A The CMS Collaboration

Search for new physics in the multijet and missing transverse momentum final state in proton-proton collisions at \TeV

Abstract

A search for new physics is performed in multijet events with large missing transverse momentum produced in proton-proton collisions at \TeVusing a data sample corresponding to an integrated luminosity of 19.5\fbinvcollected with the CMS detector at the LHC. The data sample is divided into three jet multiplicity categories (3–5, 6–7, and jets), and studied further in bins of two variables: the scalar sum of jet transverse momenta and the missing transverse momentum. The observed numbers of events in various categories are consistent with backgrounds expected from standard model processes. Exclusion limits are presented for several simplified supersymmetric models of squark or gluino pair production.

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SUS-13-012

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SUS-13-012

1 Introduction

The standard model of particle physics (SM) successfully describes a wide variety of observations in high energy physics. The recent discovery of a new scalar boson with a mass of about 125\GeV [1, 2, 3] at the CERN Large Hadron Collider (LHC) marks another success for the SM, as its properties measured so far are consistent with those of the long-sought Higgs boson. However, its mass is predicted to be unstable against quadratically divergent quantum-loop corrections, which suggests the presence of physics beyond the SM. Supersymmetry (SUSY) is a well-explored extension that addresses various shortcomings of the SM. SUSY postulates a new symmetry, relating fermionic and bosonic degrees of freedom, and introduces a superpartner for each SM particle. Radiative corrections due to SUSY particles can compensate the contribution of the SM particles and thereby stabilize the mass of the Higgs boson. In -parity-conserving models [4], SUSY particles are produced in pairs, and the lightest SUSY particle (LSP) is stable. If weakly interacting and neutral, the LSP is a potential dark matter candidate.

This paper reports an inclusive search for physics beyond the SM in multijet events with large missing transverse momentum produced in pp collisions at a centre-of-mass energy \TeVat the LHC. The data sample used corresponds to an integrated luminosity of collected by the Compact Muon Solenoid (CMS) experiment [5]. This final state is motivated by many extensions of the SM, for example those given in Refs. [6, 7, 8]. At the LHC, both the CMS and ATLAS collaborations have performed SUSY searches in all-hadronic final states [9, 10, 11, 12, 13, 14, 15, 16, 17]. For all these searches, the observed numbers of events were consistent with the expected SM background, and exclusion limits were set in the context of the constrained minimal supersymmetric extension of the standard model (CMSSM) [18, 19, 20] and various simplified models [21, 22]. Contrary to the CMSSM case, the masses of particles are free parameters in simplified models, thus allowing a generic study of the parameter space of SUSY and SUSY-like theories. Simplified models of squark and gluino pair production are used to interpret the search results in this paper.

This analysis follows previous inclusive searches [9, 10] that require at least three jets in the final state. These searches are most sensitive to the hypothetical production of pairs of squarks and gluinos, where the squarks (gluinos) each decay to one (two) jets and an undetected LSP. We extend the analyses of Refs. [9, 10] by subdividing the data into three exclusive jet multiplicity categories: \xspace= 3–5, 6–7, and 8, which renders the analysis more sensitive to a variety of final-state topologies resulting from longer cascades of squarks and gluinos, and hence in a larger number of jets. The search regions with higher jet multiplicities extend the sensitivity of the analysis to models in which the gluino often decays into top quarks. While other analyses exploit the presence of bottom-quark jets in signal events to discriminate against background [12, 13], this analysis follows a complementary strategy by requiring a large number of jets, thus helping to keep the signal efficiency for fully hadronic final states as high as possible.

The events in each jet multiplicity category are further divided according to variables that characterize the total visible hadronic activity (\HT) and the momentum imbalance (\xspace) in an event, both defined in the plane transverse to the beam. Due to the presence of a number of energetic jets and two LSPs in the final state, the signal events are expected to have large \HTand \xspace. The main SM processes contributing to this final state are \cPZ+jets events, where the \cPZ boson decays to a pair of neutrinos (+jets), and \PW+jets and events, where a \PW boson decays to an , , or lepton (+jets). The presence of at least one neutrino in these events provides a source of genuine \xspace. Another background category is quantum chromodynamics (QCD) multijet events with large \xspacefrom leptonic decays of heavy-flavour hadrons inside the jets, jet energy mismeasurement, or instrumental noise and non-functioning detector components. All these backgrounds are determined using the data, with as little reliance on simulation as possible.

2 The CMS detector and event reconstruction

The CMS detector is a multipurpose apparatus, described in detail in Ref. [5]. The CMS coordinate system is defined with the origin at the centre of the detector and the axis along the anticlockwise beam direction. The polar angle is measured with respect to the axis, and the azimuthal angle (measured in radians) in the plane perpendicular to that axis. Charged-particle trajectories are measured with a silicon pixel and strip tracker, covering , where the pseudorapidity is defined as . Immersed in the magnetic field provided by a 6\unitm diameter superconducting solenoid, which also encircles the calorimeters, the tracking system provides transverse momentum (\pt) resolution of approximately 1.5% for charged particles with \GeV. A lead-tungstate crystal electromagnetic calorimeter and a brass-and-scintillator hadron calorimeter surround the tracking volume and cover the region . Steel and quartz-fibre hadron forward calorimeters extend the coverage to . Muons are identified in gas ionization detectors embedded in the steel flux return yoke of the magnet. The events used for this search are recorded using a two-level trigger system described in Ref. [5].

The recorded events are required to have at least one well-identified interaction vertex with position within 24\unitcm from the nominal centre of the detector and transverse distance from the axis less than 2\unitcm. The primary vertex is the one with the largest sum of \pt-squared of all the associated tracks, and is assumed to correspond to the hard-scattering process. The events are reconstructed using a particle-flow (PF) algorithm [23]. This algorithm reconstructs a list of particles in each event, namely charged and neutral hadrons, photons, muons, and electrons, combining the information from the tracker, the calorimeters, and the muon system. These particles are then clustered into jets using the anti-\ktclustering algorithm [24] with a size parameter of 0.5. Contributions from additional pp collisions overlapping with the event of interest (pileup) are mitigated by discarding charged particles not associated with the primary vertex and using the Fastjet tools [25, 26] to account for the neutral pileup component. Corrections to jet energy are applied to account for the variation of the response in \ptand  [27]. Missing transverse momentum (\ETslash) is reconstructed as magnitude of the vector sum of \ptof all the reconstructed PF particles [28, 29].

3 Sample selection

The search regions are first defined using a loose baseline selection with the following requirements:

  • , where \xspaceis the number of jets with and .

  • , with , where the sum includes all jets with and .

  • , with , where in this case, jets are required to satisfy and .

  • , , and , vetoing the events where is aligned with one of the three highest \ptjets. This requirement rejects most of the QCD multijet events in which a single mismeasured jet yields high \xspace.

  • Events containing isolated muons or electrons with \GeVare vetoed in order to reject \ttbarand +jets events with leptons in the final state. Both the and are required to produce a good quality track that is matched to the primary interaction vertex [30, 31]. The isolation is measured as the scalar \ptsum of PF particles (), except the lepton itself, within a cone of width for (0.4 for ) around the lepton. The is required to be less than 20% (15%) of the \ptof the ().

  • In addition, events affected by instrumental effects, particles from non-collision sources, or poorly reconstructed kinematic variables are rejected (event cleaning) [28, 29]. Events are also rejected if a jet with has more than 95% of its energy from PF photon candidates or more than 90% from PF neutral hadron candidates.

The data sample used for this analysis was collected using trigger algorithms that required events to have \GeVand \GeV. The trigger efficiencies are measured to be greater than 99% for the offline baseline selection of \GeVand \GeVin all jet multiplicity categories used in this search. A sample of 11 753 events is selected after applying the baseline criteria. The selected events are divided into 36 non-overlapping search regions defined in terms of \xspace, \HT, and \xspace, as listed in the first three columns of Table 5.

Several Monte Carlo (MC) simulation samples are used to model the signal as well as to develop and validate the background estimation methods. The \ttbar, \PW/\cPZ+jets, +jets, and QCD multijet background samples are produced using the \MADGRAPH[32] generator at leading order (LO), interfaced with the \PYTHIA6.4.24 [33] parton-shower model, and scaled to the next-to-leading order (NLO) or next-to-next-to-leading order cross section predictions [34, 35]. The events are processed through a \GEANTfoursimulation of the detector [36]. The SUSY signal samples are generated using \MADGRAPH5, the CTEQ6L [37] parton distribution functions (PDF), and are simulated using the CMS fast simulation package [38]. The underlying event description used for the MC simulated samples is described in Ref. [39]. The effect of pileup interactions is included by adding a number of simulated minimum bias events, on top of the hard interaction, to match the distribution observed in data.

4 Background estimation

In this search, all backgrounds are measured from data using methods similar to those described in Refs. [9, 10]. The +jets background is estimated using +jets events, exploiting their electroweak correspondence to \cPZ+jets production for boson \ptabove 100\GeV. The \cPZ+jets and +jets events exhibit similar characteristics, apart from electroweak coupling differences and asymptotically vanishing residual mass effects. The \ttbaror \xspace+jets events satisfy the search selection when the / is not identified or isolated, or is out of the detector acceptance (“lost-lepton” background) or when a lepton decays hadronically ( background). The lost-lepton background is estimated by reweighting events in a +jets data control sample with measured lepton efficiencies. The estimation of the background starts from a similar +jets sample, replacing the muon with a jet sampled as a function of jet \ptfrom templates obtained from simulation. The QCD multijet background is measured using a “rebalance-and-smear” method [9, 10]. The kinematical characteristics of multijet events are predicted from data by applying a fitting procedure that imposes zero missing transverse momentum on each event, and then smearing the jets according to data-corrected jet energy resolution values. The relative contribution of the various backgrounds varies in the different search regions.

4.1 Estimation of +jets background

Photons and \cPZ bosons exhibit similar kinematic properties at high \pt, and therefore the hadronic component of an event containing either a high-\ptphoton or Z boson is similar [40, 41, 42, 43]. The +jets sample used to evaluate the \xspace+jets event rate is collected by triggering on events with a candidate and large \HT. The photon candidates are reconstructed using the energy deposited in the electromagnetic calorimeter [44, 45]. Photon candidates with and or are used in this analysis, and are required to have their lateral shower profile consistent with that of a photon produced in the hard-scattering process (a prompt photon). To veto electrons misidentified as photons, the candidates with an associated track in the pixel detector are rejected. A photon candidate is required to satisfy tight isolation requirements based on the sum over \ptvalues of the PF candidates that lie within a cone of radius around the direction of its momentum.

The contribution to the +jets control sample from events in which the photon candidate originates from the misidentification of jet fragments (background photons) is measured using a template method, which exploits the difference between the shower profile of prompt (signal) and background photons, using the distribution of a modified second moment of the electromagnetic energy cluster around its mean position [44]. The distribution (template) for background events is obtained from a sideband region defined by selecting photons that satisfy very loose photon identification and isolation requirements but fail the stringent isolation requirements. The distribution for signal events is obtained from simulation. The sum of the two templates is fit to the observed distribution, with the normalization (background and signal yields) of each template determined in the fit. On average, 93% of selected +jets candidate events are determined to originate from prompt photons.

To mimic the missing momentum due to the neutrinos from the decay of the \cPZ boson, the photon candidate is not included in the calculation of \HTand \xspacefor the +jets events. The number of \xspace+jets events is then estimated by correcting the number of +jets events for photon acceptance and reconstruction efficiency, and scaling the result with the ratio relating the production cross section of the two processes (\xspace) in the various search regions. Therefore, the ratio \xspace, which we derive from simulation, is studied as a function of \HT, \xspace, and \xspaceusing events generated with \MADGRAPH(up to four partons) that are processed through the \PYTHIAparton shower algorithm to generate additional jets. The ratio exhibits a strong dependence on \xspacefor values below around 500\GeV(Fig. 1(a)), but changes by only ()% as \HTvaries between 500 and 1500\GeV(Fig. 1(b)), which is the region of interest to this search. The ratio is parametrized as a linear function of \xspacein several \xspaceranges, \GeV, \GeV, and \GeV, as shown in Fig. 1(c). The predicted numbers of \xspace+jets events and uncertainties for various search regions are summarized in Table 5.

Figure 1: The simulated ratio \xspaceas a function of (a) \xspace, (b) \HT, (c) \xspace, where the values for three \xspacebins are shown with linear fits, and (d) the double ratio of \xspace, using events from data to those from simulation; the linear fit and its uncertainty band are overlaid.

The theoretical uncertainty associated with \xspaceis estimated using \xspace+jets events selected from data and simulation, by requiring two opposite-sign muons to satisfy the muon selection and to form an invariant mass within 20\GeVof the \cPZ boson mass. The double ratio of \xspaceusing events from data to those from simulation is parametrized as a function of \xspaceusing a linear function, as shown in Fig. 1(d), and is used to correct \xspacefor a given jet multiplicity. The fitting procedure results in uncertainties of 20%, 25%, and 45% for the background predicted in the search regions with \xspace= 3–5, 6–7, and 8, respectively. The difference in the modeling of photon identification and isolation in the simulation and data leads to uncertainties of 2–5%, 10–20%, and 20–25% on the estimated number of \xspace+jets events for the three jet multiplicity intervals, respectively. The subtraction of events with non-prompt photons from QCD multijet events amounts to less than a 5% uncertainty for the final background prediction.

4.2 Estimation of the lost-lepton background

The lost-lepton background is estimated from a +jets control sample, selected with the same criteria as used for the search, except that events are required to have exactly one well-reconstructed and isolated with 10\GeV. The events are collected with the same trigger that is used to search for the signal. The transverse mass is required to be less than 100\GeVin order to select events containing decays as well as to reject possible signal events. Here is the azimuthal angle between the and the directions.

Using the reconstruction and isolation efficiencies and of the electrons and muons, the events in the isolated muon control sample are weighted by in order to estimate the number of events with unidentified leptons, and by to estimate the number of events with non-isolated leptons in the signal region. The predicted number of lost-lepton events is corrected to account for the detector and kinematic acceptance of the muons. The lepton efficiencies and kinematic acceptance factors are obtained from the MC simulation of \PW+jets and \ttbarevents and are determined in bins of \xspace, \HT, and \xspace.

This method is validated using simulated \ttbarand \PW+jets events. The single-muon events selected from the simulated samples are used to predict the number of background events expected in the zero-lepton search regions. The resulting \HT, \xspace, and \xspacedistributions are compared in Fig. 2 to the genuine ones obtained from \ttbarand \PW+jets events simulated at the detector level. The predicted distributions closely resemble the genuine ones.

Figure 2: Predicted (a) \HT, (b) \xspace, and (c) \xspacedistributions found from applying the lost-lepton background evaluation method to simulated \ttbarand W+jets\xspaceevents (solid points) in comparison to the genuine \ttbarand W+jets\xspacebackground from simulation (shaded curves). Only statistical uncertainties are shown.

The number of lost-lepton events predicted from data using the method described above, and the corresponding uncertainties, are listed in Table 5 for each search region. The dominant uncertainties arise from the limited number of single-muon events in most of the search regions. The differences in lepton reconstruction and isolation efficiencies between data and MC simulation are evaluated using a “tag-and-probe” method [46] on \xspace+jets events. The lepton reconstruction and isolation efficiencies are measured in bins of lepton \ptand relative to the closest jet. This method renders these efficiencies insensitive to the kinematic differences between \xspace+jets events and \ttbarand \PW+jets events. Relative differences between the predictions using efficiencies extracted from data and MC simulation result in 10–25%, 10–30%, and 15–24% uncertainties for the predicted background for various \HTand \xspacesearch bins with \xspace= 3–5, 6–7, and 8, respectively. An additional uncertainty of 15% for \xspace= 3–5 and 40% for is assigned based on the statistical precision of the validation of this background estimation method. Variation of the PDFs following the procedure of Ref. [47] affects the muon acceptance, and leads to an uncertainty of less than 4% on the final prediction. Any mismodeling of anomalous \ETslash [28] affects the simulated and results in 3% uncertainty for the predicted lost-lepton background.

4.3 Estimation of the hadronic lepton background

The \xspacebackground is estimated from a sample of +jets events, selected with an inclusive single or -jet trigger, by requiring exactly one with and . As in the estimation of the lost-lepton background, only events with \GeVare considered. The +jets and \xspace+jets events arise from the same physics processes; hence the hadronic component of the two samples is the same aside from the response of the detector to a muon or a jet. To account for this difference, the muon is replaced by a simulated \xspacejet, whose \ptvalue is randomly sampled from an MC response function, . Here, the is the transverse momentum of a generated hadronically decaying lepton selected from simulated and +jets events and is that of a reconstructed jet matching the lepton in space. In order to sample the response function completely, this procedure is repeated one hundred times for each event. The \xspace, \HT, and \xspacevalues of the events are recalculated, now including this jet, and search region selection criteria are applied to predict the \xspacebackground. The predicted background is corrected for the trigger efficiency, muon selection efficiency, kinematic and detector acceptance, and the ratio of branching fractions  [48]. The muon isolation and reconstruction efficiencies are obtained from MC simulation of \PW+jets and \ttbarevents in bins of lepton \ptand relative to the closest jet. To account for the difference in efficiencies measured in data and MC simulation, the predicted numbers of +jets events are corrected by 4.9%, 4.7%, and 3.5% for \xspace=3–5, 6–7, and 8, respectively. The predicted background and uncertainties are shown in Table 5 for all the search regions.

Figure 3: Predicted (a) \HT, (b) \xspace, and (c) \xspacedistributions found from applying the background evaluation method to simulated \ttbarand W+jets\xspaceevents (solid points) in comparison to the genuine \ttbarand W+jets\xspacebackground from simulation (shaded curve). Only statistical uncertainties are shown.

The \xspacebackground estimation method is validated by applying it to simulated \PW+jets and \ttbarMC samples. The results are shown in Fig. 3 in comparison to the genuine \xspacebackground from the simulated events. To evaluate the performance of the method for events with varying hadronic activity, the method is validated in each search bin. Uncertainties of 10%, 20%, and 20% are assigned to the predicted rates for events with \xspace=3–5, 6–7, and 8 respectively, mainly to reflect the level of statistical precision for this validation. Due to the multiple sampling of the response template, the statistical uncertainty of the prediction is evaluated with a set of pseudo-experiments using a bootstrap technique [49]. Relative differences between the predictions using efficiencies extracted from data and MC result in 2–20% uncertainties across the various search bins. Other systematic uncertainties arise from the geometrical and kinematic acceptance for the muons (3%), and the -jet response function (1–15%). An uncertainty of 1–8% is assigned to account for possible differences between data and MC simulation for the acceptance of the selection.

4.4 Estimation of the QCD multijet background

The background from QCD multijet events is evaluated with the “rebalance and smear” method  [9, 10], using data samples recorded with \HTthresholds ranging from 350 to 650\GeV. The events, recorded with a trigger prescaled by a factor , are sampled times to create seed events as described below.

In the rebalance step, the momenta of the jets with \GeVcin each event are adjusted within the jet-\pt-resolution values, using a kinematic fit, such that the events are balanced in the transverse plane. Considering only jets with \ptabove a certain threshold introduces an additional imbalance in the event, which results in larger \ptfor the rebalanced jets than the expected true value. This effect is compensated by scaling the rebalanced jets by a \pt-dependent factor derived by comparing rebalanced and generator-level jets in the simulation. The scaling factors derived using either \PYTHIAor \MADGRAPH, and with different average pileup interactions, are found to be similar. The jets in the rebalanced events are then smeared using jet \ptresponse functions, which are obtained from MC simulation as a function of \ptand , and adjusted to match those determined from dijet and +jets data [27]. The QCD multijet background is predicted by applying selection criteria on the kinematic quantities calculated from the smeared jets. The procedure is repeated one hundred times to evaluate the average prediction and its statistical uncertainty in each search region.

Figure 4: Predicted (a) \HT, (b) \xspace, and (c) \xspacedistributions found from applying the “rebalance-and-smear” method to simulated QCD multijet events (solid points) in comparison with the genuine QCD multijet background from simulation (shaded curve). The distributions are shown for events that satisfy the baseline selection, except that the \xspaceselection is not applied, and in addition is required for the events used in the \xspacedistribution. The statistical uncertainties are indicated by the hatched band for the expectation and by error bars for the prediction.

The method is validated using simulated QCD multijet events. Comparisons of the \HT, \xspace, and \xspacedistributions from the MC simulation to those predicted by the rebalance-and-smear method on the same simulated events are shown in Fig. 4. A systematic uncertainty of 11–86% is assigned based on the statistical precision attributed to the validation procedure, which is performed both in the search regions and in QCD-enriched data control regions defined either by \GeVor by inverting the selection. Due to the limited number of events in individual search bins, this uncertainty is evaluated for each jet multiplicity bin for \HTsmaller or larger than 1000\GeV, inclusive over \xspace. The uncertainty due to differences in the core and tails of the jet response functions between data and simulation results in uncertainties of 10–30% and 20–35%, respectively. An uncertainty of 3%, 8%, and 35% is assigned for search regions with \xspace= 3–5, 6–7, and 8, respectively, to account for the effect of pileup. The predicted QCD multijet background contributions to the search bins along with associated uncertainties are given in Table 5.

5 Results and interpretation

The predicted background event yields and the number of observed events are summarized in Table 5 and Fig. 5 for the 36 search regions. The data are consistent with the expected background contributions from SM processes. A slight excess of events is observed in the search bin with –7, –800\GeV, and \GeV, which is insignificant when the probability to observe a statistical fluctuation as large or larger in any of the search regions is considered.

\topcaption

Predicted event yields for the different background components in the search regions defined by \HT, \xspaceand \xspace. The uncertainties of the different background sources are added in quadrature to obtain the total uncertainties. Selection QCD Total Data \xspace \HT[\GeVns] \xspace[\GeVns] X X background 3–5 500–800 200–300 1820 390 2210 450 1750 210 310 220 6090 670 6159 3–5 500–800 300–450 990 220 660 130 590 70 40 20 2280 270 2305 3–5 500–800 450–600 273 63 77 17 66.3 9.5 1.3 418 66 454 3–5 500–800 600 42 10 9.5 4.0 5.7 1.3 0.1 57.4 11.2 62 3–5 800–1000 200–300 216 46 278 62 192 33 92 66 777 107 808 3–5 800–1000 300–450 124 26 113 27 84 12 9.9 7.4 330 40 305 3–5 800–1000 450–600 47 11 36.1 9.9 24.1 3.6 0.8 108 15 124 3–5 800–1000 600 35.3 8.8 9.0 3.7 10.3 2.0 0.1 54.8 9.7 52 3–5 1000–1250 200–300 76 17 104 26 66.5 9.9 59 25 305 41 335 3–5 1000–1250 300–450 39.3 8.9 52 14 41 11 5.1 2.7 137 20 129 3–5 1000–1250 450–600 18.1 4.7 6.9 3.2 6.8 2.0 0.5 32.3 6.1 34 3–5 1000–1250 600 17.8 4.8 2.4 1.8 2.5 0.8 0.1 22.8 5.2 32 3–5 1250–1500 200–300 25.3 6.0 31.0 9.5 21.3 4.1 31 13 109 18 98 3–5 1250–1500 300–450 16.7 4.3 10.1 4.4 13.7 7.1 2.3 1.6 42.8 9.5 38 3–5 1250–1500 450 12.3 3.5 2.3 1.7 2.7 1.2 0.2 17.6 4.1 23 3–5 1500 200–300 10.5 2.9 16.7 6.2 23.5 5.6 35 14 86 17 94 3–5 1500 300 10.9 3.1 9.7 4.3 6.6 1.4 2.4 2.0 29.7 5.8 39 6–7 500–800 200–300 22.7 6.4 133 59 117 25 18.2 9.2 290 65 266 6–7 500–800 300–450 9.9 3.2 22 11 18.0 5.1 1.9 1.7 52 12 62 6–7 500–800 450 0.7 0.6 0.0 0.1 0.0 0.8 9 6–7 800–1000 200–300 9.1 3.0 56 25 46 11 13.1 6.6 124 29 111 6–7 800–1000 300–450 4.2 1.7 10.4 5.5 12.0 3.6 1.9 1.4 28.6 6.9 35 6–7 800–1000 450 1.8 1.0 2.9 2.5 1.2 0.8 0.1 6.0 2.8 4 6–7 1000–1250 200–300 4.4 1.7 24 12 29.5 7.8 11.9 6.0 70 16 67 6–7 1000–1250 300–450 3.5 1.5 8.0 4.7 8.6 2.7 1.5 1.5 21.6 5.8 20 6–7 1000–1250 450 1.4 0.8 0.0 0.6 0.1 2.2 4 6–7 1250–1500 200–300 3.3 1.4 11.5 6.5 6.4 2.7 6.8 3.9 28.0 8.2 24 6–7 1250–1500 300–450 1.4 0.8 3.5 2.6 3.5 1.9 0.9 9.4 3.6 5 6–7 1250–1500 450 0.4 0.4 0.0 0.1 0.1 0.5 2 6–7 1500 200–300 1.3 0.8 10.0 6.9 2.0 1.2 7.8 4.0 21.1 8.1 18 6–7 1500 300 1.1 0.7 3.2 2.8 2.8 1.9 0.8 7.9 3.6 3 8 500–800 200 0.0 1.9 1.5 2.8 1.4 0.1 4.8 8 8 800–1000 200 0.6 0.6 4.8 2.9 2.3 1.2 0.5 8.3 9 8 1000–1250 200 0.6 0.5 1.4 2.9 1.3 0.7 5.6 8 8 1250–1500 200 0.0 5.1 3.5 1.4 0.9 0.5 7.1 5 8 1500 200 0.0 0.0 2.4 1.4 0.9 3.3 2

Figure 5: Summary of the observed number of events in each of the 36 search regions in comparison to the corresponding background prediction. The hatched region shows the total uncertainty of the background prediction.

The results are interpreted in the context of simplified models  [21, 22] of pair production of squarks (\PSQ) or gluinos (\PSg). These particles decay directly, or via intermediate new particles, to quarks and an LSP, where the LSP is denoted as \PSGczDo\xspacein the following. The signal events are generated at LO using \MADGRAPH5, with up to two additional partons. The cross sections are determined at NLO and include the resummation of soft gluon emission at the accuracy of next-to-leading-log (NLL) calculations [50, 51, 52, 53, 54, 55]. Both for the generation of signal events and the calculation of \PSQ(\PSg) production cross section, the contribution of \PSg(\PSQ) production is effectively removed by assuming the gluino (squark) mass to be very large.

Several decay modes of gluinos are considered here, , , and where and . The branching fraction for the different decay modes is assumed, in turn, to be 100%, except for the process, where the decay proceeds via , and particles with equal probability. Squark production is studied in the decay mode . The models are studied in the parameter space of the mass of the LSP versus the mass of the gluino or squark. The \xspacedistributions observed for the three intervals of jet multiplicity are shown in Fig. 6 in comparison to the SM background prediction. The \xspacedistributions expected from gluino or squark pair production are overlaid for = 1.1 TeV and = 125\GeV, and for = 700\GeVand = 100\GeV, in various decay modes.

Figure 6: Observed \xspacedistributions compared to the predicted backgrounds for search regions with \GeVand jet multiplicity intervals of (a) 3–5, (b) 6–7, and (c) 8. The background distributions are stacked. The last bin contains the overflow. The hatched region indicates the uncertainties of the background predictions. The ratio of data to the background is shown in the lower plots. The \xspacedistributions expected from events with \PSg and \PSQpair production, with either and or and , are overlaid.

The 95% confidence level (CL) upper limits on the signal production cross section are set using the LHC-style CL criterion  [56, 57, 58]. The signal acceptance and efficiencies, and corresponding uncertainties for the 36 exclusive search regions, along with the background estimates discussed above, are combined into a likelihood that is used to construct the test statistic based on the profile likelihood ratio. The uncertainties of the signal acceptance and efficiency due to several sources are taken into account when cross section upper limits are determined. The uncertainties due to the luminosity determination (2.6%) [59], trigger inefficiency (2%), and event cleaning procedure (3%) [28] are the same for all signal models and search regions. The uncertainty from the measurement of the jet energy scale and jet energy resolution [27] leads to uncertainties of 2–8% and 1–2% in signal acceptance. The variation of PDFs [47] results in 1–8% uncertainty from the signal acceptance. The rate of initial-state radiation in the signal event simulation is corrected to correspond to that measured in data [60], leading to a corresponding uncertainty of 22% for model points with small differences between the masses of the gluino or squark and the \PSGczDo\xspace. For larger mass differences, this uncertainty is typically less than a few percent.

The observed and expected CL upper limits on the signal cross section are shown for the production of a pair with in Fig. 7(a), a pair with in Fig. 7(b), a pair with in Fig. 7(c), and a pair with in Fig. 7(d), in the (, ) and (, ) planes. The contours show the exclusion regions for the signal production cross sections obtained using the NLO+NLL calculations. The exclusion contours are also presented when the signal cross section is varied by changing the renormalization and factorization scales by a factor of two and using the PDF uncertainty based on the CTEQ6.6 [61] and MSTW2008 [62] PDF sets. Conservatively, by comparing the observed limit to the theoretical cross section minus its one-standard-deviation uncertainty, for the cases where the gluino decays as , , and , gluino masses up to 1.16, 1.13, and 1.21 TeV are excluded, respectively, for \GeV. For direct production of the first two generations of squarks (, , , ), values of below 780\GeVare excluded for \GeV. If only one of these squarks is light, then values below 400\GeVare excluded for \GeV. The expected search sensitivity is improved with respect to our similar analysis [10] based on the 7 TeV data set by up to about 200\GeVin the values of , and .

Figure 7: The observed and expected 95% CL upper limits on the (a)  and (b-d)  production cross sections in either the (, ) or the (, ) plane obtained with the simplified models. For the production the upper set of curves corresponds to the scenario when the first two generations of squarks are degenerate and light, while the lower set corresponds to only one light accessible squark.

6 Summary

An inclusive search for supersymmetry has been performed in multijet events with –5, 6–7, and 8, and large missing transverse momentum. The data sample corresponds to an integrated luminosity of collected in 8\TeVpp collisions during the year 2012 with the CMS detector at the LHC. The analysis extends the supersymmetric parameter space explored by searches in the all-hadronic final state. The observed numbers of events are found to be consistent with the expected standard model background, which is evaluated from the data. The results are presented in the context of simplified models, where final states are described by the pair production of new particles decaying to one, two, or more jets and a weakly interacting stable neutral particle, \egthe lightest supersymmetric particle (LSP). Squark masses below 780
GeV and gluino masses of up to 1.1–1.2\TeVare excluded at 95% CL within the studied models for LSP masses below 100\GeV.

Acknowledgments

We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at CERN and at other CMS institutes for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centres and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies: BMWF 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); MoER, SF0690030s09 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); NRF and WCU (Republic of Korea); LAS (Lithuania); MOE and UM (Malaysia); CINVESTAV, CONACYT, SEP, and UASLP-FAI (Mexico); MBIE (New Zealand); PAEC (Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom, RAS and RFBR (Russia); MESTD (Serbia); SEIDI and CPAN (Spain); Swiss Funding Agencies (Switzerland); NSC (Taipei); ThEPCenter, IPST, STAR and NSTDA (Thailand); TUBITAK and TAEK (Turkey); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (USA).

Individuals have received support from the Marie-Curie programme and the European Research Council and EPLANET (European Union); the Leventis Foundation; the A. P. Sloan Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science Policy Office; the Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); the Ministry of Education, Youth and Sports (MEYS) of Czech Republic; the Council of Science and Industrial Research, India; the Compagnia di San Paolo (Torino); the HOMING PLUS programme of Foundation for Polish Science, cofinanced by EU, Regional Development Fund; and the Thalis and Aristeia programmes cofinanced by EU-ESF and the Greek NSRF.

Appendix A The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
S. Chatrchyan, V. Khachatryan, A.M. Sirunyan, A. Tumasyan \cmsinstskipInstitut für Hochenergiephysik der OeAW, Wien, Austria
W. Adam, T. Bergauer, M. Dragicevic, J. Erö, C. Fabjan\cmsAuthorMark1, M. Friedl, R. Frühwirth\cmsAuthorMark1, V.M. Ghete, C. Hartl, N. Hörmann, J. Hrubec, M. Jeitler\cmsAuthorMark1, W. Kiesenhofer, V. Knünz, M. Krammer\cmsAuthorMark1, I. Krätschmer, D. Liko, I. Mikulec, D. Rabady\cmsAuthorMark2, B. Rahbaran, H. Rohringer, R. Schöfbeck, J. Strauss, A. Taurok, W. Treberer-Treberspurg, W. Waltenberger, C.-E. Wulz\cmsAuthorMark1 \cmsinstskipNational Centre for Particle and High Energy Physics, Minsk, Belarus
V. Mossolov, N. Shumeiko, J. Suarez Gonzalez \cmsinstskipUniversiteit Antwerpen, Antwerpen, Belgium
S. Alderweireldt, M. Bansal, S. Bansal, T. Cornelis, E.A. De Wolf, X. Janssen, A. Knutsson, S. Luyckx, L. Mucibello, S. Ochesanu, B. Roland, R. Rougny, H. Van Haevermaet, P. Van Mechelen, N. Van Remortel, A. Van Spilbeeck \cmsinstskipVrije Universiteit Brussel, Brussel, Belgium
F. Blekman, S. Blyweert, J. D’Hondt, N. Heracleous, A. Kalogeropoulos, J. Keaveney, T.J. Kim, S. Lowette, M. Maes, A. Olbrechts, D. Strom, S. Tavernier, W. Van Doninck, P. Van Mulders, G.P. Van Onsem, I. Villella \cmsinstskipUniversité Libre de Bruxelles, Bruxelles, Belgium
C. Caillol, B. Clerbaux, G. De Lentdecker, L. Favart, A.P.R. Gay, A. Léonard, P.E. Marage, A. Mohammadi, L. Perniè, T. Reis, T. Seva, L. Thomas, C. Vander Velde, P. Vanlaer, J. Wang \cmsinstskipGhent University, Ghent, Belgium
V. Adler, K. Beernaert, L. Benucci, A. Cimmino, S. Costantini, S. Dildick, G. Garcia, B. Klein, J. Lellouch, J. Mccartin, A.A. Ocampo Rios, D. Ryckbosch, S. Salva Diblen, M. Sigamani, N. Strobbe, F. Thyssen, M. Tytgat, S. Walsh, E. Yazgan, N. Zaganidis \cmsinstskipUniversité Catholique de Louvain, Louvain-la-Neuve, Belgium
S. Basegmez, C. Beluffi\cmsAuthorMark3, G. Bruno, R. Castello, A. Caudron, L. Ceard, G.G. Da Silveira, C. Delaere, T. du Pree, D. Favart, L. Forthomme, A. Giammanco\cmsAuthorMark4, J. Hollar, P. Jez, M. Komm, V. Lemaitre, J. Liao, O. Militaru, C. Nuttens, D. Pagano, A. Pin, K. Piotrzkowski, A. Popov\cmsAuthorMark5, L. Quertenmont, M. Selvaggi, M. Vidal Marono, J.M. Vizan Garcia \cmsinstskipUniversité de Mons, Mons, Belgium
N. Beliy, T. Caebergs, E. Daubie, G.H. Hammad \cmsinstskipCentro Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil
G.A. Alves, M. Correa Martins Junior, T. Martins, M.E. Pol, M.H.G. Souza \cmsinstskipUniversidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
W.L. Aldá Júnior, W. Carvalho, J. Chinellato\cmsAuthorMark6, A. Custódio, E.M. Da Costa, D. De Jesus Damiao, C. De Oliveira Martins, S. Fonseca De Souza, H. Malbouisson, M. Malek, D. Matos Figueiredo, L. Mundim, H. Nogima, W.L. Prado Da Silva, J. Santaolalla, A. Santoro, A. Sznajder, E.J. Tonelli Manganote\cmsAuthorMark6, A. Vilela Pereira \cmsinstskipUniversidade Estadual Paulista ,  Universidade Federal do ABC ,  São Paulo, Brazil
C.A. Bernardes, F.A. Dias\cmsAuthorMark7, T.R. Fernandez Perez Tomei, E.M. Gregores, P.G. Mercadante, S.F. Novaes, Sandra S. Padula \cmsinstskipInstitute for Nuclear Research and Nuclear Energy, Sofia, Bulgaria
V. Genchev\cmsAuthorMark2, P. Iaydjiev\cmsAuthorMark2, A. Marinov, S. Piperov, M. Rodozov, G. Sultanov, M. Vutova \cmsinstskipUniversity of Sofia, Sofia, Bulgaria
A. Dimitrov, I. Glushkov, R. Hadjiiska, V. Kozhuharov, L. Litov, B. Pavlov, P. Petkov \cmsinstskipInstitute of High Energy Physics, Beijing, China
J.G. Bian, G.M. Chen, H.S. Chen, M. Chen, R. Du, C.H. Jiang, D. Liang, S. Liang, X. Meng, R. Plestina\cmsAuthorMark8, J. Tao, X. Wang, Z. Wang \cmsinstskipState Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing, China
C. Asawatangtrakuldee, Y. Ban, Y. Guo, Q. Li, W. Li, S. Liu, Y. Mao, S.J. Qian, D. Wang, L. Zhang, W. Zou \cmsinstskipUniversidad de Los Andes, Bogota, Colombia
C. Avila, C.A. Carrillo Montoya, L.F. Chaparro Sierra, C. Florez, J.P. Gomez, B. Gomez Moreno, J.C. Sanabria \cmsinstskipTechnical University of Split, Split, Croatia
N. Godinovic, D. Lelas, D. Polic, I. Puljak \cmsinstskipUniversity of Split, Split, Croatia
Z. Antunovic, M. Kovac \cmsinstskipInstitute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, K. Kadija, J. Luetic, D. Mekterovic, S. Morovic, L. Tikvica \cmsinstskipUniversity of Cyprus, Nicosia, Cyprus
A. Attikis, G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos, P.A. Razis \cmsinstskipCharles University, Prague, Czech Republic
M. Finger, M. Finger Jr. \cmsinstskipAcademy of Scientific Research and Technology of the Arab Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
A.A. Abdelalim\cmsAuthorMark9, Y. Assran\cmsAuthorMark10, S. Elgammal\cmsAuthorMark11, A. Ellithi Kamel\cmsAuthorMark12, M.A. Mahmoud\cmsAuthorMark13, A. Radi\cmsAuthorMark11\cmsAuthorMark14 \cmsinstskipNational Institute of Chemical Physics and Biophysics, Tallinn, Estonia
M. Kadastik, M. Müntel, M. Murumaa, M. Raidal, L. Rebane, A. Tiko \cmsinstskipDepartment of Physics, University of Helsinki, Helsinki, Finland
P. Eerola, G. Fedi, M. Voutilainen \cmsinstskipHelsinki Institute of Physics, Helsinki, Finland
J. Härkönen, V. Karimäki, R. Kinnunen, M.J. Kortelainen, T. Lampén, K. Lassila-Perini, S. Lehti, T. Lindén, P. Luukka, T. Mäenpää, T. Peltola, E. Tuominen, J. Tuominiemi, E. Tuovinen, L. Wendland \cmsinstskipLappeenranta University of Technology, Lappeenranta, Finland
T. Tuuva \cmsinstskipDSM/IRFU, CEA/Saclay, Gif-sur-Yvette, France
M. Besancon, F. Couderc, M. Dejardin, D. Denegri, B. Fabbro, J.L. Faure, F. Ferri, S. Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry, E. Locci, J. Malcles, A. Nayak, J. Rander, A. Rosowsky, M. Titov \cmsinstskipLaboratoire Leprince-Ringuet, Ecole Polytechnique, IN2P3-CNRS, Palaiseau, France
S. Baffioni, F. Beaudette, P. Busson, C. Charlot, N. Daci, T. Dahms, M. Dalchenko, L. Dobrzynski, A. Florent, R. Granier de Cassagnac, P. Miné, C. Mironov, I.N. Naranjo, M. Nguyen, C. Ochando, P. Paganini, D. Sabes, R. Salerno, J.b. Sauvan, Y. Sirois, C. Veelken, Y. Yilmaz, A. Zabi \cmsinstskipInstitut Pluridisciplinaire Hubert Curien, Université de Strasbourg, Université de Haute Alsace Mulhouse, CNRS/IN2P3, Strasbourg, France
J.-L. Agram\cmsAuthorMark15, J. Andrea, D. Bloch, J.-M. Brom, E.C. Chabert, C. Collard, E. Conte\cmsAuthorMark15, F. Drouhin\cmsAuthorMark15, J.-C. Fontaine\cmsAuthorMark15, D. Gelé, U. Goerlach, C. Goetzmann, P. Juillot, 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, N. Beaupere, G. Boudoul, S. Brochet, J. Chasserat, R. Chierici, D. Contardo\cmsAuthorMark2, P. Depasse, H. El Mamouni, J. Fan, J. Fay, S. Gascon, M. Gouzevitch, B. Ille, T. Kurca, M. Lethuillier, L. Mirabito, S. Perries, J.D. Ruiz Alvarez, L. Sgandurra, V. Sordini, M. Vander Donckt, P. Verdier, S. Viret, H. Xiao \cmsinstskipInstitute of High Energy Physics and Informatization, Tbilisi State University, Tbilisi, Georgia
Z. Tsamalaidze\cmsAuthorMark16 \cmsinstskipRWTH Aachen University, I. Physikalisches Institut, Aachen, Germany
C. Autermann, S. Beranek, M. Bontenackels, B. Calpas, M. Edelhoff, L. Feld, O. Hindrichs, K. Klein, A. Ostapchuk, A. Perieanu, F. Raupach, J. Sammet, S. Schael, D. Sprenger, H. Weber, B. Wittmer, V. Zhukov\cmsAuthorMark5 \cmsinstskipRWTH Aachen University, III. Physikalisches Institut A,  Aachen, Germany
M. Ata, J. Caudron, E. Dietz-Laursonn, D. Duchardt, M. Erdmann, R. Fischer, A. Güth, T. Hebbeker, C. Heidemann, K. Hoepfner, D. Klingebiel, S. Knutzen, P. Kreuzer, M. Merschmeyer, A. Meyer, M. Olschewski, K. Padeken, P. Papacz, H. Reithler, S.A. Schmitz, L. Sonnenschein, D. Teyssier, S. Thüer, M. Weber \cmsinstskipRWTH Aachen University, III. Physikalisches Institut B,  Aachen, Germany
V. Cherepanov, Y. Erdogan, G. Flügge, H. Geenen, M. Geisler, W. Haj Ahmad, F. Hoehle, B. Kargoll, T. Kress, Y. Kuessel, J. Lingemann\cmsAuthorMark2, A. Nowack, I.M. Nugent, L. Perchalla, O. Pooth, A. Stahl \cmsinstskipDeutsches Elektronen-Synchrotron, Hamburg, Germany
I. Asin, N. Bartosik, J. Behr, W. Behrenhoff, U. Behrens, A.J. Bell, M. Bergholz\cmsAuthorMark17, A. Bethani, K. Borras, A. Burgmeier, A. Cakir, L. Calligaris, A. Campbell, S. Choudhury, F. Costanza, C. Diez Pardos, S. Dooling, T. Dorland, G. Eckerlin, D. Eckstein, T. Eichhorn, G. Flucke, A. Geiser, A. Grebenyuk, P. Gunnellini, S. Habib, J. Hauk, G. Hellwig, M. Hempel, D. Horton, H. Jung, M. Kasemann, P. Katsas, J. Kieseler, C. Kleinwort, M. Krämer, D. Krücker, W. Lange, J. Leonard, K. Lipka, W. Lohmann\cmsAuthorMark17, B. Lutz, R. Mankel, I. Marfin, I.-A. Melzer-Pellmann, A.B. Meyer, J. Mnich, A. Mussgiller, S. Naumann-Emme, O. Novgorodova, F. Nowak, H. Perrey, A. Petrukhin, D. Pitzl, R. Placakyte, A. Raspereza, P.M. Ribeiro Cipriano, C. Riedl, E. Ron, M.Ö. Sahin, J. Salfeld-Nebgen, P. Saxena, R. Schmidt\cmsAuthorMark17, T. Schoerner-Sadenius, M. Schröder, M. Stein, A.D.R. Vargas Trevino, R. Walsh, C. Wissing \cmsinstskipUniversity of Hamburg, Hamburg, Germany
M. Aldaya Martin, V. Blobel, A.R. Draeger, H. Enderle, J. Erfle, E. Garutti, K. Goebel, M. Görner, M. Gosselink, J. Haller, R.S. Höing, H. Kirschenmann, R. Klanner, R. Kogler, J. Lange, T. Lapsien, T. Lenz, I. Marchesini, J. Ott, T. Peiffer, N. Pietsch, D. Rathjens, C. Sander, H. Schettler, P. Schleper, E. Schlieckau, A. Schmidt, M. Seidel, J. Sibille\cmsAuthorMark18, V. Sola, H. Stadie, G. Steinbrück, D. Troendle, E. Usai, L. Vanelderen \cmsinstskipInstitut für Experimentelle Kernphysik, Karlsruhe, Germany
C. Barth, C. Baus, J. Berger, C. Böser, E. Butz, T. Chwalek, W. De Boer, A. Descroix, A. Dierlamm, M. Feindt, M. Guthoff\cmsAuthorMark2, F. Hartmann\cmsAuthorMark2, T. Hauth\cmsAuthorMark2, H. Held, K.H. Hoffmann, U. Husemann, I. Katkov\cmsAuthorMark5, A. Kornmayer\cmsAuthorMark2, E. Kuznetsova, P. Lobelle Pardo, D. Martschei, M.U. Mozer, Th. Müller, M. Niegel, A. Nürnberg, O. Oberst, G. Quast, K. Rabbertz, F. Ratnikov, S. Röcker, F.-P. Schilling, G. Schott, H.J. Simonis, F.M. Stober, R. Ulrich, J. Wagner-Kuhr, S. Wayand, T. Weiler, R. Wolf, M. Zeise \cmsinstskipInstitute of Nuclear and Particle Physics (INPP),  NCSR Demokritos, Aghia Paraskevi, Greece
G. Anagnostou, G. Daskalakis, T. Geralis, S. Kesisoglou, A. Kyriakis, D. Loukas, A. Markou, C. Markou, E. Ntomari, A. Psallidas, I. Topsis-giotis \cmsinstskipUniversity of Athens, Athens, Greece
L. Gouskos, A. Panagiotou, N. Saoulidou, E. Stiliaris \cmsinstskipUniversity of Ioánnina, Ioánnina, Greece
X. Aslanoglou, I. Evangelou, G. Flouris, C. Foudas, J. Jones, P. Kokkas, N. Manthos, I. Papadopoulos, E. Paradas \cmsinstskipWigner Research Centre for Physics, Budapest, Hungary
G. Bencze, C. Hajdu, P. Hidas, 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. Molnar, J. Palinkas, Z. Szillasi \cmsinstskipUniversity of Debrecen, Debrecen, Hungary
J. Karancsi, P. Raics, Z.L. Trocsanyi, B. Ujvari \cmsinstskipNational Institute of Science Education and Research, Bhubaneswar, India
S.K. Swain \cmsinstskipPanjab University, Chandigarh, India
S.B. Beri, V. Bhatnagar, N. Dhingra, R. Gupta, M. Kaur, M.Z. Mehta, M. Mittal, N. Nishu, A. Sharma, J.B. Singh \cmsinstskipUniversity of Delhi, Delhi, India
Ashok Kumar, Arun Kumar, S. Ahuja, A. Bhardwaj, B.C. Choudhary, A. Kumar, S. Malhotra, M. Naimuddin, K. Ranjan, V. Sharma, R.K. Shivpuri \cmsinstskipSaha Institute of Nuclear Physics, Kolkata, India
S. Banerjee, S. Bhattacharya, K. Chatterjee, S. Dutta, B. Gomber, Sa. Jain, Sh. Jain, R. Khurana, A. Modak, S. Mukherjee, D. Roy, S. Sarkar, M. Sharan, A.P. Singh \cmsinstskipBhabha Atomic Research Centre, Mumbai, India
A. Abdulsalam, D. Dutta, S. Kailas, V. Kumar, A.K. Mohanty\cmsAuthorMark2, L.M. Pant, P. Shukla, A. Topkar \cmsinstskipTata Institute of Fundamental Research - EHEP, Mumbai, India
T. Aziz, R.M. Chatterjee, S. Ganguly, S. Ghosh, M. Guchait\cmsAuthorMark21, A. Gurtu\cmsAuthorMark22, G. Kole, S. Kumar, M. Maity\cmsAuthorMark23, G. Majumder, K. Mazumdar, G.B. Mohanty, B. Parida, K. Sudhakar, N. Wickramage\cmsAuthorMark24 \cmsinstskipTata Institute of Fundamental Research - HECR, Mumbai, India
S. Banerjee, S. Dugad \cmsinstskipInstitute for Research in Fundamental Sciences (IPM),  Tehran, Iran
H. Arfaei, H. Bakhshiansohi, H. Behnamian, S.M. Etesami\cmsAuthorMark25, A. Fahim\cmsAuthorMark26, A. Jafari, M. Khakzad, M. Mohammadi Najafabadi, M. Naseri, S. Paktinat Mehdiabadi, B. Safarzadeh\cmsAuthorMark27, M. Zeinali \cmsinstskipUniversity College Dublin, Dublin, Ireland
M. Grunewald \cmsinstskipINFN Sezione di Bari , Università di Bari , Politecnico di Bari ,  Bari, Italy
M. Abbrescia, L. Barbone, C. Calabria, S.S. Chhibra, A. Colaleo, D. Creanza, N. De Filippis, M. De Palma, L. Fiore, G. Iaselli, G. Maggi, M. Maggi, B. Marangelli, S. My, S. Nuzzo, N. Pacifico, A. Pompili, G. Pugliese, R. Radogna, G. Selvaggi, L. Silvestris, G. Singh, R. Venditti, P. Verwilligen, G. Zito \cmsinstskipINFN Sezione di Bologna , Università di Bologna ,  Bologna, Italy
G. Abbiendi, A.C. Benvenuti, D. Bonacorsi, S. Braibant-Giacomelli, L. Brigliadori, R. Campanini, P. Capiluppi, A. Castro, F.R. Cavallo, G. Codispoti, M. Cuffiani, G.M. Dallavalle, F. Fabbri, A. Fanfani, D. Fasanella, P. Giacomelli, C. Grandi, L. Guiducci, S. Marcellini, G. Masetti, M. Meneghelli, A. Montanari, F.L. Navarria, F. Odorici, A. Perrotta, F. Primavera, A.M. Rossi, T. Rovelli, G.P. Siroli, N. Tosi, R. Travaglini \cmsinstskipINFN Sezione di Catania , Università di Catania , CSFNSM ,  Catania, Italy
S. Albergo, G. Cappello, M. Chiorboli, S. Costa, F. Giordano\cmsAuthorMark2, R. Potenza, A. Tricomi, C. Tuve \cmsinstskipINFN Sezione di Firenze , Università di Firenze ,  Firenze, Italy
G. Barbagli, V. Ciulli, C. Civinini, R. D’Alessandro, E. Focardi, E. Gallo, S. Gonzi, V. Gori, P. Lenzi, M. Meschini, S. Paoletti, G. Sguazzoni, A. Tropiano \cmsinstskipINFN Laboratori Nazionali di Frascati, Frascati, Italy
L. Benussi, S. Bianco, F. Fabbri, D. Piccolo \cmsinstskipINFN Sezione di Genova , Università di Genova ,  Genova, Italy
P. Fabbricatore, R. Ferretti, F. Ferro, M. Lo Vetere, R. Musenich, E. Robutti, S. Tosi \cmsinstskipINFN Sezione di Milano-Bicocca , Università di Milano-Bicocca ,  Milano, Italy
A. Benaglia, M.E. Dinardo, S. Fiorendi\cmsAuthorMark2, S. Gennai, R. Gerosa, A. Ghezzi, P. Govoni, M.T. Lucchini\cmsAuthorMark2, S. Malvezzi, R.A. Manzoni\cmsAuthorMark2, A. Martelli\cmsAuthorMark2, B. Marzocchi, D. Menasce, L. Moroni, M. Paganoni, D. Pedrini, S. Ragazzi, N. Redaelli, T. Tabarelli de Fatis \cmsinstskipINFN Sezione di Napoli , Università di Napoli ’Federico II’ , Università della Basilicata (Potenza) , Università G. Marconi (Roma) ,  Napoli, Italy
S. Buontempo, N. Cavallo, S. Di Guida, F. Fabozzi, A.O.M. Iorio, L. Lista, S. Meola\cmsAuthorMark2, M. Merola, P. Paolucci\cmsAuthorMark2 \cmsinstskipINFN Sezione di Padova , Università di Padova , Università di Trento (Trento) ,  Padova, Italy
P. Azzi, N. Bacchetta, D. Bisello, A. Branca, R. Carlin, P. Checchia, T. Dorigo, U. Dosselli, M. Galanti\cmsAuthorMark2, F. Gasparini, U. Gasparini, P. Giubilato, F. Gonella, A. Gozzelino, K. Kanishchev, S. Lacaprara, I. Lazzizzera, M. Margoni, A.T. Meneguzzo, F. Montecassiano, J. Pazzini, N. Pozzobon, P. Ronchese, F. Simonetto, M. Tosi, S. Vanini, P. Zotto, A. Zucchetta, G. Zumerle \cmsinstskipINFN Sezione di Pavia , Università di Pavia ,  Pavia, Italy
M. Gabusi, S.P. Ratti, C. Riccardi, P. Vitulo \cmsinstskipINFN Sezione di Perugia , Università di Perugia ,  Perugia, Italy
M. Biasini, G.M. Bilei, L. Fanò, P. Lariccia, G. Mantovani, M. Menichelli, F. Romeo, A. Saha, A. Santocchia, A. Spiezia \cmsinstskipINFN Sezione di Pisa , Università di Pisa , Scuola Normale Superiore di Pisa ,  Pisa, Italy
K. Androsov\cmsAuthorMark28, P. Azzurri, G. Bagliesi, J. Bernardini, T. Boccali, G. Broccolo, R. Castaldi, M.A. Ciocci\cmsAuthorMark28, R. Dell’Orso, F. Fiori, L. Foà, A. Giassi, M.T. Grippo\cmsAuthorMark28, A. Kraan, F. Ligabue, T. Lomtadze, L. Martini, A. Messineo, C.S. Moon\cmsAuthorMark29, F. Palla, A. Rizzi, A. Savoy-Navarro\cmsAuthorMark30, A.T. Serban, P. Spagnolo, P. Squillacioti\cmsAuthorMark28, R. Tenchini, G. Tonelli, A. Venturi, P.G. Verdini, C. Vernieri \cmsinstskipINFN Sezione di Roma , Università di Roma ,  Roma, Italy
L. Barone, F. Cavallari, D. Del Re, M. Diemoz, M. Grassi, C. Jorda, E. Longo, F. Margaroli, P. Meridiani, F. Micheli, S. Nourbakhsh, G. Organtini, R. Paramatti, S. Rahatlou, C. Rovelli, L. Soffi, P. Traczyk \cmsinstskipINFN Sezione di Torino , Università di Torino , Università del Piemonte Orientale (Novara) ,  Torino, Italy
N. Amapane, R. Arcidiacono, S. Argiro, M. Arneodo, R. Bellan, C. Biino, N. Cartiglia, S. Casasso, M. Costa, A. Degano, N. Demaria, C. Mariotti, S. Maselli, E. Migliore, V. Monaco, M. Musich, M.M. Obertino, G. Ortona, L. Pacher, N. Pastrone, M. Pelliccioni\cmsAuthorMark2, A. Potenza, A. Romero, M. Ruspa, R. Sacchi, A. Solano, A. Staiano, U. Tamponi \cmsinstskipINFN Sezione di Trieste , Università di Trieste ,  Trieste, Italy
S. Belforte, V. Candelise, M. Casarsa, F. Cossutti, G. Della Ricca, B. Gobbo, C. La Licata, M. Marone, D. Montanino, A. Penzo, A. Schizzi, T. Umer, A. Zanetti \cmsinstskipKangwon National University, Chunchon, Korea
S. Chang, T.Y. Kim, S.K. Nam \cmsinstskipKyungpook National University, Daegu, Korea
D.H. Kim, G.N. Kim, J.E. Kim, M.S. Kim, D.J. Kong, S. Lee, Y.D. Oh, H. Park, D.C. Son \cmsinstskipChonnam National University, Institute for Universe and Elementary Particles, Kwangju, Korea
J.Y. Kim, Zero J. Kim, S. Song \cmsinstskipKorea University, Seoul, Korea
S. Choi, D. Gyun, B. Hong, M. Jo, H. Kim, Y. Kim, K.S. Lee, S.K. Park, Y. Roh \cmsinstskipUniversity of Seoul, Seoul, Korea
M. Choi, J.H. Kim, C. Park, I.C. Park, S. Park, G. Ryu \cmsinstskipSungkyunkwan University, Suwon, Korea
Y. Choi, Y.K. Choi, J. Goh, E. Kwon, B. Lee, J. Lee, H. Seo, I. Yu \cmsinstskipVilnius University, Vilnius, Lithuania
A. Juodagalvis \cmsinstskipNational Centre for Particle Physics, Universiti Malaya, Kuala Lumpur, Malaysia
J.R. Komaragiri \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\cmsAuthorMark31, R. Lopez-Fernandez, J. Martínez-Ortega, A. Sanchez-Hernandez, L.M. Villasenor-Cendejas \cmsinstskipUniversidad Iberoamericana, Mexico City, Mexico
S. Carrillo Moreno, F. Vazquez Valencia \cmsinstskipBenemerita Universidad Autonoma de Puebla, Puebla, Mexico
H.A. Salazar Ibarguen \cmsinstskipUniver