2023 018 14 February

Non-prompt D-meson fraction as a function of multiplicity in pp at
$\sqrt{s}=13~{}\mathrm{TeV}$

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

The fractions of non-prompt (i.e. originating from beauty-hadron decays)
$\mathrm{D^{0}}$ and $\mathrm{D^{+}}$ mesons with respect to the inclusive
yield are measured as a function of the charged-particle multiplicity in
proton–proton collisions at a centre-of-mass energy of
$\sqrt{s}=13~{}\mathrm{TeV}$ with the ALICE detector at the LHC. The results
are reported in intervals of transverse momentum ($p_{\mathrm{T}}$) and
integrated in the range $1<p_{\mathrm{T}}<24~{}\mathrm{GeV}/c$. The fraction
of non-prompt $\mathrm{D^{0}}$ and $\mathrm{D^{+}}$ mesons is found to
increase slightly as a function of $p_{\mathrm{T}}$ in all the measured
multiplicity intervals, while no significant dependence on the charged-
particle multiplicity is observed. In order to investigate the production and
hadronisation mechanisms of charm and beauty quarks, the results are compared
to PYTHIA 8 as well as EPOS 3 and EPOS 4 Monte Carlo simulations, and to
calculations based on the colour glass condensate including three-pomeron
fusion.

##  1 Introduction

Measurements of the production of hadrons containing heavy quarks, i.e. charm
or beauty, in proton–proton (pp) collisions provide an important test of
quantum chromodynamics (QCD) calculations. Several measurements of charm- and
beauty-hadron production were carried out in pp collisions by the ALICE [1, 2,
3, 4, 5, 6, 7, 8, 9, 10, 11], ATLAS [12, 13, 14, 15, 16], CMS [17, 18, 19, 20,
21, 22, 23], and LHCb [24, 25, 26, 27, 28, 29, 30, 31, 32, 33] experiments at
the LHC, and by the STAR experiment at RHIC [34]. The measured D- and B-meson
production cross sections are generally compatible within uncertainties with
theoretical predictions based on the factorisation approach, which describe
them as the convolution of the parton distribution functions (PDFs), the
partonic cross section calculated with perturbative QCD (pQCD) calculations,
and the fragmentation functions (FFs). Calculations of the partonic cross
sections are nowadays available at next-to-leading-order accuracy (like
$k_{\mathrm{T}}$-factorisation [35, 36, 37]) or next-to-leading-order with
next-to-leading logarithm resummation (like FONLL [38, 39, 40] and GM-VFNS
[41, 42, 43, 44, 45, 46]). The FFs are typically constrained from measurements
carried out in $\mathrm{e^{+}e^{-}}$ or ep collisions [47], under the
assumption that the hadronisation of heavy quarks into hadrons is a universal
process independent of the colliding system. However, measurements of baryons
containing heavy quarks at hadronic colliders showed an enhancement of the
baryon-to-meson yield ratios relative to the values measured at
$\mathrm{e^{+}e^{-}}$ colliders [48, 32], challenging the assumption of the
universality of the fragmentation across different collision systems. Monte
Carlo (MC) generators that implement the transition from the heavy quark to
the hadron via string fragmentation (as PYTHIA 8 [49] with the Monash-13 [50]
tune) or cluster hadronisation (such as HERWIG 7 [51]), in which the heavy-
quark fragmentation is tuned to $\mathrm{e^{+}e^{-}}$ and ep measurements,
cannot reproduce the baryon-to-meson yield ratios measured in pp collisions.
When including the colour reconnection mechanism beyond the leading colour
(CR-BLC) approximation in PYTHIA 8 [52], which introduces new colour-
reconnection topologies that fragment into baryons, a much better agreement
with data is obtained [10, 8, 9]. In particular, three settings (‘Modes’ 0, 2,
and 3), characterised by different constraints on the time dilation and
causality, were defined in Ref. [52]. The time parameters are relevant in this
model, because two string pieces must be able to resolve each other during the
time between formation and hadronisation to reconnect, taking time-dilation
effects caused by relative boosts into account. However, in case of charm
baryons with strange-quark content, a significant discrepancy still remains
with data even when considering the CR-BLC modes, suggesting that additional
effects should be introduced in order to have a complete description of the
hadronisation processes [7, 6, 11]. In the light-flavour sector, it was
observed that increasing the string tension (‘colour ropes’ tune), which leads
to an increase of strangeness production, a better agreement with data for the
charged-particle multiplicity dependence of multi-strange hadron production is
obtained [53, 54].

Given that the production of heavy quarks occurs in initial hard partonic
scattering processes while the production of light particles in the underlying
event is dominated by soft processes, the measurement of heavy-flavour hadron
production as a function of the charged-particle multiplicity has the
potential to give insights into the interplay between the soft and hard
mechanisms in particle production. In particular, multi-parton interactions
(MPI) [55, 56], i.e. several hard partonic interactions occurring in a single
pp collision, influence the production of light quarks and gluons, affecting
the total event multiplicity, as well as the production of heavy quarks. In
addition, high-multiplicity events allow one to test the heavy-flavour hadron
production at small Bjorken-$x$, i.e. a kinematic region where the density of
low-momentum gluons in the colliding protons is very high and is expected to
reach saturation, which otherwise would require significantly larger energies
[57]. A faster-than-linear increase has been observed in the production of
prompt D mesons, as well as that of inclusive, prompt, and non-prompt (from
beauty-hadron decays) $\mathrm{J}/\uppsi$ mesons at midrapidity as a function
of the charged-particle multiplicity in pp collisions [58, 59]. The same
behaviour was obtained using a multiplicity estimators based on particles
measured in the same pseudorapidity interval and introducing a pseudorapidity
gap with respect to the heavy-flavour hadron [59]. A linear increase was
instead observed in the measurement of $\mathrm{J}/\uppsi$ mesons a forward
rapidity, if a pseudorapidity gap is introduced between the
$\mathrm{J}/\uppsi$ mesons and the multiplicity estimator [60]. This behaviour
is described by several MC generators including MPI, such as PYTHIA 8 [49] and
EPOS 3 [61]. EPOS is an event generator suited for various hadronic colliding
systems, from pp to nucleus–nucleus. This event generator assumes initial
conditions generated in the Gribov-Regge multiple scattering framework,
possibly followed by a hydrodynamical evolution applicable to all collision
systems. Initial conditions are generated in the Gribov-Regge multiple
scattering framework. Individual scatterings are referred to as Pomerons, and
are identified with parton ladders. Each parton ladder is composed of a pQCD
hard process with initial- and final-state radiation. Non-linear effects are
considered by means of a saturation scale. The hadronisation is performed with
a string fragmentation procedure, consisting in the decay of plasma droplets
which conserves energy, momentum, and flavour. Other models based on a colour
glass condensate (CGC) with the three-pomeron fusion mechanism [57] are also
able to describe the multiplicity dependence of the production yield of heavy-
flavour hadrons [59, 57].

It is also important to note that the charged-particle densities reached in
high-multiplicity pp collisions at LHC energies are comparable with those
measured in peripheral heavy-ion collisions. Measurements in high-multiplicity
pp collisions showed features that resemble those associated with the
formation of a colour-deconfined state of the matter called quark–gluon plasma
[62] in heavy-ion collisions [63, 64, 65]. In this context, one of the most
interesting effects is the modification of the hadronisation mechanism. Model
calculations based on statistical hadronisation [66] or hadronisation via
coalescence [67, 68] predict an enhancement of the baryon-to-meson and
strange-to-nonstrange yield ratios as a function of the charged-particle
multiplicity. The first category of models is based on the evaluation of the
population of hadron states according to statistical weights governed by the
masses of the hadrons and a universal temperature, while the second ones
implement the recombination of partons close in phase space into the final
hadrons. Recently, the ALICE Collaboration observed a multiplicity dependence
of the transverse momentum ($p_{\mathrm{T}}$) differential
$\Lambda_{\mathrm{c}}^{+}/\mathrm{D^{0}}$ ratio, smoothly evolving from pp to
Pb–Pb collisions. The same quantity measured $p_{\mathrm{T}}$ integrated was
found not to vary significantly as a function of the charged-particle
multiplicity. No modification of the $\mathrm{D_{s}^{+}}/\mathrm{D^{0}}$ ratio
with increasing multiplicity was measured in pp collisions [69, 70].
Conversely, in the beauty sector, the LHCb Collaboration found evidence of an
increase of the $\mathrm{B_{s}^{0}}/\mathrm{B^{0}}$ production ratio with the
multiplicity, in case of charged-particle multiplicity estimated with tracks
in the same pseudorapidity interval of the B mesons [71], while no
measurements of beauty-baryon production as a function of charged-particle
multiplicity are available. Finally, the fraction of
$\upchi_{\mathrm{c1}}(3872)$ and $\uppsi(2\mathrm{S})$ states promptly
produced at the collision vertex was found by the LHCb Collaboration to
decrease as charged-particle multiplicity increases [72]. This suppression is
interpreted as a consequence of the heavy-quark breakup via interactions with
comoving hadrons [73, 74].

In this article, the first measurement of the fraction of $\mathrm{D^{0}}$ and
$\mathrm{D^{+}}$ mesons originating from beauty-hadron decays
($f_{\mathrm{non\text{-}prompt}}$) at midrapidity ($|y|<0.5$) is reported as a
function of the charged-particle multiplicity in pp collisions at
$\sqrt{s}=13~{}\mathrm{TeV}$. In addition, the ratio between the fraction
measured in different multiplicity classes divided by the one measured in the
multiplicity-integrated sample is presented. The experimental apparatus and
the multiplicity determination are described in Section 2. The measurement of
$f_{\mathrm{non\text{-}prompt}}$ in six transverse momentum intervals and
integrated in $1<p_{\mathrm{T}}<24~{}\mathrm{GeV}/c$ is described in Section
3, while the evaluation of the systematic uncertainties is discussed in
Section 4. Finally, the results are presented and compared to model
calculations in Section 5.

##  2 Experimental apparatus and data sample

The ALICE apparatus is composed of several detectors for particle
reconstruction and identification at midrapidity, embedded in a large
solenoidal magnet that provides a magnetic field of $B=0.5~{}\mathrm{T}$
parallel to the beams. It also includes a forward muon spectrometer
($-4<\eta<-2.5$) and a set of forward and backward detectors for triggering
and event characterisation. A comprehensive description of the ALICE detector
and its performance is reported in Refs. [75, 76].

The Inner Tracking System (ITS), consisting of six cylindrical layers of
silicon detectors, allows for a precise reconstruction of primary and
secondary vertices, and it is used for tracking. The Time Projection Chamber
(TPC) provides up to 159 space points to reconstruct the charged-particle
trajectory, and provides particle identification (PID) via the measurement of
the specific ionisation energy loss $\mathrm{d}E/\mathrm{d}x$ of charged
particles. The Time-Of-Flight detector (TOF) extends the PID capability by
measuring the flight time of charged particles from the interaction point to
the TOF. These detectors cover the full azimuth in the pseudorapidity interval
$|\eta|<0.9$. The V0 detector arrays, covering the intervals $2.8<\eta<5.1$
(V0A) and $-3.7<\eta<-1.7$ (V0C), are used for triggering purposes and event
multiplicity measurements.

The data used for this analysis are from pp collisions at
$\sqrt{s}=13~{}\mathrm{TeV}$ collected in 2016, 2017, and 2018. A minimum-bias
(MB) trigger was used, based on coincident signals in V0A and V0C. To enrich
the data sample in the highest multiplicity regions, a high-multiplicity
trigger based on a minimum threshold for the V0 amplitudes (HMV0) was used as
well. The data sample collected with such a trigger corresponds to the 0.17%
highest-multiplicity events out of all inelastic collisions with at least one
charged particle in the pseudorapidity range $|\eta|<1$ (denoted as INEL $>$
0). Offline selections were applied to remove background from beam–gas
collisions, as described in Ref. [77]. Events with multiple reconstructed
primary vertices were rejected. The remaining pile-up events were at a percent
level and, therefore, did not affect the present analysis. Only the events
with a primary vertex reconstructed within $|z_{\rm vtx}|<10$ cm from the
nominal interaction point along the beam-line direction were considered for
the analysis. To select events in the $\text{INEL}>0$ class, at least one
track segment reconstructed with the first two ITS layers (denoted as
tracklet) within the pseudorapidity region $|\eta|<1$ was required. After
these selections, the integrated luminosities are about
$\mathcal{L}_{\mathrm{int}}\approx 32$ $\mathrm{nb}^{-1}$ for the MB triggered
events, and $\mathcal{L}_{\mathrm{int}}\approx 7.7~{}\mathrm{pb}^{-1}$ for the
HMV0 triggered events [69]. The event multiplicity was determined in the
forward rapidity region, exploiting the sum of signal amplitudes in the V0A
and V0C scintillators, V0M, and defining its percentile distribution,
$p_{\mathrm{V0M}}$. Low $p_{\mathrm{V0M}}$ values represent high-multiplicity
events. The definition of the mean multiplicity density
($\langle\mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta\rangle_{|\eta|<0.5}$) of
charged-primary particles at midrapidity is given in Ref. [78]. It was
obtained by converting the measured event multiplicities as described in Ref.
[77]. Table 1 summarises the multiplicity event classes at forward rapidity
used in this analysis ($p_{\mathrm{V0M}}(\%)$) and the corresponding values
for $\langle\mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta\rangle_{|\eta|<0.5}$,
together with the corresponding value for the multiplicity-integrated class
[77].

Monte Carlo simulations were utilised in the analysis mainly for the machine-
learning training, and to obtain the correction factors for the limited
detector acceptance as well as the reconstruction and selection efficiencies.
They were obtained by simulating pp collisions with the PYTHIA 8.243 event
generator [79, 49] (Monash-13 tune [50]). In order to enrich the simulated
data samples of prompt and non-prompt D mesons, either a
$\mathrm{c\overline{c}}$ or $\mathrm{b\overline{b}}$ quark pair was required
in each simulated PYTHIA pp event and D mesons were forced to decay into the
hadronic channels of interest for the analysis. The generated particles were
transported through the apparatus by using the GEANT3 package [80].

Table 1: Summary of the multiplicity event classes at forward rapidity expressed in percentiles of the V0M signal amplitude ($p_{\mathrm{V0M}}(\%)$). The average charged-particle densities $\langle\mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta\rangle_{|\eta|<0.5}$ at midrapidity are shown, together with the value corresponding to the multiplicity-integrated class. Multiplicity intervals are measured in experimental data down to the 0–0.1% percentile, corresponding to the highest-multiplicity interval. Multiplicity interval |  $\langle\mathrm{d}N_{\mathrm{ch}}/\mathrm{d}\eta\rangle_{|\eta|<0.5}$
---|---
$[30,100]\%$ |  $4.41\pm 0.05$
$[0.1,30]\%$ |  $13.81\pm 0.14$
$[0,0.1]\%$ |  $31.53\pm 0.38$
$\text{INEL}>0$ |  $6.93\pm 0.09$

##  3 Data analysis

The $\mathrm{D^{0}}$ and $\mathrm{D^{+}}$ mesons and their charge conjugates
were reconstructed via the hadronic decay channels $\mathrm{D^{0}\to
K^{-}\uppi^{+}}$, with branching ratio $\mathrm{BR}=(3.947\pm 0.030)\%$, and
$\mathrm{D^{+}\to K^{-}\uppi^{+}\uppi^{+}}$, with $\mathrm{BR}=(9.38\pm
0.16)\%$ [81]. D-meson candidates were built by combining pairs or triplets of
tracks with the proper charge signs, each track with
$p_{\mathrm{T}}>0.3~{}\mathrm{GeV}/c$, $|y|>0.8$, at least 70 out of a maximum
of 159 crossed TPC pad rows, a minimum number of two hits (out of six) in the
ITS, with at least one in either of the two innermost layers, and a track fit
quality $\chi^{2}/\rm{ndf}<2$ in the TPC. These track-selection criteria
reduce the D-meson acceptance in rapidity, which falls steeply to zero for
$|y|>0.5$ at low $p_{\mathrm{T}}$ and for $|y|>0.8$ at
$p_{\mathrm{T}}>5~{}\mathrm{GeV}/c$. Thus, a fiducial acceptance selection
$|y|<y_{\rm fid}(p_{\mathrm{T}})$, was applied to grant a uniform acceptance
inside the rapidity range considered. The $y_{\mathrm{fid}}(p_{\mathrm{T}})$
value was defined as a second-order polynomial function, increasing from 0.5
to 0.8 in the transverse-momentum range $0<p_{\mathrm{T}}<5~{}\mathrm{GeV}/c$,
and as a constant term, $y_{\mathrm{fid}}=0.8$, for
$p_{\mathrm{T}}>5~{}\mathrm{GeV}/c$ [2].

To suppress the large combinatorial background and to separate at the same
time the contribution of prompt and non-prompt D mesons, a machine-learning
approach with multi-class classification, based on Boosted Decision Trees
(BDT) provided by the XGBoost [82, 83] library was adopted. Signal samples of
prompt and non-prompt D mesons for the BDT training were obtained from PYTHIA
8 simulations as described in Sec. 2. The background samples were obtained
from candidates in the sideband region in the data, i.e. in the interval
$5\sigma<|\Delta M|<9\sigma$ of the invariant mass distribution, where $\Delta
M$ is the deviation between the invariant mass of the candidate and the mean
of a Gaussian function describing the signal peak and $\sigma$ is the Gaussian
width. The training procedures are the same as reported in Ref. [2]. Before
the training, loose kinematic and topological selections were applied to the
D-meson candidates. The D-meson candidate information used for training the
BDT models was mainly based on the displacement of the tracks from the primary
vertex, the impact parameter of the D-meson daughter tracks, the distance
between the D-meson decay vertex and the primary vertex, the cosine of the
pointing angle between the D-meson candidate line of flight (the vector
connecting the primary and secondary vertices) and its reconstructed momentum
vector, and the PID information of the decay tracks. Independent BDTs were
trained for each D-meson species and $p_{\mathrm{T}}$ interval in the
multiplicity-integrated sample. Subsequently, the BDTs were applied to the
real data sample in which the type of candidate is unknown. The BDT outputs
are related to the candidate probability to be a non-prompt D meson or
combinatorial background. Selections on the BDT outputs were optimised to
obtain a high non-prompt D-meson fraction while maintaining a reliable signal
extraction (with statistical significance larger than 5).

The signal extraction was performed in each $p_{\mathrm{T}}$ and multiplicity
interval via a binned maximum-likelihood fit to the candidate invariant-mass
distribution. The raw yields could be extracted in the transverse momentum
interval $1<p_{\mathrm{T}}<24~{}\mathrm{GeV}/c$ and in six subranges, for both
$\mathrm{D^{0}}$ and $\mathrm{D^{+}}$ mesons. A Gaussian function and an
exponential function were used to describe the signal peak and the background
distribution, respectively. To improve the stability of the fits, the widths
of the D-meson signal peaks were fixed to the values extracted from data
samples dominated by prompt candidates, given the naturally higher abundance
of prompt compared to non-prompt D mesons. In addition, for the
$\mathrm{D^{0}}$ meson, the contribution of signal candidates to the
invariant-mass distribution with the wrong decay-particle mass assignment
(reflections) was included in the fit. It was parameterised by fitting the
invariant-mass distribution of reflections with a double Gaussian function,
and normalised according to the reflection-to-signal ratio from the PYTHIA 8
simulations. The contribution of reflections to the raw yield is about
$0.5\%\mbox{--}4\%$, increasing with increasing $p_{\mathrm{T}}$. Examples of
invariant-mass fits with different contribution of signal from beauty-hadron
decays in the $2<p_{\mathrm{T}}<4~{}\mathrm{GeV}/c$ interval for the lowest
multiplicity class and in the $1<p_{\mathrm{T}}<24~{}\mathrm{GeV}/c$ interval
for the highest multiplicity class are shown in Fig. 1 and Fig. 2 for
$\mathrm{D^{0}}$ and $\mathrm{D^{+}}$ mesons, respectively. Based on the
selections on the BDT outputs, samples dominated by non-prompt (prompt)
candidates were selected by requiring low probability for a candidate to be
combinatorial background and a high (low) probability to be non-prompt. The
invariant-mass fits from non-prompt (prompt) enhanced samples are shown in
each right (left) panel, indicating the corresponding selection applied on the
BDT output score related to the probability to be a non-prompt D meson.

Figure 1: Invariant-mass distribution of $\mathrm{D^{0}}$ candidates and their
charge conjugates in selected $p_{\mathrm{T}}$ and multiplicity intervals. The
blue solid curves show the total fit function and the red dashed curves show
the combinatorial-background contribution. The green solid lines represent the
reflection contribution. The raw-yield ($S$) values are reported together with
their statistical uncertainties resulting from the fit. Top row:
$\mathrm{D^{0}}$ mesons in the $2<p_{\mathrm{T}}<4~{}\mathrm{GeV}/c$ interval
for the low multiplicity class. Bottom row: $\mathrm{D^{0}}$ mesons in the
$1<p_{\mathrm{T}}<24~{}\mathrm{GeV}/c$ interval for the high multiplicity
class. The corresponding BDT probability minimum threshold for the candidate
selection is reported. The left (right) column corresponds to the prompt (non-
prompt) $\mathrm{D^{0}}$ meson candidates dominated sample.

Figure 2: Invariant-mass distribution of $\mathrm{D^{+}}$ candidates and their
charge conjugates in selected $p_{\mathrm{T}}$ and multiplicity intervals. The
blue solid curves show the total fit function and the red dashed curves show
the combinatorial-background contribution. The raw-yield ($S$) values are
reported together with their statistical uncertainties resulting from the fit.
Top row: $\mathrm{D^{+}}$ mesons in the $2<p_{\mathrm{T}}<4~{}\mathrm{GeV}/c$
interval for the low multiplicity class. Bottom row: $\mathrm{D^{+}}$ mesons
in the $1<p_{\mathrm{T}}<24~{}\mathrm{GeV}/c$ interval for the high
multiplicity class. The corresponding BDT probability minimum threshold for
the candidate selection is reported. The left (right) column corresponds to
the prompt (non-prompt) $\mathrm{D^{+}}$ meson candidates dominated sample.

In each $p_{\mathrm{T}}$ and multiplicity interval, the fraction of non-prompt
D mesons, $f_{\mathrm{non\text{-}prompt}}$, was estimated by sampling the raw
yield with different BDT selections related to the candidate probability of
being a non-prompt D meson. In this way, a set of raw yields $Y_{\mathrm{i}}$
(index i refers to a given selection on the BDT output) with different
contributions from prompt and non-prompt D mesons was obtained. These raw
yields are related to the corrected yields of prompt ($N_{\mathrm{prompt}}$)
and non-prompt ($N_{\mathrm{non\text{-}prompt}}$) D mesons via the acceptance-
times-efficiency ($\mathrm{Acc}\times\epsilon$) factors as

$(\mathrm{Acc}\times\epsilon)^{\mathrm{prompt}}_{\mathrm{i}}\times
N_{\mathrm{prompt}}+(\mathrm{Acc}\times\epsilon)^{\mathrm{non\text{-}prompt}}_{\mathrm{i}}\times
N_{\mathrm{non\text{-}prompt}}-Y_{\mathrm{i}}=\delta_{\mathrm{i}}.$ (1)

In the above equation, the $\delta_{\mathrm{i}}$ term represents a residual
originating from the uncertainties on $Y_{\mathrm{i}}$,
$(\mathrm{Acc}\times\epsilon)^{\mathrm{non\text{-}prompt}}_{\mathrm{i}}$, and
$(\mathrm{Acc}\times\epsilon)^{\mathrm{prompt}}_{\mathrm{i}}$. The
$\mathrm{Acc}\times\epsilon$ factors were obtained from MC simulations as
described in Sec. 2. They are different for prompt and non-prompt D mesons due
to the different decay topology. Since the resolution of the reconstructed
primary vertex depends on the multiplicity, the simulated events were weighted
to reproduce the charged-particle multiplicity distribution measured in data
for events that contain D-meson candidates having an invariant mass compatible
with the one of the signal. After that, the $\mathrm{Acc}\times\epsilon$
factors were computed for each BDT selection for prompt and non-prompt D
mesons within the fiducial acceptance region. In the case of the number of
sets of selections $n\geq 2$, a $\chi^{2}$ function can be defined based on
the set of equations of Eq. 1, which can be minimised to obtain
$N_{\mathrm{prompt}}$ and $N_{\mathrm{non\text{-}prompt}}$. More details can
be found in Ref. [2]. The $N_{\mathrm{non\text{-}prompt}}$ and
$N_{\mathrm{prompt}}$ values can be used to calculate the corrected fraction
of non-prompt D mesons as follows

$f_{\mathrm{non\text{-}prompt}}=\frac{N_{\mathrm{non\text{-}prompt}}}{N_{\mathrm{non\text{-}prompt}}+N_{\mathrm{prompt}}}.$
(2)

In addition, the ratio between the fraction of non-prompt D mesons measured in
each multiplicity interval and the one measured in the $\text{INEL}>0$ class
of events,
$f^{\mathrm{mult}}_{\mathrm{non\text{-}prompt}}/f^{\rm{INEL>0}}_{\mathrm{non\text{-}prompt}}$,
was computed in multiplicity and $p_{\mathrm{T}}$ intervals in order to
investigate the modification of the non-prompt fraction with respect to the
one measured in the multiplicity-integrated sample.

Figure 3 shows an example of the raw-yield distribution as a function of the
BDT-based selection used in the $\chi^{2}$-minimisation procedure for
$\mathrm{D^{0}}$ (top panels) and $\mathrm{D^{+}}$ (bottom panels) mesons in
the transverse-momentum intervals $2<p_{\mathrm{T}}<4~{}\mathrm{GeV}/c$ and
$1<p_{\mathrm{T}}<24~{}\mathrm{GeV}/c$ for the low-multiplicity and high-
multiplicity classes of events, respectively. The raw yield decreases with
increasing minimum threshold for the probability to be a non-prompt D meson,
corresponding to an increasing non-prompt D fraction. Note also that the raw
yields used in this procedure are largely correlated among each other,
implying that adjacent data points are expected to fluctuate in the same
direction. The prompt and non-prompt components of the raw yields for each
BDT-based selection obtained from the $\chi^{2}$-minimisation procedure as
$(\mathrm{Acc}\times\epsilon)^{\mathrm{prompt}}_{\mathrm{i}}\times
N_{\mathrm{prompt}}$ and
$(\mathrm{Acc}\times\epsilon)^{\mathrm{non\text{-}prompt}}_{\mathrm{i}}\times
N_{\mathrm{non\text{-}prompt}}$, are reported as the red and blue
distributions, and their sum is represented by the green histogram.

Figure 3: Examples of raw-yield distribution as a function of the BDT-based
selection employed in the $\chi^{2}$-minimisation procedure adopted for the
determination of $f_{\mathrm{non\text{-}prompt}}$ of D mesons. Top row:
$\mathrm{D^{0}}$ mesons in low multiplicity (left) and high multiplicity
(right) classes. Bottom row: $\mathrm{D^{+}}$ mesons in low multiplicity
(left) and high multiplicity (right) classes.

##  4 Systematic uncertainties

The values of systematic uncertainty on the non-prompt D-meson fraction were
estimated with procedures similar to those described in Refs. [2, 69]. They
include the uncertainties on (i) the raw-yield extraction from the invariant-
mass distributions; (ii) the selection efficiency estimation; (iii) the
dependency of the efficiency on the charged-particle multiplicity; and (iv)
the D-meson $p_{\mathrm{T}}$ shape in the simulation. The estimated values of
the systematic uncertainties for some representative $p_{\mathrm{T}}$
intervals of $\mathrm{D^{0}}$ and $\mathrm{D^{+}}$ mesons are summarised in
Table 2.

Table 2: Summary of the relative systematic uncertainties on the non-prompt $\mathrm{D^{0}}$-, $\mathrm{D^{+}}$-meson fractions in various $p_{\mathrm{T}}$ and multiplicity intervals. Meson | $\mathrm{D^{0}}$ | $\mathrm{D^{+}}$ | $\mathrm{D^{0}}$ | $\mathrm{D^{+}}$
---|---|---|---|---
$p_{\mathrm{T}}~{}(\mathrm{GeV}/c)$ |  $2\mbox{--}4$ | $8\mbox{--}12$ |  $2\mbox{--}4$ | $8\mbox{--}12$ |  $1\mbox{--}2$ | $12\mbox{--}24$ |  $1\mbox{--}2$ | $12\mbox{--}24$
| $f^{30-100\%}_{\mathrm{non\text{-}prompt}}$ | $f^{0-0.1\%}_{\mathrm{non\text{-}prompt}}$
Raw-yield extraction |  2% | 2% |  3% | 3% |  5% | 2% |  6% | 6%
Efficiency estimation |  2% | 2% |  4% | 5% |  6% | 3% |  5% | 5%
MC multiplicity distribution |  2% | 1% |  4% | 0% |  0% | 0% |  0% | 0%
MC D-meson $p_{\mathrm{T}}$ distribution |  6% | 3% |  3% | 1% |  9% | 3% |  8% | 3%
| $f^{30-100\%}_{\mathrm{non\text{-}prompt}}/f^{\rm{INEL>0}}_{\mathrm{non\text{-}prompt}}$ | $f^{0-0.1\%}_{\mathrm{non\text{-}prompt}}/f^{\rm{INEL>0}}_{\mathrm{non\text{-}prompt}}$
Raw-yield extraction |  2% | 2% |  4% | 4% |  4% | 2% |  7% | 5%
Efficiency estimation |  2% | 3% |  4% | 5% |  4% | 2% |  4% | 3%
MC multiplicity distribution |  0% | 2% |  0% | 0% |  1% | 0% |  4% | 0%
MC D-meson $p_{\mathrm{T}}$ distribution |  4% | 2% |  5% | 1% |  0% | 0% |  1% | 1%

The systematic uncertainty of the raw-yield extraction was evaluated by
repeating the fits to the invariant-mass distribution varying the fit range
and the functional form of the background and signal fit functions. To further
test the sensitivity to the line shape of the signal, a bin-counting method,
in which the signal yield was obtained by integrating the background-
subtracted invariant-mass distribution within the $\pm 3\sigma$ region
relative to the peak position, was used. In the case of $\mathrm{D^{0}}$
mesons, an additional contribution due to signal reflections in the invariant-
mass distribution was estimated by varying the normalisation and the shape of
the templates used for the reflections in the invariant-mass fits. The
systematic uncertainty was defined as the RMS of the distribution of the
resulting $f_{\mathrm{non\text{-}prompt}}$ obtained from all these variations
and ranges from 2% to 6% depending on the D-meson species, multiplicity, and
$p_{\mathrm{T}}$ interval.

The systematic uncertainty of the selection-efficiency determination, arising
from possible imperfections of the description of the decay topologies or the
detector resolution in the simulation, was estimated by using alternative sets
of BDT-output selections for the procedure described in Section 3. In
particular, stricter and looser selections were tested, as well as different
combinations of selections adopted to define the system of equations described
in Eq. 1. A systematic uncertainty ranging from 2% to 6% was assigned.

To estimate the systematic uncertainty on the sensitivity of the efficiency on
the charged-particle multiplicity, due to the multiplicity dependence of the
primary-vertex reconstruction resolution, the distribution of the number of
tracklets in the MC simulation for each V0M class of events was weighted using
the one obtained in the real data considering events containing a D-meson
candidate, without requiring the invariant-mass region selection. The
resulting effect on the $f_{\mathrm{non\text{-}prompt}}$ estimation ranges
from 0% to 4%.

The systematic uncertainty on the efficiency calculation due to a possible
difference between the real and simulated D-meson transverse-momentum
distributions was estimated by evaluating the efficiency after reweighting the
$p_{\mathrm{T}}$ shape from the PYTHIA 8 generator to match the one from FONLL
calculations, in addition to the reweighting of the multiplicity distribution
mentioned above. The weights were applied to the $p_{\mathrm{T}}$
distributions of prompt D mesons and to the parent beauty-hadron
$p_{\mathrm{T}}$ distributions in case of non-prompt D mesons. The assigned
uncertainty ranges from 1% to 9%.

The aforementioned sources of systematic uncertainty were assumed to be
uncorrelated among each other. The total systematic uncertainty is defined as
the square root of the quadratic sum of the estimated values in each
$p_{\mathrm{T}}$ and multiplicity interval. In order to assess the correlation
between the systematic uncertainties on $f_{\mathrm{non\text{-}prompt}}$ in
the different multiplicity intervals with respect to the one in the
$\text{INEL}>0$ sample, the effect of the variations and the estimation of the
uncertainties were directly evaluated on the ratio
$f^{\mathrm{mult}}_{\mathrm{non\text{-}prompt}}/f^{\rm{INEL>0}}_{\mathrm{non\text{-}prompt}}$.

##  5 Results

The measured fractions of D-mesons originating from beauty-hadron decays,
$f_{\mathrm{non\text{-}prompt}}$, in pp collisions at
$\sqrt{s}=13~{}\mathrm{TeV}$ are shown in Fig. 4 as a function of
$p_{\mathrm{T}}$. The results are reported in different panels for
$\mathrm{D^{0}}$ (left) and $\mathrm{D^{+}}$ (right) mesons and for the
$\text{INEL}>0$ class (top panels) and the three multiplicity classes of
events (lower panels). The statistical and total systematic uncertainties are
shown by vertical error bars and boxes, respectively.

Figure 4: Fractions of non-prompt $\mathrm{D^{0}}$ (left column) and
$\mathrm{D^{+}}$ (right column) mesons as a function of $p_{\mathrm{T}}$ for
the $\text{INEL}>0$ class and the three multiplicity classes of events in pp
collisions at $\sqrt{s}=13~{}\mathrm{TeV}$. The measurements are compared with
the predictions obtained with PYTHIA 8 [52] and EPOS [61] event generators.

In all the event classes and for both $\mathrm{D^{0}}$ and $\mathrm{D^{+}}$
mesons, $f_{\mathrm{non\text{-}prompt}}$ increases with $p_{\mathrm{T}}$ from
5%–7% to about 10%. This increase is motivated by the harder $p_{\mathrm{T}}$
distribution of beauty hadrons compared to the charm ones, which is only
partly compensated by the $\mathrm{b}\to\mathrm{D+X}$ decay kinematics [2,
40]. The fraction of non-prompt $\mathrm{D^{0}}$ mesons is slightly larger
than that of $\mathrm{D^{+}}$ mesons, as a consequence of the different
branching ratios of B mesons with a $\mathrm{D^{0}}$ or $\mathrm{D^{+}}$ meson
in the final state, and of the different charm-quark fragmentation fractions
for the prompt D-meson production. This increasing trend is expected from pQCD
calculations, as shown in Ref. [46]. Measurements are compared to predictions
from the PYTHIA 8 [49, 84] and EPOS [61, 85] event generators. PYTHIA 8
simulations were obtained using the standard Monash 2013 tune [50] as well as
with colour reconnection settings beyond-leading-colour approximation [52],
and with colour ropes [84] using PYTHIA version 8.307. Both the version 3.448
and 4.0.0 of the EPOS MC generator were tested. In EPOS 4, parallel partonic
scatterings based on the $S$-matrix theory are implemented, leading to the
factorisation of the hard and soft scales, particularly important for heavy
quarks. This factorisation allows the computation of the PDFs within the EPOS
framework itself. The EPOS predictions presented in this paper do not include
a hydrodynamic expansion of the system. However, the results were found not to
significantly change if the latter is included. Following what was done for
data, all PYTHIA 8 and EPOS simulations were selected according to percentiles
of the $\text{INEL}>0$ cross section based on the charged-particle
multiplicity counts in the ALICE V0A and V0C acceptance. While all models
qualitatively reproduce the increase of $f_{\mathrm{non\text{-}prompt}}$ with
increasing $p_{\mathrm{T}}$, EPOS significantly underpredicts
$f_{\mathrm{non\text{-}prompt}}$ of $\mathrm{D^{+}}$ mesons and
$\mathrm{D^{0}}$ mesons in the $\text{INEL}>0$ and in the lowest multiplicity
classes of events, by up to a factor of two. Moreover, EPOS 3 predicts a
slightly stronger multiplicity dependence compared to EPOS 4. On the other
hand, PYTHIA 8 is generally closer to data but overpredicts
$f_{\mathrm{non\text{-}prompt}}$ by approximately 20–30%. No significant
difference in the various PYTHIA 8 settings tested in this work is observed,
with the exception of the CR-BLC Mode 3 setting, which predicts a lower
$\mathrm{D^{+}}$ and $\mathrm{D^{0}}$ non-prompt fraction especially in the
two highest multiplicity intervals, providing a better description of the
data.

The ratio of the D-meson non-prompt fractions in the multiplicity classes
relative to that in the $\text{INEL}>0$ class,
$f^{\mathrm{mult}}_{\mathrm{non\text{-}prompt}}/f^{\rm{INEL>0}}_{\mathrm{non\text{-}prompt}}$,
is shown in Fig. 5 as a function of transverse momentum for the three
multiplicity classes. This double ratio isolates the relative variation of
$f_{\mathrm{non\text{-}prompt}}$ as a function of the charged particle
multiplicity from absolute scaling factors. The double ratio of
$\mathrm{D^{0}}$ and $\mathrm{D^{+}}$ was found to be compatible for all the
multiplicity classes as expected. In order to improve the statistical
precision, the average $\mathrm{D^{0}}$ and $\mathrm{D^{+}}$
$f^{\mathrm{mult}}_{\mathrm{non\text{-}prompt}}/f^{\rm{INEL>0}}_{\mathrm{non\text{-}prompt}}$
was computed. The average was computed using the inverse of the quadratic sum
of the relative statistical and uncorrelated systematic uncertainties as
weights. The systematic uncertainties were propagated through the averaging
procedure considering the contributions from the raw-yield extraction and the
selection efficiency as uncorrelated, while the other sources as fully
correlated between the two D-meson species. In all multiplicity classes, the
measured ratio is compatible with unity within uncertainties. This finding
suggests similar production mechanisms of charm and beauty quarks as a
function of multiplicity. The expectation obtained with EPOS 3 shows a
modification of the $p_{\mathrm{T}}$ spectrum different for charm and beauty
hadrons due to their different mass, which is not supported by the
measurement. A qualitatively similar behaviour is obtained with EPOS 4, which
is more in agreement with the data, except for
$p_{\mathrm{T}}<4~{}\mathrm{GeV}/c$ in low-multiplicity events. All the PYTHIA
8 configurations reproduce the measurements within the uncertainties,
indicating a small influence of the hadronisation in the multiplicity
dependence, except for the CR-BLC Mode 3 setting, which underestimates the
data at low $p_{\mathrm{T}}$ in the high-multiplicity class of events. The
data points are further compared to a CGC model that includes the three-
pomeron exchange mechanism [57]. In this model, the transition from the beauty
quark to the charm hadron is modelled in a single step using
$f(\mathrm{b}\to\mathrm{H_{c}})$ fragmentation functions measured in
$\mathrm{e^{+}e^{-}}$ collisions [86]. Even though these fragmentation
functions were shown to be unable to reproduce the measured cross sections of
non-prompt D mesons in previous studies [2], they cancel in the
$f^{\mathrm{mult}}_{\mathrm{non\text{-}prompt}}/f^{\rm{INEL>0}}_{\mathrm{non\text{-}prompt}}$
ratio and for this observable the CGC predictions are consistent with the data
within uncertainties.

Figure 5: Average fractions of non-prompt $\mathrm{D^{0}}$ and
$\mathrm{D^{+}}$ mesons as a function of $p_{\mathrm{T}}$ for different
multiplicity intervals normalised to the one measured in the $\text{INEL}>0$
class of pp collisions at $\sqrt{s}=13~{}\mathrm{TeV}$. The measurements are
compared with the predictions obtained with PYTHIA 8 [52] and EPOS [61] event
generators and the CGC model. Figure 6: Average fractions of non-prompt
$\mathrm{D^{0}}$ and $\mathrm{D^{+}}$ mesons as a function of multiplicity,
both normalised to the value corresponding to the $\text{INEL}>0$ class, for
pp collisions at $\sqrt{s}=13~{}\mathrm{TeV}$ in different $p_{\mathrm{T}}$
intervals and integrated in $1<p_{\mathrm{T}}<24~{}\mathrm{GeV}/c$. The
measurements are compared with predictions obtained with the PYTHIA 8 [52] and
EPOS [61] event generators and the CGC model [57].

The specific multiplicity dependence of
$f^{\mathrm{mult}}_{\mathrm{non\text{-}prompt}}/f^{\rm{INEL>0}}_{\mathrm{non\text{-}prompt}}$
can be studied in more detail by plotting the values obtained in each
individual transverse momentum interval as a function of the charged-particle
multiplicity density normalised to the value corresponding to the
$\text{INEL}>0$ class of events, as shown in Fig. 6. In all $p_{\mathrm{T}}$
intervals, the average $\mathrm{D^{0}}$ and $\mathrm{D^{+}}$
$f^{\mathrm{mult}}_{\mathrm{non\text{-}prompt}}/f^{\rm{INEL>0}}_{\mathrm{non\text{-}prompt}}$
ratio is found to be compatible with unity, indicating a weak (if any)
dependence of $f_{\mathrm{non\text{-}prompt}}$ with the charged-particle
multiplicity. Comparisons with models reveal that the EPOS event generator
predicts a multiplicity dependence at intermediate transverse momentum
($4<p_{\mathrm{T}}<6~{}\mathrm{GeV}/c$) which is ruled out by the data. At low
$p_{\mathrm{T}}$ and multiplicity it predicts a rise of
$f_{\mathrm{non\text{-}prompt}}$ which is also not supported by the data. At
lower and higher $p_{\mathrm{T}}$ in the other multiplicity intervals,
instead, EPOS predicts a milder charged-particle multiplicity dependence and
is hence closer to the data. Moreover, the multiplicity-independence of CGC
predictions is also consistent with the data. Finally, most PYTHIA 8
predictions are consistent with the data, with the notable exception of CR-BLC
Mode 3 results, in which the double ratio is shown to decrease with
multiplicity. This behaviour can be further investigated by isolating the
double ratio for D mesons originating from beauty-meson and beauty-baryon
decays in each of the specific PYTHIA 8 configurations being used, as
represented in Fig. 7. While in all cases the
$f^{\mathrm{mult}}_{\mathrm{non\text{-}prompt}}/f^{\rm{INEL>0}}_{\mathrm{non\text{-}prompt}}$
ratio from beauty baryons increases systematically with multiplicity, the Mode
3 setting results in a decrease of this double ratio for D mesons originating
from B-meson decays. More specifically, a clean MC-only test can be performed
with the beyond-leading-colour tunes by calculating the ratio of baryons and
mesons at hadronisation time in PYTHIA 8 as a function of multiplicity in each
model, as depicted in Fig. 8. Notably, CR-BLC Mode 3 differs from other PYTHIA
8 predictions due to the fact that, in that case, beauty quarks produce
significantly more baryons, and charm quarks produce fewer baryons than in
other cases.

Figure 7: Fractions of non-prompt $\mathrm{D^{0}}$ (first row) and
$\mathrm{D^{+}}$ (second row) mesons in $1<p_{\mathrm{T}}<24~{}\mathrm{GeV}/c$
as a function of multiplicity for pp collisions at
$\sqrt{s}=13~{}\mathrm{TeV}$ compared with predictions obtained with the
PYTHIA 8 [52] event generator. The contributions from beauty meson and baryon
decays in PYTHIA 8 are displayed separately.

Consequently, the fraction of non-prompt D mesons decreases with the
multiplicity as a combination of two effects. On the one side, charm quarks
hadronise more to D mesons, increasing the prompt contribution to the D-meson
production and, on the other side, beauty quarks will tend towards being
contained in baryons, which in turn will feed preferentially into charm
baryons such as the $\Lambda_{\mathrm{c}}^{+}$ baryon. This strong preference
towards beauty baryons is not favoured by current ALICE data, which
essentially rules out the CR-BLC Mode 3 dynamics in favour of models in which
$f_{\mathrm{non\text{-}prompt}}$ tends to either remain constant or increase
slightly with multiplicity. Future studies of meson and baryon production in
the beauty sector as a function of the charged-particle multiplicity will
allow for firmer conclusions.

##  6 Summary

Figure 8: Fraction of charm and beauty quarks hadronising to baryons as a
function of the charged particle multiplicity at midrapidity in PYTHIA 8 [52]
simulations with different tunes.

The fractions of the $\mathrm{D^{0}}$ and $\mathrm{D^{+}}$ mesons originating
from beauty-hadron decays, $f_{\mathrm{non\text{-}prompt}}$, were measured at
midrapidity ($|y|<0.5$) in pp collisions at $\sqrt{s}=13~{}\mathrm{TeV}$ in
events with at least a charged particle at midrapidity ($\text{INEL}>0$ class
of events) and as a function of charged-particle multiplicity and transverse
momentum. Events with different charged-particle multiplicities were selected
as percentiles of the $\text{INEL}>0$ cross section based on the charged-
particle multiplicity counts in the ALICE V0A and V0C at forward and backward
rapidity. The $\mathrm{D^{+}}$ and $\mathrm{D^{0}}$
$f_{\mathrm{non\text{-}prompt}}$ were observed to slightly increase from about
5%–7% for $1<p_{\mathrm{T}}<3~{}\mathrm{GeV}/c$ to about 10% for
$8<p_{\mathrm{T}}<24~{}\mathrm{GeV}/c$. The ratios
$f^{\mathrm{mult}}_{\mathrm{non\text{-}prompt}}/f^{\rm{INEL>0}}_{\mathrm{non\text{-}prompt}}$
are compatible with unity both as a function of $p_{\mathrm{T}}$ and charged-
particle multiplicity, suggesting either no or only a mild multiplicity
dependence. This finding suggests a similar production mechanism of charm and
beauty quarks as a function of multiplicity.

The measured $f_{\mathrm{non\text{-}prompt}}$ values are compared to
predictions obtained with different MC generators. The EPOS 3 and EPOS 4
generators tend to underestimate the measurements, while PYTHIA 8 with
different tunes, including the colour reconnection mechanism beyond leading
colour approximation and colour ropes, slightly overestimates the data. The
variation of $f_{\mathrm{non\text{-}prompt}}$ with multiplicity is
satisfactorily described by the MC simulations except for the
$4<p_{\mathrm{T}}<6~{}\mathrm{GeV}/c$ interval, where the EPOS generator
predicts a significant increase. In all the considered $p_{\mathrm{T}}$
intervals, the CR-BLC Mode 3 tune of PYTHIA 8 foresees a decrease at high
multiplicity. In that tune, this decrease with increasing multiplicity is
motivated by an interplay between an increased fraction of charm quarks
hadronising into mesons and an increased fraction of beauty quarks hadronising
into baryons and is not favoured by data. Despite the fragmentation functions
adopted prevented to reproduce the measured cross sections of non-prompt D
mesons in previous studies [2], the ratio
$f^{\mathrm{mult}}_{\mathrm{non\text{-}prompt}}/f^{\rm{INEL>0}}_{\mathrm{non\text{-}prompt}}$
is also described well by the CGC model. The comparison between data and
theory models suggests a similar multiplicity dependence of charm- and beauty-
hadron production and in particular, a different evolution of the baryon-to-
meson ratio in the charm and beauty sectors is disfavoured.

The measurements presented in this paper provide an important test for
production and hadronisation models in the charm and beauty sectors, and they
pave the way for future studies of beauty-hadron production in pp collisions
as a function of the charged-particle multiplicity.

## 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; 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, Ministry of Research and Innovation and Institute of Atomic Physics and University Politehnica of Bucharest, 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), Thailand Science Research and Innovation (TSRI) and National Science, Research and Innovation Fund (NSRF), 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: European Research Council, Strong 2020 - Horizon 2020, Marie Skłodowska Curie (grant nos. 950692, 824093, 896850), European Union; Academy of Finland (Center of Excellence in Quark Matter) (grant nos. 346327, 346328), Finland; Programa de Apoyos para la Superación del Personal Académico, UNAM, Mexico.

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##  Appendix A The ALICE Collaboration

S. Acharya 125, D. Adamová 86, A. Adler70, G. Aglieri Rinella 33, M. Agnello
30, N. Agrawal 51, Z. Ahammed 133, S. Ahmad 16, S.U. Ahn 71, I. Ahuja 38, A.
Akindinov 141, M. Al-Turany 97, D. Aleksandrov 141, B. Alessandro 56, H.M.
Alfanda 6, R. Alfaro Molina 67, B. Ali 16, A. Alici 26, N. Alizadehvandchali
114, A. Alkin 33, J. Alme 21, G. Alocco 52, T. Alt 64, I. Altsybeev 141, M.N.
Anaam 6, C. Andrei 46, A. Andronic 136, V. Anguelov 94, F. Antinori 54, P.
Antonioli 51, N. Apadula 74, L. Aphecetche 103, H. Appelshäuser 64, C. Arata
73, S. Arcelli 26, M. Aresti 52, R. Arnaldi 56, J.G.M.C.A. Arneiro 110, I.C.
Arsene 20, M. Arslandok 138, A. Augustinus 33, R. Averbeck 97, M.D. Azmi 16,
A. Badalà 53, J. Bae 104, Y.W. Baek 41, X. Bai 118, R. Bailhache 64, Y.
Bailung 48, A. Balbino 30, A. Baldisseri 128, B. Balis 2, D. Banerjee 4, Z.
Banoo 91, R. Barbera 27, F. Barile 32, L. Barioglio 95, M. Barlou78, G.G.
Barnaföldi 137, L.S. Barnby 85, V. Barret 125, L. Barreto 110, C. Bartels 117,
K. Barth 33, E. Bartsch 64, N. Bastid 125, 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 127, A.D.C. Bell
Hechavarria 136, F. Bellini 26, R. Bellwied 114, S. Belokurova 141, V. Belyaev
141, G. Bencedi 137, S. Beole 25, Y. Berdnikov 141, A. Berdnikova 94, L.
Bergmann 94, M.G. Besoiu 63, L. Betev 33, P.P. Bhaduri 133, A. Bhasin 91, M.A.
Bhat 4, B. Bhattacharjee 42, L. Bianchi 25, N. Bianchi 49, J. Bielčík 36, J.
Bielčíková 86, J. Biernat 107, A.P. Bigot 127, A. Bilandzic 95, G. Biro 137,
S. Biswas 4, N. Bize 103, J.T. Blair 108, D. Blau 141, M.B. Blidaru 97, N.
Bluhme39, C. Blume 64, G. Boca 22,55, F. Bock 87, T. Bodova 21, A.
Bogdanov141, S. Boi 23, J. Bok 58, L. Boldizsár 137, M. Bombara 38, P.M. Bond
33, G. Bonomi 132,55, H. Borel 128, A. Borissov 141, A.G. Borquez Carcamo 94,
H. Bossi 138, E. Botta 25, Y.E.M. Bouziani 64, L. Bratrud 64, P. Braun-
Munzinger 97, M. Bregant 110, M. Broz 36, G.E. Bruno 96,32, M.D. Buckland 24,
D. Budnikov 141, H. Buesching 64, S. Bufalino 30, P. Buhler 102, Z. Buthelezi
68,121, A. Bylinkin 21, S.A. Bysiak107, M. Cai 6, H. Caines 138, A. Caliva 97,
E. Calvo Villar 101, J.M.M. Camacho 109, P. Camerini 24, F.D.M. Canedo 110, M.
Carabas 124, A.A. Carballo 33, F. Carnesecchi 33, R. Caron 126, L.A.D.
Carvalho 110, J. Castillo Castellanos 128, F. Catalano 25, C. Ceballos Sanchez
142, I. Chakaberia 74, P. Chakraborty 47, S. Chandra 133, S. Chapeland 33, M.
Chartier 117, S. Chattopadhyay 133, S. Chattopadhyay 99, T.G. Chavez 45, T.
Cheng 97,6, C. Cheshkov 126, B. Cheynis 126, V. Chibante Barroso 33, D.D.
Chinellato 111, E.S. Chizzali II,95, J. Cho 58, S. Cho 58, P. Chochula 33, P.
Christakoglou 84, C.H. Christensen 83, P. Christiansen 75, T. Chujo 123, M.
Ciacco 30, C. Cicalo 52, F. Cindolo 51, M.R. Ciupek97, G. ClaiIII,51, F.
Colamaria 50, J.S. Colburn100, D. Colella 96,32, M. Colocci 26, M. Concas
IV,56, G. Conesa Balbastre 73, Z. Conesa del Valle 129, G. Contin 24, J.G.
Contreras 36, M.L. Coquet 128, T.M. CormierI,87, P. Cortese 131,56, M.R.
Cosentino 112, F. Costa 33, S. Costanza 22,55, C. Cot 129, J. Crkovská 94, P.
Crochet 125, R. Cruz-Torres 74, P. Cui 6, A. Dainese 54, M.C. Danisch 94, A.
Danu 63, P. Das 80, P. Das 4, S. Das 4, A.R. Dash 136, S. Dash 47, R.M.H.
David45, A. De Caro 29, G. de Cataldo 50, J. de Cuveland39, A. De Falco 23, D.
De Gruttola 29, N. De Marco 56, C. De Martin 24, S. De Pasquale 29, R. Deb
132, S. Deb 48, R.J. Debski 2, K.R. Deja134, R. Del Grande 95, L. Dello
Stritto 29, W. Deng 6, P. Dhankher 19, D. Di Bari 32, A. Di Mauro 33, R.A.
Diaz 142,7, T. Dietel 113, Y. Ding 6, R. Divià 33, D.U. Dixit 19, Ø.
Djuvsland21, U. Dmitrieva 141, A. Dobrin 63, B. Dönigus 64, J.M. Dubinski 134,
A. Dubla 97, S. Dudi 90, P. Dupieux 125, M. Durkac106, N. Dzalaiova13, T.M.
Eder 136, R.J. Ehlers 74, V.N. Eikeland21, F. Eisenhut 64, D. Elia 50, B.
Erazmus 103, F. Ercolessi 26, F. Erhardt 89, M.R. Ersdal21, B. Espagnon 129,
G. Eulisse 33, D. Evans 100, S. Evdokimov 141, L. Fabbietti 95, M. Faggin 28,
J. Faivre 73, F. Fan 6, W. Fan 74, A. Fantoni 49, M. Fasel 87, P. Fecchio30,
A. Feliciello 56, G. Feofilov 141, A. Fernández Téllez 45, L. Ferrandi 110,
M.B. Ferrer 33, A. Ferrero 128, C. Ferrero 56, A. Ferretti 25, V.J.G.
Feuillard 94, V. Filova 36, D. Finogeev 141, F.M. Fionda 52, F. Flor 114, A.N.
Flores 108, S. Foertsch 68, I. Fokin 94, S. Fokin 141, E. Fragiacomo 57, E.
Frajna 137, U. Fuchs 33, N. Funicello 29, C. Furget 73, A. Furs 141, T.
Fusayasu 98, J.J. Gaardhøje 83, M. Gagliardi 25, A.M. Gago 101, C.D. Galvan
109, D.R. Gangadharan 114, P. Ganoti 78, C. Garabatos 97, J.R.A. Garcia 45, E.
Garcia-Solis 9, C. Gargiulo 33, K. Garner136, P. Gasik 97, A. Gautam 116, M.B.
Gay Ducati 66, M. Germain 103, A. Ghimouz123, C. Ghosh133, M. Giacalone 51,26,
P. Giubellino 97,56, P. Giubilato 28, A.M.C. Glaenzer 128, P. Glässel 94, E.
Glimos 120, D.J.Q. Goh76, V. Gonzalez 135, M. Gorgon 2, S. Gotovac34, V.
Grabski 67, L.K. Graczykowski 134, E. Grecka 86, A. Grelli 59, C. Grigoras 33,
V. Grigoriev 141, S. Grigoryan 142,1, F. Grosa 33, J.F. Grosse-Oetringhaus 33,
R. Grosso 97, D. Grund 36, G.G. Guardiano 111, R. Guernane 73, M. Guilbaud
103, K. Gulbrandsen 83, T. Gündem 64, T. Gunji 122, W. Guo 6, A. Gupta 91, R.
Gupta 91, R. Gupta 48, S.P. Guzman 45, K. Gwizdziel 134, L. Gyulai 137, M.K.
Habib97, C. Hadjidakis 129, F.U. Haider 91, H. Hamagaki 76, A. Hamdi 74, M.
Hamid6, Y. Han 139, R. Hannigan 108, M.R. Haque 134, J.W. Harris 138, A.
Harton 9, H. Hassan 87, D. Hatzifotiadou 51, P. Hauer 43, L.B. Havener 138,
S.T. Heckel 95, E. Hellbär 97, H. Helstrup 35, M. Hemmer 64, T. Herman 36, G.
Herrera Corral 8, F. Herrmann136, S. Herrmann 126, K.F. Hetland 35, B. Heybeck
64, H. Hillemanns 33, B. Hippolyte 127, F.W. Hoffmann 70, B. Hofman 59, B.
Hohlweger 84, G.H. Hong 139, M. Horst 95, A. Horzyk2, Y. Hou 6, P. Hristov 33,
C. Hughes 120, P. Huhn64, L.M. Huhta 115, C.V. Hulse 129, T.J. Humanic 88, A.
Hutson 114, D. Hutter 39, J.P. Iddon 117, R. Ilkaev141, H. Ilyas 14, M. Inaba
123, G.M. Innocenti 33, M. Ippolitov 141, A. Isakov 86, T. Isidori 116, M.S.
Islam 99, M. Ivanov 97, M. Ivanov13, V. Ivanov 141, M. Jablonski 2, B. Jacak
74, N. Jacazio 33, P.M. Jacobs 74, S. Jadlovska106, J. Jadlovsky106, S.
Jaelani 82, C. Jahnke 111, M.J. Jakubowska 134, M.A. Janik 134, T. Janson70,
M. Jercic89, S. Jia 10, A.A.P. Jimenez 65, F. Jonas 87, J.M. Jowett 33,97, J.
Jung 64, M. Jung 64, A. Junique 33, A. Jusko 100, M.J. Kabus 33,134, J.
Kaewjai105, P. Kalinak 60, A.S. Kalteyer 97, A. Kalweit 33, V. Kaplin 141, A.
Karasu Uysal 72, D. Karatovic 89, O. Karavichev 141, T. Karavicheva 141, P.
Karczmarczyk 134, E. Karpechev 141, U. Kebschull 70, R. Keidel 140, D.L.D.
Keijdener59, M. Keil 33, B. Ketzer 43, S.S. Khade 48, A.M. Khan 6, S. Khan 16,
A. Khanzadeev 141, Y. Kharlov 141, A. Khatun 116,16, A. Khuntia 107, M.B.
Kidson113, B. Kileng 35, B. Kim 104, C. Kim 17, D.J. Kim 115, E.J. Kim 69, J.
Kim 139, J.S. Kim 41, J. Kim 69, M. Kim 19, S. Kim 18, T. Kim 139, K. Kimura
92, S. Kirsch 64, I. Kisel 39, S. Kiselev 141, A. Kisiel 134, J.P. Kitowski 2,
J.L. Klay 5, J. Klein 33, S. Klein 74, C. Klein-Bösing 136, M. Kleiner 64, T.
Klemenz 95, A. Kluge 33, A.G. Knospe 114, C. Kobdaj 105, T. Kollegger97, A.
Kondratyev 142, N. Kondratyeva 141, E. Kondratyuk 141, J. Konig 64, S.A.
Konigstorfer 95, P.J. Konopka 33, G. Kornakov 134, S.D. Koryciak 2, A.
Kotliarov 86, V. Kovalenko 141, M. Kowalski 107, V. Kozhuharov 37, I. Králik
60, A. Kravčáková 38, L. Krcal 33,39, L. Kreis97, M. Krivda 100,60, F. Krizek
86, K. Krizkova Gajdosova 33, M. Kroesen 94, M. Krüger 64, D.M. Krupova 36, E.
Kryshen 141, V. Kučera 33, C. Kuhn 127, P.G. Kuijer 84, T. Kumaoka123, D.
Kumar133, L. Kumar 90, N. Kumar90, S. Kumar 32, S. Kundu 33, P. Kurashvili 79,
A. Kurepin 141, A.B. Kurepin 141, A. Kuryakin 141, S. Kushpil 86, J. Kvapil
100, M.J. Kweon 58, J.Y. Kwon 58, Y. Kwon 139, S.L. La Pointe 39, P. La Rocca
27, A. Lakrathok105, M. Lamanna 33, R. Langoy 119, P. Larionov 33, E. Laudi
33, L. Lautner 33,95, R. Lavicka 102, T. Lazareva 141, R. Lea 132,55, H. Lee
104, G. Legras 136, J. Lehrbach 39, T.M. Lelek2, R.C. Lemmon 85, I. León
Monzón 109, M.M. Lesch 95, E.D. Lesser 19, P. Lévai 137, X. Li10, X.L. Li6, J.
Lien 119, R. Lietava 100, I. Likmeta 114, B. Lim 25, S.H. Lim 17, V.
Lindenstruth 39, A. Lindner46, C. Lippmann 97, A. Liu 19, D.H. Liu 6, J. Liu
117, I.M. Lofnes 21, C. Loizides 87, S. Lokos 107, J. Lomker 59, P. Loncar34,
J.A. Lopez 94, X. Lopez 125, E. López Torres 7, P. Lu 97,118, J.R. Luhder 136,
M. Lunardon 28, G. Luparello 57, Y.G. Ma 40, A. Maevskaya141, M. Mager 33, A.
Maire 127, M.V. Makariev 37, M. Malaev 141, G. Malfattore 26, N.M. Malik 91,
Q.W. Malik20, S.K. Malik 91, L. Malinina VII,142, D. Mal’Kevich 141, D.
Mallick 80, N. Mallick 48, G. Mandaglio 31,53, S.K. Mandal 79, V. Manko 141,
F. Manso 125, V. Manzari 50, Y. Mao 6, G.V. Margagliotti 24, A. Margotti 51,
A. Marín 97, C. Markert 108, P. Martinengo 33, J.L. Martinez114, M.I. Martínez
45, G. Martínez García 103, S. Masciocchi 97, M. Masera 25, A. Masoni 52, L.
Massacrier 129, A. Mastroserio 130,50, O. Matonoha 75, P.F.T. Matuoka110, A.
Matyja 107, C. Mayer 107, A.L. Mazuecos 33, F. Mazzaschi 25, M. Mazzilli 33,
J.E. Mdhluli 121, A.F. Mechler64, Y. Melikyan 44,141, A. Menchaca-Rocha 67, E.
Meninno 102,29, A.S. Menon 114, M. Meres 13, S. Mhlanga113,68, Y. Miake123, L.
Micheletti 56, L.C. Migliorin126, D.L. Mihaylov 95, K. Mikhaylov 142,141, A.N.
Mishra 137, D. Miśkowiec 97, A. Modak 4, A.P. Mohanty 59, B. Mohanty80, M.
Mohisin Khan V,16, M.A. Molander 44, Z. Moravcova 83, C. Mordasini 95, D.A.
Moreira De Godoy 136, I. Morozov 141, A. Morsch 33, T. Mrnjavac 33, V.
Muccifora 49, S. Muhuri 133, J.D. Mulligan 74, A. Mulliri23, M.G. Munhoz 110,
R.H. Munzer 64, H. Murakami 122, S. Murray 113, L. Musa 33, J. Musinsky 60,
J.W. Myrcha 134, B. Naik 121, A.I. Nambrath 19, B.K. Nandi 47, R. Nania 51, E.
Nappi 50, A.F. Nassirpour 18,75, A. Nath 94, C. Nattrass 120, M.N. Naydenov
37, A. Neagu20, A. Negru124, L. Nellen 65, S.V. Nesbo35, G. Neskovic 39, D.
Nesterov 141, 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
117, N. Novitzky 123, P. Nowakowski 134, A. Nyanin 141, J. Nystrand 21, M.
Ogino 76, A. Ohlson 75, V.A. Okorokov 141, J. Oleniacz 134, A.C. Oliveira Da
Silva 120, M.H. Oliver 138, A. Onnerstad 115, C. Oppedisano 56, A. Ortiz
Velasquez 65, J. Otwinowski 107, M. Oya92, K. Oyama 76, Y. Pachmayer 94, S.
Padhan 47, D. Pagano 132,55, G. Paić 65, A. Palasciano 50, S. Panebianco 128,
H. Park 123, H. Park 104, J. Park 58, J.E. Parkkila 33, R.N. Patra91, B. Paul
23, H. Pei 6, T. Peitzmann 59, X. Peng 11,6, M. Pennisi 25, L.G. Pereira 66,
D. Peresunko 141, G.M. Perez 7, S. Perrin 128, Y. Pestov141, V. Petráček 36,
V. Petrov 141, M. Petrovici 46, R.P. Pezzi 103,66, S. Piano 57, M. Pikna 13,
P. Pillot 103, O. Pinazza 51,33, L. Pinsky114, C. Pinto 95, S. Pisano 49, M.
Płoskoń 74, M. Planinic89, F. Pliquett64, M.G. Poghosyan 87, B. Polichtchouk
141, S. Politano 30, N. Poljak 89, A. Pop 46, S. Porteboeuf-Houssais 125, V.
Pozdniakov 142, I.Y. Pozos 45, K.K. Pradhan 48, S.K. Prasad 4, S. Prasad 48,
R. Preghenella 51, F. Prino 56, C.A. Pruneau 135, I. Pshenichnov 141, M.
Puccio 33, S. Pucillo 25, Z. Pugelova106, S. Qiu 84, L. Quaglia 25, R.E.
Quishpe114, S. Ragoni 15, A. Rakotozafindrabe 128, L. Ramello 131,56, F. Rami
127, S.A.R. Ramirez 45, T.A. Rancien73, M. Rasa 27, S.S. Räsänen 44, R. Rath
51, M.P. Rauch 21, I. Ravasenga 84, K.F. Read 87,120, C. Reckziegel 112, A.R.
Redelbach 39, K. Redlich VI,79, C.A. Reetz 97, A. Rehman21, F. Reidt 33, H.A.
Reme-Ness 35, Z. Rescakova38, K. Reygers 94, A. Riabov 141, V. Riabov 141, R.
Ricci 29, M. Richter 20, A.A. Riedel 95, W. Riegler 33, C. Ristea 63, M.
Rodríguez Cahuantzi 45, K. Røed 20, R. Rogalev 141, E. Rogochaya 142, T.S.
Rogoschinski 64, D. Rohr 33, D. Röhrich 21, P.F. Rojas45, S. Rojas Torres 36,
P.S. Rokita 134, G. Romanenko 142, F. Ronchetti 49, A. Rosano 31,53, E.D.
Rosas65, K. Roslon 134, A. Rossi 54, A. Roy 48, S. Roy 47, N. Rubini 26, D.
Ruggiano 134, R. Rui 24, B. Rumyantsev142, P.G. Russek 2, R. Russo 84, A.
Rustamov 81, E. Ryabinkin 141, Y. Ryabov 141, A. Rybicki 107, H. Rytkonen 115,
W. Rzesa 134, O.A.M. Saarimaki 44, R. Sadek 103, S. Sadhu 32, S. Sadovsky 141,
J. Saetre 21, K. Šafařík 36, S.K. Saha 4, S. Saha 80, B. Sahoo 47, B. Sahoo
48, R. Sahoo 48, S. Sahoo61, D. Sahu 48, P.K. Sahu 61, J. Saini 133, K.
Sajdakova38, S. Sakai 123, M.P. Salvan 97, S. Sambyal 91, I. Sanna 33,95, T.B.
Saramela110, D. Sarkar 135, N. Sarkar133, P. Sarma 42, V. Sarritzu 23, V.M.
Sarti 95, M.H.P. Sas 138, J. Schambach 87, H.S. Scheid 64, C. Schiaua 46, R.
Schicker 94, A. Schmah94, C. Schmidt 97, H.R. Schmidt93, M.O. Schmidt 33, M.
Schmidt93, N.V. Schmidt 87, A.R. Schmier 120, R. Schotter 127, A. Schröter 39,
J. Schukraft 33, K. Schwarz97, K. Schweda 97, G. Scioli 26, E. Scomparin 56,
J.E. Seger 15, Y. Sekiguchi122, D. Sekihata 122, I. Selyuzhenkov 97,141, S.
Senyukov 127, J.J. Seo 58, D. Serebryakov 141, L. Šerkšnytė 95, A. Sevcenco
63, T.J. Shaba 68, A. Shabetai 103, R. Shahoyan33, A. Shangaraev 141, A.
Sharma90, B. Sharma 91, D. Sharma 47, H. Sharma 107, M. Sharma 91, S. Sharma
76, S. Sharma 91, U. Sharma 91, A. Shatat 129, O. Sheibani114, K. Shigaki 92,
M. Shimomura77, J. Shin12, S. Shirinkin 141, Q. Shou 40, Y. Sibiriak 141, S.
Siddhanta 52, T. Siemiarczuk 79, T.F. Silva 110, D. Silvermyr 75, T.
Simantathammakul105, R. Simeonov 37, B. Singh91, B. Singh 95, R. Singh 80, R.
Singh 91, R. Singh 48, S. Singh 16, V.K. Singh 133, V. Singhal 133, T. Sinha
99, B. Sitar 13, M. Sitta 131,56, T.B. Skaali20, G. Skorodumovs 94, M.
Slupecki 44, N. Smirnov 138, R.J.M. Snellings 59, E.H. Solheim 20, J. Song
114, A. Songmoolnak105, F. Soramel 28, A.B. Soto-hernandez 88, R. Spijkers 84,
I. Sputowska 107, J. Staa 75, J. Stachel 94, I. Stan 63, P.J. Steffanic 120,
S.F. Stiefelmaier 94, D. Stocco 103, I. Storehaug 20, P. Stratmann 136, S.
Strazzi 26, C.P. Stylianidis84, A.A.P. Suaide 110, C. Suire 129, M. Sukhanov
141, M. Suljic 33, R. Sultanov 141, V. Sumberia 91, S. Sumowidagdo 82, S.
Swain61, I. Szarka 13, M. Szymkowski 134, S.F. Taghavi 95, G. Taillepied 97,
J. Takahashi 111, G.J. Tambave 21, S. Tang 125,6, Z. Tang 118, J.D. Tapia
Takaki 116, N. Tapus124, L.A. Tarasovicova 136, M.G. Tarzila 46, G.F.
Tassielli 32, A. Tauro 33, G. Tejeda Muñoz 45, A. Telesca 33, L. Terlizzi 25,
C. Terrevoli 114, S. Thakur 4, D. Thomas 108, A. Tikhonov 141, A.R. Timmins
114, M. Tkacik106, T. Tkacik 106, A. Toia 64, R. Tokumoto92, N. Topilskaya
141, M. Toppi 49, F. Torales-Acosta19, T. Tork 129, A.G. Torres Ramos 32, A.
Trifiró 31,53, A.S. Triolo 33,31,53, S. Tripathy 51, T. Tripathy 47, S.
Trogolo 33, V. Trubnikov 3, W.H. Trzaska 115, T.P. Trzcinski 134, A. Tumkin
141, R. Turrisi 54, T.S. Tveter 20, K. Ullaland 21, B. Ulukutlu 95, A. Uras
126, M. Urioni 55,132, G.L. Usai 23, M. Vala38, N. Valle 22, L.V.R. van
Doremalen59, M. van Leeuwen 84, C.A. van Veen 94, R.J.G. van Weelden 84, P.
Vande Vyvre 33, D. Varga 137, Z. Varga 137, M. Vasileiou 78, A. Vasiliev 141,
O. Vázquez Doce 49, O. Vazquez Rueda 114, V. Vechernin 141, E. Vercellin 25,
S. Vergara Limón45, L. Vermunt 97, R. Vértesi 137, M. Verweij 59, L.
Vickovic34, Z. Vilakazi121, O. Villalobos Baillie 100, A. Villani 24, G. Vino
50, A. Vinogradov 141, T. Virgili 29, M.M.O. Virta 115, V. Vislavicius75, A.
Vodopyanov 142, B. Volkel 33, M.A. Völkl 94, K. Voloshin141, S.A. Voloshin
135, G. Volpe 32, B. von Haller 33, I. Vorobyev 95, N. Vozniuk 141, J.
Vrláková 38, C. Wang 40, D. Wang40, Y. Wang 40, A. Wegrzynek 33, F.T.
Weiglhofer39, S.C. Wenzel 33, J.P. Wessels 136, S.L. Weyhmiller 138, J.
Wiechula 64, J. Wikne 20, G. Wilk 79, J. Wilkinson 97, G.A. Willems 136, B.
Windelband 94, M. Winn 128, J.R. Wright 108, W. Wu40, Y. Wu 118, R. Xu 6, A.
Yadav 43, A.K. Yadav 133, S. Yalcin 72, Y. Yamaguchi 92, S. Yang21, S. Yano
92, Z. Yin 6, I.-K. Yoo 17, J.H. Yoon 58, S. Yuan21, A. Yuncu 94, V. Zaccolo
24, C. Zampolli 33, F. Zanone 94, N. Zardoshti 33, A. Zarochentsev 141, P.
Závada 62, N. Zaviyalov141, M. Zhalov 141, B. Zhang 6, L. Zhang 40, S. Zhang
40, X. Zhang 6, Y. Zhang118, Z. Zhang 6, M. Zhao 10, V. Zherebchevskii 141, Y.
Zhi10, D. Zhou 6, Y. Zhou 83, J. Zhu 97,6, Y. Zhu6, S.C. Zugravel 56, N. Zurlo
132,55

## Affiliation Notes

I Deceased
II Also at: Max-Planck-Institut für 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: Department of Applied Physics, Aligarh Muslim University, Aligarh,
India
VI Also at: Institute of Theoretical Physics, University of Wroclaw, Poland
VII 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 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, Università di Pavia, Pavia, Italy
23 Dipartimento di Fisica dell’Università and Sezione INFN, Cagliari, Italy
24 Dipartimento di Fisica dell’Università and Sezione INFN, Trieste, Italy
25 Dipartimento di Fisica dell’Università and Sezione INFN, Turin, Italy
26 Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN,
Bologna, Italy
27 Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN,
Catania, Italy
28 Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN,
Padova, 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 MIFT, Università di Messina, Messina, Italy
32 Dipartimento Interateneo di Fisica ‘M. Merlin’ and Sezione INFN, Bari,
Italy
33 European Organization for Nuclear Research (CERN), Geneva, Switzerland
34 Faculty of Electrical Engineering, Mechanical Engineering and Naval
Architecture, University of Split, Split, Croatia
35 Faculty of Engineering and Science, Western Norway University of Applied
Sciences, Bergen, Norway
36 Faculty of Nuclear Sciences and Physical Engineering, Czech Technical
University in Prague, Prague, Czech Republic
37 Faculty of Physics, Sofia University, Sofia, Bulgaria
38 Faculty of Science, P.J. Šafárik University, Košice, Slovak Republic
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 Horia Hulubei National Institute of Physics and Nuclear Engineering,
Bucharest, Romania
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 University of Cape Town, Cape Town, South Africa
114 University of Houston, Houston, Texas, United States
115 University of Jyväskylä, Jyväskylä, Finland
116 University of Kansas, Lawrence, Kansas, United States
117 University of Liverpool, Liverpool, United Kingdom
118 University of Science and Technology of China, Hefei, China
119 University of South-Eastern Norway, Kongsberg, Norway
120 University of Tennessee, Knoxville, Tennessee, United States
121 University of the Witwatersrand, Johannesburg, South Africa
122 University of Tokyo, Tokyo, Japan
123 University of Tsukuba, Tsukuba, Japan
124 University Politehnica of Bucharest, Bucharest, Romania
125 Université Clermont Auvergne, CNRS/IN2P3, LPC, Clermont-Ferrand, France
126 Université de Lyon, CNRS/IN2P3, Institut de Physique des 2 Infinis de
Lyon, Lyon, France
127 Université de Strasbourg, CNRS, IPHC UMR 7178, F-67000 Strasbourg, France,
Strasbourg, France
128 Université Paris-Saclay, Centre d’Etudes de Saclay (CEA), IRFU, Départment
de Physique Nucléaire (DPhN), Saclay, France
129 Université Paris-Saclay, CNRS/IN2P3, IJCLab, Orsay, France
130 Università degli Studi di Foggia, Foggia, Italy
131 Università del Piemonte Orientale, Vercelli, Italy
132 Università di Brescia, Brescia, Italy
133 Variable Energy Cyclotron Centre, Homi Bhabha National Institute, Kolkata,
India
134 Warsaw University of Technology, Warsaw, Poland
135 Wayne State University, Detroit, Michigan, United States
136 Westfälische Wilhelms-Universität Münster, Institut für Kernphysik,
Münster, Germany
137 Wigner Research Centre for Physics, Budapest, Hungary
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.