Identification techniques for highly boosted W bosons that decay into hadrons

Journal of High Energy Physics, Dec 2014

In searches for new physics in the energy regime of the LHC, it is becoming increasingly important to distinguish single-jet objects that originate from the merging of the decay products of W bosons produced with high transverse momenta from jets initiated by single partons. Algorithms are defined to identify such W jets for different signals of interest, using techniques that are also applicable to other decays of bosons to hadrons that result in a single jet, such as those from highly boosted Z and Higgs bosons. The efficiency for tagging W jets is measured in data collected with the CMS detector at a center-of-mass energy of 8 TeV, corresponding to an integrated luminosity of 19.7 fb−1. The performance of W tagging in data is compared with predictions from several Monte Carlo simulators.

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Identification techniques for highly boosted W bosons that decay into hadrons

E-mail: 0 1 2 3 0 [28] O. Antipin, D. Atwood and A. Soni , Search for RS gravitons via W 1 RWTH Aachen University, I. Physikalisches Institut , Aachen , Germany 2 University College Dublin , Dublin , Ireland 3 12: Also at British University in Egypt , Cairo , Egypt In searches for new physics in the energy regime of the LHC, it is becoming increasingly important to distinguish single-jet objects that originate from the merging of the decay products of W bosons produced with high transverse momenta from jets initiated by single partons. Algorithms are defined to identify such W jets for different signals of interest, using techniques that are also applicable to other decays of bosons to hadrons that result in a single jet, such as those from highly boosted Z and Higgs bosons. The efficiency for tagging W jets is measured in data collected with the CMS detector at a center-of-mass energy of 8 TeV, corresponding to an integrated luminosity of 19.7 fb1. The performance of W tagging in data is compared with predictions from several Monte Carlo simulators. - Summary and outlook The CMS collaboration 1 Introduction 2 3 4 Event reconstruction Data and simulated event samples Data and simulated event samples Algorithms for W jet identification Substructure observables Comparison of algorithms Performance in simulation W-polarization and quark-gluon composition Performance in data and systematic uncertainties Comparison of data and simulation Mistagging rate measurement Efficiency scale factors and mass scale/resolution measurement Systematic uncertainties The LHC at CERN probes a new energy regime in particle physics, where searches for physics beyond the standard model (SM) at high mass scale often involve objects with large transverse momenta (pT). In final states that contain the W and Z gauge bosons or Higgs bosons (H), it is possible to achieve a high selection efficiency through the use of hadronic decay channels. At sufficiently large boost above order of pT > 200 GeV, the final state hadrons from the W qq0 decay merge into a single jet, and the traditional analysis techniques relying on resolved jets are no longer applicable. However, in such cases the analysis of jet substructure can be used to identify those jets arising from decays of W, Z or H bosons. Because the values of the mass of the W and Z bosons are rather close to each other, we do not distinguish the two, and refer to such jets collectively as V jets, while the Higgs boson mass is significantly higher and can be distinguished. The focus of this paper is solely on the identification of W jets, however, we note that many of the procedures described are equally applicable for handling highly boosted Z and H bosons. Measurements of jet substructure observables related to identification of W bosons have been previously reported by CMS [1, 2] and ATLAS [3, 4]. Several searches at CMS have employed jet substructure techniques for identifying (tagging) W jets and Z jets. These include searches in all-jet tt final states [5, 6], single and pair produced V bosons in inclusive dijet final states [7, 8], and searches in the VV final states, where one of the vector bosons decays leptonically [9, 10]. In these searches, a variety of different observables have been used to identify the V jets. This paper aims to compare and measure the performance in 8 TeV pp collisions of various jet substructure techniques that can be used to distinguish V jets from more ordinary quark- and gluon-initiated jets, which we refer to as QCD jets. This paper is organized as follows. The CMS detector is described in section 2. The procedures chosen for the reconstruction of events are described in section 3. The data and simulated events used in our studies as well as the event selection criteria are presented in section 4. In section 5, through Monte Carlo (MC) simulation, we investigate the performance of jet substructure observables used to identify W jets, in order to find the best discriminants for such events. We compare these observables in different kinematic regimes, and examine factors that contribute to their performance. Their distributions in data are compared to those in MC simulations in section 6, to learn how well current MC simulations can model the physical processes responsible for jet substructure. The methods used to extract data-to-simulation scale factors needed to correct W boson tagging efficiencies obtained from MC simulation are discussed in section 6, and the mistagging rate of QCD jets in data is extracted. The goal being to provide these as reference tools for analyzing events with jets from V bosons in the final state. Finally, we give a summary of our studies in section 7. The central feature of the CMS detector is a 3.8 T superconducting solenoid of 6 m internal diameter. A complex silicon tracker, a crystal electromagnetic calorimeter (ECAL), and a hadron calorimeter (HCAL) are located within the magnetic field volume. A muon system is installed outside the solenoid, and embedded in the steel return yoke. The CMS tracker consists of 1440 silicon pixel and 15 148 silicon strip detector modules. The ECAL consists detailed description of the CMS detector, together with a definition of the coordinate system used and the relevant kinematic variables, can be found in ref. [11]. Event reconstruction Jets are reconstructed by clustering particles obtained using the particle flow (PF) algorithm [1214]. The PF procedure identifies each individual particle (a PF candidate) through an optimized combination of all subdetector information. The energy of photons is obtained directly from the ECAL measurement, corrected for suppression effects of energies from calorimetric channels with small signals (referred to as zero-suppression) [15]. The energy of an electron is determined from a combination of the track momentum at the main interaction vertex, the corresponding ECAL cluster energy, and the energy sum of all bremsstrahlung photons associated with the track. The energy of a muon is obtained from the corresponding track momentum. The energy of a charged hadron is determined from a combination of the track momentum and the corresponding ECAL and HCAL energies, corrected for zero-suppression effects, and calibrated for the nonlinear response of the calorimeters. Finally, the energy of a neutral hadron is obtained from the calibrated energies in ECAL and HCAL. The PF candidates are clustered into jets using two algorithms: the anti-kT algoversion 3.0.1 [19]. While the CA8 algorithm with a larger distance parameter is used throughout this paper to select and identify W jets, the AK5 algorithm is used to put requirements on additional QCD jets in the event selection. The choice of these algorithms is further explained in section 5. To mitigate the effect of multiple interactions in the same bunch crossing, the so-called pileup (PU), charged hadrons that are not associated with the primary vertex are removed from the list of PF candidates. The procedure is referred to as charged-hadron subtraction [20] and strongly reduces the dependence of the jet energy and substructure reconstruction on pileup. An event-by-event jet-area-based correction [2123] is applied to remove the remaining energy due to neutral particles originating from the other pp collision vertices. All jet substructure observables are computed using PF candidates calibrated prior to jet clustering. However, the resulting jets require another small correction to the jet momentum and energy that accounts for tracking inefficiencies and threshold effects. The typical jet energy resolution is 510% for jets with pT > 200 GeV. Two algorithms are used to reconstruct muons [24]: one proceeds from the inner tracker outwards, while the other starts from tracks measured in the muon chambers and matches them to those reconstructed in the silicon tracker. Muons are identified using selection criteria optimized for high-pT muons [24]. The selected muon candidates must be isolated from charged hadron activity in the detector by requiring the scaler sum of transverse track, divided by the muon pT, to be Itk/pT < 0.1. Electrons are reconstructed using a Gaussian-sum filter algorithm [15, 25], and each electron candidate must furthermore pass the identification and isolation criteria optimized for high pT electrons [25]. Data and simulated event samples This study aims to distinguish W jets from QCD jets. We use three different final state topologies to establish W jet identification in a broad region of phase space, thereby enabling a number of physics data analyses. In the tt-enriched lepton+jets event topology, the decay of two top quarks results in a final state with two b quarks and two W bosons of which one decays leptonically and the other decays to hadrons. This topology provides a relatively pure source of W jets in data, and is used to compare the efficiencies of Wtagging in data and in simulation. In contrast, the W+jet event topology, where the W boson decays leptonically, and the inclusive dijet event topology are used as a source of QCD jets to study their W-jet tagging properties in data and in simulation. These are the benchmark scenarios for searches, where the leading backgrounds are SM W+jets and dijet production. The W+jet sample accesses the low pT regime, while the dijet sample reaches higher pT, and therefore both samples are explored. To study the discrimination of W jets and QCD jets in the W+jet and dijet topologies, we use simulated samples of beyond-SM resonances decaying to the WW final state as source of W jets. Data and simulated event samples The data were collected with the CMS detector at a proton-proton (pp) center-of-mass As the default simulated signal sample, we consider a resonance X that decays to a pair of longitudinally polarized W bosons. Such samples are produced by considering either a warped extra-dimensional model, where the SM fields propagate in the bulk [2729], or models with SM-like high mass H bosons. Graviton resonance samples in the extra-dimensional model are produced with the JHUgen 3.1.8 [30, 31], interfaced with pythia 6 [32] for parton showering including the effect of hard gluon radiation. pythia 6.426 is used with Tune Z2* [33] in this paper. SM-like H boson samples are produced with powheg 1.0 [3436] interfaced with pythia 6. To study the effect of W boson polarization on the distributions of substructure variables, the model with the SM Higgslike couplings is compared to a model with a purely pseudoscalar H boson which yields only transversely polarized W bosons. These samples are produced with the JHUgen and resolution of 510%. The background is modeled using QCD multijet, W+jets, WW/WZ/ZZ, Drell-Yan samples are compared. A first sample is generated with MadGraph v5.1.3.30 [37], with showering and hadronization performed with pythia 6. The second sample is generated as well as evolved with herwig++ 2.5.0 [38] with tune version 23 [38]. The third sample is generated with pythia 8.153 [39] with Tune 4C. MadGraph, pythia 6 and pythia 8 are used with the CTEQ61L [40] parton distribution functions (PDF), while herwig++ is used with the MRST2001 [41] PDF. Two W+jets samples with different parton shower models are compared: one sample generated with MadGraph interfaced with pythia 6 and a second sample generated with herwig++. The single top quark and tt samples are simulated with powheg interfaced with pythia 6 using the CT10 [42] PDF. An alternative tt sample, generated with mc@nlo [43] and evolved with herwig++ using the CTEQ6M [40] PDF, is also used for studies of systematic effects. The Z+jets process is simulated with MadGraph interfaced with pythia 6. The VV production processes are simulated with pythia 6. All generated samples are processed through a Geant4-based [44] simulation of the CMS detector. An average of 22 supplementary interactions are added to the generated events in order to match the additional particle production observed in data from the large number of PU proton-proton interactions occurring per LHC bunch crossing. The dijet and W+jet topologies are chosen to be in the kinematic regime typically considered in searches for new phenomena [7, 9]. In both topologies we focus on the W-jet core of the jet falls within the tracker acceptance. The ranges in jet pT and the resonance masses mX are chosen to have the pT distributions similar for signal and for background. Collision data events with a dijet final state are collected using the logical OR of a set of triggers based on requirements on HT = P jets pT (scalar sum of pT of the AK5 jets), and on the invariant mass of the two jets of highest pT. Subsequent event selection follows closely the VV resonance search in ref. [7]. Events are initially selected by requiring multijet events. Finally, the dijet invariant mass is required to be larger than 890 GeV. This threshold is chosen such that the trigger selection for events with dijet masses above this threshold is 99% efficient. W-tagging is studied using the leading jet in the selected dijet events, with additional requirements set on jet pT. The main goal of the kinematic selection of the W+jet sample is to isolate a sample of events with a highly boosted topology consistent with a leptonically decaying W boson recoiling against a high pT jet. The W+jet sample, as well as the tt sample discussed below, are collected using single-lepton triggers. The lepton pT thresholds of these triggers are 40 and 80 GeV for the muon and electron channels, respectively. Offline, at least one muon or one electron, with respective pT > 50 GeV or pT > 90 GeV, is required within respective purity of W+jet events. A requirement on the imbalance in transverse momentum (ETmiss) is used to reduce the QCD multijet background. The ETmiss is computed from the negative transverse component of the vector sum of all PF candidate momenta, and is required to be above 50 GeV or 80 GeV for the muon and electron channel. The threshold is higher in the electron channel to further suppress the larger background from multijet processes. The pT of the leptonically decaying W boson and of the CA8 jet with highest pT, are required to be >200 GeV. Additional criteria are applied to ensure that the leptonic W leptonically decaying W boson and the CA8 jet must also be greater than 2.0 radians. Finally, a cutoff on additional jet activity in the event is applied to reduce the amount of CMS single CA R=0.8 jet two AK R=0.5 jets polarized W bosons, as a function of the pT of the W boson. tt background. We identify additional b jet candidates in the event by requiring that an discriminant [45] using a medium working point. To suppress tt background in the W+jet selections described above, we require that no such b jets be present in the event. To select the tt sample, we use the kinematic selection described above for the W+jet topology, but instead require that there is at least one AK5 b jet, with an angular distance Algorithms for W jet identification increases the efficiency to reconstruct W bosons with small boost as single jets, since the average angular distance between the W decay products is inversely proportional to the pT of the W. The chosen value of R provides a high efficiency for W bosons with small boost and ensures that no efficiency is lost in the transition from classical W reconstruction from two small jets at low W pT and reconstruction from a single large jet at higher W pT (see e.g. ref. [46]). Another point to consider when choosing the value of R, is the tt data sample available for validating highly boosted W jets. If R is chosen too large, the b quark from the t Wb decay tends to merge into the W jet. The chosen value of R is the result of a compromise between high efficiency for W bosons with small boost and a sufficiently large sample of W jets in tt data for validating the W jet identification algorithms. Above a pT of 200 GeV, the CA8 jet algorithm, used to identify W jets, becomes more efficient than the reconstruction of a W boson from two AK5 jets. In this paper we therefore AK or a CA algorithm is used in such comparison does not affect the overall conclusion. CMS publications, where CA was introduced in the first top tagging algorithm paper of CMS [47]. Whenever we refer to efficiency ( ) in this paper, we refer to the full efficiency to identify a W boson relative to all generated W bosons decaying to hadrons. Substructure observables As the mass of the W boson is larger than the mass of a typical QCD jet, the jet mass is the primary observable that distinguishes a W jet from a QCD jet. The bulk of the W jet mass arises from the kinematics of the two jet cores that correspond to the two decay quarks. In contrast, the QCD jet mass arises mostly from soft gluon radiation. For this reason, the use of jet grooming methods such as filtering [48], trimming [49], or pruning [50, 51], improves discrimination by removing the softer radiation, as this shifts the jet mass of QCD jets to smaller values, while maintaining the jet mass for W jets close to the W mass. Studies of these grooming methods have been performed in ref. [1], with the conclusion that the pruned jet mass provides the best separation between W signal and QCD background. In this paper, we use the grooming parameters proposed by the original authors. Pruned jet mass: is obtained by removing the softest components of a jet. The CA8 jet is reclustered from its original jet constituents, however the CA clustering sequence is modified to remove soft and wide-angle protojets (single particles, or groups of particles already combined in the previous steps). In each recombination step, its hardness z is defined as z = min{piT, pjT}/ppT, where p jT are the pT of the two protojets to be combined and p with the lower p pT is the pT of the combination of the two protojets. The protojet iT is ignored if z < zcut = 0.1, and if it forms an angle R wider than Dcut = morig/porig relative to the axis of the combination of the two protojets, where morig T and porig are the mass and pT of the original CA8 jet. The pruned jet mass distributions T for W jets and QCD jets are shown in figure 2 (upper left) at generator level and detector level with pileup. Comparing the generator level predictions for the pruned jet mass of W jets with those at detector level with pileup, the widening of the peak due to detector resolution can be observed. Further discrimination between W and QCD jets can be obtained from a more extensive use of jet substructure. Here we consider the following observables. last iteration of the CA jet clustering via pruning. The idea behind the mass drop is that the W jet is formed by merging the showers of two decay quarks, and thus the mass of each quark subjet is much smaller than the mass of the W jet. In contrast, a massive QCD jet is formed through continuous soft radiation; the subjet with larger mass contains the bulk of the jet and the ratio of the mass of the large subjet to the total mass is therefore subjet (m1) and the total pruned jet (mjet). The two subjets can also be used to estimate CMS + <PU> = 22 + sim. + <PU> = 12 + sim. + <PU> = 22 + sim. + <PU> = 12 + sim. CA R=0.8 CA R=0.8 CMS + <PU> = 22 + sim. + <PU> = 12 + sim. + <PU> = 22 + sim. + <PU> = 12 + sim. CA R=0.8 Pruned jet mass (GeV) + <PU> = 22 + sim. + <PU> = 12 + sim. + <PU> = 22 + sim. + <PU> = 12 + sim. 8 TeV + <PU> = 22 + sim. + <PU> = 12 + sim. + <PU> = 22 + sim. + <PU> = 12 + sim. CA R=0.8 CA R=0.8 tr is0.6 d Distributions of six variables characterising jet substructure in simulated samples of highly boosted and longitudinally polarized W bosons and inclusive QCD jets expected in the W+jet topology. The discriminator distributions (except for the pruned jet mass in the upper left panel) are shown after a selection on the pruned jet mass of 60 < mjet < 100 GeV. MG denotes the MadGraph generator. Thick dashed lines represent the generator predictions without pileup interactions and without CMS detector simulation. The histograms are the expected distributions after full CMS simulation with pileup corresponding to an average number of 12 and 22 interactions. left) the Qjet volatility, (lower middle) the energy correlation function double ratio C2 , and (lower left) the jet charge. figure 2 (upper middle). The differences between the generator level predictions and those at detector level with pileup are small for this observable, because the detector can resolve the two relatively well separated subjets. distance between each jet constituent and its nearest subjet axis: is consistent with having N or fewer subjets, as almost every jet constituent will be close in CA R=0.8 CMS + <PU> = 22 + sim. + <PU> = 12 + sim. + <PU> = 22 + sim. + <PU> = 12 + sim. + <PU> = 22 + sim. + <PU> = 22 + sim. + <PU> = 22 + sim. it tends to smaller values for W jets. The subjet axes are obtained by running the exclusive kT algorithm [53], and reversing the last N clustering steps. The axes can be optimized to minimize the N-subjettiness value. As default, we use a one-pass optimization of the exclusive kT axes, where one step of the iterative optimization is performed. By default jets and QCD jets after requiring 60 < mjet < 100 GeV, and demonstrates its discrimination power after the pruned jet mass selection. The distributions at detector level with pileup are shifted significantly compared to the generator level predictions, though the discrimination power is preserved. The shift was due equally to detector effects and pileup. sequences. A jet is defined by its cluster sequence, which is topologically a tree and is here referred to as jet tree. By randomizing the recombination scheme and running the pruning algorithm for each jet tree, we can define a family of trees for each jet from which we can compute a distribution of jet masses. The continuous soft radiation that forms massive QCD jets results in clustering sequences susceptible to fluctuations a small deviation in soft radiation can result in a very different order of putting the jet together. In contrast, W jets are characterized by two strong jet cores, and small perturbations usually yield nearly identical clustering sequences. Therefore a large volatility of the clustering sequence is a characteristic of QCD jets, and can be used to distinguish them from signal W jets. The procedure for quantifying the volatility of the jet clustering sequence is as follows. At every step of clustering, a weight wij is assigned to each constituent pair, and then one of the available pairs are randomly chosen and combined. The default weight is defined as: wij = exp jet algorithm. We choose to generate 50 random jet trees. Qjet volatility is defined as the root-mean-square (rms) of the jet mass distribution, divided by the average jet mass, the performance, before Qjet clustering we pre-cluster the jet constituents down to 35 where i, j and k runs over all constituent particles satisfying i < j < k. Similarly to the the denominator gives a probability for being composed of one subjet. We study C from QCD jets consistent with having a single subjet. The distribution of C figure 2 (lower middle). considered in this study. Planar flow characterises the geometric distribution of energy deposition from a jet, which discriminates W jets from QCD jets, as the latter are more isotropic. Trimmed grooming sensitivity is defined as the decrease in jet mass, when the trimming algorithm [49] is applied to the jet. of the jet. This variable has a long history in flavor tagging of neutral B mesons, and it is defined as the pT-weighted average charge of the jet: additional discrimination among quark jets, gluon jets and W jets or also to distinguish the charged W signal from that of a neutral Z. The differences between the jet charge distribution of W jets and of neutral jets can be seen in figure 2 (lower right). Detector resolution and pileup have almost no effect on this variable as it is built from charged hadrons identified using the tracker where those from PU vertices are discarded. Comparison of algorithms We compare the performance of observables used to identify W jets with the goal of establishing which provides the best signal-to-background discrimination between W jets and QCD jets. Because the pruned jet mass is the best discriminant, we examine the other variables only for jets satisfying 60 < mjet < 100 GeV. Observables highly correlated with the pruned jet mass will therefore show weaker additional improvement in performance. The figure of merit for comparing different substructure observables is the background rejection efficiency as a function of signal efficiency (receiver operating characteristic, or the ROC curve). Figure 3 shows the performance of the observables in the W+jet final state for jet pT 250350 GeV. The pruned jet mass selection is applied in both the numerator and the denominator of the efficiency, and only the additional discrimination power of the other ROC curve, a positively charged lepton is required in the event selection, and therefore the discrimination power of negatively charged W jets against QCD jets is compared. We find CA R = 0.8 low jet pT bin of 250350 GeV in the W+jet topology. The efficiencies and mistagging rates of the various discriminants are estimated on samples of W jets and QCD jets that satisfy a pruned jet mass selection of 60 < mjet < 100 GeV. eq. (5.4) between 0.3 and 1.0. In addition to the performance of individual variables, we study how their combination can improve the separation between W and QCD jets. A multivariate optimization is performed using the TMVA package [58]. A combination of observables is considered in a naive Bayes classifier and in a Multilayer Perceptron (MLP) neural network discriminant. Additional observables with respect to those shown in figure 3 are used in an attempt to increase the discrimination power. The variables used in both discriminants are the mass drop, Qjet, 2/1, C, the jet charge, the planar flow, the number of jet constituents, 2 interaction vertices. The MLP neural network is trained using a signal sample from a SM Higgs-like resonance decaying to a pair of longitudinally polarized W bosons and a background sample of W+jets generated with MadGraph, splitting the events equally in training and test event samples to compute the ROC curve. The ROC curves obtained from the multivariate methods are shown in figure 3. Compared to the performance of sensitive variable over a large range of efficiencies, and most of the other observables. We point out that, not considering systematic uncertainties, there is potential gain in using multivariate discriminators. The comparison above is performed after requiring the pruned jet mass to lie in the W boson mass window. Since all substructure variables are correlated with the jet mass, CA R=0.8 Only mjet selection <PU> = 22 Generator, PU = 0 <PU> = 12 <PU> = 22 CA R=0.8 Only mjet selection <PU> = 22 algorithm in the high jet pT bin of 400600 GeV. The performance of the pruned jet mass selection 60 < mjet < 100 GeV in the various scenarios is indicated as a filled circle. The performance of the line corresponds (in both parts) to the standard scenario with an average of 22 pileup interactions and longitudinally polarized W bosons (WL). it is important to note that the variable comparison shown in figure 3 depends strongly on the choice of the primary discriminant. When the ungroomed jet mass is the primary discriminant, a combination with other variables provides a larger increase in discrimination, although the overall performance is still inferior to the default choice of the pruned Performance in simulation In this section we examine the simulated pT and PU dependence of the W tagging efficiency. Efficiencies are defined for a pruned jet mass of 60 < mjet < 100 GeV, and N-subjettiness In figure 4, we compare systematic effects in terms of change in the ROC response in the dijet final state for 400 < pT < 600 GeV. In contrast to figure 3, where just the performance of other variables was studied relative to that of mjet, here the efficiency is discriminants. The performance for the working point requirements 60 < mjet < 100 GeV we observe that it models the pruned jet mass in data better than pythia 6 does. Each of the displayed systematic effects is discussed below. Figure 5 shows the efficiency of the baseline selection (60 < mjet < 100 GeV and (left) jet pT and (right) the number of reconstructed vertices, reflecting the contribution from pileup. At low pT, the efficiency increases with pT for the same reason as in figure 1, Background, mjet selection Number of vertices samples as a function of (left) pT and (right) the number of reconstructed vertices. The figure on the right also shows the mistagging rate for QCD jets estimated from the W+jets background sample. The error bars represent the statistical uncertainty in the MC simulation and the horizontal ones namely that at higher pT the showers from the W decay quarks are more likely to be reconstructed within a single CA8 jet. Above 600 GeV, the efficiency begins to decrease as a function of jet pT, since at larger pT the PF candidate reconstruction degrades in resolving the jet substructure and the pruning algorithm therefore removes too large a fraction of the jet mass. For Run II of the LHC, the particle flow reconstruction has been optimized by making better usage of the segmentation of the ECAL, where we expect to maintain constant efficiency up to at least pT = 3.5 TeV [59]. important to note that the same efficiency at an equivalent background rejection rate can that the ROC curve for jets with pT between 0.8 and 1.2 TeV (using a 2 TeV mass for the WW resonance) is almost indistinguishable from the ROC curve derived from the 400 at a lower signal efficiency. Consequently, a fixed working point will degrade the efficiency with increasing pT. However, by shifting the working point, the same performance can The efficiency of the mjet selection as a function of the number of reconstructed vertices, shown in figure 5 (right), decreases by 6% between 5 and 30 reconstructed vertices, whereas mistagging of the background also decreases with pileup for the same selection, yielding similar discrimination. Efficiency and mistagging rate are affected by pileup in the same background like) for both signal and background. Therefore, the same signal efficiency can be reached at the same background rejection rate for up to 30 reconstructed vertices by average pileup of 12 to 22 interactions shows almost no change in the ROC response. We also study the performance of jet substructure tagging algorithms by convolving pileup, CMS detector resolution, and efficiencies in reconstructing the particles that form the jets. In figure 4 (left), the generator level predictions without pileup are compared with the performance after full CMS simulation with pileup. A small degradation is observed relative to generator level, but the performance at detector level is almost as good as shifted up significantly by pileup and detector effects, as seen in figure 2. W-polarization and quark-gluon composition An important factor that influences the W-tagging performance is the polarization of the reconstructed W bosons. Furthermore, the W polarization can be used to identify the nature of any new phenomena, such as, for example, through studies of new WW resonances, W boson helicities at large tt masses, or WW scattering. We study the effect of W polarization by comparing simulated samples of X WW, where the W bosons are either purely longitudinally (WL) or transversely (WT) polarized. The key observable is level, where quarks are treated as final state particles, is presented in figure 6 (left). After reconstruction, the polarization in W jets can be recovered using the pruned subjets as a proxy for the W decay quarks. However, using the subjets, it is not possible to distinguish the fermion and antifermion in the W decay, which restricts the distributions to a 600 GeV X resonance, differing from figure 6 (left) in that it includes reconstruction and can cause the loss or misidentification of the subjet originating from one of the W decay partons. It appears that transversely polarized W bosons decay with the quarks emitted closer to the direction of the W, and therefore can be used to determine the polarization information. The resolution on the angular distance between two subjets in the laboratory frame is approximately 10 mrad, which translates to a resolution of approximately 65 mrad Figure 4 (right) compares the signal-to-background discrimination of the W tagger for pure WL and pure WT signal samples. We observe that the pruned jet mass selection is less efficient for WT; this is consistent with figure 6 (right), where the WT jets with higher asymmetry in the pT of the two quarks from the WT decay, such that the pruning algorithm in a considerable fraction of events rejects the particles from the lower pT quark for pure WL bosons is smaller on average than for WT bosons and is more likely to be accepted by a CA8 jet. Of the two effects, the dominant contribution depends on the CA R=0.8 + <PU>=22 + sim. + <PU>=22 + sim. W bosons. Right: subjet angular observables after a selection on pruned jet mass of WL and WT samples for jets with 250 < pT < 350 GeV. transverse momentum of the W jet. For higher jet pT, the difference in the reconstructed the transversely polarized W bosons becomes important, i.e. it is easier to distinguish WL degree than the pruned jet mass. The composition of the QCD background also influences the discrimination of the variables discussed in section 5, since the properties of quark- and gluon-initiated jets differ. For example, gluon jets tend to have a larger jet mass than quark jets and therefore fewer gluon jets are rejected by the pruned jet mass selection; this can be seen in figure 4 (right). these reasons a similar performance for quarks and gluons is achieved for the working point Performance in data and systematic uncertainties Comparison of data and simulation We compare the distributions of substructure observables between simulation and data in inclusive dijet, W+jet and tt samples. The W+jet and dijet events are compared in respective jet pT bins of 250350 GeV and 400600 GeV, and with jets in the tt sample with pT > 200 GeV. Simulation with different parton shower models of pythia 6, pythia 8 and herwig++ are also compared. In figure 7, the pruned jet mass distribution is shown for both data and simulation in the dijet and W+jet samples that probe the W-tagging variables using QCD jets. We find that the agreement is good between data and simulation, but herwig++ agrees better than pythia 6, and pythia 8 shows best agreement. Similar findings have been reported Pruned jet mass (GeV) CA R = 0.8 CA R = 0.8 Pruned jet mass (GeV) CA R=0.8 im 2 taa 1 im 2 CA R=0.8 N-subjettiness 2/1 E 2 im 2 taa 1 im 2 for W+jets events in (upper left) and (upper right) and for dijet events in (lower left) and (lower right). MG denotes the MadGraph generator. Below each figure the relative deviations are plotted between data and simulations. and best with pythia 8. To probe the description of W jets, we use the control sample of pure W bosons distributions in the tt control sample are shown in figure 8 for the muon selection. The plots include systematic and statistical uncertainties, where the band of systematic uncertainty represents the normalization uncertainties on the VV, single top quark and W+jets cross sections. The systematic uncertainty is estimated to be 20% determined from the relative s = 8 TeV at CMS and the SM expectation [60]. The agreement between simulation and data is CA R = 0.8 im 2 taa 1 im 2 taa 1 CMS CA R = 0.8 Pruned jet mass (GeV) muon selection. Below each figure the relative deviations are plotted between data and simulations. reasonable, but there are discrepancies of the order of 10%. In section 6.3 we describe the derivation of data-to-simulation scale factors to correct for these discrepancies. Generally, powheg interfaced with pythia 6 provides a better description of the tt sample than mc@nlo interfaced with herwig++. Finally, we compare the jet charge distribution of W jets in data and in simulation using the tt sample. By selecting a negatively or positively charged lepton, we can effectively distinguished on an event-by-event basis, their contributions to the tt data sample can be separated with a significance larger than 5 standard deviations. The jet charge distribution is well described by the simulation. Mistagging rate measurement A dijet sample is used to measure the rate of false positive W tags, or mistags. The mistagging rate is measured in data and compared to simulation. As discussed previously, requirements, as a function of pT and of the number of reconstructed vertices. Similarly as as a function of pT. The mistagging rate of only the mjet requirement in data is well reproduced by herwig++ and pythia 8, while MadGraph+pythia 6 underestimates reproduced better by pythia 8 than by MadGraph+pythia 6 and herwig++. The pT dependence in data is well reproduced by all generators. As a function of pileup, the mistagging rate is stable within 1% for the mjet selection. im 2 im 2 distributions reflect the sum of tt (powheg interfaced with pythia 6) and all other background processes. Below each figure the relative deviations are plotted between data and simulations. CA R=0.8 CMS CA R=0.8 CMS im 2 Number of vertices as a function of (left) pT and (right) the number of reconstructed vertices. The data over simulation of pileup as discussed in detail in section 5.3. The PU dependence is well reproduced by Efficiency scale factors and mass scale/resolution measurement The tt control sample is used to extract data-to-simulation scale factors for the W jet efficiency. These factors are meant to correct the description of the W-tagging efficiency in the simulation. They depend on the definition of the W-tagger as well as the MC generator used for simulation. We demonstrate the extraction of data-to-simulation scale factors for using powheg interfaced with pythia 6. We are concerned only with the efficiency for the pure W jet signal, and must therefore subtract background contributions to measure the scale factors. The pruned jet mass distribution is used to discriminate the pure W jet signal from background contributions. The generated W boson in the tt simulation provides a model of the contribution from the W jet peak in the pruned jet mass. The contribution from combinatorial background is derived from tt simulation as well. This model is fitted directly in the distributions of data and in their simulation. the selection efficiency on both data and simulation. The pruned jet mass distribution selection efficiency on the pure W jet component as shown in figure 11. The ratio of data and simulation efficiencies are taken as the W-tagging efficiency SF. In the tt control region we use a mass window of 65105 GeV, because of a slight shift in the mean mass of the to be primarily due to extra radiation in the W jet from the nearby b quark. Additional requirements to reduce the combinatorial background from tt improve the precision of the and the closest b-tagged AK5 jet is required to be less than 2.0, which is typical for highly boosted top quark decays [2]. This additional selection reduces the uncertainty on the scale factor by 21%. Further reduction of the combinatorial background can be achieved through requirements on top quark masses, but the limited number of tt events suggests that this can become relevant only with a larger data sample. The results of the fit are shown in figure 11. We find the pass sample agrees well between the data and simulation while the fail sample is not as well modeled, particularly when the failing jet is not a fully merged W boson but a quark or gluon jet. This is compensated in our computation of the data-to-MC scale factor. The scale factor is computed to be 0.93 0.06. The uncertainty in the SF is purely statistical. In section 6.4, we discuss systematic effects to this scale factor. The pT dependence of the scale factor was also studied at a limited statistical precision. In two pT bins between 200265 and 265600 GeV the scale factors were found to be 1.00 0.09 and 0.92 0.10, respectively. No significant pT dependence of the scale factor is observed. To extract corrections to the jet mass scale and resolution, we use the mean hmi and passed sample. Since we do not expect the jet mass scale and resolution to differ between electron and muon channels, the muon and electron data are fitted simultaneously, forcing parameters are summarized in table 1. We find that both the W jet mass scale and resolution in data are larger than that in simulation. In the simulation hmi must therefore data and simulation. ence and Technological Development of Serbia; the Secretara de Estado de Investigacion, Desarrollo e Innovacion and Programa Consolider-Ingenio 2010, Spain; the Swiss Funding Agencies (ETH Board, ETH Zurich, PSI, SNF, UniZH, Canton Zurich, and SER); the Ministry of Science and Technology, Taipei; the Thailand Center of Excellence in Physics, the Institute for the Promotion of Teaching Science and Technology of Thailand, Special Task Force for Activating Research and the National Science and Technology Development Agency of Thailand; the Scientific and Technical Research Council of Turkey, and Turkish Atomic Energy Authority; the National Academy of Sciences of Ukraine, and State Fund for Fundamental Researches, Ukraine; the Science and Technology Facilities Council, U.K.; the US Department of Energy, and the US National Science Foundation. Individuals have received support from the Marie-Curie programme and the European Research Council and EPLANET (European Union); the Leventis Foundation; the A. P. Sloan Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science Policy Office; the Fonds pour la Formation `a la Recherche dans lIndustrie et dans lAgriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Council of Science and Industrial Research, India; the HOMING PLUS programme of Foundation for Polish Science, cofinanced from European Union, Regional Development Fund; the Compagnia di San Paolo (Torino); the Consorzio per la Fisica (Trieste); MIUR project 20108T4XTM (Italy); the Thalis and Aristeia programmes cofinanced by EU-ESF and the Greek NSRF; and the National Priorities Research Program by Qatar National Research Fund. Open Access. 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Sanabria University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, Split, Croatia N. Godinovic, D. Lelas, D. Polic, I. Puljak University of Split, Faculty of Science, Split, Croatia Z. Antunovic, M. Kovac Institute Rudjer Boskovic, Zagreb, Croatia V. Brigljevic, K. Kadija, J. Luetic, D. Mekterovic, L. Sudic University of Cyprus, Nicosia, Cyprus A. Attikis, G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos, P.A. Razis Charles University, Prague, Czech Republic M. Bodlak, M. Finger, M. Finger Jr.8 Academy of Scientific Research and Technology of the Arab Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt Y. Assran9, A. Ellithi Kamel10, M.A. Mahmoud11, A. Radi12,13 National Institute of Chemical Physics and Biophysics, Tallinn, Estonia M. Kadastik, M. Murumaa, M. Raidal, A. Tiko Department of Physics, University of Helsinki, Helsinki, Finland P. Eerola, G. Fedi, M. Voutilainen Helsinki Institute of Physics, Helsinki, Finland E. Tuovinen, L. Wendland J. Talvitie, T. Tuuva Lappeenranta University of Technology, Lappeenranta, Finland DSM/IRFU, CEA/Saclay, Gif-sur-Yvette, France M. Besancon, F. Couderc, M. Dejardin, D. Denegri, B. Fabbro, J.L. Faure, C. Favaro, F. Ferri, S. Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry, E. Locci, J. Malcles, J. Rander, A. Rosowsky, M. Titov Laboratoire Leprince-Ringuet, Ecole Polytechnique, IN2P3-CNRS, Palaiseau, Institut Pluridisciplinaire Hubert Curien, Universite de Strasbourg, Universite de Haute Alsace Mulhouse, CNRS/IN2P3, Strasbourg, France J.-L. Agram14, J. Andrea, A. Aubin, D. Bloch, J.-M. Brom, E.C. Chabert, C. Collard, E. Conte14, J.-C. Fontaine14, D. Gele, U. Goerlach, C. Goetzmann, A.-C. Le Bihan, P. 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Vanelderen, A. Vanhoefer Institut fur Experimentelle Kernphysik, Karlsruhe, Germany C. Barth, C. Baus, J. Berger, C. Boser, E. Butz, T. Chwalek, W. De Boer, A. Descroix, A. Dierlamm, M. Feindt, F. Frensch, M. Giffels, F. Hartmann2, T. Hauth2, U. Husemann, I. Katkov5, A. Kornmayer2, E. Kuznetsova, P. Lobelle Pardo, M.U. Mozer, Th. Muller, A. Nurnberg, G. Quast, K. Rabbertz, F. Ratnikov, S. Rocker, H.J. Simonis, F.M. Stober, R. Ulrich, J. Wagner-Kuhr, S. Wayand, T. Weiler, R. Wolf Institute of Nuclear and Particle Physics (INPP), NCSR Demokritos, Aghia Paraskevi, Greece G. Anagnostou, G. Daskalakis, T. Geralis, V.A. Giakoumopoulou, A. Kyriakis, D. Loukas, A. Markou, C. Markou, A. Psallidas, I. Topsis-Giotis University of Athens, Athens, Greece A. Agapitos, S. Kesisoglou, A. Panagiotou, N. Saoulidou, E. Stiliaris University of Ioannina, Ioannina, Greece los, E. Paradas Wigner Research Centre for Physics, Budapest, Hungary G. Bencze, C. Hajdu, P. Hidas, D. Horvath16, F. Sikler, V. Veszpremi, G. Vesztergombi17, Institute of Nuclear Research ATOMKI, Debrecen, Hungary N. Beni, S. Czellar, J. Karancsi18, J. Molnar, J. Palinkas, Z. Szillasi University of Debrecen, Debrecen, Hungary P. Raics, Z.L. Trocsanyi, B. Ujvari National Institute of Science Education and Research, Bhubaneswar, India Panjab University, Chandigarh, India N. Nishu, J.B. Singh University of Delhi, Delhi, India S.B. Beri, V. Bhatnagar, R. Gupta, U.Bhawandeep, A.K. Kalsi, M. Kaur, M. Mittal, Ashok Kumar, Arun Kumar, S. Ahuja, A. Bhardwaj, B.C. Choudhary, A. Kumar, S. Malhotra, M. Naimuddin, K. Ranjan, V. Sharma Saha Institute of Nuclear Physics, Kolkata, India S. Banerjee, S. Bhattacharya, K. Chatterjee, S. Dutta, B. Gomber, Sa. Jain, Sh. Jain, R. Khurana, A. Modak, S. Mukherjee, D. Roy, S. Sarkar, M. Sharan Bhabha Atomic Research Centre, Mumbai, India A. Abdulsalam, D. Dutta, S. Kailas, V. Kumar, A.K. Mohanty2, L.M. Pant, P. Shukla, Tata Institute of Fundamental Research, Mumbai, India T. Aziz, S. Banerjee, S. Bhowmik19, R.M. Chatterjee, R.K. Dewanjee, S. Dugad, S. Ganguly, S. Ghosh, M. Guchait, A. Gurtu20, G. Kole, S. Kumar, M. Maity19, G. Majumder, K. Mazumdar, G.B. Mohanty, B. Parida, K. Sudhakar, N. Wickramage21 Institute for Research in Fundamental Sciences (IPM), Tehran, Iran H. Bakhshiansohi, H. Behnamian, S.M. Etesami22, A. Fahim23, R. Goldouzian, M. Khakzad, M. Mohammadi Najafabadi, M. Naseri, S. Paktinat Mehdiabadi, F. Rezaei Hosseinabadi, B. Safarzadeh24, M. Zeinali M. Felcini, M. Grunewald INFN Sezione di Bari a, Universit`a di Bari b, Politecnico di Bari c, Bari, Italy M. Abbresciaa,b, L. Barbonea,b, C. Calabriaa,b, S.S. Chhibraa,b, A. Colaleoa, D. Creanzaa,c, INFN Sezione di Bologna a, Universit`a di Bologna b, Bologna, Italy Bonacorsia,b, Braibant-Giacomellia,b, L. Brigliadoria,b, R. Campaninia,b, P. Capiluppia,b, A. Castroa,b, F.R. Cavalloa, G. Codispotia,b, M. Cuffiania,b, G.M. Dallavallea, F. Fabbria, A. Fanfania,b, D. Fasanellaa,b, P. Giacomellia, C. Grandia, L. Guiduccia,b, S. Marcellinia, G. Masettia,2, A. Montanaria, N. Tosia,b, R. Travaglinia,b INFN Sezione di Catania a, Universit`a di Catania b, CSFNSM c, Catania, Italy S. Albergoa,b, G. Cappelloa, M. Chiorbolia,b, S. Costaa,b, F. Giordanoa,2, R. Potenzaa,b, A. Tricomia,b, C. Tuvea,b INFN Sezione di Firenze a, Universit`a di Firenze b, Firenze, Italy G. Barbaglia, V. Ciullia,b, C. Civininia, R. DAlessandroa,b, E. Focardia,b, E. Galloa, S. Gonzia,b, V. Goria,b,2, P. Lenzia,b, M. Meschinia, S. Paolettia, G. Sguazzonia, A. Tropianoa,b INFN Laboratori Nazionali di Frascati, Frascati, Italy L. Benussi, S. Bianco, F. Fabbri, D. Piccolo INFN Sezione di Genova a, Universit`a di Genova b, Genova, Italy R. Ferrettia,b, F. Ferroa, M. Lo Veterea,b, E. Robuttia, S. Tosia,b INFN Sezione di Milano-Bicocca a, Universit`a di Milano-Bicocca b, Milano, M.E. Dinardoa,b, S. Fiorendia,b,2, S. Gennaia,2, R. Gerosaa,b,2, A. Ghezzia,b, P. Govonia,b, M.T. Lucchinia,b,2, S. Malvezzia, R.A. Manzonia,b, A. Martellia,b, B. Marzocchia,b, D. Menascea, L. Moronia, M. Paganonia,b, D. Pedrinia, S. Ragazzia,b, N. Redaellia, T. Tabarelli de Fatisa,b INFN Sezione di Napoli a, Universit`a di Napoli Federico II b, Universit`a della Basilicata (Potenza) c, Universit`a G. Marconi (Roma) d, Napoli, Italy S. Buontempoa, N. Cavalloa,c, S. Di Guidaa,d,2, F. Fabozzia,c, A.O.M. Iorioa,b, L. Listaa, S. Meolaa,d,2, M. Merolaa, P. Paoluccia,2 Sezione di Padova a Universit`a di Padova b Trento (Trento) c, Padova, Italy P. Azzia, N. Bacchettaa, D. Biselloa,b, A. Brancaa,b, R. Carlina,b, P. Checchiaa, M. DallOssoa,b, M. Galantia,b, F. Gasparinia,b, U. Gasparinia,b, P. Giubilatoa,b, J. Pazzinia,b, M. Pegoraroa, N. Pozzobona,b, F. Simonettoa,b, E. Torassaa, M. Tosia,b, A. Triossia, S. Venturaa, P. Zottoa,b, A. Zucchettaa,b, G. Zumerlea,b INFN Sezione di Pavia a, Universit`a di Pavia b, Pavia, Italy M. Gabusia,b, S.P. Rattia,b, C. Riccardia,b, P. Salvinia, P. Vituloa,b INFN Sezione di Perugia a, Universit`a di Perugia b, Perugia, Italy M. Biasinia,b, G.M. Bileia, D. Ciangottinia,b, L. Fan`oa,b, P. Laricciaa,b, G. Mantovania,b, M. Menichellia, F. Romeoa,b, A. Sahaa, A. Santocchiaa,b, A. Spieziaa,b,2 INFN Sezione di Pisa a, Universit`a di Pisa b, Scuola Normale Superiore di Pisa c, Pisa, Italy K. Androsova,25, P. Azzurria, G. Bagliesia, J. Bernardinia, T. Boccalia, G. Broccoloa,c, R. Castaldia, M.A. Cioccia,25, R. DellOrsoa, S. Donatoa,c, F. Fioria,c, L. Foa`a,c, A. Giassia, M.T. Grippoa,25, F. Ligabuea,c, T. Lomtadzea, L. Martinia,b, A. Messineoa,b, C.S. Moona,26, F. Pallaa,2, A. Rizzia,b, A. Savoy-Navarroa,27, A.T. Serbana, P. Spagnoloa, INFN Sezione di Roma a, Universit`a di Roma b, Roma, Italy L. Baronea,b, F. Cavallaria, G. Dimperioa,b, D. Del Rea,b, M. Diemoza, M. Grassia,b, C. Jordaa, E. Longoa,b, F. Margarolia,b, P. Meridiania, F. Michelia,b,2, S. Nourbakhsha,b, G. Organtinia,b, R. Paramattia, S. Rahatloua,b, C. Rovellia, F. Santanastasioa,b, L. Soffia,b,2, P. Traczyka,b INFN Sezione di Torino a, Universit`a di Torino b, Universit`a del Piemonte Orientale (Novara) c, Torino, Italy N. Amapanea,b, R. Arcidiaconoa,c, S. Argiroa,b,2, M. Arneodoa,c, R. Bellana,b, C. Biinoa, N. Cartigliaa, S. Casassoa,b,2, M. Costaa,b, A. Deganoa,b, N. Demariaa, L. Fincoa,b, C. Mariottia, S. Masellia, E. Migliorea,b, V. Monacoa,b, M. Musicha, M.M. Obertinoa,c,2, G. Ortonaa,b, L. Pachera,b, N. Pastronea, M. Pelliccionia, G.L. Pinna Angionia,b, A. Potenzaa,b, A. Romeroa,b, M. Ruspaa,c, R. Sacchia,b, A. Solanoa,b, A. Staianoa, U. Tamponia INFN Sezione di Trieste a, Universit`a di Trieste b, Trieste, Italy S. Belfortea, V. Candelisea,b, M. Casarsaa, F. Cossuttia, G. Della Riccaa,b, B. Gobboa, C. La Licataa,b, M. Maronea,b, D. Montaninoa,b, A. Schizzia,b,2, T. Umera,b, A. Zanettia Kangwon National University, Chunchon, Korea S. Chang, A. Kropivnitskaya, S.K. Nam Kyungpook National University, Daegu, Korea D.H. Kim, G.N. Kim, M.S. Kim, D.J. Kong, S. Lee, Y.D. Oh, H. Park, A. Sakharov, Chonbuk National University, Jeonju, Korea Chonnam National University, Institute for Universe and Elementary Particles, J.Y. Kim, S. Song Korea University, Seoul, Korea S. Choi, D. Gyun, B. Hong, M. Jo, H. Kim, Y. Kim, B. Lee, K.S. Lee, S.K. Park, Y. Roh University of Seoul, Seoul, Korea M. Choi, J.H. Kim, I.C. Park, S. Park, G. Ryu, M.S. Ryu Sungkyunkwan University, Suwon, Korea Y. Choi, Y.K. Choi, J. Goh, D. Kim, E. Kwon, J. Lee, H. Seo, I. Yu Vilnius University, Vilnius, Lithuania National Centre for Particle Physics, Universiti Malaya, Kuala Lumpur, J.R. Komaragiri, M.A.B. Md Ali Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico H. Castilla-Valdez, E. De La Cruz-Burelo, I. Heredia-de La Cruz28, R. Lopez-Fernandez, A. Sanchez-Hernandez Universidad Iberoamericana, Mexico City, Mexico S. Carrillo Moreno, F. Vazquez Valencia Benemerita Universidad Autonoma de Puebla, Puebla, Mexico I. Pedraza, H.A. Salazar Ibarguen Universidad Autonoma de San Luis Potos, San Luis Potos, Mexico E. Casimiro Linares, A. Morelos Pineda University of Auckland, Auckland, New Zealand University of Canterbury, Christchurch, New Zealand P.H. Butler, S. Reucroft National Centre for Physics, Quaid-I-Azam University, Islamabad, Pakistan A. Ahmad, M. Ahmad, Q. Hassan, H.R. Hoorani, S. Khalid, W.A. Khan, T. Khurshid, M.A. Shah, M. Shoaib National Centre for Nuclear Research, Swierk, Poland H. Bialkowska, M. Bluj, B. Boimska, T. Frueboes, M. Gorski, M. Kazana, K. Nawrocki, K. Romanowska-Rybinska, M. Szleper, P. Zalewski Institute of Experimental Physics, Faculty of Physics, University of Warsaw, G. Brona, K. Bunkowski, M. Cwiok, W. Dominik, K. Doroba, A. Kalinowski, M. Konecki, J. Krolikowski, M. Misiura, M. Olszewski, W. Wolszczak Laboratorio de Instrumentacao e Fsica Experimental de Partculas, Lisboa, Joint Institute for Nuclear Research, Dubna, Russia S. Afanasiev, P. Bunin, I. Golutvin, I. Gorbunov, V. Karjavin, V. Konoplyanikov, G. Kozlov, A. Lanev, A. Malakhov, V. Matveev29, P. Moisenz, V. Palichik, V. Perelygin, S. Shmatov, S. Shulha, N. Skatchkov, V. Smirnov, A. Zarubin Petersburg Nuclear Physics Institute, Gatchina (St. Petersburg), Russia V. Golovtsov, Y. Ivanov, V. Kim30, P. Levchenko, V. Murzin, V. Oreshkin, I. Smirnov, V. Sulimov, L. Uvarov, S. Vavilov, A. Vorobyev, An. Vorobyev Institute for Nuclear Research, Moscow, Russia Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, M. Kirsanov, N. Krasnikov, A. Pashenkov, D. Tlisov, A. Toropin Institute for Theoretical and Experimental Physics, Moscow, Russia V. Epshteyn, V. Gavrilov, N. Lychkovskaya, V. Popov, G. Safronov, S. Semenov, A. Spiridonov, V. Stolin, E. Vlasov, A. Zhokin P.N. Lebedev Physical Institute, Moscow, Russia V. Andreev, M. Azarkin, I. Dremin, M. Kirakosyan, A. Leonidov, G. Mesyats, S.V. Rusakov, A. Vinogradov Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, A. Belyaev, E. Boos, M. Dubinin31, L. Dudko, A. Ershov, A. Gribushin, A. Kaminskiy32, V. Klyukhin, O. Kodolova, I. Lokhtin, S. Obraztsov, S. Petrushanko, V. Savrin State Research Center of Russian Federation, Institute for High Energy Physics, Protvino, Russia I. Azhgirey, I. Bayshev, S. Bitioukov, V. Kachanov, A. Kalinin, D. Konstantinov, V. Krychkine, V. Petrov, R. Ryutin, A. Sobol, L. Tourtchanovitch, S. Troshin, N. Tyurin, A. Uzunian, A. Volkov University of Belgrade, Faculty of Physics and Vinca Institute of Nuclear Sciences, Belgrade, Serbia P. Adzic33, M. Ekmedzic, J. Milosevic, V. Rekovic nologicas (CIEMAT), Madrid, Spain J. Alcaraz Maestre, C. Battilana, E. Calvo, M. Cerrada, M. Chamizo Llatas, N. Colino, B. De La Cruz, A. Delgado Peris, D. Domnguez Vazquez, A. Escalante Del Valle, C. Fernandez Bedoya, J.P. Fernandez Ramos, J. Flix, M.C. Fouz, P. Garcia-Abia, O. Gonzalez Lopez, S. Goy Lopez, J.M. Hernandez, M.I. Josa, G. Merino, E. Navarro De Martino, A. Perez-Calero Yzquierdo, J. Puerta Pelayo, A. Quintario Olmeda, I. Redondo, L. Romero, Universidad Autonoma de Madrid, Madrid, Spain C. Albajar, J.F. de Troconiz, M. Missiroli, D. Moran Universidad de Oviedo, Oviedo, Spain H. Brun, J. Cuevas, J. Fernandez Menendez, S. Folgueras, I. Gonzalez Caballero, L. Lloret Instituto de Fsica de Cantabria (IFCA), CSIC-Universidad de Cantabria, J.A. Brochero Cifuentes, I.J. Cabrillo, A. Calderon, J. Duarte Campderros, M. Fernandez, G. Gomez, A. Graziano, A. Lopez Virto, J. Marco, R. Marco, C. Martinez Rivero, F. Matorras, F.J. Munoz Sanchez, J. Piedra Gomez, T. Rodrigo, A.Y. Rodrguez-Marrero, A. Ruiz-Jimeno, L. Scodellaro, I. Vila, R. Vilar Cortabitarte CERN, European Organization for Nuclear Research, Geneva, Switzerland D. Abbaneo, E. Auffray, G. Auzinger, M. Bachtis, P. Baillon, A.H. Ball, D. Barney, A. Benaglia, J. Bendavid, L. Benhabib, J.F. Benitez, C. Bernet7, G. Bianchi, P. Bloch, A. Bocci, A. Bonato, O. Bondu, C. Botta, H. Breuker, T. Camporesi, G. Cerminara, S. Colafranceschi34, M. DAlfonso, D. dEnterria, A. Dabrowski, A. David, F. De Guio, A. De Roeck, S. De Visscher, M. Dobson, M. Dordevic, N. Dupont-Sagorin, A. ElliottPeisert, J. Eugster, G. Franzoni, W. Funk, D. Gigi, K. Gill, D. Giordano, M. Girone, F. Glege, R. Guida, S. Gundacker, M. Guthoff, J. Hammer, M. Hansen, P. Harris, J. Hegeman, V. Innocente, P. Janot, K. Kousouris, K. Krajczar, P. Lecoq, C. Lourenco, N. Magini, L. Malgeri, M. Mannelli, J. Marrouche, L. Masetti, F. Meijers, S. Mersi, E. Meschi, F. Moortgat, S. Morovic, M. Mulders, P. Musella, L. Orsini, L. Pape, E. Perez, L. Perrozzi, A. Petrilli, G. Petrucciani, A. Pfeiffer, M. Pierini, M. Pimia, D. Piparo, M. Plagge, A. Racz, G. Rolandi35, M. Rovere, H. Sakulin, C. Schafer, C. Schwick, A. Sharma, P. Siegrist, P. Silva, M. Simon, P. Sphicas36, D. Spiga, J. Steggemann, B. Stieger, M. Stoye, Y. Takahashi, D. Treille, A. Tsirou, G.I. Veres17, J.R. Vlimant, N. Wardle, H.K. Wohri, H. Wollny, W.D. Zeuner Paul Scherrer Institut, Villigen, Switzerland W. Bertl, K. Deiters, W. Erdmann, R. Horisberger, Q. Ingram, H.C. Kaestli, D. Kotlinski, U. Langenegger, D. Renker, T. Rohe Institute for Particle Physics, ETH Zurich, Zurich, Switzerland F. Bachmair, L. Bani, L. Bianchini, M.A. Buchmann, B. Casal, N. Chanon, A. Deisher, G. Dissertori, M. Dittmar, M. Doneg`a, M. Dunser, P. Eller, C. Grab, D. Hits, W. Lustermann, B. Mangano, A.C. Marini, P. Martinez Ruiz del Arbol, D. Meister, N. Mohr, C. Nageli37, F. Nessi-Tedaldi, F. Pandolfi, F. Pauss, M. Peruzzi, M. Quittnat, L. Rebane, M. Rossini, A. Starodumov38, M. Takahashi, K. Theofilatos, R. Wallny, H.A. Weber C. Amsler39, M.F. Canelli, V. Chiochia, A. De Cosa, A. Hinzmann, T. Hreus, B. Kilminster, C. Lange, B. Millan Mejias, J. Ngadiuba, P. Robmann, F.J. Ronga, S. Taroni, M. Verzetti, National Central University, Chung-Li, Taiwan M. Cardaci, K.H. Chen, C. Ferro, C.M. Kuo, W. Lin, Y.J. Lu, R. Volpe, S.S. Yu National Taiwan University (NTU), Taipei, Taiwan P. Chang, Y.H. Chang, Y.W. Chang, Y. Chao, K.F. Chen, P.H. Chen, C. Dietz, U. Grundler, W.-S. Hou, K.Y. Kao, Y.J. Lei, Y.F. Liu, R.-S. Lu, D. Majumder, E. Petrakou, Y.M. Tzeng, R. Wilken Chulalongkorn University, Faculty of Science, Department of Physics, Bangkok, B. Asavapibhop, N. Srimanobhas, N. Suwonjandee Cukurova University, Adana, Turkey A. Adiguzel, M.N. Bakirci40, S. Cerci41, C. Dozen, I. Dumanoglu, E. Eskut, S. Girgis, G. Gokbulut, E. Gurpinar, I. Hos, E.E. Kangal, A. Kayis Topaksu, G. Onengut42, K. Ozdemir, S. Ozturk40, A. Polatoz, D. Sunar Cerci41, B. Tali41, H. Topakli40, M. Vergili Middle East Technical University, Physics Department, Ankara, Turkey I.V. Akin, B. Bilin, S. Bilmis, H. Gamsizkan, G. Karapinar43, K. Ocalan, S. Sekmen, U.E. Surat, M. Yalvac, M. Zeyrek Bogazici University, Istanbul, Turkey E. Gulmez, B. Isildak44, M. Kaya45, O. Kaya46 Istanbul Technical University, Istanbul, Turkey K. Cankocak, F.I. Vardarl National Scientific Center, Kharkov Institute of Physics and Technology, Kharkov, Ukraine L. Levchuk, P. Sorokin University of Bristol, Bristol, United Kingdom J.J. Brooke, E. Clement, D. Cussans, H. Flacher, R. Frazier, J. Goldstein, M. Grimes, G.P. Heath, H.F. Heath, J. Jacob, L. Kreczko, C. Lucas, Z. Meng, D.M. Newbold47, S. Paramesvaran, A. Poll, S. Senkin, V.J. Smith, T. Williams Rutherford Appleton Laboratory, Didcot, United Kingdom K.W. Bell, A. Belyaev48, C. Brew, R.M. Brown, D.J.A. Cockerill, J.A. Coughlan, K. Harder, S. Harper, E. Olaiya, D. Petyt, C.H. Shepherd-Themistocleous, A. Thea, I.R. Tomalin, W.J. Womersley, S.D. Worm Imperial College, London, United Kingdom M. Baber, R. Bainbridge, O. Buchmuller, D. Burton, D. Colling, N. Cripps, M. Cutajar, P. Dauncey, G. Davies, M. Della Negra, P. Dunne, W. Ferguson, J. Fulcher, D. Futyan, L. Lyons, A.-M. Magnan, S. Malik, B. Mathias, J. Nash, A. Nikitenko38, J. Pela, M. Pesaresi, K. Petridis, D.M. Raymond, S. Rogerson, A. Rose, C. Seez, P. Sharp, A. Tapper, M. Vazquez Acosta, T. Virdee, S.C. Zenz Brunel University, Uxbridge, United Kingdom J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, D. Leggat, D. Leslie, W. Martin, I.D. Reid, P. Symonds, L. Teodorescu, M. Turner Baylor University, Waco, U.S.A. J. Dittmann, K. Hatakeyama, A. Kasmi, H. Liu, T. Scarborough The University of Alabama, Tuscaloosa, U.S.A. O. Charaf, S.I. Cooper, C. Henderson, P. Rumerio Boston University, Boston, U.S.A. Brown University, Providence, U.S.A. A. Avetisyan, T. Bose, C. Fantasia, P. Lawson, C. Richardson, J. Rohlf, J. St. John, J. Alimena, E. Berry, S. Bhattacharya, G. Christopher, D. Cutts, Z. Demiragli, N. Dhingra, A. Ferapontov, A. Garabedian, U. Heintz, G. Kukartsev, E. Laird, G. Landsberg, M. Luk, M. Narain, M. Segala, T. Sinthuprasith, T. Speer, J. Swanson University of California, Davis, Davis, U.S.A. R. Breedon, G. Breto, M. Calderon De La Barca Sanchez, S. Chauhan, M. Chertok, J. Conway, R. Conway, P.T. Cox, R. Erbacher, M. Gardner, W. Ko, R. Lander, T. Miceli, M. Mulhearn, D. Pellett, J. Pilot, F. Ricci-Tam, M. Searle, S. Shalhout, J. Smith, M. Squires, D. Stolp, M. Tripathi, S. Wilbur, R. Yohay University of California, Los Angeles, U.S.A. R. Cousins, P. Everaerts, C. Farrell, J. Hauser, M. Ignatenko, G. Rakness, E. Takasugi, V. Valuev, M. Weber University of California, Riverside, Riverside, U.S.A. K. Burt, R. Clare, J. Ellison, J.W. Gary, G. Hanson, J. Heilman, M. Ivova Rikova, P. Jandir, E. Kennedy, F. Lacroix, O.R. Long, A. Luthra, M. Malberti, H. Nguyen, M. Olmedo Negrete, A. Shrinivas, S. Sumowidagdo, S. Wimpenny University of California, San Diego, La Jolla, U.S.A. W. Andrews, J.G. Branson, G.B. Cerati, S. Cittolin, R.T. DAgnolo, D. Evans, A. Holzner, R. Kelley, D. Klein, M. Lebourgeois, J. Letts, I. Macneill, D. Olivito, S. Padhi, C. Palmer, M. Pieri, M. Sani, V. Sharma, S. Simon, E. Sudano, M. Tadel, Y. Tu, A. Vartak, C. Welke, F. Wurthwein, A. Yagil, J. Yoo University of California, Santa Barbara, Santa Barbara, U.S.A. D. Barge, J. Bradmiller-Feld, C. Campagnari, T. Danielson, A. Dishaw, K. Flowers, M. Franco Sevilla, P. Geffert, C. George, F. Golf, L. Gouskos, J. Incandela, C. Justus, N. Mccoll, J. Richman, D. Stuart, W. To, C. West A. Apresyan, A. Bornheim, J. Bunn, Y. Chen, E. Di Marco, J. Duarte, A. Mott, H.B. Newman, C. Pena, C. Rogan, M. Spiropulu, V. Timciuc, R. Wilkinson, S. Xie, Carnegie Mellon University, Pittsburgh, U.S.A. V. Azzolini, A. Calamba, B. Carlson, T. Ferguson, Y. Iiyama, M. Paulini, J. Russ, H. Vogel, University of Colorado at Boulder, Boulder, U.S.A. J.P. Cumalat, W.T. Ford, A. Gaz, E. Luiggi Lopez, U. Nauenberg, J.G. Smith, K. Stenson, K.A. Ulmer, S.R. Wagner Cornell University, Ithaca, U.S.A. J. Alexander, A. Chatterjee, J. Chu, S. Dittmer, N. Eggert, N. Mirman, G. Nicolas Kaufman, J.R. Patterson, A. Ryd, E. Salvati, L. Skinnari, W. Sun, W.D. Teo, J. Thom, J. Thompson, J. Tucker, Y. Weng, L. Winstrom, P. Wittich Fairfield University, Fairfield, U.S.A. Fermi National Accelerator Laboratory, Batavia, U.S.A. S. Abdullin, M. Albrow, J. Anderson, G. Apollinari, L.A.T. Bauerdick, A. Beretvas, J. Berryhill, P.C. Bhat, K. Burkett, J.N. Butler, H.W.K. Cheung, F. Chlebana, S. Cihangir, V.D. Elvira, I. Fisk, J. Freeman, Y. Gao, E. Gottschalk, L. Gray, D. Green, S. Grunendahl, O. Gutsche, J. Hanlon, D. Hare, R.M. Harris, J. Hirschauer, B. Hooberman, S. Jindariani, M. Johnson, U. Joshi, K. Kaadze, B. Klima, B. Kreis, S. Kwan, J. Linacre, D. Lincoln, R. Lipton, T. Liu, J. Lykken, K. Maeshima, J.M. Marraffino, V.I. Martinez Outschoorn, S. Maruyama, D. Mason, P. McBride, K. Mishra, S. Mrenna, Y. Musienko29, S. Nahn, C. Newman-Holmes, V. ODell, O. Prokofyev, E. Sexton-Kennedy, S. Sharma, A. Soha, W.J. Spalding, L. Spiegel, L. Taylor, S. Tkaczyk, N.V. Tran, L. Uplegger, E.W. Vaandering, R. Vidal, A. Whitbeck, J. Whitmore, F. Yang University of Florida, Gainesville, U.S.A. D. Acosta, P. Avery, P. Bortignon, D. Bourilkov, M. Carver, T. Cheng, D. Curry, S. Das, M. De Gruttola, G.P. Di Giovanni, R.D. Field, M. Fisher, I.K. Furic, J. Hugon, J. Konigsberg, A. Korytov, T. Kypreos, J.F. Low, K. Matchev, P. Milenovic49, G. Mitselmakher, L. Muniz, A. Rinkevicius, L. Shchutska, M. Snowball, D. Sperka, J. Yelton, M. Zakaria Florida International University, Miami, U.S.A. S. Hewamanage, S. Linn, P. Markowitz, G. Martinez, J.L. Rodriguez Florida State University, Tallahassee, U.S.A. T. Adams, A. Askew, J. Bochenek, B. Diamond, J. Haas, S. Hagopian, V. Hagopian, K.F. Johnson, H. Prosper, V. Veeraraghavan, M. Weinberg Florida Institute of Technology, Melbourne, U.S.A. M.M. Baarmand, M. Hohlmann, H. Kalakhety, F. Yumiceva M.R. Adams, L. Apanasevich, V.E. Bazterra, D. Berry, R.R. Betts, I. Bucinskaite, R. Cavanaugh, O. Evdokimov, L. Gauthier, C.E. Gerber, D.J. Hofman, S. Khalatyan, P. Kurt, D.H. Moon, C. OBrien, C. Silkworth, P. Turner, N. Varelas The University of Iowa, Iowa City, U.S.A. E.A. Albayrak50, B. Bilki51, W. Clarida, K. Dilsiz, F. Duru, M. Haytmyradov, J.-P. Merlo, H. Mermerkaya52, A. Mestvirishvili, A. Moeller, J. Nachtman, H. Ogul, Y. Onel, F. Ozok50, A. Penzo, R. Rahmat, S. Sen, P. Tan, E. Tiras, J. Wetzel, T. Yetkin53, K. Yi Johns Hopkins University, Baltimore, U.S.A. B.A. Barnett, B. Blumenfeld, S. Bolognesi, D. Fehling, A.V. Gritsan, P. Maksimovic, C. Martin, M. Osherson, M. Swartz, Y. Xin The University of Kansas, Lawrence, U.S.A. P. Baringer, A. Bean, G. Benelli, C. Bruner, R.P. Kenny III, M. Malek, M. Murray, D. Noonan, S. Sanders, J. Sekaric, R. Stringer, Q. Wang, J.S. Wood Kansas State University, Manhattan, U.S.A. A.F. Barfuss, I. Chakaberia, A. Ivanov, S. Khalil, M. Makouski, Y. Maravin, L.K. Saini, S. Shrestha, N. Skhirtladze, I. Svintradze Lawrence Livermore National Laboratory, Livermore, U.S.A. J. Gronberg, D. Lange, F. Rebassoo, D. Wright University of Maryland, College Park, U.S.A. A. Baden, A. Belloni, B. Calvert, S.C. Eno, J.A. Gomez, N.J. Hadley, R.G. Kellogg, T. Kolberg, Y. Lu, M. Marionneau, A.C. Mignerey, K. Pedro, A. Skuja, M.B. Tonjes, Massachusetts Institute of Technology, Cambridge, U.S.A. A. Apyan, R. Barbieri, G. Bauer, W. Busza, I.A. Cali, M. Chan, L. Di Matteo, V. Dutta, G. Gomez Ceballos, M. Goncharov, D. Gulhan, M. Klute, Y.S. Lai, Y.-J. Lee, A. Levin, P.D. Luckey, T. Ma, C. Paus, D. Ralph, C. Roland, G. Roland, G.S.F. Stephans, F. Stockli, K. Sumorok, D. Velicanu, J. Veverka, B. Wyslouch, M. Yang, M. Zanetti, V. Zhukova University of Minnesota, Minneapolis, U.S.A. B. Dahmes, A. Gude, S.C. Kao, K. Klapoetke, Y. Kubota, J. Mans, N. Pastika, R. Rusack, A. Singovsky, N. Tambe, J. Turkewitz University of Mississippi, Oxford, U.S.A. J.G. Acosta, S. Oliveros University of Nebraska-Lincoln, Lincoln, U.S.A. E. Avdeeva, K. Bloom, S. Bose, D.R. Claes, A. Dominguez, R. Gonzalez Suarez, J. Keller, D. Knowlton, I. Kravchenko, J. Lazo-Flores, S. Malik, F. Meier, G.R. Snow State University of New York at Buffalo, Buffalo, U.S.A. J. Dolen, A. Godshalk, I. Iashvili, A. Kharchilava, A. Kumar, S. Rappoccio G. Alverson, E. Barberis, D. Baumgartel, M. Chasco, J. Haley, A. Massironi, D.M. Morse, D. Nash, T. Orimoto, D. Trocino, R.-J. Wang, D. Wood, J. Zhang Northwestern University, Evanston, U.S.A. K.A. Hahn, A. Kubik, N. Mucia, N. Odell, B. Pollack, A. Pozdnyakov, M. Schmitt, S. Stoynev, K. Sung, M. Velasco, S. Won University of Notre Dame, Notre Dame, U.S.A. A. Brinkerhoff, K.M. Chan, A. Drozdetskiy, M. Hildreth, C. Jessop, D.J. Karmgard, N. Kellams, K. Lannon, W. Luo, S. Lynch, N. Marinelli, T. Pearson, M. Planer, R. Ruchti, N. Valls, M. Wayne, M. Wolf, A. Woodard The Ohio State University, Columbus, U.S.A. L. Antonelli, J. Brinson, B. Bylsma, L.S. Durkin, S. Flowers, C. Hill, R. Hughes, K. Kotov, T.Y. Ling, D. Puigh, M. Rodenburg, G. Smith, B.L. Winer, H. Wolfe, H.W. Wulsin Princeton University, Princeton, U.S.A. O. Driga, P. Elmer, P. Hebda, A. Hunt, S.A. Koay, P. Lujan, D. Marlow, T. Medvedeva, M. Mooney, J. Olsen, P. Piroue, X. Quan, H. Saka, D. Stickland2, C. Tully, J.S. Werner, University of Puerto Rico, Mayaguez, U.S.A. E. Brownson, H. Mendez, J.E. Ramirez Vargas Purdue University, West Lafayette, U.S.A. V.E. Barnes, D. Benedetti, G. Bolla, D. Bortoletto, M. De Mattia, Z. Hu, M.K. Jha, M. Jones, K. Jung, M. Kress, N. Leonardo, D. Lopes Pegna, V. Maroussov, P. Merkel, D.H. Miller, N. Neumeister, B.C. Radburn-Smith, X. Shi, I. Shipsey, D. Silvers, A. Svyatkovskiy, F. Wang, W. Xie, L. Xu, H.D. Yoo, J. Zablocki, Y. Zheng Purdue University Calumet, Hammond, U.S.A. N. Parashar, J. Stupak Rice University, Houston, U.S.A. R. Redjimi, J. Roberts, J. Zabel University of Rochester, Rochester, U.S.A. A. Adair, B. Akgun, K.M. Ecklund, F.J.M. Geurts, W. Li, B. Michlin, B.P. Padley, B. Betchart, A. Bodek, R. Covarelli, P. de Barbaro, R. Demina, Y. Eshaq, T. Ferbel, A. Garcia-Bellido, P. Goldenzweig, J. Han, A. Harel, A. Khukhunaishvili, G. Petrillo, The Rockefeller University, New York, U.S.A. R. Ciesielski, L. Demortier, K. Goulianos, G. Lungu, C. Mesropian Rutgers, The State University of New Jersey, Piscataway, U.S.A. S. Arora, A. Barker, J.P. Chou, C. Contreras-Campana, E. Contreras-Campana, D. Duggan, D. Ferencek, Y. Gershtein, R. Gray, E. Halkiadakis, D. Hidas, S. Kaplan, A. Lath, P. Thomassen, M. Walker University of Tennessee, Knoxville, U.S.A. K. Rose, S. Spanier, A. York Texas A&M University, College Station, U.S.A. O. Bouhali54, A. Castaneda Hernandez, R. Eusebi, W. Flanagan, J. Gilmore, T. Kamon55, V. Khotilovich, V. Krutelyov, R. Montalvo, I. Osipenkov, Y. Pakhotin, A. Perloff, J. Roe, A. Rose, A. Safonov, T. Sakuma, I. Suarez, A. Tatarinov Texas Tech University, Lubbock, U.S.A. N. Akchurin, C. Cowden, J. Damgov, C. Dragoiu, P.R. Dudero, J. Faulkner, K. Kovitanggoon, S. Kunori, S.W. Lee, T. Libeiro, I. Volobouev Vanderbilt University, Nashville, U.S.A. M. Sharma, P. Sheldon, B. Snook, S. Tuo, J. Velkovska University of Virginia, Charlottesville, U.S.A. C. Lin, C. Neu, J. Wood Wayne State University, Detroit, U.S.A. University of Wisconsin, Madison, U.S.A. E. Appelt, A.G. Delannoy, S. Greene, A. Gurrola, W. Johns, C. Maguire, Y. Mao, A. Melo, M.W. Arenton, S. Boutle, B. Cox, B. Francis, J. Goodell, R. Hirosky, A. Ledovskoy, H. Li, C. Clarke, R. Harr, P.E. Karchin, C. Kottachchi Kankanamge Don, P. Lamichhane, D.A. Belknap, D. Carlsmith, M. Cepeda, S. Dasu, L. Dodd, S. Duric, E. Friis, R. HallWilton, M. Herndon, A. Herve, P. Klabbers, A. Lanaro, C. Lazaridis, A. Levine, R. Loveless, A. Mohapatra, I. Ojalvo, T. Perry, G.A. Pierro, G. Polese, I. Ross, T. Sarangi, A. Savin, W.H. Smith, D. Taylor, C. Vuosalo, N. Woods 1: Also at Vienna University of Technology, Vienna, Austria 2: Also at CERN, European Organization for Nuclear Research, Geneva, Switzerland 3: Also at Institut Pluridisciplinaire Hubert Curien, Universite de Strasbourg, Universite de Haute Alsace Mulhouse, CNRS/IN2P3, Strasbourg, France 4: Also at National Institute of Chemical Physics and Biophysics, Tallinn, Estonia 5: Also at Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, 6: Also at Universidade Estadual de Campinas, Campinas, Brazil 7: Also at Laboratoire Leprince-Ringuet, Ecole Polytechnique, IN2P3-CNRS, Palaiseau, France 8: Also at Joint Institute for Nuclear Research, Dubna, Russia 9: Also at Suez University, Suez, Egypt 10: Also at Cairo University, Cairo, Egypt 11: Also at Fayoum University, El-Fayoum, Egypt 13: Now at Sultan Qaboos University, Muscat, Oman 14: Also at Universite de Haute Alsace, Mulhouse, France 15: Also at Brandenburg University of Technology, Cottbus, Germany 16: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary 17: Also at Eotvos Lorand University, Budapest, Hungary 18: Also at University of Debrecen, Debrecen, Hungary 19: Also at University of Visva-Bharati, Santiniketan, India 20: Now at King Abdulaziz University, Jeddah, Saudi Arabia 21: Also at University of Ruhuna, Matara, Sri Lanka 22: Also at Isfahan University of Technology, Isfahan, Iran 23: Also at Sharif University of Technology, Tehran, Iran 24: Also at Plasma Physics Research Center, Science and Research Branch, Islamic Azad University, Tehran, Iran 25: Also at Universit`a degli Studi di Siena, Siena, Italy 26: Also at Centre National de la Recherche Scientifique (CNRS) - IN2P3, Paris, France 27: Also at Purdue University, West Lafayette, U.S.A. 28: Also at Universidad Michoacana de San Nicolas de Hidalgo, Morelia, Mexico 29: Also at Institute for Nuclear Research, Moscow, Russia 30: Also at St. Petersburg State Polytechnical University, St. Petersburg, Russia 31: Also at California Institute of Technology, Pasadena, U.S.A. 32: Also at INFN Sezione di Padova; Universit`a di Padova; Universit`a di Trento (Trento), Padova, 33: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia 34: Also at Facolt`a Ingegneria, Universit`a di Roma, Roma, Italy 35: Also at Scuola Normale e Sezione dellINFN, Pisa, Italy 36: Also at University of Athens, Athens, Greece 37: Also at Paul Scherrer Institut, Villigen, Switzerland 38: Also at Institute for Theoretical and Experimental Physics, Moscow, Russia 39: Also at Albert Einstein Center for Fundamental Physics, Bern, Switzerland 40: Also at Gaziosmanpasa University, Tokat, Turkey 41: Also at Adiyaman University, Adiyaman, Turkey 42: Also at Cag University, Mersin, Turkey 43: Also at Izmir Institute of Technology, Izmir, Turkey 44: Also at Ozyegin University, Istanbul, Turkey 45: Also at Marmara University, Istanbul, Turkey 46: Also at Kafkas University, Kars, Turkey 48: Also at School of Physics and Astronomy, University of Southampton, Southampton, United 49: Also at University of Belgrade, Faculty of Physics and Vinca Institute of Nuclear Sciences, 50: Also at Mimar Sinan University, Istanbul, Istanbul, Turkey 51: Also at Argonne National Laboratory, Argonne, U.S.A. 52: Also at Erzincan University, Erzincan, Turkey 53: Also at Yildiz Technical University, Istanbul, Turkey 54: Also at Texas A&M University at Qatar, Doha, Qatar 55: Also at Kyungpook National University, Daegu, Korea

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V. Khachatryan, A. M. Sirunyan, A. Tumasyan, W. Adam. Identification techniques for highly boosted W bosons that decay into hadrons, Journal of High Energy Physics, 2014, 17, DOI: 10.1007/JHEP12(2014)017