Studies of jet mass in dijet and W/Z+jet events

Studies of jet mass in dijet and W/Zjet events

September 28, 2019

Invariant mass spectra for jets reconstructed using the anti-\ktand Cambridge–Aachen algorithms are studied for different jet “grooming” techniques in data corresponding to an integrated luminosity of 5\fbinv, recorded with the CMS detector in proton-proton collisions at the LHC at a center-of-mass energy of 7\TeV. Leading-order QCD predictions for inclusive dijet and W/Z+jet production combined with parton-shower Monte Carlo models are found to agree overall with the data, and the agreement improves with the implementation of jet grooming methods used to distinguish merged jets of large transverse momentum from softer QCD gluon radiation.




\RCS \RCS \cmsNoteHeaderAN-12-137

0.1 Introduction

The variables most often used in analyses of jet production are jet directions and transverse momenta (). However, as jets are composite objects, their invariant masses () provide additional information that can be used to characterize their properties. One motivation for investigating jet mass is that, at the Large Hadron Collider (LHC), massive standard model (SM) particles such as \PW and \Zbosons and top quarks are often produced with large Lorentz boosts, and, when such particles decay into quarks, the masses of the evolved jets can be used to discriminate them from lighter objects generated in quantum-chromodynamic (QCD) radiative processes. The same argument also holds for any new massive particles produced at the LHC. For sufficiently large boosts, all the decay products tend to be emitted as collimated groupings into small sections of the detector, and the resulting particles can be clustered into a single jet. Jet “grooming” techniques are designed to separate such merged jets from background. These new techniques have been found to be very promising for identifying decays of highly-boosted \PW bosons and top quarks, and in searches for Higgs bosons and other massive particles [1]. The main advantage of these grooming techniques is their ability to distinguish high jets that arise from decays of massive, possibly new, particles. In addition, their robust performance is valuable in the presence of additional interactions in an event (pileup), which is likely to provide an even greater challenge to such analyses in future higher-luminosity runs at the LHC.

Only a few of these promising approaches have been studied in data at the Tevatron [2] or at the LHC [3]. To understand these techniques in the context of searches for new phenomena, the jet mass must be well-modeled through leading-order (LO) or next-to-leading-order (NLO) Monte Carlo (MC) simulations. Much recent theoretical work in QCD has focused on the computation of jet mass, including predictions using advances in an effective field theory of jets (soft collinear effective theory, SCET) [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]. Studies of the kind reported in the present analysis can provide an understanding of the extent to which MC simulations that match matrix-element partons with parton showers can model the observed internal jet structure. Results of these studies can also be used to compare data with theoretical computations of jet mass, and to provide benchmarks for the use of these algorithms in searches for highly-boosted Higgs bosons, or new objects beyond the SM, especially by investigating some of the background processes expected in such analyses.

We present a measurement of jet mass in a sample of dijet events, and the first study of such distributions in V+jet events, where V refers to a \PW or \Zboson. The data correspond to an integrated luminosity of , collected by the Compact Muon Solenoid (CMS) experiment at the LHC in pp interactions at a center-of-mass energy of 7\TeV. The analysis of these two types of final states provides complementary information because of their different parton-flavor content, since the selected dijet events are dominated by gluon-initiated jets, and the V+jet events often contain quark-initiated jets. We focus on measuring the jet mass after applying several jet grooming techniques involving “filtering” [24], “trimming” [25], and “pruning” [26, 27] of jets, as discussed in detail below. This work also presents the first attempt to measure the mass of trimmed and pruned jets.

To study the dependence of the differential distributions in on jet , we measure the distributions in intervals of jet transverse momentum. Formally, this can be expressed in terms of a double-differential cross section for jet production () that is examined as a function of for several nonoverlapping intervals in :


where refers to the interval in , and the sum of contributions over all is equal to the total observed cross section . The differential probability density as a function of for each interval can therefore be written as


The distributions in reconstructed jet mass of Eq. (2) include corrections used to unfold jets to the “particle” level; the intervals are defined for ungroomed jets, following energy corrections for the response of the detector.

For the dijet analysis, and correspond to the average transverse momentum and average jet mass of the two leading jets (\ie, of highest ): and . For the V+jet analysis, we use the and of the leading jet. Both quantities depend on the nature of the jet grooming algorithm, as discussed in Section 0.2.

This paper is organized as follows. To introduce the subject, we first discuss jet clustering algorithms in Section 0.2, focusing mainly on grooming techniques. After a brief description of the CMS detector and the MC samples in Section 0.3, we provide information pertaining to the collected data and a description of event reconstruction in Section 0.4. Selection of events is then described in Section 0.5, and the effect of pileup on jet mass is investigated in Section 0.6. This is followed in Section 0.7 by the correction and unfolding procedures that are applied to the spectra and their corresponding systematic uncertainties. In Sections 0.8 and 0.9, we present the results of the dijet and V+jet analyses, respectively. Finally, observations and remarks on the presented results are summarized in Section 0.10.

The distributions shown are also stored in HEPData format [28].

0.2 Jet clustering algorithms and grooming techniques

0.2.1 Sequential jet clustering algorithms

Jets are defined through sequential, iterative jet clustering algorithms that combine four-vectors of input pairs of particles until certain criteria are satisfied and jets are formed. For the jet algorithms considered in this paper, for each pair of particles and , a “distance” metric between the two particles (), and the so-called “beam distance” for each particle (), are computed:


where and are the transverse momenta of particles and , respectively, “” refers to the lesser of the two values, the integer depends on the specific jet algorithm, is the distance between and in rapidity () and azimuth (), and is the “size” parameter of order unity [29], with all angles expressed in radians. The particle pair with smallest is combined into a single object. All distances are recalculated using the new object, and the procedure is repeated until, for a given object , all the are greater than . Object is then classified as a jet and not considered further in the algorithm. The process is repeated until all input particles are clustered into jets.

The value for in Eqs. (3) and (4) governs the topological properties of the jets. For the procedure is referred to as the algorithm (KT). The KT jets tend to have irregular shapes and are especially useful for reconstructing jets of lower momentum [29]. For this reason, they are also sensitive to the presence of low- pileup (PU) contributions, and are used to compute the mean per unit area (in ) of an event [30]. For , the procedure is called the anti- (AK) algorithm, with features close to an idealized cone algorithm. The AK algorithm is used extensively in LHC experiments and by the theoretical community for finding well-separated jets [29]. For , the procedure is called the Cambridge–Aachen (CA) algorithm. This relies only on angular information, and, like the algorithm, provides irregularly-shaped jets in . The CA algorithm is useful in identifying jet substructure [31, 32, 33].

Jet grooming techniques [26] that reduce the impact of contributions from the underlying event (UE), PU, and low- gluon radiation can be useful irrespective of the specific nature of analysis. These kinds of contributions to jets are typically soft and diffuse, and hence contribute energy to the jet proportional to the area [30]. Because grooming techniques reduce the areas of jets without affecting the core components, the resulting jets are less sensitive to contributions from UE and PU, while still reflecting the kinematics of the hard original process. We consider three forms of grooming, referred to as filtering, trimming, and pruning. Such techniques can be applied to jets clustered through different algorithms (KT, AK, or CA). For the dijet analysis, we choose to cluster jets with the anti- algorithm with (AK7), as these are used extensively at CMS. For the V+jet analysis, in addition to AK7 jets, we also study CA jets with (CA8), considered in recent publications involving top-quark tagging [34], and with (CA12), which was proposed for analyses involving highly-boosted objects [24]. After the initial jet clustering with AK7, CA8, or CA12, the constituents of those jets are reclustered with a (possibly different) jet algorithm (\eg, KT, CA, or AK), applying additional grooming conditions to the sequence of selection criteria used for clustering. The optimal choice of this secondary clustering algorithm depends on the grooming technique, as described below. For the techniques we have investigated, the parameters chosen for the algorithms correspond to those chosen by Refs. [24, 25, 26, 27], nevertheless specific optimization would appear to be advisable for all well-defined searches for new phenomena.

0.2.2 Filtering algorithm

The “mass-drop/filtering” procedure aims to identify symmetric splitting of jets of large that have large values. It was proposed initially for use in searches for the Higgs boson [24], but we consider just the filtering aspects of this algorithm for grooming jets.

For each jet obtained in the initial clustering procedure, the filtering algorithm defines a new, groomed jet through the following algorithm: (i) the constituents of each jet are reclustered using the CA algorithm with , thereby defining new subjets , ordered in descending , and (ii) the four-momentum of the new jet is defined by the four-vector sum over the three subjets of hardest , or in the rare case that , just these remaining subjets define the new jet.

The new jet has fewer particles than the initial jet, thereby reducing the contribution from effects such as underlying event and pileup, and the new and values are therefore smaller than those of the initial jet. As will be demonstrated in Section 0.2.5, with this choice of parameters, filtering removes the fewest jet constituents, and is therefore the least aggressive of the investigated jet grooming techniques.

0.2.3 Trimming algorithm

Trimming ignores particles within a jet that fall below a dynamic threshold in  [25]. It reclusters the jet’s constituents using the algorithm with a radius , accepting only the subjets that have , where is a dimensionless cutoff parameter, and is some hard QCD scale chosen to equal the of the original jet. The and parameters of the algorithm are taken to be 0.2 and 0.03, respectively. As will be demonstrated, with this choice of parameters, trimming removes more jet constituents than the filtering procedure, but fewer jet constituents than pruning, and corresponds therefore to a moderately aggressive jet grooming technique.

0.2.4 Pruning algorithm

Following the clustering of jets using the original algorithm (either AK7, CA8, or CA12), the pruning algorithm [26, 27] reclusters the constituents of the jet through the CA algorithm, using the same distance parameter, but additional conditions beyond those given in Eq. (3). In particular, the softer of the two particles and to be merged is removed when the following conditions are met:


where and are the mass and transverse momentum of the originally-clustered jet, and and are parameters of the algorithm, chosen to be 0.1 and 0.5, respectively. In our particular choice of parameters, we have chosen to divide the jet into two “exclusive” subjets (similarly to the exclusive algorithm [29], where one clusters constituents until the jets are all separated by the parameter in Eq. 3). As will be demonstrated, with this choice of parameters, pruning removes the largest number of jet constituents, and can therefore be regarded as the most aggressive jet grooming technique investigated. It was previously used in the CMS search for resonances [34].

0.2.5 Groomed jet mass

Figure 1 shows a comparison of distributions in the dijet sample for the ratio of groomed AK7 jet mass to the mass of the matched ungroomed AK7 jet, for our three grooming techniques, for data and for pythia6 MC simulation [35], using the Z2 tune. Three distributions are shown for each grooming technique: (i) the reconstructed data (“data RECO”), (ii) the reconstructed simulated pythia6 data (“PYTHIA RECO”), and (iii) the generated particle-level jets from pythia6 (“PYTHIA GEN”). These three grooming techniques involve different jet algorithms for grooming (CA for filtering and pruning, for trimming) once the jets are found with AK7. The data and the simulation exhibit similar behavior. In general, the filtering algorithm is the least aggressive grooming technique, with groomed jet masses close to the ungroomed values. The trimming algorithm is moderately aggressive, and the pruning algorithm is the most aggressive of the three. With pruning, a bimodal distribution begins to appear, which is typical of our implementation of this algorithm as we require clustering into two exclusive subjets. In cases where the pruned jet mass is small, jets usually have most of their energy configured in “core” components, with little gluon radiation, which leads to narrow jets. When the pruned jet mass is large, the jets are split more symmetrically, which can be realized in events with gluons splitting into two nodes that fall within of the original parton.

Figure 1: Distributions in differential probability for ratios of the jet mass of groomed jets to their corresponding ungroomed values, for both dijet data and pythia6 (tune Z2) MC simulation, for the three grooming techniques discussed in the text: (i) filtering (circles, peaking near ), (ii) trimming (squares, peaking near ), and (iii) pruning (triangles, more dispersed).

0.3 The CMS detector and simulation

The CMS detector [36] is a general-purpose device with many features suited for reconstruction of energetic jets, specifically, the finely segmented electromagnetic and hadronic calorimeters and charged-particle tracking detectors.

CMS uses a right-handed coordinate system, with origin defined by the center of the CMS detector, the axis pointing to the center of the LHC ring, the axis pointing up, perpendicular to the plane of the LHC ring, and the axis along the direction of the counterclockwise beam. The polar angle is measured relative to the positive axis and the azimuthal angle relative to the axis in the - plane.

Charged particles are reconstructed in the inner silicon tracker, which is immersed in a 3.8\unitT axial magnetic field. The CMS tracking detector consists of an inner silicon pixel detector composed of three concentric central layers and two sets of disks arranged forward and backward of the center, and up to ten silicon strip central layers and three inner and nine outer strip disks forward and backward of the center. This arrangement provides full azimuthal coverage for , where is the pseudorapidity. The pseudorapidity approximates the rapidity and equals for massless particles. Since many of the reconstructed jets are not massless, we use the rapidity for characterizing jets in this analysis.

A lead tungstate crystal electromagnetic calorimeter (ECAL) and a brass/scintillator hadronic calorimeter (HCAL) surround the tracking volume and provide photon, electron, and jet reconstruction up to . The ECAL and HCAL cells are grouped into towers projecting radially outward from the center of the detector. In the central region (), the towers have dimensions of that increase at larger . ECAL and HCAL cell energies above some chosen noise-suppression thresholds are combined within each tower to define the tower energy. Muons are measured in gas-ionization detectors embedded in the steel return yoke outside the solenoid. To improve reconstruction of jets, the tracking and calorimeter information is combined in a “particle-flow” (PF) algorithm [37], which is described in Section 0.4.4.

For the analysis of dijet events, samples are simulated with pythia6.4 (Tune Z2)  [35, 38], pythia8 (Tune 4c) [39], and herwig++ (Tune 23) [40], and propagated through the simulation of the CMS detector based on \GEANTfour[41]. Underlying event (UE) and pileup (PU) are included in the simulations, which are also reweighted to have the simulated PU distribution match the observed PU distribution in the data.

For the V+jet analysis, events with a vector boson produced in association with jets are simulated using \MADGRAPH5.1 [42]. This matrix element generator is also used to simulate events. The \MADGRAPHevents are subsequently subjected to parton showering, simulated with pythia6 using the Z2 Tune [38]. To compare hadronization in different generators, we generate V+jet samples in which parton showering and hadronization are simulated with herwig++. Diboson (WW, WZ, and ZZ) events are also generated with pythia6. Single-top-quark samples are produced with \POWHEG [43], and the lepton enriched dijet samples are produced with pythia6 using the Z2 Tune. CTEQ6L1 [44] is the default set of parton distribution functions used in all these samples, except for the single-top-quark MC, which uses CTEQ6M.

0.4 Triggers and event reconstruction

0.4.1 Dijet trigger selection

Events are collected using single-jet triggers, which are based on jets reconstructed only from calorimetric information. This procedure yields inferior resolution to jets reconstructed offline with PF constituents, but provides faster reconstruction that meets trigger requirements. As the instantaneous luminosity is time-dependent, the specific jet- thresholds change with time. The triggers used to select dijet events have partial overlap. Those with lower- thresholds have high prescale settings to accommodate the higher data-acquisition rates, and some events selected with these lower- triggers are also collected at higher thresholds.

To avoid double counting of phase space, each event is assigned to a specific trigger. To do this, we compute the trigger efficiency as a function of reconstructed , select an interval in trigger efficiency where the efficiency is maximum (%) for that range of , and assign that trigger to the appropriate interval. The assignment is based on the jet values reconstructed offline (but not groomed). Table 0.4.1 shows the thresholds for each of the jet triggers used in the analysis, and the corresponding intervals of to which the triggered events are assigned.


Trigger thresholds for individual jets, and corresponding intervals used to assign the triggered events in the dijet analysis. Trigger threshold (\GeVns) range (\GeVns) 190 220–300 240 300–450 370 450

0.4.2 V+jet trigger selection

Several triggers are also used to collect events corresponding to the topology of V+jet events, where the V decays via electrons or muons in the final state. For the \PW+jet channels, the triggers consist of several single-lepton triggers, with lepton identification criteria applied online. To assure an acceptable event rate, leptons are required to be isolated from other tracks and energy depositions in the calorimeters. For the \PW channel, the trigger thresholds for the muon are in the range of 17 to 40\GeV. The higher thresholds are used at higher instantaneous luminosity. The combined trigger efficiency for signal events that pass offline requirements (described in Section 0.5) is .

For the events, the electron threshold ranges from 25 to 65\GeV. To enhance the fraction of \PW+jet events in the data, the single-electron triggers are also required to have minimum thresholds on the magnitude of the imbalance in transverse energy () and on the transverse mass () of the (electron + ) system, where , and is the angle between the directions of and . The combined efficiency for electron \PW+jet events that pass the offline criteria is .

The channel uses the same single-muon triggers as the channel. The channel uses dielectron triggers with lower thresholds for (17 and 8\GeV), and additional isolation requirements. These triggers are 99% efficient for all \Z+jet events that pass the final offline selection criteria.

0.4.3 Binning jets as a function of

The jet bins introduced in Eq. (1) are given in Table 0.4.3 for V+jet and dijet events. The jet is re-evaluated for each grooming algorithm. Because there are large biases due to jet misassignment in the dijet events, especially at small (when three particle-level jets are often reconstructed as two jets in the detector, or vice versa), the intervals for these events begin at 220\GeV. Furthermore, the smaller number of events in the V+jet samples precludes the study of these events beyond .


Intervals in ungroomed jet for the V+jet and dijet analyses. Bin interval (\GeVns) Analysis 1 125–150 V+jet 2 150–220 V+jet 3 220–300 V+jet,dijet 4 300–450 V+jet,dijet 5 450–500 dijet 6 500–600 dijet 7 600–800 dijet 8 800–1000 dijet 9 1000–1500 dijet

0.4.4 Event reconstruction

As indicated above, events are reconstructed using the particle-flow algorithm, which combines the information from all subdetectors to reconstruct the particle candidates in an event. The algorithm categorizes particles into muons, electrons, photons, charged hadrons, and neutral hadrons. The resulting PF candidates are passed through each jet clustering algorithm of Section 0.2, as implemented in FastJet (Version 3.0.1) [45, 46].

The reconstructed interaction vertex characterized by the largest value of , where is the transverse momentum of the charged track associated with the vertex, is defined as the leading primary vertex (PV) of the event. This vertex is used as the reference vertex for all PF objects in the event. A pileup interaction can affect the reconstruction of jet momenta and , as well as lepton isolation and b-tagging efficiency. To mitigate these effects, a track-based algorithm is used to remove all charged hadrons that are not consistent with originating from the leading PV.

Electron reconstruction requires the matching of an energy cluster in the ECAL with a track extrapolated from the silicon tracker [47]. Identification criteria based on the energy distribution of showers in the ECAL and consistency of tracks with the primary vertex are imposed on electron candidates. Additional requirements remove any electrons produced through conversions of photons in detector material. The analysis considers electrons only in the range of , excluding the transition region between the central and endcap ECAL detectors because of poorer resolution for electrons in this region. Muons are reconstructed using two algorithms [48]: (i) in which tracks in the silicon tracker are matched to signals in the muon chambers, and (ii) in which a global fit is performed to a track seeded by signals in the external muon system. The muon candidates are required to be reconstructed through both algorithms. Additional identification criteria are imposed on muon candidates to reduce the fraction of tracks misidentified as muons, and to reduce contamination from muon decays in flight. These criteria include the number of hits detected in the tracker and in the outer muon system, the quality of the fit to a muon track, and its consistency of originating from the leading PV.

Charged leptons from V-boson decays are expected to be isolated from other energy depositions in the event. For each lepton candidate, a cone with radius 0.3 for muons and 0.4 for electrons is chosen around the direction of the track at the event vertex. When the scalar sum of the transverse momenta of reconstructed particles within that cone, excluding the contribution from the lepton candidate, exceeds of the of the lepton candidate, that lepton is ignored. The exact isolation requirement depends on the , , and flavor of the lepton. Muons and electrons are required to have \GeVand \GeV, respectively. The large threshold for electrons ensures good trigger efficiency. To avoid double counting, isolated charged leptons are removed from the list of PF objects that are clustered into jets.

After removal of isolated leptons and charged hadrons from pileup vertices, only the neutral hadron component from pileup remains and is included in the jet clustering. This remaining component of pileup to the jet energy is removed by applying a correction based on a mean per unit area of () originating from neutral particles [30, 49]. This quantity is computed using the algorithm, and corrects the jet energy by the amount of energy expected from pileup in the jet cone. This “active area” method adds a large number of soft “ghost” particles to the clustering sequence to determine the effective area subtended by each jet. This procedure is done for all grooming algorithms just as for the ungroomed jets. The active area of a groomed jet is smaller than that of an ungroomed jet, and the pileup correction is therefore also smaller. Different responses in the endcap and central barrel calorimeters necessitate using -dependent jet corrections. The amount of energy expected from the remnants of the hard collision (the underlying event) is estimated from minimum-bias data and MC events, and is added back into the jet.

In addition, the pileup-subtracted jet four-momenta in data are corrected for nonlinearities in and by using a - and -dependent correction to account for the difference between the response in MC-simulated events and data [50]. The jet corrections are derived for the ungroomed jet algorithms but are also applied to the groomed algorithms, thereby adding additional systematic uncertainty in the energy of groomed jets.

0.5 Event selection

We apply several other selection criteria to minimize instrumental background and electronic noise. In particular, accepted events must have at least one good primary vertex (Section 0.4.4). Backgrounds from additional beam interactions are reduced by applying a variety of requirements on charged tracks. Finally, calorimeter noise is minimized through restrictions on timing and electronic pulse shapes expected for signals.

Dijet events are required to have at least two AK7 jets, each with \GeVand , and each jet must satisfy the jet quality criteria discussed in Ref. [37]. No third-jet veto is applied.

Reconstruction of \PW and \Z bosons in V+jet events begins with identification of charged leptons and a calculation of , described in the previous section. Candidates for ( or ) decays are reconstructed by combining two isolated electrons or muons and requiring the dilepton invariant mass to be in the range. An accurate measurement of is essential for distinguishing the W signal from background processes. The in the event is defined using the PF objects, and this analysis requires . Candidate decays are identified primarily through the presence of a significant and a single isolated lepton of large , with \ptand  of the \PW candidate obtained by combining the lepton and the vectors.

The analysis of V+jet events is mainly of interest for the regime of \GeV, in which the opposing jet tends to have large as well, because of momentum conservation. In fact, the leading jet in each event (independent of clustering algorithm and jet radius) is required to have and . A back-to-back topology between the vector boson and the leading jet is ensured by the additional selection of and . Requiring such highly boosted jets, in addition to the tight isolation criteria for the leptons, greatly suppresses the background from multijet production. In the +jet analysis, additional rejection of multijet background is achieved by requiring . No subleading-jet veto is applied.

Figures 2(a) and (b) show the distributions for the leading AK7 jet selected in Z+jet and W+jet candidate events, respectively. Given the unique signature for highly-boosted vector bosons recoiling from jets, the selections suffice to define very pure samples of V+jet events. In the +jet analysis, the additional constraint on dilepton mass removes almost all background contributions, yielding a purity of for \Z+jet events, with contamination from diboson production. The \PW+jet candidate sample contains \PW+jet events, with small background contributions from (13%), single top-quark (3%), and diboson and \Z+jet (1% each) events based on MC simulation. The small number of events expected from these processes are subtracted using MC predictions for the jet mass from the \PW+jet candidate events, before correcting the data for detector effects. Similarly, the small number of events expected from diboson production are subtracted from the \Z+jet candidates.

Figure 2: The distribution for the leading AK7 jet in accepted (a) \cPZ+jet and (b) \PW+jet events.

0.6 Influence of pileup on jet grooming algorithms

During the data taking the instantaneous LHC luminosity exceeded , or an average of ten interactions per bunch crossing. Such pileup collisions are not correlated with the hard-scattering process that triggers an interesting event, but present a background from low- interactions that can affect the measured energies of jets and their observed masses. Methods to mitigate these effects are part of standard event reconstruction, as discussed in Section 0.4.4, and are essential for extracting correct jet multiplicities and energies. The jet mass is expected to be particularly sensitive to pileup [1] for jets of large angular extent that contain many particles. Grooming techniques are designed to reduce the effective area of such jets and thereby minimize sensitivity to pileup. We examine this issue through studies of jet mass in the presence of pileup.

The mean jet mass for AK jets is presented for size parameters , 0.7, and 0.8, as a function of the total number of reconstructed primary vertices () in Fig. 3(a), for data and MC simulation. The mean mass for increases linearly with the jet radius from 0.5 to 0.8. A measure of the dependence of on pileup is given by the slope of a linear fit to the jet mass versus . The ratios of these slopes () are found to be roughly consistent with the ratio of the third power of the jet radius, as summarized in Table 0.6.


Slopes of linear fits of as a function of for AK jets of different values.

Ratio of slopes Measured Expected

This is in agreement with predictions for scaling of the mean mass [51]. The dependence can be understood in terms of the increase of the jet area as . Simultaneously, the contribution of these particles to the jet mass scales with the distance between them, or , yielding another power of .

In Fig. 3(b) we show the dependence of on , for AK7 jets, for different grooming algorithms. The grooming significantly reduces the impact of pileup on , as reflected by the decrease of the slope of the linear fit to the groomed-jet data points, as summarized in Table 0.6.

Figure 3: Distributions of the average jet mass for AK jets as a function of the number of reconstructed primary vertices: (a) for different jet radii, and (b) for AK7 jets, comparing the impact of grooming algorithms to results without grooming.

Values of slopes for the dependence of on for AK jets with different radii and clustering algorithms.

Jet R Clustering algorithm (\GeVns/PV)
AK5 ungroomed
AK7 ungroomed
AK7 filtered
AK7 trimmed
AK7 pruned
AK8 ungroomed

The observed agreement between data and simulation in Fig. 3 provides support for our characterization of jet grooming and pileup, and the decrease in slopes suggests that grooming is indeed an effective tool for suppressing the impact of pileup on jets with large parameters.

0.7 Corrections and systematic uncertainties

Before comparison of the jet mass distributions with QCD predictions, the data are corrected to the particle level for detector effects, such as resolution and acceptance. The simulated particle-level jets are reconstructed with the same algorithm and with the same parameters as the PF jets. We use the unfolding procedure described in Refs. [52, 53, 54, 55, 56] to correct the jet mass, through an iterative technique for finding the maximum-likelihood solution of the unfolding problem. The detector response matrix is obtained in MC studies of jets. In general, the number of iterations must be tuned to minimize the impact of statistical fluctuations on the result. In practice, however, the procedure is largely insensitive to the precise settings and binning of events and four iterations usually suffice. A larger number of iterations were found to provide the same results except for small fluctuations in the tails of distributions. A simpler bin-by-bin unfolding is used as a cross-check, and is found to provide similar results, with fluctuations in the tails of the distributions. The jet transverse momenta are not unfolded.

Systematic uncertainties are estimated by modifying the response matrix for each source of uncertainty by standard deviation, and comparing the mass distribution to the nominal results, based on simulated pythia6 events. The difference in the unfolded mass spectrum from such a change is taken as the uncertainty arising from that source.

The experimental uncertainties that can affect the unfolding of the jet mass include the jet energy scale (JES), jet energy resolution (JER), and jet angular resolution (JAR). The uncertainty from JES is estimated by raising and lowering the jet four-momenta by the measured uncertainty as a function of jet and  [50], which typically corresponds to 1–2% for the jets in this analysis. Two additional - and -independent uncertainties are included: a 1% uncertainty to account for differences observed between the measured and predicted mass for high- jets in a -enriched sample, and a 3% uncertainty to account for differences in the groomed and ungroomed energy responses found in MC simulation [34].

The impact of uncertainties in JER and JAR on are evaluated by smearing the jet energies, as well as the resolutions in and , each by 10% in the MC simulation relative to the particle-level generated jets [50]. These estimated uncertainties on JER and JAR are found to be essentially the same for all jet grooming techniques in MC studies. Since this analysis uses jets constructed from PF constituents, the charged particles have excellent energy and angular resolutions, but their use induces a dependence on tracking uncertainties, \eg, tracking efficiency. This dependence is accounted for implicitly in the 10% changes in jet energy and angular resolutions, since such changes would lead to a difference between expected and observed values of these quantities. The same is true for the neutral electromagnetic component of the jet (primarily from decays).

The remaining sources of uncertainty are estimated from MC simulation. The tracking information is not sensitive to the neutral hadronic component of jets, and this small contribution is taken directly from simulation. We estimate this remaining uncertainty by comparing the unfolded data using pythia6 and using herwig++, and assign the difference as a systematic uncertainty. This also accounts for the uncertainty from modeling parton showers. The latter effect often comprises the largest uncertainty in the unfolded jet mass distributions as described below. Other theoretical ambiguities that can affect the unfolding of the jet mass include the variation of the parton distribution functions and the modeling of initial and final-state radiation (ISR/FSR). The former was investigated and found to be much smaller than the difference between the unfolding with pythia6 and the unfolding with herwig++, and hence is neglected. The latter is included implicitly in the uncertainty between pythia6 and herwig++.

As described in Section 0.4.4, the jets used in this analysis are reconstructed after removing the charged hadrons that appear to emanate from subleading primary vertices. This procedure produces a dramatic () reduction in the pileup contribution to jets. The residual uncertainty from pileup is obtained through MC simulation, estimated by increasing and decreasing the cross section for minimum-bias events by 8%.

In the dijet analysis, there can be incorrect assignments of leading reconstructed jets relative to the generator level, \eg, two generator-level jets can be matched to three reconstructed jets, or vice versa. This effect causes a bias in the unfolding procedure, which becomes greater at small . This bias is corrected through MC studies of matching of particle-level jets to reconstructed jets, and the magnitude of the bias correction is also added to the overall systematic uncertainty. Such misassignments are negligible in the V+jet analysis.

0.8 Results from dijet final states

The differential probability distributions of Eq. (2) for of the two leading jets in dijet events, corrected for detector effects in the jet mass, are displayed in Figs. 47 for seven bins in along with the herwig++ predictions.. The is not corrected to the particle level, because the correction is expected to be negligible for the momenta considered. Results are shown for ungroomed jets and for the three categories of grooming. Each distribution is normalized to unity. The ratios of the MC simulations used in Figs. 47 to the results for data, for pythia6, pythia8, and for herwig++ are given in Figs. 811, respectively.

The largest systematic uncertainty is from the choice of parton-shower modeling used to calculate detector corrections, with small, but still significant uncertainties arising from jet energy scale and resolution, and small contributions from jet angular resolution and pileup. In the 220–300\GeVand 300–450\GeVjet- bins, the region is dominated by uncertainties from unfolding (50–100%), which are negligible for . For , the JES, JER, JAR, and pileup uncertainties each contribute . For the 450–1000\GeV bins, parton showering dominates the uncertainties, which is around 50–100% below the peak of the distribution and 5–10% for the rest of the distribution. For , statistical uncertainty dominates the entire mass range.

For clarity, the distributions in Figs. 811 are truncated where few events are recorded. Bins in with uncertainties of 100% are ignored to avoid overlap with more precise measurements in other bins. The agreement with herwig++ modeling of parton showers appears to be best for and . However, the ungroomed and filtered jets show worse agreement for when . For all generators and all bins, the agreement is better at larger jet masses. The disagreement is largest at the very lowest mass values, which correspond to the region most sensitive to the underlying event description and pileup, and where the amount of showering is apparently underestimated in the simulation.

Figure 4: Unfolded distributions for the mean mass of the two leading jets in dijet events for reconstructed AK7 jets, separated according to intervals in (the mean of the two jets). The data are shown by the symbols indicating different bins in the mean of the two jets. The statistical uncertainty is shown in light shading, and the total uncertainty in dark shading. Predictions from herwig++ are given by the dotted lines. To enhance visibility, the distributions for larger values of are scaled up by the factors given in the legend.
Figure 5: Unfolded distributions for the mean mass of the two leading jets in dijet events for reconstructed filtered AK7 jets, separated according to intervals in (the mean of the two jets). The data are shown by the symbols indicating different bins in the mean of the two jets. The statistical uncertainty is shown in light shading, and the total uncertainty in dark shading. Predictions from herwig++ are given by the dotted lines. To enhance visibility, the distributions for larger values of are scaled up by the factors given in the legend.
Figure 6: Unfolded distributions for the mean mass of the two leading jets in dijet events for reconstructed trimmed AK7 jets, separated according to intervals in (the mean of the two jets). The data are shown by the symbols indicating different bins in the mean of the two jets. The statistical uncertainty is shown in light shading, and the total uncertainty in dark shading. Predictions from herwig++ are given by the dotted lines. To enhance visibility, the distributions for larger values of are scaled up by the factors given in the legend.
Figure 7: Unfolded distributions for the mean mass of the two leading jets in dijet events for reconstructed pruned AK7 jets, separated according to intervals in (the mean of the two jets). The data are shown by the symbols indicating different bins in the mean of the two jets. The statistical uncertainty is shown in light shading, and the total uncertainty in dark shading. Predictions from herwig++ are given by the dotted lines. To enhance visibility, the distributions for larger values of are scaled up by the factors given in the legend.
Figure 8: Ratio of MC simulation to unfolded distributions of the jet mass for AK7 jets for the seven bins in . The statistical uncertainty is shown in light shading, and the total uncertainty is shown in dark shading. The comparison for pythia6 is shown in solid lines, for pythia8 in dashed lines, and for herwig++ in dotted lines.
Figure 9: Ratio of MC simulation to unfolded distributions of the jet mass for filtered AK7 jets for the seven bins in . The statistical uncertainty is shown in light shading, and the total uncertainty is shown in dark shading. The comparison for pythia6 is shown in solid lines, for pythia8 in dashed lines, and for herwig++ in dotted lines.
Figure 10: Ratio of MC simulation to unfolded distributions of the jet mass for trimmed AK7 jets for the seven bins in . The statistical uncertainty is shown in light shading, and the total uncertainty is shown in dark shading. The comparison for pythia6 is shown in solid lines, for pythia8 in dashed lines, and for herwig++ in dotted lines.
Figure 11: Ratio of MC simulation to unfolded distributions of the jet mass for pruned AK7 jets for the seven bins in . The statistical uncertainty is shown in light shading, and the total uncertainty is shown in dark shading. The comparison for pythia6 is shown in solid lines, for pythia8 in dashed lines, and for herwig++ in dotted lines.

0.9 Results from V+jet final states

This section provides the probability density distributions as functions of the mass of the leading jet in V+jet events. These distributions are corrected for detector effects in the jet mass, and are compared to MC expectations from \MADGRAPH(interfaced to pythia6) and herwig++. The jet mass distributions are studied in different ranges of between and , as given in Table 0.4.3. (Just as in the dijet results, is not corrected to the particle level.) For jets reconstructed with the CA algorithm (), we study only the events with , which is most interesting for heavy particle searches in the highly-boosted regime, where all decay products are contained within jets [24].

For clarity, the distributions are also truncated at large mass values where few events are recorded. Jet-mass bins with relative uncertainties 100% are also ignored to minimize overlap with more precise measurements in other bins.

Figures 1215 show mass distributions for the leading AK7 jet accompanying a \Zboson in +jet events for the ungroomed, filtered, trimmed, and pruned clustering of jets, respectively. Both pythia6 and herwig++ show good agreement with data for all bins, but especially so for . As in the case of the dijet analysis, the data at small jet mass are not modeled satisfactorily, but show modest improvement after applying the grooming procedures. To investigate several popular choices of jet grooming at CMS, Figs. 1617 show the distributions in for pruned CA8 and filtered CA12 jets in \Z+jet events. For groomed CA jets, both pythia6 and herwig++ provide good agreement with the data, with some possible inconsistency for and at large for for the ungroomed and filtered jets. Figures 1821 show the corresponding distributions for the mass of the leading jet accompanying the \PW boson for AK7 jets in \PW+jet events for the ungroomed, filtered, trimmed, and pruned clustering algorithms, and Figs. 2223 show the distributions for pruned CA8 and filtered CA12 jets. For CA8 and CA12 jets, only particular grooming algorithms and bins are chosen for illustration. The MC simulation shows good agreement with data, just as observed for \Z+jet events.

Figure 12: Unfolded, ungroomed AK7 distribution for +jet events. The data (black symbols) are compared to MC expectations from \MADGRAPH+pythia6 (solid lines) and herwig++ (dotted lines) on the left. The ratio of MC to data is given on the right. The statistical uncertainty is shown in light shading, and the total uncertainty in dark shading.
Figure 13: Unfolded AK7 filtered distribution for +jet events. The data (black symbols) are compared to MC expectations from \MADGRAPH+pythia6 (solid lines) and herwig++ (dotted lines) on the left. The ratio of MC to data is given on the right. The statistical uncertainty is shown in light shading, and the total uncertainty in dark shading.
Figure 14: Unfolded AK7 trimmed distribution for +jet events. The data (black symbols) are compared to MC expectations from \MADGRAPH+pythia6 (solid lines) and herwig++ (dotted lines) on the left. The ratio of MC to data is given on the right. The statistical uncertainty is shown in light shading, and the total uncertainty in dark shading.
Figure 15: Unfolded AK7 pruned distribution for +jet events. The data (black symbols) are compared to MC expectations from \MADGRAPH+pythia6 (solid lines) and herwig++ (dotted lines) on the left. The ratio of MC to data is given on the right. The statistical uncertainty is shown in light shading, and the total uncertainty in dark shading.
Figure 16: Unfolded CA8 pruned distribution for +jet events. The data (black symbols) are compared to MC expectations from \MADGRAPH+pythia6 (solid lines) and herwig++ (dotted lines) on the left. The ratio of MC to data is given on the right. The statistical uncertainty is shown in light shading, and the total uncertainty in dark shading.
Figure 17: Unfolded CA12 filtered distribution for +jet events. The data (black symbols) are compared to MC expectations from \MADGRAPH+pythia6 (solid lines) and herwig++ (dotted lines) on the left. The ratio of MC to data is given on the right. The statistical uncertainty is shown in light shading, and the total uncertainty in dark shading.
Figure 18: Distributions in for unfolded, ungroomed AK7 jets in \PW+jet events. The data (black symbols) are compared to MC expectations from \MADGRAPH+pythia6 (solid lines) and herwig++ (dotted lines) on the left. The ratios of MC to data are given on the right. The statistical uncertainty is shown in light shading, and the total uncertainty in dark shading.
Figure 19: Distributions in for unfolded, filtered AK7 jets in \PW+jet events. The data (black symbols) for different bins in are compared to MC expectations from \MADGRAPH+pythia6 (solid lines) and herwig++ (dotted lines) on the left. The ratios of MC to data are given on the right. The statistical uncertainty is shown in light shading, and the total uncertainty in dark shading.
Figure 20: Distributions in for unfolded, trimmed AK7 jets in \PW+jet events. The data (black symbols) for different bins in are compared to MC expectations from \MADGRAPH+pythia6 (solid lines) and herwig++ (dotted lines) on the left. The ratios of MC to data are given on the right. The statistical uncertainty is shown in light shading, and the total uncertainty in dark shading.
Figure 21: Distributions in for unfolded, pruned AK7 jets in \PW+jet events. The data (black symbols) for different bins in are compared to MC expectations from \MADGRAPH+pythia6 (solid lines) and herwig++ (dotted lines) on the left. The ratios of MC to data are given on the right. The statistical uncertainty is shown in light shading, and the total uncertainty in dark shading.
Figure 22: Distributions in for unfolded, pruned CA8 jets in \PW+jet events. The data (black symbols) for different bins in are compared to MC expectations from \MADGRAPH+pythia6 (solid lines) and herwig++ (dotted lines) on the left. The ratios of MC to data are given on the right. The statistical uncertainty is shown in light shading, and the total uncertainty in dark shading.
Figure 23: Distributions in for unfolded, filtered CA12 jets in \PW+jet events. The data (black symbols) for different bins in are compared to MC expectations from \MADGRAPH+pythia6 (solid lines) and herwig++ (dotted lines) on the left. The ratios of MC to data are given on the right. The statistical uncertainty is shown in light shading, and the total uncertainty in dark shading.

0.10 Summary

We have presented the differential distributions in jet mass for inclusive dijet and V+jet events, defined through the anti- algorithm for a size parameter of 0.7 for ungroomed jets, as well as for jets groomed through filtering, trimming, and pruning. In addition, similar distributions for V+jet events were given for pruned Cambridge–Aachen jets with a size parameter of 0.8, as well as for filtered Cambridge–Aachen jets with a size parameter of 1.2. The impact of pileup on jet mass was also investigated.

Higher-order QCD matrix-element predictions for partons, coupled to parton-shower Monte Carlo programs that generate jet mass in dijet and V+jet events, are found to be in good agreement with data. A comparison of data with MC simulation indicates that both pythia6 and herwig++ reproduce the data reasonably well, and that the herwig++ predictions for more aggressive grooming algorithms, \ie, those that remove larger fractions of contributions to the original ungroomed jet mass, agree somewhat better with observations. It is also observed that the more aggressive grooming procedures lead to somewhat better agreement between data and MC simulation.

In comparing the results from the V+jet analysis with those for the two leading jets in multijet events, the predictions provide slightly better agreement with the V+jet data. This observation suggests that simulation of quark jets is better than of gluon jets. Differences between data and simulation are larger at small jet mass values, which also correspond to the region more affected by pileup and soft QCD radiation.

These studies represent the first detailed investigations of techniques for characterizing jet substructure based on data collected by the CMS experiment at a center-of-mass energy of 7\TeV. For the trimming and pruning algorithms, these studies mark the first publication on this subject from the LHC, and provide an important benchmark for their use in searches for massive particles. Finally, the intrinsic stability of these algorithms to pileup effects is likely to contribute to a more rapid and widespread use of these techniques in future high-luminosity runs at the LHC.


We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at CERN and at other CMS institutes for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centres and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies: the Austrian Federal Ministry of Science and Research and the Austrian Science Fund; the Belgian Fonds de la Recherche Scientifique, and Fonds voor Wetenschappelijk Onderzoek; the Brazilian Funding Agencies (CNPq, CAPES, FAPERJ, and FAPESP); the Bulgarian Ministry of Education, Youth and Science; CERN; the Chinese Academy of Sciences, Ministry of Science and Technology, and National Natural Science Foundation of China; the Colombian Funding Agency (COLCIENCIAS); the Croatian Ministry of Science, Education and Sport; the Research Promotion Foundation, Cyprus; the Ministry of Education and Research, Recurrent financing contract SF0690030s09 and European Regional Development Fund, Estonia; the Academy of Finland, Finnish Ministry of Education and Culture, and Helsinki Institute of Physics; the Institut National de Physique Nucléaire et de Physique des Particules / CNRS, and Commissariat à l’Énergie Atomique et aux Énergies Alternatives / CEA, France; the Bundesministerium für Bildung und Forschung, Deutsche Forschungsgemeinschaft, and Helmholtz-Gemeinschaft Deutscher Forschungszentren, Germany; the General Secretariat for Research and Technology, Greece; the National Scientific Research Foundation, and National Office for Research and Technology, Hungary; the Department of Atomic Energy and the Department of Science and Technology, India; the Institute for Studies in Theoretical Physics and Mathematics, Iran; the Science Foundation, Ireland; the Istituto Nazionale di Fisica Nucleare, Italy; the Korean Ministry of Education, Science and Technology and the World Class University program of NRF, Republic of Korea; the Lithuanian Academy of Sciences; the Mexican Funding Agencies (CINVESTAV, CONACYT, SEP, and UASLP-FAI); the Ministry of Science and Innovation, New Zealand; the Pakistan Atomic Energy Commission; the Ministry of Science and Higher Education and the National Science Centre, Poland; the Fundação para a Ciência e a Tecnologia, Portugal; JINR (Armenia, Belarus, Georgia, Ukraine, Uzbekistan); the Ministry of Education and Science of the Russian Federation, the Federal Agency of Atomic Energy of the Russian Federation, Russian Academy of Sciences, and the Russian Foundation for Basic Research; the Ministry of Science and Technological Development of Serbia; the Secretaría de Estado de Investigación, Desarrollo e Innovación and Programa Consolider-Ingenio 2010, Spain; the Swiss Funding Agencies (ETH Board, ETH Zurich, PSI, SNF, UniZH, Canton Zurich, and SER); the National Science Council, Taipei; the Thailand Center of Excellence in Physics, the Institute for the Promotion of Teaching Science and Technology of Thailand and the National Science and Technology Development Agency of Thailand; the Scientific and Technical Research Council of Turkey, and Turkish Atomic Energy Authority; the Science and Technology Facilities Council, UK; 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 à la Recherche dans l’Industrie et dans l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); the Ministry of Education, Youth and Sports (MEYS) of Czech Republic; the Council of Science and Industrial Research, India; the Compagnia di San Paolo (Torino); and the HOMING PLUS programme of Foundation for Polish Science, cofinanced from European Union, Regional Development Fund.


.11 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
S. Chatrchyan, V. Khachatryan, A.M. Sirunyan, A. Tumasyan \cmsinstskipInstitut für Hochenergiephysik der OeAW, Wien, Austria
W. Adam, E. Aguilo, T. Bergauer, M. Dragicevic, J. Erö, C. Fabjan\cmsAuthorMark1, M. Friedl, R. Frühwirth\cmsAuthorMark1, V.M. Ghete, N. Hörmann, J. Hrubec, M. Jeitler\cmsAuthorMark1, W. Kiesenhofer, V. Knünz, M. Krammer\cmsAuthorMark1, I. Krätschmer, D. Liko, I. Mikulec, M. Pernicka, D. Rabady\cmsAuthorMark2, B. Rahbaran, C. Rohringer, H. Rohringer, R. Schöfbeck, J. Strauss, A. Taurok, W. Waltenberger, C.-E. Wulz\cmsAuthorMark1 \cmsinstskipNational Centre for Particle and High Energy Physics, Minsk, Belarus
V. Mossolov, N. Shumeiko, J. Suarez Gonzalez \cmsinstskipUniversiteit Antwerpen, Antwerpen, Belgium
S. Alderweireldt, M. Bansal, S. Bansal, T. Cornelis, E.A. De Wolf, X. Janssen, S. Luyckx, L. Mucibello, S. Ochesanu, B. Roland, R. Rougny, H. Van Haevermaet, P. Van Mechelen, N. Van Remortel, A. Van Spilbeeck \cmsinstskipVrije Universiteit Brussel, Brussel, Belgium
F. Blekman, S. Blyweert, J. D’Hondt, R. Gonzalez Suarez, A. Kalogeropoulos, M. Maes, A. Olbrechts, S. Tavernier, W. Van Doninck, P. Van Mulders, G.P. Van Onsem, I. Villella \cmsinstskipUniversité Libre de Bruxelles, Bruxelles, Belgium
B. Clerbaux, G. De Lentdecker, V. Dero, A.P.R. Gay, T. Hreus, A. Léonard, P.E. Marage, A. Mohammadi, T. Reis, L. Thomas, C. Vander Velde, P. Vanlaer, J. Wang \cmsinstskipGhent University, Ghent, Belgium
V. Adler, K. Beernaert, A. Cimmino, S. Costantini, G. Garcia, M. Grunewald, B. Klein, J. Lellouch, A. Marinov, J. Mccartin, A.A. Ocampo Rios, D. Ryckbosch, M. Sigamani, N. Strobbe, F. Thyssen, M. Tytgat, S. Walsh, E. Yazgan, N. Zaganidis \cmsinstskipUniversité Catholique de Louvain, Louvain-la-Neuve, Belgium
S. Basegmez, G. Bruno, R. Castello, L. Ceard, C. Delaere, T. du Pree, D. Favart, L. Forthomme, A. Giammanco\cmsAuthorMark3, J. Hollar, V. Lemaitre, J. Liao, O. Militaru, C. Nuttens, D. Pagano, A. Pin, K. Piotrzkowski, M. Selvaggi, J.M. Vizan Garcia \cmsinstskipUniversité de Mons, Mons, Belgium
N. Beliy, T. Caebergs, E. Daubie, G.H. Hammad \cmsinstskipCentro Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil
G.A. Alves, M. Correa Martins Junior, T. Martins, M.E. Pol, M.H.G. Souza \cmsinstskipUniversidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
W.L. Aldá Júnior, W. Carvalho, J. Chinellato, A. Custódio, E.M. Da Costa, D. De Jesus Damiao, C. De Oliveira Martins, S. Fonseca De Souza, H. Malbouisson, M. Malek, D. Matos Figueiredo, L. Mundim, H. Nogima, W.L. Prado Da Silva, A. Santoro, L. Soares Jorge, A. Sznajder, E.J. Tonelli Manganote, A. Vilela Pereira \cmsinstskipUniversidade Estadual Paulista ,  Universidade Federal do ABC ,  São Paulo, Brazil
T.S. Anjos, C.A. Bernardes, F.A. Dias\cmsAuthorMark4, T.R. Fernandez Perez Tomei, E.M. Gregores, C. Lagana, F. Marinho, P.G. Mercadante, S.F. Novaes, Sandra S. Padula \cmsinstskipInstitute for Nuclear Research and Nuclear Energy, Sofia, Bulgaria
V. Genchev\cmsAuthorMark2, P. Iaydjiev\cmsAuthorMark2, S. Piperov, M. Rodozov, S. Stoykova, G. Sultanov, V. Tcholakov, R. Trayanov, M. Vutova \cmsinstskipUniversity of Sofia, Sofia, Bulgaria
A. Dimitrov, R. Hadjiiska, V. Kozhuharov, L. Litov, B. Pavlov, P. Petkov \cmsinstskipInstitute of High Energy Physics, Beijing, China
J.G. Bian, G.M. Chen, H.S. Chen, C.H. Jiang, D. Liang, S. Liang, X. Meng, J. Tao, J. Wang, X. Wang, Z. Wang, H. Xiao, M. Xu, J. Zang, Z. Zhang \cmsinstskipState Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing, China
C. Asawatangtrakuldee, Y. Ban, Y. Guo, Q. Li, W. Li, S. Liu, Y. Mao, S.J. Qian, D. Wang, L. Zhang, W. Zou \cmsinstskipUniversidad de Los Andes, Bogota, Colombia
C. Avila, C.A. Carrillo Montoya, J.P. Gomez, B. Gomez Moreno, A.F. Osorio Oliveros, J.C. Sanabria \cmsinstskipTechnical University of Split, Split, Croatia
N. Godinovic, D. Lelas, R. Plestina\cmsAuthorMark5, D. Polic, I. Puljak\cmsAuthorMark2 \cmsinstskipUniversity of Split, Split, Croatia
Z. Antunovic, M. Kovac \cmsinstskipInstitute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, S. Duric, K. Kadija, J. Luetic, D. Mekterovic, S. Morovic, L. Tikvica \cmsinstskipUniversity of Cyprus, Nicosia, Cyprus
A. Attikis, M. Galanti, G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos, P.A. Razis \cmsinstskipCharles University, Prague, Czech Republic
M. Finger, M. Finger Jr. \cmsinstskipAcademy of Scientific Research and Technology of the Arab Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
Y. Assran\cmsAuthorMark6, S. Elgammal\cmsAuthorMark7, A. Ellithi Kamel\cmsAuthorMark8, M.A. Mahmoud\cmsAuthorMark9, A. Radi\cmsAuthorMark10\cmsAuthorMark11 \cmsinstskipNational Institute of Chemical Physics and Biophysics, Tallinn, Estonia
M. Kadastik, M. Müntel, M. Murumaa, M. Raidal, L. Rebane, A. Tiko \cmsinstskipDepartment of Physics, University of Helsinki, Helsinki, Finland
P. Eerola, G. Fedi, M. Voutilainen \cmsinstskipHelsinki Institute of Physics, Helsinki, Finland
J. Härkönen, A. Heikkinen, V. Karimäki, R. Kinnunen, M.J. Kortelainen, T. Lampén, K. Lassila-Perini, S. Lehti, T. Lindén, P. Luukka, T. Mäenpää, T. Peltola, E. Tuominen, J. Tuominiemi, E. Tuovinen, D. Ungaro, L. Wendland \cmsinstskipLappeenranta University of Technology, Lappeenranta, Finland
A. Korpela, T. Tuuva \cmsinstskipDSM/IRFU, CEA/Saclay, Gif-sur-Yvette, France
M. Besancon, S. Choudhury, F. Couderc, M. Dejardin, D. Denegri, B. Fabbro, J.L. Faure, F. Ferri, S. Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry, E. Locci, J. Malcles, L. Millischer, A. Nayak, J. Rander, A. Rosowsky, M. Titov \cmsinstskipLaboratoire Leprince-Ringuet, Ecole Polytechnique, IN2P3-CNRS, Palaiseau, France
S. Baffioni, F. Beaudette, L. Benhabib, L. Bianchini, M. Bluj\cmsAuthorMark12, P. Busson, C. Charlot, N. Daci, T. Dahms, M. Dalchenko, L. Dobrzynski, A. Florent, R. Granier de Cassagnac, M. Haguenauer, P. Miné, C. Mironov, I.N. Naranjo, M. Nguyen, C. Ochando, P. Paganini, D. Sabes, R. Salerno, Y. Sirois, C. Veelken, A. Zabi \cmsinstskipInstitut Pluridisciplinaire Hubert Curien, Université de Strasbourg, Université de Haute Alsace Mulhouse, CNRS/IN2P3, Strasbourg, France
J.-L. Agram\cmsAuthorMark13, J. Andrea, D. Bloch, D. Bodin, J.-M. Brom, M. Cardaci, E.C. Chabert, C. Collard, E. Conte\cmsAuthorMark13, F. Drouhin\cmsAuthorMark13, J.-C. Fontaine\cmsAuthorMark13, D. Gelé, U. Goerlach, P. Juillot, A.-C. Le Bihan, P. Van Hove \cmsinstskipUniversité de Lyon, Université Claude Bernard Lyon 1,  CNRS-IN2P3, Institut de Physique Nucléaire de Lyon, Villeurbanne, France
S. Beauceron, N. Beaupere, O. Bondu, G. Boudoul, S. Brochet, J. Chasserat, R. Chierici\cmsAuthorMark2, D. Contardo, P. Depasse, H. El Mamouni, J. Fay, S. Gascon, M. Gouzevitch, B. Ille, T. Kurca, M. Lethuillier, L. Mirabito, S. Perries, L. Sgandurra, V. Sordini, Y. Tschudi, P. Verdier, S. Viret \cmsinstskipInstitute of High Energy Physics and Informatization, Tbilisi State University, Tbilisi, Georgia
Z. Tsamalaidze\cmsAuthorMark14 \cmsinstskipRWTH Aachen University, I. Physikalisches Institut, Aachen, Germany
C. Autermann, S. Beranek, B. Calpas, M. Edelhoff, L. Feld, N. Heracleous, O. Hindrichs, R. Jussen, K. Klein, J. Merz, A. Ostapchuk, A. Perieanu, F. Raupach, J. Sammet, S. Schael, D. Sprenger, H. Weber, B. Wittmer, V. Zhukov\cmsAuthorMark15 \cmsinstskipRWTH Aachen University, III. Physikalisches Institut A,  Aachen, Germany
M. Ata, J. Caudron, E. Dietz-Laursonn, D. Duchardt, M. Erdmann, R. Fischer, A. Güth, T. Hebbeker, C. Heidemann, K. Hoepfner, D. Klingebiel, P. Kreuzer, M. Merschmeyer, A. Meyer, M. Olschewski, K. Padeken, P. Papacz, H. Pieta, H. Reithler, S.A. Schmitz, L. Sonnenschein, J. Steggemann, D. Teyssier, S. Thüer, M. Weber \cmsinstskipRWTH Aachen University, III. Physikalisches Institut B,  Aachen, Germany
M. Bontenackels, V. Cherepanov, Y. Erdogan, G. Flügge, H. Geenen, M. Geisler, W. Haj Ahmad, F. Hoehle, B. Kargoll, T. Kress, Y. Kuessel, J. Lingemann\cmsAuthorMark2, A. Nowack, I.M. Nugent, L. Perchalla, O. Pooth, P. Sauerland, A. Stahl \cmsinstskipDeutsches Elektronen-Synchrotron, Hamburg, Germany
M. Aldaya Martin, I. Asin, N. Bartosik, J. Behr, W. Behrenhoff, U. Behrens, M. Bergholz\cmsAuthorMark16, A. Bethani, K. Borras, A. Burgmeier, A. Cakir, L. Calligaris, A. Campbell, E. Castro, F. Costanza, D. Dammann, C. Diez Pardos, T. Dorland, G. Eckerlin, D. Eckstein, G. Flucke, A. Geiser, I. Glushkov, P. Gunnellini, S. Habib, J. Hauk, G. Hellwig, H. Jung, M. Kasemann, P. Katsas, C. Kleinwort, H. Kluge, A. Knutsson, M. Krämer, D. Krücker, E. Kuznetsova, W. Lange, J. Leonard, W. Lohmann\cmsAuthorMark16, B. Lutz, R. Mankel, I. Marfin, M. Marienfeld, I.-A. Melzer-Pellmann, A.B. Meyer, J. Mnich, A. Mussgiller, S. Naumann-Emme, O. Novgorodova, F. Nowak, J. Olzem, H. Perrey, A. Petrukhin, D. Pitzl, A. Raspereza, P.M. Ribeiro Cipriano, C. Riedl, E. Ron, M. Rosin, J. Salfeld-Nebgen, R. Schmidt\cmsAuthorMark16, T. Schoerner-Sadenius, N. Sen, A. Spiridonov, M. Stein, R. Walsh, C. Wissing \cmsinstskipUniversity of Hamburg, Hamburg, Germany
V. Blobel, H. Enderle, J. Erfle, U. Gebbert, M. Görner, M. Gosselink, J. Haller, T. Hermanns, R.S. Höing, K. Kaschube, G. Kaussen, H. Kirschenmann, R. Klanner, J. Lange, T. Peiffer, N. Pietsch, D. Rathjens, C. Sander, H. Schettler, P. Schleper, E. Schlieckau, A. Schmidt, M. Schröder, T. Schum, M. Seidel, J. Sibille\cmsAuthorMark17, V. Sola, H. Stadie, G. Steinbrück, J. Thomsen, L. Vanelderen \cmsinstskipInstitut für Experimentelle Kernphysik, Karlsruhe, Germany
C. Barth, C. Baus, J. Berger, C. Böser, T. Chwalek, W. De Boer, A. Descroix, A. Dierlamm, M. Feindt, M. Guthoff\cmsAuthorMark2, C. Hackstein, F. Hartmann\cmsAuthorMark2, T. Hauth\cmsAuthorMark2, M. Heinrich, H. Held, K.H. Hoffmann, U. Husemann, I. Katkov\cmsAuthorMark15, J.R. Komaragiri, P. Lobelle Pardo, D. Martschei, S. Mueller, Th. Müller, M. Niegel, A. Nürnberg, O. Oberst, A. Oehler, J. Ott, G. Quast, K. Rabbertz, F. Ratnikov, N. Ratnikova, S. Röcker, F.-P. Schilling, G. Schott, H.J. Simonis, F.M. Stober, D. Troendle, R. Ulrich, J. Wagner-Kuhr, S. Wayand, T. Weiler, M. Zeise \cmsinstskipInstitute of Nuclear and Particle Physics (INPP),  NCSR Demokritos, Aghia Paraskevi, Greece
G. Anagnostou, G. Daskalakis, T. Geralis, S. Kesisoglou, A. Kyriakis, D. Loukas, I. Manolakos, A. Markou, C. Markou, E. Ntomari \cmsinstskipUniversity of Athens, Athens, Greece
L. Gouskos, T.J. Mertzimekis, A. Panagiotou, N. Saoulidou \cmsinstskipUniversity of Ioánnina, Ioánnina, Greece
I. Evangelou, C. Foudas, P. Kokkas, N. Manthos, I. Papadopoulos \cmsinstskipKFKI Research Institute for Particle and Nuclear Physics, Budapest, Hungary
G. Bencze, C. Hajdu, P. Hidas, D. Horvath\cmsAuthorMark18, F. Sikler, V. Veszpremi, G. Vesztergombi\cmsAuthorMark19, A.J. Zsigmond \cmsinstskipInstitute of Nuclear Research ATOMKI, Debrecen, Hungary
N. Beni, S. Czellar, J. Molnar, J. Palinkas, Z. Szillasi \cmsinstskipUniversity of Debrecen, Debrecen, Hungary
J. Karancsi, P. Raics, Z.L. Trocsanyi, B. Ujvari \cmsinstskipPanjab University, Chandigarh, India
S.B. Beri, V. Bhatnagar, N. Dhingra, R. Gupta, M. Kaur, M.Z. Mehta, M. Mittal, N. Nishu, L.K. Saini, A. Sharma, J.B. Singh \cmsinstskipUniversity of Delhi, Delhi, India
Ashok Kumar, Arun Kumar, S. Ahuja, A. Bhardwaj, B.C. Choudhary, S. Malhotra, M. Naimuddin, K. Ranjan, P. Saxena, V. Sharma, R.K. Shivpuri \cmsinstskipSaha 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 \cmsinstskipBhabha Atomic Research Centre, Mumbai, India
A. Abdulsalam, D. Dutta, S. Kailas, V. Kumar, A.K. Mohanty\cmsAuthorMark2, L.M. Pant, P. Shukla \cmsinstskipTata Institute of Fundamental Research - EHEP, Mumbai, India
T. Aziz, R.M. Chatterjee, S. Ganguly, M. Guchait\cmsAuthorMark20, A. Gurtu\cmsAuthorMark21, M. Maity\cmsAuthorMark22, G. Majumder, K. Mazumdar, G.B. Mohanty, B. Parida, K. Sudhakar, N. Wickramage \cmsinstskipTata Institute of Fundamental Research - HECR, Mumbai, India
S. Banerjee, S. Dugad \cmsinstskipInstitute for Research in Fundamental Sciences (IPM),  Tehran, Iran
H. Arfaei\cmsAuthorMark23, H. Bakhshiansohi, S.M. Etesami\cmsAuthorMark24, A. Fahim\cmsAuthorMark23, M. Hashemi\cmsAuthorMark25, H. Hesari, A. Jafari, M. Khakzad, M. Mohammadi Najafabadi, S. Paktinat Mehdiabadi, B. Safarzadeh\cmsAuthorMark26, M. Zeinali \cmsinstskipINFN Sezione di Bari , Università di Bari , Politecnico di Bari ,  Bari, Italy
M. Abbrescia, L. Barbone, C. Calabria\cmsAuthorMark2, S.S. Chhibra, A. Colaleo, D. Creanza, N. De Filippis\cmsAuthorMark2, M. De Palma, L. Fiore, G. Iaselli, G. Maggi, M. Maggi, B. Marangelli, S. My, S. Nuzzo, N. Pacifico, A. Pompili, G. Pugliese, G. Selvaggi, L. Silvestris, G. Singh, R. Venditti, P. Verwilligen, G. Zito \cmsinstskipINFN Sezione di Bologna , Università di Bologna ,  Bologna, Italy
G. Abbiendi, A.C. Benvenuti, D. Bonacorsi, S. Braibant-Giacomelli, L. Brigliadori, P. Capiluppi, A. Castro, F.R. Cavallo, M. Cuffiani, G.M. Dallavalle, F. Fabbri, A. Fanfani, D. Fasanella, P. Giacomelli, C. Grandi, L. Guiducci, S. Marcellini, G. Masetti, M. Meneghelli\cmsAuthorMark2, A. Montanari, F.L. Navarria, F. Odorici, A. Perrotta, F. Primavera, A.M. Rossi, T. Rovelli, G.P. Siroli, N. Tosi, R. Travaglini \cmsinstskipINFN Sezione di Catania , Università di Catania ,  Catania, Italy
S. Albergo, G. Cappello, M. Chiorboli, S. Costa, R. Potenza, A. Tricomi, C. Tuve \cmsinstskipINFN Sezione di Firenze , Università di Firenze ,  Firenze, Italy
G. Barbagli, V. Ciulli, C. Civinini, R. D’Alessandro, E. Focardi, S. Frosali, E. Gallo, S. Gonzi, M. Meschini, S. Paoletti, G. Sguazzoni, A. Tropiano \cmsinstskipINFN Laboratori Nazionali di Frascati, Frascati, Italy
L. Benussi, S. Bianco, S. Colafranceschi\cmsAuthorMark27, F. Fabbri, D. Piccolo \cmsinstskipINFN Sezione di Genova , Università di Genova ,  Genova, Italy
P. Fabbricatore, R. Musenich, S. Tosi \cmsinstskipINFN Sezione di Milano-Bicocca , Università di Milano-Bicocca ,  Milano, Italy
A. Benaglia, F. De Guio, L. Di Matteo\cmsAuthorMark2, S. Fiorendi, S. Gennai\cmsAuthorMark2, A. Ghezzi, M.T. Lucchini\cmsAuthorMark2, S. Malvezzi, R.A. Manzoni, A. Martelli, A. Massironi, D. Menasce, L. Moroni, M. Paganoni, D. Pedrini, S. Ragazzi, N. Redaelli, T. Tabarelli de Fatis \cmsinstskipINFN Sezione di Napoli , Università di Napoli ’Federico II’ , Università della Basilicata (Potenza) , Università G. Marconi (Roma) ,  Napoli, Italy
S. Buontempo, N. Cavallo, A. De Cosa\cmsAuthorMark2, O. Dogangun, F. Fabozzi, A.O.M. Iorio, L. Lista, S. Meola\cmsAuthorMark28, M. Merola, P. Paolucci\cmsAuthorMark2 \cmsinstskipINFN Sezione di Padova , Università di Padova , Università di Trento (Trento) ,  Padova, Italy
P. Azzi, N. Bacchetta\cmsAuthorMark2, A. Branca\cmsAuthorMark2, R. Carlin, P. Checchia, T. Dorigo, F. Gasparini, U. Gasparini, A. Gozzelino, K. Kanishchev, S. Lacaprara, I. Lazzizzera, M. Margoni, A.T. Meneguzzo, J. Pazzini, N. Pozzobon, P. Ronchese, M. Sgaravatto, F. Simonetto, E. Torassa, M. Tosi, S. Vanini, P. Zotto, A. Zucchetta, G. Zumerle \cmsinstskipINFN Sezione di Pavia , Università di Pavia ,  Pavia, Italy
M. Gabusi, S.P. Ratti, C. Riccardi, P. Torre, P. Vitulo \cmsinstskipINFN Sezione di Perugia , Università di Perugia ,  Perugia, Italy
M. Biasini, G.M. Bilei, L. Fanò, P. Lariccia, G. Mantovani, M. Menichelli, A. Nappi, F. Romeo, A. Saha, A. Santocchia, A. Spiezia, S. Taroni \cmsinstskipINFN Sezione di Pisa , Università di Pisa , Scuola Normale Superiore di Pisa ,  Pisa, Italy
P. Azzurri, G. Bagliesi, J. Bernardini, T. Boccali, G. Broccolo, R. Castaldi, R.T. D’Agnolo\cmsAuthorMark2, R. Dell’Orso, F. Fiori\cmsAuthorMark2, L. Foà, A. Giassi, A. Kraan, F. Ligabue, T. Lomtadze, L. Martini\cmsAuthorMark29, A. Messineo, F. Palla, A. Rizzi, A.T. Serban\cmsAuthorMark30, P. Spagnolo, P. Squillacioti\cmsAuthorMark2, R. Tenchini, G. Tonelli, A. Venturi, P.G. Verdini \cmsinstskipINFN Sezione di Roma , Università di Roma ,  Roma, Italy
L. Barone, F. Cavallari, D. Del Re, M. Diemoz, C. Fanelli, M. Grassi\cmsAuthorMark2, E. Longo, P. Meridiani\cmsAuthorMark2, F. Micheli, S. Nourbakhsh, G. Organtini, R. Paramatti, S. Rahatlou, L. Soffi \cmsinstskipINFN Sezione di Torino , Università di Torino , Università del Piemonte Orientale (Novara) ,  Torino, Italy
N. Amapane, R. Arcidiacono, S. Argiro, M. Arneodo, C. Biino, N. Cartiglia, S. Casasso, M. Costa, N. Demaria, C. Mariotti\cmsAuthorMark2, S. Maselli, E. Migliore, V. Monaco, M. Musich\cmsAuthorMark2, M.M. Obertino, N. Pastrone, M. Pelliccioni, A. Potenza, A. Romero, M. Ruspa, R. Sacchi, A. Solano, A. Staiano \cmsinstskipINFN Sezione di Trieste , Università di Trieste ,  Trieste, Italy
S. Belforte, V. Candelise, M. Casarsa, F. Cossutti\cmsAuthorMark2, G. Della Ricca, B. Gobbo, M. Marone\cmsAuthorMark2, D. Montanino, A. Penzo, A. Schizzi \cmsinstskipKangwon National University, Chunchon, Korea
T.Y. Kim, S.K. Nam \cmsinstskipKyungpook National University, Daegu, Korea
S. Chang, D.H. Kim, G.N. Kim, D.J. Kong, H. Park, D.C. Son \cmsinstskipChonnam National University, Institute for Universe and Elementary Particles, Kwangju, Korea
J.Y. Kim, Zero J. Kim, S. Song \cmsinstskipKorea University, Seoul, Korea
S. Choi, D. Gyun, B. Hong, M. Jo, H. Kim, T.J. Kim, K.S. Lee, D.H. Moon, S.K. Park, Y. Roh \cmsinstskipUniversity of Seoul, Seoul, Korea
M. Choi, J.H. Kim, C. Park, I.C. Park, S. Park, G. Ryu \cmsinstskipSungkyunkwan University, Suwon, Korea
Y. Choi, Y.K. Choi, J. Goh, M.S. Kim, E. Kwon, B. Lee, J. Lee, S. Lee, H. Seo, I. Yu \cmsinstskipVilnius University, Vilnius, Lithuania
M.J. Bilinskas, I. Grigelionis, M. Janulis, A. Juodagalvis \cmsinstskipCentro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico
H. Castilla-Valdez, E. De La Cruz-Burelo, I. Heredia-de La Cruz, R. Lopez-Fernandez, J. Martínez-Ortega, A. Sanchez-Hernandez, L.M. Villasenor-Cendejas \cmsinstskipUniversidad Iberoamericana, Mexico City, Mexico
S. Carrillo Moreno, F. Vazquez Valencia \cmsinstskipBenemerita Universidad Autonoma de Puebla, Puebla, Mexico
H.A. Salazar Ibarguen \cmsinstskipUniversidad Autónoma de San Luis Potosí,  San Luis Potosí,  Mexico
E. Casimiro Linares, A. Morelos Pineda, M.A. Reyes-Santos \cmsinstskipUniversity of Auckland, Auckland, New Zealand
D. Krofcheck \cmsinstskipUniversity of Canterbury, Christchurch, New Zealand
A.J. Bell, P.H. Butler, R. Doesburg, S. Reucroft, H. Silverwood \cmsinstskipNational Centre for Physics, Quaid-I-Azam University, Islamabad, Pakistan
M. Ahmad, M.I. Asghar, J. Butt, H.R. Hoorani, S. Khalid, W.A. Khan, T. Khurshid, S. Qazi, M.A. Shah, M. Shoaib \cmsinstskipNational Centre for Nuclear Research, Swierk, Poland
H. Bialkowska, B. Boimska, T. Frueboes, M. Górski, M. Kazana, K. Nawrocki, K. Romanowska-Rybinska, M. Szleper, G. Wrochna, P. Zalewski \cmsinstskipInstitute of Experimental Physics, Faculty of Physics, University of Warsaw, Warsaw, Poland
G. Brona, K. Bunkowski, M. Cwiok, W. Dominik, K. Doroba, A. Kalinowski, M. Konecki, J. Krolikowski, M. Misiura, W. Wolszczak \cmsinstskipLaboratório de Instrumentação e Física Experimental de Partículas, Lisboa, Portugal
N. Almeida, P. Bargassa, A. David, P. Faccioli, P.G. Ferreira Parracho, M. Gallinaro, J. Seixas, J. Varela, P. Vischia \cmsinstskipJoint Institute for Nuclear Research, Dubna, Russia
P. Bunin, I. Golutvin, I. Gorbunov, A. Kamenev, V. Karjavin, V. Konoplyanikov, G. Kozlov, A. Lanev, A. Malakhov, P. Moisenz, V. Palichik, V. Perelygin, S. Shmatov, S. Shulha, V. Smirnov, A. Volodko, A. Zarubin \cmsinstskipPetersburg Nuclear Physics Institute, Gatchina (St. Petersburg),  Russia
S. Evstyukhin, V. Golovtsov, Y. Ivanov, V. Kim, P. Levchenko, V. Murzin, V. Oreshkin, I. Smirnov, V. Sulimov, L. Uvarov, S. Vavilov, A. Vorobyev, An. Vorobyev \cmsinstskipInstitute for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, M. Kirsanov, N. Krasnikov, V. Matveev, A. Pashenkov, D. Tlisov, A. Toropin \cmsinstskipInstitute for Theoretical and Experimental Physics, Moscow, Russia
V. Epshteyn, M. Erofeeva, V. Gavrilov, M. Kossov, N. Lychkovskaya, V. Popov, G. Safronov, S. Semenov, I. Shreyber, V. Stolin, E. Vlasov, A. Zhokin \cmsinstskipP.N. Lebedev Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin, I. Dremin, M. Kirakosyan, A. Leonidov, G. Mesyats, S.V. Rusakov, A. Vinogradov \cmsinstskipSkobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow, Russia
A. Belyaev, E. Boos, M. Dubinin\cmsAuthorMark4, L. Dudko, A. Ershov, A. Gribushin, V. Klyukhin, O. Kodolova, I. Lokhtin, A. Markina, S. Obraztsov, M. Perfilov, S. Petrushanko, A. Popov, L. Sarycheva, V. Savrin, A. Snigirev \cmsinstskipState Research Center of Russian Federation, Institute for High Energy Physics, Protvino, Russia
I. Azhgirey, I. Bayshev, S. Bitioukov, V. Grishin\cmsAuthorMark2, V. Kachanov, D. Konstantinov, V. Krychkine, V. Petrov, R. Ryutin, A. Sobol, L. Tourtchanovitch, S. Troshin, N. Tyurin, A. Uzunian, A. Volkov \cmsinstskipUniversity of Belgrade, Faculty of Physics and Vinca Institute of Nuclear Sciences, Belgrade, Serbia
P. Adzic\cmsAuthorMark31, M. Djordjevic, M. Ekmedzic, D. Krpic\cmsAuthorMark31, J. Milosevic \cmsinstskipCentro de Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT),  Madrid, Spain
M. Aguilar-Benitez, J. Alcaraz Maestre, P. Arce, C. Battilana, E. Calvo, M. Cerrada, M. Chamizo Llatas, N. Colino, B. De La Cruz, A. Delgado Peris, D. Domínguez Vázquez, C. Fernandez Bedoya, J.P. Fernández Ramos, A. Ferrando, J. Flix, M.C. Fouz, P. Garcia-Abia, O. Gonzalez Lopez, S. Goy Lopez, J.M. Hernandez, M.I. Josa, G. Merino, J. Puerta Pelayo, A. Quintario Olmeda, I. Redondo, L. Romero, J. Santaolalla, M.S. Soares, C. Willmott \cmsinstskipUniversidad Autónoma de Madrid, Madrid, Spain
C. Albajar, G. Codispoti, J.F. de Trocóniz \cmsinstskipUniversidad de Oviedo, Oviedo, Spain
H. Brun, J. Cuevas, J. Fernandez Menendez, S. Folgueras, I. Gonzalez Caballero, L. Lloret Iglesias, J. Piedra Gomez \cmsinstskipInstituto de Física de Cantabria (IFCA),  CSIC-Universidad de Cantabria, Santander, Spain
J.A. Brochero Cifuentes, I.J. Cabrillo, A. Calderon, S.H. Chuang, J. Duarte Campderros, M. Felcini\cmsAuthorMark32, M. Fernandez, G. Gomez, J. Gonzalez Sanchez, A. Graziano, C. Jorda, A. Lopez Virto, J. Marco, R. Marco, C. Martinez Rivero, F. Matorras, F.J. Munoz Sanchez, T. Rodrigo, A.Y. Rodríguez-Marrero, A. Ruiz-Jimeno, L. Scodellaro, I. Vila, R. Vilar Cortabitarte \cmsinstskipCERN, European Organization for Nuclear Research, Geneva, Switzerland
D. Abbaneo, E. Auffray, G. Auzinger, M. Bachtis, P. Baillon, A.H. Ball, D. Barney, J.F. Benitez, C. Bernet\cmsAuthorMark5, G. Bianchi, P. Bloch, A. Bocci, A. Bonato, C. Botta, H. Breuker, T. Camporesi, G. Cerminara, T. Christiansen, J.A. Coarasa Perez, D. D’Enterria, A. Dabrowski, A. De Roeck, S. De Visscher, S. Di Guida, M. Dobson, N. Dupont-Sagorin, A. Elliott-Peisert, B. Frisch, W. Funk, G. Georgiou, M. Giffels, D. Gigi, K. Gill, D. Giordano, M. Girone, M. Giunta, F. Glege, R. Gomez-Reino Garrido, P. Govoni, S. Gowdy, R. Guida, J. Hammer, M. Hansen, P. Harris, C. Hartl, J. Harvey, B. Hegner, A. Hinzmann, V. Innocente, P. Janot, K. Kaadze, E. Karavakis, K. Kousouris, P. Lecoq, Y.-J. Lee, P. Lenzi, C. Lourenço, N. Magini, T. Mäki, M. Malberti, L. Malgeri, M. Mannelli, L. Masetti, F. Meijers, S. Mersi, E. Meschi, R. Moser, M. Mulders, P. Musella, E. Nesvold, L. Orsini, E. Palencia Cortezon, E. Perez, L. Perrozzi, A. Petrilli, A. Pfeiffer, M. Pierini, M. Pimiä, D. Piparo, G. Polese, L. Quertenmont, A. Racz, W. Reece, J. Rodrigues Antunes, G. Rolandi\cmsAuthorMark33, C. Rovelli\cmsAuthorMark34, M. Rovere, H. Sakulin, F. Santanastasio, C. Schäfer, C. Schwick, I. Segoni, S. Sekmen, A. Sharma, P. Siegrist, P. Silva, M. Simon, P. Sphicas\cmsAuthorMark35, D. Spiga, A. Tsirou, G.I. Veres\cmsAuthorMark19, J.R. Vlimant, H.K. Wöhri, S.D. Worm\cmsAuthorMark36, W.D. Zeuner \cmsinstskipPaul Scherrer Institut, Villigen, Switzerland
W. Bertl, K. Deiters, W. Erdmann, K. Gabathuler, R. Horisberger, Q. Ingram, H.C. Kaestli, S. König, D. Kotlinski, U. Langenegger, F. Meier, D. Renker, T. Rohe \cmsinstskipInstitute for Particle Physics, ETH Zurich, Zurich, Switzerland
F. Bachmair, L. Bäni, P. Bortignon, M.A. Buchmann, B. Casal, N. Chanon, A. Deisher, G. Dissertori, M. Dittmar, M. Donegà, M. Dünser, P. Eller, J. Eugster, K. Freudenreich, C. Grab, D. Hits, P. Lecomte, W. Lustermann, A.C. Marini, P. Martinez Ruiz del Arbol, N. Mohr, F. Moortgat, C. Nägeli\cmsAuthorMark37, P. Nef, F. Nessi-Tedaldi, F. Pandolfi, L. Pape, F. Pauss, M. Peruzzi, F.J. Ronga, M. Rossini, L. Sala, A.K. Sanchez, A. Starodumov\cmsAuthorMark38, B. Stieger, M. Takahashi, L. Tauscher, A. Thea, K. Theofilatos, D. Treille, C. Urscheler, R. Wallny, H.A. Weber, L. Wehrli \cmsinstskipUniversität Zürich, Zurich, Switzerland
C. Amsler\cmsAuthorMark39, V. Chiochia, C. Favaro, M. Ivova Rikova, B. Kilminster, B. Millan Mejias, P. Otiougova, P. Robmann, H. Snoek, S. Tupputi, M. Verzetti \cmsinstskipNational Central University, Chung-Li, Taiwan
Y.H. Chang, K.H. Chen, C. Ferro, C.M. Kuo, S.W. Li, W. Lin, Y.J. Lu, A.P. Singh, R. Volpe, S.S. Yu \cmsinstskipNational Taiwan University (NTU),  Taipei, Taiwan
P. Bartalini, P. Chang, Y.H. Chang, Y.W. Chang, Y. Chao, K.F. Chen, C. Dietz, U. Grundler, W.-S. Hou, Y. Hsiung, K.Y. Kao, Y.J. Lei, R.-S. Lu, D. Majumder, E. Petrakou, X. Shi, J.G. Shiu, Y.M. Tzeng, X. Wan, M. Wang \cmsinstskipChulalongkorn University, Bangkok, Thailand
B. Asavapibhop, E. Simili, N. Srimanobhas, N. Suwonjandee \cmsinstskipCukurova University, Adana, Turkey
A. Adiguzel, M.N. Bakirci\cmsAuthorMark40, S. Cerci\cmsAuthorMark41, C. Dozen, I. Dumanoglu, E. Eskut, S. Girgis, G. Gokbulut, E. Gurpinar, I. Hos, E.E. Kangal, T. Karaman, G. Karapinar\cmsAuthorMark42, A. Kayis Topaksu, G. Onengut, K. Ozdemir, S. Ozturk\cmsAuthorMark43, A. Polatoz, K. Sogut\cmsAuthorMark44, D. Sunar Cerci\cmsAuthorMark41, B. Tali\cmsAuthorMark41, H. Topakli\cmsAuthorMark40, L.N. Vergili, M. Vergili \cmsinstskipMiddle East Technical University, Physics Department, Ankara, Turkey
I.V. Akin, T. Aliev, B. Bilin, S. Bilmis, M. Deniz, H. Gamsizkan, A.M. Guler, K. Ocalan, A. Ozpineci, M. Serin, R. Sever, U.E. Surat, M. Yalvac, E. Yildirim, M. Zeyrek \cmsinstskipBogazici University, Istanbul, Turkey
E. Gülmez, B. Isildak\cmsAuthorMark45, M. Kaya\cmsAuthorMark46, O. Kaya\cmsAuthorMark46, S. Ozkorucuklu\cmsAuthorMark47, N. Sonmez\cmsAuthorMark48 \cmsinstskipIstanbul Technical University, Istanbul, Turkey
H. Bahtiyar\cmsAuthorMark49, E. Barlas, K. Cankocak, Y.O. Günaydin\cmsAuthorMark50, F.I. Vardarlı, M. Yücel \cmsinstskipNational Scientific Center, Kharkov Institute of Physics and Technology, Kharkov, Ukraine
L. Levchuk \cmsinstskipUniversity 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, L. Kreczko, S. Metson, D.M. Newbold\cmsAuthorMark36, K. Nirunpong, A. Poll, S. Senkin, V.J. Smith, T. Williams \cmsinstskipRutherford Appleton Laboratory, Didcot, United Kingdom
L. Basso\cmsAuthorMark51, K.W. Bell, A. Belyaev\cmsAuthorMark51, C. Brew, R.M. Brown, D.J.A. Cockerill, J.A. Coughlan, K. Harder, S. Harper, J. Jackson, B.W. Kennedy, E. Olaiya, D. Petyt, B.C. Radburn-Smith, C.H. Shepherd-Themistocleous, I.R. Tomalin, W.J. Womersley \cmsinstskipImperial College, London, United Kingdom
R. Bainbridge, G. Ball, R. Beuselinck, O. Buchmuller, D. Colling, N. Cripps, M. Cutajar, P. Dauncey, G. Davies, M. Della Negra, W. Ferguson, J. Fulcher, D. Futyan, A. Gilbert, A. Guneratne Bryer, G. Hall, Z. Hatherell, J. Hays, G. Iles, M. Jarvis, G. Karapostoli, M. Kenzie, L. Lyons, A.-M. Magnan, J. Marrouche, B. Mathias, R. Nandi, J. Nash, A. Nikitenko\cmsAuthorMark38, J. Pela, M. Pesaresi, K. Petridis, M. Pioppi\cmsAuthorMark52, D.M. Raymond, S. Rogerson, A. Rose, C. Seez, P. Sharp, A. Sparrow, M. Stoye, A. Tapper, M. Vazquez Acosta, T. Virdee, S. Wakefield, N. Wardle, T. Whyntie \cmsinstskipBrunel University, Uxbridge, United Kingdom
M. Chadwick, 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 \cmsinstskipBaylor University, Waco, USA
K. Hatakeyama, H. Liu, T. Scarborough \cmsinstskipThe University of Alabama, Tuscaloosa, USA
O. Charaf, S.I. Cooper, C. Henderson, P. Rumerio \cmsinstskipBoston University, Boston, USA
A. Avetisyan, T. Bose, C. Fantasia, A. Heister, P. Lawson, D. Lazic, J. Rohlf, D. Sperka, J. St. John, L. Sulak \cmsinstskipBrown University, Providence, USA
J. Alimena, S. Bhattacharya, G. Christopher, D. Cutts, Z. Demiragli, A. Ferapontov, A. Garabedian, U. Heintz, S. Jabeen, G. Kukartsev, E. Laird, G. Landsberg, M. Luk, M. Narain, M. Segala, T. Sinthuprasith, T. Speer \cmsinstskipUniversity of California, Davis, Davis, USA
R. Breedon, G. Breto, M. Calderon De La Barca Sanchez, M. Caulfield, S. Chauhan, M. Chertok, J. Conway, R. Conway, P.T. Cox, J. Dolen, R. Erbacher, M. Gardner, R. Houtz, W. Ko, A. Kopecky, R. Lander, O. Mall, T. Miceli, R. Nelson, D. Pellett, F. Ricci-Tam, B. Rutherford, M. Searle, J. Smith, M. Squires, M. Tripathi, R. Vasquez Sierra, R. Yohay \cmsinstskipUniversity of California, Los Angeles, USA
V. Andreev, D. Cline, R. Cousins, J. Duris, S. Erhan, P. Everaerts, C. Farrell, J. Hauser, M. Ignatenko, C. Jarvis, G. Rakness, P. Schlein, P. Traczyk, V. Valuev, M. Weber \cmsinstskipUniversity of California, Riverside, Riverside, USA
J. Babb, R. Clare, M.E. Dinardo, J. Ellison, J.W. Gary, F. Giordano, G. Hanson, H. Liu, O.R. Long, A. Luthra, H. Nguyen, S. Paramesvaran, J. Sturdy, S. Sumowidagdo, R. Wilken, S. Wimpenny \cmsinstskipUniversity of California, San Diego, La Jolla, USA
W. Andrews, J.G. Branson, G.B. Cerati, S. Cittolin, D. Evans, A. Holzner, R. Kelley, M. Lebourgeois, J. Letts, I. Macneill, B. Mangano, S. Padhi, C. Palmer, G. Petrucciani, M. Pieri, M. Sani, V. Sharma, S. Simon, E. Sudano, M. Tadel, Y. Tu, A. Vartak, S. Wasserbaech\cmsAuthorMark53, F. Würthwein, A. Yagil, J. Yoo \cmsinstskipUniversity of California, Santa Barbara, Santa Barbara, USA
D. Barge, R. Bellan, C. Campagnari, M. D’Alfonso, T. Danielson, K. Flowers, P. Geffert, C. George, F. Golf, J. Incandela, C. Justus, P. Kalavase, D. Kovalskyi, V. Krutelyov, S. Lowette, R. Magaña Villalba, N. Mccoll, V. Pavlunin, J. Ribnik, J. Richman, R. Rossin, D. Stuart, W. To, C. West \cmsinstskipCalifornia Institute of Technology, Pasadena, USA
A. Apresyan, A. Bornheim, J. Bunn, Y. Chen, E. Di Marco, J. Duarte, M. Gataullin, D. Kcira, Y. Ma, A. Mott, H.B. Newman, C. Rogan, M. Spiropulu, V. Timciuc, J. Veverka, R. Wilkinson, S. Xie, Y. Yang, R.Y. Zhu \cmsinstskipCarnegie Mellon University, Pittsburgh, USA
V. Azzolini, A. Calamba, R. Carroll, T. Ferguson, Y. Iiyama, D.W. Jang, Y.F. Liu, M. Paulini, H. Vogel, I. Vorobiev \cmsinstskipUniversity of Colorado at Boulder, Boulder, USA
J.P. Cumalat, B.R. Drell, W.T. Ford, A. Gaz, E. Luiggi Lopez, J.G. Smith, K. Stenson, K.A. Ulmer, S.R. Wagner \cmsinstskipCornell University, Ithaca, USA
J. Alexander, A. Chatterjee, N. Eggert, L.K. Gibbons, B. Heltsley, W. Hopkins, A. Khukhunaishvili, B. Kreis, N. Mirman, G. Nicolas Kaufman, J.R. Patterson, A. Ryd, E. Salvati, W. Sun, W.D. Teo, J. Thom, J. Thompson, J. Tucker, Y. Weng, L. Winstrom, P. Wittich \cmsinstskipFairfield University, Fairfield, USA
D. Winn \cmsinstskipFermi National Accelerator Laboratory, Batavia, USA
S. Abdullin, M. Albrow, J. Anderson, G. Apollinari, L.A.T. Bauerdick, A. Beretvas, J. Berryhill, P.C. Bhat, K. Burkett, J.N. Butler, V. Chetluru, H.W.K. Cheung, F. Chlebana, S. Cihangir, V.D. Elvira, I. Fisk, J. Freeman, Y. Gao, D. Green, O. Gutsche, J. Hanlon, R.M. Harris, J. Hirschauer, B. Hooberman, S. Jindariani, M. Johnson, U. Joshi, B. Klima, S. Kunori, S. Kwan, C. Leonidopoulos\cmsAuthorMark54, J. Linacre, D. Lincoln, R. Lipton, J. Lykken, K. Maeshima, J.M. Marraffino, V.I. Martinez Outschoorn, S. Maruyama, D. Mason, P. McBride, K. Mishra, S. Mrenna, Y. Musienko\cmsAuthorMark55, C. Newman-Holmes, V. O’Dell, E. Sexton-Kennedy, S. Sharma, W.J. Spalding, L. Spiegel, L. Taylor, S. Tkaczyk, N.V. Tran, L. Uplegger, E.W. Vaandering, R. Vidal, J. Whitmore, W. Wu, F. Yang, J.C. Yun \cmsinstskipUniversity of Florida, Gainesville, USA
D. Acosta, P. Avery, D. Bourilkov, M. Chen, T. Cheng, S. Das, M. De Gruttola, G.P. Di Giovanni, D. Dobur, A. Drozdetskiy, R.D. Field, M. Fisher, Y. Fu, I.K. Furic, J. Gartner, J. Hugon, B. Kim, J. Konigsberg, A. Korytov, A. Kropivnitskaya, T. Kypreos, J.F. Low, K. Matchev, P. Milenovic\cmsAuthorMark56, G. Mitselmakher, L. Muniz, M. Park, R. Remington, A. Rinkevicius, P. Sellers, N. Skhirtladze, M. Snowball, J. Yelton, M. Zakaria \cmsinstskipFlorida International University, Miami, USA
V. Gaultney, S. Hewamanage, L.M. Lebolo, S. Linn, P. Markowitz, G. Martinez, J.L. Rodriguez \cmsinstskipFlorida State University, Tallahassee, USA
T. Adams, A. Askew, J. Bochenek, J. Chen, B. Diamond, S.V. Gleyzer, J. Haas, S. Hagopian, V. Hagopian, M. Jenkins, K.F. Johnson, H. Prosper, V. Veeraraghavan, M. Weinberg \cmsinstskipFlorida Institute of Technology, Melbourne, USA
M.M. Baarmand, B. Dorney, M. Hohlmann, H. Kalakhety, I. Vodopiyanov, F. Yumiceva \cmsinstskipUniversity of Illinois at Chicago (UIC),  Chicago, USA
M.R. Adams, L. Apanasevich, Y. Bai, V.E. Bazterra, R.R. Betts, I. Bucinskaite, J. Callner, R. Cavanaugh, O. Evdokimov, L. Gauthier, C.E. Gerber, D.J. Hofman, S. Khalatyan, F. Lacroix, C. O’Brien, C. Silkworth, D. Strom, P. Turner, N. Varelas \cmsinstskipThe University of Iowa, Iowa City, USA
U. Akgun, E.A. Albayrak, B. Bilki\cmsAuthorMark57, W. Clarida, F. Duru, S. Griffiths, J.-P. Merlo, H. Mermerkaya\cmsAuthorMark58, A. Mestvirishvili, A. Moeller, J. Nachtman, C.R. Newsom, E. Norbeck, Y. Onel, F. Ozok\cmsAuthorMark49, S. Sen, P. Tan, E. Tiras, J. Wetzel, T. Yetkin\cmsAuthorMark59, K. Yi \cmsinstskipJohns Hopkins University, Baltimore, USA
B.A. Barnett, B. Blumenfeld, S. Bolognesi, D. Fehling, G. Giurgiu, A.V. Gritsan, G. Hu, P. Maksimovic, M. Swartz, A. Whitbeck \cmsinstskipThe University of Kansas, Lawrence, USA
P. Baringer, A. Bean, G. Benelli, R.P. Kenny Iii, M. Murray, D. Noonan, S. Sanders, R. Stringer, G. Tinti, J.S. Wood \cmsinstskipKansas State University, Manhattan, USA
A.F. Barfuss, T. Bolton, I. Chakaberia, A. Ivanov, S. Khalil, M. Makouski, Y. Maravin, S. Shrestha, I. Svintradze \cmsinstskipLawrence Livermore National Laboratory, Livermore, USA
J. Gronberg, D. Lange, F. Rebassoo, D. Wright \cmsinstskipUniversity of Maryland, College Park, USA
A. Baden, B. Calvert, S.C. Eno, J.A. Gomez, N.J. Hadley, R.G. Kellogg, M. Kirn, T. Kolberg, Y. Lu, M. Marionneau, A.C. Mignerey, K. Pedro, A. Peterman, A. Skuja, J. Temple, M.B. Tonjes, S.C. Tonwar \cmsinstskipMassachusetts Institute of Technology, Cambridge, USA
A. Apyan, G. Bauer, J. Bendavid, W. Busza, E. Butz, I.A. Cali, M. Chan, V. Dutta, G. Gomez Ceballos, M. Goncharov, Y. Kim, M. Klute, K. Krajczar\cmsAuthorMark60, A. Levin, P.D. Luckey, T. Ma, S. Nahn, C. Paus, D. Ralph, C. Roland, G. Roland, M. Rudolph, G.S.F. Stephans, F. Stöckli, K. Sumorok, K. Sung, D. Velicanu, E.A. Wenger, R. Wolf, B. Wyslouch, M. Yang, Y. Yilmaz, A.S. Yoon, M. Zanetti, V. Zhukova \cmsinstskipUniversity of Minnesota, Minneapolis, USA
B. Dahmes, A. De Benedetti, G. Franzoni, A. Gude, J. Haupt, S.C. Kao, K. Klapoetke, Y. Kubota, J. Mans, N. Pastika, R. Rusack, M. Sasseville, A. Singovsky, N. Tambe, J. Turkewitz \cmsinstskipUniversity of Mississippi, Oxford, USA
L.M. Cremaldi, R. Kroeger, L. Perera, R. Rahmat, D.A. Sanders \cmsinstskipUniversity of Nebraska-Lincoln, Lincoln, USA
E. Avdeeva, K. Bloom, S. Bose, D.R. Claes, A. Dominguez, M. Eads, J. Keller, I. Kravchenko, J. Lazo-Flores, S. Malik, G.R. Snow \cmsinstskipState University of New York at Buffalo, Buffalo, USA
A. Godshalk, I. Iashvili, S. Jain, A. Kharchilava, A. Kumar, S. Rappoccio, Z. Wan \cmsinstskipNortheastern University, Boston, USA
G. Alverson, E. Barberis, D. Baumgartel, M. Chasco, J. Haley, D. Nash, T. Orimoto, D. Trocino, D. Wood, J. Zhang \cmsinstskipNorthwestern University, Evanston, USA
A. Anastassov, K.A. Hahn, A. Kubik, L. Lusito, N. Mucia, N. Odell, R.A. Ofierzynski, B. Pollack, A. Pozdnyakov, M. Schmitt, S. Stoynev, M. Velasco, S. Won \cmsinstskipUniversity of Notre Dame, Notre Dame, USA
D. Berry, A. Brinkerhoff, K.M. Chan, M. Hildreth, C. Jessop, D.J. Karmgard, J. Kolb, K. Lannon, W. Luo, S. Lynch, N. Marinelli, D.M. Morse, T. Pearson, M. Planer, R. Ruchti, J. Slaunwhite, N. Valls, M. Wayne, M. Wolf \cmsinstskipThe Ohio State University, Columbus, USA
L. Antonelli, B. Bylsma, L.S. Durkin, C. Hill, R. Hughes, K. Kotov, T.Y. Ling, D. Puigh, M. Rodenburg, C. Vuosalo, G. Williams, B.L. Winer \cmsinstskipPrinceton University, Princeton, USA
E. Berry, P. Elmer, V. Halyo, P. Hebda, J. Hegeman, A. Hunt, P. Jindal, S.A. Koay, D. Lopes Pegna, P. Lujan, D. Marlow, T. Medvedeva, M. Mooney, J. Olsen, P. Piroué, X. Quan, A. Raval, H. Saka, D. Stickland, C. Tully, J.S. Werner, S.C. Zenz, A. Zuranski \cmsinstskipUniversity of Puerto Rico, Mayaguez, USA
E. Brownson, A. Lopez, H. Mendez, J.E. Ramirez Vargas \cmsinstskipPurdue University, West Lafayette, USA
E. Alagoz, V.E. Barnes, D. Benedetti, G. Bolla, D. Bortoletto, M. De Mattia, A. Everett, Z. Hu, M. Jones, O. Koybasi, M. Kress, A.T. Laasanen, N. Leonardo, V. Maroussov, P. Merkel, D.H. Miller, N. Neumeister, I. Shipsey, D. Silvers, A. Svyatkovskiy, M. Vidal Marono, H.D. Yoo, J. Zablocki, Y. Zheng \cmsinstskipPurdue University Calumet, Hammond, USA
S. Guragain, N. Parashar \cmsinstskipRice University, Houston, USA
A. Adair, B. Akgun, C. Boulahouache, K.M. Ecklund, F.J.M. Geurts, W. Li, B.P. Padley, R. Redjimi, J. Roberts, J. Zabel \cmsinstskipUniversity of Rochester, Rochester, USA
B. Betchart, A. Bodek, Y.S. Chung, R. Covarelli, P. de Barbaro, R. Demina, Y. Eshaq, T. Ferbel, A. Garcia-Bellido, P. Goldenzweig, J. Han, A. Harel, D.C. Miner, D. Vishnevskiy, M. Zielinski \cmsinstskipThe Rockefeller University, New York, USA
A. Bhatti, R. Ciesielski, L. Demortier, K. Goulianos, G. Lungu, S. Malik, C. Mesropian \cmsinstskipRutgers, The State University of New Jersey, Piscataway, USA
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, A. Lath, S. Panwalkar, M. Park, R. Patel, V. Rekovic, J. Robles, K. Rose, S. Salur, S. Schnetzer, C. Seitz, S. Somalwar, R. Stone, S. Thomas, M. Walker \cmsinstskipUniversity of Tennessee, Knoxville, USA
G. Cerizza, M. Hollingsworth, S. Spanier, Z.C. Yang, A. York \cmsinstskipTexas A&M University, College Station, USA
R. Eusebi, W. Flanagan, J. Gilmore, T. Kamon\cmsAuthorMark61, V. Khotilovich, R. Montalvo, I. Osipenkov, Y. Pakhotin, A. Perloff, J. Roe, A. Safonov, T. Sakuma, S. Sengupta, I. Suarez, A. Tatarinov, D. Toback \cmsinstskipTexas Tech University, Lubbock, USA
N. Akchurin, J. Damgov, C. Dragoiu, P.R. Dudero, C. Jeong, K. Kovitanggoon, S.W. Lee, T. Libeiro, I. Volobouev \cmsinstskipVanderbilt University, Nashville, USA
E. Appelt, A.G. Delannoy, C. Florez, S. Greene, A. Gurrola, W. Johns, P. Kurt, C. Maguire, A. Melo, M. Sharma, P. Sheldon, B. Snook, S. Tuo, J. Velkovska \cmsinstskipUniversity of Virginia, Charlottesville, USA
M.W. Arenton, M. Balazs, S. Boutle, B. Cox, B. Francis, J. Goodell, R. Hirosky, A. Ledovskoy, C. Lin, C. Neu, J. Wood \cmsinstskipWayne State University, Detroit, USA
S. Gollapinni, R. Harr, P.E. Karchin, C. Kottachchi Kankanamge Don, P. Lamichhane, A. Sakharov \cmsinstskipUniversity of Wisconsin, Madison, USA
M. Anderson, D.A. Belknap, L. Borrello, D. Carlsmith, M. Cepeda, S. Dasu, E. Friis, L. Gray, K.S. Grogg, M. Grothe, R. Hall-Wilton, M. Herndon, A. Hervé, P. Klabbers, J. Klukas, A. Lanaro, C. Lazaridis, R. Loveless, A. Mohapatra, M.U. Mozer, I. Ojalvo, F. Palmonari, G.A. Pierro, I. Ross, A. Savin, W.H. Smith, J. Swanson \cmsinstskip†: Deceased
1:  Also at Vienna University of Technology, Vienna, Austria
2:  Also at CERN, European Organization for Nuclear Research, Geneva, Switzerland
3:  Also at National Institute of Chemical Physics and Biophysics, Tallinn, Estonia
4:  Also at California Institute of Technology, Pasadena, USA
5:  Also at Laboratoire Leprince-Ringuet, Ecole Polytechnique, IN2P3-CNRS, Palaiseau, France
6:  Also at Suez Canal University, Suez, Egypt
7:  Also at Zewail City of Science and Technology, Zewail, Egypt
8:  Also at Cairo University, Cairo, Egypt
9:  Also at Fayoum University, El-Fayoum, Egypt
10: Also at British University in Egypt, Cairo, Egypt
11: Now at Ain Shams University, Cairo, Egypt
12: Also at National Centre for Nuclear Research, Swierk, Poland
13: Also at Université de Haute Alsace, Mulhouse, France
14: Also at Joint Institute for Nuclear Research, Dubna, Russia
15: Also at Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow, Russia
16: Also at Brandenburg University of Technology, Cottbus, Germany
17: Also at The University of Kansas, Lawrence, USA
18: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary
19: Also at Eötvös Loránd University, Budapest, Hungary
20: Also at Tata Institute of Fundamental Research - HECR, Mumbai, India
21: Now at King Abdulaziz University, Jeddah, Saudi Arabia
22: Also at University of Visva-Bharati, Santiniketan, India
23: Also at Sharif University of Technology, Tehran, Iran
24: Also at Isfahan University of Technology, Isfahan, Iran
25: Also at Shiraz University, Shiraz, Iran
26: Also at Plasma Physics Research Center, Science and Research Branch, Islamic Azad University, Tehran, Iran
27: Also at Facoltà Ingegneria, Università di Roma, Roma, Italy
28: Also at Università degli Studi Guglielmo Marconi, Roma, Italy
29: Also at Università degli Studi di Siena, Siena, Italy
30: Also at University of Bucharest, Faculty of Physics, Bucuresti-Magurele, Romania
31: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia
32: Also at University of California, Los Angeles, USA
33: Also at Scuola Normale e Sezione dell’INFN, Pisa, Italy
34: Also at INFN Sezione di Roma, Roma, Italy
35: Also at University of Athens, Athens, Greece
36: Also at Rutherford Appleton Laboratory, Didcot, United Kingdom
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 Izmir Institute of Technology, Izmir, Turkey
43: Also at The University of Iowa, Iowa City, USA
44: Also at Mersin University, Mersin, Turkey
45: Also at Ozyegin University, Istanbul, Turkey
46: Also at Kafkas University, Kars, Turkey
47: Also at Suleyman Demirel University, Isparta, Turkey
48: Also at Ege University, Izmir, Turkey
49: Also at Mimar Sinan University, Istanbul, Istanbul, Turkey
50: Also at Kahramanmaras Sütcü Imam University, Kahramanmaras, Turkey
51: Also at School of Physics and Astronomy, University of Southampton, Southampton, United Kingdom
52: Also at INFN Sezione di Perugia; Università di Perugia, Perugia, Italy
53: Also at Utah Valley University, Orem, USA
54: Now at University of Edinburgh, Scotland, Edinburgh, United Kingdom
55: Also at Institute for Nuclear Research, Moscow, Russia
56: Also at University of Belgrade, Faculty of Physics and Vinca Institute of Nuclear Sciences, Belgrade, Serbia
57: Also at Argonne National Laboratory, Argonne, USA
58: Also at Erzincan University, Erzincan, Turkey
59: Also at Yildiz Technical University, Istanbul, Turkey
60: Also at KFKI Research Institute for Particle and Nuclear Physics, Budapest, Hungary
61: Also at Kyungpook National University, Daegu, Korea

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