HIG-17-018

$HeadURL:svn+ssh://svn.cern.ch/reps/tdr2/papers/HIG-17-018/trunk/HIG-17-018.tex$
$Id:HIG-17-018.tex4671212018-07-0218:24:30Zalverson$

HIG-17-018

# Evidence for associated production of a Higgs boson with a top quark pair in
final states with electrons, muons, and hadronically decaying $\PGt$ leptons
at $\sqrt{s}=13\TeV$

###### Abstract

Results of a search for the standard model Higgs boson produced in association
with a top quark pair ($\PQt\PAQt\PH$) in final states with electrons, muons,
and hadronically decaying $\PGt$ leptons are presented. The analyzed data set
corresponds to an integrated luminosity of 35.9recorded in proton-proton
collisions at $\sqrt{s}=13\TeV$ by the CMS experiment in 2016. The sensitivity
of the search is improved by using matrix element and machine learning methods
to separate the signal from backgrounds. The measured signal rate amounts to
$1.23^{+0.45}_{-0.43}$ times the production rate expected in the standard
model, with an observed (expected) significance of $3.2\sigma$ ($2.8\sigma$),
which represents evidence for $\PQt\PAQt\PH$ production in those final states.
An upper limit on the signal rate of 2.1 times the standard model production
rate is set at 95% confidence level.

##  0.1 Introduction

The observation of a Higgs boson ($\PH$) by the ATLAS and the CMS experiments
[1, 2, 3] represents a major step towards the understanding of the mechanism
for electroweak symmetry breaking (EWSB). The current most precise measurement
of the Higgs boson mass, obtained by the CMS Collaboration, is $125.26\pm
0.21\GeV$ [4]. The standard model (SM) makes precise predictions for all
properties of the Higgs boson, given its mass. Within uncertainties, all
measured properties of the discovered resonance are consistent with
expectations for the SM Higgs boson, corroborating the mechanism for EWSB in
the SM. In particular, the discovered particle is known to have zero spin and
positive parity [5, 6]. Within the present experimental uncertainties, its
coupling to fermions is found to be proportional to the fermion mass, as
predicted by the SM. In order to confirm that the mechanism for EWSB included
in the SM is indeed realized in nature, it is important to perform more
precise measurements of the Higgs boson properties.

The measurement of the Yukawa coupling of the Higgs boson to the top quark,
$y_{\cPqt}$, is of high phenomenological interest for several reasons. The
extraordinarily large value of the top quark mass, compared to the masses of
all other known fermions, may indicate that the top quark plays a still-
unknown special role in the EWSB mechanism. The measurement of the rate at
which Higgs bosons are produced in association with top quark pairs
($\cPqt\cPaqt\PH$ production) provides the most precise model-independent
determination of $y_{\cPqt}$. An example of a Feynman diagram for
$\cPqt\cPaqt\PH$ production in proton-proton ($\Pp\Pp$) collisions is shown in
Fig. 1. Since the rate for the gluon fusion Higgs boson production process is
dominated by top quark loops, a comparison of $y_{\cPqt}$ measured through
this production channel and through the $\cPqt\cPaqt\PH$ production channel
will provide powerful constraints on new physics potentially introduced into
the gluon fusion process by additional loop contributions.

Figure 1:  An example of a Feynman diagram for $\cPqt\cPaqt\PH$ production,
with subsequent decay of the Higgs boson to a pair of $\Pgt$ leptons,
producing a final state with two same-sign leptons and one reconstructed
hadronic $\tau$ lepton decay ($\Pgt_{\mathrm{h}}$).

The associated production of a Higgs boson with a top quark pair in $\Pp\Pp$
collisions at a center-of-mass energy of $\sqrt{s}=8\TeV$ has been studied in
the $\PH\to\PQb\PQb$ and $\PH\to\PGg\PGg$ decay modes as well as in
multilepton final states by the ATLAS and CMS Collaborations [7, 8, 9, 10, 11,
12]. The final states with multiple leptons cover the decay modes
$\PH\to\PW\PW$, $\PH\to\cPZ\cPZ$, and $\PH\to\Pgt\Pgt$. The ATLAS
Collaboration recently reported evidence for the $\PQt\PAQt\PH$ process
observed in the combination of several final states with data recorded at
$\sqrt{s}=13\TeV$ [13, 14]. In this paper, we present the results of a search
for $\cPqt\cPaqt\PH$ production in multilepton final states in $\Pp\Pp$
collision data recorded with the CMS detector at $\sqrt{s}=13\TeV$. The
analysis is performed in six event categories, distinguished by the number of
light charged leptons (electrons and muons, generically referred to as leptons
in the rest of this document) and the number of reconstructed hadronic $\Pgt$
lepton decays in the event. We denote by the symbol $\Pgt_{\mathrm{h}}$ the
system of charged and neutral hadrons produced in hadronic $\Pgt$ lepton
decays. The sensitivity of the analysis is enhanced by means of multivariate
analysis (MVA) techniques based on boosted decision trees (BDTs) [15, 16] and
by matrix element method (MEM) discriminants [17, 18].

This paper is structured as follows: the apparatus and the data samples are
described in Sections 2 and 3. Section 4 summarizes the event reconstruction.
The event selection and the background estimation are described in Sections 5
and 6. Section 7 focuses on the signal extraction techniques. The systematic
uncertainties are discussed in Section 8. Section 9 presents event yields,
kinematic distributions, and measured properties, while the results are
summarized in Section 10. Details about the MEM computation are provided in
Appendix .11.

##  0.2 The CMS detector

The central feature of the CMS apparatus is a superconducting solenoid of 6m
internal diameter, providing a magnetic field of 3.8T. 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, are positioned within the solenoid volume. The
silicon tracker measures charged particles within the pseudorapidity range
$\abs{\eta}<2.5$. Tracks of isolated muons of transverse momentum $\pt\geq
100\GeV$ emitted at $\abs{\eta}<1.4$ are reconstructed with an efficiency
close to 100% and resolutions of 2.8% in and 10 (30)in the transverse
(longitudinal) impact parameter [19]. The ECAL is a fine-grained hermetic
calorimeter with quasi-projective geometry, and is segmented into the barrel
region of $\abs{\eta}<1.48$ and in two endcaps that extend up to
$\abs{\eta}<3.0$. The HCAL barrel and endcaps similarly cover the region
$\abs{\eta}<3.0$. Forward calorimeters extend the coverage up to
$\abs{\eta}<5.0$. Muons are measured and identified in the range
$\abs{\eta}<2.4$ by gas-ionization detectors embedded in the steel flux-return
yoke outside the solenoid. A two-level trigger system is used to reduce the
rate of recorded events to a level suitable for data acquisition and storage
[20]. The first level of the CMS trigger system, composed of custom hardware
processors, uses information from the calorimeters and muon detectors to
select the most interesting events in a fixed time interval of less than 4.
The high-level trigger processor farm further decreases the event rate from
around 100kHz to less than 1kHz. Details of the CMS detector and its
performance, together with a definition of the coordinate system and the
kinematic variables used in the analysis, can be found in Ref. [21].

##  0.3 Data samples and Monte Carlo simulation

The analyzed data set was collected in $\Pp\Pp$ collisions at
$\sqrt{s}=13\TeV$ in 2016 and corresponds to an integrated luminosity of 35.9.
The events were recorded using a combination of triggers based on the presence
of one, two, or three electrons and muons or based on the presence of an
electron or muon and a hadronic $\Pgt$ lepton decay.

The data are compared to signal and background estimations based on Monte
Carlo (MC) simulated samples and data-driven techniques. The main irreducible
background to the analysis, arising from the associated production of a top
quark pair with one or two $\PW$ or $\cPZ$ bosons ($\PQt\PAQt\cPZ$,
$\PQt\PAQt\PW$, and $\PQt\PAQt\PW\PW$) is modeled using MC simulation. The sum
of these contributions is referred to as the $\PQt\PAQt\mathrm{V}$ background.
Other relevant backgrounds that are modeled by MC simulation include
$\cPZ\PGg$+jets, $\PW\PGg$+jets, $\cPqt\cPaqt\PGg$ and $\cPqt\cPaqt\PGg^{*}$,
single top, diboson ($\PW\PW$, $\PW\cPZ$, and $\PZ\cPZ$) and triboson
($\PW\PW\PW$, $\PW\PW\PZ$, $\PW\cPZ\cPZ$, and $\PZ\cPZ\cPZ$) production, the
production of SM Higgs bosons in association with single top quarks
($\PQt\PH$), and a few selected “rare” processes. These rare processes, such
as $\cPqt\cPaqt\cPqt\cPaqt$, and the production of same-sign $\PW$ boson
pairs, typically have very small cross sections, but may nevertheless yield
nonnegligible background contributions. The contribution to the signal regions
from the production of SM Higgs bosons through the gluon fusion and vector
boson fusion processes, as well as their production in association with $\PW$
or $\cPZ$ bosons, is negligible. Separate event samples are generated to
simulate the production of single top quarks in association with jets,
photons, and $\PW$ and $\cPZ$ bosons. The reducible $\cPZ$+jets, $\PW$+jets
and $\PQt\PAQt\text{+jets}$ backgrounds are determined from data. Simulated
$\PQt\PAQt\text{+jets}$ samples, produced using the leading order (LO) matrix
elements implemented in the $2.2.2$ program [22, 23, 24], are used solely for
the purpose of validating the data-driven background estimation methods.
Samples for other background processes and for the $\PQt\PAQt\PH$ signal are
generated using next-to-leading order (NLO) matrix elements implemented in the
programs and $v2$ [25, 26, 27, 28]. The signal events are generated for a
Higgs boson mass of $\MH=125\GeV$, while a top quark mass of
$M_{\cPqt}=172.5\GeV$ is used for all simulated processes involving a top
quark. All samples are generated using the NNPDF3.0 [29, 30, 31] set of parton
distribution functions (PDFs). Parton shower and hadronization processes are
modeled using the generator $8.212$ [32] with the CUETP8M1 tune [33]. The
decays of $\Pgt$ leptons, including polarization effects, are modeled by . All
samples containing top quark pairs as well as the $\cPZ/\PGg^{*}\to\ell\ell$
and $\PW$+jets samples are normalized to cross sections computed at next-to-
next-to-leading order accuracy in perturbative quantum chromodynamics (pQCD)
[34, 35]. The cross sections for single top quark [36, 37, 38] and diboson
[39] production are computed at NLO accuracy in pQCD.

Minimum bias events generated with are overlaid on all simulated events
according to the luminosity profile of the analyzed data. In the analyzed data
set, an average of approximately 23 inelastic $\Pp\Pp$ interactions (pileup)
occur per bunch crossing.

All generated events are passed through a detailed simulation of the CMS
apparatus, based on [40], and are processed using the same version of the CMS
event reconstruction software as used for data.

Small corrections are applied to simulated events as data-to-MC scale factors
in order to improve the modeling of the data. The efficiency of the triggers
based on the presence of one, two, or three electrons or muons, as well as the
efficiency for electrons or muons to pass the lepton reconstruction,
identification, and isolation criteria, are measured using
$\cPZ/\PGg^{*}\to\Pe\Pe$ and $\cPZ/\PGg^{*}\to\Pgm\Pgm$ events. The efficiency
of the triggers based on the presence of an electron or muon and a hadronic
$\Pgt$ lepton decay, the efficiency for hadronic $\Pgt$ lepton decays to pass
the $\Pgt_{\mathrm{h}}$ identification criteria, and the energy scale with
which hadronic $\Pgt$ lepton decays are reconstructed, are measured using
$\cPZ/\PGg^{*}\to\Pgt\Pgt$ events [41]. The $\PQb$ tagging efficiency and
mistag rate (discussed in Section 0.4.4) are measured in
$\PQt\PAQt\text{+jets}$ and $\cPZ/\PGg^{*}\to\ell\ell$ events [42],
respectively. The differences in the resolution of the missing transverse
momentum between data and simulation are measured in
$\cPZ/\PGg^{*}\to\ell\ell$ and $\PGg$+jets events [43] and corrected as
described in Ref. [44].

##  0.4 Event reconstruction

The information provided by all CMS subdetectors is employed by a particle-
flow (PF) algorithm [45] to identify and reconstruct individual particles in
the event, namely muons, electrons, photons, and charged and neutral hadrons.
These particles are then used to reconstruct jets, $\Pgt_{\mathrm{h}}$ and the
vector imbalance in the event, referred to as $\ptvecmiss$, as well as to
quantify the isolation of leptons.

###  0.4.1 Vertices

Collision vertices are reconstructed using a deterministic annealing algorithm
[46, 47]. The reconstructed vertex position is required to be compatible with
the location of the LHC beam in the $x$-$y$ plane. The reconstructed vertex
with the largest value of summed physics-object $\pt^{2}$ is taken to be the
primary $\Pp\Pp$ interaction vertex (PV). The physics objects are the jets,
clustered using the jet finding algorithm [48, 49] with the tracks assigned to
the vertex as inputs, and the associated missing transverse momentum, taken as
the negative vector sum of the of those jets. Electrons, muons, and
$\Pgt_{\mathrm{h}}$ candidates, which are subsequently reconstructed, are
required to be compatible with originating from the selected PV.

###  0.4.2 Electrons and muons

Electrons are reconstructed within $\abs{\eta}<2.5$ by an algorithm [50] that
matches tracks reconstructed in the silicon tracker to energy deposits in the
ECAL, without any significant energy deposit in the HCAL. Tracks of electron
candidates are reconstructed by a dedicated algorithm which accounts for the
emission of bremsstrahlung photons. The energy loss due to bremsstrahlung is
determined by searching for energy deposits in the ECAL located tangentially
to the track. An MVA approach based on BDTs is employed to distinguish
electrons from hadrons mimicking an electron signature. Observables that
quantify the quality of the electron track, the compactness of the electron
cluster, and the matching between the track momentum and direction with the
sum and position of energy deposits in the ECAL are used as inputs to the BDT.
This electron identification BDT has been trained on samples of electrons and
hadrons. Additional requirements are applied in order to remove electrons
originating from photon conversions [50].

The identification of muons is based on linking track segments reconstructed
in the silicon tracking detector and in the muon system [51] within
$\abs{\eta}<2.4$. The matching between track segments is done outside-in,
starting from a track in the muon system, and inside-out, starting from a
track reconstructed in the inner detector. If a link can be established, the
track parameters are recomputed using the combination of hits in the inner and
outer detectors. Quality requirements are applied on the multiplicity of hits
in the track segments, on the number of matched track segments and on the
quality of the track fit [51].

Electrons and muons in signal events are expected to be isolated, while
leptons from charm ($\PQc$) and bottom ($\PQb$) quark decays as well as from
in-flight decays of pions and kaons are often reconstructed near jets.
Isolated leptons are distinguished from nonisolated leptons using the scalar
sum over charged particles, neutral hadrons, and photons that are
reconstructed within a narrow cone centered on the lepton direction. The size
$R$ of the cone shrinks with the increasing of the lepton in order to increase
the efficiency for leptons reconstructed in signal events with high hadronic
activity to pass the isolation criteria. The narrow cone size, referred to as
“mini-isolation”, has the further advantage that it reduces the effect of
pileup. Efficiency loss due to pileup is additionally reduced by considering
only those charged particles that originate from the lepton production vertex
in the isolation sum. Residual contributions of pileup to the neutral
component of the isolation $I_{\ell}$ of the lepton are taken into account by
means of so-called effective area corrections:

$I_{\ell}=\sum_{\text{charged}}\pt+\max\left(0,\sum_{\text{neutrals}}\pt-\rho\,\mathcal{A}\,\left[\frac{R}{0.3}\right]^{2}\right),$
(1)

where $\rho$ represents the energy density of neutral particles reconstructed
within the geometric acceptance of the tracking detectors, computed as
described in Refs. [52, 53]. The effective area $\mathcal{A}$ is obtained from
the simulation, by studying the correlation between $I_{\ell}$ and $\rho$, and
is determined separately for electrons and muons and in bins of $\eta$. The
size of the cone is given by:

$R=\left\\{\begin{aligned} &0.05,&\text{if $\pt>200\GeV$}\\\
&10\GeV/\pt,&\text{if $50<\pt<200\GeV$}\\\ &0.20,&\text{if
$\pt<50\GeV$}\end{aligned}\right..$ (2)

Additional selection criteria are applied to discriminate leptons produced in
the decays of $\PW$ bosons, $\PZ$ bosons, or $\Pgt$ leptons from those
produced in the decays of $\PB$ or light mesons. We will refer to the former
as “prompt” (signal) leptons and to the latter as “nonprompt” (background)
leptons. The separation of prompt from nonprompt leptons is performed by a
BDT-based algorithm, referred to as the lepton MVA. The following observables
are used as input to the lepton MVA: the isolation of the lepton with respect
to charged and neutral particles, corrected for pileup effects; the ratio of
the of the lepton to the $\pt$ of the nearest jet; a discriminant that
quantifies the probability of this jet to originate from the hadronization of
a $\PQc$ or $\PQb$ quark (described in Section 0.4.4); the component of the
lepton momentum perpendicular to the jet axis; the transverse and longitudinal
impact parameters of the lepton track with respect to the PV; and the
significance of the impact parameter, given by the impact parameter (in three
dimensions) divided by its uncertainty, of the lepton track with respect to
the PV. The last three inputs are the and $\eta$ of the lepton and an
additional observable, which improves the discrimination of prompt leptons
from residual backgrounds in which the reconstructed lepton arises from the
misidentification of a light-quark or gluon jet. For electrons, this
additional observable is the output of the MVA that is used for electron
identification. For muons, it corresponds to the compatibility of track
segments in the muon system with the pattern expected from muon ionization.
Inputs that require the matching of the lepton to a nearby jet are set to zero
if no jet of $\pt>10\GeV$ is reconstructed within a distance $\Delta
R=\sqrt{\smash[b]{(\eta_{\mathrm{j}}-\eta_{\ell})^{2}+(\phi_{\mathrm{j}}-\phi_{\ell})^{2}}}<0.4$
from the lepton, where $\phi$ is the azimuthal angle in radians. Separate
lepton MVAs are trained for electrons and muons, using simulated samples of
prompt leptons in $\cPqt\cPaqt\PH$ signal events and nonprompt leptons in
$\cPqt\cPaqt$+jets background events. Leptons selected in the signal region
are required to pass a tight selection on the lepton MVA output. Looser
selection criteria for electrons and muons, referred to as the “relaxed lepton
selection”, are defined by relaxing the lepton MVA selection for the purpose
of estimating the contribution of background processes as detailed in Section
0.6.

###  0.4.3 Hadronic $\Pgt$ lepton decays

Hadronic $\Pgt$ lepton decays are reconstructed by the “hadrons-plus-strips”
(HPS) algorithm [54] within $\abs{\eta}<2.3$. The algorithm reconstructs
individual hadronic decay modes of the $\Pgt$ lepton:
$\Pgt^{\pm}\to\Ph^{\pm}\PGn_{\Pgt}$, $\Pgt^{\pm}\to\Ph^{\pm}\Pgpz\PGn_{\Pgt}$,
$\Pgt^{\pm}\to\Ph^{\pm}\Pgpz\Pgpz\PGn_{\Pgt}$, and
$\Pgt^{\pm}\to\Ph^{\pm}\Ph^{\mp}\Ph^{\pm}\PGn_{\Pgt}$, where $\Ph^{\pm}$
denotes either a charged pion or kaon. Hadronic $\Pgt$ candidates are built by
combining the charged hadrons reconstructed by the PF algorithm with neutral
pions. The neutral pions are reconstructed by clustering the photons and
electrons reconstructed by the PF algorithm within rectangular strips that are
narrow in $\eta$, but wide in the $\phi$ direction, to account for the
broadening of energy deposits in the ECAL if one of the photons produced in
$\Pgpz\to\PGg\PGg$ decays converts within the tracking detector. An improved
version of the strip reconstruction has been developed for data analyses at
13and beyond, replacing the one used in CMS analyses at $\sqrt{s}=7$ and 8that
was based on a fixed strip size of $0.05\times 0.20$ in $\eta$-$\phi$. In the
improved version the size of the strip is adjusted as a function of the of the
particles reconstructed within the strip [41].

Tight isolation requirements provide the most effective way to distinguish
hadronic $\Pgt$ lepton decays from a large background of light-quark and gluon
jets. The sums of scalar values of charged particles and of photons are used
as inputs to a BDT-based $\Pgt_{\mathrm{h}}$ identification discriminant.
Separate sums are used for charged particles that are compatible with
originating from the $\Pgt_{\mathrm{h}}$ production vertex and those that are
not. The final additions to the list of input variables are the reconstructed
$\Pgt_{\mathrm{h}}$ decay mode and observables that provide sensitivity to the
lifetime of the $\Pgt$ lepton. The transverse impact parameter of the highest
track of the $\Pgt_{\mathrm{h}}$ candidate with respect to the PV is used for
$\Pgt_{\mathrm{h}}$ candidates reconstructed in any decay mode. In case of
$\Pgt_{\mathrm{h}}$ candidates reconstructed in the decay mode
$\Pgt^{-}\to\Ph^{-}\Ph^{+}\Ph^{-}\PGn_{\Pgt}$, a fit of the three tracks to a
common secondary vertex is attempted and the distance to the PV is used as an
additional input variable to the BDT. The isolation is computed within a cone
of size $R=0.3$, centered on the $\Pgt_{\mathrm{h}}$ direction. Compared to
the version of the HPS algorithm used by the majority of CMS analyses with
hadronic $\Pgt$ lepton decays, which use a cone of size $R=0.5$, the size of
the cone is reduced in this analysis in order to improve the efficiency in
signal events with high hadronic activity. The BDT has been trained on samples
of hadronic $\Pgt$ lepton decays in $\PQt\PAQt\PH$ signal events and jets in
$\PQt\PAQt\text{+jets}$ background events, produced using MC simulation [41].
Loose, medium, and tight working points (WPs), corresponding respectively to a
65, 55 and 45% $\Pgt_{\mathrm{h}}$ identification efficiency and a 2, 1 and
0.5% jet $\to\Pgt_{\mathrm{h}}$ misidentification rate, are defined by varying
the selections on the BDT output. The selections are adjusted as a function of
the of the $\Pgt_{\mathrm{h}}$ candidate such that the $\Pgt_{\mathrm{h}}$
identification efficiency for each WP is constant as a function of . The loose
WP is used for the estimation of the background due to the misreconstruction
of light-quark or gluon jets as $\Pgt_{\mathrm{h}}$ candidates and is referred
to as the “relaxed $\Pgt_{\mathrm{h}}$ selection”. Contamination from events
where the reconstructed $\Pgt_{\mathrm{h}}$ originates from a misreconstructed
muon or electron is reduced by requiring the reconstructed $\Pgt_{\mathrm{h}}$
not to overlap with muons or electrons passing loose selection criteria within
$\Delta R<0.3$.

###  0.4.4 Jets

Jets are reconstructed from the PF candidates using the anti-$\kt$ algorithm
[48, 49] with a distance parameter of $0.4$, and with the constraint that the
charged particles are compatible with the selected PV. Reconstructed jets are
required not to overlap with identified electrons, muons or
$\Pgt_{\mathrm{h}}$ within $\Delta R<0.4$ and to pass identification criteria
that aim to reject spurious jets arising from calorimeter noise [55]. The
energy of reconstructed jets is calibrated as a function of jet and $\eta$
[56]. Jet energy corrections based on the algorithm [52, 53] are applied. Jets
selected for this analysis must have a $\pt>25\GeV$ and $\abs{\eta}<2.4$. Jets
originating from the hadronization of $\PQb$ quarks are identified by the
“combined secondary vertex” algorithm [42, 57], which exploits observables
related to the long lifetime of $\PQb$ hadrons and to the higher particle
multiplicity and mass of $\PQb$ jets compared to light-quark and gluon jets.
Loose and tight b tagging criteria WPs are used, respectively associated with
a mistag rate of 10 and 1% and yielding a $\PQb$ jet selection efficiency of
85 and 70%.

###  0.4.5 Missing transverse momentum

The $\ptvecmiss$ is calculated as the negative of the vector sum of all
particles reconstructed by the PF algorithm. The magnitude of the vector is
referred to as $\ptmiss$. The $\ptmiss$ resolution and response are improved
by propagating the difference between calibrated and uncalibrated jets to the
$\ptmiss$ and by applying corrections that account for pileup effects, as
described in Ref. [44].

The $\ptmiss$ is complemented by the observable
$H_{\mathrm{T}}^{\text{miss}}$, defined as the magnitude of the vectorial sum
of leptons, $\Pgt_{\mathrm{h}}$, and jets:

$H_{\mathrm{T}}^{\text{miss}}=\left|\sum_{\text{leptons}}\vec{p}_{\mathrm{T}\ell}+\sum_{\Pgt_{\mathrm{h}}}\vec{p}_{\mathrm{T}\Pgt}+\sum_{\text{jets}}\vec{p}_{\mathrm{Tj}}\right|.$
(3)

Leptons and $\Pgt_{\mathrm{h}}$ entering the sum are required to pass the
relaxed selection criteria discussed in Sections 0.4.2 and 0.4.3, while the
jets are required to satisfy $\pt>25\GeV$ and $\abs{\eta}<2.4$. The resolution
on $H_{\mathrm{T}}^{\text{miss}}$ is worse compared to the resolution on
$\ptmiss$. The advantage of the observable $H_{\mathrm{T}}^{\text{miss}}$ is
that leptons, $\Pgt_{\mathrm{h}}$, and high jets predominantly originate from
the hard scattering interaction and rarely from pileup interactions, which
makes $H_{\mathrm{T}}^{\text{miss}}$ less sensitive to variations in pileup
conditions.

The two observables $\ptmiss$ and $H_{\mathrm{T}}^{\text{miss}}$ are combined
into a single linear discriminant:

$L_{\mathrm{D}}=0.6\,\ptmiss+0.4\,H_{\mathrm{T}}^{\text{miss}},$ (4)

exploiting the fact that $\ptmiss$ and $H_{\mathrm{T}}^{\text{miss}}$ are less
correlated in events in which the reconstructed $\ptmiss$ is due to
instrumental effects compared to events with genuine $\ptmiss$ that arises
from the presence of neutrinos. The coefficients of the linear combination
have been optimized to provide the best rejection against the $\cPZ$+jets
background.

##  0.5 Event selection

This analysis focuses on final states in which one lepton is produced in one
of the top quark decays, while the additional leptons and $\Pgt_{\mathrm{h}}$
are produced in the Higgs boson or the other top quark decay. The analysis is
performed using six mutually exclusive event categories, based on the number
of reconstructed leptons and $\Pgt_{\mathrm{h}}$ candidates:

  * •

one lepton and two $\Pgt_{\mathrm{h}}$ ($1\ell+2\Pgt_{\mathrm{h}}$),

  * •

two leptons with same sign of the charge (“same-sign leptons”) and zero
$\Pgt_{\mathrm{h}}$ ($2\ell\text{ss}$),

  * •

two same-sign leptons and one $\Pgt_{\mathrm{h}}$
($2\ell\text{ss}+1\Pgt_{\mathrm{h}}$),

  * •

three leptons and zero $\Pgt_{\mathrm{h}}$ ($3\ell$),

  * •

three leptons and one $\Pgt_{\mathrm{h}}$ ($3\ell+1\Pgt_{\mathrm{h}}$), and

  * •

four leptons ($4\ell$).

The categories with no $\Pgt_{\mathrm{h}}$ are mostly sensitive to the Higgs
boson decay into $\PW$ or $\PZ$ bosons while the categories with at least one
$\Pgt_{\mathrm{h}}$ enhance the sensitivity to the Higgs boson decay into
$\tau$ leptons. The targeted $\PQt\PAQt\PH$ decays in each category are
highlighted in Tables 0.5 and 0.5.

Events in the $2\ell\text{ss}$ and $2\ell\text{ss}+1\Pgt_{\mathrm{h}}$
categories are recorded by a combination of single-lepton triggers and
triggers that select events containing lepton pairs. In the
$1\ell+2\Pgt_{\mathrm{h}}$ category, the single-lepton triggers are
complemented by triggers that select events containing an electron or muon in
combination with a $\Pgt_{\mathrm{h}}$. The efficiency to select signal events
in $3\ell$, $3\ell+1\Pgt_{\mathrm{h}}$, and $4\ell$ categories is increased by
collecting events using a combination of single-lepton and dilepton triggers,
and triggers based on the presence of three leptons.

The thresholds applied in order to select the leptons in different event
categories are dictated by trigger requirements. In the $2\ell\text{ss}$,
$3\ell$, and $4\ell$ categories, the lepton of highest (“leading” lepton) is
required to satisfy the condition $\pt>25\GeV$ and the lepton of second-
highest (“subleading” lepton) is required to satisfy $\pt>15\GeV$. The third
(fourth) lepton is required to have $\pt>15(10)\GeV$. In the
$1\ell+2\Pgt_{\mathrm{h}}$ category, the leading lepton is required to pass a
threshold of $\pt>25(20)\GeV$ if it is an electron (or muon) and is restricted
to be within $\abs{\eta}<2.1$ to match the trigger requirements. In the
$2\ell\text{ss}+1\Pgt_{\mathrm{h}}$ category, the leading lepton is required
to satisfy $\pt>25\GeV$, while the subleading lepton must satisfy
$\pt>15(10)\GeV$ if it is an electron (or muon). In the
$3\ell+1\Pgt_{\mathrm{h}}$ category, the leading (subleading and third) lepton
is required to have $\pt>20(10)\GeV$.

Hadronically decaying $\Pgt$ lepton candidates selected in the signal region
of the $2\ell\text{ss}+1\Pgt_{\mathrm{h}}$ and $3\ell+1\Pgt_{\mathrm{h}}$
categories are required to pass the medium WP and must have a reconstructed
$\pt>20\GeV$. In the $1\ell+2\Pgt_{\mathrm{h}}$ category, the tight WP is used
instead to further reduce the dominant $\PQt\PAQt\text{+jets}$ background. The
leading (subleading) $\Pgt_{\mathrm{h}}$ candidate in this category is
required to pass a threshold of $\pt>30(20)\GeV$.

In signal events selected in the $1\ell+2\Pgt_{\mathrm{h}}$ category, the
lepton predominantly originates from the leptonic decay of one of the top
quarks, while the Higgs boson decays to a pair of $\Pgt$ leptons, which both
decay hadronically. Consequently, we require the two $\Pgt_{\mathrm{h}}$ to be
of opposite sign, the combination of signs expected for a $\Pgt_{\mathrm{h}}$
pair produced in a Higgs boson decay. In the
$2\ell\text{ss}+1\Pgt_{\mathrm{h}}$ category, the sign of the reconstructed
$\Pgt_{\mathrm{h}}$ is required to be opposite to that of the leptons, while
in the $3\ell+1\Pgt_{\mathrm{h}}$ category the sum of lepton and
$\Pgt_{\mathrm{h}}$ charges is required to be zero. Finally, the modulus of
the sum of lepton charges is required to be equal to one for events selected
in the $3\ell$, matching the sum of charges expected in signal events.

Events selected in any category are required to contain at least one jet
passing tight $\PQb$ tagging criteria or at least two jets passing loose
$\PQb$ tagging criteria. Additional criteria on the multiplicity of jets are
applied. In the $1\ell+2\Pgt_{\mathrm{h}}$ and
$2\ell\text{ss}+1\Pgt_{\mathrm{h}}$ categories, the presence of at least three
jets, including the jets that pass the $\PQb$ tagging criteria, is required.
The requirement on the number of jets is tightened to at least four in the
$2\ell\text{ss}$ category, consistent with the higher jet multiplicity
expected in this category targeting events where the $\PH$ decays into
$\PW\PW\to\ell\PGn\cPq\cPq$. For events selected in the $3\ell$,
$3\ell+1\Pgt_{\mathrm{h}}$, and $4\ell$ categories, only the presence of at
least two jets is required, as those categories target events with dileptonic
decay of the $\cPqt\cPaqt$ pair.

In the $2\ell\text{ss}$ and $2\ell\text{ss}+1\Pgt_{\mathrm{h}}$ categories,
the $\PQt\PAQt\text{+jets}$ background is reduced significantly by requiring
the two leptons to have the same sign. Background contributions arising from
events containing two leptons of opposite sign, in which the sign of one
lepton is mismeasured, are reduced by applying additional quality criteria on
the charge measurement. For electrons, the consistency of the charge
measurements based on different tracking algorithms and on hits reconstructed
in either the silicon pixel detector or the combination of silicon pixel and
strip detectors, is required. For muons, the curvature of the track
reconstructed based on the combination of hits in the silicon detectors and in
the muon system is required to be measured with a relative uncertainty of less
than 20%.

The probability to mismeasure the charge is significantly higher for electrons
than for muons. Background contributions to the $2\ell\text{ss}$ and
$2\ell\text{ss}+1\Pgt_{\mathrm{h}}$ categories that arise from $\cPZ$+jets
events in which the sign of a lepton is mismeasured are reduced by requiring
events to satisfy the condition $L_{\mathrm{D}}>30\GeV$ (applied only if both
leptons are electrons in the $2\ell\text{ss}+1\Pgt_{\mathrm{h}}$ category) and
vetoing events in which the mass of the electron pair is within 10of the
$\cPZ$ boson mass. In the $3\ell$ and $3\ell+1\Pgt_{\mathrm{h}}$ categories,
the background from events containing $\cPZ$ bosons ($\cPZ$+jets, $\PW\cPZ$,
$\cPZ\cPZ$, and $\PQt\PAQt\cPZ$) is suppressed by requiring selected events to
satisfy the condition $L_{\mathrm{D}}>30\GeV$. The $\cPZ$-veto is also
extended to all events containing same-flavor opposite-sign (SFOS) lepton
pairs and the requirement on $L_{\mathrm{D}}$ is tightened to the condition
$L_{\mathrm{D}}>45\GeV$ for those. For events with four or more jets the
contamination from background processes with $\cPZ$ bosons is smaller and no
requirement on $L_{\mathrm{D}}$ is applied.

In all categories, events containing lepton pairs of mass less than 12are
rejected, as these events are not well modeled by the MC simulation.

In the $3\ell$ and $4\ell$ categories, events with two pairs of SFOS leptons
passing loose identification criteria and with a 4-lepton invariant mass lower
than 140are rejected, to avoid overlap with the dedicated $\PQt\PAQt\PH$
category from [4].

The event selections applied in the different categories are summarized in
Tables 0.5 and 0.5. Combining all the event categories and assuming the SM
$\PQt\PAQt\PH$ production, 91 signal events are expected, corresponding to
0.5% of all produced $\PQt\PAQt\PH$ events.

Event selections applied in the $2\ell\text{ss}$,
$2\ell\text{ss}+1\Pgt_{\mathrm{h}}$, $3\ell$, and $3\ell+1\Pgt_{\mathrm{h}}$
categories.  Selection $2\ell\text{ss}$ $2\ell\text{ss}+1\Pgt_{\mathrm{h}}$
Targeted $\PQt\PAQt\PH$ decay $\cPqt\to\cPqb\ell\PGn$,
$\cPqt\to\cPqb\cPq\cPq$, $\PH\to\PW\PW\to\ell\PGn\cPq\cPq$
$\cPqt\to\cPqb\ell\PGn$, $\cPqt\to\cPqb\cPq\cPq$,
$\PH\to\PGt\PGt\to\ell\Pgt_{\mathrm{h}}+\PGn^{\prime}\text{s}$ Trigger Single-
and double-lepton triggers Lepton  $\pt>25$ / $15\GeV$ $\pt>25$ / $15$ ($\Pe$)
or $10\GeV$ ($\Pgm$) Lepton $\eta$ $\abs{\eta}<2.5$ ($\Pe$) or $2.4$ ($\Pgm$)
$\Pgt_{\mathrm{h}}$ $\pt>20\GeV$ $\Pgt_{\mathrm{h}}$ $\eta$ $\abs{\eta}<2.3$
Charge requirements 2 same-sign leptons 2 same-sign leptons and charge quality
requirements and charge quality requirements
$\sum\limits_{\ell,\Pgt_{\mathrm{h}}}q=\pm 1$ Jet multiplicity $\geq$4 jets
$\geq$3 jets $\PQb$ tagging requirements $\geq$1 tight $\PQb$-tagged jet or
$\geq$2 loose $\PQb$-tagged jets Missing transverse $L_{\mathrm{D}}>30\GeV$
$L_{\mathrm{D}}>30\GeV\,^{*}$ momentum Dilepton mass $m_{\ell\ell}>12\GeV$ and
$\abs{m_{\Pe\Pe}-m_{\PZ}}>10\GeV\,^{*}$

Selection |  $3\ell$ |  $3\ell+1\Pgt_{\mathrm{h}}$
---|---|---
Targeted $\PQt\PAQt\PH$ decays |  $\cPqt\to\cPqb\ell\PGn$, $\cPqt\to\cPqb\ell\PGn$, $\PH\to\PW\PW\to\ell\PGn\cPq\cPq$ |  $\cPqt\to\cPqb\ell\PGn$, $\cPqt\to\cPqb\ell\PGn$, $\PH\to\PGt\PGt\to\ell\Pgt_{\mathrm{h}}+\PGn^{\prime}\text{s}$
|  $\cPqt\to\cPqb\ell\PGn$, $\cPqt\to\cPqb\cPq\cPq$, $\PH\to\PW\PW\to\ell\PGn\ell\PGn$ |
|  $\cPqt\to\cPqb\ell\PGn$, $\cPqt\to\cPqb\cPq\cPq$, |
|  $\PH\to\PZ\PZ\to\ell\ell\cPq\cPq$ or $\ell\ell\PGn\PGn$ |
Trigger | Single-, double- and triple-lepton triggers
Lepton  |  $\pt>25$ / $15$ / $15\GeV$ |  $\pt>20$ / $10$ / $10\GeV$
Lepton $\eta$ |  $\abs{\eta}<2.5$ ($\Pe$) or $2.4$ ($\Pgm$)
$\Pgt_{\mathrm{h}}$ |  |  $\pt>20\GeV$
$\Pgt_{\mathrm{h}}$ $\eta$ |  |  $\abs{\eta}<2.3$
Charge requirements |  $\sum\limits_{\ell}q=\pm 1$ |  $\sum\limits_{\ell,\Pgt_{\mathrm{h}}}q=0$
Jet multiplicity |  $\geq$2 jets
$\PQb$ tagging requirements |  $\geq$1 tight $\PQb$-tagged jet or $\geq$2 loose $\PQb$-tagged jets
Missing transverse | No requirement if $N_{\mathrm{j}}\geq 4$
momentum | $L_{\mathrm{D}}>45\GeV\,^{\dagger}$
|  $L_{\mathrm{D}}>30\GeV$ otherwise
Dilepton mass |  $m_{\ell\ell}>12\GeV$ and $\abs{m_{\ell\ell}-m_{\PZ}}>10\GeV\,^{\ddagger}$
Four-lepton mass |  $m_{4\ell}>140\GeV\,^{\S}$ |

∗ Applied only if both leptons are electrons.
† If the event contains a SFOS lepton pair and $N_{\mathrm{j}}\leq 3$.
‡ Applied to all SFOS lepton pairs.
§ Applied only if the event contains $2$ SFOS lepton pairs.

Event selections applied in the $1\ell+2\Pgt_{\mathrm{h}}$ and $4\ell$
categories.  Selection $1\ell+2\Pgt_{\mathrm{h}}$ $4\ell$ Targeted
$\PQt\PAQt\PH$ decays $\cPqt\to\cPqb\ell\PGn$, $\cPqt\to\cPqb\cPq\cPq$,
$\PH\to\PGt\PGt\to\Pgt_{\mathrm{h}}\Pgt_{\mathrm{h}}+\PGn^{\prime}\text{s}$
$\cPqt\to\cPqb\ell\PGn$, $\cPqt\to\cPqb\ell\PGn$,
$\PH\to\PW\PW\to\ell\PGn\ell\PGn$ $\cPqt\to\cPqb\ell\PGn$,
$\cPqt\to\cPqb\ell\PGn$, $\PH\to\PZ\PZ\to\ell\ell\cPq\cPq$ or
$\ell\ell\PGn\PGn$ Trigger Single=lepton Single-, double- and
lepton+$\Pgt_{\mathrm{h}}$ triggers and triple-lepton triggers Lepton
$\pt>25$ ($\Pe$) or $20\GeV$ ($\mu$) $\pt>25$ / $15$ / $15$ / $10\GeV$ Lepton
$\eta$ $\abs{\eta}<2.1$ $\abs{\eta}<2.5$ ($\Pe$) or $2.4$ ($\Pgm$)
$\Pgt_{\mathrm{h}}$ $\pt>30$ / $20\GeV$ $\Pgt_{\mathrm{h}}$ $\eta$
$\abs{\eta}<2.3$ Charge requirements $\sum\limits_{\Pgt_{\mathrm{h}}}q=0$ and
$\sum\limits_{\ell,\Pgt_{\mathrm{h}}}q=\pm 1$ Jet multiplicity $\geq$3 jets
$\geq$2 jets $\PQb$ tagging requirements $\geq$1 tight $\PQb$-tagged jet or
$\geq$2 loose $\PQb$-tagged jets Dilepton mass $m_{\ell\ell}>12\GeV$
$m_{\ell\ell}>12\GeV$ and $\abs{m_{\ell\ell}-m_{\PZ}}>10\GeV\,^{\ddagger}$
Four-lepton mass $m_{4\ell}>140\GeV\,^{\S}$

† If the event contains a SFOS lepton pair and $N_{\mathrm{j}}\leq 3$.
‡ Applied to all SFOS lepton pairs.
§ Applied only if the event contains $2$ SFOS lepton pairs.

##  0.6 Background estimation

Contributions of background processes to the signal region (SR) of the
analysis, defined by the event selection criteria detailed in Section 0.5,
arise from a variety of sources. Backgrounds are categorized as being either
“reducible” or “irreducible” and are estimated either from the data or modeled
using the MC simulation.

A background is considered as reducible if at least one electron or muon is
due to a nonprompt lepton (, originating from the decay of a hadron) or to the
misidentification of a hadron, or if one or more $\Pgt_{\mathrm{h}}$ is due to
the misidentification of a quark or gluon jet. In the $2\ell\text{ss}$
category, further sources of reducible backgrounds arise from events
containing lepton pairs of opposite charge in which the sign of either lepton
is mismeasured and from the production of top quark pairs in association with
either real or virtual conversion photons. The dominant reducible backgrounds,
arising from the misidentification of leptons or $\Pgt_{\mathrm{h}}$
(misidentified lepton background) or from the mismeasurement of the lepton
charge (“sign-flip” background), are determined from data. The procedures are
described in Sections 0.6.1 and 0.6.2.

The background contribution arising from $\cPqt\cPaqt$ production in
association with photons (“conversions”) is mostly relevant for the
$2\ell\text{ss}$ and $2\ell\text{ss}+1\Pgt_{\mathrm{h}}$ categories. It is
typically due to asymmetric conversions of the type $\PGg\to\Pe^{+}\Pe^{-}$,
in which one electron or positron carries most of the energy of the photon,
while the other electron or positron is of low energy and fails to get
reconstructed. Events of this type are suppressed very effectively thanks to
the electron selections used. The small remaining background is modeled using
the MC simulation. The validity of the simulation has been verified in control
regions (CRs) in data.

Irreducible background contributions are modeled using the MC simulation. The
dominant contributions are due to the production of top quark pairs in
association with $\PW$ or $\cPZ$ bosons and to the diboson production in
association with jets, dominated by the $\PW\cPZ$ and $\PZ\cPZ$ backgrounds.
Minor contributions arise from rare SM processes such as triboson production,
single top production in association with a $\cPZ$ boson, the production of
two same-sign $\PW$ bosons, and $\cPqt\cPaqt\cPqt\cPaqt$ production. Results
are presented considering the $\PQt\PH$ process as a background process
normalized to the SM expectation. The SM $\PQt\PH$ rate amounts to about 5% of
the $\PQt\PAQt\PH$ one in the signal regions of this analysis. The modeling of
the data by the simulation is validated in specific CRs, each enriched in the
contribution of one of the dominant irreducible background processes:
$\PQt\PAQt\cPZ$, $\PQt\PAQt\PW$, and $\PW\cPZ$+jets.

###  0.6.1 Background from misidentified leptons and $\Pgt_{\mathrm{h}}$

The background from misidentified leptons and $\Pgt_{\mathrm{h}}$ is estimated
from data by means of the fake factor (FF) method. The method is applied to
each event category separately. It is based on selecting a sample of events
passing all selection criteria for the respective category, detailed in
Section 0.5, except that electrons, muons, and $\Pgt_{\mathrm{h}}$ are
required to pass the relaxed, instead of nominal, selection criteria. We refer
to these event samples as the “application region” (AR) of the FF method.
Events in which all leptons and $\Pgt_{\mathrm{h}}$ pass the tight selection
criteria are vetoed in order to avoid overlap with the SR. An estimate for the
contribution of the misidentified lepton background to the SR is obtained by
applying appropriately chosen weights to the events selected in the AR.

The weights depend on the probability $f_{i}$ for a misidentified electron,
muon, or $\Pgt_{\mathrm{h}}$ that passes the relaxed selection criteria to
pass the nominal selection criteria. For the computation of the weights, the
index $i$ extends over all leptons and $\Pgt_{\mathrm{h}}$ that pass the
relaxed, but fail the nominal selection criteria. The weights differ depending
on the multiplicity of leptons and $\Pgt_{\mathrm{h}}$ passing the relaxed
selection criteria as well as on the number of those passing the nominal
selection criteria, the latter being denoted by $N_{\mathrm{p}}$. For events
containing a total of $2$ or $3$ objects, the weights are given by:

$\displaystyle w_{2}$
$\displaystyle=\begin{cases}\frac{f_{1}}{1-f_{1}}&\text{if
$N_{\mathrm{p}}=1$}\\\\[5.0pt]
-\frac{f_{1}\,f_{2}}{(1-f_{1})\,(1-f_{2})}\,&\text{if
$N_{\mathrm{p}}=0$}\end{cases}$ (5) $\displaystyle w_{3}$
$\displaystyle=\begin{cases}\frac{f_{1}}{1-f_{1}}\,&\text{if
$N_{\mathrm{p}}=2$}\\\\[5.0pt]
-\frac{f_{1}\,f_{2}}{(1-f_{1})\,(1-f_{2})}\,&\text{if
$N_{\mathrm{p}}=1$}\\\\[5.0pt]
\frac{f_{1}\,f_{2}\,f_{3}}{(1-f_{1})\,(1-f_{2})\,(1-f_{3})}\,&\text{if
$N_{\mathrm{p}}=0$}\end{cases}$

The sign of the weights alternates for events with different numbers of
leptons and $\Pgt_{\mathrm{h}}$ candidates passing the nominal selection
criteria. The alternating sign is necessary to correctly account for the
contributions of events with different numbers of prompt leptons, nonprompt
leptons, genuine $\Pgt_{\mathrm{h}}$, and hadrons to an event sample with a
given total number of reconstructed leptons and $\Pgt_{\mathrm{h}}$. For
example, in the case of events with two leptons in the $2\ell\text{ss}$
category, the negative sign in the expression
$-f_{1}\,f_{2}/[(1-f_{1})\,(1-f_{2})]$ for the weight $w_{2}$ corrects for the
contribution of events with two nonprompt leptons or misidentified hadrons to
the sample of events in which one lepton passes and the other one fails the
nominal lepton selection criteria. Application of the weights given by Eq. (5)
to events in the AR provides an unbiased estimate of the background
contribution in the SR arising from events with at least one nonprompt lepton
or hadron misidentified as prompt lepton or $\Pgt_{\mathrm{h}}$. A correction
obtained from the MC simulation is subtracted from this estimate to account
for the contamination of the AR with irreducible backgrounds, , by events in
which all leptons are prompt leptons and all $\Pgt_{\mathrm{h}}$ are genuine,
and in which a prompt lepton fails the nominal lepton selection criteria or a
genuine $\Pgt_{\mathrm{h}}$ fails the nominal $\Pgt_{\mathrm{h}}$ selection
criteria. The correction does not exceed 10% of the yield in the AR in any
category.

The factors $f_{i}$ are measured separately for electrons, muons, and
$\Pgt_{\mathrm{h}}$ and are parametrized as functions of and $\eta$. The CR in
which the $f_{i}$ are measured is referred to as “determination region” (DR)
of the FF method. The $f_{i}$ for electrons and muons are measured in multijet
events. Selected events are required to contain one electron or muon passing
the relaxed lepton selection criteria and at least one jet. The data in this
DR are collected with single lepton triggers, except at low muon , where the
presence of an additional jet with $\pt>40\GeV$ is required in the trigger.
Contamination from prompt leptons, primarily arising from the production of
single $\PW$ and $\cPZ$ bosons in association with jets, with a small
contribution from diboson production, is reduced by vetoing events with
multiple leptons. The residual contamination is subtracted based on a
likelihood fit, similar to the one used for measuring the $\PQt\PAQt\PH$
production rate in the SR described in Section 0.7, that determines the
relative contributions of different background processes with prompt leptons
to the DR. A variable closely related to the transverse mass of the electron
or muon and $\ptvecmiss$,

$m^{{}^{\prime}}_{\mathrm{T}}=\sqrt{{2p_{\mathrm{T}\ell}^{\text{fix}}\,\ptmiss\,\left(1-\cos\Delta\phi\right)}},$
(6)

is used as the discriminating observable in the fit. Here,
$p_{\mathrm{T}\ell}^{\text{fix}}=35\GeV$ is used to reduce the correlation
between $m^{{}^{\prime}}_{\mathrm{T}}$ and the of the lepton and $\Delta\phi$
denotes the angle in the transverse plane between the lepton momentum and the
$\ptvecmiss$ vector. A complication arises from the fact that the factors
$f_{i}$ are measured in multijet events, while the dominant misidentified
lepton background in the AR is due to $\PQt\PAQt\text{+jets}$ production. The
relaxed lepton selection criteria are chosen such that the $f_{i}$ are similar
for nonprompt leptons and for hadrons that are misidentified as prompt leptons
and do not differ between multijet and $\PQt\PAQt\text{+jets}$ events. The
$f_{i}$ for $\Pgt_{\mathrm{h}}$ are measured using $\cPqt\cPaqt+$jets events
in which the two $\PW$ bosons produced in the decay of the top quark pair
decay to an electron-muon pair. The events are required to contain one
electron, one muon, at least one $\Pgt_{\mathrm{h}}$ candidate passing the
relaxed $\Pgt_{\mathrm{h}}$ selection, and two or more jets, of which at least
one passes the tight or at least two pass the loose $\PQb$ tagging criteria,
and are recorded by a combination of single-lepton triggers and triggers based
on the presence of an electron-muon pair. Contributions from other background
processes are reduced by requiring the observable $L_{\mathrm{D}}$, defined by
Eq. (4), to satisfy the condition $L_{\mathrm{D}}>30\GeV$. The contamination
from background processes with genuine $\Pgt_{\mathrm{h}}$ is subtracted using
the MC simulation. Separate sets of $f_{i}$ are measured for the
$\Pgt_{\mathrm{h}}$ selection criteria applied in the
$2\ell\text{ss}+1\Pgt_{\mathrm{h}}$ and $3\ell+1\Pgt_{\mathrm{h}}$ categories
and for those applied in the $1\ell+2\Pgt_{\mathrm{h}}$ category.

For the $1\ell+2\Pgt_{\mathrm{h}}$, $2\ell\text{ss}$ and $3\ell$ categories,
the FF method is applied as described, whereas a modified version of the FF
method is utilized in the $2\ell\text{ss}+1\Pgt_{\mathrm{h}}$ and
$3\ell+1\Pgt_{\mathrm{h}}$ categories. In the modified version, only the part
of the misidentified lepton background in which at least one of the
reconstructed electrons or muons is misidentified is obtained from data,
relaxing only the selection criteria for electrons and muons when defining the
AR. On the other hand, the contribution of background events that contain
genuine prompt light leptons and in which the reconstructed
$\Pgt_{\mathrm{h}}$ is due to the misidentification of a quark or gluon jet is
obtained from the MC simulation, corrected to account for the difference in
the $\Pgt_{\mathrm{h}}$ misidentification probability in data and simulation.
In this way, $\PQt\PAQt\PH$ events where the reconstructed $\Pgt_{\mathrm{h}}$
is not due to a genuine $\Pgt_{\mathrm{h}}$ can be retained as signal, instead
of being included in the misidentified lepton background estimate. These
events amount to $\approx$30% of the total signal in the
$2\ell\text{ss}+1\Pgt_{\mathrm{h}}$ and $3\ell+1\Pgt_{\mathrm{h}}$ categories.

We have checked that the background due to nonprompt leptons was negligible in
the $4\ell$ category and the FF method is therefore not used in this category.

###  0.6.2 Sign-flip background

The sign-flip background in the $2\ell\text{ss}$ and
$2\ell\text{ss}+1\Pgt_{\mathrm{h}}$ categories is dominated by
$\PQt\PAQt\text{+jets}$ events with two prompt leptons in which the sign of
either prompt lepton is mismeasured. The background is estimated from data,
following a strategy similar to the one used for the estimation of the
misidentified lepton background. The AR used to estimate the contribution of
the sign-flip background to the SR contains events passing all selection
criteria of the SR, described in Section 0.5, of the respective category,
except that the two leptons are required to be of opposite sign. In the
$2\ell\text{ss}$ category, the sum of the probabilities to mismeasure the
charge of either one of the two leptons is then applied as an event weight. In
the $2\ell\text{ss}+1\Pgt_{\mathrm{h}}$ category, only the probability to
mismeasure the sign of the lepton with the same sign as the
$\Pgt_{\mathrm{h}}$ is used, due to the charge requirements used for the event
selection in this category. The sign misidentification rates for electrons and
muons are measured by comparing the rates of $\cPZ/\PGg^{*}\to\Pe\Pe$ and
$\cPZ/\PGg^{*}\to\Pgm\Pgm$ events with leptons of the same and of opposite
sign and are parametrized as functions of lepton and $\eta$. The probability
for mismeasuring the sign of electrons ranges from 0.02% for electrons in the
barrel to 0.4% for electrons in the endcaps, after all the object selection
criteria. The probability for mismeasuring the sign of muons is negligible in
this analysis.

##  0.7 Signal extraction

The event samples selected in the SR are still dominated by backgrounds in all
event categories. The sensitivity of the statistical analysis is enhanced by
extracting the signal rate by means of a maximum likelihood (ML) fit to the
distribution in a discriminating observable, except in the $4\ell$ category,
where we resort to event counting because of the small number of events
expected in this category. In each event category, a different discriminating
observable is chosen, in order to achieve the maximal separation in shape
between the $\PQt\PAQt\PH$ signal and backgrounds. The observables used for
the ML fit are described in Section 0.7.1, and the statistical analysis is
detailed in Section 0.7.2.

###  0.7.1 Discriminating observables

Discriminants based on the MEM approach have been developed for the
$2\ell\text{ss}+1\Pgt_{\mathrm{h}}$ and $3\ell$ categories to improve the
separation of the $\PQt\PAQt\PH$ signal with respect to the main backgrounds.
The computation of the discriminant is based on combining the knowledge of
differential theoretical cross sections for the $\PQt\PAQt\PH$ signal and for
background processes with the knowledge of the experimental resolution of the
detector. More details about their computations are provided in Appendix .11.

In the $2\ell\text{ss}+1\Pgt_{\mathrm{h}}$ category, a MEM discriminant
$\text{LR}(2\ell\text{ss}+1\Pgt_{\mathrm{h}})$ is directly used for the signal
extraction, optimized to discriminate the $\PQt\PAQt\PH$ signal from three
types of background: $\PQt\PAQt\cPZ$ events in which the $\cPZ$ boson decays
into a pair of $\Pgt$ leptons, $\PQt\PAQt\cPZ$ events in which the $\cPZ$
boson decays into a pair of electrons or muons and one lepton is misidentified
as $\Pgt_{\mathrm{h}}$, and $\PQt\PAQt\to\PQb\ell\PGn\,\PAQb\Pgt\PGn$ events
with one additional nonprompt lepton produced in either a $\PQb$ or a $\PAQb$
quark decay.

The discriminating observable used for the signal extraction in each of the
categories $2\ell\text{ss}$, $3\ell$, and $3\ell+1\Pgt_{\mathrm{h}}$ is based
on the output of two BDTs. The first BDT is trained to separate the
$\PQt\PAQt\PH$ signal from the $\PQt\PAQt\mathrm{V}$ background and the second
to separate the signal from the $\PQt\PAQt\text{+jets}$ background. In the
$1\ell+2\Pgt_{\mathrm{h}}$ category, the background is largely dominated by
$\PQt\PAQt\text{+jets}$ production and a single BDT is trained to separate the
signal from this background.

The training of the BDTs is performed on simulated events. The events used for
the training are not used elsewhere in the analysis. The observables used as
input to the BDTs are summarized in Table 0.7.1. The choice of input variables
is optimized for each category individually, and separate optimizations are
performed for the BDT that separates the signal from the $\PQt\PAQt\mathrm{V}$
background and the one that separates the signal from the
$\PQt\PAQt\text{+jets}$ background.

The input variables given in the table are defined as follows:

  * •

$\Delta R(\ell_{1},\mathrm{j})$ ($\Delta R(\ell_{2},\mathrm{j})$) refers to
the separation between the leading (subleading) lepton and the nearest jet;

  * •

$\left<\Delta R_{\mathrm{jj}}\right>$ refers to the average distance between
jets;

  * •

$\Delta R_{\Pgt\Pgt}$ refers to the distance between the leading and
subleading $\Pgt_{\mathrm{h}}$;

  * •

$N_{\mathrm{j}}$ and $N_{\mathrm{b}}$ refer to the number of jets and
$\PQb$-tagged jets of 25and $\abs{\eta}<2.4$ that do not overlap, within
$R<0.4$, with any electron, muon, or $\Pgt_{\mathrm{h}}$ passing the relaxed
selection criteria;

  * •

$m_{\Pgt\Pgt}^{\text{vis}}$ refers to the visible mass of the leading and
subleading $\Pgt_{\mathrm{h}}$;

  * •

$\mT^{\ell 1}$ refers to the transverse mass of the leading lepton and the
$\ptvecmiss$ vector, computed according to Eq. (6);

  * •

$\pt^{\ell 1}$ ($\pt^{\ell 2}$, $\pt^{\ell 3}$) refers to the of the leading
(subleading, third) lepton;

  * •

$\pt^{\Pgt 1}$ ($\pt^{\Pgt 2}$) refers to the of the leading (subleading)
$\Pgt_{\mathrm{h}}$;

  * •

$\text{LR}(3\ell)$ refers to a MEM discriminant for the $3\ell$ category,
optimized to discriminate the $\PQt\PAQt\PH$ signal from the two dominant
irreducible background processes $\PQt\PAQt\cPZ$ and $\PQt\PAQt\PW$;

  * •

The observable $\text{MVA}_{\PQt\text{had}}^{\text{max}}$ quantifies the
compatibility of jets with a hadronic decay of a top quark. The compatibility
is computed as the response of a BDT classifier and evaluated for each
possible jet and lepton permutation, using several kinematic quantities and
$\PQb$ tagging information as inputs. The maximum over all those permutations
is used as input to the BDT that separates the $\PQt\PAQt\PH$ signal from the
$\PQt\PAQt\text{+jets}$ background in the $2\ell\text{ss}$ category;

  * •

The observable $\text{MVA}_{\PH\mathrm{j}}^{\text{max}}$ quantifies the
compatibility of jets to originate from $\PH\to\PW\PW^{*}$ decays in which one
$\PW$ boson decays leptonically and the other to a pair of quarks. The
compatibility is computed as the response of a BDT classifier and evaluated
per jet, using angular variables and jet identification variables ($\PQb$
tagging and quark-gluon discriminants). The maximum over all jets is used as
input to the BDT that separates the $\PQt\PAQt\PH$ signal from the
$\PQt\PAQt\mathrm{V}$ background in the $2\ell\text{ss}$ category. Jets that
are compatible with originating from the hadronic decays of top quarks
according to $\text{MVA}_{\PQt\text{had}}^{\text{max}}$ are excluded from the
computation of $\text{MVA}_{\PH\mathrm{j}}^{\text{max}}$.

Observables used as input to the BDTs that separate the $\PQt\PAQt\PH$ signal
from the $\PQt\PAQt\mathrm{V}$ and $\PQt\PAQt\text{+jets}$ backgrounds in the
$1\ell+2\Pgt_{\mathrm{h}}$, $2\ell\text{ss}$, $3\ell$, and
$3\ell+1\Pgt_{\mathrm{h}}$ categories.  Observable $1\ell+2\Pgt_{\mathrm{h}}$
$2\ell\text{ss}$ $3\ell$ $3\ell+1\Pgt_{\mathrm{h}}$ $\Delta
R(\ell_{1},\mathrm{j})$ $\surd$ $\surd$ $\surd$ $\Delta
R(\ell_{2},\mathrm{j})$ $\surd$ $\surd$ $\surd$ $\left<\Delta
R_{\mathrm{jj}}\right>$ $\surd$ $\surd^{2}$ $\Delta R_{\Pgt\Pgt}$ $\surd$
$\max\left(\abs{\eta^{\ell 1}},\abs{\eta^{\ell 2}}\right)$ $\surd$ $\surd$
$\surd$ $H_{\mathrm{T}}^{\text{miss}}$ $\surd$ $\surd^{2}$ $N_{\mathrm{j}}$
$\surd$ $\surd$ $\surd$ $\surd$ $N_{\mathrm{b}}$ $\surd$
$m_{\Pgt\Pgt}^{\text{vis}}$ $\surd$ $\mT^{\ell 1}$ $\surd$ $\surd$ $\surd$
$\pt^{\ell 1}$ $\surd^{1}$ $\surd^{1}$ $\surd^{1}$ $\pt^{\ell 2}$ $\surd^{1}$
- - $\pt^{\ell 3}$ $\surd^{1}$ $\surd^{1}$ $\pt^{\Pgt 1}$ $\surd$ $\pt^{\Pgt
2}$ $\surd$ $\text{LR}(3\ell)$ $\surd^{1}$
$\text{MVA}_{\PQt\text{had}}^{\text{max}}$ $\surd^{2}$
$\text{MVA}_{\PH\mathrm{j}}^{\text{max}}$ $\surd^{1}$

1 Used only in BDT that separates $\PQt\PAQt\PH$ signal from
$\PQt\PAQt\mathrm{V}$ background.
2 Used only in BDT that separates $\PQt\PAQt\PH$ signal from
$\PQt\PAQt\text{+jets}$ background.

The outputs of the two BDTs that separate the $\PQt\PAQt\PH$ signal from the
$\PQt\PAQt\mathrm{V}$ and $\PQt\PAQt\text{+jets}$ backgrounds are mapped into
a single discriminant $\mathrm{D}_{\text{MVA}}$ that is used as a
discriminating observable for the signal extraction in the $2\ell\text{ss}$,
$3\ell$, and $3\ell+1\Pgt_{\mathrm{h}}$ categories. The mapping is determined
as follows. The algorithm starts by filling two-dimensional histograms of the
output of the first versus the second BDT for signal and background events.
The histograms use a fine binning. The distributions for signal and for
background are smoothed using Gaussian kernels to reduce statistical
fluctuations. The ratio of signal to background event yields is computed in
each bin and assigned to background events depending on the bins they fall in.
The cumulative distribution of this ratio is produced for background events
and partitioned, based on its quantiles, into $N$ regions of equal background
content. The number of regions is chosen using a recursive application of the
$k$-means clustering algorithm with $k=2$ [58] on the two-dimensional
distribution of the BDTs, including stopping conditions limiting the
statistical uncertainty in the signal and background templates. The output of
the algorithm that determines the mapping is a partitioning of the two-
dimensional plane spanned by the output of the two BDTs into $N$ regions and
an enumeration, used as a discriminant, of these regions by increasing signal-
to-background ratio. By construction, the distribution of the background is
approximately flat in this discriminant, while the distribution of the signal
increases from low to high values of the discriminant. In the $2\ell\text{ss}$
and $3\ell$ categories, the signal extraction is performed using this
discriminant in subcategories based on lepton flavor, lepton charges and
b-tagging requirements. In the $3\ell+1\Pgt_{\mathrm{h}}$ category, due to
limited statistics in simulation, the training of the two BDTs and the two-
dimensional mapping have been actually performed with an inclusive $3\ell$
selection, resulting in a non flat background distribution.

Events selected in the $2\ell\text{ss}+1\Pgt_{\mathrm{h}}$ category are
analyzed in two subcategories, motivated by different signal-to-background
ratios and different levels of signal-to-background separation provided by the
MEM discriminant $\text{LR}(2\ell\text{ss}+1\Pgt_{\mathrm{h}})$ in each of the
subcategories. The “no-missing-jet” subcategory contains events in which a
pair of jets compatible with originating from the hadronic decay of a $\PW$
boson is reconstructed, which allows for a full reconstruction of the decay
chain
$\PQt\PAQt\PH\to\PQb\PW\,\PAQb\PW\,\Pgt\Pgt\to\PQb\mathrm{jj}\,\PAQb\ell\PGn\,\ell\cPagn_{\ell}\PGn_{\Pgt}\,\Pgt_{\mathrm{h}}\PAGn_{\Pgt}$
in signal events, while the “missing-jet” category contains events with no
such pair of jets. The full reconstruction of the decay chain improves the
separation of the $\PQt\PAQt\PH$ signal from background events. Signal events
can contribute to the “missing-jet” category if, for example, one of the jets
produced in the $\PW$ boson decay is outside of the and $\eta$ acceptance or
if it overlaps with another jet.

###  0.7.2 Statistical analysis

The rate of the $\PQt\PAQt\PH$ signal $\mu$ is measured through a simultaneous
ML fit to the distribution in the discriminating observables or the number of
events observed in the six event categories $1\ell+2\Pgt_{\mathrm{h}}$,
$2\ell\text{ss}$, $2\ell\text{ss}+1\Pgt_{\mathrm{h}}$, $3\ell$,
$3\ell+1\Pgt_{\mathrm{h}}$, and $4\ell$. The best-fit value of this parameter
is denoted as $\hat{\mu}$. A 68% confidence interval on the parameter of
interest is obtained using a maximum likelihood fit based on the profile
likelihood ratio test statistic [59, 60]. A potential signal excess in data is
quantified by calculating the corresponding $p$-value. Upper limits on the
$\PQt\PAQt\PH$ signal rate are set via the $\text{CL}_{s}$ method [61, 62].

The nuisance parameters described in Section 0.8 are treated via the
frequentist paradigm, as described in Refs. [59, 60]. Systematic uncertainties
that affect only the normalization, but not the distribution in any
discriminating observable, are represented by $\Gamma$-function distributions
if they are statistical in origin, , corresponding to the number of events
observed in a control region, and by log-normal probability density functions
otherwise. Systematic uncertainties that affect the distribution in the
discriminating observables are incorporated into the ML fit via the technique
detailed in Ref. [63], and represented by Gaussian probability density
functions. Nuisance parameters representing systematic uncertainties of the
latter type can also affect the normalization of the $\PQt\PAQt\PH$ signal or
of the backgrounds.

##  0.8 Systematic uncertainties

Various imprecisely measured or simulated effects can affect the rates as well
as the distributions of the observables used for the signal extraction,
described in Section 0.7. We differentiate the corresponding systematic
uncertainties between experimental and theory-related sources. The
contributions of background processes that are determined from data, as
described in Section 0.6, are mostly unaffected by potential inaccuracies of
the MC simulation.

The efficiency for events to pass the combination of triggers based on the
presence of one, two, or three electrons or muons is measured in bins of
lepton multiplicity with an uncertainty between 1 and 3% using a sample of
events recorded by triggers based on $\ptmiss$. The efficiency of the trigger
that selects events containing an electron or muon in combination with a
$\Pgt_{\mathrm{h}}$ in the $1\ell+2\Pgt_{\mathrm{h}}$ category is measured
with an uncertainty of 3% in $\cPZ/\PGg^{*}\to\Pgt\Pgt$ events.

The efficiencies to reconstruct and identify electrons and muons are measured
as a function of with uncertainties ranging from 2 to 4% using
$\cPZ/\PGg^{*}\to\Pe\Pe$ and $\cPZ/\PGg^{*}\to\Pgm\Pgm$ events via the tag-
and-probe method discussed in Ref. [64]. The $\Pgt_{\mathrm{h}}$
reconstruction and identification efficiency and the $\Pgt_{\mathrm{h}}$
energy scale are measured with uncertainties of 5 and 3%, respectively, using
$\cPZ/\PGg^{*}\to\Pgt\Pgt$ events [41].

The energy scale of jets is measured using the balance of jets with $\cPZ$
bosons and photons in $\cPZ/\PGg^{*}\to\Pe\Pe$ and $\cPZ/\PGg^{*}\to\Pgm\Pgm$
and $\PGg$+jets events and the balance between jets in dijet and multijet
events [65]. The uncertainty in the jet energy scale is a few percent and
depends on and $\eta$. The impact of jet energy scale uncertainties on event
yields and on the distributions in kinematic observables is evaluated by
varying the jet energy corrections within their uncertainties and propagating
the effect to the final result by recalculating all kinematic quantities,
including $\ptmiss$, $H_{\mathrm{T}}^{\text{miss}}$, and $L_{\mathrm{D}}$, and
reapplying all event selection criteria.

The $\PQb$ tagging efficiencies are measured in multijet events, enriched in
the heavy-flavor content by requiring the presence of a muon, and in
$\PQt\PAQt\text{+jets}$ events, with uncertainties of a few percent, depending
on and $\eta$ [57]. The mistag rates for light-quark and gluon jets are
measured in $\cPZ$+jets events with an uncertainty of 5–10% for the loose and
20–30% for the tight $\PQb$ tagging criteria, again depending on and $\eta$
[57].

The uncertainty in the integrated luminosity amounts to 2.5% [66].

Uncertainties from theoretical sources are assigned to the
$\PQt\PAQt\mathrm{V}$ backgrounds and to the signal normalization. The cross
sections of the irreducible $\PQt\PAQt\cPZ$, $\PQt\PAQt\PW$, and
$\PQt\PAQt\PW\PW$ backgrounds are known with uncertainties of
${}^{+9.6\%}_{-11.2\%}$, ${}^{+12.9\%}_{-11.5\%}$, and
${}^{+8.1\%}_{-10.9\%}$, respectively, from missing higher-order corrections
on the perturbative expansion and of 3.4, 4 and 3%, respectively, from
uncertainties in the PDFs and in the strong coupling constant $\alpha_{s}$
[67]. The theoretical uncertainties in the SM expectation for the
$\PQt\PAQt\PH$ signal cross section amount to ${}^{+5.8\%}_{-9.3\%}$ from
missing higher-order corrections on the perturbative expansion and to 3.6%
from uncertainties in the PDFs and in $\alpha_{s}$ [67]. The effect of missing
higher orders on distributions in kinematic observables is evaluated through
independent changes in the renormalization and factorization scales by factors
of $2$ and $1/2$ relative to their nominal equal values [68, 69, 70].

The estimate for the misidentified lepton background, obtained from data as
described in Section 0.6.1, is subject to uncertainties in the factors $f_{i}$
that are used to compute the event weights in Eq. (5). The impact of these
uncertainties is separated into effects on the normalization and on the shape
of the distributions used for signal extraction. The effect on the
normalization ranges from 10 to 40%, depending on the multiplicity of
misidentified electrons, muons, and $\Pgt_{\mathrm{h}}$, and on their and
$\eta$. The uncertainties in the normalization include the effect of
statistical uncertainties in the sample used to measure the $f_{i}$, of
systematic uncertainties related to the subtraction of the prompt-lepton
contamination in this sample, and of the non-perfect agreement in simulation
between distributions for misidentified lepton background and those obtained
when applying the FF method. The effect on distributions in kinematic
observables is computed as follows. In case of electrons and muons, an
uncertainty band for the distributions used for signal extraction is obtained
by applying independent variations of the $f_{i}$ in different bins of and
$\eta$. In case of $\Pgt_{\mathrm{h}}$, we fit the misidentification rates
$f_{i}$ measured in the barrel and endcap region of the detector as function
of and propagate the uncertainty in the slope of the fit to the final result,
in a correlated way between all the categories with $\Pgt_{\mathrm{h}}$
candidates, with typical values around 3%.

The uncertainty in the sign misidentification rate for electrons is propagated
to the final result in a similar way. The corresponding uncertainty in the
rate of the sign-flip background amounts to $\approx$30%.

Even though the $\PW\PZ$ production is predicted theoretically at NLO accuracy
and its inclusive cross section has been measured successfully at the LHC [71,
72], this good agreement does not translate automatically to the signal
regions considered for this analysis, which require the presence of at least
one $\PQb$-tagged jet. A conservative 100% uncertainty is therefore assigned
to the diboson background in all categories but the $3\ell$ one. The
uncertainty is reduced to $\approx$40% for the $3\ell$ categories from studies
in a dedicated $3\ell$ $\PW\PZ$ CR, defined by inverting the $\PZ$ veto on the
dilepton mass and the $\PQb$ tagging requirement. The overall uncertainty
assigned to the diboson prediction in that case is estimated from the
statistical uncertainty due to the limited sample size in the CR (30%), the
residual background in the CR (20%), the uncertainties in the $\PQb$ tagging
rate (between 10 and 40%), and from the knowledge of PDFs and the theoretical
uncertainties in the flavor composition of the jets produced in association
with the electroweak bosons (up to 10%).

An uncertainty of 50% is assigned to the rate of other minor backgrounds. This
conservative uncertainty accounts for the fact that the small background
contributions from those processes have not yet been measured at the LHC.

Among all the sources of uncertainty listed above, the ones having the largest
impact on the measured $\PQt\PAQt\PH$ signal rate are related to the lepton
efficiency measurement, the estimate of the misidentified lepton background
and the theoretical sources affecting the normalization of the signal and
irreducible backgrounds, as can be seen from Table 0.8. The systematic
uncertainties related to the lepton efficiency measurement and the estimate of
the misidentified lepton background are treated as correlated between all the
categories which include leptons with a given flavor. The systematic
uncertainties in the normalization of the signal and irreducible backgrounds
are treated as correlated between all the categories.

Summary of the main sources of systematic uncertainty and their impact on the
combined measured $\PQt\PAQt\PH$ signal rate $\mu$. $\Delta\mu/\mu$
corresponds to the relative shift in signal strength obtained from varying the
systematic source within its associated uncertainty.  Source Uncertainty [%]
$\Delta\mu/\mu$ [%] , selection efficiency 2–4 11 $\Pgt_{\mathrm{h}}$
selection efficiency 5 4.5 $\PQb$ tagging efficiency 2–15 [57] 6 Reducible
background estimate 10–40 11 Jet energy calibration 2–15 [65] 5
$\Pgt_{\mathrm{h}}$ energy calibration 3 1 Theoretical sources $\approx$10 12
Integrated luminosity 2.5 5

##  0.9 Results

The number of events observed in the different categories are compared to the
SM expectations after the ML fit in Table 0.9. The event yields resulting from
the fit are consistent with those predicted by the original background and
signal estimates within the uncertainties described in Section 0.8. Most of
those uncertainties are not very constrained by the ML fit, except for the
uncertainty related to the background due to jets misidentified as
$\Pgt_{\mathrm{h}}$ candidates. This originates from the
$1\ell+2\Pgt_{\mathrm{h}}$ category which is dominated by this background.
Distributions in the discriminating observables used for the signal extraction
in the different categories after the final fit are shown in Figs. 2–4. In
Fig. 5, the different bins of the distributions are classified according to
their expected ratio of signal (S) to background (B) events. An excess of
observed events with respect to the SM backgrounds is visible in the most
sensitive bins.

Numbers of events selected in the different categories compared to the SM
expectations for the $\PQt\PAQt\PH$ signal and background processes. The event
yields expected for the $\PQt\PAQt\PH$ signal and for the backgrounds are
shown for the values of nuisance parameters obtained from the combined ML fit
and $\mu=\hat{\mu}=1.23$. Quoted uncertainties represent the combination of
statistical and systematic components.

Process | $1\ell+2\Pgt_{\mathrm{h}}$ | $2\ell\text{ss}$ | $2\ell\text{ss}+1\Pgt_{\mathrm{h}}$
---|---|---|---
$\PQt\PAQt\PH$ |  $7.1$ $\,\,\pm\,\,$ |  $2.4$ |  $66.3$ $\,\,\pm\,\,$ |  $21.0$ | $11.6\,\,\pm\,\,3.5$
$\PQt\PAQt\cPZ/\PGg^{*}$ |  $6.3$ $\,\,\pm\,\,$ |  $1.1$ |  $80.9$ $\,\,\pm\,\,$ |  $10.4$ |  $9.2$ $\,\,\pm\,\,$ |  $1.2$
$\PQt\PAQt\PW+\PQt\PAQt\PW\PW$ |  $0.5$ $\,\,\pm\,\,$ |  $0.1$ |  $150.0$ $\,\,\pm\,\,$ |  $16.9$ |  $9.1$ $\,\,\pm\,\,$ |  $1.0$
$\PW\cPZ+\PZ\cPZ$ |  $2.1$ $\,\,\pm\,\,$ |  $1.6$ |  $16.5$ $\,\,\pm\,\,$ |  $13.1$ |  $3.9$ $\,\,\pm\,\,$ |  $3.0$
$\PQt\PH$ |  $0.4$ $\,\,\pm\,\,$ |  $0.1$ |  $2.7$ $\,\,\pm\,\,$ |  $0.2$ |  $0.5$ $\,\,\pm\,\,$ |  $0.04$
Conversions | $<0.02$ |  $12.1$ $\,\,\pm\,\,$ |  $5.8$ |  $1.4$ $\,\,\pm\,\,$ |  $0.5$
Sign flip |  |  $27.5$ $\,\,\pm\,\,$ |  $8.0$ |  $0.5$ $\,\,\pm\,\,$ |  $0.1$
Misidentified leptons |  $195.7$ $\,\,\pm\,\,$ |  $13.6$ |  $94.2$ $\,\,\pm\,\,$ |  $21.2$ |  $8.6$ $\,\,\pm\,\,$ |  $2.1$
Rare backgrounds |  $1.4$ $\,\,\pm\,\,$ |  $0.7$ |  $39.0$ $\,\,\pm\,\,$ |  $21.2$ | $3.1\,\,\pm\,\,1.5$
Total expected background |  $206.3$ $\,\,\pm\,\,$ |  $14.0$ |  $423.0$ $\,\,\pm\,\,$ |  $38.0$ | $36.1\,\,\pm\,\,4.2$
Observed | $212$ | $507$ | $49$

Process | $3\ell$ | $3\ell+1\Pgt_{\mathrm{h}}$ | $4\ell$
---|---|---|---
$\PQt\PAQt\PH$ |  $22.8$ $\,\,\pm\,\,$ |  $7.4$ |  $2.6$ $\,\,\pm\,\,$ |  $0.9$ | $1.1\,\,\pm\,\,0.4$
$\PQt\PAQt\cPZ/\PGg^{*}$ |  $49.0$ $\,\,\pm\,\,$ |  $6.9$ |  $3.4$ $\,\,\pm\,\,$ |  $0.5$ |  $2.1$ $\,\,\pm\,\,$ |  $0.4$
$\PQt\PAQt\PW+\PQt\PAQt\PW\PW$ |  $35.2$ $\,\,\pm\,\,$ |  $4.2$ |  $0.4$ $\,\,\pm\,\,$ |  $0.04$ | $<2\times 10^{-3}$
$\PW\cPZ+\PZ\cPZ$ |  $9.9$ $\,\,\pm\,\,$ |  $2.4$ |  $0.3$ $\,\,\pm\,\,$ |  $0.05$ |  $0.1$ $\,\,\pm\,\,$ |  $0.1$
$\PQt\PH$ |  $1.2$ $\,\,\pm\,\,$ |  $0.2$ |  $0.1$ $\,\,\pm\,\,$ |  $0.01$ | $<4\times 10^{-4}$
Conversions |  $5.3$ $\,\,\pm\,\,$ |  $2.9$ | $<0.02$ | $<0.02$
Misidentified leptons |  $22.7$ $\,\,\pm\,\,$ |  $6.7$ |  $0.9$ $\,\,\pm\,\,$ |  $0.2$ | $<0.04$
Rare backgrounds |  $8.2$ $\,\,\pm\,\,$ |  $13.8$ |  $0.2$ $\,\,\pm\,\,$ |  $0.1$ | $0.1\,\,\pm\,\,0.2$
Total expected background |  $131.4$ $\,\,\pm\,\,$ |  $18.2$ |  $5.3$ $\,\,\pm\,\,$ |  $0.5$ | $2.4\,\,\pm\,\,0.4$
Observed | $148$ | $7$ | $3$

Figure 2:  Distributions in the discriminating observables used for the signal
extraction in the $1\ell+2\Pgt_{\mathrm{h}}$ category (top left) and in
different subcategories of the $2\ell\text{ss}$ category (top right and bottom
row), compared to the SM expectation for the $\PQt\PAQt\PH$ signal and for
background processes. A BDT trained to separate the $\PQt\PAQt\PH$ signal from
the $\PQt\PAQt\text{+jets}$ background is used in the
$1\ell+2\Pgt_{\mathrm{h}}$ category, while a $\mathrm{D}_{\text{MVA}}$
variable, combining the outputs of two BDTs trained to discriminate the
$\PQt\PAQt\PH$ signal from the $\PQt\PAQt\mathrm{V}$ and
$\PQt\PAQt\text{+jets}$ backgrounds respectively, is used in the
$2\ell\text{ss}$ subcategories. The distributions expected for signal and
background processes are shown for the values of nuisance parameters obtained
from the combined ML fit and $\mu=\hat{\mu}=1.23$, corresponding to the best-
fit value from the ML fit.

Figure 3:  Distributions in the discriminating observables used for the signal
extraction in the “no-missing-jet” (top left) and “missing-jet” (top right)
subcategories of the $2\ell\text{ss}+1\Pgt_{\mathrm{h}}$ category, the $3\ell$
category (bottom left), and the $3\ell+1\Pgt_{\mathrm{h}}$ category (bottom
right), compared to the SM expectation for the $\PQt\PAQt\PH$ signal and for
background processes. The MEM discriminant
$\text{LR}(2\ell\text{ss}+1\Pgt_{\mathrm{h}})$ is used in the
$2\ell\text{ss}+1\Pgt_{\mathrm{h}}$ subcategories, while a
$\mathrm{D}_{\text{MVA}}$ variable, combining the outputs of two BDTs trained
to discriminate the $\PQt\PAQt\PH$ signal from the $\PQt\PAQt\mathrm{V}$ and
$\PQt\PAQt\text{+jets}$ backgrounds respectively, is used in the $3\ell$ and
$3\ell+1\Pgt_{\mathrm{h}}$ categories. The distributions expected for signal
and background processes are shown for the values of nuisance parameters
obtained from the combined ML fit and $\mu=\hat{\mu}=1.23$, corresponding to
the best-fit value from the ML fit. The lowest bin of the MEM discriminant in
the “missing-jet” subcategory of the $2\ell\text{ss}+1\Pgt_{\mathrm{h}}$
category collects events for which the kinematics of the reconstructed objects
is not compatible with the $\PQt\PAQt\PH$, $\PH\to\Pgt\Pgt$ signal hypothesis.
Figure 4:  Number of events observed and expected in the $4\ell$ category. The
distributions expected for signal and background processes are shown for the
values of nuisance parameters obtained from the combined ML fit and
$\mu=\hat{\mu}=1.23$, corresponding to the best-fit value from the ML fit.
Figure 5:  Distribution of the decimal logarithm of the ratio between the
expected signal and expected background in each bin of the distributions used
for the signal extraction. The distributions expected for signal and
background processes are shown for the values of nuisance parameters obtained
from the combined ML fit and $\mu=\hat{\mu}=1.23$, corresponding to the best-
fit value from the ML fit.

Upper limits on the signal rate, computed at 95% confidence level (), are
given in Table 0.9. The limits are computed for separate fits of each
category, and for their combination. The observed limit computed from the
combination of all categories amounts to $2.1$ times the SM $\PQt\PAQt\PH$
production rate. The observed limit is compatible with the one expected if a
SM $\PQt\PAQt\PH$ signal is present at the SM predicted rate, amounting to
$1.7$ times the SM $\PQt\PAQt\PH$ production rate in the presence of a
$\PQt\PAQt\PH$ signal. In the absence of signal, an upper limit on the signal
rate of $0.8$ times the SM $\PQt\PAQt\PH$ production rate is expected.

The 95% upper limits on the $\PQt\PAQt\PH$ signal rate, in units of the SM
$\PQt\PAQt\PH$ production rate, obtained in each of the categories
individually and for the combination of all six event categories. The observed
limit is compared to the limits expected for the background-only hypothesis
($\mu=0$) and for the case that a $\PQt\PAQt\PH$ signal of SM production rate
is present in the data ($\mu=1$). The $\pm 1$ standard deviation uncertainty
intervals on the expected limits are also given in the table.

Category | Observed limit on $\mu$ | Expected limit
---|---|---
($\mu=0$) | ($\mu=1$)
$1\ell+2\Pgt_{\mathrm{h}}$ | $2.7$ | $4.1^{+1.7}_{-1.4}$ | $4.8^{+2.0}_{-1.9}$
$2\ell\text{ss}$ | $2.8$ | $1.0^{+0.4}_{-0.2}$ | $2.0^{+0.7}_{-0.3}$
$2\ell\text{ss}+1\Pgt_{\mathrm{h}}$ | $2.5$ | $1.4^{+0.7}_{-0.3}$ | $2.5^{+0.9}_{-0.5}$
$3\ell$ | $2.7$ | $1.6^{+0.8}_{-0.4}$ | $2.9^{+1.1}_{-0.4}$
$3\ell+1\Pgt_{\mathrm{h}}$ | $4.4$ | $2.8^{+1.3}_{-0.6}$ | $4.1^{+1.5}_{-0.7}$
$4\ell$ | $6.5$ | $4.9^{+2.8}_{-1.1}$ | $6.7^{+2.5}_{-0.8}$
Combined | $2.1$ | $0.8^{+0.3}_{-0.2}$ | $1.7^{+0.5}_{-0.5}$

Signal yields are extracted from a fit with $\mu$ allowed to assume different
values in each category, or constrained to assume the same value in all the
categories for the combined result. The results are shown in Fig. 6. For the
combined fit, the observed (expected) signal rate is
$\mu=1.23^{+0.45}_{-0.43}$ ($1.00^{+0.42}_{-0.38}$) times the SM
$\PQt\PAQt\PH$ production rate, with an observed (expected) significance of
$3.2\sigma$ ($2.8\sigma$), which represents evidence for $\PQt\PAQt\PH$
production in those final states. While the categories $2\ell\text{ss}$,
$3\ell$ and $4\ell$ are mostly sensitive to the $\PQt\PAQt\PH$ signal in the
$\PH\to\PW\PW$ and $\PH\to\PZ\PZ$ decay modes, the $1\ell+2\Pgt_{\mathrm{h}}$,
$2\ell\text{ss}+1\Pgt_{\mathrm{h}}$ and $3\ell+1\Pgt_{\mathrm{h}}$ categories
enhance the sensitivity to the $\PH\to\Pgt\Pgt$ decay mode. The distributions
in the discriminating observables are very similar for $\PQt\PAQt\PH$ signal
events with a $\PH$ boson decaying into $\PW$ bosons, $\PZ$ bosons, and $\Pgt$
leptons, however, causing a large anti-correlation between the corresponding
signal rates. Denoting the sum of $\PH\to\PW\PW$ and $\PH\to\PZ\PZ$ decay
modes by $\PH\to\mathrm{VV}$ and performing a two-parameter simultaneous fit
for the signal rates $\mu(\PQt\PAQt\PH,\PH\to\mathrm{VV})$ and
$\mu(\PQt\PAQt\PH,$ $\PH\to\Pgt\Pgt)$, we obtain
$\mu(\PQt\PAQt\PH,\PH\to\mathrm{VV})=1.69^{+0.68}_{-0.61}$ and
$\mu(\PQt\PAQt\PH,$ $\PH\to\Pgt\Pgt)=0.15^{+1.07}_{-0.91}$. The expected anti-
correlation between the two measured signal strengths has been explicitly
checked and is associated with a correlation factor of $-0.45$.

Figure 6:  Signal rates $\mu$, in units of the SM $\PQt\PAQt\PH$ production
rate, measured in each of the categories individually and for the combination
of all six categories. The blue (green) band corresponds to the statistical
(total) uncertainty on the combined signal rate.

As a cross check, the analysis is repeated with the $\PQt\PAQt\cPZ$ and
$\PQt\PAQt\PW(W)$ backgrounds kept freely floating in the ML fit. Control
regions enriched in the contributions of these backgrounds are added to the
fit to constrain them. The $\PQt\PAQt\cPZ$-enriched control region is defined
from the $3\ell$ signal region by inverting the $\PZ$ boson veto on the
invariant mass of SFOS lepton pairs. The $\PQt\PAQt\PW$-enriched control
region is defined from the $2\ell\text{ss}$ signal region but changing the jet
multiplicity requirement to consider events with exactly three jets. The
signal rate obtained from this fit is $\mu=1.04^{+0.50}_{-0.36}$
($1.00^{+0.42}_{-0.38}$) times the SM $\PQt\PAQt\PH$ production rate, with an
observed (expected) significance of $2.7\sigma$ ($2.7\sigma$).

##  0.10 Summary

A search has been presented for the associated production of a Higgs boson
with a top quark pair in final states with electrons, muons, and hadronically
decaying $\Pgt$ leptons ($\Pgt_{\mathrm{h}}$). The analyzed data set
corresponds to an integrated luminosity of 35.9of $\Pp\Pp$ collision data
recorded by the CMS experiment at $\sqrt{s}=13\TeV$. The analysis is performed
in six mutually exclusive event categories, based on different lepton and
$\Pgt_{\mathrm{h}}$ multiplicity requirements. The sensitivity of the analysis
is enhanced by using multivariate analysis techniques based on boosted
decision trees and on the matrix element method. The results of the analysis
are in agreement with the standard model (SM) expectation. The measured signal
rate amounts to $1.23^{+0.45}_{-0.43}$ times the SM $\PQt\PAQt\PH$ production
rate, with an observed (expected) significance of $3.2\sigma$ ($2.8\sigma$),
which represents evidence for $\PQt\PAQt\PH$ production in those final states.
An upper limit on the signal rate of $2.1$ times the SM $\PQt\PAQt\PH$
production rate is set at 95% confidence level, for an expected limit of $1.7$
times the SM production rate in the presence of a $\PQt\PAQt\PH$ signal.

###### 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: BMWFW and FWF (Austria); FNRS and FWO (Belgium); CNPq,
CAPES, FAPERJ, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST, and NSFC
(China); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RPF (Cyprus);
SENESCYT (Ecuador); MoER, ERC IUT, 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); LAS
(Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and
UASLP-FAI (Mexico); MBIE (New Zealand); PAEC (Pakistan); MSHE and NSC
(Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom, RAS, RFBR and RAEP
(Russia); MESTD (Serbia); SEIDI, CPAN, PCTI and FEDER (Spain); 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 No. 675440 (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
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 and 125105 (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 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 Welch Foundation, contract C-1845; and the
Weston Havens Foundation (USA).

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##  .11 Matrix Element Method

As mentioned in Section 0.7.1, discriminants based on the MEM approach have
been developed for the $2\ell\text{ss}+1\Pgt_{\mathrm{h}}$ and $3\ell$
categories. Additional details on their computation are given in this
Appendix. The matrix element (ME) $\mathcal{M}_{\Omega}(\mathbf{x})$
associated with a given process $\Omega$ depends on a set of kinematic
variables $\mathbf{x}$ that corresponds to the four-momenta, at parton level,
of the particles in the initial and final state. We use bold letters to
indicate vector quantities. The square of the ME is convoluted with a function
$W(\mathbf{y}|\mathbf{x})$, referred to as the “transfer function” (TF), which
represents the experimental resolution. More specifically, the function
$W(\mathbf{y}|\mathbf{x})$ corresponds to the probability for measuring a set
of observables $\mathbf{y}$ in the detector, given that the corresponding
parton-level momenta are equal to $\mathbf{x}$.

The MEM computes the differential cross section of the process $\Omega$ with
respect to the observables $\mathbf{y}$, while integrating over the unmeasured
or poorly measured parton-level quantities $\mathbf{x}$, as well as over the
Bjorken scaling variables [73] $x_{a}$ and $x_{b}$ of the colliding protons.
For each event a weight $w_{\Omega}(\textbf{y})$ is computed, which quantifies
the compatibility of the event, characterized by the measured observables
$\mathbf{y}$, with the hypotheses that the event is produced by the process
$\Omega$:

$w_{\Omega}(\textbf{y})\propto\sum\limits_{p}\int{\rd\textbf{x}\,\rd
x_{a}\,\rd
x_{b}\frac{f_{i}(x_{a},Q)f_{j}(x_{b},Q)}{x_{a}x_{b}s}\delta^{4}(x_{a}P_{a}+x_{b}P_{b}-\sum
p_{k})|\mathcal{M}_{\Omega}(\textbf{x})|^{2}W(\textbf{y}|\textbf{x})}.$ (7)

The sum $\sum\limits_{p}$ extends over all possible associations between
parton-level and reconstructed objects. The square of the ME,
$|\mathcal{M}_{\Omega}(\textbf{x})|^{2}$, is computed at LO using the program.
The symbols $f_{i}(x_{a},Q)$ and $f_{j}(x_{b},Q)$ denote the PDFs, which we
evaluate numerically using the CTEQ6.6 [74] and NNPDF3.0 LO sets. The four-
dimensional $\delta$-function $\delta^{4}(x_{a}P_{a}+x_{b}P_{b}-\sum p_{k})$
ensures the conservation of energy and momentum. The integral on the right-
hand side is first transformed analytically, in order to eliminate the
$\delta$-function and to make the computation of the integral numerically
tractable, and then computed numerically using the VEGAS algorithm [75]. A
complication arises from the fact that we use LO ME for the $\PQt\PAQt\PH$
signal and for background processes. The LO ME strictly applies only to events
in which no additional jets, besides the jets corresponding to quarks in the
LO ME, are produced in the hard scattering interaction. At the center-of-mass
energies of the LHC the phase space for quantum chromodynamics radiation is
large, however, and particles with masses up to a few hundred are typically
produced in association with a sizable hadronic activity [76]. In order to use
the LO ME, we transform the system of all particles that are present in the LO
ME into a frame in which this system has zero . In the opposite case of events
where the reconstructed jet multiplicity is lower than the number of quarks
present in the LO ME the integral on the right-hand-side of Eq. (7) is
extended by an integration over the variables associated to the missing jets.
The TFs $W(\mathbf{y}|\mathbf{x})$ are obtained from the MC simulation and are
used to model the resolution of jets and the resolution on $\ptvecmiss$.
Separate TFs are used for $\PQb$ quark and for light quark and gluon jets. The
TFs are also used to account for the energy loss due to neutrinos produced in
the decays of $\PQb$ quarks to leptons and in $\Pgt$ lepton decays. The
fraction of $\Pgt$ lepton energy carried by neutrinos is on average higher in
$\Pgt\to\ell\cPagn_{\ell}\PGn_{\Pgt}$ decays compared to
$\Pgt\to\Pgt_{\mathrm{h}}\PGn_{\Pgt}$ decays and separate TFs are determined
for both cases.

According to the Neyman lemma [77], the ratio of weights
$w_{\Omega}(\textbf{y})$ computed for the signal and for the background
hypothesis constitutes an optimal observable to separate the $\PQt\PAQt\PH$
signal from backgrounds:

$\text{LR}(\textbf{y})=\frac{w_{\PQt\PAQt\PH}(\textbf{y})}{w_{\PQt\PAQt\PH}(\textbf{y})+\sum\limits_{\mathrm{B}}\kappa_{\mathrm{B}}\,w_{\mathrm{B}}(\textbf{y})}.$
(8)

The coefficients $\kappa_{\mathrm{B}}$ that quantify the relative importance
of different background processes $\mathrm{B}$ are determined by a numerical
optimization, in order to achieve the maximal separation of the $\PQt\PAQt\PH$
signal from all background processes.

##  .12 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan, A. Tumasyan Institut für Hochenergiephysik, Wien, Austria
W. Adam, F. Ambrogi, E. Asilar, T. Bergauer, J. Brandstetter, E. Brondolin, M.
Dragicevic, J. Erö, A. Escalante Del Valle, M. Flechl, M. Friedl, R.
Frühwirth1, V.M. Ghete, J. Grossmann, J. Hrubec, M. Jeitler1, A. König, N.
Krammer, I. Krätschmer, D. Liko, T. Madlener, I. Mikulec, E. Pree, N. Rad, H.
Rohringer, J. Schieck1, R. Schöfbeck, M. Spanring, D. Spitzbart, A. Taurok, W.
Waltenberger, J. Wittmann, C.-E. Wulz1, M. Zarucki Institute for Nuclear
Problems, Minsk, Belarus
V. Chekhovsky, V. Mossolov, J. Suarez Gonzalez Universiteit Antwerpen,
Antwerpen, Belgium
E.A. De Wolf, D. Di Croce, X. Janssen, J. Lauwers, M. Pieters, M. Van De
Klundert, H. Van Haevermaet, P. Van Mechelen, N. Van Remortel Vrije
Universiteit Brussel, Brussel, Belgium
S. Abu Zeid, F. Blekman, J. D’Hondt, I. De Bruyn, J. De Clercq, K. Deroover,
G. Flouris, D. Lontkovskyi, S. Lowette, I. Marchesini, S. Moortgat, L.
Moreels, Q. Python, K. Skovpen, S. Tavernier, W. Van Doninck, P. Van Mulders,
I. Van Parijs Université Libre de Bruxelles, Bruxelles, Belgium
D. Beghin, B. Bilin, H. Brun, B. Clerbaux, G. De Lentdecker, H. Delannoy, B.
Dorney, G. Fasanella, L. Favart, R. Goldouzian, A. Grebenyuk, A.K. Kalsi, T.
Lenzi, J. Luetic, T. Seva, E. Starling, C. Vander Velde, P. Vanlaer, D.
Vannerom, R. Yonamine Ghent University, Ghent, Belgium
T. Cornelis, D. Dobur, A. Fagot, M. Gul, I. Khvastunov2, D. Poyraz, C. Roskas,
D. Trocino, M. Tytgat, W. Verbeke, B. Vermassen, M. Vit, N. Zaganidis
Université Catholique de Louvain, Louvain-la-Neuve, Belgium
H. Bakhshiansohi, O. Bondu, S. Brochet, G. Bruno, C. Caputo, A. Caudron, P.
David, S. De Visscher, C. Delaere, M. Delcourt, B. Francois, A. Giammanco, G.
Krintiras, V. Lemaitre, A. Magitteri, A. Mertens, M. Musich, K. Piotrzkowski,
L. Quertenmont, A. Saggio, M. Vidal Marono, S. Wertz, J. Zobec Centro
Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil
W.L. Aldá Júnior, F.L. Alves, G.A. Alves, L. Brito, G. Correia Silva, C.
Hensel, A. Moraes, M.E. Pol, 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, S. Fonseca De Souza, H.
Malbouisson, M. Medina Jaime5, M. Melo De Almeida, C. Mora Herrera, L. Mundim,
H. Nogima, 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
S. Ahujaa, C.A. Bernardesa, L. Calligarisa, T.R. Fernandez Perez Tomeia, E.M.
Gregoresb, P.G. Mercadanteb, S.F. Novaesa, Sandra S. Padulaa, D. Romero Abadb,
J.C. Ruiz Vargasa Institute for Nuclear Research and Nuclear Energy, Bulgarian
Academy of Sciences, Sofia, Bulgaria
A. Aleksandrov, R. Hadjiiska, P. Iaydjiev, A. Marinov, M. Misheva, M. Rodozov,
M. Shopova, G. Sultanov University of Sofia, Sofia, Bulgaria
A. Dimitrov, L. Litov, B. Pavlov, P. Petkov Beihang University, Beijing, China
W. Fang6, X. Gao6, L. Yuan Institute of High Energy Physics, Beijing, China
M. Ahmad, J.G. Bian, G.M. Chen, H.S. Chen, M. Chen, Y. Chen, C.H. Jiang, D.
Leggat, B. Li, H. Liao, Z. Liu, F. Romeo, S.M. Shaheen, A. Spiezia, J. Tao, C.
Wang, Z. Wang, E. Yazgan, H. Zhang, J. Zhao State Key Laboratory of Nuclear
Physics and Technology, Peking University, Beijing, China
Y. Ban, G. Chen, J. Li, Q. Li, S. Liu, Y. Mao, S.J. Qian, D. Wang, Z. Xu
Tsinghua University, Beijing, China
Y. Wang Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, C.A. Carrillo Montoya, L.F. Chaparro Sierra, C. Florez,
C.F. González Hernández, M.A. Segura Delgado University of Split, Faculty of
Electrical Engineering, Mechanical Engineering and Naval Architecture, Split,
Croatia
B. Courbon, N. Godinovic, D. Lelas, I. Puljak, P.M. Ribeiro Cipriano, T.
Sculac University of Split, Faculty of Science, Split, Croatia
Z. Antunovic, M. Kovac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, D. Ferencek, K. Kadija, B. Mesic, A. Starodumov7, T. Susa
University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos,
P.A. Razis, H. Rykaczewski Charles University, Prague, Czech Republic
M. Finger8, M. Finger Jr.8 Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab
Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
H. Abdalla9, Y. Assran10,11, A. Mohamed12 National Institute of Chemical
Physics and Biophysics, Tallinn, Estonia
S. Bhowmik, R.K. Dewanjee, M. Kadastik, L. Perrini, M. Raidal, C. Veelken
Department of Physics, University of Helsinki, Helsinki, Finland
P. Eerola, H. Kirschenmann, J. Pekkanen, M. Voutilainen Helsinki Institute of
Physics, Helsinki, Finland
J. Havukainen, J.K. Heikkilä, T. Järvinen, V. Karimäki, 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, J.L. Faure, F. Ferri, S.
Ganjour, S. Ghosh, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry,
C. Leloup, E. Locci, M. Machet, J. Malcles, G. Negro, J. Rander, A. Rosowsky,
M.Ö. Sahin, M. Titov Laboratoire Leprince-Ringuet, Ecole polytechnique,
CNRS/IN2P3, Université Paris-Saclay, Palaiseau, France
A. Abdulsalam13, C. Amendola, I. Antropov, S. Baffioni, F. Beaudette, P.
Busson, L. Cadamuro, C. Charlot, R. Granier de Cassagnac, M. Jo, I. Kucher, S.
Lisniak, A. Lobanov, J. Martin Blanco, M. Nguyen, C. Ochando, G. Ortona, P.
Paganini, P. Pigard, R. Salerno, J.B. Sauvan, Y. Sirois, A.G. Stahl Leiton, Y.
Yilmaz, A. Zabi, A. Zghiche Université de Strasbourg, CNRS, IPHC UMR 7178,
F-67000 Strasbourg, France
J.-L. Agram14, J. Andrea, D. Bloch, J.-M. Brom, M. Buttignol, E.C. Chabert, C.
Collard, E. Conte14, X. Coubez, F. Drouhin14, J.-C. Fontaine14, D. Gelé, U.
Goerlach, M. Jansová, P. Juillot, 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, N. Chanon, R. Chierici, D. Contardo, P.
Depasse, H. El Mamouni, J. Fay, L. Finco, S. Gascon, M. Gouzevitch, G.
Grenier, B. Ille, F. Lagarde, I.B. Laktineh, H. Lattaud, M. Lethuillier, L.
Mirabito, A.L. Pequegnot, S. Perries, A. Popov15, V. Sordini, M. Vander
Donckt, S. Viret, S. Zhang Georgian Technical University, Tbilisi, Georgia
A. Khvedelidze8 Tbilisi State University, Tbilisi, Georgia
Z. Tsamalaidze8 RWTH Aachen University, I. Physikalisches Institut, Aachen,
Germany
C. Autermann, L. Feld, M.K. Kiesel, K. Klein, M. Lipinski, M. Preuten, M.P.
Rauch, C. Schomakers, J. Schulz, M. Teroerde, B. Wittmer, V. Zhukov15 RWTH
Aachen University, III. Physikalisches Institut A, Aachen, Germany
A. Albert, D. Duchardt, M. Endres, M. Erdmann, S. Erdweg, T. Esch, R. Fischer,
A. Güth, T. Hebbeker, C. Heidemann, K. Hoepfner, S. Knutzen, M. Merschmeyer,
A. Meyer, P. Millet, S. Mukherjee, T. Pook, M. Radziej, H. Reithler, M.
Rieger, F. Scheuch, D. Teyssier, S. Thüer RWTH Aachen University, III.
Physikalisches Institut B, Aachen, Germany
G. Flügge, B. Kargoll, T. Kress, A. Künsken, T. Müller, A. Nehrkorn, A.
Nowack, C. Pistone, O. Pooth, A. Stahl16 Deutsches Elektronen-Synchrotron,
Hamburg, Germany
M. Aldaya Martin, T. Arndt, C. Asawatangtrakuldee, K. Beernaert, O. Behnke, U.
Behrens, A. Bermúdez Martínez, A.A. Bin Anuar, K. Borras17, V. Botta, A.
Campbell, P. Connor, C. Contreras-Campana, F. Costanza, V. Danilov, A. De Wit,
C. Diez Pardos, D. Domínguez Damiani, G. Eckerlin, D. Eckstein, T. Eichhorn,
E. Eren, E. Gallo18, J. Garay Garcia, A. Geiser, J.M. Grados Luyando, A.
Grohsjean, P. Gunnellini, M. Guthoff, A. Harb, J. Hauk, M. Hempel19, H. Jung,
M. Kasemann, J. Keaveney, C. Kleinwort, J. Knolle, I. Korol, D. Krücker, W.
Lange, A. Lelek, T. Lenz, K. Lipka, W. Lohmann19, R. Mankel, I.-A. Melzer-
Pellmann, A.B. Meyer, M. Meyer, M. Missiroli, G. Mittag, J. Mnich, A.
Mussgiller, D. Pitzl, A. Raspereza, M. Savitskyi, P. Saxena, R. Shevchenko, N.
Stefaniuk, H. Tholen, G.P. Van Onsem, R. Walsh, Y. Wen, K. Wichmann, C.
Wissing, O. Zenaiev University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, V. Blobel, M. Centis Vignali, T. Dreyer, E. Garutti, D.
Gonzalez, J. Haller, A. Hinzmann, M. Hoffmann, A. Karavdina, G. Kasieczka, R.
Klanner, R. Kogler, N. Kovalchuk, S. Kurz, D. Marconi, J. Multhaup, M.
Niedziela, D. Nowatschin, T. Peiffer, A. Perieanu, A. Reimers, C. Scharf, P.
Schleper, A. Schmidt, S. Schumann, J. Schwandt, J. Sonneveld, H. Stadie, G.
Steinbrück, F.M. Stober, M. Stöver, D. Troendle, E. Usai, A. Vanhoefer, B.
Vormwald Institut für Experimentelle Teilchenphysik, Karlsruhe, Germany
M. Akbiyik, C. Barth, M. Baselga, S. Baur, E. Butz, R. Caspart, T. Chwalek, F.
Colombo, W. De Boer, A. Dierlamm, N. Faltermann, B. Freund, R. Friese, M.
Giffels, M.A. Harrendorf, F. Hartmann16, S.M. Heindl, U. Husemann, F.
Kassel16, S. Kudella, H. Mildner, M.U. Mozer, Th. Müller, M. Plagge, G. Quast,
K. Rabbertz, M. Schröder, I. Shvetsov, G. Sieber, H.J. Simonis, R. Ulrich, S.
Wayand, M. Weber, T. Weiler, S. Williamson, C. Wöhrmann, R. Wolf Institute of
Nuclear and Particle Physics (INPP), NCSR Demokritos, Aghia Paraskevi, Greece
G. Anagnostou, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas, I. Topsis-
Giotis National and Kapodistrian University of Athens, Athens, Greece
G. Karathanasis, S. Kesisoglou, A. Panagiotou, N. Saoulidou, E. Tziaferi
National Technical University of Athens, Athens, Greece
K. Kousouris, I. Papakrivopoulos University of Ioánnina, Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, S. Mallios, N.
Manthos, I. Papadopoulos, E. Paradas, J. Strologas, F.A. Triantis, D.
Tsitsonis MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
M. Csanad, N. Filipovic, G. Pasztor, O. Surányi, G.I. Veres20 Wigner Research
Centre for Physics, Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath21, Á. Hunyadi, F. Sikler, T.Á. Vámi, V.
Veszpremi, G. Vesztergombi20 Institute of Nuclear Research ATOMKI, Debrecen,
Hungary
N. Beni, S. Czellar, J. Karancsi22, A. Makovec, J. Molnar, Z. Szillasi
Institute of Physics, University of Debrecen, Debrecen, Hungary
M. Bartók20, P. Raics, Z.L. Trocsanyi, B. Ujvari Indian Institute of Science
(IISc), Bangalore, India
S. Choudhury, J.R. Komaragiri National Institute of Science Education and
Research, Bhubaneswar, India
S. Bahinipati23, P. Mal, K. Mandal, A. Nayak24, D.K. Sahoo23, 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, R. Kumar, P. Kumari, M. Lohan, A. Mehta, S.
Sharma, J.B. Singh, G. Walia University of Delhi, Delhi, India
A. Bhardwaj, B.C. Choudhary, R.B. Garg, S. Keshri, A. Kumar, Ashok Kumar, S.
Malhotra, M. Naimuddin, K. Ranjan, Aashaq Shah, R. Sharma Saha Institute of
Nuclear Physics, HBNI, Kolkata, India
R. Bhardwaj25, R. Bhattacharya, S. Bhattacharya, U. Bhawandeep25, D. Bhowmik,
S. Dey, S. Dutt25, S. Dutta, S. Ghosh, N. Majumdar, K. Mondal, S.
Mukhopadhyay, S. Nandan, A. Purohit, P.K. Rout, A. Roy, S. Roy Chowdhury, S.
Sarkar, M. Sharan, B. Singh, S. Thakur25 Indian Institute of Technology
Madras, Madras, India
P.K. Behera Bhabha Atomic Research Centre, Mumbai, India
R. Chudasama, D. Dutta, V. Jha, V. Kumar, A.K. Mohanty16, P.K. Netrakanti,
L.M. Pant, P. Shukla, A. Topkar Tata Institute of Fundamental Research-A,
Mumbai, India
T. Aziz, S. Dugad, B. Mahakud, S. Mitra, G.B. Mohanty, N. Sur, B. Sutar Tata
Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, Sa. Jain, S.
Kumar, M. Maity26, G. Majumder, K. Mazumdar, N. Sahoo, T. Sarkar26, N.
Wickramage27 Indian Institute of Science Education and Research (IISER), Pune,
India
S. Chauhan, S. Dube, V. Hegde, A. Kapoor, K. Kothekar, S. Pandey, A. Rane, 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, S. Paktinat Mehdiabadi29, F. Rezaei
Hosseinabadi, B. Safarzadeh30, M. Zeinali 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, C. Calabriaa,b, A. Colaleoa, D. Creanzaa,c, L. Cristellaa,b,
N. De Filippisa,c, M. De Palmaa,b, A. Di Florioa,b, F. Erricoa,b, L. Fiorea,
A. Gelmia,b, G. Iasellia,c, S. Lezkia,b, G. Maggia,c, M. Maggia, B.
Marangellia,b, G. Minielloa,b, S. Mya,b, S. Nuzzoa,b, A. Pompilia,b, G.
Pugliesea,c, R. Radognaa, A. Ranieria, G. Selvaggia,b, A. Sharmaa, L.
Silvestrisa,16, R. Vendittia, P. Verwilligena, G. Zitoa 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,
S.S. Chhibraa,b, G. Codispotia,b, M. Cuffiania,b, G.M. Dallavallea, F.
Fabbria, A. Fanfania,b, D. Fasanellaa,b, P. Giacomellia, C. Grandia, L.
Guiduccia,b, S. Marcellinia, G. Masettia, A. Montanaria, F.L. Navarriaa,b, F.
Odoricia, A. Perrottaa, 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, S. Costaa,b, A. Di Mattiaa, F. Giordanoa,b, R. Potenzaa,b, A.
Tricomia,b, C. Tuvea,b INFN Sezione di Firenze a, Università di Firenze b,
Firenze, Italy
G. Barbaglia, K. Chatterjeea,b, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, E. Focardia,b, G. Latino, P. Lenzia,b, M. Meschinia, S.
Paolettia, L. Russoa,31, G. Sguazzonia, D. Stroma, L. Viliania INFN Laboratori
Nazionali di Frascati, Frascati, Italy
L. Benussi, S. Bianco, F. Fabbri, D. Piccolo, F. Primavera16 INFN Sezione di
Genova a, Università di Genova b, Genova, Italy
V. Calvellia,b, F. Ferroa, F. Raveraa,b, E. Robuttia, S. Tosia,b INFN Sezione
di Milano-Bicocca a, Università di Milano-Bicocca b, Milano, Italy
A. Benagliaa, A. Beschib, L. Brianzaa,b, F. Brivioa,b, V. Cirioloa,b,16, M.E.
Dinardoa,b, S. Fiorendia,b, S. Gennaia, A. Ghezzia,b, P. Govonia,b, M.
Malbertia,b, S. Malvezzia, R.A. Manzonia,b, D. Menascea, L. Moronia, M.
Paganonia,b, K. Pauwelsa,b, D. Pedrinia, S. Pigazzinia,b,32, S. Ragazzia,b, T.
Tabarelli de Fatisa,b INFN Sezione di Napoli a, Università di Napoli ’Federico
II’ b, Napoli, Italy, Università della Basilicata c, Potenza, Italy,
Università G. Marconi d, Roma, Italy
S. Buontempoa, N. Cavalloa,c, S. Di Guidaa,d,16, F. Fabozzia,c, F. Fiengaa,b,
G. Galatia,b, A.O.M. Iorioa,b, W.A. Khana, L. Listaa, S. Meolaa,d,16, P.
Paoluccia,16, C. Sciaccaa,b, F. Thyssena, E. Voevodinaa,b INFN Sezione di
Padova a, Università di Padova b, Padova, Italy, Università di Trento c,
Trento, Italy
P. Azzia, N. Bacchettaa, L. Benatoa,b, A. Bolettia,b, R. Carlina,b, A.
Carvalho Antunes De Oliveiraa,b, P. Checchiaa, M. Dall’Ossoa,b, P. De Castro
Manzanoa, T. Dorigoa, U. Dossellia, F. Gasparinia,b, U. Gasparinia,b, A.
Gozzelinoa, S. Lacapraraa, P. Lujan, A.T. Meneguzzoa,b, M. Pegoraroa, N.
Pozzobona,b, P. Ronchesea,b, R. Rossina,b, F. Simonettoa,b, A. Tiko, E.
Torassaa, M. Zanettia,b, P. Zottoa,b, G. Zumerlea,b INFN Sezione di Pavia a,
Università di Pavia b, Pavia, Italy
A. Braghieria, A. Magnania, P. Montagnaa,b, S.P. Rattia,b, V. Rea, M.
Ressegottia,b, C. Riccardia,b, P. Salvinia, I. Vaia,b, P. Vituloa,b INFN
Sezione di Perugia a, Università di Perugia b, Perugia, Italy
L. Alunni Solestizia,b, M. Biasinia,b, G.M. Bileia, C. Cecchia,b, D.
Ciangottinia,b, L. Fanòa,b, P. Laricciaa,b, R. Leonardia,b, E. Manonia, G.
Mantovania,b, V. Mariania,b, M. Menichellia, A. Rossia,b, A. Santocchiaa,b, D.
Spigaa INFN Sezione di Pisa a, Università di Pisa b, Scuola Normale Superiore
di Pisa c, Pisa, Italy
K. Androsova, P. Azzurria,16, G. Bagliesia, L. Bianchinia, T. Boccalia, L.
Borrello, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, G. Fedia, L.
Gianninia,c, A. Giassia, M.T. Grippoa,31, F. Ligabuea,c, T. Lomtadzea, E.
Mancaa,c, G. Mandorlia,c, A. Messineoa,b, F. Pallaa, A. Rizzia,b, P.
Spagnoloa, R. Tenchinia, G. Tonellia,b, A. Venturia, P.G. Verdinia INFN
Sezione di Roma a, Sapienza Università di Roma b, Rome, Italy
L. Baronea,b, F. Cavallaria, M. Cipriania,b, N. Dacia, D. Del Rea,b, E. Di
Marcoa,b, M. Diemoza, S. Gellia,b, E. Longoa,b, B. Marzocchia,b, P.
Meridiania, G. Organtinia,b, F. Pandolfia, R. Paramattia,b, F. Preiatoa,b, S.
Rahatloua,b, C. Rovellia, F. Santanastasioa,b INFN Sezione di Torino a,
Università di Torino b, Torino, Italy, Università del Piemonte Orientale c,
Novara, Italy
N. Amapanea,b, R. Arcidiaconoa,c, S. Argiroa,b, M. Arneodoa,c, N. Bartosika,
R. Bellana,b, C. Biinoa, N. Cartigliaa, R. Castelloa,b, F. Cennaa,b, M.
Costaa,b, R. Covarellia,b, A. Deganoa,b, N. Demariaa, B. Kiania,b, C.
Mariottia, S. Masellia, E. Migliorea,b, V. Monacoa,b, E. Monteila,b, M.
Montenoa, M.M. Obertinoa,b, L. Pachera,b, N. Pastronea, M. Pelliccionia, G.L.
Pinna Angionia,b, A. Romeroa,b, M. Ruspaa,c, R. Sacchia,b, K. Shchelinaa,b, V.
Solaa, A. Solanoa,b, A. Staianoa INFN Sezione di Trieste a, Università di
Trieste b, Trieste, Italy
S. Belfortea, M. Casarsaa, F. Cossuttia, G. Della Riccaa,b, A. Zanettia
Kyungpook National University
D.H. Kim, G.N. Kim, M.S. Kim, J. Lee, S. Lee, S.W. Lee, C.S. Moon, Y.D. Oh, S.
Sekmen, D.C. Son, Y.C. Yang Chonnam National University, Institute for
Universe and Elementary Particles, Kwangju, Korea
H. Kim, D.H. Moon, G. Oh Hanyang University, Seoul, Korea
J.A. Brochero Cifuentes, J. Goh, T.J. Kim Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, D. Gyun, S. Ha, B. Hong, Y. Jo, Y. Kim, K. Lee, K.S.
Lee, S. Lee, J. Lim, S.K. Park, Y. Roh Seoul National University, Seoul, Korea
J. Almond, J. Kim, J.S. Kim, H. Lee, K. Lee, K. Nam, S.B. Oh, B.C. Radburn-
Smith, S.h. Seo, U.K. Yang, H.D. Yoo, G.B. Yu University of Seoul, Seoul,
Korea
H. Kim, J.H. Kim, J.S.H. Lee, I.C. Park Sungkyunkwan University, Suwon, Korea
Y. Choi, C. Hwang, J. Lee, I. Yu Vilnius University, Vilnius, Lithuania
V. Dudenas, A. Juodagalvis, J. Vaitkus National Centre for Particle Physics,
Universiti Malaya, Kuala Lumpur, Malaysia
I. Ahmed, Z.A. Ibrahim, M.A.B. Md Ali33, F. Mohamad Idris34, W.A.T. Wan
Abdullah, M.N. Yusli, Z. Zolkapli Centro de Investigacion y de Estudios
Avanzados del IPN, Mexico City, Mexico
Duran-Osuna, M. C., H. Castilla-Valdez, E. De La Cruz-Burelo, Ramirez-Sanchez,
G., I. Heredia-De La Cruz35, Rabadan-Trejo, R. I., R. Lopez-Fernandez, J.
Mejia Guisao, Reyes-Almanza, R, A. Sanchez-Hernandez Universidad
Iberoamericana, Mexico City, Mexico
S. Carrillo Moreno, C. Oropeza Barrera, F. Vazquez Valencia Benemerita
Universidad Autonoma de Puebla, Puebla, Mexico
J. Eysermans, I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada Universidad
Autónoma de San Luis Potosí, San Luis Potosí, Mexico
A. Morelos Pineda University of Auckland, Auckland, New Zealand
D. Krofcheck University of Canterbury, Christchurch, New Zealand
S. Bheesette, P.H. Butler National Centre for Physics, Quaid-I-Azam
University, Islamabad, Pakistan
A. Ahmad, M. Ahmad, Q. Hassan, H.R. Hoorani, A. Saddique, M.A. Shah, M.
Shoaib, M. Waqas National Centre for Nuclear Research, Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, T. Frueboes, M. Górski, M. Kazana, K.
Nawrocki, M. Szleper, P. Traczyk, P. Zalewski Institute of Experimental
Physics, Faculty of Physics, University of Warsaw, Warsaw, Poland
K. Bunkowski, A. Byszuk36, K. Doroba, A. Kalinowski, M. Konecki, J.
Krolikowski, M. Misiura, M. Olszewski, A. Pyskir, M. Walczak Laboratório de
Instrumentação e Física Experimental de Partículas, Lisboa, Portugal
P. Bargassa, C. Beirão Da Cruz E Silva, A. Di Francesco, P. Faccioli, B.
Galinhas, M. Gallinaro, J. Hollar, N. Leonardo, L. Lloret Iglesias, M.V.
Nemallapudi, J. Seixas, G. Strong, O. Toldaiev, D. Vadruccio, J. Varela Joint
Institute for Nuclear Research, Dubna, Russia
S. Afanasiev, P. Bunin, M. Gavrilenko, I. Golutvin, I. Gorbunov, A. Kamenev,
V. Karjavin, A. Lanev, A. Malakhov, V. Matveev37,38, P. Moisenz, V. Palichik,
V. Perelygin, S. Shmatov, S. Shulha, N. Skatchkov, V. Smirnov, N. Voytishin,
A. Zarubin Petersburg Nuclear Physics Institute, Gatchina (St. Petersburg),
Russia
Y. Ivanov, V. Kim39, E. Kuznetsova40, P. Levchenko, V. Murzin, V. Oreshkin, I.
Smirnov, D. Sosnov, V. Sulimov, L. Uvarov, S. Vavilov, A. Vorobyev Institute