B2G-18-003

$HeadURL$ $Id$

B2G-18-003

# Search for electroweak production of a vector-like quark using fully
hadronic final states

###### Abstract

A search is performed for electroweak production of a vector-like top quark
partner of charge $2/3$ in association with a top or bottom quark, using
proton-proton collision data at $\sqrt{s}=13$collected by the CMS experiment
at the LHC in 2016. The data sample corresponds to an integrated luminosity of
35.9. The search targets quarks over a wide range of masses and fractional
widths, decaying to a top quark and either a Higgs boson or a boson in fully
hadronic final states. The search is performed using two experimentally
distinct signatures that depend on whether or not each quark from the decays
of the top quark, Higgs boson, or boson produces an individual resolved jet.
Jet substructure, tagging, and kinematic variables are used to identify the
top quark and boson jets, and also to suppress the standard model backgrounds.
The data are found to be consistent with the expected backgrounds. Upper
limits at $95\%$ confidence level are set on the cross sections for quark-
mediated production of $\PQt\PH\mathrm{Q}\PQq$, $\PQt\PZ\mathrm{Q}\PQq$, and
their sum, where Q is the associated top or bottom heavy quark and is another
associated quark. The limits are given for each search signature for various
quark widths up to 30% of the quark mass, and are between 2pb and 20fb for
quark masses in the range 0.6–2.6. These results are significantly more
sensitive than prior searches for electroweak single production of
$\PQT\to\PQt\PH$ and represent the first constraints on $\PQT\to\PQt\PZ$ using
hadronic decays of the boson with this production mode.

##  0.1 Introduction

We report on a search for electroweak production of a new heavy quark of
charge 2/3 with nonchiral couplings, referred to as a vector-like quark.
Unlike the standard model (SM) chiral fermions, such particles do not acquire
their mass from a Yukawa coupling to the Higgs boson (). Many proposed
extensions of the SM contain vector-like quarks, which usually mix with the
top quark (). Such particles could have a role in stabilizing the Higgs boson
mass, and thus offer a potential solution to the hierarchy problem. Vector-
like quarks are discussed in detail in Refs. [1, 2, 3, 4] and have been the
subject of phenomenological studies in various frameworks including those of
Refs. [5, 6, 7, 8].

Much like the top quark, a vector-like top quark partner () could be produced
either in pairs, dominantly through the strong interaction, or singly, in
association with additional quarks through the electroweak interaction. The
quark could couple to $\PQb\PW$, $\PQt\PZ$, or $\PQt\PH$; this leads to the
corresponding quark decays and to the associated electroweak production from
processes such as those depicted in Fig. 1. The branching fractions and
dominant electroweak production processes depend on the particular model; many
models have substantial branching fractions to $\PQt\PZ$ or $\PQt\PH$
resulting in signatures that are of primary relevance to this paper.
Neglecting the corrections due to decay particle masses, the branching
fractions for the singlet model of Ref. [5] are 50% ($\PQb\PW$), 25%
($\PQt\PZ$), 25% ($\PQt\PH$), while for the ($\PQT\PQB$) doublet model of Ref.
[5], the $\PQt\PZ$ and $\PQt\PH$ branching fractions tend to be approximately
equal and depend on two mixing angles, $\theta^{u}_{R}$ and $\theta^{d}_{R}$,
with each branching fraction ranging from zero to 50%. Therefore specific
models can have branching fractions as large as 50% for $\PQt\PZ$ and 50% for
$\PQt\PH$.

Figure 1: Example Feynman diagrams for electroweak production of vector-like
quarks. Charged-current (left) and neutral current (right).

We perform a search targeting the electroweak production of a vector-like top
quark partner in fully hadronic final states in proton-proton ($\Pp\Pp$)
collisions at $\sqrt{s}=13$with the CMS detector at the CERN LHC. We use two
searches that target separately lower and higher mass values for the quark.
Both searches are designed to be sensitive to the decay to a top quark and a
Higgs boson ($\PQT\to\PQt\PH$), and to the decay to a top quark and a boson
($\PQT\to\PQt\PZ$) with subsequent hadronic decays of X
($\mathrm{X}=\PH,\PZ$). Both also consider a wide range of widths of the
quark, ranging from narrow, defined as small compared to the experimental mass
resolution, to as much as 30% of the quark mass. The event selections
primarily require tagging for the Higgs and boson candidates and so are most
sensitive to $\mathrm{X}\to\PQb\PAQb$. The experimental signature is a
resonant peak in the $\PQt\mathrm{X}$ invariant mass spectrum.

The searches are designed to seek evidence of quarks produced in association
with a bottom quark, dominated by the $\PQq\Pg\to\PQT\PAQb\PQq^{\prime}$
process, and, separately, associated production with a top quark dominated by
the $\PQq\Pg\to\PQT\PAQt\PQq$ process, where the charge conjugate processes
are also implied. These are electroweak production modes, with the production
of only a single , that rely on a nonzero $\PQT\PQb\PW$ coupling for the
charged-current production, and a nonzero $\PQT\PQt\PZ$ coupling for the
neutral-current production. In order to be produced with an observable cross
section, one needs a substantial partial width for the coupling to the initial
state. As a consequence, currently accessible production cross sections in
electroweak single production are associated with particle widths exceeding
about 5%, which would affect the experimentally observable invariant mass
distributions. The total width could also be enlarged if additional decay
modes were present.

As a result of the lower requirement on the constituent center-of-mass energy
and the larger available phase space, single production via the electroweak
mechanism allows a search for vector-like top quarks with masses beyond those
already tested with pair production. The
$\PQq\Pg\to\PQT\PAQb\PQq^{\prime}\to\PQt\mathrm{X}\PAQb\PQq^{\prime}$ process,
with the top quark from the quark decaying hadronically and X decaying to two
quarks, results in up to seven jets, four of which are jets. The seven jets
are associated with the production of seven fermions, namely
$\PQq\Pg\to(\PQq\PAQq^{\prime}\PQb)(\bbbar)\PAQb\PQq^{\prime}$. Similarly the
$\PQq\Pg\to\PQT\PAQt\PQq\to\PQt\mathrm{X}\PAQt\PQq$ process results in at
least nine fermions. In each case the other associated quark ($\PQq^{\prime}$
or ) often results in a forward jet at high absolute pseudorapidity. The
$\PQq\Pg\to\PQT\PAQb\PQq^{\prime}$ process is expected to have a higher cross
section than $\PQq\Pg\to\PQT\PAQt\PQq$ from kinematic and coupling
considerations.

Recent searches at the LHC for pair production of vector-like quarks have
severely constrained the possible existence of lower-mass vector-like quarks
that couple to heavy quarks [9, 10, 11, 12, 13, 14, 15, 16, 17, 18]. These
searches use several final states arising from the $\PQb\PW$, $\PQt\PZ$, and
$\PQt\PH$ decay channels and usually model pair production under the
assumption of a narrow width. In particular, for the singlet model, the most
stringent expected lower mass limit from pair production to date is 1.2 [16]
at 95% confidence level (CL). Pair production is based on the assumed
universal strong coupling and so the quantum chromodynamics (QCD) pair
production cross sections are known and model independent, and depend only on
the quark mass, $m_{\PQT}$. On the other hand, electroweak production depends
on the strength of the quark coupling at the production vertex, either
$\PQT\PQb\PW$ or $\PQT\PQt\PZ$, and therefore the production cross sections
are model dependent. In some models, such as that of Ref. [2], the couplings
are constrained by precision observables to be quite small. In other models,
such as that of Ref. [7], cross sections two orders of magnitude higher than
in Ref. [2] may be feasible. The first direct experimental constraints on
electroweak production of vector-like quarks were published in Ref. [19].
Search results at $\sqrt{s}=13$include the search already performed by CMS for
electroweak production of with $\PQT\to\PQt\PH$ for both semileptonically
decaying top quarks and hadronically decaying top quarks using the 2015 data
set [20, 21]. Other results at $\sqrt{s}=13$targeting electroweak production
of are described in Refs. [22, 23] for $\PQT\to\PQb\PW$, and for
$\PQT\to\PQt\PZ$ with dielectron and dimuon decays of the in Refs. [24, 25,
15], and using a missing energy signature for $\PZ\to\PGn\PAGn$ decays in Ref.
[26]. The search reported here uses the 2016 data set to study fully hadronic
final states with both merged and resolved jets resulting from electroweak
production of a quark with $\PQT\to\PQt\PH$ and $\PQT\to\PQt\PZ$. This search
represents a significant advance over prior electroweak production searches
for $\PQT\to\PQt\PH$, with expected 95% CL cross section upper limits
typically 5–10 times lower than those reported in Ref. [21], and is
competitive with other searches for $\PQT\to\PQt\PZ$ in this production mode.

For low values of the quark mass, the quarks resulting from the top quark
decay and from the Higgs or boson decay can be resolved as individual jets.
However, as the quark mass increases, the larger Lorentz boost from the decay
will lead to the decay products of the top quark and the quarks from the Higgs
or boson becoming progressively less and less resolved as separate jets. The
jet multiplicity, correspondingly, is reduced. Furthermore, one can
reconstruct both the top quark and the Higgs or boson by using large-area jet
and substructure techniques, where area refers to the jet’s extent in
$\eta$-$\phi$ space. Consequently, two search signatures are defined as
follows:

  * •

Low-mass search: Reconstruction of a five-jet invariant mass signature for the
$\PQT\to\PQt\PH$ and $\PQT\to\PQt\PZ$ decay modes. This search signature is
based on multijet triggers with tagging and is effective for low masses
(0.6–1.2) where the individual jets from the decays can be resolved.

  * •

High-mass search: Reconstruction of an invariant mass signature from two
large-area jets for both the $\PQT\to\PQt\PH$ and $\PQT\to\PQt\PZ$ decay
modes. This search signature is based on triggers using high transverse
momentum jets and is effective for high mass ($>$1.0). In this mass range, the
final state particles from the decays of each of the two daughter particles
resulting from the quark decay (the and the or ) produce a single large-area
jet. This leads to events with two large-area jets.

Each search is designed to be sensitive to T quark production in association
with either a bottom quark or a top quark. Besides the primary motivation of
exploring the possibility of electroweak production of a vector-like quark,
this analysis can be viewed more broadly as two independent searches for high
mass signatures of physics beyond the SM at the LHC. As such they provide
potential for discovery of new physics, independent of the specific models
discussed here.

The paper is organized as follows: this section has given the motivation to
search for the singly produced quark with two distinct signatures and two
decay modes. The CMS detector and event reconstruction are described in
Section 0.2. The data set and the modeling of signal and background processes
are described in Section 0.3. Reconstruction methods common to the two
searches are discussed in Section 0.4. The event selection criteria,
background estimation, and results are described for the low-mass search in
Section 0.5, and for the high-mass search in Section 0.6. Systematic
uncertainties for both signatures are discussed in Section 0.7. The overall
results are presented in Section 0.8 and summarized in Section 0.9.

##  0.2 The CMS detector and event reconstruction

The central feature of the CMS apparatus is a superconducting solenoid of 6m
internal diameter, providing a magnetic field of 3.8T. Within the solenoid
volume are a silicon pixel and strip tracker, a lead tungstate crystal
electromagnetic calorimeter (ECAL), and a brass and scintillator hadron
calorimeter (HCAL), each composed of a barrel and two endcap sections. Forward
calorimeters extend the pseudorapidity coverage provided by the barrel and
endcap detectors. Muons are detected in gas-ionization chambers embedded in
the steel flux-return yoke outside the solenoid.

The silicon tracker measures charged particles within the pseudorapidity range
$\abs{\eta}<2.5$. For nonisolated particles with transverse momentum, $\pt$,
in the range $1<\pt<10\GeV$ and $\abs{\eta}<1.4$, the track resolutions are
typically 1.5% in and 25–90 (45–150)in the transverse (longitudinal) impact
parameter [27].

The ECAL consists of 75 848 crystals covering $\abs{\eta}<3.00$. The HCAL
cells have widths of 0.087 in pseudorapidity and 0.087 in azimuth ($\phi$) in
the region $\abs{\eta}<1.74$. In the $\eta$-$\phi$ plane, and for
$\abs{\eta}<1.48$, the HCAL cells map onto $5{\times}5$ 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 with $\abs{\eta}$ to a maximum of 0.174 in
$\Delta\eta$ and $\Delta\phi$. The forward calorimeters extend the
calorimetric coverage for hadronic jets to $\abs{\eta}=5.0$.

Events of interest are selected using a two-tiered trigger system [28]. 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 time interval of less than 4. The second level, known as the high-
level trigger, consists of a farm of processors running a version of the full
event reconstruction software optimized for fast processing, and reduces the
event rate to around 1kHz before data storage.

In the reconstruction, the vertex with the largest value of summed physics-
object $\pt^{2}$ is taken to be the primary $\Pp\Pp$ interaction vertex. The
physics objects are the jets, clustered using the anti-jet finding algorithm
[29, 30], with the tracks assigned to the vertex as inputs.

A particle-flow algorithm [31] aims to reconstruct and identify each
individual particle in an event, with an optimized combination of information
from the various elements of the CMS detector. The energy of photons is
directly obtained from the ECAL measurement, corrected for zero-suppression
effects. The energy of electrons is determined from a combination of the
electron momentum at the primary interaction vertex as determined by the
tracker, the energy of the corresponding ECAL cluster, and the energy sum of
all bremsstrahlung photons spatially compatible with originating from the
electron track. The momentum of muons is obtained from the fitted trajectory
of the corresponding track reconstructed from the tracker and the muon
detectors. The energy of charged hadrons is determined from a combination of
their momentum measured in the tracker and the matching ECAL and HCAL energy
deposits, corrected for zero-suppression effects and for the response function
of the calorimeters to hadronic showers. Finally, the energy of neutral
hadrons is obtained from the corresponding corrected ECAL and HCAL energies.

Jet momentum is determined as the vectorial sum of all particle momenta in the
jet, and is found from simulation to be within 5 to 10% of the true momentum
over the whole spectrum and detector acceptance. Additional $\Pp\Pp$
interactions within the same or nearby bunch crossings (pileup) can contribute
additional tracks and calorimetric energy depositions to the jet momentum. To
mitigate this effect, tracks identified to be originating from pileup vertices
are discarded, and an offset correction is applied to correct for remaining
contributions. 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, photon+jet, +jet, and multijet
events are used to estimate any residual differences in jet energy scale in
data and simulation [32]. Additional selection criteria are applied to each
jet to remove jets potentially dominated by anomalous contributions from
various subdetector components or reconstruction failures. The jet energy
resolution amounts typically to 15% at 10, 8% at 100, and 4% at 1, to be
compared to about 40, 12, and 5% obtained when the calorimeters alone are used
for jet clustering [31].

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. [33].

##  0.3 Data and modeling of signals and backgrounds

This analysis uses proton-proton collision data collected at a center-of-mass
energy of $\sqrt{s}=13$, recorded in 2016, amounting to a total integrated
luminosity of 35.9.

Simulated samples for the $2\to 3$ signal processes, $\Pp\Pp\to\PQT\PQb\PQq$
and $\Pp\Pp\to\PQT\PQt\PQq$, were generated at leading order using the Monte
Carlo (MC) event generator 2.3.3 [34] for various masses of the quark and for
the decays $\PQT\to\PQt\PH$ and $\PQT\to\PQt\PZ$. The signal generation for
these “narrow width” $2\to 3$ process samples has the width set to 10, which
is small on the scale of the experimental resolution of about 6%. Separate
samples were generated for both left- and right-handed chiralities of the
quark for each decay mode, for these narrow-width cases. In addition, 2.4.2 at
leading order was used to simulate the large width $2\to 4$ processes,
$\Pp\Pp\to\PQt\PH\PQb\PQq$, $\Pp\Pp\to\PQt\PH\PQt\PQq$,
$\Pp\Pp\to\PQt\PZ\PQb\PQq$ and $\Pp\Pp\to\PQt\PZ\PQt\PQq$, with fractional
widths $\Gamma/{m_{\PQT}}$ of 10, 20, and 30%. All of the large-width samples
assume left- (right-)handed chiralities for the $\Pp\Pp\to\PQT\PQb\PQq$
($\Pp\Pp\to\PQT\PQt\PQq$) case, as expected in the singlet (doublet) model.

The benchmark quark masses used for the results range from 0.6 to 2.6. The
NNPDF3.0 parton distribution function (PDF) set [35] was used. The samples are
generated with both the top quark and the Higgs boson decaying inclusively.
The masses of the Higgs boson and top quark are set to 125 and 172.5,
respectively. In the samples, the SM Higgs boson branching fraction
$\mathcal{B}(\PH\to\PQb\PAQb)$ of 58% is assumed. Similarly the $\PQt$ and
$\PZ$ are decayed inclusively in the $\PQT\to\PQt\PZ$ samples.

The SM background simulation samples include $\PQt\PAQt$+jets, +jets, +jets,
single top quark, $\PQt\PH\PQq$, $\ttbar\PH$, $\PQt\PAQt\PW$, $\PQt\PAQt\PZ$,
$\PW\PW$, $\PZ\PZ$, $\PW\PZ$, $\PW\PH$, and $\PZ\PH$. These processes are
generated at next-to-leading order with 2.3.3 unless otherwise specified. The
parton-level MC simulations for SM backgrounds and signal are interfaced with
8.212 [36]. The large-width signal samples and the main backgrounds involving
top quarks use the CUETP8M2T4 tune [37]. The other samples use the earlier
CUETP8M1 tune [38].

The $\PQt\PAQt$+jets events are inclusive, and are simulated using 2.0 [39,
40, 41, 42]. The +jets and +jets samples include only hadronic or boson decays
and contain an $\HT>600$requirement, where $\HT$ is the scalar sum of jet
transverse momenta. The single top quark $\PQt\PW$ process was generated using
the 2.0+ 8 generator combination. The SM $\PQt\PH\PQq$ process simulation
included all decay modes for the top quark and Higgs boson. The $\ttbar\PH$
sample was generated with the decay $\PH\to\PQb\PAQb$ with a Higgs boson mass
of 125using 2.0. The $\PW\PW$ sample was generated with hadronic decays using
2.0. The $\PZ\PZ$ sample was generated with hadronic decays using Madspin [43]
and applying the FxFx merging procedure [44] for matching jets from the matrix
element calculation with those from the parton shower. The $\PW\PZ$ sample was
inclusive and generated with 8. The $\PZ\PH$ sample was generated with
$\PZ\to\PQb\PAQb$ with 2.0.

The SM background events comprised uniquely of jets produced through the
strong interaction, referred to as QCD multijet events, were also considered
in the design of the analyses. Two sets of simulated samples were used: a
sample generated using 8 that is binned in the invariant $\pt$ associated with
the hard process and an $\HT$-binned sample using at leading order with up to
four partons in the matrix element calculations, using the MLM jet matching
scheme [45] with fragmentation and showering. All simulated event samples were
generated using the NNPDF3.0 PDF set except that based on 8 which used the
NNPDF2.3 PDF set [46].

##  0.4 Reconstruction methods and primary selection

Particle-flow anti-jets are used. Small-area jets, denoted AK4 jets, are
defined using a distance parameter of 0.4, whereas large-area jets are defined
with a distance parameter of 0.8 and are denoted as AK8 jets.

For both searches, tagging is used to identify jets that contain -flavored
hadrons ( jets). The tagging is applied to AK4 jets and also subjets
reconstructed as part of an AK8 jet. Depending on the search, tagging involves
several secondary vertexing algorithms: the online and offline CSVv2
discriminators and the DeepCSV discriminators [47]. Offline tagging working
points are defined with light-flavor jet mistag rates of approximately 0.1%
(tight), 1% (medium), and 10% (loose). For AK4 jets, the tight CSVv2 tagging
working point has an efficiency of 41% while the medium tagging working points
have CSVv2 and DeepCSV efficiencies of 63 and 68%, respectively. In the case
of AK8 jets, a grooming algorithm [48] looks for jet substructure and tagging
is applied to the resulting subjets. For AK8 jets with of around 400, the
medium tagging working point has an efficiency per subjet of about 51%,
whereas the loose tagging working point has an efficiency per subjet of about
75%. The mistag rates of jets originating from -flavored hadrons for the
tight, medium, and loose tagging working points are about 2, 12, and 37%,
respectively.

The event selection requires that the events have at least one satisfied
trigger condition among a set of unprescaled high-level trigger algorithms.
The set is specific to each search strategy. The trigger conditions for the
low-mass search strategy rely on online jet information and, for some trigger
conditions, also on tagging information. The event selection is dominated by
the trigger condition with the lowest jet multiplicity and the lowest
threshold. This condition requires at least six jets with $\pt>30$with two of
them passing the tagging online criteria. The trigger conditions of the high-
mass search strategy consist of a scalar sum trigger formed from all jets with
a summed threshold of 900and an inclusive large-area single jet trigger with a
threshold of 450. The high-mass search uses three other trigger conditions
that include some loose jet substructure requirements and lower thresholds on
either the scalar sum (700), the inclusive single-jet (360), or on two jets,
with the higher (lower) jet required to exceed 300 (200).

At least one primary vertex must be found within 24longitudinally and within
2radially of the center of the luminous region.

##  0.5 Low-mass search

The low-mass search strategy uses the invariant mass reconstructed from five
AK4 jets as the main discriminating variable. The event selection requires at
least six jets to conform with the trigger requirements. The selection
criteria are based on the properties of the signal final state, in the cases
of $\PQt\to\PQb\PW$ and $\PH/\PZ\to\bbbar$. The final state is composed of two
jets coming from the decay and three jets (two coming from the $\PH/\PZ$ and
one from the top quark decay). The main background processes consist of QCD
multijet production and top quark pair production. These backgrounds are not
expected to result in a resonance in the five-jet invariant mass variable. A
reduction of the QCD multijet background is achieved by imposing tagging, and
by requiring events to be consistent with the presence of all of the relevant
states (, $\PH/\PZ$, and top quark). The presence of only one top quark
candidate from the selected jets is used to reduce the background.

###  0.5.1 Event selection

The following criteria define the first part of the selection:

  * •

Small-area jet multiplicity. The event should have at least six AK4 jets with
$\pt>40$within $\abs{\eta}<4.5$.

  * •

Leading jets. The jets with the highest (leading jets) have larger in the
signal than in most of the backgrounds. Therefore, the leading jets must have
$\pt>170$, 130, and 80, for the leading, second-leading, and third-leading
jets, respectively.

  * •

-tagged jets. The considered quark decay leads to three quarks while most backgrounds have at most two quarks. In the signal region labeled as $3T$, at least three -tagged jets using the tight DeepCSV working point are required for jets with $\abs{\eta}<2.4$. Other tagging working points are used to estimate the background using control samples in data.

  * •

candidate identification. The correct identification of a $\PH/\PZ$ boson and
a top quark in the five-jet final state relies on a $\chi^{2}$ sorting
algorithm. Here the presence of all three states, namely the $\PH/\PZ$, the ,
and the top quark, is exploited. The algorithm loops over jet combinations and
considers two -tagged jets for the $\PH/\PZ$ candidate, two jets (potentially
-tagged) for the candidate, and a combination of the dijet candidate and a
-tagged jet for the top quark candidate. These combinations of jets are used
to construct the variables defined in Eqs. (1)–(4):

$\chi^{2}_{\PH/\PZ}=\left(\frac{m_{\PH/\PZ}^{\mathrm{meas}}-m_{\PH/\PZ}^{\mathrm{MC}}}{\sigma_{\PH/\PZ}^{\mathrm{MC}}}\right)^{2},$
(1)
$\chi^{2}_{\PW}=\left(\frac{m_{\PW}^{\text{meas}}-m_{\PW}^{\mathrm{MC}}}{\sigma_{\PW}^{\mathrm{MC}}}\right)^{2},$
(2)
$\chi^{2}_{\PQt}=\left(\frac{m_{\PQt}^{\text{meas}}-m_{\PQt}^{\mathrm{MC}}}{\sigma_{\PQt}^{\mathrm{MC}}}\right)^{2},$
(3) $\chi^{2}=\chi^{2}_{\PH/\PZ}+\chi^{2}_{\PW}+\chi^{2}_{\PQt},$ (4)

where $m^{\text{meas}}$ denotes the measured mass quantities reconstructed
from the considered combination of jets, and $m^{\mathrm{MC}}$ and
$\sigma^{\mathrm{MC}}$ denote the expected mass values and standard deviations
from Gaussian fits to simulated signal samples. The mass values fitted for
each particle are: $m_{\PH}^{\mathrm{MC}}=121.9$,
$m_{\PZ}^{\mathrm{MC}}=90.9$, $m_{\PW}^{\mathrm{MC}}=83.8$and
$m_{\PQt}^{\mathrm{MC}}=173.8$; these values differ only slightly from the
input world-average values [49]. For the decays of the Higgs and bosons, the
fitted standard deviations are $\sigma_{\PH}^{\mathrm{MC}}=13.5$and
$\sigma_{\PZ}^{\mathrm{MC}}=11.4$, and for the fully hadronic decays of the
and top quark, they are $\sigma_{\PW}^{\mathrm{MC}}=10.0$and
$\sigma_{\PQt}^{\mathrm{MC}}=16.0$. One first chooses the lowest
$\chi^{2}_{\PH/\PZ}$ -tagged jet pair as the $\PH/\PZ$ candidate and then
selects the other jets making up the and top quark candidates by minimizing
the total $\chi^{2}$. This procedure is found to improve the signal-to-
background ratio by 30% compared to simply choosing the combination with the
best total $\chi^{2}$. Finally, the total $\chi^{2}$ must not exceed 15 in
order to ensure good quality of the $\PH/\PZ$, , and top quark candidates. It
is found that the five jets are correctly identified about 73 and 64% of the
time for the narrow width $\PQt\PH\PQb\PQq$ and $\PQt\PZ\PQb\PQq$ cases,
respectively.

  * •

Second top quark mass. A large fraction of events survive the requirement on
at least three -tagged jets. These originate from incorrect tagging of the jet
arising from a charm quark from the boson part of one of the top quark decays.
In order to reduce this background, we define the second top quark mass as the
invariant mass formed by the $\PH/\PZ$ candidate and the remaining highest jet
not used in the $\chi^{2}$ calculation. For events, the second top quark mass
has a peak around 172and there are nearly no signal events expected in that
region. Therefore we require that the second top quark mass is greater than
250. This leads to about a factor of two reduction in the background.

  * •

mass. Finally, the reconstructed boson from the candidate must have a mass
larger than 100, if looking for a , and the mass must be smaller than 100, if
looking for a . This ensures that there is no overlap between the two
channels.

The second part of the selection uses the presence of a top quark and a Higgs
or boson in the event. The variables are chosen to be as model independent as
possible and the selection criteria are optimized using the figure of merit
described in [50]. The selection criteria are described below.

  * •

Relative . The relative variable is defined as
$\big{(}\pt(\PH/\PZ_{\text{cand}})+\pt({\PQt}_{\text{cand}})\big{)}/\HT$. In
single quark production, most of the momentum should be carried by the top
quark and $\PH/\PZ$ candidates, therefore the relative is an extremely good
discriminator against and multijet events. The $\PH/\PZ$ and candidates from
the candidate decay must have a relative greater than 0.40.

  * •

Max($\chi^{2}$). The maximum among the $\chi^{2}$ values defined in Eqs.
(1)–(3) is examined and is required to be less than 3.0. This criterion is
highly correlated to the $\chi^{2}$ criteria but represents a tighter
condition that ensures that each mass is identified with high quality. It is
equivalent to requiring a mass window of at most $\pm\sqrt{3}\sigma$ for each
candidate.

  * •

$\Delta R$ of jets from $\PH/\PZ$ decay. Because of the large mass of the
quark (above 0.6), the $\PH/\PZ$ decay tends to be boosted (but the jets not
completely merged). A small spatial separation of $\Delta
R(\PQb_{\PH/\PZ},\PQb_{\PH/\PZ})<1.1$ between the two -tagged jets is
required, leading to a reduction of the background. The $\Delta R$ is defined
as the inter-jet separation in $\eta$-$\phi$ space ($\Delta
R=\sqrt{\smash[b]{(\Delta\eta)^{2}+(\Delta\phi)^{2}}}$), where $\Delta\eta$
and $\Delta\phi$ are the corresponding inter-jet separations in pseudorapidity
and azimuth (in radians).

  * •

$\PH/\PZ$ $\chi^{2}$. As most of the backgrounds do not contain a genuine
Higgs boson, $\chi^{2}_{\PH/\PZ}$ is a very discriminating criterion for the
Higgs boson decay channel. We require $\chi^{2}_{\PH}<1.5$ for the case and
$\chi^{2}_{\PZ}<1.0$ for the case. It is equivalent to a mass window of $\pm
16.5$for the Higgs boson and $\pm 11.4$for the boson. A tighter $\chi^{2}$
requirement is made for candidates to avoid background contamination from
lower masses and to reduce overlap with candidates.

  * •

$\Delta R$ of jets from decay. Given the Lorentz boost of the in signal
events, a requirement of $\Delta R(j_{\PW},j_{\PW})<1.75$ reduces the QCD
multijet background, while retaining most of the signal.

  * •

$\Delta R$ of jets from top quark decay. The top quark decay products tend to
be Lorentz-boosted (but the jets do not completely merge) for the signal. A
spatial separation between the -tagged jet and the candidate that is used to
make the top quark candidate of $\Delta R({\PQb}_{\PQt},\PW)<1.2$ is required.
This further reduces the QCD multijet background.

The total number of events selected from the data sample in the $3T$ signal
region is 615 (290 for the $\PQt\PZ$ selection and 325 for the $\PQt\PH$
selection). The number of expected signal events is 7.6 for a quark mass of
0.7, $\Gamma/{m_{\PQT}}=0.01$, left-handed chirality, and a quark produced in
association with a bottom quark with a product of cross section and branching
fraction of 89fb for each channel. For this signal process, the selection
efficiency is presented in Table 0.5.1 together with various simulated
background processes for the Higgs and boson decay channels.

Cumulative efficiencies for the low-mass search after applying event
selections for the signal and main backgrounds in the Higgs boson decay
channel (upper half) and the boson decay channel (lower half). The first and
last rows of each section give the expected numbers of events normalized to
the integrated luminosity of 35.9. Uncertainties are statistical only. The
signal values are for a mass of 0.7, $\Gamma/{m_{\PQT}}=0.01$, left-handed
chirality, and a quark produced in association with a bottom quark with a
product of cross section and branching fraction of 89fb for each channel. The
“Other backgrounds” column includes +jets, +jets, single top quark, and
$\ttbar\PH$ processes. It has been checked that the $\ttbar\PH$ process does
not present a resonance in the $\PQt\PH$ channel. The number of expected
$\ttbar\PH$ events is comparable to the expected signal.  Selection for
$\PQt\PH$ Signal QCD Multijet Other backgrounds Basic selection
($m_{\bbbar}>100\GeV$) 23.1 $\pm$ 0.9 9360 $\pm$ 810 2612 $\pm$ 28 353 $\pm$
23 Relative $\HT>0.4$ 81.4% 42.8% 51.9% 52.9% $\mathrm{Max}(\chi^{2})<3.0$
54.3% 14.1% 25.1% 21.8% $\Delta R({\PQb}_{\PH},{\PQb}_{\PH})<1.1$ 44.4% 7.5%
11.9% 8.9% $\chi^{2}_{\PH}<1.5$ 39.8% 4.9% 9.3% 7.1% $\Delta
R(j_{\PW},j_{\PW})<1.75$ 33.7% 3.2% 7.2% 5.6% $\Delta
R({\PQb}_{\PQt},\PW)<1.2$ 25.7% 1.9% 4.5% 2.5% Full selection 5.9 $\pm$ 0.4
181 $\pm$ 52 116.5 $\pm$ 6.1 9.3 $\pm$ 0.6 Selection for $\PQt\PZ$ Signal QCD
Multijet Other backgrounds Basic selection ($m_{\bbbar}<100\GeV$) 5.7 $\pm$
0.2 6810 $\pm$ 630 1270 $\pm$ 17 223 $\pm$ 24 Relative $\HT>0.4$ 86.9% 48.5%
47.2% 57.5% $\mathrm{Max}(\chi^{2})<3.0$ 53.3% 15.9% 24.1% 28.8% $\Delta
R({\PQb}_{\PZ},{\PQb}_{\PZ})<1.1$ 51.1% 11.7% 16.4% 22.7% $\chi^{2}_{\PZ}<1.0$
45.0% 7.3% 11.5% 18.4% $\Delta R(j_{\PW},j_{\PW})<1.75$ 37.6% 5.2% 9.6% 9.9%
$\Delta R({\PQb}_{\PQt},\PW)<1.2$ 28.8% 1.5% 5.7% 5.5% Full selection 1.6
$\pm$ 0.1 103$\pm$ 38 72.7 $\pm$ 4.7 8.1 $\pm$3.9

###  0.5.2 Background estimation and validation

None of the SM backgrounds are expected to result in a resonance in the five-
jet invariant mass, therefore the spectrum of the background five-jet
invariant mass distribution should have a monotonically decreasing shape.
However, the second part of the selection criteria tends to shape the five-jet
invariant mass distribution. In order to evaluate the shape of the five-jet
invariant mass distribution for the background in data, two regions that are
independent from the main $3T$ signal region are defined using looser tagging
criteria. In these two regions it is important to ensure that no bias with
respect to the selection criteria is present, as all backgrounds are estimated
from data. The extraction of signal is done by fitting the signal and
background simultaneously in all three regions.

The tagging does not strongly influence the kinematic distributions of objects
used to construct the five-jet invariant mass. Therefore we relax the tagging
criteria required for three of the five jets forming the candidate. The first
new region is called the $3M$ signal region; it requires three medium -tagged
jets but excludes events with three tight -tagged jets, bringing information
on the background and possible signal shapes. The second new region is denoted
as the $2M1L$ signal region; in order to have significant numbers of events in
this background-dominated region and to keep events with similar kinematics to
the $3T$ signal region, the tagging criteria are relaxed to two medium and one
loose -tagged jets but excluding three medium -tagged jets.

Two additional samples are defined and used to validate the method; one is
enriched in QCD multijet events, the other in events. In order to define the
QCD multijet enriched control sample, the $\chi^{2}$ criterion is relaxed to
50, the Max($\chi^{2}_{\PH/\PZ}$, $\chi^{2}_{\PW}$, $\chi^{2}_{\PQt}$)
criterion is inverted, and $\chi^{2}_{\PQt}>1$ is required to reduce the
fraction of events. The QCD multijet sample is subdivided into a $3T$ region
and a $2M1L$ region (excluding $3M$) based on the -tagged jet configurations.
For the control sample, the $\chi^{2}$ criterion is relaxed to 50, the
Max($\chi^{2}_{\PH/\PZ}$, $\chi^{2}_{\PW}$, $\chi^{2}_{\PQt}$) and
$\chi^{2}_{\PH/\PZ}$ criteria are inverted, the $\Delta
R({\PQb}_{\PH/\PZ},{\PQb}_{\PH/\PZ})$ criterion is relaxed to 1.5, and
$\chi^{2}_{\PQt}<1.5$ is required. The sample is subdivided into a $2T1L$ (two
tight and one loose -tagged jets) region and a $2M1L$ region (excluding
$2T1L$) based on the -tagged jet configurations. For the $2T1L$ ($2M1L$)
region, one of the tight (medium) -tagged jets must be from the top quark
candidate. A summary of the criteria changed to define each region is
presented in Table 0.5.2. The fraction of expected signal events is of the
order of 3% in the QCD multijet region and 1% in the $2T1L$ region (for a
cross section times branching fraction of 600fb).

Criteria defining the various signal and control regions. The first line of
each section gives the tagging requirements. The criteria that differ are
preceded by an asterisk ”*”. The mass requirements are different for the
($m_{\bbbar}>100\GeV$) and channels ($m_{\bbbar}<100\GeV$).  $3T$ region $3M$
region $2M1L$ region $3T$ *$3M$ but not $3T$ *$2M1L$ but not $3M$
$\chi^{2}<15$ $\chi^{2}<15$ $\chi^{2}<15$ Relative $\HT>0.4$ Relative
$\HT>0.4$ Relative $\HT>0.4$ Max($\chi^{2})<3.0$ Max($\chi^{2})<3.0$
Max($\chi^{2})<3.0$ $\Delta R({\PQb}_{\PH/\PZ},{\PQb}_{\PH/\PZ})<1.1$ $\Delta
R({\PQb}_{\PH/\PZ},{\PQb}_{\PH/\PZ})<1.1$ $\Delta
R({\PQb}_{\PH/\PZ},{\PQb}_{\PH/\PZ})<1.1$ $\chi^{2}_{\PH/\PZ}<1.5/1.0$
$\chi^{2}_{\PH/\PZ}<1.5/1.0$ $\chi^{2}_{\PH/\PZ}<1.5/1.0$ $\Delta
R(j_{\PW},j_{\PW})<1.75$ $\Delta R(j_{\PW},j_{\PW})<1.75$ $\Delta
R(j_{\PW},j_{\PW})<1.75$ $\Delta R({\PQb}_{\PQt},\PW)<1.2$ $\Delta
R({\PQb}_{\PQt},\PW)<1.2$ $\Delta R({\PQb}_{\PQt},\PW)<1.2$ QCD $3T$ region
QCD $2M1L$ region $3T$ *$2M1L$ but not $3M$ $\chi^{2}<50$ *$\chi^{2}<50$
Relative $\HT>0.4$ Relative $\HT>0.4$ $5<\mathrm{Max}(\chi^{2})<20$ and
$\chi^{2}_{\PQt}>1.0$ *$5<\mathrm{Max}(\chi^{2})<20$ and $\chi^{2}_{\PQt}>1.0$
$\Delta R({\PQb}_{\PH/\PZ},{\PQb}_{\PH/\PZ})<1.1$ $\Delta
R({\PQb}_{\PH/\PZ},{\PQb}_{\PH/\PZ})<1.1$ $\chi^{2}_{\PH/\PZ}<1.5/1.0$
$\chi^{2}_{\PH/\PZ}<1.5/1.0$ $\Delta R(j_{\PW},j_{\PW})<1.75$ $\Delta
R(j_{\PW},j_{\PW})<1.75$ $\Delta R({\PQb}_{\PQt},\PW)<1.2$ $\Delta
R({\PQb}_{\PQt},\PW)<1.2$ $2T1L$ region $2M1L$ region *$2T1L$ *$2M1L$ but not
$2T1L$ Top -tag $T$ *Top -tag $M$ $\chi^{2}<50$ *$\chi^{2}<50$ Relative
$\HT>0.4$ Relative $\HT>0.4$ $3<\mathrm{Max}(\chi^{2})<5$
*$3<\mathrm{Max}(\chi^{2})<5$ $\Delta
R({\PQb}_{\PH/\PZ},{\PQb}_{\PH/\PZ})<1.5$ *$\Delta
R({\PQb}_{\PH/\PZ},{\PQb}_{\PH/\PZ})<1.5$ $\chi^{2}_{\PQt}<1.5$ and
$\chi^{2}_{\PH/\PZ}>3$ *$\chi^{2}_{\PQt}<1.5$ and $\chi^{2}_{\PH/\PZ}>3$
$\Delta R(j_{\PW},j_{\PW})<1.75$ $\Delta R(j_{\PW},j_{\PW})<1.75$ $\Delta
R({\PQb}_{\PQt},\PW)<1.2$ $\Delta R({\PQb}_{\PQt},\PW)<1.2$

Relaxing the tagging requirement induces a change in the tagging efficiency
depending on the $\pt$ and $\eta$ of the jet. As $\pt$ and $\eta$ are two
highly correlated variables, a reweighting procedure using $\eta$ and momentum
is used. Weights are derived jet-by-jet for each channel and for each tagging
working point using the QCD multijet control region. These weights reflect the
differences in the efficiency between loose and medium and between medium and
tight -tagged jets. For each event, the product of the weights for all three
-tagged jets is applied to correct for the change in the tagging efficiency
going from $3T$ to $3M$ and from $3M$ to $2M1L$.

The validation of the method is done with the QCD multijet and control
regions. The shape of the five-jet invariant mass distribution is compared
between the QCD $3T$ region and the QCD $2M1L$ region reweighted as $3T$; it
is found to be satisfactory using the Kolmogorov–Smirnov test. Similarly, when
comparing the shape of the five-jet invariant mass for the the $2T1L$ region
and the $2M1L$ region reweighted as $3T$, acceptable consistency is found.

The five-jet invariant mass distributions for the QCD multijet $3T$ region,
the $2T1L$ region, and the $3M$ signal region are presented in Fig. 2 together
with a potential signal for ${m_{\PQT}}=0.7$corresponding to a product of
cross section times branching fraction of 600fb. The $2M1L$ region
distribution is overlaid after applying the tagging weight computed with
respect to the $3T$, $2T1L$, and $3M$ regions, respectively. An acceptable
agreement is observed in each sample.

Figure 2: The five-jet invariant mass distribution (black points with error
bars) for the $\PQt\PH\PQb\PQq$ channel after the full selection in the QCD
multijet $3T$ control region (upper), the $2T1L$ control region (middle), and
the $3M$ signal region (lower). The superimposed blue histogram, labeled
“background”, is the reweighted $2M1L$ region distribution, used as an
estimate of the background shape, with its normalization adjusted to match the
number of entries observed in each region. A potential narrow-width signal
(dashed red histogram) is added on top of the blue histogram for
${m_{\PQT}}=0.7$and $\Gamma/{m_{\PQT}}=0.01$, for a product of signal cross
section and branching fraction of 600fb. The light blue shaded area
corresponds to the statistical uncertainties in the corresponding $2M1L$
region. The last bin in each distribution also contains events with masses
exceeding 1.3.

###  0.5.3 Low-mass search results

For each decay channel, three independent regions based on the -tagged jet
requirements are examined: $3T$ (largest signal over background ratio), $3M$,
and $2M1L$ (background dominated). The overall background shape and
normalization is driven by the observations in the $2M1L$ region. The
background shape is linked between the regions by two transfer functions;
these are derived from the tagging weights to correct for tagging differences
between the regions. One transfer function links the $3T$ region to the $3M$
region and the other links the $3M$ region to the $2M1L$ region. The transfer
functions, based on simple parametrizations of the dependence of the
reweighting values on the five-jet invariant mass, are displayed in Fig. 3.

Figure 3: Dependence of the reweighting values (product of all tagging
weights) on the five-jet invariant mass for the $2M1L$ region (left) and for
the $3M$ region (right), in the case of the $\PQt\PH$ channel (upper) and the
$\PQt\PZ$ channel (lower). These variations are fitted to obtain the transfer
functions (in red) using either a 3-parameter function with a constant term
and a slope, or a 2-parameter straight line. The light red shaded regions
represent the central 68% CL interval for each fit when taking into account
only the statistical uncertainties.

The signal is parametrized as a Gaussian shape following the fit of the quark
reconstructed mass for each of the simulation samples for each region. The
variations of the Gaussian fit parameters (mean and standard deviation) with
quark mass are fitted for each region. The parametrizations for the $\PQt\PH$
and $\PQt\PZ$ channels are found to be compatible. The systematic
uncertainties are discussed in Section 0.7. Here we note simply that they are
all taken as correlated between the channels, except the ones related to the
transfer functions and the normalization between regions. For a given channel,
the fit procedure adjusts the shape of the background bin by bin based on the
data in each of the regions, taking into account the transfer function between
regions. The overall fit uses 40wide bins; it includes three bin-independent
fit parameters, namely the signal strength and two relative normalization
factors between each region, and fit parameters for the background
contribution in each bin of the $2M1L$ region.

Figure 4: The background-only post-fit invariant mass distributions for the
$\PQt\PZ$ candidates (left) and $\PQt\PH$ candidates (right) for each region
fitted: $2M1L$ (upper row), $3M$ (middle row), and $3T$ (lower row). The
signal hypothesis shown is a with a mass of 0.7, narrow width, and a product
of the cross section and branching fraction of 600fb for the $\PQt\PZ\PQb\PQq$
and $\PQt\PH\PQb\PQq$ channels. The data are represented by the black points
with error bars, the signal hypothesis is represented by the red dashed line,
the blue histogram gives the fitted background, and the light blue band
represents the uncertainty in the background fit.

The background-only post-fit invariant mass distributions for each of the
regions ($2M1L$, $3M$, and $3T$) as well as for each channel ($\PQt\PZ$ and
$\PQt\PH$) are displayed in Fig. 4. A signal with a mass of
${m_{\PQT}}=0.7$and product of the cross section and branching fraction of
600fb is superimposed. An excess is observed when fitting the three regions
for the $\PQt\PH$ channel. The local significance is 3.0 standard deviations
for a quark mass of 0.68. For the same quark mass the local significance is
0.2 standard deviations in the $\PQt\PZ$ channel. In a search for a vector-
like quark, one expects similar branching fractions for the $\PQt\PH$ and
$\PQt\PZ$ channels. No overall excess is measured when considering the fit of
all six distributions.

The result of the median value for the limit in terms of the appropriate cross
section is calculated using the asymptotic framework [51, 52, 53].

Similarly, results are obtained in the cases where the quark has 10, 20, and
30% widths and in the case of production in association with a top quark. In
all cases, the observed results show no evidence for a signal. The studies
have also been performed for the case of right-handed chirality. No effect of
the chirality is observed, indicating that the low-mass search is insensitive
to this property. The resulting limits are reported in Section 0.8.

##  0.6 High-mass search

This search strategy focuses on reconstructing the invariant mass of
$\PQT\to\PQt\PH$ and $\PQT\to\PQt\PZ$ candidates formed from two large-area
jets in the fully hadronic channel. The large-area jets are associated with
events in which the top quark and the Higgs or $\PZ$ boson are each highly
Lorentz-boosted, and correspondingly the search targets masses of 1and above.
The background consists mostly of top quark pair production and QCD multijet
production.

###  0.6.1 Particle tagging

To identify boosted $\PQt\to\PQb\PW\to\PQb\qqbar^{\prime}$, $\PH\to\PQb\PAQb$,
and $\PZ\to\PQb\PAQb$ decays, jet substructure techniques [54] are used, which
remove soft and collinear radiation from the clustered jet constituents.
Clusters of the remaining constituents are identified with each of the quarks
from the decay of the original particle. The soft-drop algorithm [55, 48] is
used to groom the jets, using the soft radiation fraction parameter $z=0.1$
and the angular exponent parameter $\beta=0$. The algorithm yields two soft-
drop subjets. The jet mass after applying the soft-drop algorithm will be
referred to as the soft-drop mass. The pruning grooming algorithm [56] is also
used, leading to the pruned jet mass. For pruning, the minimum subjet as a
fraction of the parent jet is required to exceed 0.1 and the separation angle
in $\eta$-$\phi$ space between the two subjets must exceed 0.5. Furthermore,
the N-subjettiness algorithm [57] is used to further select jets with three or
two substructures for the top quark jets, and the $\PH$ and $\PZ$ boson jets,
respectively. Flavor tagging is applied to identify $\PQb$ quark subjets using
the CSVv2 multivariate discriminator, in order to further enhance the signal
purity and suppress backgrounds from non-$\PQt\PAQt$+jets multijet processes.
The particle tagging criteria for boosted top quark, and $\PH$ and $\PZ$ boson
jets are as follows:

  * •

$\PH$ jet: An AK8 jet with $\pt>300\GeV$ must have a pruned jet mass within
the range 105–135. The ratio of the N-subjettiness variables
$\tau_{2}/\tau_{1}$ of the jet is required to be $<$0.6. At least one of the
two soft-drop subjets must pass the medium $\PQb$ tag criterion and the other
subjet should pass at least the loose $\PQb$ tag criterion.

  * •

$\PZ$ jet: An AK8 jet with $\pt>200\GeV$ must have a pruned jet mass within
the range 65–105. The requirements on the N-subjettiness ratio
$\tau_{2}/\tau_{1}$ and subjet $\PQb$ tagging are the same as those for the
$\PH$ jet.

  * •

$\PQt$ jet: An AK8 jet with $\pt>400\GeV$ and a soft-drop mass within the
range 105–220is required. The jet should have an N-subjettiness ratio
$\tau_{3}/\tau_{2}<0.57$, indicating that the large-area jet is likely to have
three subjets. The soft-drop subjet with the highest CSVv2 discriminator value
should pass the medium $\PQb$ tag criterion [58].

In addition, reversed-$\PH$-tagged, reversed-$\PZ$-tagged, and
reversed-$\PQt$-tagged jets are defined, with the same kinematic and
N-subjettiness requirements as those for their tagged counterparts, but with
complementary $\PQb$ tagging criteria, as follows:

  * •

Reversed-$\PH$-tagged jet: Same criteria as for an $\PH$-tagged jet but with
one subjet passing the medium $\PQb$ tag criterion and the other subjet
failing the loose tag criterion.

  * •

Reversed-$\PZ$-tagged jet: Same criteria as for a $\PZ$-tagged jet but with
both subjets failing the loose $\PQb$ tag criterion.

  * •

Reversed-$\PQt$-tagged jet: Same criteria as for a $\PQt$-tagged jet but with
the highest soft-drop subjet discriminant failing the medium $\PQb$ tag
criterion.

The reversed-$\PZ$ tag is defined differently from the reversed-$\PH$ tag so
that sensitivity to a potential $\PQt\PZ$ signal, including efficiency from
$\PZ\to\PQq\cPaq(\PQq\neq\PQb)$ decays, can be retained.

###  0.6.2 Event selection

Only events satisfying the following primary selection criteria are considered
further, either as candidates for the signal or for the associated background
control regions:

  * •

Small-area jet multiplicity: At least four AK4 jets with $\pt>30\GeV$ and
$\abs{\eta}<5$.

  * •

Large-area jet multiplicity: At least two AK8 jets with $\pt>200\GeV$ and
$\abs{\eta}<2.4$.

  * •

Leading jet: The highest-AK8 jet should have $\pt>400\GeV$ and a pruned mass
greater than 50.

  * •

Scalar sum: The scalar sum of the transverse momenta of the two highest-AK8
jets should exceed 850.

  * •

Extra jet multiplicity: At least two of the AK4 jets should be separated by a
distance $\Delta R>1.2$ from the two leading AK8 jets.

  * •

Forward extra jet: At least one of the extra AK4 jets defined above should
have $\abs{\eta}>2.4$.

The last two criteria are imposed in order to ensure evidence that the
selected events contain a diquark ($\PQb\PQq$ or $\PQt\PQq$) system that is
produced in association with the quark, where the quark ($\PQq$) associated
with the vector boson tends to be forward. Events passing the selection
requirements above are by design expected to be almost fully efficient for the
trigger requirements.

Signal candidates passing the primary event selection are further categorized
according to the following criteria. At this stage, the focus is on choosing
the best pair of AK8 jets for constructing the candidate mass.

  * •

Double-tag: Each of the two highest-selected AK8 jets must have either a
$\PQt$ tag or an $\PH/\PZ$ tag. Furthermore, one of these jets must have a
$\PQt$ tag and the other an $\PH/\PZ$ tag. In the ambiguous case, where both
jets are $\PQt$-tagged and $\PH/\PZ$-tagged, the higher-jet is assigned as the
top quark candidate, and the lower-jet as the Higgs/$\PZ$ boson candidate.

  * •

$\PH/\PZ$ tag isolation: Motivated by reducing background, events are rejected
if any AK4 jet is separated from the $\PH/\PZ$ candidate jet by $0.55<\Delta
R(j,\PH/\PZ)<0.9$.

Signal regions, $\mathrm{S}_{\PH}$ and $\mathrm{S}_{\PZ}$, are defined using
these criteria for the $\PQt\PH$ and $\PQt\PZ$ searches respectively. Related
background control regions are defined and are described in Section 0.6.3.

We denote the measured AK8 jet four-vectors corresponding to the top quark,
Higgs boson and candidates as $P_{\PQt}=(E_{\PQt},\vec{p}_{\PQt})$,
$P_{\PH}=(E_{\PH},\vec{p}_{\PH})$ and $P_{\PZ}=(E_{\PZ},\vec{p}_{\PZ})$ and
construct a corrected mass-sensitive observable, $\widetilde{m}_{\PQT}$. This
observable takes advantage of the knowledge of the top quark and Higgs/boson
masses to correct the masses of the AK8 jets; it is inspired by a similar
variable used in Ref. [59] that was based on a suggestion in Ref. [60]. The
reconstructed mass of the candidate from the $\PQt\mathrm{X}$ dijet system
($m_{\PQt\mathrm{X}}^{j}$), with $\mathrm{X}=\PH/\PZ$, is adjusted for
deviations of the reconstructed top quark and Higgs/$\PZ$ boson AK8 masses
($m_{\PQt}^{j}$, $m_{\mathrm{X}}^{j}$) from the known $\PQt$ and $\PH/\PZ$
masses [49], as follows:

$\widetilde{m}_{\PQT}=\sqrt{(P_{\PQt}+P_{\mathrm{X}})^{2}}-\sqrt{P_{\PQt}^{2}}-\sqrt{P_{\mathrm{X}}^{2}}+m_{\PQt}+m_{\mathrm{X}}=m_{\PQt\mathrm{X}}^{j}-(m_{\PQt}^{j}-m_{\PQt})-(m_{\mathrm{X}}^{j}-m_{\mathrm{X}}),$
(5)

where the $j$ superscripts denote jet-based measured mass quantities. This
estimator is found to have better performance in terms of mass resolution by
about 10% compared to the uncorrected mass estimator. It has also been
verified that it is accompanied by a commensurate reduction in background
acceptance.

Example distributions of $\widetilde{m}_{\PQT}$ in the signal regions,
$\mathrm{S}_{\PH}$ and $\mathrm{S}_{\PZ}$, are shown in Fig. 5 for masses of
1.2 and 1.8for both narrow and large widths. It can be seen that in the
$\mathrm{S}_{\PZ}$ region, in addition to the expected efficiency for the
$\PQt\PZ\PQb\PQq$ process, there is also substantial efficiency for the
$\PQt\PH\PQb\PQq$ process; the reverse is not true for the $\mathrm{S}_{\PH}$
region.

Figure 5: Example $\widetilde{m}_{\PQT}$ distributions in the signal regions,
$\mathrm{S}_{\PH}$ (upper row), and $\mathrm{S}_{\PZ}$ (lower row). For
presentation purposes, the cross sections for $\PQt\PH\PQb\PQq$,
$\PQt\PZ\PQb\PQq$, $\PQt\PH\PQt\PQq$ and $\PQt\PZ\PQt\PQq$ are set equal to
1pb for all masses and fractional widths and normalized to the integrated
luminosity of the data set. The left column illustrates potential signals with
a range of masses and widths for the $\PQt\PH\PQb\PQq$ and $\PQt\PZ\PQb\PQq$
channels. The right column illustrates potential signals for one mass and a
large width for all four processes including also $\PQt\PH\PQt\PQq$ and
$\PQt\PZ\PQt\PQq$.

###  0.6.3 Background estimation

The $\widetilde{m}_{\PQT}$ distribution is used to determine the amount of
signal potentially present in the data. A fit is performed that takes
advantage of the relatively narrow signal shape in $\widetilde{m}_{\PQT}$
compared to the broader shape expected from the backgrounds. After the primary
event selection criteria, the main backgrounds are and QCD multijet events.
The background is estimated using simulated events and the QCD multijet
background is estimated using control regions in data. Other smaller
background sources, designated as “other” and consisting of +jets, +jets,
single top quark, $\PQt\PH\PQq$, $\ttbar\PH$, $\PQt\PAQt\PW$, $\PQt\PAQt\PZ$,
$\PW\PW$, $\PZ\PZ$, $\PW\PZ$, $\PW\PH$, and $\PZ\PH$, are all estimated from
simulation. Background templates are constructed using a smoothing procedure
that fits the $\widetilde{m}_{\PQT}$ distributions with an empirical
functional form. A simultaneous fit is then performed in eight regions: four
regions designed to test for tH signal contributions and four regions designed
to test for $\PQt\PZ$ signal contributions. The fit examines all eight regions
and fits the $\widetilde{m}_{\PQT}$ distributions for the amounts of signal
and QCD multijet background contributions using and other backgrounds
predicted from simulation. Fits are performed under three hypothetical signal
scenarios, $\PQt\PH$ only, $\PQt\PZ$ only, and $\PQt\PH$+$\PQt\PZ$. In the
latter case, the small difference in cross section for $\PQt\PH\PQb\PQq$
relative to $\PQt\PZ\PQb\PQq$ is taken from the singlet model calculation. For
$\PQt\PH\PQt\PQq$ relative to $\PQt\PZ\PQt\PQq$, the difference from the
($\PQT\PQB$) doublet model calculation is used.

The criteria described in Section 0.6.2 define the main signal regions
($\mathrm{S}_{\PH}$ and $\mathrm{S}_{\PZ}$) using the $\PQt$-, $\PH$-, and
$\PZ$-tagged jets. The additional six mutually exclusive regions are used as
control regions in the fit and to predict the shapes and normalization of the
QCD multijet background from data; these are denoted $\mathrm{Q}_{\PH}$,
$\mathrm{T}_{\PH}$, $\mathrm{R}_{\PH}$ for the tH signal and
$\mathrm{Q}_{\PZ}$, $\mathrm{L}_{\PZ}$, $\mathrm{R}_{\PZ}$ for the $\PQt\PZ$
signal. Regions $\mathrm{Q}_{\PH}$ and $\mathrm{Q}_{\PZ}$ are control regions
for the QCD multijet background. Region $\mathrm{T}_{\PH}$ is a $\ttbar$
enriched control region, while region $\mathrm{L}_{\PZ}$ has sensitivity to
non-$\bbbar$ decays. Regions $\mathrm{R}_{\PH}$ and $\mathrm{R}_{\PZ}$ serve
as control regions that test the rejection of QCD multijet events by the $\PH$
tag and $\PZ$ tag criteria.

For the definition of the regions, only the two highest $\pt$ AK8 jets are
examined and each jet must be either tagged or reversed-tagged. The signal
region $\mathrm{S}_{\PH}$ requires a $\PQt$-tagged jet and an $\PH$-tagged
jet. Region $\mathrm{Q}_{\PH}$ requires a reversed-$\PQt$-tagged jet and a
reversed-$\PH$-tagged jet. Region $\mathrm{T}_{\PH}$ requires a $\PQt$-tagged
jet and a reversed-$\PH$-tagged jet. Region $\mathrm{R}_{\PH}$ requires a
reversed-$\PQt$-tagged jet and an $\PH$-tagged jet. Regions $\mathrm{R}_{\PH}$
and $\mathrm{S}_{\PH}$ include the isolation requirement on the $\PH$-tagged
jet, while regions $\mathrm{Q}_{\PH}$ and $\mathrm{T}_{\PH}$ do not include an
isolation requirement around the reversed-$\PH$-tagged jet. These choices
define a $\mathrm{T}_{\PH}$ region that is enhanced in events, thus providing
a suitable control region. The order of assigning events starts with region
$\mathrm{S}_{\PH}$, then proceeds with region $\mathrm{T}_{\PH}$,
$\mathrm{R}_{\PH}$, and then $\mathrm{Q}_{\PH}$, where each subsequent region
is not allowed to contain any of the events assigned to the previous region.
In ambiguous cases, as is done for the signal region, the higher-$\pt$ jet is
assigned as the $\PQt$-tagged or reversed-$\PQt$-tagged jet.

There are four analogous regions designated by $\mathrm{Q}_{\PZ}$,
$\mathrm{L}_{\PZ}$, $\mathrm{R}_{\PZ}$, and $\mathrm{S}_{\PZ}$ for events with
$\PQt$\- and $\PZ$-tagged jets for events that do not satisfy the
$\mathrm{Q}_{\PH}$, $\mathrm{T}_{\PH}$, $\mathrm{R}_{\PH}$, or
$\mathrm{S}_{\PH}$ region definitions. The $\mathrm{Q}_{\PZ}$ region has a
reversed-$\PQt$-tagged jet and a reversed-$\PZ$-tagged jet. The
$\mathrm{L}_{\PZ}$ region has a $\PQt$-tagged jet and a reversed-$\PZ$-tagged
jet. The $\mathrm{R}_{\PZ}$ region has a reversed-$\PQt$-tagged jet and a
$\PZ$-tagged jet. These criteria make region $\mathrm{L}_{\PZ}$ sensitive to
hadronic decays of $\PZ$ bosons other than $\bbbar$ from both the
$\PQT\to\PQt\PZ$ signal and the background. Regions $\mathrm{R}_{\PZ}$ and
$\mathrm{S}_{\PZ}$ include the isolation requirement on the $\PZ$-tagged jet,
while regions $\mathrm{Q}_{\PZ}$ and $\mathrm{L}_{\PZ}$ do not include an
isolation requirement around the reversed--tagged jet. Events can only be
assigned to one of the eight regions. These criteria lead to a well-defined
$\widetilde{m}_{\PQT}$ value for each event corresponding to the mass
assignments implicit in the tagging criteria. Table 0.6.3 summarizes the
criteria for the eight regions.

Overview of the criteria used to define the mutually exclusive
$\mathrm{Q}_{\PH}$, $\mathrm{T}_{\PH}$, $\mathrm{R}_{\PH}$,
$\mathrm{S}_{\PH}$, $\mathrm{Q}_{\PZ}$, $\mathrm{L}_{\PZ}$,
$\mathrm{R}_{\PZ}$, and $\mathrm{S}_{\PZ}$ regions. These are based on the
particle tagging criteria for , , and jets and for the reversed--tagged,
reversed--tagged, and reversed--tagged jets using the two highest AK8 jets.
Region Channel First jet Second jet $\PH/\PZ$ tag isolation $\mathrm{Q}_{\PH}$
$\PQt\PH$ reversed--tagged reversed--tagged — $\mathrm{T}_{\PH}$ $\PQt\PH$ tag
reversed--tagged — $\mathrm{R}_{\PH}$ $\PQt\PH$ reversed--tagged tag required
$\mathrm{S}_{\PH}$ $\PQt\PH$ tag tag required $\mathrm{Q}_{\PZ}$ $\PQt\PZ$
reversed--tagged reversed--tagged — $\mathrm{L}_{\PZ}$ $\PQt\PZ$ tag reversed
--tagged — $\mathrm{R}_{\PZ}$ $\PQt\PZ$ reversed--tagged tag required
$\mathrm{S}_{\PZ}$ $\PQt\PZ$ tag tag required

A simultaneous fit is performed to the $\widetilde{m}_{\PQT}$ distributions in
each of the eight regions to determine the amount of signal present. The
signal templates are taken directly from the simulated signal samples. The and
other background contributions are found using the smoothed templates. The
smoothed QCD multijet background shape is determined from the data in region
$\mathrm{Q}_{\PH}$ for the regions $\mathrm{Q}_{\PH}$, $\mathrm{T}_{\PH}$,
$\mathrm{R}_{\PH}$, and $\mathrm{S}_{\PH}$ and in region $\mathrm{Q}_{\PZ}$
for the $\mathrm{Q}_{\PZ}$, $\mathrm{L}_{\PZ}$, $\mathrm{R}_{\PZ}$, and
$\mathrm{S}_{\PZ}$ regions. A binned likelihood fit is performed using the
$\widetilde{m}_{\PQT}$ variable, with 50 bins of 50width over the range
0.6–3.1. All background components, except QCD multijet, are constrained
within uncertainties using predictions from MC simulations. The numbers of QCD
multijet events in regions $\mathrm{S}_{\PH}$ and $\mathrm{S}_{\PZ}$ are
estimated using the control regions. The amount of QCD multijet background in
each of the control regions $\mathrm{Q}_{\PH}$, $\mathrm{T}_{\PH}$,
$\mathrm{R}_{\PH}$, $\mathrm{Q}_{\PZ}$, $\mathrm{L}_{\PZ}$, and
$\mathrm{R}_{\PZ}$ is found from the following. If the expected numbers of QCD
multijet events in the four regions are
$N_{\mathrm{Q}_{\PH}},N_{\mathrm{T}_{\PH}},N_{\mathrm{R}_{\PH}},N_{\mathrm{S}_{\PH}}$
then, if the Higgs boson and reversed-Higgs boson tagging are independent of
the top quark tagging and reversed-top quark tagging criteria, one may write:

$\frac{N_{\mathrm{S}_{\PH}}}{N_{\mathrm{T}_{\PH}}}=\frac{N_{\mathrm{R}_{\PH}}}{N_{\mathrm{Q}_{\PH}}}\Rightarrow
N_{\mathrm{S}_{\PH}}=N_{\mathrm{T}_{\PH}}\,\frac{N_{\mathrm{R}_{\PH}}}{N_{\mathrm{Q}_{\PH}}}.$
(6)

Using Eq. (6), one may then make a data-based prediction of the number of QCD
multijet events in the signal region $\mathrm{S}_{\PH}$. Similarly, the number
of QCD multijet background events in region $\mathrm{S}_{\PZ}$ can be
estimated from
$N_{\mathrm{S}_{\PZ}}=N_{\mathrm{L}_{\PZ}}\,(N_{\mathrm{R}_{\PZ}}/N_{\mathrm{Q}_{\PZ}})$.
In our fitting method, $N_{\mathrm{Q}_{\PH}}$, $N_{\mathrm{T}_{\PH}}$,
$N_{\mathrm{R}_{\PH}}$, $N_{\mathrm{Q}_{\PZ}}$, $N_{\mathrm{L}_{\PZ}}$, and
$N_{\mathrm{R}_{\PZ}}$ are free parameters determined by the fit. The fit
assumes that the double ratios
$(N_{\mathrm{Q}_{\PH}}/N_{\mathrm{R}_{\PH}})/(N_{\mathrm{T}_{\PH}}/N_{\mathrm{S}_{\PH}})$
and
$(N_{\mathrm{Q}_{\PZ}}/N_{\mathrm{R}_{\PZ}})/(N_{\mathrm{L}_{\PZ}}/N_{\mathrm{S}_{\PZ}})$
are consistent with unity in order to constrain the number of QCD multijet
events in regions $\mathrm{S}_{\PH}$ and $\mathrm{S}_{\PZ}$.

The double ratios measured from the QCD multijet simulation are
$(N_{\mathrm{Q}_{\PH}}/N_{\mathrm{R}_{\PH}})/(N_{\mathrm{T}_{\PH}}/N_{\mathrm{S}_{\PH}})=0.77\pm
0.39\pm 0.21$ and
$(N_{\mathrm{Q}_{\PZ}}/N_{\mathrm{R}_{\PZ}})/(N_{\mathrm{L}_{\PZ}}/N_{\mathrm{S}_{\PZ}})=0.67\pm
0.35\pm 0.24$, which are both consistent with the expected value of unity
within their combined statistical and systematic uncertainties. The systematic
uncertainties are discussed in Section 0.7. The predictions of the QCD
multijet contributions, which include the overall effect of the shape and the
normalization, have been validated using a fit that uses a different set of
eight control regions in data. These control regions are mutually exclusive to
the previously defined eight regions, contain larger numbers of events, and
are defined using loose tagging criteria on the tag and the reversed-tag in a
sample that excludes events with forward ($\abs{\eta}>2.4$) jets. The double
ratio is taken to be fully correlated between the $\PQt\PH$ and $\PQt\PZ$
regions, with a central value of 1.0, and an assigned uncertainty of 0.6. This
uncertainty is assessed based on the measured double ratios from the
relatively low number of events in the QCD multijet simulation. The fits to
data are observed to be insensitive to the exact uncertainty used in that the
preferred value for the double ratio tends to be close to unity.

Limits on the signal strength are extracted by fitting the signal and
backgrounds to the data. The fit finds the amount of signal as well as the
amounts of QCD multijet background in each of the eight regions. The QCD
multijet event yields in the regions $\mathrm{Q}_{\PH}$, $\mathrm{T}_{\PH}$,
and $\mathrm{R}_{\PH}$ are allowed to float freely. The QCD multijet event
yield in the region $\mathrm{S}_{\PH}$ is constrained using Eq. (6) with the
double ratio being modeled with a Gaussian prior. The same procedure is used
for regions $\mathrm{Q}_{\PZ}$, $\mathrm{L}_{\PZ}$, $\mathrm{R}_{\PZ}$, and
$\mathrm{S}_{\PZ}$. All other uncertainties are treated either using log-
normal priors (for those that change the event yields only), or as Gaussian
priors (with shape variations corresponding to the $\pm 1$ standard deviation
change in those uncertainties that affect the $\widetilde{m}_{\PQT}$
distributions as well as the yields). The fitting method is validated with a
data sample based on simulation. The fit uses the modified frequentist
approach for confidence levels, taking the profile likelihood ratio as the
test statistic [51, 52] and using the asymptotic approximation for limit
setting [53].

###  0.6.4 High-mass search results

Table 0.6.4 gives the total number of events in regions $\mathrm{Q}_{\PH}$,
$\mathrm{T}_{\PH}$, $\mathrm{R}_{\PH}$, and $\mathrm{S}_{\PH}$, while Table
0.6.4 gives the total number of events in regions $\mathrm{Q}_{\PZ}$,
$\mathrm{L}_{\PZ}$, $\mathrm{R}_{\PZ}$, and $\mathrm{S}_{\PZ}$. These tables
also show the fitted contributions from each background source for the
background-only hypothesis fit when fitting the observed
$\widetilde{m}_{\PQT}$ distributions in the eight regions. Also included in
the tables are the expected numbers of events and efficiencies for various
signals. The efficiencies are inclusive; they include all decay modes of the ,
, and quark.

Post-fit numbers of events for the $\mathrm{Q}_{\PH}$, $\mathrm{T}_{\PH}$,
$\mathrm{R}_{\PH}$, and $\mathrm{S}_{\PH}$ regions for the data and specified
background sources, for the overall eight-region background-only fit. The
uncertainties include both the statistical and systematic components. The
fitted background sums depend on the data. The expected event yields for
various signal samples are also listed with statistical uncertainties only,
along with the corresponding masses () and cross sections (fb). The fractional
width considered is 30%. The percent efficiency in region $\mathrm{S}_{\PH}$
is also noted in parentheses, alongside the event yield.  Data set
${m_{\PQT}}$ $\sigma$ $\mathrm{Q}_{\PH}$ $\mathrm{T}_{\PH}$ $\mathrm{R}_{\PH}$
$\mathrm{S}_{\PH}$ $\PQt\PAQt$+jets — — $140\pm 20$ $230\pm 30$ $16.8\pm 3.8$
$21.7\pm 4.8$ Other background — — $21.7\pm 9.6$ $20.5\pm 7.0$ $7.4\pm 4.4$
$3.0\pm 1.6$ QCD multijet — — $478\pm 42$ $91\pm 35$ $125\pm 12$ $28.4\pm 9.1$
Total background — — $640\pm 28$ $342\pm 23$ $149\pm 12$ $53.1\pm 7.7$ Data —
— 640 345 151 52 $\PQt\PH\PQb\PQq$ 1.2 142 $6.80\pm 0.30$ $10.7\pm 0.4$
$11.6\pm 0.4$ $20.6\pm 0.6$ (0.40) $\PQt\PZ\PQb\PQq$ 1.2 131 $1.56\pm 0.15$
$1.18\pm 0.13$ $0.49\pm 0.08$ $0.73\pm 0.11$ (0.02) $\PQt\PH\PQt\PQq$ 1.2 40.7
$2.32\pm 0.10$ $3.70\pm 0.13$ $3.21\pm 0.12$ $5.63\pm 0.16$ (0.39)
$\PQt\PZ\PQt\PQq$ 1.2 32.9 $0.47\pm 0.03$ $0.49\pm 0.03$ $0.13\pm 0.01$
$0.14\pm 0.02$ (0.01) $\PQt\PH\PQb\PQq$ 1.8 13.6 $1.08\pm 0.04$ $1.54\pm 0.05$
$1.83\pm 0.05$ $2.83\pm 0.07$ (0.58) $\PQt\PZ\PQb\PQq$ 1.8 11.0 $0.12\pm 0.01$
$0.13\pm 0.01$ $0.08\pm 0.01$ $0.07\pm 0.01$ (0.02) $\PQt\PH\PQt\PQq$ 1.8 4.0
$0.33\pm 0.01$ $0.49\pm 0.01$ $0.50\pm 0.01$ $0.76\pm 0.02$ (0.53)
$\PQt\PZ\PQt\PQq$ 1.8 3.2 $0.11\pm 0.01$ $0.11\pm 0.01$ $0.03\pm 0.01$
$0.04\pm 0.01$ (0.03)

Post-fit numbers of events for the $\mathrm{Q}_{\PZ}$, $\mathrm{L}_{\PZ}$,
$\mathrm{R}_{\PZ}$, and $\mathrm{S}_{\PZ}$ regions for the data and specified
background sources, for the overall eight-region background-only fit. The
uncertainties include both the statistical and systematic components. The
fitted background sums depend on the data. The expected event yields for
various signal samples are also listed with statistical uncertainties only,
along with the corresponding masses () and cross sections (fb). The fractional
width considered is 30%. The percent efficiency in region $\mathrm{S}_{\PZ}$
is also noted in parentheses, alongside the event yield.  Data set
${m_{\PQT}}$ $\sigma$ $\mathrm{Q}_{\PZ}$ $\mathrm{L}_{\PZ}$ $\mathrm{R}_{\PZ}$
$\mathrm{S}_{\PZ}$ $\PQt\PAQt$+jets — — $258\pm 32$ $421\pm 53$ $16.4\pm 4.4$
$30.2\pm 5.8$ Other background — — $271\pm 64$ $223\pm 94$ $12.1\pm 4.0$
$2.4\pm 1.5$ QCD multijet — — $5710\pm 150$ $830\pm 230$ $259\pm 19$ $45.0\pm
9.7$ Total background — — $6230\pm 120$ $1480\pm 180$ $288\pm 17$ $77.5\pm
9.7$ Data — — 6253 1475 286 80 $\PQt\PH\PQb\PQq$ 1.2 142 $6.44\pm 0.30$
$10.1\pm 0.4$ $3.46\pm 0.22$ $6.97\pm 0.33$ (0.14) $\PQt\PZ\PQb\PQq$ 1.2 131
$27.3\pm 0.6$ $45.6\pm 0.8$ $6.01\pm 0.29$ $9.87\pm 0.39$ (0.21)
$\PQt\PH\PQt\PQq$ 1.2 40.7 $2.22\pm 0.09$ $3.42\pm 0.12$ $0.93\pm 0.06$
$1.55\pm 0.08$ (0.11) $\PQt\PZ\PQt\PQq$ 1.2 32.9 $4.10\pm 0.08$ $6.71\pm 0.10$
$0.83\pm 0.04$ $1.41\pm 0.05$ (0.12) $\PQt\PH\PQb\PQq$ 1.8 13.6 $1.12\pm 0.04$
$1.48\pm 0.05$ $0.66\pm 0.03$ $1.09\pm 0.04$ (0.22) $\PQt\PZ\PQb\PQq$ 1.8 11.0
$3.98\pm 0.07$ $5.64\pm 0.09$ $0.89\pm 0.04$ $1.21\pm 0.04$ (0.31)
$\PQt\PH\PQt\PQq$ 1.8 4.0 $0.28\pm 0.01$ $0.44\pm 0.01$ $0.15\pm 0.01$
$0.23\pm 0.01$ (0.16) $\PQt\PZ\PQt\PQq$ 1.8 3.2 $1.27\pm 0.02$ $1.83\pm 0.03$
$0.24\pm 0.01$ $0.37\pm 0.01$ (0.32)

The resulting post-fit $\widetilde{m}_{\PQT}$ distributions in data based on
the background-only hypothesis are shown for the $\mathrm{Q}_{\PH}$,
$\mathrm{T}_{\PH}$, $\mathrm{R}_{\PH}$, $\mathrm{S}_{\PH}$ and
$\mathrm{Q}_{\PZ}$, $\mathrm{L}_{\PZ}$, $\mathrm{R}_{\PZ}$, $\mathrm{S}_{\PZ}$
regions in Figs. 6 and 7, respectively. It is found that these post-fit
distributions are consistent with the background-only model with an acceptable
goodness-of-fit.

Figure 6: The background-only post-fit distributions in data for the
$\mathrm{Q}_{\PH}$, $\mathrm{T}_{\PH}$, $\mathrm{R}_{\PH}$, and
$\mathrm{S}_{\PH}$ regions that are used as signal and control regions
primarily for the $\PQT\to\PQt\PH$ channel. The upper plots show regions
$\mathrm{Q}_{\PH}$ (left) and $\mathrm{T}_{\PH}$ (right), while the lower
plots show regions $\mathrm{R}_{\PH}$ (left) and $\mathrm{S}_{\PH}$ (right).
The dashed red histogram is an example $\PQT\to\PQt\PH$ signal for the
$\PQt\PH\PQb\PQq$ process with a 1.2quark mass and a fractional width of 30%
with a cross section from the singlet model of 142fb. The lower panels show
the ratio of observed data to fitted background per bin. The error bars on the
data represent 68% CL Poisson intervals. The light blue band in each ratio
panel shows the fractional uncertainties in the fitted background.

Figure 7: The background-only post-fit distributions in data for the
$\mathrm{Q}_{\PZ}$, $\mathrm{L}_{\PZ}$, $\mathrm{R}_{\PZ}$, and
$\mathrm{S}_{\PZ}$ regions that are used as signal and control regions
primarily for the $\PQT\to\PQt\PZ$ channel. The upper plots show regions
$\mathrm{Q}_{\PZ}$ (left) and $\mathrm{L}_{\PZ}$ (right), while the lower
plots show regions $\mathrm{R}_{\PZ}$ (left) and $\mathrm{S}_{\PZ}$ (right).
The dashed red histogram is an example $\PQT\to\PQt\PZ$ signal for the
$\PQt\PZ\PQb\PQq$ process with a 1.2quark mass and a fractional width of 30%
with a cross section from the singlet model of 131fb. The shorter dashed cyan
histogram is for $\PQT\to\PQt\PH$ signal for the $\PQt\PH\PQb\PQq$ process
with the model assumptions used in Fig. 6. The lower panels show the ratio of
observed data to fitted background per bin. The error bars on the data
represent 68% CL Poisson intervals. The light blue band in each ratio panel
shows the fractional uncertainties in the fitted background.

Upper limits are then set on the cross sections for the two production modes
($\Pp\Pp\to\PQT\PQb\PQq$ and $\Pp\Pp\to\PQT\PQt\PQq$). These upper limits are
reported in Section 0.8 together with the limits from the low-mass search for
four fractional width ($\Gamma/{m_{\PQT}}$) values and the two decay modes
($\PQt\PH$ and $\PQt\PZ$) as well as their sum ($\PQt\PH$+$\PQt\PZ$).

##  0.7 Systematic uncertainties

The systematic uncertainties can be classified into those that affect the
overall yields of the signal and the background processes, and those that
affect the invariant mass distributions $m_{\PQT}$ (for the low-mass search)
and $\widetilde{m}_{\PQT}$ (for the high-mass search). The sources of
systematic uncertainties and their effects on the signal and the background
are summarized in Table 0.7.

The systematic uncertainties in the signal and background yields for each
search. The uncertainties marked “Shape” affect both the event yields and the
distributions.  Source Low-mass High-mass Signal yield (%) Trigger efficiency
3 3 Jet energy scale and resolution Shape Shape Jet mass scale and resolution
— 2 /tagging correction factor — 10 /jet $\tau_{21}$ selection — 7+jet
-dependence Jet mass resolution — Shape Top quark jet tagging — ${}^{+5}_{-3}$
$\PQb$ tagging selection 5–7 Shape PDF 0.5–1 0.5–1 Pileup modeling 2–4 2
Integrated luminosity 2.5 2.5 Renorm./fact. scales 1–2 Shape Background yield
(%) Low-mass background Shape — $\PQt\PAQt$+jets cross section —
${}^{+6.1}_{-5.5}$ +jets cross section — 3.8 QCD multijet background — 23–25
Renorm./fact. scales — Shape

The trigger efficiency for the low-mass category is measured in data and is
found to be about 97% with an assigned uncertainty of 3%. The trigger
efficiency for the high-mass analysis is measured using hadronic triggers to
be over 99.5%. There is a mild dependence on $\widetilde{m}_{\PQT}$ that is
evaluated using a muon-based monitor trigger. The maximum variation is 3% and
this is taken as the uncertainty in the overall event yields.

The jet energy scale uncertainties depend on the and $\eta$ of the jets [61].
The jet energy resolution in data is found to be worse than that in
simulation, and the discrepancy is corrected by applying an extra smearing to
the energy of jets in simulated events. Both the jet energy scale and
resolution uncertainties affect the overall scale and shapes of the invariant
mass distributions.

The uncertainties in the $\PH$ and $\PZ$ jet mass scale and resolution and the
N-subjettiness selection [61] affect the high-mass search. The jet mass scale,
resolution, and the N-subjettiness selection efficiency were measured in a
sample of $\PQt\PAQt$+jets events with one top quark decaying leptonically and
the other top quark decaying hadronically (semileptonic $\PQt\PAQt$+jets
events). The hadronically decaying top quark is boosted enough to produce a
merged $\PW\to\qqbar^{\prime}$ jet separated from a $\PQb$ jet. The jet mass
scale was evaluated to be unity with an uncertainty of 2%. An additional
uncertainty of about 10% was derived using simulations to account for the
difference between the jet showering for a $\PW\to\qqbar^{\prime}$ jet and an
$\PH\to\PQb\PAQb$ or $\PZ\to\PQb\PAQb$ jet, using parton shower models from 8
and 2.7.1 [62]. The EE5C tune [63] is used for . The jet mass resolution was
found to be 23% larger in the data than in the simulation, with an uncertainty
of 18%. The ratio of the N-subjettiness selection efficiency in the data to
the simulation (scale factor) was found to be $1.11\pm 0.08$ with an
additional jet $\pt$-dependent uncertainty. The jet mass scale and
N-subjettiness scale factor uncertainties affect only the event yields whereas
the jet mass resolution affects both the yields and the $\widetilde{m}_{\PQT}$
distributions.

The $\PQb$ tagging [47] efficiency scale factor uncertainties for AK4 jets are
measured in multijets and $\PQt\PAQt$+jets samples, separately for $\PQb$
quark jets and for light-quark and gluon jets. These uncertainties affect only
the signal event yields for the low-mass analysis. For the high-mass analysis,
the $\PQb$ tagging efficiency scale factor uncertainty for subjets is measured
using multijet and $\PQt\PAQt$+jets samples and is applied to the subjets for
$\PH$ and $\PZ$ jets. These affect both the event yields and the
$\widetilde{m}_{\PQT}$ distributions.

For the top quark jet tagging used in the high-mass search, the scale factor
is measured using semileptonic $\PQt\PAQt$+jets events, with a boosted top
quark jet, using the method from Ref. [58]. The scale factor with uncertainty
is $1.07^{+0.05}_{-0.03}$ for each top quark jet, and affects both the event
yields and the $\widetilde{m}_{\PQT}$ distributions.

The PDF uncertainties were evaluated with the PDF4LHC procedure [64], by
reweighting the simulated events using the eigenvectors of the NNPDF3.0 PDF
set; it was found to change the overall event yields by 0.5–1%. The
renormalization and factorization scale uncertainties were estimated by
doubling and halving the nominal values used in the simulations. These were
found to affect the high-mass search event yields for both the signal and the
$\PQt\PAQt$+jets background.

The uncertainty in the measurement of the integrated luminosity amounts to
2.5% [65] and affects the overall event yields of all simulated processes. The
uncertainty associated with the mismodeling of pileup is evaluated based on a
4.6% variation on the $\Pp\Pp$ total inelastic cross section [66] and affects
the overall simulated event yields for both analyses.

The systematic uncertainties in the background estimation for the low-mass
search are taken into account by inflating the uncertainty in the slope
parameter of the transfer functions. This includes an uncertainty component
justified from the difference observed when the weights are computed on light-
flavor versus heavy-flavor jets and an uncertainty component coming from the
uncertainties in the fit arising from the $\PQb$ tagging efficiency. Based on
the observed differences, the slope parameter uncertainty is increased by a
factor of four for the $2M1L$ to $3M$ region transfer function and by a factor
of three for the $3M$ to $3T$ region transfer function. For the high-mass
search, the uncertainty in the background prediction includes the systematic
uncertainty in the double ratio used to constrain the QCD multijet event
yields in the different control and the signal regions (Section 0.6.3). While
the initial uncertainty in this double ratio is assigned to be 60%, the
postfit uncertainty ranges from 23–25%, depending on signal model.

The $\PQt\PAQt$+jets cross section uncertainty is ${}^{+6.1}_{-5.5}\%$ at
next-to-next-to-leading order [67], while that in the +jets cross section is
$3.8\%$ [68, 69]. These uncertainties affect only the high-mass search where
both these backgrounds are taken from simulations. These uncertainties are
propagated to the estimation of the QCD multijet background from the data,
where the non-QCD multijet background components are subtracted from the data.
The uncertainties also include $\widetilde{m}_{\PQT}$-dependent modeling
uncertainties for the various background components.

##  0.8 Search results

The low-mass search is sensitive to masses from 0.6 to 1.2, while the high-
mass search is sensitive to masses from 0.7 to 2.6, but with its main
sensitivity starting around 1. The limits presented here correspond to a
confidence level of 95%. They are based on whichever of the two searches for
each considered mass has the best estimated expected sensitivity for each
decay channel ($\PQt\PH$, $\PQt\PZ$, and the sum), production mode, and
fractional width. The limits from each search considered separately are
presented for completeness in Appendix .10.

Upper limits are set on the cross sections for the two production modes
($\Pp\Pp\to\PQT\PQb\PQq$ and $\Pp\Pp\to\PQT\PQt\PQq$) with the two decay modes
($\PQt\PH$ and $\PQt\PZ$) as well as their sum ($\PQt\PH$+$\PQt\PZ$), for four
fractional width ($\Gamma/{m_{\PQT}}$) values. The individual limits for the
$\PQt\PH$ decay mode neglect potential contributions from the $\PQt\PZ$ and
$\PQb\PW$ decay modes and, similarly, the individual limits for the $\PQt\PZ$
decay mode neglect potential contributions from the $\PQt\PH$ and $\PQb\PW$
decay modes. The ($\PQt\PH$+$\PQt\PZ$) sum limits are computed assuming the
relative cross sections for the two channels calculated in the respective
model for a particular width based on Ref. [70]. For $\Pp\Pp\to\PQT\PQb\PQq$,
this corresponds to the singlet model with
$\kappa_{\PW}=\kappa_{\PH}=\kappa_{\PZ}$ and for $\Pp\Pp\to\PQT\PQt\PQq$, to
the ($\PQT\PQB$) doublet model with $\kappa_{\PH}=\kappa_{\PZ}$ and
$\kappa_{\PW}=0$, where $\kappa_{\PW}$, $\kappa_{\PH}$, and $\kappa_{\PZ}$
denote the coupling parameter to $\PQb\PW$, $\PQt\PH$, and $\PQt\PZ$,
respectively. Only potential contributions from $\PQb\PW$ are neglected for
the ($\PQt\PH$+$\PQt\PZ$) experimental limits. The calculated cross sections
for $\PQt\PH$ and $\PQt\PZ$ are approximately the same but unequal. This is
due both to mass effects explicit in the decay width expressions of Ref. [5]
and to additional amplitude contributions associated with the $2\to 4$ finite
width calculation arising from quark-mediated Feynman diagrams relevant to the
large-width regime, such as the t-channel exchange of a quark. The fractional
width values include 10, 20, and 30%, and the narrow-width case. Given the
estimated effective Gaussian mass resolution of about 5%, the experimental
limits set using the narrow-width simulated signal samples are applicable to
(Breit–Wigner) fractional widths of up to about 5%. The experimental upper
limits on cross sections are generally more restrictive for smaller widths
given the narrower line shapes. The computed cross sections are found to
depend approximately linearly on the fractional width.

Figure 8 shows the cross section upper limits for $\PQT\PQb\PQq$ production
for the $\PQt\PH\PQb\PQq$ and $\PQt\PZ\PQb\PQq$ channels, and their sum. The
figure includes results for a narrow fractional width, corresponding to
$\Gamma/{m_{\PQT}}\leq 0.05$, and for a fractional width of 10%. Results for
the same quantities are shown in Fig. 9 for fractional widths of 20 and 30%.
Similar results for $\PQT\PQt\PQq$ production for the $\PQt\PH\PQt\PQq$ and
$\PQt\PZ\PQt\PQq$ channels, and their sum are shown in Fig. 10 for narrow
fractional width and for 10% fractional width, and the corresponding results
for fractional widths of 20 and 30% are shown in Fig. 11. Superimposed on
these results are the expected cross sections for the singlet model and for
the ($\PQT\PQB$) doublet model. The $\PQt\PH$ and $\PQt\PZ$ branching
fractions for a narrow width are both approximately 25% for the singlet model
and 50% for the ($\PQT\PQB$) doublet model.

The presented results are evaluated using left-handed chirality for the in the
$\Pp\Pp\to\PQT\PQb\PQq$ cases and right-handed chirality for the
$\Pp\Pp\to\PQT\PQt\PQq$ cases. Studies with the opposite chirality for narrow
width $\PQT\to\PQt\PH$ and $\PQT\to\PQt\PZ$ have shown similar sensitivity
with the differences being small for the low-mass search and at most 10% for
the high-mass search.

Figure 8: The observed and median expected upper limits at 95% CL on the cross
sections for production associated with a bottom quark for the
$\PQt\PH\PQb\PQq$ (upper row) and $\PQt\PZ\PQb\PQq$ (middle row) channels, and
their sum, $\PQt\PH\PQb\PQq+\PQt\PZ\PQb\PQq$ (lower row), for different
assumed values of the quark mass. The inner (green) band and the outer
(yellow) band indicate the regions containing 68 and 95%, respectively, of the
distribution of limits expected under the background-only hypothesis. The left
column is for a narrow fractional width ($\Gamma/{m_{\PQT}}\leq 0.05$) and the
right column is for a fractional width of $\Gamma/{m_{\PQT}}=0.1$. The
vertical dashed lines are the crossover points in sensitivity that indicate
the mass intervals used for presenting the low-mass and high-mass search
results. The dashed red and dot-dashed blue curves are for the singlet model.
Given the specified width, the couplings are implicit in the model. Two curves
corresponding to $\Gamma/{m_{\PQT}}=0.05$ (dot-dashed blue) and
$\Gamma/{m_{\PQT}}=0.01$ (dashed red) are shown for the narrow fractional
width.

Figure 9: The observed and median expected upper limits at 95% CL on the cross
sections for production associated with a bottom quark for the
$\PQt\PH\PQb\PQq$ (upper row) and $\PQt\PZ\PQb\PQq$ (middle row) channels, and
their sum, $\PQt\PH\PQb\PQq+\PQt\PZ\PQb\PQq$ (lower row), for different
assumed values of the quark mass. The inner (green) band and the outer
(yellow) band indicate the regions containing 68 and 95%, respectively, of the
distribution of limits expected under the background-only hypothesis. The left
column is for a fractional width of 20% and the right column is for a
fractional width of 30%. The vertical dashed lines are the crossover points in
sensitivity that indicate the mass intervals used for presenting the low-mass
and high-mass search results. The dashed red curves are for the singlet model.
Given the specified width, the couplings are implicit in the model.

Figure 10: The observed and median expected upper limits at 95% CL on the
cross sections for production associated with a top quark for the
$\PQt\PH\PQt\PQq$ (upper row) and $\PQt\PZ\PQt\PQq$ (middle row) channels, and
their sum, $\PQt\PH\PQt\PQq+\PQt\PZ\PQt\PQq$ (lower row), for different
assumed values of the quark mass. The inner (green) band and the outer
(yellow) band indicate the regions containing 68 and 95%, respectively, of the
distribution of limits expected under the background-only hypothesis. The left
column is for a narrow fractional width ($\Gamma/{m_{\PQT}}\leq 0.05$) and the
right column is for a fractional width of $\Gamma/{m_{\PQT}}=0.1$. The
vertical dashed lines are the crossover points in sensitivity that indicate
the mass intervals used for presenting the low-mass and high-mass search
results. The dashed red and dot-dashed blue curves are for the ($\PQT\PQB$)
doublet model. Given the specified width, the couplings are implicit in the
model. Two curves corresponding to $\Gamma/{m_{\PQT}}=0.05$ (dot-dashed blue)
and $\Gamma/{m_{\PQT}}=0.01$ (dashed red) are shown for the narrow fractional
width.

Figure 11: The observed and median expected upper limits at 95% CL on the
cross sections for production associated with a top quark for the
$\PQt\PH\PQt\PQq$ (upper row) and $\PQt\PZ\PQt\PQq$ (middle row) channels, and
their sum, $\PQt\PH\PQt\PQq+\PQt\PZ\PQt\PQq$ (lower row), for different
assumed values of the quark mass. The inner (green) band and the outer
(yellow) band indicate the regions containing 68 and 95%, respectively, of the
distribution of limits expected under the background-only hypothesis. The left
column is for a fractional width of 20% and the right column is for a
fractional width of 30%. The vertical dashed lines are the crossover points in
sensitivity that indicate the mass intervals used for presenting the low-mass
and high-mass search results. The dashed red curves are for the ($\PQT\PQB$)
doublet model. Given the specified width, the couplings are implicit in the
model.

The results of this search show that the observed limits are consistent with
the expected limits arising from the background-only hypothesis. Depending on
the fractional width, it can be seen that this search has expected sensitivity
for masses within the singlet model up to 1.28(for
$\PQt\PH\PQb\PQq+\PQt\PZ\PQb\PQq$ with 30% fractional width). For masses below
1, the models of the associated production with a bottom quark are strongly
constrained by the observed limits from the low-mass search signature, which
are generally more stringent than expected above 0.75; for the singlet model
masses in the range 0.70 to 1.00are excluded at 95% CL for some fractional
widths between 5 and 30%. For the quark masses above 1.00, the observed limits
are above model predictions, and so no exclusion at 95% CL is possible with
this data set in this mass range. The models with the associated production
with a top quark have lower cross sections with a median expected sensitivity
for quark masses within the ($\PQT\PQB$) doublet model of 0.82for the largest
fractional width of 30%. However, for this model, no range of masses is
excluded at 95% CL for any of the masses and fractional widths considered
here.

The presented experimental upper limits on the cross sections are of general
interest in more model independent approaches and demonstrate the great
potential of electroweak single production searches to test vector-like quark
production at mass scales far beyond those accessible with pair production.

##  0.9 Summary

A search for a vector-like top quark partner in the electroweak single
production mode with fully hadronic final states has been performed using
$\Pp\Pp$ collision events at $\sqrt{s}=13\TeV$ collected by the CMS experiment
in 2016. The data sample corresponds to an integrated luminosity of 35.9. The
quarks are assumed to couple only to the standard model third-generation
quarks. The decay channels exploited are $\PQT\to\PQt\PH$ and $\PQT\to\PQt\PZ$
with the hadronic decay of the top quark and primarily the $\bbbar$ decay of
the Higgs and bosons. This search is designed to be sensitive to quark
fractional widths of up to 30% and a wide range of masses. The background is
mostly due to standard model $\PQt\PAQt$+jets and QCD multijet events with
some contributions from +jets processes. No significant excess of data above
the standard model background is observed and upper limits at 95% confidence
level are set on $\sigma\>\mathcal{B}(\PQT\to\PQt\PH)$ and
$\sigma\>\mathcal{B}(\PQT\to\PQt\PZ)$, which vary between 2pb and 20fb for
masses ranging from 0.6 to 2.6in the $\PQT\PQb\PQq$ and $\PQT\PQt\PQq$
production channels. Results from combining the two decay channels assuming
equal couplings are also reported. Compared with prior electroweak single
production searches, this search is significantly more sensitive for
$\PQT\to\PQt\PH$. The search gives the first constraints using this production
mode on $\PQT\to\PQt\PZ$ for hadronic decays of the boson. These results are
competitive with those from searches for $\PQT\to\PQt\PZ$ using other decay
modes. The combined $\PQT\to\PQt\PH$ and $\PQT\to\PQt\PZ$ results for
associated production with a bottom quark lead to constraints on quarks in the
singlet model for masses below 1.00. The expected sensitivity for this model
extends to 1.28(for 30% fractional width), which is comparable to the mass
reach of the most stringent pair production searches.

###### 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); RPF
(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 and SFFR (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. Z181100004218003; the
Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; 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 Education, grant no. 3.2989.2017 (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 Nvidia Corporation;
the Welch Foundation, contract C-1845; and the Weston Havens Foundation (USA).

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##  .10 Low-mass and high-mass search limits

For information, we show here separately the limits obtained with each of the
two search signatures that were used to give the final search limit results
presented in Figs. 8–11 of the paper.

Figure 12: The observed and median expected upper limits at 95% CL on the
cross sections for production associated with a bottom quark for the
$\PQt\PH\PQb\PQq$ (upper row) and $\PQt\PZ\PQb\PQq$ (middle row) channels, and
their sum, $\PQt\PH\PQb\PQq+\PQt\PZ\PQb\PQq$ (lower row), for different
assumed values of the quark mass. The inner (green) bands and the outer
(yellow) bands indicate the regions containing 68 and 95%, respectively, of
the distribution of limits expected under the background-only hypothesis. The
left column is for a narrow fractional width ($\Gamma/{m_{\PQT}}\leq 0.05$)
and the right column is for a fractional width of $\Gamma/{m_{\PQT}}=0.1$. The
dashed red curves are for the singlet model. Given the specified width, the
couplings are implicit in the model.

Figure 13: The observed and median expected upper limits at 95% CL on the
cross sections for production associated with a bottom quark for the
$\PQt\PH\PQb\PQq$ (upper row) and $\PQt\PZ\PQb\PQq$ (middle row) channels, and
their sum, $\PQt\PH\PQb\PQq+\PQt\PZ\PQb\PQq$ (lower row), for different
assumed values of the quark mass. The inner (green) bands and the outer
(yellow) bands indicate the regions containing 68 and 95%, respectively, of
the distribution of limits expected under the background-only hypothesis. The
left column is for a fractional width of 20% and the right column is for a
fractional width of 30%. The dashed red curves are for the singlet model.
Given the specified width, the couplings are implicit in the model.

Figure 14: The observed and median expected upper limits at 95% CL on the
cross sections for production associated with a top quark for the
$\PQt\PH\PQt\PQq$ (upper row) and $\PQt\PZ\PQt\PQq$ (middle row) channels, and
their sum, $\PQt\PH\PQt\PQq+\PQt\PZ\PQt\PQq$ (lower row), for different
assumed values of the quark mass. The inner (green) bands and the outer
(yellow) bands indicate the regions containing 68 and 95%, respectively, of
the distribution of limits expected under the background-only hypothesis. The
left column is for a narrow fractional width ($\Gamma/{m_{\PQT}}\leq 0.05$)
and the right column is for a fractional width of $\Gamma/{m_{\PQT}}=0.1$. The
dashed red curves are for the ($\PQT\PQB$) doublet model. Given the specified
width, the couplings are implicit in the model.

Figure 15: The observed and median expected upper limits at 95% CL on the
cross sections for production associated with a top quark for the
$\PQt\PH\PQt\PQq$ (upper row) and $\PQt\PZ\PQt\PQq$ (middle row) channels, and
their sum, $\PQt\PH\PQt\PQq+\PQt\PZ\PQt\PQq$ (lower row), for different
assumed values of the quark mass. The inner (green) bands and the outer
(yellow) bands indicate the regions containing 68 and 95%, respectively, of
the distribution of limits expected under the background-only hypothesis. The
left column is for a fractional width of 20% and the right column is for a
fractional width of 30%. The dashed red curves are for the ($\PQT\PQB$)
doublet model. Given the specified width, the couplings are implicit in the
model.

##  .11 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan${}^{\textrm{\textdagger}}$, A. Tumasyan Institut für
Hochenergiephysik, Wien, Austria
W. Adam, F. Ambrogi, T. Bergauer, J. Brandstetter, M. Dragicevic, J. Erö, A.
Escalante Del Valle, M. Flechl, R. Frühwirth1, M. Jeitler1, N. Krammer, I.
Krätschmer, D. Liko, T. Madlener, I. Mikulec, N. Rad, J. Schieck1, R.
Schöfbeck, M. Spanring, D. Spitzbart, W. Waltenberger, C.-E. Wulz1, M. Zarucki
Institute for Nuclear Problems, Minsk, Belarus
V. Drugakov, V. Mossolov, J. Suarez Gonzalez Universiteit Antwerpen,
Antwerpen, Belgium
M.R. Darwish, E.A. De Wolf, D. Di Croce, X. Janssen, 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, Q. Python, K. Skovpen, S. Tavernier,
W. Van Doninck, P. Van Mulders Université Libre de Bruxelles, Bruxelles,
Belgium
D. Beghin, B. Bilin, H. Brun, B. Clerbaux, G. De Lentdecker, H. Delannoy, B.
Dorney, L. Favart, A. Grebenyuk, A.K. Kalsi, A. Popov, N. Postiau, E.
Starling, L. Thomas, C. Vander Velde, P. Vanlaer, D. Vannerom Ghent
University, Ghent, Belgium
T. Cornelis, D. Dobur, I. Khvastunov2, M. Niedziela, C. Roskas, D. Trocino, M.
Tytgat, W. Verbeke, B. Vermassen, M. Vit, N. Zaganidis Université Catholique
de Louvain, Louvain-la-Neuve, Belgium
O. Bondu, G. Bruno, C. Caputo, P. David, C. Delaere, M. Delcourt, A.
Giammanco, V. Lemaitre, A. Magitteri, J. Prisciandaro, A. Saggio, M. Vidal
Marono, P. Vischia, J. Zobec Centro Brasileiro de Pesquisas Fisicas, Rio de
Janeiro, Brazil
F.L. Alves, G.A. Alves, G. Correia Silva, C. Hensel, A. Moraes, P. Rebello
Teles Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
E. Belchior Batista Das Chagas, W. Carvalho, J. Chinellato3, E. Coelho, E.M.
Da Costa, G.G. Da Silveira4, D. De Jesus Damiao, C. De Oliveira Martins, S.
Fonseca De Souza, L.M. Huertas Guativa, H. Malbouisson, J. Martins5, D. Matos
Figueiredo, M. Medina Jaime6, M. Melo De Almeida, C. Mora Herrera, L. Mundim,
H. Nogima, W.L. Prado Da Silva, L.J. Sanchez Rosas, A. Santoro, A. Sznajder,
M. Thiel, E.J. Tonelli Manganote3, F. Torres Da Silva De Araujo, A. Vilela
Pereira Universidade Estadual Paulista a, Universidade Federal do ABC b, São
Paulo, Brazil
C.A. Bernardesa, L. Calligarisa, T.R. Fernandez Perez Tomeia, E.M. Gregoresb,
D.S. Lemos, P.G. Mercadanteb, S.F. Novaesa, SandraS. Padulaa Institute for
Nuclear Research and Nuclear Energy, Bulgarian Academy of Sciences, Sofia,
Bulgaria
A. Aleksandrov, G. Antchev, 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 Beihang
University, Beijing, China
W. Fang7, X. Gao7, L. Yuan Institute of High Energy Physics, Beijing, China
M. Ahmad, G.M. Chen, H.S. Chen, M. Chen, C.H. Jiang, D. Leggat, H. Liao, Z.
Liu, S.M. Shaheen8, A. Spiezia, J. Tao, E. Yazgan, H. Zhang, S. Zhang8, J.
Zhao State Key Laboratory of Nuclear Physics and Technology, Peking
University, Beijing, China
A. Agapitos, Y. Ban, G. Chen, A. Levin, J. Li, L. Li, Q. Li, Y. Mao, S.J.
Qian, D. Wang, Q. Wang Tsinghua University, Beijing, China
Z. Hu, Y. Wang Zhejiang University, Hangzhou, China
M. Xiao Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, C. Florez, C.F. González Hernández, M.A. Segura Delgado
Universidad de Antioquia, Medellin, Colombia
J. Mejia Guisao, 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. Giljanović, N. Godinovic, D. Lelas, I. Puljak, T. Sculac University of
Split, Faculty of Science, Split, Croatia
Z. Antunovic, M. Kovac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, S. Ceci, D. Ferencek, K. Kadija, B. Mesic, M. Roguljic, A.
Starodumov9, T. Susa University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, E. Erodotou, A. Ioannou, M. Kolosova, S. Konstantinou,
G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos, P.A. Razis, H.
Rykaczewski, D. Tsiakkouri Charles University, Prague, Czech Republic
M. Finger10, M. Finger Jr.10, 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. Elgammal11, E. Salama11,12 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
F. Garcia, J. Havukainen, J.K. Heikkilä, T. Järvinen, V. Karimäki, M.S. Kim,
R. Kinnunen, T. Lampén, K. Lassila-Perini, S. Laurila, S. Lehti, T. Lindén, P.
Luukka, T. Mäenpää, H. Siikonen, E. Tuominen, J. Tuominiemi Lappeenranta
University of Technology, Lappeenranta, Finland
T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
M. Besancon, F. Couderc, M. Dejardin, D. Denegri, B. Fabbro, J.L. Faure, F.
Ferri, S. Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry,
C. Leloup, E. Locci, J. Malcles, J. Rander, A. Rosowsky, M.Ö. Sahin, A. Savoy-
Navarro13, M. Titov Laboratoire Leprince-Ringuet, Ecole polytechnique,
CNRS/IN2P3, Université Paris-Saclay, Palaiseau, France
S. Ahuja, C. Amendola, F. Beaudette, P. Busson, C. Charlot, B. Diab, G.
Falmagne, R. Granier de Cassagnac, 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. Agram14, J. Andrea, D. Bloch, G. Bourgatte, J.-M. Brom, E.C. Chabert, C.
Collard, E. Conte14, J.-C. Fontaine14, D. Gelé, U. Goerlach, M. Jansová, A.-C.
Le Bihan, N. Tonon, P. Van Hove Centre de Calcul de l’Institut National de
Physique Nucleaire et de Physique des Particules, CNRS/IN2P3, Villeurbanne,
France
S. Gadrat Université de Lyon, Université Claude Bernard Lyon 1, CNRS-IN2P3,
Institut de Physique Nucléaire de Lyon, Villeurbanne, France
S. Beauceron, C. Bernet, G. Boudoul, C. Camen, A. Carle, N. Chanon, R.
Chierici, D. Contardo, P. Depasse, H. El Mamouni, J. Fay, S. Gascon, M.
Gouzevitch, B. Ille, Sa. Jain, F. Lagarde, I.B. Laktineh, H. Lattaud, A.
Lesauvage, M. Lethuillier, L. Mirabito, S. Perries, V. Sordini, L. Torterotot,
G. Touquet, M. Vander Donckt, S. Viret Georgian Technical University, Tbilisi,
Georgia
T. Toriashvili15 Tbilisi State University, Tbilisi, Georgia
Z. Tsamalaidze10 RWTH Aachen University, I. Physikalisches Institut, Aachen,
Germany
C. Autermann, L. Feld, M.K. Kiesel, K. Klein, M. Lipinski, D. Meuser, A.
Pauls, M. Preuten, M.P. Rauch, C. Schomakers, J. Schulz, M. Teroerde, B.
Wittmer RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
A. Albert, M. Erdmann, B. Fischer, S. Ghosh, T. Hebbeker, K. Hoepfner, H.
Keller, L. Mastrolorenzo, M. Merschmeyer, A. Meyer, P. Millet, G. Mocellin, S.
Mondal, S. Mukherjee, D. Noll, A. Novak, T. Pook, A. Pozdnyakov, T. Quast, M.
Radziej, 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
G. Flügge, W. Haj Ahmad16, O. Hlushchenko, T. Kress, T. Müller, A. Nehrkorn,
A. Nowack, C. Pistone, O. Pooth, D. Roy, H. Sert, A. Stahl17 Deutsches
Elektronen-Synchrotron, Hamburg, Germany
M. Aldaya Martin, P. Asmuss, I. Babounikau, H. Bakhshiansohi, K. Beernaert, O.
Behnke, A. Bermúdez Martínez, D. Bertsche, A.A. Bin Anuar, K. Borras18, V.
Botta, A. Campbell, A. Cardini, P. Connor, S. Consuegra Rodríguez, C.
Contreras-Campana, V. Danilov, A. De Wit, M.M. Defranchis, C. Diez Pardos, D.
Domínguez Damiani, G. Eckerlin, D. Eckstein, T. Eichhorn, A. Elwood, E. Eren,
E. Gallo19, A. Geiser, A. Grohsjean, M. Guthoff, M. Haranko, A. Harb, A.
Jafari, N.Z. Jomhari, H. Jung, A. Kasem18, M. Kasemann, H. Kaveh, J. Keaveney,
C. Kleinwort, J. Knolle, D. Krücker, W. Lange, T. Lenz, J. Leonard, J.
Lidrych, K. Lipka, W. Lohmann20, R. Mankel, I.-A. Melzer-Pellmann, A.B. Meyer,
M. Meyer, M. Missiroli, G. Mittag, J. Mnich, A. Mussgiller, V. Myronenko, D.
Pérez Adán, S.K. Pflitsch, D. Pitzl, A. Raspereza, A. Saibel, M. Savitskyi, V.
Scheurer, P. Schütze, C. Schwanenberger, R. Shevchenko, A. Singh, H. Tholen,
O. Turkot, A. Vagnerini, M. Van De Klundert, R. Walsh, Y. Wen, K. Wichmann, C.
Wissing, O. Zenaiev, R. Zlebcik University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, L. Benato, A. Benecke, V. Blobel, T. Dreyer, A.
Ebrahimi, F. Feindt, A. Fröhlich, C. Garbers, E. Garutti, D. Gonzalez, P.
Gunnellini, J. Haller, A. Hinzmann, A. Karavdina, G. Kasieczka, R. Klanner, R.
Kogler, N. Kovalchuk, S. Kurz, V. Kutzner, J. Lange, T. Lange, A. Malara, J.
Multhaup, C.E.N. Niemeyer, A. Perieanu, A. Reimers, O. Rieger, C. Scharf, P.
Schleper, S. Schumann, J. Schwandt, J. Sonneveld, H. Stadie, G. Steinbrück,
F.M. Stober, M. Stöver, B. Vormwald, I. Zoi Karlsruher Institut fuer
Technologie, Karlsruhe, Germany
M. Akbiyik, C. Barth, M. Baselga, S. Baur, T. Berger, E. Butz, R. Caspart, T.
Chwalek, W. De Boer, A. Dierlamm, K. El Morabit, N. Faltermann, M. Giffels, P.
Goldenzweig, A. Gottmann, M.A. Harrendorf, F. Hartmann17, U. Husemann, S.
Kudella, S. Mitra, M.U. Mozer, D. Müller, Th. Müller, M. Musich, A. Nürnberg,
G. Quast, K. Rabbertz, M. Schröder, I. Shvetsov, H.J. Simonis, R. Ulrich, M.
Wassmer, M. Weber, C. Wöhrmann, R. Wolf 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 National and Kapodistrian University of Athens, Athens, Greece
M. Diamantopoulou, G. Karathanasis, P. Kontaxakis, A. Manousakis-katsikakis,
A. Panagiotou, I. Papavergou, N. Saoulidou, A. Stakia, K. Theofilatos, K.
Vellidis, E. Vourliotis National Technical University of Athens, Athens,
Greece
G. Bakas, K. Kousouris, I. Papakrivopoulos, G. Tsipolitis University of
Ioánnina, Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, S. Mallios, K.
Manitara, N. Manthos, I. Papadopoulos, J. Strologas, F.A. Triantis, D.
Tsitsonis MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
M. Bartók21, R. Chudasama, M. Csanad, P. Major, K. Mandal, A. Mehta, M.I.
Nagy, G. Pasztor, O. Surányi, G.I. Veres Wigner Research Centre for Physics,
Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath22, F. Sikler, T.Á. Vámi, V. Veszpremi, G.
Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear Research ATOMKI,
Debrecen, Hungary
N. Beni, S. Czellar, J. Karancsi21, A. Makovec, J. Molnar, Z. Szillasi
Institute of Physics, University of Debrecen, Debrecen, Hungary
P. Raics, D. Teyssier, Z.L. Trocsanyi, B. Ujvari Eszterhazy Karoly University,
Karoly Robert Campus, Gyongyos, Hungary
T. Csorgo, W.J. Metzger, F. Nemes, T. Novak Indian Institute of Science
(IISc), Bangalore, India
S. Choudhury, J.R. Komaragiri, P.C. Tiwari National Institute of Science
Education and Research, HBNI, Bhubaneswar, India
S. Bahinipati24, C. Kar, G. Kole, P. Mal, V.K. Muraleedharan Nair Bindhu, A.
Nayak25, D.K. Sahoo24, S.K. Swain Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, S. Chauhan, R. Chawla, N. Dhingra, R.
Gupta, A. Kaur, M. Kaur, S. Kaur, P. Kumari, M. Lohan, M. Meena, K. Sandeep,
S. Sharma, J.B. Singh, A.K. Virdi, G. Walia University of Delhi, Delhi, India
A. Bhardwaj, B.C. Choudhary, R.B. Garg, M. Gola, S. Keshri, Ashok Kumar, S.
Malhotra, M. Naimuddin, P. Priyanka, K. Ranjan, Aashaq Shah, R. Sharma Saha
Institute of Nuclear Physics, HBNI, Kolkata, India
R. Bhardwaj26, M. Bharti26, R. Bhattacharya, S. Bhattacharya, U. Bhawandeep26,
D. Bhowmik, S. Dutta, S. Ghosh, M. Maity27, K. Mondal, S. Nandan, A. Purohit,
P.K. Rout, G. Saha, S. Sarkar, T. Sarkar27, M. Sharan, B. Singh26, S. Thakur26
Indian Institute of Technology Madras, Madras, India
P.K. Behera, P. Kalbhor, A. Muhammad, P.R. Pujahari, A. Sharma, A.K. Sikdar
Bhabha Atomic Research Centre, Mumbai, India
D. Dutta, V. Jha, V. Kumar, D.K. Mishra, P.K. Netrakanti, L.M. Pant, P. Shukla
Tata Institute of Fundamental Research-A, Mumbai, India
T. Aziz, M.A. Bhat, S. Dugad, G.B. Mohanty, N. Sur, RavindraKumar Verma Tata
Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, S. Karmakar,
S. Kumar, G. Majumder, K. Mazumdar, N. Sahoo, S. Sawant Indian Institute of
Science Education and Research (IISER), Pune, India
S. Chauhan, S. Dube, V. Hegde, B. Kansal, A. Kapoor, K. Kothekar, S. Pandey,
A. Rane, A. Rastogi, S. Sharma Institute for Research in Fundamental Sciences
(IPM), Tehran, Iran
S. Chenarani28, E. Eskandari Tadavani, S.M. Etesami28, M. Khakzad, M.
Mohammadi Najafabadi, M. Naseri, F. Rezaei Hosseinabadi 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,29, C. Calabriaa,b, A. Colaleoa, D. Creanzaa,c, L.
Cristellaa,b, N. De Filippisa,c, M. De Palmaa,b, A. Di Florioa,b, L. Fiorea,
A. Gelmia,b, G. Iasellia,c, M. Incea,b, S. Lezkia,b, G. Maggia,c, M. Maggia,
G. Minielloa,b, S. Mya,b, S. Nuzzoa,b, A. Pompilia,b, G. Pugliesea,c, R.
Radognaa, A. Ranieria, G. Selvaggia,b, L. Silvestrisa, F.M. Simonea, 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,b, S. Braibant-
Giacomellia,b, R. Campaninia,b, P. Capiluppia,b, A. Castroa,b, F.R. Cavalloa,
C. Cioccaa, G. Codispotia,b, M. Cuffiania,b, G.M. Dallavallea, F. Fabbria, A.
Fanfania,b, E. Fontanesia,b, P. Giacomellia, C. Grandia, L. Guiduccia,b, F.
Iemmia,b, S. Lo Meoa,30, 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,31, S. Costaa,b, A. Di Mattiaa, R. Potenzaa,b, A. Tricomia,b,31,
C. Tuvea,b INFN Sezione di Firenze a, Università di Firenze b, Firenze, Italy
G. Barbaglia, A. Cassese, R. Ceccarelli, K. Chatterjeea,b, V. Ciullia,b, C.
Civininia, R. D’Alessandroa,b, E. Focardia,b, G. Latinoa,b, P. Lenzia,b, M.
Meschinia, S. Paolettia, 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, V. Cirioloa,b,17, S. Di Guidaa,b,17,
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.