Pseudorapidity distributions of charged particles as a function of mid and forward rapidity mutiplicities in pp collisions at s √ = 5.02, 7 and 13 TeV
EEUROPEAN ORGANIZATION FOR NUCLEAR RESEARCH
CERN-EP-2020-17007 September 2020c (cid:13)
Pseudorapidity distributions of charged particles as a function of mid andforward rapidity mutiplicities in pp collisions at √ s = 5.02, 7 and 13 TeV ALICE Collaboration ∗ Abstract
The multiplicity dependence of the pseudorapidity density of charged particles in proton–proton(pp) collisions at centre-of-mass energies √ s = 5.02, 7 and 13 TeV measured by ALICE is reported.The analysis relies on track segments measured in the midrapidity range ( | η | < . | η | < > ). The multiplicity dependence of the pseudorapidy density of chargedparticles is measured with mid and forward rapidity multiplicity estimators, the latter being lessaffected by autocorrelations. A detailed comparison with predictions from the PYTHIA 8 and EPOSLHC event generators is also presented. Both generators provide a good description of the data. ∗ See Appendix A for the list of collaboration members a r X i v : . [ nu c l - e x ] S e p ultiplicity dependence study of the pseudorapidity density ALICE Collaboration The study of high-multiplicity events in proton–proton (pp) and proton–nucleus (pA) high-energy colli-sions reveals striking similarities with respect to the observations made for larger systems (AA), whichare interpreted in terms of the creation of a strongly-interacting, fluid-like QCD medium: the quark–gluon plasma (QGP). The ridge structure arising from long-range azimuthal correlations observed in ppdata [1–3] is also found in p–Pb collisions [4–7], where the presence of double-ridge structures is re-ported [4]. More recently, an ALICE measurement reported an enhancement in the relative productionof (multi-) strange particles with respect to primary charged particles as a function of multiplicity in ppcollisions [8]. This suggests that these effects might be driven just by the multiplicity.In pp and p–Pb collisions, the selection of events with large hadronic final-state multiplicities biasesthe sample towards a large average number of Multiple Parton Interactions (MPIs), as motivated by thedetermination of the number of independent scattering centres at LHC [9, 10]. In the description pro-vided by the colour reconnection (CR) mechanism [11, 12], CR in MPIs are expected to be particularlypronounced at high multiplicity. The effects of prominent CR at high multiplicity are supposed to ac-count for basic observables like the correlations between the average momentum and the multiplicityof charged particles [13] as well as for the shape of their pseudorapidity distribution [14]. Indeed, thetransverse momentum ( p T ) spectra of charged particles at high multiplicity [15, 16] can be attributed, inpp collisions, to a CR mechanism, while until now no reference multiplicity study has been publishedthat can be used for other observables as a function of multiplicity estimated at mid or forward rapidityin this collision system.This document fills this gap by providing a large set of charged-particle multiplicity density measure-ments as a function of event multiplicity in pp collisions at different centre-of-mass energies. This workcould shed light on the phenomenon of MPIs that is a key ingredient of models attempting to describelarge-multiplicity events. In any collision system, the event-averaged pseudorapidity density of primarycharged particles [17], d N ch / d η , is a key observable characterising the global properties of the collision.Especially in pp interactions, the d N ch / d η is described by the combination of the perturbative hard par-tonic processes and the underlying event [18, 19]. The underlying event includes various phenomenalike initial- and final-state radiation, colour-connected beam remnants, and infrared MPIs. In particular,its normalisation is directly connected to the MPI cross section determined by the low- x behaviour of thegluon parton-distribution function and by the consequent colour screening effects at the p T cut-off, whileits multiplicity distribution is more influenced by correlations within MPI in the fragmentation stage.The methods adopted in this analysis rely on the inclusive d N ch / d η (d N incl . ch / d η ) measurements of AL-ICE [20–24]. This study introduces exclusive event classes for two complementary multiplicity estima-tors defined in the midrapidity and in the forward regions and exploiting high-multiplicity triggers torecord a large sample of events for the highest multiplicity classes. The results are provided for an eventselection defined in a fully experimental way. Measurements are performed for inelastic collisions withat least one charged particle produced in | η | < > ), corresponding to about 75% of the totalinelastic cross section [13, 23, 25, 26]. The full description and performance of the ALICE detectors can be found elsewhere [27, 28]. Thedetectors used in this analysis are briefly presented below.The V0 detector [29] is made of two arrays (V0A and V0C) of 32 scintillating counters each. The V0Ais located at a distance of 329 cm away from the interaction point (IP) along the beam direction ( z ) andit covers the pseudorapidity range 2 . < η < .
1. The V0C is installed at z = −
88 cm, covering thepseudorapidity range − . < η < − .
7. Both counters cover the full azimuth. The V0 detector provides2ultiplicity dependence study of the pseudorapidity density ALICE Collaboration æ V0M Æ V0M/ - - - - -
10 110 N o r m a li s ed c oun t s Min. Bias data (%)1 - - - - -
15 30 - -
30 50 - -
50 100 - - - ALICE = 13 TeVspp,
Forward Multiplicity Classes (a) æ SPD Tracklet N Æ / SPD Tracklet N - - - - -
10 110 N o r m a li s ed c oun t s Min. Bias data (%)1 - - - - -
15 30 - -
30 50 - -
50 100 - ALICE = 13 TeVspp,
Central Multiplicity Classes (b)
Figure 1: The distribution of the V0M amplitude ( − . < η < − . . < η < .
1) scaled byits average value (cid:104)
V0M (cid:105) that is used to determine the forward multiplicity classes (a) and the distribu-tion of the total number of SPD tracklets in an event ( N SPD Tracklet , − < η <
2) scaled by its averagevalue (cid:104) N SPD Tracklet (cid:105) that is used to determine the midrapidity multiplicity classes (b) in pp collisions at √ s = 13 TeV. Note that the percentile values of the multiplicity classes are fractions of the visible crosssection ∆ σ / σ MB AND > (see text for details).the minimum bias and beam-gas removal trigger to ALICE. It measures the signal amplitude created bycharged particles and their arrival times with a time resolution better than 1 ns.The Silicon Pixel Detector (SPD) [30, 31] is the innermost detector of ALICE. It consists of two cylin-drical layers coaxial to the beam line at radii 3.9 and 7.6 cm. It is made of 10 million pixels distributedon 240 sensors that cover the pseudorapidity range | η | < | η | < . | η | < ±
10 cmfrom the nominal interaction point. The SPD provides a precise measurement of the position of the pri-mary interaction vertex with a spatial resolution of on average 30 µ m in the beam direction [23, 31].The multiplicity measurement of this analysis relies on the reconstruction of tracklets, which are tracksegments connecting hits on the two SPD layers and pointing to the primary vertex. The minimum bias pp data samples at √ s = 5.02, 7 and 13 TeV used in this analysis correspond to theintegrated luminosities L int = 12.4 ± ± ± − , respectively [28, 32, 33],and were collected with a magnetic field of 0.5 T provided by the ALICE solenoid magnet. The datasample at √ s = 13 TeV benefits from a high-multiplicity trigger that was implemented in ALICE at thebeginning of the LHC Run 2.The minimum bias trigger (MB AND ) requires hits in both the V0A and V0C detectors in coincidence ofbeam crossing. The contribution from diffractive interactions is minimised by requiring at least one SPDtracklet in | η | <
1; the resulting data sample is called MB
AND > . The contamination from beam-inducedbackground is removed by using the timing information of the V0 detectors and taking into account thecorrelation between tracklets and clusters in the SPD detector [28]. The events used for the analysisare required to have a reconstructed vertex in the fiducial region | z | <
10 cm. The contamination fromin-bunch pile-up events is removed offline excluding events with multiple vertices reconstructed in theSPD [23]. The pile-up probability estimated considering the beam conditions ranges from 10 − to 10 − .After the offline rejection, the remaining pile-up has a negligible impact on the final results. This wasverified by analysing separately data samples with high and low initial pile-up contamination.3ultiplicity dependence study of the pseudorapidity density ALICE CollaborationMultiplicity classes are defined by a probability (percentile) range that is interpreted as a fractionalcross section ∆ σ / σ MB AND > , with the visible cross section in pp collisions, σ MB AND > , constituting 100%.Percentile values for higher multiplicity collisions are close to 0% and for lower ones close to 100%.Forward multiplicity classes are estimated by V0M, which is the sum of the energy deposition measuredby the V0A and V0C scintillators. The distribution of the V0M amplitude scaled by its average value (cid:104) V0M (cid:105) (self-normalised V0M) is shown in Fig. 1a for MB
AND > pp collisions at √ s = 13 TeV. Dedicatedhigh-multiplicity triggers are defined by the thresholds V0M / (cid:104) V0M (cid:105) > ∼ . ∼ .
9, correspondingto σ / σ MB AND > = . | η | <
2. The distribution of the self-normalised number of SPD tracklets forMB
AND > pp collisions in | η | < AND > data samples using the mid and forward multiplicity estimators.The multiplicity percentile intervals of the visible cross section P ( MB AND > ) = ∆ σ / σ MB AND > can beconverted to fractional intervals with respect to the INEL > cross section P ( INEL > ) = ∆ σ / σ INEL > inpp collisions by following the conversion ruleP i ( INEL > ) = P i ( MB AND > ) / ε i ∑ j ( P j ( MB AND > ) / ε j ) , (1)where i indicates a specific multiplicity class, j runs over all multiplicity classes for a given collisionenergy and multiplicity estimator, and ε i ( ε j ) is the MB AND > trigger efficiency for the INEL > eventsample N MB AND > / N INEL > for the i th ( j th ) multiplicity class. The correspondence between P ( INEL > ) and P ( MB AND > ) is reported in Table 2. Forward Multiplicity Estimator Midrapidity Multiplicity Estimator √ s (TeV) √ s (TeV)5.02 7 13 5.02 7 13P ( MB AND > ) P ( INEL > ) P ( INEL > ) (%) (%) (%)0–0.01 0–0.0091 0–0.0090 0–0.00910.01–0.1 0.0091–0.0915 0.0090–0.0897 0.0091–0.09150.1–0.5 0.0915–0.4576 0.0897–0.4478 0.0915–0.45730.5–1 0.4576–0.9152 0.4478–0.8955 0.4573–0.91460–1 0–0.9152 0–0.8955 0–0.9146 0–0.9095 0–0.8887 0–0.92881–5 0.9152–4.577 0.8955–4.478 0.9146–4.574 0.9095–4.548 0.8887–4.444 0.9288–4.6440–5 0–4.577 0–4.478 0–4.574 0–4.548 0–4.444 0–4.6445–10 4.577–9.156 4.478–8.956 4.574–9.149 4.548–9.096 4.444–8.888 4.644–9.28810–15 9.156–13.74 8.956–13.44 9.149–13.73 9.096–13.65 8.888–13.33 9.288–13.9315–20 13.74–18.32 13.44–17.92 13.73–18.31 13.65–18.20 13.33–17.78 13.93–18.5820–30 18.32–27.51 17.92–26.90 18.31–27.50 18.20–27.32 17.78–26.67 18.58–27.8830–40 27.51–36.76 26.90–35.92 27.50–36.75 27.32–36.49 26.67–35.59 27.88–37.2040–50 36.76–46.11 35.92–45.02 36.75–46.12 36.49–45.77 35.59–44.53 37.20–46.5850–70 46.11–65.45 45.02–63.66 46.12–65.53 45.77–64.91 44.53–62.88 46.58–65.8270–100 65.45–100 63.66–100 65.53–100 64.91–100 62.88–100 65.82–100 Table 1: Correspondence of the multiplicity classes between P ( MB AND > ) and P ( INEL > ) . The triggerefficiency is estimated using PYTHIA 8 Monash 2013 [34–36] and GEANT3 [37].The value of d N ch / d η is obtained by correcting the number of SPD tracklets for detector acceptance aswell as reconstruction and selection efficiency following the procedure developed earlier [23, 24, 38–40].The corrections are estimated with Monte Carlo simulations based on PYTHIA 8 Monash 2013 [34, 35]for particle generation and GEANT3 [37] for the transport of particles through the geometry of AL-ICE. PYTHIA 8 has a strangeness content that underestimates the data by a p T -dependent factor, whichapproaches 2 around p T =
10 GeV/ c [41]. The discrepancy is resolved by normalising the strangenesscontent in PYTHIA 8 to match the one in the data. This corrects d N ch / d η downward by about 1%.4ultiplicity dependence study of the pseudorapidity density ALICE Collaboration Uncertainty (%) at √ s = 13 TeVForward ∆ σ / σ MB AND > Midrapidity ∆ σ / σ MB AND > ∆ σ / σ INEL > source 0–0.01% 40–50% 70–100% 0–1% 40–50% 70–100% 0–100%UncorrelatedTrigger efficiency neg. 0.2 0.2 neg. 0.2 0.2 0.2Strangeness correction 0.7 0.6 0.5 0.7 0.6 0.5 0.5Zero- p T extrapolation 0.7 0.8 1.0 0.7 0.9 1.0 1.0CorrelatedModel dependence neg 0.1 0.1 0.1 0.1 0.1 0.1Detector acceptance and efficiency 0.8 0.7 0.6 1.8 2.0 2.8 0.7Particle composition 0.5 0.5 0.5 0.5 0.5 0.5 0.5Material budget 0.2 0.2 0.2 0.2 0.2 0.2 0.2 Table 2: The first four columns report the systematic uncertainties quoted in the highest and lowestmultiplicity classes for both the mid and forward rapidity multiplicity estimators in pp collisions at √ s = 13 TeV. The last column reports the effects on the inclusive (cid:104) d N ch / d η (cid:105) .For each multiplicity class, the systematic uncertainties related to the model used in the correction proce-dure are quoted as the difference of the final results using corrections obtained with two different genera-tors: PYTHIA 8 Monash [34–36] and EPOS LHC [42, 43]. The uncertainties attributed to the descriptionof the trigger are also quoted as the difference of the simulated trigger efficiency ( N MB AND > / N INEL > ) be-tween the two event generators.The effects of the difference in particle composition between data and Monte Carlo mostly originatefrom the underestimated yield related to the weak decays of light-flavour hadrons in the simulation andare obtained with reweighting techniques: strangeness yields in the simulation are reweighted during thecorrection step by a factor of 2 to be compatible with the data; the factor is varied by ±
30% based ondata [41] that covers the whole p T region, resulting in variations of the obtained d N ch / d η ranging from ± ± ± c , the tracklet reconstructionefficiency sharply drops because of the bending in the magnetic field and to less extent due to the scatter-ing and absorption in the detector material. To estimate the uncertainty due to the extrapolation to zero p T , the number of particles below 50 MeV/ c is varied sufficiently in the event generator by + − ±
1% and slightlydependent on the multiplicity class.The effect of the limited tracking acceptance and efficiency is estimated by varying the range of primaryvertex selection along the beam direction ( z vtx ) from | z vtx | <
10 cm to the narrower | z vtx | < | z vtx | <
15 cm; the effect on d N ch / d η is below 2% in all the multiplicity classes. The effectof the detector response in different azimuthal regions is studied by measuring d N ch / d η independentlyin three different azimuthal regions of the SPD, which are then compared with the corresponding fullazimuth measurement: it varies from 0.8% to 2% with respect to the SPD configuration. The materialbudget in the ALICE central barrel is known to a precision of about 5% [28]. The corresponding sys-tematic uncertainty on d N ch / d η , obtained by varying the material budget in the simulation, is estimatedto be about 0.2%. The systematic sources for the particle-species composition, material budget, trackingacceptance and efficiency correction are treated as correlated with respect to the multiplicity classes andcollision energy. The sources for the strangeness particle correction, trigger efficiency correction andzero- p T extrapolation are considered as uncorrelated.5ultiplicity dependence study of the pseudorapidity density ALICE Collaboration - h h / d c h N d ALICE pp collisions = 5.02 TeV s - I n c l u s i v e ) h / d c h N ) / ( d h / d c h N ( d - h h / d c h N d = 7 TeV s Multiplicity estimation < 5.1 h < -1.7 and 2.8 < h -3.7 < - h I n c l u s i v e ) h / d c h N ) / ( d h / d c h N ( d - h / d c h N d = 13 TeV s - I n c l u s i v e ) h / d c h N ) / ( d h / d c h N ( d (%) AND>0 MB s / s - - - - - - -
10 20 -
15 30 -
20 40 -
30 50 -
40 70 -
50 100 - Figure 2: Charged-particle pseudorapidity density (upper panels) and the same scaled by1 / ( d N ch / d η ) incl . (lower panels) for the 0–0.01 to 70–100% multiplicity classes measured with the for-ward multiplicity estimator ( − . < η < − . . < η < .
1) in pp collisions at √ s = 5.02, 7and 13 TeV. Correlated and uncorrelated systematic uncertainties are summed in quadrature in the upperpanels and shown as boxes. Correlated systematic uncertainties are cancelled out in the lower panels. - h h / d c h N d ALICE pp collisions = 5.02 TeV s - I n c l u s i v e ) h / d c h N ) / ( d h / d c h N ( d - h h / d c h N d = 7 TeV s Multiplicity estimation < 2 h -2 < - h I n c l u s i v e ) h / d c h N ) / ( d h / d c h N ( d - h / d c h N d = 13 TeV s - I n c l u s i v e ) h / d c h N ) / ( d h / d c h N ( d (%) AND>0 MB s / s - - - -
10 20 -
15 30 -
20 40 -
30 50 -
40 70 -
50 100 - Figure 3: Charged-particle pseudorapidity density (upper panels) and the same scaled by1 / ( d N ch / d η ) incl . (lower panels) for the 0–1 to 70–100% multiplicity classes measured with the midra-pidity multiplicity estimator ( − < η <
2) in pp collisions at √ s = 5.02, 7 and 13 TeV. Correlated anduncorrelated systematic uncertainties are summed in quadrature in the upper panels and shown as boxes.Correlated systematic uncertainties are cancelled out in the lower panels. The d N ch / d η measurements at √ s = 5.02, 7 and 13 TeV for different classes of the forward multiplicityestimators are reported in Fig. 2; in the upper panels in absolute scale and in the lower panels, normalisedto the inclusive d N ch / d η (d N incl . ch / d η , d N ch / d η for 0–100%). As shown in the lower panels of Fig. 2,the pseudorapidity densities for the highest multiplicity classes (0–0.01%) are around 5 times larger than6ultiplicity dependence study of the pseudorapidity density ALICE Collaborationthose of the inclusive ones for the three different collision energies. The asymmetry of the d N ch / d η distributions for the forward multiplicity classes is due to the asymmetric pseudorapidity acceptance ofthe V0 detector. This effect is more pronounced for the highest multiplicity classes.The upper panels in Fig. 3 show the d N ch / d η measurements at √ s = 5.02, 7 and 13 TeV for differentmultiplicity classes defined by the midrapidity multiplicity estimator. For all the midrapidity multiplicityclasses, only the minimum bias trigger is used because the high-multiplicity trigger relying on V0M am-plitudes would give an additional bias. The shapes of the pseudorapidity distributions of primary chargedparticles are different when compared with those obtained with the forward multiplicity estimator. Themidrapidity multiplicity estimator is defined in a symmetric pseudorapidity region ( − < η <
2) andclearly gives rise to autocorrelations as it includes the region where the pseudorapidity distributions aremeasured ( − . < η < . η = - h I n c l . ) h / d c h N ) / ( d h / d c h N ( d ALICE pp collisions = 5.02 TeVs - M ode l / D a t a - h I n c l . ) h / d c h N ) / ( d h / d c h N ( d - M ode l / D a t a - h I n c l . ) h / d c h N ) / ( d h / d c h N ( d = 7 TeVs - : 0 AND>0 MB s / s Forward dataPYTHIA8 MonashPYTHIA8 Monash no CREPOS LHC - h M ode l / D a t a - h I n c l . ) h / d c h N ) / ( d h / d c h N ( d - : 70 AND>0 MB s / s Forward - h M ode l / D a t a - h I n c l . ) h / d c h N ) / ( d h / d c h N ( d = 13 TeVs - D a t a / m ode l - h I n c l . ) h / d c h N ) / ( d h / d c h N ( d - D a t a / m ode l Figure 4: The panels in the first and third row show the normalised pseudorapidity density distributionsof charged particles in pp collisions at √ s = 5.02, 7 and 13 TeV compared with different models for the0–1% and 70–100% multiplicity classes by the forward rapidity multiplicity estimator, respectively. Thepanels in the second and fourth row report the corresponding model/data ratio. Note that the multiplicityclasses of the models correspond to ∆ σ / σ INEL > , which is slightly different from the ∆ σ / σ MB AND > ofthe ALICE data.The measurements are compared with the predictions from PYTHIA 8 Monash 2013 [34–36] with andwithout CR and the ones from EPOS LHC [42, 43]. The multiplicity classes of the models are estimatedfor generated charged particles in the same geometrical acceptances of the forward rapidity ( − . < η < − . . < η < .
1) and midrapidity ( | η | <
2) multiplicity estimators and the percentile valueof the multiplicity class is calibrated for generated INEL > events. Figure 4 reports the comparisonof the data with these models for the 0–1% and 70–100% classes by the forward multiplicity estimator.PYTHIA 8 Monash 2013, implementing CR in the string fragmentation process, describes the data within7ultiplicity dependence study of the pseudorapidity density ALICE Collaboration5% for all the centre-of-mass energies for the 0–1% multiplicity class. For the 70–100% class, PYTHIA 8underestimates the data by up to 10%. When switching off CR, while keeping all the other modelparameters stable, PYTHIA 8 overestimates (underestimates) the data by about 30% for the 0–1% (70–100%) multiplicity class. EPOS LHC, which incorporates a collective flow-like description of the core,describes the data within 20% for both forward multiplicity classes. EPOS LHC also overestimates(underestimates) the data for the 0–1% (70–100%) multiplicity class like PYTHIA 8 Monash 2013. Forthe two classes, PYTHIA 8 describes the data better than EPOS LHC. - h I n c l . ) h / d c h N ) / ( d h / d c h N ( d ALICE pp collisions = 5.02 TeVs - M ode l / D a t a - h I n c l . ) h / d c h N ) / ( d h / d c h N ( d - M ode l / D a t a - h I n c l . ) h / d c h N ) / ( d h / d c h N ( d = 7 TeVs - : 0 AND>0 MB s / s Central dataPYTHIA8 MonashPYTHIA8 Monash no CREPOS LHC - h M ode l / D a t a - h I n c l . ) h / d c h N ) / ( d h / d c h N ( d - : 70 AND>0 MB s / s Central - h M ode l / D a t a - h I n c l . ) h / d c h N ) / ( d h / d c h N ( d = 13 TeVs - D a t a / m ode l - h I n c l . ) h / d c h N ) / ( d h / d c h N ( d - D a t a / m ode l Figure 5: The panels in the first and third row show the normalised pseudorapidity density distributionsof charged particles in pp collisions at √ s = 5.02, 7 and 13 TeV compared with different models forthe 0–1% and 70–100% multiplicity classes by the midrapidity multiplicity estimator, respectively. Thepanels in the second and fourth row report the corresponding model/data ratio. The multiplicity classes ofthe models correspond to ∆ σ / σ INEL > , which is slightly different from the ∆ σ / σ MB AND > of the ALICEdata.Figure 5 shows the comparison according to the data with these models for the 0–1% and 70–100%classes by the midrapidity multiplicity estimator. EPOS LHC describes the data within 5% for all thecentre-of-mass energies for the 0–1% multiplicity class. For the 70–100% class, EPOS LHC underesti-mates the data by up to 20%. PYTHIA 8 reproduces the data within 5% for all center-of-mass energies forthe 0–1% multiplicity class, but it is not better than EPOS LHC. For the 70–100% class, PYTHIA 8 de-scribes the data within 10% and it is better than those of EPOS LHC. When switching off CR, PYTHIA 8overestimates (underestimates) the data by about 15% (30%) for the 0–1% (70–100%) multiplicity class.The value of (cid:104) d N ch / d η (cid:105) is determined by integrating d N ch / d η in | η | < .
5. Table 3 shows the valuesof (cid:104) d N ch / d η (cid:105) for different mid and forward rapidity multiplicity classes in pp collisions at √ s = 5.02, 7and 13 TeV. The autocorrelation effect for the midrapidity estimator results in larger values of (cid:104) d N ch / d η (cid:105) in the highest multiplicity classes and in smaller ones for the lowest multiplicity classes compared withthose with the forward multiplicity estimator.The energy dependence of (cid:104) d N ch / d η (cid:105) for the multiplicity classes defined by the forward multiplicity8ultiplicity dependence study of the pseudorapidity density ALICE Collaboration Forward Multiplicity Estimator Midrapidity Multiplicity Estimator √ s (TeV) √ s (TeV)5.02 7 13 5.02 7 13 ∆ σ / σ MB AND > (cid:104) d N ch / d η (cid:105)± uncorrelated systematic uncertainty ± correlated systematic uncertainty0–0.01% 24.53 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± Table 3: Values of (cid:104) d N ch / d η (cid:105) for different multiplicity classes defined by the mid and forward multiplic-ity estimators in pp collisions at √ s = 5.02 to 13 TeV. (GeV) s æ h / d c h N d Æ (GeV) s i n c l . æ h / d c h N d Æ / æ h / d c h N d Æ Forward Multiplicity Class , %)
AND>0 MB s / s ( s (cid:181) s (cid:181) s (cid:181) s (cid:181) s (cid:181) s (cid:181) s (cid:181)
10 - 15 s (cid:181)
15 - 20 s (cid:181)
20 - 30 s (cid:181)
30 - 40 s (cid:181)
40 - 50 s (cid:181)
50 - 70 s (cid:181)
70 - 100
ALICE pp collisions
Figure 6: Energy dependence of (cid:104) d N ch / d η (cid:105) (upper) and (cid:104) d N ch / d η (cid:105) scaled by the inclusive d N ch / d η (lower) for the multiplicity classes by the forward multiplicity estimator in pp collisions. Lines show fitswith a power-law function. Corresponding bands indicate one standard deviation of the fit. Exponentsand corresponding uncertainties of the fit are listed in the legend.estimator is shown in the upper panel of Fig. 6. The LHC measurements for the (cid:104) d N ch / d η (cid:105) can be directlycompared with the ones from the NAL Bubble Chamber (pp) [45], ISR (pp) [46] , UA1 (pp) [47], UA5(pp) [48], CDF (pp) [49], STAR (pp) [50] and PHOBOS (pp) [51]. A phenomenological power law fitdescribes the centre-of-mass energy evolution of these measurements for Non-Single Diffractive (NSD),INEL and INEL > events up to LHC energies [23].Such a fit is performed practically for the values of Table 3 in different multiplicity classes to describethe dependence of (cid:104) d N ch / d η (cid:105) on the centre-of-mass energy. Corresponding exponents are shown in the9ultiplicity dependence study of the pseudorapidity density ALICE Collaborationlegend of Fig. 6. The average pseudorapidity density at midrapidity as a function of the centre-of-massenergy increases for the highest multiplicity classes. The lower panel of Fig. 6 shows (cid:104) d N ch / d η (cid:105) nor-malised to its inclusive value denoted as (cid:104) d N ch / d η (cid:105) / (cid:104) d N ch / d η (cid:105) incl . for the forward multiplicity classes.The steeper increasing trend of (cid:104) d N ch / d η (cid:105) / (cid:104) d N ch / d η (cid:105) incl . observed for higher multiplicity classes mayarise from the increase of the MPI cross sections with the centre-of-mass energy [23]. The energy and multiplicity dependence of the charged-particle pseudorapidity density d N ch / d η and theaverage charged-particle pseudorapidity density (cid:104) d N ch / d η (cid:105) in pp collisions at √ s = 5.02, 7 and 13 TeVare measured. The yields of charged particles in the 0–1% and 0–0.01% multiplicity classes for themid and forward rapidity multiplicity estimators, respectively, are up to about a factor of 5 higher withrespect to the inclusive measurements for all investigated centre-of-mass energies. The results from themultiplicity-dependent analysis presented for both the mid and forward rapidity multiplicity estimators inALICE can be used as an input for improving our understanding of Multiple Parton Interactions (MPIs)implemented in Monte Carlo models. Most of the results are described well by PYTHIA 8 with theMonash tune and by EPOS LHC. The effects of the colour reconnection (CR) is found to be importantto constrain MPIs and describe the scale of the pseudorapidity density as a function of multiplicity forboth the mid and forward multiplicity estimators as seen by the expected values for PYTHIA 8 with andwithout CR. The results can be used for further studies as a function of multiplicity estimated at mid orforward rapidity in proton–proton collisions. Acknowledgements
The ALICE Collaboration would like to thank all its engineers and technicians for their invaluable con-tributions to the construction of the experiment and the CERN accelerator teams for the outstandingperformance of the LHC complex. The ALICE Collaboration gratefully acknowledges the resources andsupport 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 buildingand running the ALICE detector: A. I. Alikhanyan National Science Laboratory (Yerevan Physics In-stitute) Foundation (ANSL), State Committee of Science and World Federation of Scientists (WFS),Armenia; Austrian Academy of Sciences, Austrian Science Fund (FWF): [M 2467-N36] and National-stiftung für Forschung, Technologie und Entwicklung, Austria; Ministry of Communications and HighTechnologies, National Nuclear Research Center, Azerbaijan; Conselho Nacional de DesenvolvimentoCientífico e Tecnológico (CNPq), Financiadora de Estudos e Projetos (Finep), Fundação de Amparo àPesquisa do Estado de São Paulo (FAPESP) and Universidade Federal do Rio Grande do Sul (UFRGS),Brazil; Ministry of Education of China (MOEC) , Ministry of Science & Technology of China (MSTC)and National Natural Science Foundation of China (NSFC), China; Ministry of Science and Educationand Croatian Science Foundation, Croatia; Centro de Aplicaciones Tecnológicas y Desarrollo Nuclear(CEADEN), Cubaenergía, Cuba; Ministry of Education, Youth and Sports of the Czech Republic, CzechRepublic; The Danish Council for Independent Research | Natural Sciences, the VILLUM FONDEN andDanish National Research Foundation (DNRF), Denmark; Helsinki Institute of Physics (HIP), Finland;Commissariat à l’Energie Atomique (CEA) and Institut National de Physique Nucléaire et de Physiquedes Particules (IN2P3) and Centre National de la Recherche Scientifique (CNRS), France; Bundesmin-isterium für Bildung und Forschung (BMBF) and GSI Helmholtzzentrum für SchwerionenforschungGmbH, Germany; General Secretariat for Research and Technology, Ministry of Education, Researchand Religions, Greece; National Research, Development and Innovation Office, Hungary; Departmentof Atomic Energy Government of India (DAE), Department of Science and Technology, Governmentof India (DST), University Grants Commission, Government of India (UGC) and Council of Scientificand Industrial Research (CSIR), India; Indonesian Institute of Science, Indonesia; Centro Fermi - Museo10ultiplicity dependence study of the pseudorapidity density ALICE CollaborationStorico della Fisica e Centro Studi e Ricerche Enrico Fermi and Istituto Nazionale di Fisica Nucleare(INFN), Italy; Institute for Innovative Science and Technology , Nagasaki Institute of Applied Science(IIST), Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT) and Japan So-ciety for the Promotion of Science (JSPS) KAKENHI, Japan; Consejo Nacional de Ciencia (CONACYT)y Tecnología, through Fondo de Cooperación Internacional en Ciencia y Tecnología (FONCICYT) andDirección General de Asuntos del Personal Academico (DGAPA), Mexico; Nederlandse Organisatievoor Wetenschappelijk Onderzoek (NWO), Netherlands; The Research Council of Norway, Norway;Commission on Science and Technology for Sustainable Development in the South (COMSATS), Pak-istan; Pontificia Universidad Católica del Perú, Peru; Ministry of Science and Higher Education, NationalScience Centre and WUT ID-UB, Poland; Korea Institute of Science and Technology Information andNational Research Foundation of Korea (NRF), Republic of Korea; Ministry of Education and ScientificResearch, Institute of Atomic Physics and Ministry of Research and Innovation and Institute of AtomicPhysics, Romania; Joint Institute for Nuclear Research (JINR), Ministry of Education and Science ofthe Russian Federation, National Research Centre Kurchatov Institute, Russian Science Foundation andRussian Foundation for Basic Research, Russia; Ministry of Education, Science, Research and Sport ofthe Slovak Republic, Slovakia; National Research Foundation of South Africa, South Africa; SwedishResearch Council (VR) and Knut & Alice Wallenberg Foundation (KAW), Sweden; European Organi-zation for Nuclear Research, Switzerland; Suranaree University of Technology (SUT), National Scienceand Technology Development Agency (NSDTA) and Office of the Higher Education Commission underNRU project of Thailand, Thailand; Turkish Atomic Energy Agency (TAEK), Turkey; National Academyof Sciences of Ukraine, Ukraine; Science and Technology Facilities Council (STFC), United Kingdom;National Science Foundation of the United States of America (NSF) and United States Department ofEnergy, Office of Nuclear Physics (DOE NP), United States of America.
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34 ,128 , R. Ilkaev ,H. Ilyas , M. Inaba , G.M. Innocenti , M. Ippolitov , A. Isakov
37 ,96 , M.S. Islam , M. Ivanov ,V. Ivanov , V. Izucheev , B. Jacak , N. Jacazio
34 ,54 , P.M. Jacobs , S. Jadlovska , J. Jadlovsky ,S. Jaelani , C. Jahnke , M.J. Jakubowska , M.A. Janik , T. Janson , M. Jercic , O. Jevons ,M. Jin , F. Jonas
97 ,145 , P.G. Jones , J. Jung , M. Jung , A. Jusko , P. Kalinak , A. Kalweit ,V. Kaplin , S. Kar , A. Karasu Uysal , D. Karatovic , O. Karavichev , T. Karavicheva ,P. Karczmarczyk , E. Karpechev , A. Kazantsev , U. Kebschull , R. Keidel , M. Keil , B. Ketzer ,Z. Khabanova , A.M. Khan , S. Khan , A. Khanzadeev , Y. Kharlov , A. Khatun , A. Khuntia ,B. Kileng , B. Kim , B. Kim , D. Kim , D.J. Kim , E.J. Kim , J. Kim , J.S. Kim , J. Kim ,J. Kim , J. Kim , M. Kim , S. Kim , T. Kim , T. Kim , S. Kirsch , I. Kisel , S. Kiselev ,A. Kisiel , J.L. Klay , C. Klein , J. Klein
34 ,59 , S. Klein , C. Klein-Bösing , M. Kleiner ,T. Klemenz , A. Kluge , M.L. Knichel , A.G. Knospe , C. Kobdaj , M.K. Köhler , T. Kollegger ,A. Kondratyev , N. Kondratyeva , E. Kondratyuk , J. Konig , S.A. Konigstorfer , P.J. Konopka ,G. Kornakov , L. Koska , O. Kovalenko , V. Kovalenko , M. Kowalski , I. Králik ,A. Kravˇcáková , L. Kreis , M. Krivda
112 ,64 , F. Krizek , K. Krizkova Gajdosova , M. Kroesen ,M. Krüger , E. Kryshen , M. Krzewicki , V. Kuˇcera
34 ,61 , C. Kuhn , P.G. Kuijer , L. Kumar ,S. Kundu , P. Kurashvili , A. Kurepin , A.B. Kurepin , A. Kuryakin , S. Kushpil , J. Kvapil ,M.J. Kweon , J.Y. Kwon , Y. Kwon , S.L. La Pointe , P. La Rocca , Y.S. Lai , A. Lakrathok ,M. Lamanna , R. Langoy , K. Lapidus , A. Lardeux , P. Larionov , E. Laudi , R. Lavicka ,T. Lazareva , R. Lea , J. Lee , S. Lee , J. Lehrbach , R.C. Lemmon , I. León Monzón ,E.D. Lesser , M. Lettrich , P. Lévai , X. Li , X.L. Li , J. Lien , R. Lietava , B. Lim ,V. Lindenstruth , A. Lindner , C. Lippmann , A. Liu , J. Liu , W.J. Llope , I.M. Lofnes ,V. Loginov , C. Loizides , P. Loncar , J.A. Lopez , X. Lopez , E. López Torres , J.R. Luhder ,M. Lunardon , G. Luparello , Y.G. Ma , A. Maevskaya , M. Mager , S.M. Mahmood , T. Mahmoud ,A. Maire , R.D. Majka ?? ,147 , M. Malaev , Q.W. Malik , L. Malinina ?? ,75 , D. Mal’Kevich , N. Mallick ,P. Malzacher , G. Mandaglio
32 ,56 , V. Manko , F. Manso , V. Manzari , Y. Mao , M. Marchisone ,J. Mareš , G.V. Margagliotti , A. Margotti , A. Marín , C. Markert , M. Marquard , N.A. Martin ,P. Martinengo , J.L. Martinez , M.I. Martínez , G. Martínez García , S. Masciocchi , M. Masera ,A. Masoni , L. Massacrier , A. Mastroserio
139 ,53 , A.M. Mathis , O. Matonoha , P.F.T. Matuoka ,A. Matyja , C. Mayer , F. Mazzaschi , M. Mazzilli , M.A. Mazzoni , A.F. Mechler , F. Meddi ,Y. Melikyan , A. Menchaca-Rocha , C. Mengke , E. Meninno
115 ,29 , A.S. Menon , M. Meres ,S. Mhlanga , Y. Miake , L. Micheletti , L.C. Migliorin , D.L. Mihaylov , K. Mikhaylov
75 ,93 ,A.N. Mishra
146 ,69 , D. Mi´skowiec , A. Modak , N. Mohammadi , A.P. Mohanty , B. Mohanty ,M. Mohisin Khan ?? ,16 , Z. Moravcova , C. Mordasini , D.A. Moreira De Godoy , L.A.P. Moreno ,I. Morozov , A. Morsch , T. Mrnjavac , V. Muccifora , E. Mudnic , D. Mühlheim , S. Muhuri ,J.D. Mulligan , A. Mulliri
23 ,55 , M.G. Munhoz , R.H. Munzer , H. Murakami , S. Murray ,L. Musa , J. Musinsky , C.J. Myers , J.W. Myrcha , B. Naik , R. Nair , B.K. Nandi , R. Nania
54 ,10 ,E. Nappi , M.U. Naru , A.F. Nassirpour , C. Nattrass , R. Nayak , T.K. Nayak , S. Nazarenko ,A. Neagu , R.A. Negrao De Oliveira , L. Nellen , S.V. Nesbo , G. Neskovic , D. Nesterov ,B.S. Nielsen , S. Nikolaev , S. Nikulin , V. Nikulin , F. Noferini
54 ,10 , P. Nomokonov , J. Norman
128 ,79 ,N. Novitzky , P. Nowakowski , A. Nyanin , J. Nystrand , M. Ogino , A. Ohlson , J. Oleniacz ,A.C. Oliveira Da Silva , M.H. Oliver , C. Oppedisano , A. Ortiz Velasquez , T. Osako ,A. Oskarsson , J. Otwinowski , K. Oyama , Y. Pachmayer , V. Pacik , S. Padhan , D. Pagano ,G. Pai´c , J. Pan , S. Panebianco , P. Pareek
142 ,50 , J. Park , J.E. Parkkila , S. Parmar ,S.P. Pathak , B. Paul , J. Pazzini , H. Pei , T. Peitzmann , X. Peng , L.G. Pereira , H. Pereira DaCosta , D. Peresunko , G.M. Perez , S. Perrin , Y. Pestov , V. Petráˇcek , M. Petrovici , R.P. Pezzi ,S. Piano , M. Pikna , P. Pillot , O. Pinazza
54 ,34 , L. Pinsky , C. Pinto , S. Pisano
10 ,52 , D. Pistone ,M. Płosko´n , M. Planinic , F. Pliquett , M.G. Poghosyan , B. Polichtchouk , N. Poljak , A. Pop ,S. Porteboeuf-Houssais , V. Pozdniakov , S.K. Prasad , R. Preghenella , F. Prino , C.A. Pruneau ,I. Pshenichnov , M. Puccio , J. Putschke , S. Qiu , L. Quaglia , R.E. Quishpe , S. Ragoni ,S. Raha , J. Rak , A. Rakotozafindrabe , L. Ramello , F. Rami , S.A.R. Ramirez , R. Raniwala ,S. Raniwala , S.S. Räsänen , R. Rath , I. Ravasenga , K.F. Read
97 ,131 , A.R. Redelbach ,K. Redlich ?? ,86 , A. Rehman , P. Reichelt , F. Reidt , R. Renfordt , Z. Rescakova , K. Reygers ,A. Riabov , V. Riabov , T. Richert
81 ,90 , M. Richter , P. Riedler , W. Riegler , F. Riggi , C. Ristea ,S.P. Rode , M. Rodríguez Cahuantzi , K. Røed , R. Rogalev , E. Rogochaya , D. Rohr , D. Röhrich , P.F. Rojas , P.S. Rokita , F. Ronchetti , A. Rosano
32 ,56 , E.D. Rosas , K. Roslon , A. Rossi ,A. Rotondi , A. Roy , P. Roy , O.V. Rueda , R. Rui , B. Rumyantsev , A. Rustamov ,E. Ryabinkin , Y. Ryabov , A. Rybicki , H. Rytkonen , O.A.M. Saarimaki , R. Sadek , S. Sadhu ,S. Sadovsky , J. Saetre , K. Šafaˇrík , S.K. Saha , S. Saha , B. Sahoo , P. Sahoo , R. Sahoo ,S. Sahoo , D. Sahu , P.K. Sahu , J. Saini , S. Sakai , S. Sambyal , V. Samsonov
99 ,94 , D. Sarkar ,N. Sarkar , P. Sarma , V.M. Sarti , M.H.P. Sas
147 ,62 , E. Scapparone , J. Schambach
97 ,120 ,H.S. Scheid , C. Schiaua , R. Schicker , A. Schmah , C. Schmidt , H.R. Schmidt ,M.O. Schmidt , M. Schmidt , N.V. Schmidt
97 ,68 , A.R. Schmier , J. Schukraft , Y. Schutz ,K. Schwarz , K. Schweda , G. Scioli , E. Scomparin , J.E. Seger , Y. Sekiguchi , D. Sekihata ,I. Selyuzhenkov
108 ,94 , S. Senyukov , J.J. Seo , D. Serebryakov , L. Šerkšnyt˙e , A. Sevcenco ,A. Shabanov , A. Shabetai , R. Shahoyan , W. Shaikh , A. Shangaraev , A. Sharma , H. Sharma ,M. Sharma , N. Sharma , S. Sharma , O. Sheibani , A.I. Sheikh , K. Shigaki , M. Shimomura ,S. Shirinkin , Q. Shou , Y. Sibiriak , S. Siddhanta , T. Siemiarczuk , D. Silvermyr , G. Simatovic ,G. Simonetti , B. Singh , R. Singh , R. Singh , R. Singh , V.K. Singh , V. Singhal , T. Sinha ,B. Sitar , M. Sitta , T.B. Skaali , M. Slupecki , N. Smirnov , R.J.M. Snellings , T.W. Snellman ,C. Soncco , J. Song , A. Songmoolnak , F. Soramel , S. Sorensen , I. Sputowska , J. Stachel ,I. Stan , P.J. Steffanic , S.F. Stiefelmaier , D. Stocco , M.M. Storetvedt , L.D. Stritto ,C.P. Stylianidis , A.A.P. Suaide , T. Sugitate , C. Suire , M. Suleymanov , M. Suljic , R. Sultanov ,M. Šumbera , V. Sumberia , S. Sumowidagdo , S. Swain , A. Szabo , I. Szarka , U. Tabassam ,S.F. Taghavi , G. Taillepied , J. Takahashi , G.J. Tambave , S. Tang
135 ,6 , M. Tarhini ,M.G. Tarzila , A. Tauro , G. Tejeda Muñoz , A. Telesca , L. Terlizzi , C. Terrevoli , S. Thakur ,D. Thomas , F. Thoresen , R. Tieulent , A. Tikhonov , A.R. Timmins , M. Tkacik , A. Toia ,N. Topilskaya , M. Toppi , F. Torales-Acosta , S.R. Torres , A. Trifiró
32 ,56 , S. Tripathy , T. Tripathy ,S. Trogolo , G. Trombetta , L. Tropp , V. Trubnikov , W.H. Trzaska , T.P. Trzcinski ,B.A. Trzeciak
37 ,62 , A. Tumkin , R. Turrisi , T.S. Tveter , K. Ullaland , E.N. Umaka , A. Uras ,G.L. Usai , M. Vala , N. Valle , S. Vallero , N. van der Kolk , L.V.R. van Doremalen , M. vanLeeuwen , P. Vande Vyvre , D. Varga , Z. Varga , M. Varga-Kofarago , A. Vargas , M. Vasileiou ,A. Vasiliev , O. Vázquez Doce , V. Vechernin , E. Vercellin , S. Vergara Limón , L. Vermunt ,R. Vernet , R. Vértesi , M. Verweij , L. Vickovic , Z. Vilakazi , O. Villalobos Baillie , G. Vino ,A. Vinogradov , T. Virgili , V. Vislavicius , A. Vodopyanov , B. Volkel , M.A. Völkl , K. Voloshin ,S.A. Voloshin , G. Volpe , B. von Haller , I. Vorobyev , D. Voscek , J. Vrláková , B. Wagner ,M. Weber , S.G. Weber , A. Wegrzynek , S.C. Wenzel , J.P. Wessels , J. Wiechula , J. Wikne ,G. Wilk , J. Wilkinson
108 ,10 , G.A. Willems , E. Willsher , B. Windelband , M. Winn , W.E. Witt ,J.R. Wright , Y. Wu , R. Xu , S. Yalcin , Y. Yamaguchi , K. Yamakawa , S. Yang , S. Yano
46 ,138 ,Z. Yin , H. Yokoyama , I.-K. Yoo , J.H. Yoon , S. Yuan , A. Yuncu , V. Yurchenko , V. Zaccolo ,A. Zaman , C. Zampolli , H.J.C. Zanoli , N. Zardoshti , A. Zarochentsev , P. Závada ,N. Zaviyalov , H. Zbroszczyk , M. Zhalov , S. Zhang , X. Zhang , Z. Zhang , V. Zherebchevskii ,Y. Zhi , D. Zhou , Y. Zhou , J. Zhu , Y. Zhu , A. Zichichi
10 ,26 , G. Zinovjev , N. Zurlo Affiliation notes i Deceased ii Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA),Bologna, Italy iii
Dipartimento DET del Politecnico di Torino, Turin, Italy iv M.V. Lomonosov Moscow State University, D.V. Skobeltsyn Institute of Nuclear, Physics, Moscow, Russia v Department of Applied Physics, Aligarh Muslim University, Aligarh, India vi Institute of Theoretical Physics, University of Wroclaw, Poland
Collaboration Institutes A.I. Alikhanyan National Science Laboratory (Yerevan Physics Institute) Foundation, Yerevan, Armenia Bogolyubov Institute for Theoretical Physics, National Academy of Sciences of Ukraine, Kiev, Ukraine Bose Institute, Department of Physics and Centre for Astroparticle Physics and Space Science (CAPSS),Kolkata, India Budker Institute for Nuclear Physics, Novosibirsk, Russia California Polytechnic State University, San Luis Obispo, California, United States Central China Normal University, Wuhan, China Centre de Calcul de l’IN2P3, Villeurbanne, Lyon, France Centro de Aplicaciones Tecnológicas y Desarrollo Nuclear (CEADEN), Havana, Cuba Centro de Investigación y de Estudios Avanzados (CINVESTAV), Mexico City and Mérida, Mexico Centro Fermi - Museo Storico della Fisica e Centro Studi e Ricerche “Enrico Fermi’, Rome, Italy Chicago State University, Chicago, Illinois, United States China Institute of Atomic Energy, Beijing, China Comenius University Bratislava, Faculty of Mathematics, Physics and Informatics, Bratislava, Slovakia COMSATS University Islamabad, Islamabad, Pakistan Creighton University, Omaha, Nebraska, United States Department of Physics, Aligarh Muslim University, Aligarh, India Department of Physics, Pusan National University, Pusan, Republic of Korea Department of Physics, Sejong University, Seoul, Republic of Korea Department of Physics, University of California, Berkeley, California, United States Department of Physics, University of Oslo, Oslo, Norway Department of Physics and Technology, University of Bergen, Bergen, Norway Dipartimento di Fisica dell’Università ’La Sapienza’ and Sezione INFN, Rome, Italy Dipartimento di Fisica dell’Università and Sezione INFN, Cagliari, Italy Dipartimento di Fisica dell’Università and Sezione INFN, Trieste, Italy Dipartimento di Fisica dell’Università and Sezione INFN, Turin, Italy Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN, Bologna, Italy Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN, Catania, Italy Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN, Padova, Italy Dipartimento di Fisica ‘E.R. Caianiello’ dell’Università and Gruppo Collegato INFN, Salerno, Italy Dipartimento DISAT del Politecnico and Sezione INFN, Turin, Italy Dipartimento di Scienze e Innovazione Tecnologica dell’Università del Piemonte Orientale and INFNSezione di Torino, Alessandria, Italy Dipartimento di Scienze MIFT, Università di Messina, Messina, Italy Dipartimento Interateneo di Fisica ‘M. Merlin’ and Sezione INFN, Bari, Italy European Organization for Nuclear Research (CERN), Geneva, Switzerland Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split,Split, Croatia Faculty of Engineering and Science, Western Norway University of Applied Sciences, Bergen, Norway Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Prague,Czech Republic Faculty of Science, P.J. Šafárik University, Košice, Slovakia Frankfurt Institute for Advanced Studies, Johann Wolfgang Goethe-Universität Frankfurt, Frankfurt,Germany Fudan University, Shanghai, China Gangneung-Wonju National University, Gangneung, Republic of Korea Gauhati University, Department of Physics, Guwahati, India Helmholtz-Institut für Strahlen- und Kernphysik, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn,Germany Helsinki Institute of Physics (HIP), Helsinki, Finland High Energy Physics Group, Universidad Autónoma de Puebla, Puebla, Mexico Hiroshima University, Hiroshima, Japan Hochschule Worms, Zentrum für Technologietransfer und Telekommunikation (ZTT), Worms, Germany Horia Hulubei National Institute of Physics and Nuclear Engineering, Bucharest, Romania Indian Institute of Technology Bombay (IIT), Mumbai, India Indian Institute of Technology Indore, Indore, India Indonesian Institute of Sciences, Jakarta, Indonesia INFN, Laboratori Nazionali di Frascati, Frascati, Italy INFN, Sezione di Bari, Bari, Italy INFN, Sezione di Bologna, Bologna, Italy INFN, Sezione di Cagliari, Cagliari, Italy INFN, Sezione di Catania, Catania, Italy INFN, Sezione di Padova, Padova, Italy INFN, Sezione di Roma, Rome, Italy INFN, Sezione di Torino, Turin, Italy INFN, Sezione di Trieste, Trieste, Italy Inha University, Incheon, Republic of Korea Institute for Gravitational and Subatomic Physics (GRASP), Utrecht University/Nikhef, Utrecht,Netherlands Institute for Nuclear Research, Academy of Sciences, Moscow, Russia Institute of Experimental Physics, Slovak Academy of Sciences, Košice, Slovakia Institute of Physics, Homi Bhabha National Institute, Bhubaneswar, India Institute of Physics of the Czech Academy of Sciences, Prague, Czech Republic Institute of Space Science (ISS), Bucharest, Romania Institut für Kernphysik, Johann Wolfgang Goethe-Universität Frankfurt, Frankfurt, Germany Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de México, Mexico City, Mexico Instituto de Física, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil Instituto de Física, Universidad Nacional Autónoma de México, Mexico City, Mexico iThemba LABS, National Research Foundation, Somerset West, South Africa Jeonbuk National University, Jeonju, Republic of Korea Johann-Wolfgang-Goethe Universität Frankfurt Institut für Informatik, Fachbereich Informatik undMathematik, Frankfurt, Germany Joint Institute for Nuclear Research (JINR), Dubna, Russia Korea Institute of Science and Technology Information, Daejeon, Republic of Korea KTO Karatay University, Konya, Turkey Laboratoire de Physique des 2 Infinis, Irène Joliot-Curie, Orsay, France Laboratoire de Physique Subatomique et de Cosmologie, Université Grenoble-Alpes, CNRS-IN2P3,Grenoble, France Lawrence Berkeley National Laboratory, Berkeley, California, United States Lund University Department of Physics, Division of Particle Physics, Lund, Sweden Moscow Institute for Physics and Technology, Moscow, Russia Nagasaki Institute of Applied Science, Nagasaki, Japan Nara Women’s University (NWU), Nara, Japan National and Kapodistrian University of Athens, School of Science, Department of Physics , Athens,Greece National Centre for Nuclear Research, Warsaw, Poland National Institute of Science Education and Research, Homi Bhabha National Institute, Jatni, India National Nuclear Research Center, Baku, Azerbaijan National Research Centre Kurchatov Institute, Moscow, Russia Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark Nikhef, National institute for subatomic physics, Amsterdam, Netherlands NRC Kurchatov Institute IHEP, Protvino, Russia NRC «Kurchatov»Institute - ITEP, Moscow, Russia NRNU Moscow Engineering Physics Institute, Moscow, Russia Nuclear Physics Group, STFC Daresbury Laboratory, Daresbury, United Kingdom Nuclear Physics Institute of the Czech Academy of Sciences, ˇRež u Prahy, Czech Republic Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States Ohio State University, Columbus, Ohio, United States Petersburg Nuclear Physics Institute, Gatchina, Russia
Physics department, Faculty of science, University of Zagreb, Zagreb, Croatia
Physics Department, Panjab University, Chandigarh, India
Physics Department, University of Jammu, Jammu, India
Physics Department, University of Rajasthan, Jaipur, India
Physikalisches Institut, Eberhard-Karls-Universität Tübingen, Tübingen, Germany
Physikalisches Institut, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany
Physik Department, Technische Universität München, Munich, Germany
Politecnico di Bari and Sezione INFN, Bari, Italy
Research Division and ExtreMe Matter Institute EMMI, GSI Helmholtzzentrum für
Schwerionenforschung GmbH, Darmstadt, Germany
Rudjer Boškovi´c Institute, Zagreb, Croatia
Russian Federal Nuclear Center (VNIIEF), Sarov, Russia
Saha Institute of Nuclear Physics, Homi Bhabha National Institute, Kolkata, India
School of Physics and Astronomy, University of Birmingham, Birmingham, United Kingdom
Sección Física, Departamento de Ciencias, Pontificia Universidad Católica del Perú, Lima, Peru
St. Petersburg State University, St. Petersburg, Russia
Stefan Meyer Institut für Subatomare Physik (SMI), Vienna, Austria
SUBATECH, IMT Atlantique, Université de Nantes, CNRS-IN2P3, Nantes, France
Suranaree University of Technology, Nakhon Ratchasima, Thailand
Technical University of Košice, Košice, Slovakia
The Henryk Niewodniczanski Institute of Nuclear Physics, Polish Academy of Sciences, Cracow, Poland
The University of Texas at Austin, Austin, Texas, United States
Universidad Autónoma de Sinaloa, Culiacán, Mexico
Universidade de São Paulo (USP), São Paulo, Brazil
Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil
Universidade Federal do ABC, Santo Andre, Brazil
University of Cape Town, Cape Town, South Africa
University of Houston, Houston, Texas, United States
University of Jyväskylä, Jyväskylä, Finland
University of Liverpool, Liverpool, United Kingdom
University of Science and Technology of China, Hefei, China
University of South-Eastern Norway, Tonsberg, Norway
University of Tennessee, Knoxville, Tennessee, United States
University of the Witwatersrand, Johannesburg, South Africa
University of Tokyo, Tokyo, Japan
University of Tsukuba, Tsukuba, Japan
Université Clermont Auvergne, CNRS/IN2P3, LPC, Clermont-Ferrand, France
Université de Lyon, Université Lyon 1, CNRS/IN2P3, IPN-Lyon, Villeurbanne, Lyon, France
Université de Strasbourg, CNRS, IPHC UMR 7178, F-67000 Strasbourg, France, Strasbourg, France
Université Paris-Saclay Centre d’Etudes de Saclay (CEA), IRFU, Départment de Physique Nucléaire(DPhN), Saclay, France
Università degli Studi di Foggia, Foggia, Italy
Università degli Studi di Pavia and Sezione INFN, Pavia, Italy
Università di Brescia and Sezione INFN, Brescia, Italy
Variable Energy Cyclotron Centre, Homi Bhabha National Institute, Kolkata, India
Warsaw University of Technology, Warsaw, Poland
Wayne State University, Detroit, Michigan, United States
Westfälische Wilhelms-Universität Münster, Institut für Kernphysik, Münster, Germany
Wigner Research Centre for Physics, Budapest, Hungary
Yale University, New Haven, Connecticut, United States