2021 006 6 January

Long-range correlations in pp collisions at $\sqrt{s}=13$ TeV

ALICE Collaboration††thanks: See Appendix A for the list of collaboration
members ALICE Collaboration

Two-particle angular correlations are measured in high-multiplicity
proton–proton collisions at $\sqrt{s}=13$ TeV by the ALICE Collaboration. The
yields of particle pairs at short-($\Delta\eta$ $\sim$ 0) and long-range
($1.6<|\Delta\eta|<1.8$) in pseudorapidity are extracted on the near-side
($\Delta\varphi$ $\sim$ 0). They are reported as a function of transverse
momentum ($p_{\mathrm{T}}$) in the range 1 $<p_{\mathrm{T}}<$ 4 GeV/$c$.
Furthermore, the event-scale dependence is studied for the first time by
requiring the presence of high-$p_{\rm T}$ leading particles or jets for
varying $p_{\rm T}$ thresholds. The results demonstrate that the long-range
“ridge” yield, possibly related to the collective behavior of the system, is
present in events with high-$p_{\mathrm{T}}$ processes as well. The magnitudes
of the short- and long-range yields are found to grow with the event scale.
The results are compared to EPOS LHC and PYTHIA 8 calculations, with and
without string-shoving interactions. It is found that while both models
describe the qualitative trends in the data, calculations from EPOS LHC show a
better quantitative agreement for the $p_{\rm T}$ dependency, while
overestimating the event-scale dependency.

##  1 Introduction

In high-energy nucleus–nucleus collisions at RHIC [1, 2, 3, 4] and LHC [5, 6,
7], significant correlations are observed between particles emitted over a
wide pseudorapidity range. The origin of these observations are collective
effects, which are related to the formation of a strongly interacting
quark–gluon plasma (QGP), which exhibits hydrodynamic behavior (see the
reviews [8, 9, 10]). Recent theoretical [11, 12, 13] and experimental [14, 15,
16, 17] advancements have contributed significantly to the understanding of
the transport properties of the QGP. Similar long-range correlations are also
observed in high-multiplicity proton–proton (pp) [18, 19, 20, 21],
proton–nucleus (pA) [22, 23, 24, 25], and light nucleus–nucleus collisions
[26, 27]. The fact that these correlations extend over a large range in
pseudorapidity implies that they originate from early times in these
collisions and thus suggest that hydrodynamic behavior is present even in
these small systems, although the volume and lifetime of the medium produced
in such a collision system are expected to be small, and there are other
mechanisms which can produce similar flow-like signals [28, 29].

Measurements of two-particle angular correlations provide information on many
physical effects, including collectivity, hadronization, fragmentation, and
femtoscopic effects [30], and are typically quantified as a function of
$\Delta\eta$, the relative pseudorapidity, and $\Delta\varphi$, the separation
in azimuthal angle, of particle pairs. The long-range structure of two-
particle angular correlations is well suited to analyze collective effects,
since it is not created by resonance decays nor fragmentation of high-momentum
partons. A typical source of long-range correlations in Monte Carlo pp
generators is the momentum conservation. The enhancement in the yield of two-
particle correlations at small $\Delta\varphi$ that extends over a large
$\Delta\eta$ is dubbed “ridge” due to its characteristic shape in the
$\Delta\eta$–$\Delta\varphi$ plane. The shape of these $\Delta\varphi$
correlations can be studied via a Fourier decomposition [31, 32]. The second
and third order terms are the dominant harmonic coefficients. In heavy-ion
collisions, harmonic coefficients can be related to the collision geometry and
density fluctuations of the colliding nuclei [33, 34, 35] and to transport
properties of the QGP in relativistic viscous hydrodynamic models [36, 11, 37,
12, 13].

The ridge structures in high-multiplicity pp and p–Pb events have been
attributed to initial-state or final-state effects. Initial-state effects,
usually attributed to gluon saturation [38, 39], can form long-range
correlations along the longitudinal direction. The final-state effects might
be parton-induced interactions [40] or collective phenomena due to
hydrodynamic behavior of the produced matter arising in a high-density system
possibly formed in these collisions [41, 42]. Hybrid models implementing both
effects are generally used in hydrodynamic simulations [43, 44]. EPOS LHC
describes collectivity in small systems with a parameterized hydrodynamic
evolution of the high-energy density region, so called “core”, formed by many
color string fields [45]. The proton shape and its fluctuations are also
important to model small systems [44]. To understand the influence of initial-
or final-state effects, and to possibly disentangle the two, a quantitative
description of the measurements in small systems [46, 47] needs to account for
details of the initial state. Systematic studies of these correlation effects
from small to large systems are being performed, both experimentally [21] and
theoretically [47]. However, the quantitative description of the full set of
experimental data has not been achieved yet. A summary of various explanations
for the observed correlations in small systems is given in [48, 49, 29, 49].

Besides the hybrid models mentioned above, alternative approaches were
developed to describe collectivity in small systems. A microscopic model for
collectivity was implemented in the PYTHIA 8 event generator, which is based
on interacting strings (string shoving) and is called the “string shoving
model” [50]. In this model, strings repel each other in the transverse
direction, which results in microscopic transverse pressure and, consequently,
in long-range correlations. PYTHIA 8 with string shoving can qualitatively
reproduce the near-side ($\Delta\varphi\sim 0$) ridge yield measured by the
CMS Collaboration [20]. This challenges the hydrodynamic picture and predicts
modifications of the jet fragmentation properties [51].

It is expected that final-state interactions affect also produced jets if they
are the source of collectivity in small systems. Proving the presence of jet
quenching [52, 53] would be another crucial evidence of the existence of a
high-density strongly-interacting system, possibly a QGP, in high-multiplicity
pp collisions. However, there is no evidence observed so far for the jet
quenching effect in high-multiplicity pp and p–Pb collisions [54, 55, 56, 57].
Jet fragmentation can be studied in two-particle angular correlations in
short-range correlations around $(\Delta\eta$, $\Delta\varphi)=(0,0)$ [58].

To further investigate the interplay of jet production and collective effects
in small systems, long- and short-range correlations are studied
simultaneously in high-multiplicity pp collisions at $\sqrt{s}=13$ TeV using
the ALICE LHC Run 2 data collected with the high-multiplicity event trigger in
2016–2018. In this article, the near-side per-trigger yield at large
pseudorapidity separation is presented as a function of transverse momentum.
The results are compared with previous measurements by the CMS Collaboration
[19]. In addition, the ridge yield and near-side jet-like correlations with
the event-scale selection are reported. The event-scale selection is done by
requiring a minimum transverse momentum of the leading particle or the
reconstructed jet at midrapidity, which is expected to bias the impact
parameter of pp collisions to be smaller on average [59, 60]. At the same
time, the transverse momentum of the leading particle or the reconstructed jet
is a measure of the momentum transfer in the hard parton scattering [61, 62].
The event-scale dependence of the second-flow harmonic $v_{2}$ has previously
been studied in pp collisions with and without a tagged $Z$ boson, where
little or no dependence was observed [63].

The experimental setup and analysis method are described in Sec. 2 and 3,
respectively. The sources of systematic uncertainties are discussed in Sec. 4.
The results and comparisons with model calculations of the measurements are
presented in Sec. 5. Finally, results are summarized in Sec. 6.

##  2 Experimental setup

The analysis is carried out with data samples of pp collisions at
$\sqrt{s}=13$ TeV collected from 2016 to 2018 during the LHC Run 2 period. The
full description of the ALICE detector and its performance in the LHC Run 2
can be found in [64, 65]. The present analysis utilizes the V0 [66], the Inner
Tracking System (ITS) [67], and the Time Projection Chamber (TPC) [68]
detectors.

The V0 detector consists of two stations placed on both sides of the
interaction point, V0A and V0C, each made of 32 plastic scintillator tiles,
covering the full azimuthal angle within the pseudorapidity intervals
$2.8<\eta<5.1$ and $-3.7<\eta<-1.7$, respectively. The V0 is used to provide a
minimum bias (MB) and a high-multiplicity (HM) trigger. The minimum bias
trigger is obtained by a time coincidence of V0A and V0C signals. The charged
particle multiplicity selection is done on the sum of the V0A and V0C signals,
which is denoted as V0M. The high-multiplicity trigger requires that the V0M
signal exceeds 5 times the mean value measured in minimum bias collisions,
selecting the 0.1% of MB events that have the largest V0 multiplicity. The
analyzed data samples of minimum bias and high-multiplicity pp events at
$\sqrt{s}=$ 13 TeV correspond to integrated luminosities of 19 nb-1 and 11
pb-1, respectively [69].

The primary vertex position is reconstructed from the measured signals in the
Silicon Pixel Detector (SPD), which forms the innermost two layers of the ITS.
Reconstructed primary vertices of selected events are required to be located
within 8 cm from the center of the detector along the beam direction. The
probability of pileup events is about 0.6% in MB events. Pileup events can be
resolved and are rejected if the longitudinal displacement of their primary
vertices is larger than 0.8 cm.

Charged-particle tracks are reconstructed by the ITS and TPC, which are
operated in a uniform solenoidal magnetic field of 0.5 T along the beam
direction. The ITS is a silicon tracker with six layers of silicon sensors
where the SPD [70] comprises the two innermost layers, the next two layers
called the Silicon Drift Detector (SDD), and the outermost layers named the
Silicon Strip Detector (SSD). The ITS and TPC, covering the full azimuthal
range, have acceptances up to $|\eta|<1.4$ and 0.9, respectively, for
detection of charged particles emitted within 8 cm from the primary vertex
position ($z_{\mathrm{vtx}}$) along the beam direction. The tracking of
charged particles is done with the combined information of the ITS and TPC
that enables the reconstruction of tracks down to 0.15 GeV/$c$, where the
efficiency is about 65%. The efficiency reaches 80% for intermediate
$p_{\mathrm{T}}$, 1 to 5 GeV/$c$. The $p_{\mathrm{T}}$ resolution is around 1%
for primary tracks with $p_{\mathrm{T}}<$ 1 GeV/$c$, and linearly increases up
to 6% at $p_{\mathrm{T}}\sim$ 40 GeV/$c$ [71].

The charged particle selection criteria are optimized to make the efficiency
uniform over the full TPC volume to mitigate the effect of small regions where
some of the ITS layers are inactive. The selection consists of two track
classes. Those belonging to the first class are required to have at least one
hit in the SPD. Tracks from the second class do not have any SPD associated
hit and their initial point is instead constrained to the primary vertex [72].

##  3 Analysis procedure

The two-particle correlation function is measured as a function of the
relative pseudorapidity ($\Delta\eta$) and the azimuthal angle difference
($\Delta\varphi$) between the trigger and the associated particles,

$\displaystyle\frac{1}{N_{\rm{trig}}}\frac{\rm{d}\it{}^{2}N_{\rm{pair}}}{\rm{d}\Delta\eta\rm{d}\Delta\varphi}=B(0,0)\frac{S(\Delta\eta,\Delta\varphi)}{B(\Delta\eta,\Delta\varphi)}\Big{\lvert}_{p_{\mathrm{T,\,trig}},\,p_{\mathrm{T,\,assoc}}}\quad,$
(1)

where $p_{\mathrm{T,\,trig}}$ and $p_{\mathrm{T,\,assoc}}$
($p_{\mathrm{T,\,trig}}>p_{\mathrm{T,\,assoc}}$) are the transverse momenta of
the trigger and associated particles, respectively, $N_{\mathrm{trig}}$ is the
number of trigger particles, and $N_{\mathrm{pair}}$ is the number of trigger-
associated particle pairs. The average number of pairs in the same event and
in mixed events are denoted as $S(\Delta\eta,\Delta\varphi)$ and
$B(\Delta\eta,\Delta\varphi)$, respectively. Normalization of
$B(\Delta\eta,\Delta\varphi)$ is done with its value at $\Delta\eta$ and
$\Delta\varphi=0$, represented as $B(0,0)$. Acceptance effects are corrected
by dividing $S(\Delta\eta,\Delta\varphi)$ with
$B(\Delta\eta,\Delta\varphi)/B(0,0)$. The right-hand side of Eq. (1) is
corrected for the track reconstruction efficiency, which is mainly relevant
for the associated particles, as a function of $p_{\mathrm{T}}$ and
pseudorapidity. Primary vertices of events to be mixed are required to be
within the same, 2 cm wide, $z_{\rm{vtx}}$ interval [73, 58] for each
multiplicity class. The final per-trigger yield is constructed by averaging
correlation functions over these primary vertex bins.

Ridge yields at large $\Delta\eta$ are extracted for various multiplicity
classes and $p_{\mathrm{T}}$ intervals. The large $\Delta\eta$ range is
selected as $1.6<|\Delta\eta|<1.8$, which is the range where the tracking
quality – efficiency and precision – is the best. The ridge yield is only
reported for $p_{\mathrm{T}}>$ 1 GeV/$c$. Below 1 GeV/$c$, the jet-like
contribution to the correlation function extends into the region where the
ridge yield is measured, 1.6 $<|\Delta\eta|<$ 1.8. In this region, the
$\Delta\varphi$ distribution, or the so-called per-trigger yield, is expressed
as

$\displaystyle\frac{1}{N_{\rm{trig}}}\frac{\rm{d}\it{}N_{\rm{pair}}}{\rm{d}\Delta\varphi}=\int_{1.6<|\Delta\eta|<1.8}\left(\frac{1}{\it{N}_{\rm{trig}}}\frac{\rm{d}\it{}^{2}N_{\rm{pair}}}{\rm{d}\Delta\eta
d\Delta\varphi}\right)\dfrac{1}{\delta_{\Delta\eta}}\rm{d}\Delta\eta-
C_{\rm{ZYAM}}\quad,$ (2)

where $\delta_{\Delta\eta}=$ 0.4 is the normalization factor to get the per-
trigger yield per unit of pseudorapidity.

The baseline of the correlation is subtracted by means of the Zero-Yield-At-
Minimum (ZYAM) procedure [74]. The minimum yield $(C_{\rm{ZYAM}})$ at
$\Delta\varphi=\Delta\varphi_{\rm{min}}$ in the $\Delta\varphi$ projection
(note that the value of $\Delta\varphi_{\rm{min}}$ can be different in data
and in models) is obtained from a fit function, which fits the data with a
Fourier series up to the third harmonic. By construction, the yield at
$\Delta\varphi_{\rm{min}}$ is zero after subtracting $C_{\rm{ZYAM}}$ from the
$\Delta\varphi$ projection. The ridge yield ($Y^{\rm{ridge}}$) is obtained by
integrating the near-side peak of the $\Delta\varphi$ projection over
$|\Delta\varphi|<|\Delta\varphi_{\rm{min}}|$ after the ZYAM procedure,

$\displaystyle
Y^{\rm{ridge}}=\int_{|\Delta\varphi|<|\Delta\varphi_{\rm{min}}|}\frac{1}{\it{N}_{\rm{trig}}}\frac{\rm{d}\it{}N_{\rm{pair}}}{\rm{d}\Delta\varphi}\rm{d}\Delta\varphi.$
(3)

The ridge yield is further studied in events having a hard jet or a
high-$p_{\mathrm{T}}$ leading particle in the midrapidity region. This event
scale is set by requiring a minimum $p_{\mathrm{T}}$ of the leading track
($p_{\mathrm{T,\,LP}}$) or the reconstructed jet
($p_{\mathrm{T,\,jet}}^{\mathrm{ch}}$) at midrapidity. The leading track is
selected within $|\eta|<0.9$ and the full azimuthal angle. Jets are
reconstructed with charged particles only (track-based jets) with the
anti-$k_{\rm{T}}$ algorithm [75, 76] and the resolution parameter $R=$ 0.4.
The recombination scheme used in this article is the $p_{\mathrm{T}}$ scheme.
Jets are selected in $|\eta_{\mathrm{jet}}|<0.4$ and in the full azimuthal
angle. The $p_{\mathrm{T}}$ of jets $p_{\mathrm{T,\,jet}}^{\mathrm{ch}}$ is
corrected for the underlying event density that is measured using the
$k_{\rm{T}}$ algorithm with $R=$ 0.2 [77].

To quantify the variation of the near-side jet-like peak with event-scale
selections with a minimum $p_{\mathrm{T,\,LP}}$ or
$p_{\mathrm{T,\,jet}}^{\mathrm{ch}}$, the near-side jet-like peak yield is
extracted from the near-side $\Delta\eta$ correlations. The near-side is
defined as $|\Delta\varphi|<$ 1.28, where the correlation function is
projected on the $\Delta\eta$ axis. The projection range, 1.28, is chosen to
fully cover $\Delta\varphi_{\rm{min}}$. The near-side $\Delta\eta$
correlations are then constructed as

$\displaystyle\frac{1}{N_{\rm{trig}}}\frac{\rm{d}\it{}N_{\rm{pair}}}{\rm{d}\Delta\eta}=\int_{|\Delta\varphi|<1.28}\left(\frac{1}{\it{N}_{\rm{trig}}}\frac{\rm{d}\it{}^{2}N_{\rm{pair}}}{\rm{d}\Delta\eta
d\Delta\varphi}\right)\dfrac{1}{\delta_{\Delta\varphi}}\rm{d}\Delta\varphi-\it{D}_{\rm{ZYAM}}\quad,$
(4)

where $\delta_{\Delta\varphi}=$ 2.56 is the normalization factor to get per-
trigger yield per unit of azimuthal angle. The minimum yield $(D_{\rm{ZYAM}})$
of the $\Delta\eta$ correlations is found within $|\Delta\eta|<$ 1.6 and used
for the subtraction from the $\Delta\eta$ correlations, which results in zero-
yield at the minimum. The near-side jet-like peak yield ($Y^{\mathrm{near}}$)
is measured by integrating the $\Delta\eta$ correlations over $|\Delta\eta|<$
1.6,

$\displaystyle
Y^{\rm{near}}=\int_{|\Delta\eta|<1.6}\left(\frac{1}{\it{N}_{\rm{trig}}}\frac{\rm{d}\it{}N_{\rm{pair}}}{\rm{d}\Delta\eta}\right)\rm{d}\Delta\eta$
(5)

##  4 Systematic uncertainties of the measured yields

The systematic uncertainties of $Y^{\rm{ridge}}$ and $Y^{\rm{near}}$ are
estimated by varying the analysis selection criteria and corrections and are
summarized in Tab. 1.

Table 1: The relative systematic uncertainty of $Y^{\rm{ridge}}$ and $Y^{\rm{near}}$. Numbers given in ranges correspond to minimum and maximum uncertainties. Sources | Systematic uncertainty (%)
---|---
$Y^{\rm{ridge}}$ | $Y^{\rm{near}}$
Pileup rejection |  $\pm$0.8–3.9 |  $\pm$0.2–2.2
Primary vertex |  $\pm$0.5–2.4 |  $\pm$1.1
Tracking |  $\pm$2.0–4.0 |  $\pm$1.5–3.4
ZYAM |  $\pm$2.1–5.1 |  $\pm$2.2–4.8
Event mixing |  $\pm$1.0–4.4 |  $\pm$0.5–1.7
Efficiency correction |  $\pm$2.5 |  $\pm$3.1
Jet contamination |  $-$18.8–25.9 ($p_{\mathrm{T}}<$ 2 GeV/$c$) | N.A.
Total (in quadrature) | ${}^{+\rm{4.9}\textrm{--}\rm{9.4}}_{-\rm{19.4}\textrm{--}\rm{21.0}}$ |  $\pm$3.9–7.3

The systematic uncertainties are independent of the event-scale selection
except for $D_{\rm{ZYAM}}$ (see below), as expected, since the multiplicity is
weakly dependent on the event scale and the ALICE detector is optimized for
much higher multiplicities (Pb–Pb collisions), this is in agreement with our
expectations.

The uncertainty associated to the pileup rejection is estimated by measuring
the changes of results with different rejection criteria from the default one.
It is mainly estimated by varying the minimal number of track contributors
required for reconstruction of pileup event vertices from 3 to 5. The
estimated uncertainty of $Y^{\rm{ridge}}$ is 0.8-3.9%. The corresponding
uncertainty of $Y^{\rm{near}}$ is estimated to be 0.2–2.2%.

Another source of systematic uncertainty is related to the selected range of
the primary vertex. The accepted range is changed from $|z_{\mathrm{vtx}}|<$ 8
cm to $|z_{\mathrm{vtx}}|<$ 6 cm. The narrower primary vertex selection allows
one to test acceptance effects on the measurement. The estimated uncertainty
of $Y^{\rm{ridge}}$ is 0.5–2.4%. The uncertainty for $Y^{\rm{near}}$ is
estimated to be 1.1%.

An additional source of systematic uncertainty is related to the track
selection criteria. The corresponding uncertainty is estimated by employing
other track selection criteria, denoted global tracks, which are optimized for
particle identification. The selection criteria of the global tracks are
almost identical to the hybrid tracks. Each global track is required to have
at least one SPD hit. Due to inefficient parts of the SPD, the azimuthal
distribution of global tracks is not uniform. The uncertainties associated
with the track selection are estimated to be 2.0–4.0% and 1.5–3.4% for
$Y^{\rm{ridge}}$ and $Y^{\rm{near}}$, respectively.

The systematic uncertainty of $Y^{\rm{ridge}}$ resulting from the ZYAM
procedure is estimated by varying the range of the fit, which is used to find
the minimum, from $|\Delta\varphi|<\pi/$2 down to $|\Delta\varphi|<1.2$. The
estimated uncertainty of $Y^{\rm{ridge}}$ is 2.1–5.1%. The corresponding
uncertainty on $Y^{\rm{near}}$ is estimated by varying the range from
$|\Delta\eta|<$ 1.6 to $|\Delta\eta|<$ 1.5 and 1.7. The estimated uncertainty
of $Y^{\rm{near}}$ is 2.2% for the unbiased case and increases to 4.8% for the
largest event-scale selections. This is the only systematic uncertainty for
which a significant dependence on the event scale is observed, reflecting a
non-negligible dependence of the near-side magnitude and shape on the event-
scale selection.

The source of systematic uncertainty is associated to the choice of the width
of $z_{\rm vtx}$ bins that are used in the event mixing method. The default
value of 2 cm is changed to 1 cm. The resulting uncertainty of
$Y^{\rm{ridge}}$ is 1.0–4.4%. The uncertainty for $Y^{\rm{near}}$ is about
0.5–1.7%. The uncertainty from the efficiency correction for charged particles
is estimated by comparing correlation functions of true particles with
correlation functions of reconstructed tracks with the efficiency correction
in simulation. The estimated uncertainties are 2.5% and 3.1% for
$Y^{\rm{ridge}}$ and $Y^{\rm{near}}$, respectively.

In the limited $\eta$-acceptance of ALICE, the ridge structure is not flat in
$\Delta\eta$ suggesting that jet-like correlations (non-flow) could
contribute, implying that they would impact the ridge-yield extraction. We
stress that the models used for comparisons also contain such a non-flow
effect, but differences in jet-like correlations between data and MC models
could influence the interpretation. To account for the related uncertainty,
the variation of the yield with $\Delta\eta$ between 1.5 and 1.8, which should
be an upper limit of the residual jet-like contamination, is used as a
systematic uncertainty of the ridge yield. The estimated upper limit of the
uncertainty is $-$25.9% for the 1.0 $<p_{\mathrm{T}}<$ 1.5 GeV/$c$ range,
$-$18.8% for the 1.5 $<p_{\mathrm{T}}<$ 2.0 GeV/$c$ range, $-$18.9% for the
1.0 $<p_{\mathrm{T}}<$ 2.0 GeV/$c$ range, and negligible for $p_{\mathrm{T}}>$
2.0 GeV/$c$. This uncertainty is considered only for the measured ridge
yields.

##  5 Results

###  5.1 Ridge yield

Figure 1:  Two-particle correlation functions as functions of $\Delta\eta$ and
$\Delta\varphi$ in minimum-bias events (0–100%, left) and high-multiplicity
(0–0.1%, right). Note that the near-side jet peaks exceed the chosen range of
the $z$-axis. The intervals of $p_{\mathrm{T,\,trig}}$ and
$p_{\mathrm{T,\,assoc}}$ are 1 $<\it{p}_{\rm{T}}<$ 2 GeV/$c$ in both cases.

Figure 1 shows the per-trigger yield obtained from Eq. (1) for 1
$<p_{\mathrm{T,\,trig}}~{}\mathrm{(}p_{\mathrm{T,\,assoc}})<$ 2 GeV/$c$ in pp
collisions at $\sqrt{\it{s}}=$13 TeV for minimum bias events (left) and high-
multiplicity events (right). It is worth noting that the $z$-axes for the
yield of the correlations is properly scaled in order to zoom in the ridge
yield, as a result, the jet peaks are sheared off in both figures. The ridge
structure is clearly observed in the high-multiplicity class while it is less
significant in the minimum bias events. The away-side yield is populated
mostly by back-to-back jet correlations.

Figure 2: One-dimensional $\Delta\varphi$ distribution in the large
$\Delta\eta$ projection for three transverse momentum intervals in minimum
bias (upper panels) and high-multiplicity (lower panels) events after ZYAM
subtraction. Transverse momentum intervals of the trigger particles and
associated particles are 1 $<p_{\mathrm{T}}<$ 2 (left), 2 $<p_{\mathrm{T}}<$ 3
(middle) and, 3 $<p_{\mathrm{T}}<$ 4 GeV/$c$ (right), respectively. The
presented model predictions were calculated using
${\mathrm{PYTHIA}\,8~{}\mathrm{String}~{}}\mathrm{Shoving}$,
$\mathrm{PYTHIA}\,8\,\mathrm{Tune\,4C}$, and
${\mathrm{EPOS}\,~{}\mathrm{LHC}}$.

Figure 2 shows $\Delta\varphi$ projections of the two-particle correlation
functions obtained in the range 1.6 $<|\Delta\eta|<$ 1.8 for several track
$p_{\mathrm{T}}$ intervals after the ZYAM subtraction (see Eq. (2)). The
results are shown for various $\it{p}_{\rm{T}}$ intervals in the minimum bias
class (upper) and the high-multiplicity class (lower) down to 1 GeV/$c$ where
the non-flow contamination is negligible. The near-side ($\Delta\varphi\sim
0$) ridge in the high-multiplicity class is clearly observed for
$p_{\mathrm{T}}<$ 3 GeV/$c$ while there is no definitive signal in the minimum
bias class. Within the range of analyzed particle $p_{\mathrm{T}}$, the yield
in the near-side ridge decreases with increasing $p_{\mathrm{T}}$ in the high-
multiplicity class.

The measurements in the high-multiplicity class are compared with the results
published by the CMS Collaboration [19]. In case of the CMS measurement, the
charged particle multiplicity was obtained by counting the number of particles
satisfying $p_{\mathrm{T}}>0.4$ GeV/$c$ in $|\eta|<$ 2.4. In our analysis,
event multiplicity is determined from the forward V0 detectors. The difference
in multiplicity selection between ALICE (forward) and CMS (midrapidity) is
studied using PYTHIA 8 simulations and it is found that the calculated
multiplicity using the CMS procedure is about 20% larger than the one from
ALICE when compared in the acceptance region of the measurements reported in
this article, $|\eta|<$ 0.9. Near-side ridges in all transverse momentum
ranges are comparable. The larger away-side yields observed in Fig. 2 for the
CMS results can be attributed to the overlap in $\eta$ acceptance between the
multiplicity selection and the correlation function measurement.

In Fig. 2, the ALICE measurements are also compared with model predictions
where a comparable high-multiplicity selection and $\Delta\eta$ projection
range are applied. The selection of high-multiplicity events in the models is
done by requiring a minimal number of charged particles emitted within the V0M
detector acceptance. In case of $\mathrm{PYTHIA}\,8\,\mathrm{Tune\,4C}$, the
0–0.1% centrality threshold is 105 charged particles. The threshold for
${\mathrm{EPOS}\,~{}\mathrm{LHC}}$ and
${\mathrm{PYTHIA}\,8~{}\mathrm{String}~{}}\mathrm{Shoving}$ are 110 and 108,
respectively. The magnitude of string shoving ($g$) is set to 3.0 in this
study. The statistical uncertainties due to the limited number of events for
the model calculations are shown as bands in each figure. The
${\mathrm{PYTHIA}\,8~{}\mathrm{String}~{}}\mathrm{Shoving}$ provides good
estimates of the near-side ridge yield and slightly overestimates the away-
side yield for the interval 1 $<\it{p}_{\rm{T}}<$ 2 GeV/$c$. However, the
${\mathrm{PYTHIA}\,8~{}\mathrm{String}~{}}\mathrm{Shoving}$ model
underestimates the near-side ridge yield for $\it{p}_{\rm{T}}>$ 2 GeV/$c$. The
$\mathrm{PYTHIA}\,8\,\mathrm{Tune\,4C}$ model does not show any near-side
ridge as expected. It slightly underestimates the away-side peak for 1
$<\it{p}_{\rm{T}}<$ 2 GeV/$c$ and provides good estimates for
$\it{p}_{\rm{T}}>$ 2 GeV/$c$. On the other hand,
${\mathrm{EPOS}\,~{}\mathrm{LHC}}$ describes the shape of the ridge yield
quantitatively better in the 2 $<p_{\mathrm{T,\,trig\,(assoc)}}<$ 4 GeV/$c$
range, while overestimating the near-side ridge yield for
$p_{\mathrm{T,\,trig\,(assoc)}}<$ 2 GeV/$c$ range.

Figure 3:  Fully corrected near-side ridge yield as a function transverse
momentum. The open blue boxes denote the measurement by ALICE. The statistical
and systematic uncertainties are shown as vertical bars and boxes,
respectively. The CMS measurement [19] is represented by filled circles and
extends down to lower $p_{\mathrm{T}}$ due to the larger $\Delta\eta$
acceptance (see Sec. 3). The three lines show model predictions from
$\mathrm{PYTHIA}\,8\,\mathrm{Tune\,4C}$ (blue dotted line),
${\mathrm{PYTHIA}\,8~{}\mathrm{String}~{}}\mathrm{Shoving}$ (orange line), and
${\mathrm{EPOS}\,~{}\mathrm{LHC}}$ (green dashed line).

Figure 3 shows the near-side ridge yield measured in high-multiplicity events
as a function of $p_{\mathrm{T,\,trig\,(assoc)}}$. The measurement is compared
with the result from CMS [19]. Considering the differences in acceptance and
the chosen multiplicity estimator of both measurements, perfect agreement
between the two sets of results is not expected. The measurement is also
compared with model calculations. As expected, the PYTHIA 8 model with Tune 4C
does not produce a near-side ridge because it is not designed to account for
this effect. The ${\mathrm{PYTHIA}\,8~{}\mathrm{String}~{}}\mathrm{Shoving}$
model describes the yield qualitatively, however the predicted yield decreases
more rapidly than the measured one for increasing
$p_{\mathrm{T,\,trig\,(assoc)}}$. The ${\mathrm{EPOS}\,~{}\mathrm{LHC}}$
model, unlike the ${\mathrm{PYTHIA}\,8~{}\mathrm{String}~{}}\mathrm{Shoving}$
model, describes well the $p_{\mathrm{T}}$ dependence of the ridge yield for
the range $\it{p}_{\rm{T}}>$ 2 GeV/$c$ , while predicting larger yields for
$\it{p}_{\rm{T}}<$ 2 GeV/$c$.

###  5.2 Event-scale dependence of the ridge yield

Figure 4:  Two-dimensional correlation functions as a function of $\Delta\eta$
and $\Delta\varphi$ in high-multiplicity events including a selection on the
event-scale. The interval of $p_{\mathrm{T,\,trig}}$ and
$p_{\mathrm{T,\,assoc}}$ is 1 $<p_{\mathrm{T,\,trig\,(assoc)}}<$ 2 GeV/$c$.
Left: HM events with a $p_{\mathrm{T,\,LP}}>$ 9 GeV/$c$ leading track. Right:
HM events with a $p_{\mathrm{T,\,jet}}^{\mathrm{ch}}>$ 10 GeV/$c$.

The ridge yield is further studied with respect to two different event-scales.
In the first measurement, the event-scale is set by requiring a minimum
$p_{\mathrm{T}}$ on the leading particle in each event (denoted as
$p_{\mathrm{T,\,LP}}$), while in the second measurement, a minimum
$p_{\mathrm{T}}$ is imposed on the leading jet (denoted as
$p_{\mathrm{T,\,jet}}^{\mathrm{ch}}$).

Figure 4 shows that the ridge structure for 1
$<p_{\mathrm{T,\,trig}}\,(p_{\mathrm{T,\,assoc}})<$ 2 GeV/$c$ still persists
in high-multiplicity pp collisions with $p_{\mathrm{T,\,LP}}>$ 9 GeV/$c$
(left) and $p_{\mathrm{T,\,jet}}^{\mathrm{ch}}>$ 10 GeV/$c$ (right). It is
worth noting that the correlation function obtained with the minimum
$p_{\mathrm{T,\,jet}}^{\mathrm{ch}}$ selection has a double peak structure
which is oriented along the $\Delta\eta$ axis at $\Delta\varphi=\pi$. This
structure emerges due to the restricted acceptance of the jet tagging,
$|\eta_{\rm{jet}}|<$ 0.4.

Figure 5:  One-dimensional $\Delta\varphi$ projections of the correlation
functions constrained to 1.6 $<|\Delta\eta|<$ 1.8 in HM events with an
additional event-scale bias. Top: with an imposed selection on the leading jet
$p_{\mathrm{T}}$, bottom: with an imposed selection on the leading particle
$p_{\mathrm{T}}$. ALICE data are compared with prediction of models.

Figure 5 shows projected $\Delta\varphi$ distributions of the correlation
functions in 1.6 $<|\Delta\eta|<$ 1.8 with the minimum $p_{\mathrm{T,\,LP}}$
(lower) and $p_{\mathrm{T,\,jet}}^{\mathrm{ch}}$ (upper) requirement. Even
with the event-scale selection, the ridge is still visible on the near-side.
The near-side ridge peak increases as the thresholds of $p_{\mathrm{T,\,LP}}$
and $p_{\mathrm{T,\,jet}}^{\mathrm{ch}}$ increase compared to the one measured
in unbiased events in Sec. 5.1. The results are compared with
${\mathrm{PYTHIA}\,8~{}\mathrm{String}~{}}\mathrm{Shoving}$,
$\mathrm{PYTHIA}\,8\,\mathrm{Tune\,4C}$, and
${\mathrm{EPOS}\,~{}\mathrm{LHC}}$ calculations. The near-side ridge peaks are
qualitatively reproduced by
${\mathrm{PYTHIA}\,8~{}\mathrm{String}~{}}\mathrm{Shoving}$ and
${\mathrm{EPOS}\,~{}\mathrm{LHC}}$ models. On the other hand, the
$\mathrm{PYTHIA}\,8\,\mathrm{Tune\,4C}$ does not show the near-side ridge peak
for neither of the two event-scale selections, but it gives compatible results
for the away-side yield just like the
${\mathrm{PYTHIA}\,8~{}\mathrm{String}~{}}\mathrm{Shoving}$ model.

Figure 6: Near-side ridge yield as a function of the
$\it{p}^{\rm{LP}}_{\rm{T,min}}$ (left) and $\it{p}^{\rm{Jet}}_{\rm{T,min}}$
(right). Data points (filled circles) show the ALICE measurement. The
statistical and systematic uncertainties are shown as vertical bars and boxes,
respectively. As the ridge yield is obtained in the same operational way for
data and models, the upper limit of the systematic uncertainty due to jet
contamination, which is 18.9%, is not included in the figure. The data are
compared with predictions of models which are represented by colored bands.
The bottom panel shows a ratio of the models to the data. The uncertainty of
the data is represented by the gray band centered around unity.

The ridge yields as function of the minimum $p_{\mathrm{T,\,LP}}$
($\it{p}^{\rm{LP}}_{\rm{T,min}}$) and $p_{\mathrm{T,\,jet}}^{\mathrm{ch}}$
($\it{p}^{\rm{jet}}_{\rm{T,min}}$) selections are shown in Fig. 6. High-
multiplicity events with imposed event-scale bias exhibit increased ridge
yields when compared to unbiased HM events. A small increase of the ridge
yields as a function of $p_{\mathrm{T,\,LP}}$ or
$p_{\mathrm{T,\,jet}}^{\mathrm{ch}}$ is observed and there is no difference
between the two event-scale selections within the uncertainties. Comparisons
to model calculations show that
${\mathrm{PYTHIA}\,8~{}\mathrm{String}~{}}\mathrm{Shoving}$ provides a
comparable trend with data, but underestimates the ridge yield. On the other
hand, ${\mathrm{EPOS}\,~{}\mathrm{LHC}}$ overestimates the ridge yield while
providing a trend comparable with the data. The origin of the enhanced ridge
yields for higher momentum jet-tagged events is not clear to date but it might
be related to the expected smaller impact parameters for dijet or multi-jet
production events as studied in [60].

Figure 7:  Near-side jet-like peak yield as a function of the
$\it{p}^{\rm{LP}}_{\rm{T,min}}$ (left) and $\it{p}^{\rm{jet}}_{\rm{T,min}}$
(right). The filled circles show measurement with ALICE. The statistical and
systematic uncertainties are shown as vertical bars and boxes, respectively.
The measurements are compared with model descriptions from
$\mathrm{PYTHIA}\,8\,\mathrm{Tune\,4C}$,
${\mathrm{PYTHIA}\,8~{}\mathrm{String}~{}}\mathrm{Shoving}$, and
${\mathrm{EPOS}\,~{}\mathrm{LHC}}$ for both selections. The total uncertainty
of the ratio is represented by the gray band centered around unity.

Finally, the near-side jet-like peak yield is measured as a function of
minimum $p_{\mathrm{T,\,LP}}$ and $p_{\mathrm{T,\,jet}}^{\mathrm{ch}}$ in Fig.
7 to further test the models that aim to describe the near-side ridge.
${\mathrm{EPOS}\,~{}\mathrm{LHC}}$ provides comparable estimates of the near-
side jet-like peak yield, while $\mathrm{PYTHIA}\,8\,\mathrm{Tune\,4C}$ and
${\mathrm{PYTHIA}\,8~{}\mathrm{String}~{}}\mathrm{Shoving}$ overestimate the
near-side yields for both event selections.

In all models if the ridge is due to final-state interactions, e.g.,
${\mathrm{EPOS}\,~{}\mathrm{LHC}}$ and PYTHIA 8 String Shoving, one also
expects the near-side jet-like peak yield to be affected. This can be observed
when comparing the measured near-side jet yields with PYTHIA 8 calculations
with and without String Shoving. The new ALICE results therefore provide
constraints beyond traditional ridge measurements that challenge existing
models.

##  6 Conclusions

Long- and short-range correlations for pairs of charged particles with 1
$<p_{\mathrm{T}}<$ 4 GeV/$c$ are studied in pp collisions at $\sqrt{s}=13$ TeV
with a focus on high-multiplicity events. The ridge and near-side jet yields
are extracted and their event scale dependence have been studied. The obtained
long-range ridge yields are compatible to those observed by the CMS
Collaboration [19]. The
${\mathrm{PYTHIA}\,8~{}\mathrm{String}~{}}\mathrm{Shoving}$ model describes
the observed yields qualitatively but the yields it predicts decrease more
rapidly with increasing $p_{\mathrm{T,\,trig\,(assoc)}}$ than those measured.
On the other hand, the ${\mathrm{EPOS}\,~{}\mathrm{LHC}}$ model gives a better
description for the $p_{\mathrm{T,\,trig\,(assoc)}}$ dependence while
overestimating the ridge yield for $p_{\mathrm{T,\,trig\,(assoc)}}<$ 2
GeV/$c$. Finally, no long-range ridge is formed in the
$\mathrm{PYTHIA}\,8\,\mathrm{Tune\,4C}$ model.

The ridge yields are further studied in high-multiplicity events biased with
additional event-scale selections, which impose a minimum transverse momentum
cutoff on a leading track or jet. The ridge structure still persists with both
selection criteria. The ridge yield increases as $p_{\mathrm{T,\,LP}}$ and
$p_{\mathrm{T,\,jet}}^{\mathrm{ch}}$ increase.
${\mathrm{PYTHIA}\,8~{}\mathrm{String}~{}}\mathrm{Shoving}$ and
${\mathrm{EPOS}\,~{}\mathrm{LHC}}$ estimate qualitatively the trends for the
event-scale selections. However, the former underestimates and the latter
overestimates it. The model predictions are also compared with the yield of
the near-side jet-like correlation measured in the biased events. The
evolution of the near-side jet yield as a function of event-scale
$p_{\mathrm{T}}$ is better captured by ${\mathrm{EPOS}\,~{}\mathrm{LHC}}$,
while the ${\mathrm{PYTHIA}\,8~{}\mathrm{String}~{}}\mathrm{Shoving}$
calculation tends to overshoot the data. The results might open a new way of
studying the impact parameter dependence of small systems with jet tagged
events in the future and will help to constrain the physical origins of long-
range correlations.

## Acknowledgements

The ALICE Collaboration would like to thank Christian Bierlich for providing
the PYTHIA8 String Shoving configurations.

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; 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; Indonesian Institute of Science, Indonesia; Istituto Nazionale di Fisica Nucleare (INFN), Italy; Institute for Innovative Science and Technology , Nagasaki Institute of Applied Science (IIST), Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT) and Japan 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 Science and Higher Education, 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 and Ministry of Research and Innovation and Institute of Atomic Physics, Romania; Joint Institute for Nuclear Research (JINR), Ministry of Education and Science of the Russian Federation, National Research Centre Kurchatov Institute, Russian Science Foundation and Russian Foundation for Basic Research, Russia; 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 (NSDTA) and Office of the Higher Education Commission under NRU project of Thailand, Thailand; Turkish Atomic Energy Agency (TAEK), 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.

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##  Appendix A The ALICE Collaboration

S. Acharya142, D. Adamová97, A. Adler75, J. Adolfsson82, G. Aglieri Rinella35,
M. Agnello31, N. Agrawal55, Z. Ahammed142, S. Ahmad16, S.U. Ahn77, Z. Akbar52,
A. Akindinov94, M. Al-Turany109, D.S.D. Albuquerque124, D. Aleksandrov90, B.
Alessandro60, H.M. Alfanda7, R. Alfaro Molina72, B. Ali16, Y. Ali14, A.
Alici26, N. Alizadehvandchali127, A. Alkin35, J. Alme21, T. Alt69, L.
Altenkamper21, I. Altsybeev115, M.N. Anaam7, C. Andrei49, D. Andreou92, A.
Andronic145, V. Anguelov106, T. Antičić110, F. Antinori58, P. Antonioli55, C.
Anuj16, N. Apadula81, L. Aphecetche117, H. Appelshäuser69, S. Arcelli26, R.
Arnaldi60, M. Arratia81, I.C. Arsene20, M. Arslandok147,106, A. Augustinus35,
R. Averbeck109, S. Aziz79, M.D. Azmi16, A. Badalà57, Y.W. Baek42, X. Bai109,
R. Bailhache69, R. Bala103, A. Balbino31, A. Baldisseri139, M. Ball44, D.
Banerjee4, R. Barbera27, L. Barioglio25, M. Barlou86, G.G. Barnaföldi146, L.S.
Barnby96, V. Barret136, C. Bartels129, K. Barth35, E. Bartsch69, F.
Baruffaldi28, N. Bastid136, S. Basu82,144, G. Batigne117, B. Batyunya76, D.
Bauri50, J.L. Bazo Alba114, I.G. Bearden91, C. Beattie147, I. Belikov138,
A.D.C. Bell Hechavarria145, F. Bellini35, R. Bellwied127, S. Belokurova115, V.
Belyaev95, G. Bencedi70,146, S. Beole25, A. Bercuci49, Y. Berdnikov100, A.
Berdnikova106, D. Berenyi146, L. Bergmann106, M.G. Besoiu68, L. Betev35, P.P.
Bhaduri142, A. Bhasin103, I.R. Bhat103, M.A. Bhat4, B. Bhattacharjee43, P.
Bhattacharya23, A. Bianchi25, L. Bianchi25, N. Bianchi53, J. Bielčík38, J.
Bielčíková97, A. Bilandzic107, G. Biro146, S. Biswas4, J.T. Blair121, D.
Blau90, M.B. Blidaru109, C. Blume69, G. Boca29, F. Bock98, A. Bogdanov95, S.
Boi23, J. Bok62, L. Boldizsár146, A. Bolozdynya95, M. Bombara39, P.M. Bond35,
G. Bonomi141, H. Borel139, A. Borissov83,95, H. Bossi147, E. Botta25, L.
Bratrud69, P. Braun-Munzinger109, M. Bregant123, M. Broz38, G.E. Bruno108,34,
M.D. Buckland129, D. Budnikov111, H. Buesching69, S. Bufalino31, O. Bugnon117,
P. Buhler116, P. Buncic35, Z. Buthelezi73,133, J.B. Butt14, S.A. Bysiak120, M.
Cai28,7, D. Caffarri92, A. Caliva109, E. Calvo Villar114, J.M.M. Camacho122,
R.S. Camacho46, P. Camerini24, F.D.M. Canedo123, A.A. Capon116, F.
Carnesecchi26, R. Caron139, J. Castillo Castellanos139, E.A.R. Casula23, F.
Catalano31, C. Ceballos Sanchez76, P. Chakraborty50, S. Chandra142, W. Chang7,
S. Chapeland35, M. Chartier129, S. Chattopadhyay142, S. Chattopadhyay112, A.
Chauvin23, T.G. Chavez46, C. Cheshkov137, B. Cheynis137, V. Chibante
Barroso35, D.D. Chinellato124, S. Cho62, P. Chochula35, P. Christakoglou92,
C.H. Christensen91, P. Christiansen82, T. Chujo135, C. Cicalo56, L.
Cifarelli26, F. Cindolo55, M.R. Ciupek109, G. ClaiII,55, J. Cleymans126, F.
Colamaria54, J.S. Colburn113, D. Colella54,146, A. Collu81, M. Colocci35,26,
M. ConcasIII,60, G. Conesa Balbastre80, Z. Conesa del Valle79, G. Contin24,
J.G. Contreras38, T.M. Cormier98, P. Cortese32, M.R. Cosentino125, F. Costa35,
S. Costanza29, P. Crochet136, E. Cuautle70, P. Cui7, L. Cunqueiro98, A.
Dainese58, F.P.A. Damas117,139, M.C. Danisch106, A. Danu68, I. Das112, P.
Das88, P. Das4, S. Das4, S. Dash50, S. De88, A. De Caro30, G. de Cataldo54, L.
De Cilladi25, J. de Cuveland40, A. De Falco23, D. De Gruttola30, N. De
Marco60, C. De Martin24, S. De Pasquale30, S. Deb51, H.F. Degenhardt123, K.R.
Deja143, L. Dello Stritto30, S. Delsanto25, W. Deng7, P. Dhankher19, D. Di
Bari34, A. Di Mauro35, R.A. Diaz8, T. Dietel126, Y. Ding7, R. Divià35, D.U.
Dixit19, Ø. Djuvsland21, U. Dmitrieva64, J. Do62, A. Dobrin68, B. Dönigus69,
O. Dordic20, A.K. Dubey142, A. Dubla109,92, S. Dudi102, M. Dukhishyam88, P.
Dupieux136, T.M. Eder145, R.J. Ehlers98, V.N. Eikeland21, D. Elia54, B.
Erazmus117, F. Ercolessi26, F. Erhardt101, A. Erokhin115, M.R. Ersdal21, B.
Espagnon79, G. Eulisse35, D. Evans113, S. Evdokimov93, L. Fabbietti107, M.
Faggin28, J. Faivre80, F. Fan7, A. Fantoni53, M. Fasel98, P. Fecchio31, A.
Feliciello60, G. Feofilov115, A. Fernández Téllez46, A. Ferrero139, A.
Ferretti25, A. Festanti35, V.J.G. Feuillard106, J. Figiel120, S. Filchagin111,
D. Finogeev64, F.M. Fionda21, G. Fiorenza54, F. Flor127, A.N. Flores121, S.
Foertsch73, P. Foka109, S. Fokin90, E. Fragiacomo61, U. Fuchs35, N.
Funicello30, C. Furget80, A. Furs64, M. Fusco Girard30, J.J. Gaardhøje91, M.
Gagliardi25, A.M. Gago114, A. Gal138, C.D. Galvan122, P. Ganoti86, C.
Garabatos109, J.R.A. Garcia46, E. Garcia-Solis10, K. Garg117, C. Gargiulo35,
A. Garibli89, K. Garner145, P. Gasik107, E.F. Gauger121, M.B. Gay Ducati71, M.
Germain117, J. Ghosh112, P. Ghosh142, S.K. Ghosh4, M. Giacalone26, P.
Gianotti53, P. Giubellino109,60, P. Giubilato28, A.M.C. Glaenzer139, P.
Glässel106, V. Gonzalez144, L.H. González-Trueba72, S. Gorbunov40, L.
Görlich120, S. Gotovac36, V. Grabski72, L.K. Graczykowski143, K.L. Graham113,
L. Greiner81, A. Grelli63, C. Grigoras35, V. Grigoriev95, A. GrigoryanI,1, S.
Grigoryan76,1, O.S. Groettvik21, F. Grosa60, J.F. Grosse-Oetringhaus35, R.
Grosso109, R. Guernane80, M. Guilbaud117, M. Guittiere117, K. Gulbrandsen91,
T. Gunji134, A. Gupta103, R. Gupta103, I.B. Guzman46, R. Haake147, M.K.
Habib109, C. Hadjidakis79, H. Hamagaki84, G. Hamar146, M. Hamid7, R.
Hannigan121, M.R. Haque143,88, A. Harlenderova109, J.W. Harris147, A.
Harton10, J.A. Hasenbichler35, H. Hassan98, D. Hatzifotiadou55, P. Hauer44,
L.B. Havener147, S. Hayashi134, S.T. Heckel107, E. Hellbär69, H. Helstrup37,
T. Herman38, E.G. Hernandez46, G. Herrera Corral9, F. Herrmann145, K.F.
Hetland37, H. Hillemanns35, C. Hills129, B. Hippolyte138, B. Hohlweger107, J.
Honermann145, G.H. Hong148, D. Horak38, S. Hornung109, R. Hosokawa15, P.
Hristov35, C. Huang79, C. Hughes132, P. Huhn69, T.J. Humanic99, H. Hushnud112,
L.A. Husova145, N. Hussain43, D. Hutter40, J.P. Iddon35,129, R. Ilkaev111, H.
Ilyas14, M. Inaba135, G.M. Innocenti35, M. Ippolitov90, A. Isakov38,97, M.S.
Islam112, M. Ivanov109, V. Ivanov100, V. Izucheev93, B. Jacak81, N.
Jacazio35,55, P.M. Jacobs81, S. Jadlovska119, J. Jadlovsky119, S. Jaelani63,
C. Jahnke123, M.J. Jakubowska143, M.A. Janik143, T. Janson75, M. Jercic101, O.
Jevons113, M. Jin127, F. Jonas98,145, P.G. Jones113, J. Jung69, M. Jung69, A.
Junique35, A. Jusko113, P. Kalinak65, A. Kalweit35, V. Kaplin95, S. Kar7, A.
Karasu Uysal78, D. Karatovic101, O. Karavichev64, T. Karavicheva64, P.
Karczmarczyk143, E. Karpechev64, A. Kazantsev90, U. Kebschull75, R. Keidel48,
M. Keil35, B. Ketzer44, Z. Khabanova92, A.M. Khan7, S. Khan16, A.
Khanzadeev100, Y. Kharlov93, A. Khatun16, A. Khuntia120, B. Kileng37, B.
Kim62, D. Kim148, D.J. Kim128, E.J. Kim74, H. Kim17, J. Kim148, J.S. Kim42, J.
Kim106, J. Kim148, J. Kim74, M. Kim106, S. Kim18, T. Kim148, S. Kirsch69, I.
Kisel40, S. Kiselev94, A. Kisiel143, J.L. Klay6, J. Klein35,60, S. Klein81, C.
Klein-Bösing145, M. Kleiner69, T. Klemenz107, A. Kluge35, A.G. Knospe127, C.
Kobdaj118, M.K. Köhler106, T. Kollegger109, A. Kondratyev76, N. Kondratyeva95,
E. Kondratyuk93, J. Konig69, S.A. Konigstorfer107, P.J. Konopka2,35, G.
Kornakov143, S.D. Koryciak2, L. Koska119, O. Kovalenko87, V. Kovalenko115, M.
Kowalski120, I. Králik65, A. Kravčáková39, L. Kreis109, M. Krivda113,65, F.
Krizek97, K. Krizkova Gajdosova38, M. Kroesen106, M. Krüger69, E. Kryshen100,
M. Krzewicki40, V. Kučera35, C. Kuhn138, P.G. Kuijer92, T. Kumaoka135, L.
Kumar102, S. Kundu88, P. Kurashvili87, A. Kurepin64, A.B. Kurepin64, A.
Kuryakin111, S. Kushpil97, J. Kvapil113, M.J. Kweon62, J.Y. Kwon62, Y.
Kwon148, S.L. La Pointe40, P. La Rocca27, Y.S. Lai81, A. Lakrathok118, M.
Lamanna35, R. Langoy131, K. Lapidus35, P. Larionov53, E. Laudi35, L.
Lautner35, R. Lavicka38, T. Lazareva115, R. Lea24, J. Lee135, J. Lehrbach40,
R.C. Lemmon96, I. León Monzón122, E.D. Lesser19, M. Lettrich35, P. Lévai146,
X. Li11, X.L. Li7, J. Lien131, R. Lietava113, B. Lim17, S.H. Lim17, V.
Lindenstruth40, A. Lindner49, C. Lippmann109, A. Liu19, J. Liu129, I.M.
Lofnes21, V. Loginov95, C. Loizides98, P. Loncar36, J.A. Lopez106, X.
Lopez136, E. López Torres8, J.R. Luhder145, M. Lunardon28, G. Luparello61,
Y.G. Ma41, A. Maevskaya64, M. Mager35, S.M. Mahmood20, T. Mahmoud44, A.
Maire138, R.D. MajkaI,147, M. Malaev100, Q.W. Malik20, L. MalininaIV,76, D.
Mal’Kevich94, N. Mallick51, P. Malzacher109, G. Mandaglio33,57, V. Manko90, F.
Manso136, V. Manzari54, Y. Mao7, J. Mareš67, G.V. Margagliotti24, A.
Margotti55, A. Marín109, C. Markert121, M. Marquard69, N.A. Martin106, P.
Martinengo35, J.L. Martinez127, M.I. Martínez46, G. Martínez García117, S.
Masciocchi109, M. Masera25, A. Masoni56, L. Massacrier79, A.
Mastroserio140,54, A.M. Mathis107, O. Matonoha82, P.F.T. Matuoka123, A.
Matyja120, C. Mayer120, A.L. Mazuecos35, F. Mazzaschi25, M. Mazzilli35,54,
M.A. Mazzoni59, A.F. Mechler69, F. Meddi22, Y. Melikyan64, A. Menchaca-
Rocha72, E. Meninno116,30, A.S. Menon127, M. Meres13, S. Mhlanga126, Y.
Miake135, L. Micheletti25, L.C. Migliorin137, D.L. Mihaylov107, K.
Mikhaylov76,94, A.N. Mishra146,70, D. Miśkowiec109, A. Modak4, N. Mohammadi35,
A.P. Mohanty63, B. Mohanty88, M. Mohisin Khan16, Z. Moravcova91, C.
Mordasini107, D.A. Moreira De Godoy145, L.A.P. Moreno46, I. Morozov64, A.
Morsch35, T. Mrnjavac35, V. Muccifora53, E. Mudnic36, D. Mühlheim145, S.
Muhuri142, J.D. Mulligan81, A. Mulliri23, M.G. Munhoz123, R.H. Munzer69, H.
Murakami134, S. Murray126, L. Musa35, J. Musinsky65, C.J. Myers127, J.W.
Myrcha143, B. Naik50, R. Nair87, B.K. Nandi50, R. Nania55, E. Nappi54, M.U.
Naru14, A.F. Nassirpour82, C. Nattrass132, S. Nazarenko111, A. Neagu20, L.
Nellen70, S.V. Nesbo37, G. Neskovic40, D. Nesterov115, B.S. Nielsen91, S.
Nikolaev90, S. Nikulin90, V. Nikulin100, F. Noferini55, S. Noh12, P.
Nomokonov76, J. Norman129, N. Novitzky135, P. Nowakowski143, A. Nyanin90, J.
Nystrand21, M. Ogino84, A. Ohlson82, J. Oleniacz143, A.C. Oliveira Da
Silva132, M.H. Oliver147, A. Onnerstad128, C. Oppedisano60, A. Ortiz
Velasquez70, T. Osako47, A. Oskarsson82, J. Otwinowski120, K. Oyama84, Y.
Pachmayer106, S. Padhan50, D. Pagano141, G. Paić70, A. Palasciano54, J.
Pan144, S. Panebianco139, P. Pareek142, J. Park62, J.E. Parkkila128, S.
Parmar102, S.P. Pathak127, B. Paul23, J. Pazzini141, H. Pei7, T. Peitzmann63,
X. Peng7, L.G. Pereira71, H. Pereira Da Costa139, D. Peresunko90, G.M. Perez8,
S. Perrin139, Y. Pestov5, V. Petráček38, M. Petrovici49, R.P. Pezzi71, S.
Piano61, M. Pikna13, P. Pillot117, O. Pinazza55,35, L. Pinsky127, C. Pinto27,
S. Pisano53, M. Płoskoń81, M. Planinic101, F. Pliquett69, M.G. Poghosyan98, B.
Polichtchouk93, N. Poljak101, A. Pop49, S. Porteboeuf-Houssais136, J.
Porter81, V. Pozdniakov76, S.K. Prasad4, R. Preghenella55, F. Prino60, C.A.
Pruneau144, I. Pshenichnov64, M. Puccio35, S. Qiu92, L. Quaglia25, R.E.
Quishpe127, S. Ragoni113, A. Rakotozafindrabe139, L. Ramello32, F. Rami138,
S.A.R. Ramirez46, A.G.T. Ramos34, R. Raniwala104, S. Raniwala104, S.S.
Räsänen45, R. Rath51, I. Ravasenga92, K.F. Read98,132, A.R. Redelbach40, K.
RedlichV,87, A. Rehman21, P. Reichelt69, F. Reidt35, R. Renfordt69, Z.
Rescakova39, K. Reygers106, A. Riabov100, V. Riabov100, T. Richert82,91, M.
Richter20, P. Riedler35, W. Riegler35, F. Riggi27, C. Ristea68, S.P. Rode51,
M. Rodríguez Cahuantzi46, K. Røed20, R. Rogalev93, E. Rogochaya76, T.S.
Rogoschinski69, D. Rohr35, D. Röhrich21, P.F. Rojas46, P.S. Rokita143, F.
Ronchetti53, A. Rosano33,57, E.D. Rosas70, A. Rossi58, A. Rotondi29, A. Roy51,
P. Roy112, N. Rubini26, O.V. Rueda82, R. Rui24, B. Rumyantsev76, A.
Rustamov89, E. Ryabinkin90, Y. Ryabov100, A. Rybicki120, H. Rytkonen128, W.
Rzesa143, O.A.M. Saarimaki45, R. Sadek117, S. Sadovsky93, J. Saetre21, K.
Šafařík38, S.K. Saha142, S. Saha88, B. Sahoo50, P. Sahoo50, R. Sahoo51, S.
Sahoo66, D. Sahu51, P.K. Sahu66, J. Saini142, S. Sakai135, S. Sambyal103, V.
SamsonovI,100,95, D. Sarkar144, N. Sarkar142, P. Sarma43, V.M. Sarti107,
M.H.P. Sas147,63, J. Schambach98,121, H.S. Scheid69, C. Schiaua49, R.
Schicker106, A. Schmah106, C. Schmidt109, H.R. Schmidt105, M.O. Schmidt106, M.
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Scomparin60, J.E. Seger15, Y. Sekiguchi134, D. Sekihata134, I.
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Šerkšnytė107, A. Sevcenco68, A. Shabanov64, A. Shabetai117, R. Shahoyan35, W.
Shaikh112, A. Shangaraev93, A. Sharma102, H. Sharma120, M. Sharma103, N.
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V. Singhal142, T. Sinha112, B. Sitar13, M. Sitta32, T.B. Skaali20, G.
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Stiefelmaier106, D. Stocco117, M.M. Storetvedt37, C.P. Stylianidis92, A.A.P.
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G.J. Tambave21, S. Tang136,7, Z. Tang130, M. Tarhini117, M.G. Tarzila49, A.
Tauro35, G. Tejeda Muñoz46, A. Telesca35, L. Terlizzi25, C. Terrevoli127, G.
Tersimonov3, S. Thakur142, D. Thomas121, R. Tieulent137, A. Tikhonov64, A.R.
Timmins127, M. Tkacik119, A. Toia69, N. Topilskaya64, M. Toppi53, F. Torales-
Acosta19, S.R. Torres38, A. Trifiró33,57, S. Tripathy70, T. Tripathy50, S.
Trogolo28, G. Trombetta34, L. Tropp39, V. Trubnikov3, W.H. Trzaska128, T.P.
Trzcinski143, B.A. Trzeciak38, A. Tumkin111, R. Turrisi58, T.S. Tveter20, K.
Ullaland21, E.N. Umaka127, A. Uras137, M. Urioni141, G.L. Usai23, M. Vala39,
N. Valle29, S. Vallero60, N. van der Kolk63, L.V.R. van Doremalen63, M. van
Leeuwen92, P. Vande Vyvre35, D. Varga146, Z. Varga146, M. Varga-Kofarago146,
A. Vargas46, M. Vasileiou86, A. Vasiliev90, O. Vázquez Doce107, V.
Vechernin115, E. Vercellin25, S. Vergara Limón46, L. Vermunt63, R. Vértesi146,
M. Verweij63, L. Vickovic36, Z. Vilakazi133, O. Villalobos Baillie113, G.
Vino54, A. Vinogradov90, T. Virgili30, V. Vislavicius91, A. Vodopyanov76, B.
Volkel35, M.A. Völkl105, K. Voloshin94, S.A. Voloshin144, G. Volpe34, B. von
Haller35, I. Vorobyev107, D. Voscek119, J. Vrláková39, B. Wagner21, M.
Weber116, A. Wegrzynek35, S.C. Wenzel35, J.P. Wessels145, J. Wiechula69, J.
Wikne20, G. Wilk87, J. Wilkinson109, G.A. Willems145, E. Willsher113, B.
Windelband106, M. Winn139, W.E. Witt132, J.R. Wright121, Y. Wu130, R. Xu7, S.
Yalcin78, Y. Yamaguchi47, K. Yamakawa47, S. Yang21, S. Yano47,139, Z. Yin7, H.
Yokoyama63, I.-K. Yoo17, J.H. Yoon62, S. Yuan21, A. Yuncu106, V. Yurchenko3,
V. Zaccolo24, A. Zaman14, C. Zampolli35, H.J.C. Zanoli63, N. Zardoshti35, A.
Zarochentsev115, P. Závada67, N. Zaviyalov111, H. Zbroszczyk143, M. Zhalov100,
S. Zhang41, X. Zhang7, Y. Zhang130, V. Zherebchevskii115, Y. Zhi11, D. Zhou7,
Y. Zhou91, J. Zhu7,109, Y. Zhu7, A. Zichichi26, G. Zinovjev3, N. Zurlo141

## Affiliation notes

I Deceased
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: M.V. Lomonosov Moscow State University, D.V. Skobeltsyn Institute
of Nuclear, Physics, Moscow, Russia
V Also at: Institute of Theoretical Physics, University of Wroclaw, Poland

## Collaboration Institutes

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