B2G-19-004

B2G-19-004

# Search for single production of a vector-like quark decaying to a top quark
and a boson in the final state with jets and missing transverse momentum at
$\sqrt{s}=13\TeV$

###### Abstract

A search is presented for single production of a vector-like quark with charge
$2/3$ $e$, in the decay channel featuring a top quark and a boson, with the
top quark decaying hadronically and the boson decaying to neutrinos. The
search uses data collected by the CMS experiment in proton-proton collisions
at a center-of-mass energy of 13, corresponding to an integrated luminosity of
137recorded at the CERN LHC in 2016–2018. The search is sensitive to a quark
mass between 0.6 and 1.8with decay widths ranging from negligibly small up to
30% of the quark mass. Reconstruction strategies for the top quark are based
on the degree of Lorentz boosting of its final state. At 95% confidence level,
the upper limit on the product of the cross section and branching fraction for
a quark of small decay width varies between 15 and 602fb, depending on its
mass. For a quark with decay widths between 10 and 30% of its mass, this upper
limit ranges between 16 and 836fb. For most of the studied range, the results
provide the best limits to date. This is the first search for single quark
production based on the full Run 2 data set of the LHC.

##  0.1 Introduction

New heavy color-triplet spin-$1/2$ fermions with nonchiral couplings, referred
to as vector-like quarks (VLQs), are predicted in many extensions of the
standard model (SM) [1, 2, 3, 4, 5, 6] in order to resolve theoretical issues,
such as the hierarchy problem. While the masses of the SM chiral quarks arise
from Yukawa couplings to the Higgs field, for VLQs non-Yukawa coupling terms
in the Lagrangian are allowed. The existence of VLQs is not yet excluded by
precision SM measurements, unlike the case of chiral quarks from a fourth
generation [7]. Pair production of VLQs occurs via strong interaction
processes while single production, which dominates for VLQ masses above
$\approx$1owing to the larger phase space, occurs via electroweak interaction
processes.

This paper presents a search for the single production of a vector-like quark
with charge $2/3$ $e$, where the quark decays to a top quark () and a boson.
The analysis is performed in proton-proton ($\Pp\Pp$) collision data at
$\sqrt{s}=13\TeV$ collected with the CMS detector in Run 2 of the CERN LHC in
2016–2018. An example of a leading-order (LO) Feynman diagram for single quark
production and decay is shown in Fig. 1.

Figure 1: A representative leading-order Feynman diagram for the production of
a single vector-like quark decaying into a boson and a top quark.

This analysis targets $\PQt\PZ$ final states where the top quark decays
hadronically via $\PQt\to\PW\PQb\to\PQq^{\prime}\PAQq\PQb$ and the boson
decays to neutrinos. Neutrinos are not detected in the experimental apparatus,
therefore a full reconstruction of the quark four-momentum cannot be
performed, and signal events are characterized by a large transverse momentum
() imbalance.

In addition to the $\PQt\PZ$ decay channel, a quark can also decay to a bottom
quark () and a boson, or a top quark and a Higgs boson ($\PH$), with branching
fractions that are a function of the model-dependent couplings with SM third-
generation quarks. For a singlet quark model [3] the branching fractions for
the $\PQb\PW$, $\PQt\PZ$, and $\PQt\PH$ decay channels are approximately 0.5,
0.25, and 0.25, respectively.

Searches for pair production of quarks at 13, targeting a variety of possible
final states from the above decay channels, have been performed by the ATLAS
[8, 9, 10, 11, 12, 13] and CMS [14, 15, 16, 17, 18] Collaborations. In
particular, the paper [13] presenting the combination of the searches at 13by
the ATLAS Collaboration has set a lower limit of 1.3at 95% confidence level on
the mass of a quark in the singlet quark model [3].

A search for single production of quarks exploiting the same final state as in
this analysis, and based on the 2015–2016 data set only, has been reported by
the ATLAS Collaboration [19]. Searches in the $\PQt\PZ$ channel using final
states where the Z boson decays to a pair of muons or electrons have been
published by both the ATLAS and CMS Collaborations [10, 20]. A search
exploiting fully hadronic final states, targeting $\PQt\PZ$ and $\PQt\PH$
channels, has been reported in a recent paper [21] by the CMS Collaboration.
Also the $\PQb\PW$ channel has been investigated at 13in final states where
the boson decays leptonically [22, 23]. No deviations have been observed in
any of these searches with respect to the expected SM background. All the
above searches are based on data sets corresponding to a maximum integrated
luminosity of about 36, which is almost a factor of four less than the 137used
for the analysis described in this paper.

As shown in Fig. 1, final states of signal events are characterized by a top
or bottom quark produced in association with the quark as well as a quark that
tends to be emitted at a low angle with respect to the beam axis, in the
forward region of the detector. For the interpretation of our search, we
assume a singlet quark model [5] in which the production process is the
associated production with a bottom quark. This process has a much larger
cross section, the production in association with a top quark being suppressed
owing to the higher top quark mass [5]. However, no specific requirement on
the associated top or bottom quark is considered in the analysis selection and
strategy, and by design the results of this search may also be interpreted in
terms of other models with a single top quark and missing transverse momentum
in the final state.

The considered mass values for the quark range from 0.6 to 1.8. Multiple
hypotheses for the quark width ($\Gamma$) are probed for the first time in
this channel, with values varying from 1 to 30% of the quark mass, by
employing simulated signal samples generated using different width hypotheses.
A novel and distinctive feature of this search is that the analysis exploits
the fact that the quark’s final state appears differently in the detector,
depending on its mass. Accordingly, different reconstruction strategies for
the top quark are employed for different mass ranges of the quark. For lower
mass values the quarks from the top quark decay tend to be reconstructed as
individual, narrow-cone jets. As the quark mass increases, the decay products
of the boosted top quark become highly collimated, producing jets that overlap
either completely or partially. Therefore, for the case of large quark masses,
large-radius jets are studied with the aid of substructure techniques in order
to identify jets originating from the top quark or the boson, enhancing the
analysis sensitivity.

Tabulated results are provided in the HEPData record for this analysis [24].

##  0.2 The CMS detector, data, and simulation

This analysis is based on $\Pp\Pp$ collision data at $\sqrt{s}=13\TeV$
collected with the CMS detector in Run 2 of the LHC during the years
2016–2018, and corresponding to integrated luminosities of 36.3, 40.7, and
59.8, for the 2016, 2017, and 2018 data sets, respectively. The central
feature of the CMS apparatus is a superconducting solenoid of 6m internal
diameter, providing a magnetic field of 3.8T. Within the solenoid volume are a
silicon pixel and strip tracker, a lead tungstate crystal electromagnetic
calorimeter (ECAL), and a brass and scintillator hadron calorimeter (HCAL),
each composed of a barrel and two endcap sections. Forward calorimeters, made
of steel and quartz-fibres, extend the pseudorapidity ($\eta$) coverage
provided by the barrel and endcap detectors. Muons are detected in gas-
ionization chambers embedded in the steel flux-return yoke outside the
solenoid. A more detailed description of the CMS detector, together with a
definition of the coordinate system used and the relevant kinematic variables,
can be found in Ref. [25].

Events of interest are selected using a two-tiered trigger system. The first
level (L1), composed of custom hardware processors, uses information from the
calorimeters and muon detectors to select events at a rate of around 100kHz
within a fixed latency of about 4 [26]. The second level, known as the high-
level trigger (HLT), consists of a farm of processors running a version of the
full event reconstruction software optimized for fast processing, and reduces
the event rate to around 1kHz before data storage [27]. Signal events of this
analysis, because of the presence of a boosted Z boson decaying to a pair of
neutrinos, are selected online by triggers which require a large transverse
momentum imbalance.

Simulated background samples are generated at LO with [28] for +jets, +jets,
and for events with jets arising exclusively from quantum chromodynamics (QCD)
interactions (multijet events). The next-to-LO (NLO) generator 2.0 [29, 30,
31] is used to simulate top quark-antiquark pairs produced in association with
jets ($\PQt\PAQt$+jets) [32] and single top quark [33] events. The version is
2.2.2 (2.4.2) for simulated events used with 2016 (2017 and 2018) data.
Diboson events with inclusive final states are generated at LO using 8.2 [34].
Diboson events with exclusive final states, generated at NLO with and 2.0, are
employed in specific signal regions defined in the analysis to improve the
statistical precision of the background estimates. Finally, at NLO is used for
+V and tZq samples, which are minor backgrounds. The 2.0 and generators are
interfaced with 8.2 with the following versions and underlying event tunes:
version 8.226 and tune CUETP8M1 [35] for the simulated events used with 2016
data, and version 8.230 and tune CP5 [36] for those used with 2017 and 2018
data. The event tune CUETP8M2T4 [37] is employed for the simulated
$\PQt\PAQt$+jets events used with 2016 data, and the version 8.240 is adopted
for simulated $\PQt\PAQt$+jets events used for 2017 and 2018 data. The
background processes are initially normalized to their theoretical cross
sections, using the highest order available. The +jets and +jets events are
further normalized to account for NLO in electroweak and next-to-NLO (NNLO) in
QCD corrections to the cross sections from Ref. [38]. The cross section for
the background is computed at NNLO in perturbative QCD using a soft-gluon
resummation at next-to-next-to-leading logarithmic accuracy with the Top++ 2.0
program [39]. For single top quark production, NLO calculations are performed
with HATHOR 2.1 [40].

Signal event samples are generated in the 4-flavor scheme at LO using
interfaced to 8.2. A singlet quark is assumed that is produced in association
with a bottom quark with suppressed right-handed coupling to SM particles
[41]. The contribution of the associated production with a top quark is within
the theory uncertainty of the dominant production with a bottom quark and is
considered negligible. The generated quark mass in the samples ranges from 0.6
up to 1.8, and several hypotheses for the quark width are considered. Narrow-
width samples are generated in mass steps of 0.1and with $\Gamma$ set to 10,
which is much smaller than the experimental resolution. Large-width samples
are generated in mass steps of 0.2with $\Gamma$ set to 10, 20, and 30% of the
resonance mass $m_{\PQT}$. Again, the version 2.2.2 (2.4.2) is used for 2016
(2017 and 2018) simulated events. The choice of the interval and granularity
for the large-width samples, both in $m_{\PQT}$ and $\Gamma$, takes into
account the sensitivity of the discriminating observables to these variables.
All possible decays of the top quark and $\PZ$ boson have been considered for
the signal process. The total cross section for a singly produced quark that
decays to $\PQt\PZ$ is computed using the interpretation framework described
in Ref. [42]. The cross section values for the $\Gamma/m_{\PQT}$ hypotheses of
1, 10, 20, and 30% are obtained from computations performed at LO QCD with the
full width calculation, based on Ref. [43]. The cross section values for the
single production of a vector-like singlet quark with $\Gamma/m_{\PQT}$ of 1,
10, 20, and 30% are reported in Table 0.2. An additional benchmark is
considered for a value of $\Gamma/m_{\PQT}$ of 5%, and the same narrow width
signal sample is used in this case as for the hypothesis with 1% for
$\Gamma/m_{\PQT}$. The cross section values for the 5% mass points are
extrapolated from the values corresponding to the 1 and 10% hypotheses using
the procedure described in Ref. [21].

Theoretical cross sections, in fb, for single production of a vector-like
singlet quark in association with a bottom quark, with the quark decaying to
$\PQt\PZ$, for mass hypotheses between 0.6 and 1.8in steps of 0.1, and for
resonance widths that are 1, 10, 20, and 30% of its mass. The framework for
the computation is described in Refs. [42, 43]. The uncertainties, given in
parentheses, are obtained by halving and doubling the values of QCD
renormalization and factorization scales.
$\sigma$($\Pp\Pp\to\PQT\PQb\PQq\to\PQt\PZ\PQb\PQq$) [fb] $m_{\PQT}$ []
$\Gamma/m_{\PQT}=1\%$ $\Gamma/m_{\PQT}=10\%$ $\Gamma/m_{\PQT}=20\%$
$\Gamma/m_{\PQT}=30\%$ 0.6 $175\,(34)$ $1750\,(340)$ $3500\,(690)$
$5200\,(1000)$ 0.7 $78\,(16)$ $780\,(160)$ $1520\,(310)$ $2240\,(710)$ 0.8
$41.5\,(8.6)$ $409\,(85)$ $790\,(170)$ $1150\,(250)$ 0.9 $23.3\,(5.0)$
$228\,(49)$ $437\,(94)$ $630\,(140)$ 1.0 $13.6\,(3.0)$ $132\,(29)$ $251\,(55)$
$360\,(80)$ 1.1 $8.2\,(1.8)$ $79\,(18)$ $150\,(34)$ $214\,(48)$ 1.2
$5.1\,(1.2)$ $49\,(11)$ $93\,(21)$ $131\,(30)$ 1.3 $3.25\,(0.75)$
$31.2\,(7.3)$ $59\,(14)$ $82\,(19)$ 1.4 $2.12\,(0.50)$ $20.3\,(4.8)$
$37.9\,(9.0)$ $54\,(13)$ 1.5 $1.41\,(0.34)$ $13.4\,(3.2)$ $25.0\,(6.0)$
$35.2\,(8.4)$ 1.6 $0.94\,(0.23)$ $9.0\,(2.2)$ $16.8\,(4.1)$ $23.5\,(5.7)$ 1.7
$0.64\,(0.16)$ $6.2\,(1.5)$ $11.5\,(2.8)$ $16.0\,(3.9)$ 1.8 $0.45\,(0.11)$
$4.2\,(1.1)$ $7.9\,(2.0)$ $11.0\,(2.8)$

The simulated samples are generated using the LO NNPDF 3.0 [44] parton
distribution function (PDF) sets for 2016, except for and single top quark
events, which adopt NLO PDF sets in this case, and using the NNLO NNPDF 3.1
[45] PDF sets for 2017 and 2018. The generated events are passed through a
simulation of the CMS detector based on [46, 47]. Additional interactions in
the same or adjacent bunch crossings (pileup) are included in the simulation,
with their frequency distribution adjusted to match that observed in data.

##  0.3 Event reconstruction and selection

The candidate vertex with the largest value of summed physics-object $\pt^{2}$
is taken to be the primary $\Pp\Pp$ interaction vertex. The physics objects
are the jets, clustered using the jet finding algorithm [48, 49] with the
tracks assigned to candidate vertices as inputs, and the associated missing
transverse momentum, taken as the negative vector sum of the of those jets.
The primary vertex must be within 24cm of the nominal interaction point along
the beam axis and within 2cm in the transverse plane.

Particle candidates in the event are reconstructed using the particle-flow
(PF) algorithm [50], which performs a global event reconstruction by combining
the information from the various elements of the CMS detector, and which
provides identification of muons, electrons, photons, and charged and neutral
hadrons. Jets are clustered from the PF candidates using the anti-algorithm
[48] with distance parameters of 0.4 (AK4 jets) and 0.8 (AK8 jets). An
algorithm [51] is applied to the AK4 jets to remove charged hadrons not
originating from the primary vertex, while for the AK8 jets the pileup per
particle identification algorithm [52] is employed, which assigns a weight to
charged and neutral PF candidates, according to the likelihood that the
particle originates from a pileup interaction. The weight is then used to
rescale the particle four-momentum. Correction factors as a function of the ,
$\eta$, energy density, and the area of the jet are applied to calibrate the
jet energy scale. The jet energy resolution for simulated jets is adjusted to
reproduce the resolution in data [53].

The missing transverse momentum vector is defined as the negative vector sum
of all the PF candidates in an event, and its magnitude is denoted as [54].
The missing hadronic activity is defined as the negative vector sum of the AK4
jets , and its magnitude is denoted as . The and are modified to account for
corrections to the energy scale of the reconstructed jets in the event. The
missing transverse momentum and the missing hadronic activity are computed
also at online level, using algorithms similar to the offline versions. Events
are selected at the HLT if they have and greater than 120.

At offline level, the AK4 jets selected for the analysis must have
$\pt>30\GeV$, while the AK8 jets must have $\pt>200\GeV$. Central AK4 or AK8
jets must have $\abs{\eta}<2.4$. Central AK4 jets are used as input to the
tagging algorithm to identify jets originating from the hadronization of a
bottom quark. Forward jets are defined as AK4 jets in the region
$2.4\leq\abs{\eta}\leq 4.0$. Jets with larger values of $\abs{\eta}$ are not
included in the count of forward jets, in order to reduce the contribution
from pileup interactions.

The mass of an AK8 jet is measured after applying jet grooming techniques, in
which the constituents of the AK8 jets are reclustered using the
Cambridge–Aachen algorithm [55, 56]. The modified mass drop tagger algorithm
[57, 58], also known as the soft-drop (SD) algorithm, with an angular exponent
$\beta=0$, soft cutoff threshold $z_{\text{cut}}<0.1$, and characteristic
radius $R_{0}=0.8$ [59], is applied to remove soft and wide-angle radiation
from the jet.

Jets containing hadrons (jets) are identified using a version of the combined
secondary vertex algorithm based on a deep neural network [60, 61]. A working
point with an identification efficiency of 70% for genuine jets and a
rejection of 99% for light-flavor jets is used for AK4 jets. The tagging
efficiencies in simulated event samples are corrected to match those measured
in data.

The AK8 jets may be tagged as originating from a boson decaying to
$\PQq^{\prime}\PAQq$ (jet) or from a top quark decaying into a fully hadronic
final state (jet). An AK8 jet is identified as a jet by using the groomed SD
mass $m_{\text{jet}}$, which is required to be between 65 and 105. A
requirement on the decorrelated $N$-subjettiness ratio denoted as
$\tau^{\mathrm{DDT}}_{21}$, which is constructed to decorrelate the dependence
on the jet scale and mass [62, 63], is also applied. We first define
$\tau_{21}=\tau_{2}/\tau_{1}$ [64], and then the decorrelated ratio is
$\tau^{\mathrm{DDT}}_{21}=\tau_{21}-M\ln[m^{2}_{\text{jet}}/(\pt^{\text{jet}}\mu)]$,
where $\pt^{\text{jet}}$ is the jet , $\mu=1\GeV$, and $M$ is a parameter
equal to $-0.080$ ($-0.082$) for 2016 (2017 and 2018) data. The requirement
$\tau^{\mathrm{DDT}}_{21}<0.43$ is applied, corresponding to an efficiency
varying between 9 and 31% over the kinematic range of this analysis for the
combined $m_{\text{jet}}$ and $\tau^{\mathrm{DDT}}_{21}$ requirements, and an
efficiency of incorrectly tagging a jet from a quark or gluon between 0.4 and
3.6%, increasing with the jet . The variables for tagging are calibrated in a
sample enriched in hadronically decaying $\PW$ bosons [65].

The combined secondary vertex algorithm for tagging is also applied to the
subjets that constitute the internal structure of an AK8 jet after the removal
of the recognized soft radiation. In this case a looser working point, with an
identification efficiency of 85% for genuine jets and a rejection of 90% for
light-flavor jets, is applied.

An AK8 jet is identified as a jet if it has a $\pt>400\GeV$, at least one
subjet tagged, a groomed SD mass between 105 and 220, and if it satisfies the
requirement on the $N$-subjettiness ratio $\tau_{32}=\tau_{3}/\tau_{2}<0.65$.
A decorrelated $N$-subjettiness ratio has not been constructed in this case,
but the overall performances of tagging criteria have been optimized targeting
specifically the identification of top quarks. The variables for top tagging
are calibrated in a -enriched sample [66]. The combined SD mass and
$\tau_{32}$ requirements correspond to a jet tagging efficiency of about 39%,
and an efficiency of incorrectly identifying a jet from another quark as a jet
of between 1.8 and 3.5%, depending on the data-taking year.

Electron and muon candidates are reconstructed in order to reject background
events containing leptons. Electron candidates are reconstructed in the
fiducial region $\abs{\eta}<2.5$ by matching the energy deposits in the ECAL
with tracks reconstructed in the tracker [67]. Electron identification
criteria are based on the energy distribution in the ECAL, on the ratio of the
energies measured in the HCAL and ECAL in the region around the electron
candidate, and on the compatibility of the track with the primary vertex of
event. Muon candidates are reconstructed within the acceptance of the CMS muon
system $\abs{\eta}<2.4$ by using information from both the muon and tracker
systems [68]. Muon identification criteria are based on the track impact
parameter, the track reconstruction quality, and the number of hits observed
in the tracker and muon systems. Tracks of leptons (electrons and muons) are
required to be isolated from unassociated energy deposits in the event. An
upper threshold is applied to an isolation variable [67, 68] defined as the
scalar sum of the charged and neutral hadrons and photons in a cone around the
lepton direction of size $\Delta
R=\sqrt{\smash[b]{(\Delta\eta)^{2}+(\Delta\phi)^{2}}}$, where $\phi$ is the
azimuthal angle in radians. The cone size is 0.4 for muons and 0.3 for
electrons. The sum is then corrected for pileup effects and divided by the
lepton .

In the offline analysis, events are selected if they have at least one AK4 jet
with $\pt>30\GeV$ and $\abs{\eta}<4.0$, and $\ptmiss>200\GeV$. In order to
suppress multijet background events, the angular separation
$\Delta\phi_{\text{jet},\ptvecmiss}$ between every AK4 jet and the must be
greater than 0.6. Events containing identified electrons or muons are vetoed.

Three different top quark candidate reconstruction strategies are exploited. A
top quark candidate can be identified as a single jet (merged case), or it can
be reconstructed from the combination of one jet and one jet (partially merged
case), or of three AK4 jets (resolved case).

In the partially merged case, if multiple jets are present in the event, the
one with the highest is chosen for the top quark reconstruction, and jets are
considered for top quark reconstruction only if they are separated from the
jet by $\Delta R_{\PW,\PQb}>1.2$. For the resolved top quark reconstruction,
at least one of the three jets used for the combination must have been tagged
as one jet. In all three cases, if multiple top quark candidates of the same
type are reconstructed, the one with the largest is selected for the
subsequent steps of the analysis.

Each selected event is assigned to one of six exclusive categories, defined
according to the type of the reconstructed top quark candidate, , merged,
partially merged, or resolved case, and to the presence or not of at least one
forward jet. If multiple types of top quark candidates are reconstructed, the
event is assigned to only one category according to the following hierarchy,
established after an optimization procedure aimed at achieving the best
expected exclusion limit over the whole mass range: first the merged category,
then the partially merged category, and finally the resolved category.
Additional requirements are applied to events in the resolved category in
order to enhance the sensitivity: the resolved top quark candidate must have
$\pt>250\GeV$, and the event , defined as the scalar sum of the AK4 jets ,
must be greater than 200.

The signal selection efficiency, including both the contribution from detector
acceptance and selection efficiency, in each of the six event categories is
reported in Fig. 2, for all the masses and widths of the quark considered in
the analysis. All possible decays of the top quark and boson are considered.
The efficiency is averaged over the three data-taking years according to the
respective integrated luminosities. The figure shows that, for the resolved
analysis, the signal efficiency reaches the highest value for the mass
hypotheses around 0.8after correction for threshold effects due to kinematic
requirements applied, and decreases as the hypothesized resonance mass
increases. The opposite trend can be seen for the merged analysis, which in
contrast displays an efficiency that increases with the mass hypothesis. This
behavior is consistent with the top quark decay products being more collimated
for higher top quark momentum regimes, corresponding to higher quark mass
hypotheses. The partially merged analysis has a lower efficiency than the
other two; nonetheless, it helps to recover signal events that are not
selected by the merged and resolved analyses. The wide width interpretations
feature similar efficiencies to the narrow width case, except for the lower
mass points, where the broader mass distribution allows more events to pass
the momentum thresholds required in the selections.

Figure 2: The product of the signal acceptance and efficiency, averaged over
the three data-taking years, for the final event selection in the resolved
(upper), partially merged (central), and merged (lower) categories, for the
zero forward jets (left), and at least one forward jet (right) categories. The
numerator is the number of events passing the respective selection, the
denominator is the total expected number of events. All possible decays of the
top quark and boson are considered in both numerator and denominator of the
efficiency computation, therefore the values are not corrected for the
branching fraction of the final state.

The signal extraction is based on a simultaneous fit to the transverse mass of
the top quark candidate and system,
$M_{\PQT}=\sqrt{\smash[b]{2\pt^{\PQt}\ptmiss(1-\cos\Delta\phi_{\PQt,\ptvecmiss})}}$,
in the six analysis categories.

##  0.4 Background estimation

The major sources of background after the event selection are
$\PQt\PAQt$+jets, +jets, and +jets events where the boson decays to neutrinos.
For the merged categories the +jets background is the largest, followed by
significant contributions from +jets and $\PQt\PAQt$+jets events. For the
partially merged categories the $\PQt\PAQt$+jets background is dominant. For
the resolved categories the $\PQt\PAQt$+jets background is the leading one,
followed by significant contributions from +jets and +jets events. The
contributions from minor backgrounds, such as single top quark and multijet
events, are estimated from simulation, since their contribution is smaller
than the other two by more than one order of magnitude.

Corrections to the simulations are derived from dedicated background-enriched
samples in data, referred to as control regions, and then applied to the
background transverse mass ($M_{\PQT}$) distributions in the signal regions
before performing the fit.

Different strategies for the determination of the correction factors are
employed for the resolved, the merged, and partially merged categories,
because the amount of data in the control regions and the mismodeling
corrections are different.

For events in the resolved category, possible discrepancies between data and
simulation distributions of the discriminating variables are likely to be due
to unaccounted higher-order corrections in perturbative QCD or quantum
electrodynamics calculations. In this case, the large numbers of events in the
control regions allow the correction factors to be determined as functions of
and .

For events in the merged and partially merged categories, background processes
can satisfy the selection requirements of the signal region because of jets
that occasionally satisfy the requirements to be identified as and jets. For
such events data and simulation can differ because of fluctuations in the
hadronic jets background and the lack of higher-order corrections in the
simulation. In the signal regions, such jets are mostly produced in
association with or bosons. In this case, the correction factors are
determined in ranges of the observable $M_{\PQT}$, and a smoothing procedure
is applied.

Independent sets of control regions are defined for the resolved, the merged,
and the partially merged categories. Starting from the baseline selection of
each category, one or more requirements are inverted in order to obtain a
properly background-enriched sample of events. In the following, only the
requirements that have been inverted with respect to the signal selection for
each category are described, while all the others stay the same.

In the resolved category, the control region is defined by requiring at least
one identified lepton in the event. The +jets control region is defined by
selecting events with at least one identified lepton and no jets. The +jets
control region is defined by selecting events with no jets.

In the merged category, the control region is defined by requiring at least
one identified lepton in the event and removing the requirement on the minimum
$\Delta\phi_{\text{jet},\ptvecmiss}$ from the selection. A single control
region is defined for +jets and +jets by requiring that no subjet of the
merged top quark candidate has been identified as one jet.

In the partially merged category, the background is largely given by events,
therefore only a control region for is used, which is defined by requiring the
minimum $\Delta\phi_{\text{jet},\ptvecmiss}<0.6$.

The strategy employed for the background prediction in the merged and
partially merged categories is tested in an independent sample, defined with
the same selection as the merged category but with the AK8 jet SD mass being
outside the interval 105–220. For the resolved categories, the background
estimation method is validated in a sample obtained from a looser selection,
defined by removing the requirement on at least one jet in the event. Figures
3 and 4 show the comparison of data and the predicted backgrounds in the above
validation samples for the merged and resolved categories, respectively.

Figure 3: Distributions of the transverse mass $M_{\PQT}$ of the reconstructed
top quark and system, for events in validation samples selected as in the
merged categories, but with the AK8 jet SD mass outside the interval 105–220,
for events with no forward jet (left) and at least one forward jet (right),
and for 2016 (upper), 2017 (central), and 2018 (lower). The overflow is
included in the last bin. The predictions of the main background components
have been determined in simulation with scale factors applied to match data
extracted from control regions. The uncertainties after the fit to data in the
validation regions are shown.

Figure 4: Distributions of the transverse mass $M_{\PQT}$ of the reconstructed
top quark and system, for events in validation samples selected as in the
resolved categories, but without the requirement of at least one jet in the
event, for events with no forward jet (left) and at least one forward jet
(right), and for 2016 (upper), 2017 (central), and 2018 (lower). The
predictions of the main background components have been determined in
simulation with scale factors applied to match data extracted from control
regions. The uncertainties before the fit to data in the validation regions
are shown.

##  0.5 Systematic uncertainties

Systematic uncertainties, both of theoretical and of experimental origin,
affect the background and signal prediction in terms of the pre-fit event
rate, or the shape of the transverse mass ($M_{\PQT}$) distributions, or both.
Most of the uncertainties are related to corrections that are applied to
simulated events in order to account for the known discrepancies with data.
The background determination procedure described in Section 0.4, including the
derivation of the correction factors, is repeated for each systematic
uncertainty variation in order to derive the corresponding modified background
$M_{\PQT}$ distributions. A summary of the systematic uncertainties and their
effect in the analysis is reported in Table 0.5.

The total 2016–2018 integrated luminosity has an uncertainty of 1.6% [69],
which applies to the overall event rates of all simulated processes.

The uncertainty associated with the mismodeling of pileup is evaluated by
varying the $\Pp\Pp$ total inelastic cross section used to compute the pileup
distribution in data by $\pm$4.6% [70], resulting in a change in the
normalization in the range 0.2–3%, depending on the category, the year, and
the signal or background component.

Systematic uncertainties due to differences in the trigger efficiency between
data and simulation are found to be 1–3%. During the 2016 and 2017 data-
taking, a gradual shift in the timing of the inputs of the ECAL L1 trigger in
the region $\abs{\eta}>2.0$ caused a specific trigger inefficiency. For events
containing a jet with $\pt\gtrsim 100\GeV$ in the region $2.5<\abs{\eta}<3.0$,
the efficiency loss is approximately 10–20%, depending on , $\eta$, and data-
taking period [26]. Correction factors were computed from data and applied to
the acceptance evaluated by simulation, resulting in a systematic uncertainty
of 0.2–3%.

The jet four-momenta are varied according to the jet energy scale and
resolution uncertainties, resulting in a systematic uncertainty of 2–18 and
1–5%, respectively. The uncertainty in the tagging efficiency for the , , and
light-flavor quarks or gluon has been evaluated by varying the corrections
applied to simulation by their uncertainties, giving a change in the
normalization in the range 0.5–1.2%. A similar procedure is done with the t
and W jet tagging uncertainties, giving a variation in the normalization of
9–10 and 7–8%, respectively.

The uncertainties from the choice of the PDF are evaluated by reweighting the
simulated signal and background events using the NNPDF 3.0 or 3.1 sets of
eigenvectors [71].

The choice of the factorization and renormalization scales, $\mu_{\mathrm{F}}$
and $\mu_{\mathrm{R}}$, affects both the cross section and the acceptance of
each background. The uncertainties are estimated separately for each
background by halving or doubling both scales together, compared to the
nominal values used in simulation. The changes in the background rates are
found to be between 10–30%. For the signal this source of uncertainty affects
only the acceptance.

The uncertainties associated with the PDF choice, the tagging efficiencies and
mistag rates, and the pileup simulation are found to have negligible effects
on the shape of the $M_{\PQT}$ distribution, so only their effects on the
overall event rates are considered.

For each background contribution, the statistical uncertainty in each bin of
the $M_{\PQT}$ distribution is taken into account. All uncertainties are
considered fully correlated across the three years of data-taking, except for
those corresponding to the ECAL L1 trigger inefficiency and the background
scale factors.

The effect on the background $M_{\PQT}$ distribution of the statistical
uncertainties in the scale factors, due to the limited number of events in the
data control regions and in simulation, is treated as follows: first the
statistical uncertainty in the scale factors is estimated, then two possible
uncertainties in the overall background distribution are considered. The first
uncertainty considers the envelope of all bin variations, thus all scale
factors variations are treated as fully correlated over each bin of the
$M_{\PQT}$ spectrum. The second uncertainty considers the scale factor
variation in each bin of $M_{\PQT}$ to linearly decrease from the maximum
value at one extreme of the $M_{\PQT}$ spectrum to the minimum value at the
other. This uncertainty corresponds to the maximum variation in the $M_{\PQT}$
slope allowed in data, and it is not uniform over the $M_{\PQT}$ spectrum,
because the statistical uncertainty grows larger at the higher end of the
spectrum. All these uncertainties are independently derived for each year and
fit simultaneously to data by employing separate nuisance parameters. This
parametrization has been chosen to reduce the dependency on bin-by-bin
statistical fluctuations, which may cause a bias on the signal yield extracted
in the statistical procedure. The number of nuisance parameters considered is
the minimum that allows closure in data validation regions, showing that the
respective variation encompass the statistical variation seen in data.

Summary of the systematic uncertainties. The maximum range of change in the
pre-fit event rate of signals and backgrounds across all years and categories
for one standard deviation change in the systematic uncertainty is reported in
the “Effect” column. All uncertainties are considered fully correlated across
the three years of data-taking, except for those corresponding to the ECAL L1
trigger inefficiency and the background scale factors. The third column
indicates whether the uncertainty affects both the rate and the shape of the
distributions or the rate only. Except for the background scale factors, all
the uncertainties affect both signal and background inputs to the fit.  Source
Effect (%) Type Integrated luminosity 1.8 rate Pileup 0.2–3 rate b tagging
0.5–1.2 rate t tagging 9–10 rate, shape W tagging 7–8 rate, shape Trigger
efficiency 1–3 rate, shape ECAL L1 trigger inefficiency 0.2–3 rate, shape Jet
energy scale 2–18 rate, shape Jet energy resolution 2–5 rate, shape PDF 1–5
rate $\mu_{\mathrm{R}}$, $\mu_{\mathrm{F}}$ 10–30 rate, shape Background scale
factors 5–50 rate, shape

##  0.6 Results

The transverse mass ($M_{\PQT}$) distributions of the selected events in the
merged, partially merged, and resolved categories are shown in Figs. 5–7,
respectively. The predictions for the background processes have been obtained
with the procedure described in Section 0.4.

Figure 5: Distributions of the transverse mass $M_{\PQT}$ of the reconstructed
top quark and system, for the selected events in the merged categories, for
events with no forward jet (left) and at least one forward jet (right), and
for 2016 (upper), 2017 (central), and 2018 (lower). The overflow is included
in the last bin. The distributions for the main background components have
been determined in simulation with scale factors extracted from control
regions. All background processes and the respective uncertainties are derived
from the fit to data, while the distributions of signal processes are
represented according to the expectation before the fit. The lines show the
signal predictions for three benchmark mass values (0.8, 1.2, and 1.6) of a
quark of negligible resonance width. Illustrative signal yields are multiplied
by a factor of 50 to improve their visibility.

Figure 6: Distributions of the transverse mass $M_{\PQT}$ of the reconstructed
top quark and system, for the selected events in the partially merged
categories, for events with no forward jet (left) and at least one forward jet
(right), and for 2016 (upper), 2017 (central), and 2018 (lower). The overflow
is included in the last bin. The distributions for the main background
components have been determined in simulation with scale factors extracted
from control regions. All background processes and the respective
uncertainties are derived from the fit to data, while the distributions of
signal processes are represented according to the expectation before the fit.
The lines show the signal predictions for three benchmark mass values (0.8,
1.2, and 1.6) of a quark of negligible resonance width. Signal yields are
multiplied by a factor of 50 to improve their visibility.

Figure 7: Distributions of the transverse mass $M_{\PQT}$ of the reconstructed
top quark and system, for the selected events in the resolved categories, for
events with no forward jet (left) and at least one forward jet (right), and
for 2016 (upper), 2017 (central), and 2018 (lower). The distributions for the
main background components have been determined in simulation with scale
factors extracted from control regions. All background processes and the
respective uncertainties are derived from the fit to data, while the
distributions of signal processes are represented according to the expectation
before the fit. The lines show the signal predictions for three benchmark mass
values (0.8, 1.2, and 1.6) of a quark of negligible resonance width. Signal
yields are multiplied by a factor of 50 to improve their visibility.

The signal extraction procedure is based on a simultaneous maximum likelihood
fit to the $M_{\PQT}$ distributions in all signal regions. Systematic
uncertainties are treated as nuisance parameters, assumed to be described by a
log-normal probability density function, for systematic uncertainties that
affect the rate, or a Gaussian probability density function, for systematic
uncertainties that affect the $M_{\PQT}$ distribution shape. Templates for
background and signal are also shown in Figs. 5–7.

No evidence of a statistically significant signal is found from the fit to the
data. The largest excess over the expected background, corresponding to a
significance of 2.5 standard deviations, is found in the narrow-width
hypothesis for a quark mass of 1.4. The global significance to have such an
excess over the entire mass range 0.6–1.8is 2.2 standard deviations. This
deviation from the expected background is mainly due to a broad excess of
events in the transverse mass distribution for $M_{\PQT}>1\TeV$, observed in
2016 data for events in the resolved category and with at least one forward
jet (see Fig. 7).

Upper limits at 95% confidence level () are set on the product of the single
quark production cross section and $\PQT\to\PQt\PZ$ channel branching
fraction. The limits are determined using the method [72, 73, 74, 75], taking
the profile likelihood ratio as the test statistic with the asymptotic limit
approximation.

The observed and expected combined upper limits from the six event categories,
as functions of the quark mass $m_{\PQT}$, and for several hypotheses on its
width, are shown in Figs. 8 and 9 respectively. The limits at low values of
$m_{\PQT}$ are mostly driven by the resolved categories, while the merged
categories are the most effective at high values, reflecting the different
sensitivities in different $m_{\PQT}$ ranges. The limits at intermediate
values of $m_{\PQT}$ benefit from the sensitivity of all the event categories
employed in the analysis. The high limit at 1.4is driven by the excess seen in
the resolved category.

Figure 8: Observed and expected 95% upper limits on the product of the single
production cross section for a singlet VLQ quark and the $\PQT\to\PQt\PZ$
branching fraction, as functions of the quark mass $m_{\PQT}$ for a narrow-
width resonance (upper left), and a width of 10% (upper right), 20% (lower
left), and 30% (lower right) of the quark mass. A singlet quark is assumed,
produced in association with a bottom quark. The inner (green) band and the
outer (yellow) band indicate the regions containing 68 and 95%, respectively,
of the distribution of limits expected under the background-only hypothesis.
The solid curves show the theoretical expectation at NLO. In the case of a
narrow-width resonance, the width of 1 (5)% of the resonance mass is indicated
with a red (blue) curve. Figure 9: Observed 95% upper limit on the product of
the single production cross section for a singlet VLQ quark and the
$\PQT\to\PQt\PZ$ branching fraction, as a function of the quark mass
$m_{\PQT}$ and width $\Gamma$, for widths from 5 to 30% of the mass. A singlet
quark that is produced in association with a bottom quark is assumed. The
solid red line indicates the boundary of the excluded region (on the hatched
side) of theoretical cross sections, as reported in Table 0.2.

Assuming a narrow-width resonance, values of the product of production cross
section and branching fraction greater than 602 to 15fb are excluded at 95%
for masses in the range 0.6–1.8.

For a resonance of width in the range 10–30% of its mass, values greater than
836 to 16fb are excluded at 95% for masses in the range 0.6–1.8.

These results set the most stringent limits on the product of the cross
section for single quark production and the branching fraction into the
$\PQt\PZ$ decay channel over the mass ranges 0.6–1.2 and 1.5–1.8. For mass
values in the range 1.2–1.5, the measurements reported in [21, 20, 19] yield
give a comparable or lower limit because of the excess of events observed in
this region in our data.

These results also set the best lower limits on the quark mass in the singlet
model, for various resonance width hypotheses: values of quark mass lower than
0.98, 1.1, 1.3, and 1.4are excluded for resonance widths 5, 10, 20, and 30% of
the mass, respectively.

##  0.7 Summary

A search for the single production of a vector-like quark with charge $2/3$
$e$ decaying to a top quark and a boson has been presented. The analysis is
based on LHC proton-proton collision data collected by the CMS experiment,
corresponding to an integrated luminosity of 137. Upper limits at 95%
confidence level are set on the product of the production cross section and
the $\PQT\to\PQt\PZ$ channel branching fraction. Values greater than 602 to
15fb for quark masses between 0.6 and 1.8are excluded at 95% confidence level
for a quark of negligible resonance width produced in association with a
bottom quark. Values greater than 836 to 16fb for masses between 0.6 and
1.8are excluded at 95% confidence level for a quark of resonance width from 10
to 30% of its mass. These results provide the best exclusion limits on the
production of single vector-like quarks in the $\PQt\PZ$ decay channel over
the mass range from 0.6 to 1.2and from 1.5 to 1.8. An interpretation of these
results within a theoretical framework in which the quark is a singlet, and
assuming a resonance width of 5% of the mass, leads to the exclusion of a
quark of mass below 0.98. The excluded mass range extends up to 1.4for a
resonance width 30% of the mass. This is the first search for single quark
production based on the full LHC Run 2 data set.

###### Acknowledgements.

We congratulate our colleagues in the CERN accelerator departments for the
excellent performance of the LHC and thank the technical and administrative
staffs at CERN and at other CMS institutes for their contributions to the
success of the CMS effort. In addition, we gratefully acknowledge the
computing cemters and personnel of the Worldwide LHC Computing Grid and other
cemters for delivering so effectively the computing infrastructure essential
to our analyses. Finally, we acknowledge the enduring support for the
construction and operation of the LHC, the CMS detector, and the supporting
computing infrastructure provided by the following funding agencies: BMBWF and
FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and
FAPESP (Brazil); MES and BNSF (Bulgaria); CERN; CAS, MoST, and NSFC (China);
MINCIENCIAS (Colombia); MSES and CSF (Croatia); RIF (Cyprus); SENESCYT
(Ecuador); MoER, ERC PUT and ERDF (Estonia); Academy of Finland, MEC, and HIP
(Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRI
(Greece); NKFIA (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland);
INFN (Italy); MSIP and NRF (Republic of Korea); MES (Latvia); LAS (Lithuania);
MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI
(Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MSHE and NSC
(Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom, RAS, RFBR, and NRC KI
(Russia); MESTD (Serbia); MCIN/AEI and PCTI (Spain); MOSTR (Sri Lanka); Swiss
Funding Agencies (Switzerland); MST (Taipei); ThEPCenter, IPST, STAR, and
NSTDA (Thailand); TUBITAK and TAEK (Turkey); NASU (Ukraine); STFC (United
Kingdom); DOE and NSF (USA). Individuals have received support from the Marie-
Curie program and the European Research Council and Horizon 2020 Grant,
contract Nos. 675440, 724704, 752730, 758316, 765710, 824093, 884104, and COST
Action CA16108 (European Union); the Leventis Foundation; the Alfred P. Sloan
Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science
Policy Office; the Fonds pour la Formation à la Recherche dans l’Industrie et
dans l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door
Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium)
under the “Excellence of Science – EOS” – be.h project n. 30820817; the
Beijing Municipal Science & Technology Commission, No. Z191100007219010; the
Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the
Deutsche Forschungsgemeinschaft (DFG), under Germany’s Excellence Strategy –
EXC 2121 “Quantum Universe” – 390833306, and under project number 400140256 -
GRK2497; the Lendület (“Momentum”) Program and the János Bolyai Research
Scholarship of the Hungarian Academy of Sciences, the New National Excellence
Program ÚNKP, the NKFIA research grants 123842, 123959, 124845, 124850,
125105, 128713, 128786, and 129058 (Hungary); the Council of Science and
Industrial Research, India; the Latvian Council of Science; the Ministry of
Science and Higher Education and the National Science Center, contracts Opus
2014/15/B/ST2/03998 and 2015/19/B/ST2/02861 (Poland); the Fundação para a
Ciência e a Tecnologia, grant CEECIND/01334/2018 (Portugal); the National
Priorities Research Program by Qatar National Research Fund; the Ministry of
Science and Higher Education, projects no. 14.W03.31.0026 and no.
FSWW-2020-0008, and the Russian Foundation for Basic Research, project
No.19-42-703014 (Russia); MCIN/AEI/10.13039/501100011033, ERDF “a way of
making Europe”, and the Programa Estatal de Fomento de la Investigación
Científica y Técnica de Excelencia María de Maeztu, grant MDM-2017-0765 and
Programa Severo Ochoa del Principado de Asturias (Spain); the Stavros Niarchos
Foundation (Greece); the Rachadapisek Sompot Fund for Postdoctoral Fellowship,
Chulalongkorn University and the Chulalongkorn Academic into Its 2nd Century
Project Advancement Project (Thailand); the Kavli Foundation; the Nvidia
Corporation; the SuperMicro Corporation; the Welch Foundation, contract
C-1845; and the Weston Havens Foundation (USA).

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##  .8 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia A. Tumasyan Institut für
Hochenergiephysik, Vienna, Austria W. Adam0000-0001-9099-4341, J.W.
Andrejkovic, T. Bergauer0000-0002-5786-0293, S. Chatterjee0000-0003-2660-0349,
M. Dragicevic0000-0003-1967-6783, A. Escalante Del Valle0000-0002-9702-6359,
R. Frühwirth1, M. Jeitler10000-0002-5141-9560, N. Krammer, L.
Lechner0000-0002-3065-1141, D. Liko, I. Mikulec, P. Paulitsch, F.M. Pitters,
J. Schieck10000-0002-1058-8093, R. Schöfbeck0000-0002-2332-8784, M.
Spanring0000-0001-6328-7887, S. Templ0000-0003-3137-5692, W.
Waltenberger0000-0002-6215-7228, C.-E. Wulz10000-0001-9226-5812 Institute for
Nuclear Problems, Minsk, Belarus V. Chekhovsky, A. Litomin, V.
Makarenko0000-0002-8406-8605 Universiteit Antwerpen, Antwerpen, Belgium M.R.
Darwish2, E.A. De Wolf, T. Janssen0000-0002-3998-4081, T. Kello3, A.
Lelek0000-0001-5862-2775, H. Rejeb Sfar, P. Van Mechelen0000-0002-8731-9051,
S. Van Putte, N. Van Remortel0000-0003-4180-8199 Vrije Universiteit Brussel,
Brussel, Belgium F. Blekman0000-0002-7366-7098, E.S. Bols0000-0002-8564-8732,
J. D’Hondt0000-0002-9598-6241, J. De Clercq0000-0001-6770-3040, M. Delcourt,
H. El Faham0000-0001-8894-2390, S. Lowette0000-0003-3984-9987, S.
Moortgat0000-0002-6612-3420, A. Morton0000-0002-9919-3492, D.
Müller0000-0002-1752-4527, A.R. Sahasransu0000-0003-1505-1743, S.
Tavernier0000-0002-6792-9522, W. Van Doninck, P. Van Mulders Université Libre
de Bruxelles, Bruxelles, Belgium D. Beghin, B. Bilin0000-0003-1439-7128, B.
Clerbaux0000-0001-8547-8211, G. De Lentdecker, L. Favart0000-0003-1645-7454,
A. Grebenyuk, A.K. Kalsi0000-0002-6215-0894, K. Lee, M. Mahdavikhorrami, I.
Makarenko0000-0002-8553-4508, L. Moureaux0000-0002-2310-9266, L. Pétré, A.
Popov0000-0002-1207-0984, N. Postiau, E. Starling0000-0002-4399-7213, L.
Thomas0000-0002-2756-3853, M. Vanden Bemden, C. Vander
Velde0000-0003-3392-7294, P. Vanlaer0000-0002-7931-4496, D.
Vannerom0000-0002-2747-5095, L. Wezenbeek Ghent University, Ghent, Belgium T.
Cornelis0000-0001-9502-5363, D. Dobur, J. Knolle0000-0002-4781-5704, L.
Lambrecht, G. Mestdach, M. Niedziela0000-0001-5745-2567, C. Roskas, A.
Samalan, K. Skovpen0000-0002-1160-0621, M. Tytgat0000-0002-3990-2074, W.
Verbeke, B. Vermassen, M. Vit Université Catholique de Louvain, Louvain-la-
Neuve, Belgium A. Bethani0000-0002-8150-7043, G. Bruno, F.
Bury0000-0002-3077-2090, C. Caputo0000-0001-7522-4808, P.
David0000-0001-9260-9371, C. Delaere0000-0001-8707-6021, I.S.
Donertas0000-0001-7485-412X, A. Giammanco0000-0001-9640-8294, K. Jaffel, Sa.
Jain0000-0001-5078-3689, V. Lemaitre, K. Mondal0000-0001-5967-1245, J.
Prisciandaro, A. Taliercio, M. Teklishyn0000-0002-8506-9714, T.T. Tran, P.
Vischia0000-0002-7088-8557, S. Wertz0000-0002-8645-3670, S. Wuyckens Centro
Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil G.A.
Alves0000-0002-8369-1446, C. Hensel, A. Moraes0000-0002-5157-5686 Universidade
do Estado do Rio de Janeiro, Rio de Janeiro, Brazil W.L. Aldá
Júnior0000-0001-5855-9817, M. Alves Gallo Pereira0000-0003-4296-7028, M.
Barroso Ferreira Filho, H. BRANDAO MALBOUISSON, W.
Carvalho0000-0003-0738-6615, J. Chinellato4, E.M. Da Costa0000-0002-5016-6434,
G.G. Da Silveira50000-0003-3514-7056, D. De Jesus Damiao0000-0002-3769-1680,
S. Fonseca De Souza0000-0001-7830-0837, D. Matos Figueiredo, C. Mora
Herrera0000-0003-3915-3170, K. Mota Amarilo, L. Mundim0000-0001-9964-7805, H.
Nogima, P. Rebello Teles0000-0001-9029-8506, A. Santoro, S.M. Silva Do
Amaral0000-0002-0209-9687, A. Sznajder0000-0001-6998-1108, M. Thiel, F. Torres
Da Silva De Araujo0000-0002-4785-3057, A. Vilela Pereira0000-0003-3177-4626
Universidade Estadual Paulista (a), Universidade Federal do ABC (b), São
Paulo, Brazil C.A. Bernardes0000-0001-5790-9563, L.
Calligaris0000-0002-9951-9448, T.R. Fernandez Perez Tomei0000-0002-1809-5226,
E.M. Gregores0000-0003-0205-1672, D.S. Lemos0000-0003-1982-8978, P.G.
Mercadante0000-0001-8333-4302, S.F. Novaes0000-0003-0471-8549, Sandra S.
Padula0000-0003-3071-0559 Institute for Nuclear Research and Nuclear Energy,
Bulgarian Academy of Sciences, Sofia, Bulgaria A. Aleksandrov, G.
Antchev0000-0003-3210-5037, R. Hadjiiska, P. Iaydjiev, M. Misheva, M. Rodozov,
M. Shopova, G. Sultanov University of Sofia, Sofia, Bulgaria A. Dimitrov, T.
Ivanov, L. Litov0000-0002-8511-6883, B. Pavlov, P. Petkov, A. Petrov Beihang
University, Beijing, China T. Cheng0000-0003-2954-9315, W.
Fang30000-0002-5247-3833, Q. Guo, T. Javaid6, M. Mittal, H. Wang, L. Yuan
Department of Physics, Tsinghua University, Beijing, China M.
Ahmad0000-0001-9933-995X, G. Bauer, C. Dozen70000-0002-4301-634X, Z.
Hu0000-0001-8209-4343, J. Martins80000-0002-2120-2782, Y. Wang, K. Yi9,10
Institute of High Energy Physics, Beijing, China E. Chapon0000-0001-6968-9828,
G.M. Chen60000-0002-2629-5420, H.S. Chen60000-0001-8672-8227, M.
Chen0000-0003-0489-9669, F. Iemmi, A. Kapoor0000-0002-1844-1504, D. Leggat, H.
Liao, Z.-A. Liu60000-0002-2896-1386, V. Milosevic0000-0002-1173-0696, F.
Monti0000-0001-5846-3655, R. Sharma0000-0003-1181-1426, J.
Tao0000-0003-2006-3490, J. Thomas-Wilsker, J. Wang0000-0002-4963-0877, T. Yu6,
H. Zhang0000-0001-8843-5209, J. Zhao0000-0001-8365-7726 State Key Laboratory
of Nuclear Physics and Technology, Peking University, Beijing, China A.
Agapitos, Y. Ban, C. Chen, Q. Huang, A. Levin0000-0001-9565-4186, Q.
Li0000-0002-8290-0517, X. Lyu, Y. Mao, S.J. Qian, D. Wang0000-0002-9013-1199,
Q. Wang0000-0003-1014-8677, J. Xiao Sun Yat-Sen University, Guangzhou, China
M. Lu, Z. You0000-0001-8324-3291 Institute of Modern Physics and Key
Laboratory of Nuclear Physics and Ion-beam Application (MOE) - Fudan
University, Shanghai, China X. Gao3, H. Okawa0000-0002-2548-6567 Zhejiang
University, Hangzhou, China, Zhejiang, China Z. Lin0000-0003-1812-3474, M.
Xiao0000-0001-9628-9336 Universidad de Los Andes, Bogota, Colombia C.
Avila0000-0002-5610-2693, A. Cabrera0000-0002-0486-6296, C.
Florez0000-0002-3222-0249, J. Fraga, A. Sarkar0000-0001-7540-7540, M.A. Segura
Delgado Universidad de Antioquia, Medellin, Colombia J. Mejia Guisao, F.
Ramirez, J.D. Ruiz Alvarez0000-0002-3306-0363, C.A. Salazar
González0000-0002-0394-4870 University of Split, Faculty of Electrical
Engineering, Mechanical Engineering and Naval Architecture, Split, Croatia D.
Giljanovic, N. Godinovic0000-0002-4674-9450, D. Lelas0000-0002-8269-5760, I.
Puljak0000-0001-7387-3812 University of Split, Faculty of Science, Split,
Croatia Z. Antunovic, M. Kovac, T. Sculac0000-0002-9578-4105 Institute Rudjer
Boskovic, Zagreb, Croatia V. Brigljevic0000-0001-5847-0062, D.
Ferencek0000-0001-9116-1202, D. Majumder0000-0002-7578-0027, M. Roguljic, A.
Starodumov110000-0001-9570-9255, T. Susa0000-0001-7430-2552 University of
Cyprus, Nicosia, Cyprus A. Attikis0000-0002-4443-3794, K. Christoforou, E.
Erodotou, A. Ioannou, G. Kole0000-0002-3285-1497, M. Kolosova, S.
Konstantinou, J. Mousa0000-0002-2978-2718, C. Nicolaou, F.
Ptochos0000-0002-3432-3452, P.A. Razis, H. Rykaczewski, H.
Saka0000-0001-7616-2573 Charles University, Prague, Czech Republic M.
Finger12, M. Finger Jr.120000-0003-3155-2484, A. Kveton Escuela Politecnica
Nacional, Quito, Ecuador E. Ayala Universidad San Francisco de Quito, Quito,
Ecuador E. Carrera Jarrin0000-0002-0857-8507 Academy of Scientific Research
and Technology of the Arab Republic of Egypt, Egyptian Network of High Energy
Physics, Cairo, Egypt H. Abdalla130000-0002-0455-3791, S. Abu
Zeid140000-0002-0820-0483 Center for High Energy Physics (CHEP-FU), Fayoum
University, El-Fayoum, Egypt M.A. Mahmoud0000-0001-8692-5458, Y.
Mohammed0000-0001-8399-3017 National Institute of Chemical Physics and
Biophysics, Tallinn, Estonia S. Bhowmik0000-0003-1260-973X, A. Carvalho
Antunes De Oliveira0000-0003-2340-836X, R.K. Dewanjee0000-0001-6645-6244, K.
Ehataht, M. Kadastik, S. Nandan, C. Nielsen, J. Pata, M.
Raidal0000-0001-7040-9491, L. Tani, C. Veelken Department of Physics,
University of Helsinki, Helsinki, Finland P. Eerola0000-0002-3244-0591, L.
Forthomme0000-0002-3302-336X, H. Kirschenmann0000-0001-7369-2536, K.
Osterberg0000-0003-4807-0414, M. Voutilainen0000-0002-5200-6477 Helsinki
Institute of Physics, Helsinki, Finland S. Bharthuar, E.
Brücken0000-0001-6066-8756, F. Garcia0000-0002-4023-7964, J.
Havukainen0000-0003-2898-6900, M.S. Kim0000-0003-0392-8691, R. Kinnunen, T.
Lampén, K. Lassila-Perini0000-0002-5502-1795, S. Lehti0000-0003-1370-5598, T.
Lindén, M. Lotti, L. Martikainen, M. Myllymäki, J. Ott0000-0001-9337-5722, H.
Siikonen, E. Tuominen0000-0002-7073-7767, J. Tuominiemi Lappeenranta
University of Technology, Lappeenranta, Finland P. Luukka0000-0003-2340-4641,
H. Petrow, T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
C. Amendola0000-0002-4359-836X, M. Besancon, F. Couderc0000-0003-2040-4099, M.
Dejardin, D. Denegri, J.L. Faure, F. Ferri0000-0002-9860-101X, S. Ganjour, A.
Givernaud, P. Gras, G. Hamel de Monchenault0000-0002-3872-3592, P. Jarry, B.
Lenzi0000-0002-1024-4004, E. Locci, J. Malcles, J. Rander, A.
Rosowsky0000-0001-7803-6650, M.Ö. Sahin0000-0001-6402-4050, A. Savoy-
Navarro15, M. Titov0000-0002-1119-6614, G.B. Yu0000-0001-7435-2963 Laboratoire
Leprince-Ringuet, CNRS/IN2P3, Ecole Polytechnique, Institut Polytechnique de
Paris, Palaiseau, France S. Ahuja0000-0003-4368-9285, F.
Beaudette0000-0002-1194-8556, M. Bonanomi0000-0003-3629-6264, A. Buchot
Perraguin, P. Busson, A. Cappati, C. Charlot, O. Davignon, B. Diab, G.
Falmagne0000-0002-6762-3937, S. Ghosh, R. Granier de
Cassagnac0000-0002-1275-7292, A. Hakimi, I. Kucher0000-0001-7561-5040, M.
Nguyen0000-0001-7305-7102, C. Ochando0000-0002-3836-1173, P.
Paganini0000-0001-9580-683X, J. Rembser, R. Salerno0000-0003-3735-2707, J.B.
Sauvan0000-0001-5187-3571, Y. Sirois0000-0001-5381-4807, A. Zabi, A.
Zghiche0000-0002-1178-1450 Université de Strasbourg, CNRS, IPHC UMR 7178,
Strasbourg, France J.-L. Agram160000-0001-7476-0158, J. Andrea, D. Apparu, D.
Bloch0000-0002-4535-5273, G. Bourgatte, J.-M. Brom, E.C. Chabert, C.
Collard0000-0002-5230-8387, D. Darej, J.-C. Fontaine16, U. Goerlach, C.
Grimault, A.-C. Le Bihan, E. Nibigira0000-0001-5821-291X, P. Van
Hove0000-0002-2431-3381 Institut de Physique des 2 Infinis de Lyon (IP2I ),
Villeurbanne, France E. Asilar0000-0001-5680-599X, S.
Beauceron0000-0002-8036-9267, C. Bernet0000-0002-9923-8734, G. Boudoul, C.
Camen, A. Carle, N. Chanon0000-0002-2939-5646, D. Contardo, P.
Depasse0000-0001-7556-2743, H. El Mamouni, J. Fay, S.
Gascon0000-0002-7204-1624, M. Gouzevitch0000-0002-5524-880X, B. Ille, I.B.
Laktineh, H. Lattaud0000-0002-8402-3263, A. Lesauvage0000-0003-3437-7845, M.
Lethuillier0000-0001-6185-2045, L. Mirabito, S. Perries, K. Shchablo, V.
Sordini0000-0003-0885-824X, L. Torterotot0000-0002-5349-9242, G. Touquet, M.
Vander Donckt, S. Viret Georgian Technical University, Tbilisi, Georgia I.
Lomidze, T. Toriashvili17, Z. Tsamalaidze12 RWTH Aachen University, I.
Physikalisches Institut, Aachen, Germany L. Feld0000-0001-9813-8646, K. Klein,
M. Lipinski, D. Meuser, A. Pauls, M.P. Rauch, N. Röwert, J. Schulz, M.
Teroerde0000-0002-5892-1377 RWTH Aachen University, III. Physikalisches
Institut A, Aachen, Germany A. Dodonova, D. Eliseev, M.
Erdmann0000-0002-1653-1303, P. Fackeldey0000-0003-4932-7162, B. Fischer, S.
Ghosh0000-0001-6717-0803, T. Hebbeker0000-0002-9736-266X, K. Hoepfner, F.
Ivone, H. Keller, L. Mastrolorenzo, M. Merschmeyer0000-0003-2081-7141, A.
Meyer0000-0001-9598-6623, G. Mocellin, S. Mondal, S.
Mukherjee0000-0001-6341-9982, D. Noll0000-0002-0176-2360, A. Novak, T.
Pook0000-0002-9635-5126, A. Pozdnyakov0000-0003-3478-9081, Y. Rath, H.
Reithler, J. Roemer, A. Schmidt0000-0003-2711-8984, S.C. Schuler, A.
Sharma0000-0002-5295-1460, L. Vigilante, S. Wiedenbeck, S. Zaleski RWTH Aachen
University, III. Physikalisches Institut B, Aachen, Germany C. Dziwok, G.
Flügge, W. Haj Ahmad180000-0003-1491-0446, O. Hlushchenko, T. Kress, A.
Nowack0000-0002-3522-5926, C. Pistone, O. Pooth, D. Roy0000-0002-8659-7762, H.
Sert0000-0003-0716-6727, A. Stahl190000-0002-8369-7506, T.
Ziemons0000-0003-1697-2130 Deutsches Elektronen-Synchrotron, Hamburg, Germany
H. Aarup Petersen, M. Aldaya Martin, P. Asmuss, I.
Babounikau0000-0002-6228-4104, S. Baxter, O. Behnke, A. Bermúdez Martínez, S.
Bhattacharya, A.A. Bin Anuar0000-0002-2988-9830, K. Borras20, V. Botta, D.
Brunner, A. Campbell0000-0003-4439-5748, A. Cardini0000-0003-1803-0999, C.
Cheng, F. Colombina, S. Consuegra Rodríguez0000-0002-1383-1837, G. Correia
Silva, V. Danilov, L. Didukh, G. Eckerlin, D. Eckstein, L.I. Estevez
Banos0000-0001-6195-3102, O. Filatov0000-0001-9850-6170, E. Gallo21, A.
Geiser, A. Giraldi, A. Grohsjean0000-0003-0748-8494, M. Guthoff, A.
Jafari220000-0001-7327-1870, N.Z. Jomhari0000-0001-9127-7408, H.
Jung0000-0002-2964-9845, A. Kasem200000-0002-6753-7254, M.
Kasemann0000-0002-0429-2448, H. Kaveh0000-0002-3273-5859, C.
Kleinwort0000-0002-9017-9504, D. Krücker0000-0003-1610-8844, W. Lange, J.
Lidrych0000-0003-1439-0196, K. Lipka, W. Lohmann23, R. Mankel, I.-A. Melzer-
Pellmann0000-0001-7707-919X, J. Metwally, A.B. Meyer0000-0001-8532-2356, M.
Meyer0000-0003-2436-8195, J. Mnich0000-0001-7242-8426, A. Mussgiller, Y.
Otarid, D. Pérez Adán0000-0003-3416-0726, D. Pitzl, A. Raspereza, B. Ribeiro
Lopes, J. Rübenach, A. Saggio0000-0002-7385-3317, A.
Saibel0000-0002-9932-7622, M. Savitskyi0000-0002-9952-9267, M. Scham, V.
Scheurer, C. Schwanenberger210000-0001-6699-6662, A. Singh, R.E. Sosa
Ricardo0000-0002-2240-6699, D. Stafford, N. Tonon0000-0003-4301-2688, O.
Turkot0000-0001-5352-7744, M. Van De Klundert0000-0001-8596-2812, R.
Walsh0000-0002-3872-4114, D. Walter, Y. Wen0000-0002-8724-9604, K. Wichmann,
L. Wiens, C. Wissing, S. Wuchterl0000-0001-9955-9258 University of Hamburg,
Hamburg, Germany R. Aggleton, S. Albrecht0000-0002-5960-6803, S.
Bein0000-0001-9387-7407, L. Benato0000-0001-5135-7489, A. Benecke, P.
Connor0000-0003-2500-1061, K. De Leo0000-0002-8908-409X, M. Eich, F. Feindt,
A. Fröhlich, C. Garbers0000-0001-5094-2256, E. Garutti0000-0003-0634-5539, P.
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Kramer, V. Kutzner, J. Lange0000-0001-7513-6330, T. Lange0000-0001-6242-7331,
A. Lobanov0000-0002-5376-0877, A. Malara0000-0001-8645-9282, A. Nigamova, K.J.
Pena Rodriguez, O. Rieger, P. Schleper, M. Schröder0000-0001-8058-9828, J.
Schwandt0000-0002-0052-597X, D. Schwarz, J. Sonneveld0000-0001-8362-4414, H.
Stadie, G. Steinbrück, A. Tews, B. Vormwald0000-0003-2607-7287, I.
Zoi0000-0002-5738-9446 Karlsruher Institut fuer Technologie, Karlsruhe,
Germany J. Bechtel0000-0001-5245-7318, T. Berger, E. Butz0000-0002-2403-5801,
R. Caspart0000-0002-5502-9412, T. Chwalek, W. De
Boer${}^{\textrm{\textdagger}}$, A. Dierlamm, A. Droll, K. El Morabit, N.
Faltermann0000-0001-6506-3107, M. Giffels, J.o. Gosewisch, A. Gottmann, F.
Hartmann190000-0001-8989-8387, C. Heidecker, U. Husemann0000-0002-6198-8388,
I. Katkov24, P. Keicher, R. Koppenhöfer, S. Maier, M. Metzler, S.
Mitra0000-0002-3060-2278, Th. Müller, M. Neukum, A. Nürnberg, G.
Quast0000-0002-4021-4260, K. Rabbertz0000-0001-7040-9846, J. Rauser, D.
Savoiu0000-0001-6794-7475, M. Schnepf, D. Seith, I. Shvetsov, H.J. Simonis, R.
Ulrich0000-0002-2535-402X, J. Van Der Linden, R.F. Von Cube, M. Wassmer, M.
Weber0000-0002-3639-2267, S. Wieland, R. Wolf0000-0001-9456-383X, S.
Wozniewski, S. Wunsch Institute of Nuclear and Particle Physics (INPP), NCSR
Demokritos, Aghia Paraskevi, Greece G. Anagnostou, P.
Asenov0000-0003-2379-9903, G. Daskalakis, T. Geralis0000-0001-7188-979X, A.
Kyriakis, D. Loukas, A. Stakia0000-0001-6277-7171 National and Kapodistrian
University of Athens, Athens, Greece M. Diamantopoulou, D. Karasavvas, G.
Karathanasis, P. Kontaxakis0000-0002-4860-5979, C.K. Koraka, A. Manousakis-
Katsikakis, A. Panagiotou, I. Papavergou, N. Saoulidou0000-0001-6958-4196, K.
Theofilatos0000-0001-8448-883X, E. Tziaferi0000-0003-4958-0408, K. Vellidis,
E. Vourliotis National Technical University of Athens, Athens, Greece G.
Bakas, K. Kousouris0000-0002-6360-0869, I. Papakrivopoulos, G. Tsipolitis, A.
Zacharopoulou University of Ioánnina, Ioánnina, Greece I.
Evangelou0000-0002-5903-5481, C. Foudas, P. Gianneios, P. Katsoulis, P.
Kokkas, N. Manthos, I. Papadopoulos0000-0002-9937-3063, J.
Strologas0000-0002-2225-7160 MTA-ELTE Lendület CMS Particle and Nuclear
Physics Group, Eötvös Loránd University, Budapest, Hungary M.
Csanad0000-0002-3154-6925, K. Farkas, M.M.A. Gadallah250000-0002-8305-6661, S.
Lökös260000-0002-4447-4836, P. Major, K. Mandal0000-0002-3966-7182, A.
Mehta0000-0002-0433-4484, G. Pasztor0000-0003-0707-9762, A.J. Rádl, O.
Surányi, G.I. Veres0000-0002-5440-4356 Wigner Research Centre for Physics,
Budapest, Hungary M. Bartók270000-0002-4440-2701, G. Bencze, C.
Hajdu0000-0002-7193-800X, D. Horvath280000-0003-0091-477X, F.
Sikler0000-0001-9608-3901, V. Veszpremi0000-0001-9783-0315, G.
Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear Research ATOMKI,
Debrecen, Hungary S. Czellar, J. Karancsi270000-0003-0802-7665, J. Molnar, Z.
Szillasi, D. Teyssier Institute of Physics, University of Debrecen, Debrecen,
Hungary P. Raics, Z.L. Trocsanyi290000-0002-2129-1279, B. Ujvari Karoly Robert
Campus, MATE Institute of Technology, Gyongyos, Hungary T.
Csorgo300000-0002-9110-9663, F. Nemes30, T. Novak Indian Institute of Science
(IISc), Bangalore, India J.R. Komaragiri0000-0002-9344-6655, D. Kumar, L.
Panwar0000-0003-2461-4907, P.C. Tiwari0000-0002-3667-3843 National Institute
of Science Education and Research, HBNI, Bhubaneswar, India S.
Bahinipati310000-0002-3744-5332, D. Dash0000-0001-9685-0226, C.
Kar0000-0002-6407-6974, P. Mal, T. Mishra0000-0002-2121-3932, V.K.
Muraleedharan Nair Bindhu32, A. Nayak320000-0002-7716-4981, P. Saha, N.
Sur0000-0001-5233-553X, S.K. Swain, D. Vats32 Panjab University, Chandigarh,
India S. Bansal0000-0003-1992-0336, S.B. Beri, V.
Bhatnagar0000-0002-8392-9610, G. Chaudhary0000-0003-0168-3336, S.
Chauhan0000-0001-6974-4129, N. Dhingra330000-0002-7200-6204, R. Gupta, A.
Kaur, M. Kaur0000-0002-3440-2767, S. Kaur, P. Kumari0000-0002-6623-8586, M.
Meena, K. Sandeep0000-0002-3220-3668, J.B. Singh0000-0001-9029-2462, A.K.
Virdi0000-0002-0866-8932 University of Delhi, Delhi, India A. Ahmed, A.
Bhardwaj0000-0002-7544-3258, B.C. Choudhary0000-0001-5029-1887, M. Gola, S.
Keshri0000-0003-3280-2350, A. Kumar0000-0003-3407-4094, M.
Naimuddin0000-0003-4542-386X, P. Priyanka0000-0002-0933-685X, K. Ranjan, A.
Shah0000-0002-6157-2016 Saha Institute of Nuclear Physics, HBNI, Kolkata,
India M. Bharti34, R. Bhattacharya, S. Bhattacharya0000-0002-8110-4957, D.
Bhowmik, S. Dutta, S. Dutta, B. Gomber350000-0002-4446-0258, M. Maity36, P.
Palit0000-0002-1948-029X, P.K. Rout0000-0001-8149-6180, G. Saha, B.
Sahu0000-0002-8073-5140, S. Sarkar, M. Sharan, B. Singh34, S. Thakur34 Indian
Institute of Technology Madras, Madras, India P.K. Behera0000-0002-1527-2266,
S.C. Behera, P. Kalbhor0000-0002-5892-3743, A. Muhammad, R. Pradhan, P.R.
Pujahari, A. Sharma0000-0002-0688-923X, A.K. Sikdar Bhabha Atomic Research
Centre, Mumbai, India D. Dutta0000-0002-0046-9568, V. Jha, V.
Kumar0000-0001-8694-8326, D.K. Mishra, K. Naskar37, P.K. Netrakanti, L.M.
Pant, P. Shukla0000-0001-8118-5331 Tata Institute of Fundamental Research-A,
Mumbai, India T. Aziz, S. Dugad, M. Kumar, U. Sarkar0000-0002-9892-4601 Tata
Institute of Fundamental Research-B, Mumbai, India S.
Banerjee0000-0002-7953-4683, R. Chudasama, M. Guchait, S. Karmakar, S. Kumar,
G. Majumder, K. Mazumdar, S. Mukherjee0000-0003-3122-0594 Indian Institute of
Science Education and Research (IISER), Pune, India K. Alpana, S.
Dube0000-0002-5145-3777, B. Kansal, A. Laha, S. Pandey0000-0003-0440-6019, A.
Rane0000-0001-8444-2807, A. Rastogi0000-0003-1245-6710, S.
Sharma0000-0001-6886-0726 Isfahan University of Technology, Isfahan, Iran H.
Bakhshiansohi380000-0001-5741-3357, M. Zeinali39 Institute for Research in
Fundamental Sciences (IPM), Tehran, Iran S. Chenarani40, S.M.
Etesami0000-0001-6501-4137, M. Khakzad0000-0002-2212-5715, M. Mohammadi
Najafabadi0000-0001-6131-5987 University College Dublin, Dublin, Ireland M.
Grunewald0000-0002-5754-0388 INFN Sezione di Bari a, Bari, Italy, Università
di Bari b, Bari, Italy, Politecnico di Bari c, Bari, Italy M.
Abbresciaa,b0000-0001-8727-7544, R. Alya,b,410000-0001-6808-1335, C. Arutaa,b,
A. Colaleoa0000-0002-0711-6319, D. Creanzaa,c0000-0001-6153-3044, N. De
Filippisa,c0000-0002-0625-6811, M. De Palmaa,b0000-0001-8240-1913, A. Di
Florioa,b, A. Di Pilatoa,b0000-0002-9233-3632, W.
Elmetenaweea,b0000-0001-7069-0252, L. Fiorea0000-0002-9470-1320, A.
Gelmia,b0000-0002-9211-2709, M. Gula0000-0002-5704-1896, G.
Iasellia,c0000-0003-2546-5341, M. Incea,b0000-0001-6907-0195, S.
Lezkia,b0000-0002-6909-774X, G. Maggia,c0000-0001-5391-7689, M.
Maggia0000-0002-8431-3922, I. Margjekaa,b, V.
Mastrapasquaa,b0000-0002-9082-5924, J.A. Merlina, S. Mya,b0000-0002-9938-2680,
S. Nuzzoa,b0000-0003-1089-6317, A. Pellecchiaa,b, A.
Pompilia,b0000-0003-1291-4005, G. Pugliesea,c0000-0001-5460-2638, A.
Ranieria0000-0001-7912-4062, G. Selvaggia,b0000-0003-0093-6741, L.
Silvestrisa0000-0002-8985-4891, F.M. Simonea,b0000-0002-1924-983X, R.
Vendittia0000-0001-6925-8649, P. Verwilligena0000-0002-9285-8631 INFN Sezione
di Bologna a, Bologna, Italy, Università di Bologna b, Bologna, Italy G.
Abbiendia0000-0003-4499-7562, C. Battilanaa,b0000-0002-3753-3068, D.
Bonacorsia,b0000-0002-0835-9574, L. Borgonovia, L. Brigliadoria, R.
Campaninia,b0000-0002-2744-0597, P. Capiluppia,b0000-0003-4485-1897, A.
Castroa,b0000-0003-2527-0456, F.R. Cavalloa0000-0002-0326-7515, M.
Cuffiania,b0000-0003-2510-5039, G.M. Dallavallea0000-0002-8614-0420, T.
Diotalevia,b0000-0003-0780-8785, F. Fabbria0000-0002-8446-9660, A.
Fanfania,b0000-0003-2256-4117, P. Giacomellia0000-0002-6368-7220, L.
Giommia,b0000-0003-3539-4313, C. Grandia0000-0001-5998-3070, L. Guiduccia,b,
S. Lo Meoa,42, L. Lunertia,b, S. Marcellinia0000-0002-1233-8100, G.
Masettia0000-0002-6377-800X, F.L. Navarriaa,b0000-0001-7961-4889, A.
Perrottaa0000-0002-7996-7139, F. Primaveraa,b0000-0001-6253-8656, A.M.
Rossia,b0000-0002-5973-1305, T. Rovellia,b0000-0002-9746-4842, G.P.
Sirolia,b0000-0002-3528-4125 INFN Sezione di Catania a, Catania, Italy,
Università di Catania b, Catania, Italy S. Albergoa,b,430000-0001-7901-4189,
S. Costaa,b,430000-0001-9919-0569, A. Di Mattiaa0000-0002-9964-015X, R.
Potenzaa,b, A. Tricomia,b,430000-0002-5071-5501, C. Tuvea,b0000-0003-0739-3153
INFN Sezione di Firenze a, Firenze, Italy, Università di Firenze b, Firenze,
Italy G. Barbaglia0000-0002-1738-8676, A. Cassesea0000-0003-3010-4516, R.
Ceccarellia,b, V. Ciullia,b0000-0003-1947-3396, C.
Civininia0000-0002-4952-3799, R. D’Alessandroa,b0000-0001-7997-0306, E.
Focardia,b0000-0002-3763-5267, G. Latinoa,b0000-0002-4098-3502, P.
Lenzia,b0000-0002-6927-8807, M. Lizzoa,b, M. Meschinia0000-0002-9161-3990, S.
Paolettia0000-0003-3592-9509, R. Seiditaa,b, G. Sguazzonia0000-0002-0791-3350,
L. Viliania0000-0002-1909-6343 INFN Laboratori Nazionali di Frascati,
Frascati, Italy L. Benussi0000-0002-2363-8889, S. Bianco0000-0002-8300-4124,
D. Piccolo0000-0001-5404-543X INFN Sezione di Genova a, Genova, Italy,
Università di Genova b, Genova, Italy M. Bozzoa,b0000-0002-1715-0457, F.
Ferroa0000-0002-7663-0805, R. Mulargiaa,b, E. Robuttia0000-0001-9038-4500, S.
Tosia,b0000-0002-7275-9193 INFN Sezione di Milano-Bicocca a, Milano, Italy,
Università di Milano-Bicocca b, Milano, Italy A. Benagliaa0000-0003-1124-8450,
F. Brivioa,b, F. Cetorellia,b, V. Cirioloa,b,19, F. De
Guioa,b0000-0001-5927-8865, M.E. Dinardoa,b0000-0002-8575-7250, P.
Dinia0000-0001-7375-4899, S. Gennaia0000-0001-5269-8517, A.
Ghezzia,b0000-0002-8184-7953, P. Govonia,b0000-0002-0227-1301, L.
Guzzia,b0000-0002-3086-8260, M. Malbertia, S. Malvezzia0000-0002-0218-4910, A.
Massironia0000-0002-0782-0883, D. Menascea0000-0002-9918-1686, L.
Moronia0000-0002-8387-762X, M. Paganonia,b0000-0003-2461-275X, D.
Pedrinia0000-0003-2414-4175, S. Ragazzia,b0000-0001-8219-2074, N.
Redaellia0000-0002-0098-2716, T. Tabarelli de Fatisa,b0000-0001-6262-4685, D.
Valsecchia,b,19, D. Zuoloa,b0000-0003-3072-1020 INFN Sezione di Napoli a,
Napoli, Italy, Università di Napoli ’Federico II’ b, Napoli, Italy, Università
della Basilicata c, Potenza, Italy, Università G. Marconi d, Roma, Italy S.
Buontempoa0000-0001-9526-556X, F. Carnevalia,b, N.
Cavalloa,c0000-0003-1327-9058, A. De Iorioa,b0000-0002-9258-1345, F.
Fabozzia,c0000-0001-9821-4151, A.O.M. Iorioa,b0000-0002-3798-1135, L.
Listaa,b0000-0001-6471-5492, S. Meolaa,d,190000-0002-8233-7277, P.
Paoluccia,190000-0002-8773-4781, B. Rossia0000-0002-0807-8772, C.
Sciaccaa,b0000-0002-8412-4072 INFN Sezione di Padova a, Padova, Italy,
Università di Padova b, Padova, Italy, Università di Trento c, Trento, Italy
P. Azzia0000-0002-3129-828X, N. Bacchettaa0000-0002-2205-5737, D.
Biselloa,b0000-0002-2359-8477, P. Bortignona0000-0002-5360-1454, A.
Bragagnoloa,b0000-0003-3474-2099, R. Carlina,b0000-0001-7915-1650, P.
Checchiaa0000-0002-8312-1531, T. Dorigoa0000-0002-1659-8727, U.
Dossellia0000-0001-8086-2863, F. Gasparinia,b0000-0002-1315-563X, U.
Gasparinia,b0000-0002-7253-2669, S.Y. Hoha,b0000-0003-3233-5123, L. Layera,44,
M. Margonia,b0000-0003-1797-4330, A.T. Meneguzzoa,b0000-0002-5861-8140, J.
Pazzinia,b0000-0002-1118-6205, M. Presillaa,b0000-0003-2808-7315, P.
Ronchesea,b0000-0001-7002-2051, R. Rossina,b, F.
Simonettoa,b0000-0002-8279-2464, G. Stronga0000-0002-4640-6108, M.
Tosia,b0000-0003-4050-1769, H. YARARa,b, M. Zanettia,b0000-0003-4281-4582, P.
Zottoa,b0000-0003-3953-5996, A. Zucchettaa,b0000-0003-0380-1172, G.
Zumerlea,b0000-0003-3075-2679 INFN Sezione di Pavia a, Pavia, Italy,
Università di Pavia b, Pavia, Italy C. Aime‘a,b, A.
Braghieria0000-0002-9606-5604, S. Calzaferria,b, D.
Fiorinaa,b0000-0002-7104-257X, P. Montagnaa,b, S.P. Rattia,b, V.
Rea0000-0003-0697-3420, C. Riccardia,b0000-0003-0165-3962, P.
Salvinia0000-0001-9207-7256, I. Vaia0000-0003-0037-5032, P.
Vituloa,b0000-0001-9247-7778 INFN Sezione di Perugia a, Perugia, Italy,
Università di Perugia b, Perugia, Italy G.M. Bileia0000-0002-4159-9123, D.
Ciangottinia,b0000-0002-0843-4108, L. Fanòa,b0000-0002-9007-629X, P.
Laricciaa,b, M. Magherinib, G. Mantovania,b, V. Mariania,b, M.
Menichellia0000-0002-9004-735X, F. Moscatellia0000-0002-7676-3106, A.
Piccinellia,b0000-0003-0386-0527, A. Rossia,b0000-0002-2031-2955, A.
Santocchiaa,b0000-0002-9770-2249, D. Spigaa0000-0002-2991-6384, T.
Tedeschia,b0000-0002-7125-2905 INFN Sezione di Pisa a, Pisa, Italy, Università
di Pisa b, Pisa, Italy, Scuola Normale Superiore di Pisa c, Pisa, Italy,
Università di Siena d, Siena, Italy P. Azzurria0000-0002-1717-5654, G.
Bagliesia0000-0003-4298-1620, V. Bertacchia,c0000-0001-9971-1176, L.
Bianchinia0000-0002-6598-6865, T. Boccalia0000-0002-9930-9299, E.
Bossinia,b0000-0002-2303-2588, R. Castaldia0000-0003-0146-845X, M.A.
Cioccia,b0000-0003-0002-5462, V. D‘amantea,d0000-0002-7342-2592, R.
Dell’Orsoa0000-0003-1414-9343, M.R. Di Domenicoa,d0000-0002-7138-7017, S.
Donatoa0000-0001-7646-4977, A. Giassia0000-0001-9428-2296, M.T.
Grippoa0000-0002-4560-1614, F. Ligabuea,c0000-0002-1549-7107, E.
Mancaa,c0000-0001-8946-655X, G. Mandorlia,c0000-0002-5183-9020, A.
Messineoa,b0000-0001-7551-5613, F. Pallaa0000-0002-6361-438X, S. Paroliaa,b,
G. Ramirez-Sancheza,c, A. Rizzia,b0000-0002-4543-2718, G.
Rolandia,c0000-0002-0635-274X, S. Roy Chowdhurya,c, A. Scribanoa, N.
Shafieia,b0000-0002-8243-371X, P. Spagnoloa0000-0001-7962-5203, R.
Tenchinia0000-0003-2574-4383, G. Tonellia,b0000-0003-2606-9156, N.
Turinia,d0000-0002-9395-5230, A. Venturia0000-0002-0249-4142, P.G.
Verdinia0000-0002-0042-9507 INFN Sezione di Roma a, Rome, Italy, Sapienza
Università di Roma b, Rome, Italy M. Campanaa,b, F.
Cavallaria0000-0002-1061-3877, M. Cipriania,b0000-0002-0151-4439, D. Del
Rea,b0000-0003-0870-5796, E. Di Marcoa0000-0002-5920-2438, M.
Diemoza0000-0002-3810-8530, E. Longoa,b0000-0001-6238-6787, P.
Meridiania0000-0002-8480-2259, G. Organtinia,b0000-0002-3229-0781, F.
Pandolfia, R. Paramattia,b0000-0002-0080-9550, C. Quarantaa,b, S.
Rahatloua,b0000-0001-9794-3360, C. Rovellia0000-0003-2173-7530, F.
Santanastasioa,b0000-0003-2505-8359, L. Soffia0000-0003-2532-9876, R.
Tramontanoa,b INFN Sezione di Torino a, Torino, Italy, Università di Torino b,
Torino, Italy, Università del Piemonte Orientale c, Novara, Italy N.
Amapanea,b0000-0001-9449-2509, R. Arcidiaconoa,c0000-0001-5904-142X, S.
Argiroa,b0000-0003-2150-3750, M. Arneodoa,c0000-0002-7790-7132, N.
Bartosika0000-0002-7196-2237, R. Bellana,b0000-0002-2539-2376, A.
Belloraa,b0000-0002-2753-5473, J. Berenguer Antequeraa,b0000-0003-3153-0891,
C. Biinoa0000-0002-1397-7246, N. Cartigliaa0000-0002-0548-9189, S.
Comettia0000-0001-6621-7606, M. Costaa,b0000-0003-0156-0790, R.
Covarellia,b0000-0003-1216-5235, N. Demariaa0000-0003-0743-9465, B.
Kiania,b0000-0001-6431-5464, F. Leggera0000-0003-1400-0709, C.
Mariottia0000-0002-6864-3294, S. Masellia0000-0001-9871-7859, E.
Migliorea,b0000-0002-2271-5192, E. Monteila,b0000-0002-2350-213X, M.
Montenoa0000-0002-3521-6333, M.M. Obertinoa,b0000-0002-8781-8192, G.
Ortonaa0000-0001-8411-2971, L. Pachera,b0000-0003-1288-4838, N.
Pastronea0000-0001-7291-1979, M. Pelliccionia0000-0003-4728-6678, G.L. Pinna
Angionia,b, M. Ruspaa,c0000-0002-7655-3475, K.
Shchelinaa,b0000-0003-3742-0693, F. Sivieroa,b0000-0002-4427-4076, V.
Solaa0000-0001-6288-951X, A. Solanoa,b0000-0002-2971-8214, D.
Soldia,b0000-0001-9059-4831, A. Staianoa0000-0003-1803-624X, M. Tornagoa,b, D.
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Trieste, Italy, Università di Trieste b, Trieste, Italy S.
Belfortea0000-0001-8443-4460, V. Candelisea,b0000-0002-3641-5983, M.
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Smith, S. Sekmen0000-0003-1726-5681, Y.C. Yang Chonnam National University,
Institute for Universe and Elementary Particles, Kwangju, Korea H.
Kim0000-0001-8019-9387, D.H. Moon0000-0002-5628-9187 Hanyang University,
Seoul, Korea B. Francois0000-0002-2190-9059, T.J. Kim0000-0001-8336-2434, J.
Park0000-0002-4683-6669 Korea University, Seoul, Korea S. Cho, S.
Choi0000-0001-6225-9876, Y. Go, B. Hong0000-0002-2259-9929, K. Lee, K.S.
Lee0000-0002-3680-7039, J. Lim, J. Park, S.K. Park, J. Yoo Kyung Hee
University, Department of Physics, Seoul, Republic of Korea, Seoul, Korea J.
Goh0000-0002-1129-2083, A. Gurtu Sejong University, Seoul, Korea H.S.
Kim0000-0002-6543-9191, Y. Kim Seoul National University, Seoul, Korea J.
Almond, J.H. Bhyun, J. Choi, S. Jeon, J. Kim, J.S. Kim, S. Ko, H. Kwon, H.
Lee0000-0002-1138-3700, S. Lee, B.H. Oh, M. Oh0000-0003-2618-9203, S.B. Oh, H.
Seo0000-0002-3932-0605, U.K. Yang, I. Yoon0000-0002-3491-8026 University of
Seoul, Seoul, Korea W. Jang, D. Jeon, D.Y. Kang, Y. Kang, J.H. Kim, S. Kim, B.
Ko, J.S.H. Lee0000-0002-2153-1519, Y. Lee, I.C. Park, Y. Roh, M.S. Ryu, D.
Song, I.J. Watson0000-0003-2141-3413, S. Yang Yonsei University, Department of
Physics, Seoul, Korea S. Ha, H.D. Yoo Sungkyunkwan University, Suwon, Korea M.
Choi, Y. Jeong, H. Lee, Y. Lee, I. Yu0000-0003-1567-5548 College of
Engineering and Technology, American University of the Middle East (AUM),
Egaila, Kuwait, Dasman, Kuwait T. Beyrouthy, Y. Maghrbi Riga Technical
University, Riga, Latvia T. Torims, V. Veckalns450000-0003-3676-9711 Vilnius
University, Vilnius, Lithuania M. Ambrozas, A. Juodagalvis0000-0002-1501-3328,
A. Rinkevicius0000-0002-7510-255X, G. Tamulaitis0000-0002-2913-9634 National
Centre for Particle Physics, Universiti Malaya, Kuala Lumpur, Malaysia N. Bin
Norjoharuddeen0000-0002-8818-7476, W.A.T. Wan Abdullah, M.N. Yusli, Z.
Zolkapli Universidad de Sonora (UNISON), Hermosillo, Mexico J.F.
Benitez0000-0002-2633-6712, A. Castaneda Hernandez0000-0003-4766-1546, M. León
Coello, J.A. Murillo Quijada0000-0003-4933-2092, A. Sehrawat, L. Valencia
Palomo0000-0002-8736-440X Centro de Investigacion y de Estudios Avanzados del
IPN, Mexico City, Mexico G. Ayala, H. Castilla-Valdez, E. De La Cruz-
Burelo0000-0002-7469-6974, I. Heredia-De La Cruz460000-0002-8133-6467, R.
Lopez-Fernandez, C.A. Mondragon Herrera, D.A. Perez Navarro, A. Sánchez
Hernández0000-0001-9548-0358 Universidad Iberoamericana, Mexico City, Mexico
S. Carrillo Moreno, C. Oropeza Barrera0000-0001-9724-0016, M. Ramírez
García0000-0002-4564-3822, F. Vazquez Valencia Benemerita Universidad Autonoma
de Puebla, Puebla, Mexico I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada
University of Montenegro, Podgorica, Montenegro J. Mijuskovic47, N. Raicevic
University of Auckland, Auckland, New Zealand D. Krofcheck0000-0001-5494-7302
University of Canterbury, Christchurch, New Zealand S. Bheesette, P.H.
Butler0000-0001-9878-2140 National Centre for Physics, Quaid-I-Azam
University, Islamabad, Pakistan A. Ahmad, M.I. Asghar, A. Awais, M.I.M. Awan,
H.R. Hoorani, W.A. Khan, M.A. Shah, M. Shoaib0000-0001-6791-8252, M.
Waqas0000-0002-3846-9483 AGH University of Science and Technology Faculty of
Computer Science, Electronics and Telecommunications, Krakow, Poland V. Avati,
L. Grzanka, M. Malawski National Centre for Nuclear Research, Swierk, Poland
H. Bialkowska, M. Bluj0000-0003-1229-1442, B. Boimska0000-0002-4200-1541, M.
Górski, M. Kazana, M. Szleper0000-0002-1697-004X, P. Zalewski Institute of
Experimental Physics, Faculty of Physics, University of Warsaw, Warsaw, Poland
K. Bunkowski, K. Doroba, A. Kalinowski0000-0002-1280-5493, M.
Konecki0000-0001-9482-4841, J. Krolikowski0000-0002-3055-0236, M.
Walczak0000-0002-2664-3317 Laboratório de Instrumentação e Física Experimental
de Partículas, Lisboa, Portugal M. Araujo, P. Bargassa0000-0001-8612-3332, D.
Bastos, A. Boletti0000-0003-3288-7737, P. Faccioli0000-0003-1849-6692, M.
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Leonardo0000-0002-9746-4594, T. Niknejad, M. Pisano, J.
Seixas0000-0002-7531-0842, O. Toldaiev0000-0002-8286-8780, J.
Varela0000-0003-2613-3146 Joint Institute for Nuclear Research, Dubna, Russia
S. Afanasiev, D. Budkouski, I. Golutvin, I. Gorbunov0000-0003-3777-6606, V.
Karjavine, V. Korenkov0000-0002-2342-7862, A. Lanev, A. Malakhov, V.
Matveev48,49, V. Palichik, V. Perelygin, M. Savina, D. Seitova, V. Shalaev, S.
Shmatov, S. Shulha, V. Smirnov, O. Teryaev, N. Voytishin, B.S. Yuldashev50, A.
Zarubin, I. Zhizhin Petersburg Nuclear Physics Institute, Gatchina (St.
Petersburg), Russia G. Gavrilov0000-0003-3968-0253, V. Golovtcov, Y. Ivanov,
V. Kim510000-0001-7161-2133, E. Kuznetsova52, V. Murzin, V. Oreshkin, I.
Smirnov, D. Sosnov0000-0002-7452-8380, V. Sulimov, L. Uvarov, S. Volkov, A.
Vorobyev Institute for Nuclear Research, Moscow, Russia Yu.
Andreev0000-0002-7397-9665, A. Dermenev, S. Gninenko0000-0001-6495-7619, N.
Golubev, A. Karneyeu0000-0001-9983-1004, D. Kirpichnikov0000-0002-7177-077X,
M. Kirsanov, N. Krasnikov, A. Pashenkov, G. Pivovarov0000-0001-6435-4463, D.
Tlisov${}^{\textrm{\textdagger}}$, A. Toropin Institute for Theoretical and
Experimental Physics named by A.I. Alikhanov of NRC ‘Kurchatov Institute’,
Moscow, Russia V. Epshteyn, V. Gavrilov, N. Lychkovskaya, A. Nikitenko53, V.
Popov, A. Spiridonov, A. Stepennov, M. Toms, E. Vlasov0000-0002-8628-2090, A.
Zhokin Moscow Institute of Physics and Technology, Moscow, Russia T. Aushev
National Research Nuclear University ’Moscow Engineering Physics Institute’
(MEPhI), Moscow, Russia O. Bychkova, R. Chistov540000-0003-1439-8390, M.
Danilov550000-0001-9227-5164, P. Parygin, S. Polikarpov550000-0001-6839-928X
P.N. Lebedev Physical Institute, Moscow, Russia V. Andreev, M. Azarkin, I.
Dremin0000-0001-7451-247X, M. Kirakosyan, A. Terkulov Skobeltsyn Institute of
Nuclear Physics, Lomonosov Moscow State University, Moscow, Russia A. Belyaev,
E. Boos0000-0002-0193-5073, V. Bunichev, M. Dubinin560000-0002-7766-7175, L.
Dudko0000-0002-4462-3192, V. Klyukhin0000-0002-8577-6531, O.
Kodolova0000-0003-1342-4251, I. Lokhtin0000-0002-4457-8678, S. Obraztsov, M.
Perfilov, V. Savrin, P. Volkov, G. Vorotnikov0000-0002-8466-9881 Novosibirsk
State University (NSU), Novosibirsk, Russia V. Blinov57, T. Dimova57, L.
Kardapoltsev57, A. Kozyrev57, I. Ovtin57, Y. Skovpen570000-0002-3316-0604
Institute for High Energy Physics of National Research Centre ‘Kurchatov
Institute’, Protvino, Russia I. Azhgirey0000-0003-0528-341X, I. Bayshev, D.
Elumakhov, V. Kachanov, D. Konstantinov0000-0001-6673-7273, P.
Mandrik0000-0001-5197-046X, V. Petrov, R. Ryutin, S.
Slabospitskii0000-0001-8178-2494, A. Sobol, S. Troshin0000-0001-5493-1773, N.
Tyurin, A. Uzunian, A. Volkov National Research Tomsk Polytechnic University,
Tomsk, Russia A. Babaev, V. Okhotnikov Tomsk State University, Tomsk, Russia
V. Borshch, V. Ivanchenko0000-0002-1844-5433, E. Tcherniaev0000-0002-3685-0635
University of Belgrade: Faculty of Physics and VINCA Institute of Nuclear
Sciences, Belgrade, Serbia P. Adzic580000-0002-5862-7397, M.
Dordevic0000-0002-8407-3236, P. Milenovic0000-0001-7132-3550, J.
Milosevic0000-0001-8486-4604 Centro de Investigaciones Energéticas
Medioambientales y Tecnológicas (CIEMAT), Madrid, Spain M. Aguilar-Benitez, J.
Alcaraz Maestre0000-0003-0914-7474, A. Álvarez Fernández, I. Bachiller, M.
Barrio Luna, Cristina F. Bedoya0000-0001-8057-9152, C.A. Carrillo
Montoya0000-0002-6245-6535, M. Cepeda0000-0002-6076-4083, M. Cerrada, N.
Colino0000-0002-3656-0259, B. De La Cruz, A. Delgado Peris0000-0002-8511-7958,
J.P. Fernández Ramos0000-0002-0122-313X, J. Flix0000-0003-2688-8047, M.C.
Fouz0000-0003-2950-976X, O. Gonzalez Lopez0000-0002-4532-6464, S. Goy
Lopez0000-0001-6508-5090, J.M. Hernandez0000-0001-6436-7547, M.I.
Josa0000-0002-4985-6964, J. León Holgado0000-0002-4156-6460, D. Moran, Á.
Navarro Tobar0000-0003-3606-1780, A. Pérez-Calero
Yzquierdo0000-0003-3036-7965, J. Puerta Pelayo0000-0001-7390-1457, I.
Redondo0000-0003-3737-4121, L. Romero, S. Sánchez Navas, L. Urda
Gómez0000-0002-7865-5010, C. Willmott Universidad Autónoma de Madrid, Madrid,
Spain J.F. de Trocóniz, R. Reyes-Almanza0000-0002-4600-7772 Universidad de
Oviedo, Instituto Universitario de Ciencias y Tecnologías Espaciales de
Asturias (ICTEA), Oviedo, Spain B. Alvarez Gonzalez0000-0001-7767-4810, J.
Cuevas0000-0001-5080-0821, C. Erice0000-0002-6469-3200, J. Fernandez
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Ramón Álvarez, J. Ripoll Sau, V. Rodríguez Bouza0000-0002-7225-7310, A.
Trapote, N. Trevisani0000-0002-5223-9342 Instituto de Física de Cantabria
(IFCA), CSIC-Universidad de Cantabria, Santander, Spain J.A. Brochero
Cifuentes0000-0003-2093-7856, I.J. Cabrillo, A. Calderon0000-0002-7205-2040,
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Martinez Ruiz del Arbol0000-0002-7737-5121, F. Matorras0000-0003-4295-5668, P.
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Scodellaro0000-0002-4974-8330, I. Vila, J.M. Vizan Garcia0000-0002-6823-8854
University of Colombo, Colombo, Sri Lanka M.K. Jayananda, B. Kailasapathy59,
D.U.J. Sonnadara, D.D.C. Wickramarathna University of Ruhuna, Department of
Physics, Matara, Sri Lanka W.G.D. Dharmaratna0000-0002-6366-837X, K. Liyanage,
N. Perera, N. Wickramage CERN, European Organization for Nuclear Research,
Geneva, Switzerland T.K. Aarrestad0000-0002-7671-243X, D. Abbaneo, J.
Alimena0000-0001-6030-3191, E. Auffray, G. Auzinger, J. Baechler, P.
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Nebgen0000-0003-3879-5622, S. Scarfi, C. Schäfer, C. Schwick, M.
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Wanczyk62, K.A. Wozniak, W.D. Zeuner Paul Scherrer Institut, Villigen,
Switzerland L. Caminada630000-0001-5677-6033, A. Ebrahimi0000-0003-4472-867X,
W. Erdmann, R. Horisberger, Q. Ingram, H.C. Kaestli, D. Kotlinski, U.
Langenegger, M. Missiroli0000-0002-1780-1344, T. Rohe ETH Zurich - Institute
for Particle Physics and Astrophysics (IPA), Zurich, Switzerland K.
Androsov620000-0003-2694-6542, M. Backhaus0000-0002-5888-2304, P. Berger, A.
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Schönenberger0000-0002-6508-5776, V. Stampf, J.
Steggemann620000-0003-4420-5510, R. Wallny0000-0001-8038-1613, D.H. Zhu
Universität Zürich, Zurich, Switzerland C. Amsler640000-0002-7695-501X, P.
Bärtschi, C. Botta0000-0002-8072-795X, D. Brzhechko, M.F.
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Robmann, S. Sanchez Cruz0000-0002-9991-195X, K. Schweiger0000-0002-5846-3919,
Y. Takahashi0000-0001-5184-2265 National Central University, Chung-Li, Taiwan
C. Adloff65, C.M. Kuo, W. Lin, A. Roy0000-0002-5622-4260, T.
Sarkar360000-0003-0582-4167, S.S. Yu National Taiwan University (NTU), Taipei,
Taiwan L. Ceard, Y. Chao, K.F. Chen0000-0003-1304-3782, P.H.
Chen0000-0002-0468-8805, W.-S. Hou0000-0002-4260-5118, Y.y. Li, R.-S. Lu, E.
Paganis0000-0002-1950-8993, A. Psallidas, A. Steen, H.y. Wu, E.
Yazgan0000-0001-5732-7950, P.r. Yu Chulalongkorn University, Faculty of
Science, Department of Physics, Bangkok, Thailand B.
Asavapibhop0000-0003-1892-7130, C. Asawatangtrakuldee0000-0003-2234-7219, N.
Srimanobhas0000-0003-3563-2959 Çukurova University, Physics Department,
Science and Art Faculty, Adana, Turkey F. Boran0000-0002-3611-390X, S.
Damarseckin66, Z.S. Demiroglu0000-0001-7977-7127, F. Dolek0000-0001-7092-5517,
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Gurpinar Guler680000-0002-6172-0285, I. Hos69, C. Isik, O. Kara, A. Kayis
Topaksu, U. Kiminsu0000-0001-6940-7800, G. Onengut, K. Ozdemir70, A. Polatoz,
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Turkcapar, I.S. Zorbakir0000-0002-5962-2221, C. Zorbilmez Middle East
Technical University, Physics Department, Ankara, Turkey B. Isildak72, G.
Karapinar73, K. Ocalan740000-0002-8419-1400, M. Yalvac750000-0003-4915-9162
Bogazici University, Istanbul, Turkey B. Akgun, I.O.
Atakisi0000-0002-9231-7464, E. Gülmez0000-0002-6353-518X, M.
Kaya760000-0003-2890-4493, O. Kaya77, Ö. Özçelik, S. Tekten78, E.A.
Yetkin790000-0002-9007-8260 Istanbul Technical University, Istanbul, Turkey A.
Cakir0000-0002-8627-7689, K. Cankocak670000-0002-3829-3481, Y. Komurcu, S.
Sen800000-0001-7325-1087 Istanbul University, Istanbul, Turkey S. Cerci71, B.
Kaynak, S. Ozkorucuklu, D. Sunar Cerci710000-0002-5412-4688 Institute for
Scintillation Materials of National Academy of Science of Ukraine, Kharkov,
Ukraine B. Grynyov National Scientific Center, Kharkov Institute of Physics
and Technology, Kharkov, Ukraine L. Levchuk0000-0001-5889-7410 University of
Bristol, Bristol, United Kingdom D. Anthony, E. Bhal0000-0003-4494-628X, S.
Bologna, J.J. Brooke0000-0002-6078-3348, A. Bundock0000-0002-2916-6456, E.
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Smith, N. Stylianou810000-0002-0113-6829, R. White Rutherford Appleton
Laboratory, Didcot, United Kingdom K.W. Bell, A. Belyaev820000-0002-1733-4408,
C. Brew0000-0001-6595-8365, R.M. Brown, D.J.A. Cockerill, K.V. Ellis, K.
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Newbold0000-0002-9015-9634, E. Olaiya, D. Petyt, T. Reis0000-0003-3703-6624,
T. Schuh, C.H. Shepherd-Themistocleous, I.R. Tomalin, T.
Williams0000-0002-8724-4678 Imperial College, London, United Kingdom R.
Bainbridge0000-0001-9157-4832, P. Bloch0000-0001-6716-979X, S. Bonomally, J.
Borg0000-0002-7716-7621, S. Breeze, O. Buchmuller, V.
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