HIG-17-022

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HIG-17-022

# Search for $\ttbar\PH$ production in the all-jet final state in proton-
proton collisions at $\sqrt{s}=13\TeV$

###### Abstract

A search is presented for the associated production of a Higgs boson with a
top quark pair in the all-jet final state. Events containing seven or more
jets are selected from a sample of proton-proton collisions at
$\sqrt{s}=13\TeV$ collected with the CMS detector at the LHC in 2016,
corresponding to an integrated luminosity of $35.9\fbinv$. To separate the
$\ttbar\PH$ signal from the irreducible $\ttbar\text{+}\bbbar$ background, the
analysis assigns leading order matrix element signal and background
probability densities to each event. A likelihood-ratio statistic based on
these probability densities is used to extract the signal. The results are
provided in terms of an observed $\ttbar\PH$ signal strength relative to the
standard model production cross section $\mu=\sigma/\sigma_{\mathrm{SM}}$,
assuming a Higgs boson mass of 125. The best fit value is $\hat{\mu}=0.9\pm
0.7\stat\pm 1.3\syst=0.9\pm 1.5\,(\text{tot})$, and the observed and expected
upper limits are, respectively, $\mu<3.8$ and $<3.1$ at 95% confidence levels.

##  0.1 Introduction

Since the observation of a new boson with a mass of approximately 125 [1, 2,
3], a major focus of the program of the ATLAS and CMS experiments at the CERN
LHC has been to measure the properties of this particle. To date, all
measurements remain consistent with the standard model (SM) Higgs boson ()
hypothesis. The new particle has been observed in $\gamma\gamma$, $\cPZ\cPZ$,
$\PW\PW$, and $\tau\tau$ decays [4, 5, 6, 7, 8, 9, 10], and evidence has been
reported in final states [11, 12]. The observed production and decay rates and
spin-parity properties are compatible with SM expectations [13, 14, 15, 16,
17] and with the measured mass of $m_{\PH}=125.26\pm 0.21\GeV$ [18].

This paper describes a search for the SM Higgs boson produced in association
with a pair of top quarks ($\ttbar\PH$ production) in all-jet events, where
the Higgs boson decays exclusively to , and each top quark decays to a bottom
quark and a boson, which in turn decays to two quarks. The analysis uses
$\sqrt{s}=13\TeV$ proton-proton () collision data collected by the CMS
detector in 2016, corresponding to an integrated luminosity of $35.9\fbinv$.

In the context of probing the properties of the Higgs boson, the $\ttbar\PH$
process is interesting for several reasons. Because of the large top quark
mass of $m_{\cPqt}=172.44\pm 0.48\GeV$ [19], the top quark Yukawa coupling
($y_{\cPqt}$) is close to unity, and it therefore plays a major role in many
beyond the SM theories. Although model-dependent indirect measurements of
$y_{\cPqt}$ have been made in processes involving top quark loops, such as in
Higgs boson production through gluon-gluon fusion and in Higgs boson decays to
photons, these loop processes can be influenced by new contributions. A direct
measurement of $y_{\cPqt}$ is therefore crucial to constrain many beyond the
SM models. As the direct measurements of $y_{\cPqt}$ have not yet reached
suitable precision in specific final-state searches nor in their combinations,
the addition of an independent search will enhance the overall sensitivity of
such measurements.

The ATLAS and CMS experiments have previously searched for $\ttbar\PH$
production in data corresponding to $\approx$5, 20, and $36\fbinv$ at centre-
of-mass energies of 7, 8, and $13\TeV$, respectively [6, 20, 21, 22]. The most
recent searches in the $\PH\to\bbbar$ decay mode have been performed in the
leptonic channels by CMS [23] and ATLAS [24]. In addition, ATLAS dedicated a
search for $\ttbar\PH$ in the all-jet final state at $\sqrt{s}=8\TeV$, in
which the observed and expected upper limits were set at multiples of 6.4 and
5.4 of the respective SM cross sections ($\sigma_{\text{SM}}$) at 95%
confidence levels (), and a best fit value was obtained for the signal
strength relative to the SM value of
$\hat{\mu}=\sigma/\sigma_{\text{SM}}=1.6\pm 2.6$ [25].

The leading order (LO) Feynman diagram for $\ttbar\PH$ production in the all-
jet final state is shown in Fig. 1. Ideally, signal events contain eight jets,
of which four are tagged as originating from quarks via the algorithm
described in Section 0.4. To accommodate jets lost due to geometrical
acceptance, merging of jets, and tagging inefficiency, events with seven or
more jets and three or more -tagged jets are included in the defined signal
region (SR). To account for extra jets from initial- or final-state radiation,
up to nine jets are considered per event.

Figure 1: An example of an LO Feynman diagram for $\ttbar\PH$ production,
including the subsequent decay of the top quark-antiquark pair, as well as
that of the Higgs boson into a bottom quark-antiquark pair.

Despite that the signature involves a large number of high transverse momentum
() final-state jets, the analysis is constrained by background, mainly from
jets produced through the strong interaction, referred to as quantum
chromodynamic (QCD) multijet production. The next largest background
contribution is from $\ttbar\text{+jets}$, which includes $\ttbar\text{+light-
flavour}$ jets, where one or more of the jets are incorrectly identified as
jets, $\ttbar\text{+}\ccbar$, and the topologically irreducible
$\ttbar\text{+}\bbbar$ background. Smaller background contributions arise from
single top quark and vector boson production in association with jets.
Although the all-jet final state has significantly more background than, for
example the lepton+jets final state, it offers a fully reconstructed event,
and a larger contribution from the signal.

Because background rates are much larger than the $\ttbar\PH$ signal, it is
important that all available information on the differences between the signal
and backgrounds is incorporated into the analysis. A quark-gluon discriminant
is used to differentiate between events containing jets originating from
light-flavour quarks and events containing jets from gluons. Given the many
combinations of jet–quark associations, it is difficult to resolve a clear,
resonant Higgs boson mass peak. Nevertheless, there are underlying kinematic
differences between the $\ttbar\PH$ signal and the multijet background and, to
a lesser extent, the $\ttbar\text{+jets}$ background. These differences are
exploited through the matrix element method (MEM) [26, 27, 28, 23] to
distinguish the signal from background. Specifically, events are assigned a
probability density according to how compatible they are with the $\ttbar\PH$
process. Although this probability density suffices to separate the signal
from most backgrounds, a second probability density is assigned to each event
according to its compatibility with the $\ttbar\text{+}\bbbar$ process, which
provides additional discrimination against the topologically irreducible
$\ttbar\text{+}\bbbar$ background. The two probability densities are combined
in a likelihood ratio to form the final discriminant between signal and
background.

The body of this paper is devoted to the details of the analysis. The CMS
detector is described in Section 0.2, while the description of the Monte Carlo
(MC) simulation follows in Section 0.3. The reconstruction of physical objects
and event selections are discussed in Section 0.4. The QCD multijet
background, estimated from control regions in data, is described in Section
0.5, followed by the description of the analysis strategy and the MEM in
Section 0.6. The treatment of the systematic uncertainties is discussed in
Section 0.7, the results are presented in Section 0.8, and a brief summary is
provided in Section 0.9.

##  0.2 The CMS detector

The central feature of the CMS apparatus is a superconducting solenoid of 6m
internal diameter, providing a magnetic field of 3.8T. A silicon pixel and
strip tracker, a lead tungstate crystal electromagnetic calorimeter (ECAL),
and a brass and scintillator hadron calorimeter (HCAL), each composed of a
barrel and two end sections, reside within the field volume. Forward
calorimeters extend the pseudorapidity ($\eta$) coverage beyond these barrel
and end sections. Muons are measured in gas-ionization detectors embedded in
the steel flux-return yoke outside the solenoid. Events of interest are
selected using a two-tiered trigger system [29]. The first level, composed of
specialized hardware processors, uses information from the calorimeters and
muon detectors to select events at a rate close to 100kHz, ascertained within
a time interval of less than 4. The second level, known as the high-level
trigger, consists of a farm of processors running a version of the full event
reconstruction software optimized for fast processing, and reduces the event
rate to $\approx$1kHz before data storage. A more detailed description of the
CMS detector, together with a definition of the coordinate system and the
kinematic variables, can be found in Ref. [30].

##  0.3 Simulated samples

This analysis uses MC event generators and a detailed detector simulation
based on (v9.4) [31] to model experimental effects, such as reconstruction,
selection efficiencies, and resolutions in the CMS detector.

The $\ttbar\PH$ signal is modelled at next-to-LO (NLO) perturbative QCD via
the (v2) event generator [32, 33, 34, 35]. The value of the Higgs boson mass
is set to $m_{\PH}=125\GeV$, the value of the top quark mass to
$m_{\cPqt}=172.5\GeV$, and the parton distribution functions (PDFs) of the
proton are described using NNPDF3.0 [36], at the same level of accuracy in
perturbative QCD as used to generate the event samples. These events are
subsequently processed through (v8.200) [37] for parton showering and
hadronization.

The generator [38, 39, 40] is used to simulate and single top quark (single )
production at NLO. The associated production of with a vector boson (referred
to as $\ttbar\text{+}\PW$ and $\ttbar\text{+}\cPZ$, or commonly as
$\ttbar\text{+V}$ in the following) is simulated at NLO using (v2.2.2) [41]
with FxFx jet matching and merging [42]. The generator is also used at LO
accuracy with the MLM matching scheme [43] to simulate the production of and
bosons with additional jets (V+jets) as well as QCD multijet events. The
multijet background is estimated from control regions in data, as described in
Section 0.5, while simulated events are used to ensure the self-consistency of
the method. Diboson ($\PW\PW$, $\PW\cPZ$, and $\cPZ\cPZ$) events are simulated
at LO using . All background events also use for parton showering and
hadronization. The underlying event in the simulation of all backgrounds,
except for , is characterized through the CUETP8M1 event tune [44, 45]. For
$\ttbar\PH$ and production, we use a custom tune CUETP8M2, developed by CMS
with an updated strong coupling $\alpha_{s}$ for initial-state radiation, to
better model the jet multiplicity spectrum.

For comparing with measured distributions, the events in the simulated samples
are normalized to the integrated luminosity of the data according to their
predicted cross sections, taken from calculations at next-to-NLO (NNLO) for
V+jets production, at approximate NNLO for single production [46], and at NLO
for diboson [47] and $\ttbar\text{+V}$ production [48]. The $\ttbar\PH$ cross
section and Higgs boson branching fractions are also generated at NLO accuracy
[49]. The simulated events are normalized to the full NNLO calculation,
including soft-gluon resummation at next-to-next-to-leading logarithmic
accuracy [50], assuming $m_{\cPqt}=172.5\GeV$. The dependence on choice of the
PDFs was also studied and found to have negligible impact on all observed
distributions; however, their impact on systematic uncertainties enters
through their normalization. As the differential measurements of top quark
pair production indicate that the measured spectrum of top quarks is softer
than in simulation [51], the top quark distribution in -generated events is
reweighted to the data using a control region enriched in lepton+jets events.
The events in the sample are also separated according to the flavour of
additional jets that do not originate from top quark decays, namely:
$\ttbar\text{+}\bbbar$, where two additional jets are within the acceptance
defined in Section 0.4 and originate from one or more quarks;
$\ttbar\text{+}\cPqb$, where only one jet is within the acceptance and
originates from a single quark; $\ttbar\text{+}2\cPqb$, where a single jet is
within the acceptance and originates from two spatially close quarks;
$\ttbar\text{+}\ccbar$, where there is at least one jet and no additional jets
within the acceptance; and $\ttbar\text{+light-flavour}$ partons
($\ttbar\text{+lf}$). The separation is motivated by the fact that the
subsamples originate from different processes and therefore have different
associated systematic uncertainties.

Effects from additional interactions in the same or neighbouring bunch
crossings (referred to as pileup) are modelled by adding simulated minimum-
bias events generated in to all simulated processes. The pileup multiplicity
distribution in simulation is reweighted to reflect the one observed in
collision data, which is distributed between about 10 and 40 and peaks at
$\approx$20\. Additional rescaling factors are applied where necessary to
improve the description of data in simulation, as discussed in the next
section.

##  0.4 Event reconstruction and selection

The particle-flow event algorithm [52] is used to reconstruct and identify
physical objects from an optimized combination of information from the various
components of the CMS detector. The reconstructed vertex with the largest
value of summed object $\pt^{2}$ is taken to be the primary interaction
vertex. These objects correspond to jets, clustered with the jet finding
algorithm [53, 54] using the tracks assigned to the vertex as inputs, and to
the associated missing transverse momentum (), defined as the negative vector
sum of the of those jets. The energy of charged hadrons is determined from a
combination of their momenta measured in the tracker and the corresponding
ECAL and HCAL energy depositions, corrected for the response function of the
calorimeters to hadronic showers, while the energy of neutral hadrons is
obtained from the corresponding corrected ECAL and HCAL energies. Charged
hadrons from pileup events are omitted in the subsequent event reconstruction.
The energy of electrons is determined from a combination of the electron
momentum at the primary interaction vertex obtained from the tracker, the
energy in the corresponding ECAL clusters, and the energy sum of all
bremsstrahlung photons spatially compatible with the electron track. The of
muons is based on the curvature of the corresponding track.

The selection criteria reflect the all-jet final state in $\ttbar\PH$
production where the Higgs boson decays into a pair. The analysis requires the
reconstruction of all objects originating from a common vertex, jets from
quark hadronization, including -tagged jets, and electrons and muons to veto
any events with leptons. The lepton veto ensures that leptonic final states in
$\ttbar\PH$ production are not considered, as these are covered by separate
searches at CMS.

Jets are reconstructed from particle-flow candidates using the anti-clustering
algorithm [53], implemented in the [54] package, using a distance parameter of
0.4. In the offline reconstruction, each jet is required to have $\pt>30\GeV$
and $\abs{\eta}<2.4$, and ECAL and HCAL energy fractions of at least 1%, which
removes jets originating from instrumental effects. In addition, jet energy
corrections are applied that depend on , $\eta$, pileup, and residual
differences between data and MC simulations [55, 56].

Jets originating from decays of quarks are identified using the combined
secondary vertex (CSVv2) algorithm [57, 58], which uses information from the
tracker. The algorithm combines information on the impact parameters at the
primary vertex with features of secondary vertices within the jet, via a
neural network discriminant. The least restrictive (“loose”) and “medium”
working points of the CSVv2 algorithm have respective efficiencies of
$\approx$80 and 65% for tagging jets originating from quarks, $\approx$35 and
10% for jets originating from quarks, and probabilities of $\approx$10 and 1%
for jets from light-flavour quarks or gluons to be misidentified as jets. In
the remainder of this paper, jets passing the loose and medium tagging working
points are referred to as CSVL and CSVM jets, respectively. The distribution
in the output of the CSVv2 discriminant in simulation is corrected using scale
factors to provide a better description of the observed features in data. This
correction is obtained separately for light-flavour and jets via a “tag-and-
probe” approach [59] in dilepton control samples enriched in events with a
boson and two jets, and in events with no additional jets, respectively. No
correction is applied for jets, although a larger systematic uncertainty is
assigned to such jets, as described in Section 0.7.

Two dedicated triggers have been developed to efficiently select signal
events, both requiring at least six jets with $\abs{\eta}<2.6$. The first
trigger has kinematic requirements of jet $\pt>40\GeV$ and $\HT>450\GeV$, and
requires at least one of the jets to be tagged; the second trigger is
complementary in that it has less stringent kinematic conditions of jet
$\pt>30\GeV$ and $\HT>400\GeV$, but requires at least two -tagged jets; the
being defined by the scalar sum of jet of all jets in the event satisfying the
given thresholds. The jets are tagged online by the triggers at an efficiency
of $\approx$70–80%, with a misidentification rate of $\approx$6% for light-
flavour quark and gluon jets. Events in data and simulation are selected if
they pass either of these two triggers. The efficiency in data and in
simulation is measured in bins of the number of CSVM jets, the of the jet with
the sixth-highest , and the in control samples collected using single-muon
triggers. A bin-by-bin scale factor, based on the ratio of this efficiency in
data to that in simulation, is applied to simulated events to correct for any
remaining differences. The overall trigger efficiency for signal events that
pass the offline event selection is 99%.

To ensure that the trigger selection is close to full efficiency relative to
the offline selection, thereby reducing the uncertainty in any efficiency
differences between data and simulation, the offline analysis selects events
that contain at least six jets with $\pt>40\GeV$, requiring $\HT>500\GeV$ and
at least 2 -tagged CSVM jets. Events with reconstructed electrons or muons in
the final state are vetoed using loose lepton reconstruction and
identification criteria, in particular, electrons and muons are required to
have $\pt>15\GeV$, $\abs{\eta}<2.4$, and to be isolated from jet activity. If
at least one reconstructed electron or muon is found in an event, the event is
rejected. The distributions in and jet multiplicity after the preselection are
shown in Fig. 2 for the data and simulation, with the signal contribution
scaled to the total background yield for illustration.

After this preselection, the events are categorized according to their
reconstructed jet and jet multiplicities. Six categories are formed: 7
reconstructed jets, out of which 3 are CSVM jets (7j, 3); 7 jets with 4 or
more CSVM jets (7j, $\geq$4); 8 jets with 3 CSVM jets (8j, 3); 8 jets with 4
or more CSVM jets (8j, $\geq$4); 9 or more jets with 3 CSVM jets ($\geq$9j,
3); and 9 or more jets with 4 or more CSVM jets ($\geq$9j, $\geq$4). Events
with 7 or more jets and 2 CSVM jets are used to form control regions for the
multijet background estimation, as described in Section 0.5.

To reject events that are unlikely to include a boson from top quark decay, a
cutoff is placed on the dijet invariant mass. Any jets that are not tagged are
considered in this calculation, and the pair with invariant mass
$m_{\cPq\cPq}$ closest to the boson mass is chosen as the boson candidate in
the event. Requirements of $60<m_{\cPq\cPq}<100$, and $70<m_{\cPq\cPq}<92\GeV$
are applied in the 7- or 8-jet, and 9-or-more-jet categories, respectively.

Jets are also classified according to a quark-gluon likelihood (QGL) [60, 61].
The QGL discriminates light-flavour quark jets from gluon jets, exploiting the
higher track multiplicity of gluon jets, the lower of their constituents, as
well as their less collimated spatial profile. A jet is assigned a QGL
discriminant value near 1 if it is quark-like, while gluon-like jets receive a
value near 0. This is used to separate events containing light-flavour jets
from $\qqbar^{\prime}$ decays of bosons from events containing gluon jets
produced in multijet interactions or via initial-state radiation. The QGL
discriminant is used only for jets not identified as originating from quarks,
and is combined in a likelihood ratio (LR) defined as:

$q_{\text{LR}(N_{1},N_{2})}=\frac{L(N_{1},0)}{L(N_{1},0)+L(N_{2},N_{1}-N_{2})},$
(1)

where $N_{1}$ is the number of jets as well as the number of quarks in the
all-quark hypothesis, and $N_{2}$ is the number of quarks in the quark+gluon
hypothesis. The individual likelihoods are defined by:

$L(N_{\cPq},N_{\cPg})=\sum_{\text{perm}}\left(\prod_{k=i_{1}}^{i_{N_{\cPq}}}f_{\cPq}(\zeta_{k})\prod_{m=i_{N_{\cPq}+1}}^{i_{N_{\cPq}+N_{\cPg}}}f_{\cPg}(\zeta_{m})\right),$
(2)

where $\zeta_{i}$ is the QGL discriminant for the $i^{\text{th}}$ jet, and
$f_{\cPq}$ and $f_{\cPg}$ are the probability density functions for
$\zeta_{i}$ when the $i^{\text{th}}$ jet originates from a quark or gluon,
respectively. The former include , , , and quarks. The sum in Eq. (2) runs
over all unique permutations in assigning $N_{\cPq}$ jets to quarks and
$N_{\cPg}$ jets to gluons. We use $q_{\text{LR}(N,0)}$ to compare the
likelihood of $N$ reconstructed light-flavour jets that arise from $N$ quarks,
to the likelihood of $N$ reconstructed light-flavour jets that arise from $N$
gluons. After excluding either the first 3 or 4 CSVM jets in an event (based
on the highest CSVv2 value), up to $N$ light-flavour jets, with $N=3$, 4, or 5
are used to calculate the LR, with a requirement of $q_{\text{LR}(N,0)}>0.5$
set for the SR. To correct the modelling of the QGL distribution in
simulation, a reweighting based on a control sample of $\mu^{+}\mu^{-}$\+ jet
and dijet events is applied to each event according to the type (quark or
gluon) and QGL value of all jets in the event. The distribution in the quark
gluon LR (QGLR), calculated excluding the first 3 -tagged jets, is shown in
Fig. 3 for data and simulation for events passing the preselection. Good
agreement is observed between prediction and data.

Figure 2: Distribution in (left) and jet multiplicity (right) in data (black
points) and in simulation (stacked histograms), after implementing the
preselection. The simulated backgrounds are first scaled to the integrated
luminosity of the data, and then the simulated QCD multijet background is
rescaled to match the yield in data. The contribution from $\ttbar\PH$ signal
(blue line) is scaled to the total background yield (equivalent to the yield
in data) to enhance readability. The hatched bands reflect the total
statistical and systematic uncertainties in the background prediction, prior
to the fit to data, which are dominated by the systematic uncertainties in the
simulated multijet background. The last bin includes event overflows. The
ratios of data to background are given below the main panels.  Figure 3:
Comparison of the distributions in the quark-gluon likelihood ratio in data
(black points) and in simulation (stacked histograms), after preselection,
excluding the first 3 -tagged jets. The simulated backgrounds are first scaled
to the integrated luminosity of the data, and then the simulated multijet
background is rescaled to match the yield in data. The contribution from
signal (blue line) is scaled to the total background yield (equivalent to the
yield in data) to enhance readability. The hatched bands reflect the total
statistical and systematic uncertainties in the background prediction, prior
to the fit to data, which are dominated by the systematic uncertainties in the
simulated multijet background. The ratio of data to background is given below
the main panel.

##  0.5 Background estimation

The main backgrounds stem from multijet and production associated with
additional gluons, light-flavour, charm, or bottom quarks
($\ttbar\text{+jets}$). The background from $\ttbar\text{+jets}$ as well as
other minor backgrounds (single , V+jets, $\ttbar\text{+V}$, and diboson
events) are estimated from MC simulation, while the background from multijet
events is obtained from control regions in data, as described below. The
approach uses a control region (CR) with low tag multiplicity to estimate the
contribution from multijet events in the SR. The CR is enriched in multijet
events, and the remaining contribution from other backgrounds (mainly
$\ttbar\text{+jets}$) is subtracted using simulation.

The CR is defined by events with two CSVM jets and at least three CSVL jets.
We define a validation region (VR) using events with $\mathrm{QGLR}<0.5$. This
definition provides four orthogonal regions, summarized in Table 0.5, from
which we can obtain and check the multijet background estimate. The use of the
VR relies on the fact that the QGLR and the number of additional CSVL jets are
uncorrelated by construction, since only the non-CSVL jets are used in the
calculation of the QGLR, except for the rare case of events with 5 or more
CSVL jets.

Definition and description of the four orthogonal regions in the analysis.
$N_{\text{CSVM}}=2$ $N_{\text{CSVM}}\geq 3$ $N_{\text{CSVL}}\geq 3$
$\textrm{QGLR}>0.5$ CR SR (to extract distribution) (final analysis)
$\textrm{QGLR}<0.5$ Validation CR VR (to validate distribution) (comparison
with data)

The four orthogonal regions are used independently in each of the six analysis
categories described in Section 0.4. For a given variable, the distribution of
the multijet events in the SR of each category is estimated from the data in
the CR, after subtracting $\ttbar\text{+jets}$ and other minor backgrounds.
Since the kinematic properties of -tagged and untagged jets differ in the CR
and SR because of different heavy-flavour composition, corrections as a
function of jet , $\eta$, and the minimum distance between the jet and the
first two -tagged jets ($\Delta R_{\text{min}}$) are applied to the CSVL jets
in the CR. The correction function is obtained from jets in events passing the
preselection, excluding the first two jets, ordered according to their CSVv2
discriminants. This is used to reweight the kinematic distributions of CSVL
jets to match those of CSVM jets. The corrected multijet distribution in the
CR is scaled to provide an estimate of the distribution in the SR.
Specifically, the multijet yield in each category is left floating in the
final fit, as discussed in Section 0.8.

A consistency check of the procedure used to estimate the multijet background
is performed through simulation. Since the power of this test is limited by
the statistical uncertainty in simulated QCD multijet events, the method is
validated in data using events with $\mathrm{QGLR}<0.5$, by applying the same
procedure used to estimate the multijet background in the SR. The
distributions in the MEM discriminant, described in Section 0.6, as well as
several kinematic variables in the VR in data, together with the multijet
background estimate obtained from data, and other simulated contributions, are
shown for the most sensitive event category, ($\geq$9j, $\geq$4), in Fig. 4.
All discrepancies are taken into account via the systematic uncertainties in
the multijet background, particularly in events with low in the (7j, 3)
category and in all $\geq$4 categories, in which two uncorrelated
uncertainties are applied, as discussed in Section 0.7.

Figure 4: Distributions in data, in simulated backgrounds, and in the
estimated multijet background for the ($\geq$9j, $\geq$4) VR category. The MEM
discriminant (upper left), (upper right), of the leading jet (middle left),
minimum $\Delta R$ between jets (middle right), invariant mass of the closest
jet pair (lower left), and minimum mass of all jet pairs (lower right). The
level of agreement between data and estimation is expressed in terms of a
$\chi^{2}$ divided by the number of degrees of freedom (dof), which are given
along with their corresponding statistical $p$-values. The differences between
data and the total estimates divided by the quadratic sum of the statistical
and systematic uncertainties in the data and in the estimates (pulls) are
given below the main panels. The numbers in parenthesis in the legends
represent the total yields for the corresponding entries. The last bin
includes event overflows.

To verify that the good performance demonstrated in the gluon jet enriched VR
($\mathrm{QGLR}<0.5$) also holds in the quark jet enriched SR
($\mathrm{QGLR}>0.5$), we investigate another CR. Specifically, the tagging
criteria are changed by selecting jets that fulfil some intermediate but not
the formal medium tagging requirement, which corresponds to an efficiency for
tagging jets of $\approx$75% with a misidentification rate of $\approx$4%. The
classified jets are then used to form jet and jet multiplicity categories in
analogy with the SR. Since these categories are orthogonal to the categories
in the SR, they are used to verify that the background estimation is valid for
$\mathrm{QGLR}>0.5$. In all validation regions, this background estimation
reproduces the kinematic distributions measured in data. In the remainder of
this paper, all multijet estimates are based on data, unless stated to the
contrary.

##  0.6 Signal extraction

A likelihood technique based on the LO matrix elements for the $\ttbar\PH$
signal and the $\ttbar\text{+}\bbbar$ background processes is used to extract
the signal. This method utilizes the full kinematic properties of each event
to provide a powerful MEM discriminant between the signal and background.
Although the discriminant is constructed to discriminate against the
$\ttbar\text{+}\bbbar$ background, it performs well against
$\ttbar\text{+light-flavour}$ jets and against multijet events. Consequently,
it is used as the single discriminant against all background sources. The MEM
algorithm is similar to the one documented in Ref. [23], but adapted to the
all-jet final state.

The MEM probability density functions for signal and background are
constructed at LO assuming that both reactions proceed via gluon-gluon fusion,
which is valid, as this production mechanism represents most of the event
yield. For example, at $\sqrt{s}=14\TeV$, the fraction of gluon-gluon
initiated $\ttbar\PH$ subprocesses corresponds to $\approx$80% of the
inclusive NLO cross section [62]. Example Feynman diagrams for these processes
are shown in Fig. 5. All inequivalent jet–quark associations in the
reconstruction of the final state are considered in the analysis.
Hadronization and detector effects are taken into account via transfer
functions obtained from simulation that map the measured jet four-momenta to
the final-state particles.

Figure 5: Examples of LO Feynman diagrams for the partonic processes of
$\cPg\cPg\to\ttbar\PH$ and $\cPg\cPg\to\ttbar\text{+}\bbbar$.

Each event contains three or four jets that most likely originate from quarks
(according to their CSVv2 discriminant values), and are considered as
candidates for quarks from the $\PH\to\bbbar$ and $\cPqt\to\PW\cPqb$ decays,
whereas untagged jets are considered as candidates for the $\PW\to\cPqu\PAQd$
or $\cPqc\PAQs$ quarks (and their charge conjugates). The accepted jet
permutations must retain the assigned “” status of their quarks. To account
for the loss of jets because of limited detector acceptance, as well as the
presence of additional jets from gluon radiation, the method integrates over
certain final-state variables in many categories, specifically over quark
directions. In events with eight jets and four jets, one light-flavour jet is
excluded from our MEM calculation in turn, and a sum is taken over the
additional permutations. This approach has been checked and shown to provide
improved performance relative to the fully reconstructed hypothesis in the
(8j, $\geq$4) category. Events with only three jets are assumed to have lost a
bottom quark from the decay of a top quark. Up to five untagged jets are
considered as other than quark candidates, while additional untagged jets are
ignored. For five such quark candidates, one is excluded in turn and the
number of permutations is increased by a factor of five. The final choice of
hypothesis for each category, optimized according to discrimination power and
computing performance, is given in Table 0.6.

Selected MEM hypotheses for each event topology. The 4W2H1T hypothesis assumes
1 quark from a top quark is lost, 3W2H2T assumes that 1 quark from a boson is
lost, and 4W2H2T represents the fully reconstructed hypothesis requiring at
least 8 jets.  Category MEM hypothesis 7 jets, 3 jets 4W2H1T 8 jets, 3 jets
4W2H1T $\geq$9 jets, 3 jets 4W2H1T 7 jets, $\geq$4 jets 3W2H2T 8 jets, $\geq$4
jets 3W2H2T $\geq$9 jets, $\geq$4 jets 4W2H2T

For each event, the MEM probability density function under the signal (S) or
background (B) hypothesis, $\mathcal{H}=\ttbar\PH$ (S) or
$\mathcal{H}=\ttbar\text{+}\bbbar$ (B), is calculated as:

$\displaystyle
w(\mathbf{y}|\mathcal{H})=\sum_{\textrm{perm}}\int\rd\mathbf{x}\int\frac{{\rd}x_{a}{\rd}x_{b}}{2x_{a}x_{b}s}g(x_{a},Q)g(x_{b},Q)\delta^{4}\left(p_{a}+p_{b}-\sum_{k=1}^{8}p_{k}\right)|\mathcal{M}_{\mathcal{H}}(p_{a},p_{b},\mathbf{p})|^{2}W(\mathbf{y}|\mathbf{p}),$
(3)

where $\mathbf{y}$ represents the set of measured observables, jet momenta,
and $s$ is the square of the centre-of-mass energy. The sum is taken over the
possible permutations of matching observed jets to final-state quarks. The
integration is performed over the phase space volume, $\mathbf{x}$, of the
final-state quarks, with momenta $\mathbf{p}=(p_{1},\ldots,p_{8})$, and over
the incident gluon momentum fractions, $x_{a}$ and $x_{b}$, using the vegas
[63] algorithm. The gluon PDF $g$ is calculated in lhapdf [64] at the
factorization scale $Q$, which is the momentum transfer in the event, using
the CTEQ6.6 set of PDFs [65]. The four-dimensional delta function is reduced
to two dimensions, which ensures conservation of energy and longitudinal
momentum, while the total of the final-state quarks is weakly constrained via
a resolution function, $\mathcal{R}(\vec{\rho}_{\mathrm{T}})$, where
$\vec{\rho}_{\mathrm{T}}$ is the measured transverse recoil, equal to the
difference between and the vector sum of the specifically selected jets. The
scattering amplitude for the given hypothesis, $\mathcal{M}_{\mathcal{H}}$, is
calculated at LO using OpenLoops (v1.3.1) [66], and all resonances are treated
using the narrow-width approximation [67]. The masses of the Higgs boson, the
boson, and the top quark are fixed, thereby constraining the energy of certain
final-state quarks. The final integrations of the fully reconstructed
$\ttbar\PH$ and $\ttbar\text{+}\bbbar$ hypotheses are therefore performed over
the incoming gluon momentum fractions and three or four respective final-state
quark energies, with an integration range of a factor of three that of the
associated jet energy resolution (JER).

The transfer function $W(\mathbf{y}|\mathbf{p})$ corresponds to the likelihood
of measuring the set of variables $\mathbf{y}$ given the final-state quark
momenta $\mathbf{p}$. Given the excellent angular resolution of reconstructed
jets, the direction of the quarks is assumed to be precisely measured, which
reduces the total transfer function to a product of quark energy transfer
functions. Each quark energy transfer function is modelled via a double
Gaussian with different parameterizations for jets associated with and other
quarks, all obtained from MC simulation.

Using Eq. (3), the MEM event discriminant is defined as:

$\mathcal{P}_{\mathrm{S}/\mathrm{B}}=\frac{w(\mathbf{y}|\mathrm{S})}{w(\mathbf{y}|\mathrm{S})+\kappa_{\mathrm{S}/\mathrm{B}}w(\mathbf{y}|\mathrm{B})},$
(4)

where $\kappa_{\mathrm{S}/\mathrm{B}}$ is a positive constant that adjusts the
relative normalization between $w(\mathbf{y}|\mathrm{S})$ and
$w(\mathbf{y}|\mathrm{B})$, given that the individual probability densities
are not normalized to unity. The $\kappa_{\mathrm{S}/\mathrm{B}}$ constant is
optimized category-by-category by minimizing the expected exclusion limit on
the signal strength discussed in Section 0.8. Starting with a value that
provides good visual discrimination between signal and background, the
optimization is performed by testing different values of
$\kappa_{\mathrm{S}/\mathrm{B}}$ above and below its original value until a
minimum in the expected limit is found in each category as well as in
combinations of categories. This optimization was proven to be equivalent to
optimizing the significance of discovery. By construction, the MEM
discriminant satisfies the condition
$0\leq\mathcal{P}_{\mathrm{S}/\mathrm{B}}\leq 1$.

The event yields expected for the signal and the different background
processes, and the yields observed in data, are listed in Table 0.6 for each
analysis category. The proportion of expected $\ttbar\PH$ events increases
with the number of jets and of jets. To better represent the signal strength
in each category, the expected yields for signal and background in the last
three bins of the MEM discriminant are listed in Table 0.6.

Expected numbers of $\ttbar\PH$ signal and background events, and the observed
event yields for the six analysis categories, following the fit to data. The
signal contributions are given at the best fit value. The quoted uncertainties
contain all the contributions described in Section 0.7 added in quadrature,
considering all correlations among the processes.  Process 7j, 3 8j, 3
$\geq$9j, 3 7j, $\geq$4 8j, $\geq$4 $\geq$9j, $\geq$4 Multijet 47 600 $\pm$ 3
000 32 700 $\pm$ 2 200 17 600 $\pm$ 1 600 3 530 $\pm$ 270 3 770 $\pm$ 360 2
280 $\pm$ 290 $\ttbar\text{+lf}$ 7 700 $\pm$ 1 600 5 700 $\pm$ 1 100 3 160
$\pm$ 550 310 $\pm$ 130 410 $\pm$ 220 244 $\pm$ 96 $\ttbar\text{+}\ccbar$ 3
100 $\pm$ 1 400 2 800 $\pm$ 1 200 2 170 $\pm$ 970 190 $\pm$ 100 270 $\pm$ 150
270 $\pm$ 150 $\ttbar\text{+}\cPqb$ 1 400 $\pm$ 620 1 240 $\pm$ 620 890 $\pm$
420 142 $\pm$ 80 160 $\pm$ 110 134 $\pm$ 73 $\ttbar\text{+}2\cPqb$ 890 $\pm$
450 760 $\pm$ 370 600 $\pm$ 290 87 $\pm$ 58 114 $\pm$ 77 101 $\pm$ 52
$\ttbar\text{+}\bbbar$ 870 $\pm$ 340 1 010 $\pm$ 370 970 $\pm$ 380 203 $\pm$
90 370 $\pm$ 150 410 $\pm$ 170 Single  750 $\pm$ 190 520 $\pm$ 130 284 $\pm$
75 43 $\pm$ 20 78 $\pm$ 68 35 $\pm$ 17 V+jets 460 $\pm$ 170 290 $\pm$ 110 240
$\pm$ 220 36 $\pm$ 33 45 $\pm$ 110 17 $\pm$ 12 $\ttbar\text{+V}$ 110 $\pm$ 20
122 $\pm$ 27 120 $\pm$ 30 14 $\pm$ 7 28 $\pm$ 14 28 $\pm$ 14 Diboson 14 $\pm$
5 5 $\pm$ 4 1 $\pm$ 1 0.6 $\pm$ 0.5 0.6 $\pm$ 0.6 0 $\pm$ 10 Total bkg 62 790
$\pm$ 900 45 220 $\pm$ 850 26 020 $\pm$ 640 4 550 $\pm$ 180 5 240 $\pm$ 340 3
520 $\pm$ 190 $\ttbar\PH$ ($\hat{\mu}=0.9$) 130 $\pm$ 210 140 $\pm$ 220 120
$\pm$ 190 32 $\pm$ 51 46 $\pm$ 75 48 $\pm$ 77 Data 62 920 45 359 26 136 4 588
5 287 3 566

Expected numbers of $\ttbar\PH$ signal and background events in the last three
bins of the MEM discriminant for the six analysis categories, following the
fit to data. The signal contributions are given at the best fit value. The
quoted uncertainties contain all the contributions described in Section 0.7
added in quadrature, considering all correlations among the processes. Also
given are the signal (S) to total background (B) ratios for the SM $\ttbar\PH$
expectation ($\mu=1$).  Process 7j, 3 8j, 3 $\geq$9j, 3 7j, $\geq$4 8j,
$\geq$4 $\geq$9j, $\geq$4 Multijet 8 560 $\pm$ 820 5 510 $\pm$ 590 3 120 $\pm$
400 608 $\pm$ 61 748 $\pm$ 74 376 $\pm$ 57 $\ttbar\text{+lf}$ 3 300 $\pm$ 470
2 220 $\pm$ 330 1 110 $\pm$ 170 121 $\pm$ 34 107 $\pm$ 31 54 $\pm$ 21
$\ttbar\text{+}\ccbar$ 1 050 $\pm$ 410 920 $\pm$ 370 660 $\pm$ 260 53 $\pm$ 27
88 $\pm$ 39 70 $\pm$ 30 $\ttbar\text{+}\cPqb$ 380 $\pm$ 160 330 $\pm$ 140 240
$\pm$ 110 44 $\pm$ 27 51 $\pm$ 25 33 $\pm$ 17 $\ttbar\text{+}2\cPqb$ 208 $\pm$
94 167 $\pm$ 79 128 $\pm$ 59 21 $\pm$ 12 36 $\pm$ 22 15 $\pm$ 8
$\ttbar\text{+}\bbbar$ 192 $\pm$ 64 249 $\pm$ 84 228 $\pm$ 80 46 $\pm$ 18 88
$\pm$ 30 95 $\pm$ 33 Single  130 $\pm$ 32 100 $\pm$ 32 50 $\pm$ 15 8 $\pm$ 5
24 $\pm$ 23 5 $\pm$ 4 V+jets 79 $\pm$ 48 39 $\pm$ 20 28 $\pm$ 21 1 $\pm$ 2 4
$\pm$ 5 0 $\pm$ 1 $\ttbar\text{+V}$ 34 $\pm$ 6 39 $\pm$ 8 32 $\pm$ 8 5 $\pm$ 3
8 $\pm$ 3 7 $\pm$ 3 Diboson 1 $\pm$ 1 0.24 $\pm$ 0.23 0.00 $\pm$ 0.00 0.33
$\pm$ 0.29 0.00 $\pm$ 0.00 0.13 $\pm$ 0.23 Total bkg 13 930 $\pm$ 260 9 580
$\pm$ 250 5 610 $\pm$ 170 910 $\pm$ 50 1 154 $\pm$ 76 656 $\pm$ 44 $\ttbar\PH$
($\hat{\mu}=0.9$) 56 $\pm$ 83 58 $\pm$ 89 47 $\pm$ 71 18 $\pm$ 27 26 $\pm$ 38
21 $\pm$ 31 Data 13 937 9 620 5 640 958 1 162 660 $\text{S}/\text{B}$
($\mu=1$) 0.005 0.007 0.009 0.023 0.025 0.036

##  0.7 Systematic uncertainties

Each source of systematic uncertainty is associated with a “nuisance”
parameter that modifies the likelihood in the final fit discussed in Section
0.8, and can either affect the yield from a process (rate uncertainty), the
distribution in the MEM discriminant, or both. In the latter case, the effects
from the rate and distribution are treated simultaneously, and can be
considered completely correlated. Each individual source of systematic
uncertainty is independent of other sources, and its effect on signal and
background is completely correlated across the relevant processes.

The uncertainty in the integrated luminosity is estimated to be 2.5% [68]. The
uncertainties in trigger scale factors are determined from the bin-by-bin
uncertainties in the ratio of efficiency in data relative to simulation, and
are $\approx$1.0% on average, with some being as large as 15%. The uncertainty
in the distribution in the number of pileup interactions is evaluated by
changing the total inelastic cross section in the MC simulation by its
uncertainty of 4.6%. The changes in the weight factors are propagated to the
MEM discriminant distributions and treated as fully correlated among all
processes. The impact of the uncertainty in the jet energy scale (JES) [55] is
evaluated for each jet in the simulated events by changing the correction
factors by their uncertainties, and propagating the effect to the MEM
discriminant by recalculating all kinematic quantities. Various independent
sources contribute to the overall JES uncertainty, and their impact is
therefore evaluated separately and they are treated as uncorrelated in the
final fit. A similar procedure is applied to account for the uncertainty
related to the JER, which ranges between about 1 and 5% of the expected energy
resolution, depending on the jet direction. Since the analysis categories are
defined in terms of jet multiplicity and kinematics, a change in either JES or
JER can induce a migration of events between categories, as well as in the SR.
The fractional changes in event yields induced by one standard deviation
shifts in JES or JER range, respectively, from 3–11% and 2–11%, depending on
the process and the category.

The scale factors applied to correct the CSVv2 discriminant, described in
Section 0.4, are affected by several components of systematic uncertainty,
which are attributed to three main sources: JES, purity of heavy- or light-
flavour jets in the control sample used to obtain the scale factors, and the
statistical uncertainty in the event sample used in their extraction [58]. A
separate, large uncertainty is applied to charm jets, owing to the lack of a
reliable calibration based on data. Each component of these systematic tagging
uncertainties is considered as a separate nuisance parameter in the final fit.

The systematic uncertainties from reweighting the simulated QGL and top quark
distributions affect both the rate and the distribution in the MEM
discriminant, and are taken into account by changing the functions used in the
reweighting procedure by their uncertainties. Many uncertainties that would
normally be related to the MC simulation of the multijet background are
avoided by estimating this contribution from data. Nevertheless, a few small
systematic uncertainties remain: an uncertainty in the correction applied to
CSVL jets that is estimated via an alternative correction obtained by applying
a second $\eta$ correction; a 2.5 or 2.0% uncertainty in the first bin of the
distribution in the MEM discriminant across $\geq$4 and 3 categories, applied
to account for respective systematic over- or underestimation of this bin in
the VR; a reweighting based on the distribution (considering the of just the
first six jets) in the (7j, 3) and all $\geq$4 categories of the VR, to
account for mismodelling at low ; and the total normalization in each category
that is left unconstrained in the final fit. The uncertainties in the multijet
background normalization have the largest impact on the sensitivity of the
analysis, and setting the normalization to a fixed value in each category
improves the expected limit by 20 to 30%.

Theoretical uncertainties are treated as process-specific if they only impact
a single simulated event sample, otherwise they are treated as correlated
across all samples to which they apply. For example, the PDF uncertainty for
quark-quark initiated processes, such as V+jets, $\ttbar\text{+}\PW$, and
diboson events, are treated as 100% correlated. The $\ttbar\text{+heavy-
flavour}$ processes represent important sources of irreducible background,
which have not yet been reliably measured, nor subjected to higher-order
calculations that constrain these contributions. In fact, the most recent
direct measurement of the $\ttbar\text{+}\bbbar$ cross section in the dilepton
final state has an accuracy of $\approx$35% [69]. A 50% uncertainty in the
production rate is therefore assigned separately to the
$\ttbar\text{+}\bbbar$, $\ttbar\text{+}2\cPqb$, $\ttbar\text{+}\cPqb$, and
$\ttbar\text{+}\ccbar$ processes. These uncertainties have a significant
impact on the expected limits, and ignoring these improves the overall limit
by about 5%.

Uncertainties from factorization ($\mu_{\text{F}}$) and renormalization
($\mu_{\text{R}}$) scales in the inclusive cross sections range from 1 to 13%,
depending on the process. The PDF uncertainties [70] range between 2 and 4%,
and are treated as fully correlated for all processes that share the same
dominant initial state ($\cPg\cPg$, $\cPg\cPq$, or $\cPq\cPq$), except for
$\ttbar\PH$, which is considered separately. Finally, the limited number of
simulated background and signal events leads to statistical fluctuations in
the nominal predictions. This is taken into account by assigning a nuisance
parameter for each bin of each sample that can be changed by its uncertainty
as described in Refs. [71, 72]. Table 0.7 summarizes the various sources of
systematic uncertainty and their impact on yields.

Summary of the systematic uncertainties affecting the signal and background
expectations. The second column indicates the range in yield of the affected
processes, caused by changing the nuisance parameters by their uncertainties.
The third column indicates whether the uncertainties impact the distribution
in the MEM discriminant. A check mark ($\checkmark$) indicates that the
uncertainty applies to the stated processes. An asterisk (*) indicates that
the uncertainty affects the multijet distribution indirectly, because of the
subtraction of directly affected backgrounds in the CR in data.  Source Range
of Distribution Process uncertainty (%) $\ttbar\PH$ Multijet
$\ttbar\text{+jets}$ Other Experimental uncertainties Integrated luminosity
2.5 No ✓ * ✓ ✓ Trigger efficiency 1–2 Yes ✓ * ✓ ✓ Pileup 0.2–5 Yes ✓ * ✓ ✓ JES
3–11 Yes ✓ * ✓ ✓ JER 2–11 Yes ✓ * ✓ ✓ tagging 4–40 Yes ✓ * ✓ ✓ QGL reweighting
4–11 Yes ✓ * ✓ ✓ Top quark reweighting 1–2 Yes * ✓ Multijet estimation CSVL
correction Yes ✓ MEM first bin Yes ✓ reweighting Yes ✓ Normalization $\infty$
No ✓ Theoretical uncertainties $\ttbar\text{+}\bbbar$ normalization 50 No * ✓
$\ttbar\text{+}2\cPqb$ normalization 50 No * ✓ $\ttbar\text{+}\cPqb$
normalization 50 No * ✓ $\ttbar\text{+}\ccbar$ normalization 50 No * ✓
$\mu_{\text{F}}$/$\mu_{\text{R}}$ scales for signal 6–9 No ✓
$\mu_{\text{F}}$/$\mu_{\text{R}}$ scales for background 1–13 No * ✓ ✓ PDFs 2–4
No ✓ * ✓ ✓ Simulated sample size 2–40 Yes ✓ * ✓ ✓

##  0.8 Results

The MEM discriminant is used to extract the signal. The results are
interpreted in terms of the signal strength modifier, defined as the ratio of
the measured $\ttbar\PH$ production cross section to the SM expectation for
$m_{\PH}=125\GeV$ ($\mu=\sigma/\sigma_{\text{SM}}$). The statistical method
used to determine the signal strength is documented in Ref. [73]. In this
method, a likelihood function $\mathcal{L}(\mu,\theta)$ is constructed from
the product of Poisson likelihoods for each bin of the MEM discriminant
distribution for all six event categories, multiplied by a probability density
function for the nuisance parameters $\theta$, discussed in Section 0.7. The
following test statistic, based on the LR, is used to compare competing
hypotheses for the value of $\mu$:

$q(\mu)=-2\ln\frac{\mathcal{L}(\mu,\hat{\theta}_{\mu})}{\mathcal{L}(\hat{\mu},\hat{\theta})},$
(5)

where $\hat{\theta}$ and $\hat{\theta}_{\mu}$ represent the best fit estimate
of $\theta$ for a floating or fixed $\mu$, respectively, following the
procedure described in Section 3.2 of Ref. [14].

The final MEM discriminants in each category, following the combined fit to
data, are displayed in Fig. 6. Each background contribution is initially
normalized to an integrated luminosity of $35.9\fbinv$, while the multijet
contribution is free to float in the fit. The fit to the nuisance parameters
constrains many uncertainties including the multijet normalization. The total
uncertainty contains the correlations among the fitted parameters that are
sampled through the covariance matrix.

Figure 6: Distributions in the fitted MEM discriminant for each analysis
category. The contributions expected from signal and background processes
(filled histograms) are shown stacked. The $\ttbar\text{+}\cPqb$ and
$\ttbar\text{+}2\cPqb$ background process are shown combined with
$\ttbar\text{+}\bbbar$, the single and $\ttbar\text{+V}$ processes are shown
combined as “Other ”, and the V+jets and diboson processes are shown combined
as “Electroweak”. The signal distributions (lines) for a Higgs boson mass of
$m_{\PH}=125\GeV$ are multiplied by a factor of 500 and superimposed on the
data. The distributions in data are indicated by the black points. The ratios
of data to background appear below the main panels.

The observed distributions are compared to the signal+background expectation
to extract a best fit value of $\hat{\mu}=0.9\pm 0.7\stat\pm 1.3\syst=0.9\pm
1.5\,(\text{tot})$, which is consistent with the SM expectation of $\mu=1$.
The statistical component of the total uncertainty is estimated by including
in the fit only a limited set of nuisance parameters that are statistical in
nature, namely the per-category multijet normalizations and the multijet bin-
by-bin uncertainties, which are dominated by the limited event count in the
CR. The systematic component is then calculated as the difference in
quadrature between the total uncertainty and the statistical component.

The best fit values of $\mu$ in each analysis category and in the combination
are listed in Table 0.8, and displayed in Fig. 7 (left). Since the value is
also compatible with the background-only hypothesis, an exclusion limit at the
95% can be set using the asymptotic approximation [74] of the modified
frequentist CL${}_{\text{s}}$ method [75, 76]. Combining all categories, we
obtain the observed and expected upper limits of $\mu<3.8$ and $<3.1$,
respectively. These limits in each category, as well as the combined limit,
are listed in Table 0.8 and displayed in Fig. 7 (right).

Best fit value of the signal-strength modifier $\hat{\mu}$ and the median
expected and observed 95% upper limits (UL) on $\mu$ in each of the six
analysis categories, as well as the combined results. The best fit values are
shown with their total uncertainties and the breakdown into the statistical
and systematic components. The expected limits are given together with their
68% intervals.  Category Best fit $\hat{\mu}$ and uncertainty Observed
Expected $\hat{\mu}$ total (stat syst) UL UL 7j, 3 $1.6$ ${}^{+9.6}_{-12.0}$
$\bigl{(}{}^{+2.7}_{-2.7}~{}~{}{}^{+9.2}_{-11.7}\bigr{)}$ 18.7
$17.6^{+6.2}_{-4.4}$ 8j, 3 $1.2$ ${}^{+5.9}_{-6.4}$
$\bigl{(}{}^{+2.2}_{-2.3}~{}~{}{}^{+5.4}_{-5.9}\bigr{)}$ 12.3
$11.5^{+4.6}_{-3.1}$ $\geq$9j, 3 $-3.5$ ${}^{+5.9}_{-6.5}$
$\bigl{(}{}^{+2.4}_{-2.4}~{}~{}{}^{+5.4}_{-6.0}\bigr{)}$ 9.0
$10.7^{+4.5}_{-3.1}$ 7j, $\geq$4 $5.4$ ${}^{+2.9}_{-2.7}$
$\bigl{(}{}^{+1.8}_{-1.8}~{}~{}{}^{+2.3}_{-2.1}\bigr{)}$ 10.6
$5.7^{+2.6}_{-1.7}$ 8j, $\geq$4 $-0.2$ ${}^{+2.8}_{-3.0}$
$\bigl{(}{}^{+1.5}_{-1.5}~{}~{}{}^{+2.3}_{-2.6}\bigr{)}$ 5.5
$5.5^{+2.6}_{-1.6}$ $\geq$9j, $\geq$4 $-0.4$ ${}^{+2.1}_{-2.2}$
$\bigl{(}{}^{+1.4}_{-1.3}~{}~{}{}^{+1.6}_{-1.8}\bigr{)}$ 4.0
$4.3^{+1.9}_{-1.3}$ Combined $0.9$ ${}^{+1.5}_{-1.5}$
$\bigl{(}{}^{+0.7}_{-0.7}~{}~{}{}^{+1.3}_{-1.3}\bigr{)}$ 3.8
$3.1^{+1.4}_{-0.9}$

Figure 7:  Best fit values in the signal strength modifiers $\hat{\mu}$, and
their 68% intervals as split into the statistical and systematic components
(left), and median expected and observed 95% upper limits on $\mu$ (right).
The expected limits are displayed with their 68% and 95% intervals, as well as
with the expectation for an injected SM signal of $\mu=1$.

The signal is displayed in Fig. 8 in terms of the distribution in
$\log_{10}(\text{S}/\text{B})$, where $\text{S}/\text{B}$ is the ratio of the
signal to background yields in each bin of the six MEM discriminant
histograms, obtained from a combined fit constrained to the SM cross section
of $\mu=1$. Good agreement is found between the data and the sum of the SM
signal and background over the whole range of this variable.

Figure 8: Distribution in $\log_{10}(\text{S}/\text{B})$, where S and B
indicate the respective bin-by-bin yields of the signal and background
expected in the MEM discriminant distributions, obtained from a combined fit
with the constraint in the cross section of $\mu=1$.

##  0.9 Summary

A search for the associated production of a Higgs boson with a top quark pair
is performed using proton-proton collision data collected by the CMS
experiment at a centre-of-mass energy of 13, corresponding to an integrated
luminosity of $35.9\fbinv$. Events are selected to be compatible with the
$\PH\to\bbbar$ decay and the all-jet final state of the pair, and are divided
into six categories according to their reconstructed jet and jet
multiplicities.

The result of the search is presented in terms of the signal strength modifier
$\mu$ for $\ttbar\PH$ production, defined as the ratio of the measured
$\ttbar\PH$ production cross section to the one expected for a standard model
Higgs boson with a mass of 125. From a combined fit of signal and background
templates to the data in all event categories, observed and expected upper
limits of $\mu<3.8$ and $<3.1$, respectively, are obtained at 95% confidence
levels. These limits correspond to a best fit value of $\hat{\mu}=0.9\pm
0.7\stat\pm 1.3\syst=0.9\pm 1.5\,(\text{tot})$, which is compatible with the
standard model expectation.

###### 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: BMWFW and FWF (Austria); FNRS and FWO (Belgium); CNPq,
CAPES, FAPERJ, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST, and NSFC
(China); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RPF (Cyprus);
SENESCYT (Ecuador); MoER, ERC IUT, and ERDF (Estonia); Academy of Finland,
MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF
(Germany); GSRT (Greece); NKFIA (Hungary); DAE and DST (India); IPM (Iran);
SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); LAS
(Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and
UASLP-FAI (Mexico); MBIE (New Zealand); PAEC (Pakistan); MSHE and NSC
(Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom, RAS, RFBR and RAEP
(Russia); MESTD (Serbia); SEIDI, CPAN, PCTI and FEDER (Spain); Swiss Funding
Agencies (Switzerland); MST (Taipei); ThEPCenter, IPST, STAR, and NSTDA
(Thailand); TUBITAK and TAEK (Turkey); NASU and SFFR (Ukraine); STFC (United
Kingdom); DOE and NSF (USA). Individuals have received support from the Marie-
Curie 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 Science 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 Estatal de Fomento de la Investigación Científica y Técnica de
Excelencia María de Maeztu, grant MDM-2015-0509 and the Programa Severo Ochoa
del Principado de Asturias; the Thalis and Aristeia 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).

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##  .10 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan, A. Tumasyan Institut für Hochenergiephysik, Wien, Austria
W. Adam, F. Ambrogi, E. Asilar, T. Bergauer, J. Brandstetter, E. Brondolin, M.
Dragicevic, J. Erö, A. Escalante Del Valle, M. Flechl, M. Friedl, R.
Frühwirth1, V.M. Ghete, J. Grossmann, J. Hrubec, M. Jeitler1, A. König, N.
Krammer, I. Krätschmer, D. Liko, T. Madlener, I. Mikulec, E. Pree, N. Rad, H.
Rohringer, J. Schieck1, R. Schöfbeck, M. Spanring, D. Spitzbart, A. Taurok, W.
Waltenberger, J. Wittmann, C.-E. Wulz1, M. Zarucki Institute for Nuclear
Problems, Minsk, Belarus
V. Chekhovsky, V. Mossolov, J. Suarez Gonzalez Universiteit Antwerpen,
Antwerpen, Belgium
E.A. De Wolf, D. Di Croce, X. Janssen, J. Lauwers, M. Pieters, M. Van De
Klundert, H. Van Haevermaet, P. Van Mechelen, N. Van Remortel Vrije
Universiteit Brussel, Brussel, Belgium
S. Abu Zeid, F. Blekman, J. D’Hondt, I. De Bruyn, J. De Clercq, K. Deroover,
G. Flouris, D. Lontkovskyi, S. Lowette, I. Marchesini, S. Moortgat, L.
Moreels, Q. Python, K. Skovpen, S. Tavernier, W. Van Doninck, P. Van Mulders,
I. Van Parijs Université Libre de Bruxelles, Bruxelles, Belgium
D. Beghin, B. Bilin, H. Brun, B. Clerbaux, G. De Lentdecker, H. Delannoy, B.
Dorney, G. Fasanella, L. Favart, R. Goldouzian, A. Grebenyuk, A.K. Kalsi, T.
Lenzi, J. Luetic, T. Seva, E. Starling, C. Vander Velde, P. Vanlaer, D.
Vannerom, R. Yonamine Ghent University, Ghent, Belgium
T. Cornelis, D. Dobur, A. Fagot, M. Gul, I. Khvastunov2, D. Poyraz, C. Roskas,
D. Trocino, M. Tytgat, W. Verbeke, B. Vermassen, M. Vit, N. Zaganidis
Université Catholique de Louvain, Louvain-la-Neuve, Belgium
H. Bakhshiansohi, O. Bondu, S. Brochet, G. Bruno, C. Caputo, A. Caudron, P.
David, S. De Visscher, C. Delaere, M. Delcourt, B. Francois, A. Giammanco, G.
Krintiras, V. Lemaitre, A. Magitteri, A. Mertens, M. Musich, K. Piotrzkowski,
L. Quertenmont, A. Saggio, M. Vidal Marono, S. Wertz, J. Zobec Centro
Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil
W.L. Aldá Júnior, F.L. Alves, G.A. Alves, L. Brito, G. Correia Silva, C.
Hensel, A. Moraes, M.E. Pol, P. Rebello Teles Universidade do Estado do Rio de
Janeiro, Rio de Janeiro, Brazil
E. Belchior Batista Das Chagas, W. Carvalho, J. Chinellato3, E. Coelho, E.M.
Da Costa, G.G. Da Silveira4, D. De Jesus Damiao, S. Fonseca De Souza, H.
Malbouisson, M. Medina Jaime5, M. Melo De Almeida, C. Mora Herrera, L. Mundim,
H. Nogima, L.J. Sanchez Rosas, A. Santoro, A. Sznajder, M. Thiel, E.J. Tonelli
Manganote3, F. Torres Da Silva De Araujo, A. Vilela Pereira Universidade
Estadual Paulista a, Universidade Federal do ABC b, São Paulo, Brazil
S. Ahujaa, C.A. Bernardesa, L. Calligarisa, T.R. Fernandez Perez Tomeia, E.M.
Gregoresb, P.G. Mercadanteb, S.F. Novaesa, Sandra S. Padulaa, D. Romero Abadb,
J.C. Ruiz Vargasa Institute for Nuclear Research and Nuclear Energy, Bulgarian
Academy of Sciences, Sofia, Bulgaria
A. Aleksandrov, R. Hadjiiska, P. Iaydjiev, A. Marinov, M. Misheva, M. Rodozov,
M. Shopova, G. Sultanov University of Sofia, Sofia, Bulgaria
A. Dimitrov, L. Litov, B. Pavlov, P. Petkov Beihang University, Beijing, China
W. Fang6, X. Gao6, L. Yuan Institute of High Energy Physics, Beijing, China
M. Ahmad, J.G. Bian, G.M. Chen, H.S. Chen, M. Chen, Y. Chen, C.H. Jiang, D.
Leggat, H. Liao, Z. Liu, F. Romeo, S.M. Shaheen, A. Spiezia, J. Tao, C. Wang,
Z. Wang, E. Yazgan, H. Zhang, J. Zhao State Key Laboratory of Nuclear Physics
and Technology, Peking University, Beijing, China
Y. Ban, G. Chen, J. Li, Q. Li, S. Liu, Y. Mao, S.J. Qian, D. Wang, Z. Xu
Tsinghua University, Beijing, China
Y. Wang Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, C.A. Carrillo Montoya, L.F. Chaparro Sierra, C. Florez,
C.F. González Hernández, M.A. Segura Delgado University of Split, Faculty of
Electrical Engineering, Mechanical Engineering and Naval Architecture, Split,
Croatia
B. Courbon, N. Godinovic, D. Lelas, I. Puljak, P.M. Ribeiro Cipriano, T.
Sculac University of Split, Faculty of Science, Split, Croatia
Z. Antunovic, M. Kovac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, D. Ferencek, K. Kadija, B. Mesic, A. Starodumov7, T. Susa
University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos,
P.A. Razis, H. Rykaczewski Charles University, Prague, Czech Republic
M. Finger8, M. Finger Jr.8 Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab
Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
A.A. Abdelalim9,10, A. Ellithi Kamel11, S. Khalil10 National Institute of
Chemical Physics and Biophysics, Tallinn, Estonia
S. Bhowmik, R.K. Dewanjee, M. Kadastik, L. Perrini, M. Raidal, C. Veelken
Department of Physics, University of Helsinki, Helsinki, Finland
P. Eerola, H. Kirschenmann, J. Pekkanen, M. Voutilainen Helsinki Institute of
Physics, Helsinki, Finland
J. Havukainen, J.K. Heikkilä, T. Järvinen, V. Karimäki, R. Kinnunen, T.
Lampén, K. Lassila-Perini, S. Laurila, S. Lehti, T. Lindén, P. Luukka, T.
Mäenpää, H. Siikonen, E. Tuominen, J. Tuominiemi Lappeenranta University of
Technology, Lappeenranta, Finland
T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
M. Besancon, F. Couderc, M. Dejardin, D. Denegri, J.L. Faure, F. Ferri, S.
Ganjour, S. Ghosh, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry,
C. Leloup, E. Locci, M. Machet, J. Malcles, G. Negro, J. Rander, A. Rosowsky,
M.Ö. Sahin, M. Titov Laboratoire Leprince-Ringuet, Ecole polytechnique,
CNRS/IN2P3, Université Paris-Saclay, Palaiseau, France
A. Abdulsalam12, C. Amendola, I. Antropov, S. Baffioni, F. Beaudette, P.
Busson, L. Cadamuro, C. Charlot, R. Granier de Cassagnac, M. Jo, I. Kucher, S.
Lisniak, A. Lobanov, J. Martin Blanco, M. Nguyen, C. Ochando, G. Ortona, P.
Paganini, P. Pigard, R. Salerno, J.B. Sauvan, Y. Sirois, A.G. Stahl Leiton, Y.
Yilmaz, A. Zabi, A. Zghiche Université de Strasbourg, CNRS, IPHC UMR 7178,
F-67000 Strasbourg, France
J.-L. Agram13, J. Andrea, D. Bloch, J.-M. Brom, E.C. Chabert, C. Collard, E.
Conte13, X. Coubez, F. Drouhin13, J.-C. Fontaine13, D. Gelé, U. Goerlach, M.
Jansová, P. Juillot, A.-C. Le Bihan, N. Tonon, P. Van Hove Centre de Calcul de
l’Institut National de Physique Nucleaire et de Physique des Particules,
CNRS/IN2P3, Villeurbanne, France
S. Gadrat Université de Lyon, Université Claude Bernard Lyon 1, CNRS-IN2P3,
Institut de Physique Nucléaire de Lyon, Villeurbanne, France
S. Beauceron, C. Bernet, G. Boudoul, N. Chanon, R. Chierici, D. Contardo, P.
Depasse, H. El Mamouni, J. Fay, L. Finco, S. Gascon, M. Gouzevitch, G.
Grenier, B. Ille, F. Lagarde, I.B. Laktineh, H. Lattaud, M. Lethuillier, L.
Mirabito, A.L. Pequegnot, S. Perries, A. 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, S. Erdweg, T. Esch, R. Fischer,
A. Güth, T. Hebbeker, C. Heidemann, K. Hoepfner, S. Knutzen, M. Merschmeyer,
A. Meyer, P. Millet, S. Mukherjee, T. Pook, M. Radziej, H. Reithler, M.
Rieger, F. Scheuch, D. Teyssier, S. Thüer RWTH Aachen University, III.
Physikalisches Institut B, Aachen, Germany
G. Flügge, B. Kargoll, T. Kress, A. Künsken, T. Müller, A. Nehrkorn, A.
Nowack, C. Pistone, O. Pooth, A. Stahl15 Deutsches Elektronen-Synchrotron,
Hamburg, Germany
M. Aldaya Martin, T. Arndt, C. Asawatangtrakuldee, K. Beernaert, O. Behnke, U.
Behrens, A. Bermúdez Martínez, A.A. Bin Anuar, K. Borras16, V. Botta, A.
Campbell, P. Connor, C. Contreras-Campana, F. Costanza, V. Danilov, A. De Wit,
C. Diez Pardos, D. Domínguez Damiani, G. Eckerlin, D. Eckstein, T. Eichhorn,
A. Elwood, E. Eren, E. Gallo17, J. Garay Garcia, A. Geiser, J.M. Grados
Luyando, A. Grohsjean, P. Gunnellini, M. Guthoff, A. Harb, J. Hauk, H. Jung,
M. Kasemann, J. Keaveney, C. Kleinwort, J. Knolle, I. Korol, 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, A.
Mussgiller, D. Pitzl, A. Raspereza, M. Savitskyi, P. Saxena, R. Shevchenko, N.
Stefaniuk, H. Tholen, G.P. Van Onsem, R. Walsh, Y. Wen, K. Wichmann, C.
Wissing, O. Zenaiev University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, V. Blobel, M. Centis Vignali, T. Dreyer, E. Garutti, D.
Gonzalez, J. Haller, A. Hinzmann, M. Hoffmann, A. Karavdina, G. Kasieczka, R.
Klanner, R. Kogler, N. Kovalchuk, S. Kurz, V. Kutzner, J. Lange, D. Marconi,
J. Multhaup, M. Niedziela, D. Nowatschin, T. Peiffer, A. Perieanu, A. Reimers,
C. Scharf, P. Schleper, A. Schmidt, S. Schumann, J. Schwandt, J. Sonneveld, H.
Stadie, G. Steinbrück, F.M. Stober, M. Stöver, D. Troendle, E. Usai, A.
Vanhoefer, B. Vormwald Institut für Experimentelle Teilchenphysik, Karlsruhe,
Germany
M. Akbiyik, C. Barth, M. Baselga, S. Baur, E. Butz, R. Caspart, T. Chwalek, F.
Colombo, W. De Boer, A. Dierlamm, N. Faltermann, B. Freund, R. Friese, M.
Giffels, M.A. Harrendorf, F. Hartmann15, S.M. Heindl, U. Husemann, F.
Kassel15, S. Kudella, H. Mildner, M.U. Mozer, Th. Müller, M. Plagge, G. Quast,
K. Rabbertz, M. Schröder, I. Shvetsov, G. Sieber, H.J. Simonis, R. Ulrich, S.
Wayand, M. Weber, T. Weiler, S. Williamson, C. Wöhrmann, R. Wolf Institute of
Nuclear and Particle Physics (INPP), NCSR Demokritos, Aghia Paraskevi, Greece
G. Anagnostou, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas, I. Topsis-
Giotis National and Kapodistrian University of Athens, Athens, Greece
G. Karathanasis, S. Kesisoglou, A. Panagiotou, N. Saoulidou, E. Tziaferi
National Technical University of Athens, Athens, Greece
K. Kousouris, I. Papakrivopoulos University of Ioánnina, Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, S. Mallios, N.
Manthos, I. Papadopoulos, E. Paradas, J. Strologas, F.A. Triantis, D.
Tsitsonis MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
M. Csanad, N. Filipovic, G. Pasztor, O. Surányi, G.I. Veres Wigner Research
Centre for Physics, Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath19, 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
M. Bartók20, P. Raics, Z.L. Trocsanyi, B. Ujvari Indian Institute of Science
(IISc), Bangalore, India
S. Choudhury, J.R. Komaragiri National Institute of Science Education and
Research, Bhubaneswar, India
S. Bahinipati22, 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, M. Kaur, S. Kaur, R. Kumar, P. Kumari, M. Lohan, A. Mehta, S.
Sharma, J.B. Singh, G. Walia University of Delhi, Delhi, India
A. Bhardwaj, B.C. Choudhary, R.B. Garg, S. Keshri, A. Kumar, Ashok Kumar, S.
Malhotra, M. Naimuddin, K. Ranjan, Aashaq Shah, R. Sharma Saha Institute of
Nuclear Physics, HBNI, Kolkata, India
R. Bhardwaj24, R. Bhattacharya, S. Bhattacharya, U. Bhawandeep24, D. Bhowmik,
S. Dey, S. Dutt24, S. Dutta, S. Ghosh, N. Majumdar, K. Mondal, S.
Mukhopadhyay, S. Nandan, A. Purohit, P.K. Rout, A. Roy, S. Roy Chowdhury, S.
Sarkar, M. Sharan, B. Singh, S. 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, A.K. Mohanty15, P.K. Netrakanti,
L.M. Pant, P. Shukla, A. Topkar Tata Institute of Fundamental Research-A,
Mumbai, India
T. Aziz, S. Dugad, B. Mahakud, S. Mitra, G.B. Mohanty, N. Sur, B. Sutar Tata
Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, Sa. Jain, S.
Kumar, M. Maity25, G. Majumder, K. Mazumdar, N. Sahoo, T. Sarkar25, N.
Wickramage26 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. Chenarani27, E. Eskandari Tadavani, S.M. Etesami27, M. Khakzad, M.
Mohammadi Najafabadi, M. Naseri, S. Paktinat Mehdiabadi28, F. Rezaei
Hosseinabadi, B. Safarzadeh29, M. Zeinali University College Dublin, Dublin,
Ireland
M. Felcini, M. Grunewald INFN Sezione di Bari a, Università di Bari b,
Politecnico di Bari c, Bari, Italy
M. Abbresciaa,b, C. Calabriaa,b, A. Colaleoa, D. Creanzaa,c, L. Cristellaa,b,
N. De Filippisa,c, M. De Palmaa,b, A. Di Florioa,b, F. Erricoa,b, L. Fiorea,
A. Gelmia,b, G. Iasellia,c, S. Lezkia,b, G. Maggia,c, M. Maggia, B.
Marangellia,b, G. Minielloa,b, S. Mya,b, S. Nuzzoa,b, A. Pompilia,b, G.
Pugliesea,c, R. Radognaa, A. Ranieria, G. Selvaggia,b, A. Sharmaa, L.
Silvestrisa,15, 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, L. Brigliadoria,b, R. Campaninia,b, P. Capiluppia,b, A.
Castroa,b, F.R. Cavalloa, S.S. Chhibraa,b, G. Codispotia,b, M. Cuffiania,b,
G.M. Dallavallea, F. Fabbria, A. Fanfania,b, D. Fasanellaa,b, P. Giacomellia,
C. Grandia, L. Guiduccia,b, S. Marcellinia, G. Masettia, A. Montanaria, F.L.
Navarriaa,b, A. Perrottaa, A.M. Rossia,b, T. Rovellia,b, G.P. Sirolia,b, N.
Tosia INFN Sezione di Catania a, Università di Catania b, Catania, Italy
S. Albergoa,b, S. Costaa,b, A. Di Mattiaa, F. Giordanoa,b, R. Potenzaa,b, A.
Tricomia,b, C. Tuvea,b INFN Sezione di Firenze a, Università di Firenze b,
Firenze, Italy
G. Barbaglia, K. Chatterjeea,b, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, E. Focardia,b, G. Latino, P. Lenzia,b, M. Meschinia, S.
Paolettia, L. Russoa,30, G. Sguazzonia, D. Stroma, L. Viliania INFN Laboratori
Nazionali di Frascati, Frascati, Italy
L. Benussi, S. Bianco, F. Fabbri, D. Piccolo, F. Primavera15 INFN Sezione di
Genova a, Università di Genova b, Genova, Italy
V. Calvellia,b, F. Ferroa, F. Raveraa,b, E. Robuttia, S. Tosia,b INFN Sezione
di Milano-Bicocca a, Università di Milano-Bicocca b, Milano, Italy
A. Benagliaa, A. Beschib, L. Brianzaa,b, F. Brivioa,b, V. Cirioloa,b,15, M.E.
Dinardoa,b, S. Fiorendia,b, S. Gennaia, A. Ghezzia,b, P. Govonia,b, M.
Malbertia,b, S. Malvezzia, R.A. Manzonia,b, D. Menascea, L. Moronia, M.
Paganonia,b, K. Pauwelsa,b, D. Pedrinia, S. Pigazzinia,b,31, S. Ragazzia,b, T.
Tabarelli de Fatisa,b INFN Sezione di Napoli a, Università di Napoli ’Federico
II’ b, Napoli, Italy, Università della Basilicata c, Potenza, Italy,
Università G. Marconi d, Roma, Italy
S. Buontempoa, N. Cavalloa,c, S. Di Guidaa,d,15, F. Fabozzia,c, F. Fiengaa,b,
G. Galatia,b, A.O.M. Iorioa,b, W.A. Khana, L. Listaa, S. Meolaa,d,15, P.
Paoluccia,15, C. Sciaccaa,b, F. Thyssena, E. Voevodinaa,b INFN Sezione di
Padova a, Università di Padova b, Padova, Italy, Università di Trento c,
Trento, Italy
P. Azzia, N. Bacchettaa, L. Benatoa,b, D. Biselloa,b, A. Bolettia,b, R.
Carlina,b, A. Carvalho Antunes De Oliveiraa,b, P. Checchiaa, P. De Castro
Manzanoa, T. Dorigoa, U. Dossellia, F. Gasparinia,b, U. Gasparinia,b, A.
Gozzelinoa, S. Lacapraraa, M. Margonia,b, A.T. Meneguzzoa,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,15, G. Bagliesia, L. Bianchinia, T. Boccalia, L.
Borrello, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, G. Fedia, L.
Gianninia,c, A. Giassia, M.T. Grippoa,30, F. Ligabuea,c, T. Lomtadzea, E.
Mancaa,c, G. Mandorlia,c, A. Messineoa,b, F. Pallaa, A. Rizzia,b, P.
Spagnoloa, R. Tenchinia, G. Tonellia,b, A. Venturia, P.G. Verdinia INFN
Sezione di Roma a, Sapienza Università di Roma b, Rome, Italy
L. Baronea,b, F. Cavallaria, M. Cipriania,b, N. Dacia, D. Del Rea,b, E. Di
Marcoa,b, M. Diemoza, S. Gellia,b, E. Longoa,b, B. Marzocchia,b, P.
Meridiania, G. Organtinia,b, F. Pandolfia, R. Paramattia,b, F. Preiatoa,b, S.
Rahatloua,b, C. Rovellia, F. Santanastasioa,b INFN Sezione di Torino a,
Università di Torino b, Torino, Italy, Università del Piemonte Orientale c,
Novara, Italy
N. Amapanea,b, R. Arcidiaconoa,c, S. Argiroa,b, M. Arneodoa,c, N. Bartosika,
R. Bellana,b, C. Biinoa, N. Cartigliaa, R. Castelloa,b, F. Cennaa,b, M.
Costaa,b, R. Covarellia,b, A. Deganoa,b, N. Demariaa, B. Kiania,b, C.
Mariottia, S. Masellia, E. Migliorea,b, V. Monacoa,b, E. Monteila,b, M.
Montenoa, M.M. Obertinoa,b, L. Pachera,b, N. Pastronea, M. Pelliccionia, G.L.
Pinna Angionia,b, A. Romeroa,b, M. Ruspaa,c, R. Sacchia,b, K. Shchelinaa,b, V.
Solaa, A. Solanoa,b, A. Staianoa INFN Sezione di Trieste a, Università di
Trieste b, Trieste, Italy
S. Belfortea, M. Casarsaa, F. Cossuttia, G. Della Riccaa,b, A. Zanettia
Kyungpook National University
D.H. Kim, G.N. Kim, M.S. Kim, J. Lee, S. Lee, S.W. Lee, C.S. Moon, Y.D. Oh, S.
Sekmen, D.C. Son, Y.C. Yang Chonnam National University, Institute for
Universe and Elementary Particles, Kwangju, Korea
H. Kim, D.H. Moon, G. Oh Hanyang University, Seoul, Korea
J.A. Brochero Cifuentes, J. Goh, T.J. Kim Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, D. Gyun, S. Ha, B. Hong, Y. Jo, Y. Kim, K. Lee, K.S.
Lee, S. Lee, J. Lim, S.K. Park, Y. Roh Seoul National University, Seoul, Korea
J. Almond, J. Kim, J.S. Kim, H. Lee, K. Lee, K. Nam, S.B. Oh, B.C. Radburn-
Smith, S.h. Seo, U.K. Yang, H.D. Yoo, G.B. Yu University of Seoul, Seoul,
Korea
H. Kim, J.H. Kim, J.S.H. Lee, I.C. Park Sungkyunkwan University, Suwon, Korea
Y. Choi, C. Hwang, J. Lee, I. Yu Vilnius University, Vilnius, Lithuania
V. Dudenas, A. Juodagalvis, J. Vaitkus National Centre for Particle Physics,
Universiti Malaya, Kuala Lumpur, Malaysia
I. Ahmed, Z.A. Ibrahim, M.A.B. Md Ali32, F. Mohamad Idris33, W.A.T. Wan
Abdullah, M.N. Yusli, Z. Zolkapli Centro de Investigacion y de Estudios
Avanzados del IPN, Mexico City, Mexico
Duran-Osuna, M. C., H. Castilla-Valdez, E. De La Cruz-Burelo, Ramirez-Sanchez,
G., I. Heredia-De La Cruz34, Rabadan-Trejo, R. I., R. Lopez-Fernandez, J.
Mejia Guisao, Reyes-Almanza, R, A. Sanchez-Hernandez Universidad
Iberoamericana, Mexico City, Mexico
S. Carrillo Moreno, C. Oropeza Barrera, F. Vazquez Valencia Benemerita
Universidad Autonoma de Puebla, Puebla, Mexico
J. Eysermans, I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada Universidad
Autónoma de San Luis Potosí, San Luis Potosí, Mexico
A. Morelos Pineda University of Auckland, Auckland, New Zealand
D. Krofcheck University of Canterbury, Christchurch, New Zealand
S. Bheesette, P.H. Butler National Centre for Physics, Quaid-I-Azam
University, Islamabad, Pakistan
A. Ahmad, M. Ahmad, Q. Hassan, H.R. Hoorani, A. Saddique, M.A. Shah, M.
Shoaib, M. Waqas National Centre for Nuclear Research, Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, T. Frueboes, M. Górski, M. Kazana, K.
Nawrocki, M. Szleper, P. Traczyk, P. Zalewski Institute of Experimental
Physics, Faculty of Physics, University of Warsaw, Warsaw, Poland
K. Bunkowski, A. Byszuk35, K. Doroba, A. Kalinowski, M. Konecki, J.
Krolikowski, M. Misiura, M. Olszewski, A. Pyskir, M. Walczak Laboratório de
Instrumentação e Física Experimental de Partículas, Lisboa, Portugal
P. Bargassa, C. Beirão Da Cruz E Silva, A. Di Francesco, P. Faccioli, B.
Galinhas, M. Gallinaro, J. Hollar, N. Leonardo, L. Lloret Iglesias, M.V.
Nemallapudi, J. Seixas, G. Strong, O. Toldaiev, D. Vadruccio, J. Varela Joint
Institute for Nuclear Research, Dubna, Russia
S. Afanasiev, P. Bunin, M. Gavrilenko, I. Golutvin, I. Gorbunov, A. Kamenev,
V. Karjavin, A. Lanev, A. Malakhov, V. Matveev36,37, P. Moisenz, V. Palichik,
V. Perelygin, S. Shmatov, S. Shulha, N. Skatchkov, V. Smirnov, N. Voytishin,
A. Zarubin Petersburg Nuclear Physics Institute, Gatchina (St. Petersburg),
Russia
Y. Ivanov, V. Kim38, E. Kuznetsova39, 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, A. Bylinkin37 National Research Nuclear University ’Moscow
Engineering Physics Institute’ (MEPhI), Moscow, Russia
R. Chistov40, M. Danilov40, P. Parygin, D. Philippov, S. Polikarpov, E.
Tarkovskii P.N. Lebedev Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin37, I. Dremin37, M. Kirakosyan37, 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. Dubinin41, L. Dudko, A.
Ershov, A. Gribushin, V. Klyukhin, O. Kodolova, I. Lokhtin, I. Miagkov, S.
Obraztsov, S. Petrushanko, V. Savrin Novosibirsk State University (NSU),
Novosibirsk, Russia
V. Blinov42, D. Shtol42, Y. Skovpen42 State Research Center of Russian
Federation, Institute for High Energy Physics of NRC &quot, Kurchatov
Institute&quot, , Protvino, Russia
I. Azhgirey, I. Bayshev, S. Bitioukov, D. Elumakhov, A. Godizov, V. Kachanov,
A. Kalinin, D. Konstantinov, P. Mandrik, V. Petrov, R. Ryutin, A. Sobol, S.
Troshin, N. Tyurin, A. Uzunian, A. Volkov National Research Tomsk Polytechnic
University, Tomsk, Russia
A. Babaev University of Belgrade, Faculty of Physics and Vinca Institute of
Nuclear Sciences, Belgrade, Serbia
P. Adzic43, P. Cirkovic, D. Devetak, M. Dordevic, J. Milosevic Centro de
Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT), Madrid,
Spain
J. Alcaraz Maestre, A. Álvarez Fernández, I. Bachiller, M. Barrio Luna, M.
Cerrada, N. Colino, B. De La Cruz, A. Delgado Peris, C. Fernandez Bedoya, J.P.
Fernández Ramos, J. Flix, M.C. Fouz, O. Gonzalez Lopez, S. Goy Lopez, J.M.
Hernandez, M.I. Josa, D. Moran, A. Pérez-Calero Yzquierdo, J. Puerta Pelayo,
I. Redondo, L. Romero, M.S. Soares, A. Triossi Universidad Autónoma de Madrid,
Madrid, Spain
C. Albajar, J.F. de Trocóniz Universidad de Oviedo, Oviedo, Spain
J. Cuevas, C. Erice, J. Fernandez Menendez, S. Folgueras, I. Gonzalez
Caballero, J.R. González Fernández, E. Palencia Cortezon, S. Sanchez Cruz, P.
Vischia, J.M. Vizan Garcia Instituto de Física de Cantabria (IFCA), CSIC-
Universidad de Cantabria, Santander, Spain
I.J. Cabrillo, A. Calderon, B. Chazin Quero, J. Duarte Campderros, M.
Fernandez, P.J. Fernández Manteca, A. García Alonso, J. Garcia-Ferrero, G.
Gomez, A. Lopez Virto, J. Marco, C. Martinez Rivero, P. Martinez Ruiz del
Arbol, F. Matorras, J. Piedra Gomez, C. Prieels, T. Rodrigo, A. Ruiz-Jimeno,
L. Scodellaro, N. Trevisani, I. Vila, R. Vilar Cortabitarte CERN, European
Organization for Nuclear Research, Geneva, Switzerland
D. Abbaneo, B. Akgun, E. Auffray, P. Baillon, A.H. Ball, D. Barney, J.
Bendavid, M. Bianco, A. Bocci, C. Botta, T. Camporesi, M. Cepeda, G.
Cerminara, E. Chapon, Y. Chen, D. d’Enterria, A. Dabrowski, V. Daponte, A.
David, M. De Gruttola, A. De Roeck, N. Deelen, M. Dobson, T. du Pree, M.
Dünser, N. Dupont, A. Elliott-Peisert, P. Everaerts, F. Fallavollita44, G.
Franzoni, J. Fulcher, W. Funk, D. Gigi, A. Gilbert, K. Gill, F. Glege, D.
Gulhan, J. Hegeman, V. Innocente, A. Jafari, P. Janot, O. Karacheban18, J.
Kieseler, V. Knünz, A. Kornmayer, M. Krammer1, C. Lange, P. Lecoq, C.
Lourenço, M.T. Lucchini, L. Malgeri, M. Mannelli, A. Martelli, F. Meijers,
J.A. Merlin, S. Mersi, E. Meschi, P. Milenovic45, F. Moortgat, M. Mulders, H.
Neugebauer, J. Ngadiuba, S. Orfanelli, L. Orsini, F. Pantaleo15, L. Pape, E.
Perez, M. Peruzzi, A. Petrilli, G. Petrucciani, A. Pfeiffer, M. Pierini, F.M.
Pitters, D. Rabady, A. Racz, T. Reis, G. Rolandi46, M. Rovere, H. Sakulin, C.
Schäfer, C. Schwick, M. Seidel, M. Selvaggi, A. Sharma, P. Silva, P.
Sphicas47, A. Stakia, J. Steggemann, M. Stoye, M. Tosi, D. Treille, A. Tsirou,
V. Veckalns48, M. Verweij, W.D. Zeuner Paul Scherrer Institut, Villigen,
Switzerland
W. Bertl${}^{\textrm{\textdagger}}$, L. Caminada49, K. Deiters, W. Erdmann, R.
Horisberger, Q. Ingram, H.C. Kaestli, D. Kotlinski, U. Langenegger, T. Rohe,
S.A. Wiederkehr ETH Zurich - Institute for Particle Physics and Astrophysics
(IPA), Zurich, Switzerland
M. Backhaus, L. Bäni, P. Berger, B. Casal, N. Chernyavskaya, G. Dissertori, M.
Dittmar, M. Donegà, C. Dorfer, C. Grab, C. Heidegger, D. Hits, J. Hoss, T.
Klijnsma, W. Lustermann, M. Marionneau, M.T. Meinhard, D. Meister, F. Micheli,
P. Musella, F. Nessi-Tedaldi, J. Pata, F. Pauss, G. Perrin, L. Perrozzi, M.
Quittnat, M. Reichmann, D. Ruini, D.A. Sanz Becerra, M. Schönenberger, L.
Shchutska, V.R. Tavolaro, K. Theofilatos, M.L. Vesterbacka Olsson, R. Wallny,
D.H. Zhu Universität Zürich, Zurich, Switzerland
T.K. Aarrestad, C. Amsler50, D. Brzhechko, M.F. Canelli, A. De Cosa, R. Del
Burgo, S. Donato, C. Galloni, T. Hreus, B. Kilminster, I. Neutelings, D.
Pinna, G. Rauco, P. Robmann, D. Salerno, K. Schweiger, C. Seitz, Y. Takahashi,
A. Zucchetta National Central University, Chung-Li, Taiwan
V. Candelise, Y.H. Chang, K.y. Cheng, T.H. Doan, Sh. Jain, R. Khurana, C.M.
Kuo, W. Lin, A. Pozdnyakov, S.S. Yu National Taiwan University (NTU), Taipei,
Taiwan
P. Chang, Y. Chao, K.F. Chen, P.H. Chen, F. Fiori, W.-S. Hou, Y. Hsiung, Arun
Kumar, Y.F. Liu, R.-S. Lu, E. Paganis, A. Psallidas, A. Steen, J.f. Tsai
Chulalongkorn University, Faculty of Science, Department of Physics, Bangkok,
Thailand
B. Asavapibhop, K. Kovitanggoon, G. Singh, N. Srimanobhas Çukurova University,
Physics Department, Science and Art Faculty, Adana, Turkey
A. Bat, F. Boran, S. Damarseckin, Z.S. Demiroglu, C. Dozen, E. Eskut, S.
Girgis, G. Gokbulut, Y. Guler, I. Hos51, E.E. Kangal52, O. Kara, A. Kayis
Topaksu, U. Kiminsu, M. Oglakci, G. Onengut, K. Ozdemir53, S. Ozturk54, A.
Polatoz, B. Tali55, U.G. Tok, S. Turkcapar, I.S. Zorbakir, C. Zorbilmez Middle
East Technical University, Physics Department, Ankara, Turkey
G. Karapinar56, K. Ocalan57, M. Yalvac, M. Zeyrek Bogazici University,
Istanbul, Turkey
I.O. Atakisi, E. Gülmez, M. Kaya58, O. Kaya59, S. Tekten, E.A. Yetkin60
Istanbul Technical University, Istanbul, Turkey
M.N. Agaras, S. Atay, A. Cakir, K. Cankocak, Y. Komurcu Institute for
Scintillation Materials of National Academy of Science of Ukraine, Kharkov,
Ukraine
B. Grynyov National Scientific Center, Kharkov Institute of Physics and
Technology, Kharkov, Ukraine
L. Levchuk University of Bristol, Bristol, United Kingdom
F. Ball, L. Beck, J.J. Brooke, D. Burns, E. Clement, D. Cussans, O. Davignon,
H. Flacher, J. Goldstein, G.P. Heath, H.F. Heath, L. Kreczko, D.M. Newbold61,
S. Paramesvaran, T. Sakuma, S. Seif El Nasr-storey, D. Smith, V.J. Smith
Rutherford Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev62, C. Brew, R.M. Brown, D. Cieri, D.J.A. Cockerill, J.A.
Coughlan, K. Harder, S. Harper, J. Linacre, E. Olaiya, D. Petyt, C.H.
Shepherd-Themistocleous, A. Thea, I.R. Tomalin, T. Williams, W.J. Womersley
Imperial College, London, United Kingdom
G. Auzinger, R. Bainbridge, P. Bloch, J. Borg, S. Breeze, O. Buchmuller, A.
Bundock, S. Casasso, D. Colling, L. Corpe, P. Dauncey, G. Davies, M. Della
Negra, R. Di Maria, Y. Haddad, G. Hall, G. Iles, T. James, M. Komm, R. Lane,
C. Laner, L. Lyons, A.-M. Magnan, S. Malik, L. Mastrolorenzo, T. Matsushita,
J. Nash63, A. Nikitenko7, V. Palladino, M. Pesaresi, A. Richards, A. Rose, E.
Scott, C. Seez, A. Shtipliyski, T. Strebler, S. Summers, A. Tapper, K. Uchida,
M. Vazquez Acosta64, T. Virdee15, N. Wardle, D. Winterbottom, J. Wright, S.C.
Zenz Brunel University, Uxbridge, United Kingdom
J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, A. Morton, I.D. Reid, L.
Teodorescu, S. Zahid Baylor University, Waco, USA
A. Borzou, K. Call, J. Dittmann, K. Hatakeyama, H. Liu, N. Pastika, C. Smith
Catholic University of America, Washington DC, USA
R. Bartek, A. Dominguez The University of Alabama, Tuscaloosa, USA
A. Buccilli, S.I. Cooper, C. Henderson, P. Rumerio, C. West Boston University,
Boston, USA
D. Arcaro, A. Avetisyan, T. Bose, D. Gastler, D. Rankin, C. Richardson, J.
Rohlf, L. Sulak, D. Zou Brown University, Providence, USA
G. Benelli, D. Cutts, M. Hadley, J. Hakala, U. Heintz, J.M. Hogan65, K.H.M.
Kwok, E. Laird, G. Landsberg, J. Lee, Z. Mao, M. Narain, J. Pazzini, S.
Piperov, S. Sagir, R. Syarif, D. Yu University of California, Davis, Davis,
USA
R. Band, C. Brainerd, R. Breedon, D. Burns, M. Calderon De La Barca Sanchez,
M. Chertok, J. Conway, R. Conway, P.T. Cox, R. Erbacher, C. Flores, G. Funk,
W. Ko, R. Lander, C. Mclean, M. Mulhearn, D. Pellett, J. Pilot, S. Shalhout,
M. Shi, J. Smith, D. Stolp, D. Taylor, K. Tos, M. Tripathi, Z. Wang, F. Zhang
University of California, Los Angeles, USA
M. Bachtis, C. Bravo, R. Cousins, A. Dasgupta, A. Florent, J. Hauser, M.
Ignatenko, N. Mccoll, S. Regnard, D. Saltzberg, C. Schnaible, V. Valuev
University of California, Riverside, Riverside, USA
E. Bouvier, K. Burt, R. Clare, J. Ellison, J.W. Gary, S.M.A. Ghiasi Shirazi,
G. Hanson, G. Karapostoli, E. Kennedy, F. Lacroix, O.R. Long, M. Olmedo
Negrete, M.I. Paneva, W. Si, L. Wang, H. Wei, S. Wimpenny, B. R. Yates
University of California, San Diego, La Jolla, USA
J.G. Branson, S. Cittolin, M. Derdzinski, R. Gerosa, D. Gilbert, B. Hashemi,
A. Holzner, D. Klein, G. Kole, V. Krutelyov, J. Letts, M. Masciovecchio, D.
Olivito, S. Padhi, M. Pieri, M. Sani, V. Sharma, S. Simon, M. Tadel, A.
Vartak, S. Wasserbaech66, J. Wood, F. Würthwein, A. Yagil, G. Zevi Della Porta
University of California, Santa Barbara - Department of Physics, Santa
Barbara, USA
N. Amin, R. Bhandari, J. Bradmiller-Feld, C. Campagnari, M. Citron, A. Dishaw,
V. Dutta, M. Franco Sevilla, L. Gouskos, R. Heller, J. Incandela, A.
Ovcharova, H. Qu, J. Richman, D. Stuart, I. Suarez, J. Yoo California
Institute of Technology, Pasadena, USA
D. Anderson, A. Bornheim, J. Bunn, J.M. Lawhorn, H.B. Newman, T. Q. Nguyen, C.
Pena, M. Spiropulu, J.R. Vlimant, R. Wilkinson, S. Xie, Z. Zhang, R.Y. Zhu
Carnegie Mellon University, Pittsburgh, USA
M.B. Andrews, T. Ferguson, T. Mudholkar, M. Paulini, J. Russ, M. Sun, H.
Vogel, I. Vorobiev, M. Weinberg University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, F. Jensen, A. Johnson, M. Krohn, S. Leontsinis, E.
MacDonald, T. Mulholland, K. Stenson, K.A. Ulmer, S.R. Wagner Cornell
University, Ithaca, USA
J. Alexander, J. Chaves, Y. Cheng, J. Chu, A. Datta, K. Mcdermott, N. Mirman,
J.R. Patterson, D. Quach, A. Rinkevicius, A. Ryd, L. Skinnari, L. Soffi, S.M.
Tan, Z. Tao, J. Thom, J. Tucker, P. Wittich, M. Zientek Fermi National
Accelerator Laboratory, Batavia, USA
S. Abdullin, M. Albrow, M. Alyari, G. Apollinari, A. Apresyan, A. Apyan, S.
Banerjee, L.A.T. Bauerdick, A. Beretvas, J. Berryhill, P.C. Bhat, G.
Bolla${}^{\textrm{\textdagger}}$, K. Burkett, J.N. Butler, A. Canepa, G.B.
Cerati, H.W.K. Cheung, F. Chlebana, M. Cremonesi, J. Duarte, V.D. Elvira, J.
Freeman, Z. Gecse, E. Gottschalk, L. Gray, D. Green, S. Grünendahl, O.
Gutsche, J. Hanlon, R.M. Harris, S. Hasegawa, J. Hirschauer, Z. Hu, B.
Jayatilaka, S. Jindariani, M. Johnson, U. Joshi, B. Klima, M.J. Kortelainen,
B. Kreis, S. Lammel, D. Lincoln, R. Lipton, M. Liu, T. Liu, R. Lopes De Sá, J.
Lykken, K. Maeshima, N. Magini, J.M. Marraffino, D. Mason, P. McBride, P.
Merkel, S. Mrenna, S. Nahn, V. O’Dell, K. Pedro, O. Prokofyev, G. Rakness, L.
Ristori, A. Savoy-Navarro67, B. Schneider, E. Sexton-Kennedy, A. Soha, W.J.
Spalding, L. Spiegel, S. Stoynev, J. Strait, N. Strobbe, L. Taylor, S.
Tkaczyk, N.V. Tran, L. Uplegger, E.W. Vaandering, C. Vernieri, M. Verzocchi,
R. Vidal, M. Wang, H.A. Weber, A. Whitbeck, W. Wu University of Florida,
Gainesville, USA
D. Acosta, P. Avery, P. Bortignon, D. Bourilkov, A. Brinkerhoff, A. Carnes, M.
Carver, D. Curry, R.D. Field, I.K. Furic, S.V. Gleyzer, B.M. Joshi, J.
Konigsberg, A. Korytov, K. Kotov, P. Ma, K. Matchev, H. Mei, G. Mitselmakher,
K. Shi, D. Sperka, N. Terentyev, L. Thomas, J. Wang, S. Wang, J. Yelton
Florida International University, Miami, USA
Y.R. Joshi, S. Linn, P. Markowitz, J.L. Rodriguez Florida State University,
Tallahassee, USA
A. Ackert, T. Adams, A. Askew, S. Hagopian, V. Hagopian, K.F. Johnson, T.
Kolberg, G. Martinez, T. Perry, H. Prosper, A. Saha, A. Santra, V. Sharma, R.
Yohay Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, V. Bhopatkar, S. Colafranceschi, M. Hohlmann, D. Noonan, T.
Roy, F. Yumiceva University of Illinois at Chicago (UIC), Chicago, USA
M.R. Adams, L. Apanasevich, D. Berry, R.R. Betts, R. Cavanaugh, X. Chen, S.
Dittmer, O. Evdokimov, C.E. Gerber, D.A. Hangal, D.J. Hofman, K. Jung, J.
Kamin, I.D. Sandoval Gonzalez, M.B. Tonjes, N. Varelas, H. Wang, Z. Wu, J.
Zhang The University of Iowa, Iowa City, USA
B. Bilki68, W. Clarida, K. Dilsiz69, S. Durgut, R.P. Gandrajula, M.
Haytmyradov, V. Khristenko, J.-P. Merlo, H. Mermerkaya70, A. Mestvirishvili,
A. Moeller, J. Nachtman, H. Ogul71, Y. Onel, F. Ozok72, A. Penzo, C. Snyder,
E. Tiras, J. Wetzel, K. Yi Johns Hopkins University, Baltimore, USA
B. Blumenfeld, A. Cocoros, N. Eminizer, D. Fehling, L. Feng, A.V. Gritsan,
W.T. Hung, P. Maksimovic, J. Roskes, U. Sarica, M. Swartz, M. Xiao, C. You The
University of Kansas, Lawrence, USA
A. Al-bataineh, P. Baringer, A. Bean, S. Boren, J. Bowen, J. Castle, S.
Khalil, A. Kropivnitskaya, D. Majumder, W. Mcbrayer, M. Murray, C. Rogan, C.
Royon, S. Sanders, E. Schmitz, J.D. Tapia Takaki, Q. Wang Kansas State
University, Manhattan, USA
A. Ivanov, K. Kaadze, Y. Maravin, A. Modak, A. Mohammadi, L.K. Saini, N.
Skhirtladze Lawrence Livermore National Laboratory, Livermore, USA
F. Rebassoo, D. Wright University of Maryland, College Park, USA
A. Baden, O. Baron, A. Belloni, S.C. Eno, Y. Feng, C. Ferraioli, N.J. Hadley,
S. Jabeen, G.Y. Jeng, R.G. Kellogg, J. Kunkle, A.C. Mignerey, F. Ricci-Tam,
Y.H. Shin, A. Skuja, S.C. Tonwar Massachusetts Institute of Technology,
Cambridge, USA
D. Abercrombie, B. Allen, V. Azzolini, R. Barbieri, A. Baty, G. Bauer, R. Bi,
S. Brandt, W. Busza, I.A. Cali, M. D’Alfonso, Z. Demiragli, G. Gomez Ceballos,
M. Goncharov, P. Harris, D. Hsu, M. Hu, Y. Iiyama, G.M. Innocenti, M. Klute,
D. Kovalskyi, Y.-J. Lee, A. Levin, P.D. Luckey, B. Maier, A.C. Marini, C.
Mcginn, C. Mironov, S. Narayanan, X. Niu, C. Paus, C. Roland, G. Roland,
G.S.F. Stephans, K. Sumorok, K. Tatar, D. Velicanu, J. Wang, T.W. Wang, B.
Wyslouch, S. Zhaozhong University of Minnesota, Minneapolis, USA
A.C. Benvenuti, R.M. Chatterjee, A. Evans, P. Hansen, S. Kalafut, Y. Kubota,
Z. Lesko, J. Mans, S. Nourbakhsh, N. Ruckstuhl, R. Rusack, J. Turkewitz, M.A.
Wadud University of Mississippi, Oxford, USA
J.G. Acosta, S. Oliveros University of Nebraska-Lincoln, Lincoln, USA
E. Avdeeva, K. Bloom, D.R. Claes, C. Fangmeier, F. Golf, R. Gonzalez Suarez,
R. Kamalieddin, I. Kravchenko, J. Monroy, J.E. Siado, G.R. Snow, B. Stieger
State University of New York at Buffalo, Buffalo, USA
A. Godshalk, C. Harrington, I. Iashvili, D. Nguyen, A. Parker, S. Rappoccio,
B. Roozbahani Northeastern University, Boston, USA
G. Alverson, E. Barberis, C. Freer, A. Hortiangtham, A. Massironi, D.M. Morse,
T. Orimoto, R. Teixeira De Lima, T. Wamorkar, B. Wang, A. Wisecarver, D. Wood
Northwestern University, Evanston, USA
S. Bhattacharya, O. Charaf, K.A. Hahn, N. Mucia, N. Odell, M.H. Schmitt, K.
Sung, M. Trovato, M. Velasco University of Notre Dame, Notre Dame, USA
R. Bucci, N. Dev, M. Hildreth, K. Hurtado Anampa, C. Jessop, D.J. Karmgard, N.
Kellams, K. Lannon, W. Li, N. Loukas, N. Marinelli, F. Meng, C. Mueller, Y.
Musienko36, M. Planer, A. Reinsvold, R. Ruchti, P. Siddireddy, G. Smith, S.
Taroni, M. Wayne, A. Wightman, M. Wolf, A. Woodard The Ohio State University,
Columbus, USA
J. Alimena, L. Antonelli, B. Bylsma, L.S. Durkin, S. Flowers, B. Francis, A.
Hart, C. Hill, W. Ji, T.Y. Ling, W. Luo, B.L. Winer, H.W. Wulsin Princeton
University, Princeton, USA
S. Cooperstein, O. Driga, P. Elmer, J. Hardenbrook, P. Hebda, S. Higginbotham,
A. Kalogeropoulos, D. Lange, J. Luo, D. Marlow, K. Mei, I. Ojalvo, J. Olsen,
C. Palmer, P. Piroué, J. Salfeld-Nebgen, D. Stickland, C. Tully University of
Puerto Rico, Mayaguez, USA
S. Malik, S. Norberg Purdue University, West Lafayette, USA
A. Barker, V.E. Barnes, S. Das, L. Gutay, M. Jones, A.W. Jung, A. Khatiwada,
D.H. Miller, N. Neumeister, C.C. Peng, H. Qiu, J.F. Schulte, J. Sun, F. Wang,
R. Xiao, W. Xie Purdue University Northwest, Hammond, USA
T. Cheng, J. Dolen, N. Parashar Rice University, Houston, USA
Z. Chen, K.M. Ecklund, S. Freed, F.J.M. Geurts, M. Guilbaud, M. Kilpatrick, W.
Li, B. Michlin, B.P. Padley, J. Roberts, J. Rorie, W. Shi, Z. Tu, J. Zabel, A.
Zhang University of Rochester, Rochester, USA
A. Bodek, P. de Barbaro, R. Demina, Y.t. Duh, T. Ferbel, M. Galanti, A.
Garcia-Bellido, J. Han, O. Hindrichs, A. Khukhunaishvili, K.H. Lo, P. Tan, M.
Verzetti The Rockefeller University, New York, USA
R. Ciesielski, K. Goulianos, C. Mesropian Rutgers, The State University of New
Jersey, Piscataway, USA
A. Agapitos, J.P. Chou, Y. Gershtein, T.A. Gómez Espinosa, E. Halkiadakis, M.
Heindl, E. Hughes, S. Kaplan, R. Kunnawalkam Elayavalli, S. Kyriacou, A. Lath,
R. Montalvo, K. Nash, M. Osherson, H. Saka, S. Salur, S. Schnetzer, D.
Sheffield, S. Somalwar, R. Stone, S. Thomas, P. Thomassen, M. Walker
University of Tennessee, Knoxville, USA
A.G. Delannoy, J. Heideman, G. Riley, K. Rose, S. Spanier, K. Thapa Texas A&M
University, College Station, USA
O. Bouhali73, A. Castaneda Hernandez73, A. Celik, M. Dalchenko, M. De Mattia,
A. Delgado, S. Dildick, R. Eusebi, J. Gilmore, T. Huang, T. Kamon74, R.
Mueller, Y. Pakhotin, R. Patel, A. Perloff, L. Perniè, D. Rathjens, A.
Safonov, A. Tatarinov Texas Tech University, Lubbock, USA
N. Akchurin, J. Damgov, F. De Guio, P.R. Dudero, J. Faulkner, E. Gurpinar, S.
Kunori, K. Lamichhane, S.W. Lee, T. Mengke, S. Muthumuni, T. Peltola, S.
Undleeb, I. Volobouev, Z. Wang Vanderbilt University, Nashville, USA
S. Greene, A. Gurrola, R. Janjam, W. Johns, C. Maguire, A. Melo, H. Ni, K.
Padeken, J.D. Ruiz Alvarez, P. Sheldon, S. Tuo, J. Velkovska, Q. Xu University
of Virginia, Charlottesville, USA
M.W. Arenton, P. Barria, B. Cox, R. Hirosky, M. Joyce, A. Ledovskoy, H. Li, C.
Neu, T. Sinthuprasith, Y. Wang, E. Wolfe, F. Xia Wayne State University,
Detroit, USA
R. Harr, P.E. Karchin, N. Poudyal, J. Sturdy, P. Thapa, S. Zaleski University
of Wisconsin - Madison, Madison, WI, USA
M. Brodski, J. Buchanan, C. Caillol, D. Carlsmith, S. Dasu, L. Dodd, S. Duric,
B. Gomber, M. Grothe, M. Herndon, A. Hervé, U. Hussain, P. Klabbers, A.
Lanaro, A. Levine, K. Long, R. Loveless, V. Rekovic, T. Ruggles, A. Savin, N.
Smith, W.H. Smith, N. Woods †: Deceased
1: Also at Vienna University of Technology, Vienna, Austria
2: Also at IRFU; CEA; Université Paris-Saclay, Gif-sur-Yvette, France
3: Also at Universidade Estadual de Campinas, Campinas, Brazil
4: Also at Federal University of Rio Grande do Sul, Porto Alegre, Brazil
5: Also at Universidade Federal de Pelotas, Pelotas, Brazil
6: Also at Université Libre de Bruxelles, Bruxelles, Belgium
7: Also at Institute for Theoretical and Experimental Physics, Moscow, Russia
8: Also at Joint Institute for Nuclear Research, Dubna, Russia
9: Also at Helwan University, Cairo, Egypt
10: Now at Zewail City of Science and Technology, Zewail, Egypt
11: Now at Cairo University, Cairo, Egypt
12: Also at Department of Physics; King Abdulaziz University, Jeddah, Saudi
Arabia
13: Also at Université de Haute Alsace, Mulhouse, France
14: Also at Skobeltsyn Institute of Nuclear Physics; Lomonosov Moscow State
University, Moscow, Russia
15: Also at CERN; European Organization for Nuclear Research, Geneva,
Switzerland
16: Also at RWTH Aachen University; III. Physikalisches Institut A, Aachen,
Germany
17: Also at University of Hamburg, Hamburg, Germany
18: Also at Brandenburg University of Technology, Cottbus, Germany
19: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary
20: Also at MTA-ELTE Lendület CMS Particle and Nuclear Physics Group; Eötvös
Loránd University, Budapest, Hungary
21: Also at Institute of Physics; University of Debrecen, Debrecen, Hungary
22: Also at Indian Institute of Technology Bhubaneswar, Bhubaneswar, India
23: Also at Institute of Physics, Bhubaneswar, India
24: Also at Shoolini University, Solan, India
25: Also at University of Visva-Bharati, Santiniketan, India
26: Also at University of Ruhuna, Matara, Sri Lanka
27: Also at Isfahan University of Technology, Isfahan, Iran
28: Also at Yazd University, Yazd, Iran
29: Also at Plasma Physics Research Center; Science and Research Branch;
Islamic Azad University, Tehran, Iran
30: Also at Università degli Studi di Siena, Siena, Italy
31: Also at INFN Sezione di Milano-Bicocca; Università di Milano-Bicocca,
Milano, Italy
32: Also at International Islamic University of Malaysia, Kuala Lumpur,
Malaysia
33: Also at Malaysian Nuclear Agency; MOSTI, Kajang, Malaysia
34: Also at Consejo Nacional de Ciencia y Tecnología, Mexico city, Mexico
35: Also at Warsaw University of Technology; Institute of Electronic Systems,
Warsaw, Poland
36: Also at Institute for Nuclear Research, Moscow, Russia
37: Now at National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
38: Also at St. Petersburg State Polytechnical University, St. Petersburg,
Russia
39: Also at University of Florida, Gainesville, USA
40: Also at P.N. Lebedev Physical Institute, Moscow, Russia
41: Also at California Institute of Technology, Pasadena, USA
42: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia
43: Also at Faculty of Physics; University of Belgrade, Belgrade, Serbia
44: Also at INFN Sezione di Pavia; Università di Pavia, Pavia, Italy
45: Also at University of Belgrade; Faculty of Physics and Vinca Institute of
Nuclear Sciences, Belgrade, Serbia
46: Also at Scuola Normale e Sezione dell’INFN, Pisa, Italy
47: Also at National and Kapodistrian University of Athens, Athens, Greece
48: Also at Riga Technical University, Riga, Latvia
49: Also at Universität Zürich, Zurich, Switzerland
50: Also at Stefan Meyer Institute for Subatomic Physics (SMI), Vienna,
Austria
51: Also at Istanbul Aydin University, Istanbul, Turkey
52: Also at Mersin University, Mersin, Turkey
53: Also at Piri Reis University, Istanbul, Turkey
54: Also at Gaziosmanpasa University, Tokat, Turkey
55: Also at Adiyaman University, Adiyaman, Turkey
56: Also at Izmir Institute of Technology, Izmir, Turkey
57: Also at Necmettin Erbakan University, Konya, Turkey
58: Also at Marmara University, Istanbul, Turkey
59: Also at Kafkas University, Kars, Turkey
60: Also at Istanbul Bilgi University, Istanbul, Turkey
61: Also at Rutherford Appleton Laboratory, Didcot, United Kingdom
62: Also at School of Physics and Astronomy; University of Southampton,
Southampton, United Kingdom
63: Also at Monash University; Faculty of Science, Clayton, Australia
64: Also at Instituto de Astrofísica de Canarias, La Laguna, Spain
65: Also at Bethel University, ST. PAUL, USA
66: Also at Utah Valley University, Orem, USA
67: Also at Purdue University, West Lafayette, USA
68: Also at Beykent University, Istanbul, Turkey
69: Also at Bingol University, Bingol, Turkey
70: Also at Erzincan University, Erzincan, Turkey
71: Also at Sinop University, Sinop, Turkey
72: Also at Mimar Sinan University; Istanbul, Istanbul, Turkey
73: Also at Texas A&M University at Qatar, Doha, Qatar
74: Also at Kyungpook National University, Daegu, Korea