Published for SISSA by Springer Received: March 8, 2019 Accepted: July 18, 2019 Published: August 6, 2019 Measurement of jet-substructure observables in top quark, W boson and light jet production in JHEP08(2019)033 √ proton-proton collisions at s = 13 TeV with the ATLAS detector The ATLAS collaboration E-mail: atlas.publications@cern.ch Abstract: A measurement of jet substructure observables is presented using data collected √ in 2016 by the ATLAS experiment at the LHC with proton-proton collisions at s = 13 TeV. Large-radius jets groomed with the trimming and soft-drop algorithms are studied. Dedicated event selections are used to study jets produced by light quarks or gluons, and hadronically decaying top quarks and W bosons. The observables measured are sensitive to substructure, and therefore are typically used for tagging large-radius jets from boosted massive particles. These include the energy correlation functions and the N -subjettiness variables. The number of subjets and the Les Houches angularity are also considered. The distributions of the substructure variables, corrected for detector effects, are compared to the predictions of various Monte Carlo event generators. They are also compared between the large-radius jets originating from light quarks or gluons, and hadronically decaying top quarks and W bosons. Keywords: Hadron-Hadron scattering (experiments) ArXiv ePrint: 1903.02942 Open Access, Copyright CERN, https://doi.org/10.1007/JHEP08(2019)033 for the benefit of the ATLAS Collaboration. Article funded by SCOAP3 . Contents 1 Introduction 1 2 ATLAS detector 2 3 Monte Carlo samples 3 4 Object and event selection 4 JHEP08(2019)033 5 Definition of the jet observables 6 6 Data-driven background estimation 9 7 Systematic uncertainties 11 7.1 Large-radius jet uncertainties 11 7.2 Other sources of uncertainties 12 8 Detector-level results 13 9 Unfolding 13 10 Particle-level results 16 11 Conclusions 23 The ATLAS collaboration 30 1 Introduction Increasing the centre-of-mass energy of proton-proton (pp) collisions from 7 and 8 TeV in Run 1 to 13 TeV. in Run 2 of the Large Hadron Collider (LHC) leads to a larger fraction of heavy particles such as top quarks, vector bosons and Higgs bosons being produced with large transverse momenta. This large transverse momentum leads to collimated decay prod- ucts. They are usually reconstructed in a large-radius jet, whose internal (sub)structure shows interesting features that can be used to identify the particle that initiated the jet formation [1, 2]. This is relevant for a host of measurements and searches, which involve identifying the large-radius jets coming from top quarks [3–7]. or Higgs bosons [8–11], for example in Run 2 in ATLAS. Usually a two step procedure is employed. In the first step, termed grooming, the effect of soft, uncorrelated radiation contained in the large-radius jet in reduced. Then jet substructure observables, which describe the spatial energy distribution inside the jets, –1– are used to classify the jets originating from different particles. This process is called jet tagging and the algorithms are referred to as taggers. Most of the grooming algorithms and jet substructure observables were developed on the basis of theoretical calculations or Monte Carlo (MC) simulation programs and then they are applied to data. Given that often large differences have been seen between predictions from MC and data, large correction factors need to be applied to simulation results. Additionally, taggers suffer from large systematic uncertainties as the modelling of the substructure observables is not well constrained [2, 12]. Most of these variables have never been measured in data, and performing a proper unfolded measurement is a common request from the theory community. Measuring these observables will help in optimising JHEP08(2019)033 and developing current and future substructure taggers, as well as tuning hadronization models in the important but still relatively unexplored regime of jet substructure. The choice of variables measured in this paper prioritized jet shapes commonly used in jet tagging, as well as those most useful for model tuning. The ATLAS Collaboration has performed measurements of jet mass and substructure √ variables at the pp centre-of-mass energies of s = 7, 8 and 13 TeV [13–19] in inclusive jet events, and the CMS Collaboration has performed measurements of jet mass and sub- √ structure in dijet, W /Z boson, and tt̄ events [20–24] at s = 7, 8 and 13 TeV. This paper presents measurements of substructure variables in large-radius jets produced in inclusive √ multijet events and in tt̄ events at s = 13 TeV using 33 fb−1 of data collected in 2016 by the ATLAS experiment. In this analysis, the lepton+jets decay mode of tt̄ events is selected, where one W boson decays into a muon and a neutrino, and the other W boson decays into a pair of quarks. Then the large-radius jets are separated into those that con- tain all the decay products of a hadronically top quark and those containing only hadronic W boson decay products. The contents of this paper are organised as follows. First, a description of the ATLAS detector is presented in section 2 and then the MC samples used in the analysis are discussed in section 3. In section 4, event and object selections are summarised. The measured jet substructure observables are defined in section 5. The background estimation is described in section 6 and the systematic uncertainties are assessed in section 7. In section 8, detector- level mass and pT distributions corresponding to selected large-radii jets are shown, and the unfolding is described in section 9. Finally, the unfolded results are presented in section 10, and the conclusions in section 11. 2 ATLAS detector The ATLAS experiment uses a multipurpose particle detector [25, 26] with a forward- backward symmetric cylindrical geometry and a near 4π coverage in solid angle.1 It con- 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 upwards. 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 p the polar angle θ as η = − ln tan(θ/2). An angular separation between two objects is defined as ∆R ≡ (∆η)2 + (∆φ)2 , where ∆η and ∆φ are the separations in η and φ. Momentum in the transverse plane is denoted by pT . –2– sists of an inner tracking detector (ID) surrounded by a thin superconducting solenoid providing a 2 T axial magnetic field, electromagnetic (EM) and hadron calorimeters, and a muon spectrometer. The ID consists of silicon pixel, silicon microstrip, and straw-tube transition-radiation tracking detectors, covering the pseudorapidity range |η| < 2.5. The calorimeter system covers the pseudorapidity range |η| < 4.9. Electromagnetic calorimetry is performed with barrel and endcap high-granularity lead/liquid-argon (LAr) sampling calorimeters, within the region |η| < 3.2. There is an additional thin LAr presampler cov- ering |η| < 1.8, to correct for energy loss in material upstream of the calorimeters. For |η| < 2.5, the LAr calorimeters are divided into three layers in depth. Hadronic calorimetry is performed with a steel/scintillator-tile calorimeter, segmented into three barrel structures JHEP08(2019)033 within |η| < 1.7, and two copper/LAr hadronic endcap calorimeters, which cover the re- gion 1.5 < |η| < 3.2. The forward solid angle up to |η| = 4.9 is covered by copper/LAr and tungsten/LAr calorimeter modules, which are optimised for energy measurements of electrons/photons and hadrons, respectively. The muon spectrometer consists of separate trigger and high-precision tracking chambers that measure the deflection of muons in a magnetic field generated by superconducting air-core toroids. The ATLAS detector selects events using a tiered trigger system [27]. The first level is implemented in custom electronics. The second level is implemented in software running on a general-purpose processor farm which processes the events and reduces the rate of recorded events to 1 kHz. 3 Monte Carlo samples Simulated events are used to optimise the event selection, correct the data for detector effects and estimate systematic uncertainties. The predictions of different phenomenological models implemented in the Monte Carlo (MC) generators are compared with the data corrected to the particle level (i.e. observables constructed from final-state particles within the detector acceptance). The generators used to produce the samples are listed in table 1. The dijet (to obtain multijet events), tt̄ and single-top-quark samples are considered to be signal processes in this analysis, corresponding to the dedicated selections. The background is estimated using Z/W +jets and diboson samples. The tt samples are scaled to next-to-next-to-leading order (NNLO) in perturbative QCD, including soft-gluon resummation to next-to-next-to- leading-log order (NNLL) [28] in cross-section, assuming a top quark mass mt = 172.5 GeV. The Powheg model [29] resummation damping parameter, hdamp , which controls the matching of matrix elements to parton showers and regulates the high-pT radiation, was set to 1.5mt [30]. The single-top-quark [31–36] and W/Z samples [37] are scaled to the NNLO theoretical cross-sections. The predicted shape of jet substructure distributions depends on the modelling of final-state radiation (FSR), and fragmentation and hadronisation, as well as on the merg- ing/matching between matrix element (ME) and parton shower (PS) generators. The Pythia8 and the Sherpa generators use a dipole shower ordered in transverse momen- tum, with the Lund string [38] and cluster hadronisation model [39] respectively. The –3– Process Generator Version PDF Tune Use Dijet Pythia8 [40, 41] 8.186 NNPDF23LO [42] A14 [43] Nominal for unfolding Sherpa [44] 2.2.1 CT10 [45] Default Validation of unfolding (with two different hadronisation models) Herwig7 [46] 7.0.4 MMHT2014 H7UE [46] Comparison tt Powheg [47] v2 NNPDF30NLO Nominal for unfolding + Pythia8 8.186 NNPDF23LO A14 Powheg v2 CT10 Validation of unfolding +Herwig++ [48] 2.7 CTEQ6L1 UE-EE-5 tune [49] Powheg v2 CT10 Comparison +Herwig7 7.0.4 MMHT2014 H7UE MG5 aMC@NLO [50] 2.6.0 NNPDF30NLO Comparison + Pythia8 8.186 NNPDF23LO A14 JHEP08(2019)033 Sherpa 2.2.1 CT10 Default Comparison Single top Powheg v1 CT10 Nominal for unfolding + Pythia6 [51, 52] 6.428 CTEQ6L1 [45] Perugia2012 [53] Z+jets Sherpa 2.2.1 CT10 Default Background estimation W +jets Sherpa 2.2.1 CT10 Default Background estimation (nominal) W +jets MG5 aMC@NLO 2.2.5 CT10 Background estimation (cross-check) + Pythia8 8.186 NNPDF23LO A14 Diboson Sherpa 2.2.1 CT10 Default Background estimation Table 1. Main features of the Monte Carlo models used to simulate signal and background samples, and to produce predictions to be compared with data. The nominal samples listed are used for comparisons with corrected data at particle level as well. For convenience, MG5 aMC@NLO is referred to as MG5 aMC in figures 3–9. Herwig7 generator uses an angle-ordered shower, with the cluster hadronisation model. For comparison purposes in dijet events, a sample was generated with Sherpa using the string hadronisation model. The MC samples were processed through the full ATLAS detector simulation [54] based on Geant4 [55], and then reconstructed and analysed using the same procedure and software that are used for the data. Additional pp collisions generated by Pythia8, with parameter values set to the A2 tune [56] and using the MSTW2008 [57] PDF set, were overlaid to simulate the effects of additional collisions from the same and nearby bunch crossings (pile-up), with a distribution of the number of extra collisions matching that of data. 4 Object and event selection √ This analysis uses pp collision data at s = 13 TeV collected by the ATLAS detector in 2016, that satisfy a number of criteria to ensure that the ATLAS detector was in good operating condition. All selected events must have at least one vertex with at least two P 2 associated tracks with pT > 400 MeV. The vertex with the highest pT,track , where pT,track is the transverse momentum of a track associated with the vertex, is chosen as the primary vertex. Jets are reconstructed from the EM-scale or locally-calibrated topological energy clus- ters [58] in both the EM and hadronic calorimeters using the anti-kt algorithm [59] with a radius parameter of R = 0.4 or R = 1.0, referred to as small-radius and large-radius jets re- spectively. These clusters are assumed to be massless when computing the jet four-vectors –4– and substructure variables. A trimming algorithm [60] is employed for the large-radius jets to mitigate the impact of initial-state radiation, underlying-event activity, and pile- up. Trimming removes subjets of radius Rsub = 0.2 with piT /pjet i T < fcut , where pT is the th jet transverse momentum of the i subjet, pT is the transverse momentum of the jet un- der consideration, and fcut = 0.05. All large-radius jets used in this paper are trimmed before applying the selection criteria. The energies of jets are calibrated by applying pT - and rapidity-dependent corrections derived from Monte Carlo simulation with additional correction factors for residual non-closure in data determined from data [58, 61]. In order to reduce the contamination by small-radius jets originating from pile-up, JHEP08(2019)033 a requirement is imposed on the output of the Jet Vertex Tagger (JVT) [62]. The JVT algorithm is a multivariate algorithm that uses tracking information to reject jets which do not originate from the primary vertex, and is applied to jets with pT < 60 GeV and |η| < 2.4. Small-radius jets containing b-hadrons are tagged using a neural-network-based algorithm [63–65] that combines information from the track impact parameters, secondary vertex location, and decay topology inside the jets. The operating point corresponds to an overall 70% b-tagging efficiency in simulated tt̄ events, and to a probability of mis-tagging light-flavour jets of approximately 1%. Muons are reconstructed from high-quality muon spectrometer track segments matched to ID tracks. Muons with a transverse momentum greater than 30 GeV and within |η| < 2.5 are selected if the associated track has a longitudinal impact parameter |z0 sin(θ)| < 0.5 mm and a transverse impact parameter significance |d0 |/σ(d0 )| < 3. The impact parameter d0 is measured relative to the beam line. The muon candidates are also required to be isolated from nearby hadronic activity [66]. The muon isolation criteria remove muons that lie a distance ∆R(µ, jet) < 0.04 + 10 GeV/pT,µ from a small-radius jet axis, where pT,µ is the pT of the muon. Since muons deposit energy in the calorimeters, an overlap removal procedure is applied in order to avoid double counting of leptons and small-radius jets. Electrons are reconstructed from energy deposits measured in the EM calorimeter which are matched to ID tracks. They are required to be isolated from nearby hadronic activity by using a set of pT - and η-dependent criteria based on calorimeter and track information as described in ref. [67]. Their selection also requires pT > 30 GeV and |η| < 2.5, excluding the region 1.37 < |η| < 1.52 which corresponds to the transition region between the barrel and end-cap calorimeters. Photon candidates are reconstructed from clusters of energy deposited in the EM calorimeter, and must have pT > 30 GeV and |η| < 2.5. Photon identification is based primarily on shower shapes in the calorimeter [68]. The missing transverse momentum, with magnitude ET miss , is calculated as the neg- ative vectorial sum of the transverse momenta of calibrated photons, electrons, muons and jets associated with the primary vertex [69]. The transverse mass of the lepton- ically q decaying W boson, mW T , is defined using the absolute value of ET miss as mW = T miss miss  2pT,µ ET 1 − cos ∆φ(µ, ET ) . In order to examine large-radius jets originating from light quarks and gluons, from top quarks and from W bosons, three event selections are defined. These are referred to as dijet, top and W selections, and are indicative of the origin of the large-radius jet. –5– In the dijet selection, the events are accepted by a single-large-radius-jet trigger that becomes fully efficient for jets with pT > 400 GeV. The offline dijet selection requires a leading trimmed large-radius jet with pT > 450 GeV and |η| < 1.5, and at least one other trimmed large-radius jet with pT > 200 GeV and |η| < 2.5, and rejects the event if an electron or muon is present. For both the top and W selections, events are collected with a set of single-muon triggers that become fully efficient for muon pT > 28 GeV. The top quarks and the W bosons are identified from their decay products. A geometrical separation between the decay products of the two top quark candidates is required. Additional requirements are applied to separate large-radius jets containing all decay products of the top quark JHEP08(2019)033 from those where the large-radius jet only contains the hadronic W boson decays, with the b-tagged small-radius jet reconstructed independently. These form the top selection and the W selection respectively. The selections are described in table 2. After these requirements the data sample contains about 3.2 × 107 events in the dijet selection, and roughly 6800 and 4500 events in the top and W selection respectively. Particle-level observables in Monte Carlo simulation are constructed from stable par- ticles, defined as those with proper lifetimes cτ & 10 mm. Muons at particle level are dressed by including contributions from photons with an angular distance ∆R < 0.1 from the muon. Particle-level jets do not include muons or neutrinos. Particle-level b-tagging is performed by requiring a prompt b-hadron to be ghost-associated [70] with the jet. 5 Definition of the jet observables All large-radius jets are trimmed before being used in the selections, and subsequently only the leading trimmed large-radius is considered in the analysis. Then the large-radius jet constructed from the original constituents of the selected jet before the trimming step is groomed using the soft-drop algorithm, and the jet substructure observables studies are constructed from that soft-dropped large-radius jet. Soft-drop [71, 72] is an extension of the original split-filtering technique [73] and relies on reclustering the jet constituents using the angle-ordered Cambridge-Aachen jet algo- rithm and then sequentially considering each splitting in order to remove soft and wide- angle radiation. At each step the jet is split into two proto-jets. The removal of proto-jets in a splitting is controlled by two parameters: a measure of the energy balance of the pair, zcut , and the significance of the angular separation of the proto-jets, β SD . These are used to define the soft-drop condition:  β SD min(pT1 , pT2 ) ∆R12 > zcut pT1 + pT2 R where R12 is the angular distance between the two proto-jets and R is the radius of the large jet. In this analysis, values of zcut = 0.1 and β SD = 0.0 are used, based on previous ATLAS studies [18], which is equivalent to modified mass drop tagger [74]. An important feature of soft-drop is that groomed observables are analytically calculable to high-order resummation accuracy [75–77]. –6– Detector level Particle level Dijet selection: pT > 200 GeV pT > 200 GeV Two trimmed anti-kt R = 1.0 jets |η| < 2.5 |η| < 2.5 Leading-pT trimmed anti-kt R = 1.0 jet pT > 450 GeV Top and W selections: pT > 30 GeV pT > 30 GeV Exactly one muon |η| < 2.5 |η| < 2.5 |z0 sin(θ)| < 0.5 mm and |d0 /σ(d0 )| < 3 pT > 25 GeV pT > 25 GeV Anti-kt R = 0.4 jets |η| < 4.4 |η| < 4.4 JHEP08(2019)033 JVT output > 0.5 (if pT < 60 GeV) If ∆R(µ, jet) < 0.04 + 10 GeV/pT,µ : Muon isolation criteria None muon is removed, so the event is discarded miss , mW ET miss > 20 GeV, E miss + mW > 60 GeV ET T T T Leptonic top At least one small-radius jet with 0.4 < ∆R(µ, jet) < 1.5 Top selection: |η| < 1.5, pT > 350 GeV, mass > 140 GeV Leading-pT trimmed anti-kt R = 1.0 jet ∆R(large-radius jet, b-tagged jet) < 1 ∆φ(µ, large-radius jet) > 2.3 W selection: |η| < 1.5, pT > 200 GeV, mass > 60 GeV and mass < 100 GeV Leading-pT trimmed anti-kt R = 1.0 jet 1 < ∆R(large-radius jet, b-tagged jet) < 1.8 ∆φ(µ, large-radius jet) > 2.3 Table 2. Summary of object event selections for detector-level and particle-level dijet and tt̄ events. “Leptonic top” refers to the top quark that decays into a leptonically decaying W boson, while “b-tagged jet” refers to small-radius jets that pass a b-tagging requirement. The top and W selections are common up to the requirement on the leptonic top, then they differ on the require- ments on the leading-pT trimmed large-radius jet. All selections are inclusive, unless otherwise mentioned. The following substructure variables are measured in this analysis: • Number of subjets with pT > 10 GeV, reconstructed from the selected large-radius jet constituents using the kt algorithm [78] with R = 0.2. • Generalised angularities defined as: LHA ziκ θiβ X λκβLHA = , i∈J where zi is the transverse momentum of jet constituent i as a fraction of the scalar sum of the pT of all constituents and θi is the angle of the ith constituent relative to the jet axis, normalised by the jet radius. The exponents κ and β LHA probe different aspects of the jet fragmentation. The (κ = 1, β LHA = 0.5) variant is termed the Les Houches angularity (LHA) [79] and used in this analysis. It is an infrared-safe version of the jet-shape angularity, and provides a measure of the broadness of a jet. –7– • Energy correlation functions ECF2 and ECF3 [80], and related ratios C2 , D2 [81]. The 1-point, 2-point and 3-point energy correlation functions for a jet J are given by: X ECF1 = pT i , i∈J X ECF ECF2(β ECF ) = pTi pTj (∆Rij )β , i 450 GeV Rtrk p T up Anti-k t R=1.0 jets, p T > 450 GeV T Rtrk p T up Soft Drop β = 0, z = 0.1 Rtrk mass up Soft Drop β = 0, z = 0.1 Rtrk mass up cut cut Rtrk mass down Rtrk mass down 10 10 5 5 0 0 −5 −5 −10 −10 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 JHEP08(2019)033 norm LHA ECF2 Percentage uncertainty Percentage uncertainty CAR CAR ATLAS Simulation CES ATLAS Simulation CES 60 s = 13 TeV CESu 60 s = 13 TeV CESu CESd W selection CESd W selection Rtrk p down Rtrk p down T T Anti-k t R=1.0 jets, p T > 200 GeV Rtrk p T up Anti-k t R=1.0 jets, p T > 200 GeV Rtrk p T up Soft Drop β = 0, z = 0.1 Rtrk mass up Soft Drop β = 0, z = 0.1 Rtrk mass up 40 cut Rtrk mass down 40 cut Rtrk mass down 20 20 0 0 −20 −20 −40 −40 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0 0.05 0.1 0.15 0.2 0.25 0.3 norm LHA ECF2 Figure 1. Bin-by-bin systematic uncertainties due to large-radius jet reconstruction uncertainties associated with cluster, Rtrk and jet mass calibrations in the dijet (top) and W (bottom) selections for the soft-drop groomed Les Houches angularity variable (left) and the normalised ECF2 variable (right). an alternative sample, as listed in table 1. The envelope is taken as an uncertainty in the subtracted shape, and results in uncertainties which are smaller than 1%. The uncertainties due to signal modelling in MC generators are accounted for in unfolding, as described in section 9. 8 Detector-level results The distributions of the trimmed large-radius jet mass and pT at detector level are shown in figure 2 for dijet, top and W selections. The peaks in the distributions due to the top and W masses are clearly visible. In general, good agreement is observed between data and simulation for the distribution of transverse momenta, while a shift is observed for the distributions of mass. This is a known effect [2], due to the lack of in situ calibrations of jet mass, and to jet mass scale uncertainties in the detector-level plots. 9 Unfolding The measured distributions are unfolded to correct for detector effects. The Iterative Bayesian (IB) unfolding method [89] with three iterations (as implemented in RooUn- fold [90]) is used to correct detector-level data to particle level, as defined in section 4. – 13 – ×103 Events / 100 [GeV] Events / 10 [GeV] 6000 ATLAS 107 ATLAS s = 13 TeV, 33 fb-1 s = 13 TeV, 33 fb-1 Dijet selection 6 Dijet selection 5000 10 Anti-k t R =1.0 jets Anti-k t R =1.0 jets Trimmed fcut = 0.05, Rsub = 0.2 Trimmed fcut = 0.05, Rsub = 0.2 Data 105 Data 4000 Pythia8 Pythia8 104 3000 103 2000 102 1000 10 JHEP08(2019)033 1.4 ↑ Data/MC 1.4 Data/MC 1.2 1.2 1 1 0.8 0.8 0.6 0.6 50 100 150 200 250 300 500 1000 1500 2000 2500 3000 3500 mjet [GeV] pjet [GeV] T Events / 60 [GeV] Events / 10 [GeV] 1600 ATLAS Data ATLAS s = 13 TeV, 33 fb-1 s = 13 TeV, 33 fb-1 tt 1400 Top selection single top 3 Top selection Anti-k t R =1.0 jets 10 Anti-k t R =1.0 jets W+jets Trimmed fcut = 0.05, Rsub = 0.2 Other backgrounds Trimmed fcut = 0.05, Rsub = 0.2 1200 Data 1000 tt 102 single top 800 W+jets Other backgrounds 600 10 400 200 1 1.4 ↑↑ Data/MC 1.4 Data/MC 1.2 1.2 1 1 0.8 0.8 0.6 0.6 ↓ 50 100 150 200 250 300 200 400 600 800 1000 1200 1400 1600 1800 2000 jet m [GeV] pjet [GeV] T Events / 60 [GeV] Events / 10 [GeV] 2200 104 ATLAS ATLAS 2000 s = 13 TeV, 33 fb-1 s = 13 TeV, 33 fb-1 W selection W selection 1800 Anti-k t R =1.0 jets Anti-k t R =1.0 jets 103 1600 Trimmed fcut = 0.05, Rsub = 0.2 Trimmed fcut = 0.05, Rsub = 0.2 Data Data 1400 tt tt 1200 single top 102 single top W+jets W+jets 1000 Other backgrounds Other backgrounds 800 10 600 400 200 1 1.4 ↑ ↑ ↑ ↑ Data/MC 1.4 ↑ ↑ Data/MC 1.2 1.2 1 1 0.8 0.8 0.6 ↓ ↓ 0.6 ↓ 50 100 150 200 250 300 200 400 600 800 1000 1200 1400 1600 1800 2000 jet m [GeV] pjet [GeV] T Figure 2. Comparison of detector-level distributions in data and MC simulation for trimmed large- radius jets for dijet (top row), top (middle row), and W (bottom row) selections. For the top and W selections, jet mass requirements have not been applied. The mass is shown in the left column, while the transverse momentum is in the right column. The shaded bands represent the combined statistical and systematic uncertainty. Contributions from dominant backgrounds are shown for the top and W selections, while the smaller contributions from other processes are grouped under other backgrounds. – 14 – Response matrices (aji ) for each distribution are derived from MC simulation and used in order to estimate the probability for a given event at particle level (T ), contributing to bin i, to be reconstructed in a given detector-level (D) bin j, also defined as P (Dj |Ti ). Rather than using a simple matrix inversion, IB unfolding uses a probabilistic approach. In order to do this, the unfolding matrix (θij ) is defined such that the number of events in a particle-level bin, Ti , is given by X Ti = θij dj (9.1) j JHEP08(2019)033 where dj is the number of data events measured in bin j. Using Bayes’ theorem, one can define the unfolding matrix as: P (Dj |Ti ) · P (Ti ) aji · P (Ti ) θij = P (Ti |Dj ) = P =P . i P (D |T j i ) · P (T i ) i aji · P (Ti ) where P (Ti ) is the input prior. The unfolding matrix can therefore be constructed using the response matrix obtained from simulation. After corrections are applied for detector acceptance and reconstruction efficiency, eq. (9.1) can be used to perform the unfolding. To ensure that the final distributions are not biased by the shape predicted by simula- tion the process is iterated, each subsequent iteration using the previous estimate for the final corrected distribution as P (Ti ). The number of iterations is chosen such that dif- ferences between multiple subsequent iterations are smaller than data-driven cross-closure uncertainties, described below. The consistency of the unfolding procedure was tested using several closure and cross- closure tests. • MC closure: a test where the distributions from the nominal MC generator are unfolded using the nominal method. Uncertainties are found to be negligible. • Cross-closure: accounts for modelling differences between two different MC genera- tors. The distributions from an alternative generator are unfolded using the nominal method and the differences account for differences in the predicted shape. These re- sult in the largest uncertainties and are typically around 5% in the dijet selection and around 14% in the top and W selections, depending on the observable and the bin. • Data-driven cross-closure: accounts for the sensitivity of the unfolding method to differences between the shape of the observable seen in data and in simulation. The particle-level substructure distributions are reweighted such that the corresponding detector-level distributions match the data. These reweighted distributions are un- folded using the nominal method and uncertainties are estimated as the differences between the reweighted particle-level and unfolded distributions. The binning of variables in the dijet selection was chosen to reduce uncertainties from the above effects by increasing the bin purity. For the top and W selections binning was determined based on the statistical uncertainty of the dominant systematic uncertainties. – 15 – σ d(Nsubjets) σ d(Nsubjets) 0.5 Top selection 0.7 ATLAS ATLAS dσ dσ s = 13 TeV, 33 fb–1 s = 13 TeV, 33 fb–1 √ √ 0.6 Dijet selection, anti-kt R = 1.0, pT > 450 GeV 0.4 Anti-kt R = 1.0, pT > 350 GeV 0.5 Soft drop β = 0 , zcut = 0.1 Soft drop β = 0 , zcut = 0.1 1 1 0.3 Data 0.4 Data Powheg+Pythia8 Pythia8 0.3 0.2 Powheg+Herwig7 Herwig7 MG5 aMC+Pythia8 0.2 Sherpa (cluster) Sherpa Sherpa (string) 0.1 0.1 0 0 1.2 1.4 1.3 MC/Data MC/Data 1.1 1.2 1 1.1 0.9 1 0.9 0.8 0.8 0.7 0.7 0.6 0.6 0.5 1 2 3 4 5 6 2 4 6 8 10 Nsubjets Nsubjets JHEP08(2019)033 σ d (Nsubjets) 0.7 σ d(Nsubjets) W selection ATLAS 0.6 ATLAS dσ 0.6 s = 13 TeV, 33 fb-1 dσ s = 13 TeV, 33 fb–1 √ 0.5 Anti-kt R = 1.0, pT > 200 GeV Anti-kt R =1.0 jets Soft drop β = 0 , zcut = 0.1 Soft Drop β = 0, zcut = 0.1 0.5 1 1 0.4 Data W selection 0.3 Powheg+Pythia8 Powheg+Herwig7 0.4 Top selection MG5 aMC+Pythia8 Dijet selection 0.2 Sherpa 0.1 0.3 0 1.4 0.2 1.3 MC/Data 1.2 1.1 1 0.9 0.1 0.8 0.7 0.6 0.5 0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7 8 9 Nsubjets Nsubjets Figure 3. Subjet multiplicity distributions compared with different MC predictions for soft- dropped large-radius jets from dijet (top left), top (top right), and W (bottom left) selections. For the dijet selection, Sherpa is tested with two different hadronisation models. Data are com- pared between the soft-dropped large-radius jets for the three selections mentioned above (bottom right). The shaded bands represent the total uncertainty, while the error bars show the statistical uncertainty, except in the bottom right plot, where the shaded areas represent the total uncertainty. 10 Particle-level results The results are presented in two sets of distributions: substructure observables in data are compared with MC predictions, and distributions measured in data corresponding to different selections are compared with each other. For the latter, it must be noted that the comparisons are performed in different large-radius jet pT ranges; however, in each instance the most inclusive selection is used. They are indicative of different substructures of the large-radius jets according to their origin even with somewhat different kinematic ranges. All plots with soft-drop grooming are shown; the trimmed versions have very similar characteristics [91]. The dominant systematic uncertainties in the measurement are the large-radius jet uncertainties resulting from the bottom-up approach using clusters, and modelling uncertainties affecting the unfolding closure and cross-closure. In figure 3, the subjet multiplicity inside the large-radius jets from the three different selections is compared with different MC predictions, and the data are compared between the three selections. While for the dijet selection most events have one subjet, for the top selection and W selection the distributions peak at three and two subjets respectively, as expected. In both cases a non-negligible fraction of events have more subjets, indicating the – 16 – 6 σ d(LHA) 3.5 σ d(LHA) Top selection ATLAS ATLAS 1 dσ 1 dσ 3 √ s = 13 TeV, 33 fb–1 5 √ s = 13 TeV, 33 fb–1 Dijet selection, anti-kt R = 1.0, pT > 450 GeV Anti-kt R = 1.0, pT > 350 GeV 2.5 4 Soft drop β = 0 , zcut = 0.1 Soft drop β = 0 , zcut = 0.1 2 Data 3 Data 1.5 Pythia8 Powheg+Pythia8 Herwig7 Powheg+Herwig7 2 1 Sherpa (cluster) MG5 aMC+Pythia8 Sherpa (string) Sherpa 1 0.5 0 0 1.4 1.4 1.3 1.3 MC/Data MC/Data 1.2 1.2 1.1 1.1 1 1 0.9 0.9 0.8 0.8 0.7 0.7 0.6 0.6 0.5 0.5 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 LHA LHA JHEP08(2019)033 σ d (LHA) 6 σ d(LHA) 6 ATLAS 1 dσ W selection ATLAS 1 dσ W selection 5 √ s = 13 TeV, 33 fb–1 s = 13 TeV, 33 fb-1 Top selection Anti-kt R = 1.0, pT > 200 GeV Anti-kt R =1.0 jets Dijet selection 5 4 Soft drop β = 0 , zcut = 0.1 Soft Drop β = 0, zcut = 0.1 3 Data 4 Powheg+Pythia8 2 Powheg+Herwig7 MG5 aMC+Pythia8 3 1 Sherpa 0 2 1.4 1.3 MC/Data 1.2 1.1 1 1 0.9 0.8 0.7 0.6 0.5 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0 0.2 0.4 0.6 0.8 1 LHA LHA Figure 4. Les Houches angularity is compared with different MC predictions for soft-dropped large-radius jets from dijet (top left), top (top right), and W (bottom left) selections. For the dijet selection, Sherpa is tested with two different hadronisation models. Data are compared between the soft-dropped large-radius jets for the three selections mentioned above (bottom right). The shaded bands represent the total uncertainty, while the error bars show the statistical uncertainty, except in the bottom right plot, where the shaded areas represent the total uncertainty. presence of semi-hard gluon radiation. In the W selection, the instances with one subjet are few, while for the top selection, some fraction of events have two subjets, indicating either non-containment of the top quark decay products, or overlapping subjets that get reconstructed as a single subjet. For the dijet selection, Pythia8 and Sherpa describe the data the best, while for the top selection and W selection, there is more spread among MC predictions. Predictions from Herwig7 are very different from data for the dijet selection, a trend which is consistent across all observables. The difference between the different hadronisation models used in Sherpa is negligible. Although these observables depend on hadronisation modelling, it can be inferred that both models can be tuned to give a good description of data. In figure 4, the Les Houches angularity (LHA) is compared between large-radius jets for the three selections and with MC model predictions. For the dijet selection, all mod- els except Herwig7 describe the data, while for the top and W selections, the level of agreement between all models and data is worse, and the peaks of the distributions in the models are shifted relative to those in data. While in the case of the top and W selections the shapes are similar, the distribution for the dijet selection peaks at the lowest value. – 17 – 8 σ d(C2 ) σ d(C2 ) 1 dσ 1 dσ Top selection 10 1 ATLAS 7 ATLAS s = 13 TeV, 33 fb–1 s = 13 TeV, 33 fb–1 √ √ Data 1 6 Powheg+Pythia8 Powheg+Herwig7 Data 5 10–1 MG5 aMC+Pythia8 Pythia8 4 Sherpa Herwig7 10–2 Sherpa (cluster) 3 Sherpa (string) 10–3 2 Dijet selection, anti-kt R = 1.0, pT > 450 GeV 10–4 Soft drop β = 0 , zcut = 0.1 1 0 1.4 1.8 Anti-kt R = 1.0, pT > 350 GeV 1.3 MC/Data MC/Data 1.6 1.2 Soft drop β = 0 , zcut = 0.1 1.1 1.4 1 1.2 0.9 1 0.8 0.7 0.8 0.6 0.6 0.5 0 0.1 0.2 0.3 0.4 0.5 0.6 0 0.1 0.2 0.3 0.4 0.5 C2 C2 JHEP08(2019)033 7 σ d (C ) σ d(C2 ) 2 1 dσ 1 dσ W selection ATLAS 6 ATLAS 6 s = 13 TeV, 33 fb–1 s = 13 TeV, 33 fb-1 √ 5 Data Anti-kt R =1.0 jets Powheg+Pythia8 Soft Drop β = 0, zcut = 0.1 5 4 Powheg+Herwig7 MG5 aMC+Pythia8 W selection 3 Sherpa Top selection 4 2 Dijet selection 1 3 0 1.8 Anti-kt R = 1.0, pT > 200 GeV 2 MC/Data 1.6 Soft drop β = 0 , zcut = 0.1 1.4 1.2 1 1 0.8 0.6 0 0.1 0.2 0.3 0.4 0.5 0 0 0.1 0.2 0.3 0.4 0.5 0.6 C2 C2 Figure 5. The distributions of C2 compared with different MC predictions for soft-dropped large- radius jets from dijet (top left), top (top right), and W (bottom left) selections. For the dijet selection, Sherpa is tested with two different hadronisation models. Data are compared between the soft-dropped large-radius jets for the three selections mentioned above (bottom right). The shaded bands represent the total uncertainty, while the error bars show the statistical uncertainty, except in the bottom right plot, where the shaded areas represent the total uncertainty. This indicates that the additional radiation in quark/gluon jets is soft, with little activity away from the large-radius jet axis, while for the large-radius jets from top quarks and W bosons, there are hard emissions separated by appreciable angles. In figure 5, a comparison of C2 among the three different selections with MC is pre- sented, as well as a comparisons of data and MC predictions for each selection. For the dijet selection, all models except Herwig7 describe the data well, while for the top and W selections, the models predict shapes that differ from data, with Powheg+Herwig7 performing somewhat worse than the rest. The three distributions have distinct peaks, corresponding to their substructure. The value of C2 increases as the number of subjets inside the large-radius jets increases. In figure 6, comparisons of the data with MC predictions for D2 reveal some interesting features. For the dijet selection, most of the models describe the data well, and for the top selection the some differences can be seen. For the W selection, all MC predictions have a peak shifted relative to data, suggesting that the models are overestimating gluon radiation. The distributions in data for the three selections are also compared in figure 6 (bottom right), where peaks at different values are observed. – 18 – 1 2.5 σ d(D2 ) σ d(D2 ) 1 dσ 1 dσ Data 0.9 ATLAS ATLAS Powheg+Pythia8 s = 13 TeV, 33 fb–1 s = 13 TeV, 33 fb–1 √ √ 0.8 2 Powheg+Herwig7 Dijet selection Top selection 0.7 MG5 aMC+Pythia8 Anti-kt R = 1.0, pT > 450 GeV 0.6 1.5 Sherpa Soft drop β = 0 , zcut = 0.1 0.5 Data 0.4 1 Pythia8 0.3 Herwig7 0.2 Sherpa (cluster) 0.5 Sherpa (string) 0.1 0 0 1.3 1.2 MC/Data MC/Data 1.2 1 1.1 0.8 1 0.6 Anti-kt R = 1.0, pT > 350 GeV 0.9 0.4 Soft drop β = 0 , zcut = 0.1 0.8 0.2 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 0 0.5 1 1.5 2 2.5 D2 D2 JHEP08(2019)033 σ d (D ) σ d(D2 ) 2 ATLAS 1 dσ 1.4 1 dσ W selection 1.6 ATLAS s = 13 TeV, 33 fb–1 √ 1.2 Data s = 13 TeV, 33 fb-1 Powheg+Pythia8 1.4 Anti-kt R =1.0 jets 1 Powheg+Herwig7 Soft Drop β = 0, zcut = 0.1 MG5 aMC+Pythia8 0.8 1.2 Sherpa W selection 0.6 Top selection 1 Dijet selection 0.4 0.8 0.2 0 0.6 1.8 Anti-kt R = 1.0, pT > 200 GeV MC/Data 1.6 Soft drop β = 0 , zcut = 0.1 0.4 1.4 1.2 1 0.2 0.8 0.6 0 0.5 1 1.5 2 2.5 0 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 D2 D2 Figure 6. The distributions of D2 compared with different MC predictions for soft-dropped large- radius jets from dijet (top left), top (top right), and W (bottom left) selections. For the dijet selection, Sherpa is tested with two different hadronisation models. Data are compared between the soft-dropped large-radius jets for the three selections mentioned above (bottom right). The shaded bands represent the total uncertainty, while the error bars show the statistical uncertainty, except in the bottom right plot, where the shaded areas represent the total uncertainty. The distributions of ECF 2norm , as shown in figure 7 for the different selections, can discriminate between events with two and three prong decays as opposed to one prong decay. Similarly to C2 , for the dijet selection, all models except Herwig7 describe the data well, while for the top and W selections, the models predict shapes that differ somewhat from data, with agreement being worse for the W selection case. The modelling of ECF 3norm in the dijet selection is better for Pythia8 than for the other generators, as shown in figure 8. For the top and W selections, none of the models describe the shape of the data distribution well, with noticeable differences at low values. The three different selections again show distinct shapes. WTA and τ WTA among top quark and W selections Finally, in figure 9, a comparison of τ21 32 is presented. The distribution of τ21WTA peaks at lower values for the W selection than for the top selection, indicating the two-prong decay of the former. In general, τ21WTA distributions are modelled well by the MC models, except Powheg + Herwig7. Although most of the models also describe the τ32 WTA distributions well, differences can be observed between them, especially in the W selection. – 19 – 10 2 12 σ d(ECF2norm ) σ d(ECF2norm ) Top selection ATLAS ATLAS JHEP08(2019)033 10 Data s = 13 TeV, 33 fb–1 s = 13 TeV, 33 fb–1 √ dσ √ dσ 10 1 Powheg+Pythia8 Dijet selection, anti-kt R = 1.0, pT > 450 GeV 8 Powheg+Herwig7 Soft drop β = 0 , zcut = 0.1 MG5 aMC+Pythia8 1 1 Sherpa 1 6 Data Pythia8 4 10–1 Herwig7 Sherpa (cluster) 2 Sherpa (string) 10–2 0 1.4 1.6 Anti-kt R = 1.0, pT > 350 GeV 1.3 MC/Data MC/Data 1.2 1.4 Soft drop β = 0 , zcut = 0.1 1.1 1.2 1 0.9 1 0.8 0.8 0.7 0.6 0.6 0.5 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 ECF2norm ECF2norm σ d (ECF2norm) σ d(ECF2norm ) 14 Data ATLAS ATLAS W selection dσ Powheg+Pythia8 √ s = 13 TeV, 33 fb–1 Top selection s = 13 TeV, 33 fb-1 dσ 12 Powheg+Herwig7 Anti-kt R =1.0 jets W selection Dijet selection 10 MG5 aMC+Pythia8 Soft Drop β = 0, zcut = 0.1 Sherpa 1 1 8 10 6 4 2 0 1 1.8 1.6 Anti-kt R = 1.0, pT > 200 GeV MC/Data 1.4 Soft drop β = 0 , zcut = 0.1 1.2 1 0.8 0.6 10−1 0 0.05 0.1 0.15 0.2 0.25 0.3 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 norm ECF2 ECF2norm Figure 7. The distributions of ECF 2norm compared with different MC predictions for soft-dropped large-radius jets from dijet (top left), top (top right), and W (bottom left) selections. For the dijet selection, Sherpa is tested with two different hadronisation models. Data are compared between the soft-dropped large-radius jets for the three selections mentioned above (bottom right). The subscript norm indicates that normalised versions of ECF 2norm are used. The shaded bands represent the total uncertainty, while the error bars show the statistical uncertainty, except in the bottom right plot, where the shaded areas represent the total uncertainty. – 20 – 60 σ d(ECF3norm ) σ d(ECF3norm ) 10 3 ATLAS ATLAS 50 √ s = 13 TeV, 33 fb–1 dσ JHEP08(2019)033 s = 13 TeV, 33 fb–1 √ dσ Top selection, anti-kt R = 1.0, pT > 350 GeV 10 2 Dijet selection, anti-kt R = 1.0, pT > 450 GeV 40 Soft drop β = 0 , zcut = 0.1 Soft drop β = 0 , zcut = 0.1 1 1 30 Data 10 1 Powheg+Pythia8 Data 20 Powheg+Herwig7 1 Pythia8 MG5 aMC+Pythia8 Herwig7 Sherpa 10 Sherpa (cluster) 10–1 Sherpa (string) 0 1.2 1.8 MC/Data 1.6 MC/Data 1.1 1 1.4 0.9 1.2 0.8 1 0.7 0.8 0.6 0.6 0.5 0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 ECF3norm ECF3norm σ d (ECF3norm) 140 10 3 σ d(ECF3norm ) ATLAS W selection ATLAS dσ 120 s = 13 TeV, 33 fb-1 s = 13 TeV, 33 fb–1 Top selection √ dσ W selection, anti-kt R = 1.0, pT > 200 GeV Dijet selection Anti-kt R =1.0 jets 100 Soft drop β = 0 , zcut = 0.1 Soft Drop β = 0, zcut = 0.1 1 80 1 Data 102 60 Powheg+Pythia8 Powheg+Herwig7 40 MG5 aMC+Pythia8 Sherpa 20 0 1.8 10 MC/Data 1.6 1.4 1.2 1 0.8 0.6 0 0.01 0.02 0.03 0.04 0.05 0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 ECF3norm ECF3norm Figure 8. The distributions of ECF 3Norm are compared with different MC predictions for soft- dropped large-radius jets from dijet (top left), top (top right), and W (bottom left) selections. For the dijet selection, Sherpa is tested with two different hadronisation models. Data are compared between the soft-dropped large-radius jets for the three selections mentioned above (bottom right The superscript “norm” indicates that normalised versions of ECF 3Norm are used. The shaded bands represent the total uncertainty, while the error bars show the statistical uncertainty, except in the bottom right plot, where the shaded areas represent the total uncertainty. – 21 – 4.5 σ d(τWTA ) σ d(τWTA ) Top selection 2.5 Top selection ATLAS ATLAS 1 dσ 4 1 dσ 32 21 Data s = 13 TeV, 33 fb–1 s = 13 TeV, 33 fb–1 √ √ 3.5 Powheg+Pythia8 2 3 Powheg+Herwig7 MG5 aMC+Pythia8 2.5 1.5 Sherpa 2 1 Data 1.5 Powheg+Pythia8 1 Powheg+Herwig7 0.5 MG5 aMC+Pythia8 0.5 Sherpa 0 0 1.2 1.4 Anti-kt R = 1.0, pT > 350 GeV MC/Data MC/Data JHEP08(2019)033 1.1 1.2 Soft drop β = 0 , zcut = 0.1 1 0.9 1 0.8 0.8 Anti-kt R = 1.0, pT > 350 GeV 0.7 0.6 Soft drop β = 0 , zcut = 0.1 0.6 0.5 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 τWTA 21 τWTA 32 4 5 σ d(τWTA ) σ d(τWTA ) W selection ATLAS ATLAS 1 dσ 32 1 dσ 21 3.5 s = 13 TeV, 33 fb–1 √ s = 13 TeV, 33 fb–1 4 √ 3 Data W selection, anti-kt R = 1.0, pT > 200 GeV Powheg+Pythia8 Soft drop β = 0 , zcut = 0.1 2.5 3 Powheg+Herwig7 2 MG5 aMC+Pythia8 Data Sherpa 2 Powheg+Pythia8 1.5 Powheg+Herwig7 1 MG5 aMC+Pythia8 1 Sherpa 0.5 0 0 1.8 1.4 Anti-kt R = 1.0, pT > 200 GeV 1.3 MC/Data MC/Data 1.6 Soft drop β = 0 , zcut = 0.1 1.2 1.4 1.1 1 1.2 0.9 1 0.8 0.7 0.8 0.6 0.6 0.5 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 τWTA 21 τWTA 32 σ d (τWTA) 4.5 σ d (τWTA) 4.5 1 dσ ATLAS 32 1 dσ ATLAS 21 W selection 4 s = 13 TeV, 33 fb-1 s = 13 TeV, 33 fb-1 Top selection 4 Anti-kt R =1.0 jets Anti-kt R =1.0 jets 3.5 Soft Drop β = 0, zcut = 0.1 Soft Drop β = 0, zcut = 0.1 3.5 3 3 W selection Top selection 2.5 2.5 2 2 1.5 1.5 1 1 0.5 0.5 0 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0 0.2 0.4 0.6 0.8 1 τWTA 21 τWTA 32 WTA WTA Figure 9. The distributions of τ21 (left) and τ32 (right) are compared with different MC pre- dictions for large-radius jets from top (top row) and W (bottom row) selections. The distributions WTA WTA of τ21 (bottom left) and τ32 (bottom right) in data are compared between the soft-dropped large-radius jets for the two selections mentioned above. The subscript WTA indicates that WTA axis was used in calculating these observables. The shaded bands represent the total uncertainty, while the error bars show the statistical uncertainty, except in the bottom plots, where the shaded areas represent the total uncertainty. – 22 – 11 Conclusions A measurement of jet substructure observables using groomed large-radius jets from light quarks or gluons, hadronically decaying top quarks and W bosons is presented using 33 fb−1 √ of s = 13 TeV proton-proton collision data taken with the ATLAS detector at the LHC. The data discriminate between the various MC models probed. In general, Pythia8 for light-quark/gluon large-radius jet observables, and Powheg+Pythia8, Sherpa as well as MG5 aMC@NLO+Pythia8 for top quark and W boson large-radius jet observables, describe the data better than other models. The different hadronisation models in Sherpa in the djiet selection result in similar predictions. For most observables, Herwig7 in JHEP08(2019)033 the dijet selection, and Powheg+Herwig7 in the top and W selections do not describe the data well. These measurements will be useful in improving the modelling of these substructure variables in MC generators. Since searches that utilise boosted topologies use these observables, or combinations of them, in tagging large-radius jets, a better modelling of them will help to increase the sensitivity of such searches. 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; 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. 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 – 23 – (U.S.A.), the Tier-2 facilities worldwide and large non-WLCG resource providers. Ma- jor contributors of computing resources are listed in ref. [92]. Open Access. 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Li46 , Y. Li46 , Z. Liang15a , B. Liberti73a , A. Liblong167 , K. Lie63c , S. Liem120 , C.Y. Lin32 , K. Lin106 , T.H. Lin99 , R.A. Linck65 , J.H. Lindon21 , A.L. Lionti54 , E. Lipeles137 , A. Lipniacka17 , M. Lisovyi61b , T.M. Liss173,as , A. Lister175 , A.M. Litke146 , J.D. Little8 , B. Liu78 , B.L Liu6 , H.B. Liu29 , H. Liu105 , J.B. Liu60a , J.K.K. Liu135 , K. Liu136 , M. Liu60a , P. Liu18 , Y. Liu15a,15d , Y.L. Liu105 , Y.W. Liu60a , M. Livan70a,70b , A. Lleres58 , J. Llorente Merino15a , S.L. Lloyd92 , C.Y. Lo63b , F. Lo Sterzo42 , E.M. Lobodzinska46 , P. Loch7 , S. Loffredo73a,73b , T. Lohse19 , K. Lohwasser149 , M. Lokajicek141 , J.D. Long173 , R.E. Long89 , L. Longo36 , K.A. Looper126 , J.A. Lopez147b , I. Lopez Paz100 , A. Lopez Solis149 , J. Lorenz114 , N. Lorenzo Martinez5 , M. Losada22 , P.J. Lösel114 , A. Lösle52 , X. Lou46 , X. Lou15a , A. Lounis132 , J. Love6 , P.A. Love89 , J.J. Lozano Bahilo174 , H. Lu63a , M. Lu60a , Y.J. Lu64 , H.J. Lubatti148 , C. Luci72a,72b , A. Lucotte58 , C. Luedtke52 , F. Luehring65 , I. Luise136 , L. Luminari72a , B. Lund-Jensen154 , M.S. Lutz102 , D. Lynn29 , R. Lysak141 , E. Lytken96 , F. Lyu15a , V. Lyubushkin79 , T. Lyubushkina79 , H. Ma29 , L.L. Ma60b , Y. Ma60b , G. Maccarrone51 , A. Macchiolo115 , C.M. Macdonald149 , J. Machado Miguens137,140b , D. Madaffari174 , R. Madar38 , W.F. Mader48 , N. Madysa48 , J. Maeda82 , K. Maekawa163 , S. Maeland17 , T. Maeno29 , M. Maerker48 , A.S. Maevskiy113 , V. Magerl52 , N. Magini78 , D.J. Mahon39 , C. Maidantchik80b , T. Maier114 , A. Maio140a,140b,140d , O. Majersky28a , S. Majewski131 , Y. Makida81 , N. Makovec132 , B. Malaescu136 , Pa. Malecki84 , V.P. Maleev138 , F. Malek58 , U. Mallik77 , D. Malon6 , C. Malone32 , S. Maltezos10 , S. Malyukov36 , J. Mamuzic174 , G. Mancini51 , I. Mandić91 , L. Manhaes de Andrade Filho80a , I.M. Maniatis162 , J. Manjarres Ramos48 , K.H. Mankinen96 , A. Mann114 , A. Manousos76 , B. Mansoulie145 , I. Manthos162 , S. Manzoni120 , A. Marantis162 , G. Marceca30 , L. Marchese135 , G. Marchiori136 , M. Marcisovsky141 , C. Marcon96 , – 35 – C.A. Marin Tobon36 , M. Marjanovic38 , Z. Marshall18 , M.U.F Martensson172 , S. Marti-Garcia174 , C.B. Martin126 , T.A. Martin178 , V.J. Martin50 , B. Martin dit Latour17 , M. Martinez14,x , V.I. Martinez Outschoorn102 , S. Martin-Haugh144 , V.S. Martoiu27b , A.C. Martyniuk94 , A. Marzin36 , L. Masetti99 , T. Mashimo163 , R. Mashinistov110 , J. Masik100 , A.L. Maslennikov122b,122a , L.H. Mason104 , L. Massa73a,73b , P. Massarotti69a,69b , P. Mastrandrea71a,71b , A. Mastroberardino41b,41a , T. Masubuchi163 , A. Matic114 , P. Mättig24 , J. Maurer27b , B. Maček91 , D.A. Maximov122b,122a , R. Mazini158 , I. Maznas162 , S.M. Mazza146 , S.P. Mc Kee105 , T.G. McCarthy115 , L.I. McClymont94 , W.P. McCormack18 , E.F. McDonald104 , J.A. Mcfayden36 , M.A. McKay42 , K.D. McLean176 , S.J. McMahon144 , P.C. McNamara104 , C.J. McNicol178 , R.A. McPherson176,ac , J.E. Mdhluli33c , Z.A. Meadows102 , S. Meehan148 , T. Megy52 , S. Mehlhase114 , A. Mehta90 , T. Meideck58 , B. Meirose43 , D. Melini174 , JHEP08(2019)033 B.R. Mellado Garcia33c , J.D. Mellenthin53 , M. Melo28a , F. Meloni46 , A. Melzer24 , S.B. Menary100 , E.D. Mendes Gouveia140a,140e , L. Meng36 , X.T. Meng105 , S. Menke115 , E. Meoni41b,41a , S. Mergelmeyer19 , S.A.M. Merkt139 , C. Merlassino20 , P. Mermod54 , L. Merola69a,69b , C. Meroni68a , O. Meshkov113 , J.K.R. Meshreki151 , A. Messina72a,72b , J. Metcalfe6 , A.S. Mete171 , C. Meyer65 , J. Meyer160 , J-P. Meyer145 , H. Meyer Zu Theenhausen61a , F. Miano156 , R.P. Middleton144 , L. Mijović50 , G. Mikenberg180 , M. Mikestikova141 , M. Mikuž91 , H. Mildner149 , M. Milesi104 , A. Milic167 , D.A. Millar92 , D.W. Miller37 , A. Milov180 , D.A. Milstead45a,45b , R.A. Mina153,p , A.A. Minaenko123 , M. Miñano Moya174 , I.A. Minashvili159b , A.I. Mincer124 , B. Mindur83a , M. Mineev79 , Y. Minegishi163 , Y. Ming181 , L.M. Mir14 , A. Mirto67a,67b , K.P. Mistry137 , T. Mitani179 , J. Mitrevski114 , V.A. Mitsou174 , M. Mittal60c , A. Miucci20 , P.S. Miyagawa149 , A. Mizukami81 , J.U. Mjörnmark96 , T. Mkrtchyan184 , M. Mlynarikova143 , T. Moa45a,45b , K. Mochizuki109 , P. Mogg52 , S. Mohapatra39 , R. Moles-Valls24 , M.C. Mondragon106 , K. Mönig46 , J. Monk40 , E. Monnier101 , A. Montalbano152 , J. Montejo Berlingen36 , M. Montella94 , F. Monticelli88 , S. Monzani68a , N. Morange132 , D. Moreno22 , M. Moreno Llácer36 , P. Morettini55b , M. Morgenstern120 , S. Morgenstern48 , D. Mori152 , M. Morii59 , M. Morinaga179 , V. Morisbak134 , A.K. Morley36 , G. Mornacchi36 , A.P. Morris94 , L. Morvaj155 , P. Moschovakos10 , B. Moser120 , M. Mosidze159b , H.J. Moss149 , J. Moss31,m , K. Motohashi165 , E. Mountricha36 , E.J.W. Moyse102 , S. Muanza101 , F. Mueller115 , J. Mueller139 , R.S.P. Mueller114 , D. Muenstermann89 , G.A. Mullier96 , J.L. Munoz Martinez14 , F.J. Munoz Sanchez100 , P. Murin28b , W.J. Murray178,144 , A. Murrone68a,68b , M. 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Nishu60c , R. Nisius115 , I. Nitsche47 , T. Nitta179 , T. Nobe163 , Y. Noguchi85 , M. Nomachi133 , I. Nomidis136 , M.A. Nomura29 , M. Nordberg36 , N. Norjoharuddeen135 , T. Novak91 , O. Novgorodova48 , R. Novotny142 , L. Nozka130 , K. Ntekas171 , E. Nurse94 , F. Nuti104 , F.G. Oakham34,av , H. Oberlack115 , J. Ocariz136 , A. Ochi82 , I. Ochoa39 , J.P. Ochoa-Ricoux147a , K. O’Connor26 , S. Oda87 , S. Odaka81 , S. Oerdek53 , A. Ogrodnik83a , A. Oh100 , S.H. Oh49 , C.C. Ohm154 , H. Oide55b,55a , M.L. Ojeda167 , H. Okawa169 , Y. Okazaki85 , Y. Okumura163 , T. Okuyama81 , A. Olariu27b , L.F. Oleiro Seabra140a , S.A. Olivares Pino147a , – 36 – D. Oliveira Damazio29 , J.L. Oliver1 , M.J.R. Olsson171 , A. Olszewski84 , J. Olszowska84 , D.C. O’Neil152 , A. Onofre140a,140e , K. Onogi117 , P.U.E. Onyisi11 , H. Oppen134 , M.J. Oreglia37 , G.E. Orellana88 , D. Orestano74a,74b , N. Orlando14 , R.S. Orr167 , B. Osculati55b,55a,∗ , V. O’Shea57 , R. Ospanov60a , G. Otero y Garzon30 , H. Otono87 , M. Ouchrif35d , F. Ould-Saada134 , A. Ouraou145 , Q. Ouyang15a , M. Owen57 , R.E. Owen21 , V.E. Ozcan12c , N. Ozturk8 , J. Pacalt130 , H.A. Pacey32 , K. Pachal49 , A. Pacheco Pages14 , C. Padilla Aranda14 , S. Pagan Griso18 , M. Paganini183 , G. Palacino65 , S. Palazzo50 , S. Palestini36 , M. Palka83b , D. Pallin38 , I. Panagoulias10 , C.E. Pandini36 , J.G. Panduro Vazquez93 , P. Pani46 , G. Panizzo66a,66c , L. Paolozzi54 , C. Papadatos109 , K. Papageorgiou9,h , A. Paramonov6 , D. Paredes Hernandez63b , S.R. Paredes Saenz135 , B. Parida166 , T.H. Park167 , A.J. Parker89 , M.A. Parker32 , F. Parodi55b,55a , E.W.P. Parrish121 , J.A. Parsons39 , U. Parzefall52 , L. Pascual Dominguez136 , V.R. Pascuzzi167 , JHEP08(2019)033 J.M.P. Pasner146 , E. Pasqualucci72a , S. Passaggio55b , F. Pastore93 , P. Pasuwan45a,45b , S. Pataraia99 , J.R. Pater100 , A. Pathak181 , T. Pauly36 , B. Pearson115 , M. Pedersen134 , L. Pedraza Diaz119 , R. Pedro140a,140b , S.V. Peleganchuk122b,122a , O. Penc141 , C. Peng15a , H. Peng60a , B.S. Peralva80a , M.M. Perego132 , A.P. Pereira Peixoto140a,140e , D.V. Perepelitsa29 , F. Peri19 , L. Perini68a,68b , H. Pernegger36 , S. Perrella69a,69b , V.D. Peshekhonov79,∗ , K. Peters46 , R.F.Y. Peters100 , B.A. Petersen36 , T.C. Petersen40 , E. Petit58 , A. Petridis1 , C. Petridou162 , P. Petroff132 , M. Petrov135 , F. Petrucci74a,74b , M. Pettee183 , N.E. Pettersson102 , K. Petukhova143 , A. Peyaud145 , R. Pezoa147b , T. Pham104 , F.H. Phillips106 , P.W. Phillips144 , M.W. Phipps173 , G. Piacquadio155 , E. Pianori18 , A. Picazio102 , R.H. Pickles100 , R. Piegaia30 , D. Pietreanu27b , J.E. Pilcher37 , A.D. Pilkington100 , M. Pinamonti73a,73b , J.L. Pinfold3 , M. Pitt180 , L. Pizzimento73a,73b , M.-A. Pleier29 , V. Pleskot143 , E. Plotnikova79 , D. Pluth78 , P. Podberezko122b,122a , R. Poettgen96 , R. Poggi54 , L. Poggioli132 , I. Pogrebnyak106 , D. Pohl24 , I. Pokharel53 , G. Polesello70a , A. Poley18 , A. Policicchio72a,72b , R. Polifka36 , A. Polini23b , C.S. Pollard46 , V. Polychronakos29 , D. Ponomarenko112 , L. Pontecorvo36 , S. Popa27a , G.A. Popeneciu27d , D.M. Portillo Quintero136 , S. Pospisil142 , K. Potamianos46 , I.N. Potrap79 , C.J. Potter32 , H. Potti11 , T. Poulsen96 , J. Poveda36 , T.D. Powell149 , G. Pownall46 , M.E. Pozo Astigarraga36 , P. Pralavorio101 , S. Prell78 , D. Price100 , M. Primavera67a , S. Prince103 , M.L. Proffitt148 , N. Proklova112 , K. Prokofiev63c , F. Prokoshin147b , S. Protopopescu29 , J. Proudfoot6 , M. Przybycien83a , A. Puri173 , P. Puzo132 , J. Qian105 , Y. Qin100 , A. Quadt53 , M. Queitsch-Maitland46 , A. Qureshi1 , P. Rados104 , F. Ragusa68a,68b , G. Rahal97 , J.A. Raine54 , S. Rajagopalan29 , A. Ramirez Morales92 , K. Ran15a,15d , T. Rashid132 , S. Raspopov5 , M.G. Ratti68a,68b , D.M. Rauch46 , F. Rauscher114 , S. Rave99 , B. Ravina149 , I. Ravinovich180 , J.H. Rawling100 , M. Raymond36 , A.L. Read134 , N.P. Readioff58 , M. Reale67a,67b , D.M. Rebuzzi70a,70b , A. Redelbach177 , G. 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Salvatore41b,41a , F. Salvatore156 , A. Salvucci63a,63b,63c , A. Salzburger36 , J. Samarati36 , D. Sammel52 , D. Sampsonidis162 , D. Sampsonidou162 , J. Sánchez174 , A. Sanchez Pineda66a,66c , H. Sandaker134 , C.O. Sander46 , M. Sandhoff182 , C. Sandoval22 , D.P.C. Sankey144 , M. Sannino55b,55a , Y. Sano117 , A. Sansoni51 , C. Santoni38 , H. Santos140a,140b , S.N. Santpur18 , JHEP08(2019)033 A. Santra174 , A. Sapronov79 , J.G. Saraiva140a,140d , O. Sasaki81 , K. Sato169 , E. Sauvan5 , P. Savard167,av , N. Savic115 , R. Sawada163 , C. Sawyer144 , L. Sawyer95,ak , C. Sbarra23b , A. Sbrizzi23a , T. Scanlon94 , J. Schaarschmidt148 , P. Schacht115 , B.M. Schachtner114 , D. Schaefer37 , L. Schaefer137 , J. Schaeffer99 , S. Schaepe36 , U. Schäfer99 , A.C. Schaffer132 , D. Schaile114 , R.D. Schamberger155 , N. Scharmberg100 , V.A. Schegelsky138 , D. Scheirich143 , F. Schenck19 , M. Schernau171 , C. Schiavi55b,55a , S. Schier146 , L.K. Schildgen24 , Z.M. Schillaci26 , E.J. Schioppa36 , M. Schioppa41b,41a , K.E. Schleicher52 , S. Schlenker36 , K.R. Schmidt-Sommerfeld115 , K. Schmieden36 , C. Schmitt99 , S. Schmitt46 , S. Schmitz99 , J.C. Schmoeckel46 , U. Schnoor52 , L. Schoeffel145 , A. Schoening61b , E. Schopf135 , M. Schott99 , J.F.P. Schouwenberg119 , J. Schovancova36 , S. Schramm54 , F. Schroeder182 , A. Schulte99 , H-C. Schultz-Coulon61a , M. Schumacher52 , B.A. Schumm146 , Ph. Schune145 , A. Schwartzman153 , T.A. Schwarz105 , Ph. Schwemling145 , R. Schwienhorst106 , A. Sciandra24 , G. Sciolla26 , M. Scornajenghi41b,41a , F. Scuri71a , F. Scutti104 , L.M. Scyboz115 , C.D. Sebastiani72a,72b , P. Seema19 , S.C. Seidel118 , A. Seiden146 , T. Seiss37 , J.M. Seixas80b , G. Sekhniaidze69a , K. Sekhon105 , S.J. Sekula42 , N. Semprini-Cesari23b,23a , S. Sen49 , S. Senkin38 , C. Serfon76 , L. Serin132 , L. Serkin66a,66b , M. Sessa60a , H. Severini128 , F. Sforza170 , A. Sfyrla54 , E. Shabalina53 , J.D. Shahinian146 , N.W. Shaikh45a,45b , D. Shaked Renous180 , L.Y. Shan15a , R. Shang173 , J.T. Shank25 , M. Shapiro18 , A. Sharma135 , A.S. Sharma1 , P.B. Shatalov111 , K. Shaw156 , S.M. Shaw100 , A. Shcherbakova138 , Y. Shen128 , N. Sherafati34 , A.D. Sherman25 , P. Sherwood94 , L. Shi158,ar , S. Shimizu81 , C.O. Shimmin183 , Y. Shimogama179 , M. Shimojima116 , I.P.J. Shipsey135 , S. Shirabe87 , M. Shiyakova79,aa , J. Shlomi180 , A. Shmeleva110 , M.J. Shochet37 , J. Shojaii104 , D.R. Shope128 , S. Shrestha126 , E. Shulga180 , P. Sicho141 , A.M. Sickles173 , P.E. Sidebo154 , E. Sideras Haddad33c , O. Sidiropoulou36 , A. Sidoti23b,23a , F. Siegert48 , Dj. Sijacki16 , M. Silva Jr.181 , M.V. Silva Oliveira80a , S.B. Silverstein45a , S. Simion132 , E. Simioni99 , M. Simon99 , R. Simoniello99 , P. Sinervo167 , N.B. Sinev131 , M. Sioli23b,23a , I. Siral105 , S.Yu. Sivoklokov113 , J. Sjölin45a,45b , E. Skorda96 , P. Skubic128 , M. Slawinska84 , K. Sliwa170 , R. Slovak143 , V. Smakhtin180 , B.H. Smart144 , J. Smiesko28a , N. Smirnov112 , S.Yu. 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Zwalinski36 1 Department of Physics, University of Adelaide, Adelaide, Australia 2 Physics Department, SUNY Albany, Albany NY, U.S.A. 3 Department of Physics, University of Alberta, Edmonton AB, Canada 4 Department of Physics(a) , Ankara University, Ankara; Istanbul Aydin University(b) , Istanbul; Division of Physics(c) , 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, U.S.A. 7 Department of Physics, University of Arizona, Tucson AZ, U.S.A. 8 Department of Physics, University of Texas at Arlington, Arlington TX, U.S.A. 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, U.S.A. 12 Bahcesehir University(a) , Faculty of Engineering and Natural Sciences, Istanbul; Istanbul Bilgi – 40 – University(b) , Faculty of Engineering and Natural Sciences, Istanbul; Department of Physics (c) , Bogazici University, Istanbul; Department of Physics Engineering(d) , 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 Institute of High Energy Physics(a) , Chinese Academy of Sciences, Beijing; Physics Department(b) , Tsinghua University, Beijing; Department of Physics(c) , Nanjing University, Nanjing; University of Chinese Academy of Science (UCAS)(d) , Beijing, China 16 Institute of Physics, University of Belgrade, Belgrade, Serbia 17 Department for Physics and Technology, University of Bergen, Bergen, Norway 18 Physics Division, Lawrence Berkeley National Laboratory and University of California, Berkeley JHEP08(2019)033 CA, U.S.A. 19 Institut für Physik, Humboldt Universität zu Berlin, Berlin, Germany 20 Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics, University of Bern, Bern, Switzerland 21 School of Physics and Astronomy, University of Birmingham, Birmingham, United Kingdom 22 Facultad de Ciencias y Centro de Investigaciónes, Universidad Antonio Nariño, Bogota, Colombia 23 INFN Bologna and Universita’ di Bologna(a) , Dipartimento di Fisica; INFN Sezione di Bologna(b) , Italy 24 Physikalisches Institut, Universität Bonn, Bonn, Germany 25 Department of Physics, Boston University, Boston MA, U.S.A. 26 Department of Physics, Brandeis University, Waltham MA, U.S.A. 27 Transilvania University of Brasov(a) , Brasov; Horia Hulubei National Institute of Physics and Nuclear Engineering(b) , Bucharest; Department of Physics(c) , Alexandru Ioan Cuza University of Iasi, Iasi; National Institute for Research and Development of Isotopic and Molecular Technologies(d) , Physics Department, Cluj-Napoca; University Politehnica Bucharest (e) , Bucharest; West University in Timisoara(f ) , Timisoara; Romania 28 Faculty of Mathematics(a) , Physics and Informatics, Comenius University, Bratislava; Department of Subnuclear Physics(b) , Institute of Experimental Physics of the Slovak Academy of Sciences, Kosice, Slovak Republic 29 Physics Department, Brookhaven National Laboratory, Upton NY, U.S.A. 30 Departamento de Fı́sica, Universidad de Buenos Aires, Buenos Aires, Argentina 31 California State University, CA, U.S.A. 32 Cavendish Laboratory, University of Cambridge, Cambridge, United Kingdom 33 Department of Physics(a) , University of Cape Town, Cape Town; Department of Mechanical Engineering Science(b) , University of Johannesburg, Johannesburg; School of Physics(c) , University of the Witwatersrand, Johannesburg, South Africa 34 Department of Physics, Carleton University, Ottawa ON, Canada 35 Faculté des Sciences Ain Chock(a) , Réseau Universitaire de Physique des Hautes Energies — Université Hassan II, Casablanca; Faculté des Sciences(b) , Université Ibn-Tofail, Kénitra; Faculté des Sciences Semlalia(c) , Université Cadi Ayyad, LPHEA-Marrakech; Faculté des Sciences(d) , Université Mohamed Premier and LPTPM, Oujda; Faculté des sciences(e) , Université Mohammed V, Rabat, Morocco 36 CERN, Geneva, Switzerland 37 Enrico Fermi Institute, University of Chicago, Chicago IL, U.S.A. 38 LPC, Université Clermont Auvergne, CNRS/IN2P3, Clermont-Ferrand, France 39 Nevis Laboratory, Columbia University, Irvington NY, U.S.A. 40 Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark 41 Dipartimento di Fisica(a) , Università della Calabria, Rende; INFN Gruppo Collegato di Cosenza (b) , Laboratori Nazionali di Frascati, Italy 42 Physics Department, Southern Methodist University, Dallas TX, U.S.A. – 41 – 43 Physics Department, University of Texas at Dallas, Richardson TX, U.S.A. 44 National Centre for Scientific Research “Demokritos”, Agia Paraskevi, Greece 45 Department of Physics(a) , Stockholm University; Oskar Klein Centre(b) , Stockholm, Sweden 46 Deutsches Elektronen-Synchrotron DESY, Hamburg and Zeuthen, Germany 47 Lehrstuhl für Experimentelle Physik IV, Technische Universität Dortmund, Dortmund, Germany 48 Institut für Kern- und Teilchenphysik, Technische Universität Dresden, Dresden, Germany 49 Department of Physics, Duke University, Durham NC, U.S.A. 50 SUPA — School of Physics and Astronomy, University of Edinburgh, Edinburgh, United Kingdom 51 INFN e Laboratori Nazionali di Frascati, Frascati, Italy 52 Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany 53 II. Physikalisches Institut, Georg-August-Universität Göttingen, Göttingen, Germany 54 Département de Physique Nucléaire et Corpusculaire, Université de Genève, Genève, Switzerland JHEP08(2019)033 55 Dipartimento di Fisica(a) , Università di Genova, Genova; INFN Sezione di Genova(b) , Italy 56 II. Physikalisches Institut, Justus-Liebig-Universität Giessen, Giessen, Germany 57 SUPA — School of Physics and Astronomy, University of Glasgow, Glasgow, United Kingdom 58 LPSC, Université Grenoble Alpes, CNRS/IN2P3, Grenoble INP, Grenoble, France 59 Laboratory for Particle Physics and Cosmology, Harvard University, Cambridge MA, U.S.A. 60 Department of Modern Physics and State Key Laboratory of Particle Detection and Electronics (a) , University of Science and Technology of China, Hefei; Institute of Frontier and Interdisciplinary Science and Key Laboratory of Particle Physics and Particle Irradiation (MOE) (b) , Shandong University, Qingdao; School of Physics and Astronomy(c) , Shanghai Jiao Tong University, KLPPAC-MoE, SKLPPC, Shanghai; Tsung-Dao Lee Institute(d) , Shanghai, China 61 Kirchhoff-Institut für Physik(a) , Ruprecht-Karls-Universität Heidelberg, Heidelberg; Physikalisches Institut(b) , Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany 62 Faculty of Applied Information Science, Hiroshima Institute of Technology, Hiroshima, Japan 63 Department of Physics(a) , Chinese University of Hong Kong, Shatin, N.T., Hong Kong; Department of Physics(b) , University of Hong Kong, Hong Kong; Department of Physics and Institute for Advanced Study(c) , Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China 64 Department of Physics, National Tsing Hua University, Hsinchu, Taiwan 65 Department of Physics, Indiana University, Bloomington IN, U.S.A. 66 INFN Gruppo Collegato di Udine(a) , Sezione di Trieste, Udine; ICTP(b) , Trieste; Dipartimento Politecnico di Ingegneria e Architettura(c) , Università di Udine, Udine, Italy 67 INFN Sezione di Lecce(a) ; Dipartimento di Matematica e Fisica(b) , Università del Salento, Lecce, Italy 68 INFN Sezione di Milano(a) ; Dipartimento di Fisica(b) , Università di Milano, Milano, Italy 69 INFN Sezione di Napoli(a) ; Dipartimento di Fisica(b) , Università di Napoli, Napoli, Italy 70 INFN Sezione di Pavia(a) ; Dipartimento di Fisica(b) , Università di Pavia, Pavia, Italy 71 INFN Sezione di Pisa(a) ; Dipartimento di Fisica E. Fermi(b) , Università di Pisa, Pisa, Italy 72 INFN Sezione di Roma(a) ; Dipartimento di Fisica(b) , Sapienza Università di Roma, Roma, Italy 73 INFN Sezione di Roma Tor Vergata(a) ; Dipartimento di Fisica(b) , Università di Roma Tor Vergata, Roma, Italy 74 INFN Sezione di Roma Tre(a) ; Dipartimento di Matematica e Fisica(b) , Università Roma Tre, Roma, Italy 75 INFN-TIFPA(a) ; Università degli Studi di Trento(b) , Trento, Italy 76 Institut für Astro- und Teilchenphysik, Leopold-Franzens-Universität, Innsbruck, Austria 77 University of Iowa, Iowa City IA, U.S.A. 78 Department of Physics and Astronomy, Iowa State University, Ames IA, U.S.A. 79 Joint Institute for Nuclear Research, Dubna, Russia 80 Departamento de Engenharia Elétrica(a) , Universidade Federal de Juiz de Fora (UFJF), Juiz de Fora; Universidade Federal do Rio De Janeiro COPPE/EE/IF(b) , Rio de Janeiro; Universidade Federal de São João del Rei (UFSJ)(c) , São João del Rei; Instituto de Fı́sica(d) , Universidade de São Paulo, São Paulo, Brazil – 42 – 81 KEK, High Energy Accelerator Research Organization, Tsukuba, Japan 82 Graduate School of Science, Kobe University, Kobe, Japan 83 AGH University of Science and Technology(a) , Faculty of Physics and Applied Computer Science, Krakow; Marian Smoluchowski Institute of Physics(b) , Jagiellonian University, Krakow, Poland 84 Institute of Nuclear Physics Polish Academy of Sciences, Krakow, Poland 85 Faculty of Science, Kyoto University, Kyoto, Japan 86 Kyoto University of Education, Kyoto, Japan 87 Research Center for Advanced Particle Physics and Department of Physics, Kyushu University, Fukuoka , Japan 88 Instituto de Fı́sica La Plata, Universidad Nacional de La Plata and CONICET, La Plata, Argentina 89 Physics Department, Lancaster University, Lancaster, United Kingdom 90 Oliver Lodge Laboratory, University of Liverpool, Liverpool, United Kingdom JHEP08(2019)033 91 Department of Experimental Particle Physics, Jožef Stefan Institute and Department of Physics, University of Ljubljana, Ljubljana, Slovenia 92 School of Physics and Astronomy, Queen Mary University of London, London, United Kingdom 93 Department of Physics, Royal Holloway University of London, Egham, United Kingdom 94 Department of Physics and Astronomy, University College London, London, United Kingdom 95 Louisiana Tech University, Ruston LA, U.S.A. 96 Fysiska institutionen, Lunds universitet, Lund, Sweden 97 Centre de Calcul de l’Institut National de Physique Nucléaire et de Physique des Particules (IN2P3), Villeurbanne, France 98 Departamento de Fı́sica Teorica C-15 and CIAFF, Universidad Autónoma de Madrid, Madrid, Spain 99 Institut für Physik, Universität Mainz, Mainz, Germany 100 School of Physics and Astronomy, University of Manchester, Manchester, United Kingdom 101 CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille, France 102 Department of Physics, University of Massachusetts, Amherst MA, U.S.A. 103 Department of Physics, McGill University, Montreal QC, Canada 104 School of Physics, University of Melbourne, Victoria, Australia 105 Department of Physics, University of Michigan, Ann Arbor MI, U.S.A. 106 Department of Physics and Astronomy, Michigan State University, East Lansing MI, U.S.A. 107 B.I. Stepanov Institute of Physics, National Academy of Sciences of Belarus, Minsk, Belarus 108 Research Institute for Nuclear Problems of Byelorussian State University, Minsk, Belarus 109 Group of Particle Physics, University of Montreal, Montreal QC, Canada 110 P.N. Lebedev Physical Institute of the Russian Academy of Sciences, Moscow, Russia 111 Institute for Theoretical and Experimental Physics of the National Research Centre Kurchatov Institute, Moscow, Russia 112 National Research Nuclear University MEPhI, Moscow, Russia 113 D.V. Skobeltsyn Institute of Nuclear Physics, M.V. Lomonosov Moscow State University, Moscow, Russia 114 Fakultät für Physik, Ludwig-Maximilians-Universität München, München, Germany 115 Max-Planck-Institut für Physik (Werner-Heisenberg-Institut), München, Germany 116 Nagasaki Institute of Applied Science, Nagasaki, Japan 117 Graduate School of Science and Kobayashi-Maskawa Institute, Nagoya University, Nagoya, Japan 118 Department of Physics and Astronomy, University of New Mexico, Albuquerque NM, U.S.A. 119 Institute for Mathematics, Astrophysics and Particle Physics, Radboud University Nijmegen/Nikhef, Nijmegen, Netherlands 120 Nikhef National Institute for Subatomic Physics and University of Amsterdam, Amsterdam, Netherlands 121 Department of Physics, Northern Illinois University, DeKalb IL, U.S.A. 122 Budker Institute of Nuclear Physics and NSU(a) , SB RAS, Novosibirsk; Novosibirsk State University Novosibirsk(b) , Russia – 43 – 123 Institute for High Energy Physics of the National Research Centre Kurchatov Institute, Protvino, Russia 124 Department of Physics, New York University, New York NY, U.S.A. 125 Ochanomizu University, Otsuka, Bunkyo-ku, Tokyo, Japan 126 Ohio State University, Columbus OH, U.S.A. 127 Faculty of Science, Okayama University, Okayama, Japan 128 Homer L. Dodge Department of Physics and Astronomy, University of Oklahoma, Norman OK, U.S.A. 129 Department of Physics, Oklahoma State University, Stillwater OK, U.S.A. 130 Palacký University, RCPTM, Joint Laboratory of Optics, Olomouc, Czech Republic 131 Center for High Energy Physics, University of Oregon, Eugene OR, U.S.A. 132 LAL, Université Paris-Sud, CNRS/IN2P3, Université Paris-Saclay, Orsay, France JHEP08(2019)033 133 Graduate School of Science, Osaka University, Osaka, Japan 134 Department of Physics, University of Oslo, Oslo, Norway 135 Department of Physics, Oxford University, Oxford, United Kingdom 136 LPNHE, Sorbonne Université, Université de Paris, CNRS/IN2P3, Paris, France 137 Department of Physics, University of Pennsylvania, Philadelphia PA, U.S.A. 138 Konstantinov Nuclear Physics Institute of National Research Centre “Kurchatov Institute”, PNPI, St. Petersburg, Russia 139 Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh PA, U.S.A. 140 Laboratório de Instrumentação e Fı́sica Experimental de Partı́culas — LIP (a) ; Departamento de Fı́sica(b) , Faculdade de Ciências, Universidade de Lisboa, Lisboa; Departamento de Fı́sica (c) , Universidade de Coimbra, Coimbra; Centro de Fı́sica Nuclear da Universidade de Lisboa (d) , Lisboa; Departamento de Fı́sica(e) , Universidade do Minho, Braga; Universidad de Granada(f ) , Granada (Spain); Dep Fı́sica and CEFITEC of Faculdade de Ciências e Tecnologia (g) , Universidade Nova de Lisboa, Caparica; Av. Rovisco Pais(h) , 1 1049-001 Lisbon, Portugal, Portugal 141 Institute of Physics of the Czech Academy of Sciences, Prague, Czech Republic 142 Czech Technical University in Prague, Prague, Czech Republic 143 Charles University, Faculty of Mathematics and Physics, Prague, Czech Republic 144 Particle Physics Department, Rutherford Appleton Laboratory, Didcot, United Kingdom 145 IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France 146 Santa Cruz Institute for Particle Physics, University of California Santa Cruz, Santa Cruz CA, U.S.A. 147 Departamento de Fı́sica(a) , Pontificia Universidad Católica de Chile, Santiago; Departamento de Fı́sica(b) , Universidad Técnica Federico Santa Marı́a, Valparaı́so, Chile 148 Department of Physics, University of Washington, Seattle WA, U.S.A. 149 Department of Physics and Astronomy, University of Sheffield, Sheffield, United Kingdom 150 Department of Physics, Shinshu University, Nagano, Japan 151 Department Physik, Universität Siegen, Siegen, Germany 152 Department of Physics, Simon Fraser University, Burnaby BC, Canada 153 SLAC National Accelerator Laboratory, Stanford CA, U.S.A. 154 Physics Department, Royal Institute of Technology, Stockholm, Sweden 155 Departments of Physics and Astronomy, Stony Brook University, Stony Brook NY, U.S.A. 156 Department of Physics and Astronomy, University of Sussex, Brighton, United Kingdom 157 School of Physics, University of Sydney, Sydney, Australia 158 Institute of Physics, Academia Sinica, Taipei, Taiwan 159 E. Andronikashvili Institute of Physics(a) , Iv. Javakhishvili Tbilisi State University, Tbilisi; High Energy Physics Institute(b) , Tbilisi State University, Tbilisi, Georgia 160 Department of Physics, Technion, Israel Institute of Technology, Haifa, Israel 161 Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University, Tel Aviv, Israel 162 Department of Physics, Aristotle University of Thessaloniki, Thessaloniki, Greece – 44 – 163 International Center for Elementary Particle Physics and Department of Physics, University of Tokyo, Tokyo, Japan 164 Graduate School of Science and Technology, Tokyo Metropolitan University, Tokyo, Japan 165 Department of Physics, Tokyo Institute of Technology, Tokyo, Japan 166 Tomsk State University, Tomsk, Russia 167 Department of Physics, University of Toronto, Toronto ON, Canada 168 TRIUMF(a) , Vancouver BC; Department of Physics and Astronomy(b) , York University, Toronto ON, Canada 169 Division of Physics and Tomonaga Center for the History of the Universe, Faculty of Pure and Applied Sciences, University of Tsukuba, Tsukuba, Japan 170 Department of Physics and Astronomy, Tufts University, Medford MA, U.S.A. 171 Department of Physics and Astronomy, University of California Irvine, Irvine CA, U.S.A. JHEP08(2019)033 172 Department of Physics and Astronomy, University of Uppsala, Uppsala, Sweden 173 Department of Physics, University of Illinois, Urbana IL, U.S.A. 174 Instituto de Fı́sica Corpuscular (IFIC), Centro Mixto Universidad de Valencia — CSIC, Valencia, Spain 175 Department of Physics, University of British Columbia, Vancouver BC, Canada 176 Department of Physics and Astronomy, University of Victoria, Victoria BC, Canada 177 Fakultät für Physik und Astronomie, Julius-Maximilians-Universität Würzburg, Würzburg, Germany 178 Department of Physics, University of Warwick, Coventry, United Kingdom 179 Waseda University, Tokyo, Japan 180 Department of Particle Physics, Weizmann Institute of Science, Rehovot, Israel 181 Department of Physics, University of Wisconsin, Madison WI, U.S.A. 182 Fakultät für Mathematik und Naturwissenschaften, Fachgruppe Physik, Bergische Universität Wuppertal, Wuppertal, Germany 183 Department of Physics, Yale University, New Haven CT, U.S.A. 184 Yerevan Physics Institute, Yerevan, Armenia a Also at Centre for High Performance Computing, CSIR Campus, Rosebank, Cape Town, South Africa b Also at CERN, Geneva, Switzerland c Also at CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille, France d Also at Département de Physique Nucléaire et Corpusculaire, Université de Genève, Genève, Switzerland e Also at Departament de Fisica de la Universitat Autonoma de Barcelona, Barcelona, Spain f Also at Departamento de Fı́sica, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal g Also at Department of Applied Physics and Astronomy, University of Sharjah, Sharjah, United Arab Emirates h Also at Department of Financial and Management Engineering, University of the Aegean, Chios, Greece i Also at Department of Physics and Astronomy, University of Louisville, Louisville, KY, U.S.A. j Also at Department of Physics and Astronomy, University of Sheffield, Sheffield, United Kingdom k Also at Department of Physics, California State University, East Bay, U.S.A. l Also at Department of Physics, California State University, Fresno, U.S.A. m Also at Department of Physics, California State University, Sacramento, U.S.A. n Also at Department of Physics, King’s College London, London, United Kingdom o Also at Department of Physics, St. Petersburg State Polytechnical University, St. Petersburg, Russia p Also at Department of Physics, Stanford University, Stanford CA, U.S.A. q Also at Department of Physics, University of Fribourg, Fribourg, Switzerland – 45 – r Also at Department of Physics, University of Michigan, Ann Arbor MI, U.S.A. s Also at Faculty of Physics, M.V. Lomonosov Moscow State University, Moscow, Russia t Also at Giresun University, Faculty of Engineering, Giresun, Turkey u Also at Graduate School of Science, Osaka University, Osaka, Japan v Also at Hellenic Open University, Patras, Greece w Also at Horia Hulubei National Institute of Physics and Nuclear Engineering, Bucharest, Romania x Also at Institucio Catalana de Recerca i Estudis Avancats, ICREA, Barcelona, Spain y Also at Institut für Experimentalphysik, Universität Hamburg, Hamburg, Germany z Also at Institute for Mathematics, Astrophysics and Particle Physics, Radboud University Nijmegen/Nikhef, Nijmegen, Netherlands aa Also at Institute for Nuclear Research and Nuclear Energy (INRNE) of the Bulgarian Academy of Sciences, Sofia, Bulgaria JHEP08(2019)033 ab Also at Institute for Particle and Nuclear Physics, Wigner Research Centre for Physics, Budapest, Hungary ac Also at Institute of Particle Physics (IPP), Canada ad Also at Institute of Physics, Academia Sinica, Taipei, Taiwan ae Also at Institute of Physics, Azerbaijan Academy of Sciences, Baku, Azerbaijan af Also at Institute of Theoretical Physics, Ilia State University, Tbilisi, Georgia ag Also at Instituto de Fisica Teorica, IFT-UAM/CSIC, Madrid, Spain ah Also at Istanbul University, Dept. of Physics, Istanbul, Turkey ai Also at Joint Institute for Nuclear Research, Dubna, Russia aj Also at LAL, Université Paris-Sud, CNRS/IN2P3, Université Paris-Saclay, Orsay, France ak Also at Louisiana Tech University, Ruston LA, U.S.A. al Also at LPNHE, Sorbonne Université, Université de Paris, CNRS/IN2P3, Paris, France am Also at Manhattan College, New York NY, U.S.A. an Also at Moscow Institute of Physics and Technology State University, Dolgoprudny, Russia ao Also at National Research Nuclear University MEPhI, Moscow, Russia ap Also at Physics Department, An-Najah National University, Nablus, Palestine aq Also at Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany ar Also at School of Physics, Sun Yat-sen University, Guangzhou, China as Also at The City College of New York, New York NY, U.S.A. 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 – 46 –