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

Journal of High Energy Physics, Dec 2016

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 fb−1, 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 Λ below 1 TeV. The use of razor variables yields results that complement those previously published.

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

Received: March Search for dark matter particles in proton-proton collisions at s F. Hartmann 0 1 2 S.M. Heindl 0 1 2 U. Husemann 0 1 2 I. Katkov 0 1 2 A. Kornmayer 0 1 2 P. Lobelle 0 1 2 0 University of Auckland , Auckland , New Zealand 1 Vanderbilt University , Nashville , U.S.A 2 25: Also at University of Ruhuna , Matara , Sri Lanka 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 fb 1, 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% con dence level for a mediator mass scale below 1 TeV. The use of razor variables yields results that complement those previously published. Hadron-Hadron scattering (experiments); Supersymmetry 8 TeV using the razor variables 1 Introduction 2 3 4 The CMS detector Data set and simulated samples Event selection Analysis strategy Background estimation Systematic uncertainties Results and interpretation Background estimation for the zero b-tag search region Background estimation for the 0 b and 0 bb samples Limits on dark matter production from the 0 sample Limits on dark matter production from the 0 b and 0 bb samples A Background estimation and observed yield The CMS collaboration 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{6], and the observed spectrum [7{10] of the cosmic microwave background [11]. A global t 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), su ciently 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{18] with R-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 (p~Tmiss). The magnitude of p~Tmiss is referred to as missing transverse energy (ETmiss). The ATLAS and CMS collaborations have reported searches for events with one high-pT jet and large ETmiss [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-pT photons [21, 22]; W, Z, or Higgs bosons [23{26]; b jets [27] and top quarks [27{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 ETmiss. The search is based on the razor variables MR and R2 [32, 33]. Given a dijet event, these variables are computed from the two jet momenta ~pj1 and ~pj2 , according to the following de nition: MR = R = MTR = In the context of SUSY, MR provides an estimate of the underlying mass scale of the event, and quantity MTR is a transverse observable that includes information about the topology of the event. The variable R2 is designed to reduce QCD multijet background; it is correlated with the angle between the two jets, where co-linear jets have large R2 while back-to-back jets have small R2. 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 R-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 R2, while categorizing events based on the value of MR improves signal to background discrimination and yields signi cantly 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 o ers 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 GeV in the gamma ray spectrum of the galactic center data collected by the Fermi-LAT { 2 { eld theory using a vector or axial-vector operator (left), and a scalar operator (right). gamma-ray space telescope [39]. The results are interpreted using an e ective eld theory approach and the Feynman diagrams for DM pair production are shown in gure 1. Unlike the SUSY razor searches [33, 35], which focus on events with large values of MR, this study also considers events with small values of MR, using R2 to discriminate between signal and background, in a kinematic region (R2 > 0:5) excluded by the baseline selection of refs. [33, 35]. A data sample corresponding to an integrated luminosity of 18.8 fb 1 of pp collisions at a center-of-mass energy of 8 TeV was collected by the CMS experiment with a trigger based on a loose selection on MR and R2. 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 pro t 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 MR values, thereby enhancing the sensitivity to direct DM production. This paper is organized as follows: the CMS detector is brie y 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. The CMS detector The central feature of the CMS apparatus is a superconducting solenoid of 6 m internal diameter, providing a magnetic eld of 3.8 T. 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 ux-return yoke outside the solenoid. Forward calorimeters extend the pseudorapidity ( ) [42] coverage provided by the barrel and endcap detectors. The rst level (L1) of the CMS trigger system, composed of custom hardware processors, uses information from the calorimeters and muon detectors MR region (GeV) Trigger e ciency (%) The uncertainty shown represents the statistical uncertainty in the measured e ciency. to select the most interesting events in a xed time interval of less than 4 s. The highlevel trigger (HLT) processor farm further decreases the event rate from around 100 kHz to around 400 Hz, before data storage. A more detailed description of the CMS detector, together with a de nition of the coordinate system used and the basic kinematic variables, can be found in ref. [42]. Data set and simulated samples The analysis is performed on events with two jets reconstructed at L1 in the central part of the detector (j j < 3:0). The L1 jet triggers are based on the sums of transverse energy approximately 1.05 1.05 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 R2 and MR are computed. In the HLT, jets are de ned 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 pT > 64 GeV are considered. Events are selected with R2 > 0:09 and R2 MR > 45 GeV. This selection rejects the majority of the background, which tends to have low R2 and low MR values, while keeping the events in the signal-sensitive regions of the (MR, R2) plane. The trigger e ciency, measured using a pre-scaled trigger with very loose thresholds, is shown in given the constraints on the maximum acceptable rate. Monte Carlo (MC) simulated signal and background samples are generated with the leading order matrix element generator MadGraph v5.1.3 [45, 46] and the CTEQ6L parton distribution function set [47]. The generation includes the pythia 6.4.26 [48] Z2* tune, which is derived from Z1 tune [49] based on the CTEQ5L set. Parton shower and hadronization e ects are included by matching the generated events to pythia, using the MLM matching algorithm [50]. The events are processed with a Geant4 [51] description of the CMS apparatus to include detector e ects. The simulation samples for SM background processes are scaled to the integrated luminosity of the data sample (18.8 fb 1), using calculations of the inclusive production cross sections at the next-to-next-to-leading order (NNLO) in the perturbative QCD expansion [52{54]. The signal processes corresponding to pair production of DM particles are simulated with up to two additional partons with pT > 80 GeV. Event selection Events are selected with at least one reconstructed interaction vertex within jzj < 24 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 e ects (such as jets near non-functioning channels in the ECAL) are removed from the analysis [55]. A particle- ow (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 e ects. 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 zerosuppression e ects 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 R = times the considered jet size or isolation cone area. )2 = 0:3, To separate signal from the main backgrounds it is necessary to identify electrons (muons) with pT > 15 GeV and j j < 2:5 (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 de ned as the scalar pT sum of all PF candidates other than the lepton itself, within a cone of size R = 0:3, and normalized to the lepton pT. A candidate is identi ed 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 pT > 10 GeV that are not consistent with muons and satisfy the same isolation requirements as those used for electrons are also identi ed to increase the lepton selection e ciency as well as to identify single-prong tau decays. Jets are formed by clustering the PF candidates, using the anti-kT algorithm 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 pT spectrum and detector acceptance. Jet energy corrections are derived from simulation, and are con rmed with in situ measurements of the energy balance in dijet and photon+jet events. Any jet whose momentum points within a cone of R < 0:3 around any identi ed 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 pT > 80 GeV and j j < 2:4, for which the corresponding L1 and HLT requirements are maximally e cient. 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%) identi cation e ciency (misidenti cation 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 pT > 40 GeV and j j < 2:4. 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 de nes 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 j (j1; j2)j is larger than 2.5 radians. The momenta of the two megajets are used to compute the razor variables, according to eq. (1.1), (1.2). Events are required to have MR > 200 GeV and R2 > 0:5. Analysis strategy To enhance the DM signal and suppress background contributions from the W+jets and tt processes, we veto events with selected electrons, muons, or isolated charged PF candidates. We de ne three di erent search regions based on the number of b-tagged jets. The zero b-tag search region contains events where no jets were identi ed 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 classi ed into four categories based on the value of MR, to enhance signal to background discrimination for a broad class of DM models: (i) very low MR (VL), dened by 200 < MR 300 GeV; (ii) low MR (L), with 300 < MR 400 GeV; (iii) high MR (H), with 400 < MR 600 GeV; and (iv) very high MR (VH), including events with MR > 600 GeV. Because of the limited size of the data sample, no further categorization based on MR is made for the one and two b-tag search regions. Within each category, the search is performed in bins of the R2 variable, with the binning chosen such that the expected background yield in each bin is larger than one event, as estimated from Monte 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 nal 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 W(` )+jets and Z( )+jets processes, while the dominant background in the one and two b-tag search regions is the tt process. To estimate the contribution of these backgrounds in the search MR category signal search region W(` ) control region no CSV loose jet Z(``) control region MR > 600 GeV (VH) regions is based on the muon multiplicity, the output of the CSV b-tagging algorithm, and the value of MR. For all the regions, R2 > 0:5 is required. signal serach region tt control region tt control region Z(``) control region 1 CSV loose jets b-tagging selection 2 CSV tight jets = 1 CSV tight jet 1 CSV tight jets MR category MR > 200 GeV based on the muon multiplicity, the output of the CSV b-tagging algorithm, and the value of MR. For all the regions, R2 > 0:5 is required. 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 W(` )+jets and Z( )+jets background in the zero b-tag search region, we de ne 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 de ne the 2 control region, enhanced in the Z+jets process, by requiring two selected muons with invariant mass between 80 GeV and 100 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 tt background in the one and two b-tag search regions, we de ne the 1 b and 2 b control regions, by requiring at least one jet satisfying the CSV tight btagging criterion along with one and two selected muons respectively. Both of these control regions are dominated by the tt process. The tt background prediction is estimated by extrapolating from the 2 b control region, while the 1 b control region is used as a crosscheck to estimate systematic uncertainties. Finally, we de ne the Z( )b control region by requiring two muons with invariant mass between 80 GeV and 100 GeV. This is used to estimate the Z( )+jets background in the one and two b-tag search regions. The de nitions of the search and control regions, and their use in this analysis are summarized in tables 2 and 3. 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 nal states with one or more neutrinos. These background processes are referred to as W(` )+jets and Z( events. Additional backgrounds arise from events involving the production of top quark pairs, and from events in which a Z boson decays to a pair of charged leptons. These processes are referred to as tt and Z(``)+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 tt events. The use of the tight working point of the CSV algorithm reduces the Z( W(` )+jets contribution as shown in table 7. 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 ETmiss direction tends to be highly aligned in the azimuthal coordinate with the razor megajets. The requirement on the razor variables and j (j1; j2)j reduces the multijet background to a negligible level, which is con rmed by checking data control regions with looser cuts on the razor variables. Background estimation for the zero b-tag search region To predict the background from W(` )+jets and Z( )+jets in the zero b-tag search region, we use a data-driven method that extrapolates the observed data yields in the 1 region to the search region. Similarly, the observed yield in the 2 control region allows the estimation of the contribution from Z(``)+jets background process. Each MR category is binned in R2. Events in which the W or Z boson decayed to muons are used to extrapolate to cases where they decay to electrons or taus. The background expected from W and Z boson production, in each R2 bin and in each MR category of the 0 sample, is computed as the corresponding yield for process X, derived from simulations. This background estimation method relies on the assumption that the kinematic properties of events in which W and Z bosons are produced are similar. To estimate the accuracy of the background estimation method, we perform a crosscheck by predicting the background in the 1 control region using the observed data yield control region. The Monte Carlo simulation is used to perform this extrapolation analogous to the calculation in Equation 6.1. The small contribution from the tt 5491 33 5288 511 2063 18 1840 233 control region in each MR category and the corresponding data-driven background estimate obtained by extrapolating from the 2 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. MR category Z( )+jets W(` )+jets Z(``)+jets VL L H VH corresponding prediction from background simulation. The quoted uncertainty in the prediction re ects only the size of the simulated sample. The contribution of each individual background process is also shown, as estimated from simulated samples. background process is also estimated using the simulated samples. In tables 4 and 5, the observed yields in the 1 and 2 control regions respectively are compared to the estimate derived from data. In tables 4{9, the contribution of each process as predicted directly by simulated samples are also given. Figure 2 shows the comparison of the R2 distributions between the observed yield and the data-driven background estimate in the 1 control region. The observed bin-by-bin di erence 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 data as well as potential di erences in the modeling of the recoil spectra between W+jets and Z+jets processes. Some bins exhibit relatively large uncertainties primarily due to statistical uctuations 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 R2. The tt background is estimated using an analogous data-driven method, where we derive corrections to the Monte Carlo simulation prediction scaled to the tt production cross-section computed to NNLO accuracy [52{54] using data in the 2 b control region for each bin in R2. The correction is then applied to the simulation prediction for the tt background contribution to the zero b-tag search region. This correction factor re ects potential mismodeling of the recoil spectrum predicted by the Monte Carlo simulation. The contribution of each background process to the 2 b sample, predicted from simulated samples, is given in table 6. The fraction of tt events in the 2 b control sample is Figure 3 shows the comparison of the observed yield and the prediction from simulation, kg 34 /taaB 21 kg 34 /taaB 21 kg 34 /taaB 21 CMS estimate derived from on the 2 control region data in the four MR 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 quoted uncertainty in the prediction only re ects the size of the simulated samThe contribution of each individual background process is also shown, as estimated from g 3 /kB 2 taa 1 in the 2 b 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 MR category Z( )+jets W(` )+jets Z(``)+jets 11655 50 12770 900 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 as a function of R2. We observe no signi cant 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 tt prediction in the zero b-tag The result of the background estimation in the zero b-tag search region is given in table 7, 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. The comparison of the data-driven background estimates and the observations for each MR category is shown in gure 4, as a function of R2. The expected event distribution is shown for two signal benchmark models, corresponding to the pair production of DM particles of mass 1 GeV in the e ective eld theory (EFT) approach with vector coupling to u or d quarks. Details on the signal benchmark models are given in section 8.1. Background estimation for the 0 b and 0 bb 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 tt events for each R2 bin in the one CMS Vu-DM m = 1 GeV Vd-DM m = 1 GeV Vu-DM m = 1 GeV Vd-DM m = 1 GeV /kB 2 taa 1 10−1 10−2 /kB 2 taa 1 Vu-DM m = 1 GeV Vd-DM m = 1 GeV Vu-DM m = 1 GeV Vd-DM m = 1 GeV /kB 2 taa 1 10−1 /kB 2 taa 1 estimates in the four MR categories: VL (top left), L (top right), H (bottom left), and VH (bottom right). The contribution of individual background processes is shown by the lled 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 GeV in the EFT approach with vector coupling to u or d quarks. The horizontal bars indicate the variable bin widths. b-tag search region, n(tt)i0 b, is computed as: n(tt)i0 b = n(tt)i2 b where n(tt)i2 b is the observed yield in the ith R2 bin in the 2 b control region, while N (tt)i0 b and N (tt)i2 b are the tt yields in the ith R2 bin predicted by the simulation for the one b-tag search region and the 2 b control region respectively. Similarly, the tt background in the two b-tag search region is derived from eq. (6.2), replacing N (tt)i0 b with N (tt)i0 bb, the tt background yield in the ith bin of the two b-tag search region predicted by the simulation. The data yield in the 2 b control region is corrected to account for g 3 /kB 2 taa 1 )b and 1 b samples, the corresponding predictions from background simulation, and (for 1 b only) the cross-check background estimate. The contribution of each individual background process is also shown, as estimated from simug 3 /kB 2 taa 1 control sample (left) and of the observed yield in the 1 b control sample and the background estimates from the 2 b and Z( )b control samples (right), shown as a function of R2. The bottom panel of each gure 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. the small contamination from Z+jets and W+jets, predicted with the simulated yields N Z(``)+jets;2 b and N W(` )+jets;2 b, respectively. The background contribution from W(` )+jets and Z( )+jets events is predicted using the Z( )b control region, and summarized in table 8. The Z+jets purity of this control region is 89%. The observed yield in the Z( )b control region is shown in the left plot of gure 5, as a function of R2, 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 R2 bin. We perform a cross-check of the method on the 1 b control region by predicting the background from the 2 b control region data. The data and prediction are compared on the right of gure 5, where we observe reasonable agreement. The di erence 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 9 and shown in gure 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. g 3 /kB 2 taa 1 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. Sample Z( )+jets W(` )+jets Z(``)+jets MC predicted Estimated 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. Systematic uncertainties For each R2 bin in each MR category, the di erence 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 di erences in the modeling of the recoil spectra between W+jets and Z+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 (MR, R2) plane, and are the dominant systematic uncertainties for the analysis. As discussed in section 6.1, a few bins at smaller values of R2 exhibit larger systematic uncertainties, primarily due to statistical uctuations 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 signi cantly better in other bins at larger values of R2. For the 0 analysis, di erences between the kinematic properties of W+jets and Z+jets events are additional sources of systematic uncertainty. These di erences arise from the choice of the PDF set, jet energy scale corrections, b tagging e ciency corrections, and trigger e ciency. These e ects largely cancel when taking the ratio of the two processes, and the resulting uncertainty is found to be smaller than one fth of the total uncertainty. The quoted uncertainty is an upper estimate of the total systematic uncertainty. For the 0 b and 0 bb samples, both the signal and control samples are dominated by tt events. The cancellation of the systematic uncertainties is even stronger in this case, since it does not involve di erent processes, and di erent PDFs. The remaining uncertainty Jet energy scale Parton distribution functions Initial-state radiation indicated represent the typical size. The dependence of these systematic uncertainties on the R2 and MR values is taken into account in the determination of the results. 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 e ects are taken into account by propagating these uncertainties into the MR category and the R2 bin. These uncertainties are considered to be fully correlated across MR categories and R2 bins. Typical values for the individual contributions are given in table 10. The total uncertainty in the signal yield is obtained by propagating the individual e ects into the MR and R2 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{64] prescription, using the CTEQ-6.6 [65] and MRST-2006-NNLO [66] PDF sets. Results and interpretation In gures 4 and 6 the estimated backgrounds are compared to the observed yield in each MR 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. Limits on dark matter production from the 0 The result is interpreted in the context of a low-energy e ective eld 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 elds. 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 e ective interaction. Two benchmark scenarios are considered in this study, axial-vector (AV), and vector (V) interactions [69], described by the following operators: O^AV = OV = rather must be estimated from the data. cuto scale, through the relation: and 5 are the Dirac matrices, eld, and q is an SM quark eld. 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 O^V will vanish in the low-energy theory. Below the cuto energy scale , DM production is described as a contact interaction between two quarks and two DM particles. In the case of s-channel production through a heavy mediator, the energy scale is identi ed with by the coupling of the mediator to quark and DM elds, gq and g , respectively. The results in tables 14{17 in the appendix are used to obtain an upper limit at 90% con dence level (CL) on the DM production cross section, UiL (where the superscript denotes the coupling to an up or down quark). The limits are obtained using the LHC CLs procedure [70, 71] and a global likelihood determined by combining the likelihoods of the di erent 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 Subsequently, the cross section ( UiL) limit is translated into a lower limit LL = UL GEN and GEN are the cuto energy scale and cross section of the simulated sample, respectively. The derived values of LL as a function of the DM mass, shown in are very similar to those derived for the CMS monojet search [61]. The exclusion limits 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 e ciency, resulting in a negligible di erence in the exclusion limit on . 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 s^ comparable to the cuto scale [68, 72{74]. For theories to be perturbative, ge is typically required to be smaller than 4 , and this condition is unlikely to be satis ed 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{77], we quantify this e ect through two EFT validity measures. The rst is a minimal kinematic constraint on obtained by requiring Qtr < ge Qtr > 2M , where Qtr is the momentum transferred from the mediator to the DM particle pair, which yields gef = 1 gef = 2 gef = 4 gef = 1 gef = 2 gef = 4 as a function of the DM mass M in the case of axial-vector (left) and vector (right) currents. The validity of the EFT is quanti ed by R contours, corresponding to di erent values of the e ective coupling ge . For completeness, regions forbidden by the EFT validity condition > 2M =ge are shown for two choices of the e ective coupling: ge = 1 (light gray) and ge = 4 (dark gray). Values of R close to unity indicate a regime in which the assumptions of the EFT approximation hold, while a deviation from unity quanti es the fraction of events for which the EFT approximation is still valid. We consider the case of s-channel production, and we compute R as a function of the e ective coupling ge in the range 0 < ge contours corresponding to R = 80% for di erent values of ge are shown in gure 7. For values of ge ' 2, the limit set by the analysis lies above the R = 80% contour. The exclusion limits on for the axial-vector and vector operators are transformed into upper limits on the spin-dependent ( NSD ) [78{84] and spin-independent ( NSI ) [80, 81, 85{ 90] DM-nucleon scattering cross section, respectively; using the following expressions [69]: with Mp and M indicating the proton and DM masses, respectively. The numerical values of the derived limits are given in tables 11 and 12. The bound on N as a function of M is shown in gure 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 11 and 12 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 ' 5 GeV. Such an e ect is expected since the spin-independent DMnucleus cross section is enhanced by the coherent scattering of DM o nucleons in the case d UL are the observed upper limits on the production cross section for u and d quarks, LL is the observed cuto energy scale lower limit; and N is the observed DMnucleon scattering cross section upper limit. d UL are the observed upper limits on the production cross section for u and d quarks, respectively; LL is the observed cuto energy scale lower limit; and N is the observed DM-nucleon scattering cross section upper limit. 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 di cult 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 bene t from the coherent enhancement. A summary of the observed limits for the axial-vector and vector operators can be found in tables 11 and 12 respectively. In order to compare our results with those from direct detection experiments, the experimental bounds in [78{80, 80, 81, 81, 85{88] are translated into bounds on comparison is shown in gure 9. This translation is well de ned since the momentum transfer in most direct detection experiments is low compared to the values of probed, and thus the EFT approximations in question are mostly valid. Limits on dark matter production from the 0 b and 0 bb samples The results from the 0 b and 0 bb 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 as a function of the DM mass M 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. m10−31 10−34 10−35 10−36 10−37 10−38 10−39 10−40 10−41 10−42 10−43 10−44 10−45 10−46 OS = Mq LL = UL as a function of the DM mass M case of axial-vector (left) and vector (right) currents. A selection of direct detection experimental bounds are also shown. considered [91], generating an operator: 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 0 b and 0 bb samples to this scenario. Unlike the case of V and AV operators, the production cross LL is then derived as Given the results of table 9 we proceed to set limits at 90% CL on the cuto scale (see table 13) using the LHC CLs procedure. To quantify the validity of the EFT we follow the transverse momentum in pp collisions at p (2014) 037 [arXiv:1407.7494] [INSPIRE]. [27] ATLAS collaboration, Search for dark matter in events with heavy quarks and missing transverse momentum in pp collisions with the ATLAS detector, Eur. Phys. J. C 75 (2015) 92 [arXiv:1410.4031] [INSPIRE]. pairs in the single-lepton nal state in proton-proton collisions at p s = 8 TeV, JHEP 06 [30] ATLAS collaboration, Search for new particles in events with one lepton and missing [31] CMS collaboration, Search for physics beyond the standard model in nal states with a D 91 (2015) 092005 [arXiv:1408.2745] [INSPIRE]. [32] C. Rogan, Kinematical variables towards new dynamics at the LHC, arXiv:1006.2727 s = 8 TeV with the ATLAS detector, JHEP 09 Phys. Rev. D 85 (2012) 012004 [arXiv:1107.1279] [INSPIRE]. TeV, Phys. Rev. D 90 (2014) 112001 [arXiv:1405.3961] [INSPIRE]. [35] CMS collaboration, Search for supersymmetry using razor variables in events with b-tagged space at the LHC, Phys. Rev. D 86 (2012) 015010 [arXiv:1203.1662] [INSPIRE]. models, JHEP 11 (2014) 024 [arXiv:1402.2285] [INSPIRE]. gamma-ray excess, Phys. Rev. D 90 (2014) 063512 [arXiv:1404.1373] [INSPIRE]. [39] D. Hooper and L. Goodenough, Dark matter annihilation in the galactic center as seen by the Fermi Gamma Ray Space Telescope, Phys. Lett. B 697 (2011) 412 [arXiv:1010.2752] [40] CMS collaboration, Data parking and data scouting at the CMS experiment, [41] CMS collaboration, Energy calibration and resolution of the CMS electromagnetic CMS-DP-2012-022 (2012). calorimeter in pp collisions at p [arXiv:1111.6097] [INSPIRE]. (2008) 063 [arXiv:0802.1189] [INSPIRE]. [42] CMS collaboration, The CMS experiment at the CERN LHC, 2008 JINST 3 S08004 (2006) 026 [hep-ph/0603175] [INSPIRE]. (2010), arXiv:1010.3558 [INSPIRE]. JHEP 06 (2011) 128 [arXiv:1106.0522] [INSPIRE]. [46] J. Alwall et al., The automated computation of tree-level and next-to-leading order di erential cross sections and their matching to parton shower simulations, JHEP 07 (2014) 079 [arXiv:1405.0301] [INSPIRE]. generation of parton distributions with uncertainties from global QCD analysis, JHEP 07 (2002) 012 [hep-ph/0201195] [INSPIRE]. Instrum. Meth. A 506 (2003) 250 [INSPIRE]. s = 8 TeV, 2015 JINST 10 P02006. and MET, CMS-PAS-PFT-09-001 (2009). Comput. Phys. Commun. 184 (2013) 208 [arXiv:1201.5896] [INSPIRE]. [arXiv:1011.3540] [INSPIRE]. at hadron colliders, Comput. Phys. Commun. 185 (2014) 2930 [arXiv:1112.5675] [INSPIRE]. [55] CMS collaboration, Performance of the CMS missing transverse momentum reconstruction [57] CMS collaboration, Commissioning of the particle- ow event reconstruction with the rst LHC collisions recorded in the CMS detector, CMS-PAS-PFT-10-001 (2010). [58] CMS collaboration, Performance of electron reconstruction and selection with the CMS s = 8 TeV, 2015 JINST 10 P06005 [60] CMS collaboration, Identi cation of b-quark jets with the CMS experiment, 2013 JINST 8 [61] CMS collaboration, Search for dark matter, extra dimensions and unparticles in monojet s = 8 TeV, Eur. Phys. J. C 75 (2015) 235 detector in proton-proton collisions at p [arXiv:1502.02701] [INSPIRE]. topology events, CMS-PAS-BTV-13-001 (2013). P04013 [arXiv:1211.4462] [INSPIRE]. events in proton-proton collisions at p [arXiv:1408.3583] [INSPIRE]. U.S.A. (2005), hep-ph/0605240 [INSPIRE]. [62] D. Bourilkov, R.C. Group and M.R. Whalley, LHAPDF: PDF use from the Tevatron to the LHC, in the proceedings of the TeV4LHC Workshop | 4th meeting, October 20{22, Batavia, Rev. D 78 (2008) 013004 [arXiv:0802.0007] [INSPIRE]. NNLO, Phys. Lett. B 652 (2007) 292 [arXiv:0706.0459] [INSPIRE]. at colliders, JHEP 09 (2010) 037 [arXiv:1002.4137] [INSPIRE]. JHEP 12 (2010) 048 [arXiv:1005.3797] [INSPIRE]. LHC, Phys. Rev. D 85 (2012) 056011 [arXiv:1109.4398] [INSPIRE]. [70] ATLAS collaboration, Procedure for the LHC Higgs boson search combination in summer 2011, ATL-PHYS-PUB-2011-011 (2011). [71] ATLAS and CMS collaborations and The LHC Higgs Combination Group, Procedure for the LHC Higgs boson search combination in Summer 2011, CMS-NOTE-2011-005 (2011). on dark matter from colliders, Phys. Rev. D 82 (2010) 116010 [arXiv:1008.1783] [INSPIRE]. searches at the LHC, JHEP 01 (2014) 025 [arXiv:1308.6799] [INSPIRE]. theory for dark matter searches at the LHC, Phys. Lett. B 728 (2014) 412 [arXiv:1307.2253] [INSPIRE]. eld theory for dark matter searches at the LHC, Part II: complete Analysis for the s-channel, JCAP 06 (2014) 060 [arXiv:1402.1275] [INSPIRE]. e ective eld theory for dark matter searches at the LHC part III: analysis for the t-channel, JCAP 09 (2014) 022 [arXiv:1405.3101] [INSPIRE]. Sun using 3109.6 days of upward-going muons in Super-Kamiokande, Astrophys. J. 742 (2011) 78 [arXiv:1108.3384] [INSPIRE]. [79] IceCube collaboration, R. Abbasi et al., Multi-year search for dark matter annihilations in the Sun with the AMANDA-II and IceCube detectors, Phys. Rev. D 85 (2012) 042002 [arXiv:1112.1840] [INSPIRE]. [arXiv:1204.3094] [INSPIRE]. [80] COUPP collaboration, E. Behnke et al., First dark matter search results from a 4 kg CF3I bubble chamber operated in a deep underground site, Phys. Rev. D 86 (2012) 052001 [81] M. Felizardo et al., Final analysis and results of the phase II SIMPLE dark matter search, Phys. Rev. Lett. 108 (2012) 201302 [arXiv:1106.3014] [INSPIRE]. bubble chamber, Phys. Rev. Lett. 114 (2015) 231302 [arXiv:1503.00008] [INSPIRE]. on 19F from PICASSO, Phys. Lett. B 711 (2012) 153 [arXiv:1202.1240] [INSPIRE]. sections from 225 live days of XENON100 data, Phys. Rev. Lett. 111 (2013) 021301 [arXiv:1301.6620] [INSPIRE]. 327 (2010) 1619 [Erratum ibid. 330 (2010) 1047]. [85] CDMS-II collaboration, Dark matter search results from the CDMS II experiment, Science massive particles using voltage-assisted calorimetric ionization detection in the SuperCDMS experiment, Phys. Rev. Lett. 112 (2014) 041302 [arXiv:1309.3259] [INSPIRE]. XENON100 data, Phys. Rev. Lett. 107 (2011) 131302 [arXiv:1104.2549] [INSPIRE]. [arXiv:1310.8214] [INSPIRE]. upgraded CRESST-II detector, Eur. Phys. J. C 74 (2014) 3184 [arXiv:1407.3146] [INSPIRE]. at the LHC, Phys. Rev. D 88 (2013) 063510 [arXiv:1303.6638] [INSPIRE]. Yerevan Physics Institute, Yerevan, Armenia V. Khachatryan, A.M. Sirunyan, A. Tumasyan Institut fur Hochenergiephysik der OeAW, Wien, Austria W. Adam, E. Asilar, T. Bergauer, J. Brandstetter, E. Brondolin, M. Dragicevic, J. Ero, M. Flechl, M. Friedl, R. Fruhwirth1, V.M. Ghete, C. Hartl, N. Hormann, J. Hrubec, National Centre for Particle and High Energy Physics, Minsk, Belarus V. Mossolov, N. Shumeiko, J. Suarez Gonzalez Universiteit 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 Vrije 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 Universite 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. Leonard, T. Maerschalk, A. Marinov, L. Pernie, A. Randle-conde, T. Seva, C. Vander Velde, P. Vanlaer, R. Yonamine, F. Zenoni, F. Zhang2 Ghent 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 Universite Catholique de Louvain, Louvain-la-Neuve, Belgium S. Basegmez, C. Belu 3 , 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. Popov4, L. Quertenmont, M. Selvaggi, M. Vidal Marono Universite de Mons, Mons, Belgium N. Beliy, G.H. Hammad Centro Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil W.L. Alda Junior, F.L. Alves, G.A. Alves, L. Brito, M. Correa Martins Junior, M. Hamer, C. Hensel, A. Moraes, M.E. Pol, P. Rebello Teles { 30 { Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil E. Belchior Batista Das Chagas, W. Carvalho, J. Chinellato5, A. Custodio, 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 Manganote5, A. Vilela Pereira Universidade Estadual Paulista a, Universidade Federal do ABC b, S~ao Paulo, E.M. Gregoresb, P.G. Mercadanteb, C.S. Moona;6, S.F. Novaesa, Sandra S. Padulaa, D. Romero Abadb, J.C. Ruiz Vargas Institute for Nuclear Research and Nuclear Energy, So a, Bulgaria A. Aleksandrov, R. Hadjiiska, P. Iaydjiev, M. Rodozov, S. Stoykova, G. Sultanov, M. VuUniversity of So a, So a, Bulgaria A. Dimitrov, I. Glushkov, L. Litov, B. Pavlov, P. Petkov Beihang University, Beijing, China Institute 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. Plestina8, F. Romeo, S.M. Shaheen, A. Spiezia, J. Tao, C. Wang, Z. Wang, Beijing, China State Key Laboratory of Nuclear Physics and Technology, Peking University, C. Asawatangtrakuldee, Y. Ban, Q. Li, S. Liu, Y. Mao, S.J. Qian, D. Wang, Z. Xu Universidad de Los Andes, Bogota, Colombia C. Avila, A. Cabrera, L.F. Chaparro Sierra, C. Florez, J.P. Gomez, B. Gomez Moreno, University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, Split, Croatia N. Godinovic, D. Lelas, I. Puljak, P.M. Ribeiro Cipriano University of Split, Faculty of Science, Split, Croatia Z. Antunovic, M. Kovac Institute Rudjer Boskovic, Zagreb, Croatia V. Brigljevic, K. Kadija, J. Luetic, S. Micanovic, L. Sudic University of Cyprus, Nicosia, Cyprus A. Attikis, G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos, P.A. Razis, Charles University, Prague, Czech Republic M. Finger9, M. Finger Jr.9 Academy of Scienti c Research and Technology of the Arab Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt A. Awad, E. El-khateeb10;10, S. Elgammal11, A. Mohamed12 National Institute of Chemical Physics and Biophysics, Tallinn, Estonia B. Calpas, M. Kadastik, M. Murumaa, M. Raidal, A. Tiko, C. Veelken Department of Physics, University of Helsinki, Helsinki, Finland P. Eerola, J. Pekkanen, M. Voutilainen Helsinki Institute of Physics, Helsinki, Finland P. Luukka, T. Peltola, J. Tuominiemi, E. Tuovinen, L. Wendland Lappeenranta University of Technology, Lappeenranta, Finland J. Talvitie, T. Tuuva DSM/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 Laboratoire Leprince-Ringuet, Ecole Polytechnique, IN2P3-CNRS, Palaiseau, Y. Yilmaz, A. Zabi Institut Pluridisciplinaire Hubert Curien, Universite de Strasbourg, Universite de Haute Alsace Mulhouse, CNRS/IN2P3, Strasbourg, France J.-L. Agram13, J. Andrea, A. Aubin, D. Bloch, J.-M. Brom, M. Buttignol, E.C. Chabert, N. Chanon, C. Collard, E. Conte13, X. Coubez, J.-C. Fontaine13, D. Gele, U. Goerlach, C. Goetzmann, A.-C. Le Bihan, J.A. Merlin14, K. Skovpen, P. Van Hove Centre de Calcul de l'Institut National de Physique Nucleaire et de Physique des Particules, CNRS/IN2P3, Villeurbanne, France Universite de Lyon, Universite Claude Bernard Lyon 1, CNRS-IN2P3, Institut de Physique Nucleaire 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 Georgian Technical University, Tbilisi, Georgia T. Toriashvili15 Z. Tsamalaidze9 Tbilisi State University, Tbilisi, Georgia RWTH 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. Zhukov4 RWTH 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. Guth, 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, RWTH Aachen University, III. Physikalisches Institut B, Aachen, Germany V. Cherepanov, Y. Erdogan, G. Flugge, H. Geenen, M. Geisler, F. Hoehle, B. Kargoll, T. Kress, A. Kunsken, J. Lingemann, A. Nehrkorn, A. Nowack, I.M. Nugent, C. Pistone, O. Pooth, A. Stahl14 Deutsches Elektronen-Synchrotron, Hamburg, Germany M. Aldaya Martin, I. Asin, N. Bartosik, O. Behnke, U. Behrens, K. Borras16, 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. Gallo17, J. Garay Garcia, A. Geiser, A. Gizhko, P. Gunnellini, J. Hauk, M. Hempel18, H. Jung, A. Kalogeropoulos, O. Karacheban18, M. Kasemann, P. Katsas, J. Kieseler, C. Kleinwort, I. Korol, W. Lange, J. Leonard, K. Lipka, A. Lobanov, W. Lohmann18, R. Mankel, I.-A. MelzerPellmann, 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.O . Sahin, P. Saxena, T. Schoerner-Sadenius, C. Seitz, S. Spannagel, N. Stefaniuk, K.D. Trippkewitz, R. Walsh, C. Wissing University of Hamburg, Hamburg, Germany V. Blobel, M. Centis Vignali, A.R. Draeger, T. Dreyer, J. Er e, E. Garutti, K. Goebel, D. Gonzalez, M. Gorner, J. Haller, M. Ho mann, R.S. Hoing, 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. Pantaleo14, T. Pei er, 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. Steinbruck, F.M. Stober, H. Tholen, D. Troendle, E. Usai, L. Vanelderen, A. Vanhoefer, B. Vormwald Institut fur Experimentelle Kernphysik, Karlsruhe, Germany C. Barth, C. Baus, J. Berger, C. Boser, E. Butz, T. Chwalek, F. Colombo, W. De Boer, A. Descroix, A. Dierlamm, S. Fink, F. Frensch, R. Friese, M. Gi els, A. Gilbert, D. Haitz, Pardo, B. Maier, H. Mildner, M.U. Mozer, T. Muller, Th. Muller, M. Plagge, G. Quast, K. Rabbertz, S. Rocker, F. Roscher, M. Schroder, G. Sieber, H.J. Simonis, R. Ulrich, J. Wagner-Kuhr, S. Wayand, M. Weber, T. Weiler, S. Williamson, C. Wohrmann, R. Wolf Institute of Nuclear and Particle Physics (INPP), NCSR Demokritos, Aghia Paraskevi, Greece A. Psallidas, I. Topsis-Giotis G. Anagnostou, G. Daskalakis, T. Geralis, V.A. Giakoumopoulou, A. Kyriakis, D. Loukas, National and Kapodistrian University of Athens, Athens, Greece A. Agapitos, S. Kesisoglou, A. Panagiotou, N. Saoulidou, E. Tziaferi University of Ioannina, Ioannina, Greece E. Paradas, J. Strologas Wigner Research Centre for Physics, Budapest, Hungary G. Bencze, C. Hajdu, A. Hazi, P. Hidas, D. Horvath19, F. Sikler, V. Veszpremi, G. Vesztergombi20, A.J. Zsigmond Institute of Nuclear Research ATOMKI, Debrecen, Hungary N. Beni, S. Czellar, J. Karancsi21, J. Molnar, Z. Szillasi14 University of Debrecen, Debrecen, Hungary M. Bartok20, A. Makovec, P. Raics, Z.L. Trocsanyi, B. Ujvari National Institute of Science Education and Research, Bhubaneswar, India S. Choudhury22, P. Mal, K. Mandal, D.K. Sahoo, N. Sahoo, S.K. Swain Panjab 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 University 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 Saha 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 Bhabha Atomic Research Centre, Mumbai, India R. Chudasama, D. Dutta, V. Jha, V. Kumar, A.K. Mohanty14, L.M. Pant, P. Shukla, Tata Institute of Fundamental Research, Mumbai, India T. Aziz, S. Banerjee, S. Bhowmik23, R.M. Chatterjee, R.K. Dewanjee, S. Dugad, S. Ganguly, S. Ghosh, M. Guchait, A. Gurtu24, Sa. Jain, G. Kole, S. Kumar, B. Mahakud, N. Sur, B. Sutar, N. Wickramage25 Indian Institute of Science Education and Research (IISER), Pune, India S. Chauhan, S. Dube, A. Kapoor, K. Kothekar, A. Rane, S. Sharma Institute for Research in Fundamental Sciences (IPM), Tehran, Iran H. Bakhshiansohi, H. Behnamian, S.M. Etesami26, A. Fahim27, M. Khakzad, M. Mohammadi Najafabadi, M. Naseri, S. Paktinat Mehdiabadi, F. Rezaei Hosseinabadi, B. Safarzadeh28, M. Zeinali University College Dublin, Dublin, Ireland M. Felcini, M. Grunewald INFN Sezione di Bari a, Universita di Bari b, Politecnico di Bari c, Bari, Italy M. Abbresciaa;b, C. Calabriaa;b, C. Caputoa;b, A. Colaleoa, D. Creanzaa;c, L. Cristellaa;b, N. De Filippisa;c, M. De Palmaa;b, L. Fiorea, G. Iasellia;c, G. Maggia;c, M. Maggia, G. Minielloa;b, S. Mya;c, S. Nuzzoa;b, A. Pompilia;b, G. Pugliesea;c, R. Radognaa;b, A. Ranieria, G. Selvaggia;b, L. Silvestrisa;14, R. Vendittia;b INFN Sezione di Bologna a, Universita di Bologna b, Bologna, Italy G. Abbiendia, C. Battilana14, D. Bonacorsia;b, S. Braibant-Giacomellia;b, L. Brigliadoria;b, R. Campaninia;b, P. Capiluppia;b, A. Castroa;b, F.R. Cavalloa, S.S. Chhibraa;b, G. Codispotia;b, M. Cu ania;b, G.M. Dallavallea, F. Fabbria, A. Fanfania;b, D. Fasanellaa;b, P. Giacomellia, C. Grandia, L. Guiduccia;b, S. Marcellinia, G. Masettia, A. Montanaria, F.L. Navarriaa;b, A. Perrottaa, A.M. Rossia;b, T. Rovellia;b, G.P. Sirolia;b, N. Tosia;b;14 INFN Sezione di Catania a, Universita di Catania b, Catania, Italy G. Cappellob, M. Chiorbolia;b, S. Costaa;b, A. Di Mattiaa, F. Giordanoa;b, R. Potenzaa;b, A. Tricomia;b, C. Tuvea;b INFN Sezione di Firenze a, Universita di Firenze b, Firenze, Italy G. Barbaglia, V. Ciullia;b, C. Civininia, R. D'Alessandroa;b, E. Focardia;b, V. Goria;b, P. Lenzia;b, M. Meschinia, S. Paolettia, G. Sguazzonia, L. Viliania;b;14 INFN Laboratori Nazionali di Frascati, Frascati, Italy L. Benussi, S. Bianco, F. Fabbri, D. Piccolo, F. Primavera14 INFN Sezione di Genova a, Universita di Genova b, Genova, Italy V. Calvellia;b, F. Ferroa, M. Lo Veterea;b, M.R. Mongea;b, E. Robuttia, S. Tosia;b INFN Sezione di Milano-Bicocca a, Universita di Milano-Bicocca b, Milano, L. Brianza, M.E. Dinardoa;b, S. Fiorendia;b, S. Gennaia, R. Gerosaa;b, A. Ghezzia;b, P. Govonia;b, S. Malvezzia, R.A. Manzonia;b;14, B. Marzocchia;b, D. Menascea, L. Moronia, M. Paganonia;b, D. Pedrinia, S. Ragazzia;b, N. Redaellia, T. Tabarelli de Fatisa;b INFN Sezione di Napoli a, Universita di Napoli 'Federico II' b, Napoli, Italy, Universita della Basilicata c, Potenza, Italy, Universita G. Marconi d, Roma, S. Buontempoa, N. Cavalloa;c, S. Di Guidaa;d;14, M. Espositoa;b, F. Fabozzia;c, A.O.M. Iorioa;b, G. Lanzaa, L. Listaa, S. Meolaa;d;14, M. Merolaa, P. Paoluccia;14, C. Sciaccaa;b, F. Thyssen Trento c, Trento, Italy INFN Sezione di Padova a, Universita di Padova b, Padova, Italy, Universita di P. Azzia;14, N. Bacchettaa, L. Benatoa;b, D. Biselloa;b, A. Bolettia;b, R. Carlina;b, P. Checchiaa, M. Dall'Ossoa;b;14, T. Dorigoa, U. Dossellia, F. Gasparinia;b, U. Gasparinia;b, A. Gozzelinoa, S. Lacapraraa, M. Margonia;b, A.T. Meneguzzoa;b, F. Montecassianoa, M. Passaseoa, J. Pazzinia;b;14, M. Pegoraroa, N. Pozzobona;b, P. Ronchesea;b, F. Simonettoa;b, E. Torassaa, M. Tosia;b, M. Zanetti, P. Zottoa;b, A. Zucchettaa;b;14, G. Zumerlea;b INFN Sezione di Pavia a, Universita di Pavia b, Pavia, Italy A. Braghieria, A. Magnania;b, P. Montagnaa;b, S.P. Rattia;b, V. Rea, C. Riccardia;b, P. Salvinia, I. Vaia;b, P. Vituloa;b INFN Sezione di Perugia a, Universita di Perugia b, Perugia, Italy L. Alunni Solestizia;b, G.M. Bileia, D. Ciangottinia;b, L. Fanoa;b, P. Laricciaa;b, G. Mantovania;b, M. Menichellia, A. Sahaa, A. Santocchiaa;b INFN Sezione di Pisa a, Universita di Pisa b, Scuola Normale Superiore di Pisa c, Pisa, Italy K. Androsova;29, P. Azzurria;14, G. Bagliesia, J. Bernardinia, T. Boccalia, R. Castaldia, M.A. Cioccia;29, R. Dell'Orsoa, S. Donatoa;c, G. Fedi, L. Foaa;cy, A. Giassia, M.T. Grippoa;29, F. Ligabuea;c, T. Lomtadzea, L. Martinia;b, A. Messineoa;b, F. Pallaa, A. Rizzia;b, A. Savoy-Navarroa;30, P. Spagnoloa, R. Tenchinia, G. Tonellia;b, A. Venturia, P.G. Verdinia INFN Sezione di Roma a, Universita di Roma b, Roma, Italy L. Baronea;b, F. Cavallaria, G. D'imperioa;b;14, D. Del Rea;b;14, M. Diemoza, S. Gellia;b, C. Jordaa, E. Longoa;b, F. Margarolia;b, P. Meridiania, G. Organtinia;b, R. Paramattia, F. Preiatoa;b, S. Rahatloua;b, C. Rovellia, F. Santanastasioa;b INFN Sezione di Torino a, Universita di Torino b, Torino, Italy, Universita del Piemonte Orientale c, Novara, Italy N. Amapanea;b, R. Arcidiaconoa;c;14, S. Argiroa;b, M. Arneodoa;c, R. Bellana;b, C. Biinoa, N. Cartigliaa, M. Costaa;b, R. Covarellia;b, A. Deganoa;b, N. Demariaa, L. Fincoa;b, B. Kiania;b, C. Mariottia, S. Masellia, E. Migliorea;b, V. Monacoa;b, E. Monteila;b, M.M. Obertinoa;b, L. Pachera;b, N. Pastronea, M. Pelliccionia, G.L. Pinna Angionia;b, F. Raveraa;b, A. Romeroa;b, M. Ruspaa;c, R. Sacchia;b, A. Solanoa;b, A. Staianoa INFN Sezione di Trieste a, Universita di Trieste b, Trieste, Italy S. Belfortea, V. Candelisea;b, M. Casarsaa, F. Cossuttia, G. Della Riccaa;b, B. Gobboa, C. La Licataa;b, A. Schizzia;b, A. Zanettia Kangwon National University, Chunchon, Korea A. Kropivnitskaya, S.K. Nam Kyungpook National University, Daegu, Korea Chonbuk National University, Jeonju, Korea J.A. Brochero Cifuentes, H. Kim, T.J. Kim31 D.H. Kim, G.N. Kim, M.S. Kim, D.J. Kong, S. Lee, S.W. Lee, Y.D. Oh, A. Sakharov, Chonnam National University, Institute for Universe and Elementary Particles, Kwangju, Korea Korea University, Seoul, Korea S. Lee, J. Lim, S.K. Park, Y. Roh Seoul National University, Seoul, Korea University of Seoul, Seoul, Korea S. Cho, S. Choi, Y. Go, D. Gyun, B. Hong, H. Kim, Y. Kim, B. Lee, K. Lee, K.S. Lee, M. Choi, H. Kim, J.H. Kim, J.S.H. Lee, I.C. Park, G. Ryu, M.S. Ryu Sungkyunkwan University, Suwon, Korea Y. Choi, J. Goh, D. Kim, E. Kwon, J. Lee, I. Yu Vilnius University, Vilnius, Lithuania V. Dudenas, A. Juodagalvis, J. Vaitkus National Centre for Particle Physics, Universiti Malaya, Kuala Lumpur, Malaysia I. Ahmed, Z.A. Ibrahim, J.R. Komaragiri, M.A.B. Md Ali32, F. Mohamad Idris33, W.A.T. Wan Abdullah, M.N. Yusli, Z. Zolkapli Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico E. Casimiro Linares, H. Castilla-Valdez, E. De La Cruz-Burelo, I. Heredia-De La Cruz34, A. Hernandez-Almada, R. Lopez-Fernandez, J. Mejia Guisao, A. Sanchez-Hernandez Universidad Iberoamericana, Mexico City, Mexico S. Carrillo Moreno, F. Vazquez Valencia Benemerita Universidad Autonoma de Puebla, Puebla, Mexico I. Pedraza, H.A. Salazar Ibarguen Universidad Autonoma de San Luis Potos , San Luis Potos , Mexico A. Morelos Pineda University of Canterbury, Christchurch, New Zealand National Centre for Physics, Quaid-I-Azam University, Islamabad, Pakistan A. Ahmad, M. Ahmad, Q. Hassan, H.R. Hoorani, W.A. Khan, T. Khurshid, M. Shoaib, National Centre for Nuclear Research, Swierk, Poland H. Bialkowska, M. Bluj, B. Boimska, T. Frueboes, M. Gorski, M. Kazana, K. Nawrocki, K. Romanowska-Rybinska, M. Szleper, P. Traczyk, P. Zalewski Institute of Experimental Physics, Faculty of Physics, University of Warsaw, G. Brona, K. Bunkowski, A. Byszuk35, K. Doroba, A. Kalinowski, M. Konecki, J. Krolikowski, M. Misiura, M. Olszewski, M. Walczak Laboratorio de Instrumentac~ao e F sica Experimental de Part culas, Lisboa, Joint Institute for Nuclear Research, Dubna, Russia I. Golutvin, N. Gorbounov, I. Gorbunov, V. Karjavin, G. Kozlov, A. Lanev, A. Malakhov, V. Matveev36;37, P. Moisenz, V. Palichik, V. Perelygin, M. Savina, S. Shmatov, S. Shulha, N. Skatchkov, V. Smirnov, E. Tikhonenko, A. Zarubin Petersburg Nuclear Physics Institute, Gatchina (St. Petersburg), Russia V. Golovtsov, Y. Ivanov, V. Kim38, E. Kuznetsova, P. Levchenko, V. Murzin, V. Oreshkin, I. Smirnov, V. Sulimov, L. Uvarov, S. Vavilov, A. Vorobyev Institute for Nuclear Research, Moscow, Russia Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov, N. Krasnikov, A. Pashenkov, D. Tlisov, A. Toropin Institute for Theoretical and Experimental Physics, Moscow, Russia V. Epshteyn, V. Gavrilov, N. Lychkovskaya, V. Popov, I. Pozdnyakov, G. Safronov, A. Spiridonov, E. Vlasov, A. Zhokin National Research Nuclear University 'Moscow Engineering Physics Institute' (MEPhI), Moscow, Russia R. Chistov, M. Danilov, O. Markin, V. Rusinov, E. Tarkovskii P.N. Lebedev Physical Institute, Moscow, Russia V. Andreev, M. Azarkin37, I. Dremin37, M. Kirakosyan, A. Leonidov37, G. Mesyats, Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow, Russia A. Baskakov, A. Belyaev, E. Boos, M. Dubinin39, L. Dudko, A. Ershov, A. Gribushin, V. Klyukhin, O. Kodolova, I. Lokhtin, I. Miagkov, S. Obraztsov, S. Petrushanko, V. Savrin, State Research Center of Russian Federation, Institute for High Energy Physics, Protvino, Russia I. Azhgirey, I. Bayshev, S. Bitioukov, V. Kachanov, A. Kalinin, D. Konstantinov, V. Krychkine, V. Petrov, R. Ryutin, A. Sobol, L. Tourtchanovitch, S. Troshin, N. Tyurin, A. Uzunian, A. Volkov University of Belgrade, Faculty of Physics and Vinca Institute of Nuclear Sciences, Belgrade, Serbia P. Adzic40, P. Cirkovic, D. Devetak, J. Milosevic, V. Rekovic nologicas (CIEMAT), Madrid, Spain J. Alcaraz Maestre, E. Calvo, M. Cerrada, M. Chamizo Llatas, N. Colino, B. De La Cruz, A. Delgado Peris, A. Escalante Del Valle, C. Fernandez Bedoya, J.P. Fernandez Ramos, J. Flix, M.C. Fouz, P. Garcia-Abia, O. Gonzalez Lopez, S. Goy Lopez, J.M. Hernandez, M.I. Josa, E. Navarro De Martino, A. Perez-Calero Yzquierdo, J. Puerta Pelayo, A. Quintario Olmeda, I. Redondo, L. Romero, M.S. Soares Universidad Autonoma de Madrid, Madrid, Spain J.F. de Troconiz, M. Missiroli, D. Moran Universidad de Oviedo, Oviedo, Spain J. Cuevas, J. Fernandez Menendez, S. Folgueras, I. Gonzalez Caballero, E. Palencia Cortezon14, J.M. Vizan Garcia Santander, Spain Instituto de F sica de Cantabria (IFCA), CSIC-Universidad de Cantabria, I.J. Cabrillo, A. Calderon, J.R. Castin~eiras De Saa, E. Curras, P. De Castro Manzano, M. Fernandez, J. Garcia-Ferrero, G. Gomez, A. Lopez Virto, J. Marco, R. Marco, C. Martinez Rivero, F. Matorras, J. Piedra Gomez, T. Rodrigo, A.Y. Rodr guez-Marrero, A. Ruiz-Jimeno, L. Scodellaro, N. Trevisani, I. Vila, R. Vilar Cortabitarte CERN, European Organization for Nuclear Research, Geneva, Switzerland D. Abbaneo, E. Au ray, G. Auzinger, M. Bachtis, P. Baillon, A.H. Ball, D. Barney, A. Benaglia, L. Benhabib, G.M. Berruti, P. Bloch, A. Bocci, A. Bonato, C. Botta, H. Breuker, T. Camporesi, R. Castello, M. Cepeda, G. Cerminara, M. D'Alfonso, D. d'Enterria, A. Dabrowski, V. Daponte, A. David, M. De Gruttola, F. De Guio, A. De Roeck, E. Di Marco41, M. Dobson, M. Dordevic, B. Dorney, T. du Pree, D. Duggan, M. Dunser, N. Dupont, A. Elliott-Peisert, G. Franzoni, J. Fulcher, W. Funk, D. Gigi, K. Gill, D. Giordano, M. Girone, F. Glege, R. Guida, S. Gundacker, M. Gutho , J. Hammer, P. Harris, J. Hegeman, V. Innocente, P. Janot, H. Kirschenmann, V. Knunz, M.J. Kortelainen, A. Martelli, L. Masetti, F. Meijers, S. Mersi, E. Meschi, F. Moortgat, S. Morovic, M. Mulders, H. Neugebauer, S. Orfanelli42, L. Orsini, L. Pape, E. Perez, M. Peruzzi, A. Petrilli, G. Petrucciani, A. Pfei er, M. Pierini, D. Piparo, A. Racz, T. Reis, G. Rolandi43, M. Rovere, M. Ruan, H. Sakulin, C. Schafer, C. Schwick, M. Seidel, A. Sharma, P. Silva, M. Simon, P. Sphicas44, J. Steggemann, M. Stoye, Y. Takahashi, D. Treille, A. Triossi, A. Tsirou, G.I. Veres20, N. Wardle, H.K. Wohri, A. Zagozdzinska35, W.D. Zeuner Paul Scherrer Institut, Villigen, Switzerland W. Bertl, K. Deiters, W. Erdmann, R. Horisberger, Q. Ingram, H.C. Kaestli, D. Kotlinski, U. Langenegger, T. Rohe Institute for Particle Physics, ETH Zurich, Zurich, Switzerland F. Bachmair, L. Bani, L. Bianchini, B. Casal, G. Dissertori, M. Dittmar, M. Donega, P. Eller, C. Grab, C. Heidegger, D. Hits, J. Hoss, G. Kasieczka, P. Lecomtey, W. Lustermann, B. Mangano, M. Marionneau, P. Martinez Ruiz del Arbol, M. Masciovecchio, M.T. Meinhard, D. Meister, F. Micheli, P. Musella, F. Nessi-Tedaldi, F. Pandol , J. Pata, F. Pauss, G. Perrin, L. Perrozzi, M. Quittnat, M. Rossini, M. Schonenberger, A. Starodumov45, M. Takahashi, V.R. Tavolaro, K. Theo latos, R. Wallny Universitat Zurich, Zurich, Switzerland T.K. Aarrestad, C. Amsler46, L. Caminada, M.F. Canelli, V. Chiochia, A. De Cosa, C. Galloni, A. Hinzmann, T. Hreus, B. Kilminster, C. Lange, J. Ngadiuba, D. Pinna, G. Rauco, P. Robmann, D. Salerno, Y. Yang National Central University, Chung-Li, Taiwan K.H. Chen, T.H. Doan, Sh. Jain, R. Khurana, M. Konyushikhin, C.M. Kuo, W. Lin, Y.J. Lu, A. Pozdnyakov, S.S. Yu National Taiwan University (NTU), Taipei, Taiwan Arun Kumar, P. Chang, Y.H. Chang, Y.W. Chang, Y. Chao, K.F. Chen, P.H. Chen, C. Dietz, F. Fiori, U. Grundler, W.-S. Hou, Y. Hsiung, Y.F. Liu, R.-S. Lu, M. Min~ano Moya, E. Petrakou, J.f. Tsai, Y.M. Tzeng Chulalongkorn University, Faculty of Science, Department of Physics, Bangkok, B. Asavapibhop, K. Kovitanggoon, G. Singh, N. Srimanobhas, N. Suwonjandee Cukurova University, Adana, Turkey A. Adiguzel, M.N. Bakirci47, S. Cerci48, S. Damarseckin, Z.S. Demiroglu, C. Dozen, I. Dumanoglu, E. Eskut, S. Girgis, G. Gokbulut, Y. Guler, E. Gurpinar, I. Hos, E.E. Kangal49, G. Onengut50, K. Ozdemir51, A. Polatoz, D. Sunar Cerci48, C. Zorbilmez Middle East Technical University, Physics Department, Ankara, Turkey B. Bilin, S. Bilmis, B. Isildak52, G. Karapinar53, M. Yalvac, M. Zeyrek Bogazici University, Istanbul, Turkey E. Gulmez, M. Kaya54, O. Kaya55, E.A. Yetkin56, T. Yetkin57 Istanbul Technical University, Istanbul, Turkey A. Cakir, K. Cankocak, S. Sen58, F.I. Vardarl Institute for Scintillation Materials of National Academy of Science of Ukraine, Kharkov, Ukraine Kharkov, Ukraine L. Levchuk, P. Sorokin National Scienti c Center, Kharkov Institute of Physics and Technology, University of Bristol, Bristol, U.K. R. Aggleton, F. Ball, L. Beck, J.J. Brooke, D. Burns, E. Clement, D. Cussans, H. Flacher, J. Goldstein, M. Grimes, G.P. Heath, H.F. Heath, J. Jacob, L. Kreczko, C. Lucas, Z. Meng, D.M. Newbold59, S. Paramesvaran, A. Poll, T. Sakuma, S. Seif El Nasr-storey, S. Senkin, D. Smith, V.J. Smith Rutherford Appleton Laboratory, Didcot, U.K. K.W. Bell, A. Belyaev60, C. Brew, R.M. Brown, L. Calligaris, D. Cieri, D.J.A. Cockerill, J.A. Coughlan, K. Harder, S. Harper, E. Olaiya, D. Petyt, C.H. Shepherd-Themistocleous, A. Thea, I.R. Tomalin, T. Williams, S.D. Worm Imperial College, London, U.K. M. Baber, R. Bainbridge, O. Buchmuller, A. Bundock, D. Burton, S. Casasso, M. Citron, D. Colling, L. Corpe, P. Dauncey, G. Davies, A. De Wit, M. Della Negra, P. Dunne, A. Elwood, D. Futyan, G. Hall, G. Iles, R. Lane, R. Lucas59, L. Lyons, A.-M. Magnan, S. Malik, J. Nash, A. Nikitenko45, J. Pela, B. Penning, M. Pesaresi, D.M. Raymond, A. Richards, A. Rose, C. Seez, A. Tapper, K. Uchida, M. Vazquez Acosta61, T. Virdee, Brunel University, Uxbridge, U.K. escu, M. Turner Baylor University, Waco, U.S.A. J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, D. Leslie, I.D. Reid, P. Symonds, L. TeodorA. Borzou, K. Call, J. Dittmann, K. Hatakeyama, H. Liu, N. Pastika The University of Alabama, Tuscaloosa, U.S.A. O. Charaf, S.I. Cooper, C. Henderson, P. Rumerio Boston University, Boston, U.S.A. D. Arcaro, A. Avetisyan, T. Bose, D. Gastler, D. Rankin, C. Richardson, J. Rohlf, L. Sulak, Brown University, Providence, U.S.A. J. Alimena, G. Benelli, E. Berry, D. Cutts, A. Ferapontov, A. Garabedian, J. Hakala, U. Heintz, O. Jesus, E. Laird, G. Landsberg, Z. Mao, M. Narain, S. Piperov, S. Sagir, R. Breedon, G. Breto, M. Calderon De La Barca Sanchez, S. Chauhan, M. Chertok, J. Conway, R. Conway, P.T. Cox, R. Erbacher, G. Funk, M. Gardner, W. Ko, R. Lander, C. Mclean, M. Mulhearn, D. Pellett, J. Pilot, F. Ricci-Tam, S. Shalhout, J. Smith, M. Squires, D. Stolp, M. Tripathi, S. Wilbur, R. Yohay University of California, Los Angeles, U.S.A. R. Cousins, P. Everaerts, A. Florent, J. Hauser, M. Ignatenko, D. Saltzberg, E. Takasugi, V. Valuev, M. Weber University of California, Riverside, Riverside, U.S.A. K. Burt, R. Clare, J. Ellison, J.W. Gary, G. Hanson, J. Heilman, M. Ivova PANEVA, P. Jandir, E. Kennedy, F. Lacroix, O.R. Long, M. Malberti, M. Olmedo Negrete, A. Shrinivas, H. Wei, S. Wimpenny, B. R. Yates University of California, San Diego, La Jolla, U.S.A. J.G. Branson, G.B. Cerati, S. Cittolin, R.T. D'Agnolo, M. Derdzinski, A. Holzner, R. Kelley, D. Klein, J. Letts, I. Macneill, D. Olivito, S. Padhi, M. Pieri, M. Sani, V. Sharma, S. Simon, M. Tadel, A. Vartak, S. Wasserbaech62, C. Welke, F. Wurthwein, A. Yagil, G. Zevi Della Porta University of California, Santa Barbara, Santa Barbara, U.S.A. J. Bradmiller-Feld, C. Campagnari, A. Dishaw, V. Dutta, K. Flowers, M. Franco Sevilla, P. Ge ert, C. George, F. Golf, L. Gouskos, J. Gran, J. Incandela, N. Mccoll, S.D. Mullin, J. Richman, D. Stuart, I. Suarez, C. West, J. Yoo California Institute of Technology, Pasadena, U.S.A. D. Anderson, A. Apresyan, J. Bendavid, A. Bornheim, J. Bunn, Y. Chen, J. Duarte, A. Mott, H.B. Newman, C. Pena, M. Spiropulu, J.R. Vlimant, S. Xie, R.Y. Zhu Carnegie Mellon University, Pittsburgh, U.S.A. M.B. Andrews, V. Azzolini, A. Calamba, B. Carlson, T. Ferguson, M. Paulini, J. Russ, M. Sun, H. Vogel, I. Vorobiev University of Colorado Boulder, Boulder, U.S.A. J.P. Cumalat, W.T. Ford, A. Gaz, F. Jensen, A. Johnson, M. Krohn, T. Mulholland, U. Nauenberg, K. Stenson, S.R. Wagner Cornell University, Ithaca, U.S.A. J. Alexander, A. Chatterjee, J. Chaves, J. Chu, S. Dittmer, N. Eggert, N. Mirman, G. Nicolas Kaufman, J.R. Patterson, A. Rinkevicius, A. Ryd, L. Skinnari, L. So , W. Sun, S.M. Tan, W.D. Teo, J. Thom, J. Thompson, J. Tucker, Y. Weng, P. Wittich Fermi National Accelerator Laboratory, Batavia, U.S.A. S. Abdullin, M. Albrow, G. Apollinari, S. Banerjee, L.A.T. Bauerdick, A. Beretvas, J. Berryhill, P.C. Bhat, G. Bolla, K. Burkett, J.N. Butler, H.W.K. Cheung, F. Chlebana, S. Cihangir, V.D. Elvira, I. Fisk, J. Freeman, E. Gottschalk, L. Gray, D. Green, S. Grunendahl, O. Gutsche, J. Hanlon, D. Hare, R.M. Harris, S. Hasegawa, J. Hirschauer, H.A. Weber, A. Whitbeck University of Florida, Gainesville, U.S.A. D. Acosta, P. Avery, P. Bortignon, D. Bourilkov, A. Brinkerho , A. Carnes, M. Carver, D. Curry, S. Das, R.D. Field, I.K. Furic, J. Konigsberg, A. Korytov, K. Kotov, P. Ma, K. Matchev, H. Mei, P. Milenovic63, G. Mitselmakher, D. Rank, R. Rossin, L. Shchutska, M. Snowball, D. Sperka, N. Terentyev, L. Thomas, J. Wang, S. Wang, J. Yelton Florida International University, Miami, U.S.A. S. Linn, P. Markowitz, G. Martinez, J.L. Rodriguez Florida State University, Tallahassee, U.S.A. A. Ackert, J.R. Adams, T. Adams, A. Askew, S. Bein, J. Bochenek, B. Diamond, J. Haas, S. Hagopian, V. Hagopian, K.F. Johnson, A. Khatiwada, H. Prosper, M. Weinberg Florida Institute of Technology, Melbourne, U.S.A. M.M. Baarmand, V. Bhopatkar, S. Colafranceschi64, M. Hohlmann, H. Kalakhety, D. Noonan, T. Roy, F. Yumiceva University of Illinois at Chicago (UIC), Chicago, U.S.A. M.R. Adams, L. Apanasevich, D. Berry, R.R. Betts, I. Bucinskaite, R. Cavanaugh, O. Evdokimov, L. Gauthier, C.E. Gerber, D.J. Hofman, P. Kurt, C. O'Brien, I.D. Sandoval Gonzalez, P. Turner, N. Varelas, Z. Wu, M. Zakaria, J. Zhang The University of Iowa, Iowa City, U.S.A. B. Bilki65, W. Clarida, K. Dilsiz, S. Durgut, R.P. Gandrajula, M. Haytmyradov, V. Khristenko, J.-P. Merlo, H. Mermerkaya66, A. Mestvirishvili, A. Moeller, J. Nachtman, H. Ogul, Y. Onel, F. Ozok67, A. Penzo, C. Snyder, E. Tiras, J. Wetzel, K. Yi Johns Hopkins University, Baltimore, U.S.A. I. Anderson, B.A. Barnett, B. Blumenfeld, A. Cocoros, N. Eminizer, D. Fehling, L. Feng, A.V. Gritsan, P. Maksimovic, M. Osherson, J. Roskes, U. Sarica, M. Swartz, M. Xiao, Y. Xin, C. You The University of Kansas, Lawrence, U.S.A. P. Baringer, A. Bean, C. Bruner, R.P. Kenny III, D. Majumder, M. Malek, W. Mcbrayer, M. Murray, S. Sanders, R. Stringer, Q. Wang Kansas State University, Manhattan, U.S.A. A. Ivanov, K. Kaadze, S. Khalil, M. Makouski, Y. Maravin, A. Mohammadi, L.K. Saini, N. Skhirtladze, S. Toda D. Lange, F. Rebassoo, D. Wright University of Maryland, College Park, U.S.A. Massachusetts Institute of Technology, Cambridge, U.S.A. A. Apyan, R. Barbieri, A. Baty, R. Bi, K. Bierwagen, S. Brandt, W. Busza, I.A. Cali, Z. Demiragli, L. Di Matteo, G. Gomez Ceballos, M. Goncharov, D. Gulhan, Y. Iiyama, G.M. Innocenti, M. Klute, D. Kovalskyi, K. Krajczar, Y.S. Lai, Y.-J. Lee, A. Levin, P.D. Luckey, A.C. Marini, C. Mcginn, C. Mironov, S. Narayanan, X. Niu, C. Paus, C. Roland, G. Roland, J. Salfeld-Nebgen, G.S.F. Stephans, K. Sumorok, K. Tatar, M. Varma, D. Velicanu, J. Veverka, J. Wang, T.W. Wang, B. Wyslouch, M. Yang, University of Minnesota, Minneapolis, U.S.A. A.C. Benvenuti, B. Dahmes, A. Evans, A. Finkel, A. Gude, P. Hansen, S. Kalafut, S.C. Kao, K. Klapoetke, Y. Kubota, Z. Lesko, J. Mans, S. Nourbakhsh, N. Ruckstuhl, R. Rusack, N. Tambe, J. Turkewitz University of Mississippi, Oxford, U.S.A. J.G. Acosta, S. Oliveros University of Nebraska-Lincoln, Lincoln, U.S.A. E. Avdeeva, R. Bartek, K. Bloom, S. Bose, D.R. Claes, A. Dominguez, C. Fangmeier, R. Gonzalez Suarez, R. Kamalieddin, D. Knowlton, I. Kravchenko, F. Meier, J. Monroy, F. Ratnikov, J.E. Siado, G.R. Snow, B. Stieger State University of New York at Bu alo, Bu alo, U.S.A. M. Alyari, J. Dolen, J. George, A. Godshalk, C. Harrington, I. Iashvili, J. Kaisen, A. Kharchilava, A. Kumar, S. Rappoccio, B. Roozbahani Northeastern University, Boston, U.S.A. G. Alverson, E. Barberis, D. Baumgartel, M. Chasco, A. Hortiangtham, A. Massironi, D.M. Morse, D. Nash, T. Orimoto, R. Teixeira De Lima, D. Trocino, R.-J. Wang, D. Wood, Northwestern University, Evanston, U.S.A. S. Bhattacharya, K.A. Hahn, A. Kubik, J.F. Low, N. Mucia, N. Odell, B. Pollack, M. Schmitt, K. Sung, M. Trovato, M. Velasco University of Notre Dame, Notre Dame, U.S.A. N. Dev, M. Hildreth, C. Jessop, D.J. Karmgard, N. Kellams, K. Lannon, N. Marinelli, F. Meng, C. Mueller, Y. Musienko36, M. Planer, A. Reinsvold, R. Ruchti, N. Rupprecht, G. Smith, S. Taroni, N. Valls, M. Wayne, M. Wolf, A. Woodard L. Antonelli, J. Brinson, B. Bylsma, L.S. Durkin, S. Flowers, A. Hart, C. Hill, R. Hughes, W. Ji, T.Y. Ling, B. Liu, W. Luo, D. Puigh, M. Rodenburg, B.L. Winer, H.W. Wulsin Princeton University, Princeton, U.S.A. O. Driga, P. Elmer, J. Hardenbrook, P. Hebda, S.A. Koay, P. Lujan, D. Marlow, T. Medvedeva, M. Mooney, J. Olsen, C. Palmer, P. Piroue, D. Stickland, C. Tully, University of Puerto Rico, Mayaguez, U.S.A. Purdue University, West Lafayette, U.S.A. A. Barker, V.E. Barnes, D. Benedetti, D. Bortoletto, L. Gutay, M.K. Jha, M. Jones, A.W. Jung, K. Jung, A. Kumar, D.H. Miller, N. Neumeister, B.C. Radburn-Smith, X. Shi, I. Shipsey, D. Silvers, J. Sun, A. Svyatkovskiy, F. Wang, W. Xie, L. Xu Purdue University Calumet, Hammond, U.S.A. N. Parashar, J. Stupak Rice University, Houston, U.S.A. A. Adair, B. Akgun, Z. Chen, K.M. Ecklund, F.J.M. Geurts, M. Guilbaud, W. Li, B. Michlin, M. Northup, B.P. Padley, R. Redjimi, J. Roberts, J. Rorie, Z. Tu, J. Zabel University of Rochester, Rochester, U.S.A. B. Betchart, A. Bodek, P. de Barbaro, R. Demina, Y. Eshaq, T. Ferbel, M. Galanti, A. Garcia-Bellido, J. Han, O. Hindrichs, A. Khukhunaishvili, K.H. Lo, P. Tan, M. Verzetti Rutgers, The State University of New Jersey, Piscataway, U.S.A. J.P. Chou, E. Contreras-Campana, D. Ferencek, Y. Gershtein, E. Halkiadakis, M. Heindl, D. Hidas, E. Hughes, S. Kaplan, R. Kunnawalkam Elayavalli, A. Lath, K. Nash, H. Saka, S. Salur, S. Schnetzer, D. She eld, S. Somalwar, R. Stone, S. Thomas, P. Thomassen, University of Tennessee, Knoxville, U.S.A. M. Foerster, G. Riley, K. Rose, S. Spanier, K. Thapa Texas A&M University, College Station, U.S.A. O. Bouhali68, A. Castaneda Hernandez68, A. Celik, M. Dalchenko, M. De Mattia, A. Delgado, S. Dildick, R. Eusebi, J. Gilmore, T. Huang, T. Kamon69, V. Krutelyov, R. Mueller, I. Osipenkov, Y. Pakhotin, R. Patel, A. Perlo , D. Rathjens, A. Rose, A. Safonov, A. Tatarinov, K.A. Ulmer Texas Tech University, Lubbock, U.S.A. N. Akchurin, C. Cowden, J. Damgov, C. Dragoiu, P.R. Dudero, J. Faulkner, S. Kunori, K. Lamichhane, S.W. Lee, T. Libeiro, S. Undleeb, I. Volobouev E. Appelt, A.G. Delannoy, S. Greene, A. Gurrola, R. Janjam, W. Johns, C. Maguire, Y. Mao, A. Melo, H. Ni, P. Sheldon, S. Tuo, J. Velkovska, Q. Xu University of Virginia, Charlottesville, U.S.A. M.W. Arenton, B. Cox, B. Francis, J. Goodell, R. Hirosky, A. Ledovskoy, H. Li, C. Neu, T. Sinthuprasith, X. Sun, Y. Wang, E. Wolfe, J. Wood, F. Xia Wayne State University, Detroit, U.S.A. C. Clarke, R. Harr, P.E. Karchin, C. Kottachchi Kankanamge Don, P. Lamichhane, University of Wisconsin - Madison, Madison, WI, U.S.A. D.A. Belknap, D. Carlsmith, S. Dasu, L. Dodd, S. Duric, B. Gomber, M. Grothe, M. Herndon, A. Herve, P. Klabbers, A. Lanaro, A. Levine, K. Long, R. Loveless, A. Mohapatra, I. Ojalvo, T. Perry, G.A. Pierro, G. Polese, T. Ruggles, T. Sarangi, A. Savin, A. Sharma, N. Smith, W.H. Smith, D. Taylor, P. Verwilligen, N. Woods 1: Also at Vienna University of Technology, Vienna, Austria 2: Also at State Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing, 3: Also at Institut Pluridisciplinaire Hubert Curien, Universite de Strasbourg, Universite de Haute Alsace Mulhouse, CNRS/IN2P3, Strasbourg, France 4: Also at Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, 5: Also at Universidade Estadual de Campinas, Campinas, Brazil 6: Also at Centre National de la Recherche Scienti que (CNRS) - IN2P3, Paris, France 7: Also at Universite Libre de Bruxelles, Bruxelles, Belgium 8: Also at Laboratoire Leprince-Ringuet, Ecole Polytechnique, IN2P3-CNRS, Palaiseau, France 9: Also at Joint Institute for Nuclear Research, Dubna, Russia 10: Also at Ain Shams University, Cairo, Egypt 11: Now at British University in Egypt, Cairo, Egypt 12: Also at Zewail City of Science and Technology, Zewail, Egypt 13: Also at Universite de Haute Alsace, Mulhouse, France 14: Also at CERN, European Organization for Nuclear Research, Geneva, Switzerland 15: Also at Tbilisi State University, Tbilisi, Georgia 16: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen, Germany 17: Also at University of Hamburg, Hamburg, Germany 18: Also at Brandenburg University of Technology, Cottbus, Germany 19: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary 20: Also at MTA-ELTE Lendulet CMS Particle and Nuclear Physics Group, Eotvos Lorand University, Budapest, Hungary 21: Also at University of Debrecen, Debrecen, Hungary 22: Also at Indian Institute of Science Education and Research, Bhopal, India 23: Also at University of Visva-Bharati, Santiniketan, India 24: Now at King Abdulaziz University, Jeddah, Saudi Arabia 26: Also at Isfahan University of Technology, Isfahan, Iran 27: Also at University of Tehran, Department of Engineering Science, Tehran, Iran 28: Also at Plasma Physics Research Center, Science and Research Branch, Islamic Azad University, Tehran, Iran 29: Also at Universita degli Studi di Siena, Siena, Italy 30: Also at Purdue University, West Lafayette, U.S.A. 31: Now at Hanyang University, Seoul, Korea 32: Also at International Islamic University of Malaysia, Kuala Lumpur, Malaysia 33: Also at Malaysian Nuclear Agency, MOSTI, Kajang, Malaysia 34: Also at Consejo Nacional de Ciencia y Tecnolog a, Mexico city, Mexico 35: Also at Warsaw University of Technology, Institute of Electronic Systems, Warsaw, Poland 36: Also at Institute for Nuclear Research, Moscow, Russia at National Research Nuclear University 'Moscow Engineering Physics Insti tute' (MEPhI), Moscow, Russia 38: Also at St. Petersburg State Polytechnical University, St. Petersburg, Russia 39: Also at California Institute of Technology, Pasadena, U.S.A. 40: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia 41: Also at INFN Sezione di Roma; Universita di Roma, Roma, Italy 42: Also at National Technical University of Athens, Athens, Greece 43: Also at Scuola Normale e Sezione dell'INFN, Pisa, Italy 44: Also at National and Kapodistrian University of Athens, Athens, Greece 45: Also at Institute for Theoretical and Experimental Physics, Moscow, Russia 46: Also at Albert Einstein Center for Fundamental Physics, Bern, Switzerland 47: Also at Gaziosmanpasa University, Tokat, Turkey 48: Also at Adiyaman University, Adiyaman, Turkey 49: Also at Mersin University, Mersin, Turkey 50: Also at Cag University, Mersin, Turkey 51: Also at Piri Reis University, Istanbul, Turkey 52: Also at Ozyegin University, Istanbul, Turkey 53: Also at Izmir Institute of Technology, Izmir, Turkey 54: Also at Marmara University, Istanbul, Turkey 55: Also at Kafkas University, Kars, Turkey 56: Also at Istanbul Bilgi University, Istanbul, Turkey 57: Also at Yildiz Technical University, Istanbul, Turkey 58: Also at Hacettepe University, Ankara, Turkey 59: Also at Rutherford Appleton Laboratory, Didcot, U.K. 60: Also at School of Physics and Astronomy, University of Southampton, Southampton, U.K. 61: Also at Instituto de Astrof sica de Canarias, La Laguna, Spain 62: Also at Utah Valley University, Orem, U.S.A. 63: Also at University of Belgrade, Faculty of Physics and Vinca Institute of Nuclear Sciences, 64: Also at Facolta Ingegneria, Universita di Roma, Roma, Italy 65: Also at Argonne National Laboratory, Argonne, U.S.A. 66: Also at Erzincan University, Erzincan, Turkey 67: Also at Mimar Sinan University, Istanbul, Istanbul, Turkey 68: Also at Texas A&M University at Qatar, Doha, Qatar 69: Also at Kyungpook National University, Daegu, Korea [28] CMS collaboration, Search for Monotop signatures in proton-proton collisions at s = 8 TeV, Phys. Rev. Lett . 114 ( 2015 ) 101801 [arXiv:1410.1149] [INSPIRE]. [29] CMS collaboration, Search for the production of dark matter in association with top-quark [36] P.J. Fox , R. Harnik , R. Primulando and C.-T. Yu , Taking a razor to dark matter parameter [37] M. Papucci , A. Vichi and K.M. Zurek , Monojet versus the rest of the world I: t-channel [38] P. Agrawal , B. Batell , D. Hooper and T. Lin , Flavored dark matter and the galactic center [43] M. Cacciari , G.P. Salam and G. Soyez , FastJet user manual , Eur. Phys. J. C 72 ( 2012 ) 1896 [44] M. Cacciari , G.P. Salam and G. Soyez , The anti-k(t) jet clustering algorithm , JHEP 04 [47] J. Pumplin , D.R. Stump , J. Huston , H.L. Lai , P.M. Nadolsky and W.K. Tung , New [48] T. Sjostrand , S. Mrenna and P.Z. Skands , PYTHIA 6.4 physics and manual , JHEP 05 [49] R. Field , Early LHC underlying event data | Findings and surprises , in the proceedings of the 22nd Hadron Collider Physics Symposium (HCP 2010 ), August 23 { 27 , Toronto, Canada [50] S. Hoche et al., Matching parton showers and matrix elements , hep-ph/ 0602031 [INSPIRE]. [51] GEANT4 collaboration , S. Agostinelli et al., GEANT4 | A Simulation toolkit, Nucl. [52] R. Gavin, Y. Li, F. Petriello and S. Quackenbush, W physics at the LHC with FEWZ 2.1, [53] R. Gavin, Y. Li, F. Petriello and S. Quackenbush, FEWZ 2.0: a code for hadronic Z production at next-to-next-to-leading order, Comput. Phys. Commun. 182 (2011) 2388 [54] M. Czakon and A. Mitov, Top++: a program for the calculation of the top-pair cross-section [56] CMS collaboration, Particle- ow event reconstruction in CMS and performance for jets, taus [63] S. Alekhin et al., The PDF4LHC Working Group Interim Report, arXiv:1101.0536 [67] M. Beltran, D. Hooper, E.W. Kolb, Z.A.C. Krusberg and T.M.P. Tait, Maverick dark matter [68] Y. Bai, P.J. Fox and R. Harnik, The Tevatron at the frontier of dark matter direct detection, [69] P.J. Fox, R. Harnik, J. Kopp and Y. Tsai, Missing energy signatures of dark matter at the [72] J. Goodman, M. Ibe, A. Rajaraman, W. Shepherd, T.M.P. Tait and H.-B. Yu, Constraints [73] A. Friedland, M.L. Graesser, I.M. Shoemaker and L. Vecchi, Probing nonstandard standard model backgrounds with LHC monojets, Phys. Lett. B 714 (2012) 267 [arXiv:1111.5331] [74] O. Buchmueller, M.J. Dolan and C. McCabe, Beyond e ective eld theory for dark matter [75] G. Busoni, A. De Simone, E. Morgante and A. Riotto, On the validity of the e ective eld [76] G. Busoni, A. De Simone, J. Gramling, E. Morgante and A. Riotto, On the validity of the [77] G. Busoni, A. De Simone, T. Jacques, E. Morgante and A. Riotto, On the validity of the [86] SuperCDMS collaboration, R. Agnese et al., Search for low-mass weakly interacting [87] XENON100 collaboration, E. Aprile et al., Dark matter results from 100 live days of [88] LUX collaboration, D.S. Akerib et al., First results from the LUX dark matter experiment at the Sanford Underground Research Facility, Phys. Rev. Lett. 112 (2014) 091303 [89] CRESST-II collaboration, G. Angloher et al., Results on low mass WIMPs using an [90] CRESST collaboration, G. Angloher et al., Results on light dark matter particles with a low-threshold CRESST-II detector, Eur. Phys. J. C 76 (2016) 25 [arXiv:1509.01515] [91] T. Lin, E.W. Kolb and L.-T. Wang, Probing dark matter couplings to top and bottom quarks M. Jeitler1, A. Konig, M. Krammer1, I. Kratschmer, D. Liko, T. Matsushita, I. Mikulec, D. Rabady, N. Rad, B. Rahbaran, H. Rohringer, J. Schieck1, R. Schofbeck, J. Strauss, W. Treberer-Treberspurg, W. Waltenberger, C.-E. Wulz1 Brazil S. Ahujaa, C.A. Bernardesb, A. De Souza Santosb, S. Dograa, T.R. Fernandez Perez Tomeia, J. Harkonen, V. Karimaki, R. Kinnunen, T. Lampen, K. Lassila-Perini, S. Lehti, T. Linden, France A. Abdulsalam, I. Antropov, S. Ba oni, 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. Mine, I.N. Naranjo, M. Nguyen, C. Ochando, G. Ortona, P. Paganini, P. Pigard, S. Regnard, R. Salerno, J.B. Sauvan, Y. Sirois, T. Strebler, I. Evangelou, G. Flouris, C. Foudas, P. Kokkas, N. Loukas, N. Manthos, I. Papadopoulos, Italy Italy Malaysia Portugal P. Bargassa, C. Beir~ao Da Cruz E Silva, A. Di Francesco, P. Faccioli, P.G. Ferreira Parracho, M. Gallinaro, J. Hollar, N. Leonardo, L. Lloret Iglesias, M.V. Nemallapudi, F. Nguyen, J. Rodrigues Antunes, J. Seixas, O. Toldaiev, D. Vadruccio, J. Varela, P. Vischia Thailand Z. Hu, B. Jayatilaka, S. Jindariani, M. Johnson, U. Joshi, B. Klima, B. Kreis, S. Lammel, J. Lewis, J. Linacre, D. Lincoln, R. Lipton, T. Liu, R. Lopes De Sa, J. Lykken, K. Maeshima, J.M. Marra no, S. Maruyama, D. Mason, P. McBride, P. Merkel, S. Mrenna, S. Nahn, C. Newman-Holmesy, V. O'Dell, K. Pedro, O. Prokofyev, G. Rakness, E. SextonKennedy, A. Soha, W.J. Spalding, L. Spiegel, S. Stoynev, N. Strobbe, L. Taylor, S. Tkaczyk, N.V. Tran, L. Uplegger, E.W. Vaandering, C. Vernieri, M. Verzocchi, R. Vidal, M. Wang, C. Anelli, A. Baden, O. Baron, A. Belloni, B. Calvert, S.C. Eno, C. Ferraioli, J.A. Gomez, N.J. Hadley, S. Jabeen, R.G. Kellogg, T. Kolberg, J. Kunkle, Y. Lu, A.C. Mignerey, Y.H. Shin, A. Skuja, M.B. Tonjes, S.C. Tonwar China


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V. Khachatryan, A. M. Sirunyan, A. Tumasyan, W. Adam. Search for dark matter particles in proton-proton collisions at \( \sqrt{s}=8 \) TeV using the razor variables, Journal of High Energy Physics, 2016, 88, DOI: 10.1007/JHEP12(2016)088