Eur. Phys. J. C (2019) 79:135 https://doi.org/10.1140/epjc/s10052-019-6632-8 Regular Article - Experimental Physics In situ calibration of large-radius jet energy and mass in 13 TeV proton–proton collisions with the ATLAS detector ATLAS Collaboration CERN, 1211 Geneva 23, Switzerland Received: 26 July 2018 / Accepted: 28 January 2019 / Published online: 13 February 2019 © CERN for the benefit of the ATLAS collaboration 2019 Abstract The response of the ATLAS detector to large- large-R energy and mass scales derived using Monte Carlo √ radius jets is measured in situ using 36.2 fb−1 of s = simulation yields uncertainties as large as 10%. The calibra- 13 TeV proton–proton collisions provided by the LHC and tion described in this paper results in a reduction of these recorded by the ATLAS experiment during 2015 and 2016. uncertainties by more than a factor of three. The jet energy scale is measured in events where the jet In this paper, a suite of in situ calibration techniques is recoils against a reference object, which can be either a described which measure the response in proton–proton ( pp) √ calibrated photon, a reconstructed Z boson, or a system of collision data at s = 13 TeV. The results of several meth- well-measured small-radius jets. The jet energy resolution ods are combined to provide a calibration that defines the and a calibration of forward jets are derived using dijet bal- nominal large-R jet energy scale (JES) and the jet mass scale ance measurements. The jet mass response is measured with (JMS). These measurements provide a significant increase in two methods: using mass peaks formed by W bosons and the precision with which the large-R jet pT and mass scales top quarks with large transverse momenta and by compar- are known across most of the kinematically accessible phase ing the jet mass measured using the energy deposited in the space. The jet energy and mass resolutions (JER, JMR) are calorimeter with that using the momenta of charged-particle also measured in situ and compared with the predictions of tracks. The transverse momentum and mass responses in sim- Monte Carlo simulations (MC). Additional uncertainties on ulations are found to be about 2–3% higher than in data. jet substructure observables used to identify boosted objects This difference is adjusted for with a correction factor. The are derived from data in Ref. [4]. results of the different methods are combined to yield a Jet reconstruction starts with clusters of topologically con- calibration over a large range of transverse momenta ( pT ). nected calorimeter cell signals. These topological clusters, or The precision of the relative jet energy scale is 1–2% for ‘topo-clusters’, are brought to the hadronic scale using the 200 GeV < pT < 2 TeV, while that of the mass scale is local hadronic cell weighting scheme (LCW) [5]. Large-R 2–10%. The ratio of the energy resolutions in data and sim- jets are reconstructed with the anti-kt algorithm [6] using a ulation is measured to a precision of 10–15% over the same radius parameter R = 1.0. The jets are groomed with the pT range. ‘trimming’ algorithm of Ref. [7], which removes regions of the jet with a small relative contribution to the jet transverse momentum. This procedure reduces the impact from addi- 1 Introduction tional pp interactions in the event and from the underlying event, improving the energy and mass resolution. Signatures with high pT , massive particles such as Higgs The several stages of the ATLAS large-R jet calibration bosons, top quarks, and W or Z bosons have become ubiq- procedure are illustrated in Fig. 1. The trimmed large-R jets uitous during Run 2 of the Large Hadron Collider (LHC). are calibrated to the energy scale of stable final-state parti- These particles most often decay hadronically. Due to their cles using corrections based on simulations. This jet-level large transverse momentum, the decay products become col- correction is referred to as the simulation-based calibration limated and may be reconstructed as a single jet with large and includes a correction to the jet mass [8]. Finally, the jets radius parameter R [1,2] (a ‘large-R’ jet). The sensitivity of are calibrated in situ using response measurements in pp col- searches and measurements that use large-R jets depends on lision data. A correction based on a statistical combination of an accurate knowledge of the transverse momentum pT and data-to-simulation ratios of these response measurements is mass m responses of the detector [3]. A calibration of the applied only to data and adjusts for the residual (typically 2– 3%) mismodelling of the response. Uncertainties in the JES  e-mail: atlas.publications@cern.ch 123 135 Page 2 of 42 Eur. Phys. J. C (2019) 79:135 Fig. 1 Overview of the large-R jet reconstruction and calibration procedure described in this paper. The calorimeter energy clusters from which jets are reconstructed have already been adjusted to point at the event’s primary hard-scatter vertex Fig. 2 Schematic representation of the events used to measure the JES and JER: a a dijet event, b a Z +jet or γ +jet event and c a multijet event with several jets recoiling against the leading (large-R) jet. The labels Ji refer to the ith leading large-R jet, while ji refers to the ith leading small-R jet that fulfils R(J1 , j) > 1.4. φ is the difference between the azimuthal angle of the jet and the reference object, while α is the difference between the azimuthal angle of the jet and the vectorial sum of the recoil system momenta (a) (b) (c) and JMS are derived by propagating uncertainties from the tical combination of three measurements from Z +jet, γ +jet, individual in situ response measurements through the statis- and multijet events in the central region of the detector. A rel- tical combination. ative intercalibration, derived using dijet events, propagates The in situ calibration is determined in two separate steps. the well-measured central JES into the forward region of the In the first step, the JES is measured with the same methods detector. The in situ calibration accounts for detector effects used to calibrate small-R jets [9]. These techniques rely on which are not captured by simulation. The JES correction the transverse momentum balance in a variety of final states, is applied as a four-momentum scale factor to jets in data; illustrated in Fig. 2. The JES correction factor is a product of therefore, it also affects the jet mass calibration. two terms. The absolute calibration is derived from a statis- 123 Eur. Phys. J. C (2019) 79:135 Page 3 of 42 135 In the second step of the in situ calibration, the jet mass the tracker. It covers the pseudorapidity1 range |η| < 2.5. response is measured using two methods following the appli- Surrounding the tracker and solenoid, a sampling calorime- cation of the in situ JES correction. The mass response is ter measures the energy of particles produced in the colli- measured in lepton+jets top quark pair production (t t¯ pro- sions with |η| < 4.9. The energies of electrons and photons duction) [10] with a fit to the peaks in the jet mass distribu- are measured precisely in a high-granularity liquid-argon tion formed by high- pT W bosons and top quarks decaying electromagnetic calorimeter. The cylindrical “barrel” covers into fully hadronic final states. A second measurement is per- |η| < 1.475, and the “endcaps” on either end of the detector formed with the Rtrk method [3], which takes advantage of the cover 1.375 < |η| < 3.2. An iron/scintillator tile calorimeter independent measurements by the calorimeter and the inner measures the energy of hadrons in the central rapidity range, tracker. This method provides a calibration for the calorime- |η| < 1.7, and a liquid-argon hadronic endcap calorimeter ter jet mass measurement over a broad pT range. The results provides coverage for 1.5 < |η| < 3.2. The forward liquid- from the two methods are combined as a smooth function of argon calorimeter measures electrons, photons, and hadrons pT in two mass bins, which could be applied to data as an for 3.2 < |η| < 4.9. Finally, a muon spectrometer in the in situ correction as outlined in Sect. 8. magnetic field of a system of superconducting air-core toroid The JER and JMR are also measured in situ and com- magnets identifies muons in the range |η| < 2.7 and mea- pared with the prediction of the simulation. The dijet balance sures their transverse momenta. The ATLAS trigger system method takes advantage of the transverse momentum balance consists of a hardware-based first-level trigger followed by in dijet events to extract the JER. The JMR is obtained from a software-based high-level trigger, which apply a real-time fits to the top quark and W boson mass peaks in high- pT selection to reduce the up to 40 MHz LHC collision rate to lepton+jets t t¯ events. an average rate of events written to storage of 1 kHz [11]. Sections 2 and 3 provide overviews of the ATLAS detec- A detailed description of the ATLAS experiment is given in tor, the data set studied, and the simulations used in this Ref. [12]. paper. Section 4 describes the reconstruction of large-R jets The data set used in this analysis consists of pp colli- in ATLAS. The following section presents the results of sions delivered by the LHC at a centre-of-mass energy of √ the balance methods that measure the jet energy scale: the s = 13 TeV during 2015 and 2016. The specific trigger intercalibration, which uses dijet events to ensure a uniform requirements vary among the various in situ analyses and response over the central and forward regions of the detec- are described in the relevant sections. All data are required tor in Sect. 5.1, the Z +jet balance method in Sect. 5.2, the to meet ATLAS standard quality criteria. Data taken during γ +jet balance method in Sect. 5.3, and the multijet balance periods in which detector subsystems were not fully func- method in Sect. 5.4. Section 6 presents the methods that are tional are discarded. Data quality criteria also reject events used to measure the jet mass response: the Rtrk method and that have significant contamination from detector noise or its results for the energy and mass scale in Section 6.1 and with issues in the read-out. The remaining data correspond the fits to the W boson and top quark mass peaks in high- pT to an integrated luminosity of 36.2 fb−1 . lepton+jets t t¯ events in Sect. 6.2, which are also used to mea- Due to the high luminosity of the LHC, multiple pp colli- sure the JMR. The measurement of the JER in dijet events sions occur during each bunch crossing. Interactions which is discussed in Sect. 7. The methodology of the combina- occur within the bunch crossing of interest (in-time pile-up) tion procedure is presented in Sect. 8, as well as the resultant or in neighbouring bunch crossings (out-of-time pile-up) may combined in situ calibration of the JES and JMS. Sect. 9 alter the measured energy or mass scale of jets or lead to summarizes the results. the reconstruction of additional ‘stochastic’ jets, seeded by upwards fluctuations in the local pile-up energy density. The average number of additional pp collisions per bunch cross- 2 The ATLAS detector and data set ing is 24 in the Run 2 data from 2015 and 2016 analysed here. The ATLAS experiment consists of three major sub-detectors: the inner detector, the calorimeters, and the muon spectrom- eter. The inner detector, closest to the interaction point, is used to track charged particles in a 2 T axial magnetic field 1 The ATLAS reference system is a Cartesian right-handed coordinate produced by a thin superconducting solenoid. It consists of a system, with the nominal collision point at the origin. The anticlockwise pixel detector, a silicon tracker equipped with micro-strip beam direction defines the positive z-axis, while the positive x-axis is detectors, and a transition radiation tracker that provides defined as pointing from the collision point to the centre of the LHC ring and the positive y-axis points upwards. The azimuthal angle φ a large number of space points in the outermost layers of is measured around the beam axis, and the polar angle θ is measured relative to the z-axis. Pseudorapidity is defined as η = − ln[tan(θ/2)], and transverse energy is defined as E T = E sin θ. 123 135 Page 4 of 42 Eur. Phys. J. C (2019) 79:135 3 Simulations vertices and charged leptons. Samples of Z +jet events are compared to a second sample generated using Sherpa 2.2.1. The data are compared with detailed simulations of the Matrix elements are calculated for up to 2 partons at NLO ATLAS detector response [13] based on the Geant4 [14] and 4 partons at LO using Comix [30] and OpenLoops [31] toolkit. Hard-scatter events for all processes studied were and merged with the Sherpa parton shower [32] according simulated with several different event generators to assess to the ME+PS@NLO prescription [33]. The NNPDF30nnlo possible systematic effects due to limitations in the physics PDF set is used in conjunction with dedicated parton shower modelling. Several different simulation packages were also tuning developed by the Sherpa authors. γ +jets events are used to hadronize final-state quarks and gluons in order to compared to a sample generated with the Sherpa 2.1.1 event compare the impact of various models of hadronization and generator. Matrix elements are calculated with up to 3 or 4 parton showering on the measurements. partons at LO and merged with the Sherpa parton shower Dijet events were generated using several different gener- according to the ME+PS@LO prescription. The CT10 PDF ator configurations. Depending on the analysis, nominal dijet set is used in conjunction with dedicated parton shower tun- samples were generated using either Pythia 8 (v8.186) [15] ing developed by the Sherpa authors. or Powheg- Box 2.0 [16–18] interfaced with Pythia 8. For γ +jet events, Pythia 8 was used as the nominal gen- These samples were generated with the A14 set of tuned erator, where the 2 → 2 matrix element is convolved with the parameters [19] and the NNPDF2.3 LO parton distribution NNPDF2.3LO PDF set. The A14 event tune was used. These function (PDF) set [20]. Samples generated with Herwig events are compared to a sample generated with Sherpa 7 [21] and Sherpa v2.1 [22] were used for comparison. The v2.1.1, which includes up to four jets in the matrix element. Herwig 7 sample used the UE-EE-5 set of tuned parame- These events were generated using the default Sherpa tune ters [23] and CTEQ6L1 PDF set [24]. The Sherpa leading- and the CT10 PDF set. order multileg generator includes 2 → 2 and 2 → 3 pro- Top quark pair production and single top production in the cesses at matrix element level, combined using the CKKW s-channel and W t final state were simulated at NLO accuracy prescription [25]. with Powheg- Box v2 [34] and the CT10 PDF set. For elec- Z +jets events are generated using Powheg- Box 2.0 troweak t-channel single top quark production, Powheg- interfaced to the Pythia 8.186 parton shower model. The Box v1 was used, which utilizes the four-flavour scheme CT10 PDF set is used in the matrix element [26]. The for NLO matrix element calculations together with the fixed AZNLO set of tuned parameters [27] is used, with PDF set four-flavour PDF set CT10f4. In all cases, the nominal sam- CTEQ6L1, for the modelling of non-perturbative effects. The ple was interfaced with Pythia 8 with the CTEQ6L1 PDF EvtGen 1.2.0 program [28] is used for the properties of b- set, which simulates the parton shower, fragmentation, and and c-hadron decays. Photos++ 3.52 [29] is used for QED underlying event. The h damp parameter in Powheg, which emissions from electroweak vertices and charged leptons. regulates the pT of the first additional emission beyond the Samples of Z +jet events are compared to a second sam- Born level and thus the pT of the recoil emission against the t t¯ ple generated using Sherpa 2.2.1. Matrix elements are cal- system, was set to the mass of the top quark (172.5 GeV). Sys- culated for up to 2 partons at NLO and 4 partons at LO tematic uncertainties in the modelling of hadronization were using Comix [30] and OpenLoops [31] and merged with the evaluated using a Powheg sample interfaced to Herwig 7. Sherpa parton shower [32] according to the ME+PS@NLO W +jet events, simulated in Sherpa v2.2.0, are considered as prescription [33]. The NNPDF30nnlo PDF set is used in a background to t t¯ production. conjunction with dedicated parton shower tuning developed The effect of pile-up on reconstructed jets was modelled by the Sherpa authors. γ +jets events are compared to a by overlaying multiple simulated minimum-bias inelastic pp sample generated with the Sherpa 2.1.1 event generator. events on the signal event. These additional events were gen- Matrix elements are calculated with up to 3 or 4 partons erated with Pythia 8, using the A2 set of tuned parame- at LO and merged with the Sherpa parton shower accord- ters [35] and MSTW2008LO PDF set [36]. The distribution ing to the ME+PS@LO prescription. The CT10 PDF set of the average number of interactions per bunch crossing in is used in conjunction with dedicated parton shower tun- simulated samples is reweighted to match that of the analyzed ing developed by the Sherpa authors. Z +jets events are dataset. generated using Powheg- Box 2.0 interfaced to the Pythia 8.186 parton shower model. The CT10 PDF set is used in the matrix element [26]. The AZNLO set of tuned parame- 4 Large-R jet reconstruction and simulation calibration ters [27] is used, with PDF set CTEQ6L1, for the modelling of non-perturbative effects. The EvtGen 1.2.0 program [28] This section describes the reconstruction of large-R jets and is used for the properties of b- and c-hadron decays. Pho- the grooming procedure. Three classes of jets are used: tos++ 3.52 [29] is used for QED emissions from electroweak calorimeter jets, particle-level (or ‘truth’) jets, and track 123 Eur. Phys. J. C (2019) 79:135 Page 5 of 42 135 jets. The large-R jets considered in this paper are recon- incorporating the grooming procedure within the jet defini- structed using the anti-kt algorithm [6] with a radius param- tion. eter R = 1.0. For balancing and veto purposes, jets recon- After reconstruction of the calorimeter jets, a correction structed with radius parameter R = 0.4 (‘small-R jets’) are derived from a sample of simulated dijet events is applied to used in some parts of the analysis with their own calibra- restore the average reconstructed calorimeter jet energy scale tion procedures applied [9]. The specific implementation of to that of particle-level jets. A correction is also applied to the jet clustering algorithm used is taken from the FastJet the η of the reconstructed jet to correct for a bias relative to package [37,38]. particle-level jets in certain regions of the detector [40]. Both corrections are applied as a function of the reconstructed jet energy and the detector pseudorapidity, ηdet , defined as the 4.1 Large-R jets pseudorapidity calculated relative to the geometrical centre of the ATLAS detector. This yields a better location of the Calorimeter jets are formed from topological clusters of energy-weighted centroid of the jet than the use of the pseu- calorimeter cells. The clusters are seeded by cells with an dorapidity calculated relative to the hard-scatter primary ver- energy significantly above the calorimeter noise. The large- tex. R jets used in this paper are reconstructed using topological Reconstructed jets are matched to particle-level jets using clusters that are calibrated to correct for response differences an angular  matching procedure that minimizes the distance between energy deposition from electromagnetic particles R = (φ)2 + (η)2 . The energy response is defined as (electrons and photons) and hadrons with the LCW scheme E reco /E truth , where E reco is the reconstructed jet energy prior of Ref. [5]. Small-R jets reconstructed from “electromagnetic to any calibration (later denoted E 0 ) and E truth is the energy scale” topo-clusters are used as a reference system in the mul- of the corresponding particle-level jets. The mass response tijet balance method of Sect. 5.4. Results are labelled with is defined as m reco /m truth , where m reco and m truth repre- “LCW” or “EM” to indicate the calibration of the clusters. sent the jet mass of the matched detector-level and particle- Topological clusters are defined to be massless. The four- level jets, respectively. The average response is determined momenta of these topo-clusters, initially defined as pointing in a Gaussian fit to the core of the response distribution. to the geometrical centre of the ATLAS detector, are adjusted The parameterization of the average jet energy response to point towards the hard-scatter primary vertex of the event, R E = E reco /E truth  used for the simulation calibration is which is defined as the primary vertex with the largest asso- presented as a function of ηdet and for several values of the ciated sum of track pT2 . truth jet energy in Fig. 3a. The correction is typically 5–10%, To reduce the effects of pile-up, soft emissions, and the with a weak dependence on the jet energy and a characteristic underlying event on jet substructure measurement, the trim- structure in ηdet that reflects the calorimeter geometry. ming algorithm is applied to the jets. Trimming reclusters The simulation-based JES correction factor cJES is deter- the jet constituents of each R = 1.0 jet using the kt algo- mined as a function of the jet energy and pseudorapidity ηdet . rithm [39] and Rsub = 0.2, producing a collection of subjets It is applied to the jet four-momentum as a multiplicative subjet jet for each jet. Subjets with pT / pT < 0.05 are removed, scale factor. The pseudorapidity correction η only changes and the jet four-momentum is recalculated from the remain- the direction. This means that the reconstructed large-R jet ing constituents. energy, mass, η, and pT become In this paper, trimmed large-R jets with pT > 200 GeV and |η| < 2.5 are studied. E reco = cJES E 0 , m reco = cJES m 0 , ηreco = η0 + η, pTreco = cJES | p0 |/ cosh (η0 + η), 4.2 Particle-level jets and the simulation-based jet where the quantities E 0 , m 0 , η0 , and p0 refer to the jet prop- calibration erties prior to any calibration, as determined by the trimming algorithm. The quantities cJES and η are smooth functions The reference for the simulation-based jet calibration is of the large-R jet kinematics. None of the calibration steps formed by particle-level jets. These are created by clustering affect the azimuthal angle φ of the jet. stable particles originating from the hard-scatter interaction The large-R jet invariant mass is calibrated in a final step. in the simulation event record which have a lifetime τ in This is important when using the jet mass in physics anal- the laboratory frame such that cτ > 10 mm. Particles that yses, because the jet mass is more sensitive than the trans- do not leave significant energy deposition in the calorimeter verse momentum to soft, wide-angle contributions and to (i.e. muons and neutrinos) are excluded. Particle-level jets cluster merging and splitting, as well as to the calorime- are reconstructed and trimmed using the same algorithms as ter geometry. For the mass correction the jet mass response those applied to large-R jets built from topological clusters, Rm = m reco /m truth  is determined using the same proce- 123 135 Page 6 of 42 Eur. Phys. J. C (2019) 79:135 Jet mass response Jet energy response 1.1 ATLAS Simulation 2.4 ATLAS Simulation E truth = 200 GeV p truth = 200 GeV T s = 13 TeV, Pythia8 dijets E truth = 400 GeV 2.2 s = 13 TeV, Pythia8 dijets p truth = 400 GeV 1.05 T Trimmed anti-k t R = 1.0, LCW E truth = 800 GeV Trimmed anti-k t R = 1.0, LCW p truth = 800 GeV 2 m truth = 40 GeV T E truth = 1500 GeV p truth = 1500 GeV T 1 E truth = 2000 GeV 1.8 p truth = 2000 GeV T 1.6 0.95 1.4 1.2 0.9 1 0.85 0.8 −2 −1.5 −1 −0.5 0 0.5 1 1.5 2 −2 −1.5 −1 −0.5 0 0.5 1 1.5 2 Large-R jet η Large-R jet η det det (a) (b) Jet mass response Jet mass response 2 1.8 ATLAS Simulation p truth = 200 GeV ATLAS Simulation p truth = 200 GeV T T 1.8 s = 13 TeV, Pythia8 dijets p truth = 400 GeV 1.6 s = 13 TeV, Pythia8 dijets p truth = 400 GeV T T Trimmed anti-k t R = 1.0, LCW p truth = 800 GeV Trimmed anti-k t R = 1.0, LCW p truth = 800 GeV T T 1.6 m truth = 80.4 GeV p truth = 1500 GeV 1.4 m truth = 172.5 GeV p truth = 1500 GeV T T p truth = 2000 GeV p truth = 2000 GeV T T 1.4 1.2 1.2 1 1 0.8 0.8 0.6 −2 −1.5 −1 −0.5 0 0.5 1 1.5 2 −2 −1.5 −1 −0.5 0 0.5 1 1.5 2 Large-R jet η Large-R jet η det det (c) (d) Fig. 3 The response for a the jet energy and b–d the jet mass of large- jet mass: b 40 GeV, representing a typical value for quark or gluon jets, R jets. The jet energy response is presented as a function of jet detector c the W boson mass, and d the top quark mass. The response is deter- pseudorapidity ηdet for several values of the truth jet energy, ranging mined in simulation of dijet events as the ratio of the reconstructed jet from 200 GeV to 2 TeV. The jet mass response is presented as a function mass to the mass of the corresponding particle-level jet. These results of jet pseudorapidity for several values of the jet transverse momentum are used to define the jet-level mass correction applied in the simulation from 200 GeV to 2 TeV and for three representative values of the truth calibration dure as for the jet energy calibration. The mass calibration is significant for jets with large particle-level mass relative to applied after the standard JES calibration. The mass response their pT (m/ pT  0.5). is presented in Fig. 3 for three representative values of the The simulation-based correction to the large-R jet mass truth jet mass: 40 GeV in panel (b), the W boson mass in panel cJMS is applied as a function of the jet E reco , ηdet , and (c), and the top quark mass in panel (d). The mass response is log(m reco /E reco ), keeping the large-R jet energy fixed and close to unity for jets with pT between 200 and 800 GeV and thus allowing the pT to vary [40]. This factor is also a smooth as large as 1.5 for very energetic jets with relatively low mass. function of the large-R jet kinematics. This has the following Several effects can impact the jet mass response. The recon- impact on the reconstructed jet kinematics: structed mass can be artificially increased by the splitting of topo-clusters during their creation. This effect is particularly E reco = cJES E 0 , m reco = cJES cJMS m 0 , ηreco = η0 + η,  important for jets with small particle-level mass relative to pTreco = cJES E 02 − cJMS2 m 20 / cosh (η0 + η). their pT (m/ pT  0.05). Similarly, when several particles form one topo-cluster, or when particles fail to produce any All results that correspond to jets that are brought to topo-cluster, the mass response is decreased. This effect is the particle-level with the simulation-based calibration are labelled with “JES+JMS”. 123 Eur. Phys. J. C (2019) 79:135 Page 7 of 42 135 4.3 Tracks and track jets such that the resolution of the combined mass measurement is always better than either of the two inputs within the sam- Tracks are reconstructed from the hits generated by charged ple from which the weights are derived. In this paper, in situ particles passing through the inner tracking detector (ID). measurements are presented for the jet mass reconstructed They are required to have pT > 500 MeV. To reduce fake from topo-clusters and for the track-assisted mass. The con- tracks, candidate tracks must be composed of at least one straint wcalo + wTA = 1 ensures that the combined mass is pixel detector hit and at least six hits in the silicon tracker. calibrated, if the scales of both mass definitions are fixed. The track transverse impact parameter |d0 | relative to the primary vertex must be less than 1.5 mm and the longitudi- nal impact parameter |z 0 | multiplied by sin θ relative to the 5 In situ pT response measurements primary vertex must be less than 3 mm [41,42]. Jets reconstructed from charged-particle tracks are used In this section, the methods used to derive the in situ cali- as a reference in calibration and uncertainty studies, taking bration for the energy (or transverse momentum) response advantage of the independence of instrumental systematic are presented. These methods use pT conservation in events effects between the ID and the calorimeter. Track jets are where a large-R jet recoils against a well-measured refer- reconstructed by applying the same jet reconstruction pro- ence object. The first method is based on the pT balance cedure to tracks as those used when constructing the topo- in dijet events with a central (|ηdet | ≤ 0.8) and a forward cluster jets described above, including the jet trimming algo- (|ηdet | > 0.8) jet. It is applied after the simulation calibra- rithm. Track jets are not calibrated. tion described in Sect. 4. The η-intercalibration corrects the pT of forward jets to make the jet energy response uniform 4.4 The combined jet mass as a function of pseudorapidity. After the η-intercalibration procedure, three further balance methods are used to pro- The jet mass resolution is improved by combining the jet vide an absolute pT scale calibration. In the Z +jet balance mass measurement in the calorimeter with the measurement method, the recoiling system is a reconstructed Z → μ+ μ− of the charged component of the jet within the ID [43– or Z → e+ e− decay, in the γ +jet balance method it is a 51]. A track jet is reconstructed from ID tracks with pT > photon, and in the multijet balance method the system is 500 MeV which are ghost-associated [52] to the topo-cluster formed by several calibrated small-R jets with low pT . These large-R jet. The measurement of this track jet’s mass is multi- three methods offer complementary coverage over a broad pT plied by the ratio of the transverse momenta of the calorimeter range. The Z +jet balance method provides the most precise jet and the track jet to obtain the track-assisted mass: results in the low- pT interval between 200 and 500 GeV, the γ +jet balance between 500 GeV and 1 TeV, and the multi- pTcalo jet balance extends to 2.5 TeV. Results of the three methods m TA = m track . (1) pTtrack are presented in this section and are combined into a global constraint on the JES in Sect. 8. where m TA is the track-assisted mass, m track the mass obtained from the tracker, and pTcalo and pTtrack are the trans- 5.1 Dijet η-intercalibration verse momenta measured respectively by the calorimeter and tracker. This alternative mass measurement has better reso- The relative η-intercalibration extends the jet calibration to lution for high- pT jets with low values of m/ pT . A weighted the forward detector region, 0.8 < |η| < 2.5. It is derived least-squares combination of the mass measurements is sub- from the differences in the pT balance between a central sequently performed with weights: reference and a forward jet in data and simulations. The η- intercalibration is determined in dijet events using a proce- m comb = wcalo m calo + wTA m TA , dure similar to that used for small-R jets [53]. The pT bal- ance of the dijet system is characterized by its asymmetry where wcalo and wTA are determined by the expected mass A, defined in terms of the forward (probe) and central (ref- resolutions σcalo and σTA of the calorimeter and track-assisted probe erence) jet pT ( pT and pTref ) as measurements, using the central 68% inter-quantile range of the jet mass response distribution in dijet events: probe pT − pTref A= avg , −2 −2 pT σcalo σTA wcalo = −2 −2 , wTA = −2 −2 , σcalo + σTA σcalo + σTA avg probe where pT = ( pT + pTref )/2. The central reference jets are required to be within |η| < 0.8. The balancing probe jet ηdet 123 135 Page 8 of 42 Eur. Phys. J. C (2019) 79:135 Table 1 Summary of the dijet topology selection and systematic vari- to be used over a wide range of pT . To ensure a 2 → 2 ations considered for the η-intercalibration analysis. The label J3 refers body topology, events with energetic additional radiation are to the third trimmed R = 1.0 jet in the event after ordering the jets in pT vetoed with an upper cut on the transverse momentum of the third jet J3 , and the leading two jets are required to sat- Variable Nominal selection Up variation Down variation isfy a minimum angular separation in azimuth. Both of these pTJ3 / pT avg < 0.4 < 0.5 < 0.3 requirements are varied in order to derive systematic uncer- φ(ref, probe) > 2.5 > 2.8 > 2.2 tainties accounting for their impact on the response measure- ments. These selections and systematic variations are sum- marized in Table 1. No pile-up jet tagging employing the Jet Vertex Tagger likelihood measure (JVT) [54,55] is applied defines the detector region whose response is being probed. for large-R jets, since in this kinematic region the contami- avg The asymmetry distribution is studied in bins of pT and the nation by pile-up jets is negligible. probe jet ηdet . In each bin, the relative response difference The relative jet- pT response Rrel is shown in Fig. 4 between the central and forward jets is as a function of the large-R jet pseudorapidity for data,   Powheg+Pythia 8, and Sherpa for two pT intervals. The probe pT 2 + A relative jet response as a function of the large-R jet pT is Rrel = = , (2) pTref 2 − A shown in Fig. 5 for two pseudorapidity ranges of the probe jet. In the central region, the relative responses of all three sam- where A is the mean value of the asymmetry. The asym- ples agree by design. The relative response in data increases metry distribution is approximately Gaussian, and the mean in the forward region due to features of the experimental value is extracted using a Gaussian fit to the core of the dis- response which are not well-reproduced in the simulation and tribution. hence not accounted for in the simulation-based JES calibra- Large-R jets with pT from 180 GeV to 2 TeV within tion factor cJES . Compared to the measured response, the pre- |η| < 2.5 are considered. Dijet events in data are selected diction remains relatively constant around unity. The differ- using several dedicated single-jet triggers based on small-R ence between the simulated and measured responses reaches jets. Their efficiency has been evaluated for large-R jets and about 5% around |η| = 2.5. Similar trends are observed for each trigger is used in its region of full efficiency for those R = 0.4 jets in Ref. [9]. In the lower panel of Figs. 4 and 5, jets. These triggers provide enough events for this technique the ratio of simulation to data is shown. An interpolation Relative jet response, R rel Relative jet response, R rel 1.2 avg 1.2 avg ATLAS 280 ≤ p < 380 GeV ATLAS 550 ≤ p < 700 GeV T T -1 -1 s = 13 TeV, 36.2 fb , dijets s = 13 TeV, 36.2 fb , dijets Trimmed anti-k t R = 1.0 (LCW+JES+JMS) Trimmed anti-k t R = 1.0 (LCW+JES+JMS) 1.1 1.1 1 1 Data Powheg+Pythia8 Sherpa2.1 Data Powheg+Pythia8 Sherpa2.1 0.9 0.9 1.1 1.1 MC / Data MC / Data 1.05 1.05 1 1 0.95 0.95 0.9 0.9 −2.5 −2 −1.5 −1 −0.5 0 0.5 1 1.5 2 2.5 −2.5 −2 −1.5 −1 −0.5 0 0.5 1 1.5 2 2.5 η η det det (a) (b) Fig. 4 The relative large-R jet response Rrel as a function of the large- ence region |ηdet | < 0.8 is unity by construction. In the lower panels, R jet detector pseudorapidity ηdet in two representative average trans- the dotted lines interpolating between Powheg+Pythia markers are avg avg verse momentum pT bins a 280 GeV < pT < 380 GeV and b obtained by smoothing with a filter using a sliding Gaussian kernel avg 550 GeV < pT < 700 GeV. The average response with in the refer- 123 Eur. Phys. J. C (2019) 79:135 Page 9 of 42 135 Relative jet response, R rel Relative jet response, R rel ATLAS 1.7 ≤ η < 1.8 ATLAS -0.6 ≤ η < -0.4 det det -1 -1 s = 13 TeV, 36.2 fb , dijets s = 13 TeV, 36.2 fb , dijets 1.1 1.1 Trimmed anti-k t R = 1.0 (LCW+JES+JMS) Trimmed anti-k t R = 1.0 (LCW+JES+JMS) 1 1 Data Powheg+Pythia8 Sherpa2.1 Data Powheg+Pythia8 Sherpa2.1 0.9 0.9 1.05 1.05 MC / Data MC / Data 1 1 0.95 0.95 3×102 4×102 5×102 103 3×102 4×102 5×102 103 Large-R jet p [GeV] Large-R jet p [GeV] T T (a) (b) Fig. 5 The relative large-R jet response Rrel as a function of the large- b −0.6 < ηdet < −0.4. In the lower panels, the lines interpolating R jet pT in two representative detector pseudorapidity ηdet bins in between Powheg+Pythia markers are obtained by smoothing with a the forward and central reference regions a 1.7 < ηdet < 1.8 and filter using a sliding Gaussian kernel Fractional Uncertainty Fractional Uncertainty 0.02 0.02 ATLAS 280 GeV < p < 380 GeV ATLAS 550 GeV < p < 700 GeV T T -1 -1 s = 13 TeV, 36.2 fb , dijets s = 13 TeV, 36.2 fb , dijets Trimmed anti-k t R = 1.0 (LCW+JES+JMS) Trimmed anti-k t R = 1.0 (LCW+JES+JMS) 0.015 0.015 Total uncertainty Statistics Total uncertainty Statistics Modelling ΔΦ down Modelling ΔΦ down ΔΦ up p J3 T down ΔΦ up p J3 T down 0.01 p J3 up 0.01 p J3 up T T 0.005 0.005 0 0 −2 −1.5 −1 −0.5 0 0.5 1 1.5 2 −2 −1.5 −1 −0.5 0 0.5 1 1.5 2 η η det det (a) (b) Fig. 6 Uncertainties associated with the large-R jet η-intercalibration using variations of the dijet topology selection are negligible relative to as a function of detector pseudorapidity ηdet in two representative aver- the simulation modelling uncertainty, which typically amounts to a 1% avg avg age transverse momentum pT bins a 280 GeV < pT < 380 GeV uncertainty for large-R jets within 0.8 < |ηdet | < 2.0 avg and b 550 GeV < pT < 700 GeV. The uncertainties evaluated using a filter with a sliding Gaussian kernel across ηdet yields The uncertainties associated with the η-intercalibration a smooth function of jet pT and ηdet . The inverse of this are shown in Fig. 6 for two representative pT bins. The smooth function is taken as the η-intercalibration correction uncertainties associated with the veto on additional radia- factor crel ( pT , ηdet ), which is applied as a jet four-momentum tion and the φ requirement placed on the dijet topology scale factor. are derived by varying these selection criteria to the values 123 135 Page 10 of 42 Eur. Phys. J. C (2019) 79:135 listed in Table 1 and re-deriving the calibration. An additional practice, there tends to be more QCD radiation in the hemi- systematic uncertainty accounts for the choice of event gen- sphere opposite to the colour-neutral Z boson, and therefore erator and parton shower models. The simulation uncertainty RDB tends to be below unity. The event selection imposes is derived by comparing the relative jet- pT response for two a veto on the pT of additional sub-leading jets. A minimum event generators: Powheg+Pythia 8 and Sherpa. In gen- requirement is also imposed on the angular separation φ eral, the uncertainties associated with the derived calibration of the large-R jet and reference Z boson. Any mismodelling are small, amounting to a ∼ 1% uncertainty within the region in the jet energy scale may be evaluated using the balance of interest for large-R jets (|η| < 2.0). Uncertainties origi- double ratio of RDB in data and simulation RDB data /R MC . If DB nating from the kinematic requirements made to select events the event selection criteria are met and the reference object avg are typically negligible, except in the highest pT bins. is well measured and correctly modelled in simulation, any deviation from unity in the double ratio can be attributed to 5.2 Z +jet balance a mismodelling of the jet response in simulation and may be taken as an in situ correction. For large-R jets within |ηdet | < 0.8, an in situ calibration Calibrated anti-kt R = 0.4 jets constructed from is derived by examining the pT balance of a large-R jet electromagnetic-scale topo-clusters are used to veto addi- and a leptonically decaying Z boson, either Z → e+ e− or tional radiation. These jets are required to be R > 1.4 Z → μ+ μ− (Fig. 2b). Both of these channels provide a pre- from the large-R jet whose response is being probed (J1 ), cise, independent reference measurement of the jet energy, which ensures that there is no overlap. Such small-R jets either from the inner detector and muon spectrometer tracks with pT < 60 GeV must also satisfy a requirement on the jet used to reconstruct muons or from the well-measured elec- vertex tagger (JVT) [54], which is designed to reject addi- tromagnetic showers and inner detector tracks used to recon- tional jets produced by pile-up interactions using informa- struct electrons. The applicable range of this calibration is tion from the inner detector. The 2 → 2 topology selection limited by the kinematic range where Z boson production is only accepts events in which any small-R jet is reconstructed relatively abundant, that is, up to a Z boson pT of about with a pT < max(0.1 pTref , 15 GeV) and the φ between the 500 GeV. Electrons used to reconstruct the Z boson are large-R jet and Z boson is greater than 2.8. A summary of required to pass ‘medium likelihood identification’ qual- the event selection is presented in Table 2. This table also ity and ‘Loose’ isolation requirements and must be recon- reports variations associated with each criterion, performed structed within |η| < 2.47 (excluding the transition region by redoing the full analysis for each such variation and taking 1.36 < |η| < 1.52 between the barrel and endcap electro- the difference between the varied and nominal results as the magnetic calorimeters) with at least 20 GeV of pT [56,57]. systematic uncertainty. Similarly, ‘VeryLoose’ quality and ‘Loose’ isolation require- Measurements of RDB are carried out separately in the ments are placed on muons, which must be reconstructed electron and muon channels. They are found to be consistent within |η| < 2.4 with pT > 20 GeV [58]. The lepton pair and thus combined to provide a single measurement of the must have opposite charge and be kinematically consistent JES. The average momentum balance in Z +jet events after with the decay of a Z boson, requiring the invariant mass of this combination is shown in Fig. 7. The balance is found to the lepton pair to satisfy 66 < m + − < 116 GeV. Large-R be consistently below unity as a function of pTref . The ratio jets studied here are calibrated with the simulation calibration of the predicted balance to the measured balance is consis- and η-intercalibration described in Sects. 4 and 5.1. tently 1–4% above unity. The uncertainties associated with The direct balance method used here closely follows the this measurement are shown in Fig. 8, where modelling sys- methodology outlined in Ref. [9]. The average momentum tematic and statistical uncertainties are the dominant source balance between the large-R jet and Z boson is of error over the pT range considered.   pTJ RDB = , (3) 5.3 γ +jet balance pTref   The large-R jet energy scale can be measured using the where pTJ is the large-R jet pT and pTref = pTZ  cos (φ)  is γ +jet final state (Fig. 2b). This method exploits the fact the component of the reference momentum collinear with the that the energy of photons is measured more precisely than jet, with φ being the azimuthal angle between the large-R that of jets. As cross-section for this process is larger than jet and reference Z boson. The average value is determined that for Z +jets production, this balance technique probes using a Gaussian fit. higher large-R jet pT . The γ +jet method is based on the Even with an ideal detector, the momentum balance RDB balance between photons and large-R jets, using the ratio of Eq. 3 will only equal unity for an ideal 2 → 2 process. In RDB defined in Eq. (3), where the reference momentum 123 Eur. Phys. J. C (2019) 79:135 Page 11 of 42 135 Table 2 Summary of the 2 → 2 topology selection and systematic variations considered for the Z +jet direct balance analysis. The labels Ji refer to the ith leading large-R jet, and ji to the ith leading small-R jet that fulfils R(J1 , ji ) > 1.4 Variable Nominal selection Up variation Down variation j pT1 max(0.1 pTref , 15 GeV) max(0.15 pTref , 20 GeV) max(0.05 pTref , 10 GeV) φ(Z , J1 ) > 2.8 > 2.9 > 2.7 Small-R jet JVT > 0.59 > 0.91 > 0.11 ATLAS 0.05 1.04 Z +jet direct balance, R DB ATLAS Fractional JES uncertainty s = 13 TeV, 36.2 fb , Z +jet, |ηJet| < 0.8 -1 1.02 s = 13 TeV, 36.2 fb-1, Z +jet Trimmed anti-k t R =1.0 (LCW+JES+JMS) 0.04 1.00 Trimmed anti-k t R =1.0 (LCW+JES+JMS) |η | < 0.8 0.98 Jet 0.03 Stat. ⊕ Syst. e E -resolution e E -scale Pile-up (JVT) MC modelling μ E -resolution ID 0.96 μ E -resolution MS μ E -scale (charge) residual μ E -scale (charge) μ E -scale Pile-up (NPV shift) Statistical 0.94 0.02 Sub-leading jet veto Δφ 0.92 0.01 0.90 Data Powheg + Pythia8 Sherpa2.2 0.00 200 300 400 500 600 MC / Data 1.05 Large-R jet p [GeV] T 1.00 0.95 Fig. 8 Breakdown of the uncertainties in the JES measurement with the Z +jet direct balance method as a function of the large-R jet trans- 200 300 400 500 600 verse momentum pT . The sources include the statistical uncertainty, variations of the generator (simulation modelling), variations of the Large-R jet p [GeV] event selection (pile-up (JVT), sub-leading jet veto, φ), the uncer- T tainties in the energy scale and resolution of electrons (e E-scale and Fig. 7 The momentum balance RDB as a function of the large-R jet e E-resolution) and muons (μ E-scale and μ E-resolution), and the transverse momentum pT in Z +jet events for the combined e+ e− and uncertainty in the pile-up conditions (NPV shift). These uncertainties μ+ μ− channels. Only statistical uncertainties are shown. For each pTref are also discussed in the context of small-R jets in Ref. [9]. The lines bin, the measured RDB is plotted against the average jet pT of the bin. are obtained by smoothing a binned representation of these uncertainties The horizontal error bars give an indication of the width of the associated using a sliding Gaussian kernel pTref bin γ  γ satisfies pT > max(0.1 pTref , 15 GeV). Small-R jets with pTref = pT  cos (φ)  is the component of pT collinear with pT < 60 GeV must also satisfy a JVT requirement. Pho- the jet. tons must be separated from reconstructed large-R jets by The double ratio of RDB data /R MC measures any residual DB at least φ(J, γ ) > 2.8. The simulation calibration and η- modelling effects in the jet energy scale calibration. If the intercalibration described in Sects. 4 and 5.1 are applied to reference photon is well measured experimentally and the the large-R jets studied here. γ +jet events are correctly modelled in simulation, any devi- A photon purity correction is applied to the mean balance ation from unity in the double ratio can be attributed to a results in data to correct for contamination from misidentified mismodelling of the jet response in the Monte Carlo simula- jets or electrons that may skew the nominal pT balance. The tion. contamination of the photon sample by fakes is derived from Events are selected using the lowest unprescaled single- data using the double-sideband, or ABCD, method [61,62] photon trigger. The offline selection requires the presence in the plane spanned by the photon isolation2 and the photon of a photon satisfying the ‘tight’ identification and isola- tion requirements [59,60] with at least 140 GeV of E T . This criterion ensures full trigger efficiency. As in the 2 The calorimeter isolation variable E Tiso is defined as the sum of the case of Z +jet balance (Sect. 5.2), the presence of sig- E T of topological clusters deposited in a cone of size R = 0.4 around nificant additional radiation in the event invalidates the the photon candidate, excluding an area of size η × φ = 0.125 × 0.175 centred on the photon cluster and subtracting the expected photon assumption of a balanced topology. Events are therefore energy deposit outside of the excluded area. Fluctuations in the ambient vetoed if a reconstructed, calibrated R = 0.4 jet built transverse energy of the event are corrected for; the typical size of this from electromagnetic-scale topo-clusters has a pT which correction is 2 GeV in the central region. 123 135 Page 12 of 42 Eur. Phys. J. C (2019) 79:135 γ +jet direct balance, R DB γ +jet direct balance, R DB ATLAS ATLAS TeV, 36.2 fb-1, γ +jet s = 13 TeV TeV, 36.2 fb-1, γ +jet s = 13 TeV 1.05 1.05 Trimmed anti-k t R = 1.0 (LCW+JES+JMS) Trimmed anti-k t R = 1.0 (LCW+JES+JMS) η jet < 0.8 150 < pref < 1000 GeV det T 1 1 0.95 0.95 0.9 Data 0.9 Data Pythia8 Pythia8 Sherpa2.1 Sherpa2.1 Stat. ⊕ Syst. Stat. 0.85 0.85 MC / Data MC / Data 1.05 1.05 1 1 0.95 0.95 200 300 400 500 600 700 800 − 2.5 − 2 − 1.5 − 1 − 0.5 0 0.5 1 1.5 2 2.5 Large-R jet p [GeV] Large-R jet η det T Fig. 9 The momentum balance RDB extracted from γ +jet events in Fig. 10 The momentum balance RDB extracted from γ +jet balance data and simulations as a function of the transverse momentum pT distributions in data and simulation as a function of the large-R jet of the large-R jet. The ratio of the results obtained from the nominal detector pseudorapidity ηdet . The ratio of the results obtained from the Pythia simulation and from data is shown in the bottom panel. The nominal Pythia simulation to the results from data is shown in the ratio of Pythia to Sherpa results, taken as a systematic uncertainty bottom panel. The ratio of Pythia to Sherpa results, taken as a sys- associated with modelling, is included in the shaded band in the ratio tematic uncertainty associated with modelling, is included in the shaded panel, which also includes statistical and systematic uncertainties from band in the ratio panel, which also includes statistical and systematic other sources. For each pTref bin, the measured RDB is plotted against the uncertainties from other sources average jet pT of the bin. The horizontal error bars give an indication of the width of the associated pTref bin to the presence of pile-up jets. The effects of extra radia- tion on the balance are assessed by varying the topological identification measure.3 The purity correction results in a selections and the overlap removal as described in Table 3. shift of the relative RDB value between data and simulation Repeating the analysis separately using φ(J, j) > 1.2 and of about 2%. φ(J, j) > 1.6 produces a negligible systematic shift relative In Fig. 9 the result is shown as a function of the refer- to the nominal result. The effects of the photon measurement ence pT for large-R jets in the region |η| < 0.8. The ratio are assessed by varying the energy scale and resolution of of the predicted response in the simulation to the measured the photon calibration, as well as by varying the measured response is shown in the inset below the main panel. As photon purity in the purity correction. The effects of pile- already observed in Sect. 5.2, the ratio of simulation to data up jets on the calibration are estimated by varying the JVT is above unity over the whole pT range. These results are selection threshold for the small-R jets. Lastly, the analysis is included in the in situ calibration that corrects the jet energy repeated with Sherpa 2.1 MC samples, in place of the nom- response in data. inal Pythia 8 samples, to assess the modelling uncertainty. The uniformity of the large-R jet response across the As shown in Fig. 11, the overall combined systematic and detector geometry is shown in Fig. 10, as a validation of the η- statistical uncertainty is approximately 1% for the pT range intercalibration procedure (Sect. 5.1). The relative response from 150 to 880 GeV. The photon energy scale uncertainty across the detector is constant and well behaved. is the dominant source over the entire pT range. There are three main categories of systematic uncertain- ties in the RDB measurement: those related to the modelling 5.4 Multijet balance of additional QCD radiation which affects the balance, uncer- tainties associated with the photons [63,64], and effects due The Z +jet and γ +jet techniques provide precise constraints 3 on the jet energy scale for jets with pT up to 1 TeV. The The photon identification decision is based on a set of shower shape energy scale of higher- pT large-R jets is measured using mul- variables computed from energy depositions in the first and second lay- ers of the electromagnetic calorimeter and from leakage in the hadronic tijet events. A schematic representation of the event topology calorimeter. used in this method is shown in Fig. 2c. The multijet balance 123 Eur. Phys. J. C (2019) 79:135 Page 13 of 42 135 Table 3 Summary of the selection and systematic variations considered for the γ +jet direct balance analysis. The labels J1 refers to the leading large-R jet and j1 to the leading small-R jet that fulfils R(J1 , j) > 1.4 Variable Nominal selection Up variation Down variation j pT1 max(0.1 pTref , 15 GeV) max(0.15 pTref , 20 GeV) max(0.05 pTref , 10 GeV) φ(J1 , γ ) > 2.8 > 2.9 > 2.7 Small-R jet JVT > 0.59 > 0.91 > 0.11 Relative uncertainty 0.02 determined in simulation in several bins of pTrecoil . The data- ATLAS Stat. ⊕ Syst. to-simulation double ratio RMJB data /R MC allows estimation of 36 fb-1, γ +jet s = 13 TeV, 36.2 Statistical MJB Trimmed anti-k t R = 1.0 (LCW+JES+JMS) γ Energy Scale the response for high- pT jets. MC Modelling 0.015 ηjet < 0.8 Other det Events are selected using single small-R jet triggers. Bins of pTrecoil are defined to correspond to a given fully efficient single small-R jet trigger. The triggers used for 200 GeV < 0.01 pTrecoil < 550 GeV are prescaled, whereas an unprescaled jet trigger is used for pTrecoil > 550 GeV. The event selection is summarized in Table 4. For small-R 0.005 jets with pT < 60 GeV within |η| < 2.4, the JVT selection is applied to suppress pile-up jets. The large-R probe jet is 0 required to have |ηdet | < 0.8, while the small-R jets that con- 200 300 400 500 600 700 800 stitute the recoil system are required to have |ηdet | < 2.8 and Large-R jet p [GeV] T pT > 25 GeV. To select events with multijet recoil systems, the leading jet in the recoil system (j1 ) is allowed to have Fig. 11 Systematic uncertainties in the in situ measurement of the jet no more than 80% of the total transverse momentum of the energy scale obtained with the γ +jet method as a function of the large-R recoil system. This selection ensures that the recoil system jet transverse momentum pT . The lines shown are obtained by smooth- ing a binned representation of these uncertainties using a sliding Gaus- consists of several jets with lower pT than the large-R jet, sian kernel which are each well-calibrated by small-R jet in situ tech- niques [9]. The angle α in the azimuthal plane between the (MJB) method takes advantage of events where an energetic leading large-R jet and the vector defining the recoil sys- large-R jet is balanced against a system that consists of mul- tem is required to satisfy |α − π | < 0.3. The R distance tiple lower- pT jets. β between the leading large-R jet and the nearest small-R For the calibration of large-R jets the reference pTrecoil is jet from the recoil system is required to be greater than 1.5. obtained as the four-vector sum of calibrated small-R anti-kt The simulation calibration and η-intercalibration described jets. The transverse momentum balance is in Sects. 4 and 5.1 are applied to the large-R jets studied using this technique.   Figure 12 shows the distribution of RMJB as a function pTJ RMJB = , of the large-R jet pT . The balance in data decreases from pTrecoil approximately 1.01 at pT = 300 GeV to about 0.99 for jets with pT = 2 TeV. The simulation shows a similar downward where pTJ is the transverse momentum of the leading large-R trend. The response in simulations is 2% higher than in data, jet and pTrecoil is the magnitude of the vectorial sum of the consistent with the findings of the other methods where they transverse momenta of the recoil system of small-R jets. The overlap. average value of the ratio is taken to be the mean value of a Gaussian fit. The value of RMJB is measured in data and Table 4 Summary of the event Variable Nominal selection Up variation Down variation selection and systematic variations considered for the Separation angle (α) |α − π | < 0.3 |α − π | < 0.4 |α − π | < 0.2 multijet direct balance analysis. The label ji refers to the ith R separation (β) >1.5 >1.9 >1.1 j leading small-R jet pT1 / pTrecoil < 0.8 < 0.9 < 0.7 Recoil system minimum pT 25 GeV 30 GeV 20 GeV 123 135 Page 14 of 42 Eur. Phys. J. C (2019) 79:135 Multijet balance, R MJB Fractional R MJB uncertainty 1.14 ATLAS ATLAS -1 0.06 s = 13 TeV, 36.2 fb-1, multijet 1.12 s = 13 TeV, 36.2 fb , multijet Trimmed anti-k t R = 1.0 (LCW+JES+JMS) Trimmed anti-k t R = 1.0 (LCW+JES+JMS) 0.05 Large-R jet η < 0.8 det 1.1 Large-R jet η < 0.8 Recoil system: anti-k t R = 0.4 EM+JES det Total uncertainty 1.08 Recoil system: anti-k t R = 0.4 EM+JES 0.04 Statistical uncertainty in-situ R = 0.4 EM JES uncertainty 1.06 Data Pythia8 Single particle uncertainty 0.03 Flavour composition, response Stat. ⊕ syst. Sherpa2.1 Pile-up, average 2016 conditions 1.04 Herwig7 MC Modelling 0.02 Event selection criteria 1.02 0.01 1 0.98 0 3×102 103 2×103 3×103 MC / Data 1.06 1.04 Large-R jet p [GeV] T 1.02 1 0.98 Fig. 13 The fractional uncertainty in RMJB as a function of the large-R 3 3 3 jet transverse momentum pT . The lines shown are obtained by smooth- 3× 102 10 2× 10 3× 10 ing a binned representation of these uncertainties using a sliding Gaus- Large-R jet p [GeV] sian kernel T Fig. 12 Mean transverse momentum balance RMJB for leading- pT large-R jets (|η| < 0.8) balanced against a system of at least two small-R ing the change in the multijet-balance variable. Using an jets ( pT ≥ 25 GeV, |η| < 2.8) as a function of the large-R jet transverse approach to systematic uncertainties similar to that in the momentum pT . The measured balance is compared with the prediction small-R in situ analysis, the transverse momentum thresh- of Monte Carlo simulations based on the event generators Pythia 8, j1 Sherpa 2.1, and Herwig 7. Below, the ratio of response measurements old for recoil jets is shifted by ± 5 GeV, the pT / pTrecoil is in data and simulation is presented. The shaded band indicates the total shifted by ± 0.1, the angle α is shifted by ± 0.1, and β is uncertainty of the measurement, described in detail in the text. For each shifted by ± 0.4. The uncertainty due to modelling of multijet pTref bin, the measured RDB is plotted against the average jet pT of the events in simulations is estimated from the largest difference bin. The horizontal error bars gives an indication of the width of the associated pTref bin between the multijet-balance results obtained from the nom- inal Pythia 8 simulation and those obtained from Sherpa v2.1 and Herwig 7. Figure 13 shows the breakdown of the The total uncertainty in the RMJB measurement is approx- fractional uncertainties in the jet energy scale derived from imately ± 2% or lower for pT < 2 TeV. The uncertainty in this method. Various uncertainties propagated from the refer- the energy scale of the jets of the recoil in situ procedure is ence jet system dominate the measurement across the entire propagated through the large-R MJB procedure. Uncertain- pT range. ties associated with high- pT jets in the recoil system which lie beyond the region covered by the R = 0.4 in situ analyses are derived from measurements of the calorimeter response 6 In situ jet mass calibration to isolated single charged particles, which are also propa- gated through this large-R jet analysis to provide coverage In this section, two methods to derive an in situ calibration for at the highest values of jet pT (> 1 TeV) [65]. No assump- the large-R jet mass are presented. The first method, known tion is made about the flavour of the recoil jets (originating as the Rtrk method, relies on the tracker to provide an inde- from a gluon, a light quark, or a heavy-flavour quark). This pendent measurement of the jet mass scale and its associated lack of knowledge is a source of systematic uncertainty. The uncertainty. The second method, known as forward folding, uncertainty in the multijet-balance observable due to the jet fits the mass peaks and jet mass response of the W boson flavour response is evaluated using a correlated propagation and top quark to measure the relative energy and mass scales of the small-R jet flavour response uncertainties, i.e. all jets and resolutions between data and simulations. Both measure- are shifted simultaneously. ments are performed after applying the in situ calibration for In addition to the jet calibration and uncertainties in the the energy scale, which also affects the jet mass scale. The reference scale, the event selection criteria and the modelling results in this section are combined into a global jet mass in the event generators directly affect the pT balance used to calibration, detailed in Sect. 8. obtain the multijet-balance results. The impact of the event selection criteria is investigated by shifting each event selec- tion criterion up and down by a specified amount and observ- 123 Eur. Phys. J. C (2019) 79:135 Page 15 of 42 135 6.1 Calorimeter-to-tracker response ratios 4 Calorimeter-to-track response, R trkT p ATLAS Data s = 13 TeV, 36.2 fb-1, dijets Pythia8 The calorimeter-to-tracker response double-ratio method (or 3.5 Trimmed anti-k t R = 1.0 (LCW+JES+JMS) Sherpa2.1 Rtrk method) is built around the fact that the ATLAS detector | | < 2.0, p > 200 GeV Herwig7 det T provides two independent measurements of the properties of Tracking unc. the same jet from the calorimeter and the tracker [3]. Jets 3 formed from inner detector tracks only take into account the hits from their charged-particle constituents. Calibrated jets 2.5 formed from energy depositions within the calorimeter pro- vide a measure of the properties from the full shower. The average calorimeter-to-track jet response 2   pTcalo 500 1000 1500 2000 2500 3000 Rtrk = MC / Data 1.06 pTtrack 1.04 1.02 1 0.98 0.96 is proportional to the average calorimeter-to-truth jet response. 0.94 Therefore, a comparison of the double ratio of Rtrk in simu- 500 1000 1500 2000 2500 3000 lations and data provides a way to validate the modelling of Large-R jet p [GeV] T large-R jet properties in situ. The ratio of Rtrk values deter- p mined in data and simulations should be equal to unity for Fig. 14 Measurement of RtrkT as a function of the large-R jet transverse momentum pT for large-R jets with m/ pT = 0.2. The large-R jet pT well-modelled observables. Any deviation from this expec- is corrected using the simulation calibration, η-intercalibration, and a tation can be taken as a scale uncertainty in the measure- combination of in situ direct balance techniques. Data are compared ment. This method is versatile and allows the determination with three generators and with three tracking variations for the default of uncertainties for several variables, such as the pT , mass, generator Pythia 8 (shown as a band around these points). The double p ratio of RtrkT measured in simulations and data is shown in the lower and substructure information of large-R jets. Moreover, the panel dijets process provides a very large sample, such that the analysis can be performed in a large number of pT and mass or m/ pT regions. Figure 14 shows Rtrk as a function of the large-R jet pT in dijet events for data and several simulation samples. The maximum spread between the two generators and three tracking variations that assume three different types of mis- modelling (resolution [66], efficiency within dense envi- ronments [67], and alignment [68]) is about 8%. A steady increase in the calorimeter-to-track jet response Rtrk with increasing large-R jet pT is observed, going well beyond the expected ratio of the total and charged transverse momenta of a jet, caused by inefficiencies in the tracker response at high jet pT . Figure 15 shows a breakdown of the uncertainties in the large-R jet pT derived from this method for the transverse momentum for large-R jets with values of m/ pT ≈ 0.2. The main source of uncertainty across the entire pT range origi- Fig. 15 The total uncertainty in the relative jet energy scale in data and nates from differences between data and the nominal Monte simulations associated with the Rtrk method is plotted as a function of Carlo generator considered in this study. As this uncertainty jet transverse momentum pT . The large-R jet pT is corrected using the simulation calibration, η-intercalibration, and a combination of in situ was expected to be large, the Rtrk method is neither included direct balance techniques. The contributions from several sources are in the in situ JES combination nor used as a source of system- indicated. The baseline uncertainty represents the deviation of the dou- atic uncertainty for the JES of large-R jets. Rather, the Rtrk ble ratio from unity for the baseline simulations. The lines shown are pT results are used as an independent cross-check to validate obtained by smoothing a binned representation of these uncertainties using a sliding Gaussian kernel the JES calibration techniques. 123 135 Page 16 of 42 Eur. Phys. J. C (2019) 79:135 trk Calorimeter-to-track response, R m 3 ATLAS Data s = 13 TeV, 36.2 fb-1, dijets Pythia8 2.8 Trimmed anti-k t R = 1.0 (LCW+JES+JMS) Sherpa2.1 | | < 2.0, p > 200 GeV Herwig7 det T 2.6 Tracking unc. 2.4 2.2 2 500 1000 1500 2000 2500 3000 MC / Data 1.06 1.04 1.02 1 0.98 0.96 Fig. 17 The total uncertainty in the relative jet mass scale between data 0.94 500 1000 1500 2000 2500 3000 and simulation associated with the Rtrk method is plotted as a function Large-R jet p [GeV] of jet transverse momentum pT for large-R jets with m/ pT = 0.2. T The large-R jet pT is corrected using the simulation calibration, η- m as a function of the large-R jet transverse intercalibration, and a combination of in situ direct balance techniques. Fig. 16 Measurement of Rtrk The contributions from several sources are indicated. The baseline momentum pT for large-R jets with m/ pT = 0.2. The large-R jet pT uncertainty represents the deviation of the double ratio from unity for is corrected using the simulation calibration, η-intercalibration, and a the baseline simulations. The lines shown are obtained by smoothing combination of in situ direct balance techniques. Data are compared a binned representation of these uncertainties using a sliding Gaussian with three generators and with three tracking variations for the default kernel generator Pythia 8 (shown as a band around these points). The double m measured in simulations and data is shown in the lower ratio of Rtrk panel There is a mild tension between the double-ratio results from the two samples. The double ratio in the t t¯ sample is sys- tematically somewhat higher than the equivalent result in the The same method is also applied to the large-R jet dijet sample. The difference is typically 1% or less, except in calorimeter mass, and is shown in Fig. 16. The largest dif- the first bin of the double ratio for jet mass. This is significant ference between the considered generators is ∼ 2 to 3%. compared to the statistical uncertainties but is small in com- Figure 17 shows the various uncertainties in the large-R jet parison with the modelling uncertainties of the Rtrk method. mass derived from the Rtrk mass response for large-R jets Some properties of these two jet populations differ, such as with m/ pT = 0.2. Again, the main source of uncertainty the distribution of their m/ pT and their flavour composition, originates from differences between data and the nominal and so it is not expected that the modelling uncertainties will simulation. cancel out exactly. No additional uncertainty is assigned to The Rtrk method can also be used to study the topology account for the topology dependence. dependence of the response modelling. The double ratio is constructed in two event samples, with different jet flavours 6.2 Forward folding (jets originating from light quarks or gluons and jets contain- ing a hadronic top quark decay). The dijet sample used for A high-purity signal sample of large-R jets with high- pT , Fig. 14 is dominated by gluon jets at low transverse momenta, hadronically decaying W bosons and top quarks is obtained while at higher momenta the fraction of light-quark jets in by selecting t t¯ events in the lepton+jets final state, where a the sample increases. The t t¯ sample of Sect. 6.2 is enriched in hadronically decaying top quark balances one which decays large-R jets that contain a complete high- pT object’s decay to a leptonically decaying W boson and b-quark. This sample (either a top quark or W boson). In Fig. 18 the double ratios is used to measure the response for jets in signal-like topolo- of the two samples are compared for jet pT and jet mass. The gies which contain jets consisting of multiple regions of high jets in the samples correspond to the same pseudorapidity energy density [69,70]. The jet mass response is determined range |η| < 2.0 and the same pT and jet mass intervals. In by fits to the W boson and top quark mass peaks in the large- both samples, the double ratio is constructed with the nominal R jet invariant mass distribution of the hadronically decaying simulation events, which rely on Pythia 8 for hadronization. top quark candidate. As systematic uncertainties are expected to partially cancel The event selection is based on the ATLAS search for t t¯ out, only statistical uncertainties are shown. resonances [71] and is summarized in Table 5. It requires a 123 Eur. Phys. J. C (2019) 79:135 Page 17 of 42 135 data data / (R trkT ) R m ) / (R trk ) ATLAS ATLAS m p s = 13 TeV, 36.2 fb-1 s = 13 TeV, 36.2 fb-1 1.05 1.05 MC ( trk MC Trimmed R = 1.0 anti-k t (LCW+JES+JMS) Trimmed R = 1.0 anti-k t (LCW+JES+JMS) (R trkT ) |η | < 2.0 |η | < 2.0 p det det Pythia8/data, t t Pythia8/data, t t Pythia8/data, dijets Pythia8/data, dijets 1 1 Stat. error only Stat. error only 0.95 0.95 0 500 1000 0 500 1000 Large-R jet p [GeV] Large-R jet p [GeV] T T (a) (b) Fig. 18 The simulation/data ratio of Rtrk for a large-R jet pT and b of in situ direct balance techniques. The jets in both samples correspond calorimeter mass as a function of the large-R jet transverse momentum to the same pseudorapidity range |η| < 2.0 and the same pT and jet mass pT . Two sets of results are derived from a dijet sample, dominated by intervals. The double ratio is constructed with the nominal Pythia 8 light-quark and gluon jets, and a t t¯ sample, where the large-R jets con- samples for dijet events and Powheg +Pythia 8 samples for the t t¯ tain a boosted W boson or top quark. The large-R jet pT is corrected sample. The error bars indicate statistical uncertainties using the simulation calibration, η-intercalibration, and a combination Table 5 Summary of the event Object Selection Description selection for the top quark events decaying into Muon (μ) Single-muon triggers Trigger lepton+jets, to be used for top μ and W mass calibration. The pT pT > 25 GeV, |η| < 2.5 Preselection bins into which events are tight muon ID Identification divided are also shown Rμ,jet > 0.4 Isolation E Tmiss E Tmiss > 20 GeV, m T > 60 GeV Small-R Jets j pT > 25 GeV Jet selection At least one jet with R( j, μ) < 1.5 Boosted top decay At least one b-tagged jet Flavour tagging Large-R Jets Rj,J > 2 Opposite hemispheres pT ∈ [200, 250] GeV, [250, 350] GeV Rb,J > 1 Boosted W sample Rb,J > 1 b-jet veto pT ∈ [350, 500] GeV, [500, 1000] GeV Rb,J < 1 Boosted top Rb,J < 1 b-jet matched central high- pT , isolated muon, and significant missing trans- of b- and c-hadrons inside the jet; the 70% signal tagging verse momentum (E Tmiss ) [72]. The W boson transverse mass efficiency working point is used here [73]. lep obtained from m 2T = 2 pT E Tmiss (1 − cos(φ)), where φ is The large-R jet mass distribution of the highest- pT large- the azimuthal angle between the charged lepton and the direc- R jet in the hemisphere opposite to the charged lepton is tion of the missing transverse momentum, must be greater shown in Fig. 19 for two categories of events, and for both than 60 GeV. A multivariate b-tagging algorithm is used to the calorimeter-only and track-assisted jet masses. For large- identify R = 0.4 jets which originate from the decays of b- R jets with intermediate pT (200 GeV < pT < 350 GeV), in quarks based on information about the impact parameters of Fig. 19a, c, the decay products of the hadronic W boson are inner detector tracks matched to the jet, the presence of dis- captured in a single large-R jet. For high- pT jets with pT > placed secondary vertices, and the reconstructed flight paths 350 GeV, in Fig. 19b, d, the complete hadronic top decay is 123 135 Page 18 of 42 Eur. Phys. J. C (2019) 79:135 Events / 5 GeV Events / 5 GeV 8000 ATLAS t t → μ + jets, |η det| < 2.0 ATLAS t t → μ + jets, |η det| < 2.0 s = 13 TeV, 36.2 fb-1 200 GeV < p T ≤ 350 GeV s = 13 TeV, 36.2 fb-1 350 GeV < p T ≤ 1000 GeV Trimmed R = 1.0 anti-k t b-jet veto: ΔR(b; jet) > 1 800 Trimmed R = 1.0 anti-k t b-jet tag: ΔR(b; jet) < 1 LCW+JES+JMS LCW+JES+JMS 6000 Data 600 Data tt tt Single top Single top 4000 W+jets 400 W+jets Other Other MC syst. error MC syst. error 2000 200 0 0 Data / MC Data / MC 2.5 1.5 2 1 1.5 1 0.5 0.5 0 20 40 60 80 100 120 140 160 180 200 0 50 100 150 200 250 300 350 Calorimeter jet mass [GeV] Calorimeter jet mass [GeV] (a) (b) Events / 5 GeV Events / 5 GeV ATLAS t t → μ + jets, |η det| < 2.0 800 ATLAS t t → μ + jets, |η det| < 2.0 8000 s = 13 TeV, 36.2 fb-1 200 GeV < p T ≤ 350 GeV s = 13 TeV, 36.2 fb-1 350 GeV < p T ≤ 1000 GeV Trimmed R = 1.0 anti-k t b-jet veto: ΔR(b; jet) > 1 Trimmed R = 1.0 anti-k t b-jet tag: ΔR(b; jet) < 1 LCW+JES+JMS LCW+JES+JMS 600 6000 Data Data tt tt Single top 400 Single top 4000 W+jets W+jets Other Other MC syst. error MC syst. error 2000 200 0 0 Data / MC Data / MC 2.5 1.5 2 1 1.5 1 0.5 0.5 0 20 40 60 80 100 120 140 160 180 200 0 50 100 150 200 250 300 350 Track-assisted jet mass [GeV] Track-assisted jet mass [GeV] (c) (d) Fig. 19 The distributions of the jet invariant mass for large-R jets in of in situ direct balance techniques. The template estimated from simula- samples enriched in a, c boosted W bosons and b, d boosted top quarks. tions is rescaled to match the observed yield. The lower panels display The distribution of the calorimeter mass is shown in (a) and (b), and the data-to-simulation ratio. The error bars on the data represent the the distribution of the track-assisted mass of the same jets is shown statistical uncertainty. The dashed uncertainty band on the simulation in (c) and (d). The large-R jet transverse momentum pT is corrected template includes the systematic uncertainties due to signal and detector using the simulation calibration, η-intercalibration, and a combination modelling captured in the main large-R jet. The high- pT W boson and The track-assisted mass (Eq. (1)) is obtained by scaling the top quark topologies are confirmed by, respectively, vetoing invariant mass of the charged-particle jet by the ratio of the or requiring a b-tagged small-R jet that overlaps with the pT of the calorimeter and charged-particle jets. The resulting large-R jet. jet mass distributions in the W boson and top quark large-R 123 Eur. Phys. J. C (2019) 79:135 Page 19 of 42 135 jet samples are presented in Fig. 19c, d. The selection for 1.15 MC / R data ATLAS s = 13 TeV, 36.2 fb-1, t t → μ + jets this second set of plots is entirely based on the properties of 1.1 m Trimmed R = 1.0 anti-k t (LCW+JES+JMS) the matched calorimeter jet, such that plots (a) and (c) and 1.05 plots (b) and (d) are populated by the same jets. The track- Rm 1 assisted mass peaks in (c) and (d) are slightly broader than 0.95 the calorimeter-based mass peaks in (a) and (b) for large-R 1.5 / σdata jets with a large invariant mass and relatively low pT . m The position and shape of the mass peaks provide infor- 1 MC mation about the large-R jet mass scale and resolution. Val- σm calorimeter (stat.+syst.) 0.5 track-assisted (stat.+syst.) ues for the ratio of the response in data and simulations m /R m ) and the ratio of the resolution in data and (s = Rdata MC [200,250] [250,350] [350,500] [500,1000] simulations (r = σdatam /σ m ) are extracted from the jet mass Large-R jet p [GeV] MC T spectrum. These two parameters are extracted simultane- Fig. 20 Summary of the in situ measurements of the large-R jet mass ously in a fit referred to as forward folding [10]. This method response in t t¯ events with a lepton+jets final state as a function of the produces simulation-based predictions of the jet mass spec- large-R jet transverse momentum pT . The large-R jet pT is corrected trum with variable response and resolution. This is achieved using the simulation calibration, η-intercalibration, and a combination by folding particle-level jets with a response function. The of in situ direct balance techniques. The closed circles correspond to the JMS and JMR of trimmed large-R jets reconstructed from calorimeter default response function is taken from the nominal simula- clusters. The open circles represent the equivalent result for the track- tions. The predicted detector-level jet mass spectrum for arbi- assisted mass. The dashed lines, corresponding to ±1% for the JMS and trary values of s and r is obtained by modifying the response ±10% for the JMR, are drawn for reference. The results in the first two function by pT bins (200 GeV < pT < 250 GeV and 250 GeV < pT < 350 GeV) correspond to a sample of high- pT W bosons, and the highest two    bins (350 GeV < pT < 500 GeV and 0.5 TeV < pT < 1 TeV) m fold = s m reco + m reco − m truth Rm m truth , pTtruth (r − s), correspond to high- pT top quarks. In each subsample, the JMS and JMR are extracted simultaneously in a two-parameter fit to the mass where m reco is the detector-level large-R jet mass and Rm is distribution. The statistical and total uncertainties are indicated with the small and large error bars on the data points, respectively the large-R jet mass response. The value of Rm is obtained from simulations, as discussed in Sect. 4. Typical values of Rm are in the range 0.8–1.5, depending on jet pT and mass. The forward-folding procedure does not require the response An in situ calibration is also derived for the track-assisted to be Gaussian. The scale factors s and r also modify the non- mass in a completely analogous fashion. The JMS and JMR Gaussian tails of the response function, if these are present results are shown with open circles in Fig. 20. The statis- in the simulations. tical and systematic uncertainties are indicated on the data The prediction from simulation is fit to the data by min- points. The systematic uncertainties are dominated by mod- imizing the χ 2 built with the predicted and observed distri- elling uncertainties and are expected to be strongly corre- butions. The best-fit values for s and r are taken as the data- lated between the two measurements. The in situ scales of to-simulation scale factors for the large-R jet mass response the two mass measurements are found to be within 1% for and jet mass resolution. This method has the advantage that all points and within 0.5% for three out of four. As the track- the response for the t t¯ events and events from other Standard assisted mass is primarily sensitive to the pT response of the Model processes is varied consistently. It was first applied to calorimeter, this level of agreement implies that the pT and 2012 data [10]. Further details of the forward-folding proce- mass scales are closely connected for these high-mass jets dure are in Refs. [43,74]. with relatively low pT . The results of the fits are shown in Fig. 20. The data sample Measurements of the pT response of high- pT W bosons is divided in several pT bins. The W boson peak is fitted in two or top quarks can be obtained directly by fitting the balance intervals: 200 GeV < pT < 250 GeV and 250 GeV < pT < distribution of the two top quark candidates. This provides 350 GeV. The top quark peak is fitted for pT between 350 and a cross-check of the direct balance methods discussed pre- 500 GeV and between 500 GeV and 1 TeV. The small error viously in Sects. 5.1–5.4 in a topology with a very different bars on the points represent the statistical uncertainty, and the radiation pattern. The reference system is formed by the b-jet, larger error bars represent the total uncertainty. The dominant the charged lepton, and the neutrino from the semileptonic systematic effect is expected to be due to the modelling of top quark decay. It is reconstructed by adding the four-vectors top quark pair production, estimated by repeating the analysis of the charged lepton, the leading (and possibly b-tagged) with Powheg + Herwig 7, Sherpa, and several variations small-R jet in a cone of size R = 1.5 around the charged of the generator settings that regulate the probability of hard lepton, and the neutrino [75]. The transverse momentum of initial- and final-state radiation. the neutrino is inferred by assigning the E Tmiss to the neu- 123 135 Page 20 of 42 Eur. Phys. J. C (2019) 79:135 1.15 events. This measurement follows the event selection crite- s = 13 TeV, 36.2 fb-1, t t → μ + jets / R data ATLAS ria outlined for the η-intercalibration provided in Sect. 5.1, T 1.1 p Trimmed R = 1.0 anti-k t (LCW+JES+JMS) 1.05 including the trigger strategy. The simulation calibration and R MC η-intercalibration described in Sects. 4 and 5.1 are applied T p 1 0.95 to the large-R jets studied here, and the large-R jet pT is 1.5 also corrected using the combination of the in situ direct bal- ance techniques discussed in Sects. 5.2, 5.3, and 5.4, which / σdata T p 1 is presented in Sect. 8. The asymmetry distribution of Sect. 5.1 is studied in dijet σMC T calorimeter (stat.+syst.) p 0.5 avg events in bins of the dijet system pT and the probe large-R [200,250] [250,350] [350,500] [500,1000] jet ηdet . The width of the asymmetry distribution depends on Large-R jet p [GeV] the resolution of the jet pT measurement and on the intrinsic T particle-level width, which arises due to balance fluctuations Fig. 21 Summary of the in situ measurements of the large-R jet and out-of-cone effects. Since the latter effect is uncorrelated response in t t¯ events with a lepton+jets final state as a function of the large-R jet transverse momentum pT . The closed circles correspond to with the detector response, the component of the asymmetry the JES and JER of trimmed large-R jets reconstructed from calorimeter width due to the detector resolution can be determined by clusters. The large-R jet pT is corrected using the simulation calibra- subtracting in quadrature the asymmetry width of particle- tion, η-intercalibration, and a combination of in situ direct balance tech- level (‘truth-level’) jets from that of reconstructed jets, giving niques. The error bars represent the total uncertainty. Statistical uncer- tainties are indicated with the inner error bar (only visible on some of  the points). The dashed lines, corresponding to ± 1% for the JES and σA,det = σA 2 ,reco − σA,truth . 2 ± 10% for the JER, are drawn for reference. The results in the first two pT bins (200 < pT < 250 GeV and 250 < pT < 350 GeV) correspond The jet energy resolution is measured in two ηdet bins: the to a sample of high- pT W bosons, and the highest two bins correspond central reference region |ηdet | < 0.8, denoted “ref”, and a to high- pT top quarks forward region 0.8 < |ηdet | < 2.0, denoted “fwd”. If both large-R jets are within the central reference region, they have trino pT , and its pz can be reconstructed using a W -mass the same pT resolution. In this case, the determination of constraint (but does not affect the balance measurement). the probe jet is arbitrary, and the assignment proceeds using The resulting balance distribution of the probe jet pT and the a random-number generator. Since both jets contribute the recoiling semileptonic top quark decay system has a distinc- same amount to the asymmetry distribution, the relative jet- tive peak around 1. The peak position is sensitive to the large- pT resolution of the reference region is defined by R jet energy scale, and its width is sensitive to the resolution. Measurements of the relative jet mass scale and resolution σ pT σA ref ,det obtained by fitting the balance distribution with the same = √ . pT ref 2 forward-folding technique are shown in Fig. 21, after the application of the in situ JES calibration derived from light The resolution of forward jets is extracted from the width quark and gluon jets (Sect. 5). The results are compatible with of the asymmetry distribution in events where a central unit JES within the precision of the measurement. This pro- reference jet balances a forward probe jet (in the region vides another confirmation that the Monte Carlo modelling 0.8 < |ηdet | < 2.0). The result is corrected for the reso- of the response of high- pT , hadronically decaying W bosons lution of central jets by subtracting the asymmetry of central or top quarks is adequate within 2–3%, and that a calibration dijet systems, giving derived from jets without hard substructure is applicable to topologies with hard substructure. σ pT (σA ref )2 ,det = (σA fwd )2 − ,det . (4) pT fwd 2 7 Measurement of the large-R jet pT resolution Figure 22 shows σA for reconstructed- and truth-level dijet avg The in situ measurement of the ATLAS jet pT resolution4 systems as a function of pT in two ηdet bins, as well as relies on a measurement that exploits the momentum bal- for data. For each of the event generators, the width of the ance between the leading and sub-leading large-R jets in dijet detector-level asymmetry is shown as a solid line, while the particle-level asymmetry is indicated by a dashed line. For forward jets, the additional correction shown in Eq. (4) is 4 The relative resolution σ ( pT )/ pT is equal to the relative energy res- olution σ (E)/E to a good approximation, and the term jet energy res- applied to account for the effect of the resolution of the large- olution is used to refer to both quantities. R jet within the central reference region. 123 Eur. Phys. J. C (2019) 79:135 Page 21 of 42 135 σA A ATLAS ATLAS σ s = 13 TeV, 36.2 fb-1, dijet s = 13 TeV, 36.2 fb-1, dijet 0.2 0.2 Trimmed anti-k t R = 1.0, (LCW+JES+JMS) Trimmed anti-k t R = 1.0, (LCW+JES+JMS) |η | < 0.8 Truth LCTopo 0.8 < |η | < 2.0 Truth LCTopo det det 0.15 Data Pythia8 0.15 Data Pythia8 avg. MC Herwig7 avg. MC Herwig7 Sherpa2.1 Sherpa2.1 0.1 0.1 0.05 0.05 1.2 1.2 MC/Data MC/Data 1.1 1.1 1 1 0.9 0.9 0.8 0.8 3 3 3× 102 10 3×102 10 avg avg p [GeV] p [GeV] T T (a) (b) Fig. 22 Width of the dijet asymmetry distribution obtained using erators Pythia 8, Herwig 7, and Sherpa 2.1. Also an unweighted reconstructed (σA,reco ) and particle-level jets (σA,truth ) as a function average of the three is shown. The large-R jet pT is corrected using the avg of the average jet transverse momentum pT . Results are shown a simulation calibration, η-intercalibration, and a combination of in situ for events where both jets have detector pseudorapidity in the range direct balance techniques. Statistical errors are usually smaller than the |ηdet | < 0.8 and b for events where the probe jet has 0.8 < |ηdet | < 2.0, size of the marker. The resolution at the particle level is also shown as and the reference jet is still within |ηdet | < 0.8. The measurement is a dashed line compared with the prediction from simulations based on the three gen- Following the correction for the particle-level width, the uncertainty is found to be a nearly constant 10% effect in the results of a fit to the asymmetry distribution obtained in data central region and to be 5–10% in the forward region. The and from several event generators (Pythia 8, Herwig 7, and φ requirement is also varied by ± 0.5, which has a small Sherpa 2.1) are shown in Fig. 23, where the measured rela- effect primarily for low- pT jets. The modelling uncertainty tive resolution σ ( pT )/ pT is plotted as a function of the aver- is estimated as the variation of the result when using differ- avg age pT of the two jets, pT . The correction for the particle- ent generators for the particle-level momentum imbalance, level resolution is estimated using the Pythia sample. The where Pythia 8 is chosen as a nominal sample and Herwig measured resolution in the central region is in fair agreement 7 and Sherpa 2.1 are chosen as the variations. with the predicted resolution. The resolution of forward jets in data and simulations is compatible within the observed uncertainties. The choice of event generator has a small effect 8 Combined large-R jet calibration results on the resolution. In Fig. 23, the relative pT resolution, pTreco / pTtrue , as pre- The measurements of the trimmed large-R jet response rel- dicted by the simulations is compared with the result of the ative to simulation obtained using the different in situ meth- extraction of the resolution from the asymmetry in simulated ods presented in Sects. 5 and 6 are combined to determine events. The difference between the two indicates a bias in the the relative jet energy and mass scales over a broad range method that is taken as an additional uncertainty (labelled of jet transverse momenta. The combination procedure is non-closure). described in detail in Ref. [76]. The total uncertainty in the determination of the JER is The data-to-simulation response ratios obtained from the shown in Fig. 24 as a function of the average pT and in γ +jet, Z +jet, and multijet balance methods are combined to the two ηdet regions. A breakdown of the uncertainties into produce a jet pT -dependent calibration curve. The uncertain- individual sources is presented. The large-R jet energy scale ties in the pT calibration are obtained by error propagation is varied according to its uncertainty, leading to a 10–15% of the uncertainties associated with the in situ methods. A variation in the measured resolution due to its impact on the jet mass calibration is derived analogously using the jet mass asymmetry (labelled as ‘JES uncertainty’). The non-closure response measurements provided by the forward-folding and Rtrk methods. 123 135 Page 22 of 42 Eur. Phys. J. C (2019) 79:135 0.15 0.15 Jet p resolution Jet p resolution ATLAS ATLAS -1 s = 13 TeV, 36.2 fb , dijet s = 13 TeV, 36.2 fb-1, dijet Trimmed anti-k t R = 1.0, (LCW+JES+JMS) Trimmed anti-k t R = 1.0, (LCW+JES+JMS) |η | < 0.8 Data 0.8 < |η | < 2.0 Data det det T T 0.1 0.1 MC (average) MC (average) Particle-level Particle-level 0.05 0.05 MC/Data MC/Data 1.2 1.2 1 1 0.8 0.8 3 3 3×102 10 3×102 10 avg avg p [GeV] p [GeV] T T (a) (b) Fig. 23 Comparison of the measured jet pT resolution with the resolu- using the in situ technique for an average of three simulations and their tion determined in simulation, averaged between different generators as envelope is also shown as a dark band. Inconsistencies between the a function of the average jet pT and in two bins of detector pseudorapid- resolution determined using the in situ technique and of the resolution ity ηdet from a |ηdet | < 0.8 and from b 0.8 < |ηdet | < 2.0. The large-R determined from the response in simulation by matching particle-level jet pT is corrected using the simulation calibration, η-intercalibration, jets to reco-level jets (light dotted line) are taken as an additional uncer- and a combination of in situ direct balance techniques. The error band, tainty in the measurement. The lines shown are obtained by smoothing drawn as a light band, represents the statistical and systematic uncer- a binned representation of these uncertainties using a sliding Gaussian tainties added in quadrature. The determination of the relative resolution kernel Fractional Jp R Uncertainty Fractional Jp R Uncertainty 0.3 ATLAS 0.3 ATLAS s = 13 TeV, 36.2 fb-1, dijet s = 13 TeV, 36.2 fb-1, dijet Trimmed anti-k t R = 1.0, (LCW+JES+JMS) Trimmed anti-k t R = 1.0, (LCW+JES+JMS) |η | < 0.8 Total Uncertainty JES uncertainty 0.8 < |η | < 2.0 Total Uncertainty JES uncertainty 0.2 det Fit Uncertainty Δφ selection 0.2 det Fit Uncertainty Δφ selection MC Modelling non-closure MC Modelling non-closure T T 0.1 0.1 0 0 3×102 4×102 103 3×102 4×102 103 p avg [GeV] p avg [GeV] T T (a) (b) Fig. 24 The relative uncertainty in the ratio of the jet transverse direct balance techniques. Contributions from three sources are esti- momentum pT resolution measured in dijet events and in simulations mated separately by propagating the uncertainty in the energy scale as a function of the average jet pT in pseudorapidity η bins a |η| < 0.8 to the measurement, by varying the φ selection, and by varying the and b 0.8 < |η| < 2.0. The large-R jet pT is corrected using the event generator. The lines shown are obtained by smoothing a binned simulation calibration, η-intercalibration, and a combination of in situ representation of these uncertainties using a sliding Gaussian kernel The measurements of the pT response are performed in transverse momentum and in two bins of the jet mass. The bins of the jet transverse momentum (the pTref values are combination proceeds in three steps which take into account translated to jet pT ) and evaluated inclusively in mass. The correlations between uncertainties and possible inconsisten- jet mass response combination is performed in bins of the jet cies between the in situ methods: 123 Eur. Phys. J. C (2019) 79:135 Page 23 of 42 135 p response ratio, R data / R MC 3 χ2/Ndof 1.04 ATLAS ATLAS s = 13 TeV, 36.2 fb-1 2.5 s = 13 TeV, 36.2 fb-1 1.02 Trimmed anti-k t R = 1.0 (LCW+JES+JMS) Trimmed anti-k t R = 1.0 (LCW+JES+JMS) 2 Z +jet, γ +jet, and Multijet 1 0.98 1.5 0.96 1 T γ +jet 0.94 Z +jet 0.5 Total uncertainty Statistical component Multijet 0.92 2×102 103 2×103 2×102 3×102 103 2×103 Large-R jet p [GeV] Large-R jet p [GeV] T T Fig. 27 The χ 2 /Ndof of the combination of in situ measurements of Fig. 25 Data-to-simulation ratio of the average jet pT response as a the relative jet- pT response of large-R jets in data and simulations as a function of large-R jet pT . The combined result (band) is based on function of the jet transverse momentum pT . The χ 2 /Ndof indicates the three in situ techniques: the Z +jet balance method (open squares), γ +jet level of tension between the results of the γ +jet and multijet balance balance method (closed triangles), and the multijet balance (open trian- methods in each pT bin. For transverse momenta beyond 1 TeV, only gles). The errors represent the statistical (inner error bars) and the total one method is available, and the χ 2 /Ndof goes to zero uncertainty (statistical and systematic uncertainties added in quadrature, outer error bars). The results apply to trimmed anti-kt jets with R = 1.0. The lines shown are smoothed using a sliding Gaussian kernel correlations by coherently shifting all correction factors by one standard deviation. The difference between the Relative weight in combination 1.4 ATLAS shifted-correction result and the nominal result provides s = 13 TeV, 36.2 fb-1 an estimate of the propagated systematic uncertainty. 1.2 Trimmed anti-k t R = 1.0 (LCW+JES+JMS) • Interpolation: The relative pT (mass) response is defined 1 in fine pT bins, separately for each in situ method using interpolating splines based on first- or second-order poly- 0.8 nomials. 0.6 • Averaging: The actual combination is carried out using a weighted average of the in situ measurements based 0.4 γ +jet on a χ 2 -minimization. The weights take into account the 0.2 Z +jet statistical and systematic uncertainties, as well as corre- Multijet lations and differing bin sizes. The local χ 2 is also useful 0 to define the level of agreement between in situ measure- 2×102 103 2×103 ments where they overlap. Large-R jet p [GeV] T Fig. 26 The weight assigned to different techniques in the combination The uncertainty sources are treated according to the Hes- of in situ measurements of the relative pT response of large-R jets in sian formalism: each uncertainty source is fully correlated data and simulations, as a function of the jet transverse momentum pT . across kinematic regions (i.e. as a function of pT and η) but For each pT bin, the weight of the Z +jet, γ +jet, and multijet balance is uncorrelated with other sources. Sources of uncertainty methods are shown. The slight discontinuities observed in the weights correspond to the onset of the multijet balance method at pT ∼ 300 GeV that affect both the small-R and large-R jet in situ calibration and to the upper end of the Z +jets method for pT ∼ 800 GeV aretreated as fully correlated. The reduced χ 2 is estimated as χ 2 /Ndof , where Ndof is the number of degrees of free- dom (in this case, the number of combined measurements • Simple Monte Carlo method: Pseudo-experiments are contributing to the average in a particular pT bin). In case created that represent the ensemble of measurements and of disagreement between different in situ measurements, i.e. contain the full data-treatment chain including interpo- when the reduced χ 2 value  is larger than 1, the uncertainty lation and averaging (described in the following steps). sources are rescaled by χ 2 /Ndof . These pseudo-experiments are used to consistently prop- A smoothing procedure using a variable-size sliding inter- agate all uncertainties into the evaluation of the aver- val with a Gaussian kernel is applied to the response ratio age. They are generated taking into account all known and its associated systematic uncertainties. This smoothing 123 135 Page 24 of 42 Eur. Phys. J. C (2019) 79:135 Relative uncertainties Relative uncertainties 0.02 0.02 ATLAS ATLAS 0.018 0.018 s = 13 TeV, 36.2 fb-1 s = 13 TeV, 36.2 fb-1 0.016 Trimmed anti-k t R = 1.0 (LCW+JES+JMS) 0.016 Trimmed anti-k t R = 1.0 (LCW+JES+JMS) 0.014 γ +jet, γ E -scale γ +jet, MC modelling 0.014 Z +jet, MC modelling Z +jet, Δφ γ +jet, Sub-leading jet veto γ +jet, γ purity Z +jet, Sub-leading jet veto Z +jet, e E -scale 0.012 0.012 Z +jet, μ E -resolution ID Z +jet, μ E -scale γ +jet, Out-of-cone γ +jet, γ E -resolution Z +jet, Pile-up (JVT) Z +jet, μ E -resolution MS 0.01 γ +jet, Δφ γ +jet, Veto overlap 0.01 Z +jet, e E -resolution Z +jet, Out-of-cone γ +jet, Pile-up (JVT) γ +jet, Statistical Z +jet, μ E -scale(charge) Z +jet, Statistical 0.008 0.008 Z +jet, μ E -scale(charge) residual 0.006 0.006 0.004 0.004 0.002 0.002 0 0 2×102 103 2×103 2×102 103 2×103 Large-R jet p [GeV] Large-R jet p [GeV] T T (a) (b) Fig. 28 Breakdown of the combined uncertainty in the large-R jet pT in combination, incorporating the weight of the method from which it response as a function of the jet transverse momentum pT , for the a originates. The nuisance parameters related to the γ +jet method (both γ +jet and b Z +jet analyses. Contributions are shown for each of the nui- directly and through their effect on the multijet balance) are shown in sance parameters of the γ +jet and Z +jet balance methods. The vertical the left panel, and those of the Z+jets method are shown in the right axis reflects the uncertainty introduced by a given nuisance parameter panel. The lines shown are smoothed using a sliding Gaussian kernel Relative uncertainties Relative uncertainties 0.02 ATLAS 0.03 ATLAS 0.018 s = 13 TeV, 36.2 fb-1 s = 13 TeV, 36.2 fb-1 0.016 Trimmed anti-k t R = 1.0 (LCW+JES+JMS) 0.025 Trimmed anti-k t R = 1.0 (LCW+JES+JMS) 0.014 Single particle high pT Flavour response MJB, MC modelling MJB, p asymmetry selection 0.012 T 0.02 Flavour composition Pile-up rho topology Dijet, modelling Dijet, Statistical MJB, p threshold T MJB, β selection 0.01 Pile-up p term T Pile-up offset NPV 0.015 Pile-up offset mu BJES response MJB, α selection MJB, Statistical 0.008 Relative non-closure Dijet, Non-closure 0.006 0.01 0.004 0.005 0.002 0 0 2×102 103 2×103 2×102 103 2×103 Large-R jet p [GeV] T Large-R jet p [GeV] T (a) (b) Fig. 29 Breakdown of the combined uncertainty in the large-R jet pT parameter in combination, incorporating the weight of the method from response as a function of the jet transverse momentum pT . Contribu- which it originates. Since the multijet balance method relies on the tions are shown for nuisance parameters of the multijet balance method small-R jet pT , nuisance parameters from all associated uncertainties for nuisance parameters a originating from the MJB selection and b are propagated. The lines shown are smoothed using a sliding Gaussian propagated from the small-R jets which constitute the recoil system. kernel The vertical axis reflects the uncertainty introduced by a given nuisance removes spikes due to statistical fluctuations in the measure- tion up to transverse momenta of approximately 500 GeV. ments, as well as discontinuities at the first and last point in Between 500 GeV and 1 TeV, the γ +jet balance receives the a given measurement. largest weight. At higher pT , the multijet balance method In Fig. 25, the ratio of the jet pT response in data and sim- acquires more weight in the combination. Beyond 1 TeV, it ulations is shown as a function of the jet transverse momen- provides the only measurement and extends the jet energy tum. Data points are shown for the γ +jet, Z +jet, and multijet scale beyond 2 TeV. balance methods, and the band corresponds to the result of The local χ 2 per degree of freedom in Fig. 27 quantifies the the combination. level of agreement between the three sets of measurements. The relative weight in the fit of the three methods is shown The results of the three methods agree in the whole pT range in Fig. 26. The Z +jet balance makes the largest contribu- 0.1 TeV < pT < 1 TeV, where all three provide results. 123 Eur. Phys. J. C (2019) 79:135 Page 25 of 42 135 The combined pT response in data is approximately 3% 1.25 Mass response ratio, R data / R MC ATLAS lower than in the simulation over most of the pT range. 1.2 s = 13 TeV, 36.2 fb-1 The deviation from unity in the data/MC ratio is significant, 1.15 Trimmed R = 1.0 anti-k t (LCW+JES+JMS) as the total uncertainty approaches 1% in the intermediate 1.1 50 < mjet < 120 GeV pT region. These observations are consistent with previous 1.05 in situ measurements of the R = 0.4 JES during Run 2 [9] with similar levels of associated uncertainty. At low pT , the 1 uncertainty reaches about 1% at 200 GeV. Above 1.5 TeV, 0.95 the uncertainty increases, reaching over 2% at 2.4 TeV. 0.9 A breakdown of the total JES uncertainty is presented 0.85 graphically in Figs. 28 and 29. This includes uncertainties in Total uncertainty R trk 0.8 Statistical component Forward Folding γ +jet, Z +jet, and multijet balance methods associated with 0.75 the simulation modelling, reference system construction and 3×102 103 2×103 calibration, and the event selection. Furthermore, as the large- Large-R jet p [GeV] R multijet balance method uses small-R jets as a reference T system, all nuisance parameters from the small-R jet cali- bration enter as uncertainties in the combination presented 1.25 Mass response ratio, R data / R MC ATLAS here. 1.2 s = 13 TeV, 36.2 fb-1 The combination of the jet mass response includes results 1.15 Trimmed R = 1.0 anti-k t (LCW+JES+JMS) from two methods. Forward folding provides four measure- 1.1 120 < mjet < 300 GeV ments in the pT range below 1 TeV. The Rtrk method takes 1.05 advantage of a large data sample and can be finely binned in 1 mass and pT , extending to over 2 TeV. The combined result is shown in Fig. 30 for two jet mass intervals: the plot in the 0.95 upper panel corresponds to the W boson mass window with 0.9 50 GeV < m < 120 GeV, and the lower panel corresponds to 0.85 the top quark mass window with 120 GeV < m < 300 GeV. 0.8 Total uncertainty R trk Statistical component Forward Folding The in situ jet mass calibration factor is defined from 0.75 the combined mass response shown in Figure 30 as cm = 3×102 103 2×103 m /R m . It is applied as a scale factor to the jet mass but RMC data Large-R jet p [GeV] T does not affect the jet momentum vector. The full calibration applied to large-R jets in data impacts the reconstructed jet Fig. 30 Data-to-simulation ratio of the average jet mass response as a energy, mass, pseudorapidity, and pT according to function of the large-R jet pT . Corrections using a combination of two in situ methods, the Rtrk and forward-folding approaches, are applied.    The fit is performed for large-R jet mass in the W mass range 50– E reco = cs E 02 + cJMS m 0 cm 2 −1 , 120 GeV (upper), and the top mass range 120–300 GeV (lower). The error bars represent the statistical and systematic uncertainties added m reco = cs cJMS cm m 0 , in quadrature. The results apply to anti-kt jets with R = 1.0 calibrated ηreco = η0 + η, with the LC+JES+JMS scheme. The lines shown are smoothed using a   sliding Gaussian kernel pTreco = cs E 02 − cJMS 2 m 20 cosh (η + η), where cs = cJES cabs crel is the product of several calibration the mass and pT ranges considered here. The level of preci- factors. The factor cJES corresponds to the simulation-based sion with which the JMS is measured depends on the kine- JES calibration, crel to the relative in situ correction obtained matic region in question. For large-R jets in the high-mass from the η-intercalibration, and cabs to the absolute in situ bin with pT between 400 GeV and 1 TeV, the uncertainties correction from the balance methods. All c-factors and the are 2–5%. In other kinematic regions the uncertainty is larger, factor η are smooth functions of the large-R jet kinematics. approaching 10% at high pT in both mass bins. The terms E 0 , m 0 , η0 and p0 refer to the jet properties prior The contributions of several sources to the uncertainty in to any calibration, as returned by the trimming algorithm. the combined jet mass scale are presented in Figs. 31 and 32. The measured JMS correction is consistent with unity In both the Rtrk and forward-folding techniques, the leading within the precision of the combined measurements. This systematic uncertainties are associated with uncertainties in suggests that the application of an in situ JES correction is the event generators across most of the pT range and for the sufficient to correct the JMS of these trimmed large-R jets in two mass intervals considered. 123 135 Page 26 of 42 Eur. Phys. J. C (2019) 79:135 Relative uncertainty Relative uncertainty 0.14 0.04 ATLAS ATLAS s = 13 TeV, 36.2 fb-1 0.035 s = 13 TeV, 36.2 fb-1 0.12 Trimmed R = 1.0 anti-k t (LCW+JES+JMS) Trimmed R = 1.0 anti-k t (LCW+JES+JMS) 50 < mjet < 120 GeV 0.03 50 < mjet < 120 GeV 0.1 R trk, MC modelling Fw. folding, MC modelling 0.025 Fw. folding, Event selection 0.08 R trk, Tracking q / p T R trk, Track efficiency Fw. folding, Parton shower 0.02 Fw. folding, Radiation 0.06 R trk, Tracking fake rates R trk, Statistical 0.015 Small-R JER 0.04 Small-R JES 0.01 Fw. folding, Statistical 0.02 0.005 0 0 3×102 103 2×103 3×102 103 2×103 Large-R jet p [GeV] Large-R jet p [GeV] T T (a) (b) Fig. 31 Breakdown of the combined JMS uncertainty shown in Fig. 30 the uncertainty introduced by a given nuisance parameter in combina- as a function of jet transverse momentum pT for the jet mass bin 50– tion, incorporating the weight of the method from which it originates. 120 GeV. Contributions are shown for each of the nuisance parameters This weight is dominated at high pT by the Rtrk method. The lines of the a Rtrk and b forward-folding methods. The vertical axis reflects shown are smoothed using a sliding Gaussian kernel Relative uncertainty Relative uncertainty 0.14 0.04 ATLAS ATLAS s = 13 TeV, 36.2 fb-1 0.035 s = 13 TeV, 36.2 fb-1 0.12 Trimmed R = 1.0 anti-k t (LCW+JES+JMS) Trimmed R = 1.0 anti-k t (LCW+JES+JMS) 120 < mjet < 300 GeV 0.03 120 < mjet < 300 GeV 0.1 R trk, MC modelling Fw. folding, MC modelling 0.025 Fw. folding, Event selection 0.08 R trk, Track efficiency R trk, Tracking fake rates Fw. folding, Parton shower 0.02 Fw. folding, Radiation 0.06 R trk, Tracking q / p T R trk, Statistical 0.015 Small-R JER 0.04 Small-R JES 0.01 Fw. folding, Statistical 0.02 0.005 0 0 3×102 103 2×103 3×102 103 2×103 Large-R jet p [GeV] Large-R jet p [GeV] T T (a) (b) Fig. 32 Breakdown of the combined JMS uncertainty shown in Fig. 30 the uncertainty introduced by a given nuisance parameter in combina- as a function of jet transverse momentum pT for the jet mass bin 120– tion, incorporating the weight of the method from which it originates. 300 GeV. Contributions are shown for each of the nuisance parameters This weight is dominated at high pT by the Rtrk method. The lines of the a Rtrk and b forward-folding methods. The vertical axis reflects shown are smoothed using a sliding Gaussian kernel 9 Conclusion the independence of measurements performed with differ- ent sub-detectors, or the position and width of known mass Several in situ calibration methods are used to measure the peaks. With this ensemble of techniques, dedicated jet energy response of the ATLAS detector to trimmed large-R jets scale and jet mass scale calibrations are derived for large-R √ using 36.2 fb−1 of s = 13 TeV proton–proton collision data jets. The results of several techniques applied to a variety of provided by the LHC and collected by the ATLAS experi- final states are consistent within the uncertainties, indicat- ment during 2015 and 2016. These methods exploit the trans- ing that after calibration, the simulations model the flavour verse momentum balance in events where a jet recoils against dependence of the jet pT and mass response to within a few a reference system with a precisely known energy scale, percent. 123 Eur. Phys. J. C (2019) 79:135 Page 27 of 42 135 The results of all methods are combined taking into entific output is published in journals, and preliminary results are made account correlations between uncertainties and possible dis- available in Conference Notes. All are openly available, without restric- tion on use by external parties beyond copyright law and the standard crepancies between the results of different in situ methods. conditions agreed by CERN. Data associated with journal publications The combined measurement of the ratio of the energy scales are also made available: tables and data from plots (e.g. cross section in data and simulations are used to derive an in situ correc- values, likelihood profiles, selection efficiencies, cross section limits, tion to the response, which determines the large-R jet energy ...) are stored in appropriate repositories such as HEPDATA (http:// hepdata.cedar.ac.uk/). ATLAS also strives to make additional material and mass scales. The residual uncertainty in the ratio of the related to the paper available that allows a reinterpretation of the data energy scales in data and simulations is 1–2% for transverse in the context of new theoretical models. For example, an extended momenta from 150 GeV to 2 TeV. The precision of the jet encapsulation of the analysis is often provided for measurements in the mass scale varies from 2 to 10% over the same pT range. framework of RIVET (http://rivet.hepforge.org/).” This information is taken from the ATLAS Data Access Policy, which is a public docu- The results of the simulations for jet pT and mass resolution ment that can be downloaded from http://opendata.cern.ch/record/413 are also validated in situ and found to agree with the mea- [opendata.cern.ch].] sured resolution within 10–15%. The in situ JES calibration, derived from light quark and gluon jets, is found to fully cor- Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecomm rect the energy and mass scales of high pT W bosons and top ons.org/licenses/by/4.0/), which permits unrestricted use, distribution, quarks to within the precision of the present measurement and reproduction in any medium, provided you give appropriate credit (1–3%). to the original author(s) and the source, provide a link to the Creative Large-R jets are a vital ingredient of the ATLAS physics Commons license, and indicate if changes were made. Funded by SCOAP3 . programme. This new in situ calibration leads to significantly reduced uncertainties in the reconstructed large-R jet pT and mass, thus increasing the sensitivity of searches and the pre- cision of Standard Model measurements using large-R jets. References 1. A.J. Larkoski, I. Moult, B. Nachman, jet substructure at the large Acknowledgements We thank CERN for the very successful oper- hadron collider: a review of recent advances in theory and machine ation of the LHC, as well as the support staff from our institutions learning (2017). arXiv:1709.04464 [hep-ph] without whom ATLAS could not be operated efficiently. We acknowl- 2. L. Asquith et al., Jet substructure at the large hadron collider: exper- edge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, imental review (2018). arXiv:1803.06991 [hep-ex] Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, 3. ATLAS Collaboration, Performance of jet substructure√ techniques Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; for large-R jets in proton–proton collisions at s = 7 TeV using CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIEN- the ATLAS detector. JHEP 09, 076 (2013). arXiv:1306.4945 [hep- CIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Repub- ex] lic; DNRF and DNSRC, Denmark; IN2P3-CNRS, CEA-DRF/IRFU, 4. ATLAS Collaboration, Performance of top-quark and W - France; SRNSFG, Georgia; BMBF, HGF, and MPG, Germany; GSRT, boson tagging with ATLAS in Run 2 of the LHC (2018). Greece; RGC, Hong Kong SAR, China; ISF and Benoziyo Center, arXiv:1808.07858 [hep-ex] Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, 5. ATLAS Collaboration, Topological cell clustering in the ATLAS Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portu- calorimeters and its performance in LHC Run 1. Eur. Phys. J. C gal; MNE/IFA, Romania; MES of Russia and NRC KI, Russian Federa- 77, 490 (2017). arXiv:1603.02934 [hep-ex] tion; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slove- 6. M. Cacciari, G.P. Salam, G. Soyez, The anti-kt jet clustering algo- nia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg rithm. JHEP 04, 063 (2008). arXiv:0802.1189 [hep-ph] Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, 7. D. Krohn, J. Thaler, L.T. Wang, Jet trimming. JHEP 02, 084 (2010). Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; arXiv:0912.1342 [hep-ph] DOE and NSF, United States of America. In addition, individual groups 8. ATLAS √ Collaboration, Jet mass and substructure of inclusive jets and members have received support from BCKDF, CANARIE, CRC in s = 7 TeV pp collisions with the ATLAS experiment. JHEP and Compute Canada, Canada; COST, ERC, ERDF, Horizon 2020, and 05, 128 (2012). arXiv:1203.4606 [hep-ex] Marie Skłodowska-Curie Actions, European Union; Investissements d’ 9. ATLAS Collaboration, Jet energy scale measurements and √ their Avenir Labex and Idex, ANR, France; DFG and AvH Foundation, Ger- systematic uncertainties in proton–proton collisions at s = many; Herakleitos, Thales and Aristeia programmes co-financed by EU- 13 TeV with the ATLAS detector. Phys. Rev. D 96, 072,002 (2017). ESF and the Greek NSRF, Greece; BSF-NSF and GIF, Israel; CERCA arXiv:1703.09665 [hep-ex] Programme Generalitat de Catalunya, Spain; The Royal Society and 10. ATLAS Collaboration, Measurement √ of large radius jet mass recon- Leverhulme Trust, United Kingdom. The crucial computing support struction performance at s = 8 TeV using the ATLAS detec- from all WLCG partners is acknowledged gratefully, in particular from tor. ATLAS-CONF-2016-008 (2016). https://cds.cern.ch/record/ CERN, the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Den- 2139642 mark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), 11. ATLAS Collaboration, Performance of the ATLAS trigger system INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Tai- in 2015. Eur. Phys. J. C 77, 317 (2017). arXiv:1611.09661 [hep-ex] wan), RAL (UK) and BNL (USA), the Tier-2 facilities worldwide and 12. ATLAS Collaboration, The ATLAS experiment at the CERN large large non-WLCG resource providers. Major contributors of computing hadron collider. JINST 3, S08003 (2008) resources are listed in Ref. [77]. 13. ATLAS Collaboration, The ATLAS simulation infrastructure. Eur. Data Availability Statement This manuscript has no associated data Phys. J. C 70, 823 (2010). arXiv:1005.4568 [physics.ins-det] or the data will not be deposited. [Authors’ comment: “All ATLAS sci- 14. S. Agostinelli et al., GEANT4—a simulation toolkit. Nucl. Instrum. Methods A 506, 250–303 (2003) 123 135 Page 28 of 42 Eur. Phys. J. C (2019) 79:135 15. T. Sjöstrand et al., An introduction to PYTHIA 8.2. Comput. Phys. and jet mass in ATLAS. ATLAS-CONF-2015-037 (2015). https:// Commun. 191, 159–177 (2015). arXiv:1410.3012 [hep-ph] cds.cern.ch/record/2044941 16. S. Alioli, P. Nason, C. Oleari, E. Re, A general framework for 41. ATLAS Collaboration, Performance of the ATLAS inner detec- implementing NLO calculations in shower Monte Carlo programs: tor track and vertex reconstruction in high pile-up LHC envi- the POWHEG BOX. JHEP 06, 043 (2010). arXiv:1002.2581 [hep- ronment. ATLAS-CONF-2012-042 (2012). https://cds.cern.ch/ ph] record/1435196 17. S. Frixione, P. Nason, C. Oleari, Matching NLO QCD computations 42. ATLAS Collaboration, √ Early inner detector tracking performance with parton shower simulations: the POWHEG method. JHEP 11, in the 2015 data at s = 13 TeV. ATL-PHYS-PUB-2015-051 070 (2007). arXiv:0709.2092 [hep-ph] (2015). https://cds.cern.ch/record/2110140 18. P. Nason, A new method for combining NLO QCD with 43. ATLAS Collaboration, Jet mass reconstruction with the ATLAS shower Monte Carlo algorithms. JHEP 11, 040 (2004). Detector in early Run 2 data. ATLAS-CONF-2016-035 (2016). arXiv:hep-ph/0409146 https://cds.cern.ch/record/2200211 19. ATLAS Collaboration, ATLAS Pythia 8 tunes to 7 TeV data. ATL- 44. M. Son, C. Spethmann, B. Tweedie, Diboson-jets and the search PHYS-PUB-2014-021 (2014). https://cds.cern.ch/record/1966419 for resonant Zh production. JHEP 08, 160 (2012). arXiv:1204.0525 20. R.D. Ball et al., Parton distributions for the LHC Run II. JHEP 04, [hep-ph] 040 (2015). arXiv:1410.8849 [hep-ph] 45. A. Katz, M. Son, B. Tweedie, Jet substructure and the search for 21. M. Bahr et al., Herwig++ physics and manual. Eur. Phys. J. C 58, neutral spin-one resonances in electroweak Boson channels. JHEP 639 (2008). arXiv:0803.0883 [hep-ph] 03, 011 (2011). arXiv:1010.5253 [hep-ph] 22. T. Gleisberg et al., Event generation with SHERPA 1.1. JHEP 02, 46. S. Schätzel, M. Spannowsky, Tagging highly boosted top quarks. 007 (2009). arXiv:0811.4622 [hep-ph] Phys. Rev. D 89, 014007 (2014). arXiv:1308.0540 [hep-ph] 23. S. Gieseke, C. Röhr, A. Siódmok, Colour reconnections in Her- 47. T. Plehn, M. Spannowsky, M. Takeuchi, D. Zerwas, Stop recon- wig++. Eur. Phys. J. C 72, 2225 (2012). arXiv:1206.0041 [hep-ph] struction with tagged tops. JHEP 10, 078 (2010). arXiv:1006.2833 24. J. Pumplin et al., New generation of parton distributions with [hep-ph] uncertainties from global QCD analysis. JHEP 07, 012 (2002). 48. T. Plehn, G.P. Salam, M. Spannowsky, Fat Jets for a Light Higgs. arXiv:hep-ph/0201195 Phys. Rev. Lett. 104, 111801 (2010). arXiv:0910.5472 [hep-ph] 25. S. Catani, F. Krauss, R. Kuhn, B.R. Webber, QCD matrix elements 49. A.J. Larkoski, F. Maltoni, M. Selvaggi, Tracking down hyper- + parton showers. JHEP 11, 063 (2001). arXiv:hep-ph/0109231 boosted top quarks. JHEP 06, 032 (2015). arXiv:1503.03347 [hep- 26. H.L. Lai et al., New parton distributions for collider physics. Phys. ph] Rev. D 82, 074024 (2010). arXiv:1007.2241 [hep-ph] 50. S. Bressler, T. Flacke, Y. Kats, S.J. Lee, G. Perez, Hadronic 27. ATLAS Collaboration, Measurement of the Z√/γ ∗ boson transverse calorimeter shower size: challenges and opportunities for jet sub- momentum distribution in pp collisions at s = 7 TeV with the structure in the superboosted regime. Phys. Lett. B 756, 137 (2016). ATLAS detector. JHEP 09, 145 (2014). arXiv:1406.3660 [hep-ex] arXiv:1506.02656 [hep-ph] 28. D.J. Lange, The EvtGen particle decay simulation package. Nucl. 51. B.T. Elder, J. Thaler, Aspects of track-assisted mass (2018). Instrum. Methods A 462, 152 (2001) arXiv:1805.11109 [hep-ph] 29. N. Davidson, T. Przedzinski, Z. Was, PHOTOS interface in C++: 52. M. Cacciari, G.P. Salam, G. Soyez, The catchment area of jets. technical and physics documentation (2010). arXiv:1011.0937 JHEP 04, 005 (2008). arXiv:0802.1188 [hep-ph] [hep-ph] 53. ATLAS Collaboration, Jet energy measurement √ and its systematic 30. T. Gleisberg, S. Höche, Comix, a new matrix element generator. uncertainty in proton–proton collisions at s = 7 TeV with the JHEP 12, 039 (2008). arXiv:0808.3674 [hep-ph] ATLAS detector. Eur. Phys. J. C 75, 17 (2015). arXiv:1406.0076 31. F. Cascioli, P. Maierhofer, S. Pozzorini, Scattering amplitudes with [hep-ex] open loops. Phys. Rev. Lett. 108, 111601 (2012). arXiv:1111.5206 54. ATLAS Collaboration, Performance √ of pile-up mitigation tech- [hep-ph] niques for jets in pp collisions at s = 8 TeV using the ATLAS 32. S. Schumann, F. Krauss, A parton shower algorithm based detector. Eur. Phys. J. C 76, 581 (2016). arXiv:1510.03823 [hep-ex] on Catani–Seymour dipole factorisation. JHEP 03, 038 (2008). 55. ATLAS Collaboration, Tagging and suppression of pileup jets with arXiv:0709.1027 [hep-ph] the ATLAS detector. ATLAS-CONF-2014-018 (2014). https://cds. 33. S. Höche, F. Krauss, M. Schönherr, F. Siegert, QCD matrix ele- cern.ch/record/1700870 ments + parton showers: the NLO case. JHEP 04, 027 (2013). 56. ATLAS Collaboration, Electron efficiency measurements with the arXiv:1207.5030 [hep-ph] ATLAS detector using 2012 LHC proton–proton collision data. 34. S. Frixione, P. Nason, G. Ridolfi, A positive-weight next-to- Eur. Phys. J. C 77, 195 (2017). arXiv:1612.01456 [hep-ex] leading-order Monte Carlo for heavy flavour hadroproduction. 57. ATLAS Collaboration, Electron efficiency measurements with the JHEP 09, 126 (2007). arXiv:0707.3088 [hep-ph] ATLAS detector using the 2015 LHC proton–proton collision 35. ATLAS Collaboration, Summary of ATLAS Pythia 8 tunes. ATL- data. ATLAS-CONF-2016-024 (2016). https://cds.cern.ch/record/ PHYS-PUB-2012-003 (2012). https://cds.cern.ch/record/1474107 2157687 36. A.D. Martin, W.J. Stirling, R.S. Thorne, G. Watt, Parton distribu- 58. ATLAS Collaboration, Muon reconstruction performance √ of the tions for the LHC. Eur. Phys. J. C 63, 189 (2009). arXiv:0901.0002 ATLAS detector in proton–proton collision data at s = 13 TeV. [hep-ph] Eur. Phys. J. C 76, 292 (2016). arXiv:1603.05598 [hep-ex] 37. M. Cacciari, G.P. Salam, G. Soyez, FastJet user manual. Eur. Phys. 59. ATLAS Collaboration, Photon identification in 2015 ATLAS data. J. C 72, 1896 (2012). arXiv:1111.6097 [hep-ph] ATL-PHYS-PUB-2016-014 (2016). https://cds.cern.ch/record/ 38. M. Cacciari, G.P. Salam, Dispelling the N 3 myth for the kt jet- 2203125 finder. Phys. Lett. B 641, 57 (2006). arXiv:hep-ph/0512210 60. ATLAS Collaboration, Measurement of the photon identification 39. S.D. Ellis, D.E. Soper, Successive combination jet algo- efficiencies with the ATLAS detector using LHC Run-1 data. Eur. rithm for hadron collisions. Phys. Rev. D 48, 3160 (1993). Phys. J. C 76, 666 (2016). arXiv:1606.01813 [hep-ex] arXiv:hep-ph/9305266 61. ATLAS Collaboration, Measurement of the inclusive √ isolated 40. ATLAS Collaboration, Monte Carlo calibration and combination prompt photon cross section in pp collisions at s = 7 TeV of in-situ measurements of jet energy scale, jet energy resolution with the ATLAS detector. Phys. Rev. D 83, 052005 (2011). arXiv:1012.4389 [hep-ex] 123 Eur. Phys. J. C (2019) 79:135 Page 29 of 42 135 66. ATLAS Collaboration, Track √ reconstruction performance of the 72. ATLAS Collaboration, Performance of missing transverse momen- ATLAS inner detector at s = 13 TeV. ATL-PHYS-PUB-2015- tum reconstruction √ with the ATLAS detector using proton–proton 018 (2015). https://cds.cern.ch/record/2037683 collisions at s = 13 TeV (2018) arXiv:1802.08168 [hep-ex] 67. ATLAS Collaboration, Performance of the ATLAS track recon- 73. ATLAS Collaboration, Measurements of b-jet √ tagging efficiency struction algorithms in dense environments in LHC Run 2. Eur. with the ATLAS detector using t t¯ events at s = 13 TeV. JHEP Phys. J. C 77, 673 (2017). arXiv:1704.07983 [hep-ex] 08, 089 (2018). arXiv:1805.01845 [hep-ex] 68. ATLAS Collaboration, Studies of radial distortions of the ATLAS 74. ATLAS Collaboration, In-situ measurements of the ATLAS large- inner detector. ATL-PHYS-PUB-2018-003 (2018). https://cds. radius jet response in 13 TeV pp collisions. ATLAS-CONF-2017- cern.ch/record/2309785 063 (2017). https://cds.cern.ch/record/2275655 69. ATLAS Collaboration, Identification of boosted, hadronically 75. ATLAS Collaboration, A search for tt resonances√ using lepton- decaying √ W bosons and comparisons with ATLAS data taken at plus-jets events in proton–proton collisions at s = 8 TeV with s = 8 TeV. Eur. Phys. J. C 76, 154 (2016). arXiv:1510.05821 the ATLAS detector. JHEP 08, 148 (2015). arXiv:1505.07018 [hep- [hep-ex] ex] 70. ATLAS Collaboration, Identification √ of high transverse momen- 76. ATLAS Collaboration, Jet energy measurement √ with the ATLAS tum top quarks in pp collisions at s = 8 TeV with the ATLAS detector in proton–proton collisions at s = 7 TeV. Eur. Phys. J. detector. JHEP 06, 093 (2016). arXiv:1603.03127 [hep-ex] C 73, 2304 (2013). arXiv:1112.6426 [hep-ex] 71. ATLAS Collaboration, Search for heavy particles decaying into 77. ATLAS Collaboration, ATLAS computing acknowledgements. top-quark√ pairs using lepton-plus-jets events in proton–proton col- ATL-GEN-PUB-2016-002. https://cds.cern.ch/record/2202407 lisions at s = 13 TeV with the ATLAS detector. Eur. Phys. J. C 78, 565 (2018). arXiv:1804.10823 [hep-ex] ATLAS Collaboration M. Aaboud34d , G. Aad99 , B. Abbott124 , O. Abdinov13,* , B. Abeloos128 , D. K. Abhayasinghe91 , S. H. Abidi164 , O. S. AbouZeid39 , N. L. Abraham153 , H. Abramowicz158 , H. Abreu157 , Y. Abulaiti6 , B. S. Acharya64a,64b,n , S. Adachi , L. Adam , L. Adamczyk , J. Adelman , M. Adersberger , A. Adiguzel12c,af , T. Adye141 , 160 97 81a 119 112 A. A. Affolder143 , Y. Afik157 , C. Agheorghiesei27c , J. A. Aguilar-Saavedra136a,136f , F. Ahmadov77,ad , G. Aielli71a,71b , S. Akatsuka83 , T. P. A. Åkesson94 , E. Akilli52 , A. V. Akimov108 , G. L. Alberghi23a,23b , J. Albert173 , P. Albicocco49 , M. J. Alconada Verzini86 , S. Alderweireldt117 , M. Aleksa35 , I. N. Aleksandrov77 , C. Alexa27b , T. Alexopoulos10 , M. Alhroob124 , B. Ali138 , G. Alimonti66a , J. Alison36 , S. P. Alkire145 , C. Allaire128 , B. M. M. Allbrooke153 , B. W. Allen127 , P. P. Allport21 , A. Aloisio67a,67b , A. Alonso39 , F. Alonso86 , C. Alpigiani145 , A. A. Alshehri55 , M. I. Alstaty99 , B. Alvarez Gonzalez35 , D. Álvarez Piqueras171 , M. G. Alviggi67a,67b , B. T. Amadio18 , Y. Amaral Coutinho78b , A. Ambler101 , L. Ambroz131 , C. Amelung26 , D. Amidei103 , S. P. Amor Dos Santos136a,136c , S. Amoroso44 , C. S. Amrouche , C. Anastopoulos , L. S. Ancu , N. Andari , T. Andeen , C. F. Anders , J. K. Anders20 , 52 146 52 142 11 59b K. J. Anderson36 , A. Andreazza66a,66b , V. Andrei59a , C. R. Anelli173 , S. Angelidakis37 , I. Angelozzi118 , A. Angerami38 , A. V. Anisenkov120a,120b , A. Annovi69a , C. Antel59a , M. T. Anthony146 , M. Antonelli49 , D. J. A. Antrim168 , 70a 79 171 35 F. Anulli , M. Aoki , J. A. Aparisi Pozo , L. Aperio Bella , G. Arabidze , J. P. Araque 104 136a , V. Araujo Ferraz78b , R. Araujo Pereira , A. T. H. Arce , R. E. Ardell , F. A. Arduh , J-F. Arguin , S. Argyropoulos , A. J. Armbruster35 , 78b 47 91 86 107 75 L. J. Armitage90 , A Armstrong168 , O. Arnaez164 , H. Arnold118 , M. Arratia31 , O. Arslan24 , A. Artamonov109,* , G. Artoni131 , S. Artz97 , S. Asai160 , N. Asbah57 , E. M. Asimakopoulou169 , L. Asquith153 , K. Assamagan29 , R. Astalos28a , R. J. Atkin32a , M. Atkinson170 , N. B. Atlay148 , K. Augsten138 , G. Avolio35 , R. Avramidou58a , M. K. Ayoub15a , G. Azuelos107,aq , A. E. Baas59a , M. J. Baca21 , H. Bachacou142 , K. Bachas65a,65b , M. Backes131 , P. Bagnaia70a,70b , M. Bahmani82 , H. Bahrasemani149 , A. J. Bailey171 , J. T. Baines141 , M. Bajic39 , C. Bakalis10 , O. K. Baker180 , P. J. Bakker118 , D. Bakshi Gupta93 , S. Balaji154 , E. M. Baldin120a,120b , P. Balek177 , F. Balli142 , W. K. Balunas133 , J. Balz97 , E. Banas82 , A. Bandyopadhyay24 , S. Banerjee178,j , A. A. E. Bannoura179 , L. Barak158 , W. M. Barbe37 , E. L. Barberio102 , D. Barberis53a,53b , M. Barbero99 , T. Barillari113 , M-S. Barisits35 , J. Barkeloo127 , T. Barklow150 , R. Barnea157 , S. L. Barnes58c , B. M. Barnett141 , R. M. Barnett18 , Z. Barnovska-Blenessy58a , A. Baroncelli72a , G. Barone26 , A. J. Barr131 , L. Barranco Navarro171 , F. Barreiro96 , J. Barreiro Guimarães da Costa15a , R. Bartoldus150 , A. E. Barton87 , P. Bartos28a , A. Basalaev134 , A. Bassalat128 , R. L. Bates55 , S. J. Batista164 , S. Batlamous34e , J. R. Batley31 , M. Battaglia143 , M. Bauce70a,70b , F. Bauer142 , K. T. Bauer168 , H. S. Bawa150,l , J. B. Beacham122 , T. Beau132 , P. H. Beauchemin167 , P. Bechtle24 , H. C. Beck51 , H. P. Beck20,p , K. Becker50 , M. Becker97 , C. Becot44 , A. Beddall12d , A. J. Beddall12a , V. A. Bednyakov77 , M. Bedognetti118 , C. P. Bee152 , T. A. Beermann35 , 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 , 123 135 Page 30 of 42 Eur. Phys. J. C (2019) 79:135 L. Beresford131 , M. Beretta49 , D. Berge44 , E. Bergeaas Kuutmann169 , N. Berger5 , L. J. Bergsten26 , J. Beringer18 , S. Berlendis7 , N. R. Bernard100 , G. Bernardi132 , C. Bernius150 , F. U. Bernlochner24 , T. Berry91 , P. Berta97 , C. Bertella15a , G. Bertoli43a,43b , I. A. Bertram87 , G. J. Besjes39 , O. Bessidskaia Bylund179 , M. Bessner44 , N. Besson142 , A. Bethani98 , S. Bethke113 , A. Betti24 , A. J. Bevan90 , J. Beyer113 , R. Bi135 , R. M. B. Bianchi135 , O. Biebel112 , D. Biedermann19 , R. Bielski35 , K. Bierwagen97 , N. V. Biesuz69a,69b , M. Biglietti72a , T. R. V. Billoud107 , M. Bindi51 , A. Bingul12d , C. Bini70a,70b , S. Biondi23a,23b , 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. Bobrovnikov120a,120b , S. S. Bocchetta94 , A. Bocci47 , D. Boerner179 , D. Bogavac112 , A. G. Bogdanchikov120a,120b , C. Bohm43a , V. Boisvert91 , P. Bokan169,w , T. Bold81a , A. S. Boldyrev111 , A. E. Bolz59b , M. Bomben132 , M. Bona90 , J. S. Bonilla127 , M. Boonekamp142 , A. Borisov140 , G. Borissov87 , J. Bortfeldt35 , D. Bortoletto131 , V. Bortolotto71a,71b , D. Boscherini23b , M. Bosman14 , J. D. Bossio Sola30 , K. Bouaouda34a , J. Boudreau135 , E. V. Bouhova-Thacker87 , D. Boumediene37 , C. Bourdarios128 , S. K. Boutle55 , A. Boveia122 , J. Boyd35 , D. Boye32b , I. R. Boyko77 , A. J. Bozson91 , J. Bracinik21 , N. Brahimi99 , A. Brandt8 , G. Brandt179 , O. Brandt59a , F. Braren44 , U. Bratzler161 , B. Brau100 , J. E. Brau127 , W. D. Breaden Madden55 , K. Brendlinger44 , L. Brenner44 , R. Brenner169 , S. Bressler177 , B. Brickwedde97 , D. L. Briglin21 , D. Britton55 , D. Britzger59b , I. Brock24 , R. Brock104 , G. Brooijmans38 , T. Brooks91 , W. K. Brooks144b , E. Brost119 , J. H Broughton21 , P. A. Bruckman de Renstrom82 , D. Bruncko28b , A. Bruni23b , G. Bruni23b , L. S. Bruni118 , S. Bruno71a,71b , B.H. Brunt31 , M. Bruschi23b , N. Bruscino135 , P. Bryant36 , L. Bryngemark44 , T. Buanes17 , Q. Buat35 , P. Buchholz148 , A. G. Buckley55 , I. A. Budagov77 , F. Buehrer50 , M. K. Bugge130 , O. Bulekov110 , D. Bullock8 , T. J. Burch119 , S. Burdin88 , C. D. Burgard118 , A. M. Burger5 , B. Burghgrave119 , K. Burka82 , S. Burke141 , I. Burmeister45 , J. T. P. Burr131 , V. Büscher97 , E. Buschmann51 , P. Bussey55 , J. M. Butler25 , C. M. Buttar55 , J. M. Butterworth92 , P. Butti35 , W. Buttinger35 , A. Buzatu155 , A. R. Buzykaev120a,120b , G. Cabras23a,23b , S. Cabrera Urbán171 , D. Caforio138 , H. Cai170 , V. M. M. Cairo2 , O. Cakir4a , N. Calace52 , P. Calafiura18 , A. Calandri99 , G. Calderini132 , P. Calfayan63 , G. Callea40a,40b , L. P. Caloba78b , S. Calvente Lopez96 , D. Calvet37 , S. Calvet37 , T. P. Calvet152 , M. Calvetti69a,69b , R. Camacho Toro132 , S. Camarda35 , P. Camarri71a,71b , D. Cameron130 , R. Caminal Armadans100 , C. Camincher35 , S. Campana35 , 92 39 M. Campanelli , A. Camplani , A. Campoverde , V. Canale 148 67a,67b , M. Cano Bret , J. Cantero , T. Cao158 , 58c 125 Y. Cao , M. D. M. Capeans Garrido , I. Caprini , M. Caprini , M. Capua40a,40b , R. M. Carbone38 , R. Cardarelli71a , 170 35 27b 27b F. C. Cardillo146 , I. Carli139 , T. Carli35 , G. Carlino67a , B. T. Carlson135 , L. Carminati66a,66b , R. M. D. Carney43a,43b , S. Caron117 , E. Carquin144b , S. Carrá66a,66b , G. D. Carrillo-Montoya35 , D. Casadei32b , M. P. Casado14,f , A. F. Casha164 , D. W. Casper168 , R. Castelijn118 , F. L. Castillo171 , V. Castillo Gimenez171 , N. F. Castro136a,136e , A. Catinaccio35 , J. R. Catmore130 , A. Cattai35 , J. Caudron24 , V. Cavaliere29 , E. Cavallaro14 , D. Cavalli66a , M. Cavalli-Sforza14 , V. Cavasinni69a,69b , E. Celebi12b , F. Ceradini72a,72b , L. Cerda Alberich171 , A. S. Cerqueira78a , A. Cerri153 , L. Cerrito71a,71b , F. Cerutti18 , A. Cervelli23a,23b , 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. Che122 , A. Chegwidden104 , S. Chekanov6 , S. V. Chekulaev165a , G. A. Chelkov77,ap , M. A. Chelstowska35 , C. Chen58a , C. H. Chen76 , H. Chen29 , J. Chen58a , J. Chen38 , S. Chen133 , S. J. Chen15c , X. Chen15b,ao , Y. Chen80 , Y-H. Chen44 , H. C. Cheng103 , H. J. Cheng15d , A. Cheplakov77 , E. Cheremushkina140 , 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. Chomont128 , S. Chouridou159 , Y. S. Chow118 , V. Christodoulou92 , M. C. Chu61a , J. Chudoba137 , A. J. Chuinard101 , J. J. Chwastowski82 , L. Chytka126 , D. Cinca45 , V. Cindro89 , I. A. Cioară24 , A. Ciocio18 , F. Cirotto67a,67b , Z. H. Citron177 , M. Citterio66a , A. Clark52 , M. R. Clark38 , P. J. Clark48 , C. Clement43a,43b , Y. Coadou99 , M. Cobal64a,64c , A. Coccaro53a,53b , J. Cochran76 , H. Cohen158 , A. E. C. Coimbra177 , L. Colasurdo117 , B. Cole38 , A. P. Colijn118 , J. Collot56 , P. Conde Muiño136a,136b , E. Coniavitis50 , S. H. Connell32b , I. A. Connelly98 , S. Constantinescu27b , F. Conventi67a,ar , A. M. Cooper-Sarkar131 , F. Cormier172 , K. J. R. Cormier164 , L. D. Corpe92 , M. Corradi70a,70b , E. E. Corrigan94 , F. Corriveau101,ab , A. Cortes-Gonzalez35 , M. J. Costa171 , F. Costanza5 , D. Costanzo146 , G. Cottin31 , G. Cowan91 , B. E. Cox98 , J. Crane98 , K. Cranmer121 , S. J. Crawley55 , R. A. Creager133 , G. Cree33 , S. Crépé-Renaudin56 , F. Crescioli132 , M. Cristinziani24 , V. Croft121 , G. Crosetti40a,40b , A. Cueto96 , T. Cuhadar Donszelmann146 , A. R. Cukierman150 , S. Czekierda82 , P. Czodrowski35 , M. J. Da Cunha Sargedas De Sousa58b,136b , C. Da Via98 , W. Dabrowski81a , T. Dado28a,w , S. Dahbi34e , T. Dai103 , F. Dallaire107 , C. Dallapiccola100 , M. Dam39 , G. D’amen23a,23b , J. Damp97 , J. R. Dandoy133 , M. F. Daneri30 , N. P. Dang178,j , N. D Dann98 , M. Danninger172 , V. Dao35 , G. Darbo53b , S. Darmora8 , O. Dartsi5 , A. Dattagupta127 , T. Daubney44 , S. D’Auria55 , W. Davey24 , C. David44 , T. Davidek139 , D. R. Davis47 , E. Dawe102 , I. Dawson146 , K. De8 , R. De Asmundis67a , A. De Benedetti124 , M. De Beurs118 , S. De Castro23a,23b , S. De Cecco70a,70b , N. De Groot117 , P. de Jong118 , H. De la Torre104 , F. De Lorenzi76 , A. De Maria51,r , D. De Pedis70a , A. De Salvo70a , U. De Sanctis71a,71b , 123 Eur. Phys. J. C (2019) 79:135 Page 31 of 42 135 M. De Santis71a,71b , A. De Santo153 , K. De Vasconcelos Corga99 , J. B. De Vivie De Regie128 , C. Debenedetti143 , D. V. Dedovich77 , N. Dehghanian3 , M. Del Gaudio40a,40b , J. Del Peso96 , Y. Delabat Diaz44 , D. Delgove128 , F. Deliot142 , C. M. Delitzsch7 , M. Della Pietra67a,67b , D. Della Volpe52 , A. Dell’Acqua35 , L. Dell’Asta25 , M. Delmastro5 , C. Delporte128 , P. A. Delsart56 , D. A. DeMarco164 , S. Demers180 , M. Demichev77 , S. P. Denisov140 , D. Denysiuk118 , L. D’Eramo132 , D. Derendarz82 , J. E. Derkaoui34d , F. Derue132 , P. Dervan88 , K. Desch24 , C. Deterre44 , K. Dette164 , M. R. Devesa30 , P. O. Deviveiros35 , A. Dewhurst141 , S. Dhaliwal26 , F. A. Di Bello52 , A. Di Ciaccio71a,71b , L. Di Ciaccio5 , W. K. Di Clemente133 , C. Di Donato67a,67b , A. Di Girolamo35 , G. Di Gregorio69a,69b , B. Di Micco72a,72b , R. Di Nardo100 , K. F. Di Petrillo57 , R. Di Sipio164 , D. Di Valentino33 , C. Diaconu99 , M. Diamond164 , F. A. Dias39 , T. Dias Do Vale136a , M. A. Diaz144a , J. Dickinson18 , E. B. Diehl103 , J. Dietrich19 , S. Díez Cornell44 , A. Dimitrievska18 , J. Dingfelder24 , F. Dittus35 , F. Djama99 , T. Djobava156b , J. I. Djuvsland59a , M. A. B. Do Vale78c , M. Dobre27b , D. Dodsworth26 , C. Doglioni94 , J. Dolejsi139 , Z. Dolezal139 , M. Donadelli78d , J. Donini37 , A. D’onofrio90 , M. D’Onofrio88 , J. Dopke141 , A. Doria67a , M. T. Dova86 , A. T. Doyle55 , E. Drechsler51 , E. Dreyer149 , T. Dreyer51 , Y. Du58b , F. Dubinin108 , M. Dubovsky28a , A. Dubreuil52 , E. Duchovni177 , G. Duckeck112 , A. Ducourthial132 , O. A. Ducu107,v , D. Duda113 , A. Dudarev35 , A. C. Dudder97 , E. M. Duffield18 , L. Duflot128 , M. Dührssen35 , C. Dülsen179 , M. Dumancic177 , A. E. Dumitriu27b,d , 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 , Y. Enari160 , J. S. Ennis175 , M. B. Epland47 , J. Erdmann45 , A. Ereditato20 , S. Errede170 , M. Escalier128 , C. Escobar171 , O. Estrada Pastor171 , A. I. Etienvre142 , E. Etzion158 , H. Evans63 , A. Ezhilov134 , M. Ezzi34e , F. Fabbri55 , L. Fabbri23a,23b , V. Fabiani117 , G. Facini92 , R. M. Faisca Rodrigues Pereira136a , R. M. Fakhrutdinov140 , S. Falciano70a , P. J. Falke5 , S. Falke5 , J. Faltova139 , Y. Fang15a , M. Fanti66a,66b , A. Farbin8 , A. Farilla72a , E. M. Farina68a,68b , T. Farooque104 , S. Farrell18 , S. M. Farrington175 , P. Farthouat35 , F. Fassi34e , P. Fassnacht35 , D. Fassouliotis9 , M. Faucci Giannelli48 , A. Favareto53a,53b , W. J. Fawcett31 , L. Fayard128 , O. L. Fedin134,o , W. Fedorko172 , M. Feickert41 , S. Feigl130 , L. Feligioni99 , C. Feng58b , E. J. Feng35 , M. Feng47 , M. J. Fenton55 , A. B. Fenyuk140 , L. Feremenga8 , J. Ferrando44 , A. Ferrari169 , P. Ferrari118 , R. Ferrari68a , D. E. Ferreira de Lima59b , A. Ferrer171 , D. Ferrere52 , C. Ferretti103 , F. Fiedler97 , A. Filipčič89 , F. Filthaut117 , K. D. Finelli25 , M. C. N. Fiolhais136a,136c,a , L. Fiorini171 , C. Fischer14 , W. C. Fisher104 , N. Flaschel44 , I. Fleck148 , P. Fleischmann103 , R. R. M. Fletcher133 , T. Flick179 , B. M. Flierl112 , L. M. Flores133 , L. R. Flores Castillo61a , F. M. Follega73a,73b , N. Fomin17 , G. T. Forcolin73a,73b , A. Formica142 , F. A. Förster14 , A. C. Forti98 , A. G. Foster21 , D. Fournier128 , H. Fox87 , S. Fracchia146 , P. Francavilla69a,69b , M. Franchini23a,23b , S. Franchino59a , D. Francis35 , L. Franconi143 , M. Franklin57 , M. Frate168 , M. Fraternali68a,68b , A. N. Fray90 , D. Freeborn92 , S. M. Fressard-Batraneanu35 , B. Freund107 , W. S. Freund78b , E. M. Freundlich45 , D. C. Frizzell124 , D. Froidevaux35 , J. A. Frost131 , C. Fukunaga161 , E. Fullana Torregrosa171 , T. Fusayasu114 , J. Fuster171 , O. Gabizon157 , A. Gabrielli23a,23b , A. Gabrielli18 , G. P. Gach81a , S. Gadatsch52 , P. Gadow113 , G. Gagliardi53a,53b , L. G. Gagnon107 , C. Galea27b , B. Galhardo136a,136c , E. J. Gallas131 , B. J. Gallop141 , P. Gallus138 , G. Galster39 , R. Gamboa Goni90 , K. K. Gan122 , S. Ganguly177 , J. Gao58a , Y. Gao88 , Y. S. Gao150,l , C. García171 , J. E. García Navarro171 , J. A. García Pascual15a , M. Garcia-Sciveres18 , R. W. Gardner36 , N. Garelli150 , V. Garonne130 , K. Gasnikova44 , A. Gaudiello53a,53b , 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. George91 , D. Gerbaudo14 , G. Gessner45 , S. Ghasemi148 , M. Ghasemi Bostanabad173 , M. Ghneimat24 , B. Giacobbe23b , S. Giagu70a,70b , N. Giangiacomi23a,23b , P. Giannetti69a , A. Giannini67a,67b , S. M. Gibson91 , M. Gignac143 , D. Gillberg33 , G. Gilles179 , D. M. Gingrich3,aq , M. P. Giordani64a,64c , F. M. Giorgi23b , P. F. Giraud142 , P. Giromini57 , G. Giugliarelli64a,64c , D. Giugni66a , F. Giuli131 , M. Giulini59b , S. Gkaitatzis159 , I. Gkialas9,i , E. L. Gkougkousis14 , P. Gkountoumis10 , L. K. Gladilin111 , C. Glasman96 , J. Glatzer14 , P. C. F. Glaysher44 , A. Glazov44 , M. Goblirsch-Kolb26 , J. Godlewski82 , S. Goldfarb102 , T. Golling52 , D. Golubkov140 , A. Gomes136a,136b,136d , R. Goncalves Gama78a , R. Gonçalo136a , G. Gonella50 , L. Gonella21 , A. Gongadze77 , F. Gonnella21 , J. L. Gonski57 , S. González de la Hoz171 , S. Gonzalez-Sevilla52 , L. Goossens35 , P. A. Gorbounov109 , H. A. Gordon29 , B. Gorini35 , E. Gorini65a,65b , A. Gorišek89 , A. T. Goshaw47 , C. Gössling45 , M. I. Gostkin77 , C. A. Gottardo24 , C. R. Goudet128 , D. Goujdami34c , A. G. Goussiou145 , N. Govender32b,b , C. Goy5 , E. Gozani157 , I. Grabowska-Bold81a , P. O. J. Gradin169 , E. C. Graham88 , J. Gramling168 , E. Gramstad130 , S. Grancagnolo19 , V. Gratchev134 , P. M. Gravila27f , F. G. Gravili65a,65b , C. Gray55 , H. M. Gray18 , Z. D. Greenwood93,ah , C. Grefe24 , K. Gregersen94 , I. M. Gregor44 , P. Grenier150 , K. Grevtsov44 , N. A. Grieser124 , J. Griffiths8 , A. A. Grillo143 , K. Grimm150 , S. Grinstein14,x , Ph. Gris37 , J.-F. Grivaz128 , S. Groh97 , E. Gross177 , J. Grosse-Knetter51 , G. C. Grossi93 , Z. J. Grout92 , C. Grud103 , A. Grummer116 , L. Guan103 , W. Guan178 , J. Guenther35 , A. Guerguichon128 , F. Guescini165a , D. Guest168 , R. Gugel50 , B. Gui122 , 123 135 Page 32 of 42 Eur. Phys. J. C (2019) 79:135 T. Guillemin5 , S. Guindon35 , U. Gul55 , C. Gumpert35 , J. Guo58c , W. Guo103 , Y. Guo58a,q , Z. Guo99 , R. Gupta41 , S. Gurbuz12c , G. Gustavino124 , B. J. Gutelman157 , P. Gutierrez124 , C. Gutschow92 , C. Guyot142 , M. P. Guzik81a , C. Gwenlan131 , C. B. Gwilliam88 , A. Haas121 , C. Haber18 , H. K. Hadavand8 , N. Haddad34e , A. Hadef58a , S. Hageböck24 , M. Hagihara166 , H. Hakobyan181,* , M. Haleem174 , J. Haley125 , G. Halladjian104 , G. D. Hallewell99 , K. Hamacher179 , P. Hamal126 , K. Hamano173 , A. Hamilton32a , G. N. Hamity146 , K. Han58a,ag , L. Han58a , S. Han15d , K. Hanagaki79,t , M. Hance143 , D. M. Handl112 , B. Haney134 , R. Hankache132 , P. Hanke59a , E. Hansen94 , J. B. Hansen39 , J. D. Hansen39 , M. C. Hansen24 , P. H. Hansen39 , K. Hara166 , A. S. Hard178 , T. Harenberg179 , S. Harkusha105 , P. F. Harrison175 , N. M. Hartmann112 , Y. Hasegawa147 , A. Hasib48 , S. Hassani142 , S. Haug20 , R. Hauser104 , L. Hauswald46 , L. B. Havener38 , M. Havranek138 , C. M. Hawkes21 , R. J. Hawkings35 , D. Hayden104 , C. Hayes152 , C. P. Hays131 , J. M. Hays90 , H. S. Hayward88 , S. J. Haywood141 , M. P. Heath48 , V. Hedberg94 , L. Heelan8 , S. Heer24 , K. K. Heidegger50 , J. Heilman33 , S. Heim44 , T. Heim18 , B. Heinemann44,al , J. J. Heinrich112 , L. Heinrich121 , C. Heinz54 , J. Hejbal137 , L. Helary35 , A. Held172 , S. Hellesund130 , S. Hellman43a,43b , C. Helsens35 , R. C. W. Henderson87 , Y. Heng178 , S. Henkelmann172 , A. M. Henriques Correia35 , G. H. Herbert19 , H. Herde26 , V. Herget174 , Y. Hernández Jiménez32c , H. Herr97 , M. G. Herrmann112 , G. Herten50 , R. Hertenberger112 , L. Hervas35 , T. C. Herwig133 , G. G. Hesketh92 , N. P. Hessey165a , J. W. Hetherly41 , 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. Hirose129 , D. Hirschbuehl179 , B. Hiti89 , O. Hladik137 , D. R. Hlaluku32c , X. Hoad48 , J. Hobbs152 , N. Hod165a , M. C. Hodgkinson146 , A. Hoecker35 , M. R. Hoeferkamp116 , F. Hoenig112 , D. Hohn24 , D. Hohov128 , T. R. Holmes36 , M. Holzbock112 , M. Homann45 , S. Honda166 , T. Honda79 , T. M. Hong135 , A. Hönle113 , B. H. Hooberman170 , W. H. Hopkins127 , Y. Horii115 , P. Horn46 , A. J. Horton149 , L. A. Horyn36 , J-Y. Hostachy56 , A. Hostiuc145 , S. Hou155 , A. Hoummada34a , J. Howarth98 , J. Hoya86 , M. Hrabovsky126 , I. Hristova19 , J. Hrivnac128 , A. Hrynevich106 , T. Hryn’ova5 , P. J. Hsu62 , S.-C. Hsu145 , Q. Hu29 , S. Hu58c , Y. Huang15a , Z. Hubacek138 , F. Hubaut99 , M. Huebner24 , F. Huegging24 , T. B. Huffman131 , E. W. Hughes38 , M. Huhtinen35 , R. F. H. Hunter33 , P. Huo152 , A. M. Hupe33 , N. Huseynov77,ad , J. Huston104 , J. Huth57 , R. Hyneman103 , G. Iacobucci52 , G. Iakovidis29 , I. Ibragimov148 , L. Iconomidou-Fayard128 , Z. Idrissi34e , P. Iengo35 , R. Ignazzi39 , O. Igonkina118,z , R. Iguchi160 , T. Iizawa52 , Y. Ikegami79 , M. Ikeno79 , D. Iliadis159 , N. Ilic150 , F. Iltzsche46 , G. Introzzi68a,68b , M. Iodice72a , K. Iordanidou38 , V. Ippolito70a,70b , M. F. Isacson169 , N. Ishijima129 , M. Ishino160 , M. Ishitsuka162 , W. Islam125 , C. Issever131 , S. Istin157 , F. Ito166 , J. M. Iturbe Ponce61a , R. Iuppa73a,73b , A. Ivina177 , H. Iwasaki79 , J. M. Izen42 , V. Izzo67a , P. Jacka137 , P. Jackson1 , R. M. Jacobs24 , V. Jain2 , G. Jäkel179 , K. B. Jakobi97 , K. Jakobs50 , S. Jakobsen74 , T. Jakoubek137 , D. O. Jamin125 , D. K. Jana93 , R. Jansky52 , J. Janssen24 , M. Janus51 , P. A. Janus81a , G. Jarlskog94 , N. Javadov77,ad , T. Javůrek35 , M. Javurkova50 , F. Jeanneau142 , L. Jeanty18 , J. Jejelava156a,ae , A. Jelinskas175 , P. Jenni50,c , J. Jeong44 , N. Jeong44 , S. Jézéquel5 , H. Ji178 , J. Jia152 , H. Jiang76 , Y. Jiang58a , Z. Jiang150 , S. Jiggins50 , F. A. Jimenez Morales37 , J. Jimenez Pena171 , S. Jin15c , A. Jinaru27b , O. Jinnouchi162 , H. Jivan32c , P. Johansson146 , K. A. Johns7 , C. A. Johnson63 , W. J. Johnson145 , K. Jon-And43a,43b , R. W. L. Jones87 , S. D. Jones153 , S. Jones7 , T. J. Jones88 , J. Jongmanns59a , P. M. Jorge136a,136b , J. Jovicevic165a , X. Ju18 , J. J. Junggeburth113 , A. Juste Rozas14,x , A. Kaczmarska82 , M. Kado128 , H. Kagan122 , M. Kagan150 , T. Kaji176 , E. Kajomovitz157 , C. W. Kalderon94 , A. Kaluza97 , S. Kama41 , A. Kamenshchikov140 , L. Kanjir89 , Y. Kano160 , V. A. Kantserov110 , J. Kanzaki79 , B. Kaplan121 , L. S. Kaplan178 , D. Kar32c , M. J. Kareem165b , E. Karentzos10 , S. N. Karpov77 , Z. M. Karpova77 , V. Kartvelishvili87 , A. N. Karyukhin140 , L. Kashif178 , R. D. Kass122 , A. Kastanas43a,43b , Y. Kataoka160 , C. Kato58c,58d , J. Katzy44 , K. Kawade80 , K. Kawagoe85 , T. Kawamoto160 , G. Kawamura51 , E. F. Kay88 , V. F. Kazanin120a,120b , R. Keeler173 , R. Kehoe41 , J. S. Keller33 , E. Kellermann94 , J. J. Kempster21 , J. Kendrick21 , O. Kepka137 , S. Kersten179 , B. P. Kerševan89 , R. A. Keyes101 , M. Khader170 , F. Khalil-Zada13 , A. Khanov125 , A. G. Kharlamov120a,120b , T. Kharlamova120a,120b , E. E. Khoda172 , A. Khodinov163 , T. J. Khoo52 , E. Khramov77 , J. Khubua156b , S. Kido80 , M. Kiehn52 , C. R. Kilby91 , Y. K. Kim36 , N. Kimura64a,64c , O. M. Kind19 , B. T. King88 , D. Kirchmeier46 , J. Kirk141 , A. E. Kiryunin113 , T. Kishimoto160 , D. Kisielewska81a , V. Kitali44 , O. Kivernyk5 , E. Kladiva28b , T. Klapdor-Kleingrothaus50 , M. H. Klein103 , M. Klein88 , U. Klein88 , K. Kleinknecht97 , P. Klimek119 , A. Klimentov29 , T. Klingl24 , T. Klioutchnikova35 , F. F. Klitzner112 , P. Kluit118 , S. Kluth113 , E. Kneringer74 , E. B. F. G. Knoops99 , A. Knue50 , A. Kobayashi160 , D. Kobayashi85 , T. Kobayashi160 , M. Kobel46 , M. Kocian150 , P. Kodys139 , P. T. Koenig24 , T. Koffas33 , E. Koffeman118 , N. M. Köhler113 , T. Koi150 , M. Kolb59b , I. Koletsou5 , T. Kondo79 , N. Kondrashova58c , K. Köneke50 , A. C. König117 , T. Kono79 , R. Konoplich121,ai , V. Konstantinides92 , N. Konstantinidis92 , B. Konya94 , R. Kopeliansky63 , S. Koperny81a , K. Korcyl82 , K. Kordas159 , G. Koren158 , A. Korn92 , I. Korolkov14 , E. V. Korolkova146 , N. Korotkova111 , O. Kortner113 , S. Kortner113 , T. Kosek139 , V. V. Kostyukhin24 , A. Kotwal47 , A. Koulouris10 , A. Kourkoumeli-Charalampidi68a,68b , C. Kourkoumelis9 , E. Kourlitis146 , V. Kouskoura29 , A. B. Kowalewska82 , R. Kowalewski173 , T. Z. Kowalski81a , C. Kozakai160 , W. Kozanecki142 , A. S. Kozhin140 , V. A. Kramarenko111 , G. Kramberger89 , D. Krasnopevtsev58a , M. W. Krasny132 , A. Krasznahorkay35 , 123 Eur. Phys. J. C (2019) 79:135 Page 33 of 42 135 D. Krauss113 , J. A. Kremer81a , J. Kretzschmar88 , P. Krieger164 , K. Krizka18 , K. Kroeninger45 , H. Kroha113 , J. Kroll137 , J. Kroll133 , J. Krstic16 , U. Kruchonak77 , H. Krüger24 , N. Krumnack76 , M. C. Kruse47 , T. Kubota102 , S. Kuday4b , J. T. Kuechler179 , S. Kuehn35 , A. Kugel59a , F. Kuger174 , T. Kuhl44 , V. Kukhtin77 , R. Kukla99 , Y. Kulchitsky105 , S. Kuleshov144b , Y. P. Kulinich170 , M. Kuna56 , T. Kunigo83 , A. Kupco137 , T. Kupfer45 , O. Kuprash158 , H. Kurashige80 , L. L. Kurchaninov165a , Y. A. Kurochkin105 , M. G. Kurth15d , E. S. Kuwertz35 , M. Kuze162 , J. Kvita126 , T. Kwan101 , A. La Rosa113 , J. L. La Rosa Navarro78d , L. La Rotonda40a,40b , F. La Ruffa40a,40b , C. Lacasta171 , F. Lacava70a,70b , J. Lacey44 , D. P. J. Lack98 , H. Lacker19 , D. Lacour132 , E. Ladygin77 , R. Lafaye5 , B. Laforge132 , T. Lagouri32c , S. Lai51 , S. Lammers63 , W. Lampl7 , E. Lançon29 , U. Landgraf50 , M. P. J. Landon90 , M. C. Lanfermann52 , V. S. Lang44 , J. C. Lange14 , R. J. Langenberg35 , A. J. Lankford168 , F. Lanni29 , K. Lantzsch24 , A. Lanza68a , A. Lapertosa53a,53b , S. Laplace132 , J. F. Laporte142 , T. Lari66a , F. Lasagni Manghi23a,23b , M. Lassnig35 , T. S. Lau61a , A. Laudrain128 , M. Lavorgna67a,67b , A. T. Law143 , M. Lazzaroni66a,66b , B. Le102 , O. Le Dortz132 , E. Le Guirriec99 , E. P. Le Quilleuc142 , M. LeBlanc7 , T. LeCompte6 , F. Ledroit-Guillon56 , C. A. Lee29 , G. R. Lee144a , L. Lee57 , S. C. Lee155 , B. Lefebvre101 , M. Lefebvre173 , F. Legger112 , C. Leggett18 , K. Lehmann149 , N. Lehmann179 , G. Lehmann Miotto35 , W. A. Leight44 , A. Leisos159,u , M. A. L. Leite78d , R. Leitner139 , D. Lellouch177 , B. Lemmer51 , K. J. C. Leney92 , T. Lenz24 , B. Lenzi35 , R. Leone7 , S. Leone69a , C. Leonidopoulos48 , G. Lerner153 , C. Leroy107 , R. Les164 , A. A. J. Lesage142 , C. G. Lester31 , M. Levchenko134 , J. Levêque5 , D. Levin103 , L. J. Levinson177 , D. Lewis90 , B. Li103 , C-Q. Li58a , H. Li58b , L. Li58c , M. Li15a , Q. Li15d , Q. Y. Li58a , S. Li58c,58d , X. Li58c , Y. Li148 , Z. Liang15a , B. Liberti71a , A. Liblong164 , K. Lie61c , S. Liem118 , A. Limosani154 , C. Y. Lin31 , K. Lin104 , T. H. Lin97 , R. A. Linck63 , J. H. Lindon21 , B. E. Lindquist152 , A. L. Lionti52 , E. Lipeles133 , A. Lipniacka17 , M. Lisovyi59b , T. M. Liss170,an , A. Lister172 , A. M. Litke143 , J. D. Little8 , B. Liu76 , B. L Liu6 , H. B. Liu29 , H. Liu103 , J. B. Liu58a , J. K. K. Liu131 , K. Liu132 , M. Liu58a , P. Liu18 , Y. Liu15a , Y. L. Liu58a , Y. W. Liu58a , M. Livan68a,68b , A. Lleres56 , J. Llorente Merino15a , S. L. Lloyd90 , C. Y. Lo61b , F. Lo Sterzo41 , E. M. Lobodzinska44 , P. Loch7 , A. Loesle50 , T. Lohse19 , K. Lohwasser146 , M. Lokajicek137 , B. A. Long25 , J. D. Long170 , R. E. Long87 , L. Longo65a,65b , K. A. Looper122 , J. A. Lopez144b , I. Lopez Paz14 , A. Lopez Solis146 , J. Lorenz112 , N. Lorenzo Martinez5 , M. Losada22 , P. J. Lösel112 , X. Lou44 , X. Lou15a , A. Lounis128 , 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. Luise132 , L. Luminari70a , B. Lund-Jensen151 , M. S. Lutz100 , P. M. Luzi132 , D. Lynn29 , R. Lysak137 , E. Lytken94 , F. Lyu15a , V. Lyubushkin77 , H. Ma29 , L. L. Ma58b , Y. Ma58b , G. Maccarrone49 , A. Macchiolo113 , C. M. Macdonald146 , J. Machado Miguens133,136b , D. Madaffari171 , R. Madar37 , W. F. Mader46 , A. Madsen44 , N. Madysa46 , J. Maeda80 , K. Maekawa160 , S. Maeland17 , T. Maeno29 , A. S. Maevskiy111 , V. Magerl50 , C. Maidantchik78b , T. Maier112 , A. Maio136a,136b,136d , O. Majersky28a , S. Majewski127 , Y. Makida79 , N. Makovec128 , B. Malaescu132 , Pa. Malecki82 , V. P. Maleev134 , F. Malek56 , U. Mallik75 , D. Malon6 , C. Malone31 , S. Maltezos10 , S. Malyukov35 , J. Mamuzic171 , G. Mancini49 , I. Mandić89 , J. Maneira136a , L. Manhaes de Andrade Filho78a , J. Manjarres Ramos46 , K. H. Mankinen94 , A. Mann112 , A. Manousos74 , B. Mansoulie142 , J. D. Mansour15a , M. Mantoani51 , S. Manzoni66a,66b , A. Marantis159 , G. Marceca30 , L. March52 , L. Marchese131 , G. Marchiori132 , M. Marcisovsky137 , C. A. Marin Tobon35 , M. Marjanovic37 , D. E. Marley103 , F. Marroquim78b , Z. Marshall18 , M. U. F Martensson169 , S. Marti-Garcia171 , C. B. Martin122 , T. A. Martin175 , V. J. Martin48 , B. Martin dit Latour17 , M. Martinez14,x , 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. Maslennikov120a,120b , L. H. Mason102 , L. Massa71a,71b , P. Massarotti67a,67b , P. Mastrandrea5 , A. Mastroberardino40a,40b , T. Masubuchi160 , P. Mättig179 , J. Maurer27b , B. Maček89 , S. J. Maxfield88 , D. A. Maximov120a,120b , R. Mazini155 , I. Maznas159 , S. M. Mazza143 , N. C. Mc Fadden116 , 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,ab , J. E. Mdhluli32c , Z. A. Meadows100 , S. Meehan145 , T. Megy50 , S. Mehlhase112 , A. Mehta88 , T. Meideck56 , B. Meirose42 , D. Melini171,g , B. R. Mellado Garcia32c , J. D. Mellenthin51 , M. Melo28a , F. Meloni44 , A. Melzer24 , S. B. Menary98 , E. D. Mendes Gouveia136a , L. Meng88 , X. T. Meng103 , A. Mengarelli23a,23b , S. Menke113 , E. Meoni40a,40b , S. Mergelmeyer19 , S. A. M. Merkt135 , C. Merlassino20 , P. Mermod52 , L. Merola67a,67b , C. Meroni66a , F. S. Merritt36 , A. Messina70a,70b , J. Metcalfe6 , A. S. Mete168 , C. Meyer133 , J. Meyer157 , J-P. Meyer142 , H. Meyer Zu Theenhausen59a , F. Miano153 , R. P. Middleton141 , L. Mijović48 , G. Mikenberg177 , M. Mikestikova137 , M. Mikuž89 , M. Milesi102 , A. Milic164 , D. A. Millar90 , D. W. Miller36 , A. Milov177 , D. A. Milstead43a,43b , A. A. Minaenko140 , M. Miñano Moya171 , I. A. Minashvili156b , A. I. Mincer121 , B. Mindur81a , M. Mineev77 , Y. Minegishi160 , Y. Ming178 , L. M. Mir14 , A. Mirto65a,65b , K. P. Mistry133 , T. Mitani176 , J. Mitrevski112 , V. A. Mitsou171 , A. Miucci20 , P. S. Miyagawa146 , A. Mizukami79 , J. U. Mjörnmark94 , T. Mkrtchyan181 , M. Mlynarikova139 , T. Moa43a,43b , K. Mochizuki107 , P. Mogg50 , S. Mohapatra38 , S. Molander43a,43b , R. Moles-Valls24 , M. C. Mondragon104 , K. Mönig44 , J. Monk39 , E. Monnier99 , A. Montalbano149 , J. Montejo Berlingen35 , F. Monticelli86 , 123 135 Page 34 of 42 Eur. Phys. J. C (2019) 79:135 S. Monzani66a , N. Morange128 , D. Moreno22 , M. Moreno Llácer35 , P. Morettini53b , M. Morgenstern118 , S. Morgenstern46 , D. Mori149 , M. Morii57 , M. Morinaga176 , V. Morisbak130 , A. K. Morley35 , G. Mornacchi35 , A. P. Morris92 , J. D. Morris90 , L. Morvaj152 , P. Moschovakos10 , M. Mosidze156b , H. J. Moss146 , J. Moss150,m , K. Motohashi162 , R. Mount150 , E. Mountricha35 , E. J. W. Moyse100 , S. Muanza99 , F. Mueller113 , J. Mueller135 , R. S. P. Mueller112 , D. Muenstermann87 , G. A. Mullier94 , F. J. Munoz Sanchez98 , P. Murin28b , W. J. Murray175,141 , A. Murrone66a,66b , M. Muškinja89 , C. Mwewa32a , A. G. Myagkov140,aj , J. Myers127 , M. Myska138 , B. P. Nachman18 , O. Nackenhorst45 , K. Nagai131 , K. Nagano79 , Y. Nagasaka60 , M. Nagel50 , E. Nagy99 , A. M. Nairz35 , Y. Nakahama115 , K. Nakamura79 , T. Nakamura160 , I. Nakano123 , H. Nanjo129 , F. Napolitano59a , R. F. Naranjo Garcia44 , R. Narayan11 , D. I. Narrias Villar59a , I. Naryshkin134 , T. Naumann44 , G. Navarro22 , R. Nayyar7 , H. A. Neal103 , P. Y. Nechaeva108 , T. J. Neep142 , A. Negri68a,68b , M. Negrini23b , S. Nektarijevic117 , C. Nellist51 , M. E. Nelson131 , S. Nemecek137 , P. Nemethy121 , M. Nessi35,e , M. S. Neubauer170 , M. Neumann179 , P. R. Newman21 , T. Y. Ng61c , Y. S. Ng19 , H. D. N. Nguyen99 , T. Nguyen Manh107 , E. Nibigira37 , R. B. Nickerson131 , R. Nicolaidou142 , D. S. Nielsen39 , J. Nielsen143 , N. Nikiforou11 , V. Nikolaenko140,aj , I. Nikolic-Audit132 , K. Nikolopoulos21 , P. Nilsson29 , Y. Ninomiya79 , A. Nisati70a , N. Nishu58c , R. Nisius113 , I. Nitsche45 , T. Nitta176 , T. Nobe160 , Y. Noguchi83 , M. Nomachi129 , I. Nomidis132 , M. A. Nomura29 , T. Nooney90 , M. Nordberg35 , N. Norjoharuddeen131 , T. Novak89 , O. Novgorodova46 , R. Novotny138 , L. Nozka126 , K. Ntekas168 , E. Nurse92 , F. Nuti102 , F. G. Oakham33,aq , H. Oberlack113 , T. Obermann24 , J. Ocariz132 , A. Ochi80 , I. Ochoa38 , J. P. Ochoa-Ricoux144a , K. O’Connor26 , S. Oda85 , S. Odaka79 , S. Oerdek51 , A. Oh98 , S. H. Oh47 , C. C. Ohm151 , H. Oide53a,53b , M. L. Ojeda164 , H. Okawa166 , Y. Okazaki83 , Y. Okumura160 , T. Okuyama79 , A. Olariu27b , L. F. Oleiro Seabra136a , S. A. Olivares Pino144a , D. Oliveira Damazio29 , J. L. Oliver1 , M. J. R. Olsson36 , A. Olszewski82 , J. Olszowska82 , D. C. O’Neil149 , A. Onofre136a,136e , K. Onogi115 , P. U. E. Onyisi11 , H. Oppen130 , M. J. Oreglia36 , G. E. Orellana86 , Y. Oren158 , D. Orestano72a,72b , E. C. Orgill98 , N. Orlando61b , A. A. O’Rourke44 , R. S. Orr164 , B. Osculati53a,53b,* , V. O’Shea55 , R. Ospanov58a , G. Otero y Garzon30 , H. Otono85 , M. Ouchrif34d , F. Ould-Saada130 , A. Ouraou142 , Q. Ouyang15a , M. Owen55 , R. E. Owen21 , V. E. Ozcan12c , N. Ozturk8 , J. Pacalt126 , H. A. Pacey31 , K. Pachal149 , A. Pacheco Pages14 , L. Pacheco Rodriguez142 , C. Padilla Aranda14 , S. Pagan Griso18 , M. Paganini180 , G. Palacino63 , S. Palazzo40a,40b , 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,i , A. Paramonov6 , D. Paredes Hernandez61b , S. R. Paredes Saenz131 , B. Parida163 , A. J. Parker87 , K. A. Parker44 , M. A. Parker31 , F. Parodi53a,53b , J. A. Parsons38 , U. Parzefall50 , V. R. Pascuzzi164 , J. M. P. Pasner143 , E. Pasqualucci70a , S. Passaggio53b , F. Pastore91 , P. Pasuwan43a,43b , S. Pataraia97 , J. R. Pater98 , A. Pathak178,j , T. Pauly35 , B. Pearson113 , M. Pedersen130 , L. Pedraza Diaz117 , R. Pedro136a,136b , S. V. Peleganchuk120a,120b , O. Penc137 , C. Peng15d , H. Peng58a , B. S. Peralva78a , M. M. Perego142 , A. P. Pereira Peixoto136a , 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. Petroff128 , M. Petrov131 , 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. Pickering131 , R. H. Pickles98 , R. Piegaia30 , J. E. Pilcher36 , A. D. Pilkington98 , M. Pinamonti71a,71b , J. L. Pinfold3 , M. Pitt177 , L. Pizzimento71a,71b , M-A. Pleier29 , V. Pleskot139 , E. Plotnikova77 , D. Pluth76 , P. Podberezko120a,120b , R. Poettgen94 , R. Poggi52 , L. Poggioli128 , I. Pogrebnyak104 , D. Pohl24 , I. Pokharel51 , G. Polesello68a , A. Poley18 , A. Policicchio70a,70b , R. Polifka35 , A. Polini23b , C. S. Pollard44 , V. Polychronakos29 , D. Ponomarenko110 , L. Pontecorvo70a , G. A. Popeneciu27d , D. M. Portillo Quintero132 , S. Pospisil138 , K. Potamianos44 , I. N. Potrap77 , C. J. Potter31 , H. Potti11 , T. Poulsen94 , J. Poveda35 , T. D. Powell146 , M. E. Pozo Astigarraga35 , P. Pralavorio99 , S. Prell76 , D. Price98 , M. Primavera65a , S. Prince101 , N. Proklova110 , K. Prokofiev61c , F. Prokoshin144b , S. Protopopescu29 , J. Proudfoot6 , M. Przybycien81a , A. Puri170 , P. Puzo128 , J. Qian103 , Y. Qin98 , A. Quadt51 , M. Queitsch-Maitland44 , A. Qureshi1 , P. Rados102 , F. Ragusa66a,66b , G. Rahal95 , J. A. Raine52 , S. Rajagopalan29 , A. Ramirez Morales90 , T. Rashid128 , S. Raspopov5 , M. G. Ratti66a,66b , D. M. Rauch44 , F. Rauscher112 , S. Rave97 , B. Ravina146 , I. Ravinovich177 , J. H. Rawling98 , M. Raymond35 , A. L. Read130 , N. P. Readioff56 , M. Reale65a,65b , D. M. Rebuzzi68a,68b , A. Redelbach174 , G. Redlinger29 , R. Reece143 , R. G. Reed32c , K. Reeves42 , L. Rehnisch19 , J. Reichert133 , D. Reikher158 , A. Reiss97 , C. Rembser35 , H. Ren15d , M. Rescigno70a , S. Resconi66a , E. D. Resseguie133 , S. Rettie172 , E. Reynolds21 , O. L. Rezanova120a,120b , P. Reznicek139 , E. Ricci73a,73b , R. Richter113 , S. Richter44 , E. Richter-Was81b , O. Ricken24 , M. Ridel132 , P. Rieck113 , C. J. Riegel179 , O. Rifki44 , M. Rijssenbeek152 , A. Rimoldi68a,68b , M. Rimoldi20 , L. Rinaldi23b , G. Ripellino151 , B. Ristić87 , E. Ritsch35 , I. Riu14 , J. C. Rivera Vergara144a , F. Rizatdinova125 , E. Rizvi90 , C. Rizzi14 , R. T. Roberts98 , S. H. Robertson101,ab , D. Robinson31 , J. E. M. Robinson44 , A. Robson55 , E. Rocco97 , C. Roda69a,69b , Y. Rodina99 , S. Rodriguez Bosca171 , A. Rodriguez Perez14 , D. Rodriguez Rodriguez171 , A. M. Rodríguez Vera165b , S. Roe35 , C. S. Rogan57 , O. Røhne130 , R. Röhrig113 , C. P. A. Roland63 , J. Roloff57 , A. Romaniouk110 , M. Romano23a,23b , N. Rompotis88 , M. Ronzani121 , L. Roos132 , 123 Eur. Phys. J. C (2019) 79:135 Page 35 of 42 135 S. Rosati70a , K. Rosbach50 , P. Rose143 , N-A. Rosien51 , B. J. Rosser133 , 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. Rousseau128 , 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,k , Y. F. Ryabov134 , M. Rybar170 , G. Rybkin128 , S. Ryu6 , A. Ryzhov140 , G. F. Rzehorz51 , P. Sabatini51 , G. Sabato118 , S. Sacerdoti128 , H. F-W. Sadrozinski143 , R. Sadykov77 , F. Safai Tehrani70a , P. Saha119 , M. Sahinsoy59a , A. Sahu179 , M. Saimpert44 , M. Saito160 , T. Saito160 , H. Sakamoto160 , A. Sakharov121,ai , D. Salamani52 , G. Salamanna72a,72b , J. E. Salazar Loyola144b , P. H. Sales De Bruin169 , D. Salihagic113 , A. Salnikov150 , J. Salt171 , D. Salvatore40a,40b , F. Salvatore153 , A. Salvucci61a,61b,61c , A. Salzburger35 , J. Samarati35 , D. Sammel50 , D. Sampsonidis159 , D. Sampsonidou159 , J. Sánchez171 , A. Sanchez Pineda64a,64c , H. Sandaker130 , C. O. Sander44 , M. Sandhoff179 , C. Sandoval22 , D. P. C. Sankey141 , M. Sannino53a,53b , Y. Sano115 , A. Sansoni49 , C. Santoni37 , H. Santos136a , I. Santoyo Castillo153 , A. Santra171 , A. Sapronov77 , J. G. Saraiva136a,136d , O. Sasaki79 , K. Sato166 , E. Sauvan5 , P. Savard164,aq , N. Savic113 , R. Sawada160 , C. Sawyer141 , L. Sawyer93,ah , C. Sbarra23b , A. Sbrizzi23a,23b , T. Scanlon92 , J. Schaarschmidt145 , P. Schacht113 , B. M. Schachtner112 , D. Schaefer36 , L. Schaefer133 , J. Schaeffer97 , S. Schaepe35 , U. Schäfer97 , A. C. Schaffer128 , D. Schaile112 , R. D. Schamberger152 , N. Scharmberg98 , V. A. Schegelsky134 , D. Scheirich139 , F. Schenck19 , M. Schernau168 , C. Schiavi53a,53b , S. Schier143 , L. K. Schildgen24 , Z. M. Schillaci26 , E. J. Schioppa35 , M. Schioppa40a,40b , K. E. Schleicher50 , S. Schlenker35 , K. R. Schmidt-Sommerfeld113 , K. Schmieden35 , C. Schmitt97 , S. Schmitt44 , S. Schmitz97 , J. C. Schmoeckel44 , U. Schnoor50 , L. Schoeffel142 , A. Schoening59b , E. Schopf131 , M. Schott97 , J. F. P. Schouwenberg117 , J. Schovancova35 , S. Schramm52 , A. Schulte97 , H-C. Schultz-Coulon59a , M. Schumacher50 , B. A. Schumm143 , Ph. Schune142 , A. Schwartzman150 , T. A. Schwarz103 , Ph. Schwemling142 , R. Schwienhorst104 , A. Sciandra24 , G. Sciolla26 , M. Scornajenghi40a,40b , F. Scuri69a , F. Scutti102 , L. M. Scyboz113 , J. Searcy103 , C. D. Sebastiani70a,70b , P. Seema19 , S. C. Seidel116 , A. Seiden143 , T. Seiss36 , J. M. Seixas78b , G. Sekhniaidze67a , K. Sekhon103 , S. J. Sekula41 , N. Semprini-Cesari23a,23b , S. Sen47 , S. Senkin37 , C. Serfon130 , L. Serin128 , L. Serkin64a,64b , M. Sessa58a , H. Severini124 , F. Sforza167 , A. Sfyrla52 , E. Shabalina51 , J. D. Shahinian143 , N. W. Shaikh43a,43b , L. Y. Shan15a , R. Shang170 , J. T. Shank25 , M. Shapiro18 , A. S. Sharma1 , A. Sharma131 , P. B. Shatalov109 , K. Shaw153 , S. M. Shaw98 , A. Shcherbakova134 , Y. Shen124 , N. Sherafati33 , A. D. Sherman25 , P. Sherwood92 , L. Shi155,am , S. Shimizu79 , C. O. Shimmin180 , M. Shimojima114 , I. P. J. Shipsey131 , S. Shirabe85 , M. Shiyakova77 , J. Shlomi177 , A. Shmeleva108 , D. Shoaleh Saadi107 , M. J. Shochet36 , S. Shojaii102 , D. R. Shope124 , S. Shrestha122 , E. Shulga110 , P. Sicho137 , A. M. Sickles170 , P. E. Sidebo151 , E. Sideras Haddad32c , O. Sidiropoulou35 , A. Sidoti23a,23b , F. Siegert46 , Dj. Sijacki16 , J. Silva136a , M. Silva Jr.178 , M. V. Silva Oliveira78a , S. B. Silverstein43a , S. Simion128 , E. Simioni97 , M. Simon97 , R. Simoniello97 , P. Sinervo164 , N. B. Sinev127 , M. Sioli23a,23b , G. Siragusa174 , I. Siral103 , S. Yu. Sivoklokov111 , J. Sjölin43a,43b , P. Skubic124 , M. Slater21 , T. Slavicek138 , M. Slawinska82 , K. Sliwa167 , R. Slovak139 , V. Smakhtin177 , B. H. Smart5 , J. Smiesko28a , N. Smirnov110 , S. Yu. Smirnov110 , Y. Smirnov110 , L. N. Smirnova111 , O. Smirnova94 , J. W. Smith51 , M. N. K. Smith38 , M. Smizanska87 , K. Smolek138 , A. Smykiewicz82 , A. A. Snesarev108 , I. M. Snyder127 , S. Snyder29 , R. Sobie173,ab , A. M. Soffa168 , A. Soffer158 , A. Søgaard48 , D. A. Soh155 , G. Sokhrannyi89 , C. A. Solans Sanchez35 , M. Solar138 , E. Yu. Soldatov110 , U. Soldevila171 , A. A. Solodkov140 , A. Soloshenko77 , O. V. Solovyanov140 , V. Solovyev134 , P. Sommer146 , H. Son167 , W. Song141 , W. Y. Song165b , A. Sopczak138 , F. Sopkova28b , C. L. Sotiropoulou69a,69b , S. Sottocornola68a,68b , R. Soualah64a,64c,h , A. M. Soukharev120a,120b , D. South44 , B. C. Sowden91 , S. Spagnolo65a,65b , M. Spalla113 , M. Spangenberg175 , F. Spanò91 , D. Sperlich19 , F. Spettel113 , T. M. Spieker59a , R. Spighi23b , G. Spigo35 , L. A. Spiller102 , D. P. Spiteri55 , M. Spousta139 , A. Stabile66a,66b , R. Stamen59a , S. Stamm19 , E. Stanecka82 , R. W. Stanek6 , C. Stanescu72a , B. Stanislaus131 , M. M. Stanitzki44 , B. Stapf118 , S. Stapnes130 , E. A. Starchenko140 , G. H. Stark36 , J. Stark56 , S. H Stark39 , P. Staroba137 , P. Starovoitov59a , S. Stärz35 , R. Staszewski82 , M. Stegler44 , P. Steinberg29 , B. Stelzer149 , H. J. Stelzer35 , O. Stelzer-Chilton165a , H. Stenzel54 , T. J. Stevenson90 , G. A. Stewart55 , M. C. Stockton127 , G. Stoicea27b , P. Stolte51 , S. Stonjek113 , A. Straessner46 , J. Strandberg151 , S. Strandberg43a,43b , M. Strauss124 , P. Strizenec28b , R. Ströhmer174 , D. M. Strom127 , R. Stroynowski41 , A. Strubig48 , S. A. Stucci29 , B. Stugu17 , J. Stupak124 , N. A. Styles44 , D. Su150 , J. Su135 , S. Suchek59a , Y. Sugaya129 , M. Suk138 , 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. Svatos137 , M. Swiatlowski36 , S. P. Swift2 , A. Sydorenko97 , I. Sykora28a , T. Sykora139 , D. Ta97 , K. Tackmann44,y , 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. Talyshev120a,120b , J. Tanaka160 , M. Tanaka162 , R. Tanaka128 , B. B. Tannenwald122 , S. Tapia Araya144b , S. Tapprogge97 , A. Tarek Abouelfadl Mohamed132 , S. Tarem157 , G. Tarna27b,d , G. F. Tartarelli66a , P. Tas139 , M. Tasevsky137 , T. Tashiro83 , E. Tassi40a,40b , A. Tavares Delgado136a,136b , 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 , 123 135 Page 36 of 42 Eur. Phys. J. C (2019) 79:135 P. K. Teng155 , J. J. Teoh118 , S. Terada79 , K. Terashi160 , J. Terron96 , S. Terzo14 , M. Testa49 , R. J. Teuscher164,ab , S. J. Thais180 , T. Theveneaux-Pelzer44 , F. Thiele39 , D. W. Thomas91 , J. P. Thomas21 , A. S. Thompson55 , P. D. Thompson21 , L. A. Thomsen180 , E. Thomson133 , Y. Tian38 , R. E. Ticse Torres51 , V. O. Tikhomirov108,ak , Yu. A. Tikhonov120a,120b , S. Timoshenko110 , P. Tipton180 , S. Tisserant99 , K. Todome162 , S. Todorova-Nova5 , S. Todt46 , J. Tojo85 , S. Tokár28a , K. Tokushuku79 , E. Tolley122 , K. G. Tomiwa32c , M. Tomoto115 , L. Tompkins150 , K. Toms116 , B. Tong57 , P. Tornambe50 , E. Torrence127 , H. Torres46 , E. Torró Pastor145 , C. Tosciri131 , J. Toth99,aa , F. Touchard99 , D. R. Tovey146 , C. J. Treado121 , T. Trefzger174 , F. Tresoldi153 , A. Tricoli29 , I. M. Trigger165a , S. Trincaz-Duvoid132 , M. F. Tripiana14 , W. Trischuk164 , B. Trocmé56 , A. Trofymov128 , C. Troncon66a , M. Trovatelli173 , F. Trovato153 , L. Truong32b , M. Trzebinski82 , A. Trzupek82 , F. Tsai44 , J. C-L. Tseng131 , P. V. Tsiareshka105 , 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,s , R. Turra66a , P. M. Tuts38 , E. Tzovara97 , G. Ucchielli23a,23b , I. Ueda79 , M. Ughetto43a,43b , F. Ukegawa166 , G. Unal35 , A. Undrus29 , G. Unel168 , F. C. Ungaro102 , Y. Unno79 , K. Uno160 , J. Urban28b , P. Urquijo102 , P. Urrejola97 , G. Usai8 , J. Usui79 , L. Vacavant99 , V. Vacek138 , B. Vachon101 , K. O. H. Vadla130 , A. Vaidya92 , C. Valderanis112 , E. Valdes Santurio43a,43b , M. Valente52 , S. Valentinetti23a,23b , 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. Varni53a,53b , T. Varol41 , D. Varouchas128 , K. E. Varvell154 , G. A. Vasquez144b , J. G. Vasquez180 , F. Vazeille37 , D. Vazquez Furelos14 , T. Vazquez Schroeder101 , J. Veatch51 , V. Vecchio72a,72b , L. M. Veloce164 , F. Veloso136a,136c , 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,aq , N. Viaux Maira144b , M. Vicente Barreto Pinto52 , I. Vichou170,* , T. Vickey146 , O. E. Vickey Boeriu146 , G. H. A. Viehhauser131 , S. Viel18 , L. Vigani131 , M. Villa23a,23b , M. Villaplana Perez66a,66b , E. Vilucchi49 , M. G. Vincter33 , V. B. Vinogradov77 , A. Vishwakarma44 , C. Vittori23a,23b , I. Vivarelli153 , S. Vlachos10 , M. Vogel179 , P. Vokac138 , G. Volpi14 , S. E. Von Buddenbrock32c , E. von Toerne24 , V. Vorobel139 , K. Vorobev110 , M. Vos171 , J. H. Vossebeld88 , N. Vranjes16 , M. Vranjes Milosavljevic16 , V. Vrba138 , M. Vreeswijk118 , T. Šfiligoj89 , R. Vuillermet35 , I. Vukotic36 , T. Ženiš28a , L. Živković16 , P. Wagner24 , W. Wagner179 , J. Wagner-Kuhr112 , H. Wahlberg86 , S. Wahrmund46 , K. Wakamiya80 , V. M. Walbrecht113 , J. Walder87 , R. Walker112 , S. D. Walker91 , W. Walkowiak148 , V. Wallangen43a,43b , A. M. Wang57 , C. Wang58b,d , F. Wang178 , H. Wang18 , H. Wang3 , J. Wang154 , J. Wang59b , P. Wang41 , Q. Wang124 , R.- J. Wang132 , R. Wang58a , R. Wang6 , S. M. Wang155 , T. Wang58a , W. Wang15c,ac , W. X. Wang58a,ac , Y. Wang58a , Z. Wang58c , C. Wanotayaroj44 , A. Warburton101 , C. P. Ward31 , D. R. Wardrope92 , A. Washbrook48 , P. M. Watkins21 , A. T. Watson21 , M. F. Watson21 , G. Watts145 , S. Watts98 , B. M. Waugh92 , A. F. Webb11 , S. Webb97 , C. Weber180 , M. S. Weber20 , S. A. Weber33 , S. M. Weber59a , A. R. Weidberg131 , B. Weinert63 , J. Weingarten51 , M. Weirich97 , C. Weiser50 , P. S. Wells35 , T. Wenaus29 , T. Wengler35 , S. Wenig35 , N. Wermes24 , M. D. Werner76 , P. Werner35 , M. Wessels59a , T. D. Weston20 , K. Whalen127 , N. L. Whallon145 , A. M. Wharton87 , A. S. White103 , A. White8 , M. J. White1 , R. White144b , D. Whiteson168 , B. W. Whitmore87 , F. J. Wickens141 , W. Wiedenmann178 , M. Wielers141 , C. Wiglesworth39 , L. A. M. Wiik-Fuchs50 , F. Wilk98 , H. G. Wilkens35 , L. J. Wilkins91 , H. H. Williams133 , S. Williams31 , C. Willis104 , S. Willocq100 , J. A. Wilson21 , I. Wingerter-Seez5 , E. Winkels153 , F. Winklmeier127 , O. J. Winston153 , B. T. Winter24 , M. Wittgen150 , M. Wobisch93 , A. Wolf97 , T. M. H. Wolf118 , R. Wolff99 , M. W. Wolter82 , H. Wolters136a,136c , V. W. S. Wong172 , N. L. Woods143 , S. D. Worm21 , B. K. Wosiek82 , K. W. Woźniak82 , K. Wraight55 , M. Wu36 , S. L. Wu178 , X. Wu52 , Y. Wu58a , T. R. Wyatt98 , B. M. Wynne48 , S. Xella39 , Z. Xi103 , L. Xia175 , D. Xu15a , H. Xu58a , L. Xu29 , T. Xu142 , W. Xu103 , B. Yabsley154 , S. Yacoob32a , K. Yajima129 , D. P. Yallup92 , D. Yamaguchi162 , Y. Yamaguchi162 , A. Yamamoto79 , T. Yamanaka160 , F. Yamane80 , M. Yamatani160 , T. Yamazaki160 , Y. Yamazaki80 , Z. Yan25 , H. J. Yang58c,58d , H. T. Yang18 , S. Yang75 , Y. Yang160 , Z. Yang17 , W-M. Yao18 , Y. C. Yap44 , Y. Yasu79 , E. Yatsenko58c,58d , J. Ye41 , S. Ye29 , I. Yeletskikh77 , E. Yigitbasi25 , E. Yildirim97 , K. Yorita176 , K. Yoshihara133 , C. J. S. Young35 , C. Young150 , J. Yu8 , J. Yu76 , X. Yue59a , S. P. Y. Yuen24 , B. Zabinski82 , G. Zacharis10 , E. Zaffaroni52 , R. Zaidan14 , A. M. Zaitsev140,aj , T. Zakareishvili156b , N. Zakharchuk33 , J. Zalieckas17 , S. Zambito57 , D. Zanzi35 , D. R. Zaripovas55 , S. V. Zeißner45 , C. Zeitnitz179 , G. Zemaityte131 , J. C. Zeng170 , Q. Zeng150 , O. Zenin140 , D. Zerwas128 , M. Zgubič131 , D. F. Zhang58b , D. Zhang103 , F. Zhang178 , G. Zhang58a , H. Zhang15c , J. Zhang6 , L. Zhang15c , L. Zhang58a , M. Zhang170 , P. Zhang15c , R. Zhang58a , R. Zhang24 , X. Zhang58b , Y. Zhang15d , Z. Zhang128 , X. Zhao41 , Y. Zhao58b,128,ag , 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. Zhulanov120a,120b , A. Zibell174 , D. Zieminska63 , N. I. Zimine77 , S. Zimmermann50 , Z. Zinonos113 , M. Zinser97 , M. Ziolkowski148 , G. Zobernig178 , A. Zoccoli23a,23b , K. Zoch51 , T. G. Zorbas146 , R. Zou36 , M. Zur Nedden19 , L. Zwalinski35 123 Eur. Phys. J. C (2019) 79:135 Page 37 of 42 135 1 Department of Physics, University of Adelaide, Adelaide, Australia 2 Physics Department, SUNY Albany, Albany, NY, USA 3 Department of Physics, University of Alberta, Edmonton, AB, Canada 4 (a) Department of Physics, Ankara University, Ankara, Turkey; (b) Istanbul Aydin University, Istanbul, Turkey; (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, USA 7 Department of Physics, University of Arizona, Tucson, AZ, USA 8 Department of Physics, University of Texas at Arlington, Arlington, TX, USA 9 Physics Department, National and Kapodistrian University of Athens, Athens, Greece 10 Physics Department, National Technical University of Athens, Zografou, Greece 11 Department of Physics, University of Texas at Austin, Austin, TX, USA 12 (a) Faculty of Engineering and Natural Sciences, Bahcesehir University, Istanbul, Turkey; (b) Faculty of Engineering and Natural Sciences, Istanbul Bilgi University, Istanbul, Turkey; (c) Department of Physics, Bogazici University, Istanbul, Turkey; (d) Department of Physics Engineering, Gaziantep University, Gaziantep, Turkey 13 Institute of Physics, Azerbaijan Academy of Sciences, Baku, Azerbaijan 14 Institut de Física d’Altes Energies (IFAE), Barcelona Institute of Science and Technology, Barcelona, Spain 15 (a) Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China; (b) Physics Department, Tsinghua University, Beijing, China; (c) Department of Physics, Nanjing University, Nanjing, China; (d) University of Chinese Academy of Science (UCAS), Beijing, China 16 Institute of Physics, University of Belgrade, Belgrade, Serbia 17 Department for Physics and Technology, University of Bergen, Bergen, Norway 18 Physics Division, Lawrence Berkeley National Laboratory and University of California, Berkeley, CA, USA 19 Institut für Physik, Humboldt Universität zu Berlin, Berlin, Germany 20 Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics, University of Bern, Bern, Switzerland 21 School of Physics and Astronomy, University of Birmingham, Birmingham, UK 22 Centro de Investigaciónes, Universidad Antonio Nariño, Bogotá, Colombia 23 (a) Dipartimento di Fisica e Astronomia, Università di Bologna, Bologna, Italy; (b) INFN Sezione di Bologna, Bologna, Italy 24 Physikalisches Institut, Universität Bonn, Bonn, Germany 25 Department of Physics, Boston University, Boston, MA, USA 26 Department of Physics, Brandeis University, Waltham, MA, USA 27 (a) Transilvania University of Brasov, Brasov, Romania; (b) Horia Hulubei National Institute of Physics and Nuclear Engineering, Bucharest, Romania; (c) Department of Physics, Alexandru Ioan Cuza University of Iasi, Iasi, Romania; (d) Physics Department, National Institute for Research and Development of Isotopic and Molecular Technologies, Cluj-Napoca, Romania; (e) University Politehnica Bucharest, Bucharest, Romania; (f) West University in Timisoara, Timisoara, Romania 28 (a) Faculty of Mathematics, Physics and Informatics, Comenius University, Bratislava, Slovakia; (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, USA 30 Departamento de Física, Universidad de Buenos Aires, Buenos Aires, Argentina 31 Cavendish Laboratory, University of Cambridge, Cambridge, UK 32 (a) Department of Physics, University of Cape Town, Cape Town, South Africa; (b) Department of Mechanical Engineering Science, University of Johannesburg, Johannesburg, South Africa; (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, Morocco; (b) Centre National de l’Energie des Sciences Techniques Nucleaires (CNESTEN), Rabat, Morocco; (c) Faculté des Sciences Semlalia, Université Cadi Ayyad, LPHEA-Marrakech, Marrakech, Morocco; (d) Faculté des Sciences, Université Mohamed Premier and LPTPM, Oujda, Morocco; (e) Faculté des sciences, Université Mohammed V, Rabat, Morocco 35 CERN, Geneva, Switzerland 123 135 Page 38 of 42 Eur. Phys. J. C (2019) 79:135 36 Enrico Fermi Institute, University of Chicago, Chicago, IL, USA 37 LPC, Université Clermont Auvergne, CNRS/IN2P3, Clermont-Ferrand, France 38 Nevis Laboratory, Columbia University, Irvington, NY, USA 39 Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark 40 (a) Dipartimento di Fisica, Università della Calabria, Rende, Italy; (b) INFN Gruppo Collegato di Cosenza, Laboratori Nazionali di Frascati, Frascati, Italy 41 Physics Department, Southern Methodist University, Dallas, TX, USA 42 Physics Department, University of Texas at Dallas, Richardson, TX, USA 43 (a) Department of Physics, Stockholm University, Stockholm, Sweden; (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, USA 48 SUPA-School of Physics and Astronomy, University of Edinburgh, Edinburgh, UK 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, Geneva, Switzerland 53 (a) Dipartimento di Fisica, Università di Genova, Genoa, Italy; (b) INFN Sezione di Genova, Genoa, Italy 54 II. Physikalisches Institut, Justus-Liebig-Universität Giessen, Giessen, Germany 55 SUPA-School of Physics and Astronomy, University of Glasgow, Glasgow, UK 56 LPSC, Université Grenoble Alpes, CNRS/IN2P3, Grenoble INP, Grenoble, France 57 Laboratory for Particle Physics and Cosmology, Harvard University, Cambridge, MA, USA 58 (a) Department of Modern Physics and State Key Laboratory of Particle Detection and Electronics, University of Science and Technology of China, Hefei, China; (b) Institute of Frontier and Interdisciplinary Science and Key Laboratory of Particle Physics and Particle Irradiation (MOE), Shandong University, Qingdao, China; (c) School of Physics and Astronomy, Shanghai Jiao Tong University, KLPPAC-MoE, SKLPPC, Shanghai, China; (d) Tsung-Dao Lee Institute, Shanghai, China 59 (a) Kirchhoff-Institut für Physik, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany; (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, NT, Hong Kong; (b) Department of Physics, University of Hong Kong, Hong Kong, China; (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, USA 64 (a) INFN Gruppo Collegato di Udine, Sezione di Trieste, Udine, Italy; (b) ICTP, Trieste, Italy; (c) Dipartimento di Chimica, Fisica e Ambiente, Università di Udine, Udine, Italy 65 (a) INFN Sezione di Lecce, Lecce, Italy; (b) Dipartimento di Matematica e Fisica, Università del Salento, Lecce, Italy 66 (a) INFN Sezione di Milano, Milan, Italy; (b) Dipartimento di Fisica, Università di Milano, Milan, Italy 67 (a) INFN Sezione di Napoli, Naples, Italy; (b) Dipartimento di Fisica, Università di Napoli, Naples, Italy 68 (a) INFN Sezione di Pavia, Pavia, Italy; (b) Dipartimento di Fisica, Università di Pavia, Pavia, Italy 69 (a) INFN Sezione di Pisa, Pisa, Italy; (b) Dipartimento di Fisica E. Fermi, Università di Pisa, Pisa, Italy 70 (a) INFN Sezione di Roma, Rome, Italy; (b) Dipartimento di Fisica, Sapienza Università di Roma, Rome, Italy 71 (a) INFN Sezione di Roma Tor Vergata, Rome, Italy; (b) Dipartimento di Fisica, Università di Roma Tor Vergata, Rome, Italy 72 (a) INFN Sezione di Roma Tre, Rome, Italy; (b) Dipartimento di Matematica e Fisica, Università Roma Tre, Rome, Italy 73 (a) INFN-TIFPA, Povo, Italy; (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, USA 76 Department of Physics and Astronomy, Iowa State University, Ames, IA, USA 77 Joint Institute for Nuclear Research, Dubna, Russia 78 (a) Departamento de Engenharia Elétrica, Universidade Federal de Juiz de Fora (UFJF), Juiz de Fora, 123 Eur. Phys. J. C (2019) 79:135 Page 39 of 42 135 Brazil; (b) Universidade Federal do Rio De Janeiro COPPE/EE/IF, Rio de Janeiro, Brazil; (c) Universidade Federal de São João del Rei (UFSJ), São João del Rei, Brazil; (d) Instituto de Física, Universidade de São Paulo, São Paulo, Brazil 79 KEK, High Energy Accelerator Research Organization, Tsukuba, Japan 80 Graduate School of Science, Kobe University, Kobe, Japan 81 (a) Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, Kraków, Poland; (b) Marian Smoluchowski Institute of Physics, Jagiellonian University, Kraków, Poland 82 Institute of Nuclear Physics Polish Academy of Sciences, Kraków, Poland 83 Faculty of Science, Kyoto University, Kyoto, Japan 84 Kyoto University of Education, Kyoto, Japan 85 Research Center for Advanced Particle Physics and Department of Physics, Kyushu University, Fukuoka, Japan 86 Instituto de Física La Plata, Universidad Nacional de La Plata and CONICET, La Plata, Argentina 87 Physics Department, Lancaster University, Lancaster, UK 88 Oliver Lodge Laboratory, University of Liverpool, Liverpool, UK 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, UK 91 Department of Physics, Royal Holloway University of London, Egham, UK 92 Department of Physics and Astronomy, University College London, London, UK 93 Louisiana Tech University, Ruston, LA, USA 94 Fysiska institutionen, Lunds universitet, Lund, Sweden 95 Centre de Calcul de l’Institut National de Physique Nucléaire et de Physique des Particules (IN2P3), Villeurbanne, France 96 Departamento de Física Teorica C-15 and CIAFF, Universidad Autónoma de Madrid, Madrid, Spain 97 Institut für Physik, Universität Mainz, Mainz, Germany 98 School of Physics and Astronomy, University of Manchester, Manchester, UK 99 CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille, France 100 Department of Physics, University of Massachusetts, Amherst, MA, USA 101 Department of Physics, McGill University, Montreal, QC, Canada 102 School of Physics, University of Melbourne, Parkville, VIC, Australia 103 Department of Physics, University of Michigan, Ann Arbor, MI, USA 104 Department of Physics and Astronomy, Michigan State University, East Lansing, MI, USA 105 B.I. Stepanov Institute of Physics, National Academy of Sciences of Belarus, Minsk, Belarus 106 Research Institute for Nuclear Problems of Byelorussian State University, Minsk, Belarus 107 Group of Particle Physics, University of Montreal, Montreal, 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, Munich, Germany 113 Max-Planck-Institut für Physik (Werner-Heisenberg-Institut), Munich, 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, USA 117 Institute for Mathematics, Astrophysics and Particle Physics, Radboud University Nijmegen/Nikhef, Nijmegen, The Netherlands 118 Nikhef National Institute for Subatomic Physics, University of Amsterdam, Amsterdam, The Netherlands 119 Department of Physics, Northern Illinois University, De Kalb, IL, USA 120 (a) Budker Institute of Nuclear Physics, SB RAS, Novosibirsk, Russia; (b) Novosibirsk State University, Novosibirsk, Russia 121 Department of Physics, New York University, New York, NY, USA 122 Ohio State University, Columbus, OH, USA 123 Faculty of Science, Okayama University, Okayama, Japan 124 Homer L. Dodge Department of Physics and Astronomy, University of Oklahoma, Norman, OK, USA 123 135 Page 40 of 42 Eur. Phys. J. C (2019) 79:135 125 Department of Physics, Oklahoma State University, Stillwater, OK, USA 126 Palacký University, RCPTM, Joint Laboratory of Optics, Olomouc, Czech Republic 127 Center for High Energy Physics, University of Oregon, Eugene, OR, USA 128 LAL, Université Paris-Sud, CNRS/IN2P3, Université Paris-Saclay, Orsay, France 129 Graduate School of Science, Osaka University, Osaka, Japan 130 Department of Physics, University of Oslo, Oslo, Norway 131 Department of Physics, Oxford University, Oxford, UK 132 LPNHE, Sorbonne Université, Paris Diderot Sorbonne Paris Cité, CNRS/IN2P3, Paris, France 133 Department of Physics, University of Pennsylvania, Philadelphia, PA, USA 134 Konstantinov Nuclear Physics Institute of National Research Centre “Kurchatov Institute”, PNPI, St. Petersburg, Russia 135 Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA, USA 136 (a) Laboratório de Instrumentação e Física Experimental de Partículas-LIP, Lisbon, Portugal; (b) Departamento de Física, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal; (c) Departamento de Física, Universidade de Coimbra, Coimbra, Portugal; (d) Centro de Física Nuclear da Universidade de Lisboa, Lisbon, Portugal; (e) Departamento de Física, Universidade do Minho, Braga, Portugal; (f) Departamento de Física Teorica y del Cosmos, Universidad de Granada, Granada, Spain; (g) Dep Física and CEFITEC of Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal 137 Institute of Physics, Academy of Sciences of the Czech Republic, Prague, Czech Republic 138 Czech Technical University in Prague, Prague, Czech Republic 139 Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic 140 State Research Center Institute for High Energy Physics, NRC KI, Protvino, Russia 141 Particle Physics Department, Rutherford Appleton Laboratory, Didcot, UK 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, USA 144 (a) Departamento de Física, Pontificia Universidad Católica de Chile, Santiago, Chile; (b) Departamento de Física, Universidad Técnica Federico Santa María, Valparaíso, Chile 145 Department of Physics, University of Washington, Seattle, WA, USA 146 Department of Physics and Astronomy, University of Sheffield, Sheffield, UK 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, USA 151 Physics Department, Royal Institute of Technology, Stockholm, Sweden 152 Departments of Physics and Astronomy, Stony Brook University, Stony Brook, NY, USA 153 Department of Physics and Astronomy, University of Sussex, Brighton, UK 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, Georgia; (b) High Energy Physics Institute, Tbilisi State University, Tbilisi, Georgia 157 Department of Physics, Technion, Israel Institute of Technology, Haifa, Israel 158 Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University, Tel Aviv, Israel 159 Department of Physics, Aristotle University of Thessaloniki, Thessaloníki, 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, Canada; (b) Department of Physics and Astronomy, York University, Toronto, ON, Canada 166 Division of Physics and Tomonaga Center for the History of the Universe, Faculty of Pure and Applied Sciences, University of Tsukuba, Tsukuba, Japan 167 Department of Physics and Astronomy, Tufts University, Medford, MA, USA 168 Department of Physics and Astronomy, University of California Irvine, Irvine, CA, USA 169 Department of Physics and Astronomy, University of Uppsala, Uppsala, Sweden 123 Eur. Phys. J. C (2019) 79:135 Page 41 of 42 135 170 Department of Physics, University of Illinois, Urbana, IL, USA 171 Instituto de Física 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, UK 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, USA 179 Fakultät für Mathematik und Naturwissenschaften, Fachgruppe Physik, Bergische Universität Wuppertal, Wuppertal, Germany 180 Department of Physics, Yale University, New Haven, CT, USA 181 Yerevan Physics Institute, Yerevan, Armenia a Also at Borough of Manhattan Community College, City University of New York, NY, USA b Also at Centre for High Performance Computing, CSIR Campus, Rosebank, Cape Town, South Africa c Also at CERN, Geneva, Switzerland d Also at CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille, France e Also at Département de Physique Nucléaire et Corpusculaire, Université de Genève, Geneva, Switzerland f Also at Departament de Fisica de la Universitat Autonoma de Barcelona, Barcelona, Spain g Also at Departamento de Física Teorica y del Cosmos, Universidad de Granada, Granada (Spain), Spain h Also at Department of Applied Physics and Astronomy, University of Sharjah, Sharjah, United Arab Emirates i Also at Department of Financial and Management Engineering, University of the Aegean, Chios, Greece j Also at Department of Physics and Astronomy, University of Louisville, Louisville, KY, USA k Also at Department of Physics and Astronomy, University of Sheffield, Sheffield, UK l Also at Department of Physics, California State University, Fresno, CA, USA m Also at Department of Physics, California State University, Sacramento, CA, USA n Also at Department of Physics, King’s College London, London, UK o Also at Department of Physics, St. Petersburg State Polytechnical University, St. Petersburg, Russia p Also at Department of Physics, University of Fribourg, Fribourg, Switzerland q Also at Department of Physics, University of Michigan, Ann Arbor, MI, USA r Also at Dipartimento di Fisica E. Fermi, Università di Pisa, Pisa, Italy s Also at Giresun University, Faculty of Engineering, Giresun, Turkey t Also at Graduate School of Science, Osaka University, Osaka, Japan u Also at Hellenic Open University, Patras, Greece v Also at Horia Hulubei National Institute of Physics and Nuclear Engineering, Bucharest, Romania w Also at II Physikalisches Institut, Georg-August-Universität Göttingen, Göttingen, Germany 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, The Netherlands aa Also at Institute for Particle and Nuclear Physics, Wigner Research Centre for Physics, Budapest, Hungary ab Also at Institute of Particle Physics (IPP), Canada ac Also at Institute of Physics, Academia Sinica, Taipei, Taiwan ad Also at Institute of Physics, Azerbaijan Academy of Sciences, Baku, Azerbaijan ae Also at Institute of Theoretical Physics, Ilia State University, Tbilisi, Georgia af Also at Istanbul University, Dept. of Physics, Istanbul, Turkey ag Also at LAL, Université Paris-Sud, CNRS/IN2P3, Université Paris-Saclay, Orsay, France ah Also at Louisiana Tech University, Ruston, LA, USA ai Also at Manhattan College, New York, NY, USA aj Also at Moscow Institute of Physics and Technology State University, Dolgoprudny, Russia ak Also at National Research Nuclear University MEPhI, Moscow, Russia al Also at Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany 123 135 Page 42 of 42 Eur. Phys. J. C (2019) 79:135 am Also at School of Physics, Sun Yat-sen University, Guangzhou, China an Also at The City College of New York, New York, NY, USA ao Also at The Collaborative Innovation Center of Quantum Matter (CICQM), Beijing, China ap Also at Tomsk State University, Tomsk, and Moscow Institute of Physics and Technology State University, Dolgoprudny, Russia aq Also at TRIUMF, Vancouver, BC, Canada ar Also at Universita di Napoli Parthenope, Naples, Italy ∗ Deceased 123