2024 142 22 May

Jet and medium production of $\phi$(1020) in p–Pb collisions

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

This work aims to differentiate strangeness produced from hard processes (jet-
like) and softer processes (underlying event) by measuring the angular
correlation between a high-momentum trigger hadron (h) acting as a jet-proxy
and a produced strange hadron ($\phi(1020)$ meson). Measuring h–$\phi$
correlations at midrapidity in p–Pb collisions at $\sqrt{s_{\rm NN}}$ = 5.02
TeV as a function of event multiplicity provides insight into the microscopic
origin of strangeness enhancement in small collision systems. The jet-like and
the underlying-event-like strangeness production are investigated as a
function of event multiplicity. They are also compared between a lower and
higher momentum region. The evolution of the per-trigger yields within the
near-side (aligned with the trigger hadron) and away-side (in the opposite
direction of the trigger hadron) jet is studied separately, allowing for the
characterization of two distinct jet-like production regimes. Furthermore, the
h–$\phi$ correlations within the underlying event give access to a production
regime dominated by soft production processes, which can be compared directly
to the in-jet production. Comparisons between h–$\phi$ and dihadron
correlations show that the observed strangeness enhancement is largely driven
by the underlying event, where the $\phi/\mathrm{h}$ ratio is significantly
larger than within the jet regions. As multiplicity increases, the fraction of
the total $\phi(1020)$ yield coming from jets decreases compared to the
underlying event production, leading to high-multiplicity events being
dominated by the increased strangeness production from the underlying event.

##  1 Introduction

The enhancement of strange-quark production in heavy-ion collisions with
respect to minimum bias proton–proton (pp) collisions has long been one of the
suggested signatures of the existence of a deconfined, partonic phase of
nuclear matter known as the quark–gluon plasma (QGP) [1]. The QGP phase is
expected to be formed in extremely high-energy-density environments, such as
those produced in heavy-ion collisions [2]. The enhancement of strange and
multi-strange hadrons in the QGP with respect to their production within a
hadronic-gas phase was one of the major predicted signatures of QGP formation
in heavy-ion collisions [3]. The measurement of strange-quark enhancement via
the production of strange hadrons has been investigated for many years in
different collision systems and at different energies by experiments at the
SPS, RHIC, and the LHC [4, 5, 6]. The production of strange particles in
heavy-ion collisions is successfully described by a grand canonical
statistical model of particle production in an equilibrated QGP medium [7, 8].

In addition to enhancement in heavy-ion collisions, more recent studies at LHC
energies have measured strangeness enhancement in high-multiplicity pp and
p–Pb collisions with respect to minimum bias pp collisions [9, 10].
Strikingly, this enhancement, quantified by the ratio of strange to non-
strange hadron (i.e. pions) yields, is seen to increase smoothly as a function
of the event multiplicity, independent of the collision system. Since the
enhancement in larger systems is attributed to a thermalized QGP state, this
experimental evidence of strangeness enhancement in small collision systems
raises questions about the early characteristics and evolution of pp and p–Pb
collision systems. While certain theoretical models — such as statistical
thermal models [11, 12], color reconnection and rope hadronization [13, 14],
and the dynamical core-corona model [15, 16] — can qualitatively explain this
behavior in small collision systems, the exact production mechanism
responsible for this increase in strangeness in these systems remains unknown.

Experimentally, strangeness enhancement can be measured by looking at the
ratio of strange particles to non-strange particles, most commonly pions. The
increase of this yield ratio with increasing multiplicity scales with the
strange-quark content of the particle species under study. Even though the
$\phi$(1020) meson ($s\overline{s}$) has hidden strangeness, the ALICE
collaboration have measured an enhancement in the $\phi$(1020) production in-
between the increase seen for hadrons containing one and two strange quarks,
such as the $\Lambda$ and the $\Xi$, respectively [17]. In this paper, the
enhancement in strange-quark production in small collision systems is
investigated by studying the $\phi$(1020) production in p–Pb collisions at
center-of-mass energy per nucleon pair $\sqrt{s_{\rm NN}}$ =
$5.02\text{\,}\mathrm{TeV}$ collected during the LHC data-taking period Run 2.
The event multiplicity classes in p–Pb collisions provide a valuable span of
collision activity, overlapping the event multiplicity of pp collisions as
well as peripheral Pb–Pb collisions. The p–Pb collision system also allows for
a balance between hard scattering and soft production processes, allowing for
both in-jet and underlying-event production to be investigated across a range
of collision multiplicities. Therefore, the p–Pb collision system is ideal for
studying the origin of strangeness enhancement.

In order to explore the mechanisms responsible for the enhancement of
$\phi$(1020) meson production as a function of system size, the analysis
presented in this paper separates the $\phi$(1020) production into event
regions that are dominated by hard-scattering processes (i.e. jets) from those
dominated by softer interactions (i.e. the underlying event). Separation of
the yields corresponding to these different production regimes can be achieved
through measurements of two-particle angular correlations [18, 19]. Previous
studies have used both jet reconstruction and two-particle angular
correlations to study strange baryon and meson production ($\Lambda^{0}$,
K${}^{0}_{\mathrm{S}}$) in jets, showing a significant difference in behavior
between jet and non-jet production [20, 21, 22]. By looking at the angular
correlation between a high-$p_{\rm T}$ trigger hadron, interpreted as a proxy
for the jet axis, and a $\phi$(1020) meson, the $\phi$(1020) meson yield can
be divided into three regions: the yield within the near-side jet (aligned
with the trigger hadron), within the away-side jet (opposite the trigger
hadron), and within the underlying event. Similar correlation measurements are
obtained using the same trigger requirements correlated with inclusive charged
hadrons (h–h) for direct comparison.

The per-trigger yield ratio of (h–$\phi$)/(h–h) pairs gives the
$\phi/\mathrm{h}$ ratio within events that contain trigger hadrons. Thus,
using angular correlations to extract pairwise h–$\phi$ and h–h yields in
different regions of correlation space the approximate $\phi/\mathrm{h}$
production ratio is extracted within the near-side jet, the away-side jet, and
the underlying-event-dominated regions. This allows one to study the
multiplicity (and system size) dependence of $\phi$(1020) production in a
region dominated by hard-scattering processes by measuring the
$\phi/\mathrm{h}$ ratio in the jet-dominated regions, and for soft processes
by measuring it in the region where the underlying event is dominant. The
separated near-side and away-side jet yields are also used to investigate
possible modification of the $\phi/\mathrm{h}$ ratio caused by differences in
jet fragmentation and potential jet-medium interactions.

The next section, Section 2, describes the ALICE sub-detectors that are used
for this analysis. Section 3 explains the data analysis details, while Section
4 describes the specific measurement procedure in more detail, followed by a
discussion of the systematic effects and corresponding uncertainties in
Section 5. Finally, the h–$\phi$ correlation results are presented in Section
6, concluding with a discussion on how these findings address the origin of
strangeness enhancement.

##  2 Experimental setup

The ALICE detector at the LHC consists of a central barrel encompassing
several sub-detectors housed within a large solenoid magnet. In addition,
several forward and backward detectors are used for event characterization and
triggering. A detailed description of the ALICE detector can be found in [23,
24]. The primary detectors used in this analysis are the Inner Tracking System
(ITS), the Time Projection Chamber (TPC), and the Time Of Flight (TOF)
detector. The ITS allows an accurate reconstruction of the primary collision
vertex and distinguishes between primary tracks originating from the primary
vertex and secondary tracks resulting from weak decays. The TPC is a
cylindrical drift chamber that acts as the main tracking detector of the ALICE
apparatus, covering the central pseudorapidity region within $|\eta|<0.9$. In
addition, the TPC track information is also used for particle identification
(PID) by measuring the specific energy loss ${\rm d}E/{\rm d}x$ of the tracks
traversing the chamber, parameterized with the Bethe-Bloch formula. Situated
outside of the TPC and covering the same $|\eta|<0.9$ range is the TOF
detector, an array of Multigap Resistive Plate Chambers (MRPC). The TOF gives
precise track timing information with a resolution of
$56\text{\,}\mathrm{p}\mathrm{s}$ that is used for additional PID constraints
[25].

In addition to the central tracking detectors, the V0 detector is used for
event characterization. The V0 detector is made of two plastic scintillator
detectors, V0A and V0C, that cover the forward ($2.8<\eta<5.1$) and backward
($-3.7<\eta<-1.7$) pseudorapidity region, respectively. The V0 detector is
used to determine the charged-particle multiplicity and for triggering
purposes.

##  3 Data analysis

###  3.1 Event selection

The dataset analyzed in this paper consists of p–Pb collisions recorded in
2016 during LHC Run 2 at $\sqrt{s_{\rm NN}}{}=$ $5.02\text{\,}\mathrm{TeV}$.
The magnetic field due to the ALICE solenoid magnet was
$0.5\text{\,}\mathrm{T}$ for all events in this period. Events were selected
using the Minimum Bias (MB) trigger, which requires a hit in both the V0A and
V0C detectors [26]. In addition, events are constrained to have their
reconstructed primary vertex position within $|z_{\rm vtx}|<$
$10\text{\,}\mathrm{c}\mathrm{m}$ from the nominal interaction point in the
beam-axis direction. To perform correlation measurements with a reconstructed
$\phi$(1020) meson, only events with at least three charged tracks with
$p_{\rm T}{}>$ $0.15\text{\,}\mathrm{GeV/\text{$c$}}$ in the central
$|\eta_{\text{lab}}|<0.9$ region are used in this analysis. This requirement
is also applied to the dihadron correlation measurement to ensure an accurate
comparison.

Due to the asymmetric energy-per-nucleon of the proton and lead-ion beams, the
nucleon–nucleon center of mass is shifted by a rapidity of $\Delta
y_{\text{NN}}=-0.465$ in the direction of the proton beam [27]. Events are
categorized based on their charged-particle multiplicity in the positive
rapidity (incoming Pb beam direction) region as detected by the V0A, and are
divided into multiplicity percentile intervals based on the multiplicity
distribution of the entire dataset. For this analysis, three multiplicity
ranges were chosen: 0–20% (highest multiplicity), 20–50%, and 50–80% (lowest
multiplicity). These intervals allow for grouping of events with similar event
characteristics while still maintaining a significant $\phi$(1020) signal in
each multiplicity range. After the trigger and offline event selection
criteria are applied, a total of $\sim 4\times 10^{8}$ events are selected for
this analysis, corresponding to an integrated luminosity of $\sim
200~{}$\text{\,}\mathrm{\SIUnitSymbolMicro b^{-1}}$$ [28]. For simplicity,
quoted rapidity and pseudorapidity ranges in this paper will refer to the
laboratory frame.

###  3.2 Track selection and $\phi$(1020) reconstruction

To measure the h–$\phi$ angular correlation function required for separating
$\phi$(1020) production in jets and in the underlying event, and the h–h
correlations to extract $\phi/\mathrm{h}$ ratios, three different types of
particles are considered: high-momentum trigger particles (jet proxy),
$\phi$(1020) meson decay products, and inclusive charged hadrons for the
dihadron correlation. Trigger particles are selected in the transverse
momentum range of $4<p_{\rm T}{}<$ $8\text{\,}\mathrm{GeV/\text{$c$}}$, while
associated particles ($\phi$(1020) and charged hadrons) are split into a lower
($1.5<p_{\rm T}{}<$ $2.5\text{\,}\mathrm{GeV/\text{$c$}}$$)$ and upper
($2.5<p_{\rm T}{}<$ $4\text{\,}\mathrm{GeV/\text{$c$}}$$)$ transverse momentum
range.

####  3.2.1 Kaon identification

For this analysis, the $\phi$(1020) meson is reconstructed through the decay
channel $\phi(1020)~{}\rightarrow~{}\text{K}^{+}\text{K}^{-}$, and the yields
are corrected for this decay branching ratio B.R. $=0.491\pm 0.005$ [29]. Kaon
candidates are selected from the central pseudorapidity region of
$|\eta|<0.8$. A standard set of quality selections is applied to ensure high
quality for the kaon candidate tracks, as described in Ref. [30]. These
include requirements on the minimum number of TPC clusters that compose the
track ($N^{\text{clust}}_{\text{TPC}}\geq 80$ out of 159), as well as an upper
limit on $\chi^{2}$ per TPC cluster in the track fitting
($\chi^{2}/N^{\text{clust}}_{\text{TPC}}\leq 4$). The tracks are also selected
with a momentum-dependent requirement on the distance of closest approach
(DCA) between the track and the reconstructed primary vertex, such that the
DCA in the transverse plane ($\text{DCA}_{XY}$) is constrained by
$\text{DCA}_{XY}<\bigl{(}0.0105+0.0350/(p_{\rm
T}/($\text{\,}\mathrm{GeV/\text{$c$}}$))^{1.1}\bigr{)}$
$\text{\,}\mathrm{c}\mathrm{m}$. This DCA requirement selects all tracks
originating from within a radius $7\sigma_{XY}$ around the primary vertex in
the $XY$ plane, where $\sigma_{XY}$ is the resolution of the distance of
closest approach from the track to the vertex in the transverse plane [24].
Since the $\phi$(1020) has a short lifetime
($\approx$46\text{\,}\mathrm{f}\mathrm{m}\mathrm{/}\text{c}$$) [29], and
therefore a decay vertex close to the primary vertex, this DCA selection
removes particle tracks coming from secondary weak decays while still
preserving the tracks of the $\phi$(1020) decay particles.

Kaon candidates for $\phi(1020)$ reconstruction are further selected using the
PID information from both the TPC and TOF detectors [31, 32]. In the TPC, the
energy loss (${\rm d}E/{\rm d}x$) of charged particles traveling through the
detector gas can be calculated using the Bethe–Bloch formula, and the
momentum-dependent ${\rm d}E/{\rm d}x$ signal of a candidate kaon can be
compared with this expected value. Due to the finite resolution of the track
momenta and energy loss in the TPC ($\sigma_{\text{TPC}}\approx 5\%$ of the
${\rm d}E/{\rm d}x$ signal [33]), the distribution of ${\rm d}E/{\rm d}x$
signals for real kaons at a given momentum is treated as a Gaussian with the
mean at the expected kaon value, and a width $\sigma_{\text{TPC}}$ that is fit
experimentally. Kaons are selected by requiring the difference between the
measured and expected ${\rm d}E/{\rm d}x$ to be within $|({\rm d}E/{\rm
d}x{})-({\rm d}E/{\rm d}x{})_{\text{Bethe--Bloch}}|/\sigma_{\text{TPC}}<3$. A
similar process is followed for the timing signal measured by the TOF
detector, selecting kaon candidates at a given momentum with the constraint
$|\beta-\beta_{\text{K}}|/\sigma_{\text{TOF}}$, where $\beta_{\text{K}}$ is
the expected track velocity given the kaon mass hypothesis and the specified
momentum. These selections for PID allow for high efficiency in detecting
produced kaons. The additional invariant mass reconstruction steps discussed
in the following section eliminate all misidentified pions and protons from
the set of candidate $\phi$ decay particles, as well as the combinatorial
background due to uncorrelated kaon pairs.

####  3.2.2 $\phi$(1020) invariant mass reconstruction

The invariant-mass distribution of the $\phi$(1020) meson is computed by
considering all unlike-sign (US) pairs of kaons satisfying the selections
described above, which includes both kaon pairs from $\phi(1020)$ decays as
well as uncorrelated combinatorial kaon pairs. The invariant-mass distribution
of like-sign (LS) kaon pairs, which is entirely composed of combinatorial kaon
pairs, is used to estimate the combinatorial background underneath the mass-
peak region of US kaon pairs (Fig. 1). For this purpose, the LS invariant-mass
distribution is scaled to match the US distribution in the left and right
sideband regions ($0.995<m_{\text{KK}}<$
$1.005\text{\,}{\mathrm{GeV/\text{$c$}}}^{2}$ and $1.04<m_{\text{KK}}<$
$1.06\text{\,}{\mathrm{GeV/\text{$c$}}}^{2}$, respectively) away from the
$\phi(1020)$ mass peak. The scale factor is defined as the sum of the yields
in the left and right sidebands in the US mass distribution divided by the
same yields in the LS mass distribution. The invariant mass distribution
obtained after subtracting the scaled LS distribution is fitted with a
Voigtian function (Gaussian convoluted with a relativistic Breit–Wigner
distribution) for the resonance mass peak added to a polynomial term to
describe the residual background, following the procedure described in Ref.
[34]. For the $p_{\rm T}$ ranges considered in this analysis, the like-sign
subtraction method leaves a residual background under the $\phi$(1020) mass
peak of less than 1% of the total signal as shown in Fig. 1.

In order to ensure a large sample purity, the invariant-mass region used to
extract the $\phi$(1020) yield in this analysis is defined as $\pm$
$6\text{\,}\mathrm{M}\mathrm{eV/\text{$c$}}^{2}$ around the nominal
$\phi(1020)$ mass (i.e. $1.014<m_{\mathrm{KK}}<$
$1.026\text{\,}{\mathrm{GeV/\text{$c$}}}^{2}$). However, this region does not
encompass the entire mass distribution of the signal, and therefore an
additional factor is needed to correct for the missing distribution tails
while calculating the yield. This factor, denoted as $k_{\text{Signal}}$, is
computed as the integral of the Voigtian fit across the entire mass range
divided by the integral in the selected mass-peak region, resulting in a
correction factor $k_{\text{Signal}}=1.22$.

To correct for the finite acceptance and inefficiencies of the detector, a
Monte Carlo simulation of minimum-bias events based on the PYTHIA 8 event
generator [35] is used to estimate the acceptance $\times$ efficiency
($A\times\varepsilon_{\rm rec}$) for the $\phi$(1020). Within the simulated
events, the $\phi$(1020) decay products are propagated through a full
simulation of the ALICE detector material using GEANT3 [36]. The
$A\times\varepsilon_{\rm rec}$ of the $\phi$(1020) is then defined as the
number of $\phi$(1020) at mid-pseudorapidity ($|\eta_{\phi}|<0.8$) that are
reconstructed after the decay products pass the track quality, PID, and
pseudorapidity requirements ($|\eta_{\mathrm{track}}|<0.8$) divided by the
total number of $\phi$(1020) that were produced in the same pseudorapidity
window in the simulated events. With the track quality selections described
above, the two-track reconstruction efficiency fraction increases from 0.3 to
0.6 with increasing $p_{\rm T}$ in the range considered in this analysis
($1.5<p^{\phi}_{\rm T}<$ $4.0\text{\,}\mathrm{GeV/\text{$c$}}$). The PID
selection and the requirement for a matching TOF hit introduces another factor
ranging from 0.25 to 0.4, leading to a final $A\times\varepsilon_{\rm rec}$
for the $\phi$(1020) ranging from 0.09 to 0.22. This efficiency is independent
of multiplicity, and is therefore calculated from the full minimum-bias sample
of events.

Figure 1: (a) Invariant-mass distribution of unlike-sign (US) kaon pairs
(black), with the scaled like-sign (LS) pair distribution (red) used to
approximate the combinatorial background (BG). (b) Background subtracted
invariant-mass distribution of unlike-sign kaon pairs (black). The
$\phi$(1020) mass peak is fit (blue dashed line) by a sum of a Voigt function
and a first-order polynomial, which accounts for the residual background. This
linear fit of the residual background is shown as a red line. Statistical
uncertainties are plotted as bars, but are too small to be visible here.

###  3.3 Associated hadrons and trigger track selection

For the associated hadrons in the dihadron correlation measurement, the same
track-quality selections are used that are required for the kaon candidates
($N^{\text{clust}}_{\text{TPC}}\geq 80$,
$\chi^{2}/N^{\text{clus}}_{\text{TPC}}\leq 4$, $|\eta|<0.8$, the $p_{\rm
T}$-dependent DCA requirement). These selection criteria are used to select
associated hadrons that are physical primary particles, defined as those
hadrons that are produced either directly from the collision or from resonance
decays [37]. After primary selection, secondary decay products contribute less
than $1\%$ to the total associated hadron yield. The tracking efficiency for
the associated charged-hadrons is approximately 0.8 for the entire $p_{\rm T}$
range considered of $1.5<p_{\rm T}{}<$ $4\text{\,}\mathrm{GeV/\text{$c$}}$.

For the high-$p_{\rm T}$ trigger hadron, tracks are selected in the transverse
momentum range $4<p_{\rm T}{}<$ $8\text{\,}\mathrm{GeV/\text{$c$}}$, and a
slightly modified set of track quality requirements is used in order to
achieve higher track reconstruction efficiency. These selections, also
detailed in Ref. [38], relax the $p_{\rm T}$-dependent DCA requirement and
replace it with a simple $2.4\text{\,}\mathrm{c}\mathrm{m}$
($3.2\text{\,}\mathrm{c}\mathrm{m}$) cutoff in the transverse (longitudinal)
distance to the primary vertex. This gives a reconstruction acceptance times
efficiency ($A\times\varepsilon_{\rm rec}$) of approximately 0.85 for the
trigger hadron.

The validity of the above efficiency corrections is checked using Monte Carlo
closure tests, where the fully efficiency corrected h–$\phi$ and h–h
measurements are built from reconstructed tracks in PYTHIA 8 generated MC
events. The reconstructed MC distributions are in good agreement with the
generated MC h–$\phi$ and h–h signal.

##  4 Angular correlation measurement

A two-particle correlation function in 2D angular space can be written as:

$C(\varphi_{t},\eta_{t},\varphi_{a},\eta_{a})=\frac{P_{t,a}(\varphi_{t},\eta_{t},\varphi_{a},\eta_{a})}{P_{t}(\varphi_{t},\eta_{t})P_{a}(\varphi_{a},\eta_{a})},$
(1)

where $P_{t,a}$ is the joint probability distribution of finding an associated
particle at azimuthal angle $\varphi_{a}$ and pseudorapidity $\eta_{a}$ along
with a trigger particle at azimuthal angle $\varphi_{t}$ and pseudorapidity
$\eta_{t}$, and $P_{t}$ and $P_{a}$ are the single-particle probability
distributions. This correlation function can be simplified experimentally by
using only the measured relative angles $\Delta\varphi$ and $\Delta\eta$
between the trigger and associated particles. Using this simplification, an
experimental correlation function can be approximated as:

$C(\Delta\varphi,\Delta\eta)\approx\frac{S(\Delta\varphi,\Delta\eta)}{B(\Delta\varphi,\Delta\eta)},$
(2)

where $S(\Delta\varphi,\Delta\eta)=\frac{{\rm d}^{2}N_{\text{pair}}}{{\rm
d}\Delta\varphi{\rm d}\Delta\eta}$ is the distribution of trigger–associated
particle pairs from the same event, and $B(\Delta\varphi,\Delta\eta)$ is the
mixed-event distribution, constructed by taking trigger and associated
particles from different events. The mixed-event distribution depends on the
single-particle distributions, and represents the two-particle acceptance of
trigger–associated pairs [39]. This correlation function can be scaled to give
the per-trigger yield of associated particles:

$C_{\rm trig}(\Delta\varphi,\Delta\eta)=\frac{1}{N_{\rm trig}}\frac{{\rm
d}^{2}N_{\text{pair}}}{{\rm d}\Delta\varphi{\rm d}\Delta\eta}=\frac{1}{N_{\rm
trig}^{\rm corr}}\frac{1}{\varepsilon_{\rm trig}\times\varepsilon_{\rm
assoc}}\frac{B(0,0)\times
S(\Delta\varphi,\Delta\eta)}{B(\Delta\varphi,\Delta\eta)},$ (3)

where $N_{\rm trig}^{\rm corr}$ is the efficiency corrected total number of
triggers, the $\varepsilon_{\rm trig}\times\varepsilon_{\rm assoc}$ denotes
the product of the $p_{\rm T}$-dependent acceptances and efficiencies for the
trigger hadron and associated particle, and $S(\Delta\varphi,\Delta\eta)$ and
$B(\Delta\varphi,\Delta\eta)$ are the same-event and mixed-event correlation
distributions, respectively [40, 41, 42]. Both the same- and mixed-event
correlations are corrected by the trigger and associated particle efficiency.
Here the mixed-event correlation is used as a pair acceptance correction, and
is normalized by the factor $B(0,0)$. This ensures that trigger and associated
particles traveling in the same direction ($\Delta\varphi\approx
0,\Delta\eta\approx 0$) have a pair acceptance equal to 1 [43]. This way of
restructuring the correlation function gives the number of correlated
trigger–associated particle pairs per trigger, or the yield of associated
particles per trigger.

When performing the correlation with a $\phi$(1020) meson, it is necessary to
remove the combinatorial background from the pool of $\phi$(1020) candidates.
To do this, the correlation measurement is performed for three different US
kaon-pair invariant mass regions: the $\phi$(1020) mass-peak region ($C_{\rm
trig}^{\text{h--(KK)}\text{ Peak}}$), the left sideband region ($C_{\rm
trig}^{\text{h--(KK)}\text{ LSB}}$), and the right sideband region ($C_{\rm
trig}^{\text{h--(KK)}\text{ RSB}}$). The correlations in the two sideband
regions are averaged together and used to estimate the shape of the
distribution produced by correlating the trigger hadron with a combinatorial
kaon pair. Taking into account these additional terms, the final correlation
function is given as:

$\displaystyle\begin{split}C_{\rm
trig}^{\text{h--}\phi}(\Delta\varphi,\Delta\eta)&=k_{\text{Signal}}\biggl{(}C_{\rm
trig}^{\text{h--(KK)}\text{ Peak}}(\Delta\varphi,\Delta\eta)\\\
&-\frac{k_{\text{LS}}}{2}\biggl{[}\frac{1}{N_{\rm Int}^{\rm LSB}}C_{\rm
trig}^{\text{h--(KK)}\text{ LSB}}(\Delta\varphi,\Delta\eta)+\frac{1}{N_{\rm
Int}^{\rm RSB}}C_{\rm trig}^{\text{h--(KK)}\text{
RSB}}(\Delta\varphi,\Delta\eta)\biggr{]}\biggr{)},\end{split}$ (4)

where the factor $k_{\text{LS}}$ is the integral of the scaled LS distribution
in the mass-peak region, $1/k_{\text{Signal}}$ is the fraction of $\phi(1020)$
in the mass-peak region (defined in Section 3.2.2), and factors $N_{\rm
Int}^{\rm RSB}$ and $N_{\rm Int}^{\rm LSB}$ are defined as the total integral
of the correlations for the right and left sideband regions, respectively.
Scaling by the inverse of these total integral factors normalizes the
correlation functions in the sideband regions to an integral of 1, before
scaling up by factor $k_{\text{LS}}$ to estimate the background under the peak
region.

Once the correlation is computed, the angular correlation is divided up into
three different regions to calculate the yields of correlated pairs: the near-
side jet (centered at $\Delta\varphi=0$), the away-side jet (centered at
$\Delta\varphi=\pi$), and the underlying event which lies under both jet
peaks. To reduce the error in this separation due to statistical noise, the
two-dimensional correlation is projected onto $\Delta\varphi$ from the range
$-1.2<\Delta\eta<1.2$. The range for $\Delta\varphi$ is chosen to be
$[-\pi/2,3\pi/2]$. In order to separate the jet peaks from the underlying
event in this one-dimensional correlation, a flat background estimate is used
by computing the average over the region farthest away from the two jet peaks
($-\pi/2<\Delta\varphi<-\pi/4$, $\pi/4<\Delta\varphi<5\pi/8$, and
$11\pi/8<\Delta\varphi<3\pi/2$). The near-side (away-side) yield is then
defined as the area above this background in the region
$-\pi/2<\Delta\varphi<\pi/2$ ($\pi/2<\Delta\varphi<3\pi/2$). The underlying-
event yield is computed as the area underneath this flat background across the
full $\Delta\varphi$ range.

##  5 Systematic uncertainties

The dominant systematic uncertainties for this analysis are divided into two
main categories: those associated with the h–$\phi$ correlation measurement
directly, which are related to the detector tracking efficiency, the
identification of produced kaons, and the $\phi(1020)$ reconstruction method;
and those associated with separating the correlation measurement into jet and
underlying-event contributions. The tracking systematic uncertainties include
contributions from the global tracking efficiency, the material budget of the
detector, and the track selection cuts. These uncertainties are estimated as
in Ref. [44], and are found to be $p_{\rm T}$ independent. Their contribution
provides the largest uncertainty to the associated yields, approximately 3.6%
for the hadron yields and 5.2% for the yields of the reconstructed
$\phi$(1020). The error associated with the PID selection of kaon candidates
was estimated by independently varying the ranges for $n\sigma_{\mathrm{TPC}}$
and $n\sigma_{\mathrm{TOF}}$ between $n=2$ and $n=3$ by steps of 0.2 and 0.5
for the TPC and TOF selection cuts, respectively.

For the h–$\phi$ correlation, additional systematic uncertainties due to the
$\phi(1020)$ reconstruction method are estimated by varying the invariant-mass
ranges used for the $\phi$(1020) mass peak, and varying the left and right
sideband mass regions used for combinatorial background subtraction. The mass-
peak region is varied from
$1.010<m_{\mathrm{KK}}<$1.030\text{\,}\mathrm{G}\mathrm{eV/\text{$c$}}^{2}$$
to
$1.015<m_{\mathrm{KK}}<$1.025\text{\,}\mathrm{G}\mathrm{eV/\text{$c$}}^{2}$$
in steps of $2\text{\,}\mathrm{M}\mathrm{eV/\text{$c$}}^{2}$, leading to a
variation of the total fraction of $\phi$(1020) signal that is measured from
90% to 76%. The systematics due to the choice of sideband regions are
estimated by independently varying the upper and lower limit of the left and
right sideband by $\pm$5\text{\,}\mathrm{M}\mathrm{eV/\text{$c$}}^{2}$$, as
well as using only the left or only the right sideband for the background
estimation. The contributions of these systematic uncertainties are detailed
in Table 1. All systematic effects considered for the correlation measurement
are $\Delta\varphi$ independent and are applied evenly to the full
$\Delta\varphi$ correlation. These uncertainties also do not vary within the
$p_{\rm T}$ ranges considered, and therefore are applied equally to the two
$p_{\rm T}$ ranges considered in this analysis. The dominant systematic
uncertainty for the correlation emerges due to uncertainties in the tracking
efficiency correction.

Table 1: Summary of the systematic uncertainty on the h–$\phi$ angular
correlation for each multiplicity interval, expressed in percent of the
central value. The table also quotes the average statistical relative error
per $\Delta\varphi$ interval for comparison. h–$\phi$ Relative Error per
$\Delta\varphi$ Bin
---
Multiplicity | 0–20% | 20–50% | 50–80%
Tracking eff. | 5.2% | 5.2% | 5.2%
PID selection | 1.0% | 1.0% | 1.0%
Mass-peak range | 0.8% | 1.3% | 2.7%
LSB mass range | 1.1% | 0.9% | 2.6%
RSB mass range | 0.2% | 0.3% | 1.2%
$k_{\text{LS}}$ scale factor | 0.9% | 0.6% | 3.0%
$k_{\text{Signal}}$ scale factor | 0.8% | 0.8% | 0.8%
Total syst. | 5.6% | 5.6% | 7.3%
Avg. statistical error | 2.2% | 3.2% | 7.3%

In addition to the sources of systematic uncertainty for the correlation
measurement in Table 1, the pairwise yield measurement includes an additional
uncertainty stemming from the method of determining the underlying-event
baseline to separate the jet and non-jet yields. This systematic is determined
using a variety of different background estimation methods: fitting a constant
to different $\Delta\varphi$ subranges around the minima of the correlation
function (i.e. only considering points around the near-side peak, only points
around the away-side peak, and combinations of both subranges), the zero yield
at minimum (ZYAM) method as detailed in Refs. [45, 46], and a full fit with
two Gaussian distributions for the near- and away-side peaks plus a constant
for the background. The systematic uncertainty on the background is determined
by taking the standard deviation of these values across the different
background methods, giving an uncertainty for the near-side yields ranging
from $\sim$2% in the high-multiplicity h–$\phi$ correlation, to $\sim$5% in
the lowest multiplicity class. The h–$\phi$ away-side yields, being more
dependent on the background estimation, are found to have a systematic
uncertainty of $\sim 7\%$ for each multiplicity class, while the effect on the
underlying event yields was negligible. For the h–h correlations, all
different background estimation methods give similar values ($<1\%$ difference
to the nominal yields), and the systematic effects from the h–h background
estimation are considered as negligible. For both the h–$\phi$ and h–h yield
measurements, the systematic uncertainties are correlated between the near-
and away-side yields, and therefore have a negligible effect on differences
between the two.

The central measurement uses a flat background estimate to separate jet and
underlying-event contributions. However, previous studies have measured
collective behavior of particles produced within p–Pb collisions [47]. In two-
particle angular correlations, this collective expansion manifests itself as
an anisotropic azimuthal distribution, which can be expanded as a Fourier
series in $\Delta\varphi$. Higher-order components of the collective flow have
a negligible effect at the momentum ranges studied here. However, the second
Fourier coefficient ($v_{2}$), referred to as elliptic flow and characterized
by the simplified equation
$a_{0}(1+2v^{\text{h}-\phi}_{2}\cos(2\Delta\varphi))$, does affect our yield
measurements. Given the absence of published direct measurement of the
$\phi$(1020) meson $v^{\phi}_{2}$ in p–Pb collisions, we have chosen the flat
background assumption as the basis for the central values and include an
additional systematic uncertainty for the near- and away-side jet yields based
on using the charged hadron $v^{\text{h}}_{2}$ measured by the ALICE
Collaboration [48]. Cross-checks performed using long-range correlations to
estimate the $v^{\phi}_{2}$ from the p–Pb data being used for this paper are
consistent with the inclusive charged-hadron $v^{\text{h}}_{2}$ that was
chosen as an estimate. As the measured $v^{\text{h}}_{2}$ is momentum
dependent, this cross-check is performed separately for the lower associated
particle $p_{\rm T}$ interval ($1.5<p_{\rm T}{}<$
$2.5\text{\,}\mathrm{GeV/\text{$c$}}$) and the higher associated particle
$p_{\rm T}$ interval ($2.5<p_{\rm T}{}<$
$4.0\text{\,}\mathrm{GeV/\text{$c$}}$). For the low momentum h–$\phi$ (h–h)
yields, the non-zero $v_{2}$ assumption lowers the jet yields between 4% (1%)
for the lowest multiplicity and 25% (16%) for the highest multiplicity.
Likewise, the high momentum h–$\phi$ (h–h) yields are lowered between 1.4%
(0.6%) for the lowest multiplicity and 16% (9%) for the highest multiplicity.
Due to the non-zero $v_{2}$ affecting the near- and away-side yields in
similar amounts, this phenomenon does not affect any differences in behavior
between the two jet peaks. Likewise, because the h–$\phi$ and h–h correlations
are affected by the $v_{2}$ in similar amounts, the impact on the final ratio
is small, with a negligible effect at low multiplicity and an approximately
10% (8%) effect on the highest-multiplicity jet-yield ratios for the
measurement in the low (high) associated $p_{\rm T}$ interval.

##  6 Results

The measured per-trigger h–$\phi$ correlation functions for the lower
($1.5<p_{\rm T}<$ $2.5\text{\,}\mathrm{GeV/\text{$c$}}$) and higher
($2.5<p_{\rm T}<$ $4.0\text{\,}\mathrm{GeV/\text{$c$}}$) associated $p_{\rm
T}$ intervals can be seen in Fig. 2. For ease of comparison, the $y$-axes of
the plots are kept at a constant scale across multiplicities, and the ranges
are set such that the non-jet background is aligned across all three
multiplicity classes. The width of the grey band represents the systematic
uncertainty on the underlying-event background, while the dotted line displays
the $v_{2}$ background estimate, as discussed in the previous section. In both
the low and high momentum correlations, the majority of h–$\phi$ pairs are
found within the underlying-event region, with this fraction increasing
significantly as a function of charged-particle multiplicity. The per-trigger
dihadron correlation functions can be seen in Fig. 3. The background and
$v_{2}$ estimates are again shown. Similarly to the h–$\phi$ correlations, the
majority of h–h pairs are found in the underlying event.

Figure 2: Per-trigger h–$\phi$ $\Delta\varphi$ angular correlations integrated
over the range $|\Delta\eta|<1.2$ for the lower $p_{\rm T}$ range of
associated $\phi$(1020), $1.5<p_{\rm T}<$
$2.5\text{\,}\mathrm{GeV/\text{$c$}}$ (top), and the higher $p_{\rm T}$ range
of associated $\phi$(1020), $2.5<p_{\rm T}<$
$4.0\text{\,}\mathrm{GeV/\text{$c$}}$ (bottom). The three multiplicity classes
are plotted in increasing order from left to right. All plots for a given
associated particle $p_{\rm T}$ have the same axis scale, with offsets set to
their respective underlying background estimation. Statistical uncertainties
are represented as error bars, while systematic uncertainties are marked with
open rectangles. The grey band shows the systematic range for the different
background estimation methods. The long dashed line shows the flat background
assumption, while the short dotted line shows the alternative background with
non-zero $v_{2}$ (used as systematic for pair yields).

Figure 3: Per-trigger dihadron $\Delta\varphi$ angular correlations integrated
over the range $|\Delta\eta|<1.2$ for the lower $p_{\rm T}$ range of
associated hadrons, $1.5<p_{\rm T}{}<$ $2.5\text{\,}\mathrm{GeV/\text{$c$}}$
(top), and the higher $p_{\rm T}$ range of associated hadrons, $2.5<p_{\rm
T}{}<$ $4.0\text{\,}\mathrm{GeV/\text{$c$}}$ (bottom). The three multiplicity
classes are plotted in increasing order from left to right. All plots for a
given associated particle $p_{\rm T}$ have the same axis scale, with offsets
set to their respective underlying background estimation. Statistical
uncertainties are represented as error bars, while systematic uncertainties
are marked as open rectangles.

Yields of per-trigger correlated pairs are extracted from the correlation
function for the near- and away-side jet peaks by integrating above the
background in the defined $\Delta\varphi$ ranges, and are shown versus
multiplicity for the lower and higher associated-particle momentum interval in
Fig. 4. Here the three multiplicity percentile bins are converted to mean
charged-particle multiplicity $\langle N_{\mathrm{ch}}\rangle$ in the range
$|\eta|<0.5$ and $p_{\rm T}>\text{$0.15\text{\,}\mathrm{GeV/\text{$c$}}$}$,
calculated using similar techniques to Ref. [49], but with the additional
requirement of a high-$p_{\rm T}$ trigger hadron present in the event. Due to
the per-trigger scaling, these pairwise yields are good proxies for the actual
$\phi$(1020) and inclusive-hadron per-trigger yields in the different
azimuthal regions. The differences between the jet yields found using the non-
zero $v_{2}$ contribution and the flat background estimates are depicted as
shaded bands in the figures. While the magnitude of the $v_{2}$ effect on the
yield grows with multiplicity, the near- and away-side yields are lowered by
similar amounts, and therefore differences between the behavior of the near-
and away-side yields are independent of the background $v_{2}$ assumption. The
other systematic uncertainties, namely those associated with the tracking
efficiency estimation and the $\phi$(1020) reconstruction, are correlated
across multiplicity. Since all of the considered sources of systematic
uncertainty are correlated between the near- and away-side yields (i.e. they
have a negligible effect on the relative difference between the near- and
away-side yields, and therefore no effect on the difference in trends with
increasing multiplicity between the two), they are not shown in these plots.

In both associated particle momentum intervals, the dihadron jet-peak yields
seen in Fig. 4 show little to no change with increasing multiplicity, with the
yields in the away-side jet consistently lower than for the near-side jet.
However, for the h–$\phi$ jet yields, there is a distinct difference in
behavior between the two momentum regions. For the higher momentum region, the
near- and away-side yields are consistent within uncertainties. They also are
consistent with a flat trend with respect to multiplicity, taking into account
uncertainties and the $v_{2}$ systematic effect. In contrast, for the lower
momentum region the data suggest an increase in the jet yields as a function
of multiplicity. To test the increase in the low momentum jet yields, the data
is fit with a flat line assuming the null hypothesis of no dependence on
$\langle N_{\mathrm{ch}}\rangle$. The $\chi^{2}$ probability of this fit gives
a p-value of 0.06 and 0.001 for the near- and away-side, respectively.
Performing the same test using the non-zero $v_{2}$ assumption for the UE
background, which lowers the increase seen in both jet yields, gives a p-value
of 0.36 and 0.03 for the near- and away-side, respectively. Figure 4 also
shows a hint that at low momentum the away-side yields grow faster as a
function of multiplicity than the near-side yields. Differences in near-side
and away-side jet yields for high-multiplicity collisions could point to
several physical effects, including differences in $\phi$(1020) production
within quark-like and gluon-like jets [50, 51], as well as possible jet-medium
interactions affecting strangeness production. However, more precise
measurements on future larger data samples are needed to demonstrate
significance in the different trends for the near- and away-side yields.

Figure 4: Per-trigger pair yields within the near- and away-side jet peaks as
a function of charged-particle multiplicity, for the low (left) and high
(right) associated momentum bins. The shaded regions represent the deviation
in the yields from using a non-zero $v_{2}$ when estimating the underlying-
event background. Closed points are the jet yields from the h–$\phi$
correlations, while open points are the jet yields from the dihadron
correlation. Only statistical uncertainties are shown. Systematic
uncertainties are on the order of 10% (15%) of the near-side (away-side)
associated $\phi$(1020) yields. As these uncertainties are correlated across
multiplicity and between the near- and away-side yields, they are not plotted
here.

To directly compare the behavior of the $\phi$(1020) production in jets and
the underlying event, it is useful to look at the pairwise yield ratio
(h–$\phi$)/(h–h) as a function of multiplicity as a proxy for the
$\phi/\mathrm{h}$ ratio. These ratios are plotted for the low-momentum and
high-momentum regions in Fig. 5 and Fig. 5, respectively. The ratios for the
near-side (red), away-side (blue), and the underlying event (green) show the
(h–$\phi$)/(h–h) yield ratio within their respective region of the measure
two-particle correlations. The total ratio (violet) represents the full
(h–$\phi$)/(h–h) yield ratio. The variation between the ratios using a flat
background estimation and using the non-zero $v_{2}$ assumption is depicted as
a shaded area below the central points. This non-zero $v_{2}$ assumption has a
small effect on the near- and away-side ratios that increases slightly with
multiplicity. The effect of the $v_{2}$ on the underlying event ratios is
negligible for all multiplicities.

Figure 5: Pairwise (h–$\phi$)/(h–h) ratio vs $\langle N_{\mathrm{ch}}\rangle$
for the lower associated momentum range, $1.5<p_{\rm
T}<$$2.5\text{\,}\mathrm{GeV/\text{$c$}}$ (top), and higher associated
momentum range, $2.5<p_{\rm T}<$$4.0\text{\,}\mathrm{GeV/\text{$c$}}$
(bottom). Statistical uncertainties are presented as error bars, while
systematic uncertainties, including uncertainties on the calculated $\langle
N_{\mathrm{ch}}\rangle$ value, are shown as open boxes. The additional
uncertainty in the ratio due to a non-zero $v_{2}$ in the underlying-event
background estimation is depicted as a gray band. Systematic uncertainties for
the (h–h) and (h–$\phi$) yields are treated as uncorrelated when calculating
the uncertainty on the ratio. Ratios from p–Pb data are depicted as solid
points, while ratios from PYTHIA 8 [52] simulations of pp events are depicted
as open points.

From the pairwise (h–$\phi$)/(h–h) ratios, the ratio in the underlying event
is observed to be significantly higher than the ratio within the jets for both
momentum intervals considered here by $\sim 5\sigma$, and is consistent with
being multiplicity independent. The away-side ratio is higher than the near-
side ratio for all multiplicities in both momentum ranges. As these pairwise
ratios are approximately equal to the per-trigger $\phi/\mathrm{h}$ ratio,
this suggests that $\phi$(1020) mesons have a higher probability to be
produced outside of the jet compared to unidentified hadrons. It can also be
seen that the total (h–$\phi$)/(h–h) ratio (violet) is predominantly
determined by the underlying event production, as the majority of h–h and
h–$\phi$ pairs do not come from the jets. In fact, the increase in the total
(h–$\phi$)/(h–h) ratio is a consequence of the change in the contribution from
jets as a function of multiplicity: for the low (high) momentum range, the
fraction of total hadrons coming from jets decreases from approximately 20%
(35%) to 10% (20%) as the multiplicity increases.

The general ordering of the ratios (e.g. jet-yield ratios are consistently
lower than the underlying-event yield ratios) is seen for both momentum
ranges. In the lower momentum interval, the away-side ratio shows a hint of an
increase of ($140\pm 90$)% from the lowest to the highest multiplicity, nearly
reaching the values of the underlying event ratio at high multiplicity. The
convergence of the ratio in the away-side jet and the underlying event at high
multiplicity is suggestive of a change in the production measured within the
away-side jet, from a more jet-like $\phi/\mathrm{h}$ production towards a
more medium-like $\phi/\mathrm{h}$ production. The near-side jet also shows
hints of an increase by ($100\pm 75$)%, though a larger data sample is needed
to better constrain this possible increase in jet production of $\phi(1020)$.
In the higher momentum interval both the near- and away-side ratios are
consistent with a flat trend with increasing multiplicity.

These results are also compared to (h–$\phi$)/(h–h) yield ratio calculated for
a pp baseline using the Monash 2013 tune of the PYTHIA 8 Monte Carlo generator
[52]. This is used to establish the modeled behavior of strangeness production
in jets and the underlying event within small collisions without an explicit
deconfined phase. Similarly to data, PYTHIA 8 events are separated into two
multiplicity classes, 0–20% and 20–80%, based on the particle production in
the forward V0A acceptance. The mean multiplicity in the central
pseudorapidity region is then measured for each of the two PYTHIA 8
multiplicity classes to compare directly with data. The PYTHIA 8 ratios within
the near- and away-side jet are consistent with the low multiplicity p–Pb
data. For both momentum bins, PYTHIA 8 under-predicts the ratio in the
underlying event, as well as the total ratio, by roughly a factor of 3. Unlike
the measured ratios, the ratios predicted by PYTHIA show no changes with
respect to multiplicity for both the jet and underlying-event contributions.

##  7 Conclusion

This paper presents new measurements of h–$\phi$ angular correlations in p–Pb
collisions at $\sqrt{s_{\rm NN}}{}=$5.02\text{\,}\mathrm{TeV}$$. The
correlations are measured for associated $\phi$(1020) mesons in two transverse
momentum intervals and in three multiplicity ranges in order to obtain the
per-trigger yields of $\phi$(1020) mesons in the near-side peak, away-side
peak, and underlying event separately. The measured yields show a distinct
difference in behavior between the two momentum ranges. While the $\phi$(1020)
yields measured in jets in the lower momentum interval tends to increase with
increasing multiplicity, the yield measured in the higher momentum interval is
roughly independent of event multiplicity. Further, the $\phi$(1020) yield
measured in the lower momentum interval in the away-side jet tends to exhibit
faster growth with multiplicity when compared to the near-side jet. This
suggests changes to the strangeness production within the away-side jet with
increasing multiplicity, though further study is needed to differentiate
between possible physical causes.

Also presented are the first measurements of differentiated pairwise
(h–$\phi$)/(h–h) yield ratios corresponding to the $\phi/\mathrm{h}$ ratio
within jets and within the underlying event in p–Pb events. These ratios show
a clear $\phi/\mathrm{h}$ ordering for both momentum ranges. The ratio is
lowest in the near-side jet, followed by the away-side, and the underlying
event is the highest. Interestingly, the (h–$\phi$)/(h–h) ratio within the
underlying event is significantly higher than the ratio in jets even within
the lowest multiplicity events, showing a $\sim 5\sigma$ difference between
the underlying-event ratio and the jet ratio for the lower momentum yields.
This difference in production at mid-$p_{\rm T}$ even in low multiplicity p–Pb
events invites further study of strangeness production at lower event
multiplicity.

It can be seen that the ratios obtained in the underlying event, as well as in
the jets within the higher momentum interval, do not exhibit significant
dependence on multiplicity. However, the away-side jet ratio in the lower
momentum range shows a slight enhancement as a function of charged-particle
multiplicity. The yield ratios in jets are consistent between PYTHIA generated
pp events and low multiplicity p–Pb events for both momentum ranges
considered. However, the total and underlying event ratio within p–Pb data is
significantly higher than the PYTHIA 8 pp baseline, across all multiplicities.
This points to the enhanced $\phi$/h production within the underlying-event
even in low multiplicity p–Pb events. The trends in these ratios point to the
need for further studies in small systems such as high-multiplicity pp
collisions, and demonstrate the usefulness of differentiated particle yields
in constraining the different sources of strangeness production and
enhancement.

## Acknowledgements

The ALICE Collaboration would like to thank all its engineers and technicians for their invaluable contributions to the construction of the experiment and the CERN accelerator teams for the outstanding performance of the LHC complex. The ALICE Collaboration gratefully acknowledges the resources and support provided by all Grid centres and the Worldwide LHC Computing Grid (WLCG) collaboration. The ALICE Collaboration acknowledges the following funding agencies for their support in building and running the ALICE detector: A. I. Alikhanyan National Science Laboratory (Yerevan Physics Institute) Foundation (ANSL), State Committee of Science and World Federation of Scientists (WFS), Armenia; Austrian Academy of Sciences, Austrian Science Fund (FWF): [M 2467-N36] and Nationalstiftung für Forschung, Technologie und Entwicklung, Austria; Ministry of Communications and High Technologies, National Nuclear Research Center, Azerbaijan; Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Financiadora de Estudos e Projetos (Finep), Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) and Universidade Federal do Rio Grande do Sul (UFRGS), Brazil; Bulgarian Ministry of Education and Science, within the National Roadmap for Research Infrastructures 2020-2027 (object CERN), Bulgaria; Ministry of Education of China (MOEC) , Ministry of Science & Technology of China (MSTC) and National Natural Science Foundation of China (NSFC), China; Ministry of Science and Education and Croatian Science Foundation, Croatia; Centro de Aplicaciones Tecnológicas y Desarrollo Nuclear (CEADEN), Cubaenergía, Cuba; Ministry of Education, Youth and Sports of the Czech Republic, Czech Republic; The Danish Council for Independent Research | Natural Sciences, the VILLUM FONDEN and Danish National Research Foundation (DNRF), Denmark; Helsinki Institute of Physics (HIP), Finland; Commissariat à l’Energie Atomique (CEA) and Institut National de Physique Nucléaire et de Physique des Particules (IN2P3) and Centre National de la Recherche Scientifique (CNRS), France; Bundesministerium für Bildung und Forschung (BMBF) and GSI Helmholtzzentrum für Schwerionenforschung GmbH, Germany; General Secretariat for Research and Technology, Ministry of Education, Research and Religions, Greece; National Research, Development and Innovation Office, Hungary; Department of Atomic Energy Government of India (DAE), Department of Science and Technology, Government of India (DST), University Grants Commission, Government of India (UGC) and Council of Scientific and Industrial Research (CSIR), India; National Research and Innovation Agency - BRIN, Indonesia; Istituto Nazionale di Fisica Nucleare (INFN), Italy; Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT) and Japan Society for the Promotion of Science (JSPS) KAKENHI, Japan; Consejo Nacional de Ciencia (CONACYT) y Tecnología, through Fondo de Cooperación Internacional en Ciencia y Tecnología (FONCICYT) and Dirección General de Asuntos del Personal Academico (DGAPA), Mexico; Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO), Netherlands; The Research Council of Norway, Norway; Pontificia Universidad Católica del Perú, Peru; Ministry of Science and Higher Education, National Science Centre and WUT ID-UB, Poland; Korea Institute of Science and Technology Information and National Research Foundation of Korea (NRF), Republic of Korea; Ministry of Education and Scientific Research, Institute of Atomic Physics, Ministry of Research and Innovation and Institute of Atomic Physics and Universitatea Nationala de Stiinta si Tehnologie Politehnica Bucuresti, Romania; Ministry of Education, Science, Research and Sport of the Slovak Republic, Slovakia; National Research Foundation of South Africa, South Africa; Swedish Research Council (VR) and Knut & Alice Wallenberg Foundation (KAW), Sweden; European Organization for Nuclear Research, Switzerland; Suranaree University of Technology (SUT), National Science and Technology Development Agency (NSTDA) and National Science, Research and Innovation Fund (NSRF via PMU-B B05F650021), Thailand; Turkish Energy, Nuclear and Mineral Research Agency (TENMAK), 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. In addition, individual groups or members have received support from: Czech Science Foundation (grant no. 23-07499S), Czech Republic; European Research Council (grant no. 950692), European Union; ICSC - Centro Nazionale di Ricerca in High Performance Computing, Big Data and Quantum Computing, European Union - NextGenerationEU; Academy of Finland (Center of Excellence in Quark Matter) (grant nos. 346327, 346328), Finland.

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##  Appendix A The ALICE Collaboration

S. Acharya 127, D. Adamová 86, A. Agarwal135, G. Aglieri Rinella 32, L.
Aglietta24, M. Agnello 29, N. Agrawal 25, Z. Ahammed 135, S. Ahmad 15, S.U.
Ahn 71, I. Ahuja 37, A. Akindinov 141, V. Akishina38, M. Al-Turany 97, D.
Aleksandrov 141, B. Alessandro 56, H.M. Alfanda 6, R. Alfaro Molina 67, B. Ali
15, A. Alici 25, N. Alizadehvandchali 116, A. Alkin 104, J. Alme 20, G. Alocco
52, T. Alt 64, A.R. Altamura 50, I. Altsybeev 95, J.R. Alvarado 44, M.N. Anaam
6, C. Andrei 45, N. Andreou 115, A. Andronic 126, E. Andronov 141, V. Anguelov
94, F. Antinori 54, P. Antonioli 51, N. Apadula 74, L. Aphecetche 103, H.
Appelshäuser 64, C. Arata 73, S. Arcelli 25, M. Aresti 22, R. Arnaldi 56,
J.G.M.C.A. Arneiro 110, I.C. Arsene 19, M. Arslandok 138, A. Augustinus 32, R.
Averbeck 97, D. Averyanov 141, M.D. Azmi 15, H. Baba124, A. Badalà 53, J. Bae
104, Y.W. Baek 40, X. Bai 120, R. Bailhache 64, Y. Bailung 48, R. Bala 91, A.
Balbino 29, A. Baldisseri 130, B. Balis 2, D. Banerjee 4, Z. Banoo 91, V.
Barbasova37, F. Barile 31, L. Barioglio 56, M. Barlou78, B. Barman41, G.G.
Barnaföldi 46, L.S. Barnby 115, E. Barreau 103, V. Barret 127, L. Barreto 110,
C. Bartels 119, K. Barth 32, E. Bartsch 64, N. Bastid 127, S. Basu 75, G.
Batigne 103, D. Battistini 95, B. Batyunya 142, D. Bauri47, J.L. Bazo Alba
101, I.G. Bearden 83, C. Beattie 138, P. Becht 97, D. Behera 48, I. Belikov
129, A.D.C. Bell Hechavarria 126, F. Bellini 25, R. Bellwied 116, S.
Belokurova 141, L.G.E. Beltran 109, Y.A.V. Beltran 44, G. Bencedi 46, A.
Bensaoula116, S. Beole 24, Y. Berdnikov 141, A. Berdnikova 94, L. Bergmann 94,
M.G. Besoiu 63, L. Betev 32, P.P. Bhaduri 135, A. Bhasin 91, B. Bhattacharjee
41, L. Bianchi 24, N. Bianchi 49, J. Bielčík 35, J. Bielčíková 86, A.P. Bigot
129, A. Bilandzic 95, G. Biro 46, S. Biswas 4, N. Bize 103, J.T. Blair 108, D.
Blau 141, M.B. Blidaru 97, N. Bluhme38, C. Blume 64, G. Boca 21,55, F. Bock
87, T. Bodova 20, J. Bok 16, L. Boldizsár 46, M. Bombara 37, P.M. Bond 32, G.
Bonomi 134,55, H. Borel 130, A. Borissov 141, A.G. Borquez Carcamo 94, H.
Bossi 138, E. Botta 24, Y.E.M. Bouziani 64, L. Bratrud 64, P. Braun-Munzinger
97, M. Bregant 110, M. Broz 35, G.E. Bruno 96,31, V.D. Buchakchiev 36, M.D.
Buckland 23, D. Budnikov 141, H. Buesching 64, S. Bufalino 29, P. Buhler 102,
N. Burmasov 141, Z. Buthelezi 68,123, A. Bylinkin 20, S.A. Bysiak107, J.C.
Cabanillas Noris 109, M.F.T. Cabrera116, M. Cai 6, H. Caines 138, A. Caliva
28, E. Calvo Villar 101, J.M.M. Camacho 109, P. Camerini 23, F.D.M. Canedo
110, S.L. Cantway 138, M. Carabas 113, A.A. Carballo 32, F. Carnesecchi 32, R.
Caron 128, L.A.D. Carvalho 110, J. Castillo Castellanos 130, M. Castoldi 32,
F. Catalano 32, S. Cattaruzzi 23, C. Ceballos Sanchez 142, R. Cerri24, I.
Chakaberia 74, P. Chakraborty 136,47, S. Chandra 135, S. Chapeland 32, M.
Chartier 119, S. Chattopadhay135, S. Chattopadhyay 135, S. Chattopadhyay 99,
M. Chen39, T. Cheng 97,6, C. Cheshkov 128, V. Chibante Barroso 32, D.D.
Chinellato 111, E.S. Chizzali II,95, J. Cho 58, S. Cho 58, P. Chochula 32,
Z.A. Chochulska136, D. Choudhury41, P. Christakoglou 84, C.H. Christensen 83,
P. Christiansen 75, T. Chujo 125, M. Ciacco 29, C. Cicalo 52, M.R. Ciupek97,
G. ClaiIII,51, F. Colamaria 50, J.S. Colburn100, D. Colella 31, M. Colocci 25,
M. Concas 32, G. Conesa Balbastre 73, Z. Conesa del Valle 131, G. Contin 23,
J.G. Contreras 35, M.L. Coquet 103,130, P. Cortese 133,56, M.R. Cosentino 112,
F. Costa 32, S. Costanza 21,55, C. Cot 131, J. Crkovská 94, P. Crochet 127, R.
Cruz-Torres 74, P. Cui 6, M.M. Czarnynoga136, A. Dainese 54, G. Dange38, M.C.
Danisch 94, A. Danu 63, P. Das 80, P. Das 4, S. Das 4, A.R. Dash 126, S. Dash
47, A. De Caro 28, G. de Cataldo 50, J. de Cuveland38, A. De Falco 22, D. De
Gruttola 28, N. De Marco 56, C. De Martin 23, S. De Pasquale 28, R. Deb 134,
R. Del Grande 95, L. Dello Stritto 32, W. Deng 6, K.C. Devereaux18, P.
Dhankher 18, D. Di Bari 31, A. Di Mauro 32, B. Diab 130, R.A. Diaz 142,7, T.
Dietel 114, Y. Ding 6, J. Ditzel 64, R. Divià 32, Ø. Djuvsland20, U. Dmitrieva
141, A. Dobrin 63, B. Dönigus 64, J.M. Dubinski 136, A. Dubla 97, P. Dupieux
127, N. Dzalaiova13, T.M. Eder 126, R.J. Ehlers 74, F. Eisenhut 64, R.
Ejima92, D. Elia 50, B. Erazmus 103, F. Ercolessi 25, B. Espagnon 131, G.
Eulisse 32, D. Evans 100, S. Evdokimov 141, L. Fabbietti 95, M. Faggin 23, J.
Faivre 73, F. Fan 6, W. Fan 74, A. Fantoni 49, M. Fasel 87, A. Feliciello 56,
G. Feofilov 141, A. Fernández Téllez 44, L. Ferrandi 110, M.B. Ferrer 32, A.
Ferrero 130, C. Ferrero IV,56, A. Ferretti 24, V.J.G. Feuillard 94, V. Filova
35, D. Finogeev 141, F.M. Fionda 52, E. Flatland32, F. Flor 138,116, A.N.
Flores 108, S. Foertsch 68, I. Fokin 94, S. Fokin 141, U. FolloIV,56, E.
Fragiacomo 57, E. Frajna 46, U. Fuchs 32, N. Funicello 28, C. Furget 73, A.
Furs 141, T. Fusayasu 98, J.J. Gaardhøje 83, M. Gagliardi 24, A.M. Gago 101,
T. Gahlaut47, C.D. Galvan 109, D.R. Gangadharan 116, P. Ganoti 78, C.
Garabatos 97, J.M. Garcia44, T. García Chávez 44, E. Garcia-Solis 9, C.
Gargiulo 32, P. Gasik 97, H.M. Gaur38, A. Gautam 118, M.B. Gay Ducati 66, M.
Germain 103, C. Ghosh135, M. Giacalone 51, G. Gioachin 29, P. Giubellino
97,56, P. Giubilato 27, A.M.C. Glaenzer 130, P. Glässel 94, E. Glimos 122,
D.J.Q. Goh76, V. Gonzalez 137, P. Gordeev 141, M. Gorgon 2, K. Goswami 48, S.
Gotovac33, V. Grabski 67, L.K. Graczykowski 136, E. Grecka 86, A. Grelli 59,
C. Grigoras 32, V. Grigoriev 141, S. Grigoryan 142,1, F. Grosa 32, J.F.
Grosse-Oetringhaus 32, R. Grosso 97, D. Grund 35, N.A. Grunwald94, G.G.
Guardiano 111, R. Guernane 73, M. Guilbaud 103, K. Gulbrandsen 83, J.J.W.K.
Gumprecht102, T. Gündem 64, T. Gunji 124, W. Guo 6, A. Gupta 91, R. Gupta 91,
R. Gupta 48, K. Gwizdziel 136, L. Gyulai 46, C. Hadjidakis 131, F.U. Haider
91, S. Haidlova 35, M. Haldar4, H. Hamagaki 76, A. Hamdi 74, Y. Han 139, B.G.
Hanley 137, R. Hannigan 108, J. Hansen 75, M.R. Haque 97, J.W. Harris 138, A.
Harton 9, M.V. Hartung 64, H. Hassan 117, D. Hatzifotiadou 51, P. Hauer 42,
L.B. Havener 138, E. Hellbär 97, H. Helstrup 34, M. Hemmer 64, T. Herman 35,
S.G. Hernandez116, G. Herrera Corral 8, S. Herrmann 128, K.F. Hetland 34, B.
Heybeck 64, H. Hillemanns 32, B. Hippolyte 129, F.W. Hoffmann 70, B. Hofman
59, G.H. Hong 139, M. Horst 95, A. Horzyk 2, Y. Hou 6, P. Hristov 32, P.
Huhn64, L.M. Huhta 117, T.J. Humanic 88, A. Hutson 116, D. Hutter 38, M.C.
Hwang 18, R. Ilkaev141, M. Inaba 125, G.M. Innocenti 32, M. Ippolitov 141, A.
Isakov 84, T. Isidori 118, M.S. Islam 99, S. Iurchenko141, M. Ivanov13, M.
Ivanov 97, V. Ivanov 141, K.E. Iversen 75, M. Jablonski 2, B. Jacak 18,74, N.
Jacazio 25, P.M. Jacobs 74, S. Jadlovska106, J. Jadlovsky106, S. Jaelani 82,
C. Jahnke 110, M.J. Jakubowska 136, M.A. Janik 136, T. Janson70, S. Ji 16, S.
Jia 10, F. Jonas 74, D.M. Jones 119, J.M. Jowett 32,97, J. Jung 64, M. Jung
64, A. Junique 32, A. Jusko 100, J. Kaewjai105, P. Kalinak 60, A. Kalweit 32,
A. Karasu Uysal V,72, D. Karatovic 89, N. Karatzenis100, O. Karavichev 141, T.
Karavicheva 141, E. Karpechev 141, M.J. Karwowska 32,136, U. Kebschull 70, R.
Keidel 140, M. Keil 32, B. Ketzer 42, S.S. Khade 48, A.M. Khan 120, S. Khan
15, A. Khanzadeev 141, Y. Kharlov 141, A. Khatun 118, A. Khuntia 35, Z.
Khuranova 64, B. Kileng 34, B. Kim 104, C. Kim 16, D.J. Kim 117, E.J. Kim 69,
J. Kim 139, J. Kim 58, J. Kim 32,69, M. Kim 18, S. Kim 17, T. Kim 139, K.
Kimura 92, A. Kirkova36, S. Kirsch 64, I. Kisel 38, S. Kiselev 141, A. Kisiel
136, J.P. Kitowski 2, J.L. Klay 5, J. Klein 32, S. Klein 74, C. Klein-Bösing
126, M. Kleiner 64, T. Klemenz 95, A. Kluge 32, C. Kobdaj 105, R. Kohara124,
T. Kollegger97, A. Kondratyev 142, N. Kondratyeva 141, J. Konig 64, S.A.
Konigstorfer 95, P.J. Konopka 32, G. Kornakov 136, M. Korwieser 95, S.D.
Koryciak 2, C. Koster84, A. Kotliarov 86, N. Kovacic89, V. Kovalenko 141, M.
Kowalski 107, V. Kozhuharov 36, I. Králik 60, A. Kravčáková 37, L. Krcal
32,38, M. Krivda 100,60, F. Krizek 86, K. Krizkova Gajdosova 32, C. Krug 66,
M. Krüger 64, D.M. Krupova 35, E. Kryshen 141, V. Kučera 58, C. Kuhn 129, P.G.
Kuijer 84, T. Kumaoka125, D. Kumar135, L. Kumar 90, N. Kumar90, S. Kumar 31,
S. Kundu 32, P. Kurashvili 79, A. Kurepin 141, A.B. Kurepin 141, A. Kuryakin
141, S. Kushpil 86, V. Kuskov 141, M. Kutyla136, A. Kuznetsov142, M.J. Kweon
58, Y. Kwon 139, S.L. La Pointe 38, P. La Rocca 26, A. Lakrathok105, M.
Lamanna 32, A.R. Landou 73, R. Langoy 121, P. Larionov 32, E. Laudi 32, L.
Lautner 32,95, R.A.N. Laveaga109, R. Lavicka 102, R. Lea 134,55, H. Lee 104,
I. Legrand 45, G. Legras 126, J. Lehrbach 38, A.M. Lejeune35, T.M. Lelek2,
R.C. Lemmon 85, I. León Monzón 109, M.M. Lesch 95, E.D. Lesser 18, P. Lévai
46, M. Li6, X. Li10, B.E. Liang-gilman 18, J. Lien 121, R. Lietava 100, I.
Likmeta 116, B. Lim 24, S.H. Lim 16, V. Lindenstruth 38, A. Lindner45, C.
Lippmann 97, D.H. Liu 6, J. Liu 119, G.S.S. Liveraro 111, I.M. Lofnes 20, C.
Loizides 87, S. Lokos 107, J. Lömker 59, X. Lopez 127, E. López Torres 7, P.
Lu 97,120, F.V. Lugo 67, J.R. Luhder 126, M. Lunardon 27, G. Luparello 57,
Y.G. Ma 39, M. Mager 32, A. Maire 129, E.M. Majerz2, M.V. Makariev 36, M.
Malaev 141, G. Malfattore 25, N.M. Malik 91, Q.W. Malik19, S.K. Malik 91, L.
Malinina I,VIII,142, D. Mallick 131, N. Mallick 48, G. Mandaglio 30,53, S.K.
Mandal 79, A. Manea 63, V. Manko 141, F. Manso 127, V. Manzari 50, Y. Mao 6,
R.W. Marcjan 2, G.V. Margagliotti 23, A. Margotti 51, A. Marín 97, C. Markert
108, P. Martinengo 32, M.I. Martínez 44, G. Martínez García 103, M.P.P.
Martins 110, S. Masciocchi 97, M. Masera 24, A. Masoni 52, L. Massacrier 131,
O. Massen 59, A. Mastroserio 132,50, O. Matonoha 75, S. Mattiazzo 27, A.
Matyja 107, A.L. Mazuecos 32, F. Mazzaschi 32,24, M. Mazzilli 116, J.E.
Mdhluli 123, Y. Melikyan 43, A. Menchaca-Rocha 67, J.E.M. Mendez 65, E.
Meninno 102, A.S. Menon 116, M.W. Menzel32,94, M. Meres 13, Y. Miake125, L.
Micheletti 32, D.L. Mihaylov 95, K. Mikhaylov 142,141, N. Minafra 118, D.
Miśkowiec 97, A. Modak 134,4, B. Mohanty80, M. Mohisin Khan VI,15, M.A.
Molander 43, S. Monira 136, C. Mordasini 117, D.A. Moreira De Godoy 126, I.
Morozov 141, A. Morsch 32, T. Mrnjavac 32, V. Muccifora 49, S. Muhuri 135,
J.D. Mulligan 74, A. Mulliri 22, M.G. Munhoz 110, R.H. Munzer 64, H. Murakami
124, S. Murray 114, L. Musa 32, J. Musinsky 60, J.W. Myrcha 136, B. Naik 123,
A.I. Nambrath 18, B.K. Nandi 47, R. Nania 51, E. Nappi 50, A.F. Nassirpour 17,
A. Nath 94, C. Nattrass 122, M.N. Naydenov 36, A. Neagu19, A. Negru113, E.
Nekrasova141, L. Nellen 65, R. Nepeivoda 75, S. Nese 19, G. Neskovic 38, N.
Nicassio 50, B.S. Nielsen 83, E.G. Nielsen 83, S. Nikolaev 141, S. Nikulin
141, V. Nikulin 141, F. Noferini 51, S. Noh 12, P. Nomokonov 142, J. Norman
119, N. Novitzky 87, P. Nowakowski 136, A. Nyanin 141, J. Nystrand 20, S. Oh
17, A. Ohlson 75, V.A. Okorokov 141, J. Oleniacz 136, A. Onnerstad 117, C.
Oppedisano 56, A. Ortiz Velasquez 65, J. Otwinowski 107, M. Oya92, K. Oyama
76, Y. Pachmayer 94, S. Padhan 47, D. Pagano 134,55, G. Paić 65, S. Paisano-
Guzmán 44, A. Palasciano 50, S. Panebianco 130, H. Park 125, H. Park 104, J.
Park 125, J.E. Parkkila 32, Y. Patley 47, B. Paul 22, M.M.D.M. Paulino 110, H.
Pei 6, T. Peitzmann 59, X. Peng 11, M. Pennisi 24, S. Perciballi 24, D.
Peresunko 141, G.M. Perez 7, Y. Pestov141, M.T. Petersen83, V. Petrov 141, M.
Petrovici 45, S. Piano 57, M. Pikna 13, P. Pillot 103, O. Pinazza 51,32, L.
Pinsky116, C. Pinto 95, S. Pisano 49, M. Płoskoń 74, M. Planinic89, F.
Pliquett64, D.K. Plociennik2, M.G. Poghosyan 87, B. Polichtchouk 141, S.
Politano 29, N. Poljak 89, A. Pop 45, S. Porteboeuf-Houssais 127, V.
Pozdniakov I,142, I.Y. Pozos 44, K.K. Pradhan 48, S.K. Prasad 4, S. Prasad 48,
R. Preghenella 51, F. Prino 56, C.A. Pruneau 137, I. Pshenichnov 141, M.
Puccio 32, S. Pucillo 24, S. Qiu 84, L. Quaglia 24, S. Ragoni 14, A. Rai 138,
A. Rakotozafindrabe 130, L. Ramello 133,56, F. Rami 129, M. Rasa 26, S.S.
Räsänen 43, R. Rath 51, M.P. Rauch 20, I. Ravasenga 32, K.F. Read 87,122, C.
Reckziegel 112, A.R. Redelbach 38, K. Redlich VII,79, C.A. Reetz 97, H.D.
Regules-Medel44, A. Rehman20, F. Reidt 32, H.A. Reme-Ness 34, Z. Rescakova37,
K. Reygers 94, A. Riabov 141, V. Riabov 141, R. Ricci 28, M. Richter 20, A.A.
Riedel 95, W. Riegler 32, A.G. Riffero 24, C. Ripoli28, C. Ristea 63, M.V.
Rodriguez 32, M. Rodríguez Cahuantzi 44, S.A. Rodríguez Ramírez 44, K. Røed
19, R. Rogalev 141, E. Rogochaya 142, T.S. Rogoschinski 64, D. Rohr 32, D.
Röhrich 20, S. Rojas Torres 35, P.S. Rokita 136, G. Romanenko 25, F. Ronchetti
49, E.D. Rosas65, K. Roslon 136, A. Rossi 54, A. Roy 48, S. Roy 47, N. Rubini
25, J.A. Rudolph84, D. Ruggiano 136, R. Rui 23, P.G. Russek 2, R. Russo 84, A.
Rustamov 81, E. Ryabinkin 141, Y. Ryabov 141, A. Rybicki 107, J. Ryu 16, W.
Rzesa 136, S. Sadhu 31, S. Sadovsky 141, J. Saetre 20, K. Šafařík 35, S.K.
Saha 4, S. Saha 80, B. Sahoo 48, R. Sahoo 48, S. Sahoo61, D. Sahu 48, P.K.
Sahu 61, J. Saini 135, K. Sajdakova37, S. Sakai 125, M.P. Salvan 97, S.
Sambyal 91, D. Samitz 102, I. Sanna 32,95, T.B. Saramela110, D. Sarkar 83, P.
Sarma 41, V. Sarritzu 22, V.M. Sarti 95, M.H.P. Sas 32, S. Sawan 80, E.
Scapparone 51, J. Schambach 87, H.S. Scheid 64, C. Schiaua 45, R. Schicker 94,
F. Schlepper 94, A. Schmah97, C. Schmidt 97, H.R. Schmidt93, M.O. Schmidt 32,
M. Schmidt93, N.V. Schmidt 87, A.R. Schmier 122, R. Schotter 129, A. Schröter
38, J. Schukraft 32, K. Schweda 97, G. Scioli 25, E. Scomparin 56, J.E. Seger
14, Y. Sekiguchi124, D. Sekihata 124, M. Selina 84, I. Selyuzhenkov 97, S.
Senyukov 129, J.J. Seo 94, D. Serebryakov 141, L. Serkin 65, L. Šerkšnytė 95,
A. Sevcenco 63, T.J. Shaba 68, A. Shabetai 103, R. Shahoyan32, A. Shangaraev
141, B. Sharma 91, D. Sharma 47, H. Sharma 54, M. Sharma 91, S. Sharma 76, S.
Sharma 91, U. Sharma 91, A. Shatat 131, O. Sheibani116, K. Shigaki 92, M.
Shimomura77, J. Shin12, S. Shirinkin 141, Q. Shou 39, Y. Sibiriak 141, S.
Siddhanta 52, T. Siemiarczuk 79, T.F. Silva 110, D. Silvermyr 75, T.
Simantathammakul105, R. Simeonov 36, B. Singh91, B. Singh 95, K. Singh 48, R.
Singh 80, R. Singh 91, R. Singh 97, S. Singh 15, V.K. Singh 135, V. Singhal
135, T. Sinha 99, B. Sitar 13, M. Sitta 133,56, T.B. Skaali19, G. Skorodumovs
94, N. Smirnov 138, R.J.M. Snellings 59, E.H. Solheim 19, J. Song 16, C.
Sonnabend 32,97, J.M. Sonneveld 84, F. Soramel 27, A.B. Soto-hernandez 88, R.
Spijkers 84, I. Sputowska 107, J. Staa 75, J. Stachel 94, I. Stan 63, P.J.
Steffanic 122, S.F. Stiefelmaier 94, D. Stocco 103, I. Storehaug 19, N.J.
Strangmann 64, P. Stratmann 126, S. Strazzi 25, A. Sturniolo 30,53, C.P.
Stylianidis84, A.A.P. Suaide 110, C. Suire 131, M. Sukhanov 141, M. Suljic 32,
R. Sultanov 141, V. Sumberia 91, S. Sumowidagdo 82, I. Szarka 13, M.
Szymkowski 136, S.F. Taghavi 95, G. Taillepied 97, J. Takahashi 111, G.J.
Tambave 80, S. Tang 6, Z. Tang 120, J.D. Tapia Takaki 118, N. Tapus113, L.A.
Tarasovicova 126, M.G. Tarzila 45, G.F. Tassielli 31, A. Tauro 32, A. Tavira
García 131, G. Tejeda Muñoz 44, A. Telesca 32, L. Terlizzi 24, C. Terrevoli
50, S. Thakur 4, D. Thomas 108, A. Tikhonov 141, N. Tiltmann 32,126, A.R.
Timmins 116, M. Tkacik106, T. Tkacik 106, A. Toia 64, R. Tokumoto92, S.
Tomassini25, K. Tomohiro92, N. Topilskaya 141, M. Toppi 49, V.V. Torres 103,
A.G. Torres Ramos 31, A. Trifiró 30,53, T. Triloki96, A.S. Triolo 32,30,53, S.
Tripathy 32, T. Tripathy 47, V. Trubnikov 3, W.H. Trzaska 117, T.P. Trzcinski
136, C. Tsolanta19, R. Tu39, A. Tumkin 141, R. Turrisi 54, T.S. Tveter 19, K.
Ullaland 20, B. Ulukutlu 95, A. Uras 128, M. Urioni 134, G.L. Usai 22, M.
Vala37, N. Valle 55, L.V.R. van Doremalen59, M. van Leeuwen 84, C.A. van Veen
94, R.J.G. van Weelden 84, P. Vande Vyvre 32, D. Varga 46, Z. Varga 46, P.
Vargas Torres65, M. Vasileiou 78, A. Vasiliev 141, O. Vázquez Doce 49, O.
Vazquez Rueda 116, V. Vechernin 141, E. Vercellin 24, S. Vergara Limón44, R.
Verma47, L. Vermunt 97, R. Vértesi 46, M. Verweij 59, L. Vickovic33, Z.
Vilakazi123, O. Villalobos Baillie 100, A. Villani 23, A. Vinogradov 141, T.
Virgili 28, M.M.O. Virta 117, A. Vodopyanov 142, B. Volkel 32, M.A. Völkl 94,
S.A. Voloshin 137, G. Volpe 31, B. von Haller 32, I. Vorobyev 32, N. Vozniuk
141, J. Vrláková 37, J. Wan39, C. Wang 39, D. Wang39, Y. Wang 39, Y. Wang 6,
A. Wegrzynek 32, F.T. Weiglhofer38, S.C. Wenzel 32, J.P. Wessels 126, J.
Wiechula 64, J. Wikne 19, G. Wilk 79, J. Wilkinson 97, G.A. Willems 126, B.
Windelband 94, M. Winn 130, J.R. Wright 108, W. Wu39, Y. Wu 120, Z. Xiong120,
R. Xu 6, A. Yadav 42, A.K. Yadav 135, Y. Yamaguchi 92, S. Yang20, S. Yano 92,
E.R. Yeats18, Z. Yin 6, I.-K. Yoo 16, J.H. Yoon 58, H. Yu12, S. Yuan20, A.
Yuncu 94, V. Zaccolo 23, C. Zampolli 32, M. Zang6, F. Zanone 94, N. Zardoshti
32, A. Zarochentsev 141, P. Závada 62, N. Zaviyalov141, M. Zhalov 141, B.
Zhang 6, C. Zhang 130, L. Zhang 39, M. Zhang127,6, S. Zhang 39, X. Zhang 6, Y.
Zhang120, Z. Zhang 6, M. Zhao 10, V. Zherebchevskii 141, Y. Zhi10, D. Zhou 6,
Y. Zhou 83, J. Zhu 54,6, S. Zhu120, Y. Zhu6, S.C. Zugravel 56, N. Zurlo 134,55

## Affiliation Notes

I Deceased
II Also at: Max-Planck-Institut fur Physik, Munich, Germany
III Also at: Italian National Agency for New Technologies, Energy and
Sustainable Economic Development (ENEA), Bologna, Italy
IV Also at: Dipartimento DET del Politecnico di Torino, Turin, Italy
V Also at: Yildiz Technical University, Istanbul, Türkiye
VI Also at: Department of Applied Physics, Aligarh Muslim University, Aligarh,
India
VII Also at: Institute of Theoretical Physics, University of Wroclaw, Poland
VIII Also at: An institution covered by a cooperation agreement with CERN

## Collaboration Institutes

1 A.I. Alikhanyan National Science Laboratory (Yerevan Physics Institute)
Foundation, Yerevan, Armenia
2 AGH University of Krakow, Cracow, Poland
3 Bogolyubov Institute for Theoretical Physics, National Academy of Sciences
of Ukraine, Kiev, Ukraine
4 Bose Institute, Department of Physics and Centre for Astroparticle Physics
and Space Science (CAPSS), Kolkata, India
5 California Polytechnic State University, San Luis Obispo, California, United
States
6 Central China Normal University, Wuhan, China
7 Centro de Aplicaciones Tecnológicas y Desarrollo Nuclear (CEADEN), Havana,
Cuba
8 Centro de Investigación y de Estudios Avanzados (CINVESTAV), Mexico City and
Mérida, Mexico
9 Chicago State University, Chicago, Illinois, United States
10 China Institute of Atomic Energy, Beijing, China
11 China University of Geosciences, Wuhan, China
12 Chungbuk National University, Cheongju, Republic of Korea
13 Comenius University Bratislava, Faculty of Mathematics, Physics and
Informatics, Bratislava, Slovak Republic
14 Creighton University, Omaha, Nebraska, United States
15 Department of Physics, Aligarh Muslim University, Aligarh, India
16 Department of Physics, Pusan National University, Pusan, Republic of Korea
17 Department of Physics, Sejong University, Seoul, Republic of Korea
18 Department of Physics, University of California, Berkeley, California,
United States
19 Department of Physics, University of Oslo, Oslo, Norway
20 Department of Physics and Technology, University of Bergen, Bergen, Norway
21 Dipartimento di Fisica, Università di Pavia, Pavia, Italy
22 Dipartimento di Fisica dell’Università and Sezione INFN, Cagliari, Italy
23 Dipartimento di Fisica dell’Università and Sezione INFN, Trieste, Italy
24 Dipartimento di Fisica dell’Università and Sezione INFN, Turin, Italy
25 Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN,
Bologna, Italy
26 Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN,
Catania, Italy
27 Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN,
Padova, Italy
28 Dipartimento di Fisica ‘E.R. Caianiello’ dell’Università and Gruppo
Collegato INFN, Salerno, Italy
29 Dipartimento DISAT del Politecnico and Sezione INFN, Turin, Italy
30 Dipartimento di Scienze MIFT, Università di Messina, Messina, Italy
31 Dipartimento Interateneo di Fisica ‘M. Merlin’ and Sezione INFN, Bari,
Italy
32 European Organization for Nuclear Research (CERN), Geneva, Switzerland
33 Faculty of Electrical Engineering, Mechanical Engineering and Naval
Architecture, University of Split, Split, Croatia
34 Faculty of Engineering and Science, Western Norway University of Applied
Sciences, Bergen, Norway
35 Faculty of Nuclear Sciences and Physical Engineering, Czech Technical
University in Prague, Prague, Czech Republic
36 Faculty of Physics, Sofia University, Sofia, Bulgaria
37 Faculty of Science, P.J. Šafárik University, Košice, Slovak Republic
38 Frankfurt Institute for Advanced Studies, Johann Wolfgang Goethe-
Universität Frankfurt, Frankfurt, Germany
39 Fudan University, Shanghai, China
40 Gangneung-Wonju National University, Gangneung, Republic of Korea
41 Gauhati University, Department of Physics, Guwahati, India
42 Helmholtz-Institut für Strahlen- und Kernphysik, Rheinische Friedrich-
Wilhelms-Universität Bonn, Bonn, Germany
43 Helsinki Institute of Physics (HIP), Helsinki, Finland
44 High Energy Physics Group, Universidad Autónoma de Puebla, Puebla, Mexico
45 Horia Hulubei National Institute of Physics and Nuclear Engineering,
Bucharest, Romania
46 HUN-REN Wigner Research Centre for Physics, Budapest, Hungary
47 Indian Institute of Technology Bombay (IIT), Mumbai, India
48 Indian Institute of Technology Indore, Indore, India
49 INFN, Laboratori Nazionali di Frascati, Frascati, Italy
50 INFN, Sezione di Bari, Bari, Italy
51 INFN, Sezione di Bologna, Bologna, Italy
52 INFN, Sezione di Cagliari, Cagliari, Italy
53 INFN, Sezione di Catania, Catania, Italy
54 INFN, Sezione di Padova, Padova, Italy
55 INFN, Sezione di Pavia, Pavia, Italy
56 INFN, Sezione di Torino, Turin, Italy
57 INFN, Sezione di Trieste, Trieste, Italy
58 Inha University, Incheon, Republic of Korea
59 Institute for Gravitational and Subatomic Physics (GRASP), Utrecht
University/Nikhef, Utrecht, Netherlands
60 Institute of Experimental Physics, Slovak Academy of Sciences, Košice,
Slovak Republic
61 Institute of Physics, Homi Bhabha National Institute, Bhubaneswar, India
62 Institute of Physics of the Czech Academy of Sciences, Prague, Czech
Republic
63 Institute of Space Science (ISS), Bucharest, Romania
64 Institut für Kernphysik, Johann Wolfgang Goethe-Universität Frankfurt,
Frankfurt, Germany
65 Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de México,
Mexico City, Mexico
66 Instituto de Física, Universidade Federal do Rio Grande do Sul (UFRGS),
Porto Alegre, Brazil
67 Instituto de Física, Universidad Nacional Autónoma de México, Mexico City,
Mexico
68 iThemba LABS, National Research Foundation, Somerset West, South Africa
69 Jeonbuk National University, Jeonju, Republic of Korea
70 Johann-Wolfgang-Goethe Universität Frankfurt Institut für Informatik,
Fachbereich Informatik und Mathematik, Frankfurt, Germany
71 Korea Institute of Science and Technology Information, Daejeon, Republic of
Korea
72 KTO Karatay University, Konya, Turkey
73 Laboratoire de Physique Subatomique et de Cosmologie, Université Grenoble-
Alpes, CNRS-IN2P3, Grenoble, France
74 Lawrence Berkeley National Laboratory, Berkeley, California, United States
75 Lund University Department of Physics, Division of Particle Physics, Lund,
Sweden
76 Nagasaki Institute of Applied Science, Nagasaki, Japan
77 Nara Women’s University (NWU), Nara, Japan
78 National and Kapodistrian University of Athens, School of Science,
Department of Physics , Athens, Greece
79 National Centre for Nuclear Research, Warsaw, Poland
80 National Institute of Science Education and Research, Homi Bhabha National
Institute, Jatni, India
81 National Nuclear Research Center, Baku, Azerbaijan
82 National Research and Innovation Agency - BRIN, Jakarta, Indonesia
83 Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark
84 Nikhef, National institute for subatomic physics, Amsterdam, Netherlands
85 Nuclear Physics Group, STFC Daresbury Laboratory, Daresbury, United Kingdom
86 Nuclear Physics Institute of the Czech Academy of Sciences, Husinec-Řež,
Czech Republic
87 Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States
88 Ohio State University, Columbus, Ohio, United States
89 Physics department, Faculty of science, University of Zagreb, Zagreb,
Croatia
90 Physics Department, Panjab University, Chandigarh, India
91 Physics Department, University of Jammu, Jammu, India
92 Physics Program and International Institute for Sustainability with Knotted
Chiral Meta Matter (SKCM2), Hiroshima University, Hiroshima, Japan
93 Physikalisches Institut, Eberhard-Karls-Universität Tübingen, Tübingen,
Germany
94 Physikalisches Institut, Ruprecht-Karls-Universität Heidelberg, Heidelberg,
Germany
95 Physik Department, Technische Universität München, Munich, Germany
96 Politecnico di Bari and Sezione INFN, Bari, Italy
97 Research Division and ExtreMe Matter Institute EMMI, GSI Helmholtzzentrum
für Schwerionenforschung GmbH, Darmstadt, Germany
98 Saga University, Saga, Japan
99 Saha Institute of Nuclear Physics, Homi Bhabha National Institute, Kolkata,
India
100 School of Physics and Astronomy, University of Birmingham, Birmingham,
United Kingdom
101 Sección Física, Departamento de Ciencias, Pontificia Universidad Católica
del Perú, Lima, Peru
102 Stefan Meyer Institut für Subatomare Physik (SMI), Vienna, Austria
103 SUBATECH, IMT Atlantique, Nantes Université, CNRS-IN2P3, Nantes, France
104 Sungkyunkwan University, Suwon City, Republic of Korea
105 Suranaree University of Technology, Nakhon Ratchasima, Thailand
106 Technical University of Košice, Košice, Slovak Republic
107 The Henryk Niewodniczanski Institute of Nuclear Physics, Polish Academy of
Sciences, Cracow, Poland
108 The University of Texas at Austin, Austin, Texas, United States
109 Universidad Autónoma de Sinaloa, Culiacán, Mexico
110 Universidade de São Paulo (USP), São Paulo, Brazil
111 Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil
112 Universidade Federal do ABC, Santo Andre, Brazil
113 Universitatea Nationala de Stiinta si Tehnologie Politehnica Bucuresti,
Bucharest, Romania
114 University of Cape Town, Cape Town, South Africa
115 University of Derby, Derby, United Kingdom
116 University of Houston, Houston, Texas, United States
117 University of Jyväskylä, Jyväskylä, Finland
118 University of Kansas, Lawrence, Kansas, United States
119 University of Liverpool, Liverpool, United Kingdom
120 University of Science and Technology of China, Hefei, China
121 University of South-Eastern Norway, Kongsberg, Norway
122 University of Tennessee, Knoxville, Tennessee, United States
123 University of the Witwatersrand, Johannesburg, South Africa
124 University of Tokyo, Tokyo, Japan
125 University of Tsukuba, Tsukuba, Japan
126 Universität Münster, Institut für Kernphysik, Münster, Germany
127 Université Clermont Auvergne, CNRS/IN2P3, LPC, Clermont-Ferrand, France
128 Université de Lyon, CNRS/IN2P3, Institut de Physique des 2 Infinis de
Lyon, Lyon, France
129 Université de Strasbourg, CNRS, IPHC UMR 7178, F-67000 Strasbourg, France,
Strasbourg, France
130 Université Paris-Saclay, Centre d’Etudes de Saclay (CEA), IRFU, Départment
de Physique Nucléaire (DPhN), Saclay, France
131 Université Paris-Saclay, CNRS/IN2P3, IJCLab, Orsay, France
132 Università degli Studi di Foggia, Foggia, Italy
133 Università del Piemonte Orientale, Vercelli, Italy
134 Università di Brescia, Brescia, Italy
135 Variable Energy Cyclotron Centre, Homi Bhabha National Institute, Kolkata,
India
136 Warsaw University of Technology, Warsaw, Poland
137 Wayne State University, Detroit, Michigan, United States
138 Yale University, New Haven, Connecticut, United States
139 Yonsei University, Seoul, Republic of Korea
140 Zentrum für Technologie und Transfer (ZTT), Worms, Germany
141 Affiliated with an institute covered by a cooperation agreement with CERN
142 Affiliated with an international laboratory covered by a cooperation
agreement with CERN.