2023 131 07 July

Charged-particle jet production in pp and p–Pb collisions

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

Measurements of inclusive charged-particle jet production in pp and p–Pb
collisions at center-of-mass energy per nucleon–nucleon collision
$\sqrt{s_{\mathrm{NN}}}~{}=~{}5.02$ TeV and the corresponding nuclear
modification factor $R_{\rm pPb}^{\rm ch~{}jet}$ are presented, using data
collected with the ALICE detector at the LHC. Jets are reconstructed in the
central rapidity region $\left|\eta_{\rm jet}\right|<0.5$ from charged
particles using the anti-$k_{\rm T}$ algorithm with resolution parameters
$R=0.2$, $0.3$, and $0.4$. The $p_{\rm T}$-differential inclusive production
cross section of charged-particle jets, as well as the corresponding cross
section ratios, are reported for pp and p–Pb collisions in the transverse
momentum range $10<p_{\rm T,jet}^{\rm ch}<140$ GeV/$c$ and $10<p_{\rm
T,jet}^{\rm ch}<160$ GeV/$c$, respectively, together with the nuclear
modification factor $R_{\rm pPb}^{\rm ch~{}jet}$ in the range $10<p_{\rm
T,jet}^{\rm ch}<140$ GeV/$c$. The analysis extends the $p_{\rm T}$ range of
the previously-reported charged-particle jet measurements by the ALICE
Collaboration. The nuclear modification factor is found to be consistent with
one and independent of the jet resolution parameter with the improved
precision of this study, indicating that the possible influence of cold
nuclear matter effects on the production cross section of charged-particle
jets in p–Pb collisions at $\sqrt{s_{\mathrm{NN}}}~{}=~{}5.02$ TeV is smaller
than the current precision. The obtained results are in agreement with other
minimum bias jet measurements available for RHIC and LHC energies, and are
well reproduced by the NLO perturbative QCD Powheg calculations with parton
shower provided by Pythia$8$ as well as by Jetscape simulations.

##  1 Introduction

In high-energy hadronic collisions, scattering processes at very large
momentum transfer $Q^{2}$ between quarks and gluons of the colliding nucleons
produce parton showers, which subsequently fragment into collimated sprays of
hadrons called jets. Studies of jet production in proton–proton (pp)
collisions allow one to test the fixed-order perturbative quantum
chromodynamics (pQCD) calculations of the jet production in the TeV domain and
tune the higher order effects in QCD-based Monte Carlo (MC) event generators
[1, 2, 3, 4]. Furthermore, such studies, especially at low $p_{\rm T}$,
constrain the non-perturbative contributions, such as the hadronization and
underlying event effects, to the inclusive jet cross section. In addition,
measurements in pp collisions also provide the baseline for similar
measurements in proton–nucleus (pA) and nucleus–nucleus (AA) collisions.
Comparing the jet production between pp and pA collisions allows for an
assessment of the effects related to the presence of bound nucleons in the
colliding system, denoted as cold nuclear matter (CNM) effects at the initial
state of the collisions, which can be described by partonic rescattering [5]
and by modification of parton distribution functions (PDFs) [6]. The study of
these CNM effects is interesting in its own right since it is necessary to
decouple the CNM effects from those related to the creation of the quark–gluon
plasma (QGP), which is a hot and dense color-deconfined QCD matter created in
AA collisions [7, 8].

The production of the QGP in AA collisions is confirmed by many observations
(see in [9] and the references therein). One of the QGP signatures is the so-
called jet quenching phenomenon. It is manifested by the suppression of
high-$p_{\rm T}$ hadron and jet yields with respect to those in pp collisions
[10, 11]. One microscopic picture of this phenomenon assumes that, while
traversing through the QGP, the initial highly-energetic parton loses energy
via medium-induced gluon radiations and elastic scatterings with constituents
of the hot and dense medium. A convenient observable to quantify these jet
quenching effects is the nuclear modification factor, defined as the ratio of
the jet (or final-state hadron) yield produced in AA or pA collisions to that
in pp collisions, scaled by the average number of nucleon–nucleon collisions
$\left\langle N_{\mathrm{coll}}\right\rangle$ [12]. A deviation from one of
this ratio at high $p_{\rm T}$ indicates the presence of nuclear effects.

Initially, the pA system was thought to be too small to create a QGP. However,
recent measurements show evidence of collective behavior in high-multiplicity
pp and p–Pb collisions at the LHC [13, 14, 15, 16] and in light nucleus–Au
collisions at RHIC [17, 18]. By contrast, jet quenching phenomena have not yet
been seen in small collision systems. The question of possible QGP formation
in small collision systems remains open and calls for further, more precise
jet quenching searches. The production of charged-particle jets and the
corresponding nuclear modification factor $R_{\rm pPb}^{\rm ch~{}jet}$ in p–Pb
collisions at center-of-mass energy per nucleon–nucleon collision
$\sqrt{s_{\mathrm{NN}}}~{}=~{}5.02$ TeV were previously reported by the ALICE
Collaboration based on Run $1$ data [19]. The scaled $p_{\rm T}$-differential
charged-particle jet production cross section from pp collisions at
$\sqrt{s}~{}=~{}7$ TeV was adopted for calculating the $R_{\rm pPb}^{\rm
ch~{}jet}$ in this study, and results show that the $R_{\rm pPb}^{\rm
ch~{}jet}$ is consistent with one within uncertainties. The measurement of
full jet production in p–Pb collisions at $\sqrt{s_{\mathrm{NN}}}=5.02$ TeV
has been presented by the ATLAS [20] and CMS Collaborations [21] at the LHC,
and in d–Au collisions at $\sqrt{s_{\mathrm{NN}}}=200$ GeV by the PHENIX
Collaboration [22] at RHIC.

In this article, we revisit previous ALICE analyses by measuring the charged-
particle jet production in the larger Run $2$ datasets of pp and p–Pb
collisions at $\sqrt{s_{\mathrm{NN}}}~{}=~{}5.02$ TeV, exploiting the
excellent tracking capabilities of ALICE [23, 24]. These large data samples
enable higher precision measurement of the charged-particle jet production
over a broader $p_{\rm T,jet}^{\rm ch}$ interval compared to the previous one
[19], extending $p_{\rm T,jet}^{\rm ch}$ down to $10$ GeV/$c$ and up to $140$
GeV/$c$. Furthermore, in contrast to the results from Run $1$, the reported
$R_{\rm pPb}^{\rm ch~{}jet}$ from Run $2$ utilizes a pp reference measured at
the same collision energy. Therefore, when constructing the nuclear
modification factor, there is no need to rely on an interpolation between
collision energies; instead, a more direct comparison can be made at the same
$\sqrt{s_{\mathrm{NN}}}$. This reduces the normalization uncertainty by a
factor of about $2.7$ as compared to the previously published results. Data
from Pb–Pb collisions are also available at the same energy [25, 26] and thus
the results from this p–Pb analysis can provide a baseline for the Pb–Pb data.
These measurements can also be used to constrain the nuclear-modified parton
distribution functions (nPDFs) [27, 28, 29, 30] and the strong coupling
constant $\alpha_{\rm s}$ [31].

This article reports the measurement of charged-particle jet production both
in pp and p–Pb collisions at $\sqrt{s_{\mathrm{NN}}}~{}=~{}5.02$ TeV and the
corresponding nuclear modification factor for jet resolution parameters
$R=0.2$, $0.3$, and $0.4$. The inclusive jet cross section is used to evaluate
ratios of jet yields obtained for different resolution parameters. These
ratios provide insight into the interplay between perturbative and non-
perturbative effects on jet transverse momentum scales [32, 33, 34, 1].

The paper is organized as follows. Section 2 describes the ALICE detector and
the dataset. Jet reconstruction approach, correction for detector and
acceptance efficiency, and systematic uncertainty assessment are discussed in
Section 3. Section 4 presents the results and compares them to theoretical
predictions and other experimental measurements. The conclusion is given in
Section 5.

##  2 Experimental setup and datasets

The ALICE detector is a general-purpose heavy-ion experiment at the LHC [35,
24]. The pp dataset used in this analysis was collected in $2017$ at
$\sqrt{s}~{}=~{}5.02$ TeV, while the p–Pb dataset was collected in $2016$ at
$\sqrt{s_{\mathrm{NN}}}~{}=~{}5.02$ TeV, during the LHC Run $2$. The analyzed
data samples were collected with a minimum bias (MB) trigger and consist of
$968\times 10^{6}$ MB events for pp collisions, corresponding to an integrated
luminosity $L_{\rm pp}=18.9\pm 0.4$ nb-1 [36], and $624\times 10^{6}$ MB
events for p–Pb collisions, corresponding to $L_{\rm pPb}=298\pm 11$ $\mu$b-1
[37]. In p–Pb collisions, a rapidity shift $\Delta y=0.465$ is needed in the
direction of the proton beam to transform from the ALICE laboratory frame to
the nucleon–nucleon center-of-mass frame due to the asymmetry of the colliding
beam energies; protons at $4$ TeV energy are collided into fully stripped
${}_{82}^{208}$Pb ions at $1.58$ TeV per nucleon energy [38].

Events were triggered using the V0 detector [39], which consists of two
scintillator arrays located at forward and backward rapidity. It covers the
pseudorapidity regions $-3.7<\eta<-1.7$ (V0C) and $2.8<\eta<5.1$ (V0A). To
select the MB trigger, coincident signals are required in both the V0A and V0C
detectors. Beam-induced background events, such as beam–gas interactions or
out-of-bunch pileup within the V0 detector readout time, are rejected offline
by using the timing information from the V0 detectors and the number of
reconstructed points and track segments in the Silicon Pixel Detector (SPD),
which are expected to be uncorrelated for background events. The SPD equips
the two innermost layers of the Inner Tracking System (ITS), a silicon tracker
with six layers, and covers the pseudorapidity interval
$\left|\eta\right|<1.4$ around midrapidity. In-bunch pileup events, where
multiple interactions occur in the same bunch crossing, are rejected by
requiring that only a single primary vertex is reconstructed with the SPD in
the event [24]. For the data samples considered in this paper, pileup events
amount to less than $1\%$ of the event sample both in pp and p–Pb collisions
[40]. Accepted events are required to have the reconstructed primary vertex
position along the beam axis within $10$ cm from the center of the detector
[24].

Charged-particle jets are reconstructed using tracks of primary-charged-
particle candidates produced in the collision. Primary charged particles are
defined as all particles with a mean proper lifetime $\tau>1~{}{\rm cm}/c$
which are either produced directly in the interaction or from decays of
particles with a mean proper lifetime $\tau<1~{}{\rm cm}/c$. This excludes
particles produced in interactions with the detector material and products of
weak decays [41]. The charged-particle trajectories are reconstructed using
information from the ITS [23] and the Time Projection Chamber (TPC) [42].
These detectors are located inside a large solenoidal magnet that provides a
uniform magnetic field of $B=0.5$ T. Tracks were selected with transverse
momenta $p_{\rm T,track}>0.15$ GeV/$c$ and in a pseudorapidity range
$\left|\eta\right|<0.9$ over the full azimuth $0<\varphi<2\pi$.

In order to achieve a uniform azimuthal angle distribution and the high-
quality momentum resolution required for jet reconstruction, the charged track
selection utilized a hybrid selection technique that compensates for local
inefficiencies in the SPD. Two distinct classes of tracks are combined in the
hybrid approach [43]. The first class consists of tracks that have at least
one hit in the SPD. The second class contains tracks without hits in the SPD,
in which case the primary interaction vertex is used to constrain the
trajectory in the track fit to improve the determination of their transverse
momentum. The charged track momentum resolution $\sigma(p_{\rm T})/p_{\rm T}$
is estimated using the covariance matrix of the track fit [24] and is
approximately $0.8\%$ at $p_{\rm T,track}=1$ GeV/$c$ and $4\%$ at $p_{\rm
T,track}=50$ GeV/$c$.

Data corrections on instrumental effects were based on Monte Carlo (MC)
simulations, which included a detailed description of the detector geometry
and response, using the Geant$3$ package [44]. The simulations were performed
using the Pythia$8$ event generator [45] with the Monash $2013$ tune [46] for
pp collisions and the Pythia$6$ [47] with the Perugia $2011$ tune [48] for
p–Pb collisions. The simulated data were analyzed in the same way as the real
data.

##  3 Data analysis

###  3.1 Jet reconstruction

The strategy for the jet reconstruction closely followed the procedures used
by the ALICE Collaboration in the Run $1$ analysis, including the background
density estimation [19]. Jet finding was performed using the FastJet $3.2.1$
[49] package. Signal jets were reconstructed from charged-particle tracks
using the anti-$k_{\rm T}$ sequential clustering algorithm [50] with
resolution parameters $R=0.2$, $0.3$, and $0.4$. The four-momenta of the jet
constituents were combined using the boost-invariant $p_{\rm T}$ recombination
scheme, treating the jet constituents as massless. To ensure that jets were
well contained in the TPC acceptance, the pseudorapidity coverage of the
reconstructed jets was constrained to $\left|\eta_{\rm jet}\right|<0.5$ for
all jet resolution parameters. The area of the jet was required to be $A_{\rm
jet}>0.6\pi R^{2}$ to suppress the contribution from pure background jet
clusters [26]. Jets which contained tracks with $p_{\rm T}$ larger than $100$
GeV/$c$ were rejected, in order to suppress contamination by fake tracks and
ensure good momentum resolution. This selection rejects less than $1\%$ of
jets in the considered kinematic region, and the impact on the raw jet
spectrum is minor.

Reconstructed jets from the hard process are always accompanied by soft
background that does not originate from the hard process, known as the
underlying event. The transverse momentum of selected signal jets in p–Pb
collisions was subsequently corrected for the average underlying event
contribution [51] according to the formula

$p_{\rm T,jet}^{\rm ch}=p_{\rm T,jet}^{\rm ch~{}raw}-\rho_{\rm ch}\times
A_{\rm jet},$ (1)

where the transverse momentum density $\rho_{\rm ch}$ of particles produced by
the underlying event in p–Pb collisions was estimated on an event-by-event
basis using the so-called improved CMS method [52]

$\rho_{\rm ch}={\rm median}\left\\{\frac{p_{\rm T,jet}^{k_{\rm T}}}{A_{\rm
jet}^{k_{\rm T}}}\right\\}\times C.$ (2)

Here $A_{\rm jet}^{k_{\rm T}}$ and $p_{\rm T,jet}^{k_{\rm T}}$ are the area
and the transverse momentum of the jet clusters found using the $k_{\rm T}$
algorithm. The $k_{\rm T}$ jets had the same resolution parameter as the
anti-$k_{\rm T}$ jets, and the pseudorapidity range of the reconstructed
$k_{\rm T}$ jets spanned $\left|\eta_{\rm jet}\right|<0.9$. The jet active
area [53] was estimated by distributing ghost particles into the
$\eta$–$\varphi$ acceptance. The ghost particle density is $200$ per unit area
and corresponds to $0.005$ area per ghost particle. The two highest-$p_{\rm
T}$ jets in the event were excluded from the estimation of the background in
order to suppress impact of physical jets on $\rho_{\rm ch}$ [54]. The scaling
factor $C$ is used to account for regions without particles. It is defined as

$C=\frac{\sum_{\rm j}A_{\rm j}}{A_{\rm{acc}}}.$ (3)

Here $A_{\rm j}$ is the area of each $k_{\rm T}$ jet with at least one real
track (i.e. excluding ghosts), and $A_{\rm{acc}}$ is the area of charged-
particle acceptance. In pp collisions, the underlying event constitutes
approximately $p_{\rm T}=1$ GeV/$c$ per jet, and was not subtracted from the
raw jet spectrum [55].

###  3.2 Corrections

The measured jet spectrum is distorted predominantly due to the finite
detector resolution and local background fluctuations with respect to the mean
underlying event density. A correction procedure known as unfolding was used
to correct for these effects [56, 57]. The response matrices for these two
effects are determined separately and combined by making a product of the two
[58].

The response matrix describing jet momentum smearing due to instrumental
effects was determined from the Pythia MC simulation. Detector-level jets were
reconstructed by transporting the generated particles through a full
simulation of the ALICE detector using the Geant$3$ transport model [59].
These jets were geometrically matched to the corresponding particle-level jets
on a jet-by-jet basis by requiring that the angular distance $\Delta
R=\sqrt{(\Delta\eta)^{2}+(\Delta\varphi)^{2}}<0.6R$, where $\Delta\eta$ and
$\Delta\varphi$ are the differences in pseudorapidity and azimuthal angle
between the detector-level and particle-level jets. The particle-level jets
were reconstructed without imposing any selection on the constituents.
Consequently, during the unfolding process, the reconstructed jets are
corrected to a constituent-charged-particle momentum of $0$ GeV/$c$.

The probability of a particle-level jet not matching any detector-level jet is
considered as the jet reconstruction efficiency, is applied to correct the
unfolded jet spectrum. Its value is about $80\%$ at jet $p_{\rm T}\approx 10$
GeV/$c$ and increases to $99\%$ for jets with $p_{\rm T}>30$ GeV/$c$ in the
considered kinematic region. Additionally, the probability that a detector-
level jet is falsely matched to a non-corresponding particle-level jet is
called the jet reconstruction purity. The jet reconstruction purity is found
to be above $99\%$ in the measured kinematic range. Hence, the raw jet
spectrum was not corrected for the reconstruction purity.

The performance of the jet reconstruction was assessed using the MC
simulation. Two variables are evaluated: the shift of the mean jet energy
scale (JES) $\Delta_{\rm JES}=\left\langle(p_{\rm T,jet}^{\rm ch~{}det}-p_{\rm
T,jet}^{\rm ch~{}truth})/p_{\rm T,jet}^{\rm ch~{}truth}\right\rangle$ and the
jet energy resolution ${\rm JER}=\sigma(p_{\rm T,jet}^{\rm ch~{}det})/p_{\rm
T,jet}^{\rm ch~{}truth}$, where $p_{\rm T,jet}^{\rm ch~{}det}$ and $p_{\rm
T,jet}^{\rm ch~{}truth}$ are the transverse momenta of the measured jet and
the corresponding truth jet, and $\sigma(p_{\rm T,jet}^{\rm ch~{}det})$
denotes the width of the $p_{\rm T,jet}^{\rm ch~{}det}-p_{\rm T,jet}^{\rm
ch~{}truth}$ distribution as a function of $p_{\rm T,jet}^{\rm ch~{}truth}$.
The $\Delta_{\rm JES}$ distribution is asymmetric and has a long negative tail
due to the reconstruction inefficiency and a sharp peak centered around
$p_{\rm T,jet}^{\rm ch~{}truth}=p_{\rm T,jet}^{\rm ch~{}det}$. The most
probable scenario is that the measured $p_{\rm T,jet}^{\rm ch}$ is close to
the jet $p_{\rm T}$ at particle level. The $\Delta_{\rm JES}$ distribution has
a mean value of $-15\%~{}(-20\%)$ and $-27\%~{}(-30\%)$ for the $p_{\rm
T,jet}^{\rm ch~{}truth}$ interval $20<p_{\rm T,jet}^{\rm ch~{}truth}<30$
GeV/$c$ and $100<p_{\rm T,jet}^{\rm ch~{}truth}<120$ GeV/$c$, respectively,
for pp (p–Pb) collisions. The value of the JER varies from $23\%~{}(20\%)$ to
$27\%~{}(38\%)$ in pp (p–Pb) collisions at $p_{\rm T,jet}^{\rm ch~{}truth}=20$
GeV/$c$ and $p_{\rm T,jet}^{\rm ch~{}truth}=150$ GeV/$c$, respectively. Both
$\Delta_{\rm JES}$ and JER exhibit a weak $R$ dependence.

The response matrix which accounts for the smearing due to local background
fluctuations, was obtained with the random cone (RC) method [60]. Cones with a
resolution parameter $R_{\rm cone}$ equal to that of the jet were placed
randomly in the $\eta-\varphi$ space in each event, within the ITS and TPC
acceptance. The background fluctuations were evaluated by comparing the sum of
the $p_{\rm T}$ of tracks inside the cone, $p_{\rm T}^{\rm RC}$, with the
expected average contribution due to the underlying event as follows:

$\delta p_{\rm T}^{\rm RC}=p_{\rm T}^{\rm RC}-\rho_{\rm ch}\pi R^{2}_{\rm
cone}.$ (4)

In this study, two definitions for the random cone were considered. First, the
random cones were required not to overlap with the leading and subleading jets
in an event. Second, the cones were placed in a perpendicular direction to the
leading jet in an event. These two approaches yield a consistent result and
their difference was considered as a source of systematic uncertainty. The
nominal result used the $\delta p_{\rm T}^{\rm RC}$ matrix obtained by the
first approach. The corresponding $\delta p_{\rm T}^{\rm RC}$ distribution has
a width of $\sigma^{\rm RC}=2.01$ GeV/$c$ for $R=0.2$, $3.01$ GeV/$c$ for
$R=0.3$, and $4.01$ GeV/$c$ for $R=0.4$. The response matrix for the local
background fluctuations was constructed row-by-row by taking the $\delta
p_{\rm T}^{\rm RC}$ distribution and shifting it along the $p_{\rm T,jet}^{\rm
ch~{}det}$ axis by the amount $p_{\rm T,jet}^{\rm ch~{}truth}$ corresponding
to each row [58]. In pp collisions, no correction for the background
fluctuations was applied, and the raw spectrum was corrected for the
instrumental effects only.

In our case the use of an unregularized unfolding method led to a non-
physically behaving solution, which wildly oscillated due to statistical
fluctuations of the measured spectrum. To suppress these fluctuations,
unfolding algorithms which impose a smoothness criterion were used in this
analysis. The regularization procedure introduces additional systematic
uncertainties on the unfolded spectrum, as discussed in Sec. 3.3.2. The
singular value decomposition (SVD-) based method [61] was used to perform the
unfolding correction for the primary analysis. The optimum regularization
parameter, $k_{\rm reg}$, was chosen such that it corresponds to the point at
which statistically significant components of the $d$-vector distribution
change to noise. The values of $k_{\rm reg}=4$ and $6$ are adopted for pp and
p–Pb collisions, respectively. The unfolding was also performed using the
iterative Bayesian method [62]. For the regularization of the Bayesian
unfolding, convergence was determined by the stability of the unfolded
solution to successive iterations, while minimizing the statistical
uncertainties [63]. The convergence was achieved after the second iteration
for both pp and p–Pb collisions. These unfolding algorithms are implemented in
the RooUnfold framework [64]. The difference between the two unfolding methods
was assigned as a source of systematic uncertainty. Input to the unfolding was
the raw jet spectrum measured in the range $10<p_{\rm T,jet}^{\rm ch}<140$
GeV/$c$ for pp collisions and $10<p_{\rm T,jet}^{\rm ch}<160$ GeV/$c$ for p–Pb
collisions. The fraction of particle-level jets that are reconstructed outside
the measured range, referred as the kinematic efficiency, is applied to the
unfolded spectrum in addition to the jet reconstruction efficiency correction.
The kinematic efficiency is above $80\%$ in the measured range. The unfolding
correction required a prior spectrum as the starting point of the algorithm.
In this analysis, by default the prior spectrum was defined by the particle-
level distribution generated using the Pythia simulation. The unfolding
correction of the raw jet spectrum ranges from $30\%$ to $60\%$ across the
analyzed $p_{\rm T}$ range.

The robustness of the unfolding procedure and the mathematical validity of the
unfolded solution were established through refolding and closure tests as done
in the previous ALICE jet measurements [63]. In the refolding test, the
unfolded spectrum was multiplied by the response matrix and the resulting
distribution was compared to the raw jet spectrum. The closure test involved
the use of two statistically independent MC samples. The first sample was
utilized to construct the response matrix and provide the prior distribution.
The second one served to generate pseudo-data, which were obtained by
resampling the central values using Poissonian smearing. The pseudo-data was
then unfolded in the same way as the real data and compared to the particle-
level distribution. Within statistical uncertainties, the solutions from both
tests were able to recover the input distribution.

Statistical uncertainties of the unfolded solutions were evaluated based on
pseudo-random experiments. In this approach, the bin contents of the input
measured spectrum were smeared according to given statistical uncertainties
obtaining an ensemble of randomized spectra. The unfolding was then applied to
each of these spectra and the resulting statistical uncertainty in each bin
was obtained from a covariance matrix corresponding to the ensemble. To cross-
check the correlation of the statistical uncertainties of the bin contents of
the jet spectrum, the statistical uncertainties were also evaluated using the
Bootstrap method [65, 66] and found to be consistent with the pseudo-random
experiments. When calculating the ratio of jet cross sections, the spectra
which appear in the numerator and denominator are from the same input data.
The degree of correlation between the same $p_{\rm T}$ bins is nevertheless
suppressed since jets reconstructed with different $R$ may fall in different
$p_{\rm T}$ bins. Thus the correlation was not taken into account; the
numerator and denominator are treated as independent, which leads to a
conservative estimate of the statistical uncertainties.

###  3.3 Systematic uncertainties

The systematic uncertainties of the $p_{\rm T}$-differential charged-particle
jet cross section and $R_{\rm pPb}^{\rm ch~{}jet}$ are quantified by varying
several parameters with respect to the primary analysis. The uncertainties are
categorized based on their point-to-point correlation into correlated
uncertainty, shape uncertainty, and normalization uncertainty. The correlated
uncertainty is positively correlated among all the $p_{\rm T,jet}^{\rm ch}$
bins. It includes the uncertainty on the tracking efficiency and the
uncertainty on the jet momentum smearing due to local background fluctuations.
The shape uncertainty is the uncertainty which is anti-correlated between
parts of the unfolded spectrum, which affects the shape of the final $p_{\rm
T,jet}^{\rm ch}$ spectrum. It arises mainly due to assumptions in the
unfolding procedure. The normalization uncertainties on the luminosity
measurement, as described in Sec. 2, were determined to be $2.34\%$ [36] and
$3.7\%$ [37] for pp and p–Pb collisions, respectively.

The influence of the statistical fluctuations on the systematic uncertainties
of the raw spectrum was suppressed by using pseudo-experiments as done in
Refs. [40, 63]. For each source of uncertainty, several randomized instances
of the raw jet $p_{\rm T,jet}^{\rm ch}$ spectrum were generated by variations
around the measured central value in each bin using a Gaussian distribution,
with $\sigma$ taken to be the uncorrelated statistical error in the bin. Each
randomized instance was analyzed using (i) corrections for the primary
analysis, and (ii) corrections that include the systematic variation. For each
randomized instance, the ratio of corrected jet $p_{\rm T,jet}^{\rm ch}$
spectra resulting from (ii) and (i) was formed. The systematic uncertainty in
each $p_{\rm T,jet}^{\rm ch}$ bin was defined as the mean value of the
distribution of ratios obtained from all randomized instances. The
uncertainties were taken as symmetric and the total uncertainty for each
category was obtained by making a quadratic sum of the uncertainties
corresponding to individual sources. The summary of the relative systematic
uncertainties discussed in this section is presented in Table 1.

####  3.3.1 Correlated uncertainties

The main sources of correlated uncertainties are described below.

  * $\bullet$

Tracking efficiency: The dominant systematic uncertainty arises from the
uncertainty on the ALICE tracking efficiency, which composes of two parts. The
first part was estimated by simultaneously varying track selection criteria in
the TPC in data and the MC simulation. The second one was determined by the
discrepancy in the TPC-ITS track matching efficiency between data and
simulations. The uncertainty on the inclusive $p_{\rm T}$ spectrum of charged
particles was found to be $3\%$ in pp collisions [67]. In p–Pb collisions, the
value was found to increase with $p_{\rm T}$ from $1\%$ at low $p_{\rm T}$
($\sim 0.5$ GeV/$c$) up to $2.5\%$ at high $p_{\rm T}$ ($\sim 14$ GeV/$c$). To
assess how this uncertainty impacts the charged-particle jet spectrum, the
unfolding was also performed with a response matrix which accounted for the
lower track reconstruction efficiency. The difference between the jet spectrum
obtained with the modified response matrix and the default one is adopted as
the uncertainty on tracking efficiency.

  * $\bullet$

Pythia fragmentation: The instrumental response matrix used in the unfolding
correction was built using Pythia simulations. The impact of the hadronization
model in Pythia was assessed using the jet angularity observable. The jet
angularity is defined as $g=\sum_{i}(p_{{\rm T},i}\times r_{i})/p_{\rm
T,jet}^{\rm ch}$, where $p_{{\rm T},i}$ is the $p_{\rm T}$ of the $i^{\rm th}$
constituent of the charged-particle jet and
$r_{i}=\sqrt{\Delta\eta_{i}^{2}+\Delta\varphi_{i}^{2}}$ is the distance of the
$i^{\rm th}$ constituent from the jet axis at the particle level. The
angularity distribution was simulated using Pythia$8$ and Herwig7 [68, 69]
simulations. The corresponding jet angularity distributions differ up to about
$30\%$. To assess the uncertainty associated with the detector effects on the
fragmentation model, the instrumental response matrix was re-weighted
according to the jet angularity. Specifically, the instrumental response
matrix was re-weighted such that the $50\%$ largest angularity jets were
weighted an additional $\pm 30\%$ relative to the $50\%$ lowest angularity
jets [26]. The modified response matrix was then used to unfold the measured
spectrum. The difference between the unfolded jet spectrum obtained with the
re-weighted response matrix and the default one is taken as the uncertainty.

  * $\bullet$

Background fluctuations: In p–Pb collisions, the $\delta p_{\rm T}$ matrix was
constructed with cones perpendicular to the leading jet, as described in
Section 3.2. The uncertainty on background fluctuations is estimated by taking
the difference between the resulting jet spectrum and the default one.

####  3.3.2 Shape uncertainties

The main sources of the shape uncertainties are described below.

  * $\bullet$

Variation of the unfolding algorithm: The $p_{\rm T,jet}^{\rm ch}$ spectrum
was unfolded with the iterative Bayesian unfolding method.

  * $\bullet$

Variation of the regularization parameter: The regularization parameter
$k_{\rm reg}$ in the SVD-based unfolding was varied by $\pm 1$ with respect to
the optimal value.

  * $\bullet$

Variation of the prior: The prior spectrum was changed to a $p_{\rm T}$
spectrum of jets calculated with Powheg+Pythia$8$ simulations.

  * $\bullet$

Variation of the lower $p_{\rm T}$ spectrum cutoff: The minimum $p_{\rm T}$ of
the measured jet spectrum used in the unfolding correction was required to be
greater than $\sigma^{\rm RC}$ of the $\delta p_{\rm T}^{\rm RC}$
distribution. The sensitivity of the unfolded result to combinatorial jets was
tested by varying the lower range of the measured jet spectrum by $\pm 3$
GeV/$c$.

The systematic uncertainties on the cross section ratios between jet spectra
obtained with different $R$ values are determined using the same strategy as
in previous ALICE measurements [26]. The numerator and denominator were varied
simultaneously and compared to the default jet cross section ratio.

The shape uncertainties between the pp and p–Pb collision systems are
considered uncorrelated and are fully propagated to the $R_{\rm pPb}^{\rm
ch~{}jet}$. Due to the partial correlation between the tracking efficiency
uncertainties in pp and p–Pb collisions [19], the tracking efficiency
uncertainty on the $R_{\rm pPb}^{\rm ch~{}jet}$ is considered to be the
maximum uncanceled part between the two collision systems.

Table 1: Summary of the contributions to the relative systematic uncertainty for the charged-particle jet cross section in pp and p–Pb collisions, and $R_{\rm pPb}^{\rm ch~{}jet}$ for $R=0.2$, $0.3$, and $0.4$. The uncertainties depend on the $p_{\rm T,jet}^{\rm ch}$ and the table shows representative values corresponding to the first and last $p_{\rm T,jet}^{\rm ch}$ interval for each jet resolution parameter. The contributions are assumed to be independent and are summed in quadrature, resulting in the total uncertainty. See text for details. | $R$ | $p_{\rm T,jet}^{\rm ch}$ (GeV/$c$) |  | Correlated uncertainty (%) |  | Shape uncertainty (%) |  | Norm. (%)
---|---|---|---|---|---|---|---|---
|  | Trk. eff. | Frag. | Bkg. fluc. | Total |  | Algo. | Reg. par. | Prior | Bin trunc. | Total |
pp | $0.2$ |  $10$ – $20$ |  | $5.3$ | $2.1$ | — | $5.7$ |  | $0.06$ | $0.07$ | $0.15$ | $0.47$ | $0.50$ |  | $2.34$
$120$ – $140$ |  | $9.7$ | $1.1$ | — | $9.8$ |  | $8.0$ | $4.3$ | $0.70$ | $4.3$ | $10$ |
$0.3$ |  $10$ – $20$ |  | $6.3$ | $1.5$ | — | $6.5$ |  | $0.04$ | $0.05$ | $0.43$ | $0.51$ | $0.67$ |
$120$ – $140$ |  | $9.6$ | $0.40$ | — | $9.6$ |  | $9.7$ | $4.1$ | $1.1$ | $2.7$ | $11$ |
$0.4$ |  $10$ – $20$ |  | $7.3$ | $2.4$ | — | $7.7$ |  | $0.08$ | $0.12$ | $0.06$ | $0.44$ | $0.47$ |
$120$ – $140$ |  | $10$ | $0.40$ | — | $10$ |  | $9.3$ | $7$ | $0.64$ | $1.2$ | $12$ |
p–Pb | $0.2$ |  $10$ – $20$ |  | $16$ | $0.70$ | $0.21$ | $16$ |  | $2.0$ | $0.34$ | $0.22$ | $0.21$ | $2.05$ |  | $3.70$
$140$ – $160$ |  | $4.6$ | $0.80$ | $1.3$ | $4.8$ |  | $11$ | $3.9$ | $1.4$ | $3.9$ | $12$ |
$0.3$ |  $10$ – $20$ |  | $16$ | $1.4$ | $0.54$ | $16$ |  | $0.51$ | $0.78$ | $0.24$ | $0.54$ | $1.1$ |
$140$ – $160$ |  | $7.7$ | $1.5$ | $0.36$ | $7.9$ |  | $8.8$ | $4.2$ | $1.7$ | $1.9$ | $10$ |
$0.4$ |  $10$ – $20$ |  | $16$ | $1.7$ | $1.2$ | $16$ |  | $3.9$ | $0.74$ | $0.19$ | $0.20$ | $4.0$ |
$140$ – $160$ |  | $9.3$ | $0.60$ | $0.16$ | $9.3$ |  | $3.3$ | $3.3$ | $3.2$ | $3.3$ | $6.6$ |
$R_{\rm pPb}^{\rm ch~{}jet}$ | $0.2$ |  $10$ – $20$ |  | $5.3$ | $2.2$ | $0.21$ | $5.8$ |  | $2.0$ | $0.35$ | $0.55$ | $0.51$ | $2.2$ |  | $4.37$
$120$ – $140$ |  | $4.8$ | $1.4$ | $1.3$ | $5.2$ |  | $11$ | $4.9$ | $3.1$ | $4.9$ | $13$ |
$0.3$ |  $10$ – $20$ |  | $6.3$ | $2.1$ | $0.54$ | $6.7$ |  | $0.51$ | $0.78$ | $0.49$ | $0.75$ | $1.3$ |
$120$ – $140$ |  | $7.3$ | $1.6$ | $0.36$ | $7.5$ |  | $12$ | $5.7$ | $1.1$ | $2.8$ | $14$ |
$0.4$ |  $10$ – $20$ |  | $7.3$ | $3.0$ | $1.2$ | $8.0$ |  | $3.9$ | $0.75$ | $0.20$ | $0.49$ | $4.0$ |
$120$ – $140$ |  | $9.1$ | $0.72$ | $0.16$ | $9.1$ |  | $10$ | $7.2$ | $1.6$ | $2.2$ | $13$ |

##  4 Results

###  4.1 Inclusive charged-particle jet production cross section in pp and
p–Pb collisions

The charged-particle jet cross sections are reported differentially in $p_{\rm
T,jet}^{\rm ch}$ and $\eta_{\rm jet}$ as

$\frac{\mathrm{d}^{2}\sigma}{\mathrm{d}p_{\rm T,jet}^{\rm
ch}\mathrm{d}\eta_{\rm
jet}}=\frac{1}{L}\frac{\mathrm{d}^{2}{N}}{\mathrm{d}p_{\rm T,jet}^{\rm
ch}\mathrm{d}\eta_{\rm jet}},$ (5)

where $\mathrm{d}^{2}{N}/\mathrm{d}p_{\rm T,jet}^{\rm ch}\mathrm{d}\eta_{\rm
jet}$ is the fully corrected $p_{\rm T}$\- and $\eta$-differential charged-
particle jet yield. The integrated luminosity for minimum bias events is
denoted by $L$, see Sec. 2.

The fully corrected charged-particle jet cross sections for $R=0.2$, $0.3$,
and $0.4$ in pp and p–Pb collisions are shown in Figs. 1 and 2, respectively.
The jet cross sections for larger jet resolution parameters are scaled by
arbitrary factors described in the legend for better visibility. These results
are compatible with the previous results from ALICE [67, 19] and cover a wider
$p_{\rm T,jet}^{\rm ch}$ interval, and are consistent with the ALICE charged-
particle jet measurements [25]. The measurements are compared to two
theoretical predictions:

  * $\bullet$

Powheg+Pythia$8$: The matrix elements are computed at NLO accuracy with the
Powheg method [70, 71] using the dijet process [72] implemented in the Powheg
Box V$2$ framework [73]. The Powheg Box is interfaced with the Lhapdf6 package
[74] to supply different (n)PDF sets for describing the non-perturbative
initial states, and Pythia$8$ [45] tune A$14$ [75] for parton shower and
fragmentation. The simulation employs the CT$14$ NLO [76], TUJU$21$ NNLO [77],
CTEQ$6.1$ NLO [78], and nNNPDF3.0 NLO [79] free-proton PDF sets. In the case
of p–Pb collisions, the corresponding nPDF sets EPPS$16$ NLO [80], nTUJU$21$
NNLO [77], nCTEQ$15$WZSIH NLO [81, 82], and nNNPDF$3.0$ NLO [79] are used,
accounting also for the shift in rapidity. The default values of $\alpha_{\rm
s}$, renormalization, and factorization scales are adopted in the results
shown in this paper.

  * $\bullet$

Jetscape: Pythia$8$ [45] is used to generate the initial hard scattering and
the underlying event. The intermediate shower is handled by the MATTER [83,
84] model that includes parton virtuality. After parton shower, QCD strings
are formed through either a colored or a colorless hadronization scheme. The
strings are subsequently fed into Pythia$8$ for string fragmentation [85, 86].
The Jetscape configuration used in this paper is referred to as the PP$19$
tune [87] as implemented in Jetscape V$3.4.1$ [88]. The predictions are only
shown for pp collisions.

As shown in the right panels of Figs. 1 and 2, within the uncertainties of the
data, the Powheg+Pythia$8$ predictions describe the data well, except for the
lowest $p_{\rm T,jet}^{\rm ch}$ interval $10$–$20$ GeV/$c$ where a maximum
discrepancy of $\sim 20\%$ is observed in both pp and p–Pb collisions. The
Jetscape prediction in pp collisions overestimates the data by $\sim 50\%$ at
low $p_{\rm T}$. The magnitude of the discrepancy depends on the jet $p_{\rm
T}$ and the resolution parameter $R$. The low-$p_{\rm T}$ region is sensitive
to non-perturbative effects such as the initial state radiation and
multiparton interactions. It is not clear to which extent can these
discrepancies be explained by improper modelling of these effects.

Figure 1: Left panel: Fully corrected charged-particle jet cross section in pp
collisions at $\sqrt{s}~{}=~{}5.02$ TeV for $R=0.2$, $0.3$, and $0.4$ (scale
factors as indicated are used for better visibility). Statistical
uncertainties are shown as vertical bars and are typically smaller than the
marker size for all points. Boxes indicate systematic uncertainties. The
additional normalization uncertainty due to luminosity is quoted separately as
$\sigma_{L}^{\rm sys}$. The data are compared to the predictions by
Powheg+Pythia$8$ and Jetscape. Right panels: Ratio of the theory predictions
to the measured data. The correlated and shape systematic uncertainties on the
ratio account for the data uncertainties and do not show the normalization
uncertainty. Figure 2: Similar results as Fig. 1 for p–Pb collisions at
$\sqrt{s_{\mathrm{NN}}}~{}=~{}5.02$ TeV.

The jet cross section ratios were evaluated by dividing the spectrum with
$R=0.2$ by those with other resolution parameters as shown in Fig. 3. The left
panel is for pp collisions, and the right panel is for p–Pb collisions. In
both pp and p–Pb collisions, the data are compared with the Powheg+Pythia$8$
predictions. General agreement between data and Powheg+Pythia$8$ predictions
is observed within uncertainties. In pp collisions, the comparison between
data and Jetscape predictions is also presented. Jetscape predictions are
consistent with data within uncertainties as well, but they show a difference
from the Powheg+Pythia$8$ predictions at low $p_{\rm T,jet}^{\rm ch}$ in
$20$–$40$ GeV/$c$. The cross section ratio is sensitive to the collimation of
particles around the jet axis and serves as an indirect measure of the jet
structure [89]. Since the jet spectra in the numerator are always with a
smaller jet resolution parameter $R$, it is expected that QCD radiation
reduces this ratio below one, and that the effect decreases with the
increasing collimation of jets at high $p_{\rm T}$ [33]. The ratios confirm
the expected trend of increasing collimation with increasing transverse
momentum of jets, corroborated also by the theoretical predictions. Figure 4
shows the comparison of the cross section ratios in pp collisions and p–Pb
collisions. The comparison shows that the energy in the jet cone distributed
transverse to the jet axis in p–Pb collisions is consistent with that in pp
collisions. No sign of a modified jet structure is observed within
uncertainties.

Figure 3: Fully corrected charged-particle jet cross section ratios with
$R=0.2$ to other jet resolution parameters. The plots also include comparison
to the Powheg+Pythia$8$ and Jetscape (in case of pp collisions) predictions.
Left panel: pp collisions at $\sqrt{s}~{}=~{}5.02$ TeV. Right panel: p–Pb
collisions at $\sqrt{s_{\mathrm{NN}}}~{}=~{}5.02$ TeV.

Figure 4: Comparison of jet cross section ratios between pp collisions and
p–Pb collisions at $\sqrt{s_{\mathrm{NN}}}~{}=~{}5.02$ TeV. The left panel
shows the ratios between jets with $R=0.2$ and $R=0.3$, the right panel shows
the ratios between jets with $R=0.2$ and $R=0.4$.

###  4.2 Nuclear modification factor $R_{\rm pPb}^{\rm ch~{}jet}$

The nuclear modification factor of charged-particle jets in minimum bias p–Pb
collisions due to nuclear matter effects is quantified by comparing the jet
cross section in p–Pb collisions normalized by the number of nucleons of the
Pb ion, $A=208$, to the jet cross section in pp collisions, known as the
$A$-scaling hypothesis [90, 12],

$R_{\rm pPb}^{\rm ch~{}jet}=\left.\frac{1}{A}\frac{\mathrm{d}^{2}\sigma_{\rm
pPb}}{\mathrm{d}p_{\rm T,jet}^{\rm ch}\mathrm{d}\eta_{\rm
jet}}\right/\frac{\mathrm{d}^{2}\sigma_{\rm pp}}{\mathrm{d}p_{\rm T,jet}^{\rm
ch}\mathrm{d}\eta_{\rm jet}}.$ (6)

The jet cross sections are measured in the laboratory frame with
$\left|\eta_{\rm jet}\right|<0.5$ in both pp and p–Pb collisions. As mentioned
above, the laboratory frame is shifted from the center-of-mass frame in
rapidity by $\Delta y=0.465$ in p–Pb collisions while it is not shifted for pp
collisions, resulting in different jet rapidity acceptances in the center-of-
mass frame between the two systems. However, this effect on $R_{\rm pPb}^{\rm
ch~{}jet}$ is smaller than $5\%$ [19], and it is not accounted in Eq. (6).

Figure 5 depicts the nuclear modification factors $R_{\rm pPb}^{\rm ch~{}jet}$
for jets with $R=0.2$, $0.3$, and $0.4$ as a function of jet transverse
momentum. The $R_{\rm pPb}^{\rm ch~{}jet}$ is compatible with one within
uncertainties in the reported transverse momentum range $10<p_{\rm T,jet}^{\rm
ch}<140$ GeV/$c$, and it is noted to be approximately independent of jet
transverse momentum and jet resolution parameter. The $R_{\rm pPb}^{\rm
ch~{}jet}$ presented in this article is in agreement with the Run $1$ result
[19] within uncertainties. The results also indicate that jet quenching, if
present, is below the sensitivity of the current measurement. The
Powheg+Pythia$8$ prediction calculated using the nCTEQ$15$WZSIH is found to
best describe the data within uncertainties. This shows that the effects of
nuclear-modified PDFs introduced by the nPDFs have minor impact on jet
production. The data from this measurement can provide an important constraint
on the global analysis of nPDFs.

The $R_{\rm pPb}^{\rm ch~{}jet}$ result reported in this paper is compared to
other published experimental results currently available. Figure 6 shows the
comparison to the measurement of full jets in p–Pb collisions at
$\sqrt{s_{\mathrm{NN}}}=5.02$ TeV by the ATLAS [20] and CMS Collaborations
[21] at the LHC, and d–Au collisions at $\sqrt{s_{\mathrm{NN}}}=200$ GeV by
the PHENIX Collaboration [22] at RHIC. Full jets are reconstructed with
charged and neutral components. It is important to realize that the energy
scales of the ATLAS, CMS, PHENIX, and ALICE measurements are different (jets
measured by ALICE do not include neutral fragments) which complicates a direct
comparison between the measurements. The ATLAS and CMS measurements show a
hint of enhancement above one, but it has to be confirmed with higher
precision measurements. It is worth noticing that by assuming the final state
particles are dominated by pions, one can roughly estimate a scaling factor of
around $1.5$ between the energy of charged-particle jets and that of full
jets. In general, the ALICE measurement is in qualitative agreement with those
from ATLAS and CMS within the current experimental precision. The ALICE
results shown here extend the measurements down to a jet $p_{\rm T}$ of $10$
GeV/$c$ and complement the measurements of the ATLAS and CMS Collaborations.

Figure 5: The nuclear modification factor $R_{\rm pPb}^{\rm ch~{}jet}$ of
inclusive charged-particle jets as a function of $p_{\rm T,jet}^{\rm ch}$ at
$\sqrt{s_{\mathrm{NN}}}~{}=~{}5.02$ TeV for $R=0.2$, $0.3$, and $0.4$. The
data measured are compared with the Powheg+Pythia$8$ predictions calculated
with various nPDFs. The bands on the predictions represent statistical
uncertainties. Systematic and statistical uncertainties of data are shown as
boxes and error bars, respectively. The normalization uncertainty of $4.37\%$
is shown as a box around one. Figure 6: Comparison of the nuclear modification
factors of jets in p–Pb and d–Au measurements at the LHC and RHIC,
respectively. The boxes around the data points denote the systematic
uncertainties, while the error bars denote the statistical uncertainties. The
systematic uncertainties on the normalization are shown as boxes at $R_{\rm
pPb}=1$.

##  5 Conclusion

The inclusive $p_{\rm T}$-differential charged-particle jet production cross
sections in pp and p–Pb collisions at $\sqrt{s_{\mathrm{NN}}}~{}=~{}5.02$ TeV
were measured using the ALICE detector at the LHC. The inclusive charged-
particle jets were reconstructed with resolution parameters $R=0.2$, $0.3$,
and $0.4$. The measured charged-particle jet cross sections are corrected for
experimental effects, such as the finite detector resolution on the jet energy
scale as well as the effects of the uncorrelated background and its
fluctuations. The ratios of jet cross sections measured for different values
of $R$ in pp collisions are consistent with those in p–Pb collisions within
uncertainties, indicating no sign of jet structure modification in p–Pb
collisions within the current measurement precision. Besides, the results
confirm that the higher-$p_{\rm T}$ jets are more collimated. The cross
section ratios also provide additional comparisons to theoretical predictions.

Within the current experimental precision and uncertainties, the nuclear
modification factor $R_{\rm pPb}^{\rm ch~{}jet}$ is observed to be consistent
with one, implying that the nuclear effects on jet production in p–Pb
collisions are below the resolution of the current measurement. The $R_{\rm
pPb}^{\rm ch~{}jet}$ is also found to be approximately independent of the jet
resolution parameter, and is consistent with the measurements of full jets by
the ATLAS, CMS, and PHENIX Collaborations within the kinematic region of
overlap among the different measurements. The ALICE results reported in this
paper extend the jet $p_{\rm T}$ reach down to $10$ GeV/$c$ and are thus
complementary to those obtained with ATLAS and CMS.

The results are well described by NLO Powheg+Pythia$8$ predictions (for pp and
p–Pb collisions), while the Jetscape (for pp collisions) prediction agrees
better with the data at high $p_{\rm T}$. These results provide a constraint
on initial- and final-state effects in nuclear collisions, global analysis of
nPDF, and provide a new baseline for the study of jet production in heavy-ion
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: A. I. Alikhanyan National Science Laboratory (Yerevan Physics Institute) Foundation (ANSL), State Committee of Science and World Federation of Scientists (WFS), Armenia; Austrian Academy of Sciences, Austrian Science Fund (FWF): [M 2467-N36] and Nationalstiftung für Forschung, Technologie und Entwicklung, Austria; Ministry of Communications and High Technologies, National Nuclear Research Center, Azerbaijan; Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Financiadora de Estudos e Projetos (Finep), Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) and Universidade Federal do Rio Grande do Sul (UFRGS), Brazil; Bulgarian Ministry of Education and Science, within the National Roadmap for Research Infrastructures 2020-2027 (object CERN), Bulgaria; Ministry of Education of China (MOEC) , Ministry of Science & Technology of China (MSTC) and National Natural Science Foundation of China (NSFC), China; Ministry of Science and Education and Croatian Science Foundation, Croatia; Centro de Aplicaciones Tecnológicas y Desarrollo Nuclear (CEADEN), Cubaenergía, Cuba; Ministry of Education, Youth and Sports of the Czech Republic, Czech Republic; The Danish Council for Independent Research | Natural Sciences, the VILLUM FONDEN and Danish National Research Foundation (DNRF), Denmark; Helsinki Institute of Physics (HIP), Finland; Commissariat à l’Energie Atomique (CEA) and Institut National de Physique Nucléaire et de Physique des Particules (IN2P3) and Centre National de la Recherche Scientifique (CNRS), France; Bundesministerium für Bildung und Forschung (BMBF) and GSI Helmholtzzentrum für Schwerionenforschung GmbH, Germany; General Secretariat for Research and Technology, Ministry of Education, Research and Religions, Greece; National Research, Development and Innovation Office, Hungary; Department of Atomic Energy Government of India (DAE), Department of Science and Technology, Government of India (DST), University Grants Commission, Government of India (UGC) and Council of Scientific and Industrial Research (CSIR), India; National Research and Innovation Agency - BRIN, Indonesia; Istituto Nazionale di Fisica Nucleare (INFN), Italy; Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT) and Japan Society for the Promotion of Science (JSPS) KAKENHI, Japan; Consejo Nacional de Ciencia (CONACYT) y Tecnología, through Fondo de Cooperación Internacional en Ciencia y Tecnología (FONCICYT) and Dirección General de Asuntos del Personal Academico (DGAPA), Mexico; Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO), Netherlands; The Research Council of Norway, Norway; Commission on Science and Technology for Sustainable Development in the South (COMSATS), Pakistan; Pontificia Universidad Católica del Perú, Peru; Ministry of Education and Science, National Science Centre and WUT ID-UB, Poland; Korea Institute of Science and Technology Information and National Research Foundation of Korea (NRF), Republic of Korea; Ministry of Education and Scientific Research, Institute of Atomic Physics, Ministry of Research and Innovation and Institute of Atomic Physics and University Politehnica of Bucharest, Romania; Ministry of Education, Science, Research and Sport of the Slovak Republic, Slovakia; National Research Foundation of South Africa, South Africa; Swedish Research Council (VR) and Knut & Alice Wallenberg Foundation (KAW), Sweden; European Organization for Nuclear Research, Switzerland; Suranaree University of Technology (SUT), National Science and Technology Development Agency (NSTDA), Thailand Science Research and Innovation (TSRI) and National Science, Research and Innovation Fund (NSRF), Thailand; Turkish Energy, Nuclear and Mineral Research Agency (TENMAK), Turkey; National Academy of Sciences of Ukraine, Ukraine; Science and Technology Facilities Council (STFC), United Kingdom; National Science Foundation of the United States of America (NSF) and United States Department of Energy, Office of Nuclear Physics (DOE NP), United States of America. In addition, individual groups or members have received support from: European Research Council, Strong 2020 - Horizon 2020 (grant nos. 950692, 824093), European Union; Academy of Finland (Center of Excellence in Quark Matter) (grant nos. 346327, 346328), Finland.

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##  Appendix A The ALICE Collaboration

S. Acharya 128, D. Adamová 87, A. Adler71, G. Aglieri Rinella 33, M. Agnello
30, N. Agrawal 52, Z. Ahammed 136, S. Ahmad 16, S.U. Ahn 72, I. Ahuja 38, A.
Akindinov 142, M. Al-Turany 98, D. Aleksandrov 142, B. Alessandro 57, H.M.
Alfanda 6, R. Alfaro Molina 68, B. Ali 16, A. Alici 26, N. Alizadehvandchali
117, A. Alkin 33, J. Alme 21, G. Alocco 53, T. Alt 65, A.R. Altamura 51, I.
Altsybeev 96, J.R. Alvarado 45, M.N. Anaam 6, C. Andrei 46, N. Andreou 116, A.
Andronic 127, 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
23, R. Arnaldi 57, J.G.M.C.A. Arneiro 111, I.C. Arsene 20, M. Arslandok 139,
A. Augustinus 33, R. Averbeck 98, M.D. Azmi 16, H. Baba125, A. Badalà 54, J.
Bae 105, Y.W. Baek 41, X. Bai 121, R. Bailhache 65, Y. Bailung 49, A. Balbino
30, A. Baldisseri 131, 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 128, L. Barreto 111, C. Bartels 120, K. Barth 33, E. Bartsch 65,
N. Bastid 128, S. Basu 76, G. Batigne 104, D. Battistini 96, B. Batyunya 143,
D. Bauri48, J.L. Bazo Alba 102, I.G. Bearden 84, C. Beattie 139, P. Becht 98,
D. Behera 49, I. Belikov 130, A.D.C. Bell Hechavarria 127, F. Bellini 26, R.
Bellwied 117, S. Belokurova 142, G. Bencedi 47, S. Beole 25, A. Bercuci 46, Y.
Berdnikov 142, A. Berdnikova 95, L. Bergmann 95, M.G. Besoiu 64, L. Betev 33,
P.P. Bhaduri 136, 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
130, A. Bilandzic 96, G. Biro 47, S. Biswas 4, N. Bize 104, J.T. Blair 109, D.
Blau 142, M.B. Blidaru 98, N. Bluhme39, C. Blume 65, G. Boca 22,56, F. Bock
88, T. Bodova 21, A. Bogdanov142, S. Boi 23, J. Bok 59, L. Boldizsár 47, M.
Bombara 38, P.M. Bond 33, G. Bonomi 135,56, H. Borel 131, A. Borissov 142,
A.G. Borquez Carcamo 95, H. Bossi 139, 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 142, H. Buesching 65, S. Bufalino 30, P.
Buhler 103, N. Burmasov 142, Z. Buthelezi 69,124, A. Bylinkin 21, S.A.
Bysiak108, M. Cai 6, H. Caines 139, A. Caliva 29, E. Calvo Villar 102, J.M.M.
Camacho 110, P. Camerini 24, F.D.M. Canedo 111, S.L. Cantway 139, M. Carabas
114, A.A. Carballo 33, F. Carnesecchi 33, R. Caron 129, L.A.D. Carvalho 111,
J. Castillo Castellanos 131, F. Catalano 33,25, C. Ceballos Sanchez 143, I.
Chakaberia 75, P. Chakraborty 48, S. Chandra 136, S. Chapeland 33, M. Chartier
120, S. Chattopadhyay 136, S. Chattopadhyay 100, T. Cheng 98,6, C. Cheshkov
129, B. Cheynis 129, 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 126, 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 132, G. Contin 24, J.G. Contreras 36, M.L.
Coquet 131, P. Cortese 134,57, M.R. Cosentino 113, F. Costa 33, S. Costanza
22,56, C. Cot 132, J. Crkovská 95, P. Crochet 128, 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 127, 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 135, R. Del Grande 96, L. Dello Stritto 29, W. Deng 6, P.
Dhankher 19, D. Di Bari 32, A. Di Mauro 33, B. Diab 131, R.A. Diaz 143,7, T.
Dietel 115, Y. Ding 6, R. Divià 33, D.U. Dixit 19, Ø. Djuvsland21, U.
Dmitrieva 142, A. Dobrin 64, B. Dönigus 65, J.M. Dubinski 137, A. Dubla 98, S.
Dudi 91, P. Dupieux 128, M. Durkac107, N. Dzalaiova13, T.M. Eder 127, R.J.
Ehlers 75, F. Eisenhut 65, R. Ejima93, D. Elia 51, B. Erazmus 104, F.
Ercolessi 26, F. Erhardt 90, M.R. Ersdal21, B. Espagnon 132, G. Eulisse 33, D.
Evans 101, S. Evdokimov 142, 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 142, A. Fernández Téllez 45, L. Ferrandi 111, M.B. Ferrer 33, A.
Ferrero 131, C. Ferrero 57, A. Ferretti 25, V.J.G. Feuillard 95, V. Filova 36,
D. Finogeev 142, F.M. Fionda 53, F. Flor 117, A.N. Flores 109, S. Foertsch 69,
I. Fokin 95, S. Fokin 142, E. Fragiacomo 58, E. Frajna 47, U. Fuchs 33, N.
Funicello 29, C. Furget 74, A. Furs 142, T. Fusayasu 99, J.J. Gaardhøje 84, M.
Gagliardi 25, A.M. Gago 102, T. Gahlaut48, C.D. Galvan 110, D.R. Gangadharan
117, P. Ganoti 79, C. Garabatos 98, T. García Chávez 45, E. Garcia-Solis 9, C.
Gargiulo 33, K. Garner127, P. Gasik 98, A. Gautam 119, M.B. Gay Ducati 67, M.
Germain 104, A. Ghimouz126, C. Ghosh136, M. Giacalone 52, G. Gioachin 30, P.
Giubellino 98,57, P. Giubilato 28, A.M.C. Glaenzer 131, P. Glässel 95, E.
Glimos 123, D.J.Q. Goh77, V. Gonzalez 138, M. Gorgon 2, K. Goswami 49, S.
Gotovac34, V. Grabski 68, L.K. Graczykowski 137, E. Grecka 87, A. Grelli 60,
C. Grigoras 33, V. Grigoriev 142, S. Grigoryan 143,1, 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 125,
W. Guo 6, A. Gupta 92, R. Gupta 92, R. Gupta 49, K. Gwizdziel 137, L. Gyulai
47, C. Hadjidakis 132, F.U. Haider 92, H. Hamagaki 77, A. Hamdi 75, Y. Han
140, B.G. Hanley 138, R. Hannigan 109, J. Hansen 76, M.R. Haque 137, J.W.
Harris 139, A. Harton 9, H. Hassan 88, D. Hatzifotiadou 52, P. Hauer 43, L.B.
Havener 139, S.T. Heckel 96, E. Hellbär 98, H. Helstrup 35, M. Hemmer 65, T.
Herman 36, G. Herrera Corral 8, F. Herrmann127, S. Herrmann 129, K.F. Hetland
35, B. Heybeck 65, H. Hillemanns 33, B. Hippolyte 130, F.W. Hoffmann 71, B.
Hofman 60, G.H. Hong 140, M. Horst 96, A. Horzyk 2, Y. Hou 6, P. Hristov 33,
C. Hughes 123, P. Huhn65, L.M. Huhta 118, T.J. Humanic 89, A. Hutson 117, D.
Hutter 39, R. Ilkaev142, H. Ilyas 14, M. Inaba 126, G.M. Innocenti 33, M.
Ippolitov 142, A. Isakov 85,87, T. Isidori 119, M.S. Islam 100, M. Ivanov13,
M. Ivanov 98, V. Ivanov 142, 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 137, M.A. Janik 137, T. Janson71, S. Ji 17, S.
Jia 10, A.A.P. Jimenez 66, F. Jonas 88,127, D.M. Jones 120, J.M. Jowett 33,98,
J. Jung 65, M. Jung 65, A. Junique 33, A. Jusko 101, M.J. Kabus 33,137, J.
Kaewjai106, P. Kalinak 61, A.S. Kalteyer 98, A. Kalweit 33, V. Kaplin 142, A.
Karasu Uysal 73, D. Karatovic 90, O. Karavichev 142, T. Karavicheva 142, P.
Karczmarczyk 137, E. Karpechev 142, U. Kebschull 71, R. Keidel 141, D.L.D.
Keijdener60, M. Keil 33, B. Ketzer 43, S.S. Khade 49, A.M. Khan 121,6, S. Khan
16, A. Khanzadeev 142, Y. Kharlov 142, A. Khatun 119, A. Khuntia 36, B. Kileng
35, B. Kim 105, C. Kim 17, D.J. Kim 118, E.J. Kim 70, J. Kim 140, J.S. Kim 41,
J. Kim 59, J. Kim 70, M. Kim 19, S. Kim 18, T. Kim 140, K. Kimura 93, S.
Kirsch 65, I. Kisel 39, S. Kiselev 142, A. Kisiel 137, J.P. Kitowski 2, J.L.
Klay 5, J. Klein 33, S. Klein 75, C. Klein-Bösing 127, M. Kleiner 65, T.
Klemenz 96, A. Kluge 33, A.G. Knospe 117, C. Kobdaj 106, T. Kollegger98, A.
Kondratyev 143, N. Kondratyeva 142, E. Kondratyuk 142, J. Konig 65, S.A.
Konigstorfer 96, P.J. Konopka 33, G. Kornakov 137, M. Korwieser 96, S.D.
Koryciak 2, A. Kotliarov 87, V. Kovalenko 142, 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 142, V. Kučera 59, C. Kuhn 130, P.G. Kuijer 85, T. Kumaoka126,
D. Kumar136, L. Kumar 91, N. Kumar91, S. Kumar 32, S. Kundu 33, P. Kurashvili
80, A. Kurepin 142, A.B. Kurepin 142, A. Kuryakin 142, S. Kushpil 87, M.J.
Kweon 59, Y. Kwon 140, S.L. La Pointe 39, P. La Rocca 27, A. Lakrathok106, M.
Lamanna 33, A.R. Landou 74,116, R. Langoy 122, P. Larionov 33, E. Laudi 33, L.
Lautner 33,96, R. Lavicka 103, R. Lea 135,56, H. Lee 105, I. Legrand 46, G.
Legras 127, 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 122, R.
Lietava 101, I. Likmeta 117, B. Lim 25, S.H. Lim 17, V. Lindenstruth 39, A.
Lindner46, C. Lippmann 98, A. Liu 19, D.H. Liu 6, J. Liu 120, G.S.S. Liveraro
112, I.M. Lofnes 21, C. Loizides 88, S. Lokos 108, J. Lömker 60, P. Loncar 34,
X. Lopez 128, E. López Torres 7, P. Lu 98,121, J.R. Luhder 127, M. Lunardon
28, G. Luparello 58, Y.G. Ma 40, M. Mager 33, A. Maire 130, E.M. Majerz2, M.V.
Makariev 37, M. Malaev 142, G. Malfattore 26, N.M. Malik 92, Q.W. Malik20,
S.K. Malik 92, L. Malinina I,VII,143, D. Mallick 132,81, N. Mallick 49, G.
Mandaglio 31,54, S.K. Mandal 80, V. Manko 142, F. Manso 128, 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 132, O. Massen 60, A. Mastroserio 133,51, 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 124, A.F. Mechler65, Y.
Melikyan 44, A. Menchaca-Rocha 68, E. Meninno 103, A.S. Menon 117, M. Meres
13, S. Mhlanga115,69, Y. Miake126, L. Micheletti 33, L.C. Migliorin129, D.L.
Mihaylov 96, K. Mikhaylov 143,142, 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, S.
Monira 137, Z. Moravcova 84, C. Mordasini 118, D.A. Moreira De Godoy 127, I.
Morozov 142, A. Morsch 33, T. Mrnjavac 33, V. Muccifora 50, S. Muhuri 136,
J.D. Mulligan 75, A. Mulliri 23, M.G. Munhoz 111, R.H. Munzer 65, H. Murakami
125, S. Murray 115, L. Musa 33, J. Musinsky 61, J.W. Myrcha 137, B. Naik 124,
A.I. Nambrath 19, B.K. Nandi 48, R. Nania 52, E. Nappi 51, A.F. Nassirpour
18,76, A. Nath 95, C. Nattrass 123, M.N. Naydenov 37, A. Neagu20, A. Negru114,
L. Nellen 66, R. Nepeivoda 76, S. Nese 20, G. Neskovic 39, N. Nicassio 51,
B.S. Nielsen 84, E.G. Nielsen 84, S. Nikolaev 142, S. Nikulin 142, V. Nikulin
142, F. Noferini 52, S. Noh 12, P. Nomokonov 143, J. Norman 120, N. Novitzky
126, P. Nowakowski 137, A. Nyanin 142, J. Nystrand 21, M. Ogino 77, S. Oh 18,
A. Ohlson 76, V.A. Okorokov 142, J. Oleniacz 137, A.C. Oliveira Da Silva 123,
M.H. Oliver 139, A. Onnerstad 118, C. Oppedisano 57, A. Ortiz Velasquez 66, J.
Otwinowski 108, M. Oya93, K. Oyama 77, Y. Pachmayer 95, S. Padhan 48, D.
Pagano 135,56, G. Paić 66, S. Paisano-Guzmán 45, A. Palasciano 51, S.
Panebianco 131, H. Park 126, H. Park 105, J. Park 59, J.E. Parkkila 33, Y.
Patley 48, R.N. Patra92, B. Paul 23, H. Pei 6, T. Peitzmann 60, X. Peng 11, M.
Pennisi 25, S. Perciballi 25, D. Peresunko 142, G.M. Perez 7, Y. Pestov142, V.
Petrov 142, M. Petrovici 46, R.P. Pezzi 104,67, S. Piano 58, M. Pikna 13, P.
Pillot 104, O. Pinazza 52,33, L. Pinsky117, C. Pinto 96, S. Pisano 50, M.
Płoskoń 75, M. Planinic90, F. Pliquett65, M.G. Poghosyan 88, B. Polichtchouk
142, S. Politano 30, N. Poljak 90, A. Pop 46, S. Porteboeuf-Houssais 128, V.
Pozdniakov 143, I.Y. Pozos 45, K.K. Pradhan 49, S.K. Prasad 4, S. Prasad 49,
R. Preghenella 52, F. Prino 57, C.A. Pruneau 138, I. Pshenichnov 142, M.
Puccio 33, S. Pucillo 25, Z. Pugelova107, S. Qiu 85, L. Quaglia 25, R.E.
Quishpe117, S. Ragoni 15, A. Rai 139, A. Rakotozafindrabe 131, L. Ramello
134,57, F. Rami 130, T.A. Rancien74, M. Rasa 27, S.S. Räsänen 44, R. Rath 52,
M.P. Rauch 21, I. Ravasenga 85, K.F. Read 88,123, 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 142, V. Riabov 142, R. Ricci 29, M. Richter 20, A.A. Riedel 96, W.
Riegler 33, A.G. Riffero 25, C. Ristea 64, M.V. Rodriguez 33, M. Rodríguez
Cahuantzi 45, S.A. Rodríguez Ramírez 45, K. Røed 20, R. Rogalev 142, E.
Rogochaya 143, T.S. Rogoschinski 65, D. Rohr 33, D. Röhrich 21, P.F. Rojas45,
S. Rojas Torres 36, P.S. Rokita 137, G. Romanenko 26, F. Ronchetti 50, A.
Rosano 31,54, E.D. Rosas66, K. Roslon 137, A. Rossi 55, A. Roy 49, S. Roy 48,
N. Rubini 26, D. Ruggiano 137, R. Rui 24, P.G. Russek 2, R. Russo 85, A.
Rustamov 82, E. Ryabinkin 142, Y. Ryabov 142, A. Rybicki 108, H. Rytkonen 118,
J. Ryu 17, W. Rzesa 137, O.A.M. Saarimaki 44, S. Sadhu 32, S. Sadovsky 142, J.
Saetre 21, K. Šafařík 36, P. Saha42, S.K. Saha 4, S. Saha 81, B. Sahoo 48, B.
Sahoo 49, R. Sahoo 49, S. Sahoo62, D. Sahu 49, P.K. Sahu 62, J. Saini 136, K.
Sajdakova38, S. Sakai 126, M.P. Salvan 98, S. Sambyal 92, D. Samitz 103, I.
Sanna 33,96, T.B. Saramela111, P. Sarma 42, V. Sarritzu 23, V.M. Sarti 96,
M.H.P. Sas 139, J. Schambach 88, H.S. Scheid 65, C. Schiaua 46, R. Schicker
95, A. Schmah98, C. Schmidt 98, H.R. Schmidt94, M.O. Schmidt 33, M. Schmidt94,
N.V. Schmidt 88, A.R. Schmier 123, R. Schotter 130, A. Schröter 39, J.
Schukraft 33, K. Schweda 98, G. Scioli 26, E. Scomparin 57, J.E. Seger 15, Y.
Sekiguchi125, D. Sekihata 125, M. Selina 85, I. Selyuzhenkov 98, S. Senyukov
130, J.J. Seo 95,59, D. Serebryakov 142, L. Šerkšnytė 96, A. Sevcenco 64, T.J.
Shaba 69, A. Shabetai 104, R. Shahoyan33, A. Shangaraev 142, 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 132, O. Sheibani117, K. Shigaki 93, M.
Shimomura78, J. Shin12, S. Shirinkin 142, Q. Shou 40, Y. Sibiriak 142, 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 136, V. Singhal
136, T. Sinha 100, B. Sitar 13, M. Sitta 134,57, T.B. Skaali20, G. Skorodumovs
95, M. Slupecki 44, N. Smirnov 139, R.J.M. Snellings 60, E.H. Solheim 20, J.
Song 117, 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 123, S.F. Stiefelmaier 95, D. Stocco 104, I. Storehaug
20, P. Stratmann 127, S. Strazzi 26, A. Sturniolo 31,54, C.P. Stylianidis85,
A.A.P. Suaide 111, C. Suire 132, M. Sukhanov 142, M. Suljic 33, R. Sultanov
142, V. Sumberia 92, S. Sumowidagdo 83, S. Swain62, I. Szarka 13, M.
Szymkowski 137, S.F. Taghavi 96, G. Taillepied 98, J. Takahashi 112, G.J.
Tambave 81, S. Tang 6, Z. Tang 121, J.D. Tapia Takaki 119, N. Tapus114, L.A.
Tarasovicova 127, M.G. Tarzila 46, G.F. Tassielli 32, A. Tauro 33, A. Tavira
García 132, G. Tejeda Muñoz 45, A. Telesca 33, L. Terlizzi 25, C. Terrevoli
117, S. Thakur 4, D. Thomas 109, A. Tikhonov 142, A.R. Timmins 117, M.
Tkacik107, T. Tkacik 107, A. Toia 65, R. Tokumoto93, K. Tomohiro93, N.
Topilskaya 142, M. Toppi 50, T. Tork 132, V.V. Torres 104, 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 118, T.P. Trzcinski 137, A. Tumkin
142, R. Turrisi 55, T.S. Tveter 20, K. Ullaland 21, B. Ulukutlu 96, A. Uras
129, 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 142, O. Vázquez Doce 50,
O. Vazquez Rueda 117, V. Vechernin 142, E. Vercellin 25, S. Vergara Limón45,
R. Verma48, L. Vermunt 98, R. Vértesi 47, M. Verweij 60, L. Vickovic34, Z.
Vilakazi124, O. Villalobos Baillie 101, A. Villani 24, G. Vino 51, A.
Vinogradov 142, T. Virgili 29, M.M.O. Virta 118, V. Vislavicius76, A.
Vodopyanov 143, B. Volkel 33, M.A. Völkl 95, K. Voloshin142, S.A. Voloshin
138, G. Volpe 32, B. von Haller 33, I. Vorobyev 96, N. Vozniuk 142, J.
Vrláková 38, J. Wan40, C. Wang 40, D. Wang40, Y. Wang 40, Y. Wang 6, A.
Wegrzynek 33, F.T. Weiglhofer39, S.C. Wenzel 33, J.P. Wessels 127, J. Wiechula
65, J. Wikne 20, G. Wilk 80, J. Wilkinson 98, G.A. Willems 127, B. Windelband
95, M. Winn 131, J.R. Wright 109, W. Wu40, Y. Wu 121, R. Xu 6, A. Yadav 43,
A.K. Yadav 136, 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 142, P. Závada
63, N. Zaviyalov142, M. Zhalov 142, B. Zhang 6, C. Zhang 131, L. Zhang 40, S.
Zhang 40, X. Zhang 6, Y. Zhang121, Z. Zhang 6, M. Zhao 10, V. Zherebchevskii
142, Y. Zhi10, D. Zhou 6, Y. Zhou 84, J. Zhu 55,6, Y. Zhu6, S.C. Zugravel 57,
N. Zurlo 135,56

## 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 Derby, Derby, United Kingdom
117 University of Houston, Houston, Texas, United States
118 University of Jyväskylä, Jyväskylä, Finland
119 University of Kansas, Lawrence, Kansas, United States
120 University of Liverpool, Liverpool, United Kingdom
121 University of Science and Technology of China, Hefei, China
122 University of South-Eastern Norway, Kongsberg, Norway
123 University of Tennessee, Knoxville, Tennessee, United States
124 University of the Witwatersrand, Johannesburg, South Africa
125 University of Tokyo, Tokyo, Japan
126 University of Tsukuba, Tsukuba, Japan
127 Universität Münster, Institut für Kernphysik, Münster, Germany
128 Université Clermont Auvergne, CNRS/IN2P3, LPC, Clermont-Ferrand, France
129 Université de Lyon, CNRS/IN2P3, Institut de Physique des 2 Infinis de
Lyon, Lyon, France
130 Université de Strasbourg, CNRS, IPHC UMR 7178, F-67000 Strasbourg, France,
Strasbourg, France
131 Université Paris-Saclay, Centre d’Etudes de Saclay (CEA), IRFU, Départment
de Physique Nucléaire (DPhN), Saclay, France
132 Université Paris-Saclay, CNRS/IN2P3, IJCLab, Orsay, France
133 Università degli Studi di Foggia, Foggia, Italy
134 Università del Piemonte Orientale, Vercelli, Italy
135 Università di Brescia, Brescia, Italy
136 Variable Energy Cyclotron Centre, Homi Bhabha National Institute, Kolkata,
India
137 Warsaw University of Technology, Warsaw, Poland
138 Wayne State University, Detroit, Michigan, United States
139 Yale University, New Haven, Connecticut, United States
140 Yonsei University, Seoul, Republic of Korea
141 Zentrum für Technologie und Transfer (ZTT), Worms, Germany
142 Affiliated with an institute covered by a cooperation agreement with CERN
143 Affiliated with an international laboratory covered by a cooperation
agreement with CERN.