HIG-17-001

$Revision:462162$
$HeadURL:svn+ssh://svn.cern.ch/reps/tdr2/papers/HIG-17-001/trunk/HIG-17-001.tex$
$Id:HIG-17-001.tex4621622018-05-2914:29:12Ztaroni$ HIG-17-001

# Search for lepton flavour violating decays of the Higgs boson to $\mu\tau$
and $\Pe\tau$ in proton-proton collisions at $\sqrt{s}=13\TeV$

###### Abstract

A search for lepton flavour violating decays of the Higgs boson in the
$\mu\tau$ and $\Pe\tau$ decay modes is presented. The search is based on a
data set corresponding to an integrated luminosity of 35.9of proton-proton
collisions collected with the CMS detector in 2016, at a centre-of-mass energy
of 13. No significant excess over the standard model expectation is observed.
The observed (expected) upper limits on the lepton flavour violating branching
fractions of the Higgs boson are $\mathcal{B}(\PH\to\mu\tau)<0.25\%\,(0.25\%)$
and $\mathcal{B}(\PH\to\Pe\tau)<0.61\%\,(0.37\%)$, at 95% confidence level.
These results are used to derive upper limits on the off-diagonal $\mu\tau$
and $\Pe\tau$ Yukawa couplings
$\sqrt{\smash[b]{|{Y_{\mu\tau}}|^{2}+|{Y_{\tau\mu}}|^{2}}}<1.43\times 10^{-3}$
and $\sqrt{|{Y_{\Pe\tau}}|^{2}+|{Y_{\tau\Pe}}|^{2}}<2.26\times 10^{-3}$ at 95%
confidence level. The limits on the lepton flavour violating branching
fractions of the Higgs boson and on the associated Yukawa couplings are the
most stringent to date.

##  0.1 Introduction

The discovery of the Higgs boson (H) at the CERN LHC [1, 2, 3] has stimulated
further precision measurements of the properties of the new particle. A
combined study of the 7 and 8data sets collected by the CMS and ATLAS
collaborations shows consistency between the measured couplings of the Higgs
boson and the standard model (SM) predictions [4]. However, the constraint on
the branching fraction to non-SM decay modes derived from these measurements,
$\mathcal{B}(\text{non-SM})<34$% at 95% confidence level (CL), still allows
for a significant contribution from exotic decays [4].

In this paper a search for lepton flavour violating (LFV) decays of the Higgs
boson in the $\Pgm\Pgt$ and $\Pe\Pgt$ channels is presented. These decays are
forbidden in the SM but occur in many new physics scenarios. These include
supersymmetric [5, 6, 7, 8, 9, 10, 11, 12, 13], composite Higgs [14, 15], or
Randall–Sundrum models [16, 17, 18], SM extensions with more than one Higgs
boson doublet [19, 20] or with flavour symmetries [21], and many other
scenarios [22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36]. The
presence of LFV Higgs boson couplings would allow $\Pgt\to\Pgm$ and
$\Pgt\to\Pe$ to proceed via a virtual Higgs boson [37, 38]. Consequently the
experimental limits on rare lepton decays, such as $\Pgt\to\Pe\gamma$ and
$\Pgt\to\Pgm\gamma$ [39], provide upper limits on
$\mathcal{B}(\PH\to\Pgm\Pgt)$ and $\mathcal{B}(\PH\to\Pe\Pgt)$ [40, 41] of
$\mathcal{O}(10\%)$. Measurements of the electron and muon magnetic moments,
and exclusion limits on the electric dipole moment of the electron also
provide complementary constraints [42]. The LFV Higgs boson decay to $\Pgm\Pe$
is strongly constrained by the $\Pgm\to\Pe\gamma$ limit,
$\mathcal{B}(\PH\to\Pe\Pgm)<\mathcal{O}(10^{-9})$ [43].

The CMS experiment published the first direct search for $\PH\to\Pgm\Pgt$
[44], followed by searches for $\PH\to\Pe\Pgt$ and $\PH\to\Pe\Pgm$ decays
[45], using proton-proton ($\Pp\Pp$) collision data corresponding to an
integrated luminosity of 19.7at a centre-of-mass energy of 8. A small excess
of data with respect to the SM background-only hypothesis at $m_{\PH}=125$was
observed in the $\PH\to\Pgm\Pgt$ channel, with a significance of $2.4$
standard deviations ($\sigma$), and the best fit for the branching fraction
was found to be $\mathcal{B}(\PH\to\Pgm\Pgt)=(0.84^{+0.39}_{-0.37})\%$. A
constraint was set on the observed (expected) branching fraction
$\mathcal{B}(\PH\to\Pgm\Pgt)<1.51\%$ (0.75%) at 95% CL. No excess of events
over the estimated background was observed in the $\PH\to\Pe\Pgt$ or
$\PH\to\Pe\Pgm$ channels, and observed (expected) upper limits on the
branching fractions $\mathcal{B}(\PH\to\Pe\Pgt)<0.69\%$ (0.75%) and
$\mathcal{B}(\PH\to\Pe\Pgm)<0.035\%$ (0.048%) at 95% CL were set. The ATLAS
Collaboration reported searches for $\PH\to\Pe\Pgt$ and $\PH\to\Pgm\Pgt$ using
$\Pp\Pp$ collision data at a centre-of-mass energy of 8, finding no
significant excess of events over the background expectation, and set observed
(expected) limits of $\mathcal{B}(\PH\to\Pgm\Pgt)<1.43\%$ (1.01%) and
$\mathcal{B}(\PH\to\Pe\Pgt)<1.04\%$ (1.21%) at 95% CL [46, 47].

The search described in this paper is performed in four decay channels,
$\PH\to\Pgm\tauh$, $\PH\to\Pgm\Pgt_{\Pe}$, $\PH\to\Pe\tauh$,
$\PH\to\Pe\Pgt_{\Pgm}$, where $\tauh$, $\Pgt_{\Pe}$, and $\Pgt_{\Pgm}$
correspond to the hadronic, electronic, and muonic decay channels of $\Pgt$
leptons, respectively. The decay channels $\PH\to\Pe\Pgt_{\Pe}$ and
$\PH\to\Pgm\Pgt_{\Pgm}$, are not considered because of the large background
contribution from $\cPZ$ boson decays. The expected final state signatures are
very similar to those for the SM $\PH\to\Pgt\Pgt$ decays, studied by CMS [48,
49, 50] and ATLAS [51], but with some significant kinematic differences. The
electron (muon) in the LFV $\PH\to\Pe(\Pgm)\Pgt$ decay is produced promptly,
and tends to have a larger momentum than in the SM
$\PH\to\Pgt_{\Pe(\Pgm)}\tauh$ decay. The search reported in this paper
improves upon the sensitivity of the earlier CMS searches [44, 45] by using a
boosted decision trees (BDT) discriminator to distinguish signal from
background events. A separate analysis, similar in strategy to the previous
CMS publications, is performed as cross check. The results of both strategies
are reported in this paper.

This paper is organized as follows. After a description of the CMS detector
(Section 0.2) and of the collision data and simulated samples used in the
analyses (Section 0.3), the event reconstruction is described in Section 0.4.
The event selection is described separately for the two Higgs boson decay
modes $\PH\to\Pe\Pgt$ and $\PH\to\Pgm\Pgt$ in Section 0.5. The backgrounds,
which are common to all channels but with different rates in each, are
described in Section 0.6. The systematic uncertainties are described in
Section 0.7 and the results are then presented in Section 0.8.

##  0.2 The CMS detector

The central feature of the CMS apparatus is a superconducting solenoid of 6m
internal diameter, providing a magnetic field of 3.8T. Within the solenoid
volume are a silicon pixel and strip tracker, a lead tungstate crystal
electromagnetic calorimeter (ECAL), and a brass and scintillator hadron
calorimeter (HCAL), each composed of a barrel and two endcap sections. Forward
calorimeters extend the pseudorapidity ($\eta$) coverage provided by the
barrel and endcap detectors. Muons are detected in gas-ionization chambers
embedded in the steel flux-return yoke outside the solenoid. The two-level CMS
trigger system selects events of interest for permanent storage [52]. The
first trigger 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 software algorithms of the
high-level trigger, executed on a farm of commercial processors, reduce the
event rate to about 1kHz using information from all detector subsystems. A
detailed description of the CMS detector, together with a definition of the
coordinate system used and the relevant kinematic variables, can be found in
Ref. [53].

##  0.3 Collision data and simulated events

The analyses presented here use samples of $\Pp\Pp$ collisions collected in
2016 by the CMS experiment at the LHC at a centre-of-mass energy of
$\sqrt{s}=13$, corresponding to an integrated luminosity of 35.9. Isolated
single muon triggers are used to collect the data samples in the
$\PH\to\Pgm\Pgt$ search. Triggers requiring a single isolated electron, or a
combination of an electron and a muon, are used in the $\PH\to\Pe\tauh$ and
$\PH\to\Pe\Pgt_{\Pgm}$ channels, respectively. Simulated samples of signal and
background events are produced with several event generators. The Higgs bosons
are produced in $\Pp\Pp$ collisions predominantly by gluon fusion () [54], but
also by vector boson fusion (VBF) [55], and in association with a $\PW$ or
$\cPZ$ boson [56]. The and VBF Higgs boson samples are generated with 2.0 [57,
58, 59, 60, 61, 62] while the minlo hvJ [63] extension of 2.0 is used for the
$\PW\PH$ and $\PZ\PH$ simulated samples. The MG5_a [64] generator is used for
$\PZ+\text{jets}$ and $\PW+\text{jets}$ processes. They are simulated at
leading order (LO) with the MLM jet matching and merging [65]. Diboson
production is simulated at next-to-LO (NLO) using MG5_a generator with the
FxFx jet matching and merging [66], whereas $\POWHEG$ 2.0 and 1.0 are used for
$\ttbar$ and single top quark production, respectively. The and generators are
interfaced with 8.212 [67] for parton showering, fragmentation, and decays.
The parameters for the underlying event description are set to the CUETP8M1
tune [68]. Due to the high instantaneous luminosities attained during data
taking, many events have multiple $\Pp\Pp$ interactions per bunch crossing
(pileup). The effect is taken into account in simulated samples, by generating
concurrent minimum bias events. All simulated samples are weighted to match
the pileup distribution observed in data, that has an average of approximately
27 interactions per bunch crossing. The CMS detector response is modelled
using [69].

##  0.4 Event reconstruction

The global event reconstruction is performed using a particle-flow (PF)
algorithm, which reconstructs and identifies each individual particle with an
optimized combination of all subdetector information [70]. In this process,
the identification of the particle type (photon, electron, muon, charged or
neutral hadron) plays an important role in the determination of the particle
direction and energy. The primary $\Pp\Pp$ vertex of the event is identified
as the reconstructed vertex with the largest value of summed physics-object
$\pt^{2}$, where is the transverse momentum. The physics objects are returned
by a jet finding algorithm [71, 72] applied to all charged tracks associated
with the vertex, plus the corresponding associated missing transverse
momentum.

A muon is identified as a track in the silicon detectors, consistent with the
primary $\Pp\Pp$ vertex and with either a track or several hits in the muon
system, associated with an energy deposit in the calorimeters compatible with
the expectations for a muon [73, 70]. Identification is based on the number of
spacial points measured in the tracker and in the muon system, the track
quality and its consistency with the event vertex location. The energy is
obtained from the corresponding track momentum.

An electron is identified as a charged particle track from the primary
$\Pp\Pp$ vertex in combination with one or more ECAL energy clusters. These
clusters correspond to the track extrapolation to the ECAL and to possible
bremsstrahlung photons emitted when interacting with the material of the
tracker [74]. Electron candidates are accepted in the range $\abs{\eta}<2.5$,
with the exception of the region $1.44<\abs{\eta}<1.57$ where service
infrastructure for the detector is located. They are identified using a
multivariate (MVA) discriminator that combines observables sensitive to the
amount of bremsstrahlung along the electron trajectory, the geometrical and
momentum matching between the electron trajectory and associated clusters as
well as various shower shape observables in the calorimeters. Electrons from
photon conversions are removed. The energy of electrons is determined from a
combination of the track momentum at the primary vertex, the corresponding
ECAL cluster energy, and the energy sum of all bremsstrahlung photons attached
to the track.

Hadronically decaying $\Pgt$ leptons are reconstructed and identified using
the hadrons-plus-strips (HPS) algorithm [75, 76]. The reconstruction starts
from a jet and searches for the products of the main $\Pgt$ lepton decay
modes: one charged hadron and up to two neutral pions, or three charged
hadrons. To improve the reconstruction efficiency in the case of conversion of
the photons from neutral-pion decay, the algorithm considers the PF photons
and electrons from a strip along the azimuthal direction $\phi$. The charges
of all the PF objects from tau lepton decay, except for the electrons from
neutral pions, are summed to reconstruct the tau lepton charge. An MVA
discriminator, based on the information of the reconstructed tau lepton and of
the charged particles in a cone around it, is used to reduce the rate for
quark- and gluon-initiated jets identified as candidates. The working point
used in the analysis has an efficiency of about 60% for a genuine , with
approximately a 0.5% misidentification rate for quark and gluon jets [76].
Additionally, muons and electrons misidentified as tau leptons are rejected
using a dedicated set of selection criteria based on the consistency between
the measurements in the tracker, calorimeters, and muon detectors. The
specific identification criteria depend on the final state studied and on the
background composition. The tau leptons that decay to muons and electrons are
reconstructed as prompt muons and electrons as described above.

Charged hadrons are identified as charged particle tracks from the primary
$\Pp\Pp$ vertex neither reconstructed as electrons nor as muons nor as $\tau$
leptons. Neutral hadrons are identified as HCAL energy clusters not assigned
to any charged hadron, or as ECAL and HCAL energy excesses with respect to the
expected charged-hadron energy deposit. All the PF candidates are clustered
into hadronic jets using the infrared and collinear safe anti-algorithm [71],
implemented in the package [77], with a distance parameter of 0.4. The jet
momentum is determined as the vector sum of all particle momenta in this jet,
and is found in the simulation to be on average within 10% of the true
momentum over the whole spectrum and detector acceptance. An offset correction
is applied to jet energies to take into account the contribution from pileup
[78]. Jet energy corrections are derived from the simulation, and are
confirmed with in situ measurements of the energy balance of dijet, multijet,
$\text{photon}+\text{jet}$, and $\PZ+\text{jet}$ events [79]. The variable
$\Delta R=\sqrt{\smash[b]{(\Delta\eta)^{2}+(\Delta\phi)^{2}}}$ is used to
measure the separation between reconstructed objects in the detector. Any jet
within $\Delta R=0.4$ of the identified leptons is removed.

Jets misidentified as electrons, muons, or tau leptons are suppressed by
imposing isolation requirements. The muon (electron) isolation is measured
relative to its $\pt^{\ell}$ ($\ell=\Pe,\Pgm$), by summing over the $\pt$ of
PF particles in a cone with $\Delta R=0.4$ (0.3) around the lepton:

$I_{\text{rel}}^{\ell}=\left(\sum\pt^{\text{charged}}+\text{max}\left[0,\sum\pt^{\text{neutral}}+\sum\pt^{\gamma}-\pt^{\text{PU}}\left(\ell\right)\right]\right)/\pt^{\ell},$

where $\pt^{\text{charged}}$, $\pt^{\text{neutral}}$, and $\pt^{\gamma}$
indicate the of a charged particle, a neutral particle, and a photon within
the cone, respectively. The neutral contribution to isolation from pileup,
$\pt^{\text{PU}}\left(\ell\right)$, is estimated from the area of the jet and
the average energy density of the event [80, 81] for the electron or from the
sum of transverse momenta of charged hadrons not originating from the primary
vertex scaled by a factor of 0.5 for the muons. The charged contribution to
isolation from pileup is rejected requiring the tracks to originate from the
primary vertex.

All the reconstructed particles in the event are used to estimate the missing
transverse momentum, $\ptvecmiss$, which is defined as the negative of the
vector sum of all identified PF objects in the event [82]. Its magnitude is
referred to as .

The transverse mass $M_{\mathrm{T}}(\ell)$ is a variable formed from the
lepton momentum and the missing transverse momentum vectors:
${M_{\mathrm{T}}}(\ell)=\sqrt{\smash[b]{2|\ptvec^{\ell}||\ptvecmiss|(1-\cos{\Delta\phi_{\ell-\ptmiss}})}}$,
where $\Delta\phi_{\ell-\ptmiss}$ is the angle in the transverse plane between
the lepton and the missing transverse momentum. It is used to discriminate the
Higgs boson signal candidates from the $\PW+\text{jets}$ background. The
collinear mass, $M_{\text{col}}$, provides an estimate of $m_{\PH}$ using the
observed decay products of the Higgs boson candidate. It is reconstructed
using the collinear approximation based on the observation that, since
$m_{\PH}\gg m_{\Pgt}$, the $\Pgt$ lepton decay products are highly Lorentz
boosted in the direction of the $\Pgt$ candidate [83]. The neutrino momenta
can be approximated to have the same direction as the other visible decay
products of the $\Pgt$ ($\vec{\Pgt}^{\text{vis}}$) and the component of the
$\ptvecmiss$ in the direction of the visible $\Pgt$ lepton decay products is
used to estimate the transverse component of the neutrino momentum
($\pt^{\nu,\,\text{est}}$). The collinear mass can then be derived from the
visible mass of the $\Pgt$-$\Pgm$ or $\Pgt$-$\Pe$ system ($M_{\text{vis}}$) as
$M_{\text{col}}=M_{\text{vis}}/\sqrt{\smash[b]{x_{\Pgt}^{\text{vis}}}}$, where
$x_{\Pgt}^{\text{vis}}$ is the fraction of energy carried by the visible decay
products of the $\Pgt$
($x_{\Pgt}^{\text{vis}}={\pt^{\vec{\Pgt}^{\text{vis}}}}/{(\pt^{\vec{\Pgt}^{\text{vis}}}+\pt^{\nu,\,\text{est}})}$),
and $M_{\text{vis}}$ is the invariant mass of the visible decay products.

##  0.5 Event selection

The signal contains a prompt isolated lepton, or , along with an oppositely
charged isolated lepton of different flavour ($\Pgt_{\Pgm}$, $\Pgt_{\Pe}$ or
$\tauh$). In each decay mode a loose selection of this signature is defined
first. The events are then divided into categories within each sample
according to the number of jets in the event. This is designed to enhance the
contribution of different Higgs boson production mechanisms. The jets are
required to have $\pt>30$and $\abs{\eta}<4.7$. The 0-jet category enhances the
contribution, while the 1-jet category enhances H production with initial-
state radiation. The 2-jet H category has a further requirement that the
invariant mass of the two jets $M_{jj}<550$while the 2-jet VBF category with
the requirement $M_{jj}\geq 550$enhances the VBF contribution. The threshold
on $M_{jj}$ has been optimized to give the best expected exclusion limits. The
definition of the categories is the same in all the channels except in the
$\PH\to\Pe\Pgt$ channels where the $M_{jj}$ threshold is 500, which optimizes
the expected limits for this channel.

After the loose selection, a binned likelihood is used to fit the distribution
of a BDT discriminator for the signal and the background contributions. This
is referred to as the BDT fit analysis. As a cross-check an analysis using a
tighter set of selection criteria is also presented. In this case, selection
requirements are placed on the kinematic variables and a fit is performed to
the $M_{\text{col}}$ distribution. This is referred to as the $M_{\text{col}}$
fit analysis. Requirements on additional kinematic variables such as
$M_{\mathrm{T}}(\ell)$ are chosen to obtain the most stringent expected
limits. The lepton has been excluded from this optimization to avoid biasing
the selection toward energetic leptons that sculpt the background
$M_{\text{col}}$ distribution to mimic the signal peak. This effect would
reduce the shape discrimination power of the signal extraction procedure.

###  0.5.1 $\PH\to\Pgm\Pgt_{\text{h}}$

The loose selection begins by requiring an isolated and an isolated of
opposite charge and separated by $\Delta R>0.3$. The muon candidate is
required to have $\pt^{\Pgm}>26$, $|\eta^{\Pgm}|<2.4$ and
$I_{\text{rel}}^{\Pgm}<0.15$. The hadronic tau candidate is required to have
$\pt^{\tauh}>30$and $|\eta^{\tauh}|<2.3$. The isolation requirement for the
candidates is included in the MVA used for the HPS identification algorithm
described in Section 0.4. Events with additional , or candidates are vetoed.
Events with at least one jet identified by the combined secondary vertex
b-tagging algorithm [84] as arising from a b quark, are also vetoed in order
to suppress the background. The tighter selection used for the
$M_{\text{col}}$ fit analysis further requires $M_{\mathrm{T}}(\tauh)<105$in
the 0-, 1- and 2-jet H categories, and $M_{\mathrm{T}}(\tauh)<85$in the 2-jet
VBF category. The selections are summarized in Table 0.5.2.

A BDT is trained after the loose selection combining all categories. The
signal training sample used is a mixture of simulated H and VBF events,
weighted according to their respective SM production cross sections. The
background training sample is a set of collision events with misidentified
leptons, as this is the dominant background in this channel. The leptons are
required to satisfy the same kinematic selection of the signal sample, be
like-sign and not isolated in order to select an orthogonal data set to the
signal sample, and yet have the same kinematic properties. The input variables
to the BDT are: $\pt^{\Pgm}$, $\pt^{\tauh}$, $M_{\text{col}}$, ,
$M_{\mathrm{T}}(\tauh)$, $\Delta\eta(\Pgm,\tauh)$, $\Delta\phi(\Pgm,\tauh)$,
and $\Delta\phi(\tauh,\ptvecmiss)$. The neutrino in the $\Pgt$ lepton decay
leads to the presence of significant missing momentum motivating the inclusion
of the $\ptmiss$ variables. The neutrino is also approximately collinear with
the visible decay products while the two leptons tend to be azimuthally
opposite leading to the inclusion of the $\Delta\phi$ variables. The BDT input
variables are shown for signal and background in Fig. 1.

Figure 1: Distributions of the input variables to the BDT for the
$\PH\to\Pgm\Pgt_{\text{h}}$ channel. The background from SM Higgs boson
production is small and not visible in the plots.

###  0.5.2 $\PH\to\Pgm\Pgt_{\Pe}$

The loose selection begins by requiring an isolated $\Pgm$ and an isolated
$\Pe$ of opposite charge and separated by $\Delta R>0.3$. The muon candidate
is required to have $\pt^{\Pgm}>26$, $|\eta^{\Pgm}|<2.4$, and
$I_{\text{rel}}^{\Pgm}<0.15$. The electron candidate is required to have
$\pt^{\Pe}>10$ , $|\eta^{\Pe}|<2.4$, and $I_{\text{rel}}^{\Pe}<0.1$. Events
with additional , or candidates, or with at least one b-tagged jet are vetoed.

The tighter selection used in the $M_{\text{col}}$ fit analysis requires
$\pt^{\Pgm}>30$for the 0-jet category and $\pt^{\Pgm}>26$in the other
categories. In the 0-, 1- , 2-jet H and 2-jet VBF categories,
$M_{\mathrm{T}}(\Pgm)$ is required to be greater than 60, 40, 15, and
15respectively. A requirement is made on the azimuthal angle between the
electron and the : $\Delta\phi(\Pe,\ptvecmiss)<$ 0.7, 0.7, 0.5, 0.3 for the
0-, 1-, 2-jet H, and 2-jet VBF categories, respectively. In the 0- and 1-jet
categories it is further required that $\Delta\phi(\Pe,\Pgm)>2.5$ and 1.0,
respectively. The selections are summarized in Table 0.5.2.

A BDT is trained after the loose selection, combining all categories. The
background is a mixed sample of and $\cPZ\to\ell\ell$ $(\ell=\Pe,\Pgm,\Pgt)$
events weighted by their production cross-sections. The background is the
dominant background in this channel for the 2-jet category and also very
significant in the 1-jet category. It has many kinematic characteristics in
common with the other backgrounds, such as diboson and single top. The
$\cPZ\to\ell\ell$ background is the dominant background in 0- and 1-jet
category. The input variables to the BDT are: $\pt^{\Pgm}$, $\pt^{\Pe}$,
$M_{\text{col}}$, $M_{\mathrm{T}}(\Pgm)$, $M_{\mathrm{T}}(\Pe)$,
$\Delta\phi(\Pe,\Pgm)$, $\Delta\phi(\Pe,\ptvecmiss)$, and
$\Delta\phi(\Pgm,\ptvecmiss)$. The distributions of these variables are shown
in Fig. 2.

Figure 2: Distributions of the input variables to the BDT for the
$\PH\to\Pgm\Pgt_{\Pe}$ channel.

Event selection criteria for the kinematic variables for the $\PH\to\Pgm\Pgt$
channels.  Variable $\PH\to\Pgm\tauh$ $\PH\to\Pgm\Pgt_{\Pe}$ 0 jet 1 jet 2 jet
0 jet 1 jet 2 jet ggH VBF ggH VBF $M_{jj}$ [] — — $<$550 $\geq$550 — — $<$550
$\geq$550 $\pt^{\Pe}$ [] — $>$10 $\pt^{\Pgm}$ [] $>$26 $>$26 $\pt^{\tauh}$ []
$>$30 — $|\eta^{\Pe}|$ — $<$2.4 $|\eta^{\Pgm}|$ $<$2.4 $<$2.4 $|\eta^{\tauh}|$
$<$2.3 — $I_{\text{rel}}^{\Pe}$ — $<$0.1 $I_{\text{rel}}^{\Pgm}$ $<$0.15
$<$0.15 $M_{\text{col}}$ fit selection $\pt^{\Pgm}$ [] — $>$30 — —- —
$M_{\mathrm{T}}(\Pgm)$ [] — $>$60 $>$40 $>$15 $>$15 $M_{\mathrm{T}}(\tauh)$ []
$<$105 $<$105 $<$105 $<$85 — $\Delta\phi(\Pe,\ptvecmiss)$ [radians] — $<$0.7
$<$0.7 $<$0.5 $<$0.3 $\Delta\phi(\Pe,\Pgm)$ [radians] — $>$2.5 $>$1.0 — —

###  0.5.3 $\PH\to\Pe\Pgt_{\text{h}}$

The loose selection begins by requiring an isolated and an isolated candidate
of opposite charge, separated by $\Delta R>0.5$. The candidate is required to
have $\pt^{\Pe}>26$, $|\eta^{\Pe}|<2.1$, and $I_{\text{rel}}^{\Pe}<0.1$. The
candidate is required to have $\pt^{\tauh}>30$and $|\eta^{\tauh}|<2.3$. Events
with additional , or candidates are vetoed. No veto is made on the number of
b-tagged jets as the contribution is small. The additional selection used for
the $M_{\text{col}}$ fit analysis further requires that
$M_{\mathrm{T}}(\tauh)<60$ . The selections are summarized in Table 0.5.4. A
BDT is trained after the loose selection. The same training samples as for the
$\PH\to\Pgm\Pgt_{\text{h}}$ channel are used, except with an electron rather
than a muon. The input variables to the BDT are also the same except for the
addition of the visible mass, $M_{\text{vis}}$, and the removal of $\ptmiss$.
The relative composition of the backgrounds in the $\PH\to\Pe\Pgt_{\text{h}}$
channel is different from the $\PH\to\Pgm\Pgt_{\text{h}}$ channel, in
particular the $\cPZ\to\Pe\Pe$ \+ jets background is larger in comparison to
the $\cPZ\to\Pgm\Pgm$ \+ jets, which leads to this change of variables.

###  0.5.4 $\PH\to\Pe\Pgt_{\Pgm}$

The loose selection begins by requiring an isolated and an isolated candidate
with opposite charge, separated by $\Delta R>0.4$. The candidate is required
to have $\pt^{\Pe}>24$ , $|\eta^{\Pe}|<2.1$, and $I_{\text{rel}}^{\Pe}<0.1$.
The candidate is required to have $\pt^{\Pgm}>10$, $|\eta^{\Pgm}|<2.4$, and
$I_{\text{rel}}^{\Pgm}<0.15$. Events with additional , or candidates, or with
at least one b-tagged jet are vetoed.

The tighter selection used in the $M_{\text{col}}$ fit analysis further
requires $\Delta\phi(\Pe,\ptvecmiss)<1.0$ and $M_{\mathrm{T}}(\Pe)>60$. The
large background is further reduced by requiring
$p_{\zeta}-0.85\,p_{\zeta}^{\rm{vis}}>-60$. This topological selection is
based on the projections

$\begin{split}p_{\zeta}=(\vec{\pt}^{\Pe}+\vec{\pt}^{\Pgm}+\vec{\pt}^{\rm{miss}})\,\frac{\vec{\zeta}}{|\vec{\zeta}|}\qquad\text{
and }\qquad
p_{\zeta}^{\rm{vis}}=(\vec{\pt}^{\Pe}+\vec{\pt}^{\Pgm})\,\frac{\vec{\zeta}}{|\vec{\zeta}|}\end{split}$

on the axis $\vec{\zeta}$ bisecting the directions of the electron,
$\vec{\pt}^{\Pe}$, and of the muon, $\vec{\pt}^{\Pgm}$. This selection
criterion is highly efficient in rejecting background as the is oriented in
the direction of the visible decay products in signal events. The selection
criteria are summarized in Table 0.5.4.

A BDT is trained after the loose selection. It uses the same input variables
as for the $\PH\to\Pgm\Pgt_{\Pe}$ channel with the addition of the visible
mass, $M_{\text{vis}}$, and the removal of $M_{\mathrm{T}}(\Pe)$. The
background used for the training is a sample of simulated events.

Event selection criteria for the kinematic variables for the $\PH\to\Pe\Pgt$
channels.  Variable $\PH\to\Pe\tauh$ $\PH\to\Pe\Pgt_{\Pgm}$ 0 jet 1 jet 2 jet
0 jet 1 jet 2 jet ggH VBF ggH VBF $M_{jj}$ [] — — $<$500 $>$500 — — $<$500
$>$500 $\pt^{\Pe}$ [] $>$26 $>$24 $\pt^{\Pgm}$ [] — $>$10 $\pt^{\tauh}$ []
$>$30 — $|\eta^{\Pe}|$ $<$2.1 $<$2.1 $|\eta^{\Pgm}|$ — $<$2.4 $|\eta^{\tauh}|$
$<$2.3 — $I_{\text{rel}}^{\Pe}$ $<$0.15 $<$0.1 $I_{\text{rel}}^{\Pgm}$ —
$<$0.1 $M_{\text{col}}$ fit selection $M_{\mathrm{T}}(\tauh)$ [] $<$60 —
$M_{\mathrm{T}}(\Pe)$ [] — $>$60 $\Delta\phi(\Pe,\ptvecmiss)$ [radians] —
$<$1.0 $p_{\zeta}-0.85\,p_{\zeta}^{vis}$ [] — ${>}-60$

##  0.6 Background estimation

Figure 3: $M_{\text{col}}$ distribution in enriched (left), like-sign lepton
(central), and $\PW+\text{jets}$ enriched (right) control samples defined in
the text. The distributions include both statistical and systematic
uncertainties.

The main background processes are $\cPZ\to\Pgt\Pgt$, in which the or arises
from a decay, and $\PW\mathrm{+jets}$ and QCD multijet production where one or
more of the jets are misidentified as leptons. Other backgrounds come from
processes in which the lepton pair is produced from the weak decays of quarks
and vector bosons. These include $\ttbar$ pairs, Higgs boson production
($\PH\to\Pgt\Pgt,\PW\PW$), $\PW\PW$, $\PW\cPZ$, and $\cPZ\cPZ$. There are also
smaller contributions from $\PW\gamma^{(*)}+\text{jets}$ processes, single top
quark production, and $Z\to\ell\ell$ ($\ell=\Pe,\Pgm)$. All the backgrounds
are estimated from simulated samples with the exception of the misidentified-
lepton backgrounds that are estimated from data with either fully data-driven
or semi data-driven methods. These techniques are described in detail below.
The background estimate is validated with control regions designed to have
enhanced contributions from the dominant backgrounds.

The $Z\to\ell\ell$ background is estimated from simulation. A reweighting is
applied to correct the generator-level $\pt$ and $m_{\ell\ell}$ distributions
in LO MG5_a samples to reduce the shape discrepancy between collision data and
simulation. The reweighting factors are extracted from a $Z\to\Pgm\Pgm$
control region and are applied to both $Z\to\Pgm\Pgm$ and $Z\to\Pe\Pe$
simulated samples in bins of $\pt$ and $m_{\ell\ell}$. Additional corrections
for $\Pgm\to\tauh$ and $\Pe\to\tauh$ misidentification rates are applied when
the reconstructed candidate is matched to a muon or an electron, respectively,
at the generator level. These corrections are measured in $Z\to\ell\ell$
events and depend on the lepton $\eta$. The $\ttbar+\text{jets}$ background is
particularly important in the $\Pe\Pgm$ final state. A correction based on the
generated of the top quark and antiquark is applied to the simulation to match
the distribution observed in a sample from collision data. The background
estimation for this contribution is validated in a enriched control sample. It
is defined by requiring the loose selection for these channels but with the
additional requirement that at least one of the jets is b-tagged. Figure 3
(left) shows the data compared to the background estimation for this control
sample in the $\PH\to\Pgm\Pgt_{\Pe}$ channel. The same samples are used in the
$\PH\to\Pe\Pgt_{\Pgm}$ channel and show similar agreement.

The Higgs boson production contributes a small but non-negligible background.
It arises predominantly from $\PH\to\Pgt\Pgt$ but also from $\PH\to\PW\PW$
decays and peaks at lower values of $M_{\text{col}}$ than the signal, because
of additional neutrinos in the decays. The event selection described in
Section 0.5 uses a BDT discriminator that combines $M_{\text{col}}$ with a set
of other kinematic variables. The Higgs boson background also peaks below the
signal in the distribution of the BDT discriminator output.

Jets misidentified as leptons are a source of background arising from two
sources, $\PW+\text{jets}$ and QCD multijet events. In $\PW+\text{jets}$
background events, one lepton candidate is a real lepton from the $\PW$ boson
decay and the other is a jet misidentified as a lepton. In QCD multijet
events, both lepton candidates are misidentified jets. In each of the four
channels for this analysis ($\Pgm\Pgt_{\text{h}}$, $\Pe\Pgt_{\text{h}}$,
$\Pgm\Pgt_{\Pe}$, $\Pe\Pgt_{\Pgm}$), the misidentified-lepton background has
been estimated using purely data-driven methods. In the $\Pgm\Pgt_{\Pe}$ and
$\Pe\Pgt_{\Pgm}$ channels it is also estimated using a technique, called semi
data-driven, partially based on control samples in data and partially on
simulation. It has been used previously in the SM $\PH\to\Pgt\Pgt$ analysis
[50]. The misidentified $\PW+\text{jets}$ background is estimated from
simulation and the QCD background with data. The two techniques give
consistent results; the semi data-driven technique is chosen for the
leptonically decaying tau channels as the fully data-driven technique is
limited by the reduced size of the sample.

### Fully data-driven technique

The misidentified lepton background is estimated from collision data samples.
The misidentification rates are evaluated with independent $\cPZ+\text{jets}$
data sets and then applied to a control region, orthogonal to the signal
region, to estimate the misidentified background in the signal region. This
control region is obtained by relaxing the signal selection requirements,
typically isolation, and excluding events passing the final selection. The
probabilities with which jets are misidentified as ($f_{\Pe}$), ($f_{\Pgm}$),
or ($f_{\Pgt}$), are estimated using events with a boson candidate plus one
jet that can be misidentified as a lepton. The boson candidate is formed from
two muons with $\pt>26$ , $\abs{\eta}<2.4$, and $I_{\text{rel}}^{\ell}<0.15$
$(0.25)$ for the $\text{jet}\to\tauh,\Pgm$ ($\text{jet}\to\Pe$)
misidentification rate. The muons are required to have opposite charge and
their invariant mass ($M_{\Pgm\Pgm}$) must satisfy $70<M_{\Pgm\Pgm}<110$. The
contribution from diboson events, where the third lepton candidate corresponds
to a genuine lepton, is subtracted using simulation. Two $\cPZ+\text{jets}$
samples are defined: the signal-like one, in which the jet satisfies the same
lepton selection criteria used in the $\PH\to\Pe\Pgt$ or $\PH\to\Pgm\Pgt$
selections, and the background-enriched $\cPZ+\text{jets}$ sample with relaxed
lepton identification on the jet but excluding events selected in the signal-
like sample. The requirements for the third lepton candidate vary depending on
the lepton flavour. The two samples are used to estimate $f_{\Pe}$, $f_{\Pgm}$
and $f_{\Pgt}$ which are obtained as

$f_{i}=\frac{N_{i}(\text{$\cPZ+\text{jets}$ signal-
like})}{N_{i}(\text{$\cPZ+\text{jets}$ background-
enriched})+N_{i}(\text{$\cPZ+\text{jets}$ signal-like})},$

where $N_{i}(\text{$\cPZ+\text{jets}$ signal-like})$ is the number of events
with a third lepton candidate that passes the signal-like sample selection,
$N_{i}(\text{$\cPZ+\text{jets}$ background-enriched})$ is the number of events
in the background-enriched sample and $i=$ , or . The lepton selection
criteria for the signal are given in Table 0.5.2 and 0.5.4. The background-
enriched lepton selection used to estimate the misidentified $\Pgm$ and $\Pe$
contribution requires an isolation of $0.15<I_{\text{rel}}^{\ell}<0.25$ and
$0.1<I_{\text{rel}}^{\ell}<0.5$, respectively. In both cases the
misidentification rate is computed as a function of the lepton . The lepton
selection for the background-enriched sample requires that the tau candidates
are identified using a loose HPS working point but are not identified by the
tight working point used for the signal selection. The loose and the tight
working points have an efficiency of 75% and 60% for genuine candidates,
respectively. The misidentification rates show a dependence that varies with
the decay mode and $\abs{\eta}$. The misidentification rates are thus obtained
as a function of for the different decay modes and $\abs{\eta}$ regions
($\abs{\eta}<1.5$ or $\abs{\eta}>1.5$).

The final misidentified lepton background in the signal region for the two
analyses (BDT and $M_{\text{col}}$ fit) is obtained from background-enriched
signal-like samples (LFV background-enriched, type $i$), where the lepton $i$
($i$ = , or ) passes the identification and isolation criteria used for the
$\cPZ+\text{jets}$ background-enriched sample but not those defining the
$\cPZ+\text{jets}$ signal-like sample, but otherwise uses the same selection
as the signal. To estimate the misidentified lepton background in the signal
sample, each event in this LFV background-enriched sample of type $i$ is
weighted by a factor ${f_{i}}/{(1-f_{i})}$, depending on the lepton for
electrons and muons or on , $\eta$, and decay mode for the lepton candidates.
Both background yield and shape distributions are thus estimated. Double-
counted events with two misidentified leptons are subtracted. For example,
events with a misidentified () and a misidentified are subtracted in the
$\PH\to\Pgm\Pgt_{\text{h}}$ ($\PH\to\Pe\Pgt_{\text{h}}$) channel using a
weight ${f_{\Pgt}\,f_{\ell}}/{[(1-f_{\Pgt})\,(1-f_{\ell})]}$ (where $\ell$ =
or ) applied to the events of a LFV background-enriched sample defined
requiring both leptons to pass the identification and isolation criteria used
for the $\cPZ+\text{jets}$ background-enriched sample but not those defining
the $\cPZ+\text{jets}$ signal-like sample.

The background estimation is validated in a like-sign sample applying the
misidentification rate $f_{i}$ to events selected inverting the charge
requirement of the lepton pair in both the background-enriched and the signal-
like samples. It is performed after the loose selection described in Section
0.5. Figure 3 (central) shows the data compared to the background estimation
in the like-sign control region for the $\PH\to\Pgm\Pgt_{\text{h}}$ channel.
The like-sign selection enhances the misidentified lepton background and there
is good agreement in the control sample. The background estimation can also be
validated in a boson enriched control sample. This data sample is obtained by
applying the signal sample requirements and
$M_{\mathrm{T}}(\ell)>60$($\ell=\Pe$ or ) and $M_{\mathrm{T}}(\tauh)>80$.
Figure 3 (right) shows the data compared to the background estimation in the
enriched sample for the $\PH\to\Pgm\Pgt_{\text{h}}$ channel. The same samples
are used in the $\PH\to\Pe\Pgt_{\text{h}}$ channel with similar agreement.

### Semi data-driven technique

The $\PW+\text{jets}$ background contribution to the misidentified-lepton
background is estimated with simulated samples. The QCD multijet contribution
is estimated with like-sign collision data events that pass the signal
requirement. The expected yield from non-QCD processes is subtracted using
simulation. The resulting sample is then rescaled to account for the
differences between the composition in the like- and opposite-sign samples.
The scaling factors are extracted from QCD multijet enriched control samples,
composed of events with the lepton candidates satisfying inverted isolation
requirements as illustrated in Ref. [50]. This technique is chosen for the
leptonically decaying tau channels as the size of the samples allows a more
precise background description.

##  0.7 Systematic uncertainties

The systematic uncertainties affect the normalization and the shape of the
distributions of the different processes, and arise from either experimental
or theoretical sources. They are summarized in Table 0.7.

Systematic uncertainties in the expected event yields. All uncertainties are
treated as correlated between the categories, except those that have two
values separated by the $\oplus$ sign. In this case, the first value is the
correlated uncertainty and the second value is the uncorrelated uncertainty
for each individual category. Theoretical uncertainties on VBF Higgs boson
production [85] are also applied to VH production. Uncertainties on acceptance
lead to migration of events between the categories, and can be correlated or
anticorrelated between categories. Ranges of uncertainties for the Higgs boson
production indicate the variation in size, from negative (anticorrelated) to
positive (correlated).  Systematic uncertainty $\PH\to\Pgm\tauh$
$\PH\to\Pgm\Pgt_{\Pe}$ $\PH\to\Pe\tauh$ $\PH\to\Pe\Pgt_{\mu}$ Muon
trigger/identification/isolation 2% 2% — 2% Electron
trigger/identification/isolation — 2% 2% 2% Hadronic tau lepton efficiency 5%
— 5% — b tagging veto 2.0–4.5% 2.0–4.5% — 2.0–4.5% $\cPZ\to\Pgm\Pgm,\Pe\Pe$ \+
jets background — 10%$\oplus$5% — 10%$\oplus$5% $\cPZ\to\Pgt\Pgt$ \+ jets
background 10%$\oplus$5% 10%$\oplus$5% 10%$\oplus$5% 10%$\oplus$5%
$\PW+\text{jets}$ background — 10% — 10% QCD multijet background — 30% — 30%
$\PW\PW,\cPZ\cPZ$ background 5%$\oplus$5% 5%$\oplus$5% 5%$\oplus$5%
5%$\oplus$5% background 10%$\oplus$5% 10%$\oplus$5% 10%$\oplus$5%
10%$\oplus$5% $\PW\gamma$ background — 10%$\oplus$5% — 10%$\oplus$5% Single
top quark background 5%$\oplus$5% 5%$\oplus$5% 5%$\oplus$5% 5%$\oplus$5%
$\Pgm\to\tauh$ background 25% — — — $\Pe\to\tauh$ background — — 12% —
$\text{Jet}\to\tauh,\Pgm,\Pe$ background 30%$\oplus$10% — 30%$\oplus$10% — Jet
energy scale 3–20% 3–20% 3-20% 3–20% energy scale 1.2% — 1.2$\%$ —
$\Pgm,\Pe\to\tauh$ energy scale 1.5% — 3% — energy scale — 0.1 – 0.5% 0.1 –
0.5% 0.1 – 0.5% energy scale 0.2% 0.2% — 0.2% Unclustered energy scale $\pm
1\sigma$ $\pm 1\sigma$ $\pm 1\sigma$ $\pm 1\sigma$ Renorm./fact. scales (H)
[85] 3.9% Renorm./fact. scales (VBF and VH) [85] 0.4% PDF + $\alpha_{s}$ (H)
[85] 3.2% PDF + $\alpha_{s}$ (VBF and VH) [85] 2.1% Renorm./fact. acceptance
(H) $-3.0$% – $+2.0$% Renorm./fact. acceptance (VBF and VH) $-0.3$% – $+1.0$%
PDF + $\alpha_{s}$ acceptance (H) $-1.5$% – $+0.5$% PDF + $\alpha_{s}$
acceptance (VBF and VH) $-1.5$% – $+1.0$% Integrated luminosity 2.5%

The uncertainties in the lepton (, , ) selection including the trigger,
identification, and isolation efficiencies are estimated using tag-and-probe
measurements in collision data sets of $\cPZ$ bosons decaying to
$\Pe\Pe,\Pgm\Pgm,\Pgt_{\Pgm}\tauh$ [86, 73, 74, 75, 76]. The b tagging
efficiency in the simulation is adjusted to match the efficiency measured in
data. The uncertainty in this measurement is taken as the systematic
uncertainty. The uncertainties on the
$\cPZ\to\Pe\Pe,\cPZ\to\Pgm\Pgm,\cPZ\to\Pgt\Pgt$, $\PW\PW$, $\cPZ\cPZ$,
$\PW\gamma$, , and single top production background contributions arise
predominantly from the uncertainties in the measured cross sections of these
processes. The uncertainties in the estimate of the misidentified-lepton
backgrounds ($\Pgm\to\tauh$, $\Pe\to\tauh$, $\text{jet}\to\tauh,\Pgm,\Pe$) are
extracted from the validation tests in control samples, described in Section
0.6.

Shape and normalization uncertainties arising from the uncertainty in the jet
energy scale are computed by propagating the effect of altering each source of
jet energy scale uncertainty by one standard deviation to the fit templates of
each process. This takes into account differences in yield and shape. The
uncertainties on the , , $\tauh$ energy scale are propagated to the
$M_{\text{col}}$ and BDT distributions. For $\tauh$, the energy scale
uncertainty is treated independently for each reconstructed hadronic decay
mode of the lepton. The systematic uncertainties in the energy resolutions of
lepton candidates have negligible effect. The energy scale of muons
(electrons) misidentified as hadronically decaying tau candidates
($\Pgm,\Pe\to\tauh$ energy scale) is considered independently from true
hadronic tau leptons. There is also an uncertainty in the unclustered energy
scale. The unclustered energy comes from jets having $\pt<10$and PF candidates
not within jets. It is propagated to . The unclustered energy scale is
considered independently for charged particles, photons, neutral hadrons, and
very forward particles which are not contained in jets. The effect of varying
the energy of each particle by its uncertainty leads to changes in both shape
of the distribution and yield. The four different systematic uncertainties are
uncorrelated.

The uncertainties in the Higgs boson production cross sections due to the
factorization and the renormalization scales, as well as the parton
distribution functions (PDF) and the strong coupling constant ($\alpha_{s}$),
result in changes in normalization and they are taken from Ref. [85]. They
also affect the acceptance and lead to the migration of events between the
categories. They are listed as acceptance uncertainties in Table 0.7 and
depend on the production process, Higgs boson decay channel, and category. For
the H production this variation on the acceptance varies from $-3$%
(anticorrelated between the categories) to 2% (correlated) for the
factorization and the renormalization scales, and from $-1.5$% to 0.5% for PDF
and $\alpha_{s}$. For the VBF and associated production (VH) the ranges go
from $-0.3$% to 1.0% for the factorization and the renormalization scales, and
from $-1.5$% to 1.0% for PDF and $\alpha_{s}$.

The bin-by-bin uncertainties account for the statistical uncertainties in
every bin of the template distributions of every process. They are
uncorrelated between bins, processes, and categories. The uncertainty of 2.5%
on the integrated luminosity [87] affects all processes with the normalization
taken directly from simulation. Shape uncertainties related to the pileup have
been considered by varying the weights applied to simulation. The weight
variation is obtained by a 5% change of the total inelastic cross section used
to estimate the number of pileup events in data. The new values are then used
to compute the weights for the simulation samples and these are applied, event
by event, to produce alternate collinear mass and BDT distributions used as
shape uncertainties in the fit. Other minimum bias event modelling and
simulation uncertainties are estimated to be much smaller than those on the
rate and are therefore neglected.

##  0.8 Results

After applying the selection criteria, a maximum likelihood fit is performed
to derive the expected and observed limits. Each systematic uncertainty is
used as a nuisance parameter in the fit. The fits are performed simultaneously
in all channels and categories. A profile likelihood ratio is used as test
statistic. The upper limits on the signal branching fraction are calculated
with the asymptotic formula, using the CLs criterion [88, 89, 90].

The BDT discriminator distributions of signal and background for each category
are shown in Fig. 4 and 7 in the $\PH\to\Pgm\Pgt$ and $\PH\to\Pe\Pgt$ channels
respectively. Figures 5 and 8 show the corresponding $M_{\text{col}}$
distributions used as cross-check. All the distributions are shown after they
have been adjusted by the fit. No excess over the background expectation is
observed. The observed and median expected $95\%$ CL upper limits, and best
fit branching fractions, for $\mathcal{B}(\PH\to\Pgm\Pgt)$ and
$\mathcal{B}(\PH\to\Pe\Pgt)$, assuming $m_{\PH}$=125, are given for each
category in Tables 0.8-0.8. The limits are also summarized graphically in
Figs. 6 and 9.

Figure 4: Distribution of the BDT discriminator for the $\PH\to\Pgm\Pgt$
process in the BDT fit analysis, in the individual channels and categories
compared to the signal and background estimation. The background is normalized
to the best fit values from the signal plus background fit while the simulated
signal corresponds to $\mathcal{B}(\PH\to\Pgm\Pgt)=5\%$. The bottom panel in
each plot shows the fractional difference between the observed data and the
fitted background. The left column of plots corresponds to the
$\PH\to\Pgm\tauh$ categories, from 0-jets (first row) to 2-jets VBF (fourth
row). The right one to their $\PH\to\Pgm\Pgt_{\Pe}$ counterparts.

Expected and observed upper limits at 95% CL, and best fit branching fractions
in percent for each individual jet category, and combined, in the
$\PH\to\Pgm\Pgt$ process obtained with the BDT fit analysis.  Expected limits
(%) 0-jet 1-jet 2-jets VBF Combined $\Pgm\Pgt_{\Pe}$ $<$0.83 $<$1.19 $<$1.98
$<$1.62 $<$0.59 $\Pgm\tauh$ $<$0.43 $<$0.56 $<$0.94 $<$0.58 $<$0.29 $\Pgm\Pgt$
$<$0.25 Observed limits (%) 0-jet 1-jet 2-jets VBF Combined $\Pgm\Pgt_{\Pe}$
$<$1.30 $<$1.34 $<$2.27 $<$1.79 $<$0.86 $\Pgm\tauh$ $<$0.51 $<$0.53 $<$0.56
$<$0.51 $<$0.27 $\Pgm\Pgt$ $<$0.25 Best fit branching fractions (%) 0-jet
1-jet 2-jets VBF Combined $\Pgm\Pgt_{\Pe}$ 0.61 $\pm$ 0.36 0.22 $\pm$ 0.46
0.39 $\pm$ 0.83 0.10 $\pm$ 1.37 0.35 $\pm$ 0.26 $\Pgm\tauh$ 0.12 $\pm$ 0.20
$-0.05$ $\pm$ 0.25 $-0.72$ $\pm$ 0.43 $-0.22$ $\pm$ 0.31 $-0.04$ $\pm$ 0.14
$\Pgm\Pgt$ 0.00 $\pm$ 0.12

Expected and observed upper limits at 95% CL, and best fit branching fractions
in percent for each individual jet category, and combined, in the
$\PH\to\Pgm\Pgt$ process obtained with the $M_{\text{col}}$ fit analysis.
Expected limits (%) 0-jet 1-jet 2-jets VBF Combined $\Pgm\Pgt_{\Pe}$ $<$1.01
$<$1.47 $<$3.23 $<$1.73 $<$0.75 $\Pgm\tauh$ $<$1.14 $<$1.26 $<$2.12 $<$1.41
$<$0.71 $\Pgm\Pgt$ $<$0.49 Observed limits (%) 0-jet 1-jet 2-jets VBF Combined
$\Pgm\Pgt_{\Pe}$ $<$1.08 $<$1.35 $<$3.33 $<$1.40 $<$0.71 $\Pgm\tauh$ $<$1.04
$<$1.74 $<$1.65 $<$1.30 $<$0.66 $\Pgm\Pgt$ $<$0.51 Best fit branching
fractions (%) 0-jet 1-jet 2-jets VBF Combined $\Pgm\Pgt_{\Pe}$ 0.13 $\pm$ 0.43
$-0.22$ $\pm$ 0.75 0.22 $\pm$ 1.39 $-1.73$ $\pm$ 1.05 $-0.04$ $\pm$ 0.33
$\Pgm\tauh$ $-0.30$ $\pm$ 0.45 0.68 $\pm$ 0.56 $-1.23$ $\pm$ 1.04 $-0.23$
$\pm$ 0.66 $-0.08$ $\pm$ 0.34 $\Pgm\Pgt$ 0.02 $\pm$ 0.20

Figure 5: Distribution of the collinear mass $M_{\text{col}}$ for the
$\PH\to\Pgm\Pgt$ process in $M_{\text{col}}$ fit analysis, in different
channels and categories compared to the signal and background estimation. The
background is normalized to the best fit values from the signal plus
background fit while the overlaid simulated signal corresponds to
$\mathcal{B}(\PH\to\Pgm\Pgt)=5\%$. The bottom panel in each plot shows the
ratio between the observed data and the fitted background. The left column of
plots corresponds to the $\PH\to\Pgm\tauh$ categories, from 0-jets (first row)
to 2-jets VBF (fourth row). The right one to their $\PH\to\Pgm\Pgt_{\Pe}$
counterparts.

Figure 6: Observed and expected 95% CL upper limits on the
$\mathcal{B}(\PH\to\Pgm\Pgt)$ for each individual category and combined. Left:
BDT fit analysis. Right: $M_{\text{col}}$ fit analysis.

Figure 7: Distribution of the BDT discriminator for the $\PH\to\Pe\Pgt$
process for the BDT fit analysis, in different channels and categories
compared to the signal and background estimation. The background is normalized
to the best fit values from the signal plus background fit while the simulated
signal corresponds to $\mathcal{B}(\PH\to\Pe\Pgt)=5\%$. The bottom panel in
each plot shows the ratio between the observed data and the fitted background.
The left column of plots corresponds to the $\PH\to\Pe\tauh$ categories, from
0-jets (first row) to 2-jets VBF (fourth row). The right one to their
$\PH\to\Pe\Pgt_{\Pgm}$ counterparts.

Expected and observed upper limits at 95% CL and best fit branching fractions
in percent for each individual jet category, and combined, in the
$\PH\to\Pe\Pgt$ process obtained with the BDT fit analysis.  Expected limits
(%) 0-jet 1-jet 2-jets VBF Combined $\Pe\Pgt_{\Pgm}$ $<$0.90 $<$1.59 $<$2.54
$<$1.84 $<$0.64 $\Pe\tauh$ $<$0.79 $<$1.13 $<$1.59 $<$0.74 $<$0.49 $\Pe\Pgt$
$<$0.37 Observed limits (%) 0-jet 1-jet 2-jets VBF Combined $\Pe\Pgt_{\Pgm}$
$<$1.22 $<$1.66 $<$2.25 $<$1.10 $<$0.78 $\Pe\tauh$ $<$0.73 $<$0.81 $<$1.94
$<$1.49 $<$0.72 $\Pe\Pgt$ $<$0.61 Best fit branching fractions (%) 0-jet 1-jet
2-jets VBF Combined $\Pe\Pgt_{\Pgm}$ 0.47 $\pm$ 0.42 0.17 $\pm$ 0.79 $-0.42$
$\pm$ 1.01 $-1.54$ $\pm$ 0.44 0.18 $\pm$ 0.32 $\Pe\tauh$ $-0.13$ $\pm$ 0.39
$-0.63$ $\pm$ 0.40 0.54 $\pm$ 0.53 0.70 $\pm$ 0.38 0.33 $\pm$ 0.24 $\Pe\Pgt$
0.30 $\pm$ 0.18

Expected and observed upper limits at 95% CL and best fit branching fractions
in percent for each individual jet category, and combined, in the
$\PH\to\Pe\Pgt$ process obtained with the $M_{\text{col}}$ fit analysis.
Expected limits (%) 0-jet 1-jet 2-jets VBF Combined $\Pe\Pgt_{\Pgm}$ $<$0.94
$<$1.21 $<$3.73 $<$2.76 $<$0.71 $\Pe\tauh$ $<$1.52 $<$1.93 $<$3.55 $<$1.76
$<$0.97 $\Pe\Pgt$ $<$0.56 Observed limits (%) 0-jet 1-jet 2-jets VBF Combined
$\Pe\Pgt_{\Pgm}$ $<$1.27 $<$1.26 $<$3.90 $<$1.78 $<$0.85 $\Pe\tauh$ $<$1.53
$<$2.07 $<$3.65 $<$3.39 $<$1.31 $\Pe\Pgt$ $<$0.72 Best fit branching fractions
(%) 0-jet 1-jet 2-jets VBF Combined $\Pe\Pgt_{\Pgm}$ 0.46 $\pm$ 0.43 0.07
$\pm$ 0.39 0.13 $\pm$ 1.13 $-1.38$ $\pm$ 1.03 0.21 $\pm$ 0.36 $\Pe\tauh$ 0.18
$\pm$ 0.35 0.45 $\pm$ 0.60 0.29 $\pm$ 1.13 2.03 $\pm$ 0.47 0.51 $\pm$ 0.41
$\Pe\Pgt$ 0.23 $\pm$ 0.24

Figure 8: Distribution of the collinear mass $M_{\text{col}}$ for the
$\PH\to\Pe\Pgt$ process in the $M_{\text{col}}$ fit analysis, in different
channels and categories compared to the signal and background estimation. The
background is normalized to the best fit values from the signal plus
background fit while the simulated signal corresponds to
$\mathcal{B}(\PH\to\Pe\Pgt)=5\%$. The lower panel in each plot shows the ratio
between the observed data and the fitted background. The left column of plots
correspond to the $\PH\to\Pe\tauh$ categories, from 0-jets (first row) to 2
jets VBF (fourth row). The right one to their $\PH\to\Pe\Pgt_{\Pgm}$
counterparts.

Figure 9: Observed and expected 95% CL upper limits on the
$\mathcal{B}(\PH\to\Pe\Pgt)$ for each individual category and combined. Left:
BDT fit analysis. Right: $M_{\text{col}}$ fit analysis.

No evidence is found for either the $\PH\to\Pgm\Pgt$ or $\PH\to\Pe\Pgt$
processes in this search. The observed exclusion limits are a significant
improvement over the 8results. The new results exclude the branching fraction
that corresponded to the best fit for the 2.4 $\sigma$ excess observed in the
8$\PH\to\Pgm\Pgt$ channel results at 95% CL, in both the $M_{\text{col}}$ fit
and BDT fit analysis. Table 0.8 shows a summary of the new 95$\%$ CL upper
limits. The BDT fit analysis is more sensitive than the $M_{\text{col}}$ fit
analysis, with expected limits reduced by about a factor of two. In both cases
the results are dominated by the systematic uncertainties.

Summary of the observed and expected upper limits at the 95% CL and the best
fit branching fractions in percent for the $\PH\to\Pgm\Pgt$ and
$\PH\to\Pe\Pgt$ processes, for the main analysis (BDT fit) and the cross check
($M_{\text{col}}$ fit) method.  Observed (expected) limits (%) Best fit
branching fraction (%) BDT fit $M_{\text{col}}$ fit BDT fit $M_{\text{col}}$
fit $\PH\to\Pgm\Pgt$ $<$0.25 (0.25)% $<$0.51 (0.49) % $0.00\pm 0.12$ %
$0.02\pm 0.20$ % $\PH\to\Pe\Pgt$ $<$0.61 (0.37) % $<$0.72 (0.56) % $0.30\pm
0.18$ % $0.23\pm 0.24$ %

The constraints on $\mathcal{B}(\PH\to\Pgm\Pgt)$ and
$\mathcal{B}(\PH\to\Pe\Pgt)$ can be interpreted in terms of LFV Yukawa
couplings [41]. The LFV decays $\Pe\Pgt$ and $\Pgm\Pgt$ arise at tree level
from the assumed flavour violating Yukawa interactions,
$Y_{\ell^{\alpha}\ell^{\beta}}$ where $\ell^{\alpha},\ell^{\beta}$ denote the
leptons, $\ell^{\alpha},\ell^{\beta}=\Pe,\Pgm,\Pgt$ and
$\ell^{\alpha}\neq\ell^{\beta}$. The decay width
$\Gamma(\PH\to\ell^{\alpha}\ell^{\beta})$ in terms of the Yukawa couplings is
given by:

$\Gamma(\PH\to\ell^{\alpha}\ell^{\beta})=\frac{m_{\PH}}{8\pi}\bigl{(}\abs{Y_{\ell^{\beta}\ell^{\alpha}}}^{2}+\abs{Y_{\ell^{\alpha}\ell^{\beta}}}^{2}\bigr{)},$

and the branching fraction by:

$\mathcal{B}(\PH\to\ell^{\alpha}\ell^{\beta})=\frac{\Gamma(\PH\to\ell^{\alpha}\ell^{\beta})}{\Gamma(\PH\to\ell^{\alpha}\ell^{\beta})+\Gamma_{\mathrm{SM}}}.$

The SM decay width is assumed to be $\Gamma_{\mathrm{SM}}=4.1$ [91] for
$m_{\PH}=125$. The 95% CL upper limit on the Yukawa couplings derived from the
expression for the branching fraction above is shown in Table 0.8. The limits
on the Yukawa couplings derived from the BDT fit analysis results are shown in
Fig. 10.

95% CL observed upper limit on the Yukawa couplings, for the main analysis
(BDT fit) and the cross check ($M_{\text{col}}$ fit) method.  BDT fit
$M_{\text{col}}$ fit
$\sqrt{\smash[b]{\abs{Y_{\Pgm\Pgt}}^{2}+\abs{Y_{\Pgt\Pgm}}^{2}}}$ $<1.43\times
10^{-3}$ $<2.05\times 10^{-3}$
$\sqrt{\abs{Y_{\Pe\Pgt}}^{2}+\abs{Y_{\Pgt\Pe}}^{2}}$ $<2.26\times 10^{-3}$
$<2.45\times 10^{-3}$

Figure 10: Constraints on the flavour violating Yukawa couplings,
$|Y_{\Pgm\Pgt}|,|Y_{\Pgt\Pgm}|$ (left) and $|Y_{\Pe\Pgt}|,|Y_{\Pgt\Pe}|$
(right), from the BDT result. The expected (red dashed line) and observed
(black solid line) limits are derived from the limit on
$\mathcal{B}(\PH\to\Pgm\Pgt)$ and $\mathcal{B}(\PH\to\Pe\Pgt)$ from the
present analysis. The flavour-diagonal Yukawa couplings are approximated by
their SM values. The green (yellow) band indicates the range that is expected
to contain 68% (95%) of all observed limit excursions from the expected limit.
The shaded regions are derived constraints from null searches for $\Pgt\to
3\Pgm$ or $\Pgt\to 3\Pe$ (dark green) [92, 93, 41] and $\Pgt\to\Pgm\gamma$ or
$\Pgt\to\Pe\gamma$ (lighter green) [93, 41]. The green hashed region is
derived by the CMS direct search presented in this paper. The blue solid lines
are the CMS limits from [44] (left) and [45](right). The purple diagonal line
is the theoretical naturalness limit $|Y_{ij}Y_{ji}|\leq m_{i}m_{j}/v^{2}$
[41].

##  0.9 Summary

The search for lepton flavour violating decays of the Higgs boson in the
$\Pgm\Pgt$ and $\Pe\Pgt$ channels, with the 2016 data collected by the CMS
detector, is presented in this paper. The data set analysed corresponds to an
integrated luminosity of 35.9of proton-proton collision data recorded at
$\sqrt{s}=13$. The results are extracted by a fit to the output of a boosted
decision trees discriminator trained to distinguish the signal from
backgrounds. The results are cross-checked with an alternate analysis that
fits the collinear mass distribution after applying selection criteria on
kinematic variables. No evidence is found for lepton flavour violating Higgs
boson decays. The observed (expected) limits on the branching fraction of the
Higgs boson to $\Pgm\Pgt$ and to $\Pe\Pgt$ are less than 0.25% (0.25%) and
0.61% (0.37%), respectively, at 95% confidence level. These limits constitute
a significant improvement over the previously obtained limits by CMS and ATLAS
using 8proton-proton collision data corresponding to an integrated luminosity
of about 20. Upper limits on the off-diagonal $\Pgm\Pgt$ and $\Pe\Pgt$ Yukawa
couplings are derived from these constraints,
$\sqrt{\smash[b]{\abs{Y_{\Pgm\Pgt}}^{2}+\abs{Y_{\Pgt\Pgm}}^{2}}}<1.43\times
10^{-3}$ and $\sqrt{\abs{Y_{\Pe\Pgt}}^{2}+\abs{Y_{\Pgt\Pe}}^{2}}<2.26\times
10^{-3}$ at 95% confidence level.

###### Acknowledgements.

We congratulate our colleagues in the CERN accelerator departments for the
excellent performance of the LHC and thank the technical and administrative
staffs at CERN and at other CMS institutes for their contributions to the
success of the CMS effort. In addition, we gratefully acknowledge the
computing centres and personnel of the Worldwide LHC Computing Grid for
delivering so effectively the computing infrastructure essential to our
analyses. Finally, we acknowledge the enduring support for the construction
and operation of the LHC and the CMS detector provided by the following
funding agencies: BMWFW and FWF (Austria); FNRS and FWO (Belgium); CNPq,
CAPES, FAPERJ, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST, and NSFC
(China); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RPF (Cyprus);
SENESCYT (Ecuador); MoER, ERC IUT, and ERDF (Estonia); Academy of Finland,
MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF
(Germany); GSRT (Greece); OTKA and NIH (Hungary); DAE and DST (India); IPM
(Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); LAS
(Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and
UASLP-FAI (Mexico); MBIE (New Zealand); PAEC (Pakistan); MSHE and NSC
(Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom, RAS, RFBR and RAEP
(Russia); MESTD (Serbia); SEIDI, CPAN, PCTI and FEDER (Spain); Swiss Funding
Agencies (Switzerland); MST (Taipei); ThEPCenter, IPST, STAR, and NSTDA
(Thailand); TUBITAK and TAEK (Turkey); NASU and SFFR (Ukraine); STFC (United
Kingdom); DOE and NSF (USA). Individuals have received support from the Marie-
Curie programme and the European Research Council and Horizon 2020 Grant,
contract No. 675440 (European Union); the Leventis Foundation; the A. P. Sloan
Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science
Policy Office; the Fonds pour la Formation à la Recherche dans l’Industrie et
dans l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door
Wetenschap en Technologie (IWT-Belgium); the Ministry of Education, Youth and
Sports (MEYS) of the Czech Republic; the Council of Science and Industrial
Research, India; the HOMING PLUS programme of the Foundation for Polish
Science, cofinanced from European Union, Regional Development Fund, the
Mobility Plus programme of the Ministry of Science and Higher Education, the
National Science Center (Poland), contracts Harmonia 2014/14/M/ST2/00428, Opus
2014/13/B/ST2/02543, 2014/15/B/ST2/03998, and 2015/19/B/ST2/02861, Sonata-bis
2012/07/E/ST2/01406; the National Priorities Research Program by Qatar
National Research Fund; the Programa Severo Ochoa del Principado de Asturias;
the Thalis and Aristeia programmes cofinanced by EU-ESF and the Greek NSRF;
the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn
University and the Chulalongkorn Academic into Its 2nd Century Project
Advancement Project (Thailand); the Welch Foundation, contract C-1845; and the
Weston Havens Foundation (USA).

## References

  * [1] ATLAS Collaboration, “Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC”,  Phys. Lett. B 716 (2012) 1, 10.1016/j.physletb.2012.08.020, arXiv:1207.7214.
  * [2] CMS Collaboration, “Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC”,  Phys. Lett. B 716 (2012) 30, 10.1016/j.physletb.2012.08.021, arXiv:1207.7235.
  * [3] CMS Collaboration, “Observation of a new boson with mass near 125 GeV in pp collisions at $\sqrt{s}$ = 7 and 8 TeV”,  JHEP 06 (2013) 081, 10.1007/JHEP06(2013)081, arXiv:1303.4571.
  * [4] ATLAS, CMS Collaboration, “Measurements of the Higgs boson production and decay rates and constraints on its couplings from a combined ATLAS and CMS analysis of the LHC pp collision data at $\sqrt{s}=7$ and 8 TeV”,  JHEP 08 (2016) 045, 10.1007/JHEP08(2016)045, arXiv:1606.02266.
  * [5] J. L. Diaz-Cruz and J. J. Toscano, “Lepton flavor violating decays of Higgs bosons beyond the standard model”,  Phys. Rev. D 62 (2000) 116005, 10.1103/PhysRevD.62.116005, arXiv:hep-ph/9910233.
  * [6] T. Han and D. Marfatia, “$h\to\mu\tau$ at hadron colliders”,  Phys. Rev. Lett. 86 (2001) 1442, 10.1103/PhysRevLett.86.1442, arXiv:hep-ph/0008141.
  * [7] E. Arganda, A. M. Curiel, M. J. Herrero, and D. Temes, “Lepton flavor violating Higgs boson decays from massive seesaw neutrinos”,  Phys. Rev. D 71 (2005) 035011, 10.1103/PhysRevD.71.035011, arXiv:hep-ph/0407302.
  * [8] A. Arhrib, Y. Cheng, and O. C. W. Kong, “Comprehensive analysis on lepton flavor violating Higgs boson to $\mu\bar{\tau}+\tau\bar{\mu}$ decay in supersymmetry without R parity”,  Phys. Rev. D 87 (2013) 015025, 10.1103/PhysRevD.87.015025, arXiv:1210.8241.
  * [9] M. Arana-Catania, E. Arganda, and M. J. Herrero, “Non-decoupling SUSY in LFV Higgs decays: a window to new physics at the LHC”,  JHEP 09 (2013) 160, 10.1007/JHEP09(2013)160, arXiv:1304.3371. [Erratum: 10.1007/JHEP10(2015)192].
  * [10] E. Arganda, M. J. Herrero, R. Morales, and A. Szynkman, “Analysis of the $h,H,A\to\tau\mu$ decays induced from SUSY loops within the Mass Insertion Approximation”,  JHEP 03 (2016) 055, 10.1007/JHEP03(2016)055, arXiv:1510.04685.
  * [11] E. Arganda, M. J. Herrero, X. Marcano, and C. Weiland, “Enhancement of the lepton flavor violating Higgs boson decay rates from SUSY loops in the inverse seesaw model”,  Phys. Rev. D 93 (2016) 055010, 10.1103/PhysRevD.93.055010, arXiv:1508.04623.
  * [12] M. E. Gomez, S. Heinemeyer, and M. Rehman, “Lepton flavor violating higgs boson decays in supersymmetric high scale seesaw models”, (2017). arXiv:1703.02229.
  * [13] H.-B. Zhang et al., “125 GeV Higgs decay with lepton flavor violation in the $\mu\nu$SSM”,  Chin. Phys. C 41 (2017) 043106, 10.1088/1674-1137/41/4/043106, arXiv:1511.08979.
  * [14] K. Agashe and R. Contino, “Composite Higgs-mediated flavor-changing neutral current”,  Phys. Rev. D 80 (2009) 075016, 10.1103/PhysRevD.80.075016, arXiv:0906.1542.
  * [15] A. Azatov, M. Toharia, and L. Zhu, “Higgs mediated flavor changing neutral currents in warped extra dimensions”,  Phys. Rev. D 80 (2009) 035016, 10.1103/PhysRevD.80.035016, arXiv:0906.1990.
  * [16] G. Perez and L. Randall, “Natural neutrino masses and mixings from warped geometry”,  JHEP 01 (2009) 077, 10.1088/1126-6708/2009/01/077, arXiv:0805.4652.
  * [17] S. Casagrande et al., “Flavor physics in the Randall-Sundrum model I. Theoretical setup and electroweak precision tests”,  JHEP 10 (2008) 094, 10.1088/1126-6708/2008/10/094, arXiv:0807.4937.
  * [18] A. J. Buras, B. Duling, and S. Gori, “The impact of Kaluza-Klein fermions on Standard Model fermion couplings in a RS model with custodial protection”,  JHEP 09 (2009) 076, 10.1088/1126-6708/2009/09/076, arXiv:0905.2318.
  * [19] J. D. Bjorken and S. Weinberg, “Mechanism for nonconservation of muon number”,  Phys. Rev. Lett. 38 (1977) 622, 10.1103/PhysRevLett.38.622.
  * [20] J.-P. Lee and K. Y. Lee, “$B_{s}\to\mu\tau$ and $h\to\mu\tau$ decays in the general two Higgs doublet model”, (2016). arXiv:1612.04057.
  * [21] H. Ishimori et al., “Non-Abelian Discrete Symmetries in Particle Physics”,  Prog. Theor. Phys. Suppl. 183 (2010) 1, 10.1143/PTPS.183.1, arXiv:1003.3552.
  * [22] M. Blanke et al., “$\Delta F=2$ observables and fine-tuning in a warped extra dimension with custodial protection ”,  JHEP 03 (2009) 001, 10.1088/1126-6708/2009/03/001, arXiv:0809.1073.
  * [23] G. F. Giudice and O. Lebedev, “Higgs-dependent Yukawa couplings”,  Phys. Lett. B 665 (2008) 79, 10.1016/j.physletb.2008.05.062, arXiv:0804.1753.
  * [24] J. A. Aguilar-Saavedra, “A minimal set of top-Higgs anomalous couplings”,  Nucl. Phys. B 821 (2009) 215, 10.1016/j.nuclphysb.2009.06.022, arXiv:0904.2387.
  * [25] M. E. Albrecht et al., “Electroweak and flavour structure of a warped extra dimension with custodial protection”,  JHEP 09 (2009) 064, 10.1088/1126-6708/2009/09/064, arXiv:0903.2415.
  * [26] A. Goudelis, O. Lebedev, and J. H. Park, “Higgs-induced lepton flavor violation”,  Phys. Lett. B 707 (2012) 369, 10.1016/j.physletb.2011.12.059, arXiv:1111.1715.
  * [27] D. McKeen, M. Pospelov, and A. Ritz, “Modified Higgs branching ratios versus CP and lepton flavor violation”,  Phys. Rev. D 86 (2012) 113004, 10.1103/PhysRevD.86.113004, arXiv:1208.4597.
  * [28] A. Pilaftsis, “Lepton flavour nonconservation in $\PH^{0}$ decays”,  Phys. Lett. B 285 (1992) 68, 10.1016/0370-2693(92)91301-O.
  * [29] J. G. Körner, A. Pilaftsis, and K. Schilcher, “Leptonic $\mathrm{CP}$ asymmetries in flavor-changing $\PH^{0}$ decays”,  Phys. Rev. D 47 (1993) 1080, 10.1103/PhysRevD.47.1080.
  * [30] E. Arganda, M. J. Herrero, X. Marcano, and C. Weiland, “Imprints of massive inverse seesaw model neutrinos in lepton flavor violating Higgs boson decays”,  Phys. Rev. D 91 (2015) 015001, 10.1103/PhysRevD.91.015001, arXiv:1405.4300.
  * [31] J. Herrero-Garcia, N. Rius, and A. Santamaria, “Higgs lepton flavour violation: UV completions and connection to neutrino masses”,  JHEP 11 (2016) 084, 10.1007/JHEP11(2016)084, arXiv:1605.06091.
  * [32] F. del Aguila et al., “Lepton flavor changing higgs decays in the littlest Higgs model with T-parity”,  JHEP 08 (2017) 028, 10.1007/JHEP08(2017)028, arXiv:1705.08827.
  * [33] N. H. Thao, L. T. Hue, H. T. Hung, and N. T. Xuan, “Lepton flavor violating Higgs boson decays in seesaw models: New discussions”,  Nucl. Phys. B 921 (2017) 159, 10.1016/j.nuclphysb.2017.05.014, arXiv:1703.00896.
  * [34] I. Galon and J. Zupan, “Dark sectors and enhanced $h\to\tau\mu$ transitions”,  JHEP 05 (2017) 083, 10.1007/JHEP05(2017)083, arXiv:1701.08767.
  * [35] E. Arganda et al., “Effective lepton flavor violating $H\ell_{i}\ell_{j}$ vertex from right-handed neutrinos within the mass insertion approximation”,  Phys. Rev. D 95 (2017) 095029, 10.1103/PhysRevD.95.095029, arXiv:1612.09290.
  * [36] D. Choudhury, A. Kundu, S. Nandi, and S. K. Patra, “Unified resolution of the $R(D)$ and $R(D^{*})$ anomalies and the lepton flavor violating decay $h\to\mu\tau$”,  Phys. Rev. D 95 (2017) 035021, 10.1103/PhysRevD.95.035021, arXiv:1612.03517.
  * [37] B. McWilliams and L.-F. Li, “Virtual effects of Higgs particles”,  Nucl. Phys. B 179 (1981) 62, 10.1016/0550-3213(81)90249-2.
  * [38] O. U. Shanker, “Flavour violation, scalar particles and leptoquarks”,  Nucl. Phys. B 206 (1982) 253, 10.1016/0550-3213(82)90534-X.
  * [39] A. Celis, V. Cirigliano, and E. Passemar, “Lepton flavor violation in the Higgs sector and the role of hadronic tau-lepton decays”,  Phys. Rev. D 89 (2014) 013008, 10.1103/PhysRevD.89.013008, arXiv:1309.3564.
  * [40] G. Blankenburg, J. Ellis, and G. Isidori, “Flavour-changing decays of a 125 GeV Higgs-like particle”,  Phys. Lett. B 712 (2012) 386, 10.1016/j.physletb.2012.05.007, arXiv:1202.5704.
  * [41] R. Harnik, J. Kopp, and J. Zupan, “Flavor violating higgs decays”,  JHEP 03 (2013) 26, 10.1007/JHEP03(2013)026, arXiv:1209.1397.
  * [42] S. M. Barr and A. Zee, “Electric dipole moment of the electron and of the neutron”,  Phys. Rev. Lett. 65 (1990) 21, 10.1103/PhysRevLett.65.21.
  * [43] MEG Collaboration, “Search for the lepton flavour violating decay $\mu^{+}\rightarrow\mathrm{e}^{+}\gamma$ with the full dataset of the MEG experiment”,  Eur. Phys. J. C 76 (2016) 434, 10.1140/epjc/s10052-016-4271-x, arXiv:1605.05081.
  * [44] CMS Collaboration, “Search for lepton-flavour-violating decays of the Higgs boson”,  Phys. Lett. B 749 (2015) 337, 10.1016/j.physletb.2015.07.053, arXiv:1502.07400.
  * [45] CMS Collaboration, “Search for lepton flavour violating decays of the higgs boson to $\mathrm{e}\tau$ and $\mathrm{e}\mu$ in proton-proton collisions at $\sqrt{s}=8$”,  Phys. Lett. B 763 (2016) 472, 10.1016/j.physletb.2016.09.062, arXiv:1607.03561.
  * [46] ATLAS Collaboration, “Search for lepton-flavour-violating decays of the Higgs and $Z$ bosons with the ATLAS detector”,  Eur. Phys. J. C 77 (2017) 70, 10.1140/epjc/s10052-017-4624-0, arXiv:1604.07730.
  * [47] ATLAS Collaboration, “Search for lepton-flavour-violating $\PH\to\mu\tau$ decays of the Higgs boson with the ATLAS detector”,  JHEP 11 (2015) 211, 10.1007/JHEP11(2015)211, arXiv:1508.03372.
  * [48] CMS Collaboration, “Evidence for the direct decay of the 125 GeV Higgs boson to fermions”,  Nature Phys. 10 (2014) 557, 10.1038/nphys3005, arXiv:1401.6527.
  * [49] CMS Collaboration, “Evidence for the 125Higgs boson decaying to a pair of $\tau$ leptons”,  JHEP 05 (2014) 104, 10.1007/JHEP05(2014)104, arXiv:1401.5041.
  * [50] CMS Collaboration, “Observation of the Higgs boson decay to a pair of $\tau$ leptons”, (2017). arXiv:1708.00373. Submitted to Phys. Lett. B.
  * [51] ATLAS Collaboration, “Evidence for the Higgs-boson Yukawa coupling to tau leptons with the ATLAS detector”,  JHEP 04 (2015) 117, 10.1007/JHEP04(2015)117, arXiv:1501.04943.
  * [52] CMS Collaboration, “The CMS trigger system”,  J. Instrum. 12 (2017) P01020, 10.1088/1748-0221/12/01/P01020, arXiv:1609.02366.
  * [53] CMS Collaboration, “The CMS experiment at the CERN LHC”,  JINST 3 (2008) S08004, 10.1088/1748-0221/3/08/S08004.
  * [54] H. M. Georgi, S. L. Glashow, M. E. Machacek, and D. V. Nanopoulos, “Higgs bosons from two gluon annihilation in proton proton collisions”,  Phys. Rev. Lett. 40 (1978) 692, 10.1103/PhysRevLett.40.692.
  * [55] R. N. Cahn, S. D. Ellis, R. Kleiss, and W. J. Stirling, “Transverse-momentum signatures for heavy Higgs bosons”,  Phys. Rev. D 35 (1987) 1626, 10.1103/PhysRevD.35.1626.
  * [56] S. L. Glashow, D. V. Nanopoulos, and A. Yildiz, “Associated production of Higgs bosons and Z particles”,  Phys. Rev. D 18 (1978) 1724, 10.1103/PhysRevD.18.1724.
  * [57] P. Nason, “A new method for combining NLO QCD with shower Monte Carlo algorithms”,  JHEP 11 (2004) 040, 10.1088/1126-6708/2004/11/040, arXiv:hep-ph/0409146.
  * [58] S. Frixione, P. Nason, and C. Oleari, “Matching NLO QCD computations with parton shower simulations: the POWHEG method”,  JHEP 11 (2007) 070, 10.1088/1126-6708/2007/11/070, arXiv:0709.2092.
  * [59] S. Alioli, P. Nason, C. Oleari, and E. Re, “A general framework for implementing NLO calculations in shower Monte Carlo programs: the POWHEG BOX”,  JHEP 06 (2010) 043, 10.1007/JHEP06(2010)043, arXiv:1002.2581.
  * [60] S. Alioli et al., “Jet pair production in POWHEG”,  JHEP 04 (2011) 081, 10.1007/JHEP04(2011)081, arXiv:1012.3380.
  * [61] S. Alioli, P. Nason, C. Oleari, and E. Re, “NLO Higgs boson production via gluon fusion matched with shower in POWHEG”,  JHEP 04 (2009) 002, 10.1088/1126-6708/2009/04/002, arXiv:0812.0578.
  * [62] E. Bagnaschi, G. Degrassi, P. Slavich, and A. Vicini, “Higgs production via gluon fusion in the POWHEG approach in the SM and in the MSSM”,  JHEP 02 (2012) 088, 10.1007/JHEP02(2012)088, arXiv:1111.2854.
  * [63] G. Luisoni, P. Nason, C. Oleari, and F. Tramontano, “HW±/HZ $+$ 0 and 1 jet at NLO with the POWHEG BOX interfaced to GoSam and their merging within MiNLO”,  JHEP 10 (2013) 083, 10.1007/JHEP10(2013)083, arXiv:1306.2542.
  * [64] J. Alwall et al., “The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations”,  JHEP 07 (2014) 079, 10.1007/JHEP07(2014)079, arXiv:1405.0301.
  * [65] J. Alwall et al., “Comparative study of various algorithms for the merging of parton showers and matrix elements in hadronic collisions”,  Eur. Phys. J. C 53 (2008) 473, 10.1140/epjc/s10052-007-0490-5, arXiv:0706.2569.
  * [66] R. Frederix and S. Frixione, “Merging meets matching in MC@NLO”,  JHEP 12 (2012) 061, 10.1007/JHEP12(2012)061, arXiv:1209.6215.
  * [67] T. Sjöstrand et al., “An introduction to PYTHIA 8.2”,  Comput. Phys. Commun. 191 (2015) 159, 10.1016/j.cpc.2015.01.024, arXiv:1410.3012.
  * [68] CMS Collaboration, “Event generator tunes obtained from underlying event and multiparton scattering measurements”,  Eur. Phys. J. C 76 (2016) 155, 10.1140/epjc/s10052-016-3988-x, arXiv:1512.00815.
  * [69] GEANT4 Collaboration, “GEANT4 — a simulation toolkit”,  Nucl. Instrum. Meth. A 506 (2003) 250, 10.1016/S0168-9002(03)01368-8.
  * [70] CMS Collaboration, “Particle-flow reconstruction and global event description with the CMS detector”,  JINST 12 (2017) P10003, 10.1088/1748-0221/12/10/P10003, arXiv:1706.04965.
  * [71] M. Cacciari, G. P. Salam, and G. Soyez, “The anti-$k_{t}$ jet clustering algorithm”,  JHEP 04 (2008) 063, 10.1088/1126-6708/2008/04/063, arXiv:0802.1189.
  * [72] M. Cacciari, G. P. Salam, and G. Soyez, “FastJet user manual”,  Eur. Phys. J. C 72 (2012) 1896, 10.1140/epjc/s10052-012-1896-2, arXiv:1111.6097.
  * [73] CMS Collaboration, “Performance of CMS muon reconstruction in pp collision events at $\sqrt{s}=7$ TeV”,  JINST 7 (2012) P10002, 10.1088/1748-0221/7/10/P10002, arXiv:1206.4071.
  * [74] CMS Collaboration, “Performance of electron reconstruction and selection with the CMS detector in proton-proton collisions at $\sqrt{s}$ = 8 TeV”,  JINST 10 (2015) P06005, 10.1088/1748-0221/10/06/P06005, arXiv:1502.02701.
  * [75] CMS Collaboration, “Reconstruction and identification of tau lepton decays to hadrons and $\nu_{\tau}$ at CMS”,  JINST 11 (2016) P01019, 10.1088/1748-0221/11/01/P01019, arXiv:1510.07488.
  * [76] CMS Collaboration, “Performance of reconstruction and identification of tau leptons in their decays to hadrons and tau neutrino in LHC Run-2”, CMS Physics Analysis Summary CMS-PAS-TAU-16-002, 2016.
  * [77] M. Cacciari, G. P. Salam, “Dispelling the $N^{3}$ myth for the $k_{t}$ jet-finder”,  Phys. Lett. B 641 (2006) 57, 10.1016/j.physletb.2006.08.037, arXiv:hep-ph/0512210.
  * [78] CMS Collaboration, “Determination of jet energy calibration and transverse momentum resolution in CMS”,  JINST 6 (2011) 11002, 10.1088/1748-0221/6/11/P11002, arXiv:1107.4277.
  * [79] CMS Collaboration, “Jet energy scale and resolution in the CMS experiment in pp collisions at 8 TeV”,  JINST 12 (2017) P02014, 10.1088/1748-0221/12/02/P02014, arXiv:1607.03663.
  * [80] M. Cacciari, G. P. Salam, and G. Soyez, “The catchment area of jets”,  JHEP 04 (2008) 005, 10.1088/1126-6708/2008/04/005, arXiv:0802.1188.
  * [81] M. Cacciari and G. P. Salam, “Pileup subtraction using jet areas”,  Phys. Lett. B 659 (2008) 119, 10.1016/j.physletb.2007.09.077, arXiv:0707.1378.
  * [82] CMS Collaboration, “Performance of the CMS missing transverse momentum reconstruction in pp data at $\sqrt{s}$ = 8 TeV”,  JINST 10 (2015) P02006, 10.1088/1748-0221/10/02/P02006, arXiv:1411.0511.
  * [83] R. K. Ellis, I. Hinchliffe, M. Soldate, and J. J. van der Bij, “Higgs decay to $\tau^{+}\tau^{-}$: A possible signature of intermediate mass Higgs bosons at high energy hadron colliders”,  Nucl. Phys. B 297 (1988) 221, 10.1016/0550-3213(88)90019-3.
  * [84] CMS Collaboration, “Identification of b quark jets at the CMS Experiment in the LHC Run 2”, CMS Physics Analysis Summary CMS-PAS-BTV-15-001, 2016.
  * [85] D. de Florian et al., “Handbook of LHC Higgs cross sections: 4. deciphering the nature of the Higgs sector”, CERN Report CERN-2017-002-M, 2016.  10.23731/CYRM-2017-002, arXiv:1610.07922.
  * [86] CMS Collaboration, “Measurements of inclusive W and Z cross sections in pp collisions at $\sqrt{s}$ = 7 TeV”,  J. High Energy Phys. 01 (2011) 080, 10.1007/JHEP01(2011)080, arXiv:1012.2466.
  * [87] CMS Collaboration, “CMS luminosity measurements for the 2016 data taking period”, CMS Physics Analysis Summary CMS-PAS-LUM-17-001, 2017.
  * [88] T. Junk, “Confidence level computation for combining searches with small statistics”,  Nucl. Instrum. Meth. A 434 (1999) 435, 10.1016/S0168-9002(99)00498-2, arXiv:hep-ex/9902006.
  * [89] A. L. Read, “Presentation of search results: the $CL_{s}$ technique”, in  Durham IPPP Workshop: Advanced Statistical Techniques in Particle Physics, p. 2693.  Durham, UK, March, 2002.  [J. Phys. G 28 (2002) 2693]. 10.1088/0954-3899/28/10/313.
  * [90] G. Cowan, K. Cranmer, E. Gross, and O. Vitells, “Asymptotic formulae for likelihood-based tests of new physics”,  Eur. Phys. J. C 71 (2011) 1554, 10.1140/epjc/s10052-011-1554-0, arXiv:1007.1727. [Erratum: 10.1140/epjc/s10052-013-2501-z].
  * [91] A. Denner et al., “Standard model Higgs-boson branching ratios with uncertainties”,  Eur. Phys. J. C 71 (2011) 1753, 10.1140/epjc/s10052-011-1753-8, arXiv:1107.5909.
  * [92] Belle Collaboration, “Search for lepton flavor violating $\tau$ decays into three leptons with 719 million produced $\tau^{+}\tau^{-}$ pairs”,  Phys. Lett. B 687 (2010) 139, 10.1016/j.physletb.2010.03.037, arXiv:1001.3221.
  * [93] Particle Data Group, C. Patrignani et al., “Review of Particle Physics”,  Chin. Phys. C 40 (2016) 100001, 10.1088/1674-1137/40/10/100001.

##  .10 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan, A. Tumasyan Institut für Hochenergiephysik, Wien, Austria
W. Adam, F. Ambrogi, E. Asilar, T. Bergauer, J. Brandstetter, E. Brondolin, M.
Dragicevic, J. Erö, M. Flechl, M. Friedl, R. Frühwirth1, V.M. Ghete, J.
Grossmann, J. Hrubec, M. Jeitler1, A. König, N. Krammer, I. Krätschmer, D.
Liko, T. Madlener, I. Mikulec, E. Pree, N. Rad, H. Rohringer, J. Schieck1, R.
Schöfbeck, M. Spanring, D. Spitzbart, 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. 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, 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, H. Brun, B. Clerbaux, G. De Lentdecker, H. Delannoy, B. Dorney, G.
Fasanella, L. Favart, R. Goldouzian, A. Grebenyuk, G. Karapostoli, T. Lenzi,
J. Luetic, T. Maerschalk, A. Marinov, A. Randle-conde, T. Seva, E. Starling,
C. Vander Velde, P. Vanlaer, D. Vannerom, R. Yonamine, F. Zenoni, F. Zhang2
Ghent University, Ghent, Belgium
A. Cimmino, T. Cornelis, D. Dobur, A. Fagot, M. Gul, I. Khvastunov3, D.
Poyraz, C. Roskas, S. Salva, M. Tytgat, W. Verbeke, N. Zaganidis Université
Catholique de Louvain, Louvain-la-Neuve, Belgium
H. Bakhshiansohi, O. Bondu, S. Brochet, G. Bruno, C. Caputo, A. Caudron, P.
David, S. De Visscher, C. Delaere, M. Delcourt, B. Francois, A. Giammanco, M.
Komm, G. Krintiras, V. Lemaitre, A. Magitteri, A. Mertens, M. Musich, K.
Piotrzkowski, L. Quertenmont, A. Saggio, M. Vidal Marono, S. Wertz, J. Zobec
Université de Mons, Mons, Belgium
N. Beliy Centro Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil
W.L. Aldá Júnior, F.L. Alves, G.A. Alves, L. Brito, M. Correa Martins Junior,
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. Chinellato4, E. Coelho, E.M.
Da Costa, G.G. Da Silveira5, D. De Jesus Damiao, S. Fonseca De Souza, L.M.
Huertas Guativa, H. Malbouisson, M. Melo De Almeida, C. Mora Herrera, L.
Mundim, H. Nogima, L.J. Sanchez Rosas, A. Santoro, A. Sznajder, M. Thiel, E.J.
Tonelli Manganote4, 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, T.R. Fernandez Perez Tomeia, E.M. Gregoresb, P.G.
Mercadanteb, S.F. Novaesa, Sandra S. Padulaa, D. Romero Abadb, J.C. Ruiz
Vargasa Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy
of Sciences, Sofia, Bulgaria
A. Aleksandrov, R. Hadjiiska, P. Iaydjiev, M. Misheva, M. Rodozov, M. Shopova,
G. Sultanov University of Sofia, Sofia, Bulgaria
A. Dimitrov, I. Glushkov, L. Litov, B. Pavlov, P. Petkov Beihang University,
Beijing, China
W. Fang6, X. Gao6, L. Yuan Institute of High Energy Physics, Beijing, China
M. Ahmad, J.G. Bian, G.M. Chen, H.S. Chen, M. Chen, Y. Chen, C.H. Jiang, D.
Leggat, H. Liao, Z. Liu, F. Romeo, S.M. Shaheen, A. Spiezia, J. Tao, C. Wang,
Z. Wang, E. Yazgan, H. Zhang, S. Zhang, J. Zhao State Key Laboratory of
Nuclear Physics and Technology, Peking University, Beijing, China
Y. Ban, G. Chen, Q. Li, S. Liu, Y. Mao, S.J. Qian, D. Wang, Z. Xu Universidad
de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, C.A. Carrillo Montoya, L.F. Chaparro Sierra, C. Florez,
C.F. González Hernández, J.D. Ruiz Alvarez University of Split, Faculty of
Electrical Engineering, Mechanical Engineering and Naval Architecture, Split,
Croatia
B. Courbon, N. Godinovic, D. Lelas, I. Puljak, P.M. Ribeiro Cipriano, T.
Sculac University of Split, Faculty of Science, Split, Croatia
Z. Antunovic, M. Kovac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, D. Ferencek, K. Kadija, B. Mesic, A. Starodumov7, T. Susa
University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos,
P.A. Razis, H. Rykaczewski Charles University, Prague, Czech Republic
M. Finger8, M. Finger Jr.8 Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab
Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
Y. Assran9,10, M.A. Mahmoud11,10, A. Mahrous12 National Institute of Chemical
Physics and Biophysics, Tallinn, Estonia
R.K. Dewanjee, M. Kadastik, L. Perrini, M. Raidal, A. Tiko, 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, H.
Siikonen, E. Tuominen, J. Tuominiemi Lappeenranta University of Technology,
Lappeenranta, Finland
J. Talvitie, T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette,
France
M. Besancon, F. Couderc, M. Dejardin, D. Denegri, J.L. Faure, F. Ferri, S.
Ganjour, S. Ghosh, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry,
I. Kucher, C. Leloup, E. Locci, M. Machet, J. Malcles, G. Negro, J. Rander, A.
Rosowsky, M.Ö. Sahin, M. Titov Laboratoire Leprince-Ringuet, Ecole
polytechnique, CNRS/IN2P3, Université Paris-Saclay, Palaiseau, France
A. Abdulsalam, C. Amendola, I. Antropov, S. Baffioni, F. Beaudette, P. Busson,
L. Cadamuro, C. Charlot, R. Granier de Cassagnac, M. Jo, S. Lisniak, A.
Lobanov, J. Martin Blanco, M. Nguyen, C. Ochando, G. Ortona, P. Paganini, P.
Pigard, R. Salerno, J.B. Sauvan, Y. Sirois, A.G. Stahl Leiton, T. Strebler, Y.
Yilmaz, A. Zabi, A. Zghiche Université de Strasbourg, CNRS, IPHC UMR 7178,
F-67000 Strasbourg, France
J.-L. Agram13, J. Andrea, D. Bloch, J.-M. Brom, M. Buttignol, E.C. Chabert, N.
Chanon, C. Collard, E. Conte13, X. Coubez, 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, 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, M. Lethuillier, L. Mirabito, A.L. Pequegnot, S.
Perries, A. Popov14, V. Sordini, M. Vander Donckt, S. Viret 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, C.
Schomakers, J. Schulz, V. Zhukov14 RWTH Aachen University, III. Physikalisches
Institut A, Aachen, Germany
A. Albert, E. Dietz-Laursonn, D. Duchardt, M. Endres, M. Erdmann, S. Erdweg,
T. Esch, R. Fischer, A. Güth, M. Hamer, T. Hebbeker, C. Heidemann, K.
Hoepfner, S. Knutzen, M. Merschmeyer, A. Meyer, P. Millet, S. Mukherjee, T.
Pook, M. Radziej, H. Reithler, M. Rieger, F. Scheuch, D. Teyssier, S. Thüer
RWTH Aachen University, III. Physikalisches Institut B, Aachen, Germany
G. Flügge, B. Kargoll, T. Kress, A. Künsken, T. Müller, A. Nehrkorn, A.
Nowack, C. Pistone, O. Pooth, A. Stahl15 Deutsches Elektronen-Synchrotron,
Hamburg, Germany
M. Aldaya Martin, T. Arndt, C. Asawatangtrakuldee, K. Beernaert, O. Behnke, U.
Behrens, A. Bermúdez Martínez, A.A. Bin Anuar, K. Borras16, V. Botta, A.
Campbell, P. Connor, C. Contreras-Campana, F. Costanza, C. Diez Pardos, G.
Eckerlin, D. Eckstein, T. Eichhorn, E. Eren, E. Gallo17, J. Garay Garcia, A.
Geiser, A. Gizhko, J.M. Grados Luyando, A. Grohsjean, P. Gunnellini, M.
Guthoff, A. Harb, J. Hauk, M. Hempel18, H. Jung, A. Kalogeropoulos, M.
Kasemann, J. Keaveney, C. Kleinwort, I. Korol, D. Krücker, W. Lange, A. Lelek,
T. Lenz, J. Leonard, K. Lipka, W. Lohmann18, R. Mankel, I.-A. Melzer-Pellmann,
A.B. Meyer, G. Mittag, J. Mnich, A. Mussgiller, E. Ntomari, D. Pitzl, A.
Raspereza, B. Roland, M. Savitskyi, P. Saxena, R. Shevchenko, S. Spannagel, N.
Stefaniuk, G.P. Van Onsem, R. Walsh, Y. Wen, K. Wichmann, C. Wissing, O.
Zenaiev University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, V. Blobel, M. Centis Vignali, T. Dreyer, E. Garutti, D.
Gonzalez, J. Haller, A. Hinzmann, M. Hoffmann, A. Karavdina, R. Klanner, R.
Kogler, N. Kovalchuk, S. Kurz, T. Lapsien, I. Marchesini, D. Marconi, M.
Meyer, M. Niedziela, D. Nowatschin, F. Pantaleo15, T. Peiffer, A. Perieanu, C.
Scharf, P. Schleper, A. Schmidt, S. Schumann, J. Schwandt, J. Sonneveld, H.
Stadie, G. Steinbrück, F.M. Stober, M. Stöver, H. Tholen, D. Troendle, E.
Usai, L. Vanelderen, A. Vanhoefer, B. Vormwald Institut für Experimentelle
Kernphysik, Karlsruhe, Germany
M. Akbiyik, C. Barth, M. Baselga, S. Baur, E. Butz, R. Caspart, T. Chwalek, F.
Colombo, W. De Boer, A. Dierlamm, N. Faltermann, B. Freund, R. Friese, M.
Giffels, D. Haitz, M.A. Harrendorf, F. Hartmann15, S.M. Heindl, U. Husemann,
F. Kassel15, S. Kudella, H. Mildner, M.U. Mozer, Th. Müller, M. Plagge, G.
Quast, K. Rabbertz, M. Schröder, I. Shvetsov, G. Sieber, H.J. Simonis, R.
Ulrich, S. Wayand, M. Weber, T. Weiler, S. Williamson, C. Wöhrmann, R. Wolf
Institute of Nuclear and Particle Physics (INPP), NCSR Demokritos, Aghia
Paraskevi, Greece
G. Anagnostou, G. Daskalakis, T. Geralis, V.A. Giakoumopoulou, A. Kyriakis, D.
Loukas, I. Topsis-Giotis National and Kapodistrian University of Athens,
Athens, Greece
G. Karathanasis, S. Kesisoglou, A. Panagiotou, N. Saoulidou National Technical
University of Athens, Athens, Greece
K. Kousouris University of Ioánnina, Ioánnina, Greece
I. Evangelou, C. Foudas, P. Kokkas, S. Mallios, N. Manthos, I. Papadopoulos,
E. Paradas, J. Strologas, F.A. Triantis MTA-ELTE Lendület CMS Particle and
Nuclear Physics Group, Eötvös Loránd University, Budapest, Hungary
M. Csanad, N. Filipovic, G. Pasztor, O. Surányi, G.I. Veres19 Wigner Research
Centre for Physics, Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath20, Á. Hunyadi, F. Sikler, V. Veszpremi, A.J.
Zsigmond Institute of Nuclear Research ATOMKI, Debrecen, Hungary
N. Beni, S. Czellar, J. Karancsi21, A. Makovec, J. Molnar, Z. Szillasi
Institute of Physics, University of Debrecen, Debrecen, Hungary
M. Bartók19, P. Raics, Z.L. Trocsanyi, B. Ujvari Indian Institute of Science
(IISc), Bangalore, India
S. Choudhury, J.R. Komaragiri National Institute of Science Education and
Research, Bhubaneswar, India
S. Bahinipati22, S. Bhowmik, P. Mal, K. Mandal, A. Nayak23, D.K. Sahoo22, N.
Sahoo, S.K. Swain Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, R. Chawla, N. Dhingra, A.K. Kalsi, A.
Kaur, M. Kaur, S. Kaur, R. Kumar, P. Kumari, A. Mehta, J.B. Singh, G. Walia
University of Delhi, Delhi, India
Ashok Kumar, Aashaq Shah, A. Bhardwaj, S. Chauhan, B.C. Choudhary, R.B. Garg,
S. Keshri, A. Kumar, S. Malhotra, M. Naimuddin, K. Ranjan, R. Sharma Saha
Institute of Nuclear Physics, HBNI, Kolkata, India
R. Bhardwaj, R. Bhattacharya, S. Bhattacharya, U. Bhawandeep, S. Dey, S. Dutt,
S. Dutta, S. Ghosh, N. Majumdar, A. Modak, K. Mondal, S. Mukhopadhyay, S.
Nandan, A. Purohit, A. Roy, D. Roy, S. Roy Chowdhury, S. Sarkar, M. Sharan, S.
Thakur Indian Institute of Technology Madras, Madras, India
P.K. Behera Bhabha Atomic Research Centre, Mumbai, India
R. Chudasama, D. Dutta, V. Jha, V. Kumar, A.K. Mohanty15, P.K. Netrakanti,
L.M. Pant, P. Shukla, A. Topkar Tata Institute of Fundamental Research-A,
Mumbai, India
T. Aziz, S. Dugad, B. Mahakud, S. Mitra, G.B. Mohanty, N. Sur, B. Sutar Tata
Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, Sa. Jain, S.
Kumar, M. Maity24, G. Majumder, K. Mazumdar, T. Sarkar24, N. Wickramage25
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, S. Paktinat Mehdiabadi27, F. Rezaei
Hosseinabadi, B. Safarzadeh28, 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, F. Erricoa,b, L. Fiorea, G. Iasellia,c, 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,15, R. Vendittia, P. Verwilligena INFN Sezione di
Bologna a, Università di Bologna b, Bologna, Italy
G. Abbiendia, C. Battilanaa,b, D. Bonacorsia,b, L. Borgonovia,b, S. Braibant-
Giacomellia,b, R. Campaninia,b, P. Capiluppia,b, A. Castroa,b, F.R. Cavalloa,
S.S. Chhibraa, G. Codispotia,b, M. Cuffiania,b, G.M. Dallavallea, F. Fabbria,
A. Fanfania,b, D. Fasanellaa,b, P. Giacomellia, C. Grandia, L. Guiduccia,b, S.
Marcellinia, G. Masettia, A. Montanaria, F.L. Navarriaa,b, A. Perrottaa, A.M.
Rossia,b, T. Rovellia,b, G.P. Sirolia,b, N. Tosia INFN Sezione di Catania a,
Università di Catania b, Catania, Italy
S. Albergoa,b, S. Costaa,b, A. Di Mattiaa, F. Giordanoa,b, R. Potenzaa,b, A.
Tricomia,b, C. Tuvea,b INFN Sezione di Firenze a, Università di Firenze b,
Firenze, Italy
G. Barbaglia, K. Chatterjeea,b, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, E. Focardia,b, P. Lenzia,b, M. Meschinia, S. Paolettia, L.
Russoa,29, G. Sguazzonia, D. Stroma, L. Viliania,b,15 INFN Laboratori
Nazionali di Frascati, Frascati, Italy
L. Benussi, S. Bianco, F. Fabbri, D. Piccolo, F. Primavera15 INFN Sezione di
Genova a, Università di Genova b, Genova, Italy
V. Calvellia,b, F. Ferroa, E. Robuttia, S. Tosia,b INFN Sezione di Milano-
Bicocca a, Università di Milano-Bicocca b, Milano, Italy
A. Benagliaa, A. Beschi, L. Brianzaa,b, F. Brivioa,b, V. Cirioloa,b, M.E.
Dinardoa,b, S. Fiorendia,b, S. Gennaia, A. Ghezzia,b, P. Govonia,b, M.
Malbertia,b, S. Malvezzia, R.A. Manzonia,b, D. Menascea, L. Moronia, M.
Paganonia,b, K. Pauwelsa,b, D. Pedrinia, S. Pigazzinia,b,30, S. Ragazzia,b, N.
Redaellia, T. Tabarelli de Fatisa,b INFN Sezione di Napoli a, Università di
Napoli ’Federico II’ b, Napoli, Italy, Università della Basilicata c, Potenza,
Italy, Università G. Marconi d, Roma, Italy
S. Buontempoa, N. Cavalloa,c, S. Di Guidaa,d,15, F. Fabozzia,c, F. Fiengaa,b,
A.O.M. Iorioa,b, W.A. Khana, L. Listaa, S. Meolaa,d,15, P. Paoluccia,15, C.
Sciaccaa,b, F. Thyssena INFN Sezione di Padova a, Università di Padova b,
Padova, Italy, Università di Trento c, Trento, Italy
P. Azzia, N. Bacchettaa, L. Benatoa,b, D. Biselloa,b, A. Bolettia,b, R.
Carlina,b, P. Checchiaa, M. Dall’Ossoa,b, P. De Castro Manzanoa, T. Dorigoa,
F. Gasparinia,b, U. Gasparinia,b, A. Gozzelinoa, S. Lacapraraa, P. Lujan, M.
Margonia,b, A.T. Meneguzzoa,b, N. Pozzobona,b, P. Ronchesea,b, R. Rossina,b,
F. Simonettoa,b, E. Torassaa, S. Venturaa, M. Zanettia,b, P. Zottoa,b, G.
Zumerlea,b INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy
A. Braghieria, A. Magnania, P. Montagnaa,b, S.P. Rattia,b, V. Rea, M.
Ressegottia,b, C. Riccardia,b, P. Salvinia, I. Vaia,b, P. Vituloa,b INFN
Sezione di Perugia a, Università di Perugia b, Perugia, Italy
L. Alunni Solestizia,b, M. Biasinia,b, G.M. Bileia, C. Cecchia,b, D.
Ciangottinia,b, L. Fanòa,b, P. Laricciaa,b, R. Leonardia,b, E. Manonia, G.
Mantovania,b, V. Mariania,b, M. Menichellia, A. Rossia,b, A. Santocchiaa,b, D.
Spigaa INFN Sezione di Pisa a, Università di Pisa b, Scuola Normale Superiore
di Pisa c, Pisa, Italy
K. Androsova, P. Azzurria,15, G. Bagliesia, T. Boccalia, L. Borrello, R.
Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, G. Fedia, L. Gianninia,c, A.
Giassia, M.T. Grippoa,29, F. Ligabuea,c, T. Lomtadzea, E. Mancaa,c, G.
Mandorlia,c, L. Martinia,b, A. Messineoa,b, F. Pallaa, A. Rizzia,b, A. Savoy-
Navarroa,31, 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,15, E. Di
Marcoa,b, M. Diemoza, S. Gellia,b, E. Longoa,b, F. Margarolia,b, B.
Marzocchia,b, P. Meridiania, G. Organtinia,b, 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, M. Costaa,b, R.
Covarellia,b, A. Deganoa,b, N. Demariaa, B. Kiania,b, C. Mariottia, S.
Masellia, E. Migliorea,b, V. Monacoa,b, E. Monteila,b, M. Montenoa, M.M.
Obertinoa,b, L. Pachera,b, N. Pastronea, M. Pelliccionia, G.L. Pinna
Angionia,b, F. Raveraa,b, A. Romeroa,b, M. Ruspaa,c, R. Sacchia,b, K.
Shchelinaa,b, V. Solaa, A. Solanoa,b, A. Staianoa, P. Traczyka,b INFN Sezione
di Trieste a, Università di Trieste b, Trieste, Italy
S. Belfortea, M. Casarsaa, F. Cossuttia, G. Della Riccaa,b, A. Zanettia
Kyungpook National University, Daegu, Korea
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 Chonbuk National University, Jeonju, Korea
A. Lee Chonnam National University, Institute for Universe and Elementary
Particles, Kwangju, Korea
H. Kim, D.H. Moon, G. Oh Hanyang University, Seoul, Korea
J.A. Brochero Cifuentes, J. Goh, T.J. Kim Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, D. Gyun, S. Ha, B. Hong, Y. Jo, Y. Kim, K. Lee, K.S.
Lee, S. Lee, J. Lim, S.K. Park, Y. Roh Seoul National University, Seoul, Korea
J. Almond, J. Kim, J.S. Kim, H. Lee, K. Lee, K. Nam, S.B. Oh, B.C. Radburn-
Smith, S.h. Seo, U.K. Yang, H.D. Yoo, G.B. Yu University of Seoul, Seoul,
Korea
M. Choi, H. Kim, J.H. Kim, J.S.H. Lee, I.C. Park Sungkyunkwan University,
Suwon, Korea
Y. Choi, C. Hwang, J. Lee, I. Yu Vilnius University, Vilnius, Lithuania
V. Dudenas, A. Juodagalvis, J. Vaitkus National Centre for Particle Physics,
Universiti Malaya, Kuala Lumpur, Malaysia
I. Ahmed, Z.A. Ibrahim, M.A.B. Md Ali32, F. Mohamad Idris33, W.A.T. Wan
Abdullah, M.N. Yusli, Z. Zolkapli Centro de Investigacion y de Estudios
Avanzados del IPN, Mexico City, Mexico
Reyes-Almanza, R, Ramirez-Sanchez, G., Duran-Osuna, M. C., H. Castilla-Valdez,
E. De La Cruz-Burelo, I. Heredia-De La Cruz34, Rabadan-Trejo, R. I., R. Lopez-
Fernandez, J. Mejia Guisao, A. Sanchez-Hernandez Universidad Iberoamericana,
Mexico City, Mexico
S. Carrillo Moreno, C. Oropeza Barrera, F. Vazquez Valencia Benemerita
Universidad Autonoma de Puebla, Puebla, Mexico
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
P.H. Butler National Centre for Physics, Quaid-I-Azam University, Islamabad,
Pakistan
A. Ahmad, M. Ahmad, Q. Hassan, H.R. Hoorani, A. Saddique, M.A. Shah, M.
Shoaib, M. Waqas National Centre for Nuclear Research, Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, T. Frueboes, M. Górski, M. Kazana, K.
Nawrocki, M. Szleper, P. Zalewski Institute of Experimental Physics, Faculty
of Physics, University of Warsaw, Warsaw, Poland
K. Bunkowski, A. Byszuk35, K. Doroba, A. Kalinowski, M. Konecki, J.
Krolikowski, M. Misiura, M. Olszewski, A. Pyskir, M. Walczak Laboratório de
Instrumentação e Física Experimental de Partículas, Lisboa, Portugal
P. Bargassa, C. Beirão Da Cruz E Silva, A. Di Francesco, P. Faccioli, B.
Galinhas, M. Gallinaro, J. Hollar, N. Leonardo, L. Lloret Iglesias, M.V.
Nemallapudi, J. Seixas, G. Strong, O. Toldaiev, D. Vadruccio, J. Varela Joint
Institute for Nuclear Research, Dubna, Russia
V. Alexakhin, P. Bunin, A. Golunov, I. Golutvin, N. Gorbounov, I. Gorbunov, A.
Kamenev, V. Karjavin, A. Lanev, A. Malakhov, V. Matveev36,37, V. Palichik, V.
Perelygin, M. Savina, S. Shmatov, N. Skatchkov, V. Smirnov, A. Zarubin
Petersburg Nuclear Physics Institute, Gatchina (St. Petersburg), Russia
Y. Ivanov, V. Kim38, E. Kuznetsova39, P. Levchenko, V. Murzin, V. Oreshkin, I.
Smirnov, 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, M. Toms, E. Vlasov, A. Zhokin Moscow
Institute of Physics and Technology, Moscow, Russia
T. Aushev, A. Bylinkin37 National Research Nuclear University ’Moscow
Engineering Physics Institute’ (MEPhI), Moscow, Russia
M. Chadeeva40, P. Parygin, D. Philippov, S. Polikarpov, E. Popova, V. Rusinov
P.N. Lebedev Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin37, I. Dremin37, M. Kirakosyan37, A. Terkulov Skobeltsyn
Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow,
Russia
A. Baskakov, A. Belyaev, E. Boos, M. Dubinin41, L. Dudko, A. Ershov, A.
Gribushin, V. Klyukhin, O. Kodolova, I. Lokhtin, I. Miagkov, S. Obraztsov, S.
Petrushanko, V. Savrin, A. Snigirev Novosibirsk State University (NSU),
Novosibirsk, Russia
V. Blinov42, D. Shtol42, Y. Skovpen42 State Research Center of Russian
Federation, Institute for High Energy Physics, Protvino, Russia
I. Azhgirey, I. Bayshev, S. Bitioukov, D. Elumakhov, V. Kachanov, A. Kalinin,
D. Konstantinov, P. Mandrik, V. Petrov, R. Ryutin, A. Sobol, S. Troshin, N.
Tyurin, A. Uzunian, A. Volkov University of Belgrade, Faculty of Physics and
Vinca Institute of Nuclear Sciences, Belgrade, Serbia
P. Adzic43, P. Cirkovic, D. Devetak, M. Dordevic, J. Milosevic, V. Rekovic
Centro de Investigaciones Energéticas Medioambientales y Tecnológicas
(CIEMAT), Madrid, Spain
J. Alcaraz Maestre, M. Barrio Luna, M. Cerrada, N. Colino, B. De La Cruz, A.
Delgado Peris, A. Escalante Del Valle, 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, A. Quintario
Olmeda, I. Redondo, L. Romero, M.S. Soares, A. Álvarez Fernández Universidad
Autónoma de Madrid, Madrid, Spain
C. Albajar, J.F. de Trocóniz, M. Missiroli Universidad de Oviedo, Oviedo,
Spain
J. Cuevas, C. Erice, J. Fernandez Menendez, I. Gonzalez Caballero, J.R.
González Fernández, E. Palencia Cortezon, S. Sanchez Cruz, P. Vischia, J.M.
Vizan Garcia Instituto de Física de Cantabria (IFCA), CSIC-Universidad de
Cantabria, Santander, Spain
I.J. Cabrillo, A. Calderon, B. Chazin Quero, E. Curras, J. Duarte Campderros,
M. Fernandez, J. Garcia-Ferrero, G. Gomez, A. Lopez Virto, J. Marco, C.
Martinez Rivero, P. Martinez Ruiz del Arbol, F. Matorras, J. Piedra Gomez, 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, P. Bloch, A. Bocci, C. Botta, T. Camporesi, R. Castello,
M. Cepeda, G. Cerminara, E. Chapon, Y. Chen, D. d’Enterria, A. Dabrowski, V.
Daponte, A. David, M. De Gruttola, A. De Roeck, N. Deelen, M. Dobson, T. du
Pree, M. Dünser, N. Dupont, A. Elliott-Peisert, P. Everaerts, F. Fallavollita,
G. Franzoni, J. Fulcher, W. Funk, D. Gigi, A. Gilbert, K. Gill, F. Glege, D.
Gulhan, P. Harris, J. Hegeman, V. Innocente, A. Jafari, P. Janot, O.
Karacheban18, J. Kieseler, V. Knünz, A. Kornmayer, M.J. Kortelainen, M.
Krammer1, C. Lange, P. Lecoq, C. Lourenço, M.T. Lucchini, L. Malgeri, M.
Mannelli, A. Martelli, F. Meijers, J.A. Merlin, S. Mersi, E. Meschi, P.
Milenovic44, F. Moortgat, M. Mulders, H. Neugebauer, J. Ngadiuba, S.
Orfanelli, L. Orsini, L. Pape, E. Perez, M. Peruzzi, A. Petrilli, G.
Petrucciani, A. Pfeiffer, M. Pierini, D. Rabady, A. Racz, T. Reis, G.
Rolandi45, M. Rovere, H. Sakulin, C. Schäfer, C. Schwick, M. Seidel, M.
Selvaggi, A. Sharma, P. Silva, P. Sphicas46, A. Stakia, J. Steggemann, M.
Stoye, M. Tosi, D. Treille, A. Triossi, A. Tsirou, V. Veckalns47, M. Verweij,
W.D. Zeuner Paul Scherrer Institut, Villigen, Switzerland
W. Bertl${}^{\textrm{\textdagger}}$, L. Caminada48, 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, L. Bianchini, B. Casal, G. Dissertori, M.
Dittmar, M. Donegà, C. Dorfer, C. Grab, C. Heidegger, D. Hits, J. Hoss, G.
Kasieczka, T. Klijnsma, W. Lustermann, B. Mangano, M. Marionneau, M.T.
Meinhard, D. Meister, F. Micheli, P. Musella, F. Nessi-Tedaldi, F. Pandolfi,
J. Pata, F. Pauss, G. Perrin, L. Perrozzi, M. Quittnat, M. Reichmann, 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. Amsler49, M.F. Canelli, A. De Cosa, R. Del Burgo, S.
Donato, C. Galloni, T. Hreus, B. Kilminster, D. Pinna, G. Rauco, P. Robmann,
D. Salerno, K. Schweiger, C. Seitz, Y. Takahashi, A. Zucchetta National
Central University, Chung-Li, Taiwan
V. Candelise, T.H. Doan, Sh. Jain, R. Khurana, C.M. Kuo, W. Lin, A.
Pozdnyakov, S.S. Yu National Taiwan University (NTU), Taipei, Taiwan
Arun Kumar, P. Chang, Y. Chao, K.F. Chen, P.H. Chen, F. Fiori, W.-S. Hou, Y.
Hsiung, Y.F. Liu, R.-S. Lu, E. Paganis, A. Psallidas, A. Steen, J.f. Tsai
Chulalongkorn University, Faculty of Science, Department of Physics, Bangkok,
Thailand
B. Asavapibhop, K. Kovitanggoon, G. Singh, N. Srimanobhas Çukurova University,
Physics Department, Science and Art Faculty, Adana, Turkey
M.N. Bakirci50, F. Boran, S. Damarseckin, Z.S. Demiroglu, C. Dozen, I.
Dumanoglu, E. Eskut, S. Girgis, G. Gokbulut, Y. Guler, I. Hos51, E.E.
Kangal52, O. Kara, U. Kiminsu, M. Oglakci, G. Onengut53, K. Ozdemir54, S.
Ozturk50, H. Topakli50, S. Turkcapar, I.S. Zorbakir, C. Zorbilmez Middle East
Technical University, Physics Department, Ankara, Turkey
B. Bilin, G. Karapinar55, K. Ocalan56, M. Yalvac, M. Zeyrek Bogazici
University, Istanbul, Turkey
E. Gülmez, M. Kaya57, O. Kaya58, S. Tekten, E.A. Yetkin59 Istanbul Technical
University, Istanbul, Turkey
M.N. Agaras, S. Atay, A. Cakir, K. Cankocak 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, J. Jacob, L. Kreczko, D.M.
Newbold60, S. Paramesvaran, T. Sakuma, S. Seif El Nasr-storey, D. Smith, V.J.
Smith Rutherford Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev61, C. Brew, R.M. Brown, L. Calligaris, D. Cieri, D.J.A.
Cockerill, J.A. Coughlan, K. Harder, S. Harper, E. Olaiya, D. Petyt, C.H.
Shepherd-Themistocleous, A. Thea, I.R. Tomalin, T. Williams Imperial College,
London, United Kingdom
G. Auzinger, R. Bainbridge, J. Borg, S. Breeze, O. Buchmuller, A. Bundock, S.
Casasso, M. Citron, D. Colling, L. Corpe, P. Dauncey, G. Davies, A. De Wit, M.
Della Negra, R. Di Maria, A. Elwood, Y. Haddad, G. Hall, G. Iles, T. James, R.
Lane, C. Laner, L. Lyons, A.-M. Magnan, S. Malik, L. Mastrolorenzo, T.
Matsushita, J. Nash, A. Nikitenko7, V. Palladino, M. Pesaresi, D.M. Raymond,
A. Richards, A. Rose, E. Scott, C. Seez, A. Shtipliyski, S. Summers, A.
Tapper, K. Uchida, M. Vazquez Acosta62, 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, I.D. Reid, P. Symonds, L.
Teodorescu, M. Turner, S. Zahid Baylor University, Waco, USA
A. Borzou, K. Call, J. Dittmann, K. Hatakeyama, H. Liu, N. Pastika, C. Smith
Catholic University of America, Washington DC, USA
R. Bartek, A. Dominguez The University of Alabama, Tuscaloosa, USA
A. Buccilli, S.I. Cooper, C. Henderson, P. Rumerio, C. West Boston University,
Boston, USA
D. Arcaro, A. Avetisyan, T. Bose, D. Gastler, D. Rankin, C. Richardson, J.
Rohlf, L. Sulak, D. Zou Brown University, Providence, USA
G. Benelli, D. Cutts, A. Garabedian, M. Hadley, J. Hakala, U. Heintz, J.M.
Hogan, K.H.M. Kwok, E. Laird, G. Landsberg, J. Lee, Z. Mao, M. Narain, J.
Pazzini, S. Piperov, S. Sagir, R. Syarif, D. Yu University of California,
Davis, Davis, USA
R. Band, C. Brainerd, R. Breedon, D. Burns, M. Calderon De La Barca Sanchez,
M. Chertok, J. Conway, R. Conway, P.T. Cox, R. Erbacher, C. Flores, G. Funk,
M. Gardner, W. Ko, R. Lander, C. Mclean, M. Mulhearn, D. Pellett, J. Pilot, S.
Shalhout, M. Shi, J. Smith, D. Stolp, K. Tos, M. Tripathi, Z. Wang University
of California, Los Angeles, USA
M. Bachtis, C. Bravo, R. Cousins, A. Dasgupta, A. Florent, J. Hauser, M.
Ignatenko, N. Mccoll, S. Regnard, D. Saltzberg, C. Schnaible, V. Valuev
University of California, Riverside, Riverside, USA
E. Bouvier, K. Burt, R. Clare, J. Ellison, J.W. Gary, S.M.A. Ghiasi Shirazi,
G. Hanson, J. Heilman, 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, I. Macneill, M.
Masciovecchio, D. Olivito, S. Padhi, M. Pieri, M. Sani, V. Sharma, S. Simon,
M. Tadel, A. Vartak, S. Wasserbaech63, 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, A. Dishaw, V. Dutta,
M. Franco Sevilla, C. George, F. Golf, L. Gouskos, J. Gran, R. Heller, J.
Incandela, S.D. Mullin, A. Ovcharova, H. Qu, J. Richman, D. Stuart, I. Suarez,
J. Yoo California Institute of Technology, Pasadena, USA
D. Anderson, A. Bornheim, J.M. Lawhorn, H.B. Newman, T. Nguyen, C. Pena, M.
Spiropulu, J.R. Vlimant, S. Xie, Z. Zhang, R.Y. Zhu Carnegie Mellon
University, Pittsburgh, USA
M.B. Andrews, T. Ferguson, T. Mudholkar, M. Paulini, J. Russ, M. Sun, H.
Vogel, I. Vorobiev, M. Weinberg University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, F. Jensen, A. Johnson, M. Krohn, S. Leontsinis, T.
Mulholland, K. Stenson, S.R. Wagner Cornell University, Ithaca, USA
J. Alexander, J. Chaves, J. Chu, S. Dittmer, 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, R.M. Harris, S. Hasegawa, J. Hirschauer, Z. Hu, B. Jayatilaka, S.
Jindariani, M. Johnson, U. Joshi, B. Klima, B. Kreis, S. Lammel, D. Lincoln,
R. Lipton, M. Liu, T. Liu, R. Lopes De Sá, J. Lykken, K. Maeshima, N. Magini,
J.M. Marraffino, D. Mason, P. McBride, P. Merkel, S. Mrenna, S. Nahn, V.
O’Dell, K. Pedro, O. Prokofyev, G. Rakness, L. Ristori, 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, A. Carnes, M.
Carver, D. Curry, R.D. Field, I.K. Furic, S.V. Gleyzer, B.M. Joshi, J.
Konigsberg, A. Korytov, K. Kotov, P. Ma, K. Matchev, H. Mei, G. Mitselmakher,
D. Rank, K. Shi, D. Sperka, N. Terentyev, L. Thomas, J. Wang, S. Wang, J.
Yelton Florida International University, Miami, USA
Y.R. Joshi, S. Linn, P. Markowitz, J.L. Rodriguez Florida State University,
Tallahassee, USA
A. Ackert, T. Adams, A. Askew, S. Hagopian, V. Hagopian, K.F. Johnson, T.
Kolberg, G. Martinez, T. Perry, H. Prosper, A. Saha, A. Santra, V. Sharma, R.
Yohay Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, V. Bhopatkar, S. Colafranceschi, M. Hohlmann, D. Noonan, T.
Roy, F. Yumiceva University of Illinois at Chicago (UIC), Chicago, USA
M.R. Adams, L. Apanasevich, D. Berry, R.R. Betts, R. Cavanaugh, X. Chen, O.
Evdokimov, C.E. Gerber, D.A. Hangal, D.J. Hofman, K. Jung, J. Kamin, I.D.
Sandoval Gonzalez, M.B. Tonjes, H. Trauger, N. Varelas, H. Wang, Z. Wu, J.
Zhang The University of Iowa, Iowa City, USA
B. Bilki64, W. Clarida, K. Dilsiz65, S. Durgut, R.P. Gandrajula, M.
Haytmyradov, V. Khristenko, J.-P. Merlo, H. Mermerkaya66, A. Mestvirishvili,
A. Moeller, J. Nachtman, H. Ogul67, Y. Onel, F. Ozok68, A. Penzo, C. Snyder,
E. Tiras, J. Wetzel, K. Yi Johns Hopkins University, Baltimore, USA
B. Blumenfeld, A. Cocoros, N. Eminizer, D. Fehling, L. Feng, A.V. Gritsan, P.
Maksimovic, J. Roskes, U. Sarica, M. Swartz, M. Xiao, C. You The University of
Kansas, Lawrence, USA
A. Al-bataineh, P. Baringer, A. Bean, S. Boren, J. Bowen, J. Castle, S.
Khalil, A. Kropivnitskaya, D. Majumder, W. Mcbrayer, M. Murray, C. Royon, S.
Sanders, E. Schmitz, J.D. Tapia Takaki, Q. Wang Kansas State University,
Manhattan, USA
A. Ivanov, K. Kaadze, Y. Maravin, A. Mohammadi, L.K. Saini, N. Skhirtladze, S.
Toda Lawrence Livermore National Laboratory, Livermore, USA
F. Rebassoo, D. Wright University of Maryland, College Park, USA
C. Anelli, A. Baden, O. Baron, A. Belloni, B. Calvert, S.C. Eno, Y. Feng, C.
Ferraioli, N.J. Hadley, S. Jabeen, G.Y. Jeng, R.G. Kellogg, J. Kunkle, A.C.
Mignerey, F. Ricci-Tam, Y.H. Shin, A. Skuja, S.C. Tonwar Massachusetts
Institute of Technology, Cambridge, USA
D. Abercrombie, B. Allen, V. Azzolini, R. Barbieri, A. Baty, R. Bi, S. Brandt,
W. Busza, I.A. Cali, M. D’Alfonso, Z. Demiragli, G. Gomez Ceballos, M.
Goncharov, D. Hsu, M. Hu, Y. Iiyama, G.M. Innocenti, M. Klute, D. Kovalskyi,
Y.S. Lai, Y.-J. Lee, A. Levin, P.D. Luckey, B. Maier, A.C. Marini, C. Mcginn,
C. Mironov, S. Narayanan, X. Niu, C. Paus, C. Roland, G. Roland, J. Salfeld-
Nebgen, G.S.F. Stephans, K. Tatar, D. Velicanu, J. Wang, T.W. Wang, B.
Wyslouch University of Minnesota, Minneapolis, USA
A.C. Benvenuti, R.M. Chatterjee, A. Evans, P. Hansen, J. Hiltbrand, 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, 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
J. Dolen, A. Godshalk, C. Harrington, I. Iashvili, D. Nguyen, A. Parker, S.
Rappoccio, B. Roozbahani Northeastern University, Boston, USA
G. Alverson, E. Barberis, A. Hortiangtham, A. Massironi, D.M. Morse, T.
Orimoto, R. Teixeira De Lima, D. Trocino, D. Wood Northwestern University,
Evanston, USA
S. Bhattacharya, O. Charaf, K.A. Hahn, N. Mucia, N. Odell, B. Pollack, M.H.
Schmitt, K. Sung, M. Trovato, M. Velasco University of Notre Dame, Notre Dame,
USA
N. Dev, M. Hildreth, K. Hurtado Anampa, C. Jessop, D.J. Karmgard, N. Kellams,
K. Lannon, N. Loukas, N. Marinelli, F. Meng, C. Mueller, Y. Musienko36, M.
Planer, A. Reinsvold, R. Ruchti, G. Smith, S. Taroni, M. Wayne, 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, B. Liu, W. Luo, D. Puigh, B.L. Winer, H.W. Wulsin
Princeton University, Princeton, USA
S. Cooperstein, O. Driga, P. Elmer, J. Hardenbrook, P. Hebda, S. Higginbotham,
D. Lange, J. Luo, D. Marlow, K. Mei, I. Ojalvo, J. Olsen, C. Palmer, P.
Piroué, 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, S. Folgueras, L. Gutay, M.K. Jha, M. Jones,
A.W. Jung, A. Khatiwada, D.H. Miller, N. Neumeister, C.C. Peng, H. Qiu, J.F.
Schulte, J. Sun, F. Wang, W. Xie Purdue University Northwest, Hammond, USA
T. Cheng, N. Parashar, J. Stupak Rice University, Houston, USA
A. Adair, Z. Chen, K.M. Ecklund, S. Freed, F.J.M. Geurts, M. Guilbaud, M.
Kilpatrick, W. Li, B. Michlin, M. Northup, B.P. Padley, J. Roberts, J. Rorie,
W. Shi, Z. Tu, J. Zabel, A. Zhang University of Rochester, Rochester, USA
A. Bodek, P. de Barbaro, R. Demina, Y.t. Duh, T. Ferbel, M. Galanti, A.
Garcia-Bellido, J. Han, O. Hindrichs, A. Khukhunaishvili, K.H. Lo, P. Tan, M.
Verzetti The Rockefeller University, New York, USA
R. Ciesielski, K. Goulianos, C. Mesropian Rutgers, The State University of New
Jersey, Piscataway, USA
A. Agapitos, J.P. Chou, Y. Gershtein, T.A. Gómez Espinosa, E. Halkiadakis, M.
Heindl, E. Hughes, S. Kaplan, R. Kunnawalkam Elayavalli, S. Kyriacou, A. Lath,
R. Montalvo, K. Nash, M. Osherson, H. Saka, S. Salur, S. Schnetzer, D.
Sheffield, S. Somalwar, R. Stone, S. Thomas, P. Thomassen, M. Walker
University of Tennessee, Knoxville, USA
A.G. Delannoy, M. Foerster, J. Heideman, G. Riley, K. Rose, S. Spanier, K.
Thapa Texas A&M University, College Station, USA
O. Bouhali69, A. Castaneda Hernandez69, A. Celik, M. Dalchenko, M. De Mattia,
A. Delgado, S. Dildick, R. Eusebi, J. Gilmore, T. Huang, T. Kamon70, R.
Mueller, Y. Pakhotin, R. Patel, A. Perloff, L. Perniè, D. Rathjens, A.
Safonov, A. Tatarinov, K.A. Ulmer Texas Tech University, Lubbock, USA
N. Akchurin, J. Damgov, F. De Guio, P.R. Dudero, J. Faulkner, E. Gurpinar, S.
Kunori, K. Lamichhane, S.W. Lee, T. Libeiro, 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, P. Sheldon, S. Tuo, J. Velkovska, Q. Xu University of Virginia,
Charlottesville, USA
M.W. Arenton, P. Barria, B. Cox, R. Hirosky, M. Joyce, A. Ledovskoy, H. Li, C.
Neu, T. Sinthuprasith, Y. Wang, E. Wolfe, F. Xia Wayne State University,
Detroit, USA
R. Harr, P.E. Karchin, N. Poudyal, J. Sturdy, P. Thapa, S. Zaleski University
of Wisconsin - Madison, Madison, WI, USA
M. Brodski, J. Buchanan, C. Caillol, S. Dasu, L. Dodd, S. Duric, B. Gomber, M.
Grothe, M. Herndon, A. Hervé, U. Hussain, P. Klabbers, A. Lanaro, A. Levine,
K. Long, R. Loveless, G. Polese, T. Ruggles, A. Savin, N. Smith, W.H. Smith,
D. Taylor, N. Woods †: Deceased
1: Also at Vienna University of Technology, Vienna, Austria
2: Also at State Key Laboratory of Nuclear Physics and Technology, Peking
University, Beijing, China
3: Also at IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
4: Also at Universidade Estadual de Campinas, Campinas, Brazil
5: Also at Universidade Federal de Pelotas, Pelotas, Brazil
6: Also at Université Libre de Bruxelles, Bruxelles, Belgium
7: Also at Institute for Theoretical and Experimental Physics, Moscow, Russia
8: Also at Joint Institute for Nuclear Research, Dubna, Russia
9: Also at Suez University, Suez, Egypt
10: Now at British University in Egypt, Cairo, Egypt
11: Also at Fayoum University, El-Fayoum, Egypt
12: Now at Helwan University, Cairo, Egypt
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 University of Visva-Bharati, Santiniketan, India
25: Also at University of Ruhuna, Matara, Sri Lanka
26: Also at Isfahan University of Technology, Isfahan, Iran
27: Also at Yazd University, Yazd, Iran
28: Also at Plasma Physics Research Center, Science and Research Branch,
Islamic Azad University, Tehran, Iran
29: Also at Università degli Studi di Siena, Siena, Italy
30: Also at INFN Sezione di Milano-Bicocca; Università di Milano-Bicocca,
Milano, Italy
31: Also at Purdue University, West Lafayette, USA
32: Also at International Islamic University of Malaysia, Kuala Lumpur,
Malaysia
33: Also at Malaysian Nuclear Agency, MOSTI, Kajang, Malaysia
34: Also at Consejo Nacional de Ciencia y Tecnología, Mexico city, Mexico
35: Also at Warsaw University of Technology, Institute of Electronic Systems,
Warsaw, Poland
36: Also at Institute for Nuclear Research, Moscow, Russia
37: Now at National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
38: Also at St. Petersburg State Polytechnical University, St. Petersburg,
Russia
39: Also at University of Florida, Gainesville, USA
40: Also at P.N. Lebedev Physical Institute, Moscow, Russia
41: Also at California Institute of Technology, Pasadena, USA
42: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia
43: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia
44: Also at University of Belgrade, Faculty of Physics and Vinca Institute of
Nuclear Sciences, Belgrade, Serbia
45: Also at Scuola Normale e Sezione dell’INFN, Pisa, Italy
46: Also at National and Kapodistrian University of Athens, Athens, Greece
47: Also at Riga Technical University, Riga, Latvia
48: Also at Universität Zürich, Zurich, Switzerland
49: Also at Stefan Meyer Institute for Subatomic Physics (SMI), Vienna,
Austria
50: Also at Gaziosmanpasa University, Tokat, Turkey
51: Also at Istanbul Aydin University, Istanbul, Turkey
52: Also at Mersin University, Mersin, Turkey
53: Also at Cag University, Mersin, Turkey
54: Also at Piri Reis University, Istanbul, Turkey
55: Also at Izmir Institute of Technology, Izmir, Turkey
56: Also at Necmettin Erbakan University, Konya, Turkey
57: Also at Marmara University, Istanbul, Turkey
58: Also at Kafkas University, Kars, Turkey
59: Also at Istanbul Bilgi University, Istanbul, 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 Instituto de Astrofísica de Canarias, La Laguna, Spain
63: Also at Utah Valley University, Orem, USA
64: Also at Beykent University, Istanbul, Turkey
65: Also at Bingol University, Bingol, Turkey
66: Also at Erzincan University, Erzincan, Turkey
67: Also at Sinop University, Sinop, Turkey
68: Also at Mimar Sinan University, Istanbul, Istanbul, Turkey
69: Also at Texas A&M University at Qatar, Doha, Qatar
70: Also at Kyungpook National University, Daegu, Korea