2017 324 10 December

Constraints on jet quenching in p–Pb collisions

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

The ALICE Collaboration reports the measurement of semi-inclusive
distributions of charged-particle jets recoiling from a high-transverse
momentum trigger hadron in p–Pb collisions at $\sqrt{s_{\rm NN}}=5.02$ TeV.
Jets are reconstructed from charged-particle tracks using the
anti-$k_{\mathrm{T}}$ algorithm with resolution parameter $R=0.2$ and 0.4. A
data-driven statistical approach is used to correct the uncorrelated
background jet yield. Recoil jet distributions are reported for jet transverse
momentum $15<p_{\mathrm{T,jet}}^{\mathrm{ch}}\ <50$ $\mathrm{GeV/}c$ and are
compared in various intervals of p–Pb event activity, based on charged-
particle multiplicity and zero-degree neutral energy in the forward (Pb-going)
direction. The semi-inclusive observable is self-normalized and such
comparisons do not require the interpretation of p–Pb event activity in terms
of collision geometry, in contrast to inclusive jet observables. These
measurements provide new constraints on the magnitude of jet quenching in
small systems at the LHC. In p–Pb collisions with high event activity, the
average medium-induced out-of-cone energy transport for jets with $R=0.4$ and
$15<p_{\mathrm{T,jet}}^{\mathrm{ch}}<50$ $\mathrm{GeV/}c$ is measured to be
less than 0.4 $\mathrm{GeV/}c$ at 90% confidence, which is over an order of
magnitude smaller than a similar measurement for central Pb–Pb collisions at
$\sqrt{s_{\rm NN}}=2.76$ TeV. Comparison is made to theoretical calculations
of jet quenching in small systems, and to inclusive jet measurements in p–Pb
collisions selected by event activity at the LHC and in d–Au collisions at
RHIC.

# Constraints on jet quenching in p–Pb collisions at $\bm{\sqrt{s_{\rm
NN}}}=5.02$ TeV measured by the event-activity dependence of semi-inclusive
hadron-jet distributions

##  1 Introduction

The collision of heavy nuclei at high energies generates a Quark-Gluon Plasma
(QGP), a dense, highly inviscid, strongly-coupled fluid governed by sub-
nucleonic degrees of freedom [1]. While the structure and dynamical behavior
of the QGP arise at the microscopic level from the interactions between quarks
and gluons that are described by Quantum Chromodynamics (QCD), the QGP also
exhibits emergent collective behavior. Current understanding of the properties
of the QGP is based primarily on two phenomena observed in high energy nuclear
collisions and their comparison to theoretical calculations: strong collective
flow [2], and jet quenching, which arises from interaction of energetic jets
with the medium [3].

Jets in hadronic collisions are generated by hard (high momentum transfer
$Q^{2}$) interactions between quarks and gluons from the projectiles, with
outgoing quarks and gluons from the interaction observed in detectors as
correlated sprays of hadrons (“jets”). Theoretical calculations of jet
production based on perturbative QCD (pQCD) are in excellent agreement over a
broad kinematic range with jet measurements in pp collisions at the Large
Hadron Collider (LHC) [4, 5, 6, 7]. Measurements in pp collisions of charged-
particle jets, which consist of the charged component of the hadronic jet
shower, are also well-described by QCD-based Monte Carlo calculations [8, 9].

In nuclear collisions, the interaction of jets with the QGP is expected to
modify the observed rate of jet production and internal jet structure. Indeed,
marked effects due to jet quenching have been observed for high transverse
momentum (high-$p_{\mathrm{T}}$) hadrons and jets in central Au–Au collisions
at the Relativistic Heavy Ion Collider (RHIC) [10, 11, 12, 13, 14, 15, 16, 17,
18, 19, 20] and in central Pb–Pb collisions at the LHC [21, 22, 23, 24, 9, 25,
26, 27, 28, 29, 30, 31, 32]. Jets therefore provide well-calibrated probes of
the QGP.

Measurements of asymmetric p–Pb collisions at the LHC and of light nucleus–Au
collisions at RHIC reveal evidence of collective effects that are similar in
magnitude to those observed in symmetric collisions of heavy nuclei [33, 34,
35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50]. These
measurements in asymmetric systems are reproduced both by model calculations
that incorporate a locally thermalized hydrodynamic medium in the final state,
and by calculations without QGP but with large fluctuations in the initial-
state wavefunctions of the projectiles (see [51] and references therein). This
raises the question whether a QGP is in fact generated in such light
asymmetric systems, which were initially thought to be too small for the
formation of a quasi-equilibrated fireball of matter in the final state [51].
Additional measurements, in particular to explore jet quenching in p–A
collisions, will help to resolve this picture and to clarify the nature of
equilibration in strongly-interacting matter.

There are several theoretical calculations currently available of jet
quenching effects in p–Pb collisions at the LHC, which differ in their
predictions. The calculation in [52] estimates the size of the region of high
energy density to have a radius that is a factor 2 smaller in p–Pb than in
central Pb–Pb collisions, with jet transport parameter $\hat{q}$, assumed to
be proportional to charged multiplicity, to be a factor 7 smaller. Jet energy
loss, which is proportional to $\hat{q}$ and depends on path length of the jet
in the medium, is consequently expected in this calculation to be much smaller
in p–Pb than in central Pb–Pb collisions. In contrast, a model calculation
based on one-dimensional Bjorken hydrodynamics predicts large initial energy
density in high multiplicity pp and p–Pb collisions [53]; this energy density
corresponds to jet energy loss of several GeV, which is similar in magnitude
to jet energy loss measured in central Pb–Pb collisions[9]. A calculation
based on pQCD at next-to-leading order (NLO) finds negligible jet quenching
effects for inclusive jet production in p–Pb collisions at $\sqrt{s_{\rm
NN}}=5$ TeV [54]. Finally, a QCD calculation of initial-state energy loss in
cold nuclear matter (CNM) finds significant suppression of inclusive jet
production for small-impact parameter p–Pb collisions at $\sqrt{s_{\rm NN}}=5$
TeV [55].

Experimental searches for jet quenching effects in d–Au collisions at RHIC and
in p–Pb collisions at the LHC have been carried out with high-$p_{\mathrm{T}}$
hadrons and reconstructed jets. These studies utilize both Minimum Bias (MB)
events and more-differential event selection, in which events are
characterized in terms of “event activity” (EA) based on central charged-
particle multiplicity (ALICE [56]); forward charged-particle multiplicity
(STAR [57], PHENIX [58, 59], ALICE [56]); forward transverse energy (ATLAS
[60], CMS [61]); or zero-degree neutral energy (STAR [57], ALICE [56]); where
“forward” and “zero-degree” refer to the direction of the heavy nuclear
projectile.

Inclusive hadron measurements in d–Au collisions at RHIC [57, 58] exhibit
yield enhancement in the region $2<p_{\mathrm{T}}<5$ $\mathrm{GeV/}c$, which
is commonly attributed to multiple scattering in the initial state, with no
significant yield modification at higher $p_{\mathrm{T}}$ and with no
significant difference observed between the MB and EA-selected distributions.
For inclusive hadron measurements in p–Pb collisions at the LHC, ALICE does
not observe significant yield modification for $p_{\mathrm{T}}>8$
$\mathrm{GeV/}c$ in both MB and EA-selected events [62, 56] while ATLAS and
CMS observe yield enhancement for $p_{\mathrm{T}}$ greater than $\sim 30$
$\mathrm{GeV/}c$ in MB events [60, 63, 64], and ATLAS observes additional
dependence on EA [60].

For inclusive jet production, no significant yield modification has been
observed in MB p–Pb collisions at the LHC and MB d–Au collisions at RHIC [65,
66, 59, 67]. However, measurements by the PHENIX collaboration at RHIC [59]
and the ATLAS collaboration at the LHC [66] find apparent enhancement of the
inclusive jet yield in EA-selected event populations thought to be biased
towards large impact parameter in such asymmetric systems (“peripheral
collisions”), with compensating suppression for event populations assigned
small impact parameter (“central collisions”), while the ALICE collaboration
finds no such yield modification as a function of event “centrality” [68].

Measurement of jet quenching effects with inclusive processes requires scaling
of the inclusive yield from a reference collision system (usually pp) by the
nuclear overlap function $\left<T_{\mathrm{aA}}\right>$, with the angle
brackets $\left<\ldots\right>$ indicating an average over the event
population; for current measurements, “aA” denotes d–Au at RHIC and p–Pb at
the LHC. For an EA-selected population, $\left<T_{\mathrm{aA}}\right>$ is
calculated by correlating EA with collision geometry and applying Glauber
modeling [69]. However, the correlation of EA with collision geometry in p–Pb
collisions is obscured by large fluctuations in the EA observables [56], and
can be biased by conservation laws and by dynamical correlations when
measuring high $Q^{2}$ processes [70, 71, 72, 73, 74, 75]. Color fluctuations
in the proton wavefunction may induce a bias in soft particle production for
p–Pb events tagged by a hard process, thereby biasing the correlation between
EA and collision geometry [76, 77, 78, 79]. A model calculation shows that
selection bias can modify the scaling factor for jet production in peripheral
$A+A$ relative to pp collisions, generating an apparent suppression of jet
production in peripheral $A+A$ collisions if the Glauber calculation does not
take this effect into account [80]; similar considerations apply to asymmetric
collision systems.

While Glauber modeling for peripheral d–Au collisions at RHIC has been
validated experimentally for moderate $Q^{2}$ processes using a proton-
stripping process and knowledge of the deuteron wavefunction [57], no such
check is possible with the proton beam at the LHC. It is therefore crucial to
measure the EA-dependence of jet quenching effects in p–Pb collisions at the
LHC with correlation observables that do not require the interpretation of EA
in terms of collision geometry.

A correlation measurement of dijet transverse-momentum balance in p–Pb
collisions at $\sqrt{s_{\rm NN}}=5.02$ TeV finds no significant difference
from a simulated pp reference distribution, independent of EA [61].
Measurements of dijet acoplanarity, which can be generated by both initial-
state and final-state effects, likewise find no significant modification due
to nuclear matter effects in EA-selected p–Pb collisions at $\sqrt{s_{\rm
NN}}=5.02$ TeV, relative to simulated distributions for pp collisions [61,
81]. While these measurements provide qualitative indications, based on
comparison to simulations, that final-state jet quenching effects in high-EA
p–Pb collisions are small, quantitative measurements or limits on jet
quenching effects in such collisions are still lacking.

In this paper we present measurements sensitive to jet quenching in p–Pb
collisions at $\sqrt{s_{\rm NN}}=5.02$ TeV, based on the semi-inclusive
distribution of charged jets recoiling from a high-$p_{\mathrm{T}}$ trigger
hadron [82]. The observable used in this analysis has been measured in pp
collisions at $\sqrt{s}=7$ TeV and compared to calculations based on PYTHIA
and on pQCD at NLO, with PYTHIA providing a better description [9]. It has
also been used to measure jet quenching effects in Pb–Pb collisions at
$\sqrt{s_{\rm NN}}=2.76$ TeV [9] and in Au–Au collisions at $\sqrt{s_{\rm
NN}}=200$ GeV [20].

The semi-inclusive recoil jet distribution is equivalent to the ratio of
inclusive cross sections [9]; comparison of such self-normalized coincidence
distributions for p–Pb event populations with different EA therefore does not
require scaling by the nuclear overlap function
$\left<T_{\mathrm{pPb}}\right>$. Measurement of this observable in p–Pb
collisions is sensitive to jet quenching effects, and indeed does not require
interpretation of the EA in terms of p–Pb collision geometry. This approach
thereby avoids potential bias due to Glauber modeling when interpreting the
measurement.

We report charged recoil jet distributions reconstructed with the
anti-$k_{\mathrm{T}}$ algorithm [83] in the range
$15<p_{\mathrm{T,jet}}^{\mathrm{ch}}<50$ $\mathrm{GeV/}c$, for jet resolution
parameters $R=0.2$ and 0.4. Correction of the jet yield for background
uncorrelated with the triggered hard process, including multi-partonic
interactions (MPI), is carried out statistically at the level of ensemble-
averaged distributions, using the data-driven method first applied in [9]. EA
in p–Pb collisions is characterized by two different observables, forward
charged-particle multiplicity and neutral energy along the beam axis, both
measured in the direction of the Pb-beam [56]. Jet quenching effects are
quantified by comparing the measured distributions in different EA classes of
the p–Pb dataset. The results are compared to other jet quenching measurements
and to theoretical calculations of jet quenching in asymmetric collision
systems.

The paper is organized as follows: Sect. 2 describes the data set and
analysis; Sect. 3 describes event selection based on trigger hadrons and event
activity; Sect. 4 describes jet reconstruction; Sect. 5 discusses the semi-
inclusive observable and presents the raw data; Sect. 6 discusses corrections;
Sect. 7 discusses systematic uncertainties; Sect. 8 presents results and
discussion; Sect. 9 compares the results to other measurements; and Sect. 10
is the summary.

##  2 Data set and analysis

The ALICE detector and performance are described in [84, 85]. The data used in
this analysis were recorded during the 2013 LHC run with p–Pb collisions at
$\sqrt{s_{\rm NN}}=5.02$ TeV. The Pb-going direction has rapidity $y>0$ and
pseudorapidity $\eta>0$ in the laboratory frame. The per-nucleon momenta of
the beams in this run were imbalanced in the laboratory frame, with the
nucleon-nucleon center-of-mass at rapidity $y_{\mathrm{NN}}=-0.465$. The
acceptance of tracks and jets in this analysis are specified in terms of
$y^{*}=y_{\mathrm{LAB}}-y_{\mathrm{NN}}$, where $y_{\mathrm{LAB}}$ denotes the
rapidity measured in the laboratory frame.

Events were selected online by an MB trigger, which requires the coincidence
of signals in the V0A and V0C forward scintillator arrays. The V0A array has
acceptance $2.8<\eta<5.1$ and the V0C array has acceptance $-3.7<\eta<-1.7$,
both covering the full azimuth. Offline event selection also utilizes the
Zero-Degree Calorimeters (ZDC), which are neutron calorimeters at zero degrees
relative to the beam direction, located at a distance 112.5 m from the nominal
interaction point. The ZDC in the Pb-going direction is labeled ZNA.

Jet reconstruction in this analysis uses charged-particle tracks. Tracks are
measured by the Inner Tracking System (ITS), a six-layer silicon vertex
tracker, and the Time Projection Chamber (TPC). The tracking system acceptance
covers $|\eta|<0.9$ over the full azimuth, with tracks reconstructed in the
range $0.15<p_{\mathrm{T}}<100$ $\mathrm{GeV/}c$. Primary vertices are
reconstructed offline by extrapolation of these tracks to the beam axis.
Primary tracks are defined as reconstructed tracks with Distance of Closest
Approach to the primary vertex in the transverse plane $DCA_{xy}<2.4$ cm.

The analysis uses high-quality primary tracks that include at least one track
point in the Silicon Pixel Detector (SPD), which comprises the two innermost
layers of ITS. The azimuthal distribution of such high-quality tracks is non-
uniform, however, due to the non-uniform acceptance of the SPD in this run.
Azimuthal uniformity in the tracking acceptance is achieved by supplementing
the high-quality tracks with complementary tracks that do not have a hit in
the SPD, which constitute 4.3% of all primary tracks. The momentum resolution
of complementary tracks, without an additional constraint, is lower than that
of high-quality tracks. Complementary tracks are therefore refit, including
the reconstructed primary vertex as a track point. Tracking efficiency for
primary tracks is about 81% for $p_{\mathrm{T}}>3$ $\mathrm{GeV/}c$. Primary-
track momentum resolution is 0.7% at $p_{\mathrm{T}}=1$ $\mathrm{GeV/}c$, 1.6%
at $p_{\mathrm{T}}=10$ $\mathrm{GeV/}c$, and 4% at $p_{\mathrm{T}}=50$
$\mathrm{GeV/}c$. Further details on the track selection and tracking
performance in this analysis are given in [23, 68].

The MB trigger efficiency for non-single diffractive (NSD) collisions is
$97.8\pm 3.1$% [86, 68]. Since this is a correlation analysis, no correction
is applied for the trigger inefficiency. Timing cuts on the V0 and ZDC
signals, which are applied offline, remove background events with vertices
outside of the nominal p–Pb interaction region that arise from beam-gas
interactions and interactions with satellite beam bunches [85].

Event pileup, due to multiple interactions in the triggered bunch crossing, is
suppressed by rejecting events with multiple primary vertex candidates. For
this procedure, a new set of primary vertex candidates is constructed from
tracklets constructed solely from SPD hits (“SPD vertices”). SPD vertices have
at least five SPD tracklets within $DCA<1$ mm and lie within the expected
envelope of p–Pb interaction points, with a distance not more than $3\sigma$
in $z$ or $2\sigma$ in the $xy$ plane from the centroid of the distribution.
The minimum distance in $z$ between SPD vertices is 8 mm. Events with multiple
SPD vertices are rejected from further analysis. The EA-bias of the pileup
rejection procedure is negligible, due to the large separation of pileup
vertices in $z$ and the requirement that each SPD vertex have at least five
contributors. In this dataset, the average number of interactions per bunch
crossing was $\mu\approx 0.3$–0.5%, and this pileup rejection procedure
removes less than 0.15% of all events.

In addition, accepted events must have the primary vertex (defined above) with
$|z_{\rm{vtx}}|<10$ cm relative to the nominal center of the ITS along the
beam axis. After all event selection cuts, the number of events in the
analysis is $96\times 10^{6}$, corresponding to an integrated luminosity of 46
$\mu{\mathrm{b}}^{-1}$.

Simulations are used to correct the raw data for instrumental effects, and to
compare the corrected measurements to expectations from an event generator.
Simulated events were generated for pp collisions at $\sqrt{s}=5.02$ TeV using
PYTHIA 6.425 with the Perugia 11 tune [87]. These events, labeled “particle-
level,” include all primary charged particles as defined in [88]. Following
the procedure in [65], instrumental effects are calculated by passing
particle-level events through a detailed model of the ALICE detector based on
GEANT3 [89]. These events are reconstructed with the same procedures that are
used for real data; the output of this process is labeled “detector-level.”
Comparison to data also uses a particle-level simulation of pp collisions at
$\sqrt{s}=5.02$ TeV generated with PYTHIA 8.215 Tune 4C [90]. All simulations
take account of the nucleon-nucleon center-of-mass rapidity shift of the p–Pb
data.

##  3 Event selection

This analysis is based on the semi-inclusive distribution of jets recoiling
from a high-$p_{\mathrm{T}}$ trigger hadron. Event selection requires the
presence of a high-$p_{\mathrm{T}}$ charged track, called the Trigger Track
(TT), in a specified $p_{\mathrm{T,trig}}$ interval. Two exclusive event sets
are defined, based on different TT intervals: $12<p_{\mathrm{T,trig}}<50$
$\mathrm{GeV/}c$, denoted TT{12,50}, and $6<p_{\mathrm{T,trig}}<7$
$\mathrm{GeV/}c$, denoted TT{6,7}.

The choice of the upper TT interval limits is driven by two competing factors:
the hardening of the recoil jet $p_{\mathrm{T}}$-spectrum with increasing
$p_{\mathrm{T,trig}}$, and the decrease of the inclusive hadron production
cross section for increasing $p_{\mathrm{T,trig}}$. The choice of TT{12,50}
provides the optimum kinematic reach and statistical precision of the
normalized recoil jet spectrum for this dataset. The criteria for the lower TT
interval, TT{6,7}, are that it be significantly lower in
$p_{\mathrm{T,trig}}$, with correspondingly softer recoil jet spectrum, while
still in the region in which inclusive hadron production can be well-described
perturbatively using collinear fragmentation functions [91, 92].

The fraction of such events in the MB population is $6.9\times 10^{-4}$ for
TT{12,50} and $6.4\times 10^{-3}$ for TT{6,7}. However, an event may satisfy
both the TT{6,7} and TT{12,50} selection criteria, since fragmentation of an
energetic jet can generate hadrons in both TT selection intervals. A procedure
is required to ensure exclusive, statistically independent datasets for the
two TT-selected populations. In addition, optimization of the statistical
precision of the analysis requires similar number of events in the two TT
classes. The MB population was therefore divided randomly into two subsets,
whose sizes are inversely proportional to the relative rate of the two TT
selections: 90% of MB events are assigned to the TT{12,50} analysis, with the
remaining 10% assigned to the TT{6,7} analysis.

An event can also contain multiple hadrons within a single TT interval,
likewise arising from jet fragmentation. For events with at least one hadron
satisfying TT{6,7}, the relative rate of two or more hadrons in an event
satisfying TT{6,7} is 2.3%; the corresponding relative rate of multiple
hadrons satisfying TT{12,50} is 5.3%. If an event contains more than one track
in the assigned TT interval, the trigger hadron is chosen as the candidate
with the highest $p_{\mathrm{T}}$. The resulting $p_{\mathrm{T}}$-distribution
of trigger tracks is consistent with the shape of the inclusive hadron
distribution within 2%. After the TT event selection procedure there are 63k
events accepted that satisfy TT{6,7} and 60k events accepted that satisfy
TT{12,50}.

A different procedure was employed in [9] for the case of multiple trigger
candidates in a TT interval, where the trigger track was chosen randomly
amongst the candidates. However, the analysis reported here has a wider range
in $p_{\mathrm{T}}$ for the upper TT class, and random selection results in
reduced level of agreement ($\sim$10%) of the trigger track
$p_{\mathrm{T}}$-distribution with the inclusive hadron spectrum shape. The
full analysis was also carried out for this choice of procedure for trigger
selection, and all resulting physics distributions agree with those of the
primary analysis within the uncertainties.

Measurement of EA uses signals from V0A and ZNA. Classification of events in
percentile intervals of the V0A and ZNA signal distributions is discussed in
[56].

About 5% of accepted events do not have a ZNA signal above the detector
threshold. The ZNA threshold is set so that the detector is fully efficient
for single neutrons. However, these events correspond to p–Pb collisions in
which the Pb-nucleus remnant is not accompanied by any beam-rapidity single
neutrons. The distribution of mid-rapidity track multiplicity for these events
resembles closely that for events with low but observable ZNA signal, and
these events are therefore assigned to the bin with lowest ZNA signal.

Figure 1: Distribution of event activity EA in decile bins measured in ZNA
(left) and V0A (right), for the MB event population and for event populations
selected with the requirement of a high-$p_{\mathrm{T}}$ trigger hadron in the
intervals $6<p_{\mathrm{T,trig}}<7$ $\mathrm{GeV/}c$ (TT{6,7}) and
$12<p_{\mathrm{T,trig}}<50$ $\mathrm{GeV/}c$ (TT{12,50}). Large EA is to the
left, with the 0-10% bin representing the largest EA, or highest amplitude
signal in ZNA or V0A.

Figure 1 shows the distribution of EA measured by ZNA and V0A, in decile bins
of signal amplitude. The decile bin limits are determined from their
distributions in the MB population, with MB events therefore distributed
uniformly in this projection by construction. The figure also shows V0A and
ZNA distributions for event populations selected by the TT{6,7} and the
TT{12,50} criteria. Requiring the presence of a high-$p_{\mathrm{T}}$ hadron
trigger in an event is seen to induce a bias towards larger EA, corresponding
to larger amplitude in both ZNA and V0A. A small dependence on the TT class
(i.e. on $p_{\mathrm{T,trig}}$) is also observed, with magnitude less than 10%
of the overall bias, and with the TT-dependence slightly larger for V0A than
for ZNA. Figure 1 shows significant correlation between EA and the presence of
a hard process in the central region.

For further analysis, events were assigned to wider percentile bins in ZNA or
V0A, based on their MB distributions: 20% of the MB population with largest
signal (“0–20%”), the next 30% (“20–50%”), and the remaining 50% with the
lowest signal (“50–100%”). The bias imposed by TT selection, shown in Fig. 1,
corresponds to different fractions of the TT-biased population: the nominal
0–20% ZNA interval corresponds to 0–35% of the TT-biased population; the
nominal 20–50% ZNA interval corresponds to 35–74% of TT-biased; and the
nominal 50–100% ZNA interval corresponds to 74–100% of TT-biased. Similar
modification of percentile fractions due to TT bias is observed for the V0A
signal.

The same events are used for the ZNA and V0A selections, so that the analyses
using the two different EA metrics are not statistically independent.

##  4 Jet reconstruction

Several types of jet are used in the analysis, which we distinguish by the
notation for jet $p_{\mathrm{T}}$: $p_{\mathrm{T,jet}}^{\mathrm{raw,ch}}$
refers to the output of the jet reconstruction algorithm;
$p_{\mathrm{T,jet}}^{\mathrm{reco,ch}}$ is
$p_{\mathrm{T,jet}}^{\mathrm{raw,ch}}$ after subtraction of an estimated
contribution to jet $p_{\mathrm{T}}$ of uncorrelated background; and
$p_{\mathrm{T,jet}}^{\mathrm{ch}}$ refers to the fully corrected jet spectrum.
For simulations, $p_{\mathrm{T,jet}}^{\mathrm{part}}$ refers to reconstructed
charged-particle jets at the particle-level, and
$p_{\mathrm{T,jet}}^{\mathrm{det}}$ refers to reconstructed charged-particle
jets at the detector-level.

Jet reconstruction is carried out using the $k_{\mathrm{T}}$ and
anti-$k_{\mathrm{T}}$ algorithms [83] with the boost-invariant
$p_{\mathrm{T}}$ recombination scheme [93], using all accepted charged tracks
with $p_{\mathrm{T}}>0.15$ $\mathrm{GeV/}c$. Jet area $A_{\mathrm{jet}}$ is
calculated using the Fastjet algorithm [94] with ghost area 0.005.

Two jet reconstruction passes are carried out for each event. The first pass
estimates the level of uncorrelated background energy in the event, while the
second pass generates the set of jet candidates used in the physics analysis,
with adjustment of their $p_{\mathrm{T}}$ using the estimated background level
from the first pass.

In the first pass, the $p_{\mathrm{T,jet}}^{\mathrm{raw,ch}}$ distribution
reconstructed by the $k_{\mathrm{T}}$ algorithm with $R=0.4$ is used to
estimate $\rho$, the magnitude of background energy per unit area [95],

$\rho={\mathrm{median}}_{k_{\mathrm{T}}\
{\mathrm{jets}}}\left\\{\frac{p_{\mathrm{T,jet}}^{\mathrm{raw,ch}}\
}{A_{\mathrm{jet}}\ }\right\\},$ (1)

where the median is calculated by excluding the jet which has the trigger
hadron as a constituent. A different $\rho$ estimator [96] is utilized to
assess the systematic uncertainties of this procedure.

The second jet reconstruction pass is carried out using the
anti-$k_{\mathrm{T}}$ algorithm with $R=0.2$ and 0.4. The value of
$p_{\mathrm{T,jet}}^{\mathrm{raw,ch}}$ for each jet candidate from this step
is then adjusted for the estimated background energy density [95],

$p_{\mathrm{T,jet}}^{\mathrm{reco,ch}}=p_{\mathrm{T,jet}}^{\mathrm{raw,ch}}-A_{\mathrm{jet}}\cdot\rho.$
(2)

A jet candidate from the second pass is accepted for further analysis if its
area satisfies $A_{\mathrm{jet}}>0.6\pi R^{2}$ [23, 9], and its jet axis lies
within $|\eta_{\rm jet}|<0.9-R$ and an azimuthal interval situated back-to-
back with respect to the TT, $\Delta\varphi_{\mathrm{recoil}}>\pi-0.6$, where
$\Delta\varphi=\varphi_{\rm TT}-\varphi_{\rm jet}$ and $0<\Delta\varphi<\pi$.
An event may have multiple accepted jet candidates.

For further analysis we follow the procedure used in [9], in which no
rejection of individual jet candidates is carried out. Recoil jet
distributions are accumulated for the selected event populations, and
corrections for uncorrelated jet yield and for smearing and residual shift of
$p_{\mathrm{T,jet}}^{\mathrm{ch}}$ due to uncorrelated background are carried
out at the level of the ensemble-averaged distributions, as discussed below.

Jet energy resolution due to instrumental effects (JER) and jet energy scale
(JES) uncertainty are similar to those in [9]. The JER is determined by
comparing simulated jets at the particle and detector levels. The distribution
of
($p_{\mathrm{T,jet}}^{\mathrm{det}}-p_{\mathrm{T,jet}}^{\mathrm{part}}$)/$p_{\mathrm{T,jet}}^{\mathrm{part}}$
is asymmetric, with a sharp peak centered at zero and a tail to negative
values [20]. Fit of a Gaussian function to the sharp peak gives $\sigma\simeq
2$–3%, while the full distribution has ${\rm RMS}\simeq 25$%, with both
quantities having no significant dependence on
$p_{\mathrm{T,jet}}^{\mathrm{part}}$ and $R$. The JES uncertainty, which is
due predominantly to uncertainty in tracking efficiency, is 4%, likewise with
no significant dependence on $p_{\mathrm{T,jet}}^{\mathrm{ch}}$ and $R$.
However, these values of JER and JES uncertainty, while helpful to
characterize the jet measurement, are not used in the analysis. Corrections
are carried out utilizing the full response matrix, which incorporates
detailed distributions of all contributions to JER and JES uncertainty. The
systematic uncertainties (Tab. 1) likewise take such factors fully into
account.

##  5 Observable and raw data

The semi-inclusive h+jet distribution corresponds to the
$p_{\mathrm{T}}$-differential distribution of recoil jets normalized by the
number of trigger hadrons, $N_{\rm{trig}}$,

$\frac{1}{N_{\rm{trig}}\ }\frac{{\mathrm{d}}^{2}N_{\rm{jets}}\
}{{\mathrm{d}}p_{\mathrm{T,jet}}^{\mathrm{ch}}\
{\mathrm{d}}\eta_{\mathrm{jet}}\
}\bigg{|}_{\begin{subarray}{c}p_{\mathrm{T,trig}}\ \in{\mathrm{TT}}\\\
\Delta\varphi\in\mathrm{recoil}\end{subarray}}=\frac{1}{\sigma^{\mathrm{pPb}\rightarrow\mathrm{h+X}}}\frac{{\mathrm{d}}^{2}\sigma^{\mathrm{pPb}\rightarrow{\mathrm{h+jet+X}}}}{{\mathrm{d}}p_{\mathrm{T,jet}}^{\mathrm{ch}}\
{\mathrm{d}}\eta_{\mathrm{jet}}\
}\bigg{|}_{\begin{subarray}{c}\mathrm{h}\in{\mathrm{TT}}\\\
\Delta\varphi\in\mathrm{recoil}\end{subarray}}$ (3)

All accepted jets contribute to the distribution on the LHS. This distribution
is equivalent to measurement of the ratio of two cross sections, as shown on
the RHS: the coincidence cross section for both trigger hadron and recoil jet
to be in the acceptance, divided by the inclusive production cross section for
trigger hadrons. This expression applies to both the MB event population, and
to event subsets selected by EA. The features of this observable and its
theoretical calculations are discussed in detail in Refs. [9, 20]. Here we
consider two specific aspects of this distribution.

The first aspect is the bias imposed by the high-$p_{\mathrm{T}}$ hadron
trigger. For collision systems in which jet quenching occurs, selection of
high-$p_{\mathrm{T}}$ hadrons is thought to bias towards the fragments of jet
that have experienced little quenching, due to the combined effect of jet
energy loss and the shapes of the inclusive jet production and the jet
fragmentation distributions [97, 98, 99, 100, 101, 102, 103, 104]. If that is
the case, then the hadron trigger bias in this measurement would be
independent of EA. This conjecture is supported by ALICE measurements of
inclusive hadron production in p–Pb collisions that find no significant yield
modification in the trigger $p_{\mathrm{T}}$-range of this measurement, for
both the MB and EA-selected event populations [62, 56]. The picture provided
by current ATLAS and CMS hadron production measurements [63, 60] is more
complex, however. Further study of this conjecture requires additional
measurements of inclusive hadron production in pp and p–Pb collisions,
together with theoretical calculations incorporating jet quenching that
accurately reproduce these measurements.

The second aspect is the effect of trigger hadron efficiency on the equality
in Eq. 3. As noted in Sect. 3, the analysis requires selection of a single
trigger hadron in each event. However, in a few percent of events there are
multiple hadrons satisfying the TT selection criteria, of which only one is
chosen as trigger. Consequently, not all hadrons that would contribute to
measurement of the inclusive hadron cross section (first term on the RHS of
Eq. 3) also contribute to $N_{\rm{trig}}$ (first term on the LHS of Eq. 3).
However, as noted above, the shape of the trigger hadron
$p_{\mathrm{T}}$-distribution is consistent with that of the inclusive hadron
spectrum within 2%. In other words, the trigger distribution used in practice
samples the inclusive hadron distribution with efficiency less than unity but
without $p_{\mathrm{T}}$-dependent bias, within a precision of 2%. This same
inefficiency also applies to the h+jet coincidence process in the second term
on the LHS of Eq. 3, and it therefore cancels identically in the ratio.
Equation 3 therefore remains valid for trigger selection efficiency less than
unity.

The study of jet quenching using inclusive yields requires comparison of the
inclusive distribution measured in heavy ion collisions to a reference
distribution measured in a system in which quenching effects are not expected,
usually pp collisions at the same $\sqrt{s_{\rm NN}}$. Such comparisons must
account for the effect of multiple nucleon-nucleon collisions in each
collision of heavy nuclei, which arises due to nuclear geometry. For inclusive
distributions in p–Pb collisions this is accomplished by scaling inclusive
cross sections for pp collisions by $\left<T_{\mathrm{pPb}}\right>$, which is
calculated by modeling based on Glauber theory under the assumption that EA is
correlated with the collision geometry [69, 57, 58, 62, 56, 60, 63, 65, 68,
59, 66].

For the semi-inclusive distribution in Eq. 3, the reference distribution
without nuclear effects is

$\displaystyle\frac{1}{\sigma_{\mathrm{ref}}^{\mathrm{pPb}\rightarrow\mathrm{h+X}}}\frac{{\mathrm{d}}^{2}\sigma_{\mathrm{ref}}^{\mathrm{pPb}\rightarrow{\mathrm{h+jet+X}}}}{{\mathrm{d}}p_{\mathrm{T,jet}}^{\mathrm{ch}}\
{\mathrm{d}}\eta_{\mathrm{jet}}\
}\bigg{|}_{\begin{subarray}{c}h\in{\mathrm{TT}}\\\
\Delta\varphi\in\mathrm{recoil}\end{subarray}}$
$\displaystyle=\frac{1}{\left<T_{\mathrm{pPb}}\right>\cdot\sigma^{\mathrm{pp}\rightarrow\mathrm{h+X}}}\frac{\left<T_{\mathrm{pPb}}\right>\cdot{\mathrm{d}}^{2}\sigma^{\mathrm{pp}\rightarrow\mathrm{h+jet+X}}}{{\mathrm{d}}p_{\mathrm{T,jet}}^{\mathrm{ch}}\
{\mathrm{d}}\eta_{\mathrm{jet}}\
}\bigg{|}_{\begin{subarray}{c}h\in{\mathrm{TT}}\\\
\Delta\varphi\in\mathrm{recoil}\end{subarray}}$
$\displaystyle=\frac{1}{\sigma^{\mathrm{pp}\rightarrow\mathrm{h+X}}}\frac{{\mathrm{d}}^{2}\sigma^{\mathrm{pp}\rightarrow\mathrm{h+jet+X}}}{{\mathrm{d}}p_{\mathrm{T,jet}}^{\mathrm{ch}}\
{\mathrm{d}}\eta_{\mathrm{jet}}\
}\bigg{|}_{\begin{subarray}{c}\mathrm{h}\in{\mathrm{TT}}\\\
\Delta\varphi\in\mathrm{recoil}\end{subarray}}$ (4)

Since the scaling factors $\left<T_{\mathrm{pPb}}\right>$ in the numerator and
denominator cancel identically, the reference distribution for this observable
has no dependence on $\left<T_{\mathrm{pPb}}\right>$. In other words, this
distribution is self-normalized, and measurement of jet quenching using this
observable does not require Glauber modeling for the reference spectrum. In
particular, the assumption that event activity is correlated with the
collision geometry is not required.

A similar approach, utilizing a coincidence observable to measure jet
quenching in high-mulitplicity pp collisions, was recently proposed in [105].

Figure 2: Uncorrected semi-inclusive distributions of charged jets recoiling
from a high-$p_{\mathrm{T}}$ hadron trigger in p–Pb collisions at
$\sqrt{s_{\rm NN}}=5.02$ TeV with the EA selection of 50–100% in ZNA for
$R=0.4$ (top panels), and with the EA selection of 0-20% in ZNA for $R=0.2$
(middle panels) and $R=0.4$ (bottom panels). The acceptance for TT and recoil
jets in the CM frame are denoted $y^{*}_{\mathrm{TT}}$ and
$y^{*}_{\mathrm{jet}}$, respectively. Left panels: raw distributions for
TT{12,50} (red circles) and TT{6,7} (blue boxes), and the corresponding
$\Delta_{\mathrm{recoil}}$ distribution (Eq. 5, black circles). Right panels:
ratio of yields for TT{12,50}/TT{6,7} measured by ALICE in p–Pb collisions and
calculated using detector level PYTHIA Perugia 11 simulation of pp collisions
at $\sqrt{s}=5.02$ TeV. The PYTHIA-generated ratios in the top right and
bottom right panels are the same. The arrow indicates the 0–1 $\mathrm{GeV/}c$
bin which is used to calculate $c_{\mathrm{Ref}}$. The uncertainties are
statistical only.

Figure 2, left panels, show recoil-jet distributions for $R=0.4$ in p–Pb
collisions with the 50–100% ZNA selection, and for $R=0.2$ and 0.4 with the
0–20% ZNA selection. Distributions in EA intervals selected with V0A and with
20–50% ZNA are similar [106]. The distributions have non-zero yield for
$p_{\mathrm{T,jet}}^{\mathrm{reco,ch}}<0$, because regions of an event can
have energy density less than $\rho$ [9]. These distributions are
significantly narrower in the region $p_{\mathrm{T,jet}}^{\mathrm{reco,ch}}<0$
than those observed in central Pb–Pb collisions, where the uncorrelated
component of the event is significantly larger [9].

Figure 2, right panels, show ratios of the distributions for the two TT
classes. The right panels also show the corresponding ratio for pp collisions
at $\sqrt{s}=5.02$ TeV, using simulated detector-level events generated with
PYTHIA Perugia 11. For $p_{\mathrm{T,jet}}^{\mathrm{reco,ch}}\sim 0$ the two
distributions agree within $\sim 10$% for both values of $R$, consistent with
the expectation that yield in this region arises predominantly from processes
that are uncorrelated with the trigger hadron [9]. At larger
$p_{\mathrm{T,jet}}^{\mathrm{reco,ch}}$, the distribution for TT{12,50}
exceeds that for TT{6,7}. This dependence of the recoil distribution on
$p_{\mathrm{T,trig}}$ is expected from QCD-based considerations, since higher
$p_{\mathrm{T,trig}}$ biases towards hard processes with higher $Q^{2}$ on
average. Indeed, hardening of the semi-inclusive recoil jet distribution with
increasing $p_{\mathrm{T,trig}}$ is also seen in the PYTHIA-generated ratios
for pp collisions at $\sqrt{s}=5.02$ TeV shown in the figure, and has been
measured in pp collisions at $\sqrt{s}=7$ TeV and observed in theoretical
calculations based on NLO pQCD and on PYTHIA [9].

The PYTHIA-generated ratio for pp collisions reproduces well the ratio
measured for low-EA p–Pb collisions (ZNA 50–100%, Fig. 2 upper right panel),
while the level of agreement between the simulation and measurements is not as
good for high-EA p–Pb collisions (ZNA 0–20%, Fig. 2, middle and bottom right
panels). This occurs because there is larger uncorrelated background in high-
EA than in low-EA p–Pb collisions.

The distribution of jet candidates that are uncorrelated with the trigger is
independent of $p_{\mathrm{T,trig}}$, by definition. The distribution of
correlated recoil jets can therefore be measured using the
$\Delta_{\mathrm{recoil}}$ observable, which is the difference of the two
normalized recoil distributions [9],

$\Delta_{\mathrm{recoil}}\left(p_{\mathrm{T,jet}}^{\mathrm{ch}}\right)=\frac{1}{N_{\rm{trig}}\
}\frac{{\mathrm{d}}^{2}N_{\rm{jets}}\
}{{\mathrm{d}}p_{\mathrm{T,jet}}^{\mathrm{ch}}\
}\bigg{|}_{p_{\mathrm{T,trig}}\ \in\mathrm{TT}_{\mathrm{Sig}}\
}-c_{\mathrm{Ref}}\ \cdot\frac{1}{N_{\rm{trig}}\
}\frac{{\mathrm{d}}^{2}N_{\rm{jets}}\
}{{\mathrm{d}}p_{\mathrm{T,jet}}^{\mathrm{ch}}\
}\bigg{|}_{p_{\mathrm{T,trig}}\ \in\mathrm{TT}_{\mathrm{Ref}}\ },$ (5)

where $\mathrm{TT}_{\mathrm{Sig}}$ and $\mathrm{TT}_{\mathrm{Ref}}$ refer to
Signal and Reference TT intervals, in this analysis corresponding to TT{12,50}
and TT{6,7} respectively. $\Delta_{\mathrm{recoil}}$ is normalized per unit
$\eta_{\mathrm{jet}}$, notation not shown.

The Reference spectrum in $\Delta_{\mathrm{recoil}}$ is scaled by the factor
$c_{\mathrm{Ref}}$ to account for the invariance of the jet density with TT-
class, as indicated by comparison of the spectrum integrals in Fig. 2 and the
larger yield of Signal spectrum at high
$p_{\mathrm{T,jet}}^{\mathrm{reco,ch}}$ [9]. The value of $c_{\mathrm{Ref}}$
in this analysis is taken as the ratio of the Signal and Reference spectra in
the bin $0<p_{\mathrm{T,jet}}^{\mathrm{reco,ch}}<1$ $\mathrm{GeV/}c$, as shown
by the arrow in Fig. 2, right panels. The value of $c_{\mathrm{Ref}}$ lies
between 0.92 and 0.99 for the various spectra. Additional variation in the
value of $c_{\mathrm{Ref}}$ was used to assess systematic uncertainties.

We note that the $\mathrm{TT}_{\mathrm{Ref}}$ distribution includes correlated
recoil jet yield, so that the subtraction in Eq. 5 removes both the trigger-
uncorrelated yield and the $\mathrm{TT}_{\mathrm{Ref}}$-correlated yield. The
$\Delta_{\mathrm{recoil}}$ observable is therefore a differential, not
absolute, measurement of the recoil spectrum [9], though the
$\mathrm{TT}_{\mathrm{Ref}}$ component is significantly smaller than that in
the $\mathrm{TT}_{\mathrm{Sig}}$ component over most of the
$p_{\mathrm{T,jet}}^{\mathrm{reco,ch}}$ range. The $\Delta_{\mathrm{recoil}}$
distributions in Fig. 2 lie significantly below the TT-specific distributions
for $p_{\mathrm{T,jet}}^{\mathrm{reco,ch}}<5$ $\mathrm{GeV/}c$ but agree with
the TT{12,50} distribution within 15% for
$p_{\mathrm{T,jet}}^{\mathrm{reco,ch}}>15$ $\mathrm{GeV/}c$. These features
indicate that the region of negative and small positive
$p_{\mathrm{T,jet}}^{\mathrm{reco,ch}}$ is dominated by uncorrelated jet
yield, while the region for large positive
$p_{\mathrm{T,jet}}^{\mathrm{reco,ch}}$ is dominated by recoil jet yield that
is correlated with $\mathrm{TT}_{\mathrm{Sig}}$.

One contribution to uncorrelated background is jet yield due to Multiple
Partonic Interactions (MPI), which can occur when two independent high-$Q^{2}$
interactions in the same p–Pb collision generate the trigger hadron and a jet
in the recoil acceptance. Since the two interactions are independent, the
recoil jet distribution generated by MPI will be independent of
$p_{\mathrm{T,trig}}$, by definition, and will be removed from
$\Delta_{\mathrm{recoil}}$ by the subtraction. No correction of
$\Delta_{\mathrm{recoil}}$ for the contribution of MPI is therefore needed in
the analysis.

The raw $\Delta_{\mathrm{recoil}}$ distributions, such as those in Fig. 2,
must still be corrected for jet momentum smearing due to instrumental effects
and local background fluctuations, and for jet reconstruction efficiency. Jet
quenching effects are measured by comparing the corrected
$\Delta_{\mathrm{recoil}}$ distributions for different EA classes, and at
different $R$.

##  6 Corrections

Corrections for instrumental effects and local background fluctuations are
carried out using unfolding methods [107, 108, 109]. The measured distribution
$\Delta^{\mathrm{M}}_{\mathrm{recoil}}$ is related to the true distribution
$\Delta^{\mathrm{T}}_{\mathrm{recoil}}$ by a linear transformation,

$\Delta^{\mathrm{M}}_{\mathrm{recoil}}(p_{\mathrm{T,jet}}^{\mathrm{reco,ch}})=R_{\mathrm{full}}(p_{\mathrm{T,jet}}^{\mathrm{reco,ch}},p_{\mathrm{T,jet}}^{\mathrm{part}})\otimes\left[\mathrm{eff}(p_{\mathrm{T,jet}}^{\mathrm{part}})\cdot\Delta^{\mathrm{T}}_{\mathrm{recoil}}(p_{\mathrm{T,jet}}^{\mathrm{part}})\right],$
(6)

where $\mathrm{eff}(p_{\mathrm{T,jet}}^{\mathrm{part}})$ is the jet
reconstruction efficiency and $R_{\mathrm{full}}$ is the cumulative response
matrix excluding jet reconstruction efficiency. The explicit specification of
jet reconstruction efficiency in this expression, distinct from the unfolding
step, makes interpretation of the unfolding procedure more transparent.
$R_{\mathrm{full}}$($p_{\mathrm{T,jet}}^{\mathrm{reco,ch}}$,$p_{\mathrm{T,jet}}^{\mathrm{part}}$)
is further assumed to factorize as the product of separate response matrices
for background fluctuations and instrumental response,

$R_{\mathrm{full}}(p_{\mathrm{T,jet}}^{\mathrm{reco,ch}},p_{\mathrm{T,jet}}^{\mathrm{part}})=R_{\mathrm{bkgd}}(p_{\mathrm{T,jet}}^{\mathrm{reco,ch}},p_{\mathrm{T,jet}}^{\mathrm{det}})\otimes
R_{\mathrm{instr}}(p_{\mathrm{T,jet}}^{\mathrm{det}},p_{\mathrm{T,jet}}^{\mathrm{part}}).$
(7)

The matrix $R_{\mathrm{full}}$ can be close to singular, in which case the
solution of Eq. 6 via direct inversion of $R_{\mathrm{full}}$ generates large
fluctuations in central values and large variance due to the statistical
variation in
$\Delta^{\mathrm{M}}_{\mathrm{recoil}}$($p_{\mathrm{T,jet}}^{\mathrm{reco,ch}}$)
and $R_{\mathrm{full}}$ [107]. An approximate solution of Eq. 6 that is
physically more meaningful is obtained by regularized unfolding, which imposes
a smoothness constraint on the solution. Unfolding in this analysis is carried
out using approaches based on Singular Value Decomposition (SVD) [108] and on
Bayes’ Theorem [109], as implemented in the RooUnfold package [110].

The instrumental response matrix, $R_{\mathrm{instr}}$, is calculated from the
simulated detector response applied to events generated by PYTHIA for pp
collisions at $\sqrt{s}=5.02$ TeV. Jets at the particle-level and detector-
level are matched in $(\eta,\phi)$ space by selecting the detector-level jet
that is closest to the particle-level jet, and vice versa. An entry in
$R_{\mathrm{instr}}$ is made for every matched pair. The $R_{\mathrm{instr}}$
matrix is normalized such that, for each bin in
$p_{\mathrm{T,jet}}^{\mathrm{part}}$, the sum over all bins in
$p_{\mathrm{T,jet}}^{\mathrm{det}}$ is unity. In practice, however, the
matching probability is less than unity, which is accounted for in Eq. 6 by
the efficiency factor $\mathrm{eff}(p_{\mathrm{T,jet}}^{\mathrm{part}})$. No
dependence of $R_{\mathrm{instr}}$ on EA of the p–Pb event population was
observed.

The background response matrix, $R_{\mathrm{bkgd}}$, is calculated by
embedding single tracks with transverse momentum $p_{\mathrm{T}}^{\rm{embed}}$
into real p–Pb events that contain a TT [9]. The relative azimuthal angle
between the embedded track and the TT is in the range
$\left[\pi/4,\,3\pi/4\right]$, to minimize overlap of the embedded track with
the jet containing TT and with true recoil jets. These hybrid events are
analyzed with the same procedures used for real data, and the jet containing
the embedded track is identified. Smearing of jet candidate $p_{\mathrm{T}}$
due to background fluctuations is quantified by the distribution of

$\delta{p_{\mathrm{T}}}\
=p_{\mathrm{T,jet}}^{\mathrm{reco,ch}}-p_{\mathrm{T}}^{\rm{embed}},$ (8)

where $p_{\mathrm{T,jet}}^{\mathrm{reco,ch}}$ refers to the jet containing the
embedded track. $R_{\mathrm{bkgd}}$, the probability distribution of
$\delta{p_{\mathrm{T}}}$, is calculated separately for the MB population and
for the various event populations selected by EA. Embedding of PYTHIA-
generated jets rather than single tracks yields very similar
$\delta{p_{\mathrm{T}}}$ distributions.

Unfolding follows the procedure described in [23]. The input to unfolding is
the measured distribution
$\Delta^{\mathrm{M}}_{\mathrm{recoil}}$($p_{\mathrm{T,jet}}^{\mathrm{reco,ch}}$)
in the range $1<p_{\mathrm{T,jet}}^{\mathrm{reco,ch}}<90$ $\mathrm{GeV/}c$.
The unfolding procedure requires specification of a prior distribution. For
the primary analysis, the prior is the $\Delta_{\mathrm{recoil}}$ distribution
calculated with PYTHIA8 tune 4C [90] for pp collisions at $\sqrt{s}=5.02$ TeV.
Regularization of SVD unfolding utilizes a statistical test to determine the
transition between random fluctuations and statistically significant
components of the $d$-vector [108], which is achieved typically with
regularization parameter $k=4$. For regularization of Bayesian unfolding,
convergence is determined by the stability of the unfolded solution for
successive iterations, which is achieved typically by the second iteration.

For both unfolding approaches, consistency of the solution is checked by
backfolding, i.e. smearing the unfolded distribution with $R_{\mathrm{full}}$
and comparing the result with the $\Delta^{\mathrm{M}}_{\mathrm{recoil}}$
distribution. Since regularization suppresses oscillating components of the
solution, the backfolded and $\Delta^{\mathrm{M}}_{\mathrm{recoil}}$
distributions will in general not be identical. Consistency of unfolding is
imposed by requiring that the difference between the backfolded and
$\Delta^{\mathrm{M}}_{\mathrm{recoil}}$ distributions in each bin be less than
3$\sigma$, based on $\Delta^{\mathrm{M}}_{\mathrm{recoil}}$ statistical
errors; otherwise, the solution is rejected.

Closure of the unfolding procedure was verified by a test in which the
response matrix, the $\Delta_{\mathrm{recoil}}$ distribution, and the prior
were generated by statistically independent sets of PYTHIA-generated events
for pp collisions at $\sqrt{s}=5.02$ TeV. The response matrix and the spectrum
were generated using PYTHIA6 Perugia-11, while the prior was generated using
PYTHIA8 tune 4C. The $\Delta_{\mathrm{recoil}}$ distribution from this test
agrees with the input particle-level distribution to better than 5%.

Correction for jet reconstruction efficiency is applied after the unfolding
step by scaling the unfolded $\Delta_{\mathrm{recoil}}$ distribution by
$1/\mathrm{eff}(p_{\mathrm{T,jet}}^{\mathrm{ch}})$. The value of
$\mathrm{eff}(p_{\mathrm{T,jet}}^{\mathrm{ch}})$ is 0.96 at
$p_{\mathrm{T,jet}}^{\mathrm{ch}}=15$ $\mathrm{GeV/}c$ and 0.98 at
$p_{\mathrm{T,jet}}^{\mathrm{ch}}=60$ $\mathrm{GeV/}c$.

##  7 Systematic uncertainties

The systematic uncertainties of the $\Delta_{\mathrm{recoil}}$ distribution
are assessed by varying the components of the correction procedure. The most
significant systematic uncertainties are due to the following:

  * •

Regularization of unfolding: for SVD, vary $k$ by $\pm 2$ relative to its
value in the primary analysis; for Bayesian unfolding, use the first three
iterations;

  * •

Unfolding prior: generate prior distributions with PYTHIA6 and PYTHIA8; for
additional variation take the difference between the priors from the two
PYTHIA versions and vary them by its magnitude but with opposite sign; use the
unfolded solution based on the iterative Bayesian approach as prior for SVD-
based unfolding;

  * •

Binning of distributions: use three different choices of binning, with
corresponding variation in spectrum limits;

  * •

Calculation of $\rho$: utilize a modified procedure [96, 65, 81] that accounts
for sparse regions of the event, instead of the area-based approach (Eq. 1);

  * •

$c_{\mathrm{Ref}}$ variation: use as upper limit $c_{\mathrm{Ref}}=1$, in
which the reference recoil jet spectrum is not scaled. For the lower limit,
double the value of $(1-c_{\mathrm{Ref}})$ from the primary analysis, giving
$c_{\mathrm{Ref}}=0.95$ for $R=0.2$ and $c_{\mathrm{Ref}}=0.90$ for $R=0.4$.
The systematic uncertainty band corresponds to the largest deviation from all
such variations of the unfolded spectrum, relative to the spectrum resulting
from the $c_{\mathrm{Ref}}$ choice of the primary analysis;

  * •

Tracking efficiency: vary $\pm 4$% relative to nominal value [65];

  * •

Track momentum resolution: extract systematic uncertainty of momentum
resolution from azimuthal variation of the inclusive charged-track
distribution; vary $R_{\mathrm{instr}}$ accordingly.

The correction for secondary vertex tracks due to weak decays makes a smaller
contribution to the systematic uncertainty than the above sources.

There is a difference in the response matrix for different selections of EA,
due to the different magnitude of uncorrelated background induced by such a
selection. The correction procedure accounts for this difference. However,
there may be a residual correlation between the EA-bias and TT-bias in the
calculation of the response. This correlation was explored by calculating the
response matrix with the appropriate EA-selected data, both with and without
TT-bias. The corrected spectra resulting from the two response matrices differ
by less than 2% for all $p_{\mathrm{T,jet}}^{\mathrm{ch}}$, $R$, and EA-
selection. This is however a check, not a systematic uncertainty, since the
response matrix for the analysis is properly calculated using the TT-bias, and
it does not contribute to the systematic uncertainty of the measurement.

The EA-bias induced by the TT{6,7} and TT{12,50} requirements are similar, and
the $\delta{p_{\mathrm{T}}}$ distributions generated for events with the two
TT requirements are likewise similar. This variation in the
$\delta{p_{\mathrm{T}}}$ distribution generates variation of less that 1% in
the corrected spectrum, after unfolding.

Statistical fluctuations of the raw data influence the quantitative assessment
of the systematic uncertainties arising from these sources. We utilize the
following procedure to minimize such effects. For each source of uncertainty,
several randomized instances of the raw $\Delta_{\mathrm{recoil}}$ spectrum
are generated by variation about the measured central value in each bin using
a Gaussian distribution, with $\sigma$ equal to the uncorrelated statistical
error in the bin. Each randomized instance is analyzed using (i) corrections
for the primary analysis (see Sect. 6), and (ii) corrections that include a
systematic variation. For each randomized instance, the ratio of corrected
$\Delta_{\mathrm{recoil}}$ spectra resulting from (ii) and (i) is formed. The
systematic uncertainty in each $p_{\mathrm{T,jet}}^{\mathrm{ch}}$-bin is
defined as the median of the distribution of ratios obtained from all
randomized instances.

|  $\Delta_{\mathrm{recoil}}$ syst. uncert. (%) |  $\Delta_{\mathrm{recoil}}$ syst. uncert. (%)
---|---|---
| ZNA 0–20% | ZNA 50–100%
$p_{\mathrm{T,jet}}^{\mathrm{ch}}$ | 15–20 $\mathrm{GeV/}c$ | 40–50 $\mathrm{GeV/}c$ | 15–20 $\mathrm{GeV/}c$ | 40–50 $\mathrm{GeV/}c$
$R$ | 0.2 | 0.4 | 0.2 | 0.4 | 0.2 | 0.4 | 0.2 | 0.4
Unfolding algorithm | $<1$ | 1.7 | 1.8 | 4.8 | 1.4 | 1.4 | 1.1 | $<1$
Unfolding prior | 0.5 | 0.2 | 1.7 | 0.5 | 0.2 | 1.2 | 1.5 | 1.2
Binning of raw spectrum | 1.1 | 2.4 | 0.5 | 1.2 | 1.0 | 1.2 | 2.1 | 2.2
$\rho$ estimator | 0.2 | 2.7 | 0.9 | 0.2 | 0.8 | 2.8 | 2.0 | 4.4
$c_{\mathrm{Ref}}$ | 2.3 | 3.6 | 1.7 | 0.5 | 1.4 | 0.9 | 1.7 | 1.3
Track reconstruction efficiency | 4.7 | 3.3 | 9.0 | 11 | 4.8 | 4.2 | 10 | 11
Track $p_{\mathrm{T}}$ resolution | 0.6 | 0.6 | 1.0 | 1.7 | 0.6 | 0.6 | 1.0 | 1.7
Weak decays | $<1$ | $<1$ | $<1$ | $<1$ | $<1$ | $<1$ | $<1$ | $<1$
Cumulative | 5.4 | 6.3 | 9.6 | 12 | 5.4 | 5.6 | 11 | 12
Table 1: Contributions to the relative systematic uncertainty of the
$\Delta_{\mathrm{recoil}}$ distribution for $R=0.2$ and 0.4 in EA-biased
events based on ZNA.

Table 1 gives representative systematic uncertainties for $R=0.2$ and $R=0.4$
in EA-biased events based on ZNA. The cumulative systematic uncertainty is
calculated by adding contributions from all systematic sources in quadrature.
For $p_{\mathrm{T,jet}}^{\mathrm{ch}}=15$–20 $\mathrm{GeV/}c$, several
components contribute with similar magnitude. For
$p_{\mathrm{T,jet}}^{\mathrm{ch}}=40$–50 $\mathrm{GeV/}c$, the cumulative
uncertainty is due predominantly to the uncertainty in tracking efficiency.
Similar uncertainties are obtained for event selection using the EA bias based
on V0A.

The systematic uncertainty of the ratio of $\Delta_{\mathrm{recoil}}$
distributions was obtained similarly, taking into account the correlated
uncertainties of numerator and denominator.

##  8 Results

Figure 3: Corrected $\Delta_{\mathrm{recoil}}$ distributions measured for p–Pb
collisions at $\sqrt{s_{\rm NN}}=5.02$ TeV, for the MB and EA-selected
populations. The acceptance for TT and recoil jets in the CM frame are denoted
$y^{*}_{\mathrm{TT}}$ and $y^{*}_{\mathrm{jet}}$, respectively. Left panels:
$R=0.2$; right panels: $R=0.4$. Also shown are $\Delta_{\mathrm{recoil}}$
distributions for pp collisions at $\sqrt{s}=5.02$ TeV simulated by PYTHIA 6
Tune Perugia 11 and PYTHIA 8 Tune 4C. The solid line in the upper panels is
the fit of an exponential function to the p–Pb distribution, with fit
parameters as specified. Lower panels: ratio of p–Pb MB and pp distributions
to the fit function.

Figure 3, upper panels, show the corrected $\Delta_{\mathrm{recoil}}$
distributions for p–Pb collisions at $\sqrt{s_{\rm NN}}=5.02$ TeV for the MB
event population and for populations selected by EA using ZNA and V0A, and for
pp collisions at $\sqrt{s}=5.02$ TeV simulated by PYTHIA. The upper panels
also show the result of a fit to the p–Pb MB distributions by an exponential
function, $a\cdot\exp\left(-p_{\mathrm{T,jet}}^{\mathrm{ch}}/b\right)$.

The PYTHIA-generated distributions for pp collisions are presented only for
comparison and are not utilized in the jet quenching analysis. Figure 3, lower
panels, show the ratio of the measured p–Pb MB and PYTHIA-generated pp
distributions to the fit distribution. The central values of the PYTHIA-
generated distributions for pp collisions lie below those of the p–Pb data,
with a difference of 25% for $p_{\mathrm{T,jet}}^{\mathrm{ch}}<20$
$\mathrm{GeV/}c$. PYTHIA 8 tune 4C agrees better with the p–Pb data, notably
at the highest $p_{\mathrm{T,jet}}^{\mathrm{ch}}$ and for $R=0.4$. For pp
collisions at $\sqrt{s}=7$ TeV, PYTHIA-generated $\Delta_{\mathrm{recoil}}$
distributions have central values that are in good agreement with data [9]. We
note, however, that the $p_{\mathrm{T,trig}}$ intervals in the two analyses
are different: this analysis uses TT{12,50}, whereas that in ref. [9] used
TT{20,50}. Reanalysis of the pp 7 TeV data with the trigger selection
TT{12,50} shows a similar level of agreement with PYTHIA as that seen in Fig.
3 [106]. We also note that at present there are significant uncertainties in
the light hadron fragmentation functions at LHC energies [91, 92], which may
affect hadron trigger selection in the PYTHIA calculation and thereby
contribute to the differences between PYTHIA and data seen in the figure.

The $\Delta_{\mathrm{recoil}}$ distributions for EA-selected event populations
and for the MB population shown in Fig. 3 are all qualitatively similar.
Measurement of the dependence of the $\Delta_{\mathrm{recoil}}$ distribution
on EA selection is therefore carried out using the ratios of such
distributions, denoted $R_{\mathrm{EA}}$, to maximize the sensitivity to
variations with EA.

Figure 4: Ratio of $\Delta_{\mathrm{recoil}}$ distributions for events with
high and low EA measured in p–Pb collisions at $\sqrt{s_{\rm NN}}=5.02$ TeV.
Left panels: V0A 0–20% / 50–100%; right panels: ZNA 0–20% / 50–100%. Upper
panels: $R=0.2$; lower panels: $R=0.4$. The grey boxes show the systematic
uncertainty of the ratio, which takes into account the correlated uncertainty
of numerator and denominator. The red line indicates the ratio for a
$p_{\mathrm{T}}$-shift of the high-EA distribution of $-0.4$ $\mathrm{GeV/}c$.

Figure 4 shows ratios of the $\Delta_{\mathrm{recoil}}$ distributions for EA-
selected event populations, with $R=0.2$ and 0.4. Since the numerator and
denominator come from different, exclusive intervals in EA, they are
statistically independent in each panel. However, some systematic
uncertainties are correlated between numerator and denominator, which has been
taken into account in the systematic uncertainty of the ratio. Note that the
same dataset is used for $R=0.2$ and $R=0.4$, and for the ZNA and V0A
selections, so that the results shown in the different panels are correlated.

Jet quenching may result in transport of jet energy out of the jet cone,
resulting in suppression of the $\Delta_{\mathrm{recoil}}$ distribution at
fixed $p_{\mathrm{T,jet}}^{\mathrm{ch}}$. Under the assumptions (i) that jet
quenching is more likely to occur in events with larger EA, and (ii) that the
hadron trigger bias is independent of EA (Sect. 5), this effect corresponds to
suppression below unity of the ratios in Fig. 4. However, in all panels the
ratio is consistent with unity within the statistical error and the systematic
uncertainty at all $p_{\mathrm{T,jet}}^{\mathrm{ch}}$, indicating that jet
quenching effects are negligible relative to the uncertainties.

These data can nevertheless provide a limit on the magnitude of medium-induced
energy transport to large angles. In order to extract a limit, we parameterize
the 0–20% and 50–100% EA-selected $\Delta_{\mathrm{recoil}}$ distributions
with the exponential function used in Fig. 3, and assume that the slope
parameter $b$ is the same for the two distributions. We also assume that the
average magnitude of energy transported out-of-cone is independent of
$p_{\mathrm{T,jet}}^{\mathrm{ch}}$, which is consistent with the observation
that the ratios $R_{\mathrm{EA}}$ in Fig. 4 are independent of
$p_{\mathrm{T,jet}}^{\mathrm{ch}}$ within uncertainties. The assumption that
the average magnitude of out-of-cone radiation is independent of
$p_{\mathrm{T,jet}}^{\mathrm{ch}}$ is likewise consistent with
$\Delta_{\mathrm{recoil}}$ measurements in Pb–Pb collisions at 2.76 TeV [9].
Consideration of a more complex dependence on
$p_{\mathrm{T,jet}}^{\mathrm{ch}}$ is beyond the scope of this
phenomenological study.

The ratios $R_{\mathrm{EA}}$ are then expressed in terms of an average shift
$\bar{s}$ in $p_{\mathrm{T,jet}}^{\mathrm{ch}}$ between low and high EA
events, where $\bar{s}=-b\cdot\ln\left(R_{\mathrm{EA}}\right)$. Fits to
$\Delta_{\mathrm{recoil}}$ for $R=0.4$ over the range
$15<p_{\mathrm{T,jet}}^{\mathrm{ch}}<50$ $\mathrm{GeV/}c$ give $b=9.26\pm
0.33$ $\mathrm{GeV/}c$ for 50–100% ZNA and $b=9.05\pm 0.30$ $\mathrm{GeV/}c$
for 50–100% V0A. Fits to the ratios in Fig. 4 then give $\bar{s}=(-0.12\pm
0.35_{\rm{stat}}\pm 0.03_{\rm{syst}})$ $\mathrm{GeV/}c$ for 0–20% ZNA, and
$\bar{s}=(-0.06\pm 0.34_{\rm{stat}}\pm 0.02_{\rm{syst}})$ $\mathrm{GeV/}c$ for
0–20% V0A, both of which are consistent with zero within uncertainties. Fits
to narrower ranges in $p_{\mathrm{T,jet}}^{\mathrm{ch}}$ give similar results.

These values are to be compared with the shift $\bar{s}=(8\pm 2_{\rm{stat}})$
$\mathrm{GeV/}c$ measured in central Pb–Pb collisions at $\sqrt{s_{\rm NN}}\
=2.76$ TeV for $R=0.5$ [9], indicating significant medium-induced energy
transport to large angles in that collision system. This comparison of out-of-
cone energy transport in p–Pb and Pb–Pb collisions supports theoretical
calculations which predict much smaller jet quenching effects in p–Pb relative
to Pb–Pb collisions [52, 54], and disfavors the calculation which predicts
strong jet quenching in small systems [53].

The measured value of $\bar{s}$ provides a constraint on the magnitude of out-
of-cone energy transport due to jet quenching in p–Pb collisions. We calculate
this constraint as the linear sum of the central value of $\bar{s}$, the one-
sided 90% confidence upper limit of its statistical error, and the absolute
value of its systematic uncertainty. For jets with $R=0.4$ in the range
$15<p_{\mathrm{T,jet}}^{\mathrm{ch}}<50$ $\mathrm{GeV/}c$, the medium-induced
charged energy transport out of the jet cone for events with high V0A or high
ZNA is less than 0.4 $\mathrm{GeV/}c$, at 90% confidence. The red line in each
panel of Fig. 4 shows the ratio for a $p_{\mathrm{T}}$-shift of $-0.4$
$\mathrm{GeV/}c$ of the high-EA distribution relative to the low-EA
distribution.

##  9 Comparison to other measurements

The EA-selected $\Delta_{\mathrm{recoil}}$ distribution ratios in Fig. 4 are
consistent with unity in the range $15<p_{\mathrm{T,jet}}^{\mathrm{ch}}<50$
$\mathrm{GeV/}c$. These distributions therefore have no significant dependence
on EA, in agreement with inclusive jet measurements for p–Pb collisions at
$\sqrt{s_{\rm NN}}=5.02$ TeV by ALICE [68], but in contrast to such
measurements in d–Au collisions at $\sqrt{s_{\rm NN}}=200$ GeV by PHENIX [59]
and in p–Pb collisions at $\sqrt{s_{\rm NN}}=5.02$ TeV by ATLAS [66], which
exhibit strong dependence on EA. In this section we explore whether these
inclusive and coincidence measurements can provide a consistent picture of jet
quenching in asymmetric systems.

We first note that these measurements differ in several aspects, and that
their detailed comparison requires calculations based on theoretical models of
jet quenching that are beyond the scope of this paper. Here we explore a more
limited question, whether the inclusive jet measurements are also consistent
with a $p_{\mathrm{T}}$-independent limit of out-of-cone charged-energy
transport of 0.4 $\mathrm{GeV/}c$. Since the ALICE inclusive jet measurement
does not find an EA-dependence, it is consistent with such a limit by
construction, without the need for additional calculation. We therefore focus
in the rest of this section on comparison to the PHENIX and ATLAS inclusive
jet measurements.

To do so, we compare the effect of a $p_{\mathrm{T}}$-independent shift of the
inclusive spectra to the measured EA-dependence of $R_{\mathrm{CP}}$, which is
the ratio of inclusive jet spectra for event populations identified as
“central” and “peripheral”, with the spectra scaled by
$\left<T_{\mathrm{pPb}}\right>$ or $\left<T_{\mathrm{dAu}}\right>$. Since the
inclusive spectra are measured with fully-reconstructed jets, including
neutral energy, we increase the 90% confidence limit for out-of-cone energy
transport to the value 0.6 $\mathrm{GeV/}c$. Note in addition that the choice
of percentile binning differs in the various measurements, which cannot be
accounted for directly in the comparison; this difference should also be borne
in mind when comparing the measurements.

The effect of the $p_{\mathrm{T}}$-shift on jet yield depends upon the
inclusive spectrum shape. In order to assess this effect we select a
representative spectrum for each data set, impose a
$p_{\mathrm{T}}$-independent shift of $-0.6$ $\mathrm{GeV/}c$ on this
spectrum, and calculate $R_{\mathrm{CP}}^{*}$, the ratio of distributions with
and without the shift. Since $R_{\mathrm{CP}}^{*}$ represents a 90% confidence
limit, no uncertainty is assigned to it.

Collision system | Comparison of spectra |  $p_{\mathrm{T,jet}}$ $\left(\mathrm{GeV/}c\right)$ |  $R_{\mathrm{CP}}$ or $R_{\mathrm{CP}}^{*}$
---|---|---|---
d–Au, pp $\sqrt{s_{\rm NN}}=0.2$ TeV [59] | d–Au 0–20%/60–88% | 16 | $R_{\mathrm{CP}}=0.71\pm 0.01_{\rm{stat}}\pm 0.03_{\rm{sys}}$
32 | $R_{\mathrm{CP}}=0.54\pm 0.04_{\rm{stat}}\pm 0.06_{\rm{sys}}$
pp w/wo $-0.6$ $\mathrm{GeV/}c$ shift | 15 | $R_{\mathrm{CP}}^{*}=0.79$
30 | $R_{\mathrm{CP}}^{*}=0.85$
p–Pb $\sqrt{s_{\rm NN}}=5.02$ TeV $y^{*}=0.3$ [66] | p–Pb 0–10%/60–90% | 57 | $R_{\mathrm{CP}}=1.09\pm 0.02_{\rm{stat}}\pm 0.03_{\rm{sys}}$
113 | $R_{\mathrm{CP}}=0.93\pm 0.01_{\rm{stat}}\pm 0.02_{\rm{sys}}$
p–Pb MB w/wo $-0.6$ $\mathrm{GeV/}c$ shift | 50 | $R_{\mathrm{CP}}^{*}=0.95$
110 | $R_{\mathrm{CP}}^{*}=0.97$
Table 2: Comparison of $R_{\mathrm{CP}}$ and $R_{\mathrm{CP}}^{*}$ for
inclusive jet production in asymmetric collisions at RHIC and the LHC. See
text for details.

Table 2 compares $R_{\mathrm{CP}}^{*}$ to the values of EA-selected
$R_{\mathrm{CP}}$ measured in asymmetric collisions at RHIC and the LHC. While
some values are in agreement, $R_{\mathrm{CP}}^{*}$ and $R_{\mathrm{CP}}$ have
opposite dependence on $p_{\mathrm{T,jet}}$ for both datasets. Within the
limits of this comparison, a $p_{\mathrm{T,jet}}^{\mathrm{ch}}$-independent
out-of-cone charged-energy transport of 0.4 $\mathrm{GeV/}c$ is not consistent
with measurements of $R_{\mathrm{CP}}$ for inclusive jet production in EA-
selected d–Au collisions at RHIC and p–Pb collisions at the LHC.

Effects other than jet quenching in the final state can modify jet yields in
nuclear collisions, in particular the initial-state effects of shadowing and
energy loss in cold matter [111, 55]. In addition, calculation of the Glauber
scaling factor for inclusive measurements may be affected by fluctuations and
dynamical correlations between a high $Q^{2}$ process and the soft observables
used to tag EA [56, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80]. Such non-
quenching effects could be the origin of the inconsistency observed here in
the EA-dependence of inclusive and coincidence jet measurements, since
inclusive and coincidence observables have different sensitivity to initial-
state effects, and Glauber scaling is required only for inclusive observables.

##  10 Summary

We have reported measurements of the semi-inclusive distribution of charged
jets recoiling from a high-$p_{\mathrm{T}}$ hadron trigger in p–Pb collisions
at $\sqrt{s_{\rm NN}}=5.02$ TeV, selected by event activity in forward (Pb-
going) charged multiplicity and zero-degree neutral energy. Interpretation of
this coincidence observable does not require the assumption that event
activity is correlated with collision geometry, with the corresponding
uncertainties of Glauber modeling. It provides a new probe of jet quenching in
p–Pb collisions that is systematically different from quenching measurements
based on inclusive jet production.

No significant difference is observed in recoil jet distributions for
different event activity. This measurement provides a limit on out-of-cone
energy transport due to jet quenching in p–Pb collisions at the LHC of less
that 0.4 $\mathrm{GeV/}c$ at 90% confidence for jet radius $R=0.4$. Comparison
of this measurement to theoretical calculations favors models with little or
no energy loss in small systems. Comparison to inclusive jet measurements in
small systems at RHIC and LHC indicates that the inclusive jet yield
modification observed in EA-selected populations is not consistent with jet
quenching. Future p–Pb measurements at the LHC, with higher statistical
precision and greater kinematic reach, will provide more stringent limits on
jet quenching in light systems.

## 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, Education and Sport and Croatian Science Foundation,
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. Acharyaorg136&D. Adamováorg92&J. Adolfssonorg33&M.M. Aggarwalorg97&G.
Aglieri Rinellaorg34&M. Agnelloorg30&N. Agrawalorg46&Z. Ahammedorg136&S.U.
Ahnorg76&S. Aiolaorg141&A. Akindinovorg62&M. Al-Turanyorg104&S.N.
Alamorg136&D.S.D. Albuquerqueorg121&D. Aleksandrovorg87&B. Alessandroorg56&R.
Alfaro Molinaorg71&Y. Aliorg15&A. Aliciorg11,org26,org51&A. Alkinorg3&J.
Almeorg21&T. Altorg68&L. Altenkamperorg21&I. Altsybeevorg135&C. Andreiorg84&D.
Andreouorg34&H.A. Andrewsorg108&A. Andronicorg104&M. Angelettiorg34&V.
Anguelovorg102&C. Ansonorg95&T. Antičićorg105&F. Antinoriorg54&P.
Antonioliorg51&N. Apadulaorg79&L. Aphecetcheorg112&H. Appelshäuserorg68&S.
Arcelliorg26&R. Arnaldiorg56&O.W. Arnoldorg103,org116&I.C. Arseneorg20&M.
Arslandokorg102&B. Audurierorg112&A. Augustinusorg34&R. Averbeckorg104&M.D.
Azmiorg16&A. Badalàorg53&Y.W. Baekorg75,org58&S. Bagnascoorg56&R.
Bailhacheorg68&R. Balaorg99&A. Baldisseriorg72&M. Ballorg43&R.C.
Baralorg65,org85&A.M. Barbanoorg25&R. Barberaorg27&F. Barileorg32&L.
Barioglioorg25&G.G. Barnaföldiorg140&L.S. Barnbyorg91&V. Barretorg130&P.
Bartaliniorg7&K. Barthorg34&E. Bartschorg68&N. Bastidorg130&S. Basuorg138&G.
Batigneorg112&B. Batyunyaorg74&P.C. Batzingorg20&J.L. Bazo Albaorg109&I.G.
Beardenorg88&H. Beckorg102&C. Beddaorg61&N.K. Beheraorg58&I. Belikovorg132&F.
Belliniorg34,org26&H. Bello Martinezorg2&R. Bellwiedorg123&L.G.E.
Beltranorg119&V. Belyaevorg90&G. Bencediorg140&S. Beoleorg25&A.
Bercuciorg84&Y. Berdnikovorg94&D. Berenyiorg140&R.A. Bertensorg126&D.
Berzanoorg56,org34&L. Betevorg34&P.P. Bhaduriorg136&A. Bhasinorg99&I.R.
Bhatorg99&B. Bhattacharjeeorg42&J. Bhomorg117&A. Bianchiorg25&L.
Bianchiorg123&N. Bianchiorg49&C. Bianchinorg138&J. Bielčíkorg37&J.
Bielčíkováorg92&A. Bilandzicorg116,org103&G. Biroorg140&R. Biswasorg4&S.
Biswasorg4&J.T. Blairorg118&D. Blauorg87&C. Blumeorg68&G. Bocaorg133&F.
Bockorg34&A. Bogdanovorg90&L. Boldizsárorg140&M. Bombaraorg38&G.
Bonomiorg134&M. Bonoraorg34&H. Borelorg72&A. Borissovorg18,org102&M.
Borriorg125&E. Bottaorg25&C. Bourjauorg88&L. Bratrudorg68&P. Braun-
Munzingerorg104&M. Bregantorg120&T.A. Brokerorg68&M. Brozorg37&E.J.
Bruckenorg44&E. Brunaorg56&G.E. Brunoorg34,org32&D. Budnikovorg106&H.
Bueschingorg68&S. Bufalinoorg30&P. Buhlerorg111&P. Buncicorg34&O.
Buschorg129&Z. Butheleziorg73&J.B. Buttorg15&J.T. Buxtonorg17&J.
Cabalaorg114&D. Caffarriorg34,org89&H. Cainesorg141&A. Calivaorg104,org61&E.
Calvo Villarorg109&R.S. Camachoorg2&P. Cameriniorg24&A.A. Caponorg111&F.
Carenaorg34&W. Carenaorg34&F. Carnesecchiorg11,org26&J. Castillo
Castellanosorg72&A.J. Castroorg126&E.A.R. Casulaorg52&C. Ceballos
Sanchezorg9&S. Chandraorg136&B. Changorg124&W. Changorg7&S. Chapelandorg34&M.
Chartierorg125&S. Chattopadhyayorg136&S. Chattopadhyayorg107&A.
Chauvinorg103,org116&C. Cheshkovorg131&B. Cheynisorg131&V. Chibante
Barrosoorg34&D.D. Chinellatoorg121&S. Choorg58&P. Chochulaorg34&M.
Chojnackiorg88&S. Choudhuryorg136&T. Chowdhuryorg130&P.
Christakoglouorg89&C.H. Christensenorg88&P. Christiansenorg33&T.
Chujoorg129&S.U. Chungorg18&C. Cicaloorg52&L. Cifarelliorg11,org26&F.
Cindoloorg51&J. Cleymansorg98&F. Colamariaorg50,org32&D.
Colellaorg63,org34,org50&A. Colluorg79&M. Colocciorg26&M. Concasorg56orgI&G.
Conesa Balbastreorg78&Z. Conesa del Valleorg59&J.G. Contrerasorg37&T.M.
Cormierorg93&Y. Corrales Moralesorg56&I. Cortés Maldonadoorg2&P.
Corteseorg31&M.R. Cosentinoorg122&F. Costaorg34&S. Costanzaorg133&J.
Crkovskáorg59&P. Crochetorg130&E. Cuautleorg69&L. Cunqueiroorg139,org93&T.
Dahmsorg116,org103&A. Daineseorg54&M.C. Danischorg102&A. Danuorg66&D.
Dasorg107&I. Dasorg107&S. Dasorg4&A. Dashorg85&S. Dashorg46&S. Deorg47&A. De
Caroorg29&G. de Cataldoorg50&C. de Contiorg120&J. de Cuvelandorg40&A. De
Falcoorg23&D. De Gruttolaorg11,org29&N. De Marcoorg56&S. De Pasqualeorg29&R.D.
De Souzaorg121&H.F. Degenhardtorg120&A. Deistingorg104,org102&A.
Delofforg83&S. Delsantoorg25&C. Deplanoorg89&P. Dhankherorg46&D. Di
Bariorg32&A. Di Mauroorg34&P. Di Nezzaorg49&B. Di Ruzzaorg54&T. Dietelorg98&P.
Dillensegerorg68&Y. Dingorg7&R. Diviàorg34&Ø. Djuvslandorg21&A. Dobrinorg34&D.
Domenicis Gimenezorg120&B. Dönigusorg68&O. Dordicorg20&L.V.R.
Doremalenorg61&A.K. Dubeyorg136&A. Dublaorg104&L. Ducrouxorg131&S.
Dudiorg97&A.K. Duggalorg97&M. Dukhishyamorg85&P. Dupieuxorg130&R.J.
Ehlersorg141&D. Eliaorg50&E. Endressorg109&H. Engelorg67&E. Eppleorg141&B.
Erazmusorg112&F. Erhardtorg96&B. Espagnonorg59&G. Eulisseorg34&J. Eumorg18&D.
Evansorg108&S. Evdokimovorg110&L. Fabbiettiorg103,org116&J. Faivreorg78&A.
Fantoniorg49&M. Faselorg93&L. Feldkamporg139&A. Felicielloorg56&G.
Feofilovorg135&A. Fernández Téllezorg2&A. Ferrettiorg25&A.
Festantiorg28,org34&V.J.G. Feuillardorg72,org130&J. Figielorg117&M.A.S.
Figueredoorg120&S. Filchaginorg106&D. Finogeevorg60&F.M. Fiondaorg21,org23&M.
Florisorg34&S. Foertschorg73&P. Fokaorg104&S. Fokinorg87&E. Fragiacomoorg57&A.
Francesconorg34&A. Franciscoorg112&U. Frankenfeldorg104&G.G. Fronzeorg25&U.
Fuchsorg34&C. Furgetorg78&A. Fursorg60&M. Fusco Girardorg29&J.J.
Gaardhøjeorg88&M. Gagliardiorg25&A.M. Gagoorg109&K. Gajdosovaorg88&M.
Gallioorg25&C.D. Galvanorg119&P. Ganotiorg82&C. Garabatosorg104&E. Garcia-
Solisorg12&K. Gargorg27&C. Gargiuloorg34&P. Gasikorg103,org116&E.F.
Gaugerorg118&M.B. Gay Ducatiorg70&M. Germainorg112&J. Ghoshorg107&P.
Ghoshorg136&S.K. Ghoshorg4&P. Gianottiorg49&P. Giubellinoorg34,org104,org56&P.
Giubilatoorg28&E. Gladysz-Dziadusorg117&P. Glässelorg102&D.M. Goméz
Coralorg71&A. Gomez Ramirezorg67&A.S. Gonzalezorg34&P. González-Zamoraorg2&S.
Gorbunovorg40&L. Görlichorg117&S. Gotovacorg115&V. Grabskiorg71&L.K.
Graczykowskiorg137&K.L. Grahamorg108&L. Greinerorg79&A. Grelliorg61&C.
Grigorasorg34&V. Grigorievorg90&A. Grigoryanorg1&S. Grigoryanorg74&J.M.
Gronefeldorg104&F. Grosaorg30&J.F. Grosse-Oetringhausorg34&R. Grossoorg104&F.
Guberorg60&R. Guernaneorg78&B. Guerzoniorg26&M. Guittiereorg112&K.
Gulbrandsenorg88&T. Gunjiorg128&A. Guptaorg99&R. Guptaorg99&I.B. Guzmanorg2&R.
Haakeorg34&C. Hadjidakisorg59&H. Hamagakiorg80&G. Hamarorg140&J.C.
Hamonorg132&M.R. Haqueorg61&J.W. Harrisorg141&A. Hartonorg12&H. Hassanorg78&D.
Hatzifotiadouorg11,org51&S. Hayashiorg128&S.T. Heckelorg68&E. Hellbärorg68&H.
Helstruporg35&A. Herghelegiuorg84&E.G. Hernandezorg2&G. Herrera Corralorg10&F.
Herrmannorg139&B.A. Hessorg101&K.F. Hetlandorg35&H. Hillemannsorg34&C.
Hillsorg125&B. Hippolyteorg132&B. Hohlwegerorg103&D. Horakorg37&S.
Hornungorg104&R. Hosokawaorg129,org78&P. Hristovorg34&C. Hughesorg126&T.J.
Humanicorg17&N. Hussainorg42&T. Hussainorg16&D. Hutterorg40&D.S.
Hwangorg19&J.P. Iddonorg125&S.A. Iga Buitronorg69&R. Ilkaevorg106&M.
Inabaorg129&M. Ippolitovorg90,org87&M.S. Islamorg107&M. Ivanovorg104&V.
Ivanovorg94&V. Izucheevorg110&B. Jacakorg79&N. Jacazioorg26&P.M.
Jacobsorg79&M.B. Jadhavorg46&S. Jadlovskaorg114&J. Jadlovskyorg114&S.
Jaelaniorg61&C. Jahnkeorg116&M.J. Jakubowskaorg137&M.A. Janikorg137&P.H.S.Y.
Jayarathnaorg123&C. Jenaorg85&M. Jercicorg96&R.T. Jimenez
Bustamanteorg104&P.G. Jonesorg108&A. Juskoorg108&P. Kalinakorg63&A.
Kalweitorg34&J.H. Kangorg142&V. Kaplinorg90&S. Karorg136&A. Karasu
Uysalorg77&O. Karavichevorg60&T. Karavichevaorg60&L. Karayanorg104,org102&P.
Karczmarczykorg34&E. Karpechevorg60&U. Kebschullorg67&R. Keidelorg143&D.L.D.
Keijdenerorg61&M. Keilorg34&B. Ketzerorg43&Z. Khabanovaorg89&P. Khanorg107&S.
Khanorg16&S.A. Khanorg136&A. Khanzadeevorg94&Y. Kharlovorg110&A.
Khatunorg16&A. Khuntiaorg47&M.M. Kielbowiczorg117&B. Kilengorg35&B.
Kimorg129&D. Kimorg142&D.J. Kimorg124&E.J. Kimorg14&H. Kimorg142&J.S.
Kimorg41&J. Kimorg102&M. Kimorg58&S. Kimorg19&T. Kimorg142&S. Kirschorg40&I.
Kiselorg40&S. Kiselevorg62&A. Kisielorg137&G. Kissorg140&J.L. Klayorg6&C.
Kleinorg68&J. Kleinorg34&C. Klein-Bösingorg139&S. Klewinorg102&A.
Klugeorg34&M.L. Knichelorg102,org34&A.G. Knospeorg123&C. Kobdajorg113&M.
Kofaragoorg140&M.K. Köhlerorg102&T. Kolleggerorg104&V. Kondratievorg135&N.
Kondratyevaorg90&E. Kondratyukorg110&A. Konevskikhorg60&M.
Konyushikhinorg138&M. Kopcikorg114&M. Kourorg99&C. Kouzinopoulosorg34&O.
Kovalenkoorg83&V. Kovalenkoorg135&M. Kowalskiorg117&I. Králikorg63&A.
Kravčákováorg38&L. Kreisorg104&M. Krivdaorg108,org63&F. Krizekorg92&E.
Kryshenorg94&M. Krzewickiorg40&A.M. Kuberaorg17&V. Kučeraorg92&C.
Kuhnorg132&P.G. Kuijerorg89&A. Kumarorg99&J. Kumarorg46&L. Kumarorg97&S.
Kumarorg46&S. Kunduorg85&P. Kurashviliorg83&A. Kurepinorg60&A.B.
Kurepinorg60&A. Kuryakinorg106&S. Kushpilorg92&M.J. Kweonorg58&Y.
Kwonorg142&S.L. La Pointeorg40&P. La Roccaorg27&C. Lagana Fernandesorg120&Y.S.
Laiorg79&I. Lakomovorg34&R. Langoyorg39&K. Lapidusorg141&C. Laraorg67&A.
Lardeuxorg20&A. Lattucaorg25&E. Laudiorg34&R. Lavickaorg37&R. Leaorg24&L.
Leardiniorg102&S. Leeorg142&F. Lehasorg89&S. Lehnerorg111&J.
Lehrbachorg40&R.C. Lemmonorg91&E. Leograndeorg61&I. León Monzónorg119&P.
Lévaiorg140&X. Liorg13&X.L. Liorg7&J. Lienorg39&R. Lietavaorg108&B.
Limorg18&S. Lindalorg20&V. Lindenstruthorg40&S.W. Lindsayorg125&C.
Lippmannorg104&M.A. Lisaorg17&V. Litichevskyiorg44&A. Liuorg79&W.J.
Llopeorg138&D.F. Lodatoorg61&P.I. Loenneorg21&V. Loginovorg90&C.
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Loweorg140&P. Luettigorg68&J.R. Luhderorg139&M. Lunardonorg28&G.
Luparelloorg24,org57&M. Lupiorg34&T.H. Lutzorg141&A. Maevskayaorg60&M.
Magerorg34&S.M. Mahmoodorg20&A. Maireorg132&R.D. Majkaorg141&M. Malaevorg94&L.
Malininaorg74orgII&D. Mal’Kevichorg62&P. Malzacherorg104&A. Mamonovorg106&V.
Mankoorg87&F. Mansoorg130&V. Manzariorg50&Y. Maoorg7&M.
Marchisoneorg131,org127,org73&J. Marešorg64&G.V. Margagliottiorg24&A.
Margottiorg51&J. Marguttiorg61&A. Marínorg104&C. Markertorg118&M.
Marquardorg68&N.A. Martinorg104&P. Martinengoorg34&J.A.L. Martinezorg67&M.I.
Martínezorg2&G. Martínez Garcíaorg112&M. Martinez Pedreiraorg34&S.
Masciocchiorg104&M. Maseraorg25&A. Masoniorg52&L. Massacrierorg59&E.
Massonorg112&A. Mastroserioorg50&A.M. Mathisorg116,org103&P.F.T.
Matuokaorg120&A. Matyjaorg126&C. Mayerorg117&J. Mazerorg126&M.
Mazzilliorg32&M.A. Mazzoniorg55&F. Meddiorg22&Y. Melikyanorg90&A. Menchaca-
Rochaorg71&E. Meninnoorg29&J. Mercado Pérezorg102&M. Meresorg36&S.
Mhlangaorg98&Y. Miakeorg129&M.M. Mieskolainenorg44&D.L. Mihaylovorg103&K.
Mikhaylovorg62,org74&A. Mischkeorg61&A.N. Mishraorg47&D. Miśkowiecorg104&J.
Mitraorg136&C.M. Mituorg66&N. Mohammadiorg34,org61&A.P. Mohantyorg61&B.
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Morenoorg2&S. Morettoorg28&A. Morrealeorg112&A. Morschorg34&V.
Mucciforaorg49&E. Mudnicorg115&D. Mühlheimorg139&S. Muhuriorg136&M.
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Munozorg79&R.H. Munzerorg68&H. Murakamiorg128&S. Murrayorg73&L. Musaorg34&J.
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Nystrandorg21&H. Oeschlerorg18,org102org*&H. Ohorg142&A. Ohlsonorg102&L.
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Velasquezorg69&A. Oskarssonorg33&J. Otwinowskiorg117&K. Oyamaorg80&Y.
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Pathakorg123&R.N. Patraorg136&B. Paulorg56&H. Peiorg7&T. Peitzmannorg61&X.
Pengorg7&L.G. Pereiraorg70&H. Pereira Da Costaorg72&D. Peresunkoorg90,org87&E.
Perez Lezamaorg68&V. Peskovorg68&Y. Pestovorg5&V. Petráčekorg37&M.
Petroviciorg84&C. Pettaorg27&R.P. Pezziorg70&S. Pianoorg57&M. Piknaorg36&P.
Pillotorg112&L.O.D.L. Pimentelorg88&O. Pinazzaorg51,org34&L. Pinskyorg123&D.B.
Piyarathnaorg123&M. Płoskońorg79&M. Planinicorg96&F. Pliquettorg68&J.
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Poghosyanorg93&B. Polichtchoukorg110&N. Poljakorg96&W. Poonsawatorg113&A.
Poporg84&H. Poppenborgorg139&S. Porteboeuf-Houssaisorg130&V.
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Redlichorg83orgIV&A. Rehmanorg21&P. Reicheltorg68&F. Reidtorg34&X. Renorg7&R.
Renfordtorg68&A. Reshetinorg60&K. Reygersorg102&V. Riabovorg94&T.
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Riggiorg27&C. Risteaorg66&M. Rodríguez Cahuantziorg2&K. Røedorg20&R.
Rogalevorg110&E. Rogochayaorg74&D. Rohrorg34,org40&D. Röhrichorg21&P.S.
Rokitaorg137&F. Ronchettiorg49&E.D. Rosasorg69&K. Roslonorg137&P.
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Rustamovorg86&E. Ryabinkinorg87&Y. Ryabovorg94&A. Rybickiorg117&S.
Saarinenorg44&S. Sadhuorg136&S. Sadovskyorg110&K. Šafaříkorg34&S.K.
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Sahuorg65&J. Sainiorg136&S. Sakaiorg129&M.A. Salehorg138&J. Salzwedelorg17&S.
Sambyalorg99&V. Samsonovorg94,org90&A. Sandovalorg71&A. Sarkarorg73&D.
Sarkarorg136&N. Sarkarorg136&P. Sarmaorg42&M.H.P. Sasorg61&E.
Scapparoneorg51&F. Scarlassaraorg28&B. Schaeferorg93&H.S. Scheidorg68&C.
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Sharmaorg97&A. Sharmaorg99&M. Sharmaorg99&M. Sharmaorg99&N. Sharmaorg97&A.I.
Sheikhorg136&K. Shigakiorg45&M. Shimomuraorg81&S. Shirinkinorg62&Q.
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Sielewiczorg34&T. Siemiarczukorg83&S. Silaevaorg87&D. Silvermyrorg33&G.
Simatovicorg89,org96&G. Simonettiorg34&R. Singarajuorg136&R. Singhorg85&V.
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Zhuorg7&A. Zichichiorg26,org11&M.B. Zimmermannorg34&G. Zinovjevorg3&J.
Zmeskalorg111&S. Zouorg7&

## Affiliation notes

org*Deceased

orgIDipartimento DET del Politecnico di Torino, Turin, Italy

orgIIM.V. Lomonosov Moscow State University, D.V. Skobeltsyn Institute of
Nuclear, Physics, Moscow, Russia

orgIIIDepartment of Applied Physics, Aligarh Muslim University, Aligarh, India

orgIVInstitute of Theoretical Physics, University of Wroclaw, Poland

## 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, Lyon, France

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

org10Centro de Investigación y de Estudios Avanzados (CINVESTAV), Mexico City
and Mérida, Mexico

org11Centro Fermi - Museo Storico della Fisica e Centro Studi e Ricerche
“Enrico Fermi’, Rome, Italy

org12Chicago State University, Chicago, Illinois, United States

org13China Institute of Atomic Energy, Beijing, China

org14Chonbuk National University, Jeonju, Republic of Korea

org15COMSATS Institute of Information Technology (CIIT), Islamabad, Pakistan

org16Department of Physics, Aligarh Muslim University, Aligarh, India

org17Department of Physics, Ohio State University, Columbus, Ohio, United
States

org18Department of Physics, Pusan National University, Pusan, Republic of
Korea

org19Department of Physics, Sejong University, Seoul, Republic of Korea

org20Department of Physics, University of Oslo, Oslo, Norway

org21Department of Physics and Technology, University of Bergen, Bergen,
Norway

org22Dipartimento di Fisica dell’Università ’La Sapienza’ and Sezione INFN,
Rome, Italy

org23Dipartimento di Fisica dell’Università and Sezione INFN, Cagliari, Italy

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

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

org26Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN,
Bologna, Italy

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

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

org29Dipartimento di Fisica ‘E.R. Caianiello’ dell’Università and Gruppo
Collegato INFN, Salerno, Italy

org30Dipartimento DISAT del Politecnico and Sezione INFN, Turin, Italy

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

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

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

org34European Organization for Nuclear Research (CERN), Geneva, Switzerland

org35Faculty of Engineering, Bergen University College, Bergen, Norway

org36Faculty of Mathematics, Physics and Informatics, Comenius University,
Bratislava, Slovakia

org37Faculty of Nuclear Sciences and Physical Engineering, Czech Technical
University in Prague, Prague, Czech Republic

org38Faculty of Science, P.J. Šafárik University, Košice, Slovakia

org39Faculty of Technology, Buskerud and Vestfold University College,
Tonsberg, Norway

org40Frankfurt Institute for Advanced Studies, Johann Wolfgang Goethe-
Universität Frankfurt, Frankfurt, Germany

org41Gangneung-Wonju National University, Gangneung, Republic of Korea

org42Gauhati University, Department of Physics, Guwahati, India

org43Helmholtz-Institut für Strahlen- und Kernphysik, Rheinische Friedrich-
Wilhelms-Universität Bonn, Bonn, Germany

org44Helsinki Institute of Physics (HIP), Helsinki, Finland

org45Hiroshima University, Hiroshima, Japan

org46Indian Institute of Technology Bombay (IIT), Mumbai, India

org47Indian Institute of Technology Indore, Indore, India

org48Indonesian Institute of Sciences, Jakarta, Indonesia

org49INFN, Laboratori Nazionali di Frascati, Frascati, Italy

org50INFN, Sezione di Bari, Bari, Italy

org51INFN, Sezione di Bologna, Bologna, Italy

org52INFN, Sezione di Cagliari, Cagliari, Italy

org53INFN, Sezione di Catania, Catania, Italy

org54INFN, Sezione di Padova, Padova, Italy

org55INFN, Sezione di Roma, Rome, Italy

org56INFN, Sezione di Torino, Turin, Italy

org57INFN, Sezione di Trieste, Trieste, Italy

org58Inha University, Incheon, Republic of Korea

org59Institut de Physique Nucléaire d’Orsay (IPNO), Université Paris-Sud,
CNRS-IN2P3, Orsay, France

org60Institute for Nuclear Research, Academy of Sciences, Moscow, Russia

org61Institute for Subatomic Physics of Utrecht University, Utrecht,
Netherlands

org62Institute for Theoretical and Experimental Physics, Moscow, Russia

org63Institute of Experimental Physics, Slovak Academy of Sciences, Košice,
Slovakia

org64Institute of Physics, Academy of Sciences of the Czech Republic, Prague,
Czech Republic

org65Institute of Physics, Bhubaneswar, India

org66Institute of Space Science (ISS), Bucharest, Romania

org67Institut für Informatik, Johann Wolfgang Goethe-Universität Frankfurt,
Frankfurt, Germany

org68Institut für Kernphysik, Johann Wolfgang Goethe-Universität Frankfurt,
Frankfurt, Germany

org69Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de México,
Mexico City, Mexico

org70Instituto de Física, Universidade Federal do Rio Grande do Sul (UFRGS),
Porto Alegre, Brazil

org71Instituto de Física, Universidad Nacional Autónoma de México, Mexico
City, Mexico

org72IRFU, CEA, Université Paris-Saclay, Saclay, France

org73iThemba LABS, National Research Foundation, Somerset West, South Africa

org74Joint Institute for Nuclear Research (JINR), Dubna, Russia

org75Konkuk University, Seoul, Republic of Korea

org76Korea Institute of Science and Technology Information, Daejeon, Republic
of Korea

org77KTO Karatay University, Konya, Turkey

org78Laboratoire de Physique Subatomique et de Cosmologie, Université
Grenoble-Alpes, CNRS-IN2P3, Grenoble, France

org79Lawrence Berkeley National Laboratory, Berkeley, California, United
States

org80Nagasaki Institute of Applied Science, Nagasaki, Japan

org81Nara Women’s University (NWU), Nara, Japan

org82National and Kapodistrian University of Athens, School of Science,
Department of Physics , Athens, Greece

org83National Centre for Nuclear Studies, Warsaw, Poland

org84National Institute for Physics and Nuclear Engineering, Bucharest,
Romania

org85National Institute of Science Education and Research, HBNI, Jatni, India

org86National Nuclear Research Center, Baku, Azerbaijan

org87National Research Centre Kurchatov Institute, Moscow, Russia

org88Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark

org89Nikhef, Nationaal instituut voor subatomaire fysica, Amsterdam,
Netherlands

org90NRNU Moscow Engineering Physics Institute, Moscow, Russia

org91Nuclear Physics Group, STFC Daresbury Laboratory, Daresbury, United
Kingdom

org92Nuclear Physics Institute, Academy of Sciences of the Czech Republic, Řež
u Prahy, Czech Republic

org93Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States

org94Petersburg Nuclear Physics Institute, Gatchina, Russia

org95Physics Department, Creighton University, Omaha, Nebraska, United States

org96Physics department, Faculty of science, University of Zagreb, Zagreb,
Croatia

org97Physics Department, Panjab University, Chandigarh, India

org98Physics Department, University of Cape Town, Cape Town, South Africa

org99Physics Department, University of Jammu, Jammu, India

org100Physics Department, University of Rajasthan, Jaipur, India

org101Physikalisches Institut, Eberhard Karls Universität Tübingen, Tübingen,
Germany

org102Physikalisches Institut, Ruprecht-Karls-Universität Heidelberg,
Heidelberg, Germany

org103Physik Department, Technische Universität München, Munich, Germany

org104Research Division and ExtreMe Matter Institute EMMI, GSI
Helmholtzzentrum für Schwerionenforschung GmbH, Darmstadt, Germany

org105Rudjer Bošković Institute, Zagreb, Croatia

org106Russian Federal Nuclear Center (VNIIEF), Sarov, Russia

org107Saha Institute of Nuclear Physics, Kolkata, India

org108School of Physics and Astronomy, University of Birmingham, Birmingham,
United Kingdom

org109Sección Física, Departamento de Ciencias, Pontificia Universidad
Católica del Perú, Lima, Peru

org110SSC IHEP of NRC Kurchatov institute, Protvino, Russia

org111Stefan Meyer Institut für Subatomare Physik (SMI), Vienna, Austria

org112SUBATECH, IMT Atlantique, Université de Nantes, CNRS-IN2P3, Nantes,
France

org113Suranaree University of Technology, Nakhon Ratchasima, Thailand

org114Technical University of Košice, Košice, Slovakia

org115Technical University of Split FESB, Split, Croatia

org116Technische Universität München, Excellence Cluster ’Universe’, Munich,
Germany

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

org118The University of Texas at Austin, Austin, Texas, United States

org119Universidad Autónoma de Sinaloa, Culiacán, Mexico

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

org121Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil

org122Universidade Federal do ABC, Santo Andre, Brazil

org123University of Houston, Houston, Texas, United States

org124University of Jyväskylä, Jyväskylä, Finland

org125University of Liverpool, Liverpool, United Kingdom

org126University of Tennessee, Knoxville, Tennessee, United States

org127University of the Witwatersrand, Johannesburg, South Africa

org128University of Tokyo, Tokyo, Japan

org129University of Tsukuba, Tsukuba, Japan

org130Université Clermont Auvergne, CNRS/IN2P3, LPC, Clermont-Ferrand, France

org131Université de Lyon, Université Lyon 1, CNRS/IN2P3, IPN-Lyon,
Villeurbanne, Lyon, France

org132Université de Strasbourg, CNRS, IPHC UMR 7178, F-67000 Strasbourg,
France, Strasbourg, France

org133Università degli Studi di Pavia, Pavia, Italy

org134Università di Brescia, Brescia, Italy

org135V. Fock Institute for Physics, St. Petersburg State University, St.
Petersburg, Russia

org136Variable Energy Cyclotron Centre, Kolkata, India

org137Warsaw University of Technology, Warsaw, Poland

org138Wayne State University, Detroit, Michigan, United States

org139Westfälische Wilhelms-Universität Münster, Institut für Kernphysik,
Münster, Germany

org140Wigner Research Centre for Physics, Hungarian Academy of Sciences,
Budapest, Hungary

org141Yale University, New Haven, Connecticut, United States

org142Yonsei University, Seoul, Republic of Korea

org143Zentrum für Technologietransfer und Telekommunikation (ZTT),
Fachhochschule Worms, Worms, Germany