B2G-19-003

B2G-19-003

# Search for a heavy resonance decaying to a top quark and a boson at
$\sqrt{s}=13\TeV$ in the fully hadronic final state

###### Abstract

A search for a heavy resonance decaying to a top quark and a boson in the
fully hadronic final state is presented. The analysis is performed using data
from proton-proton collisions at a center-of-mass energy of 13, corresponding
to an integrated luminosity of 137recorded by the CMS experiment at the LHC.
The search is focused on heavy resonances, where the decay products of each
top quark or boson are expected to be reconstructed as a single, large-radius
jet with a distinct substructure. The production of an excited bottom quark,
$\PQb^{\ast}$, is used as a benchmark when setting limits on the cross section
for a heavy resonance decaying to a top quark and a boson. The hypotheses of
$\PQb^{\ast}$ quarks with left-handed, right-handed, and vector-like
chiralities are excluded at 95% confidence level for masses below 2.6, 2.8,
and 3.1, respectively. These are the most stringent limits on the
$\PQb^{\ast}$ quark mass to date, extending the previous best limits by almost
a factor of two.

##  0.1 Introduction

The standard model (SM) has been extensively verified by experiment,
nonetheless there exists evidence that the SM is only an effective theory.
Many possibilities for physics beyond the SM have been proposed, including the
possibility that quarks are composite. Such quarks would have an internal
structure that, excited, could produce a state with higher mass [1, 2]. Such a
phenomenon is predicted by Randall–Sundrum models [3, 4] and models with a
heavy gluon partner [5, 6, 7].

In this paper, we search for a heavy resonance decaying to a top quark and a
boson in the fully hadronic final state, using proton-proton ($\Pp\Pp$)
collision data at a center-of-mass energy of 13. The search uses data
corresponding to an integrated luminosity of 137recorded by the CMS experiment
[8] at the CERN LHC during 2016–2018.

As a benchmark resonance, we consider an excited bottom quark, referred to as
a $\PQb^{\ast}$ quark [2]. The strong interaction is the dominant production
mechanism and can produce a single $\PQb^{\ast}$ quark at the LHC via the
collision of a bottom quark and a gluon, $\PQb\Pg\to\PQb^{\ast}$. The
interaction is described by the Lagrangian

$\mathcal{L}_{1}=\frac{g_{\text{s}}}{2\Lambda}G_{\mu\nu}\overline{\PQb}\sigma^{\mu\nu}(\kappa^{\PQb}_{\text{L}}P_{\text{L}}+\kappa^{\PQb}_{\text{R}}P_{\text{R}})\PQb^{\ast}+\text{h.c.},$
(1)

where $g_{\text{s}}$ is the strong coupling, $G_{\mu\nu}$ is the gauge field
tensor of the gluon, $\overline{\PQb}$ is the bottom quark field,
$\sigma^{\mu\nu}$ is the Pauli spin matrix, $\PQb^{\ast}$ is the excited
bottom quark field, and $\Lambda$ is the scale of compositeness [1], which is
chosen to be the mass of the $\PQb^{\ast}$ quark. The chiral projection
operators are represented as $P_{\text{L}}$ and $P_{\text{R}}$, and
$\kappa^{\PQb}_{\text{L}}$ and $\kappa^{\PQb}_{\text{R}}$ are the relative
coupling strengths [9].

The $\PQb^{\ast}\to\PQt\PW$ decay is the dominant decay channel, with a
branching fraction of approximately 40% for a $\PQb^{\ast}$ quark with
$m_{\PQb^{\ast}}>1.2\TeV$ [9]. The decay takes place through the weak
interaction and is described by the Lagrangian

$\mathcal{L}_{2}=\frac{g_{\text{2}}}{\sqrt{2}}\PW^{+}_{\mu}\cPaqt\gamma^{\mu}(g_{\text{L}}P_{\text{L}}+g_{\text{R}}P_{\text{R}})\PQb^{\ast}+\text{h.c.},$
(2)

where $g_{\text{2}}$ is the $SU(2)_{L}$ weak coupling and $g_{\text{L}}$ and
$g_{\text{R}}$ are the relative couplings of the boson to the left- and right-
handed $\PQb^{\ast}$ quark, respectively [9]. The full interaction chain is
then $\PQb\Pg\to\PQb^{\ast}\to\PQt\PW$. The $\PQb^{\ast}$ quark width is
expected to be less than 10% of the $\PQb^{\ast}$ quark mass, which leads to a
distinct resonant structure in the mass spectrum.

Three hypotheses for the left- and right-handed $\PQb^{\ast}$ quark couplings
are considered:

left-handed (LH): $\displaystyle\kappa^{\PQb}_{\text{L}}=g_{\text{L}}=1\text{
and }\kappa^{\PQb}_{\text{R}}=g_{\text{R}}=0,$ (3) right-handed (RH):
$\displaystyle\kappa^{\PQb}_{\text{L}}=g_{\text{L}}=0\text{ and
}\kappa^{\PQb}_{\text{R}}=g_{\text{R}}=1,\text{ and}$ (4) vector-like (LH+RH):
$\displaystyle\kappa^{\PQb}_{\text{L}}=g_{\text{L}}=1\text{ and
}\kappa^{\PQb}_{\text{R}}=g_{\text{R}}=1.$ (5)

Searches for the $\PQb^{\ast}$ quark in the $\PQt\PW$ decay mode have been
performed at the LHC by the ATLAS Collaboration at $\sqrt{s}=7\TeV$ [10] and
by the CMS Collaboration at 8 [11]. Additionally, searches for a $\PQb^{\ast}$
quark decaying to a bottom quark and a gluon were conducted by the CMS
Collaboration at 8 [12] and by the ATLAS Collaboration at 13 [13]. The CMS
$\PQt\PW$ decay mode search included a combination of fully hadronic,
lepton+jets, and dilepton final states, and excluded $\PQb^{\ast}$ quark
masses at 95% confidence level ($\CL$) below 1.4, 1.4, and 1.5, for the left-
handed, right-handed, and vector-like hypotheses, respectively.

Given the range of these exclusions, the present analysis considers a
$\PQb^{\ast}$ quark with a mass greater than 1.2. For these mass values, the
top quark and the boson are commonly produced with a high Lorentz boost.
Because of this, the hadronic decay products of the top quark and the boson
can each merge, resulting in two massive, large-radius jets, referred to as a
“top jet” and a “jet”, respectively. These jets have a distinct substructure
that is used to discriminate them from the background [14, 15]. The
$\PQb^{\ast}$ quark mass is reconstructed as the invariant mass of the top jet
and jet system, $m_{\PQt\PW}$. This variable, along with the reconstructed top
jet mass, $m_{\PQt}$, is used to search for the $\PQb^{\ast}$ quark resonance.

The background is dominated by jets produced through the strong interaction,
referred to as quantum chromodynamics (QCD) multijet production, and is
estimated using multijet-enriched control regions based on inverting the top
jet selection criteria. The SM +jets and +jets production backgrounds are also
accounted for with this technique. The background is estimated with simulation
templates fit to data simultaneously in the signal region and a dedicated
control region enhanced in production that constrains the simulation
templates.

A binned maximum likelihood fit to data is performed in the two-dimensional
$m_{\PQt\PW}$ versus $m_{\PQt}$ distribution, in a process where the signal
and background models are fit simultaneously. From this fit, $\PQb^{\ast}$
quark mass limits are derived for the three $\PQb^{\ast}$ chirality hypotheses
expressed in Eqs. (3), (4), and (5).

In addition, we interpret the results under the hypothesis of a singly
produced $\PB$ singlet vector-like quark [16, 17] decaying into $\PQt\PW$. For
$\PB$ quark masses above 1.2, the decay products would be heavily boosted with
a similar signature to the $\PQb^{\ast}$ quark decay described above. In the
model considered, the $V_{\PQt\PB}^{\text{L}}$ mixing parameter defined in
Ref. [16] is set to unity, which results in a relative signal width of less
than 5% in the B mass range of interest, with a branching fraction to tW of
approximately 50%. In contrast to the $\PQb^{\ast}$ model, the $\PB$ quark
would be produced via an electroweak interaction in association with a top or
bottom quark. We consider both scenarios, but typically the associated top or
bottom quark has a much lower transverse momentum than the $\PB$ quark decay
products, thus the effect of either on the analysis is small.

##  0.2 The CMS detector

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 extend the pseudorapidity 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. [8].

Events of interest are selected using a two-tiered trigger system [18]. The
first level, 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. The second level, known as the high-level
trigger, 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.

The analysis reflects the fact that the pixel detector was changed in the
winter of 2016/2017. The newer detector increased the number of barrel layers
from three to four and decreased the distance of the innermost layer from the
beamline in order to improve the vertex reconstruction.

##  0.3 Data and simulated samples

CMS data taking operates on annual cycles, and thus data collection and
simulation performance can change from year to year. Therefore, we categorize
both the data and simulation by year and apply dedicated scale factors before
combining the distributions from all three years to derive the final result.

We analyze events from the 2016 data set recorded by a trigger that requires
the scalar sum of transverse momenta, , of all jets in the event, , to be at
least 800 or 900, or the presence of a jet with $\pt>450\GeV$. For 2017 and
2018 data, we analyze events recorded by a trigger that requires a minimum of
1050or the presence of a jet with $\pt>500\GeV$. Additionally, 2018 data
events are recorded by a trigger that requires a jet with $\pt>400\GeV$ with a
mass of at least 30, where the jet trimming algorithm [19] has been used to
reconstruct the jet mass at the trigger level. This trigger did not exist for
the 2016 or 2017 data collection, but the addition of events recorded by this
trigger provides a higher overall selection efficiency at lower for 2018. The
choice of higher and jet thresholds used for 2017 and 2018 are due to an
increase in the instantaneous luminosity of the LHC between 2016 and 2017. The
combination of these triggers is nearly fully efficient for
$m_{\PQt\PW}>1200\GeV$.

The efficiency of the trigger selection is measured in data as the ratio of
the number of events recorded by the combined triggers to the number of events
recorded by a trigger that requires a muon candidate with $\pt>50\GeV$. A muon
trigger is used for this measurement because it is largely uncorrelated with
the triggers used for data taking.

The trigger efficiencies are parameterized as a function of dijet invariant
mass ($m_{\mathrm{jj}}$) and both the numerator and denominator of the ratio
include events that pass the preselection described in Section 0.5. The
uncertainty assigned to the efficiency measurement is one half of the trigger
inefficiency.

Figure 1 shows the trigger efficiency derived from 2016, 2017, and 2018 data
separately. Simulated samples are corrected using the efficiency measurement
from the corresponding data-taking year.

A trigger inefficiency referred to as “prefiring” occurred during the 2016 and
2017 data taking. Over that time period, a gradual shift in the timing of
triggering systems based on the ECAL in the endcap caused certain events to
not be recorded. Event corrections were calculated from data and applied to
the 2016 and 2017 simulations to model this inefficiency. The uncertainties in
these corrections are taken as systematic uncertainties.

Figure 1:  The efficiency of the full trigger selection as a function of
$m_{\mathrm{jj}}$, shown separately for 2016, 2017, and 2018 data. The minimum
$m_{\mathrm{jj}}$ considered in the analysis is 1200and is marked with a
dashed line and an arrow. The efficiency below $m_{\mathrm{jj}}$ of 1000is not
measured. The points for 2017 and 2018 data are not visible in the plateau
because they are overlapped by the points for 2016 data.

The SM and single top quark Monte Carlo (MC) simulated samples are used as
templates for background estimation in the maximum likelihood fit to data. A
scale factor is applied to the generated top quark spectrum to correct for the
differences between data and simulation. It is based on a dedicated
measurement [20, 21], in which the ratio of the distribution of the top quark
measured in data to the distribution as measured in $\POWHEG$+$\PYTHIA$ is
derived. This scale factor may be described by the expression

$w_{\PQt}(\pt)=\re^{c_{1}0.0615-c_{2}(0.0005/\GeV)\pt},$ (6)

where $c_{1}$ and $c_{2}$ are taken to be 1, as obtained in Refs. [20, 21].
The -dependent event weight is given by
$\sqrt{\smash[b]{w_{\PQt}(\pt)w_{\cPaqt}(\pt)}}$, where $w_{\PQt}(\pt)$ and
$w_{\cPaqt}(\pt)$ are evaluated using the top quark and antiquark ,
respectively. We use the same form for the scale factor but treat $c_{1}$ and
$c_{2}$ as fit parameters initialized to 1 and constrained in the fit to a
Gaussian with a width of 0.5.

To simulate the SM and single top quark production, we use the $\POWHEG$ v2
[22, 23, 24, 25, 26] matrix element event generator. For QCD multijet
simulation, we use $\MGvATNLO$ version 5 [27] with subversion 2.2.2 for 2016
and 2.4.2 for 2017 and 2018. The QCD multijet simulated samples are used to
derive a scale factor to the multijet background estimation procedure and for
cross checks of self consistency of the background estimate. The $\PQb^{\ast}$
signal samples are simulated using $\MGvATNLO$ version 5 over a mass range of
1.4 to 4.0in steps of 200. Subversion 2.2.2 is used for 2016 $\PQb^{\ast}$
signal samples with $\PQb^{\ast}$ quark masses from 1.4 to 3.0and 2016
$\PB$+and $\PB$+signal samples. Subversion 2.4.2 is used for 2017 and 2018
$\PQb^{\ast}$ signal samples with $\PQb^{\ast}$ quark masses from 1.4 to 3.0.
Subversion 2.6.5 is used for all $\PQb^{\ast}$ signal samples with
$\PQb^{\ast}$ quark masses above 3.0. For the $\PB$ signal simulations, we use
samples based on the 2016 conditions and scale the final distributions to the
integrated luminosity of the full data set, after correcting for differences
in annual selection efficiencies that are measured with the $\PQb^{\ast}$
signal samples. We simulate $\PB$ quark masses from 1.4 to 1.8in steps of 100.

Hadronization and parton showering are simulated using the $\PYTHIA$ 8
software package [28]. The NNPDF3.0 [29] parton distribution functions (PDFs)
are used with the CUETP8M1 [30] underlying event tune for the 2016 simulations
and the NNPDF3.1 [31] PDFs are used with the CP5 [32] underlying event tune
for the 2017 and 2018 simulations. The CMS detector simulation is performed
with $\GEANTfour$ [33]. Pythia version 8.212 is used for all the 2016
simulations with the exception of 2016 $\PQb^{\ast}$ signal samples with
$\PQb^{\ast}$ quark masses from 1.4 to 3.0, which use version 8.226, and 2016
$\PB$ signal samples, which use version 8.205. Pythia version 8.230 is used
for all the 2017 and 2018 simulations.

To simulate the effect of additional $\Pp\Pp$ collision data within the same
or adjacent bunch crossings (pileup), additional inelastic events are
superimposed using $\PYTHIA$. Simulated samples are then reweighted to correct
the pileup simulation, using the total inelastic cross section of
$69\unit{mb}$ [34, 35] to estimate the distribution of the number of primary
vertices in data.

##  0.4 Event reconstruction

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 anti-$\kt$ jet finding algorithm [36, 37]
with a distance parameter of $R=0.4$ and with the tracks assigned to candidate
vertices as inputs, and the associated missing transverse momentum, taken as
the negative vector sum of the transverse momentum of those jets.

A particle-flow algorithm [38] aims to reconstruct and identify each
individual particle in an event, with an optimized combination of information
from the various elements of the CMS detector. The energy of muons is obtained
from the curvature of the corresponding track. The energy of charged hadrons
is determined from a combination of their momentum measured in the tracking
detector and the matching ECAL and HCAL energy deposits, corrected for the
response function of the calorimeters to hadronic showers. Finally, the energy
of neutral hadrons is obtained from the corresponding corrected ECAL and HCAL
energies. Jets are clustered with the anti-$\kt$ jet finding algorithm, using
all particle-flow objects as input. Jet momentum is determined as the
vectorial sum of all particle momenta in the jet. Jets with a distance
parameter of $R=0.8$ are used to reconstruct the top jet and jet candidates in
an event.

The pileup per particle identification (PUPPI) algorithm [39] is used to
mitigate the effect of pileup at the reconstructed particle level, making use
of local shape information, event pileup properties, and tracking information.
A local shape variable is defined, which distinguishes between collinear and
soft diffuse distributions of other particles surrounding the particle under
consideration. The former is attributed to particles originating from the hard
scatter and the latter to particles originating from pileup interactions.
Charged particles originating from pileup vertices are discarded. For each
neutral particle, a local shape variable is computed using the surrounding
charged particles compatible with the primary vertex within the tracking
detector acceptance ($\abs{\eta}<2.5$), and using both charged and neutral
particles in the region outside of the tracking detector coverage. The momenta
of the neutral particles are then rescaled according to the probability that
they originate from the primary interaction vertex as deduced from the local
shape variable, superseding the need for jet-based pileup corrections [40].

Jet energy corrections are derived from simulation studies so that the average
measured response of jets becomes identical to that of the jets from the
reconstructed particle level. In situ measurements of the momentum balance in
dijet, photon+jet, Z+jet, and multijet events are used to determine any
residual differences between the jet energy scale in data and in simulation,
and appropriate corrections are made [41]. Additional selection criteria are
applied to each jet to remove jets potentially dominated by instrumental
effects or reconstruction failures [42].

###  0.4.1 Top quark identification

The soft-drop algorithm [43], a generalization of the modified mass drop
algorithm [44, 45], with angular exponent $\beta=0$ and soft threshold
$z=0.1$, is applied to all jets in the event to reconstruct the jet mass and
to identify subjets, and includes a grooming step to remove soft radiation,
including pileup. We only consider top jets with a minimum soft-drop mass of
65.

The $N$-subjettiness algorithm [46] defines $\tau_{N}$ variables, which
describe the consistency between the jet energy deposits and the number of
assumed subjets, $N$. When compared to jets originating from a gluon or a
light quark, a top jet is more consistent with three hard decay products, and
the ratio of $\tau_{3}$ and $\tau_{2}$ allows top jets to be distinguished
from QCD multijet background [47]. A lower ratio indicates the jet is more
consistent with a three-pronged structure than a two-pronged structure.

The $\PQb^{\ast}$ signal region selection requires $\tau_{3}/\tau_{2}<0.65$.
The $N$-subjettiness ratios are correlated with the jet mass, so a relatively
loose selection for the signal region is used to avoid biasing the mass
distribution of multijet processes.

We also require the top jet to contain a subjet from the soft-drop algorithm
to be identified as a bottom quark by the DeepCSV algorithm [48]. The
combination of the $\tau_{3}/\tau_{2}$ and DeepCSV selections has a QCD jet
misidentification rate of approximately 1% and a top tag signal efficiency of
approximately 45% [42, 49]. This selection has been chosen because it leads to
an optimal sensitivity of the cross section limits.

A jet that passes both the $\tau_{3}/\tau_{2}$ and DeepCSV $\PQb$ tagging
selection is considered “top tagged”. A -dependent correction is applied to
correct for differences in the top tagging efficiency between data and
simulation [49]. Separate corrections are used based upon the merging of the
top quark decay products in simulation. Taking the line defined by the top
quark’s trajectory as the central axis, three scenarios are considered. In the
first, the three decay products are within $R<0.8$ of the central axis and the
jet is considered “merged”. In the second, two out of three decay products are
within $R<0.8$ of the central axis and the jet is “semi-merged”. Finally, with
any other configuration of the three decay products the jet is “not merged”.
The merged component is the dominant contribution for the $\PQb^{\ast}$ signal
process among these three scenarios.

###  0.4.2  boson identification

Similar to top tagging, the boson identification algorithm requires a
selection based on $\tau_{N}$ and soft-drop mass. The jet is required to have
a soft-drop mass between 65 and 105to be consistent with the boson mass [50].
The ratio of $N$-subjettiness $\tau_{2}$ and $\tau_{1}$ variables is used to
select the characteristic two-prong structure of a hadronic boson decay since
the jet is more consistent with having two subjets than one. The $\PQb^{\ast}$
signal region selection requires $\tau_{2}/\tau_{1}<0.4$ for 2016 data and
simulation, and $\tau_{2}/\tau_{1}<0.45$ for 2017 and 2018 data and
simulation. The combined selection on the mass and $\tau_{2}/\tau_{1}$ has a
QCD jet misidentification rate of approximately 10% and a tag signal
efficiency of approximately 80%, which are consistent across the three years
[42, 49]. This selection was chosen because it leads to an optimal sensitivity
of the cross section limits.

A jet that passes the $\tau_{2}/\tau_{1}$ and soft-drop mass selections is
considered “tagged”. Differences in the tagging efficiency between data and
simulation are corrected using simulation-to-data weights [49]. Additionally,
differences in the soft-drop mass scale and resolution between data and
simulation are accounted for by scaling and smearing the soft-drop mass in
simulation [42].

##  0.5 Event selection

To select signal-like events, two jets are required with $\pt>400\GeV$ and
$\abs{\eta}<2.4$. Only the two jets with the highest are considered in the
following. The jets are required to satisfy that the difference in rapidity,
$\abs{\Delta y}$, be less than 1.6 and that $\abs{\Delta\phi}$ be greater than
$\pi/2$. The $\abs{\Delta\phi}$ requirement selects back-to-back dijet events
while the $\abs{\Delta y}$ requirement suppresses multijet events with high
$m_{\PQt\PW}$, which arise from the scattering of valence quarks. These
requirements comprise the ”preselection”, with an event then being selected as
signal if one of the two jets is tagged and the other is top tagged.

Because the background estimate relies on data in a control region defined by
inverting the top tag selection, we first require that one of the two jets can
be identified as a jet. In the case that both jets can be tagged, the jet with
lower is taken as the boson candidate in the event. If neither jet can be
tagged, the event is not selected. The jet that is taken as the boson
candidate is referred to as the initially tagged or first jet and the other
jet is called the remaining or second jet. If the event is selected, it is
categorized in either the signal region or the multijet control region
depending on whether the second jet passes the top tagging requirement. The
final selection efficiency for simulated events is calculated as the number of
events that pass the signal selection divided by the number of events
generated. Over the range of generated $\PQb^{\ast}$ quark masses, signals
with left-handed couplings are selected with an efficiency of 9–10%. Signals
with right-handed couplings have slightly higher efficiencies, ranging from
10–11%, because of their harder jet spectra.

We additionally define a dedicated measurement region. For this, events are
required to pass the preselection but the tag is changed to a top tag
selection for the initial jet tag. This jet tag also requires a top jet mass
value between 105 and 220 GeV to be consistent with the top quark mass [50].
The second top tag will be used to distribute events between the measurement
region selection and the dedicated multijet control region for the measurement
region. Additionally, for the initial jet tag, the subjet bottom quark
requirement remains the same but a tighter selection of
$\tau_{3}/\tau_{2}<0.54$ is required. The tighter selection on the initial jet
tag increases the relative contribution. The $\tau_{3}/\tau_{2}$ selection on
the second jet tag remains the same as for the $\PQb^{\ast}$ signal region
selection to avoid distorting the mass distribution because of the correlation
described in Section 0.4.1. If both jets fulfill the selection of the initial
top tag, the jet with the lower is takes as the initially top tagged jet in
the event. The background measurement region is described in more detail in
Section 0.6.2. The four tagging selection regions are summarized in Table 0.5.

A summary of the four selection regions considered in the likelihood fit to
data. The columns indicate the possible jet tag for the jet considered in the
preselection while the rows indicate the possible classification of the second
jet when using the top tagging algorithm.  W tag Top tag Top tag Signal region
(SR) measurement region ($\ttbar\mathrm{MR}$) Inverted top tag Multijet
control region (for SR) Multijet control region (for $\ttbar\mathrm{MR}$)

Comparisons of the $N$-subjettiness ratio, soft-drop mass, and DeepCSV
algorithm score in simulation between signal and background events are shown
in Fig. 2. The QCD contribution to the multijet background is shown, but the
+jets and +jets contributions are omitted since simulations of these processes
are not used in this analysis (as discussed in Section 0.6.1).

Figure 2:  The distributions of the discrimination variables used for and top
tagging for simulation samples. These plots show the top jet
$\tau_{3}/\tau_{2}$ (upper left), the jet $\tau_{2}/\tau_{1}$ (upper right),
the top tag soft-drop mass (middle left), the tag soft-drop mass (middle
right), and the subjet -tagging discriminant (lower). The $\PQb^{\ast}$ signal
sample is represented with the solid line. The area of the total background
contribution and the area of the signal component are separately normalized to
unity. All analysis selections of the signal region are applied with the
exception of the variable being plotted. Also shown are vertical dashed lines
and arrows, which indicate the optimized selection used for events in the
signal region. For the $\PQb^{\ast}$ signal sample, the top tag soft-drop mass
spectrum exhibits a resonance in the first bin near the mass, which is
comprised of jets that have been misidentified as top jets.

##  0.6 Statistical model and background estimation

The background for this analysis is comprised of multijet, , and
$\PQt\PW$-channel single top production. The multijet component is estimated
from data while the and single top components are obtained by fitting
simulation templates to data.

The $m_{\PQt}$ range considered is larger than the signal mass window of 105
to 220defined in Section 0.5. As shown in Fig. 2, an $m_{\PQt}$ selection is
not efficient at discriminating signal from background. However, by using
$m_{\PQt}$ as one of the two measurement dimensions, one can constrain the
multijet background in the $m_{\PQt}$ sidebands while distinguishing the
multijet background from the top quark backgrounds in the $m_{\PQt}$ signal
region. Thus, the $m_{\PQt}$ range comprises both the signal peak region and
the lower and upper sidebands of the peak. The signal region considers the
range of 65 to 285while the measurement region exists between 105 and 285,
where the lower mass bound of 105is used to ensure the orthogonality with the
jet mass window of the signal region.

For each bin in the two-dimensional $(m_{\PQt},m_{\PQt\PW})$ distribution, we
compare the number of expected events from both the background-only and
signal-plus-background hypotheses with the number of observed events in data.

The expected number of events from $\PQb^{\ast}$ quark production is
calculated as
$N_{\textrm{expected}}=\sigma_{\PQb^{\ast}}\mathcal{B}(\PQb^{\ast}\to\PQt\PW\to\text{hadrons})\varepsilon
L$, where $\sigma_{\PQb^{\ast}}$ is the $\PQb^{\ast}$ quark cross section,
$\mathcal{B}(\PQb^{\ast}\to\PQt\PW\to\text{hadrons})$ is the branching
fraction of $\PQb^{\ast}\to\PQt\PW$ in the fully hadronic decay mode,
$\varepsilon$ is the product of the acceptance and the efficiency, and $L$ is
the integrated luminosity of the data set.

A likelihood fit to data is used to test the signal hypothesis, where the
total background model is constructed as a sum of the individual background
contributions using a Poisson model for each bin of the
$(m_{\PQt},m_{\PQt\PW})$ distribution.

The number of expected events with failing, $n_{\text{F}}$, and passing,
$n_{\text{P}}$, top tags in a given bin is given by

$\displaystyle n_{\text{F}}(i,\vec{\theta})$
$\displaystyle=n_{\text{F}}^{\text{QCD}}(i)+n_{\text{F}}^{\ttbar}(i,\vec{\theta})+n_{\text{F}}^{\text{single
top}}(i,\vec{\theta})+n_{\text{F}}^{\text{signal}}(i,\vec{\theta})$ (7)
$\displaystyle n_{\text{P}}(i,\vec{\theta})$
$\displaystyle=n_{\text{P}}^{\text{QCD}}(i)+n_{\text{P}}^{\ttbar}(i,\vec{\theta})+n_{\text{P}}^{\text{single
top}}(i,\vec{\theta})+n_{\text{P}}^{\text{signal}}(i,\vec{\theta}),$ (8)

where $i$ is a bin in the $(m_{\PQt},m_{\PQt\PW})$ plane, and $\vec{\theta}$
is the set of all nuisance parameters that quantify the systematic
uncertainties, as described in Section 0.7. The variable
$n_{\text{F}}^{\text{QCD}}(i)$ is an unconstrained positive real number.
Finally, $n_{\text{P}}^{\text{QCD}}(i)$ is given by

$n_{\text{P}}^{\text{QCD}}(i)=n_{\text{F}}^{\text{QCD}}(i)f(m_{\PQt},m_{\PQt\PW}),$
(9)

where $f(m_{\PQt},m_{\PQt\PW})$ is a transfer function defined by the ratio of
top tagging pass and fail events, and is described in Section 0.6.1.

The negative log-likelihood is then

$\begin{split}-\ln
L(\vec{d};\vec{\theta})=\sum_{i=1}^{N_{\text{bins,F}}}\left[n_{\text{F}}(i,\vec{\theta})-d_{\text{F}}(i)\ln
n_{\text{F}}(i,\vec{\theta})\right]+\sum_{i=1}^{N_{\text{bins,P}}}\left[n_{\text{P}}(i,\vec{\theta})-d_{\text{P}}(i)\ln
n_{\text{P}}(i,\vec{\theta})\right],\end{split}$ (10)

where $N_{\text{bins,F}}$ and $N_{\text{bins,P}}$ are the total number of bins
and $d_{\text{F}}(i)$ and $d_{\text{P}}(i)$ are the number of observed events
in a given bin, for the fail and pass distributions, respectively. Thus, there
is one likelihood which combines four separate categories — signal region
“pass” and “fail” and measurement region “pass” and “fail”.

###  0.6.1 Multijet background estimate

After applying the kinematic selection along with the jet identification, we
define the ratio of the multijet background distributions that pass and fail
the top tagging requirement in data and QCD multijet MC simulation as
$R^{\text{data}}_{\mathrm{P/F}}(m_{\PQt},m_{\PQt\PW})$ and
$R^{\mathrm{MC}}_{\mathrm{P/F}}(m_{\PQt},m_{\PQt\PW})$, respectively. Because
of the combinatorial nature of multijet processes,
$R^{\text{data}}_{\mathrm{P/F}}(m_{\PQt},m_{\PQt\PW})$ and
$R^{\mathrm{MC}}_{\mathrm{P/F}}(m_{\PQt},m_{\PQt\PW})$ are both smooth as a
function of $m_{\PQt}$ and $m_{\PQt\PW}$. The data-to-simulation ratio of
these ratios is therefore also smooth and can be used to correct for
differences in simulation and data by parameterizing it with an analytic
function, $R_{\text{ratio}}(m_{\PQt},m_{\PQt\PW})$.

While $R^{\text{data}}_{\mathrm{P/F}}(m_{\PQt},m_{\PQt\PW})$ could also be
described by analytic functions, isolated features of the shape can be
factored out by using simulation. By factoring out
$R^{\mathrm{MC}}_{\mathrm{P/F}}(m_{\PQt},m_{\PQt\PW})$, the fit of the
analytic function to data is only responsible for describing the residual
differences between data and simulation that can be parameterized with fewer
parameters than the shape of
$R^{\text{data}}_{\mathrm{P/F}}(m_{\PQt},m_{\PQt\PW})$.

The number of events in a given bin of the passing category can then be
estimated from the equation

$n_{\text{P}}^{\text{QCD}}(i)=n_{\text{F}}^{\text{QCD}}(i)R^{\mathrm{MC}}_{\mathrm{P/F}}(m_{\PQt},m_{\PQt\PW})R_{\text{ratio}}(m_{\PQt},m_{\PQt\PW}),$
(11)

where $f(m_{\PQt},m_{\PQt\PW})$ has been replaced by
$R^{\mathrm{MC}}_{\mathrm{P/F}}(m_{\PQt},m_{\PQt\PW})R_{\text{ratio}}(m_{\PQt},m_{\PQt\PW})$
and $R_{\text{ratio}}(m_{\PQt},m_{\PQt\PW})$ is a surface parameterized by the
product of two one-dimensional polynomials in the $(m_{\PQt},m_{\PQt\PW})$
plane with coefficients determined from the fit to data. A second-order
polynomial was chosen for the $m_{\PQt}$ axis and a first-order polynomial was
chosen for the $m_{\PQt\PW}$ axis. These choices were based on a Fisher test
[51] where polynomial terms were added until the $p$-value obtained in the
test was less than 0.95. The parameters of the two-dimensional polynomials are
uncorrelated between years. The form of
$R_{\text{ratio}}(m_{\PQt},m_{\PQt\PW})$ is then

$(p_{0}+p_{1}m_{\PQt}+p_{2}m_{\PQt}^{2})(1+p_{3}m_{\PQt\PW}).$ (12)

To reduce the effect of statistical fluctuations when calculating
$R^{\mathrm{MC}}_{\mathrm{P/F}}(m_{\PQt},m_{\PQt\PW})$ in the QCD multijet
simulation, the pass and fail distributions are smoothed by using an adaptive
kernel density estimate [52] (KDE) prior to calculating the ratio.
Additionally, the residual contributions from the +jets and +jets backgrounds
are accounted for in this analysis, as they are absorbed by the unconstrained
$R_{\text{ratio}}(m_{\PQt},m_{\PQt\PW})$ function.

###  0.6.2 Top quark measurement region

By performing the maximum likelihood fit to data in the signal region
simultaneously with the background enriched measurement region, we further
constrain the contribution to the total background estimate. In particular,
this region is used to make measurements of the $c_{1}$ and $c_{2}$ fit
parameters of Eq. (6).

The measurement region is evaluated in the $(m_{\PQt},m_{\PQt\PQt})$ plane,
where $m_{\PQt}$ is the mass of the second jet when using the top tagging
algorithm and $m_{\PQt\PQt}$ is the invariant mass of the pair. Only the
multijet and SM processes are considered in this selection since the single
top quark contribution is negligible.

The strategy to estimate the multijet background in the measurement region is
similar to the signal region. The
$R_{\text{ratio}}^{\ttbar\mathrm{MR}}(m_{\PQt},m_{\PQt\PQt})$ in this region
is parameterized with the same polynomial form as in the signal region, but
the parameters are uncorrelated with those of the signal region. Additionally,
the $R^{\mathrm{MC}}_{\mathrm{P/F}}(m_{\PQt},m_{\PQt\PQt})$ is derived using
QCD multijet simulation events that pass the same selection as the measurement
region. Events from +jets and +jets backgrounds are suppressed by the initial
top tag requirement, and any that remain are accounted for by the multijet
background model as they are in the signal region.

The negative log-likelihood calculated from the measurement region is
constructed similarly to Eq. (10). The total negative log-likelihood is
obtained from the sum of the negative log-likelihoods of the signal region and
the measurement region. Because the fit to data can constrain the background
in both selections, the values of the free parameters that determine the shape
and normalization of the simulation are constrained by the simultaneous fit to
the - and signal-enriched selections.

##  0.7 Systematic uncertainties

This analysis takes into account several systematic uncertainties that can
affect both the shape and normalization of the simulation.

Normalization uncertainties include those in the production cross section and
in the measured integrated luminosity of the data. The uncertainties in the
and single top $\PQt\PW$-channel production are taken as 20 and 30%,
respectively, to account for the uncertainties in the cross section and in the
factorization and renormalization scales of each process. Specifically, these
values were chosen based on the largest variations in yield of the simulated
samples from varying the factorization and renormalization scales. The
uncertainty in the measured integrated luminosity is 1.8% [53, 54, 55] for the
complete Run 2 (2016–2018).

Several uncertainties exist that affect both the shape and normalization of
the $(m_{\PQt},m_{\PQt\PW})$ distributions. The uncertainties in the jet
energy scale and resolution are estimated through variations in and $\eta$ of
the PUPPI jets [41]. The uncertainty in the pileup reweighting correction is
evaluated by varying the total inelastic cross section by $\pm$4.6% [34]. The
uncertainty in the trigger correction is taken into account as a variation of
one half of the trigger inefficiency. The uncertainty in the PDFs is derived
by either evaluating the root-mean-square of the set of NNPDF MC replicas or
by evaluating the contributions of eigenvectors provided in a Hessian set
[56], depending on whether the PDF set represents variations as MC replicas or
Hessian eigenvectors. The uncertainty due to differences in the data and
simulation efficiency for the top jet tagging algorithm is evaluated by using
the variations of the top tagging scale factor [49]. The scale factors and
uncertainties vary depending on the merging scenarios defined in Section
0.4.1. The tagging uncertainty is evaluated from variations in the tagging
scale factor and includes an additional uncertainty when extrapolating to jets
outside of the region used to extract the scale factor. Additionally, the
uncertainty in the tagging soft-drop mass selection is evaluated from
variations in the jet mass scale and resolution [49]. No variations in the jet
mass scale and resolution are considered for the candidate top jet since the
effect is negligible with respect to the current results.

Unique to the simulation is the uncertainty in the top quark reweighting
procedure described in Section 0.3, which is extrapolated to high . The
uncertainty is represented as uncorrelated variations of $\pm 50\%$ in each of
the $c_{1}$ and $c_{2}$ parameters from Eq. (6).

Each uncertainty affecting both the shape and normalization is Gaussian
constrained where the $\pm 1$ standard deviation of each distribution is
mapped to the $\pm 1$ standard deviation of the corresponding unit Gaussian
constraint.

The uncertainty in the multijet background estimation is taken from the
maximum likelihood fit to data. The parameters of each two-dimensional
polynomial are uncorrelated and fitted freely with no a-priori constraints. An
additional uncertainty in the “bandwidth” parameter of the KDE algorithm is
accounted for by varying the parameter up and down by 1, where the nominal
value is 4. This parameter acts as a scale to determine the width of the
adaptive kernels.

All systematic uncertainties are considered in the simultaneous fit to data
such that all correlations are preserved. The uncertainties are always
correlated across $\PQt\PW$ and selections within a given year of data and
simulation. The cross section, PDF, and top quark reweighting $c_{1}$ and
$c_{2}$ uncertainties are individually correlated across the data-taking
years. Table 0.7 summarizes the sources of uncertainty and indicates where
correlations between samples exist.

Additionally, Table 0.7 includes a calculation of the “impact” of a parameter
on the measurement of the final signal strength for a 2.4$\PQb^{\ast}$ quark
signal. This value is calculated by comparing the measured signal strength in
the full fit against the measured signal strength in a fit where the given
nuisance parameter has been changed either “up” or “down” one standard
deviation from its post-fit value in the full fit.

As can be seen in Table 0.7, the multijet estimate from data is the dominant
source of background uncertainty in the measurement of the signal strength. In
particular, variations of one post-fit standard deviation of the linear term
in the $m_{\PQt\PW}$ axis of the signal region can change the measurement of
the signal strength by approximately 19%.

Sources of uncertainty that are taken into account in the statistical analysis
of the data. The sources affecting the normalization are given with their
percentage uncertainties, while the sources affecting the shape are listed as
“Shape” together with the dependent parameter. The rightmost column indicates
the impact of the parameter on the 2.4$\PQb^{\ast}$ signal strength when the
parameter is changed “up” and “down” by one standard deviation from its post-
fit value. For parameters where the uncertainties are uncorrelated between
data-taking years, the average impact is calculated. An impact of $+0.0$
($-0.0$) denotes an impact that is less (greater) than 0.1 ($-0.1$) but
greater (less) than 0.  Impact Source Uncertainty Samples Up Down cross
section $\pm$20% $-4.6$ $+4.4\%$ Single top cross section $\pm$30% Single top
$+1.2$ $-1.4\%$ Integrated luminosity $\pm$1.8% , single top, signal $+1.6$
$-1.1\%$ Pileup Shape ($\sigma_{mb}$) , single top, signal $+0.3$ $-0.2\%$
Trigger prefiring Shape (, $\eta$) , single top, signal $+0.0$ $+0.1\%$ Jet
energy scale Shape () , single top, signal $+0.3$ $-0.6\%$ Jet energy
resolution Shape (, $\eta$) , single top, signal $-0.4$ $-0.5\%$ Jet mass
scale Shape ($m_{\PW}$) , single top, signal $-0.1$ $-0.0\%$ Jet mass
resolution Shape ($m_{\PW}$) , single top, signal $+0.0$ $+0.9\%$ tagging
Shape () Single top, signal $+0.9$ $-0.9\%$ tagging: extrapolation Shape ()
Single top, signal $+4.9$ $-4.9\%$ Top tagging, merged Shape () , single top,
signal $+0.2$ $-0.2\%$ Top tagging, semimerged Shape () , single top, signal
$+1.1$ $-0.9\%$ Top tagging, not merged Shape () , single top, signal $-0.1$
$+0.1\%$ Trigger Shape () , single top, signal $+0.3$ $-0.4\%$ Top quark
correction $c_{1}$ Shape () $-0.3$ $+0.3\%$ Top quark correction $c_{2}$ Shape
() $-3.9$ $+3.5\%$ PDF Shape $(m_{\PQt},m_{\PQt\PW})$ Signal $+0.1$ $-0.1\%$
KDE bandwidth Shape $(m_{\PQt},m_{\PQt\PW})$ Multijet (from simulation) $-1.2$
$+0.2\%$ $R_{\text{ratio}}^{\mathrm{SR}}(m_{\PQt},m_{\PQt\PW})p_{0}$ Shape
$(m_{\PQt},m_{\PQt\PW})$ Multijet (from data) $-4.4$ $+0.0\%$
$R_{\text{ratio}}^{\mathrm{SR}}(m_{\PQt},m_{\PQt\PW})p_{1}$ Shape
$(m_{\PQt},m_{\PQt\PW})$ Multijet (from data) $-2.0$ $+2.2\%$
$R_{\text{ratio}}^{\mathrm{SR}}(m_{\PQt},m_{\PQt\PW})p_{2}$ Shape
$(m_{\PQt},m_{\PQt\PW})$ Multijet (from data) $+0.9$ $-0.8\%$
$R_{\text{ratio}}^{\mathrm{SR}}(m_{\PQt},m_{\PQt\PW})p_{3}$ Shape
$(m_{\PQt},m_{\PQt\PW})$ Multijet (from data) $+18.6$ $-18.8\%$
$R_{\text{ratio}}^{\ttbar\mathrm{MR}}(m_{\PQt},m_{\PQt\PQt})p_{0}$ Shape
$(m_{\PQt},m_{\PQt\PQt})$ Multijet (from data) $-0.4$ $+0.6\%$
$R_{\text{ratio}}^{\ttbar\mathrm{MR}}(m_{\PQt},m_{\PQt\PQt})p_{1}$ Shape
$(m_{\PQt},m_{\PQt\PQt})$ Multijet (from data) $-0.4$ $+0.6\%$
$R_{\text{ratio}}^{\ttbar\mathrm{MR}}(m_{\PQt},m_{\PQt\PQt})p_{2}$ Shape
$(m_{\PQt},m_{\PQt\PQt})$ Multijet (from data) $+0.5$ $-0.6\%$
$R_{\text{ratio}}^{\ttbar\mathrm{MR}}(m_{\PQt},m_{\PQt\PQt})p_{3}$ Shape
$(m_{\PQt},m_{\PQt\PQt})$ Multijet (from data) $-0.6$ $+0.6\%$

##  0.8 Results

The $(m_{\PQt},m_{\PQt\PW})$ and $(m_{\PQt},m_{\PQt\PQt})$ distributions are
used in a simultaneous binned maximum likelihood fit to data. The signal
strength is a free parameter in the model and the systematic uncertainties are
accounted for as nuisance parameters as described in Section 0.6.
Normalization uncertainties are modeled with log-normal priors, and
uncertainties affecting simulation shapes are modeled using a template
morphing approach with Gaussian priors.

While the fit is performed in two dimensions, evaluating the agreement of the
background model with the data is more convenient when examining projections
onto one dimension. The background estimate and measured two-dimensional
distributions from the simultaneous fit of the signal region, measurement
region, and multijet–enriched control regions are shown in Figs. 3 and 4,
respectively, as one-dimensional projections where either the $m_{\PQt\PQt}$
or $m_{\PQt}$ distribution has been separated into three regions. The lower
panels show the pull, defined as the difference between the number of events
observed in the data and the predicted background, divided by the systematic
uncertainty in the background and the statistical uncertainty in the data,
added in quadrature. All plots shown are for the signal-plus-background
hypothesis post-fit, where the 2.4$\PQb^{\ast}_{\mathrm{LH}}$ quark sample is
normalized to the post-fit signal cross section.

In Fig. 3, the left column shows distributions of $m_{\PQt}$ obtained for the
selection of the measurement region, but with a jet failing the top tagging
requirement. The right column shows the same distributions, but for jets
passing the top tagging requirement. The rows give the distributions for
separate intervals of $m_{\PQt\PQt}$. The background estimation is observed to
model the data well in both regions, validating the estimation of the multijet
background and the modeling of the simulation. The contribution from a
possible signal is negligible in this region and therefore not visible.

In Fig. 4, distributions of $m_{\PQt\PW}$, obtained for events passing the
signal region selection are shown, where the distributions in the left and
right columns have been obtained for jets failing and passing the top tagging
requirement, respectively. Plots in the row are for separate intervals of
$m_{\PQt}$. The total background estimate agrees with the data within the
uncertainties. The largest excess in data relative to the total background is
observed for a left-handed $\PQb^{\ast}$ quark with a mass of 2.4, which
results in a local significance of 2.3 standard deviations.

Additionally, the post-fit top quark reweighting measurements are consistent
with the pre-fit values, and are measured to be $c_{1}=1.01\pm 0.25$ and
$c_{2}=1.16\pm 0.16$. The agreement of the background-only model is evaluated
using the saturated test statistic [57, 58] and has a $p$-value of 0.3.
Additionally, the post-fit nuisance parameter values are consistent with the
pre-fit values and the nuisance parameter values from the background-only
model fit are consistent with those from the signal-plus-background model fit.

Asymptotic frequentist statistics are used to derive exclusion limits on
$\sigma_{\PQb^{\ast}}\mathcal{B}(\PQb^{\ast}\to\PQt\PW\to\text{hadrons})$ at
95% CL [59]. These limits are derived separately for the
$\PQb^{\ast}_{\mathrm{RH}}$, $\PQb^{\ast}_{\mathrm{LH}}$, and
$\PQb^{\ast}_{\mathrm{LH+RH}}$ quark signal hypotheses. The $\pm$1 and $\pm$2
standard deviations in the expected limit are derived from pseudo-experiments
under the background-only hypothesis in which the nuisance parameters are
randomly varied within the post-fit constraints of the maximum likelihood fit
to data.

The limits are shown in Fig. 5. The theoretical $\PQb^{\ast}$ cross sections
included in the figure as a function of $\PQb^{\ast}$ quark mass are
calculated using $\MGvATNLO$. Masses below 2.6, 2.8, and 3.1(2.9, 3.0, and
3.3) are observed (expected) to be excluded at 95% for the left-handed, right-
handed, and vector-like hypotheses, respectively. These limits nearly doubles
the mass exclusions of the previous result [11].

The sensitivity of this analysis can also be compared to the sensitivity of
the CMS dijet search [60]. The branching fraction for $\PQb^{\ast}\to\PQb\Pg$
approaches 20% asymptotically for high masses [9]. From the dijet search, the
expected upper limit on the product of the cross section and branching
fraction for a resonance decaying to a quark and a gluon is approximately
0.09pb at 2so the cross section upper limit on $\PQb^{\ast}$ quark production
is approximately 0.45pb. Using the left-handed couplings result in Fig. 5,
this analysis achieves an expected upper limit of approximately 0.015pb at 2.
With the $\PQb^{\ast}\to\PQt\PW$ branching fraction of 0.4, the cross section
upper limit on $\PQb^{\ast}_{\mathrm{LH}}$ quark production at 2is
approximately 0.0375pb. Thus, at 2, this search is about an order of magnitude
more sensitive to the excited $\PQb^{\ast}$ quark than the dijet search.

The results of this search can also be used to test models of a single $\PB$
quark produced via the electroweak interaction in association with a bottom or
top quark, and decaying into a top quark and a boson. Because the cross
section for this process is much smaller than for a $\PQb^{\ast}$ quark
produced through the strong force, and because of the selection
$m_{\PQt\PW}>1.2\TeV$, we consider the mass range 1.4 to 1.8in this
interpretation. The exclusion limits on
$\sigma_{\PB}\mathcal{B}(\PB\to\PQt\PW\to\text{hadrons})$ at 95% are shown in
Fig. 6. Over the mass range of 1.4–1.8, the observed upper limit ranges 0.027
to 0.009pb when produced in association with a bottom quark and from 0.036 to
0.012pb when produced in association with a top quark. Because of the small
theoretical cross section for the model considered, no mass limit is set. When
compared to the $\PQb^{\ast}$ quark in this mass range, the expected cross
section upper limits for a $\PB$ quark produced with an associated bottom
quark are uniformly more sensitive by approximately 22%. The equivalent
comparison for a $\PB$ quark produced with an associated top quark shows the
sensitivity is worse by no more than 7%.

These results based on 137of data can be compared directly to those of Ref.
[61], which analyzed the lepton+jets channel in 35.9of data recorded with the
CMS experiment at $\sqrt{s}=13\TeV$. At a $\PB$ quark mass of 1.4, this
analysis is less sensitive than the results from Ref. [61] by about 20% when
considering $\PB$ quark production with an associated top quark. However, this
analysis has about 20% higher sensitivity than the previous analysis when the
production is in association with a bottom quark. As the $\PB$ quark mass
increases, the sensitivity of this analysis increases faster than the analysis
described in Ref. [61]. Thus, the sensitivity of this analysis at 1.8is about
27% higher for the associated top quark hypothesis and about a factor of two
higher for the associated bottom quark hypothesis. This comparison is also
applicable to the results for a left-handed $\PB$ hypothesis in Ref. [62],
which analyzed the lepton+jets channel in 36.1of data recorded with the ATLAS
experiment at $\sqrt{s}=13\TeV$ and which has comparable results to those in
Ref. [61].

Figure 3:  Distributions of $m_{\PQt}$ in the measurement region for three
intervals of $m_{\PQt\PQt}$: 1200–1300(upper), 1300–1800(middle),
1800–3000(lower). The data are shown by points with error bars and the
individual background contributions by filled histograms. The signal is not
visible because the contamination in this region is negligible. The barely
visible shaded region is the uncertainty in the total background estimate. The
left and right columns show distributions for events with the second jet
failing and passing the top tagging requirement, respectively. The lower
panels of each figure show the pull, as a function of $m_{\PQt}$, defined as
the difference between the number of events observed in the data and the
predicted background, divided by their combined uncertainty.  Figure 4:
Distributions of $m_{\PQt\PW}$ in the $\PQb^{\ast}$ signal region for three
intervals of $m_{\PQt}$: 65–105(upper), 105–225(middle), and 225–285(lower).
The data are shown by points with error bars, the individual background
contributions by filled histograms, and a 2.4$\PQb^{\ast}_{\mathrm{LH}}$
signal is shown as a solid line. The barely visible shaded region is the
uncertainty in the total background estimate. The left and right columns show
distributions for events with a jet failing and passing the top tagging
requirement, respectively. The lower panels of each figure show the pull, as a
function of $m_{\PQt\PW}$, defined as the difference between the number of
events observed in the data and the predicted background, divided by their
combined uncertainty.

Figure 5:  Upper limits on the product of the cross section and branching
fraction at 95% $\CL$ for a $\PQb^{\ast}_{\mathrm{LH}}$ (upper),
$\PQb^{\ast}_{\mathrm{RH}}$ (middle), and $\PQb^{\ast}_{\mathrm{LH+RH}}$
(lower) quark as a function of the $\PQb^{\ast}$ quark mass. The expected
(dashed) and observed (dot-solid) limits, as well as the $\PQb^{\ast}$ quark
theoretical cross sections (shaded-solid), are shown. The vertical dashed
lines indicate the intersection of the theoretical cross sections with the
expected and observed limits. The inner and outer shaded areas around the
expected limits show the 68% and 95% CL intervals, respectively.

Figure 6:  Upper limits on the product of the cross section and branching
fraction at 95% $\CL$ for a $\PB$ produced in association with a bottom quark
(left) and top quark (right) as a function of the $\PB$ quark mass. The
expected (dashed) and observed (dot-solid) limits, as well as the $\PB$ quark
theoretical cross sections (shaded-solid), are shown. The inner and outer
shaded areas around the expected limits show the 68% and 95% CL intervals,
respectively.

##  0.9 Summary

A search for a heavy resonance decaying to a top quark and a boson in the
fully hadronic final state has been presented. The analysis uses proton-proton
collision data at a center-of-mass energy of 13corresponding to an integrated
luminosity of 137, collected by the CMS experiment at the LHC during
2016–2018.

This analysis considers the explicit case where the heavy resonance is an
excited bottom quark, $\PQb^{\ast}$. The search evaluates $\PQb^{\ast}$ quark
masses greater than 1.2, which result in highly Lorentz-boosted top quarks and
bosons that are reconstructed as single jets. Using jet substructure
algorithms designed to distinguish heavy resonance decays from light-quark and
gluon jets, the top quark and boson decays are identified as a top quark jet
and a boson jet, respectively.

The background processes in the analysis are a result of multijet processes
from the strong interaction, production, and single top quark (tW-channel)
production. The search is performed using a two-dimensional binned likelihood
fit to the data that allows all backgrounds to be fit simultaneously. The
multijet component in the signal region is estimated via a two-dimensional
transfer function method that uses a multijet-enriched control region. The and
single top background estimates are determined via a template fit to data. In
particular, a dedicated measurement region is used to constrain the shape and
yield of the background.

No statistically significant deviation from the standard model expectation is
observed. The hypotheses of $\PQb^{\ast}$ quarks with left-handed, right-
handed, and vector-like chiralities are excluded at 95% confidence level for
masses below 2.6, 2.8, and 3.1, respectively. These are the most stringent
limits on the $\PQb^{\ast}$ quark mass to date, extending the previous best
mass limits by almost a factor of two.

###### 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 centers and personnel of the Worldwide LHC Computing Grid and other
centers 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 (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); GSRT
(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); SEIDI, CPAN, PCTI, and FEDER (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, 765710 and 824093 (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 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 National Priorities
Research Program by Qatar National Research Fund; the Ministry of Science and
Higher Education, project no. 0723-2020-0041 (Russia); the Programa Estatal de
Fomento de la Investigación Científica y Técnica de Excelencia María de
Maeztu, grant MDM-2015-0509 and the Programa Severo Ochoa del Principado de
Asturias; the Thalis and Aristeia programs cofinanced by EU-ESF and the Greek
NSRF; 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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##  .10 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan${}^{\textrm{\textdagger}}$, A. Tumasyan Institut für
Hochenergiephysik, Wien, Austria
W. Adam, T. Bergauer, M. Dragicevic, A. Escalante Del Valle, R. Frühwirth1, M.
Jeitler1, N. Krammer, L. Lechner, D. Liko, I. Mikulec, F.M. Pitters, J.
Schieck1, R. Schöfbeck, M. Spanring, S. Templ, W. Waltenberger, C.-E. Wulz1,
M. Zarucki Institute for Nuclear Problems, Minsk, Belarus
V. Chekhovsky, A. Litomin, V. Makarenko Universiteit Antwerpen, Antwerpen,
Belgium
M.R. Darwish2, E.A. De Wolf, X. Janssen, T. Kello3, A. Lelek, H. Rejeb Sfar,
P. Van Mechelen, S. Van Putte, N. Van Remortel Vrije Universiteit Brussel,
Brussel, Belgium
F. Blekman, E.S. Bols, J. D’Hondt, J. De Clercq, S. Lowette, S. Moortgat, A.
Morton, D. Müller, A.R. Sahasransu, S. Tavernier, W. Van Doninck, P. Van
Mulders Université Libre de Bruxelles, Bruxelles, Belgium
D. Beghin, B. Bilin, B. Clerbaux, G. De Lentdecker, B. Dorney, L. Favart, A.
Grebenyuk, A.K. Kalsi, K. Lee, I. Makarenko, L. Moureaux, L. Pétré, A. Popov,
N. Postiau, E. Starling, L. Thomas, C. Vander Velde, P. Vanlaer, D. Vannerom,
L. Wezenbeek Ghent University, Ghent, Belgium
T. Cornelis, D. Dobur, M. Gruchala, I. Khvastunov4, G. Mestdach, M. Niedziela,
C. Roskas, K. Skovpen, M. Tytgat, W. Verbeke, B. Vermassen, M. Vit Université
Catholique de Louvain, Louvain-la-Neuve, Belgium
A. Bethani, G. Bruno, F. Bury, C. Caputo, P. David, C. Delaere, M. Delcourt,
I.S. Donertas, A. Giammanco, V. Lemaitre, K. Mondal, J. Prisciandaro, A.
Taliercio, M. Teklishyn, P. Vischia, S. Wertz, S. Wuyckens Centro Brasileiro
de Pesquisas Fisicas, Rio de Janeiro, Brazil
G.A. Alves, C. Hensel, A. Moraes Universidade do Estado do Rio de Janeiro, Rio
de Janeiro, Brazil
W.L. Aldá Júnior, E. Belchior Batista Das Chagas, H. BRANDAO MALBOUISSON, W.
Carvalho, J. Chinellato5, E. Coelho, E.M. Da Costa, G.G. Da Silveira6, D. De
Jesus Damiao, S. Fonseca De Souza, J. Martins7, D. Matos Figueiredo, C. Mora
Herrera, L. Mundim, H. Nogima, P. Rebello Teles, L.J. Sanchez Rosas, A.
Santoro, S.M. Silva Do Amaral, A. Sznajder, M. Thiel, F. Torres Da Silva De
Araujo, A. Vilela Pereira Universidade Estadual Paulista a, Universidade
Federal do ABC b, São Paulo, Brazil
C.A. Bernardesa,a, L. Calligarisa, T.R. Fernandez Perez Tomeia, E.M.
Gregoresa,b, D.S. Lemosa, P.G. Mercadantea,b, S.F. Novaesa, Sandra S. Padulaa
Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy of
Sciences, Sofia, Bulgaria
A. Aleksandrov, G. Antchev, I. Atanasov, R. Hadjiiska, P. Iaydjiev, M.
Misheva, M. Rodozov, M. Shopova, G. Sultanov University of Sofia, Sofia,
Bulgaria
A. Dimitrov, T. Ivanov, L. Litov, B. Pavlov, P. Petkov, A. Petrov Beihang
University, Beijing, China
T. Cheng, W. Fang3, Q. Guo, M. Mittal, H. Wang, L. Yuan Department of Physics,
Tsinghua University, Beijing, China
M. Ahmad, G. Bauer, Z. Hu, Y. Wang, K. Yi8,9 Institute of High Energy Physics,
Beijing, China
E. Chapon, G.M. Chen10, H.S. Chen10, M. Chen, T. Javaid10, A. Kapoor, D.
Leggat, H. Liao, Z.-A. LIU10, R. Sharma, A. Spiezia, J. Tao, J. Thomas-
wilsker, J. Wang, H. Zhang, S. Zhang10, J. Zhao State Key Laboratory of
Nuclear Physics and Technology, Peking University, Beijing, China
A. Agapitos, Y. Ban, C. Chen, Q. Huang, A. Levin, Q. Li, M. Lu, X. Lyu, Y.
Mao, S.J. Qian, D. Wang, Q. Wang, J. Xiao Sun Yat-Sen University, Guangzhou,
China
Z. You Institute of Modern Physics and Key Laboratory of Nuclear Physics and
Ion-beam Application (MOE) - Fudan University, Shanghai, China
X. Gao3, H. Okawa Zhejiang University, Hangzhou, China
M. Xiao Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, C. Florez, J. Fraga, A. Sarkar, M.A. Segura Delgado
Universidad de Antioquia, Medellin, Colombia
J. Jaramillo, J. Mejia Guisao, F. Ramirez, J.D. Ruiz Alvarez, C.A. Salazar
González, N. Vanegas Arbelaez University of Split, Faculty of Electrical
Engineering, Mechanical Engineering and Naval Architecture, Split, Croatia
D. Giljanovic, N. Godinovic, D. Lelas, I. Puljak University of Split, Faculty
of Science, Split, Croatia
Z. Antunovic, M. Kovac, T. Sculac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, D. Ferencek, D. Majumder, M. Roguljic, A. Starodumov11, T. Susa
University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, E. Erodotou, A. Ioannou, G. Kole, M. Kolosova, S.
Konstantinou, J. Mousa, C. Nicolaou, F. Ptochos, P.A. Razis, H. Rykaczewski,
H. Saka, D. Tsiakkouri Charles University, Prague, Czech Republic
M. Finger12, M. Finger Jr.12, A. Kveton, J. Tomsa Escuela Politecnica
Nacional, Quito, Ecuador
E. Ayala Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab
Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
H. Abdalla13, Y. Assran14,15, E. Salama15,16 Center for High Energy Physics
(CHEP-FU), Fayoum University, El-Fayoum, Egypt
A. Lotfy, M.A. Mahmoud National Institute of Chemical Physics and Biophysics,
Tallinn, Estonia
S. Bhowmik, A. Carvalho Antunes De Oliveira, R.K. Dewanjee, K. Ehataht, M.
Kadastik, J. Pata, M. Raidal, C. Veelken Department of Physics, University of
Helsinki, Helsinki, Finland
P. Eerola, L. Forthomme, H. Kirschenmann, K. Osterberg, M. Voutilainen
Helsinki Institute of Physics, Helsinki, Finland
E. Brücken, F. Garcia, J. Havukainen, V. Karimäki, M.S. Kim, R. Kinnunen, T.
Lampén, K. Lassila-Perini, S. Lehti, T. Lindén, H. Siikonen, E. Tuominen, J.
Tuominiemi Lappeenranta University of Technology, Lappeenranta, Finland
P. Luukka, T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
C. Amendola, M. Besancon, F. Couderc, M. Dejardin, D. Denegri, J.L. Faure, F.
Ferri, S. Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry,
B. Lenzi, E. Locci, J. Malcles, J. Rander, A. Rosowsky, M.Ö. Sahin, A. Savoy-
Navarro17, M. Titov, G.B. Yu Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole
Polytechnique, Institut Polytechnique de Paris, Palaiseau, France
S. Ahuja, F. Beaudette, M. Bonanomi, A. Buchot Perraguin, P. Busson, C.
Charlot, O. Davignon, B. Diab, G. Falmagne, R. Granier de Cassagnac, A.
Hakimi, I. Kucher, A. Lobanov, C. Martin Perez, M. Nguyen, C. Ochando, P.
Paganini, J. Rembser, R. Salerno, J.B. Sauvan, Y. Sirois, A. Zabi, A. Zghiche
Université de Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, France
J.-L. Agram18, J. Andrea, D. Apparu, D. Bloch, G. Bourgatte, J.-M. Brom, E.C.
Chabert, C. Collard, D. Darej, J.-C. Fontaine18, U. Goerlach, C. Grimault,
A.-C. Le Bihan, P. Van Hove Institut de Physique des 2 Infinis de Lyon (IP2I
), Villeurbanne, France
E. Asilar, S. Beauceron, C. Bernet, G. Boudoul, C. Camen, A. Carle, N. Chanon,
D. Contardo, P. Depasse, H. El Mamouni, J. Fay, S. Gascon, M. Gouzevitch, B.
Ille, Sa. Jain, I.B. Laktineh, H. Lattaud, A. Lesauvage, M. Lethuillier, L.
Mirabito, K. Shchablo, L. Torterotot, G. Touquet, M. Vander Donckt, S. Viret
Georgian Technical University, Tbilisi, Georgia
A. Khvedelidze12, Z. Tsamalaidze12 RWTH Aachen University, I. Physikalisches
Institut, Aachen, Germany
L. Feld, K. Klein, M. Lipinski, D. Meuser, A. Pauls, M.P. Rauch, J. Schulz, M.
Teroerde RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
D. Eliseev, M. Erdmann, P. Fackeldey, B. Fischer, S. Ghosh, T. Hebbeker, K.
Hoepfner, H. Keller, L. Mastrolorenzo, M. Merschmeyer, A. Meyer, G. Mocellin,
S. Mondal, S. Mukherjee, D. Noll, A. Novak, T. Pook, A. Pozdnyakov, Y. Rath,
H. Reithler, J. Roemer, A. Schmidt, S.C. Schuler, A. Sharma, S. Wiedenbeck, S.
Zaleski RWTH Aachen University, III. Physikalisches Institut B, Aachen,
Germany
C. Dziwok, G. Flügge, W. Haj Ahmad19, O. Hlushchenko, T. Kress, A. Nowack, C.
Pistone, O. Pooth, D. Roy, H. Sert, A. Stahl20, T. Ziemons Deutsches
Elektronen-Synchrotron, Hamburg, Germany
H. Aarup Petersen, M. Aldaya Martin, P. Asmuss, I. Babounikau, S. Baxter, O.
Behnke, A. Bermúdez Martínez, A.A. Bin Anuar, K. Borras21, V. Botta, D.
Brunner, A. Campbell, A. Cardini, P. Connor, S. Consuegra Rodríguez, V.
Danilov, M.M. Defranchis, L. Didukh, D. Domínguez Damiani, G. Eckerlin, D.
Eckstein, L.I. Estevez Banos, E. Gallo22, A. Geiser, A. Giraldi, A. Grohsjean,
M. Guthoff, A. Harb, A. Jafari23, N.Z. Jomhari, H. Jung, A. Kasem21, M.
Kasemann, H. Kaveh, C. Kleinwort, J. Knolle, D. Krücker, W. Lange, T. Lenz, J.
Lidrych, K. Lipka, W. Lohmann24, T. Madlener, R. Mankel, I.-A. Melzer-
Pellmann, J. Metwally, A.B. Meyer, M. Meyer, J. Mnich, A. Mussgiller, V.
Myronenko, Y. Otarid, D. Pérez Adán, S.K. Pflitsch, D. Pitzl, A. Raspereza, A.
Saggio, A. Saibel, M. Savitskyi, V. Scheurer, C. Schwanenberger, A. Singh,
R.E. Sosa Ricardo, N. Tonon, O. Turkot, A. Vagnerini, M. Van De Klundert, R.
Walsh, D. Walter, Y. Wen, K. Wichmann, C. Wissing, S. Wuchterl, O. Zenaiev, R.
Zlebcik University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, L. Benato, A. Benecke, K. De Leo, T. Dreyer, M. Eich, F.
Feindt, A. Fröhlich, C. Garbers, E. Garutti, P. Gunnellini, J. Haller, A.
Hinzmann, A. Karavdina, G. Kasieczka, R. Klanner, R. Kogler, V. Kutzner, J.
Lange, T. Lange, A. Malara, C.E.N. Niemeyer, A. Nigamova, K.J. Pena Rodriguez,
O. Rieger, P. Schleper, M. Schröder, J. Schwandt, D. Schwarz, J. Sonneveld, H.
Stadie, G. Steinbrück, A. Tews, B. Vormwald, I. Zoi Karlsruher Institut fuer
Technologie, Karlsruhe, Germany
J. Bechtel, T. Berger, E. Butz, R. Caspart, T. Chwalek, W. De Boer, A.
Dierlamm, A. Droll, K. El Morabit, N. Faltermann, K. Flöh, M. Giffels, J.o.
Gosewisch, A. Gottmann, F. Hartmann20, C. Heidecker, U. Husemann, I. Katkov25,
P. Keicher, R. Koppenhöfer, S. Maier, M. Metzler, S. Mitra, Th. Müller, M.
Musich, M. Neukum, G. Quast, K. Rabbertz, J. Rauser, D. Savoiu, D. Schäfer, M.
Schnepf, D. Seith, I. Shvetsov, H.J. Simonis, R. Ulrich, J. Van Der Linden,
R.F. Von Cube, M. Wassmer, M. Weber, S. Wieland, R. Wolf, S. Wozniewski, S.
Wunsch Institute of Nuclear and Particle Physics (INPP), NCSR Demokritos,
Aghia Paraskevi, Greece
G. Anagnostou, P. Asenov, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas,
G. Paspalaki, A. Stakia National and Kapodistrian University of Athens,
Athens, Greece
M. Diamantopoulou, D. Karasavvas, G. Karathanasis, P. Kontaxakis, C.K. Koraka,
A. Manousakis-katsikakis, A. Panagiotou, I. Papavergou, N. Saoulidou, K.
Theofilatos, E. Tziaferi, K. Vellidis, E. Vourliotis National Technical
University of Athens, Athens, Greece
G. Bakas, K. Kousouris, I. Papakrivopoulos, G. Tsipolitis, A. Zacharopoulou
University of Ioánnina, Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, N. Manthos, I.
Papadopoulos, J. Strologas MTA-ELTE Lendület CMS Particle and Nuclear Physics
Group, Eötvös Loránd University, Budapest, Hungary
M. Csanad, M.M.A. Gadallah26, S. Lökös27, P. Major, K. Mandal, A. Mehta, G.
Pasztor, O. Surányi, G.I. Veres Wigner Research Centre for Physics, Budapest,
Hungary
M. Bartók28, G. Bencze, C. Hajdu, D. Horvath29, F. Sikler, V. Veszpremi, G.
Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear Research ATOMKI,
Debrecen, Hungary
S. Czellar, J. Karancsi28, J. Molnar, Z. Szillasi, D. Teyssier Institute of
Physics, University of Debrecen, Debrecen, Hungary
P. Raics, Z.L. Trocsanyi30, B. Ujvari Eszterhazy Karoly University, Karoly
Robert Campus, Gyongyos, Hungary
T. Csorgo31, F. Nemes31, T. Novak Indian Institute of Science (IISc),
Bangalore, India
S. Choudhury, J.R. Komaragiri, D. Kumar, L. Panwar, P.C. Tiwari National
Institute of Science Education and Research, HBNI, Bhubaneswar, India
S. Bahinipati32, D. Dash, C. Kar, P. Mal, T. Mishra, V.K. Muraleedharan Nair
Bindhu33, A. Nayak33, N. Sur, S.K. Swain Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, G. Chaudhary, S. Chauhan, N. Dhingra34, R.
Gupta, A. Kaur, S. Kaur, P. Kumari, M. Meena, K. Sandeep, J.B. Singh, A.K.
Virdi University of Delhi, Delhi, India
A. Ahmed, A. Bhardwaj, B.C. Choudhary, R.B. Garg, M. Gola, S. Keshri, A.
Kumar, M. Naimuddin, P. Priyanka, K. Ranjan, A. Shah Saha Institute of Nuclear
Physics, HBNI, Kolkata, India
M. Bharti35, R. Bhattacharya, S. Bhattacharya, D. Bhowmik, S. Dutta, S. Ghosh,
B. Gomber36, M. Maity37, S. Nandan, P. Palit, P.K. Rout, G. Saha, B. Sahu, S.
Sarkar, M. Sharan, B. Singh35, S. Thakur35 Indian Institute of Technology
Madras, Madras, India
P.K. Behera, S.C. Behera, P. Kalbhor, A. Muhammad, R. Pradhan, P.R. Pujahari,
A. Sharma, A.K. Sikdar Bhabha Atomic Research Centre, Mumbai, India
D. Dutta, V. Jha, V. Kumar, D.K. Mishra, K. Naskar38, P.K. Netrakanti, L.M.
Pant, P. Shukla Tata Institute of Fundamental Research-A, Mumbai, India
T. Aziz, S. Dugad, G.B. Mohanty, U. Sarkar Tata Institute of Fundamental
Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, R. Chudasama, M. Guchait, S.
Karmakar, S. Kumar, G. Majumder, K. Mazumdar, S. Mukherjee, D. Roy Indian
Institute of Science Education and Research (IISER), Pune, India
S. Dube, B. Kansal, S. Pandey, A. Rane, A. Rastogi, S. Sharma Department of
Physics, Isfahan University of Technology, Isfahan, Iran
H. Bakhshiansohi39, M. Zeinali40 Institute for Research in Fundamental
Sciences (IPM), Tehran, Iran
S. Chenarani41, S.M. Etesami, M. Khakzad, M. Mohammadi Najafabadi University
College Dublin, Dublin, Ireland
M. Felcini, M. Grunewald INFN Sezione di Bari a, Università di Bari b,
Politecnico di Bari c, Bari, Italy
M. Abbresciaa,b, R. Alya,b,42, C. Arutaa,b, A. Colaleoa, D. Creanzaa,c, N. De
Filippisa,c, M. De Palmaa,b, A. Di Florioa,b, A. Di Pilatoa,b, W.
Elmetenaweea,b, L. Fiorea, A. Gelmia,b, M. Gula, G. Iasellia,c, M. Incea,b, S.
Lezkia,b, G. Maggia,c, M. Maggia, I. Margjekaa,b, V. Mastrapasquaa,b, J.A.
Merlina, S. Mya,b, S. Nuzzoa,b, A. Pompilia,b, G. Pugliesea,c, A. Ranieria, G.
Selvaggia,b, L. Silvestrisa, F.M. Simonea,b, R. Vendittia, P. Verwilligena
INFN Sezione di Bologna a, Università di Bologna b, Bologna, Italy
G. Abbiendia, C. Battilanaa,b, D. Bonacorsia,b, L. Borgonovia, S. Braibant-
Giacomellia,b, R. Campaninia,b, P. Capiluppia,b, A. Castroa,b, F.R. Cavalloa,
C. Cioccaa, M. Cuffiania,b, G.M. Dallavallea, T. Diotalevia,b, F. Fabbria, A.
Fanfania,b, E. Fontanesia,b, P. Giacomellia, L. Giommia,b, C. Grandia, L.
Guiduccia,b, F. Iemmia,b, S. Lo Meoa,43, S. Marcellinia, G. Masettia, F.L.
Navarriaa,b, A. Perrottaa, F. Primaveraa,b, A.M. Rossia,b, T. Rovellia,b, G.P.
Sirolia,b, N. Tosia INFN Sezione di Catania a, Università di Catania b,
Catania, Italy
S. Albergoa,b,44, S. Costaa,b,44, A. Di Mattiaa, R. Potenzaa,b, A.
Tricomia,b,44, C. Tuvea,b INFN Sezione di Firenze a, Università di Firenze b,
Firenze, Italy
G. Barbaglia, A. Cassesea, R. Ceccarellia,b, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, F. Fioria, E. Focardia,b, G. Latinoa,b, P. Lenzia,b, M.
Lizzoa,b, M. Meschinia, S. Paolettia, R. Seiditaa,b, G. Sguazzonia, L.
Viliania INFN Laboratori Nazionali di Frascati, Frascati, Italy
L. Benussi, S. Bianco, D. Piccolo INFN Sezione di Genova a, Università di
Genova b, Genova, Italy
M. Bozzoa,b, F. Ferroa, R. Mulargiaa,b, E. Robuttia, S. Tosia,b INFN Sezione
di Milano-Bicocca a, Università di Milano-Bicocca b, Milano, Italy
A. Benagliaa, A. Beschia,b, F. Brivioa,b, F. Cetorellia,b, V. Cirioloa,b,20,
F. De Guioa,b, M.E. Dinardoa,b, P. Dinia, S. Gennaia, A. Ghezzia,b, P.
Govonia,b, L. Guzzia,b, M. Malbertia, S. Malvezzia, A. Massironia, D.
Menascea, F. Montia,b, L. Moronia, M. Paganonia,b, D. Pedrinia, S. Ragazzia,b,
T. Tabarelli de Fatisa,b, D. Valsecchia,b,20, D. Zuoloa,b INFN Sezione di
Napoli a, Università di Napoli ’Federico II’ b, Napoli, Italy, Università
della Basilicata c, Potenza, Italy, Università G. Marconi d, Roma, Italy
S. Buontempoa, N. Cavalloa,c, A. De Iorioa,b, F. Fabozzia,c, F. Fiengaa,
A.O.M. Iorioa,b, L. Listaa,b, S. Meolaa,d,20, P. Paoluccia,20, B. Rossia, C.
Sciaccaa,b INFN Sezione di Padova a, Università di Padova b, Padova, Italy,
Università di Trento c, Trento, Italy
P. Azzia, N. Bacchettaa, D. Biselloa,b, P. Bortignona, A. Bragagnoloa,b, R.
Carlina,b, P. Checchiaa, P. De Castro Manzanoa, T. Dorigoa, F. Gasparinia,b,
U. Gasparinia,b, S.Y. Hoha,b, L. Layera,45, M. Margonia,b, A.T. Meneguzzoa,b,
M. Presillaa,b, P. Ronchesea,b, R. Rossina,b, F. Simonettoa,b, G. Stronga, M.
Tosia,b, H. YARARa,b, M. Zanettia,b, P. Zottoa,b, A. Zucchettaa,b, G.
Zumerlea,b INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy
C. Aime‘a,b, A. Braghieria, S. Calzaferria,b, D. Fiorinaa,b, P. Montagnaa,b,
S.P. Rattia,b, V. Rea, M. Ressegottia,b, C. Riccardia,b, P. Salvinia, I. Vaia,
P. Vituloa,b INFN Sezione di Perugia a, Università di Perugia b, Perugia,
Italy
G.M. Bileia, D. Ciangottinia,b, L. Fanòa,b, P. Laricciaa,b, G. Mantovania,b,
V. Mariania,b, M. Menichellia, F. Moscatellia, A. Piccinellia,b, A. Rossia,b,
A. Santocchiaa,b, D. Spigaa, T. Tedeschia,b INFN Sezione di Pisa a, Università
di Pisa b, Scuola Normale Superiore di Pisa c, Pisa Italy, Università di Siena
d, Siena, Italy
K. Androsova, P. Azzurria, G. Bagliesia, V. Bertacchia,c, L. Bianchinia, T.
Boccalia, E. Bossini, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, M.R. Di
Domenicoa,d, S. Donatoa, A. Giassia, M.T. Grippoa, F. Ligabuea,c, E. Mancaa,c,
G. Mandorlia,c, A. Messineoa,b, F. Pallaa, G. Ramirez-Sancheza,c, A. Rizzia,b,
G. Rolandia,c, S. Roy Chowdhurya,c, A. Scribanoa, N. Shafieia,b, P. Spagnoloa,
R. Tenchinia, G. Tonellia,b, N. Turinia,d, A. Venturia, P.G. Verdinia INFN
Sezione di Roma a, Sapienza Università di Roma b, Rome, Italy
F. Cavallaria, M. Cipriania,b, D. Del Rea,b, E. Di Marcoa, M. Diemoza, E.
Longoa,b, P. Meridiania, G. Organtinia,b, F. Pandolfia, R. Paramattia,b, C.
Quarantaa,b, S. Rahatloua,b, C. Rovellia, F. Santanastasioa,b, L. Soffia,b, R.
Tramontanoa,b INFN Sezione di Torino a, Università di Torino b, Torino, Italy,
Università del Piemonte Orientale c, Novara, Italy
N. Amapanea,b, R. Arcidiaconoa,c, S. Argiroa,b, M. Arneodoa,c, N. Bartosika,
R. Bellana,b, A. Belloraa,b, J. Berenguer Antequeraa,b, C. Biinoa, A.
Cappatia,b, N. Cartigliaa, S. Comettia, M. Costaa,b, R. Covarellia,b, N.
Demariaa, B. Kiania,b, F. Leggera, C. Mariottia, S. Masellia, E. Migliorea,b,
V. Monacoa,b, E. Monteila,b, M. Montenoa, M.M. Obertinoa,b, G. Ortonaa, L.
Pachera,b, N. Pastronea, M. Pelliccionia, G.L. Pinna Angionia,b, M. Ruspaa,c,
R. Salvaticoa,b, F. Sivieroa,b, V. Solaa, A. Solanoa,b, D. Soldia,b, A.
Staianoa, M. Tornagoa,b, D. Trocinoa,b INFN Sezione di Trieste a, Università
di Trieste b, Trieste, Italy
S. Belfortea, V. Candelisea,b, M. Casarsaa, F. Cossuttia, A. Da Rolda,b, G.
Della Riccaa,b, F. Vazzolera,b Kyungpook National University, Daegu, Korea
S. Dogra, C. Huh, B. Kim, D.H. Kim, G.N. Kim, J. Lee, S.W. Lee, C.S. Moon,
Y.D. Oh, S.I. Pak, B.C. Radburn-Smith, S. Sekmen, Y.C. Yang Chonnam National
University, Institute for Universe and Elementary Particles, Kwangju, Korea
H. Kim, D.H. Moon Hanyang University, Seoul, Korea
B. Francois, T.J. Kim, J. Park Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, B. Hong, K. Lee, K.S. Lee, J. Lim, J. Park, S.K. Park,
J. Yoo Kyung Hee University, Department of Physics, Seoul, Republic of Korea
J. Goh, A. Gurtu Sejong University, Seoul, Korea
H.S. Kim, 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.
Lee, S. Lee, K. Nam, B.H. Oh, M. Oh, S.B. Oh, H. Seo, U.K. Yang, I. Yoon
University of Seoul, Seoul, Korea
D. Jeon, J.H. Kim, B. Ko, J.S.H. Lee, I.C. Park, Y. Roh, D. Song, I.J. Watson
Yonsei University, Department of Physics, Seoul, Korea
S. Ha, H.D. Yoo Sungkyunkwan University, Suwon, Korea
Y. Choi, C. Hwang, Y. Jeong, H. Lee, Y. Lee, I. Yu College of Engineering and
Technology, American University of the Middle East (AUM), Egaila, Kuwait
Y. Maghrbi Riga Technical University, Riga, Latvia
V. Veckalns46 Vilnius University, Vilnius, Lithuania
M. Ambrozas, A. Juodagalvis, A. Rinkevicius, G. Tamulaitis, A. Vaitkevicius
National Centre for Particle Physics, Universiti Malaya, Kuala Lumpur,
Malaysia
W.A.T. Wan Abdullah, M.N. Yusli, Z. Zolkapli Universidad de Sonora (UNISON),
Hermosillo, Mexico
J.F. Benitez, A. Castaneda Hernandez, J.A. Murillo Quijada, L. Valencia Palomo
Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico
G. Ayala, H. Castilla-Valdez, E. De La Cruz-Burelo, I. Heredia-De La Cruz47,
R. Lopez-Fernandez, C.A. Mondragon Herrera, D.A. Perez Navarro, A. Sanchez-
Hernandez Universidad Iberoamericana, Mexico City, Mexico
S. Carrillo Moreno, C. Oropeza Barrera, M. Ramirez-Garcia, 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. Mijuskovic4, N. Raicevic University of Auckland, Auckland, New Zealand
D. Krofcheck University of Canterbury, Christchurch, New Zealand
S. Bheesette, P.H. Butler 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. Shoaib, M. Waqas 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. Bluj, B. Boimska, T. Frueboes, M. Górski, M. Kazana, M.
Szleper, P. Traczyk, P. Zalewski Institute of Experimental Physics, Faculty of
Physics, University of Warsaw, Warsaw, Poland
K. Bunkowski, K. Doroba, A. Kalinowski, M. Konecki, J. Krolikowski, M. Walczak
Laboratório de Instrumentação e Física Experimental de Partículas, Lisboa,
Portugal
M. Araujo, P. Bargassa, D. Bastos, A. Boletti, P. Faccioli, M. Gallinaro, J.
Hollar, N. Leonardo, T. Niknejad, J. Seixas, K. Shchelina, O. Toldaiev, J.
Varela Joint Institute for Nuclear Research, Dubna, Russia
S. Afanasiev, D. Budkouski, P. Bunin, M. Gavrilenko, I. Golutvin, I. Gorbunov,
A. Kamenev, V. Karjavine, 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, A. Zarubin, I. Zhizhin Petersburg Nuclear
Physics Institute, Gatchina (St. Petersburg), Russia
G. Gavrilov, V. Golovtcov, Y. Ivanov, V. Kim50, E. Kuznetsova51, V. Murzin, V.
Oreshkin, I. Smirnov, D. Sosnov, V. Sulimov, L. Uvarov, S. Volkov, A. Vorobyev
Institute for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov,
N. Krasnikov, A. Pashenkov, G. Pivovarov, 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. Nikitenko52, V. Popov, G.
Safronov, A. Spiridonov, A. Stepennov, M. Toms, E. Vlasov, A. Zhokin Moscow
Institute of Physics and Technology, Moscow, Russia
T. Aushev National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
R. Chistov53, M. Danilov54, A. Oskin, P. Parygin, S. Polikarpov53 P.N. Lebedev
Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin, I. Dremin, M. Kirakosyan, A. Terkulov Skobeltsyn
Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow,
Russia
A. Belyaev, E. Boos, V. Bunichev, M. Dubinin55, L. Dudko, V. Klyukhin, O.
Kodolova, I. Lokhtin, S. Obraztsov, M. Perfilov, S. Petrushanko, V. Savrin, P.
Volkov Novosibirsk State University (NSU), Novosibirsk, Russia
V. Blinov56, T. Dimova56, L. Kardapoltsev56, I. Ovtin56, Y. Skovpen56
Institute for High Energy Physics of National Research Centre ‘Kurchatov
Institute’, Protvino, Russia
I. Azhgirey, I. Bayshev, V. Kachanov, A. Kalinin, D. Konstantinov, V. Petrov,
R. Ryutin, A. Sobol, S. Troshin, N. Tyurin, A. Uzunian, A. Volkov National
Research Tomsk Polytechnic University, Tomsk, Russia
A. Babaev, A. Iuzhakov, V. Okhotnikov, L. Sukhikh Tomsk State University,
Tomsk, Russia
V. Borchsh, V. Ivanchenko, E. Tcherniaev University of Belgrade: Faculty of
Physics and VINCA Institute of Nuclear Sciences, Belgrade, Serbia
P. Adzic57, M. Dordevic, P. Milenovic, J. Milosevic Centro de Investigaciones
Energéticas Medioambientales y Tecnológicas (CIEMAT), Madrid, Spain
M. Aguilar-Benitez, J. Alcaraz Maestre, A. Álvarez Fernández, I. Bachiller, M.
Barrio Luna, Cristina F. Bedoya, C.A. Carrillo Montoya, M. Cepeda, M. Cerrada,
N. Colino, B. De La Cruz, A. Delgado Peris, J.P. Fernández Ramos, J. Flix,
M.C. Fouz, O. Gonzalez Lopez, S. Goy Lopez, J.M. Hernandez, M.I. Josa, J. León
Holgado, D. Moran, Á. Navarro Tobar, A. Pérez-Calero Yzquierdo, J. Puerta
Pelayo, I. Redondo, L. Romero, S. Sánchez Navas, M.S. Soares, L. Urda Gómez,
C. Willmott Universidad Autónoma de Madrid, Madrid, Spain
C. Albajar, J.F. de Trocóniz, R. Reyes-Almanza Universidad de Oviedo,
Instituto Universitario de Ciencias y Tecnologías Espaciales de Asturias
(ICTEA), Oviedo, Spain
B. Alvarez Gonzalez, J. Cuevas, C. Erice, J. Fernandez Menendez, S. Folgueras,
I. Gonzalez Caballero, E. Palencia Cortezon, C. Ramón Álvarez, J. Ripoll Sau,
V. Rodríguez Bouza, A. Trapote Instituto de Física de Cantabria (IFCA), CSIC-
Universidad de Cantabria, Santander, Spain
J.A. Brochero Cifuentes, I.J. Cabrillo, A. Calderon, B. Chazin Quero, J.
Duarte Campderros, M. Fernandez, C. Fernandez Madrazo, P.J. Fernández Manteca,
A. García Alonso, G. Gomez, C. Martinez Rivero, P. Martinez Ruiz del Arbol, F.
Matorras, J. Piedra Gomez, C. Prieels, F. Ricci-Tam, T. Rodrigo, A. Ruiz-
Jimeno, L. Scodellaro, N. Trevisani, I. Vila, J.M. Vizan Garcia University of
Colombo, Colombo, Sri Lanka
MK Jayananda, B. Kailasapathy58, D.U.J. Sonnadara, DDC Wickramarathna
University of Ruhuna, Department of Physics, Matara, Sri Lanka
W.G.D. Dharmaratna, K. Liyanage, N. Perera, N. Wickramage CERN, European
Organization for Nuclear Research, Geneva, Switzerland
T.K. Aarrestad, D. Abbaneo, E. Auffray, G. Auzinger, J. Baechler, P. Baillon,
A.H. Ball, D. Barney, J. Bendavid, N. Beni, M. Bianco, A. Bocci, E. Brondolin,
T. Camporesi, M. Capeans Garrido, G. Cerminara, S.S. Chhibra, L. Cristella, D.
d’Enterria, A. Dabrowski, N. Daci, A. David, A. De Roeck, M. Deile, R. Di
Maria, M. Dobson, M. Dünser, N. Dupont, A. Elliott-Peisert, N. Emriskova, F.
Fallavollita59, D. Fasanella, S. Fiorendi, A. Florent, G. Franzoni, J.
Fulcher, W. Funk, S. Giani, D. Gigi, K. Gill, F. Glege, L. Gouskos, M.
Haranko, J. Hegeman, Y. Iiyama, V. Innocente, T. James, P. Janot, J. Kaspar,
J. Kieseler, M. Komm, N. Kratochwil, C. Lange, S. Laurila, P. Lecoq, K. Long,
C. Lourenço, L. Malgeri, S. Mallios, M. Mannelli, F. Meijers, S. Mersi, E.
Meschi, F. Moortgat, M. Mulders, S. Orfanelli, L. Orsini, F. Pantaleo20, L.
Pape, E. Perez, M. Peruzzi, A. Petrilli, G. Petrucciani, A. Pfeiffer, M.
Pierini, M. Pitt, T. Quast, D. Rabady, A. Racz, M. Rieger, M. Rovere, H.
Sakulin, J. Salfeld-Nebgen, S. Scarfi, C. Schäfer, C. Schwick, M. Selvaggi, A.
Sharma, P. Silva, W. Snoeys, P. Sphicas60, S. Summers, V.R. Tavolaro, D.
Treille, A. Tsirou, G.P. Van Onsem, M. Verzetti, K.A. Wozniak, W.D. Zeuner
Paul Scherrer Institut, Villigen, Switzerland
L. Caminada61, A. Ebrahimi, W. Erdmann, R. Horisberger, Q. Ingram, H.C.
Kaestli, D. Kotlinski, U. Langenegger, M. Missiroli, T. Rohe ETH Zurich -
Institute for Particle Physics and Astrophysics (IPA), Zurich, Switzerland
M. Backhaus, P. Berger, A. Calandri, N. Chernyavskaya, A. De Cosa, G.
Dissertori, M. Dittmar, M. Donegà, C. Dorfer, T. Gadek, T.A. Gómez Espinosa,
C. Grab, D. Hits, W. Lustermann, A.-M. Lyon, R.A. Manzoni, M.T. Meinhard, F.
Micheli, F. Nessi-Tedaldi, J. Niedziela, F. Pauss, V. Perovic, G. Perrin, S.
Pigazzini, M.G. Ratti, M. Reichmann, C. Reissel, T. Reitenspiess, B. Ristic,
D. Ruini, D.A. Sanz Becerra, M. Schönenberger, V. Stampf, J. Steggemann62, R.
Wallny, D.H. Zhu Universität Zürich, Zurich, Switzerland
C. Amsler63, C. Botta, D. Brzhechko, M.F. Canelli, A. De Wit, R. Del Burgo,
J.K. Heikkilä, M. Huwiler, A. Jofrehei, B. Kilminster, S. Leontsinis, A.
Macchiolo, P. Meiring, V.M. Mikuni, U. Molinatti, I. Neutelings, G. Rauco, A.
Reimers, P. Robmann, S. Sanchez Cruz, K. Schweiger, Y. Takahashi National
Central University, Chung-Li, Taiwan
C. Adloff64, C.M. Kuo, W. Lin, A. Roy, T. Sarkar37, S.S. Yu National Taiwan
University (NTU), Taipei, Taiwan
L. Ceard, P. Chang, Y. Chao, K.F. Chen, P.H. Chen, W.-S. Hou, Y.y. Li, R.-S.
Lu, E. Paganis, A. Psallidas, A. Steen, E. Yazgan, P.r. Yu Chulalongkorn
University, Faculty of Science, Department of Physics, Bangkok, Thailand
B. Asavapibhop, C. Asawatangtrakuldee, N. Srimanobhas Çukurova University,
Physics Department, Science and Art Faculty, Adana, Turkey
F. Boran, S. Damarseckin65, Z.S. Demiroglu, F. Dolek, C. Dozen66, I.
Dumanoglu67, E. Eskut, G. Gokbulut, Y. Guler, E. Gurpinar Guler68, I. Hos69,
C. Isik, E.E. Kangal70, O. Kara, A. Kayis Topaksu, U. Kiminsu, G. Onengut, K.
Ozdemir71, A. Polatoz, A.E. Simsek, B. Tali72, U.G. Tok, S. Turkcapar, I.S.
Zorbakir, C. Zorbilmez Middle East Technical University, Physics Department,
Ankara, Turkey
B. Isildak73, G. Karapinar74, K. Ocalan75, M. Yalvac76 Bogazici University,
Istanbul, Turkey
B. Akgun, I.O. Atakisi, E. Gülmez, M. Kaya77, O. Kaya78, Ö. Özçelik, S.
Tekten79, E.A. Yetkin80 Istanbul Technical University, Istanbul, Turkey
A. Cakir, K. Cankocak67, Y. Komurcu, S. Sen81 Istanbul University, Istanbul,
Turkey
F. Aydogmus Sen, S. Cerci72, B. Kaynak, S. Ozkorucuklu, D. Sunar Cerci72
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. Levchuk University of Bristol, Bristol, United Kingdom
E. Bhal, S. Bologna, J.J. Brooke, A. Bundock, E. Clement, D. Cussans, H.
Flacher, J. Goldstein, G.P. Heath, H.F. Heath, L. Kreczko, B. Krikler, S.
Paramesvaran, T. Sakuma, S. Seif El Nasr-Storey, V.J. Smith, N. Stylianou82,
J. Taylor, A. Titterton Rutherford Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev83, C. Brew, R.M. Brown, D.J.A. Cockerill, K.V. Ellis, K.
Harder, S. Harper, J. Linacre, K. Manolopoulos, D.M. Newbold, E. Olaiya, D.
Petyt, T. Reis, T. Schuh, C.H. Shepherd-Themistocleous, A. Thea, I.R. Tomalin,
T. Williams Imperial College, London, United Kingdom
R. Bainbridge, P. Bloch, S. Bonomally, J. Borg, S. Breeze, O. Buchmuller, V.
Cepaitis, G.S. Chahal84, D. Colling, P. Dauncey, G. Davies, M. Della Negra, G.
Fedi, G. Hall, M.H. Hassanshahi, G. Iles, J. Langford, L. Lyons, A.-M. Magnan,
S. Malik, A. Martelli, V. Milosevic, J. Nash85, V. Palladino, M. Pesaresi,
D.M. Raymond, A. Richards, A. Rose, E. Scott, C. Seez, A. Shtipliyski, A.
Tapper, K. Uchida, T. Virdee20, N. Wardle, S.N. Webb, D. Winterbottom, A.G.
Zecchinelli Brunel University, Uxbridge, United Kingdom
J.E. Cole, A. Khan, P. Kyberd, C.K. Mackay, I.D. Reid, L. Teodorescu, S. Zahid
Baylor University, Waco, USA
S. Abdullin, A. Brinkerhoff, B. Caraway, J. Dittmann, K. Hatakeyama, A.R.
Kanuganti, B. McMaster, N. Pastika, S. Sawant, C. Smith, C. Sutantawibul, J.
Wilson Catholic University of America, Washington, DC, USA
R. Bartek, A. Dominguez, R. Uniyal, A.M. Vargas Hernandez The University of
Alabama, Tuscaloosa, USA
A. Buccilli, O. Charaf, S.I. Cooper, D. Di Croce, S.V. Gleyzer, C. Henderson,
C.U. Perez, P. Rumerio, C. West Boston University, Boston, USA
A. Akpinar, A. Albert, D. Arcaro, C. Cosby, Z. Demiragli, D. Gastler, J.
Rohlf, K. Salyer, D. Sperka, D. Spitzbart, I. Suarez, S. Yuan, D. Zou Brown
University, Providence, USA
G. Benelli, B. Burkle, X. Coubez21, D. Cutts, Y.t. Duh, M. Hadley, U. Heintz,
J.M. Hogan86, K.H.M. Kwok, E. Laird, G. Landsberg, K.T. Lau, J. Lee, J. Luo,
M. Narain, S. Sagir87, E. Usai, W.Y. Wong, X. Yan, D. Yu, W. Zhang University
of California, Davis, Davis, USA
R. Band, C. Brainerd, R. Breedon, M. Calderon De La Barca Sanchez, M. Chertok,
J. Conway, R. Conway, P.T. Cox, R. Erbacher, C. Flores, F. Jensen, O. Kukral,
R. Lander, M. Mulhearn, D. Pellett, M. Shi, D. Taylor, M. Tripathi, Y. Yao, F.
Zhang University of California, Los Angeles, USA
M. Bachtis, R. Cousins, A. Dasgupta, A. Datta, D. Hamilton, J. Hauser, M.
Ignatenko, M.A. Iqbal, T. Lam, N. Mccoll, W.A. Nash, S. Regnard, D. Saltzberg,
C. Schnaible, B. Stone, V. Valuev University of California, Riverside,
Riverside, USA
K. Burt, Y. Chen, R. Clare, J.W. Gary, G. Hanson, G. Karapostoli, O.R. Long,
N. Manganelli, M. Olmedo Negrete, W. Si, S. Wimpenny, Y. Zhang University of
California, San Diego, La Jolla, USA
J.G. Branson, P. Chang, S. Cittolin, S. Cooperstein, N. Deelen, J. Duarte, R.
Gerosa, L. Giannini, D. Gilbert, V. Krutelyov, J. Letts, M. Masciovecchio, S.
May, S. Padhi, M. Pieri, V. Sharma, M. Tadel, A. Vartak, F. Würthwein, A.
Yagil University of California, Santa Barbara - Department of Physics, Santa
Barbara, USA
N. Amin, C. Campagnari, M. Citron, A. Dorsett, V. Dutta, J. Incandela, M.
Kilpatrick, B. Marsh, H. Mei, A. Ovcharova, H. Qu, M. Quinnan, J. Richman, U.
Sarica, D. Stuart, S. Wang California Institute of Technology, Pasadena, USA
A. Bornheim, O. Cerri, I. Dutta, J.M. Lawhorn, N. Lu, J. Mao, H.B. Newman, J.
Ngadiuba, T.Q. Nguyen, M. Spiropulu, J.R. Vlimant, C. Wang, S. Xie, Z. Zhang,
R.Y. Zhu Carnegie Mellon University, Pittsburgh, USA
J. Alison, M.B. Andrews, T. Ferguson, T. Mudholkar, M. Paulini, I. Vorobiev
University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, E. MacDonald, R. Patel, A. Perloff, K. Stenson, K.A.
Ulmer, S.R. Wagner Cornell University, Ithaca, USA
J. Alexander, Y. Cheng, J. Chu, D.J. Cranshaw, K. Mcdermott, J. Monroy, J.R.
Patterson, D. Quach, A. Ryd, W. Sun, S.M. Tan, Z. Tao, J. Thom, P. Wittich, M.
Zientek Fermi National Accelerator Laboratory, Batavia, USA
M. Albrow, M. Alyari, G. Apollinari, A. Apresyan, A. Apyan, S. Banerjee,
L.A.T. Bauerdick, A. Beretvas, D. Berry, J. Berryhill, P.C. Bhat, K. Burkett,
J.N. Butler, A. Canepa, G.B. Cerati, H.W.K. Cheung, F. Chlebana, M. Cremonesi,
K.F. Di Petrillo, V.D. Elvira, J. Freeman, Z. Gecse, L. Gray, D. Green, S.
Grünendahl, O. Gutsche, R.M. Harris, R. Heller, T.C. Herwig, J. Hirschauer, B.
Jayatilaka, S. Jindariani, M. Johnson, U. Joshi, P. Klabbers, T. Klijnsma, B.
Klima, M.J. Kortelainen, S. Lammel, D. Lincoln, R. Lipton, T. Liu, J. Lykken,
C. Madrid, K. Maeshima, C. Mantilla, D. Mason, P. McBride, P. Merkel, S.
Mrenna, S. Nahn, V. O’Dell, V. Papadimitriou, K. Pedro, C. Pena55, O.
Prokofyev, F. Ravera, A. Reinsvold Hall, L. Ristori, B. Schneider, E. Sexton-
Kennedy, N. Smith, A. Soha, L. Spiegel, S. Stoynev, J. Strait, L. Taylor, S.
Tkaczyk, N.V. Tran, L. Uplegger, E.W. Vaandering, H.A. Weber University of
Florida, Gainesville, USA
D. Acosta, P. Avery, D. Bourilkov, L. Cadamuro, V. Cherepanov, F. Errico, R.D.
Field, D. Guerrero, B.M. Joshi, M. Kim, J. Konigsberg, A. Korytov, K.H. Lo, K.
Matchev, N. Menendez, G. Mitselmakher, D. Rosenzweig, K. Shi, J. Sturdy, J.
Wang, E. Yigitbasi, X. Zuo Florida State University, Tallahassee, USA
T. Adams, A. Askew, D. Diaz, R. Habibullah, S. Hagopian, V. Hagopian, K.F.
Johnson, R. Khurana, T. Kolberg, G. Martinez, H. Prosper, C. Schiber, R.
Yohay, J. Zhang Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, S. Butalla, T. Elkafrawy16, M. Hohlmann, R. Kumar Verma, D.
Noonan, M. Rahmani, M. Saunders, F. Yumiceva University of Illinois at Chicago
(UIC), Chicago, USA
M.R. Adams, L. Apanasevich, H. Becerril Gonzalez, R. Cavanaugh, X. Chen, S.
Dittmer, O. Evdokimov, C.E. Gerber, D.A. Hangal, D.J. Hofman, C. Mills, G. Oh,
T. Roy, M.B. Tonjes, N. Varelas, J. Viinikainen, X. Wang, Z. Wu, Z. Ye The
University of Iowa, Iowa City, USA
M. Alhusseini, K. Dilsiz88, S. Durgut, R.P. Gandrajula, M. Haytmyradov, V.
Khristenko, O.K. Köseyan, J.-P. Merlo, A. Mestvirishvili89, A. Moeller, J.
Nachtman, H. Ogul90, Y. Onel, F. Ozok91, A. Penzo, C. Snyder, E. Tiras92, J.
Wetzel Johns Hopkins University, Baltimore, USA
O. Amram, B. Blumenfeld, L. Corcodilos, M. Eminizer, A.V. Gritsan, S.
Kyriacou, P. Maksimovic, J. Roskes, M. Swartz, T.Á. Vámi The University of
Kansas, Lawrence, USA
C. Baldenegro Barrera, P. Baringer, A. Bean, A. Bylinkin, T. Isidori, S.
Khalil, J. King, G. Krintiras, A. Kropivnitskaya, C. Lindsey, N. Minafra, M.
Murray, C. Rogan, C. Royon, S. Sanders, E. Schmitz, J.D. Tapia Takaki, Q.
Wang, J. Williams, G. Wilson Kansas State University, Manhattan, USA
S. Duric, A. Ivanov, K. Kaadze, D. Kim, Y. Maravin, T. Mitchell, A. Modak
Lawrence Livermore National Laboratory, Livermore, USA
F. Rebassoo, D. Wright University of Maryland, College Park, USA
E. Adams, A. Baden, O. Baron, A. Belloni, S.C. Eno, Y. Feng, N.J. Hadley, S.
Jabeen, R.G. Kellogg, T. Koeth, A.C. Mignerey, S. Nabili, M. Seidel, A. Skuja,
S.C. Tonwar, L. Wang, K. Wong Massachusetts Institute of Technology,
Cambridge, USA
D. Abercrombie, R. Bi, S. Brandt, W. Busza, I.A. Cali, Y. Chen, M. D’Alfonso,
G. Gomez Ceballos, M. Goncharov, P. Harris, M. Hu, M. Klute, D. Kovalskyi, J.
Krupa, Y.-J. Lee, P.D. Luckey, B. Maier, A.C. Marini, C. Mironov, X. Niu, C.
Paus, D. Rankin, C. Roland, G. Roland, Z. Shi, G.S.F. Stephans, K. Tatar, D.
Velicanu, J. Wang, T.W. Wang, Z. Wang, B. Wyslouch University of Minnesota,
Minneapolis, USA
R.M. Chatterjee, A. Evans, P. Hansen, J. Hiltbrand, Sh. Jain, M. Krohn, Y.
Kubota, Z. Lesko, J. Mans, M. Revering, R. Rusack, R. Saradhy, N. Schroeder,
N. Strobbe, M.A. Wadud University of Mississippi, Oxford, USA
J.G. Acosta, S. Oliveros University of Nebraska-Lincoln, Lincoln, USA
K. Bloom, M. Bryson, S. Chauhan, D.R. Claes, C. Fangmeier, L. Finco, F. Golf,
J.R. González Fernández, C. Joo, I. Kravchenko, J.E. Siado, G.R.
Snow${}^{\textrm{\textdagger}}$, W. Tabb, F. Yan State University of New York
at Buffalo, Buffalo, USA
G. Agarwal, H. Bandyopadhyay, L. Hay, I. Iashvili, A. Kharchilava, C. McLean,
D. Nguyen, J. Pekkanen, S. Rappoccio Northeastern University, Boston, USA
G. Alverson, E. Barberis, C. Freer, Y. Haddad, A. Hortiangtham, J. Li, G.
Madigan, B. Marzocchi, D.M. Morse, V. Nguyen, T. Orimoto, A. Parker, L.
Skinnari, A. Tishelman-Charny, T. Wamorkar, B. Wang, A. Wisecarver, D. Wood
Northwestern University, Evanston, USA
S. Bhattacharya, J. Bueghly, Z. Chen, A. Gilbert, T. Gunter, K.A. Hahn, N.
Odell, M.H. Schmitt, K. Sung, M. Velasco University of Notre Dame, Notre Dame,
USA
R. Bucci, N. Dev, R. Goldouzian, M. Hildreth, K. Hurtado Anampa, C. Jessop, K.
Lannon, N. Loukas, N. Marinelli, I. Mcalister, F. Meng, K. Mohrman, Y.
Musienko48, R. Ruchti, P. Siddireddy, M. Wayne, A. Wightman, M. Wolf, L.
Zygala The Ohio State University, Columbus, USA
J. Alimena, B. Bylsma, B. Cardwell, L.S. Durkin, B. Francis, C. Hill, A.
Lefeld, B.L. Winer, B.R. Yates Princeton University, Princeton, USA
F.M. Addesa, B. Bonham, P. Das, G. Dezoort, P. Elmer, A. Frankenthal, B.
Greenberg, N. Haubrich, S. Higginbotham, A. Kalogeropoulos, G. Kopp, S. Kwan,
D. Lange, M.T. Lucchini, D. Marlow, K. Mei, I. Ojalvo, J. Olsen, C. Palmer, D.
Stickland, C. Tully University of Puerto Rico, Mayaguez, USA
S. Malik, S. Norberg Purdue University, West Lafayette, USA
A.S. Bakshi, V.E. Barnes, R. Chawla, S. Das, L. Gutay, M. Jones, A.W. Jung, S.
Karmarkar, M. Liu, G. Negro, N. Neumeister, C.C. Peng, S. Piperov, A. Purohit,
J.F. Schulte, M. Stojanovic17, J. Thieman, F. Wang, R. Xiao, W. Xie Purdue
University Northwest, Hammond, USA
J. Dolen, N. Parashar Rice University, Houston, USA
A. Baty, S. Dildick, K.M. Ecklund, S. Freed, F.J.M. Geurts, A. Kumar, W. Li,
B.P. Padley, R. Redjimi, J. Roberts${}^{\textrm{\textdagger}}$, W. Shi, A.G.
Stahl Leiton University of Rochester, Rochester, USA
A. Bodek, P. de Barbaro, R. Demina, J.L. Dulemba, C. Fallon, T. Ferbel, M.
Galanti, A. Garcia-Bellido, O. Hindrichs, A. Khukhunaishvili, E. Ranken, R.
Taus Rutgers, The State University of New Jersey, Piscataway, USA
B. Chiarito, J.P. Chou, A. Gandrakota, Y. Gershtein, E. Halkiadakis, A. Hart,
M. Heindl, E. Hughes, S. Kaplan, O. Karacheban24, I. Laflotte, A. Lath, R.
Montalvo, K. Nash, M. Osherson, S. Salur, S. Schnetzer, S. Somalwar, R. Stone,
S.A. Thayil, S. Thomas, H. Wang University of Tennessee, Knoxville, USA
H. Acharya, A.G. Delannoy, S. Spanier Texas A&M University, College Station,
USA
O. Bouhali93, M. Dalchenko, A. Delgado, R. Eusebi, J. Gilmore, T. Huang, T.
Kamon94, H. Kim, S. Luo, S. Malhotra, R. Mueller, D. Overton, D. Rathjens, A.
Safonov Texas Tech University, Lubbock, USA
N. Akchurin, J. Damgov, V. Hegde, S. Kunori, K. Lamichhane, S.W. Lee, T.
Mengke, S. Muthumuni, T. Peltola, S. Undleeb, I. Volobouev, Z. Wang, A.
Whitbeck Vanderbilt University, Nashville, USA
E. Appelt, S. Greene, A. Gurrola, W. Johns, C. Maguire, A. Melo, H. Ni, K.
Padeken, F. Romeo, P. Sheldon, S. Tuo, J. Velkovska University of Virginia,
Charlottesville, USA
M.W. Arenton, B. Cox, G. Cummings, J. Hakala, R. Hirosky, M. Joyce, A.
Ledovskoy, A. Li, C. Neu, B. Tannenwald, E. Wolfe Wayne State University,
Detroit, USA
P.E. Karchin, N. Poudyal, P. Thapa University of Wisconsin - Madison, Madison,
WI, USA
K. Black, T. Bose, J. Buchanan, C. Caillol, S. Dasu, I. De Bruyn, P.
Everaerts, C. Galloni, H. He, M. Herndon, A. Hervé, U. Hussain, A. Lanaro, A.
Loeliger, R. Loveless, J. Madhusudanan Sreekala, A. Mallampalli, A. Mohammadi,
D. Pinna, A. Savin, V. Shang, V. Sharma, W.H. Smith, D. Teague, S. Trembath-
reichert, W. Vetens †: Deceased
1: Also at TU Wien, Wien, Austria
2: Also at Institute of Basic and Applied Sciences, Faculty of Engineering,
Arab Academy for Science, Technology and Maritime Transport, Alexandria,
Egypt, Alexandria, Egypt
3: Also at Université Libre de Bruxelles, Bruxelles, Belgium
4: Also at IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
5: Also at Universidade Estadual de Campinas, Campinas, Brazil
6: Also at Federal University of Rio Grande do Sul, Porto Alegre, Brazil
7: Also at UFMS, Nova Andradina, Brazil
8: Also at Nanjing Normal University Department of Physics, Nanjing, China
9: Now at The University of Iowa, Iowa City, USA
10: Also at University of Chinese Academy of Sciences, Beijing, China
11: Also at Institute for Theoretical and Experimental Physics named by A.I.
Alikhanov of NRC ‘Kurchatov Institute’, Moscow, Russia
12: Also at Joint Institute for Nuclear Research, Dubna, Russia
13: Also at Cairo University, Cairo, Egypt
14: Also at Suez University, Suez, Egypt
15: Now at British University in Egypt, Cairo, Egypt
16: Now at Ain Shams University, Cairo, Egypt
17: Also at Purdue University, West Lafayette, USA
18: Also at Université de Haute Alsace, Mulhouse, France
19: Also at Erzincan Binali Yildirim University, Erzincan, Turkey
20: Also at CERN, European Organization for Nuclear Research, Geneva,
Switzerland
21: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
22: Also at University of Hamburg, Hamburg, Germany
23: Also at Department of Physics, Isfahan University of Technology, Isfahan,
Iran, Isfahan, Iran
24: Also at Brandenburg University of Technology, Cottbus, Germany
25: Also at Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State
University, Moscow, Russia
26: Also at Physics Department, Faculty of Science, Assiut University, Assiut,
Egypt
27: Also at Eszterhazy Karoly University, Karoly Robert Campus, Gyongyos,
Hungary
28: Also at Institute of Physics, University of Debrecen, Debrecen, Hungary,
Debrecen, Hungary
29: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary
30: Also at MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary, Budapest, Hungary
31: Also at Wigner Research Centre for Physics, Budapest, Hungary
32: Also at IIT Bhubaneswar, Bhubaneswar, India, Bhubaneswar, India
33: Also at Institute of Physics, Bhubaneswar, India
34: Also at G.H.G. Khalsa College, Punjab, India
35: Also at Shoolini University, Solan, India
36: Also at University of Hyderabad, Hyderabad, India
37: Also at University of Visva-Bharati, Santiniketan, India
38: Also at Indian Institute of Technology (IIT), Mumbai, India
39: Also at Deutsches Elektronen-Synchrotron, Hamburg, Germany
40: Also at Sharif University of Technology, Tehran, Iran
41: Also at Department of Physics, University of Science and Technology of
Mazandaran, Behshahr, Iran
42: Now at INFN Sezione di Bari a, Università di Bari b, Politecnico di Bari
c, Bari, Italy
43: Also at Italian National Agency for New Technologies, Energy and
Sustainable Economic Development, Bologna, Italy
44: Also at Centro Siciliano di Fisica Nucleare e di Struttura Della Materia,
Catania, Italy
45: Also at Università di Napoli ’Federico II’, NAPOLI, Italy
46: Also at Riga Technical University, Riga, Latvia, Riga, Latvia
47: Also at Consejo Nacional de Ciencia y Tecnología, Mexico City, Mexico
48: Also at Institute for Nuclear Research, Moscow, Russia
49: Now at National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
50: Also at St. Petersburg State Polytechnical University, St. Petersburg,
Russia
51: Also at University of Florida, Gainesville, USA
52: Also at Imperial College, London, United Kingdom
53: Also at P.N. Lebedev Physical Institute, Moscow, Russia
54: Also at Moscow Institute of Physics and Technology, Moscow, Russia,
Moscow, Russia
55: Also at California Institute of Technology, Pasadena, USA
56: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia
57: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia
58: Also at Trincomalee Campus, Eastern University, Sri Lanka, Nilaveli, Sri
Lanka
59: Also at INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy,
Pavia, Italy
60: Also at National and Kapodistrian University of Athens, Athens, Greece
61: Also at Universität Zürich, Zurich, Switzerland
62: Also at Ecole Polytechnique Fédérale Lausanne, Lausanne, Switzerland
63: Also at Stefan Meyer Institute for Subatomic Physics, Vienna, Austria,
Vienna, Austria
64: Also at Laboratoire d’Annecy-le-Vieux de Physique des Particules,
IN2P3-CNRS, Annecy-le-Vieux, France
65: Also at Şırnak University, Sirnak, Turkey
66: Also at Department of Physics, Tsinghua University, Beijing, China,
Beijing, China
67: Also at Near East University, Research Center of Experimental Health
Science, Nicosia, Turkey
68: Also at Beykent University, Istanbul, Turkey, Istanbul, Turkey
69: Also at Istanbul Aydin University, Application and Research Center for
Advanced Studies (App. & Res. Cent. for Advanced Studies), Istanbul, Turkey
70: Also at Mersin University, Mersin, Turkey
71: Also at Piri Reis University, Istanbul, Turkey
72: Also at Adiyaman University, Adiyaman, Turkey
73: Also at Ozyegin University, Istanbul, Turkey
74: Also at Izmir Institute of Technology, Izmir, Turkey
75: Also at Necmettin Erbakan University, Konya, Turkey
76: Also at Bozok Universitetesi Rektörlügü, Yozgat, Turkey, Yozgat, Turkey
77: Also at Marmara University, Istanbul, Turkey
78: Also at Milli Savunma University, Istanbul, Turkey
79: Also at Kafkas University, Kars, Turkey
80: Also at Istanbul Bilgi University, Istanbul, Turkey
81: Also at Hacettepe University, Ankara, Turkey
82: Also at Vrije Universiteit Brussel, Brussel, Belgium
83: Also at School of Physics and Astronomy, University of Southampton,
Southampton, United Kingdom
84: Also at IPPP Durham University, Durham, United Kingdom
85: Also at Monash University, Faculty of Science, Clayton, Australia
86: Also at Bethel University, St. Paul, Minneapolis, USA, St. Paul, USA
87: Also at Karamanoğlu Mehmetbey University, Karaman, Turkey
88: Also at Bingol University, Bingol, Turkey
89: Also at Georgian Technical University, Tbilisi, Georgia
90: Also at Sinop University, Sinop, Turkey
91: Also at Mimar Sinan University, Istanbul, Istanbul, Turkey
92: Also at Erciyes University, KAYSERI, Turkey
93: Also at Texas A&M University at Qatar, Doha, Qatar
94: Also at Kyungpook National University, Daegu, Korea, Daegu, Korea