Search for dark matter particles in proton-proton collisions at \sqrt{s}=8\TeVusing the razor variables

Search for dark matter particles in proton-proton collisions at \TeVusing the razor variables

Abstract

A search for dark matter particles directly produced in proton-proton collisions recorded by the CMS experiment at the LHC is presented. The data correspond to an integrated luminosity of 18.8\fbinv, at a center-of-mass energy of 8\TeV. The event selection requires at least two jets and no isolated leptons. The razor variables are used to quantify the transverse momentum balance in the jet momenta. The study is performed separately for events with and without jets originating from b quarks. The observed yields are consistent with the expected backgrounds and, depending on the nature of the production mechanism, dark matter production at the LHC is excluded at 90% confidence level for a mediator mass scale below 1\TeV. The use of razor variables yields results that complement those previously published.

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EXO-14-004

1 Introduction

The existence of dark matter (DM) in the universe, originally proposed [1] to reconcile observations of the Coma galaxy cluster with the prediction from the virial theorem, is commonly accepted as the explanation of many experimental phenomena in astrophysics and cosmology, such as galaxy rotation curves [2, 3], large structure formation [4, 5, 6], and the observed spectrum [7, 8, 9, 10] of the cosmic microwave background [11]. A global fit to cosmological data in the CDM model (also known as the standard model of cosmology) [12] suggests that approximately 85% of the mass of the universe is attributable to DM [10]. To accommodate these observations and the dynamics of colliding galaxy clusters [13], it has been hypothesized that DM is made mostly of weakly interacting massive particles (WIMPs), sufficiently massive to be in nonrelativistic motion following their decoupling from the hot particle plasma in the early stages of the expansion of the universe.

While the standard model (SM) of particle physics does not include a viable DM candidate, several models of physics beyond the SM, e.g., supersymmetry (SUSY) [14, 15, 16, 17, 18] with -parity conservation, can accommodate the existence of WIMPs. In these models, pairs of DM particles can be produced in proton-proton (pp) collisions at the CERN LHC. Dark matter particles would not leave a detectable signal in a particle detector. When produced in association with high-energy quarks or gluons, they could provide event topologies with jets and a transverse momentum (\pt) imbalance (). The magnitude of is referred to as missing transverse energy (). The ATLAS and CMS collaborations have reported searches for events with one high-\ptjet and large  [19, 20], which are sensitive to such topologies. In this paper, we refer to these studies as monojet searches. Complementary studies of events with high-\ptphotons [21, 22]; \PW, \cPZ, or Higgs bosons [23, 24, 25, 26]; b jets [27] and top quarks [27, 28, 29]; and leptons [30, 31] have also been performed.

This paper describes a search for dark matter particles in events with at least two jets of comparable transverse momenta and sizable . The search is based on the razor variables and  [32, 33]. Given a dijet event, these variables are computed from the two jet momenta and , according to the following definition:

(1)
with
(2)

In the context of SUSY, provides an estimate of the underlying mass scale of the event, and quantity is a transverse observable that includes information about the topology of the event. The variable is designed to reduce QCD multijet background; it is correlated with the angle between the two jets, where co-linear jets have large while back-to-back jets have small . These variables have been used to study the production of non-interacting particles in cascade decays of heavier partners, such as squarks and gluinos in SUSY models with -parity conservation [34, 35]. The sensitivity of these variables to direct DM production was suggested in Ref. [36], where it was pointed out that the dijet event topology provides good discrimination against background processes, with a looser event selection than that applied in the monojet searches. Sensitivity to DM production is most enhanced for large values of , while categorizing events based on the value of improves signal to background discrimination and yields significantly improved search sensitivity to a broader and more inclusive class of DM models. The resulting sensitivity is expected to be comparable to that of monojet searches [36, 37]. This strategy also offers the possibility to search for DM particles that couple preferentially to b quarks [38], as proposed to accommodate the observed excess of photons with energies between 1 and 4\GeVin the gamma ray spectrum of the galactic center data collected by the Fermi-LAT gamma-ray space telescope [39]. The results are interpreted using an effective field theory approach and the Feynman diagrams for DM pair production are shown in Fig. 1.

Figure 1: Feynman diagrams for the pair production of DM particles corresponding to an effective field theory using a vector or axial-vector operator (left), and a scalar operator (right).

Unlike the SUSY razor searches [35, 33], which focus on events with large values of , this study also considers events with small values of , using to discriminate between signal and background, in a kinematic region () excluded by the baseline selection of Refs. [35, 33].

A data sample corresponding to an integrated luminosity of 18.8\fbinvof pp collisions at a center-of-mass energy of 8\TeVwas collected by the CMS experiment with a trigger based on a loose selection on and . This and other special triggers were operated in 2012 to record events at a rate higher than the CMS computing system could process during data taking. The events from these triggers were stored on tape and their reconstruction was delayed until 2013, to profit from the larger availability of processing resources during the LHC shutdown. These data, referred to as “parked data” [40], enabled the exploration of events with small values, thereby enhancing the sensitivity to direct DM production.

This paper is organized as follows: the CMS detector is briefly described in Section 2. Section 3 describes the data and simulated samples of events used in the analysis. Sections 4 and 5 discuss the event selections and categorization, respectively. The estimation of the background is described in Section 6. The systematic uncertainties are discussed in Section 7, while Section 8 presents the results and the implications for several models of DM production. A summary is given in Section 9.

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. Within the solenoid volume are 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. When combining information from the entire detector, the jet energy resolution amounts typically to 15% at 10\GeV, 8% at 100\GeV, and 4% at 1\TeV [41]. Muons are measured in gas-ionization detectors embedded in the steel flux-return yoke outside the solenoid. Forward calorimeters extend the pseudorapidity ([42] coverage provided by the barrel and endcap detectors. The first level (L1) of the CMS trigger system, composed of custom hardware processors, uses information from the calorimeters and muon detectors to select the most interesting events in a fixed time interval of less than 4\mus. The high-level trigger (HLT) processor farm further decreases the event rate from around 100\unitkHz to around 400\unitHz, before data storage. A more detailed description of the CMS detector, together with a definition of the coordinate system used and the basic kinematic variables, can be found in Ref. [42].

3 Data set and simulated samples

The analysis is performed on events with two jets reconstructed at L1 in the central part of the detector (). The L1 jet triggers are based on the sums of transverse energy in regions approximately 1.051.05 in size [42] (where is the azimuthal angle in the plane transverse to the LHC beams.). At the HLT, energy deposits in ECAL and HCAL are clustered into jets and the razor variables and are computed. In the HLT, jets are defined using the \FASTJET [43] implementation of the anti-\kt [44] algorithm, with a distance parameter equal to 0.5. Events with at least two jets with \GeVare considered. Events are selected with and \GeV. This selection rejects the majority of the background, which tends to have low and low values, while keeping the events in the signal-sensitive regions of the (, ) plane. The trigger efficiency, measured using a pre-scaled trigger with very loose thresholds, is shown in Table 3. The requirements described above correspond to the least stringent event selection, given the constraints on the maximum acceptable rate.

\topcaption

Measured trigger efficiency for different regions. The selection is applied. The uncertainty shown represents the statistical uncertainty in the measured efficiency. region (\GeVns) 200–300 300–400 400–3500 Trigger efficiency (%)

Monte Carlo (MC) simulated signal and background samples are generated with the leading order matrix element generator \MADGRAPHv5.1.3 [45, 46] and the CTEQ6L parton distribution function set [47]. The generation includes the \PYTHIA6.4.26 [48] Z2* tune, which is derived from Z1 tune [49] based on the CTEQ5L set. Parton shower and hadronization effects are included by matching the generated events to \PYTHIA, using the MLM matching algorithm [50]. The events are processed with a \GEANTfour [51] description of the CMS apparatus to include detector effects. The simulation samples for SM background processes are scaled to the integrated luminosity of the data sample (18.8\fbinv), using calculations of the inclusive production cross sections at the next-to-next-to-leading order (NNLO) in the perturbative QCD expansion [52, 53, 54]. The signal processes corresponding to pair production of DM particles are simulated with up to two additional partons with \GeV.

4 Event selection

Events are selected with at least one reconstructed interaction vertex within \cm. If more than one vertex is found, the one with the highest sum of the associated track momenta squared is used as the interaction point for event reconstruction. Events containing calorimeter noise, or large missing transverse momentum due to beam halo and instrumental effects (such as jets near non-functioning channels in the ECAL) are removed from the analysis [55].

A particle-flow (PF) algorithm [56, 57] is used to reconstruct and identify individual particles with an optimized combination of information from the various elements of the CMS detector. The energy of photons is directly obtained from the ECAL measurement, corrected for zero-suppression effects. The energy of electrons is determined from a combination of the electron momentum at the primary interaction vertex as measured by the tracker, the energy of the corresponding ECAL cluster, and the energy sum of all bremsstrahlung photons (or emissions) spatially compatible with originating from the electron track. The energy of muons is obtained from the curvature of the associated 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 deposits, corrected for zero-suppression effects and for the response function of the calorimeters to hadronic showers. Finally, the energy of neutral hadrons is obtained from the corresponding corrected ECAL and HCAL energies. Contamination of the energy determinations from other pp collisions is mitigated by discarding the charged PF candidates incompatible with originating from the main vertex. Additional energy from neutral particles is subtracted on average when computing lepton (electron or muon) isolation and jet energy. This contribution is estimated as the per-event energy deposit per unit area, in the cone , times the considered jet size or isolation cone area.

To separate signal from the main backgrounds it is necessary to identify electrons (muons) with \GeVand (2.4). In order to reduce the rate for misidentifying hadrons as leptons, additional requirements based on the quality of track reconstruction and isolation are applied. Lepton isolation is defined as the scalar \ptsum of all PF candidates other than the lepton itself, within a cone of size , and normalized to the lepton \pt. A candidate is identified as a lepton if the isolation variable is found to be smaller than 15%. For electrons [58], a characteristic of the shower shape of the energy deposit in the ECAL (the shower width in the direction) is used to further reduce the contamination from hadrons. PF candidates with \GeVthat are not consistent with muons and satisfy the same isolation requirements as those used for electrons are also identified to increase the lepton selection efficiency as well as to identify single-prong tau decays.

Jets are formed by clustering the PF candidates, using the anti-\ktalgorithm with distance parameter 0.5. Jet momentum is determined as the vectorial sum of all particle momenta in the jet, and is found from simulation to be within 5% to 10% of the generated hadron level jet momentum over the whole \ptspectrum and detector acceptance. Jet energy corrections are derived from simulation, and are confirmed with in situ measurements of the energy balance in dijet and photon+jet events. Any jet whose momentum points within a cone of around any identified electron, muon, or isolated track is discarded. Additional selection criteria are applied to each event to remove spurious jet-like features originating from isolated noise patterns in certain HCAL regions. We select events containing at least two jets with \GeVand , for which the corresponding L1 and HLT requirements are maximally efficient. The combined secondary vertex (CSV) b-tagging algorithm [59, 60] is used to identify jets originating from b quarks. The loose and tight working points of the CSV algorithm, with 85% (10%) and 50% (0.1%) identification efficiency (misidentification probability) respectively, are used to assign the selected events to categories based on the number of b-tagged jets, as described below.

In order to compute the razor variables inclusively, the event is forced into a two-jet topology, by forming two megajets [34] out of all the reconstructed jets with \GeVand . All possible assignments of jets to the megajets are considered, with the requirement that a megajet consist of at least one jet. The sum of the four-momenta of the jets assigned to a megajet defines the megajet four-momentum. When more than two jets are reconstructed, more than one megajet assignment is possible. We select the assignment that minimizes the sum of the invariant masses of the two megajets. In order to reduce the contamination from multijet production, events are rejected if the angle between the two selected megajets in the transverse plane is larger than 2.5 radians. The momenta of the two megajets are used to compute the razor variables, according to Eq. (12). Events are required to have \GeVand .

5 Analysis Strategy

To enhance the DM signal and suppress background contributions from the +jets and processes, we veto events with selected electrons, muons, or isolated charged PF candidates. We define three different search regions based on the number of b-tagged jets. The zero b-tag search region contains events where no jets were identified with the CSV loose b-tagging criterion; the one b-tag search region contains events where exactly one jet passed the CSV tight criterion; and the two b-tag search region contains events where two or more jets passed the CSV tight criterion. Events in the zero b-tag search region are further classified into four categories based on the value of , to enhance signal to background discrimination for a broad class of DM models: (i) very low (VL), defined by \GeV; (ii) low (L), with \GeV; (iii) high (H), with \GeV; and (iv) very high (VH), including events with \GeV. Because of the limited size of the data sample, no further categorization based on is made for the one and two b-tag search regions. Within each category, the search is performed in bins of the variable, with the binning chosen such that the expected background yield in each bin is larger than one event, as estimated from Monte Carlo simulation.

In the H and VH categories, 3% and 35% respectively of the selected events were also selected in the monojet search [61], which used data from the same running period. The overlap in the L and VL categories is negligible, while the overlapping events in the H and VH categories were shown not to have an impact on the final sensitivity. Consequently, the results from this analysis and from the monojet analysis are largely statistically independent.

The main backgrounds in the zero b-tag search region are from the +jets and +jets processes, while the dominant background in the one and two b-tag search regions is the process. To estimate the contribution of these backgrounds in the search regions, we use a data-driven method that extrapolates from appropriately selected control regions to the search region, assisted by Monte Carlo simulation. A detailed description of the background estimation method is discussed in Section 6.

To estimate the +jets and +jets background in the zero b-tag search region, we define the 1 control region by selecting events using identical requirements to those used in the search region, with the exception of additionally requiring one selected muon. Events in this control region are extrapolated to the search region in order to estimate the background. In addition, we define the 2 control region, enhanced in the +jets process, by requiring two selected muons with invariant mass between \GeVand \GeV. The 2 control region is used to perform a cross-check prediction for the 1 control region, and the systematic uncertainties in background prediction are estimated based on this comparison.

To estimate the background in the one and two b-tag search regions, we define the 1b and 2b control regions, by requiring at least one jet satisfying the CSV tight b-tagging criterion along with one and two selected muons respectively. Both of these control regions are dominated by the process. The background prediction is estimated by extrapolating from the 2b control region, while the 1b control region is used as a cross-check to estimate systematic uncertainties. Finally, we define the b control region by requiring two muons with invariant mass between \GeVand \GeV. This is used to estimate the +jets background in the one and two b-tag search regions.

The definitions of the search and control regions, and their use in this analysis are summarized in Tables 5 and 5.

\topcaption

Analysis regions for events with zero identified b-tagged jets. The definition of these regions is based on the muon multiplicity, the output of the CSV b-tagging algorithm, and the value of . For all the regions, is required. analysis region purpose b-tagging selection category 0 signal search region \GeV(VL) 1 control region no CSV loose jet \GeV(L) \GeV(H) 2 control region \GeV(VH)

\topcaption

Analysis regions for events with identified b-tagged jets. The definition of these regions is based on the muon multiplicity, the output of the CSV b-tagging algorithm, and the value of . For all the regions, is required. analysis region purpose b-tagging selection category 0bb signal serach region 2 CSV tight jets \GeV 0b CSV tight jet 1b control region 1 CSV tight jets 2b control region b control region 1 CSV loose jets

6 Background estimation

The largest background contribution to the zero b-tag search region is from events in which a W or Z boson is produced, in association with jets, decaying to final states with one or more neutrinos. These background processes are referred to as +jets and +jets events. Additional backgrounds arise from events involving the production of top quark pairs, and from events in which a boson decays to a pair of charged leptons. These processes are referred to as and +jets, respectively. Using simulated samples, the contribution from other SM processes, such as diboson and single top production, is found to be negligible.

The main background in the one and two b-tag search regions comes from events. The use of the tight working point of the CSV algorithm reduces the +jets and +jets contribution as shown in Table 6.1. Multijet production, which is the most abundant source of events with jets and unbalanced \pt, contributes to the search region primarily due to instrumental mismeasurement of the energy of jets. As a result the \METdirection tends to be highly aligned in the azimuthal coordinate with the razor megajets. The requirement on the razor variables and reduces the multijet background to a negligible level, which is confirmed by checking data control regions with looser cuts on the razor variables.

6.1 Background estimation for the zero b-tag search region

To predict the background from +jets and +jets in the zero b-tag search region, we use a data-driven method that extrapolates the observed data yields in the 1 control region to the search region. Similarly, the observed yield in the 2 control region allows the estimation of the contribution from +jets background process. Each category is binned in . Events in which the or boson decayed to muons are used to extrapolate to cases where they decay to electrons or taus.

The background expected from and boson production, in each bin and in each category of the 0 sample, is computed as

(3)

where labels the data yield in bin for the sample with muons, and indicates the corresponding yield for process , derived from simulations. This background estimation method relies on the assumption that the kinematic properties of events in which and bosons are produced are similar.

To estimate the accuracy of the background estimation method, we perform a cross-check by predicting the background in the 1 control region using the observed data yield in the 2 control region. The Monte Carlo simulation is used to perform this extrapolation analogous to the calculation in Equation 3. The small contribution from the background process is also estimated using the simulated samples. In Tables 6.1 and 6.1, the observed yields in the 1 and 2 control regions respectively are compared to the estimate derived from data. In Tables 6.1-6.2, the contribution of each process as predicted directly by simulated samples are also given.

\topcaption

Comparison of the observed yield in the 1 control region in each category and the corresponding data-driven background estimate obtained by extrapolating from the 2 control region. The uncertainty in the estimates takes into account both the statistical and systematic components. The contribution of each individual background process is also shown, as estimated from simulated samples, as well as the total MC predicted yield. category +jets +jets +jets MC predicted Estimated Observed VL 5926 L 2110 H 923 VH 0.1 143

\topcaption

Comparison of the observed yield for the 2 control region in each category and the corresponding prediction from background simulation. The quoted uncertainty in the prediction reflects only the size of the simulated sample. The contribution of each individual background process is also shown, as estimated from simulated samples. category +jets +jets +jets MC predicted Observed VL 0.1 0.1 207 L 0.1 78 H 0.1 30 VH 0.1 0.1 7

Figure 2 shows the comparison of the distributions between the observed yield and the data-driven background estimate in the 1 control region. The observed bin-by-bin difference is propagated as a systematic uncertainty in the data-driven background method, and accounts for the statistical uncertainty in the event yield in the 2 control region data as well as potential differences in the modeling of the recoil spectra between +jets and +jets processes. Some bins exhibit relatively large uncertainties primarily due to statistical fluctuations in the 2 control region from which the background is prediction estimated. Though the uncertainties are rather large in fractional terms, sensitivity to DM signal models is still obtained, because of the enhanced signal to background ratio for the bins at large values of .

Figure 2: Comparison of observed yields in the 1 control region and the data-driven background estimate derived from on the 2 control region data in the four categories: VL (top left), L (top right), H (bottom left), and VH (bottom right). The bottom panel in each plot shows the ratio between the two distributions. The observed bin-by-bin deviation from unity is interpreted as an estimate of the systematic uncertainty associated to the background estimation methodology for the 0 search region. The dark and light bands represent the statistical and the total uncertainties in the estimates, respectively. The horizontal bars indicate the variable bin widths.

The background is estimated using an analogous data-driven method, where we derive corrections to the Monte Carlo simulation prediction scaled to the production cross-section computed to NNLO accuracy [52, 53, 54] using data in the 2b control region for each bin in . The correction is then applied to the simulation prediction for the background contribution to the zero b-tag search region. This correction factor reflects potential mismodeling of the recoil spectrum predicted by the Monte Carlo simulation. The contribution of each background process to the 2b sample, predicted from simulated samples, is given in Table 6.1. The fraction of \ttbarevents in the 2b control sample is .

\topcaption

Observed yield and predicted background from simulated samples in the 2b control region. The quoted uncertainty in the prediction only reflects the size of the simulated sample. The contribution of each individual background process is also shown, as estimated from simulated samples. Sample +jets +jets +jets MC predicted Observed 2b 0.1 60

Figure 3 shows the comparison of the observed yield and the prediction from simulation, as a function of . We observe no significant deviations between the observed data and the simulation prediction. The uncertainty derived from the data-to-simulation correction factor is propagated to the systematic uncertainty of the prediction in the zero b-tag search region.

Figure 3: Comparison of the observed yield and the prediction from simulation as a function of in the 2b control region. The uncertainties in the data and the simulated sample are represented by the vertical bars and the shaded bands, respectively. The horizontal bars indicate the variable bin widths.

The result of the background estimation in the zero b-tag search region is given in Table 6.1, where it is compared to the observed yields in data. The uncertainty in the background estimates takes into account both the statistical and systematic components.

\topcaption

Comparison of the observed yields for for the zero b-tag search region in each category and the corresponding background estimates. The uncertainty in the background estimate takes into account both the statistical and systematic components. The contribution of each individual background process is also shown, as estimated from simulated samples, as well as the total MC predicted yield. category +jets +jets +jets MC predicted Estimated Observed VL 11623 L 3785 H 1559 VH 261

The comparison of the data-driven background estimates and the observations for each category is shown in Fig. 4, as a function of . The expected event distribution is shown for two signal benchmark models, corresponding to the pair production of DM particles of mass 1\GeVin the effective field theory (EFT) approach with vector coupling to u or d quarks. Details on the signal benchmark models are given in Section 8.1.

Figure 4: Comparison of the observed yield in the zero b-tag control region and the background estimates in the four categories: VL (top left), L (top right), H (bottom left), and VH (bottom right). The contribution of individual background processes is shown by the filled histograms. The bottom panels show the ratio between the observed yields and the total background estimate. The systematic uncertainty in the ratio includes the systematic uncertainty in the background estimate. For reference, the distributions from two benchmark signal models are also shown, corresponding to the pair production of DM particles of mass 1\GeVin the EFT approach with vector coupling to u or d quarks. The horizontal bars indicate the variable bin widths.

6.2 Background estimation for the 0b and 0bb samples

A similar data-driven technique is used to determine the expected background for the one and two b-tag search regions. The background from events for each bin in the one b-tag search region, , is computed as:

(4)

where is the observed yield in the th bin in the 2b control region, while and are the yields in the th bin predicted by the simulation for the one b-tag search region and the 2b control region respectively. Similarly, the \ttbarbackground in the two b-tag search region is derived from Eq. (4), replacing with , the \ttbarbackground yield in the th bin of the two b-tag search region predicted by the simulation. The data yield in the 2b control region is corrected to account for the small contamination from +jets and +jets, predicted with the simulated yields and , respectively.

The background contribution from +jets and +jets events is predicted using the b control region, and summarized in Table 6.2. The +jets purity of this control region is 89%. The observed yield in the b control region is shown in the left plot of Fig. 5, as a function of , along with the Monte Carlo simulation prediction. The uncertainty on the simulation prediction accounts only for the statistical uncertainty of the simulated sample. This contribution, scaled by the ratio of the predicted V+jets background in the search regions to that in the control region, obtained from simulation, provides an estimate for each bin.

\topcaption

Comparison of the observed yields in the b and b samples, the corresponding predictions from background simulation, and (for b only) the cross-check background estimate. The contribution of each individual background process is also shown, as estimated from simulated samples. Sample +jets +jets +jets MC predicted Estimated Observed b 0.1 0.1 175 b 3038 17 2920

Figure 5: Comparison of the observed yield and the prediction from simulation in the b control sample (left) and of the observed yield in the b control sample and the background estimates from the 2b and b control samples (right), shown as a function of . The bottom panel of each figure shows the ratio between the data and the estimates. The shaded bands represent the statistical uncertainty in the left plot, and the total uncertainty in the right plot. The horizontal bars indicate the variable bin widths.

We perform a cross-check of the method on the 1b control region by predicting the background from the 2b control region data. The data and prediction are compared on the right of Fig. 5, where we observe reasonable agreement. The difference between the prediction and the observed data in this cross-check region is propagated as a systematic uncertainty of the method.

The estimated background in the one and two b-tag search regions is given in Table 6.2 and shown in Fig. 6, where it is compared to the observed yields in data. The uncertainty in the estimates take into account both the statistical and systematic components.

Figure 6: Comparison of observed event yields and background estimates as a function of , for the one (left) and two (right) b-tag search regions. The shaded bands represent the total uncertainty in the estimate. The horizontal bars indicate the variable bin widths.
\topcaption

Comparison of the observed yield for events in the one and two b-tag search regions and the corresponding background estimates. The uncertainty in the estimates takes into account both the statistical and systematic components. The contribution of each individual background process is also shown, as estimated from simulated samples, as well as the total MC predicted yield. Sample +jets +jets +jets MC predicted Estimated Observed bb 204 4 247 b 2282

7 Systematic uncertainties

For each bin in each category, the difference between the observed and estimated yields in the crosscheck analysis (see Section 6) is taken as the estimate of the uncertainty associated with the method, and covers the differences in the modeling of the recoil spectra between +jets and +jets processes as well as the cross section uncertainties. These uncertainties are found to be typically 20–40%, depending on the considered bin in the (, ) plane, and are the dominant systematic uncertainties for the analysis. As discussed in Section 6.1, a few bins at smaller values of exhibit larger systematic uncertainties, primarily due to statistical fluctuations in the control region. However the impact on the sensitivity to the dark matter models considered is small as the signal to background ratio is significantly better in other bins at larger values of .

For the 0 analysis, differences between the kinematic properties of +jets and +jets events are additional sources of systematic uncertainty. These differences arise from the choice of the PDF set, jet energy scale corrections, b tagging efficiency corrections, and trigger efficiency. These effects largely cancel when taking the ratio of the two processes, and the resulting uncertainty is found to be smaller than one fifth of the total uncertainty. The quoted uncertainty is an upper estimate of the total systematic uncertainty.

For the 0b and 0bb samples, both the signal and control samples are dominated by \ttbarevents. The cancellation of the systematic uncertainties is even stronger in this case, since it does not involve different processes, and different PDFs. The remaining uncertainty is dominated by the contribution arising from the small size of the control sample.

Systematic uncertainties in the signal simulation originate from the choice of the PDF set, the jet energy scale correction, the modeling of the initial-state radiation in the event generator, and the uncertainty in the integrated luminosity. The luminosity uncertainty changes the signal normalization while the other uncertainties also modify the signal shape. These effects are taken into account by propagating these uncertainties into the category and the bin. These uncertainties are considered to be fully correlated across categories and bins. Typical values for the individual contributions are given in Table 7. The total uncertainty in the signal yield is obtained by propagating the individual effects into the and variables and comparing the bin-by-bin variations with respect to the central value of the prediction based on simulation. In the particular case of the uncertainties due to the choice of the PDF set we have followed the PDF4LHC [62, 63, 64] prescription, using the CTEQ-6.6[65] and MRST-2006-NNLO [66] PDF sets.

\topcaption

Systematic uncertainties associated with the description of the DM signal. The values indicated represent the typical size. The dependence of these systematic uncertainties on the and values is taken into account in the determination of the results. Effect Uncertainty Jet energy scale 3–6% Luminosity 2.6% Parton distribution functions 3–6% Initial-state radiation 8–15%

8 Results and interpretation

In Figs. 4 and 6 the estimated backgrounds are compared to the observed yield in each region, for events without and with b-tagged jets, respectively. The background estimates agree with the observed yields, within the uncertainties. This result is interpreted in terms of exclusion limits for several models of DM production.

8.1 Limits on dark matter production from the 0 sample

The result is interpreted in the context of a low-energy effective field theory, in which the production of DM particles is mediated by six or seven dimension operators [67, 68]. This choice allows the results be compared with those of previous analyses [19, 20], and shows that a similar sensitivity is achieved.

Operators of dimension six and seven are generated assuming the existence of a heavy particle, mediating the interaction between the DM and SM fields. To describe DM production as a local interaction, the propagator of the heavy mediator is expanded through an operator product expansion. The nature of the mediator determines the nature of the effective interaction. Two benchmark scenarios are considered in this study, axial-vector (AV), and vector (V) interactions [69], described by the following operators:

(5)

Here and are the Dirac matrices, is the DM field, and is an SM quark field. The DM particle is assumed to be a Dirac fermion where both operators will contribute in the low-energy theory, while in the case of a Majorana DM particle the vector coupling will vanish in the low-energy theory. Below the cutoff energy scale , DM production is described as a contact interaction between two quarks and two DM particles. In the case of -channel production through a heavy mediator, the energy scale is identified with , where is the mediator mass and is an effective coupling, determined by the coupling of the mediator to quark and DM fields, and , respectively.

The results in Tables A-A in the Appendix are used to obtain an upper limit at 90% confidence level (CL) on the DM production cross section, (where the superscript denotes the coupling to an up or down quark). The limits are obtained using the LHC CL procedure [70, 71] and a global likelihood determined by combining the likelihoods of the different search categories. Each systematic uncertainty (see Section 7) is incorporated in the likelihood with a dedicated nuisance parameter, whose value is not known a priori but rather must be estimated from the data.

Subsequently, the cross section () limit is translated into a lower limit on the cutoff scale, through the relation:

(6)

Here and are the cutoff energy scale and cross section of the simulated sample, respectively. The derived values of as a function of the DM mass, shown in Fig. 7, are very similar to those derived for the CMS monojet search [61]. The exclusion limits on weaken at large DM masses since the cross section for DM production is reduced. The analysis has been repeated removing the events also selected by the monojet search. The reduction in background yields due to this additional requirement compensates for the reduction in signal efficiency, resulting in a negligible difference in the exclusion limit on .

Figure 7: Lower limit at 90% CL on the cutoff scale as a function of the DM mass in the case of axial-vector (left) and vector (right) currents. The validity of the EFT is quantified by contours, corresponding to different values of the effective coupling . For completeness, regions forbidden by the EFT validity condition are shown for two choices of the effective coupling: (light gray) and (dark gray).

The EFT framework provides a benchmark scenario to compare the sensitivity of this analysis with that of previous searches for similar signatures. However, the validity of an EFT approach is limited at the LHC because a fraction of events under study are generated at a comparable to the cutoff scale  [72, 68, 73, 74]. For theories to be perturbative, is typically required to be smaller than , and this condition is unlikely to be satisfied for the entire region of phase space probed by the collider searches. In addition, the range of values for the couplings being probed within the EFT may be unrealistically large. Following the study presented in Refs. [75, 76, 77], we quantify this effect through two EFT validity measures. The first is a minimal kinematic constraint on obtained by requiring and , where is the momentum transferred from the mediator to the DM particle pair, which yields . The second is more stringent and uses the quantity:

(7)

Values of close to unity indicate a regime in which the assumptions of the EFT approximation hold, while a deviation from unity quantifies the fraction of events for which the EFT approximation is still valid. We consider the case of -channel production, and we compute as a function of the effective coupling in the range . The contours corresponding to for different values of are shown in Fig. 7. For values of , the limit set by the analysis lies above the contour.

The exclusion limits on for the axial-vector and vector operators are transformed into upper limits on the spin-dependent ([78, 79, 80, 81, 82, 83, 84] and spin-independent ([81, 80, 85, 86, 87, 88, 89, 90] DM-nucleon scattering cross section, respectively; using the following expressions [69]:

(8)
(9)
where
(10)

with and indicating the proton and DM masses, respectively. The numerical values of the derived limits are given in Tables 8.1 and 8.1. The bound on as a function of is shown in Fig. 8 for spin-dependent and spin-independent DM-nucleon scattering. A summary of the observed limits for the axial-vector and vector operators can be found in Tables 8.1 and 8.1 respectively. It is observed that the spin-independent bounds obtained by direct detection experiments are more stringent than those obtained by the present result for masses above \GeV. Such an effect is expected since the spin-independent DM-nucleus cross section is enhanced by the coherent scattering of DM off nucleons in the case of spin-independent operators. We note that the present result is more sensitive for small DM mass because the recoil energy in direct detection experiments is lower in this region and therefore more difficult to detect. In the case of spin-dependent DM-nucleus scattering, the present results are more stringent that those obtained by direct detection experiments because the DM-nucleus cross section does not benefit from the coherent enhancement. A summary of the observed limits for the axial-vector and vector operators can be found in Tables 8.1 and 8.1 respectively.

In order to compare our results with those from direct detection experiments, the experimental bounds in [81, 80, 85, 86, 87, 88, 78, 79, 80, 81] are translated into bounds on . This comparison is shown in Fig. 9. This translation is well defined since the momentum transfer in most direct detection experiments is low compared to the values of being probed, and thus the EFT approximations in question are mostly valid.

\topcaption

The 90% CL limits on DM production in the case of axial-vector couplings. Here, and are the observed upper limits on the production cross section for u and d quarks, respectively; is the observed cutoff energy scale lower limit; and is the observed DM-nucleon scattering cross section upper limit. (\GeVns) (pb) (pb) (\GeVns) 1 0.39 0.45 1029 10 0.43 0.45 1012 100 0.30 0.37 1017 400 0.25 0.26 752 700 0.21 0.26 524 1000 0.17 0.22 360

\topcaption

The 90% CL limits on DM production in the case of vector couplings. Here, and are the observed upper limits on the production cross section for u and d quarks, respectively; is the observed cutoff energy scale lower limit; and is the observed DM-nucleon scattering cross section upper limit. (\GeVns) (pb) (pb) (\GeVns) 1 0.41 0.38 1038 10 0.36 0.45 1043 100 0.33 0.44 1036 400 0.23 0.35 893 700 0.22 0.27 674 1000 0.22 0.27 477

Figure 8: Upper limit at 90% CL on the DM-nucleon scattering cross section as a function of the DM mass in the case of spin-dependent axial-vector (left) and spin-independent vector (right) currents. A selection of representative direct detection experimental bounds are also shown.
Figure 9: Lower limit at 90% CL on the cutoff scale as a function of the DM mass in the case of axial-vector (left) and vector (right) currents. A selection of direct detection experimental bounds are also shown.

8.2 Limits on dark matter production from the 0b and 0bb samples

The results from the 0b and 0bb samples are interpreted in an EFT scenario, following a methodology similar to that of Section 8.1. In this case, a heavy scalar mediator is considered [91], generating an operator:

(11)

The dependence on the mass, induced by the scalar nature of the mediator, implies a stronger coupling to third-generation quarks, enhancing the sensitivity of the 0b and 0bb samples to this scenario. Unlike the case of V and AV operators, the production cross section for this process is proportional to . The value of is then derived as

(12)

Given the results of Table 6.2 we proceed to set limits at 90% CL on the cutoff scale (see Table 8.2) using the LHC CL procedure. To quantify the validity of the EFT we follow the discussion in Section 8.1, considering an interaction mediated by an -channel produced particle. The operator of Eq. (11) is suppressed by an additional factor / with respect to the operators in Eq. (5). As a result, for a given value of the coupling , smaller values of are probed in this case. The observed limit stays below the contours derived for , even when the coupling is fixed to the largest value considered, , as shown in the left plot of Fig. 10. For the same choice of coupling, the derived limit on would correspond to , as shown in the right plot of Fig. 10. Only for does the observed limit correspond to values of . This requirement implies a UV completion of the EFT beyond the perturbative regime. For this reason, this result is not interpreted in terms of an exclusion limit on .

\topcaption

The 90% CL limits on DM production in the case of scalar couplings. Here, is the observed upper limit on the production cross section, and are the observed and expected cutoff energy scale lower limit, respectively. (\GeVns) (pb) (\GeVns) (\GeVns) 0.1 5.4 43.0 48.2 1 3.8 45.3 49.9 10 6.3 43.2 48.4 100 0.8 53.7 55.1 200 0.7 47.2 48.3 300 2.8 32.5 35.8 400 2.8 28.3 30.8 1000 1.7 13.2 13.8

Figure 10: Lower limit at 90% CL on the cutoff scale for the scalar operator as a function of the DM mass . The validity of the EFT is quantified by (left) and (right) contours, corresponding to different values of the effective coupling . For completeness, regions forbidden by the EFT validity condition are shown for two choices of the effective coupling: (light gray) and (dark gray).

9 Summary

A search for dark matter has been performed studying proton-proton collisions collected with the CMS detector at the LHC at a center-of-mass energy of 8\TeV. The data correspond to an integrated luminosity of 18.8\fbinv, collected with a dedicated high-rate trigger in 2012, made possible by the creation of parked data, and processed during the LHC shutdown in 2013.

Events with at least two jets are analyzed by studying the distribution in the (, ) plane, in an event topology complementary to that of monojet searches. Events with one or two muons are used in conjunction with simulated samples, to predict the expected background from standard model processes, mainly +jets and +jets. The analysis is performed on events both with and without b-tagged jets, originating from the hadronization of a bottom quark, where in the latter case the dominant background comes from .

No significant excess is observed. The results are presented as exclusion limits on dark matter production at 90% confidence level for models based on effective operators and for different assumptions on the interaction between the dark matter particles and the colliding partons. Dark matter production at the LHC is excluded for a mediator mass scale below 1\TeVin the case of a vector or axial vector operator. While the sensitivity achieved is similar to those of previously published searches, this analysis complements those results since the use of razor variables provides more inclusive selection criteria and since the exploitation of parked data allows events with small values of to be included.

Acknowledgements.
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: the Austrian Federal Ministry of Science, Research and Economy and the Austrian Science Fund; the Belgian Fonds de la Recherche Scientifique, and Fonds voor Wetenschappelijk Onderzoek; the Brazilian Funding Agencies (CNPq, CAPES, FAPERJ, and FAPESP); the Bulgarian Ministry of Education and Science; CERN; the Chinese Academy of Sciences, Ministry of Science and Technology, and National Natural Science Foundation of China; the Colombian Funding Agency (COLCIENCIAS); the Croatian Ministry of Science, Education and Sport, and the Croatian Science Foundation; the Research Promotion Foundation, Cyprus; the Ministry of Education and Research, Estonian Research Council via IUT23-4 and IUT23-6 and European Regional Development Fund, Estonia; the Academy of Finland, Finnish Ministry of Education and Culture, and Helsinki Institute of Physics; the Institut National de Physique Nucléaire et de Physique des Particules / CNRS, and Commissariat à l’Énergie Atomique et aux Énergies Alternatives / CEA, France; the Bundesministerium für Bildung und Forschung, Deutsche Forschungsgemeinschaft, and Helmholtz-Gemeinschaft Deutscher Forschungszentren, Germany; the General Secretariat for Research and Technology, Greece; the National Scientific Research Foundation, and National Innovation Office, Hungary; the Department of Atomic Energy and the Department of Science and Technology, India; the Institute for Studies in Theoretical Physics and Mathematics, Iran; the Science Foundation, Ireland; the Istituto Nazionale di Fisica Nucleare, Italy; the Ministry of Science, ICT and Future Planning, and National Research Foundation (NRF), Republic of Korea; the Lithuanian Academy of Sciences; the Ministry of Education, and University of Malaya (Malaysia); the Mexican Funding Agencies (CINVESTAV, CONACYT, SEP, and UASLP-FAI); the Ministry of Business, Innovation and Employment, New Zealand; the Pakistan Atomic Energy Commission; the Ministry of Science and Higher Education and the National Science Center, Poland; the Fundação para a Ciência e a Tecnologia, Portugal; JINR, Dubna; the Ministry of Education and Science of the Russian Federation, the Federal Agency of Atomic Energy of the Russian Federation, Russian Academy of Sciences, and the Russian Foundation for Basic Research; the Ministry of Education, Science and Technological Development of Serbia; the Secretaría de Estado de Investigación, Desarrollo e Innovación and Programa Consolider-Ingenio 2010, Spain; the Swiss Funding Agencies (ETH Board, ETH Zurich, PSI, SNF, UniZH, Canton Zurich, and SER); the Ministry of Science and Technology, Taipei; the Thailand Center of Excellence in Physics, the Institute for the Promotion of Teaching Science and Technology of Thailand, Special Task Force for Activating Research and the National Science and Technology Development Agency of Thailand; the Scientific and Technical Research Council of Turkey, and Turkish Atomic Energy Authority; the National Academy of Sciences of Ukraine, and State Fund for Fundamental Researches, Ukraine; the Science and Technology Facilities Council, UK; the US Department of Energy, and the US National Science Foundation. Individuals have received support from the Marie-Curie programme and the European Research Council and EPLANET (European Union); the Leventis Foundation; the A. P. Sloan Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science Policy Office; the Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Council of Science and Industrial Research, India; the HOMING PLUS programme of the Foundation for Polish Science, cofinanced from European Union, Regional Development Fund; the OPUS programme of the National Science Center (Poland); the Compagnia di San Paolo (Torino); MIUR project 20108T4XTM (Italy); the Thalis and Aristeia programmes cofinanced by EU-ESF and the Greek NSRF; the National Priorities Research Program by Qatar National Research Fund; the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University (Thailand); the Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); and the Welch Foundation, contract C-1845; and the Weston Havens Foundation (USA).

Appendix

Appendix A Background estimation and observed yield

In this section, we provide the background estimate and the observed yield for each bin of the (, ) plane.

Tables A-A show the expected and observed yields in each bin of each category for the 0 sample. Tables A and A show the corresponding values for the 0b and the 0bb samples, respectively.

\topcaption

Background estimates and observed yield for each bin in the VL category. range 0.5–0.55 0.55–0.6 0.6–0.65 0.65–0.7 Observed 2049 1607 1352 1147 Estimated range 0.7–0.75 0.75–0.8 0.8–0.85 0.85–0.9 Observed 1026 896 880 744 Estimated range 0.9–0.95 0.95–1.0 1.0–2.5 Observed 688 499 735 Estimated

\topcaption

Background estimates and observed yield for each bin in the L category. range 0.5–0.575 0.575–0.65 0.65–0.75 Observed 1088 765 682 Estimated range 0.75–0.85 0.85–0.95 0.95–2.5 Observed 565 395 290 Estimated

\topcaption

Background estimates and observed yield for each bin in the H category. range 0.5–0.575 0.575–0.65 0.65–0.75 Observed 513 328 279 Estimated range 0.75–0.85 0.85–0.95 0.95–2.5 Observed 203 151 85 Estimated

\topcaption

Background estimates and observed yield for each bin in the VH category. range 0.5–0.6 0.6–0.7 0.7–0.95 0.95–2.5 Observed 117 58 75 11 Estimated

\topcaption

Background estimates and observed yield for each bin in the b signal region. range 0.5–0.6 0.6–0.75 0.75–0.9 0.9–2.5 Observed 760 807 469 246 Estimated

\topcaption

Background estimates and observed yield for each bin in the bb signal region. range 0.5–0.6 0.6–0.75 0.75–0.9 0.9–2.5 Observed 122 80 31 14 Estimated

Appendix B The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
V. Khachatryan, A.M. Sirunyan, A. Tumasyan \cmsinstskipInstitut für Hochenergiephysik der OeAW, Wien, Austria
W. Adam, E. Asilar, T. Bergauer, J. Brandstetter, E. Brondolin, M. Dragicevic, J. Erö, M. Flechl, M. Friedl, R. Frühwirth\cmsAuthorMark1, V.M. Ghete, C. Hartl, N. Hörmann, J. Hrubec, M. Jeitler\cmsAuthorMark1, A. König, M. Krammer\cmsAuthorMark1, I. Krätschmer, D. Liko, T. Matsushita, I. Mikulec, D. Rabady, N. Rad, B. Rahbaran, H. Rohringer, J. Schieck\cmsAuthorMark1, R. Schöfbeck, J. Strauss, 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, T. Cornelis, E.A. De Wolf, X. Janssen, A. Knutsson, J. Lauwers, S. Luyckx, 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, N. Daci, I. De Bruyn, K. Deroover, N. Heracleous, J. Keaveney, S. Lowette, S. Moortgat, L. Moreels, A. Olbrechts, Q. Python, D. Strom, S. Tavernier, W. Van Doninck, P. Van Mulders, G.P. Van Onsem, I. Van Parijs \cmsinstskipUniversité Libre de Bruxelles, Bruxelles, Belgium
P. Barria, H. Brun, C. Caillol, B. Clerbaux, G. De Lentdecker, G. Fasanella, L. Favart, R. Goldouzian, A. Grebenyuk, G. Karapostoli, T. Lenzi, A. Léonard, T. Maerschalk, A. Marinov, L. Perniè, A. Randle-conde, T. Seva, C. Vander Velde, P. Vanlaer, R. Yonamine, F. Zenoni, F. Zhang\cmsAuthorMark2 \cmsinstskipGhent University, Ghent, Belgium
K. Beernaert, L. Benucci, A. Cimmino, S. Crucy, D. Dobur, A. Fagot, G. Garcia, M. Gul, J. Mccartin, A.A. Ocampo Rios, D. Poyraz, D. Ryckbosch, S. Salva, M. Sigamani, M. Tytgat, W. Van Driessche, E. Yazgan, N. Zaganidis \cmsinstskipUniversité Catholique de Louvain, Louvain-la-Neuve, Belgium
S. Basegmez, C. Beluffi\cmsAuthorMark3, O. Bondu, S. Brochet, G. Bruno, A. Caudron, L. Ceard, S. De Visscher, C. Delaere, M. Delcourt, D. Favart, L. Forthomme, A. Giammanco, A. Jafari, P. Jez, M. Komm, V. Lemaitre, A. Mertens, M. Musich, C. Nuttens, L. Perrini, K. Piotrzkowski, A. Popov\cmsAuthorMark4, L. Quertenmont, M. Selvaggi, M. Vidal Marono \cmsinstskipUniversité de Mons, Mons, Belgium
N. Beliy, G.H. Hammad \cmsinstskipCentro Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil
W.L. Aldá Júnior, F.L. Alves, G.A. Alves, L. Brito, M. Correa Martins Junior, M. Hamer, 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\cmsAuthorMark5, A. Custódio, E.M. Da Costa, D. De Jesus Damiao, C. De Oliveira Martins, S. Fonseca De Souza, L.M. Huertas Guativa, H. Malbouisson, D. Matos Figueiredo, C. Mora Herrera, L. Mundim, H. Nogima, W.L. Prado Da Silva, A. Santoro, A. Sznajder, E.J. Tonelli Manganote\cmsAuthorMark5, A. Vilela Pereira \cmsinstskipUniversidade Estadual Paulista ,  Universidade Federal do ABC ,  São Paulo, Brazil
S. Ahuja, C.A. Bernardes, A. De Souza Santos, S. Dogra, T.R. Fernandez Perez Tomei, E.M. Gregores, P.G. Mercadante, C.S. Moon\cmsAuthorMark6, 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. 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\cmsAuthorMark7 \cmsinstskipInstitute of High Energy Physics, Beijing, China
M. Ahmad, J.G. Bian, G.M. Chen, H.S. Chen, M. Chen, T. Cheng, R. Du, C.H. Jiang, D. Leggat, R. Plestina\cmsAuthorMark8, F. Romeo, S.M. Shaheen, A. Spiezia, J. Tao, C. Wang, Z. Wang, H. Zhang \cmsinstskipState Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing, China
C. Asawatangtrakuldee, Y. Ban, 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, J.P. Gomez, B. Gomez Moreno, J.C. Sanabria \cmsinstskipUniversity of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, Split, Croatia
N. Godinovic, D. Lelas, I. Puljak, P.M. Ribeiro Cipriano \cmsinstskipUniversity of Split, Faculty of Science, Split, Croatia
Z. Antunovic, M. Kovac \cmsinstskipInstitute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, K. Kadija, J. Luetic, S. Micanovic, L. Sudic \cmsinstskipUniversity of Cyprus, Nicosia, Cyprus
A. Attikis, G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos, P.A. Razis, H. Rykaczewski \cmsinstskipCharles University, Prague, Czech Republic
M. Finger\cmsAuthorMark9, M. Finger Jr.\cmsAuthorMark9 \cmsinstskipAcademy of Scientific Research and Technology of the Arab Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
A. Awad, E. El-khateeb\cmsAuthorMark10\cmsAuthorMark10, S. Elgammal\cmsAuthorMark11, A. Mohamed\cmsAuthorMark12 \cmsinstskipNational Institute of Chemical Physics and Biophysics, Tallinn, Estonia
B. Calpas, M. Kadastik, M. Murumaa, 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, V. Karimäki, R. Kinnunen, T. Lampén, K. Lassila-Perini, S. Lehti, T. Lindén, P. Luukka, T. Peltola, J. Tuominiemi, E. Tuovinen, L. Wendland \cmsinstskipLappeenranta University of Technology, Lappeenranta, Finland
J. Talvitie, T. Tuuva \cmsinstskipDSM/IRFU, CEA/Saclay, Gif-sur-Yvette, France
M. Besancon, F. Couderc, M. Dejardin, D. Denegri, B. Fabbro, J.L. Faure, C. Favaro, F. Ferri, S. Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry, E. Locci, M. Machet, J. Malcles, J. Rander, A. Rosowsky, M. Titov, A. Zghiche \cmsinstskipLaboratoire Leprince-Ringuet, Ecole Polytechnique, IN2P3-CNRS, Palaiseau, France
A. Abdulsalam, I. Antropov, S. Baffioni, F. Beaudette, P. Busson, L. Cadamuro, E. Chapon, C. Charlot, O. Davignon, N. Filipovic, R. Granier de Cassagnac, M. Jo, S. Lisniak, L. Mastrolorenzo, P. Miné, I.N. Naranjo, M. Nguyen, C. Ochando, G. Ortona, P. Paganini, P. Pigard, S. Regnard, R. Salerno, J.B. Sauvan, Y. Sirois, T. Strebler, Y. Yilmaz, A. Zabi \cmsinstskipInstitut Pluridisciplinaire Hubert Curien, Université de Strasbourg, Université de Haute Alsace Mulhouse, CNRS/IN2P3, Strasbourg, France
J.-L. Agram\cmsAuthorMark13, J. Andrea, A. Aubin, D. Bloch, J.-M. Brom, M. Buttignol, E.C. Chabert, N. Chanon, C. Collard, E. Conte\cmsAuthorMark13, X. Coubez, J.-C. Fontaine\cmsAuthorMark13, D. Gelé, U. Goerlach, C. Goetzmann, A.-C. Le Bihan, J.A. Merlin\cmsAuthorMark14, K. Skovpen, 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, E. Bouvier, C.A. Carrillo Montoya, R. Chierici, D. Contardo, B. Courbon, P. Depasse, H. El Mamouni, J. Fan, J. Fay, S. Gascon, M. Gouzevitch, B. Ille, F. Lagarde, I.B. Laktineh, M. Lethuillier, L. Mirabito, A.L. Pequegnot, S. Perries, J.D. Ruiz Alvarez, D. Sabes, V. Sordini, M. Vander Donckt, P. Verdier, S. Viret \cmsinstskipGeorgian Technical University, Tbilisi, Georgia
T. Toriashvili\cmsAuthorMark15 \cmsinstskipTbilisi State University, Tbilisi, Georgia
Z. Tsamalaidze\cmsAuthorMark9 \cmsinstskipRWTH Aachen University, I. Physikalisches Institut, Aachen, Germany
C. Autermann, S. Beranek, L. Feld, A. Heister, M.K. Kiesel, K. Klein, M. Lipinski, A. Ostapchuk, M. Preuten, F. Raupach, S. Schael, J.F. Schulte, T. Verlage, H. Weber, V. Zhukov\cmsAuthorMark4 \cmsinstskipRWTH Aachen University, III. Physikalisches Institut A,  Aachen, Germany
M. Ata, M. Brodski, E. Dietz-Laursonn, D. Duchardt, M. Endres, M. Erdmann, S. Erdweg, T. Esch, R. Fischer, A. Güth, T. Hebbeker, C. Heidemann, K. Hoepfner, S. Knutzen, M. Merschmeyer, A. Meyer, P. Millet, S. Mukherjee, M. Olschewski, K. Padeken, P. Papacz, 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
V. Cherepanov, Y. Erdogan, G. Flügge, H. Geenen, M. Geisler, F. Hoehle, B. Kargoll, T. Kress, A. Künsken, J. Lingemann, A. Nehrkorn, A. Nowack, I.M. Nugent, C. Pistone, O. Pooth, A. Stahl\cmsAuthorMark14 \cmsinstskipDeutsches Elektronen-Synchrotron, Hamburg, Germany
M. Aldaya Martin, I. Asin, N. Bartosik, O. Behnke, U. Behrens, K. Borras\cmsAuthorMark16, A. Burgmeier, A. Campbell, C. Contreras-Campana, F. Costanza, C. Diez Pardos, G. Dolinska, S. Dooling, T. Dorland, G. Eckerlin, D. Eckstein, T. Eichhorn, G. Flucke, E. Gallo\cmsAuthorMark17, J. Garay Garcia, A. Geiser, A. Gizhko, P. Gunnellini, J. Hauk, M. Hempel\cmsAuthorMark18, H. Jung, A. Kalogeropoulos, O. Karacheban\cmsAuthorMark18, M. Kasemann, P. Katsas, J. Kieseler, C. Kleinwort, I. Korol, W. Lange, J. Leonard, K. Lipka, A. Lobanov, W. Lohmann\cmsAuthorMark18, R. Mankel, I.-A. Melzer-Pellmann, A.B. Meyer, G. Mittag, J. Mnich, A. Mussgiller, S. Naumann-Emme, A. Nayak, E. Ntomari, H. Perrey, D. Pitzl, R. Placakyte, A. Raspereza, B. Roland, M.Ö. Sahin, P. Saxena, T. Schoerner-Sadenius, C. Seitz, S. Spannagel, N. Stefaniuk, K.D. Trippkewitz, R. Walsh, C. Wissing \cmsinstskipUniversity of Hamburg, Hamburg, Germany
V. Blobel, M. Centis Vignali, A.R. Draeger, T. Dreyer, J. Erfle, E. Garutti, K. Goebel, D. Gonzalez, M. Görner, J. Haller, M. Hoffmann, R.S. Höing, A. Junkes, R. Klanner, R. Kogler, N. Kovalchuk, T. Lapsien, T. Lenz, I. Marchesini, D. Marconi, M. Meyer, M. Niedziela, D. Nowatschin, J. Ott, F. Pantaleo\cmsAuthorMark14, T. Peiffer, A. Perieanu, N. Pietsch, J. Poehlsen, C. Sander, C. Scharf, P. Schleper, E. Schlieckau, A. Schmidt, S. Schumann, J. Schwandt, V. Sola, H. Stadie, G. Steinbrück, F.M. Stober, H. Tholen, D. Troendle, E. Usai, L. Vanelderen, A. Vanhoefer, B. Vormwald \cmsinstskipInstitut für Experimentelle Kernphysik, Karlsruhe, Germany
C. Barth, C. Baus, J. Berger, C. Böser, E. Butz, T. Chwalek, F. Colombo, W. De Boer, A. Descroix, A. Dierlamm, S. Fink, F. Frensch, R. Friese, M. Giffels, A. Gilbert, D. Haitz, F. Hartmann\cmsAuthorMark14, S.M. Heindl, U. Husemann, I. Katkov\cmsAuthorMark4, A. Kornmayer\cmsAuthorMark14, P. Lobelle Pardo, B. Maier, H. Mildner, M.U. Mozer, T. Müller, Th. Müller, M. Plagge, G. Quast, K. Rabbertz, S. Röcker, F. Roscher, M. Schröder, G. Sieber, H.J. Simonis, R. Ulrich, J. Wagner-Kuhr, 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, A. Psallidas, I. Topsis-Giotis \cmsinstskipNational and Kapodistrian University of Athens, Athens, Greece
A. Agapitos, S. Kesisoglou, A. Panagiotou, N. Saoulidou, E. Tziaferi \cmsinstskipUniversity of Ioánnina, Ioánnina, Greece
I. Evangelou, G. Flouris, C. Foudas, P. Kokkas, N. Loukas, N. Manthos, I. Papadopoulos, E. Paradas, J. Strologas \cmsinstskipWigner Research Centre for Physics, Budapest, Hungary
G. Bencze, C. Hajdu, A. Hazi, 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. Karancsi\cmsAuthorMark21, J. Molnar, Z. Szillasi\cmsAuthorMark14 \cmsinstskipUniversity of Debrecen, Debrecen, Hungary
M. Bartók\cmsAuthorMark20, A. Makovec, P. Raics, Z.L. Trocsanyi, B. Ujvari \cmsinstskipNational Institute of Science Education and Research, Bhubaneswar, India
S. Choudhury\cmsAuthorMark22, P. Mal, K. Mandal, D.K. Sahoo, N. Sahoo, S.K. Swain \cmsinstskipPanjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, R. Chawla, R. Gupta, U.Bhawandeep, A.K. Kalsi, A. Kaur, M. Kaur, R. Kumar, A. Mehta, M. Mittal, J.B. Singh, G. Walia \cmsinstskipUniversity of Delhi, Delhi, India
Ashok Kumar, A. Bhardwaj, B.C. Choudhary, R.B. Garg, S. Malhotra, M. Naimuddin, N. Nishu, K. Ranjan, R. Sharma, V. Sharma \cmsinstskipSaha Institute of Nuclear Physics, Kolkata, India
R. Bhattacharya, S. Bhattacharya, K. Chatterjee, S. Dey, 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 \cmsinstskipBhabha Atomic Research Centre, Mumbai, India
R. Chudasama, D. Dutta, V. Jha, V. Kumar, A.K. Mohanty\cmsAuthorMark14, L.M. Pant, P. Shukla, A. Topkar \cmsinstskipTata Institute of Fundamental Research, Mumbai, India
T. Aziz, S. Banerjee, S. Bhowmik\cmsAuthorMark23, R.M. Chatterjee, R.K. Dewanjee, S. Dugad, S. Ganguly, S. Ghosh, M. Guchait, A. Gurtu\cmsAuthorMark24, Sa. Jain, G. Kole, S. Kumar, B. Mahakud, M. Maity\cmsAuthorMark23, G. Majumder, K. Mazumdar, S. Mitra, G.B. Mohanty, B. Parida, T. Sarkar\cmsAuthorMark23, N. Sur, B. Sutar, N. Wickramage\cmsAuthorMark25 \cmsinstskipIndian Institute of Science Education and Research (IISER),  Pune, India
S. Chauhan, S. Dube, A. Kapoor, K. Kothekar, A. Rane, S. Sharma \cmsinstskipInstitute for Research in Fundamental Sciences (IPM),  Tehran, Iran
H. Bakhshiansohi, H. Behnamian, S.M. Etesami\cmsAuthorMark26, A. Fahim\cmsAuthorMark27, M. Khakzad, M. Mohammadi Najafabadi, M. Naseri, S. Paktinat Mehdiabadi, 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, L. Silvestris\cmsAuthorMark14, R. Venditti \cmsinstskipINFN Sezione di Bologna , Università di Bologna ,  Bologna, Italy
G. Abbiendi, C. Battilana\cmsAuthorMark14, D. Bonacorsi, S. Braibant-Giacomelli, L. Brigliadori, R. Campanini, P. Capiluppi, A. Castro, F.R. Cavallo, S.S. Chhibra, G. Codispoti, M. Cuffiani, G.M. Dallavalle, F. Fabbri, A. Fanfani, D. Fasanella, P. Giacomelli, C. Grandi, L. Guiducci, S. Marcellini, G. Masetti, A. Montanari, F.L. Navarria, A. Perrotta, A.M. Rossi, T. Rovelli, G.P. Siroli, N. Tosi\cmsAuthorMark14 \cmsinstskipINFN Sezione di Catania , Università di Catania ,  Catania, Italy
G. Cappello, M. Chiorboli, S. Costa, A. Di Mattia, F. Giordano, 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, V. Gori, P. Lenzi, M. Meschini, S. Paoletti, G. Sguazzoni, L. Viliani\cmsAuthorMark14 \cmsinstskipINFN Laboratori Nazionali di Frascati, Frascati, Italy
L. Benussi, S. Bianco, F. Fabbri, D. Piccolo, F. Primavera\cmsAuthorMark14 \cmsinstskipINFN Sezione di Genova , Università di Genova ,  Genova, Italy
V. Calvelli, F. Ferro, M. Lo Vetere, M.R. Monge, E. Robutti, S. Tosi \cmsinstskipINFN Sezione di Milano-Bicocca , Università di Milano-Bicocca ,  Milano, Italy
L. Brianza, M.E. Dinardo, S. Fiorendi, S. Gennai, R. Gerosa, A. Ghezzi, P. Govoni, S. Malvezzi, R.A. Manzoni\cmsAuthorMark14, 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’ , Napoli, Italy, Università della Basilicata , Potenza, Italy, Università G. Marconi , Roma, Italy
S. Buontempo, N. Cavallo, S. Di Guida\cmsAuthorMark14, M. Esposito, F. Fabozzi, A.O.M. Iorio, G. Lanza, L. Lista, S. Meola\cmsAuthorMark14, M. Merola, P. Paolucci\cmsAuthorMark14, C. Sciacca, F. Thyssen \cmsinstskipINFN Sezione di Padova , Università di Padova , Padova, Italy, Università di Trento , Trento, Italy
P. Azzi\cmsAuthorMark14, N. Bacchetta, L. Benato, D. Bisello, A. Boletti, R. Carlin, P. Checchia, M. Dall’Osso\cmsAuthorMark14, T. Dorigo, U. Dosselli, F. Gasparini, U. Gasparini, A. Gozzelino, S. Lacaprara, M. Margoni, A.T. Meneguzzo, F. Montecassiano, M. Passaseo, J. Pazzini\cmsAuthorMark14, M. Pegoraro, N. Pozzobon, P. Ronchese, F. Simonetto, E. Torassa, M. Tosi, M. Zanetti, P. Zotto, A. Zucchetta\cmsAuthorMark14, G. Zumerle \cmsinstskipINFN Sezione di Pavia , Università di Pavia ,  Pavia, Italy
A. Braghieri