HIG-17-026

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$Id:HIG-17-026.tex4890372019-02-1410:09:15Zmschrode$

HIG-17-026

# Search for $\ttbar\PH$ production in the $\PH\to\bbbar$ decay channel with
leptonic decays in proton-proton collisions at $\sqrt{s}=13\TeV$

###### Abstract

A search is presented for the associated production of a standard model Higgs
boson with a top quark-antiquark pair ($\ttbar\PH$), in which the Higgs boson
decays into a quark-antiquark pair, in proton-proton collisions at a centre-
of-mass energy $\sqrt{s}=13\TeV$. The data correspond to an integrated
luminosity of 35.9recorded with the CMS detector at the CERN LHC. Candidate
$\ttbar\PH$ events are selected that contain either one or two electrons or
muons from the decays and are categorised according to the number of jets.
Multivariate techniques are employed to further classify the events and
eventually discriminate between signal and background. The results are
characterised by an observed $\ttbar\PH$ signal strength relative to the
standard model cross section, $\mu=\sigma/\sigma_{\mathrm{SM}}$, under the
assumption of a Higgs boson mass of 125. A combined fit of multivariate
discriminant distributions in all categories results in an observed (expected)
upper limit on $\mu$ of 1.5 (0.9) at 95% confidence level, and a best fit
value of $0.72\pm 0.24\stat\pm 0.38\syst$, corresponding to an observed
(expected) signal significance of 1.6 (2.2) standard deviations above the
background-only hypothesis.

##  0.1 Introduction

The observation [1, 2, 3] of a Higgs boson with a mass of approximately 125
[4, 5] at the CERN LHC marked the starting point of a broad experimental
programme to determine the properties of the newly discovered particle. Decays
into $\gamma\gamma$, $\PZ\PZ$, $\PW\PW$, and $\Pgt\Pgt$ final states have been
observed, and there is evidence for the direct decay of the particle to the
bottom quark-antiquark () final state [6, 7, 8, 9, 10]. The measured rates for
various production and decay channels are consistent with the standard model
(SM) expectations [11, 12], and the hypothesis of a spin-0 particle is
favoured over other hypotheses [13, 14].

In the SM, the Higgs boson couples to fermions with a Yukawa-type interaction,
with a coupling strength proportional to the fermion mass. Probing the
coupling of the Higgs boson to the heaviest known fermion, the top quark, is
therefore very important for testing the SM and for constraining various
models of physics beyond the SM (BSM), some of which predict a different
coupling strength than the SM. Indirect constraints on the coupling between
the top quark and the Higgs boson are available from processes including
virtual top quark loops, for example Higgs boson production through gluon-
gluon fusion [11, 12], as well as from production of four top quarks [15]. On
the other hand, the associated production of a Higgs boson and a top quark-
antiquark pair ($\ttbar\PH$ production) as illustrated by the Feynman diagrams
in Fig. 1 is a direct probe of the Higgs boson coupling to fermions with weak
isospin $+1/2$. The Higgs boson decay into , also shown in Fig. 1, is
experimentally attractive as a final state because it features the largest
branching fraction of $0.58\pm 0.02$ for a 125Higgs boson [16].

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Figure 1: Representative leading-order Feynman diagrams for $\ttbar\PH$
production, including the subsequent decay of the Higgs boson into a quark-
antiquark pair, and the decay of the top quark-antiquark pair into final
states with either one (single-lepton channel, left) or two (dilepton channel,
right) electrons or muons.

Several BSM physics scenarios predict a significantly enhanced production rate
of events with $\ttbar\PH$ final states, while not modifying the branching
fractions of Higgs boson decays by a measurable amount [17, 18, 19, 20, 21,
22, 23, 24, 25, 26]. In this context, a measurement of the $\ttbar\PH$
production cross section has the potential to distinguish the SM Higgs
mechanism of generating fermion masses from alternative ones.

Various dedicated searches for $\ttbar\PH$ production have been conducted
during Run 1 of the LHC. The CMS Collaboration searches employed proton-proton
($\Pp\Pp$) collision data corresponding to an integrated luminosity of 5at a
centre-of-mass energy of $\sqrt{s}=7\TeV$ and 19.5at $\sqrt{s}=8\TeV$. These
searches have been performed by studying Higgs boson decays to quarks,
photons, and leptons using multivariate analysis (MVA) techniques, showing a
mild excess of the observed $\ttbar\PH$ cross section relative to the SM
expectation of $\mu=\sigma/\sigma_{\text{SM}}=2.8\pm 1.0$ [27]. A similar
excess of $\mu=2.1^{+1.4}_{-1.2}$ was observed in a search for $\ttbar\PH$
production in multilepton final states by the ATLAS Collaboration using data
at $\sqrt{s}=8\TeV$, corresponding to an integrated luminosity of 20.3 [28].
The searches in the $\PH\to\bbbar$ decay channel were performed with several
analysis techniques [27, 29, 30], yielding a most stringent observed
(expected) upper limit on $\mu$ of 3.4 (2.2) at the 95% confidence level ().

The increased centre-of-mass energy of $\sqrt{s}=13\TeV$ results in a
$\ttbar\PH$ production cross section 3.9 times larger than at $\sqrt{s}=8\TeV$
based on next-to-leading-order (NLO) calculations; while the cross section for
the most important background, production, is increased by a factor of 3.3
[31], resulting in a more favourable signal-to-background ratio. The CMS
Collaboration has performed searches in the all-jets [32] and multilepton [33]
final states with 35.9of data, achieving evidence for $\ttbar\PH$ production
with an observed (expected) significance of 3.2 (2.8) standard deviations in
the latter case. Recently, the ATLAS Collaboration reported observed
(expected) evidence for $\ttbar\PH$ production with a significance of 4.2
(3.8) standard deviations, based on an integrated luminosity of 36.1and
combining several Higgs boson decay channels [34]; in the $\PH\to\bbbar$
channel alone, an observed (expected) upper limit on $\mu$ of 2.0 (1.2) at 95%
and a best fit value of $\mu=0.84^{+0.64}_{-0.61}$ were obtained [35].

In this paper, a search for $\ttbar\PH$ production in the $\PH\to\bbbar$ final
state is presented that has been performed using $35.9\fbinv$ of data recorded
with the CMS detector at $\sqrt{s}=13\TeV$ in 2016. In the SM, the top quark
is expected to decay into a boson and a quark almost exclusively. Hence
different decay modes can be identified according to the subsequent decays of
the bosons. The event selection is based on the decay topology of $\ttbar\PH$
events in which the Higgs boson decays into and the decay involves at least
one lepton, resulting in either $\ell\Pgn\,\cPq\cPaq^{\prime}\,\bbbar$
(single-lepton) or $\ell^{+}\Pgn\,\ell^{-}\Pagn\,\bbbar$ (dilepton) final
states, where $\ell=\Pe,\Pgm$ arising either from the prompt decay of a boson
or from leptonic $\tau$ decays. Analysis methods established in Run 1 [27, 29]
have been significantly improved, and novel methods have been added. In
particular, two multivariate techniques—namely boosted decision trees (BDTs)
and the matrix element method (MEM) [36, 37, 38, 39, 40]—that utilise event
information differently in order to discriminate signal from background events
have been employed in combination. Since the two methods aim at separating
signal from different background processes, their combined usage helps to
obtain a better sensitivity. In addition, a new multivariate technique based
on deep neural networks (DNNs) has been employed to separate signal from
background events. The best fit value of the signal strength modifier $\mu$ is
obtained from a combined profile likelihood fit of the classifier output
distributions to the data, correlating processes and their uncertainties where
appropriate.

This document is structured as follows. The CMS detector is described in
Section 0.2. In Section 0.3, the simulated signal and background samples are
described. The basic selection of analysis objects and events is discussed in
Section 0.4. The general analysis strategy and background estimation methods
are introduced in Section 0.5. The effect of systematic uncertainties is
studied in Section 0.6. Results of the analysis are presented in Section 0.7,
followed by a summary in Section 0.8.

##  0.2 The CMS detector

The central feature of the CMS apparatus is a superconducting solenoid of 6m
internal diameter, providing a magnetic field of 3.8T. Within the solenoid
volume are a silicon pixel and strip tracker, a lead tungstate crystal
electromagnetic calorimeter, and a brass and scintillator hadron calorimeter,
each composed of a barrel and two endcap sections. Forward calorimeters extend
the pseudorapidity ($\eta$) coverage provided by the barrel and endcap
detectors. Muons are detected in gas-ionisation chambers embedded in the steel
magnetic flux-return yoke outside the solenoid. A more detailed description of
the CMS detector, together with a definition of the coordinate system used and
the relevant kinematic variables, can be found in Ref. [41]. Events of
interest are selected using a two-tiered trigger system [42]. The first level,
composed of custom hardware processors, uses information from the calorimeters
and muon detectors to select events, while the second level selects events by
running a version of the full event reconstruction software optimised for fast
processing on a farm of computer processors.

##  0.3 Simulation of signal and background

Several Monte Carlo event generators, interfaced with a detailed detector
simulation, are used to model experimental effects, such as reconstruction and
selection efficiencies, as well as detector resolutions. The CMS detector
response is simulated using (v.9.4) [43].

For the simulation of the $\ttbar\PH$ signal sample, the NLO event generator
(v.2) [44, 45, 46, 47] is used. Standard model backgrounds are simulated using
(v.2), (v.8.200) [48], or (v.2.2.2) [49], depending on the process. The value
of the Higgs boson mass is assumed to be 125, while the top quark mass value
is set to 172.5. The proton structure is described by the parton distribution
functions (PDF) NNPDF3.0 [50].

The main background contribution originates from production, the production of
and /$\gamma^{*}$ bosons with additional jets (referred to as
$\PW\text{+jets}$ and $\PZ\text{+jets}$, or commonly as V+jets), single top
quark production ( and $t$-channel production), diboson (, , and $\PZ\PZ$)
processes, and production in association with a or boson (referred to as
$\ttbar\text{+}\PW$ and $\ttbar\text{+}\PZ$, or commonly as
$\ttbar\text{+}\text{V}$). Both the and the single top quark processes in the
$t$\- and -channels are simulated with [51, 52]. The $s$-channel single top
quark processes, as well as V+jets and $\ttbar\text{+}\text{V}$ processes are
simulated at NLO with , where for the V+jets processes the matching of matrix-
element (ME) jets to parton showers (PS) is performed using the FxFx [53]
prescription. The event generator is used to simulate diboson events.

Parton showering and hadronisation are simulated with (v. 8.200) for all
signal and background processes. The CUETP8M2T4 [54] tune is used to
characterise the underlying event in the $\ttbar\PH$ signal and and single top
quark background processes, while the CUETP8M1 [55] tune is used for all other
background processes.

For comparison with the observed distributions, the events in the simulated
samples are normalised to the same integrated luminosity of the data sample,
according to their predicted cross sections. These are taken from theoretical
calculations at next-to-next-to-leading order (NNLO, for V+jets production),
approximate NNLO (single top quark channel [56]), and NLO (single top quark
$t$\- and $s$-channels [57, 58], $\ttbar\text{+}\text{V}$ production [59], and
diboson production [60]). The $\ttbar\PH$ cross section of
$507^{+35}_{-50}\,\text{fb}$ and Higgs boson branching fractions used in the
analysis also correspond to NLO accuracy [16]. The simulated sample is
normalised to the full NNLO calculation with resummation to next-to-next-to-
leading-logarithmic accuracy [61, 62, 63, 64, 65, 66, 67], assuming a top
quark mass value of 172.5and using the NNPDF3.0 PDF set. This sample is
further separated into the following processes based on the flavour of
additional jets that do not originate from the top quark decays in the event:
$\ttbar\text{+}\bbbar$, defined at generator level as the events in which two
additional jets are generated within the acceptance requirements (see Section
0.4), each of which originates from one or more hadrons;
$\ttbar\text{+}\cPqb$, for which only one additional jet within the acceptance
originates from a single hadron; $\ttbar\text{+}2\cPqb$, which corresponds to
events with two additional hadrons that are close enough in direction to
produce a single jet; $\ttbar\text{+}\ccbar$, for which events have at least
one additional jet within the acceptance and no additional jets; \+ light
flavour jets ($\ttbar\text{+}\text{lf}$), which corresponds to events that do
not belong to any of the above processes. The $\ttbar\text{+}\bbbar$,
$\ttbar\text{+}\cPqb$, $\ttbar\text{+}2\cPqb$, and $\ttbar\text{+}\ccbar$
processes are collectively referred to as $\ttbar\text{+}\text{hf}$ in the
following. This categorisation is important because the subsamples originate
from different physics processes and have different systematic uncertainties.

Effects from additional $\Pp\Pp$ interactions in the same bunch crossings
(pileup) are modelled by adding simulated minimum-bias events (generated with
v.8.212, tune CUETP8M1) to all simulated processes. The pileup multiplicity
distribution in simulation is reweighted to reflect the luminosity profile of
the observed $\Pp\Pp$ collisions. Correction factors described in Section 0.4
are applied to the simulation where necessary to improve the description of
the data.

##  0.4 Object and event reconstruction

The event selection is optimised to identify events from the production of a
Higgs boson in association with events, where the Higgs boson decays into .
Two decay modes are considered: the single-lepton mode
($\ttbar\to\ell\Pgn\,\cPq\cPaq^{\prime}\,\bbbar$), where one boson decays into
a charged lepton and a neutrino, and the dilepton mode
($\ttbar\to\ell^{+}\Pgn\,\ell^{-}\Pagn\,\bbbar$), where both bosons decay into
a charged lepton and a neutrino. These signatures imply the presence of
isolated leptons ($\ell=\Pe,\,\Pgm$), missing transverse momentum due to the
neutrinos from boson decays, and highly energetic jets originating from the
final-state quarks. Jets originating from the hadronisation of quarks are
identified through tagging techniques [68].

Online, events in the single-lepton channel were selected by single-lepton
triggers which require the presence of one electron (muon) with a transverse
momentum () threshold of $\pt>27(24)\GeV$. Events in the dilepton channel were
selected either by the single-lepton trigger (retaining events with an
additional lepton) or by dilepton triggers that require the presence of two
electrons or muons. The same-flavour dilepton triggers required two electrons
with $\pt>23$ and 12, or two muons with $\pt>17$ and 8, respectively. The
different-flavour dilepton triggers required either a muon with $\pt>23\GeV$
and an electron with $\pt>12\GeV$, or an electron with $\pt>23\GeV$ and a muon
with $\pt>8\GeV$.

Events are reconstructed using a particle-flow (PF) technique [69], which
combines information from all subdetectors to enhance the reconstruction
performance by identifying individual particle candidates in $\Pp\Pp$
collisions. An interaction vertex [70] is required within 24of the detector
centre along the beam line direction, and within 2of the beam line in the
transverse plane. Among all such vertices, the reconstructed vertex with the
largest value of summed physics-object $\pt^{2}$ is taken to be the primary
$\Pp\Pp$ interaction vertex. The physics objects are the jets, clustered using
a jet finding algorithm [71, 72] 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. All other interaction vertices are considered
as pileup vertices. Charged tracks identified as hadrons from pileup vertices
are omitted in the subsequent event reconstruction.

The electron and muon candidates are required to be sufficiently isolated from
nearby jet activity as follows. For each electron (muon) candidate, a cone of
$\Delta R=0.3\,(0.4)$ is constructed around the direction of the track at the
event vertex, where $\Delta R$ is defined as
$\sqrt{\smash[b]{(\Delta\eta)^{2}+(\Delta\phi)^{2}}}$, and $\Delta\eta$ and
$\Delta\phi$ are the distances in the pseudorapidity and azimuthal angle.
Excluding the contribution from the lepton candidate, the scalar sum of all
particle candidates inside the cone consistent with arising from the chosen
primary event vertex is calculated. The neutral component from pileup
interactions is subtracted event-by-event, based on the average transverse
energy deposited by neutral particles in the event in the case of electrons,
and half the transverse momentum carried by charged-particles identified to
come from pileup vertices in the case of muons. A relative isolation
discriminant $I_{\text{rel}}$ is defined as the ratio of this sum to the of
the lepton candidate. Electron candidates are selected if they have values of
$I_{\text{rel}}<0.06$, while muons are selected if they fulfil the requirement
$I_{\text{rel}}<0.15$ in the single-lepton channel and $I_{\text{rel}}<0.25$
in the dilepton channel. In addition, electrons from identified photon
conversions are rejected [73]. To further increase the purity of muons
originating from the primary interaction and to suppress misidentified muons
or muons from decay-in-flight processes, additional quality criteria, such as
a minimal number of hits associated with the muon track, are required in both
the silicon tracker and the muon system [74].

For the single-lepton channel, events are selected containing exactly one
energetic, isolated lepton ( or ), which is required to have $\pt>30(26)\GeV$
in the case of the electron (muon), and $\abs{\eta}<2.1$. Electron candidates
in the transition region between the barrel and endcap calorimeters,
$1.4442<\abs{\eta}<1.5560$, are excluded. The flavour of the lepton must match
the flavour of the trigger that accepted the event (if an electron is
identified, the single-electron trigger must have accepted the event). For the
dilepton channel, events are required to have a pair of oppositely charged
energetic leptons ($\Pep\Pem$, $\PGmpm\Pemp$, $\PGmp\PGmm$). The lepton with
the highest out of the pair is required to have $\pt>25\GeV$, and the other
lepton $\pt>15\GeV$; both leptons are required to fulfil the requirement
$\abs{\eta}<2.4$, excluding electrons in the transition region. The flavours
of the lepton pair must match the flavour of the trigger that accepted the
event. The events are unambiguously classified as $\Pep\Pem$, $\PGmpm\Pemp$,
or $\PGmp\PGmm$, depending on the type of the selected lepton pair, and there
is no overlap with the other channels under study. The invariant mass of the
selected lepton pair, $m_{\ell\ell}$, is required to be larger than 20to
suppress events from heavy-flavour resonance decays and low-mass Drell–Yan
processes. In the same-flavour channels, events are also rejected if
$76<m_{\ell\ell}<106\GeV$, thereby suppressing further contribution from
$\PZ\text{+jets}$ events. In both the single- and dilepton channel, events
with additional isolated leptons with $\pt>15\GeV$ and $\abs{\eta}<2.4$ are
excluded from further analysis.

The missing transverse momentum vector is defined as the projection of the
negative vector sum of the momenta of all reconstructed PF objects in an event
on the plane perpendicular to the beams. Its magnitude is referred to as .
Events are required to fulfil $\ptmiss>20\GeV$ in the single-lepton and
$\ptmiss>40\GeV$ in the dilepton same-flavour channels to further suppress
background contribution.

Jets are reconstructed from the PF particle candidates using the anti-
clustering algorithm [71] with a distance parameter of 0.4, as implemented in
[72]. Charged hadrons that are associated to pileup vertices are discarded
from the clustering. The jet energy is corrected for the remaining neutral-
hadron pileup component in a manner similar to that used to find the energy
within the lepton isolation cone [75]. Jet energy corrections are also applied
as a function of jet and $\eta$ [76] to data and simulation. All reconstructed
jets in the single-lepton channel and the two jets leading in in the dilepton
channel are required to satisfy $\abs{\eta}<2.4$ and $\pt>30\GeV$. Other jets
in the dilepton channel are selected if $\pt>20\GeV$. Events are selected if
they contain at least four jets in the single-lepton channel or at least two
jets in the dilepton channel.

Jets originating from the hadronisation of quarks are identified using a
combined secondary vertex algorithm (CSVv2) [68], which provides a tagging
discriminant by combining identified secondary vertices and track-based
lifetime information. A discriminant value is chosen such that the probability
of tagging jets originating from light-flavour quarks ($\cPqu$, $\cPqd$, or
$\cPqs$) or gluons is about 1%, and the corresponding efficiency for tagging
jets from () quarks is ${\approx}65\%$ (10%). The shape of the CSVv2
discriminant distribution in simulation is corrected by scale factors to
better describe the data. This correction is derived separately for light-
flavour and jets with a tag-and-probe approach. Control samples enriched in
events with a boson and exactly two jets where a jet veto is applied are used
to obtain the correction for light-flavour jets. The correction for jets is
estimated using a sample enriched in events with no additional jets [68]. For
jets, the data-to-simulation scale factor is set to unity with an uncertainty
twice the one of the correction for jets. Events are required to have at least
two (one) -tagged jets in the single-lepton (dilepton) channels.

Event yields observed in data and predicted by the simulation after this
selection (referred to as baseline selection in the following) are listed in
Table 0.4 for the single-lepton and dilepton channels. The corresponding jet
and -tagged jet multiplicity distributions are shown in Figs. 2 and 3,
respectively. The $\ttbar\PH$ signal includes $\PH\to\bbbar$ and all other
Higgs boson decay modes. Background contributions from QCD multijet
production, estimated using a low-control region in data, have been found to
be negligible in this analysis.

Event yields observed in data and predicted by the simulation after the
baseline selection requirements in the single-lepton (SL) and dilepton (DL)
channels. The $\ttbar\PH$ signal includes $\PH\to\bbbar$ and all other Higgs
boson decay modes. The quoted uncertainties are statistical only.  Process SL
channel DL channel $\ttbar\text{+}\text{lf}$ 463 658 $\pm$ 174 241 032 $\pm$
99 $\ttbar\text{+}\ccbar$ 5pt76 012 $\pm$ 70 5pt 24 550 $\pm$ 32
$\ttbar\text{+}\cPqb$ 22 416 $\pm$ 38 5 979 $\pm$ 16 $\ttbar\text{+}2\cPqb$ 9
052 $\pm$ 24 1 785 $\pm$ 9 $\ttbar\text{+}\bbbar$ 10 897 $\pm$ 27 1 840 $\pm$
9 Single  25 215 $\pm$ 166 12 206 $\pm$ 125 V+jets 12 309 $\pm$ 58 5 684 $\pm$
209 $\ttbar\text{+}\text{V}$ 2 457 $\pm$ 12 2 570 $\pm$ 23 Diboson 449 $\pm$
14 430 $\pm$ 15 Total bkg. 622 466 $\pm$ 263 296 077 $\pm$ 266 $\ttbar\PH$ 1
232 $\pm$ 2 314.0 $\pm$ 0.9 Data 610 556 283 942

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Figure 2:  Jet (left) and -tagged jet (right) multiplicity in the single-lepton (SL) channel after the baseline selection. The expected background contributions (filled histograms) are stacked, and the expected signal distribution (line), which includes $\PH\to\bbbar$ and all other Higgs boson decay modes, is superimposed. Each contribution is normalised to an integrated luminosity of $35.9\fbinv$, and the signal distribution is additionally scaled by a factor of 15 for better visibility. The hatched uncertainty bands correspond to the total statistical and systematic uncertainties (excluding uncertainties that affect only the normalisation of the distribution) added in quadrature. The distributions observed in data (markers) are overlayed. The last bin includes overflow events. The lower plots show the ratio of the data to the background prediction.  |
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Figure 3:  Jet (left) and -tagged jet (right) multiplicity in the dilepton
(DL) channel after the baseline selection. The expected background
contributions (filled histograms) are stacked, and the expected signal
distribution (line), which includes $\PH\to\bbbar$ and all other Higgs boson
decay modes, is superimposed. Each contribution is normalised to an integrated
luminosity of $35.9\fbinv$, and the signal distribution is additionally scaled
by a factor of 15 for better visibility. The hatched uncertainty bands
correspond to the total statistical and systematic uncertainties (excluding
uncertainties that affect only the normalisation of the distribution) added in
quadrature. The distributions observed in data (markers) are overlayed. The
last bin includes overflow events. The lower plots show the ratio of the data
to the background prediction.

##  0.5 Analysis strategy and event classification

In both the single-lepton and dilepton channels, events with at least four
jets of which at least three are -tagged are selected among those passing the
baseline selection described in Section 0.4. These events are then further
divided into categories with varying signal purity and different background
composition. In each category, combinations of several multivariate
discriminants are optimised to separate signal from background. The signal is
extracted in a simultaneous template fit of the discriminant output obtained
from the simulation to the data across all the categories, correlating
processes and their uncertainties where appropriate. In this way, the
different background composition in the different categories helps to
constrain the uncertainties of the different processes and increases the
overall sensitivity of the search.

Several methods that classify events as signal- or background-like were
explored to achieve optimal sensitivity: DNNs and BDTs, combined with a MEM.
In the DNN approach, the jet multiplicity and the DNN classification output,
described below, are used for the event categorisation (“jet-process
categories”). In the BDT approach, events are divided into categories based on
their jet and -tagged jet multiplicity (“jet-tag categories”). The approach
that provided the best expected sensitivity in each channel, evaluated on fits
to simulated data, was chosen for obtaining the final result from data.
Therefore, in the single-lepton channel the DNN approach is used, while in the
dilepton channel a BDT+MEM classification is chosen. The methods and the
corresponding categorisation are illustrated in Fig. 4 and described in the
following.

Figure 4: Illustration of the analysis strategy.

In the single-lepton channel, events are separated depending on the jet
multiplicity into three categories with ${(4\,\text{jets},\geq 3\,\text{\cPqb\
tags})}$, ${(5\,\text{jets},\geq 3\,\text{\cPqb\ tags})}$, and ${(\geq
6\,\text{jets},\geq 3\,\text{\cPqb\ tags})}$. Dedicated multi-classification
DNNs [77] are trained in each jet multiplicity category to separate signal and
each of the five $\ttbar\text{+}\text{jets}$ background processes
$\ttbar\text{+}\bbbar$, $\ttbar\text{+}2\cPqb$, $\ttbar\text{+}\cPqb$,
$\ttbar\text{+}\ccbar$, or $\ttbar\text{+}\text{lf}$.

The DNN training is performed using simulated $\ttbar\PH$ and
$\ttbar\text{+}\text{jets}$ events as signal and background, respectively. The
overall set of events is split into a training set (30%), an independent set
(20%) for validation and optimisation of the DNN configuration (hyper
parameters), such as the number of nodes per layer, and a set that is reserved
for the fit to the data (50%). The hyper parameters and input variables are
detailed in Appendix .9.

The training is conducted in two stages. In the first stage, a DNN is trained
to predict which of the reconstructed physics objects originate from the
expected underlying hard process, such as for example the quark jet from the
decay of a top quark. In the second stage, the initial network is extended by
adding hidden layers, which take as input the variables and the output values
of the first stage, and the resulting network is trained to predict the
physics process of an event. The values obtained in the output nodes of the
second stage are normalised to unity using a “softmax” function [77], and, as
a result, can be interpreted as probabilities describing the likelihood of the
event being a $\ttbar\PH$ signal or one of the five
$\ttbar\text{+}\text{jets}$ background processes. Events are divided into
subcategories of the most probable process according to this DNN
classification. Thus, there are in total 18 jet-process categories in the
single-lepton channel. In each of the jet-process categories, the DNN
classifier output distribution of the node that matches the process category
is used as the final discriminant.

The DNNs utilise input variables related to kinematic properties of individual
objects, event shape, and the jet CSVv2 tagging discriminant, and additionally
the MEM discriminant output, described in the following.

The MEM discriminant is constructed as the ratio of the probability density
values for the signal ($\ttbar\PH$) and background ($\ttbar\text{+}\bbbar$)
hypotheses, following the algorithm described in Ref. [29]. Each event is
assigned a probability density value computed from the four-momenta of the
reconstructed particles, which is based on the leading order scattering
amplitudes for the $\ttbar\PH$ and $\ttbar\text{+}\bbbar$ processes and
integrated over the particle-level quantities that are either unknown or
poorly measured. The probability density functions are constructed at leading
order, assuming gluon-gluon fusion production both for signal and background
processes as it represents the majority of the event rate. In each event, the
four jets that are most likely to originate from quarks are considered
explicitly as candidates for the quarks from the decay of the Higgs boson and
the top quarks. All permutations of jets, regardless of their tagging
discriminant, are considered when associating the -quark-like jets to the top
quark or Higgs boson decays in the matrix element. The four -like jets are
selected using a likelihood ratio criterion as follows. The likelihoods are
computed under either the hypothesis that four jets or that two jets in the
event originate from quarks, based on the expected tagging discriminant
probability densities from simulation. The used ratio is computed as the four
--jets likelihood, normalised to the sum of the four- and the two--jets
likelihoods. When computing the MEM in the single-lepton channel, up to four
additional light jets, ordered in , are permuted over as candidates for the
light quarks from the hadronic decay of the boson.

In the dilepton channel, events are separated into two jet-tag categories with
${(\geq 4\,\text{jets},3\,\text{\cPqb\ tags})}$ and ${(\geq
4\,\text{jets},\geq 4\,\text{\cPqb\ tags})}$. In each jet-tag category, a
dedicated BDT is trained to separate signal from background processes. The
BDTs utilise input variables related to kinematic properties of individual
objects, event shape, and the jet CSVv2 tagging discriminant, similar as the
DNNs, but no MEM information. The training is performed using simulated
$\ttbar\PH$ and $\ttbar\text{+}\text{jets}$ events as signal and background,
respectively, which are weighted to achieve equal yields of signal and
background events. In order to avoid a biased performance estimate, the events
are separated in half for training and validation. The specific BDT boosting
method used is the stochastic gradient boost [78, 36], available as part of
the TMVA package [38]. The choice of the BDT architecture and the input
variables was optimised with a procedure based on the particle swarm algorithm
[79, 80], selecting the configuration and set of variables that yields the
highest discrimination power. They are detailed in Appendix .9.

In the ${(\geq 4\,\text{jets},3\,\text{\cPqb\ tags})}$ category, the BDT
output distribution is used as the final discriminant. The ${(\geq
4\,\text{jets},\geq 4\,\text{\cPqb\ tags})}$ category is further divided into
two subcategories, one with small values of the BDT output (background-like)
and one with large output values (signal-like). The division is taken at the
median of the BDT output distribution for simulated signal events. In each
subcategory, the MEM discriminant output is used as the final discriminant.
The high BDT output subcategory is expected to be enhanced with signal events
and residual $\ttbar\text{+}\bbbar$ background events, and the MEM
discriminant achieves by construction particularly powerful additional
separation against the $\ttbar\text{+}\bbbar$ background contributions. The
choice of the median contributes to a robust result by ensuring a sufficient
number of events in each subcategory. Including the low tag multiplicity and
the low BDT output subcategories into the fit constrains the background
contributions and systematic uncertainties for each of the different event
topologies. Thus, there are in total three categories in the dilepton channel.

In summary, in the single-lepton channel events are subdivided into 18 jet-
process categories and the DNN output distribution of the most probable
process is used as the final discriminant. In the dilepton channel events are
subdivided into three jet-tag categories and either the BDT or MEM output
distribution is used as the final discriminant.

##  0.6 Systematic uncertainties

In Table 0.6, all sources of systematic uncertainties considered in the
analysis are listed. They affect either the rate of the signal or background
processes, or the discriminant shape, or both. In the last case, the rate and
shape effects are treated as entirely correlated and are varied
simultaneously. The uncertainties are taken into account via nuisance
parameters in the final fit procedure described in Section 0.7, where the
effects from the same source are treated as fully correlated among the
different categories. The impact of the uncertainties on the final result is
discussed in Section 0.7.

Systematic uncertainties considered in the analysis, their corresponding type
(affecting rate or shape of the distributions), and additional remarks.
Source Type Remarks Integrated luminosity rate Signal and all backgrounds
Lepton identification/isolation shape Signal and all backgrounds Trigger
efficiency shape Signal and all backgrounds Pileup shape Signal and all
backgrounds Jet energy scale shape Signal and all backgrounds Jet energy
resolution shape Signal and all backgrounds tag hf fraction shape Signal and
all backgrounds tag hf stats (linear) shape Signal and all backgrounds tag hf
stats (quadratic) shape Signal and all backgrounds tag lf fraction shape
Signal and all backgrounds tag lf stats (linear) shape Signal and all
backgrounds tag lf stats (quadratic) shape Signal and all backgrounds tag
charm (linear) shape Signal and all backgrounds tag charm (quadratic) shape
Signal and all backgrounds Renorm./fact. scales ($\ttbar\PH$) rate Scale
uncertainty of NLO $\ttbar\PH$ prediction Renorm./fact. scales () rate Scale
uncertainty of NNLO prediction Renorm./fact. scales
($\ttbar\text{+}\text{hf}$) rate Additional 50% rate uncertainty of
$\ttbar\text{+}\text{hf}$ predictions Renorm./fact. scales () rate Scale
uncertainty of NLO single prediction Renorm./fact. scales (V) rate Scale
uncertainty of NNLO and prediction Renorm./fact. scales (VV) rate Scale
uncertainty of NLO diboson prediction PDF ($\Pg\Pg$) rate PDF uncertainty for
$\Pg\Pg$ initiated processes except $\ttbar\PH$ PDF ($\Pg\Pg$ $\ttbar\PH$)
rate PDF uncertainty for $\ttbar\PH$ PDF ($\Pq\Paq$) rate PDF uncertainty of
$\Pq\Paq$ initiated processes ($\ttbar\text{+}\PW$,,) PDF ($\Pq\Pg$) rate PDF
uncertainty of $\Pq\Pg$ initiated processes (single t) $\mu_{\mathrm{R}}$
scale () shape Renormalisation scale uncertainty of the ME generator (), same
for additional jet flavours $\mu_{\mathrm{F}}$ scale () shape Factorisation
scale uncertainty of the ME generator (), same for additional jet flavours PS
scale: ISR () rate Initial state radiation uncertainty of the PS (for events),
jet multiplicity dependent rate uncertainty, independent for additional jet
flavours PS scale: FSR () rate Final state radiation uncertainty (for events),
jet multiplicity dependent rate uncertainty, independent for additional jet
flavours ME-PS matching () rate NLO ME to PS matching, hdamp [54] (for
events), jet multiplicity dependent rate uncertainty, independent for
additional jet flavours Underlying event () rate Underlying event (for
events), jet multiplicity dependent rate uncertainty, independent for
additional jet flavours NNPDF3.0NLO ($\ttbar\PH$, ) shape Based on the NNPDF
replicas, same for $\ttbar\PH$ and additional jet flavours Bin-by-bin event
count shape Statistical uncertainty of the signal and background prediction
due to the limited sample size

The uncertainty in the integrated luminosity estimate is 2.5% [81]. The
trigger efficiency in the single-lepton channel and the electron and muon
identification efficiency uncertainties are estimated by comparing variations
in measured efficiency between data and simulation using a high-purity sample
of boson decays. In the dilepton channel, the trigger efficiency is measured
in data with a method based on triggers that are uncorrelated with those used
in the analysis, in particular based on requirements. These uncertainties are
found to be small, typically below 1–2%. Effects of the uncertainty in the
distribution of the number of pileup interactions are evaluated by varying the
total inelastic cross section used to predict the number of pileup
interactions in the simulated events by $\pm 4.6\%$ from its nominal value
[82]. The uncertainty due to the limited knowledge of the jet energy scale
(resolution) is determined by variations of the energy scale (resolution)
correction of all jets in the signal and background predictions by one
standard deviation. In the case of the jet energy scale uncertainty, these
variations are divided into 26 sources, which include uncertainties owing to
the extrapolation between samples of different jet-flavour composition and the
presence of pileup collisions in the derivation of the corrections [76]. The
effect of each source is evaluated individually. The uncertainty of the CSVv2
tagging scale factors is evaluated by applying alternative scale factors based
on varying the following systematic effects [68] by one standard deviation,
separately for the different jet flavours: the contamination of background
processes in the control samples, the jet energy scale uncertainty—which is
correlated with the overall jet energy scale uncertainty—and the statistical
uncertainty in the scale factor evaluation. The impact of the statistical
uncertainty is parameterised as the sum of two contributions: one term with
linear dependence on the tagging discriminant value, allowing an overall tilt
of the discriminant distribution, and another term with quadratic dependence,
allowing an overall shift of the discriminant distribution.

Theoretical uncertainties of the cross sections used to predict the rates of
various processes are propagated to the yield estimates. All rates are
estimated using cross sections with at least NLO accuracy, which have
uncertainties arising primarily from PDFs and the choice of factorisation and
renormalisation scales (both in the ME and the PS). The cross section
uncertainties are each separated into their PDF and scale components
(renorm./fact. scales) and are correlated where appropriate between processes.
For example, the PDF uncertainties for background processes originating
primarily from gluon-gluon initial states are treated as 100% correlated. The
PDF uncertainty of the $\ttbar\PH$ signal production is treated separately
from the background processes.

The $\ttbar\text{+}\bbbar$ process, and to lesser extent the
$\ttbar\text{+}2\cPqb$, $\ttbar\text{+}\cPqb$, and $\ttbar\text{+}\ccbar$
production, represent important sources of irreducible background. Neither
previous measurements of $\ttbar\text{+}\text{hf}$ production [83, 84, 85, 86]
nor higher-order theoretical calculations can currently constrain the
normalisation of these contributions to better than 35% accuracy [87, 88]. The
shape of the final discriminant distributions as well as important input
variable distributions of the sum of the $\ttbar\text{+}\bbbar$,
$\ttbar\text{+}2\cPqb$, and $\ttbar\text{+}\cPqb$ processes obtained with the
nominal simulation were compared to those obtained from a 4-flavour scheme
(v.2.2.2) [89] $\ttbar\text{+}\bbbar$ simulation combined with OpenLoops
(v.1.3.1) [90]. The shapes agree within the statistical precision. Therefore,
an additional 50% rate uncertainty is assigned to each of the
$\ttbar\text{+}\text{hf}$ processes to account also for differences in the
phase space with respect to Ref. [86]. Moreover, the robustness of the fit
model was verified using simulated toy data, which were sampled from the
templates of the fit model. The background templates were modified in the
following ways to sample the toy data: increasing the normalisation of the
$\ttbar\text{+}\bbbar$ background template by 30% in accordance with the
results in Ref. [86] or replacing the sum of the templates of the
$\ttbar\text{+}\bbbar$, $\ttbar\text{+}2\cPqb$, and $\ttbar\text{+}\cPqb$
processes obtained with the nominal simulation by those obtained from the
4-flavour scheme plus OpenLoops mentioned above. In each case, a fit of the
nominal model to the toy data is performed as described in Section 0.7,
including the full set of systematic uncertainties. The injected signal is
recovered within a few percent, well within the uncertainties assigned to
these processes.

The uncertainty arising from the missing higher-order terms in the simulation
with of the $\ttbar\text{+}\text{jets}$ process at the ME level is assessed by
varying the renormalisation and factorisation scales in the simulation up and
down by factors of two with respect to the nominal values, using event weights
obtained directly from the generator. At the PS level, the corresponding
uncertainty is estimated by varying the parameters controlling the amount of
initial- and final-state radiation independently by factors of 0.5 and 2 [91].
These sources of uncertainties are treated as uncorrelated. The uncertainty
originating from the scheme used to match the ME level calculation to the PS
simulation is derived by comparing the reference $\ttbar\text{+}\text{jets}$
simulation with two samples with varied hdamp parameter [54], which controls
the ME and PS matching and effectively regulates the high-radiation. The
effect on the final discriminators owing to uncertainties in the underlying
event tune of the $\ttbar\text{+}\text{jets}$ event generator are estimated
using simulations with varied parameters with respect to those used to derive
the CUETP8M2T4 tune in the default setup. The event count in the additional
samples required to estimate the modelling uncertainties was small and induced
changes to the discriminant distributions comparable in size to the
statistical fluctuations of the additional samples. For this reason, the
uncertainties were estimated conservatively as the changes in the rates of the
different subprocesses independently for different jet multiplicities. If the
statistical uncertainty owing to the size of the simulated samples was larger
than the rate change, the former was assigned as uncertainty. The derived rate
uncertainties were then correlated between jet multiplicities to account for
migration effects and are treated as uncorrelated among the subprocesses.
Possible shape variations of the final discriminant distributions due to the
PDF uncertainty have been estimated by evaluating the PDF replicas provided
with the NNPDF set [50]. The impact of the mismodelling of the top quark
spectrum in the simulation [92] was found to be negligible.

The impact of statistical fluctuations in the signal and background prediction
due to the limited number of simulated events is accounted for using the
Barlow–Beeston approach described in Refs. [93, 94].

##  0.7 Results

The numbers of events selected in the jet-process categories of the single-
lepton channel and in the jet-tag categories of the dilepton channel, before
and after the fit of the signal strength modifier and the nuisance parameters,
are listed in Tables 0.7–0.7. The final discriminants in some example
categories in the single-lepton channel and the three dilepton categories
before and after the fit to data are displayed in Figs. 5–6 and Figs. 7–8,
respectively. All final discriminants in the single-lepton channel before and
after the fit to data are displayed in Appendices .10 and .11.

Observed and expected event yields per jet-process category (node) in the
single-lepton channel with 4 jets and at least 3 tags, prior to the fit to
data (after the fit to data). The quoted uncertainties denote the total
statistical and systematic components.  pre-fit (post-fit) yields Process
$\ttbar\PH$ node $\ttbar\text{+}\bbbar$ node $\ttbar\text{+}2\cPqb$ node
$\ttbar\text{+}\cPqb$ node $\ttbar\text{+}\ccbar$ node
$\ttbar\text{+}\text{lf}$ node $\ttbar\text{+}\text{lf}$ $1249$ ($962$) $727$
($572$) $1401$ ($1090$) $1035$ ($823$) $2909$ ($2296$) $8463$ ($6829$)
$\ttbar\text{+}\ccbar$ $298$ ($458$) $232$ ($359$) $428$ ($678$) $251$ ($400$)
$686$ ($1068$) $1022$ ($1652$) $\ttbar\text{+}\cPqb$ $253$ ($356$) $215$
($311$) $370$ ($530$) $326$ ($484$) $308$ ($437$) $469$ ($683$)
$\ttbar\text{+}2\cPqb$ $124$ ($96$) $77$ ($62$) $317$ ($254$) $90$ ($73$)
$100$ ($79$) $134$ ($108$) $\ttbar\text{+}\bbbar$ $139$ ($137$) $191$ ($192$)
$149$ ($140$) $105$ ($103$) $119$ ($114$) $133$ ($128$) Single  $96$ ($96$)
$117$ ($109$) $167$ ($162$) $93$ ($96$) $231$ ($232$) $304$ ($307$) V+jets
$37$ ($37$) $76$ ($74$) $48$ ($46$) $27$ ($27$) $97$ ($89$) $69$ ($69$)
$\ttbar\text{+}\text{V}$ $13$ ($13$) $6$ ($6$) $12$ ($11$) $6$ ($6$) $10$
($10$) $16$ ($16$) Diboson $4$ ($4$) $5$ ($5$) $0.9$ ($0.8$) $0.6$ ($0.7$) $2$
($2$) $4$ ($4$) Total bkg. $2213$ ($2158$) $1645$ ($1688$) $2892$ ($2911$)
$1935$ ($2012$) $4462$ ($4328$) $10614$ ($9795$) $\pm$ tot unc. $\pm 508$
($\pm 58$) $\pm 415$ ($\pm 53$) $\pm 588$ ($\pm 89$) $\pm 402$ ($\pm 67$) $\pm
1051$ ($\pm 120$) $\pm 2359$ ($\pm 270$) $\ttbar\PH$ $27$ ($21$) $9$ ($7$)
$16$ ($12$) $7$ ($5$) $9$ ($7$) $16$ ($13$) $\pm$ tot unc. $\pm 4$ ($\pm 3$)
$\pm 1$ ($\pm 1$) $\pm 2$ ($\pm 2$) $\pm 1$ ($\pm 1$) $\pm 1$ ($\pm 1$) $\pm
2$ ($\pm 2$) Data $2125$ $1793$ $2896$ $2027$ $4366$ $9693$

Observed and expected event yields per jet-process category (node) in the
single-lepton channel with 5 jets and at least 3 tags, prior to the fit to
data (after the fit to data). The quoted uncertainties denote the total
statistical and systematic uncertainty.  pre-fit (post-fit) yields Process
$\ttbar\PH$ node $\ttbar\text{+}\bbbar$ node $\ttbar\text{+}2\cPqb$ node
$\ttbar\text{+}\cPqb$ node $\ttbar\text{+}\ccbar$ node
$\ttbar\text{+}\text{lf}$ node $\ttbar\text{+}\text{lf}$ $785$ ($570$) $647$
($467$) $830$ ($604$) $683$ ($525$) $1148$ ($848$) $4903$ ($3697$)
$\ttbar\text{+}\ccbar$ $336$ ($455$) $341$ ($469$) $445$ ($633$) $264$ ($382$)
$552$ ($756$) $1207$ ($1726$) $\ttbar\text{+}\cPqb$ $257$ ($351$) $290$
($399$) $355$ ($494$) $321$ ($477$) $219$ ($301$) $494$ ($692$)
$\ttbar\text{+}2\cPqb$ $136$ ($104$) $128$ ($99$) $324$ ($253$) $89$ ($73$)
$85$ ($65$) $184$ ($143$) $\ttbar\text{+}\bbbar$ $266$ ($251$) $410$ ($397$)
$224$ ($207$) $150$ ($143$) $144$ ($132$) $228$ ($212$) Single  $62$ ($63$)
$82$ ($84$) $98$ ($96$) $45$ ($58$) $114$ ($113$) $189$ ($193$) V+jets $25$
($23$) $54$ ($53$) $34$ ($31$) $11$ ($12$) $46$ ($41$) $54$ ($51$)
$\ttbar\text{+}\text{V}$ $20$ ($20$) $14$ ($13$) $17$ ($16$) $7$ ($7$) $11$
($10$) $25$ ($24$) Diboson $1$ ($1$) $3$ ($3$) $0.4$ ($0.4$) () $0.6$ ($0.4$)
$3$ ($3$) Total bkg. $1889$ ($1838$) $1969$ ($1985$) $2326$ ($2332$) $1570$
($1676$) $2320$ ($2268$) $7287$ ($6742$) $\pm$ tot unc. $\pm 459$ ($\pm 57$)
$\pm 485$ ($\pm 70$) $\pm 489$ ($\pm 71$) $\pm 334$ ($\pm 47$) $\pm 597$ ($\pm
79$) $\pm 1655$ ($\pm 219$) $\ttbar\PH$ $53$ ($41$) $21$ ($17$) $20$ ($15$)
$8$ ($6$) $11$ ($8$) $28$ ($22$) $\pm$ tot unc. $\pm 7$ ($\pm 6$) $\pm 3$
($\pm 3$) $\pm 2$ ($\pm 2$) $\pm 1$ ($\pm 1$) $\pm 1$ ($\pm 1$) $\pm 3$ ($\pm
3$) Data $1848$ $2040$ $2299$ $1690$ $2302$ $6918$

Observed and expected event yields per jet-process category (node) in the
single-lepton channel with at least 6 jets and at least 3 tags, prior to the
fit to data (after the fit to data). The quoted uncertainties denote the total
statistical and systematic uncertainty.  pre-fit (post-fit) yields Process
$\ttbar\PH$ node $\ttbar\text{+}\bbbar$ node $\ttbar\text{+}2\cPqb$ node
$\ttbar\text{+}\cPqb$ node $\ttbar\text{+}\ccbar$ node
$\ttbar\text{+}\text{lf}$ node $\ttbar\text{+}\text{lf}$ $1982$ ($1381$)
$1280$ ($897$) $852$ ($595$) $916$ ($661$) $243$ ($172$) $50$ ($36$)
$\ttbar\text{+}\ccbar$ $1150$ ($1415$) $998$ ($1230$) $636$ ($805$) $444$
($567$) $115$ ($147$) $16$ ($19$) $\ttbar\text{+}\cPqb$ $549$ ($705$) $575$
($746$) $314$ ($409$) $253$ ($338$) $28$ ($35$) $4$ ($5$)
$\ttbar\text{+}2\cPqb$ $306$ ($233$) $282$ ($215$) $372$ ($293$) $78$ ($62$)
$10$ ($8$) $1$ ($0.8$) $\ttbar\text{+}\bbbar$ $834$ ($769$) $1156$ ($1082$)
$299$ ($266$) $145$ ($129$) $17$ ($15$) $3$ ($2$) Single  $110$ ($116$) $146$
($145$) $92$ ($82$) $53$ ($53$) $4$ ($4$) $3$ ($3$) V+jets $38$ ($37$) $78$
($76$) $34$ ($30$) $10$ ($9$) $7$ ($6$) $0.6$ ($0.6$) $\ttbar\text{+}\text{V}$
$80$ ($75$) $58$ ($54$) $31$ ($28$) $11$ ($11$) $4$ ($4$) $0.4$ ($0.4$)
Diboson $0.9$ ($0.9$) $0.5$ ($0.5$) $0.4$ ($0.4$) $0.4$ ($0.4$) () () Total
bkg. $5049$ ($4733$) $4575$ ($4447$) $2629$ ($2509$) $1911$ ($1831$) $429$
($392$) $77$ ($67$) $\pm$ tot unc. $\pm 1216$ ($\pm 186$) $\pm 1156$ ($\pm
142$) $\pm 603$ ($\pm 80$) $\pm 422$ ($\pm 65$) $\pm 107$ ($\pm 14$) $\pm 18$
($\pm 3$) $\ttbar\PH$ $142$ ($108$) $53$ ($40$) $24$ ($18$) $10$ ($7$) $2.1$
($1.5$) $0.30$ ($0.23$) $\pm$ tot unc. $\pm 19$ ($\pm 15$) $\pm 8$ ($\pm 6$)
$\pm 3$ ($\pm 2$) $\pm 1$ ($\pm 1$) $\pm 0.2$ ($\pm 0.2$) $\pm 0.03$ ($\pm
0.03$) Data $4822$ $4400$ $2484$ $1852$ $422$ $76$

Observed and expected event yields per jet-tag category in the dilepton
channel, prior to the fit to data (after the fit to data). The quoted
uncertainties denote the total statistical and systematic uncertainty.  pre-
fit (post-fit) yields Process $\geq 4$ jets, 3 tags $\geq 4$ jets, $\geq 4$
tags BDT-low BDT-high $\ttbar\text{+}\text{lf}$ $845$ ($637$) $16$ ($11$)
$0.7$ ($0.5$) $\ttbar\text{+}\ccbar$ $712$ ($966$) $25$ ($31$) $3$ ($4$)
$\ttbar\text{+}\cPqb$ $546$ ($747$) $26$ ($35$) $4$ ($6$)
$\ttbar\text{+}2\cPqb$ $252$ ($196$) $11$ ($8$) $2$ ($1$)
$\ttbar\text{+}\bbbar$ $439$ ($415$) $103$ ($109$) $33$ ($32$) Single  $47$
($51$) $5$ ($3$) $1$ ($2$) V+jets $10$ ($8$) () () $\ttbar\text{+}\text{V}$
$40$ ($38$) $4$ ($4$) $2$ ($2$) Diboson $0.9$ ($0.7$) () () Total bkg. $2893$
($3058$) $190$ ($201$) $46$ ($48$) $\pm$ tot unc. $\pm 705$ ($\pm 98$) $\pm
67$ ($\pm 10$) $\pm 17$ ($\pm 3$) $\ttbar\PH$ $42$ ($32$) $6$ ($5$) $6$ ($5$)
$\pm$ tot unc. $\pm 6$ ($\pm 5$) $\pm 1$ ($\pm 1$) $\pm 1$ ($\pm 1$) Data
$3077$ $207$ $58$

|
---|---
|
Figure 5: Final discriminant shapes in the single-lepton (SL) channel before
the fit to data: DNN discriminant in the jet-process categories with $\geq$6
jets-$\ttbar\PH$ (upper left); 5 jets-$\ttbar\text{+}\bbbar$ (upper right); 4
jets-$\ttbar\text{+}\text{lf}$ (lower left); and $\geq$6
jets-$\ttbar\text{+}\ccbar$ (lower right). The expected background
contributions (filled histograms) are stacked, and the expected signal
distribution (line), which includes $\PH\to\bbbar$ and all other Higgs boson
decay modes, is superimposed. Each contribution is normalised to an integrated
luminosity of $35.9\fbinv$, and the signal distribution is additionally scaled
by a factor of 15 for better visibility. The hatched uncertainty bands include
the total uncertainty of the fit model. The distributions observed in data
(markers) are overlayed. The first and the last bins include underflow and
overflow events, respectively. The lower plots show the ratio of the data to
the background prediction.

|
---|---

Figure 6:  Final discriminant shapes in the dilepton (DL) channel before the fit to data: BDT discriminant in the analysis category with ${(\geq 4\,\text{jets},3\,\text{\cPqb\ tags})}$ (upper row) and MEM discriminant in the analysis categories with ${(\geq 4\,\text{jets},\geq 4\,\text{\cPqb\ tags})}$ (lower row) with low (left) and high (right) BDT output. The expected background contributions (filled histograms) are stacked, and the expected signal distribution (line), which includes $\PH\to\bbbar$ and all other Higgs boson decay modes, is superimposed. Each contribution is normalised to an integrated luminosity of $35.9\fbinv$, and the signal distribution is additionally scaled by a factor of 15 for better visibility. The hatched uncertainty bands include the total uncertainty of the fit model. The distributions observed in data (markers) are overlayed. The first and the last bins include underflow and overflow events, respectively. The lower plots show the ratio of the data to the background prediction.  |
---|---
|
Figure 7:  Final discriminant shapes in the single-lepton (SL) channel after
the fit to data: DNN discriminant in the jet-process categories with $\geq$6
jets-$\ttbar\PH$ (upper left); 5 jets-$\ttbar\text{+}\bbbar$ (upper right); 4
jets-$\ttbar\text{+}\text{lf}$ (lower left); and $\geq$6
jets-$\ttbar\text{+}\ccbar$ (lower right). The hatched uncertainty bands
include the total uncertainty after the fit to data. The distributions
observed in data (markers) are overlayed. The first and the last bins include
underflow and overflow events, respectively. The lower plots show the ratio of
the data to the post-fit background plus signal distribution.

|
---|---

Figure 8:  Final discriminant shapes in the dilepton (DL) channel after the
fit to data: BDT discriminant in the analysis category with ${(\geq
4\,\text{jets},3\,\text{\cPqb\ tags})}$ (upper row) and MEM discriminant in
the analysis categories with ${(\geq 4\,\text{jets},\geq 4\,\text{\cPqb\
tags})}$ (lower row) with low (left) and high (right) BDT output. The hatched
uncertainty bands include the total uncertainty after the fit to data. The
distributions observed in data (markers) are overlayed. The first and the last
bins include underflow and overflow events, respectively. The lower plots show
the ratio of the data to the post-fit background plus signal distribution.

The signal strength modifier $\mu=\sigma/\sigma_{\mathrm{SM}}$ of the
$\ttbar\PH$ production cross section is determined in a simultaneous binned
maximum likelihood fit to the data across all analysis categories. The fit
procedure takes into account systematic uncertainties that modify the shape
and normalisation of the final discriminant distributions, as described in
Section 0.6. The best fit values of the nuisance parameters are within 1
standard deviation of the prior uncertainty for more than 95% of the total
number of nuisance parameters. The best fit values of the 20 parameters ranked
highest in impact are presented in Fig. 9. As expected, the fit constrains the
nuisance parameters related to the conservatively assigned 50% prior
uncertainties on the $\ttbar\text{+}\text{hf}$ cross section to 40–60% of the
prior. A few other nuisance parameters that are related to jet energy scale
and tagging uncertainties are constrained up to a factor of 50%. These
constraints are not due to conservatively assigned prior uncertainties but are
attributed to the fact that events are selected according to different, large
multiplicities of jets and -tagged jets, thus increasing the sensitivity of
the analysis to changes of the jet energy scale and tagging efficiency, by
their effect on the event yield per analysis category. Furthermore, the impact
on $\mu$ of the most relevant sources of uncertainty is shown in Fig. 9, which
is computed as the difference of the nominal best fit value of $\mu$ and the
best fit value obtained when fixing the nuisance parameter under scrutiny to
its best fit value plus/minus its post-fit uncertainty. In particular, the 20
parameters with the highest impact are shown, excluding nuisance parameters
describing the statistical uncertainties due to the size of the simulated
samples. The nuisance parameters with the highest impact are related to the
uncertainty in the $\ttbar\text{+}\text{hf}$ and signal cross sections, as
well as in the tagging scale factors.

Figure 9:  Post-fit pull and impact on the signal strength $\mu$ of the
nuisance parameters included in the fit, ordered by their impact. Only the 20
highest ranked parameters are shown, not including nuisance parameters
describing the uncertainty due to the size of the simulated samples. The four
highest-ranked nuisance parameters related to the jet energy scale uncertainty
sources are shown as indicated in parentheses. The pulls of the nuisance
parameters (black markers) are computed relative to their pre-fit values
$\theta_{0}$ and uncertainties $\Delta\theta$. The impact $\Delta\mu$ is
computed as the difference of the nominal best fit value of $\mu$ and the best
fit value obtained when fixing the nuisance parameter under scrutiny to its
best fit value $\hat{\theta}$ plus/minus its post-fit uncertainty (coloured
areas).

The obtained best fit value of $\mu$ is $0.72\pm 0.24\stat\pm 0.38\syst$ with
a total uncertainty of $\pm 0.45$. This corresponds to an observed (expected)
significance of 1.6 (2.2) standard deviations above the background-only
hypothesis. The observed and predicted event yields in all the bins of the
final discriminants, ordered by the pre-fit expected signal-to-background
ratio (S/B) are shown in Fig. 10 (left). The best fit values in each analysis
channel separately and in the combination are listed in Table 0.7 and
displayed in Fig. 10 (right).

---
Figure 10:  Bins of the final discriminants as used in the fit (left),
reordered by the pre-fit expected signal-to-background ratio (S/B). Each of
the shown bins includes multiple bins of the final discriminants with similar
S/B. The fitted signal (cyan) is compared to the expectation for the SM Higgs
boson $\mu=1$ (red). Best fit values of the signal strength modifiers $\mu$
(right) with their 68% expected confidence intervals (outer error bar), also
split into their statistical (inner error bar) and systematic components.

Best fit value of the signal strength modifier $\mu$ and the observed and
median expected 95% upper limits in the single-lepton and the dilepton
channels as well as the combined results. The one standard deviation
confidence intervals of the expected limit and the best fit value are also
quoted, split into the statistical and systematic components in the latter
case.  Channel 95% upper limit Best-fit $\mu$ observed expected
$\pm\text{tot}\,(\pm\text{stat}\,\,\pm\text{syst})$ Single-lepton $1.75$
$1.03^{+0.44}_{-0.29}$
$0.84^{+0.52}_{-0.50}\;\left({}^{+0.27}_{-0.26}\,\,{}^{+0.44}_{-0.43}\right)$
Dilepton $2.34$ $2.48^{+1.17}_{-0.76}$
$-0.24^{+1.21}_{-1.12}\;\left({}^{+0.63}_{-0.60}\,\,{}^{+1.04}_{-0.95}\right)$
Combined $1.51$ $0.92^{+0.39}_{-0.26}$
$0.72^{+0.45}_{-0.45}\;\left({}^{+0.24}_{-0.24}\,\,{}^{+0.38}_{-0.38}\right)$

The contributions of the statistical and various systematic uncertainties to
the uncertainty in $\mu$ are listed in Table 0.7. The statistical uncertainty
is evaluated by fixing all nuisance parameters to their post-fit values. The
impact of the systematic uncertainties is evaluated by repeating the fit
fixing only the nuisance parameters related to the uncertainty under scrutiny
to their post-fit values and subtracting the obtained uncertainty in
quadrature from the total uncertainty of the fit where no parameters are
fixed. The total uncertainty of the full fit (0.45) is different from the
quadratic sum of the listed contributions because of correlations between the
nuisance parameters.

Contributions of different sources of uncertainties to the result for the fit
to the data (observed) and to the expectation from simulation (expected). The
quoted uncertainties $\Delta\mu$ in $\mu$ are obtained by fixing the listed
sources of uncertainties to their post-fit values in the fit and subtracting
the obtained result in quadrature from the result of the full fit. The
statistical uncertainty is evaluated by fixing all nuisance parameters to
their post-fit values. The quadratic sum of the contributions is different
from the total uncertainty because of correlations between the nuisance
parameters.  Uncertainty source $\pm\Delta\mu$ (observed) $\pm\Delta\mu$
(expected) Total experimental $+0.15$/$-0.16$ $+0.19$/$-0.17$ tagging
$+0.11$/$-0.14$ $+0.12$/$-0.11$ jet energy scale and resolution
$+0.06$/$-0.07$ $+0.13$/$-0.11$ Total theory $+0.28$/$-0.29$ $+0.32$/$-0.29$
$\ttbar\text{+}\text{hf}$ cross section and parton shower $+0.24$/$-0.28$
$+0.28$/$-0.28$ Size of the simulated samples $+0.14$/$-0.15$ $+0.16$/$-0.16$
Total systematic $+0.38$/$-0.38$ $+0.45$/$-0.42$ Statistical $+0.24$/$-0.24$
$+0.27$/$-0.27$ Total $+0.45$/$-0.45$ $+0.53$/$-0.49$

The total uncertainty of 0.45 is dominated by contributions from systematic
effects, while the statistical component is 0.24. The largest contributions
originate from the theoretical uncertainties amounting to $+0.28$/$-0.29$,
where the $\ttbar\text{+}\text{hf}$ modelling uncertainties have a major
contribution. Experimental uncertainties amount to $+0.15$/$-0.16$, dominated
by the tagging related uncertainties. Systematic uncertainties due to the size
of the various simulated samples used to model the background and signal
templates are at the same order and amount to $+0.14$/$-0.15$.

An upper limit on $\mu$ under the background-only hypothesis is also
determined, using a modified frequentist $\text{CL}_{\text{S}}$ procedure [95,
96] with the asymptotic method [97]. When combining all categories and
channels, an observed (expected) upper limit at 95% on $\mu$ of 1.5 (0.9) is
obtained. The observed and expected upper limits in each channel and in the
combination are listed in Table 0.7 and visualised in Fig. 11.

Figure 11:  Median expected (dashed line) and observed (markers) 95% upper
limits on $\mu$. The inner (green) band and the outer (yellow) band indicate
the regions containing 68 and 95%, respectively, of the distribution of limits
expected under the background-only hypothesis. Also shown is the limit that is
expected in case a SM $\ttbar\PH$ signal ($\mu=1$) is present in the data
(solid red line).

In addition, the statistical analysis has been performed using the jet-process
categorisation and DNN output in both channels and their combination, as well
as using the jet-tag categorisation and the BDT or MEM in both channels. The
results obtained in each channel and the combination are compatible within 1.7
standard deviations or better, evaluated using a jackknife procedure [98].
This serves as an important cross check and validation of the complex analysis
methods.

##  0.8 Summary

A search for the associated production of a Higgs boson and a top quark-
antiquark pair ($\ttbar\PH$) is performed using $\Pp\Pp$ collision data
recorded with the CMS detector at a centre-of-mass energy of 13in 2016,
corresponding to an integrated luminosity of $35.9\fbinv$. Candidate events
are selected in final states compatible with the Higgs boson decaying into a
quark-antiquark pair and the single-lepton and dilepton decay channels of the
system. Selected events are split into mutually exclusive categories according
to their decay channel and jet content. In each category a powerful
discriminant is constructed to separate the $\ttbar\PH$ signal from the
dominant $\ttbar\text{+}\text{jets}$ background, based on several multivariate
analysis techniques (boosted decision trees, matrix element method, and deep
neural networks). An observed (expected) upper limit on the $\ttbar\PH$
production cross section $\mu$ relative to the SM expectations of $1.5\,(0.9)$
at 95% confidence level is obtained. The best fit value of $\mu$ is $0.72\pm
0.24\stat\pm 0.38\syst$. These results correspond to an observed (expected)
significance of 1.6 (2.2) standard deviations above the background-only
hypothesis.

###### 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 centres 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: the Austrian Federal Ministry of Science, Research and
Economy and the Austrian Science Fund; the Belgian Fonds de la Recherche
Scientifique, and Fonds voor Wetenschappelijk Onderzoek; the Brazilian Funding
Agencies (CNPq, CAPES, FAPERJ, and FAPESP); the Bulgarian Ministry of
Education and Science; CERN; the Chinese Academy of Sciences, Ministry of
Science and Technology, and National Natural Science Foundation of China; the
Colombian Funding Agency (COLCIENCIAS); the Croatian Ministry of Science,
Education and Sport, and the Croatian Science Foundation; the Research
Promotion Foundation, Cyprus; the Secretariat for Higher Education, Science,
Technology and Innovation, Ecuador; the Ministry of Education and Research,
Estonian Research Council via IUT23-4 and IUT23-6 and European Regional
Development Fund, Estonia; the Academy of Finland, Finnish Ministry of
Education and Culture, and Helsinki Institute of Physics; the Institut
National de Physique Nucléaire et de Physique des Particules / CNRS, and
Commissariat à l’Énergie Atomique et aux Énergies Alternatives / CEA, France;
the Bundesministerium für Bildung und Forschung, Deutsche
Forschungsgemeinschaft, and Helmholtz-Gemeinschaft Deutscher
Forschungszentren, Germany; the General Secretariat for Research and
Technology, Greece; the National Research, Development and Innovation Fund,
Hungary; the Department of Atomic Energy and the Department of Science and
Technology, India; the Institute for Studies in Theoretical Physics and
Mathematics, Iran; the Science Foundation, Ireland; the Istituto Nazionale di
Fisica Nucleare, Italy; the Ministry of Science, ICT and Future Planning, and
National Research Foundation (NRF), Republic of Korea; the Lithuanian Academy
of Sciences; the Ministry of Education, and University of Malaya (Malaysia);
the Mexican Funding Agencies (BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-
FAI); the Ministry of Business, Innovation and Employment, New Zealand; the
Pakistan Atomic Energy Commission; the Ministry of Science and Higher
Education and the National Science Centre, Poland; the Fundação para a Ciência
e a Tecnologia, Portugal; JINR, Dubna; the Ministry of Education and Science
of the Russian Federation, the Federal Agency of Atomic Energy of the Russian
Federation, Russian Academy of Sciences and the Russian Foundation for Basic
Research; the Ministry of Education, Science and Technological Development of
Serbia; the Secretaría de Estado de Investigación, Desarrollo e Innovación,
Programa Consolider-Ingenio 2010, Plan Estatal de Investigación Científica y
Técnica y de Innovación 2013-2016, Plan de Ciencia, Tecnología e Innovación
2013-2017 del Principado de Asturias and Fondo Europeo de Desarrollo Regional,
Spain; the Swiss Funding Agencies (ETH Board, ETH Zurich, PSI, SNF, UniZH,
Canton Zurich, and SER); the Ministry of Science and Technology, Taipei; the
Thailand Center of Excellence in Physics, the Institute for the Promotion of
Teaching Science and Technology of Thailand, Special Task Force for Activating
Research and the National Science and Technology Development Agency of
Thailand; the Scientific and Technical Research Council of Turkey, and Turkish
Atomic Energy Authority; the National Academy of Sciences of Ukraine, and
State Fund for Fundamental Researches, Ukraine; the Science and Technology
Facilities Council, UK; the US Department of Energy, and the US National
Science Foundation. Individuals have received support from the Marie-Curie
programme 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”) Programme 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 Scientific and Industrial Research, India; the HOMING PLUS
programme of the Foundation for Polish Science, cofinanced from European
Union, Regional Development Fund, the Mobility Plus programme 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 de Excelencia María de Maeztu and the Programa Severo Ochoa del
Principado de Asturias; the Thalis and Aristeia programmes 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).

## References

  * [1] ATLAS Collaboration, “Observation of a new particle in the search for the standard model Higgs boson with the ATLAS detector at the LHC”,  Phys. Lett. B 716 (2012) 1, 10.1016/j.physletb.2012.08.020, arXiv:1207.7214.
  * [2] CMS Collaboration, “Observation of a new boson at a mass of 125with the CMS experiment at the LHC”,  Phys. Lett. B 716 (2012) 30, 10.1016/j.physletb.2012.08.021, arXiv:1207.7235.
  * [3] CMS Collaboration, “A new boson with a mass of 125observed with the CMS experiment at the Large Hadron Collider”,  Science 338 (2012) 1569, 10.1126/science.1230816.
  * [4] ATLAS and CMS Collaborations, “Combined measurement of the Higgs boson mass in pp collisions at $\sqrt{s}=7$ and 8 with the ATLAS and CMS experiments”,  Phys. Rev. Lett. 114 (2015) 191803, 10.1103/PhysRevLett.114.191803, arXiv:1503.07589.
  * [5] CMS Collaboration, “Measurements of properties of the Higgs boson decaying into the four-lepton final state in pp collisions at $\sqrt{s}=13\TeV$”,  JHEP 11 (2017) 047, 10.1007/JHEP11(2017)047, arXiv:1706.09936.
  * [6] CMS Collaboration, “Evidence for the direct decay of the 125Higgs boson to fermions”,  Nature Phys. 10 (2014) 557, 10.1038/nphys3005, arXiv:1401.6527.
  * [7] ATLAS Collaboration, “Evidence for the Higgs-boson Yukawa coupling to tau leptons with the ATLAS detector”,  JHEP 04 (2015) 117, 10.1007/JHEP04(2015)117, arXiv:1501.04943.
  * [8] CMS Collaboration, “Observation of the Higgs boson decay to a pair of $\tau$ leptons with the CMS detector”,  Phys. Lett. B 779 (2018) 283, 10.1016/j.physletb.2018.02.004, arXiv:1708.00373.
  * [9] ATLAS Collaboration, “Evidence for the $\PH\to\bbbar$ decay with the ATLAS detector”,  JHEP 12 (2017) 024, 10.1007/JHEP12(2017)024, arXiv:1708.03299.
  * [10] CMS Collaboration, “Evidence for the Higgs boson decay to a bottom quark-antiquark pair”, (2017). arXiv:1709.07497. Submitted to Phys. Lett. B.
  * [11] ATLAS Collaboration, “Measurements of Higgs boson production and couplings in diboson final states with the ATLAS detector at the LHC”,  Phys. Lett. B 726 (2013) 88, 10.1016/j.physletb.2013.08.010, arXiv:1307.1427. [Erratum: 10.1016/j.physletb.2014.05.011].
  * [12] CMS Collaboration, “Precise determination of the mass of the Higgs boson and tests of compatibility of its couplings with the standard model predictions using proton collisions at 7 and 8”,  Eur. Phys. J. C 75 (2015) 212, 10.1140/epjc/s10052-015-3351-7, arXiv:1412.8662.
  * [13] ATLAS Collaboration, “Evidence for the spin-0 nature of the Higgs boson using ATLAS data”,  Phys. Lett. B 726 (2013) 120, 10.1016/j.physletb.2013.08.026, arXiv:1307.1432.
  * [14] CMS Collaboration, “Constraints on the spin-parity and anomalous HVV couplings of the Higgs boson in proton collisions at 7 and 8”,  Phys. Rev. D 92 (2015) 012004, 10.1103/PhysRevD.92.012004, arXiv:1411.3441.
  * [15] CMS Collaboration, “Search for standard model production of four top quarks with same-sign and multilepton final states in proton-proton collisions at $\sqrt{s}=13\TeV$”,  Eur. Phys. J. C 78 (2018) 140, 10.1140/epjc/s10052-018-5607-5, arXiv:1710.10614.
  * [16] LHC Higgs Cross Section Working Group, “Handbook of LHC Higgs cross sections: 4. deciphering the nature of the Higgs sector”,  CERN (2016) 10.23731/CYRM-2017-002, arXiv:1610.07922.
  * [17] G. Burdman, M. Perelstein, and A. Pierce, “Large Hadron Collider tests of the little Higgs model”,  Phys. Rev. Lett. 90 (2003) 241802, 10.1103/PhysRevLett.90.241802, arXiv:hep-ph/0212228. [Erratum: 10.1103/PhysRevLett.92.049903].
  * [18] T. Han, H. E. Logan, B. McElrath, and L.-T. Wang, “Phenomenology of the little Higgs model”,  Phys. Rev. D 67 (2003) 095004, 10.1103/PhysRevD.67.095004, arXiv:hep-ph/0301040.
  * [19] M. Perelstein, M. E. Peskin, and A. Pierce, “Top quarks and electroweak symmetry breaking in little Higgs models”,  Phys. Rev. D 69 (2004) 075002, 10.1103/PhysRevD.69.075002, arXiv:hep-ph/0310039.
  * [20] H.-C. Cheng, I. Low, and L.-T. Wang, “Top partners in little Higgs theories with T-parity”,  Phys. Rev. D 74 (2006) 055001, 10.1103/PhysRevD.74.055001, arXiv:hep-ph/0510225.
  * [21] H.-C. Cheng, B. A. Dobrescu, and C. T. Hill, “Electroweak symmetry breaking and extra dimensions”,  Nucl. Phys. B 589 (2000) 249, 10.1016/S0550-3213(00)00401-6, arXiv:hep-ph/9912343.
  * [22] M. Carena, E. Ponton, J. Santiago, and C. E. M. Wagner, “Light Kaluza-Klein states in Randall-Sundrum models with custodial SU(2)”,  Nucl. Phys. B 759 (2006) 202, 10.1016/j.nuclphysb.2006.10.012, arXiv:hep-ph/0607106.
  * [23] R. Contino, L. Da Rold, and A. Pomarol, “Light custodians in natural composite Higgs models”,  Phys. Rev. D 75 (2007) 055014, 10.1103/PhysRevD.75.055014, arXiv:hep-ph/0612048.
  * [24] G. Burdman and L. Da Rold, “Electroweak symmetry breaking from a holographic fourth generation”,  JHEP 12 (2007) 086, 10.1088/1126-6708/2007/12/086, arXiv:0710.0623.
  * [25] C. T. Hill, “Topcolor: Top quark condensation in a gauge extension of the standard model”,  Phys. Lett. B 266 (1991) 419, 10.1016/0370-2693(91)91061-Y.
  * [26] A. Carmona, M. Chala, and J. Santiago, “New Higgs production mechanism in composite Higgs models”,  JHEP 07 (2012) 049, 10.1007/JHEP07(2012)049, arXiv:1205.2378.
  * [27] CMS Collaboration, “Search for the associated production of the Higgs boson with a top-quark pair”,  JHEP 09 (2014) 087, 10.1007/JHEP09(2014)087, arXiv:1408.1682. [Erratum: 10.1007/JHEP10(2014)106].
  * [28] ATLAS Collaboration, “Search for the associated production of the Higgs boson with a top quark pair in multilepton final states with the ATLAS detector”,  Phys. Lett. B 749 (2015) 519, 10.1016/j.physletb.2015.07.079, arXiv:1506.05988.
  * [29] CMS Collaboration, “Search for a standard model Higgs boson produced in association with a top-quark pair and decaying to bottom quarks using a matrix element method”,  Eur. Phys. J. C 75 (2015) 251, 10.1140/epjc/s10052-015-3454-1, arXiv:1502.02485.
  * [30] ATLAS Collaboration, “Search for the standard model Higgs boson produced in association with top quarks and decaying into $\bbbar$ in pp collisions at $\sqrt{s}=8\TeV$ with the ATLAS detector”,  Eur. Phys. J. C 75 (2015) 349, 10.1140/epjc/s10052-015-3543-1, arXiv:1503.05066.
  * [31] LHC Higgs Cross Section Working Group, “Handbook of LHC Higgs cross sections: 1. inclusive observables”,  CERN (2011) 10.5170/CERN-2011-002, arXiv:1101.0593.
  * [32] CMS Collaboration, “Search for $\mathrm{t}\overline{\mathrm{t}}$H production in the all-jet final state in proton-proton collisions at $\sqrt{s}=13\TeV$”,  JHEP 06 (2018) 101, 10.1007/JHEP06(2018)101, arXiv:1803.06986.
  * [33] CMS Collaboration, “Evidence for associated production of a Higgs boson with a top quark pair in final states with electrons, muons, and hadronically decaying $\tau$ leptons at $\sqrt{s}=13\TeV$”, (2018). arXiv:1803.05485. Submitted to JHEP.
  * [34] ATLAS Collaboration, “Evidence for the associated production of the Higgs boson and a top quark pair with the ATLAS detector”, (2017). arXiv:1712.08891. Submitted to Phys. Rev. D.
  * [35] ATLAS Collaboration, “Search for the standard model Higgs boson produced in association with top quarks and decaying into a $b\bar{b}$ pair in $pp$ collisions at $\sqrt{s}=13\TeV$ with the ATLAS detector”,  Phys. Rev. D 97 (2018) 072016, 10.1103/PhysRevD.97.072016, arXiv:1712.08895.
  * [36] T. J. Hastie, R. J. Tibshirani, and J. H. Friedman, “The elements of statistical learning : data mining, inference, and prediction”.  Springer series in statistics. Springer, New York, NY, 2. ed., corr. at 10. print. edition, 2013.  ISBN 978-0-387-84857-0.
  * [37] P. C. Bhat, “Multivariate analysis methods in particle physics”,  Ann. Rev. Nucl. Part. Sci. 61 (2011) 281, 10.1146/annurev.nucl.012809.104427.
  * [38] A. Höcker et al., “TMVA: Toolkit for multivariate data analysis”,  PoS ACAT (2007) 040, arXiv:physics/0703039.
  * [39] K. Kondo, “Dynamical likelihood method for reconstruction of events with missing momentum. 1: Method and toy models”,  J. Phys. Soc. Jap. 57 (1988) 4126, 10.1143/JPSJ.57.4126.
  * [40] D0 Collaboration, “A precision measurement of the mass of the top quark”,  Nature 429 (2004) 638, 10.1038/nature02589, arXiv:hep-ex/0406031.
  * [41] CMS Collaboration, “The CMS experiment at the CERN LHC”,  JINST 3 (2008) S08004, 10.1088/1748-0221/3/08/S08004.
  * [42] CMS Collaboration, “The CMS trigger system”,  JINST 12 (2017) P01020, 10.1088/1748-0221/12/01/P01020, arXiv:1609.02366.
  * [43] GEANT4 Collaboration, “—a simulation toolkit”,  Nucl. Instrum. Meth. A 506 (2003) 250, 10.1016/S0168-9002(03)01368-8.
  * [44] P. Nason, “A new method for combining NLO QCD with shower Monte Carlo algorithms”,  JHEP 11 (2004) 040, 10.1088/1126-6708/2004/11/040, arXiv:hep-ph/0409146.
  * [45] S. Frixione, P. Nason, and C. Oleari, “Matching NLO QCD computations with parton shower simulations: the POWHEG method”,  JHEP 11 (2007) 070, 10.1088/1126-6708/2007/11/070, arXiv:0709.2092.
  * [46] S. Alioli, P. Nason, C. Oleari, and E. Re, “A general framework for implementing NLO calculations in shower Monte Carlo programs: the POWHEG BOX”,  JHEP 06 (2010) 043, 10.1007/JHEP06(2010)043, arXiv:1002.2581.
  * [47] H. B. Hartanto, B. Jager, L. Reina, and D. Wackeroth, “Higgs boson production in association with top quarks in the POWHEG BOX”,  Phys. Rev. D 91 (2015) 094003, 10.1103/PhysRevD.91.094003, arXiv:1501.04498.
  * [48] T. Sjöstrand et al., “An introduction to PYTHIA 8.2”,  Comput. Phys. Commun. 191 (2015) 159, 10.1016/j.cpc.2015.01.024, arXiv:1410.3012.
  * [49] J. Alwall et al., “The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations”,  JHEP 07 (2014) 079, 10.1007/JHEP07(2014)079, arXiv:1405.0301.
  * [50] NNPDF Collaboration, “Parton distributions for the LHC Run II”,  JHEP 04 (2015) 040, 10.1007/JHEP04(2015)040, arXiv:1410.8849.
  * [51] S. Alioli, P. Nason, C. Oleari, and E. Re, “NLO single-top production matched with shower in POWHEG: $s$\- and $t$-channel contributions”,  JHEP 09 (2009) 111, 10.1088/1126-6708/2009/09/111, arXiv:0907.4076. [Erratum: 10.1007/JHEP02(2010)011].
  * [52] E. Re, “Single-top Wt-channel production matched with parton showers using the POWHEG method”,  Eur. Phys. J. C 71 (2011) 1547, 10.1140/epjc/s10052-011-1547-z, arXiv:1009.2450.
  * [53] R. Frederix and S. Frixione, “Merging meets matching in MC@NLO”,  JHEP 12 (2012) 061, 10.1007/JHEP12(2012)061, arXiv:1209.6215.
  * [54] CMS Collaboration, “Investigations of the impact of the parton shower tuning in Pythia 8 in the modelling of at $\sqrt{s}=8$ and 13”, CMS Physics Analysis Summary CMS-PAS-TOP-16-021, 2016.
  * [55] CMS Collaboration, “Event generator tunes obtained from underlying event and multiparton scattering measurements”,  Eur. Phys. J. C 76 (2016) 155, 10.1140/epjc/s10052-016-3988-x, arXiv:1512.00815.
  * [56] N. Kidonakis, “Two-loop soft anomalous dimensions for single top quark associated production with $\mathrm{W^{-}}$ or $\mathrm{H^{-}}$”,  Phys. Rev. D 82 (2010) 054018, 10.1103/PhysRevD.82.054018, arXiv:1005.4451.
  * [57] M. Aliev et al., “HATHOR: HAdronic Top and Heavy quarks crOss section calculatoR”,  Comput. Phys. Commun. 182 (2011) 1034, 10.1016/j.cpc.2010.12.040, arXiv:1007.1327.
  * [58] P. Kant et al., “HatHor for single top-quark production: Updated predictions and uncertainty estimates for single top-quark production in hadronic collisions”,  Comput. Phys. Commun. 191 (2015) 74, 10.1016/j.cpc.2015.02.001, arXiv:1406.4403.
  * [59] F. Maltoni, D. Pagani, and I. Tsinikos, “Associated production of a top-quark pair with vector bosons at NLO in QCD: impact on $\mathrm{t}\overline{\mathrm{t}}\mathrm{H}$ searches at the LHC”,  JHEP 02 (2016) 113, 10.1007/JHEP02(2016)113, arXiv:1507.05640.
  * [60] J. M. Campbell, R. K. Ellis, and C. Williams, “Vector boson pair production at the LHC”,  JHEP 07 (2011) 018, 10.1007/JHEP07(2011)018, arXiv:1105.0020.
  * [61] M. Cacciari et al., “Top-pair production at hadron colliders with next-to-next-to-leading logarithmic soft-gluon resummation”,  Phys. Lett. B 710 (2012) 612, 10.1016/j.physletb.2012.03.013, arXiv:1111.5869.
  * [62] P. Bärnreuther, M. Czakon, and A. Mitov, “Percent-level-precision physics at the Tevatron: next-to-next-to-leading order QCD corrections to $\cPq\cPaq\to\ttbar\text{+X}$”,  Phys. Rev. Lett. 109 (2012) 132001, 10.1103/PhysRevLett.109.132001, arXiv:1204.5201.
  * [63] M. Czakon and A. Mitov, “NNLO corrections to top-pair production at hadron colliders: the all-fermionic scattering channels”,  JHEP 12 (2012) 054, 10.1007/JHEP12(2012)054, arXiv:1207.0236.
  * [64] M. Czakon and A. Mitov, “NNLO corrections to top pair production at hadron colliders: the quark-gluon reaction”,  JHEP 01 (2013) 080, 10.1007/JHEP01(2013)080, arXiv:1210.6832.
  * [65] M. Beneke, P. Falgari, S. Klein, and C. Schwinn, “Hadronic top-quark pair production with NNLL threshold resummation”,  Nucl. Phys. B 855 (2012) 695, 10.1016/j.nuclphysb.2011.10.021, arXiv:1109.1536.
  * [66] M. Czakon, P. Fiedler, and A. Mitov, “Total top-quark pair-production cross section at hadron colliders through $o({\alpha_{s}}^{4})$”,  Phys. Rev. Lett. 110 (2013) 252004, 10.1103/PhysRevLett.110.252004, arXiv:1303.6254.
  * [67] M. Czakon and A. Mitov, “Top++: a program for the calculation of the top-pair cross-section at hadron colliders”,  Comput. Phys. Commun. 185 (2014) 2930, 10.1016/j.cpc.2014.06.021, arXiv:1112.5675.
  * [68] CMS Collaboration, “Identification of heavy-flavour jets with the CMS detector in pp collisions at 13”, (2017). arXiv:1712.07158. Submitted to JINST.
  * [69] CMS Collaboration, “Particle-flow reconstruction and global event description with the CMS detector”,  JINST 12 (2017) P10003, 10.1088/1748-0221/12/10/P10003, arXiv:1706.04965.
  * [70] CMS Collaboration, “Description and performance of track and primary-vertex reconstruction with the CMS tracker”,  JINST 9 (2014) P10009, 10.1088/1748-0221/9/10/P10009, arXiv:1405.6569.
  * [71] M. Cacciari, G. P. Salam, and G. Soyez, “The anti-jet clustering algorithm”,  JHEP 04 (2008) 063, 10.1088/1126-6708/2008/04/063, arXiv:0802.1189.
  * [72] M. Cacciari, G. P. Salam, and G. Soyez, “FastJet user manual”,  Eur. Phys. J. C 72 (2012) 1896, 10.1140/epjc/s10052-012-1896-2, arXiv:1111.6097.
  * [73] CMS Collaboration, “Performance of electron reconstruction and selection with the CMS detector in proton-proton collisions at $\sqrt{s}=8\TeV$”,  JINST 10 (2015) P06005, 10.1088/1748-0221/10/06/P06005, arXiv:1502.02701.
  * [74] CMS Collaboration, “Performance of the CMS muon detector and muon reconstruction with proton-proton collisions at $\sqrt{s}=13\TeV$”,  JINST 13 (2018) P06015, 10.1088/1748-0221/13/06/P06015, arXiv:1804.04528.
  * [75] M. Cacciari, G. P. Salam, and G. Soyez, “The catchment area of jets”,  JHEP 04 (2008) 005, 10.1088/1126-6708/2008/04/005, arXiv:0802.1188.
  * [76] CMS Collaboration, “Jet energy scale and resolution in the CMS experiment in pp collisions at 8”,  JINST 12 (2017) P02014, 10.1088/1748-0221/12/02/P02014, arXiv:1607.03663.
  * [77] I. Goodfellow, Y. Bengio, and A. Courville, “Deep Learning”.  MIT Press, 2016.  http://www.deeplearningbook.org.
  * [78] J. H. Friedman, “Stochastic gradient boosting”,  Comput. Stat. Data Anal. 38 (2002) 367, 10.1016/S0167-9473(01)00065-2.
  * [79] J. Kennedy and R. Eberhart, “Particle swarm optimization”, in  Proceedings of the IEEE International Conference on neural networks, volume 4, p. 1942.  1995\.  10.1109/ICNN.1995.488968.
  * [80] K. El Morabit, “A study of the multivariate analysis of Higgs boson production in association with a top quark-antiquark pair in the boosted regime at the CMS experiment”, Master’s thesis, Karlsruher Institut für Technologie (KIT), 2015.  EKP-2016-00035.
  * [81] CMS Collaboration, “CMS luminosity measurements for the 2016 data taking period”, CMS Physics Analysis Summary CMS-PAS-LUM-17-001, 2017.
  * [82] ATLAS Collaboration, “Measurement of the inelastic proton-proton cross section at $\sqrt{s}=13\TeV$ with the ATLAS detector at the LHC”,  Phys. Rev. Lett. 117 (2016) 182002, 10.1103/PhysRevLett.117.182002, arXiv:1606.02625.
  * [83] CMS Collaboration, “Measurement of the cross section ratio $\sigma_{\mathrm{t\bar{t}b\bar{b}}}/\sigma_{\mathrm{t\bar{t}jj}}$ in pp collisions at $\sqrt{s}$ = 8 TeV”,  Phys. Lett. B 746 (2015) 132, 10.1016/j.physletb.2015.04.060, arXiv:1411.5621.
  * [84] ATLAS Collaboration, “Measurements of fiducial cross-sections for $t\bar{t}$ production with one or two additional b-jets in pp collisions at $\sqrt{s}=8\TeV$ using the ATLAS detector”,  Eur. Phys. J. C 76 (2016) 11, 10.1140/epjc/s10052-015-3852-4, arXiv:1508.06868.
  * [85] CMS Collaboration, “Measurement of $\mathrm{t}\overline{\mathrm{t}}$ production with additional jet activity, including $\mathrm{b}$ quark jets, in the dilepton decay channel using pp collisions at $\sqrt{s}=$ 8 TeV”,  Eur. Phys. J. C 76 (2016) 379, 10.1140/epjc/s10052-016-4105-x, arXiv:1510.03072.
  * [86] CMS Collaboration, “Measurements of cross sections in association with b jets and inclusive jets and their ratio using dilepton final states in pp collisions at $\sqrt{s}=13\TeV$”,  Phys. Lett. B 776 (2018) 355, 10.1016/j.physletb.2017.11.043, arXiv:1705.10141.
  * [87] G. Bevilacqua, M. V. Garzelli, and A. Kardos, “$\text{t}\overline{\text{t}}\text{b}\overline{\text{b}}$ hadroproduction with massive bottom quarks with PowHel”, (2017). arXiv:1709.06915.
  * [88] T. Jezo, J. M. Lindert, N. Moretti, and S. Pozzorini, “New NLOPS predictions for $\ttbar\text{+b-jet}$ production at the LHC”, (2018). arXiv:1802.00426.
  * [89] T. Gleisberg et al., “Event generation with SHERPA 1.1”,  JHEP 02 (2009) 007, 10.1088/1126-6708/2009/02/007, arXiv:0811.4622.
  * [90] F. Cascioli, P. Maierhofer, and S. Pozzorini, “Scattering amplitudes with Open Loops”,  Phys. Rev. Lett. 108 (2012) 111601, 10.1103/PhysRevLett.108.111601, arXiv:1111.5206.
  * [91] P. Skands, S. Carrazza, and J. Rojo, “Tuning PYTHIA 8.1: the Monash 2013 tune”,  Eur. Phys. J. C 74 (2014) 3024, 10.1140/epjc/s10052-014-3024-y, arXiv:1404.5630.
  * [92] CMS Collaboration, “Measurement of differential cross sections for the production of top quark pairs and of additional jets in lepton+jets events from pp collisions at $\sqrt{s}=13\TeV$”, (2018). arXiv:1803.08856. Submitted to Phys. Rev. D.
  * [93] R. J. Barlow and C. Beeston, “Fitting using finite Monte Carlo samples”,  Comput. Phys. Commun. 77 (1993) 219, 10.1016/0010-4655(93)90005-W.
  * [94] J. S. Conway, “Incorporating nuisance parameters in likelihoods for multisource spectra”, in  Proceedings, PHYSTAT 2011 Workshop on Statistical Issues Related to Discovery Claims in Search Experiments and Unfolding, CERN,Geneva, Switzerland 17-20 January 2011.  2011\.  arXiv:1103.0354.
  * [95] T. Junk, “Confidence level computation for combining searches with small statistics”,  Nucl. Instrum. Meth. A 434 (1999) 435, 10.1016/S0168-9002(99)00498-2, arXiv:hep-ex/9902006.
  * [96] A. L. Read, “Presentation of search results: the $\text{CL}_{\mathrm{s}}$ technique”,  J. Phys. G 28 (2002) 2693, 10.1088/0954-3899/28/10/313.
  * [97] G. Cowan, K. Cranmer, E. Gross, and O. Vitells, “Asymptotic formulae for likelihood-based tests of new physics”,  Eur. Phys. J. C 71 (2011) 1554, 10.1140/epjc/s10052-011-1554-0, arXiv:1007.1727. [Erratum: 10.1140/epjc/s10052-013-2501-z].
  * [98] B. Efron, “The Jackknife, the Bootstrap and Other Resampling Plans”.  Society for Industrial and Applied Mathematics, 1982.  10.1137/1.9781611970319.
  * [99] J. D. Bjorken and S. J. Brodsky, “Statistical model for electron-positron annihilation into hadrons”,  Phys. Rev. D 1 (1970) 1416, 10.1103/PhysRevD.1.1416.
  * [100] G. C. Fox and S. Wolfram, “Event shapes in $e^{+}e^{-}$ annihilation”,  Nucl. Phys. B 157 (1979) 543, 10.1016/0550-3213(79)90120-2.

##  .9 BDT and DNN input variables and configuration

All input variables used in the DNNs and BDTs are listed in Tables .9-.9.

Input variables used in the DNNs or BDTs in the different categories of the
single-lepton and dilepton channels. Variables used in a specific multivariate
method and analysis category are denoted by a “$+$” and unused variables by a
“$-$”. (Continued in Tables .9 and .9.)  Variable Definition SL
${(4\,\text{jets},\geq 3\,\text{\cPqb\ tags})}$ SL ${(5\,\text{jets},\geq
3\,\text{\cPqb\ tags})}$ SL ${(\geq 6\,\text{jets},\geq 3\,\text{\cPqb\
tags})}$ DL ${(\geq 4\,\text{jets},3\,\text{\cPqb\ tags})}$ DL ${(\geq
4\,\text{jets},\geq 4\,\text{\cPqb\ tags})}$ $\pt(\text{jet 1})$ of the
highest-jet + + - - - $\eta(\text{jet 1})$ $\eta$ of the highest-jet - + + - -
$d(\text{jet 1})$ tagging discriminant of the highest-jet + + + - -
$\pt(\text{jet 2})$ of the second highest-jet - + - - - $\eta(\text{jet 2})$
$\eta$ of the second highest-jet + + + - - $d(\text{jet 2})$ tagging
discriminant of the second highest-jet + + + - - $\pt(\text{jet 3})$ of the
third highest-jet - + - - - $\eta(\text{jet 3})$ $\eta$ of the third highest-
jet + + + - - $d(\text{jet 3})$ tagging discriminant of the third highest-jet
+ + + - - $\pt(\text{jet 4})$ of the fourth highest-jet + + - - -
$\eta(\text{jet 4})$ $\eta$ of the fourth highest-jet + + + - - $d(\text{jet
4})$ tagging discriminant of the fourth highest-jet + - + - - $\pt(\text{lep
1})$ of the highest-lepton - + + - - $\eta(\text{lep 1})$ $\eta$ of the
highest-lepton + - + - - $d^{\text{avg}}_{\text{j}}$ average tagging
discriminant value of all jets + + + - - $d^{\text{avg}}_{\text{b}}$ average
tagging discriminant value of -tagged jets + + + + +
$d^{\text{avg}}_{\text{non-b}}$ average tagging discriminant value of non--
tagged jets - - - + +
$\tfrac{1}{N_{\text{b}}}\sum^{N_{\text{b}}}_{b}\left(d-d_{\text{b}}^{\text{avg}}\right)^{2}$
squared difference between the tagging discriminant value of a -tagged jet and
the average tagging discriminant values of all -tagged jets, summed over all
-tagged jets + + + - - $d_{\text{j}}^{\text{max}}$ maximal tagging
discriminant value of all jets + + + - - $d_{\text{b}}^{\text{max}}$ maximal
tagging discriminant value of -tagged jets + + + - -
$d_{\text{j}}^{\text{min}}$ minimal tagging discriminant value of all jets + +
+ - - $d_{\text{j}}^{\text{min}}$ minimal tagging discriminant value of
-tagged jets + + + - - $d_{2}$ second highest tagging discriminant value of
all jets + + + - -

Continued from Table .9 and continued in Table .9.  Variable Definition SL
${(4\,\text{jets},\geq 3\,\text{\cPqb\ tags})}$ SL ${(5\,\text{jets},\geq
3\,\text{\cPqb\ tags})}$ SL ${(\geq 6\,\text{jets},\geq 3\,\text{\cPqb\
tags})}$ DL ${(\geq 4\,\text{jets},3\,\text{\cPqb\ tags})}$ DL ${(\geq
4\,\text{jets},\geq 4\,\text{\cPqb\ tags})}$ $N_{\text{b}}(\text{tight})$
number of -tagged jets at a working point with a 0.1% probability of tagging
gluon and light-flavour jets + + + - - BLR likelihood ratio discriminating
between 4 quark jets and 2 quark jets events + + + - -
$\text{BLR}^{\text{trans}}$ transformed BLR defined as
$\ln[\text{BLR}/(1.0-\text{BLR})]$ + + + - - $\Delta
R_{\text{j},\text{j}}^{\text{min}}$ $\Delta R$ between the two closest jets +
+ + - - $\Delta R_{\text{b},\text{b}}^{\text{min}}$ $\Delta R$ between the two
closest -tagged jets + + + - - $\Delta R_{\text{j},\text{j}}^{\text{max}}$
$\Delta R$ between the two jets furthest apart - + - - - $\Delta
R_{\text{b},\text{b}}^{\text{max}}$ $\Delta R$ between the two -tagged jets
furthest apart - - + - - $\Delta\eta_{\text{j},\text{j}}^{\text{max}}$
$\Delta\eta$ between the two jets furthest apart in $\eta$ - - - - +
$\Delta\eta_{\text{b},\text{b}}^{\text{max}}$ $\Delta\eta$ between the two
-tagged jets furthest apart in $\eta$ - - - + +
$\Delta\eta_{\text{b},\text{b}}^{\text{avg}}$ average $\Delta\eta$ between
-tagged jets - - + - - $\Delta R_{\text{b},\text{b}}^{\text{avg}}$ average
$\Delta R$ between -tagged jets - + + - - $\Delta
R_{\text{j},\text{b}}^{\text{avg}}$ average $\Delta R$ between jets of which
at least one is -tagged - - - + - $\Delta
R_{\text{lep},\text{j}}^{\text{min}\Delta R}$ $\Delta R$ between lepton and
closest jet + + - - - $\Delta R_{\text{lep},\text{b}}^{\text{min}\Delta R}$
$\Delta R$ between lepton and closest -tagged jet - + + - -
$m_{\text{lep},\text{b}}^{\text{min}\Delta R}$ mass of lepton and closest
-tagged jet + + + - - $m_{\text{b},\text{b}}^{\text{min}\Delta R}$ mass of
closest -tagged jets + + + - + $m_{\text{j},\text{b}}^{\text{min}\Delta R}$
mass of closest jets of which at least one is -tagged - - - + -
$m_{\text{b},\text{b}}^{\text{max mass}}$ maximal mass of pairs of -tagged
jets - - - + + $\pt{}_{\text{b},\text{b}}^{\text{min}\Delta R}$ combined of
closest -tagged jets - - - + - $\pt{}_{\text{j},\text{b}}^{\text{min}\Delta
R}$ combined of closest jets of which at least one is -tagged - - - - +
$m_{\text{j}}^{\text{avg}}$ average mass of all jets + + + - -
$(m^{2}){}_{\text{b}}^{\text{avg}}$ average squared mass of all -tagged jets +
- + - - $m_{\text{b},\text{b}}^{\text{closest to 125}}$ mass of pair of
-tagged jets closest to 125 - + + - - $N^{\text{j},\text{b}}$ number of pairs
of jets (with at least one -tagged jet) with an invariant mass within 110–140
- - - + + MEM matrix element method discriminant + + + - -

Continued from Table .9.  Variable Definition SL ${(4\,\text{jets},\geq
3\,\text{\cPqb\ tags})}$ SL ${(5\,\text{jets},\geq 3\,\text{\cPqb\ tags})}$ SL
${(\geq 6\,\text{jets},\geq 3\,\text{\cPqb\ tags})}$ DL ${(\geq
4\,\text{jets},3\,\text{\cPqb\ tags})}$ DL ${(\geq 4\,\text{jets},\geq
4\,\text{\cPqb\ tags})}$ $H_{\text{T}}^{\text{j}}$ scalar sum of jet  - + - +
- $H_{\text{T}}^{\text{b}}$ scalar sum of -tagged jet  + + + - -
$A^{\text{j}}$ $\frac{3}{2}\lambda_{3}$ where $\lambda_{i}$ are the
eigenvalues of the momentum tensor built with jets [99] - + + - -
$A^{\text{b}}$ $\frac{3}{2}\lambda_{3}$ where $\lambda_{i}$ are the
eigenvalues of the momentum tensor built with -tagged jets [99] + + + - -
$C^{\text{j}}$ $H_{\text{T}}^{\text{j}}$ divided by the sum of the energies of
all jets - - + - - $C^{\text{b}}$ $H_{\text{T}}^{\text{b}}$ divided by the sum
of the energies of all -tagged jets - - + - + $S^{\text{j}}$
$\frac{3}{2}(\lambda_{2}+\lambda_{3})$ where $\lambda_{i}$ are the eigenvalues
of the momentum tensor built with jets [99] + + + - - $S^{\text{b}}$
$\frac{3}{2}(\lambda_{2}+\lambda_{3})$ where $\lambda_{i}$ are the eigenvalues
of the momentum tensor built with -tagged jets [99] - + + - -
$S^{\text{j}}_{\text{T}}$ $\frac{2\lambda_{2}}{\lambda_{2}+\lambda_{1}}$ where
$\lambda_{i}$ are the eigenvalues of the momentum tensor built with jets [99]
+ + + - - $S^{\text{b}}_{\text{T}}$
$\frac{2\lambda_{2}}{\lambda_{2}+\lambda_{1}}$ where $\lambda_{i}$ are the
eigenvalues of the momentum tensor built with -tagged jets [99] + + + - -
$I^{\text{b}}$ a measure of how spherical or linear in $r-\phi$ space -tagged
jets are in the event - - - + - $H_{2}$ second Fox–Wolfram moment [100] - + -
- - $H_{3}$ third Fox–Wolfram moment [100] + + - - - $H_{3}^{\text{b}}$ third
Fox–Wolfram moment calculated with -tagged jets [100] - - - - + $R_{3}$ ratio
of Fox–Wolfram moments $H_{3}/H_{0}$ [100] - - - + - $H_{4}$ fourth
Fox–Wolfram moment [100] + - + - -

The BDTs employed in the dilepton channel were trained using the stochastic
gradient boost method [78, 36], available as part of the TMVA package [38].
The number of trees ($N_{\text{trees}}$), the learning rate (shrinkage), the
fraction of events used for the training of an individual tree (bagging
fraction), the granularity of the cuts at each node splitting
($N_{\text{cuts}}$), and the number of node splittings per tree (depth) are
listed in Table .9.

Configuration of the BDTs used in the dilepton channel.  Category
$N_{\text{trees}}$ shrinkage bagging fraction $N_{\text{cuts}}$ depth ${(\geq
4\,\text{jets},3\,\text{\cPqb\ tags})}$ 955 0.022 0.42 30 2 ${(\geq
4\,\text{jets},\geq 4\,\text{\cPqb\ tags})}$ 638 0.006 0.41 42 2

The DNNs used in the single-lepton channel comprise two layers with 100 nodes
each in each of the two network stages. Overtraining is suppressed by random
node dropout with a probability of 30% and an L2 weight normalisation factor
of $10^{-5}$. All networks are optimised using the ADAM optimiser with a
learning rate of $10^{-4}$, and the ELU activation function is used to add
non-linearity to the response of the network [77].

##  .10 Pre-fit discriminant shapes (single-lepton channel)

|
---|---
|
|
Figure 12: Final discriminant (DNN) shapes in the single-lepton (SL) channel before the fit to data, in the jet-process categories with ${(4\,\text{jets},\geq 3\,\text{\cPqb\ tags})}$ and (from upper left to lower right) $\ttbar\PH$, $\ttbar\text{+}\bbbar$, $\ttbar\text{+}2\cPqb$, $\ttbar\text{+}\cPqb$, $\ttbar\text{+}\ccbar$, and $\ttbar\text{+}\text{lf}$. The expected background contributions (filled histograms) are stacked, and the expected signal distribution (line), which includes $\PH\to\bbbar$ and all other Higgs boson decay modes, is superimposed. Each contribution is normalised to an integrated luminosity of $35.9\fbinv$, and the signal distribution is additionally scaled by a factor of 15 for better visibility. The hatched uncertainty bands include the total uncertainty of the fit model. The first and the last bins include underflow and overflow events, respectively. The lower plots show the ratio of the data to the background prediction.  |
---|---
|
|
Figure 13: Final discriminant (DNN) shapes in the single-lepton (SL) channel before the fit to data, in the jet-process categories with ${(5\,\text{jets},\geq 3\,\text{\cPqb\ tags})}$ and (from upper left to lower right) $\ttbar\PH$, $\ttbar\text{+}\bbbar$, $\ttbar\text{+}2\cPqb$, $\ttbar\text{+}\cPqb$, $\ttbar\text{+}\ccbar$, and $\ttbar\text{+}\text{lf}$. The expected background contributions (filled histograms) are stacked, and the expected signal distribution (line), which includes $\PH\to\bbbar$ and all other Higgs boson decay modes, is superimposed. Each contribution is normalised to an integrated luminosity of $35.9\fbinv$, and the signal distribution is additionally scaled by a factor of 15 for better visibility. The hatched uncertainty bands include the total uncertainty of the fit model. The first and the last bins include underflow and overflow events, respectively. The lower plots show the ratio of the data to the background prediction.  |
---|---
|
|
Figure 14: Final discriminant (DNN) shapes in the single-lepton (SL) channel
before the fit to data, in the jet-process categories with ${(\geq
6\,\text{jets},\geq 3\,\text{\cPqb\ tags})}$ and (from upper left to lower
right) $\ttbar\PH$, $\ttbar\text{+}\bbbar$, $\ttbar\text{+}2\cPqb$,
$\ttbar\text{+}\cPqb$, $\ttbar\text{+}\ccbar$, and $\ttbar\text{+}\text{lf}$.
The expected background contributions (filled histograms) are stacked, and the
expected signal distribution (line), which includes $\PH\to\bbbar$ and all
other Higgs boson decay modes, is superimposed. Each contribution is
normalised to an integrated luminosity of $35.9\fbinv$, and the signal
distribution is additionally scaled by a factor of 15 for better visibility.
The hatched uncertainty bands include the total uncertainty of the fit model.
The first and the last bins include underflow and overflow events,
respectively. The lower plots show the ratio of the data to the background
prediction.

##  .11 Post-fit discriminant shapes (single-lepton channel)

|
---|---
|
|
Figure 15: Final discriminant (DNN) shapes in the single-lepton (SL) channel after the fit to data, in the jet-process categories with ${(4\,\text{jets},\geq 3\,\text{\cPqb\ tags})}$ and (from upper left to lower right) $\ttbar\PH$, $\ttbar\text{+}\bbbar$, $\ttbar\text{+}2\cPqb$, $\ttbar\text{+}\cPqb$, $\ttbar\text{+}\ccbar$, and $\ttbar\text{+}\text{lf}$. The error bands include the total uncertainty after the fit to data. The first and the last bins include underflow and overflow events, respectively. The lower plots show the ratio of the data to the post-fit background plus signal distribution.  |
---|---
|
|
Figure 16: Final discriminant (DNN) shapes in the single-lepton (SL) channel after the fit to data, in the jet-process categories with ${(5\,\text{jets},\geq 3\,\text{\cPqb\ tags})}$ and (from upper left to lower right) $\ttbar\PH$, $\ttbar\text{+}\bbbar$, $\ttbar\text{+}2\cPqb$, $\ttbar\text{+}\cPqb$, $\ttbar\text{+}\ccbar$, and $\ttbar\text{+}\text{lf}$. The error bands include the total uncertainty after the fit to data. The first and the last bins include underflow and overflow events, respectively. The lower plots show the ratio of the data to the post-fit background plus signal distribution.  |
---|---
|
|
Figure 17: Final discriminant (DNN) shapes in the single-lepton (SL) channel
after the fit to data, in the jet-process categories with ${(\geq
6\,\text{jets},\geq 3\,\text{\cPqb\ tags})}$ and (from upper left to lower
right) $\ttbar\PH$, $\ttbar\text{+}\bbbar$, $\ttbar\text{+}2\cPqb$,
$\ttbar\text{+}\cPqb$, $\ttbar\text{+}\ccbar$, and $\ttbar\text{+}\text{lf}$.
The error bands include the total uncertainty after the fit to data. The first
and the last bins include underflow and overflow events, respectively. The
lower plots show the ratio of the data to the post-fit background plus signal
distribution.

##  .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, M. Dragicevic,
J. Erö, A. Escalante Del Valle, M. Flechl, R. Frühwirth1, V.M. Ghete, J.
Hrubec, M. Jeitler1, N. Krammer, I. Krätschmer, D. Liko, T. Madlener, I.
Mikulec, 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, N. Postiau, E. Starling, L. Thomas, C. Vander Velde, P.
Vanlaer, D. Vannerom, Q. Wang 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, P. David, C.
Delaere, M. Delcourt, B. Francois, A. Giammanco, G. Krintiras, V. Lemaitre, A.
Magitteri, A. Mertens, M. Musich, K. Piotrzkowski, A. Saggio, M. Vidal Marono,
S. Wertz, J. Zobec Centro Brasileiro de Pesquisas Fisicas, Rio de Janeiro,
Brazil
F.L. Alves, G.A. Alves, L. Brito, M. Correa Martins Junior, 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, C. De Oliveira Martins, S.
Fonseca De Souza, H. Malbouisson, D. Matos Figueiredo, M. Melo De Almeida, C.
Mora Herrera, L. Mundim, H. Nogima, W.L. Prado Da Silva, L.J. Sanchez Rosas,
A. Santoro, A. Sznajder, M. Thiel, E.J. Tonelli Manganote3, F. Torres Da Silva
De Araujo, A. Vilela Pereira Universidade Estadual Paulista a, Universidade
Federal do ABC b, São Paulo, Brazil
S. Ahujaa, C.A. Bernardesa, L. Calligarisa, T.R. Fernandez Perez Tomeia, E.M.
Gregoresb, P.G. Mercadanteb, S.F. Novaesa, SandraS. Padulaa, D. Romero Abadb
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. Fang5, X. Gao5, 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, H. Liao, Z. Liu, F. Romeo, S.M. Shaheen6, 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, A. Levin, J. Li, L. Li, Q. Li, 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, 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, M. Kolosova, G. Mavromanolakis, J. Mousa, C. Nicolaou,
F. Ptochos, P.A. Razis, H. Rykaczewski Charles University, Prague, Czech
Republic
M. Finger8, M. Finger Jr.8 Escuela Politecnica Nacional, Quito, Ecuador
E. Ayala Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab
Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
H. Abdalla9, A.A. Abdelalim10,11, A. Mohamed11 National Institute of Chemical
Physics and Biophysics, Tallinn, Estonia
S. Bhowmik, A. Carvalho Antunes De Oliveira, R.K. Dewanjee, K. Ehataht, M.
Kadastik, M. Raidal, C. Veelken Department of Physics, University of Helsinki,
Helsinki, Finland
P. Eerola, 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, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry, C. Leloup,
E. Locci, 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. Abdulsalam12, C. Amendola, I. Antropov, F. Beaudette, P. Busson, C.
Charlot, R. Granier de Cassagnac, I. Kucher, S. Lisniak, A. Lobanov, J. Martin
Blanco, M. Nguyen, C. Ochando, G. Ortona, P. Pigard, R. Salerno, J.B. Sauvan,
Y. Sirois, A.G. Stahl Leiton, A. Zabi, A. Zghiche Université de Strasbourg,
CNRS, IPHC UMR 7178, Strasbourg, France
J.-L. Agram13, J. Andrea, D. Bloch, J.-M. Brom, E.C. Chabert, V. Cherepanov,
C. Collard, E. Conte13, J.-C. Fontaine13, D. Gelé, U. Goerlach, M. Jansová,
A.-C. Le Bihan, N. Tonon, P. Van Hove Centre de Calcul de l’Institut National
de Physique Nucleaire et de Physique des Particules, CNRS/IN2P3, Villeurbanne,
France
S. Gadrat Université de Lyon, Université Claude Bernard Lyon 1, CNRS-IN2P3,
Institut de Physique Nucléaire de Lyon, Villeurbanne, France
S. Beauceron, C. Bernet, G. Boudoul, 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. Popov14, 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. Zhukov14 RWTH
Aachen University, III. Physikalisches Institut A, Aachen, Germany
A. Albert, D. Duchardt, M. Endres, M. Erdmann, T. Esch, R. Fischer, S. Ghosh,
A. Güth, T. Hebbeker, C. Heidemann, K. Hoepfner, H. Keller, S. Knutzen, L.
Mastrolorenzo, M. Merschmeyer, A. Meyer, P. Millet, S. Mukherjee, T. Pook, M.
Radziej, Y. Rath, H. Reithler, M. Rieger, F. Scheuch, A. Schmidt, D. Teyssier
RWTH Aachen University, III. Physikalisches Institut B, Aachen, Germany
G. Flügge, O. Hlushchenko, B. Kargoll, T. Kress, A. Künsken, T. Müller, A.
Nehrkorn, A. Nowack, C. Pistone, O. Pooth, H. Sert, A. Stahl15 Deutsches
Elektronen-Synchrotron, Hamburg, Germany
M. Aldaya Martin, T. Arndt, C. Asawatangtrakuldee, I. Babounikau, K.
Beernaert, O. Behnke, U. Behrens, A. Bermúdez Martínez, D. Bertsche, A.A. Bin
Anuar, K. Borras16, V. Botta, A. Campbell, P. Connor, C. Contreras-Campana, F.
Costanza, V. Danilov, A. De Wit, M.M. Defranchis, C. Diez Pardos, D. Domínguez
Damiani, G. Eckerlin, T. Eichhorn, A. Elwood, E. Eren, E. Gallo17, A. Geiser,
J.M. Grados Luyando, A. Grohsjean, P. Gunnellini, M. Guthoff, M. Haranko, A.
Harb, J. Hauk, H. Jung, M. Kasemann, J. Keaveney, C. Kleinwort, J. Knolle, D.
Krücker, W. Lange, A. Lelek, T. Lenz, K. Lipka, W. Lohmann18, R. Mankel, I.-A.
Melzer-Pellmann, A.B. Meyer, M. Meyer, M. Missiroli, G. Mittag, J. Mnich, V.
Myronenko, S.K. Pflitsch, D. Pitzl, A. Raspereza, A. Saibel, M. Savitskyi, P.
Saxena, P. Schütze, C. Schwanenberger, R. Shevchenko, A. Singh, N. Stefaniuk,
H. Tholen, O. Turkot, A. Vagnerini, G.P. Van Onsem, R. Walsh, Y. Wen, K.
Wichmann, C. Wissing, O. Zenaiev University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, L. Benato, A. Benecke, V. Blobel, M. Centis Vignali, T.
Dreyer, E. Garutti, D. Gonzalez, J. Haller, A. Hinzmann, A. Karavdina, G.
Kasieczka, R. Klanner, R. Kogler, N. Kovalchuk, S. Kurz, V. Kutzner, J. Lange,
D. Marconi, J. Multhaup, M. Niedziela, D. Nowatschin, A. Perieanu, A. Reimers,
O. Rieger, C. Scharf, P. Schleper, S. Schumann, J. Schwandt, J. Sonneveld, H.
Stadie, G. Steinbrück, F.M. Stober, M. Stöver, D. Troendle, A. Vanhoefer, B.
Vormwald Karlsruher Institut fuer Technologie, Karlsruhe, Germany
M. Akbiyik, C. Barth, M. Baselga, S. Baur, E. Butz, R. Caspart, T. Chwalek, F.
Colombo, W. De Boer, A. Dierlamm, K. El Morabit, N. Faltermann, B. Freund, M.
Giffels, M.A. Harrendorf, F. Hartmann15, S.M. Heindl, U. Husemann, F.
Kassel15, I. Katkov14, P. Keicher, S. Kudella, H. Mildner, S. Mitra, M.U.
Mozer, Th. Müller, M. Plagge, G. Quast, K. Rabbertz, M. Schröder, I. Shvetsov,
G. Sieber, H.J. Simonis, R. Ulrich, M. Waßmer, 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, G.
Paspalaki, I. Topsis-Giotis National and Kapodistrian University of Athens,
Athens, Greece
G. Karathanasis, S. Kesisoglou, P. Kontaxakis, A. Panagiotou, N. Saoulidou, E.
Tziaferi, K. Vellidis National Technical University of Athens, Athens, Greece
K. Kousouris, I. Papakrivopoulos, G. Tsipolitis 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. Bartók19, M. Csanad, N. Filipovic, P. Major, M.I. Nagy, G. Pasztor, O.
Surányi, G.I. Veres Wigner Research Centre for Physics, Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath20, Á. Hunyadi, F. Sikler, T.Á. Vámi, V.
Veszpremi, G. Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear
Research ATOMKI, Debrecen, Hungary
N. Beni, S. Czellar, J. Karancsi21, A. Makovec, J. Molnar, Z. Szillasi
Institute of Physics, University of Debrecen, Debrecen, Hungary
P. Raics, Z.L. Trocsanyi, B. Ujvari Indian Institute of Science (IISc),
Bangalore, India
S. Choudhury, J.R. Komaragiri, P.C. Tiwari National Institute of Science
Education and Research, HBNI, Bhubaneswar, India
S. Bahinipati22, C. Kar, P. Mal, K. Mandal, A. Nayak23, D.K. Sahoo22, S.K.
Swain Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, S. Chauhan, R. Chawla, N. Dhingra, R.
Gupta, A. Kaur, A. Kaur, M. Kaur, S. Kaur, R. Kumar, P. Kumari, M. Lohan, A.
Mehta, K. Sandeep, S. Sharma, J.B. Singh, G. Walia University of Delhi, Delhi,
India
A. Bhardwaj, B.C. Choudhary, R.B. Garg, M. Gola, S. Keshri, Ashok Kumar, S.
Malhotra, M. Naimuddin, P. Priyanka, K. Ranjan, Aashaq Shah, R. Sharma Saha
Institute of Nuclear Physics, HBNI, Kolkata, India
R. Bhardwaj24, M. Bharti, R. Bhattacharya, S. Bhattacharya, U. Bhawandeep24,
D. Bhowmik, S. Dey, S. Dutt24, S. Dutta, S. Ghosh, K. Mondal, S. Nandan, A.
Purohit, P.K. Rout, A. Roy, S. Roy Chowdhury, S. Sarkar, M. Sharan, B. Singh,
S. Thakur24 Indian Institute of Technology Madras, Madras, India
P.K. Behera Bhabha Atomic Research Centre, Mumbai, India
R. Chudasama, D. Dutta, V. Jha, V. Kumar, P.K. Netrakanti, L.M. Pant, P.
Shukla Tata Institute of Fundamental Research-A, Mumbai, India
T. Aziz, M.A. Bhat, S. Dugad, G.B. Mohanty, N. Sur, B. Sutar, RavindraKumar
Verma Tata Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, Sa. Jain, S.
Karmakar, S. Kumar, M. Maity25, G. Majumder, K. Mazumdar, N. Sahoo, T.
Sarkar25 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. Chenarani26, E. Eskandari Tadavani, S.M. Etesami26, M. Khakzad, M.
Mohammadi Najafabadi, M. Naseri, F. Rezaei Hosseinabadi, B. Safarzadeh27, 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, M. Incea,b, S. Lezkia,b, G. Maggia,c, M. Maggia,
G. Minielloa,b, S. Mya,b, S. Nuzzoa,b, A. Pompilia,b, G. Pugliesea,c, R.
Radognaa, A. Ranieria, G. Selvaggia,b, A. Sharmaa, L. Silvestrisa, 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, C. Cioccaa, G. Codispotia,b, M. Cuffiania,b, G.M.
Dallavallea, F. Fabbria, A. Fanfania,b, P. Giacomellia, C. Grandia, L.
Guiduccia,b, F. Iemmia,b, S. Marcellinia, G. Masettia, A. Montanaria, F.L.
Navarriaa,b, A. Perrottaa, F. Primaveraa,b,15, 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, A. Di Mattiaa, 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,28, G. Sguazzonia, D. Stroma, L. Viliania INFN Laboratori
Nazionali di Frascati, Frascati, Italy
L. Benussi, S. Bianco, F. Fabbri, D. Piccolo INFN Sezione di Genova a,
Università di Genova b, Genova, Italy
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,15, S. Di
Guidaa,d,15, M.E. Dinardoa,b, S. Fiorendia,b, S. Gennaia, A. Ghezzia,b, P.
Govonia,b, M. Malbertia,b, S. Malvezzia, A. Massironia,b, D. Menascea, L.
Moronia, M. Paganonia,b, D. Pedrinia, 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, A. Di Crescenzoa,b, F. Fabozzia,c, F. Fiengaa,
G. Galatia, A.O.M. Iorioa,b, W.A. Khana, L. Listaa, S. Meolaa,d,15, P.
Paoluccia,15, C. Sciaccaa,b, E. Voevodinaa,b INFN Sezione di Padova a,
Università di Padova b, Padova, Italy, Università di Trento c, Trento, Italy
P. Azzia, N. Bacchettaa, M. Benettonia, A. Bolettia,b, A. Bragagnolo, R.
Carlina,b, P. Checchiaa, P. De Castro Manzanoa, T. Dorigoa, U. Dossellia, F.
Gasparinia,b, U. Gasparinia,b, A. Gozzelinoa, S. Lacapraraa, P. Lujan, M.
Margonia,b, A.T. Meneguzzoa,b, J. Pazzinia,b, 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, G. Bagliesia, L. Bianchinia, T. Boccalia, L.
Borrello, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, G. Fedia, F. Fioria,c,
L. Gianninia,c, A. Giassia, M.T. Grippoa, F. Ligabuea,c, 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, F. Cennaa,b, S. Cometti, M. Costaa,b,
R. Covarellia,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, D. Soldi,
A. Staianoa INFN Sezione di Trieste a, Università di Trieste b, Trieste, Italy
S. Belfortea, V. Candelisea,b, M. Casarsaa, F. Cossuttia, G. Della Riccaa,b,
F. Vazzolera,b, A. Zanettia Kyungpook National University, Daegu, Korea
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. Goh29, T.J. Kim Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, D. Gyun, S. Ha, B. Hong, Y. Jo, K. Lee, K.S. Lee, S.
Lee, J. Lim, S.K. Park, Y. Roh Sejong University, Seoul, Korea
H.S. Kim 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
D. Jeon, 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 Ali30, F. Mohamad Idris31, W.A.T. Wan
Abdullah, M.N. Yusli, Z. Zolkapli Universidad de Sonora (UNISON), Hermosillo,
Mexico
A. Castaneda Hernandez, J.A. Murillo Quijada Centro de Investigacion y de
Estudios Avanzados del IPN, Mexico City, Mexico
H. Castilla-Valdez, E. De La Cruz-Burelo, M.C. Duran-Osuna, I. Heredia-De La
Cruz32, R. Lopez-Fernandez, J. Mejia Guisao, R.I. Rabadan-Trejo, M. Ramirez-
Garcia, G. Ramirez-Sanchez, R Reyes-Almanza, 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, M.I. Asghar, 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. Byszuk33, 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, V. Alexakhin, P. Bunin, M. Gavrilenko, A. Golunov, I. Golutvin,
N. Gorbounov, V. Karjavin, A. Lanev, A. Malakhov, V. Matveev34,35, P. Moisenz,
V. Palichik, V. Perelygin, M. Savina, S. Shmatov, V. Smirnov, N. Voytishin, A.
Zarubin Petersburg Nuclear Physics Institute, Gatchina (St. Petersburg),
Russia
V. Golovtsov, Y. Ivanov, V. Kim36, E. Kuznetsova37, P. Levchenko, V. Murzin,
V. Oreshkin, I. Smirnov, D. Sosnov, V. Sulimov, L. Uvarov, S. Vavilov, A.
Vorobyev Institute for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov,
N. Krasnikov, A. Pashenkov, D. Tlisov, A. Toropin Institute for Theoretical
and Experimental Physics, Moscow, Russia
V. Epshteyn, V. Gavrilov, N. Lychkovskaya, V. Popov, I. Pozdnyakov, G.
Safronov, A. Spiridonov, A. Stepennov, V. Stolin, M. Toms, E. Vlasov, A.
Zhokin Moscow Institute of Physics and Technology, Moscow, Russia
T. Aushev National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
R. Chistov38, M. Danilov38, P. Parygin, D. Philippov, S. Polikarpov38, E.
Tarkovskii P.N. Lebedev Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin35, I. Dremin35, M. Kirakosyan35, S.V. Rusakov, A.
Terkulov Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State
University, Moscow, Russia
A. Baskakov, A. Belyaev, E. Boos, V. Bunichev, M. Dubinin39, L. Dudko, A.
Ershov, V. Klyukhin, O. Kodolova, I. Lokhtin, I. Miagkov, S. Obraztsov, S.
Petrushanko, V. Savrin, A. Snigirev Novosibirsk State University (NSU),
Novosibirsk, Russia
V. Blinov40, T. Dimova40, L. Kardapoltsev40, D. Shtol40, Y. Skovpen40
Institute for High Energy Physics of National Research Centre ’Kurchatov
Institute’, Protvino, Russia
I. Azhgirey, I. Bayshev, S. Bitioukov, D. Elumakhov, A. Godizov, V. Kachanov,
A. Kalinin, D. Konstantinov, P. Mandrik, V. Petrov, R. Ryutin, S.
Slabospitskii, A. Sobol, S. Troshin, N. Tyurin, A. Uzunian, A. Volkov National
Research Tomsk Polytechnic University, Tomsk, Russia
A. Babaev, S. Baidali, V. Okhotnikov University of Belgrade: Faculty of
Physics and VINCA Institute of Nuclear Sciences
P. Adzic41, P. Cirkovic, D. Devetak, M. Dordevic, J. Milosevic Centro de
Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT), Madrid,