\PHyear2023 \PHnumber196 \PHdate30 August

\ShortTitleLong-range two-particle correlation over a wide $\eta$ range in p–Pb collisions

\CollaborationALICE Collaboration \ShortAuthorALICE Collaboration

Correlations in azimuthal angle extending over a long range in pseudorapidity between particles, usually called the “ridge” phenomenon, were discovered in heavy-ion collisions, and later found in pp and p–Pb collisions. In large systems, they are thought to arise from the expansion (collective flow) of the produced particles. Extending these measurements over a wider range in pseudorapidity and final-state particle multiplicity is important to understand better the origin of these long-range correlations in small collision systems. In this Letter, measurements of the long-range correlations in p–Pb collisions at $\sqrt{s_{\mathrm{NN}}}=5.02$ TeV are extended to a pseudorapidity gap of $\Delta\eta$ $\sim$ 8 between particles using the ALICE forward multiplicity detectors. After suppressing non-flow correlations, e.g., from jet and resonance decays, the ridge structure is observed to persist up to a very large gap of $\Delta\eta$ $\sim$ 8 for the first time in p–Pb collisions. This shows that the collective flow-like correlations extend over an extensive pseudorapidity range also in small collision systems such as p–Pb collisions. The pseudorapidity dependence of the second-order anisotropic flow coefficient, $v_{2}(\eta)$ , is extracted from the long-range correlations. The $v_{2}(\eta)$ results are presented for a wide pseudorapidity range of $-3.1<\eta<4.8$ in various centrality classes in p–Pb collisions. To gain a comprehensive understanding of the source of anisotropic flow in small collision systems, the $v_{2}(\eta)$ measurements are compared with hydrodynamic and transport model calculations. The comparison suggests that the final-state interactions play a dominant role in developing the anisotropic flow in small collision systems.

1 Introduction

High-energy heavy-ion collisions can produce a deconfined state of quarks and gluons, the so-called quark–gluon plasma (QGP). Measurements of azimuthal anisotropic flow, for example, via long-range two-particle correlations, are sensitive to key properties of the QGP [1]. The two-particle correlations between an associated particle and a trigger particle are commonly measured as a function of the differences in pseudorapidity ( $\Delta\eta=\eta_{\rm trig}-\eta_{\rm asso}$ ) and azimuthal angle ( $\Delta\varphi=\varphi_{\rm trig}-\varphi_{\rm asso}$ ). Striking correlations over a long range in $\Delta\eta$ on the near side ( $\Delta\varphi\sim 0$ ), the so-called “ridge”, have been observed in heavy-ion collisions [2, 3, 4, 5, 6, 7, 8, 9, 10, 11], where they are well understood as a consequence of strong, final-state interactions in the dense system created in heavy-ion collisions and the resulting fluid-like collective expansion of the matter created. The correlation function, associate yield as a function of differences in pseudorapidity and azimuthal angle, projected onto the $\Delta\varphi$ direction, can be expressed in terms of a Fourier series,

\frac{{\rm{d}}N_{\rm pair}}{\rm d\Delta\varphi}\propto 1+\sum_{n=1}^{\infty}2v^{2}_{n}\cos{(n\Delta\varphi)},

(1)

where $v_{n}$ is the Fourier coefficient of $n$ -th flow harmonics. The $v_{n}$ coefficients emerge due to the anisotropic hydrodynamic expansion of the medium and fluctuate along with the collision geometry in heavy-ion collisions. The $v_{n}$ coefficients and their multiplicity and transeverse-momentum ( $p_{\rm T}$ ) dependence are well described by relativistic hydrodynamic models at midrapidity [12, 13]. The pseudorapidity dependence of $v_{n}$ coefficients is sensitive to a temperature dependence of the shear viscosity to entropy density ratio $\eta/s$ of the QGP [14, 15, 16]. It has been measured over a large pseudorapidity region ( $|\eta|<5$ ) in Au–Au and Pb–Pb collisions at RHIC [17] and the LHC [18]. In small collision systems, such as pp and p–Pb collisions, a “ridge” structure was also observed similar to heavy-ion collisions [19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31]. The $p_{\rm T}$ dependence of $v_{n}$ coefficients in small collision systems, which shows a characteristic mass dependence at low $p_{\rm T}$ , is found to be similar to heavy-ion collisions [31, 29, 27, 32]. Both hydrodynamic (macroscopic) and kinetic transport (microscopic) models can describe collective-flow observables fairly well in small systems as well as in heavy-ion collisions [33, 34, 35, 36, 37, 38]. However, the underlying physics of the observed anisotropic flow in small collision systems is still under debate. One possible scenario predicts that both contributions from a momentum anisotropy arising in the initial state as well as final interactions are essential in small collision systems [39, 40, 41]. Furthermore, the relative contributions of soft collective processes, concentrated in the dense “core” and modeled with hydrodynamics, and of hard processes such as hard scattering and jet fragmentation (described as a pp-like “corona”) are also not well understood. The charged-particle pseudorapidity distribution is asymmetric in p–Pb collisions [42]: the multiplicity is larger in the Pb-going direction compared to the p-going direction. Since the mean free path depends on the charged-particle multiplicity, the pseudorapidity dependence of $v_{2}$ reflects the underlying dynamical evolution in p–Pb collisions and is a direct indicator of how local particle densities modulate the collective flow. CMS results on $v_{2}(\eta)$ in p–Pb collisions at the LHC [43, 44] show a significant pseudorapidity dependence, beyond what would be expected from the pseudorapidity dependence of the mean $p_{\rm T}$ . However, the acceptance is limited to midrapidity $|\eta|<2$ . Various small asymmetric collision systems were used to extract $v_{2}(\eta)$ at RHIC by means of the event plane method. The measurements were carried out within the pseudorapidity range of $|\eta|<3$ [45, 46]. A hydrodynamic model [47] describes the result of $v_{2}(\eta)$ qualitatively except for the pseudorapidity regions ( $-3<\eta<-2$ ) in p–Au collisions, where non-flow effects appear to be significant because the rapidity gap from the detector that determines the event plane is small.

In this Letter, the measurements of long-range two-particle correlations and $v_{2}(\eta)$ in p–Pb collisions at $\sqrt{s_{\mathrm{NN}}}=5.02$ TeV with the ALICE detector at the LHC are presented. These measurements utilize the Forward Multiplicity Detector (FMD) to extract $v_{2}(\eta)$ over the unprecedented range of about 8 units of pseudorapidity ( $-3.1<\eta<4.8$ ). The results are compared with a hydrodynamic-model calculation and the AMPT transport model.

2 Experimental setup

A comprehensive description of the ALICE detector can be found in Refs. [48, 49]. The main detectors used in this analysis are the Forward Multiplicity Detector (FMD), the Inner Tracking System (ITS), and the Time Projection Chamber (TPC). The FMD is a silicon strip detector that measures charged particles with a fine granularity of $\Delta\varphi=\pi/20$ and $\Delta\eta=0$ .05. The FMD comprises three sub-detectors: FMD1, FMD2, and FMD3. The combined acceptance of FMD1 and FMD2 is $1.7<\eta<5.1$ . The pseudorapidity coverage of FMD3 is $-3.4<\eta<-1.7$ . FMD1 and FMD3 consist of an inner and an outer ring, and FMD2 consists of only one ring, all placed around the beam pipe. The inner and outer rings are divided into 20 and 40 sectors in the azimuthal direction, respectively, and each ring is composed of hexagonal silicon sensors. Each inner and outer ring is segmented into 512 and 256 strips in the radial direction, respectively. Charged-particle tracking at midrapidity is provided using the TPC and ITS, both covering the full azimuthal angle and $|\eta|<0.8$ . They are placed inside the L3 solenoid magnet, which provides a magnetic field of $B=0.5$ T along the beam direction. The vertex detector, ITS, consists of six layers of silicon detectors; two layers each are equipped with the Silicon Pixel Detector (SPD), the Silicon Drift Detector (SDD), and the Silicon Strip Detector (SSD). The TPC provides track reconstruction using up to 159 space points along a charged-particle trajectory and particle identification via the measurement of specific energy loss $\textrm{d}E/\textrm{d}x$ . The V0 is used for event triggering and centrality determination. It is composed of two arrays of 32 scintillator tiles each and covers $-3.7<\eta<-1.7$ (V0C) and $2.8<\eta<5.1$ (V0A). In addition, two neutron Zero Degree Calorimeters (ZDCs) located at $112.5$ m (ZNA) and $-112.5$ m (ZNC) from the interaction point along the beam direction are used for the event selection.

3 Data analysis

3.1 Event and Track selection

This analysis uses ALICE data taken for p–Pb collisions at a centre-of-mass energy per nucleon pair of $\sqrt{s_{\mathrm{NN}}}=5.02$ TeV provided by the LHC in 2016, corresponding to a proton beam energy of 4 TeV and a lead beam energy of 1.58 TeV per nucleon. Due to the asymmetric collision system, the nucleon–nucleon centre-of-mass system was shifted by 0.465 rapidity units in the proton beam direction with respect to the ALICE laboratory system. In this Letter, $\eta$ denotes the pseudorapidity in the laboratory system, and the positive pseudorapidity points in the Pb-going direction.

This analysis uses 5 $\times 10^{8}$ events acquired using a minimum bias trigger. The minimum bias trigger requires the in-time coincidence of signals from V0A and V0C. The first step of the event selection is performed on the amplitude and timing in the V0 and ZDC as described in Ref. [50]. The efficiency of the event selection is 99.2 $\%$ for non-single-diffractive collisions. The primary vertex position is determined with reconstructed tracks in the TPC and ITS by using an analytic $\chi^{2}$ minimization method as described in Ref. [51]. The primary vertex in the beam axis is required to be within 10 cm of the nominal interaction point along the beam line. In addition, pile-up events from beam-induced background are rejected by using correlations between the FMD and V0 multiplicities.

Charged-particle tracks are reconstructed in the ITS and TPC within $|\eta|<0.8$ for $p_{\rm T}>0.2$ GeV/ $c$ as follows. First, the tracks are selected on the number of space points and the quality of the track fit in the TPC. In addition, the tracks are required to have a distance of closest approach (DCA) to the reconstructed primary vertices less than 2 cm in the beam-axis direction. The DCA in the transverse direction is required to be less than 7 $\sigma_{\rm DCA}$ , where $\sigma_{\rm DCA}$ is a $p_{\rm T}$ -dependent transverse impact parameter resolution. The efficiency of the charged-particle track selection is estimated with a Monte Carlo (MC) simulation using the DPMJET event generator [52] and GEANT3 [53] to simulate particle transport through the detector. The efficiency is about 65% at $p_{\rm T}$ =0.2 GeV/ $c$ , increases to about 79% at $p_{\rm T}$ =0.8 GeV/ $c$ , and decreases to about 76% at $p_{\rm T}$ =3 GeV/ $c$ .

The selected events are divided into several centrality classes based on the V0A amplitude. Table 1 shows the event classes and corresponding average charged-particle pseudorapidity densities within $-3.25<\eta<-2.5$ , $|\eta|<0.8$ , $2<\eta<3.75$ , and $3.75<\eta<5$ . The multiplicities were measured by the innermost two layers of the ITS at midrapidity and the FMD at forward and backward rapidity [42].

Table 1: Average charged-particle pseudorapidity density for

p_{\rm T}>0

GeV/

c

in different pseudorapidity regions.

Centrality	$\langle\mathrm{d}\it{N}_{\mathrm{ch}}/\mathrm{d}\eta\rangle_{\rm-3.25<\eta<\rm-2.5}$	$\langle\mathrm{d}\it{N}_{\mathrm{ch}}/\mathrm{d}\eta\rangle_{\|\eta\|<\rm 0.8}$	$\langle\mathrm{d}\it{N}_{\mathrm{ch}}/\mathrm{d}\eta\rangle_{\rm 2<\eta<\rm 3.75}$	$\langle\mathrm{d}\it{N}_{\mathrm{ch}}/\mathrm{d}\eta\rangle_{\rm 3.75<\eta<\rm 5}$
0–5 $\%$	$34\pm 2.2$	$45\pm 1.4$	$60\pm 3.8$	$52\pm 3.5$
5–10 $\%$	$29\pm 1.9$	$36\pm 1.1$	$46\pm 2.9$	$39\pm 2.6$
10–20 $\%$	$26\pm 1.6$	$31\pm 0.94$	$37\pm 2.4$	$31\pm 2.1$
20–40 $\%$	$21\pm 1.4$	$23\pm 0.72$	$27\pm 1.7$	$22\pm 1.5$
40–60 $\%$	$16\pm 1.0$	$16\pm 0.54$	$17\pm 1.1$	$14\pm 0.95$
60–80 $\%$	$11\pm 0.68$	$9.7\pm 0.33$	$9.9\pm 0.63$	$7.8\pm 0.52$
80–100 $\%$	$5.4\pm 0.34$	$4.2\pm 0.14$	$3.5\pm 0.22$	$2.7\pm 0.18$

3.2 Two-particle correlation and extraction of $v_{2}(\eta)$

For a given event class, two-particle correlations between a trigger and associated particle are measured as a function of the pseudorapidity difference $\Delta\eta$ and the azimuthal angle difference $\Delta\varphi$ . The associated yield to a trigger particle as a function of $\Delta\eta$ and $\Delta\varphi$ is defined as

\frac{1}{N_{\rm trig}}\frac{\rm d^{2}\it N_{\rm assoc}}{\rm d\Delta\eta d\Delta\varphi}=\frac{S(\Delta\eta,\Delta\varphi)}{B(\Delta\eta,\Delta\varphi)},

(2)

where $N_{\rm trig}$ is the total number of trigger particles in the given event class, the signal function $S(\Delta\eta,\Delta\varphi)=\frac{1}{\it N_{\rm trig}}\frac{{\rm d^{2}}N_{\rm same}}{\rm d\Delta\eta\rm d\Delta\varphi}$ is the associated yield per trigger particle in the same event, and the background function $B(\Delta\eta,\Delta\varphi)=\alpha\frac{\rm d^{2}\it N_{\rm mixed}}{\rm d\Delta\eta\rm d\Delta\varphi}$ is the pair yield associated to a trigger particle when the associated particles are taken from other events which fall in the same event class. The $\alpha$ factor is chosen such that $B(\Delta\eta,\Delta\varphi)$ is unity at its maximum. By dividing $S(\Delta\eta,\Delta\varphi)$ by $B(\Delta\eta,\Delta\varphi)$ , pair acceptance and single particle efficiency for both particles are corrected. To take into account the vertex position dependence of the above acceptance and efficiency, the correlation function is extracted in 2 cm wide intervals of the vertex position.

Refer to caption — Figure 1: The associated yield per trigger as a function of $\Delta\eta$ and $\Delta\varphi$ as measured for TPC–FMD1,2 (left), TPC–FMD3 (central), and FMD1,2–FMD3 (right) correlations in the 0–5% (top) and 60–100% (bottom) p–Pb collisions.

Figure 1 shows the correlation function in $\Delta\eta$ and $\Delta\varphi$ between trigger and associated particles in TPC ( $|\eta|<0.8$ ) – FMD1,2 ( $2.9<\eta<3.1$ ) (left), TPC– FMD3 ( $-3.1<\eta<-2.9$ ) (center), and FMD1,2 ( $4.6<\eta<4.8$ ) – FMD3 ( $-3.1<\eta<-2.9$ ) (right) in the 0–5% (top) and 60–100 $\%$ (bottom) p–Pb collisions at $\sqrt{s_{\mathrm{NN}}}$ = 5.02 TeV, respectively. For all three combinations, a long-range correlation on the near side ( $-\pi/2<\Delta\varphi<\pi/2$ ), the so-called “ridge”, is observed in the 0–5% event class, while no significant “ridge” is observed in 60–100%. The long-range correlation in the away side ( $\pi/2<\Delta\varphi<3\pi/2$ ) mainly results from jets recoiling opposite to the trigger particle and is visible in all event classes. Since the pseudorapidity gap between the trigger and associated particles is large, the pronounced peak structure on the near side due to jets [28], which is centred on $\Delta\eta=0$ , is not present. In central p–Pb collisions at the LHC, the near-side “ridge” structure is observed to extend up to a pseudorapidity separation of $\Delta\eta\sim 8$ , which is the largest range measured.

To estimate and subtract the non-flow effects due to recoil jets and resonance decays, the template fit procedure, which was introduced by the ATLAS collaboration, is employed [54, 55]. The correlation function $Y(\Delta\varphi)$ is assumed to be a superposition of a non-flow contribution, which is estimated by scaling the correlation function from peripheral events, and the flow contribution. The template fit function is defined as

Y(\Delta\varphi)=FY^{\rm peri}(\Delta\varphi)+G^{\rm tmp}\{1+2\sum_{n=2}^{3}V^{\rm tmp}_{n,n}\cos(n\Delta\varphi)\},

(3)

where $F,G^{\rm tmp}$ , and $V^{\rm tmp}_{n,n}$ are free parameters. $Y^{\rm peri}(\Delta\varphi)$ is the correlation function in peripheral events. $V^{\rm tmp}_{n,n}$ represents the $n$ -th flow coefficient, and $F$ and $G$ are the scaling factors of the non-flow distribution on the away side and a flat, azimuth-independent baseline, respectively. This method assumes no flow components in the peripheral event used for the template and no away-side jet modifications between central and peripheral events.

Figure 2 shows the projection of the correlation functions, i.e., TPC–FMD1,2 (left), TPC–FMD3 (center), FMD1,2–FMD3 (right), onto the $\Delta\varphi$ axis in the 0–5% event class. These one-dimensional correlations are fitted by the template fit function using Eq. (3). The fit describes the data well with a $\chi^{2}$ /ndf of about 0.8–2.5 for all correlation functions corresponding to all $\eta$ gap combinations. The flow-like part of the fit is dominated by the second harmonic for all three correlations. The modulation for the pair correlation is extracted for each harmonic in the template fit.

Assuming that the relative modulation of the two-particle correlation function is solely due to the modulation of the single-particle distribution, the modulation of the two-particle correlation measured in two different pseudorapidity ranges for particles a and b can be factorized as:

V_{n,n}(\eta_{a},\eta_{b})=v_{n}(\eta_{a})v_{n}(\eta_{b}).

(4)

If this factorization holds, the flow component of a single particle at a certain pseudorapidity can be extracted from three dihadron correlations between different pseudorapidity regions given by

v_{n}(\eta_{a})=\sqrt{\frac{V_{n,n}(\eta_{a},\eta_{b})V_{n,n}(\eta_{a},\eta_{c})}{V_{n,n}(\eta_{b},\eta_{c})}},

(5)

where each $V_{n,n}$ is the modulation extracted by TPC–FMD1,2, TPC–FMD3, and FMD1,2–FMD3 correlation, respectevily. This method is similar to the “3 $\times$ 2PC” method used by PHENIX [21, 56]. The factorization breaks down if the event plane and/or the flow amplitude depend on pseudorapidity, for example because of initial longitudinal fluctuations or thermal fluctuations [57, 58, 59, 60, 61, 62]. Therefore, the uncertainty due to those decorrelation effects is estimated by changing the $\eta$ gap, as it will be discussed in Section 3.3.

Since the FMD is not a tracking detector, it is difficult to separate primary particles from secondary particles inside the FMD. Secondary particles are generated around the primary particle and might distort its distribution. The effects of secondary particles on the flow harmonics are estimated using MC simulations based on the AMPT and EPOS event generators [63, 36, 64]. The correction is performed based on the change in the reconstructed particle distribution after particle transport and interaction within the detector material from the original distribution. The correction factor is extracted as the ratio of the $v_{2}$ of primary particles over the $v_{2}$ of all reconstructed particles (i.e. including primary and secondary particles). In order to match the range of the FMD, which has acceptance down to $p_{\rm T}=0$ , the charged-particle $v_{2}$ at midrapidity is extrapolated to $p_{\rm T}=0$ based on the data of the $p_{\rm T}$ spectrum and the $p_{\rm T}$ differential $v_{2}$ of charged-particles. The factor is about 0.86 for all four centralities.

3.3 Systematic uncertainties

The systematic uncertainties relate to the event selection, the track selection, the correction of secondary particles in the FMD, the material budget in the FMD, the choice of the peripheral event class for non-flow subtraction, the reference pseudorapidity choice of $\eta_{\rm b},\eta_{\rm c}$ to extract $v_{2}(\eta_{\rm a})$ , and the jet modification. A systematic uncertainty is only assigned when the difference between the nominal data points and variations is statistically significant according to the Barlow criterium [65]. Table 2 shows the summary of systematic uncertainties for $v_{2}$ , which depend on centrality and pseudorapidity as indicated by the range given. The uncertainty due to the event selection is investigated by changing the selection parameters for V0–FMD multiplicity correlations. The uncertainty slightly depends on centrality, and it is the largest in the 20–40% centrality class. The uncertainty due to track selection is evaluated by varying track selection parameters. This systematic uncertainty is 0.28–0.45% and is also the largest in the 20–40% centrality class. The systematic uncertainty related to the correction of the contamination by secondary particles is estimated using different event generators, EPOS and AMPT. The magnitude of collective-like signal in EPOS and AMPT is very different, and this check investigates how stable the correction is with respect to the magnitude of the flow. This uncertainty is found to be larger in the p-going direction than in the Pb-going direction. The number of secondary particles depends on the material budget in the ALICE environment. This systematic uncertainty is 2.3 $\%$ estimated using MC simulations with increased or reduced material budget of the detector descriptions in GEANT simulation by $\pm$ 10%. Uncertainties associated with secondary correction and material budget are assigned only for the FMD acceptance. The systematic uncertainty of the peripheral event choice used for non-flow subtraction is estimated using the 80–100 $\%$ event class instead of 60–100 $\%$ . The systematic uncertainty on the reference pseudorapidity choice is investigated by changing the $\eta$ gap between $\eta_{\rm b}$ and $\eta_{\rm c}$ to extract $v_{2}(\eta_{\rm a})$ . The uncertainty ranges from 2.6 $\%$ to 5.1 $\%$ depending on the centrality and the choice of reference pseudorapidity. The shape of the jet is modified depending on the centrality. The uncertainty is evaluated by varying the away-side width of the peripheral collisions and is 0.8–2.7%. These systematic uncertainties are added in quadrature.

Table 2: The summary of systematic uncertainties, which is absolute value on

v_{2}

. The uncertainties take values within the given range depending on the centrality and pseudorapidity for each source.

Source of uncertainty	Systematic uncertainty (%)
Event selection	0.56–3.1 $\%$
Track selection	0.28–0.45 $\%$
Secondary correction	0.74–3.5 $\%$
Material budget	2.3 $\%$
Non-flow sub	0.76–4.6 $\%$
Jet modification	0.8–2.7%
$\eta$ gap selection	2.6–5.1 $\%$
Total	3.9–8.3 $\%$

4 Results

The $v_{2}$ for charged-particles in a specific pseudorapidity region can be extracted using the template fit procedure and the three dihadron correlations of TPC–FMD1,2, TPC–FMD3, and FMD1,2–FMD3, as described in Section 3.2. The corresponding results of the $p_{\rm T}$ -integrated $v_{2}$ as a function of $\eta$ for the 0–5%, 5–10%, 10–20%, and 20–40% centrality classes are shown in Fig. 3. After applying the non-flow subtraction with the template fit approach, a non-zero $v_{2}$ is observed over a wide rapidity range for the first time in p–Pb collisions. The $v_{2}$ results in p–Pb collisions were measured previously for the pseudorapidity range of $|\eta|<2$ by CMS [43, 44]. The $v_{2}$ measurements presented in this Letter significantly extend the measurements to a much wider pseudorapidity range, $-3.1<\eta<4.8$ . This result confirms the emergence of anisotropic flow over a wide rapidity region, as in high-energy heavy-ion collisions [18]. In addition, the $v_{2}$ measurements show a significant pseudorapidity dependence for all four centrality classes. It is more prominent in the Pb-going direction (positive $\eta$ ) than in the p-going direction (negative $\eta$ ). Additionally, a stronger centrality dependence of $v_{2}$ is found in the Pb-going direction than in the p-going direction.

Previous $v_{2}(\eta)$ measurements showed that the magnitude of $v_{2}$ is correlated with charged-particle pseudorapidity density [45]; $v_{2}(\eta)$ increases with increasing multiplicity. However, $v_{2}(\eta)$ might not be linearly correlated with $\mathrm{d}\it{N}_{\mathrm{ch}}/\mathrm{d}\eta$ [42]. Figure 4 shows $v_{2}$ as a function of charged-particle multiplicity density for five different pseudorapidity regions and for different centrality classes: 0–5%, 5–10%, 10–20%, and 20–40%. Figure 4 demonstrates that the pseudorapidity dependence of $v_{2}$ is not just simply driven by the local multiplicity, $v_{2}$ independently depends on both $\eta$ and $\mathrm{d}\it{N}_{\mathrm{ch}}/\mathrm{d}\eta$ . In a fixed pseudorapidity range, $v_{2}$ depends on local multiplicity, but at fixed local multiplicity, there is still a significant dependence on the pseudorapidity.

Figure 5 shows the comparisons of $v_{2}$ in 0–5% p–Pb collisions and in 60–70% and 70–80% Pb–Pb collisions, where the multiplicity is similar in the Pb-going direction between the two collision systems [42, 66]. The charged-particle pseudorapidity density at $\eta\sim 3$ is about 60 in p–Pb collisions and 80 and 37 in 60–70% and 70–80% Pb–Pb collisions, respectively. Here, the results from Pb–Pb collisions are based on the 2-particle cumulant method [18]. The $v_{2}$ results in p–Pb collisions are compatible with the $v_{2}$ in 60–70% and 70–80% Pb–Pb collisions over the entire $\eta$ range within the sizable uncertainties of the Pb–Pb results. Somewhat unexpectedly, given the potential differences in the initial collision overlap geometry, it is observed that peripheral Pb–Pb collisions and central p–Pb collisions have comparable $v_{2}$ at similar multiplicities.

To further investigate the origin of the flow in small collision systems, the $v_{2}(\eta)$ measured in p–Pb collisions is compared with hydrodynamical calculations [67] in Fig. 6, and with the AMPT transport model in Fig. 7. The 3+1 hydrodynamical model employs 3D Glauber initial conditions, viscous hydrodynamics based on MUSIC, and the UrQMD model to simulate the dynamics in the hadronic phase. The shear viscosity and color string width in the transverse plane are adjusted to $\eta_{T}/(e+P)=0.08$ and $\sigma_{x}=0.4$ fm, respectively, to reproduce the mean transverse momentum of identified particles and the $p_{\rm T}$ dependence of charged-particle $v_{2}$ measured with the template fit procedure by ATLAS at midrapidity in p–Pb collisions at $\sqrt{s_{\mathrm{NN}}}=5.02$ TeV [67]. The hydrodynamical model underestimates the pseudorapidity dependence of charged-particle multiplicity density when centrality is determined at forward pseudorapidity, while it is reproduced when centrality is determined at midrapidity [68]. The correlation between the multiplicities at forward and midrapidity is weaker in the model than in the data. In this model, $v_{2}$ mainly originates from the 3D initial geometry and develops in the course of the hydrodynamical evolution. The model describes the $v_{2}(\eta)$ measurement in 0–5% and 5–10%, while it somewhat overestimates the data in 10–20% and 20–40% at both forward and backward rapidity.

Similarly, Fig. 7 shows the comparisons with calculations by the AMPT model in the string-melting configuration. Unlike the hydrodynamical model, the AMPT with string melting is a transport model that produces collective behaviour microscopically by final-state scattering in both the partonic and hadronic phases. The $v_{2}(\eta)$ calculation from AMPT describes the data qualitatively in the 0–5% centrality class and reproduces the asymmetry between the Pb-going and p-going directions. However, the centrality dependence is less significant than observed in the data.

The comparisons between the $v_{2}(\eta)$ measurements and the calculations from the hydrodynamical and AMPT transport models suggest that strong final-state interactions are possibly the origin of a significant $v_{2}$ over a wide pseudorapidity range in small collision systems such as p–Pb collisions. Initial momentum anisotropy from Colour-Glass Condensate (CGC) could also play a role in high-multiplicity p–Pb collisions. However, a recent study using gluon saturation from the IP-Glasma model shows that the initial momentum anisotropy results to short-range correlation [69]. The observed finite $v_{2}$ extracted from ultra-long correlations will likely not be influenced by contributions from initial momentum anisotropy from CGC but more likely originates from the fluctuating initial geometry giving rise to final-state interactions.

5 Summary

In this Letter, the two-particle correlation function is presented as a function of $\Delta\eta$ and $\Delta\varphi$ in p–Pb collisions at $\sqrt{s_{\mathrm{NN}}}=5.02$ TeV with ALICE. The “ridge” structure is observed up to a rapidity gap of 8 units between the trigger and the associate particles, in central events. A double-ridge structure is visible after the non-flow subtraction. The $p_{\rm T}$ -integrated $v_{2}$ is extracted from the correlation function and presented as a function of pseudorapidity and centrality class. Non-zero $v_{2}$ is observed over a wide pseudorapidity range in central p–Pb collisions for the first time. The pseudorapidity dependence as well as its asymmetric shape of $v_{2}$ could be well explained by charged-particle multiplicity distributions. In addition, the $v_{2}(\eta)$ in p–Pb central events is comparable with the $v_{2}(\eta)$ in peripheral Pb–Pb collisions at similar multiplicity. Finally, both hydrodynamical [67] and AMPT transport model calculations [36] describe the data qualitatively over a wide pseudorapidity region. The comparison with the model calculations suggests the emergence of collective flow at very forward pseudorapidity ( $|\eta|\sim 5$ ) region in p–Pb collisions, just like in high-energy heavy-ion collisions. The results suggest an important role of final-state interactions in developing anisotropic flow in small collision systems.

Acknowledgements

The ALICE Collaboration would like to thank all its engineers and technicians for their invaluable contributions to the construction of the experiment and the CERN accelerator teams for the outstanding performance of the LHC complex. The ALICE Collaboration gratefully acknowledges the resources and support provided by all Grid centres and the Worldwide LHC Computing Grid (WLCG) collaboration. The ALICE Collaboration acknowledges the following funding agencies for their support in building and running the ALICE detector: A. I. Alikhanyan National Science Laboratory (Yerevan Physics Institute) Foundation (ANSL), State Committee of Science and World Federation of Scientists (WFS), Armenia; Austrian Academy of Sciences, Austrian Science Fund (FWF): [M 2467-N36] and Nationalstiftung für Forschung, Technologie und Entwicklung, Austria; Ministry of Communications and High Technologies, National Nuclear Research Center, Azerbaijan; Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Financiadora de Estudos e Projetos (Finep), Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) and Universidade Federal do Rio Grande do Sul (UFRGS), Brazil; Bulgarian Ministry of Education and Science, within the National Roadmap for Research Infrastructures 2020–2027 (object CERN), Bulgaria; Ministry of Education of China (MOEC) , Ministry of Science & Technology of China (MSTC) and National Natural Science Foundation of China (NSFC), China; Ministry of Science and Education and Croatian Science Foundation, Croatia; Centro de Aplicaciones Tecnológicas y Desarrollo Nuclear (CEADEN), Cubaenergía, Cuba; Ministry of Education, Youth and Sports of the Czech Republic, Czech Republic; The Danish Council for Independent Research — Natural Sciences, the VILLUM FONDEN and Danish National Research Foundation (DNRF), Denmark; Helsinki Institute of Physics (HIP), Finland; Commissariat à l’Energie Atomique (CEA) and Institut National de Physique Nucléaire et de Physique des Particules (IN2P3) and Centre National de la Recherche Scientifique (CNRS), France; Bundesministerium für Bildung und Forschung (BMBF) and GSI Helmholtzzentrum für Schwerionenforschung GmbH, Germany; General Secretariat for Research and Technology, Ministry of Education, Research and Religions, Greece; National Research, Development and Innovation Office, Hungary; Department of Atomic Energy Government of India (DAE), Department of Science and Technology, Government of India (DST), University Grants Commission, Government of India (UGC) and Council of Scientific and Industrial Research (CSIR), India; National Research and Innovation Agency - BRIN, Indonesia; Istituto Nazionale di Fisica Nucleare (INFN), Italy; Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT) and Japan Society for the Promotion of Science (JSPS) KAKENHI, Japan; Consejo Nacional de Ciencia (CONACYT) y Tecnología, through Fondo de Cooperación Internacional en Ciencia y Tecnología (FONCICYT) and Dirección General de Asuntos del Personal Academico (DGAPA), Mexico; Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO), Netherlands; The Research Council of Norway, Norway; Commission on Science and Technology for Sustainable Development in the South (COMSATS), Pakistan; Pontificia Universidad Católica del Perú, Peru; Ministry of Education and Science, National Science Centre and WUT ID-UB, Poland; Korea Institute of Science and Technology Information and National Research Foundation of Korea (NRF), Republic of Korea; Ministry of Education and Scientific Research, Institute of Atomic Physics, Ministry of Research and Innovation and Institute of Atomic Physics and University Politehnica of Bucharest, Romania; Ministry of Education, Science, Research and Sport of the Slovak Republic, Slovakia; National Research Foundation of South Africa, South Africa; Swedish Research Council (VR) and Knut & Alice Wallenberg Foundation (KAW), Sweden; European Organization for Nuclear Research, Switzerland; Suranaree University of Technology (SUT), National Science and Technology Development Agency (NSTDA) and National Science, Research and Innovation Fund (NSRF via PMU-B B05F650021), Thailand; Turkish Energy, Nuclear and Mineral Research Agency (TENMAK), Turkey; National Academy of Sciences of Ukraine, Ukraine; Science and Technology Facilities Council (STFC), United Kingdom; National Science Foundation of the United States of America (NSF) and United States Department of Energy, Office of Nuclear Physics (DOE NP), United States of America. In addition, individual groups or members have received support from: European Research Council, Strong 2020 - Horizon 2020 (grant nos. 950692, 824093), European Union; Academy of Finland (Center of Excellence in Quark Matter) (grant nos. 346327, 346328), Finland.

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Appendix A Non-flow subtraction with the improved-template-fit method and peripheral subtraction procedure

To examine the robustness of the observation, two different derived template-fit methods for non-flow suppression are employed: the zero-yield-at-minimum (ZYAM) and the improved-template-fit method [70, 54]. Henceforth, the peripheral subtraction will denote the ZYAM template-fit method. The term of $FY^{\rm peri}(\Delta\varphi)$ in Eq. (3) is substituted by $FY^{\rm peri}(\Delta\varphi)-Y_{0}$ , where $Y_{0}$ is the baseline of the correlation function in peripheral events, which is determined at $\Delta\varphi\sim 0$ . The peripheral subtraction method assumes that the 2nd-order modulation is zero when the multiplicity is zero. An improved-template-fit method was developed to estimate the non-flow effects in peripheral collisions more realistically. This method assumes that there are no flow components in the peripheral events. On the other hand, this procedure takes into account the residual flow components in peripheral events. The flow harmonics in the second-most peripheral events are extracted by the template fit using the correlation function in the most peripheral events given by

Y^{\rm peri}(\Delta\varphi)=Y_{\rm non\mathchar 45flow}(\Delta\varphi)+G^{\rm peri}\{1+2\sum_{n=2}^{3}v^{\rm peri}_{n,n}\cos{(n\Delta\varphi)}\},

(6)

where $Y^{\rm peri}(\Delta\varphi)$ and $Y_{\rm non-flow}(\Delta\varphi)$ are the correlation functions in the second-most and the most peripheral events, respectively. By substituting Eq. (A.1) with Eq. (3), it can be seen that

Y(\Delta\varphi)=FY_{\rm non\mathchar 45flow}(\Delta\varphi)+(G^{\rm tmp}+FG^{\rm peri})\{1+2\sum_{n=2}^{3}\frac{G^{\rm tmp}v^{\rm tmp}_{n,n}+FG^{\rm peri}v^{\rm peri}_{n,n}}{G^{\rm tmp}+FG^{\rm peri}}\cos(n\Delta\varphi)\}.

(7)

Finally, the non-flow subtracted flow coefficient from the improved-template-fit method, $v^{\rm imp}_{n,n}$ , can be obtained as

v^{\rm imp}_{n,n}=v^{\rm tmp}_{n,n}-\frac{FG^{\rm peri}}{G^{\rm tmp}+FG^{\rm peri}}(v^{\rm tmp}_{n,n}-v^{\rm peri}_{n,n}).

(8)

Figure 8 shows the $p_{\rm T}$ -integrated $v_{2}$ as a function of $\eta$ with two different non-flow suppression methods for 0–5%, 5–10%, 10–20%, and 20–40% p–Pb collisions. The improved-template-fit method, which considers $v_{2}$ in peripheral collisions, gives smaller $v_{2}$ than the template-fit method; however, the difference is insignificant for the presented pseudorapidity regions. The effect of the second-order component in the peripheral collision is small. The result of $v_{2}$ from the peripheral subtraction method, which assumes no elliptic flow in the peripheral collision, is about 15 $\%$ smaller than the template procedure for the 0–5 $\%$ centrality class. Regardless of which non-flow subtraction method is applied, non-zero $v_{2}$ results are observed for the entire presented pseudorapidity region with more than 5- $\sigma$ confidence, further confirming the observations of anisotropic flow in high-multiplicity p–Pb collisions. Figure 9 shows the $v_{2}$ as a function of local charged-particle density for five different pseudorapidity regions with the peripheral subtraction (left) and the improved-template-fit (right) methods. Similar to the template fit results shown in Fig. 4, $v_{2}$ from peripheral subtraction and improved-templated-fit methods show a charged multiplicity density dependence. Fig. 10 also shows the comparisons of $v_{2}$ in 0–5% central p–Pb collisions and in peripheral Pb–Pb collisions with the improved-template-fit method and the peripheral subtraction. The $v_{2}$ of the improved-template-fit method is comparable to the $v_{2}$ in peripheral Pb–Pb collisions with similar multiplicity at forward rapidity as well as the $v_{2}$ of the template fit. At the same time, the $v_{2}$ result from the peripheral subtraction is consistent with the results in Pb–Pb in the Pb-going direction within the uncertainty; however, it is smaller in the p-going direction. Figure 11 compares the AMPT calculation and the results with the improved-template-fit and the peripheral subtraction methods. Similar to the experimental measurements, improved-template-fit and peripheral subtraction methods are also applied in these AMPT calculations. It is found that the AMPT calculations also exhibit the differences in $v_{2}$ from the improved-template-fit method and the peripheral subtraction, just as the data. This is because both template-fit and improved-template-fit methods consider possible flow generated in peripheral collisions, which is the case in the AMPT model, while such flow contributions are treated as non-flow and subtracted in the peripheral subtraction method.

Appendix B The ALICE Collaboration

S. Acharya ¹²⁶, D. Adamová ⁸⁶, G. Aglieri Rinella ³³, M. Agnello ³⁰, N. Agrawal ⁵¹, Z. Ahammed ¹³⁴, S. Ahmad¹⁶, S.U. Ahn ⁷¹, I. Ahuja ³⁸, A. Akindinov ¹⁴², M. Al-Turany ⁹⁷, D. Aleksandrov ¹⁴², B. Alessandro ⁵⁶, H.M. Alfanda ⁶, R. Alfaro Molina ⁶⁷, B. Ali ¹⁶, A. Alici ²⁶, N. Alizadehvandchali ¹¹⁵, A. Alkin ³³, J. Alme ²¹, G. Alocco ⁵², T. Alt ⁶⁴, A.R. Altamura ⁵⁰, I. Altsybeev ⁹⁵, M.N. Anaam ⁶, C. Andrei ⁴⁶, N. Andreou ¹¹⁴, A. Andronic ¹³⁷, V. Anguelov ⁹⁴, F. Antinori ⁵⁴, P. Antonioli ⁵¹, N. Apadula ⁷⁴, L. Aphecetche ¹⁰³, H. Appelshäuser ⁶⁴, C. Arata ⁷³, S. Arcelli ²⁶, M. Aresti ²³, R. Arnaldi ⁵⁶, J.G.M.C.A. Arneiro ¹¹⁰, I.C. Arsene ²⁰, M. Arslandok ¹³⁹, A. Augustinus ³³, R. Averbeck ⁹⁷, M.D. Azmi¹⁶, H. Baba¹²³, A. Badalà ⁵³, J. Bae ¹⁰⁴, Y.W. Baek ⁴¹, X. Bai ¹¹⁹, R. Bailhache ⁶⁴, Y. Bailung ⁴⁸, A. Balbino ³⁰, A. Baldisseri ¹²⁹, B. Balis ², D. Banerjee ⁴, Z. Banoo ⁹¹, R. Barbera ²⁷, F. Barile ³², L. Barioglio ⁹⁵, M. Barlou⁷⁸, B. Barman⁴², G.G. Barnaföldi ¹³⁸, L.S. Barnby ⁸⁵, V. Barret ¹²⁶, L. Barreto ¹¹⁰, C. Bartels ¹¹⁸, K. Barth ³³, E. Bartsch ⁶⁴, N. Bastid ¹²⁶, S. Basu ⁷⁵, G. Batigne ¹⁰³, D. Battistini ⁹⁵, B. Batyunya ¹⁴³, D. Bauri⁴⁷, J.L. Bazo Alba ¹⁰¹, I.G. Bearden ⁸³, C. Beattie ¹³⁹, P. Becht ⁹⁷, D. Behera ⁴⁸, I. Belikov ¹²⁸, A.D.C. Bell Hechavarria ¹³⁷, F. Bellini ²⁶, R. Bellwied ¹¹⁵, S. Belokurova ¹⁴², Y.A.V. Beltran ⁴⁵, G. Bencedi ¹³⁸, S. Beole ²⁵, Y. Berdnikov ¹⁴², A. Berdnikova ⁹⁴, L. Bergmann ⁹⁴, M.G. Besoiu ⁶³, L. Betev ³³, P.P. Bhaduri ¹³⁴, A. Bhasin ⁹¹, M.A. Bhat ⁴, B. Bhattacharjee ⁴², L. Bianchi ²⁵, N. Bianchi ⁴⁹, J. Bielčík ³⁶, J. Bielčíková ⁸⁶, J. Biernat ¹⁰⁷, A.P. Bigot ¹²⁸, A. Bilandzic ⁹⁵, G. Biro ¹³⁸, S. Biswas ⁴, N. Bize ¹⁰³, J.T. Blair ¹⁰⁸, D. Blau ¹⁴², M.B. Blidaru ⁹⁷, N. Bluhme³⁹, C. Blume ⁶⁴, G. Boca ^22,55, F. Bock ⁸⁷, T. Bodova ²¹, A. Bogdanov¹⁴², S. Boi ²³, J. Bok ⁵⁸, L. Boldizsár ¹³⁸, M. Bombara ³⁸, P.M. Bond ³³, G. Bonomi ^133,55, H. Borel ¹²⁹, A. Borissov ¹⁴², A.G. Borquez Carcamo ⁹⁴, H. Bossi ¹³⁹, E. Botta ²⁵, Y.E.M. Bouziani ⁶⁴, L. Bratrud ⁶⁴, P. Braun-Munzinger ⁹⁷, M. Bregant ¹¹⁰, M. Broz ³⁶, G.E. Bruno ^96,32, M.D. Buckland ²⁴, D. Budnikov ¹⁴², H. Buesching ⁶⁴, S. Bufalino ³⁰, P. Buhler ¹⁰², N. Burmasov ¹⁴², Z. Buthelezi ^68,122, A. Bylinkin ²¹, S.A. Bysiak¹⁰⁷, M. Cai ⁶, H. Caines ¹³⁹, A. Caliva ²⁹, E. Calvo Villar ¹⁰¹, J.M.M. Camacho ¹⁰⁹, P. Camerini ²⁴, F.D.M. Canedo ¹¹⁰, M. Carabas ¹²⁵, A.A. Carballo ³³, F. Carnesecchi ³³, R. Caron ¹²⁷, L.A.D. Carvalho ¹¹⁰, J. Castillo Castellanos ¹²⁹, F. Catalano ^33,25, C. Ceballos Sanchez ¹⁴³, I. Chakaberia ⁷⁴, P. Chakraborty ⁴⁷, S. Chandra ¹³⁴, S. Chapeland ³³, M. Chartier ¹¹⁸, S. Chattopadhyay ¹³⁴, S. Chattopadhyay ⁹⁹, T.G. Chavez ⁴⁵, T. Cheng ^97,6, C. Cheshkov ¹²⁷, B. Cheynis ¹²⁷, V. Chibante Barroso ³³, D.D. Chinellato ¹¹¹, E.S. Chizzali ^I,⁹⁵, J. Cho ⁵⁸, S. Cho ⁵⁸, P. Chochula ³³, D. Choudhury⁴², P. Christakoglou ⁸⁴, C.H. Christensen ⁸³, P. Christiansen ⁷⁵, T. Chujo ¹²⁴, M. Ciacco ³⁰, C. Cicalo ⁵², F. Cindolo ⁵¹, M.R. Ciupek⁹⁷, G. Clai^II,⁵¹, F. Colamaria ⁵⁰, J.S. Colburn¹⁰⁰, D. Colella ^96,32, M. Colocci ²⁶, M. Concas ^III,³³, G. Conesa Balbastre ⁷³, Z. Conesa del Valle ¹³⁰, G. Contin ²⁴, J.G. Contreras ³⁶, M.L. Coquet ¹²⁹, P. Cortese ^132,56, M.R. Cosentino ¹¹², F. Costa ³³, S. Costanza ^22,55, C. Cot ¹³⁰, J. Crkovská ⁹⁴, P. Crochet ¹²⁶, R. Cruz-Torres ⁷⁴, P. Cui ⁶, A. Dainese ⁵⁴, M.C. Danisch ⁹⁴, A. Danu ⁶³, P. Das ⁸⁰, P. Das ⁴, S. Das ⁴, A.R. Dash ¹³⁷, S. Dash ⁴⁷, R.M.H. David⁴⁵, A. De Caro ²⁹, G. de Cataldo ⁵⁰, J. de Cuveland³⁹, A. De Falco ²³, D. De Gruttola ²⁹, N. De Marco ⁵⁶, C. De Martin ²⁴, S. De Pasquale ²⁹, R. Deb ¹³³, R. Del Grande ⁹⁵, L. Dello Stritto ²⁹, W. Deng ⁶, P. Dhankher ¹⁹, D. Di Bari ³², A. Di Mauro ³³, B. Diab ¹²⁹, R.A. Diaz ^143,7, T. Dietel ¹¹³, Y. Ding ⁶, J. Ditzel ⁶⁴, R. Divià ³³, D.U. Dixit ¹⁹, Ø. Djuvsland²¹, U. Dmitrieva ¹⁴², A. Dobrin ⁶³, B. Dönigus ⁶⁴, J.M. Dubinski ¹³⁵, A. Dubla ⁹⁷, S. Dudi ⁹⁰, P. Dupieux ¹²⁶, M. Durkac¹⁰⁶, N. Dzalaiova¹³, T.M. Eder ¹³⁷, R.J. Ehlers ⁷⁴, F. Eisenhut ⁶⁴, R. Ejima⁹², D. Elia ⁵⁰, B. Erazmus ¹⁰³, F. Ercolessi ²⁶, B. Espagnon ¹³⁰, G. Eulisse ³³, D. Evans ¹⁰⁰, S. Evdokimov ¹⁴², L. Fabbietti ⁹⁵, M. Faggin ²⁸, J. Faivre ⁷³, F. Fan ⁶, W. Fan ⁷⁴, A. Fantoni ⁴⁹, M. Fasel ⁸⁷, A. Feliciello ⁵⁶, G. Feofilov ¹⁴², A. Fernández Téllez ⁴⁵, L. Ferrandi ¹¹⁰, M.B. Ferrer ³³, A. Ferrero ¹²⁹, C. Ferrero ⁵⁶, A. Ferretti ²⁵, V.J.G. Feuillard ⁹⁴, V. Filova ³⁶, D. Finogeev ¹⁴², F.M. Fionda ⁵², E. Flatland³³, F. Flor ¹¹⁵, A.N. Flores ¹⁰⁸, S. Foertsch ⁶⁸, I. Fokin ⁹⁴, S. Fokin ¹⁴², E. Fragiacomo ⁵⁷, E. Frajna ¹³⁸, U. Fuchs ³³, N. Funicello ²⁹, C. Furget ⁷³, A. Furs ¹⁴², T. Fusayasu ⁹⁸, J.J. Gaardhøje ⁸³, M. Gagliardi ²⁵, A.M. Gago ¹⁰¹, T. Gahlaut⁴⁷, C.D. Galvan ¹⁰⁹, D.R. Gangadharan ¹¹⁵, P. Ganoti ⁷⁸, C. Garabatos ⁹⁷, A.T. Garcia ¹³⁰, J.R.A. Garcia ⁴⁵, E. Garcia-Solis ⁹, C. Gargiulo ³³, P. Gasik ⁹⁷, A. Gautam ¹¹⁷, M.B. Gay Ducati ⁶⁶, M. Germain ¹⁰³, A. Ghimouz¹²⁴, C. Ghosh¹³⁴, M. Giacalone ⁵¹, G. Gioachin ³⁰, P. Giubellino ^97,56, P. Giubilato ²⁸, A.M.C. Glaenzer ¹²⁹, P. Glässel ⁹⁴, E. Glimos ¹²¹, D.J.Q. Goh⁷⁶, V. Gonzalez ¹³⁶, P. Gordeev¹⁴², M. Gorgon ², K. Goswami ⁴⁸, S. Gotovac³⁴, V. Grabski ⁶⁷, L.K. Graczykowski ¹³⁵, E. Grecka ⁸⁶, A. Grelli ⁵⁹, C. Grigoras ³³, V. Grigoriev ¹⁴², S. Grigoryan ^143,1, F. Grosa ³³, J.F. Grosse-Oetringhaus ³³, R. Grosso ⁹⁷, D. Grund ³⁶, N.A. Grunwald⁹⁴, G.G. Guardiano ¹¹¹, R. Guernane ⁷³, M. Guilbaud ¹⁰³, K. Gulbrandsen ⁸³, T. Gündem ⁶⁴, T. Gunji ¹²³, W. Guo ⁶, A. Gupta ⁹¹, R. Gupta ⁹¹, R. Gupta ⁴⁸, S.P. Guzman ⁴⁵, K. Gwizdziel ¹³⁵, L. Gyulai ¹³⁸, C. Hadjidakis ¹³⁰, F.U. Haider ⁹¹, S. Haidlova ³⁶, H. Hamagaki ⁷⁶, A. Hamdi ⁷⁴, Y. Han ¹⁴⁰, B.G. Hanley ¹³⁶, R. Hannigan ¹⁰⁸, J. Hansen ⁷⁵, M.R. Haque ¹³⁵, J.W. Harris ¹³⁹, A. Harton ⁹, H. Hassan ¹¹⁶, D. Hatzifotiadou ⁵¹, P. Hauer ⁴³, L.B. Havener ¹³⁹, S.T. Heckel ⁹⁵, E. Hellbär ⁹⁷, H. Helstrup ³⁵, M. Hemmer ⁶⁴, T. Herman ³⁶, G. Herrera Corral ⁸, F. Herrmann¹³⁷, S. Herrmann ¹²⁷, K.F. Hetland ³⁵, B. Heybeck ⁶⁴, H. Hillemanns ³³, B. Hippolyte ¹²⁸, F.W. Hoffmann ⁷⁰, B. Hofman ⁵⁹, G.H. Hong ¹⁴⁰, M. Horst ⁹⁵, A. Horzyk², Y. Hou ⁶, P. Hristov ³³, C. Hughes ¹²¹, P. Huhn⁶⁴, L.M. Huhta ¹¹⁶, T.J. Humanic ⁸⁸, A. Hutson ¹¹⁵, D. Hutter ³⁹, R. Ilkaev¹⁴², H. Ilyas ¹⁴, M. Inaba ¹²⁴, G.M. Innocenti ³³, M. Ippolitov ¹⁴², A. Isakov ^84,86, T. Isidori ¹¹⁷, M.S. Islam ⁹⁹, M. Ivanov ⁹⁷, M. Ivanov¹³, V. Ivanov ¹⁴², K.E. Iversen ⁷⁵, M. Jablonski ², B. Jacak ⁷⁴, N. Jacazio ²⁶, P.M. Jacobs ⁷⁴, S. Jadlovska¹⁰⁶, J. Jadlovsky¹⁰⁶, S. Jaelani ⁸², C. Jahnke ¹¹⁰, M.J. Jakubowska ¹³⁵, M.A. Janik ¹³⁵, T. Janson⁷⁰, S. Ji ¹⁷, S. Jia ¹⁰, A.A.P. Jimenez ⁶⁵, F. Jonas ⁸⁷, D.M. Jones ¹¹⁸, J.M. Jowett ^33,97, J. Jung ⁶⁴, M. Jung ⁶⁴, A. Junique ³³, A. Jusko ¹⁰⁰, M.J. Kabus ^33,135, J. Kaewjai¹⁰⁵, P. Kalinak ⁶⁰, A.S. Kalteyer ⁹⁷, A. Kalweit ³³, V. Kaplin ¹⁴², A. Karasu Uysal ⁷², D. Karatovic ⁸⁹, O. Karavichev ¹⁴², T. Karavicheva ¹⁴², P. Karczmarczyk ¹³⁵, E. Karpechev ¹⁴², U. Kebschull ⁷⁰, R. Keidel ¹⁴¹, D.L.D. Keijdener⁵⁹, M. Keil ³³, B. Ketzer ⁴³, S.S. Khade ⁴⁸, A.M. Khan ¹¹⁹, S. Khan ¹⁶, A. Khanzadeev ¹⁴², Y. Kharlov ¹⁴², A. Khatun ¹¹⁷, A. Khuntia ³⁶, B. Kileng ³⁵, B. Kim ¹⁰⁴, C. Kim ¹⁷, D.J. Kim ¹¹⁶, E.J. Kim ⁶⁹, J. Kim ¹⁴⁰, J.S. Kim ⁴¹, J. Kim ⁵⁸, J. Kim ⁶⁹, M. Kim ¹⁹, S. Kim ¹⁸, T. Kim ¹⁴⁰, K. Kimura ⁹², S. Kirsch ⁶⁴, I. Kisel ³⁹, S. Kiselev ¹⁴², A. Kisiel ¹³⁵, J.P. Kitowski ², J.L. Klay ⁵, J. Klein ³³, S. Klein ⁷⁴, C. Klein-Bösing ¹³⁷, M. Kleiner ⁶⁴, T. Klemenz ⁹⁵, A. Kluge ³³, A.G. Knospe ¹¹⁵, C. Kobdaj ¹⁰⁵, T. Kollegger⁹⁷, A. Kondratyev ¹⁴³, N. Kondratyeva ¹⁴², E. Kondratyuk ¹⁴², J. Konig ⁶⁴, S.A. Konigstorfer ⁹⁵, P.J. Konopka ³³, G. Kornakov ¹³⁵, M. Korwieser ⁹⁵, S.D. Koryciak ², A. Kotliarov ⁸⁶, V. Kovalenko ¹⁴², M. Kowalski ¹⁰⁷, V. Kozhuharov ³⁷, I. Králik ⁶⁰, A. Kravčáková ³⁸, L. Krcal ^33,39, M. Krivda ^100,60, F. Krizek ⁸⁶, K. Krizkova Gajdosova ³³, M. Kroesen ⁹⁴, M. Krüger ⁶⁴, D.M. Krupova ³⁶, E. Kryshen ¹⁴², V. Kučera ⁵⁸, C. Kuhn ¹²⁸, P.G. Kuijer ⁸⁴, T. Kumaoka¹²⁴, D. Kumar¹³⁴, L. Kumar ⁹⁰, N. Kumar⁹⁰, S. Kumar ³², S. Kundu ³³, P. Kurashvili ⁷⁹, A. Kurepin ¹⁴², A.B. Kurepin ¹⁴², A. Kuryakin ¹⁴², S. Kushpil ⁸⁶, V. Kuskov¹⁴², M.J. Kweon ⁵⁸, Y. Kwon ¹⁴⁰, S.L. La Pointe ³⁹, P. La Rocca ²⁷, A. Lakrathok¹⁰⁵, M. Lamanna ³³, R. Langoy ¹²⁰, P. Larionov ³³, E. Laudi ³³, L. Lautner ^33,95, R. Lavicka ¹⁰², R. Lea ^133,55, H. Lee ¹⁰⁴, I. Legrand ⁴⁶, G. Legras ¹³⁷, J. Lehrbach ³⁹, T.M. Lelek², R.C. Lemmon ⁸⁵, I. León Monzón ¹⁰⁹, M.M. Lesch ⁹⁵, E.D. Lesser ¹⁹, P. Lévai ¹³⁸, X. Li¹⁰, J. Lien ¹²⁰, R. Lietava ¹⁰⁰, I. Likmeta ¹¹⁵, B. Lim ²⁵, S.H. Lim ¹⁷, V. Lindenstruth ³⁹, A. Lindner⁴⁶, C. Lippmann ⁹⁷, D.H. Liu ⁶, J. Liu ¹¹⁸, G.S.S. Liveraro ¹¹¹, I.M. Lofnes ²¹, C. Loizides ⁸⁷, S. Lokos ¹⁰⁷, J. Lomker ⁵⁹, P. Loncar ³⁴, X. Lopez ¹²⁶, E. López Torres ⁷, P. Lu ^97,119, F.V. Lugo ⁶⁷, J.R. Luhder ¹³⁷, M. Lunardon ²⁸, G. Luparello ⁵⁷, Y.G. Ma ⁴⁰, M. Mager ³³, A. Maire ¹²⁸, M.V. Makariev ³⁷, M. Malaev ¹⁴², G. Malfattore ²⁶, N.M. Malik ⁹¹, Q.W. Malik²⁰, S.K. Malik ⁹¹, L. Malinina ^VI,¹⁴³, D. Mallick ^130,80, N. Mallick ⁴⁸, G. Mandaglio ^31,53, S.K. Mandal ⁷⁹, V. Manko ¹⁴², F. Manso ¹²⁶, V. Manzari ⁵⁰, Y. Mao ⁶, R.W. Marcjan ², G.V. Margagliotti ²⁴, A. Margotti ⁵¹, A. Marín ⁹⁷, C. Markert ¹⁰⁸, P. Martinengo ³³, M.I. Martínez ⁴⁵, G. Martínez García ¹⁰³, M.P.P. Martins ¹¹⁰, S. Masciocchi ⁹⁷, M. Masera ²⁵, A. Masoni ⁵², L. Massacrier ¹³⁰, O. Massen ⁵⁹, A. Mastroserio ^131,50, O. Matonoha ⁷⁵, S. Mattiazzo ²⁸, A. Matyja ¹⁰⁷, C. Mayer ¹⁰⁷, A.L. Mazuecos ³³, F. Mazzaschi ²⁵, M. Mazzilli ³³, J.E. Mdhluli ¹²², Y. Melikyan ⁴⁴, A. Menchaca-Rocha ⁶⁷, J.E.M. Mendez ⁶⁵, E. Meninno ^102,29, A.S. Menon ¹¹⁵, M. Meres ¹³, S. Mhlanga^113,68, Y. Miake¹²⁴, L. Micheletti ³³, D.L. Mihaylov ⁹⁵, K. Mikhaylov ^143,142, A.N. Mishra ¹³⁸, D. Miśkowiec ⁹⁷, A. Modak ⁴, B. Mohanty⁸⁰, M. Mohisin Khan^IV,¹⁶, M.A. Molander ⁴⁴, S. Monira ¹³⁵, C. Mordasini ¹¹⁶, D.A. Moreira De Godoy ¹³⁷, I. Morozov ¹⁴², A. Morsch ³³, T. Mrnjavac ³³, V. Muccifora ⁴⁹, S. Muhuri ¹³⁴, J.D. Mulligan ⁷⁴, A. Mulliri²³, M.G. Munhoz ¹¹⁰, R.H. Munzer ⁶⁴, H. Murakami ¹²³, S. Murray ¹¹³, L. Musa ³³, J. Musinsky ⁶⁰, J.W. Myrcha ¹³⁵, B. Naik ¹²², A.I. Nambrath ¹⁹, B.K. Nandi ⁴⁷, R. Nania ⁵¹, E. Nappi ⁵⁰, A.F. Nassirpour ¹⁸, A. Nath ⁹⁴, C. Nattrass ¹²¹, M.N. Naydenov ³⁷, A. Neagu²⁰, A. Negru¹²⁵, E. Nekrasova¹⁴², L. Nellen ⁶⁵, R. Nepeivoda ⁷⁵, S. Nese ²⁰, G. Neskovic ³⁹, N. Nicassio ⁵⁰, B.S. Nielsen ⁸³, E.G. Nielsen ⁸³, S. Nikolaev ¹⁴², S. Nikulin ¹⁴², V. Nikulin ¹⁴², F. Noferini ⁵¹, S. Noh ¹², P. Nomokonov ¹⁴³, J. Norman ¹¹⁸, N. Novitzky ⁸⁷, P. Nowakowski ¹³⁵, A. Nyanin ¹⁴², J. Nystrand ²¹, M. Ogino ⁷⁶, S. Oh ¹⁸, A. Ohlson ⁷⁵, V.A. Okorokov ¹⁴², J. Oleniacz ¹³⁵, A.C. Oliveira Da Silva ¹²¹, A. Onnerstad ¹¹⁶, C. Oppedisano ⁵⁶, A. Ortiz Velasquez ⁶⁵, J. Otwinowski ¹⁰⁷, M. Oya⁹², K. Oyama ⁷⁶, Y. Pachmayer ⁹⁴, S. Padhan ⁴⁷, D. Pagano ^133,55, G. Paić ⁶⁵, A. Palasciano ⁵⁰, S. Panebianco ¹²⁹, H. Park ¹²⁴, H. Park ¹⁰⁴, J. Park ⁵⁸, J.E. Parkkila ³³, Y. Patley ⁴⁷, R.N. Patra⁹¹, B. Paul ²³, H. Pei ⁶, T. Peitzmann ⁵⁹, X. Peng ¹¹, M. Pennisi ²⁵, S. Perciballi ²⁵, D. Peresunko ¹⁴², G.M. Perez ⁷, Y. Pestov¹⁴², V. Petrov ¹⁴², M. Petrovici ⁴⁶, R.P. Pezzi ^103,66, S. Piano ⁵⁷, M. Pikna ¹³, P. Pillot ¹⁰³, O. Pinazza ^51,33, L. Pinsky¹¹⁵, C. Pinto ⁹⁵, S. Pisano ⁴⁹, M. Płoskoń ⁷⁴, M. Planinic⁸⁹, F. Pliquett⁶⁴, M.G. Poghosyan ⁸⁷, B. Polichtchouk ¹⁴², S. Politano ³⁰, N. Poljak ⁸⁹, A. Pop ⁴⁶, S. Porteboeuf-Houssais ¹²⁶, V. Pozdniakov ¹⁴³, I.Y. Pozos ⁴⁵, K.K. Pradhan ⁴⁸, S.K. Prasad ⁴, S. Prasad ⁴⁸, R. Preghenella ⁵¹, F. Prino ⁵⁶, C.A. Pruneau ¹³⁶, I. Pshenichnov ¹⁴², M. Puccio ³³, S. Pucillo ²⁵, Z. Pugelova¹⁰⁶, S. Qiu ⁸⁴, L. Quaglia ²⁵, S. Ragoni ¹⁵, A. Rai ¹³⁹, A. Rakotozafindrabe ¹²⁹, L. Ramello ^132,56, F. Rami ¹²⁸, S.A.R. Ramirez ⁴⁵, T.A. Rancien⁷³, M. Rasa ²⁷, S.S. Räsänen ⁴⁴, R. Rath ⁵¹, M.P. Rauch ²¹, I. Ravasenga ⁸⁴, K.F. Read ^87,121, C. Reckziegel ¹¹², A.R. Redelbach ³⁹, K. Redlich ^V,⁷⁹, C.A. Reetz ⁹⁷, A. Rehman²¹, F. Reidt ³³, H.A. Reme-Ness ³⁵, Z. Rescakova³⁸, K. Reygers ⁹⁴, A. Riabov ¹⁴², V. Riabov ¹⁴², R. Ricci ²⁹, M. Richter ²⁰, A.A. Riedel ⁹⁵, W. Riegler ³³, A.G. Riffero ²⁵, C. Ristea ⁶³, M.V. Rodriguez ³³, M. Rodríguez Cahuantzi ⁴⁵, K. Røed ²⁰, R. Rogalev ¹⁴², E. Rogochaya ¹⁴³, T.S. Rogoschinski ⁶⁴, D. Rohr ³³, D. Röhrich ²¹, P.F. Rojas⁴⁵, S. Rojas Torres ³⁶, P.S. Rokita ¹³⁵, G. Romanenko ²⁶, F. Ronchetti ⁴⁹, A. Rosano ^31,53, E.D. Rosas⁶⁵, K. Roslon ¹³⁵, A. Rossi ⁵⁴, A. Roy ⁴⁸, S. Roy ⁴⁷, N. Rubini ²⁶, D. Ruggiano ¹³⁵, R. Rui ²⁴, P.G. Russek ², R. Russo ⁸⁴, A. Rustamov ⁸¹, E. Ryabinkin ¹⁴², Y. Ryabov ¹⁴², A. Rybicki ¹⁰⁷, H. Rytkonen ¹¹⁶, J. Ryu ¹⁷, W. Rzesa ¹³⁵, O.A.M. Saarimaki ⁴⁴, S. Sadhu ³², S. Sadovsky ¹⁴², J. Saetre ²¹, K. Šafařík ³⁶, P. Saha⁴², S.K. Saha ⁴, S. Saha ⁸⁰, B. Sahoo ⁴⁷, B. Sahoo ⁴⁸, R. Sahoo ⁴⁸, S. Sahoo⁶¹, D. Sahu ⁴⁸, P.K. Sahu ⁶¹, J. Saini ¹³⁴, K. Sajdakova³⁸, S. Sakai ¹²⁴, M.P. Salvan ⁹⁷, S. Sambyal ⁹¹, D. Samitz ¹⁰², I. Sanna ^33,95, T.B. Saramela¹¹⁰, P. Sarma ⁴², V. Sarritzu ²³, V.M. Sarti ⁹⁵, M.H.P. Sas ³³, S. Sawan⁸⁰, J. Schambach ⁸⁷, H.S. Scheid ⁶⁴, C. Schiaua ⁴⁶, R. Schicker ⁹⁴, F. Schlepper ⁹⁴, A. Schmah⁹⁷, C. Schmidt ⁹⁷, H.R. Schmidt⁹³, M.O. Schmidt ³³, M. Schmidt⁹³, N.V. Schmidt ⁸⁷, A.R. Schmier ¹²¹, R. Schotter ¹²⁸, A. Schröter ³⁹, J. Schukraft ³³, K. Schweda ⁹⁷, G. Scioli ²⁶, E. Scomparin ⁵⁶, J.E. Seger ¹⁵, Y. Sekiguchi¹²³, D. Sekihata ¹²³, M. Selina ⁸⁴, I. Selyuzhenkov ⁹⁷, S. Senyukov ¹²⁸, J.J. Seo ^94,58, D. Serebryakov ¹⁴², L. Šerkšnytė ⁹⁵, A. Sevcenco ⁶³, T.J. Shaba ⁶⁸, A. Shabetai ¹⁰³, R. Shahoyan³³, A. Shangaraev ¹⁴², A. Sharma⁹⁰, B. Sharma ⁹¹, D. Sharma ⁴⁷, H. Sharma ⁵⁴, M. Sharma ⁹¹, S. Sharma ⁷⁶, S. Sharma ⁹¹, U. Sharma ⁹¹, A. Shatat ¹³⁰, O. Sheibani¹¹⁵, K. Shigaki ⁹², M. Shimomura⁷⁷, J. Shin¹², S. Shirinkin ¹⁴², Q. Shou ⁴⁰, Y. Sibiriak ¹⁴², S. Siddhanta ⁵², T. Siemiarczuk ⁷⁹, T.F. Silva ¹¹⁰, D. Silvermyr ⁷⁵, T. Simantathammakul¹⁰⁵, R. Simeonov ³⁷, B. Singh⁹¹, B. Singh ⁹⁵, K. Singh ⁴⁸, R. Singh ⁸⁰, R. Singh ⁹¹, R. Singh ⁴⁸, S. Singh ¹⁶, V.K. Singh ¹³⁴, V. Singhal ¹³⁴, T. Sinha ⁹⁹, B. Sitar ¹³, M. Sitta ^132,56, T.B. Skaali²⁰, G. Skorodumovs ⁹⁴, M. Slupecki ⁴⁴, N. Smirnov ¹³⁹, R.J.M. Snellings ⁵⁹, E.H. Solheim ²⁰, J. Song ¹⁷, C. Sonnabend ^33,97, F. Soramel ²⁸, A.B. Soto-hernandez ⁸⁸, R. Spijkers ⁸⁴, I. Sputowska ¹⁰⁷, J. Staa ⁷⁵, J. Stachel ⁹⁴, I. Stan ⁶³, P.J. Steffanic ¹²¹, S.F. Stiefelmaier ⁹⁴, D. Stocco ¹⁰³, I. Storehaug ²⁰, P. Stratmann ¹³⁷, S. Strazzi ²⁶, A. Sturniolo ^31,53, C.P. Stylianidis⁸⁴, A.A.P. Suaide ¹¹⁰, C. Suire ¹³⁰, M. Sukhanov ¹⁴², M. Suljic ³³, R. Sultanov ¹⁴², V. Sumberia ⁹¹, S. Sumowidagdo ⁸², S. Swain⁶¹, I. Szarka ¹³, M. Szymkowski ¹³⁵, S.F. Taghavi ⁹⁵, G. Taillepied ⁹⁷, J. Takahashi ¹¹¹, G.J. Tambave ⁸⁰, S. Tang ⁶, Z. Tang ¹¹⁹, J.D. Tapia Takaki ¹¹⁷, N. Tapus¹²⁵, L.A. Tarasovicova ¹³⁷, M.G. Tarzila ⁴⁶, G.F. Tassielli ³², A. Tauro ³³, G. Tejeda Muñoz ⁴⁵, A. Telesca ³³, L. Terlizzi ²⁵, C. Terrevoli ¹¹⁵, S. Thakur ⁴, D. Thomas ¹⁰⁸, A. Tikhonov ¹⁴², N. Tiltmann ^33,137, A.R. Timmins ¹¹⁵, M. Tkacik¹⁰⁶, T. Tkacik ¹⁰⁶, A. Toia ⁶⁴, R. Tokumoto⁹², K. Tomohiro⁹², N. Topilskaya ¹⁴², M. Toppi ⁴⁹, T. Tork ¹³⁰, P.V. Torres⁶⁵, V.V. Torres ¹⁰³, A.G. Torres Ramos ³², A. Trifiró ^31,53, A.S. Triolo ^33,31,53, S. Tripathy ⁵¹, T. Tripathy ⁴⁷, S. Trogolo ³³, V. Trubnikov ³, W.H. Trzaska ¹¹⁶, T.P. Trzcinski ¹³⁵, A. Tumkin ¹⁴², R. Turrisi ⁵⁴, T.S. Tveter ²⁰, K. Ullaland ²¹, B. Ulukutlu ⁹⁵, A. Uras ¹²⁷, G.L. Usai ²³, M. Vala³⁸, N. Valle ²², L.V.R. van Doremalen⁵⁹, M. van Leeuwen ⁸⁴, C.A. van Veen ⁹⁴, R.J.G. van Weelden ⁸⁴, P. Vande Vyvre ³³, D. Varga ¹³⁸, Z. Varga ¹³⁸, M. Vasileiou ⁷⁸, A. Vasiliev ¹⁴², O. Vázquez Doce ⁴⁹, O. Vazquez Rueda ¹¹⁵, V. Vechernin ¹⁴², E. Vercellin ²⁵, S. Vergara Limón⁴⁵, R. Verma⁴⁷, L. Vermunt ⁹⁷, R. Vértesi ¹³⁸, M. Verweij ⁵⁹, L. Vickovic³⁴, Z. Vilakazi¹²², O. Villalobos Baillie ¹⁰⁰, A. Villani ²⁴, A. Vinogradov ¹⁴², T. Virgili ²⁹, M.M.O. Virta ¹¹⁶, V. Vislavicius⁷⁵, A. Vodopyanov ¹⁴³, B. Volkel ³³, M.A. Völkl ⁹⁴, K. Voloshin¹⁴², S.A. Voloshin ¹³⁶, G. Volpe ³², B. von Haller ³³, I. Vorobyev ⁹⁵, N. Vozniuk ¹⁴², J. Vrláková ³⁸, J. Wan⁴⁰, C. Wang ⁴⁰, D. Wang⁴⁰, Y. Wang ⁴⁰, Y. Wang ⁶, A. Wegrzynek ³³, F.T. Weiglhofer³⁹, S.C. Wenzel ³³, J.P. Wessels ¹³⁷, S.L. Weyhmiller ¹³⁹, J. Wiechula ⁶⁴, J. Wikne ²⁰, G. Wilk ⁷⁹, J. Wilkinson ⁹⁷, G.A. Willems ¹³⁷, B. Windelband ⁹⁴, M. Winn ¹²⁹, J.R. Wright ¹⁰⁸, W. Wu⁴⁰, Y. Wu ¹¹⁹, R. Xu ⁶, A. Yadav ⁴³, A.K. Yadav ¹³⁴, S. Yalcin ⁷², Y. Yamaguchi ⁹², S. Yang²¹, S. Yano ⁹², E.R. Yeats¹⁹, Z. Yin ⁶, I.-K. Yoo ¹⁷, J.H. Yoon ⁵⁸, H. Yu¹², S. Yuan²¹, A. Yuncu ⁹⁴, V. Zaccolo ²⁴, C. Zampolli ³³, F. Zanone ⁹⁴, N. Zardoshti ³³, A. Zarochentsev ¹⁴², P. Závada ⁶², N. Zaviyalov¹⁴², M. Zhalov ¹⁴², B. Zhang ⁶, C. Zhang ¹²⁹, L. Zhang ⁴⁰, S. Zhang ⁴⁰, X. Zhang ⁶, Y. Zhang¹¹⁹, Z. Zhang ⁶, M. Zhao ¹⁰, V. Zherebchevskii ¹⁴², Y. Zhi¹⁰, D. Zhou ⁶, Y. Zhou ⁸³, J. Zhu ^54,6, Y. Zhu⁶, S.C. Zugravel ⁵⁶, N. Zurlo ^133,55

Affiliation Notes

^I Also at: Max-Planck-Institut für Physik, Munich, Germany
^II Also at: Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), Bologna, Italy
^III Also at: Dipartimento DET del Politecnico di Torino, Turin, Italy
^IV Also at: Department of Applied Physics, Aligarh Muslim University, Aligarh, India
^V Also at: Institute of Theoretical Physics, University of Wroclaw, Poland
^VI Also at: An institution covered by a cooperation agreement with CERN

Collaboration Institutes

¹ A.I. Alikhanyan National Science Laboratory (Yerevan Physics Institute) Foundation, Yerevan, Armenia
² AGH University of Krakow, Cracow, Poland
³ Bogolyubov Institute for Theoretical Physics, National Academy of Sciences of Ukraine, Kiev, Ukraine
⁴ Bose Institute, Department of Physics and Centre for Astroparticle Physics and Space Science (CAPSS), Kolkata, India
⁵ California Polytechnic State University, San Luis Obispo, California, United States
⁶ Central China Normal University, Wuhan, China
⁷ Centro de Aplicaciones Tecnológicas y Desarrollo Nuclear (CEADEN), Havana, Cuba
⁸ Centro de Investigación y de Estudios Avanzados (CINVESTAV), Mexico City and Mérida, Mexico
⁹ Chicago State University, Chicago, Illinois, United States
¹⁰ China Institute of Atomic Energy, Beijing, China
¹¹ China University of Geosciences, Wuhan, China
¹² Chungbuk National University, Cheongju, Republic of Korea
¹³ Comenius University Bratislava, Faculty of Mathematics, Physics and Informatics, Bratislava, Slovak Republic
¹⁴ COMSATS University Islamabad, Islamabad, Pakistan
¹⁵ Creighton University, Omaha, Nebraska, United States
¹⁶ Department of Physics, Aligarh Muslim University, Aligarh, India
¹⁷ Department of Physics, Pusan National University, Pusan, Republic of Korea
¹⁸ Department of Physics, Sejong University, Seoul, Republic of Korea
¹⁹ Department of Physics, University of California, Berkeley, California, United States
²⁰ Department of Physics, University of Oslo, Oslo, Norway
²¹ Department of Physics and Technology, University of Bergen, Bergen, Norway
²² Dipartimento di Fisica, Università di Pavia, Pavia, Italy
²³ Dipartimento di Fisica dell’Università and Sezione INFN, Cagliari, Italy
²⁴ Dipartimento di Fisica dell’Università and Sezione INFN, Trieste, Italy
²⁵ Dipartimento di Fisica dell’Università and Sezione INFN, Turin, Italy
²⁶ Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN, Bologna, Italy
²⁷ Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN, Catania, Italy
²⁸ Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN, Padova, Italy
²⁹ Dipartimento di Fisica ‘E.R. Caianiello’ dell’Università and Gruppo Collegato INFN, Salerno, Italy
³⁰ Dipartimento DISAT del Politecnico and Sezione INFN, Turin, Italy
³¹ Dipartimento di Scienze MIFT, Università di Messina, Messina, Italy
³² Dipartimento Interateneo di Fisica ‘M. Merlin’ and Sezione INFN, Bari, Italy
³³ European Organization for Nuclear Research (CERN), Geneva, Switzerland
³⁴ Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, Split, Croatia
³⁵ Faculty of Engineering and Science, Western Norway University of Applied Sciences, Bergen, Norway
³⁶ Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Prague, Czech Republic
³⁷ Faculty of Physics, Sofia University, Sofia, Bulgaria
³⁸ Faculty of Science, P.J. Šafárik University, Košice, Slovak Republic
³⁹ Frankfurt Institute for Advanced Studies, Johann Wolfgang Goethe-Universität Frankfurt, Frankfurt, Germany
⁴⁰ Fudan University, Shanghai, China
⁴¹ Gangneung-Wonju National University, Gangneung, Republic of Korea
⁴² Gauhati University, Department of Physics, Guwahati, India
⁴³ Helmholtz-Institut für Strahlen- und Kernphysik, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
⁴⁴ Helsinki Institute of Physics (HIP), Helsinki, Finland
⁴⁵ High Energy Physics Group, Universidad Autónoma de Puebla, Puebla, Mexico
⁴⁶ Horia Hulubei National Institute of Physics and Nuclear Engineering, Bucharest, Romania
⁴⁷ Indian Institute of Technology Bombay (IIT), Mumbai, India
⁴⁸ Indian Institute of Technology Indore, Indore, India
⁴⁹ INFN, Laboratori Nazionali di Frascati, Frascati, Italy
⁵⁰ INFN, Sezione di Bari, Bari, Italy
⁵¹ INFN, Sezione di Bologna, Bologna, Italy
⁵² INFN, Sezione di Cagliari, Cagliari, Italy
⁵³ INFN, Sezione di Catania, Catania, Italy
⁵⁴ INFN, Sezione di Padova, Padova, Italy
⁵⁵ INFN, Sezione di Pavia, Pavia, Italy
⁵⁶ INFN, Sezione di Torino, Turin, Italy
⁵⁷ INFN, Sezione di Trieste, Trieste, Italy
⁵⁸ Inha University, Incheon, Republic of Korea
⁵⁹ Institute for Gravitational and Subatomic Physics (GRASP), Utrecht University/Nikhef, Utrecht, Netherlands
⁶⁰ Institute of Experimental Physics, Slovak Academy of Sciences, Košice, Slovak Republic
⁶¹ Institute of Physics, Homi Bhabha National Institute, Bhubaneswar, India
⁶² Institute of Physics of the Czech Academy of Sciences, Prague, Czech Republic
⁶³ Institute of Space Science (ISS), Bucharest, Romania
⁶⁴ Institut für Kernphysik, Johann Wolfgang Goethe-Universität Frankfurt, Frankfurt, Germany
⁶⁵ Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de México, Mexico City, Mexico
⁶⁶ Instituto de Física, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
⁶⁷ Instituto de Física, Universidad Nacional Autónoma de México, Mexico City, Mexico
⁶⁸ iThemba LABS, National Research Foundation, Somerset West, South Africa
⁶⁹ Jeonbuk National University, Jeonju, Republic of Korea
⁷⁰ Johann-Wolfgang-Goethe Universität Frankfurt Institut für Informatik, Fachbereich Informatik und Mathematik, Frankfurt, Germany
⁷¹ Korea Institute of Science and Technology Information, Daejeon, Republic of Korea
⁷² KTO Karatay University, Konya, Turkey
⁷³ Laboratoire de Physique Subatomique et de Cosmologie, Université Grenoble-Alpes, CNRS-IN2P3, Grenoble, France
⁷⁴ Lawrence Berkeley National Laboratory, Berkeley, California, United States
⁷⁵ Lund University Department of Physics, Division of Particle Physics, Lund, Sweden
⁷⁶ Nagasaki Institute of Applied Science, Nagasaki, Japan
⁷⁷ Nara Women’s University (NWU), Nara, Japan
⁷⁸ National and Kapodistrian University of Athens, School of Science, Department of Physics , Athens, Greece
⁷⁹ National Centre for Nuclear Research, Warsaw, Poland
⁸⁰ National Institute of Science Education and Research, Homi Bhabha National Institute, Jatni, India
⁸¹ National Nuclear Research Center, Baku, Azerbaijan
⁸² National Research and Innovation Agency - BRIN, Jakarta, Indonesia
⁸³ Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark
⁸⁴ Nikhef, National institute for subatomic physics, Amsterdam, Netherlands
⁸⁵ Nuclear Physics Group, STFC Daresbury Laboratory, Daresbury, United Kingdom
⁸⁶ Nuclear Physics Institute of the Czech Academy of Sciences, Husinec-Řež, Czech Republic
⁸⁷ Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States
⁸⁸ Ohio State University, Columbus, Ohio, United States
⁸⁹ Physics department, Faculty of science, University of Zagreb, Zagreb, Croatia
⁹⁰ Physics Department, Panjab University, Chandigarh, India
⁹¹ Physics Department, University of Jammu, Jammu, India
⁹² Physics Program and International Institute for Sustainability with Knotted Chiral Meta Matter (SKCM2), Hiroshima University, Hiroshima, Japan
⁹³ Physikalisches Institut, Eberhard-Karls-Universität Tübingen, Tübingen, Germany
⁹⁴ Physikalisches Institut, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany
⁹⁵ Physik Department, Technische Universität München, Munich, Germany
⁹⁶ Politecnico di Bari and Sezione INFN, Bari, Italy
⁹⁷ Research Division and ExtreMe Matter Institute EMMI, GSI Helmholtzzentrum für Schwerionenforschung GmbH, Darmstadt, Germany
⁹⁸ Saga University, Saga, Japan
⁹⁹ Saha Institute of Nuclear Physics, Homi Bhabha National Institute, Kolkata, India
¹⁰⁰ School of Physics and Astronomy, University of Birmingham, Birmingham, United Kingdom
¹⁰¹ Sección Física, Departamento de Ciencias, Pontificia Universidad Católica del Perú, Lima, Peru
¹⁰² Stefan Meyer Institut für Subatomare Physik (SMI), Vienna, Austria
¹⁰³ SUBATECH, IMT Atlantique, Nantes Université, CNRS-IN2P3, Nantes, France
¹⁰⁴ Sungkyunkwan University, Suwon City, Republic of Korea
¹⁰⁵ Suranaree University of Technology, Nakhon Ratchasima, Thailand
¹⁰⁶ Technical University of Košice, Košice, Slovak Republic
¹⁰⁷ The Henryk Niewodniczanski Institute of Nuclear Physics, Polish Academy of Sciences, Cracow, Poland
¹⁰⁸ The University of Texas at Austin, Austin, Texas, United States
¹⁰⁹ Universidad Autónoma de Sinaloa, Culiacán, Mexico
¹¹⁰ Universidade de São Paulo (USP), São Paulo, Brazil
¹¹¹ Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil
¹¹² Universidade Federal do ABC, Santo Andre, Brazil
¹¹³ University of Cape Town, Cape Town, South Africa
¹¹⁴ University of Derby, Derby, United Kingdom
¹¹⁵ University of Houston, Houston, Texas, United States
¹¹⁶ University of Jyväskylä, Jyväskylä, Finland
¹¹⁷ University of Kansas, Lawrence, Kansas, United States
¹¹⁸ University of Liverpool, Liverpool, United Kingdom
¹¹⁹ University of Science and Technology of China, Hefei, China
¹²⁰ University of South-Eastern Norway, Kongsberg, Norway
¹²¹ University of Tennessee, Knoxville, Tennessee, United States
¹²² University of the Witwatersrand, Johannesburg, South Africa
¹²³ University of Tokyo, Tokyo, Japan
¹²⁴ University of Tsukuba, Tsukuba, Japan
¹²⁵ University Politehnica of Bucharest, Bucharest, Romania
¹²⁶ Université Clermont Auvergne, CNRS/IN2P3, LPC, Clermont-Ferrand, France
¹²⁷ Université de Lyon, CNRS/IN2P3, Institut de Physique des 2 Infinis de Lyon, Lyon, France
¹²⁸ Université de Strasbourg, CNRS, IPHC UMR 7178, F-67000 Strasbourg, France, Strasbourg, France
¹²⁹ Université Paris-Saclay, Centre d’Etudes de Saclay (CEA), IRFU, Départment de Physique Nucléaire (DPhN), Saclay, France
¹³⁰ Université Paris-Saclay, CNRS/IN2P3, IJCLab, Orsay, France
¹³¹ Università degli Studi di Foggia, Foggia, Italy
¹³² Università del Piemonte Orientale, Vercelli, Italy
¹³³ Università di Brescia, Brescia, Italy
¹³⁴ Variable Energy Cyclotron Centre, Homi Bhabha National Institute, Kolkata, India
¹³⁵ Warsaw University of Technology, Warsaw, Poland
¹³⁶ Wayne State University, Detroit, Michigan, United States
¹³⁷ Westfälische Wilhelms-Universität Münster, Institut für Kernphysik, Münster, Germany
¹³⁸ Wigner Research Centre for Physics, Budapest, Hungary
¹³⁹ Yale University, New Haven, Connecticut, United States
¹⁴⁰ Yonsei University, Seoul, Republic of Korea
¹⁴¹ Zentrum für Technologie und Transfer (ZTT), Worms, Germany
¹⁴² Affiliated with an institute covered by a cooperation agreement with CERN
¹⁴³ Affiliated with an international laboratory covered by a cooperation agreement with CERN.