2016 052 29 February

Centrality-dependent charged jets in p–Pb ALICE Collaboration††thanks: See
Appendix A for the list of collaboration members ALICE Collaboration

Measurements of charged jet production as a function of centrality are
presented for p–Pb collisions recorded at $\sqrt{s_{\mathrm{NN}}}=5.02$ TeV
with the ALICE detector. Centrality classes are determined via the energy
deposit in neutron calorimeters at zero degree, close to the beam direction,
to minimise dynamical biases of the selection. The corresponding number of
participants or binary nucleon–nucleon collisions is determined based on the
particle production in the Pb-going rapidity region. Jets have been
reconstructed in the central rapidity region from charged particles with the
anti-$k_{\mathrm{T}}$ algorithm for resolution parameters $R=0.2$ and $R=0.4$
in the transverse momentum range 20 to 120 GeV/$c$. The reconstructed jet
momentum and yields have been corrected for detector effects and underlying-
event background. In the five centrality bins considered, the charged jet
production in p–Pb collisions is consistent with the production expected from
binary scaling from pp collisions. The ratio of jet yields reconstructed with
the two different resolution parameters is also independent of the centrality
selection, demonstrating the absence of major modifications of the radial jet
structure in the reported centrality classes.

##  1 Introduction

The measurement of benchmark processes in proton–nucleus collisions plays a
crucial role for the interpretation of nucleus–nucleus collision data, where
one expects to create a system with high temperature in which the elementary
constituents of hadronic matter, quarks and gluons, are deconfined for a short
time: the quark-gluon plasma (QGP) [1]. Proton–lead collisions are important
to investigate cold nuclear initial and final state effects, in particular to
disentangle them from effects of the hot medium created in the final state of
Pb–Pb collisions [2].

The study of hard parton scatterings and their subsequent fragmentation via
reconstructed jets plays a crucial role in the characterisation of the hot and
dense medium produced in Pb–Pb collisions while jet measurements in p–Pb and
pp collisions provide allow to constrain the impact of cold nuclear matter
effects in heavy-ion collisions. In the initial state, the nuclear parton
distribution functions can be modified with respect to the quark and gluon
distributions in free nucleons, e.g. via shadowing effects and gluon
saturation [2, 3]. In addition, jet production may be influenced, already in
p–Pb collisions, by multiple scattering of partons and hadronic re-interaction
in the initial and final state [4, 5].

In the absence of any modification in the initial state, the partonic
scattering rate in nuclear collisions compared to pp collisions is expected to
increase linearly with the average number of binary nucleon–nucleon collisions
$\left<N_{\mathrm{coll}}\right>$. This motivates the definition of the nuclear
modification factor $R_{\mathrm{pPb}}$, as the ratio of particle or jet
transverse momentum ($p_{\mathrm{T}}$) spectra in nuclear collisions to those
in pp collisions scaled by $\left<N_{\mathrm{coll}}\right>$.

In heavy-ion collisions at the LHC, binary ($N_{\mathrm{coll}}$) scaling is
found to hold for probes that do not interact strongly, i.e. isolated prompt
photons [6] and electroweak bosons [7, 8]. On the contrary, the yields of
hadrons and jets in central Pb–Pb collisions are strongly modified compared to
the scaling assumptions. For hadrons, the yield is suppressed by up to a
factor of seven at $p_{\mathrm{T}}\approx 6$ GeV$/c$, approaching a factor of
two at high $p_{\mathrm{T}}$ ($\gtrsim$ 30 GeV/$c$) [9, 10, 11]. A similar
suppression is observed for jets [12, 13, 14, 15, 16]. This observation, known
as jet quenching, is attributed to the formation of a QGP in the collision,
where the hard scattered partons radiate gluons due to strong interaction with
the medium, as first predicted in [17, 18].

In minimum bias p–Pb collisions at $\sqrt{s_{\mathrm{NN}}}=5.02$ TeV the
production of unidentified charged particles [19, 20, 21, 22] and jets [23,
24, 25] is consistent with the absence of a strong final state suppression.
However, multiplicity dependent studies in p–Pb collisions on the production
of low-$p_{\mathrm{T}}$ identified particles and long range correlations [26,
27, 28, 29] show similar features as measured in Pb–Pb collisions, where they
are attributed to the collective behaviour following the creation of a QGP.
These features in p–Pb collisions become more pronounced for higher
multiplicity events, which in Pb–Pb are commonly associated with more central
collisions or higher initial energy density.

The measurement of jets, compared to single charged hadrons, tests the parton
fragmentation beyond the leading particle with the inclusion of large-angle
and low-$p_{\mathrm{T}}$ fragments. Thus jets are potentially sensitive to
centrality-dependent modifications of low-$p_{\mathrm{T}}$ fragments.

This work extends the analysis of the charged jet production in minimum bias
p–Pb collisions recorded with the ALICE detector at
$\sqrt{s_{\mathrm{NN}}}=5.02$ TeV to a centrality-differential study for jet
resolution parameters $R=0.2$ and 0.4 in the $p_{\mathrm{T}}$ range from 20 to
120 GeV/$c$ [25]. Section 2 describes the event and track selection, the
centrality determination, as well as the jet reconstruction, the corrections
for uncorrelated background contributing to the jet momentum [30, 15, 31] and
the corrections for detector effects. The impact of different centrality
selections on the nuclear modification factor has been studied in detail in
[32]. We estimate the centrality using zero-degree neutral energy and the
charged particle multiplicity measured by scintillator array detectors at
rapidities along the direction of the Pb beam to determine
$N_{\mathrm{coll}}$. The correction procedures specific to the centrality-
dependent jet measurement are discussed in detail. Section 3 introduces the
three main observables: the centrality-dependent jet production cross section,
the nuclear modification factor, and ratio of jet cross sections for two
different resolution parameters. Systematic uncertainties are discussed in
Sec. 4 and results are presented in Sec. 5.

##  2 Data analysis

###  2.1 Event selection

The data used for this analysis were collected with the ALICE detector [33]
during the p–Pb run of the LHC at $\sqrt{s_{\mathrm{NN}}}=5.02\textnormal{
TeV}$ at the beginning of 2013\. The ALICE experimental setup and its
performance during the LHC Run 1 are described in detail in [34, 33].

For the analysis presented in this paper, the main detectors used for event
and centrality selection are two scintillator detectors (V0A and V0C),
covering the pseudo-rapidity range of $2.8<\eta_{\mathrm{lab}}<5.1$ and
$-3.7<\eta_{\mathrm{lab}}<-1.7$, respectively [35], and the Zero Degree
Calorimeters (ZDCs), composed of two sets of neutron (ZNA and ZNC) and proton
calorimeters (ZPA and ZPC) located at a distance $\pm 112.5$ m from the
interaction point. Here and in the following $\eta_{\mathrm{lab}}$ denotes the
pseudo-rapidity in the ALICE laboratory frame.

The minimum bias trigger used in p–Pb collisions requires signal coincidence
in the V0A and V0C scintillators. In addition, offline selections on timing
and vertex-quality are used to remove events with multiple interactions within
the same bunch crossing and (pile-up) and background events, such as beam-gas
interactions. The event sample used for the analysis presented in this
manuscript was collected exclusively in the beam configuration where the
proton travels towards negative $\eta_{\mathrm{lab}}$ (from V0A to V0C). The
nucleon–nucleon center-of-mass system moves in the direction of the proton
beam corresponding to a rapidity of $y_{\mathrm{NN}}=-0.465$.

A van der Meer scan was performed to measure the visible cross section for the
trigger and beam configuration used in this analysis:
$\sigma_{\mathrm{V0}}=2.09\pm 0.07$ b [36]. Studies with Monte Carlo
simulations show that the sample collected in the configuration explained
above consists mainly of non-single diffractive (NSD) interactions and a
negligible contribution from single diffractive and electromagnetic
interactions (see [37] for details). The trigger is not fully efficient for
NSD events and the inefficiency is observed mainly for events without a
reconstructed vertex, i.e. with no particles produced at central rapidity.
Given the fraction of events without a reconstructed vertex in the data the
corresponding inefficiency for NSD events is estimated to ($2.2\pm 3.1$)%.
This inefficiency is expected to mainly affect the most peripheral centrality
class. Following the prescriptions of [32], centrality classes are defined as
percentiles of the visible cross section and are not corrected for trigger
efficiency.

The further analysis requires a reconstructed vertex, in addition to the
minimum bias trigger selection. The fraction of events with a reconstructed
vertex is 98.3% for minimum bias events and depends on the centrality class.
In the analysis events with a reconstructed vertex $|z|>10~{}\mathrm{cm}$
along the beam axis are rejected. In total, about $96\cdot 10^{6}$ events,
corresponding to an integrated luminosity of 46 $\mu$b-1, are used for the
analysis and classified into five centrality classes

###  2.2 Centrality determination

Centrality classes can be defined by dividing the multiplicity distribution
measured in a certain pseudo-rapidity interval into fractions of the cross
section, with the highest multiplicities corresponding to the most central
collisions (smallest impact parameter $b$). The corresponding number of
participants, as well as $N_{\mathrm{coll}}$ and $b$, can be estimated with a
Glauber model [38], e.g. by fitting the measured multiplicity distribution
with the $N_{\mathrm{part}}$ distribution from the model, convoluted with a
Negative Binomial Distribution (NBD). Details on this procedure for Pb–Pb and
p–Pb collisions in ALICE are found in [39] and [32], respectively.

In $\textnormal{p--}A$ collisions centrality selection is susceptible to a
variety of biases. In general, relative fluctuations of $N_{\mathrm{part}}$
and of event multiplicity are large, due to their small numerical value, in
p–Pb collisions [32]
$\left<N_{\mathrm{part}}\right>=\left<N_{\mathrm{coll}}\right>+1=7.9\pm 0.6$
and $\frac{\mathrm{d}N_{\mathrm{ch}}}{\mathrm{d}\eta}=16.81\pm 0.71$,
respectively. Using either of these quantities to define centrality, in the
Glauber model or the in experimental method, already introduces a bias
compared to a purely geometrical selection based on the impact parameter $b$.

In addition, a kinematic bias exists for events containing
high-$p_{\mathrm{T}}$ particles, originating from parton fragmentation as
discussed above. The contribution of these jet fragments to the overall
multiplicity rises with the jet energy and thus can introduce a trivial
correlation between the multiplicity and presence of a high-$p_{\mathrm{T}}$
particle, and a selection on multiplicity will bias the jet population. High
multiplicity events are more likely created in collisions with multiple-parton
interactions, which can lead to a nuclear modification factor larger than
unity. On the contrary, the selection of low multiplicity (peripheral) events
can pose an effective veto on hard processes, which would lead to a nuclear
modification factor smaller than unity. As shown in [32] the observed
suppression and enhancement for charged particles in bins of multiplicity with
respect to the binary scaling assumption can be explained by this selection
bias alone. The bias can be fully reproduced by an independent superposition
of simulated pp events and the farther the centrality estimator is separated
in rapidity from the measurement region at mid-rapidity, the smaller the bias.
We do not repeat the analysis for the centrality estimators with known biases
here.

In this work, centrality classification is based solely on the zero-degree
energy measured in the lead-going neutron detector ZNA, since it is expected
to have only a small dynamical selection bias. However, the ZNA signal cannot
be related directly to the produced multiplicity for the $N_{\mathrm{coll}}$
determination via NBD. As discussed in detail in [32] an alternative hybrid
approach is used to connect the centrality selection based on the ZNA signal
to another $N_{\mathrm{coll}}$ determination via the charged particle
multiplicity in the lead-going direction measured with the V0A
($\left<N_{\mathrm{coll}}\right>_{c}^{\mathrm{Pb-side}}$). This approach
assumes that the V0 signal is proportional to the number of wounded lead
(target) nucleons
($N_{\mathrm{part}}^{\mathrm{target}}=N_{\mathrm{part}}-1=N_{\mathrm{coll}}$).
The average number of collisions for a given centrality, selected with the
ZNA, is then given by scaling the minimum bias value
$\left<N_{\mathrm{coll}}\right>_{\mathrm{MB}}=6.9$ with the ratio of the
average raw signal $S$ of the innermost ring of the V0A:

$\left<N_{\mathrm{coll}}^{\mathrm{Pb-
side}}\right>_{c}=\left<N_{\mathrm{coll}}\right>_{\mathrm{MB}}\cdot\frac{\left<S\right>_{c}}{\left<S\right>_{\mathrm{MB}}}.$
(1)

The values of $N_{\mathrm{coll}}$ obtained with this method are shown in Tab.
1 for different ZNA centrality classes [32].

ZNA centrality class | $\left<N_{\mathrm{coll}}^{\mathrm{Pb-side}}\right>$ | $\rho$ | $\sigma(\rho)$ | $\sigma(\delta p_{\mathrm{T,\,ch}})(R=0.4)$
---|---|---|---|---
(%) of visible cross section |  |  (GeV/$c$) |  (GeV/$c$) |  (GeV/$c$)
0-20 |  12.1$\pm$ | 1.0 | 1.60 | 1.17 | 1.43
20-40 |  9.6$\pm$ | 0.8 | 1.27 | 1.04 | 1.30
40-60 |  6.7$\pm$ | 0.5 | 0.88 | 0.84 | 1.11
60-80 |  4.0$\pm$ | 0.3 | 0.70 | 0.52 | 0.90
80-100 |  2.1$\pm$ | 0.3 | 0.26 | 0.37 | 0.71
Minimum bias (0-100) |  6.9$\pm$ | 0.6 | 0.98 | 1.02 | 0.91
Table 1:  Average $N_{\mathrm{coll}}$ values for centrality classes selected
with the ZNA determined with the hybrid approach
($N_{\mathrm{coll}}^{\mathrm{Pb-side}}$) [32], as well as moments of the
background density and background fluctuation distributions shown in Fig. 1
(negligible statistical uncertainty).

###  2.3 Jet reconstruction and event-by-event corrections

The reported measurements are performed using _charged jets_ , clustered
starting from charged particles only, as described in [15, 40, 25] for
different collision systems. Charged particles are reconstructed using
information from the Inner Tracking System (ITS) [41] and the Time Projection
Chamber (TPC) which cover the full azimuth and $|\eta_{\mathrm{lab}}|<0.9$ for
tracks reconstructed with full length in the TPC [42].

The azimuthal distribution of high-quality tracks with reconstructed track
points in the Silicon Pixel Detector (SPD), the two innermost layers of the
ITS, is not completely uniform due to inefficient regions in the SPD. This can
be compensated by considering in addition tracks without reconstructed points
in the SPD. The additional tracks constitute approximately 4.3% of the track
sample used for analysis. For these tracks, the primary vertex is used as an
additional constraint in the track fitting to improve the momentum resolution.
This approach yields a uniform tracking efficiency within the acceptance,
which is needed to avoid geometrical biases of the jet reconstruction
algorithm caused by a non-uniform density of reconstructed tracks. The
procedure is described first and in detail in the context of jet
reconstruction with ALICE in Pb–Pb collisions [15].

The anti-$k_{\mathrm{T}}$ algorithm from the FastJet package [43] is employed
to reconstruct jets from these tracks using the $p_{\mathrm{T}}$ recombination
scheme. The resolution parameters used in the present analysis are $R=0.2$ and
$R=0.4$. Reconstructed jets are further corrected for contributions from the
underlying event to the jet momentum as

$p_{\mathrm{T,\,ch\;jet}}=p_{\mathrm{T,\,ch\;jet}}^{\mathrm{raw}}-A_{\mathrm{ch\;jet}}\cdot\rho_{\mathrm{ch}},$
(2)

where $A_{\mathrm{ch\;jet}}$ is the area of the jet and $\rho_{\mathrm{ch}}$
the event-by-event background density [44]. The area is estimated by counting
the so-called _ghost particles_ in the jet. These are defined as particles
with a finite area and vanishing momentum, which are distributed uniformly in
the event and included in the jet reconstruction [45]. Their vanishing
momentum ensures that the jet momentum is not influenced when they are
included, while the number of ghost particles assigned to the jet provides a
direct measure of its area. The background density $\rho_{\mathrm{ch}}$ is
estimated via the median of the individual momentum densities of jets
reconstructed with the $k_{\mathrm{T}}$ algorithm in the event

$\rho_{\mathrm{ch}}=\mathrm{median}\left\\{\frac{p_{\\\
\mathrm{T},\,k}}{A_{k}}\right\\}\cdot C,$ (3)

where $k$ runs over all reconstructed $k_{\mathrm{T}}$ jets with momentum
$p_{\mathrm{T},\,i}$ and area $A_{i}$. Reconstructed $k_{\mathrm{T}}$ jets are
commonly chosen for the estimate of the background density, since they provide
a more robust sampling of low momentum particles. $C$ is the occupancy
correction factor, defined as

$C=\frac{\sum_{j}{A_{j}}}{A_{\textnormal{acc}}},$ (4)

where $A_{j}$ is the area of each $k_{\mathrm{T}}$ jet with at least one real
track, i.e. excluding ghosts, and $A_{\mathrm{acc}}$ is the area of the
charged-particle acceptance, namely $(2\times 0.9)\times 2\pi$. The typical
values for $C$ range from $0.72$ for most central collisions (0-20%) to $0.15$
for most peripheral collisions (80-100%). This procedure takes into account
the more sparse environment in p–Pb collisions compared to Pb–Pb and is
described in more detail in [25]. The probability distribution for
$\rho_{\mathrm{ch}}$ for the five centrality classes and minimum bias is shown
in Fig. 1 (left) and the mean and width of the distributions are given in Tab.
1. The event activity and thus the background density increases for more
central collisions, though on average the background density is still two
orders of magnitude smaller than in Pb–Pb collisions where
$\rho_{\mathrm{ch}}$ is $\approx 140$ GeV/$c$ for central collisions [31].

Figure 1: (Color online) Left: Centrality dependence of the background
momentum density $\rho_{\mathrm{ch}}$ determined with $k_{\mathrm{T}}$ jets
and $R=0.4$. Right: $\delta p_{\mathrm{T,\,ch}}$ distributions for different
centralities obtained with random cones and $R=0.4$.

###  2.4 Jet spectrum unfolding

Residual background fluctuations and instrumental effects can smear the jet
$p_{\mathrm{T}}$. Their impact on the jet spectrum needs to be corrected on a
statistical basis using unfolding, which is performed using the approach of
Singular-Value-Decomposition (SVD) [46]. The response matrix employed in the
unfolding is the combination of the (centrality-dependent) jet response to
background fluctuations and the detector response. The general correction
techniques are discussed in detail in the context of the minimum bias charged
jet measurement in p–Pb [25].

Region-to-region fluctuations of the background density compared to the event
median, contain purely statistical fluctuations of particle number and
momentum and in addition also intra-event correlations, e.g. those
characterised by the azimuthal anisotropy $v_{2}$ and higher harmonics, which
induce additional variations of the local background density. The impact of
these fluctuations on the jet momentum is determined by probing the transverse
momentum density in randomly distributed cones in $(\eta,\phi)$ and comparing
it to the average background via [31]:

$\delta
p_{\mathrm{T,\,ch}}=\sum_{\mathrm{i}}{p_{\mathrm{T,\,i}}-\rho_{\mathrm{ch}}\cdot
A},~{}~{}~{}A=\pi R^{2}$ (5)

where $p_{\mathrm{T,\,i}}$ is the transverse momentum of each track $i$ inside
a cone of radius $R$, where $R$ corresponds to the resolution parameter in the
jet reconstruction. $\rho_{\mathrm{ch}}$ is the background density, and $A$
the area of the cone. The distribution of residuals, as defined by Eq. 5, is
shown for different centralities in Fig. 1 (right). The corresponding widths
are given in Tab. 1. The background fluctuations increase for more central
events, which is expected from the general increase of statistical
fluctuations ($\propto\sqrt{N}$) with the particle multiplicity. The $\delta
p_{\mathrm{T,\,ch}}$ distributions measured for $R=0.2$ and 0.4 are used in
the unfolding procedure.

In addition to the background fluctuations the unfolding procedure takes into
account the instrumental response. The dominating instrumental effects on the
reconstructed jet spectrum are the single-particle tracking efficiency and
momentum resolution. These effects are encoded in a response matrix, which is
determined with a full detector simulation using PYTHIA6 [47] to generate jets
and GEANT3 [48] for the transport through the ALICE setup. The detector
response matrix links the jet momentum at the charged particle level to the
one reconstructed from tracks after particle transport through the detector.
No correction for the missing energy of neutral jet constituents is applied.

##  3 Observables

###  3.1 Jet production cross sections

The jet production cross sections
$\frac{\mathrm{d}\sigma^{c}}{\mathrm{d}p_{\mathrm{T}}}$, for different
centralities $c$, are provided as fractions of the visible cross section
$\sigma_{\mathrm{V0}}$. The fraction of the cross section is determined with
the number of selected events in each centrality bin $N^{c}_{\mathrm{ev}}$ and
takes into account the vertex reconstruction efficiency
$\epsilon^{c}_{\mathrm{vtx}}$ determined for each centrality

$\frac{\mathrm{d}\sigma^{c}}{\mathrm{d}p_{\mathrm{T}}}=\frac{\epsilon^{c}_{\mathrm{vtx}}}{N^{c}_{\mathrm{ev}}}\frac{\mathrm{d}N}{\mathrm{d}p_{\mathrm{T}}}\cdot\sigma_{\mathrm{V0}}\cdot\frac{N^{c}_{\mathrm{ev}}}{N^{\mathrm{MB}}_{\mathrm{ev}}}=\frac{\epsilon^{c}_{\mathrm{vtx}}}{N^{\mathrm{MB}}_{\mathrm{ev}}}\frac{\mathrm{d}N}{\mathrm{d}p_{\mathrm{T}}}\cdot\sigma_{\mathrm{V0}},$
(6)

where $\epsilon^{c}_{\mathrm{vtx}}$ decreases from 99.9% for the most central
selection (0-20%) to 95.4% in peripheral.

###  3.2 Quantifying nuclear modification

The nuclear modification factor compares the $p_{\mathrm{T}}$-differential
per-event yield, e.g. in p–Pb or Pb–Pb collisions, to the differential yield
in pp collisions at the same center-of-mass energy in order to quantify
nuclear effects. Under the assumption that the jet or particle production at
high $p_{\mathrm{T}}$ scales with the number of binary collisions, the nuclear
modification factor is unity in the absence of nuclear effects.

In p–Pb collisions the jet population can be biased, depending on the
centrality selection and $N_{\mathrm{coll}}$ determination, hence the nuclear
modification factor may vary from unity even in the absence of nuclear effects
as described in detail in Sec. 2.2 (see also [32]). To reflect this ambiguity
the centrality-differential nuclear modification factor in p–Pb collisions is
called $Q_{\mathrm{pPb}}$, instead of $R_{\mathrm{pPb}}$ as in the minimum
bias case. $Q_{\mathrm{pPb}}$ is defined as

$Q_{\mathrm{pPb}}=\frac{\mathrm{d^{2}}N^{c}_{\mathrm{pPb}}/\mathrm{d}\eta\mathrm{d}p_{\mathrm{T}}}{\left<N_{\mathrm{coll}}^{c}\right>\cdot\mathrm{d^{2}}N_{\mathrm{pp}}/\mathrm{d}\eta\mathrm{d}p_{\mathrm{T}}}.$
(7)

Here, $\left<N^{c}_{\mathrm{coll}}\right>$ is number of binary collisions for
centrality $c$, shown in Tab. 1.

For the construction of $Q_{\mathrm{pPb}}$, we use the same pp reference as
for the study of charged jet production in minimum bias p–Pb collisions [25].
This reference has been determined from the ALICE charged jet measurement at 7
TeV [40] via scaling to the p–Pb center-of-mass energy and taking into account
the rapidity shift of the colliding nucleons. The scaling behaviour of the
charged jet spectra is determined based on pQCD calculations using the POWHEG
framework [49] and PYTHIA parton shower (see [25] for details). This procedure
fixes the normalisation based on the measured data at 7 TeV, while the
evolution of the cross section with beam energy is calculated, taking into
account all dependences implemented in POWHEG and PYTHIA, e.g. the larger
fraction of quark initiated jets at lower collision energy.

###  3.3 Jet production cross section ratio

The angular broadening or narrowing of the parton shower with respect to the
original parton direction can have an impact on the jet production cross
section determined with different resolution parameters. This can be tested
via the ratio of cross sections or yields reconstructed with different radii,
e.g. $R=0.2$ and $0.4$, in a common rapidity interval, here
$|\eta_{\mathrm{lab}}|<0.5$:

$\mathscr{R}(0.2,\,0.4)=\frac{\mathrm{d}\sigma_{\mathrm{pPb,\,R=0.2}}/\mathrm{d}p_{\mathrm{T}}}{\mathrm{d}\sigma_{\mathrm{pPb,\,R=0.4}}/\mathrm{d}p_{\mathrm{T}}}.$
(8)

Consider for illustration the extreme scenario where all fragments are already
contained within $R=0.2$. In this case the ratio would be unity. In addition,
the statistical uncertainties between $R=0.2$ and $R=0.4$ would be fully
correlated and they would cancel completely in the ratio, when the jets are
reconstructed from the same data set. If the jets are less collimated, the
ratio decreases and the statistical uncertainties cancel only partially. For
the analysis presented in this paper, the conditional probability varies
between 25% and 50% for reconstructing a $R=0.2$ jet in the same
$p_{\mathrm{T}}$-bin as a geometrically close $R=0.4$ jet. This leads to a
reduction of the statistical uncertainty on the ratio of about 5-10% compared
to the case of no correlation.

The measurement and comparison of fully corrected jet cross sections for
different radii provides an observable sensitive to the radial redistribution
of momentum that is also theoretically well defined [50]. Other observables
that test the structure of jets, such as the fractional transverse momentum
distribution of jet constituents in radial and longitudinal direction or jet-
hadron correlations [10, 51, 52, 53, 54], are potentially more sensitive to
modified jet fragmentation in p–Pb and Pb–Pb. However, in these cases the
specific choices of jet reconstruction parameters, particle $p_{\mathrm{T}}$
thresholds and the treatment of background particles often limit the
quantitative comparison between experimental observables and to theory
calculations.

##  4 Systematic uncertainties

The different sources of systematic uncertainties for the three observables
presented in this paper are listed in Tab. 2 for 0-20% and 60-80% most central
collisions.

Observable |  Jet cross section ($R=0.4$) |  |  |  $Q_{\mathrm{pPb}}$ ($R=0.4$) |  |  | $\mathscr{R}$ |
---|---|---|---|---|---|---|---|---
ZNA centrality class (%) | 0-20 | 60-80 |  | 0-20 | 60-80 |  | 0-20 | 60-80
Single-particle efficiency (%) | $10.2-14.0$ | $10.0-12.7$ |  | $4.9-6.3$ | $4.9-6.4$ |  | $2.0-2.0$ | $1.8-4.7$
Unfolding (%) | 4.3 | 4.6 |  | 4.5 | 4.8 |  | 1.4 | -3.1
Unfolding prior steepness (%) | $0.9-7.0$ | $0.3-3.6$ |  | $1.1-7.2$ | $0.8-4.0$ |  | $0.7-1.4$ | $0.3-2.2$
Regularisation strength (%) | $2.8-6.4$ | $0.4-3.7$ |  | $2.8-7.3$ | $0.5-3.9$ |  | $1.8-7.0$ | $0.3-3.7$
Minimum $p_{\mathrm{T}}$ cut-off (%) | $3.7-9.2$ | $0.6-2.9$ |  | $4.1-9.8$ | $1.7-3.8$ |  | $2.2-0.8$ | $0.5-1.8$
Background estimate (%) | $3.5-1.8$ | $3.8-3.0$ |  | $3.5-1.8$ | $3.8-3.0$ |  | $1.7-1.8$ | $2.6-1.2$
$\delta p_{\mathrm{T,\,ch}}$ estimate (%) | $0.1-0.0$ | $0.2-2.3$ |  | $0.1-0.0$ | $0.2-2.3$ |  | $0.1-0.0$ | $0.2-1.1$
Combined uncertainty (%) | $12.5-19.8$ | $11.6-15.2$ |  | $9.0-16.3$ | $8.1-11.1$ |  | $4.2-7.8$ | $4.4-7.5$
Combined uncertainty ($R=0.2$) (%) | $10.4-19.5$ | $8.2-12.5$ |  | $8.6-18.0$ | $5.8-9.4$ |  | - | -
$\left<N_{\mathrm{coll}}^{\mathrm{Pb-side}}\right>$ (%) | - | - |  | 8.0 | 8.0 |  | - | -
Visible cross section (%) | 3.3 | 3.3 |  | - | - |  | - | -
Reference scaling pp 7 TeV (%) | - | - |  | 9.0 | 9.0 |  | - | -
NSD selection efficiency p–Pb (%) | - | - |  | 3.1 | 3.1 |  | - | -
Combined scaling uncertainty (%) | - | - |  | 12.4 | 12.4 |  | - | -
Table 2:  Summary of systematic uncertainties on the fully corrected jet
spectrum, the corresponding nuclear modification factor, and the jet
production cross section ratio in 0-20% central and 60-80% peripheral events
for the resolution parameter $R=0.4$. The range of percentages provides the
variation from the minimum to the maximum momentum in each centrality. For
$R=0.2$ only the combined uncertainty is provided for, the difference to
$R=0.4$ is mainly due to the smaller impact of the single particle efficiency
for smaller radii.

The dominant source of uncertainty for the $p_{\mathrm{T}}$-differential jet
production cross section is the uncertainty of the single-particle tracking
efficiency that has a direct impact on the correction of the jet momentum in
the unfolding, as discussed in Sec. 2.4. In p–Pb collisions, the single-
particle efficiency is known with a relative uncertainty of 4%, which is
equivalent to a 4% uncertainty on the jet momentum scale. To estimate the
effect of the tracking efficiency uncertainty on the jet yield, the tracking
efficiency is artificially lowered by randomly discarding the corresponding
fraction of tracks (4%) used as input for the jet finder. Depending on the
shape of the spectrum, the uncertainty on the single-particle efficiency (jet
momentum scale) translates into an uncertainty on the jet yield ranging from 8
to 15%.

To estimate the effect of the single-particle efficiency on the p–Pb nuclear
modification factor for jets, one has to consider that the uncertainty on the
efficiency is partially correlated between the pp and p–Pb data set. The
correction is determined with the same description of the ALICE detector in
the Monte Carlo and for similar track quality cuts, but changes of detector
conditions between run periods reduce the degree of correlation between the
data sets. The uncorrelated uncertainty on the single-particle efficiency has
been estimated to 2% by varying the track quality cuts in data and
simulations. Consequently, the resulting uncertainty for the nuclear
modification factor is basically half the uncertainty due to the single
particle efficiency in the jet spectrum (cf. Tab. 2). It was determined by
discarding 2% of the tracks in one of the two collision systems, as also
described in [25].

Uncertainties introduced by the unfolding procedure, e.g. choice of unfolding
method, prior, regularisation strength, and minimum $p_{\mathrm{T}}$ cut-off,
are determined by varying those methods and parameters within reasonable
boundaries. Bayesian [55, 56] and $\chi^{2}$ [57] unfolding have been tested
and compared to the default SVD unfolding to estimate the systematic
uncertainty of the chosen method. The quality of the unfolded result is
evaluated by inspecting the Pearson coefficients, where a large
(anti-)correlation between neighbouring bins indicates that the regularisation
is not optimal.

The overall uncertainty on the jet yield due to the background subtraction is
estimated by comparing various background estimates: track-based and jet-based
density estimates, as well as pseudo-rapidity-dependent corrections. The
estimated uncertainty amounts to 3.8% at low $p_{\mathrm{T}}$ and decreases
for higher reconstructed jet momenta.

The main uncertainty related to the background fluctuation estimate is given
by the choice of excluding reconstructed jets in the random cone sampling.
While the probability of a jet to overlap with another jet in the event scales
with $N_{\mathrm{coll}}-1$, it scales in the case of the random cone sampling
with $N_{\mathrm{coll}}$. This can be emulated by rejecting a given fraction
of cones overlapping with signal jets, which introduces an additional
dependence on the definition of a signal jet. The resulting uncertainty due to
the treatment of jet overlaps is of the order of 0.1% and can be considered
negligible.

In addition, several normalisation uncertainties need to be considered: the
uncertainty on $N_{\mathrm{coll}}$ (8% in the hybrid approach), on the visible
cross section $\sigma_{\mathrm{V0}}$ (3.3%) and from the assumptions made to
obtain the scaled pp reference from 7 to 5 TeV (9%).

Further details on the evaluation of the centrality-independent systematic
uncertainties can be found in [25].

##  5 Results

Figure 2: (Color online) $p_{\mathrm{T}}$-differential production cross
sections of charged jet production in p–Pb collisions at $5.02$ TeV for
several centrality classes. Top and bottom panels show the result for $R=0.4$
and $R=0.2$, respectively. In these and the following plots, the coloured
boxes represent systematic uncertainties, the error bars represent statistical
uncertainties. The overall normalisation uncertainty on the visible cross
section is $3.3\%$ in p–Pb. The corresponding reference pp spectrum is shown
for both radii, it was obtained by scaling down the measured charged jets at 7
TeV to the reference energy.

The $p_{\mathrm{T}}$-differential cross sections for jets reconstructed from
charged particles for five centrality classes in p–Pb collisions at
$\sqrt{s_{\mathrm{NN}}}=5.02$ TeV are shown in Fig. 2. For both resolution
parameters, the measured yields are higher for more central collisions, as
expected from the increase of the binary interactions (cf. Tab. 1). The pp
reference at $\sqrt{s}=5.02$ TeV is also shown. In addition to the increase in
binary collisions the larger total cross section in p–Pb compared to pp
further separates the data from the two collision systems; by an additional
factor of
$20\%\cdot\sigma^{\mathrm{pPb}}_{\mathrm{V0}}/\sigma^{\mathrm{pp}}_{\mathrm{inel}}\approx
6$.

Figure 3: (Color online) Nuclear modification factors $Q_{\mathrm{pPb}}$ of
charged jets for several centrality classes. $N_{\mathrm{coll}}$ has been
determined with the hybrid model. Top and bottom panels show the result for
$R=0.4$ and $R=0.2$, respectively. The combined global normalisation
uncertainty from $N_{\mathrm{coll}}$, the measured pp cross section, and the
reference scaling is indicated by the box around unity.

The scaling behaviour of the p–Pb spectra with respect to the pp reference is
quantified by the nuclear modification factor $Q_{\mathrm{pPb}}$ (Eq.7). The
nuclear modification factor with the hybrid approach, shown in Fig. 3, is
compatible with unity for all centrality classes, indicating the absence of
centrality-dependent nuclear effects on the jet yield in the kinematic regime
probed by our measurement. This result is consistent with the measurement of
single charged particles in p–Pb collisions presented in [32], where the same
hybrid approach is used.

For other centrality selections, closer to mid-rapidity, a separation of
$Q_{\mathrm{pPb}}$ for jets is observed for the different centralities that is
caused by dynamical biases of the selection, similar to the $Q_{\mathrm{pPb}}$
for charged particles. If we use e.g. the centrality selection based on the
multiplicity in the V0A, $Q_{\mathrm{pPb}}$ decreases from about 1.2 in
central to approximately 0.5 in peripheral collisions [58].

The centrality dependence of full jet production in p–Pb collisions, i.e.
using charged and neutral jet fragments, has been reported by the ATLAS
collaboration in [23] over a broad range of the center-of-mass rapidity
($y^{*}$) and transverse momentum. Centrality-dependent deviations of jet
production have been found for large rapidities in the proton-going direction
and $p_{\mathrm{T,jet}}\gtrsim 100$ GeV/$c$. In the nucleon–nucleon center-of-
mass system as defined by ATLAS, our measurement in
$\left|\eta_{\mathrm{lab}}\right|<0.5$ corresponds to $-0.96<y^{*}<-0.04$. As
shown in Fig. 4, the measurement of the nuclear modification factor of charged
jets in central and peripheral collisions is consistent with the full jet
measurement of ATLAS, where the kinematical selection of jet momentum and
rapidity overlap, note however that the underlying parton $p_{\mathrm{T}}$ at
a given reconstructed $p_{\mathrm{T}}$ is higher for charged jets.

The centrality evolution for $Q_{\mathrm{pPb}}$ as measured by ALICE is shown
for three $p_{\mathrm{T}}$-regions and $R=0.4$ in Fig. 5. No significant
variation is observed with centrality for a fixed $p_{\mathrm{T}}$ interval.
The same holds for $R=0.2$ (not shown).

Recently, the PHENIX collaboration reported on a centrality dependent
modification of the jet yield in d–Au collisions at
$\sqrt{s_{\mathrm{NN}}}=200$ GeV in the range of
$20<p_{\mathrm{T}}<50~{}\mbox{GeV}/c$ [59]: a suppression of 20% in central
events and corresponding enhancement in peripheral events is observed. Even
when neglecting the impact of any possible biases in the centrality selection,
the measurement of the nuclear modification at lower $\sqrt{s_{\mathrm{NN}}}$
cannot be directly compared to the measurements at LHC for two reasons. First,
in case of a possible final state energy loss the scattered parton momentum is
the relevant scale. Here, the nuclear modification factor at lower energies is
more sensitive to energy loss, due to the steeper spectrum of scattered
partons. Second, for initial state effects the nuclear modification should be
compared in the probed Bjorken-$x$, which can be estimated at mid-rapidity to
$x_{\mathrm{T}}\approx 2p_{\mathrm{T}}/\sqrt{s_{\mathrm{NN}}}$, and is at a
given $p_{\mathrm{T}}$ approximately a factor of 25 smaller in p–Pb collisions
at the LHC.

Figure 4: (Color online) Nuclear modification factor of charged jets compared
to the nuclear modification factor for full jets as measured by the ATLAS
collaboration [23]. Note that the underlying parton $p_{\mathrm{T}}$ for fixed
reconstructed jet $p_{\mathrm{T}}$ is higher in the case of charged jets.
Figure 5: (Color online) Centrality evolution of $Q_{\mathrm{pPb}}$ for
selected $p_{\mathrm{T,\,ch\;jet}}$-bins and $R=0.4$.  Figure 6: (Color
online) Charged jet production cross section ratio for different resolution
parameters as defined in Eq. 8. Different centrality classes are shown
together with the result for minimum bias collisions. Note that the systematic
uncertainties are partially correlated between centrality classes. The ratio
for minimum collisions is compared in more detail to pp collisions at higher
energy and NLO calculations at $\sqrt{s}=5.02$ TeV in [25], where no
significant deviations are found.

The ratio of jet production cross sections reconstructed with $R=0.2$ and 0.4
is shown in Fig. 6. For all centrality classes, the ratio shows the expected
stronger jet collimation towards higher $p_{\mathrm{T}}$. Moreover, the ratio
is for all centralities consistent with the result obtained in minimum bias
p–Pb collisions, which agrees with the jet cross section ratio in pp
collisions as shown in [25]. The result is fully compatible with the
expectation, since even in central Pb–Pb collisions, where a significant jet
suppression in the nuclear modification factor is measured, the cross section
ratio remains unaffected [15].

##  6 Summary

Centrality-dependent results on charged jet production in p–Pb collisions at
$\sqrt{s_{\mathrm{NN}}}=5.02$ TeV have been shown for transverse momentum
range $20<p_{\mathrm{T,\,ch\;jet}}<120~{}\mbox{GeV/}c$ and for resolution
parameters $R=0.2$ and $R=0.4$. The centrality selection is performed using
the forward neutron energy, and the corresponding number of binary collisions
$N_{\mathrm{coll}}$ is estimated via the correlation to the multiplicity
measured in the lead-going direction, in order use a rapidity region well
separated from the one where jets are reconstructed.

With this choice of centrality and data driven $N_{\mathrm{coll}}$ estimate,
the nuclear modification factor $Q_{\mathrm{pPb}}$ is consistent with unity
and does not indicate a significant centrality dependence within the
statistical and systematical uncertainties. In the measured kinematic range
momentum between 20 $\mbox{GeV/}c$ and up to 120 $\mbox{GeV/}c$ and close to
mid-rapidity, the observed nuclear modification factor is consistent with
results from full jet measurements by the ATLAS collaboration in the same
kinematic region. The jet cross section ratio for $R=0.2$ and $0.4$ shows no
centrality dependence, indicating no modification of the degree of collimation
of the jets at different centralities.

These measurements show the absence of strong nuclear effects on the jet
production at mid-rapidity for all centralities.

## Acknowledgements

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

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##  Appendix A The ALICE Collaboration

J. Adamorg39&D. Adamováorg84&M.M. Aggarwalorg88&G. Aglieri Rinellaorg35&M.
Agnelloorg110&N. Agrawalorg47&Z. Ahammedorg133&S. Ahmadorg19&S.U. Ahnorg68&S.
Aiolaorg137&A. Akindinovorg58&S.N. Alamorg133&D.S.D. Albuquerqueorg121&D.
Aleksandrovorg80&B. Alessandroorg110&D. Alexandreorg101&R. Alfaro
Molinaorg64&A. Aliciorg12,org104&A. Alkinorg3&J.R.M. Almarazorg119&J.
Almeorg37,org18&T. Altorg42&S. Altinpinarorg18&I. Altsybeevorg132&C. Alves
Garcia Pradoorg120&C. Andreiorg78&A. Andronicorg97&V. Anguelovorg94&T.
Antičićorg98&F. Antinoriorg107&P. Antonioliorg104&L. Aphecetcheorg113&H.
Appelshäuserorg53&S. Arcelliorg27&R. Arnaldiorg110&O.W. Arnoldorg93,org36&I.C.
Arseneorg22&M. Arslandokorg53&B. Audurierorg113&A. Augustinusorg35&R.
Averbeckorg97&M.D. Azmiorg19&A. Badalàorg106&Y.W. Baekorg67&S.
Bagnascoorg110&R. Bailhacheorg53&R. Balaorg91&S. Balasubramanianorg137&A.
Baldisseriorg15&R.C. Baralorg61&A.M. Barbanoorg26&R. Barberaorg28&F.
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Volpeorg32,org136&B. von Hallerorg35&I. Vorobyevorg93,org36&D.
Vranicorg97,org35&J. Vrlákováorg40&B. Vulpescuorg70&B. Wagnerorg18&J.
Wagnerorg97&H. Wangorg57&M. Wangorg7,org113&D. Watanabeorg128&Y.
Watanabeorg127&M. Weberorg112,org35&S.G. Weberorg97&D.F. Weiserorg94&J.P.
Wesselsorg54&U. Westerhofforg54&A.M. Whiteheadorg90&J. Wiechulaorg34&J.
Wikneorg22&G. Wilkorg77&J. Wilkinsonorg94&M.C.S. Williamsorg104&B.
Windelbandorg94&M. Winnorg94&P. Yangorg7&S. Yanoorg46&Z. Yasinorg16&Z.
Yinorg7&H. Yokoyamaorg128&I.-K. Yooorg96&J.H. Yoonorg50&V. Yurchenkoorg3&A.
Zaborowskaorg134&V. Zaccoloorg81&A. Zamanorg16&C. Zampolliorg104,org35&H.J.C.
Zanoliorg120&S. Zaporozhetsorg66&N. Zardoshtiorg101&A. Zarochentsevorg132&P.
Závadaorg60&N. Zaviyalovorg99&H. Zbroszczykorg134&I.S. Zguraorg62&M.
Zhalovorg86&H. Zhangorg18&X. Zhangorg74,org7&Y. Zhangorg7&C. Zhangorg57&Z.
Zhangorg7&C. Zhaoorg22&N. Zhigarevaorg58&D. Zhouorg7&Y. Zhouorg81&Z.
Zhouorg18&H. Zhuorg18&J. Zhuorg7,org113&A. Zichichiorg27,org12&A.
Zimmermannorg94&M.B. Zimmermannorg54,org35&G. Zinovjevorg3&M. Zyzakorg42

## Affiliation notes

0Deceased

idp1778112Also at: Georgia State University, Atlanta, Georgia, United States

idp3133376Also at: Also at Department of Applied Physics, Aligarh Muslim
University, Aligarh, India

idp3847728Also at: M.V. Lomonosov Moscow State University, D.V. Skobeltsyn
Institute of Nuclear, Physics, Moscow, Russia

## Collaboration Institutes

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

org2Benemérita Universidad Autónoma de Puebla, Puebla, Mexico

org3Bogolyubov Institute for Theoretical Physics, Kiev, Ukraine

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

org5Budker Institute for Nuclear Physics, Novosibirsk, Russia

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

org7Central China Normal University, Wuhan, China

org8Centre de Calcul de l’IN2P3, Villeurbanne, France

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

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

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

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

org13Chicago State University, Chicago, Illinois, USA

org14China Institute of Atomic Energy, Beijing, China

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

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

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

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

org19Department of Physics, Aligarh Muslim University, Aligarh, India

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

org21Department of Physics, Sejong University, Seoul, South Korea

org22Department of Physics, University of Oslo, Oslo, Norway

org23Dipartimento di Fisica dell’Università ’La Sapienza’ and Sezione INFN
Rome, Italy

org24Dipartimento di Fisica dell’Università and Sezione INFN, Cagliari, Italy

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

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

org27Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN,
Bologna, Italy

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

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

org30Dipartimento di Fisica ‘E.R. Caianiello’ dell’Università and Gruppo
Collegato INFN, Salerno, Italy

org31Dipartimento di Scienze e Innovazione Tecnologica dell’Università del
Piemonte Orientale and Gruppo Collegato INFN, Alessandria, Italy

org32Dipartimento Interateneo di Fisica ‘M. Merlin’ and Sezione INFN, Bari,
Italy

org33Division of Experimental High Energy Physics, University of Lund, Lund,
Sweden

org34Eberhard Karls Universität Tübingen, Tübingen, Germany

org35European Organization for Nuclear Research (CERN), Geneva, Switzerland

org36Excellence Cluster Universe, Technische Universität München, Munich,
Germany

org37Faculty of Engineering, Bergen University College, Bergen, Norway

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

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

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

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

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

org43Gangneung-Wonju National University, Gangneung, South Korea

org44Gauhati University, Department of Physics, Guwahati, India

org45Helsinki Institute of Physics (HIP), Helsinki, Finland

org46Hiroshima University, Hiroshima, Japan

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

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

org49Indonesian Institute of Sciences, Jakarta, Indonesia

org50Inha University, Incheon, South Korea

org51Institut de Physique Nucléaire d’Orsay (IPNO), Université Paris-Sud,
CNRS-IN2P3, Orsay, France

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

org53Institut für Kernphysik, Johann Wolfgang Goethe-Universität Frankfurt,
Frankfurt, Germany

org54Institut für Kernphysik, Westfälische Wilhelms-Universität Münster,
Münster, Germany

org55Institut Pluridisciplinaire Hubert Curien (IPHC), Université de
Strasbourg, CNRS-IN2P3, Strasbourg, France

org56Institute for Nuclear Research, Academy of Sciences, Moscow, Russia

org57Institute for Subatomic Physics of Utrecht University, Utrecht,
Netherlands

org58Institute for Theoretical and Experimental Physics, Moscow, Russia

org59Institute of Experimental Physics, Slovak Academy of Sciences, Košice,
Slovakia

org60Institute of Physics, Academy of Sciences of the Czech Republic, Prague,
Czech Republic

org61Institute of Physics, Bhubaneswar, India

org62Institute of Space Science (ISS), Bucharest, Romania

org63Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de México,
Mexico City, Mexico

org64Instituto de Física, Universidad Nacional Autónoma de México, Mexico
City, Mexico

org65iThemba LABS, National Research Foundation, Somerset West, South Africa

org66Joint Institute for Nuclear Research (JINR), Dubna, Russia

org67Konkuk University, Seoul, South Korea

org68Korea Institute of Science and Technology Information, Daejeon, South
Korea

org69KTO Karatay University, Konya, Turkey

org70Laboratoire de Physique Corpusculaire (LPC), Clermont Université,
Université Blaise Pascal, CNRS–IN2P3, Clermont-Ferrand, France

org71Laboratoire de Physique Subatomique et de Cosmologie, Université
Grenoble-Alpes, CNRS-IN2P3, Grenoble, France

org72Laboratori Nazionali di Frascati, INFN, Frascati, Italy

org73Laboratori Nazionali di Legnaro, INFN, Legnaro, Italy

org74Lawrence Berkeley National Laboratory, Berkeley, California, United
States

org75Moscow Engineering Physics Institute, Moscow, Russia

org76Nagasaki Institute of Applied Science, Nagasaki, Japan

org77National Centre for Nuclear Studies, Warsaw, Poland

org78National Institute for Physics and Nuclear Engineering, Bucharest,
Romania

org79National Institute of Science Education and Research, Bhubaneswar, India

org80National Research Centre Kurchatov Institute, Moscow, Russia

org81Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark

org82Nikhef, Nationaal instituut voor subatomaire fysica, Amsterdam,
Netherlands

org83Nuclear Physics Group, STFC Daresbury Laboratory, Daresbury, United
Kingdom

org84Nuclear Physics Institute, Academy of Sciences of the Czech Republic, Řež
u Prahy, Czech Republic

org85Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States

org86Petersburg Nuclear Physics Institute, Gatchina, Russia

org87Physics Department, Creighton University, Omaha, Nebraska, United States

org88Physics Department, Panjab University, Chandigarh, India

org89Physics Department, University of Athens, Athens, Greece

org90Physics Department, University of Cape Town, Cape Town, South Africa

org91Physics Department, University of Jammu, Jammu, India

org92Physics Department, University of Rajasthan, Jaipur, India

org93Physik Department, Technische Universität München, Munich, Germany

org94Physikalisches Institut, Ruprecht-Karls-Universität Heidelberg,
Heidelberg, Germany

org95Purdue University, West Lafayette, Indiana, United States

org96Pusan National University, Pusan, South Korea

org97Research Division and ExtreMe Matter Institute EMMI, GSI Helmholtzzentrum
für Schwerionenforschung, Darmstadt, Germany

org98Rudjer Bošković Institute, Zagreb, Croatia

org99Russian Federal Nuclear Center (VNIIEF), Sarov, Russia

org100Saha Institute of Nuclear Physics, Kolkata, India

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

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

org103Sezione INFN, Bari, Italy

org104Sezione INFN, Bologna, Italy

org105Sezione INFN, Cagliari, Italy

org106Sezione INFN, Catania, Italy

org107Sezione INFN, Padova, Italy

org108Sezione INFN, Rome, Italy

org109Sezione INFN, Trieste, Italy

org110Sezione INFN, Turin, Italy

org111SSC IHEP of NRC Kurchatov institute, Protvino, Russia

org112Stefan Meyer Institut für Subatomare Physik (SMI), Vienna, Austria

org113SUBATECH, Ecole des Mines de Nantes, Université de Nantes, CNRS-IN2P3,
Nantes, France

org114Suranaree University of Technology, Nakhon Ratchasima, Thailand

org115Technical University of Košice, Košice, Slovakia

org116Technical University of Split FESB, Split, Croatia

org117The Henryk Niewodniczanski Institute of Nuclear Physics, Polish Academy
of Sciences, Cracow, Poland

org118The University of Texas at Austin, Physics Department, Austin, Texas,
USA

org119Universidad Autónoma de Sinaloa, Culiacán, Mexico

org120Universidade de São Paulo (USP), São Paulo, Brazil

org121Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil

org122University of Houston, Houston, Texas, United States

org123University of Jyväskylä, Jyväskylä, Finland

org124University of Liverpool, Liverpool, United Kingdom

org125University of Tennessee, Knoxville, Tennessee, United States

org126University of the Witwatersrand, Johannesburg, South Africa

org127University of Tokyo, Tokyo, Japan

org128University of Tsukuba, Tsukuba, Japan

org129University of Zagreb, Zagreb, Croatia

org130Université de Lyon, Université Lyon 1, CNRS/IN2P3, IPN-Lyon,
Villeurbanne, France

org131Università di Brescia

org132V. Fock Institute for Physics, St. Petersburg State University, St.
Petersburg, Russia

org133Variable Energy Cyclotron Centre, Kolkata, India

org134Warsaw University of Technology, Warsaw, Poland

org135Wayne State University, Detroit, Michigan, United States

org136Wigner Research Centre for Physics, Hungarian Academy of Sciences,
Budapest, Hungary

org137Yale University, New Haven, Connecticut, United States

org138Yonsei University, Seoul, South Korea

org139Zentrum für Technologietransfer und Telekommunikation (ZTT),
Fachhochschule Worms, Worms, Germany