EUROPEAN ORGANIZATION FOR NUCLEAR RESEARCH (CERN)

 CERN-EP-2019-065 LHCb-PAPER-2019-012

Measurement of charged hadron production in $Z$-tagged jets in proton-proton
collisions at $\sqrt{s}=8\text{\,Te\kern-2.07413ptV}$

LHCb collaboration†††Authors are listed at the end of this paper.

The production of charged hadrons within jets recoiling against a $Z$ boson is
measured in proton-proton collision data at
$\sqrt{s}=8\text{\,Te\kern-1.00006ptV}$ recorded by the LHCb experiment. The
charged-hadron structure of the jet is studied longitudinally and transverse
to the jet axis for jets with transverse momentum
$p_{\mathrm{T}}>20\text{\,Ge\kern-1.00006ptV}$ and in the pseudorapidity range
$2.5<\eta<4$. These are the first measurements of jet hadronization at these
forward rapidities and also the first where the jet is produced in association
with a $Z$ boson. In contrast to previous hadronization measurements at the
Large Hadron Collider, which are dominated by gluon jets, these measurements
probe predominantly light-quark jets which are found to be more longitudinally
and transversely collimated with respect to the jet axis when compared to the
previous gluon dominated measurements. Therefore, these results provide
valuable information on differences between quarks and gluons regarding
nonperturbative hadronization dynamics.

Published in Phys. Rev. Lett. 123 (2019) 232001

© 2024 CERN for the benefit of the LHCb collaboration. CC-BY-4.0 licence.

Quantum chromodynamics (QCD), the theory of the strong interaction, is unique
amongst the fundamental forces due to the nonperturbative processes that
confine quarks and gluons, collectively referred to as partons, within bound-
state hadrons. The parton structure of protons has been the focus of intense
research efforts; however, the understanding of how hadrons arise from
scattered partons is limited in comparison. Perturbative QCD calculations
utilize fragmentation functions to determine cross-sections of hadron
production from scattered partons. Fragmentation functions describe the
probability for a particular parton to transform into a particular hadron [1,
2, 3]. Several global fits to experimental data have provided parameterized
fragmentation functions (see e.g. Ref. [4] and references therein). However,
there is a significant lack of understanding in the mechanisms through which
hadrons are formed in the nonperturbative hadronization process and therefore
additional data are required.

Fragmentation function studies have been performed using inclusive hadron
production at $e^{+}e^{-}$ colliders, which benefit from a simpler environment
free of initial-state gluon radiation [5, 6, 7, 8, 9, 10, 11, 12, 13]. Semi-
inclusive deep-inelastic-scattering measurements have also been used to
constrain fragmentation functions at smaller values of $Q^{2}$, the momentum
transfer [14, 15]. Additionally, inclusive hadron production measurements have
been used to study fragmentation functions in the more complex environment,
relative to interactions involving leptons, of proton-proton ($pp$) collisions
[16, 17, 18]. However, such measurements are limited by the lack of an
explicit way to relate the scattered parton to the final-state hadron.
Measuring fragmentation functions with respect to high transverse momentum
($p_{\mathrm{T}}$) jets offers a unique opportunity to study hadron production
relative to an object that is correlated to the scattered parton. For example,
the transverse profile, in addition to the longitudinal dynamics of hadrons
within jets, can be used to study fragmentation functions in the longitudinal
and transverse directions with respect to the jet axis. Such multidimensional
measurements that go beyond inclusive hadrons, or those that consider
correlations between particles, have the potential to answer unique questions
within QCD related to universality, factorization, and the importance of
color-charge flow [19, 20].

This Letter reports a study of charged hadrons produced in jets recoiling
against a $Z$ boson, also referred to as $Z$-tagged jets, in the forward
region of $pp$ collisions.111Throughout this Letter the notation $Z$ includes
both the $Z^{0}$ and virtual $\gamma^{*}$ contributions. The longitudinal
momentum fraction, $z$, the momentum transverse to the jet axis,
$j_{\textrm{T}}$, and the radial distribution, $r$, of charged hadrons are
measured with respect to the jet axis in the laboratory frame, defined as

$z\equiv\frac{{\bf{p}}_{\textrm{jet}}\cdot{\bf{p}}_{\textrm{hadron}}}{|{\bf{p}}_{\textrm{jet}}|^{2}},$
(1)
$\mbox{$j_{\textrm{T}}$}\equiv\frac{|{\bf{p}}_{\textrm{jet}}\times{\bf{p}}_{\textrm{hadron}}|}{|{\bf{p}}_{\textrm{jet}}|},$
(2)

and

$r\equiv\sqrt{(\phi_{\textrm{jet}}-\phi_{\textrm{hadron}})^{2}+(y_{\textrm{jet}}-y_{\textrm{hadron}})^{2}}.$
(3)

Here, ${\bf{p}}$ is the 3-momentum vector, $\phi$ is the azimuthal angle, and
$y$ is the rapidity. The data sample is selected from an integrated luminosity
of approximately 2$\text{\,fb}^{-1}$ collected at a center-of-mass energy
$\sqrt{s}=8\text{\,Te\kern-1.00006ptV}$ with the LHCb detector in 2012. Events
with only one reconstructed primary vertex are analyzed to better identify
signatures of a hard two-to-two partonic scattering. Jets are clustered with
the anti-$k_{T}$ algorithm [21] using a distance parameter $R=0.5$ and are
measured differentially in $p_{\mathrm{T}}$ for
$p_{\mathrm{T}}>20\text{\,Ge\kern-1.00006ptV}$, and in the pseudorapidity
range $2.5<\eta<4$.222In this Letter, natural units ($c=\hbar=1$) are used.
Charged hadrons within the jet are required to have
$p_{\mathrm{T}}>0.25\text{\,Ge\kern-1.00006ptV}$, momentum
$p>4\text{\,Ge\kern-1.00006ptV}$, and to lie within the jet cone such that
$\Delta R<0.5$, where $\Delta
R\equiv\sqrt{(\phi_{\textrm{jet}}-\phi_{\textrm{hadron}})^{2}+(\eta_{\textrm{jet}}-\eta_{\textrm{hadron}})^{2}}$.
The distributions are unfolded to account for the detector response and to
facilitate comparisons with theoretical and numerical predictions. This is the
first measurement of charged hadrons within jets produced in association with
a $Z$ boson, as well as the first measurement of charged hadrons in jets at
these forward pseudorapidities. The $Z$+jet process is primarily sensitive to
light quark jets, as demonstrated by PYTHIA in this kinematic range [22, 23].
Thus, these data provide new and complementary information to previous jet
substructure measurements in the inclusive jet channel at midrapidity in
hadronic collisions, which are sensitive to primarily gluon jets [24, 25, 26,
27, 28, 29]. Recent results at midrapidity in the isolated photon-jet channel
can also probe fragmentation differences when a photon, rather than a massive
vector boson, is measured opposite the jet [30].

The LHCb detector is a single-arm forward spectrometer covering the
pseudorapidity range $2<\eta<5$, described in detail in Refs. [31, 32].
Simulations are used to evaluate the detector performance with regard to the
jet reconstruction, track-in-jet reconstruction, and to validate the analysis
methods. The simulated $pp\rightarrow Z+\textrm{jet}+X$ events are generated
using Pythia 8 [23] with a specific LHCb configuration [33]. Decays of
hadronic particles are described by EvtGen [34], and final-state radiation in
the simulation is generated using Photos [35]. Finally, the Geant4 toolkit
[36, *Allison:2006ve] is used to simulate the interactions of the particles
with the detector, as described in Ref. [38].

This analysis uses the same data set as that used for the $Z$+jet cross
section measurement, where events are selected and $Z$ bosons are measured via
their dimuon decay as described in Ref. [39]. Candidate events are required to
pass a trigger [40] which selects muons with
$p_{\mathrm{T}}>10\text{\,Ge\kern-1.00006ptV}$. Only events that contain two
high-$p_{\mathrm{T}}$ muons are retained. The muons are required to satisfy
track-reconstruction and muon-identification criteria, as in Refs. [41, 39],
and are also required to fall within the fiducial region of $2<\eta<4.5$,
where the detector performance is well understood. Finally, the dimuon system
must have an invariant mass, $M_{\mu\mu}$, within the range
$60<M_{\mu\mu}<120\text{\,Ge\kern-1.00006ptV}$.

Jet reconstruction is performed using a particle flow algorithm [42], where
the charged and neutral particles are clustered using the anti-kT algorithm as
implemented in Ref. [43]. Reconstructed jets with
$p_{\mathrm{T}}>15\text{\,Ge\kern-1.00006ptV}$ that lie within $2.5<\eta<4$
are analyzed. The $15<p_{\mathrm{T}}<20\text{\,Ge\kern-1.00006ptV}$ region is
included to avoid inefficiencies at the lower $p_{\mathrm{T}}$ limit of the
measurement in the unfolding procedure. The pseudorapidity requirement ensures
that the full jet cone lies within the fiducial area of the LHCb detector.
Selection requirements are placed on the jets to reduce the rate of jets not
associated with the partonic process producing the $Z$ boson, which is already
suppressed by the requirement of a single reconstructed primary vertex in the
event. Additionally, the decay muons from the $Z$ boson must not be contained
within the jet cone. Only jets that are on the azimuthal away-side of the $Z$
boson, defined by
$\Delta\phi_{{Z}-\textrm{jet}}\equiv|\phi_{{Z}}-\phi_{\textrm{jet}}|>7\pi/8$,
are analyzed. The jet energy calibrations are the same as those used in Ref.
[39]. Charged hadrons within the jet are identified by the particle flow
algorithm utilizing the particle-identification systems and several track-
quality criteria [32]. The charged hadrons must also satisfy $\Delta R<0.5$,
which ensures that the corresponding tracks fall within the tracking
acceptance.

The methods used to determine the charged-hadron fragmentation distributions
are as described in Refs. [24, 44, 27]. The fragmentation distributions are
corrected for tracking inefficiencies and two-dimensionally unfolded for
resolution effects, which primarily occur due to the jet energy resolution.
The unfolded fragmentation distributions are then normalized by the total
number of $Z$+jet events in a given jet $p_{\mathrm{T}}$ bin, which is
determined separately from the hadron-in-jet unfolding procedure. A test of
the method described here and below, performed with the reconstructed
simulation samples, confirmed that the generated distributions were reproduced
for all observables studied, within the statistical uncertainties of the
simulated sample. In this analysis, the $Z$-boson $p_{\mathrm{T}}$ is
integrated to provide the statistical precision to measure the fragmentation
as a function of jet $p_{\mathrm{T}}$. The integral of the fragmentation
distributions then corresponds to the mean multiplicity of charged hadrons
within the jet.

The number of $Z$+jet pairs in each jet $p_{\mathrm{T}}$ bin is corrected to
account for reconstruction and selection inefficiencies, and is determined
independently from and normalizes the fragmentation distributions. The muon
detection efficiencies are determined in data using the technique employed in
the inclusive weak boson cross-section measurements of LHCb [45, 41]. The jet
reconstruction efficiency is evaluated from simulation, and is greater than
90% for jets with $p_{\mathrm{T}}>20\text{\,Ge\kern-1.00006ptV}$. A correction
is also applied to account for differences between the number of events
produced and measured in a given $p_{\mathrm{T}}$ bin due to the jet
$p_{\mathrm{T}}$ resolution. This correction is determined from simulation,
and is less than 10%. The method described above is cross checked by comparing
the results to a full Bayesian unfolding [46] as implemented in Ref. [47]. The
two methods agree to within 1%.

Simulation is used to determine the tracking efficiency and to account for
effects from misreconstructed tracks that are incorrectly measured inside or
outside of the jet cone. The efficiency is evaluated as a function of momentum
and pseudorapidity and applied on a per-track basis. The efficiency decreases
for $p>150\text{\,Ge\kern-1.00006ptV}$ due to a requirement on the uncertainty
of the track bending radius which is part of the particle flow algorithm and
has a larger effect at high momentum. The efficiencies were found to be
independent of the observables $z$, $j_{\textrm{T}}$ and $r$ in the simulated
sample. To validate the efficiency corrections procedure, the simulation
sample is split in half and the efficiencies are determined with one half and
applied to the other. Good recovery of the generated charged hadron
distributions in $p$ and $\eta$ is observed. Within the statistical precision
of the sample the tracking efficiency does not depend on the jet
$p_{\mathrm{T}}$.

The effects of bin migration in jet $p_{\mathrm{T}}$ and in the fragmentation
observables on the fragmentation distributions, primarily due to the jet
energy and momentum resolutions, are corrected using the two-dimensional
Bayesian unfolding method. Response matrices are constructed for each
fragmentation observable using simulated samples that study the correlations
between the generated and reconstructed yields in bins of
$[z,p_{\textrm{T}}^{\textrm{jet}}]$,
$[\mbox{$j_{\textrm{T}}$},p_{\textrm{T}}^{\textrm{jet}}]$, and
$[r,p_{\textrm{T}}^{\textrm{jet}}]$. Typically the bin migration is less than
5%; however, it can be larger for more extreme values of the fragmentation
variables, for example at large $z$. The number of iterations in the Bayesian
unfolding procedure is selected to be the minimum number for which the
relative change in the fragmentation functions at $z\approx 0.05$ is smaller
than 0.2% per additional iteration in all of the jet $p_{\mathrm{T}}$ bins.
Based on this criterion, the unfolding is iterated seven times for each
observable.

Systematic uncertainties that arise from the uncertainties on the various
efficiencies are assigned to the number of $Z$+jet pairs measured in each jet
$p_{\mathrm{T}}$ bin. The uncertainty in the muon reconstruction efficiency is
negligible. Systematic uncertainties on the jet reconstruction are evaluated
as in Ref. [39] by comparing the jet reconstruction quality requirements in
simulation and data. Similarly to the muon efficiencies, the precision with
which the uncertainty of the jet reconstruction corrections are determined,
due to the limited simulation sample size, is also evaluated; however, this is
found to be negligible compared to the jet reconstruction quality requirement
uncertainty of 1.9%. The normalization is not corrected for $Z$+jet background
events, and thus a systematic uncertainty of 1.7% is assigned for the impurity
of both $Z$ bosons and jets in the measurement, as determined in Refs. [41,
39]. Effects from pile up are also studied and found to be negligible. The
total normalization uncertainty of 2.7% is determined by adding these
components in quadrature.

The jet-energy scale and resolution are also considered as sources of
systematic uncertainty. The jet-energy scale and its uncertainty have been
studied in previous measurements of the $Z$+jet cross-section [42, 39]. To
estimate these effects, the scale is varied by one standard deviation of its
uncertainty. New unfolding matrices are constructed with this modification,
and the difference in the fragmentation distributions determined with the
modified and nominal response matrices is taken as a systematic uncertainty.
Similarly, the systematic uncertainty due to the jet-energy resolution is
evaluated by smearing each component of the jet momentum by an additional term
corresponding to the uncertainty on the jet resolution and constructing new
response matrices. The difference between the nominal and smeared unfolded
charged hadron-in-jet distributions is taken as the systematic uncertainty on
the jet-energy resolution.

The unfolding method is validated with two different tests. The first test is
performed by splitting the simulated sample in two and using one half to
generate the response matrices with which the other half is unfolded. Recovery
of the generator-level fragmentation distributions is observed and average
deviations from perfect agreement are 2%, which is assigned as an uncertainty
related to the unfolding procedure. A second test is performed by splitting
the simulated sample in half by $Z$-boson $p_{\mathrm{T}}$, and performing a
similar test to the previous one to check for any uncertainty associated with
the assumed prior. The results again deviate from perfect agreement by about
2%, confirming that a 2% systematic uncertainty on the unfolding procedure is
appropriate.

The track selection requirements, tracking efficiency, and charged-hadron
identification are also studied as sources of systematic uncertainty. The
track selection uncertainty is assigned by requiring a tight fake-track
removal criterion and repeating the analysis. The differences in the final
fragmentation distributions with and without this requirement are taken as
systematic uncertainties. The track selection uncertainty is typically less
than 5%; however, it reaches a maximum of approximately 8% at some values of
$z$, $j_{\textrm{T}}$, and $r$ in the highest jet $p_{\mathrm{T}}$ bin
studied. The systematic uncertainty on the tracking efficiency is determined
by smoothing the two-dimensional efficiency and repeating the analysis, which
accounts for the statistical precision with which the efficiency is
determined. The resulting distributions are compared to the nominal
distributions and the differences are taken as uncertainties on the tracking
efficiency; these are generally less than 3% but rise up to 10% in some bins.
Uncertainties associated to misidentifying charged hadrons are also considered
by comparing the nominal fragmentation functions to those obtained when
hadron-to-lepton (and vice versa) misidentification probabilities are
considered. These uncertainties are less than 5%, except at large $z$ where
the charged pion-to-electron misidentification probability becomes larger
[32].

Figure 1: Distributions of the longitudinal momentum fraction of the hadron
with respect to the jet in three bins of jet $p_{\mathrm{T}}$. The bars
(boxes) show the statistical (systematic) uncertainties.

Figure 1 shows the distributions of $z$ in three jet $p_{\mathrm{T}}$ bins.
These illustrate that the longitudinal momentum fraction is approximately
constant as a function of jet $p_{\mathrm{T}}$ at high $z$. At low $z$ the
fragmentation functions differ, which is a kinematic effect due to the
requirement that the track momentum be greater than
$4\text{\,Ge\kern-1.00006ptV}$; therefore, higher $p_{\mathrm{T}}$ jets can
probe smaller $z$. This also reflects that the charged hadron multiplicity
increases with jet $p_{\mathrm{T}}$. Comparing these forward measurements to
inclusive jet measurements at central rapidity from ATLAS [24] indicates that
the fragmentation functions are not as steeply falling at high $z$ [48]. This
may reflect differences between light-quark and gluon fragmentation.

Figures 2 and 3 show the $j_{\textrm{T}}$ and $r$ distributions of charged
hadrons within jets. The $j_{\textrm{T}}$ profiles show a rounded peak at
small $j_{\textrm{T}}$ which transitions to a perturbative tail at larger
$j_{\textrm{T}}$ as also seen in Ref. [49]. This is indicative of an
observable that can be treated in the so-called transverse-momentum-dependent
framework [1, 2, 50, 3], where sensitivity to both a large and small
transverse momentum scale is necessary. The radial profiles show that the
number of charged hadrons at small $r$ is highly dependent on jet
$p_{\mathrm{T}}$; however, the values are relatively constant as a function of
jet $p_{\mathrm{T}}$ at nearly all other values of $r$. Interestingly, the
$j_{\textrm{T}}$ fragmentation distributions are similar to the central
pseudorapidity inclusive jet results; however, these measurements are more
collimated in $r$ than the inclusive jet measurements [48]. This behavior in
$r$ is correlated to the flatter fragmentation in $z$ and may be a reflection
of the different pseudorapidity region or differences in light-quark and gluon
fragmentation. We note that the comparisons to the measurements by ATLAS
should be qualitative in nature, rather than quantitative, due to the slightly
different kinematic criteria placed on each of the measurements. The
distributions in $j_{\textrm{T}}$ and $r$ offer the opportunity to study the
interplay between perturbative parton shower and nonperturbative hadronization
dynamics. For example, the steeply falling tail of the $j_{\textrm{T}}$
distributions results from a combination of perturbative radiation and
nonperturbative hadronization processes.

Figure 2: Distributions of the transverse momentum of charged hadrons with
respect to the jet axis in three bins of jet $p_{\mathrm{T}}$. The bars
(boxes) show the statistical (systematic) uncertainties. Figure 3: Radial
profile distributions of hadrons with respect to the jet axis in three bins of
jet $p_{\mathrm{T}}$. The bars (boxes) show the statistical (systematic)
uncertainties.

The fragmentation functions are compared to predictions from Pythia 8 $Z$+jet
events, where the details of the Pythia 8 configuration can be found in Ref.
[48]. These comparisons are made since the specific LHCb tune contains
realistic experimental conditions [33] and also shows that the unfolding
procedure is not simply correcting the measured distributions to the
predictions from Pythia 8\. An example of the comparison as a function of $z$
is shown in Fig. 4; all of the comparisons described in this text can be found
in Ref. [48]. In general, Pythia 8 underestimates the number of charged
hadrons at high $z$; Pythia 8 also underestimates the number of charged
hadrons at small $r$. Comparisons of the data to predictions from Pythia 8 as
a function of $j_{\textrm{T}}$ show a consistent shape, but in general Pythia
underestimates the number of charged hadrons in each bin by approximately 20%.
The integral of the ratio of the Pythia 8 predictions to the data is always
less than unity, which is a reflection of the underestimation of the mean
charged hadron multiplicity in Pythia 8.

Figure 4: The $z$ distribution for jets with
$30<p_{\mathrm{T}}<50\text{\,Ge\kern-1.00006ptV}$ compared to Pythia 8
simulation predictions. The bars (boxes) show the statistical (systematic)
uncertainties.

In summary, the production of charged hadrons in jets recoiling against a $Z$
boson is measured in $\sqrt{s}=8$ TeV $pp$ collisions by the LHCb experiment.
The jets are measured in the fiducial region of
$20<p_{\mathrm{T}}<100\text{\,Ge\kern-1.00006ptV}$ and $2.5<\eta<4$, while the
hadrons are required to have $p_{\mathrm{T}}>0.25\text{\,Ge\kern-1.00006ptV}$,
$p>4\text{\,Ge\kern-1.00006ptV}$, and to be located within the jet cone of
distance parameter $R=0.5$. The longitudinal momentum fraction, momentum
transverse to the jet axis, and radial profile of the charged hadrons are
measured with respect to the jet axis. These results provide insight into
hadronization mechanisms as they probe a new kinematic regime. They
additionally probe a high fraction of light-quark jets versus gluon jets when
compared to midrapidity inclusive jet measurements in the same jet
$p_{\mathrm{T}}$ range. The results are compared to predictions from the
Pythia 8 event generator with a specific LHCb configuration, and show that the
simulation underestimates the number of high momentum hadrons. Additionally,
comparisons with inclusive midrapidity gluon-dominated jet measurements
indicate that light quark-dominated jets recoiling against a $Z$ boson at
forward rapidity are more collimated in both $z$ and $r$ [48]. This work lays
the foundation for a broader hadronization research program at LHCb, utilizing
the excellent tracking, particle identification, and heavy-flavor jet tagging
capabilities already demonstrated by the LHCb detector [32, 51].

## Acknowledgements

We express our gratitude to our colleagues in the CERN accelerator departments
for the excellent performance of the LHC. We thank the technical and
administrative staff at the LHCb institutes. We acknowledge support from CERN
and from the national agencies: CAPES, CNPq, FAPERJ and FINEP (Brazil); MOST
and NSFC (China); CNRS/IN2P3 (France); BMBF, DFG and MPG (Germany); INFN
(Italy); NWO (Netherlands); MNiSW and NCN (Poland); MEN/IFA (Romania); MSHE
(Russia); MinECo (Spain); SNSF and SER (Switzerland); NASU (Ukraine); STFC
(United Kingdom); NSF (USA). We acknowledge the computing resources that are
provided by CERN, IN2P3 (France), KIT and DESY (Germany), INFN (Italy), SURF
(Netherlands), PIC (Spain), GridPP (United Kingdom), RRCKI and Yandex LLC
(Russia), CSCS (Switzerland), IFIN-HH (Romania), CBPF (Brazil), PL-GRID
(Poland) and OSC (USA). We are indebted to the communities behind the multiple
open-source software packages on which we depend. Individual groups or members
have received support from AvH Foundation (Germany); EPLANET, Marie
Skłodowska-Curie Actions and ERC (European Union); ANR, Labex P2IO and OCEVU,
and Région Auvergne-Rhône-Alpes (France); Key Research Program of Frontier
Sciences of CAS, CAS PIFI, and the Thousand Talents Program (China); RFBR, RSF
and Yandex LLC (Russia); GVA, XuntaGal and GENCAT (Spain); the Royal Society
and the Leverhulme Trust (United Kingdom); Laboratory Directed Research and
Development program of LANL (USA).

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LHCb Collaboration

R. Aaij29, C. Abellán Beteta46, B. Adeva43, M. Adinolfi50, C.A. Aidala77, Z.
Ajaltouni7, S. Akar61, P. Albicocco20, J. Albrecht12, F. Alessio44, M.
Alexander55, A. Alfonso Albero42, G. Alkhazov35, P. Alvarez Cartelle57, A.A.
Alves Jr43, S. Amato2, Y. Amhis9, L. An19, L. Anderlini19, G. Andreassi45, M.
Andreotti18, J.E. Andrews62, F. Archilli29, J. Arnau Romeu8, A. Artamonov41,
M. Artuso63, K. Arzymatov39, E. Aslanides8, M. Atzeni46, B. Audurier24, S.
Bachmann14, J.J. Back52, S. Baker57, V. Balagura9,b, W. Baldini18,44, A.
Baranov39, R.J. Barlow58, S. Barsuk9, W. Barter57, M. Bartolini21, F.
Baryshnikov73, V. Batozskaya33, B. Batsukh63, A. Battig12, V. Battista45, A.
Bay45, F. Bedeschi26, I. Bediaga1, A. Beiter63, L.J. Bel29, S. Belin24, N.
Beliy4, V. Bellee45, N. Belloli22,i, K. Belous41, I. Belyaev36, G.
Bencivenni20, E. Ben-Haim10, S. Benson29, S. Beranek11, A. Berezhnoy37, R.
Bernet46, D. Berninghoff14, E. Bertholet10, A. Bertolin25, C. Betancourt46, F.
Betti17,e, M.O. Bettler51, Ia. Bezshyiko46, S. Bhasin50, J. Bhom31, M.S.
Bieker12, S. Bifani49, P. Billoir10, A. Birnkraut12, A. Bizzeti19,u, M.
Bjørn59, M.P. Blago44, T. Blake52, F. Blanc45, S. Blusk63, D. Bobulska55, V.
Bocci28, O. Boente Garcia43, T. Boettcher60, A. Bondar40,x, N. Bondar35, S.
Borghi58,44, M. Borisyak39, M. Borsato14, M. Boubdir11, T.J.V. Bowcock56, C.
Bozzi18,44, S. Braun14, M. Brodski44, J. Brodzicka31, A. Brossa Gonzalo52, D.
Brundu24,44, E. Buchanan50, A. Buonaura46, C. Burr58, A. Bursche24, J.S.
Butter29, J. Buytaert44, W. Byczynski44, S. Cadeddu24, H. Cai67, R.
Calabrese18,g, S. Cali20, R. Calladine49, M. Calvi22,i, M. Calvo Gomez42,m, A.
Camboni42,m, P. Campana20, D.H. Campora Perez44, L. Capriotti17,e, A.
Carbone17,e, G. Carboni27, R. Cardinale21, A. Cardini24, P. Carniti22,i, K.
Carvalho Akiba2, G. Casse56, M. Cattaneo44, G. Cavallero21, R. Cenci26,p, M.G.
Chapman50, M. Charles10,44, Ph. Charpentier44, G. Chatzikonstantinidis49, M.
Chefdeville6, V. Chekalina39, C. Chen3, S. Chen24, S.-G. Chitic44, V.
Chobanova43, M. Chrzaszcz44, A. Chubykin35, P. Ciambrone20, X. Cid Vidal43, G.
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Closier44, V. Coco44, J.A.B. Coelho9, J. Cogan8, E. Cogneras7, L. Cojocariu34,
P. Collins44, T. Colombo44, A. Comerma-Montells14, A. Contu24, G. Coombs44, S.
Coquereau42, G. Corti44, C.M. Costa Sobral52, B. Couturier44, G.A. Cowan54,
D.C. Craik60, A. Crocombe52, M. Cruz Torres1, R. Currie54, C.L. Da Silva78, E.
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J.M. De Miranda1, L. De Paula2, M. De Serio16,d, P. De Simone20, J.A. de
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O. Leroy8, T. Lesiak31, B. Leverington14, H. Li66, P.-R. Li4,ab, X. Li78, Y.
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C. Marin Benito9, M. Marinangeli45, P. Marino45, J. Marks14, P.J. Marshall56,
G. Martellotti28, M. Martinelli44,22, D. Martinez Santos43, F. Martinez
Vidal76, A. Massafferri1, M. Materok11, R. Matev44, A. Mathad46, Z. Mathe44,
V. Matiunin36, C. Matteuzzi22, K.R. Mattioli77, A. Mauri46, E. Maurice9,b, B.
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C. Meaux8, N. Meinert70, D. Melnychuk33, M. Merk29, A. Merli23,q, E.
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I. Nasteva2, M. Needham54, N. Neri23,q, S. Neubert14, N. Neufeld44, R.
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C. Parkes58,44, G. Passaleva19,44, A. Pastore16, M. Patel57, C.
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1Centro Brasileiro de Pesquisas Físicas (CBPF), Rio de Janeiro, Brazil
2Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
3Center for High Energy Physics, Tsinghua University, Beijing, China
4University of Chinese Academy of Sciences, Beijing, China
5Institute Of High Energy Physics (ihep), Beijing, China
6Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, IN2P3-LAPP, Annecy,
France
7Université Clermont Auvergne, CNRS/IN2P3, LPC, Clermont-Ferrand, France
8Aix Marseille Univ, CNRS/IN2P3, CPPM, Marseille, France
9LAL, Univ. Paris-Sud, CNRS/IN2P3, Université Paris-Saclay, Orsay, France
10LPNHE, Sorbonne Université, Paris Diderot Sorbonne Paris Cité, CNRS/IN2P3,
Paris, France
11I. Physikalisches Institut, RWTH Aachen University, Aachen, Germany
12Fakultät Physik, Technische Universität Dortmund, Dortmund, Germany
13Max-Planck-Institut für Kernphysik (MPIK), Heidelberg, Germany
14Physikalisches Institut, Ruprecht-Karls-Universität Heidelberg, Heidelberg,
Germany
15School of Physics, University College Dublin, Dublin, Ireland
16INFN Sezione di Bari, Bari, Italy
17INFN Sezione di Bologna, Bologna, Italy
18INFN Sezione di Ferrara, Ferrara, Italy
19INFN Sezione di Firenze, Firenze, Italy
20INFN Laboratori Nazionali di Frascati, Frascati, Italy
21INFN Sezione di Genova, Genova, Italy
22INFN Sezione di Milano-Bicocca, Milano, Italy
23INFN Sezione di Milano, Milano, Italy
24INFN Sezione di Cagliari, Monserrato, Italy
25INFN Sezione di Padova, Padova, Italy
26INFN Sezione di Pisa, Pisa, Italy
27INFN Sezione di Roma Tor Vergata, Roma, Italy
28INFN Sezione di Roma La Sapienza, Roma, Italy
29Nikhef National Institute for Subatomic Physics, Amsterdam, Netherlands
30Nikhef National Institute for Subatomic Physics and VU University Amsterdam,
Amsterdam, Netherlands
31Henryk Niewodniczanski Institute of Nuclear Physics Polish Academy of
Sciences, Kraków, Poland
32AGH - University of Science and Technology, Faculty of Physics and Applied
Computer Science, Kraków, Poland
33National Center for Nuclear Research (NCBJ), Warsaw, Poland
34Horia Hulubei National Institute of Physics and Nuclear Engineering,
Bucharest-Magurele, Romania
35Petersburg Nuclear Physics Institute NRC Kurchatov Institute (PNPI NRC KI),
Gatchina, Russia
36Institute of Theoretical and Experimental Physics NRC Kurchatov Institute
(ITEP NRC KI), Moscow, Russia, Moscow, Russia
37Institute of Nuclear Physics, Moscow State University (SINP MSU), Moscow,
Russia
38Institute for Nuclear Research of the Russian Academy of Sciences (INR RAS),
Moscow, Russia
39Yandex School of Data Analysis, Moscow, Russia
40Budker Institute of Nuclear Physics (SB RAS), Novosibirsk, Russia
41Institute for High Energy Physics NRC Kurchatov Institute (IHEP NRC KI),
Protvino, Russia, Protvino, Russia
42ICCUB, Universitat de Barcelona, Barcelona, Spain
43Instituto Galego de Física de Altas Enerxías (IGFAE), Universidade de
Santiago de Compostela, Santiago de Compostela, Spain
44European Organization for Nuclear Research (CERN), Geneva, Switzerland
45Institute of Physics, Ecole Polytechnique Fédérale de Lausanne (EPFL),
Lausanne, Switzerland
46Physik-Institut, Universität Zürich, Zürich, Switzerland
47NSC Kharkiv Institute of Physics and Technology (NSC KIPT), Kharkiv, Ukraine
48Institute for Nuclear Research of the National Academy of Sciences (KINR),
Kyiv, Ukraine
49University of Birmingham, Birmingham, United Kingdom
50H.H. Wills Physics Laboratory, University of Bristol, Bristol, United
Kingdom
51Cavendish Laboratory, University of Cambridge, Cambridge, United Kingdom
52Department of Physics, University of Warwick, Coventry, United Kingdom
53STFC Rutherford Appleton Laboratory, Didcot, United Kingdom
54School of Physics and Astronomy, University of Edinburgh, Edinburgh, United
Kingdom
55School of Physics and Astronomy, University of Glasgow, Glasgow, United
Kingdom
56Oliver Lodge Laboratory, University of Liverpool, Liverpool, United Kingdom
57Imperial College London, London, United Kingdom
58School of Physics and Astronomy, University of Manchester, Manchester,
United Kingdom
59Department of Physics, University of Oxford, Oxford, United Kingdom
60Massachusetts Institute of Technology, Cambridge, MA, United States
61University of Cincinnati, Cincinnati, OH, United States
62University of Maryland, College Park, MD, United States
63Syracuse University, Syracuse, NY, United States
64Laboratory of Mathematical and Subatomic Physics , Constantine, Algeria,
associated to 2
65Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio), Rio de
Janeiro, Brazil, associated to 2
66South China Normal University, Guangzhou, China, associated to 3
67School of Physics and Technology, Wuhan University, Wuhan, China, associated
to 3
68Institute of Particle Physics, Central China Normal University, Wuhan,
Hubei, China, associated to 3
69Departamento de Fisica , Universidad Nacional de Colombia, Bogota, Colombia,
associated to 10
70Institut für Physik, Universität Rostock, Rostock, Germany, associated to 14
71Van Swinderen Institute, University of Groningen, Groningen, Netherlands,
associated to 29
72National Research Centre Kurchatov Institute, Moscow, Russia, associated to
36
73National University of Science and Technology “MISIS”, Moscow, Russia,
associated to 36
74National Research University Higher School of Economics, Moscow, Russia,
associated to 39
75National Research Tomsk Polytechnic University, Tomsk, Russia, associated to
36
76Instituto de Fisica Corpuscular, Centro Mixto Universidad de Valencia -
CSIC, Valencia, Spain, associated to 42
77University of Michigan, Ann Arbor, United States, associated to 63
78Los Alamos National Laboratory (LANL), Los Alamos, United States, associated
to 63

aUniversidade Federal do Triângulo Mineiro (UFTM), Uberaba-MG, Brazil
bLaboratoire Leprince-Ringuet, Palaiseau, France
cP.N. Lebedev Physical Institute, Russian Academy of Science (LPI RAS),
Moscow, Russia
dUniversità di Bari, Bari, Italy
eUniversità di Bologna, Bologna, Italy
fUniversità di Cagliari, Cagliari, Italy
gUniversità di Ferrara, Ferrara, Italy
hUniversità di Genova, Genova, Italy
iUniversità di Milano Bicocca, Milano, Italy
jUniversità di Roma Tor Vergata, Roma, Italy
kUniversità di Roma La Sapienza, Roma, Italy
lAGH - University of Science and Technology, Faculty of Computer Science,
Electronics and Telecommunications, Kraków, Poland
mLIFAELS, La Salle, Universitat Ramon Llull, Barcelona, Spain
nHanoi University of Science, Hanoi, Vietnam
oUniversità di Padova, Padova, Italy
pUniversità di Pisa, Pisa, Italy
qUniversità degli Studi di Milano, Milano, Italy
rUniversità di Urbino, Urbino, Italy
sUniversità della Basilicata, Potenza, Italy
tScuola Normale Superiore, Pisa, Italy
uUniversità di Modena e Reggio Emilia, Modena, Italy
vH.H. Wills Physics Laboratory, University of Bristol, Bristol, United Kingdom
wMSU - Iligan Institute of Technology (MSU-IIT), Iligan, Philippines
xNovosibirsk State University, Novosibirsk, Russia
ySezione INFN di Trieste, Trieste, Italy
zSchool of Physics and Information Technology, Shaanxi Normal University
(SNNU), Xi’an, China
aaPhysics and Micro Electronic College, Hunan University, Changsha City, China
abLanzhou University, Lanzhou, China
†Deceased