2020 220 10 November

Jet fragmentation transverse momentum

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

Jet fragmentation transverse momentum ($j_{\mathrm{T}}$) distributions are
measured in proton-proton (pp) and proton-lead (p–Pb) collisions at
$\sqrt{s_{\mathrm{NN}}}=5.02\leavevmode\nobreak\ \mathrm{TeV}$ with the ALICE
experiment at the LHC. Jets are reconstructed with the ALICE tracking
detectors and electromagnetic calorimeter using the anti-$k_{\rm T}$ algorithm
with resolution parameter $R=0.4$ in the pseudorapidity range $|\eta|<0.25$.
The $j_{\mathrm{T}}$ values are calculated for charged particles inside a
fixed cone with a radius $R=0.4$ around the reconstructed jet axis. The
measured $j_{\mathrm{T}}$ distributions are compared with a variety of parton-
shower models. Herwig and Pythia 8 based models describe the data well for the
higher $j_{\mathrm{T}}$ region, while they underestimate the lower
$j_{\mathrm{T}}$ region. The $j_{\mathrm{T}}$ distributions are further
characterised by fitting them with a function composed of an inverse gamma
function for higher $j_{\mathrm{T}}$ values (called the “wide component”),
related to the perturbative component of the fragmentation process, and with a
Gaussian for lower $j_{\mathrm{T}}$ values (called the “narrow component”),
predominantly connected to the hadronisation process. The width of the
Gaussian has only a weak dependence on jet transverse momentum, while that of
the inverse gamma function increases with increasing jet transverse momentum.
For the narrow component, the measured trends are successfully described by
all models except for Herwig. For the wide component, Herwig and Pythia 8
based models slightly underestimate the data for the higher jet transverse
momentum region. These measurements set constraints on models of jet
fragmentation and hadronisation.

##  1 Introduction

Jets are groups of collimated particles mainly resulting from fragmentation of
hard scattered partons produced in high-energy particle collisions. Jet
production in quantum chromodynamics (QCD) [1, 2, 3, 4, 5] can be thought as a
two-stage process [6]. After being produced in the hard scattering, partons
reduce their virtuality by emitting gluons [7]. Since the momentum transfer
scale ($Q^{2}$) is large during the showering, perturbative QCD calculations
can be applied. When $Q^{2}$ becomes of the order of $\Lambda_{\mathrm{QCD}}$,
partons hadronise into final-state particles through higher order processes
that cannot be calculated perturbatively [8, 9, 10, 11, 12, 13, 14]. Instead,
the implementation of specific hadronisation models in Monte Carlo event
generators such as Pythia [8] and Herwig [10] can be used.

In this work the fragmentation of partons is studied using the jet
fragmentation transverse momentum, $j_{\mathrm{T}}$. The $j_{\mathrm{T}}$ is
defined as the perpendicular component of the momentum of the constituent
particle with respect to reconstructed jet momentum, $\vec{p}_{\mathrm{jet}}$.
The length of the $\vec{j}_{\rm T}$ vector is

$j_{\mathrm{T}}=\frac{|\vec{p}_{\mathrm{jet}}\times\vec{p}_{\mathrm{track}}|}{|\vec{p}_{\mathrm{jet}}|}\quad,$
(1)

where $\vec{p}_{\mathrm{track}}$ is the momentum of the constituent particles.
It is one of many jet shape observables to study the properties of fragmenting
particles with respect to the initial hard momentum during the fragmentation
process. The $j_{\mathrm{T}}$ provides a measurement of the transverse
momentum spread of the jet fragments.

Previously, $j_{\mathrm{T}}$ has been studied using two-particle correlations
where $j_{\mathrm{T}}$ is calculated for particles with respect to the highest
transverse momentum particle in each event instead of reconstructed jet. The
study using the correlation method was done by the CCOR collaboration at ISR
in pp collisions at centre-of-mass energies $\sqrt{s}=31,\;45$ and
$63\leavevmode\nobreak\ \mathrm{GeV}$ [15] and by the PHENIX collaboration at
RHIC in pp collisions at $\sqrt{s}=200\leavevmode\nobreak\ \mathrm{GeV}$ [16]
and d–Au collisions at a center-of-mass energy per nucleon pair
$\sqrt{s_{\mathrm{NN}}}=200\leavevmode\nobreak\ \mathrm{GeV}$ [17]. The
results showed no clear dependence on the transverse momentum
($p_{\mathrm{T}}$) of the trigger particle. Jet measurements to study
$j_{\mathrm{T}}$ were done by the CDF collaboration in
$\mathrm{p\kern-0.50003pt\mathrm{bar}{p}}$ collisions at
$\sqrt{s}=1.96\leavevmode\nobreak\ \mathrm{TeV}$ [18] at Tevatron, by the
ATLAS collaboration in pp at $\sqrt{s}=7\leavevmode\nobreak\ \mathrm{TeV}$
[19] and by the LHCb collaboration in pp collisions at
$\sqrt{s}=8\leavevmode\nobreak\ \mathrm{TeV}$ [20] at the LHC. The results
show a dependence of the width of $j_{\mathrm{T}}$ distributions with respect
to the $p_{\mathrm{T}}$ of jets at the LHC energies.

Jets are used as an important probe for the study of the deconfined phase of
strongly interacting matter, the quark–gluon plasma (QGP) that is formed in
high-energy collisions of heavy nuclei. There exists plenty of experimental
evidence of jet energy loss, such as the suppression of inclusive hadron
spectra at high transverse momentum [21, 22, 23, 24, 25], the modification of
back-to-back hadron-hadron [26, 27] and direct photon-hadron correlations
[28], hadron–jet correlations [29, 30], the modification of reconstructed jet
spectra [31, 32] and jet substructure [33, 34, 35, 36], as compared to the
expectations from elementary proton-proton collisions.

Jet quenching in heavy-ion collisions evolves multi-scale steps from hard to
soft processes [37, 38]. Hard scales dominate in the elementary hard
scattering. The hard scattering is followed by the subsequent branching
process down to non-perturbative scales. Soft scales, of the order of the
temperature of the medium, characterise interactions of soft partons produced
in the shower with the QGP. Soft scales also govern hadronisation, which is
expected to take place in vacuum for sufficiently energetic probes, even
though some changes can persist from modifications of colour flow [39, 40,
41]. Understanding the contributions from the different processes to the jet
shower evolution in medium and their scale dependence is crucial to constrain
the dynamics of jet energy loss in the expanding medium [42], and fundamental
medium properties like the temperature-dependent transport coefficient [43,
44]. Besides heavy-ion collisions one should study also smaller systems such
as p–Pb in order to get an important baseline. Cold nuclear matter effects
[45, 46, 47] in p–Pb collisions need to be considered to interpret the
measurements in heavy-ion collisions.

The results for $j_{\mathrm{T}}$ distributions obtained using two-particle
correlations were recently reported by the ALICE Collaboration [48] in pp and
p–Pb collisions. In this paper, jet reconstruction provides a better estimate
of the initial parton momentum than the leading hadron in two-particle
correlations. Additionally, contrary to the correlation studies, the
$j_{\mathrm{T}}$ distribution is not smeared by hadrons decaying from a short
living resonance.

The $j_{\mathrm{T}}$ distributions are studied by reconstructing jets with the
ALICE tracking detectors and electromagnetic calorimeter using the
anti-$k_{\rm T}$ algorithm [49] with resolution parameter $R=0.4$ in the
pseudorapidity range $|\eta|<0.25$ in pp collisions at $\sqrt{s}$ = 5.02 TeV
and p–Pb minimum bias collisions at
$\sqrt{s_{\mathrm{NN}}}=5.02\leavevmode\nobreak\ \mathrm{TeV}$. It is worth
noting that there is a shift in the centre-of-mass rapidity of $\Delta
y=0.465$ in the direction of the proton beam because of the asymmetric
collision system. The $j_{\mathrm{T}}$ distribution is further analysed by
fitting and separating it into two distinct components that are assigned to
the parton shower and the hadronisation process. The attempt to separate the
two components is presented for the first time using jets in various jet
transverse momentum ($p_{\mathrm{T,\,jet}}$) ranges. We also compare the
results with those obtained from Pythia and Herwig simulations.

##  2 Experimental setup and data samples

The data presented here were recorded by the ALICE detector in 2017 for pp
collisions at $\sqrt{s}=5.02\,\mathrm{TeV}$ with $7.6\times 10^{8}$ minimum-
bias events ($\mathcal{L}_{\mathrm{int}}=15.7\,\mathrm{nb}^{-1}$) and in 2013
for p–Pb collisions at $\sqrt{s_{\mathrm{NN}}}=5.02\leavevmode\nobreak\
\mathrm{TeV}$ with $1.3\times 10^{8}$ events
($\mathcal{L}_{\mathrm{int}}=620\,\mathrm{nb}^{-1}$). Detailed information
about the ALICE detector during LHC Run 1 and Run 2 can be found in Refs. [50,
51].

The V0 detector [52] provides the information for event triggering. The V0
detector consists of two scintillator hodoscopes that are located on each side
of the interaction point along the beam direction. It covers the
pseudorapidity region $-3.7<\eta<-1.7$ (V0C) and $2.8<\eta<5.1$ (V0A). To
select the minimum-bias trigger signals are required in both the V0A and V0C .
This condition is used to reduce the contamination of data from beam-gas
events using the timing difference of the signals between the V0A and V0C
detectors [51].

The analysis is performed with events that have a primary vertex within
$|z_{\mathrm{vtx}}|<10\leavevmode\nobreak\ \mathrm{cm}$ of the nominal
interaction point at $z_{\mathrm{vtx}}=0$ along the beam direction. Charged
particles are used for reconstruction of the primary vertex and jets. The
charged particles are reconstructed with the Inner Tracking System (ITS) [53]
and the Time Projection Chamber (TPC) [54]. These detectors are located inside
a large solenoidal magnet that provides a homogeneous magnetic field of 0.5 T.
Tracks within a pseudorapidity range $|\eta|<0.9$ over the full azimuth are
accepted. The ITS is made up of the Silicon Pixel Detector (SPD) in the
innermost layers, the Silicon Drift Detector (SDD) in the middle layers and
the Silicon Strip Detector (SSD) in the outermost layers, each consisting of
two layers. The tracks are selected following the hybrid approach [55] which
ensures a uniform distribution of tracks as a function of azimuthal angle
($\varphi$). The hybrid approach combines two different classes of tracks. The
first class consists of tracks that have at least one hit in the SPD. The
tracks from the second class do not have any SPD associated hit and mainly
rely on the position information of the primary vertex when reconstructing the
tracks. Combining the information from the ITS and TPC provides a $p_{\rm T}$
resolution ranging from $1$ to $10\,\%$ for charged particles from $0.15$ and
$100\,\mathrm{\mathrm{GeV}\kern-0.50003pt/\kern-0.20004pt\textit{c}}$. For
tracks without the ITS information, the momentum resolution is comparable to
that of ITS+TPC tracks below transverse momentum $p_{\rm
T}=10\,\mathrm{\mathrm{GeV}\kern-0.50003pt/\kern-0.20004pt\textit{c}}$, but
for higher momenta the resolution reaches $20\,\%$ at $p_{\rm
T}=50\,\mathrm{\mathrm{GeV}\kern-0.50003pt/\kern-0.20004pt\textit{c}}$ [51,
56].

The EMCal covers an area with a range of $|\eta|<0.7$ in pseudorapidity and
107 degrees in azimuth and is made up of 12288 towers in total. Each tower
consists of 76 alternating layers of 1.44 mm lead and 77 layers of 1.76 mm
scintillator material. The EMCal is also used to provide a high-energy photon
trigger for a high-$p_{\mathrm{T,\,jet}}$ data sample that is complementary to
the minimum bias trigger for a low $p_{\mathrm{T,\,jet}}$ data sample. The
EMCal can be used to trigger on single shower deposits or energy deposits
integrated over a larger area. The latter is used for the high-energy photon
trigger. The EMCal trigger definition for p–Pb collisions in 2013 requires an
energy deposit in a group of the towers of either 10
$\mathrm{GeV}\kern-0.50003pt$ for the low threshold trigger or 20
$\mathrm{GeV}\kern-0.50003pt$ for the high threshold trigger. A sample of
$3\times 10^{6}$ events ( $\mathcal{L}_{\mathrm{int}}\leavevmode\nobreak\
=\leavevmode\nobreak\ 5\,\mathrm{nb}^{-1}$) with the EMCal trigger provides
increased statistics for $p_{\mathrm{T,\,jet}}>60{\leavevmode\nobreak\
\mathrm{GeV}\kern-0.50003pt/\kern-0.20004pt\textit{c}}$ where the trigger bias
disappears in the analysis [57]. The energy of the electromagnetic shower
clusters is reconstructed in the EMCal by searching for a tower with an energy
deposit greater than a defined seed energy and merging all towers that share
the energy cluster. To avoid double counting, when a cluster is matched with
charged particles measured by the ITS and TPC, the sum of the transverse
momentum of all the matched tracks are subtracted from the cluster energy.

##  3 Analysis method

For each collision event, jets are reconstructed with the anti-$k_{\rm T}$
algorithm [49] and resolution parameter $R=0.4$ using FastJet [58]. The
$p_{\mathrm{T}}$-recombination scheme is used when reconstructing jets. Jets
are selected in $\left|\eta\right|<0.25$ to satisfy the fiducial acceptance of
the EMCal. In the jet reconstruction both charged particles with $p_{\rm
T}>0.15\,\mathrm{\mathrm{GeV}\kern-0.50003pt/\kern-0.20004pt\textit{c}}$ and
EMCal clusters with $p_{\rm
T}>0.30\,\mathrm{\mathrm{GeV}\kern-0.50003pt/\kern-0.20004pt\textit{c}}$ are
considered. All charged particles within a fixed cone with a resolution
parameter $R$ are taken as jet constituents, instead of using the list of jet
constituents provided by the jet algorithm [19, 59]. Results are presented in
terms of the jet transverse momentum $p_{\mathrm{T,\,jet}}$.

The resulting $j_{\mathrm{T}}$ distributions are corrected for detector
effects using the unfolding method in Ref. [60]. The response matrix used for
the unfolding is obtained from Pythia6 Perugia 2011 [61] events transported
through the ALICE experimental set up described with GEANT3 [62, 63]. This
response matrix
$(j_{\mathrm{T}}^{\mathrm{rec}},p_{\mathrm{T,jet}}^{\mathrm{rec}},j_{\mathrm{T}}^{\mathrm{true}},p_{\mathrm{T,jet}}^{\mathrm{true}})$
has $2\times 2$ dimensions to correct the detector inefficiency for jet
transverse momentum ($p_{\mathrm{T,\,jet}}$) and $j_{\mathrm{T}}$
simultaneously, where $j_{\mathrm{T}}^{\mathrm{true}}$ and
$p_{\mathrm{T,jet}}^{\mathrm{true}}$ are obtained from particle level jets by
Pythia6 and $j_{\mathrm{T}}^{\mathrm{rec}}$ and
$p_{\mathrm{T,jet}}^{\mathrm{rec}}$ are the corresponding measured values in
ALICE, respectively. As a primary method the unfolding is performed with an
iterative (Bayesian) algorithm as implemented in the RooUnfold package [60].
The number of iterations 4 is used as a regularisation parameter. The
unfolding procedure is tested by dividing the Pythia6 data sample into two
halves. The first half is used to fill the response matrix. The second half is
used to test the closure of the unfolding method. For
$40<p_{\mathrm{T,\,jet}}<150\,\mathrm{\mathrm{GeV}\kern-0.50003pt/\kern-0.20004pt\textit{c}}$,
the generated $p_{\mathrm{T,\,jet}}$ distribution is recovered. For
$j_{\mathrm{T}}>0.1\,\mathrm{\mathrm{GeV}\kern-0.50003pt/\kern-0.20004pt\textit{c}}$,
the $j_{\mathrm{T}}$ distribution is also recovered.

The effect of the underlying event background is estimated by looking at a
cone perpendicular to the observed jet axis ($\frac{\pi}{2}$ rotation in
$\varphi$, for details see Refs [64, 65]). The background $j_{\mathrm{T}}$ is
calculated for any track that is found within this cone and the rotated jet
axis is used as reference for $j_{\mathrm{T}}$. The background obtained in
this manner is subtracted from the unfolded inclusive $j_{\mathrm{T}}$
distribution, which gives the resulting signal distribution as shown in Eq. 2.
The probability of events with jets inside the perpendicular cone are
estimated as 1–2% of the total number of jets. Jets reconstructed with charged
particles only (charged jet) for $R=0.4$ and
$p_{\mathrm{T,\,jet}}^{\mathrm{ch}}>5$ GeV/$c$ are used to check other jets
inside the perpendicular since charged jets can cover the full azimuthal angle
contrary to the case of jets in the EMCal acceptance. To make sure there is no
jet contribution in the background, those events are not used for background
estimation. Because of this reason, $N_{\textrm{perpendicular jets}}$ is less
than $N_{\textrm{jets}}$ by about 1–2% in Eq. 2.

$\frac{1}{N_{\mathrm{jets}}}\frac{\mathrm{d}N}{j_{\mathrm{T,\,ch}}\mathrm{d}j_{\mathrm{T,\,ch}}}\bigg{|}_{\mathrm{signal}}=\frac{1}{N_{\mathrm{jets}}}\frac{\mathrm{d}N}{j_{\mathrm{T,\,ch}}\mathrm{d}j_{\mathrm{T,\,ch}}}\bigg{|}_{\mathrm{inclusive}}-\frac{1}{N_{\textrm{perpendicular
jets}}}\frac{\mathrm{d}N}{j_{\mathrm{T,\,ch}}\mathrm{d}j_{\mathrm{T,\,ch}}}\bigg{|}_{\mathrm{background}}$
(2)

The resulting signal distribution is fitted with the two-component function
shown in Eq. 3. A Gaussian distribution centered at
$j_{\mathrm{T}}\leavevmode\nobreak\ =\leavevmode\nobreak\
0\,\mathrm{\leavevmode\nobreak\
}{\mathrm{GeV}\kern-0.50003pt/\kern-0.20004pt\textit{c}}$ is used for lower
$j_{\mathrm{T}}$ and an inverse gamma function is used for $j_{\mathrm{T}}$
above $1\,\mathrm{\mathrm{GeV}\kern-0.50003pt/\kern-0.20004pt\textit{c}}$,
where $B_{1}$ to $B_{5}$ are parameters [48].

$\frac{1}{N_{\mathrm{jets}}}\frac{\mathrm{d}N}{j_{\mathrm{T,\,ch}}\mathrm{d}j_{\mathrm{T,\,ch}}}=\frac{B_{2}}{B_{1}\sqrt{2\pi}}e^{-\frac{j_{\mathrm{T}}^{2}}{2B_{1}^{2}}}+\frac{B_{3}B_{5}^{B_{4}}}{\Gamma\left(B_{4}\right)}\frac{e^{-\frac{B_{5}}{j_{\mathrm{T}}}}}{j_{\mathrm{T}}^{B_{4}+1}}$
(3)

To achieve stable results the fitting is performed in two steps. First, lower
and higher parts of the $j_{\mathrm{T}}$ distribution are fitted with a
Gaussian and inverse gamma function, respectively. After getting the results
from the individual fits, they are combined into a single function with
initial values from the individual results and then an additional fit is
performed. After getting the fit function,
$\sqrt{\left<j_{\mathrm{T}}^{2}\right>}$ (RMS) and yield values are extracted
separately from each component. The narrow component RMS from the Gaussian
part is determined as

$\sqrt{\left<j_{\mathrm{T}}^{2}\right>}=\sqrt{2}B_{1}$ (4)

and the wide component RMS value from the inverse gamma function is calculated
as

$\sqrt{\left<j_{\mathrm{T}}^{2}\right>}=\frac{B_{5}}{\sqrt{\left(B_{4}-2\right)\left(B_{4}-3\right)}}\quad,$
(5)

where it is required that $B_{4}>3$.

##  4 Systematic uncertainties

The systematic uncertainties in this analysis come from the background
estimation, the unfolding procedure and the uncertainties related to track and
cluster selection. The effect originating from uncertainty in the tracking
efficiency is estimated with a Pythia simulation by removing 4% of tracks
randomly from each event corresponding to a mismatching probability of tracks
between the ITS and TPC. The resulting variations in the RMS values are less
than 4% and 5% for the wide and narrow components, respectively. The
uncertainty related to the EMCal energy scale was estimated by scaling cluster
energies up and down by 2% in the PYTHIA particle level generation in order to
reflect a non-linearity correction of the EMCal energy scale ranging from
about 7% at
$0.5\,\mathrm{\mathrm{GeV}\kern-0.50003pt/\kern-0.20004pt\textit{c}}$ to a
negligible value above
$3\,\mathrm{\mathrm{GeV}\kern-0.50003pt/\kern-0.20004pt\textit{c}}$.
Similarly, the jet momentum was scaled by $\pm 2\%$ when determining
$p_{\mathrm{T,\,jet}}$ to check how the cluster energy affects
$j_{\mathrm{T}}$ distributions. The variation of both RMS components is seen
to be less than $2\%$.

The systematic uncertainty on the background estimation was studied using the
“random background” method as an alternative to that of the perpendicular-
cone. This method assigns new random $\eta$ and $\varphi$ of the existing
tracks in the event using a uniform distribution without changing their
$p_{\mathrm{T}}$ values. A random jet cone is also from uniform $\eta$ and
$\varphi$ distributions covering $|\eta|<0.25$ and $0<\varphi<2\pi$ and tracks
near the jet axis are not used. The resulting uncertainty is below 5% for the
wide component RMS and below 9% for the narrow component RMS in p–Pb
collisions. In pp collisions, the background estimation with the random
background method is overestimated as the underlying events are expected to be
smaller than the p–Pb data. As a result, the signal $j_{\mathrm{T}}$
distribution is underestimated. To study the effect of background fluctuations
in p–Pb collisions, a study based on embedding particles generated with Pythia
in real events was performed. The embedded particles are simulated by
following the multiplicity density information [66] and $p_{\rm T}$
distribution [67] of charged particles in p–Pb collisions in ALICE. The effect
in RMS is negligible for both RMS components.

The systematic uncertainty introduced by the unfolding procedure was
determined by repeating the unfolding using the Singular-Value Decomposition
(SVD) method as an alternative. Given that the SVD method does not allow for
multi-dimensional unfolding, the unfolding is performed separately for
different $p_{\mathrm{T,\,jet}}$ intervals. In a Pythia closure test, the true
distribution for $j_{\mathrm{T}}<0.1\,\mathrm{GeV}/c$ was in general found to
be between the unfolded distributions from the iterative and SVD methods. The
difference between the methods when unfolding data is used as an estimate of
the unfolding uncertainty. The resulting uncertainty is below 8% for both wide
and narrow component RMS in p–Pb collisions. In pp collisions it is 13% and
15% for the wide and narrow components, respectively. The iterative unfolding
algorithm permits the change of the number of iterations as a regularisation
parameter. The stability of the results was verified by using 3 and 5
iterations instead of the default 4. The effect of the unfolding for different
ranges of $p_{\mathrm{T,\,jet}}$ is tested by varying the first value of
$p_{\mathrm{T,\,jet}}$ from 5 to 10 GeV/$c$. These effects are found
negligible compared to that for the two different unfolding methods. The model
dependence of the unfolding procedure was explored by weighting the response
matrix with PYTHIA. The jet yield in the response matrix is varied by $\pm
30\%$ for the angularity $g=\Sigma_{i}\left(p_{\mathrm{T},i}\times
r_{i}\right)/p_{\mathrm{T,jet}}>0.1$, where $p_{\mathrm{T},i}$ is the $p_{\rm
T}$ of the $i^{\mathrm{th}}$ constituent of the jet and
$r_{i}=\sqrt{\Delta\eta^{2}_{i}+\Delta\varphi^{2}_{i}}$ is the distance of the
$i^{\mathrm{th}}$ constituent from the jet axis [68, 32]. The effect is found
to be below 2% for the wide component and below 1% for the narrow component.

The different sources of systematic uncertainty are considered as uncorrelated
and the values are summed in quadrature. The summary table in Table 1 shows an
overview of systematic uncertainties for $40<p_{\mathrm{T,\,jet}}<60$ GeV/$c$
in pp and p–Pb collisions.

Table 1: Summary of systematic uncertainties for $40<p_{\mathrm{T,\,jet}}<60$ GeV/$c$ in pp and p–Pb collisions. |  $j_{\mathrm{T}}$ distribution at $j_{\mathrm{T}}$ = 0.2–0.8–2 GeV/$c$ | Wide RMS | Narrow RMS
---|---|---|---
source | pp | p–Pb | pp | p–Pb | pp | p–Pb
Background | 20–17–20% | neg.–2–5% | 2.8% | 5% | 0.5% | 9%
Unfolding | 10–neg.–20% | 10–neg.–12% | 13% | 8% | 15% | 8%
Tracking | 4–4–4% | 2–1–neg.% | 0.5 | 4 % | neg. | 5 %
EMCal | 4–3–10% | 2–2–2% | 1.8 | 1% | 1.1% | 1%
Model dependence | neg.–2–5% | neg.–neg.–10% | 1.2 | 2% | 1% | 0%
Total | 23–18–31% | 10–3–16% | 14% | 10 % | 15% | 13%

##  5 Results

Figure 1: Comparison of the $j_{\mathrm{T}}$ distributions in pp and p–Pb
collisions at $\sqrt{s}$, $\sqrt{s_{\mathrm{NN}}}$ = 5.02 TeV in
$40<p_{\mathrm{T,\,jet}}<60$ GeV/$c$. The centre-of-mass rapidity in p–Pb
collisions is shifted by $\Delta y=0.465$ in the direction of the proton beam.

The $j_{\mathrm{T}}$ distribution in pp collisions at
$\sqrt{s}=5.02\,\mathrm{TeV}$ is compared with that in p–Pb collisions at
$\sqrt{s_{\mathrm{NN}}}=5.02\,\mathrm{TeV}$ in Fig. 1 for jet transverse
momentum in $40<p_{\mathrm{T,\,jet}}<60$ GeV/$c$. The ratio of the
$j_{\mathrm{T}}$ distributions represents the consistence of the result in pp
and p–Pb collisions and implies no clear cold nuclear matter effects in p–Pb
collisions. For higher $p_{\mathrm{T,\,jet}}$ intervals, the comparison is not
provided because of the lack of enough statistics for higher
$p_{\mathrm{T,\,jet}}$ in minimum-bias pp collisions and the absence of the
data sample with the EMCal trigger in the corresponding pp data taking period.

Figure 2: The $j_{\mathrm{T}}$ distributions of charged particles in $R=0.4$
anti-$k_{\mathrm{T}}$ jets as measured in p–Pb collisions at
$\sqrt{s_{\mathrm{NN}}}=5.02\leavevmode\nobreak\ \mathrm{TeV}$ for different
ranges of $p_{\mathrm{T,\,jet}}$. The centre-of-mass rapidity is shifted by
$\Delta y=0.465$ in the direction of the proton beam. The bottom panel shows
ratios of the $j_{\mathrm{T}}$ distributions with respect to that in
$40<p_{\mathrm{T,\,jet}}<60$ GeV/$c$.

Figure 2 shows the distributions of $j_{\mathrm{T}}$ for charged particles in
different $p_{\mathrm{T,\,jet}}$ intervals after applying the unfolding
correction and background subtraction in p–Pb collisions at
$\sqrt{s_{\mathrm{NN}}}=5.02\leavevmode\nobreak\ \mathrm{TeV}$. The yield at
low $j_{\mathrm{T}}$ stays constant with increasing $p_{\mathrm{T,\,jet}}$. At
high $j_{\mathrm{T}}$ the yield increases and the distributions become wider
with increasing $p_{\mathrm{T,\,jet}}$ as indicated by the ratios of the
$j_{\mathrm{T}}$ distributions shown in the bottom panel. Notably, this is due
to kinematical limits. At midrapidity, within a fixed cone the maximum
$j_{\mathrm{T}}$ depends on the track momentum by the relation of
$j_{\mathrm{T,\,max}}\approx R\times p_{\mathrm{T,\,track}}$, resulting in an
increase of the possible $j_{\mathrm{T}}$ as $p_{\mathrm{T,\,jet}}$ increases.
Though jets with larger momenta are more collimated, the net effect is an
increase of $\langle j_{\mathrm{T}}\rangle$ as $p_{\mathrm{T,\,jet}}$
increases. These measurements are consistent with the findings by the ATLAS
[19] and LHCb collaborations [20].

Figure 3: The $j_{\mathrm{T}}$ distribution in p–Pb collisions at
$\sqrt{s_{\mathrm{NN}}}=5.02\leavevmode\nobreak\ \mathrm{TeV}$ for jets with
transverse momentum in $60<p_{\mathrm{T,\,jet}}<80$ GeV/$c$. The measured data
are compared to calculations by theoretical models in pp collisions at
$\sqrt{s}=5.02$ TeV.

Figure 3 shows the $j_{\mathrm{T}}$ distribution in p–Pb collisions at
$\sqrt{s_{\mathrm{NN}}}=5.02\leavevmode\nobreak\ \mathrm{TeV}$ for jets with
$60<p_{\mathrm{T,\,jet}}<80$ GeV/$c$ compared with expectations from various
generators in pp collisions at $\sqrt{s}=5.02$ TeV. Pythia and Herwig handle
both the showering process and hadronisation differently. Pythia uses the Lund
string model [69] to perform the hadronisation stage. Herwig uses a cluster
model for the hadronisation [9, 10]. Pythia 8 has $p_{\rm T}$-ordered showers
by default while Herwig implements a parton shower using the coherent
branching algorithm [70], which has angular ordering as a central feature. The
$p_{\rm T}$-ordering in a Pythia 8 shower is a compromise [71]: ordering in
the $p_{\rm T}$ at splitting ensures the ordering in the hardness and also
effectively favours large angles. Herwig describes the $j_{\mathrm{T}}$
distribution better than other models for the lower $j_{\mathrm{T}}$ region;
however, it still underestimates the data by about 20%. It describes the
result well for higher $j_{\mathrm{T}}$ region. Other Pythia 8 based models
describe the data at high $j_{\mathrm{T}}$ but not in the low $j_{\mathrm{T}}$
region. The results for the other $p_{\mathrm{T,\,jet}}$ intervals are
reported in Figs. A1, A2 and A3 that derive the same conclusion. All models
describe the data better at higher $j_{\mathrm{T}}$ as $p_{\mathrm{T,\,jet}}$
increase, while those underestimate the data at lower $j_{\mathrm{T}}$ for the
entire $p_{\mathrm{T,\,jet}}$.

Pythia 8 Monash Tune [72] adopted LHC data to constrain the initial-state
radiation and multiparton interaction parameters based on the default
parameters of Pythia tune 4C [73]. There is no clear separation of the
$j_{\mathrm{T}}$ distributions originating from the different tunes of Pythia.
As of version 8.3 Pythia 8 implemented two more shower models as part of the
code. Those are VINCIA and Dire Showers that are based on the $k_{\mathrm{T}}$
(transverse momentum of a dipole)-ordered picture of QCD splitting [74, 75].
The $j_{\mathrm{T}}$ distributions generated by the two shower models were
obtained by using the default parameters of Pythia tune 4C. In order to study
the effect of the NLO calculation accuracy for the parton showering in Pythia
8 (POWHEG NLO + PYTHIA PS), the $j_{\mathrm{T}}$ distribution generated with
the combined POWHEG [76] and Pythia simulation is also compared to the data.
The $j_{\mathrm{T}}$ distributions obtained with the POWHEG NLO calculation
and Dire Shower display themselves as upper and lower bounds of the Pythia
8-based models for the higher $j_{\mathrm{T}}$ region; however, they are
within the systematic uncertainty of the data for the higher $j_{\mathrm{T}}$
region.

Figure 4: The $j_{\mathrm{T}}$ distribution of charged particles with a two-
component fit for $60<p_{\mathrm{T,\,jet}}<80$
$\mathrm{GeV}\kern-0.50003pt/\kern-0.20004pt\textit{c}$. The distribution is
fitted with the two-component fit described in Sec. 3.

The distributions are fitted with the two-component fit motivated by [48]. The
function forms are given in Eq. 3. An example of the fitted distribution is
shown in Fig. 4 for $60<p_{\mathrm{T,\,jet}}<80$
$\mathrm{GeV}\kern-0.50003pt/\kern-0.20004pt\textit{c}$. The Gaussian term
corresponds to the narrow part that can be associated with the hadronisation
process, while the inverse gamma corresponds to the wide component
characterising the QCD shower. The $j_{\mathrm{T}}$ distributions are
described well by the two-component model fit. The corresponding statistical
uncertainties are calculated via the general error propagation formulas in Eq.
6

$\delta\sqrt{\left<j_{\mathrm{T}}^{2}\right>}=\sqrt{2}\delta
B_{1}\leavevmode\nobreak\ \leavevmode\nobreak\ \leavevmode\nobreak\
\textrm{and}\leavevmode\nobreak\ \leavevmode\nobreak\ \leavevmode\nobreak\
\sqrt{\left(\frac{(5-2B_{4})B_{5}\delta
B_{4}}{(2(B_{4}-2)(B_{4}-3))^{\frac{3}{2}}}\right)^{2}+\left(\frac{\delta
B_{5}}{\sqrt{\left(B_{4}-2\right)\left(B_{4}-3\right)}}\right)^{2}}$ (6)

for the narrow and wide component RMS values, respectively.

Figure 5: RMS values extracted from the fits for the Gaussian (narrow) and
inverse gamma (wide) components. The middle and bottom plots show ratios of
models to data for the wide and narrow components, respectively. The grey
filled bands with (without) a hatched line in the ratio plots represent the
statistical (systematic) uncertainties of the data. Note that pp data points
are shifted by -2 GeV/$c$ on the horizontal axis to be distinguished from p–Pb
data points.

The widths of the $j_{\mathrm{T}}$ distributions are determined as a function
of the transverse momentum of jet. The RMS
$\left(\sqrt{\left<j_{\mathrm{T}}^{2}\right>}\,\right)$ values for the two
components are shown in Fig. 5 along with comparisons to Monte Carlo
simulations. There is clear separation in the width of the wide and narrow
components of the $j_{\mathrm{T}}$ distributions. The RMS values of the wide
component are 3-4 times larger than the narrow component RMS. The wide
component RMS shows an increasing trend with increasing $p_{\mathrm{T,\,jet}}$
that is parameterised by a linear function as
$\sqrt{\left<j_{\mathrm{T}}^{2}\right>}=0.005\,(\pm 0.004)\times
p_{\mathrm{T,\,jet}}+0.497\,(\pm 0.255)$, while the narrow component RMS stays
constant with the fitted value of $0.253\,(\pm 0.009)$. Both of these trends
are qualitatively consistent with the results in the dihadron $j_{\mathrm{T}}$
analysis [48].

All models except for Herwig describe the RMS values relatively well for the
narrow RMS component. For the wide RMS component Herwig describes the data
best as $p_{\mathrm{T,\,jet}}$ increases. Dire Shower shows clearly lower
values compared to data up to 18% for the wide RMS components. Other Pythia-
based models show a good description for the lower $j_{\mathrm{T}}$ region,
however, they underestimate the data for the higher $p_{\mathrm{T,\,jet}}$
region.

Figure 6: Comparison of results from the jet-based and dihadron-based
$j_{\mathrm{T}}$ analyses [48]. Ranges of dihadron trigger $p_{\rm T}$
($p_{\mathrm{T,\,trigger}}$) are converted to corresponding
$p_{\mathrm{T,\,jet}}$ ranges using observed mean $p_{\mathrm{T,\,jet}}$
values in $p_{\mathrm{T,\,trigger}}$ bins. Dihadron results are shown for
$0.2<x_{||}<0.4$, where $x_{||}$ is the longitudinal component fraction of the
associated track momentum with respect to the momentum of the trigger track.
The difference of the two analyses originates from the different kinematic
selections and the choice of the axis used for the $j_{\mathrm{T}}$
calculation. See text for more details.

##  6 Discussion

The comparison with the results from the dihadron analysis [48] performed for
the same collision system and energy is shown in Fig. 6. Different
$p_{\mathrm{T}}$ regions of leading particles used in the dihadron analysis
are converted to the corresponding average momentum of the jets which contain
those leading particles. The wide and narrow components of the dihadron
results are for $0.2<x_{||}<0.4$, where $x_{||}$ is the projection of the
momentum of the associated track to that of the trigger particles. Wide
component RMS values tend to increase with increasing
$p_{\mathrm{T,\,trigger}}$ and $p_{\mathrm{T,\,jet}}$, whereas narrow
component RMS values of both results show a weak dependence on
$p_{\mathrm{T,\,jet}}$ above $20\,$ GeV/$c$. The trends are similar for
dihadron and jet $j_{\mathrm{T}}$ results. However, the RMS values of the
dihadron analysis are larger than those for the jet analysis both for the
narrow and wide components.

The difference in the narrow and wide RMS components can be explained by the
following two factors. The first one is due to the different kinematic
selections on the charged particles in the same jet from which the
$j_{\mathrm{T}}$ values are calculated. The other one is due to the choice of
the axis used for the $j_{\mathrm{T}}$ calculation. In the dihadron analysis
$j_{\mathrm{T}}$ is calculated for all near-side tracks if the associated
tracks satisfy the condition
$\vec{p}_{\mathrm{leading}}\times\vec{p}_{\mathrm{a}}>0$. Here
$\vec{p}_{\mathrm{leading}}$ and $\vec{p}_{\mathrm{a}}$ are the momentum
vectors of the leading and associated tracks, respectively. Thus, the
kinematical limit $j_{\mathrm{T,max}}$ can be larger in the dihadron analysis
than in the jet analysis in which only particles in a cone with $R=0.4$ are
considered.

The effect of the $R$ parameter choice and $p_{\mathrm{T,\,jet}}$ dependence
on $j_{\mathrm{T}}$ was studied using Pythia 8 and the results are shown in
Fig. 7a. The usage of a fixed cone sets stringent limits on the possible
$j_{\mathrm{T}}$ values. Increasing the cone size loosens these limits and
allows for higher $j_{\mathrm{T}}$ values. The effect on the wide and narrow
components of the $j_{\mathrm{T}}$ distributions for Pythia 8 is shown in Fig.
7b, where the wide component RMS gets larger by about 10% when going from
$R=0.3$ to 0.4 and from 0.4 to 0.5, indicating that the kinematic limit
introduced by increasing $R$ results in a widening of the $j_{\mathrm{T}}$
distribution. For the narrow component the effect is relatively small and they
appear independent of the $R$ parameter and $p_{\mathrm{T,\,jet}}$. There can
also be a broadening effect for jets caused by the increasing gluon jet
fraction as the kinematical limit increases [68]. Additionally, there is an
effect originating from the kinematic cut on $x_{||}$ values in the dihadron
analysis that can alter the $j_{\mathrm{T}}$ distributions – but that is not
further investigated here.

(a) (a) (b) (a)

Figure 7: The effect of changing the $R$ parameter in jet finding on
$j_{\mathrm{T}}$ distributions obtained with Pythia 8 simulations. Comparison
of (a) $j_{\mathrm{T}}$ signal distributions for different $R$ parameters and
their ratios to that of $R=0.3$ and (b) RMS values of the wide and narrow
components and their ratios to that of $R=0.3$ for the wide component only.
Figure 8:  The $j_{\mathrm{T}}$ distributions with respect to the leading
track momentum (leading track reference) and the jet axis (jet-axis reference)
within the same jet for three different $p_{\mathrm{T,\,jet}}$ intervals with
$R=0.4$.

It is worth noting that the leading-track momentum vector provides an
imperfect estimate of the jet axis. Because the leading track in general is at
an angle compared to the jet axis, the resulting $j_{\mathrm{T}}$ values based
on the leading track are biased from the axis of the jet. Practically, the jet
axis found by the jet finding algorithm tends to minimise the $j_{\mathrm{T}}$
of jet constituents. Moreover, in the dihadron correlation analysis the usage
of the leading hadron as the trigger particle imposes a trigger bias favouring
quark jets resulting in jet narrowing. The impact of the different axes
adopted in the two analyses is investigated by measuring $j_{\mathrm{T}}$ with
respect to the leading track momentum (leading track reference), instead of
the jet axis (jet-axis reference) within the same jet for $R=0.4$. The results
are shown in Fig. 8. The widths of the $j_{\mathrm{T}}$ distributions for the
jet-axis reference overall are smaller than those of the leading track
reference. The bias of the choice of axis becomes small as
$p_{\mathrm{T,\,jet}}$ increases. As shown in the bottom panels, the ratios of
the distributions increase monotonically, implying that the leading track
reference makes both the wide and narrow components wider as the ratio
distributions show a monotonic increase.

Dihadron $j_{\mathrm{T}}$ distributions [48] are compared to those of jet
$j_{\mathrm{T}}$. Although a direct comparison between jet and dihadron
$j_{\mathrm{T}}$ measurements is not possible because of the effects of the
different kinematic selection and choice of the axis, RMS values of the wide
and narrow components can be quantitatively understood by considering the good
agreement between Pythia and data.

##  7 Conclusion

In this work the jet fragmentation transverse momentum ($j_{\mathrm{T}}$)
distribution of charged particles
$\frac{1}{N_{\mathrm{jets}}}\frac{\mathrm{d}N}{j_{\mathrm{T,\,ch}}\mathrm{d}j_{\mathrm{T,\,ch}}}$
is studied using jet reconstruction in pp and p–Pb collisions at $\sqrt{s}$,
$\sqrt{s_{\mathrm{NN}}}=5.02\leavevmode\nobreak\ \mathrm{TeV}$. The
$j_{\mathrm{T}}$ distributions of charged particles in p–Pb collisions become
wider as the jet transverse momentum $p_{\mathrm{T,\,jet}}$ increases. This is
understood as an effect of the reduction of the kinematical limit with
increasing $p_{\mathrm{T,\,jet}}$, allowing for higher $j_{\mathrm{T}}$
values. The $j_{\mathrm{T}}$ distribution in p–Pb collision is compared with
that in pp collisions for jet transverse momentum in
$40<p_{\mathrm{T,\,jet}}<60$ GeV/$c$, which shows no clear modification of the
$j_{\mathrm{T}}$ distribution for the p–Pb collision system. No significant
cold nuclear matter effects are observed in the previous and current
$j_{\mathrm{T}}$ measurements using dihadron correlations [48] and jet
reconstruction. For the jet study, higher statistics in pp collisions for both
minimum bias and EMCal trigger is demanded to interpret the effect in lower
$j_{\mathrm{T}}$ and higher $p_{\mathrm{T,\,jet}}$. The $j_{\mathrm{T}}$
distributions in p–Pb collisions are compared with various parton shower and
fragmentation models. All models describe the data well for the higher
$j_{\mathrm{T}}$ region, while they underestimate the data by about 20% at
lower $j_{\mathrm{T}}$.

Two distinct components of the jet fragmentation transverse momentum
$j_{\mathrm{T}}$ are extracted for narrow and wide contributions to quantify
the $j_{\mathrm{T}}$ distribution further in pp and p–Pb collisions. The width
of the narrow component has only a weak dependence on jet transverse momentum,
while that of the wide component increases with increasing jet transverse
momentum. The results are qualitatively consistent as a function of
$p_{\mathrm{T,\,jet}}$ with the previous $j_{\mathrm{T}}$ study performed with
dihadron correlations [48]. We also present a comparison to Pythia and Herwig
simulations to figure out if the two distinct components are described well by
models or differences are present. For the wide component, Herwig and Pythia 8
based models slightly underestimate the data for the higher jet transverse
momentum region. For the narrow component, the measured trends are
successfully described by all models except for Herwig. This is opposite to
the case of the $j_{\mathrm{T}}$ distributions at lower $j_{\mathrm{T}}$ where
the narrow component corresponds. This indicates that the shape of the
$j_{\mathrm{T}}$ distribution in models is also important to describe the
data.

In addition to the result in p–Pb collisions, a high statistics in pp
collisions will further constrain predictions in model calculations for jet
fragmentation and hadronisation. Future studies of the $j_{\mathrm{T}}$
distribution performed differentially in the longitudinal momentum fraction
$z$ can be used to constrain transverse-momentum dependent fragmentation
functions [12].

## 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; Ministry of Education of China (MOEC) , Ministry of Science & Technology of China (MSTC) and National Natural Science Foundation of China (NSFC), China; Ministry of Science and Education and Croatian Science Foundation, Croatia; Centro de Aplicaciones Tecnológicas y Desarrollo Nuclear (CEADEN), Cubaenergía, Cuba; Ministry of Education, Youth and Sports of the Czech Republic, Czech Republic; The Danish Council for Independent Research | Natural Sciences, the VILLUM FONDEN and Danish National Research Foundation (DNRF), Denmark; Helsinki Institute of Physics (HIP), Finland; Commissariat à l’Energie Atomique (CEA) and Institut National de Physique Nucléaire et de Physique des Particules (IN2P3) and Centre National de la Recherche Scientifique (CNRS), France; Bundesministerium für Bildung und Forschung (BMBF) and GSI Helmholtzzentrum für Schwerionenforschung GmbH, Germany; General Secretariat for Research and Technology, Ministry of Education, Research and Religions, Greece; National Research, Development and Innovation Office, Hungary; Department of Atomic Energy Government of India (DAE), Department of Science and Technology, Government of India (DST), University Grants Commission, Government of India (UGC) and Council of Scientific and Industrial Research (CSIR), India; Indonesian Institute of Science, Indonesia; Istituto Nazionale di Fisica Nucleare (INFN), Italy; Institute for Innovative Science and Technology , Nagasaki Institute of Applied Science (IIST), Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT) and Japan Society for the Promotion of Science (JSPS) KAKENHI, Japan; Consejo Nacional de Ciencia (CONACYT) y Tecnología, through Fondo de Cooperación Internacional en Ciencia y Tecnología (FONCICYT) and Dirección General de Asuntos del Personal Academico (DGAPA), Mexico; Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO), Netherlands; The Research Council of Norway, Norway; Commission on Science and Technology for Sustainable Development in the South (COMSATS), Pakistan; Pontificia Universidad Católica del Perú, Peru; Ministry of Science and Higher Education, National Science Centre and WUT ID-UB, Poland; Korea Institute of Science and Technology Information and National Research Foundation of Korea (NRF), Republic of Korea; Ministry of Education and Scientific Research, Institute of Atomic Physics and Ministry of Research and Innovation and Institute of Atomic Physics, Romania; Joint Institute for Nuclear Research (JINR), Ministry of Education and Science of the Russian Federation, National Research Centre Kurchatov Institute, Russian Science Foundation and Russian Foundation for Basic Research, Russia; Ministry of Education, Science, Research and Sport of the Slovak Republic, Slovakia; National Research Foundation of South Africa, South Africa; Swedish Research Council (VR) and Knut & Alice Wallenberg Foundation (KAW), Sweden; European Organization for Nuclear Research, Switzerland; Suranaree University of Technology (SUT), National Science and Technology Development Agency (NSDTA) and Office of the Higher Education Commission under NRU project of Thailand, Thailand; Turkish Atomic Energy Agency (TAEK), Turkey; National Academy of Sciences of Ukraine, Ukraine; Science and Technology Facilities Council (STFC), United Kingdom; National Science Foundation of the United States of America (NSF) and United States Department of Energy, Office of Nuclear Physics (DOE NP), United States of America.

## References

  * [1] D. J. Gross and F. Wilczek, “Ultraviolet behavior of non-abelian gauge theories”, Physical Review Letters 30 no. 26, (1973) 1343–1346.
  * [2] H. D. Politzer, “Reliable perturbative results for strong interactions?”, Physical Review Letters 30 no. 26, (1973) 1346–1349.
  * [3] D. Gross and F. Wilczek, “Asymptotically Free Gauge Theories - I”, Phys. Rev. D 8 (1973) 3633–3652.
  * [4] D. J. Gross and F. Wilczek, “Asymptotically Free Gauge Theories - II”, Phys. Rev. D 9 (1974) 980–993.
  * [5] H. Georgi and H. D. Politzer, “Electroproduction scaling in an asymptotically free theory of strong interactions”, Physical Review D 9 no. 2, (1974) 416.
  * [6] A. Buckley et al., “General-purpose event generators for LHC physics”, Phys. Rept. 504 (2011) 145–233, arXiv:1101.2599 [hep-ph].
  * [7] Y. L. Dokshitzer, V. A. Khoze, A. H. Mueller, and S. Troian, Basics of perturbative QCD.  1991\.
  * [8] T. Sjöstrand, S. Mrenna, and P. Z. Skands, “A Brief Introduction to PYTHIA 8.1”, Comput. Phys. Commun. 178 (2008) 852–867, arXiv:0710.3820 [hep-ph].
  * [9] M. Bahr et al., “Herwig++ Physics and Manual”, Eur. Phys. J. C58 (2008) 639–707, arXiv:0803.0883 [hep-ph].
  * [10] J. Bellm et al., “Herwig 7.0/Herwig++ 3.0 release note”, Eur. Phys. J. C76 no. 4, (2016) 196, arXiv:1512.01178 [hep-ph].
  * [11] R. Perez-Ramos, F. Arleo, and B. Machet, “Next-to-MLLA corrections to single inclusive $k_{\perp}$ distributions and two-particle correlations in a jet”, Phys. Rev. D 78 (2008) 014019, arXiv:0712.2212 [hep-ph].
  * [12] Z.-B. Kang, X. Liu, F. Ringer, and H. Xing, “The transverse momentum distribution of hadrons within jets”, JHEP 11 (2017) 068, arXiv:1705.08443 [hep-ph].
  * [13] Y. I. Azimov, Y. L. Dokshitzer, V. A. Khoze, and S. I. Troyan, “Similarity of Parton and Hadron Spectra in QCD Jets”, Z. Phys. C27 (1985) 65–72.
  * [14] D. Gutierrez-Reyes, I. Scimemi, W. J. Waalewijn, and L. Zoppi, “Transverse momentum dependent distributions with jets”, Phys. Rev. Lett. 121 no. 16, (2018) 162001, arXiv:1807.07573 [hep-ph].
  * [15] CCOR Collaboration, A. Angelis et al., “A Measurement of the Transverse Momenta of Partons, and of Jet Fragmentation as a Function of $\sqrt{s}$ in $pp$ Collisions”, Phys. Lett. B97 (1980) 163–168.
  * [16] PHENIX Collaboration, S. S. Adler et al., “Jet properties from dihadron correlations in p+p collisions at $\sqrt{s}=$ 200 GeV”, Phys. Rev. D74 (2006) 072002, arXiv:hep-ex/0605039 [hep-ex].
  * [17] PHENIX Collaboration, S. S. Adler et al., “Jet structure from dihadron correlations in d+Au collisions at s(NN)**(1/2) = 200-GeV”, Phys. Rev. C73 (2006) 054903, arXiv:nucl-ex/0510021 [nucl-ex].
  * [18] CDF Collaboration, T. Aaltonen et al., “Measurement of the $k_{T}$ Distribution of Particles in Jets Produced in $p\mathrm{bar}{p}$ Collisions at $\sqrt{s}=1.96$-TeV”, Phys. Rev. Lett. 102 (2009) 232002, arXiv:0811.2820 [hep-ex].
  * [19] ATLAS Collaboration, G. Aad et al., “Measurement of the jet fragmentation function and transverse profile in proton-proton collisions at a center-of-mass energy of 7 TeV with the ATLAS detector”, Eur. Phys. J. C71 (2011) 1795, arXiv:1109.5816 [hep-ex].
  * [20] LHCb Collaboration, R. Aaij et al., “Measurement of charged hadron production in $Z$-tagged jets in proton-proton collisions at $\sqrt{s}=8$ TeV”, Phys. Rev. Lett. 123 no. 23, (2019) 232001, arXiv:1904.08878 [hep-ex].
  * [21] PHENIX Collaboration, K. Adcox et al., “Suppression of hadrons with large transverse momentum in central Au+Au collisions at $\sqrt{s_{NN}}$ = 130-GeV”, Phys. Rev. Lett. 88 (2002) 022301, arXiv:nucl-ex/0109003 [nucl-ex].
  * [22] STAR Collaboration, J. Adams et al., “Evidence from d + Au measurements for final state suppression of high p(T) hadrons in Au+Au collisions at RHIC”, Phys. Rev. Lett. 91 (2003) 072304, arXiv:nucl-ex/0306024 [nucl-ex].
  * [23] BRAHMS Collaboration, I. Arsene et al., “Transverse momentum spectra in Au+Au and d+Au collisions at $\sqrt{s_{\mathrm{NN}}}$ = 200 GeV and the pseudorapidity dependence of high $p_{\mathrm{T}}$ suppression”, Phys. Rev. Lett. 91 (2003) 072305, arXiv:nucl-ex/0307003 [nucl-ex].
  * [24] CMS Collaboration, V. Khachatryan et al., “Charged-particle nuclear modification factors in PbPb and pPb collisions at $\sqrt{s_{\mathrm{N}\;\mathrm{N}}}=5.02$ TeV”, JHEP 04 (2017) 039, arXiv:1611.01664 [nucl-ex].
  * [25] ALICE Collaboration, S. Acharya et al., “Transverse momentum spectra and nuclear modification factors of charged particles in pp, p–Pb and Pb–Pb collisions at the LHC”, JHEP 11 (2018) 013, arXiv:1802.09145 [nucl-ex].
  * [26] PHENIX Collaboration, A. Adare et al., “Transverse momentum and centrality dependence of dihadron correlations in Au+Au collisions at $\sqrt{s_{\mathrm{NN}}}$ = 200 GeV: Jet-quenching and the response of partonic matter”, Phys. Rev. C77 (2008) 011901, arXiv:0705.3238 [nucl-ex].
  * [27] ALICE Collaboration, K. Aamodt et al., “Particle-yield modification in jet-like azimuthal di-hadron correlations in Pb-Pb collisions at $\sqrt{s_{\mathrm{NN}}}=2.76$ TeV”, Phys. Rev. Lett. 108 (2012) 092301, arXiv:1110.0121 [nucl-ex].
  * [28] PHENIX Collaboration, A. Adare et al., “Medium modification of jet fragmentation in Au + Au collisions at $\sqrt{s_{NN}}=200$ GeV measured in direct photon-hadron correlations”, Phys. Rev. Lett. 111 no. 3, (2013) 032301, arXiv:1212.3323 [nucl-ex].
  * [29] ALICE Collaboration, J. Adam et al., “Measurement of jet quenching with semi-inclusive hadron-jet distributions in central Pb-Pb collisions at $\sqrt{s_{\mathrm{NN}}}=2.76$ TeV”, JHEP 09 (2015) 170, arXiv:1506.03984 [nucl-ex].
  * [30] STAR Collaboration, L. Adamczyk et al., “Measurements of jet quenching with semi-inclusive hadron+jet distributions in Au+Au collisions at $\sqrt{s_{NN}}$ = 200 GeV”, Phys. Rev. C 96 no. 2, (2017) 024905, arXiv:1702.01108 [nucl-ex].
  * [31] ALICE Collaboration, J. Adam et al., “Measurement of jet suppression in central Pb–Pb collisions at $\sqrt{s_{\rm NN}}$ = 2.76 TeV”, Phys. Lett. B746 (2015) 1–14, arXiv:1502.01689 [nucl-ex].
  * [32] ALICE Collaboration, S. Acharya et al., “Measurements of inclusive jet spectra in pp and central Pb–Pb collisions at $\sqrt{s_{\rm{NN}}}$ = 5.02 TeV”, arXiv:1909.09718 [nucl-ex].
  * [33] CMS Collaboration, A. M. Sirunyan et al., “Observation of Medium-Induced Modifications of Jet Fragmentation in Pb-Pb Collisions at $\sqrt{s_{NN}}=$ 5.02 TeV Using Isolated Photon-Tagged Jets”, Phys. Rev. Lett. 121 no. 24, (2018) 242301, arXiv:1801.04895 [hep-ex].
  * [34] CMS Collaboration, S. Chatrchyan et al., “Measurement of jet fragmentation in PbPb and pp collisions at $\sqrt{s_{NN}}=2.76$ TeV”, Phys. Rev. C90 no. 2, (2014) 024908, arXiv:1406.0932 [nucl-ex].
  * [35] ALICE Collaboration, S. Acharya et al., “Medium modification of the shape of small-radius jets in central Pb-Pb collisions at $\sqrt{s_{\mathrm{NN}}}=2.76\,\rm{TeV}$”, JHEP 10 (2018) 139, arXiv:1807.06854 [nucl-ex].
  * [36] ALICE Collaboration, S. Acharya et al., “Exploration of jet substructure using iterative declustering in pp and Pb–Pb collisions at LHC energies”, Phys. Lett. B802 (2020) 135227, arXiv:1905.02512 [nucl-ex].
  * [37] A. Kurkela and U. A. Wiedemann, “Picturing perturbative parton cascades in QCD matter”, Phys. Lett. B740 (2015) 172–178, arXiv:1407.0293 [hep-ph].
  * [38] JETSCAPE Collaboration, Y. Tachibana et al., “Jet substructure modifications in a QGP from multi-scale description of jet evolution with JETSCAPE”, PoS HardProbes2018 (2018) 099, arXiv:1812.06366 [nucl-th].
  * [39] P. Aurenche and B. G. Zakharov, “Jet color chemistry and anomalous baryon production in $AA$-collisions”, Eur. Phys. J. C71 (2011) 1829, arXiv:1109.6819 [hep-ph].
  * [40] A. Beraudo, J. G. Milhano, and U. A. Wiedemann, “Medium-induced color flow softens hadronization”, Phys. Rev. C85 (2012) 031901, arXiv:1109.5025 [hep-ph].
  * [41] A. Beraudo, J. G. Milhano, and U. A. Wiedemann, “The Contribution of Medium-Modified Color Flow to Jet Quenching”, JHEP 07 (2012) 144, arXiv:1204.4342 [hep-ph].
  * [42] J. Casalderrey-Solana, Y. Mehtar-Tani, C. A. Salgado, and K. Tywoniuk, “New picture of jet quenching dictated by color coherence”, Phys. Lett. B725 (2013) 357–360, arXiv:1210.7765 [hep-ph].
  * [43] F. D’Eramo, M. Lekaveckas, H. Liu, and K. Rajagopal, “Momentum Broadening in Weakly Coupled Quark-Gluon Plasma (with a view to finding the quasiparticles within liquid quark-gluon plasma)”, JHEP 05 (2013) 031, arXiv:1211.1922 [hep-ph].
  * [44] A. Ayala, I. Dominguez, J. Jalilian-Marian, and M. E. Tejeda-Yeomans, “Relating $\hat{q}$, $\eta/s$ and $\Delta E$ in an expanding quark-gluon plasma”, Phys. Rev. C94 no. 2, (2016) 024913, arXiv:1603.09296 [hep-ph].
  * [45] R. Baier, Y. L. Dokshitzer, A. H. Mueller, S. Peigne, and D. Schiff, “Radiative energy loss and p(T) broadening of high-energy partons in nuclei”, Nucl. Phys. B484 (1997) 265–282, arXiv:hep-ph/9608322 [hep-ph].
  * [46] L. D. McLerran and R. Venugopalan, “Computing quark and gluon distribution functions for very large nuclei”, Phys. Rev. D49 (1994) 2233–2241, arXiv:hep-ph/9309289 [hep-ph].
  * [47] K. J. Eskola, H. Paukkunen, and C. A. Salgado, “EPS09: A New Generation of NLO and LO Nuclear Parton Distribution Functions”, JHEP 04 (2009) 065, arXiv:0902.4154 [hep-ph].
  * [48] ALICE Collaboration, S. Acharya et al., “Jet fragmentation transverse momentum measurements from di-hadron correlations in $\sqrt{s}$ = 7 TeV pp and $\sqrt{s_{\rm{NN}}}$ = 5.02 TeV p-Pb collisions”, JHEP 03 (2019) 169, arXiv:1811.09742 [nucl-ex].
  * [49] M. Cacciari, G. P. Salam, and G. Soyez, “The anti-$k_{t}$ jet clustering algorithm”, JHEP 04 (2008) 063, arXiv:0802.1189 [hep-ph].
  * [50] ALICE Collaboration, K. Aamodt et al., “The ALICE experiment at the CERN LHC”, JINST 3 (2008) S08002.
  * [51] ALICE Collaboration, B. Abelev et al., “Performance of the ALICE Experiment at the CERN LHC”, Int. J. Mod. Phys. A29 (2014) 1430044, arXiv:1402.4476 [nucl-ex].
  * [52] ALICE Collaboration, P. Cortese et al., “ALICE technical design report on forward detectors: FMD, T0 and V0”, tech. rep., 9, 2004.
  * [53] ALICE Collaboration, K. Aamodt et al., “Alignment of the ALICE Inner Tracking System with cosmic-ray tracks”, JINST 5 (2010) P03003, arXiv:1001.0502 [physics.ins-det].
  * [54] J. Alme et al., “The ALICE TPC, a large 3-dimensional tracking device with fast readout for ultra-high multiplicity events”, Nucl. Instrum. Meth. A622 (2010) 316–367, arXiv:1001.1950 [physics.ins-det].
  * [55] ALICE Collaboration, B. Abelev et al., “Long-range angular correlations on the near and away side in p–Pb collisions at $\sqrt{s_{\mathrm{NN}}}=5.02$ TeV”, Phys. Lett. B719 (2013) 29–41, arXiv:1212.2001 [nucl-ex].
  * [56] ALICE Collaboration, B. Abelev et al., “Measurement of Event Background Fluctuations for Charged Particle Jet Reconstruction in Pb–Pb collisions at $\sqrt{s_{\mathrm{NN}}}=2.76$ TeV”, JHEP 03 (2012) 053, arXiv:1201.2423 [hep-ex].
  * [57] ALICE Collaboration, B. B. Abelev et al., “Performance of the ALICE Experiment at the CERN LHC”, Int. J. Mod. Phys. A 29 (2014) 1430044, arXiv:1402.4476 [nucl-ex].
  * [58] M. Cacciari, G. P. Salam, and G. Soyez, “FastJet User Manual”, Eur. Phys. J. C72 (2012) 1896, arXiv:1111.6097 [hep-ph].
  * [59] CMS Collaboration, S. Chatrchyan et al., “Shape, Transverse Size, and Charged Hadron Multiplicity of Jets in pp Collisions at 7 TeV”, JHEP 06 (2012) 160, arXiv:1204.3170 [hep-ex].
  * [60] T. Adye, “Unfolding algorithms and tests using RooUnfold”, in Proceedings, PHYSTAT 2011 Workshop on Statistical Issues Related to Discovery Claims in Search Experiments and Unfolding, CERN,Geneva, Switzerland 17-20 January 2011, pp. 313–318, CERN.  CERN, Geneva, 2011.  arXiv:1105.1160 [physics.data-an].
  * [61] P. Z. Skands, “Tuning Monte Carlo Generators: The Perugia Tunes”, Phys. Rev. D82 (2010) 074018, arXiv:1005.3457 [hep-ph].
  * [62] GEANT4 Collaboration, S. Agostinelli et al., “GEANT4: A Simulation toolkit”, Nucl. Instrum. Meth. A506 (2003) 250–303.
  * [63] Geant4 Collaboration, M. Asai, A. Dotti, M. Verderi, and D. H. Wright, “Recent developments in Geant4”, Annals Nucl. Energy 82 (2015) 19–28.
  * [64] ALICE Collaboration, B. Abelev et al., “Charged jet cross sections and properties in proton-proton collisions at $\sqrt{s}=7$ TeV”, Phys. Rev. D91 no. 11, (2015) 112012, arXiv:1411.4969 [nucl-ex].
  * [65] ALICE Collaboration, S. Acharya et al., “Charged jet cross section and fragmentation in proton-proton collisions at $\sqrt{s}=7$ TeV”, Phys. Rev. D99 no. 1, (2019) 012016, arXiv:1809.03232 [nucl-ex].
  * [66] ALICE Collaboration, B. Abelev et al., “Pseudorapidity density of charged particles in p–Pb collisions at $\sqrt{s_{\mathrm{NN}}}=5.02$ TeV”, Phys. Rev. Lett. 110 no. 3, (2013) 032301, arXiv:1210.3615 [nucl-ex].
  * [67] ALICE Collaboration, B. Abelev et al., “Transverse momentum dependence of inclusive primary charged-particle production in p-Pb collisions at $\sqrt{s_{\mathrm{{NN}}}}=5.02\leavevmode\nobreak\ \text{TeV}$”, Eur. Phys. J. C74 no. 9, (2014) 3054, arXiv:1405.2737 [nucl-ex].
  * [68] A. J. Larkoski, J. Thaler, and W. J. Waalewijn, “Gaining (Mutual) Information about Quark/Gluon Discrimination”, JHEP 11 (2014) 129, arXiv:1408.3122 [hep-ph].
  * [69] B. Andersson, G. Gustafson, G. Ingelman, and T. Sjöstrand, “Parton Fragmentation and String Dynamics”, Phys. Rept. 97 (1983) 31–145.
  * [70] S. Gieseke, P. Stephens, and B. Webber, “New formalism for QCD parton showers”, JHEP 12 (2003) 045, arXiv:hep-ph/0310083 [hep-ph].
  * [71] T. Sjostrand and P. Z. Skands, “Transverse-momentum-ordered showers and interleaved multiple interactions”, Eur. Phys. J. C39 (2005) 129–154, arXiv:hep-ph/0408302 [hep-ph].
  * [72] P. Skands, S. Carrazza, and J. Rojo, “Tuning PYTHIA 8.1: the Monash 2013 Tune”, Eur. Phys. J. C74 no. 8, (2014) 3024, arXiv:1404.5630 [hep-ph].
  * [73] R. Corke and T. Sjostrand, “Interleaved Parton Showers and Tuning Prospects”, JHEP 03 (2011) 032, arXiv:1011.1759 [hep-ph].
  * [74] N. Fischer, S. Prestel, M. Ritzmann, and P. Skands, “Vincia for Hadron Colliders”, Eur. Phys. J. C76 no. 11, (2016) 589, arXiv:1605.06142 [hep-ph].
  * [75] S. Höche and S. Prestel, “The midpoint between dipole and parton showers”, Eur. Phys. J. C75 no. 9, (2015) 461, arXiv:1506.05057 [hep-ph].
  * [76] C. Oleari, “The POWHEG-BOX”, Nucl. Phys. Proc. Suppl. 205-206 (2010) 36–41, arXiv:1007.3893 [hep-ph].

##  Appendix A The ALICE Collaboration

S. Acharya142, D. Adamová97, A. Adler75, J. Adolfsson82, G. Aglieri Rinella35,
M. Agnello31, N. Agrawal55, Z. Ahammed142, S. Ahmad16, S.U. Ahn77, Z. Akbar52,
A. Akindinov94, M. Al-Turany109, D.S.D. Albuquerque124, D. Aleksandrov90, B.
Alessandro60, H.M. Alfanda7, R. Alfaro Molina72, B. Ali16, Y. Ali14, A.
Alici26, N. Alizadehvandchali127, A. Alkin35, J. Alme21, T. Alt69, L.
Altenkamper21, I. Altsybeev115, M.N. Anaam7, C. Andrei49, D. Andreou92, A.
Andronic145, V. Anguelov106, T. Antičić110, F. Antinori58, P. Antonioli55, N.
Apadula81, L. Aphecetche117, H. Appelshäuser69, S. Arcelli26, R. Arnaldi60, M.
Arratia81, I.C. Arsene20, M. Arslandok147,106, A. Augustinus35, R.
Averbeck109, S. Aziz79, M.D. Azmi16, A. Badalà57, Y.W. Baek42, X. Bai109, R.
Bailhache69, R. Bala103, A. Balbino31, A. Baldisseri139, M. Ball44, D.
Banerjee4, R. Barbera27, L. Barioglio25, M. Barlou86, G.G. Barnaföldi146, L.S.
Barnby96, V. Barret136, C. Bartels129, K. Barth35, E. Bartsch69, F.
Baruffaldi28, N. Bastid136, S. Basu82,144, G. Batigne117, B. Batyunya76, D.
Bauri50, J.L. Bazo Alba114, I.G. Bearden91, C. Beattie147, I. Belikov138,
A.D.C. Bell Hechavarria145, F. Bellini35, R. Bellwied127, S. Belokurova115, V.
Belyaev95, G. Bencedi70,146, S. Beole25, A. Bercuci49, Y. Berdnikov100, A.
Berdnikova106, D. Berenyi146, L. Bergmann106, M.G. Besoiu68, L. Betev35, P.P.
Bhaduri142, A. Bhasin103, I.R. Bhat103, M.A. Bhat4, B. Bhattacharjee43, P.
Bhattacharya23, A. Bianchi25, L. Bianchi25, N. Bianchi53, J. Bielčík38, J.
Bielčíková97, A. Bilandzic107, G. Biro146, S. Biswas4, J.T. Blair121, D.
Blau90, M.B. Blidaru109, C. Blume69, G. Boca29, F. Bock98, A. Bogdanov95, S.
Boi23, J. Bok62, L. Boldizsár146, A. Bolozdynya95, M. Bombara39, G. Bonomi141,
H. Borel139, A. Borissov83,95, H. Bossi147, E. Botta25, L. Bratrud69, P.
Braun-Munzinger109, M. Bregant123, M. Broz38, G.E. Bruno108,34, M.D.
Buckland129, D. Budnikov111, H. Buesching69, S. Bufalino31, O. Bugnon117, P.
Buhler116, P. Buncic35, Z. Buthelezi73,133, J.B. Butt14, S.A. Bysiak120, D.
Caffarri92, A. Caliva109, E. Calvo Villar114, J.M.M. Camacho122, R.S.
Camacho46, P. Camerini24, F.D.M. Canedo123, A.A. Capon116, F. Carnesecchi26,
R. Caron139, J. Castillo Castellanos139, E.A.R. Casula56, F. Catalano31, C.
Ceballos Sanchez76, P. Chakraborty50, S. Chandra142, W. Chang7, S.
Chapeland35, M. Chartier129, S. Chattopadhyay142, S. Chattopadhyay112, A.
Chauvin23, C. Cheshkov137, B. Cheynis137, V. Chibante Barroso35, D.D.
Chinellato124, S. Cho62, P. Chochula35, P. Christakoglou92, C.H.
Christensen91, P. Christiansen82, T. Chujo135, C. Cicalo56, L. Cifarelli26, F.
Cindolo55, M.R. Ciupek109, G. ClaiII,55, J. Cleymans126, F. Colamaria54, J.S.
Colburn113, D. Colella54, A. Collu81, M. Colocci35,26, M. ConcasIII,60, G.
Conesa Balbastre80, Z. Conesa del Valle79, G. Contin24, J.G. Contreras38, T.M.
Cormier98, P. Cortese32, M.R. Cosentino125, F. Costa35, S. Costanza29, P.
Crochet136, E. Cuautle70, P. Cui7, L. Cunqueiro98, A. Dainese58, F.P.A.
Damas117,139, M.C. Danisch106, A. Danu68, D. Das112, I. Das112, P. Das88, P.
Das4, S. Das4, S. Dash50, S. De88, A. De Caro30, G. de Cataldo54, L. De
Cilladi25, J. de Cuveland40, A. De Falco23, D. De Gruttola30, N. De Marco60,
C. De Martin24, S. De Pasquale30, S. Deb51, H.F. Degenhardt123, K.R. Deja143,
S. Delsanto25, W. Deng7, P. Dhankher19, D. Di Bari34, A. Di Mauro35, R.A.
Diaz8, T. Dietel126, P. Dillenseger69, Y. Ding7, R. Divià35, D.U. Dixit19, Ø.
Djuvsland21, U. Dmitrieva64, J. Do62, A. Dobrin68, B. Dönigus69, O. Dordic20,
A.K. Dubey142, A. Dubla109,92, S. Dudi102, M. Dukhishyam88, P. Dupieux136,
T.M. Eder145, R.J. Ehlers98, V.N. Eikeland21, D. Elia54, B. Erazmus117, F.
Ercolessi26, F. Erhardt101, A. Erokhin115, M.R. Ersdal21, B. Espagnon79, G.
Eulisse35, D. Evans113, S. Evdokimov93, L. Fabbietti107, M. Faggin28, J.
Faivre80, F. Fan7, A. Fantoni53, M. Fasel98, P. Fecchio31, A. Feliciello60, G.
Feofilov115, A. Fernández Téllez46, A. Ferrero139, A. Ferretti25, A.
Festanti35, V.J.G. Feuillard106, J. Figiel120, S. Filchagin111, D. Finogeev64,
F.M. Fionda21, G. Fiorenza54, F. Flor127, A.N. Flores121, S. Foertsch73, P.
Foka109, S. Fokin90, E. Fragiacomo61, U. Fuchs35, C. Furget80, A. Furs64, M.
Fusco Girard30, J.J. Gaardhøje91, M. Gagliardi25, A.M. Gago114, A. Gal138,
C.D. Galvan122, P. Ganoti86, C. Garabatos109, J.R.A. Garcia46, E. Garcia-
Solis10, K. Garg117, C. Gargiulo35, A. Garibli89, K. Garner145, P. Gasik107,
E.F. Gauger121, M.B. Gay Ducati71, M. Germain117, J. Ghosh112, P. Ghosh142,
S.K. Ghosh4, M. Giacalone26, P. Gianotti53, P. Giubellino109,60, P.
Giubilato28, A.M.C. Glaenzer139, P. Glässel106, V. Gonzalez144, L.H. González-
Trueba72, S. Gorbunov40, L. Görlich120, S. Gotovac36, V. Grabski72, L.K.
Graczykowski143, K.L. Graham113, L. Greiner81, A. Grelli63, C. Grigoras35, V.
Grigoriev95, A. GrigoryanI,1, S. Grigoryan76, O.S. Groettvik21, F. Grosa60,
J.F. Grosse-Oetringhaus35, R. Grosso109, R. Guernane80, M. Guilbaud117, M.
Guittiere117, K. Gulbrandsen91, T. Gunji134, A. Gupta103, R. Gupta103, I.B.
Guzman46, R. Haake147, M.K. Habib109, C. Hadjidakis79, H. Hamagaki84, G.
Hamar146, M. Hamid7, R. Hannigan121, M.R. Haque143,88, A. Harlenderova109,
J.W. Harris147, A. Harton10, J.A. Hasenbichler35, H. Hassan98, D.
Hatzifotiadou55, P. Hauer44, L.B. Havener147, S. Hayashi134, S.T. Heckel107,
E. Hellbär69, H. Helstrup37, T. Herman38, E.G. Hernandez46, G. Herrera
Corral9, F. Herrmann145, K.F. Hetland37, H. Hillemanns35, C. Hills129, B.
Hippolyte138, B. Hohlweger107, J. Honermann145, G.H. Hong148, D. Horak38, S.
Hornung109, R. Hosokawa15, P. Hristov35, C. Huang79, C. Hughes132, P. Huhn69,
T.J. Humanic99, H. Hushnud112, L.A. Husova145, N. Hussain43, D. Hutter40, J.P.
Iddon35,129, R. Ilkaev111, H. Ilyas14, M. Inaba135, G.M. Innocenti35, M.
Ippolitov90, A. Isakov38,97, M.S. Islam112, M. Ivanov109, V. Ivanov100, V.
Izucheev93, B. Jacak81, N. Jacazio35,55, P.M. Jacobs81, S. Jadlovska119, J.
Jadlovsky119, S. Jaelani63, C. Jahnke123, M.J. Jakubowska143, M.A. Janik143,
T. Janson75, M. Jercic101, O. Jevons113, M. Jin127, F. Jonas98,145, P.G.
Jones113, J. Jung69, M. Jung69, A. Jusko113, P. Kalinak65, A. Kalweit35, V.
Kaplin95, S. Kar7, A. Karasu Uysal78, D. Karatovic101, O. Karavichev64, T.
Karavicheva64, P. Karczmarczyk143, E. Karpechev64, A. Kazantsev90, U.
Kebschull75, R. Keidel48, M. Keil35, B. Ketzer44, Z. Khabanova92, A.M. Khan7,
S. Khan16, A. Khanzadeev100, Y. Kharlov93, A. Khatun16, A. Khuntia120, B.
Kileng37, B. Kim62, D. Kim148, D.J. Kim128, E.J. Kim74, H. Kim17, J. Kim148,
J.S. Kim42, J. Kim106, J. Kim148, J. Kim74, M. Kim106, S. Kim18, T. Kim148, T.
Kim148, S. Kirsch69, I. Kisel40, S. Kiselev94, A. Kisiel143, J.L. Klay6, J.
Klein35,60, S. Klein81, C. Klein-Bösing145, M. Kleiner69, T. Klemenz107, A.
Kluge35, A.G. Knospe127, C. Kobdaj118, M.K. Köhler106, T. Kollegger109, A.
Kondratyev76, N. Kondratyeva95, E. Kondratyuk93, J. Konig69, S.A.
Konigstorfer107, P.J. Konopka2,35, G. Kornakov143, S.D. Koryciak2, L.
Koska119, O. Kovalenko87, V. Kovalenko115, M. Kowalski120, I. Králik65, A.
Kravčáková39, L. Kreis109, M. Krivda113,65, F. Krizek97, K. Krizkova
Gajdosova38, M. Kroesen106, M. Krüger69, E. Kryshen100, M. Krzewicki40, V.
Kučera35, C. Kuhn138, P.G. Kuijer92, T. Kumaoka135, L. Kumar102, S. Kundu88,
P. Kurashvili87, A. Kurepin64, A.B. Kurepin64, A. Kuryakin111, S. Kushpil97,
J. Kvapil113, M.J. Kweon62, J.Y. Kwon62, Y. Kwon148, S.L. La Pointe40, P. La
Rocca27, Y.S. Lai81, A. Lakrathok118, M. Lamanna35, R. Langoy131, K.
Lapidus35, P. Larionov53, E. Laudi35, L. Lautner35, R. Lavicka38, T.
Lazareva115, R. Lea24, J. Lee135, S. Lee148, J. Lehrbach40, R.C. Lemmon96, I.
León Monzón122, E.D. Lesser19, M. Lettrich35, P. Lévai146, X. Li11, X.L. Li7,
J. Lien131, R. Lietava113, B. Lim17, S.H. Lim17, V. Lindenstruth40, A.
Lindner49, C. Lippmann109, A. Liu19, J. Liu129, I.M. Lofnes21, V. Loginov95,
C. Loizides98, P. Loncar36, J.A. Lopez106, X. Lopez136, E. López Torres8, J.R.
Luhder145, M. Lunardon28, G. Luparello61, Y.G. Ma41, A. Maevskaya64, M.
Mager35, S.M. Mahmood20, T. Mahmoud44, A. Maire138, R.D. MajkaI,147, M.
Malaev100, Q.W. Malik20, L. MalininaIV,76, D. Mal’Kevich94, N. Mallick51, P.
Malzacher109, G. Mandaglio33,57, V. Manko90, F. Manso136, V. Manzari54, Y.
Mao7, M. Marchisone137, J. Mareš67, G.V. Margagliotti24, A. Margotti55, A.
Marín109, C. Markert121, M. Marquard69, N.A. Martin106, P. Martinengo35, J.L.
Martinez127, M.I. Martínez46, G. Martínez García117, S. Masciocchi109, M.
Masera25, A. Masoni56, L. Massacrier79, A. Mastroserio140,54, A.M. Mathis107,
O. Matonoha82, P.F.T. Matuoka123, A. Matyja120, C. Mayer120, F. Mazzaschi25,
M. Mazzilli35,54, M.A. Mazzoni59, A.F. Mechler69, F. Meddi22, Y. Melikyan64,
A. Menchaca-Rocha72, C. Mengke7, E. Meninno116,30, A.S. Menon127, M. Meres13,
S. Mhlanga126, Y. Miake135, L. Micheletti25, L.C. Migliorin137, D.L.
Mihaylov107, K. Mikhaylov76,94, A.N. Mishra146,70, D. Miśkowiec109, A. Modak4,
N. Mohammadi35, A.P. Mohanty63, B. Mohanty88, M. Mohisin Khan16, Z.
Moravcova91, C. Mordasini107, D.A. Moreira De Godoy145, L.A.P. Moreno46, I.
Morozov64, A. Morsch35, T. Mrnjavac35, V. Muccifora53, E. Mudnic36, D.
Mühlheim145, S. Muhuri142, J.D. Mulligan81, A. Mulliri23,56, M.G. Munhoz123,
R.H. Munzer69, H. Murakami134, S. Murray126, L. Musa35, J. Musinsky65, C.J.
Myers127, J.W. Myrcha143, B. Naik50, R. Nair87, B.K. Nandi50, R. Nania55, E.
Nappi54, M.U. Naru14, A.F. Nassirpour82, C. Nattrass132, S. Nazarenko111, A.
Neagu20, L. Nellen70, S.V. Nesbo37, G. Neskovic40, D. Nesterov115, B.S.
Nielsen91, S. Nikolaev90, S. Nikulin90, V. Nikulin100, F. Noferini55, S.
Noh12, P. Nomokonov76, J. Norman129, N. Novitzky135, P. Nowakowski143, A.
Nyanin90, J. Nystrand21, M. Ogino84, A. Ohlson82, J. Oleniacz143, A.C.
Oliveira Da Silva132, M.H. Oliver147, B.S. Onnerstad128, C. Oppedisano60, A.
Ortiz Velasquez70, T. Osako47, A. Oskarsson82, J. Otwinowski120, K. Oyama84,
Y. Pachmayer106, S. Padhan50, D. Pagano141, G. Paić70, J. Pan144, S.
Panebianco139, P. Pareek142, J. Park62, J.E. Parkkila128, S. Parmar102, S.P.
Pathak127, B. Paul23, J. Pazzini141, H. Pei7, T. Peitzmann63, X. Peng7, L.G.
Pereira71, H. Pereira Da Costa139, D. Peresunko90, G.M. Perez8, S. Perrin139,
Y. Pestov5, V. Petráček38, M. Petrovici49, R.P. Pezzi71, S. Piano61, M.
Pikna13, P. Pillot117, O. Pinazza55,35, L. Pinsky127, C. Pinto27, S. Pisano53,
M. Płoskoń81, M. Planinic101, F. Pliquett69, M.G. Poghosyan98, B.
Polichtchouk93, N. Poljak101, A. Pop49, S. Porteboeuf-Houssais136, J.
Porter81, V. Pozdniakov76, S.K. Prasad4, R. Preghenella55, F. Prino60, C.A.
Pruneau144, I. Pshenichnov64, M. Puccio35, S. Qiu92, L. Quaglia25, R.E.
Quishpe127, S. Ragoni113, J. Rak128, A. Rakotozafindrabe139, L. Ramello32, F.
Rami138, S.A.R. Ramirez46, A.G.T. Ramos34, R. Raniwala104, S. Raniwala104,
S.S. Räsänen45, R. Rath51, I. Ravasenga92, K.F. Read98,132, A.R. Redelbach40,
K. RedlichV,87, A. Rehman21, P. Reichelt69, F. Reidt35, R. Renfordt69, Z.
Rescakova39, K. Reygers106, A. Riabov100, V. Riabov100, T. Richert82,91, M.
Richter20, P. Riedler35, W. Riegler35, F. Riggi27, C. Ristea68, S.P. Rode51,
M. Rodríguez Cahuantzi46, K. Røed20, R. Rogalev93, E. Rogochaya76, T.S.
Rogoschinski69, D. Rohr35, D. Röhrich21, P.F. Rojas46, P.S. Rokita143, F.
Ronchetti53, A. Rosano33,57, E.D. Rosas70, A. Rossi58, A. Rotondi29, A. Roy51,
P. Roy112, N. Rubini26, O.V. Rueda82, R. Rui24, B. Rumyantsev76, A.
Rustamov89, E. Ryabinkin90, Y. Ryabov100, A. Rybicki120, H. Rytkonen128,
O.A.M. Saarimaki45, R. Sadek117, S. Sadovsky93, J. Saetre21, K. Šafařík38,
S.K. Saha142, S. Saha88, B. Sahoo50, P. Sahoo50, R. Sahoo51, S. Sahoo66, D.
Sahu51, P.K. Sahu66, J. Saini142, S. Sakai135, S. Sambyal103, V.
Samsonov100,95, D. Sarkar144, N. Sarkar142, P. Sarma43, V.M. Sarti107, M.H.P.
Sas147,63, J. Schambach98,121, H.S. Scheid69, C. Schiaua49, R. Schicker106, A.
Schmah106, C. Schmidt109, H.R. Schmidt105, M.O. Schmidt106, M. Schmidt105,
N.V. Schmidt98,69, A.R. Schmier132, R. Schotter138, J. Schukraft35, Y.
Schutz138, K. Schwarz109, K. Schweda109, G. Scioli26, E. Scomparin60, J.E.
Seger15, Y. Sekiguchi134, D. Sekihata134, I. Selyuzhenkov109,95, S.
Senyukov138, J.J. Seo62, D. Serebryakov64, L. Šerkšnytė107, A. Sevcenco68, A.
Shabanov64, A. Shabetai117, R. Shahoyan35, W. Shaikh112, A. Shangaraev93, A.
Sharma102, H. Sharma120, M. Sharma103, N. Sharma102, S. Sharma103, O.
Sheibani127, A.I. Sheikh142, K. Shigaki47, M. Shimomura85, S. Shirinkin94, Q.
Shou41, Y. Sibiriak90, S. Siddhanta56, T. Siemiarczuk87, D. Silvermyr82, G.
Simatovic92, G. Simonetti35, B. Singh107, R. Singh88, R. Singh103, R. Singh51,
V.K. Singh142, V. Singhal142, T. Sinha112, B. Sitar13, M. Sitta32, T.B.
Skaali20, M. Slupecki45, N. Smirnov147, R.J.M. Snellings63, C. Soncco114, J.
Song127, A. Songmoolnak118, F. Soramel28, S. Sorensen132, I. Sputowska120, J.
Stachel106, I. Stan68, P.J. Steffanic132, S.F. Stiefelmaier106, D. Stocco117,
M.M. Storetvedt37, L.D. Stritto30, C.P. Stylianidis92, A.A.P. Suaide123, T.
Sugitate47, C. Suire79, M. Suljic35, R. Sultanov94, M. Šumbera97, V.
Sumberia103, S. Sumowidagdo52, S. Swain66, A. Szabo13, I. Szarka13, U.
Tabassam14, S.F. Taghavi107, G. Taillepied136, J. Takahashi124, G.J.
Tambave21, S. Tang136,7, Z. Tang130, M. Tarhini117, M.G. Tarzila49, A.
Tauro35, G. Tejeda Muñoz46, A. Telesca35, L. Terlizzi25, C. Terrevoli127, G.
Tersimonov3, S. Thakur142, D. Thomas121, R. Tieulent137, A. Tikhonov64, A.R.
Timmins127, M. Tkacik119, A. Toia69, N. Topilskaya64, M. Toppi53, F. Torales-
Acosta19, S.R. Torres38,9, A. Trifiró33,57, S. Tripathy70, T. Tripathy50, S.
Trogolo28, G. Trombetta34, L. Tropp39, V. Trubnikov3, W.H. Trzaska128, T.P.
Trzcinski143, B.A. Trzeciak38, A. Tumkin111, R. Turrisi58, T.S. Tveter20, K.
Ullaland21, E.N. Umaka127, A. Uras137, G.L. Usai23, M. Vala39, N. Valle29, S.
Vallero60, N. van der Kolk63, L.V.R. van Doremalen63, M. van Leeuwen92, P.
Vande Vyvre35, D. Varga146, Z. Varga146, M. Varga-Kofarago146, A. Vargas46, M.
Vasileiou86, A. Vasiliev90, O. Vázquez Doce107, V. Vechernin115, E.
Vercellin25, S. Vergara Limón46, L. Vermunt63, R. Vértesi146, M. Verweij63, L.
Vickovic36, Z. Vilakazi133, O. Villalobos Baillie113, G. Vino54, A.
Vinogradov90, T. Virgili30, V. Vislavicius91, A. Vodopyanov76, B. Volkel35,
M.A. Völkl105, K. Voloshin94, S.A. Voloshin144, G. Volpe34, B. von Haller35,
I. Vorobyev107, D. Voscek119, J. Vrláková39, B. Wagner21, M. Weber116, A.
Wegrzynek35, S.C. Wenzel35, J.P. Wessels145, J. Wiechula69, J. Wikne20, G.
Wilk87, J. Wilkinson109, G.A. Willems145, E. Willsher113, B. Windelband106, M.
Winn139, W.E. Witt132, J.R. Wright121, Y. Wu130, R. Xu7, S. Yalcin78, Y.
Yamaguchi47, K. Yamakawa47, S. Yang21, S. Yano47,139, Z. Yin7, H. Yokoyama63,
I.-K. Yoo17, J.H. Yoon62, S. Yuan21, A. Yuncu106, V. Yurchenko3, V. Zaccolo24,
A. Zaman14, C. Zampolli35, H.J.C. Zanoli63, N. Zardoshti35, A.
Zarochentsev115, P. Závada67, N. Zaviyalov111, H. Zbroszczyk143, M. Zhalov100,
S. Zhang41, X. Zhang7, Y. Zhang130, V. Zherebchevskii115, Y. Zhi11, D. Zhou7,
Y. Zhou91, J. Zhu7,109, Y. Zhu7, A. Zichichi26, G. Zinovjev3, N. Zurlo141

Affiliation Notes

I Deceased

II Also at: Italian National Agency for New Technologies, Energy and
Sustainable Economic Development (ENEA), Bologna, Italy

III Also at: Dipartimento DET del Politecnico di Torino, Turin, Italy

IV Also at: M.V. Lomonosov Moscow State University, D.V. Skobeltsyn Institute
of Nuclear, Physics, Moscow, Russia

V Also at: Institute of Theoretical Physics, University of Wroclaw, Poland

Collaboration Institutes

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

2 AGH University of Science and Technology, Cracow, Poland

3 Bogolyubov Institute for Theoretical Physics, National Academy of Sciences
of Ukraine, Kiev, Ukraine

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

5 Budker Institute for Nuclear Physics, Novosibirsk, Russia

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

7 Central China Normal University, Wuhan, China

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

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

10 Chicago State University, Chicago, Illinois, United States

11 China Institute of Atomic Energy, Beijing, China

12 Chungbuk National University, Cheongju, Republic of Korea

13 Comenius University Bratislava, Faculty of Mathematics, Physics and
Informatics, Bratislava, Slovakia

14 COMSATS University Islamabad, Islamabad, Pakistan

15 Creighton University, Omaha, Nebraska, United States

16 Department of Physics, Aligarh Muslim University, Aligarh, India

17 Department of Physics, Pusan National University, Pusan, Republic of Korea

18 Department of Physics, Sejong University, Seoul, Republic of Korea

19 Department of Physics, University of California, Berkeley, California,
United States

20 Department of Physics, University of Oslo, Oslo, Norway

21 Department of Physics and Technology, University of Bergen, Bergen, Norway

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

23 Dipartimento di Fisica dell’Università and Sezione INFN, Cagliari, Italy

24 Dipartimento di Fisica dell’Università and Sezione INFN, Trieste, Italy

25 Dipartimento di Fisica dell’Università and Sezione INFN, Turin, Italy

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

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

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

29 Dipartimento di Fisica e Nucleare e Teorica, Università di Pavia and
Sezione INFN, Pavia, Italy

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

31 Dipartimento DISAT del Politecnico and Sezione INFN, Turin, Italy

32 Dipartimento di Scienze e Innovazione Tecnologica dell’Università del
Piemonte Orientale and INFN Sezione di Torino, Alessandria, Italy

33 Dipartimento di Scienze MIFT, Università di Messina, Messina, Italy

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

35 European Organization for Nuclear Research (CERN), Geneva, Switzerland

36 Faculty of Electrical Engineering, Mechanical Engineering and Naval
Architecture, University of Split, Split, Croatia

37 Faculty of Engineering and Science, Western Norway University of Applied
Sciences, Bergen, Norway

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

39 Faculty of Science, P.J. Šafárik University, Košice, Slovakia

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

41 Fudan University, Shanghai, China

42 Gangneung-Wonju National University, Gangneung, Republic of Korea

43 Gauhati University, Department of Physics, Guwahati, India

44 Helmholtz-Institut für Strahlen- und Kernphysik, Rheinische Friedrich-
Wilhelms-Universität Bonn, Bonn, Germany

45 Helsinki Institute of Physics (HIP), Helsinki, Finland

46 High Energy Physics Group, Universidad Autónoma de Puebla, Puebla, Mexico

47 Hiroshima University, Hiroshima, Japan

48 Hochschule Worms, Zentrum für Technologietransfer und Telekommunikation
(ZTT), Worms, Germany

49 Horia Hulubei National Institute of Physics and Nuclear Engineering,
Bucharest, Romania

50 Indian Institute of Technology Bombay (IIT), Mumbai, India

51 Indian Institute of Technology Indore, Indore, India

52 Indonesian Institute of Sciences, Jakarta, Indonesia

53 INFN, Laboratori Nazionali di Frascati, Frascati, Italy

54 INFN, Sezione di Bari, Bari, Italy

55 INFN, Sezione di Bologna, Bologna, Italy

56 INFN, Sezione di Cagliari, Cagliari, Italy

57 INFN, Sezione di Catania, Catania, Italy

58 INFN, Sezione di Padova, Padova, Italy

59 INFN, Sezione di Roma, Rome, Italy

60 INFN, Sezione di Torino, Turin, Italy

61 INFN, Sezione di Trieste, Trieste, Italy

62 Inha University, Incheon, Republic of Korea

63 Institute for Gravitational and Subatomic Physics (GRASP), Utrecht
University/Nikhef, Utrecht, Netherlands

64 Institute for Nuclear Research, Academy of Sciences, Moscow, Russia

65 Institute of Experimental Physics, Slovak Academy of Sciences, Košice,
Slovakia

66 Institute of Physics, Homi Bhabha National Institute, Bhubaneswar, India

67 Institute of Physics of the Czech Academy of Sciences, Prague, Czech
Republic

68 Institute of Space Science (ISS), Bucharest, Romania

69 Institut für Kernphysik, Johann Wolfgang Goethe-Universität Frankfurt,
Frankfurt, Germany

70 Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de México,
Mexico City, Mexico

71 Instituto de Física, Universidade Federal do Rio Grande do Sul (UFRGS),
Porto Alegre, Brazil

72 Instituto de Física, Universidad Nacional Autónoma de México, Mexico City,
Mexico

73 iThemba LABS, National Research Foundation, Somerset West, South Africa

74 Jeonbuk National University, Jeonju, Republic of Korea

75 Johann-Wolfgang-Goethe Universität Frankfurt Institut für Informatik,
Fachbereich Informatik und Mathematik, Frankfurt, Germany

76 Joint Institute for Nuclear Research (JINR), Dubna, Russia

77 Korea Institute of Science and Technology Information, Daejeon, Republic of
Korea

78 KTO Karatay University, Konya, Turkey

79 Laboratoire de Physique des 2 Infinis, Irène Joliot-Curie, Orsay, France

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

81 Lawrence Berkeley National Laboratory, Berkeley, California, United States

82 Lund University Department of Physics, Division of Particle Physics, Lund,
Sweden

83 Moscow Institute for Physics and Technology, Moscow, Russia

84 Nagasaki Institute of Applied Science, Nagasaki, Japan

85 Nara Women’s University (NWU), Nara, Japan

86 National and Kapodistrian University of Athens, School of Science,
Department of Physics , Athens, Greece

87 National Centre for Nuclear Research, Warsaw, Poland

88 National Institute of Science Education and Research, Homi Bhabha National
Institute, Jatni, India

89 National Nuclear Research Center, Baku, Azerbaijan

90 National Research Centre Kurchatov Institute, Moscow, Russia

91 Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark

92 Nikhef, National institute for subatomic physics, Amsterdam, Netherlands

93 NRC Kurchatov Institute IHEP, Protvino, Russia

94 NRC «Kurchatov»Institute - ITEP, Moscow, Russia

95 NRNU Moscow Engineering Physics Institute, Moscow, Russia

96 Nuclear Physics Group, STFC Daresbury Laboratory, Daresbury, United Kingdom

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

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

99 Ohio State University, Columbus, Ohio, United States

100 Petersburg Nuclear Physics Institute, Gatchina, Russia

101 Physics department, Faculty of science, University of Zagreb, Zagreb,
Croatia

102 Physics Department, Panjab University, Chandigarh, India

103 Physics Department, University of Jammu, Jammu, India

104 Physics Department, University of Rajasthan, Jaipur, India

105 Physikalisches Institut, Eberhard-Karls-Universität Tübingen, Tübingen,
Germany

106 Physikalisches Institut, Ruprecht-Karls-Universität Heidelberg,
Heidelberg, Germany

107 Physik Department, Technische Universität München, Munich, Germany

108 Politecnico di Bari and Sezione INFN, Bari, Italy

109 Research Division and ExtreMe Matter Institute EMMI, GSI Helmholtzzentrum
für Schwerionenforschung GmbH, Darmstadt, Germany

110 Rudjer Bošković Institute, Zagreb, Croatia

111 Russian Federal Nuclear Center (VNIIEF), Sarov, Russia

112 Saha Institute of Nuclear Physics, Homi Bhabha National Institute,
Kolkata, India

113 School of Physics and Astronomy, University of Birmingham, Birmingham,
United Kingdom

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

115 St. Petersburg State University, St. Petersburg, Russia

116 Stefan Meyer Institut für Subatomare Physik (SMI), Vienna, Austria

117 SUBATECH, IMT Atlantique, Université de Nantes, CNRS-IN2P3, Nantes, France

118 Suranaree University of Technology, Nakhon Ratchasima, Thailand

119 Technical University of Košice, Košice, Slovakia

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

121 The University of Texas at Austin, Austin, Texas, United States

122 Universidad Autónoma de Sinaloa, Culiacán, Mexico

123 Universidade de São Paulo (USP), São Paulo, Brazil

124 Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil

125 Universidade Federal do ABC, Santo Andre, Brazil

126 University of Cape Town, Cape Town, South Africa

127 University of Houston, Houston, Texas, United States

128 University of Jyväskylä, Jyväskylä, Finland

129 University of Liverpool, Liverpool, United Kingdom

130 University of Science and Technology of China, Hefei, China

131 University of South-Eastern Norway, Tonsberg, Norway

132 University of Tennessee, Knoxville, Tennessee, United States

133 University of the Witwatersrand, Johannesburg, South Africa

134 University of Tokyo, Tokyo, Japan

135 University of Tsukuba, Tsukuba, Japan

136 Université Clermont Auvergne, CNRS/IN2P3, LPC, Clermont-Ferrand, France

137 Université de Lyon, CNRS/IN2P3, Institut de Physique des 2 Infinis de Lyon
, Lyon, France

138 Université de Strasbourg, CNRS, IPHC UMR 7178, F-67000 Strasbourg, France,
Strasbourg, France

139 Université Paris-Saclay Centre d’Etudes de Saclay (CEA), IRFU, Départment
de Physique Nucléaire (DPhN), Saclay, France

140 Università degli Studi di Foggia, Foggia, Italy

141 Università di Brescia and Sezione INFN, Brescia, Italy

142 Variable Energy Cyclotron Centre, Homi Bhabha National Institute, Kolkata,
India

143 Warsaw University of Technology, Warsaw, Poland

144 Wayne State University, Detroit, Michigan, United States

145 Westfälische Wilhelms-Universität Münster, Institut für Kernphysik,
Münster, Germany

146 Wigner Research Centre for Physics, Budapest, Hungary

147 Yale University, New Haven, Connecticut, United States

148 Yonsei University, Seoul, Republic of Korea

##  Appendix B  Comparison of the $j_{\mathrm{T}}$ distributions with models
for other $p_{\mathrm{T,\,jet}}$ regions

Figure 9: The $j_{\mathrm{T}}{}$ distribution in p–Pb collisions at
$\sqrt{s_{\mathrm{NN}}}=5.02\leavevmode\nobreak\ \mathrm{TeV}$ for
$40<p_{\mathrm{T,\,jet}}<60$ GeV/$c$ comparing to theoretical models in pp
collisions at $\sqrt{s}=5.02$ TeV. Figure 10: The $j_{\mathrm{T}}{}$
distribution in p–Pb collisions at
$\sqrt{s_{\mathrm{NN}}}=5.02\leavevmode\nobreak\ \mathrm{TeV}$ for
$80<p_{\mathrm{T,\,jet}}<100$ GeV/$c$ comparing to theoretical models in pp
collisions at $\sqrt{s}=5.02$ TeV.  Figure 11: The $j_{\mathrm{T}}{}$
distribution in p–Pb collisions at
$\sqrt{s_{\mathrm{NN}}}=5.02\leavevmode\nobreak\ \mathrm{TeV}$ for
$100<p_{\mathrm{T,\,jet}}<150$ GeV/$c$ comparing to theoretical models in pp
collisions at $\sqrt{s}=5.02$ TeV.

##  Appendix C  Two-component model fit in pp collisions

Figure 12: The $j_{\mathrm{T}}{}$ distribution of charged particles with a
two-component fit for $40<p_{\mathrm{T,\,jet}}<60$
$\mathrm{GeV}\kern-0.50003pt/\kern-0.20004pt\textit{c}$ in pp collisions. The
distribution is fitted with the two-component fit described in Sec. 3.