HIG-17-027

$HeadURL$ $Id$

HIG-17-027

# Search for heavy Higgs bosons decaying to a top quark pair in proton-proton
collisions at $\sqrt{s}=13$

###### Abstract

A search is presented for additional scalar () or pseudoscalar () Higgs bosons
decaying to a top quark pair in proton-proton collisions at a center-of-mass
energy of 13. The data set analyzed corresponds to an integrated luminosity of
35.9 collected by the CMS experiment at the LHC. Final states with one or two
charged leptons are considered. The invariant mass of the reconstructed top
quark pair system and variables that are sensitive to the spin of the
particles decaying into the top quark pair are used to search for signatures
of the or bosons. The interference with the standard model top quark pair
background is taken into account. A moderate signal-like deviation compatible
with an boson with a mass of 400 is observed with a global significance of 1.9
standard deviations. New stringent constraints are reported on the strength of
the coupling of the hypothetical bosons to the top quark, with the mass of the
bosons ranging from 400 to 750and their total relative width from 0.5 to 25%.
The results of the search are also interpreted in a minimal supersymmetric
standard model scenario. Values of $m_{\PSA}$ from 400 to 700are probed, and a
region with values of $\tan\beta$ below 1.0 to 1.5, depending on $m_{\PSA}$,
is excluded at 95% confidence level.

##  0.1 Introduction

The observation of a Higgs boson with a mass of 125by the ATLAS and CMS
experiments [1, 2, 3] was a milestone in particle physics, confirming the
existence of a crucial ingredient of the standard model (SM) of particle
physics. Multiple extensions of the SM predict new spin-0 states. These
include two-Higgs-doublet models (2HDMs) [4], of which the minimal
supersymmetric standard model (MSSM) [5, 6] is a particular realization,
models predicting a new electroweak singlet [7], and other models with a
combination of singlet and doublet fields [8]. The additional bosons may also
provide a portal to dark matter, by acting as a mediator between SM and dark
matter particles [9, 10].

The new states introduced in these extensions of the SM may include charged
Higgs bosons, , scalar (CP-even) neutral and bosons (here denotes the lighter
of the two states), and a pseudoscalar (CP-odd) neutral boson. For convenience
and depending on the context, a common symbol $\Phi$ is used in this paper to
represent the and bosons.

Top quarks play a key role in searches for new physics because of their high
mass and large coupling to the SM Higgs boson. Provided that additional Higgs
bosons couple to fermions via a Yukawa interaction, the top quark’s high mass
suggests the size of the coupling to these new bosons to be large as well.
Hence, assuming the masses of the new $\Phi$ bosons are sufficiently high,
their possible decay to a top quark pair is particularly interesting. Decays
of CP-odd Higgs bosons to weak vector bosons, ${\PSA}\to\PW\PW$ and
${\PSA}\to\PZ\PZ$, are forbidden (at tree level) if the CP symmetry is
assumed. Such decays are also strongly suppressed for bosons in the vicinity
of the alignment limit of 2HDMs, in which the properties of the boson approach
those of the SM Higgs boson [11]. The aligned scenario may naturally occur as
a result of a broken symmetry, and such models imply the existence of
additional Higgs bosons at relatively low mass ($\lesssim 550$) [12]. In this
paper, however, we do not rely on the assumption of alignment and its
naturalness, and probe masses between 400 and 750.

We consider a Yukawa-like coupling between the new spin-0 bosons and the top
quark. The corresponding terms in the Lagrangian for the two CP eigenstates
read as

$\mathcal{L}_{\text{Yukawa,\,{\PH}}}=-g_{{\PH}\ttbar}\,\frac{m_{\cPqt}}{v}\,\cPaqt\cPqt{\PH},\qquad\mathcal{L}_{\text{Yukawa,\,{\PSA}}}=ig_{{\PSA}\ttbar}\,\frac{m_{\cPqt}}{v}\,\cPaqt{\gamma}_{5}\cPqt{\PSA},$
(1)

where $m_{\cPqt}$ is the top quark mass, $v$ is the SM Higgs vacuum
expectation value, and the strength of the couplings is controlled by real-
valued coupling modifiers $g_{\Phi\ttbar}\geqslant 0$.

A special case of a Type-II 2HDM [4] is the Higgs sector in the hMSSM [13],
where the boson is identified with the Higgs boson with mass of 125. The hMSSM
can be fully described by two tree-level parameters: $\tan\beta$, the ratio of
the vacuum expectation values of the two Higgs fields, and $m_{\PSA}$, the
mass of the pseudoscalar boson. The parameter region at low values of
$\tan\beta$ is of particular interest, since the coupling of the additional
Higgs bosons to top quarks is enhanced in this regime.

We consider the production of new $\Phi$ bosons through the gluon fusion
process, with only top quarks in the loop. When the heavy Higgs boson decays
into a top quark pair, this mode interferes at the quantum level with the SM
production of top quark pairs. Example Feynman diagrams are shown in Fig. 1.
As a consequence, the signal consists of a resonant and an interference
component. The resonant component corresponds to the square of the amplitude
given by the signal diagram, and results in a Breit–Wigner peak in the
distribution of the invariant mass of the system, $m_{\ttbar}$. The
interference component may be either destructive or constructive, depending on
the phase space region and signal model. The sum of the components may result
in a peak-dip structure in the $m_{\ttbar}$ distribution [14, 15, 16]. It is
worth noting that the shape and magnitude of the interference depends on the
specific signal model, and can be significantly modified by new particles
appearing in the loop of the production diagram [17, 18].

Figure 1: The Feynman diagram for the signal process (left) and an example
diagram for the SM production of top quark pairs (right).

Decays of the scalar and pseudoscalar Higgs bosons produce pairs in the
${}^{3}P_{0}$ and ${}^{1}S_{0}$ states respectively [16], while the SM
$gg\to\ttbar$ production results in a mixture of states that changes with the
partonic center-of-mass energy. Consequently, the signal and the background
exhibit different angular properties, providing an additional handle to
distinguish them.

A search for or bosons decaying to a top quark pair was performed at a center-
of-mass energy $\sqrt{s}=8$by the ATLAS experiment [19]. The results were
interpreted within the context of a Type-II 2HDM. The CMS experiment performed
a search for top quark associated production of an or boson decaying to a top
quark pair at $\sqrt{s}=13$ [20]. The ATLAS and CMS Collaborations also
searched for spin-1 and spin-2 resonances decaying to a top quark pair [21,
22], generally probing very high masses and Lorentz-boosted topologies, and
without considering quantum interference with SM top quark pair production. In
addition, both collaborations performed searches for decaying to a top and a
bottom quark [23, 24], which are also sensitive to the region of low
$\tan\beta$ in the hMSSM parameter space.

This paper describes a search for or bosons decaying to a top quark pair in
proton-proton collisions at $\sqrt{s}=13$using the CMS detector at the CERN
LHC. The data set analyzed corresponds to an integrated luminosity of about
35.9, collected in 2016. Events are selected in which the top quark pair
decays into a final state with one or two leptons, where a lepton refers to an
electron or a muon throughout this article. This analysis exploits both
$m_{\ttbar}$ and angular variables sensitive to the spin of the heavy Higgs
bosons. Constraints on the coupling modifier $g_{\Phi\ttbar}$ are derived as a
function of the boson mass and width. The results are also interpreted in the
hMSSM context, putting constraints in the $(m_{\PSA},\tan\beta)$ plane.

##  0.2 The CMS detector and event reconstruction

The central feature of the CMS apparatus is a superconducting solenoid of 6m
internal diameter, providing a magnetic field of 3.8T. Within the solenoid
volume are a silicon pixel and strip tracker, a lead tungstate crystal
electromagnetic calorimeter (ECAL), and a brass and scintillator hadron
calorimeter (HCAL), each composed of a barrel and two endcap sections. Forward
calorimeters extend the pseudorapidity coverage provided by the barrel and
endcap detectors. Muons are detected in gas-ionization chambers embedded in
the steel flux-return yoke outside the solenoid. A more detailed description
of the CMS detector, together with a definition of the coordinate system used
and the relevant kinematic variables, can be found in Ref. [25].

The reconstructed vertex with the largest value of summed physics-object
$\pt^{2}$ is taken to be the primary $\Pp\Pp$ interaction vertex. The physics
objects are the jets, clustered using the jet finding algorithm [26, 27] with
the tracks assigned to the vertex as inputs, and the associated missing
transverse momentum, taken as the negative vector sum of the of those jets.

The particle-flow (PF) algorithm [28] aims to reconstruct and identify each
individual particle in an event, with an optimized combination of information
from the various elements of the CMS detector. The reconstructed particles are
referred to as PF candidates in the following. The energy of photons is
obtained from the ECAL measurement. The energy of electrons is determined from
a combination of the electron momentum at the primary interaction vertex as
determined by the tracker, the energy of the corresponding ECAL cluster, and
the energy sum of all bremsstrahlung photons spatially compatible with
originating from the electron track. The energy of muons is obtained from the
curvature of the corresponding track. The energy of charged hadrons is
determined from a combination of their momentum measured in the tracker and
the matching ECAL and HCAL energy deposits, corrected for zero-suppression
effects and for the response function of the calorimeters to hadronic showers.
Finally, the energy of neutral hadrons is obtained from the corresponding
corrected ECAL and HCAL energies.

Jets are reconstructed from the PF candidates with the infrared and collinear
safe anti-algorithm [27] operated with a distance parameter $R=0.4$. Jet
momentum is determined as the vectorial sum of all particle momenta in the
jet, and is found from simulation to be, on average, within 5 to 10% of the
true momentum over the whole spectrum and detector acceptance. Additional
$\Pp\Pp$ interactions within the same or nearby bunch crossings, referred to
as pileup, can contribute additional tracks and calorimetric energy
depositions to the jet momentum. To mitigate this effect, tracks identified to
be originating from pileup vertices are discarded, and an offset correction is
applied to correct for remaining contributions. Jet momentum corrections are
derived from simulation to bring the measured response of jets to that of
particle level jets on average. In situ measurements of the momentum balance
in dijet, $\text{photon}+\text{jet}$, $\PZ+\text{jet}$, and quantum
chromodynamics (QCD) multijet events (consisting uniquely of jets produced
through the strong interaction), are used to account for any residual
differences in jet scale in data and simulation [29]. The relative jet
resolution amounts typically to 15–20% at 30, 10% at 100, and 5% at 1.
Additional selection criteria are applied to each jet to remove jets
potentially dominated by anomalous contributions from various subdetector
components or reconstruction failures [30].

Jets originating from quarks are identified with the cMVAv2 algorithm [31],
combining six different jet discriminators, which exploit displaced track and
secondary vertex information. The collection of -tagged jets is defined by an
operating point that corresponds to a tagging efficiency of about 66% for
jets, 13% for jets, and a misidentification probability (“mistag rate”) of
about 1% for light-flavor jets. Differences between data and simulation in the
tagging efficiency and mistag rate are accounted for by scale factors that
depend on the jet and $\eta$.

The electron momentum is estimated by combining the energy measurement in the
ECAL with the momentum measurement in the tracker. The momentum resolution for
electrons with $\pt\approx 45\GeV$ from $\PZ\to\Pe\Pe$ decays ranges from 1.7%
for nonshowering electrons in the barrel region to 4.5% for showering
electrons in the endcaps [32]. Muons are measured in the pseudorapidity range
$\abs{\eta}<2.4$, with detection planes made using three technologies: drift
tubes, cathode strip chambers, and resistive plate chambers. Matching muons to
tracks measured in the silicon tracker results in a relative transverse
momentum resolution for muons with $20<\pt<100\GeV$ of 1.3–2.0% in the barrel
and better than 6% in the endcaps. The resolution in the barrel is better than
10% for muons with up to 1 [33]. Simulation-to-data scale factors that depend
on the lepton and $\eta$ are used to correct for small differences in lepton
trigger, identification, and isolation efficiency.

We define tight and loose collections of electron and muon candidates,
corresponding to the stringency of the lepton identification criteria. For
electrons, an updated version of the criteria from Ref. [32] is utilized,
while the muon identification is as described in Ref. [33]. Tight and loose
electrons are furthermore required to satisfy $\pt>20$, while tight (loose)
muons have $\pt>20~{}(10)$. The relative lepton isolation, $I_{\text{rel}}$,
is calculated as the sum of the transverse momenta of charged-hadron, neutral-
hadron, and photon PF candidates, inside a cone of $\Delta
R=\sqrt{\smash[b]{(\Delta\eta)^{2}+(\Delta\phi)^{2}}}=0.4$ around the lepton,
divided by the lepton . An estimated contribution from pileup is subtracted in
this calculation. Tight electrons must satisfy $I_{\text{rel}}\lesssim 0.06$,
while loose ones are required to have $I_{\text{rel}}\lesssim 0.18$ (0.16) in
the barrel (endcap) region. Tight (loose) muon candidates must satisfy
$I_{\text{rel}}<0.15$ (0.25).

The variable , referred to as the missing transverse momentum [34], is defined
as the magnitude of the missing transverse momentum vector , which is the
projection on the plane perpendicular to the beams of the negative vector sum
of the momenta of all reconstructed PF candidates in the event. The energy
corrections applied to the jets are propagated to the calculation.

##  0.3 Data and simulated event samples

This analysis is performed on a $\Pp\Pp$ collision data set recorded during
2016, at a center-of-mass energy of 13. The total integrated luminosity of the
collected data sample is $35.9\pm 0.9$ [35].

The single-electron (single-muon) data sample is selected with triggers [36]
based on the presence of an isolated electron (muon). The dielectron,
electron-muon, and dimuon data samples are selected with triggers that require
the presence of two leptons of the corresponding flavors. In order to increase
the selection efficiency for dilepton events in which the subleading lepton
has a relatively low , all the dilepton samples are further extended with
events that pass the single-lepton but not the dilepton triggers.

In order to compare the collected data to theoretical predictions, Monte Carlo
(MC) samples are produced with events simulating the $\Phi\to\ttbar$ signal
and SM background processes. The signal is simulated at leading order (LO)
accuracy in perturbative QCD using a custom model in the 2.5.1 event generator
[37] that implements the top quark loop of the gluon fusion production via an
effective coupling between the new boson and gluons [38]. The generator
employs the NNPDF3.0 parton distribution functions (PDFs) [39], and is
interfaced with 8.212 [40] for fragmentation and hadronization, with the
CUETP8M1 underlying event tune [41, 42]. Signal event samples are produced for
(pseudo)scalar boson masses of 400, 500, 600, and 750, with relative total
decay widths $\Gamma_{\Phi}/m_{\Phi}$ of 2.5, 5, 10, and 25% for each mass
scenario. The factorization and renormalization scales, $\mu_{\text{F}}$ and
$\mu_{\text{R}}$, are set on an event-by-event basis to $m_{\ttbar}/2$,
following the choice in Ref. [43]. The top quarks from the heavy Higgs boson
decay are decayed in , preserving their spin correlations. Samples are
generated for events corresponding to the resonant heavy boson signal, and for
events corresponding to interference terms in the matrix element calculation
between the signal and SM background. Events in the interference samples can
receive negative weights, which reflects the sign of the corresponding part of
the squared matrix element in the presence of a destructive interference.
Since the heavy Higgs boson is produced via gluon fusion with a top quark
loop, the coupling between the boson and the top quark appears twice in the
matrix element. As a result, events originating from the resonance
(interference) matrix element terms correspond to a cross section that is
proportional to $g^{4}_{\Phi\ttbar}$ ($g^{2}_{\Phi\ttbar}$).

We calculate the next-to-next-to-LO (NNLO) cross sections for the resonant
part of a given signal using the SusHi 1.6.1 program [44]. The ratio of the
NNLO cross section over the LO cross section computed with determines the $K$
factor, typically of size ${\approx}2$, applied to the resonant part of the
signal. The $K$ factors applied to the interference component of the signal
are obtained as the geometric mean of the $K$ factors of the resonant signal
and the SM process [43]. The SM $K$ factor is 1.6, calculated as the ratio
between the NNLO cross section used for the simulated sample, as described
below, and the LO cross section obtained in a similar setup. The $K$ factors
for the resonant part of the signal and the interference are applied
throughout this analysis. In the hMSSM interpretation, we also use the 2hdmc
program [45] to calculate, for given $m_{\PSA}$ and $\tan\beta$, the mass of
the boson and the widths of both heavy Higgs bosons, as well as other MSSM
parameters.

The main SM background contribution in this analysis originates from
production. Other background events originate from single top quark
production, single boson production (Drell–Yan $\PZ/\gamma^{*}+\text{jets}$
and $\PW+\text{jets}$), diboson processes ($\PW\PW$, $\PW\PZ$, and $\PZ\PZ$),
production in association with a or boson (commonly referred to as
$\ttbar\PV$), and QCD multijet processes.

The process is simulated to next-to-LO (NLO) using the v2 generator [46, 47,
48, 49], assuming a top quark mass of 172.5. The factorization and
renormalization scales are set to
$\sqrt{\smash[b]{m_{\cPqt}^{2}+p_{\text{T},\cPqt}^{2}}}$, where $m_{\cPqt}$
and $p_{\text{T},\cPqt}$ are the mass and the transverse momentum of the top
quarks in the underlying Born-level configuration. The NNPDF3.0 PDF set is
used, and the events are passed to with the CUETP8M2T4 event tune [50]. The
predicted production cross section is $831.8^{+19.8}_{-29.2}$ (scale) ${}\pm
35.1$ (PDF + ) pb, as calculated with the Top++2.0 program to NNLO in
perturbative QCD, including soft-gluon resummation to next-to-next-to-leading
logarithmic (NNLL) order (as discussed in Ref. [51] and references therein),
and assuming a top quark mass of 172.5 GeV. The first uncertainty comes from
the independent variation of the factorization and renormalization scales,
while the second one is associated to variations in the PDF and strong
coupling , following the PDF4LHC prescription with the MSTW2008 68% confidence
level NNLO, CT10 NNLO, and NNPDF2.3 5f FFN PDF sets (as discussed in Ref. [52]
and references therein, and Refs. [53, 54, 55]). The modeling of SM production
in is known to predict a harder spectrum of the top quarks than observed in
the data. An empirical reweighting for top quark pairs based on the spectrum
of generator-level top quarks is applied to obtain a better agreement with the
measured differential cross section [56, 57].

The single top quark production processes, via the $t$, $\PQt\PW$, and $s$
channels, are generated at NLO using v2, , and , respectively. The samples are
normalized using the NLO cross section predictions for the $t$ and $s$
channels [58, 59], and approximate NNLO prediction for the $\PQt\PW$ channel
[60]. The $\PZ/\gamma^{*}+\text{jets}$, $\PW+\text{jets}$, and $\ttbar\PV$
event samples are generated using . For the single boson production processes,
events with up to four additional partons are generated at LO, and the MLM
matching scheme [61] is employed to combine the different parton
multiplicities. The single-boson production cross sections are calculated at
NNLO [62, 63]. In the dilepton analysis, the normalization of the
$\PZ/\gamma^{*}+\text{jets}$ contribution is determined from a control region
in data. The $\ttbar\PV$ events are generated at NLO, applying [64] merging,
and are normalized using NLO cross section predictions. Events simulating the
diboson processes are generated using and normalized to the respective NNLO
(for WW production) [65] or NLO (for WZ and ZZ production) [66] cross
sections. The modeling of QCD multijet events is obtained from a control
region in data, but events simulated with are used to validate the modeling.
All events are interfaced with for the fragmentation and hadronization, using
the CUETP8M2T4 (single top processes) or CUETP8M1 (others) tunes.

The simulated events are processed through the CMS detector simulation based
on the program [67]. Pileup events generated with are overlaid in all samples,
to simulate additional interactions in the same bunch crossing. The simulated
events are weighted to reproduce the distribution of the number of pileup
interactions observed in data. On average, there are 23 collisions per bunch
crossing.

##  0.4 Data analysis

We search for heavy Higgs bosons decaying into a top quark pair, in final
states with either one or two leptons, where the lepton is either an electron
or a muon. The analysis strategy for single-lepton final states differs from
the one for dilepton final states, due to differences in the event selection,
SM background composition, kinematic top quark pair reconstruction, and
definition of observables that discriminate between the SM background and the
signal.

###  0.4.1 Single-lepton final state

In the single-lepton channel we aim to select events originating from top
quark pair decays to a leptonically decaying boson and a hadronically decaying
boson. The targeted topology is therefore
$\PQt\PAQt\to\ell^{+}\PGn\PQb~{}\PQq\PAQq^{\prime}\PAQb$, where $\ell$ denotes
an electron or a muon and the leptonic and hadronic boson decays may be
swapped. Events in the single-electron (single-muon) channel in data and
simulation are required to pass a single-electron (single-muon) trigger, as
explained in Section 0.3. Selected events must have exactly one tight electron
(muon) with $\pt>30~{}(26)$and $\abs{\eta_{\text{SC}}}<2.5$
($\abs{\eta}<2.4$), where $\eta_{\text{SC}}$ is the pseudorapidity of the ECAL
supercluster associated with the electron [32]. To suppress the contribution
from $\PZ/\gamma^{*}+\text{jets}$ and other processes in which multiple prompt
leptons are produced, an event is rejected if an additional loose electron or
loose muon is found. This also ensures orthogonality to the event selection of
the dilepton analysis outlined in Section 0.4.2. An event must contain at
least four jets with $\pt>20$and $\abs{\eta}<2.4$, at least two of which are
required to be tagged. To further suppress the QCD multijet background, only
events with $\mT^{\PW}>50$are selected. The transverse mass variable is
defined as
$\mT^{\PW}=\sqrt{\smash[b]{2\pt^{\ell}\ptmiss[1-\cos\Delta\phi(\ptvec^{\ell},\ptvecmiss)]}}$,
with $\ptvec^{\ell}$ being the transverse momentum of the only tight electron
or muon in the event.

Each event that passes the selection described above is reconstructed under
the assumption that it has been produced in the process
$\PQt\PAQt\to\ell\PGn\PQb\PAQb\PQq\PAQq^{\prime}$. The system is reconstructed
using an approach similar to the one adopted in Ref. [56]. All possible ways
to assign four reconstructed jets to the four quarks in the final state are
considered. To reduce the number of combinations, each of the two quarks is
required to be associated to a -tagged jet. For each considered choice of a
jet assigned to a quark stemming from the semileptonically decaying top quark,
an attempt is made to reconstruct the transverse momentum of the neutrino,
$\ptvec^{\PGn}$ [68]. This is achieved by imposing a constraint on the mass of
the semileptonically decaying top quark and on the mass of the leptonically
decaying boson, and choosing the unique solution of the neutrino momentum that
minimizes the distance $D_{\PGn}=\abs{\ptvec^{\PGn}-\ptvecmiss}$. Next, a
likelihood function is constructed from the probability density function of
the minimal value of $D_{\PGn}$, and the two-dimensional (2D) probability
density function of the reconstructed mass of the top quark and the boson in
the hadronic side of the decay. The jet assignment with the largest value of
this likelihood is chosen to reconstruct the system.

The performance of the reconstruction algorithm is studied using SM
simulation, considering only events with the targeted decays at the generator
level, $\PQt\PAQt\to\ell\PGn\PQb\PAQb\PQq\PAQq^{\prime}$. A correct jet-quark
assignment exists for 44% of such events that pass the event selection. For
those events where a correct assignment exists and a solution can be found for
the neutrino momentum, the probability that all four jets are correctly
assigned to the quarks varies from around 60 to 80%, depending on the value of
the invariant mass of the generator-level top quark pair,
$m_{\ttbar}^{\text{gen}}$. The relative $m_{\ttbar}$ resolution, as computed
with all selected events with targeted decays, changes from about 17 to 21%,
depending on $m_{\ttbar}^{\text{gen}}$.

The reconstruction results in a solution in about 85% of observed events, and
only the events with a solution are considered for further analysis. The
search for the $\Phi\to\ttbar$ signal is performed using two observables. The
first one is the invariant mass $m_{\ttbar}$, as obtained from the
reconstruction algorithm, and probes the mass of the heavy (pseudo)scalar
boson. The second observable is $\abs{\cos\theta^{*}_{\cPqt_{\ell}}}$, where
$\theta^{*}_{\cPqt_{\ell}}$ denotes the angle between the momentum of the
semileptonically decaying top quark in the rest frame and the momentum of the
system in the laboratory frame, as illustrated in Fig. 2. In a $\Phi\to\ttbar$
signal process, the heavy Higgs boson would decay into top quarks
isotropically, resulting in a flat distribution of
$\cos\theta^{*}_{\cPqt_{\ell}}$ at the generator level. The SM production, on
the other hand, yields a distribution peaking at $\pm 1$. As a result, the
distribution of $\abs{\cos\theta^{*}_{\cPqt_{\ell}}}$ is relatively enriched
in signal events towards $\abs{\cos\theta^{*}_{\cPqt_{\ell}}}=0$.

Figure 2: Definition of angle $\theta^{*}_{\cPqt_{\ell}}$ used in the single-
lepton final state. Momenta in different coordinate systems are depicted with
arrows of different colors and styles.

The QCD multijet background is estimated from dedicated control regions in the
data, independently in the electron and muon channels. The total event yield
for this background is obtained from data using a variant of the ABCD method
[69]. Four regions are defined, based on the relative isolation of the lepton
(smaller or greater than the isolation threshold imposed on the tight lepton),
and the $\mT^{\PW}$ variable (smaller or greater than 50). The three regions
complementary to the signal region are relatively enriched in the multijet
background. The overall rate of QCD multijet background in the signal region
is estimated with a simultaneous fit to the numbers of events observed in the
four regions, exploiting the factorization of the distribution of this
background in $(\mT^{\PW},I_{\text{rel}})$. The shape of the multijet
distribution of the observables $m_{\ttbar}$ and
$\abs{\cos\theta^{*}_{\cPqt_{\ell}}}$ is determined from data using events
with an inverted lepton isolation selection applied, after subtracting the
contributions from other backgrounds.

The data and the expected SM background yields are shown in Table 0.4.1, for
selected events that have a solution of the reconstruction algorithm. The
background predictions are computed with the help of a maximum likelihood fit
to the data using the background-only version of the full statistical model
that will be described in Section 0.6. The uncertainties obtained in this fit,
referred to as post-fit uncertainties, are reported.

Event yields and composition of SM background in the single-electron and
single-muon channels. Expected yields are computed after the background-only
fit to the data as explained in the text.  Process Electron channel Muon
channel Event yield Observed 274 821 416 254 Total background
$\left(274.8^{+0.8}_{-0.9}\right)\ten{3}$
$\left(416.3^{+1.1}_{-1.2}\right)\ten{3}$ Fraction w. r. t. total background
91.9% 92.1% Single top quark 3.9% 4.0% $\PW+\text{jets}$ 1.9% 2.1%
$\PZ/\gamma^{*}+\text{jets}$ 0.4% 0.3% $\ttbar\PV$ 0.2% 0.2% Diboson 0.1% 0.1%
QCD multijet 1.5% 1.0%

Figure 3: Observed and expected distributions of $m_{\ttbar}$ in different
$\abs{\cos\theta^{*}_{\cPqt_{\ell}}}$ regions in the $\Pe+\text{jets}$ (upper)
and $\PGm+\text{jets}$ (lower) channels. The expected distributions have been
obtained with a background-only fit to the data, and an approximate post-fit
uncertainty is shown with a gray band. The impact of the best-fit signal is
included in the lower panels for illustration.

The observed and post-fit predicted distributions of $m_{\ttbar}$ in different
$\abs{\cos\theta^{*}_{\cPqt_{\ell}}}$ regions are shown in Fig. 3. The impact
of the signal process (including the interference) for the best-fit signal
hypothesis, which will be discussed in Section 0.6.2, is shown in the lower
panels. It demonstrates the characteristic peak-dip lineshape discussed in
Section 0.1. For this benchmark and also in general, the contributions from
both the resonant part and the interference are important. The relative
importance of the latter increases with the total width or as the coupling
modifier decreases.

###  0.4.2 Dilepton final state

In the dilepton channel we aim to select signal events where both top quarks
decay to a leptonically decaying boson. Hence, the targeted decay topology is
$\PQt\PAQt\to\ell^{+}\PGn\PQb~{}\ell^{-}\PAGn\PAQb$. Events in the dielectron
($\Pe\Pe$), electron-muon ($\Pe\Pgm$), and dimuon ($\Pgm\Pgm$) channel in data
and simulation are required to pass a dielectron, electron-muon, and dimuon
trigger, respectively, or a single-lepton trigger, as explained in Section
0.3. The subsequent event selection closely follows Ref. [70]. Events are
required to contain exactly one pair of oppositely charged tight leptons, in
which the leading (subleading) lepton has $\pt>25$ (20). Events are rejected
if they contain additional tight electrons or additional muons that satisfy
the tight identification criteria with the exception of a looser selection on
isolation, $I_{\text{rel}}<0.25$. The selected dilepton pair is further
required to have an invariant mass of at least 20, to suppress events from
low-mass dilepton resonances. In the $\Pe\Pe$ and $\mu\mu$ channels, events
are rejected if they contain a dilepton pair consistent with a decay of a
boson, namely, if they have an invariant mass in the range 76–106. Each event
must have at least two jets with $\pt>30$and $\abs{\eta}<2.4$. Additional jets
with $\pt>20$and $\abs{\eta}<2.4$ in the event are also considered for further
analysis. At least one of the jets is required to be tagged. In the $\Pe\Pe$
and $\Pgm\Pgm$ channels, the must exceed 40to further suppress
$\PZ/\gamma^{*}$ background events.

The contribution from the $\PZ/\gamma^{*}+\text{jets}$ background process to
the selected event yield is estimated using control regions in data, following
the procedure described in Ref. [71]. The yield outside the boson mass window
is estimated based on the observed event yield within the window, using the
knowledge from simulation of the ratio of $\PZ/\gamma^{*}+\text{jets}$ inside
and outside of the mass window. In this estimation, the
non-$\PZ/\gamma^{*}+\text{jets}$ background contribution within the boson mass
window is taken from the $\Pe\mu$ channel, where the
$\PZ/\gamma^{*}+\text{jets}$ contribution is negligible, and corrected for
lepton reconstruction effects before being subtracted from the observed number
of events in the boson mass window. Using this method, the theoretically
predicted $\PZ/\gamma^{*}+\text{jets}$ event yield is scaled by a factor 1.22,
1.20, and 1.19 in the $\Pe\Pe$, $\Pe\PGm$, and $\PGm\PGm$ channels
respectively.

Each event that passes the selection described above is reconstructed under
the assumption that it has been produced in a process
$\PQt\PAQt\to\ell^{+}\PGn\PQb~{}\ell^{-}\PAGn\PAQb$. A kinematic
reconstruction algorithm [72] is applied to reconstruct the system. All jets
with $\pt>20$are considered in the reconstruction of the system. Given an
assignment of jets and leptons to the underlying expected decay products, a
system of equations is constructed that imposes constraints on the
reconstructed boson mass and the reconstructed top quark and antiquark masses.
The transverse momentum imbalance, represented by , is assumed to originate
solely from the two neutrinos. Detector resolution effects are taken into
account by sampling both the measured energy and the direction of the leptons
and jet candidates within their respective experimental resolutions. For each
sampling, the solution of the equations that results in the smallest
$m_{\ttbar}$ is chosen. Per event, 100 samplings are performed, and each is
assigned a weight based on the probability density of the invariant mass of
the lepton and jet from the top quark decay. The kinematic properties of the
top quark and antiquark are obtained as a weighted average over all samplings.
Finally, the assignment of jets resulting in the maximum sum of weights over
all samplings is chosen, and preference is given to a jet assignment that
contains two -tagged jets.

The performance of the reconstruction algorithm is studied using simulated SM
events with targeted decays at the generator level,
$\PQt\PAQt\to\ell^{+}\PGn\PQb~{}\ell^{-}\PAGn\PAQb$. In 75% of the selected
events the two generator-level jets are within the acceptance. For those
events for which the algorithm has a solution, the probability to correctly
match both jets as chosen by the algorithm to jets originating from quarks
from the top quark decays is 55 to 85%, depending on the value of
$m_{\ttbar}^{\text{gen}}$. The $m_{\ttbar}$ resolution, computed using all
selected SM events with targeted decays, ranges from 20 to 28%, depending on
$m_{\ttbar}^{\text{gen}}$.

The events for which the reconstruction results in a solution, which is the
case in about 95% of observed events, are considered for further analysis. The
resulting event yields for data and SM background expectations are shown in
Table 0.4.2.

Event yields and composition of SM background in the dilepton channel.
Expected yields are computed in the same way as in Table 0.4.1.  Process Event
yield Observed 230 233 Total background $\left(231.1\pm 0.8\right)\ten{3}$
Fraction w. r. t. total background 93.3% Single top quark 3.3%
$\PZ/\gamma^{*}+\text{jets}$ 2.9% $\ttbar\PV$ 0.3% Diboson 0.1%

The search for the $\Phi\to\ttbar$ signal is performed using two observables.
The first one is the invariant mass $m_{\ttbar}$, obtained from the
reconstruction algorithm. The second observable is a spin correlation variable
constructed from the charged leptons in the event. Charged leptons have the
highest spin analyzing power amongst the top quark decay products [73], and
their properties can be measured precisely. The chosen variable is the cosine
of the angle between the charged lepton momenta in their respective helicity
frames, and is denoted by $c_{\text{hel}}$. The four-momenta of the leptons in
their helicity frames are obtained by first boosting the leptons into the rest
frame and then boosting them along their parent top quark or antiquark
directions in this frame. The distribution of $c_{\text{hel}}$ is sensitive to
the spin and CP state of the system. At the generator level and with no
requirements on acceptance, the distribution is linear in shape; its slope is
maximally positive for the resonance and is mildly negative for the resonance.
On the other hand, the slope for the SM production, integrated over the entire
$m_{\ttbar}$ range, is mildly positive. This allows discriminating both
between the signal and background processes and between the and states. The
observed and post-fit predicted distributions used in the dilepton channel are
shown in Fig. 4.

Figure 4: Observed and expected distributions of the observables exploited in
the dilepton channel. The expected distributions have been obtained with a
background-only fit to the data, and an approximate post-fit uncertainty is
shown with a gray band. The impact of the best-fit signal is included in the
lower panel for illustration.

##  0.5 Systematic uncertainties

Various sources of uncertainty affect the distributions of the observables
used to search for a heavy Higgs boson signal. Below we describe the
experimental and theoretical systematic effects considered in the analysis. In
the statistical evaluation discussed in Section 0.6, each source of
uncertainty corresponds to a nuisance parameter in a binned maximum-likelihood
fit to the distributions of the observables in data. Uncertainties that affect
only the normalization are modeled using log-normal constraints, while
Gaussian constraints are imposed for nuisance parameters that control all
other uncertainties. Unless stated otherwise, all uncertainties are evaluated
on signal as well as background processes and treated as fully correlated
among the processes and lepton channels. The uncertainties are summarized in
Table 0.5.

The uncertainty due to the jet scale [29] is evaluated by varying the
corresponding corrections within their uncertainties. The events are
reanalyzed, by reapplying the event selection and recalculating all kinematic
quantities. A total of 19 independent jet momentum correction uncertainties
affecting jets in the tracker acceptance are considered. As the jet resolution
in simulation is smeared to match the resolution observed in data, a
corresponding uncertainty is evaluated. An uncertainty in the unclustered
component of is computed by shifting the energies of PF candidates not
clustered into jets with $\pt>15$according to the energy resolution for each
type of PF candidate [34]. Uncertainties in the tagging efficiency scale
factors applied to simulated events are evaluated by varying them within the
respective uncertainties [31]. The scale factors for heavy-flavor ( and ) jets
are varied independently of those for light-flavor jets. The uncertainties in
the trigger scale factors as well as the electron and muon identification
scale factors are considered [32, 33], where the lepton identification also
includes effects originating from the isolation requirement and the track
reconstruction. The uncertainties in the trigger efficiency scale factors for
the single-electron and single-muon channels are considered not correlated
with each other, but each of them is independently correlated with the
uncertainty in the trigger scale factors in the dilepton channel, with a 50%
correlation coefficient. Effects due to the uncertainty in the distribution of
the number of pileup interactions are evaluated by varying the effective
inelastic proton-proton cross section in the simulation by 4.6% from its
nominal value. The uncertainty in the integrated luminosity amounts to 2.5%
[35] and affects the normalization of all simulated processes.

The prediction of the SM production, the main background process in the
analysis, is affected by various sources of theoretical uncertainties. The
overall normalization of the SM background is assigned an uncertainty of 6%,
from the $\text{NNLO}+\text{NNLL}$ QCD cross section calculation [51, 52] that
is used to normalize the events. The effect of the choice of the
renormalization and factorization scales, $\mu_{\text{R}}$ and
$\mu_{\text{F}}$, in the matrix element is evaluated by varying these scales
independently by a factor of 2 and $1/2$. The effect on the acceptance is
considered, but the effect on the cross section is ignored as it is already
included in the considered uncertainty in the cross section. The
renormalization scales used in the parton shower simulation of initial-state
radiation (ISR) and final-state radiation (FSR) are also varied independently
by a factor of 2 in each direction. The effect of the uncertainty in the
amount of ISR, as well as in the underlying event tune used in the simulation,
was found not to be statistically significant. The uncertainty in the top
quark mass is considered by shifting $m_{\cPqt}$ in the simulation by $\pm
3$and rescaling the induced variations by a factor of $1/6$ to emulate a more
realistic top quark mass uncertainty of 0.5 [74]. The uncertainty in the
matching scale between the matrix element and the parton shower is evaluated
by varying the parameter, $h_{\text{damp}}$, that controls the suppression of
radiation of additional high-jets [50]. The nominal value of $h_{\text{damp}}$
in the simulation is $1.58~{}m_{\cPqt}$, and the varied values are
$0.99~{}m_{\cPqt}$ and $2.24~{}m_{\cPqt}$. The uncertainty arising from the
choice of the PDF set is evaluated by reweighting the simulated events using
100 replicas of the NNPDF3.0 set. A principal component analysis is performed
on the variations from the PDF replicas to construct two base variations, such
that the deviation from the nominal distribution given by each replica can be
described as a linear combination of the base variations. The uncertainty in
the parameter used in the PDF set induces a third independent PDF variation.
The uncertainty accounting for the mismodeling of the spectrum of top quarks
is evaluated by varying the two parameters used in the top quark reweighting
function.

The renormalization and factorization scale uncertainties in the heavy Higgs
boson signal simulation are treated independently for the resonant and
interference components. Compared to the alternative of varying the scales for
the two components simultaneously, we found this to be the more conservative
option. The effect on the acceptance as well as on the cross section is
considered. Since the simulated samples have been generated at LO accuracy,
the total effect on the cross section reaches values in excess of 30%. Other
theoretical uncertainties in the signal, such as the uncertainties in
$m_{\cPqt}$ or PDF, are neglected as they are expected to be small compared to
this variation.

The expected yields for most of the non- background processes are derived
using theoretical predictions for the cross sections at NLO or higher
accuracy. The uncertainties assumed in the normalization of these processes
are conservative and always exceed those of the corresponding theoretical
computations. For the single top quark production in the $t$ ($\cPqt\PW$)
channel we assign an uncertainty of 20 (15)%, which is based on the
measurements of the cross sections of these processes [75, 76, 77]. The
uncertainty in the $\ttbar\PV$ production is taken to be 30%, which covers the
uncertainties of the experimental measurements [78, 79]. To account for the
fact that this search probes a restricted region of the phase space of the
corresponding processes, we assign uncertainties of 50% for $\PW+\text{jets}$
and $\PZ/\gamma^{*}+\text{jets}$ production (only in the single-lepton
channel) and 30% for the diboson production. Finally, a 20% uncertainty is
used for the $s$-channel single top quark production, for which no measurement
at the LHC exists. The adopted conservative normalization uncertainties have
little impact on the sensitivity of this search due to the small contribution
of these processes.

In cases where the normalization of a background process is estimated using a
data-driven method, the corresponding uncertainty is determined by the same
method. For the Drell–Yan background in the dilepton channel we assign a 30%
uncertainty, from the variation in the scale factors when derived with and
without the requirements on , the jet tag decisions, and the reconstruction.
The normalization of the QCD multijet background, which is only relevant in
the single-lepton channel, is assigned an uncertainty of ${+100}/{-50}$%,
independently in the single-electron and single-muon channels. It covers the
statistical uncertainty in the underlying fit and the difference between the
data-driven and MC-based estimations.

The nominal background prediction is affected by the limited size of the
simulated MC event samples. This statistical uncertainty is evaluated using
the “light” Barlow–Beeston method [80], by introducing one additional nuisance
parameter per bin of the 2D distribution of the observables.

The systematic uncertainties considered in the analysis, indicating the number
of corresponding nuisance parameters (when more than one) in the statistical
model, the type (affecting shape or only normalization), the affected
processes, and the correlation among the lepton channels. Uncertainties tagged
in the last column with “All” are correlated among the single-lepton and
dilepton channels. In case an uncertainty is only applicable to the single-
electron, the single-muon, the single-lepton, or the dilepton channel, they
are indicated with , , $\ell$, $\ell\ell$, respectively.  Uncertainty (# of
parameters) Type Affected process Correlation Jet scale (19) shape All All Jet
resolution shape All All Unclustered  shape All All tagging heavy-flavor jets
shape All All tagging light-flavor jets shape All All Pileup shape All All
Electron identification shape All All Muon identification shape All All
Single-electron trigger shape All , $\ell\ell$ Single-muon trigger shape All ,
$\ell\ell$ Luminosity calibration norm. All All Renorm. scale SM  shape SM
All Fact. scale SM  shape SM  All Parton shower FSR  shape SM  All
$h_{\text{damp}}$ shape SM  All Top quark mass shape SM  All Top quark (2)
shape SM  All PDF (3) shape SM  All Renorm. scale res. signal shape Resonant
signal All Renorm. scale int. signal shape Interference signal All Fact. scale
res. signal shape Resonant signal All Fact. scale int. signal shape
Interference signal All SM norm. norm. SM  All Single top $t$ channel norm.
norm. Single top $t$ channel $\ell$ Single top $s$ channel norm. norm. Single
top $s$ channel $\ell$ Single top $\cPqt\PW$ channel norm. norm. Single top tW
channel All $\PW+\text{jets}$ norm. norm. $\PW+\text{jets}$ $\ell$
$\PZ/\gamma^{*}+\text{jets}$ norm. norm. $\PZ/\gamma^{*}+\text{jets}$ $\ell$
$\PZ/\gamma^{*}+\text{jets}$ norm. from data norm.
$\PZ/\gamma^{*}+\text{jets}$ $\ell\ell$ Diboson norm. norm. Diboson All
$\ttbar\PV$ norm. norm. $\ttbar\PV$ All QCD multijet norm. from data,  norm.
QCD multijet QCD multijet norm. from data,  norm. QCD multijet MC statistical
uncertainty (365) shape All No

Several systematic variations in the background, most notably those
constructed from dedicated MC samples, are affected by statistical
fluctuations. We suppress these fluctuations by smoothing the relative
deviations from the nominal background distribution of $m_{\ttbar}$ and the
angular variable. The up and down deviations for each independent uncertainty
are assumed to be symmetric in shape, but allowed to differ in the overall
size. The symmetrized deviation is smoothed using a version of the LOWESS
algorithm (LOcally WEighted Scatterplot Smoothing) [81, 82]. In the vicinity
of each bin of the 2D distribution, the symmetrized deviation is fitted with a
plane using a weighted least squares fit, in which nearby bins receive larger
weights. The smoothed deviation obtained in this way is rescaled to account
for the overall size of the input up or down deviation, and applied to the
nominal background expectation in the given bin. A similar procedure is also
applied to all signal distributions. The resulting distributions are then used
in the subsequent analysis to evaluate the systematic uncertainty under
consideration.

In general, the relative importance of different systematic uncertainties
depends greatly on the signal hypothesis, especially the mass of the heavy
Higgs boson. Typically, among the uncertainties with the largest impact are
the signal theoretical uncertainties and some of the jet momentum correction
uncertainties. Close to the production threshold, the variations in
$m_{\cPqt}$ and the $h_{\text{damp}}$ parameter become important, while for
larger $m_{\Phi}$ the PDF, $\mu_{\text{R}}$, and $\mu_{\text{F}}$ variations
in the SM background can have significant impacts. For certain signal
hypotheses, the signal distribution partially resembles one of a minor
background; in such cases the variation of the normalization of the respective
background becomes relevant. In addition, MC statistical uncertainties, when
grouped together, often outweigh every other individual uncertainty.

##  0.6 Results

To evaluate the consistency of the observed data with the presence of a
signal, we perform a statistical analysis using the 2D binned distribution of
$(m_{\ttbar},\abs{\cos\theta^{*}_{\cPqt_{\ell}}})$ in the single-electron and
the single-muon channels separately, and the 2D binned distribution of
$(m_{\ttbar},c_{\text{hel}})$ in the combined dilepton channel. The single-
lepton and dilepton channels do not overlap as they correspond to orthogonal
lepton selection criteria.

The statistical model is defined by the likelihood function

$\begin{gathered}L(\mu,\mathbf{p},\boldsymbol{\nu})=\left(\prod_{i}\frac{\lambda_{i}^{n_{i}}(\mu,\mathbf{p},\boldsymbol{\nu})}{n_{i}!}\,\re^{-\lambda_{i}(\mu,\mathbf{p},\boldsymbol{\nu})}\right)\,G(\boldsymbol{\nu}),\\\
\lambda_{i}(\mu,\mathbf{p},\boldsymbol{\nu})=\mu\sum_{\Phi={\PH},{\PSA}}\left(g_{\Phi\ttbar}^{4}\,s_{R,i}^{\Phi}(m_{\Phi},\Gamma_{\Phi},\boldsymbol{\nu})+g_{\Phi\ttbar}^{2}\,s_{I,i}^{\Phi}(m_{\Phi},\Gamma_{\Phi},\boldsymbol{\nu})\right)+b_{i}(\boldsymbol{\nu}),\end{gathered}$
(2)

with $b_{i}$ denoting the combined background yield in a given bin $i$,
$s_{R,i}^{\Phi}$ and $s_{I,i}^{\Phi}$ the signal yields in a given bin for the
resonant and interference part, respectively, $\boldsymbol{\nu}$ the vector of
nuisance parameters (on which the signal and background yields generally
depend), $n_{i}$ the observed yield, and $g_{\Phi\ttbar}$ the coupling
strength modifiers given by Eq. (1). The parameters of the signal model (mass
$m_{\Phi}$, width $\Gamma_{\Phi}$, and $g_{\Phi\ttbar}$) are collectively
denoted by vector $\mathbf{p}$. Eq. (2) is kept generic by including
contributions from both CP states. As there is no interference between them,
the corresponding signal distributions are trivially added together. We also
introduce an auxiliary overall signal strength modifier $\mu$, which rescales
the full beyond the SM (BSM) contribution. This allows testing different
signal hypotheses in a computationally efficient way, as will be detailed
below. The external constraints on the nuisance parameters are taken into
account in this likelihood via a product of corresponding probability density
functions, $G(\boldsymbol{\nu})$.

The background-only model is constructed by setting $\mu=0$ in Eq. (2). To
quantify the level of agreement between it and observed data, we perform a
goodness-of-fit test based on the so-called “saturated model” [83]. This
yields a $p$-value of 0.43, indicating a good overall agreement.

We perform scans over the parameters of the signal models, $\mathbf{p}$. A
variant of the LHC profile likelihood ratio test statistic $\tilde{q}_{\mu}$
from Refs. [84, 85] is utilized:

$\tilde{q}_{\mu,\mathbf{p}}=-2\ln\frac{L(\mu,\mathbf{p},\hat{\boldsymbol{\nu}}_{\mu,\mathbf{p}})}{L(\hat{\mu}_{\mathbf{p}},\mathbf{p},\hat{\boldsymbol{\nu}}_{\mathbf{p}})},\quad
0\leqslant\hat{\mu}_{\mathbf{p}}\leqslant\mu.$ (3)

The test statistic is expressed in terms of the auxiliary parameter $\mu$ in
Eq. (2), in which the statistical model is linear, while the parameters
$\mathbf{p}$ are kept fixed at their values being probed in the scan. The
likelihood in the numerator is maximized with respect to the nuisance
parameters, and $\hat{\boldsymbol{\nu}}_{\mu,\mathbf{p}}$ denotes the vector
of their values at the maximum. A similar notation is used in the denominator,
where the likelihood is maximized with respect to both $\mu$ and
$\boldsymbol{\nu}$, under the additional constraint
$0\leqslant\hat{\mu}_{\mathbf{p}}\leqslant\mu$. The requirement
$\hat{\mu}_{\mathbf{p}}\geqslant 0$ excludes from the consideration cases in
which the shape of the overall BSM contribution gets flipped, resulting in a
qualitatively different effect from what is targeted in this search. The
condition $\hat{\mu}_{\mathbf{p}}\leqslant\mu$ prevents the exclusion of a
signal hypothesis if the data are more compatible with a model that predicts
the BSM contribution of a similar shape but a larger overall size.

For each signal hypothesis $\mathbf{p}$, we perform a test according to the
criterion [86, 87]. This is done for $\mu=1$ in Eq. (3), which reproduces the
nominal signal expectation. We profit from the known asymptotic approximation
[84] for distributions of the adopted test statistic to construct these
distributions in a computationally efficient way. If the value computed for
$\mu=1$ and given $\mathbf{p}$ is found to be smaller than 0.05, the point
$\mathbf{p}$ is said to be excluded at 95% confidence level ().

###  0.6.1 Interpolation and extrapolation of signal masses and widths

To construct expected signal distributions for every point encountered in the
scans, we apply an interpolation in mass and width of the heavy Higgs boson,
starting from the reference generated points. This is done independently for
the $m_{\ttbar}$ distribution in each bin of the angular variable. We consider
the resonant part of the signal and the interference separately and further
split the interference contribution in two according to the sign of the per-
event generator weight. A change in the mass results in a horizontal shift of
the $m_{\ttbar}$ distribution. The interpolation in this observable is
implemented with a nonlinear morphing algorithm [88]. On the other hand, the
effect of a change in $\Gamma_{\Phi}$ is evaluated with an independent
interpolation in each bin.

The signal model parameter scan may reach values of $\Gamma_{\Phi}/m_{\Phi}$
below 2.5%, the lowest value considered in the simulated signal samples. Since
the reconstructed $m_{\ttbar}$ resolution is about 17% or worse, the shape of
the $m_{\ttbar}$ distribution for a signal with such low widths does not
differ from the one corresponding to $\Gamma_{\Phi}/m_{\Phi}=2.5\%$. Hence,
for scan points with $\Gamma_{\Phi}/m_{\Phi}<2.5\%$ it is sufficient to use
the distributions for $\Gamma_{\Phi}/m_{\Phi}=2.5\%$ and only scale the cross
sections appropriately.

###  0.6.2 Model-independent interpretation

Constraints on the coupling strength modifier $g_{\Phi\ttbar}$ are derived as
a function of the mass and width of the heavy Higgs boson, for each CP state
independently. The coupling modifier for the other CP state in Eq. (2) is set
to zero to exclude it from the statistical model. The scan is performed for
$m_{\Phi}$ between 400 and 750and $\Gamma_{\Phi}/m_{\Phi}$ between 0.5 and
25%. The mass and width interpolation described in Section 0.6.1 is performed
in scan points other than those corresponding to the generated signal samples.
Coupling strength values up to 3 are probed to guarantee that the amplitudes
preserve perturbative unitarity for all calculations, in accordance with the
lower bound $\tan\beta=1/g_{{\PSA}\ttbar}\gtrsim 0.3$ given in Ref. [4] in the
context of 2HDMs.

The constraints obtained on $g_{\Phi\ttbar}$ are presented in Figs. 5 and 6
for the scalar and the pseudoscalar scenarios, respectively. Since the total
width $\Gamma_{\Phi}$ is kept fixed during the scans and the partial width of
$\Phi\to\ttbar$ is proportional to $g_{\Phi\ttbar}^{2}$, in some regions the
partial width can exceed $\Gamma_{\Phi}$. These unphysical regions are marked
in the figures with hatched lines. In some cases the observed exclusion for a
given mass does not extend continuously all the way to the largest probed
$g_{\Phi\ttbar}=3$ (, $m_{{\PH}}\approx 700$in the panel for
$\Gamma_{{\PH}}/m_{{\PH}}=25\%$ in Fig. 5). This is due to the strong
dependence of the shape of the signal distribution on the value of the
coupling strength modifier. For some values of $g_{\Phi\ttbar}$ the shape
becomes compatible with systematic variations in the background.

Figure 5: Model-independent constraints on the coupling strength modifier as a
function of the heavy scalar boson mass, for relative widths of 0.5, 1, 2.5,
5, 10, and 25%. The observed constraints are indicated by the blue shaded
area. The inner (green) band and the outer (yellow) band indicate the regions
containing 68 and 95%, respectively, of the distribution of constraints
expected under the background-only hypothesis. The unphysical region of phase
space in which the partial width $\Gamma_{{\PH}\to\ttbar}$ becomes larger than
the total width is indicated by the hatched lines.

Figure 6: Model-independent constraints on the coupling strength modifier as a
function of the heavy pseudoscalar boson mass, for relative widths of 0.5, 1,
2.5, 5, 10, and 25%. The observed constraints are indicated by the blue shaded
area. The inner (green) band and the outer (yellow) band indicate the regions
containing 68 and 95%, respectively, of the distribution of constraints
expected under the background-only hypothesis. The unphysical region of phase
space in which the partial width $\Gamma_{{\PSA}\to\ttbar}$ becomes larger
than the total width is indicated by the hatched lines.

As evident from Fig. 6, there is a signal-like excess for the pseudoscalar
hypotheses with low masses. The largest deviation from the SM background is
observed for a pseudoscalar Higgs boson with a mass of 400 and a total
relative width of 4%, with a local significance of $3.5\pm 0.3$ standard
deviations. Figure 7 shows scans of $-2\ln[L(g_{{\PSA}\ttbar})/L_{\text{SM}}]$
for this hypothesis, as a function of the coupling modifier
$g_{{\PSA}\ttbar}$. The likelihoods $L(g_{{\PSA}\ttbar})$ and $L_{\text{SM}}$
are given by Eq. (2). They are computed for $\mu=1$ and 0 respectively and in
both cases maximized with respect to all nuisance parameters. The scans are
shown for the observed data, as well as for the expectations under the
background-only hypothesis and in the presence of the signal. In the latter
case the coupling modifier is set to the value obtained in the combined fit,
$g_{{\PSA}\ttbar}\approx 0.9$. In this case and in general, the expected
sensitivity is comparable between the single-lepton and dilepton channels. The
single-lepton channel is slightly more sensitive for the scalar hypotheses and
in the case of the pseudoscalar hypotheses with larger masses. However, Fig. 7
demonstrates that the observed excess is driven by the dilepton channel, which
is also supported by the comparisons between the observed and expected
distributions in Figs. 3 and 4.

Figure 7: Scans of profiled likelihood for the pseudoscalar hypothesis with
$m_{\PSA}=400$ and $\Gamma_{\PSA}/m_{\PSA}=4\%$. The scans are shown for the
single- and dilepton channels separately, as well as for the combination.

When accounting for the look-elsewhere effect [89] in the mass, total width,
and CP state of the heavy Higgs boson, the significance of the excess is 1.9
standard deviations, which corresponds to a $p$-value of 0.028. We note that
higher-order electroweak corrections to the SM production can become important
in the vicinity of the pair production threshold [90] and may account for the
excess.

###  0.6.3 Interpretation in the hMSSM

For the hMSSM, we perform a scan over the two model parameters, $m_{\PSA}$ and
$\tan\beta$. Both and bosons are included. For each point, the coupling
strength modifiers $g_{{\PSA}\ttbar}$ and $g_{{\PH}\ttbar}$, the mass
$m_{\PH}$, and the widths of the two heavy Higgs bosons are determined with
the 2HDMC program. The CP-even state is more massive of the two, but the mass
separation $\Delta m=m_{{\PH}}-m_{{\PSA}}$ decreases with $m_{{\PSA}}$ and
$\tan\beta$. Typical values of $\Delta m/m_{{\PSA}}$ vary from ${\approx}20$%
for $m_{{\PSA}}=400$, $\tan\beta=0.5$ to ${\approx}1$% for $m_{{\PSA}}=700$,
$\tan\beta=2$. The scan is performed for $m_{\PSA}$ between 400 and 700in
steps of 12.5, and $\tan\beta$ between 0.4 and 5.0 in steps of 0.2. Similarly
to what was done above, the lower boundary $\tan\beta>0.4$ is imposed to
assure perturbative unitarity [4]. The mass and width interpolation described
in Section 0.6.1 is performed in scan points other than those corresponding to
the generated signal samples.

The expected and observed exclusions in the $(m_{\PSA},\tan\beta)$ plane are
presented in Fig. 8. The upper boundary of the observed (expected) exclusion
in $\tan\beta$ varies from 1.0 (2.3) at $m_{\PSA}=400$to 1.5 (0.8) at
$m_{\PSA}=700$. The tension between the observed exclusion and the expectation
at low $m_{\PSA}$ is a manifestation of the excess discussed above. These
results can be compared to those of the search for
$\PHpm\to\cPqt\cPaqb/\cPaqt\cPqb$ in Ref. [23], which were also interpreted in
the hMSSM benchmark, setting constraints in the $(m_{\PHpm},\tan\beta)$ plane.
Translating the results from Ref. [23] in terms of $m_{{\PSA}}$, the present
analysis observes a more stringent exclusion in $\tan\beta$ for
$m_{{\PSA}}\approx 700$ GeV, while the exclusion for $m_{{\PSA}}\approx 400$
GeV is substantially weaker than in the reference due to the observed signal-
like deviation. The expected exclusion is tighter than in Ref. [23] throughout
the considered $m_{{\PSA}}$ range.

Figure 8: Exclusion in the $(m_{\PSA},\tan\beta)$ plane of the hMSSM. The
inner (dark gray) band and the outer (light gray) band indicate the regions
containing 68 and 95%, respectively, of the distribution of constraints
expected under the background-only hypothesis. The observed excluded region is
indicated by the blue shaded area. Both and boson signals are included with
masses and widths that correspond to a given point in the plane.

##  0.7 Summary

Results are presented for the search for additional heavy Higgs bosons
decaying to a pair of top quarks. A data sample recorded with the CMS detector
at $\sqrt{s}=13$, corresponding to an integrated luminosity of 35.9, is
analyzed. The final states with one or two leptons are utilized. The invariant
mass of the reconstructed system as well as angular variables sensitive to the
spin of the new boson are used to search for the signal, while taking into
account the interference with the standard model production.

A moderate signal-like deviation is observed for the hypothesis of a
pseudoscalar Higgs boson with the mass $m_{{\PSA}}\approx 400$. After
accounting for the look-elsewhere effect, its significance is 1.9 standard
deviations. Further improvements of the theoretical description of the
standard model process in the vicinity of the production threshold will be
needed to clarify the origin of this deviation.

Constraints on the strength of the coupling of the sought-for boson to top
quarks are reported, separately for the scalar and pseudoscalar cases, for the
mass ranging from 400 to 750 and the total relative width from 0.5 to 25%.
These are the most stringent constraints on this coupling to date. The results
are also interpreted in the hMSSM scenario in the minimal supersymmetric
standard model. This search probes the values of $m_{\PSA}$ from 400 to 700
and excludes, at 95% confidence level, the region with values of $\tan\beta$
below 1.0 to 1.5, depending on $m_{\PSA}$. This extends the exclusion obtained
in previous searches.

###### Acknowledgements.

We congratulate our colleagues in the CERN accelerator departments for the
excellent performance of the LHC and thank the technical and administrative
staffs at CERN and at other CMS institutes for their contributions to the
success of the CMS effort. In addition, we gratefully acknowledge the
computing centers and personnel of the Worldwide LHC Computing Grid for
delivering so effectively the computing infrastructure essential to our
analyses. Finally, we acknowledge the enduring support for the construction
and operation of the LHC and the CMS detector provided by the following
funding agencies: BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq,
CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST,
and NSFC (China); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RPF
(Cyprus); SENESCYT (Ecuador); MoER, ERC IUT, PUT and ERDF (Estonia); Academy
of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG,
and HGF (Germany); GSRT (Greece); NKFIA (Hungary); DAE and DST (India); IPM
(Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); MES
(Latvia); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT,
LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC
(Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom,
RAS, RFBR, and NRC KI (Russia); MESTD (Serbia); SEIDI, CPAN, PCTI, and FEDER
(Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST
(Taipei); ThEPCenter, IPST, STAR, and NSTDA (Thailand); TUBITAK and TAEK
(Turkey); NASU and SFFR (Ukraine); STFC (United Kingdom); DOE and NSF (USA).
Individuals have received support from the Marie-Curie program and the
European Research Council and Horizon 2020 Grant, contract Nos. 675440,
752730, and 765710 (European Union); the Leventis Foundation; the A.P. Sloan
Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science
Policy Office; the Fonds pour la Formation à la Recherche dans l’Industrie et
dans l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door
Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium)
under the “Excellence of Science – EOS” – be.h project n. 30820817; the
Beijing Municipal Science & Technology Commission, No. Z181100004218003; the
Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the
Lendület (“Momentum”) Program and the János Bolyai Research Scholarship of the
Hungarian Academy of Sciences, the New National Excellence Program ÚNKP, the
NKFIA research grants 123842, 123959, 124845, 124850, 125105, 128713, 128786,
and 129058 (Hungary); the Council of Science and Industrial Research, India;
the HOMING PLUS program of the Foundation for Polish Science, cofinanced from
European Union, Regional Development Fund, the Mobility Plus program of the
Ministry of Science and Higher Education, the National Science Center
(Poland), contracts Harmonia 2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543,
2014/15/B/ST2/03998, and 2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406;
the National Priorities Research Program by Qatar National Research Fund; the
Ministry of Science and Education, grant no. 3.2989.2017 (Russia); the
Programa Estatal de Fomento de la Investigación Científica y Técnica de
Excelencia María de Maeztu, grant MDM-2015-0509 and the Programa Severo Ochoa
del Principado de Asturias; the Thalis and Aristeia programs cofinanced by EU-
ESF and the Greek NSRF; the Rachadapisek Sompot Fund for Postdoctoral
Fellowship, Chulalongkorn University and the Chulalongkorn Academic into Its
2nd Century Project Advancement Project (Thailand); the Welch Foundation,
contract C-1845; and the Weston Havens Foundation (USA).

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##  .8 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan${}^{\textrm{\textdagger}}$, A. Tumasyan Institut für
Hochenergiephysik, Wien, Austria
W. Adam, F. Ambrogi, T. Bergauer, J. Brandstetter, M. Dragicevic, J. Erö, A.
Escalante Del Valle, M. Flechl, R. Frühwirth1, M. Jeitler1, N. Krammer, I.
Krätschmer, D. Liko, T. Madlener, I. Mikulec, N. Rad, J. Schieck1, R.
Schöfbeck, M. Spanring, D. Spitzbart, W. Waltenberger, C.-E. Wulz1, M. Zarucki
Institute for Nuclear Problems, Minsk, Belarus
V. Drugakov, V. Mossolov, J. Suarez Gonzalez Universiteit Antwerpen,
Antwerpen, Belgium
M.R. Darwish, E.A. De Wolf, D. Di Croce, X. Janssen, J. Lauwers, A. Lelek, M.
Pieters, H. Rejeb Sfar, H. Van Haevermaet, P. Van Mechelen, S. Van Putte, N.
Van Remortel Vrije Universiteit Brussel, Brussel, Belgium
F. Blekman, E.S. Bols, S.S. Chhibra, J. D’Hondt, J. De Clercq, D. Lontkovskyi,
S. Lowette, I. Marchesini, S. Moortgat, 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, L. Favart, A. Grebenyuk, A.K. Kalsi, J. Luetic, A. Popov, N. Postiau,
E. Starling, L. Thomas, C. Vander Velde, P. Vanlaer, D. Vannerom Ghent
University, Ghent, Belgium
T. Cornelis, D. Dobur, I. Khvastunov2, M. Niedziela, C. Roskas, D. Trocino, M.
Tytgat, W. Verbeke, B. Vermassen, M. Vit, N. Zaganidis Université Catholique
de Louvain, Louvain-la-Neuve, Belgium
O. Bondu, G. Bruno, C. Caputo, P. David, C. Delaere, M. Delcourt, A.
Giammanco, V. Lemaitre, A. Magitteri, J. Prisciandaro, A. Saggio, M. Vidal
Marono, P. Vischia, J. Zobec Centro Brasileiro de Pesquisas Fisicas, Rio de
Janeiro, Brazil
F.L. Alves, G.A. Alves, G. Correia Silva, C. Hensel, A. Moraes, P. Rebello
Teles Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
E. Belchior Batista Das Chagas, W. Carvalho, J. Chinellato3, E. Coelho, E.M.
Da Costa, G.G. Da Silveira4, D. De Jesus Damiao, C. De Oliveira Martins, S.
Fonseca De Souza, L.M. Huertas Guativa, H. Malbouisson, J. Martins5, D. Matos
Figueiredo, M. Medina Jaime6, M. Melo De Almeida, C. Mora Herrera, L. Mundim,
H. Nogima, W.L. Prado Da Silva, L.J. Sanchez Rosas, A. Santoro, A. Sznajder,
M. Thiel, E.J. Tonelli Manganote3, F. Torres Da Silva De Araujo, A. Vilela
Pereira Universidade Estadual Paulista a, Universidade Federal do ABC b, São
Paulo, Brazil
S. Ahujaa, C.A. Bernardesa, L. Calligarisa, T.R. Fernandez Perez Tomeia, E.M.
Gregoresb, D.S. Lemos, P.G. Mercadanteb, S.F. Novaesa, SandraS. Padulaa
Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy of
Sciences, Sofia, Bulgaria
A. Aleksandrov, G. Antchev, R. Hadjiiska, P. Iaydjiev, A. Marinov, M. Misheva,
M. Rodozov, M. Shopova, G. Sultanov University of Sofia, Sofia, Bulgaria
M. Bonchev, A. Dimitrov, T. Ivanov, L. Litov, B. Pavlov, P. Petkov Beihang
University, Beijing, China
W. Fang7, X. Gao7, L. Yuan Department of Physics, Tsinghua University,
Beijing, China
Z. Hu, Y. Wang Institute of High Energy Physics, Beijing, China
M. Ahmad, G.M. Chen, H.S. Chen, M. Chen, C.H. Jiang, D. Leggat, H. Liao, Z.
Liu, S.M. Shaheen8, A. Spiezia, J. Tao, E. Yazgan, H. Zhang, S. Zhang8, J.
Zhao State Key Laboratory of Nuclear Physics and Technology, Peking
University, Beijing, China
A. Agapitos, Y. Ban, G. Chen, A. Levin, J. Li, L. Li, Q. Li, Y. Mao, S.J.
Qian, D. Wang, Q. Wang Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, L.F. Chaparro Sierra, C. Florez, C.F. González
Hernández, M.A. Segura Delgado Universidad de Antioquia, Medellin, Colombia
J. Mejia Guisao, J.D. Ruiz Alvarez, C.A. Salazar González, N. Vanegas Arbelaez
University of Split, Faculty of Electrical Engineering, Mechanical Engineering
and Naval Architecture, Split, Croatia
D. Giljanović, N. Godinovic, D. Lelas, I. Puljak, T. Sculac University of
Split, Faculty of Science, Split, Croatia
Z. Antunovic, M. Kovac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, S. Ceci, D. Ferencek, K. Kadija, B. Mesic, M. Roguljic, A.
Starodumov9, T. Susa University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, E. Erodotou, A. Ioannou, M. Kolosova, S. Konstantinou,
G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos, P.A. Razis, H.
Rykaczewski, D. Tsiakkouri Charles University, Prague, Czech Republic
M. Finger10, M. Finger Jr.10, A. Kveton, J. Tomsa Escuela Politecnica
Nacional, Quito, Ecuador
E. Ayala Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab
Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
H. Abdalla11, A. Mohamed12 National Institute of Chemical Physics and
Biophysics, Tallinn, Estonia
S. Bhowmik, A. Carvalho Antunes De Oliveira, R.K. Dewanjee, K. Ehataht, M.
Kadastik, M. Raidal, C. Veelken Department of Physics, University of Helsinki,
Helsinki, Finland
P. Eerola, L. Forthomme, H. Kirschenmann, K. Osterberg, M. Voutilainen
Helsinki Institute of Physics, Helsinki, Finland
F. Garcia, J. Havukainen, J.K. Heikkilä, T. Järvinen, V. Karimäki, R.
Kinnunen, T. Lampén, K. Lassila-Perini, S. Laurila, S. Lehti, T. Lindén, P.
Luukka, T. Mäenpää, H. Siikonen, E. Tuominen, J. Tuominiemi Lappeenranta
University of Technology, Lappeenranta, Finland
T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
M. Besancon, F. Couderc, M. Dejardin, D. Denegri, B. Fabbro, J.L. Faure, F.
Ferri, S. Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry,
C. Leloup, E. Locci, J. Malcles, J. Rander, A. Rosowsky, M.Ö. Sahin, A. Savoy-
Navarro13, M. Titov Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole
Polytechnique, Institut Polytechnique de Paris
C. Amendola, F. Beaudette, P. Busson, C. Charlot, B. Diab, G. Falmagne, R.
Granier de Cassagnac, I. Kucher, A. Lobanov, C. Martin Perez, M. Nguyen, C.
Ochando, P. Paganini, J. Rembser, R. Salerno, J.B. Sauvan, Y. Sirois, A. Zabi,
A. Zghiche Université de Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, France
J.-L. Agram14, J. Andrea, D. Bloch, G. Bourgatte, J.-M. Brom, E.C. Chabert, C.
Collard, E. Conte14, J.-C. Fontaine14, D. Gelé, U. Goerlach, M. Jansová, A.-C.
Le Bihan, N. Tonon, P. Van Hove Centre de Calcul de l’Institut National de
Physique Nucleaire et de Physique des Particules, CNRS/IN2P3, Villeurbanne,
France
S. Gadrat Université de Lyon, Université Claude Bernard Lyon 1, CNRS-IN2P3,
Institut de Physique Nucléaire de Lyon, Villeurbanne, France
S. Beauceron, C. Bernet, G. Boudoul, C. Camen, N. Chanon, R. Chierici, D.
Contardo, P. Depasse, H. El Mamouni, J. Fay, S. Gascon, M. Gouzevitch, B.
Ille, Sa. Jain, F. Lagarde, I.B. Laktineh, H. Lattaud, M. Lethuillier, L.
Mirabito, S. Perries, V. Sordini, G. Touquet, M. Vander Donckt, S. Viret
Georgian Technical University, Tbilisi, Georgia
A. Khvedelidze10 Tbilisi State University, Tbilisi, Georgia
Z. Tsamalaidze10 RWTH Aachen University, I. Physikalisches Institut, Aachen,
Germany
C. Autermann, L. Feld, M.K. Kiesel, K. Klein, M. Lipinski, D. Meuser, A.
Pauls, M. Preuten, M.P. Rauch, C. Schomakers, J. Schulz, M. Teroerde, B.
Wittmer RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
A. Albert, M. Erdmann, S. Erdweg, T. Esch, B. Fischer, R. Fischer, S. Ghosh,
T. Hebbeker, K. Hoepfner, H. Keller, L. Mastrolorenzo, M. Merschmeyer, A.
Meyer, P. Millet, G. Mocellin, S. Mondal, S. Mukherjee, D. Noll, A. Novak, T.
Pook, A. Pozdnyakov, T. Quast, M. Radziej, Y. Rath, H. Reithler, M. Rieger, J.
Roemer, A. Schmidt, S.C. Schuler, A. Sharma, S. Thüer, S. Wiedenbeck RWTH
Aachen University, III. Physikalisches Institut B, Aachen, Germany
G. Flügge, W. Haj Ahmad15, O. Hlushchenko, T. Kress, T. Müller, A. Nehrkorn,
A. Nowack, C. Pistone, O. Pooth, D. Roy, H. Sert, A. Stahl16 Deutsches
Elektronen-Synchrotron, Hamburg, Germany
M. Aldaya Martin, P. Asmuss, I. Babounikau, H. Bakhshiansohi, K. Beernaert, O.
Behnke, U. Behrens, A. Bermúdez Martínez, D. Bertsche, A.A. Bin Anuar, K.
Borras17, V. Botta, A. Campbell, A. Cardini, P. Connor, S. Consuegra
Rodríguez, C. Contreras-Campana, V. Danilov, A. De Wit, M.M. Defranchis, C.
Diez Pardos, D. Domínguez Damiani, G. Eckerlin, D. Eckstein, T. Eichhorn, A.
Elwood, E. Eren, E. Gallo18, A. Geiser, J.M. Grados Luyando, A. Grohsjean, M.
Guthoff, M. Haranko, A. Harb, A. Jafari, N.Z. Jomhari, H. Jung, A. Kasem17, M.
Kasemann, H. Kaveh, J. Keaveney, C. Kleinwort, J. Knolle, D. Krücker, W.
Lange, T. Lenz, J. Leonard, J. Lidrych, K. Lipka, W. Lohmann19, R. Mankel,
I.-A. Melzer-Pellmann, A.B. Meyer, M. Meyer, M. Missiroli, G. Mittag, J.
Mnich, A. Mussgiller, V. Myronenko, D. Pérez Adán, S.K. Pflitsch, D. Pitzl, A.
Raspereza, A. Saibel, M. Savitskyi, V. Scheurer, P. Schütze, C.
Schwanenberger, R. Shevchenko, A. Singh, H. Tholen, O. Turkot, A. Vagnerini,
M. Van De Klundert, G.P. Van Onsem, R. Walsh, Y. Wen, K. Wichmann, C. Wissing,
O. Zenaiev, R. Zlebcik University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, L. Benato, A. Benecke, V. Blobel, T. Dreyer, A.
Ebrahimi, A. Fröhlich, C. Garbers, E. Garutti, D. Gonzalez, P. Gunnellini, J.
Haller, A. Hinzmann, A. Karavdina, G. Kasieczka, R. Klanner, R. Kogler, N.
Kovalchuk, S. Kurz, V. Kutzner, J. Lange, T. Lange, A. Malara, D. Marconi, J.
Multhaup, C.E.N. Niemeyer, D. Nowatschin, A. Perieanu, A. Reimers, O. Rieger,
C. Scharf, P. Schleper, S. Schumann, J. Schwandt, J. Sonneveld, H. Stadie, G.
Steinbrück, F.M. Stober, M. Stöver, B. Vormwald, I. Zoi Karlsruher Institut
fuer Technologie, Karlsruhe, Germany
M. Akbiyik, C. Barth, M. Baselga, S. Baur, T. Berger, E. Butz, R. Caspart, T.
Chwalek, W. De Boer, A. Dierlamm, K. El Morabit, N. Faltermann, M. Giffels, P.
Goldenzweig, A. Gottmann, M.A. Harrendorf, F. Hartmann16, U. Husemann, S.
Kudella, S. Mitra, M.U. Mozer, Th. Müller, M. Musich, A. Nürnberg, G. Quast,
K. Rabbertz, M. Schröder, I. Shvetsov, H.J. Simonis, R. Ulrich, M. Weber, C.
Wöhrmann, R. Wolf Institute of Nuclear and Particle Physics (INPP), NCSR
Demokritos, Aghia Paraskevi, Greece
G. Anagnostou, P. Asenov, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas,
G. Paspalaki National and Kapodistrian University of Athens, Athens, Greece
M. Diamantopoulou, G. Karathanasis, P. Kontaxakis, A. Panagiotou, I.
Papavergou, N. Saoulidou, A. Stakia, K. Theofilatos, K. Vellidis National
Technical University of Athens, Athens, Greece
G. Bakas, K. Kousouris, I. Papakrivopoulos, G. Tsipolitis University of
Ioánnina, Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, S. Mallios, K.
Manitara, N. Manthos, I. Papadopoulos, J. Strologas, F.A. Triantis, D.
Tsitsonis MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
M. Bartók20, M. Csanad, P. Major, K. Mandal, A. Mehta, M.I. Nagy, G. Pasztor,
O. Surányi, G.I. Veres Wigner Research Centre for Physics, Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath21, F. Sikler, T.Á. Vámi, V. Veszpremi, G.
Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear Research ATOMKI,
Debrecen, Hungary
N. Beni, S. Czellar, J. Karancsi20, A. Makovec, J. Molnar, Z. Szillasi
Institute of Physics, University of Debrecen, Debrecen, Hungary
P. Raics, D. Teyssier, Z.L. Trocsanyi, B. Ujvari Eszterhazy Karoly University,
Karoly Robert Campus, Gyongyos, Hungary
T. Csorgo, W.J. Metzger, F. Nemes, T. Novak Indian Institute of Science
(IISc), Bangalore, India
S. Choudhury, J.R. Komaragiri, P.C. Tiwari National Institute of Science
Education and Research, HBNI, Bhubaneswar, India
S. Bahinipati23, C. Kar, G. Kole, P. Mal, V.K. Muraleedharan Nair Bindhu, A.
Nayak24, D.K. Sahoo23, S.K. Swain Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, S. Chauhan, R. Chawla, N. Dhingra, R.
Gupta, A. Kaur, M. Kaur, S. Kaur, P. Kumari, M. Lohan, M. Meena, K. Sandeep,
S. Sharma, J.B. Singh, A.K. Virdi, G. Walia University of Delhi, Delhi, India
A. Bhardwaj, B.C. Choudhary, R.B. Garg, M. Gola, S. Keshri, Ashok Kumar, S.
Malhotra, M. Naimuddin, P. Priyanka, K. Ranjan, Aashaq Shah, R. Sharma Saha
Institute of Nuclear Physics, HBNI, Kolkata, India
R. Bhardwaj25, M. Bharti25, R. Bhattacharya, S. Bhattacharya, U. Bhawandeep25,
D. Bhowmik, S. Dey, S. Dutta, S. Ghosh, M. Maity26, K. Mondal, S. Nandan, A.
Purohit, P.K. Rout, G. Saha, S. Sarkar, T. Sarkar26, M. Sharan, B. Singh25, S.
Thakur25 Indian Institute of Technology Madras, Madras, India
P.K. Behera, P. Kalbhor, A. Muhammad, P.R. Pujahari, A. Sharma, A.K. Sikdar
Bhabha Atomic Research Centre, Mumbai, India
R. Chudasama, D. Dutta, V. Jha, V. Kumar, D.K. Mishra, P.K. Netrakanti, L.M.
Pant, P. Shukla Tata Institute of Fundamental Research-A, Mumbai, India
T. Aziz, M.A. Bhat, S. Dugad, G.B. Mohanty, N. Sur, RavindraKumar Verma Tata
Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, S. Karmakar,
S. Kumar, G. Majumder, K. Mazumdar, N. Sahoo, S. Sawant Indian Institute of
Science Education and Research (IISER), Pune, India
S. Chauhan, S. Dube, V. Hegde, A. Kapoor, K. Kothekar, S. Pandey, A. Rane, A.
Rastogi, 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, F. Rezaei Hosseinabadi University College
Dublin, Dublin, Ireland
M. Felcini, M. Grunewald INFN Sezione di Bari a, Università di Bari b,
Politecnico di Bari c, Bari, Italy
M. Abbresciaa,b, R. Alya,b,28, C. Calabriaa,b, A. Colaleoa, D. Creanzaa,c, L.
Cristellaa,b, N. De Filippisa,c, M. De Palmaa,b, A. Di Florioa,b, L. Fiorea,
A. Gelmia,b, G. Iasellia,c, M. Incea,b, S. Lezkia,b, G. Maggia,c, M. Maggia,
G. Minielloa,b, S. Mya,b, S. Nuzzoa,b, A. Pompilia,b, G. Pugliesea,c, R.
Radognaa, A. Ranieria, G. Selvaggia,b, L. Silvestrisa, R. Vendittia, P.
Verwilligena INFN Sezione di Bologna a, Università di Bologna b, Bologna,
Italy
G. Abbiendia, C. Battilanaa,b, D. Bonacorsia,b, L. Borgonovia,b, S. Braibant-
Giacomellia,b, R. Campaninia,b, P. Capiluppia,b, A. Castroa,b, F.R. Cavalloa,
C. Cioccaa, G. Codispotia,b, M. Cuffiania,b, G.M. Dallavallea, F. Fabbria, A.
Fanfania,b, E. Fontanesi, P. Giacomellia, C. Grandia, L. Guiduccia,b, F.
Iemmia,b, S. Lo Meoa,29, S. Marcellinia, G. Masettia, F.L. Navarriaa,b, A.
Perrottaa, F. Primaveraa,b, A.M. Rossia,b, T. Rovellia,b, G.P. Sirolia,b, N.
Tosia INFN Sezione di Catania a, Università di Catania b, Catania, Italy
S. Albergoa,b,30, S. Costaa,b, A. Di Mattiaa, R. Potenzaa,b, A. Tricomia,b,30,
C. Tuvea,b INFN Sezione di Firenze a, Università di Firenze b, Firenze, Italy
G. Barbaglia, R. Ceccarelli, K. Chatterjeea,b, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, E. Focardia,b, G. Latino, P. Lenzia,b, M. Meschinia, S.
Paolettia, G. Sguazzonia, D. Stroma, L. Viliania INFN Laboratori Nazionali di
Frascati, Frascati, Italy
L. Benussi, S. Bianco, D. Piccolo INFN Sezione di Genova a, Università di
Genova b, Genova, Italy
M. Bozzoa,b, F. Ferroa, R. Mulargiaa,b, E. Robuttia, S. Tosia,b INFN Sezione
di Milano-Bicocca a, Università di Milano-Bicocca b, Milano, Italy
A. Benagliaa, A. Beschia,b, F. Brivioa,b, V. Cirioloa,b,16, S. Di Guidaa,b,16,
M.E. Dinardoa,b, P. Dinia, S. Fiorendia,b, S. Gennaia, A. Ghezzia,b, P.
Govonia,b, L. Guzzia,b, M. Malbertia, S. Malvezzia, D. Menascea, F. Montia,b,
L. Moronia, G. Ortonaa,b, M. Paganonia,b, D. Pedrinia, S. Ragazzia,b, T.
Tabarelli de Fatisa,b, D. Zuoloa,b INFN Sezione di Napoli a, Università di
Napoli ’Federico II’ b, Napoli, Italy, Università della Basilicata c, Potenza,
Italy, Università G. Marconi d, Roma, Italy
S. Buontempoa, N. Cavalloa,c, A. De Iorioa,b, A. Di Crescenzoa,b, F.
Fabozzia,c, F. Fiengaa, G. Galatia, A.O.M. Iorioa,b, L. Listaa,b, S.
Meolaa,d,16, P. Paoluccia,16, B. Rossia, C. Sciaccaa,b, E. Voevodinaa,b INFN
Sezione di Padova a, Università di Padova b, Padova, Italy, Università di
Trento c, Trento, Italy
P. Azzia, N. Bacchettaa, D. Biselloa,b, A. Bolettia,b, A. Bragagnolo, R.
Carlina,b, P. Checchiaa, P. De Castro Manzanoa, T. Dorigoa, U. Dossellia, F.
Gasparinia,b, U. Gasparinia,b, A. Gozzelinoa, S.Y. Hoh, P. Lujan, M.
Margonia,b, A.T. Meneguzzoa,b, J. Pazzinia,b, M. Presillab, P. Ronchesea,b, R.
Rossina,b, F. Simonettoa,b, A. Tiko, M. Tosia,b, M. Zanettia,b, P. Zottoa,b,
G. Zumerlea,b INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy
A. Braghieria, 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
M. Biasinia,b, G.M. Bileia, C. Cecchia,b, D. Ciangottinia,b, L. Fanòa,b, P.
Laricciaa,b, R. Leonardia,b, E. Manonia, G. Mantovania,b, V. Mariania,b, M.
Menichellia, A. Rossia,b, A. Santocchiaa,b, D. Spigaa INFN Sezione di Pisa a,
Università di Pisa b, Scuola Normale Superiore di Pisa c, Pisa, Italy
K. Androsova, P. Azzurria, G. Bagliesia, V. Bertacchia,c, L. Bianchinia, T.
Boccalia, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, G. Fedia, L.
Gianninia,c, A. Giassia, M.T. Grippoa, F. Ligabuea,c, E. Mancaa,c, G.
Mandorlia,c, A. Messineoa,b, F. Pallaa, A. Rizzia,b, G. Rolandi31, S. Roy
Chowdhury, A. Scribanoa, P. Spagnoloa, R. Tenchinia, G. Tonellia,b, N. Turini,
A. Venturia, P.G. Verdinia INFN Sezione di Roma a, Sapienza Università di Roma
b, Rome, Italy
F. Cavallaria, M. Cipriania,b, D. Del Rea,b, E. Di Marcoa,b, M. Diemoza, E.
Longoa,b, B. Marzocchia,b, P. Meridiania, G. Organtinia,b, F. Pandolfia, R.
Paramattia,b, C. Quarantaa,b, S. Rahatloua,b, C. Rovellia, F.
Santanastasioa,b, L. Soffia,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, A. Cappatia,b, N. Cartigliaa, S. Comettia, M.
Costaa,b, R. Covarellia,b, N. Demariaa, B. Kiania,b, C. Mariottia, S.
Masellia, E. Migliorea,b, V. Monacoa,b, E. Monteila,b, M. Montenoa, M.M.
Obertinoa,b, L. Pachera,b, N. Pastronea, M. Pelliccionia, G.L. Pinna
Angionia,b, A. Romeroa,b, M. Ruspaa,c, R. Sacchia,b, R. Salvaticoa,b, V.
Solaa, A. Solanoa,b, D. Soldia,b, A. Staianoa INFN Sezione di Trieste a,
Università di Trieste b, Trieste, Italy
S. Belfortea, V. Candelisea,b, M. Casarsaa, F. Cossuttia, A. Da Rolda,b, G.
Della Riccaa,b, F. Vazzolera,b, A. Zanettia Kyungpook National University,
Daegu, Korea
B. Kim, D.H. Kim, G.N. Kim, M.S. Kim, J. Lee, S.W. Lee, C.S. Moon, Y.D. Oh,
S.I. Pak, 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
B. Francois, T.J. Kim, J. Park Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, D. Gyun, S. Ha, B. Hong, K. Lee, K.S. Lee, J. Lim, J.
Park, S.K. Park, Y. Roh Kyung Hee University, Department of Physics
J. Goh Sejong University, Seoul, Korea
H.S. Kim Seoul National University, Seoul, Korea
J. Almond, J.H. Bhyun, J. Choi, S. Jeon, J. Kim, J.S. Kim, H. Lee, K. Lee, S.
Lee, K. Nam, M. Oh, S.B. Oh, B.C. Radburn-Smith, U.K. Yang, H.D. Yoo, I. Yoon,
G.B. Yu University of Seoul, Seoul, Korea
D. Jeon, H. Kim, J.H. Kim, J.S.H. Lee, I.C. Park, I. Watson Sungkyunkwan
University, Suwon, Korea
Y. Choi, C. Hwang, Y. Jeong, J. Lee, Y. Lee, I. Yu Riga Technical University,
Riga, Latvia
V. Veckalns32 Vilnius University, Vilnius, Lithuania
V. Dudenas, A. Juodagalvis, G. Tamulaitis, J. Vaitkus National Centre for
Particle Physics, Universiti Malaya, Kuala Lumpur, Malaysia
Z.A. Ibrahim, F. Mohamad Idris33, W.A.T. Wan Abdullah, M.N. Yusli, Z. Zolkapli
Universidad de Sonora (UNISON), Hermosillo, Mexico
J.F. Benitez, A. Castaneda Hernandez, J.A. Murillo Quijada, L. Valencia Palomo
Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico
H. Castilla-Valdez, E. De La Cruz-Burelo, I. Heredia-De La Cruz34, R. Lopez-
Fernandez, A. Sanchez-Hernandez Universidad Iberoamericana, Mexico City,
Mexico
S. Carrillo Moreno, C. Oropeza Barrera, M. Ramirez-Garcia, 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 Montenegro, Podgorica, Montenegro
N. Raicevic University of Auckland, Auckland, New Zealand
D. Krofcheck University of Canterbury, Christchurch, New Zealand
S. Bheesette, P.H. Butler National Centre for Physics, Quaid-I-Azam
University, Islamabad, Pakistan
A. Ahmad, M. Ahmad, M. Gul, Q. Hassan, H.R. Hoorani, W.A. Khan, M.A. Shah, M.
Shoaib, M. Waqas AGH University of Science and Technology Faculty of Computer
Science, Electronics and Telecommunications, Krakow, Poland
V. Avati, L. Grzanka, M. Malawski National Centre for Nuclear Research,
Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, M. Górski, M. Kazana, M. Szleper, 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
M. Araujo, P. Bargassa, D. Bastos, A. Di Francesco, P. Faccioli, B. Galinhas,
M. Gallinaro, J. Hollar, N. Leonardo, J. Seixas, K. Shchelina, G. Strong, O.
Toldaiev, J. Varela Joint Institute for Nuclear Research, Dubna, Russia
V. Alexakhin, Y. Ershov, M. Gavrilenko, A. Golunov, I. Golutvin, N. Gorbounov,
I. Gorbunov, V. Karjavine, V. Korenkov, A. Lanev, A. Malakhov, V.
Matveev36,37, P. Moisenz, V. Palichik, V. Perelygin, M. Savina, S. Shmatov, S.
Shulha, B.S. Yuldashev38, A. Zarubin Petersburg Nuclear Physics Institute,
Gatchina (St. Petersburg), Russia
L. Chtchipounov, V. Golovtsov, Y. Ivanov, V. Kim39, E. Kuznetsova40, P.
Levchenko, V. Murzin, V. Oreshkin, I. Smirnov, D. Sosnov, V. Sulimov, L.
Uvarov, 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 named by A.I. Alikhanov of NRC ‘Kurchatov Institute’,
Moscow, Russia
V. Epshteyn, V. Gavrilov, N. Lychkovskaya, A. Nikitenko41, V. Popov, I.
Pozdnyakov, G. Safronov, A. Spiridonov, A. Stepennov, M. Toms, E. Vlasov, A.
Zhokin Moscow Institute of Physics and Technology, Moscow, Russia
T. Aushev National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
O. Bychkova, R. Chistov42, M. Danilov42, S. Polikarpov42, E. Tarkovskii P.N.
Lebedev Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin, I. Dremin, M. Kirakosyan, A. Terkulov Skobeltsyn
Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow,
Russia
A. Baskakov, A. Belyaev, E. Boos, V. Bunichev, M. Dubinin43, L. Dudko, V.
Klyukhin, O. Kodolova, I. Lokhtin, S. Obraztsov, M. Perfilov, S. Petrushanko,
V. Savrin Novosibirsk State University (NSU), Novosibirsk, Russia
A. Barnyakov44, V. Blinov44, T. Dimova44, L. Kardapoltsev44, Y. Skovpen44
Institute for High Energy Physics of National Research Centre ‘Kurchatov
Institute’, Protvino, Russia
I. Azhgirey, I. Bayshev, S. Bitioukov, V. Kachanov, D. Konstantinov, P.
Mandrik, V. Petrov, R. Ryutin, S. Slabospitskii, A. Sobol, S. Troshin, N.
Tyurin, A. Uzunian, A. Volkov National Research Tomsk Polytechnic University,
Tomsk, Russia
A. Babaev, A. Iuzhakov, V. Okhotnikov Tomsk State University, Tomsk, Russia
V. Borchsh, V. Ivanchenko, E. Tcherniaev University of Belgrade: Faculty of
Physics and VINCA Institute of Nuclear Sciences
P. Adzic45, P. Cirkovic, D. Devetak, M. Dordevic, P. Milenovic, J. Milosevic,
M. Stojanovic Centro de Investigaciones Energéticas Medioambientales y
Tecnológicas (CIEMAT), Madrid, Spain
M. Aguilar-Benitez, J. Alcaraz Maestre, A. Álvarez Fernández, I. Bachiller, M.
Barrio Luna, J.A. Brochero Cifuentes, C.A. Carrillo Montoya, M. Cepeda, 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, Á. Navarro Tobar, A. Pérez-Calero Yzquierdo,
J. Puerta Pelayo, I. Redondo, L. Romero, S. Sánchez Navas, M.S. Soares, A.
Triossi, C. Willmott Universidad Autónoma de Madrid, Madrid, Spain
C. Albajar, J.F. de Trocóniz Universidad de Oviedo, Instituto Universitario de
Ciencias y Tecnologías Espaciales de Asturias (ICTEA), Oviedo, Spain
B. Alvarez Gonzalez, J. Cuevas, C. Erice, J. Fernandez Menendez, S. Folgueras,
I. Gonzalez Caballero, J.R. González Fernández, E. Palencia Cortezon, V.
Rodríguez Bouza, S. Sanchez Cruz 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, G. Gomez, C. Martinez
Rivero, P. Martinez Ruiz del Arbol, F. Matorras, J. Piedra Gomez, C. Prieels,
T. Rodrigo, A. Ruiz-Jimeno, L. Russo46, L. Scodellaro, N. Trevisani, I. Vila,
J.M. Vizan Garcia University of Colombo, Colombo, Sri Lanka
K. Malagalage University of Ruhuna, Department of Physics, Matara, Sri Lanka
W.G.D. Dharmaratna, N. Wickramage CERN, European Organization for Nuclear
Research, Geneva, Switzerland
D. Abbaneo, B. Akgun, E. Auffray, G. Auzinger, J. Baechler, P. Baillon, A.H.
Ball, D. Barney, J. Bendavid, M. Bianco, A. Bocci, E. Bossini, C. Botta, E.
Brondolin, T. Camporesi, A. Caratelli, G. Cerminara, E. Chapon, G. Cucciati,
D. d’Enterria, A. Dabrowski, N. Daci, V. Daponte, A. David, O. Davignon, A. De
Roeck, N. Deelen, M. Deile, M. Dobson, M. Dünser, N. Dupont, A. Elliott-
Peisert, F. Fallavollita47, D. Fasanella, G. Franzoni, J. Fulcher, W. Funk, S.
Giani, D. Gigi, A. Gilbert, K. Gill, F. Glege, M. Gruchala, M. Guilbaud, D.
Gulhan, J. Hegeman, C. Heidegger, Y. Iiyama, V. Innocente, P. Janot, O.
Karacheban19, J. Kaspar, J. Kieseler, M. Krammer1, C. Lange, P. Lecoq, C.
Lourenço, L. Malgeri, M. Mannelli, A. Massironi, F. Meijers, J.A. Merlin, S.
Mersi, E. Meschi, F. Moortgat, M. Mulders, J. Ngadiuba, S. Nourbakhsh, S.
Orfanelli, L. Orsini, F. Pantaleo16, L. Pape, E. Perez, M. Peruzzi, A.
Petrilli, G. Petrucciani, A. Pfeiffer, M. Pierini, F.M. Pitters, D. Rabady, A.
Racz, M. Rovere, H. Sakulin, C. Schäfer, C. Schwick, M. Selvaggi, A. Sharma,
P. Silva, W. Snoeys, P. Sphicas48, J. Steggemann, V.R. Tavolaro, D. Treille,
A. Tsirou, A. Vartak, M. Verzetti, W.D. Zeuner Paul Scherrer Institut,
Villigen, Switzerland
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, P. Berger, N. Chernyavskaya, G. Dissertori, M. Dittmar, M.
Donegà, C. Dorfer, T.A. Gómez Espinosa, C. Grab, D. Hits, T. Klijnsma, W.
Lustermann, R.A. Manzoni, M. Marionneau, M.T. Meinhard, F. Micheli, P.
Musella, F. Nessi-Tedaldi, F. Pauss, G. Perrin, L. Perrozzi, S. Pigazzini, M.
Reichmann, C. Reissel, T. Reitenspiess, D. Ruini, D.A. Sanz Becerra, M.
Schönenberger, L. Shchutska, 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, B. Kilminster, S. Leontsinis, V.M. Mikuni, I. Neutelings, G.
Rauco, P. Robmann, D. Salerno, K. Schweiger, C. Seitz, Y. Takahashi, S. Wertz,
A. Zucchetta National Central University, Chung-Li, Taiwan
T.H. Doan, C.M. Kuo, W. Lin, A. Roy, S.S. Yu National Taiwan University (NTU),
Taipei, Taiwan
P. Chang, Y. Chao, K.F. Chen, P.H. Chen, W.-S. Hou, Y.y. Li, R.-S. Lu, E.
Paganis, A. Psallidas, A. Steen Chulalongkorn University, Faculty of Science,
Department of Physics, Bangkok, Thailand
B. Asavapibhop, C. Asawatangtrakuldee, N. Srimanobhas, N. Suwonjandee Çukurova
University, Physics Department, Science and Art Faculty, Adana, Turkey
A. Bat, F. Boran, S. Damarseckin51, Z.S. Demiroglu, F. Dolek, C. Dozen, I.
Dumanoglu, E. Eskut, G. Gokbulut, EmineGurpinar Guler52, Y. Guler, I. Hos53,
C. Isik, E.E. Kangal54, O. Kara, A. Kayis Topaksu, U. Kiminsu, M. Oglakci, G.
Onengut, K. Ozdemir55, A. Polatoz, A.E. Simsek, D. Sunar Cerci56, U.G. Tok, S.
Turkcapar, I.S. Zorbakir, C. Zorbilmez Middle East Technical University,
Physics Department, Ankara, Turkey
B. Isildak57, G. Karapinar58, M. Yalvac Bogazici University, Istanbul, Turkey
I.O. Atakisi, E. Gülmez, M. Kaya59, O. Kaya60, B. Kaynak, Ö. Özçelik, S.
Tekten, E.A. Yetkin61 Istanbul Technical University, Istanbul, Turkey
A. Cakir, K. Cankocak, Y. Komurcu, S. Sen62 Istanbul University, Istanbul,
Turkey
S. Ozkorucuklu 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, E. Bhal, S. Bologna, J.J. Brooke, D. Burns, E. Clement, D. Cussans,
H. Flacher, J. Goldstein, G.P. Heath, H.F. Heath, L. Kreczko, S. Paramesvaran,
B. Penning, T. Sakuma, S. Seif El Nasr-Storey, D. Smith, V.J. Smith, J.
Taylor, A. Titterton Rutherford Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev63, C. Brew, R.M. Brown, D. Cieri, D.J.A. Cockerill, J.A.
Coughlan, K. Harder, S. Harper, J. Linacre, K. Manolopoulos, D.M. Newbold, E.
Olaiya, D. Petyt, T. Reis, T. Schuh, C.H. Shepherd-Themistocleous, A. Thea,
I.R. Tomalin, T. Williams, W.J. Womersley Imperial College, London, United
Kingdom
R. Bainbridge, P. Bloch, J. Borg, S. Breeze, O. Buchmuller, A. Bundock,
GurpreetSingh CHAHAL64, D. Colling, P. Dauncey, G. Davies, M. Della Negra, R.
Di Maria, P. Everaerts, G. Hall, G. Iles, T. James, M. Komm, C. Laner, L.
Lyons, A.-M. Magnan, S. Malik, A. Martelli, V. Milosevic, J. Nash65, V.
Palladino, M. Pesaresi, D.M. Raymond, A. Richards, A. Rose, E. Scott, C. Seez,
A. Shtipliyski, M. Stoye, T. Strebler, S. Summers, A. Tapper, K. Uchida, T.
Virdee16, N. Wardle, D. Winterbottom, J. Wright, A.G. Zecchinelli, S.C. Zenz
Brunel University, Uxbridge, United Kingdom
J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, C.K. Mackay, A. Morton, I.D. Reid,
L. Teodorescu, S. Zahid Baylor University, Waco, USA
K. Call, J. Dittmann, K. Hatakeyama, C. Madrid, B. McMaster, N. Pastika, C.
Smith Catholic University of America, Washington, DC, USA
R. Bartek, A. Dominguez, R. Uniyal The University of Alabama, Tuscaloosa, USA
A. Buccilli, S.I. Cooper, C. Henderson, P. Rumerio, C. West Boston University,
Boston, USA
D. Arcaro, T. Bose, Z. Demiragli, D. Gastler, S. Girgis, D. Pinna, C.
Richardson, J. Rohlf, D. Sperka, I. Suarez, L. Sulak, D. Zou Brown University,
Providence, USA
G. Benelli, B. Burkle, X. Coubez, D. Cutts, Y.t. Duh, M. Hadley, J. Hakala, U.
Heintz, J.M. Hogan66, K.H.M. Kwok, E. Laird, G. Landsberg, J. Lee, Z. Mao, M.
Narain, S. Sagir67, R. Syarif, E. Usai, D. Yu University of California, Davis,
Davis, USA
R. Band, C. Brainerd, R. Breedon, M. Calderon De La Barca Sanchez, M. Chertok,
J. Conway, R. Conway, P.T. Cox, R. Erbacher, C. Flores, G. Funk, F. Jensen, W.
Ko, O. Kukral, R. Lander, M. Mulhearn, D. Pellett, J. Pilot, M. Shi, 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, W.A. Nash, S. Regnard, D. Saltzberg, C. Schnaible, B.
Stone, V. Valuev University of California, Riverside, Riverside, USA
K. Burt, R. Clare, J.W. Gary, S.M.A. Ghiasi Shirazi, G. Hanson, G.
Karapostoli, E. Kennedy, O.R. Long, M. Olmedo Negrete, M.I. Paneva, W. Si, L.
Wang, H. Wei, S. Wimpenny, B.R. Yates, Y. Zhang University of California, San
Diego, La Jolla, USA
J.G. Branson, P. Chang, S. Cittolin, M. Derdzinski, R. Gerosa, D. Gilbert, B.
Hashemi, D. Klein, V. Krutelyov, J. Letts, M. Masciovecchio, S. May, S. Padhi,
M. Pieri, V. Sharma, M. Tadel, F. Würthwein, A. Yagil, G. Zevi Della Porta
University of California, Santa Barbara - Department of Physics, Santa
Barbara, USA
N. Amin, R. Bhandari, C. Campagnari, M. Citron, V. Dutta, M. Franco Sevilla,
L. Gouskos, J. Incandela, B. Marsh, H. Mei, A. Ovcharova, H. Qu, J. Richman,
U. Sarica, D. Stuart, S. Wang, J. Yoo California Institute of Technology,
Pasadena, USA
D. Anderson, A. Bornheim, O. Cerri, I. Dutta, J.M. Lawhorn, N. Lu, J. Mao,
H.B. Newman, T.Q. Nguyen, J. Pata, M. Spiropulu, J.R. Vlimant, S. Xie, Z.
Zhang, R.Y. Zhu Carnegie Mellon University, Pittsburgh, USA
M.B. Andrews, T. Ferguson, T. Mudholkar, M. Paulini, M. Sun, I. Vorobiev, M.
Weinberg University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, A. Johnson, E. MacDonald, T. Mulholland, R. Patel, A.
Perloff, K. Stenson, K.A. Ulmer, S.R. Wagner Cornell University, Ithaca, USA
J. Alexander, J. Chaves, Y. Cheng, J. Chu, A. Datta, A. Frankenthal, K.
Mcdermott, N. Mirman, J.R. Patterson, D. Quach, A. Rinkevicius68, A. Ryd, S.M.
Tan, Z. Tao, J. Thom, 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, 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, AllisonReinsvold Hall, J. Hanlon, R.M.
Harris, S. Hasegawa, R. Heller, J. Hirschauer, B. Jayatilaka, S. Jindariani,
M. Johnson, U. Joshi, B. Klima, M.J. Kortelainen, B. Kreis, S. Lammel, J.
Lewis, D. Lincoln, R. Lipton, M. Liu, T. Liu, J. Lykken, K. Maeshima, J.M.
Marraffino, D. Mason, P. McBride, P. Merkel, S. Mrenna, S. Nahn, V. O’Dell, V.
Papadimitriou, K. Pedro, C. Pena, G. Rakness, F. Ravera, L. Ristori, B.
Schneider, E. Sexton-Kennedy, N. Smith, 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
University of Florida, Gainesville, USA
D. Acosta, P. Avery, P. Bortignon, D. Bourilkov, A. Brinkerhoff, L. Cadamuro,
A. Carnes, V. Cherepanov, D. Curry, F. Errico, R.D. Field, S.V. Gleyzer, B.M.
Joshi, M. Kim, J. Konigsberg, A. Korytov, K.H. Lo, P. Ma, K. Matchev, N.
Menendez, G. Mitselmakher, D. Rosenzweig, K. Shi, J. Wang, S. Wang, X. Zuo
Florida International University, Miami, USA
Y.R. Joshi Florida State University, Tallahassee, USA
T. Adams, A. Askew, S. Hagopian, V. Hagopian, K.F. Johnson, R. Khurana, T.
Kolberg, G. Martinez, T. Perry, H. Prosper, C. Schiber, R. Yohay, J. Zhang
Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, V. Bhopatkar, M. Hohlmann, D. Noonan, M. Rahmani, M. Saunders,
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, C.
Mills, T. Roy, M.B. Tonjes, N. Varelas, H. Wang, X. Wang, Z. Wu The University
of Iowa, Iowa City, USA
M. Alhusseini, B. Bilki52, W. Clarida, K. Dilsiz69, S. Durgut, R.P.
Gandrajula, M. Haytmyradov, V. Khristenko, O.K. Köseyan, J.-P. Merlo, A.
Mestvirishvili70, A. Moeller, J. Nachtman, H. Ogul71, Y. Onel, F. Ozok72, A.
Penzo, C. Snyder, E. Tiras, J. Wetzel 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, M. Swartz, M. Xiao The University of
Kansas, Lawrence, USA
C. Baldenegro Barrera, P. Baringer, A. Bean, S. Boren, J. Bowen, A. Bylinkin,
T. Isidori, S. Khalil, J. King, G. Krintiras, A. Kropivnitskaya, C. Lindsey,
D. Majumder, W. Mcbrayer, N. Minafra, M. Murray, C. Rogan, C. Royon, S.
Sanders, E. Schmitz, J.D. Tapia Takaki, Q. Wang, J. Williams, G. Wilson Kansas
State University, Manhattan, USA
S. Duric, A. Ivanov, K. Kaadze, D. Kim, Y. Maravin, D.R. Mendis, T. Mitchell,
A. Modak, A. Mohammadi 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, N.J. Hadley, S. Jabeen,
G.Y. Jeng, R.G. Kellogg, J. Kunkle, A.C. Mignerey, S. Nabili, F. Ricci-Tam, M.
Seidel, Y.H. Shin, A. Skuja, S.C. Tonwar, K. Wong Massachusetts Institute of
Technology, Cambridge, USA
D. Abercrombie, B. Allen, A. Baty, R. Bi, S. Brandt, W. Busza, I.A. Cali, M.
D’Alfonso, G. Gomez Ceballos, M. Goncharov, P. Harris, D. Hsu, M. Hu, M.
Klute, D. Kovalskyi, Y.-J. Lee, P.D. Luckey, B. Maier, A.C. Marini, C. Mcginn,
C. Mironov, S. Narayanan, X. Niu, C. Paus, D. Rankin, C. Roland, G. Roland, Z.
Shi, G.S.F. Stephans, K. Sumorok, K. Tatar, D. Velicanu, J. Wang, T.W. Wang,
B. Wyslouch University of Minnesota, Minneapolis, USA
A.C. Benvenuti${}^{\textrm{\textdagger}}$, R.M. Chatterjee, A. Evans, S. Guts,
P. Hansen, J. Hiltbrand, Sh. Jain, S. Kalafut, Y. Kubota, Z. Lesko, J. Mans,
R. Rusack, M.A. Wadud University of Mississippi, Oxford, USA
J.G. Acosta, S. Oliveros University of Nebraska-Lincoln, Lincoln, USA
K. Bloom, D.R. Claes, C. Fangmeier, L. Finco, F. Golf, R. Gonzalez Suarez, R.
Kamalieddin, I. Kravchenko, J.E. Siado, G.R. Snow, B. Stieger State University
of New York at Buffalo, Buffalo, USA
G. Agarwal, C. Harrington, I. Iashvili, A. Kharchilava, C. Mclean, D. Nguyen,
A. Parker, J. Pekkanen, S. Rappoccio, B. Roozbahani Northeastern University,
Boston, USA
G. Alverson, E. Barberis, C. Freer, Y. Haddad, A. Hortiangtham, G. Madigan,
D.M. Morse, T. Orimoto, L. Skinnari, A. Tishelman-Charny, T. Wamorkar, B.
Wang, A. Wisecarver, D. Wood Northwestern University, Evanston, USA
S. Bhattacharya, J. Bueghly, T. Gunter, K.A. Hahn, N. Odell, M.H. Schmitt, K.
Sung, M. Trovato, M. Velasco University of Notre Dame, Notre Dame, USA
R. Bucci, N. Dev, R. Goldouzian, M. Hildreth, K. Hurtado Anampa, C. Jessop,
D.J. Karmgard, K. Lannon, W. Li, N. Loukas, N. Marinelli, I. Mcalister, F.
Meng, C. Mueller, Y. Musienko36, M. Planer, R. Ruchti, P. Siddireddy, G.
Smith, S. Taroni, M. Wayne, A. Wightman, M. Wolf, A. Woodard The Ohio State
University, Columbus, USA
J. Alimena, B. Bylsma, L.S. Durkin, S. Flowers, B. Francis, C. Hill, W. Ji, A.
Lefeld, T.Y. Ling, B.L. Winer Princeton University, Princeton, USA
S. Cooperstein, G. Dezoort, P. Elmer, J. Hardenbrook, N. Haubrich, S.
Higginbotham, A. Kalogeropoulos, S. Kwan, D. Lange, M.T. Lucchini, J. Luo, D.
Marlow, K. Mei, I. Ojalvo, J. Olsen, C. Palmer, P. Piroué, J. Salfeld-Nebgen,
D. Stickland, C. Tully, Z. Wang 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,
B. Mahakud, D.H. Miller, G. Negro, N. Neumeister, C.C. Peng, S. Piperov, 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
K.M. Ecklund, S. Freed, F.J.M. Geurts, M. Kilpatrick, Arun Kumar, W. Li, B.P.
Padley, R. Redjimi, J. Roberts, J. Rorie, W. Shi, A.G. Stahl Leiton, Z. Tu, A.
Zhang University of Rochester, Rochester, USA
A. Bodek, P. de Barbaro, R. Demina, J.L. Dulemba, C. Fallon, T. Ferbel, M.
Galanti, A. Garcia-Bellido, J. Han, O. Hindrichs, A. Khukhunaishvili, E.
Ranken, P. Tan, R. Taus Rutgers, The State University of New Jersey,
Piscataway, USA
B. Chiarito, J.P. Chou, A. Gandrakota, Y. Gershtein, E. Halkiadakis, A. Hart,
M. Heindl, E. Hughes, S. Kaplan, S. Kyriacou, I. Laflotte, A. Lath, R.
Montalvo, K. Nash, M. Osherson, H. Saka, S. Salur, S. Schnetzer, D. Sheffield,
S. Somalwar, R. Stone, S. Thomas, P. Thomassen University of Tennessee,
Knoxville, USA
H. Acharya, A.G. Delannoy, J. Heideman, G. Riley, S. Spanier Texas A&M
University, College Station, USA
O. Bouhali73, A. Celik, M. Dalchenko, M. De Mattia, A. Delgado, S. Dildick, R.
Eusebi, J. Gilmore, T. Huang, T. Kamon74, S. Luo, D. Marley, R. Mueller, D.
Overton, L. Perniè, D. Rathjens, A. Safonov Texas Tech University, Lubbock,
USA
N. Akchurin, J. Damgov, F. De Guio, S. Kunori, K. Lamichhane, S.W. Lee, T.
Mengke, S. Muthumuni, T. Peltola, S. Undleeb, I. Volobouev, Z. Wang, A.
Whitbeck Vanderbilt University, Nashville, USA
S. Greene, A. Gurrola, R. Janjam, W. Johns, C. Maguire, A. Melo, H. Ni, K.
Padeken, F. Romeo, P. Sheldon, S. Tuo, J. Velkovska, M. Verweij University of
Virginia, Charlottesville, USA
M.W. Arenton, P. Barria, B. Cox, G. Cummings, R. Hirosky, M. Joyce, A.
Ledovskoy, C. Neu, B. Tannenwald, 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
J. Buchanan, C. Caillol, D. Carlsmith, S. Dasu, I. De Bruyn, L. Dodd, F.
Fiori, C. Galloni, B. Gomber75, M. Herndon, A. Hervé, U. Hussain, P. Klabbers,
A. Lanaro, A. Loeliger, K. Long, R. Loveless, J. Madhusudanan Sreekala, T.
Ruggles, A. Savin, V. Sharma, W.H. Smith, D. Teague, S. Trembath-reichert, 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 UFMS, Nova Andradina, Brazil
6: Also at Universidade Federal de Pelotas, Pelotas, Brazil
7: Also at Université Libre de Bruxelles, Bruxelles, Belgium