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Published for SISSA by Springer

Received: March 4, 2013

Accepted: May 7, 2013

Published: May 28, 2013

Search for the standard model Higgs boson produced

in association with a top-quark pair in pp collisions at

the LHC

The CMS collaboration

E-mail: cms-publication-committee-chair@cern.ch

Abstract: A search for the standard model Higgs boson produced in association with a

top-quark pair is presented using data samples corresponding to an integrated luminosity of

5.0 fb−1 (5.1 fb−1) collected in pp collisions at the center-of-mass energy of 7 TeV (8 TeV).

Events are considered where the top-quark pair decays to either one lepton+jets (tt →
`νqq′bb) or dileptons (tt → `+ν`−νbb), ` being an electron or a muon. The search is

optimized for the decay mode H → bb. The largest background to the ttH signal is

top-quark pair production with additional jets. Artificial neural networks are used to

discriminate between signal and background events. Combining the results from the 7 TeV

and 8 TeV samples, the observed (expected) limit on the cross section for Higgs boson

production in association with top-quark pairs for a Higgs boson mass of 125 GeV is 5.8

(5.2) times the standard model expectation.

Keywords: Hadron-Hadron Scattering, Higgs physics

Open Access, Copyright CERN,

for the benefit of the CMS collaboration

doi:10.1007/JHEP05(2013)145

mailto:cms-publication-committee-chair@cern.ch
http://dx.doi.org/10.1007/JHEP05(2013)145


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Contents

1 Introduction 1

2 The CMS detector 3

3 Data and simulation samples 3

4 Event reconstruction and selection 4

5 Signal extraction 7

6 Systematic uncertainties 12

7 Results 21

8 Summary 22

The CMS collaboration 30

1 Introduction

With the recent observation [1, 2] at the Large Hadron Collider (LHC) of a new, Higgs-

like particle with a mass of approximately 125 GeV, the focus of searches for the standard

model (SM) Higgs boson has shifted to evaluating the consistency of this new particle with

SM expectations. A key component in this effort will be to determine whether the new

particle’s observed couplings to other fundamental particles match the predictions for a

SM Higgs boson. A deviation from expectations could provide hints of physics beyond the

standard model.

In the SM, the dominant production mechanism for the Higgs boson at the LHC

arises from gluon fusion, via the Higgs boson coupling to gluons through a heavy quark

loop. However, with sufficient data, other production mechanisms, such as Higgs boson

production via vector boson fusion or in association with a W boson, Z boson, or tt pair,

should also be observable. Furthermore, there are a number of decay channels available to

a SM Higgs boson with a mass of approximately 125 GeV. Although the dominant decay

mode at this mass is to a pair of bottom quarks, decays to WW, ZZ, ττ , and γγ are

also experimentally accessible. The SM provides precise predictions for these production

and decay rates that depend on the coupling strength of the Higgs boson to the other

fundamental particles of the SM.

To date, the only combinations of production mechanism and decay mode that have

been established at greater than three standard deviation (σ) significance for this newly

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observed particle are direct production, with the new particle decaying either to a pair of

photons or a pair of W or Z bosons. In all three of these cases, the observed rates are

in agreement with SM expectations for Higgs boson production within the experimental

uncertainties. However, establishing the complete consistency of the couplings of this newly

observed particle with SM expectations for the Higgs boson involves measuring the rate of

production across all the various possible production and decay channels discussed above.

The analysis described herein focuses on the search for a Higgs boson produced in

association with a pair of top quarks (ttH production) conducted at the Compact Muon

Solenoid (CMS) experiment. The analysis considers Higgs boson masses between 110 and

140 GeV. The search is optimized for Higgs boson decays to a bottom-quark pair, but we

do not exclude events from other Higgs boson decay modes. The rate at which this process

occurs depends on the largest of the fermionic couplings to the Higgs boson, namely the

couplings to the top and bottom quarks. These two key couplings will be particularly

important in probing the new particle’s consistency with SM expectations.

The ttH vertex is the most challenging one to probe directly. Measuring the rate of

Higgs boson production through the gluon fusion process provides an indirect measurement

of the coupling between the top quark and the Higgs boson because this production mech-

anism is dominated by a top-quark loop that couples the gluons to the Higgs boson [3].

Likewise, the decay of the Higgs boson to two photons receives a significant contribution

from a top-quark loop, although the loop involving W bosons dominates in this process [4].

However, extraction of the coupling between the top quark and the Higgs boson in this

way relies on the assumption that there are no new massive fundamental particles beyond

those of the SM that contribute in the loop. Unless the Higgs boson is very heavy, it will

not decay to top quarks. Therefore, for the mass range most favored for the SM Higgs [5],

and for 125 GeV in particular, ttH production is the only way to probe the ttH vertex in a

model-independent manner [6, 7].

In contrast, there are several processes that can be used to probe the coupling of

this new particle to bottom quarks. Because of the large bb background from multijet

production, it is not experimentally feasible to probe H → bb in Higgs boson production

via gluon fusion. Instead, the search is typically made using associated production involving

either a W or a Z boson (VH production). Although ttH production has a smaller expected

cross section, this signature provides a probe that is complementary to the VH channel:

they both provide information about the coupling between the bottom quark and the Higgs

boson, but the dominant backgrounds are very different, tt + jets production instead of

W + jets production.

An observation of ttH production, depending on the measured properties, might be

consistent with the SM Higgs boson or could indicate something more exotic [8, 9]. Since

the expected SM rates in this channel are very small, a sizeable excess would be clear

evidence for new physics. A previous search at the Tevatron [10], the first such search

conducted at a hadron collider, showed no significant excess over SM expectation.

This paper is organized as follows. Section 2 describes the CMS apparatus. Section 3

describes the data and simulation samples utilized in the analysis, while section 4 discusses

the object identification, event reconstruction and selection. The extraction of the ttH

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signal is discussed in section 5, followed by a description of the impact of systematic

uncertainties encountered in the analysis in section 6. The results of this search are reported

in section 7 and followed by a summary in section 8.

2 The CMS detector

The CMS detector consists of the following main components. A superconducting solenoid

occupies the central region of the CMS detector, providing an axial magnetic field of 3.8 T

parallel to the beam direction. The silicon pixel and strip tracker, the crystal electromag-

netic calorimeter and the brass/scintillator hadron calorimeter are located in concentric

layers within the solenoid. These layers provide coverage out to |η| = 2.5, where pseu-

dorapidity is defined as η = − ln [tan (θ/2)]. A quartz-fiber Cherenkov hadron forward

calorimeter extends further to |η| < 5.2. The CMS experiment uses a right-handed coor-

dinate system, with the origin at the nominal interaction point, the x axis pointing to the

center of the LHC ring, the y axis pointing up (perpendicular to the LHC plane), and the

z axis along the counterclockwise beam direction. The polar angle θ is measured from the

positive z axis and the azimuthal angle φ is measured in the x-y plane in radians. Muons

are detected by gas-ionization detectors embedded in the steel flux return yoke outside the

solenoid. The first level of the CMS trigger system, composed of custom hardware proces-

sors, is designed to select the most interesting events in less than 3µs using information

from the calorimeters and muon detectors. The high-level trigger processor farm further

decreases the event rate to a few hundred Hz for data storage. More details about the

CMS detector can be found in ref. [11].

3 Data and simulation samples

This search is performed with samples of proton-proton collisions at
√
s = 7 TeV and 8 TeV,

collected with the CMS detector in 2011 and 2012, respectively. These data correspond to

a total integrated luminosity of 5.0 fb−1 at 7 TeV and 5.1 fb−1 at 8 TeV.

All background and signal processes are modeled using Monte Carlo (MC) simulations

from MadGraph 5.1.1 [12], pythia 6.4.24 [13], and powheg 1.0 [14] event generators,

depending on the physics process. The MC samples use CTEQ6L1 [15] parton distribution

functions (PDFs) of the proton, except for the powheg samples, which use CTEQ6M. The

ttH signal events are generated using pythia. The main background tt sample is generated

with MadGraph, with matrix elements corresponding to up to three additional partons

which are then matched to parton showers produced by pythia. The additional partons

generated with the tt sample include b and c quarks in addition to light flavored quarks

and gluons. Decays of τ leptons are handled with tauola 2.75 [16]. MadGraph is also

used to simulate ttW, ttZ, W + jets, and Drell-Yan (DY) processes, with up to 4 partons

in the final state. The DY contribution includes all Z/γ∗ → `` processes with the dilepton

invariant mass m`` > 10 GeV. Single-top production is modeled with the next-to-leading

order (NLO) generator powheg combined with pythia. Electroweak diboson processes

(WW, WZ, and ZZ) are simulated using pythia.

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All background and signal process rates are estimated using NLO or higher theoretical

predictions. The ttH cross section [17–24] and Higgs branching fractions [25–28] used in the

analysis have NLO accuracy. The tt and diboson cross sections are calculated at NLO with

mcfm [29–31]. The single-top-quark production rates are normalized to an approximately

next-to-next-to-leading order (NNLO) calculation [32–35]. The W+jets and DY+jets rates

are normalized to inclusive NNLO cross sections from fewz [36, 37]. The ttW and ttZ

rates are normalized to the NLO predictions from refs. [38, 39]. These cross sections are

allowed to vary within their uncertainties in the fit we use to calculate the limit.

Effects from additional pp interactions in the same bunch crossing (pileup) are modeled

by adding simulated minimum-bias events (generated with pythia) to the simulated pro-

cesses. The CMS detector response is simulated using the Geant4 software package [40].

The pileup multiplicity distribution in MC is reweighted to reflect the luminosity profile of

the observed pp collisions. We apply an additional correction factor to account for residual

differences in the jet transverse momentum (pT) spectrum due to pileup; the event-by-event

correction factor is based on the difference between simulation and data in the distribution

of the scalar sum of the transverse momenta of the jets in the event. We include a system-

atic shape uncertainty in association with this correction factor. In addition to correcting

the MC due to pileup, we also apply jet energy resolution corrections [41] and lepton and

trigger efficiency scale factors to the MC events.

4 Event reconstruction and selection

This analysis selects events consistent with the production of a Higgs boson in association

with a top-quark pair (see figure 1). In the SM, the top quark is expected to decay to a

W boson and a bottom quark nearly 100% of the time. Hence different tt decay modes

can be identified according to the subsequent decays of the W bosons. Here we consider

two tt decay modes: the lepton+jets mode (tt → `νqq′bb), where one W boson decays

leptonically, and the dilepton mode (tt→ `+ν`−νbb), where both W bosons do so. For the

lepton+jets case, we select events containing an energetic, isolated, electron or muon, and

at least four energetic jets, two or more of which should be identified as originating from

a b quark (b-tagged) [42]. For the dilepton case, we require a pair of oppositely charged

energetic leptons (two electrons, two muons, or one electron and one muon) and two or

more jets, with at least two of the jets being b-tagged.

Object reconstruction is based on the particle flow (PF) algorithm [43], which combines

the information from all CMS subdetectors to identify and reconstruct individual objects

including muons, electrons, photons, and charged and neutral hadrons produced in an

event. To minimize the impact of pileup, charged particles are required to originate from

the primary vertex, which is identified as the reconstructed vertex with the largest value

of Σp2T, where the summation includes all tracks associated with that vertex. In both

channels, a significant amount of missing transverse energy (Emiss
T ) should be present due

to the presence of neutrinos, however no explicit requirement on the Emiss
T is used in the

event selection. The Emiss
T vector is calculated as the negative of the vectorial sum of the

transverse momenta of all particles. For both channels, we use a common set of criteria

for selecting individual objects (electrons, muons, and jets) which is described below.

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g

g

t̄

t

t

t̄

H

W+

W−

b

b̄

b

b̄

`+

ν`

q̄′, ν̄`

q, `−

Figure 1. A leading-order Feynman diagram for ttH production, illustrating the two top-quark

pair system decay channels considered here, and the H → bb decay mode for which the analysis

is optimized.

In the lepton+jets channel, the data were recorded with triggers requiring the presence

of either a single muon or electron. The trigger muon candidate was required to be isolated

from other activity in the event and to have pT > 24 GeV for both the 2011 and 2012 data-

taking periods. In 2011, the trigger electron candidate was required to have transverse

energy ET > 25 GeV and to be produced in association with at least three jets with

pT > 30 GeV, whereas in 2012, a single-electron trigger with minimum ET threshold of

27 GeV was used. In the dilepton channel, the data were recorded with triggers requiring

any combination of electrons and muons, one lepton with pT > 17 GeV and another with

pT > 8 GeV. The offline object selection detailed below is designed to select events in the

plateau of the trigger efficiency turn-on curve.

Muons are reconstructed using information from the tracking detectors and the muon

chambers [44]. Tight muons must satisfy additional quality criteria based on the number

of hits associated with the muon candidate in the pixel, strip, and muon detectors. For

lepton+jets events, tight muons are required to have pT > 30 GeV and |η| < 2.1 to ensure

the full trigger efficiency. For dilepton events, tight muons are required to have pT > 20 GeV

and |η| < 2.1. Loose muons in both channels are required to have pT > 10 GeV and

|η| < 2.4. The muon isolation is assessed by calculating the scalar sum of the pT of

charged particles from the same primary vertex and neutral particles in a cone of ∆R =√
(∆η)2 + (∆φ)2 = 0.4 around the muon direction, excluding the muon itself; the resulting

sum is corrected for the effects of neutral hadrons from pileup interactions. The ratio of

this corrected isolation sum to the muon pT is the relative isolation of the muon. For tight

muons, the relative isolation is required to be less than 0.12. For loose muons, this ratio

must be less than 0.2.

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Electrons are reconstructed using both calorimeter and tracking information [45]. Any

electron that can be paired with an oppositely charged particle consistent with the conver-

sion of an energetic photon is rejected. Tight electrons in lepton+jets events are required to

have ET > 30 GeV, while in dilepton events they must have ET > 20 GeV. Loose electrons

must have ET > 10 GeV. All electrons are required to have |η| < 2.5. Electrons that fall

into the transition region between the barrel and endcap of the electromagnetic calorimeter

(1.442 < |η| < 1.566) are rejected because the reconstruction of an electron object in this

region is not optimal. The isolation for electrons is calculated in a similar manner to muon

isolation; however, for electrons the isolation sum is calculated in a cone of ∆R = 0.3. In

the same way as for muons, the relative isolation is the ratio of this corrected isolation sum

to the electron ET. Tight electrons must have a relative isolation less than 0.1, while loose

electrons must have a relative isolation less than 0.2.

In both channels of this search, all events are required to contain at least one tight

lepton, either a muon or an electron. The second lepton in the dilepton channel may

be loose or tight, while in the lepton+jets channel events with a second loose lepton are

rejected to ensure the same events do not enter both channels.

Jets are reconstructed by clustering the charged and neutral PF particles using the

anti-kT algorithm with a distance parameter of 0.5 [46, 47]. Particles identified as isolated

muons and electrons are expected to come from W decays and are excluded from the

clustering. Non-isolated muons and electrons are expected to come from b-decays and are

included in the clustering. The momentum of a jet is determined from the vector sum of

all particle momenta in the jet candidate and is scaled according to jet energy corrections,

based on simulation, jet plus photon data events and dijet data events [41]. Charged PF

particles not associated with the primary event vertex are ignored when reconstructing

jets. The neutral component coming from pileup events is removed by applying a residual

energy correction following the area-based procedure described in refs. [48, 49]. In the

lepton+jets channel, we require at least three jets with pT > 40 GeV and a fourth jet with

pT > 30 GeV. In the dilepton analysis, we require at least two jets with pT > 30 GeV. All

jets must have a pseudorapidity in the range |η| < 2.4.

Jets are identified as originating from a b quark using the combined secondary vertex

(CSV) algorithm [42]. This algorithm combines information about the impact parameter

of tracks and reconstructed secondary vertices within the jets in a multivariate algorithm

designed to separate jets containing the decay products of bottom-flavored hadrons from

jets originating from charm quarks, light quarks, or gluons. The CSV algorithm provides

a continuous output discriminant; high values of the CSV discriminant indicate that the

jet is more consistent with being a b jet, while low values indicate the jet is more likely

a light-quark jet. To select b-tagged jets, a selection is placed on the CSV discriminant

distribution such that the efficiency is 70% (20%) for jets originating from a b (c) quark and

the probability of tagging jets originating from light quarks or gluons is 2%. In addition, the

CSV discriminant values for the selected jets are used in the signal extraction as described

in section 5. For MC events, the CSV discriminant values of each jet are adjusted so that

the proportion of b jets, c jets, and light-quark jets of different η and pT values passing each

of three CSV working points (tight, medium, and loose) is the same in data and MC. The

adjustment factor is computed using a linear interpolation between CSV working points.

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Figure 2 shows the jet and b-tagged jet multiplicities for events selected in the lep-

ton+jets channel. For both lepton+jets and dilepton channels, signal ttH events are gen-

erally characterized by having more jets and more tags than the background processes. To

increase the sensitivity of this analysis, we separate the selected events into different cate-

gories based on the number of jets and tags. For lepton+jets events, we use the following

seven categories: ≥6 jets + 2 b-tags, 4 jets + 3 b-tags, 5 jets + 3 b-tags, ≥6 jets + 3 b-tags,

4 jets + 4 b-tags, 5 jets + ≥4 b-tags, and ≥6 jets + ≥4 b-tags. For dilepton events, only

two categories are used: 2 jets + 2 b-tags and ≥3 jets + ≥3 b-tags. Tables 1–3 show the

predicted signal, background, and observed yields in each category for the lepton+jets and

dilepton channels. Background estimates are obtained from MC after the appropriate cor-

rections and scale factors have been applied, as described above. Given the event selection

criteria and the large jet and b-tag multiplicity requirements in the lepton+jets channel,

the background from QCD multijet production is negligible. Uncertainties in signal and

background yields include both statistical and systematic sources. Sources of systematic

uncertainty are described in section 6. In tables 1–3, the tt + jets background is separated

into the tt+bb, tt+cc, and tt+light flavor (lf) components. The categories with higher jet

and tag multiplicities are the most sensitive to signal. We include less sensitive categories

in order to better constrain the background.

The choice of event selection categories outlined above is optimized for the H → bb

decay mode. However, in the higher end of our search range — including mH = 125 GeV

—other decay modes, especially WW and ττ , can have significant standard model branch-

ing fractions. For the purposes of this search, we define any ttH event as signal, regardless

of the Higgs boson decay. For most of the event selection categories defined above, the

contribution from the decay modes other than H → bb is less than 10%. The largest

contribution from the non-bb decay modes arises in the ≥6 jets + 2 b-tags lepton+jets

category where almost 50% of the events come from decay modes other than H → bb. In

that category H → WW dominates the non-bb contribution. With the current optimiza-

tion, the impact of the non-bb decay modes to the analysis sensitivity is negligible as the

contribution from H→ bb in the most sensitive categories is > 95%.

5 Signal extraction

Artificial neural networks (ANNs) [50] are used in all categories of the analysis to further

discriminate signal from background and improve signal sensitivity. Separate ANNs are

trained for each jet-tag category, and the choice of input variables is optimized for each as

well. The ANN input variables considered are related to object kinematics, event shape,

and the discriminant output from the b-tagging algorithm. A total of 24 input variables

has been considered and are listed in column 1 of table 4. The inputs are selected from

a ranked list based on initial separation between signal and background. The separation

of the individual variables is evaluated using a separation benchmark 〈S2〉 [51] defined

as follows:

〈S2〉 =
1

2

∫
(ŷS(y)− ŷB(y))2

ŷS(y) + ŷB(y)
dy, (5.1)

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+lftt c+ctt b+btt

Single t +Vtt EWK

Bkg. Unc. Data  30×H(125) tt

Number of Jets
4 5 6 7 8 9 10

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ve

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ts

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410

-1 = 7 TeV, L = 5.0 fbsCMS                              
2 b-tags≥4 jets + ≥Lepton + 

Number of jets
4 5 6 7 8 9 10

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2 Number of Tags
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2 b-tags≥4 jets + ≥Lepton + 

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2 b-tags≥4 jets + ≥Lepton + 

-1 = 8 TeV, L = 5.1 fbsCMS                              

Number of jets
4 5 6 7 8 9 10

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2 b-tags≥4 jets + ≥Lepton + 

-1 = 8 TeV, L = 5.1 fbsCMS                              

Number of tags
2 3 4 5

D
at

a/
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1

2

Figure 2. Number of jets (left) and number of b-tagged jets (right) in data and simulation for

events with ≥4 jets + ≥2 b-tags in the lepton+jets channel at 7 TeV (top) and 8 TeV (bottom). The

background is normalized to the SM expectation; the uncertainty band (shown as a hatched band

in the stack plot and a green band in the ratio plot) includes statistical and systematic uncertainties

that affect both the rate and shape of the background distributions. The ttH signal (mH = 125 GeV)

is normalized to 30 × SM expectation.

where y is the input variable, and ŷS and ŷB are the signal and background probability

density functions for that input variable in the signal and background samples, respectively.

The maximum number of input variables considered is determined by the statistics in the

simulated samples used for ANN training. The number of variables per category is deter-

mined by reducing the number of variables until the minimum number of variables needed

to maintain roughly the same ANN performance is reached. In the lepton+jets categories,

the use of approximately 10 input variables yields stable performance; using fewer inputs

exhibits degraded discrimination power, and using more inputs exhibits little improvement

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≥6 jets 4 jets 5 jets ≥6 jets 4 jets 5 jets ≥6 jets

2 b-tags 3 b-tags 3 b-tags 3 b-tags 4 b-tags ≥4 b-tags ≥4 b-tags

ttH(125) 6.1 ± 0.9 2.7 ± 1.1 4.0 ± 1.6 3.8 ± 1.6 0.4 ± 0.2 1.1 ± 0.4 1.4 ± 0.6

tt+lf 2040 ± 520 940 ± 170 590 ± 120 346 ± 92 15.7 ± 3.3 22.8 ± 5.3 26.1 ± 7.7

tt + bb 31 ± 17 26 ± 13 28 ± 15 24 ± 13 2.1 ± 1.1 5.7 ± 3.1 8.4 ± 4.8

tt + cc 37.5 ± 9.5 10.1 ± 1.9 12.8 ± 2.7 11.8 ± 3.2 0.5 ± 0.1 1.0 ± 0.3 1.5 ± 0.5

tt V 18.4 ± 3.5 3.2 ± 0.6 4.3 ± 0.8 4.5 ± 0.9 0.2 ± 0.0 0.5 ± 0.1 0.7 ± 0.2

Single t 54.8 ± 7.0 40.0 ± 5.1 21.8 ± 3.3 9.6 ± 1.6 1.2 ± 0.4 1.0 ± 0.3 0.8 ± 0.3

V+jets 41 ± 26 21 ± 11 4.9 ± 4.8 0.5 ± 0.6 0.0 ± 0.0 0.0 ± 0.0 0.1 ± 0.1

Diboson 0.6 ± 0.2 0.7 ± 0.2 0.7 ± 0.2 0.3 ± 0.2 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0

Total bkg 2230 ± 540 1040 ± 180 660 ± 130 396 ± 99 19.7 ± 4.1 30.9 ± 7.3 38 ± 11

Data 2137 1214 736 413 18 37 49

Table 1. Expected event yields for backgrounds (bkg), signal, and number of observed events in

the lepton+jets channel in 7 TeV data.

≥6 jets 4 jets 5 jets ≥6 jets 4 jets 5 jets ≥6 jets

2 b-tags 3 b-tags 3 b-tags 3 b-tags 4 b-tags ≥4 b-tags ≥4 b-tags

ttH(125) 11.7 ± 1.9 3.9 ± 1.8 6.1 ± 2.8 6.9 ± 3.1 0.6 ± 0.3 1.5 ± 0.7 2.5 ± 1.2

tt+lf 3460 ± 940 1320 ± 280 870 ± 210 570 ± 170 18.0 ± 5.1 27.6 ± 8.6 41 ± 15

tt + bb 61 ± 34 35 ± 19 43 ± 24 35 ± 20 2.5 ± 1.7 8.4 ± 5.3 15.4 ± 9.4

tt + cc 62 ± 17 19.6 ± 5.1 25.0 ± 6.9 25.9 ± 7.7 0.6 ± 0.4 0.8 ± 0.9 3.7 ± 1.8

tt V 35.7 ± 7.5 4.5 ± 1.1 6.1 ± 1.4 8.6 ± 2.1 0.1 ± 0.1 0.7 ± 0.2 1.5 ± 0.4

Single t 79 ± 18 56 ± 11 25.6 ± 6.2 10.3 ± 2.9 0.3 ± 0.6 3.1 ± 2.2 1.0 ± 0.6

V+jets 53 ± 40 5.9 ± 6.0 0.8 ± 0.9 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0

Diboson 1.2 ± 0.4 1.8 ± 0.6 0.5 ± 0.2 0.2 ± 0.1 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0

Total bkg 3760 ± 980 1440 ± 300 970 ± 230 650 ± 190 21.5 ± 6.1 41 ± 12 63 ± 21

Data 3503 1646 1116 686 28 56 74

Table 2. Expected event yields for backgrounds (bkg), signal, and number of observed events in

the lepton+jets channel in 8 TeV data.

7 TeV Data 8 TeV Data

2 jets + 2 b-tags ≥3 jets + ≥3 b-tags 2 jets + 2 b-tags ≥3 jets + ≥3 b-tags

ttH(125) 0.5 ± 0.2 2.1 ± 0.9 0.7 ± 0.3 3.3 ± 1.5

tt+lf 3280 ± 590 109 ± 25 4100 ± 780 135 ± 34

tt + bb 6.5 ± 3.4 16.1 ± 8.6 7.6 ± 4.2 25 ± 14

tt + cc 5.1 ± 1.0 7.5 ± 1.8 10.1 ± 2.8 14.1 ± 4.1

tt V 2.6 ± 0.5 2.3 ± 0.5 3.5 ± 0.8 3.8 ± 0.9

Single t 99 ± 11 3.9 ± 0.8 129 ± 18 6.2 ± 2.4

V+jets 810 ± 190 23.5 ± 9.7 830 ± 200 29 ± 13

Diboson 25.8 ± 2.7 0.6 ± 0.1 29.2 ± 3.7 0.7 ± 0.2

Total bkg 4230 ± 660 163 ± 35 5110 ± 860 215 ± 48

Data 4303 185 5406 251

Table 3. Expected event yields for backgrounds (bkg), signal, and number of observed events in

the dilepton channel in 7 TeV and 8 TeV data.

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Lepton+Jets Dilepton

Jets ≥6 4 5 ≥6 4 5 ≥6 2 ≥3

Tags 2 3 3 3 4 ≥4 ≥4 2 ≥3

Jet 1 pT X X X F X
Jet 2 pT X X
Jet 3 pT X X X X
Jet 4 pT X X X X
Njets X
pT(`, Emiss

T , jets) F X X X X X
M(`, Emiss

T , jets) X X X X X
Average M((juntagm , juntagn )) X X
M((jtagm , jtagn )closest) X
M((jtagm , jtagn )best) X
Average ∆R(jtagm , jtagn ) X X X X
Minimum ∆R(jtagm , jtagn ) X X X
∆R(`, jclosest) X X X X
Sphericity X X X
Aplanarity X X
H0 X
H1 X X
H2 X X
H3 F X X
µCSV X X F F F F F X F
(σCSV

n )2 X X X X X
Highest CSV value X
2nd-highest CSV value X X X X X X
Lowest CSV value X X X X X X

Table 4. The ANN inputs for the nine jet-tag categories in the 8 TeV ttH analysis in the lepton+jets

and dilepton channels. The choice of inputs is optimized for each category. Definitions of the

variables are given in the text. The best input variable for each jet-tag category is denoted by F.

in performance in most categories. A similar exercise was done for the dilepton categories.

The choice of input variables for each jet-tag category used in the 8 TeV analysis is sum-

marized in table 4; the input variables for each category in the 7 TeV analysis are very

similar. The input variables used in the ANN can be broken down into several classes, as

detailed below.

The first class of variables are those that are basic kinematic properties of single objects

in the event or combinations of objects. These variables include the pT of the leading

four jets, and the pT and mass of the system defined by the vector sum of the lepton(s)

momenta, the Emiss
T vector, and the momenta of the jets in the event (pT(`, Emiss

T , jets)

and M(`, Emiss
T , jets), respectively), all of which favor larger values for ttH signal than for

the backgrounds. The number of jets is used in the ≥3 jets + ≥3 b-tags category in the

dilepton analysis since ttH signal favors larger jet multiplicity than background.

A related class of variables involves looking at the kinematic properties of pairs of jets.

The H → bb decay produces jets that have a large invariant mass even if the jets fail the

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b-tag selection. Other untagged jets in the event tend to come from hadronic W decay and

initial- or final-state radiation, and tend to have a small invariant mass compared to the

jets from the Higgs boson decay. For this reason, some signal discrimination is provided

by examining the invariant mass of pairs of untagged jets in lepton+jets categories with

six or more jets but fewer than four b-tagged jets.

Likewise, the 6-jet category with four or more tags uses two variables that rely specifi-

cally on the H→ bb hypothesis: the invariant mass of the tagged-jet pair with the smallest

opening angle (M((jtagm , jtagn )closest)), and the “best Higgs mass” (M((jtagm , jtagn )best)), the in-

variant mass constructed from the two tagged jets least likely to be a part of the tt system

as determined by a minimum χ2 search among all the jet, lepton, and Emiss
T combinations

in the event, using the W and top masses as kinematic constraints. The M((jtagm , jtagn )closest)

distribution for both signal and background has a peak near the same value; however, the

distribution is wider in the case of signal, offering some discriminating power. In signal

events, the “best Higgs mass” is highly correlated with the Higgs boson mass. Although

the peak is broadened by events where the wrong jets are associated with the Higgs boson

decay, this variable still provides some power in discriminating signal from background.

The ≥6 jets + ≥4 b-tags uses 11 variables instead of the typical 10 because it was shown

that the addition of the “best Higgs mass” variable, uniquely designed for this jet-tag

category, offers a non-negligible increase in expected ANN performance.

Another class of variables exploits differences in the “shape” of events between signal

and background. In general, production of an extra massive object, in addition to top

quarks tends to make ttH events more spherical in shape, while the background events

are more collimated or have more jet activity. Variables in this class include angular

correlations, like the opening angle between the tagged jets (∆R(jtagm , jtagn )) or between the

lepton and closest jet (∆R(`, jclosest)), where in the dilepton analysis the angle is calculated

with respect to the lepton leading in pT. More complex event shape variables like sphericity

and aplanarity [52], as well as the Fox-Wolfram moments H0, H1, H2, H3 [53], also exhibit

differences between signal and background.

The last class of variables used in the ANN involves the CSV discriminant values of

the tagged jets. The signal events tend to have more b jets than the dominant tt + jets

background. Beyond the simple multiplicity of tagged jets we can, however, exploit the

overall b-jet content of the signal in several ways. For instance, the average and squared-

deviation from this average of the CSV discriminant values for the tagged jets (µCSV,

(σCSV
n )2 for the n-th tagged jet) are powerful variables. Events with genuine b jets will

have higher average CSV discriminant values and the b jets themselves will have CSV

values more tightly clustered around high values than those from light-flavour or charm

jets which are tagged.

Using the procedure discussed above, different variables are chosen for use in each

of the different event selection categories. This is motivated by the fact that although

the tt+jets background is dominant throughout, the kinematics of the events can be very

distinct in different jet multiplicity bins. Similarly, the tagging discriminant of the b jets

clearly is different in events with 2, 3 or ≥4 b-tags. Finally, the overall breakdown of the

tt+jets background into tt + bb, tt + cc and tt+light-flavor is different across the jet-tag

categories, implying different variables will be more effective in some categories than others.

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In nearly all event selection categories, the variables that discriminate best between

signal and background directly involve b-tagging information, such as the average CSV

output value for b-tagged jets. This is natural, since the largest fraction of the backgrounds

in all categories involve events with fewer b jets than ttH generally has. However, when

considering specifically tt + bb, a background very similar to the signal, the b-tagging

information alone is not as powerful, and additional information from kinematic variables

and angular correlations, such as the minimum ∆R between all pairs of b-tagged jets,

become important. Even so, the tt + bb background remains difficult to separate from the

ttH signal.

Figures 3 through 5 show the variables used in the ANN for the 5 jets + 3 b-tags

category (lepton+jets channel) and the 2 jets + 2 b-tags (dilepton channel). The 5 jets +

3 b-tags category is chosen for lepton+jets as a compromise between signal sensitivity and

adequate statistics for display purposes. Also shown, in figure 6, are data-to-simulation

comparisons of the best input variables for each jet-tag category considered in the 8 TeV

analysis. The data-to-simulation ratio plots in figures 3 through 6 show that, within uncer-

tainties, the simulation reproduces well the shape and normalization of the distributions

of the variables used in the ANN before the final maximum likelihood fit is performed (as

discussed in section 7). Correlations between input variables are also well reproduced by

simulation.

For ANN training, we use ttH (mH = 120 GeV) as the signal and tt+jets as the

background, such that there is an equal amount of both for each category. The mass

mH = 120 GeV sample was chosen in the analysis of the 7 TeV data before the observation

of a Higgs-like particle at mH = 125 GeV was announced. This mass point was preserved

in the 8 TeV ANN training for consistency. The signal and background events used to

train an ANN are split in half: one half is used to do the training itself, while the other

is used as an independent test sample to monitor performance during training. The ANN

method used is the “multilayer perceptron”, available as part of the tmva [51] package

in root [54]. A multilayer perceptron is a specific kind of neural network in which the

neurons in each layer only have connections to neurons in the following layer. The network

architecture used here consists of two hidden layers, with N neurons in the first layer and

N − 1 neurons in the second layer, where N is the number of input variables. Standard

tests were completed during ANN training to look for evidence of overtraining; no such

evidence was found in any jet-tag category, providing confidence that our training statistics

were satisfactory given the number of input variables used in each.

The ANN output provides better discrimination between signal and background than

any one of the input variables individually. Figures 7 and 8 show the ANN output for

all the categories of the lepton+jets channel in 7 TeV and 8 TeV data, respectively, and

figures 9 and 10 show output distributions for dilepton events. We use these ANN output

distributions for the signal extraction as described in section 7.

6 Systematic uncertainties

Table 5 lists the systematic uncertainties that affect signal and background yields, the shape

of the ANN output, or both. The effects of these uncertainties are evaluated specifically

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Figure 3. Distributions of the five ANN input variables with rankings 1 through 5, in terms of

separation, for the 5 jets + 3 b-tags category of the lepton+jets channel at 8 TeV. Definitions

of the variables are given in the text. The background is normalized to the SM expectation;

the uncertainty band (shown as a hatched band in the stack plot and a green band in the ratio

plot) includes statistical and systematic uncertainties that affect both the rate and shape of the

background distributions. The ttH signal (mH = 125 GeV) is normalized to ∼150 × SM expectation,

equal to the total background yield, for easier comparison of the shapes.

for each event selection category, and the effects from the same source are treated as

completely correlated across the categories. The impact on the rate is the relative change

in expected yield due to each uncertainty. Some sources of uncertainty affect predicted

yields for all processes in each category uniformly, while in some cases the uncertainty

affects the predicted yield of some processes in certain categories more than others; in the

latter cases the range of the effect on the predicted yield is given across all processes in all

categories. Hence large relative rate changes listed in table 5 can typically be attributed

to processes with small expected yields in a single category that change significantly when

considering a source of uncertainty.

Lepton identification and trigger efficiency uncertainties were found to have a small

impact on the analysis. The uncertainties were estimated by comparing variations in the

difference in performance between data and MC simulation using a high-purity sample of

Z-boson decays. The largest variations were at most 4% for a small fraction of events,

such as electrons at low pT. The analysis conservatively uses 4% uncertainty on the lepton

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Figure 4. Distributions of the five ANN input variables with rankings 6 through 10, in terms

of separation, for the 5 jets + 3 b-tags category of the lepton+jets channel at 8 TeV. Definitions

of the variables are given in the text. The background is normalized to the SM expectation;

the uncertainty band (shown as a hatched band in the stack plot and a green band in the ratio

plot) includes statistical and systematic uncertainties that affect both the rate and shape of the

background distributions. The ttH signal (mH = 125 GeV) is normalized to ∼150 × SM expectation,

equal to the total background yield, for easier comparison of the shapes.

scale overall. To ascertain the effects of the uncertainty on the pileup distribution, the cross

section used to predict the distribution of pileup interactions in MC is varied by 8% from its

nominal value, and the resulting change in the number of pileup interactions is propagated

through the analysis. The systematic uncertainty due to the additional pileup correction,

based on the scalar sum of the pT of the jets, is evaluated by doubling or removing the

correction applied. The uncertainty on the luminosity estimate corresponding to the 7 TeV

dataset is 2.2% [55] and, for the 8 TeV dataset, 4.4% [56].

The uncertainty from the jet energy scale [41] is evaluated by varying the energy scale

for all jets in the signal and background predictions up and down by one standard deviation

as a function of jet pT and η and re-evaluating the yields and ANN shapes of all processes.

Similarly, the uncertainty on the jet energy resolution is obtained by varying the jet energy

resolution correction up and down by one standard deviation, although in this case the

effect on shape is negligible and therefore not included.

The b-tagging scale factor corrects the b-tagging efficiency in simulation to match that

measured in data [42]. The uncertainty on this scale factor is evaluated by varying it up

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Figure 5. Distributions of ANN input variables for the 2 jets + 2 b-tags category of the dilepton

channel at 8 TeV. Definitions of the variables are given in the text. The background is normalized

to the SM expectation; the uncertainty band (shown as a hatched band in the stack plot and a

green band in the ratio plot) includes statistical and systematic uncertainties that affect both the

rate and shape of the background distributions. The ttH signal (mH = 125 GeV) is normalized to

∼7000 × SM expectation, equal to the total background yield, for easier comparison of the shapes.

and down by one standard deviation and the new CSV output value corresponding to that

uncertainty is recalculated. This new CSV value is used to determine both the number

of tags associated with that systematic and the new shape of variables that use the CSV

output, such as the average CSV value for b-tagged jets. This uncertainty affects both

rate and shape estimates. Since the b-tagging scale factor uncertainty affects the ANN

shape differently for events with different number of jets or number of b-tagged jets, we

conservatively assume no correlations among all the categories.

We account for the effect of background MC statistics in our analysis using the ap-

proach described in [57, 58]. To make the limit computation more efficient and stable, we

do not evaluate this uncertainty for any bin in the ANN shapes for which the MC statistical

uncertainty is negligible compared to the data statistics or where there is no appreciable

contribution from signal. In total, there are 64 nuisance parameters used to describe the

MC statistics for the 8 TeV results, but only five are needed for 7 TeV, due to the larger

MC statistics available for those samples. Tests show that the effect of neglecting bins as

described above is smaller than 5%.

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Figure 6. Input variables that give the best signal-background separation power for each of the

lepton + jets and dilepton jet, b-tag categories used in the analysis at 8 TeV. Definitions of the

variables are given in the text. The background is normalized to the SM expectation; the uncertainty

band (shown as a hatched band in the stack plot and a green band in the ratio plot) includes

statistical and systematic uncertainties that affect both the rate and shape of the background

distributions. The ttH signal (mH = 125 GeV) is normalized to ∼25–7000 × SM expectation, equal

to the total background yield for that category, for easier comparison of the shapes.

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Figure 7. The distributions of the ANN output for lepton+jets events at 7 TeV in the various

analysis categories. The top, middle, and bottom rows are events with 4, 5, and ≥6 jets, respectively,

while the left, middle, and right-hand columns are events with 2, 3, and ≥4 b-tags, respectively.

Background-like events have a low ANN output value. Signal-like events have a high ANN output

value. The background is normalized to the SM expectation; the uncertainty band (shown as a

hatched band in the stack plot and a green band in the ratio plot) includes statistical and systematic

uncertainties that affect both the rate and shape of the background distributions. The ttH signal

(mH = 125 GeV) is normalized to 30 × SM expectation.

Theoretical uncertainties on the cross sections used to predict the rates of various

processes are propagated to the yield estimates. All rates are estimated using cross sections

of at least NLO accuracy, which have uncertainties arising primarily from PDFs and the

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Figure 8. The distributions of the ANN output for lepton+jets events at 8 TeV in the various

analysis categories. The top, middle and, bottom rows are events with 4, 5, and ≥6 jets, respectively,

while the left, middle, and right-hand columns are events with 2, 3, and ≥4 b-tags, respectively.

Background-like events have a low ANN output value. Signal-like events have a high ANN output

value. The background is normalized to the SM expectation; the uncertainty (shown as a hatched

band in the stack plot and a green band in the ratio plot) includes statistical and systematic

uncertainties that affect both the rate and shape of the background distributions. The ttH signal

(mH = 125 GeV) is normalized to 30 × SM expectation.

choice of factorization and renormalization scales. The cross section uncertainties are each

separated into their PDF and scale components and correlated where appropriate between

processes. For example, the PDF uncertainty for processes originating primarily from

gluon-gluon initial states, e.g., tt and ttH production, are treated as 100% correlated.

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Figure 9. The distributions of the ANN output for dilepton events at 7 TeV in the various analysis

categories. The left plot shows events with 2 jets + 2 b-tags and right plot shows events with

≥3 jets + ≥3 b-tags. The background is normalized to the SM expectation; the uncertainty (shown

as a hatched band in the stack plot and a green band in the ratio plot) band includes statistical and

systematic uncertainties that affect both the rate and shape of the background distributions. The

ttH signal (mH = 125 GeV) is normalized to 300 or 30 × SM expectation for the 2 jets + 2 b-tags

and the ≥3 jets + ≥3 b-tags categories, respectively.

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Figure 10. The distributions of the ANN output for dilepton events at 8 TeV in the various

analysis categories. The left plot shows events with 2 jets + 2 b-tags and right plot shows events

with ≥3 jets + ≥3 b-tags. The background is normalized to the SM expectation; the uncertainty

(shown as a hatched band in the stack plot and a green band in the ratio plot) includes statistical

and systematic uncertainties that affect both the rate and shape of the background distributions.

The ttH signal (mH = 125 GeV) is normalized to 300 or 30 × SM expectation for the 2 jets +

2 b-tags and the ≥3 jets + ≥3 b-tags categories, respectively.

In addition, for the tt + jets (including tt + bb and tt + cc) and the V+jets processes,

the inclusive NLO or better cross section predictions are extrapolated to exclusive rates for

particular jet or tag categories using the MadGraph tree-level matrix element generator

matched to the pythia parton shower MC program. Although MadGraph incorporates

contributions from higher-order diagrams, because it does so only at tree-level, it is subject

to fairly large uncertainties arising from the choice of scale. These uncertainties are evalu-

ated using samples for which the factorization and renormalization scales have been varied

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Source Rate Uncertainty Shape Remarks

Luminosity (7 TeV) 2.2% No All signal and backgrounds

Luminosity (8 TeV) 4.4% No All signal and backgrounds

Lepton ID/Trig 4% No All signal and backgrounds

Pileup 1% No All signal and backgrounds

Additional Pileup Corr. — Yes All signal and backgrounds

Jet Energy Resolution 1.5% No All signal and backgrounds

Jet Energy Scale 0–60% Yes All signal and backgrounds

b-Tag SF (b/c) 0–33.6% Yes All signal and backgrounds

b-Tag SF (mistag) 0–23.5% Yes All signal and backgrounds

MC Statistics — Yes All backgrounds

PDF (gg) 9% No For gg initiated processes (tt, ttZ, ttH)

PDF (qq) 4.2–7% No For qq initiated processes (ttW, W, Z).

PDF (qg) 4.6% No For qg initiated processes (single top)

QCD Scale (ttH) 15% No For NLO ttH prediction

QCD Scale (tt) 2–12% No For NLO tt and single top predictions

QCD Scale (V) 1.2–1.3% No For NNLO W and Z prediction

QCD Scale (VV) 3.5% No For NLO diboson prediction

Madgraph Scale (tt) 0–20% Yes tt + jets/bb/cc uncorrelated. Varies by jet bin.

Madgraph Scale (V) 20–60% No Varies by jet bin.

tt + bb 50% No Only tt + bb.

Table 5. Summary of the systematic uncertainties considered on the inputs to the limit calcula-

tion. Except where noted, each row in this table will be treated as a single, independent nuisance

parameter.

up and down by a factor of two. The rate uncertainty arising from this source varies with

the number of additional jets in the production diagram, and is larger for events with more

jets. The effect of scale variations on the ANN output shape is also included for the tt+jets

sample. Scale variations are treated as uncorrelated for the tt+light flavour, tt + bb, and

tt + cc components to cover the uncertainty in the relative yields of those processes; the

impact on the ANN output shape from scale variation in the V+jets processes is neglected,

since this contribution is small in most categories. The scale variations for W + jets and

Z + jets are treated as correlated with each other, but uncorrelated with tt + jets.

As the background due to the tt + bb contribution is very similar to the signal, the

uncertainty on its rate and shape will have a substantial impact on our search. Due to the

lack of more accurate higher order theoretical predictions for this process, we estimated

this background and assessed its uncertainty based on the inclusive tt sample and the

most important contribution to the uncertainty comes from the factorization and renor-

malization scale systematics. Neither control region studies nor higher-order theoretical

calculations [59] can currently constrain the normalization of the tt + bb contribution to

better than 50% accuracy. Therefore, to be conservative, an extra 50% rate uncertainty is

assigned to tt + bb for both 7 TeV and 8 TeV.

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7 Results

A maximum likelihood fit is performed on the ANN output distributions from the nine

jet-tag categories considered in the analysis. We consider the model including the SM

backgrounds and a Higgs boson signal, as well as a model with only SM backgrounds but

no Higgs boson signal. As we currently lack sensitivity to detect a SM Higgs boson signal,

and observe no significant excess in the data, we focus here on setting 95% confidence level

(CL) upper limits on the possible presence of a SM-like signal.

The statistical methodology employed by this analysis is identical to that used for other

CMS searches [2, 60, 61]. In brief, we use a modified frequentist CLs [62, 63] approach in

which the test statistic involves the ratio of the likelihood functions constructed from the

background expectations plus the SM Higgs boson signal scaled by an arbitrary parameter

µ, where µ ≥ 0. The parameter µ = σ/σSM is the ratio of the cross section of our signal

process (σ) to the expected SM Higgs boson cross section (σSM). The likelihood function

describes the expected yield of signal and background in bins of the ANN output for each

event selection category. The systematic uncertainties described in section 6 are incorpo-

rated into the likelihood by means of nuisance parameters that affect each background’s

rate, shape or both. Shape variations are handled by means of template morphing. A

vertical template morphing approach is used where the shapes are smoothly interpolated

between the ±1σ varied shapes and linearly extrapolated outside that region. This is the

standard template morphing approach used by all CMS Higgs analyses. As appropriate for

the frequentist approach taken here, the nuisance parameters are profiled during the limit

extraction. The nuisance parameter correlations are implemented in a way that accounts

for event migrations between the selection categories. Furthermore, in cases involving

shape systematics, where high-statistics, background-rich categories might overconstrain

certain systematic effects in the lower-statistics, higher-sensitivity categories, we take the

approach of decorrelating the nuisance parameters to avoid overly aggressive constraints.

When combining the results from the 7 TeV and 8 TeV datasets, the proper correlation

in systematic effects must be represented in the nuisance parameter choices. Given that for

all theoretical predictions and many experimental uncertainties, exactly the same calcula-

tion or calibration is applied to the 7 TeV and 8 TeV datasets, the associated systematic

uncertainties are treated as completely correlated and a single nuisance parameter is used

to implement the effect. There are two exceptions to this approach. The luminosity is

evaluated separately for the two analyses and the dominant uncertainties are largely inde-

pendent, so the luminosity uncertainty is treated as uncorrelated between 7 TeV and 8 TeV.

Furthermore, as separate MC samples are used for the two datasets, the MC statistical

uncertainties are treated as uncorrelated between the two datasets.

Background-dominated categories are used to constrain the fitted background contri-

butions in the signal-enhanced categories. The prediction from the fit for the composition

of the selected sample in each category more accurately describes the data than the predic-

tion directly from simulation, and the uncertainties on the final composition are reduced.

The resulting distributions are driven by the shape from tt+light flavor, the dominant

background in each category. No significant excesses of data above the background-only

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predictions are observed, and we use our statistical treatment to extract upper limits on

the amount of ttH production consistent with our data. Figure 11 shows the 95% CL upper

limit on the ratio µ of the ttH cross section with respect to that predicted by the SM as a

function of mH for the 7 TeV and 8 TeV samples separately, combining both lepton+jets and

dilepton channels in each dataset. Figure 12 shows the upper limit obtained by combining

both data samples. Table 6 shows the expected and observed limits for 7 TeV, 8 TeV, and

combined analysis, using both the lepton+jets and the dilepton channels. The expected

limit is extracted from the background-only hypothesis with no Higgs signal present. In

addition to the median expected limit, the bands that contain 68% (1 standard deviation)

and 95% (2 standard deviations) around the median are also quoted. The median expected

limit for a Higgs boson mass of 125 GeV is 5.2× σSM while the observed limit is 5.8× σSM.

As a cross check, we extracted the limit using the best single variable according to

table 4 and plotted in figure 6 instead of the ANN output. Otherwise, the analysis was

performed in exactly the same way as the version based on the ANN, including the event

selection categories, systematic uncertainties, and treatment of the nuisance parameters.

The resulting median expected limit, for a Higgs boson mass of 125 GeV is 6.6 × σSM,

approximately 27% higher than the limit obtained with the ANN. The primary reason for

this decrease in sensitivity is the loss of separating power and the increased susceptibility

to individual systematic effects coming from using fewer variables. The observed limit

obtained using the best single variable analysis is 10.4×σSM, which is beyond the 68% CL

range (on µ) of the expected ([5.0, 9.2]) but within the 95% CL range ([4.0, 12.7]).

8 Summary

A search for the standard model Higgs boson produced in association with a top-quark

pair has been performed at the CMS experiment using data samples corresponding to an

integrated luminosity of 5.0 fb−1 (5.1 fb−1) collected in pp collisions at the center-of-mass

energy of 7 TeV (8 TeV). Events are considered where the top-quark pair decays to either

one lepton+jets (tt → `νqq′bb) or dileptons (tt → `+ν`−νbb), ` being an electron or a

muon. The search has been optimized for the decay mode H→ bb, however sensitivity to

other decay modes has been preserved. Artificial neural networks are used to discriminate

between signal and background events. Combining the results from the 7 TeV and 8 TeV

samples, the observed (expected) limit on the cross section for Higgs boson production in

association with top-quark pairs for a Higgs boson mass of 125 GeV is 5.8 (5.2) times the

standard model expectation. This is the first such search at the LHC.

Acknowledgments

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 ad-

dition, 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

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Figure 11. The observed and expected 95% CL upper limits on the signal strength parameter

µ = σ/σSM for lepton+jets and dilepton channels combined using the 2011 dataset at 7 TeV (above)

and the 2012 dataset at 8 TeV (below).

operation of the LHC and the CMS detector provided by the following funding agencies:

the Austrian Federal Ministry of Science and Research; 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, Youth

and Science; CERN; the Chinese Academy of Sciences, Ministry of Science and Technol-

ogy, and National Natural Science Foundation of China; the Colombian Funding Agency

(COLCIENCIAS); the Croatian Ministry of Science, Education and Sport; the Research

Promotion Foundation, Cyprus; the Ministry of Education and Research, Recurrent fi-

nancing contract SF0690030s09 and European Regional Development Fund, Estonia; the

Academy of Finland, Finnish Ministry of Education and Culture, and Helsinki Institute of

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7 TeV Lepton+Jets and Dilepton

Expected

mH Observed Median 68% CL Range 95% CL Range

110 GeV 4.1 4.4 [3.3, 6.3] [2.7, 8.7]

115 GeV 4.7 5.0 [3.7, 7.1] [2.9, 10.0]

120 GeV 5.5 5.8 [4.3, 8.2] [3.4, 11.4]

125 GeV 6.2 6.9 [5.2, 9.7] [4.1, 13.6]

130 GeV 7.3 8.8 [6.5, 12.5] [5.0, 17.3]

135 GeV 8.8 10.8 [7.9, 15.4] [6.3, 21.3]

140 GeV 10.4 14.2 [10.2, 20.0] [8.2, 27.8]

8 TeV Lepton+Jets and Dilepton

Expected

mH Observed Median 68% CL Range 95% CL Range

110 GeV 5.6 3.3 [2.4, 4.7] [2.0, 6.9]

115 GeV 6.6 4.1 [3.0, 5.8] [2.5, 8.1]

120 GeV 8.2 5.1 [3.8, 7.1] [3.1, 10.0]

125 GeV 8.7 5.7 [4.3, 8.2] [3.5, 11.3]

130 GeV 10.4 6.9 [5.2, 9.8] [4.2, 13.9]

135 GeV 13.7 9.4 [7.1, 13.1] [5.7, 18.3]

140 GeV 15.2 11.3 [8.5, 16.2] [6.8, 22.5]

7 TeV + 8 TeV Lepton+Jets and Dilepton combined

Expected

mH Observed Median 68% CL Range 95% CL Range

110 GeV 4.0 3.2 [2.4, 4.6] [1.8, 6.5]

115 GeV 4.5 3.8 [2.8, 5.4] [2.2, 7.5]

120 GeV 5.5 4.5 [3.3, 6.4] [2.7, 8.9]

125 GeV 5.8 5.2 [3.7, 7.3] [2.9, 10.1]

130 GeV 6.8 6.5 [4.8, 9.2] [3.6, 12.9]

135 GeV 8.3 8.4 [6.1, 11.9] [4.8, 16.3]

140 GeV 8.6 10.4 [7.5, 14.9] [5.9, 20.5]

Table 6. Observed and expected 95% CL upper limits on µ = σ/σSM for the SM Higgs boson in

the lepton+jets and dilepton channels combined, using the 7 TeV dataset, 8 TeV dataset, and both

datasets combined.

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 Scientific Research Foundation, and

National Office for Research and Technology, Hungary; the Department of Atomic En-

ergy 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 Korean Ministry of Education, Science and Technol-

ogy and the World Class University program of NRF, Republic of Korea; the Lithuanian

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 (GeV)Hm
110 115 120 125 130 135 140

S
M

σ/σ
95

%
 C

L 
lim

it 
on

 

0

2

4

6

8

10

12

14

16

18

20 Observed 

σ 1±Expected 

σ 2±Expected 

Lepton+Jets and Dilepton -1 = 8 TeV, L = 5.1 fbs; -1 = 7 TeV, L = 5.0 fbsCMS  

Figure 12. Using the 2011+2012 datasets, the observed and expected 95% CL upper limits on the

signal strength parameter µ = σ/σSM for lepton+jets and dilepton channels combined.

Academy of Sciences; the Mexican Funding Agencies (CINVESTAV, CONACYT, SEP,

and UASLP-FAI); the Ministry of Science and Innovation, New Zealand; the Pakistan

Atomic Energy Commission; the Ministry of Science and Higher Education and the Na-

tional Science Centre, Poland; the Fundação para a Ciência e a Tecnologia, Portugal; JINR

(Armenia, Belarus, Georgia, Ukraine, Uzbekistan); the Ministry of Education and Science

of the Russian Federation, the Federal Agency of Atomic Energy of the Russian Federa-

tion, Russian Academy of Sciences, and the Russian Foundation for Basic Research; the

Ministry of Science and Technological Development of Serbia; the Secretaŕıa de Estado

de Investigación, Desarrollo e Innovación and Programa Consolider-Ingenio 2010, Spain;

the Swiss Funding Agencies (ETH Board, ETH Zurich, PSI, SNF, UniZH, Canton Zurich,

and SER); the National Science Council, Taipei; the Thailand Center of Excellence in

Physics, the Institute for the Promotion of Teaching Science and Technology of Thailand

and the National Science and Technology Development Agency of Thailand; the Scientific

and Technical Research Council of Turkey, and Turkish Atomic Energy Authority; 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 (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 Ministry of Education, Youth and Sports (MEYS) of Czech Republic; the Council of

Science and Industrial Research, India; the Compagnia di San Paolo (Torino); and the

HOMING PLUS programme of Foundation for Polish Science, cofinanced from European

Union, Regional Development Fund.

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Open Access. This article is distributed under the terms of the Creative Commons

Attribution License which permits any use, distribution and reproduction in any medium,

provided the original author(s) and source are credited.

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The CMS collaboration

Yerevan Physics Institute, Yerevan, Armenia

S. Chatrchyan, V. Khachatryan, A.M. Sirunyan, A. Tumasyan

Institut für Hochenergiephysik der OeAW, Wien, Austria

W. Adam, T. Bergauer, M. Dragicevic, J. Erö, C. Fabjan1, M. Friedl, R. Frühwirth1,

V.M. Ghete, N. Hörmann, J. Hrubec, M. Jeitler1, W. Kiesenhofer, V. Knünz,

M. Krammer1, I. Krätschmer, D. Liko, I. Mikulec, D. Rabady2, B. Rahbaran, C. Rohringer,

H. Rohringer, R. Schöfbeck, J. Strauss, A. Taurok, W. Treberer-treberspurg, W. Wal-

tenberger, C.-E. Wulz1

National Centre for Particle and High Energy Physics, Minsk, Belarus

V. Mossolov, N. Shumeiko, J. Suarez Gonzalez

Universiteit Antwerpen, Antwerpen, Belgium

S. Alderweireldt, M. Bansal, S. Bansal, T. Cornelis, E.A. De Wolf, X. Janssen, A. Knutsson,

S. Luyckx, L. Mucibello, S. Ochesanu, B. Roland, R. Rougny, H. Van Haevermaet, P. Van

Mechelen, N. Van Remortel, A. Van Spilbeeck

Vrije Universiteit Brussel, Brussel, Belgium

F. Blekman, S. Blyweert, J. D’Hondt, R. Gonzalez Suarez, A. Kalogeropoulos, J. Keaveney,

M. Maes, A. Olbrechts, S. Tavernier, W. Van Doninck, P. Van Mulders, G.P. Van Onsem,

I. Villella

Université Libre de Bruxelles, Bruxelles, Belgium

B. Clerbaux, G. De Lentdecker, V. Dero, A.P.R. Gay, T. Hreus, A. Léonard, P.E. Marage,

A. Mohammadi, T. Reis, L. Thomas, C. Vander Velde, P. Vanlaer, J. Wang

Ghent University, Ghent, Belgium

V. Adler, K. Beernaert, L. Benucci, A. Cimmino, S. Costantini, G. Garcia, M. Grunewald,

B. Klein, J. Lellouch, A. Marinov, J. Mccartin, A.A. Ocampo Rios, D. Ryckbosch,

M. Sigamani, N. Strobbe, F. Thyssen, M. Tytgat, S. Walsh, E. Yazgan, N. Zaganidis

Université Catholique de Louvain, Louvain-la-Neuve, Belgium

S. Basegmez, G. Bruno, R. Castello, L. Ceard, C. Delaere, T. du Pree, D. Favart,

L. Forthomme, A. Giammanco3, J. Hollar, V. Lemaitre, J. Liao, O. Militaru, C. Nuttens,

D. Pagano, A. Pin, K. Piotrzkowski, A. Popov4, M. Selvaggi, J.M. Vizan Garcia

Université de Mons, Mons, Belgium

N. Beliy, T. Caebergs, E. Daubie, G.H. Hammad

Centro Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil

G.A. Alves, M. Correa Martins Junior, T. Martins, M.E. Pol, M.H.G. Souza

Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil

W.L. Aldá Júnior, W. Carvalho, J. Chinellato5, A. Custódio, E.M. Da Costa, D. De Jesus

Damiao, C. De Oliveira Martins, S. Fonseca De Souza, H. Malbouisson, M. Malek, D. Matos

– 30 –



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Figueiredo, L. Mundim, H. Nogima, W.L. Prado Da Silva, A. Santoro, L. Soares Jorge,

A. Sznajder, E.J. Tonelli Manganote5, A. Vilela Pereira

Universidade Estadual Paulista a, Universidade Federal do ABC b, São Paulo,

Brazil

T.S. Anjosb, C.A. Bernardesb, F.A. Diasa,6, T.R. Fernandez Perez Tomeia, E.M. Gregoresb,

C. Laganaa, F. Marinhoa, P.G. Mercadanteb, S.F. Novaesa, Sandra S. Padulaa

Institute for Nuclear Research and Nuclear Energy, Sofia, Bulgaria

V. Genchev2, P. Iaydjiev2, S. Piperov, M. Rodozov, S. Stoykova, G. Sultanov, V. Tcholakov,

R. Trayanov, M. Vutova

University of Sofia, Sofia, Bulgaria

A. Dimitrov, R. Hadjiiska, V. Kozhuharov, L. Litov, B. Pavlov, P. Petkov

Institute of High Energy Physics, Beijing, China

J.G. Bian, G.M. Chen, H.S. Chen, C.H. Jiang, D. Liang, S. Liang, X. Meng, J. Tao,

J. Wang, X. Wang, Z. Wang, H. Xiao, M. Xu

State Key Laboratory of Nuclear Physics and Technology, Peking University,

Beijing, China

C. Asawatangtrakuldee, Y. Ban, Y. Guo, Q. Li, W. Li, S. Liu, Y. Mao, S.J. Qian, D. Wang,

L. Zhang, W. Zou

Universidad de Los Andes, Bogota, Colombia

C. Avila, C.A. Carrillo Montoya, J.P. Gomez, B. Gomez Moreno, J.C. Sanabria

Technical University of Split, Split, Croatia

N. Godinovic, D. Lelas, R. Plestina7, D. Polic, I. Puljak

University of Split, Split, Croatia

Z. Antunovic, M. Kovac

Institute Rudjer Boskovic, Zagreb, Croatia

V. Brigljevic, S. Duric, K. Kadija, J. Luetic, D. Mekterovic, S. Morovic, L. Tikvica

University of Cyprus, Nicosia, Cyprus

A. Attikis, G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos, P.A. Razis

Charles University, Prague, Czech Republic

M. Finger, M. Finger Jr.

Academy of Scientific Research and Technology of the Arab Republic of Egypt,

Egyptian Network of High Energy Physics, Cairo, Egypt

Y. Assran8, A. Ellithi Kamel9, A.M. Kuotb Awad10, M.A. Mahmoud10, A. Radi11,12

National Institute of Chemical Physics and Biophysics, Tallinn, Estonia

M. Kadastik, M. Müntel, M. Murumaa, M. Raidal, L. Rebane, A. Tiko

Department of Phy