EXO-16-055

$HeadURL:svn+ssh://svn.cern.ch/reps/tdr2/papers/EXO-16-055/trunk/EXO-16-055.tex$
$Id:EXO-16-055.tex4723272018-08-2018:01:12Zalverson$

EXO-16-055

# Search for dark matter produced in association with a Higgs boson decaying
to $\gamma\gamma$ or $\tau^{+}\tau^{-}$ at $\sqrt{s}=13\TeV$

###### Abstract

A search for dark matter particles is performed by looking for events with
large transverse momentum imbalance and a recoiling Higgs boson decaying to
either a pair of photons or a pair of $\Pgt$ leptons. The search is based on
proton-proton collision data at a center-of-mass energy of 13collected at the
CERN LHC in 2016 and corresponding to an integrated luminosity of
$35.9\fbinv$. No significant excess over the expected standard model
background is observed. Upper limits at 95% confidence level are presented for
the product of the production cross section and branching fraction in the
context of two benchmark simplified models. For the -two-Higgs-doublet model
(where is a new massive boson mediator) with an intermediate heavy
pseudoscalar particle of mass $m_{\PSA}=300\GeV$ and
$m_{\mathrm{DM}}=100\GeV$, the masses from 550to 1265are excluded. For a
baryonic model, with $m_{\mathrm{DM}}=1\GeV$, masses up to 615are excluded.
Results are also presented for the spin-independent cross section for the dark
matter-nucleon interaction as a function of the mass of the dark matter
particle. This is the first search for dark matter particles produced in
association with a Higgs boson decaying to two $\Pgt$ leptons.

##  0.1 Introduction

Astrophysical evidence strongly suggests the existence of dark matter (DM) in
the universe [1]. Whether the DM has a particle origin remains a mystery [2].
There are a number of well-motivated theories beyond the standard model (SM)
of particle physics that predict the existence of a particle, , that could
serve as a DM candidate. To date, only gravitational interactions between DM
and SM particles have been observed. However, the discovery of a Higgs boson
by both the ATLAS and CMS Collaborations at the CERN LHC in 2012 [3, 4, 5]
provides a new way to probe DM-SM particle interactions.

Collider experiment searches have typically looked for DM recoiling against an
associated SM particle. Since any produced DM is unlikely to interact with the
detector material, it creates an imbalance in the recorded momentum yielding a
large amount of missing transverse momentum, . This paper presents a search
for DM recoiling against an SM-like Higgs boson () using the $\Ph+\ptmiss$
signature. This SM-like Higgs boson can be produced from initial- or final-
state radiation, or from a new interaction between DM and SM particles.
However, initial-state radiation of an SM-like Higgs boson from a quark or
gluon is suppressed by Yukawa or loop processes, respectively [6, 7, 8].

Previous searches for $\Ph+\ptmiss$ have been performed at both the ATLAS and
CMS experiments. No excesses were observed in either $\Ph\to\bbbar$ or
$\Ph\to\gamma\gamma$ decay channels with 20.3 (36)of data at
$\sqrt{s}=8\,(13)\TeV$ [9, 10, 11] or with 2.3–36.1of data at
$\sqrt{s}=13\TeV$ [12, 13, 14]. This paper examines two Higgs boson decay
channels: $\Ph\to\gamma\gamma$ and $\Ph\to\Pgt^{+}\Pgt^{-}$.

Two simplified models for DM+production are used as benchmarks for this
search, both of which were recommended by the LHC Dark Matter Forum [15]. The
leading order (LO) Feynman diagrams for these models are shown in Fig. 1. The
first benchmark model (Fig. 1 left) is a -two-Higgs-doublet model (-2HDM) [7].
In this scenario, the SM is extended by a $U(1)_{\PZpr}$ group, with a new
massive boson mediator, while a Type-2 2HDM framework [16, 17] is used to
formulate the extended Higgs sector. At LO, the boson is produced resonantly
and decays into an SM-like Higgs boson and an intermediate heavy pseudoscalar
particle (). The then decays into a pair of Dirac fermionic DM particles. This
analysis does not consider the contribution of the decay $\PZpr\to\PZ\Ph$
which can have a $\Ph+\ptmiss$ signature if $\PZ\to\nu\nu$. The second diagram
(Fig. 1 right) describes a baryonic model [8]. In this scenario, a new massive
vector mediator emits a Higgs boson and then decays to a pair of Dirac
fermionic DM particles. Here, the baryonic gauge boson arises from a new
$U(1)_{\mathrm{B}}$ baryon number symmetry. A baryonic Higgs boson
($\Ph_{\mathrm{B}}$) is introduced to spontaneously break the new symmetry and
generates the boson mass via a coupling that is dependent on the
$\Ph_{\mathrm{B}}$ vacuum expectation value. The couplings to quarks and DM
are proportional to the $U(1)_{\mathrm{B}}$ gauge couplings. There is a mixing
between $\Ph_{\mathrm{B}}$ and the SM Higgs boson, allowing the to radiate an
SM-like Higgs boson. The stable baryonic states in this model are the
candidate DM particles.

Figure 1: Leading order Feynman diagrams for DM associated production with a
Higgs boson for two theoretical models: -2HDM (left) and baryonic (right).

In the -2HDM, there are several parameters that affect the predicted cross
section. However, when the is on-shell, only the and masses affect the
kinematic distributions of the final state particles studied in this analysis.
This paper considers a resonance with mass between 450 and 2000and an
pseudoscalar with mass between 300 and 700, in accordance with the LHC Dark
Matter Forum recommendations [15]. The ratio of the vacuum expectation values
of the Higgs doublets ($\tan{\beta}$) in this model is fixed to 1. As given in
Ref. [13], the DM particle mass is fixed to 100, the DM-coupling strength
$g_{\mathrm{DM}}$ is fixed to 1, and the coupling strength $g_{\PZpr}$ is
fixed to 0.8.

For the baryonic model, this paper considers a resonance with a mass between
100 and 2500and DM particle masses between 1 and 900. As suggested for this
model [18], the mediator-DM coupling is fixed to 1 and the mediator-quark
coupling ($g_{\cPq}$) is fixed to 0.25. The mixing angle between the baryonic
Higgs boson and the SM-like Higgs boson is set to 0.3 and the coupling between
the boson and the SM-like Higgs boson is proportional to the mass of the
boson.

For both models, values of the couplings and mixing angle are chosen to
maximize the predicted cross section. Results for other values can be obtained
by rescaling the cross section since these parameters do not affect the
kinematic distributions of the final state particles. The SM-like Higgs boson
is assumed to be the already observed 125Higgs boson, since the SM-like Higgs
boson has similar properties to the SM Higgs boson. Therefore, in this paper
the observed 125Higgs boson is denoted by .

Although the SM Higgs boson branching fractions to $\gamma\gamma$ and
$\Pgt^{+}\Pgt^{-}$ are smaller than the branching fraction to $\bbbar$, the
analysis presented here exploits these two decay channels because they have
unique advantages compared with the $\Ph\to\bbbar$ channel. The
$\Ph\to\gamma\gamma$ channel benefits from higher precision in reconstructed
invariant mass and the $\Ph\to\Pgt^{+}\Pgt^{-}$ channel benefits from smaller
SM background. Additionally, the $\Ph\to\gamma\gamma$ and
$\Ph\to\Pgt^{+}\Pgt^{-}$ channels are not dependent on trigger thresholds, as
such searches in these channels are complementary to those in the
$\Ph\to\bbbar$ channel since they can probe DM scenarios with lower . The
search in the $\Ph\to\gamma\gamma$ channel uses a fit in the diphoton
invariant mass spectrum to extract the signal yield. In addition to a high-
category, a low-category is also considered to extend the phase space of the
search. In the $\Ph\to\Pgt^{+}\Pgt^{-}$ channel, the three decay channels of
the $\tau$ lepton with the highest branching fractions are analyzed. After
requiring an amount of in order to sufficiently suppress the quantum
chromodynamic (QCD) multijet background, the signal is extracted by performing
a simultaneous fit to the transverse mass of the and the two $\tau$ lepton
candidates in the signal region (SR) and control regions (CRs).

The paper is organized as follows. Section 0.2 gives a brief description of
the CMS detector and the event reconstruction. Section 0.3 details the data
set and the simulated samples used in the analysis. Then Sections 0.4 and 0.5
present the event selection and analysis strategy for each decay channel,
respectively. The systematic uncertainties affecting the analysis are
presented in Section 0.6. Section 0.7 details the results of the analysis and
their interpretations. A summary is given in Section 0.8.

##  0.2 The CMS detector and event reconstruction

The central feature of the CMS detector is a superconducting solenoid, of 6m
internal diameter, providing an axial magnetic field of 3.8T along the beam
direction. 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). Extensive forward calorimetry
complements the coverage provided by the barrel and endcap detectors. Charged
particle trajectories are measured by the silicon pixel and strip tracker
system, covering $0\leq\phi\leq 2\pi$ in azimuth and $\abs{\eta}<2.50$, where
the pseudorapidity is $\eta=-\ln{\left(\tan{\theta/2}\right)}$, and $\theta$
is the polar angle with respect to the counterclockwise beam direction. Muons
are measured in gas-ionization detectors embedded in the steel flux-return
yoke. A more detailed description of the CMS detector can be found in Ref.
[19].

Events of interest are selected using a two-tiered trigger system [20]. The
first level, composed of custom hardware processors, uses information from the
calorimeters and muon detectors to select events at a rate of around 100kHz
within a time interval of less than 4. The second level, known as the high-
level trigger, consists of a farm of processors running a version of the full
event reconstruction software optimized for fast processing, and reduces the
event rate to around 1kHz before data storage.

Using information from all CMS subdetectors, a global event reconstruction is
performed using the particle-flow (PF) algorithm [21]. The PF algorithm
optimally combines all of the detector information and generates a list of
stable particles (PF candidates), namely photons, electrons, muons, and
charged and neutral hadrons. The reconstructed vertex with the largest value
of summed physics-object $\pt^{2}$ is taken to be the primary $\Pp\Pp$
interaction vertex (PV). The physics objects are the jets, clustered using the
jet finding algorithm [22, 23] with the tracks assigned to the vertex as
inputs, and the negative vector sum of the of those jets. The PV is used as
the reference vertex for all objects reconstructed with the PF algorithm.

Photons are reconstructed from their energy deposits in the ECAL, which can
involve several crystals [24]. A photon that converts to an electron-positron
pair in the tracker will yield a shower spread out in azimuth due to the
deflection of the electron and positron in the strong magnetic field. In order
to achieve the best photon energy resolution, corrections are applied to
overcome energy losses including those from photon conversions [24].
Additional corrections, calculated from the mass distribution of
$\PZ\to\Pe^{+}\Pe^{-}$ events, are applied to the measured energy scale of the
photons in data (${\leq}1$%) and to the energy resolution in simulation
(${\leq}2$%).

Electron reconstruction requires the matching of the cluster of energy
deposits in the ECAL with a track in the silicon tracker. Electron
identification is based on the ECAL shower shape, matching between the track
and ECAL cluster, and consistency with the PV. Muons are reconstructed by
combining two complementary algorithms [25]: one that matches tracks in the
silicon tracker with signals in the muon system, and another in which a global
track fit seeded by the muon track segment is performed.

Jets are reconstructed from PF candidates using the anti-clustering algorithm
[22] as implemented in [23] with a distance parameter of 0.4. Jet energy
corrections are derived from simulation to bring the average measured response
of jets to that of particle-level jets. Hadronically decaying $\tau$ leptons
are reconstructed from jets using the hadrons-plus-strips (HPS) algorithm
[26]. The HPS algorithm uses combinations of reconstructed charged hadrons and
energy deposits in the ECAL to reconstruct the $\tau$ lepton’s three most
common hadronic decay modes: 1-prong, 1-prong + $\PGpz\mathrm{(s)}$, and
3-prong. In the $\Ph\to\Pgt^{+}\Pgt^{-}$ channel, events with jets originating
from quark decays are excluded in order to reduce the background from $\ttbar$
events. The combined secondary vertex algorithm [27] is used to identify jets
originating from quarks by their characteristic displaced vertices.

The missing transverse momentum vector (), with magnitude , is the negative
vector sum of the of all PF candidates in an event. Jet energy corrections are
propagated to the for a more accurate measurement [28]. Events may have
anomalously large from sources such as detector noise, cosmic ray muons, and
beam halo particles, which are not well modeled in simulation. Event filters
[29] are applied to remove such events.

##  0.3 Observed and simulated data samples

The analysis is performed with $\Pp\Pp$ collision data at $\sqrt{s}=13\TeV$
collected with the CMS detector in 2016. The data correspond to an integrated
luminosity of 35.9.

The analysis strategy and event selection were optimized using Monte Carlo
(MC) simulated samples of associated DM+production via the two benchmark
models discussed in Section 0.1. The v2.3.0 [30] generator is used to generate
both the -2HDM and baryonic signals at LO. The decay of the SM-like Higgs
boson is simulated by 8.205 [31].

A small but irreducible background for both decay channels in this analysis
comes from events in which the SM Higgs boson is produced in association with
a boson that decays to two neutrinos. Other SM Higgs boson production
mechanisms are associated with resonant but reducible backgrounds. These
include gluon-gluon fusion ($\cPg\cPg\Ph$), vector boson fusion (VBF), and
production in association with a pair of top quarks ($\ttbar$). The production
in association with a vector boson (V) and other SM Higgs boson backgrounds
are all generated using v2.2.2 at next-to-leading order (NLO) in perturbative
QCD.

The dominant nonresonant backgrounds for the $\Ph\to\gamma\gamma$ channel are
events with mismeasured and two photons that happen to have an invariant mass
close to the mass of the SM Higgs boson. The largest contributions to this are
nonresonant $\gamma\gamma$, , and QCD multijet production. The simulated
$\gamma\gamma$ sample is generated at LO with v2.2.2 [32] while the and QCD
multijet samples are modeled at LO with . Additional backgrounds originate
from electroweak (EW) processes such as single top, $\ttbar$, , or boson
production in association with one or two photons, and Drell–Yan (DY)
production where the boson decays to pairs of electrons or muons. The DY and
all other EW backgrounds considered in the analysis are generated at NLO with
. These nonresonant background samples are used for optimizing the analysis
selection, however, they are not used for the ultimate background estimation.

The largest backgrounds for the $\Ph\to\Pgt^{+}\Pgt^{-}$ channel are
$\PW+\text{jets}$, $\ttbar$, and multiboson processes. The v2.3.0 generator is
used for $\PW+\text{jets}$ processes, which are generated at LO in
perturbative QCD with the MLM jet matching and merging scheme [33]. A
$\pt$-dependent correction factor is applied to the $\PW+\text{jets}$ sample
to account for next-to-next-to-leading order QCD and NLO EW effects [34, 35,
36, 37]. The $\ttbar$ process is generated at NLO with the 2.0 [38, 39, 40,
41] generator. Single top quark production is modeled at NLO with the 1.0 [42]
generator. The FxFx [43] merging scheme is used to generate some smaller
diboson backgrounds (including $\PW\PZ$ samples) with the generator at NLO,
while the dominant diboson backgrounds, $\PW\PW$ and $\PZ\PZ$ in two lepton
final states, are generated using 2.0. Another reducible background considered
in this analysis is $\cPZ/\gamma^{*}\to\ell\ell/\Pgt\Pgt$, where $\ell$ is
$\Pe$ or $\Pgm$. The Drell–Yan background is corrected for differences in the
dilepton mass $m_{\ell\ell/\Pgt\Pgt}$ and dilepton transverse momentum
$\pt(\ell\ell/\Pgt\Pgt)$ distributions using dimuon events in data [44].

All simulated samples mentioned above use the NNPDF 3.0 parton distribution
function (PDF) sets [45, 46] with the order matching that used in the matrix
element calculations. For parton showering and hadronization, as well as for
$\tau$ lepton decays, the samples are interfaced with using the CUETP8M1 tune
[47] for all samples except , for which the M2 tune is used. The MC samples
are processed through a full simulation of the CMS detector based on [48] and
are reconstructed with the same algorithms that are used for the data. All
samples include the simulation of additional inelastic $\Pp\Pp$ interactions
in the same or neighboring bunch crossings (pileup). Minimum-bias collision
events generated with are added to the simulated samples to reproduce the
pileup effects in the data. Additionally, the simulated events are weighted so
that the pileup vertex distribution matches that of the data, with an average
of 27 interactions per bunch crossing.

##  0.4 Analysis strategy in the $\Ph\to\gamma\gamma$ channel

The search for DM+in the $\Ph\to\gamma\gamma$ channel is performed by
selecting events with two photons and a large amount of . The set of
requirements detailed in Section 0.4.1 is applied to select well-identified
photons and to enhance the signal significance. A fit to the diphoton
invariant mass distribution, described in Section 0.4.2, is performed to
extract the background and signal yields.

###  0.4.1 Event selection

The events used in this analysis were selected by a diphoton trigger with
asymmetric $\pt$ thresholds of 30 and 18and diphoton invariant mass above 90.
The trigger also has loose photon identification criteria based on the cluster
shower shape, isolation requirements, and a selection on the ratio of hadronic
to electromagnetic energy deposits of the photon candidates.

The photons that enter the analysis are required to fall within the fiducial
range of the ECAL ($\abs{\eta}<1.44$ or $1.57<\abs{\eta}<2.50$) and to satisfy
various preselection criteria that are slightly more stringent than the
trigger requirements. An additional veto on the presence of a track pointing
to the ECAL cluster is applied to reject electrons that could be reconstructed
as photons. Scale factors, extracted from $\PZ\to\Pe^{+}\Pe^{-}$ events using
the tag-and-probe method [49], are applied to the simulated samples to account
for any discrepancy in identification efficiency between data and simulation.

The isolation variables that are used in the photon identification are
calculated by summing the of PF photons, neutral hadrons, or charged hadrons
associated with the PV in a cone of radius $\Delta
R=\sqrt{\smash[b]{(\Delta\eta)^{2}+(\Delta\phi)^{2}}}=0.3$. The isolation
variables are corrected by the median transverse momentum density of the event
to mitigate the effects of pileup [50]. Some of the signals considered can
have Lorentz-boosted topologies. For example, high-mass mediators could result
in a large boost to the Higgs boson. When a boosted Higgs boson decays to two
photons, the resulting photons hit the ECAL close to each other. This effect
leads to large contributions from one photon to the photon isolation sum of
the other. In order to maintain high efficiency for high-mass mediator
signals, the photon isolation requirement is not applied to photons that are
within $\Delta R<0.3$ of each other.

Preselected photons are required to have leading (subleading) photon above 30
(20)and diphoton invariant mass $m_{\gamma\gamma}$ above 95. Simulated signal
and background samples that pass the preselection were used to study the
discriminating power of variables such as , the of the diphoton system
$p_{\mathrm{T}\gamma\gamma}$, and the ratio /$m_{\gamma\gamma}$ for each
photon. A selection on that scales with $m_{\gamma\gamma}$ is chosen so that
it does not distort the shape of the $m_{\gamma\gamma}$ distribution. The
$p_{\mathrm{T}\gamma\gamma}$ variable is included in the selection because it
has higher resolution than the event’s measured and is expected to be large
for signal events, since the Higgs boson is produced back-to-back with . A
high-category ($\ptmiss\geq 130\GeV$) is optimal for the two benchmark models
presented in this paper. A low-category ($50<\ptmiss<130\GeV$), optimized
using as reference the baryonic signal model, is also included to probe softer
signals, namely signals that may not be observed in other $\Ph+\ptmiss$
searches because they rely heavily on for background rejection. The chosen
requirements, found to optimize the signal sensitivity for both models in the
low- and high-categories, are given in Table 0.4.1.

Optimized kinematic requirements for the low- and high-categories.  Variable
Low-category High-category ${>}50\GeV$, ${<}130\GeV$ ${>}130\GeV$
$p_{\mathrm{T}1}/m_{\gamma\gamma}$ ${>}0.45$ ${>}0.5$
$p_{\mathrm{T}2}/m_{\gamma\gamma}$ ${>}0.25$ ${>}0.25$
$p_{\mathrm{T}\gamma\gamma}$ ${>}75\GeV$ ${>}90\GeV$

Further background rejection is achieved using two topological requirements.
The azimuthal separation $\abs{\Delta\phi(p_{\gamma\gamma},\ptvecmiss)}$
between and the Higgs boson direction reconstructed from the two photons must
be greater than 2.1 to select events in which the Higgs boson and are back-to-
back. Events with highly energetic jets collinear to are removed by the
requirement that the $\min\abs{\Delta\phi(p_{\text{jet}},\ptvecmiss)}$ be
greater than 0.5 for any jet with above 50. This rejects events with a large
misreconstructed arising from mismeasured jet . Finally, events are vetoed if
they have three or more jets each with above 30, to reject multijet
backgrounds while maintaining a high efficiency for the two benchmark signal
models. The distribution of the selected events is shown in Fig. 2.

Figure 2:  Distribution of for events passing the requirements given in Table
0.4.1. Events with below 50are not used in the analysis. The cross sections of
the signals are set to 1pb. The total simulated background is normalized to
the integral of the data. The statistical uncertainty in the total background
is shown by the hatched bands. The data-to-simulation ratio is shown in the
lower panel.

###  0.4.2 Background estimation and signal extraction

A narrow resonance search similar to the SM Higgs boson diphoton analysis of
Ref. [51] is performed. The diphoton invariant mass between 105 and 180is fit
with a model that is the sum of the signal and background shapes. The signal
shape, taken from the simulated events, is allowed to change independently in
each of the two categories. The background shape includes a smooth function,
estimated from the data, to model the continuum background, and a resonant
contribution from the SM Higgs boson. The fit is performed with an unbinned
maximum-likelihood technique in both the low- and high-categories discussed
above.

The resonant background, arising from the SM Higgs boson decays to two
photons, appears as a peak under the expected signal peak. This contribution
from all SM Higgs boson production modes is estimated with the simulated
events by including a mass distribution template, scaled to the NLO cross
section, as a resonant component in the final fitting probability density
function (pdf).

The nonresonant background contribution, mostly due to $\gamma\gamma$ and to
various EW processes, is estimated using data. The nonresonant diphoton
$m_{\gamma\gamma}$ distribution in the data is fit, in each category, with an
analytic function. Because the exact functional form of the background is
unknown, the parametric model must be flexible enough to describe a variety of
potential underlying functions. Using an incorrect background model can lead
to biases in the measured signal yield that can artificially modify the
sensitivity of the analysis. Three functions are considered as possible models
for the nonresonant background; they are analytical forms that are frequently
used in dijet [52] and diphoton [53] resonance searches. The best functional
form found to fit the nonresonant diphoton $m_{\gamma\gamma}$ distribution, in
both categories, is a power law function $f(x)=ax^{-b}$ where $a$ and $b$ are
free parameters constrained to be positive.

A detailed bias study has been performed in order to choose this function. The
$m_{\gamma\gamma}$ shape of the simulated nonresonant events is used as a
template to generate 1000 pseudo-experiments for each category. For each
pseudo-experiment, the number of events generated is equal to the number of
events observed in data in that category. The resulting $m_{\gamma\gamma}$
distribution is fit with each analytic function considered. The exercise is
also repeated injecting a potential signal contribution. The pulls of each
pseudo-experiment, defined as the difference in the number of simulated events
and those predicted by the fit function divided by the statistical
uncertainties of the fit, are calculated. If the bias (the median of the
pulls) is five times smaller than the statistical uncertainty in the number of
fitted signal events, any potential bias from the choice of background model
is considered negligible. Since this criterion is satisfied for the power law
function, any systematic uncertainty in the bias from the background fit
function is neglected in this analysis.

The final background-only fit for both categories is shown in Fig. 3. Both the
resonant and nonresonant background pdf contributions are shown. The slight
excess of events observed in data around 125in the low-category is compatible
with the SM Higgs boson expectation within 2.0 standard deviations.

Figure 3:  The background-only fit to data is performed, for low-(left) and
high-(right) categories, with the sum of a power law (dashed black) fit
function to describe the nonresonant contribution, and a resonant shape
(dashed red), taken from simulation, to take into account the SM
$\Ph\to\gamma\gamma$ contribution. The SM contribution is fixed to the
theoretical prediction in the statistical analysis. The sum of the nonresonant
and resonant shapes (solid blue) is used to estimate the total background in
this analysis.

##  0.5 Analysis strategy in the $\Ph\to\Pgt^{+}\Pgt^{-}$ channel

###  0.5.1 Event selection

The three final states of $\Pgt$ lepton pairs with the highest $\Pgt\Pgt$
branching fractions ($\Pe\tauh$, $\Pgm\tauh$, and $\tauh\tauh$) are considered
in this analysis. In the $\Pe\tauh$ and $\Pgm\tauh$ channels, one of the
$\Pgt$ leptons decays leptonically to an electron or a muon and two neutrinos,
while the other $\Pgt$ lepton decays hadronically ($\tauh$) with one neutrino.
In the third channel, $\tauh\tauh$, both $\Pgt$ leptons decay hadronically.
The $\Pe\Pgm$, $\Pe\Pe$, and $\Pgm\Pgm$ final states are not included because
of the low branching fraction of the $\Pgt\Pgt$ pair to purely leptonic final
states. The $\Pe\Pe$ and $\Pgm\Pgm$ final states are not considered, since
they are overwhelmed by DY background.

Triggers based on the presence of a single electron (muon) are used to select
events in the $\Pe\tauh$ ($\Pgm\tauh$) channel. In the $\tauh\tauh$ channel,
the triggers require the presence of two isolated $\tauh$ objects. Each
$\tauh$ candidate reconstructed offline is required to match a $\tauh$
candidate at the trigger level, with a $\Delta R$ separation less than 0.5.

The electrons and muons in the $\Pe\tauh$ and $\Pgm\tauh$ channels are
required to have $\pt$ greater than $26\GeV$, exceeding the trigger thresholds
for the single-electron and single-muon triggers. Electrons (muons) with
$\abs{\eta}<2.1$ (2.4) are used. An $\Pe\tauh$ ($\Pgm\tauh$) event is required
to have an electron (muon) passing a multivariate MVA identification
discriminator [26] and an isolation requirement of $I^{\Pe}_{\text{rel}}<0.10$
($I^{\Pgm}_{\text{rel}}<0.15$), where $I^{\ell}_{\text{rel}}$ is defined as in
Eq. (1), with an isolation cone of size $\Delta R=0.3$ $(0.4)$ surrounding the
electron (muon):

$I^{\ell}_{\text{rel}}=\left(\sum\pt^{\text{charged}}+\max[0,\sum\pt^{\text{neutral}}+\sum\pt^{\gamma}-0.5\times\sum\pt^{\mathrm{{PU}}}]\right)/\pt^{\ell}.$
(1)

Here $\sum\pt^{\text{charged}}$, $\sum\pt^{\text{neutral}}$, and
$\sum\pt^{\gamma}$ are the scalar sums of transverse momentum from charged
hadrons associated with the primary vertex, neutral hadrons, and photons,
respectively. The term $\sum\pt^{\mathrm{PU}}$ is the sum of transverse
momentum of charged hadrons not associated with the primary vertex and
$\pt^{\ell}$ is the $\pt$ of the electron or muon.

Hadronically decaying $\tau$ leptons in all channels are required to satisfy a
loose ($\tauh\tauh$ channel) or a tight ($\Pe\tauh$ and $\Pgm\tauh$ channels)
working point of an MVA isolation measure. The loose (tight) working point
corresponds to a 65 (50)% efficiency with a 0.8 (0.2)% misidentification
probability. The $\tau$ leptons are required to be identified as decaying via
one of the three modes recognized with the HPS algorithm, and also pass
discriminators that reduce the rate of electrons and muons misreconstructed as
$\tauh$ candidates [54]. For the $\Pe\tauh$ and $\Pgm\tauh$ channels, the
$\tauh$ candidates are required to have $\pt>20\GeV$ and $\abs{\eta}<2.3$. In
the $\tauh\tauh$ channel, the leading (subleading) $\tau$ lepton is required
to be greater than 55 (40), both $\tauh$ transverse momenta exceeding the
double-hadronic $\tau$ lepton trigger thresholds of 35. The selection criteria
are summarized in Table 0.5.1 for all three final states.

Selection requirements for the three $\Pgt\Pgt$ decay channels. The thresholds
for the triggers are given in the second column in parentheses.

|  | Lepton selection
---|---|---
Final state | Trigger type |  $[{\GeVns}]$ | $\eta$ | Isolation
$\Pe\tauh$ |  (25) | $\pt^{\Pe}>26$ | $\abs{\eta^{\Pe}}<2.1$ | $I^{\Pe}_{\text{rel}}<0.1$
|  | $\pt^{\tauh}>20$ | $\abs{\eta^{\tauh}}<2.3$ | Tight MVA $\tauh$
$\Pgm\tauh$ |  (24) | $\pt^{\Pgm}>26$ | $\abs{\eta^{\Pgm}}<2.4$ | $I^{\Pgm}_{\text{rel}}<0.15$
|  | $\pt^{\tauh}>20$ | $\abs{\eta^{\tauh}}<2.3$ | Tight MVA $\tauh$
$\tauh\tauh$ |  (35) & (35) | $\pt^{\tauh}>55\,\&\,40$ | $\abs{\eta^{\tauh}}<2.1$ | Loose MVA $\tauh$

The is further required to be greater than 105and the visible of the
$\Pgt\Pgt$ system is required to be greater than 65. These stringent criteria
reduce the need for tighter isolation in the $\tauh\tauh$ channel.
Additionally, the mass reconstructed from the visible of the $\Pgt\Pgt$ system
is required to be less than 125, to ensure that the $\Pgt\Pgt$ system is
compatible with an SM Higgs boson. In order to minimize diboson and
$\PW+\text{jets}$ contributions, the two $\Pgt$ lepton candidates must pass a
loose collinearity criterion of $\Delta R_{\Pgt\Pgt}<2.0$.

Two types of event veto are employed for background reduction. Events with
jets tagged as originating from hadronization of quarks are vetoed, to reduce
and single top processes. The working point used in the tagging algorithm
corresponds to about a 66% efficiency for a 1% misidentification probability.
In addition, events with additional muons or electrons beyond those from the
$\Pgt$ lepton candidates are discarded, to reduce the contribution of
multilepton backgrounds.

###  0.5.2 Signal extraction and background estimation

The signal is extracted from a maximum-likelihood fit to the total transverse
mass ($M_{\mathrm{T}}^{\text{tot}}$) distributions in the different channels
for the SR, and for the $\PW+\text{jets}$ and QCD multijet background CRs. The
$M_{\mathrm{T}}^{\text{tot}}$ is defined as:

$M_{\mathrm{T}}^{\text{tot}}=\sqrt{\smash[b]{(p_{\mathrm{T}}^{\Pgt_{1}}+p_{\mathrm{T}}^{\Pgt_{2}}+\ptmiss)^{2}-(p_{x}^{\Pgt_{1}}+p_{x}^{\Pgt_{2}}+p_{x}^{\text{miss}})^{2}-(p_{y}^{\Pgt_{1}}+p_{y}^{\Pgt_{2}}+p_{y}^{\text{miss}})^{2}}},$
(2)

where $p_{x}^{\text{miss}}$ and $p_{y}^{\text{miss}}$ are the magnitudes of
the $x$ and $y$ components of , respectively.

The $\PW+\text{jets}$ and the QCD multijet background are estimated directly
from the data. The procedure to estimate these processes relies on CRs, which
are included in the maximum-likelihood fit, to extract the results. The other
backgrounds, $\ttbar$, $\PZ+\text{jets}$, SM Higgs boson, single top quark,
and diboson production processes, are extracted from simulation.

The shape of the $M_{\mathrm{T}}^{\text{tot}}$ distribution of the
$\PW+\text{jets}$ background is estimated from simulation by requiring the
same selection as for the SR, but the isolation of the $\Pgt$ lepton
candidates is relaxed to increase the statistical precision of the
distribution. To constrain the normalization of the $\PW+\text{jets}$
background, a CR enriched in $\PW+\text{jets}$ events is constructed by
inverting the isolation criteria on the candidates while keeping a loose
isolation. The CR obtained by inverting the isolation criterion is included in
the maximum-likelihood fit to constrain the normalization of the
$\PW+\text{jets}$ background in the SR.

To estimate the QCD multijet background, a CR in data is obtained by requiring
the $\Pgt$ lepton candidates to have the same sign. No significant amount of
signal and of background with opposite-sign is expected in this CR because the
charge misidentification is of order 1% and the charge misidentification for
electrons and muons is even smaller. All simulated backgrounds are subtracted
from observed events in the CR, and the remaining contribution is classified
as QCD multijet background. The contribution of QCD multijet events with
opposite-sign $\Pgt$ lepton candidates in the SR is obtained by multiplying
the QCD multijet background, obtained in the same-sign CR, by a scale factor.
The scale factor, approximately unity with an uncertainty of 20%, is
determined from events with candidates failing the isolation requirement and
with low , which do not overlap with events selected in the SR. To increase
the statistical precision of the QCD multijet distribution, the isolation of
the $\Pgt$ lepton candidates is relaxed for the $\ell\tauh$ channels, while
conserving the normalization obtained as detailed above. The same-sign $\Pgt$
lepton candidate CR constrains the QCD multijet background normalization in
the SR and the other CRs in the maximum-likelihood fit.

The normalizations of the $\PW+\text{jets}$ and QCD multijet background are
strongly correlated since both processes contribute to both CRs. The
simultaneous fit of the SR and CRs takes into account this correlation. The SR
distributions included in the simultaneous maximum-likelihood fit are shown in
Fig. 4. For the signal extraction, $\PW+\text{jets}$ and QCD multijet
background CRs are considered separately, whereas in Fig. 4, the two
backgrounds are presented merged together.

Figure 4:  Distributions of the total transverse mass
$M_{\mathrm{T}}^{\text{tot}}$ in the SR for the $\Pe\tauh$ (upper left),
$\Pgm\tauh$ (upper right), and $\tauh\tauh$ (lower) final states are shown
after the simultaneous maximum-likelihood fit. Representative signal
distributions are shown with cross sections normalized to 1pb. The data points
are shown with their statistical uncertainties, and the point in the final bin
includes overflow. The statistical uncertainty of the observed distribution is
represented by the error bars on the data points. The overflow of each
distribution is included in the final 400–500bin. Single top processes are
included in the “Diboson” contribution. The “Other DY” contribution includes
background from $\PZ\to\ell\ell$. The systematic uncertainty related to the
background prediction is indicated by the shaded band.

##  0.6 Systematic uncertainties

In both analysis channels, an uncertainty of 2.5% is used for the
normalization of simulated samples to reflect the uncertainty in the
integrated luminosity measurement in 2016 [55]. Common to both analysis
channels are systematic uncertainties related to the theoretical production
cross section of the Higgs boson. The PDF, and renormalization and
factorization scale uncertainties are addressed using the recommendations of
PDF4LHC [56] and LHC Higgs Cross Section [57] working groups, respectively.
The value of these uncertainties range from 0.3 to 9.0%. The systematic
uncertainties associated with each of the analysis channels are detailed
below. Uncertainties affecting normalizations are represented by log-normal
pdfs in the statistical analysis.

###  0.6.1 The $\Ph\to\gamma\gamma$ channel

In the $\Ph\to\gamma\gamma$ channel, there are several sources of experimental
and theoretical uncertainties that affect the signal and the SM
$\Ph\to\gamma\gamma$ yields. However, the largest source of uncertainty is
statistical. As mentioned in Section 0.4.2, no systematic uncertainties are
applied to the nonresonant background, which is extracted from a fit to data,
since the bias of the fit is negligible compared to the statistical
uncertainty of the data set. The systematic uncertainties for the
$\Ph\to\gamma\gamma$ channel are summarized in Table 0.6.1. In addition to the
theoretical uncertainties mentioned above, a 20% cross section uncertainty is
included for the $\Pg\Pg\Ph$ sample, based on the CMS differential
measurements of $\Ph\to\gamma\gamma$, for diphoton above 70 [58]. The
branching fraction uncertainty [57] of 1.73% is also included.

In addition to the integrated luminosity uncertainty, several other
experimental sources of systematic uncertainty are included in this analysis.
The trigger efficiency uncertainty (approximately 1%) is extracted from
$\PZ\to\Pe^{+}\Pe^{-}$ events using a tag-and-probe technique [49]. The photon
identification uncertainty of 2% arises from the observed difference in
efficiencies between data and simulation. A 0.5% energy scale uncertainty is
assigned to take into account the knowledge of the photon energy scale at the
boson mass peak and its extrapolation to the Higgs boson mass. Additionally,
several -related uncertainties are applied. The systematic uncertainty from
mismeasured is evaluated by comparing the tail of the distributions in data
and simulation in a enriched CR. The efficiencies with which data and
simulated events pass the selection are compared. The difference in efficiency
is 50% and is included as a systematic uncertainty associated with mismeasured
. However, the contribution of simulated backgrounds with mismeasured is quite
small since only the $\Pg\Pg\Ph$ and VBF SM $\Ph\to\gamma\gamma$ production
modes contribute. Finally, a systematic uncertainty, which is less than 4%, is
applied to take into account the difference in efficiency between data and
simulation when applying the topological $\Delta\phi$ requirements in the low-
region. This uncertainty is evaluated using $\PZ\to\Pe^{+}\Pe^{-}$ events, and
only affects the $\Pg\Pg\Ph$ and VBF simulated samples.

Systematic uncertainties affecting the signal and resonant backgrounds in the
$\Ph\to\gamma\gamma$ channel.  Signal [%] SM [%] Theoretical sources PDF — 2–4
Renorm. and fact. scale — 0.3–9 Cross section ($\Pg\Pg\Ph$) — 20 Higgs boson
branching fraction 1.73 Experimental sources Integrated luminosity 2.5 Trigger
efficiency 1.0 Photon identification efficiency 2.0 Photon energy scale Shape,
0.5 mismeasurement ($\Pg\Pg\Ph$ and VBF) — 50 $\Delta\phi$ selection
efficiency ($\Pg\Pg\Ph$ and VBF) — 1–4

###  0.6.2 The $\Ph\to\Pgt^{+}\Pgt^{-}$ channel

The systematic uncertainties in the $\Ph\to\Pgt^{+}\Pgt^{-}$ channel are
related to the normalization of signal and background processes and, in
several instances, the shapes of the signal and background distributions. As
mentioned earlier, the simultaneous maximum-likelihood fit is performed in the
SR and CRs, where the shape and normalization uncertainties are represented by
nuisance parameters in the likelihood. Uncertainties affecting the
distribution of $M_{\mathrm{T}}^{\text{tot}}$ (shape uncertainties) are
represented by Gaussian pdfs, whereas log-normal pdfs are used for
normalization, as stated above. The largest overall uncertainty is
statistical. Table 0.6.2 summarizes the different sources of systematic
uncertainty in this channel.

Systematic uncertainties affecting signal and background in the
$\Ph\to\Pgt^{+}\Pgt^{-}$ channel.  Change in acceptance or shape Source
Affected processes $\Pe\tauh$ $\Pgm\tauh$ $\tauh\tauh$ $\tauh$ identification
(correlated) simulation 4.5% 4.5% — $\tauh$ identification (uncorrelated)
simulation 2% 2% 9% High $\tauh$ simulation Shape, up to 8% $\Pe$
identification & trigger simulation 2% — — $\Pgm$ identification & trigger
simulation — 2% — $\tauh$ trigger simulation — — Shape only $\Pe$
misidentified as $\tauh$ $\PZ\to\Pe\Pe$ 12% — — $\Pgm$ misidentified as
$\tauh$ $\PZ\to\Pgm\Pgm$ — 25% — Jet misidentified as $\tauh$
$\PZ+\text{jets}$ Shape only $\tauh$ energy scale (per decay mode) simulation
1.2% on energy scale Jet energy scale and effect on $\ptmiss$ simulation
Shape, up to 10% energy scale simulation Shape, up to 11% Integrated
luminosity simulation 2.5% Norm. $\PW+\text{jets}$/QCD multijet
$\PW+\text{jets}$/QCD multijet up to 20% Norm. $\ttbar$ $\ttbar$ 6% Norm.
diboson Diboson 5% Norm. single top Single top 5% Norm. SM Higgs boson SM
Higgs boson up to 5% $\PZ+\text{jets}$ LO-NLO reweighting $\PZ+\text{jets}$
Shape, up to 26% $\PW+\text{jets}$ NLO EW correction $\PW+\text{jets}$ Shape,
up to 6% $\PW\PW$ NLO EW correction $\PW\PW$ Shape, up to 12% $\PZ\PZ$ NLO EW
correction $\PZ\PZ$ Shape, up to 2% Top quark reweighting $\ttbar$ Shape, up
to 5% Theory: Higgs boson branching Signal + SM Higgs boson 1.7% fraction
Theory: renorm. and fact. scale Signal 4% Theory: PDF Signal 2% Limited number
of events All processes Shape only (bin-by-bin)

An uncertainty of 2% is assigned to simulated events containing an electron or
muon candidate. In simulated events with a $\tauh$ candidate, an additional
uncertainty of 5% per $\tauh$ is applied. These uncertainties account for the
observed differences in the performance of electron, muon, and $\tauh$
identification, isolation, and trigger algorithms, between data and
simulation. The hadronic $\tauh$ efficiency is not fully correlated across all
$\Pgt\Pgt$ final states because there are different discriminators used in
each channel. The $\tauh\tauh$ channel has a 9% $\tauh$ uncertainty due to a
correlation with the $\tauh\tauh$ trigger systematic uncertainty. An
uncertainty of 12% is assigned to simulated events containing an electron
misidentified as a $\tauh$ candidate, and 25% for a muon misidentified as a
$\tauh$ candidate [44]. A 2 (4)% uncertainty is assigned to the yield of
multiboson and single top () processes to account for changes in overall
normalization arising from uncertainties in the tagging performance.
Similarly, a 5% tagging uncertainty is assigned to $\PZ+\text{jets}$ and SM
Higgs boson processes, while all other processes, including signal, receive a
2% uncertainty. A systematic uncertainty of up to 20% is applied to QCD
multijet background to account for yield differences in the same-sign CR. All
of the background systematic uncertainties in the same-sign region are
propagated to the total QCD multijet background uncertainty, which is taken to
be 40%.

The $\PW+\text{jets}$ background has a -dependent uncertainty, which
approaches 10%, from predicted NLO EW K-factors where the full EW correction
is treated as the systematic uncertainty [34, 35, 36, 37]. Cross section
uncertainties of the order of 5% are applied to the $\ttbar$ (6%), top quark
(5%), and diboson (5%) processes [59, 60, 61, 62]. In simulated
$\PZ+\text{jets}$ samples, a shape uncertainty of 10% of the $\PZ$ boson
reweighting correction, to account for higher-order effects, is used. The
uncertainty in the $\PZ+\text{jets}$ background contribution is about 12% in
the SR. The contribution includes a shape systematic uncertainty equivalent to
5% related to the top quark spectrum, since there is evidence that the
spectrum is softer in data than in simulation [62].

A 1.2% uncertainty in the $\tau$ lepton energy scale [44] is propagated
through to the final signal extraction variables. The $\tau$ lepton energy
scale depends on the decay mode and is correlated across all channels. A shape
uncertainty is used for the uncertainty in the double trigger. A shift of 3%
of the of the trigger-level candidate leads to a 12% normalization difference
at 40, and a 2% difference at 60. For $\pt>60\GeV$, a constant 2% systematic
uncertainty is applied.

To account for potentially different rates of jets misidentified as candidates
between data and simulation, an uncertainty, applied as a function of the of
the candidate, is used for background events where the reconstructed candidate
is matched to a jet at generator-level. The uncertainty increases to about 20%
near a candidate $\pt=200\GeV$, and acts to change the shape of the
$M_{\mathrm{T}}^{\text{tot}}$ distribution. An asymmetric uncertainty related
to the identification of with a high is applied to signal and background
simulation. The high-efficiency measurement uses selected highly virtual
bosons and has limited statistical precision in comparison to the lower
$\PZ\to\tau\tau$ and efficiency studies. Therefore the asymmetric uncertainty
is used in combination with a constant scale factor. It is proportional to and
has a value of $+5\%$ and $-35\%$ per 1. For the application of all of the
aforementioned $\Pgt$ lepton uncertainties, simulated backgrounds are
separated depending on whether the reconstructed candidates are matched to
generated $\Pgt$ leptons.

In all simulated samples, uncertainties in the calculation related to
unclustered energy deposits are taken into account. Uncertainties in the jet
energy scale are included on an event-by-event basis and propagated to the
calculation. Lastly, an uncertainty in the statistical precision of each
process in each bin of the distribution is also included.

##  0.7 Results

The results of the analysis are derived from the maximum-likelihood fits
presented in Sections 0.4 and 0.5 for the $\Ph\to\gamma\gamma$ and
$\Ph\to\Pgt^{+}\Pgt^{-}$ channels, respectively.

###  0.7.1 Observed yields

For the $\Ph\to\gamma\gamma$ channel, from the signal plus background fit to
$m_{\gamma\gamma}$, the number of expected events from background processes
are determined. The background yields and the observed number of events within
3of the SM Higgs boson mass are listed in Table 0.7.1 for both the low- and
high-categories. The excess at low-has negligible effect on the results when
combined with the high-category for the benchmark signals considered in this
paper.

Expected background yields and observed numbers of events for the
$\Ph\to\gamma\gamma$ channel in the $m_{\gamma\gamma}$ range of 122–128are
shown for the low- and high-categories. The nonresonant background includes
QCD multijet, $\gamma\gamma$, , and EW backgrounds and is estimated from the
analytic function fit to data. The SM Higgs boson background is presented
separately for the irreducible Vproduction and for the other production modes.
For the resonant background contributions, both the statistical and the
systematic uncertainties are listed. As detailed in Section 0.4.2, the
systematic uncertainty associated with the nonresonant background is
negligible.  Expected background Low-category High-category SM
$\Ph\to\gamma\gamma$ (V) $2.9\pm 0.1\stat\pm 0.2\syst$ $1.26\pm 0.05\stat\pm
0.09\syst$ SM $\Ph\to\gamma\gamma$ ($\Pg\Pg\Ph$, $\ttbar$, VBF) $5.3\pm
0.3\stat\pm 1.2\syst$ $0.11\pm 0.01\stat\pm 0.01\syst$ Nonresonant background
$125.1\pm 11.2\stat$ $4.5\pm 2.1\stat$ Total background $133\pm 11\stat\pm
1\syst$ $5.9\pm 2.1\stat\pm 0.1\syst$ Observed events 159 6

In the $\Ph\to\Pgt^{+}\Pgt^{-}$ channel, the final simultaneous fit to the
$M_{\mathrm{T}}^{\text{tot}}$ distributions for the SR, and $\PW+\text{jets}$
and QCD multijet CRs is performed in each of the three considered $\Pgt$ decay
channels (, , and $\tauh\tauh$). The extracted post-fit yields for the
expected number of background events and the number of events observed in data
are shown in Table 0.7.1. The number of events observed is in good agreement
with the number of events predicted by the SM backgrounds.

Estimated background yields and observed numbers of events for
$M_{\mathrm{T}}^{\text{tot}}>260\GeV$, in the SR of the
$\Ph\to\Pgt^{+}\Pgt^{-}$ channel. The uncertainties in the total expected
yields include the statistical and systematic contributions.  Expected
background $\Pe\tauh$ $\Pgm\tauh$ $\tauh\tauh$ $\PW+\text{jets}$/QCD multijet
13.1 $\pm$ 2.2 32.5 $\pm$ 6.2 3.8 $\pm$ 2.6 $\ttbar$ 13.7 $\pm$ 1.6 24.8 $\pm$
2.0 4.2 $\pm$ 1.3 SM Higgs boson 0.48 $\pm$ 0.08 0.72 $\pm$ 0.06 1.21 $\pm$
0.08 Diboson 12.3 $\pm$ 1.0 21.5 $\pm$ 1.5 7.3 $\pm$ 0.6 $\PZ\to\Pgt\Pgt$ 0.00
$\pm$ 0.01 0.0 $\pm$ 0.5 3.6 $\pm$ 1.2 $\PZ\to\ell\ell$ 0.9 $\pm$ 1.9 2.0
$\pm$ 1.3 — $\PZ\to\nu\nu$ — — 0.4 $\pm$ 0.3 Total background 40.5 $\pm$ 3.3
81.8 $\pm$ 6.3 20.5 $\pm$ 3.0 Observed events 38 81 26

Aside from the small excess in the low-category of the $\Ph\to\gamma\gamma$
channel, the observed numbers of events are consistent with SM expectations.
All of the results presented here are interpreted in terms of the two
benchmark models of DM production mentioned earlier. Expected signal yields
and the product of the predicted signal acceptances and their efficiencies
($\mathrm{A}\epsilon$) are summarized in Table 0.7.1 for selected mass points,
in both $\Ph\to\gamma\gamma$ and $\Ph\to\Pgt^{+}\Pgt^{-}$ channels.

The expected signal yields and the product of acceptance and efficiency
($\mathrm{A}\epsilon$) for the two benchmark models. The -2HDM signal is shown
for the parameters $m_{\PSA}$ = 300and $m_{\PZpr}$ = 1000, and the baryonic
signal, for the parameters $m_{\mathrm{DM}}$ = 1and $m_{\PZpr}$ = 100.
$\Ph\to\gamma\gamma$ channel $\Ph\to\Pgt^{+}\Pgt^{-}$ channel Signal Low-
High- -2HDM Expected yield 0.1 $\pm$ 0.4 4.5 $\pm$ 0.6 6.5 $\pm$ 0.3 11.1
$\pm$ 0.5 14.3 $\pm$ 1.2 $\mathrm{A}\epsilon$ [%] 0.1 42.6 2.2 3.6 4.4
Baryonic  Expected yield 14.7 $\pm$ 6.7 13.8 $\pm$ 6.4 8.6 $\pm$ 0.3 16.8
$\pm$ 0.5 20.9 $\pm$ 0.8 $\mathrm{A}\epsilon$ [%] 6.4 6.0 0.1 0.3 0.3

A discussion of the results for the -2HDM interpretation is presented in
Section 0.7.2. The results in the context of the baryonic interpretation are
given in Section 0.7.3. The baryonic results are also reinterpreted for
comparison with direct detection experiments in Section 0.7.4 by looking at
simplified DM models proposed by the ATLAS-CMS Dark Matter Forum [15].

###  0.7.2 Interpretation in the -2HDM model

For the event selection given in Sections 0.4 and 0.5, the results interpreted
in terms of the -2HDM associated production of DM and a Higgs boson are
presented here. The expected and observed yields are used to calculate an
upper limit on the production cross section of DM+production via the -2HDM
mechanism. Upper limits are computed [63] at 95% confidence level () using a
profile likelihood ratio and the modified frequentist criterion [64, 65] with
an asymptotic approximation [66]. The upper limits are obtained for each Higgs
boson decay channel separately and for the statistical combination of the two.
The two decay channels are combined using the Higgs boson branching fractions
predicted by the SM [57]. In the combination of the two analyses, the
theoretical uncertainties in the Higgs boson cross section and the systematic
uncertainty in the integrated luminosity are assumed to be fully correlated
between the two decay channels.

Figure 5 shows the 95% expected and observed upper limits on the DM production
cross section ($\sigma_{\mathrm{95\%\,\CL}}$) as a function of mass. Both the
$\Ph\to\gamma\gamma$ and $\Ph\to\Pgt^{+}\Pgt^{-}$ channels, as well as the
combination of the two, are shown for $m_{\PSA}=300\GeV$. These upper limits,
although obtained with a DM mass of 100, can be considered valid for any DM
mass below 100since the branching fraction for decays of to DM particles
decreases as the dark matter mass increases. The theoretical cross section
($\sigma_{\mathrm{th}}$) is calculated with $m_{\mathrm{DM}}=100\GeV$,
$g_{\PZpr}=0.8$, and $g_{\mathrm{DM}}=\tan{\beta}=1$, as mentioned in Section
0.1.

Figure 5:  Expected and observed 95% upper limits on the -2HDM cross section
for dark matter associated production with a Higgs boson
($\PZpr\to\PGc\PGc\Ph$) are shown. Limits are given for the
$\Ph\to\gamma\gamma$ channel, $\Ph\to\Pgt^{+}\Pgt^{-}$ channel, and their
combined exclusion.

To produce exclusion limits in the two-dimensional plane of mass and mass, an
interpolation is performed. Fully simulated signal samples (mentioned in
Section 0.3) were generated in a coarse grid of $m_{\PSA}$ and $m_{\PZpr}$.
For the $\Ph\to\gamma\gamma$ channel, the $m_{\gamma\gamma}$ shape does not
depend on the mass of these particles, only the expected yield is affected by
these masses. Therefore, the product $\mathrm{A}\epsilon$ of the fully
simulated samples is parametrized and used to extract the expected number of
events for intermediate mass points. In the $\Ph\to\Pgt^{+}\Pgt^{-}$ channel,
this is not sufficient because the $M_{\mathrm{T}}^{\text{tot}}$ shape does
depend on the particle masses. A reweighting technique is used to extract the
yields for the intermediate mass points. Simulation samples were produced at
generator-level for $m_{\PZpr}$ between 450 and 2000in steps of 50and for
$m_{\PSA}$ between 300 and 700in steps of 25. These are compared with the
full-simulation samples at generator-level. The bin-by-bin ratio of the SM-
like Higgs boson between the two samples is used to weight the full-simulation
samples. This method was validated by applying the same procedure at the
generator-level among the samples for which full-simulation is available.

The interpolation between mass points is improved using kernel algorithms to
display smooth, continuous exclusion contours. The resulting two-dimensional
exclusion for the -2HDM signal is shown in Fig. 6. The 95% expected and
observed upper limits on signal strength
($\sigma_{\mathrm{95\%\,\CL}}$/$\sigma_{\mathrm{th}}$) are shown. Regions of
the parameter space with $\sigma_{\mathrm{95\%\,\CL}}/\sigma_{\mathrm{th}}<1$
are excluded at 95% under the nominal $\sigma_{\mathrm{th}}$ hypothesis. For
$m_{\PSA}=300\GeV$, the $\Ph\to\gamma\gamma$ channel alone excludes at 95% the
masses from 550to 860, while the $\Ph\to\Pgt^{+}\Pgt^{-}$ channel excludes the
$m_{\PZpr}$ masses from 750to 1200. The combination of these two decay
channels excludes the masses from 550to 1265for $m_{\PSA}=300\GeV$. The mass
range considered is extended from previous CMS searches to include $450\leq
m_{\PZpr}<600\GeV$.

Figure 6:  Observed 95% upper limits on the -2HDM signal strength for the
$\Ph\to\gamma\gamma$ (left), $\Ph\to\Pgt^{+}\Pgt^{-}$ (right), and combination
of the two channels (lower center). The observed (expected) two-dimensional
exclusion curves are shown with thick red (dashed black) lines. The plus and
minus one standard deviation expected exclusion curves are also shown as thin
black lines. The region below the lines is excluded.

###  0.7.3 Baryonic model interpretation

Here the results presented in Section 0.7.1 are interpreted in the context of
the baryonic model. This paper presents the first baryonic model
interpretation of $\Ph+\ptmiss$ searches with the CMS detector. The 95% upper
limits on DM+cross section are calculated for the baryonic production
mechanism. The upper limits for each decay channel and the combination of the
two channels are shown in Fig. 7. The $\sigma_{\mathrm{th}}$ is calculated
assuming the choice of parameters detailed in Section 0.1. Results in the two-
dimensional plane of $m_{\mathrm{DM}}$ and $m_{\PZpr}$ are produced using an
interpolated grid produced in the same way as described in Section 0.7.2. The
two-dimensional exclusion for this model is shown in Fig. 8, where the 95%
upper limits on the signal strength are shown for each decay channel and for
the combination of the $\Ph\to\gamma\gamma$ and $\Ph\to\Pgt^{+}\Pgt^{-}$
channels. For $m_{\mathrm{DM}}=1\GeV$, the $\Ph\to\gamma\gamma$ channel
excludes $m_{\PZpr}$ masses up to 574. The $\Ph\to\Pgt^{+}\Pgt^{-}$ channel
similarly excludes $m_{\PZpr}$ masses up to 450. The combination of the two
decay channels excludes $m_{\PZpr}$ up to 615for $m_{\mathrm{DM}}=1\GeV$.

Figure 7:  Expected and observed 95% upper limits on the baryonic cross
section for dark matter associated production with a Higgs boson
($\PZpr\to\PGc\PGc\Ph$) are shown. Limits are given for the
$\Ph\to\gamma\gamma$ channel, $\Ph\to\Pgt^{+}\Pgt^{-}$ channel, and their
combined exclusion.

Figure 8:  Observed 95% upper limits on the baryonic signal strength for the
$\Ph\to\gamma\gamma$ (left), $\Ph\to\Pgt^{+}\Pgt^{-}$ (right), and combination
of the two channels (lower center). The observed (expected) two-dimensional
exclusion curves are shown with thick red (dashed black) lines. The plus and
minus one standard deviation expected exclusion curves are also shown as thin
black lines. The region below the lines is excluded.

###  0.7.4 Simplified DM model interpretation

Limits from the baryonic model are reinterpreted to infer limits on the
$s$-channel simplified DM models that were proposed by the ATLAS-CMS Dark
Matter Forum [15] for comparison with direct detection experiments. In the
model considered in this analysis, Dirac DM particles couple to a vector
mediator, which in turn couples to the SM quarks. A point in the parameter
space of this model is determined by four variables: the DM particle mass
$m_{\mathrm{DM}}$, the mediator mass $m_{\mathrm{med}}$, the mediator-DM
coupling $g_{\mathrm{DM}}$, and the universal mediator-quark coupling
$g_{\cPq}$. The couplings for this analysis are fixed to $g_{\mathrm{DM}}=1.0$
and $g_{\cPq}=0.25$, following the recommendation of Ref. [18].

The results are interpreted in the spin-independent (SI) cross section
$\sigma^{\mathrm{SI}}$ for DM scattering off a nucleus. The value of
$\sigma^{\mathrm{SI}}$ for a given point in the $s$-channel simplified DM
model is determined by the equation [18]:

$\sigma^{\mathrm{SI}}=\frac{f^{2}(g_{\cPq})g_{\mathrm{DM}}^{2}\mu^{2}_{\mathrm{nDM}}}{\pi
m_{\mathrm{med}}^{4}},$ (3)

where $\mu_{\mathrm{nDM}}$ is the reduced mass of the DM-nucleon system and
$f(g_{\cPq})$ is the mediator-nucleon coupling, which is dependent on
$g_{\cPq}$. The resulting $\sigma^{\mathrm{SI}}$ limits as a function of DM
mass are shown in Fig. 9. In the same plot, exclusions from several direct
detection experiments are shown. For the baryonic model, the limits are more
stringent than direct detection experiments for $m_{\mathrm{DM}}<2.5\GeV$.

Figure 9:  The 90% exclusion limits on the DM-nucleon SI scattering cross
section as a function of $m_{\mathrm{DM}}$. Results obtained in this analysis
are compared with those from a selection of direct detection (DD) experiments.
The latter exclude the regions above the curves. Limits from CDMSLite [67],
LUX [68], XENON-1T [69], PandaX-II [70], and CRESST-II [71] are shown.

##  0.8 Summary

A search for dark matter particles produced in association with a Higgs boson
has been performed. The study focuses on the case where the 125Higgs boson
decays to either two photons or two $\tau$ leptons. This analysis is based on
proton-proton collision data collected with the CMS detector during 2016 at
$\sqrt{s}$ = 13, corresponding to an integrated luminosity of 35.9. The
results of the search are interpreted in terms of a -two-Higgs-doublet model
(-2HDM) and a baryonic simplified model of dark matter production.

A statistical combination of the two channels was performed and these results
were used to produce upper limits on dark matter production. Limits on the
signal production cross section are calculated for both simplified models. For
the -2HDM signal, with an intermediate pseudoscalar of mass $m_{\PSA}=300\GeV$
and $m_{\mathrm{DM}}=100\GeV$, the masses from 550to 1265are excluded at 95%
confidence level. For the baryonic model, with $m_{\mathrm{DM}}=1\GeV$, masses
up to 615are excluded. This is the first search for dark matter produced in
association with a Higgs boson decaying to two $\tau$ leptons and the first to
combine results from the $\gamma\gamma$ and $\Pgt^{+}\Pgt^{-}$ decay channels.
The -2HDM interpretation extended the mass range compared with previous CMS
searches. The interpretation of the results include the baryonic model
interpretation for CMS and an extrapolation to limits on the spin-independent
cross section for the dark matter-nucleon interaction.

###### Acknowledgements.

We congratulate our colleagues in the CERN accelerator departments for the
excellent performance of the LHC and thank the technical and administrative
staffs at CERN and at other CMS institutes for their contributions to the
success of the CMS effort. In addition, we gratefully acknowledge the
computing centers and personnel of the Worldwide LHC Computing Grid for
delivering so effectively the computing infrastructure essential to our
analyses. Finally, we acknowledge the enduring support for the construction
and operation of the LHC and the CMS detector provided by the following
funding agencies: BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq,
CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST,
and NSFC (China); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RPF
(Cyprus); SENESCYT (Ecuador); MoER, ERC IUT, and ERDF (Estonia); Academy of
Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and
HGF (Germany); GSRT (Greece); NKFIA (Hungary); DAE and DST (India); IPM
(Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); MES
(Latvia); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT,
LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC
(Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom,
RAS, RFBR, and NRC KI (Russia); MESTD (Serbia); SEIDI, CPAN, PCTI, and FEDER
(Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST
(Taipei); ThEPCenter, IPST, STAR, and NSTDA (Thailand); TUBITAK and TAEK
(Turkey); NASU and SFFR (Ukraine); STFC (United Kingdom); DOE and NSF (USA).
Individuals have received support from the Marie-Curie program and the
European Research Council and Horizon 2020 Grant, contract 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”) Program and the János Bolyai Research Scholarship of the
Hungarian Academy of Sciences, the New National Excellence Program ÚNKP, the
NKFIA research grants 123842, 123959, 124845, 124850 and 125105 (Hungary); the
Council of Science and Industrial Research, India; the HOMING PLUS program of
the Foundation for Polish Science, cofinanced from European Union, Regional
Development Fund, the Mobility Plus program of the Ministry of Science and
Higher Education, the National Science Center (Poland), contracts Harmonia
2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543, 2014/15/B/ST2/03998, and
2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406; the National Priorities
Research Program by Qatar National Research Fund; the Programa Estatal de
Fomento de la Investigación Científica y Técnica de Excelencia María de
Maeztu, grant MDM-2015-0509 and the Programa Severo Ochoa del Principado de
Asturias; the Thalis and Aristeia programs cofinanced by EU-ESF and the Greek
NSRF; the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn
University and the Chulalongkorn Academic into Its 2nd Century Project
Advancement Project (Thailand); the Welch Foundation, contract C-1845; and the
Weston Havens Foundation (USA).

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##  .9 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan, A. Tumasyan Institut für Hochenergiephysik, Wien, Austria
W. Adam, F. Ambrogi, E. Asilar, T. Bergauer, J. Brandstetter, M. Dragicevic,
J. Erö, A. Escalante Del Valle, M. Flechl, R. Frühwirth1, V.M. Ghete, J.
Hrubec, M. Jeitler1, N. Krammer, I. Krätschmer, D. Liko, T. Madlener, I.
Mikulec, N. Rad, H. Rohringer, J. Schieck1, R. Schöfbeck, M. Spanring, D.
Spitzbart, A. Taurok, W. Waltenberger, J. Wittmann, C.-E. Wulz1, M. Zarucki
Institute for Nuclear Problems, Minsk, Belarus
V. Chekhovsky, V. Mossolov, J. Suarez Gonzalez Universiteit Antwerpen,
Antwerpen, Belgium
E.A. De Wolf, D. Di Croce, X. Janssen, J. Lauwers, M. Pieters, M. Van De
Klundert, H. Van Haevermaet, P. Van Mechelen, N. Van Remortel Vrije
Universiteit Brussel, Brussel, Belgium
S. Abu Zeid, F. Blekman, J. D’Hondt, I. De Bruyn, J. De Clercq, K. Deroover,
G. Flouris, D. Lontkovskyi, S. Lowette, I. Marchesini, S. Moortgat, L.
Moreels, Q. Python, K. Skovpen, S. Tavernier, W. Van Doninck, P. Van Mulders,
I. Van Parijs Université Libre de Bruxelles, Bruxelles, Belgium
D. Beghin, B. Bilin, H. Brun, B. Clerbaux, G. De Lentdecker, H. Delannoy, B.
Dorney, G. Fasanella, L. Favart, R. Goldouzian, A. Grebenyuk, A.K. Kalsi, T.
Lenzi, J. Luetic, N. Postiau, E. Starling, L. Thomas, C. Vander Velde, P.
Vanlaer, D. Vannerom, Q. Wang Ghent University, Ghent, Belgium
T. Cornelis, D. Dobur, A. Fagot, M. Gul, I. Khvastunov2, D. Poyraz, C. Roskas,
D. Trocino, M. Tytgat, W. Verbeke, B. Vermassen, M. Vit, N. Zaganidis
Université Catholique de Louvain, Louvain-la-Neuve, Belgium
H. Bakhshiansohi, O. Bondu, S. Brochet, G. Bruno, C. Caputo, P. David, C.
Delaere, M. Delcourt, B. Francois, A. Giammanco, G. Krintiras, V. Lemaitre, A.
Magitteri, A. Mertens, M. Musich, K. Piotrzkowski, A. Saggio, M. Vidal Marono,
S. Wertz, J. Zobec Centro Brasileiro de Pesquisas Fisicas, Rio de Janeiro,
Brazil
F.L. Alves, G.A. Alves, L. Brito, M. Correa Martins Junior, G. Correia Silva,
C. Hensel, A. Moraes, M.E. Pol, P. Rebello Teles Universidade do Estado do Rio
de Janeiro, Rio de Janeiro, Brazil
E. Belchior Batista Das Chagas, W. Carvalho, J. Chinellato3, E. Coelho, E.M.
Da Costa, G.G. Da Silveira4, D. De Jesus Damiao, C. De Oliveira Martins, S.
Fonseca De Souza, H. Malbouisson, D. Matos Figueiredo, M. Melo De Almeida, C.
Mora Herrera, L. Mundim, H. Nogima, W.L. Prado Da Silva, L.J. Sanchez Rosas,
A. Santoro, A. Sznajder, M. Thiel, E.J. Tonelli Manganote3, F. Torres Da Silva
De Araujo, A. Vilela Pereira Universidade Estadual Paulista a, Universidade
Federal do ABC b, São Paulo, Brazil
S. Ahujaa, C.A. Bernardesa, L. Calligarisa, T.R. Fernandez Perez Tomeia, E.M.
Gregoresb, P.G. Mercadanteb, S.F. Novaesa, SandraS. Padulaa, D. Romero Abadb
Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy of
Sciences, Sofia, Bulgaria
A. Aleksandrov, R. Hadjiiska, P. Iaydjiev, A. Marinov, M. Misheva, M. Rodozov,
M. Shopova, G. Sultanov University of Sofia, Sofia, Bulgaria
A. Dimitrov, L. Litov, B. Pavlov, P. Petkov Beihang University, Beijing, China
W. Fang5, X. Gao5, L. Yuan Institute of High Energy Physics, Beijing, China
M. Ahmad, J.G. Bian, G.M. Chen, H.S. Chen, M. Chen, Y. Chen, C.H. Jiang, D.
Leggat, H. Liao, Z. Liu, F. Romeo, S.M. Shaheen6, A. Spiezia, J. Tao, C. Wang,
Z. Wang, E. Yazgan, H. Zhang, J. Zhao State Key Laboratory of Nuclear Physics
and Technology, Peking University, Beijing, China
Y. Ban, G. Chen, A. Levin, J. Li, L. Li, Q. Li, Y. Mao, S.J. Qian, D. Wang, Z.
Xu Tsinghua University, Beijing, China
Y. Wang Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, C.A. Carrillo Montoya, L.F. Chaparro Sierra, C. Florez,
C.F. González Hernández, M.A. Segura Delgado University of Split, Faculty of
Electrical Engineering, Mechanical Engineering and Naval Architecture, Split,
Croatia
B. Courbon, N. Godinovic, D. Lelas, I. Puljak, T. Sculac University of Split,
Faculty of Science, Split, Croatia
Z. Antunovic, M. Kovac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, D. Ferencek, K. Kadija, B. Mesic, A. Starodumov7, T. Susa
University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, M. Kolosova, G. Mavromanolakis, J. Mousa, C. Nicolaou,
F. Ptochos, P.A. Razis, H. Rykaczewski Charles University, Prague, Czech
Republic
M. Finger8, M. Finger Jr.8 Escuela Politecnica Nacional, Quito, Ecuador
E. Ayala Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab
Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
Y. Assran9,10, S. Elgammal10, S. Khalil11 National Institute of Chemical
Physics and Biophysics, Tallinn, Estonia
S. Bhowmik, A. Carvalho Antunes De Oliveira, R.K. Dewanjee, K. Ehataht, M.
Kadastik, M. Raidal, C. Veelken Department of Physics, University of Helsinki,
Helsinki, Finland
P. Eerola, H. Kirschenmann, J. Pekkanen, M. Voutilainen Helsinki Institute of
Physics, Helsinki, Finland
J. Havukainen, J.K. Heikkilä, T. Järvinen, V. Karimäki, R. Kinnunen, T.
Lampén, K. Lassila-Perini, S. Laurila, S. Lehti, T. Lindén, P. Luukka, T.
Mäenpää, H. Siikonen, E. Tuominen, J. Tuominiemi Lappeenranta University of
Technology, Lappeenranta, Finland
T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
M. Besancon, F. Couderc, M. Dejardin, D. Denegri, J.L. Faure, F. Ferri, S.
Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry, C. Leloup,
E. Locci, J. Malcles, G. Negro, J. Rander, A. Rosowsky, M.Ö. Sahin, M. Titov
Laboratoire Leprince-Ringuet, Ecole polytechnique, CNRS/IN2P3, Université
Paris-Saclay, Palaiseau, France
A. Abdulsalam12, C. Amendola, I. Antropov, F. Beaudette, P. Busson, C.
Charlot, R. Granier de Cassagnac, I. Kucher, S. Lisniak, A. Lobanov, J. Martin
Blanco, M. Nguyen, C. Ochando, G. Ortona, P. Pigard, R. Salerno, J.B. Sauvan,
Y. Sirois, A.G. Stahl Leiton, A. Zabi, A. Zghiche Université de Strasbourg,
CNRS, IPHC UMR 7178, Strasbourg, France
J.-L. Agram13, J. Andrea, D. Bloch, J.-M. Brom, E.C. Chabert, V. Cherepanov,
C. Collard, E. Conte13, J.-C. Fontaine13, D. Gelé, U. Goerlach, M. Jansová,
A.-C. Le Bihan, N. Tonon, P. Van Hove Centre de Calcul de l’Institut National
de Physique Nucleaire et de Physique des Particules, CNRS/IN2P3, Villeurbanne,
France
S. Gadrat Université de Lyon, Université Claude Bernard Lyon 1, CNRS-IN2P3,
Institut de Physique Nucléaire de Lyon, Villeurbanne, France
S. Beauceron, C. Bernet, G. Boudoul, N. Chanon, R. Chierici, D. Contardo, P.
Depasse, H. El Mamouni, J. Fay, L. Finco, S. Gascon, M. Gouzevitch, G.
Grenier, B. Ille, F. Lagarde, I.B. Laktineh, H. Lattaud, M. Lethuillier, L.
Mirabito, A.L. Pequegnot, S. Perries, A. Popov14, V. Sordini, M. Vander
Donckt, S. Viret, S. Zhang Georgian Technical University, Tbilisi, Georgia
A. Khvedelidze8 Tbilisi State University, Tbilisi, Georgia
Z. Tsamalaidze8 RWTH Aachen University, I. Physikalisches Institut, Aachen,
Germany
C. Autermann, L. Feld, M.K. Kiesel, K. Klein, M. Lipinski, M. Preuten, M.P.
Rauch, C. Schomakers, J. Schulz, M. Teroerde, B. Wittmer, V. Zhukov14 RWTH
Aachen University, III. Physikalisches Institut A, Aachen, Germany
A. Albert, D. Duchardt, M. Endres, M. Erdmann, T. Esch, R. Fischer, S. Ghosh,
A. Güth, T. Hebbeker, C. Heidemann, K. Hoepfner, H. Keller, S. Knutzen, L.
Mastrolorenzo, M. Merschmeyer, A. Meyer, P. Millet, S. Mukherjee, T. Pook, M.
Radziej, H. Reithler, M. Rieger, F. Scheuch, A. Schmidt, D. Teyssier RWTH
Aachen University, III. Physikalisches Institut B, Aachen, Germany
G. Flügge, O. Hlushchenko, B. Kargoll, T. Kress, A. Künsken, T. Müller, A.
Nehrkorn, A. Nowack, C. Pistone, O. Pooth, H. Sert, A. Stahl15 Deutsches
Elektronen-Synchrotron, Hamburg, Germany
M. Aldaya Martin, T. Arndt, C. Asawatangtrakuldee, I. Babounikau, K.
Beernaert, O. Behnke, U. Behrens, A. Bermúdez Martínez, D. Bertsche, A.A. Bin
Anuar, K. Borras16, V. Botta, A. Campbell, P. Connor, C. Contreras-Campana, F.
Costanza, V. Danilov, A. De Wit, M.M. Defranchis, C. Diez Pardos, D. Domínguez
Damiani, G. Eckerlin, T. Eichhorn, A. Elwood, E. Eren, E. Gallo17, A. Geiser,
J.M. Grados Luyando, A. Grohsjean, P. Gunnellini, M. Guthoff, M. Haranko, A.
Harb, J. Hauk, H. Jung, M. Kasemann, J. Keaveney, C. Kleinwort, J. Knolle, D.
Krücker, W. Lange, A. Lelek, T. Lenz, K. Lipka, W. Lohmann18, R. Mankel, I.-A.
Melzer-Pellmann, A.B. Meyer, M. Meyer, M. Missiroli, G. Mittag, J. Mnich, V.
Myronenko, S.K. Pflitsch, D. Pitzl, A. Raspereza, M. Savitskyi, P. Saxena, P.
Schütze, C. Schwanenberger, R. Shevchenko, A. Singh, N. Stefaniuk, H. Tholen,
O. Turkot, A. Vagnerini, G.P. Van Onsem, R. Walsh, Y. Wen, K. Wichmann, C.
Wissing, O. Zenaiev University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, L. Benato, A. Benecke, V. Blobel, M. Centis Vignali, T.
Dreyer, E. Garutti, D. Gonzalez, J. Haller, A. Hinzmann, A. Karavdina, G.
Kasieczka, R. Klanner, R. Kogler, N. Kovalchuk, S. Kurz, V. Kutzner, J. Lange,
D. Marconi, J. Multhaup, M. Niedziela, D. Nowatschin, A. Perieanu, A. Reimers,
O. Rieger, C. Scharf, P. Schleper, S. Schumann, J. Schwandt, J. Sonneveld, H.
Stadie, G. Steinbrück, F.M. Stober, M. Stöver, D. Troendle, A. Vanhoefer, B.
Vormwald Karlsruher Institut fuer Technology
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, M. Giffels, M.A.
Harrendorf, F. Hartmann15, S.M. Heindl, U. Husemann, F. Kassel15, I. Katkov14,
S. Kudella, H. Mildner, S. Mitra, M.U. Mozer, Th. Müller, M. Plagge, G. Quast,
K. Rabbertz, M. Schröder, I. Shvetsov, G. Sieber, H.J. Simonis, R. Ulrich, S.
Wayand, M. Weber, T. Weiler, S. Williamson, C. Wöhrmann, R. Wolf Institute of
Nuclear and Particle Physics (INPP), NCSR Demokritos, Aghia Paraskevi, Greece
G. Anagnostou, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas, G.
Paspalaki, I. Topsis-Giotis National and Kapodistrian University of Athens,
Athens, Greece
G. Karathanasis, S. Kesisoglou, P. Kontaxakis, A. Panagiotou, N. Saoulidou, E.
Tziaferi, K. Vellidis National Technical University of Athens, Athens, Greece
K. Kousouris, I. Papakrivopoulos, G. Tsipolitis University of Ioánnina,
Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, S. Mallios, N.
Manthos, I. Papadopoulos, E. Paradas, J. Strologas, F.A. Triantis, D.
Tsitsonis MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
M. Bartók19, M. Csanad, N. Filipovic, P. Major, M.I. Nagy, G. Pasztor, O.
Surányi, G.I. Veres Wigner Research Centre for Physics, Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath20, Á. Hunyadi, F. Sikler, T.Á. Vámi, V.
Veszpremi, G. Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear
Research ATOMKI, Debrecen, Hungary
N. Beni, S. Czellar, J. Karancsi21, A. Makovec, J. Molnar, Z. Szillasi
Institute of Physics, University of Debrecen, Debrecen, Hungary
P. Raics, Z.L. Trocsanyi, B. Ujvari Indian Institute of Science (IISc),
Bangalore, India
S. Choudhury, J.R. Komaragiri, P.C. Tiwari National Institute of Science
Education and Research, HBNI, Bhubaneswar, India
S. Bahinipati22, C. Kar, P. Mal, K. Mandal, A. Nayak23, D.K. Sahoo22, S.K.
Swain Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, S. Chauhan, R. Chawla, N. Dhingra, R.
Gupta, A. Kaur, A. Kaur, M. Kaur, S. Kaur, R. Kumar, P. Kumari, M. Lohan, A.
Mehta, K. Sandeep, S. Sharma, J.B. Singh, G. Walia University of Delhi, Delhi,
India
A. Bhardwaj, B.C. Choudhary, R.B. Garg, M. Gola, S. Keshri, Ashok Kumar, S.
Malhotra, M. Naimuddin, P. Priyanka, K. Ranjan, Aashaq Shah, R. Sharma Saha
Institute of Nuclear Physics, HBNI, Kolkata, India
R. Bhardwaj24, M. Bharti, R. Bhattacharya, S. Bhattacharya, U. Bhawandeep24,
D. Bhowmik, S. Dey, S. Dutt24, S. Dutta, S. Ghosh, K. Mondal, S. Nandan, A.
Purohit, P.K. Rout, A. Roy, S. Roy Chowdhury, S. Sarkar, M. Sharan, B. Singh,
S. Thakur24 Indian Institute of Technology Madras, Madras, India
P.K. Behera Bhabha Atomic Research Centre, Mumbai, India
R. Chudasama, D. Dutta, V. Jha, V. Kumar, P.K. Netrakanti, L.M. Pant, P.
Shukla Tata Institute of Fundamental Research-A, Mumbai, India
T. Aziz, M.A. Bhat, S. Dugad, G.B. Mohanty, N. Sur, B. Sutar, RavindraKumar
Verma Tata Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, Sa. Jain, S.
Karmakar, S. Kumar, M. Maity25, G. Majumder, K. Mazumdar, N. Sahoo, T.
Sarkar25 Indian Institute of Science Education and Research (IISER), Pune,
India
S. Chauhan, S. Dube, V. Hegde, A. Kapoor, K. Kothekar, S. Pandey, A. Rane, S.
Sharma Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
S. Chenarani26, E. Eskandari Tadavani, S.M. Etesami26, M. Khakzad, M.
Mohammadi Najafabadi, M. Naseri, F. Rezaei Hosseinabadi, B. Safarzadeh27, M.
Zeinali University College Dublin, Dublin, Ireland
M. Felcini, M. Grunewald INFN Sezione di Bari a, Università di Bari b,
Politecnico di Bari c, Bari, Italy
M. Abbresciaa,b, C. Calabriaa,b, A. Colaleoa, D. Creanzaa,c, L. Cristellaa,b,
N. De Filippisa,c, M. De Palmaa,b, A. Di Florioa,b, F. Erricoa,b, L. Fiorea,
A. Gelmia,b, G. Iasellia,c, M. Incea,b, S. Lezkia,b, G. Maggia,c, M. Maggia,
G. Minielloa,b, S. Mya,b, S. Nuzzoa,b, A. Pompilia,b, G. Pugliesea,c, R.
Radognaa, A. Ranieria, G. Selvaggia,b, A. Sharmaa, L. Silvestrisa, R.
Vendittia, P. Verwilligena, G. Zitoa INFN Sezione di Bologna a, Università di
Bologna b, Bologna, Italy
G. Abbiendia, C. Battilanaa,b, D. Bonacorsia,b, L. Borgonovia,b, S. Braibant-
Giacomellia,b, R. Campaninia,b, P. Capiluppia,b, A. Castroa,b, F.R. Cavalloa,
S.S. Chhibraa,b, C. Cioccaa, G. Codispotia,b, M. Cuffiania,b, G.M.
Dallavallea, F. Fabbria, A. Fanfania,b, P. Giacomellia, C. Grandia, L.
Guiduccia,b, F. Iemmia,b, S. Marcellinia, G. Masettia, A. Montanaria, F.L.
Navarriaa,b, A. Perrottaa, F. Primaveraa,b,15, A.M. Rossia,b, T. Rovellia,b,
G.P. Sirolia,b, N. Tosia INFN Sezione di Catania a, Università di Catania b,
Catania, Italy
S. Albergoa,b, A. Di Mattiaa, R. Potenzaa,b, A. Tricomia,b, C. Tuvea,b INFN
Sezione di Firenze a, Università di Firenze b, Firenze, Italy
G. Barbaglia, K. Chatterjeea,b, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, E. Focardia,b, G. Latino, P. Lenzia,b, M. Meschinia, S.
Paolettia, L. Russoa,28, G. Sguazzonia, D. Stroma, L. Viliania INFN Laboratori
Nazionali di Frascati, Frascati, Italy
L. Benussi, S. Bianco, F. Fabbri, D. Piccolo INFN Sezione di Genova a,
Università di Genova b, Genova, Italy
F. Ferroa, F. Raveraa,b, E. Robuttia, S. Tosia,b INFN Sezione di Milano-
Bicocca a, Università di Milano-Bicocca b, Milano, Italy
A. Benagliaa, A. Beschib, L. Brianzaa,b, F. Brivioa,b, V. Cirioloa,b,15, S. Di
Guidaa,d,15, M.E. Dinardoa,b, S. Fiorendia,b, S. Gennaia, A. Ghezzia,b, P.
Govonia,b, M. Malbertia,b, S. Malvezzia, A. Massironia,b, D. Menascea, L.
Moronia, M. Paganonia,b, D. Pedrinia, S. Ragazzia,b, T. Tabarelli de Fatisa,b
INFN Sezione di Napoli a, Università di Napoli ’Federico II’ b, Napoli, Italy,
Università della Basilicata c, Potenza, Italy, Università G. Marconi d, Roma,
Italy
S. Buontempoa, N. Cavalloa,c, A. Di Crescenzoa,b, F. Fabozzia,c, F. Fiengaa,
G. Galatia, A.O.M. Iorioa,b, W.A. Khana, L. Listaa, S. Meolaa,d,15, P.
Paoluccia,15, C. Sciaccaa,b, E. Voevodinaa,b INFN Sezione di Padova a,
Università di Padova b, Padova, Italy, Università di Trento c, Trento, Italy
P. Azzia, N. Bacchettaa, D. Biselloa,b, A. Bolettia,b, A. Bragagnolo, R.
Carlina,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, J. Pazzinia,b, P. Ronchesea,b, R.
Rossina,b, F. Simonettoa,b, A. Tiko, E. Torassaa, M. Zanettia,b, P. Zottoa,b,
G. Zumerlea,b INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy
A. Braghieria, A. Magnania, P. Montagnaa,b, S.P. Rattia,b, V. Rea, M.
Ressegottia,b, C. Riccardia,b, P. Salvinia, I. Vaia,b, P. Vituloa,b INFN
Sezione di Perugia a, Università di Perugia b, Perugia, Italy
L. Alunni Solestizia,b, M. Biasinia,b, G.M. Bileia, C. Cecchia,b, D.
Ciangottinia,b, L. Fanòa,b, P. Laricciaa,b, R. Leonardia,b, E. Manonia, G.
Mantovania,b, V. Mariania,b, M. Menichellia, A. Rossia,b, A. Santocchiaa,b, D.
Spigaa INFN Sezione di Pisa a, Università di Pisa b, Scuola Normale Superiore
di Pisa c, Pisa, Italy
K. Androsova, P. Azzurria, G. Bagliesia, L. Bianchinia, T. Boccalia, L.
Borrello, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, G. Fedia, F. Fioria,c,
L. Gianninia,c, A. Giassia, M.T. Grippoa, F. Ligabuea,c, E. Mancaa,c, G.
Mandorlia,c, A. Messineoa,b, F. Pallaa, A. Rizzia,b, P. Spagnoloa, R.
Tenchinia, G. Tonellia,b, A. Venturia, P.G. Verdinia INFN Sezione di Roma a,
Sapienza Università di Roma b, Rome, Italy
L. Baronea,b, F. Cavallaria, M. Cipriania,b, N. Dacia, D. Del Rea,b, E. Di
Marcoa,b, M. Diemoza, S. Gellia,b, E. Longoa,b, B. Marzocchia,b, P.
Meridiania, G. Organtinia,b, F. Pandolfia, R. Paramattia,b, F. Preiatoa,b, S.
Rahatloua,b, C. Rovellia, F. Santanastasioa,b INFN Sezione di Torino a,
Università di Torino b, Torino, Italy, Università del Piemonte Orientale c,
Novara, Italy
N. Amapanea,b, R. Arcidiaconoa,c, S. Argiroa,b, M. Arneodoa,c, N. Bartosika,
R. Bellana,b, C. Biinoa, N. Cartigliaa, F. Cennaa,b, S. Cometti, M. Costaa,b,
R. Covarellia,b, N. Demariaa, B. Kiania,b, C. Mariottia, S. Masellia, E.
Migliorea,b, V. Monacoa,b, E. Monteila,b, M. Montenoa, M.M. Obertinoa,b, L.
Pachera,b, N. Pastronea, M. Pelliccionia, G.L. Pinna Angionia,b, A. Romeroa,b,
M. Ruspaa,c, R. Sacchia,b, K. Shchelinaa,b, V. Solaa, A. Solanoa,b, D. Soldi,
A. Staianoa INFN Sezione di Trieste a, Università di Trieste b, Trieste, Italy
S. Belfortea, V. Candelisea,b, M. Casarsaa, F. Cossuttia, G. Della Riccaa,b,
F. Vazzolera,b, A. Zanettia Kyungpook National University
D.H. Kim, G.N. Kim, M.S. Kim, J. Lee, S. Lee, S.W. Lee, C.S. Moon, Y.D. Oh, S.
Sekmen, D.C. Son, Y.C. Yang Chonnam National University, Institute for
Universe and Elementary Particles, Kwangju, Korea
H. Kim, D.H. Moon, G. Oh Hanyang University, Seoul, Korea
J. Goh29, T.J. Kim Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, D. Gyun, S. Ha, B. Hong, Y. Jo, K. Lee, K.S. Lee, S.
Lee, J. Lim, S.K. Park, Y. Roh Sejong University, Seoul, Korea
H.S. Kim Seoul National University, Seoul, Korea
J. Almond, J. Kim, J.S. Kim, H. Lee, K. Lee, K. Nam, S.B. Oh, B.C. Radburn-
Smith, S.h. Seo, U.K. Yang, H.D. Yoo, G.B. Yu University of Seoul, Seoul,
Korea
D. Jeon, H. Kim, J.H. Kim, J.S.H. Lee, I.C. Park Sungkyunkwan University,
Suwon, Korea
Y. Choi, C. Hwang, J. Lee, I. Yu Vilnius University, Vilnius, Lithuania
V. Dudenas, A. Juodagalvis, J. Vaitkus National Centre for Particle Physics,
Universiti Malaya, Kuala Lumpur, Malaysia
I. Ahmed, Z.A. Ibrahim, M.A.B. Md Ali30, F. Mohamad Idris31, W.A.T. Wan
Abdullah, M.N. Yusli, Z. Zolkapli Universidad de Sonora (UNISON), Hermosillo,
Mexico
A. Castaneda Hernandez, J.A. Murillo Quijada Centro de Investigacion y de
Estudios Avanzados del IPN, Mexico City, Mexico
H. Castilla-Valdez, E. De La Cruz-Burelo, M.C. Duran-Osuna, I. Heredia-De La
Cruz32, R. Lopez-Fernandez, J. Mejia Guisao, R.I. Rabadan-Trejo, M. Ramirez-
Garcia, G. Ramirez-Sanchez, R Reyes-Almanza, A. Sanchez-Hernandez Universidad
Iberoamericana, Mexico City, Mexico
S. Carrillo Moreno, C. Oropeza Barrera, F. Vazquez Valencia Benemerita
Universidad Autonoma de Puebla, Puebla, Mexico
J. Eysermans, I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada Universidad
Autónoma de San Luis Potosí, San Luis Potosí, Mexico
A. Morelos Pineda University of Auckland, Auckland, New Zealand
D. Krofcheck University of Canterbury, Christchurch, New Zealand
S. Bheesette, P.H. Butler National Centre for Physics, Quaid-I-Azam
University, Islamabad, Pakistan
A. Ahmad, M. Ahmad, M.I. Asghar, Q. Hassan, H.R. Hoorani, S. Qazi, M.A. Shah,
M. Shoaib, M. Waqas National Centre for Nuclear Research, Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, T. Frueboes, M. Górski, M. Kazana, K.
Nawrocki, M. Szleper, P. Traczyk, P. Zalewski Institute of Experimental
Physics, Faculty of Physics, University of Warsaw, Warsaw, Poland
K. Bunkowski, A. Byszuk33, K. Doroba, A. Kalinowski, M. Konecki, J.
Krolikowski, M. Misiura, M. Olszewski, A. Pyskir, M. Walczak Laboratório de
Instrumentação e Física Experimental de Partículas, Lisboa, Portugal
P. Bargassa, C. Beirão Da Cruz E Silva, A. Di Francesco, P. Faccioli, B.
Galinhas, M. Gallinaro, J. Hollar, N. Leonardo, L. Lloret Iglesias, M.V.
Nemallapudi, J. Seixas, G. Strong, O. Toldaiev, D. Vadruccio, J. Varela Joint
Institute for Nuclear Research, Dubna, Russia
V. Alexakhin, A. Golunov, I. Golutvin, N. Gorbounov, I. Gorbunov, A. Kamenev,
V. Karjavin, A. Lanev, A. Malakhov, V. Matveev34,35, 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
V. Golovtsov, Y. Ivanov, V. Kim36, E. Kuznetsova37, P. Levchenko, V. Murzin,
V. Oreshkin, I. Smirnov, D. Sosnov, V. Sulimov, L. Uvarov, S. Vavilov, A.
Vorobyev Institute for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov,
N. Krasnikov, A. Pashenkov, D. Tlisov, A. Toropin Institute for Theoretical
and Experimental Physics, Moscow, Russia
V. Epshteyn, V. Gavrilov, N. Lychkovskaya, V. Popov, I. Pozdnyakov, G.
Safronov, A. Spiridonov, A. Stepennov, V. Stolin, M. Toms, E. Vlasov, A.
Zhokin Moscow Institute of Physics and Technology, Moscow, Russia
T. Aushev National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
M. Chadeeva38, P. Parygin, D. Philippov, S. Polikarpov38, E. Popova, V.
Rusinov P.N. Lebedev Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin35, I. Dremin35, M. Kirakosyan35, S.V. Rusakov, A.
Terkulov Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State
University, Moscow, Russia
A. Baskakov, A. Belyaev, E. Boos, V. Bunichev, M. Dubinin39, L. Dudko, A.
Ershov, A. Gribushin, V. Klyukhin, O. Kodolova, I. Lokhtin, I. Miagkov, S.
Obraztsov, V. Savrin, A. Snigirev Novosibirsk State University (NSU),
Novosibirsk, Russia
V. Blinov40, T. Dimova40, L. Kardapoltsev40, D. Shtol40, Y. Skovpen40 State
Research Center of Russian Federation, Institute for High Energy Physics of
NRC “Kurchatov Institute”, Protvino, Russia
I. Azhgirey, I. Bayshev, S. Bitioukov, D. Elumakhov, A. Godizov, V. Kachanov,
A. Kalinin, D. Konstantinov, P. Mandrik, V. Petrov, R. Ryutin, S.
Slabospitskii, A. Sobol, S. Troshin, N. Tyurin, A. Uzunian, A. Volkov National
Research Tomsk Polytechnic University, Tomsk, Russia
A. Babaev, S. Baidali University of Belgrade, Faculty of Physics and Vinca
Institute of Nuclear Sciences, Belgrade, Serbia
P. Adzic41, 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, J.A.
Brochero Cifuentes, 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, V. Rodríguez Bouza,
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, E. Brondolin, T. Camporesi, M.
Cepeda, G. Cerminara, E. Chapon, Y. Chen, G. Cucciati, D. d’Enterria, A.
Dabrowski, V. Daponte, A. David, A. De Roeck, N. Deelen, M. Dobson, T. du
Pree, M. Dünser, N. Dupont, A. Elliott-Peisert, P. Everaerts, F.
Fallavollita42, D. Fasanella, G. Franzoni, J. Fulcher, W. Funk, D. Gigi, A.
Gilbert, K. Gill, F. Glege, M. Guilbaud, D. Gulhan, J. Hegeman, V. Innocente,
A. Jafari, P. Janot, O. Karacheban18, J. Kieseler, A. Kornmayer, M. Krammer1,
C. Lange, P. Lecoq, C. Lourenço, L. Malgeri, M. Mannelli, F. Meijers, J.A.
Merlin, S. Mersi, E. Meschi, P. Milenovic43, F. Moortgat, M. Mulders, 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. Rolandi44, M. Rovere, H. Sakulin, C. Schäfer,
C. Schwick, M. Seidel, M. Selvaggi, A. Sharma, P. Silva, P. Sphicas45, A.
Stakia, J. Steggemann, M. Tosi, D. Treille, A. Tsirou, V. Veckalns46, W.D.
Zeuner Paul Scherrer Institut, Villigen, Switzerland
L. Caminada47, 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, N. Chernyavskaya, G. Dissertori, M. Dittmar,
M. Donegà, C. Dorfer, C. Grab, C. Heidegger, D. Hits, J. Hoss, T. Klijnsma, W.
Lustermann, R.A. Manzoni, M. Marionneau, M.T. Meinhard, F. Micheli, P.
Musella, F. Nessi-Tedaldi, J. Pata, F. Pauss, G. Perrin, L. Perrozzi, S.
Pigazzini, M. Quittnat, 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. Amsler48, 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
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, W.-S. Hou, Arun Kumar, Y.y. Li, 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, N. Srimanobhas, N. Suwonjandee Çukurova University, Physics
Department, Science and Art Faculty, Adana, Turkey
A. Bat, F. Boran, S. Cerci49, S. Damarseckin, Z.S. Demiroglu, F. Dolek, C.
Dozen, I. Dumanoglu, S. Girgis, G. Gokbulut, Y. Guler, E. Gurpinar, I. Hos50,
C. Isik, E.E. Kangal51, O. Kara, A. Kayis Topaksu, U. Kiminsu, M. Oglakci, G.
Onengut, K. Ozdemir52, S. Ozturk53, D. Sunar Cerci49, B. Tali49, U.G. Tok, S.
Turkcapar, I.S. Zorbakir, C. Zorbilmez Middle East Technical University,
Physics Department, Ankara, Turkey
B. Isildak54, G. Karapinar55, M. Yalvac, M. Zeyrek Bogazici University,
Istanbul, Turkey
I.O. Atakisi, E. Gülmez, M. Kaya56, O. Kaya57, S. Tekten, E.A. Yetkin58
Istanbul Technical University, Istanbul, Turkey
M.N. Agaras, S. Atay, A. Cakir, K. Cankocak, Y. Komurcu, S. Sen59 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, B. Penning, T. Sakuma, D. Smith, V.J. Smith, J. Taylor, A.
Titterton 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, C. Laner,
L. Lyons, A.-M. Magnan, S. Malik, A. Martelli, J. Nash62, A. Nikitenko7, V.
Palladino, M. Pesaresi, A. Richards, A. Rose, E. Scott, C. Seez, A.
Shtipliyski, G. Singh, M. Stoye, T. Strebler, S. Summers, A. Tapper, K.
Uchida, 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, C.K. Mackay, A. Morton, I.D. Reid,
L. Teodorescu, S. Zahid Baylor University, Waco, USA
K. Call, J. Dittmann, K. Hatakeyama, H. Liu, C. Madrid, B. Mcmaster, 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, T. Bose, D. Gastler, D. Rankin, C. Richardson, J. Rohlf, L. Sulak,
D. Zou Brown University, Providence, USA
G. Benelli, X. Coubez, D. Cutts, M. Hadley, J. Hakala, U. Heintz, J.M.
Hogan63, K.H.M. Kwok, E. Laird, G. Landsberg, J. Lee, Z. Mao, M. Narain, S.
Piperov, S. Sagir64, R. Syarif, E. Usai, 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, O. Kukral, R. Lander, C. Mclean, M. Mulhearn, D. Pellett, J. Pilot, S.
Shalhout, M. Shi, D. Stolp, D. Taylor, K. Tos, M. Tripathi, Z. Wang, F. Zhang
University of California, Los Angeles, USA
M. Bachtis, C. Bravo, R. Cousins, A. Dasgupta, A. Florent, J. Hauser, M.
Ignatenko, N. Mccoll, S. Regnard, D. Saltzberg, C. Schnaible, V. Valuev
University of California, Riverside, Riverside, USA
E. Bouvier, K. Burt, R. Clare, 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, S. Wang, J. Yoo California
Institute of Technology, Pasadena, USA
D. Anderson, A. Bornheim, J.M. Lawhorn, H.B. Newman, T.Q. Nguyen, 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, M. Sun, I. Vorobiev, M.
Weinberg University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, F. Jensen, A. Johnson, M. Krohn, S. Leontsinis, E.
MacDonald, T. Mulholland, K. Stenson, K.A. Ulmer, S.R. Wagner Cornell
University, Ithaca, USA
J. Alexander, J. Chaves, Y. Cheng, J. Chu, A. Datta, K. Mcdermott, N. Mirman,
J.R. Patterson, D. Quach, A. Rinkevicius, A. Ryd, L. Skinnari, L. Soffi, S.M.
Tan, Z. Tao, J. Thom, J. Tucker, P. Wittich, M. Zientek Fermi National
Accelerator Laboratory, Batavia, USA
S. Abdullin, M. Albrow, M. Alyari, G. Apollinari, A. Apresyan, A. Apyan, S.
Banerjee, L.A.T. Bauerdick, A. Beretvas, J. Berryhill, P.C. Bhat, G.
Bolla${}^{\textrm{\textdagger}}$, K. Burkett, J.N. Butler, A. Canepa, G.B.
Cerati, H.W.K. Cheung, F. Chlebana, M. Cremonesi, J. Duarte, V.D. Elvira, J.
Freeman, Z. Gecse, E. Gottschalk, L. Gray, D. Green, S. Grünendahl, O.
Gutsche, J. Hanlon, R.M. Harris, S. Hasegawa, J. Hirschauer, Z. Hu, B.
Jayatilaka, S. Jindariani, M. Johnson, U. Joshi, B. Klima, M.J. Kortelainen,
B. Kreis, S. Lammel, D. Lincoln, R. Lipton, M. Liu, T. Liu, J. Lykken, K.
Maeshima, J.M. Marraffino, D. Mason, P. McBride, P. Merkel, S. Mrenna, S.
Nahn, V. O’Dell, K. Pedro, C. Pena, O. Prokofyev, G. Rakness, L. Ristori, A.
Savoy-Navarro66, B. Schneider, E. Sexton-Kennedy, A. Soha, W.J. Spalding, L.
Spiegel, S. Stoynev, J. Strait, N. Strobbe, L. Taylor, S. Tkaczyk, N.V. Tran,
L. Uplegger, E.W. Vaandering, C. Vernieri, M. Verzocchi, R. Vidal, M. Wang,
H.A. Weber, A. Whitbeck University of Florida, Gainesville, USA
D. Acosta, P. Avery, P. Bortignon, D. Bourilkov, A. Brinkerhoff, L. Cadamuro,
A. Carnes, M. Carver, D. Curry, R.D. Field, S.V. Gleyzer, B.M. Joshi, J.
Konigsberg, A. Korytov, P. Ma, K. Matchev, H. Mei, G. Mitselmakher, K. Shi, D.
Sperka, J. Wang, S. Wang Florida International University, Miami, USA
Y.R. Joshi, S. Linn Florida State University, Tallahassee, USA
A. Ackert, T. Adams, A. Askew, S. Hagopian, V. Hagopian, K.F. Johnson, T.
Kolberg, G. Martinez, T. Perry, H. Prosper, A. Saha, V. Sharma, R. Yohay
Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, V. Bhopatkar, S. Colafranceschi, M. Hohlmann, D. Noonan, M.
Rahmani, T. Roy, F. Yumiceva University of Illinois at Chicago (UIC), Chicago,
USA
M.R. Adams, L. Apanasevich, D. Berry, R.R. Betts, R. Cavanaugh, X. Chen, S.
Dittmer, O. Evdokimov, C.E. Gerber, D.A. Hangal, D.J. Hofman, K. Jung, J.
Kamin, C. Mills, I.D. Sandoval Gonzalez, M.B. Tonjes, N. Varelas, H. Wang, X.
Wang, Z. Wu, J. Zhang The University of Iowa, Iowa City, USA
M. Alhusseini, B. Bilki67, W. Clarida, K. Dilsiz68, S. Durgut, R.P.
Gandrajula, M. Haytmyradov, V. Khristenko, J.-P. Merlo, A. Mestvirishvili, A.
Moeller, J. Nachtman, H. Ogul69, Y. Onel, F. Ozok70, A. Penzo, C. Snyder, E.
Tiras, J. Wetzel Johns Hopkins University, Baltimore, USA
B. Blumenfeld, A. Cocoros, N. Eminizer, D. Fehling, L. Feng, A.V. Gritsan,
W.T. Hung, P. Maksimovic, J. Roskes, U. Sarica, M. Swartz, M. Xiao, C. You The
University of Kansas, Lawrence, USA
A. Al-bataineh, P. Baringer, A. Bean, S. Boren, J. Bowen, A. Bylinkin, J.
Castle, S. Khalil, A. Kropivnitskaya, D. Majumder, W. Mcbrayer, M. Murray, C.
Rogan, S. Sanders, E. Schmitz, J.D. Tapia Takaki, Q. Wang Kansas State
University, Manhattan, USA
S. Duric, A. Ivanov, K. Kaadze, D. Kim, Y. Maravin, D.R. Mendis, T. Mitchell,
A. Modak, A. Mohammadi, L.K. Saini, N. Skhirtladze Lawrence Livermore National
Laboratory, Livermore, USA
F. Rebassoo, D. Wright University of Maryland, College Park, USA
A. Baden, O. Baron, A. Belloni, S.C. Eno, Y. Feng, C. Ferraioli, N.J. Hadley,
S. Jabeen, G.Y. Jeng, R.G. Kellogg, J. Kunkle, A.C. Mignerey, F. Ricci-Tam,
Y.H. Shin, A. Skuja, S.C. Tonwar, K. Wong Massachusetts Institute of
Technology, Cambridge, USA
D. Abercrombie, B. Allen, V. Azzolini, A. Baty, G. Bauer, R. Bi, S. Brandt, W.
Busza, I.A. Cali, M. D’Alfonso, Z. Demiragli, G. Gomez Ceballos, M. Goncharov,
P. Harris, D. Hsu, M. Hu, Y. Iiyama, G.M. Innocenti, M. Klute, D. Kovalskyi,
Y.-J. Lee, P.D. Luckey, B. Maier, A.C. Marini, C. Mcginn, C. Mironov, S.
Narayanan, X. Niu, C. Paus, C. Roland, G. Roland, G.S.F. Stephans, K. Sumorok,
K. Tatar, D. Velicanu, J. Wang, T.W. Wang, B. Wyslouch, S. Zhaozhong
University of Minnesota, Minneapolis, USA
A.C. Benvenuti, R.M. Chatterjee, A. Evans, P. Hansen, S. Kalafut, Y. Kubota,
Z. Lesko, J. Mans, S. Nourbakhsh, N. Ruckstuhl, R. Rusack, J. Turkewitz, M.A.
Wadud University of Mississippi, Oxford, USA
J.G. Acosta, S. Oliveros University of Nebraska-Lincoln, Lincoln, USA
E. Avdeeva, K. Bloom, D.R. Claes, C. Fangmeier, F. Golf, R. Gonzalez Suarez,
R. Kamalieddin, I. Kravchenko, J. Monroy, J.E. Siado, G.R. Snow, B. Stieger
State University of New York at Buffalo, Buffalo, USA
A. Godshalk, C. Harrington, I. Iashvili, A. Kharchilava, D. Nguyen, A. Parker,
S. Rappoccio, B. Roozbahani Northeastern University, Boston, USA
G. Alverson, E. Barberis, C. Freer, A. Hortiangtham, D.M. Morse, T. Orimoto,
R. Teixeira De Lima, T. Wamorkar, B. Wang, A. Wisecarver, D. Wood Northwestern
University, Evanston, USA
S. Bhattacharya, O. Charaf, K.A. Hahn, N. Mucia, N. Odell, M.H. Schmitt, K.
Sung, M. Trovato, M. Velasco University of Notre Dame, Notre Dame, USA
R. Bucci, N. Dev, M. Hildreth, K. Hurtado Anampa, C. Jessop, D.J. Karmgard, N.
Kellams, K. Lannon, W. Li, N. Loukas, N. Marinelli, F. Meng, C. Mueller, Y.
Musienko34, M. Planer, A. Reinsvold, R. Ruchti, P. Siddireddy, G. Smith, S.
Taroni, M. Wayne, A. Wightman, M. Wolf, A. Woodard The Ohio State University,
Columbus, USA
J. Alimena, L. Antonelli, B. Bylsma, L.S. Durkin, S. Flowers, B. Francis, A.
Hart, C. Hill, W. Ji, T.Y. Ling, W. Luo, B.L. Winer, H.W. Wulsin Princeton
University, Princeton, USA
S. Cooperstein, P. Elmer, J. Hardenbrook, P. Hebda, S. Higginbotham, A.
Kalogeropoulos, D. Lange, M.T. Lucchini, J. Luo, D. Marlow, K. Mei, I. Ojalvo,
J. Olsen, C. Palmer, P. Piroué, J. Salfeld-Nebgen, D. Stickland, C. Tully
University of Puerto Rico, Mayaguez, USA
S. Malik, S. Norberg Purdue University, West Lafayette, USA
A. Barker, V.E. Barnes, S. Das, L. Gutay, M. Jones, A.W. Jung, A. Khatiwada,
B. Mahakud, D.H. Miller, N. Neumeister, C.C. Peng, H. Qiu, J.F. Schulte, J.
Sun, F. Wang, R. Xiao, W. Xie Purdue University Northwest, Hammond, USA
T. Cheng, J. Dolen, N. Parashar Rice University, Houston, USA
Z. Chen, K.M. Ecklund, S. Freed, F.J.M. Geurts, M. Kilpatrick, W. Li, B.
Michlin, B.P. Padley, J. Roberts, J. Rorie, W. Shi, Z. Tu, J. Zabel, A. Zhang
University of Rochester, Rochester, USA
A. Bodek, P. de Barbaro, R. Demina, Y.t. Duh, J.L. Dulemba, C. Fallon, T.
Ferbel, M. Galanti, A. Garcia-Bellido, J. Han, O. Hindrichs, A.
Khukhunaishvili, K.H. Lo, P. Tan, R. Taus, M. Verzetti Rutgers, The State
University of New Jersey, Piscataway, USA
A. Agapitos, J.P. Chou, Y. Gershtein, T.A. Gómez Espinosa, E. Halkiadakis, M.
Heindl, E. Hughes, S. Kaplan, R. Kunnawalkam Elayavalli, S. Kyriacou, A. Lath,
R. Montalvo, K. Nash, M. Osherson, H. Saka, S. Salur, S. Schnetzer, D.
Sheffield, S. Somalwar, R. Stone, S. Thomas, P. Thomassen, M. Walker
University of Tennessee, Knoxville, USA
A.G. Delannoy, J. Heideman, G. Riley, K. Rose, S. Spanier, K. Thapa Texas A&M
University, College Station, USA
O. Bouhali71, A. Celik, M. Dalchenko, M. De Mattia, A. Delgado, S. Dildick, R.
Eusebi, J. Gilmore, T. Huang, T. Kamon72, S. Luo, R. Mueller, Y. Pakhotin, R.
Patel, A. Perloff, L. Perniè, D. Rathjens, A. Safonov, A. Tatarinov Texas Tech
University, Lubbock, USA
N. Akchurin, J. Damgov, F. De Guio, P.R. Dudero, S. Kunori, K. Lamichhane,
S.W. Lee, T. Mengke, S. Muthumuni, T. Peltola, S. Undleeb, I. Volobouev, Z.
Wang Vanderbilt University, Nashville, USA
S. Greene, A. Gurrola, R. Janjam, W. Johns, C. Maguire, A. Melo, H. Ni, K.
Padeken, J.D. Ruiz Alvarez, P. Sheldon, S. Tuo, J. Velkovska, M. Verweij, Q.
Xu University of Virginia, Charlottesville, USA
M.W. Arenton, P. Barria, B. Cox, R. Hirosky, M. Joyce, A. Ledovskoy, H. Li, C.
Neu, T. Sinthuprasith, Y. Wang, E. Wolfe, F. Xia Wayne State University,
Detroit, USA
R. Harr, P.E. Karchin, N. Poudyal, J. Sturdy, P. Thapa, S. Zaleski University
of Wisconsin - Madison, Madison, WI, USA
M. Brodski, J. Buchanan, C. Caillol, D. Carlsmith, S. Dasu, L. Dodd, B.
Gomber, M. Grothe, M. Herndon, A. Hervé, U. Hussain, P. Klabbers, A. Lanaro,
A. Levine, K. Long, R. Loveless, T. Ruggles, A. Savin, N. Smith, W.H. Smith,
N. Woods †: Deceased
1: Also at Vienna University of Technology, Vienna, Austria
2: Also at IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
3: Also at Universidade Estadual de Campinas, Campinas, Brazil
4: Also at Federal University of Rio Grande do Sul, Porto Alegre, Brazil
5: Also at Université Libre de Bruxelles, Bruxelles, Belgium
6: Also at University of Chinese Academy of Sciences, Beijing, China
7: Also at Institute for Theoretical and Experimental Physics, Moscow, Russia
8: Also at Joint Institute for Nuclear Research, Dubna, Russia
9: Also at Suez University, Suez, Egypt
10: Now at British University in Egypt, Cairo, Egypt
11: Also at Zewail City of Science and Technology, Zewail, Egypt
12: Also at Department of Physics, King Abdulaziz University, Jeddah, Saudi
Arabia
13: Also at Université de Haute Alsace, Mulhouse, France
14: Also at Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State
University, Moscow, Russia
15: Also at CERN, European Organization for Nuclear Research, Geneva,
Switzerland
16: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
17: Also at University of Hamburg, Hamburg, Germany
18: Also at Brandenburg University of Technology, Cottbus, Germany
19: Also at MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
20: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary
21: Also at Institute of Physics, University of Debrecen, Debrecen, Hungary
22: Also at Indian Institute of Technology Bhubaneswar, Bhubaneswar, India
23: Also at Institute of Physics, Bhubaneswar, India
24: Also at Shoolini University, Solan, India
25: Also at University of Visva-Bharati, Santiniketan, India
26: Also at Isfahan University of Technology, Isfahan, Iran
27: Also at Plasma Physics Research Center, Science and Research Branch,
Islamic Azad University, Tehran, Iran
28: Also at Università degli Studi di Siena, Siena, Italy
29: Also at Kyunghee University, Seoul, Korea
30: Also at International Islamic University of Malaysia, Kuala Lumpur,
Malaysia
31: Also at Malaysian Nuclear Agency, MOSTI, Kajang, Malaysia
32: Also at Consejo Nacional de Ciencia y Tecnología, Mexico city, Mexico
33: Also at Warsaw University of Technology, Institute of Electronic Systems,
Warsaw, Poland
34: Also at Institute for Nuclear Research, Moscow, Russia
35: Now at National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
36: Also at St. Petersburg State Polytechnical University, St. Petersburg,
Russia
37: Also at University of Florida, Gainesville, USA
38: Also at P.N. Lebedev Physical Institute, Moscow, Russia
39: Also at California Institute of Technology, Pasadena, USA
40: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia
41: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia
42: Also at INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy
43: Also at University of Belgrade, Faculty of Physics and Vinca Institute of
Nuclear Sciences, Belgrade, Serbia
44: Also at Scuola Normale e Sezione dell’INFN, Pisa, Italy
45: Also at National and Kapodistrian University of Athens, Athens, Greece
46: Also at Riga Technical University, Riga, Latvia
47: Also at Universität Zürich, Zurich, Switzerland
48: Also at Stefan Meyer Institute for Subatomic Physics (SMI), Vienna,
Austria
49: Also at Adiyaman University, Adiyaman, Turkey
50: Also at Istanbul Aydin University, Istanbul, Turkey
51: Also at Mersin University, Mersin, Turkey
52: Also at Piri Reis University, Istanbul, Turkey
53: Also at Gaziosmanpasa University, Tokat, Turkey
54: Also at Ozyegin University, Istanbul, Turkey
55: Also at Izmir Institute of Technology, Izmir, Turkey
56: Also at Marmara University, Istanbul, Turkey
57: Also at Kafkas University, Kars, Turkey
58: Also at Istanbul Bilgi University, Istanbul, Turkey
59: Also at Hacettepe University, Ankara, Turkey
60: Also at Rutherford Appleton Laboratory, Didcot, United Kingdom
61: Also at School of Physics and Astronomy, University of Southampton,
Southampton, United Kingdom
62: Also at Monash University, Faculty of Science, Clayton, Australia
63: Also at Bethel University, St. Paul, USA
64: Also at Karamanoğlu Mehmetbey University, Karaman, Turkey
65: Also at Utah Valley University, Orem, USA
66: Also at Purdue University, West Lafayette, USA
67: Also at Beykent University, Istanbul, Turkey
68: Also at Bingol University, Bingol, Turkey
69: Also at Sinop University, Sinop, Turkey
70: Also at Mimar Sinan University, Istanbul, Istanbul, Turkey
71: Also at Texas A&M University at Qatar, Doha, Qatar
72: Also at Kyungpook National University, Daegu, Korea