Forward-backward multiplicity correlations in pp collisions at √s = 0.9, 2.76 and 7 TeV

JHEP05(2015)097 Published for SISSA by Springer Received:February 3, 2015 Accepted:April 24, 2015 Published:May 20, 2015 Forward-backward multiplicity correlations in pp collisions at √s=0.9, 2.76 and 7 TeV The ALICE collaboration E-mail: [email protected] Abstract: The strength of forward-backward (FB) multiplicity correlations is measured by the ALICE detector in proton-proton (pp) collisions at √s= 0.9, 2.76 and 7 TeV. The measurement is performed in the central pseudorapidity region (|η|<0.8) for the transverse momentum pT>0.3 GeV/c. Two separate pseudorapidity windows of width (δη) ranging from 0.2 to 0.8 are chosen symmetrically around η= 0. The multiplicity correlation strength (bcorr) is studied as a function of the pseudorapidity gap (ηgap) between the two windows as well as the width of these windows. The correlation strength is found to decrease with increasing ηgap and shows a non-linear increase with δη. A sizable increase of the correlation strength with the collision energy, which cannot be explained exclusively by the increase of the mean multiplicity inside the windows, is observed. The correlation coefficient is also measured for multiplicities in different configurations of two azimuthal sectors selected within the symmetric FB η-windows. Two different contributions, the short-range (SR) and the long-range (LR), are observed. The energy dependence of bcorr is found to be weak for the SR component while it is strong for the LR component. Moreover, the correlation coefficient is studied for particles belonging to various transverse momentum intervals chosen to have the same mean multiplicity. Both SR and LR contributions to bcorr are found to increase with pTin this case. Results are compared to PYTHIA and PHOJET event generators and to a string-based phenomenological model. The observed dependencies of bcorr add new constraints on phenomenological models. Keywords: Hadron-Hadron Scattering ArXiv ePrint: 1502.00230 Open Access, Copyright CERN, for the benefit of the ALICE Collaboration. Article funded by SCOAP3. doi:10.1007/JHEP05(2015)097 JHEP05(2015)097 Contents 1 Introduction 1 2 Data analysis 3 2.1 Experimental setup, event and track selection 3 2.2 Definition of counting windows 4 2.3 Experimental procedures of the FB correlation coefficient measurement 5 2.4 Corrections and systematic uncertainties 5 3 Multiplicity correlations in windows separated in pseudorapidity 7 3.1 Dependence on the gap between windows 7 3.2 Dependence on the width of windows 7 3.3 Dependence on the collision energy 9 4 Multiplicity correlations in windows separated in pseudorapidity and azimuth 9 5 Dependence of FB multiplicity correlation strength on the choice of pT intervals 12 6 Conclusion 15 A A model with random uniform distribution of produced particles in pseudorapidity 16 The ALICE collaboration 21 1 Introduction We report a detailed study of correlations between multiplicities in pp collisions at 0.9, 2.76 and 7 TeV. The correlations are obtained from event-by-event multiplicity measurements in pseudorapidity (η) and azimuth (ϕ) separated intervals. The intervals are selected one in the forward and another in the backward hemispheres in the center-of-mass system, therefore the correlations are referred to as forward-backward (FB) correlations. The FB correlation strength is characterized by the correlation coefficient, bcorr, which is obtained from a linear regression analysis of the average multiplicity measured in the backward rapidity hemisphere (hnBinF) as a function of the event multiplicity in the forward hemisphere (nF): hnBinF=a+bcorr ·nF.(1.1) – 1 – JHEP05(2015)097 This linear relation (1.1) has been observed experimentally [1–4] and is discussed in [5–7]. Under the assumption of linear correlation between nFand nB, the Pearson correlation coefficient bcorr =hnBnFi−hnBihnFi hn2 Fi−hnFi2(1.2) can be used for the experimental determination of bcorr [2]. Since the parameter ais given by a=hnBi−bcorrhnFi, it adds no additional information and usually is not considered [5–7]. Heretofore, FB multiplicity correlations were studied experimentally in a large number of collision systems including e+e−,µ+p, pp, pp and A–A interactions [3,4,8–13]. No FB multiplicity correlations were observed in e+e−annihilation at √s= 29 GeV. This was interpreted as the consequence of independent fragmentation of the forward and backward jets produced in this process [14]. In contrast, in pp collisions at the ISR [13] at √s= 52.6 GeV [4] and in pp interactions at the SppS collider [15] sizeable positive FB multiplicity correlations have been observed. Their strength was found to increase strongly with collision energy [3], which was confirmed later at much higher energies (√s&1 TeV) in pp collisions by the E735 collaboration at the Tevatron [12] and in pp collisions by the ATLAS experiment at the LHC (√s= 0.9 and 7 TeV) [16]. One of the observations reported by ATLAS is the decrease of bcorr with the increase of the minimum transverse momentum of charged particles. The STAR collaboration at RHIC analysed the FB multiplicity correlations in pp and Au–Au collisions at √sNN = 200 GeV [17]. Strong correlation was observed in case of Au–Au collisions, while in pp collisions bcorr was found to be rather small (∼0.1). In the present paper we relate this to the use of smaller pseudorapidity windows as compared to previous pp and pp measurements. Forward-backward multiplicity correlations in high energy pp and A–A collisions also raise a considerable theoretical interest. First attempts to explain this phenomenon [7,18– 20] were made in the framework of the Dual Parton Model (DPM) [2] and the Quark Gluon String Model (QGSM) [21,22]. They provide a quantitative description of multiparticle production in soft processes. In improved versions of the models, collectivity effects arising due to the interactions between strings, which are particularly important in the case of A–A interactions, were taken into account [23–26]. These effects are based on the String Fusion Model (SFM) proposed in [27,28]. It was shown that these string interactions lead to a considerable modification of the FB correlation strength, along with the reduction of multiplicities, the increase of mean particle pT, and the enhancement of heavy flavour production in central A–A collisions [23,29,30]. FB correlations are usually divided into short and long-range components [2,7]. In phenomenological models, short-range correlations (SRC) are assumed to be localized over a small range of η-differences, up to one unit. They are induced by various short-range effects from single source fragmentation, including particles produced from decays of clusters or resonances, jet and mini-jet induced correlations. Long-range correlations (LRC) extend over a wider range in η. They originate from fluctuations in the number and properties of particle emitting sources (clusters, cut pomerons, strings, mini-jets etc.) [2,7,19,23–26]. – 2 – JHEP05(2015)097 The SFM predicts that the variance of the number of particle-emitting sources (strings) should be damped by their fusion, implying a reduction of multiplicity long-range correlations [23,25,26]. Contrary to this prediction, long-range correlations arising in the Color Glass Condensate model (CGC) [31] have been shown to increase with the centrality of the collision [32]. Therefore, the investigation of correlations between various observables, measured in two different, sufficiently separated η-intervals, is considered to be a powerful tool for the exploration of the initial conditions of hadronic interactions [33]. In the case of A–A collisions, these correlations induced across a wide range in ηare expected to reflect the earliest stages of the collisions, almost free from final state effects [32,34]. The reference for the analysis of A–A collision dynamics can be obtained in pp collisions by studying the dependence of FB correlations on collision energy, particle pseudorapidity, azimuth and transverse momenta. This paper is organized as follows: section 2provides experimental details, including the description of the procedures used for the event and track selection, the efficiency corrections and systematic uncertainties estimates. Sections 3and 4discuss the results on FB multiplicity correlation measurements in ηin pp collisions at √s= 0.9, 2.76 and 7 TeV and in η–φwindows at √s= 0.9 and 7 TeV. In section 3, we present dependences of the correlation coefficient on the gap between windows, their widths and the collision energy. In section 4, multiplicity correlations in windows separated in pseudorapidity and azimuth are studied, and the comparison with Monte Carlo generators PYTHIA6 and PHOJET is discussed. Results on multiplicity correlations in different pTranges in pp collisions at √s= 7 TeV are presented in section 5. 2 Data analysis 2.1 Experimental setup, event and track selection The data presented in this paper were recorded with the ALICE detector [35] in pp collisions at √s= 0.9, 2.76 and 7 TeV. Charged primary particles are reconstructed with the central barrel detectors combining information from the Inner Tracking System (ITS) and the Time Projection Chamber (TPC). Both detectors are located inside the 0.5 T solenoidal field. The ITS is composed of 3 different types of coordinate-sensitive Si-detectors. It consists of 2 silicon pixel innermost layers (SPD), 2 silicon drift (SDD) and 2 silicon strip (SSD) outer detector layers. The design allows for two-particle separation in events with multiplicity up to 100 charged particles per cm2. The SPD detector covers the pseudorapidity ranges |η|<2 for inner and |η|<1.4 for outer layers, acceptances of SDD and SSD are |η|<0.9 and |η|<1, respectively. All ITS elements have a radiation length of about 1.1% X0per layer. The ITS provides reliable charged particle tracking down to transverse momenta of 0.1 GeV/c, ideal for the study of low-pT(soft) phenomena. The ALICE TPC is the main tracking detector of the central rapidity region. The TPC, together with the ITS, provides charged particle momentum measurement, particle identification and vertex determination with good momentum and dE/dxresolution as well as two-track separation of identified hadrons and leptons in the pTregion below 10 GeV/c. – 3 – JHEP05(2015)097 Figure 1. Illustration of the variables δη,ηgap and ηsep used in the present analysis. Figure 2. Illustration of sets of η-windows with different widths δη and separation gaps ηgap. The TPC has an acceptance of |η|<0.9 for tracks which reach the outer radius of the TPC and up to |η|<1.5 for tracks that exit through the endcap of the TPC. For the present analysis, minimum bias pp events are used. The minimum-bias trigger required a hit in one of the forward scintillator counters (VZERO) or in one of the two SPD layers. The VZERO timing signal was used to reject beam-gas and beam-halo collisions. The primary vertex was reconstructed using the combined track information from the TPC and ITS, and only events with primary vertices lying within ±10 cm from the centre of the apparatus are selected. In this way a uniform acceptance in the central pseudorapidity region |η|<0.8 is ensured. The data samples for √s= 0.9, 2.76 and 7 TeV comprise 2×106, 10 ×106, and 6.5×106events, respectively. Only runs with low probability to produce several separate events per one bunch crossing (so-called pile-up events) were used in this analysis. To obtain high tracking efficiency and to reduce efficiency losses due to detector boundaries, tracks are selected with pT>0.3 GeV/c in the pseudorapidity range |η|<0.8. Employing a Kalman filter technique, tracks are reconstructed using space-time points measured by the TPC. Tracks with at least 70 space-points associated and track fitting χ2/ndof less than 2 are accepted. Additionally, at least two hits in the ITS must be associated with the track. Tracks are also rejected if their distance of closest approach (DCA) to the reconstructed event vertex is larger than 0.3 cm in either the transverse or the longitudinal plane. For the chosen selection criteria, the tracking efficiency for charged particles with pT>0.3 GeV/c is about 80%. 2.2 Definition of counting windows Two intervals separated symmetrically around η= 0 with variable width δη ranging from 0.2 to 0.8 are defined as “forward” (F, η > 0) and “backward” (B, η < 0) . Correlations between multiplicities of charged particles (n) are studied as a function of the gap between the windows (denoted as ηgap). Another convenient variable is ηsep which is the separation in pseudorapidity between centres of the windows. These variables are illustrated in figure 1, and all configurations of window pairs chosen for the analysis are drawn in figure 2. – 4 – JHEP05(2015)097 Figure 3. Illustration of 8 configurations of azimuthal sectors. Forward and backward pseudorapidity windows of the width δη = 0.2 are additionally split into 8 azimuthal sectors with the width δϕ =π/4. The red sectors correspond to the first window of the FB pair, the green sectors to the second one. The variable ϕsep is the separation in azimuthal angle between centres of the sectors. The analysis is extended to correlations between separated regions in the η–ϕplane (sectors). The ϕ-angle space is split into 8 sectors with the width δϕ =π/4 as shown in figure 3. This selection is motivated by a compromise between granularity and statistical uncertainty. The definitions and equations, described in section 1, remain the same for the η–ϕwindows. The acceptance of the windows is determined by their widths δη and δϕ as the ALICE acceptance is approximately uniform in the selected ranges of ηand ϕ. 2.3 Experimental procedures of the FB correlation coefficient measurement The present paper focuses on the study of FB correlation phenomena related to soft particle production. Therefore we restrict pTin 0.3< pT<1.5 GeV/c, except for the study of the pTdependence presented in section 5, where the pTrange is 0.3< pT<6 GeV/c. The correlation coefficients, bcorr, for each window pair can be calculated using two methods [1–4]. In the first method values of hnBnFi,hnBi,hnFiand hn2 Fiare accumulated event-by-event and then bcorr is determined using eq. (1.2). In the second method, bcorr is calculated using linear regression. The 2-dimensional distributions (nB,nF) are obtained integrating over all selected events, then the average backward multiplicity is calculated for each fixed value of the forward multiplicity, and bcorr is obtained from a linear fit to the correlation function (see illustration in figure 4). Deviations from linear behavior may provide additional information, however, a detailed study of non-linearity in the correlation function is beyond the scope of this paper. It has been shown that the results obtained with the two methods agree within statistical uncertainty. In this work, results using the first method are presented. 2.4 Corrections and systematic uncertainties Acceptance and tracking efficiency corrections are extracted from Monte Carlo simulations using PYTHIA6 [36] (Perugia 0 tune) and PHOJET [37,38] as particle generators followed by a full detector response simulation based on GEANT3 [39]. Corrections are done to primary charged particle correlations and multiplicities. Correction factors obtained with these two generators are found to agree within 1% and the difference is neglected. Three independent correction procedures are investigated. In the first procedure, the correction factors for bcorr are obtained as the ratio of bcorr obtained at generator level (true value) to bcorr after detector response simulation – 5 – JHEP05(2015)097 Figure 4. Illustrative example of forward versus backward raw multiplicity distribution for windows with δη = 0.6 and ηgap = 0.4 at √s= 7 TeV (left) and corresponding correlation function (right). The correlation strength bcorr is obtained from a linear fit according to eq. (1.1). Since most of the statistics is at low multiplicities, the fit is mainly determined by the first points. Error source 0.9 TeV 2.76 TeV 7 TeV Number of TPC space-points 0.5–3.0 0–0.1 0.2–0.7 Number of ITS space-points 0.6–1.9 — 0.2–1.4 DCA 3.0–4.0 1.0–1.8 0.1–1.0 Vertex position along the beam line 0.2–1.1 0–1.0 0–0.7 bcorr correction procedure 2.5–4.0 2.2–4.2 1.6–2.8 Event pile-up <1<1<1 Total (%) 3.4–4.5 2.8–4.2 2.0–3.0 Table 1. Sources of systematic errors of bcorr measurements in η-windows of width δη = 0.2, and their contributions (in %). The minimal and maximal estimated values are indicated for each given source. (measured value). In the second procedure the correction factors are obtained for hnBnFi, hnBi,hnFiand hn2 Fiseparately and bcorr is obtained from the corrected moments. The third procedure takes into account approximately linear dependence of bcorr on hnFiwhen hnFivaries with cuts, and each corrected value of bcorr is found by extrapolation to the corrected value of hnFi. It was found that results of all three procedures agree within 1.6–4.2% (see table 1), thus proving the robustness of bcorr determination. The second procedure was chosen as the most direct and commonly used to produce the final corrected value of bcorr. Correction factors increase the values of bcorr, obtained for standard cuts, by 6–10 % for analysis in η-windows and 9–18 % for analysis in η–φwindows and in pTintervals. By varying the – 6 – JHEP05(2015)097 Figure 5. Forward-backward correlation strength bcorr as function of ηgap and for different windows widths δη = 0.2, 0.4, 0.6 and 0.8 in pp collisions at √s= 0.9, 2.76 and 7 TeV. selection cuts (vertex-, DCAand track selection cuts), correction procedures, and by comparison of the high and low pile-up runs, the systematic uncertainties on bcorr have been estimated. Adding all contributions in quadrature, the total systematic uncertainties are below 4.5% (4.2%, 3%) at √s= 0.9 (2.76, 7) TeV for the bcorr analysis in η-separated windows, and 6% for analysis in η–φseparated windows at √s= 0.9 and 7 TeV. For the bcorr analysis in pTintervals for 7 TeV, the systematic uncertainties are less than 8%. Statistical errors are small and within the symbol sizes for data points in the figures. A summary of the contributions of systematic uncertainties for bcorr in η-separated windows with the width δη = 0.2 is presented in table 1. 3 Multiplicity correlations in windows separated in pseudorapidity 3.1 Dependence on the gap between windows Figure 5shows the FB multiplicity correlation coefficient bcorr as a function of ηgap and for different widths of the ηwindows (δη) in pp collisions at the three collision energies. For each √s,bcorr is found to decrease slowly with increasing ηgap, while maintaining a substantial pedestal value throughout the full ηgap range. 3.2 Dependence on the width of windows The δη-dependence for adjacent (ηgap = 0), symmetrical windows with respect to η= 0 is shown in figure 6. For all collision energies, the correlation coefficient increases nonlinearly with δη. This trend is quite well described by PYTHIA6 and PHOJET, although the agreement worsens with increasing √s. This δη-dependence can be understood, along with other approaches [7,25,40], in a simple model with event-by-event multiplicity fluctuations and random distribution of produced particles in pseudorapidity. In this model, the multiplicity in an ηinterval containing the fraction pof the mean multiplicity hNiin the full η-acceptance is binomially distributed and its mean square is given by hn2 Fi=hn2 Bi=p(1 −p)hNi+p2hN2i,(3.1) – 7 – JHEP05(2015)097 Figure 6. Correlation strength bcorr as a function of δη for ηgap = 0 in pp collisions for √s= 0.9, 2.76 and 7 TeV. The MC results from PYTHIA6 Perugia 0 (solid line), Perugia 2011 (dotted line) and PHOJET (dashed line), calculated at generator level, are shown for comparison. The bottom panels show the ratio of bcorr between data and MC. The red dashed curves correspond to the model of independent particle emission from a fluctuating source (see text). where Nis the charged particle multiplicity measured in the pseudorapidity interval Yand p=hnFi hNi=hnBi hNi=δη Y.(3.2) One can connect the multiplicity fluctuations in the full η-acceptance considered in this analysis (Y= 1.6) with the correlation strength bcorr (see appendix A): bmod corr =αδη/Y 1 + αδη/Y ,(3.3) where α=σ2 N hNi−1.(3.4) Note that using eq. (3.1) and eq. (3.2) one can write the eq. (3.3) also in the following form: bmod corr = 1 −hnFi σ2 nF .(3.5) From the measured ratio of the multiplicity variance σ2 N≡ hN2i − hNi2in Y= 1.6 to the mean value hNiwe obtain the value of αat √s= 0.9, 2.76 and 7 TeV to be 2.03, 3.25 and 4.42, respectively, with a systematic uncertainty of about 5%. The bmod corr (δη)- dependences calculated by eq. (3.3) are shown in figure 6as red dashed lines. At ηgap = 0 the bcorr(δη) dependence is well described by this simple model. However, this model is not able to describe the dependence of bcorr on ηgap in figure 5because it does not take into account the SRC contribution mentioned above. – 8 – JHEP05(2015)097 Figure 14. Correlation strength bcorr at √s= 7 TeV for separated η–ϕwindows in different pT intervals with same hnchi. Five ϕsep values are shown as a function of ηsep. Windows of δη = 0.2 and δϕ =π/4. Top and bottom panels and contain the same experimental data, lines correspond to PYTHIA6 Perugia 2011 (top) and to PHOJET (bottom). PHOJET discrepancy with the data is especially dramatic at ϕsep =π/2, where PHOJET shows no dependence of bcorr on the pTrange. It was shown already in [47] that PHOJET has difficulties in description of underlying event measurements. Figure 14 shows that for higher pTintervals a near-side peak appears (see panels for ϕsep = 0 and π/4), at the same time the bcorr in the flat region at ηsep >1 increases with pT for all ϕsep values (compare panels for ϕsep =π/2, 3π/4 and π). It should be emphasized that the value of the pedestal (the common constant component in all panels) increases with pT. In nearand away-side azimuthal regions the increase of bcorr with pmin Tcan be explained by an enhanced number of back-to-back decays and jets. The general rise of bcorr can be related to the increase of the variance σ2 Nin eq. (3.4), discussed in the framework of the simple model in section 3.2. 6 Conclusion The strengths of forward-backward (FB) multiplicity correlations have been measured in minimum bias pp collisions at √s= 0.9, 2.76 and 7 TeV using multiplicities determined in two separated pseudorapidity windows separated by a variable gap, ηgap, of up to 1.2 units. The dependences of the correlation coefficient bcorr on the collision energy, the width and the position of pseudorapidity windows have been investigated. For the first time, the – 15 – JHEP05(2015)097 analysis has been also applied for various configurations of the azimuthal sectors selected within these pseudorapidity windows in events at √s= 0.9 and 7 TeV. A considerable increase of the FB correlation strength with the growth of the collision energy from √s= 0.9 to 7 TeV is observed. It is shown that this cannot be explained by the increase of the mean multiplicity alone. The correlation strength grows with the width of pseudorapidity windows, while it decreases slightly with increasing pseudorapidity gap between the windows. It is shown that there is a strong non-linear dependence of the correlation strength on the width of the pseudorapidity windows and hence on the mean multiplicity value. Measurements of the correlation strength for various configurations of azimuthal sectors enable the distinction of two contributions: short-range (SR) and long-range (LR) correlations. A weak dependence on the collision energy is observed for the SR component while the LR component has a strong dependence. For η-gaps larger than one unit of pseudorapidity and π/2 in azimuth the LR contribution dominates. This contribution forms a pedestal value (the common constant component) of bcorr increasing with collision energy. Moreover, pseudorapidity and pseudorapidity-azimuthal distributions of bcorr have been obtained in pp events at √s= 7 TeV for various particle transverse momentum intervals. It is found that the FB correlation strength increases with the transverse momentum if pT-intervals with the same mean multiplicity are chosen. The measurements have been compared to calculations using the PYTHIA and PHOJET MC event generators. These generators are able to describe the general trends of bcorr as a function of δη,ηgap and ϕsep and its dependence on the collision energy. In pT-dependent analysis of bcorr, PYTHIA describes data reasonably well, while PHOJET fails to describe bcorr in azimuthal sectors. The observed dependences of bcorr add new constraints on phenomenological models. In particular the transition between soft and hard processes in pp collisions can be investigated in detail using the pTdependence of azimuthal and pseudorapidity distributions of forward-backward multiplicity correlation strength bcorr. A A model with random uniform distribution of produced particles in pseudorapidity In a simple model with event-by-event multiplicity fluctuations and random uniform distribution of produced particles in pseudorapidity the probability to observe nFparticles in some subinterval δη from the total number of Ncharged particles produced in the whole pseudorapidity interval Yis given by the binomial distribution: PN(nF) = CnF NpnF(1 −p)N−nF,(A.1) with hnFiN=pN and hn2 FiN=p(1 −p)N+p2N2, where p≡δη/Y . (We consider the case of symmetric windows δηF=δηB=δη.) Averaging then over events with different values of N, P(nF) = X N P(N)PN(nF),(A.2) – 16 – JHEP05(2015)097 we have hnFi=X nF P(nF)nF=X nF X N P(N)PN(nF)nF=X N P(N)pN =phNi(A.3) and hence p=hnFi hNi=hnBi hNi=δη Y.(A.4) In the same way we find hn2 Fi=hn2 Bi=p(1 −p)hNi+p2hN2i,(A.5) h(nF+nB)2i= 2p(1 −2p)hNi+ (2p)2hN2i.(A.6) One can rewrite (A.4)–(A.6) also as σ2 nF+nB−hnF+nBi hnF+nBi2=σ2 nF−hnFi hnFi2=σ2 N−hNi hNi2≡RN,(A.7) since the so-called robust variance RNis the same for any subinterval of Yin the case of the independent homogeneous distribution of the particles along Y[43]. Using the presentation for the covariance hnFnBi−hnFihnBi ≡ 1 2(σ2 nF+nB−σ2 nF−σ2 nB),(A.8) we can write for the correlation coefficient in a model-independent way: bcorr =σ2 nF+nB−σ2 nF−σ2 nB 2σ2 nF .(A.9) Then combining (A.7) and (A.9) we find bmod corr =hnFiRN 1 + hnFiRN .(A.10) Using (A.4) we can write (A.10) also as bmod corr =αδη/Y 1 + αδη/Y ,(A.11) where α=hNiRN=σ2 N hNi−1.(A.12) Substituting the expression RN=σ2 nF−hnFi hnFi2(A.13) from (A.7) into (A.10) one finds another presentation for bmod corr : bmod corr = 1 −hnFi σ2 nF .(A.14) – 17 – JHEP05(2015)097 Open Access. This article is distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited. References [1] UA5 collaboration, G.J. Alner et al., UA5: A general study of proton-antiproton physics at √s= 546 GeV,Phys. Rept. 154 (1987) 247 [INSPIRE]. [2] A. Capella, U. 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Senosi65 , J. Seo67 ,96 , E. Serradilla64 ,10 , A. Sevcenco62 , A. Shabanov56 , A. Shabetai113 , O. Shadura3, R. Shahoyan36 , A. Shangaraev112 , A. Sharma90 , N. Sharma61 ,124 , K. Shigaki47 , K. Shtejer27 ,9, Y. Sibiriak100 , S. Siddhanta106 , K.M. Sielewicz36 , T. Siemiarczuk77 , D. Silvermyr84 ,34 , C. Silvestre71 , G. Simatovic128 , R. Singaraju131 , R. Singh90 ,79 , S. Singha79 ,131 , V. Singhal131 , B.C. Sinha131 , T. Sinha101 , B. Sitar39 , M. Sitta32 , T.B. Skaali22 , K. Skjerdal18 , M. Slupecki122 , N. Smirnov136 , R.J.M. Snellings57 , T.W. Snellman122 , C. Søgaard34 , R. Soltz75 , J. Song96 , M. Song137 , Z. Song7, F. Soramel30 , S. Sorensen124 , M. Spacek40 , E. Spiriti72 , I. Sputowska116 , M. Spyropoulou-Stassinaki88 , B.K. Srivastava95 , J. Stachel93 , I. Stan62 , G. Stefanek77 , M. Steinpreis20 , E. Stenlund34 , G. Steyn65 , J.H. Stiller93 , D. Stocco113 , P. Strmen39 , A.A.P. Suaide119 , T. Sugitate47 , – 23 – JHEP05(2015)097 C. Suire51 , M. Suleymanov16 , R. Sultanov58 , M. ˇ Sumbera83 , T.J.M. Symons74 , A. Szabo39 , A. Szanto de Toledo119 ,i, I. Szarka39 , A. Szczepankiewicz36 , M. Szymanski133 , J. Takahashi120 , N. Tanaka127 , M.A. Tangaro33 , J.D. Tapia Takaki,ii,51 , A. Tarantola Peloni53 , M. Tariq19 , M.G. Tarzila78 , A. Tauro36 , G. Tejeda Mu˜noz2, A. Telesca36 , K. Terasaki126 , C. Terrevoli30 ,25 , B. Teyssier129 , J. Th¨ader97 ,74 , D. Thomas57 ,117 , R. Tieulent129 , A.R. Timmins121 , A. Toia53 , S. Trogolo111 , V. Trubnikov3, W.H. Trzaska122 , T. Tsuji126 , A. Tumkin99 , R. Turrisi108 , T.S. Tveter22 , K. Ullaland18 , A. Uras129 , G.L. Usai25 , A. Utrobicic128 , M. Vajzer83 , M. Vala59 , L. Valencia Palomo70 , S. Vallero27 , J. Van Der Maarel57 , J.W. Van Hoorne36 , M. van Leeuwen57 , T. Vanat83 , P. Vande Vyvre36 , D. Varga135 , A. Vargas2, M. Vargyas122 , R. Varma48 , M. Vasileiou88 , A. Vasiliev100 , A. Vauthier71 , V. Vechernin130 , A.M. Veen57 , M. Veldhoen57 , A. Velure18 , M. Venaruzzo73 , E. Vercellin27 , S. Vergara Lim´on2, R. Vernet8, M. Verweij134 , L. Vickovic115 , G. Viesti30 ,i, J. Viinikainen122 , Z. Vilakazi125 ,65 , O. Villalobos Baillie102 , A. Vinogradov100 , L. Vinogradov130 , Y. Vinogradov99 , T. Virgili31 , V. Vislavicius34 , Y.P. Viyogi131 , A. Vodopyanov66 , M.A. V¨olkl93 , K. Voloshin58 , S.A. Voloshin134 , G. Volpe36 , B. von Haller36 , I. Vorobyev92 ,37 , D. Vranic97 ,36 , J. Vrl´akov´a41 , B. Vulpescu70 , A. Vyushin99 , B. Wagner18 , J. Wagner97 , H. Wang57 , M. Wang7,113 , Y. Wang93 , D. Watanabe127 , M. Weber36 ,121 , S.G. Weber97 , J.P. Wessels54 , U. Westerhoff54 , J. Wiechula35 , J. Wikne22 , M. Wilde54 , G. Wilk77 , J. Wilkinson93 , M.C.S. Williams105 , B. Windelband93 , M. Winn93 , C.G. Yaldo134 , Y. Yamaguchi126 , H. Yang57 , P. Yang7, S. Yano47 , S. Yasnopolskiy100 , Z. Yin7, H. Yokoyama127 , I.-K. Yoo96 , V. Yurchenko3, I. Yushmanov100 , A. Zaborowska133 , V. Zaccolo80 , A. Zaman16 , C. Zampolli105 , H.J.C. Zanoli119 , S. Zaporozhets66 , A. Zarochentsev130 , P. Z´avada60 , N. Zaviyalov99 , H. Zbroszczyk133 , I.S. Zgura62 , M. Zhalov85 , H. Zhang7, X. Zhang74 , Y. Zhang7, C. Zhao22 , N. Zhigareva58 , D. Zhou7, Y. Zhou57 , Z. Zhou18 , H. Zhu7, J. Zhu7,113 , X. Zhu7, A. Zichichi12 ,28 , A. Zimmermann93 , M.B. Zimmermann54 ,36 , G. Zinovjev3, M. Zyzak43 iDeceased ii Also at: University of Kansas, Lawrence, Kansas, United States 1A.I. Alikhanyan National Science Laboratory (Yerevan Physics Institute) Foundation, Yerevan, Armenia 2Benem´erita Universidad Aut´onoma de Puebla, Puebla, Mexico 3Bogolyubov Institute for Theoretical Physics, Kiev, Ukraine 4Bose Institute, Department of Physics and Centre for Astroparticle Physics and Space Science (CAPSS), Kolkata, India 5Budker Institute for Nuclear Physics, Novosibirsk, Russia 6California Polytechnic State University, San Luis Obispo, California, United States 7Central China Normal University, Wuhan, China 8Centre de Calcul de l’IN2P3, Villeurbanne, France 9Centro de Aplicaciones Tecnol´ogicas y Desarrollo Nuclear (CEADEN), Havana, Cuba 10 Centro de Investigaciones Energ´eticas Medioambientales y Tecnol´ogicas (CIEMAT), Madrid, Spain 11 Centro de Investigaci´on y de Estudios Avanzados (CINVESTAV), Mexico City and M´erida, Mexico 12 Centro Fermi - Museo Storico della Fisica e Centro Studi e Ricerche “Enrico Fermi”, Rome, Italy 13 Chicago State University, Chicago, Illinois, U.S.A. 14 China Institute of Atomic Energy, Beijing, China 15 Commissariat `a l’Energie Atomique, IRFU, Saclay, France 16 COMSATS Institute of Information Technology (CIIT), Islamabad, Pakistan 17 Departamento de F´ısica de Part´ıculas and IGFAE, Universidad de Santiago de Compostela, Santiago de Compostela, Spain – 24 –