Multiplicity dependence of two-particle azimuthal correlations in pp collisions at the LHC

Abstract

(ProQuest: ... denotes formulae and/or non-USASCII text omitted; see image)

We present the measurements of particle pair yields per trigger particle obtained from di-hadron azimuthal correlations in pp collisions at ... = 0.9, 2.76, and 7 TeV recorded with the ALICE detector. The yields are studied as a function of the charged particle multiplicity. Taken together with the single particle yields the pair yields provide information about parton fragmentation at low transverse momenta, as well as on the contribution of multiple parton interactions to particle production. Data are compared to calculations using the PYTHIA6, PYTHIA8, and PHOJET event generators. [Figure not available: see fulltext.]

Published for SISSA by Springer

Received: July 4, 2013 Accepted: August 12, 2013

Published: September 10, 2013

Multiplicity dependence of two-particle azimuthal correlations in pp collisions at the LHC

JHEP09(2013)049

The ALICE collaboration

E-mail: mailto:[email protected]

Web End [email protected]

Abstract: We present the measurements of particle pair yields per trigger particle obtained from di-hadron azimuthal correlations in pp collisions at s = 0.9, 2.76, and 7 TeV recorded with the ALICE detector. The yields are studied as a function of the charged particle multiplicity. Taken together with the single particle yields the pair yields provide information about parton fragmentation at low transverse momenta, as well as on the contribution of multiple parton interactions to particle production. Data are compared to calculations using the PYTHIA6, PYTHIA8, and PHOJET event generators.

Keywords: Hadron-Hadron Scattering

Open Access, Copyright CERN,for the benet of the ALICE collaboration

doi:http://dx.doi.org/10.1007/JHEP09(2013)049

Web End =10.1007/JHEP09(2013)049

Contents

1 Introduction 1

2 Experimental setup 3

3 Event and track selection 43.1 Trigger and oine event selection 43.2 Track cuts 5

4 Analysis method 64.1 Denitions 64.2 Relation between experimental observables and the PYTHIA MPI model 8

5 Correction procedure 9

6 Systematic uncertainties 14

7 Results 177.1 Yields 187.2 Centre-of-mass energy dependence 237.3 Multiple parton interactions 23

8 Conclusions 29

1 Introduction

The multiplicity distribution of particles produced in proton-proton (pp) collisions and the multiplicity dependence of other global event characteristics represent fundamental observables reecting the properties of the underlying particle production mechanisms. In the Feynman picture, the strongly interacting hadrons can be seen as bunches of point-like partons producing particles in interactions with small (soft) and large (hard) momentum transfer. As expected from Feynman scaling [1], at low centre-of-mass energies (s), where particle production is dominated by soft interactions, the mean number of particles hMi

was found to rise logarithmically with s. Moreover, the evolution of the charged particle multiplicity distribution P (M) as a function of s follows the Koba-Nielsen-Oleson (KNO) scaling [2] with scaling variable z = M/hMi and P (M)hMi = (z), where (z)

is an energy independent function. Experimentally one nds that KNO scaling is violated for s > 200 GeV [3]. This scaling violation which increases with s has been interpreted as a consequence of particle production through multiple parton-parton interactions (MPI) [46]. Further, at the LHC, already at a transverse momentum transfer of a

1

JHEP09(2013)049

few GeV/c the cross section for leading order (LO) parton-parton scatterings exceeds the total pp inelastic cross section. This apparent inconsistency can be resolved by aggregating several quasi independent scatterings in the same pp collision [7, 8]. If multiple semi-hard scatterings play a dominant role in the production of high multiplicity events, this should lead to distinct experimentally observable eects. The search for these is the aim of the present analysis of pp collisions recorded with the ALICE detector at the LHC.

Each parton-parton scattering produces partons almost back-to-back in azimuth, '. They fragment producing two correlated bundles of particles. With increasing multiplicity we expect that both the number of sources of correlated particles and the number of correlated particles per source increase. Thus, we have designed our analysis methods in a way that the two eects can be separated as much as possible. Since many of the bundles of particles (low transverse-momentum jets) overlap in the same event, they can not be identied and separated event-by-event. An alternative method, pursued in this analysis, is to study two-particle angular correlations as a function of the event multiplicity [9].

Such studies involve measuring the distributions of the relative angle ' between particle pairs consisting of a trigger particle in a certain transverse momentum pT,trig

interval and an associated particle in a pT,assoc interval, where ' is the dierence in azimuth ' between the two particles. The pT ranges chosen for the analysis (pT,trig > 0.7 GeV/c and pT,assoc > 0.4(0.7) GeV/c) are a compromise between being high enough to decrease the sensitivity to low energy phenomena such as the breaking of individual strings (pminT QCD) and suciently low such that the correlations are sensitive to the bulk of

the particle production. These cuts have been also used by the CDF collaboration to dene so-called track-clusters: a track with pT > 0.7 GeV/c with at least one other track with pT > 0.4 GeV/c in a cone of radius

p'2 + 2 < 0.7, where is the pseudo-rapidity dierence [10]. In the CDF analysis, the presence of a track-cluster has been used for an event-by-event identication of hard events. In the present correlation analysis the ' distributions are averaged over all events of a given sample. This has the advantage that random correlations, which become dominant at high multiplicities, can be subtracted. The mean number of trigger particles per event and the correlated pair-yield per trigger are measured and combined in a way that they can provide information about the number of semi-hard scatterings in the event of a given charged particle multiplicity as well as the fragmentation properties of low-pT partons biased by the multiplicity selection.

Although the full nal state of pp collisions cannot be calculated in perturbative QCD, pQCD-inspired models based on multiple parton interactions provide a consistent way to describe high multiplicity pp collisions, and have been implemented in recent Monte Carlo (MC) generators like PYTHIA6 [7, 11], PYTHIA8 [12], PHOJET [13] and HERWIG [14]. Using the QCD factorisation theorem [15] cross sections are calculated from a convolution of the short-distance parton-parton cross section and the long-distance parton distribution function (pdf ) of the proton. Approaching zero momentum transfer, the leading order short distance cross sections diverge and the models have to implement regularisation mechanisms to control this divergence. Moreover, parton distribution functions are only known for single parton scatterings and, hence, extensions for multiple interactions are needed. Furthermore, each partonic interaction produces coloured strings between the

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nal state partons which overlap in the case of many interactions. It is possible that in this case partons do not hadronise independently and phenomenological models have been developed to account for so-called colour connections and reconnections. Measurements that can provide information on multiple parton interactions and fragmentation properties are important to constrain such models. Consequently, we compare our results for pp collisions at s = 0.9, 2.76 and 7 TeV among each other and to the outcome of Monte Carlo simulations on generator level with dierent PYTHIA6 tunes (Perugia-0 and Perugia-2011 [16]), PYTHIA8 and PHOJET. We have chosen this set of generators and tunes since they have been already compared to previous ALICE measurements based on azimuthal correlations: the underlying event [17] and transverse sphericity [18]; ref. [17] contains a short description of them.

The paper is organised in the following way: the ALICE sub-systems used in the analysis are described in section 2 and the data samples, event and track selection in section 3. Section 4 introduces the analysis strategy. In sections 5 and 6 we focus on the data correction procedure and systematic uncertainties, respectively. Final results are presented in section 7 and in section 8 we draw conclusions.

2 Experimental setup

The pp collision data used for this analysis were recorded by the ALICE detector at the LHC. The detector is described in detail in ref. [19]. In the following, only the sub-detectors used in this analysis are described in detail. These are the VZERO detector, the Inner Tracking System (ITS) including the Silicon Pixel Detector (SPD), the Silicon Drift Detector (SDD), and the Silicon Strip Detector (SSD), as well as the Time Projection Chamber (TPC). The VZERO detector and the SPD are used to trigger on minimum bias events. The track reconstruction of charged particles is performed with the combined information from the ITS and the TPC.

The VZERO scintillator hodoscope is divided into two arrays of counters, VZERO-A and VZERO-C located at 3.4 m and -0.9 m from the nominal interaction point along the beam axis, respectively. VZERO-A covers the pseudorapidity range of 2.8 < < 5.1 and VZERO-C 3.7 < < 1.7.

The Inner Tracking System (ITS) comprises 6 cylindrical layers of silicon detectors of three dierent detector types, each contributing with two layers. The Silicon Pixel Detector constitutes the rst two layers of the ITS. The sensitive part of the detector is made of high granularity 250 m-thick hybrid silicon pixels consisting of a 2-dimensional matrix of reversed-biased silicon detector diodes with 107 read-out channels. The pseudorapidity coverage is | | < 1.98 for the rst layer and | | < 1.4 for the second layer. The SPD

contributes to the minimum bias trigger as well as to the reconstruction of tracks left by charged particles, and the vertex reconstruction. The Silicon Drift Detector comprises the two intermediate layers of the ITS. The sensitive part consists of homogeneous high-resistivity 300 m-thick n-type silicon wafers with 133000 read-out channels. The SDD contributes to the reconstruction of tracks of charged particles as well as to the particle identication using energy loss information. The Silicon Strip Detector composes the two

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outermost layers of the ITS. The double-sided SSD has 2.6 million read-out channels and contributes like the SDD to the track reconstruction and the particle identication. Furthermore, it is optimised for track matching between the ITS and the Time Projection Chamber. The total material budget of the ITS traversed by straight tracks perpendicular to the detector surface amounts to 7.2% X0.

The main tracking detector of the ALICE central barrel is the Time Projection Chamber. It is a cylindrical detector lled with 90 m3 of gaseous Ne/CO2/N2 at a mixing ratio of (85.7/9.5/4.8). High-voltage is applied to the central membrane, resulting in an electric eld between the central electrode and the end caps, which are each equipped with multi-wire proportional chambers. The TPC provides full azimuthal acceptance for particles produced in the pseudo-rapidity interval | | < 0.9. It is used to perform charged-particle

momentum measurements with a good two-track separation adequate to cope with the extreme particle densities present in central heavy-ion collisions. Hence, in pp collisions, two-particle reconstruction eects like track merging and track splitting are small and manageable. The ITS and TPC cover the full azimuth and a combined pseudo-rapidity interval

| | < 0.9. All detectors are operated inside the L3 magnet which generates a homogeneous

magnetic eld of B = 0.5 T in the detector region.

3 Event and track selection

The present analysis uses pp collisions collected with ALICE minimum bias triggers at the collision energies s = 0.9, 2.76, and 7 TeV. In May 2010, 7 million events were collected at s = 0.9 TeV, in March 2011, 27 million events were collected at 2.76 TeV, and from April to August 2010, 204 million events were collected at 7 TeV. The probability for pile-up events is negligible for the s = 0.9 TeV data taking period but sizeable for the s = 2.76 and 7 TeV data taking periods. The impact of pile-up events on the nal analysis results has been tested and quantied using a high pile-up data set as well as by performing a comparison of results obtained with sub-sets of the nominal data sets at relatively high and relatively low pile-up probability.

ALICE data are compared to model predictions of PYTHIA6.4 [7, 11] (tune Perugia-0 [16] and tune Perugia-2011 [16]), PYTHIA8.1 [12] (tune 4C [20]), and PHOJET [13] (version 1.12). The detector response in full detector simulations has been modeled using GEANT3 [21] as well as GEANT4 [22, 23].

3.1 Trigger and o ine event selection

Minimum bias events were selected using the following trigger requirements: at least one charged particle needs to be detected in either the SPD or in one of the two VZERO detectors in coincidence with signals from the two BPTX beam pick-up counters indicating the presence of two intersecting proton bunches [24]. In addition to the online trigger selection, the trigger decision is reprocessed oine using the same selection criteria; however, the reconstructed information are used instead of the online signals.

Only events having exactly one good quality reconstructed primary collision vertex are used in the analysis. Collision vertices are reconstructed using either reconstructed

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Events (million) Fraction of all (%) pp @ s = 0.9 TeVTriggered 6.96 100.0 Vertex cuts 4.91 70.6 Track in acceptance 4.64 66.7 pp @ s = 2.76 TeVTriggered 26.65 100.0 Vertex cuts 19.42 72.9 Track in acceptance 18.49 69.4 pp @ s = 7 TeVTriggered 203.96 100.0 Vertex cuts 157.89 77.4 Track in acceptance 152.02 74.5

Table 1. Number of pp minimum bias events after event selection for the data sets at s = 0.9,2.76, and 7 TeV. The track selection used in the last event selection step is described in section 3.2.

tracks or so-called tracklets [24] based on correlated hits measured in the two SPD layers. A vertex passes the quality selection if it is located within |z

| < 10 cm with respect

to the nominal interaction point in beam direction and if at least one track contributes to the reconstruction of the vertex. Pile-up events with more than one reconstructed collision vertex are rejected from the analysis. Furthermore, we require at least one reconstructed high-quality track (see section 3.2) in the combined ITS-TPC acceptance of pT > 0.2 GeV/c and | | < 0.9. The discussed event selection cuts eciently suppress events from beam-gas

and beam-halo interactions as well as from cosmic rays. Table 1 shows the number of recorded minimum bias events that pass the event selection cuts. The vertex-cut eciency is dominated by the vertex quality requirements. The single vertex requirement after vertex quality cuts removes up to 0.5 % additional events.

3.2 Track cuts

In the analysis, we consider only charged primary particles which are dened as prompt particles produced in the collision and their decay products except products of weak decays of strange particles. The data analysis is performed using track selection cuts optimised for a uniform azimuth (') acceptance and for a minimal contamination of tracks by particles originating from secondary vertices (secondary particles) [17]. The track selection comprises the following cuts: tracks are required to have at least three associated hits in the ITS, one of which has to be located in the rst three ITS layers. Furthermore, each track needs to have at least 70 associated TPC clusters measured in the 159 TPC pad rows. The quality of the track parameter tting is measured by the ~2 per TPC cluster and tracks passing our selection have ~2 per cluster < 4. No tracks with a kink topology indicating a particle decay are accepted. A pT-dependent DCAxy-cut corresponding to 7 times the of the expected primary track distribution (DCAxy, max 0.2 cm) assures that the tracks passing the selection criteria are predominantly those from the primary vertex. In addition, a cut

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vertex

JHEP09(2013)049

Criterion Value Minimum number of ITS hits 3 Minimum number of ITS hits in rst 3 layers 1 Minimum number of TPC clusters 70 Maximum ~2 per TPC cluster 4 Maximum DCAxy(pT) 7 (DCAxy, max 0.2 cm)

Maximum DCAz 2 cm

Table 2. Track selection criteria.

on the distance of closest approach in the z-direction of maximal DCAz = 2 cm improves the selection of primary particles and rejects particles from secondary vertices originating from, for example, the decay of long-lived particles or hadronic interaction in the detector material. Moreover, this cut removes tracks originating from displaced pile-up vertices. Out of the selected high quality tracks, the data analysis accepts tracks within the ITS-TPC acceptance | | < 0.9 and with pT > 0.2 GeV/c. The track selection cuts are summarised

in table 2.

4 Analysis method

4.1 Denitions

We are analysing the sample-averaged probability distribution of the azimuthal dierence ' = 'trig '

assoc between trigger particles (pT,trig > pminT,trig, | | < 0.9) and associated

particles (pT,assoc > pminT,assoc, | | < 0.9). The pair-yield per trigger as a function of ' is

dened as dN d' =

1 Ntrig

JHEP09(2013)049

dNassoc

d' , (4.1) where Ntrig is the number of trigger particles and Nassoc is the number of associated particles. We study the pair-yield per trigger as a function of the charged particle multiplicity Ncharged, | | < 0.9, pT > 0.2 GeV/c, as well as for dierent transverse momentum thresholds pminT,trig and pminT,assoc.

The left panel of gure 1 shows an example of the measured per-trigger pair yield as a function of ' for pT,trig > 0.7 GeV/c and pT,assoc > 0.4 GeV/c and

Ncharged, | | < 0.9, pT > 0.2 GeV/c = 30. The two structures at the near-side (' 0) and away-side (' ) of the trigger particle are dominantly induced by the fragmentation

of back-to-back parton pairs. In order to extract the per-trigger pair-yields for all multiplicity and pT-cut classes, a t function is introduced which allows us to decompose the azimuthal correlation into its main components. Whereas the away-side peak can be tted using a single Gaussian, the near-side peak shows an enhanced tail-region and needs the superposition of two Gaussians with dierent widths. Including a constant C to describe the combinatorial background, we obtained the tting function

f(') = C + A1 exp

[parenleftbigg]

'22 21 [parenrightbigg]

+ A2 exp

[parenleftbigg]

'22 22 [parenrightbigg]

+ A3 exp

[parenleftbigg]

(' )2

2 23 [parenrightbigg]

. (4.2)

6

j

DdN/d

trig

1/N

j

DdN/d

trig

1/N

4

3.5

ALICE pp @

| < 0.9

h

|

= 7 TeV

s

p

> 0.7 GeV/

c

p

> 0.4 GeV/

c

N

= 30

3.5

3

Fit:

c

/NDF = 1.63

ALICE dataSum of fit components- Constant- 1. Gaussian NS - 2. Gaussian NS- Gaussian AS

pp @

| < 0.9

h

|

= 7 TeV

s

p

> 0.7 GeV/

c

p

> 0.4 GeV/

c

N

= 30

3

2.5

2.5

2

2

1.5

1.5

1

1

0.5

0.5

0 -1 0 1 2 3 4

0 -1 0 1 2 3 4

JHEP09(2013)049

j

D

(rad)

j

D

(rad)

Figure 1. Left panel: illustration of the contributions to the per-trigger pair yield as a function of '. Right panel: the per-trigger pair yield as a function of ' described by the t function and its sub-components (see text).

To increase the stability of the t, the rst near-side Gaussian and the away-side Gaussian are restricted to /2 < ' < /2 and /2 < ' < 3/2, respectively. The second

near-side Gaussian is tted in the region /5 < ' < /5.

The right panel of gure 1 shows the measured azimuthal correlation, the parametrisation of the correlation based on the t function, and the sub-components of the t function. The ~2 per degree of freedom for this t is 1.63.

Pair yield for asymmetric and symmetric pT-bins. In the case of non-overlapping pT intervals for the trigger and associated particles, the pair yield per trigger measures the conditional yield of associated particles under the trigger condition. Beside non-overlapping pT intervals (asymmetric bins), we are using symmetric bins for which the two intervals are identical. In this case, for n trigger particles, n(n1)/2 unique pairs with pT,trig > pT,assoc

can be formed and, hence, the pair yield per trigger particle measures:

hNpairi

hNtrigi

= 1

hni

hn(n 1)i

2 =

1

2

hn2i

hni

. (4.3)

In general, without the knowledge of the second moment hn2i of the number dis

tribution function Pn, the mean number of correlated particles hni cannot be determined.

However, for small hni and monotonically falling Pn, the expression has a well dened limit:

1

2

hn2i

hni

1

1

[parenrightbigg] h

ni

1 P0

1. (4.4)

Since hni/(1 P0) is the mean value of the distribution Pn under the condition that at

least one particle has been produced, the right-hand side represents the number of particles associated with a trigger particle. Note that for jet-like self-similar particle emission (geometric series) the approximation is exact.

Pair yield extraction. Based on the t function of equation (4.2), ve observables can be derived. Three of the observables are directly related to the decomposed pair yield per trigger:

7

Per-trigger pair yield in the combinatorial background

hNisotropi =

1Ntrigger C, (4.5)

Per-trigger pair yield in the near-side peak

hNassoc, nearsidei =

2

Ntrigger (A1 1 + A2 2), (4.6)

Per-trigger pair yield in the away-side peak

hNassoc, awaysidei =

2

Ntrigger (A3 3). (4.7)

The yields in the near-side and away-side peaks measure fragmentation properties of low-pT partons. In addition, the average number of trigger particles hN

trigger

JHEP09(2013)049

i is determined:

Ntrigger

Nevents . (4.8)

The average number of trigger particles depends on the number of semi-hard scatterings per event and the fragmentation properties of partons. With the aim to reduce the fragmentation dependence and to increase the sensitivity to the number of scatterings per event we dene for symmetric pT-bins a new observable, average number of uncorrelated seeds, by combining the average number of trigger particles with the near-side and away-side yield of trigger particles (pT > pT, trig).

hNuncorrelated seedsi = h

hNtriggeri =

Ntriggeri

h1 + Nassoc, near+away, pT>pT, trig i

. (4.9)

where

hNassoc, near+away, pT>pT, trig i = hNassoc, nearsidei + hNassoc, awaysidei (4.10)

and also the associated particles have pT > pT, trig.

Model studies (see section 4.2) show that the ratio eectively corrects for the multiplicative eect of fragmentation so that the obtained quantity provides information about the number of uncorrelated sources of particle production.

4.2 Relation between experimental observables and the PYTHIA MPI model

The PYTHIA MC for pp collisions includes a model for multiple parton interactions. Within the PYTHIA model, the dependence between the number of uncorrelated seeds

hNuncorrelated seedsi and the average number of multiple parton interactions hNMPIi can be studied. Here, the number of multiple parton interactions NMPI is dened as the number of hard or semi-hard scatterings that occurred in a single pp collision [11]. The number of multiple parton interactions NMPI is shown in gure 2 for the PYTHIA6 tunes Perugia-0 and

Pro-Q2O [16]. Both MC tunes predate LHC data and give a good description of Tevatron

8

1

-1

10

-2

10

-3

10

-4

10

-5

)

MPI

Probability(N

= 7 TeV

s

pp @

PYTHIA6

Perugia-0 (320)

Pro-Q2O (129)

JHEP09(2013)049

10 0 10 20 30 40 50 60 70

N

MPI

Figure 2. Number of multiple parton interactions NMPI in PYTHIA6 tune Perugia-0 and tune Pro-Q2O.

(pp at s = 2 TeV) results. However, they have very dierent probability distributions for NMPI. Whereas Pro-Q2O features a wide plateau, that of Perugia-0 is much narrower.

The dependence between the number of uncorrelated seeds and the number of multiple parton interactions in PYTHIA6 tune Perugia-0 simulations on generator level is shown in gure 3 for dierent | |-ranges and pT,trig-thresholds. For all cases, we see a linear

dependence. The same is observed for the tune Pro-Q2O (not shown). However, the dierence in width of the MPI distributions has direct consequences for the experimental observables dened in the previous subsection 4.1 demonstrating their sensitivity to MPI and fragmentation properties. Figure 4 (left panel) shows the near-side pair-yield per trigger as a function of multiplicity. In the case of tune Pro-Q2O the yield reaches a plateau at Nch > 15 after which it rises only very slowly. In contrast, tune Perugia-0 shows a rather steep rise with a change to an even steeper slope at Nch 50. The reason is the

limited NMPI in this tune. In order to reach high multiplicities the number of fragments per parton has to increase together with NMPI. This can also be observed in gure 4 (right panel) where the number of uncorrelated seeds as a function of charged multiplicity is shown. For the tune Pro-Q2O an almost linear rise as a function of charged multiplicity is observed up to the highest multiplicities, whereas for the tune Perugia-0, it starts to level o at about Nch 50.

5 Correction procedure

We corrected for the relevant ineciencies such as detector acceptance, reconstruction, two-track and vertex reconstruction eciency. In addition, the contamination of the sample of primary tracks by secondary particles was also corrected for. The trigger ineciency is not part of these corrections as it is negligible for events with at least one track in the considered acceptance | | < 0.9. In the following paragraphs, the correction steps

9

50

uncorrelated seeds

N

uncorrelated seeds

N

p

> 0.7 GeV/

T

c

| < 10.0

h

, |

50

p

> 0.7 GeV/

c

| < 10.0

h

, |

p

> 0.8 GeV/

T

c

| < 10.0

h

, |

p

> 0.7 GeV/

c

| < 0.9

h

, |

40

p

> 0.9 GeV/

T

c

| < 10.0

h

, |

40

p

> 1.0 GeV/

T

c

| < 10.0

h

, |

Pythia6 Perugia-0 = 7 TeV

s

pp @

30

30

20

20

10

10

JHEP09(2013)049

Pythia6 Perugia-0 = 7 TeV

s

pp @

0 5 10 15 20

0 5 10 15 20

N

N

MPI

MPI

Figure 3. Linear dependence between Nuncorrelated seeds and NMPI in PYTHIA6 Perugia-0 simulations on generator level. Left panel: Nuncorrelated seeds versus NMPI for dierent | |-ranges. Right

panel: Nuncorrelated seeds versus NMPI for dierent pT, trig-thresholds.

20

PYTHIA6 Perugia-0 (320)

PYTHIA6 Pro-Q2O (129)

pp @

| < 0.9

h

= 7 TeV

s

p

| > 0.7 GeV/

c

p

> 0.7 GeV/

c

0.9

assoc, near

N

18

0.8

16

PYTHIA6 Perugia-0 (350)

PYTHIA6 Pro-Q2O (129)

pp @

| < 0.9

h

= 7 TeV

s

p

| > 0.7 GeV/

c

0.7

14

0.6

uncorrelated seeds

N

12

0.5

10

0.4

8

0.3

6

0.2

4

0.1

2

Pro-Q2O / Perugia-0

0.5

Pro-Q2O / Perugia-0

0.5

1.5

1.5

1

1

0 10 20 30 40 50 60 70

0 10 20 30 40 50 60 70

N

N

charged, |

| < 0.9,

h

p

> 0.2 GeV/

c

charged, |

| < 0.9,

h

p

> 0.2 GeV/

c

Figure 4. Comparison of PYTHIA6 tunes Perugia-0 and Pro-Q2O for near-side pair-yield per trigger particle (left panel) and number of uncorrelated seeds (right panel).

are discussed in detail. Table 3 shows a breakdown of the main correction steps and corresponding eciencies or contamination for the dierent collision energies. They have been estimated from full transport and detector response simulations of PHOJET and PYTHIA6 tune Perugia-0 events using GEANT3 and a data driven correction procedure. We show the eciencies for the lowest pT -cut used in the analysis (pT > 0.2 GeV/c for the charged particle multiplicity), because it corresponds to the largest ineciency and contamination.

Tracking e ciency. The tracking eciency is given by the ratio of the number of reconstructed tracks from primary particles after track quality cuts to the number of primary particles. The tracking eciency depends on the kinematic properties of the particle (pT,

10

Correction s = 0.9 TeV s = 2.76 TeV s = 7 TeV Tracking eciency 76.4 % 75.5 % 76.8 % Contamination (MC based) 5.0 % 5.2 % 4.9 % Contamination (data-driven) 1.1 % 1.0 % 1.1 % Two-track and detector eects 0.5 % 0.6 % 0.5 % Vertex reconstruction eciency 97.5 % 98.3 % 98.8 %

Table 3. Main contributions to the track-to-particle correction averaged over pT > 0.2 GeV/c,

| | < 0.9, and charged particle multiplicities N charged.

JHEP09(2013)049

1

1

pp @

= 2.76 TeV

s

pp @ = 0.9 TeV

s

pp @

= 7 TeV

s

pp @

= 2.76 TeV

s

pp @

= 0.9 TeV

s

pp @

PYTHIA6 Perugia-0 (320)

GEANT3 c

> 0.2 GeV/

= 7 TeV

s

Tracking efficiency

0.9

Tracking efficiency

0.9

0.8

0.8

0.7

0.7

PYTHIA6 Perugia-0 (320) GEANT3

| < 0.9

h

|

p

0.6

1 2 3 4 5 6 7 8 9 10

0.6

-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1

p

(GeV/

T

c

)

h

Figure 5. Reconstruction eciency for primary particles. Left panel: reconstruction eciency versus transverse momentum (| | < 0.9). Right panel: reconstruction eciency versus pseudorapidity

(pT > 0.2 GeV/c).

, ') and is inuenced by the detector geometry, the probability of particle absorption in the detector material and particle decays. Figure 5 shows the tracking eciency for the dierent centre-of-mass energies obtained by projecting 2-dimensional pT-correction

maps and integrating over '. For the analysed data sets, the integrated tracking eciency lies in the range 76 % to 77 %.

Secondary particle contamination. The standard Monte Carlo based contamination correction is given by the ratio of the number of reconstructed tracks after track quality cuts to the number of reconstructed tracks of primary particles. The contamination of the reconstructed tracks passing the quality cuts is mainly due to decay products from strange particles, photon conversions, and hadronic interactions with the detector material. Figure 6 shows the contamination correction as a function of the transverse momentum and the pseudorapidity. For the analysed data sets, the integrated contamination correction amounts to approximately 5 %.

In addition to the Monte Carlo based contamination correction, a data driven correction has been applied. This correction is based on the results of ref. [25, 26] which show that the generators PHOJET and PYTHIA6 tune Perugia-0 used in the correction procedure strongly underestimate strange particle yields. This underestimation leads to an incomplete correction of the contamination in ALICE data when using Monte Carlo based

11

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Figure 6. Contamination correction. Left panel: contamination correction versus transverse momentum (| | < 0.9). Right panel: contamination correction versus pseudorapidity (pT >

0.2 GeV/c).

correction maps only. Based on the measured yields of strange particles, an additional correction factor of approximately 1 % has been added to the 5 % obtained from the standard MC contamination correction.

Two-track and detector e ects. Eects such as track splitting, track merging, decay of long-lived particles, hadronic interactions with the detector material, gamma conversions as well as a non-uniform '-acceptance induce modulations of the '-distributions that have to be taken into account. These modications can not be corrected in single-track-corrections only. Figure 7 shows the ratio

dN'(paircorrected tracks)/

dN'(pairMC particles).

The ratio is presented for all tracks, for tracks from primary particles only, and for tracks of mixed events each after single track correction. An enhanced number of particle pairs peaked around ' = 0 is found after single track correction for the three cases. The ratio of corrected pairs to Monte Carlo particle pairs including secondary particles also shows a small enhancement around ' = . To correct for this eect, a two-track post-correction is performed after the single track correction, using Monte Carlo based correction factors which depend on ', pminT,trig, and pminT,assoc. The correction decreases with increasing transverse momentum thresholds. For the analysed data sets, the maximum eect from this correction (5 %) is observed for the lowest values of pminT,trig = 0.7 GeV/c and pminT,assoc =

0.4 GeV/c and at the highest Ncharged, where the ratio near-side yield over combinatorial

background is lowest.

Vertex reconstruction e ciency. The vertex reconstruction eciency is the ratio between the number of triggered events with a reconstructed accepted vertex of good quality and the number of triggered events. The vertex reconstruction eciency has not only an impact on the number of events but also on the total number of particles entering the data sample. The eect of the vertex reconstruction eciency contributes with 1.2 %

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Figure 7. Ratio between the track pair distribution of reconstructed and corrected tracks using single track corrections and the pair distribution of MC primary particles (pT, trig > 0.7 GeV/c, pT, assoc > 0.4 GeV/c, and | | < 0.9) as a function of the dierence in azimuthal angle '

track1

'track2 = '. The full detector simulations have been performed for s = 7 TeV.

to 2.5 % to the multiplicity integrated track-to-particle correction for the analysed data sets. The impact of the vertex reconstruction eciency depends strongly on the charged particle multiplicity eecting only the low Ncharged bins. For Ncharged > 10, the vertex

reconstruction eciency is consistent with unity.

Trigger e ciency. The correction of the trigger eciency takes into account the fact that the number of triggered events is only a subset of the produced events of a given event class. However, the trigger is fully ecient for events with at least one charged track in the considered ITS-TPC acceptance. Hence, no correction for the trigger eciency is applied.

Charged particle multiplicity correction. The present analysis studies the evolution of the integrated yields of the azimuthal correlation as a function of the true charged particle multiplicity Ncharged in the range pT > 0.2 GeV/c and | | < 0.9. Our approach to a full correction of detector eects on the multiplicity is a two-step procedure: rst, the correction of the raw two-particle correlation observables Ounc from Ounc(Nrec,charged) to its corrected value Ocorr(Nrec,charged) is performed as a function of the reconstructed uncorrected multiplicty Nrec,charged. Then, the correction of the charged particle multiplicity from Ocorr(Nrec,charged) to Ocorr(Ncharged) is carried out to obtain the corresponding ob

servable at the corrected charged particle multiplicity Ncharged. The same procedure has

also been used for the measurement of the mean transverse momentum and the transverse sphericity as a function of the true multiplicity as described in refs. [18, 27]. The correction employs the correlation matrices R(Ncharged, Nrec,charged) which are proportional to the

probability of reconstructing Nrec,charged particles under the condition that Ncharged particles have been produced. They are obtained from full detector simulations quantifying the relation between the number of charged primary particles and the number of reconstructed tracks both in pT > 0.2 GeV/c and | | < 0.9 as shown in the left panel of gure 8.

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Figure 8. Left panel: simulated correlation matrix. Right panel: normalized and extended correlation matrix. Input for the extension are Gaussian distributions with extrapolated hN

charged

i and

[angbracketleft]Ncharged[angbracketright].

The columns of the correlation matrix have to be normalised to one

Ncharged :

XNrec, chargedR1(Ncharged, Nrec,charged) = 1. (5.1)

The normalised correlation matrix represents the conditional probability for measuring an event of a given true multiplicity, Ncharged, for a given reconstructed track multiplicity of

Nrec,charged. In a second step, the correlation matrix is extrapolated to the highest multiplicities not covered in the detector simulation due to the limited number of simulated events. To this end, the distribution of each matrix column at low multiplicities is tted with a Gaussian function. As expected, the width of the Gaussian functions grows approximately as [radicalbig]N

rec, charged and the mean grows as hNchargedi Nrec,charged. This scaling is used to extrapolate the correlation matrix to higher multiplicities. An extrapolated correlation matrix with normalised columns is shown in the right panel of gure 8. Based on the normalised and extended correlation matrix, the observable Ocorr(Nrec,charged) can be

converted to Ocorr(Ncharged) using

O(Ncharged) =

XNrec, chargedO(Nrec,charged) R1(Ncharged, Nrec,charged). (5.2)

6 Systematic uncertainties

A comprehensive study of the systematic uncertainties of the nal analysis results has been performed. In the following, the sources of systematic uncertainties and their impact on the analysis results are described. Representative for all nal analysis results, the systematic uncertainties of the per-trigger near-side pair yield measured using pT,trig > 0.7 GeV/c and pT,assoc > 0.4 GeV/c as a function of the charged particle multiplicity are discussed in the text and summarised in table 4.

14

s = 0.9 TeV s = 2.76 TeV s = 7 TeV N=2 N=2hNi N=2 N=2hNi N=2 N=2hNi

Signal extraction 0.3 % 0.1 % 0.2 % 0.1 % 0.5 % 0.1 %

Bin width 0.2 % 0.1 % 0.2 % 0.1 % 0.2 % 0.1 %

Correction procedure 1.9% 0.9 % 5.0 % 3.0 % 12.8 % 1.2 %

Event generator 1.1 % 1.8 % 1.8 % 2.0 % 1.9 % 0.1 %

Transport MC 0.3 % 0.1 % 0.3 % 0.1 % 0.3 % 0.1 %

Track cut 15.0 % 2.5 % 16.9 % 2.3 % 10.6 % 2.0 %

Vertex cut 2.7 % 0.5 % 1.5 % - 2.1 % -

Detector eciency 3.0 % 3.0 % 4.1 % 4.1 % 4.1 % 4.1 %

Material budget 0.4 % 0.3 % 0.4 % 0.3 % 0.4 % 0.3 %

Particle composition 2.0 % 1.0 % 2.1 % 1.3 % 2.0 % 1.5 %

Pileup - - - - 5.0 % 1.0 %

Extrapol. of S.-Corr. - - 2 % - 2 % -Table 4. Systematic uncertainties for the per-trigger near-side pair yield measured using pT, trig >

0.7 GeV/c, pT, assoc > 0.4 GeV/c, and | | < 0.9 exemplary for all nal analysis results for two

charged particle multiplicity bins. The full charged particle multiplicity dependence can be found in ref. [28].

Per-trigger pair yield measurement based on a t function. The per-trigger pair yield of the azimuthal correlation is extracted utilizing the t function of equation (4.2). A good agreement between the data distribution and the t function has been found using residuals as well as the ~2/NDF test. The stability of the t results has been veried based on various tests. For example, it has been checked that a modication of the combinatorial background of the azimuthal correlation does not change the extracted yields of the near and the away-side peaks. Moreover, it has been veried that the combination of events results in the expected modication of the per-trigger pair yield components. In addition, the minimum number of events needed for a stable t result as well as the optimised resolution of the '-distribution in terms of the bin-size have been determined.

Correction procedure. In section 5, a full correction procedure of detector eects has been introduced. When correcting event generator data after full detector simulations with correction maps obtained with the same event generator, it is expected to recover the Monte Carlo input. A remaining disagreement between the corrected results and the input Monte Carlo results represents the systematic uncertainty of the correction procedure. As an example, the per-trigger near-side pair yield obtained from the MC input and the corrected results dier from each other by up to 12.8 % for the rst charged particle multiplicity bin and by less than 3.0 % for higher charged particle multiplicity bins.

Correction maps can be estimated with dierent Monte Carlo generators. When using correction maps of one Monte Carlo generator for the correction of data of a second Monte Carlo generator, further discrepancies can emerge. The per-trigger near-side pair yields of corrected data obtained using PYTHIA6 tune Perugia-0 correction maps and using PHOJET correction maps dier by less than 2 % for all charged particle multiplicities.

15

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We have estimated the impact of the transport Monte Carlo choice on the nal analysis results. For this purpose, in addition to the default GEANT3 [21] detector simulations, a sample of pp events has been simulated using GEANT4 [22, 23]. The results obtained with the GEANT3 and the GEANT4 based correction maps are in very good agreement. The results dier from each other by a maximum of 0.3 % for all charged particle multiplicities.

Track and vertex selection. The systematic uncertainty related to the choice of the track selection cuts introduced in section 3.2 is estimated by performing a full correction and analysis chain using varying track selection cuts. For this purpose, the default ITSTPC track cuts have been loosened and tightened within reasonable limits. In addition, tracks measured exclusively with the TPC have been analysed. The per-trigger near-side pair yield shows a sizable dierence when using the dierent track cuts of up to 16 % for the rst charged particle multiplicity bin, however, the impact decreases to less than 2.5 % for higher charged particle multiplicities.

The impact of the vertex selection choice is tested by varying the vertex quality cuts. Instead of requiring at least one track associated to the collision vertex, two tracks are required. The impact of this modication on the per-trigger near-side pair yield is 2 % for the lowest charged particle multiplicity bin and compatible with zero for charged particle multiplicities above Ncharged > 10.

Tracking e ciency. The ITS-TPC tracking eciency uncertainty has been estimated by comparing the track matching eciency between ITS and TPC and vice versa for simulated data and real data [27, 29]. The disagreement between the matching eciencies is then converted into a transverse momentum dependent reconstruction eciency uncertainty. By varying the reconstruction eciency accordingly, the systematic uncertainty on the nal analysis results can be estimated. The impact of this uncertainty on the per-trigger near-side pair yield is about 4 % for all charged particle multiplicities.

The material budget of ALICE has been measured with the help of photon conversions in the detector material. The remaining uncertainty in the knowledge of the material budget can be converted into a transverse momentum dependent uncertainty of the tracking eciency. The eect of this uncertainty results in a small variation of analysis results. For example, the per-trigger near-side pair yield is modied by below 0.4 % for all charged particle multiplicities.

The ITS-TPC tracking eciency estimated in full detector simulations depends to some extent on the composition of the particle yields. This is due to the fact that the particle decay length and the probability to be absorbed in the detector material depends on the particle type. The systematic uncertainty related to the particle composition has already been studied in ref. [17]. Motivated by a disagreement of measured particle yields to predictions of PYTHIA6 and PHOJET [25, 26], the yields of pions, kaons, and protons used in the calculation of correction maps of detector eects have been modied by 30 % [17].

The eect of this modication accounts for a variation of the nal results of at most 2.0 %.

Pile-up events. The impact of pile-up events on the analysis results has been tested by analysing high pile-up data sets. A quantitative estimation of the systematic uncertainty

16

JHEP09(2013)049

related to pile-up events in the data analysis has been performed by splitting the default data sets into sub-sets of relatively low and relatively high pileup-probability. The dierence between the analysis results of the two sub-sets accounts for about 5 % for the lowest charged particle multiplicity bin and below 1 % for all higher charged particle multiplicities.

Extrapolation of strangeness correction. As part of the contamination correction procedure described in section 5, a data driven contamination correction has been performed accounting for the underestimated strangeness yield in the Monte Carlo generators. This correction is based on ALICE measurements at s = 0.9 TeV [25, 26], however, these corrections were also used to correct collision data measured at s = 2.76 and 7 TeV. The uncertainty related to the extrapolation of this correction to higher centre-of-mass energies can be estimated using measurements of strange particle yields performed by the CMS experiment at s = 0.9 and 7 TeV [30]. When performing the same data driven contamination correction based on the CMS measurements, small modication of the nal results can be observed. The systematic uncertainty of the per-trigger near-side pair yield related to the extrapolation of the strangeness correction is below 2 % for the rst charged particle multiplicity and compatible with zero for charged particle multiplicities above Ncharged > 8.

7 Results

The two-particle correlation analysis are now presented, after having included the corresponding corrections described in the previous sections. Results are discussed for the three dierent centre-of-mass energies and two sets of pT-cuts: pT,trig > 0.7 GeV/c, pT,assoc >

0.4 GeV/c and pT-cuts: pT,trig > 0.7 GeV/c, pT,assoc > 0.7 GeV/c. The second, symmetric, bin is used to analyse the number of uncorrelated seeds.

ALICE data are presented as black points and the results of Monte Carlo calculations as coloured symbols. The error bars represent the statistical errors and the boxes the systematic uncertainties. The horizontal error bars correspond to the bin-width. For measurements as a function of the charged particle multiplicity, the upper part of the gures shows the analysis results and the lower part shows the ratio between data and the Monte Carlo calculations.

Before discussing in detail the multiplicity and centre-of-mass energy dependence and their implications for multiple parton interactions, we present in gure 9 an example of a measured azimuthal correlation function. In the gure, the data are compared to various MC simulations on generator level for the charged particle multiplicity bin Ncharged = 10 at

s = 7 TeV. The part of the systematic uncertainty that has the same relative contribution for all '-bins is presented as a box on the left side of the data points. The height of the box corresponds to the value of the leftmost data point (at ' = /2) and must be

scaled for all other data points according to their absolute values.

Within the systematic uncertainties, the constant combinatorial background is of the same height for data and all PYTHIA tunes. PHOJET shows a lower combinatorial background. The near-side peak centred around ' = 0 is overestimated by all Monte Carlo generators in terms of its height and its integral above the combinatorial background. Here,

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PYTHIA6 tune Perugia-2011 shows the best agreement with data. The width of the near-side peak is roughly reproduced by the Monte Carlo generators. PHOJET and PYTHIA8 tune 4C produce an away-side peak (' = ) with a higher absolute height than in data. The PYTHIA6 tunes Perugia-0 and Perugia-2011 both agree with data in terms of the height of the away-side peak. PYTHIA8 4C and PHOJET overestimate the integral of the away-side peak above the constant combinatorial background. Here, PYTHIA6 Perugia-0 agrees with data, and PYTHIA6 tune Perugia-2011 underestimates the data slightly. The width of the away-side peak is much narrower in PHOJET than in data while the PYTHIA tunes give only a slightly narrower away-side peak.

7.1 Yields

First, the analysis results for the highest analysed collision energy s = 7 TeV are presented. Next, we discuss the collision energies s = 2.76 TeV and 0.9 TeV.

Near-side. The per-trigger near-side pair-yield which provides information on the fragmentation of partons is presented in the top left panel of gure 10 for pT,trig > 0.7 GeV/c

and pT,assoc > 0.4 GeV/c. The measured near-side pair yield grows as a function of the charged particle multiplicity indicating a fragmentation bias as characteristic for a MPI distribution with a narrow plateau (tune Perugia-0, see section 4.2). This general trend is reproduced by the MC generators. As expected PYTHIA6 tune Perugia-2011 and PYTHIA8 tune 4C, which already include LHC data, are closest to the data. For Ncharged > 20

(Perugia-2011) and Ncharged > 30 (4C) the agreement is within the systematic errors, while

in this region, all other models overestimate the data by up to 50 %. For all MCs, the agreement becomes worse moving to lower multiplicities. Here, Perugia-2011 also overestimates the data by up to 30 %. The largest deviations (up to 120 %) are found in the comparison with PHOJET.

18

For the higher pT,assoc-cut (> 0.7 GeV) the agreement is with the exception of PYTHIA6 tune Perugia-2011 and PYTHIA8 at high Nch worse (gure 10 (top right)). In particular, for low multiplicities the deviation is between 40 % and 150 %.

Away-side. The per-trigger away-side pair yield which provides information about the fragments produced back-to-back within the detector acceptance is presented for pT,trig > 0.7 GeV/c and pT,assoc > 0.4 GeV/c in the left panel of the second row of gure 10.

As with the near-side yield, the measured away-side pair yield grows as a function of the charged particle multiplicity. Above Ncharged = 10, the growth is signicantly stronger on

the away-side. Surprisingly, tune Perugia-0 now agrees with the data within uncertainties over the whole multiplicity range, whereas Perugia-2011 and PYTHIA8, which have the best agreement for the near-side yield, signicantly underestimates the away-side yield. The deviations of PHOJET is similar to the ones observed for the near-side. When increasing the pT,assoc-threshold to 0.7 GeV/c (right panel of the second row of gure 10), also

PYTHIA6 tune Perugia-0 overestimates the away-side pair yield by about 30 %, whereas tune Perugia-2011 and PYTHIA8 show the best agreement at high Ncharged.

Combinatorial background. The per-trigger pair yield in the constant combinatorial background of the correlation grows linearly as a function of the charged particle multiplicity as shown in the third row of gure 10. The data are well described by all models within the systematic uncertainties for all charged particle multiplicities for pT,assoc > 0.4 GeV/c

(left panel). When increasing the pT,assoc-threshold to 0.7 GeV/c (right panel), PHOJET underestimates the combinatorial background by approximately 20 %.

Trigger particles per event. The average number of trigger particles with pT,trig >

0.7 GeV/c as a function of the charged particle multiplicity is presented in the bottom-left panel of gure 10. The average number of trigger particles grows stronger than linearly as a function of the charged particle multiplicity. This can be understood from the pair yield results. As the multiplicity increases, both the number of semi-hard scatterings per event and the number of fragments per scattering increase, leading to a greater than linear increase in the number of particles above a given pT-threshold. This observation is also consistent with the observed increase of the mean transverse momentum with multiplicity [27]. The PYTHIA6 tunes slightly overestimate the ALICE results while PHOJET underestimates the data. The agreement with PYTHIA8 is excellent for Ncharged > 15.

Number of uncorrelated seeds. The average number of uncorrelated seeds (cf. equation (4.9)) is presented in the bottom right panel of gure 10. At low multiplicities, the number of uncorrelated seeds grows almost linearly. At high multiplicities, the growth decreases. All models reproduce the qualitative development of the number of correlated seeds as a function of the charged particle multiplicity. While the data are signicantly underestimated by PHOJET, PYTHIA6 and PYTHIA8 reproduce the results reasonably well.

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Figure 10. Per-trigger near-side pair yield (top row), per-trigger away-side pair yield (second row), per-trigger pair yield in the combinatorial background (third row), average number of trigger particles and average number of uncorrelated seeds (bottom row) measured at s = 7 TeV.

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> 0.2 GeV/

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| < 0.9,

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> 0.2 GeV/

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Figure 11. Per-trigger near-side pair yield (top row), per-trigger away-side pair yield (second row), per-trigger pair yield in the combinatorial background (third row), average number of trigger particles and average number of uncorrelated seeds (bottom row) measured at s = 2.76 TeV.

21

assoc, near side

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JHEP09(2013)049

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uncorrelated seeds

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ALICEPHOJETPYTHIA8 4CPYTHIA6 Perugia-0 (320)

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PYTHIA6 Perugia-2011 (350)

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charged, |

| < 0.9,

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> 0.2 GeV/

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charged, |

| < 0.9,

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p

> 0.2 GeV/

c

Figure 12. Per-trigger near-side pair yield (top row), per-trigger away-side pair yield (second row), per-trigger pair yield in the combinatorial background (third row), average number of trigger particles and average number of uncorrelated seeds (bottom row) measured at s = 0.9 TeV.

22

7.2 Centre-of-mass energy dependence

Figures 11 and 12 show the observables discussed above measured at the two lower centreof-mass energies s = 2.76 and 0.9 TeV. On average, the agreement between the model calculations and the ALICE results improves with decreasing collision energy. However, qualitatively the behaviour of the dierent models is similar. Tune Perugia-2011 agrees best with the measured near-side yield and under-predicts the away-side yield, for which Perugia-0 has the best agreement. PHOJET generally shows the worst agreement. However, the agreement between PHOJET and the ALICE results in terms of the near- and away-side yields is good for s = 900 GeV at high multiplicity, whereas PYTHIA8 has the largest disagreement in this region.

To allow for a more direct comparison of the trends as a function of centre-of-mass energy, gures 1317 show in the same plots the multiplicity dependence for the three energies for data (top left) and for the various MC generators. We note that the colors now indicate the dierent beam energies. In data, the near-side pair yield in a xed charged particle multiplicity bin (gure 13) grows as a function of s. While all event generators reproduce this increase qualitatively, PHOJET shows a signicantly stronger energy dependence than the data and the PYTHIA results. The away-side pair yield in a xed charged particle multiplicity bin measured by ALICE decreases as a function of the centre-of-mass energy as shown in gure 14. This decrease is explained by the limited -acceptance. Due to the longitudinal momentum distribution of partons in the colliding protons, the scattered partons have a wide relative distribution that increases with increasing s. While all PYTHIA tunes reproduce the away-side yield decrease, PHOJET does not show a clear energy dependence of the yield in the studied centre-of-mass energy range.

The combinatorial background in a xed charged particle multiplicity bin does not show any centre-of-mass energy dependence (gure 15). This behaviour is well reproduced by all Monte Carlo generators. The average number of trigger particle shown in gure 16 grows slowly as a function of the centre-of-mass energy. The average number of uncorrelated seeds (gure 17) also grows slowly as a function of the centre-of-mass energy. This increase is smallest for PHOJET. The qualitative centre-of-mass energy dependence of the average number of trigger particle and the average number of uncorrelated seeds is well reproduced by the Monte Carlo generators.

7.3 Multiple parton interactions

Interpreted in the context of the PYTHIA model, the number of uncorrelated seeds (cf. equation (4.9)) provides information about the number of semi-hard parton-parton interactions per event as discussed in section 4. In the top left panel of gure 17, the average number of uncorrelated seeds as a function of the charged particle multiplicity is presented for the centre-of-mass energies s = 0.9, 2.76, and 7 TeV. Figure 18 shows the residuals between the data points and linear t functions ((data-t)/data). It can be observed that the charged particle multiplicity increases approximately linearly with the number of uncorrelated seeds. However, it deviates from the linear dependence at large charged

23

JHEP09(2013)049

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> 0.2 GeV/

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c

Figure 13. Per-trigger near-side pair yield as a function of the charged particle multiplicity measured for s = 0.9, 2.76, and 7 TeV.

24

1

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ALICE (pp @

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= 2.76 TeV)

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= 7 TeV)

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= 0.9 TeV)

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> 0.2 GeV/

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Figure 14. Per-trigger away-side pair yield as a function of the charged particle multiplicity measured for s = 0.9, 2.76, and 7 TeV.

25

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= 7 TeV)

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Figure 15. Per-trigger pair yield in the combinatorial background as a function of the charged particle multiplicity measured for s = 0.9, 2.76, and 7 TeV.

26

ALICE (pp @

= 2.76 TeV)

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= 7 TeV)

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Figure 16. Average number of trigger particles per event as a function of the charged particle multiplicity measured for s = 0.9, 2.76, and 7 TeV.

27

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uncorrelated seeds

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= 2.76 TeV)

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c

Figure 17. Average number of uncorrelated seeds per event as a function of the charged particle multiplicity measured for s = 0.9, 2.76, and 7 TeV.

28

(data-fit)/data

0.6

ALICE (pp @

= 2.76 TeV)

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= 7 TeV)

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ALICE (pp @

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JHEP09(2013)049

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c

Figure 18. Residual between the number of uncorrelated seeds and linear t functions.

particle multiplicities. Here, the rise of the number of uncorrelated seeds levels o. This observation is consistent with the assumption that at highest multiplicities a further increase of the number of multiple parton interactions becomes extremely improbable. In this scenario, high charged particle multiplicities can only be reached by selecting events with many high-multiplicity jets.

8 Conclusions

We have studied the pair-yields per trigger in two-particle azimuthal correlations between charged trigger and associated particles in pp collisions at s = 0.9, 2.76, and 7 TeV. The correlations have been measured for charged particles recorded with the ALICE central barrel detectors ITS and TPC covering the full azimuth and a pseudorapidity range of

| | < 0.9. The analysis has been performed as a function of the charged particle multiplicity

and for the transverse momentum thresholds for trigger particles of pT,trig > 0.7 GeV/c

and for associated particles of pT,assoc > 0.4 and 0.7 GeV/c.

The azimuthal correlations have been decomposed into the pair yield in the combinatorial background, the pair yield in the near-side peak (' 0), and the pair yield in the

away-side peak (' ). Furthermore, the average number of trigger particles per event

have been measured. While the per-trigger near-side and away-side pair yield provide information about fragmentation properties of low-pT partons, the average number of trigger particles includes information from both the number of sources of particle production and the fragmentation. In order to increase the sensitivity to the number of sources of particle production, we have dened an observable, number of uncorrelated seeds, in which the impact of the fragmentation is reduced. Using PYTHIA simulations on generator level, we have shown that the number of uncorrelated seeds is proportional to the number of

29

semi-hard parton-parton interactions in pp collision. However, the factor of proportionality depends on the tune and, hence, no absolute number of interactions can be derived from this procedure.

The per-trigger near- and away-side pair-yields as a function of the charged particle multiplicity increase with multiplicity. This increase can be explained by the fact that the correlations and the multiplicity are measured in the same pseudo-rapidity region and that the probability distribution of the number of multi-parton interactions is steeply falling. Under these conditions, high multiplicities are reached through a high number of multi-parton interactions and a higher than average number of fragments per parton. This is also consistent with our observation that the number of trigger particles above a pT threshold (0.7 GeV considered here) increases stronger than linearly with multiplicity.

The symmetric bin pT,trig, pT,assoc > 0.7 GeV/c has been used to reduce the multiplicative eect of fragmentation and to determine the number of uncorrelated trigger particles. The latter increases linearly with multiplicity up to the highest multiplicities where it starts to level o. This eect is observed for all centre-of-mass energies. Interpreted within the PYTHIA model of multi-parton interactions this is evidence for a limitation of the number of MPIs above a certain threshold. Independent of its physical interpretation the observed systematics are important for any study performed as a function of multiplicity.

We have compared our results to the event generators PYTHIA6, PYTHIA8, and PHOJET. While the constant, combinatorial background of the correlation is described fairly well by all models, the models have diculties to describe the per-trigger pair-yields in the near-side peak and the away-side peaks. The PYTHIA tunes reproduce the centreof-mass dependence of the near and the away-side pair yield. PHOJET overestimates the increase of the near-side yield with the centre-of-mass energy, while it does not show any centre-of-mass dependence of the away-side yield. The development of the number of uncorrelated seeds with charged particle multiplicity is described well by all models. These ndings are expected to provide important input for future Monte Carlo tunes and will help to constrain the models used in these generators.

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 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 Cientco e Tecnolgico (CNPq), Financiadora de Estudos e Projetos (FINEP), Fundaao de Amparo Pesquisa do Estado de Sao 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);

30

JHEP09(2013)049

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 Communitys Seventh Framework Programme;

Helsinki Institute of Physics and the Academy of Finland;

French CNRS-IN2P3, the Region Pays de Loire, Region Alsace, Region Auvergne and CEA, France;

German BMBF and the Helmholtz Association;

General Secretariat for Research and Technology, Ministry of Development, Greece; Hungarian OTKA and National Oce for Research and Technology (NKTH); Department of Atomic Energy and Department of Science and Technology of the Government of India;

Istituto Nazionale di Fisica Nucleare (INFN) and Centro Fermi - Museo Storico della Fisica e Centro Studi e Ricerche Enrico Fermi, Italy;

MEXT Grant-in-Aid for Specially Promoted Research, Japan;

Joint Institute for Nuclear Research, Dubna;

National Research Foundation of Korea (NRF);

CONACYT, DGAPA, Mxico, ALFA-EC and the EPLANET Program (European Particle Physics Latin American Network)

Stichting voor Fundamenteel Onderzoek der Materie (FOM) and the Nederlandse Organ-isatie voor Wetenschappelijk Onderzoek (NWO), Netherlands;

Research Council of Norway (NFR);

Polish Ministry of Science and Higher Education;

National Authority for Scientic Research - NASR (Autoritatea National pentru Cercetare Stiintic - ANCS);

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;

CIEMAT, EELA, Ministerio de Economa y Competitividad (MINECO) of Spain, Xunta de Galicia (Consellera de Educacin), CEADEN, Cubaenerga, Cuba, and IAEA (International Atomic Energy Agency);

Swedish Research Council (VR) and Knut & 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.

Open Access. This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited.

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The ALICE collaboration

B. Abelev, 72 J. Adam, 38 D. Adamov, 79 A.M. Adare, 130 M.M. Aggarwal, 83G. Aglieri Rinella, 34 M. Agnello, 100 ,89 A.G. Agocs, 129 A. Agostinelli, 28 Z. Ahammed, 124A. Ahmad Masoodi, 18 N. Ahmad, 18 I. Ahmed, 16 S.U. Ahn, 65 S.A. Ahn, 65I. Aimo, 25 ,100 ,89 M. Ajaz, 16 A. Akindinov, 51 D. Aleksandrov, 95 B. Alessandro, 100D. Alexandre, 97 A. Alici, 102 ,13 A. Alkin, 4 J. Alme, 36 T. Alt, 40 V. Altini, 32S. Altinpinar, 19 I. Altsybeev, 126 C. Andrei, 75 A. Andronic, 92 V. Anguelov, 88J. Anielski, 59 C. Anson, 20 T. Antii, 93 F. Antinori, 101 P. Antonioli, 102 L. Aphecetche, 108H. Appelshauser, 57 N. Arbor, 68 S. Arcelli, 28 A. Arend, 57 N. Armesto, 17 R. Arnaldi, 100T. Aronsson, 130 I.C. Arsene, 92 M. Arslandok, 57 A. Asryan, 126 A. Augustinus, 34R. Averbeck, 92 T.C. Awes, 80 J.yst, 43 M.D. Azmi, 18 ,85 M. Bach, 40 A. Badal, 99 Y.W. Baek, 67 ,41 R. Bailhache, 57 R. Bala, 86 ,100 A. Baldisseri, 15F. Baltasar Dos Santos Pedrosa, 34 J. Bn, 52 R.C. Baral, 53 R. Barbera, 27 F. Barile, 32

G.G. Barnafldi, 129 L.S. Barnby, 97 V. Barret, 67 J. Bartke, 112 M. Basile, 28 N. Bastid, 67S. Basu, 124 B. Bathen, 59 G. Batigne, 108 B. Batyunya, 63 P.C. Batzing, 22 C. Baumann, 57I.G. Bearden, 77 H. Beck, 57 N.K. Behera, 45 I. Belikov, 62 F. Bellini, 28 R. Bellwied, 118E. Belmont-Moreno, 61 G. Bencedi, 129 S. Beole, 25 I. Berceanu, 75 A. Bercuci, 75Y. Berdanikov, 81 D. Berenyi, 129 A.A.E. Bergognon, 108 R.A. Bertens, 50 D. Berzano, 25 ,100L. Betev, 34 A. Bhasin, 86 A.K. Bhati, 83 J. Bhom, 122 L. Bianchi, 25 N. Bianchi, 69C. Bianchin, 50 J. Bielk, 38 J. Bielkov, 79 A. Bilandzic, 77 S. Bjelogrlic, 50 F. Blanco, 11F. Blanco, 118 D. Blau, 95 C. Blume, 57 M. Boccioli, 34 F. Bock, 64 ,71 S. Bttger, 56A. Bogdanov, 73 H. Bggild, 77 M. Bogolyubsky, 48 L. Boldizsr, 129 M. Bombara, 39J. Book, 57 H. Borel, 15 A. Borissov, 128 J. Bornschein, 40 F. Boss, 85 M. Botje, 78E. Botta, 25 E. Braidot, 71 P. Braun-Munzinger, 92 M. Bregant, 108 T. Breitner, 56T.A. Broker, 57 T.A. Browning, 90 M. Broz, 37 R. Brun, 34 E. Bruna, 25 ,100 G.E. Bruno, 32D. Budnikov, 94 H. Buesching, 57 S. Bufalino, 25 ,100 P. Buncic, 34 O. Busch, 88Z. Buthelezi, 85 D. Caarri, 29 ,101 X. Cai, 8 H. Caines, 130 A. Caliva, 50 E. Calvo Villar, 98P. Camerini, 23 V. Canoa Roman, 12 G. Cara Romeo, 102 F. Carena, 34 W. Carena, 34N. Carlin Filho, 115 F. Carminati, 34 A. Casanova Daz, 69 J. Castillo Castellanos, 15 J.F. Castillo Hernandez, 92 E.A.R. Casula, 24 V. Catanescu, 75 C. Cavicchioli, 34C. Ceballos Sanchez, 10 J. Cepila, 38 P. Cerello, 100 B. Chang, 43 ,132 S. Chapeland, 34 J.L. Charvet, 15 S. Chattopadhyay, 124 S. Chattopadhyay, 96 M. Cherney, 82C. Cheshkov, 34 ,117 B. Cheynis, 117 V. Chibante Barroso, 34 D.D. Chinellato, 118P. Chochula, 34 M. Chojnacki, 77 S. Choudhury, 124 P. Christakoglou, 78 C.H. Christensen, 77P. Christiansen, 33 T. Chujo, 122 S.U. Chung, 91 C. Cicalo, 103 L. Cifarelli, 28 ,13F. Cindolo, 102 J. Cleymans, 85 F. Colamaria, 32 D. Colella, 32 A. Collu, 24G. Conesa Balbastre, 68 Z. Conesa del Valle, 34 ,47 M.E. Connors, 130 G. Contin, 23 J.G. Contreras, 12 T.M. Cormier, 128 Y. Corrales Morales, 25 P. Cortese, 31I. Corts Maldonado, 3 M.R. Cosentino, 71 F. Costa, 34 M.E. Cotallo, 11 E. Crescio, 12P. Crochet, 67 E. Cruz Alaniz, 61 R. Cruz Albino, 12 E. Cuautle, 60 L. Cunqueiro, 69T.R. Czopowicz, 127 A. Dainese, 29 ,101 R. Dang, 8 A. Danu, 55 I. Das, 47 S. Das, 5 D. Das, 96K. Das, 96 S. Dash, 45 A. Dash, 116 S. De, 124 G.O.V. de Barros, 115 A. De Caro, 30 ,13G. de Cataldo, 105 J. de Cuveland, 40 A. De Falco, 24 D. De Gruttola, 30 ,13 H. Delagrange, 108A. Delo, 74 N. De Marco, 100 E. Dnes, 129 S. De Pasquale, 30 A. Deppman, 115G. D Erasmo, 32 R. de Rooij, 50 M.A. Diaz Corchero, 11 D. Di Bari, 32 T. Dietel, 59C. Di Giglio, 32 S. Di Liberto, 106 A. Di Mauro, 34 P. Di Nezza, 69 R. Divi, 34. Djuvsland, 19 A. Dobrin, 128 ,33 ,50 T. Dobrowolski, 74 B. Dnigus, 92 ,57 O. Dordic, 22 A.K. Dubey, 124 A. Dubla, 50 L. Ducroux, 117 P. Dupieux, 67 A.K. Dutta Majumdar, 96D. Elia, 105 B.G. Elwood, 14 D. Emschermann, 59 H. Engel, 56 B. Erazmus, 34 ,108

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H.A. Erdal, 36 D. Eschweiler, 40 B. Espagnon, 47 M. Estienne, 108 S. Esumi, 122 D. Evans, 97S. Evdokimov, 48 G. Eyyubova, 22 D. Fabris, 29 ,101 J. Faivre, 68 D. Falchieri, 28 A. Fantoni, 69M. Fasel, 88 D. Fehlker, 19 L. Feldkamp, 59 D. Felea, 55 A. Feliciello, 100 B. Fenton-Olsen, 71G. Feolov, 126 A. Fernndez Tllez, 3 A. Ferretti, 25 A. Festanti, 29 J. Figiel, 112 M.A.S. Figueredo, 115 S. Filchagin, 94 D. Finogeev, 49 F.M. Fionda, 32 E.M. Fiore, 32E. Floratos, 84 M. Floris, 34 S. Foertsch, 85 P. Foka, 92 S. Fokin, 95 E. Fragiacomo, 104A. Francescon, 34 ,29 U. Frankenfeld, 92 U. Fuchs, 34 C. Furget, 68 M. Fusco Girard, 30 J.J. Gaardhje, 77 M. Gagliardi, 25 A. Gago, 98 M. Gallio, 25 D.R. Gangadharan, 20P. Ganoti, 80 C. Garabatos, 92 E. Garcia-Solis, 14 C. Gargiulo, 34 I. Garishvili, 72J. Gerhard, 40 M. Germain, 108 M. Gheata, 55 ,34 A. Gheata, 34 B. Ghidini, 32 P. Ghosh, 124P. Gianotti, 69 P. Giubellino, 34 E. Gladysz-Dziadus, 112 P. Glassel, 88 L. Goerlich, 112R. Gomez, 114 ,12 E.G. Ferreiro, 17 P. Gonzlez-Zamora, 11 S. Gorbunov, 40 A. Goswami, 87S. Gotovac, 110 L.K. Graczykowski, 127 R. Grajcarek, 88 A. Grelli, 50 A. Grigoras, 34C. Grigoras, 34 V. Grigoriev, 73 A. Grigoryan, 2 S. Grigoryan, 63 B. Grinyov, 4 N. Grion, 104P. Gros, 33 J.F. Grosse-Oetringhaus, 34 J.-Y. Grossiord, 117 R. Grosso, 34 F. Guber, 49R. Guernane, 68 B. Guerzoni, 28 M. Guilbaud, 117 K. Gulbrandsen, 77 H. Gulkanyan, 2T. Gunji, 121 A. Gupta, 86 R. Gupta, 86 R. Haake, 59 . Haaland, 19 C. Hadjidakis, 47M. Haiduc, 55 H. Hamagaki, 121 G. Hamar, 129 B.H. Han, 21 L.D. Hanratty, 97 A. Hansen, 77 J.W. Harris, 130 A. Harton, 14 D. Hatzifotiadou, 102 S. Hayashi, 121 A. Hayrapetyan, 34 ,2 S.T. Heckel, 57 M. Heide, 59 H. Helstrup, 36 A. Herghelegiu, 75 G. Herrera Corral, 12N. Herrmann, 88 B.A. Hess, 123 K.F. Hetland, 36 B. Hicks, 130 B. Hippolyte, 62 Y. Hori, 121P. Hristov, 34 I. Hivnov, 47 M. Huang, 19 T.J. Humanic, 20 D. Hutter, 40 D.S. Hwang, 21R. Ichou, 67 R. Ilkaev, 94 I. Ilkiv, 74 M. Inaba, 122 E. Incani, 24 P.G. Innocenti, 34G.M. Innocenti, 25 C. Ionita, 34 M. Ippolitov, 95 M. Irfan, 18 A. Ivanov, 126 V. Ivanov, 81M. Ivanov, 92 O. Ivanytskyi, 4 A. Jacho lkowski, 27 P. M. Jacobs, 71 C. Jahnke, 115H.J. Jang, 65 M.A. Janik, 127 P.H.S.Y. Jayarathna, 118 S. Jena, 118 ,45 D.M. Jha, 128 R.T. Jimenez Bustamante, 60 P.G. Jones, 97 H. Jung, 41 A. Jusko, 97 A.B. Kaidalov, 51S. Kalcher, 40 P. Kalik, 52 T. Kalliokoski, 43 A. Kalweit, 34 J.H. Kang, 132 V. Kaplin, 73S. Kar, 124 A. Karasu Uysal, 66 O. Karavichev, 49 T. Karavicheva, 49 E. Karpechev, 49A. Kazantsev, 95 U. Kebschull, 56 R. Keidel, 133 B. Ketzer, 57 ,111 K. H. Khan, 16 P. Khan, 96 S.A. Khan, 124 M.M. Khan, 18 A. Khanzadeev, 81 Y. Kharlov, 48 B. Kileng, 36 J.H. Kim, 21 J.S. Kim, 41 T. Kim, 132 M. Kim, 41 M. Kim, 132 D.J. Kim, 43 S. Kim, 21 B. Kim, 132D.W. Kim, 41 ,65 S. Kirsch, 40 I. Kisel, 40 S. Kiselev, 51 A. Kisiel, 127 G. Kiss, 129 J.L. Klay, 7J. Klein, 88 C. Klein-Bsing, 59 M. Kliemant, 57 A. Kluge, 34 M.L. Knichel, 92A.G. Knospe, 113 M.K. Khler, 92 T. Kollegger, 40 A. Kolojvari, 126 M. Kompaniets, 126V. Kondratiev, 126 N. Kondratyeva, 73 A. Konevskikh, 49 V. Kovalenko, 126 M. Kowalski, 112S. Kox, 68 G. Koyithatta Meethaleveedu, 45 J. Kral, 43 I. Krlik, 52 F. Kramer, 57A. Kravkov, 39 M. Krelina, 38 M. Kretz, 40 M. Krivda, 97 ,52 F. Krizek, 43 ,38 ,, 79M. Krus, 38 E. Kryshen, 81 M. Krzewicki, 92 V. Kucera, 79 Y. Kucheriaev, 95T. Kugathasan, 34 C. Kuhn, 62 P.G. Kuijer, 78 I. Kulakov, 57 J. Kumar, 45 P. Kurashvili, 74A. Kurepin, 49 A.B. Kurepin, 49 A. Kuryakin, 94 V. Kushpil, 79 S. Kushpil, 79 H. Kvaerno, 22 M.J. Kweon, 88 Y. Kwon, 132 P. Ladrn de Guevara, 60 C. Lagana Fernandes, 115I. Lakomov, 47 R. Langoy, 125 S.L. La Pointe, 50 C. Lara, 56 A. Lardeux, 108 P. La Rocca, 27R. Lea, 23 M. Lechman, 34 S.C. Lee, 41 G.R. Lee, 97 I. Legrand, 34 J. Lehnert, 57 R.C. Lemmon, 107 M. Lenhardt, 92 V. Lenti, 105 H. Len, 61 M. Leoncino, 25I. Len Monzn, 114 P. Lvai, 129 S. Li, 67 ,8 J. Lien, 19 ,125 R. Lietava, 97 S. Lindal, 22V. Lindenstruth, 40 C. Lippmann, 92 ,34 M.A. Lisa, 20 H.M. Ljunggren, 33 D.F. Lodato, 50 P.I. Loenne, 19 V.R. Loggins, 128 V. Loginov, 73 D. Lohner, 88 C. Loizides, 71 K.K. Loo, 43

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X. Lopez, 67 E. Lpez Torres, 10 G. Lvhiden, 22 X.-G. Lu, 88 P. Luettig, 57 M. Lunardon, 29J. Luo, 8 G. Luparello, 50 C. Luzzi, 34 R. Ma, 130 K. Ma, 8 D.M. Madagodahettige-Don, 118A. Maevskaya, 49 M. Mager, 58 ,, 34 D.P. Mahapatra, 53 A. Maire, 88 M. Malaev, 81I. Maldonado Cervantes, 60 L. Malinina, 63 , ii D. MalKevich, 51 P. Malzacher, 92A. Mamonov, 94 L. Manceau, 100 L. Mangotra, 86 V. Manko, 95 F. Manso, 67 V. Manzari, 105M. Marchisone, 67 ,25 J. Mare, 54 G.V. Margagliotti, 23 ,104 A. Margotti, 102 A. Marn, 92C. Markert, 34 ,113 M. Marquard, 57 I. Martashvili, 120 N.A. Martin, 92 J. Martin Blanco, 108P. Martinengo, 34 M.I. Martnez, 3 G. Martnez Garca, 108 Y. Martynov, 4 A. Mas, 108S. Masciocchi, 92 M. Masera, 25 A. Masoni, 103 L. Massacrier, 108 A. Mastroserio, 32A. Matyja, 112 C. Mayer, 112 J. Mazer, 120 R. Mazumder, 46 M.A. Mazzoni, 106 F. Meddi, 26A. Menchaca-Rocha, 61 J. Mercado Prez, 88 M. Meres, 37 Y. Miake, 122 K. Mikhaylov, 63 ,51L. Milano, 34 ,25 J. Milosevic, 22 , iii A. Mischke, 50 A.N. Mishra, 87 ,46 D. Mikowiec, 92C. Mitu, 55 J. Mlynarz, 128 B. Mohanty, 124 ,76 L. Molnar, 129 ,62 L. Montano Zetina, 12M. Monteno, 100 E. Montes, 11 T. Moon, 132 M. Morando, 29 D.A. Moreira De Godoy, 115S. Moretto, 29 A. Morreale, 43 A. Morsch, 34 V. Muccifora, 69 E. Mudnic, 110 S. Muhuri, 124M. Mukherjee, 124 H. Mller, 34 M.G. Munhoz, 115 S. Murray, 85 L. Musa, 34 J. Musinsky, 52 B.K. Nandi, 45 R. Nania, 102 E. Nappi, 105 M. Nasar, 1 C. Nattrass, 120 T.K. Nayak, 124S. Nazarenko, 94 A. Nedosekin, 51 M. Nicassio, 32 ,92 M. Niculescu, 55 ,34 B.S. Nielsen, 77S. Nikolaev, 95 V. Nikolic, 93 S. Nikulin, 95 V. Nikulin, 81 B.S. Nilsen, 82 M.S. Nilsson, 22F. Noferini, 102 ,13 P. Nomokonov, 63 G. Nooren, 50 A. Nyanin, 95 A. Nyatha, 45C. Nygaard, 77 J. Nystrand, 19 A. Ochirov, 126 H. Oeschler, 58 ,34 ,, 88 S. Oh, 130 S.K. Oh, 41L. Olah, 129 J. Oleniacz, 127 A.C. Oliveira Da Silva, 115 J. Onderwaater, 92 C. Oppedisano, 100A. Ortiz Velasquez, 33 ,60 A. Oskarsson, 33 P. Ostrowski, 127 J. Otwinowski, 92 K. Oyama, 88K. Ozawa, 121 Y. Pachmayer, 88 M. Pachr, 38 F. Padilla, 25 P. Pagano, 30 G. Pai, 60F. Painke, 40 C. Pajares, 17 S.K. Pal, 124 A. Palaha, 97 A. Palmeri, 99 V. Papikyan, 2

G.S. Pappalardo, 99 W.J. Park, 92 A. Passfeld, 59 D.I. Patalakha, 48 V. Paticchio, 105B. Paul, 96 A. Pavlinov, 128 T. Pawlak, 127 T. Peitzmann, 50 H. Pereira Da Costa, 15E. Pereira De Oliveira Filho, 115 D. Peresunko, 95 C.E. Prez Lara, 78 D. Perrino, 32W. Peryt, 127 , i A. Pesci, 102 Y. Pestov, 6 V. Petrek, 38 M. Petran, 38 M. Petris, 75P. Petrov, 97 M. Petrovici, 75 C. Petta, 27 S. Piano, 104 M. Pikna, 37 P. Pillot, 108O. Pinazza, 102 ,34 L. Pinsky, 118 N. Pitz, 57 D.B. Piyarathna, 118 M. Planinic, 93M. P losko, 71 J. Pluta, 127 T. Pocheptsov, 63 S. Pochybova, 129 P.L.M. Podesta-Lerma, 114 M.G. Poghosyan, 34 K. Polk, 54 B. Polichtchouk, 48 N. Poljak, 50 ,93 A. Pop, 75S. Porteboeuf-Houssais, 67 V. Pospil, 38 B. Potukuchi, 86 S.K. Prasad, 128R. Preghenella, 102 ,13 F. Prino, 100 C.A. Pruneau, 128 I. Pshenichnov, 49 G. Puddu, 24V. Punin, 94 J. Putschke, 128 H. Qvigstad, 22 A. Rachevski, 104 A. Rademakers, 34 J. Rak, 43A. Rakotozandrabe, 15 L. Ramello, 31 R. Raniwala, 87 S. Raniwala, 87 S.S. Rasanen, 43 B.T. Rascanu, 57 D. Rathee, 83 W. Rauch, 34 A.W. Rauf, 16 V. Razazi, 24 K.F. Read, 120 J.S. Real, 68 K. Redlich, 74 , iv R.J. Reed, 130 A. Rehman, 19 P. Reichelt, 57 M. Reicher, 50F. Reidt, 88 R. Renfordt, 57 A.R. Reolon, 69 A. Reshetin, 49 F. Rettig, 40 J.-P. Revol, 34K. Reygers, 88 L. Riccati, 100 R.A. Ricci, 70 T. Richert, 33 M. Richter, 22 P. Riedler, 34W. Riegler, 34 F. Riggi, 27 ,99 A. Rivetti, 100 M. Rodrguez Cahuantzi, 3A. Rodriguez Manso, 78 K. Red, 19 ,22 E. Rogochaya, 63 D. Rohr, 40 D. Rhrich, 19R. Romita, 92 ,107 F. Ronchetti, 69 P. Rosnet, 67 S. Rossegger, 34 A. Rossi, 34 C. Roy, 62P. Roy, 96 A.J. Rubio Montero, 11 R. Rui, 23 R. Russo, 25 E. Ryabinkin, 95 A. Rybicki, 112S. Sadovsky, 48 K.afak, 34 R. Sahoo, 46 P.K. Sahu, 53 J. Saini, 124 H. Sakaguchi, 44S. Sakai, 71 ,69 D. Sakata, 122 C.A. Salgado, 17 J. Salzwedel, 20 S. Sambyal, 86 V. Samsonov, 81X. Sanchez Castro, 62 L.ndor, 52 A. Sandoval, 61 M. Sano, 122 G. Santagati, 27

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R. Santoro, 34 ,13 D. Sarkar, 124 E. Scapparone, 102 F. Scarlassara, 29 R.P. Scharenberg, 90C. Schiaua, 75 R. Schicker, 88 C. Schmidt, 92 H.R. Schmidt, 123 S. Schuchmann, 57J. Schukraft, 34 M. Schulc, 38 T. Schuster, 130 Y. Schutz, 34 ,108 K. Schwarz, 92 K. Schweda, 92G. Scioli, 28 E. Scomparin, 100 P.A. Scott, 97 R. Scott, 120 G. Segato, 29 I. Selyuzhenkov, 92S. Senyukov, 62 J. Seo, 91 S. Serci, 24 E. Serradilla, 11 ,61 A. Sevcenco, 55 A. Shabetai, 108G. Shabratova, 63 R. Shahoyan, 34 S. Sharma, 86 N. Sharma, 120 S. Rohni, 86 K. Shigaki, 44K. Shtejer, 10 Y. Sibiriak, 95 E. Sicking, 59 ,34 S. Siddhanta, 103 T. Siemiarczuk, 74D. Silvermyr, 80 C. Silvestre, 68 G. Simatovic, 60 ,93 G. Simonetti, 34 R. Singaraju, 124R. Singh, 86 S. Singha, 124 ,76 V. Singhal, 124 B.C. Sinha, 124 T. Sinha, 96 B. Sitar, 37M. Sitta, 31 T.B. Skaali, 22 K. Skjerdal, 19 R. Smakal, 38 N. Smirnov, 130 R.J.M. Snellings, 50C. Sgaard, 33 R. Soltz, 72 M. Song, 132 J. Song, 91 C. Soos, 34 F. Soramel, 29 M. Spacek, 38I. Sputowska, 112 M. Spyropoulou-Stassinaki, 84 B.K. Srivastava, 90 J. Stachel, 88 I. Stan, 55G. Stefanek, 74 M. Steinpreis, 20 E. Stenlund, 33 G. Steyn, 85 J.H. Stiller, 88 D. Stocco, 108M. Stolpovskiy, 48 P. Strmen, 37 A.A.P. Suaide, 115 M.A. Subieta Vsquez, 25 T. Sugitate, 44C. Suire, 47 M. Suleymanov, 16 R. Sultanov, 51 M.umbera, 79 T. Susa, 93 T.J.M. Symons, 71A. Szanto de Toledo, 115 I. Szarka, 37 A. Szczepankiewicz, 34 M. Szymaski, 127J. Takahashi, 116 M.A. Tangaro, 32 J.D. Tapia Takaki, 47 A. Tarantola Peloni, 57A. Tarazona Martinez, 34 A. Tauro, 34 G. Tejeda Munoz, 3 A. Telesca, 34 A. Ter Minasyan, 95C. Terrevoli, 32 J. Thader, 92 D. Thomas, 50 R. Tieulent, 117 A.R. Timmins, 118 D. Tlusty, 38A. Toia, 40 ,29 ,, 101 H. Torii, 121 L. Toscano, 100 V. Trubnikov, 4 D. Truesdale, 20W.H. Trzaska, 43 T. Tsuji, 121 A. Tumkin, 94 R. Turrisi, 101 T.S. Tveter, 22 J. Ulery, 57K. Ullaland, 19 J. Ulrich, 64 ,56 A. Uras, 117 G.M. Urciuoli, 106 G.L. Usai, 24 M. Vajzer, 38 ,79M. Vala, 63 ,52 L. Valencia Palomo, 47 S. Vallero, 25 P. Vande Vyvre, 34 J.W. Van Hoorne, 34M. van Leeuwen, 50 L. Vannucci, 70 A. Vargas, 3 R. Varma, 45 M. Vasileiou, 84 A. Vasiliev, 95V. Vechernin, 126 M. Veldhoen, 50 M. Venaruzzo, 23 E. Vercellin, 25 S. Vergara, 3 R. Vernet, 9M. Verweij, 128 ,50 L. Vickovic, 110 G. Viesti, 29 J. Viinikainen, 43 Z. Vilakazi, 85O. Villalobos Baillie, 97 Y. Vinogradov, 94 L. Vinogradov, 126 A. Vinogradov, 95 T. Virgili, 30 Y.P. Viyogi, 124 A. Vodopyanov, 63 M.A. Vlkl, 88 S. Voloshin, 128 K. Voloshin, 51G. Volpe, 34 B. von Haller, 34 I. Vorobyev, 126 D. Vranic, 92 ,34 J. Vrlkov, 39 B. Vulpescu, 67A. Vyushin, 94 V. Wagner, 38 B. Wagner, 19 J. Wagner, 92 M. Wang, 8 Y. Wang, 88Y. Wang, 8 K. Watanabe, 122 D. Watanabe, 122 M. Weber, 118 J.P. Wessels, 59U. Westerho, 59 J. Wiechula, 123 D. Wielanek, 127 J. Wikne, 22 M. Wilde, 59 G. Wilk, 74J. Wilkinson, 88 M.C.S. Williams, 102 B. Windelband, 88 M. Winn, 88 C. Xiang, 8 C.G. Yaldo, 128 Y. Yamaguchi, 121 H. Yang, 15 ,50 S. Yang, 19 P. Yang, 8 S. Yano, 44S. Yasnopolskiy, 95 J. Yi, 91 Z. Yin, 8 I.-K. Yoo, 91 J. Yoon, 132 I. Yushmanov, 95V. Zaccolo, 77 C. Zach, 38 C. Zampolli, 102 S. Zaporozhets, 63 A. Zarochentsev, 126P. Zvada, 54 N. Zaviyalov, 94 H. Zbroszczyk, 127 P. Zelnicek, 56 I.S. Zgura, 55 M. Zhalov, 81H. Zhang, 8 X. Zhang, 71 ,67 ,, 8 F. Zhang, 8 Y. Zhang, 8 D. Zhou, 8 F. Zhou, 8 Y. Zhou, 50H. Zhu, 8 X. Zhu, 8 J. Zhu, 8 J. Zhu, 8 A. Zichichi, 28 ,13 A. Zimmermann, 88 G. Zinovjev, 4Y. Zoccarato, 117 M. Zynovyev, 4 M. Zyzak, 57

i Deceased

ii Also at: M.V.Lomonosov Moscow State University, D.V.Skobeltsyn Institute of Nuclear Physics,Moscow, Russia

iii Also at: University of Belgrade, Faculty of Physics and Vina Institute of Nuclear Sciences,Belgrade, Serbia

iv Also at: Institute of Theoretical Physics, University of Wroclaw, Wroclaw, Poland

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JHEP09(2013)049

1 Academy of Scientic Research and Technology (ASRT), Cairo, Egypt

2 A. I. Alikhanyan National Science Laboratory (Yerevan Physics Institute) Foundation, Yerevan, Armenia

3 Benemrita Universidad Autnoma de Puebla, Puebla, Mexico

4 Bogolyubov Institute for Theoretical Physics, Kiev, Ukraine

5 Bose Institute, Department of Physics and Centre for Astroparticle Physics and Space Science (CAPSS), Kolkata, India

6 Budker Institute for Nuclear Physics, Novosibirsk, Russia

7 California Polytechnic State University, San Luis Obispo, California, United States

8 Central China Normal University, Wuhan, China

9 Centre de Calcul de lIN2P3, Villeurbanne, France

10 Centro de Aplicaciones Tecnolgicas y Desarrollo Nuclear (CEADEN), Havana, Cuba

11 Centro de Investigaciones Energticas Medioambientales y Tecnolgicas (CIEMAT), Madrid, Spain

12 Centro de Investigacin y de Estudios Avanzados (CINVESTAV), Mexico City and Mrida, Mexico

13 Centro Fermi - Museo Storico della Fisica e Centro Studi e Ricerche Enrico Fermi, Rome, Italy

14 Chicago State University, Chicago, United States

15 Commissariat lEnergie Atomique, IRFU, Saclay, France

16 COMSATS Institute of Information Technology (CIIT), Islamabad, Pakistan

17 Departamento de Fsica de Partculas and IGFAE, Universidad de Santiago de Compostela, Santiago de Compostela, Spain

18 Department of Physics Aligarh Muslim University, Aligarh, India

19 Department of Physics and Technology, University of Bergen, Bergen, Norway

20 Department of Physics, Ohio State University, Columbus, Ohio, United States

21 Department of Physics, Sejong University, Seoul, South Korea

22 Department of Physics, University of Oslo, Oslo, Norway

23 Dipartimento di Fisica dellUniversit and Sezione INFN, Trieste, Italy

24 Dipartimento di Fisica dellUniversit and Sezione INFN, Cagliari, Italy

25 Dipartimento di Fisica dellUniversit and Sezione INFN, Turin, Italy

26 Dipartimento di Fisica dellUniversit La Sapienza and Sezione INFN, Rome, Italy

27 Dipartimento di Fisica e Astronomia dellUniversit and Sezione INFN, Catania, Italy

28 Dipartimento di Fisica e Astronomia dellUniversit and Sezione INFN, Bologna, Italy

29 Dipartimento di Fisica e Astronomia dellUniversit and Sezione INFN, Padova, Italy

30 Dipartimento di Fisica E.R. Caianiello dellUniversit and Gruppo Collegato INFN, Salerno, Italy

31 Dipartimento di Scienze e Innovazione Tecnologica dellUniversit del Piemonte Orientale and Gruppo Collegato INFN, Alessandria, Italy

32 Dipartimento Interateneo di Fisica M. Merlin and Sezione INFN, Bari, Italy

33 Division of Experimental High Energy Physics, University of Lund, Lund, Sweden

34 European Organization for Nuclear Research (CERN), Geneva, Switzerland

35 Fachhochschule Kln, Kln, Germany

36 Faculty of Engineering, Bergen University College, Bergen, Norway

37 Faculty of Mathematics, Physics and Informatics, Comenius University, Bratislava, Slovakia

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38 Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Prague, Czech Republic

39 Faculty of Science, P.J.afrik University, Koice, Slovakia

40 Frankfurt Institute for Advanced Studies, Johann Wolfgang Goethe-Universitat Frankfurt, Frankfurt, Germany

41 Gangneung-Wonju National University, Gangneung, South Korea

42 Gauhati University, Department of Physics, Guwahati, India

43 Helsinki Institute of Physics (HIP) and University of Jyvaskyla, Jyvaskyla, Finland

44 Hiroshima University, Hiroshima, Japan

45 Indian Institute of Technology Bombay (IIT), Mumbai, India

46 Indian Institute of Technology Indore, Indore, India (IITI)

47 Institut de Physique Nuclaire dOrsay (IPNO), Universit Paris-Sud, CNRS-IN2P3, Orsay, France

48 Institute for High Energy Physics, Protvino, Russia

49 Institute for Nuclear Research, Academy of Sciences, Moscow, Russia

50 Nikhef, National Institute for Subatomic Physics and Institute for Subatomic Physics of Utrecht University, Utrecht, Netherlands

51 Institute for Theoretical and Experimental Physics, Moscow, Russia

52 Institute of Experimental Physics, Slovak Academy of Sciences, Koice, Slovakia

53 Institute of Physics, Bhubaneswar, India

54 Institute of Physics, Academy of Sciences of the Czech Republic, Prague, Czech Republic

55 Institute of Space Sciences (ISS), Bucharest, Romania

56 Institut fr Informatik, Johann Wolfgang Goethe-Universitat Frankfurt, Frankfurt, Germany

57 Institut fr Kernphysik, Johann Wolfgang Goethe-Universitat Frankfurt, Frankfurt, Germany

58 Institut fr Kernphysik, Technische Universitat Darmstadt, Darmstadt, Germany

59 Institut fr Kernphysik, Westfalische Wilhelms-Universitat Mnster, Mnster, Germany

60 Instituto de Ciencias Nucleares, Universidad Nacional Autnoma de Mxico, Mexico City, Mexico

61 Instituto de Fsica, Universidad Nacional Autnoma de Mxico, Mexico City, Mexico

62 Institut Pluridisciplinaire Hubert Curien (IPHC), Universit de Strasbourg, CNRS-IN2P3, Strasbourg, France

63 Joint Institute for Nuclear Research (JINR), Dubna, Russia

64 Kirchho -Institut fr Physik, Ruprecht-Karls-Universitat Heidelberg, Heidelberg, Germany

65 Korea Institute of Science and Technology Information, Daejeon, South Korea

66 KTO Karatay University, Konya, Turkey

67 Laboratoire de Physique Corpusculaire (LPC), Clermont Universit, Universit Blaise Pascal, CNRSIN2P3, Clermont-Ferrand, France

68 Laboratoire de Physique Subatomique et de Cosmologie (LPSC), Universit Joseph Fourier, CNRS-IN2P3, Institut Polytechnique de Grenoble, Grenoble, France

69 Laboratori Nazionali di Frascati, INFN, Frascati, Italy

70 Laboratori Nazionali di Legnaro, INFN, Legnaro, Italy

71 Lawrence Berkeley National Laboratory, Berkeley, California, United States

72 Lawrence Livermore National Laboratory, Livermore, California, United States

73 Moscow Engineering Physics Institute, Moscow, Russia

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JHEP09(2013)049

74 National Centre for Nuclear Studies, Warsaw, Poland

75 National Institute for Physics and Nuclear Engineering, Bucharest, Romania

76 National Institute of Science Education and Research, Bhubaneswar, India

77 Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark

78 Nikhef, National Institute for Subatomic Physics, Amsterdam, Netherlands

79 Nuclear Physics Institute, Academy of Sciences of the Czech Republic,e u Prahy, Czech Republic

80 Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States

81 Petersburg Nuclear Physics Institute, Gatchina, Russia

82 Physics Department, Creighton University, Omaha, Nebraska, United States

83 Physics Department, Panjab University, Chandigarh, India

84 Physics Department, University of Athens, Athens, Greece

85 Physics Department, University of Cape Town and iThemba LABS, National Research Foundation, Somerset West, South Africa

86 Physics Department, University of Jammu, Jammu, India

87 Physics Department, University of Rajasthan, Jaipur, India

88 Physikalisches Institut, Ruprecht-Karls-Universitat Heidelberg, Heidelberg, Germany

89 Politecnico di Torino, Turin, Italy

90 Purdue University, West Lafayette, Indiana, United States

91 Pusan National University, Pusan, South Korea

92 Research Division and ExtreMe Matter Institute EMMI, GSI Helmholtzzentrum fr Schwerionenforschung, Darmstadt, Germany

93 Rudjer Bokovi Institute, Zagreb, Croatia

94 Russian Federal Nuclear Center (VNIIEF), Sarov, Russia

95 Russian Research Centre Kurchatov Institute, Moscow, Russia

96 Saha Institute of Nuclear Physics, Kolkata, India

97 School of Physics and Astronomy, University of Birmingham, Birmingham, United Kingdom

98 Seccin Fsica, Departamento de Ciencias, Ponticia Universidad Catlica del Per, Lima, Peru

99 Sezione INFN, Catania, Italy

100 Sezione INFN, Turin, Italy

101 Sezione INFN, Padova, Italy

102 Sezione INFN, Bologna, Italy

103 Sezione INFN, Cagliari, Italy

104 Sezione INFN, Trieste, Italy

105 Sezione INFN, Bari, Italy

106 Sezione INFN, Rome, Italy

107 Nuclear Physics Group, STFC Daresbury Laboratory, Daresbury, United Kingdom

108 SUBATECH, Ecole des Mines de Nantes, Universit de Nantes, CNRS-IN2P3, Nantes, France

109 Suranaree University of Technology, Nakhon Ratchasima, Thailand

110 Technical University of Split FESB, Split, Croatia

111 Technische Universitat Mnchen, Munich, Germany

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112 The Henryk Niewodniczanski Institute of Nuclear Physics, Polish Academy of Sciences, Cracow,Poland

113 The University of Texas at Austin, Physics Department, Austin, TX, United States

114 Universidad Autnoma de Sinaloa, Culiacn, Mexico

115 Universidade de So Paulo (USP), So Paulo, Brazil

116 Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil

117 Universit de Lyon, Universit Lyon 1, CNRS/IN2P3, IPN-Lyon, Villeurbanne, France

118 University of Houston, Houston, Texas, United States

119 University of Technology and Austrian Academy of Sciences, Vienna, Austria

120 University of Tennessee, Knoxville, Tennessee, United States

121 University of Tokyo, Tokyo, Japan

122 University of Tsukuba, Tsukuba, Japan

123 Eberhard Karls Universitat Tbingen, Tbingen, Germany

124 Variable Energy Cyclotron Centre, Kolkata, India

125 Vestfold University College, Tonsberg, Norway

126 V. Fock Institute for Physics, St. Petersburg State University, St. Petersburg, Russia

127 Warsaw University of Technology, Warsaw, Poland

128 Wayne State University, Detroit, Michigan, United States

129 Wigner Research Centre for Physics, Hungarian Academy of Sciences, Budapest, Hungary

130 Yale University, New Haven, Connecticut, United States

131 Yildiz Technical University, Istanbul, Turkey

132 Yonsei University, Seoul, South Korea

133 Zentrum fr Technologietransfer und Telekommunikation (ZTT), Fachhochschule Worms, Worms,Germany

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JHEP09(2013)049