Search for new phenomena with the \(M_{\mathrm {T2}}\) variable in the all-hadronic final state produced in proton–proton collisions at \(\sqrt{s} = 13\) \(\,\text {TeV}\)

The European Physical Journal C, Oct 2017

A search for new phenomena is performed using events with jets and significant transverse momentum imbalance, as inferred through the \(M_{\mathrm {T2}}\) variable. The results are based on a sample of proton–proton collisions collected in 2016 at a center-of-mass energy of 13\(\,\text {TeV}\) with the CMS detector and corresponding to an integrated luminosity of 35.9\(\,\text {fb}^\text {-1}\). No excess event yield is observed above the predicted standard model background, and the results are interpreted as exclusion limits at 95% confidence level on the masses of predicted particles in a variety of simplified models of R-parity conserving supersymmetry. Depending on the details of the model, 95% confidence level lower limits on the gluino (light-flavor squark) masses are placed up to 2025 (1550)\(\,\text {GeV}\). Mass limits as high as 1070 (1175)\(\,\text {GeV}\) are set on the masses of top (bottom) squarks. Information is provided to enable re-interpretation of these results, including model-independent limits on the number of non-standard model events for a set of simplified, inclusive search regions.

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Search for new phenomena with the \(M_{\mathrm {T2}}\) variable in the all-hadronic final state produced in proton–proton collisions at \(\sqrt{s} = 13\) \(\,\text {TeV}\)

Eur. Phys. J. C Search for new phenomena with the MT2 variable in the all-hadronic final state produced in proton-proton collisions at √ s = 13 TeV CMS Collaboration 0 1 2 0 2 The CMS detector 1 CERN , 1211 Geneva 23 , Switzerland 2 Institute of Experimental Physics, Faculty of Physics, University of Warsaw , Warsaw , Poland K. Bunkowski, A. Byszuk 3 , K. Doroba , A. Kalinowski, M. Konecki, J. Krolikowski, M. Misiura, M. Olszewski, A. Pyskir, M. Walczak A search for new phenomena is performed using events with jets and significant transverse momentum imbalance, as inferred through the MT2 variable. The results are based on a sample of proton-proton collisions collected in 2016 at a center-of-mass energy of 13 TeV with the CMS detector and corresponding to an integrated luminosity of 35.9 fb−1. No excess event yield is observed above the predicted standard model background, and the results are interpreted as exclusion limits at 95% confidence level on the masses of predicted particles in a variety of simplified models of R-parity conserving supersymmetry. Depending on the details of the model, 95% confidence level lower limits on the gluino (light-flavor squark) masses are placed up to 2025 (1550) GeV. Mass limits as high as 1070 (1175) GeV are set on the masses of top (bottom) squarks. Information is provided to enable re-interpretation of these results, including model-independent limits on the number of non-standard model events for a set of simplified, inclusive search regions. 1 Introduction We present results of a search for new phenomena in events with jets and significant transverse momentum imbalance in proton–proton collisions at √s = 13 TeV. Such searches were previously conducted by both the ATLAS [ 1–5 ] and CMS [ 6–9 ] Collaborations. Our search builds on the work presented in Ref. [6], using improved methods to estimate the background from standard model (SM) processes and a data set corresponding to an integrated luminosity of 35.9 fb−1 of pp collisions collected during 2016 with the CMS detector at the CERN LHC. Event counts in bins of the number of jets (Nj), the number of b-tagged jets (Nb), the scalar sum of the transverse momenta pT of all selected jets (HT), and the MT2 variable [ 6,10 ] are compared against estimates of the background from SM processes derived from dedicated data control samples. We observe no evidence for a significant excess above the expected background event yield and interpret the results as exclusion limits at 95% confidence level on the production of pairs of gluinos and squarks using simplified models of supersymmetry (SUSY) [ 11–18 ]. Modelindependent limits on the number of non-SM events are also provided for a simpler set of inclusive search regions. The central feature of the CMS apparatus is a superconducting solenoid of 6 m internal diameter, providing a magnetic field of 3.8 T. Within the solenoid volume are a silicon pixel and strip tracker, a lead tungstate crystal electromagnetic calorimeter, and a brass and scintillator hadron calorimeter, each composed of a barrel and two endcap sections. Forward calorimeters extend the pseudorapidity (η) coverage provided by the barrel and endcap detectors. Muons are measured in gas-ionization detectors embedded in the steel fluxreturn yoke outside the solenoid. The first level of the CMS trigger system, composed of custom hardware processors, uses information from the calorimeters and muon detectors to select the most interesting events in a fixed time interval of less than 4 μs. The high-level trigger processor farm further decreases the event rate from around 100 kHz to less than 1 kHz, before data storage. A more detailed description of the CMS detector and trigger system, together with a definition of the coordinate system used and the relevant kinematic variables, can be found in Refs. [ 19,20 ]. 3 Event selection and Monte Carlo simulation Events are processed using the particle-flow (PF) algorithm [21], which is designed to reconstruct and identify all particles using the optimal combination of information from the elements of the CMS detector. Physics objects reconstructed with this algorithm are hereafter referred to as particle-flow candidates. The physics objects and the event preselection are similar to those described in Ref. [ 6 ], and are summarized in Table 1. We select events with at least one jet, and veto events with an isolated lepton (e or μ) or charged PF candidate. The isolated charged PF candidate selection is designed to provide additional rejection against events with electrons and muons, as well as to reject hadronic tau decays. Jets are formed by clustering PF candidates using the anti-kT algorithm [ 22, 23 ] and are corrected for contributions from event pileup [24] and the effects of non-uniform detector response. Only jets passing the selection criteria in Table 1 are used for counting and the determination of kinematic variables. Jets consistent with originating from a heavy-flavor hadron are identified using the combined secondary vertex tagging algorithm [ 25 ], with a working point chosen such that the efficiency to identify a b quark jet is in the range 50–65% for jet pT between 20 and 400 GeV. The misidentification rate is approximately 1% for light-flavor and gluon jets and 10% for charm jets. A more detailed discussion of the algorithm performance is given in Ref. [ 25 ]. The negative of the vector sum of the pT of all selected jets is denoted by H miss, while p miss is defined as the nega T T tive of the vector pT sum of all reconstructed PF candidates. The jet corrections are also used to correct pTmiss. Events with possible contributions from beam-halo processes or anomalous noise in the calorimeter are rejected using dedicated filters [ 26, 27 ]. For events with at least two jets, we start with the pair having the largest dijet invariant mass and iteratively cluster all selected jets using a hemisphere algorithm that minimizes the Lund distance measure [ 28, 29 ] until two stable pmiss > 120 GeV and H miss > 120 GeV or T T HT > 300 GeV and pTmiss > 110 GeV or HT > 900 GeV or jet pT > 450 GeV R = 0.4, pT > 30 GeV, |η| < 2.4 pT > 20 GeV, |η| < 2.4 pmiss > 250 GeV for HT < 1000 GeV, else pTmiss > 30 GeV T Δφmin = Δφ pmiss, j1,2,3,4 > 0.3 T | pTmiss − H miss|/ pTmiss < 0.5 T MT2 > 200 GeV for HT < 1500 GeV, else MT2 > 400 GeV pT > 10 GeV, |η| < 2.4, pTsum < 0.2 pTlep or pT > 5 GeV, |η| < 2.4, MT < 100 GeV, pTsum < 0.2 pTlep pT > 10 GeV, |η| < 2.4, pTsum < 0.1 pTlep or pT > 5 GeV, |η| < 2.4, MT < 100 GeV, pTsum < 0.2 pTlep pT > 10 GeV, |η| < 2.4, MT < 100 GeV, pTsum < 0.1 pTtrack Veto e or μ: ΔR = min(0.2, max(10 GeV/ pTlep, 0.05)) Veto track: ΔR = 0.3 pseudo-jets are obtained. The resulting pseudo-jets together with the p miss are used to calculate the kinematic variable T MT2 as: MT2 = min pTmissX(1)+ pTmissX(2) = pTmiss max M T(1), M (2) T (1) where pTmissX(i) (i = 1,2) are trial vectors obtained by decomposing p miss, and M (i) are the transverse masses T T obtained by pairing either of the trial vectors with one of the two pseudo-jets. The minimization is performed over all trial momenta satisfying the p miss constraint. The background T from multijet events (discussed in Sect. 4) is characterized by small values of MT2, while larger MT2 values are obtained in processes with significant, genuine p miss. T Collision events are selected using triggers with requirements on HT, pmiss, H miss, and jet pT. The combined trig T T ger efficiency, as measured in a data sample of events with an isolated electron, is found to be > 98% across the full kinematic range of the search. To suppress background from multijet production, we require MT2 > 200 GeV in events with Nj ≥ 2 and HT < 1500 GeV. This MT2 threshold is increased to 400 GeV for events with HT > 1500 GeV to maintain multijet processes as a subdominant background in all search regions. To protect against jet mismeasurement, we require the minimum difference in azimuthal angle between the p miss vector and each of the leading four jets, Δφmin, T to be greater than 0.3, and the magnitude of the difference between p miss and H miss to be less than half of pmiss. For T T T the determination of Δφmin we consider jets with |η| < 4.7. If less than four such jets are found, all are considered in the Δφmin calculation. Events containing at least two jets are categorized by the values of Nj, Nb, and HT. Each such bin is referred to as a topological region. Signal regions are defined by further dividing topological regions into bins of MT2. Events with only one jet are selected if the pT of the jet is at least 250 GeV, and are classified according to the pT of this jet and whether the event contains a b-tagged jet. The search regions are summarized in Tables 5, 6, 7 in Appendix A. We also define super signal regions, covering a subset of the kinematic space of the full analysis with simpler inclusive selections. The super signal regions can be used to obtain approximate interpretations of our result, as discussed in Sect. 5, where these regions are defined. Monte Carlo (MC) simulations are used to design the search, to aid in the estimation of SM backgrounds, and to evaluate the sensitivity to gluino and squark pair production in simplified models of SUSY. The main background samples (Z+jets, W+jets, and tt+jets), as well as signal samples of gluino and squark pair production, are generated at leading order (LO) precision with the MadGraph 5 generator [ 30, 31 ] interfaced with pythia 8.2 [32] for fragmentation and parton showering. Up to four, three, or two additional partons are considered in the matrix element calculations for the generation of the V+jets (V = Z, W), tt+jets, and signal samples, respectively. Other background processes are also considered: ttV(V = Z, W) samples are generated at LO precision with the MadGraph 5 generator, with up to two additional partons in the matrix element calculations, while single top samples are generated at next-to-leading order (NLO) precision with the MadGraph_aMC@NLO [ 30 ] or powheg [ 33, 34 ] generators. Contributions from rarer processes such as diboson, triboson, and four top production, are found to be negligible. Standard model samples are simulated with a detailed Geant4 [35] based detector simulation and processed using the same chain of reconstruction programs as collision data, while the CMS fast simulation program [ 36 ] is used for the signal samples. The most precise available cross section calculations are used to normalize the simulated samples, corresponding most often to NLO or next-to-NLO accuracy [ 30, 33, 34, 37– 40 ]. To improve on the MadGraph modeling of the multiplicity of additional jets from initial state radiation (ISR), MadGraph tt MC events are weighted based on the number of ISR jets (NjISR) so as to make the jet multiplicity agree with data. The same reweighting procedure is applied to SUSY MC events. The weighting factors are obtained from a control region enriched in tt, obtained by selecting events with two leptons and exactly two b-tagged jets, and vary between 0.92 for NjISR = 1 and 0.51 for NjISR ≥ 6. We take one half of the deviation from unity as the systematic uncertainty in these reweighting factors, to cover for differences between tt and SUSY production. CMS CMS n i /B106 s t n ve105 E 104 103 102 10 1 2 C1.5 M /a 1 ta0.5 D 0 10−2100 in106 B / tsn105 e v E104 103 102 10 1 10−1 2 C1.5 M /a 1 ta0.5 D 0 35.9 fb-1 (13 TeV) Data tt+jets events, with smaller contributions from rarer processes such as diboson or ttV(V = Z, W) production. – “irreducible”, i.e., Z+jets events, where the Z boson decays to neutrinos. This background is most similar to potential signals. It is a major background in nearly all search regions, its importance decreasing with increasing Nb. – “instrumental background”, i.e., mostly multijet events with no genuine pmiss. These events enter a search region T due to either significant jet momentum mismeasurements, or sources of anomalous noise. 4.1 Estimation of the background from events with leptonic W boson decays Control regions with exactly one lepton candidate are selected using the same triggers and preselections used for the signal regions, with the exception of the lepton veto, which is inverted. Selected events are binned according to the same criteria as the search regions, and the background in each signal bin, NLSLR, is obtained from the number of events in the control region, N1CR, using transfer factors according to: NLSLR The single-lepton control region typically has 1–2 times as many events as the corresponding signal region. The factor RM0C/1 HT, Nj, Nb, MT2 accounts for lepton acceptance and efficiency and the expected contribution from the decay of W bosons to hadrons through an intermediate τ lepton. It is obtained from MC simulation, and corrected for measured differences in lepton efficiencies between data and simulation. The factor k (MT2) accounts for the distribution, in bins of MT2, of the estimated background in each topological region. It is obtained using both data and simulation as follows. In each topological region, the control region corresponding to the highest MT2 bin is successively combined with the next highest bin until the expected SM yield in combined bins is at least 50 events. When two or more control region bins are combined, the fraction of events expected to populate a particular MT2 bin, k (MT2), is determined using the expectation from SM simulated samples, including all relevant processes. The modeling of MT2 is checked in data using single-lepton control samples enriched in events originating from either W+jets or tt+jets, as shown in the upper and lower panels of Fig. 1, respectively. The predicted distributions in the comparison are obtained by summing all control regions after normalizing MC yields to data and distributing events among MT2 bins according to the expectation from simulation, as is done for the estimate of the lost-lepton background. For 35.9 fb-1 (13 TeV) Data Stat. unc. Syst. unc. 2 F O / S 1.8 F R CMS events with Nj = 1, a control region is defined for each bin of jet pT. Uncertainties from the limited size of the control sample and from theoretical and experimental sources are evaluated and propagated to the final estimate. The dominant uncertainty in RM0C/1 HT, Nj, Nb, MT2 arises from the modeling of the lepton efficiency (for electrons, muons, and 2 3 4 5 6 7 8 9 10 Nj 1.2 CMS 1 0.8 0.6 0.4 0.2 02 rφ 100 hadronically-decaying tau leptons) and jet energy scale (JES) and is of order 15–20%. The uncertainty in the MT2 extrapolation, which is as large as 40%, arises primarily from the JES, the relative fractions of W+jets and tt+jets, and variations of the renormalization and factorization scales assumed for their simulation. These and other uncertainties are similar to those in Ref. [ 6 ]. 4.2 Estimation of the background from Z(νν)+jets The Z → νν background is estimated from a dilepton con trol sample selected using triggers requiring two leptons. The trigger efficiency, measured with a data sample of events with large HT, is found to be greater than 97% in the selected kinematic range. To obtain a control sample enriched in Z → + − events ( = e, μ), we require that the leptons are of the same flavor, opposite charge, that the pT of the leading and trailing leptons are at least 100 and 30 GeV, respectively, and that the invariant mass of the lepton pair is consistent with the mass of a Z boson within 20 GeV. After requiring that the pT of the dilepton system is at least 200 GeV, the preselection requirements are applied based on kinematic variables recalculated after removing the dilepton system from the event to replicate the Z → νν kinematics. For events with Nj = 1, one control region is defined for each bin of jet pT. For events with at least two jets, the selected events are binned in HT, Nj, and Nb, but not in MT2, to increase the dilepton event yield in each control region. The contribution to each control region from flavorsymmetric processes, most importantly tt, is estimated using opposite-flavor (OF) eμ events obtained with the same selections as same-flavor (SF) ee and μμ events. The background in each signal bin is then obtained using transfer factors and the associated fit uncertainty. Values of fj, the fraction of events in bin Nj, (middle) and rb, the fraction of events in bin Nj that fall in bin Nb, (right) are measured in data after requiring Δφmin < 0.3 and 100 < MT2 < 200 GeV. The hatched bands represent both statistical and systematic uncertainties according to: N SR Z→νν N CRSF = HT, Nj, Nb, MT2 HT, Nj, Nb − N CROF HT, Nj, Nb RSF/OF × RZ→νν/Z → + − MC HT, Nj, Nb k (MT2) . (3) Here N CRSF and N CROF are the number of SF and OF events in the control region, while RMZ→Cνν/Z→ + − and k (MT2) are defined below. The factor RSF/OF accounts for the difference in acceptance and efficiency between SF and OF events. It is determined as the ratio of the number of SF events to OF events in a tt enriched control sample, obtained with the same selections as the Z → + − sample, but inverting the requirements on the pT and the invariant mass of the lepton pair. A measured value of RSF/OF = 1.13 ± 0.15 is observed to be stable with respect to event kinematics, and is applied in all regions. Figure 2 (left) shows RSF/OF measured as a function of the number of jets. An estimate of the Z → νν background in each topological region is obtained from the corresponding dilepton control region via the factor RMZ→Cνν/Z→ + − , which accounts for the acceptance and efficiency to select the dilepton pair and the ratio of branching fractions for Z → + − and Z → νν decays. This factor is obtained from simulation, including corrections for differences in the lepton efficiencies between data and simulation. The factor k (MT2) accounts for the distribution, in bins of MT2, of the estimated background in each topological region. This distribution is constructed using the MT2 shape from dilepton data and Z → νν simulation in each topological region. Studies with simulated samples indicate that the MT2 shape for Z → νν events is indeCMS 1 Jet 0b ≥1b [ 1 [ [2 [3 [4 [6 [2 [3 [4 [6 [2 [3 [4 [6 [2 [3 [4 [6 [2 [3 [4 [6 [2 [3 [4 [6 [2 [3 [4 [6 [2 [3 [4 [2 [3 [4 [2 [3 [4 [2 [3 [4 Prediction of direct top squark production with different decay modes. For mixed decay scenarios, we assume a 50% branching fraction for each decay mode at least 50 expected events from simulation. The fraction of malizing the simulation to the data yield in the same group events in each uncombined bin is determined using the corof bins. responding MT2 template from dilepton data, corrected by The modeling of MT2 is validated in data using control the ratio R MC Z →νν/Z → + − . The MT2 shape from simulation is used to distribute events among the combined bins, after norsamples enriched in γ , W → ν, and Z → + − events in each bin of HT. The lower panel of Fig. 2 shows agreement between the MT2 distributions obtained from γ , W, and Z data control samples with that from Z → νν simulation for events with 1000 < HT < 1500 GeV. In this comparison, the γ sample is obtained by selecting events with pTγ > 180 GeV and is corrected for contributions from multijet events and RZ/γ , the W sample is corrected for RMZ/CW, MC both the W and Z samples are corrected for contributions from top quark events, and the Z sample is further corrected for RMZ→Cνν/Z→ + − . Here RMZ/Cγ ( RMZ/CW) is the ratio of the MT2 distributions for Z boson and γ (W) boson events derived in simulation. The largest uncertainty in the estimate of the invisible Z background in most regions results from the limited size of the dilepton control sample. This uncertainty, as well as all other relevant theoretical and experimental uncertainties, are evaluated and propagated to the final estimate. The dominant uncertainty in the ratio R MZ→Cνν/Z → + − is obtained from measured differences in lepton efficiency between data and simulation, and is about 5%. The uncertainty in the k (MT2) factor arises from data statistics for uncombined bins, while for combined bins it is due to uncertainties in the JES and variations in the renormalization and factorization scales. These can result in effects as large as 40%. 4.3 Estimation of the multijet background For events with at least two jets, a multijet-enriched control region is obtained in each HT bin by inverting the Δφmin requirement described in Sect. 3. Events are selected using HT triggers, and the extrapolation from low- to high-Δφmin is based on the following ratio: rφ (MT2) = N (Δφmin > 0.3)/N (Δφmin < 0.3). (4) Studies with simulated samples show that the ratio can be described by a power law as rφ (MT2) = a MTb2. The parameters a and b are determined separately in each HT bin by fitting rφ in an MT2 sideband in data after subtracting nonmultijet contributions using simulation. The sideband spans MT2 values of 60–100 GeV for events with HT < 1000 GeV, and 70–100 GeV for events with larger values of HT. The fit to the rφ distribution in the 1000 < HT < 1500 GeV region is shown in Fig. 3 (left). The inclusive multijet contribution in each signal region, NjS,bR (MT2), is estimated using the ratio rφ (MT2) measured in the MT2 sideband and the number of events in the low-Δφmin control region, NiCncR (MT2), according to NjS,bR(MT2) = NiCncR (MT2) rφ (MT2) fj ( HT) rb Nj , (5) where fj is the fraction of multijet events in bin Nj, and rb is the fraction of events in bin Nj that are in bin Nb. (Here, Nj denotes a jet multiplicity bin, and Nb denotes a b jet multiplicity bin within Nj). The values of fj and rb are measured using events with MT2 between 100 and 200 GeV in the low Δφmin sideband, where fj is measured separately in each HT bin, while rb is measured in bins of Nj integrated over HT, as rb is found to be independent of the latter. Values of fj and rb measured in data are shown in Fig. 3 (center and right) compared to simulation. The largest uncertainties in the estimate in most regions result from the statistical uncertainty in the fit and from the sensitivity of the rφ value to variations in the fit window. These variations result in an uncertainty that increases with MT2 and ranges from 20–50%. The total uncertainty in the estimate is found to be of similar size as in Ref. [ 6 ], varying between 40–180% depending on the search region. An estimate based on rφ (MT2) is not viable in the monojet search regions, which therefore require a different strategy. A control region is obtained by selecting events with a second jet with 30 < pT < 60 GeV and inverting the Δφmin requirement. After subtracting non-multijet contributions using simulation, the data yield in the control region is taken as an estimate of the background in the corresponding monojet search region. Tests in simulation show the method provides a conservative estimate of the multijet background, which is less than 8% in all monojet search regions. In all monojet bins, a 50% uncertainty in the non-multijet subtraction is combined with the statistical uncertainty from the data yield in the control region with a second jet. 5 Results The data yields in the search regions are statistically compatible with the estimated backgrounds from SM processes. A summary of the results of this search is shown in Fig. 4. Each bin in the upper panel corresponds to a single HT, Nj, Nb 710 observed exclusion region, while the dashed red lines indicate the expected limits and their ±1 standard deviation ranges. The thin black lines show the effect of the theoretical uncertainties on the signal cross section topological region, integrated over MT2. The lower panel further breaks down the background estimates and observed data yields into MT2 bins for the region 575 < HT < 1000 GeV. Distributions for the other HT regions can be found in Appendix B. The background estimates and corresponding uncertainties shown in these plots rely exclusively on the inputs from control samples and simulation described in Sect. 4, and are referred to in the rest of the text as “prefit background” results. To allow simpler reinterpretation, we also provide results for super signal regions, which cover subsets of the full analysis with simpler inclusive selections and that can be used to obtain approximate interpretations of this search. The definitions of these regions are given in Table 2, with the predicted and observed number of events and the 95% confidence level (CL) upper limit on the number of signal events contributing to each region. Limits are set using a modified frequentist approach, employing the CLs criterion and relying on uncertainties on the signal cross section. The white diagonal band in the upper right plot corresponds to the region |mt − mt − mχ0 | < 25 GeV 1 and small mχ0 . Here the efficiency of the selection is a strong func1 tion of mt − mχ0 , and as a result the precise determination of the cross 1 section upper limit is uncertain because of the finite granularity of the available MC samples in this region of the (mt, mχ0 ) plane 1 asymptotic approximations to calculate the distribution of the profile likelihood test-statistic used [ 41–44 ]. 5.1 Interpretation The results of the search can be interpreted by performing a maximum likelihood fit to the data in the signal regions. The fit is carried out under either a backgroundonly or a background+signal hypothesis. The uncertainties in the modeling of the backgrounds, summarized in Sect. 4, are inputs to the fitting procedure. The likelihood is constructed as the product of Poisson probability density functions, one for each signal region, with constraint terms that account for uncertainties in the background esti710 mates and, if considered, the signal yields. The result of the background-only fit, denoted as “post-fit background”, is given in Appendix B. If the magnitude and correlation model of the uncertainties associated to the pre-fit estimates are properly assigned, and the data are found to be in agreement with the estimates, then the fit has the effect of constraining the background and reducing the associated uncertainties. t → bχ1+, χ1+ → W∗+χ10, is also considered, with the chargino mass chosen such that Δm χ1±, χ10 = 5 GeV. Finally, we also consider a compressed scenario (below) where pp → tt → ccχ10χ 0. The area 1 enclosed by the thick black curve represents the observed exclusion region, while the dashed red lines indicate the expected limits and their ±1 standard deviation ranges. The thin black lines show the effect of the theoretical uncertainties on the signal cross section The results of the search are used to constrain the simplified models of SUSY [ 45 ] shown in Fig. 5. For each scenario of gluino (squark) pair production, the simplified models assume that all SUSY particles other than the gluino (squark) and the lightest neutralino are too heavy to be produced directly, and that the gluino (squark) decays promptly. The models assume that each gluino (squark) decays with a 100% branching fraction into the decay products depicted Table 4 Summary of 95% CL observed exclusion limits on the masses of SUSY particles (sparticles) in different simplified model scenarios. The limit on the mass of the produced sparticle is quoted for a massless χ10, while for the mass of the χ10 we quote the highest limit that is obtained mχ10 = 0 GeV Limit on produced Highest limit sparticle on the mass (GeV) for χ10 mass (GeV) in Fig. 5. For models where the decays of the two squarks differ, we assume a 50% branching fraction for each decay mode. For the scenario of top squark pair production, the polarization of the top quark is model dependent and is a function of the top-squark and neutralino mixing matrices. To remain agnostic to a particular model realization, events are generated without polarization. Signal cross sections are calculated at NLO + NLL order in αs [ 46–50 ]. Typical values of the uncertainties in the signal yield for the simplified models considered are listed in Table 3. The sources of uncertainties and the methods used to evaluate their effect on the interpretation are the same as those discussed in Ref. [ 6 ]. Uncertainties due to the luminosity [ 51 ], ISR and pileup modeling, and b tagging and lepton efficiencies are treated as correlated across search bins. Remaining uncertainties are taken as uncorrelated. Figure 6 shows the exclusion limits at 95% CL for gluinomediated bottom squark, top squark, and light-flavor squark production. Exclusion limits at 95% CL for the direct production of bottom, top, and light-flavor squark pairs are shown in Fig. 7. Direct production of top squarks for three alternate decay scenarios are also considered, and exclusion limits at 95% CL are shown in Fig. 8. Table 4 summarizes the limits on the masses of the SUSY particles excluded in the simplified model scenarios considered. These results extend the constraints on gluinos and squarks by about 300 GeV and on χ10 by 200 GeV with respect to those in Ref. [ 6 ]. The largest differences between the observed and expected limits are found for scenarios of top squark pair production with moderate mass splittings and result from observed yields that are less than the expected background in topological regions with HT between 575 and 1500 GeV, at least 7 jets, and either one or two b-tagged jets. We note that the 95% CL upper limits on signal cross sections obtained using the most sensitive super signal regions of Table 2 are typically less stringent by a factor of ∼1.5– 3 compared to those obtained in the fully-binned analysis. The full analysis performs better because of its larger signal acceptance and because it splits the events into bins with more favorable signal-to-background ratio. 6 Summary This paper presents the results of a search for new phenomena using events with jets and large MT2. Results are based on a 35.9 fb−1 data sample of proton–proton collisions at √s = 13 TeV collected in 2016 with the CMS detector. No significant deviations from the standard model expectations are observed. The results are interpreted as limits on the production of new, massive colored particles in simplified models of supersymmetry. This search probes gluino masses up to 2025 GeV and χ10 masses up to 1400 GeV. Constraints 590 550 475 775 1400 1010 1100 Simplified model Direct squark production Bottom squark Top squark Single light squark g → bbχ10 g → ttχ10 g → qqχ10 Eight degenerate light squarks 1550 Gluino-mediated production 1175 1070 1050 2025 1900 1860 are also obtained on the pair production of light-flavor, bottom, and top squarks, probing masses up to 1550, 1175, and 1070 GeV, respectively, and χ10 masses up to 775, 590, and 550 GeV in each scenario. Acknowledgements We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at CERN and at other CMS institutes for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies: BMWFW and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST, and NSFC (China); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RPF (Cyprus); SENESCYT (Ecuador); MoER, ERC IUT, and ERDF (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRT (Greece); OTKA and NIH (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MBIE (New Zealand); PAEC (Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom, RAS, RFBR and RAEP (Russia); MESTD (Serbia); SEIDI, CPAN, PCTI and FEDER (Spain); Swiss Funding Agencies (Switzerland); MST (Taipei); ThEPCenter, IPST, STAR, and NSTDA (Thailand); TUBITAK and TAEK (Turkey); NASU and SFFR (Ukraine); STFC (UK); DOE and NSF (USA). Individuals have received support from the Marie-Curie program and the European Research Council and EPLANET (European Union); the Leventis Foundation; the A. P. Sloan Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science Policy Office; the Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Council of Science and Industrial Research, India; the HOMING PLUS program of the Foundation for Polish Science, cofinanced from European Union, Regional Development Fund, the Mobility Plus program of the Ministry of Science and Higher Education, the National Science Center (Poland), contracts Harmonia 2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543, 2014/15/B/ST2/03998, and 2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406; the National Priorities Research Program by Qatar National Research Fund; the Programa Clarín-COFUND del Principado de Asturias; the Thalis and Aristeia programs cofinanced by EUESF and the Greek NSRF; the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University and the Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); and the Welch Foundation, contract C-1845. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecomm ons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Funded by SCOAP3. A Definition of search regions The 213 exclusive search regions are defined in Tables 5, 6 and 7. Table 5 Summary of signal regions for the monojet selection Jet pT binning (GeV) 2 − 3j, 0b 2 − 3j, 1b CMS f o n i b s e i E . t s E / a t D 2 T M f o s n i n i e i r t E / a t 1.5 0.5 2 1 0 10 10 10 10 10 6 5 4 3 2 10 1 10 −1 1.5 0.5 2 1 0 2-3j 0b ] 5 7 5 , 0 5 4 [ 2-3j 1b 1j 0b ] 0 0 3 , 0 0 2 [ ] 0 0 4 , 0 0 3 [ ] 0 0 3 , 0 0 2 [ ] 0 0 4 , 0 0 3 [ ] 0 0 3 , 0 0 2 [ ] 0 0 4 , 0 0 3 [ Fig. 9 (Upper) Comparison of the estimated background and observed data events in each signal bin in the monojet region. On the x-axis, the p binning is shown in units of GeV. Hatched bands represent the full T uncertainty in the background estimate. (Lower) Same for the very low H region. On the x-axis, the M T T2 binning is shown in units of GeV Fig. 10 (Upper) Comparison of the estimated background and observed data events in each signal bin in the low-H region. T Hatched bands represent the full uncertainty in the background estimate. Same for the high(middle) and extreme- (lower) H regions. On the x-axis, the T M binning is shown in units T2 of GeV. For the extreme-H T region, the last bin is left empty for visualization purposes CMS e i r t n E . t s E / a t a D n i s e i r t n E . t s a t a D f o s n i E / a t a D 3 10 10 1 Pre-fit background H [450, 575] GeV T 2-3j 1b 2-3j 2b 4-6j 0b 4-6j 1b Data 500 ]00 ]004 040> 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ,1 ,1 > 0 0 0 ,1 ,1 > 0 0 0 ,1 > 0 0 0 ,1 ,1 > 0 0 0 ,1 ,1 > 0 0 0 ,1 > 0 0 0 ,1 > 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [2 [4 [6 80 00 [2 [4 [6 80 00 [2 [4 [6 80 [2 [4 [6 80 00 [2 [4 [6 80 00 [2 [4 [6 80 [2 [4 [6 80 [2 [4 [6 [2 [4 [6 [2 [4 [2 [4 [ 1 [ 1 [ [ 1 [ 1 [ [ [ [ [ [ n 5 E 10 4 10 3 10 2 10 . t a t [2 [3 [4 [5 [70 100 [2 [3 [4 [5 [ jet1 Fig. 11 Comparison of post-fit background prediction and observed the x-axis the p binning is shown in units of GeV, whereas for the T data events in each topological region. Hatched bands represent the multijet signal regions, the notations j, b indicate Nj, Nb labeling post-fit uncertainty in the background prediction. For the monojet, on Fig. 13 (Upper) Comparison of the post-fit background prediction and observed data events in each signal bin in the 10 1 −1 10 −2 10 f o 105 2-3j 2-3j 2-3j 4-6j 4-6j 4-6j ≥7j ≥7j s ,]00800 ,]00010 ,]00410 ,]00810 8001> ,]00800 ,]00010 ,]00510 5001> 5001> ,]00800 ,]00010 ,]00410 ,]00810 8001> ,]00800 ,]00010 ,]40010 ,]80010 8001> ,]80000 ,]50010 5001> ,]80000 ,]00010 ,]50010 5001> ,]80000 ,]50010 5001> ,]80000 ,]50010 5001> ,]50010 5001> 5001> 6 0 0 0 6 0 0 [ 8 0 4 [ 8 0 [ 1 1 [ 1 [ [ [ [6 [80 010 410 [6 [80 010 410 [6 [80 [ [ [ [ 6 0 0 6 0 [ 8 0 [ 8 [ 1 [ [ [030 [040 [060 ,[0801 > [030 [040 [060 ,[0801 > [030 [040 [060 ,[0801 > [030 [040 [060 ,[0801 > [030 [040 [060 ,[0801 > [030 [040 [060 ,[0801 > [030 [040 [060 ,[0801 > [030 [040 ,[0601 > [030 [040 ,[0601 > [030 [040 ,[0601 > [300 [40 [600 CMS Fig. 14 (Upper) The post-fit background prediction and observed data events in the analysis binning, for all topological regions with the expected yield for the signal model of gluino mediated bottom-squark production (mg = 1000 GeV, mχ10 = 800 GeV) stacked on top of the expected background. For the monojet regions, the pjet1 binning is in T units of GeV. (Lower) Same for the extreme-HT region for the same signal with (mg = 1900 GeV, mχ10 = 100 GeV). On the x-axis, the MT2 binning is shown in units of GeV. The hatched bands represent the postfit uncertainty in the background prediction. For the extreme-HT region, the last bin is left empty for visualization purposes Page 20 of 34 CMS Collaboration Yerevan Physics Institute, Yerevan, Armenia A. M. Sirunyan, A. Tumasyan, A. Johnson41 Institut für Hochenergiephysik, Wien, Austria W. Adam, F. Ambrogi, E. Asilar, T. Bergauer, J. Brandstetter, E. Brondolin, M. Dragicevic, J. Erö, M. Flechl, M. Friedl, R. Frühwirth1, V. M. Ghete, J. Grossmann, J. Hrubec, M. Jeitler1, A. König, N. Krammer, I. Krätschmer, D. Liko, T. Madlener, I. Mikulec, E. Pree, D. Rabady, N. Rad, H. Rohringer, J. Schieck1, R. Schöfbeck, M. Spanring, D. Spitzbart, J. Strauss, W. Waltenberger, J. Wittmann, C.-E. Wulz1, M. Zarucki Institute for Nuclear Problems, Minsk, Belarus V. Chekhovsky, V. Mossolov, J. Suarez Gonzalez Universiteit Antwerpen, Antwerp, Belgium N. Van Remortel, A. Van Spilbeeck Vrije Universiteit Brussel, Brussels, Belgium E. A. De Wolf, D. Di Croce, X. Janssen, J. Lauwers, M. Van De Klundert, H. Van Haevermaet, P. Van Mechelen, S. Abu Zeid, F. Blekman, J. D’Hondt, I. De Bruyn, J. De Clercq, K. Deroover, G. Flouris, D. Lontkovskyi, S. Lowette, S. Moortgat, L. Moreels, A. Olbrechts, Q. Python, K. Skovpen, S. Tavernier, W. Van Doninck, P. Van Mulders, I. Van Parijs Université Libre de Bruxelles, Brussels, Belgium H. Brun, B. Clerbaux, G. De Lentdecker, H. Delannoy, G. Fasanella, L. Favart, R. Goldouzian, A. Grebenyuk, G. Karapostoli, T. Lenzi, J. Luetic, T. Maerschalk, A. Marinov, A. Randle-conde, T. Seva, C. Vander Velde, P. Vanlaer, D. Vannerom, R. Yonamine, F. Zenoni, F. Zhang2 Ghent University, Ghent, Belgium A. Cimmino, T. Cornelis, D. Dobur, A. Fagot, M. Gul, I. Khvastunov, D. Poyraz, C. Roskas, S. Salva, M. Tytgat, W. Verbeke, N. Zaganidis Université Catholique de Louvain, Louvain-la-Neuve, Belgium H. Bakhshiansohi, O. Bondu, S. Brochet, G. Bruno, A. Caudron, S. De Visscher, C. Delaere, M. Delcourt, B. Francois, A. Giammanco, A. Jafari, M. Komm, G. Krintiras, V. Lemaitre, A. Magitteri, A. Mertens, M. Musich, K. Piotrzkowski, L. Quertenmont, M. Vidal Marono, S. Wertz Université de Mons, Mons, Belgium N. Beliy Centro Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil W. L. Aldá Júnior, F. L. Alves, G. A. Alves, L. Brito, M. Correa Martins Junior, C. Hensel, A. Moraes, M. E. Pol, P. Rebello Teles Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil E. Belchior Batista Das Chagas, W. Carvalho, J. Chinellato3, A. Custódio, E. M. Da Costa, G. G. Da Silveira4, D. De Jesus Damiao, S. Fonseca De Souza, L. M. Huertas Guativa, H. Malbouisson, M. Melo De Almeida, C. Mora Herrera, L. Mundim, H. Nogima, A. Santoro, A. Sznajder, E. J. Tonelli Manganote3, F. Torres Da Silva De Araujo, A. Vilela Pereira Universidade Estadual Paulistaa , Universidade Federal do ABCb, São Paulo, Brazil S. Ahujaa , C. A. Bernardesa , T. R. Fernandez Perez Tomeia , E. M. Gregoresb, P. G. Mercadanteb, C. S. Moona , S. F. Novaesa , Sandra S. Padulaa , D. Romero Abadb, J. C. Ruiz Vargasa Institute for Nuclear Research and Nuclear Energy of Bulgaria Academy of Sciences, Sofia, Bulgaria A. Aleksandrov, R. Hadjiiska, P. Iaydjiev, M. Misheva, M. Rodozov, M. Shopova, S. Stoykova, G. Sultanov University of Sofia, Sofia, Bulgaria A. Dimitrov, I. Glushkov, L. Litov, B. Pavlov, P. Petkov Beihang University, Beijing, China W. Fang5, X. Gao5 Institute of High Energy Physics, Beijing, China M. Ahmad, J. G. Bian, G. M. Chen, H. S. Chen, M. Chen, Y. Chen, C. H. Jiang, D. Leggat, Z. Liu, F. Romeo, S. M. Shaheen, A. Spiezia, J. Tao, C. Wang, Z. Wang, E. Yazgan, H. Zhang, J. Zhao State Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing, China Y. Ban, G. Chen, Q. Li, S. Liu, Y. Mao, S. J. Qian, D. Wang, Z. Xu Universidad de Los Andes, Bogota, Colombia C. Avila, A. Cabrera, L. F. Chaparro Sierra, C. Florez, C. F. González Hernández, J. D. Ruiz Alvarez Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, Split, Croatia B. Courbon, N. Godinovic, D. Lelas, I. Puljak, P. M. Ribeiro Cipriano, T. Sculac Faculty of Science, University of Split, Split, Croatia Z. Antunovic, M. Kovac Institute Rudjer Boskovic, Zagreb, Croatia V. Brigljevic, D. Ferencek, K. Kadija, B. Mesic, T. Susa Charles University, Prague, Czech Republic M. Finger6, M. Finger Jr.6 Universidad San Francisco de Quito, Quito, Ecuador E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt A. Ellithi Kamel7, S. Khalil8, A. Mohamed8 National Institute of Chemical Physics and Biophysics, Tallinn, Estonia R. K. Dewanjee, M. Kadastik, L. Perrini, M. Raidal, A. Tiko, C. Veelken Department of Physics, University of Helsinki, Helsinki, Finland P. Eerola, J. Pekkanen, M. Voutilainen Lappeenranta University of Technology, Lappeenranta, Finland J. Talvitie, T. Tuuva Laboratoire Leprince-Ringuet, Ecole Polytechnique, CNRS/IN2P3, Université Paris-Saclay, Palaiseau, France A. Abdulsalam, I. Antropov, S. Baffioni, F. Beaudette, P. Busson, L. Cadamuro, E. Chapon, C. Charlot, O. Davignon, R. Granier de Cassagnac, M. Jo, S. Lisniak, A. Lobanov, J. Martin Blanco, M. Nguyen, C. Ochando, G. Ortona, P. Paganini, P. Pigard, S. Regnard, R. Salerno, J. B. Sauvan, Y. Sirois, A. G. Stahl Leiton, T. Strebler, Y. Yilmaz, A. Zabi Université de Strasbourg, CNRS, IPHC UMR 7178, 67000 Strasbourg, France J.-L. Agram9, J. Andrea, A. Aubin, J.-M. Brom, M. Buttignol, E. C. Chabert, N. Chanon, C. Collard, E. Conte9, X. Coubez, J.-C. Fontaine9, D. Gelé, U. Goerlach, M. Jansová, A.-C. Le Bihan, N. Tonon, P. Van Hove Georgian Technical University, Tbilisi, Georgia A. Khvedelidze6 Tbilisi State University, Tbilisi, Georgia Z. Tsamalaidze6 RWTH Aachen University, I. Physikalisches Institut, Aachen, Germany C. Autermann, S. Beranek, L. Feld, M. K. Kiesel, K. Klein, M. Lipinski, M. Preuten, C. Schomakers, J. Schulz, T. Verlage RWTH Aachen University, III. Physikalisches Institut A, Aachen, Germany A. Albert, M. Brodski, E. Dietz-Laursonn, D. Duchardt, M. Endres, M. Erdmann, S. Erdweg, T. Esch, R. Fischer, A. Güth, M. Hamer, T. Hebbeker, C. Heidemann, K. Hoepfner, S. Knutzen, M. Merschmeyer, A. Meyer, P. Millet, S. Mukherjee, M. Olschewski, K. Padeken, T. Pook, M. Radziej, H. Reithler, M. Rieger, F. Scheuch, D. Teyssier, S. Thüer University of Hamburg, Hamburg, Germany S. Bein, V. Blobel, M. Centis Vignali, A. R. Draeger, T. Dreyer, E. Garutti, D. Gonzalez, J. Haller, A. Hinzmann, M. Hoffmann, A. Junkes, A. Karavdina, R. Klanner, R. Kogler, N. Kovalchuk, S. Kurz, T. Lapsien, I. Marchesini, D. Marconi, M. Meyer, M. Niedziela, D. Nowatschin, F. Pantaleo11, T. Peiffer, A. Perieanu, C. Scharf, P. Schleper, A. Schmidt, S. Schumann, J. Schwandt, J. Sonneveld, H. Stadie, G. Steinbrück, F. M. Stober, M. Stöver, H. Tholen, D. Troendle, E. Usai, L. Vanelderen, A. Vanhoefer, B. Vormwald Institut für Experimentelle Kernphysik, Karlsruhe, Germany M. Akbiyik, C. Barth, S. Baur, E. Butz, R. Caspart, T. Chwalek, F. Colombo, W. De Boer, A. Dierlamm, B. Freund, R. Friese, M. Giffels, A. Gilbert, D. Haitz, F. Hartmann11, S. M. Heindl, U. Husemann, F. Kassel11, S. Kudella, H. Mildner, M. U. Mozer, Th. Müller, M. Plagge, G. Quast, K. Rabbertz, M. Schröder, I. Shvetsov, G. Sieber, H. J. Simonis, R. Ulrich, S. Wayand, M. Weber, T. Weiler, S. Williamson, C. Wöhrmann, R. Wolf Institute of Nuclear and Particle Physics (INPP), NCSR Demokritos, Aghia Paraskevi, Greece G. Anagnostou, G. Daskalakis, T. Geralis, V. A. Giakoumopoulou, A. Kyriakis, D. Loukas, I. Topsis-Giotis National and Kapodistrian University of Athens, Athens, Greece S. Kesisoglou, A. Panagiotou, N. Saoulidou MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös Loránd University, Budapest, Hungary M. Csanad, N. Filipovic, G. Pasztor Wigner Research Centre for Physics, Budapest, Hungary G. Bencze, C. Hajdu, D. Horvath15, Á. Hunyadi, F. Sikler, V. Veszpremi, G. Vesztergombi16, A. J. Zsigmond Institute of Nuclear Research ATOMKI, Debrecen, Hungary N. Beni, S. Czellar, J. Karancsi17, A. Makovec, J. Molnar, Z. Szillasi Institute of Physics, University of Debrecen, Debrecen, Hungary M. Bartók16, P. Raics, Z. L. Trocsanyi, B. Ujvari Indian Institute of Science (IISc), Bangalore, India S. Choudhury, J. R. Komaragiri National Institute of Science Education and Research, Bhubaneswar, India S. Bahinipati18, S. Bhowmik, P. Mal, K. Mandal, A. Nayak19, D. K. Sahoo18, N. Sahoo, S. K. Swain Bhabha Atomic Research Centre, Mumbai, India R. Chudasama, D. Dutta, V. Jha, V. Kumar, A. K. Mohanty11, P. K. Netrakanti, L. M. Pant, P. Shukla, A. Topkar Tata Institute of Fundamental Research-A, Mumbai, India T. Aziz, S. Dugad, B. Mahakud, S. Mitra, G. B. Mohanty, B. Parida, N. Sur, B. Sutar Tata Institute of Fundamental Research-B, Mumbai, India S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, Sa. Jain, S. Kumar, M. Maity20, G. Majumder, K. Mazumdar, T. Sarkar20, N. Wickramage21 Indian Institute of Science Education and Research (IISER), Pune, India S. Chauhan, S. Dube, V. Hegde, A. Kapoor, K. Kothekar, S. Pandey, A. Rane, S. Sharma Institute for Research in Fundamental Sciences (IPM), Tehran, Iran S. Chenarani22, E. Eskandari Tadavani, S. M. Etesami22, M. Khakzad, M. Mohammadi Najafabadi, M. Naseri, S. Paktinat Mehdiabadi23, F. Rezaei Hosseinabadi, B. Safarzadeh24, M. Zeinali University College Dublin, Dublin, Ireland M. Felcini, M. Grunewald INFN Sezione di Baria , Università di Barib, Politecnico di Baric, 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, F. Erricoa ,b, S. Lezkia ,b, L. Fiorea , G. Iasellia ,c, G. Maggia ,c, M. Maggia , G. Minielloa ,b, S. Mya ,b, S. Nuzzoa ,b, A. Pompilia ,b, G. Pugliesea ,c, R. Radognaa ,b, A. Ranieria , G. Selvaggia ,b, A. Sharmaa , L. Silvestrisa ,11, R. Vendittia , P. Verwilligena INFN Sezione di Bolognaa , Università di Bolognab, Bologna, Italy G. Abbiendia , C. Battilana, 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. Cuffiania ,b, G. M. Dallavallea , F. Fabbria , A. Fanfania ,b, D. Fasanellaa ,b, P. Giacomellia , L. Guiduccia ,b, S. Marcellinia , G. Masettia , F. L. Navarriaa ,b, A. Perrottaa , A. M. Rossia ,b, T. Rovellia ,b, G. P. Sirolia ,b, N. Tosia ,b,11 INFN Sezione di Cataniaa , Università di Cataniab, Catania, Italy S. Albergoa ,b, S. Costaa ,b, A. Di Mattiaa , F. Giordanoa ,b, R. Potenzaa ,b, A. Tricomia ,b, C. Tuvea ,b INFN Sezione di Firenzea , Università di Firenzeb, Firenze, Italy G. Barbaglia , K. Chatterjeea ,b, V. Ciullia ,b, C. Civininia , R. D’Alessandroa ,b, E. Focardia ,b, P. Lenzia ,b, M. Meschinia , S. Paolettia , L. Russoa ,25, G. Sguazzonia , D. Stroma , L. Viliania ,b,11 INFN Laboratori Nazionali di Frascati, Frascati, Italy L. Benussi, S. Bianco, F. Fabbri, D. Piccolo, F. Primavera11 INFN Sezione di Genovaa , Università di Genovab, Genova, Italy V. Calvellia ,b, F. Ferroa , E. Robuttia , S. Tosia ,b INFN Sezione di Milano-Bicoccaa , Università di Milano-Bicoccab, Milan, Italy L. Brianzaa ,b, F. Brivioa ,b, V. Cirioloa ,b, M. E. Dinardoa ,b, S. Fiorendia ,b, S. Gennaia , A. Ghezzia ,b, P. Govonia ,b, M. Malbertia ,b, S. Malvezzia , R. A. Manzonia ,b, D. Menascea , L. Moronia , M. Paganonia ,b, K. Pauwelsa ,b, D. Pedrinia , S. Pigazzinia ,b,26, S. Ragazzia ,b, T. Tabarelli de Fatisa ,b INFN Sezione di Napolia , Università di Napoli ’Federico II’ b, Naples, Italy, Università della Basilicatac, Potenza, Italy, Università G. Marconid , Rome, Italy S. Buontempoa , N. Cavalloa ,c, S. Di Guidaa ,d ,11, M. Espositoa ,b, F. Fabozzia ,c, F. Fiengaa ,b, A. O. M. Iorioa ,b, W. A. Khana , G. Lanzaa , L. Listaa , S. Meolaa ,d ,11, P. Paoluccia ,11, C. Sciaccaa ,b, F. Thyssena INFN Sezione di Padovaa , Università di Padovab, Padua, Italy, Università di Trentoc, Trento, Italy P. Azzia ,11, N. Bacchettaa , L. Benatoa ,b, M. Biasottoa ,27, D. Biselloa ,b, A. Bolettia ,b, R. Carlina ,b, A. Carvalho Antunes De Oliveiraa ,b, P. Checchiaa , M. Dall’Ossoa ,b, P. De Castro Manzanoa , T. Dorigoa , U. Dossellia , S. Fantinela , F. Fanzagoa , U. Gasparinia ,b, S. Lacapraraa , M. Margonia ,b, A. T. Meneguzzoa ,b, N. Pozzobona ,b, P. Ronchesea ,b, R. Rossina ,b, F. Simonettoa ,b, E. Torassaa , M. Zanettia ,b, P. Zottoa ,b INFN Sezione di Paviaa , Università di Paviab, Pavia, Italy A. Braghieria , F. Fallavollitaa ,b, A. Magnania ,b, P. Montagnaa ,b, S. P. Rattia ,b, V. Rea , M. Ressegotti, C. Riccardia ,b, P. Salvinia , I. Vaia ,b, P. Vituloa ,b INFN Sezione di Perugiaa , Università di Perugiab, Perugia, Italy L. Alunni Solestizia ,b, G. M. Bileia , D. Ciangottinia ,b, L. Fanòa ,b, P. Laricciaa ,b, R. Leonardia ,b, G. Mantovania ,b, V. Mariania ,b, M. Menichellia , A. Sahaa , A. Santocchiaa ,b, D. Spiga INFN Sezione di Pisaa , Università di Pisab, Scuola Normale Superiore di Pisac, Pisa, Italy K. Androsova , P. Azzurria ,11, G. Bagliesia , J. Bernardinia , T. Boccalia , L. Borrello, R. Castaldia , M. A. Cioccia ,b, R. Dell’Orsoa , G. Fedia , L. Gianninia ,c, A. Giassia , M. T. Grippoa ,25, F. Ligabuea ,c, T. Lomtadzea , E. Mancaa ,c, G. Mandorlia ,c, L. Martinia ,b, A. Messineoa ,b, F. Pallaa , A. Rizzia ,b, A. Savoy-Navarroa ,28, P. Spagnoloa , R. Tenchinia , G. Tonellia ,b, A. Venturia , P. G. Verdinia INFN Sezione di Romaa , Sapienza Università di Romab, Rome, Italy L. Baronea ,b, F. Cavallaria , M. Cipriania ,b, D. Del Rea ,b,11, M. Diemoza , S. Gellia ,b, E. Longoa ,b, F. Margarolia ,b, B. Marzocchia ,b, P. Meridiania , G. Organtinia ,b, R. Paramattia ,b, F. Preiatoa ,b, S. Rahatloua ,b, C. Rovellia , F. Santanastasioa ,b INFN Sezione di Torinoa , Università di Torinob, Tourin, Italy, Università del Piemonte Orientalec, Novara, Italy N. Amapanea ,b, R. Arcidiaconoa ,c,11, S. Argiroa ,b, M. Arneodoa ,c, N. Bartosika , R. Bellana ,b, C. Biinoa , N. Cartigliaa , F. Cennaa ,b, M. Costaa ,b, R. Covarellia ,b, A. Deganoa ,b, N. Demariaa , B. Kiania ,b, C. Mariottia , S. Masellia , E. Migliorea ,b, V. Monacoa ,b, E. Monteila ,b, M. Montenoa , 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, K. Shchelinaa ,b, V. Solaa , A. Solanoa ,b, A. Staianoa , P. Traczyka ,b INFN Sezione di Triestea , Università di Triesteb, Trieste, Italy S. Belfortea , M. Casarsaa , F. Cossuttia , G. Della Riccaa ,b, A. Zanettia Kyungpook National University, Daegu, Korea D. H. Kim, G. N. Kim, M. S. Kim, J. Lee, S. Lee, S. W. Lee, Y. D. Oh, S. Sekmen, D. C. Son, Y. C. Yang Chonbuk National University, Jeonju, Korea A. Lee Hanyang University, Seoul, Korea J. A. Brochero Cifuentes, J. Goh, T. J. Kim Korea University, Seoul, Korea S. Cho, S. Choi, Y. Go, D. Gyun, S. Ha, B. Hong, Y. Jo, Y. Kim, K. Lee, K. S. Lee, S. Lee, J. Lim, S. K. Park, Y. Roh University of Seoul, Seoul, Korea M. Choi, H. Kim, J. H. Kim, J. S. H. Lee, I. C. Park, G. Ryu Sungkyunkwan University, Suwon, Korea Y. Choi, C. Hwang, J. Lee, I. Yu Vilnius University, Vilnius, Lithuania V. Dudenas, A. Juodagalvis, J. Vaitkus National Centre for Particle Physics, Universiti Malaya, Kuala Lumpur, Malaysia I. Ahmed, Z. A. Ibrahim, M. A. B. Md Ali29, F. Mohamad Idris30, W. A. T. Wan Abdullah, M. N. Yusli, Z. Zolkapli Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico H. Castilla-Valdez, E. De La Cruz-Burelo, I. Heredia-De La Cruz31, R. Lopez-Fernandez, J. Mejia Guisao, A. Sanchez-Hernandez Universidad Iberoamericana, Mexico City, Mexico S. Carrillo Moreno, C. Oropeza Barrera, F. Vazquez Valencia Benemerita Universidad Autonoma de Puebla, Puebla, Mexico I. Pedraza, H. A. Salazar Ibarguen, C. Uribe Estrada Universidad Autónoma de San Luis Potosí, San Luis Potosí, Mexico A. Morelos Pineda University of Auckland, Auckland, New Zealand D. Krofcheck University of Canterbury, Christchurch, New Zealand P. H. Butler National Centre for Physics, Quaid-I-Azam University, Islamabad, Pakistan A. Ahmad, M. Ahmad, Q. Hassan, H. R. Hoorani, S. Qazi, A. Saddique, M. Shoaib, M. Waqas Laboratório de Instrumentação e Física Experimental de Partículas, Lisbon, Portugal P. Bargassa, C. Beirão Da Cruz E Silva, B. Calpas, A. Di Francesco, P. Faccioli, M. Gallinaro, J. Hollar, N. Leonardo, L. Lloret Iglesias, M. V. Nemallapudi, J. Seixas, O. Toldaiev, D. Vadruccio, J. Varela National Research Nuclear University ‘Moscow Engineering Physics Institute’ (MEPhI), Moscow, Russia M. Chadeeva37, O. Markin, P. Parygin, D. Philippov, S. Polikarpov, V. Rusinov P.N. Lebedev Physical Institute, Moscow, Russia V. Andreev, M. Azarkin34, I. Dremin34, M. Kirakosyan34, A. Terkulov Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow, Russia A. Baskakov, A. Belyaev, E. Boos, M. Dubinin38, L. Dudko, A. Ershov, A. Gribushin, V. Klyukhin, O. Kodolova, I. Lokhtin, I. Miagkov, S. Obraztsov, S. Petrushanko, V. Savrin, A. Snigirev Novosibirsk State University (NSU), Novosibirsk, Russia V. Blinov39, Y. Skovpen39, D. Shtol39 State Research Center of Russian Federation, Institute for High Energy Physics, Protvino, Russia I. Azhgirey, I. Bayshev, S. Bitioukov, D. Elumakhov, V. Kachanov, A. Kalinin, D. Konstantinov, V. Krychkine, V. Petrov, R. Ryutin, A. Sobol, S. Troshin, N. Tyurin, A. Uzunian, A. Volkov Faculty of Physics and Vinca Institute of Nuclear Sciences, University of Belgrade, Belgrade, Serbia P. Adzic40, P. Cirkovic, D. Devetak, M. Dordevic, J. Milosevic, V. Rekovic Universidad Autónoma de Madrid, Madrid, Spain J. F. de Trocóniz, M. Missiroli, D. Moran Instituto de Física de Cantabria (IFCA), CSIC-Universidad de Cantabria, Santander, Spain I. J. Cabrillo, A. Calderon, B. Chazin Quero, E. Curras, M. Fernandez, J. Garcia-Ferrero, G. Gomez, A. Lopez Virto, J. Marco, C. Martinez Rivero, P. Martinez Ruiz del Arbol, F. Matorras, J. Piedra Gomez, T. Rodrigo, A. Ruiz-Jimeno, L. Scodellaro, N. Trevisani, I. Vila, R. Vilar Cortabitarte CERN, European Organization for Nuclear Research, Geneva, Switzerland D. Abbaneo, E. Auffray, P. Baillon, A. H. Ball, D. Barney, M. Bianco, P. Bloch, A. Bocci, C. Botta, T. Camporesi, R. Castello, M. Cepeda, G. Cerminara, E. Chapon, Y. Chen, D. d’Enterria, A. Dabrowski, V. Daponte, A. David, M. De Gruttola, A. De Roeck, E. Di Marco41, M. Dobson, B. Dorney, T. du Pree, M. Dünser, N. Dupont, A. Elliott-Peisert, P. Everaerts, G. Franzoni, J. Fulcher, W. Funk, D. Gigi, K. Gill, F. Glege, D. Gulhan, S. Gundacker, M. Guthoff, P. Harris, J. Hegeman, V. Innocente, P. Janot, O. Karacheban14, J. Kieseler, H. Kirschenmann, V. Knünz, A. Kornmayer11, M. J. Kortelainen, C. Lange, P. Lecoq, C. Lourenço, M. T. Lucchini, L. Malgeri, M. Mannelli, A. Martelli, F. Meijers, J. A. Merlin, S. Mersi, E. Meschi, P. Milenovic42, F. Moortgat, M. Mulders, H. Neugebauer, S. Orfanelli, L. Orsini, L. Pape, E. Perez, M. Peruzzi, A. Petrilli, G. Petrucciani, A. Pfeiffer, M. Pierini, A. Racz, T. Reis, G. Rolandi43, M. Rovere, H. Sakulin, C. Schäfer, C. Schwick, M. Seidel, M. Selvaggi, A. Sharma, P. Silva, P. Sphicas44, J. Steggemann, M. Stoye, M. Tosi, D. Treille, A. Triossi, A. Tsirou, V. Veckalns45, G. I. Veres16, M. Verweij, N. Wardle, W. D. Zeuner National Taiwan University (NTU), Taipei, Taiwan Arun Kumar, P. Chang, Y. Chao, K. F. Chen, P. H. Chen, F. Fiori, W.-S. Hou, Y. Hsiung, Y. F. Liu, R.-S. Lu, M. Miñano Moya, E. Paganis, A. Psallidas, J. f. Tsai Department of Physics, Faculty of Science, Chulalongkorn University, Bangkok, Thailand B. Asavapibhop, K. Kovitanggoon, G. Singh, N. Srimanobhas Cukurova University-Physics Department, Science and Art Faculty, Adana, Turkey A. Adiguzel48, F. Boran, S. Cerci49, S. Damarseckin, Z. S. Demiroglu, C. Dozen, I. Dumanoglu, S. Girgis, G. Gokbulut, Y. Guler, I. Hos50, E. E. Kangal51, O. Kara, A. Kayis Topaksu, U. Kiminsu, M. Oglakci, G. Onengut52, K. Ozdemir53, D. Sunar Cerci49, H. Topakli54, S. Turkcapar, I. S. Zorbakir, C. Zorbilmez Physics Department, Middle East Technical University, Ankara, Turkey B. Bilin, G. Karapinar55, K. Ocalan56, M. Yalvac, M. Zeyrek Bogazici University, Istanbul, Turkey E. Gülmez, M. Kaya57, O. Kaya58, S. Tekten, E. A. Yetkin59 Istanbul Technical University, Istanbul, Turkey M. N. Agaras, S. Atay, A. Cakir, K. Cankocak Institute for Scintillation Materials of National Academy of Science of Ukraine, Kharkov, Ukraine B. Grynyov National Scientific Center, Kharkov Institute of Physics and Technology, Kharkov, Ukraine L. Levchuk, P. Sorokin Rutherford Appleton Laboratory, Didcot, UK K. W. Bell, A. Belyaev61, 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 Brunel University, Uxbridge, UK J. E. Cole, P. R. Hobson, A. Khan, P. Kyberd, I. D. Reid, P. Symonds, L. Teodorescu, M. Turner Baylor University, Waco, USA A. Borzou, K. Call, J. Dittmann, K. Hatakeyama, H. Liu, N. Pastika Catholic University of America, Washington, DC, USA R. Bartek, A. Dominguez The University of Alabama, Tuscaloosa, USA A. Buccilli, S. I. Cooper, C. Henderson, P. Rumerio, C. West Boston University, Boston, USA D. Arcaro, A. Avetisyan, T. Bose, D. Gastler, D. Rankin, C. Richardson, J. Rohlf, L. Sulak, D. Zou Brown University, Providence, USA G. Benelli, D. Cutts, A. Garabedian, J. Hakala, U. Heintz, J. M. Hogan, K. H. M. Kwok, E. Laird, G. Landsberg, Z. Mao, M. Narain, S. Piperov, S. Sagir, R. Syarif, D. Yu University of California, Davis, CA, USA R. Band, C. Brainerd, D. Burns, M. Calderon De La Barca Sanchez, M. Chertok, J. Conway, R. Conway, P. T. Cox, R. Erbacher, C. Flores, G. Funk, M. Gardner, W. Ko, R. Lander, C. Mclean, M. Mulhearn, D. Pellett, J. Pilot, S. Shalhout, M. Shi, J. Smith, M. Squires, D. Stolp, K. Tos, M. Tripathi, Z. Wang University of California, Riverside, CA, USA E. Bouvier, K. Burt, R. Clare, J. Ellison, J. W. Gary, S. M. A. Ghiasi Shirazi, G. Hanson, J. Heilman, P. Jandir, E. Kennedy, F. Lacroix, O. R. Long, M. Olmedo Negrete, M. I. Paneva, A. Shrinivas, W. Si, L. Wang, H. Wei, S. Wimpenny, B. R. Yates University of California, Santa Barbara - Department of Physics, Santa Barbara, USA N. Amin, R. Bhandari, J. Bradmiller-Feld, C. Campagnari, A. Dishaw, V. Dutta, M. Franco Sevilla, C. George, F. Golf, L. Gouskos, J. Gran, R. Heller, J. Incandela, S. D. Mullin, A. Ovcharova, H. Qu, J. Richman, D. Stuart, I. Suarez, J. Yoo Carnegie Mellon University, Pittsburgh, USA M. B. Andrews, T. Ferguson, T. Mudholkar, M. Paulini, J. Russ, M. Sun, H. Vogel, I. Vorobiev, M. Weinberg University of Colorado Boulder, Boulder, USA J. P. Cumalat, W. T. Ford, F. Jensen, M. Krohn, S. Leontsinis, T. Mulholland, K. Stenson, S. R. Wagner Fermi National Accelerator Laboratory, Batavia, USA S. Abdullin, M. Albrow, G. Apollinari, A. Apresyan, A. Apyan, S. Banerjee, L. A. T. Bauerdick, A. Beretvas, J. Berryhill, P. C. Bhat, G. Bolla, K. Burkett, J. N. Butler, A. Canepa, G. B. Cerati, H. W. K. Cheung, F. Chlebana, M. Cremonesi, J. Duarte, V. D. Elvira, J. Freeman, Z. Gecse, E. Gottschalk, L. Gray, D. Green, S. Grünendahl, O. Gutsche, R. M. Harris, S. Hasegawa, J. Hirschauer, Z. Hu, B. Jayatilaka, S. Jindariani, M. Johnson, U. Joshi, B. Klima, B. Kreis, S. Lammel, D. Lincoln, R. Lipton, M. Liu, T. Liu, R. Lopes De Sá, J. Lykken, K. Maeshima, N. Magini, J. M. Marraffino, S. Maruyama, D. Mason, P. McBride, P. Merkel, S. Mrenna, S. Nahn, V. O’Dell, K. Pedro, O. Prokofyev, G. Rakness, L. Ristori, B. Schneider, E. Sexton-Kennedy, A. Soha, W. J. Spalding, L. Spiegel, S. Stoynev, J. Strait, N. Strobbe, L. Taylor, S. Tkaczyk, N. V. Tran, L. Uplegger, E. W. Vaandering, C. Vernieri, M. Verzocchi, R. Vidal, M. Wang, H. A. Weber, A. Whitbeck Florida International University, Miami, USA Y. R. Joshi, S. Linn, P. Markowitz, G. Martinez, J. L. Rodriguez Florida Institute of Technology, Melbourne, USA M. M. Baarmand, V. Bhopatkar, S. Colafranceschi, M. Hohlmann, D. Noonan, T. Roy, F. Yumiceva The University of Kansas, Lawrence, USA A. Al-bataineh, P. Baringer, A. Bean, S. Boren, J. Bowen, J. Castle, S. Khalil, A. Kropivnitskaya, D. Majumder, W. Mcbrayer, M. Murray, C. Royon, S. Sanders, E. Schmitz, R. Stringer, J. D. Tapia Takaki, Q. Wang Kansas State University, Manhattan, USA A. Ivanov, K. Kaadze, Y. Maravin, A. Mohammadi, L. K. Saini, N. Skhirtladze, S. Toda Lawrence Livermore National Laboratory, Livermore, USA F. Rebassoo, D. Wright University of Maryland, College Park, USA C. Anelli, A. Baden, O. Baron, A. Belloni, B. Calvert, S. C. Eno, C. Ferraioli, N. J. Hadley, S. Jabeen, G. Y. Jeng, R. G. Kellogg, J. Kunkle, A. C. Mignerey, F. Ricci-Tam, Y. H. Shin, A. Skuja, S. C. Tonwar Massachusetts Institute of Technology, Cambridge, USA D. Abercrombie, B. Allen, V. Azzolini, R. Barbieri, A. Baty, R. Bi, S. Brandt, W. Busza, I. A. Cali, M. D’Alfonso, Z. Demiragli, G. Gomez Ceballos, M. Goncharov, D. Hsu, Y. Iiyama, G. M. Innocenti, M. Klute, D. Kovalskyi, Y. S. Lai, Y.-J. Lee, A. Levin, P. D. Luckey, B. Maier, A. C. Marini, C. Mcginn, C. Mironov, S. Narayanan, X. Niu, C. Paus, C. Roland, G. Roland, J. Salfeld-Nebgen, G. S. F. Stephans, K. Tatar, D. Velicanu, J. Wang, T. W. Wang, B. Wyslouch University of Mississippi, Oxford, USA J. G. Acosta, S. Oliveros University of Nebraska-Lincoln, Lincoln, USA E. Avdeeva, K. Bloom, D. R. Claes, C. Fangmeier, R. Gonzalez Suarez, R. Kamalieddin, I. Kravchenko, J. Monroy, J. E. Siado, G. R. Snow, B. Stieger State University of New York at Buffalo, Buffalo, USA M. Alyari, J. Dolen, A. Godshalk, C. Harrington, I. Iashvili, D. Nguyen, A. Parker, S. Rappoccio, B. Roozbahani Northeastern University, Boston, USA G. Alverson, E. Barberis, A. Hortiangtham, A. Massironi, D. M. Morse, D. Nash, T. Orimoto, R. Teixeira De Lima, D. Trocino, R.-J. Wang, D. Wood Northwestern University, Evanston, USA S. Bhattacharya, O. Charaf, K. A. Hahn, N. Mucia, N. Odell, B. Pollack, M. H. Schmitt, K. Sung, M. Trovato, M. Velasco The Ohio State University, Columbus, USA J. Alimena, L. Antonelli, B. Bylsma, L. S. Durkin, S. Flowers, B. Francis, A. Hart, C. Hill, W. Ji, B. Liu, W. Luo, D. Puigh, B. L. Winer, H. W. Wulsin University of Puerto Rico, Mayagüez, USA S. Malik, S. Norberg Purdue University, West Lafayette, USA A. Barker, V. E. Barnes, S. Folgueras, L. Gutay, M. K. Jha, M. Jones, A. W. Jung, A. Khatiwada, D. H. Miller, N. Neumeister, J. F. Schulte, J. Sun, F. Wang, W. Xie Purdue University Northwest, Hammond, USA T. Cheng, N. Parashar, J. Stupak The Rockefeller University, New York, USA R. Ciesielski, K. Goulianos, C. Mesropian Rutgers, The State University of New Jersey, Piscataway, USA A. Agapitos, J. P. Chou, Y. Gershtein, T. A. Gómez Espinosa, E. Halkiadakis, M. Heindl, E. Hughes, S. Kaplan, R. Kunnawalkam Elayavalli, S. Kyriacou, A. Lath, R. Montalvo, K. Nash, M. Osherson, H. Saka, S. Salur, S. Schnetzer, D. Sheffield, S. Somalwar, R. Stone, S. Thomas, P. Thomassen, M. Walker University of Tennessee, Knoxville, USA M. Foerster, J. Heideman, G. Riley, K. Rose, S. Spanier, K. Thapa Texas Tech University, Lubbock, USA N. Akchurin, J. Damgov, F. De Guio, P. R. Dudero, J. Faulkner, E. Gurpinar, S. Kunori, K. Lamichhane, S. W. Lee, T. Libeiro, T. Peltola, S. Undleeb, I. Volobouev, Z. Wang Vanderbilt University, Nashville, USA S. Greene, A. Gurrola, R. Janjam, W. Johns, C. Maguire, A. Melo, H. Ni, P. Sheldon, S. Tuo, J. Velkovska, Q. Xu Wayne State University, Detroit, USA C. Clarke, R. Harr, P. E. Karchin, J. Sturdy, S. Zaleski † Deceased 1: Also at Vienna University of Technology, Vienna, Austria 2: Also at State Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing, China 3: Also at Universidade Estadual de Campinas, Campinas, Brazil 4: Also at Universidade Federal de Pelotas, Pelotas, Brazil 5: Also at Université Libre de Bruxelles, Brussels, Belgium 6: Also at Joint Institute for Nuclear Research, Dubna, Russia 7: Now at Cairo University, Cairo, Egypt 8: Also at Zewail City of Science and Technology, Zewail, Egypt 9: Also at Université de Haute Alsace, Mulhouse, France 10: Also at Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow, Russia 11: Also at CERN, European Organization for Nuclear Research, Geneva, Switzerland 12: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen, Germany 13: Also at University of Hamburg, Hamburg, Germany 14: Also at Brandenburg University of Technology, Cottbus, Germany 15: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary 16: Also at MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös Loránd University, Budapest, Hungary 17: Also at Institute of Physics, University of Debrecen, Debrecen, Hungary 18: Also at Indian Institute of Technology Bhubaneswar, Bhubaneswar, India 19: Also at Institute of Physics, Bhubaneswar, India 20: Also at University of Visva-Bharati, Santiniketan, India 21: Also at University of Ruhuna, Matara, Sri Lanka 22: Also at Isfahan University of Technology, Isfahan, Iran 23: Also at Yazd University, Yazd, Iran 24: Also at Plasma Physics Research Center, Science and Research Branch, Islamic Azad University, Tehran, Iran 25: Also at Università degli Studi di Siena, Siena, Italy 26: Also at INFN Sezione di Milano-Bicocca; Università di Milano-Bicocca, Milan, Italy 27: Also at Laboratori Nazionali di Legnaro dell’INFN, Legnaro, Italy 28: Also at Purdue University, West Lafayette, USA 29: Also at International Islamic University of Malaysia, Kuala Lumpur, Malaysia 30: Also at Malaysian Nuclear Agency, MOSTI, Kajang, Malaysia 31: Also at Consejo Nacional de Ciencia y Tecnología, Mexico City, Mexico 32: Also at Warsaw University of Technology, Institute of Electronic Systems, Warsaw, Poland 33: Also at Institute for Nuclear Research, Moscow, Russia 34: Now at National Research Nuclear University ‘Moscow Engineering Physics Institute’ (MEPhI), Moscow, Russia 35: Also at St. Petersburg State Polytechnical University, St. Petersburg, Russia 1. 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