2023 054 22 March

Inclusive J$/\psi$ production in Pb–Pb collisions at
$\sqrt{s_{\mathrm{NN}}}~{}=~{}5.02$ TeV

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

The measurements of the inclusive ${\rm J}/\psi$ yield at midrapidity
($\left|y\right|<0$.9) and forward rapidity (2.5 $<y<$ 4) in Pb–Pb collisions
at $\sqrt{s_{\mathrm{NN}}}~{}=~{}5.02$ TeV with the ALICE detector at the LHC
are reported. The inclusive ${\rm J}/\psi$ production yields and nuclear
modification factors, $R_{\rm AA}$, are measured as a function of the
collision centrality, ${\rm J}/\psi$ transverse momentum ($p_{\rm T}$), and
rapidity. The ${\rm J}/\psi$ average transverse momentum and squared
transverse momentum ($\langle p_{\mathrm{T}}\rangle$ and $\langle
p_{\mathrm{T}}^{\mathrm{2}}\rangle$) are evaluated as a function of the
centrality at midrapidity. Compared to the previous ALICE publications, here
the entire Pb–Pb collisions dataset collected during the LHC Run 2 is used,
which improves the precision of the measurements and extends the $p_{\rm T}$
coverage. The $p_{\rm T}$-integrated $R_{\rm AA}$ shows a hint of an
increasing trend towards unity from semicentral to central collisions at
midrapidity, while it is flat at forward rapidity. The $p_{\rm
T}$-differential $R_{\rm AA}$ shows a strong suppression at high $p_{\rm T}$
with less suppression at low $p_{\rm T}$ where it reaches a larger value at
midrapidity compared to forward rapidity. The ratio of the $p_{\rm
T}$-integrated yields of ${\rm J}/\psi$ to those of D0 mesons is reported for
the first time for the central and semicentral event classes at midrapidity.
Model calculations implementing charmonium production via the coalescence of
charm quarks and antiquarks during the fireball evolution (transport models)
or in a statistical approach with thermal weights are in good agreement with
the data at low $p_{\rm T}$. At higher $p_{\rm T}$, the data are well
described by transport models and a model based on energy loss in the
strongly-interacting medium produced in nuclear collisions at the LHC.

##  1 Introduction

Quantum chromodynamics (QCD) is the theory describing the strong interaction.
Lattice QCD, i.e. the discrete formulation of QCD, predicts the existence of a
state of deconfined matter at high energy density that is characterised by
quark and gluon degrees of freedom [1, 2]. This quark–gluon plasma (QGP) is
created during the early hot and dense stage of heavy-ion collisions at ultra-
relativistic energies.

The heavy quarks, charm (c) and beauty (b), are unique probes for this phase
of matter [3, 4]. Due to their large masses, they are produced as
quark–antiquark pairs in hard partonic scattering processes in the early stage
of the collision, and they thus experience the full evolution of the system.
While the majority of the produced heavy quarks and antiquarks hadronise
independently into open heavy-flavour hadrons, bound quarkonium states can
also be formed [5]. However, it has been predicted that the formation of bound
states should be suppressed due to the mechanism of colour screening where the
large density of colour charges in the QGP hinders the production of bound
quarkonia [6, 7]. The degree of suppression of the various quarkonium states
depends on their binding energy along with the medium properties, such as its
temperature. Consequently, the measurements of quarkonium production rates in
heavy-ion collisions have been considered as a potential thermometer of the
medium.

The production of ${\rm J}/\psi$, the charmonium ground state with quantum
numbers $J^{PC}=1^{--}$, has been studied extensively in heavy-ion collisions
over the last several decades. Suppression of the ${\rm J}/\psi$ yield was
observed in nucleus–nucleus collisions with respect to the expectation from
proton–proton collisions at the SPS (up to centre-of-mass energy per nucleon
pair $\sqrt{s_{\mathrm{NN}}}$ $\mathrm{=}$ 17 GeV) [8, 9, 10], at RHIC
($\sqrt{s_{\mathrm{NN}}}$ up to 200 GeV) [11, 12, 13, 14] and at the LHC
($\sqrt{s_{\mathrm{NN}}}$ $\mathrm{=}$ 2.76 TeV [15, 16, 17, 18] and 5.02 TeV
[19, 20, 21, 22, 23, 24]). However, contrary to the prediction from the
colour-screening scenario, the measured suppression does not increase with
increasing collision energy from RHIC to LHC despite of an increased energy
density of the produced QGP. At LHC energies, ${\rm J}/\psi$ production is
found to be less suppressed than at the lower RHIC energies, in particular at
low transverse momentum, $p_{\rm T}$, of the ${\rm J}/\psi$ [18, 25, 26, 27].
In addition, a significant azimuthal anisotropy in the ${\rm J}/\psi$
production was observed via the elliptic and triangular flow measurements
reported in Refs. [28, 29]. These observations are explained by an additional
${\rm J}/\psi$ production mechanism, referred to as (re)generation in the
following, in which copiously produced uncorrelated charm quarks and
antiquarks bind into ${\rm J}/\psi$ mesons [30, 31]. This process can only
take place in a deconfined medium, and its contribution to the measured ${\rm
J}/\psi$ yield increases with the density of c$\overline{\rm c}$ pairs and,
therefore, with increasing collision energy and decreasing $p_{\rm T}$ [32,
33, 34]. With increasing $p_{\rm T}$, (re)generation becomes less relevant for
${\rm J}/\psi$ production and, instead, charmonium dissociation and the
fragmentation of high-energy partons into charmonia become dominant. In the
latter case, the suppression of high-$p_{\rm T}$ ${\rm J}/\psi$ yields should
reflect the energy loss of partons [35], which is mostly of radiative nature
in this kinematic regime.

The formation process of charmonia in heavy-ion collisions is complex and
various phenomenological approaches are considered. In the statistical
hadronization scenario, the relative abundances of charmomium states with
respect to other charmed hadrons are determined by thermal weights [30, 32] at
the system chemical freeze-out. In microscopic transport and comover
interaction models, charmonia are continuously produced and broken up during
their propagation through the QGP [31, 33, 34, 36]. Furthermore, it is
important to consider cold nuclear matter (CNM) effects. In particular, the
modification of the parton distribution functions in nuclei with respect to
nucleons [37] has to be taken into account for the interpretation of the
results. These CNM effects were investigated in ALICE especially with
proton–nucleus collisions [38, 39, 40, 41, 42, 43, 44].

For a better assessment of the production mechanisms, systematic measurements
of the centrality, $p_{\rm T}$, and rapidity dependence of ${\rm J}/\psi$
production are pivotal. In this article, the ALICE results on inclusive ${\rm
J}/\psi$ production at midrapidity ($\left|y\right|<0$.9) for $0.15<p_{\rm
T}<15$ GeV/c and forward rapidity (2.5 $<y<$ 4) for $0.3<p_{\rm T}<20$ GeV/c,
from the full Run 2 data sample at the LHC, are reported. Inclusive ${\rm
J}/\psi$ measurements contain a prompt ${\rm J}/\psi$ contribution from direct
${\rm J}/\psi$ and decay from heavier charmonium states, and a non-prompt
${\rm J}/\psi$ contribution from the decay of beauty hadrons. The precision of
the measurements, using the entire Run 2 data sample, improved significantly
compared to previous ones [21, 20] and the measurements could be extended up
to 15 GeV/c and 20 GeV/c at mid and forward rapidity, respectively. The
$p_{\rm T}$-differential ${\rm J}/\psi$ yields in Pb–Pb collisions at
$\sqrt{s_{\mathrm{NN}}}~{}=~{}5.02$ TeV are measured in various centrality
classes. At midrapidity, the average transverse momentum $\langle p_{\rm
T}\rangle$ as well as squared transverse momentum $\langle p_{\rm
T}^{2}\rangle$ is determined, which provide a quantitative estimation of the
evolution of the $p_{\rm T}$ spectra as a function of centrality. The nuclear
modification factor $R_{\rm AA}$ defined, as the ratio of the yield in Pb–Pb
to the corresponding yield in pp collisions scaled by the number of binary
nucleon–nucleon collisions is calculated. The results as a function of $p_{\rm
T}$ and collision centrality are compared with model calculations employing
the statistical hadronisation [32], microscopic parton transport [33, 34],
comover [36], and energy loss [35] approaches.

##  2 Apparatus and data sample

A complete description of the ALICE apparatus and its performance is given in
Refs. [45, 46]. The central barrel detectors, for the dielectron analysis, and
the muon spectrometer, for the dimuon analysis, covering midrapidity and
forward rapidity, respectively, were used in the analyses reported in this
paper.

At midrapidity, the main detectors employed in the analysis are the Time
Projection Chamber (TPC) [47] and the Inner Tracking System (ITS) [48], both
immersed in a uniform magnetic field of 0.5 T provided by a solenoid magnet.
The TPC is used for tracking and particle identification. It covers the
pseudorapidity range $\left|\eta\right|~{}<~{}0$.9 for tracks with full radial
length and has full coverage in azimuth. It provides excellent momentum
resolution and electron–hadron separation in a wide range of track transverse
momentum. The ITS is a cylindrical six-layer silicon detector, with the
innermost layer located at 3.9 cm from the beam pipe, providing additional
space points for tracking that enhance the spatial resolution in the
reconstruction of primary and secondary vertices.

At forward rapidity, the muon spectometer [49, 50] detects muons in the range
$-4<\eta<-2.5$. It consists of a 3 Tm dipole associated with a tracking and a
trigger system. A front absorber with a thickness of 10 interaction lengths is
placed before the tracking system in order to filter out hadrons produced in
the interaction. The tracking system consists of five tracking stations, each
one made of two planes of cathode pad chambers. An iron wall with 7.2
interaction length thickness is located between the tracking and trigger
stations in order to stop secondary hadrons escaping the front absorber and
low momentum muons produced predominantly from $\pi$ and $K$ decays. The
trigger system consists of two stations, each one made of two planes of
resistive plate chambers. Finally, a conical absorber around the beam pipe
protects the spectrometer against secondary particles produced by the
interaction of primary particles with large pseudorapidity at the beam pipe.
In the analysis at forward rapidity, the determination of the primary vertex
of the collision is provided by the Silicon Pixel Detector (SPD) that
constitutes the two innermost layers of the ITS.

Both analyses, at midrapidity and forward rapidity, use the V0 [51] and Zero
Degree Calorimeter (ZDC) [52] detectors. The V0 detector consists of two
scintillator detector arrays and covers the full azimuth in the pseudorapidity
regions $-3.7<\eta<-1.7$ and 2.8 $<\eta<$ 5.1, respectively. It is used for
triggering, beam–gas background rejection, and characterisation of the event
centrality. The ZDC detectors are located at a distance of 112.5 m on both
sides of the interaction region along the beam direction, and they detect
spectator nucleons emitted at zero degree with respect to the LHC beam axis.
They are used to reject electromagnetic Pb–Pb interactions.

The trigger for minimum-bias (MB) events was provided by the coincidence of
signals in the two scintillator arrays of the V0 detector. The dimuon analysis
relies on a dimuon trigger which requires, in addition to the MB trigger, the
detection of two opposite-sign tracks in the muon trigger system. The muon
trigger selects muon candidates with a $p_{\rm T}$ larger than a threshold of
$\sim 1$ GeV/$c$. The trigger efficiency reaches 50% at this threshold value
and a plateau value of 98% at $p_{\rm T}\sim 2.5$ GeV/$c$ [53].

The results presented in this article are based on the data sample collected
by ALICE from Pb–Pb collisions at $\sqrt{s_{\mathrm{NN}}}~{}=~{}5.02$ TeV in
2015 and 2018 during Run 2 at the LHC. During the 2018 data taking, the Pb–Pb
dataset for the central barrel was enhanced with central (0–10%) and
semicentral (30–50%) events. In total, the integrated luminosity corresponding
to the analysed data sample was about 105 $\rm\mu\rm b^{-1}$ and 51 $\rm\mu\rm
b^{-1}$ for the central and semicentral events, respectively. For the other
centrality intervals, the integrated luminosity of the data sample was 22
$\rm\mu\rm b^{-1}$. For the analysis at forward rapidity, the dimuon triggered
sample corresponds to an integrated luminosity of 756 $\rm\mu b^{-1}$. At
midrapidity, triggered events containing collisions that overlap within a time
window smaller than the readout time of the TPC were removed to preserve a
uniform particle identification performance of the TPC, which is sensitive to
the total charge produced by the ionising tracks in the sensitive volume. Only
events with the primary vertex, reconstructed within $\pm$10 cm from the
nominal interaction point in the beam direction, were considered for further
analysis at midrapidity. In the forward analysis, there was no selection on
the primary vertex.

##  3 Analysis details

The primary observable is the $p_{\rm T}$-differential ${\rm J}/\psi$ yield
per unit of rapidity $\mathrm{d}^{2}N/(\mathrm{d}y\;\mathrm{d}p_{\rm T})$. For
a given interval of centrality, rapidity ($\mathrm{\Delta}y$), and transverse
momentum ($\mathrm{\Delta}p_{\rm T}$), this is obtained as

$\frac{\mathrm{d}^{2}N}{\mathrm{~{}d}y\mathrm{~{}d}p_{\rm T}}=\frac{N_{{\rm
J}/\psi}}{N_{\rm{ev}}\times\mathrm{BR}_{{\rm
J}/\psi\rightarrow\mathrm{l}^{+}\mathrm{l}^{-}}\times(A\times\epsilon)\times\Delta
y\times\Delta p_{\rm T}},$ (1)

where $N_{{\rm J}/\psi}$ is the number of reconstructed ${\rm J}/\psi$ mesons,
$N_{\mathrm{ev}}$ is the number of events corresponding to the analysed
centrality interval, and ($A\times\epsilon$) is the acceptance times
efficiency factor. The branching ratio (BR) corresponds to either the
dielectron or the dimuon ${\rm J}/\psi$ decay channel. Since the analysis at
forward rapidity was based on a sample of dimuon-triggered events, the
equivalent $N_{\mathrm{ev}}$ was obtained as the product of the number of
dimuon-triggered events times the inverse of the probability of having a
dimuon trigger in a MB triggered event, $F_{\mathrm{norm}}$ [40, 54]. The
number of equivalent $N_{\mathrm{ev}}$ was first obtained for the 0–90%
centrality class and was then scaled to the centrality classes considered in
the analysis.

The nuclear modification factor, $R_{\rm AA}$, is obtained as

$R_{\rm AA}=\frac{\mathrm{d}^{2}N/(\mathrm{d}y\mathrm{~{}d}p_{\rm T})}{\langle
T_{\mathrm{AA}}\rangle\,\mathrm{d}^{2}\sigma_{\mathrm{pp}}/(\mathrm{d}y\mathrm{~{}d}p_{\rm
T})},$ (2)

where $\langle$$T_{\text{AA}}$$\rangle$ is the average nuclear overlap
function as described in Ref. [55] and given in Table 1 for the centrality
intervals used for the analyses at midrapidity and forward rapidity. The
$R_{\rm AA}$ is evaluated as a function of the average number of participant
nucleons, $N_{\mathrm{part}}$, corresponding to a given centrality class (as
shown in Table 1), and as a function of the ${\rm J}/\psi$ $p_{\rm T}$. The
differential ${\rm J}/\psi$ production cross section in pp collisions,
$\mathrm{d}^{2}\sigma_{\mathrm{pp}}/(\mathrm{d}y\mathrm{~{}d}p_{\rm T})$, was
measured at both midrapidity and forward rapidity as reported in Refs. [56]
and [57], respectively. At midrapidity, the data sample size does not allow to
obtain the cross section for $p_{\rm T}>10$ GeV/c and an extrapolation is
applied for the last $p_{\rm T}$ interval (10 $<$ $p_{\rm T}$ $<$ 15 GeV/$c$),
the result of the extrapolation is 6.82 $\pm$ 0.91 nb. The details of this
approach are described in Ref. [58, 59], and the corresponding $R_{\rm AA}$ is
shown as the open points in Figs. 6, 7 and 9.

The $p_{\rm T}$-differential ${\rm J}/\psi$ yields at midrapidity were studied
further by extracting the $\langle p_{\mathrm{T}}\rangle$ and $\langle
p_{\mathrm{T}}^{\mathrm{2}}\rangle$ in fine centrality intervals, as described
later in this section. For quantitative comparisons of the $p_{\rm T}$
distributions, the ratio $r_{\rm AA}$ of the ${\rm J}/\psi$ $\langle
p_{\mathrm{T}}^{\mathrm{2}}\rangle$ measured in Pb–Pb collisions to the one
obtained in pp collisions at the same energy is calculated as

$r_{\rm AA}=\frac{\langle p_{\mathrm{T}}^{\mathrm{2}}\rangle_{\rm
PbPb}}{\langle p_{\mathrm{T}}^{\mathrm{2}}\rangle_{{\rm pp}}}.$ (3)

Table 1: Average nuclear overlap function $\langle$$T_{\text{AA}}$$\rangle$ and the average number of participants $\langle$$N_{\mathrm{part}}$ $\rangle$ in Pb–Pb collisions at $\sqrt{s_{\rm{NN}}}=5.02$ TeV for the centrality classes used in the analyses at midrapidity (upper) and forward rapidity (lower). Centrality |  $\langle$$T_{\rm{AA}}$$\rangle$ (1/mb) |  $\langle$$N_{\mathrm{part}}$ $\rangle$
---|---|---
0–5% | 26.08 $\pm$ 0.18 | 383.40 $\pm$ 0.57
5–10% | 20.44 $\pm$ 0.17 | 331.20 $\pm$ 1.03
10–20% | 14.4 $\pm$ 0.13 | 262.00 $\pm$ 1.15
20–30% | 8.77 $\pm$ 0.10 | 187.90 $\pm$ 1.34
30–40% | 5.09 $\pm$ 0.08 | 130.80 $\pm$ 1.33
40–50% | 2.75 $\pm$ 0.05 | 87.14 $\pm$ 0.93
50–70% | 0.98 $\pm$ 0.02 | 42.65 $\pm$ 0.69
70–90% | 0.16 $\pm$ 0.004 | 11.34 $\pm$ 0.16
0–20% | 18.83 $\pm$ 0.14 | 309.7 $\pm$ 0.89
20–40% | 6.93 $\pm$ 0.09 | 159.4 $\pm$ 1.32
40–90% | 1.00 $\pm$ 0.02 | 39.03 $\pm$ 0.53

###  3.1  J/$\psi$ raw yield extraction

The ${\rm J}/\psi$ mesons are reconstructed employing the
$\rm{e}^{+}\rm{e}^{-}$ decay channel at midrapidity and the
$\rm{\mu}^{+}\rm{\mu}^{-}$ decay channel at forward rapidity. The analysis
techniques are discussed in detail in Refs. [21, 18, 20]. Here, only a brief
overview is given and differences with respect to previous analyses are
highlighted.

Electron candidates for the analysis at midrapidity are tracks reconstructed
in both the ITS and TPC in the pseudorapidity range
$\left|\eta\right|~{}<~{}0$.9 and with a $p_{\rm T}$ $>$ 1 GeV/$c$ to suppress
combinatorial background. All tracks are required to have at least one hit in
the SPD layers and at least 70 out of a maximum of 159 clusters reconstructed
in the TPC. These and other quality criteria that were applied in addition
(see Ref. [20]) ensure good tracking resolution and particle identification.
They reduce the background electrons from the conversion of photons in the
detector material or from long lived weakly-decaying hadrons as well as tracks
from pileup collisions, occuring within the readout time of the TPC. Electrons
and positrons are identified using selections on the specific energy loss,
$\textrm{d}E/\textrm{d}x$, in the TPC gaseous volume. The measured
$\textrm{d}E/\textrm{d}x$ is required to be within 3 standard deviations
($\sigma$) relative to the expected electron specific energy loss
corresponding to the track momentum, and more than 3.5$\sigma$ different from
either the $\pi$ or proton specific energy loss hypotheses. Electrons from
photon conversions surviving the track quality criteria are rejected using a
technique where candidate electrons are paired with other electrons selected
with less strict criteria to enhance the probability of finding the conversion
partner, as described in detail in Ref. [21].

Muon candidates were selected such that the track pseudorapidity is within the
geometrical acceptance of the muon spectrometer, $-4<\eta<-2.5$, and that the
reconstructed track matches a track segment reconstructed in the trigger
system. The transverse position of the tracks at the end of the absorber is
required to be $17.6<R_{\rm abs}<89.5$ cm in order to reject tracks crossing
the thickest part of the absorber. In addition, a selection was applied on the
product of the track momentum and the transverse distance to the primary
vertex in order to reduce the contamination produced by particles that do not
originate from the interaction point.

The number of reconstructed ${\rm J}/\psi$ mesons, i.e. the raw ${\rm J}/\psi$
yield, was obtained by constructing the invariant-mass distribution of all the
possible opposite-sign dileptons with rapidity selections of $2.5<y<4$ for
dimuons and $|y|<0.9$ for dielectrons. At midrapidity, the signal extraction
was performed in two steps. First, the combinatorial background was estimated
using an event-mixing technique [21] and subtracted from the invariant-mass
distribution. In the second step, the remaining distribution was fitted with a
two-component function, one corresponding to the ${\rm J}/\psi$ signal and the
other to the residual background, which mainly arises from correlated
semileptonic decays of heavy-flavour hadrons. The ${\rm J}/\psi$ signal line
shape was obtained from Monte Carlo (MC) simulations of ${\rm J}/\psi$ mesons
decaying in the dielectron channel embedded in simulated Pb–Pb collisions, as
described below, while for the residual background a second-order polynomial
function was employed. The raw ${\rm J}/\psi$ yield was obtained by first
counting the dielectron pairs in the mass range $2.92<m_{\rm e^{+}e^{-}}<3.16$
GeV/$c^{2}$ in the combinatorial-background subtracted invariant-mass
distribution, and then subtracting the residual background based on a two-
component fit. Finally, the raw ${\rm J}/\psi$-meson yield is corrected for
the fraction of ${\rm J}/\psi$ reconstructed outside of the counting mass
interval, as described in more detail in Section 3.3. This procedure is
illustrated in the left panels of Fig. 1 for the collision centrality interval
0–5% and $p_{\rm T}>0.15$ GeV/$c$. The upper left panel shows the invariant-
mass distributions for the opposite-sign dielectrons constructed from the same
event (black) and mixed events (red). The fitting procedure of the
combinatorial background subtracted invariant-mass distribution discussed
above is illustrated in the lower left panel.

Figure 1:  Upper panels: invariant-mass distribution of opposite-sign lepton
pairs from the same event (black points) and mixed events (red histograms) at
midrapidity (left) and forward rapidity (right) in Pb–Pb collisions at
$\sqrt{s_{\mathrm{NN}}}~{}=~{}5.02$ TeV. Lower panels: invariant-mass
distribution after the background subtraction with the event-mixing technique.
The fit curves, shown in red, represent the sum of the signal and background
shapes and the blue curves correspond to the residual background.

At forward rapidity, two different methods were used to extract the number of
${\rm J}/\psi$ counts. In the first method, the invariant-mass distributions
were fitted with a sum of a signal and a background function, while in the
second method the event-mixing technique was employed, as described in Ref.
[18]. The fit functions corresponding to the signal are either a double-sided
Crystal Ball function (CB2) or a pseudo-Gaussian with a mass-dependent width
[60]. In both cases, the ${\rm J}/\psi$ pole mass and width were free
parameters of the fit, while the non-Gaussian tail parameters were fixed. Two
sets of tail parameters were obtained, one based on MC simulations and one
extracted from a large data sample of pp collisions at $\sqrt{s}$ = 13 TeV
[61]. The MC simulations were embedded into real MB events in order to
properly account for the effect of the detector occupancy. The $\psi(\rm 2S)$
resonance was also included in the fit to the invariant-mass spectrum, using
the same signal function as for the ${\rm J}/\psi$ with mass and width bound
to those of the ${\rm J}/\psi$ [61, 62]. The background functions employed in
the first method were either a variable-width Gaussian [60] or a ratio of a
second order to a third order polynomial. The residual background in the
second method was parameterised with a sum of two exponential functions.
Finally, two invariant-mass ranges were considered for the fit procedure:
$2.2<m_{\mu^{+}\mu^{-}}<4.5$ and $2.4<m_{\mu^{+}\mu^{-}}<4.7$ GeV/$c^{2}$. An
example of the signal extraction fit is shown in the right panel of Fig. 1
before (upper plot) and after (lower plot) the subtraction of the
combinatorial background estimated with the event-mixing technique. For each
$p_{\rm T}$ and centrality interval, several fits were performed with the two
different approaches, different combinations of signal and background
functions, signal tail parameters, and fitting ranges. The number of ${\rm
J}/\psi$ was obtained as the average of the results from the various fitting
methods [57]. These various fitting methods are used to determine the
systematic uncertainties on the yield extraction as described in Section 3.4.

About 9.0$\times$105 and 8.2$\times$$10^{4}$ raw ${\rm J}/\psi$ counts are
measured at forward rapidity and midrapidity, respectively, integrated over
all available centrality and $p_{\rm T}$ intervals.

###  3.2  J/$\psi$ $\langle p_{\mathrm{T}}\rangle$ and $\langle
p_{\mathrm{T}}^{\mathrm{2}}\rangle$ extraction

At midrapidity, a quantitative study of the ${\rm J}/\psi$ $p_{\rm T}$
spectrum in fine centrality intervals is conducted by extracting the ${\rm
J}/\psi$ mean $p_{\rm T}$, $\langle p_{\mathrm{T}}\rangle_{{\rm J}/\psi}$, and
mean $p_{\rm T}$ squared, $\langle p_{\mathrm{T}}^{\mathrm{2}}\rangle_{{\rm
J}/\psi}$. For a given centrality interval, these quantities are obtained
based on a fit to the mass dependent $\langle p_{\mathrm{T}}\rangle$ and
$\langle p_{\mathrm{T}}^{\mathrm{2}}\rangle$ distributions after efficiency
correction, using a function defined as:

$X(m_{\rm e^{+}e^{-}})=f(m_{\rm e^{+}e^{-}})\times X_{{\rm
J}/\psi}+(1-f(m_{\rm e^{+}e^{-}}))\times X_{\mathrm{bkg}}(m_{\rm e^{+}e^{-}})$
(4)

where $X$ stands for either the $\langle p_{\mathrm{T}}\rangle$ or $\langle
p_{\mathrm{T}}^{\mathrm{2}}\rangle$, and $f$ is the invariant-mass dependent
fraction of ${\rm J}/\psi$ signal determined in the signal-extraction
procedure explained above. The invariant-mass dependent background component
$X_{\mathrm{bkg(m_{e^{+}e^{-})}}}$ is determined from the event-mixing
procedure plus a second order polynomial function for the residual background.
Examples of these fits for the $\langle p_{\mathrm{T}}\rangle$ observable are
shown in Fig. 2.

Figure 2: ${\rm J}/\psi$ $\langle p_{\mathrm{T}}\rangle$ extraction in Pb–Pb
collisions at $\sqrt{s_{\mathrm{NN}}}~{}=~{}5.02$ TeV at midrapidity for the
0–5% (left panel) and the 70–90% (right panel) centrality interval. The data
points correspond to opposite-sign $\rm{e}^{+}\rm{e}^{-}$ pairs from the same
event, the blue line to the $\rm{e}^{+}\rm{e}^{-}$ pairs from mixed events,
and the red line is the combined fit that includes the mixed events and
residual background which is described by the polynomial function.

###  3.3 Acceptance and efficiency correction

The acceptance times reconstruction efficiency factor (${\rm
A}\times\epsilon$), which enters into Eq. 1, was computed employing both MC
and data-driven methods. At midrapidity, this factor includes the kinematic
acceptance, track-reconstruction and particle-identification efficiencies, and
the fraction of ${\rm J}/\psi$ with an invariant mass in the signal counting
range. With the exception of the particle identification, obtained with a
data-driven method, the corrections were obtained using a MC simulation of
${\rm J}/\psi$ embedded in simulated Pb–Pb collisions. The Pb–Pb collisions
were generated using the HIJING 1.0 model [63], while the ${\rm J}/\psi$ were
generated using a cocktail of prompt ${\rm J}/\psi$ with a kinematic
distribution tuned to existing measurements and non-prompt ${\rm J}/\psi$ from
beauty hadrons forced to decay into channels containing ${\rm J}/\psi$, using
PYTHIA 6.4 [64]. The $\rm{e}^{+}\rm{e}^{-}$ decay of the embedded ${\rm
J}/\psi$ was handled using PHOTOS [65]. Both the prompt ${\rm J}/\psi$ and the
beauty hadrons forced to decay into non-prompt ${\rm J}/\psi$ were assumed to
be unpolarised, in agreement with existing measurements, which indicate small
or no polarisation [66, 67]. All generated particles were transported through
the ALICE detector setup using GEANT3 [68], taking into account the time
dependence of detector conditions during the 2015 and 2018 data-taking
periods. For the determination of the particle-identification efficiency, a
clean sample of electrons from photon conversions, passing similar quality
selection criteria as primary electrons in the TPC, is used to compute
differential maps in pseudorapidity, azimuthal angle $\varphi$ and momentum
$p$ for the single-electron selection efficiency. These were then propagated
to the ${\rm J}/\psi$ dielectron pairs using the phase-space distribution of
the ${\rm J}/\psi$ decay simulated in the above mentioned MC simulations. The
total ($A\times\varepsilon$) for the $p_{\rm T}$-integrated ${\rm J}/\psi$
yields is about 6.5% in the 0–10% centrality interval, and it slightly
increases towards more peripheral collisions. As a function of $p_{\rm T}$,
the ($A\times\varepsilon$) has a non-monotonic behaviour, with a minimum value
of 5.6% around $p_{\rm T}$ = 2 GeV/$c$, and a maximum of about 9% towards zero
and high $p_{\rm T}$.

At forward rapidity, the acceptance and reconstruction efficiency values were
determined using simulated ${\rm J}/\psi$ mesons forced to decay via the
dimuon channel, embedded into real events. The ${\rm J}/\psi$ $p_{\rm T}$\-
and $y$-differential distributions used in the simulation were adjusted to
measurements via an iterative procedure, and separately for all centrality
intervals employed in this analysis. The ${\rm J}/\psi$ were assumed to be
unpolarised, in agreement with the small polarisation, compatible with zero,
measured in Pb–Pb collisions for $2<p_{\rm T}<10$ GeV/$c$ [67]. As in the
analysis at midrapidity, the simulations take into account the time dependence
of the detector conditions, such as the status of the tracking chambers and
the residual detector element misalignment. The trigger-chamber efficiency was
determined from data and used as input in the simulations. The (${\rm
A}\times\epsilon$) reaches a minimum of 11% at $p_{\rm T}$ $\approx$ 2 GeV/c
and increases up to 13% at low $p_{\rm T}$ and up to 46% at high $p_{\rm T}$
in the 0–20% centrality interval. It increases towards peripheral collisions
by a few percent [46].

###  3.4 Systematic uncertainties

The considered sources of systematic uncertainty for the analysis at
midrapidity include central-barrel tracking, electron identification, signal
extraction, and the kinematics of the ${\rm J}/\psi$ injected in the MC
simulations. For the analysis at forward rapidity, the main systematic
uncertainties originate from the signal extraction, the muon tracking and
trigger efficiencies, and the kinematics of the ${\rm J}/\psi$ used in the
embedded MC simulations. In addition, for the $R_{\rm AA}$, uncertainties on
the pp reference and the nuclear overlap function are included in both
analyses [55]. The uncertainty on the ${\rm J}/\psi$ decay branching ratio and
the evaluated systematic uncertainties are summarised in Table 2 and Table 3
for the analysis at midrapidity and forward rapidity, respectively.

At midrapidity, the tracking uncertainty is the largest source of systematic
uncertainty and it is dominated by the ITS–TPC track matching. This was
determined based on the difference in the matching efficiency observed for
single tracks between data and MC simulations. The systematic uncertainty due
to the electron identification takes into account the residual miscalibration
of the TPC particle identification (PID) response and also the statistical
uncertainty of the clean electron sample used to compute the identification
efficiency. For its estimation, the PID selection criteria (both electron
inclusion and hadron rejection) are varied, each time obtaining a new set of
raw yields and corresponding PID efficiencies. The assigned systematic
uncertainty is taken as the standard deviation of the distribution of the
corrected results obtained in this procedure. The systematic uncertainty of
the signal extraction includes one component from the ${\rm J}/\psi$ signal
shape obtained from MC simulations and one component related to the
description of the dielectron background. The former was determined by varying
the mass range in which the signal is counted and recomputing each time the
corresponding signal fraction correction, while the latter was determined by
repeating the fit to the invariant-mass distribution in different mass ranges.
The standard deviation of the corrected yield distribution was then taken as
the systematic uncertainty. Since the ${\rm J}/\psi$ efficiency depends on
$p_{\rm T}$, the average efficiency computed over wide $p_{\rm T}$ intervals
depends in turn on the underlying ${\rm J}/\psi$ $p_{\rm T}$ distribution used
in the MC simulations. The corresponding uncertainty was minimised by
iteratively tuning the injected $p_{\rm T}$ spectrum to match the corrected
spectrum measured in this analysis. A systematic uncertainty which takes into
account possible variations of the $p_{\rm T}$ spectrum, statistically
compatible with the finally measured $p_{\rm T}$ spectrum, was assigned and it
is typically below 1%. The total systematic uncertainty on the ${\rm J}/\psi$
corrected yield varies in the range 6–10% in different $p_{\rm T}$ and
centrality intervals. The systematic uncertainties, which are dominated by the
tracking uncertainties, are correlated over centrality and $p_{\rm T}$ to a
very large extent. The systematic uncertainties for $\langle
p_{\mathrm{T}}\rangle$ and $\langle p_{\mathrm{T}}^{\mathrm{2}}\rangle$ are
evaluated via similar procedures as for the $p_{\rm T}$ integrated yields. The
uncertainties from signal extraction and track selection criteria range,
respectively, from 0.2 to 1.2% and from 0.5 to 1.3%. The electron
identification and ITS–TPC matching systematic uncertainties are calculated by
propagating the $p_{\rm T}$-differential systematic uncertainties to the
$\langle p_{\mathrm{T}}\rangle$ based on the measured $p_{\rm T}$-differential
spectrum.

In the analysis at forward rapidity, the systematic uncertainty corresponding
to the signal extraction was determined using several variations of the fit to
the invariant-mass spectra, including the fit method, the signal and
background functions, and the fitted mass range. This uncertainty varies in
the range 1.5–10.7% depending on the $p_{\rm T}$ interval and centrality
class. The systematic uncertainty on (${\rm A}\times\epsilon$) depends on the
uncertainty on the $p_{\rm T}$ and $y$ distributions of the simulated ${\rm
J}/\psi$, and on the tracking, trigger, and matching efficiency. The first two
were evaluated by varying the $p_{\rm T}$ and $y$ spectrum for each centrality
interval, taking into account the correlations between the $p_{\rm T}$ and $y$
distributions. The systematic uncertainty was estimated as the largest
difference between the nominal (${\rm A}\times\epsilon$) and the one estimated
from the variations. It ranges between 0.2% and 4.1%. The systematic
uncertainty on the muon tracking efficiency was estimated based on the
difference between the single-muon tracking efficiency obtained in data and MC
with a method that uses the redundancy of the tracking information in each
station. The corresponding uncertainty for dimuons was evaluated to be 3% and
constant over $p_{\rm T}$. An additional systematic uncertainty is ascribed to
the loss of tracking efficiency due to occupancy effects in the most central
events and was estimated to range between 0.5 and 1%, increasing towards more
central events. The systematic uncertainty on the trigger efficiency has two
components, one due to the intrinsic efficiency of the trigger chambers and
another one due to the trigger response. The first component was estimated
from the uncertainties on the single-muon trigger efficiency measured from
data and used in the simulations. The second component was evaluated by
comparing the $p_{\rm T}$ dependence of the trigger response function of the
single muon between data and MC. The two sources were added in quadrature and
the obtained uncertainty ranges between 1.5 and 2%. Finally, a 1% systematic
uncertainty is assigned, related to the choice of the $\chi^{2}$ selection
used to define the matching between the tracks reconstructed in the tracking
system and the track segments reconstructed in the trigger system. The
uncertainty on $F_{\mathrm{norm}}$ was estimated by using two methods. First,
the opposite-sign dimuon trigger condition was applied when analysing recorded
MB events and, second, the counting rate of the dimuon and MB triggers were
compared. The estimated uncertainty on $F_{\mathrm{norm}}$ was obtained by
comparing the two methods and it amounts to 0.7%.

The systematic uncertainties on $\langle$$T_{\text{AA}}$$\rangle$ were
obtained as described in Ref. [55] and the values are listed in Table 1 for
both analyses, at midrapidity and forward rapidity. The systematic uncertainty
on the definition of the centrality interval was estimated using variations of
$\pm 1\%$ of the V0 signal amplitude corresponding to 90% of the hadronic
Pb–Pb cross section and redefining correspondingly the centrality intervals.
The systematic uncertainty of the centrality limit depends on the width of the
centrality classes and it ranges from 1 to 6% and from 0 to 2.8%, as shown in
Tables 2 and 3 for the analyses at midrapidity and forward rapidity,
respectively.

The systematic uncertainties on the ${\rm J}/\psi$ reference cross section in
pp collisions at $\sqrt{s}~{}=~{}5.02$ TeV as obtained in Refs. [69, 57] are
provided in Table 2 and Table 3. The correlations of the systematic
uncertainties over centrality and $p_{\rm T}$ depend on the mid and forward
rapidity analysis and they are indicated in Table 2 and 3.

Table 2: Systematic uncertainties on the $p_{\rm T}$-integrated ($0.15<p_{\textrm{T}}<15~{}\textrm{GeV/}c$) measurement at midrapidity for different centrality intervals. The individual contributions and the total uncertainties are given in percentage. It is considered that all the uncertainties are correlated over centrality and $p_{\rm T}$ to a very large extent. Centrality (%) | 0–5 | 5–10 | 10–20 | 20–30 | 30–40 | 40–50 | 50–70 | 70–90
---|---|---|---|---|---|---|---|---
Signal extraction | 2.5 | 2.7 | 1.9 | 6.6 | 2.5 | 1.0 | 1.8 | 2.5
MC input | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 | 0.5
Tracking | 10.1 | 10.1 | 8.5 | 8.5 | 8.0 | 8.0 | 7.9 | 7.9
PID | 1.6 | 1.3 | 1.4 | 1.4 | 1.1 | 1.2 | 1.1 | 1.3
Centrality limit | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 5.7 | 6.0
Total | 10.6 | 10.5 | 8.8 | 10.8 | 9.0 | 8.2 | 10.0 | 10.3
$T_{\text{AA}}$(only on $R_{\rm AA}$) | 0.7 | 0.8 | 0.9 | 1.2 | 1.6 | 1.7 | 2.0 | 2.3
pp reference (only on $R_{\rm AA}$) | 5.8
Branching ratio (only on yield) | 0.5
Table 3:  Systematic uncertainties on the $p_{\rm T}$-differential measurement at forward rapidity for various centrality intervals. The individual contributions are given in percentage. When a range is given, it corresponds to the minimum and maximum values obtained in the $p_{\rm T}$ interval. Values marked with an asterisk correspond to the uncertainties correlated over $p_{\rm T}$. These uncertainties are considered as global ones for $R_{\rm AA}$ and added in quadrature to the uncorrelated uncertainties for the yields. Centrality (%) | 0–20% | 20–40% | 40–90%
---|---|---|---
$F_{\rm{norm}}$ | 0.7*
Signal extraction | 1.5–5.8 | 1.9–4.5 | 1.6–10.7
MC input | 1.8–4.1 | 0.2–2.1 | 0.9–1.8
Tracking efficiency | 3.0 + 1.0* | 3.0 + 0.5* | 3.0
Trigger efficiency | 1.5–2.0 + 1.0* | 1.5–2.0 + 0.5* | 1.5–2.0
Matching efficiency | 1.0 | 1.0 | 1.0
Centrality limit | – | 0.8* | 2.8*
$T_{\rm{AA}}$ (only on $R_{\rm AA}$) | 0.8* | 1.3* | 2.0*
pp reference (only on $R_{\rm AA}$) | 3.5–5.6 + 1.9*
Branching ratio (only on yield) | 0.5*

##  4 Results

###  4.1 Inclusive J/$\psi$ yields

The fully corrected inclusive ${\rm J}/\psi$ $p_{\rm T}$-differential yields,
$\mathrm{d^{2}}N/(\mathrm{d}y\mathrm{d}p_{\mathrm{T}}$), were obtained
according to Eq.1 in centrality intervals in Pb–Pb collisions at
$\sqrt{s_{\mathrm{NN}}}~{}=~{}5.02$ TeV at midrapidity ($\left|y\right|<0.9$)
and at forward rapidity ($2.5<y<4$). Figure 3 shows the ${\rm J}/\psi$ yields
obtained at midrapidity for the $0$–$10$% and $30$–$50$% centrality intervals,
while Fig. 4 shows the yields measured at forward rapidity in the $0$–$20$%,
$20$–$40$% and $40$–$90$% centrality intervals. For all of the results, the
statistical and systematic uncertainties are indicated by the vertical error
bars and the open boxes around the data points, respectively. These results
are compared with calculations performed using the statistical hadronisation
model (SHMc) by Andronic et al. [32], and two microscopic transport models by
Rapp et al. [70] and Zhuang et al. [33]. The physics assumptions in these
model calculations are discussed in more detail in the next section. The lower
panels of the Figs. 3 and 4 depict the ratio between the experimental data and
the different model calculations, with the width of the bands representing the
model uncertainties. These uncertainties are due to uncertainties on input
parameters, mainly the total charm-quark production cross section and CNM
effects. The filled boxes around unity represent the uncertainties of the
measured results, shown as the quadratic sum of statistical and systematic
uncertainties. Both transport models describe the $p_{\rm T}$-differential
yields in central and semicentral collisions, while they overestimate them at
forward rapidity in peripheral collisions. The SHMc calculations coupled with
a hydrodynamics inspired freeze-out parameterisation are in good agreement
with the data in the low-$p_{\rm T}$ region, but underestimate the
measurements at higher $p_{\rm T}$.

Figure 3: ${\rm J}/\psi$ $p_{\rm T}$-differential production yields in Pb–Pb
collisions at $\sqrt{s_{\mathrm{NN}}}~{}=~{}5.02$ TeV at midrapidity in the
$0$–$10$% (left panel) and $30$–$50$% (right panel) centrality intervals. The
statistical and systematic uncertainties are indicated, respectively, by the
vertical error bars and the open boxes. The horizontal bars indicate the
$p_{\rm T}$ intervals. Data are compared to model calculations from Refs. [32,
70, 33]. The ratios between data and models are shown in the lower panels. The
filled boxes around unity depict the quadratic sum of statistical and
systematic uncertainties from the measurement, while the bands indicate model
uncertainties.

Figure 4: ${\rm J}/\psi$ $p_{\rm T}$-differential production yields in Pb–Pb
collisions at $\sqrt{s_{\mathrm{NN}}}~{}=~{}5.02$ TeV at forward rapidity in
the $0$–$20$%, $20$–$40$%, and $40$–$90$% centrality intervals. The
statistical and systematic uncertainties are indicated, respectively, by the
vertical error bars and the open boxes. The horizontal bars indicate the
$p_{\rm T}$ intervals. Data are compared to model calculations from Refs. [32,
70, 33]. The ratio between data and models is shown in the lower panels. The
filled boxes around unity depict the quadratic sum of statistical and
systematic uncertainties from the measurement, while the bands indicate model
uncertainties.

###  4.2 The J/$\psi$ nuclear modification factor $R_{\rm AA}$

The nuclear modification factor $R_{\rm AA}$ was obtained using the measured
yields, according to Eq. 2. Figure 5 shows the $p_{\rm T}$-integrated ${\rm
J}/\psi$ $R_{\rm AA}$ as a function of $N_{\mathrm{part}}$ in Pb–Pb collisions
at $\sqrt{s_{\mathrm{NN}}}~{}=~{}5.02$ TeV, obtained in the current analysis
at midrapidity in comparison to the results at forward rapidity, previously
reported by the ALICE Collaboration in Ref. [19]. Global uncertainties
represented the centrality-correlated uncertainties, shown as filled boxes
around unity, and are largely uncorrelated between the forward and midrapidity
analyses. Both results exclude low-$p_{\rm T}$ ${\rm J}/\psi$, with a
selection of $p_{\rm T}>0.15$ GeV/$c$ and $p_{\rm T}>0.3$ GeV/$c$ at
midrapidity and forward rapidity, respectively, in order to reject ${\rm
J}/\psi$ produced via photoproduction processes [71, 72, 73], which contribute
significantly to the ${\rm J}/\psi$ yield in particular in peripheral
collisions [74, 75]. The $R_{\rm AA}$ is compatible with unity in the most
peripheral collisions, while a suppression of the ${\rm J}/\psi$ production in
Pb–Pb collisions with respect to binary scaled pp collisions is observed in
semicentral and central collisions, in particular at forward rapidity. At
midrapidity, $R_{\rm AA}$ exhibits a slightly increasing trend from
approximately $\langle N_{\mathrm{part}}\rangle$ = 100 towards the most
central collisions, with slightly larger $R_{\rm AA}$ values at midrapidity
than at forward rapidity, which confirms previous observations reported by
ALICE [21]. The results at midrapidity are larger than those measured at
forward rapidity, with a significance of the difference of $2.2\sigma$ when
considering the data points in the centrality range 0–10%. The larger $R_{\rm
AA}$ in central collisions at midrapidity is expected in phenomenological
models due to the larger ${\rm d}\sigma_{\mathrm{c}\rm\overline{c}}/{\rm d}y$
at midrapidity which leads to larger fraction of ${\rm J}/\psi$ produced via
(re)generation [32, 33, 76].

Figure 5: Inclusive ${\rm J}/\psi$ nuclear modification factor at midrapidity
and forward rapidity [19], integrated over $p_{\rm T}$, as a function of the
number of participants in Pb–Pb collisions at
$\sqrt{s_{\mathrm{NN}}}~{}=~{}5.02$ TeV. The statistical and systematic
uncertainties are indicated, respectively, by the vertical error bars and the
open boxes around the data points. The filled boxes around unity show the
global uncertainties.

The $p_{\rm T}$-differential $R_{\rm AA}$ results are shown in Fig. 6 for
midrapidity (left panel) and forward rapidity (right panel) in various
centrality intervals. The main feature of these measurements is that the
$R_{\rm AA}$ values are relatively large at low $p_{\rm T}$ ($p_{\rm T}<5$
GeV/$c$), in contrast with the strong suppression in the $p_{\rm T}>5$ GeV/$c$
range, for central and semicentral collisions. A weaker $p_{\rm T}$ dependence
of the $R_{\rm AA}$ values is observed from central to peripheral collisions,
up to a constant $R_{\rm AA}$, within uncertainties, in the 40–90% centrality
interval at forward rapidity. Such a behaviour in the data can be
qualitatively understood by the dominance of hot nuclear matter effects for
central and semicentral collisions, acting on top of the CNM ones, which were
discussed in Refs. [39, 44]. In the low-$p_{\rm T}$ region and in particular
in central and semicentral collisions, where the density of charm quarks is
larger, the coalescence of charm quark pairs has an important contribution
counterbalancing the impact of quarkonium suppression in the QGP. At higher
$p_{\rm T}$, the ${\rm J}/\psi$ production is dominated by effects such as
dissociation and energy loss, which are expected to be stronger in the most
central collisions. A comparison of the ${\rm J}/\psi$ $p_{\rm
T}$-differential $R_{\rm AA}$ in the most central Pb–Pb collisions at
$\sqrt{s_{\mathrm{NN}}}~{}=~{}5.02$ TeV between midrapidity and forward
rapidity is shown in Fig. 7. Neglecting the slight difference in centrality
intervals, the $R_{\rm AA}$ is higher at midrapidity with respect to forward
rapidity at low $p_{\rm T}$ ($p_{\rm T}<3$ GeV/$c$) with a 2.7$\sigma$
significance, highlighting the strong dependence of $R_{\rm AA}$ on the local
charm quark density, further supporting the picture of quarkonium production
via coalescing charm quarks. The two measurements converge to similar values
at higher $p_{\rm T}$ suggesting a weaker dependence on rapidity for the
suppression effects.

Figure 6: Inclusive ${\rm J}/\psi$ $p_{\rm T}$-differential $R_{\rm AA}$ in
Pb–Pb collisions at $\sqrt{s_{\mathrm{NN}}}~{}=~{}5.02$ TeV in various
centrality intervals. The left panel shows the comparison of $R_{\rm AA}$
measured in central (0–10%) and semicentral (30–50%) collisions at
midrapidity. For the data point in the $p_{\rm T}$ bin 10 $<$ $p_{\rm T}$ $<$
15 GeV/$c$ an open symbol is used to highlight the usage of pp reference from
the extrapolation approach. The right panel shows the measured $R_{\rm AA}$ in
three centrality classes, 0–20%, 20–40%, and 40–90%, at forward rapidity. The
statistical and systematic uncertainties are indicated, respectively, by the
vertical error bars and the open boxes around the data points. The filled
boxes around unity show the global uncertainties. Figure 7: Inclusive ${\rm
J}/\psi$ $R_{\rm AA}$ as a function of $p_{\rm T}$ in Pb–Pb collisions at
$\sqrt{s_{\mathrm{NN}}}~{}=~{}5.02$ TeV at midrapidity and forward rapidity,
in the 0–10% centrality class and 0–20% centrality class, respectively.For the
data point in the $p_{\rm T}$ bin 10 $<$ $p_{\rm T}$ $<$ 15 GeV/$c$ an open
symbol is used to highlight the usage of pp reference from the extrapolation
approach. The statistical and systematic uncertainties are indicated,
respectively, by the vertical error bars and the open boxes around the data
points. The filled boxes around unity show the global uncertainties.

The measurements presented so far are discussed in the rest of this section in
comparison to calculations which employ different approaches in modelling the
collision fireball and the hot medium effects having an impact on ${\rm
J}/\psi$ production in Pb–Pb collisions.

The statistical hadronisation model by Andronic et al. [32] assumes that all
charm quarks are produced during the initial hard partonic interactions and
then thermalize in the QGP. The relative yields of the charmed hadrons are
then determined solely by the equilibrium thermodynamical parameters at the
chemical freeze-out, which are fixed from fits to the yields of light-
flavoured hadrons. A recent extension of the model [32, 30] implements a
hydro-inspired freeze-out hypersurface which allows the calculation of $p_{\rm
T}$-differential yields in addition to the integrated ones.

The microscopic transport model of Zhuang et al. [33, 77] implements a real
time evolution of the ${\rm J}/\psi$, $\psi(2S)$ and $\chi_{\rm c}$ production
using a Boltzmann-type rate equation that includes both dissociation and
coalescence terms. The dissociation term includes contributions from the
temperature dependent colour screening effect and scatterings with thermal
partons, i.e. gluon dissociation. The (re)generation of charmonia is
implemented by exploiting the detailed balance of the gluon dissociation
process. The space–time evolution of the fireball is described using the
equations for (2+1)D ideal hydrodynamics. This model includes also the
production of non-prompt ${\rm J}/\psi$, with the precursor beauty quarks
being propagated through the QGP using the Langevin equation.

Similarly to the previously described model, the transport model proposed by
Rapp et al. [78, 79, 80, 81, 76], is also based on a kinetic rate equation to
compute the time evolution of charmonium (${\rm J}/\psi$, $\psi(2S)$, and
$\chi_{c}$) yields. The dissociation term of the rate equation employs an
inelastic parton scattering cross section of charmonia in the QGP, computed
using next-to-leading order perturbative QCD Feynman diagrams, and also
includes the effect of in-medium reduced binding energy. The (re)generation
rate depends on the charmonium dissociation temperature, which is extracted
from lattice QCD calculations, and equilibrium limits computed based on the
thermal model. The space–time evolution of heavy-ion collisions is simulated
by a cylindrically expanding fireball model with the regenerated charmonium
$p_{\rm T}$-spectra being calculated in a thermal blast-wave approximation at
a temperature and flow velocity reflecting the average production time of each
charmonium state [82].

All of the models described above consider the initial state of the nuclear
collisions by making assumptions on the total charm quark density produced
during the hard partonic collisions and modified by the CNM effects. The two
transport models obtain the total charm density based on the measured total
charm cross section in pp collisions [83] multiplied by the number of binary
nucleon–nucleon collisions. The CNM effects are introduced via different
approaches. The microscopic transport model of Rapp et al. [78, 79] estimates
CNM effects using fits of the measured p–A data, while the transport model of
Zhuang et al. [80, 81] uses the nuclear parton distribution functions and
their uncertainties from EPS09 [84]. The SHMc extracts the total charm cross
section from the ALICE measurements of D meson production in Pb–Pb collisions
[85]. The large uncertainty on the estimation of CNM effects is inherited by
these model calculations.

At large $p_{\rm T}$, the fragmentation of high-energy partons may become the
main mechanism for ${\rm J}/\psi$ production. In that case, energy loss of
partons due to multiple scattering in the QGP leads to ${\rm J}/\psi$
suppression at high $p_{\rm T}$. In the model by Arleo et al. [35] , the
quenching of large-$p_{\rm T}$ particles ($p_{\rm T}>10$ GeV/$c$) is assumed
to be mostly due to radiative parton energy loss. In this approach, the
$p_{\rm T}$ dependence of $R_{\rm AA}$ is fully predicted from the model
proposed by Baier et al. [86, 87], which employed a medium-induced gluon
spectrum. The $R_{\rm AA}$ value is computed from the mean energy loss, which
is extracted from a fit to the charged hadron $R_{\rm AA}$, measured in
various collision systems, the average fragmentation function, and the colour
coupling factor of the parton. At forward rapidity, the mean energy loss is
further corrected for the charged-hadron multiplicity difference between
midrapidity and forward rapidity. The model uncertainties arise from the
uncertainties on these inputs. This model does not include the production of
non-prompt ${\rm J}/\psi$, but the $R_{\rm AA}$ variation, when accounting for
this contribution, is expected to lie within the theoretical uncertainties.

The $p_{\rm T}$-integrated nuclear modification factor measured in Pb–Pb
collisions at $\sqrt{s_{\mathrm{NN}}}~{}=~{}5.02$ TeV at midrapidity is shown
in Fig. 8 in comparison with results from the SHMc and the two transport-model
calculations. The calculations are shown as coloured bands, illustrating the
uncertainties on the initial effects, mainly CNM effects, described above.
Within the model uncertainties, all three predictions agree with the data. One
can note though that the data lie on the upper edge of the transport-model
calculations, while they are in good agreement with the central values from
the SHMc calculations for semicentral and central collisions.

Figures 9 and 10 show the $p_{\rm T}$-differential $R_{\rm AA}$ measurements
for various centrality intervals at midrapidity and forward rapidity,
respectively, in comparison with the available model calculations. With the
exception of the energy-loss calculations, available only for $p_{\rm T}>10$
GeV/$c$, all of the models cover the full $p_{\rm T}$ range in which these
measurements were performed. The SHMc model calculations are in good agreement
with the data at low $p_{\rm T}$ at both midrapidity and forward rapidity.
However, the $R_{\rm AA}$ is underestimated for $p_{\rm T}>5$ GeV/$c$ in all
centrality intervals in both rapidity ranges. This might be attributed to
physical sources missing in this approach, such as the contributions from
surviving primordial ${\rm J}/\psi$ or non-prompt ${\rm J}/\psi$ from beauty-
hadron decays, but also to an underestimated amount of radial flow acquired by
the charm quarks during the system evolution. A similar conclusion can also be
drawn from the comparison of the $p_{\rm T}$-differential yields in Pb–Pb
collisions shown in Fig. 3 where the measured spectrum is harder than the one
from the SHMc calculations. The two transport models are in better
quantitative agreement with data than the SHMc model. Both of the transport
models provide a good description of the $R_{\rm AA}$ at both low and high
$p_{\rm T}$. However, the model calculations in the low-$p_{\rm T}$ region,
where ${\rm J}/\psi$ production is dominated by coalescence in these models,
do not describe the detailed shape of the $p_{\rm T}$ dependence of $R_{\rm
AA}$, in particular in semicentral collisions, which points to a still not
perfectly understood dynamics of charm-quark coalescence.

The energy-loss calculations by Arleo et al [35], performed in all studied
centrality ranges for $p_{\rm T}>10$ GeV/$c$, are in good agreement with the
measurements, which, based on the model assumptions, suggests that the
dominant mechanism in this kinematic regime is indeed energy loss, similar to
that of the other hadrons measured at LHC energies.

Figure 8: Inclusive ${\rm J}/\psi$ $R_{\rm AA}$ at midrapidity, integrated
over $p_{\rm T}$, as a function of $N_{\mathrm{part}}$ in Pb–Pb collisions at
$\sqrt{s_{\mathrm{NN}}}~{}=~{}5.02$ TeV and compared to model calculations
from Refs. [32, 70, 33].

Figure 9: Transverse-momentum dependence of the ${\rm J}/\psi$ $R_{\rm AA}$ in
Pb–Pb collisions at $\sqrt{s_{\mathrm{NN}}}~{}=~{}5.02$ TeV at midrapidity in
the $0$–$10$% (left panel) and $30$–$50$% (right panel) centrality intervals.
For the data point in the $p_{\rm T}$ bin 10 $<$ $p_{\rm T}$ $<$ 15 GeV/$c$ an
open symbol is used to highlight the usage of pp reference from the
extrapolation approach. The data are compared with model calculations from
Refs. [32, 70, 33, 35].

Figure 10: Transverse-momentum dependence of the ${\rm J}/\psi$ $R_{\rm AA}$
at forward rapidity in the $0$–$20$%, $20$–$40$% and $40$–$90$% centrality
intervals. The data are compared with model calculations from Refs. [32, 70,
33, 35]

###  4.3 The inclusive J/$\psi$ $\langle p_{\mathrm{T}}\rangle$ and $\langle
p_{\mathrm{T}}^{\mathrm{2}}\rangle$

Observables that allow for a more differential study of the ${\rm J}/\psi$
$p_{\rm T}$ spectrum with respect to the centrality of the collisions are the
${\rm J}/\psi$ $\langle p_{\mathrm{T}}\rangle$ and $\langle
p_{\mathrm{T}}^{\mathrm{2}}\rangle$. The latter is typically quantified using
the $r_{\rm AA}$, defined in Eq.3, which is related to the broadening or
narrowing of the ${\rm J}/\psi$ $p_{\rm T}$ spectrum relative to that in pp
collisions. The $\langle p_{\mathrm{T}}\rangle$ and the $r_{\rm AA}$ measured
at midrapidity are shown in Fig. 11 in the left and right panels,
respectively, as a function of $\langle N_{\mathrm{part}}\rangle$. Similar
measurements were done at the forward rapidity as well [20]. These results are
compared with similar measurements in heavy-ion collisions at RHIC [11, 26,
88] and SPS [89] energies. The $\langle p_{\mathrm{T}}\rangle$ results are
also compared with the value reported by the ALICE Collaboration in pp
collisions at midrapidity at $\sqrt{s}=5.02$ TeV [69], showing a good
agreement with the value measured in the most peripheral Pb–Pb collisions. The
data show that for a given $N_{\mathrm{part}}$, the ${\rm J}/\psi$ $\langle
p_{\mathrm{T}}\rangle$ grows with increasing collision energy. However, while
the data indicate no centrality dependence of the $\langle
p_{\mathrm{T}}\rangle$ at the SPS and RHIC energies, a monotonically
decreasing trend from the most peripheral to the most central collisions is
observed in the ALICE measurements, which reflects the gradual increase of the
low $p_{\rm T}$ (re)generation component. The $r_{\rm AA}$ results support the
observations for the $\langle p_{\mathrm{T}}\rangle$, showing a decrease from
unity towards central collisions for the ALICE measurements. The RHIC results
are compatible with unity over the whole covered centrality range, while the
SPS data indicate a strong increase from peripheral to central collisions,
suggesting that CNM effects such as the Cronin effect [90] have an impact on
the ${\rm J}/\psi$ $p_{\rm T}$ shape.

The $\langle p_{\mathrm{T}}\rangle$ and $\langle
p_{\mathrm{T}}^{\mathrm{2}}\rangle$ results are also compared with the
aforementioned transport model calculations, which show a good agreement with
the trends observed in data as demonstrated in Fig. 12. Although overall in
good quantitative agreement, the calculations by Rapp et al. overestimate the
${\rm J}/\psi$ $\langle p_{\mathrm{T}}\rangle$ for the most central
collisions, while the $\langle p_{\mathrm{T}}^{\mathrm{2}}\rangle$ results are
slightly underestimated by both models in the semicentral and peripheral range
($50<N_{\mathrm{part}}<150$).

Figure 11: Left panel: Inclusive ${\rm J}/\psi$ $\langle
p_{\mathrm{T}}\rangle$ as a function of the mean number of participants in
Pb–Pb collisions at $\sqrt{s_{\mathrm{NN}}}~{}=~{}5.02$ TeV at midrapidity.
Right panel: Inclusive ${\rm J}/\psi$ $r_{\rm AA}$ as a function of centrality
at $\sqrt{s_{\mathrm{NN}}}~{}=~{}5.02$ TeV and compared with measurements at
lower energies from RHIC [11, 91, 88] and the SPS [89]. The statistical and
systematic uncertainties are indicated, respectively, by the vertical error
bars and the open boxes around the data points. The filled box around unity on
the right panel shows the global uncertainty.

Figure 12: Inclusive ${\rm J}/\psi$ $\langle p_{\mathrm{T}}\rangle$ as a
function of the mean number of participants in Pb–Pb collisions at
$\sqrt{s_{\mathrm{NN}}}~{}=~{}5.02$ TeV at midrapidity (left panel), and ${\rm
J}/\psi$ $r_{\rm AA}$ as a function of centrality (right panel). The results
are compared with transport model calculations [70, 33]

###  4.4 The J/$\psi$ to D0 yield ratio

A long awaited measurement which helps understanding the details of the ${\rm
J}/\psi$ production in heavy-ion collisions is the ratio between the ${\rm
J}/\psi$ and the ${\rm D}^{0}$ yields, both measured in the same collision
system. Such a measurement provides a tight constraint to models because some
of the model parameters and most model uncertainties related to the
$\mathrm{c}\rm\overline{c}$ cross section cancel in the ratio. This ratio is
sensitive to the hadronisation mechanisms of the different charm hadrons.
While a model independent measurement of the $\mathrm{c}\rm\overline{c}$
production density in heavy-ion collisions is not available, it is still
useful to compare the ${\rm J}/\psi$ yield with the recently published ALICE
measurements of the ${\rm D}^{0}$ yield down to zero $p_{\rm T}$ [85].

Figure 13 shows the measured $p_{\rm T}$-integrated ${\rm J}/\psi$ to ${\rm
D}^{0}$ yield ratio in central (0–10%) and semicentral (30–50%) collisions.
The largest source of systematic uncertainty for both measurements comes from
tracking efficiency and it is considered correlated between the ${\rm D}^{0}$
and ${\rm J}/\psi$ measurements, and consequently cancels in the ratio.
Without this uncertainty, the numerical values of the ${\rm J}/\psi$ yields
are $0.12\pm 0.005$ (stat.) $\pm 0.012$ (syst.) and $0.016\pm 0.0008$ (stat.)
$\pm 0.0004$ (syst.) for (0–10%) and (30–50%) centrality intervals,
respectively. The statistical (systematical) uncertainty of the ratio is the
quadratic sum of the statistical (systematical) uncertainties of the two
measurements. The results suggest a higher value for this ratio in central
compared to semicentral collisions. This is supported by the SHMc calculations
[32], which suggests both the ${\rm J}/\psi$ and ${\rm D}^{0}$ are produced
via the coalescence of charm quarks at the phase boundary, the ratio being
determined by the charm fugacity. The SHMc model gives a good description of
the data. The model uncertainty from the SHMc model is due to uncertainties on
the charm fugacity parameter, which is fitted to the ALICE D0 data [85].

Figure 13: Inclusive ${\rm J}/\psi$ to $D^{0}$ yield [85] ratio at
$\sqrt{s_{\mathrm{NN}}}~{}=~{}5.02$ TeV at midrapidity for the 0–10% and
30–50% centrality intervals. Vertical lines and open boxes represent the
statistical and systematical uncertainties, respectively. The measurements are
compared with SHMc model predictions [32].

##  5 Conclusions

The ${\rm J}/\psi$ $p_{\rm T}$-differential yields and nuclear modification
factors $R_{\rm AA}$ measured from $p_{\rm T}=0.15$ GeV/c up to $15$ GeV/c in
the 0–10% and 30–50% centrality ranges at midrapidity and from $p_{\rm T}=0.3$
GeV/c up to $p_{\rm T}=20$ GeV/c in the 0–20%, 20–40% and 40–90% centrality
ranges at forward rapidity, and the centrality dependent ${\rm J}/\psi$
$\langle p_{\mathrm{T}}\rangle$ and $r_{\rm AA}$ measured at midrapidity, are
reported and discussed in comparison with model calculations.

The centrality dependent $p_{\rm T}$-integrated $R_{\rm AA}$ in peripheral
collisions shows similar values at both midrapidity and forward rapidity,
while a hint of an increasing trend of the $R_{\rm AA}$ is observed at
midrapidity towards central collisions. When looking at the $p_{\rm
T}$-differential $R_{\rm AA}$, relatively large values are observed at low
$p_{\rm T}$, which are compatible with unity for $p_{\rm T}<3$ GeV/$c$ in the
0–10% centrality interval at $|y|<0.9$, while a strong nuclear suppression is
seen at higher $p_{\rm T}$ in central and semicentral collisions. In addition,
$R_{\rm AA}$ is higher at midrapidity than at forward rapidity for $p_{\rm
T}<3$ GeV/c in the most central collisions. A weaker $p_{\rm T}$ dependence of
the $R_{\rm AA}$ values is observed for more peripheral collisions. Such a
behaviour, for the central and semicentral collisions, can be explained by a
large contribution from (re)generation to the ${\rm J}/\psi$ yields. This is
supported by the statistical hadronisation model and by two microscopic
transport model calculations when compared to the data. However, the large
model uncertainties, which are mainly due to the assumptions on the collision
initial conditions, prevent from drawing a clear conclusion on the
phenomenology of the ${\rm J}/\psi$ production in heavy-ion collisions at LHC
energies. The ${\rm J}/\psi$ nuclear modification factor at high $p_{\rm T}$
is well described by the transport models and also by a ${\rm J}/\psi$ energy
loss model, while it is largely underestimated in the hydro-inspired freeze-
out approach implemented together with the SHMc model. For most central
events, the $R_{\rm AA}$ converge to similar values at high $p_{\rm T}$ at mid
and forward rapidity suggesting a weaker dependence on rapidity for the
suppression effects. The centrality dependent ${\rm J}/\psi$ $\langle
p_{\mathrm{T}}\rangle$ and $r_{\rm AA}$ measurements are compared with similar
results at lower energies from RHIC [11, 91, 88] and SPS [89], with the
centrality trends showing an opposite behaviour between the LHC and the lower
energy results. This behaviour is compatible with a strong contribution from
the regeneration component which tends to soften the $p_{\rm T}$
distributions. The two microscopic transport models describe the data within
the uncertainties.

The ratio of inclusive $p_{\rm T}$-integrated ${\rm J}/\psi$ to ${\rm D}^{0}$
yields measured by ALICE at midrapidity in Pb–Pb collisions is presented for
the first time in the 0–10% and 30–50% centrality ranges. The data shows a
larger value of this ratio in 0–10% compared to 30–50% collisions, which is in
good agreement with the expectation from the SHMc model that fixes the charm
fugacity parameter based on the ${\rm D}^{0}$ yields measured by ALICE.

The large improvements in experimental accuracy expected for the LHC Run 3 and
4 for charmonium measurements and more in general for heavy-quark production,
the improved measurements of total charm quark cross section and CNM effects
are critical to constrain the phenomenological model calculations, will allow
to settle the longstanding questions regarding the mechanisms behind
charmonium production in heavy-ion collisions.

## Acknowledgements

We would like to thank the theory groups who kindly provided us with their
calculations and for the fruitful discussions.

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 Universitatea Nationala de Stiinta si Tehnologie Politehnica Bucuresti, 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; Programa de Apoyos para la Superación del Personal Académico, UNAM, Mexico.

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##  Appendix A The ALICE Collaboration

S. Acharya 127, D. Adamová 87, A. Adler71, G. Aglieri Rinella 33, M. Agnello
30, N. Agrawal 52, Z. Ahammed 135, S. Ahmad 16, S.U. Ahn 72, I. Ahuja 38, A.
Akindinov 141, M. Al-Turany 98, D. Aleksandrov 141, B. Alessandro 57, H.M.
Alfanda 6, R. Alfaro Molina 68, B. Ali 16, A. Alici 26, N. Alizadehvandchali
116, A. Alkin 33, J. Alme 21, G. Alocco 53, T. Alt 65, I. Altsybeev 141, J.R.
Alvarado 45, M.N. Anaam 6, C. Andrei 46, A. Andronic 126, V. Anguelov 95, F.
Antinori 55, P. Antonioli 52, N. Apadula 75, L. Aphecetche 104, H.
Appelshäuser 65, C. Arata 74, S. Arcelli 26, M. Aresti 53, R. Arnaldi 57,
J.G.M.C.A. Arneiro 111, I.C. Arsene 20, M. Arslandok 138, A. Augustinus 33, R.
Averbeck 98, M.D. Azmi 16, A. Badalà 54, J. Bae 105, Y.W. Baek 41, X. Bai 120,
R. Bailhache 65, Y. Bailung 49, A. Balbino 30, A. Baldisseri 130, B. Balis 2,
D. Banerjee 4, Z. Banoo 92, R. Barbera 27, F. Barile 32, L. Barioglio 96, M.
Barlou79, G.G. Barnaföldi 47, L.S. Barnby 86, V. Barret 127, L. Barreto 111,
C. Bartels 119, K. Barth 33, E. Bartsch 65, N. Bastid 127, S. Basu 76, G.
Batigne 104, D. Battistini 96, B. Batyunya 142, D. Bauri48, J.L. Bazo Alba
102, I.G. Bearden 84, C. Beattie 138, P. Becht 98, D. Behera 49, I. Belikov
129, A.D.C. Bell Hechavarria 126, F. Bellini 26, R. Bellwied 116, S.
Belokurova 141, G. Bencedi 47, S. Beole 25, Y. Berdnikov 141, A. Berdnikova
95, L. Bergmann 95, M.G. Besoiu 64, L. Betev 33, P.P. Bhaduri 135, A. Bhasin
92, M.A. Bhat 4, B. Bhattacharjee 42, L. Bianchi 25, N. Bianchi 50, J. Bielčík
36, J. Bielčíková 87, J. Biernat 108, A.P. Bigot 129, A. Bilandzic 96, G. Biro
47, S. Biswas 4, N. Bize 104, J.T. Blair 109, D. Blau 141, M.B. Blidaru 98, N.
Bluhme39, C. Blume 65, G. Boca 22,56, F. Bock 88, T. Bodova 21, A.
Bogdanov141, S. Boi 23, J. Bok 59, L. Boldizsár 47, M. Bombara 38, P.M. Bond
33, G. Bonomi 134,56, H. Borel 130, A. Borissov 141, A.G. Borquez Carcamo 95,
H. Bossi 138, E. Botta 25, Y.E.M. Bouziani 65, L. Bratrud 65, P. Braun-
Munzinger 98, M. Bregant 111, M. Broz 36, G.E. Bruno 97,32, M.D. Buckland 24,
D. Budnikov 141, H. Buesching 65, S. Bufalino 30, P. Buhler 103, Z. Buthelezi
69,123, A. Bylinkin 21, S.A. Bysiak108, M. Cai 6, H. Caines 138, A. Caliva 29,
E. Calvo Villar 102, J.M.M. Camacho 110, P. Camerini 24, F.D.M. Canedo 111,
S.L. Cantway 138, M. Carabas 114, A.A. Carballo 33, F. Carnesecchi 33, R.
Caron 128, L.A.D. Carvalho 111, J. Castillo Castellanos 130, F. Catalano
33,25, C. Ceballos Sanchez 142, I. Chakaberia 75, P. Chakraborty 48, S.
Chandra 135, S. Chapeland 33, M. Chartier 119, S. Chattopadhyay 135, S.
Chattopadhyay 100, T. Cheng 98,6, C. Cheshkov 128, B. Cheynis 128, V. Chibante
Barroso 33, D.D. Chinellato 112, E.S. Chizzali II,96, J. Cho 59, S. Cho 59, P.
Chochula 33, P. Christakoglou 85, C.H. Christensen 84, P. Christiansen 76, T.
Chujo 125, M. Ciacco 30, C. Cicalo 53, F. Cindolo 52, M.R. Ciupek98, G.
ClaiIII,52, F. Colamaria 51, J.S. Colburn101, D. Colella 97,32, M. Colocci 26,
M. Concas IV,57, G. Conesa Balbastre 74, Z. Conesa del Valle 131, G. Contin
24, J.G. Contreras 36, M.L. Coquet 130, T.M. CormierI,88, P. Cortese 133,57,
M.R. Cosentino 113, F. Costa 33, S. Costanza 22,56, C. Cot 131, J. Crkovská
95, P. Crochet 127, R. Cruz-Torres 75, P. Cui 6, A. Dainese 55, M.C. Danisch
95, A. Danu 64, P. Das 81, P. Das 4, S. Das 4, A.R. Dash 126, S. Dash 48, A.
De Caro 29, G. de Cataldo 51, J. de Cuveland39, A. De Falco 23, D. De Gruttola
29, N. De Marco 57, C. De Martin 24, S. De Pasquale 29, R. Deb 134, S. Deb 49,
K.R. Deja136, R. Del Grande 96, L. Dello Stritto 29, W. Deng 6, P. Dhankher
19, D. Di Bari 32, A. Di Mauro 33, B. Diab 130, R.A. Diaz 142,7, T. Dietel
115, Y. Ding 6, R. Divià 33, D.U. Dixit 19, Ø. Djuvsland21, U. Dmitrieva 141,
A. Dobrin 64, B. Dönigus 65, J.M. Dubinski 136, A. Dubla 98, S. Dudi 91, P.
Dupieux 127, M. Durkac107, N. Dzalaiova13, T.M. Eder 126, R.J. Ehlers 75, F.
Eisenhut 65, D. Elia 51, B. Erazmus 104, F. Ercolessi 26, F. Erhardt 90, M.R.
Ersdal21, B. Espagnon 131, G. Eulisse 33, D. Evans 101, S. Evdokimov 141, L.
Fabbietti 96, M. Faggin 28, J. Faivre 74, F. Fan 6, W. Fan 75, A. Fantoni 50,
M. Fasel 88, P. Fecchio30, A. Feliciello 57, G. Feofilov 141, A. Fernández
Téllez 45, L. Ferrandi 111, M.B. Ferrer 33, A. Ferrero 130, C. Ferrero 57, A.
Ferretti 25, V.J.G. Feuillard 95, V. Filova 36, D. Finogeev 141, F.M. Fionda
53, F. Flor 116, A.N. Flores 109, S. Foertsch 69, I. Fokin 95, S. Fokin 141,
E. Fragiacomo 58, E. Frajna 47, U. Fuchs 33, N. Funicello 29, C. Furget 74, A.
Furs 141, T. Fusayasu 99, J.J. Gaardhøje 84, M. Gagliardi 25, A.M. Gago 102,
C.D. Galvan 110, D.R. Gangadharan 116, P. Ganoti 79, C. Garabatos 98, T.
García Chávez 45, E. Garcia-Solis 9, C. Gargiulo 33, K. Garner126, P. Gasik
98, A. Gautam 118, M.B. Gay Ducati 67, M. Germain 104, A. Ghimouz125, C.
Ghosh135, M. Giacalone 52,26, P. Giubellino 98,57, P. Giubilato 28, A.M.C.
Glaenzer 130, P. Glässel 95, E. Glimos 122, D.J.Q. Goh77, V. Gonzalez 137, M.
Gorgon 2, K. Goswami 49, S. Gotovac34, V. Grabski 68, L.K. Graczykowski 136,
E. Grecka 87, A. Grelli 60, C. Grigoras 33, V. Grigoriev 141, S. Grigoryan
142,1, F. Grosa 33, J.F. Grosse-Oetringhaus 33, R. Grosso 98, D. Grund 36,
G.G. Guardiano 112, R. Guernane 74, M. Guilbaud 104, K. Gulbrandsen 84, T.
Gündem 65, T. Gunji 124, W. Guo 6, A. Gupta 92, R. Gupta 92, R. Gupta 49, K.
Gwizdziel 136, L. Gyulai 47, M.K. Habib98, C. Hadjidakis 131, F.U. Haider 92,
H. Hamagaki 77, A. Hamdi 75, M. Hamid6, Y. Han 139, B.G. Hanley 137, R.
Hannigan 109, J. Hansen 76, M.R. Haque 136, J.W. Harris 138, A. Harton 9, H.
Hassan 88, D. Hatzifotiadou 52, P. Hauer 43, L.B. Havener 138, S.T. Heckel 96,
E. Hellbär 98, H. Helstrup 35, M. Hemmer 65, T. Herman 36, G. Herrera Corral
8, F. Herrmann126, S. Herrmann 128, K.F. Hetland 35, B. Heybeck 65, H.
Hillemanns 33, B. Hippolyte 129, F.W. Hoffmann 71, B. Hofman 60, B. Hohlweger
85, G.H. Hong 139, M. Horst 96, A. Horzyk 2, Y. Hou 6, P. Hristov 33, C.
Hughes 122, P. Huhn65, L.M. Huhta 117, T.J. Humanic 89, A. Hutson 116, D.
Hutter 39, R. Ilkaev141, H. Ilyas 14, M. Inaba 125, G.M. Innocenti 33, M.
Ippolitov 141, A. Isakov 87, T. Isidori 118, M.S. Islam 100, M. Ivanov13, M.
Ivanov 98, V. Ivanov 141, K.E. Iversen 76, M. Jablonski 2, B. Jacak 75, N.
Jacazio 26, P.M. Jacobs 75, S. Jadlovska107, J. Jadlovsky107, S. Jaelani 83,
C. Jahnke 112, M.J. Jakubowska 136, M.A. Janik 136, T. Janson71, M. Jercic90,
S. Ji 17, S. Jia 10, A.A.P. Jimenez 66, F. Jonas 88,126, J.M. Jowett 33,98, J.
Jung 65, M. Jung 65, A. Junique 33, A. Jusko 101, M.J. Kabus 33,136, J.
Kaewjai106, P. Kalinak 61, A.S. Kalteyer 98, A. Kalweit 33, V. Kaplin 141, A.
Karasu Uysal 73, D. Karatovic 90, O. Karavichev 141, T. Karavicheva 141, P.
Karczmarczyk 136, E. Karpechev 141, U. Kebschull 71, R. Keidel 140, D.L.D.
Keijdener60, M. Keil 33, B. Ketzer 43, S.S. Khade 49, A.M. Khan 120,6, S. Khan
16, A. Khanzadeev 141, Y. Kharlov 141, A. Khatun 118, A. Khuntia 108, M.B.
Kidson115, B. Kileng 35, B. Kim 105, C. Kim 17, D.J. Kim 117, E.J. Kim 70, J.
Kim 139, J.S. Kim 41, J. Kim 59, J. Kim 70, M. Kim 19, S. Kim 18, T. Kim 139,
K. Kimura 93, S. Kirsch 65, I. Kisel 39, S. Kiselev 141, A. Kisiel 136, J.P.
Kitowski 2, J.L. Klay 5, J. Klein 33, S. Klein 75, C. Klein-Bösing 126, M.
Kleiner 65, T. Klemenz 96, A. Kluge 33, A.G. Knospe 116, C. Kobdaj 106, T.
Kollegger98, A. Kondratyev 142, N. Kondratyeva 141, E. Kondratyuk 141, J.
Konig 65, S.A. Konigstorfer 96, P.J. Konopka 33, G. Kornakov 136, M. Korwieser
96, S.D. Koryciak 2, A. Kotliarov 87, V. Kovalenko 141, M. Kowalski 108, V.
Kozhuharov 37, I. Králik 61, A. Kravčáková 38, L. Krcal 33,39, M. Krivda
101,61, F. Krizek 87, K. Krizkova Gajdosova 33, M. Kroesen 95, M. Krüger 65,
D.M. Krupova 36, E. Kryshen 141, V. Kučera 59, C. Kuhn 129, P.G. Kuijer 85, T.
Kumaoka125, D. Kumar135, L. Kumar 91, N. Kumar91, S. Kumar 32, S. Kundu 33, P.
Kurashvili 80, A. Kurepin 141, A.B. Kurepin 141, A. Kuryakin 141, S. Kushpil
87, J. Kvapil 101, M.J. Kweon 59, Y. Kwon 139, S.L. La Pointe 39, P. La Rocca
27, A. Lakrathok106, M. Lamanna 33, A.R. Landou 74, R. Langoy 121, P. Larionov
33, E. Laudi 33, L. Lautner 33,96, R. Lavicka 103, R. Lea 134,56, H. Lee 105,
I. Legrand 46, G. Legras 126, J. Lehrbach 39, T.M. Lelek2, R.C. Lemmon 86, I.
León Monzón 110, M.M. Lesch 96, E.D. Lesser 19, P. Lévai 47, X. Li10, X.L.
Li6, J. Lien 121, R. Lietava 101, I. Likmeta 116, B. Lim 25, S.H. Lim 17, V.
Lindenstruth 39, A. Lindner46, C. Lippmann 98, A. Liu 19, D.H. Liu 6, J. Liu
119, G.S.S. Liveraro 112, I.M. Lofnes 21, C. Loizides 88, S. Lokos 108, J.
Lomker 60, P. Loncar 34, J.A. Lopez 95, X. Lopez 127, E. López Torres 7, P. Lu
98,120, J.R. Luhder 126, M. Lunardon 28, G. Luparello 58, Y.G. Ma 40, M. Mager
33, A. Maire 129, M.V. Makariev 37, M. Malaev 141, G. Malfattore 26, N.M.
Malik 92, Q.W. Malik20, S.K. Malik 92, L. Malinina I,VII,142, D. Mallick 81,
N. Mallick 49, G. Mandaglio 31,54, S.K. Mandal 80, V. Manko 141, F. Manso 127,
V. Manzari 51, Y. Mao 6, R.W. Marcjan 2, G.V. Margagliotti 24, A. Margotti 52,
A. Marín 98, C. Markert 109, P. Martinengo 33, M.I. Martínez 45, G. Martínez
García 104, M.P.P. Martins 111, S. Masciocchi 98, M. Masera 25, A. Masoni 53,
L. Massacrier 131, A. Mastroserio 132,51, O. Matonoha 76, S. Mattiazzo 28,
P.F.T. Matuoka111, A. Matyja 108, C. Mayer 108, A.L. Mazuecos 33, F. Mazzaschi
25, M. Mazzilli 33, J.E. Mdhluli 123, A.F. Mechler65, Y. Melikyan 44,141, A.
Menchaca-Rocha 68, E. Meninno 103, A.S. Menon 116, M. Meres 13, S.
Mhlanga115,69, Y. Miake125, L. Micheletti 33, L.C. Migliorin128, D.L. Mihaylov
96, K. Mikhaylov 142,141, A.N. Mishra 47, D. Miśkowiec 98, A. Modak 4, A.P.
Mohanty 60, B. Mohanty81, M. Mohisin Khan V,16, M.A. Molander 44, Z. Moravcova
84, C. Mordasini 96, D.A. Moreira De Godoy 126, I. Morozov 141, A. Morsch 33,
T. Mrnjavac 33, V. Muccifora 50, S. Muhuri 135, J.D. Mulligan 75, A.
Mulliri23, M.G. Munhoz 111, R.H. Munzer 65, H. Murakami 124, S. Murray 115, L.
Musa 33, J. Musinsky 61, J.W. Myrcha 136, B. Naik 123, A.I. Nambrath 19, B.K.
Nandi 48, R. Nania 52, E. Nappi 51, A.F. Nassirpour 18,76, A. Nath 95, C.
Nattrass 122, M.N. Naydenov 37, A. Neagu20, A. Negru114, L. Nellen 66, G.
Neskovic 39, B.S. Nielsen 84, E.G. Nielsen 84, S. Nikolaev 141, S. Nikulin
141, V. Nikulin 141, F. Noferini 52, S. Noh 12, P. Nomokonov 142, J. Norman
119, N. Novitzky 125, P. Nowakowski 136, A. Nyanin 141, J. Nystrand 21, M.
Ogino 77, A. Ohlson 76, V.A. Okorokov 141, J. Oleniacz 136, A.C. Oliveira Da
Silva 122, M.H. Oliver 138, A. Onnerstad 117, C. Oppedisano 57, A. Ortiz
Velasquez 66, J. Otwinowski 108, M. Oya93, K. Oyama 77, Y. Pachmayer 95, S.
Padhan 48, D. Pagano 134,56, G. Paić 66, S. Paisano-Guzmán 45, A. Palasciano
51, S. Panebianco 130, H. Park 125, H. Park 105, J. Park 59, J.E. Parkkila 33,
R.N. Patra92, B. Paul 23, H. Pei 6, T. Peitzmann 60, X. Peng 11, M. Pennisi
25, D. Peresunko 141, G.M. Perez 7, S. Perrin 130, Y. Pestov141, V. Petrov
141, M. Petrovici 46, R.P. Pezzi 104,67, S. Piano 58, M. Pikna 13, P. Pillot
104, O. Pinazza 52,33, L. Pinsky116, C. Pinto 96, S. Pisano 50, M. Płoskoń 75,
M. Planinic90, F. Pliquett65, M.G. Poghosyan 88, B. Polichtchouk 141, S.
Politano 30, N. Poljak 90, A. Pop 46, S. Porteboeuf-Houssais 127, V.
Pozdniakov 142, I.Y. Pozos 45, K.K. Pradhan 49, S.K. Prasad 4, S. Prasad 49,
R. Preghenella 52, F. Prino 57, C.A. Pruneau 137, I. Pshenichnov 141, M.
Puccio 33, S. Pucillo 25, Z. Pugelova107, S. Qiu 85, L. Quaglia 25, R.E.
Quishpe116, S. Ragoni 15, A. Rakotozafindrabe 130, L. Ramello 133,57, F. Rami
129, T.A. Rancien74, M. Rasa 27, S.S. Räsänen 44, R. Rath 52, M.P. Rauch 21,
I. Ravasenga 85, K.F. Read 88,122, C. Reckziegel 113, A.R. Redelbach 39, K.
Redlich VI,80, C.A. Reetz 98, H.D. Regules-Medel45, A. Rehman21, F. Reidt 33,
H.A. Reme-Ness 35, Z. Rescakova38, K. Reygers 95, A. Riabov 141, V. Riabov
141, R. Ricci 29, M. Richter 20, A.A. Riedel 96, W. Riegler 33, C. Ristea 64,
M.V. Rodriguez 33, M. Rodríguez Cahuantzi 45, S.A. Rodríguez Ramírez 45, K.
Røed 20, R. Rogalev 141, E. Rogochaya 142, T.S. Rogoschinski 65, D. Rohr 33,
D. Röhrich 21, P.F. Rojas45, S. Rojas Torres 36, P.S. Rokita 136, G. Romanenko
142, F. Ronchetti 50, A. Rosano 31,54, E.D. Rosas66, K. Roslon 136, A. Rossi
55, A. Roy 49, S. Roy 48, N. Rubini 26, D. Ruggiano 136, R. Rui 24, P.G.
Russek 2, R. Russo 85, A. Rustamov 82, E. Ryabinkin 141, Y. Ryabov 141, A.
Rybicki 108, H. Rytkonen 117, J. Ryu 17, W. Rzesa 136, O.A.M. Saarimaki 44, R.
Sadek 104, S. Sadhu 32, S. Sadovsky 141, J. Saetre 21, K. Šafařík 36, P.
Saha42, S.K. Saha 4, S. Saha 81, B. Sahoo 48, B. Sahoo 49, R. Sahoo 49, S.
Sahoo62, D. Sahu 49, P.K. Sahu 62, J. Saini 135, K. Sajdakova38, S. Sakai 125,
M.P. Salvan 98, S. Sambyal 92, I. Sanna 33,96, T.B. Saramela111, D. Sarkar
137, N. Sarkar135, P. Sarma 42, V. Sarritzu 23, V.M. Sarti 96, M.H.P. Sas 138,
J. Schambach 88, H.S. Scheid 65, C. Schiaua 46, R. Schicker 95, A. Schmah95,
C. Schmidt 98, H.R. Schmidt94, M.O. Schmidt 33, M. Schmidt94, N.V. Schmidt 88,
A.R. Schmier 122, R. Schotter 129, A. Schröter 39, J. Schukraft 33, K.
Schwarz98, K. Schweda 98, G. Scioli 26, E. Scomparin 57, J.E. Seger 15, Y.
Sekiguchi124, D. Sekihata 124, I. Selyuzhenkov 98, S. Senyukov 129, J.J. Seo
59, D. Serebryakov 141, L. Šerkšnytė 96, A. Sevcenco 64, T.J. Shaba 69, A.
Shabetai 104, R. Shahoyan33, A. Shangaraev 141, A. Sharma91, B. Sharma 92, D.
Sharma 48, H. Sharma 55,108, M. Sharma 92, S. Sharma 77, S. Sharma 92, U.
Sharma 92, A. Shatat 131, O. Sheibani116, K. Shigaki 93, M. Shimomura78, J.
Shin12, S. Shirinkin 141, Q. Shou 40, Y. Sibiriak 141, S. Siddhanta 53, T.
Siemiarczuk 80, T.F. Silva 111, D. Silvermyr 76, T. Simantathammakul106, R.
Simeonov 37, B. Singh92, B. Singh 96, K. Singh 49, R. Singh 81, R. Singh 92,
R. Singh 49, S. Singh 16, V.K. Singh 135, V. Singhal 135, T. Sinha 100, B.
Sitar 13, M. Sitta 133,57, T.B. Skaali20, G. Skorodumovs 95, M. Slupecki 44,
N. Smirnov 138, R.J.M. Snellings 60, E.H. Solheim 20, J. Song 116, A.
Songmoolnak106, C. Sonnabend 33,98, F. Soramel 28, A.B. Soto-hernandez 89, R.
Spijkers 85, I. Sputowska 108, J. Staa 76, J. Stachel 95, I. Stan 64, P.J.
Steffanic 122, S.F. Stiefelmaier 95, D. Stocco 104, I. Storehaug 20, P.
Stratmann 126, S. Strazzi 26, C.P. Stylianidis85, A.A.P. Suaide 111, C. Suire
131, M. Sukhanov 141, M. Suljic 33, R. Sultanov 141, V. Sumberia 92, S.
Sumowidagdo 83, S. Swain62, I. Szarka 13, M. Szymkowski 136, S.F. Taghavi 96,
G. Taillepied 98, J. Takahashi 112, G.J. Tambave 81, S. Tang 6, Z. Tang 120,
J.D. Tapia Takaki 118, N. Tapus114, L.A. Tarasovicova 126, M.G. Tarzila 46,
G.F. Tassielli 32, A. Tauro 33, G. Tejeda Muñoz 45, A. Telesca 33, L. Terlizzi
25, C. Terrevoli 116, S. Thakur 4, D. Thomas 109, A. Tikhonov 141, A.R.
Timmins 116, M. Tkacik107, T. Tkacik 107, A. Toia 65, R. Tokumoto93, N.
Topilskaya 141, M. Toppi 50, T. Tork 131, A.G. Torres Ramos 32, A. Trifiró
31,54, A.S. Triolo 33,31,54, S. Tripathy 52, T. Tripathy 48, S. Trogolo 33, V.
Trubnikov 3, W.H. Trzaska 117, T.P. Trzcinski 136, A. Tumkin 141, R. Turrisi
55, T.S. Tveter 20, K. Ullaland 21, B. Ulukutlu 96, A. Uras 128, M. Urioni
56,134, G.L. Usai 23, M. Vala38, N. Valle 22, L.V.R. van Doremalen60, M. van
Leeuwen 85, C.A. van Veen 95, R.J.G. van Weelden 85, P. Vande Vyvre 33, D.
Varga 47, Z. Varga 47, M. Vasileiou 79, A. Vasiliev 141, O. Vázquez Doce 50,
O. Vazquez Rueda 116, V. Vechernin 141, E. Vercellin 25, S. Vergara Limón45,
L. Vermunt 98, R. Vértesi 47, M. Verweij 60, L. Vickovic34, Z. Vilakazi123, O.
Villalobos Baillie 101, A. Villani 24, G. Vino 51, A. Vinogradov 141, T.
Virgili 29, M.M.O. Virta 117, V. Vislavicius76, A. Vodopyanov 142, B. Volkel
33, M.A. Völkl 95, K. Voloshin141, S.A. Voloshin 137, G. Volpe 32, B. von
Haller 33, I. Vorobyev 96, N. Vozniuk 141, J. Vrláková 38, J. Wan40, C. Wang
40, D. Wang40, Y. Wang 40, A. Wegrzynek 33, F.T. Weiglhofer39, S.C. Wenzel 33,
J.P. Wessels 126, J. Wiechula 65, J. Wikne 20, G. Wilk 80, J. Wilkinson 98,
G.A. Willems 126, B. Windelband 95, M. Winn 130, J.R. Wright 109, W. Wu40, Y.
Wu 120, R. Xu 6, A. Yadav 43, A.K. Yadav 135, S. Yalcin 73, Y. Yamaguchi 93,
S. Yang21, S. Yano 93, Z. Yin 6, I.-K. Yoo 17, J.H. Yoon 59, H. Yu12, S.
Yuan21, A. Yuncu 95, V. Zaccolo 24, C. Zampolli 33, F. Zanone 95, N. Zardoshti
33, A. Zarochentsev 141, P. Závada 63, N. Zaviyalov141, M. Zhalov 141, B.
Zhang 6, L. Zhang 40, S. Zhang 40, X. Zhang 6, Y. Zhang120, Z. Zhang 6, M.
Zhao 10, V. Zherebchevskii 141, Y. Zhi10, D. Zhou 6, Y. Zhou 84, J. Zhu 98,6,
Y. Zhu6, S.C. Zugravel 57, N. Zurlo 134,56

## Affiliation Notes

I Deceased
II Also at: Max-Planck-Institut fur Physik, Munich, Germany
III Also at: Italian National Agency for New Technologies, Energy and
Sustainable Economic Development (ENEA), Bologna, Italy
IV Also at: Dipartimento DET del Politecnico di Torino, Turin, Italy
V Also at: Department of Applied Physics, Aligarh Muslim University, Aligarh,
India
VI Also at: Institute of Theoretical Physics, University of Wroclaw, Poland
VII 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 HUN-REN Wigner Research Centre for Physics, Budapest, Hungary
48 Indian Institute of Technology Bombay (IIT), Mumbai, India
49 Indian Institute of Technology Indore, Indore, India
50 INFN, Laboratori Nazionali di Frascati, Frascati, Italy
51 INFN, Sezione di Bari, Bari, Italy
52 INFN, Sezione di Bologna, Bologna, Italy
53 INFN, Sezione di Cagliari, Cagliari, Italy
54 INFN, Sezione di Catania, Catania, Italy
55 INFN, Sezione di Padova, Padova, Italy
56 INFN, Sezione di Pavia, Pavia, Italy
57 INFN, Sezione di Torino, Turin, Italy
58 INFN, Sezione di Trieste, Trieste, Italy
59 Inha University, Incheon, Republic of Korea
60 Institute for Gravitational and Subatomic Physics (GRASP), Utrecht
University/Nikhef, Utrecht, Netherlands
61 Institute of Experimental Physics, Slovak Academy of Sciences, Košice,
Slovak Republic
62 Institute of Physics, Homi Bhabha National Institute, Bhubaneswar, India
63 Institute of Physics of the Czech Academy of Sciences, Prague, Czech
Republic
64 Institute of Space Science (ISS), Bucharest, Romania
65 Institut für Kernphysik, Johann Wolfgang Goethe-Universität Frankfurt,
Frankfurt, Germany
66 Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de México,
Mexico City, Mexico
67 Instituto de Física, Universidade Federal do Rio Grande do Sul (UFRGS),
Porto Alegre, Brazil
68 Instituto de Física, Universidad Nacional Autónoma de México, Mexico City,
Mexico
69 iThemba LABS, National Research Foundation, Somerset West, South Africa
70 Jeonbuk National University, Jeonju, Republic of Korea
71 Johann-Wolfgang-Goethe Universität Frankfurt Institut für Informatik,
Fachbereich Informatik und Mathematik, Frankfurt, Germany
72 Korea Institute of Science and Technology Information, Daejeon, Republic of
Korea
73 KTO Karatay University, Konya, Turkey
74 Laboratoire de Physique Subatomique et de Cosmologie, Université Grenoble-
Alpes, CNRS-IN2P3, Grenoble, France
75 Lawrence Berkeley National Laboratory, Berkeley, California, United States
76 Lund University Department of Physics, Division of Particle Physics, Lund,
Sweden
77 Nagasaki Institute of Applied Science, Nagasaki, Japan
78 Nara Women’s University (NWU), Nara, Japan
79 National and Kapodistrian University of Athens, School of Science,
Department of Physics , Athens, Greece
80 National Centre for Nuclear Research, Warsaw, Poland
81 National Institute of Science Education and Research, Homi Bhabha National
Institute, Jatni, India
82 National Nuclear Research Center, Baku, Azerbaijan
83 National Research and Innovation Agency - BRIN, Jakarta, Indonesia
84 Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark
85 Nikhef, National institute for subatomic physics, Amsterdam, Netherlands
86 Nuclear Physics Group, STFC Daresbury Laboratory, Daresbury, United Kingdom
87 Nuclear Physics Institute of the Czech Academy of Sciences, Husinec-Řež,
Czech Republic
88 Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States
89 Ohio State University, Columbus, Ohio, United States
90 Physics department, Faculty of science, University of Zagreb, Zagreb,
Croatia
91 Physics Department, Panjab University, Chandigarh, India
92 Physics Department, University of Jammu, Jammu, India
93 Physics Program and International Institute for Sustainability with Knotted
Chiral Meta Matter (SKCM2), Hiroshima University, Hiroshima, Japan
94 Physikalisches Institut, Eberhard-Karls-Universität Tübingen, Tübingen,
Germany
95 Physikalisches Institut, Ruprecht-Karls-Universität Heidelberg, Heidelberg,
Germany
96 Physik Department, Technische Universität München, Munich, Germany
97 Politecnico di Bari and Sezione INFN, Bari, Italy
98 Research Division and ExtreMe Matter Institute EMMI, GSI Helmholtzzentrum
für Schwerionenforschung GmbH, Darmstadt, Germany
99 Saga University, Saga, Japan
100 Saha Institute of Nuclear Physics, Homi Bhabha National Institute,
Kolkata, India
101 School of Physics and Astronomy, University of Birmingham, Birmingham,
United Kingdom
102 Sección Física, Departamento de Ciencias, Pontificia Universidad Católica
del Perú, Lima, Peru
103 Stefan Meyer Institut für Subatomare Physik (SMI), Vienna, Austria
104 SUBATECH, IMT Atlantique, Nantes Université, CNRS-IN2P3, Nantes, France
105 Sungkyunkwan University, Suwon City, Republic of Korea
106 Suranaree University of Technology, Nakhon Ratchasima, Thailand
107 Technical University of Košice, Košice, Slovak Republic
108 The Henryk Niewodniczanski Institute of Nuclear Physics, Polish Academy of
Sciences, Cracow, Poland
109 The University of Texas at Austin, Austin, Texas, United States
110 Universidad Autónoma de Sinaloa, Culiacán, Mexico
111 Universidade de São Paulo (USP), São Paulo, Brazil
112 Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil
113 Universidade Federal do ABC, Santo Andre, Brazil
114 Universitatea Nationala de Stiinta si Tehnologie Politehnica Bucuresti,
Bucharest, Romania
115 University of Cape Town, Cape Town, South Africa
116 University of Houston, Houston, Texas, United States
117 University of Jyväskylä, Jyväskylä, Finland
118 University of Kansas, Lawrence, Kansas, United States
119 University of Liverpool, Liverpool, United Kingdom
120 University of Science and Technology of China, Hefei, China
121 University of South-Eastern Norway, Kongsberg, Norway
122 University of Tennessee, Knoxville, Tennessee, United States
123 University of the Witwatersrand, Johannesburg, South Africa
124 University of Tokyo, Tokyo, Japan
125 University of Tsukuba, Tsukuba, Japan
126 Universität Münster, Institut für Kernphysik, Münster, Germany
127 Université Clermont Auvergne, CNRS/IN2P3, LPC, Clermont-Ferrand, France
128 Université de Lyon, CNRS/IN2P3, Institut de Physique des 2 Infinis de
Lyon, Lyon, France
129 Université de Strasbourg, CNRS, IPHC UMR 7178, F-67000 Strasbourg, France,
Strasbourg, France
130 Université Paris-Saclay, Centre d’Etudes de Saclay (CEA), IRFU, Départment
de Physique Nucléaire (DPhN), Saclay, France
131 Université Paris-Saclay, CNRS/IN2P3, IJCLab, Orsay, France
132 Università degli Studi di Foggia, Foggia, Italy
133 Università del Piemonte Orientale, Vercelli, Italy
134 Università di Brescia, Brescia, Italy
135 Variable Energy Cyclotron Centre, Homi Bhabha National Institute, Kolkata,
India
136 Warsaw University of Technology, Warsaw, Poland
137 Wayne State University, Detroit, Michigan, United States
138 Yale University, New Haven, Connecticut, United States
139 Yonsei University, Seoul, Republic of Korea
140 Zentrum für Technologie und Transfer (ZTT), Worms, Germany
141 Affiliated with an institute covered by a cooperation agreement with CERN
142 Affiliated with an international laboratory covered by a cooperation
agreement with CERN.