loops, Phys. Rev. Lett. 108 (2012) 111601 [arXiv:1111.5206] [INSPIRE]. [49] S. Schumann and F. Krauss, A parton shower algorithm based on Catani-Seymour dipole factorisation, JHEP 03 (2008) 038 [arXiv:0709.1027] [INSPIRE]. [50] D.J. Lange, The EvtGen particle decay simulation package, Nucl. Instrum. Meth. A 462 JHEP04(2019)046 (2001) 152 [INSPIRE]. [51] S. Frixione, E. Laenen, P. Motylinski and B.R. Webber, Angular correlations of lepton pairs from vector boson and top quark decays in Monte Carlo simulations, JHEP 04 (2007) 081 [hep-ph/0702198] [INSPIRE]. [52] P. Artoisenet, R. Frederix, O. Mattelaer and R. Rietkerk, Automatic spin-entangled decays of heavy resonances in Monte Carlo simulations, JHEP 03 (2013) 015 [arXiv:1212.3460] [INSPIRE]. [53] S. Frixione, E. Laenen, P. Motylinski, B.R. Webber and C.D. White, Single-top hadroproduction in association with a W boson, JHEP 07 (2008) 029 [arXiv:0805.3067] [INSPIRE]. [54] T. Sjöstrand, S. Mrenna and P.Z. Skands, PYTHIA 6.4 physics and manual, JHEP 05 (2006) 026 [hep-ph/0603175] [INSPIRE]. [55] J. Pumplin, D.R. Stump, J. Huston, H.L. Lai, P.M. Nadolsky and W.K. Tung, New generation of parton distributions with uncertainties from global QCD analysis, JHEP 07 (2002) 012 [hep-ph/0201195] [INSPIRE]. [56] P.Z. Skands, Tuning Monte Carlo generators: the Perugia tunes, Phys. Rev. D 82 (2010) 074018 [arXiv:1005.3457] [INSPIRE]. [57] M. Aliev, H. Lacker, U. Langenfeld, S. Moch, P. Uwer and M. Wiedermann, HATHOR: HAdronic Top and Heavy quarks crOss section calculatoR, Comput. Phys. Commun. 182 (2011) 1034 [arXiv:1007.1327] [INSPIRE]. [58] P. Kant et al., HatHor for single top-quark production: updated predictions and uncertainty estimates for single top-quark production in hadronic collisions, Comput. Phys. Commun. 191 (2015) 74 [arXiv:1406.4403] [INSPIRE]. [59] N. Kidonakis, Two-loop soft anomalous dimensions for single top quark associated production with a W − or H − , Phys. Rev. D 82 (2010) 054018 [arXiv:1005.4451] [INSPIRE]. [60] C. Anastasiou, L.J. Dixon, K. Melnikov and F. Petriello, High precision QCD at hadron colliders: electroweak gauge boson rapidity distributions at NNLO, Phys. Rev. D 69 (2004) 094008 [hep-ph/0312266] [INSPIRE]. [61] ATLAS collaboration, Summary of ATLAS PYTHIA 8 tunes, ATL-PHYS-PUB-2012-003, CERN, Geneva, Switzerland (2012). [62] A.D. Martin, W.J. Stirling, R.S. Thorne and G. Watt, Parton distributions for the LHC, Eur. Phys. J. C 63 (2009) 189 [arXiv:0901.0002] [INSPIRE]. – 46 – [63] ATLAS collaboration, Electron efficiency measurements with the ATLAS detector using the 2015 LHC proton-proton collision data, ATLAS-CONF-2016-024, CERN, Geneva, Switzerland (2016). [64] ATLAS collaboration, Muon reconstruction performance of the ATLAS detector in √ proton-proton collision data at s = 13 TeV, Eur. Phys. J. C 76 (2016) 292 [arXiv:1603.05598] [INSPIRE]. [65] M. Cacciari, G.P. Salam and G. Soyez, The anti-kt jet clustering algorithm, JHEP 04 (2008) 063 [arXiv:0802.1189] [INSPIRE]. [66] ATLAS collaboration, Topological cell clustering in the ATLAS calorimeters and its JHEP04(2019)046 performance in LHC run 1, Eur. Phys. J. C 77 (2017) 490 [arXiv:1603.02934] [INSPIRE]. [67] ATLAS collaboration, Jet energy scale measurements and their systematic uncertainties in √ proton-proton collisions at s = 13 TeV with the ATLAS detector, Phys. Rev. D 96 (2017) 072002 [arXiv:1703.09665] [INSPIRE]. [68] ATLAS collaboration, Tagging and suppression of pileup jets with the ATLAS detector, ATLAS-CONF-2014-018, CERN, Geneva, Switzerland (2014). [69] ATLAS collaboration, Optimisation of the ATLAS b-tagging performance for the 2016 LHC run, ATL-PHYS-PUB-2016-012, CERN, Geneva, Switzerland (2016). [70] M. Cacciari, G.P. Salam and G. Soyez, The catchment area of jets, JHEP 04 (2008) 005 [arXiv:0802.1188] [INSPIRE]. [71] ATLAS collaboration, Measurement of the tt̄ production cross-section using eµ events with √ b-tagged jets in pp collisions at s = 13 TeV with the ATLAS detector, Phys. Lett. B 761 (2016) 136 [Erratum ibid. B 772 (2017) 879] [arXiv:1606.02699] [INSPIRE]. [72] ATLAS collaboration, Estimation of non-prompt and fake lepton backgrounds in final states √ with top quarks produced in proton-proton collisions at s = 8 TeV with the ATLAS detector, ATLAS-CONF-2014-058, CERN, Geneva, Switzerland (2014). [73] ATLAS collaboration, Measurement of jet activity produced in top-quark events with an √ electron, a muon and two b-tagged jets in the final state in pp collisions at s = 13 TeV with the ATLAS detector, Eur. Phys. J. C 77 (2017) 220 [arXiv:1610.09978] [INSPIRE]. [74] G. D’Agostini, A multidimensional unfolding method based on Bayes’ theorem, Nucl. Instrum. Meth. A 362 (1995) 487 [INSPIRE]. [75] T. Adye, Unfolding algorithms and tests using RooUnfold, in Proceedings, PHYSTAT 2011 Workshop on Statistical Issues Related to Discovery Claims in Search Experiments and Unfolding, CERN, Geneva, Switzerland, 17–20 January 2011, CERN-2011-006, CERN, Geneva, Switzerland (2011), pg. 313 [arXiv:1105.1160] [INSPIRE]. √ [76] ATLAS collaboration, Luminosity determination in pp collisions at s = 8 TeV using the ATLAS detector at the LHC, Eur. Phys. J. C 76 (2016) 653 [arXiv:1608.03953] [INSPIRE]. [77] G. Avoni et al., The new LUCID-2 detector for luminosity measurement and monitoring in ATLAS, 2018 JINST 13 P07017 [INSPIRE]. [78] ATLAS collaboration, Electron and photon energy calibration with the ATLAS detector √ using data collected in 2015 at s = 13 TeV, ATL-PHYS-PUB-2016-015, CERN, Geneva, Switzerland (2016). – 47 – [79] ATLAS collaboration, Performance of pile-up mitigation techniques for jets in pp collisions √ at s = 8 TeV using the ATLAS detector, Eur. Phys. J. C 76 (2016) 581 [arXiv:1510.03823] [INSPIRE]. √ [80] ATLAS collaboration, Jet energy resolution in proton-proton collisions at s = 7 TeV recorded in 2010 with the ATLAS detector, Eur. Phys. J. C 73 (2013) 2306 [arXiv:1210.6210] [INSPIRE]. [81] ATLAS collaboration, Calibration of light-flavour jet b-tagging rates on ATLAS √ proton-proton collision data at s = 13 TeV, ATLAS-CONF-2018-006, CERN, Geneva, Switzerland (2018). [82] ATLAS collaboration, Measurement of b-tagging efficiency of c-jets in tt̄ events using a JHEP04(2019)046 likelihood approach with the ATLAS detector, ATLAS-CONF-2018-001, CERN, Geneva, Switzerland (2018). [83] J. Butterworth et al., PDF4LHC recommendations for LHC run II, J. Phys. G 43 (2016) 023001 [arXiv:1510.03865] [INSPIRE]. [84] ATLAS collaboration, Measurement of the tt̄Z and tt̄W production cross sections in √ multilepton final states using 3.2 fb−1 of pp collisions at s = 13 TeV with the ATLAS detector, Eur. Phys. J. C 77 (2017) 40 [arXiv:1609.01599] [INSPIRE]. [85] ATLAS collaboration, ATLAS computing acknowledgements, ATL-GEN-PUB-2016-002, CERN, Geneva, Switzerland (2016). – 48 – The ATLAS collaboration M. Aaboud34d , G. Aad99 , B. Abbott125 , O. Abdinov13,∗ , B. Abeloos129 , D.K. Abhayasinghe91 , S.H. Abidi164 , O.S. AbouZeid39 , N.L. Abraham153 , H. Abramowicz158 , H. Abreu157 , Y. Abulaiti6 , B.S. Acharya64a,64b,p , S. Adachi160 , L. Adam97 , L. Adamczyk81a , J. Adelman119 , M. Adersberger112 , A. Adiguzel12c,aj , T. Adye141 , A.A. Affolder143 , Y. Afik157 , C. Agheorghiesei27c , J.A. Aguilar-Saavedra137f,137a,ai , F. Ahmadov77,ag , G. Aielli71a,71b , S. Akatsuka83 , T.P.A. Åkesson94 , E. Akilli52 , A.V. Akimov108 , G.L. Alberghi23b,23a , J. Albert173 , P. Albicocco49 , M.J. Alconada Verzini86 , S. Alderweireldt117 , M. Aleksa35 , I.N. Aleksandrov77 , C. Alexa27b , T. Alexopoulos10 , M. Alhroob125 , B. Ali139 , G. Alimonti66a , J. Alison36 , JHEP04(2019)046 S.P. Alkire145 , C. Allaire129 , B.M.M. Allbrooke153 , B.W. Allen128 , P.P. Allport21 , A. Aloisio67a,67b , A. Alonso39 , F. Alonso86 , C. Alpigiani145 , A.A. Alshehri55 , M.I. Alstaty99 , B. Alvarez Gonzalez35 , D. Álvarez Piqueras171 , M.G. Alviggi67a,67b , B.T. Amadio18 , Y. Amaral Coutinho78b , A. Ambler101 , L. Ambroz132 , C. Amelung26 , D. Amidei103 , S.P. Amor Dos Santos137a,137c , S. Amoroso44 , C.S. Amrouche52 , C. Anastopoulos146 , L.S. Ancu52 , N. Andari142 , T. Andeen11 , C.F. Anders59b , J.K. Anders20 , K.J. Anderson36 , A. Andreazza66a,66b , V. Andrei59a , C.R. Anelli173 , S. Angelidakis37 , I. Angelozzi118 , A. Angerami38 , A.V. Anisenkov120b,120a , A. Annovi69a , C. Antel59a , M.T. Anthony146 , M. Antonelli49 , D.J.A. Antrim168 , F. Anulli70a , M. Aoki79 , J.A. Aparisi Pozo171 , L. Aperio Bella35 , G. Arabidze104 , J.P. Araque137a , V. Araujo Ferraz78b , R. Araujo Pereira78b , A.T.H. Arce47 , R.E. Ardell91 , F.A. Arduh86 , J-F. Arguin107 , S. Argyropoulos75 , A.J. Armbruster35 , L.J. Armitage90 , A. Armstrong168 , O. Arnaez164 , H. Arnold118 , M. Arratia31 , O. Arslan24 , A. Artamonov109,∗ , G. Artoni132 , S. Artz97 , S. Asai160 , N. Asbah57 , E.M. Asimakopoulou169 , L. Asquith153 , K. Assamagan29 , R. Astalos28a , R.J. Atkin32a , M. Atkinson170 , N.B. Atlay148 , K. Augsten139 , G. Avolio35 , R. Avramidou58a , M.K. Ayoub15a , A.M. Azoulay165b , G. Azuelos107,av , A.E. Baas59a , M.J. Baca21 , H. Bachacou142 , K. Bachas65a,65b , M. Backes132 , P. Bagnaia70a,70b , M. Bahmani82 , H. Bahrasemani149 , A.J. Bailey171 , J.T. Baines141 , M. Bajic39 , C. Bakalis10 , O.K. Baker180 , P.J. Bakker118 , D. Bakshi Gupta8 , S. Balaji154 , E.M. Baldin120b,120a , P. Balek177 , F. Balli142 , W.K. Balunas134 , J. Balz97 , E. Banas82 , A. Bandyopadhyay24 , S. Banerjee178,l , A.A.E. Bannoura179 , L. Barak158 , W.M. Barbe37 , E.L. Barberio102 , D. Barberis53b,53a , M. Barbero99 , T. Barillari113 , M-S. Barisits35 , J. Barkeloo128 , T. Barklow150 , R. Barnea157 , S.L. Barnes58c , B.M. Barnett141 , R.M. Barnett18 , Z. Barnovska-Blenessy58a , A. Baroncelli72a , G. Barone29 , A.J. Barr132 , L. Barranco Navarro171 , F. Barreiro96 , J. Barreiro Guimarães da Costa15a , R. Bartoldus150 , A.E. Barton87 , P. Bartos28a , A. Basalaev135 , A. Bassalat129 , R.L. Bates55 , S.J. Batista164 , S. Batlamous34e , J.R. Batley31 , M. Battaglia143 , M. Bauce70a,70b , F. Bauer142 , K.T. Bauer168 , H.S. Bawa150,n , J.B. Beacham123 , T. Beau133 , P.H. Beauchemin167 , P. Bechtle24 , H.C. Beck51 , H.P. Beck20,s , K. Becker50 , M. Becker97 , C. Becot44 , A. Beddall12d , A.J. Beddall12a , V.A. Bednyakov77 , M. Bedognetti118 , C.P. Bee152 , T.A. Beermann74 , M. Begalli78b , M. Begel29 , A. Behera152 , J.K. Behr44 , A.S. Bell92 , G. Bella158 , L. Bellagamba23b , A. Bellerive33 , M. Bellomo157 , P. Bellos9 , K. Belotskiy110 , N.L. Belyaev110 , O. Benary158,∗ , D. Benchekroun34a , M. Bender112 , N. Benekos10 , Y. Benhammou158 , E. Benhar Noccioli180 , J. Benitez75 , D.P. Benjamin47 , M. Benoit52 , J.R. Bensinger26 , S. Bentvelsen118 , L. Beresford132 , M. Beretta49 , D. Berge44 , E. Bergeaas Kuutmann169 , N. Berger5 , B. Bergmann139 , L.J. Bergsten26 , J. Beringer18 , S. Berlendis7 , N.R. Bernard100 , G. Bernardi133 , C. Bernius150 , F.U. Bernlochner24 , T. Berry91 , P. Berta97 , C. Bertella15a , G. Bertoli43a,43b , I.A. Bertram87 , G.J. Besjes39 , O. Bessidskaia Bylund179 , M. Bessner44 , N. Besson142 , A. Bethani98 , S. Bethke113 , A. Betti24 , A.J. Bevan90 , J. Beyer113 , R. Bi136 , R.M. Bianchi136 , O. Biebel112 , D. Biedermann19 , R. Bielski35 , K. Bierwagen97 , N.V. Biesuz69a,69b , – 49 – M. Biglietti72a , T.R.V. Billoud107 , M. Bindi51 , A. Bingul12d , C. Bini70a,70b , S. Biondi23b,23a , M. Birman177 , T. Bisanz51 , J.P. Biswal158 , C. Bittrich46 , D.M. Bjergaard47 , J.E. Black150 , K.M. Black25 , T. Blazek28a , I. Bloch44 , C. Blocker26 , A. Blue55 , U. Blumenschein90 , Dr. Blunier144a , G.J. Bobbink118 , V.S. Bobrovnikov120b,120a , S.S. Bocchetta94 , A. Bocci47 , D. Boerner179 , D. Bogavac112 , A.G. Bogdanchikov120b,120a , C. Bohm43a , V. Boisvert91 , P. Bokan169 , T. Bold81a , A.S. Boldyrev111 , A.E. Bolz59b , M. Bomben133 , M. Bona90 , J.S. Bonilla128 , M. Boonekamp142 , A. Borisov121 , G. Borissov87 , J. Bortfeldt35 , D. Bortoletto132 , V. Bortolotto71a,71b , D. Boscherini23b , M. Bosman14 , J.D. Bossio Sola30 , K. Bouaouda34a , J. Boudreau136 , E.V. Bouhova-Thacker87 , D. Boumediene37 , C. Bourdarios129 , S.K. Boutle55 , A. Boveia123 , J. Boyd35 , D. Boye32b , I.R. Boyko77 , A.J. Bozson91 , J. Bracinik21 , N. Brahimi99 , A. Brandt8 , G. Brandt179 , O. Brandt59a , F. Braren44 , U. Bratzler161 , B. Brau100 , J.E. Brau128 , JHEP04(2019)046 W.D. Breaden Madden55 , K. Brendlinger44 , L. Brenner44 , R. Brenner169 , S. Bressler177 , B. Brickwedde97 , D.L. Briglin21 , D. Britton55 , D. Britzger113 , I. Brock24 , R. Brock104 , G. Brooijmans38 , T. Brooks91 , W.K. Brooks144b , E. Brost119 , J.H Broughton21 , P.A. Bruckman de Renstrom82 , D. Bruncko28b , A. Bruni23b , G. Bruni23b , L.S. Bruni118 , S. Bruno71a,71b , B.H. Brunt31 , M. Bruschi23b , N. Bruscino136 , P. Bryant36 , L. Bryngemark44 , T. Buanes17 , Q. Buat35 , P. Buchholz148 , A.G. Buckley55 , I.A. Budagov77 , M.K. Bugge131 , F. Bührer50 , O. Bulekov110 , D. Bullock8 , T.J. Burch119 , S. Burdin88 , C.D. Burgard118 , A.M. Burger5 , B. Burghgrave119 , K. Burka82 , S. Burke141 , I. Burmeister45 , J.T.P. Burr132 , V. Büscher97 , E. Buschmann51 , P. Bussey55 , J.M. Butler25 , C.M. Buttar55 , J.M. Butterworth92 , P. Butti35 , W. Buttinger35 , A. Buzatu155 , A.R. Buzykaev120b,120a , G. Cabras23b,23a , S. Cabrera Urbán171 , D. Caforio139 , H. Cai170 , V.M.M. Cairo2 , O. Cakir4a , N. Calace52 , P. Calafiura18 , A. Calandri99 , G. Calderini133 , P. Calfayan63 , G. Callea40b,40a , L.P. Caloba78b , S. Calvente Lopez96 , D. Calvet37 , S. Calvet37 , T.P. Calvet152 , M. Calvetti69a,69b , R. Camacho Toro133 , S. Camarda35 , D. Camarero Munoz96 , P. Camarri71a,71b , D. Cameron131 , R. Caminal Armadans100 , C. Camincher35 , S. Campana35 , M. Campanelli92 , A. Camplani39 , A. Campoverde148 , V. Canale67a,67b , M. Cano Bret58c , J. Cantero126 , T. Cao158 , Y. Cao170 , M.D.M. Capeans Garrido35 , I. Caprini27b , M. Caprini27b , M. Capua40b,40a , R.M. Carbone38 , R. Cardarelli71a , F.C. Cardillo146 , I. Carli140 , T. Carli35 , G. Carlino67a , B.T. Carlson136 , L. Carminati66a,66b , R.M.D. Carney43a,43b , S. Caron117 , E. Carquin144b , S. Carrá66a,66b , G.D. Carrillo-Montoya35 , D. Casadei32b , M.P. Casado14,g , A.F. Casha164 , D.W. Casper168 , R. Castelijn118 , F.L. Castillo171 , V. Castillo Gimenez171 , N.F. Castro137a,137e , A. Catinaccio35 , J.R. Catmore131 , A. Cattai35 , J. Caudron24 , V. Cavaliere29 , E. Cavallaro14 , D. Cavalli66a , M. Cavalli-Sforza14 , V. Cavasinni69a,69b , E. Celebi12b , F. Ceradini72a,72b , L. Cerda Alberich171 , A.S. Cerqueira78a , A. Cerri153 , L. Cerrito71a,71b , F. Cerutti18 , A. Cervelli23b,23a , S.A. Cetin12b , A. Chafaq34a , D. Chakraborty119 , S.K. Chan57 , W.S. Chan118 , Y.L. Chan61a , J.D. Chapman31 , B. Chargeishvili156b , D.G. Charlton21 , C.C. Chau33 , C.A. Chavez Barajas153 , S. Che123 , A. Chegwidden104 , S. Chekanov6 , S.V. Chekulaev165a , G.A. Chelkov77,au , M.A. Chelstowska35 , C. Chen58a , C.H. Chen76 , H. Chen29 , J. Chen58a , J. Chen38 , S. Chen134 , S.J. Chen15c , X. Chen15b,at , Y. Chen80 , Y-H. Chen44 , H.C. Cheng103 , H.J. Cheng15d , A. Cheplakov77 , E. Cheremushkina121 , R. Cherkaoui El Moursli34e , E. Cheu7 , K. Cheung62 , L. Chevalier142 , V. Chiarella49 , G. Chiarelli69a , G. Chiodini65a , A.S. Chisholm35,21 , A. Chitan27b , I. Chiu160 , Y.H. Chiu173 , M.V. Chizhov77 , K. Choi63 , A.R. Chomont129 , S. Chouridou159 , Y.S. Chow118 , V. Christodoulou92 , M.C. Chu61a , J. Chudoba138 , A.J. Chuinard101 , J.J. Chwastowski82 , L. Chytka127 , D. Cinca45 , V. Cindro89 , I.A. Cioară24 , A. Ciocio18 , F. Cirotto67a,67b , Z.H. Citron177 , M. Citterio66a , A. Clark52 , M.R. Clark38 , P.J. Clark48 , C. Clement43a,43b , Y. Coadou99 , M. Cobal64a,64c , A. Coccaro53b,53a , J. Cochran76 , H. Cohen158 , A.E.C. Coimbra177 , L. Colasurdo117 , B. Cole38 , A.P. Colijn118 , J. Collot56 , P. Conde Muiño137a,i , E. Coniavitis50 , – 50 – S.H. Connell32b , I.A. Connelly98 , S. Constantinescu27b , F. Conventi67a,aw , A.M. Cooper-Sarkar132 , F. Cormier172 , K.J.R. Cormier164 , L.D. Corpe92 , M. Corradi70a,70b , E.E. Corrigan94 , F. Corriveau101,ae , A. Cortes-Gonzalez35 , M.J. Costa171 , F. Costanza5 , D. Costanzo146 , G. Cottin31 , G. Cowan91 , B.E. Cox98 , J. Crane98 , K. Cranmer122 , S.J. Crawley55 , R.A. Creager134 , G. Cree33 , S. Crépé-Renaudin56 , F. Crescioli133 , M. Cristinziani24 , V. Croft122 , G. Crosetti40b,40a , A. Cueto96 , T. Cuhadar Donszelmann146 , A.R. Cukierman150 , S. Czekierda82 , P. Czodrowski35 , M.J. Da Cunha Sargedas De Sousa58b , C. Da Via98 , W. Dabrowski81a , T. Dado28a,z , S. Dahbi34e , T. Dai103 , F. Dallaire107 , C. Dallapiccola100 , M. Dam39 , G. D’amen23b,23a , J. Damp97 , J.R. Dandoy134 , M.F. Daneri30 , N.P. Dang178,l , N.D Dann98 , M. Danninger172 , V. Dao35 , G. Darbo53b , S. Darmora8 , O. Dartsi5 , A. Dattagupta128 , T. Daubney44 , S. D’Auria66a,66b , W. Davey24 , C. David44 , T. Davidek140 , D.R. Davis47 , JHEP04(2019)046 E. Dawe102 , I. Dawson146 , K. De8 , R. De Asmundis67a , A. De Benedetti125 , M. De Beurs118 , S. De Castro23b,23a , S. De Cecco70a,70b , N. De Groot117 , P. de Jong118 , H. De la Torre104 , F. De Lorenzi76 , A. De Maria51,u , D. De Pedis70a , A. De Salvo70a , U. De Sanctis71a,71b , M. De Santis71a,71b , A. De Santo153 , K. De Vasconcelos Corga99 , J.B. De Vivie De Regie129 , C. Debenedetti143 , D.V. Dedovich77 , N. Dehghanian3 , M. Del Gaudio40b,40a , J. Del Peso96 , Y. Delabat Diaz44 , D. Delgove129 , F. Deliot142 , C.M. Delitzsch7 , M. Della Pietra67a,67b , D. Della Volpe52 , A. Dell’Acqua35 , L. Dell’Asta25 , M. Delmastro5 , C. Delporte129 , P.A. Delsart56 , D.A. DeMarco164 , S. Demers180 , M. Demichev77 , S.P. Denisov121 , D. Denysiuk118 , L. D’Eramo133 , D. Derendarz82 , J.E. Derkaoui34d , F. Derue133 , P. Dervan88 , K. Desch24 , C. Deterre44 , K. Dette164 , M.R. Devesa30 , P.O. Deviveiros35 , A. Dewhurst141 , S. Dhaliwal26 , F.A. Di Bello52 , A. Di Ciaccio71a,71b , L. Di Ciaccio5 , W.K. Di Clemente134 , C. Di Donato67a,67b , A. Di Girolamo35 , G. Di Gregorio69a,69b , B. Di Micco72a,72b , R. Di Nardo100 , K.F. Di Petrillo57 , R. Di Sipio164 , D. Di Valentino33 , C. Diaconu99 , M. Diamond164 , F.A. Dias39 , T. Dias Do Vale137a , M.A. Diaz144a , J. Dickinson18 , E.B. Diehl103 , J. Dietrich19 , S. Dı́ez Cornell44 , A. Dimitrievska18 , J. Dingfelder24 , F. Dittus35 , F. Djama99 , T. Djobava156b , J.I. Djuvsland59a , M.A.B. Do Vale78c , M. Dobre27b , D. Dodsworth26 , C. Doglioni94 , J. Dolejsi140 , Z. Dolezal140 , M. Donadelli78d , J. Donini37 , A. D’onofrio90 , M. D’Onofrio88 , J. Dopke141 , A. Doria67a , M.T. Dova86 , A.T. Doyle55 , E. Drechsler51 , E. Dreyer149 , T. Dreyer51 , Y. Du58b , F. Dubinin108 , M. Dubovsky28a , A. Dubreuil52 , E. Duchovni177 , G. Duckeck112 , A. Ducourthial133 , O.A. Ducu107,y , D. Duda113 , A. Dudarev35 , A.C. Dudder97 , E.M. Duffield18 , L. Duflot129 , M. Dührssen35 , C. Dülsen179 , M. Dumancic177 , A.E. Dumitriu27b,e , A.K. Duncan55 , M. Dunford59a , A. Duperrin99 , H. Duran Yildiz4a , M. Düren54 , A. Durglishvili156b , D. Duschinger46 , B. Dutta44 , D. Duvnjak1 , M. Dyndal44 , S. Dysch98 , B.S. Dziedzic82 , C. Eckardt44 , K.M. Ecker113 , R.C. Edgar103 , T. Eifert35 , G. Eigen17 , K. Einsweiler18 , T. Ekelof169 , M. El Kacimi34c , R. El Kosseifi99 , V. Ellajosyula99 , M. Ellert169 , F. Ellinghaus179 , A.A. Elliot90 , N. Ellis35 , J. Elmsheuser29 , M. Elsing35 , D. Emeliyanov141 , A. Emerman38 , Y. Enari160 , J.S. Ennis175 , M.B. Epland47 , J. Erdmann45 , A. Ereditato20 , S. Errede170 , M. Escalier129 , C. Escobar171 , O. Estrada Pastor171 , A.I. Etienvre142 , E. Etzion158 , H. Evans63 , A. Ezhilov135 , M. Ezzi34e , F. Fabbri55 , L. Fabbri23b,23a , V. Fabiani117 , G. Facini92 , R.M. Faisca Rodrigues Pereira137a , R.M. Fakhrutdinov121 , S. Falciano70a , P.J. Falke5 , S. Falke5 , J. Faltova140 , Y. Fang15a , M. Fanti66a,66b , A. Farbin8 , A. Farilla72a , E.M. Farina68a,68b , T. Farooque104 , S. Farrell18 , S.M. Farrington175 , P. Farthouat35 , F. Fassi34e , P. Fassnacht35 , D. Fassouliotis9 , M. Faucci Giannelli48 , A. 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Freeborn92 , S.M. Fressard-Batraneanu35 , B. Freund107 , W.S. Freund78b , E.M. Freundlich45 , D.C. Frizzell125 , D. Froidevaux35 , J.A. Frost132 , C. Fukunaga161 , E. Fullana Torregrosa171 , T. Fusayasu114 , J. Fuster171 , O. Gabizon157 , A. Gabrielli23b,23a , A. Gabrielli18 , G.P. Gach81a , S. Gadatsch52 , P. Gadow113 , G. Gagliardi53b,53a , L.G. Gagnon107 , C. Galea27b , B. Galhardo137a,137c , E.J. Gallas132 , B.J. Gallop141 , P. Gallus139 , G. Galster39 , R. Gamboa Goni90 , K.K. Gan123 , S. Ganguly177 , J. Gao58a , Y. Gao88 , Y.S. Gao150,n , C. Garcı́a171 , J.E. Garcı́a Navarro171 , JHEP04(2019)046 J.A. Garcı́a Pascual15a , M. Garcia-Sciveres18 , R.W. Gardner36 , N. Garelli150 , V. Garonne131 , K. Gasnikova44 , A. Gaudiello53b,53a , G. Gaudio68a , I.L. Gavrilenko108 , A. Gavrilyuk109 , C. Gay172 , G. Gaycken24 , E.N. Gazis10 , C.N.P. Gee141 , J. Geisen51 , M. Geisen97 , M.P. Geisler59a , K. Gellerstedt43a,43b , C. Gemme53b , M.H. Genest56 , C. Geng103 , S. Gentile70a,70b , S. 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Gostkin77 , C.A. Gottardo24 , C.R. Goudet129 , D. Goujdami34c , A.G. Goussiou145 , N. Govender32b,c , C. Goy5 , E. Gozani157 , I. Grabowska-Bold81a , P.O.J. Gradin169 , E.C. Graham88 , J. Gramling168 , E. Gramstad131 , S. Grancagnolo19 , V. Gratchev135 , P.M. Gravila27f , F.G. Gravili65a,65b , C. Gray55 , H.M. Gray18 , Z.D. Greenwood93,al , C. Grefe24 , K. Gregersen94 , I.M. Gregor44 , P. Grenier150 , K. Grevtsov44 , N.A. Grieser125 , J. Griffiths8 , A.A. Grillo143 , K. Grimm150,b , S. Grinstein14,aa , Ph. Gris37 , J.-F. Grivaz129 , S. Groh97 , E. Gross177 , J. Grosse-Knetter51 , G.C. Grossi93 , Z.J. Grout92 , C. Grud103 , A. Grummer116 , L. Guan103 , W. Guan178 , J. Guenther35 , A. Guerguichon129 , F. Guescini165a , D. Guest168 , R. Gugel50 , B. Gui123 , T. Guillemin5 , S. Guindon35 , U. Gul55 , C. Gumpert35 , J. Guo58c , W. Guo103 , Y. Guo58a,t , Z. Guo99 , R. Gupta44 , S. Gurbuz12c , G. Gustavino125 , B.J. Gutelman157 , P. Gutierrez125 , C. Gutschow92 , C. Guyot142 , M.P. Guzik81a , C. Gwenlan132 , C.B. Gwilliam88 , A. Haas122 , C. Haber18 , H.K. Hadavand8 , N. Haddad34e , A. Hadef58a , S. Hageböck24 , M. Hagihara166 , H. Hakobyan181,∗ , M. Haleem174 , J. Haley126 , G. Halladjian104 , G.D. Hallewell99 , K. Hamacher179 , P. Hamal127 , K. Hamano173 , A. Hamilton32a , G.N. Hamity146 , K. Han58a,ak , L. Han58a , S. Han15d , K. Hanagaki79,w , M. Hance143 , D.M. Handl112 , B. Haney134 , R. Hankache133 , P. Hanke59a , E. Hansen94 , J.B. Hansen39 , J.D. Hansen39 , M.C. Hansen24 , P.H. Hansen39 , K. Hara166 , A.S. Hard178 , T. Harenberg179 , S. Harkusha105 , P.F. Harrison175 , N.M. Hartmann112 , Y. Hasegawa147 , A. Hasib48 , S. Hassani142 , S. Haug20 , R. Hauser104 , L. Hauswald46 , L.B. Havener38 , M. Havranek139 , C.M. Hawkes21 , R.J. Hawkings35 , D. Hayden104 , C. Hayes152 , C.P. Hays132 , J.M. Hays90 , H.S. Hayward88 , S.J. Haywood141 , M.P. Heath48 , V. Hedberg94 , L. Heelan8 , S. Heer24 , K.K. Heidegger50 , J. Heilman33 , S. Heim44 , T. Heim18 , B. Heinemann44,aq , J.J. Heinrich112 , L. Heinrich122 , C. Heinz54 , J. Hejbal138 , L. Helary35 , A. Held172 , S. Hellesund131 , S. Hellman43a,43b , C. Helsens35 , – 52 – R.C.W. Henderson87 , Y. Heng178 , S. Henkelmann172 , A.M. Henriques Correia35 , G.H. Herbert19 , H. Herde26 , V. Herget174 , Y. Hernández Jiménez32c , H. Herr97 , M.G. Herrmann112 , T. Herrmann46 , G. Herten50 , R. Hertenberger112 , L. Hervas35 , T.C. Herwig134 , G.G. Hesketh92 , N.P. Hessey165a , S. Higashino79 , E. Higón-Rodriguez171 , K. Hildebrand36 , E. Hill173 , J.C. Hill31 , K.K. Hill29 , K.H. Hiller44 , S.J. Hillier21 , M. Hils46 , I. Hinchliffe18 , M. Hirose130 , D. Hirschbuehl179 , B. Hiti89 , O. Hladik138 , D.R. Hlaluku32c , X. Hoad48 , J. Hobbs152 , N. Hod165a , M.C. Hodgkinson146 , A. Hoecker35 , M.R. Hoeferkamp116 , F. Hoenig112 , D. Hohn24 , D. Hohov129 , T.R. Holmes36 , M. Holzbock112 , M. Homann45 , S. Honda166 , T. Honda79 , T.M. Hong136 , A. Hönle113 , B.H. Hooberman170 , W.H. Hopkins128 , Y. Horii115 , P. Horn46 , A.J. Horton149 , L.A. Horyn36 , J-Y. Hostachy56 , A. Hostiuc145 , S. Hou155 , A. Hoummada34a , J. Howarth98 , J. Hoya86 , M. Hrabovsky127 , I. Hristova19 , J. Hrivnac129 , A. Hrynevich106 , T. Hryn’ova5 , JHEP04(2019)046 P.J. Hsu62 , S.-C. Hsu145 , Q. Hu29 , S. Hu58c , Y. Huang15a , Z. Hubacek139 , F. Hubaut99 , M. Huebner24 , F. Huegging24 , T.B. Huffman132 , M. Huhtinen35 , R.F.H. Hunter33 , P. Huo152 , A.M. Hupe33 , N. Huseynov77,ag , J. Huston104 , J. Huth57 , R. Hyneman103 , G. Iacobucci52 , G. Iakovidis29 , I. Ibragimov148 , L. Iconomidou-Fayard129 , Z. Idrissi34e , P. Iengo35 , R. Ignazzi39 , O. Igonkina118,ac , R. Iguchi160 , T. Iizawa52 , Y. Ikegami79 , M. Ikeno79 , D. Iliadis159 , N. Ilic117 , F. Iltzsche46 , G. Introzzi68a,68b , M. Iodice72a , K. Iordanidou38 , V. Ippolito70a,70b , M.F. Isacson169 , N. Ishijima130 , M. Ishino160 , M. Ishitsuka162 , W. Islam126 , C. Issever132 , S. Istin157 , F. Ito166 , J.M. Iturbe Ponce61a , R. Iuppa73a,73b , A. Ivina177 , H. Iwasaki79 , J.M. Izen42 , V. Izzo67a , P. Jacka138 , P. Jackson1 , R.M. Jacobs24 , V. Jain2 , G. Jäkel179 , K.B. Jakobi97 , K. Jakobs50 , S. Jakobsen74 , T. Jakoubek138 , D.O. Jamin126 , R. Jansky52 , J. Janssen24 , M. Janus51 , P.A. Janus81a , G. Jarlskog94 , N. Javadov77,ag , T. Javůrek35 , M. Javurkova50 , F. Jeanneau142 , L. Jeanty18 , J. Jejelava156a,ah , A. Jelinskas175 , P. Jenni50,d , J. Jeong44 , N. Jeong44 , S. Jézéquel5 , H. Ji178 , J. Jia152 , H. Jiang76 , Y. Jiang58a , Z. Jiang150,r , S. Jiggins50 , F.A. Jimenez Morales37 , J. Jimenez Pena171 , S. Jin15c , A. Jinaru27b , O. Jinnouchi162 , H. Jivan32c , P. Johansson146 , K.A. Johns7 , C.A. Johnson63 , W.J. Johnson145 , K. Jon-And43a,43b , R.W.L. Jones87 , S.D. Jones153 , S. Jones7 , T.J. Jones88 , J. Jongmanns59a , P.M. Jorge137a,137b , J. Jovicevic165a , X. Ju18 , J.J. Junggeburth113 , A. Juste Rozas14,aa , A. Kaczmarska82 , M. Kado129 , H. Kagan123 , M. Kagan150 , T. Kaji176 , E. Kajomovitz157 , C.W. Kalderon94 , A. Kaluza97 , S. Kama41 , A. Kamenshchikov121 , L. Kanjir89 , Y. Kano160 , V.A. Kantserov110 , J. Kanzaki79 , B. Kaplan122 , L.S. Kaplan178 , D. Kar32c , M.J. Kareem165b , E. Karentzos10 , S.N. Karpov77 , Z.M. Karpova77 , V. Kartvelishvili87 , A.N. Karyukhin121 , L. Kashif178 , R.D. Kass123 , A. Kastanas43a,43b , Y. Kataoka160 , C. Kato58d,58c , J. Katzy44 , K. Kawade80 , K. Kawagoe85 , T. Kawamoto160 , G. Kawamura51 , E.F. Kay88 , V.F. Kazanin120b,120a , R. Keeler173 , R. Kehoe41 , J.S. Keller33 , E. Kellermann94 , J.J. Kempster21 , J. Kendrick21 , O. Kepka138 , S. Kersten179 , B.P. Kerševan89 , S. Ketabchi Haghighat164 , R.A. Keyes101 , M. Khader170 , F. Khalil-Zada13 , A. Khanov126 , A.G. Kharlamov120b,120a , T. Kharlamova120b,120a , E.E. Khoda172 , A. Khodinov163 , T.J. Khoo52 , E. Khramov77 , J. Khubua156b , S. Kido80 , M. Kiehn52 , C.R. Kilby91 , Y.K. Kim36 , N. Kimura64a,64c , O.M. Kind19 , B.T. King88 , D. Kirchmeier46 , J. Kirk141 , A.E. Kiryunin113 , T. Kishimoto160 , D. Kisielewska81a , V. Kitali44 , O. Kivernyk5 , E. Kladiva28b,∗ , T. Klapdor-Kleingrothaus50 , M.H. Klein103 , M. Klein88 , U. Klein88 , K. Kleinknecht97 , P. Klimek119 , A. Klimentov29 , T. Klingl24 , T. Klioutchnikova35 , F.F. Klitzner112 , P. Kluit118 , S. Kluth113 , E. Kneringer74 , E.B.F.G. Knoops99 , A. Knue50 , A. Kobayashi160 , D. Kobayashi85 , T. Kobayashi160 , M. Kobel46 , M. Kocian150 , P. Kodys140 , P.T. Koenig24 , T. Koffas33 , E. Koffeman118 , N.M. Köhler113 , T. Koi150 , M. Kolb59b , I. Koletsou5 , T. Kondo79 , N. Kondrashova58c , K. Köneke50 , A.C. König117 , T. Kono79 , R. Konoplich122,an , V. Konstantinides92 , N. Konstantinidis92 , B. Konya94 , R. Kopeliansky63 , S. Koperny81a , K. Korcyl82 , K. Kordas159 , G. Koren158 , A. Korn92 , I. Korolkov14 , E.V. Korolkova146 , – 53 – N. Korotkova111 , O. Kortner113 , S. Kortner113 , T. Kosek140 , V.V. Kostyukhin24 , A. Kotwal47 , A. Koulouris10 , A. Kourkoumeli-Charalampidi68a,68b , C. Kourkoumelis9 , E. Kourlitis146 , V. Kouskoura29 , A.B. Kowalewska82 , R. Kowalewski173 , T.Z. Kowalski81a , C. Kozakai160 , W. Kozanecki142 , A.S. Kozhin121 , V.A. Kramarenko111 , G. Kramberger89 , D. Krasnopevtsev58a , M.W. Krasny133 , A. Krasznahorkay35 , D. Krauss113 , J.A. Kremer81a , J. Kretzschmar88 , P. Krieger164 , K. Krizka18 , K. Kroeninger45 , H. Kroha113 , J. Kroll138 , J. Kroll134 , J. Krstic16 , U. Kruchonak77 , H. Krüger24 , N. Krumnack76 , M.C. Kruse47 , T. Kubota102 , S. Kuday4b , J.T. Kuechler179 , S. Kuehn35 , A. Kugel59a , F. Kuger174 , T. Kuhl44 , V. Kukhtin77 , R. Kukla99 , Y. Kulchitsky105 , S. Kuleshov144b , Y.P. Kulinich170 , M. Kuna56 , T. Kunigo83 , A. Kupco138 , T. Kupfer45 , O. Kuprash158 , H. Kurashige80 , L.L. Kurchaninov165a , Y.A. Kurochkin105 , A. Kurova110 , M.G. Kurth15d , E.S. Kuwertz35 , M. Kuze162 , J. Kvita127 , T. Kwan101 , JHEP04(2019)046 A. La Rosa113 , J.L. La Rosa Navarro78d , L. La Rotonda40b,40a , F. La Ruffa40b,40a , C. Lacasta171 , F. Lacava70a,70b , J. Lacey44 , D.P.J. Lack98 , H. Lacker19 , D. Lacour133 , E. Ladygin77 , R. Lafaye5 , B. Laforge133 , T. Lagouri32c , S. Lai51 , S. Lammers63 , W. Lampl7 , E. Lançon29 , U. Landgraf50 , M.P.J. Landon90 , M.C. Lanfermann52 , V.S. Lang44 , J.C. Lange51 , R.J. Langenberg35 , A.J. Lankford168 , F. Lanni29 , K. Lantzsch24 , A. Lanza68a , A. Lapertosa53b,53a , S. Laplace133 , J.F. Laporte142 , T. Lari66a , F. Lasagni Manghi23b,23a , M. Lassnig35 , T.S. Lau61a , A. Laudrain129 , M. Lavorgna67a,67b , A.T. Law143 , M. Lazzaroni66a,66b , B. Le102 , O. Le Dortz133 , E. Le Guirriec99 , E.P. Le Quilleuc142 , M. LeBlanc7 , T. LeCompte6 , F. Ledroit-Guillon56 , C.A. Lee29 , G.R. Lee144a , L. Lee57 , S.C. Lee155 , B. Lefebvre101 , M. Lefebvre173 , F. Legger112 , C. Leggett18 , K. Lehmann149 , N. Lehmann179 , G. Lehmann Miotto35 , W.A. Leight44 , A. Leisos159,x , M.A.L. Leite78d , R. Leitner140 , D. Lellouch177 , K.J.C. Leney92 , T. Lenz24 , B. Lenzi35 , R. Leone7 , S. Leone69a , C. Leonidopoulos48 , G. Lerner153 , C. Leroy107 , R. Les164 , A.A.J. Lesage142 , C.G. Lester31 , M. Levchenko135 , J. Levêque5 , D. Levin103 , L.J. Levinson177 , D. Lewis90 , B. Li103 , C-Q. Li58a,am , H. Li58b , L. Li58c , M. Li15a , Q. Li15d , Q.Y. Li58a , S. Li58d,58c , X. Li58c , Y. Li148 , Z. Liang15a , B. Liberti71a , A. Liblong164 , K. Lie61c , S. Liem118 , A. Limosani154 , C.Y. Lin31 , K. Lin104 , T.H. Lin97 , R.A. Linck63 , J.H. Lindon21 , B.E. Lindquist152 , A.L. Lionti52 , E. Lipeles134 , A. Lipniacka17 , M. Lisovyi59b , T.M. Liss170,as , A. Lister172 , A.M. Litke143 , J.D. Little8 , B. Liu76 , B.L Liu6 , H.B. Liu29 , H. Liu103 , J.B. Liu58a , J.K.K. Liu132 , K. Liu133 , M. Liu58a , P. Liu18 , Y. Liu15a , Y.L. Liu58a , Y.W. Liu58a , M. Livan68a,68b , A. Lleres56 , J. Llorente Merino15a , S.L. Lloyd90 , C.Y. Lo61b , F. Lo Sterzo41 , E.M. Lobodzinska44 , P. Loch7 , T. Lohse19 , K. Lohwasser146 , M. Lokajicek138 , J.D. Long170 , R.E. Long87 , L. Longo65a,65b , K.A. Looper123 , J.A. Lopez144b , I. Lopez Paz98 , A. Lopez Solis146 , J. Lorenz112 , N. Lorenzo Martinez5 , M. Losada22 , P.J. Lösel112 , A. Lösle50 , X. Lou44 , X. Lou15a , A. Lounis129 , J. Love6 , P.A. Love87 , J.J. Lozano Bahilo171 , H. Lu61a , M. Lu58a , N. Lu103 , Y.J. Lu62 , H.J. Lubatti145 , C. Luci70a,70b , A. Lucotte56 , C. Luedtke50 , F. Luehring63 , I. Luise133 , L. Luminari70a , B. Lund-Jensen151 , M.S. Lutz100 , P.M. Luzi133 , D. Lynn29 , R. Lysak138 , E. Lytken94 , F. Lyu15a , V. Lyubushkin77 , T. Lyubushkina77 , H. Ma29 , L.L. Ma58b , Y. Ma58b , G. Maccarrone49 , A. Macchiolo113 , C.M. Macdonald146 , J. Machado Miguens134,137b , D. Madaffari171 , R. Madar37 , W.F. Mader46 , A. Madsen44 , N. Madysa46 , J. Maeda80 , K. Maekawa160 , S. Maeland17 , T. Maeno29 , M. Maerker46 , A.S. Maevskiy111 , V. Magerl50 , D.J. Mahon38 , C. Maidantchik78b , T. Maier112 , A. Maio137a,137b,137d , O. Majersky28a , S. Majewski128 , Y. Makida79 , N. Makovec129 , B. Malaescu133 , Pa. Malecki82 , V.P. Maleev135 , F. Malek56 , U. Mallik75 , D. Malon6 , C. Malone31 , S. Maltezos10 , S. Malyukov35 , J. Mamuzic171 , G. Mancini49 , I. Mandić89 , J. Maneira137a , L. Manhaes de Andrade Filho78a , J. Manjarres Ramos46 , K.H. Mankinen94 , A. Mann112 , A. Manousos74 , B. Mansoulie142 , J.D. Mansour15a , M. Mantoani51 , S. Manzoni66a,66b , A. Marantis159 , G. Marceca30 , L. March52 , L. Marchese132 , G. Marchiori133 , M. Marcisovsky138 , C.A. Marin Tobon35 , M. Marjanovic37 , D.E. Marley103 , F. Marroquim78b , Z. Marshall18 , – 54 – M.U.F Martensson169 , S. Marti-Garcia171 , C.B. Martin123 , T.A. Martin175 , V.J. Martin48 , B. Martin dit Latour17 , M. Martinez14,aa , V.I. Martinez Outschoorn100 , S. Martin-Haugh141 , V.S. Martoiu27b , A.C. Martyniuk92 , A. Marzin35 , L. Masetti97 , T. Mashimo160 , R. Mashinistov108 , J. Masik98 , A.L. Maslennikov120b,120a , L.H. Mason102 , L. Massa71a,71b , P. Massarotti67a,67b , P. Mastrandrea5 , A. Mastroberardino40b,40a , T. Masubuchi160 , P. Mättig179 , J. Maurer27b , B. Maček89 , S.J. Maxfield88 , D.A. Maximov120b,120a , R. Mazini155 , I. Maznas159 , S.M. Mazza143 , G. Mc Goldrick164 , S.P. Mc Kee103 , A. McCarn103 , T.G. McCarthy113 , L.I. McClymont92 , E.F. McDonald102 , J.A. Mcfayden35 , G. Mchedlidze51 , M.A. McKay41 , K.D. McLean173 , S.J. McMahon141 , P.C. McNamara102 , C.J. McNicol175 , R.A. McPherson173,ae , J.E. Mdhluli32c , Z.A. Meadows100 , S. Meehan145 , T.M. Megy50 , S. Mehlhase112 , A. Mehta88 , T. Meideck56 , B. Meirose42 , D. Melini171,h , B.R. Mellado Garcia32c , J.D. Mellenthin51 , JHEP04(2019)046 M. Melo28a , F. Meloni44 , A. Melzer24 , S.B. Menary98 , E.D. Mendes Gouveia137a , L. Meng88 , X.T. Meng103 , A. Mengarelli23b,23a , S. Menke113 , E. Meoni40b,40a , S. Mergelmeyer19 , S.A.M. Merkt136 , C. Merlassino20 , P. Mermod52 , L. Merola67a,67b , C. Meroni66a , F.S. Merritt36 , A. Messina70a,70b , J. Metcalfe6 , A.S. Mete168 , C. Meyer134 , J. Meyer157 , J-P. Meyer142 , H. Meyer Zu Theenhausen59a , F. Miano153 , R.P. Middleton141 , L. Mijović48 , G. Mikenberg177 , M. Mikestikova138 , M. Mikuž89 , M. Milesi102 , A. Milic164 , D.A. Millar90 , D.W. Miller36 , A. Milov177 , D.A. Milstead43a,43b , A.A. Minaenko121 , M. Miñano Moya171 , I.A. Minashvili156b , A.I. Mincer122 , B. Mindur81a , M. Mineev77 , Y. Minegishi160 , Y. Ming178 , L.M. Mir14 , A. Mirto65a,65b , K.P. Mistry134 , T. Mitani176 , J. Mitrevski112 , V.A. Mitsou171 , A. Miucci20 , P.S. Miyagawa146 , A. Mizukami79 , J.U. Mjörnmark94 , T. Mkrtchyan181 , M. Mlynarikova140 , T. Moa43a,43b , K. Mochizuki107 , P. Mogg50 , S. Mohapatra38 , S. Molander43a,43b , R. Moles-Valls24 , M.C. Mondragon104 , K. Mönig44 , J. Monk39 , E. Monnier99 , A. Montalbano149 , J. Montejo Berlingen35 , F. Monticelli86 , S. Monzani66a , N. Morange129 , D. Moreno22 , M. Moreno Llácer35 , P. Morettini53b , M. Morgenstern118 , S. Morgenstern46 , D. Mori149 , M. Morii57 , M. Morinaga176 , V. Morisbak131 , A.K. Morley35 , G. Mornacchi35 , A.P. Morris92 , J.D. Morris90 , L. 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Olszowska82 , D.C. O’Neil149 , A. Onofre137a,137e , K. Onogi115 , – 55 – P.U.E. Onyisi11 , H. Oppen131 , M.J. Oreglia36 , G.E. Orellana86 , Y. Oren158 , D. Orestano72a,72b , E.C. Orgill98 , N. Orlando61b , A.A. O’Rourke44 , R.S. Orr164 , B. Osculati53b,53a,∗ , V. O’Shea55 , R. Ospanov58a , G. Otero y Garzon30 , H. Otono85 , M. Ouchrif34d , F. Ould-Saada131 , A. Ouraou142 , Q. Ouyang15a , M. Owen55 , R.E. Owen21 , V.E. Ozcan12c , N. Ozturk8 , J. Pacalt127 , H.A. Pacey31 , K. Pachal149 , A. Pacheco Pages14 , L. Pacheco Rodriguez142 , C. Padilla Aranda14 , S. Pagan Griso18 , M. Paganini180 , G. Palacino63 , S. Palazzo40b,40a , S. Palestini35 , M. Palka81b , D. Pallin37 , I. Panagoulias10 , C.E. Pandini35 , J.G. Panduro Vazquez91 , P. Pani35 , G. Panizzo64a,64c , L. Paolozzi52 , T.D. Papadopoulou10 , K. Papageorgiou9,k , A. Paramonov6 , D. Paredes Hernandez61b , S.R. Paredes Saenz132 , B. Parida163 , T.H. Park33 , A.J. Parker87 , K.A. Parker44 , M.A. Parker31 , F. Parodi53b,53a , J.A. Parsons38 , U. Parzefall50 , V.R. Pascuzzi164 , J.M.P. Pasner143 , E. Pasqualucci70a , S. Passaggio53b , F. Pastore91 , P. Pasuwan43a,43b , JHEP04(2019)046 S. Pataraia97 , J.R. Pater98 , A. Pathak178,l , T. Pauly35 , B. Pearson113 , M. Pedersen131 , L. Pedraza Diaz117 , R. Pedro137a,137b , S.V. Peleganchuk120b,120a , O. Penc138 , C. Peng15d , H. Peng58a , B.S. Peralva78a , M.M. Perego129 , A.P. Pereira Peixoto137a , D.V. Perepelitsa29 , F. Peri19 , L. Perini66a,66b , H. Pernegger35 , S. Perrella67a,67b , V.D. Peshekhonov77,∗ , K. Peters44 , R.F.Y. Peters98 , B.A. Petersen35 , T.C. Petersen39 , E. Petit56 , A. Petridis1 , C. Petridou159 , P. Petroff129 , M. Petrov132 , F. Petrucci72a,72b , M. Pettee180 , N.E. Pettersson100 , A. Peyaud142 , R. Pezoa144b , T. Pham102 , F.H. Phillips104 , P.W. Phillips141 , M.W. Phipps170 , G. Piacquadio152 , E. Pianori18 , A. Picazio100 , M.A. Pickering132 , R.H. Pickles98 , R. Piegaia30 , J.E. Pilcher36 , A.D. Pilkington98 , M. Pinamonti71a,71b , J.L. 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Rodriguez Bosca171 , A. Rodriguez Perez14 , D. Rodriguez Rodriguez171 , A.M. Rodrı́guez Vera165b , S. Roe35 , C.S. Rogan57 , O. Røhne131 , R. Röhrig113 , C.P.A. Roland63 , J. Roloff57 , A. Romaniouk110 , M. Romano23b,23a , N. Rompotis88 , M. Ronzani122 , L. Roos133 , S. Rosati70a , K. Rosbach50 , N-A. Rosien51 , B.J. Rosser134 , E. Rossi44 , E. Rossi72a,72b , E. Rossi67a,67b , L.P. Rossi53b , L. Rossini66a,66b , J.H.N. Rosten31 , R. Rosten14 , M. Rotaru27b , J. Rothberg145 , D. Rousseau129 , D. Roy32c , A. Rozanov99 , Y. Rozen157 , X. Ruan32c , F. Rubbo150 , F. Rühr50 , A. Ruiz-Martinez171 , Z. Rurikova50 , N.A. Rusakovich77 , H.L. Russell101 , J.P. Rutherfoord7 , E.M. Rüttinger44,m , – 56 – Y.F. Ryabov135 , M. Rybar170 , G. Rybkin129 , S. Ryu6 , A. Ryzhov121 , G.F. Rzehorz51 , P. Sabatini51 , G. Sabato118 , S. Sacerdoti129 , H.F-W. Sadrozinski143 , R. Sadykov77 , F. Safai Tehrani70a , P. Saha119 , M. Sahinsoy59a , A. Sahu179 , M. Saimpert44 , M. Saito160 , T. Saito160 , H. Sakamoto160 , A. Sakharov122,an , D. Salamani52 , G. Salamanna72a,72b , J.E. Salazar Loyola144b , P.H. Sales De Bruin169 , D. Salihagic113 , A. Salnikov150 , J. Salt171 , D. Salvatore40b,40a , F. Salvatore153 , A. Salvucci61a,61b,61c , A. Salzburger35 , J. Samarati35 , D. Sammel50 , D. Sampsonidis159 , D. Sampsonidou159 , J. Sánchez171 , A. Sanchez Pineda64a,64c , H. Sandaker131 , C.O. Sander44 , M. Sandhoff179 , C. Sandoval22 , D.P.C. Sankey141 , M. Sannino53b,53a , Y. Sano115 , A. Sansoni49 , C. Santoni37 , H. Santos137a , I. Santoyo Castillo153 , A. Santra171 , A. Sapronov77 , J.G. Saraiva137a,137d , O. Sasaki79 , K. Sato166 , E. Sauvan5 , P. Savard164,av , N. Savic113 , R. Sawada160 , C. Sawyer141 , L. Sawyer93,al , C. Sbarra23b , JHEP04(2019)046 A. Sbrizzi23a , T. Scanlon92 , J. Schaarschmidt145 , P. Schacht113 , B.M. Schachtner112 , D. Schaefer36 , L. Schaefer134 , J. Schaeffer97 , S. Schaepe35 , U. Schäfer97 , A.C. Schaffer129 , D. Schaile112 , R.D. Schamberger152 , N. 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Spiteri55 , M. Spousta140 , A. Stabile66a,66b , R. Stamen59a , S. Stamm19 , E. Stanecka82 , R.W. Stanek6 , C. Stanescu72a , B. Stanislaus132 , M.M. Stanitzki44 , B. Stapf118 , S. Stapnes131 , E.A. Starchenko121 , G.H. Stark36 , J. Stark56 , S.H Stark39 , P. Staroba138 , P. Starovoitov59a , S. Stärz35 , R. Staszewski82 , M. Stegler44 , P. Steinberg29 , – 57 – B. Stelzer149 , H.J. Stelzer35 , O. Stelzer-Chilton165a , H. Stenzel54 , T.J. Stevenson90 , G.A. Stewart35 , M.C. Stockton35 , G. Stoicea27b , P. Stolte51 , S. Stonjek113 , A. Straessner46 , J. Strandberg151 , S. Strandberg43a,43b , M. Strauss125 , P. Strizenec28b , R. Ströhmer174 , D.M. Strom128 , R. Stroynowski41 , A. Strubig48 , S.A. Stucci29 , B. Stugu17 , J. Stupak125 , N.A. Styles44 , D. Su150 , J. Su136 , S. Suchek59a , Y. Sugaya130 , M. Suk139 , V.V. Sulin108 , M.J. Sullivan88 , D.M.S. Sultan52 , S. Sultansoy4c , T. Sumida83 , S. Sun103 , X. Sun3 , K. Suruliz153 , C.J.E. Suster154 , M.R. Sutton153 , S. Suzuki79 , M. Svatos138 , M. Swiatlowski36 , S.P. Swift2 , A. Sydorenko97 , I. Sykora28a , T. Sykora140 , D. Ta97 , K. Tackmann44,ab , J. Taenzer158 , A. Taffard168 , R. Tafirout165a , E. Tahirovic90 , N. Taiblum158 , H. Takai29 , R. Takashima84 , E.H. Takasugi113 , K. Takeda80 , T. Takeshita147 , Y. Takubo79 , M. Talby99 , A.A. Talyshev120b,120a , J. Tanaka160 , M. Tanaka162 , R. Tanaka129 , B.B. Tannenwald123 , S. Tapia Araya144b , JHEP04(2019)046 S. Tapprogge97 , A. Tarek Abouelfadl Mohamed133 , S. Tarem157 , G. Tarna27b,e , G.F. Tartarelli66a , P. Tas140 , M. Tasevsky138 , T. Tashiro83 , E. Tassi40b,40a , A. Tavares Delgado137a,137b , Y. Tayalati34e , A.C. Taylor116 , A.J. Taylor48 , G.N. Taylor102 , P.T.E. Taylor102 , W. Taylor165b , A.S. Tee87 , P. Teixeira-Dias91 , H. Ten Kate35 , J.J. Teoh118 , S. Terada79 , K. Terashi160 , J. Terron96 , S. Terzo14 , M. Testa49 , R.J. Teuscher164,ae , S.J. Thais180 , T. Theveneaux-Pelzer44 , F. Thiele39 , D.W. Thomas91 , J.P. Thomas21 , A.S. Thompson55 , P.D. Thompson21 , L.A. Thomsen180 , E. Thomson134 , Y. Tian38 , R.E. Ticse Torres51 , V.O. Tikhomirov108,ap , Yu.A. Tikhonov120b,120a , S. Timoshenko110 , P. Tipton180 , S. Tisserant99 , K. Todome162 , S. Todorova-Nova5 , S. Todt46 , J. Tojo85 , S. Tokár28a , K. Tokushuku79 , E. Tolley123 , K.G. Tomiwa32c , M. Tomoto115 , L. Tompkins150,r , K. Toms116 , B. Tong57 , P. Tornambe50 , E. Torrence128 , H. Torres46 , E. Torró Pastor145 , C. Tosciri132 , J. Toth99,ad , F. Touchard99 , D.R. Tovey146 , C.J. Treado122 , T. Trefzger174 , F. Tresoldi153 , A. Tricoli29 , I.M. Trigger165a , S. Trincaz-Duvoid133 , M.F. Tripiana14 , W. Trischuk164 , B. Trocmé56 , A. Trofymov129 , C. Troncon66a , M. Trovatelli173 , F. Trovato153 , L. Truong32b , M. Trzebinski82 , A. Trzupek82 , F. Tsai44 , J.C-L. Tseng132 , P.V. Tsiareshka105 , A. Tsirigotis159 , N. Tsirintanis9 , V. Tsiskaridze152 , E.G. Tskhadadze156a , I.I. Tsukerman109 , V. Tsulaia18 , S. Tsuno79 , D. Tsybychev152,163 , Y. Tu61b , A. Tudorache27b , V. Tudorache27b , T.T. Tulbure27a , A.N. Tuna57 , S. Turchikhin77 , D. Turgeman177 , I. Turk Cakir4b,v , R. Turra66a , P.M. Tuts38 , E. Tzovara97 , G. Ucchielli23b,23a , I. Ueda79 , M. Ughetto43a,43b , F. Ukegawa166 , G. Unal35 , A. Undrus29 , G. Unel168 , F.C. Ungaro102 , Y. Unno79 , K. Uno160 , J. Urban28b , P. Urquijo102 , P. Urrejola97 , G. Usai8 , J. Usui79 , L. Vacavant99 , V. Vacek139 , B. Vachon101 , K.O.H. Vadla131 , A. Vaidya92 , C. Valderanis112 , E. Valdes Santurio43a,43b , M. Valente52 , S. Valentinetti23b,23a , A. Valero171 , L. Valéry44 , R.A. Vallance21 , A. Vallier5 , J.A. Valls Ferrer171 , T.R. Van Daalen14 , H. Van der Graaf118 , P. Van Gemmeren6 , J. Van Nieuwkoop149 , I. Van Vulpen118 , M. Vanadia71a,71b , W. Vandelli35 , A. Vaniachine163 , P. Vankov118 , R. Vari70a , E.W. Varnes7 , C. Varni53b,53a , T. Varol41 , D. Varouchas129 , K.E. Varvell154 , G.A. Vasquez144b , J.G. Vasquez180 , F. Vazeille37 , D. Vazquez Furelos14 , T. Vazquez Schroeder35 , J. Veatch51 , V. Vecchio72a,72b , L.M. Veloce164 , F. Veloso137a,137c , S. Veneziano70a , A. Ventura65a,65b , M. Venturi173 , N. Venturi35 , V. Vercesi68a , M. Verducci72a,72b , C.M. Vergel Infante76 , C. Vergis24 , W. Verkerke118 , A.T. Vermeulen118 , J.C. Vermeulen118 , M.C. Vetterli149,av , N. Viaux Maira144b , M. Vicente Barreto Pinto52 , I. Vichou170,∗ , T. Vickey146 , O.E. Vickey Boeriu146 , G.H.A. Viehhauser132 , S. Viel18 , L. Vigani132 , M. Villa23b,23a , M. Villaplana Perez66a,66b , E. Vilucchi49 , M.G. Vincter33 , V.B. Vinogradov77 , A. Vishwakarma44 , C. Vittori23b,23a , I. Vivarelli153 , S. Vlachos10 , M. Vogel179 , P. Vokac139 , G. Volpi14 , S.E. von Buddenbrock32c , E. Von Toerne24 , V. Vorobel140 , K. Vorobev110 , M. Vos171 , J.H. Vossebeld88 , N. Vranjes16 , M. Vranjes Milosavljevic16 , V. Vrba139 , M. Vreeswijk118 , T. Šfiligoj89 , R. Vuillermet35 , I. Vukotic36 , T. Ženiš28a , L. Živković16 , P. Wagner24 , W. Wagner179 , J. Wagner-Kuhr112 , – 58 – H. Wahlberg86 , S. Wahrmund46 , K. Wakamiya80 , V.M. Walbrecht113 , J. Walder87 , R. Walker112 , S.D. Walker91 , W. Walkowiak148 , V. Wallangen43a,43b , A.M. Wang57 , C. Wang58b,e , F. Wang178 , H. Wang18 , H. Wang3 , J. Wang154 , J. Wang59b , P. Wang41 , Q. Wang125 , R.-J. Wang133 , R. Wang58a , R. Wang6 , S.M. Wang155 , W.T. Wang58a , W. Wang15c,af , W.X. Wang58a,af , Y. Wang58a,am , Z. Wang58c , C. Wanotayaroj44 , A. Warburton101 , C.P. Ward31 , D.R. Wardrope92 , A. Washbrook48 , P.M. Watkins21 , A.T. Watson21 , M.F. Watson21 , G. Watts145 , S. Watts98 , B.M. Waugh92 , A.F. Webb11 , S. Webb97 , C. Weber180 , M.S. Weber20 , S.A. Weber33 , S.M. Weber59a , A.R. Weidberg132 , B. Weinert63 , J. Weingarten45 , M. Weirich97 , C. Weiser50 , P.S. Wells35 , T. Wenaus29 , T. Wengler35 , S. Wenig35 , N. Wermes24 , M.D. Werner76 , P. Werner35 , M. Wessels59a , T.D. Weston20 , K. Whalen128 , N.L. Whallon145 , A.M. Wharton87 , A.S. White103 , A. White8 , M.J. White1 , R. White144b , D. Whiteson168 , B.W. Whitmore87 , F.J. Wickens141 , JHEP04(2019)046 W. Wiedenmann178 , M. Wielers141 , C. Wiglesworth39 , L.A.M. Wiik-Fuchs50 , F. Wilk98 , H.G. Wilkens35 , L.J. Wilkins91 , H.H. Williams134 , S. Williams31 , C. Willis104 , S. Willocq100 , J.A. Wilson21 , I. Wingerter-Seez5 , E. Winkels153 , F. Winklmeier128 , O.J. Winston153 , B.T. Winter50 , M. Wittgen150 , M. Wobisch93 , A. Wolf97 , T.M.H. Wolf118 , R. Wolff99 , M.W. Wolter82 , H. Wolters137a,137c , V.W.S. Wong172 , N.L. Woods143 , S.D. Worm21 , B.K. Wosiek82 , K.W. Woźniak82 , K. Wraight55 , M. Wu36 , S.L. Wu178 , X. Wu52 , Y. Wu58a , T.R. Wyatt98 , B.M. Wynne48 , S. Xella39 , Z. Xi103 , L. Xia175 , D. Xu15a , H. Xu58a,e , L. Xu29 , T. Xu142 , W. Xu103 , B. Yabsley154 , S. Yacoob32a , K. Yajima130 , D.P. Yallup92 , D. Yamaguchi162 , Y. Yamaguchi162 , A. Yamamoto79 , T. Yamanaka160 , F. Yamane80 , M. Yamatani160 , T. Yamazaki160 , Y. Yamazaki80 , Z. Yan25 , H.J. Yang58c,58d , H.T. Yang18 , S. Yang75 , Y. Yang160 , Z. Yang17 , W-M. Yao18 , Y.C. Yap44 , Y. Yasu79 , E. Yatsenko58c,58d , J. Ye41 , S. Ye29 , I. Yeletskikh77 , E. Yigitbasi25 , E. Yildirim97 , K. Yorita176 , K. Yoshihara134 , C.J.S. Young35 , C. Young150 , J. Yu8 , J. Yu76 , X. Yue59a , S.P.Y. Yuen24 , B. Zabinski82 , G. Zacharis10 , E. Zaffaroni52 , R. Zaidan14 , A.M. Zaitsev121,ao , T. Zakareishvili156b , N. Zakharchuk33 , J. Zalieckas17 , S. Zambito57 , D. Zanzi35 , D.R. Zaripovas55 , S.V. Zeißner45 , C. Zeitnitz179 , G. Zemaityte132 , J.C. Zeng170 , Q. Zeng150 , O. Zenin121 , D. Zerwas129 , M. Zgubič132 , D.F. Zhang58b , D. Zhang103 , F. Zhang178 , G. Zhang58a , H. Zhang15c , J. Zhang6 , L. Zhang15c , L. Zhang58a , M. Zhang170 , P. Zhang15c , R. Zhang58a , R. Zhang24 , X. Zhang58b , Y. Zhang15d , Z. Zhang129 , P. Zhao47 , Y. Zhao58b,129,ak , Z. Zhao58a , A. Zhemchugov77 , Z. Zheng103 , D. Zhong170 , B. Zhou103 , C. Zhou178 , L. Zhou41 , M.S. Zhou15d , M. Zhou152 , N. Zhou58c , Y. Zhou7 , C.G. Zhu58b , H.L. Zhu58a , H. Zhu15a , J. Zhu103 , Y. Zhu58a , X. Zhuang15a , K. Zhukov108 , V. Zhulanov120b,120a , A. Zibell174 , D. Zieminska63 , N.I. Zimine77 , S. Zimmermann50 , Z. Zinonos113 , M. Zinser97 , M. Ziolkowski148 , G. Zobernig178 , A. Zoccoli23b,23a , K. Zoch51 , T.G. Zorbas146 , R. Zou36 , M. Zur Nedden19 and L. Zwalinski35 1 Department of Physics, University of Adelaide, Adelaide; Australia 2 Physics Department, SUNY Albany, Albany NY; United States of America 3 Department of Physics, University of Alberta, Edmonton AB; Canada 4 (a) Department of Physics, Ankara University, Ankara;(b) Istanbul Aydin University, Istanbul;(c) Division of Physics, TOBB University of Economics and Technology, Ankara; Turkey 5 LAPP, Université Grenoble Alpes, Université Savoie Mont Blanc, CNRS/IN2P3, Annecy; France 6 High Energy Physics Division, Argonne National Laboratory, Argonne IL; United States of America 7 Department of Physics, University of Arizona, Tucson AZ; United States of America 8 Department of Physics, University of Texas at Arlington, Arlington TX; United States of America 9 Physics Department, National and Kapodistrian University of Athens, Athens; Greece 10 Physics Department, National Technical University of Athens, Zografou; Greece 11 Department of Physics, University of Texas at Austin, Austin TX; United States of America – 59 – 12 (a) Bahcesehir University, Faculty of Engineering and Natural Sciences, Istanbul; (b) Istanbul Bilgi University, Faculty of Engineering and Natural Sciences, Istanbul;(c) Department of Physics, Bogazici University, Istanbul;(d) Department of Physics Engineering, Gaziantep University, Gaziantep; Turkey 13 Institute of Physics, Azerbaijan Academy of Sciences, Baku; Azerbaijan 14 Institut de Fı́sica d’Altes Energies (IFAE), Barcelona Institute of Science and Technology, Barcelona; Spain 15 (a) Institute of High Energy Physics, Chinese Academy of Sciences, Beijing;(b) Physics Department, Tsinghua University, Beijing;(c) Department of Physics, Nanjing University, Nanjing;(d) University of Chinese Academy of Science (UCAS), Beijing; China 16 Institute of Physics, University of Belgrade, Belgrade; Serbia 17 Department for Physics and Technology, University of Bergen, Bergen; Norway JHEP04(2019)046 18 Physics Division, Lawrence Berkeley National Laboratory and University of California, Berkeley CA; United States of America 19 Institut für Physik, Humboldt Universität zu Berlin, Berlin; Germany 20 Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics, University of Bern, Bern; Switzerland 21 School of Physics and Astronomy, University of Birmingham, Birmingham; United Kingdom 22 Centro de Investigaciónes, Universidad Antonio Nariño, Bogota; Colombia 23 (a) Dipartimento di Fisica e Astronomia, Università di Bologna, Bologna; (b) INFN Sezione di Bologna; Italy 24 Physikalisches Institut, Universität Bonn, Bonn; Germany 25 Department of Physics, Boston University, Boston MA; United States of America 26 Department of Physics, Brandeis University, Waltham MA; United States of America 27 (a) Transilvania University of Brasov, Brasov;(b) Horia Hulubei National Institute of Physics and Nuclear Engineering, Bucharest;(c) Department of Physics, Alexandru Ioan Cuza University of Iasi, Iasi;(d) National Institute for Research and Development of Isotopic and Molecular Technologies, Physics Department, Cluj-Napoca;(e) University Politehnica Bucharest, Bucharest;(f ) West University in Timisoara, Timisoara; Romania 28 (a) Faculty of Mathematics, Physics and Informatics, Comenius University, Bratislava;(b) Department of Subnuclear Physics, Institute of Experimental Physics of the Slovak Academy of Sciences, Kosice; Slovak Republic 29 Physics Department, Brookhaven National Laboratory, Upton NY; United States of America 30 Departamento de Fı́sica, Universidad de Buenos Aires, Buenos Aires; Argentina 31 Cavendish Laboratory, University of Cambridge, Cambridge; United Kingdom 32 (a) Department of Physics, University of Cape Town, Cape Town;(b) Department of Mechanical Engineering Science, University of Johannesburg, Johannesburg;(c) School of Physics, University of the Witwatersrand, Johannesburg; South Africa 33 Department of Physics, Carleton University, Ottawa ON; Canada 34 (a) Faculté des Sciences Ain Chock, Réseau Universitaire de Physique des Hautes Energies — Université Hassan II, Casablanca;(b) Centre National de l’Energie des Sciences Techniques Nucleaires (CNESTEN), Rabat;(c) Faculté des Sciences Semlalia, Université Cadi Ayyad, LPHEA-Marrakech;(d) Faculté des Sciences, Université Mohamed Premier and LPTPM, Oujda;(e) Faculté des sciences, Université Mohammed V, Rabat; Morocco 35 CERN, Geneva; Switzerland 36 Enrico Fermi Institute, University of Chicago, Chicago IL; United States of America 37 LPC, Université Clermont Auvergne, CNRS/IN2P3, Clermont-Ferrand; France 38 Nevis Laboratory, Columbia University, Irvington NY; United States of America 39 Niels Bohr Institute, University of Copenhagen, Copenhagen; Denmark 40 (a) Dipartimento di Fisica, Università della Calabria, Rende;(b) INFN Gruppo Collegato di Cosenza, Laboratori Nazionali di Frascati; Italy 41 Physics Department, Southern Methodist University, Dallas TX; United States of America – 60 – 42 Physics Department, University of Texas at Dallas, Richardson TX; United States of America 43 (a) Department of Physics, Stockholm University;(b) Oskar Klein Centre, Stockholm; Sweden 44 Deutsches Elektronen-Synchrotron DESY, Hamburg and Zeuthen; Germany 45 Lehrstuhl für Experimentelle Physik IV, Technische Universität Dortmund, Dortmund; Germany 46 Institut für Kern- und Teilchenphysik, Technische Universität Dresden, Dresden; Germany 47 Department of Physics, Duke University, Durham NC; United States of America 48 SUPA — School of Physics and Astronomy, University of Edinburgh, Edinburgh; United Kingdom 49 INFN e Laboratori Nazionali di Frascati, Frascati; Italy 50 Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg; Germany 51 II. Physikalisches Institut, Georg-August-Universität Göttingen, Göttingen; Germany 52 Département de Physique Nucléaire et Corpusculaire, Université de Genève, Genève; Switzerland 53 (a) Dipartimento di Fisica, Università di Genova, Genova;(b) INFN Sezione di Genova; Italy JHEP04(2019)046 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 MA; United States of America 58 (a) Department of Modern Physics and State Key Laboratory of Particle Detection and Electronics, University of Science and Technology of China, Hefei;(b) Institute of Frontier and Interdisciplinary Science and Key Laboratory of Particle Physics and Particle Irradiation (MOE), Shandong University, Qingdao;(c) School of Physics and Astronomy, Shanghai Jiao Tong University, KLPPAC-MoE, SKLPPC, Shanghai;(d) Tsung-Dao Lee Institute, Shanghai; China 59 (a) Kirchhoff-Institut für Physik, Ruprecht-Karls-Universität Heidelberg, Heidelberg; (b) Physikalisches Institut, Ruprecht-Karls-Universität Heidelberg, Heidelberg; Germany 60 Faculty of Applied Information Science, Hiroshima Institute of Technology, Hiroshima; Japan 61 (a) Department of Physics, Chinese University of Hong Kong, Shatin, N.T., Hong Kong;(b) Department of Physics, University of Hong Kong, Hong Kong;(c) Department of Physics and Institute for Advanced Study, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong; China 62 Department of Physics, National Tsing Hua University, Hsinchu; Taiwan 63 Department of Physics, Indiana University, Bloomington IN; United States of America 64 (a) INFN Gruppo Collegato di Udine, Sezione di Trieste, Udine;(b) ICTP, Trieste;(c) Dipartimento di Chimica, Fisica e Ambiente, Università di Udine, Udine; Italy 65 (a) INFN Sezione di Lecce;(b) Dipartimento di Matematica e Fisica, Università del Salento, Lecce; Italy 66 (a) INFN Sezione di Milano;(b) Dipartimento di Fisica, Università di Milano, Milano; Italy 67 (a) INFN Sezione di Napoli;(b) Dipartimento di Fisica, Università di Napoli, Napoli; Italy 68 (a) INFN Sezione di Pavia;(b) Dipartimento di Fisica, Università di Pavia, Pavia; Italy 69 (a) INFN Sezione di Pisa;(b) Dipartimento di Fisica E. Fermi, Università di Pisa, Pisa; Italy 70 (a) INFN Sezione di Roma;(b) Dipartimento di Fisica, Sapienza Università di Roma, Roma; Italy 71 (a) INFN Sezione di Roma Tor Vergata;(b) Dipartimento di Fisica, Università di Roma Tor Vergata, Roma; Italy 72 (a) INFN Sezione di Roma Tre;(b) Dipartimento di Matematica e Fisica, Università Roma Tre, Roma; Italy 73 (a) INFN-TIFPA;(b) Università degli Studi di Trento, Trento; Italy 74 Institut für Astro- und Teilchenphysik, Leopold-Franzens-Universität, Innsbruck; Austria 75 University of Iowa, Iowa City IA; United States of America 76 Department of Physics and Astronomy, Iowa State University, Ames IA; United States of America 77 Joint Institute for Nuclear Research, Dubna; Russia 78 (a) Departamento de Engenharia Elétrica, Universidade Federal de Juiz de Fora (UFJF), Juiz de Fora;(b) Universidade Federal do Rio De Janeiro COPPE/EE/IF, Rio de Janeiro; (c) Universidade Federal de São João del Rei (UFSJ), São João del Rei;(d) Instituto de Fı́sica, Universidade de São Paulo, São Paulo; Brazil – 61 – 79 KEK, High Energy Accelerator Research Organization, Tsukuba; Japan 80 Graduate School of Science, Kobe University, Kobe; Japan 81 (a) AGH University of Science and Technology, Faculty of Physics and Applied Computer Science, Krakow;(b) Marian Smoluchowski Institute of Physics, Jagiellonian University, Krakow; Poland 82 Institute of Nuclear Physics Polish Academy of Sciences, Krakow; Poland 83 Faculty of Science, Kyoto University, Kyoto; Japan 84 Kyoto University of Education, Kyoto; Japan 85 Research Center for Advanced Particle Physics and Department of Physics, Kyushu University, Fukuoka; Japan 86 Instituto de Fı́sica La Plata, Universidad Nacional de La Plata and CONICET, La Plata; Argentina 87 Physics Department, Lancaster University, Lancaster; United Kingdom 88 Oliver Lodge Laboratory, University of Liverpool, Liverpool; United Kingdom JHEP04(2019)046 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 LA; United States of America 94 Fysiska institutionen, Lunds universitet, Lund; Sweden 95 Centre de Calcul de l’Institut National de Physique Nucléaire et de Physique des Particules (IN2P3), Villeurbanne; France 96 Departamento de Fı́sica Teorica C-15 and CIAFF, Universidad Autónoma de Madrid, Madrid; Spain 97 Institut für Physik, Universität Mainz, Mainz; Germany 98 School of Physics and Astronomy, University of Manchester, Manchester; United Kingdom 99 CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille; France 100 Department of Physics, University of Massachusetts, Amherst MA; United States of America 101 Department of Physics, McGill University, Montreal QC; Canada 102 School of Physics, University of Melbourne, Victoria; Australia 103 Department of Physics, University of Michigan, Ann Arbor MI; United States of America 104 Department of Physics and Astronomy, Michigan State University, East Lansing MI; United States of America 105 B.I. Stepanov Institute of Physics, National Academy of Sciences of Belarus, Minsk; Belarus 106 Research Institute for Nuclear Problems of Byelorussian State University, Minsk; Belarus 107 Group of Particle Physics, University of Montreal, Montreal QC; Canada 108 P.N. Lebedev Physical Institute of the Russian Academy of Sciences, Moscow; Russia 109 Institute for Theoretical and Experimental Physics (ITEP), Moscow; Russia 110 National Research Nuclear University MEPhI, Moscow; Russia 111 D.V. Skobeltsyn Institute of Nuclear Physics, M.V. Lomonosov Moscow State University, Moscow; Russia 112 Fakultät für Physik, Ludwig-Maximilians-Universität München, München; Germany 113 Max-Planck-Institut für Physik (Werner-Heisenberg-Institut), München; Germany 114 Nagasaki Institute of Applied Science, Nagasaki; Japan 115 Graduate School of Science and Kobayashi-Maskawa Institute, Nagoya University, Nagoya; Japan 116 Department of Physics and Astronomy, University of New Mexico, Albuquerque NM; United States of America 117 Institute for Mathematics, Astrophysics and Particle Physics, Radboud University Nijmegen/Nikhef, Nijmegen; Netherlands 118 Nikhef National Institute for Subatomic Physics and University of Amsterdam, Amsterdam; Netherlands 119 Department of Physics, Northern Illinois University, DeKalb IL; United States of America – 62 – 120 (a) Budker Institute of Nuclear Physics and NSU, SB RAS, Novosibirsk;(b) 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 NY; United States of America 123 Ohio State University, Columbus OH; United States of America 124 Faculty of Science, Okayama University, Okayama; Japan 125 Homer L. Dodge Department of Physics and Astronomy, University of Oklahoma, Norman OK; United States of America 126 Department of Physics, Oklahoma State University, Stillwater OK; United States of America 127 Palacký University, RCPTM, Joint Laboratory of Optics, Olomouc; Czech Republic 128 Center for High Energy Physics, University of Oregon, Eugene OR; United States of America JHEP04(2019)046 129 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 PA; United States of America 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 PA; United States of America 137 (a) Laboratório de Instrumentação e Fı́sica Experimental de Partı́culas — LIP; (b) Departamento de Fı́sica, Faculdade de Ciências, Universidade de Lisboa, Lisboa; (c) Departamento de Fı́sica, Universidade de Coimbra, Coimbra;(d) Centro de Fı́sica Nuclear da Universidade de Lisboa, Lisboa;(e) Departamento de Fı́sica, Universidade do Minho, Braga;(f ) Departamento de Fı́sica Teorica y del Cosmos, Universidad de Granada, Granada (Spain); (g) Dep Fı́sica and CEFITEC of Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica; Portugal 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 CA; United States of America 144 (a) Departamento de Fı́sica, Pontificia Universidad Católica de Chile, Santiago; (b) Departamento de Fı́sica, Universidad Técnica Federico Santa Marı́a, Valparaı́so; Chile 145 Department of Physics, University of Washington, Seattle WA; United States of America 146 Department of Physics and Astronomy, University of Sheffield, Sheffield; United Kingdom 147 Department of Physics, Shinshu University, Nagano; Japan 148 Department Physik, Universität Siegen, Siegen; Germany 149 Department of Physics, Simon Fraser University, Burnaby BC; Canada 150 SLAC National Accelerator Laboratory, Stanford CA; United States of America 151 Physics Department, Royal Institute of Technology, Stockholm; Sweden 152 Departments of Physics and Astronomy, Stony Brook University, Stony Brook NY; United States of America 153 Department of Physics and Astronomy, University of Sussex, Brighton; United Kingdom 154 School of Physics, University of Sydney, Sydney; Australia 155 Institute of Physics, Academia Sinica, Taipei; Taiwan 156 (a) E. Andronikashvili Institute of Physics, Iv. Javakhishvili Tbilisi State University, Tbilisi; (b) High Energy Physics Institute, Tbilisi State University, Tbilisi; Georgia 157 Department of Physics, Technion, Israel Institute of Technology, Haifa; Israel – 63 – 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 ON; Canada 165 (a) TRIUMF, Vancouver BC;(b) Department of Physics and Astronomy, York University, Toronto ON; Canada 166 Division of Physics and Tomonaga Center for the History of the Universe, Faculty of Pure and JHEP04(2019)046 Applied Sciences, University of Tsukuba, Tsukuba; Japan 167 Department of Physics and Astronomy, Tufts University, Medford MA; United States of America 168 Department of Physics and Astronomy, University of California Irvine, Irvine CA; United States of America 169 Department of Physics and Astronomy, University of Uppsala, Uppsala; Sweden 170 Department of Physics, University of Illinois, Urbana IL; United States of America 171 Instituto de Fı́sica Corpuscular (IFIC), Centro Mixto Universidad de Valencia — CSIC, Valencia; Spain 172 Department of Physics, University of British Columbia, Vancouver BC; Canada 173 Department of Physics and Astronomy, University of Victoria, Victoria BC; Canada 174 Fakultät für Physik und Astronomie, Julius-Maximilians-Universität Würzburg, Würzburg; Germany 175 Department of Physics, University of Warwick, Coventry; United Kingdom 176 Waseda University, Tokyo; Japan 177 Department of Particle Physics, Weizmann Institute of Science, Rehovot; Israel 178 Department of Physics, University of Wisconsin, Madison WI; United States of America 179 Fakultät für Mathematik und Naturwissenschaften, Fachgruppe Physik, Bergische Universität Wuppertal, Wuppertal; Germany 180 Department of Physics, Yale University, New Haven CT; United States of America 181 Yerevan Physics Institute, Yerevan; Armenia a Also at Borough of Manhattan Community College, City University of New York, NY; United States of America b Also at California State University, East Bay; United States of America c Also at Centre for High Performance Computing, CSIR Campus, Rosebank, Cape Town; South Africa d Also at CERN, Geneva; Switzerland e Also at CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille; France f Also at Département de Physique Nucléaire et Corpusculaire, Université de Genève, Genève; Switzerland g Also at Departament de Fisica de la Universitat Autonoma de Barcelona, Barcelona; Spain h Also at Departamento de Fı́sica Teorica y del Cosmos, Universidad de Granada, Granada (Spain); Spain i Also at Departamento de Fı́sica, Instituto Superior Técnico, Universidade de Lisboa, Lisboa; Portugal j Also at Department of Applied Physics and Astronomy, University of Sharjah, Sharjah; United Arab Emirates k Also at Department of Financial and Management Engineering, University of the Aegean, Chios; Greece – 64 – l Also at Department of Physics and Astronomy, University of Louisville, Louisville, KY; United States of America m Also at Department of Physics and Astronomy, University of Sheffield, Sheffield; United Kingdom n Also at Department of Physics, California State University, Fresno CA; United States of America o Also at Department of Physics, California State University, Sacramento CA; United States of America p Also at Department of Physics, King’s College London, London; United Kingdom q Also at Department of Physics, St. Petersburg State Polytechnical University, St. Petersburg; Russia r Also at Department of Physics, Stanford University; United States of America s Also at Department of Physics, University of Fribourg, Fribourg; Switzerland t Also at Department of Physics, University of Michigan, Ann Arbor MI; United States of America JHEP04(2019)046 u Also at Dipartimento di Fisica E. Fermi, Università di Pisa, Pisa; Italy v Also at Giresun University, Faculty of Engineering, Giresun; Turkey w Also at Graduate School of Science, Osaka University, Osaka; Japan x Also at Hellenic Open University, Patras; Greece y Also at Horia Hulubei National Institute of Physics and Nuclear Engineering, Bucharest; Romania z Also at II. Physikalisches Institut, Georg-August-Universität Göttingen, Göttingen; Germany aa Also at Institucio Catalana de Recerca i Estudis Avancats, ICREA, Barcelona; Spain ab Also at Institut für Experimentalphysik, Universität Hamburg, Hamburg; Germany ac Also at Institute for Mathematics, Astrophysics and Particle Physics, Radboud University Nijmegen/Nikhef, Nijmegen; Netherlands ad Also at Institute for Particle and Nuclear Physics, Wigner Research Centre for Physics, Budapest; Hungary ae Also at Institute of Particle Physics (IPP); Canada af Also at Institute of Physics, Academia Sinica, Taipei; Taiwan ag Also at Institute of Physics, Azerbaijan Academy of Sciences, Baku; Azerbaijan ah Also at Institute of Theoretical Physics, Ilia State University, Tbilisi; Georgia ai Also at Instituto de Fı́sica Teórica de la Universidad Autónoma de Madrid; Spain aj Also at Istanbul University, Dept. of Physics, Istanbul; Turkey ak Also at LAL, Université Paris-Sud, CNRS/IN2P3, Université Paris-Saclay, Orsay; France al Also at Louisiana Tech University, Ruston LA; United States of America am Also at LPNHE, Sorbonne Université, Paris Diderot Sorbonne Paris Cité, CNRS/IN2P3, Paris; France an Also at Manhattan College, New York NY; United States of America ao Also at Moscow Institute of Physics and Technology State University, Dolgoprudny; Russia ap Also at National Research Nuclear University MEPhI, Moscow; Russia aq Also at Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg; Germany ar Also at School of Physics, Sun Yat-sen University, Guangzhou; China as Also at The City College of New York, New York NY; United States of America at Also at The Collaborative Innovation Center of Quantum Matter (CICQM), Beijing; China au Also at Tomsk State University, Tomsk, and Moscow Institute of Physics and Technology State University, Dolgoprudny; Russia av Also at TRIUMF, Vancouver BC; Canada aw Also at Universita di Napoli Parthenope, Napoli; Italy ∗ Deceased – 65 –