2021 205 06 October

Production of b jets in pp and p–Pb collisions

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

A measurement of the inclusive b-jet production cross section is presented in
pp and p–Pb collisions at $\sqrt{s_{\mathrm{NN}}}~{}=~{}5.02$ TeV, using data
collected with the ALICE detector at the LHC. The jets were reconstructed in
the central rapidity region $|\eta|<0.5$ from charged particles using the
anti-$k_{\rm T}$ algorithm with resolution parameter $R=0.4$. Identification
of b jets exploits the long lifetime of b hadrons, using the properties of
secondary vertices and impact parameter distributions. The
$p_{\mathrm{T}}$-differential inclusive production cross section of b jets, as
well as the corresponding inclusive b-jet fraction, are reported for pp and
p–Pb collisions in the jet transverse momentum range $10\leq
p_{\mathrm{T,ch\,jet}}\leq 100$ GeV/$c$, together with the nuclear
modification factor, $R_{\rm pPb}^{\text{b-jet}}$. The analysis thus extends
the lower $p_{\mathrm{T}}$ limit of b-jet measurements at the LHC. The nuclear
modification factor is found to be consistent with unity, indicating that the
production of b jets in p–Pb at $\sqrt{s_{\mathrm{NN}}}~{}=~{}5.02$ TeV is not
affected by cold nuclear matter effects within the current precision. The
measurements are well reproduced by POWHEG NLO pQCD calculations with PYTHIA
fragmentation.

##  1 Introduction

Charm and beauty quarks arise from hard scattering processes with large four-
momentum transfer ($Q^{2}$). In the subsequent hadronization process they lose
their initial virtuality and produce short-lived heavy-flavor hadrons, which
can be reconstructed either through their weak hadronic decays or indirectly
via their semi-leptonic decay channels. In case of proton–proton collisions,
the inclusive production cross section of heavy-flavor hadrons can be
calculated with perturbative quantum chromodynamics (QCD) using the
factorization approach, which assumes that the collision process can be
described by a convolution of parton distribution functions (PDFs), a short-
distance parton-level cross section, and a fragmentation function. This
factorization was proven to be valid at the leading power of $Q$ [1], as well
as the leading power corrections $O(1/Q^{2})$ [2, 3]. The concept of the QCD
factorization is often extrapolated to proton–nucleus collisions by replacing
the usual PDFs with nuclear PDFs (nPDFs), while keeping the short-distance
parton-level cross section and the fragmentation function the same [4, 5, 6,
7, 8, 9]. However, there are also additional phenomena which may or may not be
incorporated into the nPDFs, for instance soft gluon interactions between the
incoming and/or outgoing hadrons causing $k_{\rm T}$-broadening and energy
loss of partons in the cold nuclear matter [10, 11, 12, 13]. These effects may
break the QCD factorization in nuclear collisions, thus making the nPDFs
process dependent. They are often accounted for as extra modification factors
or convolutions with extra functions in various models [14, 15, 16, 17]. The
differences between the factorization of proton–proton and proton–nucleus
collisions are in general referred to as cold-nuclear-matter (CNM) effects.
The overall impact of the CNM effects on the resulting
$p_{\mathrm{T}}$-differential inclusive production cross section spectrum can
be quantified by means of the nuclear modification factor, defined as the
ratio of the particle yield measured in proton–nucleus collisions and the
expected yield that would be obtained from a superposition of independent pp
collisions. The sensitivity of heavy-flavor probes to CNM effects can be
expected to differ from that of light-flavor probes due to the mass-dependent
jet fragmentation [18, 19, 20].

Small collision systems such as pp or p–A provide a natural reference for the
more complex nucleus–nucleus collisions. Nuclear matter in these ultra-
relativistic heavy-ion collisions can reach extremely high energy densities
and temperatures, and transform into its hot and dense deconfined phase, the
quark–gluon plasma (QGP) [21, 22, 23]. Initial parton showers interact with
the medium via collisional and radiative processes that cause dissipation and
redistribution of energy inside the parton shower. This results in the
suppression of high-$p_{\mathrm{T}}$ hadrons and jets [24, 25, 26, 27, 28] in
nucleus–nucleus collisions and the modification of the jet substructure [29,
30, 31, 32], the so-called jet quenching. Since heavy-flavor quarks are mainly
produced in initial hard processes and since their numbers remain largely
unchanged in the later stages of the reaction [33, 34], they provide a unique
opportunity to study the space–time evolution of the QGP. In this context,
small collision systems represent an important test for theoretical models
that account for the system-size-dependent evolution of the QGP signatures as
well as CNM effects. Understanding CNM effects is therefore essential for the
accurate quantification of the effects of a hot and dense medium in heavy-ion
measurements.

The reconstruction of jets containing heavy-flavor hadrons provides more
direct access to the primary heavy-flavor parton kinematics than an inclusive
measurement of heavy-flavor hadrons. By measuring heavy-flavor jets,
production and fragmentation effects can be studied separately. The ALICE
Collaboration reported production of charm-tagged jets in pp collisions at
$\sqrt{s}=7$ TeV [35]. Measurements of beauty-tagged jets (b jets) in pp and
p–Pb collisions were performed by the CMS experiment [36]. They reported the
nuclear modification factor for b jets with transverse momentum larger than 50
GeV/$c$. The ALICE detector has excellent tracking capabilities for
low-$p_{\mathrm{T}}$ charged particles, which makes it possible to measure b
jets at low transverse momenta. This provides a unique opportunity at the LHC
to study nuclear modification of b jets down to the region where the energy
scale of the jets is of similar magnitude compared to the b-quark mass, which
increases sensitivity to mass dependent effects. In this paper, we present the
first measurement of inclusive charged-particle b-jet
$p_{\mathrm{T}}$-differential cross section and the b-jet fraction, down to
jet-transverse momentum $p_{\mathrm{T,ch\,jet}}=10$ GeV/$c$ in p–Pb and pp
collisions at $\sqrt{s_{\mathrm{NN}}}~{}=~{}5.02$ TeV. The measured
$p_{\mathrm{T}}$ distributions were used to obtain the nuclear modification
factor of b jets, $R_{\rm pPb}^{\text{b-jet}}$, in the transverse momentum
range $10\leq p_{\mathrm{T,ch\,jet}}\leq 100$ GeV/$c$.

The paper is organized as follows: the next section introduces the
experimental setup and data sets used for these measurements. Jet
reconstruction and the b-jet tagging procedures are described in Sec. 3.
Section 4 deals with the correction steps that were applied in the analysis.
These include corrections for b-jet tagging efficiency, b-jet tagging purity,
and unfolding of the jet momentum smearing due to underlying event
fluctuations and instrumental effects. Systematic uncertainties are discussed
in Sec. 5. Section 6 is devoted to the discussion of the final results. The
paper is summarized in Sec. 7.

##  2 Experimental setup and data sets

The ALICE detector [37, 38] consists of a central barrel, a forward muon arm,
and a set of forward detectors that are used for triggering and event
characterization. The central barrel hosts detection systems that provide
tracking and particle identification. The most important ones for this
analysis are the Inner Tracking System (ITS) and the Time Projection Chamber
(TPC). The ITS is a 6-layer silicon tracker, which allows for precise
reconstruction of primary interaction and secondary decay vertices. The two
innermost layers of the ITS are formed by the Silicon Pixel Detector (SPD).
All detectors of the central barrel are placed in a solenoidal magnet that
provides a field of 0.5 T along the beam direction.

The present analysis is based on the p–Pb and pp collisions at
$\sqrt{s_{\mathrm{NN}}}~{}=~{}5.02$ TeV taken by ALICE in 2016 and 2017,
respectively. For p–Pb collisions, the beam energies of colliding protons and
Pb nuclei were asymmetric: the protons had 4 TeV, while Pb nuclei had an
energy of 1.59 TeV per nucleon. This resulted in the laboratory frame in a
rapidity ($y$) shift of the nucleon–nucleon center-of-mass system by $\Delta
y=0.465$ in the direction of the proton beam.

The main triggering device for the data sets used here is the V0 detector
[39], consisting of two scintillator arrays V0A and V0C. They cover the full
azimuth angle in the forward and backward pseudorapidity ranges $2.8<\eta<5.1$
and $-3.7<\eta<-1.7$, respectively. The minimum bias trigger (MB) is defined
by a coincidence of V0A and V0C signals. Timing of the V0A and V0C signals is
also used to reject background from beam–gas interactions.

Pile-up events constitute less than 1% (0.5%) of triggered events in pp (p–Pb)
collisions. They were identified and rejected using an algorithm that utilizes
track segments, formed by hits in the SPD, to recognize events with multiple
primary vertices. The remaining undetected pile-up events constitute a
negligible fraction of the analysed sample.

The p–Pb data set corresponds to an integrated luminosity of $\mathcal{L}_{\rm
pPb}=(298\pm 11)\,{\upmu}$b-1 ($624\times 10^{6}$ MB events) [40], and the pp
data set to $\mathcal{L}_{\rm pp}=(18.9\pm 0.4)$ nb-1 ($968\times 10^{6}$ MB
events) [41]. Only events with the location of the reconstructed primary
vertex along the beam axis within $|z_{\rm vtx}|<10$ cm were retained to
assure a uniform detector coverage at midrapidity.

##  3 Jet reconstruction and b-jet identification

The analysis uses high-quality tracks [35] reconstructed in the pseudorapidity
range $|\eta_{\rm track}|<0.9$ that have at least one hit in either of the two
SPD layers. In the regions where the SPD was inefficient, high-quality tracks
were supplemented with complementary tracks that do not have a hit in the SPD,
to achieve azimuthal uniformity in the tracking acceptance. The momentum
resolution of complementary tracks is improved by constraining the origin of
the track to the primary vertex. Complementary tracks constitute about 3.5% of
all primary tracks. The tracking efficiency for primary tracks with
$p_{\mathrm{T}}>1$ GeV/$c$ varies with $p_{\mathrm{T}}$ between 70 and 85%.
Primary-track momentum resolution is about 0.7% at $p_{\mathrm{T}}=1$ GeV/$c$,
1.6% at $p_{\mathrm{T}}=10$ GeV/$c$, and 4% at $p_{\mathrm{T}}=50$ GeV/$c$.
The spatial resolution of the track impact parameter with respect to the
primary vertex is better than $75\,\upmu$m for charged-particle tracks with
transverse momentum $p_{\mathrm{T}}>1$ GeV/$c$ and better than $20\,\upmu$m
for tracks with $p_{\mathrm{T}}>20$ GeV/$c$ [35, 38]. More information about
the track selection can be found in Ref. [35].

Jets were reconstructed using the infrared and collinear safe anti-$k_{\rm T}$
algorithm [42] from the FastJet package [43]. The resolution parameter was set
to $R=0.4$, which ensures that most of the momentum of the initial parton
(approximately 70% to 90% in the range of the current measurement) falls
within the jet cone [44]. The jets were constructed from charged particles
having $p_{\rm T,track}>0.15$ GeV/$c$ and pseudorapidity $|\eta_{\rm
track}|<0.9$. Their four-momenta were combined using the $p_{\mathrm{T}}$
recombination scheme, which considers all particles to be massless [43]. The
pseudorapidity coverage of the reconstructed jets was constrained to
$|\eta_{\rm jet}|<0.9-R=0.5$ to select only jets that are fully contained
within the TPC acceptance.

The reconstructed transverse momentum for jets $p_{\mathrm{T,ch\,jet}}^{\rm
reco}$ is obtained using the measured transverse momentum of charged-particle
jets $p_{\mathrm{T,ch\,jet}}^{\rm raw}$, corrected for the mean contribution
of the underlying event using the formula $p_{\mathrm{T,ch\,jet}}^{\rm
reco}=p_{\mathrm{T,ch\,jet}}^{\rm raw}\,-\,\rho\times A_{\rm jet}$ [45]. Here
$A_{\rm jet}$ denotes the area of the jet and $\rho$ is the mean underlying
event $p_{\mathrm{T}}$ density. The mean underlying event $p_{\mathrm{T}}$
density was calculated on an event-by-event basis using the estimator
introduced by CMS [46].

Identification of b jets is based on kinematic variables related to the
lifetime of b-hadrons ($c\tau\approx 500$ $\upmu$m), and the large impact
parameter of beauty-hadron-decay daughters. Several discriminator variables
were defined and applied in two distinct b-jet tagging methods that are
presented in this paper, the impact parameter (IP) method (based on the
distance of closest approach, DCA, of the individual jet tracks to the primary
vertex), and the displaced secondary vertex (SV) method (based on the topology
of a reconstructed secondary vertex using a subset of the jet tracks). The
tagging of the b-jet candidates utilizes global tracks only and exploits the
high spatial resolution of the SPD. The b-jet $p_{\mathrm{T}}$-differential
spectra were separately obtained with the two tagging algorithms and
eventually combined to improve the accuracy of the measurement. While the IP
method generally provides better b-jet tagging efficiency, the SV method has
been proven to be more stable at low $p_{\mathrm{T}}$. Both methods are
discussed in detail below. For more information on b-jet tagging algorithms,
the reader may refer to Refs. [47, 48, 49].

###  3.1 b-jet tagging based on impact parameter

The impact parameter of a track can be measured either in three dimensions or
in the projection on the plane perpendicular to the beam axis. This analysis
used the latter definition (denoted $d_{xy}$) to exploit the better resolution
of the ITS in this plane.

The sign of the impact parameter is determined as the sign of the scalar
product of the jet axis and the impact parameter vector pointing from the
primary vertex to the point of closest approach. Tracks originating from a
secondary vertex tend to have positive impact parameter values because of the
mother particle decay length. On the other hand, the tracks originating from
the primary vertex can have both positive and negative impact parameter values
due to finite resolution which smears the impact parameter of primary tracks
symmetrically around the primary vertex. Discrimination among different jet
flavors was based on the impact parameter significance ($Sd_{xy}$), defined as
the ratio of the impact parameter to its estimated resolution. The impact
parameter resolution largely depends on the $\eta$ and $p_{\rm T}$ of the
tracks. Figure 1 (top left) shows the probability distribution of the impact
parameter significance for tracks belonging to different jet flavors, as
determined from a detector-level simulation, where PYTHIA 8 Monash 2013 [50]
events were processed with an ALICE GEANT 3-based particle transport model
[51].

On average, tracks associated to b jets have larger $Sd_{xy}$ values when
compared to c jets and light-flavor jets. This means that the impact parameter
has a strong discriminating power in distinguishing between the different jet
flavors.

Figure 1: Top left: The normalized impact parameter significance ($Sd_{xy}$)
distribution for all tracks inside light-flavor, charm, and beauty jets as
determined from PYTHIA 8 (Monash 2013 tune [52]) detector-level simulations.
Top right: The distribution of the second largest impact parameter
significance in the jet. Bottom left: The distribution of the third largest
$Sd_{xy}$ in the jet. Bottom right: Distribution of $Sd_{xy}$ for data in pp
and p–Pb collisions.

This analysis uses the track counting algorithm [47], which arranges the
$Sd_{xy}$ values of tracks in a jet in descending order. A jet was tagged as a
b jet if the second largest impact parameter significance value (see Fig. 1
top right) was greater than a certain threshold parameter $Sd_{xy}^{\rm min}$.
The default threshold parameter that was chosen in this analysis is
$Sd_{xy}^{\rm min}=2.5$, which gives an average tagging efficiency of $55\%$
with average purity of $42\%$ for b jets with $20<p_{\mathrm{T,ch\,jet}}^{\rm
reco}<40$ GeV/$c$. This choice provided an optimum balance between good
efficiency and good background rejection. Discrimination based on the tracks
with the first largest as well as the third largest impact parameter
significance value (see Fig. 1 bottom left) were used for consistency checks.

The purity and b-jet tagging efficiency of the selected b-jet sample presented
in Sec. 4.1.1 were determined using the jet probability algorithm [49, 53,
47]. This algorithm evaluates a combined impact parameter significance of
tracks inside the jet and estimates a likelihood that all tracks associated
with the jet originated from the primary vertex.

Figure 2: The logarithmic jet probability $-\ln(JP)$ distribution for light-
flavor, charm, and beauty jets in pp collisions at $\sqrt{s}~{}=~{}5.02$ TeV.

Reconstructed tracks were classified based on different geometric and tracking
features. The algorithm defines a resolution function $R_{\rm IP}$ for each
category, by fitting the negative side of the signed $Sd_{xy}$ distribution
(bottom right panel of Fig. 1). This fit is carried out on the negative part
of the distribution because in this range it is predominantly populated by
primary tracks originating from the primary vertex.

The resolution functions corresponding to the different track categories were
used to calculate the track probability $P_{\rm tr}$. This $P_{\rm tr}$
corresponds to the probability that a high-quality jet constituent track with
an impact parameter significance $Sd_{xy}$ is coming from the primary vertex:

$P_{\rm tr}(Sd_{xy})=\frac{\int_{-\infty}^{-|Sd_{xy}|}R_{\rm IP}(S){\rm
d}S}{\int_{-\infty}^{0}R_{\rm IP}(S){\rm d}S}\,,$ (1)

where the integration is done over the negative side of the impact parameter
significance distribution. A large impact parameter value results in a small
$P_{\rm tr}$.

The jet probability ($JP$) is then calculated by combining the $P_{\rm tr}$
values of tracks within a given jet according to the equation [49, 53, 47]:

$JP=\prod\times\sum_{k=0}^{N_{\rm
track}-1}\frac{(-\log\prod)^{k}}{k!}\;\;\;\mbox{,\,where}\;\;\;\prod=\prod_{i=1}^{N_{\rm
track}}P_{{\rm tr},i}\,.$ (2)

Only tracks with positive $Sd_{xy}$ are selected to calculate the jet
probability. The $JP$ discriminates over different jet flavors only in a very
narrow interval ($0<JP<0.2$). This distribution is therefore not convenient
for discrimination. For this reason, the $-\ln(JP)$ quantity was used as a
discriminator in our analysis to determine the b-jet tagging efficiency using
a data-driven method. As shown in Fig. 2, the $-\ln(JP)$ decreases much faster
for light-flavor jets and c jets when compared to b jets, allowing for an
effective statistical discrimination of b jets.

###  3.2 b-jet tagging based on secondary vertex reconstruction

Secondary vertices (SV), where the weak decay of the beauty hadrons take
place, are in most cases well displaced from the primary vertex of the
collision due to the lifetime of beauty hadrons. Beauty hadrons primarily
decay to non-prompt charm particles which typically have similarly long
lifetime. The SV algorithm reconstructs the secondary vertices inside the jets
from triplets of jet-constituent tracks. This choice was motivated by the
typical decay patterns of beauty hadrons. From all of these reconstructed
secondary vertices, this algorithm selects for the b-jet tagging the vertex
that is most displaced. The vertex reconstruction quality is described by the
dispersion of the reconstructed secondary vertex, $\sigma_{\rm
SV}=\sqrt{d_{1}^{2}+d_{2}^{2}+d_{3}^{2}}$, where $d_{1,2,3}$ are the distances
of closest approach of the three tracks to the secondary vertex.

This algorithm uses the decay length $L_{xy}$ as a discriminator. The decay
length is the distance between the primary vertex and the secondary vertex
measured in the plane transverse to the beam axis. The significance is then
defined by dividing $L_{xy}$ by its uncertainty,
$SL_{xy}=L_{xy}/\sigma_{L_{xy}}$. The b-tagging is then performed by
considering both the SV dispersion $\sigma_{\rm SV}$ and the decay length
significance $SL_{xy}$.

The default operating point of the tagging in the analysis is $SL_{xy}>7$ and
$\sigma_{\rm SV}<0.03$ cm. These selection values were determined by
optimizing for high b-tagging efficiency and low c-quark and light-flavor
mistagging rates based on simulations. Figure 3 shows examples of the
$SL_{xy}$ and $\sigma_{\rm SV}$ distributions for jets having different
flavors as obtained from PYTHIA 8 simulations using the Monash tune [52]
followed by an ALICE detector level MC simulation and reconstruction. Figure 4
shows examples of the $SL_{xy}$ and $\sigma_{\rm SV}$ probability
distributions in pp and p–Pb collision data.

Figure 3: Distributions of the tagging discriminators used in the SV method,
$SL_{xy}$ (left) and $\sigma_{\rm SV}$ (right), for b jets, c jets, and light-
flavor jets as obtained from a MC simulation of the ALICE apparatus, using
PYTHIA as an event generator.

Figure 4: Distributions of the tagging discriminators used in the SV method,
$SL_{xy}$ (left) and $\sigma_{\rm SV}$ (right), for pp and p–Pb collisions.

##  4 Corrections to the b-tagged jet spectrum

The raw $p_{\mathrm{T}}$ spectrum of b-jet candidates ($\mathrm{d}N^{\rm
tagged}/\mathrm{d}p_{\mathrm{T,ch\,jet}}^{\rm reco}$) that was obtained after
applying the tagging algorithms was corrected for the b-jet tagging
efficiency, $\epsilon_{\rm b}$, and the purity of the selected b-jet sample,
$P_{\rm b}$,

$\frac{{\rm d}N^{\rm b\,\,jet}_{\rm det.\,level}}{{\rm
d}p_{\mathrm{T,ch\,jet}}^{\rm reco}}=\frac{\mathrm{d}N^{\rm
tagged}}{\mathrm{d}p_{\mathrm{T,ch\,jet}}^{\rm reco}}\times\frac{P_{\rm
b}}{\epsilon_{\rm b}}\,.$ (3)

The resulting spectrum is then corrected for jet reconstruction efficiency and
momentum smearing due to detector effects and background fluctuations by means
of unfolding. All corrections are discussed below in detail.

###  4.1 Tagging efficiency

The b-jet tagging algorithms discussed in Section 3 do not identify all
produced b jets. The probability that a given tagging algorithm correctly
identifies a jet originating from a b quark as a b jet is called the tagging
efficiency. Similarly, one can also define the mistagging efficiency as the
probability that a jet originating from a charm quark or a light-flavor parton
is falsely tagged as a b jet. The efficiency of a given algorithm for tagging
or mistagging is defined as

$\epsilon_{i}(p_{\mathrm{T,ch\,jet}}^{\rm reco})=\frac{N_{i}^{\rm
tagged}(p_{\mathrm{T,ch\,jet}}^{\rm reco})}{N_{i}^{\rm
total}(p_{\mathrm{T,ch\,jet}}^{\rm reco})}\ ,$ (4)

where $i$ is the jet flavor (b, c or light-flavor), $N_{i}^{\rm tagged}$ is
the number of tagged $i$ jets, and $N_{i}^{\rm total}$ is the total number of
$i$ jets.

####  4.1.1 Tagging efficiency of the IP algorithm

The tagging efficiency of the IP algorithm was estimated based on the semi-
data-driven method outlined in Refs. [36, 47], where the $-\ln(JP)$
distributions are fitted with a set of detector-level MC templates, which
describe the shape of the jet probability distributions corresponding to b
jets, c jets, and light-flavor jets. The templates for p–Pb were obtained from
a MC simulation based on the EPOS event generator [54] with embedded PYTHIA 6
events, where particles are propagated through a model of the ALICE detector
using GEANT 3 [51]. The simulated events were then reconstructed as events in
data. The templates for pp collisions were obtained similarly, using the
PYTHIA 8 MC event generator.

Two jet samples were created: a sample that contains the jets satisfying the
tagging requirement (tagged sample), and another sample that contains the
inclusive jets before applying the tagging algorithm (untagged sample). The
associated $-\ln(JP)$ distributions from data were fitted with the
corresponding b, c, and light-flavor jet templates using a binned maximum
likelihood fit. The fitting procedure was done separately for the tagged jet
(with $Sd_{xy}^{\rm min}=2.5$) and the inclusive (untagged) jet samples, see
Fig. 5.

(a) Untagged jets (b) Tagged jets

Figure 5: Fit of the measured $-\ln(JP)$ discriminator distribution with a
linear combination of b, c, and light-flavor jet templates for the untagged
sample (left) and for the tagged sample (right).

The b-jet tagging efficiency is then obtained as the ratio of the number of
identified b jets to the number of b jets before identification:

$\epsilon_{\rm b}=\frac{C_{\rm b}\times f_{\rm b}^{\rm tag}\times N_{\rm
data}^{\rm tag}}{f_{\rm b}^{\rm untag}\times N_{\rm data}^{\rm untag}}\,.$ (5)

Here $f_{\rm b}^{\rm untag}$ and $f_{\rm b}^{\rm tag}$ denote the b-jet
fractions before and after tagging, respectively, which are extracted from the
fits; $N_{\rm data}^{\rm untag}$ and $N_{\rm data}^{\rm tag}$ give the numbers
of jets before and after tagging, which were extracted from data; finally,
$C_{\rm b}$ is the fraction of b jets for which the jet probability can be
defined, i.e. b jets having at least two constituent tracks with positive
$Sd_{xy}$. This factor was estimated from MC. The $C_{\rm b}$ is $\approx$80%
at 10 GeV/$c$ and increases to 98% at 40 GeV/$c$ and remains at that value for
$p_{\mathrm{T}}>40$ GeV/$c$.

Figure 6: The b-jet tagging efficiency extracted from the data-driven method
using the IP algorithm in pp and p–Pb collisions.

Figure 6 shows the b-jet tagging efficiency of the IP method in pp and p–Pb
collisions. As an alternative for $-\ln(JP)$ in the template fitting, other
discriminators were also used to check consistency and estimate systematic
uncertainties. The alternative discriminators were the jet mass distribution
[55] and the distribution of energy fraction $f_{E}$ carried by the secondary
vertex in the jet. Both of them provide results that are consistent with the
standard analysis within one standard deviation. The systematic uncertainty on
the tagging efficiency is estimated by fitting the $f_{E}$ distribution
instead of $-\ln(JP)$. While $JP$ may be correlated with the IP, there is no
such correlation in the case of $f_{E}$. The good match between efficiencies
and purities obtained with the different methods excludes the possibility that
any such correlation affects the results. Finally, it is worth noting that the
template fit procedure yields results with large systematic uncertainties for
$p_{\mathrm{T,ch\,jet}}<20$ GeV/$c$, so the interval between
$10<p_{\mathrm{T,ch\,jet}}<20$ GeV/$c$ was omitted in the IP analysis. The
reason for these uncertainties is that the individual templates have rather
similar shapes, causing instabilities in the fitting algorithm and thus
reducing the discrimination power of the fit.

####  4.1.2 Tagging efficiency of the SV algorithm

For the SV method, tagging and mistagging efficiencies of beauty, charm, and
light-flavor jets were estimated based on the same detector-level MC
simulation data sets that were used in the IP method. While the IP algorithm
used the MC simulation to get templates and assesses the reconstruction
efficiency with a data-driven method, the SV algorithm obtained the efficiency
directly from the MC simulation via Eq. (4). In particle-level simulations, a
jet was counted as a b jet if there was a beauty hadron present with a three-
momentum vector contained within the jet cone. An analogous definition was
also used for c jets and the remaining jets were considered to be light-flavor
jets. Figure 7 presents the efficiencies as a function of jet momentum in pp
and p–Pb collisions. The figure shows that tagging with the default selection
criteria yields similar performance in both systems, ensuring suppression of
light-flavor jets by two orders of magnitude. Comparing the tagging
efficiencies of the IP and SV methods, it can be seen that the efficiency of
the IP method tagging is about a factor two higher because of the less
stringent selections that are applied.

Figure 7: Beauty-jet tagging efficiencies, as well as charm-jet, and light-
flavor jet mistagging efficiencies for the SV method in pp (solid markers) and
p–Pb (open markers) collisions at $\sqrt{s_{\mathrm{NN}}}=5.02$ TeV, shown as
a function of jet transverse momentum.

###  4.2 Purity of the b-jet sample

The b-jet tagging algorithms introduced in Sec. 3 select not only b jets but
also a certain fraction of charm and light-flavor jets, cf. Sec. 4.1. Given
the higher production cross section of light-flavor and charmed jets, this
leads to a significant sample contamination that needs to be corrected for.
The purity of the tagged sample of b-jet candidates, $P_{\rm b}$, is defined
as the fraction of true b jets over the total number of tagged jets,

$P_{\rm b}(p_{\mathrm{T,ch\,jet}}^{\rm reco})=\frac{N_{\rm b\,jet}^{\rm
tagged}(p_{\mathrm{T,ch\,jet}}^{\rm reco})}{N^{\rm
tagged}(p_{\mathrm{T,ch\,jet}}^{\rm reco})}\ .$ (6)

Here $N_{\rm b\,jet}^{\rm tagged}$ is the number of tagged true b jets and
$N^{\rm tagged}$ is the number of all tagged jets. One of the biggest
challenges in the b-jet analysis is to obtain an accurate purity estimate.

####  4.2.1 b-jet purity from the IP tagging

In the IP method analysis, b-jet purity is estimated using a data-driven
method based on the jet probability discriminator. A linear combination of
detector-level MC templates corresponding to pure beauty, charm, and light-
flavor jets were fitted to the $-\ln(JP)$ distribution measured in data in a
similar way as discussed in Sec 4.1.1. Figure 8 shows the resulting b-jet
purity for the IP method with $Sd_{xy}^{\rm min}=2.5$ in pp and p–Pb
collisions. The template fitting procedure was repeated with other
discriminators to assess the corresponding systematic uncertainty, as detailed
in Sec 4.1.1.

Figure 8: The b-jet purity as obtained from the IP method in pp and p–Pb
collisions.

On figure 8, one can see that the purity in p–Pb collisions is slightly higher
than that in pp collisions. This effect arises from small differences between
the two systems. Let us note that this difference is much smaller than the
systematic uncertainties corresponding to the purity calculation.

####  4.2.2 b-jet purity from the SV tagging

The purity of the b-jet candidate sample tagged with the SV method was
estimated based on a hybrid method that utilizes both data-driven template
fitting and simulations. In p–Pb data, the purities were primarily determined
by fitting the invariant mass distribution of the most displaced secondary
vertex with beauty, charm, and light-flavor templates. The invariant mass was
calculated from the three prongs that were used to reconstruct the secondary
vertex, assuming that all tracks have the mass of a charged pion. These
templates were obtained from the detector-level EPOS simulation with embedded
PYTHIA 6 events. Analogous fits were done also for the pp data using detector-
level PYTHIA templates. The fits were done in several $p_{\mathrm{T}}$
intervals. Figure 9 shows a typical example of template fit in pp and p–Pb
collisions. The small statistical samples, however, prevented the use of the
template fitting method for jets with momenta larger than 30–40 GeV/$c$.
Therefore, the purity was also estimated based on POWHEG HVQ simulations [56]
with the CTEQ6M parton distribution function (PDF) set [57]. In the case of
the p–Pb system, the EPS09 nPDF set was applied in addition [58], and the
rapidity shift was taken into account. Simulated particle-level charm and
beauty jet $p_{\mathrm{T}}$ spectra were subjected to instrumental (efficiency
and detector effects) and background fluctuation effects to estimate the c-
and b-jet contributions in the inclusive raw jet spectrum before tagging. The
purity was then estimated in each $p_{\mathrm{T,ch\,jet}}^{\rm reco}$ bin as

$P_{\rm b}=\frac{\epsilon_{\rm b}N_{\rm b}}{\epsilon_{\rm b}N_{\rm
b}+\epsilon_{\rm c}N_{\rm c}+\epsilon_{\rm lf}\left(N_{\rm incl}-N_{\rm
b}-N_{\rm c}\right)}\,,$ (7)

where $\epsilon_{\rm b}$, $\epsilon_{\rm c}$, and $\epsilon_{\rm lf}$ are
tagging and mistagging efficiencies for beauty, charm, and light-flavor jets,
respectively; and $N_{\rm b}$ ($N_{\rm c}$) is the estimated contribution of
beauty (charm) jets in the raw inclusive untagged jets $N_{\rm incl}$.
Nevertheless, this purity estimate relies on model parameters that cannot be
directly validated, i.e, quark masses as well as renormalization and
factorization scales used in the computation of the beauty and the charm
production cross section. Hence, a statistical analysis was carried out
comparing simulated purities with purities obtained by the data-driven
invariant mass template fit method simultaneously in a broad range of tagging
selection criteria. This was done in order to determine the simulation
configurations that are consistent with the results of the data-driven method.
Consistency was defined with a $\chi^{2}/{\rm NDF}<10$ goodness-of-description
test taking into account the total number-of-degrees-of-freedom (NDF) in the
simultaneous comparison. The configuration space covered variations of the QCD
renormalization and factorization scales by factors 0.5–2 with respect to the
default values, and variations of the quark masses in the range 4.5–5
GeV/$c^{2}$ for b-quarks and 1.3–1.7 GeV/$c^{2}$ for c-quarks. The variation
of the heavy quark masses only has a small effect on the observed b-jet sample
purity, below 2% for the b-quark and negligible for the c-quark. Changing the
factorization (renormalization) scales in the simulation of the b-quark
spectrum by a factor of 2 affects the purity in the same (opposite) direction
by 4 to 8%, while a factor of 2 change in the renormalization or factorization
scales in the simulation of the c-quark spectrum causes a 2 to 6% effect on
the resulting purity in either direction.

Simulations with accepted configurations were then used to determine the
purities in the p–Pb as well as the pp data. Figure 10 shows a comparison of
the b-jet sample purity obtained for the default tagging with the template fit
method and the POWHEG-simulation-based approach. All accepted configurations
were used to assess the systematic uncertainty related to the purity of the
tagged b-jet candidate sample.

Figure 9: Invariant mass distribution of the combination of three prongs,
forming the most displaced secondary vertex in jets with
$20<p_{\mathrm{T,ch\,jet}}^{\rm reco}<30$ GeV/$c$, tagged with the default
selection $SL_{xy}>7$ and $\sigma_{\rm SV}<0.03$ cm for pp (left) and p–Pb
(right) collisions. The data (black points) are fitted with detector-level MC
templates corresponding to beauty, charm, and light-flavor jets to assess the
purity of the b-jet candidate sample. See text for further information on MC.

Figure 10: Purity of the b-jet candidates selected with the SV method when
using the default tagging selection criteria. The purity was estimated with
the data-driven template fit method (red points) and with the POWHEG-
simulation based approach. The POWHEG scale variations accepted by the
statistical analysis are colored green, the rejected ones are gray. Results
for pp and p–Pb are shown in the left and right panel, respectively.

###  4.3 Detector effects and unfolding

The measured jet spectra were affected by distortions stemming from two main
sources: instrumental effects and local background fluctuations with respect
to the mean underlying event density. These two effects smeared the true jet
spectrum and can be corrected for via an unfolding procedure. The corrections
were assumed to factorize; thus, were handled with a product of two matrices
that were determined separately [59]. The instrumental effects were accounted
for by constructing a response matrix that is based on a b-jet sample
generated with PYTHIA 8 [50], and subsequently processed with an ALICE GEANT
3-based particle transport model [51]. The detector-level jets were matched to
the particle-level jets based on geometry. This was done by minimizing their
angular distance $\Delta R=\sqrt{\Delta\varphi^{2}+\Delta\eta^{2}}$, where
$\Delta\varphi$ and $\Delta\eta$ are, respectively, the differences in
azimuthal angle and pseudorapidity between given particle-level and detector-
level jets. One-to-one correspondence between particle-level and detector-
level jets was required, and $\Delta R$ was constrained to be less than 0.25
[60]. The instrumental effects cause a similar shift in the jet energy scale
of reconstructed charged-particle b jets and inclusive untagged jets; untagged
jets being shifted by about $\approx 1$% more. The shift is
$p_{\mathrm{T}}$-dependent and for b jets with 10 GeV/$c$ reaches about 2% and
increases to about 18% for 100 GeV/$c$ b jets. The jet energy scale resolution
of b jets and inclusive untagged jets is likewise similar; b jets having by
about 1% smaller resolution than the untagged jets. The resolution for 10
GeV/$c$ b jets is about 17% and increases to approximately 22% for 100 GeV/$c$
b jets.

The matrix that describes momentum smearing due to background fluctuations was
obtained with two methods based on track embedding and the random cone
technique (RC) [61]. In the track-embedding approach, a track was embedded
perpendicular in azimuth to the axis of the tagged b-jet candidate. This
region is expected to be dominated by the underlying event. The resulting
momentum smearing is

$\delta p_{\mathrm{T}}^{\rm emb}=p_{\mathrm{T,ch\,jet}}^{\rm raw,\,emb}-\rho
A_{\rm jet}-p_{\mathrm{T}}^{\rm emb}\,,$ (8)

where $p_{\mathrm{T,ch\,jet}}^{\rm raw,\,emb}$ is the reconstructed momentum
of the jet with the embedded track, $A_{\rm jet}$ is its area, $\rho$ is the
estimated underlying event $p_{\mathrm{T}}$ density, and $p_{\mathrm{T}}^{\rm
emb}$ is the transverse momentum of the embedded track.

In the RC approach, momentum smearing was calculated using a cone with radius
$R_{\rm cone}=0.4$ placed in a random position in the $\eta-\varphi$ plane in
an event. This cone must not overlap with the leading and the sub-leading jets
in the event and must be fully inside the acceptance of the central barrel.

The momentum smearing is calculated from tracks which are inside the cone as:

$\delta p_{\rm T}^{\rm RC}=p_{\rm T}^{\rm RC}\,-\,\rho\pi R^{2}_{\rm cone}\,,$
(9)

where $p_{\rm T}^{\rm RC}$ denotes the sum of the $p_{\mathrm{T}}$ of the
tracks inside the cone. Only events which contained a tagged b-jet candidate
were selected for the calculation of $\delta p_{\mathrm{T}}$.

The $\delta p_{\rm T}$ matrices obtained with the track embedding and RC
techniques provided consistent unfolded b-jet spectra and the difference is
accounted for as a systematic uncertainty. In this analysis, the track
embedding technique was used in the standard analysis, and the RC method as a
systematic variation.

By default, unfolding of the raw b-jet spectrum defined in Eq. (3) was
performed using the singular value decomposition (SVD) method [62] implemented
in the RooUnfold package [63]. The optimal regularization parameter value was
found to be four for the SV analysis and eight for the IP analysis. Stability
of the unfolded solutions was tested also with the Bayesian unfolding [64] and
the $\chi^{2}$ unfolding. These algorithms provided consistent results with
the SVD and the differences were taken into account in the systematic
uncertainties.

###  4.4 b-jet cross section and nuclear modification factor

The $p_{\rm T}$-differential b-jet production cross section was calculated as

$\frac{\mathrm{d}^{2}\sigma^{\rm
b\,\,jet}}{\mathrm{d}p_{\mathrm{T,ch\,jet}}\mathrm{d}\eta_{\rm
jet}}=\frac{1}{\mathcal{L}}\times\frac{{\rm d}^{2}N^{\rm b\,\,jet}_{\rm
unfolded}}{{\rm d}p_{\mathrm{T,ch\,jet}}{\rm d}\eta_{\rm jet}}\,,$ (10)

where ${\rm d}^{2}N^{\rm b\,\,jet}_{\rm unfolded}/{\rm
d}p_{\mathrm{T,ch\,jet}}{\rm d}\eta_{\rm jet}$ is the unfolded $p_{\rm T}$
differential yield of b jets and $\mathcal{L}$ is the integrated luminosity
corresponding to minimum bias events, which was quoted for the pp and p–Pb
data samples in Sec. 2.

Modification of the b-jet spectrum in p–Pb collisions due to nuclear matter
effects was then quantified with the nuclear modification factor [65], which
compares the $p_{\mathrm{T}}$-dependent production rates in p–Pb to the rates
expected from the independent superposition of pp collisions.

$R_{\rm pPb}^{\text{b-jet}}=\frac{1}{A}\frac{\mathrm{d}^{2}\sigma_{\rm
pPb}^{\rm b\,\,jet}/\mathrm{d}p_{\mathrm{T,ch\,jet}}\mathrm{d}\eta_{\rm
jet}}{\mathrm{d}^{2}\sigma_{\rm pp}^{\rm
b\,\,jet}/\mathrm{d}p_{\mathrm{T,ch\,jet}}\mathrm{d}\eta_{\rm jet}}\ ,$ (11)

where $A=208$ is the number of nucleons in the Pb nucleus.

###  4.5 Combining the results of the IP and SV methods

The $p_{\rm T}$-differential b-jet production cross sections obtained from the
IP and SV methods were combined using the Best Linear Unbiased Estimator
(BLUE) method [66, 67]. The BLUE method is used to combine different
measurements of the same physical quantity, where the uncertainties of the
individual measurements are correlated between the measurements to some
extent. Besides the b-jet cross section in pp and p–Pb collisions, the BLUE
method was also used to obtain the combined nuclear modification factor
$R_{\rm pPb}^{\text{b-jet}}$ given that correlated systematic uncertainties
cancel to a different degree in the individual ratios for the IP and SV
analyses.

The combined results were obtained under the following considerations. The
systematic uncertainties from tagging, and purity extraction were assumed to
be uncorrelated between the two methods. The contributions to the systematic
uncertainty from the tracking efficiency and $p_{\mathrm{T}}$ resolution, as
well as from the contamination by secondary tracks, were treated as fully
correlated. Since the same data set was used in the two methods, the
statistical uncertainty is partially correlated. The correlation coefficient
$\rho_{\rm stat}$ was estimated as

$\rho_{\rm stat}=\frac{{\rm Cov}({\rm IP},{\rm SV})}{\sigma_{\rm
IP}\sigma_{\rm SV}}\;\;\;\mbox{with}\;\;\;{\rm Cov}({\rm IP},{\rm
SV})=\frac{\sigma_{\rm IP}^{2}\sigma_{\rm SV}^{2}}{\sigma_{{\rm IP}\cap{\rm
SV}}^{2}}\,,$ (12)

where $\sigma_{\rm IP}$ ($\sigma_{\rm SV}$) is the statistical uncertainty
corresponding to the jet sample from the IP (SV) method, and $\sigma_{{\rm
IP}\cap{\rm SV}}$ is the statistical uncertainty corresponding to the sample
selected by both the IP and the SV methods. The correlation coefficients for
statistical uncertainty are $\rho_{\rm stat}=0.35$ for pp collisions and
$\rho_{\rm stat}=0.27$ for p–Pb collisions. For the background fluctuations
and unfolding uncertainties, which were partially correlated between both
methods, an arbitrarily chosen correlation coefficient value of 0.5 was used,
with values 0 and 1 used as consistency checks. Correlation coefficients
between other parameters were varied similarly and the resulting systematic
uncertainty from these choices was found to be negligible.

##  5 Sources of systematic uncertainties

Systematic uncertainties of the $p_{\rm T}$-differential b-jet cross section
and $R_{\rm pPb}^{\text{b-jet}}$ were assessed by varying the selection and
correction procedures. Table 1 lists the possible sources of systematic
uncertainties, and the adopted variations, with respect to the standard
selection procedures and methods used to obtain the central values of the
results. These variations are discussed in more detail below. Table 2 provides
a summary of all uncertainties, reported separately for the IP and SV
analyses, as well as for the combined results obtained with the BLUE method.
The two analyses were developed largely independently from each other. In the
IP analysis, all uncertainties were considered as symmetrical, while in the SV
analysis, most of the uncertainties were considered as asymmetrical.
Systematic uncertainties due to the tracking efficiency and $p_{\mathrm{T}}$
resolution, tagging, contamination by secondary tracks, and background
fluctuations were treated as correlated between the pp and p–Pb systems.
Hence, these were partially propagated into $R_{\rm pPb}^{\text{b-jet}}$,
taking the correlation into account. All the other uncertainties were
considered uncorrelated and were fully propagated. The different types of
correlated systematic uncertainties on the $R_{\rm pPb}^{\text{b-jet}}$ were
determined by simultaneously varying the pp and the p–Pb results to make sure
that the correlations cancel out. Since the combination with the BLUE method
requires symmetric uncertainties, two SV spectra were made, one with the lower
and one with the upper uncertainties. These spectra were combined with the IP
spectrum separately, and a conservative choice was made by taking the maximum
of the lower and upper boundaries point-by-point in the combined result. The
individual uncertainty sources are discussed in detail in the following
paragraphs.

Table 1: Summary of sources of systematic uncertainty and adopted variations to estimate their effects in the cases of the SV and the IP methods. Source | Standard analysis | Variations
---|---|---
Common | Tracking efficiency | default reconstruction | 4% of tracks removed
| Track $p_{\mathrm{T}}$ resolution | default reconstruction |  $p_{\mathrm{T}}$-smeared reconstruction
| Secondary track contamination | default MC correction | data-driven estimate
| Underlying event fluctuations | embedding | random cone
IP method……….. | Unfolding | Method | SVD | Bayesian–$\chi^{2}$
| Regularization | 8 | 7–9
| Matrix | full | truncated at 5 GeV/$c$
| Binning | default | limits shifted by 2 GeV/$c$
| Prior function | POWHEG b-jet spectrum | measured–unfolded $\chi^{2}$ spectra
| Tagging efficiency/purity | $Sd_{xy}$ | 2.5 | 1–4
| Fit distribution | $-\ln(JP)$ | $f_{E}$
SV method…………. | Tagging efficiency | $SL_{xy}$ | 7 | 6–8
| $\sigma_{\rm SV}$ | 0.03 cm | 0.02–0.05 cm
| Purity | POWHEG b and c spectra | “hybrid” scale variations
| Unfolding | Method | SVD | Bayesian
| Regularization | 4 | 3–5
| Matrix | full | truncated at 5 GeV/$c$
| Binning | default | limits shifted by 1 GeV/$c$
| Prior function | POWHEG b-jet spectrum | all scale variations
Table 2: Statistical and systematic uncertainties, in percent, corresponding to three representative $p_{\mathrm{T,ch\,jet}}$ ranges for the pp and p–Pb cross sections, as well as for the $R_{\rm pPb}^{\text{b-jet}}$. Uncertainties of the IP and SV methods are quoted separately. Wherever applicable, the table also reports the resulting combined uncertainties. Both the upper and lower values are listed for the asymmetric SV systematic uncertainties. An additional uncertainty from the normalization by the integrated luminosity [41, 40] is quoted in the last row.  $p_{\mathrm{T,ch\,jet}}$ interval |  10–20 GeV/$c$ |  40–50 GeV/$c$ |  80–100 GeV/$c$
---|---|---|---
analysis | SV | IP | SV | comb. | IP | SV | comb.
Statistical uncertainty | pp | 1.9 | 6.3 | 5.2 | 6.3 | 23.5 | 18.4 | 22.9
p–Pb | 1.9 | 3.7 | 3.6 | 3.8 | 9.0 | 12.9 | 9.3
$R_{\rm pPb}^{\text{b-jet}}$ | 2.6 | 8.9 | 6.4 | 5.8 | 31.2 | 22.5 | 20.6
Tracking efficiency | pp | 7.9 | 11.1 | 8.4 | 11.1 | 16.2 | 9.5 | 16.0
p–Pb | 6.7 | 12.1 | 9.2 | 12.2 | 14.2 | 8.6 | 14.5
$R_{\rm pPb}^{\text{b-jet}}$ | 1.4 | 0.9 | 1.0 | 1.0 | 1.9 | 1.3 | 1.6
Tracking resolution | pp | $+1.2/-1.2$ | 1.4 | $+3.9/-3.9$ | 1.4 | 3.1 | $+6.0/-6.0$ | 3.2
p–Pb | $+3.3/-3.3$ | 1.6 | $+4.5/-4.5$ | 1.5 | 2.1 | $+5.3/-5.3$ | 1.9
$R_{\rm pPb}^{\text{b-jet}}$ | $+2.1/-2.2$ | 0.2 | $+0.6/-0.6$ | 0.5 | 1.0 | $+0.8/-0.7$ | 0.9
Secondary vertex contamination | pp | $+1.6/-0.0$ | 2.3 | $+2.4/-0.0$ | 2.6 | 4.0 | $+2.9/-0.0$ | 7.2
p–Pb | $+4.1/-0.0$ | 6.1 | $+5.4/-0.0$ | 1.5 | 7.6 | $+7.8/-0.0$ | 2.2
$R_{\rm pPb}^{\text{b-jet}}$ | $+0.0/-2.5$ | 3.6 | $+0.0/-3.0$ | 1.0 | 3.3 | $+0.0/-5.1$ | 4.2
Background fluctuation | pp | $+0.0/-5.4$ | 6.9 | $+0.0/-10.3$ | 6.8 | 3.2 | $+3.2/-0.0$ | 3.1
p–Pb | $+0.0/-3.1$ | 2.7 | $+0.0/-5.4$ | 2.8 | 1.7 | $+3.1/-0.0$ | 1.8
$R_{\rm pPb}^{\text{b-jet}}$ | $+2.6/-0.0$ | 4.2 | $+6.7/-0.0$ | 5.5 | 1.4 | $+1.4/-0.0$ | 0.7
b-jet tagging | pp | $+0.9/-2.8$ | 0.2 | $+3.4/-6.5$ | 0.2 | 3.4 | $+6.8/-13.4$ | 3.3
p–Pb | $+3.4/-1.6$ | 0.4 | $+4.6/-8.6$ | 0.5 | 0.8 | $+6.0/-15.3$ | 1.2
$R_{\rm pPb}^{\text{b-jet}}$ | $+2.5/-2.2$ | 0.4 | $+5.0/-5.3$ | 3.6 | 3.5 | $+10.7/-13.8$ | 7.2
Purity | pp | $+13.0/-21.8$ | 15.3 | $+16.4/-16.8$ | 12.0 | 15.3 | $+21.8/-17.3$ | 11.7
p–Pb | $+13.1/-21.0$ | 8.9 | $+11.9/-16.3$ | 9.4 | 8.9 | $+21.1/-15.7$ | 9.5
$R_{\rm pPb}^{\text{b-jet}}$ | $+5.2/-9.4$ | 14.7 | $+5.2/-6.6$ | 5.6 | 14.7 | $+8.2/-9.7$ | 8.8
Unfolding | pp | $+7.2/-0.9$ | 2.1 | $+1.0/-1.9$ | 2.1 | 7.9 | $+27.1/-6.1$ | 7.8
p–Pb | $+9.5/-5.6$ | 0.9 | $+0.5/-4.5$ | 0.9 | 1.6 | $+11.4/-14.3$ | 1.5
$R_{\rm pPb}^{\text{b-jet}}$ | $+2.5/-5.4$ | 2.2 | $+3.1/-4.7$ | 2.6 | 8.1 | $+4.4/-15.9$ | 10.6
Total systematic uncertainty | pp | $+17.0/-24.0$ | 18.1 | $+19.3/-22.8$ | 18.1 | 22.8 | $+37.4/-25.4$ | 23.1
p–Pb | $+18.6/-23.2$ | 16.5 | $+17.2/-22.2$ | 15.8 | 18.7 | $+28.0/-28.1$ | 17.7
$R_{\rm pPb}^{\text{b-jet}}$ | $+7.3/-11.6$ | 15.9 | $+10.4/-10.2$ | 9.1 | 17.7 | $+14.3/-23.8$ | 16.2
Normalization uncertainty | pp | 2.34
p–Pb | 3.7
$R_{\rm pPb}^{\text{b-jet}}$ | 4.37

###  5.1 Tracking efficiency

The systematic uncertainty on tracking efficiency is about 4% [68]. This
uncertainty translates into an uncertainty on the energy scale of
reconstructed jets. The resulting effect on the b-jet spectra was estimated by
constructing an instrumental response matrix from which 4% of tracks were
randomly removed. This matrix represents the downward uncertainty on the
reconstruction efficiency. It is assumed that a 4% variation towards higher
tracking efficiency would affect the results symmetrically. The tracking
efficiency uncertainty is one of the major sources of systematic uncertainties
on the b-jet cross section. It tends to increase with increasing b-jet $p_{\rm
T}$.

###  5.2 $p_{\mathrm{T}}$ resolution of tracks

The $p_{\mathrm{T}}$ resolution of tracks was discussed briefly in Sec. 3 and
more details can be found in Ref. [38]. The systematic uncertainty on track
transverse momentum resolution was estimated from the azimuthal variation of
the $p_{\mathrm{T}}$ spectrum of positively and negatively charged particles
following the procedure described in Ref. [69]. The resulting effect of these
variations on the b-jet cross section spectra was investigated by unfolding
the b-jet spectrum with an instrumental response matrix that reflected the
observed local variations in track $p_{\mathrm{T}}$ smearing.

###  5.3 Contamination from secondary tracks

Contamination of jets from secondary tracks, due to weak decays, was corrected
for using the instrumental matrix. This correction is MC based and relies on
the secondary track fractions from the simulations. As a systematic variation,
these fractions were taken from a data-driven approach where DCA distributions
of tracks to the primary vertex were fitted with templates corresponding to
primary tracks and secondary tracks. This resulted in a systematic shift in
jet energy scale. In the SV analysis this uncertainty was treated as one-sided
since the true fraction of secondary tracks is expected to fall between the
two calculations.

The production of long-lived strange particles is known to be poorly described
by the PYTHIA MC event generator [70, 71, 72, 73]. Decays of ${\rm
K}^{0}_{\rm{S}}$ and strange baryons were however found to contribute by less
than 1% to the constructed light-flavor SV invariant mass templates that are
used for the data driven purity estimate. Possible variations of the
strangeness in simulations would; therefore, have negligible impact on the
shape of this template and should have negligible impact on the extracted
purity. A similar situation holds also for the IP templates, where decays of
long-lived strange particles contribute on the percent level only. Omission of
the long-lived strange particles from construction of the templates led to
negligible changes of the fit results.

###  5.4 Underlying event fluctuations

This uncertainty was estimated by comparing the spectra unfolded using $\delta
p_{\mathrm{T}}$ matrices constructed with the track embedding and the random
cone methods. This resulted in a one-sided uncertainty on the SV spectra.

###  5.5 b-jet tagging efficiency and purity in the IP method

The uncertainty was estimated by varying the default impact parameter
significance and template fit discriminator. The working point of the tagging
selection criterion, set by default as $Sd_{xy}^{\rm min}=2.5$, was varied in
the range from 1 to 4. This resulted in variations in the data driven b-jet
tagging efficiency and purity that were propagated to the b-jet cross
sections.

Similarly, The energy fraction carried by charged tracks associated to the
secondary jet vertex, $f_{E}$ was used as template fitting discriminator.
Differences between these methods were added up in quadrature with the
uncertainties from the fitting method to establish the overall uncertainty on
the template fitting.

The purity of the selected b-jet candidates can be in principle affected also
by the admixture of the long-lived strange V0 particles (K${}^{0}_{\rm s}$ and
$\Lambda$/$\overline{\Lambda}$), which result in decay-daughter tracks with
large impact parameters. The possible effect of these daughter tracks on the
purity and efficiency of the IP tagging was tested by ignoring those tracks
that, when combined with other tracks of the same event, yield an invariant
mass compatible with the K${}^{0}_{\rm s}$ or $\Lambda$ hypothesis. The
corresponding systematic effect on the resulting b-jet spectrum was found to
be negligible.

###  5.6 b-jet tagging efficiency and purity in the SV method

The default tagging selection, $SL_{xy}>7$ and $\sigma_{\rm SV}<0.03$ cm, was
chosen to fall into a region where the simulation adequately describes the
data. The variations were performed such that one parameter was kept at its
default value while the other parameter was altered. In this study, $SL_{xy}$
was varied from 6 to 8, and $\sigma_{\rm SV}$ was varied from 0.02 to 0.05 cm.
Since these two parameters are correlated, the envelope of the systematic
variations was considered, constructed using the point-by-point maximal upper
and lower variations.

In the SV method, the major source of systematic uncertainty on the b-jet
cross sections stems from the purity assessment of the tagged b-jet candidate
sample. The uncertainty was evaluated by repeating the analysis with each of
the accepted POWHEG purity curves shown in Fig. 10. The uncertainty is defined
by the envelope of the resulting spectrum variations.

The POWHEG configurations that provide a statistically acceptable description
of the purity are determined based on template fits in the p–Pb system. Since
the same configurations are used in the pp system, the assumptions on the CNM
effects in POWHEG p–Pb simulations will, counter-intuitively, affect the
purity estimation in the pp system. This effect was estimated based on the
comparison of the POWHEG simulations to the existing heavy-flavor $R_{\rm
pPb}$ measurements [74, 75]. This additional, independent uncertainty on the
SV-method purity in the pp system was found to be a few percents at low
$p_{\rm T}$ and is vanishing towards higher $p_{\rm T}$.

In the SV method, since a three-prong secondary vertex is required and the
purity is determined based on template fitting of the invariant mass
distribution, a possible incorrect modelling of V0 particles poses negligible
impact on the purity.

###  5.7 Unfolding

Both the IP and SV methods use SVD unfolding in the standard analysis. To
establish the uncertainty stemming from the choice of the unfolding method,
the spectra were also unfolded with the Bayesian method, and in the IP
analysis, with the $\chi^{2}$ method in addition. The sensitivity to the
choice of regularization parameter was investigated by changing its value
within $\pm 1$. The unfolding was also repeated with a modified lower
$p_{\mathrm{T}}$ limit of the input spectrum from $p_{\mathrm{T}}=5$ GeV/$c$
to $p_{\mathrm{T}}=1$ GeV/$c$. The SV analysis also considered a different
input $p_{\mathrm{T}}$ spectrum binning. Both methods used the b-jet POWHEG
spectrum as the default prior function in the respective standard analyses. In
the IP analysis, the unfolding was repeated using the measured, as well as the
$\chi^{2}$-unfolded spectra as priors. In the SV analysis, the unfolding was
repeated by taking as priors the POWHEG b-jet spectra resulting from different
scale and mass variations. The root mean square (RMS) of the differences
between these variations and the standard analysis spectra was taken as
uncertainty in the IP analysis. In the SV analysis, the statistical and
systematic parts were separated using pseudo-experiments with randomized input
spectra. The pseudo-experiments were carried out for the standard analysis
configuration as well as for each systematic variation. The maximum deviations
at each $p_{\mathrm{T,ch\,jet}}$ value were taken as asymmetric uncertainties.

An additional systematic uncertainty stems from the limited knowledge of very
low momentum jet production, determined from PYTHIA simulations when
constructing the response matrix. This was estimated by using a matrix that
was truncated below $p_{\mathrm{T,ch\,jet}}=5$ GeV/$c$, and the resulting
deviation with respect to the standard analysis spectrum was added up in
quadrature to the total uncertainty.

###  5.8 Normalization

There are also uncertainties on the normalization of the differential cross
section which will be propagated to the nuclear modification factor. The
normalization uncertainties are discussed in detail in Ref. [40] for pp
collisions and in Ref. [41] for p–Pb collisions.

##  6 Results and discussion

###  6.1 b-jet production cross section in pp and p–Pb collisions

Figure 11: Comparison of the $p_{\mathrm{T}}$ differential production cross
section of charged-particle anti-$k_{\rm T}$ $R=0.4$ b jets measured in pp and
p–Pb collisions at $\sqrt{s_{\mathrm{NN}}}=5.02$ TeV using the IP and SV
methods. Systematic and statistical uncertainties are shown as boxes and error
bars respectively. The additional common normalization uncertainty due to
luminosity is denoted $\sigma_{\mathcal{L}}^{\rm Sys}$ and it is quoted
separately.

Figure 12: Top panels: The combined differential production cross section of
charged-particle anti-$k_{\rm T}$ $R=0.4$ b jets measured in pp (left) and
p–Pb (right) collisions at $\sqrt{s_{\mathrm{NN}}}=5.02$ TeV. The data are
compared with a NLO pQCD prediction by the POWHEG dijet tune with PYTHIA 8
fragmentation [76, 77]. Systematic and statistical uncertainties are shown as
boxes and error bars, respectively. The additional common normalization
uncertainty due to luminosity, $\sigma_{\mathcal{L}}^{\rm Sys}$, is quoted
separately. Bottom panels: Ratio of the theory calculations to the data.

Figure 11 presents the $p_{\rm T}$-differential production cross section of b
jets obtained from the IP and SV analyses in pp and p–Pb collisions at
$\sqrt{s_{\mathrm{NN}}}=5.02$ TeV. For easier comparison across the two
systems, the p–Pb cross section is normalized by the number of Pb nucleons
$A=208$. The results obtained with the two methods are consistent within
uncertainties.

The combined b-jet cross sections are compared with NLO pQCD calculations by
the POWHEG dijet tune with PYTHIA 8 fragmentation [76, 77], see Fig. 12. The
measured b-jet cross section is described by the calculations within the
experimental and theoretical uncertainties. The quoted theoretical
uncertainties on the POWHEG data contain uncertainties obtained by changing
the renormalization and factorization scales by a factor 0.5–2, variation of
$\alpha_{s}$, and variation of the PDFs of the CT14NLO parton distribution
function [78] and the EPPS16 nPDF [79] in the POWHEG calculations. The
uncertainties from CT14NLO and EPPS16 were propagated according to the Hessian
prescription of the authors of these parameterizations (Eq. 53 of Ref. [79]).
The uncertainty on $\alpha_{s}$ was estimated by varying the strong coupling
from 0.111 to 0.123.

###  6.2 b-jet fraction

Figure 13 shows the fraction of charged-particle b-jets among inclusive
charged-particle jets in pp and p–Pb collisions. The reference
$p_{\mathrm{T}}$-differential cross sections of inclusive charged-particle jet
production in pp and p–Pb were taken from Refs. [80] and [81], respectively.
The inclusive-jet and the b-jet measurements were obtained from different data
samples, collected in different periods. Although the uncertainties
corresponding to track reconstruction may be partly correlated, as a
conservative approach, both the statistical and systematic uncertainties of
the inclusive and b-jet cross sections were considered as uncorrelated. The
measured b-jet fractions are compared with calculations of the POWHEG dijet
tune with PYTHIA 8 fragmentation [76, 77]. In the p–Pb case, the EPPS16
nuclear PDF set was also applied. The measured b-jet fraction is described by
these calculations within uncertainties.

Figure 13: The b-jet fraction in pp collisions at $\sqrt{s}~{}=~{}5.02$ TeV
(left) and p–Pb collisions at $\sqrt{s_{\mathrm{NN}}}~{}=~{}5.02$ TeV (right),
compared with POWHEG NLO pQCD calculations with PYTHIA 8 fragmentation.

###  6.3 The b-jet nuclear modification factor $R_{\rm pPb}^{\text{b-jet}}$

Figure 14: Left: The nuclear modification factor $R_{\rm pPb}^{\text{b-jet}}$
of the inclusive charged-particle anti-$k_{\rm T}$ $R=0.4$ b jets as a
function of $p_{\rm T}$ from the IP and SV method. Right: The nuclear
modification factor $R_{\rm pPb}^{\text{b-jet}}$ obtained from combining the
IP and SV method results as a function of $p_{\mathrm{T,ch\,jet}}$ compared
with the calculation by the POWHEG dijet tune with the PYTHIA 8 fragmentation
[76, 77]. Systematic and statistical uncertainties are shown as boxes and
error bars, respectively. There is an additional normalization uncertainty of
$4.37\%$ due to luminosity, which is quoted separately.

Figure 14 (left) shows the nuclear modification factor of charged-particle b
jets obtained from the IP and SV methods. The $R_{\rm pPb}^{\text{b-jet}}$ of
the two methods are consistent within uncertainties. Figure 14 (right)
displays the combined b-jet nuclear modification factor $R_{\rm
pPb}^{\text{b-jet}}$ as a function of $p_{\mathrm{T,ch\,jet}}$, compared to
the NLO pQCD, POWHEG dijet tune with PYTHIA 8 fragmentation calculation [76,
77]. The NLO $R_{\rm pPb}^{\text{b-jet}}$ was estimated from the ratio of the
b-jet spectra obtained with EPPS16 and CT14NLO parton distribution functions.
The $R_{\rm pPb}^{\text{b-jet}}$ is consistent with unity within
uncertainties, as well as with a mild modification of $R_{\rm
pPb}^{\text{b-jet}}\approx 1.1\pm 0.1$ predicted by antishadowing in the
EPPS16 nuclear PDFs, in the full $10<p_{\mathrm{T,ch\,jet}}<100$ GeV/$c$ range
of the measurement. The pQCD calculations describe the data within
uncertainties. These results indicate that there are no strong nuclear matter
effects present in b-jet production at midrapidity in p–Pb collisions at
$\sqrt{s_{\mathrm{NN}}}~{}=~{}5.02$ TeV.

Figure 15: The nuclear modification factor $R_{\rm pPb}^{\text{b-jet}}$ for
charged-particle b jets measured by the ALICE experiment, compared with the
b-jet measurement from the CMS experiment [36]. The CMS measurement represents
$R=0.3$ fully reconstructed b jets within $-2.5<\eta_{\rm jet}<1.5$. There is
an additional 22% scaling uncertainty from the PYTHIA pp reference on the CMS
data that is not shown in the figure. The ALICE $R_{\rm pPb}^{\text{b-jet}}$
data have an additional normalization uncertainty of $4.37\%$.

Figure 15 shows the $R_{\rm pPb}^{\text{b-jet}}$ for charged-particle b jets
measured by ALICE as a function of jet $p_{\rm T}$, compared with the
measurement of the CMS collaboration for full-jet b jets [36]. Since the jets
from CMS also include the neutral particles, the $p_{\mathrm{T}}$ scales do
not compare directly. Note that there is an additional $\approx 22$% scaling
uncertainty on the CMS data from the pp reference that was computed using
PYTHIA simulations. Despite the different jet definitions and rapidity ranges
used in the two measurements, the ALICE and CMS data are fully compatible in
the overlap region. A substantial nuclear modification of b-jet production by
cold nuclear matter can be excluded in the whole range from
$p_{\mathrm{T,ch\,jet}}>10$ GeV/$c$ (approximately corresponding to $p_{\rm
T,fulljet}\gtrsim 15$ GeV/$c$) up to $p_{\rm T,fulljet}<400$ GeV/$c$.

Figure 16: The nuclear modification factor of b jets compared to that of
inclusive jets from Ref. [81]. There is a global normalization uncertainty of
$4.37\%$ on the $R_{\rm pPb}^{\text{b-jet}}$ data from luminosity calculation,
and $11.6\%$ on the inclusive-jet $R_{\rm pPb}$ data due to luminosity
calculation and the scaling of the pp reference.

Fig. 16 shows the nuclear modification factor of the b jets compared to that
of inclusive jets from Ref. [81]. The inclusive-jet $R_{\rm pPb}$, dominated
by jets originating from light-flavor gluons and quarks, is consistent with
unity as well as with $R_{\rm pPb}^{\text{b-jet}}$. This suggests that jets in
the given $p_{\mathrm{T,ch\,jet}}$ may only be subject to mild cold nuclear
matter effects, regardless of the jet-initiating parton.

##  7 Summary

A measurement of the $p_{\rm T}$-differential b-jet production cross sections
in pp and p–Pb collisions at $\sqrt{s_{\rm NN}}=5.02$ TeV is presented in this
paper, in the transverse momentum range $10\leq p_{\mathrm{T,ch\,jet}}\leq
100$ GeV/$c$ and the central rapidity region. The lower $p_{\mathrm{T}}$ reach
of the current measurements is unprecedented at the LHC. The fraction of b
jets compared to inclusive jets in pp collisions are around 0.02 in the lowest
$10\leq p_{\mathrm{T,ch\,jet}}<20$ GeV/$c$ interval, saturating at about 0.03
from $p_{\mathrm{T,ch\,jet}}\geq 30$ GeV/$c$. There is no significant
difference between the b-jet fractions measured in pp and p–Pb collisions. The
nuclear modification factor $R_{\rm pPb}^{\text{b-jet}}$ is found to be
consistent with unity within the current precision, implying no strong cold
nuclear effects on the b-jet production in p–Pb collisions at
$\sqrt{s_{\mathrm{NN}}}~{}=~{}5.02$ TeV. The b-jet measurements are described
by NLO pQCD POWHEG calculations with PYTHIA 8 fragmentation within
uncertainties.

In the low jet transverse momentum range, jet energy loss by radiative and
collisional mechanisms in a hot and dense medium is expected to be strongly
mass dependent. The current results, which exploit the excellent tracking
capabilities of ALICE and reach down to $p_{\mathrm{T,ch\,jet}}=10$ GeV/$c$,
provide a baseline for future measurements of nuclear modification in Pb–Pb
collisions.

## 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; 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; Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan Society for the Promotion of Science (JSPS) KAKENHI and Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT)of Applied Science (IIST), 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. In addition, individual groups and members have received support from the Lebanese National Council for Scientific Research (CNRS-L) and Lebanese University, Lebanon.

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##  Appendix A The ALICE Collaboration

S. Acharya142, D. Adamová97, A. Adler75, J. Adolfsson82, G. Aglieri Rinella34,
M. Agnello30, N. Agrawal54, Z. Ahammed142, S. Ahmad16, S.U. Ahn77, I. Ahuja38,
Z. Akbar51, A. Akindinov94, M. Al-Turany109, S.N. Alam16, D. Aleksandrov90, B.
Alessandro60, H.M. Alfanda7, R. Alfaro Molina72, B. Ali16, Y. Ali14, A.
Alici25, N. Alizadehvandchali126, A. Alkin34, J. Alme21, G. Alocco55, T.
Alt69, I. Altsybeev114, M.N. Anaam7, C. Andrei48, D. Andreou92, A.
Andronic145, M. Angeletti34, V. Anguelov106, F. Antinori57, P. Antonioli54, C.
Anuj16, N. Apadula81, L. Aphecetche116, H. Appelshäuser69, S. Arcelli25, R.
Arnaldi60, I.C. Arsene20, M. Arslandok147, A. Augustinus34, R. Averbeck109, S.
Aziz79, M.D. Azmi16, A. Badalà56, Y.W. Baek41, X. Bai130,109, R. Bailhache69,
Y. Bailung50, R. Bala103, A. Balbino30, A. Baldisseri139, B. Balis2, D.
Banerjee4, R. Barbera26, L. Barioglio107, M. Barlou86, G.G. Barnaföldi146,
L.S. Barnby96, V. Barret136, C. Bartels129, K. Barth34, E. Bartsch69, F.
Baruffaldi27, N. Bastid136, S. Basu82, G. Batigne116, B. Batyunya76, D.
Bauri49, J.L. Bazo Alba113, I.G. Bearden91, C. Beattie147, P. Becht109, I.
Belikov138, A.D.C. Bell Hechavarria145, F. Bellini25, R. Bellwied126, S.
Belokurova114, V. Belyaev95, G. Bencedi146,70, S. Beole24, A. Bercuci48, Y.
Berdnikov100, A. Berdnikova106, L. Bergmann106, M.G. Besoiu68, L. Betev34,
P.P. Bhaduri142, A. Bhasin103, I.R. Bhat103, M.A. Bhat4, B. Bhattacharjee42,
P. Bhattacharya22, L. Bianchi24, N. Bianchi52, J. Bielčík37, J. Bielčíková97,
J. Biernat119, A. Bilandzic107, G. Biro146, S. Biswas4, J.T. Blair120, D.
Blau90,83, M.B. Blidaru109, C. Blume69, G. Boca28,58, F. Bock98, A.
Bogdanov95, S. Boi22, J. Bok62, L. Boldizsár146, A. Bolozdynya95, M.
Bombara38, P.M. Bond34, G. Bonomi141,58, H. Borel139, A. Borissov83, H.
Bossi147, E. Botta24, L. Bratrud69, P. Braun-Munzinger109, M. Bregant122, M.
Broz37, G.E. Bruno108,33, M.D. Buckland23,129, D. Budnikov110, H. Buesching69,
S. Bufalino30, O. Bugnon116, P. Buhler115, Z. Buthelezi73,133, J.B. Butt14, A.
Bylinkin128, S.A. Bysiak119, M. Cai27,7, H. Caines147, A. Caliva109, E. Calvo
Villar113, J.M.M. Camacho121, R.S. Camacho45, P. Camerini23, F.D.M. Canedo122,
F. Carnesecchi34,25, R. Caron139, J. Castillo Castellanos139, E.A.R. Casula22,
F. Catalano30, C. Ceballos Sanchez76, P. Chakraborty49, S. Chandra142, S.
Chapeland34, M. Chartier129, S. Chattopadhyay142, S. Chattopadhyay111, A.
Chauvin22, T.G. Chavez45, T. Cheng7, C. Cheshkov137, B. Cheynis137, V.
Chibante Barroso34, D.D. Chinellato123, S. Cho62, P. Chochula34, P.
Christakoglou92, C.H. Christensen91, P. Christiansen82, T. Chujo135, C.
Cicalo55, L. Cifarelli25, F. Cindolo54, M.R. Ciupek109, G. ClaiII,54, J.
CleymansI,125, F. Colamaria53, J.S. Colburn112, D. Colella53,108,33, A.
Collu81, M. Colocci34, M. ConcasIII,60, G. Conesa Balbastre80, Z. Conesa del
Valle79, G. Contin23, J.G. Contreras37, M.L. Coquet139, T.M. Cormier98, P.
Cortese31, M.R. Cosentino124, F. Costa34, S. Costanza28,58, P. Crochet136, R.
Cruz-Torres81, E. Cuautle70, P. Cui7, L. Cunqueiro98, A. Dainese57, M.C.
Danisch106, A. Danu68, P. Das88, P. Das4, S. Das4, S. Dash49, A. De Caro29, G.
de Cataldo53, L. De Cilladi24, J. de Cuveland39, A. De Falco22, D. De
Gruttola29, N. De Marco60, C. De Martin23, S. De Pasquale29, S. Deb50, H.F.
Degenhardt122, K.R. Deja143, L. Dello Stritto29, W. Deng7, P. Dhankher19, D.
Di Bari33, A. Di Mauro34, R.A. Diaz8, T. Dietel125, Y. Ding137,7, R. Divià34,
D.U. Dixit19, Ø. Djuvsland21, U. Dmitrieva64, J. Do62, A. Dobrin68, B.
Dönigus69, A.K. Dubey142, A. Dubla109,92, S. Dudi102, P. Dupieux136, N.
Dzalaiova13, T.M. Eder145, R.J. Ehlers98, V.N. Eikeland21, F. Eisenhut69, D.
Elia53, B. Erazmus116, F. Ercolessi25, F. Erhardt101, A. Erokhin114, M.R.
Ersdal21, B. Espagnon79, G. Eulisse34, D. Evans112, S. Evdokimov93, L.
Fabbietti107, M. Faggin27, J. Faivre80, F. Fan7, A. Fantoni52, M. Fasel98, P.
Fecchio30, A. Feliciello60, G. Feofilov114, A. Fernández Téllez45, A.
Ferrero139, A. Ferretti24, V.J.G. Feuillard106, J. Figiel119, S. Filchagin110,
D. Finogeev64, F.M. Fionda55,21, G. Fiorenza34,108, F. Flor126, A.N.
Flores120, S. Foertsch73, S. Fokin90, E. Fragiacomo61, E. Frajna146, U.
Fuchs34, N. Funicello29, C. Furget80, A. Furs64, J.J. Gaardhøje91, M.
Gagliardi24, A.M. Gago113, A. Gal138, C.D. Galvan121, P. Ganoti86, C.
Garabatos109, J.R.A. Garcia45, E. Garcia-Solis10, K. Garg116, C. Gargiulo34,
A. Garibli89, K. Garner145, P. Gasik109, E.F. Gauger120, A. Gautam128, M.B.
Gay Ducati71, M. Germain116, P. Ghosh142, S.K. Ghosh4, M. Giacalone25, P.
Gianotti52, P. Giubellino109,60, P. Giubilato27, A.M.C. Glaenzer139, P.
Glässel106, D.J.Q. Goh84, V. Gonzalez144, L.H. González-Trueba72, S.
Gorbunov39, M. Gorgon2, L. Görlich119, S. Gotovac35, V. Grabski72, L.K.
Graczykowski143, L. Greiner81, A. Grelli63, C. Grigoras34, V. Grigoriev95, S.
Grigoryan76,1, F. Grosa34,60, J.F. Grosse-Oetringhaus34, R. Grosso109, G.G.
Guardiano123, R. Guernane80, M. Guilbaud116, K. Gulbrandsen91, T. Gunji134, W.
Guo7, A. Gupta103, R. Gupta103, S.P. Guzman45, L. Gyulai146, M.K. Habib109, C.
Hadjidakis79, H. Hamagaki84, M. Hamid7, R. Hannigan120, M.R. Haque143, A.
Harlenderova109, J.W. Harris147, A. Harton10, J.A. Hasenbichler34, H.
Hassan98, D. Hatzifotiadou54, P. Hauer43, L.B. Havener147, S.T. Heckel107, E.
Hellbär109, H. Helstrup36, T. Herman37, E.G. Hernandez45, G. Herrera Corral9,
F. Herrmann145, K.F. Hetland36, H. Hillemanns34, C. Hills129, B. Hippolyte138,
B. Hofman63, B. Hohlweger92, J. Honermann145, G.H. Hong148, D. Horak37, S.
Hornung109, A. Horzyk2, R. Hosokawa15, Y. Hou7, P. Hristov34, C. Hughes132, P.
Huhn69, L.M. Huhta127, C.V. Hulse79, T.J. Humanic99, H. Hushnud111, L.A.
Husova145, A. Hutson126, J.P. Iddon34,129, R. Ilkaev110, H. Ilyas14, M.
Inaba135, G.M. Innocenti34, M. Ippolitov90, A. Isakov37,97, T. Isidori128,
M.S. Islam111, M. Ivanov109, V. Ivanov100, V. Izucheev93, M. Jablonski2, B.
Jacak81, N. Jacazio34, P.M. Jacobs81, S. Jadlovska118, J. Jadlovsky118, S.
Jaelani63, C. Jahnke123,122, M.J. Jakubowska143, A. Jalotra103, M.A. Janik143,
T. Janson75, M. Jercic101, O. Jevons112, A.A.P. Jimenez70, F. Jonas98,145,
P.G. Jones112, J.M. Jowett 34,109, J. Jung69, M. Jung69, A. Junique34, A.
Jusko112, J. Kaewjai117, P. Kalinak65, A.S. Kalteyer109, A. Kalweit34, V.
Kaplin95, A. Karasu Uysal78, D. Karatovic101, O. Karavichev64, T.
Karavicheva64, P. Karczmarczyk143, E. Karpechev64, V. Kashyap88, A.
Kazantsev90, U. Kebschull75, R. Keidel47, D.L.D. Keijdener63, M. Keil34, B.
Ketzer43, Z. Khabanova92, A.M. Khan7, S. Khan16, A. Khanzadeev100, Y.
Kharlov93,83, A. Khatun16, A. Khuntia119, B. Kileng36, B. Kim17,62, C. Kim17,
D.J. Kim127, E.J. Kim74, J. Kim148, J.S. Kim41, J. Kim106, J. Kim74, M.
Kim106, S. Kim18, T. Kim148, S. Kirsch69, I. Kisel39, S. Kiselev94, A.
Kisiel143, J.P. Kitowski2, J.L. Klay6, J. Klein34, S. Klein81, C. Klein-
Bösing145, M. Kleiner69, T. Klemenz107, A. Kluge34, A.G. Knospe126, C.
Kobdaj117, M.K. Köhler106, T. Kollegger109, A. Kondratyev76, N. Kondratyeva95,
E. Kondratyuk93, J. Konig69, S.A. Konigstorfer107, P.J. Konopka34, G.
Kornakov143, S.D. Koryciak2, A. Kotliarov97, O. Kovalenko87, V. Kovalenko114,
M. Kowalski119, I. Králik65, A. Kravčáková38, L. Kreis109, M. Krivda112,65, F.
Krizek97, K. Krizkova Gajdosova37, M. Kroesen106, M. Krüger69, E. Kryshen100,
M. Krzewicki39, V. Kučera34, C. Kuhn138, P.G. Kuijer92, T. Kumaoka135, D.
Kumar142, L. Kumar102, N. Kumar102, S. Kundu34, P. Kurashvili87, A. Kurepin64,
A.B. Kurepin64, A. Kuryakin110, S. Kushpil97, J. Kvapil112, M.J. Kweon62, J.Y.
Kwon62, Y. Kwon148, S.L. La Pointe39, P. La Rocca26, Y.S. Lai81, A.
Lakrathok117, M. Lamanna34, R. Langoy131, K. Lapidus34, P. Larionov34,52, E.
Laudi34, L. Lautner34,107, R. Lavicka115,37, T. Lazareva114, R. Lea141,23,58,
J. Lehrbach39, R.C. Lemmon96, I. León Monzón121, E.D. Lesser19, M.
Lettrich34,107, P. Lévai146, X. Li11, X.L. Li7, J. Lien131, R. Lietava112, B.
Lim17, S.H. Lim17, V. Lindenstruth39, A. Lindner48, C. Lippmann109, A. Liu19,
D.H. Liu7, J. Liu129, I.M. Lofnes21, V. Loginov95, C. Loizides98, P. Loncar35,
J.A. Lopez106, X. Lopez136, E. López Torres8, J.R. Luhder145, M. Lunardon27,
G. Luparello61, Y.G. Ma40, A. Maevskaya64, M. Mager34, T. Mahmoud43, A.
Maire138, M. Malaev100, N.M. Malik103, Q.W. Malik20, S.K. Malik103, L.
MalininaIV,76, D. Mal’Kevich94, D. Mallick88, N. Mallick50, G. Mandaglio32,56,
V. Manko90, F. Manso136, V. Manzari53, Y. Mao7, G.V. Margagliotti23, A.
Margotti54, A. Marín109, C. Markert120, M. Marquard69, N.A. Martin106, P.
Martinengo34, J.L. Martinez126, M.I. Martínez45, G. Martínez García116, S.
Masciocchi109, M. Masera24, A. Masoni55, L. Massacrier79, A.
Mastroserio140,53, A.M. Mathis107, O. Matonoha82, P.F.T. Matuoka122, A.
Matyja119, C. Mayer119, A.L. Mazuecos34, F. Mazzaschi24, M. Mazzilli34, M.A.
MazzoniI,59, J.E. Mdhluli133, A.F. Mechler69, Y. Melikyan64, A. Menchaca-
Rocha72, E. Meninno115,29, A.S. Menon126, M. Meres13, S. Mhlanga125,73, Y.
Miake135, L. Micheletti60, L.C. Migliorin137, D.L. Mihaylov107, K.
Mikhaylov76,94, A.N. Mishra146, D. Miśkowiec109, A. Modak4, A.P. Mohanty63, B.
Mohanty88, M. Mohisin KhanV,16, M.A. Molander44, Z. Moravcova91, C.
Mordasini107, D.A. Moreira De Godoy145, I. Morozov64, A. Morsch34, T.
Mrnjavac34, V. Muccifora52, E. Mudnic35, D. Mühlheim145, S. Muhuri142, J.D.
Mulligan81, A. Mulliri22, M.G. Munhoz122, R.H. Munzer69, H. Murakami134, S.
Murray125, L. Musa34, J. Musinsky65, J.W. Myrcha143, B. Naik133,49, R. Nair87,
B.K. Nandi49, R. Nania54, E. Nappi53, A.F. Nassirpour82, A. Nath106, C.
Nattrass132, A. Neagu20, L. Nellen70, S.V. Nesbo36, G. Neskovic39, D.
Nesterov114, B.S. Nielsen91, S. Nikolaev90, S. Nikulin90, V. Nikulin100, F.
Noferini54, S. Noh12, P. Nomokonov76, J. Norman129, N. Novitzky135, P.
Nowakowski143, A. Nyanin90, J. Nystrand21, M. Ogino84, A. Ohlson82, V.A.
Okorokov95, J. Oleniacz143, A.C. Oliveira Da Silva132, M.H. Oliver147, A.
Onnerstad127, C. Oppedisano60, A. Ortiz Velasquez70, T. Osako46, A.
Oskarsson82, J. Otwinowski119, M. Oya46, K. Oyama84, Y. Pachmayer106, S.
Padhan49, D. Pagano141,58, G. Paić70, A. Palasciano53, J. Pan144, S.
Panebianco139, J. Park62, J.E. Parkkila127, S.P. Pathak126, R.N. Patra103,34,
B. Paul22, H. Pei7, T. Peitzmann63, X. Peng7, L.G. Pereira71, H. Pereira Da
Costa139, D. Peresunko90,83, G.M. Perez8, S. Perrin139, Y. Pestov5, V.
Petráček37, M. Petrovici48, R.P. Pezzi116,71, S. Piano61, M. Pikna13, P.
Pillot116, O. Pinazza54,34, L. Pinsky126, C. Pinto26, S. Pisano52, M.
Płoskoń81, M. Planinic101, F. Pliquett69, M.G. Poghosyan98, B. Polichtchouk93,
S. Politano30, N. Poljak101, A. Pop48, S. Porteboeuf-Houssais136, J. Porter81,
V. Pozdniakov76, S.K. Prasad4, R. Preghenella54, F. Prino60, C.A. Pruneau144,
I. Pshenichnov64, M. Puccio34, S. Qiu92, L. Quaglia24, R.E. Quishpe126, S.
Ragoni112, A. Rakotozafindrabe139, L. Ramello31, F. Rami138, S.A.R. Ramirez45,
A.G.T. Ramos33, T.A. Rancien80, R. Raniwala104, S. Raniwala104, S.S.
Räsänen44, R. Rath50, I. Ravasenga92, K.F. Read98,132, A.R. Redelbach39, K.
RedlichVI,87, A. Rehman21, P. Reichelt69, F. Reidt34, H.A. Reme-ness36, Z.
Rescakova38, K. Reygers106, A. Riabov100, V. Riabov100, T. Richert82, M.
Richter20, W. Riegler34, F. Riggi26, C. Ristea68, M. Rodríguez Cahuantzi45, K.
Røed20, R. Rogalev93, E. Rogochaya76, T.S. Rogoschinski69, D. Rohr34, D.
Röhrich21, P.F. Rojas45, S. Rojas Torres37, P.S. Rokita143, F. Ronchetti52, A.
Rosano32,56, E.D. Rosas70, A. Rossi57, A. Roy50, P. Roy111, S. Roy49, N.
Rubini25, O.V. Rueda82, D. Ruggiano143, R. Rui23, B. Rumyantsev76, P.G.
Russek2, R. Russo92, A. Rustamov89, E. Ryabinkin90, Y. Ryabov100, A.
Rybicki119, H. Rytkonen127, W. Rzesa143, O.A.M. Saarimaki44, R. Sadek116, S.
Sadovsky93, J. Saetre21, K. Šafařík37, S.K. Saha142, S. Saha88, B. Sahoo49, P.
Sahoo49, R. Sahoo50, S. Sahoo66, D. Sahu50, P.K. Sahu66, J. Saini142, S.
Sakai135, M.P. Salvan109, S. Sambyal103, V. SamsonovI,100,95, D. Sarkar144, N.
Sarkar142, P. Sarma42, V.M. Sarti107, M.H.P. Sas147, J. Schambach98, H.S.
Scheid69, C. Schiaua48, R. Schicker106, A. Schmah106, C. Schmidt109, H.R.
Schmidt105, M.O. Schmidt34,106, M. Schmidt105, N.V. Schmidt98,69, A.R.
Schmier132, R. Schotter138, J. Schukraft34, K. Schwarz109, K. Schweda109, G.
Scioli25, E. Scomparin60, J.E. Seger15, Y. Sekiguchi134, D. Sekihata134, I.
Selyuzhenkov109,95, S. Senyukov138, J.J. Seo62, D. Serebryakov64, L.
Šerkšnytė107, A. Sevcenco68, T.J. Shaba73, A. Shabanov64, A. Shabetai116, R.
Shahoyan34, W. Shaikh111, A. Shangaraev93, A. Sharma102, H. Sharma119, M.
Sharma103, N. Sharma102, S. Sharma103, U. Sharma103, O. Sheibani126, A.I.
Sheikh146, K. Shigaki46, M. Shimomura85, S. Shirinkin94, Q. Shou40, Y.
Sibiriak90, S. Siddhanta55, T. Siemiarczuk87, T.F. Silva122, D. Silvermyr82,
T. Simantathammakul117, G. Simonetti34, B. Singh107, R. Singh88, R. Singh103,
R. Singh50, V.K. Singh142, V. Singhal142, T. Sinha111, B. Sitar13, M. Sitta31,
T.B. Skaali20, G. Skorodumovs106, M. Slupecki44, N. Smirnov147, R.J.M.
Snellings63, C. Soncco113, J. Song126, A. Songmoolnak117, F. Soramel27, S.
Sorensen132, I. Sputowska119, J. Stachel106, I. Stan68, P.J. Steffanic132,
S.F. Stiefelmaier106, D. Stocco116, I. Storehaug20, M.M. Storetvedt36, P.
Stratmann145, C.P. Stylianidis92, A.A.P. Suaide122, C. Suire79, M. Sukhanov64,
M. Suljic34, R. Sultanov94, V. Sumberia103, S. Sumowidagdo51, S. Swain66, A.
Szabo13, I. Szarka13, U. Tabassam14, S.F. Taghavi107, G. Taillepied136, J.
Takahashi123, G.J. Tambave21, S. Tang136,7, Z. Tang130, J.D. Tapia
TakakiVII,128, M. Tarhini116, M.G. Tarzila48, A. Tauro34, G. Tejeda Muñoz45,
A. Telesca34, L. Terlizzi24, C. Terrevoli126, G. Tersimonov3, S. Thakur142, D.
Thomas120, R. Tieulent137, A. Tikhonov64, A.R. Timmins126, M. Tkacik118, A.
Toia69, N. Topilskaya64, M. Toppi52, F. Torales-Acosta19, T. Tork79, A.
Trifiró32,56, S. Tripathy54,70, T. Tripathy49, S. Trogolo34,27, V. Trubnikov3,
W.H. Trzaska127, T.P. Trzcinski143, A. Tumkin110, R. Turrisi57, T.S. Tveter20,
K. Ullaland21, A. Uras137, M. Urioni58,141, G.L. Usai22, M. Vala38, N.
Valle28,58, S. Vallero60, L.V.R. van Doremalen63, M. van Leeuwen92, P. Vande
Vyvre34, D. Varga146, Z. Varga146, M. Varga-Kofarago146, M. Vasileiou86, A.
Vasiliev90, O. Vázquez Doce52,107, V. Vechernin114, E. Vercellin24, S. Vergara
Limón45, L. Vermunt63, R. Vértesi146, M. Verweij63, L. Vickovic35, Z.
Vilakazi133, O. Villalobos Baillie112, G. Vino53, A. Vinogradov90, T.
Virgili29, V. Vislavicius91, A. Vodopyanov76, B. Volkel34,106, M.A. Völkl106,
K. Voloshin94, S.A. Voloshin144, G. Volpe33, B. von Haller34, I. Vorobyev107,
D. Voscek118, N. Vozniuk64, J. Vrláková38, B. Wagner21, C. Wang40, D. Wang40,
M. Weber115, R.J.G.V. Weelden92, A. Wegrzynek34, S.C. Wenzel34, J.P.
Wessels145, J. Wiechula69, J. Wikne20, G. Wilk87, J. Wilkinson109, G.A.
Willems145, B. Windelband106, M. Winn139, W.E. Witt132, J.R. Wright120, W.
Wu40, Y. Wu130, R. Xu7, A.K. Yadav142, S. Yalcin78, Y. Yamaguchi46, K.
Yamakawa46, S. Yang21, S. Yano46, Z. Yin7, I.-K. Yoo17, J.H. Yoon62, S.
Yuan21, A. Yuncu106, V. Zaccolo23, C. Zampolli34, H.J.C. Zanoli63, N.
Zardoshti34, A. Zarochentsev114, P. Závada67, N. Zaviyalov110, M. Zhalov100,
B. Zhang7, S. Zhang40, X. Zhang7, Y. Zhang130, V. Zherebchevskii114, Y. Zhi11,
N. Zhigareva94, D. Zhou7, Y. Zhou91, J. Zhu109,7, Y. Zhu7, G. Zinovjev3, N.
Zurlo141,58

## 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
VII Also at: University of Kansas, Lawrence, Kansas, United States

## 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 Roma, Rome, Italy
60 INFN, Sezione di Torino, Turin, Italy
61 INFN, Sezione di Trieste, Trieste, Italy
62 Inha University, Incheon, Republic of Korea
63 Institute for Gravitational and Subatomic Physics (GRASP), Utrecht
University/Nikhef, Utrecht, Netherlands
64 Institute for Nuclear Research, Academy of Sciences, Moscow, Russia
65 Institute of Experimental Physics, Slovak Academy of Sciences, Košice,
Slovakia
66 Institute of Physics, Homi Bhabha National Institute, Bhubaneswar, India
67 Institute of Physics of the Czech Academy of Sciences, Prague, Czech
Republic
68 Institute of Space Science (ISS), Bucharest, Romania
69 Institut für Kernphysik, Johann Wolfgang Goethe-Universität Frankfurt,
Frankfurt, Germany
70 Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de México,
Mexico City, Mexico
71 Instituto de Física, Universidade Federal do Rio Grande do Sul (UFRGS),
Porto Alegre, Brazil
72 Instituto de Física, Universidad Nacional Autónoma de México, Mexico City,
Mexico
73 iThemba LABS, National Research Foundation, Somerset West, South Africa
74 Jeonbuk National University, Jeonju, Republic of Korea
75 Johann-Wolfgang-Goethe Universität Frankfurt Institut für Informatik,
Fachbereich Informatik und Mathematik, Frankfurt, Germany
76 Joint Institute for Nuclear Research (JINR), Dubna, Russia
77 Korea Institute of Science and Technology Information, Daejeon, Republic of
Korea
78 KTO Karatay University, Konya, Turkey
79 Laboratoire de Physique des 2 Infinis, Irène Joliot-Curie, Orsay, France
80 Laboratoire de Physique Subatomique et de Cosmologie, Université Grenoble-
Alpes, CNRS-IN2P3, Grenoble, France
81 Lawrence Berkeley National Laboratory, Berkeley, California, United States
82 Lund University Department of Physics, Division of Particle Physics, Lund,
Sweden
83 Moscow Institute for Physics and Technology, Moscow, Russia
84 Nagasaki Institute of Applied Science, Nagasaki, Japan
85 Nara Women’s University (NWU), Nara, Japan
86 National and Kapodistrian University of Athens, School of Science,
Department of Physics , Athens, Greece
87 National Centre for Nuclear Research, Warsaw, Poland
88 National Institute of Science Education and Research, Homi Bhabha National
Institute, Jatni, India
89 National Nuclear Research Center, Baku, Azerbaijan
90 National Research Centre Kurchatov Institute, Moscow, Russia
91 Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark
92 Nikhef, National institute for subatomic physics, Amsterdam, Netherlands
93 NRC Kurchatov Institute IHEP, Protvino, Russia
94 NRC «Kurchatov»Institute - ITEP, Moscow, Russia
95 NRNU Moscow Engineering Physics Institute, Moscow, Russia
96 Nuclear Physics Group, STFC Daresbury Laboratory, Daresbury, United Kingdom
97 Nuclear Physics Institute of the Czech Academy of Sciences, Řež u Prahy,
Czech Republic
98 Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States
99 Ohio State University, Columbus, Ohio, United States
100 Petersburg Nuclear Physics Institute, Gatchina, Russia
101 Physics department, Faculty of science, University of Zagreb, Zagreb,
Croatia
102 Physics Department, Panjab University, Chandigarh, India
103 Physics Department, University of Jammu, Jammu, India
104 Physics Department, University of Rajasthan, Jaipur, India
105 Physikalisches Institut, Eberhard-Karls-Universität Tübingen, Tübingen,
Germany
106 Physikalisches Institut, Ruprecht-Karls-Universität Heidelberg,
Heidelberg, Germany
107 Physik Department, Technische Universität München, Munich, Germany
108 Politecnico di Bari and Sezione INFN, Bari, Italy
109 Research Division and ExtreMe Matter Institute EMMI, GSI Helmholtzzentrum
für Schwerionenforschung GmbH, Darmstadt, Germany
110 Russian Federal Nuclear Center (VNIIEF), Sarov, Russia
111 Saha Institute of Nuclear Physics, Homi Bhabha National Institute,
Kolkata, India
112 School of Physics and Astronomy, University of Birmingham, Birmingham,
United Kingdom
113 Sección Física, Departamento de Ciencias, Pontificia Universidad Católica
del Perú, Lima, Peru
114 St. Petersburg State University, St. Petersburg, Russia
115 Stefan Meyer Institut für Subatomare Physik (SMI), Vienna, Austria
116 SUBATECH, IMT Atlantique, Université de Nantes, CNRS-IN2P3, Nantes, France
117 Suranaree University of Technology, Nakhon Ratchasima, Thailand
118 Technical University of Košice, Košice, Slovakia
119 The Henryk Niewodniczanski Institute of Nuclear Physics, Polish Academy of
Sciences, Cracow, Poland
120 The University of Texas at Austin, Austin, Texas, United States
121 Universidad Autónoma de Sinaloa, Culiacán, Mexico
122 Universidade de São Paulo (USP), São Paulo, Brazil
123 Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil
124 Universidade Federal do ABC, Santo Andre, Brazil
125 University of Cape Town, Cape Town, South Africa
126 University of Houston, Houston, Texas, United States
127 University of Jyväskylä, Jyväskylä, Finland
128 University of Kansas, Lawrence, Kansas, United States
129 University of Liverpool, Liverpool, United Kingdom
130 University of Science and Technology of China, Hefei, China
131 University of South-Eastern Norway, Tonsberg, Norway
132 University of Tennessee, Knoxville, Tennessee, United States
133 University of the Witwatersrand, Johannesburg, South Africa
134 University of Tokyo, Tokyo, Japan
135 University of Tsukuba, Tsukuba, Japan
136 Université Clermont Auvergne, CNRS/IN2P3, LPC, Clermont-Ferrand, France
137 Université de Lyon, CNRS/IN2P3, Institut de Physique des 2 Infinis de
Lyon, Lyon, France
138 Université de Strasbourg, CNRS, IPHC UMR 7178, F-67000 Strasbourg, France,
Strasbourg, France
139 Université Paris-Saclay Centre d’Etudes de Saclay (CEA), IRFU, Départment
de Physique Nucléaire (DPhN), Saclay, France
140 Università degli Studi di Foggia, Foggia, Italy
141 Università di Brescia, Brescia, Italy
142 Variable Energy Cyclotron Centre, Homi Bhabha National Institute, Kolkata,
India
143 Warsaw University of Technology, Warsaw, Poland
144 Wayne State University, Detroit, Michigan, United States
145 Westfälische Wilhelms-Universität Münster, Institut für Kernphysik,
Münster, Germany
146 Wigner Research Centre for Physics, Budapest, Hungary
147 Yale University, New Haven, Connecticut, United States
148 Yonsei University, Seoul, Republic of Korea