Measurement of pion, kaon and proton production in proton–proton collisions at √s = 7 TeV

This is an electronic reprint of the original article. This reprint may differ from the original in pagination and typographic detail. Author(s): Title: Year: Version: Please cite the original version: All material supplied via JYX is protected by copyright and other intellectual property rights, and duplication or sale of all or part of any of the repository collections is not permitted, except that material may be duplicated by you for your research use or educational purposes in electronic or print form. You must obtain permission for any other use. Electronic or print copies may not be offered, whether for sale or otherwise to anyone who is not an authorised user. Measurement of pion, kaon and proton production in proton–proton collisions at √s = 7 TeV ALICE Collaboration ALICE Collaboration. (2015). Measurement of pion, kaon and proton production in proton–proton collisions at √s = 7 TeV. European Physical Journal C, 75(5), Article 226. https://doi.org/10.1140/epjc/s10052-015-3422-9 2015 Eur. Phys. J. C (2015) 75:226 DOI 10.1140/epjc/s10052-015-3422-9 Regular Article - Experimental Physics Measurement of pion, kaon and proton production in proton–proton collisions at √s=7TeV ALICE Collaboration CERN, 1211 Geneva 23, Switzerland Received: 2 April 2015 / Accepted: 20 April 2015 © CERN for the benefit of the ALICE collaboration 2015. This article is published with open access at Springerlink.com Abstract The measurement of primary π±,K±,pand p production at mid-rapidity (|y|<0.5) in proton–proton collisions at √s=7 TeV performed with a large ion collider experiment at the large hadron collider (LHC) is reported. Particle identification is performed using the specific ionisation energy-loss and time-of-flight information, the ringimagingCherenkovtechniqueandthekink-topologyidentification of weak decays of charged kaons. Transverse momentum spectra are measured from 0.1 up to 3 GeV/cfor pions, from 0.2 up to 6 GeV/cfor kaons and from 0.3 up to 6 GeV/c for protons. The measured spectra and particle ratios are compared with quantum chromodynamics-inspired models, tuned to reproduce also the earlier measurements performed at the LHC. Furthermore, the integrated particle yields and ratios as well as the average transverse momenta are compared with results at lower collision energies. 1 Introduction The majority of the particles produced at mid-rapidity in proton–proton collisions are low-momentum hadrons not originating from the fragmentation of partons produced in scattering processes with large momentum transfer. Their production, therefore, cannot be computed from first principles via perturbative quantum chromodynamics (pQCD). Currently available models describing hadronhadron collisions at high energy, such as the event generators PYTHIA6 [1], PYTHIA8 [2,3], EPOS [4,5] and PHOJET [6], combine pQCD calculations for the description of hard processes with phenomenological models for the description of the soft component. The measurement of lowmomentum particle production and species composition is therefore important as it provides crucial input for the modelling of the soft component and of the hadronisation processes.Furthermore,itservesasareferenceforthesamemeasurementinPb–Pbcollisions tostudytheproperties ofthehot and dense strongly interacting medium with partonic degrees e-mail: [email protected] offreedom,thequark–gluonplasma,whichiscreatedinthese collisions. In this paper, the measurement of primary π±, K±,pand pproduction at mid-rapidity in proton–proton collisions at √s=7 TeV using the ALICE detector [7–10] is presented. Primary particles are defined as prompt particles produced in the collision including decay products, except those from weak decays of light flavour hadrons and muons. Pions, kaons and protons are identified over a wide momentum range by combining the information extracted from the specific ionisation energy loss (dE/dx) measured in the inner tracking system (ITS) [11] and in the time projection chamber (TPC) [12], the time of flight measured in the time-of-flight (TOF) detector [13], the Cherenkov radiation measured in the high-momentum particle identification detector (HMPID) [14] and the kink-topology identification of the weak decays of charged kaons. Similar measurements in proton–proton collisions at √s=900 GeV and 2.76 TeV are reported in [15–17] and are included, together with lower energy data [18–24], in the discussion of the evolution of particle production with collision energy. Similar measurement at the LHC have also been performed in the forward region [25]. The paper is organised as follows. In Sect. 2the ALICE experimental setup is described, focusing on the detectors and the corresponding particle identification (PID) techniques relevant for the present measurement. Details of the event and track selection criteria and the corrections applied to the measured raw yields are also presented. In Sect. 3the results on the production of primary π±,K±,pand pare shown. These include the transverse momentum (pT)distributions and the pT-integrated production yields of each particle species and the K/πand p/πratios. The evolution with collision energy of the pT-integrated particle yields, of their ratios and of their average transverse momenta pT is also presented. In Sect. 4particle spectra and their ratios (K/πand p/π) are compared with models, in particular with different PYTHIA tunes [1–3,26,27], EPOS [4,5] and PHOJET [6]. Section 5concludes the paper summarizing the results. 123 226 Page 2 of 23 Eur. Phys. J. C (2015) 75:226 2 Experimental setup and data analysis 2.1 The ALICE detector The ALICE detector was specifically optimised to reconstruct and identify particles over a wide momentum range thanks to the low material budget, the moderate magnetic field and the presence of detectors exploiting all the known PID techniques. A comprehensive description of the ALICE experimental setup and performance can be found in [7–10]. In the following, the PID detectors relevant for the analysis presented in this paper are briefly described, namely ITS, TPC, TOF and HMPID. They are located in the ALICE central barrel in a B=0.5 T solenoidal magnetic field directed along the beam axis. The ITS, TPC and TOF detectors cover the full azimuth (ϕ) and have a pseudorapidity coverage of |η|<0.9, while the HMPID covers the pseudorapidity interval |η|<0.55 and the azimuthal angle range 1.2◦<ϕ<58.5◦. The ITS [11] is the innermost central barrel detector. It is composed of six cylindrical layers of silicon detectors, located at radial distances between 3.9 and 43 cm from the beam axis. The two innermost layers are equipped with silicon pixel detectors (SPD), the two intermediate ones are silicon drift detectors (SDD), while the two outermost ones are silicon strip detectors (SSD). The ITS provides high resolution tracking points close to the beam line, which allows us to reconstruct primary and secondary vertices with high precision, to measure with excellent resolution the distance of closest approach (DCA) of a track to the primary vertex, and to improve the track pTresolution. It is also used as a stand-alone tracker to reconstruct particles that do not reach the TPC or do not cross its sensitive areas. The SDD and SSD are equipped with analogue readout enabling PID via dE/dx measurements with a relative resolution of about 10 %. The TPC [12] is the main tracking detector of the ALICE central barrel. It is a large volume cylindrical chamber with high-granularity readout that surrounds the ITS covering the region85<r<247and−250 <z<+250cm intheradialr and longitudinal zdirections, respectively. It provides threedimensional space points and specific ionisation energy loss dE/dxwith up to 159 samples per track. The relative dE/dx resolution is measured to be about 5.5 % for tracks that cross from the centre of the outer edge of the detector. The TOF detector [13] is a large-area array of multigap resistive plate chambers with an intrinsic time resolution of 50 ps, including the electronic readout contribution. It is a cylindrical detector located at a radial distance 370 <r< 399 cm from the beam axis. Particles are identified using simultaneouslytheTOFinformationwiththemomentumand track length measured with the ITS and the TPC. The HMPID [14] is a single-arm proximity-focusing ring imaging Cherenkov (RICH) detector located at 475 cm from thebeamaxis.TheCherenkovradiatorisa15-mm-thicklayer of liquid C6F14 (perfluorohexane) with a refractive index of n=1.2989 at a photon wave length λ=175 nm, corresponding to a minimum particle velocity βmin =0.77. In addition to the detectors described above that provide PID information, the VZERO system [28] is used for trigger and event selection. It is composed of two scintillator arrays, which cover the pseudorapidity ranges 2.8<η<5.1 and −3.7<η<−1.7. 2.2 Data sample, event and track selection The results presented in this paper are obtained combining five independent analyses, namely ITS stand-alone, TPC– TOF, TOF, HMPID, kink, using different PID methods. The analysed data are proton–proton collisions at √s=7TeV collectedin 2010. During thatperiod, the instantaneousluminosity at the ALICE interaction point was kept within the range 0.6–1.2×1029 cm−2s−1to limit the collision pile-up probability. Only runs with a collision pile-up probability smaller than 4 % are used in this analysis, leading to an average pile-up rate of 2.5 %. The number of events used in the five independent analyses is reported in Table 1. The data were collected using a minimum-bias trigger, which required a hit in the SPD or in at least one of the VZERO scintillator arrays in coincidence with the arrival of proton bunches from bothdirections.Thistriggerselectionessentiallycorresponds to the requirement of having at least one charged particle in 8 units of pseudorapidity. The contamination due to beaminduced background is removed off-line by using the timing information from the VZERO detector, which measures the event time with a resolution of about 1 ns, and the correlation between the number of clusters and track segments (tracklets) in the SPD [15]. Selectedeventsarefurtherrequiredtohaveareconstructed primary vertex. For 87 % of the triggered events, the interaction vertex position is determined from the tracks reconstructed in TPC and ITS. For events that do not have a vertex reconstructed from tracks, which are essentially collisions with low multiplicity of charged particles, the primary vertex is reconstructed from the SPD tracklets, which are track segments built from pairs of hits in the two innermost layers of the ITS. Overall, the fraction of events with reconstructedprimaryvertex,eitherfromtracksorfromSPDtracklets, is of 91 %. Accepted events are required to have the reconstructed vertex position along the beam direction, z, within ±10 cm from the centre of the ALICE central barrel. This ensures good rapidity coverage, uniformity of the particle reconstruction efficiency in ITS and TPC and reduction of the remaining beam-gas contamination. In the following analyses two different sets of tracks are used: the global tracks, reconstructed using information from both ITS and TPC, and the ITS-sa tracks, reconstructed by using 123 Eur. Phys. J. C (2015) 75:226 Page 3 of 23 226 only the hits in the ITS. To limit the contamination due to secondary particles and tracks with wrongly associated hits and to ensure high tracking efficiency, tracks are selected according to the following criteria. The global tracks are required to cross over at least 70 TPC readout rows with a value of χ2/Nclusters of the momentum fit in the TPC lower than 4, to have at least two clusters reconstructed in the ITS out of which at least one is in the SPD layers and to have a DCA to the interaction vertex in the longitudinal plane, DCAz<2 cm. Furthermore, the daughter tracks of reconstructed kinks are rejected. This last cut is not applied in the kink analysis where a further pT-dependent selection on the DCA of the selected tracks to the primary vertex in the transverse plane (DCAxy) is requested. The global tracks that satisfy these selection criteria have a pTresolution of 1%atpT=1GeV/cand 2 % at pT=10 GeV/c.The ITS-sa tracks are required to have at least four ITS clusters out of which at least one in the SPD layers and three in the SSD and SDD, χ2/Nclusters <2.5 and a DCAxy satisfying a pT-dependent upper cut corresponding to 7 times the DCA resolution. The selected ITS-sa tracks have a maximum pT resolution of 6 % for pions, 8 % for kaons and 10 % for protons in the pTrange used in the analysis. Global and ITSsa tracks have a similar resolution in the DCAxy parameter, that is, 75 µmatpT=1GeV/cand 20 µmatpT=15 GeV/c[29], which is well reproduced in the simulation of the detector performance. The final spectra are calculated for |y|<0.5. 2.3 Particle identification strategy To measure the production of π±,K±,pand pover awidepTrange, results from five independent analyses, namely ITS-sa, TPC–TOF, TOF, HMPID and kink, are combined. Each analysis uses different PID signals in order to identify particles in the complementary pTranges reported inTable1.Inthefollowing,thePIDstrategiesusedbyITS-sa, TPC–TOF and TOF analyses are briefly summarised since they are already discussed in detail in [15,30], while the HMPID analysis, presented here for the first time, and the kink analysis, modified with respect to that described in [15], are presented in more detail. 2.3.1 ITS stand-alone analysis In this analysis ITS-sa tracks are used and particles are identified by comparing the dE/dxmeasurement provided by the ITSdetector withthe expected valuesat agivenmomentum p under the corresponding mass hypotheses. In Fig. 1, the measured dE/dxvalues are shown as a function of track momentum together with the curves of the energy loss for the different particle species, which are calculated using the PHOBOS parametrisation [31] of the Bethe–Bloch curves at large βγ and with a polynomial to correct for instrumental effects. A single identity is assigned to each track according to the mass hypothesis for which the expected specific energy-loss value is the closest to the measured dE/dxfor a track with momentum p. No explicit selection on the difference between the measured and expected values is applied except for a lower limit on pions set to two times the dE/dxresolution (σ) and an upper limit on protons given by the mid-point between the proton and the deuteron expected dE/dx.TheITSdE/dxis calculated as a truncated mean of three or four dE/dxvalues provided by the SDD and SSD layers. The truncated mean is the average of the lowest two dE/dxvalues in case signals in all the four layers are available, or as a weighted average of the lowest (weight 1) and the second lowest (weight 1/2) values in the case where only three dE/dxsamFig. 1 Distribution of dE/dxas a function of momentum (p) measured in the ITS using ITS-sa tracks in |η|<0.9. The continuous curves represent the parametrisation of dE/dxfor e, π,Kand pwhile the dashed curves are the bands used in the PID procedure Table 1 Number of analysed events and pTrange (GeV/c) covered by each analysis Analysis # of events πKp ITS-sa 5.4×1070.1–0.6 0.2–0.5 0.3–0.6 TPC–TOF 5.4×1070.25–1.2 0.3–1.2 0.45–2.0 TOF 5.4×1070.5–2.5 0.5–2.4 0.8–4.0 HMPID 8.1×1071.5–3.0 1.5–3.0 1.5–6.0 Kink 16.9×107– 0.2–6.0 – 123 226 Page 4 of 23 Eur. Phys. J. C (2015) 75:226 ples are measured. Even with this truncated mean approach, used to reduce the effect of the tail of the Landau distribution at large dE/dx, the small number of samples results in residual non-Gaussian tails in the dE/dxdistribution, which are partially reproduced in simulation. These non-Gaussian tails increase the misidentification rate, e.g. pions falling in the kaon identification bands. The misidentification probability is estimated using a Monte-Carlo simulations where the particle abundances were adjusted to those observed in data. This correction is at most 10 % in the pTrange of this analysis. In order to check possible systematic effects due to these non-Gaussian tails and their imperfect description in Monte-Carlo simulations, the analysis was repeated withdifferentstrategiesfortheparticleidentification, namely using a 3σcompatibility band around the expected dE/dx curves and extracting the yields of pions, kaons and protons using the unfolding method described in [15], which is based on fits to the dE/dxdistributions in each pTinterval. The difference among the results from these different analysis strategies is assigned as a systematic uncertainty due to the PID. 2.3.2 TPC–TOF analysis In this analysis global tracks are used and particle identification is performed by comparing the measured PID signals in the TPC and TOF detectors (dE/dx, time of flight) with the expected values for different mass hypotheses. An identity is assigned to a track if the measured signal differs from the expected value by less than three times its resolution σ. For pions and protons with pT<0.6 GeV/cand kaons with pT<0.5 GeV/c, a compatibility within 3σis required on the dE/dxmeasurement provided by the TPC computed as a truncated mean of the lowest 60 % of the availabledE/dxsamples. ThedE/dxresultingfrom thistruncated mean approach is Gaussian and it is shown in Fig. 2as a function of the track momentum together with the expected energy-loss curves (see [32] for a discussion of the dE/dx parametrisation). Above these pTthresholds, i.e. pT≥0.6 GeV/cfor pions and protons and pT≥0.5 GeV/cfor kaons, a three σrequirement is applied to both the dE/dxmeasurement provided by the TPC and the time of flight ttof provided by the TOF detector. The time of flight ttof, as will be described in more detail in the next section, is the difference between the arrival time τTOF measured with the TOF detector and the event start time t0, namely ttof =τTOF −t0. The additional condition on the TOF signal helps in extending the particle identification on a track-by-track basis to higher pTwhere the TPC separation power decreases. The particles for which the TOF signal is available are a sub-sample of the global tracks reconstructed using ITS and TPC information. The TOF information is not available for tracks that cross inactive regions of the TOF Fig. 2 Distribution of dE/dxas a function of momentum (p) measured in the TPC using global tracks for |η|<0.9. The continuous curves represent the Bethe–Bloch parametrisation Fig. 3 Particle velocity βmeasured by the TOF detector as a function of the rigidity p/z,wherezis the particle charge, for |η|<0.9 detector, for particles that decay or interact with the material before the TOF and for tracks whose trajectory, after prolongation from the TPC outer radius, is not matched with a hit in the TOF detector. The fraction of global tracks with associated TOF information (TOF matching efficiency) depends on the particle species and pTas well as on the fraction of the TOF active readout channels. For the data analysis presented in this paper the matching efficiency increases with increasing pTuntil it saturates, e.g. at about 65 % for pions with pT >1GeV/c.InFig.3the velocity βof the tracks, computed from the trajectory length measured with the ITS and TPC and the time of flight measured with the TOF, is reported as a function of the rigidity p/z, where zis the charge assigned based on the measured direction of the track curvature. More than one identity can be assigned to a track if it fulfils PID and rapidity selection criteria for different particle species. The frequency of such cases is at most 0.5 % in the momentum range used in this analysis. The misidentification of primary particles is computed and corrected for using Monte-Carlo simulations. It is at most 2 % for pions and protons and 8 % for kaons in the considered pTranges. The 123 Eur. Phys. J. C (2015) 75:226 Page 5 of 23 226 correction of the raw spectra for the misidentified particles provides also a way to remove the overestimation of the total number of particles introduced by the possibility, described above, to assign more than one identity to a track. 2.3.3 TOF analysis Thisanalysisuses the sub-sample ofglobal tracks for whicha TOF measurement is available. The PID procedure utilises a statistical unfolding approach that provides a pTreach higher than the three σapproach described in the previous section. The procedure is based on the comparison between the measured time of flight from the primary vertex to the TOF detector, ttof, and the time expected under a given mass hypothesis, texp i(i=π,K,p), namely on the variable ti=ttof −texp i. As mentioned in the previous section, the time of flight ttof is defined as the difference between the time measured with the TOF detector τTOF and the event start time t0.Thet0 value is computed from the analysed tracks themselves on an event-by-event basis, using a combinatorial algorithm which compares the measured τTOF with the expected ones for different mass hypotheses. The track under study is excluded to avoid any bias in the PID procedure [13,15]. In case the TOF t0algorithm fails, the average beam-beam interaction time is used. The former approach provides a better t0resolution, but it requires at least three reconstructed tracks with an associated TOF timing measurement. The yield of particles of species iinagivenpTinterval is obtained by fitting the distribution of the variable tiobtained from all the tracks regardless of the method used to compute the t0.Thisdistribution is composed of the signal from particles of species i, which is centred at ti=0, and two distinct populations corresponding to the other two hadron species, j,k= i. The tidistribution is therefore fitted with the sum of three functions f(ti), one for the signal and two for the other hadron species, as shown in Fig. 4.The f(ti)functional forms are defined using the data in the region of clear species separation. The TOF signal is not purely Gaussian and it is described by a function f(ti)that is composed of a Gaussian term and an exponential tail at high timainly due to tracks inducing signals in more than one elementary detector readout element [13]. The raw yield of the species iis given by the integral of the signal fit function. The reach in pTof this PID method depends on the resolution of ti, that is, the combination of the TOF detector intrinsic resolution, the uncertainty on the start time and the tracking and momentum resolution. Its value, for the data used in this analysis, is about 120 ps leading to 2σpion– kaon and kaon–proton separation at pT=2.5 GeV/cand pT=4.0 GeV/c, respectively. This PID procedure has the advantage of not requiring a Monte-Carlo-based correction for misidentification because the contamination under the Fig. 4 Distribution of tiassuming the pion mass hypothesis in the transverse momentum interval 1.9 <pT<2.0 GeV/c. The data (black points) are fitted with a function (light blue line) that is the sum of the signal due to pions (green dotted line) and the two populations corresponding to kaons (red dotted line) and protons (purple dashed line) Fig. 5 Display of a Cherenkov ring detected in a module of HMPID for an inclined track crossing the detector. The colours are proportional to the pad charge signal signal of particles of species idue to other particle species is accounted for by the background fit functions. 2.3.4 HMPID analysis The HMPID is a RICH detector in a proximity focusing layout in which the primary ionizing charged particle generates Cherenkov light inside a liquid C6F14 radiator [14]. The UV photonsare convertedintophotoelectrons inathinCsIfilmof the PhotoCathodes (PCs) and the photoelectrons are amplified in an avalanche process inside a multi-wire proportional chamber operated with CH4. To obtain the position sensitivity for the reconstruction of the Cherenkov rings, the PCs are segmented into pads. The final image of a Cherenkov ring is then formed by a cluster of pads (called a “MIP” cluster) associated to the primary ionisation of the particle and the photoelectron clusters associated to Cherenkov photons. In Fig. 5a typical Cherenkov ring is shown. 123 226 Page 6 of 23 Eur. Phys. J. C (2015) 75:226 In this analysis, the sub-sample of global tracks that reach the HMPID detector and produce the Cherenkov rings is used. Starting from the photoelectron cluster coordinates on the photocathode, a back-tracking algorithm calculates the corresponding single photon Cherenkov angle by using the impact angle of a track extrapolated from the central tracking detectors up to the radiator volume. A selection on the distance (dMIP−trk) computed on the cathode plane between the centroid of the MIP cluster and the track extrapolation, set to dMIP−trk <5 cm, rejects fake associations in the detector. Background discrimination is performed using the Hough transform method (HTM) [33]. The mean Cherenkov angle θckovis obtained if at least three photoelectron clusters are detected. For a given track, θckovis computed as the weighted average of the single photon angles (if any) selected by HTM. Pions, kaons and protons become indistinguishable at high momentum when the resolution on θckovreaches 3.5 mrad. The angle θckovas a function of the track momentum is shown in Fig. 6, where the solid lines represent the θckov dependence on the particle momentum θckov =cos−1p2+m2 np ,(1) where nis the refractive index of the liquid radiator, mthe mass of the particle and pits momentum. Thisanalysisis performedfor p>1.5GeV/c,wherepions, kaons and protons produce a ring with enough photoelectron clusters to be reconstructed. If the track momentum is below the threshold to produce Cherenkov photons, background clusters could be wrongly associated to the track. As an example the few entries visible in Fig. 6between the pion and kaon bands at low θckovcorrespond to wrong associations of clusters with a kaon or a proton below the threshold to produce Cherenkov photons. Fig. 6 Mean Cherenkov angle θckovmeasured with HMPID in its full geometrical acceptance as a function of the particle momentum p for positively and negatively charged tracks. The solid lines represent the theoretical curves for each particle species Fig. 7 Distributions of θckovmeasured with the HMPID in the two narrow pTintervals3.4<pT<3.6 GeV/c(top)and5<pT<5.5GeV/c (bottom) for tracks from negatively charged particles. Solid lines represent the total fit (sum of three Gaussian functions). Dotted lines correspond to pion, kaon and proton signals. The background is negligible The particle yields are extracted from a fit to the Cherenkov angle distribution in narrow transverse momentum intervals. In Fig. 7, examples of the reconstructed Cherenkov angle distributions in two narrow pTintervals (3.4 <pT<3.6 GeV/cand 5 <pT<5.5 GeV/c) for negatively charged tracks are shown. The background, mainly due to noisy pads and photoelectron clusters from other rings overlapping to the reconstructed one, is negligible in the momentum range considered in this analysis. The fit function (shown as a solid line in Fig. 7) is a sum of three Gaussian functions, one for each particle species (dashed lines), whose mean and sigma are fixed to the Monte-Carlo values. The extracted separation power of hadron identification in the HMPID as a function of pTis shown in Fig. 8.The separation between pions and kaons (kaons and protons) is expressed as the difference between the means of the θckovangle Gaussian distributions for the two given particle species (π,Kor K,p) divided by the average of the Gaussian widths of the two distributions, i.e. (σπ+σK)/2 or (σK+σp)/2. A separation at 3σlevel in θckovis achieved up to pT=3GeV/cfor K–πand up to pT=5GeV/cfor K–p. 123 Eur. Phys. J. C (2015) 75:226 Page 7 of 23 226 Fig. 8 Separation power (nσ) of hadron identification in the HMPID as a function of pT. The separation nσof pions and kaons (kaons and protons)is definedas thedifferencebetweenthe averageof theGaussian distributions of θckovforthe twohadron speciesdividedbythe average of the Gaussian widths of the two distributions The separation at 6 GeV/cfor K–pcan be extrapolated from the curve and it is about 2.5σ. The HMPID geometrical acceptance is about 5 % for tracks with high momentum. Therefore the analysis of HMPID required one to analyse a larger data sample with respect to the other PID methods, as reported in Table 1.The total efficiency is the convolution of the tracking, matching and PID efficiencies. The PID efficiency of this method is determined by the Cherenkov angle reconstruction efficiency. It has been computed by means of Monte-Carlo simulations and it reaches 90 % for particles with velocity β∼1. As a cross check, the PID efficiency has been determined using clean samples of protons and pions from and K0 sdecays. The measured efficiency agrees within the statistical uncertainties with the Monte-Carlo estimates, in the momentum range 1.5 <pT<6GeV/c. Moreover, the correction due to the dMIP−trk cut is computed from the same sample of identified protons and pions from and K0 s decays. 2.3.5 Kink analysis Charged kaons can also be identified in the TPC by reconstructing their weak-decay vertices, which exhibit a characteristic kink topology defined by a decay vertex with two tracks (mother and daughter) having the same charge. This procedure extends the measurement of charged kaons on a track-by-track basis to pT=6GeV/c. The algorithm for the kink reconstruction is applied inside a fiducial volume of the TPC, namely 130 <R<200 cm, needed to reconstruct both the mother and the daughter tracks. The mother track is selected with similar criteria to the global tracks (Sect. 2.2), but with a looser selection on the minimum number of TPC clusters, which is set to 20, and a wider rapidity range Fig. 9 Kink invariant mass Mμν in data (red circles) and Monte-Carlo (black line) for summed particles and antiparticles, integrated over the mother transverse momentum range 0.2 <pT<6.0GeV/cand |y|<0.7before(top panel)andafter(bottom panel) the topological selections, based mainly on the qTand the maximum decay opening angle set to |y|<0.7 to increase the statistics of kink candidates. No selections are applied on the charged daughter track. The reconstructed invariant mass Mμν is calculated assuming the charged daughter track to be a muon and the undetected neutral daughter track to be a neutrino. The neutrino momentum is the difference between the measured momenta of the mother particle and of the charged daughter. The Mμν distribution, for summed positive and negative charges, integrated over the mother transverse momentum range 0.2 <pT<6.0GeV/cis reported in the top panel of Fig. 9for both data and PYTHIA simulations normalised to the same number of entries. Three peaks are present: one centred on the kaon mass due to the kaon decays K→μ+νμ (branching ratio BR =63.55 %), one centred at Mμν =0.43 GeV/c2due to the K→π+π0decay (BR =20.66 %), whose kinematics is calculated with wrong mass assumptions, and the peak due to pion decays π→μ+νμ(BR =99.99 %). The width of the peaks reflects the momentum resolution of the detector, which is well reproduced in Monte-Carlo simulations. The two-body kinematics of the kink topology allows one to separate kaon decays from the main source of background due to charged pion decays [15]. In the μ+νμchannel, the upper limit of the qTvari123 226 Page 8 of 23 Eur. Phys. J. C (2015) 75:226 able, where qTis defined as the transverse momentum of the daughter track with respect to the mother’s direction, is 236 MeV/cfor muons from kaon decays and 30 MeV/cfor muons from pion decays. To remove most of the pion decays, aqT>120 MeV/cselection is applied. The background is further reduced by rejecting kink decays for which the decay angle, namely the angle between the momenta of the mother and the charged daughter tracks is larger than the maximum angle allowed under the hypothesis K→μ+νμ. The bottom panel of Fig. 9shows the invariant mass distribution of the kaon candidates with mother transverse momentum 0.2 <pT<6.0GeV/cafter the topological selection criteria for kaon identification (mainly the qTand decay angle cuts) are applied. It is evident that only the two peaks coming from kaon decays are present, while the pion background peak is removed. The broad structure on the left originates fromthethree-bodydecaysofkaons.Theagreementbetween data and simulations in this figure (Fig. 9) is better than 8 %. Most of the selected mother tracks have a dE/dxin the TPC which is compatible with the values expected for kaons. Tracks outside 3.5σfrom the expected kaon dE/dx have been removed to attain a purity >97 % in the pTrange studied in this analysis. These rejected tracks are <4%,have pT<0.8 GeV/cand are, according to Monte-Carlo studies, pions. The raw kaon spectra are obtained from the integral of the invariant mass distribution computed in narrow pT intervals after the topological selection criteria on the qT,the decay opening angle and the compatibility with the expected dE/dxfor kaons are applied. The kaon misidentification is computed and corrected for by using Monte-Carlo simulations. It depends on the mother’s transverse momentum with a maximum value of 3.6 % at 0.8 GeV/cand a minimum of 2 % at 1 GeV/c, remaining almost flat up to pT=6GeV/c. Its average value in the pTrange considered in this analysis is 2.1 %. 2.4 Correction of raw spectra To obtain the pTdistributions of primary π,Kand p,the contribution of secondaries is subtracted from the raw spectra. Then the spectra are corrected for the PID efficiency, the misidentification probability, the acceptance, the reconstruction and the selection efficiencies according to d2N dpTdy=Nraw(pT)1 pTy 1−s(pT) ε(pT)·f(pT), (2) where Nraw(pT)1 pTyis the raw yield in a given pTinterval, s(pT)is the total contamination including effects of secondary and misidentified particles, ε(pT)is the acceptance ×efficiency including PID efficiency, detector acceptance, reconstruction and selection efficiencies and f(pT)is an additional factor to correct for imperfections of the cross sections for antiparticle interactions with the material used in the GEANT3 code. The contamination due to weak decays of light flavour hadrons (mainly K0 saffecting πspectra and and + affecting pspectra) and interactions with the material has to be computed and subtracted from the raw spectra. Since strangeness production is underestimated in the event generators and the interactions of low pTparticles with the material are not properly modelled in the transport codes, the secondary-particle contribution is evaluated with a datadriven approach. This approach exploits the high resolution determination of the track impact parameter in the transverse plane, DCAxy, and the fact that secondary particles from strange hadron decays and interactions with the detector material, originate from secondary vertices significantly displaced from the interaction point and, therefore, their tracks have, on average, larger absolute values of DCAxy with respect to primary particles. Hence, for each of the PID techniques described in the previous sections, the contribution of secondary particles to the measured raw yield of a given hadron species in a given pTinterval is extracted by fitting the measured distributions of DCAxy of the tracks identified as particles of the considered hadron species. The DCAxy distributions are modelled with three contributions, called templates. Their shapes are extracted for each pT interval and particle species from simulations, as described in [30], and represent the DCAxy distributions of primary particles, secondary particles from weak decays of strange hadrons and secondary particles produced in the interactions with the detector material, respectively. An example for protons in the interval 0.55 <pT<0.60 GeV/cis shown in Fig. 10. The correction for secondary-particle contamination is relevant for π±(from 10 % at low pTto <2 % at high pT), Fig. 10 Proton DCAxy distribution in the range 0.55 <pT<0.60 GeV/ctogether with the Monte-Carlo templates for primary protons (green dotted line), secondary protons from weak decays (red dotted line) and secondary protons produced in interactions with the detector material (blue dashed line) which are fitted to the data. The light blue line represents the combined fit, while the black dots are the data 123 Eur. Phys. J. C (2015) 75:226 Page 15 of 23 226 Fig. 18 Top panel measured pTspectra of pions, kaons and protons, sum of particles and antiparticles, compared to PYTHIA6-Z2, PYTHIA6-CentralPerugia2011, PYTHIA8, EPOS LHC and PHOJET Monte-Carlo calculations. Statistical (vertical error bars) and systematic (open boxes) uncertainties are reported for the measured spectra. Bottom panels ratios between data and Monte-Carlo calculations GeV/cfor pions and kaons and up to 1.5 GeV/cfor protons). The PHOJET parameters are not retuned using the LHC data. The measured pion pTspectrum is reproduced by EPOS within 15 % over the whole pTrange. PYTHIA6-Z2, PYTHIA6-CentralPerugia2011 and PYTHIA8 show similar trends. They correctly predict the shapes of the pion spectra for pT>500 MeV/c, overestimating the data by about 10, 20 and 25 %, respectively, while the shapes differ from data for pT<200 MeV/c(the ratios are not flat) and the yields are underestimated by up to 30 %. The PHOJET generator does not provide a satisfactory description of the measured spectrum shape for any of the particle species. The deviations from the data show a maximum for pT∼1.2 GeV/cand are more pronounced for kaons and protons than for pions. All the tested Monte-Carlo generators underestimate the kaon yield by about 20–30 % for pT>600 MeV/c, while for pT <400 MeV/cthey overestimate the data by up to 30 %. A similar deviation is observed by the ALICE Collaboration also for other strange particle species with a hierarchy depending on the strangeness content [46]. The proton yield is well described by EPOS only at low transverse momenta (pT<1GeV/c),whilethe generatortendsto overestimate the Fig. 19 Measured(K++K−)/(π++π−)(left)and(p+p)/(π++π−) (right)ratiosasafunctionofpTcomparedtoPYTHIA6-Z2, PYTHIA6CentralPerugia2011,PYTHIA8,EPOS LHCandPHOJET calculations. Statistical (vertical error bars) and systematic (open boxes) uncertainties are reported for the measured spectra data by up to 30 % at higher pT. None of the three PYTHIA tunes describes the shape of the proton spectrum in the full pTrange. All of them give a reasonable description of the yield in the range 1 <pT<2GeV/c, but they overestimate the data at lower and higher pTby up to 40 %. The comparison of the pT-dependent particle ratios with models allows the hadronisation and soft parton interaction mechanisms implemented in the event generators to be tested. In the left and right panels of Fig. 19, the measured (K++K−)/(π++π−)and (p+p)/(π++π−)ratios as a function of pTare compared with the same event generators shown in Fig. 18. The measured (K++K−)/(π++π−) ratio increases from 0.05 at pT=0.2 GeV/cup to 0.45 at pT ∼3GeV/cwith a slope that decreases with increasing pT. All the models underestimate the data at high momenta, with EPOS exhibiting the smallest deviation. The measured (p+ p)/(π++π−)showsanincrease from0.03at pT=0.3GeV/c up to 0.25 at pT∼1.5 GeV/c, while above this pTit tends to flatten. The data are well described by PYTHIA6-Z2, while PYTHIA6-CentralPerugia2011, PHOJET and EPOS show a large deviation at high momenta. PYTHIA8 shows a smaller deviation over the whole momentum range even if, as seen in Fig. 18, it overestimates both pion and proton spectra. The comparison between data and Monte-Carlo calculations shows that the tunes of the generators based only on few global observables, such as the integrated charged hadron multiplicity, allow only for a partial description of the data. The high-precision measurements of the identified charged hadron pTspectra reported here, which cover a wide momentum range in the central rapidity region, give useful information for a fine tuning of the Monte-Carlo generators and a better understanding of soft particle production mechanisms at LHC energies. 123 226 Page 16 of 23 Eur. Phys. J. C (2015) 75:226 5 Summary A detailed analysis of primary π±,K±,pand pproduction in proton–proton collisions at √s=7 TeV with the ALICE detector has been performed. Particle identification is performed using several techniques namely the specific ionisation energy loss measured in the ITS and TPC, the time of flight measured with the TOF detector, the Cherenkov radiation measured in the HMPID and the kink-topology identification of the weak decays of charged kaons. The combination of these techniques allows for precision measurements of the pTspectra over a wide momentum range: from 0.1upto3GeV/cfor pions, from 0.2 up to 6 GeV/cfor kaons and from 0.3 up to 6 GeV/cfor protons. A comparison of the ALICE results with similar measurements performed by the PHENIX Collaboration at RHIC shows that the pTintegrated yields increase with collision energy for all the measured particle species. A slight increase of the pTwith √sis also observed. This rising trend that becomes apparent at √s>0.9 TeV is established by the higher √sLHC data. It could be related to the increasing importance of hard processes at these energies. The pT-integrated K/πand p/π ratios extend the measurements available at lower collision energies from SPS, SppS and RHIC experiments showing a saturation above √s=0.9 TeV. Finally, the pTspectra and particle ratios have been compared with the calculations of QCD-inspired Monte-Carlo models namely PYTHIA6Z2, PYTHIA6-CentralPerugia2011, PYTHIA8, EPOS LHC and PHOJET. Even though the shapes of the spectra are fairly well reproduced by all models (except PHOJET that fails to describe the spectrum shape of all the three hadron species), none of them can describe simultaneously the measured yields of pions, kaons and protons. These results can be used for a better understanding of the hadron production mechanisms in pp interactions at LHC energies and could further constrain the parameters of the models. Acknowledgments The ALICE Collaboration would like to thank all its engineers and technicians for their invaluable contributions to the construction of the experiment and the CERN accelerator teams for the outstanding performance of the LHC complex. The ALICE Collaboration gratefully acknowledges the resources and support provided by all Grid centres and the Worldwide LHC Computing Grid (WLCG) Collaboration. The ALICE Collaboration acknowledges the following funding agencies for their support in building and running the ALICE detector: State Committee of Science, World Federation of Scientists (WFS) and Swiss Fonds Kidagan, Armenia, Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Financiadora de Estudos e Projetos (FINEP), Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP); National Natural Science Foundation of China (NSFC), the Chinese Ministry of Education (CMOE) and the Ministry of Science and Technology of China (MSTC); Ministry of Education and Youth of the Czech Republic; Danish Natural Science Research Council, the Carlsberg Foundation and the Danish National Research Foundation; The European Research Council under the European Community’s Seventh Framework Programme; Helsinki Institute of Physics andthe Academy ofFinland; French CNRS-IN2P3,the ‘RegionPaysde Loire’, ‘Region Alsace’, ‘Region Auvergne’ and CEA, France; German Bundesministerium fur Bildung, Wissenschaft, Forschung und Technologie (BMBF) and the Helmholtz Association; General Secretariat for Research and Technology, Ministry of Development, Greece; Hungarian Orszagos Tudomanyos Kutatasi Alappgrammok (OTKA) and National Office for Research and Technology (NKTH); Department of Atomic Energy and Department of Science and Technology of the Governmentof India; IstitutoNazionale di FisicaNucleare (INFN) andCentroFermi–MuseoStoricodellaFisicaeCentroStudi eRicerche“Enrico Fermi”, Italy; MEXT Grant-in-Aid for Specially Promoted Research, Japan; Joint Institute for Nuclear Research, Dubna; National Research Foundation of Korea (NRF); Consejo Nacional de Cienca y Tecnologia (CONACYT), Direccion General de Asuntos del Personal Academico (DGAPA), México; Amerique Latine Formation academique European Commission (ALFA-EC) and the EPLANET Program (European Particle Physics Latin American Network) Stichting voor Fundamenteel Onderzoek der Materie (FOM) and the Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO), Netherlands; Research Council of Norway (NFR); National Science Centre, Poland; Ministry of National Education/Institute for Atomic Physics and Consiliul National alCercettriitiinifice–ExecutiveAgencyforHigher EducationResearch Development and Innovation Funding (CNCS-UEFISCDI) – Romania; Ministry of Education and Science of Russian Federation, Russian Academy of Sciences, Russian Federal Agency of Atomic Energy, Russian Federal Agency for Science and Innovations and The Russian Foundation for Basic Research; Ministry of Education of Slovakia; Department of Science and Technology, South Africa; Centro de Investigaciones Energeticas, Medioambientales y Tecnologicas (CIEMAT), E-Infrastructure shared between Europe and Latin America (EELA), Ministerio de Economía y Competitividad (MINECO) of Spain, Xunta de Galicia (Consellería de Educación), Centro de Aplicaciones Tecnolgicas y Desarrollo Nuclear (CEADEN), Cubaenergía, Cuba, and IAEA (International Atomic Energy Agency); Swedish Research Council (VR) and Knut and Alice Wallenberg Foundation (KAW); Ukraine Ministry of Education and Science; United Kingdom Science and Technology Facilities Council (STFC); The United States Department of Energy, the United States National Science Foundation, the State of Texas, and the State of Ohio; Ministry of Science, Education and Sports of Croatia and Unity through Knowledge Fund, Croatia. 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Alikhanyan National Science Laboratory (Yerevan Physics Institute) Foundation, Yerevan, Armenia 2Benemérita Universidad Autónoma de Puebla, Puebla, Mexico 3Bogolyubov Institute for Theoretical Physics, Kiev, Ukraine 4Department of Physics and Centre for Astroparticle Physics and Space Science (CAPSS), Bose Institute, Kolkata, India 5Budker Institute for Nuclear Physics, Novosibirsk, Russia 6California Polytechnic State University, San Luis Obispo, California, USA 7Central China Normal University, Wuhan, China 8Centre de Calcul de l’IN2P3, Villeurbanne, France 9Centro de Aplicaciones Tecnológicas y Desarrollo Nuclear (CEADEN), Havana, Cuba 10 Centro de Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT), Madrid, Spain 11 Centro de Investigación y de Estudios Avanzados (CINVESTAV), Mexico City and Mérida, Mexico 12 Centro Fermi-Museo Storico della Fisica e Centro Studi e Ricerche “Enrico Fermi”, Rome, Italy 13 Chicago State University, Chicago, IL, USA 14 China Institute of Atomic Energy, Beijing, China 15 Commissariat à l’Energie Atomique, IRFU, Saclay, France 16 COMSATS Institute of Information Technology (CIIT), Islamabad, Pakistan 17 Departamento de Física de Partículas and IGFAE, Universidad de Santiago de Compostela, Santiago de Compostela, Spain 18 Department of Physics and Technology, University of Bergen, Mons, Norway 19 Department of Physics, Aligarh Muslim University, Aligarh, India 20 Department of Physics, Ohio State University, Columbus, OH, USA 21 Department of Physics, Sejong University, Seoul, South Korea 22 Department of Physics, University of Oslo, Oslo, Norway 23 Dipartimento di Elettrotecnica ed Elettronica del Politecnico, Bari, Italy 24 Dipartimento di Fisica dell’Università ‘La Sapienza’ and Sezione INFN, Rome, Italy 25 Dipartimento di Fisica dell’Università and Sezione INFN, Cagliari, Italy 26 Dipartimento di Fisica dell’Università and Sezione INFN, Trieste, Italy 123 Eur. Phys. J. C (2015) 75:226 Page 21 of 23 226 27 Dipartimento di Fisica dell’Università and Sezione INFN, Turin, Italy 28 Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN, Bologna, Italy 29 Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN, Catania, Italy 30 Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN, Padua, Italy 31 Dipartimento di Fisica ‘E.R. Caianiello’ dell’Università and Gruppo Collegato INFN, Salerno, Italy 32 Dipartimento di Scienze e Innovazione Tecnologica dell’Università del Piemonte Orientale and Gruppo Collegato INFN, Alessandria, Italy 33 Dipartimento Interateneo di Fisica ‘M. Merlin’ and Sezione INFN, Bari, Italy 34 Division of Experimental High Energy Physics, University of Lund, Lund, Sweden 35 Eberhard Karls Universität Tübingen, Tübingen, Germany 36 European Organization for Nuclear Research (CERN), Geneva, Switzerland 37 Excellence Cluster Universe, Technische Universität München, Munich, Germany 38 Faculty of Engineering, Bergen University College, Mons, Norway 39 Faculty of Mathematics, Physics and Informatics, Comenius University, Bratislava, Slovakia 40 Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Prague, Czech Republic 41 Faculty of Science, P.J. Šafárik University, Kosice, Slovakia 42 Faculty of Technology, Buskerud and Vestfold University College, Vestfold, Norway 43 Frankfurt Institute for Advanced Studies, Johann Wolfgang Goethe-Universität Frankfurt, Frankfurt, Germany 44 Gangneung-Wonju National University, Gangneung, South Korea 45 Department of Physics, Gauhati University, Guwahati, India 46 Helsinki Institute of Physics (HIP), Helsinki, Finland 47 Hiroshima University, Hiroshima, Japan 48 Indian Institute of Technology Bombay (IIT), Mumbai, India 49 Indian Institute of Technology Indore (IITI), Indore, India 50 Inha University, Incheon, South Korea 51 Institut de Physique Nucléaire d’Orsay (IPNO), Université Paris-Sud, CNRS-IN2P3, Orsay, France 52 Institut für Informatik, Johann Wolfgang Goethe-Universität Frankfurt, Frankfurt, Germany 53 Institut für Kernphysik, Johann Wolfgang Goethe-Universität Frankfurt, Frankfurt, Germany 54 Institut für Kernphysik, Westfälische Wilhelms-Universität Münster, Münster, Germany 55 Institut Pluridisciplinaire Hubert Curien (IPHC), Université de Strasbourg, CNRS-IN2P3, Strasbourg, France 56 Institute for Nuclear Research, Academy of Sciences, Moscow, Russia 57 Institute for Subatomic Physics of Utrecht University, Utrecht, The Netherlands 58 Institute for Theoretical and Experimental Physics, Moscow, Russia 59 Institute of Experimental Physics, Slovak Academy of Sciences, Kosice, Slovakia 60 Institute of Physics, Academy of Sciences of the Czech Republic, Prague, Czech Republic 61 Institute of Physics, Bhubaneswar, India 62 Institute of Space Science (ISS), Bucharest, Romania 63 Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de México, Mexico City, Mexico 64 Instituto de Física, Universidad Nacional Autónoma de México, Mexico City, Mexico 65 iThemba LABS, National Research Foundation, Somerset West, South Africa 66 Joint Institute for Nuclear Research (JINR), Dubna, Russia 67 Konkuk University, Seoul, South Korea 68 Korea Institute of Science and Technology Information, Daejeon, South Korea 69 KTO Karatay University, Konya, Turkey 70 Laboratoire de Physique Corpusculaire (LPC), Clermont Université, Université Blaise Pascal, CNRS-IN2P3, Clermont-Ferrand, France 71 Laboratoire de Physique Subatomique et de Cosmologie, Université Grenoble-Alpes, CNRS-IN2P3, Grenoble, France 72 Laboratori Nazionali di Frascati, INFN, Frascati, Italy 73 Laboratori Nazionali di Legnaro, INFN, Legnaro, Italy 74 Lawrence Berkeley National Laboratory, Berkeley, California, USA 75 Lawrence Livermore National Laboratory, Livermore, CA, USA 76 Moscow Engineering Physics Institute, Moscow, Russia 77 National Centre for Nuclear Studies, Warsaw, Poland 123 226 Page 22 of 23 Eur. Phys. J. C (2015) 75:226 78 National Institute for Physics and Nuclear Engineering, Bucharest, Romania 79 National Institute of Science Education and Research, Bhubaneswar, India 80 Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark 81 Nikhef, National Institute for Subatomic Physics, Amsterdam, The Netherlands 82 Nuclear Physics Group, STFC Daresbury Laboratory, Daresbury, UK 83 Nuclear Physics Institute, Academy of Sciences of the Czech Republic, ˇ Rež u Prahy, Czech Republic 84 Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA 85 Petersburg Nuclear Physics Institute, Gatchina, Russia 86 Physics Department, Creighton University, Omaha, NE, USA 87 Physics Department, Panjab University, Chandigarh, India 88 Physics Department, University of Athens, Athens, Greece 89 Physics Department, University of Cape Town, Cape Town, South Africa 90 Physics Department, University of Jammu, Jammu, India 91 Physics Department, University of Rajasthan, Jaipur, India 92 Physik Department, Technische Universität München, Munich, Germany 93 Physikalisches Institut, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany 94 Politecnico di Torino, Turin, Italy 95 Purdue University, West Lafayette, IN, USA 96 Pusan National University, Pusan, South Korea 97 Research Division and ExtreMe Matter Institute EMMI, GSI Helmholtzzentrum für Schwerionenforschung, Darmstadt, Germany 98 Rudjer Boškovi´c Institute, Zagreb, Croatia 99 Russian Federal Nuclear Center (VNIIEF), Sarov, Russia 100 Russian Research Centre Kurchatov Institute, Moscow, Russia 101 Saha Institute of Nuclear Physics, Kolkata, India 102 School of Physics and Astronomy, University of Birmingham, Birmingham, UK 103 Sección Física, Departamento de Ciencias, Pontificia Universidad Católica del Perú, Lima, Peru 104 Sezione INFN, Bari, Italy 105 Sezione INFN, Bologna, Italy 106 Sezione INFN, Cagliari, Italy 107 Sezione INFN, Catania, Italy 108 Sezione INFN, Padova, Italy 109 Sezione INFN, Rome, Italy 110 Sezione INFN, Trieste, Italy 111 Sezione INFN, Turin, Italy 112 SSC IHEP of NRC Kurchatov institute, Protvino, Russia 113 SUBATECH, Ecole des Mines de Nantes, Université de Nantes, CNRS-IN2P3, Nantes, France 114 Suranaree University of Technology, Nakhon Ratchasima, Thailand 115 Technical University of Split FESB, Split, Croatia 116 The Henryk Niewodniczanski Institute of Nuclear Physics, Polish Academy of Sciences, Cracow, Poland 117 Physics Department, The University of Texas at Austin, Austin, TX, USA 118 Universidad Autónoma de Sinaloa, Culiacán, Mexico 119 Universidade de São Paulo (USP), São Paulo, Brazil 120 Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil 121 University of Houston, Houston, TX, USA 122 University of Jyväskylä, Jyväskylä, Finland 123 University of Liverpool, Liverpool, UK 124 University of Tennessee, Knoxville, TN, USA 125 University of the Witwatersrand, Johannesburg, South Africa 126 University of Tokyo, Tokyo, Japan 127 University of Tsukuba, Tsukuba, Japan 128 University of Zagreb, Zagreb, Croatia 129 Université de Lyon, Université Lyon 1, CNRS/IN2P3, IPN-Lyon, Villeurbanne, France 123 Eur. Phys. J. C (2015) 75:226 Page 23 of 23 226 130 V. Fock Institute for Physics, St. Petersburg State University, St. Petersburg, Russia 131 Variable Energy Cyclotron Centre, Kolkata, India 132 Vinˇca Institute of Nuclear Sciences, Belgrade, Serbia 133 Warsaw University of Technology, Warsaw, Poland 134 Wayne State University, Detroit, MI, USA 135 Wigner Research Centre for Physics, Hungarian Academy of Sciences, Budapest, Hungary 136 Yale University, New Haven, CT, USA 137 Yonsei University, Seoul, South Korea 138 Zentrum für Technologietransfer und Telekommunikation (ZTT), Fachhochschule Worms, Worms, Germany aDeceased bAlso at: University of Kansas, Lawrence, Kansas, USA 123