Search for t t-bar resonances in highly boosted lepton+jets and fully hadronic final states in proton-proton collisions at sqrt(s) = 13 TeV
EEUROPEAN ORGANIZATION FOR NUCLEAR RESEARCH (CERN)
CERN-EP/2017-0492017/07/12
CMS-B2G-16-015
Search for tt resonances in highly boosted lepton+jets andfully hadronic final states in proton-proton collisions at √ s =
13 TeV
The CMS Collaboration ∗ Abstract
A search for the production of heavy resonances decaying into top quark-antiquarkpairs is presented. The analysis is performed in the lepton+jets and fully hadronicchannels using data collected in proton-proton collisions at √ s =
13 TeV using theCMS detector at the LHC, corresponding to an integrated luminosity of 2.6 fb − .The selection is optimized for massive resonances, where the top quarks have largeLorentz boosts. No evidence for resonant tt production is found in the data, and up-per limits on the production cross section of heavy resonances are set. The exclusionlimits for resonances with masses above 2 TeV are significantly improved comparedto those of previous analyses at √ s = Published in the Journal of High Energy Physics as doi:10.1007/JHEP07(2017)001. c (cid:13) ∗ See Appendix A for the list of collaboration members a r X i v : . [ h e p - e x ] J u l Numerous extensions of the standard model (SM) predict the existence of new interactions withenhanced couplings to third-generation quarks, especially the top quark. The associated mas-sive new particle contained in these theories could be observed as a tt resonance in experimentsat the CERN LHC. Examples of such resonances are: massive color-singlet Z-like bosons (Z (cid:48) )in extended gauge theories [1–3], colorons [4–7] and axigluons [8–10] in models with extendedstrong interaction sectors, heavier Higgs siblings in models with extended Higgs sectors [11],and Kaluza–Klein (KK) excitations of gluons [12], electroweak gauge bosons [13], and gravi-tons [14] in various extensions of the Randall–Sundrum (RS) model [15, 16]. These modelspredict the existence of TeV-scale resonances with production cross sections of a few picobarnsat √ s =
13 TeV. In all of these examples, resonant tt production would be observable in thereconstructed invariant mass spectrum of the top quark-antiquark pair ( M tt ).Searches performed at the Tevatron have set upper limits on the production cross section ofnarrow Z (cid:48) resonances with masses below 900 GeV that decay into tt and have a relative decaywidth Γ / M of 1.2% [17, 18]. Similarly, searches at the LHC have set sub-picobarn limits on theproduction cross section of resonances in the 1–3 TeV mass range [19–26] at √ s = (cid:48) bosons with masses of up to 2.4 and 2.9 TeV,respectively, and an RS KK gluon with mass of up to 2.8 TeV, at the 95% CL.In this paper, we present a search for the production of heavy spin-1 or spin-2 resonancesdecaying into tt pairs using the analysis methods described in Ref. [27]. We use data recordedin 2015 with the CMS detector in proton-proton (pp) collisions at √ s =
13 TeV at the LHC,corresponding to an integrated luminosity of 2.6 fb − . Four benchmark models are considered:a Z (cid:48) boson decaying exclusively to tt with relative decay widths of 1%, 10%, and 30%, and a KKgluon resonance in the RS model (having a relative decay width of approximately 17%). The Z (cid:48) events are generated in the framework of the sequential SM (SSM) [28]. Although the 1% and30% widths are unphysical for various masses in that model, assuming SM-like couplings toquarks, this approach enables us to present limits as a function of width, allowing the resultsto be reinterpreted in models with different resonance widths. The RS KK gluon model isprovided as an example of a specific, well-motivated model with a predicted physical width.A search is performed using the M tt spectrum for resonances with masses greater than 500 GeV,where the top quarks from the resonance decay have large Lorentz boosts. The analysis isperformed using the lepton+jets and fully hadronic tt decay modes. The lepton+jets channel istt → ( W + b )( W − b ) → ( q q b )( (cid:96) − ν (cid:96) b ) ( or charge conjugate ) ,where one W boson decays hadronically, and the other decays to a muon or an electron, andthe associated neutrino. The fully hadronic channel istt → ( W + b )( W − b ) → ( q q b )( q q b ) ,where both W bosons decay hadronically. The sensitivity of the search is improved by identify-ing jets originating from the hadronization of b quarks (b jets), and separating the samples intocategories that depend on the number of leptons (0 or 1), the lepton flavor (electron or muon),the number of jets consistent with a hadronic top quark decay (“t-tagged” jets), and the numberof b jets or b subjets (where subjets are smaller jets found within a given jet). In the lepton+jetschannel, the resulting samples consist mainly of events from SM tt production or from W bo-son production in association with jets. In the fully hadronic channel, the resulting samples are dominated by SM tt and non-top multijet production. We refer to the latter as NTMJ, and thiscategory comprises events from quantum chromodynamic (QCD) interactions as well as fromother processes that result in jet production. The term “QCD multijet” is used to describe theclass of interactions considered in the generation of samples of simulated events arising solelyfrom QCD processes.In this paper, Section 2 describes the CMS detector, while Sections 3 and 4 describe the tech-niques used for object reconstruction and the properties of simulated events utilized in theanalysis, respectively. Section 5 describes the event selections applied in each channel of theanalysis, and Section 6 outlines the methods developed to estimate the various backgroundcomponents using fitting procedures. Finally, Section 7 contains the results of the analysis inthe form of cross section limits on new physics models, and Section 8 summarizes the work. The central feature of the CMS apparatus [29] is a superconducting solenoid of 6 m internaldiameter, providing a magnetic field of 3.8 T. Within the solenoid volume are a silicon pixeland strip tracker, a lead tungstate crystal electromagnetic calorimeter (ECAL), and a brass andscintillator hadron calorimeter (HCAL). In the region | η | < η ) and 0.087 radians in azimuth ( φ ). In the η – φ plane, and for | η | < × | η | > ∆ η and ∆ φ . Withineach tower, the energy deposits in ECAL and HCAL cells are summed to define the calori-meter tower energies, subsequently used to provide the energies and directions of hadronicjets. Electron momenta are estimated by combining the energy measurement in the ECAL withthe momentum measurement in the tracker. Extensive forward calorimetry complements thecoverage provided by the barrel and endcap detectors. Muons are measured in gas-ionizationdetectors embedded in the steel flux-return yoke outside the solenoid. A more detailed de-scription of the CMS detector, together with a definition of the coordinate system used and therelevant kinematic variables, can be found in Ref. [29]. Event reconstruction is based on the CMS particle-flow (PF) algorithm [30, 31], which takesinto account information from all subdetectors, including measurements from the tracking sys-tem, energy deposits in the ECAL and HCAL, and tracks reconstructed in the muon detectors.Given this information, all particles in the event are reconstructed as electrons, muons, photons,charged hadrons, or neutral hadrons.Primary vertices are reconstructed using a deterministic annealing filtering algorithm [32]. Theleading primary vertex of the event is defined as the primary vertex with the largest squaredsum of transverse momenta ( p T ) of associated charged particles. Charged particles associ-ated with other primary vertices due to additional interactions within the same bunch crossing(“pileup”) are removed from further consideration.Muons are reconstructed using the information collected in the muon detectors and the innertracking detectors, and are measured in the range | η | < tracks measured in the silicon tracker results in a p T resolution of 1.3–2.0% in the barrel andbetter than 6% in the endcaps for muons with 20 < p T <
100 GeV. The p T resolution in thebarrel is better than 10% for muons with p T up to 1 TeV [33].Electron candidates are reconstructed in the range | η | < p T ≈
45 GeV from Z → ee decays ranges from 1.7% for nonshowering electronsin the barrel region to 4.5% for electrons showering in the endcaps [34].Jets are clustered using PF candidates as inputs to the anti- k T algorithm [35] in the F AST J ET R = η and p T [38]. The jet energy resolution varies from 15% at 10 GeV to 8% at 100 GeV to 4% at1 TeV for the small-radius jets, and degrades by a few percent for the large-radius jets. Thesmall-radius jets associated with b quarks are identified using the Combined Secondary Vertexv2 (CSVv2) algorithm [39, 40]. The working point used for jet b tagging in this analysis has anefficiency of ≈
65% (in tt simulated events) and a mistag rate (the fraction of light-flavor jetsthat are incorrectly tagged) of ≈
1% [40].The large-radius jets with p T >
500 GeV are taken as hadronic top quark candidates. To identifytrue top quark decays, the “CMS top tagger v2” algorithm [41] is used. In this algorithm, theconstituents of the AK8 jets are reclustered using the Cambridge–Aachen algorithm [42, 43].The “modified mass drop tagger” algorithm [44], also known as the “soft drop” (SD) algo-rithm, recursively declusters a jet into two subjets, discarding soft and wide-angle radiation jetcomponents until a hard splitting criterion is met, to obtain jets consistent with boosted heavy-object decays. This algorithm has been shown to improve jet mass resolution by approximately40% relative to standard reconstruction techniques [45]. The algorithm is used with angu-lar exponent β =
0, soft cutoff threshold z cut < R = τ N are calculated using all PF candidates in the AK8 jet. Each corresponds to a p T -weighted minimum distance from one of N hypothesized subjet axes, defined by the one-pass minimization procedure. These observables are used to quantify the consistency of theparticles of a jet with an N-prong decay topology. The variable τ = τ / τ [47, 48] is employedto identify the three-pronged substructure of a hadronically decaying top quark. The specificworking point used in this analysis is defined by requiring that the soft-dropped mass of thejet satisfies 110 < M SD <
210 GeV and the N-subjettiness variable satisfies τ < p T is above 500 GeV. Jets selected by the jet mass and N-subjettiness criteria arereferred to as “t-tagged”. Additionally, t-tagged jets are considered to have a subjet b tag if they contain at least one soft-dropped subjet identified as b-tagged using the working pointdescribed above.The missing p T in the plane transverse to the beam direction is reconstructed as the negativevector sum of the p T of all PF candidates reconstructed in the event [38]. Its magnitude isdenoted by p missT . Corrections to the jet energy scale and jet energy resolution are propagatedto the measurement of p missT . The simulation of Z (cid:48) resonances is performed with the leading-order M AD G RAPH v5.2.2.2 [49]Monte Carlo (MC) program using SM values for the left- and right-handed Z (cid:48) couplings to topquarks. The simulation is performed for a range of Z (cid:48) masses between 0.5 and 4.0 TeV, and forthe three relative width hypotheses of 1%, 10%, and 30%. Higher-order QCD multijet processesfor up to three extra partons are simulated at tree level. The Z (cid:48) boson is required to decay intoa tt pair in all generated events. The parton showering and hadronization is modeled with
PYTHIA
PYTHIA program. The KKgluon excitations are simulated with resonance masses between 0.5 and 4.0 TeV, assuming thebranching fraction of the KK gluon into top quark pairs is ≈ < M tt distri-butions for resonance masses of 2 TeV and 4 TeV, for the various signal hypotheses considered.For the highest-mass samples considered, the resonance production is dominated by off-shellcontributions, giving the long tail toward low values of M tt seen in the distributions. [GeV] tt Generated M0 2000 4000 F r a c t i on o f e v en t s Z' 2 TeV, 1% widthZ' 2 TeV, 10% widthZ' 2 TeV, 30% widthRS gluon 2 TeV (13 TeV)
CMS
Simulation [GeV] tt Generated M0 2000 4000 F r a c t i on o f e v en t s Z' 4 TeV, 1% widthZ' 4 TeV, 10% widthZ' 4 TeV, 30% widthRS gluon 4 TeV (13 TeV)
CMS
Simulation
Figure 1: Distributions of generator-level M tt for the production of new particles with massesof 2 TeV (left) and 4 TeV (right), for the four signal hypotheses considered in this analysis.Background events from tt production via QCD interactions and electroweak production ofsingle top quarks in the tW channel are simulated with the next-to-leading order (NLO) gen-erator POWHEG (v2) [53–57]. The s - and t -channel processes of single top quark production aresimulated with M AD G RAPH MC @ NLO v5.2.2.2 [49]. All events are interfaced with
PYTHIA for the description of fragmentation and hadronization.
The associated production of W or Z boson and jets is simulated using M AD G RAPH . The MLMmatching scheme is applied to match the showers generated with
PYTHIA . Up to four addi-tional partons in the matrix element calculations are included. The tt, W/Z+jets, and single-top-quark samples are normalized to the theoretical predictions described in Refs. [58–61]. Di-boson processes (VV = WW, WZ, and ZZ) are simulated with
PYTHIA for both the matrixelement and parton showering calculations. The event rates are normalized to the NLO crosssections from Ref. [62].Simulated QCD multijet events, generated with
PYTHIA , are used to validate the background-estimation procedure in the fully hadronic channel, but not in the search, where the NTMJbackground is estimated from sideband regions in data.All events are generated at the center of mass energy of 13 TeV and use the NNPDF 3.0 partondistribution functions (PDF) [63]. In the parton shower simulated with
PYTHIA , the underlyingevent tune CUETP8M1 [64, 65] has been used. All simulated samples include the effects ofadditional inelastic proton-proton interactions within the same or adjacent bunch crossings.
Events in the muon channel are collected with a single-muon trigger, which requires the pres-ence of a muon with p T >
45 GeV and | η | < p T >
45 GeV, | η | < p T >
200 (50) GeV for the leading (subleading) AK4 jet reconstructed at trigger level. Thesetrigger choices ensure an efficiency of about 99% for high-mass signal events.In the lepton+jets analysis, we select events offline containing one muon with p T >
50 GeVand | η | < p T >
50 GeV and | η | < | η | < p T > p T >
50 (70) GeV. Additional reconstructedjets, utilized in the reconstruction of the tt system, are required to have p T >
30 GeV. Giventhe highly-boosted topology of the final-state objects, no isolation requirements are applied tothe leptons at the trigger level or in the analysis stages. However, events are required to pass atwo-dimensional selection of ∆ R ( (cid:96) , j ) > p relT ( (cid:96) , j ) >
20 GeV, where j is the small-radius jetwith minimal angular separation ∆ R = √ ( ∆ η ) + ( ∆ φ ) from the lepton (cid:96) (electron or muon),and p relT ( (cid:96) , j ) is the component of the lepton momentum orthogonal to the axis of jet j . The val-ues of ∆ R ( (cid:96) , j ) and p relT ( (cid:96) , j ) are calculated considering small-radius jets with p T >
15 GeV and | η | < p missT >
50 GeV and ( p missT + p (cid:96) T ) >
150 GeV.In the electron channel, where jets are often misidentified as electrons, we find that the mosteffective approach for rejecting NTMJ events is to require only p missT >
120 GeV. After theserequirements, the contributions from NTMJ production in both lepton channels are found tobe negligible. We also reject events that contain a second lepton to ensure there is no overlapbetween the event samples and to maintain a clear distinction between lepton+jets and dilep-ton+jets analyses. Finally, we veto events with two t-tagged jets to ensure orthogonality tothe fully hadronic analysis. This veto has a negligible impact on the signal efficiency of the lepton+jets analysis.The kinematic reconstruction of the tt system in the lepton+jets channel is performed by as-signing the products in the final state to either the leptonic or hadronic branch of the tt system.We first assign the charged lepton and p missT to the leptonic branch of the event, where p missT is interpreted as the p T of the neutrino, p z ( ν ) . The longitudinal component of the neutrinomomentum is inferred by constraining the invariant mass of the (cid:96) + ν system to match the Wboson mass. This procedure leads to a quadratic equation in p z ( ν ) . If two real solutions arefound, hypotheses are built for both cases. If no real solutions are available, the real part istaken as p z ( ν ) . In events without t-tagged jets, only small-radius jets are used to reconstructboth the leptonic and hadronic top decays.In events containing a t-tagged jet, the large-radius jet is assigned to the hadronically decayingtop quark. Only small-radius jets with a separation of ∆ R > χ discriminator is used to quantify the compatibility of eachhypothesis with a tt decay. The discriminator is defined as χ = (cid:32) M lep − M lep σ M lep (cid:33) + (cid:32) M had − M had σ M had (cid:33) , (1)where M lep and M had are the invariant masses of the reconstructed semileptonically and hadron-ically decaying top quark, respectively. The quantities σ M lep and σ M had are the resolutions of theleptonic and hadronic top quark reconstruction, respectively, and M lep and M had are the meansof the corresponding mass distributions. The values of M lep , σ M lep , M had , and σ M had are derivedusing a sample of simulated events in which all four partons of the final state top quark decayproducts are matched to a reconstructed jet used in the hypothesis. In each event, the tt pairreconstructed with the smallest value of χ (labeled χ ) is chosen. In events with a t-taggedjet, M had is given by the mass of the large-radius jet calculated using the soft drop algorithm.This choice is made because, compared to the conventional jet mass, the soft dropped massis much less dependent on the jet p T , and therefore on the resonance mass in a given signalhypothesis. Moreover, this provides greater discrimination between background and signal.Events in the signal region are required to have χ <
30 for all lepton+jets categories. Thisupper threshold on χ reduces the contribution of events from non-tt background processesand maximizes the expected sensitivity of the analysis to new resonances.Finally, to further enhance sensitivity, events are categorized according to the number of t-taggedand b-tagged jets as follows : events with one t-tagged jet ( ) ; events with zero t-taggedjets and at least one b-tagged jet ( ) ; and events with zero t-tagged and b-taggedjets ( ) . The fully hadronic channel requires that at least two jets satisfy kinematic and t tagging selec-tion criteria. The data were collected online with a trigger requiring the scalar sum of the AK4jet energies ( H T ) to be larger than 800 GeV. The trigger selection has an efficiency of above 95%,as measured in simulation, for events that satisfy the offline requirement H T > p T >
500 GeV, rapidity | y | < | ∆ φ | > .3 Tagging variables in lepton+jets and fully hadronic channels Events are further categorized into six regions based on two criteria: the rapidity difference( ∆ y ) between the two AK8 jets and the number of jets with at least one b-tagged subjet forthe two highest p T jets. Events can contain 0, 1, or 2 jets with a b-tagged subjet, and they areseparated into bins of | ∆ y | < | ∆ y | > The distributions of the two variables used in the t tagging algorithm, τ and M SD , are shownin Fig. 2 for the lepton+jets channel (upper row) and the fully hadronic channel (lower row).Each of the figures is obtained after removing the selection on the quantity being plotted, whilemaintaining all other analysis-level selections. We observe good agreement between data andsimulation in the lepton+jets decay channel, where simulated events are divided into contri-butions from generator-level top quarks and other jets from tt events and subdominant back-ground processes. The fully hadronic channel also shows good agreement between the simu-lated distribution and data. The small discrepancies do not affect the analysis, as it relies ondata to estimate the NTMJ contribution to the background. Some discrepancy is visible at highvalues of τ , however this region is excluded by the selection used for t tagging. In this section, we describe the sources of the SM background and methods of backgroundestimation for both the lepton+jets and fully hadronic channels. We then introduce the sourcesof systematic uncertainty considered in this analysis. Finally, we describe the treatment of thebackgrounds and uncertainties in the maximum likelihood fit that is used to determine the totalyield of SM processes and in the statistical analysis of data.
Several SM processes contribute to the sample obtained from the lepton+jets selection de-scribed in Section 5. The two main background processes are tt and W+jets production. Thelatter accounts for a sizeable portion of the background in the ( ) category, whereasthe former fully dominates the ( ) and ( ) categories. Single top quark, Z+jets,and diboson production contribute only a small fraction of the background.The distributions obtained from simulation are corrected to account for known discrepanciesin the observed number of data and simulated events. In particular, we derive a scale factor(SF) between data and simulation for the t tagging mistag (t mistag) rate for AK8 jets from asample dominated by W+jets, selected by requiring events to have χ min >
30. The remainingcontamination from tt is removed by subtracting the distribution of tt events in simulation.The t mistag rate is measured separately for the muon and electron channels, in data and sim-ulation. The resulting values, together with the data-to-simulation SFs, are shown in Table 1.As the SFs for the muon and electron channels are consistent, the weighted average is used:SF (cid:96) = ± ± ± ± µ +jets 0.043 ± ± ± E v en t s / b i n DataMatched to top quarkUnmatched to top quark=1 pb) s Z' 3 TeV (
CMS (13 TeV) -1 < 210 GeV SD | < 2.4, 110 GeV < M h > 500 GeV, | T AK8 jets with p lepton+jets t Jet D a t a / b k g E v en t s / G e V DataMatched to top quarkUnmatched to top quark=1 pb) s Z' 3 TeV (
CMS (13 TeV) -1 < 0.69 t | < 2.4, h > 500 GeV, | T AK8 jets with p lepton+jets
Jet soft-drop mass [GeV] D a t a / b k g E v en t s / b i n CMS (13 TeV) -1 < 210 GeV SD subjet b-tag, 110 < M | < 2.4, h > 400 GeV, | T AK8 jets with p fully hadronic
DatattQCD =1 pb) s Z' 3 TeV ( t Jet D a t a / b k g E v en t s / G e V CMS (13 TeV) -1 < 0.69 t | < 2.4, subjet b-tag, h > 400 GeV, | T AK8 jets with p fully hadronic
DatattQCD =1 pb) s Z' 3 TeV (
Jet soft-drop mass [GeV] D a t a / b k g Figure 2: Distributions of the N-subjettiness ratio, τ , and the soft dropped mass, M SD , forAK8 jets in data and simulation, after the signal selection. For lepton+jets, with p T >
500 GeV(upper row). For the fully hadronic final state, with p T >
400 GeV and subjet b tag (lowerrow). The distribution of τ (left) is shown after the selection 110 < M SD <
210 GeV, and thedistribution of M SD (right) is shown after the selection τ < (cid:48) signal model are shown with the black dashed lines. In obtainingthe final results, NTMJ production is estimated from data, and simulated QCD multijet eventsare not used. In all plots, the error bars include only statistical contributions. .2 Fully hadronic channel hypothesis to data [66]. Distributions defined in samples dominated by various backgroundsare used simultaneously in a binned maximum likelihood fit to constrain the different uncer-tainties in the background model using the data. The reconstructed M tt distribution is used inregions dominated by tt and W+jets, and the dimuon invariant mass is used in a region domi-nated by Z+jets. The tt-dominated region is defined by M tt < χ min <
30. The regiondominated by W+jets events is defined by χ min >
30. For each of these two latter regions, sixexclusive categories are defined based on lepton flavor and number of b-tagged and t-taggedjets ( ( ) ; ( ) ; ( ) ), giving a total of 12 control regions (CRs). Oneadditional CR, dominated by Z+jets, is defined by removing the lepton veto from the µ + jetsselection and adding the Z boson mass window requirement 71 < M µµ <
111 GeV. The Z → eechannel is not used because of the stringent requirement on p missT . The fully hadronic channel has two primary sources of SM background: tt events and NTMJproduction. The shape of the M tt distribution for tt events is taken from simulation. The nor-malization of this distribution is initially set to the theoretical cross section, but is allowed tovary within both rate and shape uncertainties during the statistical analysis. The shape andnormalization are both fitted and extracted for each of the six event categories. The variationof the tt contribution to the total background predominantly affects the signal regions with twosubjet b tags, which have tt as the dominant background component.For the NTMJ estimate, we use a data-driven technique similar to that described in Ref. [25].The method involves selecting a sample of data events with low SM tt contribution by invert-ing the t tagging N-subjettiness requirement on one selected jet (anti-tag), and determining thet tagging rate for the second jet (probe). The anti-tag jet is required to satisfy 110 < M SD <
210 GeV and τ > p T ) and is measured separately for events falling into each of the six b tag and | ∆ y | categories (Fig. 3). The anti-tag requirement is designed to select a sample in data dominated byNTMJ events. A small number of genuine tt events survive this selection. This contaminationis removed by subtracting the distributions measured in tt simulation from those measured inthe anti-tag and probe selection in data.Once the t mistag rate has been determined from the NTMJ control sample, it is used to estimatethe normalization and shape of NTMJ events passing the final event selection. To do this, weuse a “single-tagged” region that contains events with at least one t-tagged jet. To avoid bias,we randomly select one of the two leading top quark jet candidates and require that it pass the ttagging selection described above. If the randomly chosen jet is t tagged, we include this eventand weight it by the appropriate t mistag rate based on the momentum of the jet opposite thetagged jet, their rapidity difference, and the number of subjet b tags, as shown in Fig. 3.This singly-tagged control region without any requirements on the second jet has an overlapwith the signal region, and is used to estimate the NTMJ background. To remove the effectsof double-counting, the tt contribution is subtracted from the NTMJ estimate. This is done byevaluating the t mistag weighting procedure described above on the simulated tt events, tofind the contribution of tt events that would enter the NTMJ background estimate when themethod is applied to data. This contribution amounts to a tt contamination of about 1–2% ofthe NTMJ background estimate in the 0 b-tag event regions (about 6–10% in the other regions),and is subtracted from the NTMJ background estimate.As a final step in determining the shape of the NTMJ background estimate, we correct for Jet momentum [GeV]
500 1000 2000 3000 t m i s t ag r a t e - -
10 1
CMS (13 TeV) -1 y| < 1.0 D | Jet momentum [GeV]
500 1000 2000 3000 t m i s t ag r a t e - -
10 1
CMS (13 TeV) -1 y| > 1.0 D | Figure 3: The mistag rate for the t tagging algorithm in the fully hadronic channel, measuredwith data for the six event categories by an anti-tag and probe procedure. The round, square,and triangular points indicate the t mistag rate for events in the 0, 1, and 2 b tag categories, re-spectively. The left (right) plot contains events with | ∆ y | < > < M SD <
210 GeV. This method is validated using simulated QCDmultijet events.
Several sources of systematic uncertainties are considered in this search. Each of these is relatedto an experimental uncertainty introduced in the reconstruction of the event or to a theoreticaluncertainty affecting the simulation of certain background or signal processes. In particular,we quantify the effect of each of these uncertainties on the measurement of the invariant massof the reconstructed tt system. These uncertainties are taken into account in the maximumlikelihood fit to determine the total yield of SM processes, and in the statistical interpretationof the data. The complete list of systematic uncertainties is given below, and Table 2 lists thesources of uncertainty and the channels they affect.The effect of the uncertainties in the theoretical SM cross sections for tt, W+jets and Z+jetsproduction are obtained from the background fit described above, and are 8% for tt, 6% forW+jets, and 20% for Z+jets production. Small contributions to the event yields arise fromsingle top quark and diboson production. Their normalization is taken from theory [60, 67–70] and assigned a 20% uncertainty. The effect due to missing higher-order corrections in thesimulation of tt and W+jets production in the SM is estimated by varying the renormalizationand factorization scales used in the simulation up and down independently by a factor of 2.Additionally, we account for uncertainties in the simulation of initial- and final-state radiationon the reconstruction of the tt system by using tt events simulated with different Q scales usedfor the parton shower generation and evolution. Simulated samples for both background andsignal processes are generated using PDFs from the NNPDF 3.0 set [63]. The correspondingsystematic uncertainty is determined according to the procedure described in Ref. [71]. Theuncertainty in the total integrated luminosity at √ s =
13 TeV is 2.7% [72]. The systematic .4 Fitting procedure uncertainty associated with the yield of simulated pileup events is evaluated by varying theinelastic pp cross section [73] by ±
5% ( σ inel = ± p T and η [33]. The uncertain-ties are obtained by varying each corresponding data-to-simulation SF by one standard devi-ation. Additional systematic uncertainties of 1% and 0.5% are attributed to the identificationand trigger efficiency SF measurements, respectively. Similarly, the uncertainty in the electronidentification efficiency is applied as a function of the electron p T and η [34]. An uncertaintyof 2% is assigned to the efficiency of the electron trigger selection, and is determined from acomplementary measurement of the e+jets trigger efficiency in a dilepton (e µ ) control region.The uncertainties in the data-to-simulation corrections for jet energy scale and jet energy reso-lution are evaluated by varying these corrections within their uncertainties, as functions of thejet p T and η . Both systematic variations are also propagated to the measurement of p missT andthe jet mass. A SF is applied to account for differing efficiencies and misidentification rates ofthe b tagging selection between data and simulation. Uncertainties in the SFs are measuredas functions of the jet p T and treated as uncorrelated. The data-to-simulation correction forthe subjet b tagging algorithm efficiency is also included as an independent uncertainty and isevaluated by varying the correction within its uncertainties, as a function of jet p T and η . Thedata-to-simulation correction for the efficiency of the t tagging selection for AK8 jets is mea-sured in situ in the statistical analysis. This is done by leaving this parameter unconstrained inthe fit. The t mistag efficiency in the lepton+jets channel (dominated by quarks from W+jets) ismeasured directly in a control region dominated by W+jets production with an uncertainty of19%. The t mistag rate in the fully hadronic channel (dominated by gluons from QCD interac-tions) is measured as described above, with a momentum-dependent uncertainty ranging from5 to 100%. These uncertainties are estimated by varying the anti-tag criterion for the construc-tion of the anti-tag and probe sample. Systematic uncertainties due to the t tagging efficiencyand t mistag rate are treated as uncorrelated.The systematic uncertainty associated with the “mass-modified” procedure, which is used tocorrect the kinematic bias in the background estimation, is computed by taking half the dif-ference between the uncorrected and “mass-modified” background estimates. This affects theshape and normalization of the M tt distribution. Simulated QCD multijet events are used in aclosure test to verify that the background estimation procedure accurately predicts the doublet-tagged M tt distribution. An additional systematic uncertainty is assigned to the NTMJ back-ground estimate based on small disagreements (up to 10%) observed in the closure test, in theshape of the kinematic threshold at low values of M tt . To improve the flexibility of the background model, we estimate the central values and uncer-tainties in several parameters through a maximum likelihood fit to data using the top quarkpair invariant mass distribution, as follows. The normalizations for the background estimatesusing simulated events are left unconstrained in the fit. The data-to-simulation SF for thet tagging efficiency is also unconstrained and extracted from the fit. The SF for the subjet b tag-ging efficiency as well as the yield of events from the NTMJ background estimation method,having both p T and η dependence, are allowed to vary within uncertainties, with their finalvalues estimated by the fit. The NTMJ background is constrained using the procedure outlinedin Section 6.2. All other systematic uncertainties are included as nuisance parameters in the fit,and are allowed to vary within their corresponding rate and shape uncertainties, as described Table 2: Sources of uncertainty and the channels they affect. Uncorrelated uncertainties appliedto a given channel are labeled with a (cid:12) . Uncertainties that are correlated between channelsare labeled with a ⊕ . In this table, σ denotes the uncertainty in the given prior value in thelikelihood fit. Uncertainty ChannelSource Prior uncertainty Lepton+jets Fully hadronictt cross section ± ⊕ ⊕ W+jets cross section ± (cid:12) Z+jets cross section ± (cid:12) Single-top cross section ± (cid:12) Diboson cross section ± (cid:12) Integrated luminosity ± ⊕ ⊕ Pileup modeling ± σ ⊕ ⊕ Muon identification ± σ ( p T , η ) (cid:12) Muon trigger ± σ ( p T , η ) (cid:12) Electron identification ± σ ( p T , η ) (cid:12) Electron trigger ± (cid:12) Jet energy scale ± σ ( p T , η ) ⊕ ⊕ Jet energy resolution ± σ ( η ) ⊕ ⊕ Jet b tagging efficiency ± σ ( p T , η ) (cid:12) Jet b mistag rate ± σ ( p T , η ) (cid:12) Subjet b tagging efficiency ± σ ( p T , η ) (cid:12) Jet t tagging efficiency unconstrained ⊕ ⊕
Lepton+jets channel t mistag rate ± (cid:12) Fully hadronic channel t mistag rate ± σ ( p ) (cid:12) PDFs ± σ ⊕ ⊕ tt matrix element scale ± σ ⊕ ⊕ tt parton shower scale ± σ ⊕ ⊕ W+jets matrix element scale ± σ (cid:12) NTMJ background kinematics ± σ (cid:12) NTMJ background closure test ± σ (cid:12) above, using log-normal prior distributions. The best fit values obtained from this maximumlikelihood evaluation are used to correct the distributions of background and signal processes.A Bayesian statistical method [66, 74] is used to extract the upper limits at 95% confidence level(CL) on the product of the cross section and branching fraction, i.e. σ ( pp → X ) B ( X → tt ) ,for heavy resonances decaying to a tt pair. In order to maximize the expected sensitivity of thesearch, twelve exclusive categories are employed simultaneously in the statistical analysis, asdescribed above. For each category, the observable used in the limit-setting procedure is M tt .A template-based shape analysis is performed using the Theta software package [66] for these M tt distributions. The systematic uncertainties listed in Table 2 are introduced as individualnuisance parameters in the limit calculation. For the signal cross section parameter, we use auniform prior distribution. The uncertainty in the data-to-simulation correction for t taggingefficiency is left unconstrained, whereas each of the other nuisance parameters correspondingto a systematic uncertainty is modeled with a log normal prior distribution. The uncertaintydue to the finite size of the simulated samples is introduced in the statistical analysis accord-ing to the “Barlow–Beeston lite” method [75]. The impact of the statistical uncertainty in thesimulated samples is limited by rebinning each M tt distribution to ensure that the statisticaluncertainty associated with the expected background is less than 30% in each bin. .4 Fitting procedure E v en t s / G e V E v en t s / G e V Data Othert t Z’ 2.0 TeV, 1% width +jets, 1 t tag µ (13 TeV) CMS [GeV] tt M D a t a / B k g E v en t s / G e V E v en t s / G e V Data Othert t Z’ 2.0 TeV, 1% width e+jets, 1 t tag (13 TeV) CMS [GeV] tt M D a t a / B k g E v en t s / G e V E v en t s / G e V Data Othert t Z’ 2.0 TeV, 1% width +jets, 0 t tag, 1 b tag µ (13 TeV) CMS [GeV] tt M D a t a / B k g E v en t s / G e V E v en t s / G e V Data Othert t Z’ 2.0 TeV, 1% width e+jets, 0 t tag, 1 b tag (13 TeV) CMS [GeV] tt M D a t a / B k g E v en t s / G e V E v en t s / G e V Data Othert t Z’ 2.0 TeV, 1% width +jets, 0 t tag, 0 b tag µ (13 TeV) CMS [GeV] tt M D a t a / B k g E v en t s / G e V E v en t s / G e V Data Othert t Z’ 2.0 TeV, 1% width e+jets, 0 t tag, 0 b tag (13 TeV) CMS [GeV] tt M D a t a / B k g Figure 4: Distributions in M tt for data and expected background, for events passing the signalselection of the lepton+jets analysis ( χ <
30) after the maximum likelihood fit. Distribu-tions are shown for the muon (left) and electron (right) channel. For each lepton flavor, eventsare split into three exclusive categories (from uppermost to lowest): ( ) , ( ) ,and ( ) . The signal templates are normalized to a cross section of 1 pb. The un-certainties associated with the background expectation include the statistical and all post-fitsystematic uncertainties. The lower panel in each figure shows the ratio of data to predictedSM background, with the statistical (light gray) and total (dark gray) uncertainties shown sep-arately. E v en t s / G e V E v en t s / G e V Data NTMJt t Z’ 2.0 TeV, 1% width y| < 1.0; 0 b tag ∆ | (13 TeV) CMS [GeV] tt M D a t a / B k g E v en t s / G e V E v en t s / G e V Data NTMJt t Z’ 2.0 TeV, 1% width y| > 1.0; 0 b tag ∆ | (13 TeV) CMS [GeV] tt M D a t a / B k g E v en t s / G e V E v en t s / G e V Data NTMJt t Z’ 2.0 TeV, 1% width y| < 1.0; 1 b tag ∆ | (13 TeV) CMS [GeV] tt M D a t a / B k g E v en t s / G e V E v en t s / G e V Data NTMJt t Z’ 2.0 TeV, 1% width y| > 1.0; 1 b tag ∆ | (13 TeV) CMS [GeV] tt M D a t a / B k g E v en t s / G e V E v en t s / G e V Data NTMJt t Z’ 2.0 TeV, 1% width y| < 1.0; 2 b tag ∆ | (13 TeV) CMS [GeV] tt M
500 1000 1500 2000 2500 D a t a / B k g E v en t s / G e V E v en t s / G e V Data NTMJt t Z’ 2.0 TeV, 1% width y| > 1.0; 2 b tag ∆ | (13 TeV) CMS [GeV] tt M D a t a / B k g Figure 5: Distributions in M tt for data and expected background, for events passing the signalselection of the fully hadronic analysis after the maximum likelihood fit. Distributions areshown for the regions with | ∆ y | < | ∆ y | > Table 3: Numbers of events in the signal region for the lepton+jets analysis. The expectedyields for SM backgrounds are obtained from the maximum likelihood fit to the data describedin Section 6.4. The uncertainties reported in the total expected background include the sta-tistical uncertainties in the simulation and all the posterior systematic uncertainties. For theW+jets background, LF (HF) indicates contributions from W bosons produced in associationwith light-flavor (heavy-flavor) jets. µ +jets signal regionProcess 1 t tag 0 t tags, 1 b tag 0 t tags, 0 b tagstt 218 ±
28 7602 ±
826 1965 ± ± ±
54 4675 ± ± ±
30 780 ± ± ±
111 635 ± ±
29 9164 ±
856 8055 ± ±
15 1016 ±
124 248 ± ± ±
10 684 ± ± ± ± ± ±
18 74 ± ±
16 1260 ±
129 1090 ± | ∆ y | > ± ± ± ± ± ± ± ± ± | ∆ y | < ± ±
10 60 ± ± ± ± ±
11 369 ±
12 79 ± The number of events observed in data and expected from SM processes after the backgroundfit are given in Tables 3 and 4 for the six categories in the signal region of the lepton+jets andfully hadronic channels, respectively. The invariant mass distribution of the reconstructed tt pair is shown in Fig. 4 (Fig. 5) for data and the expected SM backgrounds in the lepton+jets(fully hadronic) signal-region categories after the background fit. Good agreement betweendata and background prediction is observed within the estimated systematic uncertainties. Themodeling of the data in background-enriched samples is verified using kinematic distributionsfor leptons, jets, and the reconstructed leptonically and hadronically decaying top quarks ineach of the individual categories considered in the analysis. The small differences are coveredby the systematic uncertainties. For the lepton+jets channel, some discrepancies are observedat large M tt in the distributions in categories where the W+jets background dominates. Thesediscrepancies are related to missing higher-order corrections in the simulated events, and havelittle impact on the results, as these categories are less sensitive than those dominated by tt.Dedicated cross checks have confirmed that the localized discrepancies visible in Figs. 4 and5 may be attributed to statistical fluctuations. The sensitivity of this analysis is driven by the1 t tag categories in the lepton+jets channel, and the 2 b tag categories in the fully hadronicchannel, which have the highest signal-to-background ratios.We proceed to set exclusion limits on different benchmark models for tt resonances. Four ex-tensions to the SM are considered in the statistical analysis: a Z (cid:48) boson decaying exclusively tott with a relative decay width ( Γ / M ) of 1%, 10%, or 30%, and a KK gluon resonance in the RSmodel. The cross sections for Z (cid:48) production are taken from NLO order calculations [76]. Theleading order (LO) predictions for the KK gluon cross sections are multiplied by a factor of 1.3to account for higher-order corrections [77].Limits are extracted on the cross sections for the various signal hypotheses using the distribu-tions in Figs. 4 and 5. By varying the nuisance parameters within their prior distribution func-tions, pseudo-experiments are performed to estimate the 68% and 95% CL (1 and 2 standarddeviations) expected limits in the median results. The combined results, including observedlimits on the resonant production cross sections, are shown in Fig. 6, and tabulated in Tables5–9. The combination of the lepton+jets and fully hadronic channels significantly improves theexclusion limits relative to previous results for all models, except for those using a width of 1%.Starting from the lower mass exclusion limit of 0.5 TeV, masses are excluded up to 4 TeV for the30% width Z (cid:48) samples, up to 3.9 TeV for the 10% width Z (cid:48) , and up to 3.3 TeV for the RS KKgluon hypotheses, at the 95% CL. These limits are close to the point where the parton luminos-ity at low tt mass dominates the mass distribution by enhancing the off-shell contribution andreducing the resonant contribution, modifying the behavior of the signal model from resonant-like to nonresonant-like. Because of this, a different analysis strategy should be considered infuture searches, in order to be sensitive to such non-resonant production at large M tt . Table 5shows the exclusion limits obtained for the two channels and for their combination. Figure 7presents the Z (cid:48) limits as a function of width instead of mass.Table 5: Comparison of mass exclusion results (in TeV) for the individual channels and for theircombination. Excluded mass ranges [TeV]Z (cid:48) ( Γ / M = ) Z (cid:48) ( Γ / M = ) Z (cid:48) ( Γ / M = ) RS KK GluonResult Exp. Obs. Exp. Obs. Exp. Obs. Exp. Obs.Lepton+jets 0.6 – 2.1 0.6 – 2.3 0.5 – 3.5 0.5 – 3.4 0.5 – 4.0 0.5 – 4.0 0.5 – 2.9 0.5 – 2.9Fully hadronic 1.2 – 1.8 1.4 – 1.8 1.0 – 3.2 1.0 – 3.5 1.0 – 3.7 1.0 – 4.0 1.0 – 2.6 1.0 – 2.4Combined 0.6 – 2.4 0.6 – 2.5 0.5 – 3.7 0.5 – 3.9 0.5 – 4.0 0.5 – 4.0 0.5 – 3.1 0.5 – 3.3 [TeV] Z' M ) [ pb ]tt fi ( Z ' B · Z ' s - - - -
10 110 (13 TeV) -1 CMS
ObservedExpected1 s.d. exp. – – Z' 1% width (NLO) [TeV] Z' M ) [ pb ]tt fi ( Z ' B · Z ' s - - - -
10 110 (13 TeV) -1 CMS
ObservedExpected1 s.d. exp. – – Z' 10% width (NLO) [TeV] Z' M ) [ pb ]tt fi ( Z ' B · Z ' s - - - -
10 110 (13 TeV) -1 CMS
ObservedExpected1 s.d. exp. – – Z' 30% width (NLO) [TeV] KK g M ) [ pb ]tt fi KK ( g B · KK g s - - - -
10 110 (13 TeV) -1 CMS
ObservedExpected1 s.d. exp. – – · RS gluon (LO
Figure 6: Observed and expected upper limits at 95% CL on the product of the productioncross section and branching fractions for the full combination of the analysis results, shown asfunction of the resonance mass. Limits are set using four extensions to the SM : (upper left) a Z (cid:48) boson with Γ / M of 1%, (upper right) a Z (cid:48) boson with Γ / M of 10%, (lower left) a Z (cid:48) boson with Γ / M of 30% and (lower right) a KK excitation of a gluon in the RS model. The correspondingtheoretical prediction as a function of the resonance mass is shown as a dot-dashed curve. A model-independent search for the production of heavy spin-1 or spin-2 resonances decayinginto tt final states has been conducted. The data correspond to an integrated luminosity of2.6 fb − collected with the CMS detector in proton-proton collisions at √ s =
13 TeV at the LHC.The analysis is designed to have high sensitivity at resonance masses above 1 TeV, where final-state decay products become collimated because of the large Lorentz boosts of the top quarks.The analysis method provides an in-situ measurement of the data-to-simulation scale factorfor the t tagging efficiency and the normalization of the main backgrounds. No evidence formassive resonances that decay to tt is found. Limits at 95% CL are set on the production crosssection of new spin-1 particles decaying to tt with relative decay widths that are either narrowor wide compared with the detector resolution.In addition, limits are set on the production of particles in benchmark models beyond the stan- Z' / M Z' G - - ) [ pb ]tt fi ( Z ' B · Z ' s -
10 110 (13 TeV) -1 CMS
Observed Expected1 s.d. exp. – – Z' 1 TeV (NLO) Z' / M Z' G - - ) [ pb ]tt fi ( Z ' B · Z ' s - -
10 1
Observed Expected1 s.d. exp. – – Z' 2 TeV (NLO) (13 TeV) -1 CMS Z' / M Z' G - - ) [ pb ]tt fi ( Z ' B · Z ' s - -
10 1
Observed Expected1 s.d. exp. – – Z' 3 TeV (NLO) (13 TeV) -1 CMS Z' / M Z' G - - ) [ pb ]tt fi ( Z ' B · Z ' s - -
10 1
Observed Expected1 s.d. exp. – – Z' 4 TeV (NLO) (13 TeV) -1 CMS
Figure 7: Expected and observed limits presented as a function of width, for M Z (cid:48) =
1, 2, 3,4 TeV. The corresponding theoretical prediction as a function of width is shown as a dot-dashedcurve in each case.dard model. Topcolor Z (cid:48) bosons with relative widths Γ / M of 1%, 10%, and 30% are excludedfor mass ranges of 0.6–2.5, 0.5–3.9, and 0.5–4.0 TeV, respectively. Kaluza–Klein excitations of agluon with masses in the range 0.5–3.3 TeV in the Randall–Sundrum model are also excluded.This search presents limits on Z (cid:48) bosons as a function of the relative width of the resonance inthe range from 1–30%, for the first time in CMS.This analysis yields approximately the same sensitivity as the previous search based on 8 TeVdata [27] (corresponding to an integrated luminosity of 19.7 fb − ) for resonance masses in therange 1.0–2.0 TeV. At higher resonance masses, the present analysis is significantly more sensi-tive. Previous lower mass limits on the Z (cid:48) with 10% relative width and the Kaluza–Klein gluonwere 2.9 and 2.8 TeV, respectively. The present analysis extends the lower mass limits to 3.9and 3.3 TeV, respectively, for these models. Table 6: Expected and observed cross section limits at 95% CL, for the 1% width Z (cid:48) resonancehypothesis.Mass [TeV] Observed limits [pb] Expected limits [pb] − σ − σ Median + σ + σ (cid:48) resonancehypothesis.Mass [TeV] Observed limits [pb] Expected limits [pb] − σ − σ Median + σ + σ (cid:48) resonancehypothesis.Mass [TeV] Observed limits [pb] Expected limits [pb] − σ − σ Median + σ + σ Table 9: Expected and observed cross section limits at 95% CL, for the RS KK gluon hypothesis.Mass [TeV] Observed limits [pb] Expected limits [pb] − σ − σ Median + σ + σ Acknowledgments
We congratulate our colleagues in the CERN accelerator departments for the excellent perfor-mance of the LHC and thank the technical and administrative staffs at CERN and at otherCMS institutes for their contributions to the success of the CMS effort. In addition, we grate-fully acknowledge the computing centers and personnel of the Worldwide LHC ComputingGrid for delivering so effectively the computing infrastructure essential to our analyses. Fi-nally, we acknowledge the enduring support for the construction and operation of the LHCand the CMS detector provided by the following funding agencies: the Austrian Federal Min-istry of Science, Research and Economy and the Austrian Science Fund; the Belgian Fonds dela Recherche Scientifique, and Fonds voor Wetenschappelijk Onderzoek; the Brazilian Fund-ing Agencies (CNPq, CAPES, FAPERJ, and FAPESP); the Bulgarian Ministry of Education andScience; CERN; the Chinese Academy of Sciences, Ministry of Science and Technology, and Na-tional Natural Science Foundation of China; the Colombian Funding Agency (COLCIENCIAS);the Croatian Ministry of Science, Education and Sport, and the Croatian Science Foundation;the Research Promotion Foundation, Cyprus; the Secretariat for Higher Education, Science,Technology and Innovation, Ecuador; the Ministry of Education and Research, Estonian Re-search Council via IUT23-4 and IUT23-6 and European Regional Development Fund, Estonia;the Academy of Finland, Finnish Ministry of Education and Culture, and Helsinki Institute ofPhysics; the Institut National de Physique Nucl´eaire et de Physique des Particules / CNRS, andCommissariat `a l’ ´Energie Atomique et aux ´Energies Alternatives / CEA, France; the Bundes-ministerium f ¨ur Bildung und Forschung, Deutsche Forschungsgemeinschaft, and Helmholtz-Gemeinschaft Deutscher Forschungszentren, Germany; the General Secretariat for Researchand Technology, Greece; the National Scientific Research Foundation, and National Innova-tion Office, Hungary; the Department of Atomic Energy and the Department of Science andTechnology, India; the Institute for Studies in Theoretical Physics and Mathematics, Iran; theScience Foundation, Ireland; the Istituto Nazionale di Fisica Nucleare, Italy; the Ministry ofScience, ICT and Future Planning, and National Research Foundation (NRF), Republic of Ko-rea; the Lithuanian Academy of Sciences; the Ministry of Education, and University of Malaya(Malaysia); the Mexican Funding Agencies (BUAP, CINVESTAV, CONACYT, LNS, SEP, andUASLP-FAI); the Ministry of Business, Innovation and Employment, New Zealand; the Pak-istan Atomic Energy Commission; the Ministry of Science and Higher Education and the Na-tional Science Centre, Poland; the Fundac¸ ˜ao para a Ciˆencia e a Tecnologia, Portugal; JINR,Dubna; the Ministry of Education and Science of the Russian Federation, the Federal Agencyof Atomic Energy of the Russian Federation, Russian Academy of Sciences, and the RussianFoundation for Basic Research; the Ministry of Education, Science and Technological Devel-opment of Serbia; the Secretar´ıa de Estado de Investigaci ´on, Desarrollo e Innovaci ´on and Pro-grama Consolider-Ingenio 2010, Spain; the Swiss Funding Agencies (ETH Board, ETH Zurich,PSI, SNF, UniZH, Canton Zurich, and SER); the Ministry of Science and Technology, Taipei; theThailand Center of Excellence in Physics, the Institute for the Promotion of Teaching Scienceand Technology of Thailand, Special Task Force for Activating Research and the National Sci-ence and Technology Development Agency of Thailand; the Scientific and Technical ResearchCouncil of Turkey, and Turkish Atomic Energy Authority; the National Academy of Sciencesof Ukraine, and State Fund for Fundamental Researches, Ukraine; the Science and TechnologyFacilities 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 Re-search Council and EPLANET (European Union); the Leventis Foundation; the A. P. SloanFoundation; the Alexander von Humboldt Foundation; the Belgian Federal Science Policy Of-fice; the Fonds pour la Formation `a la Recherche dans l’Industrie et dans l’Agriculture (FRIA- References
Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); theMinistry of Education, Youth and Sports (MEYS) of the Czech Republic; the Council of Sci-ence and Industrial Research, India; the HOMING PLUS programme of the Foundation forPolish Science, cofinanced from European Union, Regional Development Fund, the Mobil-ity Plus programme of the Ministry of Science and Higher Education, the National ScienceCenter (Poland), contracts Harmonia 2014/14/M/ST2/00428, Opus 2013/11/B/ST2/04202,2014/13/B/ST2/02543 and 2014/15/B/ST2/03998, Sonata-bis 2012/07/E/ST2/01406; the Thalisand Aristeia programmes cofinanced by EU-ESF and the Greek NSRF; the National PrioritiesResearch Program by Qatar National Research Fund; the Programa Clar´ın-COFUND del Princi-pado de Asturias; the Rachadapisek Sompot Fund for Postdoctoral Fellowship, ChulalongkornUniversity and the Chulalongkorn Academic into Its 2nd Century Project Advancement Project(Thailand); and the Welch Foundation, contract C-1845.
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A. Harb, J. Hauk, M. Hempel , H. Jung, A. Kalogeropoulos, O. Karacheban , M. Kasemann,J. Keaveney, C. Kleinwort, I. Korol, D. Kr ¨ucker, W. Lange, A. Lelek, T. Lenz, J. Leonard,K. Lipka, W. Lohmann , R. Mankel, I.-A. Melzer-Pellmann, A.B. Meyer, G. Mittag, J. Mnich,A. Mussgiller, E. Ntomari, D. Pitzl, R. Placakyte, A. Raspereza, B. Roland, M. Savitskyi,P. Saxena, R. Shevchenko, S. Spannagel, N. Stefaniuk, G.P. Van Onsem, R. Walsh, Y. Wen,K. Wichmann, C. Wissing University of Hamburg, Hamburg, Germany
V. Blobel, M. Centis Vignali, A.R. Draeger, T. Dreyer, E. Garutti, D. Gonzalez, J. Haller,M. Hoffmann, A. Junkes, R. Klanner, R. Kogler, N. Kovalchuk, S. Kurz, T. Lapsien,I. Marchesini, D. Marconi, M. Meyer, M. Niedziela, D. Nowatschin, F. Pantaleo , T. Peiffer,A. Perieanu, C. Scharf, P. Schleper, A. Schmidt, S. Schumann, J. Schwandt, J. Sonneveld,H. Stadie, G. Steinbr ¨uck, F.M. Stober, M. St ¨over, H. Tholen, D. Troendle, E. Usai, L. Vanelderen,A. Vanhoefer, B. Vormwald Institut f ¨ur Experimentelle Kernphysik, Karlsruhe, Germany
M. Akbiyik, C. Barth, S. Baur, C. Baus, J. Berger, E. Butz, R. Caspart, T. Chwalek, F. Colombo,W. De Boer, A. Dierlamm, B. Freund, R. Friese, M. Giffels, A. Gilbert, D. Haitz, F. Hartmann ,S.M. Heindl, U. Husemann, F. Kassel , S. Kudella, H. Mildner, M.U. Mozer, Th. M ¨uller,M. Plagge, G. Quast, K. Rabbertz, M. Schr ¨oder, I. Shvetsov, G. Sieber, H.J. Simonis, R. Ulrich,S. Wayand, M. Weber, T. Weiler, S. Williamson, C. W ¨ohrmann, R. Wolf Institute of Nuclear and Particle Physics (INPP), NCSR Demokritos, Aghia Paraskevi,Greece
G. Anagnostou, G. Daskalakis, T. Geralis, V.A. Giakoumopoulou, A. Kyriakis, D. Loukas,I. Topsis-Giotis
National and Kapodistrian University of Athens, Athens, Greece
S. Kesisoglou, A. Panagiotou, N. Saoulidou
University of Io´annina, Io´annina, Greece
I. Evangelou, G. Flouris, C. Foudas, P. Kokkas, N. Manthos, I. Papadopoulos, E. Paradas,J. Strologas, F.A. Triantis
MTA-ELTE Lend ¨ulet CMS Particle and Nuclear Physics Group, E ¨otv ¨os Lor´and University,Budapest, Hungary
M. Csanad, N. Filipovic, G. Pasztor
Wigner Research Centre for Physics, Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath , F. Sikler, V. Veszpremi, G. Vesztergombi , A.J. Zsigmond Institute of Nuclear Research ATOMKI, Debrecen, Hungary
N. Beni, S. Czellar, J. Karancsi , A. Makovec, J. Molnar, Z. Szillasi Institute of Physics, University of Debrecen
M. Bart ´ok , P. Raics, Z.L. Trocsanyi, B. Ujvari Indian Institute of Science (IISc)
S. Choudhury, J.R. Komaragiri
National Institute of Science Education and Research, Bhubaneswar, India
S. Bahinipati , S. Bhowmik, P. Mal, K. Mandal, A. Nayak , D.K. Sahoo , N. Sahoo, S.K. Swain Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, R. Chawla, N. Dhingra, U.Bhawandeep, A.K. Kalsi, A. Kaur,M. Kaur, R. Kumar, P. Kumari, A. Mehta, M. Mittal, J.B. Singh, G. Walia
University of Delhi, Delhi, India
Ashok Kumar, A. Bhardwaj, S. Chauhan, B.C. Choudhary, R.B. Garg, S. Keshri, S. Malhotra,M. Naimuddin, K. Ranjan, A. Shah, R. Sharma, V. Sharma
Saha Institute of Nuclear Physics, Kolkata, India
R. Bhattacharya, S. Bhattacharya, K. Chatterjee, S. Dey, S. Dutt, S. Dutta, S. Ghosh,N. Majumdar, A. Modak, K. Mondal, S. Mukhopadhyay, S. Nandan, A. Purohit, A. Roy, D. Roy,S. Roy Chowdhury, S. Sarkar, M. Sharan, S. Thakur
Indian Institute of Technology Madras, Madras, India
P.K. Behera
Bhabha Atomic Research Centre, Mumbai, India
R. Chudasama, D. Dutta, V. Jha, V. Kumar, A.K. Mohanty , P.K. Netrakanti, L.M. Pant,P. Shukla, A. Topkar Tata Institute of Fundamental Research-A, Mumbai, India
T. Aziz, S. Dugad, B. Mahakud, S. Mitra, G.B. Mohanty, B. Parida, N. Sur, B. Sutar
Tata Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, R.K. Dewanjee, S. Ganguly, M. Guchait,Sa. Jain, S. Kumar, M. Maity , G. Majumder, K. Mazumdar, T. Sarkar , N. Wickramage Indian Institute of Science Education and Research (IISER), Pune, India
S. Chauhan, S. Dube, V. Hegde, A. Kapoor, K. Kothekar, S. Pandey, A. Rane, S. Sharma
Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
S. Chenarani , E. Eskandari Tadavani, S.M. Etesami , M. Khakzad, M. MohammadiNajafabadi, M. Naseri, S. Paktinat Mehdiabadi , F. Rezaei Hosseinabadi, B. Safarzadeh ,M. Zeinali University College Dublin, Dublin, Ireland
M. Felcini, M. Grunewald
INFN Sezione di Bari a , Universit`a di Bari b , Politecnico di Bari c , Bari, Italy M. Abbrescia a , b , C. Calabria a , b , C. Caputo a , b , A. Colaleo a , D. Creanza a , c , L. Cristella a , b , N. DeFilippis a , c , M. De Palma a , b , L. Fiore a , G. Iaselli a , c , G. Maggi a , c , M. Maggi a , G. Miniello a , b ,S. My a , b , S. Nuzzo a , b , A. Pompili a , b , G. Pugliese a , c , R. Radogna a , b , A. Ranieri a , G. Selvaggi a , b ,A. Sharma a , L. Silvestris a ,13 , R. Venditti a , P. Verwilligen a INFN Sezione di Bologna a , Universit`a di Bologna b , Bologna, Italy G. Abbiendi a , C. Battilana, D. Bonacorsi a , b , S. Braibant-Giacomelli a , b , L. Brigliadori a , b ,R. Campanini a , b , P. Capiluppi a , b , A. Castro a , b , F.R. Cavallo a , S.S. Chhibra a , b , M. Cuffiani a , b ,G.M. Dallavalle a , F. Fabbri a , A. Fanfani a , b , D. Fasanella a , b , P. Giacomelli a , L. Guiducci a , b ,S. Marcellini a , G. Masetti a , F.L. Navarria a , b , A. Perrotta a , A.M. Rossi a , b , T. Rovelli a , b ,G.P. Siroli a , b , N. Tosi a , b ,13 INFN Sezione di Catania a , Universit`a di Catania b , Catania, Italy S. Albergo a , b , S. Costa a , b , A. Di Mattia a , F. Giordano a , b , R. Potenza a , b , A. Tricomi a , b , C. Tuve a , b A The CMS Collaboration
INFN Sezione di Firenze a , Universit`a di Firenze b , Firenze, Italy G. Barbagli a , V. Ciulli a , b , C. Civinini a , R. D’Alessandro a , b , E. Focardi a , b , P. Lenzi a , b ,M. Meschini a , S. Paoletti a , L. Russo a ,27 , G. Sguazzoni a , L. Viliani a , b ,13 INFN Laboratori Nazionali di Frascati, Frascati, Italy
L. Benussi, S. Bianco, F. Fabbri, D. Piccolo, F. Primavera INFN Sezione di Genova a , Universit`a di Genova b , Genova, Italy V. Calvelli a , b , F. Ferro a , M.R. Monge a , b , E. Robutti a , S. Tosi a , b INFN Sezione di Milano-Bicocca a , Universit`a di Milano-Bicocca b , Milano, Italy L. Brianza a , b ,13 , F. Brivio a , b , V. Ciriolo, M.E. Dinardo a , b , S. Fiorendi a , b ,13 , S. Gennai a ,A. Ghezzi a , b , P. Govoni a , b , M. Malberti a , b , S. Malvezzi a , R.A. Manzoni a , b , D. Menasce a ,L. Moroni a , M. Paganoni a , b , K. Pauwels, D. Pedrini a , S. Pigazzini a , b , S. Ragazzi a , b , T. Tabarellide Fatis a , b INFN Sezione di Napoli a , Universit`a di Napoli ’Federico II’ b , Napoli, Italy, Universit`a dellaBasilicata c , Potenza, Italy, Universit`a G. Marconi d , Roma, Italy S. Buontempo a , N. Cavallo a , c , S. Di Guida a , d ,13 , M. Esposito a , b , F. Fabozzi a , c , F. Fienga a , b ,A.O.M. Iorio a , b , G. Lanza a , L. Lista a , S. Meola a , d ,13 , P. Paolucci a ,13 , C. Sciacca a , b , F. Thyssen a INFN Sezione di Padova a , Universit`a di Padova b , Padova, Italy, Universit`a di Trento c ,Trento, Italy P. Azzi a ,13 , N. Bacchetta a , L. Benato a , b , M. Biasotto a ,28 , D. Bisello a , b , A. Boletti a , b , R. Carlin a , b ,A. Carvalho Antunes De Oliveira a , b , P. Checchia a , M. Dall’Osso a , b , P. De Castro Manzano a ,T. Dorigo a , S. Fantinel a , U. Gasparini a , b , A. Gozzelino a , S. Lacaprara a , M. Margoni a , b ,A.T. Meneguzzo a , b , N. Pozzobon a , b , P. Ronchese a , b , R. Rossin a , b , F. Simonetto a , b , E. Torassa a ,S. Ventura a , M. Zanetti a , b , P. Zotto a , b INFN Sezione di Pavia a , Universit`a di Pavia b , Pavia, Italy A. Braghieri a , F. Fallavollita a , b , A. Magnani a , b , P. Montagna a , b , S.P. Ratti a , b , V. Re a , M. Ressegotti,C. Riccardi a , b , P. Salvini a , I. Vai a , b , P. Vitulo a , b INFN Sezione di Perugia a , Universit`a di Perugia b , Perugia, Italy L. Alunni Solestizi a , b , G.M. Bilei a , D. Ciangottini a , b , L. Fan `o a , b , P. Lariccia a , b , R. Leonardi a , b ,G. Mantovani a , b , V. Mariani a , b , M. Menichelli a , A. Saha a , A. Santocchia a , b , D. Spiga INFN Sezione di Pisa a , Universit`a di Pisa b , Scuola Normale Superiore di Pisa c , Pisa, Italy K. Androsov a , P. Azzurri a ,13 , G. Bagliesi a , J. Bernardini a , T. Boccali a , L. Borrello, R. Castaldi a ,M.A. Ciocci a , b , R. Dell’Orso a , G. Fedi a , A. Giassi a , M.T. Grippo a ,27 , F. Ligabue a , c , T. Lomtadze a ,L. Martini a , b , A. Messineo a , b , F. Palla a , A. Rizzi a , b , A. Savoy-Navarro a ,29 , P. Spagnolo a ,R. Tenchini a , G. Tonelli a , b , A. Venturi a , P.G. Verdini a INFN Sezione di Roma a , Sapienza Universit`a di Roma b L. Barone, F. Cavallari, M. Cipriani, D. Del Re , M. Diemoz, S. Gelli, E. Longo, F. Margaroli,B. Marzocchi, P. Meridiani, G. Organtini, R. Paramatti, F. Preiato, S. Rahatlou, C. Rovelli,F. Santanastasio INFN Sezione di Torino a , Universit`a di Torino b , Torino, Italy, Universit`a del PiemonteOrientale c , Novara, Italy N. Amapane a , b , R. Arcidiacono a , c ,13 , S. Argiro a , b , M. Arneodo a , c , N. Bartosik a , R. Bellan a , b ,C. Biino a , N. Cartiglia a , F. Cenna a , b , M. Costa a , b , R. Covarelli a , b , A. Degano a , b , N. Demaria a ,B. Kiani a , b , C. Mariotti a , S. Maselli a , E. Migliore a , b , V. Monaco a , b , E. Monteil a , b , M. Monteno a ,M.M. Obertino a , b , L. Pacher a , b , N. Pastrone a , M. Pelliccioni a , G.L. Pinna Angioni a , b , F. Ravera a , b , A. Romero a , b , M. Ruspa a , c , R. Sacchi a , b , K. Shchelina a , b , V. Sola a , A. Solano a , b , A. Staiano a ,P. Traczyk a , b INFN Sezione di Trieste a , Universit`a di Trieste b , Trieste, Italy S. Belforte a , M. Casarsa a , F. Cossutti a , G. Della Ricca a , b , A. Zanetti a Kyungpook National University, Daegu, Korea
D.H. Kim, G.N. Kim, M.S. Kim, J. Lee, S. Lee, S.W. Lee, Y.D. Oh, S. Sekmen, D.C. Son, Y.C. Yang
Chonbuk National University, Jeonju, Korea
A. Lee
Chonnam National University, Institute for Universe and Elementary Particles, Kwangju,Korea
H. Kim, D.H. Moon
Hanyang University, Seoul, Korea
J.A. Brochero Cifuentes, J. Goh, T.J. Kim
Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, D. Gyun, S. Ha, B. Hong, Y. Jo, Y. Kim, K. Lee, K.S. Lee, S. Lee, J. Lim,S.K. Park, Y. Roh
Seoul National University, Seoul, Korea
J. Almond, J. Kim, H. Lee, S.B. Oh, B.C. Radburn-Smith, S.h. Seo, U.K. Yang, H.D. Yoo, G.B. Yu
University of Seoul, Seoul, Korea
M. Choi, H. Kim, J.H. Kim, J.S.H. Lee, I.C. Park, G. Ryu
Sungkyunkwan University, Suwon, Korea
Y. Choi, C. Hwang, J. Lee, I. Yu
Vilnius University, Vilnius, Lithuania
V. Dudenas, A. Juodagalvis, J. Vaitkus
National Centre for Particle Physics, Universiti Malaya, Kuala Lumpur, Malaysia
I. Ahmed, Z.A. Ibrahim, M.A.B. Md Ali , F. Mohamad Idris , W.A.T. Wan Abdullah,M.N. Yusli, Z. Zolkapli Centro 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. MejiaGuisao, A. Sanchez-Hernandez Universidad Iberoamericana, Mexico City, Mexico
S. Carrillo Moreno, C. Oropeza Barrera, F. Vazquez Valencia
Benemerita Universidad Autonoma de Puebla, Puebla, Mexico
S. Carpinteyro, I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada
Universidad Aut ´onoma de San Luis Potos´ı, San Luis Potos´ı, Mexico
A. Morelos Pineda
University of Auckland, Auckland, New Zealand
D. Krofcheck
University of Canterbury, Christchurch, New Zealand
P.H. Butler A The CMS Collaboration
National Centre for Physics, Quaid-I-Azam University, Islamabad, Pakistan
A. Ahmad, M. Ahmad, Q. Hassan, H.R. Hoorani, W.A. Khan, A. Saddique, M.A. Shah,M. Shoaib, M. Waqas
National Centre for Nuclear Research, Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, T. Frueboes, M. G ´orski, M. Kazana, K. Nawrocki,K. Romanowska-Rybinska, M. Szleper, P. Zalewski
Institute of Experimental Physics, Faculty of Physics, University of Warsaw, Warsaw, Poland
K. Bunkowski, A. Byszuk , K. Doroba, A. Kalinowski, M. Konecki, J. Krolikowski, M. Misiura,M. Olszewski, A. Pyskir, M. Walczak Laborat ´orio de Instrumenta¸c˜ao e F´ısica Experimental de Part´ıculas, Lisboa, Portugal
P. Bargassa, C. Beir˜ao Da Cruz E Silva, B. Calpas, A. Di Francesco, P. Faccioli, M. Gallinaro,J. Hollar, N. Leonardo, L. Lloret Iglesias, M.V. Nemallapudi, J. Seixas, O. Toldaiev, D. Vadruccio,J. Varela
Joint Institute for Nuclear Research, Dubna, Russia
S. Afanasiev, P. Bunin, M. Gavrilenko, I. Golutvin, I. Gorbunov, A. Kamenev, V. Karjavin,A. Lanev, A. Malakhov, V. Matveev , V. Palichik, V. Perelygin, S. Shmatov, S. Shulha,N. Skatchkov, V. Smirnov, N. Voytishin, A. Zarubin
Petersburg Nuclear Physics Institute, Gatchina (St. Petersburg), Russia
Y. Ivanov, V. Kim , E. Kuznetsova , P. Levchenko, V. Murzin, V. Oreshkin, I. Smirnov,V. Sulimov, L. Uvarov, S. Vavilov, A. Vorobyev Institute for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov, N. Krasnikov,A. Pashenkov, D. Tlisov, A. Toropin
Institute for Theoretical and Experimental Physics, Moscow, Russia
V. Epshteyn, V. Gavrilov, N. Lychkovskaya, V. Popov, I. Pozdnyakov, G. Safronov,A. Spiridonov, M. Toms, E. Vlasov, A. Zhokin
Moscow Institute of Physics and Technology, Moscow, Russia
T. Aushev, A. Bylinkin National Research Nuclear University ’Moscow Engineering Physics Institute’ (MEPhI),Moscow, Russia
M. Chadeeva , E. Popova, E. Tarkovskii P.N. Lebedev Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin , I. Dremin , M. Kirakosyan, A. Terkulov Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow,Russia
A. Baskakov, A. Belyaev, E. Boos, V. Bunichev, M. Dubinin , L. Dudko, A. Ershov, A. Gribushin,V. Klyukhin, N. Korneeva, I. Lokhtin, I. Miagkov, S. Obraztsov, M. Perfilov, V. Savrin Novosibirsk State University (NSU), Novosibirsk, Russia
V. Blinov , Y.Skovpen , D. Shtol State Research Center of Russian Federation, Institute for High Energy Physics, Protvino,Russia
I. Azhgirey, I. Bayshev, S. Bitioukov, D. Elumakhov, V. Kachanov, A. Kalinin, D. Konstantinov,V. Krychkine, V. Petrov, R. Ryutin, A. Sobol, S. Troshin, N. Tyurin, A. Uzunian, A. Volkov University of Belgrade, Faculty of Physics and Vinca Institute of Nuclear Sciences, Belgrade,Serbia
P. Adzic , P. Cirkovic, D. Devetak, M. Dordevic, J. Milosevic, V. Rekovic Centro de Investigaciones Energ´eticas Medioambientales y Tecnol ´ogicas (CIEMAT),Madrid, Spain
J. Alcaraz Maestre, M. Barrio Luna, M. Cerrada, M. Chamizo Llatas, N. Colino, B. De LaCruz, A. Delgado Peris, A. Escalante Del Valle, C. Fernandez Bedoya, J.P. Fern´andez Ramos,J. Flix, M.C. Fouz, P. Garcia-Abia, O. Gonzalez Lopez, S. Goy Lopez, J.M. Hernandez, M.I. Josa,E. Navarro De Martino, A. P´erez-Calero Yzquierdo, J. Puerta Pelayo, A. Quintario Olmeda,I. Redondo, L. Romero, M.S. Soares
Universidad Aut ´onoma de Madrid, Madrid, Spain
J.F. de Troc ´oniz, M. Missiroli, D. Moran
Universidad de Oviedo, Oviedo, Spain
J. Cuevas, C. Erice, J. Fernandez Menendez, I. Gonzalez Caballero, J.R. Gonz´alez Fern´andez,E. Palencia Cortezon, S. Sanchez Cruz, I. Su´arez Andr´es, P. Vischia, J.M. Vizan Garcia
Instituto de F´ısica de Cantabria (IFCA), CSIC-Universidad de Cantabria, Santander, Spain
I.J. Cabrillo, A. Calderon, B. Chazin Quero, E. Curras, M. Fernandez, J. Garcia-Ferrero,G. Gomez, A. Lopez Virto, J. Marco, C. Martinez Rivero, F. Matorras, J. Piedra Gomez,T. Rodrigo, A. Ruiz-Jimeno, L. Scodellaro, N. Trevisani, I. Vila, R. Vilar Cortabitarte
CERN, European Organization for Nuclear Research, Geneva, Switzerland
D. Abbaneo, E. Auffray, P. Baillon, A.H. Ball, D. Barney, M. Bianco, P. Bloch, A. Bocci,C. Botta, T. Camporesi, R. Castello, M. Cepeda, G. Cerminara, Y. Chen, D. d’Enterria,A. Dabrowski, V. Daponte, A. David, M. De Gruttola, A. De Roeck, E. Di Marco , M. Dobson,B. Dorney, T. du Pree, M. D ¨unser, N. Dupont, A. Elliott-Peisert, P. Everaerts, G. Franzoni,J. Fulcher, W. Funk, D. Gigi, K. Gill, F. Glege, D. Gulhan, S. Gundacker, M. Guthoff, P. Harris,J. Hegeman, V. Innocente, P. Janot, J. Kieseler, H. Kirschenmann, V. Kn ¨unz, A. Kornmayer ,M.J. Kortelainen, M. Krammer , C. Lange, P. Lecoq, C. Lourenc¸o, M.T. Lucchini, L. Malgeri,M. Mannelli, A. Martelli, F. Meijers, J.A. Merlin, S. Mersi, E. Meschi, P. Milenovic , F. Moortgat,M. Mulders, H. Neugebauer, S. Orfanelli, L. Orsini, L. Pape, E. Perez, M. Peruzzi, A. Petrilli,G. Petrucciani, A. Pfeiffer, M. Pierini, A. Racz, T. Reis, G. Rolandi , M. Rovere, H. Sakulin,J.B. Sauvan, C. Sch¨afer, C. Schwick, M. Seidel, A. Sharma, P. Silva, P. Sphicas , J. Steggemann,M. Stoye, M. Tosi, D. Treille, A. Triossi, A. Tsirou, V. Veckalns , G.I. Veres , M. Verweij,N. Wardle, A. Zagozdzinska , W.D. Zeuner Paul Scherrer Institut, Villigen, Switzerland
W. Bertl, K. Deiters, W. Erdmann, R. Horisberger, Q. Ingram, H.C. Kaestli, D. Kotlinski,U. Langenegger, T. Rohe, S.A. Wiederkehr
Institute for Particle Physics, ETH Zurich, Zurich, Switzerland
F. Bachmair, L. B¨ani, L. Bianchini, B. Casal, G. Dissertori, M. Dittmar, M. Doneg`a, C. Grab,C. Heidegger, D. Hits, J. Hoss, G. Kasieczka, W. Lustermann, B. Mangano, M. Marionneau,P. Martinez Ruiz del Arbol, M. Masciovecchio, M.T. Meinhard, D. Meister, F. Micheli,P. Musella, F. Nessi-Tedaldi, F. Pandolfi, J. Pata, F. Pauss, G. Perrin, L. Perrozzi, M. Quittnat,M. Rossini, M. Sch ¨onenberger, A. Starodumov , V.R. Tavolaro, K. Theofilatos, R. Wallny Universit¨at Z ¨urich, Zurich, Switzerland
T.K. Aarrestad, C. Amsler , L. Caminada, M.F. Canelli, A. De Cosa, S. Donato, C. Galloni, A The CMS Collaboration
A. Hinzmann, T. Hreus, B. Kilminster, J. Ngadiuba, D. Pinna, G. Rauco, P. Robmann, D. Salerno,C. Seitz, Y. Yang, A. Zucchetta
National Central University, Chung-Li, Taiwan
V. Candelise, T.H. Doan, Sh. Jain, R. Khurana, M. Konyushikhin, C.M. Kuo, W. Lin,A. Pozdnyakov, S.S. Yu
National Taiwan University (NTU), Taipei, Taiwan
Arun Kumar, P. Chang, Y.H. Chang, Y. Chao, K.F. Chen, P.H. Chen, F. Fiori, W.-S. Hou,Y. Hsiung, Y.F. Liu, R.-S. Lu, M. Mi ˜nano Moya, E. Paganis, A. Psallidas, J.f. Tsai
Chulalongkorn University, Faculty of Science, Department of Physics, Bangkok, Thailand
B. Asavapibhop, K. Kovitanggoon, G. Singh, N. Srimanobhas
Cukurova University - Physics Department, Science and Art Faculty
A. Adiguzel, F. Boran, S. Damarseckin, Z.S. Demiroglu, C. Dozen, E. Eskut, S. Girgis,G. Gokbulut, Y. Guler, I. Hos , E.E. Kangal , O. Kara, A. Kayis Topaksu, U. Kiminsu,M. Oglakci, G. Onengut , K. Ozdemir , S. Ozturk , A. Polatoz, B. Tali , S. Turkcapar,I.S. Zorbakir, C. Zorbilmez Middle East Technical University, Physics Department, Ankara, Turkey
B. Bilin, G. Karapinar , K. Ocalan , M. Yalvac, M. Zeyrek Bogazici University, Istanbul, Turkey
E. G ¨ulmez, M. Kaya , O. Kaya , E.A. Yetkin Istanbul Technical University, Istanbul, Turkey
A. Cakir, K. Cankocak
Institute for Scintillation Materials of National Academy of Science of Ukraine, Kharkov,Ukraine
B. Grynyov
National Scientific Center, Kharkov Institute of Physics and Technology, Kharkov, Ukraine
L. Levchuk, P. Sorokin
University of Bristol, Bristol, United Kingdom
R. Aggleton, F. Ball, L. Beck, J.J. Brooke, D. Burns, E. Clement, D. Cussans, H. Flacher,J. Goldstein, M. Grimes, G.P. Heath, H.F. Heath, J. Jacob, L. Kreczko, C. Lucas, D.M. Newbold ,S. Paramesvaran, A. Poll, T. Sakuma, S. Seif El Nasr-storey, D. Smith, V.J. Smith Rutherford Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev , C. Brew, R.M. Brown, L. Calligaris, D. Cieri, D.J.A. Cockerill,J.A. Coughlan, K. Harder, S. Harper, E. Olaiya, D. Petyt, C.H. Shepherd-Themistocleous,A. Thea, I.R. Tomalin, T. Williams Imperial College, London, United Kingdom
M. Baber, R. Bainbridge, O. Buchmuller, A. Bundock, S. Casasso, M. Citron, D. Colling,L. Corpe, P. Dauncey, G. Davies, A. De Wit, M. Della Negra, R. Di Maria, P. Dunne, A. Elwood,D. Futyan, Y. Haddad, G. Hall, G. Iles, T. James, R. Lane, C. Laner, L. Lyons, A.-M. Magnan,S. Malik, L. Mastrolorenzo, J. Nash, A. Nikitenko , J. Pela, M. Pesaresi, D.M. Raymond,A. Richards, A. Rose, E. Scott, C. Seez, S. Summers, A. Tapper, K. Uchida, M. Vazquez Acosta ,T. Virdee , J. Wright, S.C. Zenz Brunel University, Uxbridge, United Kingdom
J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, I.D. Reid, P. Symonds, L. Teodorescu, M. Turner Baylor University, Waco, USA
A. Borzou, K. Call, J. Dittmann, K. Hatakeyama, H. Liu, N. Pastika
Catholic University of America
R. Bartek, A. Dominguez
The University of Alabama, Tuscaloosa, USA
A. Buccilli, S.I. Cooper, C. Henderson, P. Rumerio, C. West
Boston University, Boston, USA
D. Arcaro, A. Avetisyan, T. Bose, D. Gastler, D. Rankin, C. Richardson, J. Rohlf, L. Sulak, D. Zou
Brown University, Providence, USA
G. Benelli, D. Cutts, A. Garabedian, J. Hakala, U. Heintz, J.M. Hogan, K.H.M. Kwok, E. Laird,G. Landsberg, Z. Mao, M. Narain, S. Piperov, S. Sagir, E. Spencer, R. Syarif
University of California, Davis, Davis, USA
D. Burns, M. Calderon De La Barca Sanchez, M. Chertok, J. Conway, R. Conway, P.T. Cox,R. Erbacher, C. Flores, G. Funk, M. Gardner, W. Ko, R. Lander, C. Mclean, M. Mulhearn,D. Pellett, J. Pilot, S. Shalhout, M. Shi, J. Smith, M. Squires, D. Stolp, K. Tos, M. Tripathi
University of California, Los Angeles, USA
M. Bachtis, C. Bravo, R. Cousins, A. Dasgupta, A. Florent, J. Hauser, M. Ignatenko, N. Mccoll,D. Saltzberg, C. Schnaible, V. Valuev
University of California, Riverside, Riverside, USA
E. Bouvier, K. Burt, R. Clare, J. Ellison, J.W. Gary, S.M.A. Ghiasi Shirazi, G. Hanson, J. Heilman,P. Jandir, E. Kennedy, F. Lacroix, O.R. Long, M. Olmedo Negrete, M.I. Paneva, A. Shrinivas,W. Si, H. Wei, S. Wimpenny, B. R. Yates
University of California, San Diego, La Jolla, USA
J.G. Branson, G.B. Cerati, S. Cittolin, M. Derdzinski, A. Holzner, D. Klein, G. Kole, V. Krutelyov,J. Letts, I. Macneill, D. Olivito, S. Padhi, M. Pieri, M. Sani, V. Sharma, S. Simon, M. Tadel,A. Vartak, S. Wasserbaech , F. W ¨urthwein, A. Yagil, G. Zevi Della Porta University of California, Santa Barbara - Department of Physics, Santa Barbara, USA
N. Amin, R. Bhandari, J. Bradmiller-Feld, C. Campagnari, A. Dishaw, V. Dutta, M. FrancoSevilla, C. George, F. Golf, L. Gouskos, J. Gran, R. Heller, J. Incandela, S.D. Mullin,A. Ovcharova, H. Qu, J. Richman, D. Stuart, I. Suarez, J. Yoo
California Institute of Technology, Pasadena, USA
D. Anderson, J. Bendavid, A. Bornheim, J.M. Lawhorn, H.B. Newman, C. Pena, M. Spiropulu,J.R. Vlimant, S. Xie, R.Y. Zhu
Carnegie Mellon University, Pittsburgh, USA
M.B. Andrews, T. Ferguson, M. Paulini, J. Russ, M. Sun, H. Vogel, I. Vorobiev, M. Weinberg
University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, F. Jensen, A. Johnson, M. Krohn, S. Leontsinis, T. Mulholland,K. Stenson, S.R. Wagner
Cornell University, Ithaca, USA
J. Alexander, J. Chaves, J. Chu, S. Dittmer, K. Mcdermott, N. Mirman, J.R. Patterson,A. Rinkevicius, A. Ryd, L. Skinnari, L. Soffi, S.M. Tan, Z. Tao, J. Thom, J. Tucker, P. Wittich,M. Zientek A The CMS Collaboration
Fairfield University, Fairfield, USA
D. Winn
Fermi National Accelerator Laboratory, Batavia, USA
S. Abdullin, M. Albrow, G. Apollinari, A. Apresyan, S. Banerjee, L.A.T. Bauerdick, A. Beretvas,J. Berryhill, P.C. Bhat, G. Bolla, K. Burkett, J.N. Butler, A. Canepa, H.W.K. Cheung, F. Chlebana,M. Cremonesi, J. Duarte, V.D. Elvira, I. Fisk, J. Freeman, Z. Gecse, E. Gottschalk, L. Gray,D. Green, S. Gr ¨unendahl, O. Gutsche, R.M. Harris, S. Hasegawa, J. Hirschauer, Z. Hu,B. Jayatilaka, S. Jindariani, M. Johnson, U. Joshi, B. Klima, B. Kreis, S. Lammel, D. Lincoln,R. Lipton, M. Liu, T. Liu, R. Lopes De S´a, J. Lykken, K. Maeshima, N. Magini, J.M. Marraffino,S. Maruyama, D. Mason, P. McBride, P. Merkel, S. Mrenna, S. Nahn, V. O’Dell, K. Pedro,O. Prokofyev, G. Rakness, L. Ristori, B. Schneider, E. Sexton-Kennedy, A. Soha, W.J. Spalding,L. Spiegel, S. Stoynev, J. Strait, N. Strobbe, L. Taylor, S. Tkaczyk, N.V. Tran, L. Uplegger,E.W. Vaandering, C. Vernieri, M. Verzocchi, R. Vidal, M. Wang, H.A. Weber, A. Whitbeck
University of Florida, Gainesville, USA
D. Acosta, P. Avery, P. Bortignon, A. Brinkerhoff, A. Carnes, M. Carver, D. Curry, S. Das,R.D. Field, I.K. Furic, J. Konigsberg, A. Korytov, K. Kotov, P. Ma, K. Matchev, H. Mei,G. Mitselmakher, D. Rank, L. Shchutska, D. Sperka, N. Terentyev, L. Thomas, J. Wang, S. Wang,J. Yelton
Florida International University, Miami, USA
S. Linn, P. Markowitz, G. Martinez, J.L. Rodriguez
Florida State University, Tallahassee, USA
A. Ackert, T. Adams, A. Askew, S. Bein, S. Hagopian, V. Hagopian, K.F. Johnson, T. Kolberg,T. Perry, H. Prosper, A. Santra, R. Yohay
Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, V. Bhopatkar, S. Colafranceschi, M. Hohlmann, D. Noonan, T. Roy,F. Yumiceva
University of Illinois at Chicago (UIC), Chicago, USA
M.R. Adams, L. Apanasevich, D. Berry, R.R. Betts, R. Cavanaugh, X. Chen, O. Evdokimov,C.E. Gerber, D.A. Hangal, D.J. Hofman, K. Jung, J. Kamin, I.D. Sandoval Gonzalez, M.B. Tonjes,H. Trauger, N. Varelas, H. Wang, Z. Wu, J. Zhang
The University of Iowa, Iowa City, USA
B. Bilki , W. Clarida, K. Dilsiz, S. Durgut, R.P. Gandrajula, M. Haytmyradov, V. Khristenko,J.-P. Merlo, H. Mermerkaya , A. Mestvirishvili, A. Moeller, J. Nachtman, H. Ogul, Y. Onel,F. Ozok , A. Penzo, C. Snyder, E. Tiras, J. Wetzel, K. Yi Johns Hopkins University, Baltimore, USA
B. Blumenfeld, A. Cocoros, N. Eminizer, D. Fehling, L. Feng, A.V. Gritsan, P. Maksimovic,J. Roskes, U. Sarica, M. Swartz, M. Xiao, C. You
The University of Kansas, Lawrence, USA
A. Al-bataineh, P. Baringer, A. Bean, S. Boren, J. Bowen, J. Castle, S. Khalil, A. Kropivnitskaya,D. Majumder, W. Mcbrayer, M. Murray, C. Royon, S. Sanders, R. Stringer, J.D. Tapia Takaki,Q. Wang
Kansas State University, Manhattan, USA
A. Ivanov, K. Kaadze, Y. Maravin, A. Mohammadi, L.K. Saini, N. Skhirtladze, S. Toda Lawrence Livermore National Laboratory, Livermore, USA
F. Rebassoo, D. Wright
University of Maryland, College Park, USA
C. Anelli, A. Baden, O. Baron, A. Belloni, B. Calvert, S.C. Eno, C. Ferraioli, N.J. Hadley,S. Jabeen, G.Y. Jeng, R.G. Kellogg, J. Kunkle, A.C. Mignerey, F. Ricci-Tam, Y.H. Shin, A. Skuja,S.C. Tonwar
Massachusetts Institute of Technology, Cambridge, USA
D. Abercrombie, B. Allen, A. Apyan, V. Azzolini, R. Barbieri, A. Baty, R. Bi, K. Bierwagen,S. Brandt, W. Busza, I.A. Cali, M. D’Alfonso, Z. Demiragli, G. Gomez Ceballos, M. Goncharov,D. Hsu, Y. Iiyama, G.M. Innocenti, M. Klute, D. Kovalskyi, Y.S. Lai, Y.-J. Lee, A. Levin,P.D. Luckey, B. Maier, A.C. Marini, C. Mcginn, C. Mironov, S. Narayanan, X. Niu, C. Paus,C. Roland, G. Roland, J. Salfeld-Nebgen, G.S.F. Stephans, K. Tatar, D. Velicanu, J. Wang,T.W. Wang, B. Wyslouch
University of Minnesota, Minneapolis, USA
A.C. Benvenuti, R.M. Chatterjee, A. Evans, P. Hansen, S. Kalafut, S.C. Kao, Y. Kubota, Z. Lesko,J. Mans, S. Nourbakhsh, N. Ruckstuhl, R. Rusack, N. Tambe, J. Turkewitz
University of Mississippi, Oxford, USA
J.G. Acosta, S. Oliveros
University of Nebraska-Lincoln, Lincoln, USA
E. Avdeeva, K. Bloom, D.R. Claes, C. Fangmeier, R. Gonzalez Suarez, R. Kamalieddin,I. Kravchenko, J. Monroy, J.E. Siado, G.R. Snow, B. Stieger
State University of New York at Buffalo, Buffalo, USA
M. Alyari, J. Dolen, A. Godshalk, C. Harrington, I. Iashvili, A. Kharchilava, A. Parker,S. Rappoccio, B. Roozbahani
Northeastern University, Boston, USA
G. Alverson, E. Barberis, A. Hortiangtham, A. Massironi, D.M. Morse, D. Nash, T. Orimoto,R. Teixeira De Lima, D. Trocino, R.-J. Wang, D. Wood
Northwestern University, Evanston, USA
S. Bhattacharya, O. Charaf, K.A. Hahn, N. Mucia, N. Odell, B. Pollack, M.H. Schmitt, K. Sung,M. Trovato, M. Velasco
University of Notre Dame, Notre Dame, USA
N. Dev, M. Hildreth, K. Hurtado Anampa, C. Jessop, D.J. Karmgard, N. Kellams, K. Lannon,N. Loukas, N. Marinelli, F. Meng, C. Mueller, Y. Musienko , M. Planer, A. Reinsvold, R. Ruchti,N. Rupprecht, G. Smith, S. Taroni, M. Wayne, M. Wolf, A. Woodard The Ohio State University, Columbus, USA
J. Alimena, L. Antonelli, B. Bylsma, L.S. Durkin, S. Flowers, B. Francis, A. Hart, C. Hill, W. Ji,B. Liu, W. Luo, D. Puigh, B.L. Winer, H.W. Wulsin
Princeton University, Princeton, USA
A. Benaglia, S. Cooperstein, O. Driga, P. Elmer, J. Hardenbrook, P. Hebda, D. Lange, J. Luo,D. Marlow, K. Mei, I. Ojalvo, J. Olsen, C. Palmer, P. Pirou´e, D. Stickland, A. Svyatkovskiy,C. Tully
University of Puerto Rico, Mayaguez, USA
S. Malik, S. Norberg A The CMS Collaboration
Purdue University, West Lafayette, USA
A. Barker, V.E. Barnes, S. Folgueras, L. Gutay, M.K. Jha, M. Jones, A.W. Jung, A. Khatiwada,D.H. Miller, N. Neumeister, J.F. Schulte, J. Sun, F. Wang, W. Xie
Purdue University Northwest, Hammond, USA
T. Cheng, N. Parashar, J. Stupak
Rice University, Houston, USA
A. Adair, B. Akgun, Z. Chen, K.M. Ecklund, F.J.M. Geurts, M. Guilbaud, W. Li, B. Michlin,M. Northup, B.P. Padley, J. Roberts, J. Rorie, Z. Tu, J. Zabel
University of Rochester, Rochester, USA
B. Betchart, A. Bodek, P. de Barbaro, R. Demina, Y.t. Duh, T. Ferbel, M. Galanti, A. Garcia-Bellido, J. Han, O. Hindrichs, A. Khukhunaishvili, K.H. Lo, P. Tan, M. Verzetti
The Rockefeller University, New York, USA
R. Ciesielski, K. Goulianos, C. Mesropian
Rutgers, The State University of New Jersey, Piscataway, USA
A. Agapitos, J.P. Chou, Y. Gershtein, T.A. G ´omez Espinosa, E. Halkiadakis, M. Heindl,E. Hughes, S. Kaplan, R. Kunnawalkam Elayavalli, S. Kyriacou, A. Lath, R. Montalvo, K. Nash,M. Osherson, H. Saka, S. Salur, S. Schnetzer, D. Sheffield, S. Somalwar, R. Stone, S. Thomas,P. Thomassen, M. Walker
University of Tennessee, Knoxville, USA
M. Foerster, J. Heideman, G. Riley, K. Rose, S. Spanier, K. Thapa
Texas A&M University, College Station, USA
O. Bouhali , A. Castaneda Hernandez , A. Celik, M. Dalchenko, M. De Mattia, A. Delgado,S. Dildick, R. Eusebi, J. Gilmore, T. Huang, T. Kamon , R. Mueller, Y. Pakhotin, R. Patel,A. Perloff, L. Perni`e, D. Rathjens, A. Safonov, A. Tatarinov, K.A. Ulmer Texas Tech University, Lubbock, USA
N. Akchurin, J. Damgov, F. De Guio, C. Dragoiu, P.R. Dudero, J. Faulkner, E. Gurpinar,S. Kunori, K. Lamichhane, S.W. Lee, T. Libeiro, T. Peltola, S. Undleeb, I. Volobouev, Z. Wang
Vanderbilt University, Nashville, USA
S. Greene, A. Gurrola, R. Janjam, W. Johns, C. Maguire, A. Melo, H. Ni, P. Sheldon, S. Tuo,J. Velkovska, Q. Xu
University of Virginia, Charlottesville, USA
M.W. Arenton, P. Barria, B. Cox, R. Hirosky, A. Ledovskoy, H. Li, C. Neu, T. Sinthuprasith,X. Sun, Y. Wang, E. Wolfe, F. Xia
Wayne State University, Detroit, USA
C. Clarke, R. Harr, P.E. Karchin, J. Sturdy, S. Zaleski
University of Wisconsin - Madison, Madison, WI, USA
D.A. Belknap, J. Buchanan, C. Caillol, S. Dasu, L. Dodd, S. Duric, B. Gomber, M. Grothe,M. Herndon, A. Herv´e, U. Hussain, P. Klabbers, A. Lanaro, A. Levine, K. Long, R. Loveless,G.A. Pierro, G. Polese, T. Ruggles, A. Savin, N. Smith, W.H. Smith, D. Taylor, N. Woods1: Also at Vienna University of Technology, Vienna, Austria2: Also at State Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing,China3: Also at Universidade Estadual de Campinas, Campinas, Brazil