201 16 July 2018

Small-radius jet shapes in pp and Pb–Pb collisions at ALICE

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

We present the measurement of a new set of jet shape observables for track-
based jets in central Pb–Pb collisions at $\sqrt{s_{\mathrm{NN}}}=2.76$ TeV.
The set of jet shapes includes the first radial moment or angularity, $g$; the
momentum dispersion, $p_{\rm T}D$; 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
$\sqrt{s}=7$ 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.

# Medium modification of the shape of small-radius jets in central Pb–Pb
collisions at $\mathbf{\sqrt{s_{\mathrm{NN}}}}=2.76\,\rm{TeV}$

##  1 Introduction

The objective of the heavy-ion jet physics program at RHIC and LHC is to
understand the behaviour of QCD matter at the limit of high energy density and
temperature by studying the dynamics of jet-medium interactions. Jet physics
in heavy-ion collisions is a multiscale problem. Hard scales govern the
elementary scattering and the subsequent branching process down to non-
perturbative scales, in the vacuum as well as in the medium. Soft scales, of
the order of the temperature of the medium, characterise the interactions of
soft partons produced in the shower with the strongly coupled medium. Soft
scales also rule hadronisation, which is expected to take place in vacuum for
sufficiently energetic probes. A detailed discussion of the different
processes contributing to the jet shower evolution in medium and their onset
scales can be found in Ref. [1]. The interplay between these processes can
lead to modifications of the longitudinal and transverse distributions of the
constituents of the jet with respect to jet fragmentation in vacuum. These jet
structure modifications can be investigated with jet shape observables and
have the potential to constrain the dynamics of jet energy loss in medium, the
role of colour coherence [2], and fundamental medium properties like
temperature, density or the evolution of the medium degrees of freedom with
the resolution scale [3].

The jet shape observables measured so far in heavy-ion collisions at the LHC
can be classified into three groups: inclusive, jet-by-jet shapes using
constituents information, and jet shapes using the clustering history. The
first group consists on inclusive observables that measure intra or inter-jet
distributions. The ratios of jet yields with different resolution parameter
$R$ are an example. Such ratios are infrared and collinear (IRC) safe [4] and
are sensitive to the transverse energy profile of the jets [5, 6, 7]. ATLAS
measured central to peripheral inclusive jet yield ratios for different jet
radii up to $R=0.5$ showing differences of the order of $30\%$ at
$p_{\rm{T,jet}}<100$ GeV/$c$ [8], which indicate energy redistribution within
the jet in medium relative to vacuum. In ALICE, such ratios were measured for
inclusive and semi-inclusive samples of jets recoiling from high-$p_{\rm T}$
hadrons [9, 10]. In the case of recoil jets, larger $R$ were accessible and
the results showed no indication of medium modifications when changing the jet
resolution up to $R=0.5$. ALICE and ATLAS measurements are characterised by
different jet selections and different minimum constituent cutoffs. Another
example of shapes belonging to this category are the fragmentation fuctions
[11, 12]. The fragmentation functions give information on the longitudinal
share of energy within the jet. The experimental results show an enhancement
of the low and high-$z$ component and a depletion at intermediate $z$ in Pb–Pb
relative to pp collisions, where $z$ is the fraction of the jet momentum
carried by the particles in the jet [11, 12]. The modifications are small and
they were quantified as an excess of approximately $0.9$ particles at low
momentum, in the difference between the integrals of the fragmentation
functions in Pb–Pb and pp collisions.

In order to probe the jet shape at large angles relative to the jet axis, two
observables were designed. The CMS missing $p_{\rm T}$ method [13] considers
the projection of all particle momentum vectors in the event onto the axis of
a selected dijet pair. This method is insensitive to the uncorrelated
background, and particles correlated with the dijet reveal that momentum
balance of the system is totally recovered only by very soft particles
($p_{\mathrm{T}}\leq 1$ GeV/$c$) at large angles ($\Delta R_{\rm jet}>0.8$).
Jet-track angular correlations [14] explore the large-angle component
differentially with similar conclusions. Similarly, the jet profile [15]
measures the radial distribution of energy relative to the jet axis. The
results indicate an enhancement of momentum relative to pp collisions at
distances to the jet axis $\Delta R_{\rm jet}\gtrsim 0.3$. This enhancement is
accompanied by a reduction of momentum at short distances to the jet axis
$0.1<\Delta R_{\rm jet}<0.2$.

The second group of shape observables are the jet shapes built as a jet-by-jet
function of the jet constituent 4-momenta. The jet mass [16] is an example.
The jet mass is related to the virtuality of the parton that originated the
jet. It increases with large-angle soft particle emission. The ALICE
measurement of the jet mass in heavy-ion collisions [16] for jets of $R=0.4$
showed a hint of reduction relative to the vacuum reference. Theoretical
models show that energy loss effects reduce the jet mass while the medium
response increases it, resulting in a mass that is shifted to higher values
than what was found by ALICE results [17].

The third category of jet shape observables uses the clustering history to
select certain parts of the particle shower using well-defined jet clustering
techniques, for instance grooming [18, 19], to amplify or suppress a region of
the splitting phase space where medium-induced effects are expected. Examples
are the 2-subjetiness [20] or the soft drop subjet momentum balance, $z_{\rm
g}$ [21, 22], designed to explore changes in the rate of 2-prong jets and the
momentum balance of semi-hard subjets in heavy-ion collisions relative to pp
collisions. New ideas and applications for this third category of jet shapes
are being discussed in the literature for beyond Standard Model searches and
QCD studies in pp as well as heavy-ion collisions.

The shapes analysed in this paper belong to the second category and are
described in detail in Section 2. They probe complementary aspects of the jet
fragmentation such as the transverse energy profile or the dispersion of the
jet constituents transverse momentum distribution. Our aim was to perform a
systematic exploration of the intrajet distributions to pose constraints on
key aspects of the theory of jet quenching. A clean connection to the theory
was pursued via the selection of observables that are well defined and
calculable from first principles in pQCD and via the full correction of the
observables to particle level. The considered small resolution $R=0.2$ and
ALICE instrumental capabilities allowed us to obtain fully corrected particle-
level jet measurements, in a unique range at the LHC of low jet momentum and
low constituent momentum cutoff of $0.15$ GeV/$c$. Our measurements give
insight on whether the jet substructure is resolved by the medium at small
angular scales and on the role of the medium response.

The rest of the paper is organized as follows: Section 3 presents the data
sets and event selection used for the analysis, Sections 4 and 5 describe the
jet finding procedure and the underlying event subtraction, while Sections 6
and 7 present the response of the shapes to detector effects and background
fluctuations and the 2-dimensional unfolding procedure that simultaneously
corrects the shape and jet $p_{\rm T}$ distributions. Section 8 describes the
different contributions to the systematic uncertainty and finally, Section 9
presents the fully corrected results and their interpretation with comparisons
to theoretical models.

##  2 The set of jet shape observables

In this analysis, we focus on three jet shape observables that probe
complementary aspects of the jet fragmentation, namely the first radial moment
or angularity (or girth), $g$, the momentum dispersion, $p_{\mathrm{T}}D$, and
the difference between the leading and sub-leading track transverse momentum,
$LeSub$.

The angularity is defined as

$g=\sum_{i\in\rm jet}\frac{p_{\mathrm{T,}i}}{p_{\mathrm{T,jet}}}\Delta
R_{\mathrm{jet},i},$ (1)

where $p_{\mathrm{T},i}$ is the transverse momentum of the $i$-th constituent
and $\Delta R_{\mathrm{jet},i}$ is the distance in ($\eta$, $\varphi$) space
between constituent $i$ and the jet axis. This shape is sensitive to the
radial energy profile of the jet.

The momentum dispersion $p_{\mathrm{T}}D$ is defined as

$p_{\rm T}D=\frac{\sqrt{\sum_{i\in\rm jet}p_{\mathrm{T,}i}^{2}}}{\sum_{i\in\rm
jet}{p_{\mathrm{T,}i}}}.$ (2)

This shape measures the second moment of the constituent $p_{\rm T}$
distribution in the jet and is connected to how hard or soft the jet
fragmentation is. For example, in the extreme case of few constituents
carrying a large fraction of the jet momentum, $p_{\mathrm{T}}D$ will be close
to 1, while in the case of jets with a large number of constituents and softer
momentum, $p_{\mathrm{T}}D$ would end up closer to 0.

The two previous shapes are related to the moments of the so-called
generalized angularities defined as:
$\lambda_{\beta}^{\kappa}=\sum_{i}(\frac{p_{\rm{T},\it{i}}}{p_{\rm{T,jet}}})^{\kappa}(\frac{\Delta
R_{\rm{jet},\it{i}}}{R})^{\beta}$ [23]. The number of jet constituents
corresponds to ($\kappa$,$\beta$) $=$ (0,0), the square of $p_{\rm T}D$
corresponds to (2,0), the angularity $g$ corresponds to (1,1), and the square
of the mass scaled by the jet $p_{\rm T}$ is related to (1,2).

$LeSub$ is defined as the difference of the leading track $p_{\rm T}$ (
$p_{\mathrm{T,track}}^{\rm{lead}}$) and sub-leading track $p_{\rm T}$
($p_{\mathrm{T,track}}^{\rm{sublead}}$):

$LeSub=p_{\mathrm{T,track}}^{\rm{lead}}-p_{\mathrm{T,track}}^{\rm{sublead}}.$
(3)

$LeSub$ is not an IRC-safe observable but shows robustness against
contributions of soft background particles as we will discuss in Section 5. In
order to give an illustrative example for the sensitivity of these observables
to different types of jet fragmentation, Fig. 1 compares the behaviour of the
shapes distributions for quark and gluon initiated jets as obtained by PYTHIA
[24] in pp collisions. At the same transverse momentum, gluon jets are broader
and produce more fragments with a softer momentum spectrum than quark jets.
Consequently, their first radial moment ($g$) is on average higher, whereas
the momentum dispersion ($p_{\rm T}D$) and $LeSub$ are lower. The momentum
dependence of the three shapes in vacuum is illustrated in Fig. 2. As the jet
momentum increases, the angularity and the $p_{\rm{T}}D$ decrease while
$LeSub$ shifts to higher values. These changes are consistent with jets
becoming narrower and with larger differences among constituents’ transverse
momentum at higher jet $p_{\rm{T}}$.

Figure 1: $g$, $p_{\rm T}D$, and $LeSub$ for quark and gluon jets as obtained
from PYTHIA Perugia 2011 simulations of pp collisions at $\sqrt{s}=2.76$ TeV
in the transverse momentum interval $40\leq
p_{\mathrm{T,jet}}^{\rm{part,ch}}\leq 60$ GeV/$c$.

Figure 2: $g$, $p_{\rm T}D$, and $LeSub$ as obtained from PYTHIA Perugia 2011
simulations of pp collisions at $\sqrt{s}=2.76$ TeV for three different
transverse momentum intervals.

##  3 Data sets, event selection, and simulations

The ALICE detector and its performance are described in Refs. [25, 26].

The data were taken during the 2011 LHC Pb–Pb run at $\sqrt{s_{\rm NN}}=2.76$
TeV. This analysis uses events recorded with minimum-bias (MB) triggers, based
on the signal measured in the V0 scintillators detectors that cover the full
azimuth in the pseudo-rapidity intervals $-3.7<\eta<-1.7$ and $2.8<\eta<5.1$.
The online information of the V0 detector was also used to enhance the
fraction of the 10% most-central Pb–Pb collisions. The online centrality
selection has an efficiency of 100% for the $0$–$7\%$ interval in centrality
percentile and drops to about 80% efficiency for the $7$–$10\%$ interval.

Events are reconstructed offline as described in Ref. [27]. Charged tracks are
measured in the ALICE central barrel via the Inner Tracking System (ITS),
which consists of six cylindrical layers of silicon detectors, and the Time
Projection Chamber (TPC) with acceptance $|\eta|<0.9$ over the full azimuth.
Accepted tracks were required to have $0.15<p_{\rm T}<100$ $\mathrm{GeV/}c$,
with at least 70 space-points and at least 80% of the geometrically findable
space-points in the TPC. To account for the azimuthally non-uniform response
of the ITS, in this dataset, two exclusive classes of tracks were used [26]:
tracks with Silicon Pixel Detector (SPD) hits (70% of all tracks in central
Pb–Pb collisions and 95% in pp collisions) and, when the SPD information is
not present, TPC tracks with at least one hit in the ITS, with their
trajectory refitted to the primary vertex to improve the momentum resolution.
The primary vertex was required to lie within 10 cm of the nominal center of
the detector along the beam axis and within 1 cm of it in the transverse
plane. After offline event selection, the Pb–Pb dataset consisted of 17M
events in the $0$–$10\%$ centrality percentile interval ($L_{\rm
int}\approx$21.3\text{\,}\mathrm{\SIUnitSymbolMicro}\mathrm{b}$^{-1}$).

The pp collision data used to validate PYTHIA [24] were recorded during the
2010 low-luminosity pp run at $\sqrt{s}=7$ TeV with a MB trigger selection.
The trigger configuration, offline event selection and tracking are described
in Ref. [28]. After the event selection, the pp dataset consisted of 168M
events ($L_{\rm int}\approx$2.5\text{\,}\mathrm{n}\mathrm{b}$^{-1}$).

For central Pb–Pb collisions, the tracking efficiency is about $80\%$ for
$p_{\rm T}>1$ $\mathrm{GeV/}c$, decreasing to $\approx 56\%$ at $p_{\rm
T}=0.15$ $\mathrm{GeV/}c$. The track momentum resolution is around $1\%$ at
$p_{\rm T}\,=1$ $\mathrm{GeV/}c$ and $\approx 2.5\%$ at $p_{\rm T}\,=40$
$\mathrm{GeV/}c$. For pp collisions, the tracking efficiency is about
$2$–$3\%$ higher than in central Pb–Pb collisions. The track momentum
resolution is about $1\%$ for reconstructed tracks with $p_{\rm T}\,=1$
$\mathrm{GeV/}c$ and of the order of $4.1\%$ for $p_{\rm T}\,=40$
$\mathrm{GeV/}c$ [26, 28].

Simulations of pp collisions were carried out using PYTHIA 6.425 and PYTHIA 8,
with the Perugia 2011 and 4C tunes [29]. They were used as particle-level
references to our fully corrected jet shapes. Moreover, instrumental effects
were simulated using PYTHIA Perugia 0 for the primary collision followed by a
detailed particle transport and detector response simulation using GEANT3
[30]. Simulated events, which include primary particles and the daughters of
strong and electromagnetic decays but not secondaries from interactions in the
detector material or the daughters of weak decays from strange hadrons, are
denoted as “particle level”. Simulated events, which also include instrumental
effects and weak decay daughters, where reconstructed tracks are selected
using the experimental cuts, are denoted as “detector level”.

##  4 Jet reconstruction

Jet reconstruction for both the pp and Pb–Pb analysis was carried out using
the $k_{\mathrm{T}}$ and anti-$k_{\mathrm{T}}$ [31] algorithms applied to all
accepted tracks. The $E$-scheme for recombination was used [32] and the mass
of the charged pion was assumed for each track.

The jet area, $A_{\rm jet}$, was calculated by the FastJet algorithm [32]
using ghost particles (nearly zero-$p_{\rm T}$ particles that participate in
the clustering procedure but do not modify the jet momentum) with area $A_{\rm
g}=0.005$ [33]. A cut on the jet area was applied to suppress combinatorial
jets while preserving high efficiency for true hard jets [34, 35]. Jet
candidates were rejected if $A_{\rm jet}<0.07$ for $R=0.2$. Jet candidates
were accepted if fully contained in the acceptance, meaning that their
centroids laid within $|\eta_{\rm{jet}}|<0.7$, where $\eta_{\rm{jet}}$ is the
pseudo-rapidity of the jet axis.

In pp collisions and for the considered $R=0.2$, the change of the jet
momentum due to the underlying event background is negligible. For the Pb–Pb
analysis, corrections of jet $p_{\rm T}$ and jet shapes are needed due to the
presence of large background from the underlying event. For that purpose, the
jet reconstruction was carried out twice for each event. The first pass
applies the $k_{\mathrm{T}}$ algorithm with $R=0.2$ to estimate the density of
jet-like transverse-momentum and mass due to the background, $\rho$ and
$\rho_{\rm m}$, respectively, defined as:

$\rho=\mathrm{median}\left(\frac{p_{\rm{T,jet}}^{\rm{raw},\it{i}}}{A_{\rm{jet}}^{\it{i}}}\right),\,\,\,\,\,\rho_{m}=\mathrm{median}\left(\frac{\it{m}_{\it{i}}}{A_{\rm
jet}^{\it{i}}}\right)$ (4)

where the index $i$ runs over all jet candidates in an event and
$p_{\rm{T,jet}}^{{\rm raw,}i}$, $m_{i}$, and $A_{\rm jet}^{i}$ are the
transverse momentum, mass, and area of the $i^{\textup{th}}$ reconstructed
jet. The two jets with highest $p_{\rm{T,jet}}^{\rm{raw},i}$ were excluded
from the calculation of the median to suppress the impact of signal jets on
the underlying event background estimate. The second pass, which generates jet
candidates for the reported distributions, applied the anti-$k_{\mathrm{T}}$
algorithm with resolution parameter $R=0.2$.

##  5 Average background subtraction and fake jet suppression

In order to correct the candidate jet $p_{\rm T}$ and shape distributions
simultaneously for the average underlying event background, two different
techniques were applied.

  * •

Area-derivatives method [36]. This method is valid for any infrared and
collinear safe jet reconstruction algorithm and jet shape. The event is
characterised by $\rho$ and $\rho_{\rm m}$. Ghost particles are added
uniformly in the acceptance, each of them mimicking a pileup-like component in
a region of area $A_{\rm g}$. The sensitivity of the shape to background is
determined by calculating its derivatives with respect to the transverse
momentum and mass of the ghosts. Given $\rho$, $\rho_{\rm m}$, and the
information on the derivatives, the value of the jet shape is then
extrapolated by a Taylor series to zero background.

  * •

Constituent subtraction method [37]. In this method, the subtraction operates
particle-by-particle. Ghost particles are added uniformly to the acceptance,
with finite $p_{\rm T}$ and mass given by: $p_{\rm{T,g}}=A_{\rm{g}}\rho$ and
$m_{\rm{g}}=A_{\rm{g}}\rho_{\rm m}$. The distance between each real jet
constituent and each ghost is then computed and an iterative procedure starts,
which consists of finding the closest pair. If the transverse momentum of
particle $i$ is larger than that of the ghost, the ghost is discarded and its
transverse momentum is subtracted from that of the real particle. In case the
transverse momentum of the ghost is larger than that of particle $i$, the real
particle is discarded and the transverse momentum of the real particle is
subtracted from the ghost transverse momentum. The same procedure applies to
the mass. The procedure terminates when all jet constituents are analysed.

We note that in the case of $\rho_{\rm m}=0$, the jet $p_{\rm T}$ correction
obtained with these methods coincides exactly with the standard area-based
subtraction approach where $p_{\rm{T,jet}}^{sub}=p_{\rm{T,jet}}-\rho\times
A_{\rm{jet}}$. The $\rho_{\rm m}$ term was introduced to take into account
that low-$p_{\rm{T}}$ particles from the underlying event have masses that are
not negligible compared to their momenta. This component has impact on the
observables related to differences between jet energy and 3-momentum like the
jet mass [36] but negligible impact on the jet momentum.

The jet-by-jet constituent subtraction technique [37] was used as the primary
method and the area-derivatives method was used as a systematic variation. To
study the performance of the subtraction methods, PYTHIA events at detector
level were embedded into Pb–Pb events. Embedding means superimposing the
PYTHIA and Pb–Pb events at track level. Figure 3 shows the shape distribution
for embedded unsubtracted jets (squares), the average background-subtracted
jet shapes (open circles and crosses for the two methods), and the PYTHIA
detector-level distributions (full circles). The average background-subtracted
embedded distributions get closer to the PYTHIA detector-level distributions
than without background subtraction. The comparison was performed in the
interval of reconstructed and subtracted embedded momentum,
$p_{\rm{T,Jet}}^{\rm{rec,ch}}$, of $40$–$60$ GeV/$c$. Figure 3 reveals that
$LeSub$ is rather insensitive to modifications induced by the background.
Residual differences, due to background fluctuations, were corrected using an
unfolding procedure (see Section 7).

Figure 3: Background subtraction performance for jet shapes studied with jets
from PYTHIA events embedded into real Pb–Pb events, in the background
subtracted transverse momentum interval $40\leq
p_{\mathrm{T,jet}}^{\rm{rec,ch}}\leq 60$ GeV/$c$ for the area derivatives and
constituent subtraction methods.

The smearing of jet $p_{\rm T}$ due to the local background fluctuations,
quantified as $\delta p_{\mathrm{T}}$ [38], has a width of $\sigma=$ 4 GeV/$c$
for $R=0.2$ in central Pb–Pb collisions [38]. The truncation of the raw yield
at 30 GeV/$c$ prior to unfolding sets our working point more than 7$\sigma$
away from zero and thus contributions from purely combinatorial background
jets to the raw spectrum are negligible, allowing for a stable unfolding
correction [9].

##  6 Detector and background response

A 4-dimensional response matrix was built with axes
$shape^{\mathrm{part,ch}}$, $p_{\mathrm{T,jet}}^{\mathrm{part,ch}}$,
$shape^{\mathrm{rec,ch}}$, and $p_{\mathrm{T,jet}}^{\mathrm{rec,ch}}$. The
upper index ’part’ refers to particle level and ’rec’ refers to reconstructed
level quantities. In pp collisions, index ’rec’ refers to detector-level
quantities, while in Pb–Pb collisions it refers to embedded and background
subtracted quantities. In order to associate a reconstructed-level jet to a
particle-level jet, a matching criterion needs to be defined.

The response matrix for pp collisions is purely instrumental and was
constructed using PYTHIA events at particle level and after full detector
simulation. The matching criterion between the corresponding jets at particle
and detector level is purely geometrical and was based on requiring that they
are univocally the closest in the ($\eta,\varphi$) space. The response matrix
for the Pb–Pb case considers both the effects of the detector and the effects
of the background fluctuations. To construct it, we embedded PYTHIA detector-
level events into Pb–Pb events and we applied two successive matchings, the
first between the background-subtracted, embedded jets and detector-level
PYTHIA jets and the second between the detector and particle-level jets. The
matching between embedded and detector-level jets is not purely geometrical
but also required that more than $50\%$ of the detector-level jet momentum is
contained in the embedded reconstructed jet. The matching efficiency is
consistent with unity for jets with $p_{\rm T}$ above 30 GeV/$c$. We note that
since our embedding is a superposition of PYTHIA and Pb–Pb events at track
level, two-track effects are not present, however their impact in data is
small due to the large required number of clusters per track. The jet energy
scale shift in pp collisions is about 15$\%$ at
$p_{\rm{T,jet}}^{\rm{part,ch}}=50$ GeV/$c$. In Pb–Pb collisions, this shift is
about $12\%$ in the same transverse momentum range at the particle level [9].
The instrumental jet energy resolution (JER), which characterises the detector
response relative to charged jets at particle level, varies from $20\%$ at
$p_{\mathrm{T,jet}}^{\rm{rec,ch}}=20$ GeV/$c$ to $25\%$ at
$p_{\mathrm{T,jet}}^{\rm{rec,ch}}=100$ GeV/$c$, similarly for pp and Pb–Pb
collisions. The JER is dominated by tracking effects and shows no dependence
with jet $R$ [9].

The jet shape resolution can be studied via the distribution of residuals,
which gives the relative difference between the jet shapes measured at
particle and reconstructed level. In Fig. 4, the left panels show the
distribution of residuals for each of the three shapes for jets in pp and for
PYTHIA embedded jets in Pb–Pb collisions, in the particle-level jet
$p_{\rm{T,jet}}^{\rm{part,ch}}$ range of $40$–$60$ GeV/$c$. Tracking
inefficiency induces a negative tail in the angularity (narrower jets due to
missing constituents), while a positive tail is induced on average by
background fluctuations and, to a lower extend, also by track momentum
resolution. The trend is the opposite in the case of $p_{\rm T}D$: losses due
to tracking efficiency shift the distribution of residuals to positive values
(fewer constituents) while the background fluctuations induce a negative
shift. For $LeSub$, the distributions in PYTHIA and PYTHIA embedded
simulations are similar due to the resilience of the observable to background
fluctuations.

The right panels of Fig. 4 show the resolution of the shapes, quantified as
the standard deviation $\sigma$ of the distribution of residuals, as a
function of the shape at particle level for pp and Pb–Pb collisions. At low
angularity, the resolution is poor because these jets are more collimated and
typically have fewer constituents. In this region, this shape is thus more
sensitive to tracking inefficiency and background fluctuations. At higher
angularities the resolution improves up to 20$\%$. The resolution of $p_{\rm
T}D$ is overall below $15\%$ and improves for harder jets when
$p_{\mathrm{T}}D$ approaches unity. A similar case holds for $LeSub$, for
which the resolution improves at higher values of the shape and worsens at low
values where detector effects have a larger impact.

Figure 4: Left plots show the distributions of residuals for the set of three
jet shapes in a given interval of $p_{\rm{T,jet}}^{\rm{part,ch}}$ $40$–$60$
GeV/$c$ using the PYTHIA and PYTHIA embedded simulations. Right plots show the
width (quantified as the standard deviation) of the distributions on the left
as a function of the values of the shapes at particle level. The black and red
curves correspond to pp and Pb–Pb simulations, respectively. The line
connecting the points on the right is drawn to guide the eye.

##  7 Two-dimensional unfolding procedure

Residual background fluctuations and detector effects were unfolded to
simultaneously correct the reconstructed jet transverse momentum and shape
distributions back to the particle level. Bayesian unfolding in two dimensions
as implemented in the RooUnfold package [39] was used. Several considerations
needed to be taken into account. The 2D correlation
($p_{\rm{T,jet}}^{\rm{rec,ch}}$, $shape_{\rm jet}^{\rm{rec,ch}}$), which is
the input to the unfolding, was binned such that there are at least 10 counts
per bin, to guarantee statistical stability of the correction procedure, which
also sets the upper limit of the input $p_{\rm{T,jet}}^{\rm{rec,ch}}$ range
(80 GeV/$c$ both in pp and Pb–Pb collisions). The shape input ranges are
$0.3$–$1$, $0.02$–$0.12$, and $0$–$30$ GeV/$c$ for $g$, $p_{\rm T}D$ and
$LeSub$, respectively, for both collision systems. The raw input correlation
should not contain combinatorial background, which was suppressed by
truncating it at sufficiently high values of $p_{\rm{T,jet}}^{\rm{rec,ch}}$.
The lower limit of the input $p_{\rm{T,jet}}^{\rm{rec,ch}}$ range for
unfolding in Pb–Pb collisions is $30$ GeV/$c$. As argued in Section 4, fake
jet contamination above this limit for jets measured with $R=0.2$ is
negligible. In pp collisions, the cutoff is set at
$p_{\rm{T,jet}}^{\rm{rec,ch}}=20$ GeV/$c$.

The particle-level $p_{\rm T,jet}^{\rm part,ch}$ range of the response matrix
is from 0 to 200 GeV/$c$. The shape ranges at the particle level are $0$–$1$,
$0$–$0.12$ and $0$–$50$ GeV/$c$ for $g$, $p_{\rm T}D$, and $LeSub$,
respectively. The particle-level ranges were extended beyond the input ranges
to allow for jet migration into the reconstructed level range due to
background fluctuations and tracking efficiency losses. When the data input is
truncated, feed-in from detector-level jets outside the truncated range had to
be considered and corrected for. However, this correction (referred to as
kinematic efficiency) is purely based on MC and has to be limited by
considering unfolded bins far away from the truncation thresholds. Thus, our
final results are presented for the jet momentum interval $40$–$60$ GeV/$c$.

We tested the stability of the unfolding by refolding the solution back and
checking the agreement with the raw distribution. In pp (Pb–Pb) collisions,
both distributions agree within 1$\%$ (5$\%$) after the second (third)
iteration. The unfolding solutions converged after few iterations (note that
convergence occurs globally in 2D and not just in a given interval of jet
$p_{\rm T}$). We also performed a closure test, where two statistically
independent MC samples are used to fill the response and the pseudo-data. In
this case, the unfolded solution agrees with the MC truth within less than
10$\%$ in pp and Pb–Pb collisions.

##  8 Systematic uncertainties

The systematic uncertainties for the shapes were determined by varying the
analysis settings for instrumental response and background fluctuations. The
systematic uncertainties are listed below:

  * •

Tracking efficiency uncertainty for the used track selection is $\pm 4\%$ and
this was used as source to estimate the jet energy scale uncertainty [16].

  * •

The prior in the 2D Bayesian implementation of RooUnfold was taken as the
projection of the response matrix onto the true axes. The default prior was
PYTHIA Perugia 0. We considered three different variations. As the systematic
uncertainty in a given bin we take the maximal deviation out of the three
variations. The first variation was to re-weight the response matrix such that
the prior coincides with the unfolding solution. The second considered
variation was obtained by re-weighting the response matrix such that the
projection onto true axis was that of purely gluonic jets. The third variation
was obtained by re-weighting the response matrix such that the projection onto
the true axis was that of purely quark jets.

  * •

The regularization was given by the number of iterations considered, which was
4 (8) for pp (Pb–Pb) collisions in the default solutions. The uncertainty in
the regularization was estimated by considering differences to solutions for
one less and two more iterations.

  * •

The minimum accepted jet $p_{\mathrm{T,jet}}^{\rm ch}$ as input to the
unfolding was 20 (30) GeV/$c$ in pp (Pb–Pb) collisions. As a variation, we
lowered the truncation by 10 GeV/$c$.

  * •

The binning of the raw input was changed arbitrarily (but keeping the
statistical requirements of at least 10 counts per bin) in both the
$p_{\rm{T,jet}}^{\rm ch}$ and shape dimensions.

  * •

The choice of the background subtraction method in Pb–Pb collisions affected
mostly the tails of the distribution and resulted in a variation of 10$\%$ at
most.

  * •

In Pb–Pb collisions, the matching criterion in the tagging algorithm was
relaxed so that the response was filled with pairs of jets where the
reconstructed embedded jet contained at least $40\%$ of the probe jet
momentum.

The different components of the systematic uncertainties for the different
shapes are summarized in Tables 1 and 2 for pp and Pb–Pb collisions,
respectively. The largest contribution to the systematic uncertainties on the
fully corrected pp data comes from the tracking efficiency uncertainty, yet
the total systematic uncertainty is smaller when compared to the statistical
one. In Pb–Pb collisions, systematic uncertainties due to prior and
subtraction method choice dominate over statistical uncertainties. All the
uncertainties induce changes in the shape of our observables and the applied
normalization causes long range anti-correlations. The total uncertainty is
obtained by adding the different components in quadrature.

Shape | $p_{\mathrm{T}}D$ | $g$ |  $LeSub$ (GeV/$c$)
---|---|---|---
Shape interval | 0.3-0.4 | 0.5-0.6 | 0.8-1 | 0-0.02 | 0.05-0.06 | 0.08-0.12 | 0-5 | 10-15 | 20-30
Tracking | $10\%$ | $0.70\%$ | $11\%$ | $10\%$ | $1.7\%$ | $4.2\%$ | $1.8\%$ | $0.5\%$ | $6.6\%$
Prior | ${}_{-0.0}^{+0.3}\%$ | ${}_{-0.0}^{+0.9}\%$ | ${}_{-0.0}^{+0.0}\%$ | ${}_{-3.0}^{+0.0}\%$ | ${}_{-1.2}^{+0.0}\%$ | ${}_{-0.0}^{+3.0}\%$ | ${}_{-0.0}^{+0.9}\%$ | ${}_{-0.0}^{+0.6}\%$ | ${}_{-0.0}^{+0.5}\%$
Regularization | ${}_{-0.3}^{+0.1}\%$ | ${}_{-1.2}^{+0.7}\%$ | ${}_{-0.1}^{+0.4}\%$ | ${}_{-2.7}^{+5.9}\%$ | ${}_{-1.0}^{+2.3}\%$ | ${}_{-4.5}^{+2.6}\%$ | ${}_{-1.3}^{+0.8}\%$ | ${}_{-0.6}^{+0.6}\%$ | ${}_{-0.0}^{+0.6}\%$
Truncation | ${}_{-0.7}^{+0.0}\%$ | ${}_{-0.1}^{+0.0}\%$ | ${}_{-0.0}^{+0.5}\%$ | ${}_{-0.0}^{+0.3}\%$ | ${}_{-0.2}^{+0.0}\%$ | ${}_{-0.0}^{+0.3}\%$ | ${}_{-0.0}^{+0.1}\%$ | ${}_{-0.1}^{+0.0}\%$ | ${}_{-0.0}^{+0.1}\%$
Binning | $1.4\%$ | $1.6\%$ | $4.2\%$ | $0.2\%$ | $6.4\%$ | $2.5\%$ | $2.1\%$ | $1.8\%$ | $0.9\%$
Total | ${}_{-10}^{+10}\%$ | ${}_{-2.2}^{+2.1}\%$ | ${}_{-11}^{+11}\%$ | ${}_{-11}^{+12}\%$ | ${}_{-6.8}^{+7.0}\%$ | ${}_{-6.7}^{+6.3}\%$ | ${}_{-3.1}^{+3.0}\%$ | ${}_{-2.0}^{+2.1}\%$ | ${}_{-6.6}^{+6.7}\%$
Table 1: Relative systematic uncertainties on the measured jet shapes in pp collisions for three selected jet shape intervals in the jet $p_{\mathrm{T,jet}}^{\rm ch}$ range of $40$–$60$ GeV/$c$. Shape | $p_{\mathrm{T}}D$ | $g$ |  $LeSub$ (GeV/$c$)
---|---|---|---
Shape interval | 0.3-0.4 | 0.5-0.6 | 0.8-1 | 0-0.02 | 0.05-0.06 | 0.08-0.12 | 0-5 | 10-15 | 20-30
Tracking | $0.7\%$ | $1.1\%$ | $3.3\%$ | $9.6\%$ | $2.9\%$ | $4.9\%$ | $0.6\%$ | $1.7\%$ | $0.8\%$
Prior | $20\%$ | $2.6\%$ | $7.4\%$ | $7.6\%$ | $8.1\%$ | $20\%$ | $7.5\%$ | $7.9\%$ | $9.0\%$
Regularization | ${}_{-1.5}^{+0.6}\%$ | ${}_{-0.8}^{+0.3}\%$ | ${}_{-0.3}^{+0.1}\%$ | ${}_{-0.9}^{+0.3}\%$ | ${}_{-0.8}^{+0.5}\%$ | ${}_{-0.0}^{+0.1}\%$ | ${}_{-1.1}^{+0.4}\%$ | ${}_{-0.1}^{+0.2}\%$ | ${}_{-1.7}^{+4.3}\%$
Truncation | ${}_{-18}^{+0.0}\%$ | ${}_{-0.0}^{+1.6}\%$ | ${}_{-0.0}^{+3.9}\%$ | ${}_{-0.0}^{+3.7}\%$ | ${}_{-1.0}^{+0.0}\%$ | ${}_{-39}^{+0.0}\%$ | ${}_{-25}^{+0.0}\%$ | ${}_{-0.0}^{+10}\%$ | ${}_{-0.0}^{+18}\%$
Binning | $1.3\%$ | $2.3\%$ | $4.2\%$ | $2.3\%$ | $3.6\%$ | $3.5\%$ | $0.9\%$ | $7.9\%$ | $3.4\%$
Bkg.Sub | ${}_{-0.0}^{+5.5}\%$ | ${}_{-2.1}^{+0.0}\%$ | ${}_{-0.3}^{+0.0}\%$ | ${}_{-2.5}^{+0.0}\%$ | ${}_{-9.5}^{+0.0}\%$ | ${}_{-13}^{+0.0}\%$ | ${}_{-1.0}^{+0.0}\%$ | ${}_{-6.7}^{+0.0}\%$ | ${}_{-1.6}^{+0.0}\%$
Matching | ${}_{-0.5}^{+0.0}\%$ | ${}_{-0.0}^{+0.2}\%$ | ${}_{-0.0}^{+9.4}\%$ | ${}_{-0.0}^{+2.6}\%$ | ${}_{-0.0}^{+1.9}\%$ | ${}_{-0.0}^{+23}\%$ | ${}_{-4.3}^{+0.0}\%$ | ${}_{-0.3}^{+0.0}\%$ | ${}_{-0.7}^{+0.0}\%$
Total | ${}_{-27}^{+21}\%$ | ${}_{-4.3}^{+4.0}\%$ | ${}_{-9.2}^{+14}\%$ | ${}_{-13}^{+13}\%$ | ${}_{-13}^{+9.5}\%$ | ${}_{-47}^{+31}\%$ | ${}_{-26}^{+7.6}\%$ | ${}_{-13}^{+15}\%$ | ${}_{-10}^{+21}\%$
Table 2: Relative systematic uncertainties on the measured jet shapes in Pb–Pb
collisions for three selected jet shape intervals in the jet
$p_{\rm{T,jet}}^{\rm ch}$ range $40$–$60$ GeV/$c$.

##  9 Results and discussion

Figure 5 presents the fully corrected jet shape distributions measured in pp
collisions at $\sqrt{s}=7$ TeV in the jet $p_{\mathrm{T}}$ range $40$–$60$
GeV/$c$. The results are compared to PYTHIA Perugia 2011 and PYTHIA 8 tune 4C
jet shape distributions obtained at the same collision energy. The ratio plots
in the lower panels indicate a reasonable agreement within $20\%$. Large non-
perturbative effects are expected for small-$R$ jets [40] and seem to be well
accounted for by the simulations.

Figure 5: Fully corrected jet shape distributions measured in pp collisions at
$\sqrt{s}=7$ TeV for $R=0.2$ in the range of jet $p_{\mathrm{T,jet}}^{\rm ch}$
of $40$–$60$ GeV$/c$. The results are compared to PYTHIA. The coloured boxes
represent the uncertainty on the jet shape (upper panels) and its propagation
to the ratio (lower panels)

Figure 6 shows the fully corrected jet shape distributions in Pb–Pb collisions
at $\sqrt{s_{\mathrm{NN}}}=$ 2.76 TeV compared to PYTHIA Perugia 2011 and
PYTHIA 8 tune 4C at the same collision energy and in the same jet $p_{\rm T}$
range of $40$–$60$ GeV/$c$. The radial moment (upper left plot) appears to be
shifted to lower values in the measured data compared to PYTHIA. The
$p_{\mathrm{T}}D$ (upper right plot) is shifted to higher values in the
measured data compared to PYTHIA. $LeSub$ (bottom) shows no indication of
modifications relative to PYTHIA. These results indicate that the
fragmentation in Pb–Pb collisions is harder and more collimated than in vacuum
at the same reconstructed energy.

The observed hardening of the fragmentation is qualitatively consistent with
the observed enhancement of the high-$z$ component of the fragmentation
functions of inclusive jets measured by ATLAS and CMS in Pb–Pb collisions[11,
12]. More recent measurements of fragmentation functions of jets recoiling
from photons at CMS [41] do not show an enhancement at high-$z$ but rather
indicate a depletion of the high-$z$ component accompanied by an enhancement
of the soft modes. When the jet fragmentation is studied as a function of the
photon energy in gamma-jet events, where the transverse momentum of the photon
balances the initial parton momentum from the hard scattering to good
approximation, there is no bias towards higher $Q^{2}$ in Pb–Pb relative to pp
compared to the case when the recoiling jet energy is used. To quantitatively
compare the different observables that select different samples of jets
(inclusive vs recoil) and that are subject to different kinematic cuts,
modeling within the same theoretical framework is required.

Figure 6: Fully corrected jet shape distributions in $0$–$10\%$ central Pb–Pb
collisions at $\sqrt{s_{\rm NN}}=2.76$ TeV for $R=0.2$ in the range of jet
$p_{\mathrm{T,jet}}^{\rm ch}$ of $40$–$60$ GeV$/c$. The results are compared
to PYTHIA. The coloured boxes represent the uncertainty on the jet shape
(upper panels) and its propagation to the ratio (lower panels).

In Fig. 7 we compared quark and gluon vacuum jet shape distributions from
PYTHIA to our data. Since quark-initiated jets radiate less, their
fragmentation is harder and less broad. Gluon-initiated jets can be thought of
as an approximation to modified jets in the hypothetical case where quenching
accelerates the shower evolution just by increasing the number of splittings.
This scenario would lead to a broadening and softening of the in-cone shower
(see differences in the shape between inclusive jets and gluon jets in the
plot) as opposed to the data. The comparison in Fig. 7 indicates that the
Pb–Pb fragmentation agrees more with a vacuum quark-like fragmentation than
with a vacuum gluon-like fragmentation. It is worth noting that in the case
where gluon jets interact more strongly with the medium than quark jets, their
relative fractions might change for a given jet $p_{\rm T}$ in favour of more
quark-initiated jets. In line with this argument, the simple toy model
calculations described in Ref. [42] can explain qualitatively some aspects of
the data like the hardening of the fragmentation function and $p_{\rm T}$
dependence of the jet suppression, just by using a varying quark fraction and
a greater quenching for gluon jets.

Figure 7: Jet shape distributions in $0$–$10\%$ central Pb–Pb collisions at
$\sqrt{s_{\rm NN}}=2.76$ TeV for $R=0.2$ in range of jet
$p_{\mathrm{T,jet}}^{\rm ch}$ of $40$–$60$ GeV$/c$ compared to quark and gluon
vacuum generated jet shape distributions. The coloured boxes represent the
experimental uncertainty on the jet shapes.

Another ingredient that might contribute to the observed differences between
jet shapes in Pb–Pb and pp collisions at the same $p_{\rm{T,jet}}^{\rm ch}$ is
that the original energy of the parton initiating the jet shower is different
in both systems. The significant suppression of jet rates at high
$p_{\rm{T}}$, $R_{\rm AA}<1$, suggests that the jet energy that is
reconstructed in Pb–Pb collisions is smaller than the original parton energy;
this could lead to a larger virtuality of jets in Pb–Pb than in pp collisions
for a given momentum. Let’s consider the case where a fraction $X\%$ of the
jet momentum is lost coherently, meaning that the jet substructure is not
resolved by the medium and the jet radiates as a single colour charge [2].
Since $g$ and $p_{\rm{T}}D$ are normalized to the jet $p_{\rm{T}}$, a simple
rescaling by a momentum fraction $X\%$ of each jet constituent leaves Eqs. 3
and 2 unmodified. In this scenario, the modified jet shapes, for a given
reconstructed jet $p_{\rm{T}}$, are simply the vacuum-like shapes of jets with
a momentum higher by a fraction $1/(1-X\%)$. As seen in Fig. 2, both $g$ and
$p_{\rm{T}}D$ decrease with jet momentum in vacuum. Our experimental results
show that the $g$ distribution shifts to lower values in Pb–Pb collisions
relative to the vacuum-like one. The $p_{\rm{T}}D$ distribution, instead,
increases, contrary to what is expected from a fully coherent energy loss
scenario. Following these considerations, the medium seems to be able to
resolve the jet structure at angular scales below $R=0.2$.

We also compared our results to JEWEL calculations [43], which is a
perturbative framework for jet evolution in the presence of a dense medium.
The detailed description of the jet-medium interaction includes elastic
scattering off medium constituents, inelastic medium-induced gluon radiation,
and medium recoil. The medium recoil refers to the response of the medium to
the jet. This component is a correlated background that cannot be
experimentally suppressed. An extensive comparison of the model to the
existing jet shapes was done (see Ref. [17]), showing that the contribution of
the medium recoil to the modification of the jet shapes is large, in
particular in those shapes that are most sensitive to the soft, large-angle
quanta such as the jet mass or the subjet momentum imbalance $z_{\rm g}$.
Figure 8 shows the measured jet shape distributions compared to JEWEL
calculations. The effects of the medium recoil are small, as expected for the
small considered $R$ and thus the measurement constrains the purely radiative
aspects of the JEWEL shower modification. There is good agreement between the
model and the data.

The ALICE measurement of the jet mass [16] for jets of $R=0.4$ showed some
hints of a reduction relative to the vacuum reference. The jet mass, as
discussed in Section 2, differs parametrically from the angularity only in the
power of the angle dependence, so it is also sensitive to the broadening or
collimation of the jet shower. Comparisons to JEWEL revealed that the effect
of jet mass reduction due to energy loss is obscured by the broadening due to
the medium response or recoil[17], which contributes more strongly to jets
with $R=0.4$ than to the jet core measurements with $R=0.2$ reported here.

Figure 8: Jet shape distributions in $0$–$10\%$ central Pb–Pb collisions at
$\sqrt{s_{\rm NN}}=2.76$ TeV for $R=0.2$ in range of jet
$p_{\mathrm{T,jet}}^{\rm ch}$ of $40$–$60$ GeV$/c$ compared to JEWEL with and
without recoils with different subtraction methods. The coloured boxes
represent the experimental uncertainty on the jet shapes.

##  10 Conclusions

In this paper, the first measurement of a new set of track-based jet shape
distributions has been presented. The measurements were performed in pp
collisions at $\sqrt{s}=7$ TeV and in $0$–$10\%$ central Pb–Pb collisions at
$\sqrt{s_{\rm NN}}=2.76$ TeV in the low jet transverse momentum interval
$40\leq p_{\mathrm{T,jet}}^{\rm{ch}}\leq 60$ GeV/$c$ and using small jet
resolution $R=$ 0.2. The full correction to particle level and the measurement
of an unbiased sample of jets with a constituent transverse momentum cutoff of
$0.15$ GeV/$c$ are key aspects of the analysis that allow exploring possible
medium modifications in a wide dynamical range including soft modes.

The jet shapes reported here probe complementary aspects of the jet
fragmentation and are used to test possible scenarios and ingredients of the
theoretical description of jet quenching.

The measurements of $g$, $p_{\mathrm{T}}D$, and $LeSub$ in pp collisions are
within 20% in agreement with PYTHIA Perugia 2011 and PYTHIA 8 4C tunes.

In central Pb–Pb collisions, the measurements of $g$ and $p_{\mathrm{T}}D$
show that the jet core is more collimated and fragments harder than in pp
collisions. The picture is qualitatively consistent with a more quark-like jet
fragmentation, suggesting e.g. either a modified fragmentation pattern of all
jets or a selection on quark-like jet properties imposed by the medium for
these observables.

The role of colour coherence in the jet-medium interactions and the scales at
which it dominates is a key ingredient in the characterisation of the medium.
We argue that the medium-modification of $g$ and $p_{\rm{T}}D$ is not
consistent with the scenario where the jets interact with the medium
coherently, as single colour charges. This suggests that the medium is able to
resolve the jet structure at angular scales smaller than $R=0.2$.

Comparison to calculations using the JEWEL jet quenching model shows that the
contribution of the medium response to the small-radius jets reported here is
small and thus the data can constrain the effects due to energy loss, contrary
to other measurements at larger $R$ where the medium recoil can obscure the
radiative effects.

## 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 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), Universidade Federal do
Rio Grande do Sul (UFRGS), Financiadora de Estudos e Projetos (Finep) and
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP), Brazil;
Ministry of Science & Technology of China (MSTC), National Natural Science
Foundation of China (NSFC) and Ministry of Education of China (MOEC) , China;
Ministry of Science and Education, Croatia; Ministry of Education, Youth and
Sports of the Czech Republic, Czech Republic; The Danish Council for
Independent Research — Natural Sciences, the Carlsberg Foundation 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, Wissenschaft, Forschung und Technologie (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; Centro Fermi -
Museo Storico della Fisica e Centro Studi e Ricerche Enrico Fermi and Istituto
Nazionale di Fisica Nucleare (INFN), Italy; Institute for Innovative Science
and Technology , Nagasaki Institute of Applied Science (IIST), Japan Society
for the Promotion of Science (JSPS) KAKENHI and Japanese Ministry of
Education, Culture, Sports, Science and Technology (MEXT), 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 and National Science Centre, 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 Romanian National Agency
for Science, Technology and Innovation, Romania; Joint Institute for Nuclear
Research (JINR), Ministry of Education and Science of the Russian Federation
and National Research Centre Kurchatov Institute, Russia; Ministry of
Education, Science, Research and Sport of the Slovak Republic, Slovakia;
National Research Foundation of South Africa, South Africa; Centro de
Aplicaciones Tecnológicas y Desarrollo Nuclear (CEADEN), Cubaenergía, Cuba and
Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas
(CIEMAT), Spain; Swedish Research Council (VR) and Knut & Alice Wallenberg
Foundation (KAW), Sweden; European Organization for Nuclear Research,
Switzerland; National Science and Technology Development Agency (NSDTA),
Suranaree University of Technology (SUT) 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.

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##  Appendix A The ALICE Collaboration

S. Acharya139, F.T.-. Acosta20, D. Adamová93, A. Adler74, J. Adolfsson80, M.M.
Aggarwal98, G. Aglieri Rinella34, M. Agnello31, N. Agrawal48, Z. Ahammed139,
S.U. Ahn76, S. Aiola144, A. Akindinov64, M. Al-Turany104, S.N. Alam139, D.S.D.
Albuquerque121, D. Aleksandrov87, B. Alessandro58, R. Alfaro Molina72, Y.
Ali15, A. Alici10,27,53, A. Alkin2, J. Alme22, T. Alt69, L. Altenkamper22, I.
Altsybeev111, M.N. Anaam6, C. Andrei47, D. Andreou34, H.A. Andrews108, A.
Andronic104,142, M. Angeletti34, V. Anguelov102, C. Anson16, T. Antičić105, F.
Antinori56, P. Antonioli53, R. Anwar125, N. Apadula79, L. Aphecetche113, H.
Appelshäuser69, S. Arcelli27, R. Arnaldi58, I.C. Arsene21, M. Arslandok102, A.
Augustinus34, R. Averbeck104, M.D. Azmi17, A. Badalà55, Y.W. Baek40,60, S.
Bagnasco58, R. Bailhache69, R. Bala99, A. Baldisseri135, M. Ball42, R.C.
Baral85, A.M. Barbano26, R. Barbera28, F. Barile52, L. Barioglio26, G.G.
Barnaföldi143, L.S. Barnby92, V. Barret132, P. Bartalini6, K. Barth34, E.
Bartsch69, N. Bastid132, S. Basu141, G. Batigne113, B. Batyunya75, P.C.
Batzing21, J.L. Bazo Alba109, I.G. Bearden88, H. Beck102, C. Bedda63, N.K.
Behera60, I. Belikov134, F. Bellini34, H. Bello Martinez44, R. Bellwied125,
L.G.E. Beltran119, V. Belyaev91, G. Bencedi143, S. Beole26, A. Bercuci47, Y.
Berdnikov96, D. Berenyi143, R.A. Bertens128, D. Berzano34,58, L. Betev34, P.P.
Bhaduri139, A. Bhasin99, I.R. Bhat99, H. Bhatt48, B. Bhattacharjee41, J.
Bhom117, A. Bianchi26, L. Bianchi125, N. Bianchi51, J. Bielčík37, J.
Bielčíková93, A. Bilandzic103,116, G. Biro143, R. Biswas3, S. Biswas3, J.T.
Blair118, D. Blau87, C. Blume69, G. Boca137, F. Bock34, A. Bogdanov91, L.
Boldizsár143, M. Bombara38, G. Bonomi138, M. Bonora34, H. Borel135, A.
Borissov102,142, M. Borri127, E. Botta26, C. Bourjau88, L. Bratrud69, P.
Braun-Munzinger104, M. Bregant120, T.A. Broker69, M. Broz37, E.J. Brucken43,
E. Bruna58, G.E. Bruno33,34, D. Budnikov106, H. Buesching69, S. Bufalino31, P.
Buhler112, P. Buncic34, O. BuschI,131, Z. Buthelezi73, J.B. Butt15, J.T.
Buxton95, J. Cabala115, D. Caffarri89, H. Caines144, A. Caliva104, E. Calvo
Villar109, R.S. Camacho44, P. Camerini25, A.A. Capon112, W. Carena34, F.
Carnesecchi10,27, J. Castillo Castellanos135, A.J. Castro128, E.A.R. Casula54,
C. Ceballos Sanchez8, S. Chandra139, B. Chang126, W. Chang6, S. Chapeland34,
M. Chartier127, S. Chattopadhyay139, S. Chattopadhyay107, A. Chauvin24, C.
Cheshkov133, B. Cheynis133, V. Chibante Barroso34, D.D. Chinellato121, S.
Cho60, P. Chochula34, T. Chowdhury132, P. Christakoglou89, C.H. Christensen88,
P. Christiansen80, T. Chujo131, S.U. Chung18, C. Cicalo54, L. Cifarelli10,27,
F. Cindolo53, J. Cleymans124, F. Colamaria52, D. Colella52, A. Collu79, M.
Colocci27, M. ConcasII,58, G. Conesa Balbastre78, Z. Conesa del Valle61, J.G.
Contreras37, T.M. Cormier94, Y. Corrales Morales58, P. Cortese32, M.R.
Cosentino122, F. Costa34, S. Costanza137, J. Crkovská61, P. Crochet132, E.
Cuautle70, L. Cunqueiro94,142, T. Dahms103,116, A. Dainese56, F.P.A.
Damas113,135, S. Dani66, M.C. Danisch102, A. Danu68, D. Das107, I. Das107, S.
Das3, A. Dash85, S. Dash48, S. De49, A. De Caro30, G. de Cataldo52, C. de
Conti120, J. de Cuveland39, A. De Falco24, D. De Gruttola10,30, N. De Marco58,
S. De Pasquale30, R.D. De Souza121, H.F. Degenhardt120, A. Deisting102,104, A.
Deloff84, S. Delsanto26, C. Deplano89, P. Dhankher48, D. Di Bari33, A. Di
Mauro34, B. Di Ruzza56, R.A. Diaz8, T. Dietel124, P. Dillenseger69, Y. Ding6,
R. Divià34, Ø. Djuvsland22, A. Dobrin34, D. Domenicis Gimenez120, B.
Dönigus69, O. Dordic21, A.K. Dubey139, A. Dubla104, L. Ducroux133, S. Dudi98,
A.K. Duggal98, M. Dukhishyam85, P. Dupieux132, R.J. Ehlers144, D. Elia52, E.
Endress109, H. Engel74, E. Epple144, B. Erazmus113, F. Erhardt97, M.R.
Ersdal22, B. Espagnon61, G. Eulisse34, J. Eum18, D. Evans108, S. Evdokimov90,
L. Fabbietti103,116, M. Faggin29, J. Faivre78, A. Fantoni51, M. Fasel94, L.
Feldkamp142, A. Feliciello58, G. Feofilov111, A. Fernández Téllez44, A.
Ferretti26, A. Festanti34, V.J.G. Feuillard102, J. Figiel117, M.A.S.
Figueredo120, S. Filchagin106, D. Finogeev62, F.M. Fionda22, G. Fiorenza52, F.
Flor125, M. Floris34, S. Foertsch73, P. Foka104, S. Fokin87, E. Fragiacomo59,
A. Francescon34, A. Francisco113, U. Frankenfeld104, G.G. Fronze26, U.
Fuchs34, C. Furget78, A. Furs62, M. Fusco Girard30, J.J. Gaardhøje88, M.
Gagliardi26, A.M. Gago109, K. Gajdosova88, M. Gallio26, C.D. Galvan119, P.
Ganoti83, C. Garabatos104, E. Garcia-Solis11, K. Garg28, C. Gargiulo34, P.
Gasik103,116, E.F. Gauger118, M.B. Gay Ducati71, M. Germain113, J. Ghosh107,
P. Ghosh139, S.K. Ghosh3, P. Gianotti51, P. Giubellino58,104, P. Giubilato29,
P. Glässel102, D.M. Goméz Coral72, A. Gomez Ramirez74, V. Gonzalez104, P.
González-Zamora44, S. Gorbunov39, L. Görlich117, S. Gotovac35, V. Grabski72,
L.K. Graczykowski140, K.L. Graham108, L. Greiner79, A. Grelli63, C.
Grigoras34, V. Grigoriev91, A. Grigoryan1, S. Grigoryan75, J.M. Gronefeld104,
F. Grosa31, J.F. Grosse-Oetringhaus34, R. Grosso104, R. Guernane78, B.
Guerzoni27, M. Guittiere113, K. Gulbrandsen88, T. Gunji130, A. Gupta99, R.
Gupta99, I.B. Guzman44, R. Haake34,144, M.K. Habib104, C. Hadjidakis61, H.
Hamagaki81, G. Hamar143, M. Hamid6, J.C. Hamon134, R. Hannigan118, M.R.
Haque63, A. Harlenderova104, J.W. Harris144, A. Harton11, H. Hassan78, D.
Hatzifotiadou10,53, S. Hayashi130, S.T. Heckel69, E. Hellbär69, H. Helstrup36,
A. Herghelegiu47, E.G. Hernandez44, G. Herrera Corral9, F. Herrmann142, K.F.
Hetland36, T.E. Hilden43, H. Hillemanns34, C. Hills127, B. Hippolyte134, B.
Hohlweger103, D. Horak37, S. Hornung104, R. Hosokawa78,131, J. Hota66, P.
Hristov34, C. Huang61, C. Hughes128, P. Huhn69, T.J. Humanic95, H. Hushnud107,
N. Hussain41, T. Hussain17, D. Hutter39, D.S. Hwang19, J.P. Iddon127, R.
Ilkaev106, M. Inaba131, M. Ippolitov87, M.S. Islam107, M. Ivanov104, V.
Ivanov96, V. Izucheev90, B. Jacak79, N. Jacazio27, P.M. Jacobs79, M.B.
Jadhav48, S. Jadlovska115, J. Jadlovsky115, S. Jaelani63, C. Jahnke116,120,
M.J. Jakubowska140, M.A. Janik140, C. Jena85, M. Jercic97, O. Jevons108, R.T.
Jimenez Bustamante104, M. Jin125, P.G. Jones108, A. Jusko108, P. Kalinak65, A.
Kalweit34, J.H. Kang145, V. Kaplin91, S. Kar6, A. Karasu Uysal77, O.
Karavichev62, T. Karavicheva62, P. Karczmarczyk34, E. Karpechev62, U.
Kebschull74, R. Keidel46, D.L.D. Keijdener63, M. Keil34, B. Ketzer42, Z.
Khabanova89, A.M. Khan6, S. Khan17, S.A. Khan139, A. Khanzadeev96, Y.
Kharlov90, A. Khatun17, A. Khuntia49, M.M. Kielbowicz117, B. Kileng36, B.
Kim131, D. Kim145, D.J. Kim126, E.J. Kim13, H. Kim145, J.S. Kim40, J. Kim102,
M. Kim60,102, S. Kim19, T. Kim145, T. Kim145, K. Kindra98, S. Kirsch39, I.
Kisel39, S. Kiselev64, A. Kisiel140, J.L. Klay5, C. Klein69, J. Klein58, C.
Klein-Bösing142, S. Klewin102, A. Kluge34, M.L. Knichel34, A.G. Knospe125, C.
Kobdaj114, M. Kofarago143, M.K. Köhler102, T. Kollegger104, N. Kondratyeva91,
E. Kondratyuk90, A. Konevskikh62, P.J. Konopka34, M. Konyushikhin141, L.
Koska115, O. Kovalenko84, V. Kovalenko111, M. Kowalski117, I. Králik65, A.
Kravčáková38, L. Kreis104, M. Krivda65,108, F. Krizek93, M. Krüger69, E.
Kryshen96, M. Krzewicki39, A.M. Kubera95, V. Kučera60,93, C. Kuhn134, P.G.
Kuijer89, J. Kumar48, L. Kumar98, S. Kumar48, S. Kundu85, P. Kurashvili84, A.
Kurepin62, A.B. Kurepin62, S. Kushpil93, J. Kvapil108, M.J. Kweon60, Y.
Kwon145, S.L. La Pointe39, P. La Rocca28, Y.S. Lai79, I. Lakomov34, R.
Langoy123, K. Lapidus144, A. Lardeux21, P. Larionov51, E. Laudi34, R.
Lavicka37, R. Lea25, L. Leardini102, S. Lee145, F. Lehas89, S. Lehner112, J.
Lehrbach39, R.C. Lemmon92, I. León Monzón119, P. Lévai143, X. Li12, X.L. Li6,
J. Lien123, R. Lietava108, B. Lim18, S. Lindal21, V. Lindenstruth39, S.W.
Lindsay127, C. Lippmann104, M.A. Lisa95, V. Litichevskyi43, A. Liu79, H.M.
Ljunggren80, W.J. Llope141, D.F. Lodato63, V. Loginov91, C. Loizides79,94, P.
Loncar35, X. Lopez132, E. López Torres8, P. Luettig69, J.R. Luhder142, M.
Lunardon29, G. Luparello59, M. Lupi34, A. Maevskaya62, M. Mager34, S.M.
Mahmood21, A. Maire134, R.D. Majka144, M. Malaev96, Q.W. Malik21, L.
MalininaIII,75, D. Mal’Kevich64, P. Malzacher104, A. Mamonov106, V. Manko87,
F. Manso132, V. Manzari52, Y. Mao6, M. Marchisone129,133, J. Mareš67, G.V.
Margagliotti25, A. Margotti53, J. Margutti63, A. Marín104, C. Markert118, M.
Marquard69, N.A. Martin102,104, P. Martinengo34, J.L. Martinez125, M.I.
Martínez44, G. Martínez García113, M. Martinez Pedreira34, S. Masciocchi104,
M. Masera26, A. Masoni54, L. Massacrier61, E. Masson113, A. Mastroserio52,136,
A.M. Mathis103,116, P.F.T. Matuoka120, A. Matyja117,128, C. Mayer117, M.
Mazzilli33, M.A. Mazzoni57, F. Meddi23, Y. Melikyan91, A. Menchaca-Rocha72, E.
Meninno30, J. Mercado Pérez102, M. Meres14, S. Mhlanga124, Y. Miake131, L.
Micheletti26, M.M. Mieskolainen43, D.L. Mihaylov103, K. Mikhaylov64,75, A.
Mischke63, A.N. Mishra70, D. Miśkowiec104, J. Mitra139, C.M. Mitu68, N.
Mohammadi34, A.P. Mohanty63, B. Mohanty85, M. Mohisin KhanIV,17, D.A. Moreira
De Godoy142, L.A.P. Moreno44, S. Moretto29, A. Morreale113, A. Morsch34, T.
Mrnjavac34, V. Muccifora51, E. Mudnic35, D. Mühlheim142, S. Muhuri139, M.
Mukherjee3, J.D. Mulligan144, M.G. Munhoz120, K. Münning42, M.I.A. Munoz79,
R.H. Munzer69, H. Murakami130, S. Murray73, L. Musa34, J. Musinsky65, C.J.
Myers125, J.W. Myrcha140, B. Naik48, R. Nair84, B.K. Nandi48, R. Nania10,53,
E. Nappi52, A. Narayan48, M.U. Naru15, A.F. Nassirpour80, H. Natal da Luz120,
C. Nattrass128, S.R. Navarro44, K. Nayak85, R. Nayak48, T.K. Nayak139, S.
Nazarenko106, R.A. Negrao De Oliveira34,69, L. Nellen70, S.V. Nesbo36, G.
Neskovic39, F. Ng125, M. Nicassio104, J. Niedziela34,140, B.S. Nielsen88, S.
Nikolaev87, S. Nikulin87, V. Nikulin96, F. Noferini10,53, P. Nomokonov75, G.
Nooren63, J.C.C. Noris44, J. Norman78, A. Nyanin87, J. Nystrand22, M. Ogino81,
H. Oh145, A. Ohlson102, J. Oleniacz140, A.C. Oliveira Da Silva120, M.H.
Oliver144, J. Onderwaater104, C. Oppedisano58, R. Orava43, M. Oravec115, A.
Ortiz Velasquez70, A. Oskarsson80, J. Otwinowski117, K. Oyama81, Y.
Pachmayer102, V. Pacik88, D. Pagano138, G. Paić70, P. Palni6, J. Pan141, S.
Panebianco135, V. Papikyan1, P. Pareek49, J. Park60, J.E. Parkkila126, S.
Parmar98, A. Passfeld142, S.P. Pathak125, R.N. Patra139, B. Paul58, H. Pei6,
T. Peitzmann63, X. Peng6, L.G. Pereira71, H. Pereira Da Costa135, D.
Peresunko87, E. Perez Lezama69, V. Peskov69, Y. Pestov4, V. Petráček37, M.
Petrovici47, C. Petta28, R.P. Pezzi71, S. Piano59, M. Pikna14, P. Pillot113,
L.O.D.L. Pimentel88, O. Pinazza34,53, L. Pinsky125, S. Pisano51, D.B.
Piyarathna125, M. Płoskoń79, M. Planinic97, F. Pliquett69, J. Pluta140, S.
Pochybova143, P.L.M. Podesta-Lerma119, M.G. Poghosyan94, B. Polichtchouk90, N.
Poljak97, W. Poonsawat114, A. Pop47, H. Poppenborg142, S. Porteboeuf-
Houssais132, V. Pozdniakov75, S.K. Prasad3, R. Preghenella53, F. Prino58, C.A.
Pruneau141, I. Pshenichnov62, M. Puccio26, V. Punin106, J. Putschke141, S.
Raha3, S. Rajput99, J. Rak126, A. Rakotozafindrabe135, L. Ramello32, F.
Rami134, R. Raniwala100, S. Raniwala100, S.S. Räsänen43, B.T. Rascanu69, R.
Rath49, V. Ratza42, I. Ravasenga31, K.F. Read94,128, K. RedlichV,84, A.
Rehman22, P. Reichelt69, F. Reidt34, X. Ren6, R. Renfordt69, A. Reshetin62,
J.-P. Revol10, K. Reygers102, V. Riabov96, T. Richert63,80,88, M. Richter21,
P. Riedler34, W. Riegler34, F. Riggi28, C. Ristea68, S.P. Rode49, M. Rodríguez
Cahuantzi44, K. Røed21, R. Rogalev90, E. Rogochaya75, D. Rohr34, D. Röhrich22,
P.S. Rokita140, F. Ronchetti51, E.D. Rosas70, K. Roslon140, P. Rosnet132, A.
Rossi29,56, A. Rotondi137, F. Roukoutakis83, C. Roy134, P. Roy107, O.V.
Rueda70, R. Rui25, B. Rumyantsev75, A. Rustamov86, E. Ryabinkin87, Y.
Ryabov96, A. Rybicki117, S. Saarinen43, S. Sadhu139, S. Sadovsky90, K.
Šafařík34, S.K. Saha139, B. Sahoo48, P. Sahoo49, R. Sahoo49, S. Sahoo66, P.K.
Sahu66, J. Saini139, S. Sakai131, M.A. Saleh141, S. Sambyal99, V.
Samsonov91,96, A. Sandoval72, A. Sarkar73, D. Sarkar139, N. Sarkar139, P.
Sarma41, M.H.P. Sas63, E. Scapparone53, F. Scarlassara29, B. Schaefer94, H.S.
Scheid69, C. Schiaua47, R. Schicker102, C. Schmidt104, H.R. Schmidt101, M.O.
Schmidt102, M. Schmidt101, N.V. Schmidt69,94, J. Schukraft34, Y. Schutz34,134,
K. Schwarz104, K. Schweda104, G. Scioli27, E. Scomparin58, M. Šefčík38, J.E.
Seger16, Y. Sekiguchi130, D. Sekihata45, I. Selyuzhenkov91,104, S.
Senyukov134, E. Serradilla72, P. Sett48, A. Sevcenco68, A. Shabanov62, A.
Shabetai113, R. Shahoyan34, W. Shaikh107, A. Shangaraev90, A. Sharma98, A.
Sharma99, M. Sharma99, N. Sharma98, A.I. Sheikh139, K. Shigaki45, M.
Shimomura82, S. Shirinkin64, Q. Shou6,110, Y. Sibiriak87, S. Siddhanta54, K.M.
Sielewicz34, T. Siemiarczuk84, D. Silvermyr80, G. Simatovic89, G.
Simonetti34,103, R. Singaraju139, R. Singh85, R. Singh99, V. Singhal139, T.
Sinha107, B. Sitar14, M. Sitta32, T.B. Skaali21, M. Slupecki126, N.
Smirnov144, R.J.M. Snellings63, T.W. Snellman126, J. Sochan115, C. Soncco109,
J. Song18, A. Songmoolnak114, F. Soramel29, S. Sorensen128, F. Sozzi104, I.
Sputowska117, J. Stachel102, I. Stan68, P. Stankus94, E. Stenlund80, D.
Stocco113, M.M. Storetvedt36, P. Strmen14, A.A.P. Suaide120, T. Sugitate45, C.
Suire61, M. Suleymanov15, M. Suljic34, R. Sultanov64, M. Šumbera93, S.
Sumowidagdo50, K. Suzuki112, S. Swain66, A. Szabo14, I. Szarka14, U.
Tabassam15, J. Takahashi121, G.J. Tambave22, N. Tanaka131, M. Tarhini113, M.G.
Tarzila47, A. Tauro34, G. Tejeda Muñoz44, A. Telesca34, C. Terrevoli29, B.
Teyssier133, D. Thakur49, S. Thakur139, D. Thomas118, F. Thoresen88, R.
Tieulent133, A. Tikhonov62, A.R. Timmins125, A. Toia69, N. Topilskaya62, M.
Toppi51, S.R. Torres119, S. Tripathy49, S. Trogolo26, G. Trombetta33, L.
Tropp38, V. Trubnikov2, W.H. Trzaska126, T.P. Trzcinski140, B.A. Trzeciak63,
T. Tsuji130, A. Tumkin106, R. Turrisi56, T.S. Tveter21, K. Ullaland22, E.N.
Umaka125, A. Uras133, G.L. Usai24, A. Utrobicic97, M. Vala115, L. Valencia
Palomo44, N. Valle137, L.V.R. van Doremalen63, J.W. Van Hoorne34, M. van
Leeuwen63, P. Vande Vyvre34, D. Varga143, A. Vargas44, M. Vargyas126, R.
Varma48, M. Vasileiou83, A. Vasiliev87, A. Vauthier78, O. Vázquez Doce103,116,
V. Vechernin111, A.M. Veen63, E. Vercellin26, S. Vergara Limón44, L.
Vermunt63, R. Vernet7, R. Vértesi143, L. Vickovic35, J. Viinikainen126, Z.
Vilakazi129, O. Villalobos Baillie108, A. Villatoro Tello44, A. Vinogradov87,
T. Virgili30, V. Vislavicius80,88, A. Vodopyanov75, M.A. Völkl101, K.
Voloshin64, S.A. Voloshin141, G. Volpe33, B. von Haller34, I. Vorobyev103,116,
D. Voscek115, D. Vranic34,104, J. Vrláková38, B. Wagner22, H. Wang63, M.
Wang6, Y. Watanabe131, M. Weber112, S.G. Weber104, A. Wegrzynek34, D.F.
Weiser102, S.C. Wenzel34, J.P. Wessels142, U. Westerhoff142, A.M.
Whitehead124, J. Wiechula69, J. Wikne21, G. Wilk84, J. Wilkinson53, G.A.
Willems34,142, M.C.S. Williams53, E. Willsher108, B. Windelband102, W.E.
Witt128, R. Xu6, S. Yalcin77, K. Yamakawa45, S. Yano45,135, Z. Yin6, H.
Yokoyama78,131, I.-K. Yoo18, J.H. Yoon60, V. Yurchenko2, V. Zaccolo58, A.
Zaman15, C. Zampolli34, H.J.C. Zanoli120, N. Zardoshti108, A. Zarochentsev111,
P. Závada67, N. Zaviyalov106, H. Zbroszczyk140, M. Zhalov96, X. Zhang6, Y.
Zhang6, Z. Zhang6,132, C. Zhao21, V. Zherebchevskii111, N. Zhigareva64, D.
Zhou6, Y. Zhou88, Z. Zhou22, H. Zhu6, J. Zhu6, Y. Zhu6, A. Zichichi10,27, M.B.
Zimmermann34, G. Zinovjev2, J. Zmeskal112

Affiliation Notes

I Deceased

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

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

IV Also at: Department of Applied Physics, Aligarh Muslim University, Aligarh,
India

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

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

4 Budker Institute for Nuclear Physics, Novosibirsk, Russia

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

6 Central China Normal University, Wuhan, China

7 Centre de Calcul de l’IN2P3, Villeurbanne, Lyon, France

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

11 Chicago State University, Chicago, Illinois, United States

12 China Institute of Atomic Energy, Beijing, China

13 Chonbuk National University, Jeonju, Republic of Korea

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

15 COMSATS Institute of Information Technology (CIIT), Islamabad, Pakistan

16 Creighton University, Omaha, Nebraska, United States

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

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

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

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

21 Department of Physics, University of Oslo, Oslo, Norway

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

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

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

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

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

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

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

29 Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN,
Padova, 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 Interateneo di Fisica ‘M. Merlin’ and Sezione INFN, Bari,
Italy

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

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

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

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

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

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

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

41 Gauhati University, Department of Physics, Guwahati, India

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

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

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

45 Hiroshima University, Hiroshima, Japan

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

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

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

49 Indian Institute of Technology Indore, Indore, India

50 Indonesian Institute of Sciences, Jakarta, Indonesia

51 INFN, Laboratori Nazionali di Frascati, Frascati, Italy

52 INFN, Sezione di Bari, Bari, Italy

53 INFN, Sezione di Bologna, Bologna, Italy

54 INFN, Sezione di Cagliari, Cagliari, Italy

55 INFN, Sezione di Catania, Catania, Italy

56 INFN, Sezione di Padova, Padova, Italy

57 INFN, Sezione di Roma, Rome, Italy

58 INFN, Sezione di Torino, Turin, Italy

59 INFN, Sezione di Trieste, Trieste, Italy

60 Inha University, Incheon, Republic of Korea

61 Institut de Physique Nucléaire d’Orsay (IPNO), Institut National de
Physique Nucléaire et de Physique des Particules (IN2P3/CNRS), Université de
Paris-Sud, Université Paris-Saclay, Orsay, France

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

63 Institute for Subatomic Physics, Utrecht University/Nikhef, Utrecht,
Netherlands

64 Institute for Theoretical and Experimental Physics, 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 Johann-Wolfgang-Goethe Universität Frankfurt Institut für Informatik,
Fachbereich Informatik und Mathematik, Frankfurt, Germany

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

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

77 KTO Karatay University, Konya, Turkey

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

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

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

81 Nagasaki Institute of Applied Science, Nagasaki, Japan

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

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

84 National Centre for Nuclear Research, Warsaw, Poland

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

86 National Nuclear Research Center, Baku, Azerbaijan

87 National Research Centre Kurchatov Institute, Moscow, Russia

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

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

90 NRC Kurchatov Institute IHEP, Protvino, Russia

91 NRNU Moscow Engineering Physics Institute, Moscow, Russia

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

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

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

95 Ohio State University, Columbus, Ohio, United States

96 Petersburg Nuclear Physics Institute, Gatchina, Russia

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

98 Physics Department, Panjab University, Chandigarh, India

99 Physics Department, University of Jammu, Jammu, India

100 Physics Department, University of Rajasthan, Jaipur, India

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

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

103 Physik Department, Technische Universität München, Munich, Germany

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

105 Rudjer Bošković Institute, Zagreb, Croatia

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

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

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

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

110 Shanghai Institute of Applied Physics, Shanghai, China

111 St. Petersburg State University, St. Petersburg, Russia

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

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

114 Suranaree University of Technology, Nakhon Ratchasima, Thailand

115 Technical University of Košice, Košice, Slovakia

116 Technische Universität München, Excellence Cluster ’Universe’, Munich,
Germany

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

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

119 Universidad Autónoma de Sinaloa, Culiacán, Mexico

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

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

122 Universidade Federal do ABC, Santo Andre, Brazil

123 University College of Southeast Norway, Tonsberg, Norway

124 University of Cape Town, Cape Town, South Africa

125 University of Houston, Houston, Texas, United States

126 University of Jyväskylä, Jyväskylä, Finland

127 University of Liverpool, Liverpool, United Kingdom

128 University of Tennessee, Knoxville, Tennessee, United States

129 University of the Witwatersrand, Johannesburg, South Africa

130 University of Tokyo, Tokyo, Japan

131 University of Tsukuba, Tsukuba, Japan

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

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

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

135 Université Paris-Saclay Centre d¿Études de Saclay (CEA), IRFU, Department
de Physique Nucléaire (DPhN), Saclay, France

136 Università degli Studi di Foggia, Foggia, Italy

137 Università degli Studi di Pavia and Sezione INFN, Pavia, Italy

138 Università di Brescia and Sezione INFN, Brescia, Italy

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

140 Warsaw University of Technology, Warsaw, Poland

141 Wayne State University, Detroit, Michigan, United States

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

143 Wigner Research Centre for Physics, Hungarian Academy of Sciences,
Budapest, Hungary

144 Yale University, New Haven, Connecticut, United States

145 Yonsei University, Seoul, Republic of Korea