2015-030 February 12, 2015

Rapidity and transverse-momentum dependence of the inclusive J/$\psi$ $R_{\rm
pPb}$ ALICE CollaborationALICE Collaboration

We have studied the transverse-momentum ($p_{{\mathrm{T}}}$) dependence of the
inclusive J/$\psi$ production in p–Pb collisions at $\sqrt{s_{\rm NN}}=5.02$
TeV, in three center-of-mass rapidity ($y_{\rm cms}$) regions, down to zero
$p_{{\mathrm{T}}}$. Results in the forward and backward rapidity ranges
($2.03<y_{\rm cms}<3.53$ and $-4.46<y_{\rm cms}<-2.96$) are obtained by
studying the J/$\psi$ decay to $\mu^{+}\mu^{-}$, while the mid-rapidity region
($-1.37<y_{\rm cms}<0.43$) is investigated by measuring the ${\rm e}^{+}{\rm
e}^{-}$ decay channel. The $p_{{\mathrm{T}}}$ dependence of the J/$\psi$
production cross section and nuclear modification factor are presented for
each of the rapidity intervals, as well as the J/$\psi$ mean $p_{\rm T}$
values. Forward and mid-rapidity results show a suppression of the J/$\psi$
yield, with respect to pp collisions, which decreases with increasing
$p_{{\mathrm{T}}}$. At backward rapidity no significant J/$\psi$ suppression
is observed. Theoretical models including a combination of cold nuclear matter
effects such as shadowing and partonic energy loss, are in fair agreement with
the data, except at forward rapidity and low transverse momentum. The
implications of the p–Pb results for the evaluation of cold nuclear matter
effects on J/$\psi$ production in Pb–Pb collisions are also discussed.

The suppression of charmonia, bound states of $c$ and $\bar{c}$ quarks, and in
particular of the J/$\psi$ state, has long been proposed as a signature for
the formation of a plasma of quarks and gluons (QGP) [1] in ultrarelativistic
nucleus-nucleus collisions. However, it was soon realized that charmonium
production can also be modified by nuclear effects not necessarily related to
QGP formation [2]. These so-called cold nuclear matter (CNM) effects can be
investigated by studying charmonium production in proton-nucleus (p–A)
collisions as confirmed by the analysis of results obtained by several fixed-
target (SPS [3, 4], HERA [5] and Tevatron [6]) and collider (RHIC [7] and LHC
[8, 9]) experiments.

Theoretical models have studied the production of charmonium in p–A collisions
and the effects of the surrounding cold nuclear medium by introducing various
mechanisms which include nuclear shadowing, gluon saturation, energy loss and
nuclear absorption. Models [10, 11, 12] inspired by Quantum ChromoDynamics
(QCD) describe charmonium production as a two-step process, with the
$c\overline{c}$ pair created in a hard parton scattering, followed by its
evolution into a bound state with specific quantum numbers. The pair creation
is sensitive to the Parton Distribution Functions (PDFs) in both colliding
partners and, at high energy, occurs mainly via gluon fusion. Although PDFs
are known to be modified in a nuclear environment, information on the
dependence of such modifications on the fraction $x$ (Bjorken-$x$) of the
nucleon momentum carried by the gluons and on the four-momentum squared
$Q^{2}$ transferred in the scattering is still limited [13, 14, 15].
Charmonium production measurements can therefore provide insight into the so-
called nuclear shadowing, i.e., on how the nucleon gluon PDFs are modified in
a nucleus.
Modifications of the initial state of the nucleus are also addressed by
approaches assuming that at sufficiently high energies, when the quark pair is
produced from a dense gluon system carrying small $x$-values in the nuclear
target, a coherent effect known as gluon saturation sets in. Such an effect
can be described by the Color Glass Condensate (CGC) effective theory, which
is characterized by a saturation momentum scale ($Q^{2}_{s}$). When combined
with a specific quarkonium production model [16, 17], it is able to provide
predictions for charmonium production in p–A collisions. In the context of
shadowing and CGC models, a measurement of the charmonium yield as a function
of transverse momentum ($p_{\rm T}$) and rapidity ($y$) is important as it
gives access to specific ranges of values of the gluon $x$ and/or $Q^{2}$.
In addition to these purely initial state effects, both the incoming partons
and the $c\overline{c}$ pair propagating through the nucleus may lose energy
by gluon radiation at the various stages of the charmonium formation process
[18]. The interference of gluons radiated before and after the hard production
vertex can lead to coherent energy loss effects, expected to induce a
modification of the charmonium kinematic distributions [19].
Finally, while travelling through nuclear matter, the evolving $c\overline{c}$
pair or, if crossing times are sufficiently large, the fully formed resonance,
may break-up into open charm meson pairs. Although this mechanism, known as
nuclear absorption, plays an important role at lower collision energies [4],
at the LHC the contribution of this effect to the production cross section is
expected to be small, due to the very short crossing time of the pair through
the nuclear environment.

Understanding the role of the cold nuclear matter effects outlined above is
essential to further our knowledge of various aspects of the physics of strong
interactions, and it is crucial for the interpretation of the results on
charmonium production in heavy-ion collisions, where the formation of a QGP is
expected. In such a hot and dense deconfined medium the color screening
mechanism (the QCD analogue of the Debye screening in QED) can prevent the
formation of the heavy-quark bound states, leading to a suppression of
quarkonium production [1]. In addition, at LHC energies, the large charm quark
density may lead to a (re)generation of charmonium by (re)combination of charm
quarks [20, 21] in the QGP phase and/or when the system cools down and the
formation of hadrons occurs. This effect enhances charmonium production and is
expected to be particularly sizeable at low $p_{{\mathrm{T}}}$. In heavy-ion
collisions, a superposition of hot and cold nuclear matter effects is
expected, and a quantitative evaluation of the latter is an important
prerequisite for a detailed understanding of the former. At lower energy, both
at SPS[22, 23, 24] and RHIC [25, 26], a suppression of J/$\psi$ production, in
addition to the CNM effects estimated from p–A(d-A) collisions, was indeed
observed.

A suppression of J/$\psi$ production has been measured in Pb–Pb collisions at
the LHC [27, 28, 29, 30, 31]. It was quantified via the nuclear modification
factor, i.e., the ratio of the Pb–Pb yields with respect to those measured in
pp at the same energy, scaled by the number of binary nucleon-nucleon
collisions. The suppression has been found to be stronger at forward rapidity
and at high $p_{{\mathrm{T}}}$ [30, 31], in agreement with expectations from
(re)combination models. Similar to the lower energy experiments, accurate
measurements in p–A collisions are needed to quantitatively assess the
contribution of hot and cold nuclear matter effects in Pb–Pb.

The first measurements of inclusive J/$\psi$ production in p–Pb collisions at
the LHC at $\sqrt{s_{\rm NN}}=5.02$ TeV [8, 9] have shown a sizeable
suppression, with respect to binary-scaled pp collisions, at forward rapidity
(p-going side) and no suppression at backward rapidity (Pb-going side). The
nuclear modification factors are in fair agreement with models based on
nuclear shadowing [32, 33]. Calculations including a contribution from
coherent energy loss [19] also reproduce the data. Corresponding measurements
for the less strongly bound $\psi(2S)$ charmonium state are presented in [34].
In addition, an extrapolation to Pb–Pb collisions of the J/$\psi$ suppression
measured in p–Pb showed that the effects observed in Pb–Pb cannot be ascribed
only to CNM [8].

In this situation, a study of the transverse-momentum dependence of J/$\psi$
production at LHC energies for various rapidity regions is particularly
interesting in order to: (i) reach a deeper understanding and better quantify
the complicated interplay of CNM effects, which are expected to exhibit a
well-defined kinematical dependence [33, 35, 36]; (ii) determine if the
differential features of the Pb–Pb results that suggest the presence of
(re)combination effects are still present when the contribution of CNM is
considered.

In this paper, we present ALICE results on the transverse-momentum dependence
of the inclusive J/$\psi$ production in p–Pb collisions at $\sqrt{s_{\rm
NN}}=5.02$ TeV, measured in three center-of-mass rapidity ($y_{\rm cms}$)
ranges: backward ($-4.46<y_{\rm cms}<-2.96$), mid- ($-1.37<y_{\rm cms}<0.43$)
and forward ($2.03<y_{\rm cms}<3.53$). The data are from the 2013 LHC p-Pb
run.

At mid-rapidity, J/$\psi$ are reconstructed in the ${\rm e}^{+}{\rm e}^{-}$
decay channel with the ALICE central barrel detectors, covering the
pseudorapidity range $|\eta_{\rm lab}|<$0.9. For the backward and forward
rapidity analysis, J/$\psi$ are detected, through their $\mu^{+}\mu^{-}$ decay
channel in the muon spectrometer, in the pseudorapidity range $-4<\eta_{\rm
lab}<-2.5$.

Due to the energy asymmetry of the LHC beams ($E_{\rm p}=4$ TeV and $E_{\rm
Pb}=1.58\cdot A_{\rm Pb}$ TeV, where $A_{\rm Pb}$= 208 is the Pb atomic mass
number), the nucleon-nucleon center-of-mass is shifted, with respect to the
laboratory frame, by $\Delta y=0.465$ in the direction of the proton beam.
Since data were collected in two configurations, interchanging the direction
of the proton and the Pb beams in the LHC, the muon spectrometer acceptance
covers the forward and backward $y_{\rm cms}$ regions quoted above, where
positive (negative) rapidities refer to the direction of the proton (Pb) beam.
In the following, the notation p–Pb (Pb–p) will refer to the first (second)
configuration.

For the dielectron analysis, the central barrel detectors used for the
J/$\psi$ reconstruction are the Inner Tracking System (ITS) [37] and the Time
Projection Chamber (TPC) [38]. The ITS contains six cylindrical layers of
silicon detectors, with the innermost layer at a radius of 3.9 cm with respect
to the beam axis and the outermost layer at 43 cm. This detector is used for
reconstructing the primary interaction vertex as well as vertices from
different interactions and secondary vertices from decays of heavy-flavored
particles. The TPC has a cylindrical geometry with an active volume that
extends from 85 to 247 cm in the radial direction and 500 cm longitudinally.
It is the main central barrel tracking detector and also provides particle
identification via the measurement of the specific energy loss (${\rm d}E/{\rm
d}x$) in the detector gas.

The muon spectrometer [39] is the main detector used in the dimuon analysis.
It consists of a 3 T$\cdot$m dipole magnet, coupled with a tracking and a
triggering system. Between the interaction point and the muon spectrometer, a
ten interaction-length ($\lambda_{\rm I}$) front absorber filters out the
hadrons produced in the interaction. Muon tracking is performed by means of
five tracking stations, each one made of two planes of Cathode Pad Chambers. A
7.2 $\lambda_{\rm I}$ iron wall, which stops secondary hadrons escaping the
front absorber and low momentum muons, is placed after the tracking stations.
It is followed by a muon trigger system, based on two stations equipped with
Resistive Plate Chambers. A conical absorber made of tungsten, lead and steel
protects the spectrometer against secondary particles produced by the
interaction of large-$\eta$ primary particles in the beam pipe. In the dimuon
analysis, the determination of the interaction vertex is provided by the two
innermost Si-pixel layers of the ITS (Silicon Pixel Detector, SPD).

For both analyses, timing information from the Zero Degree Calorimeters [40],
placed symmetrically at 112.5 m with respect to the interaction point, is used
to remove de-bunched proton-lead collisions. Furthermore, two scintillator
hodoscopes (VZERO) [41], with pseudorapidity coverage $2.8<\eta_{\rm lab}<5.1$
and $-3.7<\eta_{\rm lab}<-1.7$, are used to remove beam-induced background.
More details on the ALICE apparatus can be found in [39].

A coincidence of signals in the two VZERO detectors provides the minimum bias
(MB) trigger, which has a $>99$% efficiency for selecting non single-
diffractive p–Pb collisions [42]. While the dielectron analysis is based on
MB-triggered events, the study of J/$\psi$ in the $\mu^{+}\mu^{-}$ decay
channel relies on a dimuon trigger which requires, in addition to the MB
condition, the detection of two opposite-sign tracks in the trigger system.
The dimuon trigger selects two muon candidates with transverse momenta
$p_{{\mathrm{T}},\mu}$ larger than 0.5 GeV/$c$. The trigger threshold is not
sharp, and the single muon trigger efficiency reaches its plateau value
($\sim$ 96%) at $p_{{\mathrm{T}},\mu}\sim 1.5$ GeV/$c$. The dielectron
analysis was performed on a data sample corresponding to the p–Pb
configuration, with an integrated luminosity $\mathcal{L}_{\rm int}=51.4\pm
1.9$ $\mu$b-1, while for the dimuon analysis the corresponding values are
$5.01\pm 0.19$ nb-1 for p–Pb and $5.81\pm 0.20$ nb-1 for Pb–p (the quoted
uncertainties are systematic) [43].

The dielectron analysis is based on 1.07$\times 10^{8}$ events, collected with
a low MB interaction rate ($\sim$10 kHz), with a negligible amount of events
having more than one interaction per bunch crossing (pile-up events). The
interaction vertex is required to lie within $\pm 10$ cm from the nominal
collision point along the beam axis, in order to obtain a uniform acceptance
of the central barrel detector system in the fiducial range $|\eta_{\rm
lab}|<0.9$. Electron candidates are selected with criteria very similar to
those used in previous analyses of pp collisions at $\sqrt{s}=$7 TeV [44] and
Pb–Pb collisions at $\sqrt{s_{\rm NN}}=2.76$ TeV [30]. To ensure a uniform
tracking efficiency and particle identification resolution in the TPC, only
tracks within $|\eta_{\rm lab}|<0.9$ are used. Electron identification is
performed using the TPC, as shown in Fig. 1, by requiring the ${\rm d}E/{\rm
d}x$ signal to be compatible with the electron assumption within 3$\sigma$,
where $\sigma$ denotes the resolution of the ${\rm d}E/{\rm d}x$ measurement.
Furthermore, the TPC tracks that are compatible with the pion and proton
assumptions within 3.5$\sigma$ are rejected. A slightly looser rejection
condition (3$\sigma$) is applied when considering tracks corresponding to
dielectron candidates with $p_{\rm T}>$ 5 GeV/$c$ in order to enhance the
statistics. A cut on the transverse momentum ($p_{\rm T,e}>1.0$ GeV/$c$) is
applied to remove combinatorial background from low-momentum electrons. The
efficiency loss induced by this cut amounts to only $\sim$20%, due to the
relatively large momentum of the J/$\psi$ decay products. The electron
candidates must have at least one hit in the innermost two layers of the ITS,
thus rejecting a large fraction of background electrons from photon
conversions. For dielectrons with $p_{\rm T}<3$ GeV/$c$ the electron
candidates are required to have a hit in the first layer, to further reduce
background. The tracks are required to have at least 70 out of a maximum of
159 clusters in the TPC and a $\chi^{2}$ normalized to the number of clusters
attached to the track smaller than 4.

Figure 1: Charged particle specific energy loss (${\rm d}E/{\rm d}x$) as a
function of momentum, as measured in the TPC in p–Pb collisions. The black
lines are the corresponding Bethe-Bloch parametrizations for the various
particle species. Figure 2: Opposite-sign dielectron invariant mass spectra
(blue symbols) for various $p_{\rm T}$ intervals, compared to the background
(black curve) estimated through mixed events. The background is scaled to
match the data in the mass ranges $2.0<m_{{\rm e}^{+}{\rm e}^{-}}<2.5$
GeV/$c^{2}$ and $3.2<m_{{\rm e}^{+}{\rm e}^{-}}<3.7$ GeV/$c^{2}$.

The J/$\psi$ yields are obtained by counting the number of entries in the
invariant mass range $2.92<m_{{\rm e}^{+}{\rm e}^{-}}<3.16$ GeV/$c^{2}$ after
background subtraction. The J/$\psi$ radiative decay channel and the energy
loss of the electrons due to bremsstrahlung in the detector material produce a
long tail towards low invariant masses. A fit using a Crystal Ball (CB) [45]
function for the J/$\psi$ signal gives compatible values in Monte-Carlo (MC)
and data ($\sim$20 MeV/$c^{2}$ for the width of the Gaussian component of the
CB). Taking into account such a mass resolution and the presence of the
bremsstrahlung tail, $67-73\%$ of the signal, depending on $p_{{\mathrm{T}}}$,
falls within the counting window. The background shape is obtained from event
mixing. Event mixing is performed by pairing leptons from different events
having similar global characteristics such as the primary-vertex position and
the track multiplicity (the result being quite insensitive to the rapidity
range, either forward or central, chosen for the multiplicity measurements).
The mixed-event background is then scaled to match the same-event opposite-
sign distribution in the mass ranges $2.0<m_{{\rm e}^{+}{\rm e}^{-}}<2.5$
GeV/$c^{2}$ and $3.2<m_{{\rm e}^{+}{\rm e}^{-}}<3.7$ GeV/$c^{2}$ (the
contribution of the bremsstrahlung tail in the former range and of the
$\psi(2S)$ in the latter are negligible). Consistent results are found when
the same-event like-sign distributions are used, instead of event mixing, to
estimate the background. The systematic uncertainty on the signal extraction
comes from the variation of the mass range where the normalization of the
mixed-event background shape is performed and from the choice of the mass
window where the signal is counted. The signal extraction has been performed
in five transverse-momentum bins, $p_{\rm T}<1.3$, $1.3<p_{\rm T}<3$,
$3<p_{\rm T}<5$, $5<p_{\rm T}<7$ and $7<p_{\rm T}<10$ GeV/$c$. The J/$\psi$
counts in these bins vary from 25 to 132, with a significance, computed in the
$2.92<m_{{\rm e}^{+}{\rm e}^{-}}<3.16$ GeV/$c^{2}$ mass region, ranging from
4.6 to 8.7. An analysis of the $p_{{\mathrm{T}}}$-integrated data sample,
using the procedure detailed above, gives $465\pm 37({\rm stat.})\pm 16({\rm
syst.})$ J/$\psi$ signal counts. The systematic uncertainty on the signal
extraction is largest at low $p_{{\mathrm{T}}}$ (10% for $p_{\rm T}<1.3$
GeV/$c$ and 12% for $1.3<p_{\rm T}<3$ GeV/$c$), due to a less favorable signal
over background ratio, and decreases to $\sim$5.5–8.4% in the other three
$p_{{\mathrm{T}}}$ bins. Figure 2 shows the invariant mass distributions for
the opposite-sign dielectrons compared with the mixed-event background for the
different intervals of $p_{{\mathrm{T}}}$.

The dimuon analysis is performed as detailed in [8], and is shortly summarized
hereafter. Data were collected with the dimuon trigger, and the MB interaction
rate (up to 200 kHz) was much higher than in the sample used for the
dielectron analysis. This leads to a $\sim$2% interaction pile-up probability.
However, the probability of having more than one dimuon in the same bunch
crossing satisfying the trigger condition is negligible. Muon candidate tracks
are reconstructed in the tracking system by using the standard reconstruction
algorithm [44]. The quality of the tracks is ensured by requiring the single
muon pseudorapidity to be in the range $-4<\eta_{{\rm lab,}\mu}<-2.5$, in
order to remove particles at the edges of the muon spectrometer acceptance. In
addition, a cut on the radial coordinate of the track at the end of the front
absorber ($17.6<R_{\rm abs}<89.5$ cm) is performed, ensuring rejection of
muons crossing its high-density part, where energy loss and multiple
scattering effects are more important. The tracks reconstructed in the
tracking system that are not matched to a corresponding track in the
triggering system are rejected [44]. Finally, the reconstructed dimuons are
required to be in $2.03<y_{\rm cms}<3.53$ ($-4.46<y_{\rm cms}<-2.96$) for the
forward (backward) rapidity analysis. The number of J/$\psi$ is extracted in
transverse-momentum bins, in the range $p_{\rm T}<15$ GeV/$c$, through fits to
the invariant mass spectra of opposite-sign dimuons. The spectra are fitted
with a superposition of background and resonance shapes. The background is
described with a Gaussian function with a mass-dependent width or,
alternatively, with an exponential function times a fourth-order polynomial
function. For the J/$\psi$ shape an extended Crystal Ball function, which
accommodates a non-Gaussian tail both on the right and on the left side of the
resonance peak, is adopted. Alternatively, a pseudo-Gaussian function [46] is
used, corresponding to a Gaussian core around the J/$\psi$ pole, and tails on
the right and left side of it, parameterized by varying the width of the
Gaussian as a function of the mass. The value of the J/$\psi$ mass and its
width ($\sigma$) at the pole position are free parameters of the fit. The mass
coincides with the PDG value within less than 5 MeV/$c^{2}$ and the width is
$\sim$70 MeV/$c^{2}$, slightly increasing with $p_{{\mathrm{T}}}$, due to a
small relative decrease in the tracking resolution for harder muons. Although
the signal over background ratios, calculated for a $\pm 3\sigma$ interval
around the resonance peak, are relatively large (ranging from 1.4 to $\sim 6$
moving from low to high $p_{{\mathrm{T}}}$), the parameters of the tails of
the J/$\psi$ distributions cannot be reliably tuned on the data (in particular
at large $p_{{\mathrm{T}}}$, where statistics is limited), but are fixed, for
each $p_{{\mathrm{T}}}$ bin, to the values extracted from fits to
reconstructed samples from a signal-only MC generation. The contribution of
the $\psi$(2S) resonance is also included in the fitting procedure, even if
its influence on the determination of the J/$\psi$ yield is negligible.
Finally, all the fits are performed in two different invariant mass ranges,
either $2<m_{\mu\mu}<5$ GeV/$c^{2}$ or $2.2<m_{\mu\mu}<4.5$ GeV/$c^{2}$.
Examples of fits to the invariant mass spectra, in the $p_{{\mathrm{T}}}$ bins
under study, are shown in Fig. 3.

Figure 3:  The opposite-sign dimuon invariant mass spectra for the various
$p_{{\mathrm{T}}}$ bins, relative to the p–Pb data sample (blue symbols). The
fits shown in this Figure (blue curves) were performed by using the sum of
extended Crystal Ball functions for the J/$\psi$ and $\psi$(2S) signals, and a
variable width Gaussian for the background. The signal and background
components are shown separately as red curves.

For each $p_{{\mathrm{T}}}$ bin, the number of J/$\psi$ is evaluated as the
average of the integrals of the resonance functions obtained in the various
fits. The RMS of the corresponding yield distributions ($0.2-3$%, depending on
$p_{{\mathrm{T}}}$) provides the systematic uncertainty on the signal
extraction. Additional sets of tails, obtained from the MC, but referring to
other $y_{\rm cms}$ and $p_{\rm T}$ phase space regions, have also been tested
and the dependence of the extracted yields on the variation of the tails (2%)
is included in the systematic uncertainty on the signal extraction. As a
function of $p_{{\mathrm{T}}}$, the number of J/$\psi$ in the p–Pb (Pb–p)
configuration ranges between $\sim$16100 ($\sim$16000) in the most populated
bin ($1<p_{\rm T}<2$ GeV/$c$) and less than $\sim$900 ($\sim$300) in the
highest $p_{{\mathrm{T}}}$ bin ($10<p_{\rm T}<15$ GeV/$c$).

The J/$\psi$ yields are then corrected for the product of acceptance times
efficiency ($A\times\varepsilon$), evaluated by means of a MC simulation.
J/$\psi$ production is assumed to be unpolarized, as motivated by the small
degree of polarization measured in pp collisions at $\sqrt{s}=7$ TeV [47, 48,
49]. In the ${\rm e}^{+}{\rm e}^{-}$ decay channel, $A\times\varepsilon$ is
calculated using a MC simulation where J/$\psi$ are injected into p–Pb
collisions simulated with HIJING [50]. The decay products of the J/$\psi$ are
then propagated through a realistic description of the ALICE set-up, based on
GEANT3.21 [51], taking into account the time evolution of the detector
performance. Finally, J/$\psi$ candidates are reconstructed with the same
procedure applied to data. The $p_{{\mathrm{T}}}$-integrated
$A\times\varepsilon$ factor amounts to 8.9%. Its $p_{{\mathrm{T}}}$-dependence
exhibits a minimum ($\sim$7.5%) around $p_{\rm T}=2$ GeV/$c$, due to the
kinematical acceptance, and it reaches $\sim$12% at high $p_{{\mathrm{T}}}$.
The integrated value of $A\times\varepsilon$ is affected by a 3% systematic
uncertainty related to the choice of the J/$\psi$ $p_{{\mathrm{T}}}$\- and
$y$-distributions used in the MC simulation. This value is obtained using as
input several distributions, determined by varying within uncertainties the
differential spectra extracted from the ALICE p–Pb data themselves. For
$p_{{\mathrm{T}}}$-differential studies, the values of $A\times\varepsilon$
are found to be sensitive only at a sub-percent level to the adopted input
$p_{{\mathrm{T}}}$\- and $y$-distributions. A further small systematic
uncertainty reaching 1.5% in the highest $p_{{\mathrm{T}}}$ interval and
related to the statistical uncertainty of the MC sample is also introduced.
The systematic uncertainty on the dielectron reconstruction efficiency is
strongly dominated by the particle identification uncertainty and amounts to
4%. It was obtained by comparing the single track reconstruction efficiency
for topologically identified positrons and electrons from photon conversions
with the corresponding MC quantities. In the dimuon analysis, the J/$\psi$
$A\times\varepsilon$ is obtained with a MC simulation, by generating signal-
only samples, tracking them in the experimental set-up modeled with GEANT3.21
and using the same reconstruction procedure applied to data. The use of a pure
signal MC is justified, since the tracking efficiency does not show a
dependence on the hadronic multiplicity of the collision. A realistic
description of the set-up is adopted, including the time evolution of the
efficiencies of tracking and triggering detectors. As for the dielectron
analysis, the differential distributions used as an input to the MC are tuned
directly on the data. The J/$\psi$ $A\times\varepsilon$ values, integrated
over $p_{{\mathrm{T}}}$, are 25.4% and 17.1% for p–Pb and Pb–p respectively
[8], and exhibit a dependence on transverse momentum, being of the order of
$\sim$24% ($\sim$16%) for p–Pb (Pb–p) at low $p_{{\mathrm{T}}}$ and reaches
$\sim$50% ($\sim$35%) in the highest $p_{{\mathrm{T}}}$ bin ($10<p_{\rm T}<15$
GeV/$c$). The systematically lower $A\times\varepsilon$ values in Pb–p reflect
the smaller detector efficiency in the corresponding data taking period. The
systematic uncertainty on the integrated $A\times\varepsilon$ due to the input
shapes is 1.5% for both p–Pb and Pb–p, and has been estimated using various
distributions obtained from data and corresponding to smaller intervals in
$y$, $p_{{\mathrm{T}}}$ and centrality (see [8] for details). For
$p_{{\mathrm{T}}}$-differential studies, the corresponding uncertainties are
below 1.5%. The uncertainty on the dimuon tracking efficiency amounts to 4%
(6%) for p–Pb (Pb–p) and is taken as constant for the full $p_{{\mathrm{T}}}$
range. It is evaluated by combining the uncertainties on single muon tracking
efficiencies, considered as uncorrelated. The efficiency of each tracking
plane is obtained using the redundancy of the tracking system (two independent
planes per station) and then single muon efficiencies for the full tracking
system are calculated according to the tracking algorithm [52]. Their
uncertainty is determined by comparing the efficiency obtained with tracks
from MC and real data. The systematic uncertainty on the dimuon trigger
efficiency includes: (i) a contribution due to the uncertainty in the
evaluation of the trigger detector efficiency ($\sim$ 2%, independent of
$p_{{\mathrm{T}}}$); (ii) a $0.5-3$% $p_{{\mathrm{T}}}$-dependent contribution
(2% for the integrated efficiency), related to small differences in the
trigger response function between data and MC in the region close to the
trigger threshold; (iii) a $0.5-3.5$% $p_{{\mathrm{T}}}$-dependent
contribution due to a small fraction of opposite-sign pairs which were
misidentified as like-sign by the trigger system. Finally, a $\sim$1%
uncertainty, independent of $p_{{\mathrm{T}}}$, is included, due to the choice
of the value of the $\chi^{2}$ cut applied to the matching of tracks
reconstructed in the muon tracking and triggering systems.

Source |  $\sigma^{{\rm J/}\psi}_{\rm pPb}$, $R_{\rm pPb}$ |  $\sigma^{{\rm J/}\psi}_{\rm pPb}$, $R_{\rm pPb}$ |  $\sigma^{{\rm J/}\psi}_{\rm Pbp}$, $R_{\rm Pbp}$
---|---|---|---
| -1.37$<y_{\rm cms}<$0.43 | 2.03$<y_{\rm cms}<$3.53 | -4.46$<y_{\rm cms}<$-2.96
Uncorrelated |  |  |
Tracking efficiency ($\mu^{+}\mu^{-}$) | - | 4 | 6
Trigger efficiency ($\mu^{+}\mu^{-}$) | - | 2.7 $-$ 4.1 | 2.7 $-$ 4.1
Matching efficiency ($\mu^{+}\mu^{-}$) | - | 1 | 1
Reconstruction efficiency (${\rm e}^{+}{\rm e}^{-})$ | 4 | - | -
Signal extraction | 5.5 $-$ 12.6 | 2 $-$ 2.5 | 2 $-$ 3.6
MC input | 0.3 $-$ 1.5 | 0.1 $-$ 0.4 | 0.1 $-$ 1.4
$\sigma^{{\rm J/}\psi}_{\rm pp}$ | 4.8 $-$ 15.7 | 5.2 $-$ 9.2 | 5.2 $-$ 9.2
Partially correlated |  |  |
$\sigma^{{\rm J/}\psi}_{\rm pp}$ (corr. vs $y$ and $p_{{\mathrm{T}}}$) | - | 2.8 $-$ 5.9 | 2 $-$ 5.6
Correlated |  |  |
B.R. (J/$\psi$ $\rightarrow l^{+}l^{-}$) | 1 | 1 | 1
$\mathcal{L}_{int}$(corr. vs. $p_{{\mathrm{T}}}$, uncorr. vs. $y$) | 3.3 | 3.4 | 3.1
$\mathcal{L}_{int}$(corr. vs. $y$ and $p_{{\mathrm{T}}}$) | 1.6 | 1.6 | 1.6
$\sigma^{{\rm J/}\psi}_{\rm pp}$ | 16.6 | 5.2 | 5.2
Table 1: Systematic uncertainties (in percent) on the measurement of inclusive
J/$\psi$ cross sections and nuclear modification factors. For
$p_{{\mathrm{T}}}$-dependent uncertainties, the minimum and maximum values are
given. The degree of correlation (uncorrelated, partially correlated,
correlated) refers to the $p_{\rm T}$-dependence, unless specified otherwise.
It cannot be excluded that a degree of correlation, difficult to quantify, is
present also in uncertainties currently labelled as uncorrelated.
Uncertainties on $\mathcal{L}_{int}$ and branching ratios are relevant for
cross sections, while those on $\sigma^{{\rm J/}\psi}_{\rm pp}$ contribute
only to the uncertainty on the nuclear modification factors.
$\mathcal{L}_{int}$ uncertainties are split into two components, respectively
uncorrelated and correlated between p–Pb and Pb–p, as detailed in [43].

The differential cross section for inclusive J/$\psi$ production is defined
as:

$\frac{{\rm d}^{2}\sigma^{\rm J/\psi}_{\rm pPb}}{{\rm d}y{\rm d}p_{\rm
T}}=\frac{N_{\rm J/\psi}(\Delta y,\Delta p_{\rm T})}{\mathcal{L}_{\rm
int}^{\rm pPb}\cdot(A\times\varepsilon)_{(\Delta y,\Delta p_{\rm T})}\cdot{\rm
B.R.}({\rm J}/\psi\rightarrow l{{}^{+}}l{{}^{-}})\cdot\Delta y\cdot\Delta
p_{\rm T}}$ (1)

where $N_{\rm J/\psi}(\Delta y,\Delta p_{\rm T})$ is the number of J/$\psi$
for a given $\Delta y$ and $\Delta$$p_{{\mathrm{T}}}$ interval. The branching
ratio to dileptons, ${\rm B.R.}({\rm J}/\psi\rightarrow l{{}^{+}}l{{}^{-}})$,
is $5.94\pm 0.06$% ($5.93\pm 0.06$%) for the dielectron (dimuon) decay [53].
The integrated luminosity, $\mathcal{L}_{\rm int}^{\rm pPb}$, is the ratio
between $N_{\rm MB}$, the number of MB collisions, and $\sigma^{\rm MB}_{\rm
pPb}$, the corresponding cross section, measured in a van der Meer scan to be
2.09 $\pm$ 0.07 b for the p–Pb configuration and 2.12 $\pm$ 0.07 b for the
Pb–p case [43]. The luminosity is also independently determined by means of a
second signal based on a Čherenkov counter [39], as described in [43]. The two
measurements differ by at most 1% throughout the whole data-taking period and
such a value is quadratically added to the luminosity uncertainty. Finally,
since the dimuon analysis is based on a sample of $N_{\rm DIMU}$ dimuon
triggered events, the number of equivalent MB collisions is computed as
$N_{\rm MB}=F\cdot N_{\rm DIMU}$, where $F$ is a factor accounting for the
probability of having a dimuon trigger when the MB condition is satisfied and
for the small ($\sim 2\%$) pile-up probability in the corresponding data
sample. The systematic uncertainty on this quantity, quadratically added to
the other luminosity uncertainties, is 1% and originates from the comparison
between the different approaches used for its evaluation [8]. A summary of the
systematic uncertainties can be found in Table 1. The differential inclusive
J/$\psi$ cross sections are shown in Fig. 4, in the ranges $p_{\rm T}<10$
GeV/$c$ for the dielectron analysis and $p_{\rm T}<15$ GeV/$c$ for the dimuon
analysis. The numerical values can be found in Table 2.

$p_{\rm T}$ | $\mathrm{d}^{2}\sigma^{\rm J/\psi}_{\rm pPb}/\mathrm{d}y\mathrm{d}p_{\rm T}$ | $p_{\rm T}$ | $R_{\rm pPb}$ |  $\mathrm{d}^{2}\sigma^{\rm J/\psi}_{\rm pp}/\mathrm{d}y\mathrm{d}p_{\rm T}$ (interpol.)
---|---|---|---|---
(GeV/$c$) |  ($\mu$b/(GeV/$c$)) |  (GeV/$c$) |  |  ($\mu$b/(GeV/$c$))
$-4.46<y_{\rm cms}<-2.96$ ($\mu^{+}\mu^{-}$)
$[0;1]$ |  97.7$\pm$2.0$\pm$7.2$\pm$3.5 | $[0;1]$ |  0.96$\pm$0.02$\pm$0.09$\pm$0.03$\pm$0.06 |  0.490$\pm$0.029$\pm$0.017$\pm$0.026
$[1;2]$ |  196.8$\pm$2.7$\pm$14.3$\pm$7.1 | $[1;2]$ |  1.06$\pm$0.01$\pm$0.10$\pm$0.04$\pm$0.07 |  0.892$\pm$0.048$\pm$0.030$\pm$0.046
$[2;3]$ |  159.6$\pm$2.1$\pm$11.6$\pm$5.8 | $[2;3]$ |  1.11$\pm$0.01$\pm$0.10$\pm$0.04$\pm$0.07 |  0.693$\pm$0.036$\pm$0.025$\pm$0.036
$[3;4]$ |  93.3$\pm$1.6$\pm$6.7$\pm$3.4 | $[3;4]$ |  1.16$\pm$0.02$\pm$0.10$\pm$0.04$\pm$0.07 |  0.388$\pm$0.021$\pm$0.012$\pm$0.020
$[4;5]$ |  45.7$\pm$1.0$\pm$3.2$\pm$1.7 | $[4;5]$ |  1.17$\pm$0.02$\pm$0.11$\pm$0.03$\pm$0.07 |  0.187$\pm$0.011$\pm$0.004$\pm$0.010
$[5;6]$ |  22.1$\pm$0.5$\pm$1.6$\pm$0.8 | $[5;6]$ |  1.13$\pm$0.03$\pm$0.12$\pm$0.02$\pm$0.07 |  0.094$\pm$0.007$\pm$0.002$\pm$0.005
$[6;7]$ |  11.2$\pm$0.4$\pm$0.8$\pm$0.4 | $[6;8]$ |  1.27$\pm$0.03$\pm$0.14$\pm$0.08$\pm$0.08 |  0.032$\pm$0.003$\pm$0.002$\pm$0.002
$[7;8]$ |  5.7$\pm$0.3$\pm$0.4$\pm$0.2 |  |  |
$[8;10]$ |  2.3$\pm$0.1$\pm$0.2$\pm$0.1 |  |  |
$[10;15]$ |  0.33$\pm$0.03$\pm$0.03$\pm$0.01 |  |  |
$-1.37<y_{\rm cms}<0.43$ (${\rm e}^{+}{\rm e}^{-}$)
$[0;1.3]$ |  158$\pm$33$\pm$17$\pm$6 | $[0;1.3]$ |  0.81$\pm$0.17$\pm$0.10$\pm$0.14 |  0.94$\pm$0.07$\pm$0.16
$[1.3;3]$ |  211$\pm$33$\pm$26$\pm$8 | $[1.3;3]$ |  0.64$\pm$0.10$\pm$0.09$\pm$0.11 |  1.60$\pm$0.08$\pm$0.26
$[3;5]$ |  126$\pm$15$\pm$9$\pm$5 | $[3;5]$ |  0.77$\pm$0.09$\pm$0.07$\pm$0.13 |  0.79$\pm$0.05$\pm$0.13
$[5;7]$ |  43.4$\pm$6.5$\pm$3.4$\pm$1.7 | $[5;7]$ |  0.89$\pm$0.13$\pm$0.13$\pm$0.15 |  0.23$\pm$0.03$\pm$0.04
$[7;10]$ |  10.2$\pm$2.4$\pm$1.0$\pm$0.4 | $[7;10]$ |  0.89$\pm$0.21$\pm$0.16$\pm$0.15 |  0.06$\pm$0.01$\pm$0.01
$2.03<y_{\rm cms}<3.53$ ($\mu^{+}\mu^{-}$)
$[0;1]$ |  78.8$\pm$1.5$\pm$4.6$\pm$3.1 | $[0;1]$ |  0.61$\pm$0.01$\pm$0.05$\pm$0.02$\pm$0.04 |  0.624$\pm$0.036$\pm$0.025$\pm$0.032
$[1;2]$ |  158.4$\pm$2.2$\pm$9.0$\pm$6.2 | $[1;2]$ |  0.64$\pm$0.01$\pm$0.05$\pm$0.02$\pm$0.04 |  1.197$\pm$0.064$\pm$0.046$\pm$0.062
$[2;3]$ |  138.2$\pm$1.9$\pm$7.9$\pm$5.4 | $[2;3]$ |  0.68$\pm$0.01$\pm$0.05$\pm$0.03$\pm$0.04 |  0.980$\pm$0.051$\pm$0.039$\pm$0.051
$[3;4]$ |  91.3$\pm$1.4$\pm$5.0$\pm$3.6 | $[3;4]$ |  0.76$\pm$0.01$\pm$0.06$\pm$0.03$\pm$0.05 |  0.579$\pm$0.032$\pm$0.022$\pm$0.030
$[4;5]$ |  53.0$\pm$0.9$\pm$2.8$\pm$2.1 | $[4;5]$ |  0.87$\pm$0.02$\pm$0.07$\pm$0.02$\pm$0.06 |  0.294$\pm$0.017$\pm$0.008$\pm$0.015
$[5;6]$ |  29.7$\pm$0.6$\pm$1.6$\pm$1.1 | $[5;6]$ |  0.91$\pm$0.02$\pm$0.08$\pm$0.03$\pm$0.06 |  0.156$\pm$0.011$\pm$0.005$\pm$0.008
$[6;7]$ |  14.9$\pm$0.4$\pm$0.8$\pm$0.6 | $[6;8]$ |  0.98$\pm$0.02$\pm$0.09$\pm$0.05$\pm$0.06 |  0.057$\pm$0.005$\pm$0.003$\pm$0.003
$[7;8]$ |  8.3$\pm$0.3$\pm$0.5$\pm$0.3 |  |  |
$[8;10]$ |  3.7$\pm$0.1$\pm$0.2$\pm$0.1 |  |  |
$[10;15]$ |  0.77$\pm$0.03$\pm$0.05$\pm$0.03 |  |  |
Table 2:  Summary of the results on the inclusive J/$\psi$ differential cross
sections and nuclear modification factors for p–Pb collisions. The results of
the cross section interpolation for pp collisions are also shown. For p–Pb
cross section results, the first quoted uncertainty is statistical. The
following uncertainties are systematic, the second one being $p_{\rm
T}$-uncorrelated and the third one $p_{\rm T}$-correlated. For $R_{\rm pPb}$
the first quoted uncertainty is statistical. The following uncertainties are
systematic, the second one being $p_{\rm T}$-uncorrelated. For dielectron
results the third uncertainty is $p_{\rm T}$-correlated, while for dimuon
results the third uncertainty is partially $p_{\rm T}$-correlated and the
fourth is $p_{\rm T}$-correlated. For the results on the interpolated pp cross
section, the first quoted uncertainty combines statistical and $p_{\rm
T}$-uncorrelated systematic uncertainties. For dielectron results the second
uncertainty is $p_{\rm T}$-correlated systematic, while for dimuon results the
second uncertainty is partially $p_{\rm T}$-correlated, and the third is
$p_{\rm T}$-correlated.

For the dielectron analysis, the $p_{\rm T}$-integrated cross section was also
determined, obtaining

${\rm d}\sigma^{{\rm J}/\psi}_{\rm pPb}/{\rm d}y(-1.37<y_{\rm
cms}<0.43)=909\pm 78({\rm stat.})\pm 71({\rm syst.})\mu{\rm b}.$

The corresponding $p_{{\mathrm{T}}}$-integrated cross sections for the dimuon
analysis were published in [8].

Figure 4: $p_{{\mathrm{T}}}$-differential inclusive J/$\psi$ cross sections
for the various rapidity regions under study. The vertical error bars
correspond to the statistical uncertainties, while open boxes represent the
uncorrelated systematic uncertainties and the shaded boxes the quadratic sum
of the fully and partially correlated ones. The numerical values can be read
in Table 2. The horizontal bars correspond to the widths of the $p_{\rm T}$
bins.

Starting from the $p_{{\mathrm{T}}}$-differential J/$\psi$ cross sections it
is possible to evaluate, as additional information, the mean $p_{\rm T}$
($\langle p_{{\mathrm{T}}}\rangle$) for the various $y$-ranges, by means of
fits based on the empirical function:

$\frac{{\rm d}^{2}\sigma^{\rm J/\psi}_{\rm pPb}}{{\rm d}y{\rm d}p_{\rm
T}}=C\times\frac{p_{\rm T}}{\left[1+\left(\frac{p_{\rm
T}}{p_{0}}\right)^{2}\right]^{n}}$ (2)

where $C$, $p_{0}$ and $n$ are free parameters. The quality of the fits is
satisfactory ($\chi^{2}/{\rm ndf}\sim 1$) and the resulting $\langle
p_{{\mathrm{T}}}\rangle$ values, computed for the measured $p_{{\mathrm{T}}}$
ranges, are

$\displaystyle\langle p_{{\mathrm{T}}}\rangle(-4.46<y_{\rm cms}<-2.96)=2.47\pm
0.01{\rm(stat.)}\pm 0.03{\rm(syst.)}\,{\rm GeV}/c$ $\displaystyle\langle
p_{{\mathrm{T}}}\rangle(-1.37<y_{\rm cms}<0.43)=2.86\pm 0.15{\rm(stat.)}\pm
0.10{\rm(syst.)}\,{\rm GeV}/c$ $\displaystyle\langle
p_{{\mathrm{T}}}\rangle(2.03<y_{\rm cms}<3.53)=2.77\pm 0.01{\rm(stat.)}\pm
0.03{\rm(syst.)}\,{\rm GeV}/c$

The quoted uncertainties were obtained by performing fits including only
statistical (or uncorrelated systematic) uncertainties on differential cross
sections.

In order to perform a meaningful comparison of $\langle
p_{{\mathrm{T}}}\rangle$ results in the dielectron and dimuon analysis, the
values from the dimuon analysis have also been extracted, with the same
procedure detailed above, in the range $p_{\rm T}<10$ GeV/$c$, obtaining
results which are smaller by less than 2% with respect to the full
$p_{{\mathrm{T}}}$ range. It is found that $\langle p_{{\mathrm{T}}}\rangle$
is larger at central rapidity. Furthermore, the $\langle
p_{{\mathrm{T}}}\rangle$ measured at forward $y_{\rm cms}$ is significantly
larger than at backward $y_{\rm cms}$. This difference, which could be partly
due to the slightly different $|y|$-coverage, persists when $\langle
p_{{\mathrm{T}}}\rangle$ is calculated in the $|y_{\rm cms}|$ region common to
p–Pb and Pb–p ($2.96<|y_{\rm cms}|<3.53$). The values obtained in this case
are $2.58\pm 0.02{\rm(stat.)}\pm 0.04{\rm(syst.)}\,{\rm GeV}/c$ and $2.69\pm
0.02{\rm(stat.)}\pm 0.03{\rm(syst.)}\,{\rm GeV}/c$, respectively at backward
and forward $y_{\rm cms}$, and differ by $\sim 2\sigma$.

The J/$\psi$ nuclear modification factor $R_{\mathrm{pPb}}$ is obtained as the
ratio of the differential cross sections between proton-nucleus and proton-
proton collisions, normalized to $A_{\rm Pb}$:

$R_{\rm pPb}(y,p_{\rm T})=\frac{{\rm d}^{2}\sigma^{\rm J/\psi}_{\rm pPb}/{\rm
d}y{\rm d}p_{\rm T}}{A_{\rm Pb}\cdot{\rm d}^{2}\sigma^{\rm J/\psi}_{\rm
pp}/{\rm d}y{\rm d}p_{\rm T}}$ (3)

Since no pp data are available at $\sqrt{s}=5.02$ TeV, the ${\rm
d}^{2}\sigma^{\rm J/\psi}_{\rm pp}/{\rm d}y{\rm d}p_{\rm T}$ reference cross
sections were obtained by means of an interpolation/extrapolation procedure.
For the dielectron analysis, the starting point of the interpolation procedure
is the determination of $\textrm{d}\sigma/\textrm{d}y$ for inclusive J/$\psi$
in pp collisions at $y_{\rm cms}\sim 0$ and $\sqrt{s}=5.02$ TeV, carried out
as for the analysis described in [30]. Available mid-rapidity data at
$\sqrt{s}$ = 0.2[54], 1.96[55], 2.76[56] and 7 TeV[44] are interpolated using
several empirical functions (exponential, logarithmic and power-law, covering
in this way the various possibilities for the curvature of the
$\sqrt{s}$-dependence) obtaining $\textrm{d}\sigma/\textrm{d}y=6.19\pm 1.03$
$\mu$b. Even if the $y_{\rm cms}$ range covered in this analysis is shifted by
0.465 units with respect to mid-rapidity, the rapidity-dependence of the cross
section is negligible compared to the uncertainty on the interpolation
procedure. Then, a method similar to the one in [57] is applied to derive the
$p_{{\mathrm{T}}}$-differential cross section. It is based on the empirical
observation that pp and p$\overline{\textrm{p}}$ results on differential
spectra obtained at various collision energies and in different rapidity
ranges [44, 55, 54, 58, 48] exhibit scaling properties when plotted as a
function of $p_{{\mathrm{T}}}$/$\langle p_{{\mathrm{T}}}\rangle$. The
normalized spectra, with the statistical and the bin-by-bin uncorrelated
systematic uncertainties added in quadrature, can be fitted with a one-
parameter function described in [57]. The $p_{{\mathrm{T}}}$-differential
cross sections at mid-rapidity and $\sqrt{s}=5.02$ TeV can then be obtained by
rescaling the fitted universal distribution using the previously estimated
$\textrm{d}\sigma/\textrm{d}y$ and its corresponding $\langle
p_{{\mathrm{T}}}\rangle$. The latter value is obtained by an interpolation of
the energy-dependence of $\langle p_{{\mathrm{T}}}\rangle$ values evaluated
fitting the available experimental mid-rapidity results [44, 55, 54] with
exponential, logarithmic and power-law functions. One obtains in this way, in
the range $p_{\rm T}<10$ GeV/$c$, $\langle p_{{\mathrm{T}}}\rangle=2.81\pm
0.10$ GeV/$c$ as an average of the results calculated with the various
empirical functions. As outlined above for $\textrm{d}\sigma/\textrm{d}y$, the
0.465 $y$-unit shift of the data with respect to mid-rapidity has a negligible
effect also on $\langle p_{{\mathrm{T}}}\rangle$.

For the dimuon analysis, thanks to the smaller uncertainties with respect to
mid-rapidity results, an approach equivalent to that described in [59],
exclusively based on the ALICE data collected at $\sqrt{s}=2.76$ TeV [56] and
7 TeV [60] in $2.5<y_{\rm cms}<4$, $p_{\rm T}<8$ GeV/$c$ has been used. The
reference cross sections are obtained with a two-step procedure, corresponding
to an energy interpolation followed by a rapidity extrapolation. In the first
step, for each $p_{{\mathrm{T}}}$ bin, the ${\rm d}^{2}\sigma^{\rm
J/\psi}_{\rm pp}/{\rm d}y{\rm d}p_{\rm T}$ values at $\sqrt{s}=2.76$ and 7 TeV
are interpolated, using three different empirical functions (linear, power-law
and exponential) to estimate the cross section values at $\sqrt{s}=5.02$ TeV.
The central values are calculated as the average of the results obtained with
the three functions, while the associated uncertainties come from the
experimental uncertainties on the points used for the interpolation, added in
quadrature to a contribution chosen as the maximum spread of the results from
the different interpolating functions. In the second step, this result is
extrapolated from $2.5<y_{\rm cms}<4$ to the p–Pb and Pb–p $y_{\rm cms}$
ranges, using the scaling factors for the $p_{{\mathrm{T}}}$-integrated cross
sections computed in [59]. Finally, since the LHCb Collaboration has shown
that the J/$\psi$ $p_{{\mathrm{T}}}$ distributions slightly depend on $y_{\rm
cms}$ [48] in the rapidity range covered in the dimuon analysis, a
$p_{{\mathrm{T}}}$-dependent correction tuned on these data (10% maximum at
large $p_{{\mathrm{T}}}$) is applied.

The inclusive J/$\psi$ nuclear modification factor is shown in Fig. 5 for the
three rapidity regions under study. The numerical values of $R_{\rm pPb}$, as
well as the results of the interpolation procedure for the estimate of the pp
cross sections, can be found in Table 2. For the dimuon analysis, the
evaluation of $R_{\rm pPb}$ is restricted to $p_{\rm T}<8$ GeV/$c$, the region
covered by the pp measurements used in the evaluation of the reference cross
sections.

Figure 5: The J/$\psi$ nuclear modification factor as a function of
$p_{{\mathrm{T}}}$ at backward (top), mid (center) and forward (bottom)
rapidities. Statistical uncertainties are represented by vertical error bars,
while open boxes correspond to uncorrelated uncertainties and the shaded areas
to uncertainties partially correlated in $p_{{\mathrm{T}}}$. The boxes around
$R_{\rm pPb}=1$ show the size of the correlated uncertainties. The horizontal
bars correspond to the widths of the $p_{{\mathrm{T}}}$ bins. Results from
various models are also shown, including a pure shadowing calculation [33]
based on the EPS09 parameterization, a CGC-inspired model [36], and the
results of the coherent energy loss calculation [35], with or without the
inclusion of an EPS09 shadowing contribution.

The sources of systematic uncertainties on $R_{\rm pPb}$ and their values are
summarized in Table 1. The terms related to the pp reference cross sections
contribute to uncorrelated, partially or fully correlated uncertainties on
$R_{\rm pPb}$, depending on their origin. In particular, for the dimuon
analysis: (i) the statistical and $p_{{\mathrm{T}}}$-uncorrelated systematic
uncertainties on the $\sqrt{s}=2.76$ and 7 TeV pp data contribute to the
uncorrelated uncertainty; (ii) the spread of the results obtained with various
interpolating/extrapolating functions in $\sqrt{s}$ and $y_{\rm cms}$
contribute to the partially correlated uncertainty; (iii) the
$\sqrt{s}$-correlated uncertainties between the $\sqrt{s}=2.76$ and 7 TeV pp
data contribute to the correlated uncertainty. At forward and mid-rapidity the
J/$\psi$ $R_{\mathrm{pPb}}$ shows a clear suppression at low
$p_{{\mathrm{T}}}$, vanishing at high $p_{{\mathrm{T}}}$. At backward rapidity
no suppression is present, within uncertainties.

For the dielectron analysis, the $p_{\rm T}$-integrated nuclear modification
factor was also calculated, carrying out the signal extraction procedure on
the $p_{\rm T}$-integrated invariant mass spectrum. The obtained value

$R_{\rm pPb}=0.71\pm 0.06({\rm stat.})\pm 0.13({\rm syst.})$

is consistent with the forward rapidity ($2.03<y_{cms}<3.53$) dimuon result,
and smaller than the backward one ($-4.46<y_{cms}<-2.96$) by $\sim 2\sigma$
[8].

In Fig. 5 predictions from various models are compared to the data. A
calculation based on the next-to-leading order (NLO) Color Evaporation Model
(CEM) for the prompt J/$\psi$ production and the EPS09 shadowing
parametrization [33] reproduces within uncertainties the
$p_{{\mathrm{T}}}$-dependence and the amplitude of the suppression for
$p_{{\mathrm{T}}}$ $>1.5$ GeV/$c$ in the three rapidity regions under study.
The theoretical uncertainties arise from the uncertainties on EPS09 as well as
on the values of charm quark mass and of the renormalization and factorization
scales used for the cross section calculation. Data are also compared to two
calculations based on a parametrization of experimental results on prompt
J/$\psi$ production in pp collisions and including the effects of coherent
energy loss [35] in the cold nuclear medium. One of the calculations includes
only coherent energy loss, while the other combines coherent energy loss with
EPS09 shadowing. The uncertainty bands include, for the coherent energy loss
mechanism, a variation of both the $q_{\rm 0}$ parameter (gluon transport
coefficient evaluated at $x=0.01$) and the parametrization of the production
cross section. At forward rapidity the pure energy loss scenario predicts a
much steeper $p_{{\mathrm{T}}}$-dependence, while better agreement is found
when the EPS09 contribution is included. However, at low $p_{{\mathrm{T}}}$, a
discrepancy between data and both calculations is observed. Also at mid-
rapidity the coherent energy loss model including the EPS09 contribution
better describes the data, although the larger uncertainties prevent a firm
conclusion. The same features can be observed at backward rapidity, where the
calculation including coherent energy loss and shadowing agrees with the data
in showing weak nuclear effects on J/$\psi$ production. Finally, the results
at central and forward rapidities are compared with a prediction based on the
CGC framework and using CEM for the prompt J/$\psi$ production [36]. In the
backward rapidity region, higher gluon $x$ in the nucleus are probed and the
CGC model is out of its range of applicability. The quoted uncertainties are
related to the choices of $Q^{2}_{s}$ and of the charm quark mass. While the
model is in fair agreement with mid-rapidity data, it clearly underpredicts
the J/$\psi$ $R_{\rm pPb}$ in the full $p_{{\mathrm{T}}}$ range at forward
rapidity.

The theoretical calculations discussed above are carried out for prompt
J/$\psi$ (i.e., direct J/$\psi$ and the contribution from $\chi_{c}$ and
$\psi$(2S) decays), while the measurements are for inclusive J/$\psi$ which
include a non-prompt contribution from B-hadron decays. The contribution of
the latter source to $R^{\rm incl}_{\rm pPb}$ can be evaluated from the
measured fraction $f_{B}$ of non-prompt to prompt J/$\psi$ production in pp
collisions and on the suppression $R^{\rm non-prompt}_{\rm pPb}$ of non-prompt
J/$\psi$ in p–Pb collisions. More in detail, in the range $2<y_{\rm cms}<4.5$,
the fraction $f_{B}$ measured by LHCb in pp collisions at $\sqrt{s}=7$ TeV,
increases from 0.08 to 0.22 from $p_{\rm T}=0$ to 8 GeV/$c$ [48]. This
quantity has a small variation within the $y_{\rm cms}$ range covered and is
also not strongly $\sqrt{s}$-dependent (similar values are obtained for
$\sqrt{s}=8$ TeV [58]). At mid-rapidity, $f_{\rm B}$ was measured by ALICE in
pp collisions at $\sqrt{s}=7$ TeV and ranges from 0.10 to 0.44 for
$p_{{\mathrm{T}}}$ increasing from 1.3 to 10 GeV/$c$ [61]. $R^{\rm non-
prompt}_{\rm pPb}$ was measured at $\sqrt{s_{\rm NN}}=5.02$ TeV by LHCb,
integrated over $p_{{\mathrm{T}}}$, obtaining 0.83 $\pm$ 0.02 $\pm$ 0.08 for
$2.5<y_{\rm cms}<4$ and 0.98 $\pm$ 0.06 $\pm$ 0.10 for $-4<y_{\rm cms}<-2.5$
[9]. Assuming for each $p_{{\mathrm{T}}}$-bin a variation of $R^{\rm non-
prompt}_{\rm pPb}$ between 0.6 and 1.3, a conservative choice due to the
unavailability of a $p_{{\mathrm{T}}}$-differential result, and considering
the $p_{{\mathrm{T}}}$-dependence of $f_{B}$ at $\sqrt{s}=7$ TeV, one can
extract $R^{\rm prompt}_{\rm pPb}$ as $R^{\rm prompt}_{\rm pPb}=R^{\rm
incl}_{\rm pPb}+f_{\rm B}\cdot(R^{\rm incl}_{\rm pPb}-R^{\rm non-prompt}_{\rm
pPb})$. The maximum differences between the inclusive and prompt
$R_{\mathrm{pPb}}$ obtained in this way are, for low and high
$p_{{\mathrm{T}}}$: (i) 3 and 10% at backward rapidity; (ii) 11 and 16% at
central rapidity; (iii) 10 and 8% at forward rapidity. These variations are,
at most, of the same order of magnitude as the quoted uncertainties on
inclusive $R_{\mathrm{pPb}}$.

The $R_{\mathrm{pPb}}$ results shown in this paper can be considered as a
valuable tool to improve our understanding of the contribution of CNM to the
suppression of the J/$\psi$ yields observed in Pb–Pb [30, 31]. Indeed, as
verified in [8] for the dimuon analysis, in Pb–Pb collisions the Bjorken-$x$
ranges probed by the J/$\psi$ production process in the two colliding nuclei,
assuming a $gg\rightarrow J/\psi$ (2$\rightarrow$1) [62] mechanism, are
shifted by only $\sim$10% with respect to the corresponding intervals for p–Pb
and Pb–p, despite the different energy ($\sqrt{s_{\rm NN}}$ = 2.76 TeV) and
the slightly different $y_{\rm cms}$ range ($2.5<y<4$) for Pb–Pb. A similar
conclusion holds at mid-rapidity, where the covered $x$-intervals, calculated
for $p_{\rm T}=\langle p_{\rm T}\rangle$, are $6.1\times 10^{-4}<x<3.0\times
10^{-3}$ and $7.0\times 10^{-4}<x<3.5\times 10^{-3}$ for p–Pb and Pb–Pb
collisions, respectively. Under the assumption that shadowing is the main CNM-
related mechanism that plays a role in the J/$\psi$ production and that its
effect on the two colliding nuclei in Pb–Pb collisions can be factorized, the
product $R_{\mathrm{pPb}}\times R_{\mathrm{Pbp}}$ ($R^{2}_{\mathrm{pPb}}$) can
be considered as an estimate of CNM effects in Pb–Pb collisions at forward
(central) rapidity [63, 64]. This conclusion holds not only for the
$2\rightarrow 1$ production process but also when the more general
$2\rightarrow 2$ mechanism ($gg\rightarrow J/\psi g$) is considered.

In Fig. 6 the comparison of the measured $R_{\rm PbPb}$ with the quantities
defined above is carried out. Such a comparison should be considered as
qualitative, in view of the slight $x$-mismatch detailed above and of the fact
that, at mid-rapidity, the centrality ranges probed in p–Pb and Pb–Pb are not
the same (0-100% and 0-50%, respectively). In both rapidity regions, the
extrapolation of CNM effects shows a clear $p_{{\mathrm{T}}}$-dependence,
corresponding to a strong suppression at low $p_{{\mathrm{T}}}$, which
vanishes for large transverse momenta. At low $p_{{\mathrm{T}}}$ and central
rapidity, there might be an indication for a Pb–Pb suppression smaller than
the CNM extrapolation, consistent with the presence of a contribution related
to the (re)combination of $c\bar{c}$ pairs [30], taking place in the hot
medium. A similar effect can be seen at forward rapidity. At large
$p_{{\mathrm{T}}}$ and forward rapidity, the observed suppression in Pb–Pb
collisions is much larger than CNM extrapolations, showing that, in this
transverse-momentum region, suppression effects in hot matter, possibly
related to color screening, become dominant.

Figure 6:  The estimate of the $p_{{\mathrm{T}}}$-dependence of CNM effects in
Pb–Pb, calculated as $R^{2}_{\mathrm{pPb}}$ for mid-rapidity data (top) and as
$R_{\mathrm{pPb}}\times R_{\mathrm{Pbp}}$ (bottom) at forward rapidity. The
quantities are compared to $R_{\mathrm{PbPb}}$ measured in Pb–Pb collisions in
the (approximately) corresponding $y$-ranges [30, 31]. The vertical error bars
correspond to the statistical uncertainties, the open boxes (shaded areas)
represent $p_{\rm T}$-uncorrelated (partially correlated) systematic
uncertainties, while the boxes around $R_{\rm pPb}=1$ show the size of the
correlated uncertainties. The horizontal bars correspond to the widths of the
$p_{{\mathrm{T}}}$ bins. The Pb–Pb points in the bottom panel were slightly
displaced in $p_{{\mathrm{T}}}$, to improve visibility.

Finally, a more direct comparison of Pb–Pb results with the CNM extrapolation
can be obtained by defining the ratio $S_{\rm
J/\psi}=R_{\mathrm{PbPb}}/(R_{\mathrm{pPb}}\times R_{\mathrm{Pbp}})$. Such a
quantity, for forward rapidity results, is shown in Fig. 7 and confirms the
main features detailed above, i.e., a strong suppression of J/$\psi$ at large
$p_{{\mathrm{T}}}$, and a hint for an enhancement at low $p_{{\mathrm{T}}}$.
At central rapidity, due to the sizeable uncertainties on both p–Pb and Pb–Pb
results, only the $p_{\rm T}$-integrated ratio can be obtained. Using the
$R_{\rm PbPb}$ in the 0-90% centrality range [30], and the integrated $R_{\rm
pPb}$ given above, one gets $1.43\pm 0.26{\rm(stat)}\pm 0.56{\rm(syst)}$. More
precise measurements are needed to draw a firm conclusion in this rapidity
range.

Figure 7: The ratio between $R_{\mathrm{PbPb}}$ for inclusive J/$\psi$ at
forward rapidity and the product $R_{\mathrm{pPb}}\times R_{\mathrm{Pbp}}$ of
the nuclear modification factors at forward and backward rapidity. None of the
uncertainties cancels out in the ratio. Statistical uncertainties are shown as
vertical error bars, while the boxes around the points represent a quadratic
combination of uncorrelated and partially correlated systematic uncertainties.
The box around $S_{\rm J/\psi}=1$ corresponds to correlated uncertainties. The
horizontal bars coincide with the widths of the $p_{{\mathrm{T}}}$ bins.

In summary, we have presented results on the inclusive J/$\psi$ production in
p–Pb collisions at $\sqrt{s_{\rm NN}}=5.02$ TeV. The
$p_{{\mathrm{T}}}$-differential cross sections, the $\langle p_{\rm T}\rangle$
and the nuclear modification factors have been evaluated in three rapidity
regions: $-4.46<y_{\rm cms}<-2.96$, $-1.37<y_{\rm cms}<0.43$ and $2.03<y_{\rm
cms}<3.53$. At forward and mid-rapidity a significant suppression is observed
at low $p_{{\mathrm{T}}}$, with a vanishing trend at high $p_{{\mathrm{T}}}$.
At backward rapidity no significant suppression or enhancement is visible.
Comparisons with theoretical models based on a combination of nuclear
shadowing and coherent energy loss effects provide a fair description of the
observed patterns, except at forward rapidity and low transverse momentum.
These results can be used to provide a qualitative estimate of the influence
of cold nuclear matter effects on the J/$\psi$ suppression observed in Pb–Pb
collisions. Under the assumption that shadowing represents the main CNM
contribution, we find that it cannot account for the observed suppression in
Pb–Pb at high $p_{{\mathrm{T}}}$. At low $p_{{\mathrm{T}}}$, the observed CNM
effects alone may suggest a suppression larger than that observed in Pb–Pb,
which is consistent with the presence of a charm quark (re)combination
component to the J/$\psi$ production in nucleus-nucleus collisions.

## Acknowledgements

The ALICE Collaboration would like to thank all its engineers and technicians
for their invaluable contributions to the construction of the experiment and
the CERN accelerator teams for the outstanding performance of the LHC complex.
The ALICE Collaboration gratefully acknowledges the resources and support
provided by all Grid centres and the Worldwide LHC Computing Grid (WLCG)
collaboration. The ALICE Collaboration acknowledges the following funding
agencies for their support in building and running the ALICE detector: State
Committee of Science, World Federation of Scientists (WFS) and Swiss Fonds
Kidagan, Armenia, 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); National Natural Science
Foundation of China (NSFC), the Chinese Ministry of Education (CMOE) and the
Ministry of Science and Technology of China (MSTC); Ministry of Education and
Youth of the Czech Republic; Danish Natural Science Research Council, the
Carlsberg Foundation and the Danish National Research Foundation; The European
Research Council under the European Community’s Seventh Framework Programme;
Helsinki Institute of Physics and the Academy of Finland; French CNRS-IN2P3,
the ‘Region Pays de Loire’, ‘Region Alsace’, ‘Region Auvergne’ and CEA,
France; German Bundesministerium fur Bildung, Wissenschaft, Forschung und
Technologie (BMBF) and the Helmholtz Association; General Secretariat for
Research and Technology, Ministry of Development, Greece; Hungarian Orszagos
Tudomanyos Kutatasi Alappgrammok (OTKA) and National Office for Research and
Technology (NKTH); Department of Atomic Energy and Department of Science and
Technology of the Government of India; Istituto Nazionale di Fisica Nucleare
(INFN) and Centro Fermi - Museo Storico della Fisica e Centro Studi e Ricerche
”Enrico Fermi”, Italy; MEXT Grant-in-Aid for Specially Promoted Research,
Japan; Joint Institute for Nuclear Research, Dubna; National Research
Foundation of Korea (NRF); Consejo Nacional de Cienca y Tecnologia (CONACYT),
Direccion General de Asuntos del Personal Academico(DGAPA), México, :Amerique
Latine Formation academique – European Commission(ALFA-EC) and the EPLANET
Program (European Particle Physics Latin American Network) Stichting voor
Fundamenteel Onderzoek der Materie (FOM) and the Nederlandse Organisatie voor
Wetenschappelijk Onderzoek (NWO), Netherlands; Research Council of Norway
(NFR); National Science Centre, Poland; Ministry of National
Education/Institute for Atomic Physics and Consiliul Naţional al Cercetării
Ştiinţifice - Executive Agency for Higher Education Research Development and
Innovation Funding (CNCS-UEFISCDI) - Romania; Ministry of Education and
Science of Russian Federation, Russian Academy of Sciences, Russian Federal
Agency of Atomic Energy, Russian Federal Agency for Science and Innovations
and The Russian Foundation for Basic Research; Ministry of Education of
Slovakia; Department of Science and Technology, South Africa; Centro de
Investigaciones Energeticas, Medioambientales y Tecnologicas (CIEMAT),
E-Infrastructure shared between Europe and Latin America (EELA), Ministerio de
Economía y Competitividad (MINECO) of Spain, Xunta de Galicia (Consellería de
Educación), Centro de Aplicaciones Tecnológicas y Desarrollo Nuclear (CEADEN),
Cubaenergía, Cuba, and IAEA (International Atomic Energy Agency); Swedish
Research Council (VR) and Knut $\&$ Alice Wallenberg Foundation (KAW); Ukraine
Ministry of Education and Science; United Kingdom Science and Technology
Facilities Council (STFC); The United States Department of Energy, the United
States National Science Foundation, the State of Texas, and the State of Ohio;
Ministry of Science, Education and Sports of Croatia and Unity through
Knowledge Fund, Croatia. Council of Scientific and Industrial Research (CSIR),
New Delhi, India

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##  Appendix A The ALICE Collaboration

J. Adamorg39&D. Adamováorg82&M.M. Aggarwalorg86&G. Aglieri Rinellaorg36&M.
Agnelloorg110&N. Agrawalorg47&Z. Ahammedorg130&I. Ahmedorg16&S.U. Ahnorg67&I.
Aimoorg93,org110&S. Aiolaorg135&M. Ajazorg16&A. Akindinovorg57&S.N.
Alamorg130&D. Aleksandrovorg99&B. Alessandroorg110&D. Alexandreorg101&R.
Alfaro Molinaorg63&A. Aliciorg104,org12&A. Alkinorg3&J. Almeorg37&T.
Altorg42&S. Altinpinarorg18&I. Altsybeevorg129&C. Alves Garcia Pradoorg118&C.
Andreiorg77&A. Andronicorg96&V. Anguelovorg92&J. Anielskiorg53&T.
Antičićorg97&F. Antinoriorg107&P. Antonioliorg104&L. Aphecetcheorg112&H.
Appelshäuserorg52&S. Arcelliorg28&N. Armestoorg17&R. Arnaldiorg110&T.
Aronssonorg135&I.C. Arseneorg22&M. Arslandokorg52&A. Augustinusorg36&R.
Averbeckorg96&M.D. Azmiorg19&M. Bachorg42&A. Badalàorg106&Y.W. Baekorg43&S.
Bagnascoorg110&R. Bailhacheorg52&R. Balaorg89&A. Baldisseriorg15&F. Baltasar
Dos Santos Pedrosaorg36&R.C. Baralorg60&A.M. Barbanoorg110&R. Barberaorg29&F.
Barileorg33&G.G. Barnaföldiorg134&L.S. Barnbyorg101&V. Barretorg69&P.
Bartaliniorg7&J. Bartkeorg115&E. Bartschorg52&M. Basileorg28&N. Bastidorg69&S.
Basuorg130&B. Bathenorg53&G. Batigneorg112&A. Batista Camejoorg69&B.
Batyunyaorg65&P.C. Batzingorg22&I.G. Beardenorg79&H. Beckorg52&C.
Beddaorg110&N.K. Beheraorg47&I. Belikovorg54&F. Belliniorg28&H. Bello
Martinezorg2&R. Bellwiedorg120&R. Belmontorg133&E. Belmont-Morenoorg63&V.
Belyaevorg75&G. Bencediorg134&S. Beoleorg27&I. Berceanuorg77&A.
Bercuciorg77&Y. Berdnikovorg84&D. Berenyiorg134&R.A. Bertensorg56&D.
Berzanoorg36,org27&L. Betevorg36&A. Bhasinorg89&I.R. Bhatorg89&A.K.
Bhatiorg86&B. Bhattacharjeeorg44&J. Bhomorg126&L. Bianchiorg27,org120&N.
Bianchiorg71&C. Bianchinorg133,org56&J. Bielčíkorg39&J. Bielčíkováorg82&A.
Bilandzicorg79&S. Biswasorg78&S. Bjelogrlicorg56&F. Blancoorg10&D.
Blauorg99&C. Blumeorg52&F. Bockorg73,org92&A. Bogdanovorg75&H. Bøggildorg79&L.
Boldizsárorg134&M. Bombaraorg40&J. Bookorg52&H. Borelorg15&A. Borissovorg95&M.
Borriorg81&F. Bossúorg64&M. Botjeorg80&E. Bottaorg27&S. Böttgerorg51&P. Braun-
Munzingerorg96&M. Bregantorg118&T. Breitnerorg51&T.A. Brokerorg52&T.A.
Browningorg94&M. Brozorg39&E.J. Bruckenorg45&E. Brunaorg110&G.E. Brunoorg33&D.
Budnikovorg98&H. Bueschingorg52&S. Bufalinoorg36,org110&P. Buncicorg36&O.
Buschorg92&Z. Butheleziorg64&J.T. Buxtonorg20&D. Caffarriorg36,org30&X.
Caiorg7&H. Cainesorg135&L. Calero Diazorg71&A. Calivaorg56&E. Calvo
Villarorg102&P. Cameriniorg26&F. Carenaorg36&W. Carenaorg36&J. Castillo
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Chochulaorg36&K. Choiorg95&M. Chojnackiorg79&S. Choudhuryorg130&P.
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Chujoorg126&S.U. Chungorg95&C. Cicaloorg105&L. Cifarelliorg12,org28&F.
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Colluorg25&M. Colocciorg28&G. Conesa Balbastreorg70&Z. Conesa del
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Corrales Moralesorg27&I. Cortés Maldonadoorg2&P. Corteseorg32&M.R.
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Cuautleorg62&L. Cunqueiroorg36&T. Dahmsorg91&A. Daineseorg107&A. Danuorg61&D.
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Deorg118&A. De Caroorg31,org12&G. de Cataldoorg103&J. de Cuvelandorg42&A. De
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Deistingorg96,org92&A. Delofforg76&E. Dénesorg134&G. D’Erasmoorg33&D. Di
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Dietelorg88&P. Dillensegerorg52&R. Diviàorg36&Ø. Djuvslandorg18&A.
Dobrinorg56,org80&T. Dobrowolskiorg760&D. Domenicis Gimenezorg118&B.
Dönigusorg52&O. Dordicorg22&A.K. Dubeyorg130&A. Dublaorg56&L. Ducrouxorg128&P.
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## Affiliation notes

0Deceased

## Collaboration Institutes

org1A.I. Alikhanyan National Science Laboratory (Yerevan Physics Institute)
Foundation, Yerevan, Armenia

org2Benemérita Universidad Autónoma de Puebla, Puebla, Mexico

org3Bogolyubov Institute for Theoretical Physics, Kiev, Ukraine

org4Bose Institute, Department of Physics and Centre for Astroparticle Physics
and Space Science (CAPSS), Kolkata, India

org5Budker Institute for Nuclear Physics, Novosibirsk, Russia

org6California Polytechnic State University, San Luis Obispo, California,
United States

org7Central China Normal University, Wuhan, China

org8Centre de Calcul de l’IN2P3, Villeurbanne, France

org9Centro de Aplicaciones Tecnológicas y Desarrollo Nuclear (CEADEN), Havana,
Cuba

org10Centro de Investigaciones Energéticas Medioambientales y Tecnológicas
(CIEMAT), Madrid, Spain

org11Centro de Investigación y de Estudios Avanzados (CINVESTAV), Mexico City
and Mérida, Mexico

org12Centro Fermi - Museo Storico della Fisica e Centro Studi e Ricerche
“Enrico Fermi”, Rome, Italy

org13Chicago State University, Chicago, Illinois, USA

org14China Institute of Atomic Energy, Beijing, China

org15Commissariat à l’Energie Atomique, IRFU, Saclay, France

org16COMSATS Institute of Information Technology (CIIT), Islamabad, Pakistan

org17Departamento de Física de Partículas and IGFAE, Universidad de Santiago
de Compostela, Santiago de Compostela, Spain

org18Department of Physics and Technology, University of Bergen, Bergen,
Norway

org19Department of Physics, Aligarh Muslim University, Aligarh, India

org20Department of Physics, Ohio State University, Columbus, Ohio, United
States

org21Department of Physics, Sejong University, Seoul, South Korea

org22Department of Physics, University of Oslo, Oslo, Norway

org23Dipartimento di Elettrotecnica ed Elettronica del Politecnico, Bari,
Italy

org24Dipartimento di Fisica dell’Università ’La Sapienza’ and Sezione INFN
Rome, Italy

org25Dipartimento di Fisica dell’Università and Sezione INFN, Cagliari, Italy

org26Dipartimento di Fisica dell’Università and Sezione INFN, Trieste, Italy

org27Dipartimento di Fisica dell’Università and Sezione INFN, Turin, Italy

org28Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN,
Bologna, Italy

org29Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN,
Catania, Italy

org30Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN,
Padova, Italy

org31Dipartimento di Fisica ‘E.R. Caianiello’ dell’Università and Gruppo
Collegato INFN, Salerno, Italy

org32Dipartimento di Scienze e Innovazione Tecnologica dell’Università del
Piemonte Orientale and Gruppo Collegato INFN, Alessandria, Italy

org33Dipartimento Interateneo di Fisica ‘M. Merlin’ and Sezione INFN, Bari,
Italy

org34Division of Experimental High Energy Physics, University of Lund, Lund,
Sweden

org35Eberhard Karls Universität Tübingen, Tübingen, Germany

org36European Organization for Nuclear Research (CERN), Geneva, Switzerland

org37Faculty of Engineering, Bergen University College, Bergen, Norway

org38Faculty of Mathematics, Physics and Informatics, Comenius University,
Bratislava, Slovakia

org39Faculty of Nuclear Sciences and Physical Engineering, Czech Technical
University in Prague, Prague, Czech Republic

org40Faculty of Science, P.J. Šafárik University, Košice, Slovakia

org41Faculty of Technology, Buskerud and Vestfold University College,
Vestfold, Norway

org42Frankfurt Institute for Advanced Studies, Johann Wolfgang Goethe-
Universität Frankfurt, Frankfurt, Germany

org43Gangneung-Wonju National University, Gangneung, South Korea

org44Gauhati University, Department of Physics, Guwahati, India

org45Helsinki Institute of Physics (HIP), Helsinki, Finland

org46Hiroshima University, Hiroshima, Japan

org47Indian Institute of Technology Bombay (IIT), Mumbai, India

org48Indian Institute of Technology Indore, Indore (IITI), India

org49Inha University, Incheon, South Korea

org50Institut de Physique Nucléaire d’Orsay (IPNO), Université Paris-Sud,
CNRS-IN2P3, Orsay, France

org51Institut für Informatik, Johann Wolfgang Goethe-Universität Frankfurt,
Frankfurt, Germany

org52Institut für Kernphysik, Johann Wolfgang Goethe-Universität Frankfurt,
Frankfurt, Germany

org53Institut für Kernphysik, Westfälische Wilhelms-Universität Münster,
Münster, Germany

org54Institut Pluridisciplinaire Hubert Curien (IPHC), Université de
Strasbourg, CNRS-IN2P3, Strasbourg, France

org55Institute for Nuclear Research, Academy of Sciences, Moscow, Russia

org56Institute for Subatomic Physics of Utrecht University, Utrecht,
Netherlands

org57Institute for Theoretical and Experimental Physics, Moscow, Russia

org58Institute of Experimental Physics, Slovak Academy of Sciences, Košice,
Slovakia

org59Institute of Physics, Academy of Sciences of the Czech Republic, Prague,
Czech Republic

org60Institute of Physics, Bhubaneswar, India

org61Institute of Space Science (ISS), Bucharest, Romania

org62Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de México,
Mexico City, Mexico

org63Instituto de Física, Universidad Nacional Autónoma de México, Mexico
City, Mexico

org64iThemba LABS, National Research Foundation, Somerset West, South Africa

org65Joint Institute for Nuclear Research (JINR), Dubna, Russia

org66Konkuk University, Seoul, South Korea

org67Korea Institute of Science and Technology Information, Daejeon, South
Korea

org68KTO Karatay University, Konya, Turkey

org69Laboratoire de Physique Corpusculaire (LPC), Clermont Université,
Université Blaise Pascal, CNRS–IN2P3, Clermont-Ferrand, France

org70Laboratoire de Physique Subatomique et de Cosmologie, Université
Grenoble-Alpes, CNRS-IN2P3, Grenoble, France

org71Laboratori Nazionali di Frascati, INFN, Frascati, Italy

org72Laboratori Nazionali di Legnaro, INFN, Legnaro, Italy

org73Lawrence Berkeley National Laboratory, Berkeley, California, United
States

org74Lawrence Livermore National Laboratory, Livermore, California, United
States

org75Moscow Engineering Physics Institute, Moscow, Russia

org76National Centre for Nuclear Studies, Warsaw, Poland

org77National Institute for Physics and Nuclear Engineering, Bucharest,
Romania

org78National Institute of Science Education and Research, Bhubaneswar, India

org79Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark

org80Nikhef, National Institute for Subatomic Physics, Amsterdam, Netherlands

org81Nuclear Physics Group, STFC Daresbury Laboratory, Daresbury, United
Kingdom

org82Nuclear Physics Institute, Academy of Sciences of the Czech Republic, Řež
u Prahy, Czech Republic

org83Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States

org84Petersburg Nuclear Physics Institute, Gatchina, Russia

org85Physics Department, Creighton University, Omaha, Nebraska, United States

org86Physics Department, Panjab University, Chandigarh, India

org87Physics Department, University of Athens, Athens, Greece

org88Physics Department, University of Cape Town, Cape Town, South Africa

org89Physics Department, University of Jammu, Jammu, India

org90Physics Department, University of Rajasthan, Jaipur, India

org91Physik Department, Technische Universität München, Munich, Germany

org92Physikalisches Institut, Ruprecht-Karls-Universität Heidelberg,
Heidelberg, Germany

org93Politecnico di Torino, Turin, Italy

org94Purdue University, West Lafayette, Indiana, United States

org95Pusan National University, Pusan, South Korea

org96Research Division and ExtreMe Matter Institute EMMI, GSI Helmholtzzentrum
für Schwerionenforschung, Darmstadt, Germany

org97Rudjer Bošković Institute, Zagreb, Croatia

org98Russian Federal Nuclear Center (VNIIEF), Sarov, Russia

org99Russian Research Centre Kurchatov Institute, Moscow, Russia

org100Saha Institute of Nuclear Physics, Kolkata, India

org101School of Physics and Astronomy, University of Birmingham, Birmingham,
United Kingdom

org102Sección Física, Departamento de Ciencias, Pontificia Universidad
Católica del Perú, Lima, Peru

org103Sezione INFN, Bari, Italy

org104Sezione INFN, Bologna, Italy

org105Sezione INFN, Cagliari, Italy

org106Sezione INFN, Catania, Italy

org107Sezione INFN, Padova, Italy

org108Sezione INFN, Rome, Italy

org109Sezione INFN, Trieste, Italy

org110Sezione INFN, Turin, Italy

org111SSC IHEP of NRC Kurchatov institute, Protvino, Russia

org112SUBATECH, Ecole des Mines de Nantes, Université de Nantes, CNRS-IN2P3,
Nantes, France

org113Suranaree University of Technology, Nakhon Ratchasima, Thailand

org114Technical University of Split FESB, Split, Croatia

org115The Henryk Niewodniczanski Institute of Nuclear Physics, Polish Academy
of Sciences, Cracow, Poland

org116The University of Texas at Austin, Physics Department, Austin, Texas,
USA

org117Universidad Autónoma de Sinaloa, Culiacán, Mexico

org118Universidade de São Paulo (USP), São Paulo, Brazil

org119Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil

org120University of Houston, Houston, Texas, United States

org121University of Jyväskylä, Jyväskylä, Finland

org122University of Liverpool, Liverpool, United Kingdom

org123University of Tennessee, Knoxville, Tennessee, United States

org124University of the Witwatersrand, Johannesburg, South Africa

org125University of Tokyo, Tokyo, Japan

org126University of Tsukuba, Tsukuba, Japan

org127University of Zagreb, Zagreb, Croatia

org128Université de Lyon, Université Lyon 1, CNRS/IN2P3, IPN-Lyon,
Villeurbanne, France

org129V. Fock Institute for Physics, St. Petersburg State University, St.
Petersburg, Russia

org130Variable Energy Cyclotron Centre, Kolkata, India

org131Vinča Institute of Nuclear Sciences, Belgrade, Serbia

org132Warsaw University of Technology, Warsaw, Poland

org133Wayne State University, Detroit, Michigan, United States

org134Wigner Research Centre for Physics, Hungarian Academy of Sciences,
Budapest, Hungary

org135Yale University, New Haven, Connecticut, United States

org136Yonsei University, Seoul, South Korea

org137Zentrum für Technologietransfer und Telekommunikation (ZTT),
Fachhochschule Worms, Worms, Germany