2021 146 21 July

K${}^{0}_{\rm S}$\- and (anti-)$\Lambda$-hadron correlations in pp collisions
at $\sqrt{s}=13$ TeV

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

Two-particle azimuthal correlations are measured with the ALICE apparatus in
pp collisions at $\sqrt{s}=13$ TeV to explore strangeness- and multiplicity-
related effects in the fragmentation of jets and the transition regime between
bulk and hard production, probed with the condition that a strange meson
($\mathrm{K_{S}^{0}}$) or baryon ($\Lambda$) with transverse momentum
$p_{\mathrm{T}}>3$ GeV/$c$ is produced. Azimuthal correlations between kaons
or $\Lambda$ hyperons with other hadrons are presented at midrapidity for a
broad range of the trigger ($3<p_{\mathrm{T}}^{\mathrm{trigg}}<20$ GeV/$c$)
and associated particle $p_{\mathrm{T}}$ (1 GeV/$c$
$<p_{\mathrm{T}}^{\mathrm{assoc}}<p_{\mathrm{T}}^{\mathrm{trigg}}$), for
minimum-bias events and as a function of the event multiplicity. The near- and
away-side peak yields are compared for the case of either $\mathrm{K_{S}^{0}}$
or $\Lambda$($\overline{\Lambda}$) being the trigger particle with that of
inclusive hadrons (a sample dominated by pions). In addition, the measurements
are compared with predictions from PYTHIA 8 and EPOS LHC event generators.

##  1 Introduction

Particle production as a function of the event charged-particle multiplicity
in proton-proton (pp) collisions at the LHC has revealed interesting patterns.
Clearly, in the soft (bulk) particle production domain with low transverse
momentum ($p_{\mathrm{T}}\lesssim\ $4 GeV/$c$), several experimental
measurements indicate features in high-multiplicity pp collisions similar to
those observed in nucleus–nucleus collisions. These include long-range
correlations in pseudorapidity [1, 2], large azimuthal anisotropies [3, 4] and
strangeness production [5, 6].

These measurements are theoretically interpreted in terms of a combination of
initial-state collective dynamics (colour-glass condensate) [7] or as a
hydrodynamic-like (final-state) collective flow [8]. Quantifying the relative
contributions of initial- and final-state phenomena is a challenge, both
experimentally and theoretically (see reviews [9, 10]). These phenomena are
also modelled in Monte Carlo (MC) event generators, like PYTHIA8 [11] or EPOS
LHC [12]. For example, a basic experimental finding in pp collisions, the
increase of the average transverse momentum with the event multiplicity is
realized in these two models very differently. In PYTHIA 8, in events with
several partonic scatterings, termed Multiple Parton Interactions (MPI), the
respective color strings cut (reconnect) each other, leading to a
redistribution of energy from particle production to transverse momentum. In
the EPOS LHC model, a parametrised hydrodynamic evolution of a small volume
with high density of thermalised matter (core) is used. In both models the
respective parameters were tuned using the Run 1 data at the LHC, without
explicit inclusion of particle correlations [13, 12].

In PYTHIA8, correlations among the final state hadrons are realized through
transversely extended strings, exerting on each other transverse shoves [14]
that mimic collective dynamics, akin to that of a (long-lived) quark–gluon
medium. The shoving model of hadronisation was recently used to discern within
a PYTHIA8 study [15] possible effects of jet quenching in pp collisions. This
prominent characteristic of nucleus–nucleus collisions, remains undetected in
high-multiplicity pp or p–Pb collisions [16], perhaps not surprisingly, given
the much smaller spatial extension of the dense system, compared to the
nucleus–nucleus case. Experimentally, it was recently shown that in pp
collisions the near-side long-range (in pseudorapidity) ridge yield (of bulk
particles) in high-multiplicity events remains present for events which are
additionally biased, through either a $Z$ boson [17], a leading
high-$p_{\mathrm{T}}$ particle or a jet [18]. These findings are interesting
per se and also motivate the quest to find or exclude jet quenching in high-
multiplicity events in small collision systems, with differences in the
observed effects on gluon, light and heavy quark jets.

At LEP, differences between quark- and gluon-initiated jets in e+-e-
annihilations have been revealed in several measurements. Gluon jets are
characterised by a larger charged-particle multiplicity than quark jets [19,
20]. Moreover, in the relative production of $\mathrm{K_{S}^{0}}$ mesons and
$\Lambda$ hyperons to charged particles, it was found that the relative
production of $\Lambda$ is $\approx$30% higher in gluon than in quark jets,
while the relative $\mathrm{K_{S}^{0}}$ production was found to be
approximately the same [21].

In the present article, such studies of particle production and correlations
are continued, exploring the effect of a strangeness bias, both in form of a
meson ($\mathrm{K_{S}^{0}}$) or a baryon ($\Lambda$) with $p_{\mathrm{T}}>3$
GeV/$c$. The correlations between kaons or $\Lambda$($\overline{\Lambda}$)
hyperons with other hadrons are studied at midrapidity in pp collisions at
$\sqrt{s}=13$ TeV for a broad range of the trigger
($3<p_{\mathrm{T}}^{\mathrm{trigg}}<20$ GeV/$c$) and associated particle
$p_{\mathrm{T}}$ (1 GeV/$c$
$<p_{\mathrm{T}}^{\mathrm{assoc}}<p_{\mathrm{T}}^{\mathrm{trigg}}$), for
minimum bias events and as a function of the event multiplicity measured at
forward rapidities. Such correlations encode effects of fragmentation,
hadronisation and parton showering as well as possible collectivity and jet
quenching. The complex overlap of these aspects is probed through the
experimental handles of particle species and $p_{\mathrm{T}}$, inducing
different kinematic and flavour biases. The near- and away-side peak yields
are compared for the case of either $\mathrm{K_{S}^{0}}$ or (anti-)$\Lambda$
as a trigger particle with that of inclusive hadrons (a sample dominated by
pions). The measurements are, in addition, compared with the PYTHIA 8 and EPOS
LHC event generators.

The article is organised as follows: Section 2 outlines the experimental setup
and the data sample; Section 3 describes the analysis, while Section 4
presents the results; a summary and an outlook are given in Section 5.

##  2 Experiment and data sample

The inclusive charged hadron, $\mathrm{K_{S}^{0}}$ meson and (anti-)$\Lambda$
hyperon identification at midrapidity is performed using the tracking
detectors of the ALICE central barrel located in a solenoidal magnet, which
provides a magnetic field of 0.5 T oriented along the beam direction. A
detailed description of the ALICE experiment and its performance can be found
in [22, 23].

###  2.1 Event selection

For the data taking, a minimum bias (MB) trigger is employed, provided by the
V0 detector, which consists of two forward scintillator arrays covering the
pseudorapidity ranges $-3.7<\eta<-1.7$ and $2.8~{}<~{}\eta~{}<~{}5.1$. The MB
trigger signal consists of a coincident signal in both arrays. All events
selected in this analysis are required to have a reconstructed primary
collision vertex (PV) within the longitudinal interval $|z_{\mathrm{vtx}}|<10$
cm from the nominal interaction point in order to ensure uniform detector
performance. Beam-gas events are rejected using timing cuts with the V0
detector. Moreover pile-up events are rejected based on the Silicon Pixel
Detector (SPD) information. The total number of analysed pp collisions at
$\sqrt{s}=13$ TeV, measured during the LHC Run 2 data-taking period in years
2016–2018 by ALICE is $1.58\times 10^{9}$ corresponding to integrated
luminosity of about 27 nb-1.

###  2.2 Multiplicity selection

The correlation functions are calculated for six event classes (0–1%, 1–3%,
3–7%, 7–15%, 15–50%, 50–100%), selected on the event activity via the
multiplicity in the forward and backward direction, measured with the V0
detector within the acceptance described above. The events are selected based
on percentiles of the summed signal in the two V0 detectors (V0M), for
instance the 0–1% and 50–100% classes are the events with the highest and
lowest range of the V0M signal. The trigger efficiency is not accounted for in
the above multiplicity ranges. The intervals corrected for the trigger
efficiency are: respectively; 0–0.92%, 0.92–2.74%, 2.74–6.40%, 6.40–13.44%,
13.44–46.12%, 46.12–100% [24].

For the MC event generators, the multiplicity classes are selected with the
trigger-corrected percentile calculation, applied to the distribution of
charged primary particles produced in the $\eta$ acceptance of the V0
detectors.

###  2.3 Primary hadron and $\mathrm{V^{0}}$ selection

Primary charged tracks (denoted as h) are reconstructed in the pseudorapidity
range $|\eta|<0.8$ using the Inner Tracking System (ITS), which consists of
six layers of silicon detectors around the beam pipe, and the Time Projection
Chamber (TPC), consisting of a large cylindrical drift volume filled with
nearly $90\text{\,}\mathrm{m}^{3}$ either of Ar/CO2 (88/12, in 2016 and 2018)
or Ne/CO2/N2 (90/10/5 in 2017) and read out by multi-wire proportional
chambers. Combining the information from these two detectors, the primary
charged-track sample is created by applying selection criteria in order to
suppress the contamination from secondary particles following previous studies
[25]. The number of crossed pad rows in the TPC is required to be at least 70
(out of a maximum of 159) and the minimal ratio to the number of findable
clusters (geometrically possible assignable clusters to a track) is 0.8. Only
tracks with a fraction of shared clusters with other tracks smaller than 0.4
are accepted. The distance of closest approach (DCA) to the PV is required to
be within an elipsoid with semi-axes of 2.4 cm and 3.2 cm in the $xy$-plane
and $z$-direction, respectively. Every track is required to have a fit quality
for both TPC and ITS, characterised by goodness-of-fit values $\chi^{2}$ per
cluster smaller than 4 and 36 for the TPC and ITS, respectively. Only tracks
with a hit in the two most inner layers of ITS are selected. The kink
topologies produced by decays are rejected. The selected sample of primary
charged particles is dominated by hadrons. The electrons constitute less than
1%.

Table 1:  Selection criteria for $\rm V^{0}$ candidates based on the topological variables.  Selection criterion | $\rm K_{S}^{0}$ | $\Lambda(\overline{\Lambda})$
---|---|---
Absolute value of rapidity | $<0.5$ | $<0.5$
Decay radius (cm) | $>0.5$ | $>0.5$
${\rm DCA}_{xy}$ of daughter track to PV (cm) | $>0.06$ | $>0.06$
${\rm DCA}_{xy}$ between daughter tracks ($n_{\sigma}$) | $<1$ | $<1$
cos($\theta_{\mathrm{PA}}$) | $>0.97$ | $>0.995$
Proper lifetime (cm) | $<20$ | $<30$
Competing rejection (GeV/$c^{2}$) | $>0.005$ | $>0.01$
Invariant mass (GeV/$c^{2}$) | $m_{\mathrm{K_{S}^{0}}}\pm 3\sigma$ | $m_{\Lambda(\overline{\Lambda})}\pm 3\sigma$

The $\rm K_{S}^{0}$ mesons and $\Lambda$($\overline{\Lambda}$) baryons
($\mathrm{V^{0}}$ particles) are reconstructed in the rapidity range $|y|<0.5$
via their most probable decay channels [26] and exploiting their
characteristic ($\mathrm{V^{0}}$) decay topology:

$\mathrm{K_{S}^{0}}\rightarrow\pi^{+}+\pi^{-}(69.2\%),$
$\Lambda\rightarrow\mathrm{p}+\pi^{-}(63.9\%),$
$\overline{\Lambda}\rightarrow\mathrm{\overline{p}}+\pi^{+}(63.9\%).$

The identification and reconstruction follows previous measurements presented
in [27] and [28]. The identification of the daughter tracks is performed via
specific energy loss d$E$/d$x$ in the TPC, which is required to be within
$\pm$3$\sigma$ from the expected mean value for pions or protons (for more
details see [23]). The track quality criteria are the same as for the primary
global tracks described above. The pairs of identified daughter tracks are
combined to $\mathrm{V^{0}}$ candidates, which are accepted if their invariant
mass is within 3$\sigma$ (1$\sigma$ for $\mathrm{K_{S}^{0}}$ is in the range
0.0039-0.0075 GeV/$c^{2}$ and for $\Lambda$ in 0.0021-0.0033 GeV/$c^{2}$
depending on $p_{\mathrm{T}}$) from the nominal value. The combinatorial
contribution is suppressed by applying selection criteria based on the
topological variables summarised in Table 1. Here, the $\mathrm{V^{0}}$ decay
radius is the distance between the point where the $\mathrm{V^{0}}$ decays
(secondary vertex) and the PV. The ${\rm DCA}_{xy}$ to PV is the distance of
closest approach between the daughter track and the PV. The
$\theta_{\mathrm{PA}}$ refers to the pointing angle, which is the angle
between the momentum vector of the $\mathrm{V^{0}}$ candidate and the line
connecting the primary and secondary vertex. The reconstructed proper lifetime
of an individual particle is defined as $mL/p$, where $m$ is the particle
mass, $L$ is the distance between primary and secondary vertex and $p$ is the
particle momentum. The mean life $c\tau$ is listed in [26] and its value is
2.68 cm and 7.89 cm for $\mathrm{K_{S}^{0}}$ and
$\Lambda(\overline{\Lambda})$, respectively. It can happen that a certain pair
reconstructed as $\rm K_{S}^{0}$ candidate can have an invariant mass of
$\Lambda(\overline{\Lambda})$ under the p$\pi$ assumption for the daughter
tracks. Such pairs are not accepted neither as $\mathrm{K_{S}^{0}}$ nor as
$\Lambda(\overline{\Lambda})$ candidates. Table 1 quotes under the competing
rejection entry the mass on which this criterion is applied for both
$\mathrm{K_{S}^{0}}$ and $\Lambda$. Besides the topological selections, a
bunch-off pile-up (by the high frequency collisions, some tracks from previous
bunch-crossing remain in TPC when current collision happen) removal criterion
is required where at least one of the $\mathrm{V^{0}}$ daughter tracks is
reconstructed both in ITS and TPC or has a signal in the Time-Of-Flight
detector.

Figure 1: An example of the raw same-event (left), mixed-event (middle) and
final (mixed-event scaled, right) two-dimensional correlation function for
$\mathrm{K_{S}^{0}}$-hadrons. The correlation functions were scaled with
1/1000 for better visibility. The plateau in the left and middle plot is
caused by non-equal selection in $\eta$ of the trigger and associated
particle.

##  3 Analysis

###  3.1 The correlation function

In the dihadron correlation approach, the first hadron is the trigger
particle, here either a primary charged particle (hadron) or an identified
$\mathrm{K_{S}^{0}}$ meson or $\Lambda$($\overline{\Lambda}$) hyperon with
$p_{\mathrm{T}}$ in range 3-20 GeV/$c$. Since the $\Lambda$-h and
$\overline{\Lambda}$-h correlation functions are compatible, as expected for
this collision energy, the results are combined and reported in the following
as $(\Lambda+\overline{\Lambda})$-h. The second particle is the associated
particle, in this case, always a primary charged particle with a kinematic
requirement 1 GeV/$c$ $<$$p_{\mathrm{T}}^{\mathrm{assoc}}$ $<$
$p_{\mathrm{T}}^{\mathrm{trigg}}$. By calculating the differences in the
azimuthal angle and pseudorapidity for each of such pairs, three types of
correlation functions are constructed: h-h, $\mathrm{K_{S}^{0}}$-h and
$(\Lambda+\overline{\Lambda})$-h. For h-h correlations, pairs with invariant
mass (IM) within $\pm$5 MeV/$c^{2}$ of the mass of $\mathrm{K_{S}^{0}}$ or
$\Lambda$($\overline{\Lambda}$) or from $\gamma$ conversions are not accepted.
The contribution from decays of the K∗(892) and $\phi$ mesons, $\Delta$
resonances and D mesons was checked and found negligible. In the case of
$(\Lambda+\overline{\Lambda})$-h correlations, this restriction is applied to
pairs with IM of a cascade ($\Sigma$, $\Xi$, $\Omega$).An example of a raw
$\mathrm{K_{S}^{0}}$-h correlation function is shown in Fig. 1 (left panel).
At $(\Delta\varphi,\Delta\eta)=(0,0)$, one can observe the near-side peak,
which originates mostly from particle pairs fragmented within the same jet.
Bose-Einstein correlations, strong decays of high-mass resonances and final
state interactions may have also a small contribution for the h-h case. Due to
momentum conservation, jets are produced back-to-back in the transverse plane.
Thus, a second peak around $\pi$ in $\Delta\varphi$ is expected, which is
smeared in the $\Delta\eta$ direction, because the particles can obtain an
additional longitudinal boost related to the varied center-of-mass frame of
the partonic collision. In the selection of the trigger particle, the near-
side jet is fully reconstructed in the longitudinal direction, but the away-
side jet is not necessarily (fully) within the detector acceptance. The
procedure of getting fully corrected 2-dimensional per-trigger yield is
schematically written in Eq 1. Here, $\frac{{\rm d}^{2}N_{\mathrm{pair}}^{\rm
raw}}{{\rm d}\Delta\varphi{\rm d}\Delta\eta}(\Delta\varphi,\Delta\eta)$ is the
uncorrected correlation function,
$\varepsilon_{\mathrm{trigg}}$,$\varepsilon_{\mathrm{assoc}}$ and
$\varepsilon_{\mathrm{pair}}$ are correction factors further described in Sec.
3.2 and $N_{\mathrm{trigg}}$ is the number of the trigger particles.
Afterwards, the 2-dimensional per-trigger yield is projected on the
$\Delta\varphi$ axis and integrated (see Eq. 2) in the intervals
$|\Delta\varphi|<0.9$ and $|\Delta\varphi-\pi|<1.4$ to obtain the near-side
and away-side yield, respectively, denoted as $Y_{\Delta\varphi}$ in Eq. 2.

$\frac{{\rm d}^{2}N_{\mathrm{pair}}}{{\rm d}\Delta\varphi{\rm
d}\Delta\eta}(\Delta\varphi,\Delta\eta)=\frac{1}{N_{\mathrm{trigg}}}\frac{1}{\varepsilon_{\mathrm{trigg}}}\frac{1}{\varepsilon_{\mathrm{assoc}}}\frac{{\rm
d}^{2}N_{\mathrm{pair}}^{\rm raw}}{{\rm d}\Delta\varphi{\rm
d}\Delta\eta}(\Delta\varphi,\Delta\eta)\frac{1}{\varepsilon_{\mathrm{pair}}}$
(1) $Y_{\Delta\varphi}=\int_{\Delta\varphi_{1}}^{\Delta\varphi_{2}}\frac{{\rm
d}N}{{\rm d}\Delta\varphi}{\rm d}\Delta\varphi$ (2)

###  3.2 Corrections

The corrections are described in the same order as they were applied to the
data.

All MC-based corrections are calculated using events from PYTHIA8.210 (Monash
2013 tune) [29, 13], with particle propagation through the detector by means
of GEANT3 [30]. The detection inefficiencies are corrected with the single
particle efficiency factor, calculated in MC and applied as weight
($1/\varepsilon_{\mathrm{trigg}}\times 1/\varepsilon_{\mathrm{assoc}}$) for
each pair. This factor was calculated separately for trigger and associated
particles as a function of $p_{\mathrm{T}}$, $\eta$, $\varphi$ and PV
position. In the case of primary charged particles, a
$p_{\mathrm{T}}$-dependent contamination factor is also part of the weight to
account for the amount of secondary particles in the sample. This is defined
as a ratio of only primary tracks to all reconstructed ones.

Imperfect detector acceptance within $|\eta|\ <\ $ 0.8 range is corrected with
the mixed-event method, where trigger particles from one event are correlated
with associated particles from different events. Thus, no physical
correlations are present. The mixed-event correlation function has a typical
triangular shape determined by the $\eta$ acceptance. An example of this
function is shown in the middle plot of Fig. 1 where a plateau is visible.
This is caused by different ranges in $\eta$ for trigger
($\mathrm{K_{S}^{0}}$) and associated particles (h). The mixed-event
correlation is already scaled to unity with a scaling factor equal to the
average of bins with $\Delta\eta$ = 0. In the following, the actual
correlation function is divided by the mixed-event one to eliminate the
detector acceptance effects as illustrated in Fig. 1. This correction is
schematically written as $1/\varepsilon_{\mathrm{pair}}$ in Eq. 1. In some
cases, due to the finite binning in multiplicity and PV position in
z-direction, the mixed-event correlation does not match the shape of the
background perfectly. For this reason, a so called “wing“ correction is
performed. Here the correlation function is scaled once more with a 2D
distribution constant in $\Delta\varphi$ and dependent on $\Delta\eta$ in
order to get a flat distribution in $\Delta\eta$ at the away-side. This
correction is never larger than 2% and only affects the h-h correlation
function. A similar effect was observed also in a previous analysis [31].

For the reconstruction of $\mathrm{K_{S}^{0}}$ mesons and
$\Lambda$($\overline{\Lambda}$) baryons, some of the candidates selected with
the topological criteria are in fact combinatorial background. Since the shape
of the correlation function does not need to be the same for the signal and
background, a second correlation function is calculated, where candidates from
two intervals from outside the invariant mass peak
($m_{\mathrm{V^{0}}}-9\sigma$ to $m_{\mathrm{V^{0}}}-6\sigma$ and
$m_{\mathrm{V^{0}}}+6\sigma$ to $m_{\mathrm{V^{0}}}+9\sigma$) are taken as
trigger particles. These give the same width as the signal region in the
invariant mass spectrum. The second “side-band“ correlation function is
subtracted from the signal one. The number of trigger particles is in addition
corrected for purity, defined as a ratio of number of signal $\mathrm{V^{0}}$
candidates over all candidates within the invariant mass acceptance region.

In the case of $\Lambda$($\overline{\Lambda}$) being the trigger particle, the
feed-down contribution from decays of $\Xi$ baryons (reconstructed following
[6]) baryons is subtracted in a similar way as for the combinatorial
background. For this case, the $(\Xi^{-}+\overline{\Xi}^{+})$-h correlation
function in every $p_{\mathrm{T}}$ and multiplicity bin is calculated, scaled
with the detection efficiency of $\Lambda$($\overline{\Lambda}$) from $\Xi$
decays and subtracted from the $(\Lambda+\overline{\Lambda})$-h correlation
function. Similarly, the feed-down fraction is subtracted from the number of
trigger particles. It is assumed that the production rates of charged and
neutral $\Xi$ baryons are equal and the feed-down fraction from $\Omega$ is
negligible. This correction has an effect of 5% on the final near-side yields
for low $p_{\mathrm{T}}$ and smaller than 1% for high $p_{\mathrm{T}}$.

After projecting the per-trigger yield on the $\Delta\varphi$ axis, the
underlying event background is subtracted with the ZYAM (Zero Yield At
Minimum) method [32]. The background is assumed to be flat and estimated as
the average value of six bins outside the jet peaks to reduce the statistical
fluctuations.

###  3.3 Systematic uncertainties

Table 2:  Summary of the main sources and values of the relative systematic uncertainties (expressed in %) for the per-trigger yields in the MB sample. The abbreviation “negl.“ stands for negligible (smaller than 0.1%) and “rej.“ means that this variation was rejected due to the Barlow criterion. | h-h |  $\rm K_{S}^{0}$-h |  $\Lambda(\overline{\Lambda})$-h
---|---|---|---
| near | away | near | away | near | away
$\Delta\varphi$ window | 0.3 | 0.4 | 0.5 | 0.7 | 0.7 | 0.5
PV along the z-axis ($z_{\mathrm{vtx}}$) | negl. | negl. | 1.2 | 1.7 | 0.6 | 0.7
Binning in $z_{\mathrm{vtx}}$ | negl. | 0.4 | 0.8 | 1.6 | 0.5 | 1.2
Yield calculation | 1.0 | negl. | 1.1 | 0.7 | 0.3 | 0.4
Pedestal subtraction | 0.8 | 1.9 | 0.4 | 1.6 | 1.0 | 2.0
$\Delta\eta$ range | 0.5 | – | 1.2 | – | 1.0 | –
Mixing scale | negl. | negl. | 0.7 | 0.9 | 0.3 | 0.5
Topological variables | – | – | 1.5 | 3.5 | 3.0 | 3.1
Invariant mass range | – | – | rej. | rej. | 1.2 | 1.8
Primary track selection | 0.3 | 0.7 | 1.1 | 1.8 | 2.2 | 0.4
Wing correction | 1.2 | 1.8 | – | – | 0.7 | 0.8
$\Xi$ topological variables | – | – | – | – | 0.4 | 1.6
MC closure | negl. | negl. | negl. | negl. | 2.5 | negl.
Total | 1.9 | 2.7 | 3.1 | 5.1 | 5.1 | 4.8

The sources of systematic uncertainties of the per-trigger yields in the
minimum bias sample are listed in Tab. 2. These are estimated by varying
track-selection criteria and other parameters in the analysis. The
significance of each source of systematic uncertainty was checked according to
the Barlow criterion [33]. Within this procedure a threshold value (1
$\sigma$) is set, based on which each variation can be checked, whether it is
within statistical fluctuations or a real systematic difference. If a certain
variation did not pass the test, this contribution was not accounted for in
the total systematic uncertainty, which was calculated as a quadrature sum of
the individual contributions. For the ratios of yields, the systematic
uncertainties are calculated separately which causes cancellation of
correlated uncertainties.

For the uncertainty related to the $\Delta\varphi$ integration window, the
window is varied around the nominal values ($|\Delta\varphi|<~{}0.9$ and
$|\Delta\varphi-\pi|<1.4$) within $\pm$0.1. For the yields for the h-h
correlations, on both near- and away-side, the contribution to the total
uncertainty is around 0.4% for all multiplicity classes. For the yields for
$\mathrm{K_{S}^{0}}$-h and $(\Lambda+\overline{\Lambda})$-h correlations, the
value varies within 0.4–2% for both near- and away-side.

The PV selection along the $z$-axis ($z_{\mathrm{vtx}}$) is decreased from
$\pm 10$ cm to $\pm 7$ cm from the interaction point in order to estimate the
uncertainty connected to the detector acceptance effects. The uncertainty is
smaller than 0.3% in all multiplicity classes for the yields from h-h
correlation function. It is in the range 0.7–2.3% and 0.7–2.7% for the near-
side yield in case of $\mathrm{K_{S}^{0}}$-h and
$(\Lambda+\overline{\Lambda})$-h yields, respectively. For the away-side, this
source contributes with 1.7–4.5% and 0.7–4.9% in case of
$\mathrm{K_{S}^{0}}$-h and $(\Lambda+\overline{\Lambda})$-h yields,
respectively.

The number of bins in $z_{\mathrm{vtx}}$ used for the event-mixing classes is
changed from 9 to 7 to account for the uncertainty connected with the detector
acceptance. For the yields triggered with an unidentified hadron, the
contribution from this source is smaller than 0.5% at both sides for all
multiplicity classes. This uncertainty is in the range 0.5–2.7% and 0.4–1.5%
for the near-side yield triggered with $\mathrm{K_{S}^{0}}$ and
$(\Lambda+\overline{\Lambda})$, respectively and within 1.2–5.2% and 0.8–2.8%
for the away-side.

The contribution to the systematic uncertainty resulting from the yield
calculation method is estimated by fitting each jet peak with a double-
gaussian function and integrating the fit function to calculate the per-
trigger yield instead of calculating the yield directly by the bin counting
method as default. This leads to an uncertainty around 1% for the near-side
and to a value smaller than 0.2% for the away-side for the h-h yields in all
multiplicity classes. For most multiplicity classes this source was rejected
by the Barlow criterion for the $\mathrm{K_{S}^{0}}$ trigger. The non-rejected
contribution is 1.1% and 0.7% for the near- and away-side, respectively. The
accounted contribution to the uncertainty of yields triggered with
$(\Lambda+\overline{\Lambda})$ is in the range 0.3–0.8% and 0.2–3.4% for the
near-side and away-side yields, respectively.

For the variation of the underlying event subtraction method, which takes the
average value of 6 bins from the left and right side of the near-side peak, a
constant fit in ranges $[-\pi/2,-1]$ and $[1,\pi/2]$ is used, leading to an
estimated uncertainty around 0.6% (1.5%), 2% (2.2%) and 1.8% (4.5%) for the
near- (away- ) side yield from the unidentified hadron-,
$\mathrm{K_{S}^{0}}$-, and $(\Lambda+\overline{\Lambda})$-triggered
correlation functions, respectively.

The $\Delta\eta$ range is varied within 0.1 around its nominal value
$|\Delta\eta|<1$ in order to estimate the uncertainty related to the near-side
jet acceptance. This is estimated to be within 0.3–0.9%, 0.6–1.9%, 0.4-2.4%
for h-h, $\mathrm{K_{S}^{0}}$-h and $(\Lambda+\overline{\Lambda})$-h yields in
all multiplicity classes, respectively.

The scale factor for the mixed-event correlation function is varied, which
gives a negligible contribution to the total systematic uncertainty for h-h
yields for both sides. This contribution for $\mathrm{K_{S}^{0}}$-h
($(\Lambda+\overline{\Lambda})$-h) yields is estimated as 0.7–1.5% (0.2–0.4%)
and 0.9–2% (0.2–0.5%) for the near and away-side yields, respectively, in
different multiplicity classes.

In order to estimate the systematic uncertainty connected to the
$\mathrm{V^{0}}$ reconstruction, the values for the topological selection are
varied around the nominal values. Its value is, for different multiplicity
classes, in the range 1.5–5.8% (1.9–5.6%) and 2.2–7.5% (2–5.5%) for the
$\mathrm{K_{S}^{0}}$ $(\Lambda+\overline{\Lambda})$ triggered yields at the
near- and away-side, respectively.

The ranges for the signal and for background in the invariant mass
distributions are varied in order to estimate the uncertainty related to the
subtraction of the contribution from misidentified $\mathrm{K_{S}^{0}}$ or
$(\Lambda+\overline{\Lambda})$. This source is rejected by the Barlow
criterion for the $\mathrm{K_{S}^{0}}$-h yields and has a value in the range
0.5–3.9% and 1.1–4.3% for the $(\Lambda+\overline{\Lambda})$-h triggered
yields, for the near- and away-side, respectively.

The systematic uncertainty associated with the primary track selections is
estimated by selecting tracks with slightly varied criteria. These are the
same as the ones used for global tracks, but there is a tighter and
$p_{\mathrm{T}}$-dependent DCA requirement in the $xy$-plane, which means that
tracks with a DCA in the $xy$-plane larger than
$0.0105+0.0350/p_{\mathrm{T}}^{1.1}$ are rejected. This uncertainty is smaller
than 0.7% for both the near- and away-side yield for h-h for all multiplicity
classes. The uncertainty intervals for $\mathrm{K_{S}^{0}}$
($(\Lambda+\overline{\Lambda})$) triggered yields are estimated as 0.9–2.4%
(0.4–3.5%) and 1.3–3.9% (0.2–3.1%) for the near- and away-side yields,
respectively.

The range used for the estimation of the wing correction scaling factor is
varied in order to calculate the uncertainty related to this method. This
contribution is not dependent on the event multiplicity.

The $\Xi^{-}(\overline{\Xi}^{+})$ reconstruction uncertainty contributes to
the uncertainty of yields triggered by $(\Lambda+\overline{\Lambda})$. This
contribution is estimated by varying the topological selection of
$\Xi^{-}(\bar{\Xi}^{+})$ hyperons around their nominal values. This
uncertainty is in the range 0.2–4% (0.2–3.9%) for the near(away)-side yields
for events in all multiplicity classes.

The correction procedure is checked with a Monte Carlo closure test. Two
correlation functions are calculated, the first one with generated MC
particles and the second one with MC particles reconstructed after GEANT3
propagation using the full reconstruction and correction chain as for the
experimental data. The ratio of these two correlation functions is expected to
be unity. This is the case for the h-h and $\mathrm{K_{S}^{0}}$-h correlation
functions, but there is a residual departure from unity for
$(\Lambda+\overline{\Lambda})$-h correlation function at the near-side of up
to 2.5%, which is accounted as a systematic uncertainty.

##  4 Results and discussion

Figure 2: $\Delta\varphi$ projection of the h-h (left), $\mathrm{K_{S}^{0}}$-h
(middle) and $(\Lambda+\overline{\Lambda})$-h (right) correlation functions
compared with MC event generators for low (top) and high (bottom)
$p_{\mathrm{T}}^{\mathrm{trigg}}$. Error bars and colored boxes represent
statistical and systematic uncertainties, respectively. The data are compared
with MC event generators.

The $\Delta\varphi$ projections of the correlation functions for the three
different trigger particles are shown for two
$p_{\mathrm{T}}^{\mathrm{trigg}}$ intervals,
$3<p_{\mathrm{T}}^{\mathrm{trigg}}<4$ GeV/$c$ and $9<\
p_{\mathrm{T}}^{\mathrm{trigg}}<11$ GeV/$c$, in Fig. 2. Included are also the
correlation functions predicted by MC event generators widely used by the LHC
collaborations: PYTHIA8 with the standard Monash tune, which includes colour
re-connection as final-state effects [13], PYTHIA8 Monash tune with shoving
[14] and EPOS LHC [12]. It is important to note that the PYTHIA 8 Monash and
EPOS LHC tunings were based on single-particle spectra and underlying event
observables, but did not include particle correlations in azimuth. The shoving
strength parameter g is here set to g=3 and in addition the upper
$p_{\mathrm{T}}$ cut for the shoving mechanism is turned off. None of the
models describes quantitatively the correlation functions consistently for the
three trigger particle species. For the low $p_{\mathrm{T}}^{\mathrm{trigg}}$,
both PYTHIA8 tunes overestimate the peaks on the near- and away-side, while
EPOS LHC underestimates them significantly for all trigger particles except
for the $\mathrm{K_{S}^{0}}$. In the high $p_{\mathrm{T}}^{\mathrm{trigg}}$
interval, the shoving tune of PYTHIA8 is underestimating the near-side peak
for all trigger particles except for $\mathrm{K_{S}^{0}}$-h case, but
describes well the away-side peak. The description of EPOS LHC and PYTHIA8
Monash is similar in both $p_{\mathrm{T}}^{\mathrm{trigg}}$ intervals, where
EPOS LHC is underestimating both peaks of h-h and
$(\Lambda+\overline{\Lambda})$-h correlation functions and can reasonably well
describe the $\Delta\varphi$ projection of the $\mathrm{K_{S}^{0}}$-h
correlation function. PYTHIA8 Monash tune overestimates both peaks for all
three types of correlation functions.

The per-trigger yields, obtained by integrating the $\Delta\varphi$
projections of the correlation function for the intervals
$|\Delta\varphi|<0.9$ (near-side) and $|\Delta\varphi-\pi|<1.4$ (away-side)
are studied as a function of $p_{\mathrm{T}}^{\mathrm{trigg}}$,
$p_{\mathrm{T}}^{\mathrm{assoc}}$ and event-activity class for the three
trigger particle species and compared with the MC event generators. The
results are described in the following sections.

###  4.1 Per-trigger yields

The per-trigger yields are shown in Fig. 3 as a function of the
$p_{\mathrm{T}}^{\mathrm{trigg}}$ for different event-activity classes. An
increasing trend with $p_{\mathrm{T}}^{\mathrm{trigg}}$ is observed, as
expected, as higher energetic jets have increasingly more associated
particles.

For a more quantitative inspection of the event-activity dependence, the ratio
between the yield in each event class and the yield for minimum bias is given
in Fig. 4. Different trends are visible for the near- and away-side peaks. A
clear multiplicity ordering for the near-side can be observed, events with
higher multiplicity exhibiting the highest yields. This behaviour is most
obvious for the h-h correlations, but it is significant on the near-side also
for the $\mathrm{V^{0}}$-triggered yields.

Figure 3: Per-trigger yields of h-h (left), $\mathrm{K_{S}^{0}}$-h (middle)
and $(\Lambda+\overline{\Lambda})$-h (right) correlation functions as a
function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ on the near-side (upper row) and
away-side (lower row) for different multiplicity classes. For visibility, the
values in the various event classes are scaled with the factors indicated in
the legend. Error bars and colored boxes represent statistical and systematic
uncertainties, respectively, which are in most cases within the data points.
Figure 4: Ratio of the per-trigger yields in different multiplicity classes to
the corresponding minimum bias yield for h-h (left), $\mathrm{K_{S}^{0}}$-h
(middle) and $(\Lambda+\overline{\Lambda})$-h (right) correlations on the
near-side (upper row) and away-side (lower row). Error bars and colored boxes
represent statistical and systematic uncertainties, respectively. Figure 5:
Per-trigger yields of h-h (left), $\mathrm{K_{S}^{0}}$-h (middle) and
$(\Lambda+\overline{\Lambda})$-h (right) correlation functions as a function
of $p_{\mathrm{T}}^{\mathrm{assoc}}$ on the near-side (upper row) and away-
side (lower row) for different $p_{\mathrm{T}}^{\mathrm{trigg}}$ intervals.
Error bars and colored boxes represent statistical and systematic
uncertainties, respectively.

For the away-side this ordering is reverted, in particular with the increase
of the $p_{\mathrm{T}}$ of the trigger particle. Given the uncertainties, this
trend is less significant for the $\mathrm{K_{S}^{0}}$-h and
$(\Lambda+\overline{\Lambda})$-h correlations. The finding is qualitatively
reproduced in PYTHIA8 simulations and can be understood considering that the
location of the away-side jet is not fixed in $\eta$. The requirement of a low
(high) multiplicity in the V0 detectors at larger rapidity, biases the events
towards configurations where the away-side jet is within (outside) the central
acceptance, thus increasing (decreasing) the per-trigger yield at the away-
side. The near-side jet is by construction in the central pseudorapidity
acceptance, though the multiplicity measurement in the V0 detectors may still
be influenced by long range correlations (flow-like) and fragmentation biases.
This jet bias, although expected and roughly understood, is interesting, as it
gives insights on particle production mechanisms. It is further investigated
through the comparison with model predictions and through normalisation of the
yields for $\mathrm{K_{S}^{0}}$-h and $(\Lambda+\overline{\Lambda})$-h to
those for h-h.

The $p_{\mathrm{T}}^{\mathrm{assoc}}$ per-trigger yield spectra for different
$p_{\mathrm{T}}^{\mathrm{trigg}}$ intervals are shown in Fig. 5. A decreasing
trend with $p_{\mathrm{T}}^{\mathrm{assoc}}$ is observed, as expected, as the
probability for creation of associated particle with high $p_{\mathrm{T}}$
decreases with $p_{\mathrm{T}}$. Note, that due to the kinematic requirement
$p_{\mathrm{T}}^{\mathrm{assoc}}$ $<$$p_{\mathrm{T}}^{\mathrm{trigg}}$, the
yields triggered with lower $p_{\mathrm{T}}$ particle do not cover the full
$p_{\mathrm{T}}^{\mathrm{assoc}}$ range.

###  4.2 Comparison with model predictions

The ratios of model predictions to data for the integrated per-trigger yields
are shown in Fig. 6 for the near- and away-side yields as a function of
$p_{\mathrm{T}}^{\mathrm{trigg}}$ for minimum bias events and event classes
selected on their activity. The first finding is that the trends in the model
descriptions of the data are largely the same for h-h, $\mathrm{K_{S}^{0}}$-h
and $(\Lambda+\overline{\Lambda})$-h correlations. The event-activity
dependence is reproduced by PYTHIA8 calculations, for both the standard tune
and with shoving, the latter clearly offering a better description. The
dependence is not reproduced by EPOS LHC.

Figure 6: Models to data ratio of integrated per-trigger yields as a function
of $p_{\mathrm{T}}^{\mathrm{trigg}}$ for different multiplicity classes on the
near-side (top panels) and away-side (bottom panels) for three MC event
generators: PYTHIA8 Monash with colour re-connection (left), PYTHIA8 with
shoving (middle) and EPOS LHC (right). Note, for plotting purpose, the
arbitrary shifts for the vertical axis are given in the legend. The bands
represent combined systematic and statistical uncertainties from data and
models.

For the near-side, PYTHIA8 with colour re-connection describes well the yields
for high $p_{\mathrm{T}}$ trigger particles while it overestimates the yields
at low-$p_{\mathrm{T}}$. This is valid for all multiplicity classes and holds
for the three species of trigger particle. This is a consequence of the good
description of the hard QCD processes in PYTHIA8, which do not depend on the
event multiplicity. The shoving mechanism improves the intermediate sector, at
the price of degrading the harder sector. It is apparent that allowing shoving
for large $p_{\mathrm{T}}$, as done for the simulations presented here, is not
entirely physical. The medium-hard and softer regime remains a challenge and
is a subject of current theoretical attention [34]. For the away-side, the
shoving mechanism describes the data better than the standard PYTHIA8. In EPOS
LHC the event-activity dependence seen in data is not described well: a
stronger event-activity dependence is observed in the model, which
underpredicts the data for higher event multiplicities and either overpredicts
it (for the near-side) or describes it well (for the away-side) for the lower
event-activity classes.

The features of the data-model comparison are dramatically different in Fig.
7, where the model to data per-trigger yield ratio is shown as a function of
$p_{\mathrm{T}}^{\mathrm{assoc}}$ for minimum bias collisions. Here, the
trigger-particle dependence is rather different in the models. PYTHIA8
predicts, for both the standard settings and for the shoving mechanism, in
particular for the near-side and in dependence of
$p_{\mathrm{T}}^{\mathrm{trigg}}$, a much stronger dependence on the trigger
particle than seen in the data, while EPOS LHC describes the data well. For
high values of $p_{\mathrm{T}}^{\mathrm{trigg}}$, the data are described well
in the whole $p_{\mathrm{T}}^{\mathrm{assoc}}$ range. This reflects that the
full fragmentation process in a hard scattering is well modelled. It is
important to note that PYTHIA8 Monash describes quite well the measured
$p_{\mathrm{T}}$ dependence of the ratio
$(\Lambda+\overline{\Lambda})/\mathrm{K_{S}^{0}}$ in jets with
$p_{\mathrm{T}}~{}>~{}10$ GeV/$c$. However, the ratio in MB collisions is not
described well, in particular for $p_{\mathrm{T}}<4$ GeV/$c$ [35].

It is interesting to examine the differences between PYTHIA8 with the standard
settings and with the shoving mechanism. Clearly, the shoving mechanism
describes the medium-hard processes better, corresponding roughly to
$3<p_{\mathrm{T}}^{\mathrm{trigg}}<5$ GeV/$c$ and
$1<p_{\mathrm{T}}^{\mathrm{assoc}}<5$ GeV/$c$, while the harder processes are
underestimated. This is visible in particular in Fig. 6 for all trigger-
particle species for different event-activity classes, while in case of the
$p_{\mathrm{T}}^{\mathrm{assoc}}$ dependence shown in Fig. 7 the shoving
mechanism leads to a poorer description of the data compared to the standard
settings.

Figure 7: Models to data ratio of integrated per-trigger yields for minimum
bias collisions as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ for
different $p_{\mathrm{T}}^{\mathrm{trigg}}$ intervals on the near-side (top
panels) and away-side (bottom panels) for three MC models: PYTHIA8 Monash with
colour re-connection (left), PYTHIA8 with shoving (middle) and EPOS LHC
(right). Note that for plotting purpose, the arbitrary shifts for the vertical
axis are given in the legend. The bands represent combined systematical and
statistical uncertainties for data and statistical for models.

The EPOS LHC model gives a rather different description of the data compared
to PYTHIA8. In contrast to the rather poor description of the
$p_{\mathrm{T}}^{\mathrm{trigg}}$ and event-class dependence (Fig. 6) the
dependence on $p_{\mathrm{T}}^{\mathrm{assoc}}$ (Fig. 7) is rather well
described for different $p_{\mathrm{T}}^{\mathrm{trigg}}$ intervals and for
the three trigger-particle species.

###  4.3 Comparison of hadron- and strangeness-triggered yields

In order to compare more quantitatively the correlation yields for different
trigger-particle species, ratios to the per-trigger yields for h-h are
calculated. These ratios are shown as a function of
$p_{\mathrm{T}}^{\mathrm{trigg}}$ in Fig. 8 for the near- and away-side, while
Fig. 9 shows them as a function of $p_{\mathrm{T}}^{\mathrm{assoc}}$. The data
indicate that the differences between charged-hadron (a sample dominated by
pions) triggered yields and either $\mathrm{K_{S}^{0}}$ or
$(\Lambda+\overline{\Lambda})$ triggered yields are small and have a weak
dependence on the event-activity class. All three models describe the trends
seen in the ratios better than it was the case for yields itself, a
consequence of the earlier finding that the model-data disagreements are very
similar for the three trigger-particle species.

At closer examination, one observes that a difference between different
trigger-particle species is visible for the near-side. The yield ratios $\rm
Y_{\Delta\varphi}^{\mathrm{K_{S}^{0}}-h}/Y_{\Delta\varphi}^{h-h}$ are smaller
than unity and flat with $p_{\mathrm{T}}^{\mathrm{trigg}}$ in all multiplicity
classes, which indicates that jets triggered with $\mathrm{K_{S}^{0}}$ mesons
contain less particles than the unbiased (inclusive) jets and this feature
does not depend on the hardness of the process
($p_{\mathrm{T}}^{\mathrm{trigg}}$) or event multiplicity. In contrast to
that, the $\rm
Y_{\Delta\varphi}^{(\Lambda+\overline{\Lambda})-h}/Y_{\Delta\varphi}^{h-h}$
ratio increases with $p_{\mathrm{T}}^{\mathrm{trigg}}$. Potentially, this
could be explained with a bias towards gluon jets, which contain more
particles [19] and have relative enhanced production of $\Lambda$ hyperons
[21]. In the right column in the left plot of Fig. 9, it is visible that this
effect in the data is pronounced for the soft part of harder jets (low
$p_{\mathrm{T}}^{\mathrm{assoc}}$ for high $p_{\mathrm{T}}^{\mathrm{trigg}}$).
A decreasing trend of the ratio can also be observed with increasing
$p_{\mathrm{T}}^{\mathrm{assoc}}$. This suggests that a jet triggered with
either a $\mathrm{K_{S}^{0}}$ or a $\Lambda$ or $\overline{\Lambda}$ particle
has a smaller amount of associated particles with higher $p_{\mathrm{T}}$ than
a jet triggered with an unidentified hadron with the same $p_{\mathrm{T}}$.
This decreasing trend is reproduced by the considered models.

Figure 8:  Ratios of integrated per-trigger yield of $\mathrm{K_{S}^{0}}$-h
(left column) or $(\Lambda+\overline{\Lambda})$-h (right column) to h-h as a
function of $p_{\mathrm{T}}^{\mathrm{trigg}}$, for the near-side in the left
plot and for the away-side in the right plot, for different event multiplicity
classes. Error bars and colored boxes represent statistical and systematic
uncertainties, respectively. The bands around model curves stand for their
statistical uncertainty.

Figure 9: Per-trigger yield ratios of $\mathrm{K_{S}^{0}}$-h (left column) or
$(\Lambda+\overline{\Lambda})$-h (right column) to h-h as a function of
$p_{\mathrm{T}}^{\mathrm{assoc}}$, for the near-side in the left plot and for
the away-side in the right plot, for different
$p_{\mathrm{T}}^{\mathrm{trigg}}$ intervals in the MB sample. Error bars and
colored boxes represent statistical and systematic uncertainties,
respectively. The bands around model curves stand for their statistical
uncertainty.

To check if the difference in the ratios for the near side triggered with
$\mathrm{K_{S}^{0}}$ and $(\Lambda+\overline{\Lambda})$ can be caused by the
differences between quark and gluon jets, a separated PYTHIA 8 study was
performed, selecting exclusive hard processes containing only quarks
($q+\overline{q}\rightarrow q+\overline{q}$) or gluons ($g+g\rightarrow g+g$)
in the final state. The results of this study are shown in Fig. 10 for MB
collisions in comparison with the data. It is visible that the ratio is almost
identical for the $\mathrm{K_{S}^{0}}$-triggered yields for both quark and
gluon jets. In this case, for both processes the ratio is flat as a function
of $p_{\mathrm{T}}^{\mathrm{trigg}}$ and decreasing with
$p_{\mathrm{T}}^{\mathrm{assoc}}$. Nevertheless, a clear difference between
the two processes is observed in the ratios of yields triggered with
$(\Lambda+\overline{\Lambda})$. The ratio for the gluon-jet process is
increasing with $p_{\mathrm{T}}^{\mathrm{trigg}}$ and is higher, while it is
flat and smaller for the quark jets. A systematic difference is visible also
in the $p_{\mathrm{T}}^{\mathrm{assoc}}$ dependence, where the ratio from
gluon jets is significantly higher than the one from quark jets. Although the
$p_{\mathrm{T}}$ distributions of hadrons in the simulated exclusive processes
may be different than in MB collisions, it is expected that the observed
difference is generic and explains at least some of the trends seen in the
data.

Figure 10: Per-trigger yield ratios of $\mathrm{K_{S}^{0}}$-h (left column) or
$(\Lambda+\overline{\Lambda})$-h (right column) to h-h at the near-side as a
function of $p_{\mathrm{T}}^{\mathrm{trigg}}$ in the left plot and as a
function of $p_{\mathrm{T}}^{\mathrm{assoc}}$ in the right plot, compared with
the PYTHIA8 calculation of the quark and gluon jets. Error bars and colored
boxes represent statistical and systematic uncertainties, respectively. The
bands around model curves stand for their statistical uncertainty.

In contrast, for the away-side, the ratios as a function of
$p_{\mathrm{T}}^{\mathrm{trigg}}$ are compatible with unity within the
uncertainties as shown in the right part of Fig. 8, thus no dependence on the
trigger particle species ($\mathrm{K_{S}^{0}}$ or
$(\Lambda+\overline{\Lambda})$) is seen. The deviation of some bins from unity
is caused by the fluctuations affecting the underlying event estimation. A
similar trend is also observed in the $p_{\mathrm{T}}^{\mathrm{assoc}}$
dependence in the right plot of Fig. 9. This fits to the expectation that
there is no bias in the away-side jet, since the associated production of
strangeness is not dominated by hard processes, i.e. initial production of a
$\mathrm{s\bar{s}}$ pair leading to two strangeness induced jets. This is
confirmed by the comparison with models, which predict ratios for the away-
side around unity (Fig. 8).

##  5 Summary and conclusions

Charged-hadron, $\mathrm{K_{S}^{0}}$ and $(\Lambda+\overline{\Lambda})$
triggered correlation functions are measured at the LHC in pp collisions at
$\sqrt{s}$ = 13 TeV with the ALICE apparatus. The integrated per-trigger
yields are extracted for both near- and away-side and studied as a function of
event multiplicity, $p_{\mathrm{T}}^{\mathrm{trigg}}$ and
$p_{\mathrm{T}}^{\mathrm{assoc}}$. A dependence on the event-multiplicity is
observed, which may be explained at the near-side by an interplay of
fragmentation and of collective-like effects such as the long-range ridge. For
the away-side the primary effects are likely due to a selection bias, where
the away-side jet is within the V0 detector acceptance causing smaller away-
side yields for high-multiplicity classes. This bias was overcome by studying
the ratios of yields for $\mathrm{K_{S}^{0}}$ or
$(\Lambda+\overline{\Lambda})$ as trigger particle over that for inclusive
hadrons. For the near-side, different trends were observed for yield ratios
triggered by $\mathrm{K_{S}^{0}}$ and $(\Lambda+\overline{\Lambda})$. While
the $\rm Y_{\Delta\varphi}^{K_{S}^{0}-h}/Y_{\Delta\varphi}^{h-h}$ is flat, the
$\rm
Y_{\Delta\varphi}^{(\Lambda+\overline{\Lambda})-h}/Y_{\Delta\varphi}^{h-h}$
increases with $p_{\mathrm{T}}^{\mathrm{trigg}}$. This difference is most
pronounced for high $p_{\mathrm{T}}^{\mathrm{trigg}}$ and low
$p_{\mathrm{T}}^{\mathrm{assoc}}$. A PYTHIA8 study of the respective exclusive
processes reveals that the difference is caused by the bias towards gluon jets
through triggering with $\Lambda$ or $\overline{\Lambda}$ hyperon. In the
$p_{\mathrm{T}}^{\mathrm{assoc}}$ dependence of these yield ratios, a
decreasing trend of the ratio can be observed with increasing
$p_{\mathrm{T}}^{\mathrm{assoc}}$, suggesting a smaller amount of associated
particles with higher $p_{\mathrm{T}}$ in the jets triggered with a
$\mathrm{V^{0}}$ particle than in jets triggered with an unidentified hadron
with the same $p_{\mathrm{T}}$. This trend can be reproduced by the considered
models. However, on the away-side, no clear difference between the trigger-
particle species is observed.

All the measured quantities are compared with model predictions. PYTHIA8 with
the standard Monash tune can describe the data for high
$p_{\mathrm{T}}^{\mathrm{trigg}}$ (hard processes) while the shoving mechanism
works better at intermediate $p_{\mathrm{T}}^{\mathrm{trigg}}$. The comparison
of data with the EPOS LHC model reveals that the model predicts a
significantly stronger multiplicity dependence than seen in the data. The
ratios of yields for $\mathrm{K_{S}^{0}}$ or $(\Lambda+\overline{\Lambda})$
trigger particles to those for inclusive (charged) hadrons are qualitatively
well described by all the considered models, except for EPOS LHC at the near-
side for the $(\Lambda+\overline{\Lambda})$ triggered yield ratios.

## Acknowledgements

The ALICE Collaboration would like to thank Christian Bierlich for providing
the PYTHIA8 String Shoving configurations and discussions around this and
Tanguy Pierog and Klaus Werner for helpful discussions around the EPOS LHC
model.

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; 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; Indonesian Institute of Science, Indonesia; Istituto Nazionale di Fisica Nucleare (INFN), Italy; Institute for Innovative Science and Technology , Nagasaki Institute of Applied Science (IIST), 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; Commission on Science and Technology for Sustainable Development in the South (COMSATS), Pakistan; Pontificia Universidad Católica del Perú, Peru; Ministry of Education and Science, 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 and Ministry of Research and Innovation and Institute of Atomic Physics, Romania; Joint Institute for Nuclear Research (JINR), Ministry of Education and Science of the Russian Federation, National Research Centre Kurchatov Institute, Russian Science Foundation and Russian Foundation for Basic Research, Russia; Ministry of Education, Science, Research and Sport of the Slovak Republic, Slovakia; National Research Foundation of South Africa, South Africa; Swedish Research Council (VR) and Knut & Alice Wallenberg Foundation (KAW), Sweden; European Organization for Nuclear Research, Switzerland; Suranaree University of Technology (SUT), National Science and Technology Development Agency (NSDTA) and Office of the Higher Education Commission under NRU project of Thailand, Thailand; Turkish 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.

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##  Appendix A The ALICE Collaboration

S. Acharya141, D. Adamová96, A. Adler74, G. Aglieri Rinella34, M. Agnello30,
N. Agrawal54, Z. Ahammed141, S. Ahmad16, S.U. Ahn76, I. Ahuja38, Z. Akbar51,
A. Akindinov93, M. Al-Turany108, S.N. Alam16,40, D. Aleksandrov89, B.
Alessandro59, H.M. Alfanda7, R. Alfaro Molina71, B. Ali16, Y. Ali14, A.
Alici25, N. Alizadehvandchali125, A. Alkin34, J. Alme21, T. Alt68, L.
Altenkamper21, I. Altsybeev113, M.N. Anaam7, C. Andrei48, D. Andreou91, A.
Andronic144, M. Angeletti34, V. Anguelov105, F. Antinori57, P. Antonioli54, C.
Anuj16, N. Apadula80, L. Aphecetche115, H. Appelshäuser68, S. Arcelli25, R.
Arnaldi59, I.C. Arsene20, M. Arslandok146,105, A. Augustinus34, R.
Averbeck108, S. Aziz78, M.D. Azmi16, A. Badalà56, Y.W. Baek41, X. Bai129,108,
R. Bailhache68, Y. Bailung50, R. Bala102, A. Balbino30, A. Baldisseri138, B.
Balis2, M. Ball43, D. Banerjee4, R. Barbera26, L. Barioglio106, M. Barlou85,
G.G. Barnaföldi145, L.S. Barnby95, V. Barret135, C. Bartels128, K. Barth34, E.
Bartsch68, F. Baruffaldi27, N. Bastid135, S. Basu81, G. Batigne115, B.
Batyunya75, D. Bauri49, J.L. Bazo Alba112, I.G. Bearden90, C. Beattie146, I.
Belikov137, A.D.C. Bell Hechavarria144, F. Bellini25, R. Bellwied125, S.
Belokurova113, V. Belyaev94, G. Bencedi69, S. Beole24, A. Bercuci48, Y.
Berdnikov99, A. Berdnikova105, L. Bergmann105, M.G. Besoiu67, L. Betev34, P.P.
Bhaduri141, A. Bhasin102, I.R. Bhat102, M.A. Bhat4, B. Bhattacharjee42, P.
Bhattacharya22, L. Bianchi24, N. Bianchi52, J. Bielčík37, J. Bielčíková96, J.
Biernat118, A. Bilandzic106, G. Biro145, S. Biswas4, J.T. Blair119, D. Blau89,
M.B. Blidaru108, C. Blume68, G. Boca28,58, F. Bock97, A. Bogdanov94, S. Boi22,
J. Bok61, L. Boldizsár145, A. Bolozdynya94, M. Bombara38, P.M. Bond34, G.
Bonomi140,58, H. Borel138, A. Borissov82, H. Bossi146, E. Botta24, L.
Bratrud68, P. Braun-Munzinger108, M. Bregant121, M. Broz37, G.E. Bruno107,33,
M.D. Buckland128, D. Budnikov109, H. Buesching68, S. Bufalino30, O. Bugnon115,
P. Buhler114, Z. Buthelezi72,132, J.B. Butt14, S.A. Bysiak118, M. Cai27,7, H.
Caines146, A. Caliva108, E. Calvo Villar112, J.M.M. Camacho120, R.S.
Camacho45, P. Camerini23, F.D.M. Canedo121, F. Carnesecchi34,25, R. Caron138,
J. Castillo Castellanos138, E.A.R. Casula22, F. Catalano30, C. Ceballos
Sanchez75, P. Chakraborty49, S. Chandra141, S. Chapeland34, M. Chartier128, S.
Chattopadhyay141, S. Chattopadhyay110, A. Chauvin22, T.G. Chavez45, T. Cheng7,
C. Cheshkov136, B. Cheynis136, V. Chibante Barroso34, D.D. Chinellato122, S.
Cho61, P. Chochula34, P. Christakoglou91, C.H. Christensen90, P.
Christiansen81, T. Chujo134, C. Cicalo55, L. Cifarelli25, F. Cindolo54, M.R.
Ciupek108, G. ClaiII,54, J. CleymansI,124, F. Colamaria53, J.S. Colburn111, D.
Colella107,53,33,145, A. Collu80, M. Colocci34, M. ConcasIII,59, G. Conesa
Balbastre79, Z. Conesa del Valle78, G. Contin23, J.G. Contreras37, M.L.
Coquet138, T.M. Cormier97, P. Cortese31, M.R. Cosentino123, F. Costa34, S.
Costanza28,58, P. Crochet135, R. Cruz-Torres80, E. Cuautle69, P. Cui7, L.
Cunqueiro97, A. Dainese57, M.C. Danisch105, A. Danu67, I. Das110, P. Das87, P.
Das4, S. Das4, S. Dash49, S. De87, A. De Caro29, G. de Cataldo53, L. De
Cilladi24, J. de Cuveland39, A. De Falco22, D. De Gruttola29, N. De Marco59,
C. De Martin23, S. De Pasquale29, S. Deb50, H.F. Degenhardt121, K.R. Deja142,
L. Dello Stritto29, S. Delsanto24, W. Deng7, P. Dhankher19, D. Di Bari33, A.
Di Mauro34, R.A. Diaz8, T. Dietel124, Y. Ding136,7, R. Divià34, D.U. Dixit19,
Ø. Djuvsland21, U. Dmitrieva63, J. Do61, A. Dobrin67, B. Dönigus68, O.
Dordic20, A.K. Dubey141, A. Dubla108,91, S. Dudi101, M. Dukhishyam87, P.
Dupieux135, N. Dzalaiova13, T.M. Eder144, R.J. Ehlers97, V.N. Eikeland21, F.
Eisenhut68, D. Elia53, B. Erazmus115, F. Ercolessi25, F. Erhardt100, A.
Erokhin113, M.R. Ersdal21, B. Espagnon78, G. Eulisse34, D. Evans111, S.
Evdokimov92, L. Fabbietti106, M. Faggin27, J. Faivre79, F. Fan7, A. Fantoni52,
M. Fasel97, P. Fecchio30, A. Feliciello59, G. Feofilov113, A. Fernández
Téllez45, A. Ferrero138, A. Ferretti24, V.J.G. Feuillard105, J. Figiel118, S.
Filchagin109, D. Finogeev63, F.M. Fionda55,21, G. Fiorenza34,107, F. Flor125,
A.N. Flores119, S. Foertsch72, P. Foka108, S. Fokin89, E. Fragiacomo60, E.
Frajna145, U. Fuchs34, N. Funicello29, C. Furget79, A. Furs63, J.J.
Gaardhøje90, M. Gagliardi24, A.M. Gago112, A. Gal137, C.D. Galvan120, P.
Ganoti85, C. Garabatos108, J.R.A. Garcia45, E. Garcia-Solis10, K. Garg115, C.
Gargiulo34, A. Garibli88, K. Garner144, P. Gasik108, E.F. Gauger119, A.
Gautam127, M.B. Gay Ducati70, M. Germain115, P. Ghosh141, S.K. Ghosh4, M.
Giacalone25, P. Gianotti52, P. Giubellino108,59, P. Giubilato27, A.M.C.
Glaenzer138, P. Glässel105, D.J.Q. Goh83, V. Gonzalez143, L.H. González-
Trueba71, S. Gorbunov39, M. Gorgon2, L. Görlich118, S. Gotovac35, V.
Grabski71, L.K. Graczykowski142, L. Greiner80, A. Grelli62, C. Grigoras34, V.
Grigoriev94, A. GrigoryanI,1, S. Grigoryan75,1, O.S. Groettvik21, F.
Grosa34,59, J.F. Grosse-Oetringhaus34, R. Grosso108, G.G. Guardiano122, R.
Guernane79, M. Guilbaud115, K. Gulbrandsen90, T. Gunji133, W. Guo7, A.
Gupta102, R. Gupta102, S.P. Guzman45, L. Gyulai145, M.K. Habib108, C.
Hadjidakis78, G. Halimoglu68, H. Hamagaki83, G. Hamar145, M. Hamid7, R.
Hannigan119, M.R. Haque142,87, A. Harlenderova108, J.W. Harris146, A.
Harton10, J.A. Hasenbichler34, H. Hassan97, D. Hatzifotiadou54, P. Hauer43,
L.B. Havener146, S. Hayashi133, S.T. Heckel106, E. Hellbär108, H. Helstrup36,
T. Herman37, E.G. Hernandez45, G. Herrera Corral9, F. Herrmann144, K.F.
Hetland36, H. Hillemanns34, C. Hills128, B. Hippolyte137, B. Hofman62, B.
Hohlweger91, J. Honermann144, G.H. Hong147, D. Horak37, S. Hornung108, A.
Horzyk2, R. Hosokawa15, Y. Hou7, P. Hristov34, C. Hughes131, P. Huhn68, T.J.
Humanic98, H. Hushnud110, A. Hutson125, D. Hutter39, J.P. Iddon34,128, R.
Ilkaev109, H. Ilyas14, M. Inaba134, G.M. Innocenti34, M. Ippolitov89, A.
Isakov37,96, M.S. Islam110, M. Ivanov108, V. Ivanov99, V. Izucheev92, M.
Jablonski2, B. Jacak80, N. Jacazio34, P.M. Jacobs80, S. Jadlovska117, J.
Jadlovsky117, S. Jaelani62, C. Jahnke122,121, M.J. Jakubowska142, A.
Jalotra102, M.A. Janik142, T. Janson74, M. Jercic100, O. Jevons111, A.A.P.
Jimenez69, F. Jonas97,144, P.G. Jones111, J.M. Jowett 34,108, J. Jung68, M.
Jung68, A. Junique34, A. Jusko111, J. Kaewjai116, P. Kalinak64, A. Kalweit34,
V. Kaplin94, S. Kar7, A. Karasu Uysal77, D. Karatovic100, O. Karavichev63, T.
Karavicheva63, P. Karczmarczyk142, E. Karpechev63, A. Kazantsev89, U.
Kebschull74, R. Keidel47, D.L.D. Keijdener62, M. Keil34, B. Ketzer43, Z.
Khabanova91, A.M. Khan7, S. Khan16, A. Khanzadeev99, Y. Kharlov92, A.
Khatun16, A. Khuntia118, B. Kileng36, B. Kim17,61, C. Kim17, D.J. Kim126, E.J.
Kim73, J. Kim147, J.S. Kim41, J. Kim105, J. Kim147, J. Kim73, M. Kim105, S.
Kim18, T. Kim147, S. Kirsch68, I. Kisel39, S. Kiselev93, A. Kisiel142, J.P.
Kitowski2, J.L. Klay6, J. Klein34, S. Klein80, C. Klein-Bösing144, M.
Kleiner68, T. Klemenz106, A. Kluge34, A.G. Knospe125, C. Kobdaj116, M.K.
Köhler105, T. Kollegger108, A. Kondratyev75, N. Kondratyeva94, E.
Kondratyuk92, J. Konig68, S.A. Konigstorfer106, P.J. Konopka34,2, G.
Kornakov142, S.D. Koryciak2, L. Koska117, A. Kotliarov96, O. Kovalenko86, V.
Kovalenko113, M. Kowalski118, I. Králik64, A. Kravčáková38, L. Kreis108, M.
Krivda111,64, F. Krizek96, K. Krizkova Gajdosova37, M. Kroesen105, M.
Krüger68, E. Kryshen99, M. Krzewicki39, V. Kučera34, C. Kuhn137, P.G.
Kuijer91, T. Kumaoka134, D. Kumar141, L. Kumar101, N. Kumar101, S. Kundu34,87,
P. Kurashvili86, A. Kurepin63, A.B. Kurepin63, A. Kuryakin109, S. Kushpil96,
J. Kvapil111, M.J. Kweon61, J.Y. Kwon61, Y. Kwon147, S.L. La Pointe39, P. La
Rocca26, Y.S. Lai80, A. Lakrathok116, M. Lamanna34, R. Langoy130, K.
Lapidus34, P. Larionov34,52, E. Laudi34, L. Lautner34,106, R. Lavicka37, T.
Lazareva113, R. Lea140,23,58, J. Lehrbach39, R.C. Lemmon95, I. León Monzón120,
E.D. Lesser19, M. Lettrich34,106, P. Lévai145, X. Li11, X.L. Li7, J. Lien130,
R. Lietava111, B. Lim17, S.H. Lim17, V. Lindenstruth39, A. Lindner48, C.
Lippmann108, A. Liu19, D.H. Liu7, J. Liu128, I.M. Lofnes21, V. Loginov94, C.
Loizides97, P. Loncar35, J.A. Lopez105, X. Lopez135, E. López Torres8, J.R.
Luhder144, M. Lunardon27, G. Luparello60, Y.G. Ma40, A. Maevskaya63, M.
Mager34, T. Mahmoud43, A. Maire137, M. Malaev99, N.M. Malik102, Q.W. Malik20,
L. MalininaIV,75, D. Mal’Kevich93, N. Mallick50, P. Malzacher108, G.
Mandaglio32,56, V. Manko89, F. Manso135, V. Manzari53, Y. Mao7, J. Mareš66,
G.V. Margagliotti23, A. Margotti54, A. Marín108, C. Markert119, M. Marquard68,
N.A. Martin105, P. Martinengo34, J.L. Martinez125, M.I. Martínez45, G.
Martínez García115, S. Masciocchi108, M. Masera24, A. Masoni55, L.
Massacrier78, A. Mastroserio139,53, A.M. Mathis106, O. Matonoha81, P.F.T.
Matuoka121, A. Matyja118, C. Mayer118, A.L. Mazuecos34, F. Mazzaschi24, M.
Mazzilli34, J.E. Mdhluli132, A.F. Mechler68, Y. Melikyan63, A. Menchaca-
Rocha71, E. Meninno114,29, A.S. Menon125, M. Meres13, S. Mhlanga124,72, Y.
Miake134, L. Micheletti59,24, L.C. Migliorin136, D.L. Mihaylov106, K.
Mikhaylov75,93, A.N. Mishra145, D. Miśkowiec108, A. Modak4, A.P. Mohanty62, B.
Mohanty87, M. Mohisin KhanV,16, M.A. Molander44, Z. Moravcova90, C.
Mordasini106, D.A. Moreira De Godoy144, L.A.P. Moreno45, I. Morozov63, A.
Morsch34, T. Mrnjavac34, V. Muccifora52, E. Mudnic35, D. Mühlheim144, S.
Muhuri141, J.D. Mulligan80, A. Mulliri22, M.G. Munhoz121, R.H. Munzer68, H.
Murakami133, S. Murray124, L. Musa34, J. Musinsky64, J.W. Myrcha142, B.
Naik132,49, R. Nair86, B.K. Nandi49, R. Nania54, E. Nappi53, M.U. Naru14, A.F.
Nassirpour81, A. Nath105, C. Nattrass131, A. Neagu20, L. Nellen69, S.V.
Nesbo36, G. Neskovic39, D. Nesterov113, B.S. Nielsen90, S. Nikolaev89, S.
Nikulin89, V. Nikulin99, F. Noferini54, S. Noh12, P. Nomokonov75, J.
Norman128, N. Novitzky134, P. Nowakowski142, A. Nyanin89, J. Nystrand21, M.
Ogino83, A. Ohlson81, V.A. Okorokov94, J. Oleniacz142, A.C. Oliveira Da
Silva131, M.H. Oliver146, A. Onnerstad126, C. Oppedisano59, A. Ortiz
Velasquez69, T. Osako46, A. Oskarsson81, J. Otwinowski118, M. Oya46, K.
Oyama83, Y. Pachmayer105, S. Padhan49, D. Pagano140,58, G. Paić69, A.
Palasciano53, J. Pan143, S. Panebianco138, P. Pareek141, J. Park61, J.E.
Parkkila126, S.P. Pathak125, R.N. Patra102,34, B. Paul22, H. Pei7, T.
Peitzmann62, X. Peng7, L.G. Pereira70, H. Pereira Da Costa138, D. Peresunko89,
G.M. Perez8, S. Perrin138, Y. Pestov5, V. Petráček37, M. Petrovici48, R.P.
Pezzi115,70, S. Piano60, M. Pikna13, P. Pillot115, O. Pinazza54,34, L.
Pinsky125, C. Pinto26, S. Pisano52, M. Płoskoń80, M. Planinic100, F.
Pliquett68, M.G. Poghosyan97, B. Polichtchouk92, S. Politano30, N. Poljak100,
A. Pop48, S. Porteboeuf-Houssais135, J. Porter80, V. Pozdniakov75, S.K.
Prasad4, R. Preghenella54, F. Prino59, C.A. Pruneau143, I. Pshenichnov63, M.
Puccio34, S. Qiu91, L. Quaglia24, R.E. Quishpe125, S. Ragoni111, A.
Rakotozafindrabe138, L. Ramello31, F. Rami137, S.A.R. Ramirez45, A.G.T.
Ramos33, T.A. Rancien79, R. Raniwala103, S. Raniwala103, S.S. Räsänen44, R.
Rath50, I. Ravasenga91, K.F. Read97,131, A.R. Redelbach39, K. RedlichVI,86, A.
Rehman21, P. Reichelt68, F. Reidt34, H.A. Reme-ness36, R. Renfordt68, Z.
Rescakova38, K. Reygers105, A. Riabov99, V. Riabov99, T. Richert81, M.
Richter20, W. Riegler34, F. Riggi26, C. Ristea67, M. Rodríguez Cahuantzi45, K.
Røed20, R. Rogalev92, E. Rogochaya75, T.S. Rogoschinski68, D. Rohr34, D.
Röhrich21, P.F. Rojas45, P.S. Rokita142, F. Ronchetti52, A. Rosano32,56, E.D.
Rosas69, A. Rossi57, A. Rotondi28,58, A. Roy50, P. Roy110, S. Roy49, N.
Rubini25, O.V. Rueda81, R. Rui23, B. Rumyantsev75, P.G. Russek2, A.
Rustamov88, E. Ryabinkin89, Y. Ryabov99, A. Rybicki118, H. Rytkonen126, W.
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Šafařík37, S.K. Saha141, S. Saha87, B. Sahoo49, P. Sahoo49, R. Sahoo50, S.
Sahoo65, D. Sahu50, P.K. Sahu65, J. Saini141, S. Sakai134, S. Sambyal102, V.
SamsonovI,99,94, D. Sarkar143, N. Sarkar141, P. Sarma42, V.M. Sarti106, M.H.P.
Sas146, J. Schambach97,119, H.S. Scheid68, C. Schiaua48, R. Schicker105, A.
Schmah105, C. Schmidt108, H.R. Schmidt104, M.O. Schmidt34, M. Schmidt104, N.V.
Schmidt97,68, A.R. Schmier131, R. Schotter137, J. Schukraft34, Y. Schutz137,
K. Schwarz108, K. Schweda108, G. Scioli25, E. Scomparin59, J.E. Seger15, Y.
Sekiguchi133, D. Sekihata133, I. Selyuzhenkov108,94, S. Senyukov137, J.J.
Seo61, D. Serebryakov63, L. Šerkšnytė106, A. Sevcenco67, T.J. Shaba72, A.
Shabanov63, A. Shabetai115, R. Shahoyan34, W. Shaikh110, A. Shangaraev92, A.
Sharma101, H. Sharma118, M. Sharma102, N. Sharma101, S. Sharma102, U.
Sharma102, O. Sheibani125, K. Shigaki46, M. Shimomura84, S. Shirinkin93, Q.
Shou40, Y. Sibiriak89, S. Siddhanta55, T. Siemiarczuk86, T.F. Silva121, D.
Silvermyr81, G. Simonetti34, B. Singh106, R. Singh87, R. Singh102, R. Singh50,
V.K. Singh141, V. Singhal141, T. Sinha110, B. Sitar13, M. Sitta31, T.B.
Skaali20, G. Skorodumovs105, M. Slupecki44, N. Smirnov146, R.J.M. Snellings62,
C. Soncco112, J. Song125, A. Songmoolnak116, F. Soramel27, S. Sorensen131, I.
Sputowska118, J. Stachel105, I. Stan67, P.J. Steffanic131, S.F.
Stiefelmaier105, D. Stocco115, I. Storehaug20, M.M. Storetvedt36, C.P.
Stylianidis91, A.A.P. Suaide121, T. Sugitate46, C. Suire78, M. Sukhanov63, M.
Suljic34, R. Sultanov93, M. Šumbera96, V. Sumberia102, S. Sumowidagdo51, S.
Swain65, A. Szabo13, I. Szarka13, U. Tabassam14, S.F. Taghavi106, G.
Taillepied135, J. Takahashi122, G.J. Tambave21, S. Tang135,7, Z. Tang129, L.A.
Tarasovičová144, M. Tarhini115, M.G. Tarzila48, A. Tauro34, G. Tejeda Muñoz45,
A. Telesca34, L. Terlizzi24, C. Terrevoli125, G. Tersimonov3, S. Thakur141, D.
Thomas119, R. Tieulent136, A. Tikhonov63, A.R. Timmins125, M. Tkacik117, A.
Toia68, N. Topilskaya63, M. Toppi52, F. Torales-Acosta19, T. Tork78, S.R.
Torres37, A. Trifiró32,56, S. Tripathy54,69, T. Tripathy49, S. Trogolo34,27,
G. Trombetta33, V. Trubnikov3, W.H. Trzaska126, T.P. Trzcinski142, B.A.
Trzeciak37, A. Tumkin109, R. Turrisi57, T.S. Tveter20, K. Ullaland21, A.
Uras136, M. Urioni58,140, G.L. Usai22, M. Vala38, N. Valle58,28, S. Vallero59,
N. van der Kolk62, L.V.R. van Doremalen62, M. van Leeuwen91, P. Vande Vyvre34,
D. Varga145, Z. Varga145, M. Varga-Kofarago145, A. Vargas45, M. Vasileiou85,
A. Vasiliev89, O. Vázquez Doce52,106, V. Vechernin113, E. Vercellin24, S.
Vergara Limón45, L. Vermunt62, R. Vértesi145, M. Verweij62, L. Vickovic35, Z.
Vilakazi132, O. Villalobos Baillie111, G. Vino53, A. Vinogradov89, T.
Virgili29, V. Vislavicius90, A. Vodopyanov75, B. Volkel34, M.A. Völkl105, K.
Voloshin93, S.A. Voloshin143, G. Volpe33, B. von Haller34, I. Vorobyev106, D.
Voscek117, N. Vozniuk63, J. Vrláková38, B. Wagner21, C. Wang40, D. Wang40, M.
Weber114, R.J.G.V. Weelden91, A. Wegrzynek34, S.C. Wenzel34, J.P. Wessels144,
J. Wiechula68, J. Wikne20, G. Wilk86, J. Wilkinson108, G.A. Willems144, B.
Windelband105, M. Winn138, W.E. Witt131, J.R. Wright119, W. Wu40, Y. Wu129, R.
Xu7, A.K. Yadav141, S. Yalcin77, Y. Yamaguchi46, K. Yamakawa46, S. Yang21, S.
Yano46, Z. Yin7, H. Yokoyama62, I.-K. Yoo17, J.H. Yoon61, S. Yuan21, A.
Yuncu105, V. Zaccolo23, A. Zaman14, C. Zampolli34, H.J.C. Zanoli62, N.
Zardoshti34, A. Zarochentsev113, P. Závada66, N. Zaviyalov109, M. Zhalov99, B.
Zhang7, S. Zhang40, X. Zhang7, Y. Zhang129, V. Zherebchevskii113, Y. Zhi11, N.
Zhigareva93, D. Zhou7, Y. Zhou90, J. Zhu7,108, Y. Zhu7, A. Zichichi25, G.
Zinovjev3, N. Zurlo140,58

## Affiliation notes

I Deceased
II Also at: Italian National Agency for New Technologies, Energy and
Sustainable Economic Development (ENEA), Bologna, Italy
III Also at: Dipartimento DET del Politecnico di Torino, Turin, Italy
IV Also at: M.V. Lomonosov Moscow State University, D.V. Skobeltsyn Institute
of Nuclear, Physics, Moscow, Russia
V Also at: Department of Applied Physics, Aligarh Muslim University, Aligarh,
India
VI Also at: Institute of Theoretical Physics, University of Wroclaw, Poland

## Collaboration Institutes

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