Strange particle production in pp collisions at root{s}=0.9 and 7 TeV

EUROPEAN ORGANIZATION FOR NUCLEAR RESEARCH (CERN) CERN-PH-EP/2010-094 2011/05/30 CMS-QCD-10-007 Strange Particle Production in pp Collisions at √s= 0.9 and 7 TeV The CMS Collaboration∗ Abstract The spectra of strange hadrons are measured in proton-proton collisions, recorded by the CMS experiment at the CERN LHC, at centre-of-mass energies of 0.9 and 7 TeV. The K0 S,Λ, and Ξ−particles and their antiparticles are reconstructed from their decay topologies and the production rates are measured as functions of rapidity and transverse momentum, pT. The results are compared to other experiments and to predictions of the PYTHIA Monte Carlo program. The pTdistributions are found to differ substantially from the PYTHIA results and the production rates exceed the predictions by up to a factor of three. Submitted to the Journal of High Energy Physics ∗See Appendix A for the list of collaboration members arXiv:1102.4282v2 [hep-ex] 27 May 2011 1 1 Introduction Measurements of particle yields and spectra are an essential step in understanding protonproton collisions at the Large Hadron Collider (LHC). The Compact Muon Solenoid (CMS) Collaboration has published results on spectra of charged particles at centre-of-mass energies of 0.9, 2.36, and 7 TeV [1, 2]. In this analysis the measurement is extended to strange mesons and baryons (K0 S,Λ,Ξ−)1at centre-of-mass energies of 0.9 and 7 TeV. The investigation of strange hadron production is an important ingredient in understanding the nature of the strong force. The LHC experiments ALICE and LHCb have recently reported results on strange hadron production at √s=0.9 TeV [3, 4]. In addition to results at √s=0.9 TeV, we also present results at √s=7 TeV, opening up a new energy regime in which to study the strong interaction. As the strange quark is heavier than up and down quarks, production of strange hadrons is generally suppressed relative to hadrons containing only up and down quarks. The amount of strangeness suppression is an important component in Monte Carlo (MC) models such as PYTHIA [5] and HIJING/BB [6]. Because the threshold for strange quark production in a quark-gluon plasma is much smaller than in a hadron gas, an enhancement in strange particle production has frequently been suggested as an indication of quark-gluon plasma formation [7]. This effect would be further enhanced in baryons with multiple strange quarks. While a quark-gluon plasma is more likely to be found in collisions of heavy nuclei, the enhancement of strange quark production in high energy pp collisions would be a sign of a collective effect, according to some models [8, 9]. In contrast, recent Regge-theory calculations indicate little change in the ratio of K0 Sto charge particle production with increasing collision energy [10, 11]. Thus, these measurements can be used to constrain theories, provide input for tuning of Monte Carlo models, and serve as a reference for the interpretation of strangeness production results in heavy-ion collisions. Minimum bias collisions at the LHC can be classified as elastic scattering, inelastic singlediffractive dissociation (SD), inelastic double-diffractive dissociation, and inelastic non-diffractive scattering. The results presented here are normalized to the sum of double-diffractive and non-diffractive interactions, referred to as non-single-diffractive (NSD) interactions [1, 2]. This choice is made to most closely match the event selection and to compare with previous experiments, which often used similar criteria. The K0 S,Λ, and Ξ−are long-lived particles (cτ>1 cm) and can be identified from their decay products originating from a displaced vertex. The particles are reconstructed from their decays: K0 S→π+π−,Λ→pπ−, and Ξ−→Λπ−over the rapidity range |y|<2, where the rapidity is defined as y=1 2ln E+pL E−pL,Eis the particle energy, and pLis the particle momentum along the anticlockwise beam direction. For each particle species, we measure the production rate versus rapidity and transverse momentum pT, the average pT, the central production rate dN dy |y≈0, and the integrated yield for |y|<2 per NSD event. We compare our measurements to results from Monte Carlo models and lower energy data. 2 CMS experiment and collected data CMS is a general purpose experiment at the LHC [12]. The silicon tracker, lead-tungstate crystal electromagnetic calorimeter, and brass-scintillator hadron calorimeter are all immersed in a 3.8 T axial magnetic field while muon detectors are interspersed with flux return steel outside of the 6 m diameter superconducting solenoid. The silicon tracker is used to reconstruct charged particle trajectories with |η|<2.5, where the pseudorapidity is defined as η=−ln tan θ 2,θ 1Particle-conjugate states are implied throughout this paper. 23 Strange particle reconstruction being the polar angle with respect to the anticlockwise beam. The tracker consists of layers of 100×150 µm2pixel sensors at radii less than 15 cm and layers of strip sensors, with pitch ranging from 80 to 183 µm, covering radii from 25 to 110 cm. In addition to barrel and endcap detectors, CMS has extensive forward calorimetry including a steel and quartz-fibre hadron calorimeter (HF), which covers 2.9 <|η|<5.2. The data presented in this paper were collected by the CMS experiment in spring 2010 from proton-proton collisions at centre-of-mass energies of 0.9 and 7 TeV during a period in which the probability for two collisions in the same bunch crossing was negligible and the bunch crossings were well separated. The online selection of events required activity in the beam scintillator counters at 3.23 <|η|< 4.65 in coincidence with colliding proton bunches. The offline selection required deposits of at least 3 GeV of energy in each end of the HF [1], preferentially selecting NSD events. A primary vertex reconstructed in the tracker was required and beam-halo and other beam-related background events were rejected as described in Ref. [1]. The data selected with these criteria contain 9.08 and 23.86 million events at 0.9 and 7 TeV, corresponding to approximate integrated luminosities of 240 and 480 µb−1, respectively. To determine the acceptance and efficiency, minimum-bias Monte Carlo samples were generated at both centre-of-mass energies using PYTHIA 6.422 [5] with tune D6T [13]. These events were passed through a CMS detector simulation package based on GEANT 4 [14]. 3 Strange particle reconstruction Ionization deposits recorded by the silicon tracker are used to reconstruct tracks. To maximize reconstruction efficiency, we use a combined track collection formed from merging tracks found with the standard tracking described in Ref. [15] and the minimum bias tracking described in Ref. [1]. Both tracking collections use the same basic algorithm; the differences are in the requirements for seeding, propagating, and filtering tracks. As described in Ref. [15], the K0 Sand Λ(generically referred to as V0) reconstruction combines pairs of oppositely charged tracks; if the normalized χ2of the fit to a common vertex is less than 7, the candidate is kept. The primary vertex is refit for each candidate, removing the two tracks associated with the V0candidate. The next two paragraphs describe the selection of candidates for measurement of V0and Ξ−properties, respectively. Selection variables are measured in units of σ, the calculated uncertainty including all correlations. To remove K0 Sparticles misidentified as Λparticles and vice versa, the K0 S(Λ)candidates must have a corresponding pπ−(π+π−)mass more than 2.5σaway from the world-average Λ(K0 S) mass. The production cross sections we measure are intended to represent the prompt production of K0 Sand Λ, including strong and electromagnetic decays. However, V0particles can also be produced from weak decays and from secondary nuclear interactions. These unwanted contributions are reduced by requiring that the V0momentum vector points back to the primary vertex. This is done by requiring the 3D distance of closest approach of the V0to the primary vertex to be less than 3σ. To remove generic prompt backgrounds, the 3D V0vertex separation from the primary vertex must be greater than 5σand both V0daughter tracks must have a 3D distance of closest approach to the primary vertex greater than 3σ. With the above selection, the background level for low transverse-momentum Λcandidates remains high. Therefore, additional cuts are applied to Λcandidates with pT<0.6 GeV/c: •3D separation between the primary and Λvertices >10σ(instead of >5σ), •transverse (2D) separation between the pp collision region (beamspot) and Λvertex >10σ(instead of no cut), where the uncertainty is dominated by the Λvertex, and 3 •3D impact parameter of the pion and proton tracks with respect to the primary vertex >(7−2|y|)σ(instead of >3σ) where yis the rapidity of the Λcandidate. The rapidity dependence is a consequence of the observation that, for the low transverse momentum candidates, large backgrounds dominate at small rapidity, while low efficiency characterizes the large rapidity behaviour. The resulting mass distributions of K0 Sand Λcandidates from the 0.9 and 7 TeV data are shown in Figs. 1 and 2. The π+π−mass distribution is fit with a double Gaussian (with a common mean) signal function plus a quadratic background. The pπ−mass distribution is fit with a double Gaussian (common mean) signal function and a background function of the form AqB, where q=Mpπ−−(mp+mπ−),Mpπ−is the pπ−invariant mass, and Aand Bare free parameters. The fitted K0 S(Λ)yields at √s=0.9 and 7 TeV are 1.4×106(2.8×105)and 6.5× 106(1.5×106), respectively. ] 2 invariant mass [MeV/c − π + π 450 500 550 600 2 Candidates / 1 MeV/c 0 20 40 60 80 100 3 10× 3 10×Yield: 1393 2 Mean: 497.8 MeV/c 2 : 8.4 MeV/cσAvg CMS = 0.9 TeVs ] 2 invariant mass [MeV/c − π + π 450 500 550 600 2 Candidates / 1 MeV/c 0 100 200 300 400 3 10× 3 10×Yield: 6534 2 Mean: 497.8 MeV/c 2 : 8.2 MeV/cσAvg CMS = 7 TeVs Figure 1: The π+π−invariant mass distributions from data collected at √s=0.9 TeV (left) and 7 TeV (right). The solid curves are fits to a double Gaussian and quadratic polynomial. The dashed curves show the quadratic background contribution. To reconstruct the Ξ−, charged tracks of the correct sign are combined with Λcandidates. The χ2probability of the fit to a common vertex for the Λand the charged track must be greater than 5%. In this fit, the Λcandidate is constrained to have the correct world-average mass [16]. The primary vertex is refit for each Ξ−candidate, removing all tracks associated with the Ξ−. The Ξ−candidates must then pass the following selection criteria: •3D impact parameter with respect to the primary vertex >2σfor the proton track from the Λdecay, >3σfor the π−track from the Λdecay, and >4σfor the π−track from the Ξ−decay, •invariant mass from the π+π−hypothesis for the tracks associated with the Λcandidate at least 20 MeV/c2away from the world-average K0 Smass, •3D impact parameter of the Ξ−candidate with respect to the primary vertex <3σ, •3D separation between Λvertex and primary vertex >10σ, and •3D separation between Ξ−vertex and primary vertex >2σ. The mass distributions of Ξ−candidates from the √s=0.9 and 7 TeV data are shown in Fig. 3. The Λπ−mass is fit with a double Gaussian (with a common mean) signal function and a 44 Efficiency correction ] 2 invariant mass [MeV/c − πp 1100 1120 1140 2 Candidates / 0.5 MeV/c 0 5 10 15 20 3 10× 3 10×Yield: 276 2 Mean: 1116.1 MeV/c 2 : 3.6 MeV/cσAvg CMS = 0.9 TeVs ] 2 invariant mass [MeV/c − πp 1100 1120 1140 2 Candidates / 0.5 MeV/c 0 20 40 60 80 100 120 3 10× 3 10×Yield: 1460 2 Mean: 1116.0 MeV/c 2 : 3.4 MeV/cσAvg CMS = 7 TeVs Figure 2: The pπ−invariant mass distributions from data collected at √s=0.9 TeV (left) and 7 TeV (right). The solid curves are fits to a double Gaussian signal and a background function given by AqB, where q=Mpπ−−(mp+mπ−). The dashed curves show the background contribution. background function of the form Aq1/2 +Bq3/2, where q=MΛπ−−(mΛ+mπ−)and MΛπ−is the Λπ−invariant mass. The fitted Ξ−yields at √s=0.9 and 7 TeV are 6.2×103and 3.4×104, respectively. 4 Efficiency correction The efficiency correction is determined from a Monte Carlo simulation which is used to measure the effects of acceptance and the efficiency for event selection (including the trigger) and particle reconstruction. The Monte Carlo samples are reweighted to match the observed track multiplicity in data, as this has been shown to be an important component of the trigger efficiency [1, 2]. This is referred to as track weighting. The efficiency correction also accounts for the other decay channels of the strange particles that we do not attempt to reconstruct, such as K0 S→π0π0. The efficiency is given by the number of reconstructed particles divided by the number of generated particles, subject to two modifications. Firstly, the efficiency correction is used to account for candidates from SD events. As the results are normalized to NSD events, candidates from SD events which pass the event selection must be removed. This is done by defining the efficiency as the number of reconstructed candidates in all events divided by the number of generated candidates in NSD events. Secondly, the efficiency is modified to account for the small contribution of reconstructed non-prompt strange particles which pass the selection criteria. This is only an issue for the Λparticles which receive contributions from Ξand Ωdecays. Since these non-prompt Λparticles are present in both the MC and data, we modify the efficiency to remove this contribution by calculating the numerator using all of the reconstructed strange particles and the denominator with only the prompt generated strange particles. As the MC fails to produce enough Ξparticles (see Section 6), the non-prompt Λ’s are weighted more than prompt Λ’s in the efficiency calculation. The results of this analysis are presented in terms of two kinematic distributions: transverse 5 ] 2 invariant mass [MeV/c − πΛ 1300 1350 1400 2 Candidates / 2 MeV/c 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 3 10× 3 10×Yield: 6.2 2 Mean: 1322.2 MeV/c 2 : 4.3 MeV/cσAvg CMS = 0.9 TeVs ] 2 invariant mass [MeV/c − πΛ 1300 1350 1400 2 Candidates / 2 MeV/c 0 2 4 6 8 3 10× 3 10×Yield: 34.4 2 Mean: 1322.1 MeV/c 2 : 4.1 MeV/cσAvg CMS = 7 TeVs Figure 3: The Λπ−invariant mass distributions from data collected at √s=0.9 TeV (left) 7 TeV (right). The solid curves are fits to a double Gaussian signal and a background function given by Aq1/2 +Bq3/2, where q=MΛπ−−(mΛ+mπ−). The dashed curves show the background contribution. momentum and rapidity. For all modes, |y|is divided into 10 equal size bins from 0 to 2 and pTis divided into 20 equal size bins from 0 to 4 GeV/cplus one bin each from 4 to 5 GeV/cand 5 to 6 GeV/c. In addition, the V0modes also have 6–8 GeV/cand 8–10 GeV/c pTbins. All results are for particles with |y|<2. The efficiency correction for the V0modes uses a two-dimensional binning in pTand |y|. Thus, the data are divided into 240 bins in the |y|,pTplane. The invariant mass histograms in each bin are fit to a double Gaussian signal function (with a common mean) and a background function. In bins with few entries, a single Gaussian signal function is used. For the Λsample, some bins are merged due to sparse populations in |y|,pTspace. The merging is performed separately when measuring |y|and pTsuch that the merging occurs across pTand |y|bins, respectively. The efficiency from MC is evaluated in each bin and applied to the measured yield to obtain the corrected yield. The two-dimensional binning used for the V0efficiency correction greatly reduces problems arising from remaining differences in production dynamics between the data and the simulation. The much smaller sample of Ξ−candidates prevents the use of 2D binning. Thus, the data are divided into |y|bins to measure the |y|distribution and into pTbins to measure the pTdistribution. However, the MC spectra do not match the data. Therefore, each Monte Carlo Ξ−particle is weighted in pT(|y|) to match the distribution in data when measuring the efficiency versus |y|(pT). Thus, the MC and data distributions are forced to match in the variable over which we integrate to determine the efficiency. We refer to this as kinematic weighting. The efficiencies for all three particles are shown versus |y| and pTin Fig. 4. The efficiencies (for particles with |y|<2) include the acceptance, event selection, reconstruction and selection, and also account for other decay channels. The increase in efficiency with pTis due to the improvement in tracking efficiency as track pTincreases and to the selection criteria designed to remove prompt decays. The slight decrease at high pTis due to particles decaying too far out to have reconstructed tracks. While there is no centre-ofmass energy dependence on the efficiency versus pT, particles produced at √s=7 TeV have a higher average-pT, resulting in a higher efficiency when plotted versus rapidity. As a check on the ability of the Monte Carlo simulation to reproduce the efficiency, the (well- 64 Efficiency correction |y| 0 0.5 1 1.5 2 Efficiency 0 0.05 0.1 0.15 0.2 0.25 0.3 0 S = 7 TeV: Ks 0 S = 0.9 TeV: Ks Λ = 7 TeV: s Λ = 0.9 TeV: s − Ξ = 7 TeV: s − Ξ = 0.9 TeV: s CMS [GeV/c] T p 0 2 4 6 8 10 Efficiency 0 0.1 0.2 0.3 0.4 0 S = 7 TeV: Ks 0 S = 0.9 TeV: Ks Λ = 7 TeV: s Λ = 0.9 TeV: s − Ξ = 7 TeV: s − Ξ = 0.9 TeV: s CMS Figure 4: Total efficiencies, including acceptance, trigger and event selection, reconstruction and particle selection, and other decay modes, as a function of |y|(left) and pT(right) for K0 S, Λ, and Ξ−produced promptly in the range |y|<2. Error bars come from MC statistics. known) K0 S,Λ, and Ξ−lifetimes are measured. For the K0 Smeasurement, the data are divided into bins of pTand ct, where ct is calculated as ct =cmL/pwhere m,L, and pare, respectively, the mass, decay length, and momentum of the particle. In each bin the data is corrected by the MC efficiency and the corrected yields summed in pTto obtain the ct distribution. Due to smaller sample sizes, the Λand Ξ−yields are only measured in bins of ct. Using the kinematic weighting technique, the MC efficiency in each bin of ct is calculated with the pTspectrum correctly weighted to match data. The corrected lifetime distributions, shown in Fig. 5, display exponential behaviour. The vertex separation requirements result in very low efficiencies and low yields in the first lifetime bin and are thus expected to have some discrepancies. An actual measurement of the lifetime would remove this issue by using the reduced proper time, where one measures the lifetime relative to the point at which the particle had a chance to be reconstructed. The measured values of the lifetimes are also reasonably consistent with the world averages [16] (shown in Fig. 5) considering that only statistical uncertainties are reported and that this is not the optimal method for a lifetime measurement. To convert the efficiency corrected yields to per event yields requires the true number of NSD events, which is obtained by correcting the number of selected events for the event selection inefficiency. The event selection includes both the online trigger and offline selection described in Section 2. The event selection efficiency is determined in two ways. In the default method, it is calculated directly from the Monte Carlo simulation (appropriately weighted by the track multiplicity to reproduce the data). In the alternative method, the event selection efficiency versus track multiplicity is derived from the Monte Carlo. Then, each measured event is weighted by the inverse of the event selection efficiency based on its number of tracks. The number of events divided by the number of weighted events gives the event selection efficiency. However, since the event selection requires a primary vertex, no events will have fewer than two tracks. Therefore, the Monte Carlo is also used to determine the fraction of NSD events which have fewer than two tracks and the event selection efficiency is adjusted to include this effect. In both methods, the event selection efficiency accounts for unwanted SD events which pass the event selection. The numerator in the efficiency ratio contains all selected events, including 7 ct [cm] 0 S K 0 2 4 6 8 10 12 -1 ) dN / dct (cm) NSD (1/N -2 10 -1 10 0.1 ps± = 89.0 τ = 7 TeV: s 0.2 ps± = 89.3 τ = 0.9 TeV: s CMS 0.05 ps± = 89.53 PDG τ Statistical uncertainties only ct [cm]Λ 0 2 4 6 8 10 12 14 16 18 20 -1 ) dN / dct (cm) NSD (1/N -2 10 -1 10 1.1 ps± = 261.4 τ = 7 TeV: s 3.1 ps± = 264.6 τ = 0.9 TeV: s CMS 2.0 ps± = 263.1 PDG τ Statistical uncertainties only ct [cm] − Ξ 0 1 2 3 4 5 6 7 8 9 10 -1 ) dN / dct (cm) NSD (1/N -3 10 -2 10 5 ps± = 167 τ = 7 TeV: s 8 ps± = 178 τ = 0.9 TeV: s CMS 1.5 ps± = 163.9 PDG τ Statistical uncertainties only Figure 5: K0 S(left), Λ(middle), and Ξ−(right) corrected decay time distributions at √s=0.9 and 7 TeV. The values of the lifetimes, derived from a fit with an exponential function (solid line), are shown in the legend along with the world-average value. The error bars and uncertainties on the lifetimes refer to the statistical uncertainty only. single-diffractive events, while the denominator contains all NSD events. 5 Systematic uncertainties The systematic uncertainties, reported in Table 1, are divided into two categories: normalization uncertainties, which only affect the overall normalization, and point-to-point uncertainties, which may also affect the shape of the pTand |y|distributions. The list below summarizes the source and evaluation of the point-to-point systematic uncertainties. •Kinematic weighting versus 2D binning: The efficiency corrections using the 1D kinematic weighting technique (used for the Ξ−analysis) and the 2D binning technique (used for the V0analysis) were compared by measuring the efficiency with both methods on the highest statistics channel (K0 Sat 7 TeV). •Non-prompt Λ: The contribution of non-prompt Λdecays is varied by a factor of two in the simulation. •MC tune: The nominal efficiency calculated from the default PYTHIA 6 D6T tune [13] is compared to the efficiency obtained from the PYTHIA 6 Perugia0 (P0) tune [17] and PYTHIA 8 [18]. •Variation of reconstruction cuts: The following cuts are varied for all three modes: V0vertex separation significance (±2σ), 3D impact parameter of V0and Ξ−(±2σ), 3D impact parameter of tracks (±2σ), cut on K0 S(Λ)mass for Λ(K0 S)candidates (±1.5σ), and increase of number of hits required on each track from 3 to 5. For the Ξ−, additional cuts were varied: the Ξ−vertex separation significance (±1σ) and Ξ− vertex fit probability (±3%). •Detached particle reconstruction: Finding that the corrected lifetime distributions are exponential with the correct lifetime is a verification of our understanding of the reconstruction efficiency versus decay length. The systematic uncertainty is taken as the difference between the fitted lifetimes and the world-average lifetimes [16]. While the K0 Sand Λlifetimes are within 1% of the world-average, a 2% systematic uncertainty is conservatively assigned. 14 6 Results [GeV/c] T p 0 2 4 6 8 10 ) 0 S ) / N(KΛN( 0 0.2 0.4 0.6 0.8 1 = 7 TeVs PYTHIA6 D6T PYTHIA6 P0 PYTHIA8 = 0.9 TeVs PYTHIA6 D6T PYTHIA6 P0 PYTHIA8 CMS [GeV/c] T p 0 1 2 3 4 5 6 )Λ) / N( − ΞN( 0 0.05 0.1 0.15 0.2 0.25 = 7 TeVs PYTHIA6 D6T PYTHIA6 P0 PYTHIA8 = 0.9 TeVs PYTHIA6 D6T PYTHIA6 P0 PYTHIA8 CMS Figure 8: N(Λ)/N(K0 S)(left) and N(Ξ−)/N(Λ)(right) in NSD events versus pT. The inner vertical error bars (when visible) show the statistical uncertainties, the outer the statistical and all systematic uncertainties summed in quadrature. Results are shown for three PYTHIA predictions at each centre-of-mass energy. from STAR [24] and at √s=0.9 TeV results from ALICE [3]. These three results show a remarkable consistency across a wide variety of collision energies. In contrast, the CDF values for N(Λ)/N(K0 S)[26] are significantly higher than the CMS results while the CDF measurements of N(Ξ−)/N(Λ)[25] are lower, albeit with less significance. Reducing the pTdistributions to a single value, the average pT, we compare the CMS results with earlier results at lower energies in Fig. 11 [3, 24, 26–32]. The CMS results are in excellent agreement with the recent ALICE measurements at 0.9 TeV. The CMS results continue the overall trend of increasing average pTwith increasing particle mass and increasing centre-ofmass energy. 6.3 Analysis of production rate As a measure of the overall production rate in NSD events, dN dy |y≈0and the total yield for |y|<2 were extracted and tabulated in Table 4. The quantity dN dy |y≈0is the average value of dN dy over the region |y|<0.2. The integrated yields for |y|<2 are obtained by integrating the pTspectra, using the Tsallis function fit to account for particles above the measured pTrange. The central production rates of K0 S,Λ, and Ξ−are compared to previous results in Fig. 12. The results show the expected increase in production with centre-of-mass energy with little evidence of a difference due to beam particles. As the ALICE results are normalized to all inelastic collisions, they are expected to be somewhat lower than the CMS results. The production ratios N(K0 S)/N(Λ)and N(Ξ−)/N(Λ)versus |y|are shown in Fig. 13. The rapidity distributions are very flat and, as observed in the pTdistributions of Fig. 8, show no dependence on centre-of-mass energy. Three PYTHIA predictions at each centre-of-mass energy are also shown in Fig. 13. These results confirm what can already be seen in the comparisons shown in the left panes of Fig. 6; PYTHIA underestimates the production of strange particles and the discrepancy grows with particle mass. 6.3 Analysis of production rate 15 [GeV/c] T p 0 1 2 3 4 5 6 -1 (GeV/c) T /dp − Ξ ) dN NSD (1/N -5 10 -4 10 -3 10 -2 10 CMS: pp @ 7 TeV CMS: pp @ 0.9 TeV @ 1.96 TeVpCDF: p ALICE: pp @ 0.9 TeV STAR: pp @ 0.2 TeV 0 1 2 3 4 5 6 -1 (GeV/c) T /dp Λ ) dN NSD (1/N -4 10 -3 10 -2 10 -1 10 0 1 2 3 4 5 6 -1 (GeV/c) T /dp 0 S K ) dN NSD (1/N -4 10 -3 10 -2 10 -1 10 1CMS Figure 9: K0 S(top), Λ(middle), and Ξ−(bottom) production per event versus pT. The error bars on the CMS results show the combined statistical, point-to-point systematic, and normalization systematic uncertainties. The error bars on the CDF [25], ALICE [3], and STAR [24] results show the combined statistical and systematic uncertainties. The CMS, CDF, and STAR results are normalized to NSD events while the ALICE results are normalized to all inelastic events. Table 4: dN dy |y≈0and integrated yields (|y|<2.0)per NSD event from data. In each data column, the first uncertainty is statistical and the second is systematic. √s=0.9 TeV √s=7 TeV Particle dN dy |y≈0NdN dy |y≈0N K0 S0.205±0.001±0.015 0.784±0.002±0.056 0.346±0.001±0.025 1.341±0.001±0.097 Λ0.108±0.001±0.012 0.404±0.004±0.046 0.189±0.001±0.022 0.717±0.005±0.082 Ξ−0.011±0.001±0.001 0.043±0.001±0.006 0.021±0.001±0.003 0.080±0.001±0.011 16 6 Results [GeV/c] T p 0 1 2 3 4 5 6 )Λ ) / N( − ΞN( 0 0.1 0.2 0.3 @ 1.96 TeVpCDF: p @ 1.8 TeVpCDF: p @ 0.63 TeVpCDF: p ALICE: pp @ 0.9 TeV STAR: pp @ 0.2 TeV CMS: pp @ 7 TeV CMS: pp @ 0.9 TeV 0 1 2 3 4 5 6 ) 0 S ) / N(KΛN( 0.5 1 1.5 CMS Figure 10: Ratio of Λto K0 Sproduction (top) and Ξ−to Λproduction (bottom) versus pT. The CMS, ALICE [3], and STAR [24] error bars include the statistical and systematic uncertainties. The CDF error bars include the statistical uncertainties for N(Λ)/N(K0 S)[26] and the statistical and systematic uncertainties for N(Ξ−)/N(Λ)[25]. The CDF N(Λ)/N(K0 S)bin sizes are doubled to reduced fluctuations. For experiments in which the binning for Λand Ξ−is different (ALICE and STAR), bins are merged to provide common bin ranges in the N(Ξ−)/N(Λ) distribution. 6.3 Analysis of production rate 17 [TeV] s -1 10 1 10 [GeV/c] − Ξ 〉 T p〈 0.6 0.8 1 1.2 − Ξ CMS (pp))pE735 (p ALICE (pp) )pUA5 (p STAR (pp) [GeV/c] Λ 〉 T p〈 0.6 0.7 0.8 0.9 1 1.1 Λ CMS (pp) )pCDF (p )pE735 (p ALICE (pp) )pUA5 (p STAR (pp) [GeV/c] 0 S K 〉 T p〈 0.5 0.6 0.7 0.8 0 S K CMS (pp) )pCDF (p ALICE (pp) )pUA5 (p STAR (pp) CMS Figure 11: Average pTfor K0 S(top), Λ(middle), and Ξ−(bottom), as a function of the centre-ofmass energy. The CMS measurements are for |y|<2. The other results are from UA5 [27–31] (p¯ p collisions covering |y|<2.5, |y|<2, and |y|<3 for K0 S,Λ, and Ξ−, respectively), E735 [32] (p¯ p collisions using tracks with −0.36 <η<1.0), CDF [26] (p¯ p collisions covering |η|<1.0), STAR [24] (pp collisions covering |y|<0.5), and ALICE [3] (pp collisions covering |y|<0.75 for K0 Sand Λand |y|<0.8 for Ξ−). Some points have been slightly offset from the true energy to improve visibility. The vertical bars indicate the statistical and systematic uncertainties (when available) summed in quadrature. 18 6 Results [TeV] s -1 10 1 10 0≈y /dy − Ξ ) dN ev (1/N 0 0.005 0.01 0.015 0.02 0.025 − Ξ CMS NSD (pp) ALICE INEL (pp) STAR NSD (pp) 0≈y /dy Λ ) dN ev (1/N 0.1 0.15 0.2 Λ CMS NSD (pp) ALICE INEL (pp) STAR NSD (pp) 0≈y /dy 0 S K ) dN ev (1/N 0.1 0.15 0.2 0.25 0.3 0.35 S 0 K CMS NSD (pp) )pCDF MB (p ALICE INEL (pp) )pUA5 NSD (p STAR NSD (pp) CMS Figure 12: The central rapidity production rate for K0 S(top), Λ(middle), and Ξ−(bottom), as a function of the centre-of-mass energy. The previous results are from UA5 [29, 30] (p¯ p), CDF [33] (p¯ p), STAR [24] (pp), and ALICE [3] (pp). The CMS, UA5, and STAR results are normalized to NSD events. The CDF results are normalized to events passing their trigger and event selection defined chiefly by activity in both sides of the detector, at least four tracks, and a primary vertex. The ALICE results are normalized to all inelastic events. Some points have been slightly offset from the true energy to improve visibility. The vertical bars indicate the statistical uncertainties for the UA5 and CDF results and the combined statistical and systematic uncertainties for the CMS, ALICE, and STAR results. 19 |y| 0 0.5 1 1.5 2 ) 0 S ) / N(KΛN( 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 = 7 TeVs PYTHIA6 D6T PYTHIA6 P0 PYTHIA8 = 0.9 TeVs PYTHIA6 D6T PYTHIA6 P0 PYTHIA8 CMS |y| 0 0.5 1 1.5 2 )Λ) / N( − ΞN( 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 = 7 TeVs PYTHIA6 D6T PYTHIA6 P0 PYTHIA8 = 0.9 TeVs PYTHIA6 D6T PYTHIA6 P0 PYTHIA8 CMS Figure 13: The production ratios N(Λ)/N(K0 S)(left) and N(Ξ−)/N(Λ)(right) in NSD events versus |y|. The inner vertical error bars (when visible) show the statistical uncertainties, the outer the statistical and all systematic uncertainties summed in quadrature. Results are shown for three PYTHIA predictions at each centre-of-mass energy. Table 5 shows a comparison of the production rate of data to PYTHIA 6 with the D6T tune. The left column shows a large increase in the strange particle production cross section as the centre-of-mass energy increases from 0.9 to 7 TeV. The systematic uncertainties for this ratio are reduced as the same uncertainty affects both samples nearly equally. The results for K0 S and Λare consistent with the increase observed in inclusive charged particle production [1, 2] (5.82 3.48 =1.67)while the Ξ−results show a slightly greater increase. The increase in particle production from 0.9 to 7 TeV is not well modelled by PYTHIA 6. Another feature, seen in the right column, is the deficit of strange particles produced by PYTHIA 6. The deficit of K0 Sparticles in the MC, 15% (28%) low at 0.9 (7) TeV, is consistent with the results found in the production of charged particles [1, 2]. However, the deficit is much worse as the mass increases, resulting in a 63% reduction in Ξ−particles in MC compared to data at √s=7 TeV. While values are only presented for PYTHIA 6 with the D6T tune, the same features are also evident for the other two PYTHIA comparisons in the rapidity distribution plots in Fig. 6. Table 5: Comparison of strangeness production rates between PYTHIA 6 Monte Carlo (D6T) and data. In each column, the first uncertainty is statistical and the second is systematic. Particle "dN dy |y≈0(7 TeV) dN dy |y≈0(0.9 TeV)# "dN dy |y≈0(MCD6T) dN dy |y≈0(Data)# Data MC (D6T) √s=0.9 TeV √s=7 TeV K0 S1.69 ±0.01 ±0.06 1.42 0.852 ±0.005 ±0.061 0.717 ±0.001 ±0.052 Λ1.75 ±0.02 ±0.08 1.48 0.606 ±0.007 ±0.070 0.514 ±0.003 ±0.059 Ξ−1.93 ±0.10 ±0.09 1.51 0.477 ±0.021 ±0.064 0.373 ±0.010 ±0.050 7 Conclusions This article presents a study of the production of K0 S,Λ, and Ξ−particles in proton-proton collisions at centre-of-mass energies 0.9 and 7 TeV. By fully exploiting the low-momentum track 20 7 Conclusions reconstruction capabilities of CMS, we have measured the transverse-momentum distribution of these strange particles down to zero. From this sample of 10 million strange particles, the transverse momentum distributions were measured out to 10 GeV/cfor K0 Sand Λand out to 6 GeV/cfor Ξ−. We fit these distributions with a Tsallis function to obtain information on the exponential decay at low pTand the power-law behaviour at high pT. All species show a flattening of the exponential decay as the centre-of-mass energy increases. While the baryons show little change in the high-pTregion, the K0 Spower-law parameter decreases from 7.8 to 6.9. The average pTvalues, calculated directly from the data, are found to increase with particle mass and centre-of-mass energy, in agreement with predictions and other experimental results. While the PYTHIA pTdistributions used in this analysis show significant variation based on tune and version, they are all broader than the data distributions. We have also measured the production versus rapidity and extracted the value of dN/dy in the central rapidity region. The increase in production of strange particles as the centre-ofmass energy increases from 0.9 to 7 TeV is approximately consistent with the results for inclusive charged particles. However, as in the inclusive charged particle case, PYTHIA fails to match this increase. For K0 Sproduction, the discrepancy is similar to what has been found in charged particles. However, the deficit between PYTHIA and data is significantly larger for the two hyperons at both energies, reaching a factor of three discrepancy for Ξ−production at √s=7 TeV. If a quark-gluon plasma or other collective effects were present, we might expect an enhancement of double-strange baryons to single-strange baryons and/or an enhancement of strange baryons to strange mesons. However, the production ratios N(Λ)/N(K0 S)and N(Ξ−)/N(Λ)versus rapidity and transverse momentum show no change with centre-of-mass energy. Thus, the deficiency in PYTHIA is likely originating from parameters regulating the frequency of strange quarks appearing in colour strings. The variety of measurements presented here can be used to tune PYTHIA and other models as well as a baseline to understand measurements of strangeness production in heavy-ion collisions. Acknowledgements We wish to congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC machine. We thank the technical and administrative staff at CERN and other CMS institutes, and acknowledge support from: FMSR (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST, and NSFC (China); COLCIENCIAS (Colombia); MSES (Croatia); RPF (Cyprus); Academy of Sciences and NICPB (Estonia); Academy of Finland, ME, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRT (Greece); OTKA and NKTH (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); NRF and WCU (Korea); LAS (Lithuania); CINVESTAV, CONACYT, SEP, and UASLP-FAI (Mexico); PAEC (Pakistan); SCSR (Poland); FCT (Portugal); JINR (Armenia, Belarus, Georgia, Ukraine, Uzbekistan); MST and MAE (Russia); MSTD (Serbia); MICINN and CPAN (Spain); Swiss Funding Agencies (Switzerland); NSC (Taipei); TUBITAK and TAEK (Turkey); STFC (United Kingdom); DOE and NSF (USA). 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Blekman, S. Blyweert, J. D’Hondt, O. Devroede, R. Gonzalez Suarez, A. Kalogeropoulos, J. Maes, M. Maes, S. Tavernier, W. Van Doninck, P. Van Mulders, G.P. Van Onsem, I. Villella Universit´e Libre de Bruxelles, Bruxelles, Belgium O. Charaf, B. Clerbaux, G. De Lentdecker, V. Dero, A.P.R. Gay, G.H. Hammad, T. Hreus, P.E. Marage, L. Thomas, C. Vander Velde, P. Vanlaer, J. Wickens Ghent University, Ghent, Belgium V. Adler, S. Costantini, M. Grunewald, B. Klein, A. Marinov, J. Mccartin, D. Ryckbosch, F. Thyssen, M. Tytgat, L. Vanelderen, P. Verwilligen, S. Walsh, N. Zaganidis Universit´e Catholique de Louvain, Louvain-la-Neuve, Belgium S. Basegmez, G. Bruno, J. Caudron, L. Ceard, J. De Favereau De Jeneret, C. Delaere, P. Demin, D. Favart, A. Giammanco, G. Gr´ egoire, J. Hollar, V. Lemaitre, J. Liao, O. Militaru, S. Ovyn, D. Pagano, A. Pin, K. Piotrzkowski, N. Schul Universit´e de Mons, Mons, Belgium N. Beliy, T. Caebergs, E. Daubie Centro Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil G.A. Alves, D. De Jesus Damiao, M.E. Pol, M.H.G. Souza Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil W. Carvalho, E.M. Da Costa, C. De Oliveira Martins, S. Fonseca De Souza, L. Mundim, H. Nogima, V. Oguri, W.L. Prado Da Silva, A. Santoro, S.M. Silva Do Amaral, A. Sznajder, F. Torres Da Silva De Araujo Instituto de Fisica Teorica, Universidade Estadual Paulista, Sao Paulo, Brazil F.A. Dias, M.A.F. Dias, T.R. Fernandez Perez Tomei, E. M. Gregores2, F. Marinho, S.F. Novaes, Sandra S. Padula Institute for Nuclear Research and Nuclear Energy, Sofia, Bulgaria N. Darmenov1, L. Dimitrov, V. Genchev1, P. Iaydjiev1, S. Piperov, M. Rodozov, S. Stoykova, G. Sultanov, V. Tcholakov, R. Trayanov, I. Vankov 30 A The CMS Collaboration S. Goy Lopez, J.M. Hernandez, M.I. Josa, G. Merino, J. Puerta Pelayo, I. Redondo, L. Romero, J. Santaolalla, C. Willmott Universidad Aut´onoma de Madrid, Madrid, Spain C. Albajar, G. Codispoti, J.F. de Troc´ oniz Universidad de Oviedo, Oviedo, Spain J. Cuevas, J. Fernandez Menendez, S. Folgueras, I. Gonzalez Caballero, L. Lloret Iglesias, J.M. Vizan Garcia Instituto de F´ısica de Cantabria (IFCA), CSIC-Universidad de Cantabria, Santander, Spain J.A. Brochero Cifuentes, I.J. Cabrillo, A. Calderon, M. Chamizo Llatas, S.H. Chuang, J. Duarte Campderros, M. Felcini21, M. Fernandez, G. Gomez, J. Gonzalez Sanchez, C. Jorda, P. Lobelle Pardo, A. Lopez Virto, J. Marco, R. Marco, C. Martinez Rivero, F. Matorras, F.J. Munoz Sanchez, J. Piedra Gomez22, T. Rodrigo, A. Ruiz-Jimeno, L. Scodellaro, M. Sobron Sanudo, I. Vila, R. Vilar Cortabitarte CERN, European Organization for Nuclear Research, Geneva, Switzerland D. Abbaneo, E. Auffray, G. Auzinger, P. Baillon, A.H. Ball, D. Barney, A.J. Bell23, D. Benedetti, C. Bernet3, W. Bialas, P. Bloch, A. Bocci, S. Bolognesi, H. Breuker, G. Brona, K. Bunkowski, T. Camporesi, E. Cano, G. Cerminara, T. Christiansen, J.A. Coarasa Perez, B. 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Schmidt, H. Snoek National Central University, Chung-Li, Taiwan Y.H. Chang, K.H. Chen, W.T. Chen, S. Dutta, A. Go, C.M. Kuo, S.W. Li, W. Lin, M.H. Liu, Z.K. Liu, Y.J. Lu, D. Mekterovic, J.H. Wu, S.S. Yu 31 National Taiwan University (NTU), Taipei, Taiwan P. Bartalini, P. Chang, Y.H. Chang, Y.W. Chang, Y. Chao, K.F. Chen, W.-S. Hou, Y. Hsiung, K.Y. Kao, Y.J. Lei, R.-S. Lu, J.G. Shiu, Y.M. Tzeng, M. Wang Cukurova University, Adana, Turkey A. Adiguzel, M.N. Bakirci31, S. Cerci32, Z. Demir, C. Dozen, I. Dumanoglu, E. Eskut, S. Girgis, G. Gokbulut, Y. Guler, E. Gurpinar, I. Hos, E.E. Kangal, T. Karaman, A. Kayis Topaksu, A. Nart, G. Onengut, K. Ozdemir, S. Ozturk, A. Polatoz, K. Sogut33, B. Tali, H. Topakli31, D. Uzun, L.N. Vergili, M. Vergili, C. Zorbilmez Middle East Technical University, Physics Department, Ankara, Turkey I.V. Akin, T. Aliev, S. Bilmis, M. Deniz, H. Gamsizkan, A.M. Guler, K. Ocalan, A. Ozpineci, M. Serin, R. Sever, U.E. Surat, E. Yildirim, M. Zeyrek Bogazici University, Istanbul, Turkey M. Deliomeroglu, D. Demir34, E. G¨ ulmez, A. Halu, B. Isildak, M. Kaya35, O. Kaya35, S. Ozkorucuklu36, N. Sonmez37 National Scientific Center, Kharkov Institute of Physics and Technology, Kharkov, Ukraine L. Levchuk University of Bristol, Bristol, United Kingdom P. Bell, F. Bostock, J.J. Brooke, T.L. Cheng, E. Clement, D. Cussans, R. Frazier, J. Goldstein, M. Grimes, M. Hansen, D. Hartley, G.P. Heath, H.F. Heath, B. Huckvale, J. Jackson, L. Kreczko, S. Metson, D.M. Newbold38, K. Nirunpong, A. Poll, S. Senkin, V.J. Smith, S. Ward Rutherford Appleton Laboratory, Didcot, United Kingdom L. Basso39, K.W. Bell, A. Belyaev39, C. Brew, R.M. Brown, B. Camanzi, D.J.A. Cockerill, J.A. Coughlan, K. Harder, S. Harper, B.W. Kennedy, E. Olaiya, D. Petyt, B.C. Radburn-Smith, C.H. Shepherd-Themistocleous, I.R. Tomalin, W.J. Womersley, S.D. Worm Imperial College, London, United Kingdom R. Bainbridge, G. Ball, J. Ballin, R. Beuselinck, O. Buchmuller, D. Colling, N. Cripps, M. Cutajar, G. Davies, M. Della Negra, J. Fulcher, D. Futyan, A. Guneratne Bryer, G. Hall, Z. Hatherell, J. Hays, G. Iles, G. Karapostoli, L. Lyons, A.-M. Magnan, J. Marrouche, R. Nandi, J. Nash, A. Nikitenko28, A. Papageorgiou, M. Pesaresi, K. Petridis, M. Pioppi40, D.M. Raymond, N. Rompotis, A. Rose, M.J. Ryan, C. Seez, P. Sharp, A. Sparrow, A. Tapper, S. Tourneur, M. Vazquez Acosta, T. Virdee, S. Wakefield, D. Wardrope, T. Whyntie Brunel University, Uxbridge, United Kingdom M. Barrett, M. Chadwick, J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, D. Leslie, W. Martin, I.D. Reid, L. Teodorescu Baylor University, Waco, USA K. Hatakeyama Boston University, Boston, USA T. Bose, E. Carrera Jarrin, C. Fantasia, A. Heister, J. St. John, P. Lawson, D. Lazic, J. Rohlf, D. Sperka, L. Sulak Brown University, Providence, USA A. Avetisyan, S. Bhattacharya, J.P. Chou, D. Cutts, A. Ferapontov, U. Heintz, S. Jabeen, G. Kukartsev, G. Landsberg, M. Narain, D. Nguyen, M. Segala, T. Speer, K.V. Tsang 32 A The CMS Collaboration University of California, Davis, Davis, USA M.A. Borgia, R. Breedon, M. Calderon De La Barca Sanchez, D. Cebra, S. Chauhan, M. Chertok, J. Conway, P.T. Cox, J. Dolen, R. Erbacher, E. Friis, W. Ko, A. Kopecky, R. Lander, H. Liu, S. Maruyama, T. Miceli, M. Nikolic, D. Pellett, J. Robles, S. Salur, T. Schwarz, M. Searle, J. Smith, M. Squires, M. Tripathi, R. Vasquez Sierra, C. Veelken University of California, Los Angeles, Los Angeles, USA V. Andreev, K. Arisaka, D. Cline, R. Cousins, A. Deisher, J. Duris, S. Erhan, C. Farrell, J. Hauser, M. Ignatenko, C. Jarvis, C. Plager, G. Rakness, P. Schlein†, J. Tucker, V. Valuev University of California, Riverside, Riverside, USA J. Babb, R. Clare, J. Ellison, J.W. Gary, F. Giordano, G. Hanson, G.Y. Jeng, S.C. Kao, F. Liu, H. Liu, A. Luthra, H. Nguyen, B.C. Shen†, R. Stringer, J. Sturdy, S. Sumowidagdo, R. Wilken, S. Wimpenny University of California, San Diego, La Jolla, USA W. Andrews, J.G. Branson, G.B. Cerati, E. Dusinberre, D. Evans, F. Golf, A. Holzner, R. Kelley, M. Lebourgeois, J. Letts, B. Mangano, J. Muelmenstaedt, S. Padhi, C. Palmer, G. Petrucciani, H. Pi, M. Pieri, R. Ranieri, M. Sani, V. Sharma1, S. Simon, Y. Tu, A. Vartak, F. W¨ urthwein, A. Yagil University of California, Santa Barbara, Santa Barbara, USA D. Barge, R. Bellan, C. Campagnari, M. D’Alfonso, T. Danielson, K. Flowers, P. Geffert, J. Incandela, C. Justus, P. Kalavase, S.A. Koay, D. Kovalskyi, V. Krutelyov, S. Lowette, N. Mccoll, V. Pavlunin, F. Rebassoo, J. Ribnik, J. Richman, R. Rossin, D. Stuart, W. To, J.R. Vlimant California Institute of Technology, Pasadena, USA A. Bornheim, J. Bunn, Y. Chen, M. Gataullin, D. Kcira, V. Litvine, Y. Ma, A. Mott, H.B. Newman, C. Rogan, V. Timciuc, P. Traczyk, J. Veverka, R. Wilkinson, Y. Yang, R.Y. Zhu Carnegie Mellon University, Pittsburgh, USA B. Akgun, R. Carroll, T. Ferguson, Y. Iiyama, D.W. Jang, S.Y. Jun, Y.F. Liu, M. Paulini, J. Russ, N. Terentyev, H. Vogel, I. Vorobiev University of Colorado at Boulder, Boulder, USA J.P. Cumalat, M.E. Dinardo, B.R. Drell, C.J. Edelmaier, W.T. Ford, A. Gaz, B. Heyburn, E. Luiggi Lopez, U. Nauenberg, J.G. Smith, K. Stenson, K.A. Ulmer, S.R. Wagner, S.L. Zang Cornell University, Ithaca, USA L. Agostino, J. Alexander, A. Chatterjee, S. Das, N. Eggert, L.J. Fields, L.K. Gibbons, B. Heltsley, W. Hopkins, A. Khukhunaishvili, B. Kreis, V. Kuznetsov, G. Nicolas Kaufman, J.R. Patterson, D. Puigh, D. Riley, A. Ryd, X. Shi, W. Sun, W.D. Teo, J. Thom, J. Thompson, J. Vaughan, Y. Weng, L. Winstrom, P. Wittich Fairfield University, Fairfield, USA A. Biselli, G. Cirino, D. Winn Fermi National Accelerator Laboratory, Batavia, USA S. Abdullin, M. Albrow, J. Anderson, G. Apollinari, M. Atac, J.A. Bakken, S. Banerjee, L.A.T. Bauerdick, A. Beretvas, J. Berryhill, P.C. Bhat, I. Bloch, F. Borcherding, K. Burkett, J.N. Butler, V. Chetluru, H.W.K. Cheung, F. Chlebana, S. Cihangir, M. Demarteau, D.P. Eartly, V.D. Elvira, S. Esen, I. Fisk, J. Freeman, Y. Gao, E. Gottschalk, D. Green, K. Gunthoti, O. Gutsche, A. Hahn, J. Hanlon, R.M. Harris, J. Hirschauer, B. Hooberman, E. James, H. Jensen, M. Johnson, U. Joshi, R. Khatiwada, B. Kilminster, B. Klima, K. Kousouris, S. Kunori, S. Kwan, 33 C. Leonidopoulos, P. Limon, R. Lipton, J. Lykken, K. Maeshima, J.M. Marraffino, D. Mason, P. McBride, T. McCauley, T. Miao, K. Mishra, S. Mrenna, Y. Musienko41, C. Newman-Holmes, V. O’Dell, S. Popescu42, R. Pordes, O. Prokofyev, N. Saoulidou, E. Sexton-Kennedy, S. Sharma, A. Soha, W.J. Spalding, L. Spiegel, P. Tan, L. Taylor, S. Tkaczyk, L. Uplegger, E.W. Vaandering, R. Vidal, J. Whitmore, W. Wu, F. Yang, F. Yumiceva, J.C. Yun University of Florida, Gainesville, USA D. Acosta, P. Avery, D. Bourilkov, M. Chen, G.P. Di Giovanni, D. Dobur, A. Drozdetskiy, R.D. Field, M. Fisher, Y. Fu, I.K. Furic, J. Gartner, S. Goldberg, B. Kim, S. Klimenko, J. Konigsberg, A. Korytov, A. Kropivnitskaya, T. Kypreos, K. Matchev, G. Mitselmakher, L. Muniz, Y. Pakhotin, C. Prescott, R. Remington, M. Schmitt, B. Scurlock, P. Sellers, N. Skhirtladze, D. Wang, J. Yelton, M. Zakaria Florida International University, Miami, USA C. Ceron, V. Gaultney, L. Kramer, L.M. Lebolo, S. Linn, P. Markowitz, G. Martinez, J.L. Rodriguez Florida State University, Tallahassee, USA T. Adams, A. Askew, D. Bandurin, J. Bochenek, J. Chen, B. Diamond, S.V. Gleyzer, J. Haas, S. Hagopian, V. Hagopian, M. Jenkins, K.F. Johnson, H. Prosper, L. Quertenmont, S. Sekmen, V. Veeraraghavan Florida Institute of Technology, Melbourne, USA M.M. Baarmand, B. Dorney, S. Guragain, M. Hohlmann, H. Kalakhety, R. Ralich, I. Vodopiyanov University of Illinois at Chicago (UIC), Chicago, USA M.R. Adams, I.M. Anghel, L. Apanasevich, Y. Bai, V.E. Bazterra, R.R. Betts, J. Callner, R. Cavanaugh, C. Dragoiu, E.J. Garcia-Solis, L. Gauthier, C.E. Gerber, D.J. Hofman, S. Khalatyan, F. Lacroix, M. Malek, C. O’Brien, C. Silvestre, A. Smoron, D. Strom, N. Varelas The University of Iowa, Iowa City, USA U. Akgun, E.A. Albayrak, B. Bilki, K. Cankocak43, W. Clarida, F. Duru, C.K. Lae, E. McCliment, J.-P. Merlo, H. Mermerkaya, A. Mestvirishvili, A. Moeller, J. Nachtman, C.R. Newsom, E. Norbeck, J. Olson, Y. Onel, F. Ozok, S. Sen, J. Wetzel, T. Yetkin, K. Yi Johns Hopkins University, Baltimore, USA B.A. Barnett, B. Blumenfeld, A. Bonato, C. Eskew, D. Fehling, G. Giurgiu, A.V. Gritsan, Z.J. Guo, G. Hu, P. Maksimovic, S. Rappoccio, M. Swartz, N.V. Tran, A. Whitbeck The University of Kansas, Lawrence, USA P. Baringer, A. Bean, G. Benelli, O. Grachov, M. Murray, D. Noonan, V. Radicci, S. Sanders, J.S. Wood, V. Zhukova Kansas State University, Manhattan, USA T. Bolton, I. Chakaberia, A. Ivanov, M. Makouski, Y. Maravin, S. Shrestha, I. Svintradze, Z. Wan Lawrence Livermore National Laboratory, Livermore, USA J. Gronberg, D. Lange, D. Wright University of Maryland, College Park, USA A. Baden, M. Boutemeur, S.C. Eno, D. Ferencek, J.A. Gomez, N.J. Hadley, R.G. Kellogg, M. Kirn, Y. Lu, A.C. Mignerey, K. Rossato, P. Rumerio, F. Santanastasio, A. Skuja, J. Temple, M.B. Tonjes, S.C. Tonwar, E. Twedt 34 A The CMS Collaboration Massachusetts Institute of Technology, Cambridge, USA B. Alver, G. Bauer, J. Bendavid, W. Busza, E. Butz, I.A. Cali, M. Chan, V. Dutta, P. Everaerts, G. Gomez Ceballos, M. Goncharov, K.A. Hahn, P. Harris, Y. Kim, M. Klute, Y.-J. Lee, W. Li, C. Loizides, P.D. Luckey, T. Ma, S. Nahn, C. Paus, D. Ralph, C. Roland, G. Roland, M. Rudolph, G.S.F. Stephans, K. Sumorok, K. Sung, E.A. Wenger, S. Xie, M. Yang, Y. Yilmaz, A.S. Yoon, M. Zanetti University of Minnesota, Minneapolis, USA P. Cole, S.I. Cooper, P. Cushman, B. Dahmes, A. De Benedetti, P.R. Dudero, G. Franzoni, J. Haupt, K. Klapoetke, Y. Kubota, J. Mans, V. Rekovic, R. Rusack, M. Sasseville, A. Singovsky University of Mississippi, University, USA L.M. Cremaldi, R. Godang, R. Kroeger, L. Perera, R. Rahmat, D.A. Sanders, D. Summers University of Nebraska-Lincoln, Lincoln, USA K. Bloom, S. Bose, J. Butt, D.R. Claes, A. Dominguez, M. Eads, J. Keller, T. Kelly, I. Kravchenko, J. Lazo-Flores, C. Lundstedt, H. Malbouisson, S. Malik, G.R. Snow State University of New York at Buffalo, Buffalo, USA U. Baur, A. Godshalk, I. Iashvili, S. Jain, A. Kharchilava, A. Kumar, S.P. Shipkowski, K. Smith Northeastern University, Boston, USA G. Alverson, E. Barberis, D. Baumgartel, O. Boeriu, M. Chasco, S. Reucroft, J. Swain, D. Wood, J. Zhang Northwestern University, Evanston, USA A. Anastassov, A. Kubik, N. Odell, R.A. Ofierzynski, B. Pollack, A. Pozdnyakov, M. Schmitt, S. Stoynev, M. Velasco, S. Won University of Notre Dame, Notre Dame, USA L. Antonelli, D. Berry, M. Hildreth, C. Jessop, D.J. Karmgard, J. Kolb, T. Kolberg, K. Lannon, W. Luo, S. Lynch, N. Marinelli, D.M. Morse, T. Pearson, R. Ruchti, J. Slaunwhite, N. Valls, J. Warchol, M. Wayne, J. Ziegler The Ohio State University, Columbus, USA B. Bylsma, L.S. Durkin, J. Gu, C. Hill, P. Killewald, K. Kotov, T.Y. Ling, M. Rodenburg, G. Williams Princeton University, Princeton, USA N. Adam, E. Berry, P. Elmer, D. Gerbaudo, V. Halyo, P. Hebda, A. Hunt, J. Jones, E. Laird, D. Lopes Pegna, D. Marlow, T. Medvedeva, M. Mooney, J. Olsen, P. Pirou´ e, X. Quan, H. Saka, D. Stickland, C. Tully, J.S. Werner, A. Zuranski University of Puerto Rico, Mayaguez, USA J.G. Acosta, X.T. Huang, A. Lopez, H. Mendez, S. Oliveros, J.E. Ramirez Vargas, A. Zatserklyaniy Purdue University, West Lafayette, USA E. Alagoz, V.E. Barnes, G. Bolla, L. Borrello, D. Bortoletto, A. Everett, A.F. Garfinkel, Z. Gecse, L. Gutay, Z. Hu, M. Jones, O. Koybasi, M. Kress, A.T. Laasanen, N. Leonardo, C. Liu, V. Maroussov, P. Merkel, D.H. Miller, N. Neumeister, I. Shipsey, D. Silvers, A. Svyatkovskiy, H.D. Yoo, J. Zablocki, Y. Zheng Purdue University Calumet, Hammond, USA P. Jindal, N. Parashar 35 Rice University, Houston, USA C. Boulahouache, V. Cuplov, K.M. Ecklund, F.J.M. Geurts, J.H. Liu, B.P. Padley, R. Redjimi, J. Roberts, J. Zabel University of Rochester, Rochester, USA B. Betchart, A. Bodek, Y.S. Chung, R. Covarelli, P. de Barbaro, R. Demina, Y. Eshaq, H. Flacher, A. Garcia-Bellido, P. Goldenzweig, Y. Gotra, J. Han, A. Harel, D.C. Miner, D. Orbaker, G. Petrillo, D. Vishnevskiy, M. Zielinski The Rockefeller University, New York, USA A. Bhatti, R. Ciesielski, L. Demortier, K. Goulianos, G. Lungu, C. Mesropian, M. Yan Rutgers, the State University of New Jersey, Piscataway, USA O. Atramentov, A. Barker, D. Duggan, Y. Gershtein, R. Gray, E. Halkiadakis, D. Hidas, D. Hits, A. Lath, S. Panwalkar, R. Patel, A. Richards, K. Rose, S. Schnetzer, S. Somalwar, R. Stone, S. Thomas University of Tennessee, Knoxville, USA G. Cerizza, M. Hollingsworth, S. Spanier, Z.C. Yang, A. York Texas A&M University, College Station, USA J. Asaadi, R. Eusebi, J. Gilmore, A. Gurrola, T. Kamon, V. Khotilovich, R. Montalvo, C.N. Nguyen, I. Osipenkov, J. Pivarski, A. Safonov, S. Sengupta, A. Tatarinov, D. Toback, M. Weinberger Texas Tech University, Lubbock, USA N. Akchurin, J. Damgov, C. Jeong, K. Kovitanggoon, S.W. Lee, Y. Roh, A. Sill, I. Volobouev, R. Wigmans, E. Yazgan Vanderbilt University, Nashville, USA E. Appelt, E. Brownson, D. Engh, C. Florez, W. Gabella, W. Johns, P. Kurt, C. Maguire, A. Melo, P. Sheldon, S. Tuo, J. Velkovska University of Virginia, Charlottesville, USA M.W. Arenton, M. Balazs, S. Boutle, M. Buehler, S. Conetti, B. Cox, B. Francis, R. Hirosky, A. Ledovskoy, C. Lin, C. Neu, R. Yohay Wayne State University, Detroit, USA S. Gollapinni, R. Harr, P.E. Karchin, P. Lamichhane, M. Mattson, C. Milst` ene, A. Sakharov University of Wisconsin, Madison, USA M. Anderson, M. Bachtis, J.N. Bellinger, D. Carlsmith, S. Dasu, J. Efron, L. Gray, K.S. Grogg, M. Grothe, R. Hall-Wilton1, M. Herndon, P. Klabbers, J. Klukas, A. Lanaro, C. Lazaridis, J. Leonard, R. Loveless, A. Mohapatra, D. Reeder, I. Ross, A. Savin, W.H. Smith, J. Swanson, M. Weinberg †: Deceased 1: Also at CERN, European Organization for Nuclear Research, Geneva, Switzerland 2: Also at Universidade Federal do ABC, Santo Andre, Brazil 3: Also at Laboratoire Leprince-Ringuet, Ecole Polytechnique, IN2P3-CNRS, Palaiseau, France 4: Also at Suez Canal University, Suez, Egypt 5: Also at Fayoum University, El-Fayoum, Egypt 6: Also at Soltan Institute for Nuclear Studies, Warsaw, Poland 7: Also at Massachusetts Institute of Technology, Cambridge, USA 8: Also at Universit´ e de Haute-Alsace, Mulhouse, France 36 A The CMS Collaboration 9: Also at Brandenburg University of Technology, Cottbus, Germany 10: Also at Moscow State University, Moscow, Russia 11: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary 12: Also at E¨ otv¨ os Lor´ and University, Budapest, Hungary 13: Also at Tata Institute of Fundamental Research - HECR, Mumbai, India 14: Also at University of Visva-Bharati, Santiniketan, India 15: Also at Facolt` a Ingegneria Universit` a di Roma ”La Sapienza”, Roma, Italy 16: Also at Universit` a della Basilicata, Potenza, Italy 17: Also at Laboratori Nazionali di Legnaro dell’ INFN, Legnaro, Italy 18: Also at Universit` a degli studi di Siena, Siena, Italy 19: Also at California Institute of Technology, Pasadena, USA 20: Also at Faculty of Physics of University of Belgrade, Belgrade, Serbia 21: Also at University of California, Los Angeles, Los Angeles, USA 22: Also at University of Florida, Gainesville, USA 23: Also at Universit´ e de Gen` eve, Geneva, Switzerland 24: Also at Scuola Normale e Sezione dell’ INFN, Pisa, Italy 25: Also at INFN Sezione di Roma; Universit` a di Roma ”La Sapienza”, Roma, Italy 26: Also at University of Athens, Athens, Greece 27: Also at The University of Kansas, Lawrence, USA 28: Also at Institute for Theoretical and Experimental Physics, Moscow, Russia 29: Also at Paul Scherrer Institut, Villigen, Switzerland 30: Also at University of Belgrade, Faculty of Physics and Vinca Institute of Nuclear Sciences, Belgrade, Serbia 31: Also at Gaziosmanpasa University, Tokat, Turkey 32: Also at Adiyaman University, Adiyaman, Turkey 33: Also at Mersin University, Mersin, Turkey 34: Also at Izmir Institute of Technology, Izmir, Turkey 35: Also at Kafkas University, Kars, Turkey 36: Also at Suleyman Demirel University, Isparta, Turkey 37: Also at Ege University, Izmir, Turkey 38: Also at Rutherford Appleton Laboratory, Didcot, United Kingdom 39: Also at School of Physics and Astronomy, University of Southampton, Southampton, United Kingdom 40: Also at INFN Sezione di Perugia; Universit` a di Perugia, Perugia, Italy 41: Also at Institute for Nuclear Research, Moscow, Russia 42: Also at Horia Hulubei National Institute of Physics and Nuclear Engineering (IFIN-HH), Bucharest, Romania 43: Also at Istanbul Technical University, Istanbul, Turkey