A The CMS Collaboration

Searches for bosons decaying to a top quark and a bottom quark in proton-proton collisions at 13\TeV


Searches are presented for heavy gauge bosons decaying into a top and a bottom quark in data collected by the CMS experiment at that correspond to an integrated luminosity of 2.2 and 2.6\fbinvin the leptonic and hadronic analyses, respectively. Two final states are analyzed, one containing a single electron, or muon, and missing transverse momentum, and the other containing multiple jets and no electrons or muons. No evidence is found for a right-handed boson () and the combined analyses exclude at 95% confidence level with masses below 2.4\TeVif (mass of the right handed neutrino), and below 2.6\TeVif . The results provide the most stringent limits for right-handed bosons in the top and bottom quark decay channel.




\RCS \RCS \cmsNoteHeaderB2G-16-016

1 Introduction

Many theories that extend the standard model (SM) predict additional charged gauge bosons [1, 2, 3, 4, 5], often referred to as bosons. In models where the resonance is sufficiently massive, it is common to postulate that the coupling to third generation quarks might be enhanced relative to the second and first generations [6, 7], making a search for the decay or highly appropriate. A particular advantage of this kind of search is that this channel is more easily distinguished from the large continuum of multijet background than searches in the decays to light quarks (). The search in top and bottom quark (tb) systems complements searches in (where denotes a charged lepton and denotes a neutrino) and (where denotes an SM or \Zboson) channels. The tb final state also benefits from the fact that its mass can be fully determined, whereas in the leptonic mode there is a two-fold ambiguity in its mass.

This paper presents the first search performed for a right-handed () decaying to a top and a bottom quark at , using data collected by the CMS experiment corresponding to an integrated luminosity of up to 2.6\fbinv. In scenarios where a theoretical right-handed neutrino () is heavier than the , the decay is forbidden and the branching fraction is enhanced. This makes the decay an important channel in the search for bosons. Previous searches in the tb channel have been performed at the Fermilab Tevatron [8, 9, 10] and at the CERN LHC by both the CMS [11, 12] and ATLAS [13, 14] Collaborations. The most stringent limits to date on the production of bosons with purely right-handed couplings come from the CMS search performed at  [12]. Relative to this 8\TeVsearch, the expected production cross section of the boson at \TeVis enhanced by a factor of approximately 7 (13) for a 2 (3)\TeVresonance.

We separately analyze events with and without a lepton in the final state (referred to as leptonic and hadronic analyses), and then combine the results. In both analyses, the invariant mass of the tb system () is used to conduct searches for the boson. The achieved sensitivity after combining the results is better than in each individual channel, thereby providing improved exclusion limits compared to previous results.

In this paper, Section 2 contains a description of the CMS detector. Section 3 provides details of the simulated samples and their production, while Section 4 discusses the techniques used for object reconstruction and event selection. The methods used for estimation of backgrounds are given in Section 5. Section 6 provides information on systematic uncertainties, and Section 7 presents results of the individual and combined analyses. A summary is given in Section 8.

2 The CMS detector

The central feature of the CMS apparatus is a superconducting solenoid of 6\unitm internal diameter, providing a magnetic field of 3.8\unitT. A silicon pixel and strip tracker, a lead tungstate crystal electromagnetic calorimeter (ECAL), and a brass and scintillator hadron calorimeter (HCAL), each composed of a barrel and two endcap sections, reside within the solenoid field. Forward calorimeters extend the pseudorapidity () coverage [15] provided by the barrel and endcap detectors. Muons are measured in gas-ionization detectors embedded in the steel flux-return yoke outside the solenoid.

The electron momentum is estimated by combining the energy measurement in the ECAL with the momentum measurement in the tracker. The momentum resolution for electrons with from decays ranges from 1.7% for electrons without an accompanying shower in the barrel region, to 4.5% for electrons showering in the endcaps [16].

Muons are measured in the range , with detection planes based on drift tubes, cathode strip chambers, and resistive plate chambers. Matching muons to tracks in the silicon tracker yields a relative resolution for muons with of 1.3–2.0% in the barrel and better than 6% in the endcaps. The \ptresolution in the barrel is better than 10% for muons with \ptup to 1\TeV [17].

Events of interest are selected using a two-tiered trigger system [18]. The first level (L1), composed of custom hardware processors, uses information from the calorimeters and muon detectors to select events at a rate of around 100\unitkHz within a time interval of less than 4\mus. The second level, known as the high-level trigger (HLT), consists of a farm of processors running a version of the full event reconstruction software optimized for fast processing, and reduces the event rate to less than 1\unitkHz before data storage.

The particle-flow event algorithm [19, 20, 21] reconstructs and identifies each individual particle candidate using an optimized combination of information from the various elements of the CMS detector. The energy of photons is obtained from the ECAL measurement, and corrected for the online suppression of signals close to threshold. The energy of electrons is determined from a combination of the electron momentum at the primary interaction vertex determined by the tracker, the energy of the corresponding ECAL cluster, and the energy sum of all bremsstrahlung photons spatially compatible with originating from the electron track. The energy of muons is obtained from the curvature of the corresponding track. The energy of charged hadrons is determined from a combination of their momentum measured in the tracker and the matching ECAL and HCAL energy deposition, corrected for suppression of small signals and for the response of hadron showers in the calorimeters. Finally, the energy of neutral hadrons is obtained from the corresponding corrected ECAL and HCAL energies.

The missing transverse momentum vector, \ptvecmiss, is defined as the projection on the plane perpendicular to the beams of the negative vector sum of the momenta of all reconstructed particles in an event.

A more detailed description of the CMS detector, together with a definition of the coordinate system and the kinematic variables, can be found in Ref. [15].

3 Modeling of signal and background

All signal events are generated at leading order (LO) using the CompHEP 4.5.2 [22] package and their cross sections are scaled to next-to-leading order (NLO) with an approximate K-factor of 1.2 [23, 24]. All signal samples are generated with purely right-handed couplings, according to the following model-independent, lowest-order, effective Lagrangian:


where is the element of the Cabibbo–Kobayashi–Maskawa matrix when is a quark, and when is a lepton, and is the SM weak coupling constant. Since we consider bosons (with right-handed couplings), there is no interference at production with the SM W boson. The simulation for leptonic decays of the boson includes decays involving a lepton, and no distinction is made in the analysis between an electron or muon directly from the W boson decay and an electron or muon from a subsequent leptonic decay. Signal samples are generated for signal masses between 1 and 3\TeVin 100\GeVsteps. The width of the generated by CompHEP is narrow, and varies with the mass, but is approximately 3% for all masses considered in this analysis. This is smaller than the invariant mass resolution of the detector, and therefore the precise values of the width does not have a significant effect on our results.

For right-handed \PWprbosons, the leptonic decays necessarily produce right-handed neutrinos (). When the mass of the is larger than that of the boson () then the decays are kinematically forbidden and only decays are allowed (of which is a subset). On the other hand, if the is lighter than the boson () then decays are allowed. Consequently, the product of the cross section and its branching fraction ( tb) is enhanced for heavy neutrinos by approximately one third. When calculating the distribution in the number of expected signal events, it is always assumed that . When displaying upper limits at 95% confidence levels (CL), we consider both scenarios.

The SM processes that contribute significantly to the background in the leptonic analysis are W+jets and \ttbarevents. The background in the hadronic analysis is dominated by multijet and \ttbarproduction. Although it is a much smaller contribution to the total background, both analyses also consider associated production of a top quark and a W boson as background, while the leptonic analysis further considers both - and -channel single top quark, Z or +jets, and diboson (WW, WZ and ZZ) production. The hadronic and leptonic analyses employ different methods of background estimation because of differences in the final states. All background predictions from nondominant sources are estimated from simulation.

Simulated samples for +jets, -, and -channel single top, and W+jets are generated at NLO using the \MGvATNLO [25, 26, 27] v2.2.2 generator. The and single top quark in the tW channel samples are generated using the \POWHEG v2 generator [28, 29, 30, 31, 32], and all other backgrounds are generated at LO using the \PYTHIA8.2 [33] generator. In all cases, NNPDF 3.0 parton distribution functions (PDFs) are used [34].

Both hadronic and leptonic analyses use the MC simulated \ttbarbackground prediction. In the leptonic analysis, the \ttbarsimulation is assigned a correction based on the top quark , which is known to be improperly modeled [35]. This correction is not necessary in the hadronic analysis because of differences in the phase space resulting from the specific event selections, and is confirmed in a enriched control region. The predictions from both analyses are checked in control regions that are independent with respect to the signal region and contain minimal contamination from signal. In both cases, the agreement between the data and prediction from simulation is good.

For the W+jets background in the leptonic analysis, the initial prediction is estimated from simulation. The agreement with data is then checked in a control region dominated by W+jets events. The same region is also used to extract correction factors for different W+jets components, e.g., W+light-quark or gluon jets and W+charm or bottom quark jets. The relative composition of these components in simulation is known to differ [36] from the composition in data, and we apply these correction factors to the predictions.

The multijet background in the hadronic analysis is determined from data in independent control regions. The validity of the estimation procedure is then checked using simulated multijet events.

More details on the background estimation methods can be found in Section 5.

All simulated signal and background events are processed through \PYTHIA8.2 for parton fragmentation and hadronization, where the underlying event tune CUETP8M1 [37] has been used. The simulation of the CMS detector is performed using \GEANTfour [38]. Also, all simulated event samples include additional overlapping proton-proton interactions in the same or adjacent bunch crossings (pileup) that are weighted such that the distribution in the number of interactions agrees with that expected in data.

4 Event reconstruction and selection

The two analyses employ different selections targeted at their respective signal topologies. Details on specific aspects of the selections are given below.

4.1 Jet reconstruction

Jets are reconstructed offline from the particle-flow candidates, clustered using the anti-\ktalgorithm [39, 40] with distance parameters of 0.4 (AK4 jets) and 0.8 (AK8 jets).

The jet momentum is defined by the vectorial sum of all particle-flow candidate momenta in the jet, and is found from simulation to be within 5 to 10% of the true momentum. An offset correction is applied to jet momenta to take into account the contribution from pileup. Jet energy corrections [41] are obtained from simulation, and are confirmed with in situ measurements of the energy balance in dijet and photon+jet events. Additional selection criteria are applied to each event to remove spurious jet-like features originating from isolated noise patterns in certain HCAL regions.

Both the leptonic and hadronic analyses use the charged-hadron subtraction method, which removes from the event any charged hadrons not associated with the leading vertex, defined as the vertex with the highest sum. The estimated contribution from pileup to the neutral hadron component of jets is also subtracted, based on the jet area [42].

The leptonic analysis uses AK4 jets because their smaller area makes them less sensitive to pileup, and the hadronic analysis uses AK8 jets whose larger area makes them more suited to the jet substructure-based techniques used to identify highly Lorentz-boosted top quark decays. These techniques are discussed in Section 4.1.2.

Identification of b jets

The combined secondary vertex version 2 (CSVv2) algorithm [43, 44], which combines secondary vertex and track based lifetime information in order to identify b jets, is used by both analyses. They use an operating point which has a b jet identification (b tagging) efficiency of 80% and a light-flavor jet misidentification (mistag) probability of 10%. A scale factor is applied as a function of \ptto correct observed differences in performance between data and simulation. In the hadronic analysis, an additional uncertainty is used to account for small differences in b tagging which arise from the larger jet-cone size. Details on the systematic uncertainty in b tagging can be found in Section 6.

Tagging of top quarks

The large Lorentz boost of the top quark from heavy boson () decays causes the three jets from hadronic decays to merge into a single large-radius jet with distinct substructure. Variables that are sensitive to characteristics of this substructure can be used to discriminate signal from background. The hadronic analysis uses a top tagging algorithm that is based on three such variables: jet mass, N-subjettiness [45, 46], and subjet b tagging.

The jet mass is calculated after applying the modified mass-drop tagger, also known as the “soft drop” algorithm [47, 48], which reclusters the AK8 jet with the Cambridg–Aachen algorithm [49] and declusters until the following requirement is met:


where are the magnitude of the transverse momenta of the two subjet candidates, is the distance (, where is the azimuthal angle in radians) between candidates, and is the jet size parameter. For this analysis, we use and , and require the mass of the soft-drop declustered jet to be between 110 and 210\GeV, i.e. consistent with the top quark mass, . For this operating point, the soft drop algorithm is equivalent to the modified mass-drop tagger [50, 47].

The N-subjettiness algorithm defines a series of variables that describe the consistency between the jet energy and the number of assumed subjets (N):


where is the distance between the axis of the subjet candidate () and a specific constituent particle (), and is the normalization factor,


where is the distance parameter used in the jet clustering algorithm. The axes of the subjet candidate used to calculate N-subjettiness are found using the exclusive \ktalgorithm [51], after which an optimization procedure is applied to minimize the N-subjettiness value, calculated using all particle-flow constituents of the AK8 jet. A jet with a low value will have energy deposited close to the axes of the N subjet candidates, which is a characteristic of a jet containing N subjets. A top quark jet is likely to be more consistent with three subjets than two, while a jet from a gluon or light quark will typically be consistent with either two or three subjets. Therefore, the ratio of and is characteristically smaller for top quark jets than for the multijet background. We select jets with .

Finally, we apply the CSVv2 b tagging algorithm to the two soft-drop subjets of the candidate jet, and require the maximum b tagging discriminator value to be at least 0.76. The above selection criteria correspond to the working point of the CMS top quark tagging algorithm defined by a 0.3% top-quark mistagging rate [52], with a corresponding top-quark efficiency of approximately 30%.

Scale factors resulting from small differences in t tagging efficiencies in data and simulation are derived in a pure semileptonic \ttbarsample separately for jets with \ptgreater or less than 550\GeV. These are applied as corrections to simulated events, and are consistent with unity.

4.2 Identification of electrons and muons

Electron candidates are selected using a multivariate identification technique, specifically, a boosted decision tree. The multivariate discriminant is based on the spatial energy distribution of the shower, the quality of the track, the match between the track and electromagnetic cluster, the fraction of total cluster energy deposited in the HCAL, the amount of energy appearing in the regions surrounding the tracker and calorimeters, and the probability of the electron to have originated from a converted photon. The track associated with a muon candidate is required to have hits in the pixel and muon detectors, good quality, and transverse and longitudinal impact parameters (distance of closest approach) with respect to the leading vertex close to zero.

Both the leptonic and hadronic analyses use the same criteria for muon identification, while the criteria used for electron identification are less restrictive in the hadronic analysis than in the leptonic analysis. The choice of lepton identification and use of a veto ensure that there is no overlap between events in the two analyses, and makes combining their results straightforward.

Scale factors arising from small differences between lepton identification efficiencies in data and simulation are obtained from a data sample of events as a function of . These scale factors are then applied as corrections to simulated events.

In highly boosted semileptonic top quark decays from heavy bosons, the lepton and jet may not be well separated. For this reason, no isolation requirement is applied to the lepton. Instead, a two-dimensional requirement is placed on the and for the lepton and the closest jet with \GeVand , where the is given by the magnitude of the component of the lepton momentum orthogonal to the jet axis. For electrons (muons), we require that either or \GeV. These requirements help remove the multijet contribution from the background in the leptonic analysis, while maintaining high efficiency for signal events. The four-momenta of identified lepton-candidate particles are subtracted from the four-momentum of the jets that contain them, which helps ensure that jets considered in the leptonic analysis are not contaminated by nearby high-energy leptons.

4.3 Mass reconstruction

The methods of reconstructing boson candidates differ in the two analyses. In the leptonic channel, the tb invariant mass is reconstructed from the charged lepton, \ptvecmiss, and two jets in the event. The - and -components of neutrino \ptare determined from \ptvecmissand the -component is calculated by constraining the invariant mass of the lepton and neutrino to the mass of the W boson. This leads to a quadratic equation in . When the two solutions are real numbers, both are used to reconstruct W boson candidates. If both solutions contain imaginary parts, we set to the real part of the solutions, and recompute , which yields a different quadratic ambiguity. In the latter case, we use only the solution with mass closest to 80.4\GeV. Once we have all components of the neutrino momentum, we combine the viable neutrino momentum solutions with the charged lepton momentum to create W boson candidates. We then reconstruct the top quark by combining the four-momenta of each of the W boson candidates with each jet with \GeVand . Whichever jet yields a top quark candidate mass closest to 172.5\GeVis labeled as the “best jet“ and is used to reconstruct the top quark candidate. In the case of two W candidates, we use the candidate that yields the top quark mass closest to its nominal value of 172.5\GeV. Finally, we combine the top quark candidate with the highest \ptjet, that is not the “best jet,” yielding the reconstructed candidate.

In the hadronic channel, the tb invariant mass is reconstructed from the two leading AK8 jets in the event.

4.4 Analysis selections in the leptonic channel

Candidate events in the leptonic analysis are selected in the HLT with single-lepton triggers that require a \ptof at least 105 (45)\GeVfor electrons (muons) and have no isolation requirement. Scale factors to account for differences in efficiency between data and simulation are obtained through the procedure outlined in Section 4.2. Events must contain a reconstructed lepton with \GeVand in the electron (muon) channel. Events are rejected if they contain more than one identified lepton with \GeVand in the electron (muon) channel.

Events are also required to have at least two jets with \GeVand , and the jet with leading \ptmust have \GeVin the electron (muon) channel, where at least one of these jets must be b tagged. Events must have \GeVin the electron (muon) channel. In addition, events in the electron channel must have an opening angle in the transverse plane between the electron and the \ptvecmissvector radians. In both channels, the top quark candidate is required to have 250\GeVand 350\GeV, where is the \ptof the vector sum of the two leading \ptjets. In addition, in the muon channel, the mass of the top quark candidate must satisfy the condition 100 250\GeV. These requirements all serve to reject events which are not consistent with the decay of a heavy resonance to a top and bottom quark. The selections in both channels are optimized separately, thereby leading to slight differences in certain requirements. Event yields after the selection for the leptonic analysis are shown in Table 4.4.


Number of selected events, and the number of signal and background events expected from simulation in the leptonic analysis. The expectations for signal and background correspond to an integrated luminosity of 2.2\fbinv. “Full selection” refers to the additional requirements of \GeVand \GeVfor both channels, and also 100 250\GeVin the muon channel, while ”Object selection” omits these requirements. The quoted uncertainty does not include systematic uncertainties that affect the shape of distributions (a complete description of sources of uncertainty can be found in Section 6). Electron channel Muon channel Object selection Full selection Object selection Full selection b tag b tags b tag b tags b tag b tags b tag b tags Signal = 1400\GeV 30 22 28 20 35 31 26 24 = 2000\GeV 9 6 9 6 11 9 9 7 = 2600\GeV 3 1 3 1 3 2 3 1 Background \ttbar 71 26 56 19 68 27 49 18 tqb 5 2 4 1 4 1 3 1 tW 11 6 10 5 9 3 4 1 W 11 4 9 4 9 4 5 2 tb 1 0 1 0 0 0 0 0 +jj 89 8 77 7 80 6 25 1 +bb/cc 139 22 119 18 128 23 45 7 +jets 3 0 4 0 21 0 12 0 WW, WZ, ZZ 9 0 7 0 3 0 0 0 Total background 33922 675 28719 534 32224 645 14311 303 Data 309 58 256 44 281 58 143 30

4.5 Analysis selections in the hadronic channel

Candidate events in the hadronic channel are required to satisfy one of two HLT selections. The first demands at least two AK8 jets with \GeV, one of which must have a trimmed [53] jet mass greater than 30\GeV, and also requires the leading \ptjet to have \GeV. In addition, this trigger requires that the event contains at least one b-tagged jet. The second trigger requires that the scalar \ptsum of reconstructed jets be at least 800\GeV. The efficiency of the combination of these two triggers is measured with data collected using a trigger with a lower scalar \ptsum threshold, and is extracted as a function of the scalar \ptsum of the two jets with leading \pt(), which provides a way to account for this effect.

We require events to have at least two jets with \GeV, one of which must be identified as a top jet using the t tagging algorithm, and the other must be tagged as a bottom jet. Furthermore, the b jet must have a soft-drop mass less than 70\GeV. Finally, the two jets are required to be separated by radians and to have , where is the rapidity difference between the two jets.

The event yields after implementing the selections in the hadronic analysis are shown in Table 4.5.


Number of selected events, and the number of signal and background events expected from simulation in the hadronic analysis. The expectations for signal and background correspond to an integrated luminosity of 2.6\fbinv. The quoted uncertainty does not include systematic uncertainties that affect the shape of distributions (a complete description of sources of uncertainty can be found in Section 6). Signal = 1400\GeV 228 = 2000\GeV 27 = 2600\GeV 4 Background Multijets 6134 \ttbar 376 tW 32 Total background Data 6491

5 Backgrounds

5.1 Backgrounds in the leptonic analysis

Top quark pair production background

The predicted \ttbarbackground is estimated from simulation and checked in two distinct control regions, both of which do not apply the requirements on , , , nor the number of b jets. The first region is defined by relaxing the leading jet \ptand \ptvecmissrequirements, and requiring events to have at least four jets, two of which are b-tagged, and have \GeV. The latter requirement ensures that the signal contamination in this region is less than 1%. The second region is defined by requiring events to have two leptons, which must have for the leading (subleading) \ptlepton. This requirement ensures that there is no overlap between the signal region and the second control region. In addition, we relax the requirements on the leading jet \ptand \ptvecmiss, and reject events for which the invariant mass of the dilepton system (if they are of the same flavor) is between 70 and 110\GeV, which ensures that the control region does not contain a significant fraction of +jets events.

In both control regions, we compare simulated distributions and overall yields with data. We observe significantly better agreement between data and simulation when a correction is applied to the top quark \ptspectrum in the \ttbarsimulation. The correction factor is obtained from measurements of the differential top quark \ptdistribution [35]. We apply this correction factor to the \ttbarsimulation, as a function of the generator-level top quark \pt, and use the differences from the distributions without the correction as estimates of the systematic uncertainty in the expected \ttbarbackground.

W+jets background

The prediction for the W+jets background is estimated from simulation. It is then corrected for known discrepancies in the relative fraction of W+jets events with light-flavor jets compared to bottom or charm quark jets. This correction is obtained from data using a modified event selection that does not include the requirements on , , and , and also removes the requirement of a b-tagged jet. This sample is referred to as the pre-tag sample. A subset of these events, in which neither of the two leading \ptjets are b tagged, is referred to as the 0-tag sample. The 0-tag sample is dominated by the W+jets background and contains contributions from other background sources, which comprise less than 20% of the total. The difference between data and simulation in the 0-tag sample is used to obtain a first-order scale factor for W+jets light-flavor events, which is applied to the W+jets simulation, and the difference between data and simulation in the pre-tag distribution is used to calculate a first-order scale factor for W+jets heavy-flavor events. This procedure is repeated until following iterations do not cause the scale factors to shift by more than 0.1%. We also check this calculation by analytically solving the system of equations from the iteration, and confirm that the two methods yield identical results.

We require that the total number of predicted events is unaffected by the simultaneous application of the two scale factors. We assign uncertainties to these factors by repeating the procedure with the b tagging scale factors varied within their uncertainties. The procedure is identical to the procedure used in Ref. [11].

5.2 Backgrounds in the hadronic analysis

Multijet background

The multijet background is estimated from data, and the method is verified through simulation. The procedure uses the distribution of multijet events that fail the b tagging requirement, weighted by a transfer factor (average b tagging rate) to predict the multijet yield in the signal region.

To estimate the average b tagging rate in multijet events, we define modified t tagging criteria. Specifically, we now select events that contains jets with 0.75, and shift the soft-drop jet mass window to be between 50 and 170\GeV. These requirements ensure that the control region is orthogonal to the signal region and has contributions from both signal and \ttbarevents that are less than 1%. Using the standard requirement in the signal region, we favor a similar parton flavor composition. A control region is then defined by applying the signal selection with the modified t tagging requirements, omitting the b tagging requirement.

We calculate the average b tagging rate as a function of b candidate jet \ptin three regions: (low), (transition), (high). The denominator contains all events in the control region, while the numerator includes only those that pass the signal region b tagging requirement. The average b tagging rate in each range is fitted using a bifurcated polynomial that models the distribution. The functional form is


The parameters to are free coefficients determined in the fit. The value of is chosen separately for each region, and is 500, 500, and 550\GeVin the low, transition, and high- regions, respectively.

The uncertainty related to the average b tagging rate is obtained from the full covariance matrix of the fitting algorithm. The functional form is chosen to optimize agreement between sideband and Monte Carlo estimates. We estimate an uncertainty related to the choice of the fit function by comparing the results of the nominal fit with those determined using other functional forms. These other forms include the following: a constant, a second-degree polynomial, a third-degree polynomial, and an exponential function.

We observe that there is a correlation between the b tag rate and the soft-drop mass of the b candidate. To account for this correlation, we extract a correction factor for the multijet background as a function of the soft-drop mass of the b jet candidate. This factor is calculated by taking the ratio of the soft-drop mass distributions for the b tagging pass and b tagging fail samples in the control region of the multijet simulation. The factor is then used as an event weight along with the fit to the average b tagging rate to estimate the multijet background from data. An uncertainty in the factor, equal to half the difference between the factor and unity, is included in the analysis.

We check the closure of this procedure using both multijet simulation and an additional control region in data. The control region is defined by inverting the requirement in the signal region. This provides a much purer multijet sample in data, which is orthogonal to both the signal region and the control region used to estimate the multijet contribution.

The closure test using the prediction from simulation shows a small residual discrepancy in the \xspacedistribution, which is used to extract a correction for the multijet prediction. We include an uncertainty in this correction equal to the difference between the correction and unity. After this correction, the corresponding closure test in the data control region shows good agreement between the multijet prediction and observed data.

Top quark pair production background

In the hadronic analysis, the \ttbarbackground prediction is estimated from simulation and checked in a region defined through selections identical to those used in the signal region, except that the b jet soft-drop mass requirement is inverted. This region contains an increased fraction of \ttbarevents relative to the signal region (approximately a factor of six), and does not overlap with the signal region or any other control regions used in the analysis. The prediction for the multijet background in this region is estimated from data using the same method as the signal region. The prediction for the \ttbarbackground is found to be consistent with that observed in the data, and no other correction is required.

6 Systematic uncertainties

Systematic uncertainties fall into two categories: those that affect only the total event yield, and those that affect both the event yield and the \xspacedistribution. Unless otherwise specified, the uncertainties are common both the leptonic and hadronic analyses.

The uncertainty in the measured integrated luminosity (2.7%) [54] belongs to the first category. The leptonic analysis includes uncertainties on the modeling of the lepton trigger (2-4%). The hadronic analysis includes uncertainties in the AK4 vs. AK8 jet b tagging rates (3%), t tagging efficiency (20%) , and in the theoretical \ttbarand single top quark cross sections ().

Since the two analyses use the same criteria to identify muons, but different criteria for electrons, the uncertainty in the muon reconstruction and identification (2%) is included in both analyses, while the uncertainty in electron reconstruction and identification (5%) is included only in the leptonic analysis.

Other uncertainties belong to the second category and are detailed below. Unless otherwise specified, the uncertainties are assigned to all samples for which the prediction is estimated from simulation.

The uncertainties due to the choice in the renormalization and factorization scales ( and , respectively) are evaluated at the matrix element level using event weights to change the scales up or down relative to the nominal scale by a factor of two, while restricting to  [55, 56]. The uncertainty from changes in both scales at the parton shower level are evaluated for the \ttbarbackground using samples generated with twice or half the nominal scale.

Uncertainties on the b tagging, jet energy scale, and jet energy resolution are calculated by varying the relevant scale factors within their uncertainties. For the jet energy scale and resolution, nominal factors and uncertainties are obtained for both AK4 and AK8 jets and applied appropriately in the leptonic and hadronic analyses.

A correction is applied to all simulated event samples to provide better matching of the distribution of pileup interactions in data. This procedure uses a minimum bias interaction cross section () of 69\unitmb, and uncertainties are calculated by varying the minimum bias cross section by .

To estimate the uncertainty arising from the choice of the PDF, we use the NNPDF 3.0 PDF set uncertainty defined in Ref. [57].

In the leptonic analysis, the uncertainties in the W+jets heavy- and light-flavor factors are included as a variation in the W+jets background, and the \ttbarbackground with an uncorrected top quark \ptspectrum is included as a one-sided systematic uncertainty.

In the hadronic analysis, the uncertainty in the trigger efficiency is taken to be one half of the measured trigger inefficiency, and applied as a function of the scalar \ptsum of the two leading jets. Uncertainties in the multijet background estimation procedure are also applied. These result from choice of functional form in the fit to the average b tagging rate, corrections due to correlations between the average b tagging rate and soft-drop jet mass, and differences obtained from a closure test in simulation.

In the leptonic analysis, the dominant uncertainty sources are from the correction to the \ptspectrum of the top quark in \ttbarevents, and and at the matrix element level. In the hadronic analysis, the dominant uncertainty sources are from the multijet background estimation and t tagging efficiency. Both analyses are also affected by the subdominant uncertainties related to the choice of PDF and b tagging. All systematic uncertainties for both analyses are summarized separately in Table 6.


Sources of systematic uncertainty affecting the distribution taken into account when setting 95% CL upper limits. The three right-most columns indicate the channels to which the uncertainty applies (noted by ), and whether it also applies to signals (noted by ). When a source applies to both channels, it is treated as fully correlated in the combination. Sources that list the changes as 1 standard deviation (s.d.) depend on the distribution of the variable given in the parentheses, while those that list the variation as a percent are rate uncertainties. Source Variation Leptonic Hadronic Signal Integrated luminosity 2.7% \checkmark Muon identification efficiency 2% \checkmark Electron identification efficiency 5% \checkmark Single-lepton trigger () 4%/2% \checkmark AK4 to AK8 b tagging 3% \checkmark Top quark tagging 20% \checkmark cross section %, % tW cross section 5.4% Matrix element scales \ttbarparton shower scale Jet energy scale \checkmark Jet energy resolution \checkmark b tagging \checkmark Light quark mistag rate \checkmark Pileup () \checkmark PDFs \checkmark W+jets heavy-flavor fraction Top reweighting trigger \checkmark Average b tagging rate fit Alternative functional forms b candidate mass Multijet simulation nonclosure

7 Results

Comparisons of the \xspacedistribution between the predicted background and observed data for both analyses are shown in Figs. 1 and 2. We observe good agreement between the predicted SM background processes and the observed data, and proceed to set upper limits at 95% CL on the boson production cross section for masses between 1 and 3\TeV. Limits on the cross section of boson production are calculated using a Bayesian method with a flat signal prior, using the theta package [58]. The Bayesian approach uses a binned likelihood to calculate 95% CL upper limits on the product of the signal production cross section and the branching fraction . The computation takes into account all systematic uncertainties given in Section 6, as well as statistical uncertainties related to the backgrounds, which are incorporated using the ”Barlow–Beeston lite” method [59, 60]. All rate uncertainties are included as nuisance parameters with log-normal priors.

Figure 1: Reconstructed \xspacedistributions from the leptonic analysis in the 1 b tag (upper) and 2 b tag (lower) categories, for the electron (left) and muon (right) channels. The “LF” and “HF” notations indicate the light- and heavy-flavor components of the W+jets contribution, respectively. The simulated signal and background samples are normalized to the integrated luminosity of the analyzed data set. The distributions are shown after the application of all selections. The 68% uncertainty in the background estimate includes all contributions to the predicted background, while the total uncertainty is the combined uncertainty of the background and data.
Figure 2: Reconstructed \xspacedistribution from the hadronic analysis. The simulated signal and backgrounds are normalized to the integrated luminosity of the analyzed data set. The distribution is shown after the application of all selections. The 68% uncertainty in the background estimate includes all contributions to the predicted background, while the total uncertainty is the combined uncertainty of the background and data.

The leptonic analysis separates events into four independent categories according to the lepton type (electron or muon) and the number of b-tagged jets in the first two leading \ptjets (1 or 2). This improves the sensitivity of the analysis. In the leptonic analysis, the \xspacedistribution is binned to reduce uncertainties from the number of events in each sample. The binning is as follows: 9 bins with widths of 200\GeVfrom 400 to 2200\GeV, 1 bin of width 400\GeVfrom 2200 to 2600\GeV, and 1 bin for 2600\GeVand above. In the hadronic analysis, the \xspacedistribution is binned using 50\GeVbins from 0 to 2100\GeV, 100\GeVbins from 2100 to 2500\GeV, and 1 bin for 2500\GeVand above.

Results from the two analyses are shown separately in Fig. 3. The leptonic and hadronic analyses are able to exclude boson masses below 2.4 and 2.0\TeV, respectively.

Figure 3: The 95% CL upper limit on the boson production cross section, separately for the leptonic (left) and hadronic (right) analyses. Masses for which the theoretical cross section is above the observed upper limit are excluded at 95% CL.

In combining the two analyses, a joint likelihood is used to simultaneously consider all categories. We treat the uncertainties related to jet energy scale and resolution, luminosity, pileup, b tagging scale factors, and PDF as fully correlated. All other uncertainties are considered to be uncorrelated.

The combined upper limit on boson production cross section at 95% CL is shown in Fig. 4. The observed and expected 95% CL upper limits are 2.5 and 2.4\TeV, respectively. This represents a significant improvement over the results from the individual analyses.

Figure 4: The 95% CL upper limit on the boson production cross section for the combined leptonic and hadronic analyses. Masses for which the theoretical cross section is above the observed upper limit are excluded at 95% CL.

8 Summary

Searches have been reported for a heavy boson resonance decaying into a top and a bottom quark in data from proton-proton collisions at \TeVcollected with the CMS detector. Analysis of the leptonic and hadronic channels is based on an integrated luminosity of 2.2 and 2.6\fbinv, respectively. No evidence is observed for the production of a boson, and upper limits at 95% confidence level on are determined as a function of the boson mass. After combining the two analyses, the upper limits at 95% confidence level are compared to the predicted boson production cross sections. bosons are excluded for masses less than 2.4\TeVif , and less than 2.6\TeVif . These results represents the most stringent limits published in the tb decay channel.

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 centers 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: BMWFW and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST, and NSFC (China); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RPF (Cyprus); SENESCYT (Ecuador); MoER, ERC IUT, and ERDF (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRT (Greece); OTKA and NIH (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MBIE (New Zealand); PAEC (Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom, RAS, RFBR and RAEP (Russia); MESTD (Serbia); SEIDI, CPAN, PCTI and FEDER (Spain); Swiss Funding Agencies (Switzerland); MST (Taipei); ThEPCenter, IPST, STAR, and NSTDA (Thailand); TUBITAK and TAEK (Turkey); NASU and SFFR (Ukraine); STFC (United Kingdom); DOE and NSF (USA). Individuals have received support from the Marie-Curie program and the European Research Council and Horizon 2020 Grant, contract No. 675440 (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 the Czech Republic; the Council of Science and Industrial Research, India; the HOMING PLUS program of the Foundation for Polish Science, cofinanced from European Union, Regional Development Fund, the Mobility Plus program of the Ministry of Science and Higher Education, the National Science Center (Poland), contracts Harmonia 2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543, 2014/15/B/ST2/03998, and 2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406; the National Priorities Research Program by Qatar National Research Fund; the Programa Clarín-COFUND del Principado de Asturias; the Thalis and Aristeia programs cofinanced by EU-ESF and the Greek NSRF; the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University and the Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); and the Welch Foundation, contract C-1845.

Appendix A The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan, A. Tumasyan \cmsinstskipInstitut für Hochenergiephysik, Wien, Austria
W. Adam, F. Ambrogi, E. Asilar, T. Bergauer, J. Brandstetter, E. Brondolin, M. Dragicevic, J. Erö, M. Flechl, M. Friedl, R. Frühwirth\cmsAuthorMark1, V.M. Ghete, J. Grossmann, N. Hörmann, J. Hrubec, M. Jeitler\cmsAuthorMark1, A. König, I. Krätschmer, D. Liko, T. Madlener, T. Matsushita, I. Mikulec, E. Pree, D. Rabady, N. Rad, H. Rohringer, J. Schieck\cmsAuthorMark1, M. Spanring, D. Spitzbart, J. Strauss, W. Waltenberger, J. Wittmann, C.-E. Wulz\cmsAuthorMark1, M. Zarucki \cmsinstskipInstitute for Nuclear Problems, Minsk, Belarus
V. Chekhovsky, V. Mossolov, J. Suarez Gonzalez \cmsinstskipNational Centre for Particle and High Energy Physics, Minsk, Belarus
N. Shumeiko \cmsinstskipUniversiteit Antwerpen, Antwerpen, Belgium
E.A. De Wolf, X. Janssen, J. Lauwers, M. Van De Klundert, H. Van Haevermaet, P. Van Mechelen, N. Van Remortel, A. Van Spilbeeck \cmsinstskipVrije Universiteit Brussel, Brussel, Belgium
S. Abu Zeid, F. Blekman, J. D’Hondt, I. De Bruyn, J. De Clercq, K. Deroover, S. Lowette, S. Moortgat, L. Moreels, A. Olbrechts, Q. Python, K. Skovpen, S. Tavernier, W. Van Doninck, P. Van Mulders, I. Van Parijs \cmsinstskipUniversité Libre de Bruxelles, Bruxelles, Belgium
H. Brun, B. Clerbaux, G. De Lentdecker, H. Delannoy, G. Fasanella, L. Favart, R. Goldouzian, A. Grebenyuk, G. Karapostoli, T. Lenzi, J. Luetic, T. Maerschalk, A. Marinov, A. Randle-conde, T. Seva, C. Vander Velde, P. Vanlaer, D. Vannerom, R. Yonamine, F. Zenoni, F. Zhang\cmsAuthorMark2 \cmsinstskipGhent University, Ghent, Belgium
A. Cimmino, T. Cornelis, D. Dobur, A. Fagot, M. Gul, I. Khvastunov, D. Poyraz, S. Salva, R. Schöfbeck, M. Tytgat, W. Van Driessche, W. Verbeke, N. Zaganidis \cmsinstskipUniversité Catholique de Louvain, Louvain-la-Neuve, Belgium
H. Bakhshiansohi, O. Bondu, S. Brochet, G. Bruno, A. Caudron, S. De Visscher, C. Delaere, M. Delcourt, B. Francois, A. Giammanco, A. Jafari, M. Komm, G. Krintiras, V. Lemaitre, A. Magitteri, A. Mertens, M. Musich, K. Piotrzkowski, L. Quertenmont, M. Vidal Marono, S. Wertz \cmsinstskipUniversité de Mons, Mons, Belgium
N. Beliy \cmsinstskipCentro Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil
W.L. Aldá Júnior, F.L. Alves, G.A. Alves, L. Brito, C. Hensel, A. Moraes, M.E. Pol, P. Rebello Teles \cmsinstskipUniversidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
E. Belchior Batista Das Chagas, W. Carvalho, J. Chinellato\cmsAuthorMark3, A. Custódio, E.M. Da Costa, G.G. Da Silveira\cmsAuthorMark4, D. De Jesus Damiao, S. Fonseca De Souza, L.M. Huertas Guativa, H. Malbouisson, C. Mora Herrera, L. Mundim, H. Nogima, A. Santoro, A. Sznajder, E.J. Tonelli Manganote\cmsAuthorMark3, F. Torres Da Silva De Araujo, A. Vilela Pereira \cmsinstskipUniversidade Estadual Paulista ,  Universidade Federal do ABC ,  São Paulo, Brazil
S. Ahuja, C.A. Bernardes, T.R. Fernandez Perez Tomei, E.M. Gregores, P.G. Mercadante, C.S. Moon, S.F. Novaes, Sandra S. Padula, D. Romero Abad, J.C. Ruiz Vargas \cmsinstskipInstitute for Nuclear Research and Nuclear Energy, Sofia, Bulgaria
A. Aleksandrov, R. Hadjiiska, P. Iaydjiev, M. Misheva, M. Rodozov, S. Stoykova, G. Sultanov, M. Vutova \cmsinstskipUniversity of Sofia, Sofia, Bulgaria
A. Dimitrov, I. Glushkov, L. Litov, B. Pavlov, P. Petkov \cmsinstskipBeihang University, Beijing, China
W. Fang\cmsAuthorMark5, X. Gao\cmsAuthorMark5 \cmsinstskipInstitute of High Energy Physics, Beijing, China
M. Ahmad, J.G. Bian, G.M. Chen, H.S. Chen, M. Chen, Y. Chen, C.H. Jiang, D. Leggat, Z. Liu, F. Romeo, S.M. Shaheen, A. Spiezia, J. Tao, C. Wang, Z. Wang, E. Yazgan, H. Zhang, J. Zhao \cmsinstskipState Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing, China
Y. Ban, G. Chen, Q. Li, S. Liu, Y. Mao, S.J. Qian, D. Wang, Z. Xu \cmsinstskipUniversidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, L.F. Chaparro Sierra, C. Florez, C.F. González Hernández, J.D. Ruiz Alvarez \cmsinstskipUniversity of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, Split, Croatia
N. Godinovic, D. Lelas, I. Puljak, P.M. Ribeiro Cipriano, T. Sculac \cmsinstskipUniversity of Split, Faculty of Science, Split, Croatia
Z. Antunovic, M. Kovac \cmsinstskipInstitute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, D. Ferencek, K. Kadija, B. Mesic, T. Susa \cmsinstskipUniversity of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos, P.A. Razis, H. Rykaczewski \cmsinstskipCharles University, Prague, Czech Republic
M. Finger\cmsAuthorMark6, M. Finger Jr.\cmsAuthorMark6 \cmsinstskipUniversidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin \cmsinstskipAcademy of Scientific Research and Technology of the Arab Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
A.A. Abdelalim\cmsAuthorMark7\cmsAuthorMark8, Y. Mohammed\cmsAuthorMark9, E. Salama\cmsAuthorMark10\cmsAuthorMark11 \cmsinstskipNational Institute of Chemical Physics and Biophysics, Tallinn, Estonia
R.K. Dewanjee, M. Kadastik, L. Perrini, M. Raidal, A. Tiko, C. Veelken \cmsinstskipDepartment of Physics, University of Helsinki, Helsinki, Finland
P. Eerola, J. Pekkanen, M. Voutilainen \cmsinstskipHelsinki Institute of Physics, Helsinki, Finland
J. Härkönen, T. Järvinen, V. Karimäki, R. Kinnunen, T. Lampén, K. Lassila-Perini, S. Lehti, T. Lindén, P. Luukka, E. Tuominen, J. Tuominiemi, E. Tuovinen \cmsinstskipLappeenranta University of Technology, Lappeenranta, Finland
J. Talvitie, T. Tuuva \cmsinstskipIRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
M. Besancon, F. Couderc, M. Dejardin, D. Denegri, J.L. Faure, F. Ferri, S. Ganjour, S. Ghosh, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry, I. Kucher, E. Locci, M. Machet, J. Malcles, J. Rander, A. Rosowsky, M.Ö. Sahin, M. Titov \cmsinstskipLaboratoire Leprince-Ringuet, Ecole polytechnique, CNRS/IN2P3, Université Paris-Saclay, Palaiseau, France
A. Abdulsalam, I. Antropov, S. Baffioni, F. Beaudette, P. Busson, L. Cadamuro, E. Chapon, C. Charlot, O. Davignon, R. Granier de Cassagnac, M. Jo, S. Lisniak, A. Lobanov, M. Nguyen, C. Ochando, G. Ortona, P. Paganini, P. Pigard, S. Regnard, R. Salerno, Y. Sirois, A.G. Stahl Leiton, T. Strebler, Y. Yilmaz, A. Zabi, A. Zghiche \cmsinstskipUniversité de Strasbourg, CNRS, IPHC UMR 7178, F-67000 Strasbourg, France
J.-L. Agram\cmsAuthorMark12, J. Andrea, D. Bloch, J.-M. Brom, M. Buttignol, E.C. Chabert, N. Chanon, C. Collard, E. Conte\cmsAuthorMark12, X. Coubez, J.-C. Fontaine\cmsAuthorMark12, D. Gelé, U. Goerlach, A.-C. Le Bihan, P. Van Hove \cmsinstskipCentre de Calcul de l’Institut National de Physique Nucleaire et de Physique des Particules, CNRS/IN2P3, Villeurbanne, France
S. Gadrat \cmsinstskipUniversité de Lyon, Université Claude Bernard Lyon 1,  CNRS-IN2P3, Institut de Physique Nucléaire de Lyon, Villeurbanne, France
S. Beauceron, C. Bernet, G. Boudoul, R. Chierici, D. Contardo, B. Courbon, P. Depasse, H. El Mamouni, J. Fay, L. Finco, S. Gascon, M. Gouzevitch, G. Grenier, B. Ille, F. Lagarde, I.B. Laktineh, M. Lethuillier, L. Mirabito, A.L. Pequegnot, S. Perries, A. Popov\cmsAuthorMark13, V. Sordini, M. Vander Donckt, S. Viret \cmsinstskipGeorgian Technical University, Tbilisi, Georgia
T. Toriashvili\cmsAuthorMark14 \cmsinstskipTbilisi State University, Tbilisi, Georgia
Z. Tsamalaidze\cmsAuthorMark6 \cmsinstskipRWTH Aachen University, I. Physikalisches Institut, Aachen, Germany
C. Autermann, S. Beranek, L. Feld, M.K. Kiesel, K. Klein, M. Lipinski, M. Preuten, C. Schomakers, J. Schulz, T. Verlage \cmsinstskipRWTH Aachen University, III. Physikalisches Institut A,  Aachen, Germany
A. Albert, M. Brodski, E. Dietz-Laursonn, D. Duchardt, M. Endres, M. Erdmann, S. Erdweg, T. Esch, R. Fischer, A. Güth, M. Hamer, T. Hebbeker, C. Heidemann, K. Hoepfner, S. Knutzen, M. Merschmeyer, A. Meyer, P. Millet, S. Mukherjee, M. Olschewski, K. Padeken, T. Pook, M. Radziej, H. Reithler, M. Rieger, F. Scheuch, L. Sonnenschein, D. Teyssier, S. Thüer \cmsinstskipRWTH Aachen University, III. Physikalisches Institut B,  Aachen, Germany
G. Flügge, B. Kargoll, T. Kress, A. Künsken, J. Lingemann, T. Müller, A. Nehrkorn, A. Nowack, C. Pistone, O. Pooth, A. Stahl\cmsAuthorMark15 \cmsinstskipDeutsches Elektronen-Synchrotron, Hamburg, Germany
M. Aldaya Martin, T. Arndt, C. Asawatangtrakuldee, K. Beernaert, O. Behnke, U. Behrens, A.A. Bin Anuar, K. Borras\cmsAuthorMark16, V. Botta, A. Campbell, P. Connor, C. Contreras-Campana, F. Costanza, C. Diez Pardos, G. Eckerlin, D. Eckstein, T. Eichhorn, E. Eren, E. Gallo\cmsAuthorMark17, J. Garay Garcia, A. Geiser, A. Gizhko, J.M. Grados Luyando, A. Grohsjean, P. Gunnellini, A. Harb, J. Hauk, M. Hempel\cmsAuthorMark18, H. Jung, A. Kalogeropoulos, M. Kasemann, J. Keaveney, C. Kleinwort, I. Korol, D. Krücker, W. Lange, A. Lelek, T. Lenz, J. Leonard, K. Lipka, W. Lohmann\cmsAuthorMark18, 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 \cmsinstskipUniversity of Hamburg, Hamburg, Germany
S. Bein, 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\cmsAuthorMark15, T. Peiffer, A. Perieanu, C. Scharf, P. Schleper, A. Schmidt, S. Schumann, J. Schwandt, J. Sonneveld, H. Stadie, G. Steinbrück, F.M. Stober, M. Stöver, H. Tholen, D. Troendle, E. Usai, L. Vanelderen, A. Vanhoefer, B. Vormwald \cmsinstskipInstitut für 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\cmsAuthorMark15, S.M. Heindl, U. Husemann, F. Kassel\cmsAuthorMark15, S. Kudella, H. Mildner, M.U. Mozer, Th. Müller, M. Plagge, G. Quast, K. Rabbertz, M. Schröder, I. Shvetsov, G. Sieber, H.J. Simonis, R. Ulrich, S. Wayand, M. Weber, T. Weiler, S. Williamson, C. Wöhrmann, R. Wolf \cmsinstskipInstitute 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 \cmsinstskipNational and Kapodistrian University of Athens, Athens, Greece
S. Kesisoglou, A. Panagiotou, N. Saoulidou \cmsinstskipUniversity of Ioánnina, Ioánnina, Greece
I. Evangelou, G. Flouris, C. Foudas, P. Kokkas, N. Manthos, I. Papadopoulos, E. Paradas, J. Strologas, F.A. Triantis \cmsinstskipMTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös Loránd University, Budapest, Hungary
M. Csanad, N. Filipovic, G. Pasztor \cmsinstskipWigner Research Centre for Physics, Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath\cmsAuthorMark19, F. Sikler, V. Veszpremi, G. Vesztergombi\cmsAuthorMark20, A.J. Zsigmond \cmsinstskipInstitute of Nuclear Research ATOMKI, Debrecen, Hungary
N. Beni, S. Czellar, J. Karancsi\cmsAuthorMark21, A. Makovec, J. Molnar, Z. Szillasi \cmsinstskipInstitute of Physics, University of Debrecen, Debrecen, Hungary
M. Bartók\cmsAuthorMark20, P. Raics, Z.L. Trocsanyi, B. Ujvari \cmsinstskipIndian Institute of Science (IISc),  Bangalore, India
S. Choudhury, J.R. Komaragiri \cmsinstskipNational Institute of Science Education and Research, Bhubaneswar, India
S. Bahinipati\cmsAuthorMark22, S. Bhowmik, P. Mal, K. Mandal, A. Nayak\cmsAuthorMark23, D.K. Sahoo\cmsAuthorMark22, N. Sahoo, S.K. Swain \cmsinstskipPanjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, U. Bhawandeep, R. Chawla, N. Dhingra, A.K. Kalsi, A. Kaur, M. Kaur, R. Kumar, P. Kumari, A. Mehta, M. Mittal, J.B. Singh, G. Walia \cmsinstskipUniversity of Delhi, Delhi, India
Ashok Kumar, Aashaq Shah, A. Bhardwaj, S. Chauhan, B.C. Choudhary, R.B. Garg, S. Keshri, A. Kumar, S. Malhotra, M. Naimuddin, K. Ranjan, R. Sharma, V. Sharma \cmsinstskipSaha Institute of Nuclear Physics, HBNI, Kolkata, India
R. Bhardwaj, R. Bhattacharya, S. Bhattacharya, 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 \cmsinstskipIndian Institute of Technology Madras, Madras, India
P.K. Behera \cmsinstskipBhabha Atomic Research Centre, Mumbai, India
R. Chudasama, D. Dutta, V. Jha, V. Kumar, A.K. Mohanty\cmsAuthorMark15, P.K. Netrakanti, L.M. Pant, P. Shukla, A. Topkar \cmsinstskipTata Institute of Fundamental Research-A, Mumbai, India
T. Aziz, S. Dugad, B. Mahakud, S. Mitra, G.B. Mohanty, B. Parida, N. Sur, B. Sutar \cmsinstskipTata Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, Sa. Jain, S. Kumar, M. Maity\cmsAuthorMark24, G. Majumder, K. Mazumdar, T. Sarkar\cmsAuthorMark24, N. Wickramage\cmsAuthorMark25 \cmsinstskipIndian 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 \cmsinstskipInstitute for Research in Fundamental Sciences (IPM),  Tehran, Iran
S. Chenarani\cmsAuthorMark26, E. Eskandari Tadavani, S.M. Etesami\cmsAuthorMark26, M. Khakzad, M. Mohammadi Najafabadi, M. Naseri, S. Paktinat Mehdiabadi\cmsAuthorMark27, F. Rezaei Hosseinabadi, B. Safarzadeh\cmsAuthorMark28, M. Zeinali \cmsinstskipUniversity College Dublin, Dublin, Ireland
M. Felcini, M. Grunewald \cmsinstskipINFN Sezione di Bari , Università di Bari , Politecnico di Bari ,  Bari, Italy
M. Abbrescia, C. Calabria, C. Caputo, A. Colaleo, D. Creanza, L. Cristella, N. De Filippis, M. De Palma, L. Fiore, G. Iaselli, G. Maggi, M. Maggi, G. Miniello, S. My, S. Nuzzo, A. Pompili, G. Pugliese, R. Radogna, A. Ranieri, G. Selvaggi, A. Sharma, L. Silvestris\cmsAuthorMark15, R. Venditti, P. Verwilligen \cmsinstskipINFN Sezione di Bologna , Università di Bologna ,  Bologna, Italy
G. Abbiendi, C. Battilana, D. Bonacorsi, S. Braibant-Giacomelli, L. Brigliadori, R. Campanini, P. Capiluppi, A. Castro, F.R. Cavallo, S.S. Chhibra, M. Cuffiani, G.M. Dallavalle, F. Fabbri, A. Fanfani, D. Fasanella, P. Giacomelli, L. Guiducci, S. Marcellini, G. Masetti, F.L. Navarria, A. Perrotta, A.M. Rossi, T. Rovelli, G.P. Siroli, N. Tosi\cmsAuthorMark15 \cmsinstskipINFN Sezione di Catania , Università di Catania ,  Catania, Italy
S. Albergo, S. Costa, A. Di Mattia, F. Giordano, R. Potenza, A. Tricomi, C. Tuve \cmsinstskipINFN Sezione di Firenze , Università di Firenze ,  Firenze, Italy
G. Barbagli, K. Chatterjee, V. Ciulli, C. Civinini, R. D’Alessandro, E. Focardi, P. Lenzi, M. Meschini, S. Paoletti, L. Russo\cmsAuthorMark29, G. Sguazzoni, D. Strom, L. Viliani\cmsAuthorMark15 \cmsinstskipINFN Laboratori Nazionali di Frascati, Frascati, Italy
L. Benussi, S. Bianco, F. Fabbri, D. Piccolo, F. Primavera\cmsAuthorMark15 \cmsinstskipINFN Sezione di Genova , Università di Genova ,  Genova, Italy
V. Calvelli, F. Ferro, E. Robutti, S. Tosi \cmsinstskipINFN Sezione di Milano-Bicocca , Università di Milano-Bicocca ,  Milano, Italy
L. Brianza, F. Brivio, V. Ciriolo, M.E. Dinardo, S. Fiorendi, S. Gennai, A. Ghezzi, P. Govoni, M. Malberti, S. Malvezzi, R.A. Manzoni, D. Menasce, L. Moroni, M. Paganoni, K. Pauwels, D. Pedrini, S. Pigazzini\cmsAuthorMark30, S. Ragazzi, T. Tabarelli de Fatis \cmsinstskipINFN Sezione di Napoli , Università di Napoli ’Federico II’ , Napoli, Italy, Università della Basilicata , Potenza, Italy, Università G. Marconi , Roma, Italy
S. Buontempo, N. Cavallo, S. Di Guida\cmsAuthorMark15, F. Fabozzi, F. Fienga, A.O.M. Iorio, W.A. Khan, L. Lista, S. Meola\cmsAuthorMark15, P. Paolucci\cmsAuthorMark15, C. Sciacca, F. Thyssen \cmsinstskipINFN Sezione di Padova , Università di Padova , Padova, Italy, Università di Trento , Trento, Italy
P. Azzi\cmsAuthorMark15, N. Bacchetta, S. Badoer, L. Benato, A. Boletti, R. Carlin, A. Carvalho Antunes De Oliveira, P. Checchia, M. Dall’Osso, P. De Castro Manzano, T. Dorigo, U. Gasparini, A. Gozzelino, S. Lacaprara, M. Margoni, A.T. Meneguzzo, M. Pegoraro, N. Pozzobon, P. Ronchese, R. Rossin, F. Simonetto, E. Torassa, S. Ventura, M. Zanetti, P. Zotto, G. Zumerle \cmsinstskipINFN Sezione di Pavia , Università di Pavia ,  Pavia, Italy
A. Braghieri, F. Fallavollita, A. Magnani, P. Montagna, S.P. Ratti, V. Re, M. Ressegotti, C. Riccardi, P. Salvini, I. Vai, P. Vitulo \cmsinstskipINFN Sezione di Perugia , Università di Perugia ,  Perugia, Italy
L. Alunni Solestizi, G.M. Bilei, D. Ciangottini, L. Fanò, P. Lariccia, R. Leonardi, G. Mantovani, V. Mariani, M. Menichelli, A. Saha, A. Santocchia, D. Spiga \cmsinstskipINFN Sezione di Pisa , Università di Pisa , Scuola Normale Superiore di Pisa ,  Pisa, Italy
K. Androsov, P. Azzurri\cmsAuthorMark15, G. Bagliesi, J. Bernardini, T. Boccali, L. Borrello, R. Castaldi, M.A. Ciocci, R. Dell’Orso, G. Fedi, A. Giassi, M.T. Grippo\cmsAuthorMark29, F. Ligabue, T. Lomtadze, L. Martini, A. Messineo, F. Palla, A. Rizzi, A. Savoy-Navarro\cmsAuthorMark31, P. Spagnolo, R. Tenchini, G. Tonelli, A. Venturi, P.G. Verdini \cmsinstskipINFN Sezione di Roma , Sapienza Università di Roma ,  Rome, Italy
L. Barone, F. Cavallari, M. Cipriani, D. Del Re\cmsAuthorMark15, M. Diemoz, S. Gelli, E. Longo, F. Margaroli, B. Marzocchi, P. Meridiani, G. Organtini, R. Paramatti, F. Preiato, S. Rahatlou, C. Rovelli, F. Santanastasio \cmsinstskipINFN Sezione di Torino , Università di Torino , Torino, Italy, Università del Piemonte Orientale , Novara, Italy
N. Amapane, R. Arcidiacono\cmsAuthorMark15, S. Argiro, M. Arneodo, N. Bartosik, R. Bellan, C. Biino, N. Cartiglia, F. Cenna, M. Costa, R. Covarelli, A. Degano, N. Demaria, B. Kiani, C. Mariotti, S. Maselli, E. Migliore, V. Monaco, E. Monteil, M. Monteno, M.M. Obertino, L. Pacher, N. Pastrone, M. Pelliccioni, G.L. Pinna Angioni, F. Ravera, A. Romero, M. Ruspa, R. Sacchi, K. Shchelina, V. Sola, A. Solano, A. Staiano, P. Traczyk \cmsinstskipINFN Sezione di Trieste , Università di Trieste ,  Trieste, Italy
S. Belforte, M. Casarsa, F. Cossutti, G. Della Ricca, A. Zanetti \cmsinstskipKyungpook 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 \cmsinstskipChonbuk National University, Jeonju, Korea
A. Lee \cmsinstskipChonnam National University, Institute for Universe and Elementary Particles, Kwangju, Korea
H. Kim, D.H. Moon \cmsinstskipHanyang University, Seoul, Korea
J.A. Brochero Cifuentes, J. Goh, T.J. Kim \cmsinstskipKorea 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 \cmsinstskipSeoul 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 \cmsinstskipUniversity of Seoul, Seoul, Korea
M. Choi, H. Kim, J.H. Kim, J.S.H. Lee, I.C. Park, G. Ryu \cmsinstskipSungkyunkwan University, Suwon, Korea
Y. Choi, C. Hwang, J. Lee, I. Yu \cmsinstskipVilnius University, Vilnius, Lithuania
V. Dudenas, A. Juodagalvis, J. Vaitkus \cmsinstskipNational Centre for Particle Physics, Universiti Malaya, Kuala Lumpur, Malaysia
I. Ahmed, Z.A. Ibrahim, M.A.B. Md Ali\cmsAuthorMark32, F. Mohamad Idris\cmsAuthorMark33, W.A.T. Wan Abdullah, M.N. Yusli, Z. Zolkapli \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\cmsAuthorMark34, R. Lopez-Fernandez, J. Mejia Guisao, A. Sanchez-Hernandez \cmsinstskipUniversidad Iberoamericana, Mexico City, Mexico
S. Carrillo Moreno, C. Oropeza Barrera, F. Vazquez Valencia \cmsinstskipBenemerita Universidad Autonoma de Puebla, Puebla, Mexico
I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada \cmsinstskipUniversidad Autónoma de San Luis Potosí,  San Luis Potosí,  Mexico
A. Morelos Pineda \cmsinstskipUniversity of Auckland, Auckland, New Zealand
D. Krofcheck \cmsinstskipUniversity of Canterbury, Christchurch, New Zealand
P.H. Butler \cmsinstskipNational Centre for Physics, Quaid-I-Azam University, Islamabad, Pakistan
A. Ahmad, M. Ahmad, Q. Hassan, H.R. Hoorani, A. Saddique, M.A. Shah, M. Shoaib, M. Waqas \cmsinstskipNational Centre for Nuclear Research, Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, T. Frueboes, M. Górski, M. Kazana, K. Nawrocki, K. Romanowska-Rybinska, M. Szleper, P. Zalewski \cmsinstskipInstitute of Experimental Physics, Faculty of Physics, University of Warsaw, Warsaw, Poland
K. Bunkowski, A. Byszuk\cmsAuthorMark35, K. Doroba, A. Kalinowski, M. Konecki, J. Krolikowski, M. Misiura, M. Olszewski, A. Pyskir, M. Walczak \cmsinstskipLaboratório de Instrumentação e Física Experimental de Partículas, Lisboa, Portugal
P. Bargassa, C. Beirão 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 \cmsinstskipJoint 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\cmsAuthorMark36\cmsAuthorMark37, V. Palichik, V. Perelygin, S. Shmatov, S. Shulha, N. Skatchkov, V. Smirnov, N. Voytishin, A. Zarubin \cmsinstskipPetersburg Nuclear Physics Institute, Gatchina (St. Petersburg),  Russia
Y. Ivanov, V. Kim\cmsAuthorMark38, E. Kuznetsova\cmsAuthorMark39, P. Levchenko, V. Murzin, V. Oreshkin, I. Smirnov, V. Sulimov, L. Uvarov, S. Vavilov, A. Vorobyev \cmsinstskipInstitute 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 \cmsinstskipInstitute 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 \cmsinstskipMoscow Institute of Physics and Technology, Moscow, Russia
T. Aushev, A. Bylinkin\cmsAuthorMark37 \cmsinstskipNational Research Nuclear University ’Moscow Engineering Physics Institute’ (MEPhI),  Moscow, Russia
M. Chadeeva\cmsAuthorMark40, E. Popova, E. Tarkovskii \cmsinstskipP.N. Lebedev Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin\cmsAuthorMark37, I. Dremin\cmsAuthorMark37, M. Kirakosyan, A. Terkulov \cmsinstskipSkobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow, Russia
A. Baskakov, A. Belyaev, E. Boos, V. Bunichev, M. Dubinin\cmsAuthorMark41, L. Dudko, A. Ershov, A. Gribushin, V. Klyukhin, O. Kodolova, I. Lokhtin, I. Miagkov, S. Obraztsov, M. Perfilov, V. Savrin \cmsinstskipNovosibirsk State University (NSU),  Novosibirsk, Russia
V. Blinov\cmsAuthorMark42, Y.Skovpen\cmsAuthorMark42, D. Shtol\cmsAuthorMark42 \cmsinstskipState 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 \cmsinstskipUniversity of Belgrade, Faculty of Physics and Vinca Institute of Nuclear Sciences, Belgrade, Serbia
P. Adzic\cmsAuthorMark43, P. Cirkovic, D. Devetak, M. Dordevic, J. Milosevic, V. Rekovic \cmsinstskipCentro de Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT),  Madrid, Spain
J. Alcaraz Maestre, M. Barrio Luna, M. Cerrada, N. Colino, B. De La Cruz, A. Delgado Peris, A. Escalante Del Valle, C. Fernandez Bedoya, J.P. Fernández Ramos, J. Flix, M.C. Fouz, P. Garcia-Abia, O. Gonzalez Lopez, S. Goy Lopez, J.M. Hernandez, M.I. Josa, A. Pérez-Calero Yzquierdo, J. Puerta Pelayo, A. Quintario Olmeda, I. Redondo, L. Romero, M.S. Soares \cmsinstskipUniversidad Autónoma de Madrid, Madrid, Spain
J.F. de Trocóniz, M. Missiroli, D. Moran \cmsinstskipUniversidad de Oviedo, Oviedo, Spain
J. Cuevas, C. Erice, J. Fernandez Menendez, I. Gonzalez Caballero, J.R. González Fernández, E. Palencia Cortezon, S. Sanchez Cruz, I. Suárez Andrés, P. Vischia, J.M. Vizan Garcia \cmsinstskipInstituto de Física 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 \cmsinstskipCERN, 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\cmsAuthorMark44, M. Dobson, B. Dorney, T. du Pree, M. Dünser, 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, O. Karacheban\cmsAuthorMark18, J. Kieseler, H. Kirschenmann, V. Knünz, A. Kornmayer\cmsAuthorMark15, M.J. Kortelainen, C. Lange, P. Lecoq, C. Lourenço, M.T. Lucchini, L. Malgeri, M. Mannelli, A. Martelli, F. Meijers, J.A. Merlin, S. Mersi, E. Meschi, P. Milenovic\cmsAuthorMark45, 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\cmsAuthorMark46, M. Rovere, H. Sakulin, J.B. Sauvan, C. Schäfer, C. Schwick, M. Seidel, A. Sharma, P. Silva, P. Sphicas\cmsAuthorMark47, J. Steggemann, M. Stoye, M. Tosi, D. Treille, A. Triossi, A. Tsirou, V. Veckalns\cmsAuthorMark48, G.I. Veres\cmsAuthorMark20, M. Verweij, N. Wardle, W.D. Zeuner \cmsinstskipPaul 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 \cmsinstskipInstitute for Particle Physics, ETH Zurich, Zurich, Switzerland
F. Bachmair, L. Bäni, P. Berger, L. Bianchini, B. Casal, G. Dissertori, M. Dittmar, M. Donegà, C. Grab, C. Heidegger, D. Hits, J. Hoss, G. Kasieczka, T. Klijnsma, 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. Schönenberger, L. Shchutska, A. Starodumov\cmsAuthorMark49, V.R. Tavolaro, K. Theofilatos, M.L. Vesterbacka Olsson, R. Wallny, A. Zagozdzinska\cmsAuthorMark35, D.H. Zhu \cmsinstskipUniversität Zürich, Zurich, Switzerland
T.K. Aarrestad, C. Amsler\cmsAuthorMark50, L. Caminada, M.F. Canelli, A. De Cosa, S. Donato, C. Galloni, A. Hinzmann, T. Hreus, B. Kilminster, J. Ngadiuba, D. Pinna, G. Rauco, P. Robmann, D. Salerno, C. Seitz, Y. Yang, A. Zucchetta \cmsinstskipNational 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 \cmsinstskipNational 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. Miñano Moya, E. Paganis, A. Psallidas, J.f. Tsai \cmsinstskipChulalongkorn University, Faculty of Science, Department of Physics, Bangkok, Thailand
B. Asavapibhop, K. Kovitanggoon, G. Singh, N. Srimanobhas \cmsinstskipCukurova University, Physics Department, Science and Art Faculty, Adana, Turkey
A. Adiguzel\cmsAuthorMark51, F. Boran, S. Cerci\cmsAuthorMark52, S. Damarseckin, Z.S. Demiroglu, C. Dozen, I. Dumanoglu, S. Girgis, G. Gokbulut, Y. Guler, I. Hos\cmsAuthorMark53, E.E. Kangal\cmsAuthorMark54, O. Kara, U. Kiminsu, M. Oglakci, G. Onengut\cmsAuthorMark55, K. Ozdemir\cmsAuthorMark56, D. Sunar Cerci\cmsAuthorMark52, B. Tali\cmsAuthorMark52, H. Topakli\cmsAuthorMark57, S. Turkcapar, I.S. Zorbakir, C. Zorbilmez \cmsinstskipMiddle East Technical University, Physics Department, Ankara, Turkey
B. Bilin, G. Karapinar\cmsAuthorMark58, K. Ocalan\cmsAuthorMark59, M. Yalvac, M. Zeyrek \cmsinstskipBogazici University, Istanbul, Turkey
E. Gülmez, M. Kaya\cmsAuthorMark60, O. Kaya\cmsAuthorMark61, E.A. Yetkin\cmsAuthorMark62 \cmsinstskipIstanbul Technical University, Istanbul, Turkey
A. Cakir, K. Cankocak \cmsinstskipInstitute for Scintillation Materials of National Academy of Science of Ukraine, Kharkov, Ukraine
B. Grynyov \cmsinstskipNational Scientific Center, Kharkov Institute of Physics and Technology, Kharkov, Ukraine
L. Levchuk, P. Sorokin \cmsinstskipUniversity 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\cmsAuthorMark63, S. Paramesvaran, A. Poll, T. Sakuma, S. Seif El Nasr-storey, D. Smith, V.J. Smith \cmsinstskipRutherford Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev\cmsAuthorMark64, 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 \cmsinstskipImperial 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\cmsAuthorMark49, J. Pela, M. Pesaresi, D.M. Raymond, A. Richards, A. Rose, E. Scott, C. Seez, S. Summers, A. Tapper, K. Uchida, M. Vazquez Acosta\cmsAuthorMark65, T. Virdee\cmsAuthorMark15, J. Wright, S.C. Zenz \cmsinstskipBrunel University, Uxbridge, United Kingdom
J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, I.D. Reid, P. Symonds, L. Teodorescu, M. Turner \cmsinstskipBaylor University, Waco, USA
A. Borzou, K. Call, J. Dittmann, K. Hatakeyama, H. Liu, N. Pastika \cmsinstskipCatholic University of America, Washington, USA
R. Bartek, A. Dominguez \cmsinstskipThe University of Alabama, Tuscaloosa, USA
A. Buccilli, S.I. Cooper, C. Henderson, P. Rumerio, C. West \cmsinstskipBoston University, Boston, USA
D. Arcaro, A. Avetisyan, T. Bose, D. Gastler, D. Rankin, C. Richardson, J. Rohlf, L. Sulak, D. Zou \cmsinstskipBrown 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, R. Syarif \cmsinstskipUniversity of California, Davis, Davis, USA
R. Band, C. Brainerd, 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, Z. Wang \cmsinstskipUniversity 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 \cmsinstskipUniversity 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 \cmsinstskipUniversity 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\cmsAuthorMark66, F. Würthwein, A. Yagil, G. Zevi Della Porta \cmsinstskipUniversity of California, Santa Barbara - Department of Physics, Santa Barbara, USA
N. Amin, R. Bhandari, J. Bradmiller-Feld, C. Campagnari, A. Dishaw, V. Dutta, M. Franco Sevilla, 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 \cmsinstskipCalifornia Institute of Technology, Pasadena, USA
D. Anderson, J. Bendavid, A. Bornheim, J.M. Lawhorn, H.B. Newman, T. Nguyen, C. Pena, M. Spiropulu, J.R. Vlimant, S. Xie, Z. Zhang, R.Y. Zhu \cmsinstskipCarnegie Mellon University, Pittsburgh, USA
M.B. Andrews, T. Ferguson, M. Paulini, J. Russ, M. Sun, H. Vogel, I. Vorobiev, M. Weinberg \cmsinstskipUniversity 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 \cmsinstskipCornell 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 \cmsinstskipFairfield University, Fairfield, USA
D. Winn \cmsinstskipFermi National Accelerator Laboratory, Batavia, USA
S. Abdullin, M. Albrow, G. Apollinari, A. Apresyan, A. Apyan, 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. Grünendahl, 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 Sá, 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 \cmsinstskipUniversity 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, D. Sperka, N. Terentyev, L. Thomas, J. Wang, S. Wang, J. Yelton \cmsinstskipFlorida International University, Miami, USA
S. Linn, P. Markowitz, G. Martinez, J.L. Rodriguez \cmsinstskipFlorida State University, Tallahassee, USA
A. Ackert, T. Adams, A. Askew, S. Hagopian, V. Hagopian, K.F. Johnson, T. Kolberg, T. Perry, H. Prosper, A. Santra, R. Yohay \cmsinstskipFlorida Institute of Technology, Melbourne, USA
M.M. Baarmand, V. Bhopatkar, S. Colafranceschi, M. Hohlmann, D. Noonan, T. Roy, F. Yumiceva \cmsinstskipUniversity 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 \cmsinstskipThe University of Iowa, Iowa City, USA
B. Bilki\cmsAuthorMark67, W. Clarida, K. Dilsiz\cmsAuthorMark68, S. Durgut, R.P. Gandrajula, M. Haytmyradov, V. Khristenko, J.-P. Merlo, H. Mermerkaya\cmsAuthorMark69, A. Mestvirishvili, A. Moeller, J. Nachtman, H. Ogul\cmsAuthorMark70, Y. Onel, F. Ozok\cmsAuthorMark71, A. Penzo, C. Snyder, E. Tiras, J. Wetzel, K. Yi \cmsinstskipJohns 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 \cmsinstskipThe 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, E. Schmitz, R. Stringer, J.D. Tapia Takaki, Q. Wang \cmsinstskipKansas State University, Manhattan, USA
A. Ivanov, K. Kaadze, Y. Maravin, A. Mohammadi, L.K. Saini, N. Skhirtladze, S. Toda \cmsinstskipLawrence Livermore National Laboratory, Livermore, USA
F. Rebassoo, D. Wright \cmsinstskipUniversity 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 \cmsinstskipMassachusetts Institute of Technology, Cambridge, USA
D. Abercrombie, B. Allen, V. Azzolini, R. Barbieri, A. Baty, R. Bi, 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 \cmsinstskipUniversity 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 \cmsinstskipUniversity of Mississippi, Oxford, USA
J.G. Acosta, S. Oliveros \cmsinstskipUniversity 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 \cmsinstskipState 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 \cmsinstskipNortheastern 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 \cmsinstskipNorthwestern 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 \cmsinstskipUniversity 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\cmsAuthorMark36, M. Planer, A. Reinsvold, R. Ruchti, N. Rupprecht, G. Smith, S. Taroni, M. Wayne, M. Wolf, A. Woodard \cmsinstskipThe 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 \cmsinstskipPrinceton 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. Piroué, D. Stickland, A. Svyatkovskiy, C. Tully \cmsinstskipUniversity of Puerto Rico, Mayaguez, USA
S. Malik, S. Norberg \cmsinstskipPurdue 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 \cmsinstskipPurdue University Northwest, Hammond, USA
T. Cheng, N. Parashar, J. Stupak \cmsinstskipRice 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 \cmsinstskipUniversity 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 \cmsinstskipThe Rockefeller University, New York, USA
R. Ciesielski, K. Goulianos, C. Mesropian \cmsinstskipRutgers, The State University of New Jersey, Piscataway, USA
A. Agapitos, J.P. Chou, Y. Gershtein, T.A. Gómez 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 \cmsinstskipUniversity of Tennessee, Knoxville, USA
M. Foerster, J. Heideman, G. Riley, K. Rose, S. Spanier, K. Thapa \cmsinstskipTexas A&M University, College Station, USA
O. Bouhali\cmsAuthorMark72, A. Castaneda Hernandez\cmsAuthorMark72, A. Celik, M. Dalchenko, M. De Mattia, A. Delgado, S. Dildick, R. Eusebi, J. Gilmore, T. Huang, T. Kamon\cmsAuthorMark73, R. Mueller, Y. Pakhotin, R. Patel, A. Perloff, L. Perniè, D. Rathjens, A. Safonov, A. Tatarinov, K.A. Ulmer \cmsinstskipTexas 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 \cmsinstskipVanderbilt 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 \cmsinstskipUniversity 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 \cmsinstskipWayne State University, Detroit, USA
C. Clarke, R. Harr, P.E. Karchin, J. Sturdy, S. Zaleski \cmsinstskipUniversity 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. Hervé, 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. Woods \cmsinstskip1:  Also at Vienna University of Technology, Vienna, Austria
2:  Also at State Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing, China
3:  Also at Universidade Estadual de Campinas, Campinas, Brazil
4:  Also at Universidade Federal de Pelotas, Pelotas, Brazil
5:  Also at Université Libre de Bruxelles, Bruxelles, Belgium
6:  Also at Joint Institute for Nuclear Research, Dubna, Russia
7:  Also at Helwan University, Cairo, Egypt
8:  Now at Zewail City of Science and Technology, Zewail, Egypt
9:  Now 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 Université de Haute Alsace, Mulhouse, France
13: Also at Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow, Russia
14: Also at Tbilisi State University, Tbilisi, Georgia
15: Also at CERN, European Organization for Nuclear Research, Geneva, Switzerland
16: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen, Germany
17: Also at University of Hamburg, Hamburg, Germany
18: Also at Brandenburg University of Technology, Cottbus, Germany
19: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary
20: Also at MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös Loránd University, Budapest, Hungary
21: Also at Institute of Physics, University of Debrecen, Debrecen, Hungary
22: Also at Indian Institute of Technology Bhubaneswar, Bhubaneswar, India
23: Also at Institute of Physics, Bhubaneswar, India
24: Also at University of Visva-Bharati, Santiniketan, India
25: Also at University of Ruhuna, Matara, Sri Lanka
26: Also at Isfahan University of Technology, Isfahan, Iran
27: Also at Yazd University, Yazd, Iran
28: Also at Plasma Physics Research Center, Science and Research Branch, Islamic Azad University, Tehran, Iran
29: Also at Università degli Studi di Siena, Siena, Italy
30: Also at INFN Sezione di Milano-Bicocca; Università di Milano-Bicocca, Milano, Italy
31: Also at Purdue University, West Lafayette, USA
32: Also at International Islamic University of Malaysia, Kuala Lumpur, Malaysia
33: Also at Malaysian Nuclear Agency, MOSTI, Kajang, Malaysia
34: Also at Consejo Nacional de Ciencia y Tecnología, Mexico city, Mexico
35: Also at Warsaw University of Technology, Institute of Electronic Systems, Warsaw, Poland
36: Also at Institute for Nuclear Research, Moscow, Russia
37: Now at National Research Nuclear University ’Moscow Engineering Physics Institute’ (MEPhI), Moscow, Russia
38: Also at St. Petersburg State Polytechnical University, St. Petersburg, Russia
39: Also at University of Florida, Gainesville, USA
40: Also at P.N. Lebedev Physical Institute, Moscow, Russia
41: Also at California Institute of Technology, Pasadena, USA
42: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia
43: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia
44: Also at INFN Sezione di Roma; Sapienza Università di Roma, Rome, Italy
45: Also at University of Belgrade, Faculty of Physics and Vinca Institute of Nuclear Sciences, Belgrade, Serbia
46: Also at Scuola Normale e Sezione dell’INFN, Pisa, Italy
47: Also at National and Kapodistrian University of Athens, Athens, Greece
48: Also at Riga Technical University, Riga, Latvia
49: Also at Institute for Theoretical and Experimental Physics, Moscow, Russia
50: Also at Albert Einstein Center for Fundamental Physics, Bern, Switzerland
51: Also at Istanbul University, Faculty of Science, Istanbul, Turkey
52: Also at Adiyaman University, Adiyaman, Turkey
53: Also at Istanbul Aydin University, Istanbul, Turkey
54: Also at Mersin University, Mersin, Turkey
55: Also at Cag University, Mersin, Turkey
56: Also at Piri Reis University, Istanbul, Turkey
57: Also at Gaziosmanpasa University, Tokat, Turkey
58: Also at Izmir Institute of Technology, Izmir, Turkey
59: Also at Necmettin Erbakan University, Konya, Turkey
60: Also at Marmara University, Istanbul, Turkey
61: Also at Kafkas University, Kars, Turkey
62: Also at Istanbul Bilgi University, Istanbul, Turkey
63: Also at Rutherford Appleton Laboratory, Didcot, United Kingdom
64: Also at School of Physics and Astronomy, University of Southampton, Southampton, United Kingdom
65: Also at Instituto de Astrofísica de Canarias, La Laguna, Spain
66: Also at Utah Valley University, Orem, USA
67: Also at BEYKENT UNIVERSITY, Istanbul, Turkey
68: Also at Bingol University, Bingol, Turkey
69: Also at Erzincan University, Erzincan, Turkey
70: Also at Sinop University, Sinop, Turkey
71: Also at Mimar Sinan University, Istanbul, Istanbul, Turkey
72: Also at Texas A&M University at Qatar, Doha, Qatar
73: Also at Kyungpook National University, Daegu, Korea


  1. M. Schmaltz and D. Tucker-Smith, “Little Higgs review”, Ann. Rev. Nucl. Part. Sci. 55 (2005) 229, doi:10.1146/annurev.nucl.55.090704.151502, arXiv:hep-ph/0502182.
  2. T. Appelquist, H.-C. Cheng, and B. A. Dobrescu, “Bounds on universal extra dimensions”, Phys. Rev. D 64 (2001) 035002, doi:10.1103/PhysRevD.64.035002, arXiv:hep-ph/0012100.
  3. H.-C. Cheng, C. T. Hill, S. Pokorski, and J. Wang, “Standard model in the latticized bulk”, Phys. Rev. D 64 (2001) 065007, doi:10.1103/PhysRevD.64.065007, arXiv:hep-th/0104179.
  4. R. S. Chivukula, E. H. Simmons, and J. Terning, “Limits on noncommuting extended technicolor”, Phys. Rev. D 53 (1996) 5258, doi:10.1103/PhysRevD.53.5258, arXiv:hep-ph/9506427.
  5. R. N. Mohapatra and J. C. Pati, “Left-right gauge symmetry and an ’isoconjugate’ model of violation”, Phys. Rev. D 11 (1975) 566, doi:10.1103/PhysRevD.11.566.
  6. D. J. Muller and S. Nandi, “Topflavor: a separate SU(2) for the third family”, Phys. Lett. B 383 (1996) 345, doi:10.1016/0370-2693(96)00745-9, arXiv:hep-ph/9607328.
  7. E. Malkawi, T. Tait, and C.-P. Yuan, “A model of strong flavor dynamics for the top quark”, Phys. Lett. B 385 (1996) 304, doi:10.1016/0370-2693(96)00859-3, arXiv:hep-ph/9603349.
  8. D0 Collaboration, “Search for boson resonances decaying to a top quark and a bottom quark”, Phys. Rev. Lett. 100 (2008) 211803, doi:10.1103/PhysRevLett.100.211803, arXiv:0803.3256.
  9. D0 Collaboration, “Search for \PWpr resonances with left- and right-handed couplings to fermions”, Phys. Lett. B 699 (2011) 145, doi:10.1016/j.physletb.2011.03.066, arXiv:1101.0806.
  10. CDF Collaboration, “Search for resonances decaying to top and bottom quarks with the cdf experiment”, Phys. Rev. Lett. 115 (2015) 061801, doi:10.1103/PhysRevLett.115.061801, arXiv:1504.01536.
  11. CMS Collaboration, “Search for W’ tb decays in the lepton+jets final state in pp collisions at = 8 TeV”, JHEP 05 (2014) 108, doi:10.1007/JHEP05(2014)108, arXiv:1402.2176.
  12. CMS Collaboration, “Search for W’ tb decays in proton-proton collisions at = 8 TeV”, JHEP 02 (2016) 122, doi:10.1007/JHEP02(2016)122, arXiv:1509.06051.
  13. ATLAS Collaboration, “Search for W’ in the lepton plus jets final state in proton-proton collisions at a centre-of-mass energy of = 8 TeV with the ATLAS detector”, Phys. Lett. B 743 (2015) 235, doi:10.1016/j.physletb.2015.02.051, arXiv:1410.4103.
  14. ATLAS Collaboration, “Search for decays in pp collisions at TeV with the ATLAS detector”, Eur. Phys. J. C 75 (2015) 165, doi:10.1140/epjc/s10052-015-3372-2, arXiv:1408.0886.
  15. CMS Collaboration, “The CMS experiment at the CERN LHC”, JINST 3 (2008) S08004, doi:10.1088/1748-0221/3/08/S08004.
  16. CMS Collaboration, “Performance of electron reconstruction and selection with the CMS detector in proton-proton collisions at \TeV”, JINST 10 (2015) P06005, doi:10.1088/1748-0221/10/06/P06005, arXiv:1502.02701.
  17. CMS Collaboration, “Performance of CMS muon reconstruction in pp collision events at \TeV”, JINST 7 (2012) P10002, doi:10.1088/1748-0221/7/10/P10002, arXiv:1206.4071.
  18. CMS Collaboration, “The CMS trigger system”, JINST 12 (2017) P01020, doi:10.1088/1748-0221/12/01/P01020, arXiv:1609.02366.
  19. CMS Collaboration, “Particle-flow event reconstruction in CMS and performance for jets, taus, and \MET”, CMS Physics Analysis Summary CMS-PAS-PFT-09-001, 2009.
  20. CMS Collaboration, “Commissioning of the particle-flow event reconstruction with the first LHC collisions recorded in the CMS detector”, CMS Physics Analysis Summary CMS-PAS-PFT-10-001, 2010.
  21. CMS Collaboration, “Particle-flow reconstruction and global event description with the CMS detector”, (2017). arXiv:1706.04965. Submitted to JINST.
  22. CompHEP Collaboration, “CompHEP 4.4: Automatic computations from Lagrangians to events”, Nucl. Instrum. Meth. A 534 (2004) 250, doi:10.1016/j.nima.2004.07.096, arXiv:hep-ph/0403113.
  23. Z. Sullivan, “Fully differential production and decay at next-to-leading order in QCD”, Phys. Rev. D 66 (2002) 075011, doi:10.1103/PhysRevD.66.075011, arXiv:hep-ph/0207290.
  24. D. Duffty and Z. Sullivan, “Model independent reach for W-prime bosons at the LHC”, Phys. Rev. D 86 (2012) 075018, doi:10.1103/PhysRevD.86.075018, arXiv:1208.4858.
  25. J. Alwall et al., “The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations”, JHEP 07 (2014) 079, doi:10.1007/JHEP07(2014)079, arXiv:1405.0301.
  26. R. Frederix and S. Frixione, “Merging meets matching in MC@NLO”, JHEP 12 (2012) 061, doi:10.1007/JHEP12(2012)061, arXiv:1209.6215.
  27. J. Alwall et al., “Comparative study of various algorithms for the merging of parton showers and matrix elements in hadronic collisions”, Eur. Phys. J. C 53 (2008) 473, doi:10.1140/epjc/s10052-007-0490-5, arXiv:0706.2569.
  28. P. Nason, “A new method for combining NLO QCD with shower Monte Carlo algorithms”, JHEP 11 (2004) 040, doi:10.1088/1126-6708/2004/11/040, arXiv:hep-ph/0409146.
  29. S. Frixione, P. Nason, and C. Oleari, “Matching NLO QCD computations with Parton Shower simulations: the POWHEG method”, JHEP 11 (2007) 070, doi:10.1088/1126-6708/2007/11/070.
  30. S. Alioli, P. Nason, C. Oleari, and E. Re, “A general framework for implementing NLO calculations in shower Monte Carlo programs: the POWHEG BOX”, JHEP 06 (2010) 043, doi:10.1007/JHEP06(2010)043, arXiv:1002.2581.
  31. S. Frixione, P. Nason, and G. Ridolfi, “A positive-weight next-to-leading-order Monte Carlo for heavy flavour hadroproduction”, JHEP 09 (2007) 126, doi:10.1088/1126-6708/2007/09/126, arXiv:0707.3088.
  32. E. Re, “Single-top -channel production matched with parton showers using the POWHEG method”, Eur. Phys. J. C 71 (2011) 1547, doi:10.1140/epjc/s10052-011-1547-z, arXiv:1009.2450.
  33. T. Sjöstrand et al., “An introduction to PYTHIA 8.2”, Comput. Phys. Commun. 191 (2015) 159, doi:10.1016/j.cpc.2015.01.024, arXiv:1410.3012.
  34. NNPDF Collaboration, “Parton distributions for the LHC Run II”, JHEP 04 (2015) 040, doi:10.1007/JHEP04(2015)040, arXiv:1410.8849.
  35. CMS Collaboration, “Measurement of the differential cross section for top quark pair production in pp collisions at ”, Eur. Phys. J. C 75 (2015) 542, doi:10.1140/epjc/s10052-015-3709-x, arXiv:1505.04480.
  36. CMS Collaboration, “Measurement of the production cross section in pp collisions at 7\TeV in lepton+jets events using b-quark jet identification”, Phys. Rev. D 84 (2011) 092004, doi:10.1103/PhysRevD.84.092004, arXiv:1108.3773.
  37. CMS Collaboration, “Event generator tunes obtained from underlying event and multiparton scattering measurements”, Eur. Phys. J. C 76 (2016) 155, doi:10.1140/epjc/s10052-016-3988-x, arXiv:1512.00815.
  38. S. J. Allison et al., “Geant4 developments and applications”, IEEE Trans. Nucl. Sci. 53 (2006) 270, doi:10.1109/TNS.2006.869826.
  39. M. Cacciari, G. P. Salam, and G. Soyez, “The anti- jet clustering algorithm”, JHEP 04 (2008) 063, doi:10.1088/1126-6708/2008/04/063, arXiv:0802.1189.
  40. M. Cacciari, G. P. Salam, and G. Soyez, “FastJet user manual”, Eur. Phys. J. C 72 (2012) 1896, doi:10.1140/epjc/s10052-012-1896-2, arXiv:1111.6097.
  41. CMS Collaboration, “Determination of jet energy calibration and transverse momentum resolution in CMS”, JINST 6 (2011) P11002, doi:10.1088/1748-0221/6/11/P11002, arXiv:1107.4277.
  42. M. Cacciari and G. P. Salam, “Pileup subtraction using jet areas”, Phys. Lett. B 659 (2008) 119, doi:10.1016/j.physletb.2007.09.077, arXiv:0707.1378.
  43. CMS Collaboration, “Identification of b-quark jets with the CMS experiment”, JINST 8 (2013) P04013, doi:10.1088/1748-0221/8/04/P04013, arXiv:1211.4462.
  44. CMS Collaboration, “Identification of b quark jets at the CMS Experiment in the LHC Run 2”, CMS Physics Analysis Summary CMS-PAS-BTV-15-001, 2015.
  45. J. Thaler and K. Van Tilburg, “Identifying boosted objects with N-subjettiness”, JHEP 03 (2011) 015, doi:10.1007/JHEP03(2011)015, arXiv:1011.2268.
  46. J. Thaler and K. Van Tilburg, “Maximizing boosted top identification by minimizing N-subjettiness”, JHEP 02 (2012) 093, doi:10.1007/JHEP02(2012)093, arXiv:1108.2701.
  47. M. Dasgupta, A. Fregoso, S. Marzani, and G. P. Salam, “Towards an understanding of jet substructure”, JHEP 09 (2013) 029, doi:10.1007/JHEP09(2013)029, arXiv:1307.0007.
  48. A. J. Larkoski, S. Marzani, G. Soyez, and J. Thaler, “Soft drop”, JHEP 05 (2014) 146, doi:10.1007/JHEP05(2014)146, arXiv:1402.2657.
  49. Y. L. Dokshitzer, G. D. Leder, S. Moretti, and B. R. Webber, “Better jet clustering algorithms”, JHEP 08 (1997) 001, doi:10.1088/1126-6708/1997/08/001, arXiv:hep-ph/9707323.
  50. M. Dasgupta, A. Fregoso, S. Marzani, and A. Powling, “Jet substructure with analytical methods”, Eur. Phys. J. C 73 (2013) 2623, doi:10.1140/epjc/s10052-013-2623-3, arXiv:1307.0013.
  51. S. D. Ellis and D. E. Soper, “Successive combination jet algorithm for hadron collisions”, Phys. Rev. D 48 (1993) 3160, doi:10.1103/PhysRevD.48.3160, arXiv:hep-ph/9305266.
  52. CMS Collaboration, “Top tagging with new approaches”, CMS Physics Analysis Summary CMS-PAS-JME-15-002, 2016.
  53. D. Krohn, J. Thaler, and L.-T. Wang, “Jet trimming”, JHEP 02 (2010) 84, doi:10.1007/JHEP02(2010)084, arXiv:0912.1342.
  54. CMS Collaboration, “CMS luminosity measurement for the 2015 data taking period”, CMS Physics Analysis Summary CMS-PAS-LUM-15-001, 2015.
  55. M. Cacciari et al., “The cross-section at 1.8 TeV and 1.96 TeV: a study of the systematics due to parton densities and scale dependence”, JHEP 04 (2004) 068, doi:10.1088/1126-6708/2004/04/068, arXiv:hep-ph/0303085.
  56. S. Catani, D. de Florian, M. Grazzini, and P. Nason, “Soft gluon resummation for Higgs boson production at hadron colliders”, JHEP 07 (2003) 028, doi:10.1088/1126-6708/2003/07/028, arXiv:hep-ph/0306211.
  57. M. Botje et al., “The PDF4LHC Working Group Interim Recommendations”, (2011). arXiv:1101.0538.
  58. T. Müller, J. Ott, and J. Wagner-Kuhr, “theta - a framework for template-based modeling and inference”, 2010.
  59. R. Barlow and C. Beeston, “Fitting using finite Monte Carlo samples”, Comp. Phys. Comm. 77 (1993) 219, doi:10.1016/0010-4655(93)90005-W.
  60. J. S. Conway, “Incorporating nuisance parameters in likelihoods for multisource spectra”, in Proceedings, PHYSTAT 2011 Workshop on Statistical Issues Related to Discovery Claims in Search Experiments and Unfolding, p. 115. CERN,Geneva, Switzerland, January, 2011. arXiv:1103.0354. doi:10.5170/CERN-2011-006.115.