HIG-16-040

$HeadURL:svn+ssh://svn.cern.ch/reps/tdr2/papers/HIG-16-040/trunk/HIG-16-040.tex$
$Id:HIG-16-040.tex4755242018-09-1922:58:01Zferrif$

HIG-16-040

# Measurements of Higgs boson properties in the diphoton decay channel in
proton-proton collisions at $\sqrt{s}=13\TeV$

###### Abstract

Measurements of Higgs boson properties in the $\HGG$ decay channel are
reported. The analysis is based on data collected by the CMS experiment in
proton-proton collisions at $\sqrt{s}=13\TeV$ during the 2016 LHC running
period, corresponding to an integrated luminosity of 35.9. Allowing the Higgs
mass to float, the measurement yields a signal strength relative to the
standard model prediction of $1.18^{+0.17}_{-0.14}=1.18\
^{+0.12}_{-0.11}\stat^{+0.09}_{-0.07}\syst^{+0.07}_{-0.06}\thy$, which is
largely insensitive to the exact Higgs mass around 125. Signal strengths
associated with the different Higgs boson production mechanisms, couplings to
bosons and fermions, and effective couplings to photons and gluons are also
measured.

##  0.1 Introduction

The standard model of particle physics (SM) [1, 2, 3] has been very successful
in explaining the interactions between elementary particles. During the Run 1
period (2010-2012) of the CERN LHC, with proton-proton collisions at centre-
of-mass energies of $7$ and $8\TeV$, a new particle was discovered by the
ATLAS [4] and CMS [5, 6] Collaborations. The discovery was followed by a
comprehensive set of studies of the properties of this new boson in the decay
channels and production modes accessible with the LHC Run 1 data set.
Measurements from ATLAS and CMS [7, 8] have shown that the properties of the
new boson are consistent with expectations for the SM Higgs boson [9, 10, 11,
12, 13, 14].

Despite the small branching fraction predicted by the SM (${\approx}0.2\%$),
the $\HGG$ decay channel provides a clean final state with an invariant mass
peak that can be reconstructed with high precision. As a consequence, $\HGG$
was one of the most important channels for the discovery of the Higgs boson
and first measurements of its properties [15, 16]. In Run 2, with proton-
proton collisions at $\sqrt{s}=13\TeV$, this channel remains one of the most
sensitive to continue the precise characterization of the Higgs boson.

In this paper, measurements of the Higgs boson production rates with respect
to the SM prediction (signal strength modifiers) are presented, along with
measurements of the coupling modifiers to fermions and bosons, and effective
coupling modifiers to photons and gluons, in the so-called $\kappa$ framework
[17]. Improved precision on these parameters constrains possible deviations in
the Higgs sector of the SM. The analysis is based on proton-proton collision
data collected at $\sqrt{s}=13\TeV$ by the CMS experiment in 2016,
corresponding to an integrated luminosity of 35.9.

##  0.2 The CMS detector

The central feature of the CMS apparatus is a superconducting solenoid, 13m in
length and with an inner diameter of 6m, which provides an axial 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
($\eta$) 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.

Charged-particle trajectories are measured by the silicon pixel and strip
tracker, with full azimuthal coverage within $\abs{\eta}<2.5$. The ECAL and
HCAL surround the tracking volume and cover the region $\abs{\eta}<3.0$. The
ECAL barrel extends to $\abs{\eta}<1.48$, while the endcaps cover the region
$1.48<\abs{\eta}<3.0$. A lead/silicon-strip preshower detector is located in
front of the ECAL endcap in the region $1.65<\abs{\eta}<2.6$. The preshower
detector includes two planes of silicon sensors measuring the $x$ and $y$
coordinates of the impinging particles. A steel/quartz-fibre Cherenkov forward
calorimeter extends the calorimetric coverage to $\abs{\eta}<5.0$. In the
region $\abs{\eta}<1.74$, the HCAL cells have widths of 0.087 in both
pseudorapidity and azimuth ($\phi$). In the $(\eta,\phi)$ plane, and for
$\abs{\eta}<1.48$, the HCAL cells map on to $5{\times}5$ ECAL crystal arrays
to form calorimeter towers projecting radially outwards from points slightly
offset from the nominal interaction point. In the endcap, the ECAL arrays
matching the HCAL cells contain fewer crystals. The calibration of the ECAL
uses the azimuthal symmetry of the energy flow in minimum-bias events,
$\pi^{0}\to\Pgg\Pgg$, $\eta\to\Pgg\Pgg$, $\PW\to\Pe\nu$, and ${\PZ\to\EE}$
decays. Changes in the response of the ECAL crystals due to irradiation during
the LHC running periods and their subsequent recovery are monitored
continuously and corrected for, using light injected from a laser system. More
details on the methods employed are given in Ref. [18].

The global event reconstruction algorithm, also called particle-flow event
reconstruction [19], attempts to reconstruct and identify individual particles
using an optimized combination of information from the various elements of the
CMS detector. The energy of photons is directly obtained from the ECAL
measurement with a procedure described in greater detail in Section 0.5.1. 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 energy.

Hadronic jets are clustered from these reconstructed particles using the
infrared- and collinear-safe anti-$\kt$ algorithm [20], with a distance
parameter of 0.4. The jet momentum is determined as the vectorial sum of all
particle momenta in the jet. An offset correction is applied to jet energies
to take into account the contribution from additional proton-proton
interactions within the same or nearby bunch crossings. Jet energy corrections
are derived from simulation, and are confirmed with in situ measurements of
the energy balance in dijet, multijet, photon $+$ jet, and leptonically
decaying $\PZ+\text{jets}$ events [21]. The jet momentum is found from
simulation to be within 5 to 10% of the true momentum over the entire jet
transverse momentum () spectrum and detector acceptance. Additional selection
criteria are applied to each event to remove spurious jet-like features
originating from isolated noise patterns in certain HCAL regions.

To identify jets originating from the hadronization of bottom quarks, the
combined secondary vertex (CSV) $\PQb$ tagging algorithm is used [22, 23]. The
algorithm tags jets from hadron decays by their displaced decay vertex,
providing a numerical discriminant value that is higher for jets likely to be
initiated by quarks. Two tagging algorithm working points, medium and loose,
are used in this analysis: the medium (loose) point provides an efficiency for
identifying quark jets of about 70% (85%) and a misidentification probability
for jets from light quarks and gluons of about 1% (10%).

The missing transverse momentum vector is taken as the negative vector sum of
all reconstructed particle candidate transverse momenta in the event
reconstruction, and its magnitude is referred to as $\ptmiss$.

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. [24].

##  0.3 Analysis strategy

The dominant Higgs boson production mechanism in proton-proton collisions is
gluon-gluon fusion ($\Pg\Pg\PH$), with additional contributions from vector
boson fusion ($\mathrm{VBF}$), and production in association with a vector
boson ($\mathrm{V}\PH$) or with a top quark pair ($\ttbar\PH$).

To maximise the sensitivity of the analysis, specific production modes with
reduced background contamination are targeted. Events are categorized by
requiring specific features in the final state: forward jets for
$\mathrm{VBF}$, top decay products such as muons, electrons, missing
transverse energy from neutrinos, jets arising from the hadronization of
quarks for $\ttbar\PH$, and vector-boson decay products such as muons,
electrons, missing transverse energy, or dijets with a characteristic
invariant mass for $\mathrm{V}\PH$ production. The events with no specific
features, mostly coming from $\Pg\Pg\PH$, are categorized according to their
expected probability to be signal rather than background.

Several multivariate techniques are used in the analysis. An initial set is
used to improve the event reconstruction, and particularly the photon energy
estimate, the photon identification, the identification of the diphoton
primary vertex and the estimate of its probability of being the true diphoton
vertex. In the subsequent steps of the analysis, the event classification
benefits from multivariate techniques to categorize $\Pg\Pg\PH$ events, to
enhance the identification of forward jets in $\mathrm{VBF}$ events and the
separation of such events from $\Pg\Pg\PH$ events, to enhance the tagging and
the separation of $\ttbar\PH$ jets in events with multiple jets.

Measurements are extracted by a simultaneous maximum-likelihood fit to the
diphoton invariant mass distributions in all event categories. Simulated
samples are used to derive the signal model, while the background is obtained
from the fit to the data. The latter aspect is particularly important, as it
makes the use of simulated samples only relevant to the optimization of the
multivariate classifiers used in the different steps of the analysis. While
imperfect simulation might induce suboptimal performance, the use of
multivariate inputs uncorrelated with the diphoton invariant mass ensures that
no bias is introduced. The impact of the choice of the event generator on the
multivariate discriminators has also been checked and found to be negligible.

##  0.4 Data sample and simulated events

The events used in this analysis were selected by diphoton triggers with
asymmetric transverse energy ($\ET$) thresholds of 30 and 18. The trigger
selection requires a loose calorimetric identification using the shape of the
electromagnetic showers, a loose isolation requirement, and a selection on the
ratio of the HCAL and ECAL deposits of the photon candidates. The $\RNINE$
shower shape variable is used in the trigger to identify photons that convert
to an $\EE$ pair in the tracker material before reaching the ECAL surface. The
$\RNINE$ variable is defined as the energy sum of the $3\times 3$ crystals
centred on the most energetic crystal in the candidate electromagnetic cluster
divided by the energy of the candidate. The electromagnetic showers from
photons that convert before reaching the calorimeter have wider transverse
profiles and lower values of $\RNINE$ than those of unconverted photons. The
trigger efficiency is measured from ${\PZ\to\EE}$ events using the tag-and-
probe technique [25]. Efficiencies in simulation are corrected to match those
measured in data.

Simulated signal events are generated using v2.2.2 at next-to-leading order
(NLO) [26] in perturbative quantum chromodynamics (QCD) with FxFx merging
[27], the parton level samples being interfaced to $\PYTHIA 8.205$ [28] for
parton showering and hadronization. The CUETP8M1 underlying event tune
parameter set is used [29]. Events produced via the gluon fusion mechanism are
weighted as a function of the Higgs boson and the number of jets in the event,
to match the prediction from the nnlops program [30]. Parton distribution
functions (PDFs) are taken from the NNPDF3.0 [31] set. The signal cross
sections and branching fraction recommended by the LHC Higgs cross section
working group are used [32].

The dominant background to consists of the irreducible prompt diphoton
production, and the reducible backgrounds from $\GAMJET$ and dijet events
where the jets are misidentified as isolated photons. Background events, used
for the trainings of multivariate discriminants and for category optimization,
have been simulated using various event generators. The diphoton background is
modeled with the v.2.2.1 [33] generator. It includes the Born processes with
up to 3 additional jets as well as the box processes at leading order.
Multijet and backgrounds are modeled with $\PYTHIA$, with a filter applied to
enhance the production of jets with a large fraction of electromagnetic
energy. The $\PW\Pgg$ and $\PZ\Pgg$ samples are generated with at leading
order, while Drell–Yan events are simulated with the same generator at NLO
precision.

The detailed response of the CMS detector is simulated using the [34] package.
This includes the simulation of the multiple proton-proton interactions taking
place in each bunch crossing, referred to as pileup. These can occur at the
nominal bunch crossing (in-time pileup) or at the crossing of previous and
subsequent bunches (out-of-time pileup), and the simulation accounts for both.
Simulated events are weighted to reproduce the distribution of the number of
interactions in data. The average number of pileup interactions measured in
data amounts to 23, with a root-mean-square (rms) of about 6.

##  0.5 Photon reconstruction and identification

Photon candidates are reconstructed as part of the global event
reconstruction, as described in Section 0.2. Photons are identified as ECAL
energy clusters not linked to the extrapolation of any charged-particle
trajectory to the ECAL. The clustering algorithm allows an almost complete
collection of the energy of the photons, even for those converting in the
material upstream of the calorimeter. First, cluster “seeds” are identified as
local energy maxima above a given threshold. Second, clusters are grown from
the seeds by aggregating crystals with at least one side in common with a
clustered crystal and with an energy in excess of a given threshold. This
threshold represents about two standard deviations of the electronic noise in
the ECAL and amounts to 80in the barrel and, depending on $\abs{\eta}$, up to
300in the endcaps. The energy of each crystal can be shared among adjacent
clusters assuming a Gaussian transverse profile of the electromagnetic shower.
Finally, clusters are merged into “superclusters”, to allow good energy
containment, accounting for geometrical variations of the detector along
$\eta$, and optimizing robustness against pileup.

###  0.5.1 Photon energy

The energy of photons is computed from the sum of the energy of the clustered
crystals, calibrated and corrected for changes in the response over time [18]
and considered in the clustering procedure. The preshower energy is added to
that of the superclusters in the region covered by this detector. To optimize
the resolution, the photon energy is corrected for the containment of the
electromagnetic shower in the superclusters and the energy losses from
converted photons [35]. The correction is computed with a multivariate
regression technique that estimates simultaneously the energy of the photon
and its uncertainty. This regression is trained on simulated photons using as
the target the ratio of the true photon energy and the sum of the energy of
the clustered crystals. The inputs are shower shapes and position variables –
both sensitive to shower containment and possible unclustered energy –
preshower information, and global event observables sensitive to pileup.

A multistep procedure has been implemented to correct the energy scale in
data, and to determine the additional smearing to be applied to the
reconstructed photon energy in simulated events so as to reproduce the energy
resolution observed in data. First, the energy scale in data is equalized with
that in simulated events, and residual long-term drifts in the response are
corrected, using $\PZ\to\EE$ decays in which the electron showers are
reconstructed as photons. Then, the photon energy resolution predicted by the
simulation is improved by adding a Gaussian smearing determined from the
comparison between the $\PZ\to\EE$ line-shape in data and simulation (Fig. 1).
The corrections to the energy scale are extracted differentially in time,
$\abs{\eta}$ (two categories in the barrel and two in the endcaps) and
$\RNINE$ (two categories). They range from about 0.1 to about 0.3% in the
barrel and from about 0.2 to about 2% in the endcap, depending on the
category. The amount of smearing required is extracted differentially in the
same $\abs{\eta}$ and $\RNINE$ categories as the energy scale corrections and
ranges from about 0.1 to about 2.7%, depending on the category.

Figure 1: Comparison of the dielectron invariant mass distributions in data
and simulation (after energy smearing) for ${\PZ\to\EE}$ events where
electrons are reconstructed as photons. The comparison is shown requiring
$\RNINE>0.94$ for both “photons” and for (left) events with both photons in
the barrel, and (right) the remaining events. The simulated distributions are
normalized to the integral of the data distribution in the range
$87<m_{\Pe\Pe}<93\GeV$ to highlight the agreement in the bulk of the
distributions.

###  0.5.2 Photon preselection

The photons considered further in this analysis are required to satisfy
preselection criteria similar to, but slightly more stringent than, the
trigger requirements. The preselection requirements consist of:

  * •

$\pt^{\Pgg 1}>30\GeV$ and $\pt^{\Pgg 2}>20\GeV$, where $\pt^{\Pgg 1}$ and
$\pt^{\Pgg 2}$ are the transverse momenta of the leading (in $\pt$) and
subleading photons, respectively;

  * •

$\abs{\eta}<2.5$, excluding the barrel-endcap transition region
$1.44<\abs{\eta}<1.57$, where the photon energy reconstruction is affected by
a suboptimal containment of the electromagnetic shower;

  * •

a selection on the $\RNINE$ variable and on $\sigma_{\eta\eta}$ – the lateral
extension of the shower, defined as the energy-weighted spread within the
$5{\times}5$ crystal matrix centred on the crystal with the largest energy
deposit in the supercluster – to reject ECAL energy deposits incompatible with
a single isolated electromagnetic shower, such as those coming from neutral
mesons;

  * •

a selection on the ratio of the energy in the HCAL cells behind the
supercluster to the energy in the supercluster (H/E), to reject hadrons;

  * •

an electron veto, which rejects the photon candidate if its supercluster is
matched to an electron track with no missing hits in the innermost tracker
layers;

  * •

a requirement on the photon isolation ($\mathcal{I}_{\text{ph}}$), defined as
the sum of the transverse energy of the particles identified as photons and
falling inside a cone of radius
${R=\sqrt{(\Delta\eta)^{2}+(\Delta\varphi)^{2}}}=0.3$ around the photon
candidate direction; the sum is corrected for the contribution of the pileup
estimated from the median energy density in the event [36];

  * •

a requirement on the track isolation in a hollow cone
($\mathcal{I}_{\text{tk}}$), the sum of the transverse momenta of all tracks
in a cone of radius $R=0.3$ around the photon candidate direction (with tracks
in an inner cone of size $R=0.04$ not included in the sum); the cone is hollow
to use the same isolation definition also for electrons;

  * •

a loose requirement on charged-hadron isolation ($\mathcal{I}_{\text{ch}}$),
the sum of the transverse momenta of charged particles inside a cone of radius
$R=0.3$ around the photon candidate; this requirement is added to the one on
track isolation to match the selection applied to photon candidates as part of
data reconstruction;

  * •

a loose requirement on the photon identification (as described in Section
0.5.3).

The selection thresholds are reported in Table 0.5.2. Additionally, both
photons must satisfy either (a) $\RNINE>0.8$ and
$\mathcal{I}_{\text{ch}}<20\GeV$, or (b)
$\mathcal{I}_{\text{ch}}/\pt^{\Pgg}<0.3$.

Schema of the photon preselection requirements.  H/E $\sigma_{\eta\eta}$
$\mathcal{I}_{\text{ph}}$ () $\mathcal{I}_{\text{tk}}$ () Barrel $[0.5,0.85]$
$<$0.08 $<$0.015 $<$4.0 $<$6.0 $>$0.85 $<$0.08 Endcaps $[0.8,0.90]$ $<$0.08
$<$0.035 $<$4.0 $<$6.0 $>$0.90 $<$0.08

The efficiency of all preselection criteria, except the electron veto
requirement, is measured with a tag-and-probe technique using ${\PZ\to\EE}$
events. The efficiency for photons to satisfy the electron veto requirement,
which cannot be measured with ${\PZ\to\EE}$ events, is obtained from
$\PZ\to\MM\Pgg$ events, in which the photon is produced by final-state
radiation and provides a sample of prompt photons with a purity higher than
99%. The photon in this sample ranges from about 20 to about 60. The measured
efficiency for photons to satisfy the electron veto requirement has no
dependency on the photon within about $\pm 1\%$, and is well reproduced in
simulated events.

Table 0.5.2 shows the preselection efficiencies measured in data,
$\epsilon_{\text{data}}$, and simulation, $\epsilon_{\text{MC}}$, along with
their ratio $\epsilon_{\text{data}}/\epsilon_{\text{MC}}$. Statistical and
systematic uncertainties are included both in the efficiencies and in their
ratio. The measured ratios are used to correct the signal efficiency in
simulated signal samples and the associated uncertainties are propagated to
the expected signal yields.

Photon preselection efficiencies as measured in four photon categories,
obtained with tag-and-probe techniques using ${\PZ\to\EE}$ and $\PZ\to\MM\Pgg$
events. The quoted uncertainties include the statistical and systematic
components.  Preselection category $\epsilon_{\text{data}}$ (%)
$\epsilon_{\text{MC}}$ (%) ${\epsilon_{\text{data}}}/{\epsilon_{\text{MC}}}$
Barrel; $\RNINE>0.85$ $94.2\pm 0.9$ $94.7\pm 0.9$ $0.995\pm 0.001$ Barrel;
$\RNINE<0.85$ $82.5\pm 0.7$ $82.5\pm 0.7$ $1.000\pm 0.003$ Endcap;
$\RNINE>0.90$ $90.1\pm 0.2$ $91.3\pm 0.1$ $0.987\pm 0.005$ Endcap;
$\RNINE<0.90$ $49.7\pm 1.4$ $53.8\pm 1.5$ $0.923\pm 0.010$

###  0.5.3 Photon identification

A boosted decision tree (BDT) is used to separate prompt photons from photon
candidates that arise from misidentified jet fragments, but which satisfy the
preselection. This photon identification BDT is trained using simulated
$\GAMJET$ events where prompt photons are considered as signal and non-prompt
photons as background.

The photon identification BDT is trained with the following input variables:

  * •

shower shape observables, corrected to mitigate data and simulation
discrepancies;

  * •

isolation variables, $\mathcal{I}_{\text{ph}}$ and $\mathcal{I}_{\text{ch}}$;
two kinds of $\mathcal{I}_{\text{ch}}$ are computed, including hadrons
associated with the chosen primary vertex (described in Section 0.6), and
including hadrons associated with the vertex providing the largest isolation
sum; the latter is effective in rejecting misidentified photon candidates
originating from jets coming from a vertex other than the chosen one;

  * •

photon $\eta$ and energy, which are correlated with the shower topology and
isolation variables;

  * •

the median energy density per unit area in the event, $\rho$, to minimize the
impact of pileup on the above inputs.

Figure 2: (Left) Distribution of the photon identification BDT score of the
lowest scoring photon of diphoton pairs with an invariant mass in the range
$100<{m}_{\Pgg\Pgg}<180\GeV$, for events passing the preselection in the 13
TeV data set (points), and for simulated background events (blue histogram).
Histograms are also shown for different components of the simulated
background. The sum of all background distributions is scaled up to data. The
red histogram corresponds to simulated Higgs boson signal events. (Right)
Distribution of the photon identification BDT score for $\PZ\to\EE$ events in
data and simulation, where the electrons are reconstructed as photons. The
systematic uncertainty applied to the shape from simulation (hashed region) is
also shown.

Figure 2 (left) shows the photon identification BDT score of the lowest-
scoring photon from all diphoton pairs with an invariant mass in the range
$100<{m}_{\Pgg\Pgg}<180\GeV$, for events passing the preselection in data and
simulated background events.

The photon identification BDT score is also shown in Fig. 2 (right) for
electrons reconstructed as photons in $\PZ\to\EE$ events, in data and
simulation. The systematic uncertainty in the photon identification score,
represented by the hashed region, is conservatively assigned to cover the
largest observed discrepancy between data and simulation for electrons in the
ECAL endcaps.

##  0.6 Diphoton vertex

The determination of the primary vertex from which the two photons originate
has a direct impact on the diphoton invariant mass resolution. If the position
along the beam axis ($z$) of the interaction producing the diphoton is known
to better than about 10, the invariant mass resolution is dominated by the
photon energy resolution. For comparison, the distribution in $z$ of the
position of the vertices reconstructed from the observed tracks has an rms
spread of about 3.4.

The diphoton vertex assignment relies on a BDT (the vertex identification BDT)
whose inputs are observables related to tracks recoiling against the diphoton
system:

  * •

$\sum_{i}\abs{{\ptvec}^{\,i}}^{2}$,

  * •

$-\sum_{i}{\ptvec}^{\,i}\cdot{\ptvec}^{\,\Pgg\Pgg}/\abs{{\ptvec}^{\,\Pgg\Pgg}}$,

  * •

$(\abs{\sum_{i}{\ptvec}^{\,i}}-\pt^{\Pgg\Pgg})/(\abs{\sum_{i}{{\ptvec}^{\,i}}}+\pt^{\Pgg\Pgg})$,

where $\vec{\pt}^{i}$ is the transverse momentum of the $i$th track associated
with a given vertex and ${\ptvec}^{\,\Pgg\Pgg}$ is the transverse momentum of
the diphoton system measured with respect to the same vertex. The sum runs
over all charged particle-flow candidates associated with the given vertex.

In the presence of tracks from photons converted in the tracker material, two
additional input variables are used:

  * •

the number of conversions,

  * •

the pull $\abs{z_{\text{vtx}}-z_{\Pe}}/\sigma_{z}$ between the longitudinal
position of the reconstructed vertex, $z_{\text{vtx}}$, and the longitudinal
position of the vertex estimated using conversion track(s), $z_{\Pe}$, where
the variable $\sigma_{z}$ denotes the uncertainty in $z_{\Pe}$.

A second vertex-related multivariate discriminant (vertex probability BDT),
used in the diphoton BDT (discussed in Section 0.7), is designed to estimate,
event-by-event, the probability for the vertex assignment to be within 10of
the diphoton interaction point. The vertex probability BDT is trained on
simulated $\HGG$ events using the following input variables:

  * •

the number of vertices in each event;

  * •

the values of the vertex identification BDT score for the three most probable
vertices in each event;

  * •

the distances between the chosen vertex and the second and third choices;

  * •

the magnitude of the transverse momentum of the diphoton system,
$\pt^{\Pgg\Pgg}$;

  * •

the number of photons with an associated conversion track.

The performance of the vertex identification BDT is validated using
$\PZ\to\MM$ events (Fig. 3), where the vertices are fitted omitting the muon
tracks to mimic a diphoton system. In addition, the use of tracks from
converted photons to locate the vertex is validated using $\GAMJET$ events.
Discrepancies between data and simulation are corrected for in the analysis
and a corresponding uncertainty is considered.

In the simulated samples the width of the beam spot was about a factor 1.5
larger than what was subsequently observed in data. To correct for this,
simulated events in which the selected vertex is more than 1away from the
generated one are weighted such that the width of the distribution of the
primary vertices is the same as the beam spot width in data.

The efficiency of correctly assigning the diphoton vertex to be within 10of
the true vertex in simulated events is shown in Fig. 4 as a function of the of
the diphoton pair and as a function of the number of primary vertices in the
event, and compared with the average estimated vertex probability BDT. The
overall efficiency is about 81%.

Figure 3: Validation of the $\HGG$ vertex identification algorithm on
$\PZ\to\MM$ events omitting the muon tracks. Simulated events are weighted to
match the distributions of pileup and location of primary vertices in data.

Figure 4: Comparison of the true vertex identification efficiency and the
average estimated vertex probability as a function of the reconstructed
diphoton (left) and of the number of primary vertices (right) in simulated
$\HGG$ events with $m_{\PH}=125\GeV$. Events are weighted according to the
cross sections of the different production modes and to match the
distributions of pileup and location of primary vertices in data.

##  0.7 Event classification

The event selection requires two preselected photon candidates with $\pt^{\Pgg
1}>{m}_{\Pgg\Pgg}/3$ and $\pt^{\Pgg 2}>{m}_{\Pgg\Pgg}/4$, in the mass range
$100<{m}_{\Pgg\Pgg}<180\GeV$. The use of thresholds scaled by ${m}_{\Pgg\Pgg}$
prevents a distortion of the low end of the invariant mass spectrum. The
requirement on the photon $\pt$ is applied after the vertex assignment.

To improve the sensitivity of the analysis, events are classified targeting
different production mechanisms and according to their mass resolution and
predicted signal-to-background ratio. In each category, the selections are
optimized to maximize the significance of the expected signal with respect to
the background. As the first step of the classification, exclusive event
categories are defined by dedicated selections on additional reconstructed
objects to select Higgs boson production mechanisms other than $\Pg\Pg\PH$:
$\mathrm{VBF}$, $\mathrm{V}\PH$ or $\ttbar\PH$.

All objects are reconstructed as described in Section 0.2 and (for photons)
Section 0.5. In addition, electrons are required to be within $\abs{\eta}<2.5$
and outside the barrel-endcap transition region. Muons are required to be
within $\abs{\eta}<2.4$.

A dedicated diphoton BDT is used in the event categorization. The diphoton BDT
assigns a high score to events with photons showing signal-like kinematics,
good mass resolution, and high photon identification BDT score. The input
variables to the classifier are:

  * •

$\pt^{\Pgg}/{m}_{\Pgg\Pgg}$ for each photon;

  * •

the pseudorapidity of the two photons;

  * •

the cosine of the angle between the two photons in the transverse plane;

  * •

photon identification BDT scores for both photons;

  * •

two per-event relative mass resolution estimates, one under the hypothesis
that the mass has been reconstructed using the correct primary vertex, and the
other under the hypothesis that the mass has been reconstructed using an
incorrect vertex;

  * •

the per-event probability estimate that the correct primary vertex has been
assigned to the diphoton.

The relative mass resolution is computed from the propagation of the photon
energy resolution estimates, assuming the functional forms of the photon
resolutions are Gaussian. Figure 5 (left) shows the transformed score from the
diphoton multivariate classifier for data and simulated signal and
backgrounds, for events with two photons satisfying the preselection
requirements. The classifier score has been transformed such that the sum of
signal events from all the production modes has a uniform distribution. A
validation of the score from the diphoton multivariate classifier obtained in
${\PZ\to\EE}$ events, where the electrons are reconstructed as photons, is
shown in Fig. 5 (right) for data and simulation.

Figure 5:  (Left) Transformed score distribution from the diphoton
multivariate classifier for events with two photons satisfying the
preselection requirements in data (points), simulated signal (red shades), and
simulated background (coloured histograms). Both signal and background are
stacked together. The vertical dashed lines show the boundaries of the
untagged categories, the grey shade indicates events discarded from the
analysis. (Right) Score distribution of the diphoton multivariate classifier
in ${\PZ\to\EE}$ events where the electrons are reconstructed as photons. The
points show the distribution for data, the histogram shows the distribution
for simulated Drell–Yan events. The pink band indicates the statistical and
systematic uncertainties in simulation. The grey shade indicates events
discarded from the analysis.

###  0.7.1 Event categories for $\ttbar\PH$ production

Events produced in association with a top quark pair feature two quarks from
the decay of the top quarks, and may be accompanied by charged leptons or
additional jets. In the latter case, to enhance the tagging of $\ttbar\PH$
multijet events, a multivariate discriminant is built upon the following
inputs:

  * •

the number of jets with $\pt>25\GeV$;

  * •

the leading jet $\pt$;

  * •

the two highest scores from the tag CSV discriminator.

The output of this discriminant is shown in Fig. 6. The threshold on the
discriminant is optimized jointly with the requirement on the diphoton BDT
score by maximizing the expected sensitivity to $\ttbar\PH$ production.

To cross-check the performance of this BDT observable, a control sample in
data is defined by selecting events with a pair of photons, one of which
passes the preselection and photon identification requirements, while the
other has no preselection applied and an inverted criterion on the score from
the photon identification BDT. As the efficiency for selecting such photons is
not the same as for the signal region, events in the control samples are
weighted according to the $\eta$ and $\pt$ of the photons so as to obtain a
control sample with similar kinematic properties as the signal region, but
statistically independent.

Figure 6: Score distribution of the jet multivariate discriminant used to
enhance jet tagging in the ttH multijet category. The points show the
distribution for data in the signal region sidebands, ${m}_{\Pgg\Pgg}<115\GeV$
or ${m}_{\Pgg\Pgg}>135\GeV$; the histogram shows the distribution for events
in the data control sample; the filled histogram shows the distribution for
simulated signal events. The distributions in the simulated and control
samples are scaled as to match the integral of that from the data sidebands.

Depending on the type of the top quark decay, the following categories are
defined:

  * •

semileptonic top quark decays ($\cPqt\cPqt\PH$ Leptonic):

    * –

leading photon $\pt>{m}_{\Pgg\Pgg}/2$, subleading photon
$\pt>{m}_{\Pgg\Pgg}/4$;

    * –

diphoton classifier BDT score greater than 0.11;

    * –

at least one lepton with $\pt>20\GeV$; electrons must satisfy loose
requirements on the same observables as described in Ref. [37]; muons are
required to pass a tight selection based on the quality of the track, the
number of hits in the tracker and muon system, and the longitudinal and
transverse impact parameters of the track with respect to the muon vertex, and
to satisfy a requirement on the relative isolation (after correction for
pileup) based on transverse energy of the charged hadrons, neutral hadrons,
and photons, in a cone around the muon with a radius between 0.05 and 0.2,
depending on the $\pt$ of the muon;

    * –

all selected leptons $\ell$ are required to have $R(\ell,\Pgg)>0.35$, where
$R$ is the distance between the objects in the $\eta-\phi$ plane;

    * –

specifically for electrons: $\abs{m_{\Pe,\Pgg}-m_{\PZ}}>5\GeV$, where
$m_{\Pe,\Pgg}$ is the invariant mass of any pair of electron and photon and
$m_{\PZ}$ refers to the mass of the $\PZ$ boson;

    * –

at least two jets in the event with $\pt>25\GeV$, $\abs{\eta}<2.4$, and
$R(\text{jet},\Pgg)>0.4$ and $R(\text{jet},\ell)>0.4$;

    * –

at least one of the jets in the event identified as a jet according to the CSV
tagger medium requirement;

  * •

hadronic top quark decays ($\cPqt\cPqt\PH$ Hadronic):

    * –

leading photon $\pt>{m}_{\Pgg\Pgg}/3$, subleading photon
$\pt>{m}_{\Pgg\Pgg}/4$;

    * –

diphoton classifier BDT score greater than 0.58;

    * –

no leptons, defined according to the criteria of the $\cPqt\cPqt\PH$ Leptonic
category;

    * –

at least three jets in the event with $\pt>25\GeV$ and $\abs{\eta}<2.4$;

    * –

at least one of the jets in the event identified as a jet according to the CSV
tagger loose requirement;

    * –

score from the $\cPqt\cPqt\PH$ Hadronic multivariate discriminant greater than
0.75.

###  0.7.2 Event categories for $\mathrm{V}\PH$ production

The selection criteria targeting the associated production of the Higgs boson
with a vector or boson exploit the presence of leptons, missing transverse
momentum, and jets. To reduce contamination from Drell–Yan events with an
electron misreconstructed as a photon, or with photons radiated in the final
state, photon candidates are required to be separated in angle from the
closest lepton. The criteria are the following:

  * •

leptonic $\PZ$ decays ($\PZ\PH$ Leptonic):

    * –

leading photon $\pt>3{m}_{\Pgg\Pgg}/8$, subleading photon
$\pt>{m}_{\Pgg\Pgg}/4$;

    * –

diphoton classifier BDT score greater than 0.11;

    * –

two same-flavour leptons within the fiducial region, $\pt>20\GeV$; electrons
and muons are required to satisfy the same identification criteria as for the
$\cPqt\cPqt\PH$ Leptonic category;

    * –

dilepton invariant mass $m_{\ell\ell}$ in the range $70<m_{\ell\ell}<110\GeV$;

    * –

$R(\Pgg,\Pe)>1.0$, $R(\Pgg,\PGm)>0.5$, for each of the leptons;

    * –

in addition, a conversion veto is applied to the electrons to reduce the
number of electrons originating from photon conversions, by requiring that,
when an electron and a photon candidate share a supercluster, the electron
track is well separated from the centre of the supercluster:
$R(\mathrm{supercluster},\Pe\text{-track})>0.4$.

  * •

leptonic decays ($\PW\PH$ Leptonic):

    * –

leading photon $\pt>3{m}_{\Pgg\Pgg}/8$, subleading photon
$\pt>{m}_{\Pgg\Pgg}/4$;

    * –

diphoton classifier BDT score greater than 0.28;

    * –

at least one lepton with $\pt>20\GeV$; electrons and muons are required to
satisfy the same identification criteria as for the $\PZ\PH$ Leptonic
category;

    * –

$R(\Pgg,\ell)>1.0$ and conversion veto as in the $\PZ\PH$ Leptonic category;

    * –

missing transverse momentum $\ptmiss>45\GeV$;

    * –

up to two jets each satisfying $\pt>20\GeV$, $\abs{\eta}<2.4$,
$R(\text{jet},\ell)>0.4$, and $R(\text{jet},\Pgg)>0.4$;

  * •

or leptonic decays, relaxed selection ($\mathrm{V}\PH$ LeptonicLoose):

    * –

as for $\PW\PH$ Leptonic with the requirement on the missing transverse
momentum to be $\ptmiss<45\GeV$;

  * •

or leptonic decays, with at least one missing lepton ($\mathrm{V}\PH$ MET):

    * –

leading photon $\pt>3{m}_{\Pgg\Pgg}/8$, subleading photon
$\pt>{m}_{\Pgg\Pgg}/4$;

    * –

diphoton classifier BDT score greater than 0.79;

    * –

missing transverse momentum $\ptmiss>85\GeV$;

    * –

angle in the transverse plane between the direction of the diphoton and the
$\ptvecmiss$ $\Delta\phi(\Pgg\Pgg,\ptvecmiss)>2.4$;

  * •

hadronic decays of and ($\mathrm{V}\PH$ Hadronic):

    * –

leading photon $\pt>{m}_{\Pgg\Pgg}/2$, subleading photon
$\pt>{m}_{\Pgg\Pgg}/4$;

    * –

diphoton classifier BDT score greater than 0.79;

    * –

at least two jets, each with $\pt>40\GeV$ and $\abs{\eta}<2.4$,
$R(\text{jet},\Pgg)>0.4$;

    * –

dijet invariant mass in the range $60<m_{jj}<120\GeV$;

    * –

$\abs{\cos\theta^{\star}}<0.5$, where $\theta^{\star}$ is the angle that the
diphoton system makes, in the diphoton-dijet centre-of-mass frame, with
respect to the direction of motion of the diphoton-dijet system in the lab
frame. The distribution of this variable is rather uniform for $\mathrm{V}\PH$
events, while it is strongly peaked at 1 for background and events from
$\Pg\Pg\PH$ production.

###  0.7.3 Event categories for $\mathrm{VBF}$ production

Events produced via the $\mathrm{VBF}$ process feature two jets in the final
state separated by a large rapidity gap. A multivariate discriminant is
trained to tag the distinctive kinematics of the $\mathrm{VBF}$ jets,
considering as background the production process of $\Pg\Pg\PH$ \+ jets. This
discriminant is given as input to an additional multivariate classifier
($\mathrm{VBF}$ combined BDT) along with the score from the diphoton BDT, and
the ratio $\pt^{\Pgg\Pgg}/{m}_{\Pgg\Pgg}$. Figure 7 (left) shows the
transformed score from the $\mathrm{VBF}$ combined BDT for data in the mass
sideband regions from 105–115and 135–145, along with the predicted
$\mathrm{VBF}$ and $\Pg\Pg\PH$ distributions. The $\mathrm{VBF}$ combined BDT
score has been transformed such that the signal events from the $\mathrm{VBF}$
production mode has a uniform distribution. A validation of the score from the
combined multivariate classifier obtained in ${\PZ\to\EE}$ \+ jets events,
where the electrons are reconstructed as photons and at least two jets satisfy
the requirements listed below to enter the $\mathrm{VBF}$ category, is shown
in Fig. 7 (right) for data and simulation.

Figure 7: Score distribution from the $\mathrm{VBF}$ combined BDT for (left)
$\Pg\Pg\PH$ and $\mathrm{VBF}$ signal distributions, compared to background
taken from data in the mass sideband regions, and (right) ${\PZ\to\EE}$ \+
jets events. On the left, the signal region selection is applied to the
simulated $\Pg\Pg\PH$ and $\mathrm{VBF}$ events; these are compared to points
representing the background, as determined from data using the signal region
selection in mass sidebands. On the right, the signal selection is applied to
electrons reconstructed as photons, with points showing the distribution for
data and the histogram showing the distribution for simulated Drell–Yan
events, including statistical and systematic uncertainties (pink band). In
both plots, dotted lines delimit the three $\mathrm{VBF}$ categories, while
the grey region is discarded from the analysis.

The selections targeting the $\mathrm{VBF}$ production mechanism are the
following:

  * •

leading photon $\pt>{m}_{\Pgg\Pgg}/3$, subleading photon
$\pt>{m}_{\Pgg\Pgg}/4$;

  * •

photon identification BDT score greater than $-0.2$, to provide additional
rejection against background events whose kinematics yield a high diphoton BDT
score despite one reconstructed photon with a relatively low photon
identification BDT score;

  * •

one jet with $\pt>40\GeV$ and one with $\pt>30\GeV$, both with
$\abs{\eta}<4.7$ and with a tight requirement on the pileup jet
identification;

  * •

invariant mass of the two jets $m_{jj}>250\GeV$;

  * •

$\mathrm{VBF}$ combined multivariate discriminant greater than 0.43.

Three categories are defined using the score from the combined discriminant,
and are optimized to maximize the expected signal significance in the
$\mathrm{VBF}$ production channel.

###  0.7.4 Event categories for $\Pg\Pg\PH$ production

Events not passing any exclusive category are classified using the
multivariate discriminator described in the introduction of this section. The
score from this classifier is used to select and divide the events into four
“untagged” categories according to the diphoton mass resolution and predicted
signal over background ratio. The number of categories is determined by
maximizing the expected signal significance. The boundaries of these
categories are shown in Fig. 5.

###  0.7.5 Final classification

Each event is classified exclusively by applying the category selections in
order and choosing the highest-priority category satisfied by the event.
Category selections targeting specific production processes are applied first,
ranked by expected signal significance, then untagged categories. The final
ordering is thus $\cPqt\cPqt\PH$ Leptonic, $\cPqt\cPqt\PH$ Hadronic, $\PZ\PH$
Leptonic, $\PW\PH$ Leptonic, $\mathrm{V}\PH$ LeptonicLoose, $\mathrm{VBF}$
categories, $\mathrm{V}\PH$ MET, $\mathrm{V}\PH$ Hadronic, and untagged. The
fraction of events with multiple diphoton pairs satisfying one or more
category selections is less than $2\times 10^{-4}$. In this case, the diphoton
in the highest-priority category is selected or, in case of ambiguities, the
diphoton pair with the highest sum of photon $\pt$ is selected.

##  0.8 Signal model

The signal shape for the diphoton invariant mass distribution in each category
and for a nominal Higgs boson mass ${m}_{\PH}$ is constructed from simulation
using events from the different production modes.

The simulation includes the tuning of the photon shower variables to the data,
and accounts for trigger, reconstruction and identification efficiencies as
measured with data-driven techniques (as discussed in Section 0.5). It also
weights the events so that the distribution of the number of interactions and
the primary vertex location reproduce those observed in data, as explained in
Sections 0.4 and 0.6.

Since the shape of the ${m}_{\Pgg\Pgg}$ distribution changes considerably
depending on whether the vertex associated with the candidate diphoton was
correctly identified within 10, distributions for the correct vertex and wrong
vertex assignments are fit separately when constructing the signal model. For
each process, category, and vertex scenario, the ${m}_{\Pgg\Pgg}$
distributions are fitted using a sum of at most five Gaussian functions.

For each process, category, and vertex scenario, a simultaneous fit of signal
samples at mass values in the range from 120 to 130is performed to obtain
parametric variations of the Gaussian function parameters used in the signal
model fit. Polynomials are used to describe these variations.

The final fit function for each category is obtained by summing the functions
for all production modes normalized to the expected signal yields in that
category. Figure 8 shows the signal model corresponding to ${m}_{\PH}=125\GeV$
for the best resolution category and also for all categories combined
together, weighted by the $\textrm{S}/(\textrm{S}+\textrm{B})$ ratio, where S
is the number of signal events, and B the number of background events in a
window around the signal peak, in each category.

The product of efficiency and acceptance of the signal model as a function of
${m}_{\PH}$ for all categories combined is shown in Fig. 9.

Figure 8: Parametrized signal shape for the best resolution category (left)
and for all categories combined together and weighted by the
$\textrm{S}/(\textrm{S}+\textrm{B})$ ratio (right) for a simulated $\HGG$
signal sample with ${m}_{\PH}=125\GeV$. The open squares represent weighted
simulated events and the blue lines are the corresponding models. Also shown
are the $\sigma_{\text{eff}}$ value (half the width of the narrowest interval
containing 68.3% of the invariant mass distribution) and the corresponding
interval as a grey band, and the full width at half maximum (FWHM) and the
corresponding interval as a double arrow. Figure 9: The product of efficiency
and acceptance of the signal model as a function of ${m}_{\PH}$ for all
categories combined. The black line represents the yield from the signal
model. The yellow band indicates the effect of the $\pm$1 standard deviation
of the systematic uncertainties for trigger, photon identification and
selection, photon energy scale and modelling of the photon energy resolution,
and vertex identification (described in Section 0.10).

##  0.9 Background model

The model used to describe the background is extracted from data with the
discrete profiling method [38] as implemented in Ref. [15]. This technique was
designed as a way to estimate the systematic uncertainty associated with
choosing a particular analytic function to fit the background ${m}_{\Pgg\Pgg}$
distribution. The method treats the choice of the background function as a
discrete nuisance parameter in the likelihood fit to the data.

No assumptions are made about the particular processes composing the
background nor the functional form of their smoothly falling diphoton
invariant mass distribution. A large set of candidate function families is
considered, including exponentials, Bernstein polynomials, Laurent series, and
power law functions. For each family of functions, an F-test [39] is performed
to determine the maximum order to be used, while the minimum order is
determined by requiring a reasonable fit to the data. The background is
assumed to be a smoothly falling distribution; this is supported by the shape
of background distributions both in simulated events and in data, in the
latter case those of the events rejected by the diphoton BDT.

When fitting these functions to the background ${m}_{\Pgg\Pgg}$ distribution,
the value of twice the negative logarithm of the likelihood (2NLL) is
minimized. A penalty of $n_{\mathrm{p}}$ is added to 2NLL to take into account
the number of floating parameters $n_{\mathrm{p}}$ in each candidate function
and avoid favouring functions with a greater number of free parameters. When
making a measurement of a given parameter of interest, the discrete profiling
method determines the minimal 2NLL by considering all allowed functions for
each value of the parameter.

##  0.10 Systematic uncertainties

The systematic uncertainties are treated differently depending on how they
affect the ${m}_{\Pgg\Pgg}$ signal distribution. The parameters of the signal
model shape are allowed to vary, within the constraints set by the
measurements described in Section 0.5.1, to account for systematic
uncertainties in the photon scale and resolution. Additional nuisance
parameters are included to account for systematic uncertainties which affect
the overall rate and migration of signal events between the categories, and
are log-normal constrained. For cases where the systematic uncertainty has an
effect on the input to one of the classification discriminants, the
uncertainty takes the form of a variation in the category yield, representing
event migration between categories.

###  0.10.1 Theoretical uncertainties

Theoretical uncertainties in the signal yield associated with QCD calculations
typically have an overall normalization uncertainty, taken from Ref. [32],
along with an additional uncertainty accounting for the migration of events
between the analysis categories. The category migration uncertainties are
factorized from the overall yield uncertainty by scaling them appropriately so
that the overall yield (including events outside the acceptance of the
analysis) is unchanged. The uncertainties computed in this way are:

  * •

QCD scale uncertainty: related to variations of the renormalization and
factorization scales, has two nuisance parameters affecting the overall
normalization uncertainty and depending on the number of jets in the event.
Variations are found to be typically less then 5%.

  * •

PDF uncertainties: have an overall normalization from the PDF4LHC prescription
[40, 32], while the bin-to-bin migrations are calculated from the NNPDF3.0
[31] PDF set using the MC2hessian procedure [41]. The category migrations are
found to be typically less than 1%, depending on the category.

  * •

$\alpha_{\mathrm{s}}$ uncertainty: the uncertainty in the value of the strong
force coupling constant $\alpha_{\mathrm{s}}$ is evaluated following the
PDF4LHC prescription. The overall variation in the relative event yield due to
the $\alpha_{\mathrm{s}}$ uncertainty is at most 2.6%.

Further theoretical uncertainties are:

  * •

Underlying event and parton shower uncertainty: is obtained using samples
where the choice and tuning of the generator has been modified. This
systematic uncertainty is treated as an event migration systematic as it will
mainly affect the jets in the analysis. The possibility that an event could
move from one $\mathrm{VBF}$ category to another or from any $\mathrm{VBF}$
category to an inclusive category is assigned a systematic uncertainty of 7
and 9%, respectively.

  * •

Gluon fusion contamination in the ttH tagged categories: the theoretical
predictions for gluon fusion are less reliable in a regime where the Higgs
boson is produced in association with a large number of jets. The systematic
uncertainty in the gluon fusion contamination in the ttH tagged categories has
been estimated taking into account several contributions:

    * –

uncertainty due to the limited size of the simulated sample: 10%.

    * –

uncertainty from the jet modelling. This uncertainty is estimated as the
observed difference in the jet multiplicity between predictions and data in
$\ttbar+\mathrm{jets}$ events (which are dominated by gluon fusion production
$\Pg\Pg\to\ttbar$), with fully leptonic decays. This uncertainty is about 35%
in the bins with the largest discrepancy ($N_{\textrm{jets}}\geq 5$).

    * –

uncertainty in the gluon splitting modelling. This is estimated by scaling the
fraction of events from gluon fusion with real jets by the observed difference
between data and simulation in the ratio
$\sigma(\ttbar\bbbar)/\sigma(\ttbar\mathrm{jj})$ at 13 TeV [42]. This
uncertainty implies a variation of about 50% in the yield of gluon fusion
events.

  * •

Gluon fusion contamination in categories with additional jets and a high-Higgs
boson: particularly important for estimating the yield in the $\mathrm{VBF}$
categories. A total of seven nuisance parameters account for different
systematic effects:

    * –

uncertainties in jet multiplicities: two nuisance parameters account for
missing higher-order corrections and two for migrations between categories
with different jet multiplicity. These are based on the STWZ [43] and BLPTW
[43, 44, 45] predictions.

    * –

uncertainties in the Higgs boson modelling: two nuisance parameters include
migrations between regions with in the range between 60 and $120\GeV$ and
above $120\GeV$. A third nuisance parameter accounts for the impact of top
quark mass effects, which are negligible for a Higgs boson below $150\GeV$ and
rise to about 35% at $500\GeV$; these impact primarily the tightest untagged
and $\mathrm{VBF}$ categories, where the resulting uncertainty in the
predicted gluon fusion yield is 6–8%.

    * –

uncertainties in the acceptance of gluon fusion events in the $\mathrm{VBF}$
categories, due to missing higher-order QCD effects in the calculations: these
are estimated by variations of the renormalization and factorization scales in
MCFM 5.8 [46]. Two nuisance parameters account for the uncertainty in the
overall normalizations of Higgs boson events with 2 extra jets, or with 3 or
more extra jets, allowing one to propagate the impact of jet suppression from
the kinematic selections in the $\mathrm{VBF}$ BDT scores. An extension of the
Stewart–Tackmann method [47, 48] is used. The impact on the yield of gluon
fusion events in $\mathrm{VBF}$ categories is 8–13%.

  * •

Uncertainty in the $\HGG$ branching fraction: is estimated to be about 2%
[32].

###  0.10.2 Experimental uncertainties in the photon energy scale

The experimental uncertainties in the photon energy scale and resolution are
propagated through to the signal model in the final statistical fit, allowing
the shape to vary. These uncertainties are:

  * •

Energy scale and resolution: The uncertainties in the overall photon energy
scale and resolution corrections are assessed with ${\PZ\to\EE}$ events and
applied to photons. These uncertainties account for varying the $\RNINE$
distribution, the regression training (using electrons instead of photons) and
the electron selection used to derive the corrections. The uncertainty in the
additional energy smearing is assigned propagating the uncertainties in the
various $\abs{\eta}$ and $\RNINE$ bins to the Higgs boson signal phase space.
In both cases dedicated nuisance parameters are included as additional
systematic terms in the signal model and amount to a 0.15 to 0.5% effect on
the photon energy depending on the photon category. The effect on the
measurement of the inclusive signal strength modifier is found to be about
2.5%.

  * •

Nonlinearity of the photon energy: An additional uncertainty accounts for the
possible residual differences in the linearity of the energy scale between
data and simulation. This effect is studied using Lorentz-boosted boson
dielectron decays. The effect is found to be at most 0.1% on the photon energy
in all categories, except in the untagged category with highest signal-to-
background ratio, for which it is 0.2%.

Additional uncertainties are assigned based on studies accounting for
differences between electrons and photons on the following points.

  * •

Nonuniformity of the light collection: The uncertainty in the modelling of the
fraction of scintillation light reaching the photodetector as a function of
the longitudinal depth in the crystal at which it was emitted. The uncertainty
has been slightly increased with respect to Run 1 to account for the larger
loss in transparency of the ECAL crystals. The size of the effect on the
photon energy scale for 2016 data is estimated to be 0.07%.

  * •

Electromagnetic shower modelling: A further small uncertainty is added to
account for imperfect electromagnetic shower simulation in . A simulation made
with a previous version of the shower description, not using the
Seltzer–Berger model for the bremsstrahlung energy spectrum [49], changes the
energy scale for both electrons and photons. Although mostly consistent with
zero, the variation is interpreted as a limitation on our knowledge of the
correct simulation of the showers, leading to a further uncertainty of 0.05%
in the photon energy.

  * •

Modelling of the material budget: The uncertainty in the material budget
between the interaction point and the ECAL, which affects the behaviour of
electron and photon showers, is estimated with specially simulated samples
where the material budget is uniformly varied by $\pm 5$ %. This accounts for
the difference in the estimate of the material budget between data and
simulation, using methods based on electron bremsstrahlung, multiple
scattering of pions, and energy flow in ECAL. The effect on the energy scale
is at most 0.24%.

  * •

Shower shape corrections: The uncertainty deriving from the imperfect shower
shape modelling in simulation. It is estimated using simulation with and
without the corrections on the shower shape variables applied to mitigate
discrepancies between data and simulation (as described in Section 0.5.3).
This uncertainty in the energy scale is at most 0.01–0.15%, depending on the
photon category.

###  0.10.3 Additional experimental uncertainties

Other experimental uncertainties are accounted for by propagating the
uncertainties in the efficiencies, scale factors, and selection variables
through the analysis and applying them to the per-category signal yield:

  * •

Trigger efficiency: the trigger efficiency is measured from ${\PZ\to\EE}$
events using the tag-and-probe technique; the impact on the event yields is at
most 0.1%.

  * •

Photon preselection: the systematic uncertainty is taken as the uncertainty in
the ratio between the efficiency measured in data and in simulation; it ranges
from 0.1 to 0.7%, according to the photon category, and results in an event
yield variation from 0.2 to 0.5%, depending on the event category.

  * •

Photon identification BDT score: to cover the observed discrepancies between
data and simulation, the uncertainty in the signal yields in the different
categories of the analysis is estimated conservatively by propagating the
uncertainty described in Section 0.5 through the diphoton BDT and
categorization.

  * •

Per-photon energy resolution estimate: this uncertainty is parameterized
conservatively as a rescaling of the resolution by $\pm 5\%$ about its nominal
value, to cover all differences between data and simulation in the output
distribution of the estimator. The variation is propagated through the
diphoton BDT and categorization procedure.

  * •

Jet energy scale and smearing corrections: this uncertainty is implemented as
migration within $\mathrm{VBF}$ categories, within ttH categories, within
$\mathrm{V}\PH$ categories, and from tagged to untagged categories. Jet energy
scale corrections account for an 8 to 18% migration between the $\mathrm{VBF}$
categories and 11% from the $\mathrm{VBF}$ to untagged categories. The
migration due to the energy scale is about 5% in $\ttbar\PH$ categories and up
to about 15% in $\mathrm{V}\PH$ categories. The jet energy resolution has an
impact on the event migration of less than 3% in all categories except VH, for
which the effect can be as large as 20%. However, the processes contributing
to the $\mathrm{V}\PH$ categories and showing the largest migrations represent
a marginal fraction of events, so that their effect on the results is
negligible. Processes contributing to the majority of the events in the
$\mathrm{V}\PH$ categories show migrations of about 3%.

  * •

Missing transverse energy: this uncertainty is computed by shifting the
reconstructed $\pt$ of the particle candidates entering the computation of
$\ptmiss$ within the momentum scale and resolution uncertainties appropriate
to each type of reconstructed object, as described in Ref. [50]. It results in
a 10 to 15% migration from the $\Pg\Pg\PH$ categories into the $\mathrm{V}\PH$
MET category.

  * •

Pileup jet identification: this uncertainty is estimated by comparing in data
and simulation the identification score of jets in events with a boson and one
balanced jet. The full discrepancy between data and simulation is used to
estimate the event migration, which is of the order of 1% or less.

  * •

Lepton isolation and identification: for both electrons and muons the
uncertainty is computed by varying the ratio of the efficiency measured in
data and simulation within its uncertainty. The measurement is done using the
tag-and-probe technique on events. The resulting differences in the selection
efficiency are less than 1% for the $\cPqt\cPqt\PH$ Leptonic category, 1.5%
for the $\PW\PH$ Leptonic category, and 3% for the $\PZ\PH$ Leptonic category.

  * •

tagging efficiency: uncertainties have been evaluated by comparing data and
simulated distributions for the CSV tagging discriminant, as described in
Section 0.2. The uncertainties include the statistical component in the
estimate of the fraction of heavy- and light-flavour jets in data and
simulation, and the corresponding mutual contaminations. These are propagated
differently for the hadron-tagged category and the lepton-tagged category,
because the former uses the tagging discriminant distribution as input to a
specialized ttH BDT, whereas the latter uses a fixed working point, as
described in Section 0.7. For the lepton-tagged category, the uncertainty is
evaluated by varying the measured tagging efficiencies in data and simulation
within their uncertainties [22]. For the hadron-tagged category, the
uncertainty is evaluated by modifying the shape of the tagging discriminant in
the simulation. The resulting uncertainty in the signal yields is about 2% in
the lepton-tagged category and less than 5% in the hadron-tagged category.

  * •

Vertex finding efficiency: the largest contribution to this uncertainty comes
from the modelling of the underlying event, plus the uncertainty in the ratio
of data and simulation obtained using $\PZ\to\mu^{+}\mu^{-}$ events. It is
handled as an additional nuisance parameter built into the signal model, which
allows the fraction of events in the correct and wrong vertex scenario to
change. The size of the uncertainty in the vertex selection efficiency is 2%.

  * •

Integrated luminosity: it amounts to a 2.5% uncertainty in the signal yield
[51].

The choice of the background parametrization is handled using the discrete
profiling method, described in Section 0.9, which propagates the uncertainty
on the choice of function through the fits.

The dominant systematic uncertainties on the signal strengths and couplings
are the photon shower shape modelling (which affects the photon identification
and per-photon energy resolution estimate), the photon energy scale and
smearing, the jet energy scale, the integrated luminosity. The most important
theoretical uncertainties are the branching fraction, and the renormalization
and factorization scale uncertainties. Each of these uncertainties has an
impact of a few percent on the overall signal strength, with some dependence
on the targeted production mechanism, as shown in Fig. 10.

Figure 10: Summary of the impact of the different systematic uncertainties on
the overall signal strength modifier and on the signal strength modifiers for
the $\mathrm{VBF}$ and $\ttbar\PH$ production processes. The observed
(expected) results are shown by the solid (empty) bars.

##  0.11 Results

To extract the results, binned maximum-likelihood fits are performed to the
${m}_{\Pgg\Pgg}$ distributions of all categories, in the range
$100<{m}_{\Pgg\Pgg}<180\GeV$, with a single overall signal strength modifier
and a single value of ${m}_{\PH}$ free to vary in the fit (profiled). Binned
fits are used for speed of computation, and the chosen bin size of 250is
sufficiently small compared to the mass resolution that no information is
lost. The signal strength modifier $\mu$ is defined as the ratio of the
observed Higgs boson rate in the decay channel to the SM expectation. The data
and the signal-plus-background model fit for each category are shown in Figs.
11–13. The ${m}_{\Pgg\Pgg}$ distribution for the sum of all the categories is
shown in Fig. 14. The one (green) and two (yellow) standard deviation bands
shown for the background component of the fit include the uncertainty in the
fitted parameters.

Figure 11: Data and signal-plus-background model fits in the four untagged
categories are shown. The one (green) and two (yellow) standard deviation
bands include the uncertainties in the background component of the fit. The
lower panel in each plot shows the residuals after the background subtraction.

Figure 12: Data and signal-plus-background model fits in $\mathrm{VBF}$ and
$\ttbar\PH$ categories are shown. The one (green) and two (yellow) standard
deviation bands include the uncertainties in the background component of the
fit. The lower panel in each plot shows the residuals after the background
subtraction.

Figure 13: Data and signal-plus-background model fits in $\mathrm{V}\PH$
categories are shown. The one (green) and two (yellow) standard deviation
bands include the uncertainties in the background component of the fit. The
lower panel in each plot shows the residuals after the background subtraction.

Figure 14: Data and signal-plus-background model fits for all categories
summed (left) and where the categories are summed weighted by their
sensitivity (right). The one (green) and two (yellow) standard deviation bands
include the uncertainties in the background component of the fit. The lower
panel in each plot show the residuals after the background subtraction.

Table 0.11 and Fig. 15 show the expected number of signal events for each
category. The total number is broken down by the contribution (in percent) of
each production mode to any particular event category. The
$\sigma_{\text{eff}}$ and $\sigma_{\text{HM}}$ are also listed: the former is
defined as the smallest interval containing 68.3% of the invariant mass
distribution, while the latter represents the width of the distribution at
half of its highest point (FWHM), divided by 2.35. The table also reports the
expected number of background events per in the corresponding
$\pm\sigma_{\text{eff}}$ window around 125, using the best fit background
function.

The expected number of signal events per category and the percentage breakdown
per production mode in that category. The $\sigma_{\text{eff}}$, computed as
the smallest interval containing 68.3% of the invariant mass distribution, and
$\sigma_{\text{HM}}$, computed as the width of the distribution at half of its
highest point divided by 2.35, are also shown as an estimate of the
${m}_{\Pgg\Pgg}$ resolution in that category. The expected number of
background events per around 125is also listed.  Event categories Expected SM
125Higgs boson signal Bkg Total $\Pg\Pg\PH$ $\mathrm{VBF}$ ttH bbH tHq tHW
$\PW\PH$ lep $\PZ\PH$ lep $\PW\PH$ had $\PZ\PH$ had $\sigma_{\text{eff}}$
$\sigma_{\text{HM}}$ ($\GeV^{-1}$) () () Untagged 0 32.5 72.0 % 16.6 % 2.6 %
0.6 % 0.7 % 0.3 % 0.6 % 0.3 % 4.2 % 2.2 % 1.32 1.26 21.8 Untagged 1 469.3 86.5
% 7.9 % 0.6 % 1.2 % 0.1 % $<$0.05 % 0.5 % 0.3 % 1.9 % 1.1 % 1.46 1.32 925.1
Untagged 2 678.3 89.9 % 5.4 % 0.4 % 1.2 % 0.1 % $<$0.05 % 0.5 % 0.3 % 1.4 %
0.8 % 1.93 1.67 2391.7 Untagged 3 624.3 91.3 % 4.4 % 0.5 % 1.0 % 0.1 % $<$0.05
% 0.5 % 0.3 % 1.2 % 0.7 % 2.61 2.27 4855.1 $\mathrm{VBF}$ 0 9.3 15.5 % 83.2 %
0.4 % 0.4 % 0.3 % $<$0.05 % $<$0.05 % $<$0.05 % 0.2 % $<$0.05 % 1.52 1.31 1.6
$\mathrm{VBF}$ 1 8.0 28.4 % 69.7 % 0.4 % 0.6 % 0.4 % $<$0.05 % 0.1 % $<$0.05 %
0.3 % 0.1 % 1.66 1.38 3.3 $\mathrm{VBF}$ 2 25.2 45.1 % 51.2 % 0.9 % 0.8 % 0.6
% 0.1 % 0.2 % 0.1 % 0.8 % 0.3 % 1.64 1.37 18.9 $\cPqt\cPqt\PH$ Hadronic 5.6
7.0 % 0.7 % 81.1 % 2.1 % 4.3 % 2.1 % 0.1 % 0.1 % 0.7 % 1.9 % 1.48 1.30 2.4
$\cPqt\cPqt\PH$ Leptonic 3.8 1.5 % $<$0.05 % 87.8 % 0.1 % 4.7 % 3.1 % 1.5 %
1.2 % $<$0.05 % $<$0.05 % 1.60 1.35 1.5 $\PZ\PH$ Leptonic 0.5 $<$0.05 %
$<$0.05 % 2.6 % $<$0.05 % $<$0.05 % 0.1 % $<$0.05 % 97.3 % $<$0.05 % $<$0.05 %
1.65 1.43 0.1 $\PW\PH$ Leptonic 3.6 1.3 % 0.6 % 5.2 % 0.2 % 3.0 % 0.7 % 84.5 %
4.3 % 0.1 % 0.1 % 1.64 1.43 2.1 $\mathrm{V}\PH$ LeptonicLoose 2.7 8.1 % 2.7 %
2.4 % 0.6 % 1.8 % 0.1 % 64.4 % 19.1 % 0.6 % 0.2 % 1.67 1.56 3.5
$\mathrm{V}\PH$ Hadronic 7.9 47.6 % 4.5 % 4.4 % 0.4 % 1.7 % 0.3 % 0.2 % 0.5 %
25.2 % 15.1 % 1.38 1.30 7.2 $\mathrm{V}\PH$ MET 4.0 18.7 % 2.6 % 15.4 % 0.4 %
2.1 % 1.2 % 26.8 % 30.4 % 1.4 % 0.9 % 1.56 1.39 3.5 Total 1875.0 86.9 % 7.1 %
1.0 % 1.1 % 0.2 % $<$0.05 % 0.8 % 0.4 % 1.6 % 0.9 % 1.96 1.62 8237.8

Figure 15: Expected fraction of signal events per production mode in the
different categories. For each category, the $\sigma_{\text{eff}}$ and
$\sigma_{\text{HM}}$ of the signal model, as described in the text, are given.
The ratio of the number of signal events (S) to the number of signal-plus-
background events (S+B) is shown on the right hand side.

A likelihood scan of the signal strength modifier is performed, with other
parameters of the signal and background models allowed to vary. Systematic
uncertainties are included in the form of nuisance parameters and the results
are obtained using an asymptotic approach [52, 53, 54] with a test statistic
based on the profile likelihood ratio ($q$) [55]. The individual contributions
of the statistical and systematic uncertainties are separated by performing a
likelihood scan removing the systematic uncertainties to determine the
statistical uncertainty. The systematic uncertainty is then taken as the
difference in quadrature between the total uncertainty and the statistical
uncertainty. The results can be found in Fig. 16. The best fit signal strength
modifier measured for all categories combined using this method is
$\widehat{\mu}=1.18^{+0.17}_{-0.14}=1.18\
^{+0.12}_{-0.11}\stat^{+0.09}_{-0.07}\syst^{+0.07}_{-0.06}\thy$. The best fit
mass is found at $\widehat{{m}}_{\PH}=125.4\pm 0.3\GeV=125.4\pm 0.2\stat\pm
0.2\syst\GeV$, compatible with the combined mass measurement from ATLAS and
CMS [7]. A precise determination of the systematic uncertainties affecting the
best fit mass is not within the scope of this analysis. The maximum relative
variation of $\widehat{\mu}$ for ${m}_{\PH}$ within a range of $\pm 1\GeV$
around $125\GeV$ is less than 2%.

The results of a fit to the signal strength modifier for each production mode,
defined analogously to the overall $\mu$ above, are shown in Fig. 17 and
summarized in Table 0.11. The observed rates of the $\mathrm{VBF}$,
$\ttbar\PH$, and $\mathrm{V}\PH$ production modes correspond respectively to
$p$-values of 4.2, 0.074, and 0.47%, with respect to the absence of the
considered production mode. The expected $p$-values are 1.8, 7.3, and 12%,
respectively, for an SM Higgs boson, with the current data set.

Results of the fit to the signal strength modifier for each production mode.
The total uncertainties as well as a their statistical, systematic, and theory
components are shown. The last two columns report the $p$-value relative to
the observed rates and referred to the abscence of the considered production
mode, and its corresponding estimated significance.  Process $\widehat{\mu}$
Uncertainties $p$-value Estimated significance tot stat syst theo (standard
deviations) $\Pg\Pg\PH$ $1.10$ ${}^{+0.20}_{-0.18}$ ${}^{+0.15}_{-0.15}$
${}^{+0.09}_{-0.08}$ ${}^{+0.08}_{-0.06}$ 3.1$\ten{-12}$ 6.9 $\mathrm{VBF}$
$0.8$ ${}^{+0.6}_{-0.5}$ ${}^{+0.5}_{-0.4}$ ${}^{+0.3}_{-0.2}$
${}^{+0.2}_{-0.1}$ 4.2$\ten{-2}$ 1.7 $\ttbar\PH$ $2.2$ ${}^{+0.9}_{-0.8}$
${}^{+0.9}_{-0.8}$ ${}^{+0.2}_{-0.1}$ ${}^{+0.2}_{-0.1}$ 7.4$\ten{-4}$ 3.2
$\mathrm{V}\PH$ $2.4$ ${}^{+1.1}_{-1.0}$ ${}^{+1.0}_{-1.0}$ ${}^{+0.2}_{-0.1}$
${}^{+0.2}_{-0.1}$ 4.7$\ten{-3}$ 2.6

A similar fit is performed to extract the ratios of observed cross sections to
the SM prediction in the stage 0 of the simplified template cross section
(STXS) framework [32]. These cross sections are for a reduced fiducial volume,
defined by requiring the Higgs boson rapidity to be less than 2.5. Outside of
this volume the analysis has a negligible acceptance. The ratios are measured
for the $\Pg\Pg\PH$, $\mathrm{VBF}$, $\ttbar\PH$, and $\mathrm{V}\PH$
production processes. $\mathrm{V}\PH$ is further split considering the decay
of the associated boson into $\PW\PH$ leptonic, $\PZ\PH$ leptonic, and
$\mathrm{V}\PH$ hadronic, which groups hadronic decays of both the and bosons.
The STXS approach differs from the signal strength modifier measurements in
the splitting of the production modes, and reduces the dependence of the
measurements on the theoretical uncertainties in the SM predictions, by
avoiding the sizeable uncertainty associated with the extrapolation to the
full phase space. The measured cross section ratios, where the SM prediction
[32] is denoted as $\sigma_{\mathrm{theo}}$, are shown in Fig. 18.

Figure 16: The likelihood scan for the signal strength modifier where the
value of the SM Higgs boson mass is profiled in the fit. Figure 17: Signal
strength modifiers measured for each process (black points), with the SM Higgs
boson mass profiled, compared to the overall signal strength modifier (green
band) and to the SM expectation (dashed red line).  Figure 18: Cross section
ratios measured for each process (black points) in the Higgs simplified
template cross section framework [32], with the SM Higgs boson mass profiled,
compared to the SM expectations and their uncertainties (blue band). The
signal strength modifiers are constrained to be nonnegative, as indicated by
the vertical line and hashed pattern at zero.

A two-dimensional likelihood scan of the signal strength modifier
$\mu_{\Pg\Pg\PH,\ttbar\PH}$ for fermionic production modes ($\Pg\Pg\PH$ and
$\ttbar\PH$) and $\mu_{\mathrm{VBF},\mathrm{V}\PH}$ for vector boson
production modes ($\mathrm{VBF}$, $\PZ\PH$, $\PW\PH$), with the value of the
parameter ${m}_{\PH}$ profiled in the fit, is performed. Figure 19 shows the
68 and 95% confidence level () contours. The best fit values for each modifier
are $\widehat{\mu}_{\Pg\Pg\PH,\ttbar\PH}=1.19^{+0.22}_{-0.18}$ and
$\widehat{\mu}_{\mathrm{VBF},\mathrm{V}\PH}=1.21^{+0.58}_{-0.51}$.

Deviations from the SM expectation in the couplings of the Higgs boson can be
parameterized using coupling modifiers in the so-called $\kappa$ framework
[17]. Two-dimensional likelihood scans of the Higgs boson coupling modifiers
are produced: $\kappa_{\mathrm{f}}$ versus $\kappa_{\mathrm{V}}$, the coupling
modifiers to fermions and bosons; and $\kappa_{\mathrm{g}}$ versus
$\kappa_{\Pgg}$, the effective coupling modifiers to gluons and photons. The
$\kappa$ parameters other than those varied are fixed to 1 in each case.
Figure 20 shows the test statistic $q$ and the 68% and 95% contours for each
scan. The point $(\kappa_{\mathrm{V}},\kappa_{\mathrm{f}})=(1,-1)$ has an
observed (expected) $q$ value of 35.2 (53.7), inconsistent with the observed
(expected) best fit value at the level of 5.8 (7.0) standard deviations.

Figure 19: The two-dimensional best fit (black cross) of the signal strength
modifiers for fermionic ($\Pg\Pg\PH$, $\ttbar\PH$) and bosonic
($\mathrm{VBF}$, $\PZ\PH$, $\PW\PH$) production modes compared to the SM
expectation (red diamond). The Higgs boson mass is profiled in the fit. The
solid (dashed) line represents the 68 (95)% confidence region.

Figure 20: Two-dimensional likelihood scans of $\kappa_{\mathrm{f}}$ versus
$\kappa_{\mathrm{V}}$ (left) and $\kappa_{\mathrm{g}}$ versus $\kappa_{\Pgg}$
(right). All four variables are expressed relative to the SM expectations. The
mass of the Higgs boson is profiled in the fits. The crosses indicate the best
fit values, the diamonds indicate the standard model expectations. The colour
maps indicate the value of the test statistic $q$ as described in the text.

##  0.12 Summary

We report measurements of the production cross section and couplings of the
Higgs boson using its diphoton decay: the overall signal strength modifier;
the signal strength modifier for each production mode separately; cross
section ratios for the stage 0 simplified template cross section framework;
the best fit rates in the
$\mu_{\mathrm{VBF},\mathrm{V}\PH}$-$\mu_{\Pg\Pg\PH,\ttbar\PH}$ plane with
$\mathrm{VBF}$ and $\mathrm{V}\PH$ production, and $\Pg\Pg\PH$ and $\ttbar\PH$
production, varied together; and the best fit coupling modifiers in the
$\kappa_{\mathrm{f}}$-$\kappa_{\mathrm{V}}$ and
$\kappa_{\mathrm{g}}$-$\kappa_{\Pgg}$ planes. The analysis is based on proton-
proton collision data collected at $\sqrt{s}=13\TeV$ by the CMS experiment at
the LHC in 2016, corresponding to an integrated luminosity of 35.9. The best
fit signal strength modifier obtained after profiling ${m}_{\PH}$ is
${\widehat{\mu}=1.18^{+0.17}_{-0.14}=1.18\
^{+0.12}_{-0.11}\stat^{+0.09}_{-0.07}\syst^{+0.07}_{-0.06}\thy}$. The best fit
values in the $\mu_{\mathrm{VBF},\mathrm{V}\PH}$–$\mu_{\Pg\Pg\PH,\ttbar\PH}$
plane are $\widehat{\mu}_{\Pg\Pg\PH,\ttbar\PH}=1.19^{+0.22}_{-0.18}$ and
$\widehat{\mu}_{\mathrm{VBF},\mathrm{V}\PH}=1.21^{+0.58}_{-0.51}$. When
$\mu_{\ttbar\PH}$ is considered separately, the best fit value is
$\widehat{\mu}_{\ttbar\PH}=2.2^{+0.9}_{-0.8}$, corresponding to a $p$-value of
0.074% with respect to the absence of $\ttbar\PH$ production. Stage 0
simplified template cross sections are compatible with the standard model.

###### Acknowledgements.

We congratulate our colleagues in the CERN accelerator departments for the
excellent performance of the LHC and thank the technical and administrative
staffs at CERN and at other CMS institutes for their contributions to the
success of the CMS effort. In addition, we gratefully acknowledge the
computing centres and personnel of the Worldwide LHC Computing Grid for
delivering so effectively the computing infrastructure essential to our
analyses. Finally, we acknowledge the enduring support for the construction
and operation of the LHC and the CMS detector provided by the following
funding agencies: the Austrian Federal Ministry of Science, Research and
Economy and the Austrian Science Fund; the Belgian Fonds de la Recherche
Scientifique, and Fonds voor Wetenschappelijk Onderzoek; the Brazilian Funding
Agencies (CNPq, CAPES, FAPERJ, and FAPESP); the Bulgarian Ministry of
Education and Science; CERN; the Chinese Academy of Sciences, Ministry of
Science and Technology, and National Natural Science Foundation of China; the
Colombian Funding Agency (COLCIENCIAS); the Croatian Ministry of Science,
Education and Sport, and the Croatian Science Foundation; the Research
Promotion Foundation, Cyprus; the Secretariat for Higher Education, Science,
Technology and Innovation, Ecuador; the Ministry of Education and Research,
Estonian Research Council via IUT23-4 and IUT23-6 and European Regional
Development Fund, Estonia; the Academy of Finland, Finnish Ministry of
Education and Culture, and Helsinki Institute of Physics; the Institut
National de Physique Nucléaire et de Physique des Particules / CNRS, and
Commissariat à l’Énergie Atomique et aux Énergies Alternatives / CEA, France;
the Bundesministerium für Bildung und Forschung, Deutsche
Forschungsgemeinschaft, and Helmholtz-Gemeinschaft Deutscher
Forschungszentren, Germany; the General Secretariat for Research and
Technology, Greece; the National Research, Development and Innovation Fund,
Hungary; the Department of Atomic Energy and the Department of Science and
Technology, India; the Institute for Studies in Theoretical Physics and
Mathematics, Iran; the Science Foundation, Ireland; the Istituto Nazionale di
Fisica Nucleare, Italy; the Ministry of Science, ICT and Future Planning, and
National Research Foundation (NRF), Republic of Korea; the Lithuanian Academy
of Sciences; the Ministry of Education, and University of Malaya (Malaysia);
the Mexican Funding Agencies (BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-
FAI); the Ministry of Business, Innovation and Employment, New Zealand; the
Pakistan Atomic Energy Commission; the Ministry of Science and Higher
Education and the National Science Centre, Poland; the Fundação para a Ciência
e a Tecnologia, Portugal; JINR, Dubna; the Ministry of Education and Science
of the Russian Federation, the Federal Agency of Atomic Energy of the Russian
Federation, Russian Academy of Sciences and the Russian Foundation for Basic
Research; the Ministry of Education, Science and Technological Development of
Serbia; the Secretaría de Estado de Investigación, Desarrollo e Innovación,
Programa Consolider-Ingenio 2010, Plan Estatal de Investigación Científica y
Técnica y de Innovación 2013-2016, Plan de Ciencia, Tecnología e Innovación
2013-2017 del Principado de Asturias and Fondo Europeo de Desarrollo Regional,
Spain; the Swiss Funding Agencies (ETH Board, ETH Zurich, PSI, SNF, UniZH,
Canton Zurich, and SER); the Ministry of Science and Technology, Taipei; the
Thailand Center of Excellence in Physics, the Institute for the Promotion of
Teaching Science and Technology of Thailand, Special Task Force for Activating
Research and the National Science and Technology Development Agency of
Thailand; the Scientific and Technical Research Council of Turkey, and Turkish
Atomic Energy Authority; the National Academy of Sciences of Ukraine, and
State Fund for Fundamental Researches, Ukraine; the Science and Technology
Facilities Council, UK; the US Department of Energy, and the US National
Science Foundation. Individuals have received support from the Marie-Curie
programme and the European Research Council and Horizon 2020 Grant, contract
No. 675440 (European Union); the Leventis Foundation; the A. P. Sloan
Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science
Policy Office; the Fonds pour la Formation à la Recherche dans l’Industrie et
dans l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door
Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium)
under the “Excellence of Science - EOS” - be.h project n. 30820817; the
Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the
Lendület (“Momentum”) Programme and the János Bolyai Research Scholarship of
the Hungarian Academy of Sciences, the New National Excellence Program ÚNKP,
the NKFIA research grants 123842, 123959, 124845, 124850 and 125105 (Hungary);
the Council of Scientific and Industrial Research, India; the HOMING PLUS
programme of the Foundation for Polish Science, cofinanced from European
Union, Regional Development Fund, the Mobility Plus programme of the Ministry
of Science and Higher Education, the National Science Center (Poland),
contracts Harmonia 2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543,
2014/15/B/ST2/03998, and 2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406;
the National Priorities Research Program by Qatar National Research Fund; the
Programa de Excelencia María de Maeztu and the Programa Severo Ochoa del
Principado de Asturias; the Thalis and Aristeia programmes cofinanced by EU-
ESF and the Greek NSRF; the Rachadapisek Sompot Fund for Postdoctoral
Fellowship, Chulalongkorn University and the Chulalongkorn Academic into Its
2nd Century Project Advancement Project (Thailand); the Welch Foundation,
contract C-1845; and the Weston Havens Foundation (USA).

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##  .13 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan, A. Tumasyan Institut für Hochenergiephysik, Wien, Austria
W. Adam, F. Ambrogi, E. Asilar, T. Bergauer, J. Brandstetter, E. Brondolin, M.
Dragicevic, J. Erö, A. Escalante Del Valle, M. Flechl, M. Friedl, R.
Frühwirth1, V.M. Ghete, J. Grossmann, J. Hrubec, M. Jeitler1, A. König, N.
Krammer, I. Krätschmer, D. Liko, T. Madlener, I. Mikulec, E. Pree, N. Rad, H.
Rohringer, J. Schieck1, R. Schöfbeck, M. Spanring, D. Spitzbart, A. Taurok, W.
Waltenberger, J. Wittmann, C.-E. Wulz1, M. Zarucki Institute for Nuclear
Problems, Minsk, Belarus
V. Chekhovsky, V. Mossolov, J. Suarez Gonzalez Universiteit Antwerpen,
Antwerpen, Belgium
E.A. De Wolf, D. Di Croce, X. Janssen, J. Lauwers, M. Pieters, M. Van De
Klundert, H. Van Haevermaet, P. Van Mechelen, N. Van Remortel Vrije
Universiteit Brussel, Brussel, Belgium
S. Abu Zeid, F. Blekman, J. D’Hondt, I. De Bruyn, J. De Clercq, K. Deroover,
G. Flouris, D. Lontkovskyi, S. Lowette, I. Marchesini, S. Moortgat, L.
Moreels, Q. Python, K. Skovpen, S. Tavernier, W. Van Doninck, P. Van Mulders,
I. Van Parijs Université Libre de Bruxelles, Bruxelles, Belgium
D. Beghin, B. Bilin, H. Brun, B. Clerbaux, G. De Lentdecker, H. Delannoy, B.
Dorney, G. Fasanella, L. Favart, R. Goldouzian, A. Grebenyuk, A.K. Kalsi, T.
Lenzi, J. Luetic, T. Seva, E. Starling, C. Vander Velde, P. Vanlaer, D.
Vannerom, R. Yonamine Ghent University, Ghent, Belgium
T. Cornelis, D. Dobur, A. Fagot, M. Gul, I. Khvastunov2, D. Poyraz, C. Roskas,
D. Trocino, M. Tytgat, W. Verbeke, B. Vermassen, M. Vit, N. Zaganidis
Université Catholique de Louvain, Louvain-la-Neuve, Belgium
H. Bakhshiansohi, O. Bondu, S. Brochet, G. Bruno, C. Caputo, A. Caudron, P.
David, S. De Visscher, C. Delaere, M. Delcourt, B. Francois, A. Giammanco, G.
Krintiras, V. Lemaitre, A. Magitteri, A. Mertens, M. Musich, K. Piotrzkowski,
L. Quertenmont, A. Saggio, M. Vidal Marono, S. Wertz, J. Zobec Centro
Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil
W.L. Aldá Júnior, F.L. Alves, G.A. Alves, L. Brito, G. Correia Silva, C.
Hensel, A. Moraes, M.E. Pol, P. Rebello Teles Universidade do Estado do Rio de
Janeiro, Rio de Janeiro, Brazil
E. Belchior Batista Das Chagas, W. Carvalho, J. Chinellato3, E. Coelho, E.M.
Da Costa, G.G. Da Silveira4, D. De Jesus Damiao, S. Fonseca De Souza, H.
Malbouisson, M. Medina Jaime5, M. Melo De Almeida, C. Mora Herrera, L. Mundim,
H. Nogima, L.J. Sanchez Rosas, A. Santoro, A. Sznajder, M. Thiel, E.J. Tonelli
Manganote3, F. Torres Da Silva De Araujo, A. Vilela Pereira Universidade
Estadual Paulista a, Universidade Federal do ABC b, São Paulo, Brazil
S. Ahujaa, C.A. Bernardesa, L. Calligarisa, T.R. Fernandez Perez Tomeia, E.M.
Gregoresb, P.G. Mercadanteb, S.F. Novaesa, Sandra S. Padulaa, D. Romero Abadb,
J.C. Ruiz Vargasa Institute for Nuclear Research and Nuclear Energy, Bulgarian
Academy of Sciences, Sofia, Bulgaria
A. Aleksandrov, R. Hadjiiska, P. Iaydjiev, A. Marinov, M. Misheva, M. Rodozov,
M. Shopova, G. Sultanov University of Sofia, Sofia, Bulgaria
A. Dimitrov, L. Litov, B. Pavlov, P. Petkov Beihang University, Beijing, China
W. Fang6, X. Gao6, L. Yuan Institute of High Energy Physics, Beijing, China
M. Ahmad, J.G. Bian, G.M. Chen, H.S. Chen, M. Chen, Y. Chen, C.H. Jiang, D.
Leggat, H. Liao, Z. Liu, F. Romeo, S.M. Shaheen, A. Spiezia, J. Tao, C. Wang,
Z. Wang, E. Yazgan, H. Zhang, J. Zhao State Key Laboratory of Nuclear Physics
and Technology, Peking University, Beijing, China
Y. Ban, G. Chen, J. Li, Q. Li, S. Liu, Y. Mao, S.J. Qian, D. Wang, Z. Xu
Tsinghua University, Beijing, China
Y. Wang Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, C.A. Carrillo Montoya, L.F. Chaparro Sierra, C. Florez,
C.F. González Hernández, M.A. Segura Delgado University of Split, Faculty of
Electrical Engineering, Mechanical Engineering and Naval Architecture, Split,
Croatia
B. Courbon, N. Godinovic, D. Lelas, I. Puljak, P.M. Ribeiro Cipriano, T.
Sculac University of Split, Faculty of Science, Split, Croatia
Z. Antunovic, M. Kovac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, D. Ferencek, K. Kadija, B. Mesic, A. Starodumov7, T. Susa
University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos,
P.A. Razis, H. Rykaczewski Charles University, Prague, Czech Republic
M. Finger8, M. Finger Jr.8 Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab
Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
A.A. Abdelalim9,10, A. Ellithi Kamel11, A. Mohamed10 National Institute of
Chemical Physics and Biophysics, Tallinn, Estonia
S. Bhowmik, R.K. Dewanjee, M. Kadastik, L. Perrini, M. Raidal, C. Veelken
Department of Physics, University of Helsinki, Helsinki, Finland
P. Eerola, H. Kirschenmann, J. Pekkanen, M. Voutilainen Helsinki Institute of
Physics, Helsinki, Finland
J. Havukainen, J.K. Heikkilä, T. Järvinen, V. Karimäki, R. Kinnunen, T.
Lampén, K. Lassila-Perini, S. Laurila, S. Lehti, T. Lindén, P. Luukka, T.
Mäenpää, H. Siikonen, E. Tuominen, J. Tuominiemi Lappeenranta University of
Technology, Lappeenranta, Finland
T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
M. Besancon, F. Couderc, M. Dejardin, D. Denegri, J.L. Faure, F. Ferri, S.
Ganjour, S. Ghosh, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry,
C. Leloup, E. Locci, M. Machet, J. Malcles, G. Negro, J. Rander, A. Rosowsky,
M.Ö. Sahin, M. Titov Laboratoire Leprince-Ringuet, Ecole polytechnique,
CNRS/IN2P3, Université Paris-Saclay, Palaiseau, France
A. Abdulsalam12, C. Amendola, I. Antropov, S. Baffioni, F. Beaudette, P.
Busson, L. Cadamuro, C. Charlot, R. Granier de Cassagnac, M. Jo, I. Kucher, S.
Lisniak, A. Lobanov, J. Martin Blanco, M. Nguyen, C. Ochando, G. Ortona, P.
Paganini, P. Pigard, R. Salerno, J.B. Sauvan, Y. Sirois, A.G. Stahl Leiton, Y.
Yilmaz, A. Zabi, A. Zghiche Université de Strasbourg, CNRS, IPHC UMR 7178,
F-67000 Strasbourg, France
J.-L. Agram13, J. Andrea, D. Bloch, J.-M. Brom, E.C. Chabert, C. Collard, E.
Conte13, X. Coubez, F. Drouhin13, J.-C. Fontaine13, D. Gelé, U. Goerlach, M.
Jansová, P. Juillot, A.-C. Le Bihan, N. Tonon, P. Van Hove Centre de Calcul de
l’Institut National de Physique Nucleaire et de Physique des Particules,
CNRS/IN2P3, Villeurbanne, France
S. Gadrat Université de Lyon, Université Claude Bernard Lyon 1, CNRS-IN2P3,
Institut de Physique Nucléaire de Lyon, Villeurbanne, France
S. Beauceron, C. Bernet, G. Boudoul, N. Chanon, R. Chierici, D. Contardo, P.
Depasse, H. El Mamouni, J. Fay, L. Finco, S. Gascon, M. Gouzevitch, G.
Grenier, B. Ille, F. Lagarde, I.B. Laktineh, H. Lattaud, M. Lethuillier, L.
Mirabito, A.L. Pequegnot, S. Perries, A. Popov14, V. Sordini, M. Vander
Donckt, S. Viret, S. Zhang Georgian Technical University, Tbilisi, Georgia
A. Khvedelidze8 Tbilisi State University, Tbilisi, Georgia
Z. Tsamalaidze8 RWTH Aachen University, I. Physikalisches Institut, Aachen,
Germany
C. Autermann, L. Feld, M.K. Kiesel, K. Klein, M. Lipinski, M. Preuten, M.P.
Rauch, C. Schomakers, J. Schulz, M. Teroerde, B. Wittmer, V. Zhukov14 RWTH
Aachen University, III. Physikalisches Institut A, Aachen, Germany
A. Albert, D. Duchardt, M. Endres, M. Erdmann, S. Erdweg, T. Esch, R. Fischer,
A. Güth, T. Hebbeker, C. Heidemann, K. Hoepfner, S. Knutzen, M. Merschmeyer,
A. Meyer, P. Millet, S. Mukherjee, T. Pook, M. Radziej, H. Reithler, M.
Rieger, F. Scheuch, D. Teyssier, S. Thüer RWTH Aachen University, III.
Physikalisches Institut B, Aachen, Germany
G. Flügge, B. Kargoll, T. Kress, A. Künsken, T. Müller, A. Nehrkorn, A.
Nowack, C. Pistone, O. Pooth, A. Stahl15 Deutsches Elektronen-Synchrotron,
Hamburg, Germany
M. Aldaya Martin, T. Arndt, C. Asawatangtrakuldee, K. Beernaert, O. Behnke, U.
Behrens, A. Bermúdez Martínez, A.A. Bin Anuar, K. Borras16, V. Botta, A.
Campbell, P. Connor, C. Contreras-Campana, F. Costanza, V. Danilov, A. De Wit,
C. Diez Pardos, D. Domínguez Damiani, G. Eckerlin, D. Eckstein, T. Eichhorn,
A. Elwood, E. Eren, E. Gallo17, J. Garay Garcia, A. Geiser, J.M. Grados
Luyando, A. Grohsjean, P. Gunnellini, M. Guthoff, A. Harb, J. Hauk, H. Jung,
M. Kasemann, J. Keaveney, C. Kleinwort, J. Knolle, I. Korol, D. Krücker, W.
Lange, A. Lelek, T. Lenz, K. Lipka, W. Lohmann18, R. Mankel, I.-A. Melzer-
Pellmann, A.B. Meyer, M. Meyer, M. Missiroli, G. Mittag, J. Mnich, A.
Mussgiller, D. Pitzl, A. Raspereza, M. Savitskyi, P. Saxena, R. Shevchenko, N.
Stefaniuk, H. Tholen, G.P. Van Onsem, R. Walsh, Y. Wen, K. Wichmann, C.
Wissing, O. Zenaiev University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, V. Blobel, M. Centis Vignali, T. Dreyer, E. Garutti, D.
Gonzalez, J. Haller, A. Hinzmann, M. Hoffmann, A. Karavdina, G. Kasieczka, R.
Klanner, R. Kogler, N. Kovalchuk, S. Kurz, V. Kutzner, J. Lange, D. Marconi,
J. Multhaup, M. Niedziela, D. Nowatschin, T. Peiffer, A. Perieanu, A. Reimers,
C. Scharf, P. Schleper, A. Schmidt, S. Schumann, J. Schwandt, J. Sonneveld, H.
Stadie, G. Steinbrück, F.M. Stober, M. Stöver, D. Troendle, E. Usai, A.
Vanhoefer, B. Vormwald Institut für Experimentelle Teilchenphysik, Karlsruhe,
Germany
M. Akbiyik, C. Barth, M. Baselga, S. Baur, E. Butz, R. Caspart, T. Chwalek, F.
Colombo, W. De Boer, A. Dierlamm, N. Faltermann, B. Freund, R. Friese, M.
Giffels, M.A. Harrendorf, F. Hartmann15, S.M. Heindl, U. Husemann, F.
Kassel15, S. Kudella, H. Mildner, M.U. Mozer, Th. Müller, M. Plagge, G. Quast,
K. Rabbertz, M. Schröder, I. Shvetsov, G. Sieber, H.J. Simonis, R. Ulrich, S.
Wayand, M. Weber, T. Weiler, S. Williamson, C. Wöhrmann, R. Wolf Institute of
Nuclear and Particle Physics (INPP), NCSR Demokritos, Aghia Paraskevi, Greece
G. Anagnostou, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas, I. Topsis-
Giotis National and Kapodistrian University of Athens, Athens, Greece
G. Karathanasis, S. Kesisoglou, A. Panagiotou, N. Saoulidou, E. Tziaferi
National Technical University of Athens, Athens, Greece
K. Kousouris, I. Papakrivopoulos University of Ioánnina, Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, S. Mallios, N.
Manthos, I. Papadopoulos, E. Paradas, J. Strologas, F.A. Triantis, D.
Tsitsonis MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
M. Csanad, N. Filipovic, G. Pasztor, O. Surányi, G.I. Veres Wigner Research
Centre for Physics, Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath19, Á. Hunyadi, F. Sikler, T.Á. Vámi, V.
Veszpremi, G. Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear
Research ATOMKI, Debrecen, Hungary
N. Beni, S. Czellar, J. Karancsi21, A. Makovec, J. Molnar, Z. Szillasi
Institute of Physics, University of Debrecen, Debrecen, Hungary
M. Bartók20, P. Raics, Z.L. Trocsanyi, B. Ujvari Indian Institute of Science
(IISc), Bangalore, India
S. Choudhury, J.R. Komaragiri National Institute of Science Education and
Research, Bhubaneswar, India
S. Bahinipati22, P. Mal, K. Mandal, A. Nayak23, D.K. Sahoo22, S.K. Swain
Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, S. Chauhan, R. Chawla, N. Dhingra, R.
Gupta, A. Kaur, M. Kaur, S. Kaur, R. Kumar, P. Kumari, M. Lohan, A. Mehta, S.
Sharma, J.B. Singh, G. Walia University of Delhi, Delhi, India
A. Bhardwaj, B.C. Choudhary, R.B. Garg, S. Keshri, A. Kumar, Ashok Kumar, S.
Malhotra, M. Naimuddin, K. Ranjan, Aashaq Shah, R. Sharma Saha Institute of
Nuclear Physics, HBNI, Kolkata, India
R. Bhardwaj24, R. Bhattacharya, S. Bhattacharya, U. Bhawandeep24, D. Bhowmik,
S. Dey, S. Dutt24, S. Dutta, S. Ghosh, N. Majumdar, K. Mondal, S.
Mukhopadhyay, S. Nandan, A. Purohit, P.K. Rout, A. Roy, S. Roy Chowdhury, S.
Sarkar, M. Sharan, B. Singh, S. Thakur24 Indian Institute of Technology
Madras, Madras, India
P.K. Behera Bhabha Atomic Research Centre, Mumbai, India
R. Chudasama, D. Dutta, V. Jha, V. Kumar, A.K. Mohanty15, P.K. Netrakanti,
L.M. Pant, P. Shukla, A. Topkar Tata Institute of Fundamental Research-A,
Mumbai, India
T. Aziz, S. Dugad, B. Mahakud, S. Mitra, G.B. Mohanty, N. Sur, B. Sutar Tata
Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, Sa. Jain, S.
Kumar, M. Maity25, G. Majumder, K. Mazumdar, N. Sahoo, T. Sarkar25, N.
Wickramage26 Indian Institute of Science Education and Research (IISER), Pune,
India
S. Chauhan, S. Dube, V. Hegde, A. Kapoor, K. Kothekar, S. Pandey, A. Rane, S.
Sharma Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
S. Chenarani27, E. Eskandari Tadavani, S.M. Etesami27, M. Khakzad, M.
Mohammadi Najafabadi, M. Naseri, S. Paktinat Mehdiabadi28, F. Rezaei
Hosseinabadi, B. Safarzadeh29, M. Zeinali University College Dublin, Dublin,
Ireland
M. Felcini, M. Grunewald INFN Sezione di Bari a, Università di Bari b,
Politecnico di Bari c, Bari, Italy
M. Abbresciaa,b, C. Calabriaa,b, A. Colaleoa, D. Creanzaa,c, L. Cristellaa,b,
N. De Filippisa,c, M. De Palmaa,b, A. Di Florioa,b, F. Erricoa,b, L. Fiorea,
A. Gelmia,b, G. Iasellia,c, S. Lezkia,b, G. Maggia,c, M. Maggia, B.
Marangellia,b, G. Minielloa,b, S. Mya,b, S. Nuzzoa,b, A. Pompilia,b, G.
Pugliesea,c, R. Radognaa, A. Ranieria, G. Selvaggia,b, A. Sharmaa, L.
Silvestrisa,15, R. Vendittia, P. Verwilligena, G. Zitoa INFN Sezione di
Bologna a, Università di Bologna b, Bologna, Italy
G. Abbiendia, C. Battilanaa,b, D. Bonacorsia,b, L. Borgonovia,b, S. Braibant-
Giacomellia,b, L. Brigliadoria,b, R. Campaninia,b, P. Capiluppia,b, A.
Castroa,b, F.R. Cavalloa, S.S. Chhibraa,b, G. Codispotia,b, M. Cuffiania,b,
G.M. Dallavallea, F. Fabbria, A. Fanfania,b, D. Fasanellaa,b, P. Giacomellia,
C. Grandia, L. Guiduccia,b, F. Iemmi, S. Marcellinia, G. Masettia, A.
Montanaria, F.L. Navarriaa,b, A. Perrottaa, T. Rovellia,b, G.P. Sirolia,b, N.
Tosia INFN Sezione di Catania a, Università di Catania b, Catania, Italy
S. Albergoa,b, S. Costaa,b, A. Di Mattiaa, F. Giordanoa,b, R. Potenzaa,b, A.
Tricomia,b, C. Tuvea,b INFN Sezione di Firenze a, Università di Firenze b,
Firenze, Italy
G. Barbaglia, K. Chatterjeea,b, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, E. Focardia,b, G. Latino, P. Lenzia,b, M. Meschinia, S.
Paolettia, L. Russoa,30, G. Sguazzonia, D. Stroma, L. Viliania INFN Laboratori
Nazionali di Frascati, Frascati, Italy
L. Benussi, S. Bianco, F. Fabbri, D. Piccolo, F. Primavera15 INFN Sezione di
Genova a, Università di Genova b, Genova, Italy
V. Calvellia,b, F. Ferroa, F. Raveraa,b, E. Robuttia, S. Tosia,b INFN Sezione
di Milano-Bicocca a, Università di Milano-Bicocca b, Milano, Italy
A. Benagliaa, A. Beschib, L. Brianzaa,b, F. Brivioa,b, V. Cirioloa,b,15, M.E.
Dinardoa,b, S. Fiorendia,b, S. Gennaia, A. Ghezzia,b, P. Govonia,b, M.
Malbertia,b, S. Malvezzia, R.A. Manzonia,b, D. Menascea, L. Moronia, M.
Paganonia,b, K. Pauwelsa,b, D. Pedrinia, S. Pigazzinia,b,31, S. Ragazzia,b, T.
Tabarelli de Fatisa,b INFN Sezione di Napoli a, Università di Napoli ’Federico
II’ b, Napoli, Italy, Università della Basilicata c, Potenza, Italy,
Università G. Marconi d, Roma, Italy
S. Buontempoa, N. Cavalloa,c, S. Di Guidaa,d,15, F. Fabozzia,c, F. Fiengaa,b,
G. Galatia,b, A.O.M. Iorioa,b, W.A. Khana, L. Listaa, S. Meolaa,d,15, P.
Paoluccia,15, C. Sciaccaa,b, F. Thyssena, E. Voevodinaa,b INFN Sezione di
Padova a, Università di Padova b, Padova, Italy, Università di Trento c,
Trento, Italy
P. Azzia, N. Bacchettaa, L. Benatoa,b, D. Biselloa,b, A. Bolettia,b, R.
Carlina,b, A. Carvalho Antunes De Oliveiraa,b, P. Checchiaa, M. Dall’Ossoa,b,
P. De Castro Manzanoa, T. Dorigoa, U. Dossellia, F. Gasparinia,b, U.
Gasparinia,b, A. Gozzelinoa, S. Lacapraraa, P. Lujan, M. Margonia,b, A.T.
Meneguzzoa,b, N. Pozzobona,b, P. Ronchesea,b, R. Rossina,b, F. Simonettoa,b,
A. Tiko, E. Torassaa, M. Zanettia,b, P. Zottoa,b INFN Sezione di Pavia a,
Università di Pavia b, Pavia, Italy
A. Braghieria, A. Magnania, P. Montagnaa,b, S.P. Rattia,b, V. Rea, M.
Ressegottia,b, C. Riccardia,b, P. Salvinia, I. Vaia,b, P. Vituloa,b INFN
Sezione di Perugia a, Università di Perugia b, Perugia, Italy
L. Alunni Solestizia,b, M. Biasinia,b, G.M. Bileia, C. Cecchia,b, D.
Ciangottinia,b, L. Fanòa,b, P. Laricciaa,b, R. Leonardia,b, E. Manonia, G.
Mantovania,b, V. Mariania,b, M. Menichellia, A. Rossia,b, A. Santocchiaa,b, D.
Spigaa INFN Sezione di Pisa a, Università di Pisa b, Scuola Normale Superiore
di Pisa c, Pisa, Italy
K. Androsova, P. Azzurria, G. Bagliesia, L. Bianchinia, T. Boccalia, L.
Borrello, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, G. Fedia, L.
Gianninia,c, A. Giassia, M.T. Grippoa, F. Ligabuea,c, T. Lomtadzea, E.
Mancaa,c, G. Mandorlia,c, A. Messineoa,b, F. Pallaa, A. Rizzia,b, P.
Spagnoloa, R. Tenchinia, G. Tonellia,b, A. Venturia, P.G. Verdinia INFN
Sezione di Roma a, Sapienza Università di Roma b, Rome, Italy
L. Baronea,b, F. Cavallaria, M. Cipriania,b, N. Dacia, D. Del Rea,b, E. Di
Marcoa,b, M. Diemoza, S. Gellia,b, E. Longoa,b, B. Marzocchia,b, P.
Meridiania, G. Organtinia,b, F. Pandolfia, R. Paramattia,b, F. Preiatoa,b, S.
Rahatloua,b, C. Rovellia, F. Santanastasioa,b INFN Sezione di Torino a,
Università di Torino b, Torino, Italy, Università del Piemonte Orientale c,
Novara, Italy
N. Amapanea,b, R. Arcidiaconoa,c, S. Argiroa,b, M. Arneodoa,c, N. Bartosika,
R. Bellana,b, C. Biinoa, N. Cartigliaa, R. Castelloa,b, F. Cennaa,b, M.
Costaa,b, R. Covarellia,b, A. Deganoa,b, N. Demariaa, B. Kiania,b, C.
Mariottia, S. Masellia, E. Migliorea,b, V. Monacoa,b, E. Monteila,b, M.
Montenoa, M.M. Obertinoa,b, L. Pachera,b, N. Pastronea, M. Pelliccionia, G.L.
Pinna Angionia,b, A. Romeroa,b, M. Ruspaa,c, R. Sacchia,b, K. Shchelinaa,b, V.
Solaa, A. Solanoa,b, A. Staianoa INFN Sezione di Trieste a, Università di
Trieste b, Trieste, Italy
S. Belfortea, M. Casarsaa, F. Cossuttia, G. Della Riccaa,b, A. Zanettia
Kyungpook National University
D.H. Kim, G.N. Kim, M.S. Kim, J. Lee, S. Lee, S.W. Lee, C.S. Moon, Y.D. Oh, S.
Sekmen, D.C. Son, Y.C. Yang Chonnam National University, Institute for
Universe and Elementary Particles, Kwangju, Korea
H. Kim, D.H. Moon, G. Oh Hanyang University, Seoul, Korea
J.A. Brochero Cifuentes, J. Goh, T.J. Kim Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, D. Gyun, S. Ha, B. Hong, Y. Jo, Y. Kim, K. Lee, K.S.
Lee, S. Lee, J. Lim, S.K. Park, Y. Roh Seoul National University, Seoul, Korea
J. Almond, J. Kim, J.S. Kim, H. Lee, K. Lee, K. Nam, S.B. Oh, B.C. Radburn-
Smith, S.h. Seo, U.K. Yang, H.D. Yoo, G.B. Yu University of Seoul, Seoul,
Korea
H. Kim, J.H. Kim, J.S.H. Lee, I.C. Park Sungkyunkwan University, Suwon, Korea
Y. Choi, C. Hwang, J. Lee, I. Yu Vilnius University, Vilnius, Lithuania
V. Dudenas, A. Juodagalvis, J. Vaitkus National Centre for Particle Physics,
Universiti Malaya, Kuala Lumpur, Malaysia
I. Ahmed, Z.A. Ibrahim, M.A.B. Md Ali32, F. Mohamad Idris33, W.A.T. Wan
Abdullah, M.N. Yusli, Z. Zolkapli Centro de Investigacion y de Estudios
Avanzados del IPN, Mexico City, Mexico
Duran-Osuna, M. C., H. Castilla-Valdez, E. De La Cruz-Burelo, Ramirez-Sanchez,
G., I. Heredia-De La Cruz34, Rabadan-Trejo, R. I., R. Lopez-Fernandez, J.
Mejia Guisao, Reyes-Almanza, R, A. Sanchez-Hernandez Universidad
Iberoamericana, Mexico City, Mexico
S. Carrillo Moreno, C. Oropeza Barrera, F. Vazquez Valencia Benemerita
Universidad Autonoma de Puebla, Puebla, Mexico
J. Eysermans, I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada Universidad
Autónoma de San Luis Potosí, San Luis Potosí, Mexico
A. Morelos Pineda University of Auckland, Auckland, New Zealand
D. Krofcheck University of Canterbury, Christchurch, New Zealand
S. Bheesette, P.H. Butler National Centre for Physics, Quaid-I-Azam
University, Islamabad, Pakistan
A. Ahmad, M. Ahmad, Q. Hassan, H.R. Hoorani, A. Saddique, M.A. Shah, M.
Shoaib, M. Waqas National Centre for Nuclear Research, Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, T. Frueboes, M. Górski, M. Kazana, K.
Nawrocki, M. Szleper, P. Traczyk, P. Zalewski Institute of Experimental
Physics, Faculty of Physics, University of Warsaw, Warsaw, Poland
K. Bunkowski, A. Byszuk35, K. Doroba, A. Kalinowski, M. Konecki, J.
Krolikowski, M. Misiura, M. Olszewski, A. Pyskir, M. Walczak Laboratório de
Instrumentação e Física Experimental de Partículas, Lisboa, Portugal
P. Bargassa, C. Beirão Da Cruz E Silva, A. Di Francesco, P. Faccioli, B.
Galinhas, M. Gallinaro, J. Hollar, N. Leonardo, L. Lloret Iglesias, M.V.
Nemallapudi, J. Seixas, G. Strong, O. Toldaiev, D. Vadruccio, J. Varela Joint
Institute for Nuclear Research, Dubna, Russia
V. Alexakhin, A. Golunov, I. Golutvin, N. Gorbounov, I. Gorbunov, A. Kamenev,
V. Karjavin, A. Lanev, A. Malakhov, V. Matveev36,37, P. Moisenz, V. Palichik,
V. Perelygin, M. Savina, S. Shmatov, S. Shulha, N. Skatchkov, V. Smirnov, A.
Zarubin Petersburg Nuclear Physics Institute, Gatchina (St. Petersburg),
Russia
Y. Ivanov, V. Kim38, E. Kuznetsova39, P. Levchenko, V. Murzin, V. Oreshkin, I.
Smirnov, D. Sosnov, V. Sulimov, L. Uvarov, S. Vavilov, A. Vorobyev Institute
for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov,
N. Krasnikov, A. Pashenkov, D. Tlisov, A. Toropin Institute for Theoretical
and Experimental Physics, Moscow, Russia
V. Epshteyn, V. Gavrilov, N. Lychkovskaya, V. Popov, I. Pozdnyakov, G.
Safronov, A. Spiridonov, A. Stepennov, V. Stolin, M. Toms, E. Vlasov, A.
Zhokin Moscow Institute of Physics and Technology, Moscow, Russia
T. Aushev, A. Bylinkin37 National Research Nuclear University ’Moscow
Engineering Physics Institute’ (MEPhI), Moscow, Russia
R. Chistov40, M. Danilov40, P. Parygin, D. Philippov, S. Polikarpov, E.
Tarkovskii P.N. Lebedev Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin37, I. Dremin37, M. Kirakosyan37, S.V. Rusakov, A.
Terkulov Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State
University, Moscow, Russia
A. Baskakov, A. Belyaev, E. Boos, V. Bunichev, M. Dubinin41, L. Dudko, A.
Ershov, A. Gribushin, V. Klyukhin, O. Kodolova, I. Lokhtin, I. Miagkov, S.
Obraztsov, S. Petrushanko, V. Savrin Novosibirsk State University (NSU),
Novosibirsk, Russia
V. Blinov42, D. Shtol42, Y. Skovpen42 State Research Center of Russian
Federation, Institute for High Energy Physics of NRC &quot, Kurchatov
Institute&quot, , Protvino, Russia
I. Azhgirey, I. Bayshev, S. Bitioukov, D. Elumakhov, A. Godizov, V. Kachanov,
A. Kalinin, D. Konstantinov, P. Mandrik, V. Petrov, R. Ryutin, A. Sobol, S.
Troshin, N. Tyurin, A. Uzunian, A. Volkov National Research Tomsk Polytechnic
University, Tomsk, Russia
A. Babaev University of Belgrade, Faculty of Physics and Vinca Institute of
Nuclear Sciences, Belgrade, Serbia
P. Adzic43, P. Cirkovic, D. Devetak, M. Dordevic, J. Milosevic Centro de
Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT), Madrid,
Spain
J. Alcaraz Maestre, A. Álvarez Fernández, I. Bachiller, M. Barrio Luna, M.
Cerrada, N. Colino, B. De La Cruz, A. Delgado Peris, C. Fernandez Bedoya, J.P.
Fernández Ramos, J. Flix, M.C. Fouz, O. Gonzalez Lopez, S. Goy Lopez, J.M.
Hernandez, M.I. Josa, D. Moran, A. Pérez-Calero Yzquierdo, J. Puerta Pelayo,
I. Redondo, L. Romero, M.S. Soares, A. Triossi Universidad Autónoma de Madrid,
Madrid, Spain
C. Albajar, J.F. de Trocóniz Universidad de Oviedo, Oviedo, Spain
J. Cuevas, C. Erice, J. Fernandez Menendez, S. Folgueras, I. Gonzalez
Caballero, J.R. González Fernández, E. Palencia Cortezon, S. Sanchez Cruz, P.
Vischia, J.M. Vizan Garcia Instituto de Física de Cantabria (IFCA), CSIC-
Universidad de Cantabria, Santander, Spain
I.J. Cabrillo, A. Calderon, B. Chazin Quero, J. Duarte Campderros, M.
Fernandez, P.J. Fernández Manteca, A. García Alonso, J. Garcia-Ferrero, G.
Gomez, A. Lopez Virto, J. Marco, C. Martinez Rivero, P. Martinez Ruiz del
Arbol, F. Matorras, J. Piedra Gomez, C. Prieels, T. Rodrigo, A. Ruiz-Jimeno,
L. Scodellaro, N. Trevisani, I. Vila, R. Vilar Cortabitarte CERN, European
Organization for Nuclear Research, Geneva, Switzerland
D. Abbaneo, B. Akgun, E. Auffray, P. Baillon, A.H. Ball, D. Barney, J.
Bendavid, M. Bianco, A. Bocci, C. Botta, T. Camporesi, M. Cepeda, G.
Cerminara, E. Chapon, Y. Chen, D. d’Enterria, A. Dabrowski, V. Daponte, A.
David, M. De Gruttola, A. De Roeck, N. Deelen, M. Dobson, T. du Pree, M.
Dünser, N. Dupont, A. Elliott-Peisert, P. Everaerts, F. Fallavollita44, G.
Franzoni, J. Fulcher, W. Funk, D. Gigi, A. Gilbert, K. Gill, F. Glege, D.
Gulhan, J. Hegeman, V. Innocente, A. Jafari, P. Janot, O. Karacheban18, J.
Kieseler, V. Knünz, A. Kornmayer, M. Krammer1, C. Lange, P. Lecoq, C.
Lourenço, M.T. Lucchini, L. Malgeri, M. Mannelli, A. Martelli, F. Meijers,
J.A. Merlin, S. Mersi, E. Meschi, P. Milenovic45, F. Moortgat, M. Mulders, H.
Neugebauer, J. Ngadiuba, S. Orfanelli, L. Orsini, F. Pantaleo15, L. Pape, E.
Perez, M. Peruzzi, A. Petrilli, G. Petrucciani, A. Pfeiffer, M. Pierini, F.M.
Pitters, D. Rabady, A. Racz, T. Reis, G. Rolandi46, M. Rovere, H. Sakulin, C.
Schäfer, C. Schwick, M. Seidel, M. Selvaggi, A. Sharma, P. Silva, P.
Sphicas47, A. Stakia, J. Steggemann, M. Stoye, M. Tosi, D. Treille, A. Tsirou,
V. Veckalns48, M. Verweij, W.D. Zeuner Paul Scherrer Institut, Villigen,
Switzerland
W. Bertl${}^{\textrm{\textdagger}}$, L. Caminada49, K. Deiters, W. Erdmann, R.
Horisberger, Q. Ingram, H.C. Kaestli, D. Kotlinski, U. Langenegger, T. Rohe,
S.A. Wiederkehr ETH Zurich - Institute for Particle Physics and Astrophysics
(IPA), Zurich, Switzerland
M. Backhaus, L. Bäni, P. Berger, B. Casal, N. Chernyavskaya, G. Dissertori, M.
Dittmar, M. Donegà, C. Dorfer, C. Grab, C. Heidegger, D. Hits, J. Hoss, T.
Klijnsma, W. Lustermann, M. Marionneau, M.T. Meinhard, D. Meister, F. Micheli,
P. Musella, F. Nessi-Tedaldi, J. Pata, F. Pauss, G. Perrin, L. Perrozzi, M.
Quittnat, M. Reichmann, D. Ruini, D.A. Sanz Becerra, M. Schönenberger, L.
Shchutska, V.R. Tavolaro, K. Theofilatos, M.L. Vesterbacka Olsson, R. Wallny,
D.H. Zhu Universität Zürich, Zurich, Switzerland
T.K. Aarrestad, C. Amsler50, D. Brzhechko, M.F. Canelli, A. De Cosa, R. Del
Burgo, S. Donato, C. Galloni, T. Hreus, B. Kilminster, I. Neutelings, D.
Pinna, G. Rauco, P. Robmann, D. Salerno, K. Schweiger, C. Seitz, Y. Takahashi,
A. Zucchetta National Central University, Chung-Li, Taiwan
V. Candelise, Y.H. Chang, K.y. Cheng, T.H. Doan, Sh. Jain, R. Khurana, C.M.
Kuo, W. Lin, A. Pozdnyakov, S.S. Yu National Taiwan University (NTU), Taipei,
Taiwan
P. Chang, Y. Chao, K.F. Chen, P.H. Chen, F. Fiori, W.-S. Hou, Y. Hsiung, Arun
Kumar, Y.F. Liu, R.-S. Lu, E. Paganis, A. Psallidas, A. Steen, J.f. Tsai
Chulalongkorn University, Faculty of Science, Department of Physics, Bangkok,
Thailand
B. Asavapibhop, K. Kovitanggoon, G. Singh, N. Srimanobhas Çukurova University,
Physics Department, Science and Art Faculty, Adana, Turkey
A. Bat, F. Boran, S. Cerci51, S. Damarseckin, Z.S. Demiroglu, C. Dozen, I.
Dumanoglu, S. Girgis, G. Gokbulut, Y. Guler, I. Hos52, E.E. Kangal53, O. Kara,
A. Kayis Topaksu, U. Kiminsu, M. Oglakci, G. Onengut, K. Ozdemir54, D. Sunar
Cerci51, B. Tali51, U.G. Tok, S. Turkcapar, I.S. Zorbakir, C. Zorbilmez Middle
East Technical University, Physics Department, Ankara, Turkey
G. Karapinar55, K. Ocalan56, M. Yalvac, M. Zeyrek Bogazici University,
Istanbul, Turkey
I.O. Atakisi, E. Gülmez, M. Kaya57, O. Kaya58, S. Tekten, E.A. Yetkin59
Istanbul Technical University, Istanbul, Turkey
M.N. Agaras, S. Atay, A. Cakir, K. Cankocak, Y. Komurcu Institute for
Scintillation Materials of National Academy of Science of Ukraine, Kharkov,
Ukraine
B. Grynyov National Scientific Center, Kharkov Institute of Physics and
Technology, Kharkov, Ukraine
L. Levchuk University of Bristol, Bristol, United Kingdom
F. Ball, L. Beck, J.J. Brooke, D. Burns, E. Clement, D. Cussans, O. Davignon,
H. Flacher, J. Goldstein, G.P. Heath, H.F. Heath, L. Kreczko, D.M. Newbold60,
S. Paramesvaran, T. Sakuma, S. Seif El Nasr-storey, D. Smith, V.J. Smith
Rutherford Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev61, C. Brew, R.M. Brown, D. Cieri, D.J.A. Cockerill, J.A.
Coughlan, K. Harder, S. Harper, J. Linacre, E. Olaiya, D. Petyt, C.H.
Shepherd-Themistocleous, A. Thea, I.R. Tomalin, T. Williams, W.J. Womersley
Imperial College, London, United Kingdom
G. Auzinger, R. Bainbridge, P. Bloch, J. Borg, S. Breeze, O. Buchmuller, A.
Bundock, S. Casasso, D. Colling, L. Corpe, P. Dauncey, G. Davies, M. Della
Negra, R. Di Maria, Y. Haddad, G. Hall, G. Iles, T. James, M. Komm, R. Lane,
C. Laner, L. Lyons, A.-M. Magnan, S. Malik, L. Mastrolorenzo, T. Matsushita,
J. Nash62, A. Nikitenko7, V. Palladino, M. Pesaresi, A. Richards, A. Rose, E.
Scott, C. Seez, A. Shtipliyski, T. Strebler, S. Summers, A. Tapper, K. Uchida,
M. Vazquez Acosta63, T. Virdee15, N. Wardle, D. Winterbottom, J. Wright, S.C.
Zenz Brunel University, Uxbridge, United Kingdom
J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, A. Morton, I.D. Reid, L.
Teodorescu, S. Zahid Baylor University, Waco, USA
A. Borzou, K. Call, J. Dittmann, K. Hatakeyama, H. Liu, N. Pastika, C. Smith
Catholic University of America, Washington DC, USA
R. Bartek, A. Dominguez The University of Alabama, Tuscaloosa, USA
A. Buccilli, S.I. Cooper, C. Henderson, P. Rumerio, C. West Boston University,
Boston, USA
D. Arcaro, A. Avetisyan, T. Bose, D. Gastler, D. Rankin, C. Richardson, J.
Rohlf, L. Sulak, D. Zou Brown University, Providence, USA
G. Benelli, D. Cutts, M. Hadley, J. Hakala, U. Heintz, J.M. Hogan64, K.H.M.
Kwok, E. Laird, G. Landsberg, J. Lee, Z. Mao, M. Narain, J. Pazzini, S.
Piperov, S. Sagir, R. Syarif, D. Yu University of California, Davis, Davis,
USA
R. Band, C. Brainerd, R. Breedon, D. Burns, M. Calderon De La Barca Sanchez,
M. Chertok, J. Conway, R. Conway, P.T. Cox, R. Erbacher, C. Flores, G. Funk,
W. Ko, R. Lander, C. Mclean, M. Mulhearn, D. Pellett, J. Pilot, S. Shalhout,
M. Shi, J. Smith, D. Stolp, D. Taylor, K. Tos, M. Tripathi, Z. Wang, F. Zhang
University of California, Los Angeles, USA
M. Bachtis, C. Bravo, R. Cousins, A. Dasgupta, A. Florent, J. Hauser, M.
Ignatenko, N. Mccoll, S. Regnard, D. Saltzberg, C. Schnaible, V. Valuev
University of California, Riverside, Riverside, USA
E. Bouvier, K. Burt, R. Clare, J. Ellison, J.W. Gary, S.M.A. Ghiasi Shirazi,
G. Hanson, G. Karapostoli, E. Kennedy, F. Lacroix, O.R. Long, M. Olmedo
Negrete, M.I. Paneva, W. Si, L. Wang, H. Wei, S. Wimpenny, B. R. Yates
University of California, San Diego, La Jolla, USA
J.G. Branson, S. Cittolin, M. Derdzinski, R. Gerosa, D. Gilbert, B. Hashemi,
A. Holzner, D. Klein, G. Kole, V. Krutelyov, J. Letts, M. Masciovecchio, D.
Olivito, S. Padhi, M. Pieri, M. Sani, V. Sharma, S. Simon, M. Tadel, A.
Vartak, S. Wasserbaech65, J. Wood, F. Würthwein, A. Yagil, G. Zevi Della Porta
University of California, Santa Barbara - Department of Physics, Santa
Barbara, USA
N. Amin, R. Bhandari, J. Bradmiller-Feld, C. Campagnari, M. Citron, A. Dishaw,
V. Dutta, M. Franco Sevilla, L. Gouskos, R. Heller, J. Incandela, A.
Ovcharova, H. Qu, J. Richman, D. Stuart, I. Suarez, J. Yoo California
Institute of Technology, Pasadena, USA
D. Anderson, A. Bornheim, J. Bunn, J.M. Lawhorn, H.B. Newman, T. Q. Nguyen, C.
Pena, M. Spiropulu, J.R. Vlimant, R. Wilkinson, S. Xie, Z. Zhang, R.Y. Zhu
Carnegie Mellon University, Pittsburgh, USA
M.B. Andrews, T. Ferguson, T. Mudholkar, M. Paulini, J. Russ, M. Sun, H.
Vogel, I. Vorobiev, M. Weinberg University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, F. Jensen, A. Johnson, M. Krohn, S. Leontsinis, E.
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