Identification of high transverse momentum top quarks in pp collisions at \( \sqrt{s}=8 \) TeV with the ATLAS detector

Journal of High Energy Physics, Jun 2016

This paper presents studies of the performance of several jet-substructure techniques, which are used to identify hadronically decaying top quarks with high transverse momentum contained in large-radius jets. The efficiency of identifying top quarks is measured using a sample of top-quark pairs and the rate of wrongly identifying jets from other quarks or gluons as top quarks is measured using multijet events collected with the ATLAS experiment in 20.3 fb−1 of 8 TeV proton-proton collisions at the Large Hadron Collider. Predictions from Monte Carlo simulations are found to provide an accurate description of the performance. The techniques are compared in terms of signal efficiency and background rejection using simulations, covering a larger range in jet transverse momenta than accessible in the dataset. Additionally, a novel technique is developed that is optimized to reconstruct top quarks in events with many jets.

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Identification of high transverse momentum top quarks in pp collisions at \( \sqrt{s}=8 \) TeV with the ATLAS detector

JHE Identification of high transverse momentum top quarks in pp collisions at √s = 8 TeV with the ATLAS detector This paper presents studies of the performance of several jet-substructure techniques, which are used to identify hadronically decaying top quarks with high transverse momentum contained in large-radius jets. The efficiency of identifying top quarks is measured using a sample of top-quark pairs and the rate of wrongly identifying jets from other quarks or gluons as top quarks is measured using multijet events collected with the ATLAS experiment in 20.3 fb−1 of 8 TeV proton-proton collisions at the Large Hadron Collider. Predictions from Monte Carlo simulations are found to provide an accurate description of the performance. The techniques are compared in terms of signal efficiency and background rejection using simulations, covering a larger range in jet transverse momenta than accessible in the dataset. Additionally, a novel technique is developed that is optimized to reconstruct top quarks in events with many jets. Hadron-Hadron scattering (experiments) - E P 0 6 1 Introduction The ATLAS detector Monte-Carlo simulation Object reconstruction and event selection 4.1 Object reconstruction 4.2 Event selection 4.2.1 Signal sample 4.2.2 Background sample Top-tagging techniques 5.1 Substructure-variable taggers 5.2 Shower Deconstruction 5.3 HEPTopTagger 6 Systematic uncertainties 6.1 Experimental uncertainties 6.2 In situ determination of the subjet energy scale for the HEPTopTagger 6.3 Uncertainties in the modelling of physics processes 7 Study of top-tagging performance using Monte-Carlo simulation 7.1 Comparison of top-tagging performance 7.2 HEPTopTagger04 performance Measurement of the top-tagging efficiency and mistag rate 8.1 Top-tagging efficiency 8.1.1 Efficiency of the substructure-variable taggers 8.1.2 Efficiency of Shower Deconstruction 8.1.3 Efficiency of the HEPTopTagger 8.2 Mistag rate 8.2.1 Mistag rate for the substructure-variable taggers 8.2.2 Mistag rate for Shower Deconstruction 8.2.3 Mistag rate for the HEPTopTagger 9 Summary and conclusions A Additional distributions for the signal-sample selection The ATLAS collaboration – 1 – Introduction Conventional top-quark identification methods reconstruct the products of a hadronic topquark decay (t → bW → bq′q¯) as jets with a small radius parameter R (typically R = 0.4 or 0.5).1 There are usually several of these small-R jets in a high-energy, hard proton-proton (pp) collision event at the Large Hadron Collider (LHC). Hadronic top-quark decays are reconstructed by taking those jets which, when combined, best fit the kinematic properties of the top-quark decay, such as the top-quark mass and the W -boson mass. These kinematic constraints may also be fulfilled for a collection of jets which do not all originate from the same top-quark decay chain. In analyses of LHC pp collisions, conventional top-quark identification methods are inefficient at high top-quark energies because the top-quark decay products are collimated and the probability of resolving separate small-R jets is reduced. Top quarks with high transverse momentum (pT & 200 GeV) may instead be reconstructed as a jet with large radius parameter, R ≥ 0.8 (large-R jet) [1–13]. An analysis of the internal jet structure is then performed to identify and reconstruct hadronically decaying top quarks (top tagging). Since a single jet that contains all of the decay products of a massive particle has different properties from a jet of the same transverse momentum originating from a light quark or gluon, it is possible to use the substructure of large-R jets to distinguish top quarks with high pT from jets from other sources, for example from multijet production. These differences in the jet substructure can be better resolved after contributions from soft gluon radiation or from additional pp interactions in the same or adjacent bunch crossings (pile-up) are removed from the jets. Such methods are referred to as jet grooming and consist of either an adaptive modification of the jet algorithm or a selective removal of soft radiation during the process of iterative recombination in jet reconstruction [14–16]. The jet-substructure approach aims to reduce combinatorial background from assigning small-R jets to top-quark candidates in order to achieve a more precise reconstruction of the top-quark four-momentum and a higher background rejection. In searches for top-antitop quark (tt¯) resonances, the improved kinematic reconstruction leads to a better mass resolution for large resonance masses (≥ 1 TeV) compared to the conventional approach, resulting in an increased sensitivity to physics beyond the Standard Model (SM) [17]. ATLAS has published performance studies of jet-substructure methods for top tagging at a pp centre-of-mass energy of √s = 7 TeV [18]. In the paper presented here, the performance of several approaches to top tagging at √s = 8 TeV is documented. Top tagging based on the combination of jet-substructure variables, Shower Deconstruction [ 19, 20 ], 1The ATLAS experiment uses a right-handed coordinate system with its origin at the nominal interaction point (IP) in the centre of the detector and the z-axis along the beam line. The x-axis points from the IP to the centre of the LHC ring, and the y-axis points upwards. Cylindrical coordinates (r, φ) are used in the transverse plane, φ being the azimuthal angle around the beam line. Observables labelled “transverse” are projected into the x–y plane. The pseudorapidity is defined in terms of the polar angle θ as η = − ln tan θ/2. The transverse momentum is defined as pT = p sin θ = p/ cosh η, and the transverse energy ET has an analogous definition. The distance in η–φ space is referred to as ΔR = p(Δη)2 + (Δφ)2. The rapidity of a particle is defined as y = 12 ln EE−+ppzz , in which E and pz are the energy and momentum z-component of the particle. The jet radius parameter R sets the range in y–φ space over which clustering to form jets occurs. – 2 – J H E P 0 6 ( 2 0 1 6 ) 0 9 3 and the HEPTopTagger [ 21, 22 ] is studied, as described in section 5. A new method, HEPTopTagger04, is introduced. Optimised for top tagging in events with many jets, it uses a preselection of small-R jets as input to the HEPTopTagger algorithm. Monte-Carlo (MC) simulation is used to compare the efficiencies and misidentification rates of all approaches over a large kinematic range. The performance of the different methods is studied in data using two different event samples: a signal sample enriched with top quarks and a background sample dominated by multijet production. The signal sample is used to measure top-tagging efficiencies from data, which are compared to the predictions obtained from MC simulations. Quantifying the degree to which MC simulations correctly model the top-tagging efficiency observed in data is crucial for any physics analysis in which top-tagging methods are used because MC simulations are commonly used to model signal and background processes. The signal sample is also used to determine the energy scale of subjets in situ from the reconstructed top-quark mass distribution. Top-tagging misidentification rates are measured in the background sample and are also compared to the prediction of MC simulations. 2 The ATLAS detector The ATLAS detector consists of an inner tracking detector system (ID), which is surrounded by electromagnetic (EM) and hadronic calorimeters and a muon spectrometer (MS). The ID consists of silicon pixel and strip detectors and a transition-radiation tracker covering |η| < 2.5, and it is immersed in a 2 T axial magnetic field. The EM calorimeters use lead/liquid argon (LAr) technology to provide calorimetry for |η| < 3.2, with copper/LAr used in the forward region 3.1 < |η| < 4.9. In the region |η| < 1.7, hadron calorimetry is provided by steel/scintillator calorimeters. In the forward region, copper/LAr and tungsten/LAr calorimeters are used for 1.5 < |η| < 3.2 and 3.1 < |η| < 4.9, respectively. The MS surrounds the calorimeter system and consists of multiple layers of trigger and tracking chambers within a toroidal magnetic field generated by air-core superconducting magnets, which allows for the measurement of muon momenta for |η| < 2.7. ATLAS uses a three-level trigger system [ 23 ] with a hardware-based first-level trigger, which is followed by two software-based trigger levels with an increasingly fine-grained selection of events at lower rates. A detailed description of the ATLAS detector is given in ref. [24]. 3 Monte-Carlo simulation MC simulations are used to model different SM contributions to the signal and background samples. They are also used to study and compare the performance of top-tagging algorithms over a larger kinematic range than accessible in the data samples. Top-quark pair production is simulated with POWHEG-BOX r2330.3 [ 25–28 ] interfaced with PYTHIA v6.426 [ 29 ] with the set of tuned parameters (tune) Perugia 2011C [ 30 ] and the CT10 [ 31 ] set of parton distribution functions (PDFs). The hdamp parameter, which effectively regulates the high-pT gluon radiation in POWHEG, is left at the default value of hdamp = ∞. This MC sample is referred to as the POWHEG+PYTHIA tt¯ sample. – 3 – H E P 0 6 ( 2 0 1 6 ) 0 9 3 Alternative tt¯ samples are used to evaluate systematic uncertainties. A sample generated with MC@NLO v4.01 [ 32, 33 ] interfaced to HERWIG v6.520 [34] and JIMMY v4.31 [ 35 ] with the AUET2 tune [ 36 ], again simulated using the CT10 PDF set, is used to estimate the uncertainty related to the choice of generator. To evaluate the impact of variations in the parton shower and hadronization models, a sample is generated with POWHEG-BOX interfaced to HERWIG and JIMMY. The effects of variations in the QCD (quantum chromodynamics) initial- and final-state radiation (ISR and FSR) modelling are estimated with samples generated with ACERMC v3.8 [ 37 ] interfaced to PYTHIA v.6.426 with the AUET2B tune and the CTEQ6L1 PDF set [ 38 ], where the parton-shower parameters are varied in the range allowed by data [ 39 ]. For the study of systematic uncertainties on kinematic distributions resulting from PDF uncertainties, a sample is generated using POWHEG-BOX interfaced with PYTHIA v.6.427 and using the HERAPDF set [ 40 ]. For all tt¯ samples, a top-quark mass of 172.5 GeV is used. The tt¯ cross section for pp collisions at a centre-of-mass energy of √s = 8 TeV is σtt¯ = 253−+1135 pb for a top-quark mass of 172.5 GeV. It has been calculated at next-tonext-to-leading order (NNLO) in QCD including resummation of next-to-next-to-leading logarithmic (NNLL) soft gluon terms with top++2.0 [ 41–47 ]. The PDF and αs uncertainties were calculated using the PDF4LHC prescription [ 48 ] with the MSTW2008 68% CL NNLO [ 49, 50 ], CT10 NNLO [ 31, 51 ] and NNPDF2.3 5f FFN [ 52 ] PDF sets, and their effect is added in quadrature to the effect of factorization- and renormalization-scale uncertainties. The NNLO+NNLL value is about 3% larger than the exact NNLO prediction, as implemented in Hathor 1.5 [53]. In measurements of the differential tt¯ production cross section as a function of the topquark pT, a discrepancy between data and MC predictions was observed in 7 TeV data [ 54 ]. Based on this measurement, a method of sequential reweighting of the top-quark-pT and tt¯-system-pT distributions was developed [ 55 ], which gives better agreement between the MC predictions and 8 TeV data. In this paper, this reweighting technique is applied to the POWHEG+PYTHIA tt¯ sample, for which the technique was developed. The predicted total tt¯ cross section at NNLO+NNLL is not changed by the reweighting procedure. Single-top-quark production in the s- and W t-channel is modelled with POWHEGBOX and the CT10 PDF set interfaced to PYTHIA v6.426 using Perugia 2011C. Singletop-quark production in the t-channel is generated with POWHEG-BOX in the fourflavour scheme (in which b-quarks are generated in the hard scatter and the PDF does not contain b-quarks) using the four-flavour CT10 PDF set interfaced to PYTHIA v6.427. The overlap between W t production and tt¯ production is removed with the diagram-removal scheme [ 56 ] and the different single-top-production processes are normalized to the approximate NNLO cross-section predictions [ 57–59 ]. Events with a W or a Z boson produced in association with jets (W+jets or Z+jets) are generated with ALPGEN [ 60 ] interfaced to PYTHIA v6.426 using the CTEQ6L1 PDF set and Perugia 2011C. Up to five additional partons are included in the calculation of the matrix element, as well as additional c-quarks, cc¯-quark pairs, and b¯b-quark pairs, taking into account the masses of these heavy quarks. The W+jets contribution is normalized using the charge asymmetry in W -boson production in data [ 61, 62 ] by selecting μ+jets – 4 – H E P 0 6 ( 2 0 1 6 ) 0 9 3 events and comparing to the prediction from MC simulations. The Z+jets contribution is normalized to the calculation of the inclusive cross section at NNLO in QCD obtained with FEWZ [63]. For the comparison of the different top-tagging techniques using MC simulation only, multijet samples are generated with PYTHIA v8.160 with the CT10 PDF set and AU2. As a source of high-transverse-momentum top quarks, samples of events with a hypothetical massive Z′ resonance decaying to top-quark pairs, Z′ → tt¯, are generated with resonance masses ranging from 400 GeV to 3000 GeV and a resonance width of 1.2% of the resonance mass [ 64 ] using PYTHIA v8.175 with the MSTW2008 68% CL LO PDF set [ 49, 50 ] and AU2. For a study of top-quark reconstruction in a final state with many jets, the process2 pp → H+t¯(b) → t¯bt¯(b) is generated in a type-II 2HDM model [ 65 ] with a mass of 1400 GeV of the charged Higgs boson using POWHEG-BOX interfaced to PYTHIA v8.165 with AU2 and the CT10 PDF set. The width of the charged Higgs boson is set to zero and the five-flavour scheme is used. The additional b-quark (in parentheses above) can be present or not, depending on whether the underlying process is gg → H+t¯b or g¯b → H+t¯. All MC samples are passed through a full simulation of the ATLAS detector [ 66 ] based on GEANT4 [67], except for the tt¯ samples used to estimate systematic uncertainties due to the choice of MC generator, parton shower, and amount of ISR/FSR, which are passed through a faster detector simulation with reduced complexity in the description of the calorimeters [68]. All MC samples are reconstructed using the same algorithms as used for data and have minimum-bias events simulated with PYTHIA v8.1 [ 69 ] overlaid to match the pile-up conditions of the collision data sample. 4 4.1 Object reconstruction and event selection Object reconstruction Electron candidates are reconstructed [ 70, 71 ] from clusters in the EM calorimeter and are required to have a track in the ID, associated with the main primary vertex [72], which is 2 defined as the one with the largest P pT,track. They must have ET > 25 GeV and |ηcluster| < 2.47 excluding the barrel/end-cap-calorimeter transition region 1.37 < |ηcluster| < 1.52, where ηcluster is the pseudorapidity of the cluster in the EM calorimeter. The shape of the cluster in the calorimeter must be consistent with the typical energy deposition of an electron and the electron candidate must satisfy the mini-isolation [ 17, 73 ] requirement to reduce background contributions from non-prompt electrons and hadronic showers: the scalar sum of track transverse momenta within a cone of size ∆ R = 10 GeV/ETel around the electron track must be less than 5% of the electron transverse energy ETel (only tracks with pT > 1 GeV are considered in the sum, excluding the track matched to the electron cluster). Muons are reconstructed [ 74 ] using both the ID and the MS and must be associated with the main primary vertex of the event. Muons are required to have pT > 25 GeV 2The process pp → H−t(¯b) → t¯bt(¯b) is also simulated. For simplicity only the positively charged Higgs boson is indicated explicitly in this paper, but it should be understood to denote both signs of the electric charge. – 5 – H E P 0 6 ( 2 0 1 6 ) 0 9 3 and |η| < 2.5 and are required to be isolated with requirements similar to those used for electron candidates: the scalar sum of the track transverse momenta within a cone of size ∆ R = 10 GeV/pµT around the muon track must be less than 5% of pµT, where pµT is the transverse momentum of the muon. Jets are built [ 75 ] from topological clusters of calorimeter cells, which are calibrated to the hadronic energy scale [ 76 ] using a local cell-weighting scheme [ 77 ]. The clusters are treated as massless and are combined by adding their four-momenta, leading to massive jets. The reconstructed jet energy is calibrated using energy- and η-dependent corrections obtained from MC simulations. These corrections are obtained by comparing reconstructed jets with geometrically matched jets built from stable particles (particle level). The corrections are validated using in situ measurements of small-R jets [ 78 ]. Jets reconstructed with the anti-kt [ 79 ] algorithm using a radius parameter R = 0.4 must satisfy pT > 25 GeV and |η| < 2.5. The jet vertex fraction (JVF) uses the tracks matched to a jet and is defined as the ratio of the scalar sum of the transverse momenta of tracks from the main primary vertex to that of all matched tracks. A jet without any matched track is assigned a JVF value of −1. For anti-kt R = 0.4 jets with pT < 50 GeV and |η| < 2.4, the JVF must be larger than 0.5 [ 80 ] to suppress jets from pile-up. Large-R jets are reconstructed with the anti-kt algorithm using R = 1.0 and with the Cambridge/Aachen algorithm [ 81 ] (C/A) using R = 1.5. Anti-kt R = 1.0 jets are groomed using a trimming procedure [16]: the constituents of the anti-kt R = 1.0 jet are reclustered using the kt algorithm [ 82 ] with R = 0.3. Subjets with a pT of less than 5% of the largeR jet pT are removed [18]. The properties of the trimmed jet are recalculated from the constituents of the remaining subjets. The trimmed jet mass, pT, and pseudorapidity are corrected to be, on average, equal to the particle-level jet mass, pT, and pseudorapidity using MC simulations [18, 83]. An illustration of trimming is given in figure 4 of ref. [18]. The C/A R = 1.5 jets are required to satisfy pT > 200 GeV. These jets are used as input to the HEPTopTagger, which employs an internal pile-up suppression, and are therefore left ungroomed. For trimmed anti-kt R = 1.0 jets, the minimum pT is raised to 350 GeV to reduce the fraction of jets not containing all top-quark decay products due to the smaller jet radius parameter. All large-R jets must satisfy |η| < 2.0. The missing transverse momentum is calculated from the vector sum of the transverse energy of clusters in the calorimeters, and it is corrected for identified electrons, muons and anti-kt R = 0.4 jets, for which specific object-identification criteria are applied [84]. The magnitude of the missing transverse momentum is denoted by ETmiss. 4.2 Event selection The data used in this paper were taken in 2012 at a centre-of-mass-energy √s = 8 TeV and correspond to an integrated luminosity of 20.3 fb−1 [85]. Data are used only if all subsystems of the detector as well as the trigger system were fully functional. Baseline quality criteria are imposed to reject contamination from detector noise, non-collision beam backgrounds, and other spurious effects. Events are required to have at least one reconstructed primary vertex with at least five associated ID tracks, each with a pT larger than 400 MeV. This vertex must be consistent with the LHC beam spot [ 72 ]. In addition, all anti-kt – 6 – H E P 0 6 ( 2 0 1 6 ) 0 9 3 R = 0.4 jets in the event which have pT > 20 GeV are required to satisfy the “looser” quality criteria discussed in detail in ref. [ 78 ], otherwise the event is rejected. Two different event samples are used to study the performance of top-tagging algorithms in data: a signal sample enriched in hadronically decaying top quarks and a background sample consisting mainly of multijet events. 4.2.1 Signal sample For the signal sample, a selection of tt¯ events in the lepton+jets channel is used, in which one of the W bosons from tt¯ → W +bW −¯b decays hadronically and the other W boson decays leptonically. The selection is performed in the muon channel and the electron channel. The selection criteria for the muon and electron channels differ only in the requirements imposed on the reconstructed leptons. For the muon channel, the events are required to pass at least one of two muon triggers, where one is optimized to select isolated muons with a transverse momentum of at least 24 GeV and the other selects muons with at least 36 GeV without the isolation requirement. Exactly one muon with pT > 25 GeV is required as defined in section 4.1. Muons are rejected if they are close to an anti-kt R = 0.4 jet that has pT > 25 GeV. The rejection occurs if ∆ R(μ, jet) < (0.04 + 10 GeV/pµT). Events in the muon channel are rejected if they contain an additional electron candidate. For the electron channel, events are required to pass at least one of two triggers. The first is designed for isolated electrons with pT > 24 GeV and the second trigger requires electrons with pT > 60 GeV without the isolation requirement. Exactly one electron is required with ET > 25 GeV as defined in section 4.1. An electron-jet overlap removal is applied based on the observation that the electron pT contributes a significant fraction of the pT of close-by anti-kt R = 0.4 jets. Therefore, the electron momentum is subtracted from the jet momentum before kinematic requirements are applied to the jet, so that jets close to an electron often fall below the jet pT threshold. If the electron-subtracted jet still fulfils the kinematic requirements for anti-kt R = 0.4 jets and the electron is still close, the electron is considered not isolated. In this case, the electron is removed from the event and the original non-subtracted jet is kept. Events in the electron channel are rejected if they also contain a muon candidate. To select events with a leptonically decaying W boson, the following requirements are imposed. The events are required to have missing transverse momentum ETmiss > 20 GeV. Additionally, the scalar sum of ETmiss and the transverse mass of the leptonic W -boson candidate must satisfy ETmiss + mTW > 60 GeV, where mTW = q2pℓTETmiss(1 − cos ∆ φ) is calculated from the transverse momentum of the lepton, pℓT, and ETmiss in the event. The variable ∆ φ is the azimuthal angle between the lepton momentum and the ETmiss direction. To reduce contamination from W+jets events, each event must contain at least two b-tagged anti-kt R = 0.4 jets with pT > 25 GeV and |η| < 2.5. A neural-network-based b-tagging algorithm [86] is employed, which uses information on the impact parameters of the tracks associated with the jet, the secondary vertex, and the decay topology as its input. The operating point chosen for this analysis corresponds to a b-tagging identi– 7 – J H E P 0 6 ( 2 0 1 6 ) 0 9 3 Radius parameter pT range |η| range fication efficiency of 70% in simulated tt¯ events. In tt¯ events with high-momentum top quarks, the direction of the b-quark from the leptonic decay of a top quark is often close to the lepton direction. Hence, at least one b-tagged jet is required to be within ∆ R = 1.5 of the lepton direction. A second b-tag away from the lepton is required that fulfils ∆ R(lepton, b-tag) > 1.5. This b-tagged jet is expected to originate from the b-quark from the hadronic top-quark decay, and is expected to be well separated from the decay products of the leptonically decaying top quark. Each event is required to contain at least one large-R jet that fulfils the requirement ∆ R(lepton, large-R jet) > 1.5. This criterion increases the probability that the large-R jet originates from a hadronically decaying top quark. The large-R jet has to fulfil |η| < 2 and exceed a pT threshold. The jet algorithm, the radius parameter, and the pT threshold depend on the top tagger under study. An overview is given in table 1. The top taggers are introduced in section 5 where also the choice of particular large-R jet types is motivated. If several large-R jets in an event satisfy the mentioned criteria, only the jet with the highest pT is considered. This choice does not bias the measurements presented in this paper, because the top-tagging efficiencies and misidentification rates are measured as a function of the large-R jet kinematics. In simulated events containing top quarks, large-R jets are classified as matched or not matched to a hadronically decaying top quark. The classification is based on the distance ∆ R between the axis of the large-R jet and the flight direction of a generated hadronically decaying top quark. The top-quark flight direction at the top-quark decay vertex is chosen, so as to take into account radiation from the top quark changing its direction. Matched jets are those with ∆ R smaller than a predefined value Rmatch, while not-matched jets are those with ∆ R > Rmatch. The radius Rmatch is 0.75 for the anti-kt R = 1.0 jets and 1.0 for the C/A R = 1.5 jets. Changing Rmatch to 1.0 for the anti-kt R = 1.0 jets has a negligible impact on the size of the not-matched tt¯ contribution (less than 1%). Alternative matching schemes were tested but did not show improved matching properties, such as a higher matching efficiency. Distributions for the signal selection with at least one trimmed anti-kt R = 1.0 jet with pT > 350 GeV are shown in figure 1. The top-quark purity in this sample is 97%, with a small background contribution from W+jets production (3%). Single-top production accounts for 4% of the event yield and the tt¯prediction accounts for 93% (62% from matched and 31% from not-matched events). Not-matched tt¯ events are an intrinsic feature of the signal selection. With different selection criteria the fraction of not-matched tt¯ events J H E P 0 6 ( 2 0 1 6 ) 0 9 3 varies, as does the total number of selected events. The chosen signal selection in the lepton+jets channel was found to be a good compromise between a reduced fraction of not-matched tt¯ events and a sizeable number of selected events. The mass and the transverse momentum of the highest-pT trimmed anti-kt R = 1.0 jet are shown in figures 1(a) and 1(b), respectively. The systematic uncertainties shown in these plots are described in detail in section 6. The mass distribution shows three peaks: one at the top-quark mass, a second at the W -boson mass and a third around 35 GeV. According to simulation, which describes the measured distribution within uncertainties, the top-quark purity in the region near the top-quark mass is very high, with the largest contribution being matched tt¯. The peak at the position of the W -boson mass originates from hadronically decaying top quarks where the b-jet from the decay is not contained in the large-R jet. Even smaller masses are obtained if one of the decay products of the hadronically decaying W boson is not contained in the large-R jet or if only one top-quarkdecay product is captured in the large-R jet. In these cases, a small mass is obtained due to the kinematic requirements imposed during trimming. The fraction of not-matched tt¯ increases for decreasing large-R jet mass indicating a decreasing fraction of jets with a close-by hadronically decaying top quark. Only a small fraction of the peak at small mass is due to matched tt¯. The large-R jet pT exhibits a falling spectrum, and the application of the sequential pT reweighting to the simulation (cf. section 3) yields a good description of the data. The dominant systematic uncertainties in figure 1 result from uncertainties in the largeR jet energy scale (JES), the PDF, and the tt¯ generator. The contributions from these sources are approximately equal in size, except for large-R jets with pT > 500 GeV where the choice of tt¯ generator dominates. These uncertainties affect mostly the normalization of the distributions. For the PDF and tt¯ generator uncertainties, this normalization uncertainty comes about as follows: while the total tt¯ cross section is fixed when the different MC event samples are compared, the pT dependence of the cross section varies from sample to sample, leading to a change in normalization for the phase space considered here (pT > 350 GeV). Distributions for events fulfilling the signal selection with at least one C/A R = 1.5 jet with pT > 200 GeV, to be used in the HEPTopTagger studies, are shown in figure 2. According to the simulation, the top quark purity in this sample is 97%. The only nonnegligible background process is W+jets production (3%). The tt¯ prediction is split into a matched part (59%) and a not-matched part (29%). Single-top production contributes 9% to the total event yield. The mass of the highest-pT C/A R = 1.5 jet with pT > 200 GeV is shown in figure 2(a) and it exhibits a broad peak around 190 GeV. The large-R-jet mass distributions from not-matched tt¯, single-top production, and W+jets production have their maxima at smaller values than the distribution from matched tt¯. No distinct W -boson peak is visible, because the C/A R = 1.5 jets are ungroomed. The pT spectrum of the highest-pT C/A R = 1.5 jet is smoothly falling and well described by simulation after the sequential pT reweighting is applied (figure 2(b)). The C/A R = 1.5 jet distributions are described by the simulation within the uncertainties. The systematic uncertainties are slightly smaller than those in the distributions shown in figure 1 for anti-kt R = 1.0 jets with pT > 350 GeV because the tt¯ modelling – 9 – H E P 0 6 ( 2 0 1 6 ) 0 9 3 Top-tagging techniques Top tagging classifies a given large-R jet as a top jet if its substructure satisfies certain criteria. This paper examines several top-tagging methods, which differ in their substructure analysis and which are described in the following subsections. Due to the different substructure criteria applied, the methods have different efficiencies for tagging signal jets and different misidentification rates for background jets. High efficiency is obtained for loose criteria and implies a high misidentification rate. The performance of the taggers in terms of efficiencies and misidentification rates is provided in section 7.1. The choice of trimmed anti-kt R = 1.0 jets (as defined in section 4.1) for substructurebased analyses has been previously studied in detail [18], including comparisons of different grooming techniques and parameters. The following jet-substructure variables are used for top tagging in this analysis: • trimmed mass — The mass, m, of the trimmed anti-kt R = 1.0 jets is less susceptible to energy depositions from pile-up and the underlying event than the mass of the untrimmed jet. On average, large-R jets containing top-quark decay products have a larger mass than background jets. • kt splitting scales — The kt splitting scales [ 87 ] are a measure of the scale of the last recombination steps in the kt algorithm, which clusters high-momentum and large-angle proto-jets last. Hence, the kt splitting scales are sensitive to whether the last recombination steps correspond to the merging of the decay products of massive particles. They are determined by reclustering the constituents of the trimmed largeR jet with the kt algorithm and are defined as pdij = min(pTi, pTj ) × ∆ Rij , (5.1) in which ∆ Rij is the distance between two subjets i and j in η–φ space, and pTi and pTj are the corresponding subjet transverse momenta. Subjets merged in the last kt clustering step provide the √d12 observable, and √d23 is the splitting scale of the second-to-last merging. The expected value of the first splitting scale √d12 for hadronic top-quark decays captured fully in a large-R jet is approximately mt/2, where mt is the top quark mass. The second splitting scale √d23 targets the hadronic decay of the W boson with an expected value of approximately mW /2. The use of the splitting scale for W -boson tagging in 8 TeV ATLAS data is explored in ref. [ 88 ]. Background jets initiated by hard gluons or light quarks tend to have smaller values of the splitting scales and exhibit a steeply falling spectrum. • N-subjettiness — The N-subjettiness variables τN [ 89, 90 ] quantify how well jets can be described as containing N or fewer subjets. The N subjets found by an exclusive kt clustering of the constituents of the trimmed large-R jet define axes within the jet. J H E P 0 6 ( 2 0 1 6 ) 0 9 3 The quantity τN is given by the pT-weighted sum of the distances of the constituents from the subjet axes: τN = 1 X pTk × ∆ Rkmin d0 k with d0 ≡ X pTk × R , k (5.2) in which pTk is the transverse momentum of constituent k, ∆ Rmin is the distance k between constituent k and the axis of the closest subjet, and R is the radius parameter of the large-R jet. The ratio τ3/τ2 (denoted τ32) provides discrimination between large-R jets formed from hadronically decaying top quarks with high transverse momentum (top jets) which have a 3-prong subjet structure (small values of τ32) and non-top jets with two or fewer subjets (large values of τ32). Similarly, the ratio τ2/τ1 ≡ τ21 is used to separate large-R jets with a 2-prong structure (hadronic decays of Z or W bosons) from jets with only one hard subjet, such as those produced from light quarks or gluons. The variable τ21 is studied in the context of W -boson tagging with the ATLAS and CMS detectors in ref. [ 88 ] and ref. [ 91 ], respectively. A method that distinguishes hadronically decaying high-pT Z bosons from W bosons is studied in ref. [ 92 ]. Distributions of the kt splitting scales and N-subjettiness variables for large-R jets in a top-quark-enriched event sample (cf. section 4.2.1) are shown in figure 3. The √d12 distribution shows a broad shoulder at values above 40 GeV and the matched tt¯contribution exhibits a peak near mt/2 as expected. For the not-matched tt¯ contribution and the W+jets process, √d12 takes on smaller values and the requirement of a minimum value of √d12 can be used to increase the ratio of top-quark signal to background (S/B). For the second splitting scale √d23, signal and background are less well separated than for √d12, but √d23 also provides signal-background discrimination. The distribution of τ32 shows the expected behaviour, with the matched tt¯ contribution having small values, because the hadronic top-quark decay is better described by a three-subjet structure than by two subjets. For not-matched tt¯ and W+jets production, the distribution peaks at ≈ 0.75. Requiring a maximum value of τ32 increases the signal-to-background ratio. For τ21, the separation of signal and background is less pronounced, but values above 0.8 are obtained primarily for background. Thus, τ21 also provides signal-background discrimination. The distributions are well described by the simulation of SM processes within systematic uncertainties, which are described in section 6. For all distributions shown, the large-R JES, tt¯ generator, and parton-shower uncertainties give sizeable contributions, as do the uncertainties of the modelling of the respective substructure variables shown. The uncertainties for √d12 and √d23 are dominated by the tt¯ generator and ISR/FSR uncertainties, respectively, for low values of the substructure variable. Low values of these variables are mainly present for not-matched tt¯, for which the modelling is particularly sensitive to the amount of high-pT radiation in addition to tt¯, because these large-R jets do not primarily originate from hadronically decaying top quarks. The modelling of additional radiation in tt¯ events is also an important uncertainty for the number of events at low values of τ32 and τ21, for which the tt¯ ISR/FSR uncertainties dominate the total uncertainty. The modJ H E P 0 6 ( 2 0 1 6 ) 0 9 3 Tagger Substructure tagger I Substructure tagger II Substructure tagger III Substructure tagger IV Substructure tagger V W ′ top tagger Top-tagging criterion √d12 > 40 GeV m > 100 GeV m > 100 GeV and √d12 > 40 GeV m > 100 GeV and √d12 > 40 GeV and √d23 > 10 GeV m > 100 GeV and √d12 > 40 GeV and √d23 > 20 GeV √d12 > 40 GeV and 0.4 < τ21 < 0.9 and τ32 < 0.65 requirement or the requirement on √d12 further increases the efficiency (taggers I and II). The W ′ top tagger was optimized for a search for tb resonances (W ′) in the fully-hadronic decay mode [2], where a high background suppression is required. The efficiency of this tagger is therefore lower than that of taggers I to III. Taggers IV and V are introduced to study the effect of a requirement on √d23 in addition to the requirements of tagger III. Distributions of the pT and mass of trimmed anti-kt R = 1.0 jets after applying the six different taggers based on substructure variables are shown in figures 4 and 5, respectively, for events passing the full signal selection of section 4.2.1. While the pT spectra look similar after tagging by the different taggers, the mass spectra differ significantly due to the different substructure-variable requirements imposed by the taggers. Taggers II to V require the mass to be greater than 100 GeV, and this cut-off is visible in the distributions. The mass distribution after the √d12 > 40 GeV requirement of Tagger I (figure 5(a)) differs from that of the pre-tag distribution (figure 1(a)), because √d12 is strongly correlated with the trimmed mass. The impact of the √d12 > 40 GeV requirement plus the N-subjettiness requirements of the W ′ top tagger on the mass spectrum is visible by comparing figure 5(f) with the pre-tag distribution (figure 1(a)). The prominent peak around the top-quark mass shows that the sample after tagging is pure in jets which contain all three decay products of the hadronic top-quark decay. All distributions are described by the MC simulation within uncertainties, indicating that the kinematics and the substructure of tagged large-R jets are well modelled by simulation. The uncertainty in the large-R jet pT requiring a top tag is dominated by the large-R JES and the parton-shower and tt¯ generator uncertainties. Hence, the same uncertainties dominate in the different regions of the pT spectrum as before requiring a top tag (section 4.2.1). The uncertainty on the large-R-jet mass distributions is dominated by the jet-mass scale uncertainty for all substructure taggers. The large-R JES as well as tt¯ modelling uncertainties also contribute, but have a smaller impact. For all substructure taggers, the uncertainties in the substructure variables used in the respective taggers have a non-negligible impact, in particular for low large-R jet masses, i.e. in the regime which is sensitive to the modelling of not-matched tt¯ and extra radiation. 5.2 Shower Deconstruction In Shower Deconstruction (SD) [ 19, 20 ], likelihoods are separately calculated for the scenario that a given large-R jet originates from a hadronic top-quark decay and for the scenario that it originates from a background process. The likelihoods are calculated from theoretical hypotheses, which for the application in this paper correspond to the SM. The signal process is the hadronic decay of a top quark and for the background process, the splitting of hard gluons into qq¯ is considered. For signal and background, the effect of the parton shower is included in the calculation of the likelihood. Subjets of the large-R jet are used as proxies for partons in the underlying model and a weight is calculated for each possible shower that leads to the observed subjet configuration. This weight is proportional to the probability that the assumed initial particle generates the final configuration, taking into account the SM amplitude for the underlying hard process and the Sudakov form factors for the parton shower. A discriminating variable χ is calculated as the ratio of the sum of the signal-hypothesis weights to the sum of the background-hypothesis weights. For a set {piκ} of N observed subjet four-momenta piκ, in which i ∈ [1, N ], the value of χ is given by χ({piκ}) = Pperm. P({piκ}|signal) Pperm. P({piκ}|background) , (5.3) with P({piκ}|signal) being the weight for the hypothesis that a signal process leads to the observed configuration {piκ} and the sum in the numerator is over all showers, in which signal processes lead to this configuration. Similarly, the denominator sums the weights for the background processes. If χ is larger than a certain cut value, the large-R jet is tagged as a top jet. By adjusting the threshold value for χ, the tagging efficiency can be changed continuously. The inputs to SD are the four-momenta of the subjets in the large-R jet. SD has an internal mechanism to suppress pile-up, which is based on the fact that the weights of the likelihood ratio contain the probability that a subset of the subjets did not originate from the hard interaction but are the result of pile-up. Details can be found in refs. [ 19, 20 ]. In this paper, trimmed anti-kt R = 1.0 jets are used as input to SD, but the subjets of the untrimmed jet are fed to the SD algorithm, and the kinematic properties (pT, η) of the trimmed jet are only used to preselect the signal sample. This procedure avoids interference of the trimming with the SD-internal pile-up suppression. To obtain the best SD performance, the smallest structures in the flow of particles should be resolved by the subjets used as input to SD. Therefore, C/A R = 0.2 subjets are used, as they are the jets with the smallest radius parameter for which ATLAS calibrations and calibration uncertainties have been derived [ 18, 76 ]. Only the nine hardest subjets of the large-R jet are used in the present study to reduce the processing time per event, which grows with the number of subjets considered in the calculation. The signal weight is zero for large-R jets with fewer than three subjets because a finite signal weight requires the existence of at least three subjets which are identified with the three partons from the top-quark decay. To speed up the computation of the signal weights, the signal weight is set to zero if no combination of at least three subjets can be found that has an invariant mass within a certain range around the top-quark mass. The rationale for this mass requirement is that subjet combinations outside of this mass range would receive only a very small (but finite) weight due to the Breit-Wigner distribution assumed for the signal H E P 0 6 ( 2 0 1 6 ) 0 9 3 hypothesis. Similarly, a subset of the subjets which have a combined invariant mass close to the top-quark mass must give an invariant mass within a given range around the W -boson mass. Due to detector effects, the values of these ranges around the top-quark mass and the W -boson mass must be tuned to optimize the performance and cannot be extracted directly from the model. The values used in this study are a range of 40 GeV around a top-quark mass of 172 GeV and a range of 20 GeV around a W -boson mass of 80.4 GeV. For the background hypothesis, no constraint on the subjet multiplicity is present and also no mass-range requirements are imposed. Distributions of the multiplicity and pT of C/A R = 0.2 subjets found in the untrimmed anti-kt R = 1.0 jets from the signal selection are shown in figure 6. These subjets are used as input to SD and must satisfy the kinematic constraints pT > 20 GeV and |η| < 2.1. The subjet multiplicity of the large-R jet is shown in figure 6(a). Most of the large-R jets have two or three subjets and only a small fraction have more than four subjets. Of the large-R jets, 41% have fewer than three subjets and are hence assigned a SD signal weight of zero. The simulation describes the data within statistical and systematic uncertainties indicating that the input to the SD algorithm, the subjet multiplicity and kinematics, are well described. For two and three subjets, the uncertainty is dominated by uncertainties in the large-R JES and the PDF. For one subjet and for four or more subjets, as well, the uncertainty is dominated by the subjet energy-resolution uncertainty. The source of most events with only one subjet is not-matched tt¯, for which the modelling of additional low-pT radiation exceeding the minimum subjet pT depends on the precision of the subjet energy scale and resolution. The same effect is present for four or more subjets, because hadronically decaying top quarks are expected to give rise to a distinct three-subjet structure and additional subjets may be due to additional low-pT radiation close to the top quark. The pT distributions of the three hardest subjets are shown in figures 6(b)–6(d). The pT of the highest-pT subjet is larger than ≈ 100 GeV and has a broad peak from 200 to 400 GeV. The shoulder at 370 GeV is caused by large-R jets from not-matched tt¯ and W+jets background, as many of these jets have only one subjet, as shown in figure 6(a), and in that case the single subjet carries most of the momentum of the large-R jet, i.e. most of the momentum is concentrated in the core of the jet. Therefore, the shoulder at 370 GeV is due to the requirement pT > 350 GeV for the large-R jet. The systematic uncertainty in the region mainly populated by jets with one dominant subjet (pT > 350 GeV) or by jets with many subjets (100 < pT < 150 GeV) in figure 6(a) has sizeable contributions from the modelling of the subjet properties, here the subjet energy scale. While the large-R JES also contributes for 100 < pT < 150 GeV, it is dominant for jets mainly showing the expected distinct two-subjet or three-subjet structure (150 < pT < 350 GeV). For pT > 500 GeV, the largest uncertainty results from the difference between the tt¯ generators, as this is the main source of uncertainties for the modelling of tt¯ events in the upper range of the pT spectrum studied. For the second-highest subjet pT, the background distribution peaks near the 20 GeV threshold. These are subjets in large-R jets with only two subjets where the highestpT subjet carries most of the large-R jet momentum. These asymmetric configurations, where the highest-pT subjet carries a much larger pT than the second-highest-pT subjet, H E P 0 6 ( 2 0 1 6 ) 0 9 3 [16] D. Krohn, J. Thaler and L.-T. Wang, Jet Trimming, JHEP 02 (2010) 084 [arXiv:0912.1342] [INSPIRE]. 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Zwalinski31. 1 Department of Physics, University of Adelaide, Adelaide, Australia 2 Physics Department, SUNY Albany, Albany NY, U.S.A. 3 Department of Physics, University of Alberta, Edmonton AB, Canada 4 (a) Department of Physics, Ankara University, Ankara; (b) Istanbul Aydin University, Istanbul; (c) Division of Physics, TOBB University of Economics and Technology, Ankara, Turkey 5 LAPP, CNRS/IN2P3 and Universit´e Savoie Mont Blanc, Annecy-le-Vieux, France 6 High Energy Physics Division, Argonne National Laboratory, Argonne IL, U.S.A. 7 Department of Physics, University of Arizona, Tucson AZ, U.S.A. 8 Department of Physics, The University of Texas at Arlington, Arlington TX, U.S.A. 9 Physics Department, University of Athens, Athens, Greece 10 Physics Department, National Technical University of Athens, Zografou, Greece 11 Institute of Physics, Azerbaijan Academy of Sciences, Baku, Azerbaijan 12 Institut de F´ısica d’Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Barcelona, Spain, Spain 13 Institute of Physics, University of Belgrade, Belgrade, Serbia 14 Department for Physics and Technology, University of Bergen, Bergen, Norway 15 Physics Division, Lawrence Berkeley National Laboratory and University of California, Berkeley CA, U.S.A. 16 Department of Physics, Humboldt University, Berlin, Germany 17 Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics, University of Bern, Bern, Switzerland 18 School of Physics and Astronomy, University of Birmingham, Birmingham, U.K. H E P 0 6 ( 2 0 1 6 ) 0 9 3 H E P 0 6 ( 2 0 1 6 ) 0 9 3 53 II Physikalisches Institut, Justus-Liebig-Universita¨t Giessen, Giessen, Germany 54 SUPA - School of Physics and Astronomy, University of Glasgow, Glasgow, U.K. 55 II Physikalisches Institut, Georg-August-Universit¨at, Go¨ttingen, Germany 56 Laboratoire de Physique Subatomique et de Cosmologie, Universit´e Grenoble-Alpes, CNRS/IN2P3, Grenoble, France 57 Department of Physics, Hampton University, Hampton VA, U.S.A. 58 Laboratory for Particle Physics and Cosmology, Harvard University, Cambridge MA, U.S.A. 59 (a) Kirchhoff-Institut fu¨r Physik, Ruprecht-Karls-Universita¨t Heidelberg, Heidelberg; (b) Physikalisches Institut, Ruprecht-Karls-Universita¨t Heidelberg, Heidelberg; (c) ZITI Institut fu¨r technische Informatik, Ruprecht-Karls-Universita¨t Heidelberg, Mannheim, Germany 60 Faculty of Applied Information Science, Hiroshima Institute of Technology, Hiroshima, Japan 61 (a) Department of Physics, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong; (b) Department of Physics, The University of Hong Kong, Hong Kong; (c) Department of Physics, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China 62 Department of Physics, Indiana University, Bloomington IN, U.S.A. 63 Institut fu¨r Astro- und Teilchenphysik, Leopold-Franzens-Universita¨t, Innsbruck, Austria 64 University of Iowa, Iowa City IA, U.S.A. 65 Department of Physics and Astronomy, Iowa State University, Ames IA, U.S.A. 66 Joint Institute for Nuclear Research, JINR Dubna, Dubna, Russia 67 KEK, High Energy Accelerator Research Organization, Tsukuba, Japan 68 Graduate School of Science, Kobe University, Kobe, Japan 69 Faculty of Science, Kyoto University, Kyoto, Japan 70 Kyoto University of Education, Kyoto, Japan 71 Department of Physics, Kyushu University, Fukuoka, Japan 72 Instituto de F´ısica La Plata, Universidad Nacional de La Plata and CONICET, La Plata, Argentina 73 Physics Department, Lancaster University, Lancaster, U.K. 74 (a) INFN Sezione di Lecce; (b) Dipartimento di Matematica e Fisica, Universita` del Salento, Lecce, Italy 75 Oliver Lodge Laboratory, University of Liverpool, Liverpool, U.K. 76 Department of Physics, Joˇzef Stefan Institute and University of Ljubljana, Ljubljana, Slovenia 77 School of Physics and Astronomy, Queen Mary University of London, London, U.K. 78 Department of Physics, Royal Holloway University of London, Surrey, U.K. 79 Department of Physics and Astronomy, University College London, London, U.K. 80 Louisiana Tech University, Ruston LA, U.S.A. 81 Laboratoire de Physique Nucl´eaire et de Hautes Energies, UPMC and Universit´e Paris-Diderot and CNRS/IN2P3, Paris, France 82 Fysiska institutionen, Lunds universitet, Lund, Sweden 83 Departamento de Fisica Teorica C-15, Universidad Autonoma de Madrid, Madrid, Spain 84 Institut fu¨r Physik, Universita¨t Mainz, Mainz, Germany 85 School of Physics and Astronomy, University of Manchester, Manchester, U.K. 86 CPPM, Aix-Marseille Universit´e and CNRS/IN2P3, Marseille, France 87 Department of Physics, University of Massachusetts, Amherst MA, U.S.A. 88 Department of Physics, McGill University, Montreal QC, Canada 89 School of Physics, University of Melbourne, Victoria, Australia 90 Department of Physics, The University of Michigan, Ann Arbor MI, U.S.A. 91 Department of Physics and Astronomy, Michigan State University, East Lansing MI, U.S.A. 92 (a) INFN Sezione di Milano; (b) Dipartimento di Fisica, Universita` di Milano, Milano, Italy 93 B.I. Stepanov Institute of Physics, National Academy of Sciences of Belarus, Minsk, Republic of Belarus 94 National Scientific and Educational Centre for Particle and High Energy Physics, Minsk, Republic of Belarus 95 Department of Physics, Massachusetts Institute of Technology, Cambridge MA, U.S.A. H E P 0 6 ( 2 0 1 6 ) 0 9 3 96 Group of Particle Physics, University of Montreal, Montreal QC, Canada 97 P.N. Lebedev Physical Institute of the Russian Academy of Sciences, Moscow, Russia 98 Institute for Theoretical and Experimental Physics (ITEP), Moscow, Russia 99 National Research Nuclear University MEPhI, Moscow, Russia 100 D.V. Skobeltsyn Institute of Nuclear Physics, M.V. Lomonosov Moscow State University, Moscow, Russia 101 Fakulta¨t fu¨r Physik, Ludwig-Maximilians-Universita¨t Mu¨nchen, Mu¨nchen, Germany 102 Max-Planck-Institut fu¨r Physik (Werner-Heisenberg-Institut), Mu¨nchen, Germany 103 Nagasaki Institute of Applied Science, Nagasaki, Japan 104 Graduate School of Science and Kobayashi-Maskawa Institute, Nagoya University, Nagoya, Japan 105 (a) INFN Sezione di Napoli; (b) Dipartimento di Fisica, Universita` di Napoli, Napoli, Italy 106 Department of Physics and Astronomy, University of New Mexico, Albuquerque NM, U.S.A. 107 Institute for Mathematics, Astrophysics and Particle Physics, Radboud University Nijmegen/Nikhef, Nijmegen, Netherlands 108 Nikhef National Institute for Subatomic Physics and University of Amsterdam, Amsterdam, Netherlands 109 Department of Physics, Northern Illinois University, DeKalb IL, U.S.A. 110 Budker Institute of Nuclear Physics, SB RAS, Novosibirsk, Russia 111 Department of Physics, New York University, New York NY, U.S.A. 112 Ohio State University, Columbus OH, U.S.A. 113 Faculty of Science, Okayama University, Okayama, Japan 114 Homer L. Dodge Department of Physics and Astronomy, University of Oklahoma, Norman OK, U.S.A. 115 Department of Physics, Oklahoma State University, Stillwater OK, U.S.A. 116 Palack´y University, RCPTM, Olomouc, Czech Republic 117 Center for High Energy Physics, University of Oregon, Eugene OR, U.S.A. 118 LAL, Univ. Paris-Sud, CNRS/IN2P3, Universit´e Paris-Saclay, Orsay, France 119 Graduate School of Science, Osaka University, Osaka, Japan 120 Department of Physics, University of Oslo, Oslo, Norway 121 Department of Physics, Oxford University, Oxford, U.K. 122 (a) INFN Sezione di Pavia; (b) Dipartimento di Fisica, Universita` di Pavia, Pavia, Italy 123 Department of Physics, University of Pennsylvania, Philadelphia PA, U.S.A. 124 National Research Centre ”Kurchatov Institute” B.P.Konstantinov Petersburg Nuclear Physics Institute, St. Petersburg, Russia 125 (a) INFN Sezione di Pisa; (b) Dipartimento di Fisica E. Fermi, Universita` di Pisa, Pisa, Italy 126 Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh PA, U.S.A. 127 (a) Laborato´rio de Instrumentac¸a˜o e F´ısica Experimental de Part´ıculas - LIP, Lisboa; (b) Faculdade de Ciˆencias, Universidade de Lisboa, Lisboa; (c) Department of Physics, University of Coimbra, Coimbra; (d) Centro de F´ısica Nuclear da Universidade de Lisboa, Lisboa; (e) Departamento de Fisica, Universidade do Minho, Braga; (f) Departamento de Fisica Teorica y del Cosmos and CAFPE, Universidad de Granada, Granada (Spain); (g) Dep Fisica and CEFITEC of Faculdade de Ciencias e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal 128 Institute of Physics, Academy of Sciences of the Czech Republic, Praha, Czech Republic 129 Czech Technical University in Prague, Praha, Czech Republic 130 Faculty of Mathematics and Physics, Charles University in Prague, Praha, Czech Republic 131 State Research Center Institute for High Energy Physics (Protvino), NRC KI, Russia 132 Particle Physics Department, Rutherford Appleton Laboratory, Didcot, U.K. 133 (a) INFN Sezione di Roma; (b) Dipartimento di Fisica, Sapienza Universita` di Roma, Roma, Italy 134 (a) INFN Sezione di Roma Tor Vergata; (b) Dipartimento di Fisica, Universita` di Roma Tor Vergata, Roma, Italy 135 (a) INFN Sezione di Roma Tre; (b) Dipartimento di Matematica e Fisica, Universita` Roma Tre, Roma, Italy H E P 0 6 ( 2 0 1 6 ) 0 9 3 H E P 0 6 ( 2 0 1 6 ) 0 9 3 a Also at Department of Physics, King’s College London, London, U.K. b Also at Institute of Physics, Azerbaijan Academy of Sciences, Baku, Azerbaijan c Also at Novosibirsk State University, Novosibirsk, Russia d Also at TRIUMF, Vancouver BC, Canada e Also at Department of Physics, California State University, Fresno CA, U.S.A. f Also at Department of Physics, University of Fribourg, Fribourg, Switzerland g Also at Departament de Fisica de la Universitat Autonoma de Barcelona, Barcelona, Spain h Also at Departamento de Fisica e Astronomia, Faculdade de Ciencias, Universidade do Porto, Portugal i Also at Tomsk State University, Tomsk, Russia j Also at CPPM, Aix-Marseille Universit´e and CNRS/IN2P3, Marseille, France k Also at Universita di Napoli Parthenope, Napoli, Italy l Also at Institute of Particle Physics (IPP), Canada m Also at Particle Physics Department, Rutherford Appleton Laboratory, Didcot, U.K. n Also at Department of Physics, St. Petersburg State Polytechnical University, St. Petersburg, Russia o Also at Louisiana Tech University, Ruston LA, U.S.A. p Also at Institucio Catalana de Recerca i Estudis Avancats, ICREA, Barcelona, Spain q Also at Department of Physics, The University of Michigan, Ann Arbor MI, U.S.A. r Also at Graduate School of Science, Osaka University, Osaka, Japan s Also at Department of Physics, National Tsing Hua University, Taiwan t Also at Department of Physics, The University of Texas at Austin, Austin TX, U.S.A. u Also at Institute of Theoretical Physics, Ilia State University, Tbilisi, Georgia v Also at CERN, Geneva, Switzerland w Also at Georgian Technical University (GTU),Tbilisi, Georgia x Also at Ochadai Academic Production, Ochanomizu University, Tokyo, Japan y Also at Manhattan College, New York NY, U.S.A. z Also at Hellenic Open University, Patras, Greece aa Also at Institute of Physics, Academia Sinica, Taipei, Taiwan ab Also at LAL, Univ. 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D 89 ( 2014 ) 014007 [arXiv: 1308 .0540] [INSPIRE]. 19 (a ) Department of Physics, Bogazici University, Istanbul; (b) Department of Physics Engineering, Gaziantep University, Gaziantep; (c) Department of Physics, Dogus University, Istanbul, Turkey 20 Centro de Investigaciones, Universidad Antonio Narino, Bogota, Colombia 21 (a) INFN Sezione di Bologna; (b) Dipartimento di Fisica e Astronomia , Universita` di Bologna, Bologna, Italy 22 Physikalisches Institut , University of Bonn, Bonn, Germany 23 Department of Physics, Boston University, Boston MA, U.S.A. 24 Department of Physics, Brandeis University, Waltham MA, U.S.A. 25 ( a) Universidade Federal do Rio De Janeiro COPPE /EE/IF, Rio de Janeiro; (b) Electrical Circuits Department , Federal University of Juiz de Fora (UFJF), Juiz de Fora; (c) Federal University of Sao Joao del Rei (UFSJ), Sao Joao del Rei; (d ) Instituto de Fisica, Universidade de Sao Paulo, Sao Paulo, Brazil 26 Physics Department, Brookhaven National Laboratory, Upton NY , U.S.A. 27 (a ) Transilvania University of Brasov, Brasov, Romania; (b) National Institute of Physics and Nuclear Engineering , Bucharest; (c) National Institute for Research and Development of Isotopic and Molecular Technologies , Physics Department, Cluj Napoca; (d) University Politehnica Bucharest, Bucharest; (e)] West University in Timisoara, Timisoara, Romania 28 Departamento de F´ısica, Universidad de Buenos Aires, Buenos Aires, Argentina 29 Cavendish Laboratory, University of Cambridge, Cambridge, U.K. 30 Department of Physics, Carleton University, Ottawa ON, Canada 31 CERN , Geneva, Switzerland 32 Enrico Fermi Institute , University of Chicago, Chicago IL, U.S.A. 33 ( a) Departamento de F ´ısica , Pontificia Universidad Cato´lica de Chile, Santiago; (b) Departamento de F´ısica, Universidad T´ecnica Federico Santa Mar´ıa, Valpara´ıso, Chile 34 ] (a) Institute of High Energy Physics , Chinese Academy of Sciences, Beijing; (b) Department of Modern Physics, University of Science and Technology of China, Anhui; (c) Department of Physics, Nanjing University, Jiangsu; (d) School of Physics, Shandong University, Shandong; (e) Department of Physics and Astronomy, Shanghai Key Laboratory for Particle Physics and Cosmology , Shanghai Jiao Tong University, Shanghai; ( also affiliated with PKU-CHEP); (f) Physics Department , Tsinghua University, Beijing 100084, China 35 Laboratoire de Physique Corpusculaire , Clermont Universit´e and Universit´e Blaise Pascal and CNRS/IN2P3, Clermont-Ferrand, France 36 Nevis Laboratory , Columbia University, Irvington NY, U.S.A. 37 Niels Bohr Institute , University of Copenhagen, Kobenhavn, Denmark 38 (a) INFN Gruppo Collegato di Cosenza, Laboratori Nazionali di Frascati; (b) Dipartimento di Fisica , Universita` della Calabria, Rende, Italy 39 (a ) AGH University of Science and Technology, Faculty of Physics and Applied Computer Science, Krakow; (b) Marian Smoluchowski Institute of Physics , Jagiellonian University, Krakow, Poland 40 Institute of Nuclear Physics Polish Academy of Sciences, Krakow , Poland 41 Physics Department, Southern Methodist University, Dallas TX, U.S.A. 42 Physics Department, University of Texas at Dallas, Richardson TX, U.S.A. 43 DESY , Hamburg and Zeuthen, Germany 44 Institut fu¨r Experimentelle Physik IV , Technische Universita¨t Dortmund, Dortmund, Germany 45 Institut fu¨r Kern- und Teilchenphysik , Technische Universita¨t Dresden, Dresden, Germany 46 Department of Physics, Duke University, Durham NC , U.S.A. 47 SUPA - School of Physics and Astronomy, University of Edinburgh, Edinburgh, U.K. 48 INFN Laboratori Nazionali di Frascati , Frascati, Italy 49 Fakulta¨t fu¨r Mathematik und Physik, Albert- Ludwigs-Universita¨t, Freiburg, Germany 50 Section de Physique, Universit´e de Gen`eve, Geneva, Switzerland 51 (a) INFN Sezione di Genova; (b) Dipartimento di Fisica , Universita` di Genova, Genova, Italy 52 (a ) E. Andronikashvili Institute of Physics, Iv. Javakhishvili Tbilisi State University, Tbilisi; (b) High Energy Physics Institute , Tbilisi State University, Tbilisi, Georgia 136 (a) Facult´e des Sciences Ain Chock , R´eseau Universitaire de Physique des Hautes Energies - Universit´e Hassan II , Casablanca; (b) Centre National de l' Energie des Sciences Techniques Nucleaires, Rabat; (c) Facult´e des Sciences Semlalia, Universit´e Cadi Ayyad, LPHEA-Marrakech; (d) Facult´e des Sciences, Universit´e Mohamed Premier and LPTPM, Oujda; (e) Facult´e des sciences , Universit´e Mohammed V , Rabat , Morocco 137 DSM/IRFU (Institut de Recherches sur les Lois Fondamentales de l' Univers), CEA Saclay (Commissariat a` l'Energie Atomique et aux Energies Alternatives), Gif-sur- Yvette , France 138 Santa Cruz Institute for Particle Physics, University of California Santa Cruz, Santa Cruz CA, U.S.A. 139 Department of Physics, University of Washington, Seattle WA, U.S.A. 140 Department of Physics and Astronomy, University of Sheffield, Sheffield, U.K. 141 Department of Physics, Shinshu University, Nagano, Japan 142 Fachbereich Physik , Universita¨t Siegen, Siegen, Germany 143 Department of Physics, Simon Fraser University, Burnaby BC , Canada 144 SLAC National Accelerator Laboratory , Stanford CA, U.S.A. 145 (a ) Faculty of Mathematics, Physics & Informatics, Comenius University, Bratislava; (b) Department of Subnuclear Physics, Institute of Experimental Physics of the Slovak Academy of Sciences, Kosice , Slovak Republic 146 (a ) Department of Physics, University of Cape Town, Cape Town; (b ) Department of Physics, University of Johannesburg, Johannesburg; (c) School of Physics, University of the Witwatersrand, Johannesburg, South Africa 147 (a ) Department of Physics, Stockholm University; (b) The Oskar Klein Centre , Stockholm, Sweden 148 Physics Department, Royal Institute of Technology, Stockholm, Sweden 149 Departments of Physics & Astronomy and Chemistry , Stony Brook University, Stony Brook NY, U.S.A. 150 Department of Physics and Astronomy, University of Sussex, Brighton, U.K. 151 School of Physics, University of Sydney, Sydney, Australia 152 Institute of Physics, Academia Sinica, Taipei, Taiwan 153 Department of Physics, Technion: Israel Institute of Technology, Haifa, Israel 154 Raymond and Beverly Sackler School of Physics and Astronomy , Tel Aviv University, Tel Aviv, Israel 155 Department of Physics, Aristotle University of Thessaloniki, Thessaloniki, Greece 156 International Center for Elementary Particle Physics and Department of Physics, The University of Tokyo, Tokyo, Japan 157 Graduate School of Science and Technology, Tokyo Metropolitan University, Tokyo, Japan 158 Department of Physics, Tokyo Institute of Technology, Tokyo, Japan 159 Department of Physics, University of Toronto, Toronto ON, Canada 160 (a ) TRIUMF, Vancouver BC ; (b ) Department of Physics and Astronomy, York University, Toronto ON, Canada 161 Faculty of Pure and Applied Sciences, and Center for Integrated Research in Fundamental Science and Engineering , University of Tsukuba, Tsukuba, Japan 162 Department of Physics and Astronomy, Tufts University, Medford MA, U.S.A. 163 Department of Physics and Astronomy, University of California Irvine, Irvine CA, U.S.A. 164 (a) INFN Gruppo Collegato di Udine, Sezione di Trieste, Udine; (b) ICTP, Trieste; (c) Dipartimento di Chimica , Fisica e Ambiente, Universita` di Udine, Udine, Italy 165 Department of Physics and Astronomy, University of Uppsala, Uppsala, Sweden 166 Department of Physics, University of Illinois, Urbana IL , U.S.A. 167 Instituto de F´ısica Corpuscular (IFIC) and Departamento de F ´ısica Ato´mica , Molecular y Nuclear and Departamento de Ingenier´ ıa Electro´nica and Instituto de Microelectro´nica de Barcelona (IMB-CNM), University of Valencia and CSIC, Valencia, Spain 168 Department of Physics, University of British Columbia, Vancouver BC , Canada 169 Department of Physics and Astronomy, University of Victoria, Victoria BC , Canada

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G. Aad, B. Abbott, J. Abdallah, O. Abdinov, R. Aben. Identification of high transverse momentum top quarks in pp collisions at \( \sqrt{s}=8 \) TeV with the ATLAS detector, Journal of High Energy Physics, 2016, 93, DOI: 10.1007/JHEP06(2016)093