B2G-19-005

B2G-19-005

# A search for bottom-type, vector-like quark pair production in a fully
hadronic final state in proton-proton collisions at $\sqrt{s}=13\TeV$

###### Abstract

A search is described for the production of a pair of bottom-type vector-like
quarks (VLQs), each decaying into a or quark and either a Higgs or a boson,
with a mass greater than 1000. The analysis is based on data from proton-
proton collisions at a 13center-of-mass energy recorded at the CERN LHC,
corresponding to a total integrated luminosity of 137. As the predominant
decay modes of the Higgs and bosons are to a pair of quarks, the analysis
focuses on final states consisting of jets resulting from the six quarks
produced in the events. Since the two jets produced in the decay of a highly
Lorentz-boosted Higgs or boson can merge to form a single jet, nine
independent analyses are performed, categorized by the number of observed jets
and the reconstructed event mode. No signal in excess of the expected
background is observed. Lower limits are set on the VLQ mass at 95% confidence
level equal to 1570in the case where the VLQ decays exclusively to a quark and
a Higgs boson, 1390for when it decays exclusively to a quark and a boson, and
1450for when it decays equally in these two modes. These limits represent
significant improvements over the previously published VLQ limits.

##  0.1 Introduction

One of the biggest puzzles in elementary particle physics concerns the large
difference between the electroweak scale and the Planck scale, and the related
problem of the unexpectedly low value of the Higgs boson mass [1]. In the
standard model (SM), the Higgs boson is assumed to be a fundamental scalar
(spin-0) particle. Unlike the fundamental fermions (leptons and quarks) and
the vector gauge bosons, the corrections to the Higgs boson mass due to vacuum
energy fluctuations are quadratic, driving the Higgs boson mass to the cutoff
value of the vacuum energy fluctuations. In the absence of any new physics
below the Planck scale, this cutoff is about $10^{19}\GeV$. In that case, the
Higgs boson mass would naturally be expected to be seventeen orders of
magnitude greater than its measured mass of 125.

Although supersymmetry provides an elegant solution to this problem [2, 3],
the lack of evidence for the production of supersymmetric particles at the
CERN LHC indicates that, if supersymmetry is realized in nature, it is broken
at an energy scale greater than a few and, therefore, does not solve the fine
tuning of the 125Higgs boson mass. Several alternative theories have been
proposed for solving this fine tuning problem. These theories include
composite Higgs models [4, 5, 6], in which the Higgs boson is not a
fundamental particle, but rather contains constituents bound by a new type of
gauge interaction, and little Higgs models [7, 8], in which the Higgs boson is
a pseudo-Nambu–Goldstone boson that arises from spontaneous breaking of a
global symmetry at the energy scale. Both of these types of models predict a
new class of vector-like fermions [9] with the same charges as the SM
fermions, but with purely vector current couplings to the weak gauge bosons.
In composite Higgs models, the vector-like quarks (VLQs) are excited bound-
state resonances, while in little Higgs models they are fundamental particles
that cancel loop divergences.

Since the VLQs are nonchiral, Lagrangian mass terms not arising from Yukawa
couplings to the Higgs field are allowed, thereby avoiding the constraints on
heavy, sequential fourth-generation quarks set by the measured cross section
for Higgs boson production at the LHC [10, 11]. Requiring VLQs to have
renormalizable couplings to the SM quarks permits only four types of VLQs,
defined by their charge $q$: $q=-1/3$ (), $q=+2/3$ (T), $q=-4/3$ (), and
$q=+5/3$ (Y) [12]. These are arranged into seven multiplets: two singlets
(Tand ), three doublets (${\HepParticle{T}{}{}}\Xspace\PB$,
$\PX{\HepParticle{T}{}{}}\Xspace$, and ${\HepParticle{Y}{}{}}\Xspace\PB$), and
two triplets ($\PX{\HepParticle{T}{}{}}\Xspace\PB$ and
${\HepParticle{T}{}{}}\Xspace\PB{\HepParticle{Y}{}{}}\Xspace$) [13]. This
analysis focuses on the $q=-1/3$ () type of VLQ.

The branching fractions $\mathcal{B}$ of the Tand are model specific and
depend upon the VLQ multiplet configuration, the mass of the VLQ, and the
coupling of the VLQ to chiral quarks [14]. In general, up-type quark mass
eigenstates will be mixtures of the chiral up-type quarks with the TVLQ, while
down-type quark mass eigenstates will be mixtures of the chiral down-type
quarks and the VLQ. Precision measurements of the couplings of the first and
second generation SM quarks constrain their mixings with VLQs and indicate
that the only sizable couplings of the Tand VLQs allowed are to SM quarks of
the third generation, although couplings to other quarks are not excluded [15,
16, 13]. In this analysis, we assume the VLQ has three decay modes:
$\PB\to\PQb\PZ$, $\PB\to\PQb\PH$, and $\PB\to\PQt\PW$. In most models, for a
VLQ mass greater than the current limit of approximately 1000, there is a
small difference between $\mathcal{B}(\PB\to\PQb\PZ)$ and
$\mathcal{B}(\PB\to\PQb\PH)$, depending on the VLQ mass, but the difference is
essentially zero for masses greater than 2000. The expected values of
$\mathcal{B}(\PB\to\PQb\PZ)$ and $\mathcal{B}(\PB\to\PQb\PH)$ also depend upon
the multiplet configuration. They are 50% for both the
$\PX{\HepParticle{T}{}{}}\Xspace\PB$ triplet and the
$\PB{\HepParticle{Y}{}{}}\Xspace$ doublet, and 25% for the
${\HepParticle{T}{}{}}\Xspace\PB{\HepParticle{Y}{}{}}\Xspace$ triplet and the
singlet. The branching fractions for the ${\HepParticle{T}{}{}}\Xspace\PB$
doublet depend upon the mixing of the Tand VLQs with chiral quarks. If the
${\HepParticle{T}{}{}}\Xspace\PQt$ mixing is zero, the $\PB\to\PQb\PH$ and
$\PB\to\PQb\PZ$ branching fractions are 50%. If the
${\HepParticle{T}{}{}}\Xspace\PQt$ and $\PB\PQb$ mixing are equal, these
branching fractions are 25%. If the $\PB\PQb$ mixing is zero, these branching
fractions are zero [12].

Results from both the ATLAS and CMS Collaborations using events with fully
hadronic final states, based on data from proton-proton ($\Pp\Pp$) collisions
at $\sqrt{s}=13\TeV$ with an integrated luminosity of 36, have excluded a
pair-produced with mass up to approximately 1100. The ATLAS analysis [17] was
based on a classification of event signatures using neural networks, while the
CMS analysis [18] used event shapes to identify Lorentz-boosted objects. The
ATLAS (CMS) results exclude masses, at 95% confidence level (), up to 1010
(980), 710 (1070), and 950 (1025)for the 100% $\PB\to\PQb\PH$, 100%
$\PB\to\PQb\PZ$, and the $\PB{\HepParticle{Y}{}{}}\Xspace$ doublet cases,
respectively. In addition, an ATLAS analysis [19] combining both fully
hadronic and leptonic channels excludes values of the mass up to 1140for the
$\PB{\HepParticle{Y}{}{}}\Xspace$ doublet case. The analysis presented here
improves on these results by using the full 137data set collected by CMS in
2016–2018, and by fully reconstructing the event kinematics, thereby allowing
the mass of the to be reconstructed.

##  0.2 Analysis overview

This analysis involves a search for the production of a pair of bottom-type
VLQs with mass greater than 1000, using data from $\Pp\Pp$ collisions at
$\sqrt{s}=13\TeV$ at the LHC collected by the CMS detector during 2016–2018,
corresponding to an integrated luminosity of 137. The analysis is focused on
events in which each of the VLQs decays to a or quark and to either a Higgs or
boson. Since the dominant decay modes of the Higgs and bosons are to a quark
and antiquark pair, we select final states consisting of jets resulting from
the quarks and antiquarks produced in the decays of the VLQs and subsequent
decays of the two bosons. The events are categorized into three modes,
depending on the daughter bosons: $\PQb\PH\PQb\PH$, $\PQb\PH\PQb\PZ$, and
$\PQb\PZ\PQb\PZ$. Figure 1 shows the dominant Feynman diagrams for these three
modes.

Figure 1: Dominant diagrams of the pair production of bottom-type VLQs () that
subsequently decay to a or quark and either a Higgs or boson. In events
targeted by this analysis, the boson then decays to a pair of quarks, where
denotes any quark other than a top quark, while the Higgs boson decays to
quarks. Upper left: $\PQb\PH\PQb\PH$ mode, upper right: $\PQb\PH\PQb\PZ$ mode,
lower: $\PQb\PZ\PQb\PZ$ mode.

Background from SM processes (predominantly “multijet” events, consisting
solely of jets produced through the strong interaction) is reduced by
requiring that the jets are consistent with the production of a pair of bosons
(either Higgs or ), that the reconstructed VLQs have equal masses, and that
some of the jets are tagged as originating from quarks. For a highly boosted
Higgs or boson, the two jets resulting from its daughter quarks might merge
into a single reconstructed jet. In order to include these events, three
orthogonal, fully independent analyses are carried out using exclusive sets of
events categorized by the observed jet multiplicity: 4, 5, or 6 jets. The
final result is obtained by combining these three independent analyses. In
this paper, we use “jet tagging requirements” to refer to both single jets
tagged as being from a quark, and merged jets tagged as containing a pair.

To select the correct assignment of reconstructed jets to parent particles, a
modified $\chi^{2}$ metric, $\chi^{2}_{\text{mod}}$, is used. The
$\chi^{2}_{\text{mod}}$ value is determined by the differences between the
masses of the two reconstructed bosons and the mass of the Higgs or boson,
normalized by their resolutions, and by the reconstructed fractional mass
difference of the two VLQs. The event mode is assigned as $\PQb\PH\PQb\PH$,
$\PQb\PH\PQb\PZ$, or $\PQb\PZ\PQb\PZ$, depending on which gives the smallest
value of $\chi^{2}_{\text{mod}}$. An upper cutoff on the value of
$\chi^{2}_{\text{mod}}$ is applied to remove background.

The expected background is first determined by fitting the distribution of the
number of events as a function of the reconstructed VLQ mass, before jet
tagging requirements are applied, so this sample is overwhelmingly background
dominated. The fraction of background expected to remain after jet tagging
requirements are applied, called the background jet-tagged fraction, is
measured using events with VLQ candidate masses in the range 500–800, in which
a VLQ signal has already been excluded, and then corrected for a possible
dependence on the VLQ mass by using a control region with a higher
$\chi^{2}_{\text{mod}}$ value. Both the $\chi^{2}_{\text{mod}}$ selection and
jet tagging requirements are simultaneously optimized for maximal sensitivity
to a potential signal. This optimization is done separately for each event
mode and jet multiplicity. For the final result, all event mode and jet
multiplicity analyses are combined using the procedure in Ref. [20] to obtain
VLQ mass limits as a function of $\mathcal{B}(\PB\to\PQb\PH)$ and
$\mathcal{B}(\PB\to\PQb\PZ)$, as described further in Section 0.10.

##  0.3 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 ($\eta$) coverage provided by the
barrel and endcap detectors. Muons are detected in gas-ionization chambers
embedded in the steel flux-return yoke outside the solenoid.

The electromagnetic calorimeter consists of 75 848 lead tungstate crystals,
which provide coverage in $\abs{\eta}<1.48$ in a barrel region and
$1.48<\abs{\eta}<3.0$ in two endcap regions. In the region $\abs{\eta}<1.74$,
the HCAL cells have widths of 0.087 in pseudorapidity and 0.087 in azimuth
($\phi$). In the $\eta$-$\phi$ plane, and for $\abs{\eta}<1.48$, the HCAL
cells map on to $5{\times}5$ arrays of ECAL crystals to form calorimeter
towers projecting radially outwards from close to the nominal interaction
point. For $\abs{\eta}>1.74$, the coverage of the towers increases
progressively to a maximum of 0.174 in $\Delta\eta$ and $\Delta\phi$. Within
each tower, the energy deposits in ECAL and HCAL cells are summed to define
the calorimeter tower energies, which are subsequently used to provide the
energies and directions of hadronic jets. When combining information from the
entire detector, the jet energy resolution amounts typically to 15–20% at 30,
10% at 100, and 5% at 1 [21].

Events of interest are selected using a two-tiered trigger system [22]. 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 time interval of about 4. The second level, known as the high-
level trigger (HLT), consists of a farm of processors running a version of the
full event reconstruction software optimized for fast processing, and reduces
the event rate to around 1kHz before data storage.

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. [23].

##  0.4 Data and simulated events

The data used in this analysis were collected during the 2016–2018 LHC running
periods and correspond to an integrated luminosity of 137 [24, 25, 26].

Signal events with pair production of VLQs were simulated using the Monte
Carlo generator [27], version v2.3.3 (v2.4.2) for samples corresponding to
2016 (2017–2018) data, at leading order with the NNPDF3.0 parton distribution
functions (PDFs) [28]. The generated VLQ masses $m_{\PB}$ cover the range
1000–1800in steps of 100. Hadronization of the underlying partons was
simulated using v8.212 [29] with the CUETP8M1 tune [30] for samples
corresponding to 2016 data, and with the CP5 tune [31] for samples
corresponding to 2017 and 2018 data. Corrections of the cross sections to
next-to-next-to-leading order and next-to-next-to-leading logarithmic soft-
gluon resummation were obtained using Top++ 2.0 [32] with the MSTW2008NNLO68CL
parton distribution set from the lhapdf 5.9.0 library [33, 34]. To simulate
the effect of additional $\Pp\Pp$ interactions within the same or nearby bunch
crossings (“pileup”), v8.226 with a total inelastic $\Pp\Pp$ cross section of
69.2mb [35] was used. Following event generation, the package [36, 37] was
used to simulate the CMS detector response. Scale factors corresponding to jet
energy corrections, jet energy resolutions [21], pileup, and jet tagging [38,
39] are applied to the simulated signal events so that the corresponding
distributions agree with those in data.

##  0.5 Jet reconstruction and tagging

The global event reconstruction, also called the particle-flow event
reconstruction [40], aims to reconstruct and identify each individual particle
in an event, with an optimized combination of all subdetector information. In
this process, the identification of the particle type (photon, electron, muon,
charged hadron, or neutral hadron) plays an important role in the
determination of the particle direction and energy. First, photons, electrons,
and muons are identified using ECAL energy clusters, tracks in the tracker,
and hits in the muon system. Then, charged hadrons are identified as charged
particle tracks neither identified as electrons, nor as muons. Finally,
neutral hadrons are identified as HCAL energy clusters not linked to any
charged hadron trajectory, or as a combined ECAL and HCAL energy excess with
respect to the expected charged hadron energy deposit. The energy of charged
hadrons is determined from a combination of the track momentum and the
corresponding ECAL and HCAL energies, corrected for the response function of
the calorimeters to hadronic showers, and the energy of neutral hadrons is
obtained from the corresponding corrected ECAL and HCAL energies.

For this analysis, two types of hadronic jets are clustered from these
reconstructed particles, using the infrared and collinear safe anti-algorithm
[41, 42]. The first type, “AK4 jets”, uses a distance parameter $\Delta
R=\sqrt{\smash[b]{(\Delta\eta)^{2}+(\Delta\phi)^{2}}}$ of 0.4. However, since
merged jets from a boosted Higgs or boson decay may be wider, a second set,
using a distance parameter of 0.8 (“AK8 jets”) is also reconstructed from the
same set of input particles. Jet momentum is determined as the vectorial sum
of all particle momenta in the jet, and is found from simulation to be, on
average, within 5 to 10% of the true momentum over the whole transverse
momentum () spectrum and detector acceptance. Pileup can contribute additional
tracks and calorimetric energy depositions to the jet momentum. The pileup per
particle identification algorithm (PUPPI) [43] 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 identified as 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
tracker acceptance ($\abs{\eta}<2.5$), and using both charged and neutral
particles in the region outside of the tracker coverage. The momenta of the
neutral particles are then rescaled according to their probability to
originate from the primary interaction vertex deduced from the local shape
variable, superseding the need for jet-based pileup corrections [44]. Jet
energy corrections are derived from simulation to bring the measured response
of jets to that of particle level jets on average. In situ measurements of the
momentum balance in dijet, $\text{photon}+\text{jet}$, $\PZ+\text{jet}$, and
multijet events are used to account for any residual differences in the jet
energy scale between data and simulation [21]. Additional selection criteria
are applied to each jet to remove jets potentially dominated by anomalous
contributions from various subdetector components or reconstruction failures.

This analysis only uses AK4 jets with $\pt>50\GeV$ and AK8 jets with
$\pt>200\GeV$, both within $\abs{\eta}<2.4$. For AK8 jets, the constituents
are reclustered using the Cambridge–Aachen algorithm [45, 46]. The “modified
mass drop tagger” algorithm [47, 48], also known as the “soft drop” algorithm,
with angular exponent $\beta=0$, soft cutoff threshold $z_{\text{cut}}<0.1$,
and characteristic radius $R_{0}=0.8$ [49], is applied to remove soft, wide-
angle radiation from the jet. This results in a jet mass that, in the case of
large mass, more accurately corresponds to the mass of the mother particle
from which the jet originated. For events with boosted Higgs and bosons, the
AK8 soft-drop mass is used to obtain the mass of the merged jet.

The event jet multiplicity is determined by the number of AK4 jets passing the
requirements above. In signal events, decays of the VLQ pair and subsequent
decays of the Higgs and boson daughters yield a total of six quarks. In all
three event modes, at least two of these are quarks. Considering the
predominant $\PH\to\bbbar$ decay, for the $\PQb\PH\PQb\PH$ event mode, all six
are quarks; for the $\PQb\PH\PQb\PZ$ event mode, four or six are quarks; while
for the $\PQb\PZ\PQb\PZ$ event mode, two, four or six are quarks. We note that
of the hadronic boson decays, 15% are to a $\bbbar$ pair; however,
$\PZ\to\ccbar$ decays also have a significant chance of passing the tagging
requirements, as discussed in Section 0.6, and are also included (along with
other possible boson decay modes) in the signal efficiency. If all six quark
jets are individually reconstructed, a 6-jet event is produced; if two jets
merge into a single reconstructed jet, this produces a 5-jet event; and if two
merged jets are produced, then a 4-jet event results. Note that the VLQ
reconstruction does not consider the possibility of additional jets produced
by initial state or final state radiation.

Because of the large number of jets in signal events, tagging is a powerful
tool to significantly reduce the background from SM processes. Individual jets
are tagged using the DeepJet discriminant [38] applied to AK4 jets, while
merged jets from $\bbbar$ pairs are double tagged using the algorithm in Ref.
[39], developed in the context of $\PH\to\bbbar$ searches, applied to AK8
jets.

##  0.6 Event selection

The events used in the analysis are first selected online by the CMS trigger
system. The HLT trigger used requires the total measured in the calorimeters
to be at least 900 (1050)for the 2016 (2017–2018) data set. Offline, events
with $\HT>1350\GeV$ are selected, where is defined as the scalar sum of the
jet for all AK4 jets with $\pt>50\GeV$ and $\abs{\eta}<2.4$. The requirement
is set higher than the trigger threshold to avoid effects due to trigger turn
on. In order to minimize bias when measuring the efficiency of the HLT
triggers, the efficiencies are measured in a data set collected by an
orthogonal trigger, which requires the event to have a single muon. For all
years, the measured trigger efficiency for events with $\HT>1350\GeV$ is at
least 99.6%. Table 0.6 shows the efficiency for simulated VLQ signal events
after the requirement for each of the three jet multiplicity channels and for
each of three VLQ masses (1000, 1200, and 1400).

Signal efficiencies of the offline selection, in %, for each of the jet
multiplicity channels, for three VLQ masses (1000, 1200, and 1400). The
efficiency is the fraction of events in each jet multiplicity category
satisfying the $\HT>1350\GeV$ selection. Statistical uncertainties are
negligible and therefore omitted. lccc VLQ mass [] & 4 jets 5 jets 6 jets
1000 95.1 89.4 81.4
1200 98.5 96.2 91.3
1400 99.5 98.4 95.0

The number of tagged jets required to select an event, as well as the working
points for the taggers used, are optimized separately for each of the three
jet multiplicities and event modes, in order to maximize the expected signal
sensitivity. For the working points selected, the single tagger has an
efficiency of 82% for jets in simulated events with $\pt>30\GeV$ and a mistag
rate of 1% for light quarks (, , or ) and approximately 17% for charm quarks
[38]. The double tagger has an efficiency of 75% in simulated $\PH\to\bbbar$
events, and a mistag rate of 10% in simulated inclusive multijet events and
33% in simulated $\PH\to\ccbar$ events [39], where a mistag in the double tag
case means that at least one non-quark subjet is present in the tagged jet.
The number of tags required depends on the jet multiplicity as follows: in the
6-jet case, four AK4 jets are required to have a tag, except in the
$\PQb\PZ\PQb\PZ$ event mode, for which three tags are required. In the 5-jet
case, three of the AK4 jets not associated with the merged decay products are
required to be tagged; no double tag requirement is applied to the AK8 jet
associated with the merged decay. In the 4-jet case, two of the nonmerged AK4
jets are required to have a tag, and one of the merged jets is required to
have a double tag, except in the $\PQb\PZ\PQb\PZ$ event mode, for which no
double tag is required. These requirements are summarized in Table 0.6.

Summary of the minimum number of single and double tags required for each jet
multiplicity and event mode. clccc Jet multiplicity Tag $\PQb\PH\PQb\PH$
$\PQb\PH\PQb\PZ$ $\PQb\PZ\PQb\PZ$
4 jets Single 2 2 2
Double 1 1 0

5 jets Single 3 3 3
Double 0 0 0

6 jets Single 4 4 3

##  0.7 Event reconstruction

In the case when the two jets produced from a /boson decay are individually
resolved, the mass of the parent boson can be estimated from the invariant
mass of the two jets. In the case where the two jets are merged, the parent
boson mass is instead estimated using the soft-drop mass of the AK8 jet. Only
those AK8 jets that are within $\Delta R<0.3$ of an AK4 jet are used. However,
if a second AK4 jet is within $\Delta R<0.6$ of the AK8 jet, this overlap
could cause the AK8 jet mass to be misreconstructed, so in this case the AK8
jet is discarded and the two AK4 jets are treated as a resolved dijet boson
candidate.

A central feature in the analysis is the selection of the correct way of
combining jets in order to reconstruct the parent particles; this is a
difficult task because of the large number of jets in the event. In 6-jet
events, there are two pairs of jets originating from /boson decay; and three
jets (including two from the /decay) associated with each VLQ decay. In 5-jet
events, there is a pair of jets associated with one /boson and a merged jet
associated with the other /boson; each of these is associated with one of the
remaining two jets to form a VLQ candidate. In 4-jet events, there is a merged
jet associated with each /boson, each of which is paired to one of the
remaining two jets to form a VLQ candidate. The final reconstructed VLQ mass,
$m_{\text{VLQ}}$, is defined as the average mass of the two individual
reconstructed VLQs in the event.

The number of possible ways to combine the jets to reconstruct the two /bosons
and then to combine these with the two remaining jets to form the VLQ
candidates is 720, 120, and 24 for the 6-, 5-, and 4-jet multiplicities,
respectively. However, many of these different combinations simply involve
different permutations among the jets that constitute the individual VLQs, and
these permutations do not affect the reconstructed VLQ mass. The numbers of
combinations that give distinct VLQ masses are only 10, 10, and 3 for the 6-,
5-, and 4-jet multiplicities, respectively. For the 5- and 4-jet
multiplicities, however, the jet associated with the merged /boson decay
products distinguishes different combinations, since this jet is treated
differently from the others by having a double tag, rather than a single
tagging requirement. This doubles the number of distinct 5-jet combinations
and quadruples the number of distinct 4-jet combinations. The final number of
distinct combinations is then 10, 20, and 12 for the 6-, 5-, and 4-jet
multiplicities, respectively.

For each jet combination, $\chi^{2}_{\text{mod}}$ is determined using Eqs. 1–3
below, which depend on the measured mass of a dijet Higgs or boson candidate
($m_{\text{dijet}}$), the measured soft-drop mass of a merged-jet Higgs or
boson candidate ($m_{\text{merged}}$), and the fractional mass difference of
the two VLQ candidates ($\Delta m_{\text{VLQ}}$), where $\Delta
m_{\text{VLQ}}$ is the difference of the masses of the two VLQ candidates
divided by the average mass of the two. The only use of AK8 jets in this
calculation is to determine the mass of the merged /candidates using the soft-
drop mass of the matched AK8 jet. All other quantities are determined using
AK4 jet kinematics.

$\displaystyle{\text{6-jet events: }}\chi^{2}_{\text{mod}}$
$\displaystyle=\frac{(m_{\text{dijet}_{1}}-\overline{m}_{\text{dijet}})^{2}}{\sigma^{2}_{m_{\text{dijet}}}}+\frac{(m_{\text{dijet}_{2}}-\overline{m}_{\text{dijet}})^{2}}{\sigma^{2}_{m_{\text{dijet}}}}+\frac{(\Delta
m_{\text{VLQ}}-\overline{\Delta m}_{\text{VLQ}})^{2}}{\sigma^{2}_{\Delta
m_{\text{VLQ}}}},$ (1) $\displaystyle{\text{5-jet events:
}}\chi^{2}_{\text{mod}}$
$\displaystyle=\frac{(m_{\text{dijet}}-\overline{m}_{\text{dijet}})^{2}}{\sigma^{2}_{m_{\text{dijet}}}}+\frac{(m_{\text{merged}}-\overline{m}_{\text{merged}})^{2}}{\sigma^{2}_{m_{\text{merged}}}}+\frac{(\Delta
m_{\text{VLQ}}-\overline{\Delta m}_{\text{VLQ}})^{2}}{\sigma^{2}_{\Delta
m_{\text{VLQ}}}},$ (2) $\displaystyle{\text{4-jet events:
}}\chi^{2}_{\text{mod}}$
$\displaystyle=\frac{(m_{\text{merged}_{1}}-\overline{m}_{\text{merged}})^{2}}{\sigma^{2}_{m_{\text{merged}}}}+\frac{(m_{\text{merged}_{2}}-\overline{m}_{\text{merged}})^{2}}{\sigma^{2}_{m_{\text{merged}}}}+\frac{(\Delta
m_{\text{VLQ}}-\overline{\Delta m}_{\text{VLQ}})^{2}}{\sigma^{2}_{\Delta
m_{\text{VLQ}}}}.$ (3)

The means ($\overline{m}$ and $\overline{\Delta m}_{\text{VLQ}}$) and standard
deviations ($\sigma_{m}$ and $\sigma_{\Delta m_{\text{VLQ}}}$) of the
parameters used in these expressions are determined from simulated signal
events in which the jets are matched to the generator-level quarks and
/bosons. These quantities are derived separately for each jet multiplicity,
but do not depend on the simulated signal mass. For each parameter, the
central core of the distribution is fit with a Gaussian function, whose mean
and standard deviation are then used as the parameters in the expressions for
$\chi^{2}_{\text{mod}}$. As the distribution of the merged Higgs boson mass is
asymmetrical, two Gaussian functions are separately fit above and below the
peak of the distribution. Since the underlying distributions used in these
expressions have non-Gaussian tails and are in some cases asymmetric, the
values of $\chi^{2}_{\text{mod}}$ are not exactly distributed as a $\chi^{2}$
variable. However, the difference is small, and $\chi^{2}_{\text{mod}}$ is
only used to select events, so these deviations do not affect the analysis.
Choosing the jet combination that has the lowest value of
$\chi^{2}_{\text{mod}}$ gives a high probability of identifying the correct
jet combination, and allows $m_{\text{VLQ}}$ to be reconstructed. In
simulation, this can then be compared with the generated mass $m_{\PB}$. This
is indicated in Fig. 2, which shows the average value of the reconstructed VLQ
mass for the jet combination with the lowest $\chi^{2}_{\text{mod}}$ for
simulated signal events with $m_{\PB}=1200\GeV$. In most cases, the VLQ mass
is correctly reconstructed. We observe that the reconstructed mass
distribution is consistently peaked at a value about 5% lower than the
generated mass, for all generated signal mass values. The low-side tail is due
to the presence of incorrectly reconstructed events.

Figure 2: Distributions of $m_{\text{VLQ}}$ for simulated signal events with a
generated VLQ mass $m_{\PB}=1200\GeV$. A requirement of
$\chi^{2}_{\text{mod}}/\text{ndf}<2$ is applied to the events. Mass
distributions for 4-jet (left), 5-jet (center), and 6-jet (right) events are
shown for the three event modes: $\PQb\PH\PQb\PH$ (upper row),
$\PQb\PH\PQb\PZ$ (middle row), and $\PQb\PZ\PQb\PZ$ (lower row).

Figure 3 shows the distributions of $\chi^{2}_{\text{mod}}/\text{ndf}$, where
ndf is the number of degrees of freedom, for the best jet combination (, the
combination with the lowest $\chi^{2}_{\text{mod}}$), from simulated 1200VLQ
signal events, at each jet multiplicity. Each $\chi^{2}_{\text{mod}}$
expression has three degrees of freedom, one for each term. The distributions
for the data are also shown for comparison. In these plots, the simulated
signal and data distributions are normalized to the same integral value within
the displayed $\chi^{2}_{\text{mod}}$ range. This figure demonstrates that
requiring a small $\chi^{2}_{\text{mod}}$ value for the best jet combination
provides an effective method for removing background.

Figure 3: Distribution of $\chi^{2}_{\text{mod}}/\text{ndf}$ for the best jet
combination for simulated 1200VLQ events (red histogram) and data (black
points), for 4-jet (left), 5-jet (center), and 6-jet events (right). The
simulated signal events and data events are normalized to the same integral
value within the displayed $\chi^{2}_{\text{mod}}$ range.

The $\chi^{2}_{\text{mod}}$ value is also used to select the event mode. There
are three possible decays of the bottom-type VLQ: $\PB\to\PQb\PH$,
$\PB\to\PQb\PZ$, and $\PB\to\PQt\PW$. This results in six possible modes for
the $\PB\PAB$ pair production events: $\PQb\PH\PQb\PH$, $\PQb\PH\PQb\PZ$,
$\PQb\PZ\PQb\PZ$, $\PQt\PW\PQb\PH$, $\PQt\PW\PQb\PZ$, and $\PQt\PW\PQt\PW$.
The latter three modes involve one or two decays of a VLQ to a quark and a
boson. These events either have a jet multiplicity greater than six, or
contain leptons and missing transverse energy from the decays. Although this
analysis is not optimized for sensitivity to these events, events with
$\PB\to\PQt\PW$, if present, have some probability to be selected as one of
the other three event modes and can affect the sensitivity of the analysis.
These events are included in the signal simulation and are added according to
their reconstruction efficiency. For events that satisfy the requirement and
that are categorized as either 4-, 5-, or 6-jet multiplicity events, the
$\chi^{2}_{\text{mod}}$ described above is calculated for each of the three
event modes: $\PQb\PH\PQb\PH$, $\PQb\PH\PQb\PZ$, or $\PQb\PZ\PQb\PZ$, and the
mode of the event is selected as the one that has the best
$\chi^{2}_{\text{mod}}$ value. Events are categorized by their jet
multiplicity and their reconstructed mode, regardless of the underlying decay
mode for simulated signal events.

Before examining the data in the potential signal region, the event selection
parameters (the jet tagging parameters and $\chi^{2}_{\text{mod}}$) are
optimized. This optimization is performed by varying the parameters and
selecting the values that maximize the sensitivity to a 1600VLQ signal. The
mass of 1600is chosen because it is the point with maximum sensitivity of the
analysis; however, the optimized parameters are largely independent of the
point chosen. The optimized jet tagging parameters are described in Section
0.6, and the optimized $\chi^{2}_{\text{mod}}/\text{ndf}$ values are shown
below in Table 0.7. With the optimized selection, the overall signal
efficiency measured in simulation for a generated VLQ mass of 1600is
approximately 5% in the $\mathcal{B}(\PB\to\PQb\PZ)=100\%$ scenario,
increasing to 10% for $\mathcal{B}(\PB\to\PQb\PH)=100\%$.

Optimized values of the $\chi^{2}_{\text{mod}}/\text{ndf}$ selection as a
function of jet multiplicity and event mode. ccccccc Jet multiplicity
Event mode 4 5 6
$\PQb\PH\PQb\PH$ 5.5 2 2.75
$\PQb\PH\PQb\PZ$ 2.75 2.5 2.5
$\PQb\PZ\PQb\PZ$ 2 2 2.25

Figure 4: Distributions of $m_{\text{VLQ}}$ for the jet combination with the
lowest $\chi^{2}_{\text{mod}}$ in 4-jet (left), 5-jet (center), and 6-jet
(right) multiplicity events. The red lines show the exponential fit in the
range 1000–2000. The lower panels show the fractional difference,
$(\text{data}-\text{fit})/\text{fit}$.

##  0.8 Background estimation

The expected background is estimated in a low-mass sideband region in data,
using the ratio of the number of events passing the tag requirements to the
number before these requirements are applied. The estimation is done
separately for each of the three event modes, jet multiplicities, and three
data-taking years, for a total of 27 cases.

Figure 4 shows the distribution of $m_{\text{VLQ}}$ for the jet combination
with the lowest $\chi^{2}_{\text{mod}}$ for each of the three jet
multiplicities. All events shown in this plot are required to pass a selection
of $\chi^{2}_{\text{mod}}/\text{ndf}<4$. The falloff in the distribution at
lower masses is due to the $\HT>1350\GeV$ requirement. The distributions are
then fit with an exponential function for VLQ candidate masses greater than
1000; in all three cases, the function (shown by the red line) agrees with the
data. An $F$-test [50] shows that a more complex model, namely an exponential
plus constant background, offers no significant improvement over the
exponential distribution. The lower plots show the fractional difference
between the data and the fit. At this stage, since there is no requirement
made on jet tagging, the ratio of background to signal event acceptance is
more than two orders of magnitude larger than after jet tagging, so the fits
are insensitive to any possible signal events in the data.

The background jet-tagged fraction (BJTF) is the fraction of background events
that remain after the jet tagging requirements, as described in Section 0.5,
are applied. Since the BJTF for events with $m_{\text{VLQ}}>1000\GeV$ could be
biased due to signal events that might be in the data, the BJTF is initially
determined only for events in which $m_{\text{VLQ}}$ is between 500 and 800,
which is below the current lower exclusion limit on the VLQ mass [18, 17].
Table 0.8 shows the BJTF for data events with $m_{\text{VLQ}}$ in the range
500–800for each of the three event modes and three jet multiplicities.

Values of the BJTF for data events with $m_{\text{VLQ}}$ in the range
500–800for each of the three event modes and three jet multiplicities. cccc
$\PQb\PH\PQb\PH$ $\PQb\PH\PQb\PZ$ $\PQb\PZ\PQb\PZ$
4 jets $0.0042\pm 0.0014$ $0.0019\pm 0.0004$ $0.0025\pm 0.0004$
5 jets $0.0041\pm 0.0003$ $0.0036\pm 0.0002$ $0.0048\pm 0.0009$
6 jets $0.0019\pm 0.0002$ $0.0019\pm 0.0002$ $0.0020\pm 0.0005$

Because the jet tagging efficiency depends on the of the jet, the BJTF might
depend on the mass of the VLQ candidate, since events with greater VLQ mass
generally have higher jets. A control region is therefore used to determine
the VLQ mass dependence of the BJTF by offsetting the window of the
$\chi^{2}_{\text{mod}}$ selection. The signal $\chi^{2}_{\text{mod}}$ region
depends on the event mode and multiplicity, as determined by the optimization
procedure described in Section 0.7, but in all cases is at most
$\chi^{2}_{\text{mod}}/\text{ndf}<5.5$. A control region is defined by using a
region of $12<\chi^{2}_{\text{mod}}/\text{ndf}<48$. Figure 5 shows the mass
dependence of the BJTF for data events in the $\chi^{2}_{\text{mod}}$ control
region. A first-order polynomial fit is used to determine the BJTF mass
dependence. Another $F$-test shows that there is no improvement for a second-
order polynomial fit compared to a first-order one, and also that the first-
order polynomial fit performs better than a constant fit. A systematic
uncertainty is assigned by comparing the first-order polynomial fit to an
exponential fit, and using the average difference of these two fits over the
mass range as the uncertainty. This covers the uncertainty due to the choice
of the BJTF shape.

Figure 5: Dependence of the BJTF on $m_{\text{VLQ}}$ in the control region
$12<\chi^{2}_{\text{mod}}/\text{ndf}<48$, for 4-jet (left column), 5-jet
(center column), and 6-jet (right column) multiplicities, and for the
$\PQb\PH\PQb\PH$ (upper row), $\PQb\PH\PQb\PZ$ (middle row), and
$\PQb\PZ\PQb\PZ$ (lower row) event modes. The data are shown as black points
with vertical error bars, and the linear fit and associated uncertainty are
shown as a solid red line and the shaded red band.

In order to validate that the control region used has the same BJTF behavior
as the signal region, we perform a test where the BJTF in the low VLQ mass
range (500–800) is plotted as a function of
$\chi^{2}_{\text{mod}}/\text{ndf}$, in twelve equally spaced regions for
$\chi^{2}_{\text{mod}}/\text{ndf}$ from 0 to 48. This is shown in Fig. 6. The
slope of this plot is consistent with zero, indicating no statistically
significant dependence on $\chi^{2}_{\text{mod}}/\text{ndf}$.

Figure 6: Dependence of the BJTF on $\chi^{2}_{\text{mod}}/\text{ndf}$ in the
low-mass (500–800) VLQ region, for 4-jet (left column), 5-jet (center column),
and 6-jet (right column) multiplicities, and for the $\PQb\PH\PQb\PH$ (upper
row), $\PQb\PH\PQb\PZ$ (middle row), and $\PQb\PZ\PQb\PZ$ (lower row) event
modes. The data are shown as black points with vertical error bars, and the
linear fit and associated uncertainty are shown as a solid red line and the
shaded red band.

Figure 7 shows the two-dimensional dependence of the BJTF in data on
$m_{\text{VLQ}}$ and $\chi^{2}_{\text{mod}}/\text{ndf}$, and Fig. 8 shows the
corresponding distributions for simulated VLQ signal events with a generated
VLQ mass of 1200. The signal region is indicated in these plots by the red
rectangle, and is excluded from the data plots.

Figure 7: Dependence of the BJTF on $m_{\text{VLQ}}$ and the best
$\chi^{2}_{\text{mod}}/\text{ndf}$ in data events, for 4-jet (left column),
5-jet (center column), and 6-jet (right column) multiplicities, and for the
$\PQb\PH\PQb\PH$ (upper row), $\PQb\PH\PQb\PZ$ (middle row), and
$\PQb\PZ\PQb\PZ$ (lower row) event modes. The red box indicates the signal
region, which is excluded from these plots.

Figure 8: Dependence of the BJTF on $m_{\text{VLQ}}$ and the best
$\chi^{2}_{\text{mod}}/\text{ndf}$ in simulated VLQ signal events with
$m_{\PB}=1200\GeV$, for 4-jet (left column), 5-jet (center column), and 6-jet
(right column) multiplicities, and for the $\PQb\PH\PQb\PH$ (upper row),
$\PQb\PH\PQb\PZ$ (middle row), and $\PQb\PZ\PQb\PZ$ (lower row) event modes.
The red box indicates the signal region.

The final estimate of the number of background events $n_{\mathrm{b}}$ as a
function of VLQ mass $m$ is given by the following expression:

$n_{\mathrm{b}}(m)=n(m)\varepsilon_{0}\frac{\varepsilon(m)}{(\int_{500\GeV}^{800\GeV}\varepsilon(m^{\prime})\,\rd
m^{\prime})/(300\GeV)},$ (4)

where $n(m)$ is the number of candidates as a function of $m_{\text{VLQ}}$
before jet tagging for candidates passing the $\chi^{2}_{\text{mod}}$
selection shown in Fig. 4, $\varepsilon_{0}$ is the BJTF at low VLQ mass as
shown in Table 0.8, and the last factor accounts for the potential mass
dependence of the BJTF, with $\varepsilon(m)$ the distribution of the BJTF as
a function of mass, as shown in Fig. 5; the factor of 300is to normalize over
the range considered. The final estimate is also validated by comparison with
the region $8<\chi^{2}_{\text{mod}}/\text{ndf}<12$ and with simulated events.

##  0.9 Systematic uncertainties

We consider two types of systematic uncertainties, those that are common to
all event modes and jet multiplicities, and those that depend on the
particular channel. The uncertainties in the first category are listed in
Table 0.9; these are the integrated luminosity, trigger efficiency, and the
choice of fit function for the dependence of the BJTF on VLQ mass. The
integrated luminosities of the 2016, 2017, and 2018 data-taking periods are
individually known with uncertainties in the range 2.3–2.5% [24, 25, 26],
while the total 2016–2018 integrated luminosity has an uncertainty of 1.8%,
the improvement in precision reflecting the (uncorrelated) time evolution of
some systematic effects. The uncertainty associated with the choice of fit
function for the $m_{\text{VLQ}}$ dependence of the BJTF is determined by
finding the average difference between the fit functions used for each mode
and multiplicity combination, and using the maximum one as the common
uncertainty for all modes and multiplicities. Table 0.9 also indicates whether
an uncertainty affects the signal efficiency or the background estimate.

Systematic uncertainties common to all three event modes and all three jet
multiplicities. All uncertainties listed here are rate uncertainties, meaning
they affect only the normalization. lcc Type Signal/Background Uncertainty
Integrated luminosity Signal 1.8%
Trigger efficiency Signal 0.02%
Choice of fit function Background 4.9%

The uncertainties that depend on event mode and jet multiplicity are those due
to the background estimation, jet tag scale factors, jet energy resolution and
scale, choice of PDF, and pileup.

There are several sources of uncertainty in the background estimation,
corresponding to the three terms in Eq. 4. The first uncertainty arises from
the exponential fit to the distribution $n(m)$, the number of events as a
function of mass before jet tagging is applied. The uncertainties in the fit
parameters $p_{0}$ and $p_{1}$ are used to determine the uncertainty in the
fit value for a given mass. The second is the uncertainty in the BJTF
determined for low-mass VLQ candidates, $\varepsilon_{0}$, as shown in Table
0.8. Finally, the third uncertainty arises from the third term, to account for
a potential mass dependence of the BJTF, and is obtained from the
uncertainties in the fit parameters, as in the first case.

The efficiencies for jet tagging are measured in simulated events and then
corrected to data events using a data-to-simulation scale factor. The
uncertainty in this scale factor is propagated to the signal reconstruction
efficiency by varying the scale factors within their uncertainties [39]. The
uncertainties due to the scale factors for jet energy scale and resolution
[21] are determined similarly. The uncertainty due to the choice of PDF
weighting is calculated from a set of 100 weights selected from the NNPDF3.0
distribution, following the prescription in Ref. [51]. The pileup
uncertainties are due to a 4.6% systematic uncertainty in the $\Pp\Pp$
inelastic cross section.

Table 0.9 summarizes these uncertainties, and indicates whether they affect
the signal efficiency or the background estimate, and whether the uncertainty
affects the overall rate or the shape of the mass distribution. For the PDF
systematic uncertainties, the values refer only to the event acceptance rate.
There is in addition an uncertainty in the VLQ pair production cross section.
This uncertainty depends only weakly on VLQ mass and an average value of 6% is
used for all masses [34].

Table of systematic uncertainties for each event mode and jet multiplicity.
The reported values indicate the uncertainty in the event yield in a
$\pm$75window about the signal peak for a generated signal mass
$m_{\PB}=1600\GeV$. lccccc Type Signal/Background Rate/Shape 4 jets 5 jets 6
jets

$\PQb\PH\PQb\PH$ event mode
Background fit $p_{0}$ Background Shape 59% 14% 13%
Background fit $p_{1}$ Background Shape 78% 18% 16%
BJTF $m$ dependence $p_{0}$ Background Shape 1.3% 5.9% 4.5%
BJTF $m$ dependence $p_{1}$ Background Shape 19% 25% 17%
Low-mass BJTF Background Rate 34% 9.7% 11%
Jet tag scale factors Signal Shape 16% 15% 17%
Jet energy scale Signal Shape 4.0% 5.3% 6.4%
Jet energy resolution Signal Shape 2.4% 1.5% 1.6%
Pileup Signal Shape 28% 28% 27%
PDF Signal Rate 1.5% 1.5% 1.5%
$\PQb\PH\PQb\PZ$ event mode
Background fit $p_{0}$ Background Shape 21% 12% 10%
Background fit $p_{1}$ Background Shape 21% 14% 12%
BJTF $m$ dependence $p_{0}$ Background Shape 2.1% 7.7% 3.5%
BJTF $m$ dependence $p_{1}$ Background Shape 21% 30% 27%
Low-mass BJTF Background Rate 22% 7.7% 11%
Jet tag scale factors Signal Shape 15% 13% 17%
Jet energy scale Signal Shape 4.9% 5.7% 5.1%
Jet energy resolution Signal Shape 1.8% 2.7% 3.2%
Pileup Signal Shape 33% 28% 21%
PDF Signal Rate 1.6% 1.5% 1.5%
$\PQb\PZ\PQb\PZ$ event mode
Background fit $p_{0}$ Background Shape 26% 17% 24%
Background fit $p_{1}$ Background Shape 28% 21% 32%
BJTF $m$ dependence $p_{0}$ Background Shape 3.7% 0.6% 11%
BJTF $m$ dependence $p_{1}$ Background Shape 15% 7.8% 21%
Low-mass BJTF Background Rate 16% 19% 25%
Jet tag scale factors Signal Shape 8.9% 8.0% 11%
Jet energy scale Signal Shape 4.0% 2.9% 1.6%
Jet energy resolution Signal Shape 2.5% 2.5% 3.2%
Pileup Signal Shape 28% 28% 10%
PDF Signal Rate 1.5% 1.5% 1.5%

##  0.10 Results

Figure 9 shows the distribution of the reconstructed VLQ mass, after the
optimized selections described in Section 0.7 have been applied, for data, the
expected background, and for simulated signal events with a VLQ mass of 1200,
1400, 1600, and 1800and $\mathcal{B}(\PB\to\PQb\PH)=100\%$. The signal
distributions are normalized to the expected number of events as determined by
the VLQ production cross section. No statistically significant excess of data
over the background expectation is observed; the largest difference across all
nine mass points and branching fraction scenarios is slightly less than
$2\sigma$.

Figure 9: Data (black points), expected background (solid blue histogram), and
expected background plus a VLQ signal for different VLQ masses (colored
lines), for 4-jet (left column), 5-jet (center column), and 6-jet (right
column) multiplicities and for $\PQb\PH\PQb\PH$ (upper row), $\PQb\PH\PQb\PZ$
(middle row), and $\PQb\PZ\PQb\PZ$ (lower row) event modes. For the signal,
$\mathcal{B}(\PB\to\PQb\PH)=100\%$ is assumed. The hatched regions for the
background and background plus signal distributions indicate the systematic
uncertainties. All three data-taking years are combined.

We proceed to set exclusion limits on the VLQ mass as a function of the
branching fractions. The signal extraction procedure is based on a binned
maximum likelihood fit. All systematic uncertainties are incorporated into the
fit as nuisance parameters, where the effect of each systematic uncertainty is
included as a lognormal probability distribution per bin. A limit at 95% is
calculated using the method [52, 53] using the profile likelihood test
statistic [54] with the asymptotic limit approximation. Figure 10 shows the
final expected and observed limits on the VLQ mass as a function of
$\mathcal{B}(\PB\to\PQb\PH)$ and $\mathcal{B}(\PB\to\PQb\PZ)$, after all of
the individual jet multiplicities and event modes have been combined. Points
for which the exclusion limit is less than 1000are not shown.

Figure 11 shows the expected limits at 95% on the cross section of VLQ pair
production as a function of VLQ mass assuming three different branching
fraction combinations: $\mathcal{B}(\PB\to\PQb\PH)=100\%$,
$\mathcal{B}(\PB\to\PQb\PZ)=100\%$, and
$\mathcal{B}(\PB\to\PQb\PH)=\mathcal{B}(\PB\to\PQb\PZ)=50\%$. The observed
limits at 95% are: 1570in the 100% $\PQb\PH$ case, 1390in the 100% $\PQb\PZ$
case, and 1450in the 50% $\PQb\PH$ plus 50% $\PQb\PZ$ case. In the fully
$\PB\to\PQb\PH$ and $\PB\to\PQb\PZ$ modes, as well as the mixed
$\PQb\PH\PQb\PZ$ mode, where this analysis is most sensitive, these limits
represent significant improvements over previously published VLQ limits (1010,
1070, and 1025respectively), extending the existing limits by several hundred
. These improvements can be attributed to the use of the
$\chi^{2}_{\text{mod}}/\text{ndf}$ method, which allows the hadronic final
state to be fully reconstructed, as well as to the increased size of the data
sample.

Figure 10: Expected (upper) and observed (lower) limits on the VLQ mass at 95%
as a function of the branching fractions $\mathcal{B}(\PB\to\PQb\PH)$ and
$\mathcal{B}(\PB\to\PQb\PZ)$.

Figure 11: The 95% confidence limit on the cross section for VLQ pair
production as a function of VLQ mass for three branching fraction hypotheses:
$\mathcal{B}(\PB\to\PQb\PH)=100\%$ (upper left),
$\mathcal{B}(\PB\to\PQb\PZ)=100\%$ (upper right), and
$\mathcal{B}(\PB\to\PQb\PH)=\mathcal{B}(\PB\to\PQb\PZ)=50\%$ (lower). The
solid black line indicates the observed limit and the dashed line indicates
the expected limit with 1 sigma (green band) and 2 sigma (yellow band)
uncertainties. The theoretical cross section and its uncertainty are shown as
the red line and pale red band; the band is only slightly visible outside the
line.

##  0.11 Summary

This paper describes a search for bottom-type, vector-like quark (VLQ) pair
production in data collected by the CMS detector in 2016–2018 at
$\sqrt{s}=13\TeV$, where the VLQ decays into a or quark and either a Higgs
boson or a boson. The analysis targets the fully hadronic $\PB\to\PQb\PH$ and
$\PB\to\PQb\PZ$ decays by tagging jets and using a modified $\chi^{2}$ metric
to reconstruct the event. Different jet multiplicity categories were used to
account for the fact that Higgs or boson decays can produce either two
distinct jets or, if highly Lorentz boosted, a single merged jet. Backgrounds
were estimated from a region of low VLQ mass and extrapolated into the signal
region using a modified $\chi^{2}$ control region. Limits were set on the VLQ
mass at 95% confidence level as a function of the branching fractions for
$\PB\to\PQb\PH$ and $\PB\to\PQb\PZ$. Compared to previous measurements [18,
17], limits on the VLQ mass have been increased from 1010 to 1570in the
$\mathcal{B}(\PB\to\PQb\PH)=100\%$ case, from 1070 to 1390in the
$\mathcal{B}(\PB\to\PQb\PZ)=100\%$ case, and from 1025 to 1450in the
$\mathcal{B}(\PB\to\PQb\PH)=\mathcal{B}(\PB\to\PQb\PZ)=50\%$ case.

###### 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 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 and the CMS detector 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); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RIF
(Cyprus); SENESCYT (Ecuador); MoER, ERC IUT, 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,
752730, and 765710 (European Union); the Leventis Foundation; the A.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; 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 HOMING PLUS program of
the Foundation for Polish Science, cofinanced from European Union, Regional
Development Fund, the Mobility Plus program of the Ministry of Science and
Higher Education, the National Science Center (Poland), contracts Harmonia
2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543, 2014/15/B/ST2/03998, and
2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406; the National Priorities
Research Program by Qatar National Research Fund; the Ministry of Science and
Higher Education, project no. 02.a03.21.0005 (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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##  .12 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, J. Erö, A. Escalante Del Valle, R.
Frühwirth1, M. Jeitler1, N. Krammer, L. Lechner, D. Liko, T. Madlener, I.
Mikulec, F.M. Pitters, N. Rad, 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, J. Suarez Gonzalez Universiteit
Antwerpen, Antwerpen, Belgium
M.R. Darwish2, E.A. De Wolf, D. Di Croce, X. Janssen, T. Kello3, A. Lelek, M.
Pieters, H. Rejeb Sfar, H. Van Haevermaet, P. Van Mechelen, S. Van Putte, N.
Van Remortel Vrije Universiteit Brussel, Brussel, Belgium
F. Blekman, E.S. Bols, S.S. Chhibra, J. D’Hondt, J. De Clercq, D. Lontkovskyi,
S. Lowette, I. Marchesini, S. Moortgat, A. Morton, Q. Python, 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, 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, M. Niedziela, C. Roskas,
K. Skovpen, M. Tytgat, W. Verbeke, B. Vermassen, M. Vit Université Catholique
de Louvain, Louvain-la-Neuve, Belgium
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, M. Medina
Jaime8, 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
M. Bonchev, A. Dimitrov, T. Ivanov, L. Litov, B. Pavlov, P. Petkov, A. Petrov
Beihang University, Beijing, China
W. Fang3, Q. Guo, H. Wang, L. Yuan Department of Physics, Tsinghua University,
Beijing, China
M. Ahmad, Z. Hu, Y. Wang Institute of High Energy Physics, Beijing, China
E. Chapon, G.M. Chen9, H.S. Chen9, M. Chen, T. Javaid9, A. Kapoor, D. Leggat,
H. Liao, Z. Liu, R. Sharma, A. Spiezia, J. Tao, J. Thomas-wilsker, J. Wang, H.
Zhang, S. Zhang9, 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 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. Starodumov10, 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. Finger11, M. Finger Jr.11, 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
S. Abu Zeid12, S. Khalil13, E. Salama14,12 Center for High Energy Physics
(CHEP-FU), Fayoum University, El-Fayoum, Egypt
M.A. Mahmoud, Y. Mohammed15 National Institute of Chemical Physics and
Biophysics, Tallinn, Estonia
S. Bhowmik, A. Carvalho Antunes De Oliveira, R.K. Dewanjee, K. Ehataht, M.
Kadastik, 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-
Navarro16, M. Titov, G.B. Yu Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole
Polytechnique, Institut Polytechnique de Paris, Paris, 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. Agram17, J. Andrea, D. Bloch, G. Bourgatte, J.-M. Brom, E.C. Chabert, C.
Collard, J.-C. Fontaine17, D. Gelé, U. Goerlach, C. Grimault, A.-C. Le Bihan,
P. Van Hove Université de Lyon, Université Claude Bernard Lyon 1, CNRS-IN2P3,
Institut de Physique Nucléaire de Lyon, 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, L. Torterotot, G. Touquet, M. Vander Donckt, S. Viret Georgian
Technical University, Tbilisi, Georgia
A. Khvedelidze11, Z. Tsamalaidze11 RWTH Aachen University, I. Physikalisches
Institut, Aachen, Germany
L. Feld, K. Klein, M. Lipinski, D. Meuser, A. Pauls, M. Preuten, 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 Ahmad18, O. Hlushchenko, T. Kress, A. Nowack, C.
Pistone, O. Pooth, D. Roy, H. Sert, A. Stahl19, 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. Borras20, V. Botta, D.
Brunner, A. Campbell, A. Cardini, P. Connor, S. Consuegra Rodríguez, V.
Danilov, A. De Wit, M.M. Defranchis, L. Didukh, D. Domínguez Damiani, G.
Eckerlin, D. Eckstein, T. Eichhorn, L.I. Estevez Banos, E. Gallo21, A. Geiser,
A. Giraldi, A. Grohsjean, M. Guthoff, A. Harb, A. Jafari22, N.Z. Jomhari, H.
Jung, A. Kasem20, M. Kasemann, H. Kaveh, C. Kleinwort, J. Knolle, D. Krücker,
W. Lange, T. Lenz, J. Lidrych, K. Lipka, W. Lohmann23, R. Mankel, I.-A.
Melzer-Pellmann, J. Metwally, A.B. Meyer, M. Meyer, M. Missiroli, 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, A.
Ebrahimi, 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, S. Schumann, J.
Schwandt, D. Schwarz, J. Sonneveld, H. Stadie, G. Steinbrück, 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, A.
Gottmann, F. Hartmann19, C. Heidecker, U. Husemann, M.A. Iqbal, I. Katkov24,
P. Keicher, R. Koppenhöfer, S. Maier, M. Metzler, S. Mitra, D. Müller, Th.
Müller, M. Musich, G. Quast, K. Rabbertz, J. Rauser, D. Savoiu, D. Schäfer, M.
Schnepf, M. Schröder, D. Seith, I. Shvetsov, H.J. Simonis, R. Ulrich, M.
Wassmer, M. Weber, R. Wolf, S. Wozniewski 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, 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, K. Manitara,
N. Manthos, I. Papadopoulos, J. Strologas MTA-ELTE Lendület CMS Particle and
Nuclear Physics Group, Eötvös Loránd University, Budapest, Hungary
M. Bartók25, 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
G. Bencze, C. Hajdu, D. Horvath28, F. Sikler, V. Veszpremi, G.
Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear Research ATOMKI,
Debrecen, Hungary
S. Czellar, J. Karancsi25, J. Molnar, Z. Szillasi, D. Teyssier Institute of
Physics, University of Debrecen, Debrecen, Hungary
P. Raics, Z.L. Trocsanyi, B. Ujvari Eszterhazy Karoly University, Karoly
Robert Campus, Gyongyos, Hungary
T. Csorgo, F. Nemes, 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. Bahinipati29, D. Dash, C. Kar, P. Mal, T. Mishra, V.K. Muraleedharan Nair
Bindhu, A. Nayak30, D.K. Sahoo29, N. Sur, S.K. Swain Panjab University,
Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, G. Chaudhary, S. Chauhan, N. Dhingra31, R.
Gupta, A. Kaur, S. Kaur, P. Kumari, M. Meena, K. Sandeep, S. Sharma, 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. Bharti32, R. Bhattacharya, S. Bhattacharya, D. Bhowmik, S. Dutta, S. Ghosh,
B. Gomber33, M. Maity34, S. Nandan, P. Palit, P.K. Rout, G. Saha, B. Sahu, S.
Sarkar, M. Sharan, B. Singh32, S. Thakur32 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. Kumar, K. Naskar35, P.K. Netrakanti, L.M. Pant, P. Shukla Tata
Institute of Fundamental Research-A, Mumbai, India
T. Aziz, M.A. Bhat, S. Dugad, R. Kumar Verma, 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. Bakhshiansohi36, M. Zeinali37 Institute for Research in Fundamental
Sciences (IPM), Tehran, Iran
S. Chenarani38, 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,39, 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, C. Grandia, L. Guiduccia,b, F.
Iemmia,b, S. Lo Meoa,40, 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,41, S. Costaa,b, A. Di Mattiaa, R. Potenzaa,b, A. Tricomia,b,41,
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,19,
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, D. Menascea, F. Montia,b,
L. Moronia, M. Paganonia,b, D. Pedrinia, S. Ragazzia,b, T. Tabarelli de
Fatisa,b, D. Valsecchia,b,19, 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,19, P. Paoluccia,19, B. Rossia, C.
Sciaccaa,b, E. Voevodinaa,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,42, 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
M. Biasinia,b, 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
K. Androsova, P. Azzurria, G. Bagliesia, V. Bertacchia,c, L. Bianchinia, T.
Boccalia, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, M.R. Di Domenicoa,b, S.
Donatoa, L. Gianninia,c, 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, 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, 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, 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, S. Ha, 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, K. 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
H.D. Yoo Sungkyunkwan University, Suwon, Korea
Y. Choi, C. Hwang, Y. Jeong, H. Lee, Y. Lee, I. Yu American University of the
Middle East (AUM), Dasman, Kuwait
Y. Maghrbi Riga Technical University, Riga, Latvia
V. Veckalns43 Vilnius University, Vilnius, Lithuania
A. Juodagalvis, A. Rinkevicius, G. Tamulaitis 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 Cruz44,
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
J. Eysermans, I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada Universidad
Autónoma de San Luis Potosí, San Luis Potosí, Mexico
A. Morelos Pineda 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, A. Byszuk45, K. Doroba, A. Kalinowski, M. Konecki, J.
Krolikowski, M. Olszewski, 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, P. Bunin, M. Gavrilenko, I. Golutvin, I. Gorbunov, A. Kamenev,
V. Karjavine, A. Lanev, A. Malakhov, V. Matveev46,47, 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. Kim48, E. Kuznetsova49, 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. Nikitenko50, 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
O. Bychkova, M. Chadeeva51, D. Philippov, E. Popova, V. Rusinov 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. Dubinin52, L. Dudko, A. Ershov, A.
Gribushin, V. Klyukhin, O. Kodolova, I. Lokhtin, S. Obraztsov, M. Perfilov, V.
Savrin Novosibirsk State University (NSU), Novosibirsk, Russia
V. Blinov53, T. Dimova53, L. Kardapoltsev53, I. Ovtin53, Y. Skovpen53
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. Adzic54, P. Cirkovic, 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, J.A. Brochero Cifuentes, 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, A. García Alonso, 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, A. Triossi, 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, S. Sanchez Cruz, A. Trapote Instituto de Física de
Cantabria (IFCA), CSIC-Universidad de Cantabria, Santander, Spain
I.J. Cabrillo, A. Calderon, B. Chazin Quero, J. Duarte Campderros, M.
Fernandez, P.J. Fernández Manteca, 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, I. Vila, J.M. Vizan Garcia University
of Colombo, Colombo, Sri Lanka
MK Jayananda, B. Kailasapathy55, 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, B. Akgun, E. Auffray, G. Auzinger, J. Baechler, P.
Baillon, A.H. Ball, D. Barney, J. Bendavid, N. Beni, M. Bianco, A. Bocci, E.
Bossini, E. Brondolin, T. Camporesi, G. Cerminara, L. Cristella, D.
d’Enterria, A. Dabrowski, N. Daci, V. Daponte, A. David, A. De Roeck, M.
Deile, R. Di Maria, M. Dobson, M. Dünser, N. Dupont, A. Elliott-Peisert, N.
Emriskova, F. Fallavollita56, D. Fasanella, S. Fiorendi, A. Florent, G.
Franzoni, J. Fulcher, W. Funk, S. Giani, D. Gigi, K. Gill, F. Glege, L.
Gouskos, M. Guilbaud, D. Gulhan, 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, A. Massironi, F. Meijers, S. Mersi, E. Meschi, F. Moortgat, M.
Mulders, J. Niedziela, S. Orfanelli, L. Orsini, F. Pantaleo19, L. Pape, E.
Perez, M. Peruzzi, A. Petrilli, G. Petrucciani, A. Pfeiffer, M. Pierini, 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. Sphicas57, S. Summers, V.R. Tavolaro, D. Treille, A. Tsirou,
G.P. Van Onsem, A. Vartak, M. Verzetti, K.A. Wozniak, W.D. Zeuner Paul
Scherrer Institut, Villigen, Switzerland
L. Caminada58, W. Erdmann, R. Horisberger, Q. Ingram, H.C. Kaestli, D.
Kotlinski, U. Langenegger, 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, 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, M.L. Vesterbacka Olsson, R. Wallny,
D.H. Zhu Universität Zürich, Zurich, Switzerland
C. Amsler59, C. Botta, D. Brzhechko, M.F. Canelli, 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, K. Schweiger, Y. Takahashi National Central University, Chung-Li,
Taiwan
C. Adloff60, C.M. Kuo, W. Lin, A. Roy, T. Sarkar34, 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 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. Damarseckin61, Z.S. Demiroglu, F. Dolek, C. Dozen62, I.
Dumanoglu63, E. Eskut, G. Gokbulut, Y. Guler, E. Gurpinar Guler64, I. Hos65,
C. Isik, E.E. Kangal66, O. Kara, A. Kayis Topaksu, U. Kiminsu, G. Onengut, K.
Ozdemir67, A. Polatoz, A.E. Simsek, B. Tali68, U.G. Tok, S. Turkcapar, I.S.
Zorbakir, C. Zorbilmez Middle East Technical University, Physics Department,
Ankara, Turkey
B. Isildak69, G. Karapinar70, K. Ocalan71, M. Yalvac72 Bogazici University,
Istanbul, Turkey
I.O. Atakisi, E. Gülmez, M. Kaya73, O. Kaya74, Ö. Özçelik, S. Tekten75, E.A.
Yetkin76 Istanbul Technical University, Istanbul, Turkey
A. Cakir, K. Cankocak63, Y. Komurcu, S. Sen77 Istanbul University, Istanbul,
Turkey
F. Aydogmus Sen, S. Cerci68, B. Kaynak, S. Ozkorucuklu, D. Sunar Cerci68
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, 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, J. Taylor, A. Titterton Rutherford
Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev78, 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, A.
Bundock, V. Cepaitis, G.S. Chahal79, D. Colling, P. Dauncey, G. Davies, M.
Della Negra, G. Fedi, G. Hall, G. Iles, J. Langford, L. Lyons, A.-M. Magnan,
S. Malik, A. Martelli, V. Milosevic, J. Nash80, V. Palladino, M. Pesaresi,
D.M. Raymond, A. Richards, A. Rose, E. Scott, C. Seez, A. Shtipliyski, M.
Stoye, A. Tapper, K. Uchida, T. Virdee19, N. Wardle, S.N. Webb, D.
Winterbottom, A.G. Zecchinelli Brunel University, Uxbridge, United Kingdom
J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, C.K. Mackay, I.D. Reid, L.
Teodorescu, S. Zahid Baylor University, Waco, USA
S. Abdullin, A. Brinkerhoff, K. Call, B. Caraway, J. Dittmann, K. Hatakeyama,
A.R. Kanuganti, C. Madrid, B. McMaster, N. Pastika, S. Sawant, C. Smith, J.
Wilson Bethel University, St. Paul, Minneapolis, USA
S. Johnson 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, S.V. Gleyzer, C. Henderson, 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. Coubez20, D. Cutts, Y.t. Duh, M. Hadley, U. Heintz,
J.M. Hogan81, K.H.M. Kwok, E. Laird, G. Landsberg, K.T. Lau, J. Lee, M.
Narain, S. Sagir82, R. Syarif, E. Usai, W.Y. Wong, 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, G. Funk, F. Jensen, W.
Ko${}^{\textrm{\textdagger}}$, O. Kukral, R. Lander, M. Mulhearn, D. Pellett,
J. Pilot, M. Shi, D. Taylor, K. Tos, M. Tripathi, Y. Yao, F. Zhang University
of California, Los Angeles, USA
M. Bachtis, R. Cousins, A. Dasgupta, D. Hamilton, J. Hauser, M. Ignatenko, 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, M.I. Paneva, 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, D. Gilbert, V. Krutelyov, J. Letts, M. Masciovecchio, S. May, S.
Padhi, M. Pieri, V. Sharma, M. Tadel, 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, 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, J. Pata, 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, M. Sun, I.
Vorobiev University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, E. MacDonald, T. Mulholland, 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, A. Datta, A. Frankenthal, 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,
V.D. Elvira, J. Freeman, Z. Gecse, E. Gottschalk, L. Gray, D. Green, S.
Grünendahl, O. Gutsche, R.M. Harris, S. Hasegawa, 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, M.
Liu, T. Liu, J. Lykken, K. Maeshima, D. Mason, P. McBride, P. Merkel, S.
Mrenna, S. Nahn, V. O’Dell, V. Papadimitriou, K. Pedro, C. Pena52, O.
Prokofyev, F. Ravera, A. Reinsvold Hall, L. Ristori, B. Schneider, E. Sexton-
Kennedy, N. Smith, A. Soha, W.J. Spalding, L. Spiegel, S. Stoynev, J. Strait,
L. Taylor, S. Tkaczyk, N.V. Tran, L. Uplegger, E.W. Vaandering, H.A. Weber, A.
Woodard 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, S. Wang, 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. Elkafrawy12, M. Hohlmann, 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 The University
of Iowa, Iowa City, USA
M. Alhusseini, K. Dilsiz83, S. Durgut, R.P. Gandrajula, M. Haytmyradov, V.
Khristenko, O.K. Köseyan, J.-P. Merlo, A. Mestvirishvili84, A. Moeller, J.
Nachtman, H. Ogul85, Y. Onel, F. Ozok86, A. Penzo, C. Snyder, E. Tiras, J.
Wetzel, K. Yi87 Johns Hopkins University, Baltimore, USA
O. Amram, B. Blumenfeld, L. Corcodilos, M. Eminizer, A.V. Gritsan, S.
Kyriacou, P. Maksimovic, C. Mantilla, 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, A.
Mohammadi 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, G.Y. Jeng, 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, B. Allen, R. Bi, S. Brandt, W. Busza, I.A. Cali, Y. Chen, M.
D’Alfonso, G. Gomez Ceballos, M. Goncharov, P. Harris, D. Hsu, M. Hu, M.
Klute, D. Kovalskyi, J. Krupa, Y.-J. Lee, P.D. Luckey, B. Maier, A.C. Marini,
C. Mcginn, C. Mironov, S. Narayanan, X. Niu, C. Paus, D. Rankin, C. Roland, G.
Roland, Z. Shi, G.S.F. Stephans, K. Sumorok, 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, S. Chauhan, D.R. Claes, C. Fangmeier, L. Finco, F. Golf, J.R.
González Fernández, 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, C. Harrington, L. Hay, I. Iashvili, A.
Kharchilava, C. McLean, D. Nguyen, J. Pekkanen, S. Rappoccio, B. Roozbahani
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,
D.J. Karmgard, K. Lannon, N. Loukas, N. Marinelli, I. Mcalister, F. Meng, K.
Mohrman, Y. Musienko46, R. Ruchti, P. Siddireddy, S. Taroni, 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
P. Das, G. Dezoort, P. Elmer, B. Greenberg, N. Haubrich, S. Higginbotham, A.
Kalogeropoulos, G. Kopp, S. Kwan, D. Lange, M.T. Lucchini, J. Luo, D. Marlow,
K. Mei, I. Ojalvo, J. Olsen, C. Palmer, P. Piroué, D. Stickland, C. Tully
University of Puerto Rico, Mayaguez, USA
S. Malik, S. Norberg Purdue University, West Lafayette, USA
V.E. Barnes, R. Chawla, S. Das, L. Gutay, M. Jones, A.W. Jung, G. Negro, N.
Neumeister, C.C. Peng, S. Piperov, A. Purohit, H. Qiu, J.F. Schulte, M.
Stojanovic16, N. Trevisani, F. Wang, A. Wildridge, R. Xiao, W. Xie Purdue
University Northwest, Hammond, USA
T. Cheng, J. Dolen, N. Parashar Rice University, Houston, USA
A. Baty, S. Dildick, K.M. Ecklund, S. Freed, F.J.M. Geurts, M. Kilpatrick, A.
Kumar, W. Li, B.P. Padley, R. Redjimi, J. Roberts${}^{\textrm{\textdagger}}$,
J. Rorie, 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, R. Gambhir, A. Gandrakota, Y. Gershtein, E.
Halkiadakis, A. Hart, M. Heindl, E. Hughes, S. Kaplan, O. Karacheban23, I.
Laflotte, A. Lath, R. Montalvo, K. Nash, M. Osherson, P. Pajarillo, N.
Paladino, S. Salur, S. Schnetzer, S. Somalwar, R. Stone, S.A. Thayil, S.
Thomas, A. Venugopal, H. Wang University of Tennessee, Knoxville, USA
H. Acharya, A.G. Delannoy, S. Spanier Texas A&M University, College Station,
USA
O. Bouhali88, M. Dalchenko, A. Delgado, R. Eusebi, J. Gilmore, T. Huang, T.
Kamon89, H. Kim, S. Luo, S. Malhotra, R. Mueller, D. Overton, L. Perniè, 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, R. Janjam, 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, Y. Wang, E. Wolfe, F. Xia 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, D. Pinna, A.
Savin, V. Shang, V. Sharma, W.H. Smith, J. Steggemann, D. Teague, S. Trembath-
reichert, W. Vetens †: Deceased
1: Also at Vienna University of Technology, Vienna, Austria
2: Also at Institute of Basic and Applied Sciences, Faculty of Engineering,
Arab Academy for Science, Technology and Maritime Transport, 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 Universidade Federal de Pelotas, Pelotas, Brazil
9: Also at University of Chinese Academy of Sciences, Beijing, China
10: Also at Institute for Theoretical and Experimental Physics named by A.I.
Alikhanov of NRC ‘Kurchatov Institute’, Moscow, Russia
11: Also at Joint Institute for Nuclear Research, Dubna, Russia
12: Also at Ain Shams University, Cairo, Egypt
13: Also at Zewail City of Science and Technology, Zewail, Egypt
14: Also at British University in Egypt, Cairo, Egypt
15: Now at Fayoum University, El-Fayoum, Egypt
16: Also at Purdue University, West Lafayette, USA
17: Also at Université de Haute Alsace, Mulhouse, France
18: Also at Erzincan Binali Yildirim University, Erzincan, Turkey
19: Also at CERN, European Organization for Nuclear Research, Geneva,
Switzerland
20: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
21: Also at University of Hamburg, Hamburg, Germany
22: Also at Department of Physics, Isfahan University of Technology, Isfahan,
Iran, Isfahan, Iran
23: Also at Brandenburg University of Technology, Cottbus, Germany
24: Also at Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State
University, Moscow, Russia
25: Also at Institute of Physics, University of Debrecen, Debrecen, Hungary,
Debrecen, Hungary
26: Also at Physics Department, Faculty of Science, Assiut University, Assiut,
Egypt
27: Also at MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary, Budapest, Hungary
28: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary
29: Also at IIT Bhubaneswar, Bhubaneswar, India, Bhubaneswar, India
30: Also at Institute of Physics, Bhubaneswar, India
31: Also at G.H.G. Khalsa College, Punjab, India
32: Also at Shoolini University, Solan, India
33: Also at University of Hyderabad, Hyderabad, India
34: Also at University of Visva-Bharati, Santiniketan, India
35: Also at Indian Institute of Technology (IIT), Mumbai, India
36: Also at Deutsches Elektronen-Synchrotron, Hamburg, Germany
37: Also at Sharif University of Technology, Tehran, Iran
38: Also at Department of Physics, University of Science and Technology of
Mazandaran, Behshahr, Iran
39: Now at INFN Sezione di Bari a, Università di Bari b, Politecnico di Bari
c, Bari, Italy
40: Also at Italian National Agency for New Technologies, Energy and
Sustainable Economic Development, Bologna, Italy
41: Also at Centro Siciliano di Fisica Nucleare e di Struttura Della Materia,
Catania, Italy
42: Also at Università di Napoli ’Federico II’, NAPOLI, Italy
43: Also at Riga Technical University, Riga, Latvia, Riga, Latvia
44: Also at Consejo Nacional de Ciencia y Tecnología, Mexico City, Mexico
45: Also at Warsaw University of Technology, Institute of Electronic Systems,
Warsaw, Poland
46: Also at Institute for Nuclear Research, Moscow, Russia
47: Now at National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
48: Also at St. Petersburg State Polytechnical University, St. Petersburg,
Russia
49: Also at University of Florida, Gainesville, USA
50: Also at Imperial College, London, United Kingdom
51: Also at P.N. Lebedev Physical Institute, Moscow, Russia
52: Also at California Institute of Technology, Pasadena, USA
53: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia
54: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia
55: Also at Trincomalee Campus, Eastern University, Sri Lanka, Nilaveli, Sri
Lanka
56: Also at INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy,
Pavia, Italy
57: Also at National and Kapodistrian University of Athens, Athens, Greece
58: Also at Universität Zürich, Zurich, Switzerland
59: Also at Stefan Meyer Institute for Subatomic Physics, Vienna, Austria,
Vienna, Austria
60: Also at Laboratoire d’Annecy-le-Vieux de Physique des Particules,
IN2P3-CNRS, Annecy-le-Vieux, France
61: Also at Şırnak University, Sirnak, Turkey
62: Also at Department of Physics, Tsinghua University, Beijing, China,
Beijing, China
63: Also at Near East University, Research Center of Experimental Health
Science, Nicosia, Turkey
64: Also at Beykent University, Istanbul, Turkey, Istanbul, Turkey
65: Also at Istanbul Aydin University, Application and Research Center for
Advanced Studies (App. & Res. Cent. for Advanced Studies), Istanbul, Turkey
66: Also at Mersin University, Mersin, Turkey
67: Also at Piri Reis University, Istanbul, Turkey
68: Also at Adiyaman University, Adiyaman, Turkey
69: Also at Ozyegin University, Istanbul, Turkey
70: Also at Izmir Institute of Technology, Izmir, Turkey
71: Also at Necmettin Erbakan University, Konya, Turkey
72: Also at Bozok Universitetesi Rektörlügü, Yozgat, Turkey
73: Also at Marmara University, Istanbul, Turkey
74: Also at Milli Savunma University, Istanbul, Turkey
75: Also at Kafkas University, Kars, Turkey
76: Also at Istanbul Bilgi University, Istanbul, Turkey
77: Also at Hacettepe University, Ankara, Turkey
78: Also at School of Physics and Astronomy, University of Southampton,
Southampton, United Kingdom
79: Also at IPPP Durham University, Durham, United Kingdom
80: Also at Monash University, Faculty of Science, Clayton, Australia
81: Also at Bethel University, St. Paul, Minneapolis, USA, St. Paul, USA
82: Also at Karamanoğlu Mehmetbey University, Karaman, Turkey
83: Also at Bingol University, Bingol, Turkey
84: Also at Georgian Technical University, Tbilisi, Georgia
85: Also at Sinop University, Sinop, Turkey
86: Also at Mimar Sinan University, Istanbul, Istanbul, Turkey
87: Also at Nanjing Normal University Department of Physics, Nanjing, China
88: Also at Texas A&M University at Qatar, Doha, Qatar
89: Also at Kyungpook National University, Daegu, Korea, Daegu, Korea