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Jet fragmentation transverse momentum distributions in pp and p-Pb collisions at $$ \sqrt{s} $$, $$ \sqrt{s_{\mathrm{NN}}} $$ = 5.02 TeV

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A bstract Jet fragmentation transverse momentum ( j T ) distributions are measured in proton-proton (pp) and proton-lead (p-Pb) collisions at $$ \sqrt{s_{\mathrm{NN}}} $$ s NN = 5 . 02 TeV with the ALICE experiment at the LHC. Jets are reconstructed with the ALICE tracking detectors and electromagnetic calorimeter using the anti- k T algorithm with resolution parameter R = 0 . 4 in the pseudorapidity range |η| < 0 . 25. The j 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 T distributions are compared with a variety of parton-shower models. Herwig and P ythia 8 based models describe the data well for the higher j T region, while they underestimate the lower j T region. The j T distributions are further characterised by fitting them with a function composed of an inverse gamma function for higher j T values (called the “wide component”), related to the perturbative component of the fragmentation process, and with a Gaussian for lower j 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.
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JHEP09(2021)211
Published for SISSA by Springer
Received:December 3, 2020
Revised:August 20, 2021
Accepted:August 31, 2021
Published:September 30, 2021
Jet fragmentation transverse momentum distributions
in pp and p-Pb collisions at s,sNN = 5.02 TeV
The ALICE collaboration
E-mail: ALICE-publications@cern.ch
Abstract: Jet fragmentation transverse momentum (jT) distributions are measured in
proton-proton (pp) and proton-lead (p-Pb) collisions at sNN = 5.02 TeV with the ALICE
experiment at the LHC. Jets are reconstructed with the ALICE tracking detectors and
electromagnetic calorimeter using the anti-kTalgorithm with resolution parameter R= 0.4
in the pseudorapidity range |η|<0.25. The jTvalues are calculated for charged particles
inside a fixed cone with a radius R= 0.4around the reconstructed jet axis. The measured
jTdistributions are compared with a variety of parton-shower models. Herwig and Pythia
8 based models describe the data well for the higher jTregion, while they underestimate
the lower jTregion. The jTdistributions are further characterised by fitting them with
a function composed of an inverse gamma function for higher jTvalues (called the “wide
component”), related to the perturbative component of the fragmentation process, and with
a Gaussian for lower jTvalues (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.
Keywords: Heavy Ion Experiments
ArXiv ePrint: 2011.05904
Open Access, Copyright CERN,
for the benefit of the ALICE Collaboration.
Article funded by SCOAP3.
https://doi.org/10.1007/JHEP09(2021)211
JHEP09(2021)211
Contents
1 Introduction 1
2 Experimental setup and data samples 3
3 Analysis method 4
4 Systematic uncertainties 5
5 Results 7
6 Discussion 13
7 Conclusion 15
A Comparison of the jTdistributions with models for other pT,jet regions 18
The ALICE collaboration 24
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 chromo-
dynamics (QCD) [15] 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 mo-
mentum transfer scale (Q2) is large during the showering, perturbative QCD calculations
can be applied. When Q2becomes of the order of ΛQCD, partons hadronise into final-state
particles through processes that cannot be calculated perturbatively [814]. 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 trans-
verse momentum, jT. The jTis defined as the perpendicular component of the momentum
of the constituent particle with respect to reconstructed jet momentum, ~pjet. The length
of the ~
jTvector is
jT=|~pjet ×~ptrack|
|~pjet|,(1.1)
where ~ptrack 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 jTprovides a measurement of the
transverse momentum spread of the jet fragments.
– 1 –
JHEP09(2021)211
Previously, jThas been studied using two-particle correlations where jTis 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 collab-
oration at ISR in pp collisions at centre-of-mass energies s= 31,45 and 63 GeV [15] and
by the PHENIX collaboration at RHIC in pp collisions at s= 200 GeV [16] and d-Au colli-
sions at a center-of-mass energy per nucleon pair sNN = 200 GeV [17]. The results showed
no clear dependence on the transverse momentum (pT) of the trigger particle. Jet measure-
ments to study jTwere done by the CDF collaboration in p collisions at s= 1.96 TeV [18]
at Tevatron, by the ATLAS collaboration in pp at s= 7 TeV [19] and by the LHCb col-
laboration in pp collisions at s= 8 TeV [20] at the LHC. The results show a dependence
of the width of jTdistributions with respect to the pTof 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 [2125], the modifi-
cation 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 [3336], as compared to the expectations from elementary proton-proton
collisions.
Jet quenching in heavy-ion collisions evolves multi-scale steps from hard to soft pro-
cesses [37,38]. Hard scales dominate in the elementary hard scattering. The hard scat-
tering 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 [3941]. 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 trans-
port 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 [4547] in
p-Pb collisions need to be considered to interpret the measurements in heavy-ion collisions.
The results for jTdistributions 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
jTdistribution is not smeared by hadrons decaying from a short living resonance.
The jTdistributions are studied by reconstructing jets with the ALICE tracking detec-
tors and electromagnetic calorimeter using the anti-kTalgorithm [49] with resolution pa-
rameter R= 0.4in the pseudorapidity range |η|<0.25 in pp collisions at s= 5.02 TeV and
p-Pb minimum bias collisions at sNN = 5.02 TeV. It is worth noting that there is a shift in
the centre-of-mass rapidity of y= 0.465 in the direction of the proton beam because of the
asymmetric collision system. The jTdistribution is further analysed by fitting and separat-
– 2 –
JHEP09(2021)211
ing it into two distinct components that are assigned to the parton shower and the hadroni-
sation process. The attempt to separate the two components is presented for the first time
using jets in various jet transverse momentum (pT,jet) ranges. We also compare the results
with those obtained from PYTHIA (PYTHIA 8.3) and Herwig (Herwig 7.2) simulations.
2 Experimental setup and data samples
The data presented here were recorded by the ALICE detector in 2017 for pp collisions at
s= 5.02 TeV with 7.6×108minimum-bias events (Lint = 15.7 nb1) and in 2013 for p-Pb
collisions at sNN = 5.02 TeV with 1.3×108events (Lint = 620 nb1). 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< η < 1.7(V0C) and
2.8< η < 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 |zvtx|<10 cm
of the nominal interaction point at zvtx = 0 along the beam direction. Charged particles
are used for reconstruction of the primary vertex and jets. The charged particles are re-
constructed 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 |η|<0.9over
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 (ϕ). 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 in-
formation of the primary vertex when reconstructing the tracks. Combining the information
from the ITS and TPC provides a pTresolution ranging from 1to 10 % for charged particles
from 0.15 and 100 GeV/c. For tracks without the ITS information, the momentum resolu-
tion is comparable to that of ITS+TPC tracks below transverse momentum pT= 10 GeV/c,
but for higher momenta the resolution reaches 20 % at pT= 50 GeV/c[51,56].
The EMCal covers an area with a range of |η|<0.7in pseudorapidity and 107 degrees
in azimuth and is made up of 12288 towers in total. Each tower consists of 76 alternating
layers of 1.44mm 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-pT,jet data sample that is com-
plementary to the minimum bias trigger for a low pT,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
– 3 –
JHEP09(2021)211
10 GeV for the low threshold trigger or 20 GeV for the high threshold trigger. A sample
of 3×106events ( Lint = 5 nb1) with the EMCal trigger provides increased statistics
for pT,jet >60 GeV/cwhere 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-kTalgorithm [49] and reso-
lution parameter R= 0.4using FastJet [58]. The pT-recombination scheme is used when
reconstructing jets. Jets are selected in |η|<0.25 to satisfy the fiducial acceptance of the
EMCal. The jet energy resolution JER = σ(preco
T,jet)/ptrue
T,jet is calculated as 20% (18%) at
ptrue
T,jet = 20 GeV/cand 21% (19%) at 100 GeV/cin pp (p-Pb) collisions. The jet angular
resolution is estimated as 29% (28%) and 2% (2%) at pT,jet = 20 GeV/c 20% (19%) and
1.2% (1.2%) at pT,jet = 100 GeV/c in pp (p-Pb) collisions for pseudorapidity and azimuthal
angle, respectively. In the jet reconstruction both charged particles with pT>0.15 GeV/c
and EMCal clusters with pT>0.3 GeV/care considered. All charged particles within a
fixed cone with a resolution parameter Rare 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 pT,jet.
The resulting jTdistributions are corrected for detector effects using the unfolding
method in ref. [60]. The response matrix used for the unfolding is obtained from events
generated by PYTHIA 8 Monash 2013 (PYTHIA 8.2) [61] for the correction of the data
sample in pp collisions and PYTHIA 6 Perugia 2011 (PYTHIA 6.4) [62] for the correction
of the one in p-Pb collisions. The events are transported through the ALICE experimental
set up described with GEANT 3 [63,64]. This response matrix (jrec
T, prec
T,jet, j true
T, ptrue
T,jet)
has 2×2dimensions to correct the detector inefficiency for jet transverse momentum
(pT,jet) and jTsimultaneously, where jtrue
Tand ptrue
T,jet are obtained from particle level jets
by PYTHIA 6 and 8 and jrec
Tand prec
T,jet 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 unfolding procedure is tested
by dividing the generated 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 < pT,jet <150 GeV/c, the generated pT,jet distribution is recovered. For jT>0.1 GeV/c,
the jTdistribution is also recovered.
The effect of the underlying event background is estimated by looking at a cone per-
pendicular to the observed jet axis (π
2rotation in ϕ, for details see refs. [65,66]). The
background jTis calculated for any track that is found within this cone and the rotated
jet axis is used as reference for jT. The background obtained in this manner is subtracted
from the unfolded inclusive jTdistribution, which gives the resulting signal distribution as
– 4 –
JHEP09(2021)211
shown in eq. (3.1). 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= 0.4and pch
T,jet >5GeV/care 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,
Nperpendicular jets is less than Njets by about 1–2% in eq. (3.1).
1
Njets
dN
jT,chdjT,ch signal
=1
Njets
dN
jT,chdjT,ch inclusive 1
Nperpendicular jets
dN
jT,chdjT,ch background
(3.1)
The resulting signal distribution is fitted with the two-component function shown in
eq. (3.2). A Gaussian distribution centered at jT= 0 GeV/cis used for lower jTand an
inverse gamma function is used for jTabove 1 GeV/c, where B1to B5are parameters [48].
1
Njets
dN
jT,chdjT,ch
=B2
B12πej2
T
2B2
1+B3BB4
5
Γ (B4)
eB5
jT
jB4+1
T
(3.2)
To achieve stable results the fitting is performed in two steps. First, lower and higher parts
of the jTdistribution 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, qj2
T(RMS) and yield values are extracted separately from each
component. The narrow component RMS from the Gaussian part is determined as
qj2
T=2B1(3.3)
and the wide component RMS value from the inverse gamma function is calculated as
qj2
T=B5
p(B42) (B43) ,(3.4)
where it is required that B4>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 simula-
tion 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 uncer-
tainty 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 GeV/cto a negligible
value above 3 GeV/c. Similarly, the jet momentum was scaled by ±2% when determining
– 5 –
JHEP09(2021)211
pT,jet to check how the cluster energy affects jTdistributions. The variation of both RMS
components is seen to be less than 2%.
The systematic uncertainty on the background estimation was studied using the “ran-
dom background” method as an alternative to that of the perpendicular-cone. This method
assigns new random ηand ϕof the existing tracks in the event using a uniform distribu-
tion without changing their pTvalues. A random jet cone is also from uniform ηand ϕ
distributions covering |η|<0.25 and 0< ϕ < 2πand 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. 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 [67] and pTdistribution [68] 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 [69]. Given that the SVD method does not allow for multi-dimensional un-
folding, the unfolding is performed separately for different pT,jet intervals. In a PYTHIA
closure test, the true distribution for jT>0.1 GeV/c was in general found to be between
the unfolded distributions from the iterative and SVD methods within 2%. The difference
between the methods when unfolding data is used as an estimate of the unfolding uncer-
tainty. 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 one iteration
above and below instead of the default value, where the default value is chosen by checking
that unfolded jTdistributions converge. Also, the regularisation parameter kis varied by
one unit above and below with respect to the default solution of the SVD method that is
determined by following the guideline [69]. The iterative algorithm requires a prior esti-
mate of the shape of the distribution. As a default prior, generated PYTHIA distribution
is used. To estimate the effect of the prior, the unfolded jTdistribution is used as a prior
instead. The effect of the unfolding for different ranges of pT,jet is tested by varying the
first value of pT,jet from 5 to 15GeV/c. These effects are found negligible compared to
that for the two different unfolding methods. The resulting uncertainty by the unfolding
procedure is below 8% for both wide and narrow component RMS in p-Pb collisions. In
pp collisions it is 9% and 12% for the wide and narrow components, respectively.
The model dependence of the unfolding procedure was explored by weighting the re-
sponse matrix with PYTHIA. The jet yield in the response matrix is varied by ±30% for
the angularity g > 0.1. The angularity is defined as g= Σi(pT,i ×ri)/pT,jet, where pT,i
is the pTof the ith constituent of the jet and ri=qη2
i+ ∆ϕ2
iis the distance of the ith
constituent from the jet axis [32,70]. The effect is found to be below 2% for the wide
component and negligible 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 1shows an overview of
systematic uncertainties for 40 < pT,jet <60 GeV/cin pp and p-Pb collisions.
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JHEP09(2021)211
jTdistribution at jT= 0.2–0.8–2 GeV/cWide RMS Narrow RMS
source pp p-Pb pp p-Pb pp p-Pb
Background 2–2–5% neg.–2–5% 1.1% 5% 2.9% 9%
Unfolding 10–neg.–20% 10–neg.–12% 9% 8% 12% 8%
Tracking 2–2–2% 2–1–neg.% 0.4% 4 % 0.2% 5%
EMCal 2–2–5% 2–2–2% 1.8% 1% 0.2% 1%
Model dependence neg.–2–5% neg.–neg.–10% 0.5% 2% neg. neg.
Total 11–4–22% 10–3–16% 9% 10 % 12% 13%
Table 1. Summary of systematic uncertainties for 40 < pT,jet <60 GeV/cin pp and p-Pb collisions.
5 Results
The jTdistribution in pp collisions at s= 5.02 TeV is compared with that in p-Pb
collisions at sNN = 5.02 TeV in figure 1for jet transverse momentum in 40 < pT,jet <
60 GeV/c. The ratio of the jTdistributions 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
the interval in 100 < pT,jet <150 GeV/c, the comparison is not provided because of the
lack of enough statistics in minimum-bias pp collisions and the absence of the data sample
with the EMCal trigger in the corresponding pp data taking period.
Figure 2shows the distributions of jTfor charged particles in different pT,jet intervals
after applying the unfolding correction and background subtraction in p-Pb collisions at
sNN = 5.02 TeV. The yield at low jTstays constant with increasing pT,jet. At high jT
the yield increases and the distributions become wider with increasing pT,jet as indicated
by the ratios of the jTdistributions shown in the bottom panel. Notably, this is due to
kinematical limits. At midrapidity, within a fixed cone the maximum jTdepends on the
track momentum by the relation of jT,max R×pT,track, resulting in an increase of the
possible jTas pT,jet increases. Though jets with larger momenta are more collimated, the
net effect is an increase of hjTias pT,jet increases. These measurements are consistent with
the findings by the ATLAS [19] and LHCb collaborations [20].
Figure 3shows the jTdistribution in p-Pb collisions at sNN = 5.02 TeV for jets
with 60 < pT,jet <80 GeV/ccompared with expectations from various generators in pp
collisions at s= 5.02 TeV. PYTHIA 8 based models (PYTHIA 8.3) and Herwig (Herwig
7.2) handle both the showering process and hadronisation differently. PYTHIA 8 uses the
Lund string model [71] to perform the hadronisation stage. Herwig uses a cluster model
for the hadronisation [9,10]. PYTHIA 8 has pT-ordered showers by default while Herwig
implements a parton shower using the coherent branching algorithm [72], which has angular
ordering as a central feature. The pT-ordering in a PYTHIA 8 shower is a compromise [73]:
ordering in the pTat splitting ensures the ordering in the hardness and also effectively
favours large angles. Herwig describes the jTdistribution better than other models for the
– 7 –
JHEP09(2021)211
Figure 1. Comparison of the jTdistributions in pp and p-Pb collisions at s,sNN = 5.02 TeV
in 40 < pT,jet <60 GeV/c. The centre-of-mass rapidity in p-Pb collisions is shifted by y= 0.465
in the direction of the proton beam.
whole jTregion. Other PYTHIA 8 based models describe the data at high jTbut not in
the low jTregion. The results for the other pT,jet intervals are reported in figures B1, B2
and B3 that derive the same conclusion. Models describe the data better as pT,jet increases
in pp collisions. This is also true at higher jT, however, models underestimate the data at
lower jTconsistently for all pT,jet ranges in p-Pb collisions.
PYTHIA 8 Monash 2013 [61] adopted LHC data to constrain the initial-state radiation
and multi-parton interaction parameters based on the default parameters of PYTHIA 8
tune 4C [74]. There is no clear separation of the jTdistributions originating from the
different tunes of PYTHIA 8. As of version 8.3 PYTHIA 8 implemented two more shower
– 8 –
JHEP09(2021)211
1
10
3
10
7
10
8
10
)
2
/GeV
2
c (
T, ch
jd
Nd
T
j
1
jets
N
1
)
3
10× (c < 150 GeV/
T, jet
p100 <
)
2
10× (c < 100 GeV/
T, jet
p80 <
)
1
10× (c < 80 GeV/
T, jet
p60 <
)
0
10× (c < 60 GeV/
T, jet
p40 <
= 5.02 TeV
NN
sPb p
= 0.465
cms
y
= 0.4R,
T
kAnti-
| < 0.25
jet
η
|
ALICE
1
10 1
)c (GeV/
T, ch
j
1
10
c60 GeV/Ratio to 40
Figure 2. The jTdistributions of charged particles in R= 0.4anti-kTjets as measured in p-Pb
collisions at sNN = 5.02 TeV for different ranges of pT,jet. The centre-of-mass rapidity is shifted
by y= 0.465 in the direction of the proton beam. The bottom panel shows ratios of the jT
distributions with respect to that in 40 < pT,jet <60 GeV/c.
models as part of the code. Those are VINCIA and Dire Showers that are based on the
kT(transverse momentum of a dipole)-ordered picture of QCD splitting [75,76]. The
jTdistributions generated by the two shower models were obtained by using the default
parameters of PYTHIA 8 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 jT
distribution generated with the combined POWHEG [77] and PYTHIA simulation is also
compared to the data. The jTdistributions 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 jTregion; however, they are within the systematic uncertainty of
– 9 –
JHEP09(2021)211
2
10
1
2
10
4
10
)
2
/GeV
2
c (
T, ch
jd
Nd
T, ch
j
1
jets
N
1
ppPbp
PYTHIA8 Monash
PYTHIA8 4C
Herwig 7
VINCIA
Dire
POWHEG NLO + PYTHIA PS
PYTHIA8 Angantyr + Nuclear PDF
= 5.02 TeV
NN
s, s
Pb) = 0.465 (p
cms
y
= 0.4R,
T
kAnti-
| < 0.25
jet
η
|
c < 80 GeV/
T, jet
p60 <
ALICE
0.5
1
1.5
Model/pp
1
10 1
)c (GeV/
T, ch
j
0.5
1
1.5
Pb
Model/p
Figure 3. The jTdistribution in p-Pb collisions at sNN = 5.02 TeV for jets with transverse
momentum in 60 < pT,jet <80 GeV/c. The measured data are compared to calculations by
theoretical models in pp collisions at s= 5.02 TeV.
the data for the higher jTregion. PYTHIA 8 Angantyr extends pp simulation of PYTHIA
8 to the case of heavy-ion collisions [78]. PYTHIA 8 Angantyr is used to simulate p-Pb
collisions with the nuclear parton distribution function (PDF) EPS09LO [47] for the Pb-ion
beam. The resulting jTdistribution is almost the same with those by pp simulations with
a proton PDF and it does not describe the data for the lower jTregion at all.
The distributions are fitted with the two-component fit motivated by [48]. The function
forms are given in eq. (3.2). An example of the fitted distribution is shown in figure 4for
60 < pT,jet <80 GeV/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 jTdistributions are described well
– 10 –
JHEP09(2021)211
1
10
2
10
5
10
)
2
/GeV
2
c (
T, ch
jd
Nd
T, ch
j
1
jets
N
1
Data
Total
Narrow
Wide
= 5.02 TeV
NN
sPb p
= 0.465
cms
y
= 0.4R,
T
kAnti-
| < 0.25
jet
η
|
c < 80 GeV/
T, jet
p60 <
ALICE
1
10 1
)c (GeV/
T, ch
j
0.5
1
1.5
Ratio (data/fit)
Figure 4. The jTdistribution of charged particles with a two-component fit for 60 < pT,jet <
80 GeV/c. The distribution is fitted with the two-component fit described in section 3.
by the two-component model fit. The corresponding statistical uncertainties are calculated
via the general error propagation formulas in eq. (5.1)
δqj2
T=2δB1and v
u
u
t (5 2B4)B5δB4
(2(B42)(B43))3
2!2
+ δB5
p(B42) (B43)!2
(5.1)
for the narrow and wide component RMS values, respectively.
The widths of the jTdistributions are determined as a function of the transverse
momentum of jet. The RMS qj2
Tvalues for the two components are shown in figure 5
along with comparisons to Monte Carlo simulations. There is clear separation in the width
of the wide and narrow components of the jTdistributions. The RMS values of the wide
component are 3-4 times larger than the narrow component RMS. The wide component
– 11 –
JHEP09(2021)211
40 60 80 100 120
0
1
2
)c (GeV/
T
2
j
Pb Widep
Pb Narrowp
pp Wide
pp Narrow
PYTHIA 8 Monash
PYTHIA 8 4C
Herwig 7
VINCIA
Dire
POWHEG NLO + PYTHIA PS
PYTHIA8 Angantyr + Nuclear PDF
= 5.02 TeV
NN
s, s
Pb) = 0.465 (p
cms
y
= 0.4R,
T
kAnti-
| < 0.25
jet
η
|
ALICE
40 60 80 100 120
0.8
1
Ratio (W)
40 60 80 100 120
)c (GeV/
T, jet
p
0.6
0.8
1
1.2
Ratio (N)
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 p-Pb data. Note that pp data points are
shifted by -2 GeV/con the horizontal axis to be distinguished from p-Pb data points.
RMS shows an increasing trend with increasing pT,jet that is parameterised by a linear
function as qj2
T= 0.005 (±0.004) ×pT,jet + 0.497 (±0.255), while the narrow component
RMS stays constant with the fitted value of 0.253 (±0.009). Both of these trends are
qualitatively consistent with the results in the dihadron jTanalysis [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 pT,jet
increases. Dire Shower shows clearly lower values compared to data up to 18% for the wide
RMS components. Other PYTHIA 8 based models show a good description for the lower
jTregion, however, they underestimate the data for the higher pT,jet region.
– 12 –
JHEP09(2021)211
20 40 60 80 100 120
)c (GeV/
T, jet
p
0
1
2
)c (GeV/
T
2
j
= 5.02 TeV
NN
sp-Pb
= 0.465
cms
y
= 0.4R,
T
kAnti-
| < 0.25
jet
η
|
ALICE
, Wide
T
jJet
, Narrow
T
jJet
, Wide
T
jDihadron
, Narrow
T
jDihadron
Figure 6. Comparison of results from the jet-based and dihadron-based jTanalyses [48]. Ranges of
dihadron trigger pT(pT,trigger) are converted to corresponding pT,jet ranges using observed mean
pT,jet values in pT,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 jTcalculation. 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 figure 6. Different pTregions 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 pT,trigger and pT,jet, whereas narrow component RMS values of
both results show a weak dependence on pT,jet above 20 GeV/c. The trends are similar for
dihadron and jet jTresults. 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 jTvalues are calculated. The other one
is due to the choice of the axis used for the jTcalculation. In the dihadron analysis jTis
calculated for all near-side tracks if the associated tracks satisfy the condition ~pleading ×~pa>
0. Here ~pleading and ~paare the momentum vectors of the leading and associated tracks,
respectively. Thus, the kinematical limit jT,max can be larger in the dihadron analysis than
in the jet analysis in which only particles in a cone with R= 0.4are considered.
– 13 –
JHEP09(2021)211
1
10
3
10
7
10
8
10
)
2
/GeV
2
c (
T
jd
Nd
T
j
1
jets
N
1
)
0
10× = 0.3 (R
)
1
10× = 0.4 (R
)
2
10× = 0.5 (R
PYTHIA 8
= 5.02 TeVs
T
kAnti-
c < 80 GeV/
T, jet
p60 <
1
10 1
)c (GeV/
T
j
1
2
3
4
= 0.3R
Ratio to
(a)
40 60 80 100 120
0
1
2
)c (GeV/
T
2
j
Wide
= 0.3R
= 0.4R
= 0.5R
Narrow
= 0.3R
= 0.4R
= 0.5R
PYTHIA 8
= 5.02 TeVs
T
kAnti-
40 60 80 100 120
)c (GeV/
T, jet
p
1
1.2
Ratio (wide)
(b)
Figure 7. The effect of changing the Rparameter in jet finding on jTdistributions obtained with
PYTHIA 8 simulations. Comparison of (a) jTsignal distributions for different Rparameters and
their ratios to that of R= 0.3and (b) RMS values of the wide and narrow components and their
ratios to that of R= 0.3for the wide component only.
The effect of the Rparameter choice and pT,jet dependence on jTwas studied using
PYTHIA 8 and the results are shown in figure 7a. The usage of a fixed cone sets stringent
limits on the possible jTvalues. Increasing the cone size loosens these limits and allows for
higher jTvalues. The effect on the wide and narrow components of the jTdistributions
for PYTHIA 8 is shown in figure 7b, where the wide component RMS gets larger by
about 10% when going from R= 0.3to 0.4 and from 0.4 to 0.5, indicating that the
kinematic limit introduced by increasing Rresults in a widening of the jTdistribution.
For the narrow component the effect is relatively small and they appear independent of
the Rparameter and pT,jet. There can also be a broadening effect for jets caused by the
increasing gluon jet fraction as the kinematical limit increases [70]. Additionally, there is
an effect originating from the kinematic cut on x|| values in the dihadron analysis that can
alter the jTdistributions — but that is not further investigated here.
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 jTvalues 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 jT
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
– 14 –
JHEP09(2021)211
3
10
1
3
10
5
10
)
2
/GeV
2
c (
T
jd
Nd
T
j
1
jets
N
1
ALICE
c < 60 GeV/
T, jet
p40 < c < 60 GeV/
T, jet
p40 <
1
10 1
)c (GeV/
T
j
1
Leading track / jet-axis
3
1
3
5
c < 80 GeV/
T, jet
p60 <
Jet-axis reference
Leading track reference
Jet-axis reference
Leading track reference
1
10 1
1
3
1
3
5 = 5.02 TeV
NN
sPb p
= 0.465
cms
y
= 0.4R,
T
kAnti-
| < 0.25
jet
η
|
c < 100 GeV/
T, jet
p80 < c < 100 GeV/
T, jet
p80 <
1
10 1
1
Figure 8. The jTdistributions with respect to the leading track momentum (leading track ref-
erence) and the jet axis (jet-axis reference) within the same jet for three different pT,jet intervals
with R= 0.4.
narrowing. The impact of the different axes adopted in the two analyses is investigated
by measuring jTwith 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 figure 8. The widths of the jTdistributions 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 pT,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 jTdistributions [48] are compared to those of jet jT. Although a direct
comparison between jet and dihadron jTmeasurements 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 (jT) distribution of charged par-
ticles 1
Njets
dN
jT,chdjT,ch is studied using jet reconstruction in pp and p-Pb collisions at s,
sNN = 5.02 TeV. The jTdistributions of charged particles in p-Pb collisions become
wider as the jet transverse momentum pT,jet increases. This is understood as an effect of
the reduction of the kinematical limit with increasing pT,jet, allowing for higher jTvalues.
The jTdistribution in p-Pb collision is compared with that in pp collisions for jet trans-
verse momentum in 40 < pT,jet <100 GeV/c, which shows no clear modification of the jT
– 15 –
JHEP09(2021)211
distribution for the p-Pb collision system. No significant cold nuclear matter effects are
observed in the previous and current jTmeasurements 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 jTand higher pT,jet. The jT
distributions in p-Pb collisions are compared with various parton shower and fragmentation
models. All models describe the data well for the higher jTregion, while they underesti-
mate the data by about 20% and 40% at lower jTin pp and p-Pb collisions, respectively.
Two distinct components of the jet fragmentation transverse momentum jTare ex-
tracted for narrow and wide contributions to quantify the jTdistribution 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 trans-
verse momentum. The results are qualitatively consistent as a function of pT,jet with the
previous jTstudy performed with dihadron correlations [48]. We also present a comparison
to PYTHIA 8 (PYTHIA 8.3) and Herwig (Herwig 7.2) 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 jTdistributions at lower jTwhere the narrow component corresponds. This indicates
that the shape of the jTdistribution 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 fur-
ther constrain predictions in model calculations for jet fragmentation and hadronisation.
Future studies of the jTdistribution performed differentially in the longitudinal momen-
tum fraction zcan be used to constrain transverse-momentum dependent fragmentation
functions [12].
Acknowledgments
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 ac-
knowledges the following funding agencies for their support in building and running the
ALICE detector: A. I. Alikhanyan National Science Laboratory (Yerevan Physics Insti-
tute) 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
– 16 –
JHEP09(2021)211
National Natural Science Foundation of China (NSFC), China; Ministry of Science and
Education and Croatian Science Foundation, Croatia; Centro de Aplicaciones Tecnológi-
cas 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 Foun-
dation (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; Bun-
desministerium für Bildung und Forschung (BMBF) and GSI Helmholtzzentrum für Schw-
erionenforschung GmbH, Germany; General Secretariat for Research and Technology, Min-
istry 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 Nu-
cleare (INFN), Italy; Institute for Innovative Science and Technology , Nagasaki Institute of
Applied Science (IIST), Japanese Ministry of Education, Culture, Sports, Science and Tech-
nology (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), Pak-
istan; Pontificia Universidad Católica del Perú, Peru; Ministry of Science and Higher Ed-
ucation, National Science Centre and WUT ID-UB, Poland; Korea Institute of Science
and Technology Information and National Research Foundation of Korea (NRF), Repub-
lic 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.
– 17 –
JHEP09(2021)211
A Comparison of the jTdistributions with models for other pT,jet regions
2
10
1
2
10
4
10
)
2
/GeV
2
c (
T, ch
jd
Nd
T, ch
j
1
jets
N
1
ppPbp
PYTHIA8 Monash
PYTHIA8 4C
Herwig 7
VINCIA
Dire
POWHEG NLO + PYTHIA PS
PYTHIA8 Angantyr + Nuclear PDF
= 5.02 TeV
NN
s, s
Pb) = 0.465 (p
cms
y
= 0.4R,
T
kAnti-
| < 0.25
jet
η
|
c < 60 GeV/
T, jet
p40 <
ALICE
0.5
1
1.5
Model/pp
1
10 1
)c (GeV/
T, ch
j
0.5
1
1.5
Pb
Model/p
Figure 9. The jTdistribution in pp and p-Pb collisions at s, sNN = 5.02 TeV for 40 < pT,jet <
60 GeV/ccomparing to theoretical models in pp and p-Pb collisions.
2
10
1
2
10
4
10
)
2
/GeV
2
c (
T, ch
jd
Nd
T, ch
j
1
jets
N
1
ppPbp
PYTHIA8 Monash
PYTHIA8 4C
Herwig 7
VINCIA
Dire
POWHEG NLO + PYTHIA PS
PYTHIA8 Angantyr + Nuclear PDF
= 5.02 TeV
NN
s, s
Pb) = 0.465 (p
cms
y
= 0.4R,
T
kAnti-
| < 0.25
jet
η
|
c < 100 GeV/
T, jet
p80 <
ALICE
0.5
1
1.5
Model/pp
1
10 1
)c (GeV/
T, ch
j
0.5
1
1.5
Pb
Model/p
Figure 10. The jTdistribution in p-Pb collisions at s, sNN = 5.02 TeV for 80 < pT,jet <
100 GeV/ccomparing to theoretical models in pp and p-Pb collisions.
– 18 –
JHEP09(2021)211
2
10
1
2
10
4
10
)
2
/GeV
2
c (
T, ch
jd
Nd
T, ch
j
1
jets
N
1
Data
PYTHIA8 Monash
PYTHIA8 4C
Herwig 7
VINCIA
Dire
POWHEG NLO + PYTHIA PS
PYTHIA8 Angantyr + Nuclear PDF
= 5.02 TeV
NN
sPb p
= 0.465
cms
y
= 0.4R,
T
kAnti-
| < 0.25
jet
η
|
c < 150 GeV/
T, jet
p100 <
ALICE
1
10 1
)c (GeV/
T, ch
j
0.5
1
1.5
PbModel/p
Figure 11. The jTdistribution in p-Pb collisions at sNN = 5.02 TeV for 100 < pT,jet <150 GeV/c
comparing to theoretical models in pp and p-Pb collisions.
Open Access. This article is distributed under the terms of the Creative Commons
Attribution License (CC-BY 4.0), which permits any use, distribution and reproduction in
any medium, provided the original author(s) and source are credited.
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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,
– 24 –
JHEP09(2021)211
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. Kim17,62 ,
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 ,
– 25 –
JHEP09(2021)211
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 ,
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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. Šerkšnyt˙e107 ,
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 , T.W. Snellman128,
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,
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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
IDeceased
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
VAlso at: Institute of Theoretical Physics, University of Wroclaw, Poland
Collaboration Institutes
1A.I. Alikhanyan National Science Laboratory (Yerevan Physics Institute) Foundation, Yerevan,
Armenia
2AGH University of Science and Technology, Cracow, Poland
3Bogolyubov Institute for Theoretical Physics, National Academy of Sciences of Ukraine, Kiev,
Ukraine
4Bose Institute, Department of Physics and Centre for Astroparticle Physics and Space Science
(CAPSS), Kolkata, India
5Budker Institute for Nuclear Physics, Novosibirsk, Russia
6California Polytechnic State University, San Luis Obispo, California, United States
7Central China Normal University, Wuhan, China
8Centro de Aplicaciones Tecnológicas y Desarrollo Nuclear (CEADEN), Havana, Cuba
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9Centro 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
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JHEP09(2021)211
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
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JHEP09(2021)211
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
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JHEP09(2021)211
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
– 31 –
Preprint
Full-text available
Jet fragmentation functions are measured for the first time in proton-proton collisions for charged pions, kaons, and protons within jets recoiling against a $Z$ boson. The charged-hadron distributions are studied longitudinally and transversely to the jet direction for jets with transverse momentum 20 $< p_{\textrm{T}} < 100$ GeV and in the pseudorapidity range $2.5 < \eta < 4$. The data sample was collected with the LHCb experiment at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 1.64 fb$^{-1}$. Triple differential distributions as a function of the hadron longitudinal momentum fraction, hadron transverse momentum, and jet transverse momentum are also measured for the first time. This helps constrain transverse-momentum-dependent fragmentation functions. Differences in the shapes and magnitudes of the measured distributions for the different hadron species provide insights into the hadronization process for jets predominantly initiated by light quarks.
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This article reports measurements of the pT-differential inclusive jet cross section in pp collisions at s=5.02TeV and the pT-differential inclusive jet yield in Pb-Pb 0–10% central collisions at sNN=5.02TeV. Jets were reconstructed at midrapidity with the ALICE tracking detectors and electromagnetic calorimeter using the anti-kT algorithm. For pp collisions, we report jet cross sections for jet resolution parameters R=0.1–0.6 over the range 20<pT,jet<140GeV/c, as well as the jet cross-section ratios of different R and comparisons to two next-to-leading-order (NLO)–based theoretical predictions. For Pb-Pb collisions, we report the R=0.2 and R=0.4 jet spectra for 40<pT,jet<140GeV/c and 60<pT,jet<140GeV/c, respectively. The scaled ratio of jet yields observed in Pb-Pb to pp collisions, RAA, is constructed, and exhibits strong jet quenching and a clear pT dependence for R=0.2. No significant R dependence of the jet RAA is observed within the uncertainties of the measurement. These results are compared to several theoretical predictions.
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The ALICE collaboration at the CERN LHC reports novel measurements of jet substructure in pp collisions at s=7 TeV and central Pb–Pb collisions at sNN=2.76 TeV. Jet substructure of track-based jets is explored via iterative declustering and grooming techniques. We present the measurement of the momentum sharing of two-prong substructure exposed via grooming, the zg, and its dependence on the opening angle, in both pp and Pb–Pb collisions. We also present the measurement of the distribution of the number of branches obtained in the iterative declustering of the jet, which is interpreted as the number of its hard splittings. In Pb–Pb collisions, we observe a suppression of symmetric splittings at large opening angles and an enhancement of splittings at small opening angles relative to pp collisions, with no significant modification of the number of splittings. The results are compared to predictions from various Monte Carlo event generators to test the role of important concepts in the evolution of the jet in the medium such as colour coherence.
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A bstract The transverse structure of jets was studied via jet fragmentation transverse momentum ( j T ) distributions, obtained using two-particle correlations in proton-proton and proton-lead collisions, measured with the ALICE experiment at the LHC. The highest transverse momentum particle in each event is used as the trigger particle and the region 3 < p Tt < 15GeV/ c is explored in this study. The measured distributions show a clear narrow Gaussian component and a wide non-Gaussian one. Based on Pythia simulations, the narrow component can be related to non-perturbative hadronization and the wide component to quantum chromodynamical splitting. The width of the narrow component shows a weak dependence on the transverse momentum of the trigger particle, in agreement with the expectation of universality of the hadronization process. On the other hand, the width of the wide component shows a rising trend suggesting increased branching for higher transverse momentum. The results obtained in pp collisions at $$ \sqrt{s}=7 $$ s = 7 TeV and in p–Pb collisions at $$ \sqrt{s_{\mathrm{NN}}}=5.02 $$ s N N = 5.02 TeV are compatible within uncertainties and hence no significant cold nuclear matter effects are observed. The results are compared to previous measurements from CCOR and PHENIX as well as to P ythia 8 and Herwig 7 simulations.
Article
Full-text available
We report the differential charged jet cross section and jet fragmentation distributions measured with the ALICE detector in proton-proton collisions at a center-of-mass energy s=7 TeV. Jets with pseudorapidity |η|<0.5 are reconstructed from charged particles using the anti-kT jet-finding algorithm with a resolution parameter R=0.4. The jet cross section is measured in the transverse momentum interval 5≤pTch jet<100 GeV/c. Jet fragmentation is studied measuring the scaled transverse momentum spectra of the charged constituents of jets in four intervals of jet transverse momentum between 5 and 30 GeV/c. The measurements are compared to calculations from the pythia model as well as next-to-leading-order perturbative QCD calculations with powheg+pythia8. The charged jet cross section is well described by powheg for the entire measured range of pTch jet. For pTch jet>40 GeV/c, the pythia calculations also agree with the measured charged jet cross section. pythia6 simulations describe the fragmentation distributions to 15%. Larger discrepancies are observed for pythia8.
Article
Full-text available
Measurements of fragmentation functions for jets associated with an isolated photon are presented for the first time in pp and Pb-Pb collisions. The analysis uses data collected with the CMS detector at the CERN LHC at a nucleon-nucleon center-of-mass energy of 5.02 TeV. Fragmentation functions are obtained for jets with pTjet>30 GeV/c in events containing an isolated photon with pTγ>60 GeV/c, using charged tracks with transverse momentum pTtrk>1 GeV/c in a cone around the jet axis. The association with an isolated photon constrains the initial pT and azimuthal angle of the parton whose shower produced the jet. For central Pb-Pb collisions, modifications of the jet fragmentation functions are observed when compared to those measured in pp collisions, while no significant differences are found in the 50% most peripheral collisions. Jets in central Pb-Pb events show an excess (depletion) of low (high) pT particles, with a transition around 3 GeV/c. This measurement shows for the first time the in-medium shower modifications of partons (quark dominated) with well-defined initial kinematics. It constitutes a new well-controlled reference for testing theoretical models of the parton passage through the quark-gluon plasma.
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Full-text available
A bstract We present the measurement of a new set of jet shape observables for track-based jets in central Pb-Pb collisions at $$ \sqrt{s_{\mathrm{NN}}}=2.76 $$ s N N = 2.76 TeV. The set of jet shapes includes the first radial moment or angularity, g ; the momentum dispersion, p T D ; and the difference between the leading and sub-leading constituent track transverse momentum, LeSub . These observables provide complementary information on the jet fragmentation and can constrain different aspects of the theoretical description of jet-medium interactions. The jet shapes were measured for a small resolution parameter R = 0 . 2 and were fully corrected to particle level. The observed jet shape modifications indicate that in-medium fragmentation is harder and more collimated than vacuum fragmentation as obtained by PYTHIA calculations, which were validated with the measurements of the jet shapes in proton-proton collisions at $$ \sqrt{s}=7 $$ s = 7 TeV. The comparison of the measured distributions to templates for quark and gluon-initiated jets indicates that in-medium fragmentation resembles that of quark jets in vacuum. We further argue that the observed modifications are not consistent with a totally coherent energy loss picture where the jet loses energy as a single colour charge, suggesting that the medium resolves the jet structure at the angular scales probed by our measurements ( R = 0 . 2). Furthermore, we observe that small- R jets can help to isolate purely energy loss effects from other effects that contribute to the modifications of the jet shower in medium such as the correlated background or medium response.
Article
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A bstract We present a new model for building up complete exclusive hadronic final states in high energy nucleus collisions. It is a direct extrapolation of high energy pp collisions (as described by P ythia ), and thus bridges a large part of the existing gap between heavy ion and high energy physics phenomenology. The model is inspired by the old Fritiof model and the notion of wounded nucleons. Two essential features are the treatment of multi-parton interactions and diffractive excitation in each NN sub-collision. Diffractive excitation is related to fluctuations in the nucleon partonic sub-structure, and fluctuations in both projectile and target are here included for the first time. The model is able to give a good description of general final-state properties such as multiplicity and transverse momentum distributions, both in p A and AA collisions. The model can therefore serve as a baseline for understanding the non-collective background to observables sensitive to collective behaviour. As P ythia does not include a mechanism to reproduce the collective effects seen in pp collisions, such effects are also not reproduced by the present version of Angantyr. Effects of high string density, shown to be able to reproduce e.g. higher strangeness ratios and the ridge in pp, will be added in future studies.