HIG-18-017

HIG-18-017

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

###### Abstract

A search for lepton flavour violating decays of a neutral non-standard-model
Higgs boson in the $\mu\tau$ and $\Pe\tau$ decay modes is presented. The
search is based on proton-proton collisions at a center of mass energy
$\sqrt{s}=13$collected with the CMS detector in 2016, corresponding to an
integrated luminosity of $35.9\fbinv$. The $\tau$ leptons are reconstructed in
the leptonic and hadronic decay modes. No signal is observed in the mass range
200–900. At 95% confidence level, the observed (expected) upper limits on the
production cross section multiplied by the branching fraction vary from 51.9
(57.4)fb to 1.6 (2.1)fb for the $\mu\tau$ and from 94.1 (91.6)fb to 2.3
(2.3)fb for the $\Pe\tau$ decay modes.

##  0.1 Introduction

The discovery of the 125Higgs boson, $\PH(125)$, at the CERN LHC in 2012 [1,
2, 3] was a major breakthrough in particle physics. A combined study of data
from collisions at $\sqrt{s}$ = 7 and 8collected by the ATLAS and CMS
Collaborations shows the particle to have properties consistent with the
standard model (SM) Higgs boson [4, 5, 6, 7, 8, 9] including the spin,
couplings, and charge-parity assignment [10, 11]. Lepton flavour violating
(LFV) decays of the $\PH(125)$ are forbidden in the SM. However, the presence
of new physics in the Higgs sector is not excluded [12] and there exist many
possible extensions of the SM that allow LFV decays of the $\PH(125)$. These
include the two Higgs doublet model [13], supersymmetric models [14, 15, 16,
17, 18, 19, 20], composite Higgs models [21, 22], models with flavour
symmetries [23], Randall–Sundrum models [24, 25, 26], and others [27, 28, 29,
30, 31, 32, 33, 34, 35]. A common feature of many of these models is the
presence of additional neutral Higgs bosons ($\PH$ and A) that would also have
LFV decays [36, 37].

The most recent search for LFV decays of the $\PH(125)$ was performed by the
CMS Collaboration in the $\PGm\PGt$ and $\Pe\PGt$ channels, using proton-
proton ($\Pp\Pp$) collision data recorded at a centre-of-mass energy of
$\sqrt{s}=13$, and corresponding to an integrated luminosity of 35.9 [38]. The
observed (expected) upper limits set on the branching fractions were
$\mathcal{B}(\PH(125)\to\PGm\PGt)<0.25\,(0.25)$% and
$\mathcal{B}(\PH(125)\to\Pe\PGt)<0.61\,(0.37)$% at 95% confidence level (CL).
These constraints were a significant improvement over the previously set
limits by the CMS and ATLAS Collaborations using the 8$\Pp\Pp$ collision data
set, corresponding to an integrated luminosity of 20 [39, 40, 41, 42]. Results
from the previous CMS $\PH(125)\to\PGm\PGt$ search, performed using 8$\Pp\Pp$
collision data, were used to set limits on high mass LFV decays in a
phenomenological study [12]. Limits on the product of the production cross
section with branching fraction for the $\PH\to\PGm\PGt$ channel were obtained
for mass, $m_{\PH}$, less than 300.

This paper describes the first direct search for LFV $\PH\to\mu\tau$ and
$\PH\to\Pe\tau$ decays for an mass in the range $200<m_{\PH}<900$. The search
is performed in four decay channels, $\PH\to\PGm\tauh$,
$\PH\to\PGm\PGt_{\Pe}$, $\PH\to\Pe\tauh$, and $\PH\to\Pe\PGt_{\PGm}$ where
$\tauh$, $\PGt_{\Pe}$, and $\PGt_{\PGm}$ correspond to the hadronic,
electronic and muonic decay channels of $\tau$ leptons, respectively. The
final-state signatures are very similar to those of the $\PH\to\PGt\PGt$
decays, studied by CMS [43, 44, 45, 46] and ATLAS [47]. However, there are
some significant kinematic differences. The primary difference is that the
muon (electron) in the LFV $\PH\to\PGm(\Pe)\PGt$ decay is produced promptly,
and tends to have a higher momentum than in the $\PH\to\PGt_{\PGm(\Pe)}\PGt$
decay. Only the gluon fusion production process is considered in this search
and the signal is modelled assuming a narrow width of the Higgs boson. The
strategy is similar to the previous LFV $\PH(125)$ searches performed by the
CMS Collaboration, but optimised for higher mass Higgs boson decays.

This paper is organized as follows. After a brief overview of the CMS detector
in Section 0.2 and the description of the collision data and simulated samples
used in the analysis in Section 0.3, the event reconstruction is described in
Section 0.4. The event selection is outlined in Section 0.5 and the background
processes are described in Section 0.6. This is followed by a description of
the systematic uncertainties in Section 0.7. Finally, the results are
presented in Section 0.8, and the paper is summarized in Section 0.9.

##  0.2 The CMS detector

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. [48]. 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. Events of
interest are selected using a two-tiered trigger system [49]. The first level,
composed of custom hardware processors, uses information from the calorimeters
and muon detectors to select events at a rate of around 100kHz within a time
interval of less than 4. The second level, known as the high-level trigger,
consists of a farm of processors running a version of the full event
reconstruction software optimised for fast processing, and reduces the event
rate to around 1kHz before data storage.

##  0.3 Collision data and event simulation

The data used in this analysis have been collected in $\Pp\Pp$ collisions at
the LHC, at a centre-of-mass energy of $13$, with the CMS detector in 2016,
and correspond to an integrated luminosity of 35.9 [50]. A trigger requiring
at least one muon is used to collect the data sample in the $\PH\to\PGm\tauh$
and $\PH\to\PGm\PGt_{\Pe}$ channels. Triggers requiring at least one electron,
or a combination of an electron and a muon are used for the $\PH\to\Pe\tauh$
and $\PH\to\Pe\PGt_{\PGm}$ channels respectively. Simulated samples of signal
and background events are produced with different event generators. The
$\PH\to\PGm\PGt$ and $\PH\to\Pe\PGt$ decay samples are generated with 2.0 [51,
52, 53, 54, 55, 56] at next-to-leading-order (NLO) in perturbative quantum
chromodynamics. Only the gluon fusion ($\Pg\Pg\PH$) [57] production mode has
been considered in this analysis. These scalar boson samples are generated
assuming the narrow width approximation for a range of masses from 200 to 900.
The $\PZ\\!+\\!\text{jets}$ and $\PW\\!+\\!\text{jets}$ processes are
simulated using the 2.2.2 [58] generator at leading order with the MLM jet
matching and merging [59]. The generator is also used for diboson production
which is simulated at NLO with the FxFx jet matching and merging scheme [60].
The $\POWHEG$ 2.0 and 1.0 at NLO are used for top quark-antiquark ($\ttbar$)
and single top quark production, respectively. The and generators are
interfaced with 8.212 [61] for parton showering and fragmentation. The
parameters for the underlying event description are set to the CUETP8M1 tune
[62]. The set of parton distribution functions (PDFs) used is NNPDF30nloas0118
[63]. The CMS detector response is modelled using [64].

Because of the high instantaneous luminosities attained during data taking,
events have multiple $\Pp\Pp$ interactions per bunch crossing (pileup). This
effect is taken into account in simulated samples, by generating concurrent
minimum bias events, and overlapping them with simulated hard events. All
simulated samples are weighted to match the pileup distribution observed in
data, which has an average of approximately 23 interactions per bunch
crossing.

##  0.4 Event reconstruction

The event reconstruction is performed using a particle-flow (PF) algorithm,
which aims to reconstruct and identify each individual particle in an event
(PF candidate), with an optimised combination of information from the various
elements of the CMS detector [65]. In this process, the identification of the
particle type for each PF candidate (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 the jets,
clustered using the jet finding algorithm [66, 67] with the tracks assigned to
the vertex as inputs, and the associated missing transverse momentum, taken as
the negative vector sum of the of those jets.

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 [68, 65]. Identification is based on the number of
spatial points measured in the tracker and in the muon system, the track
quality, and its consistency with the event vertex location. The
identification working point chosen for this analysis reconstructs muons with
an efficiency above 98% and a hadron misidentification rate of 0.1% for pions
and 0.3% for kaons. The energy is obtained from the corresponding track
momentum. An important aspect of muon reconstruction is the lepton isolation
that is described later in this section.

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 are matched with the track extrapolation to the ECAL and with
possible bremsstrahlung photons emitted when interacting with the material of
the tracker [69]. Electron candidates are accepted in the pseudorapidity 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-analysis (MVA) discriminator that combines observables
sensitive to the amount of bremsstrahlung along the electron trajectory, the
geometric 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 chosen working point for
selecting the electrons assures an average identification efficiency of 80%
with a misidentification probability of 5%. 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 associated with the track.

Charged hadrons are identified as charged-particle tracks neither identified
as electrons, nor as muons. Finally, neutral hadrons are identified as HCAL
energy clusters not linked to any charged hadron trajectory, or as a combined
ECAL and HCAL energy excess 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 [66], implemented in the package
[70], with a distance parameter of 0.4. Jet momentum is determined as the
vectorial sum of all particle momenta in the jet, and is found from simulation
to be, on average, within 5 to 10% of the true momentum over the whole
spectrum and detector acceptance. Additional proton-proton interactions within
the same or nearby bunch crossings can contribute additional tracks and
calorimetric energy depositions, increasing the apparent jet momentum. To
mitigate this effect, tracks identified to be originating from pileup vertices
are discarded and an offset correction is applied to correct for remaining
contributions. Jet energy corrections are derived from simulation studies so
that the average measured response of jets becomes identical to that of
particle level jets. In situ measurements of the momentum balance in dijet,
photon+jet, $\PZ\\!+\\!\text{jets}$, and multijet events are used to determine
any residual differences between the jet energy scale in data and in
simulation, and appropriate corrections are made [71]. Additional selection
criteria are applied to each jet to remove jets potentially dominated by
instrumental effects or reconstruction failures.

Hadronically decaying $\PGt$ leptons () are reconstructed and identified using
the hadrons-plus-strips algorithm [72, 73]. 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 a neutral-pion decay, the algorithm considers the PF photons and
electrons from a strip along $\phi$. The sign of the candidate is determined
through its decay products.

An MVA discriminator, based on variables such as lifetime information, decay
mode, multiplicity of neutral, charged and pileup particles in a cone around
the reconstructed , is used to reduce the rate for quark- and gluon-initiated
jets identified as candidates. The working point used in the analysis is a
“tight” one, with an efficiency of about 50% for a genuine , and approximately
a 0.2% misidentification rate for quark and gluon jets [73]. Additionally,
muons and electrons misidentified as are rejected by considering 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 $\PGt$ leptons that decay to
muons and electrons are reconstructed in the same manner as prompt muons and
electrons, respectively, as described above.

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,
where $\eta$ and $\phi$ are the pseudorapidity and azimuthal directions,
respectively.

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

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

where $\pt^{\text{charged}}$, $\pt^{\text{neutral}}$, and $\pt^{\gamma}$
indicate the of a charged and of a neutral particle, and a photon within the
cone, respectively. The neutral particle contribution to isolation from
pileup, $\pt^{\text{PU}}\left(\ell\right)$, is estimated from the $\pt$ sum of
charged hadrons not originating from the primary vertex scaled by a factor of
0.5 [68] for the muons. For the electrons, this contribution is estimated from
the area of the jet and the average energy density of the event [74, 75]. The
charged-particle contribution to isolation from pileup is rejected by
requiring the tracks to originate from the primary vertex. Jet arising from a
quark are identified by the combined secondary vertex tagging algorithm [76]
using the working point characterised by a jet identification efficiency
around 65% and a misidentification probability around 1% for light quark and
gluon jets.

All the reconstructed particles in the event are used to estimate the missing
transverse momentum, $\ptvecmiss$, which is defined as the projection onto the
plane perpendicular to the beam axis of the negative vector sum of the momenta
of all reconstructed PF candidates in an event [77]. The effect of the jet
energy corrections described earlier in this section is then propagated to
this $\ptvecmiss$. The magnitude of the final vector is referred to as . The
transverse mass $\mT(\ell)$ is a variable formed from the lepton transverse
momentum and the missing transverse momentum vectors:
${\mT}(\ell)=\sqrt{\smash[b]{2|\ptvec^{\ell}||\ptvecmiss|(1-\cos{\Delta\phi_{\ell-\ptmiss}})}}$,
where $\Delta\phi_{\ell-\ptmiss}$ is the angle between the lepton transverse
momentum and the missing transverse momentum. 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 boosted in the direction of the $\PGt$
candidate [78]. The neutrino momenta can be approximated to have the same
direction as the other visible decay products of the $\PGt$ lepton
($\vec{\PGt}^{\text{vis}}$) 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 is then
$M_{\text{col}}=M_{\text{vis}}/\sqrt{\smash[b]{x_{\PGt}^{\text{vis}}}}$, where
$x_{\PGt}^{\text{vis}}$ is the fraction of momentum carried by the visible
decay products of the $\PGt$ lepton,
$x_{\PGt}^{\text{vis}}={\pt^{\vec{\PGt}^{\text{vis}}}}/{(\pt^{\vec{\PGt}^{\text{vis}}}+\pt^{\nu,\,\text{est}})}$,
and $M_{\text{vis}}$ is the visible mass of the $\PGt-\Pe$ or $\PGt-\PGm$
system.

Dedicated performance studies on data validate the reconstruction and
identification techniques described in this section. When necessary,
corrections have been applied to the simulated samples to ensure they
correctly describe the behaviour of the data [68, 69, 71, 73, 76, 77].

##  0.5 Event selection

The event selection is performed in two steps. An initial selection is
followed by another, final, set of requirements on kinematic variables that
exploit the distinct event topology of the signal. The event sample defined by
the initial selection is used in the background estimation described in
Section 0.6. The event selection begins by requiring two isolated leptons of
opposite charge, different flavour, and separated by $\Delta R>0.3$. The
isolation of the candidates is included in the MVA discriminator described in
Section 0.4. Events with additional , , or $\tauh$ candidates respectively
with $\pt>10$, 5, or 20are discarded. The kinematic requirements applied are
dictated by the triggers or detector acceptance and are summarized in Table
0.5.

The events are then divided into two categories according to the number of
jets in the event. The jets are required to have $\pt>30$and $\abs{\eta}<4.7$.
Events with no jets form the 0-jet category while events with exactly one jet
form the 1-jet category. The 1-jet category includes $\Pg\Pg\PH$ production
with initial state radiation. Events with more than one jet are discarded.

Initial selection criteria applied to the kinematic variables for the
$\PH\to\mu\tau$ and $\PH\to\Pe\tau$ analyses. The selected sample is used in
the background estimation from control samples in data.
$~{}\PH\to\PGm\PGt_{\PSh}~{}$ $~{}\PH\to\PGm\PGt_{\Pe}~{}$
$~{}\PH\to\Pe\PGt_{\PSh}~{}$ $~{}\PH\to\Pe\PGt_{\PGm}~{}$ $\pt^{\PGm}$ $>$53
$>$53 — $>$10 $\pt^{\Pe}$ — $>$10 $>$26 $>$26 $\pt^{\PGt}$ $>$30 — $>$30 —
$\abs{\eta^{\PGm}}$ $<$2.4 $<$2.4 — $<$2.4 $\abs{\eta^{\Pe}}$ — $<$2.4 $<$2.1
$<$2.4 $\abs{\eta^{\PGt}}$ $<$2.3 — $<$2.3 — $I_{\text{rel}}^{\PGm}$ 0.15
$<$0.15 — $<$0.15 $I_{\text{rel}}^{\Pe}$ — $<$0.1 $<$0.1 $<$0.1 $\Delta
R(\PGm,\Pe)$ — $>$0.3 — $>$0.3 $\Delta R(\PGm,\PGt)$ $>$0.3 — — — $\Delta
R(\Pe,\PGt)$ — — $>$0.3 —

The final selection is given in Table 0.5. It begins by tightening the
requirement of the prompt lepton from the Higgs boson decay, as it provides a
powerful discriminant against the background. The $\PGt$ lepton in the decay
is highly boosted, leading to a collimation of the decay products. This can be
exploited by either limiting the azimuthal separation of the decay products
including the , or imposing a requirement on the transverse mass $\mT(\PGt)$,
which is strongly correlated with the azimuthal separation. These selection
criteria are optimised for each decay mode in two $m_{\PH}$ ranges to obtain
the most stringent expected upper limits. The low- and high-mass regions are
defined to be $200<m_{\PH}<450$and $450<m_{\PH}<900$, respectively. A binned
likelihood fit to the $M_{\text{col}}$ distributions is then used to extract
signal and background contributions. The $M_{\text{col}}$ approximates the
Higgs mass better than the widely used $M_{\text{vis}}$, and therefore
improves the separation of the signal from the background. This improvement is
larger in the high mass regime, with up to a factor of three gain in
sensitivity when compared to the use of $M_{\text{vis}}$.

Final event selection criteria for the low-mass range, $200<m_{\PH}<450$, and
the high-mass range, $450<m_{\PH}<900$, considered in the $\PH\to\mu\tau$ and
$\PH\to\Pe\tau$ analyses.  Low-mass range High-mass range
$\PH\to\PGm\PGt_{\PSh}$ 0-jet $\pt^{\PGm}>60$ $\pt^{\PGm}>150$ $\pt^{\PGt}>30$
$\pt^{\PGt}>45$ $\mT(\tauh)<105$ $\mT(\tauh)<200$ 1-jet $\pt^{\PGm}>60$
$\pt^{\PGm}>150$ $\pt^{\PGt}>30$ $\pt^{\PGt}>45$ $\mT(\tauh)<120$
$\mT(\tauh)<230$ $\PH\to\PGm\PGt_{\Pe}$ 0-jet $\pt^{\PGm}>60$ $\pt^{\PGm}>150$
$\pt^{\Pe}>10$ $\pt^{\Pe}>10$ $\Delta\phi(\Pe,\ptvecmiss)<0.7$rad
$\Delta\phi(\Pe,\ptvecmiss)<0.3$rad $\Delta\phi(\Pe,\PGm)>2.2$rad
$\Delta\phi(\Pe,\PGm)>2.2$rad 1-jet $\pt^{\PGm}>60$ $\pt^{\PGm}>150$
$\pt^{\Pe}>10$ $\pt^{\Pe}>10$ $\Delta\phi(\Pe,\ptvecmiss)<0.7$rad
$\Delta\phi(\Pe,\ptvecmiss)<0.3$rad $\Delta\phi(\Pe,\PGm)>2.2$rad
$\Delta\phi(\Pe,\PGm)>2.2$rad $\PH\to\Pe\PGt_{\PSh}$ 0-jet $\pt^{\Pe}>60$
$\pt^{\Pe}>150$ $\pt^{\PGt}>30$ $\pt^{\PGt}>45$ $\mT(\tauh)<105$
$\mT(\tauh)<200$ 1-jet $\pt^{\Pe}>60$ $\pt^{\Pe}>150$ $\pt^{\PGt}>30$
$\pt^{\PGt}>45$ $\mT(\tauh)<120$ $\mT(\tauh)<230$ $\PH\to\Pe\PGt_{\PGm}$ 0-jet
$\pt^{\Pe}>60$ $\pt^{\Pe}>150$ $\pt^{\PGm}>10$ $\pt^{\PGm}>10$
$\Delta\phi(\PGm,\ptvecmiss)<0.7$rad $\Delta\phi(\PGm,\ptvecmiss)<0.3$rad
$\Delta\phi(\Pe,\PGm)>2.2$rad $\Delta\phi(\Pe,\PGm)>2.2$rad 1-jet
$\pt^{\Pe}>60$ $\pt^{\Pe}>150$ $\pt^{\PGm}>10$ $\pt^{\PGm}>10$
$\Delta\phi(\PGm,\ptvecmiss)<0.7$rad $\Delta\phi(\PGm,\ptvecmiss)<0.3$rad
$\Delta\phi(\Pe,\PGm)>2.2$rad $\Delta\phi(\Pe,\PGm)>2.2$rad

##  0.6 Background estimation

The most significant background in the $\PGm\PGt_{\PSh}$ and $\Pe\PGt_{\PSh}$
channels comes from the $\PW\\!+\\!\text{jets}$ process and from events
comprised uniquely of jets produced through the strong interaction, referred
to as quantum chromodynamics (QCD) multijet events. In these processes, jets
are misidentified as electrons, muons or leptons. This background is estimated
with the collected data. The main background in the $\PGm\PGt_{\Pe}$ and
$\Pe\PGt_{\PGm}$ channels is $\ttbar$ production. It is estimated using
simulations. Other smaller backgrounds include electroweak diboson ($\PW\PW$,
$\PW\PZ$, and $\PZ\PZ$), Drell-Yan (DY)$\to\ell\ell$
($\ell=\Pe,\PGm)+\text{jets}$, DY$\to\PGt\PGt+\text{jets}$, SM Higgs boson
($\PH\to\PGt\PGt,\PW\PW$), $\PW\gamma^{(*)}\\!+\\!\text{jets}$, and single top
quark production processes. These are estimated using simulations. Gluon
fusion, vector boson fusion, and associated production mechanisms are
considered for the SM Higgs boson background. The background estimation
techniques are described in detail below, and are validated with control
regions that are enhanced with the dominant backgrounds.

The DY$\to\ell\ell,\PGt\PGt$ background is estimated from simulation. A
reweighting is applied to the generator-level boson $\pt$ and invariant mass,
$m_{\ell\ell,\PGt\PGt}$, distributions to correct for a shape discrepancy
between data and simulation. The reweighting factors, extracted from a control
region enriched in $\PZ\to\PGm\PGm$ events, are applied in bins of boson $\pt$
and $m_{\ell\ell,\PGt\PGt}$ as explained in [45]. Additional corrections for
$\Pe\to\tauh$ and $\mu\to\tauh$ misidentification rates are applied to the
simulated DY sample when the reconstructed $\tauh$ candidate is matched to an
electron for the $\PH\to\Pe\PGt_{\PSh}$ channel or a muon for the
$\PH\to\PGm\PGt_{\PSh}$ channel, respectively, at the generator level. These
corrections depend on the lepton $\eta$ and are measured in $Z\to\ell\ell$
data events.

The $\ttbar$ background is also estimated using simulation. The overall
normalisation of this estimate in the signal region is corrected with a
rescaling factor derived from a control region enriched in $\ttbar$ events,
defined by requiring the initial selection with the additional requirement
that at least one of the jets is tagged. Figure 1 (upper left) shows the data
compared to the background estimate in the $\ttbar$-enriched region in the
$\PH\to\PGm\PGt_{\Pe}$ channel.

Jets from $\PW\\!+\\!\text{jets}$ and QCD multijet events that are
misidentified as electrons, muons and, mainly, leptons, are leading source of
background in the $\PGm\PGt_{\PSh}$ and $\Pe\PGt_{\PSh}$ channels. In
$\PW\\!+\\!\text{jets}$ events, one lepton candidate is expected to be a
genuine lepton from the $\PW$ decay and the other a jet misidentified as a
lepton. In QCD multijet events, both lepton candidates are misidentified jets.
A technique fully based on control samples in data is used to estimate the
misidentified lepton background in the $\PGm\PGt_{\PSh}$ and $\Pe\PGt_{\PSh}$
channels, for which it is the dominant contribution. In the $\PGm\PGt_{\Pe}$
and $\Pe\PGt_{\PGm}$ channels, this background is estimated using a
combination of simulated samples and control regions in data. These methods
have been used in Refs. [38] and [45], and a detailed description can be found
in those publications. However, we are briefly describing the techniques in
the following subsections.

Figure 1: The $M_{\text{col}}$ distribution in the $\ttbar$ enriched (upper
left), like-sign lepton (upper right), and $\PW\\!+\\!\text{jets}$ enriched
(lower) control samples defined in the text. The uncertainty bands include
both statistical and systematic uncertainties from Section 0.7. No fit is
performed for these distributions. The different background processes shown
are normalised to the luminosity of the data either using the theoretical
prediction of the corresponding production cross section or directly from the
data driven technique described in the text.

###  0.6.1 Misidentified lepton background estimation from control samples in
data

The misidentified-lepton background is estimated from data. The
misidentification probabilities, $f_{i}$, where $i=\PGm$, $\Pe$, or $\tauh$,
are evaluated with independent $\PZ\\!+\\!\text{jets}$ data sets and then
applied to a control sample. The control sample is obtained by relaxing the
signal selection requirements, the $\PGm$, $\Pe$, or $\tauh$ isolation, and
excluding events passing the signal selection. The $f_{i}$ are estimated using
events with a boson candidate and one jet that can be misidentified as , , or
. The boson candidate is formed requiring two muons with $\pt>26$,
$\abs{\eta}<2.4$, and $I_{\text{rel}}^{\PGm}<0.15$. The muons are required to
have opposite charges and the dimuon invariant mass, $m_{\PGm\PGm}$, must
satisfy $76<m_{\PGm\PGm}<106$. The contribution from diboson events, where the
third lepton candidate corresponds to a genuine , , or , is subtracted using
simulation. Two $\PZ\\!+\\!\text{jets}$ samples are defined: a signal-like
one, in which the jet satisfies the same , , or selection criteria used in the
$\PH\to\PGm\PGt$ or $\PH\to\Pe\PGt$ selections, and a background-enriched
$\PZ\\!+\\!\text{jets}$ sample with relaxed identification on the jet
misidentified as , , or , but excluding events selected in the signal-like
sample. The requirements on the third candidate, the misidentified jet, depend
on the lepton flavour. The two samples are used to estimate $f_{i}$ as

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

where $N_{i}(\text{$\PZ\\!+\\!\text{jets}$ signal-like})$ is the number of
events with a third candidate (, , or ) that passes the signal-like sample
selection, and $N_{i}(\text{$\PZ\\!+\\!\text{jets}$ background-enriched})$ is
the number of events in the background-enriched sample. The background-
enriched selection used to estimate the misidentified $\PGm$ and $\Pe$
contribution requires an isolation of $0.15<I_{\text{rel}}^{\PGm}<0.25$ and
$0.1<I_{\text{rel}}^{\Pe}<0.5$, respectively. In both cases the
misidentification rate is computed and applied as a function of the lepton .
The lepton selection for the background-enriched sample requires that the
lepton candidates are identified using a loose $\tauh$ identification and
isolation working point but are not identified by the tight working point used
for the signal selection. The loose and tight working points have an
efficiencies of 70 and 50% for genuine candidates, respectively.

The misidentification rates have a dependence that varies with the number of
charged pions in the decay. They are estimated and applied as a function of
and for either one or three charged pions in the decay. The misidentified
background in the signal sample is obtained from control samples for each
lepton flavour. The selection requirements for these samples are the same as
for the signal sample except that the , , or should pass the identification
and isolation criteria used for the $\PZ\\!+\\!\text{jets}$ background-
enriched sample, but not those defining the $\PZ\\!+\\!\text{jets}$ signal-
like sample. To estimate the misidentified background in the signal sample,
each event in this background enriched sample is weighted by a factor
${f_{i}}/{(1-f_{i})}$. The background from misidentified muons and electrons
is estimated to be less than 5% of the misidentified $\tauh$ lepton background
and is neglected.

The background estimate is validated in a like-sign sample by applying the
misidentification rate $f_{i}$ to events selected by requiring the , , or in
the pairs having the same charge in both the background-enriched and the
signal-like samples. This validation is performed after the initial selection
described in Section 0.5. Figure 1 (upper right) shows the data compared to
the background estimate in the like-sign control region for the
$\PH\to\PGm\PGt_{\PSh}$ channel. The like-sign selection enhances the
misidentified-lepton background, and this sample is expected to be composed of
a similar fraction of $\PW\\!+\\!\text{jets}$ and QCD multijet events. The
background estimate is also validated in a boson enriched control sample. This
data sample is obtained by applying the signal sample requirements and cuts,
$50<\mT(\ell)<110$($\ell=\PGm$ or ) and $\mT(\tau)>50$. The misidentified
background in the signal region and boson enriched control sample are both
dominated by $\PW\\!+\\!\text{jets}$ events, with QCD multijet events forming
a small fraction of the samples. Figure 1 (lower) shows the data compared to
the background estimate in the $\PW\\!+\\!\text{jets}$ enriched sample for the
$\PH\to\PGm\PGt_{\PSh}$ channel. The background expectation for the
$\PH\to\Pe\PGt_{\PSh}$ channel is also validated with the same samples and
gives similar agreement.

###  0.6.2 $\PW\\!+\\!\text{jets}$ and QCD background estimation in
$\mu\tau_{\Pe}$ and $\Pe\tau_{\mu}$ channels

The $\PW\\!+\\!\text{jets}$ background contribution to the misidentified
background is estimated with simulations. The QCD multijet contribution is
estimated with like-sign data events that pass all the other signal
requirements. The remaining non-QCD background is estimated using simulation.
The resulting sample is then rescaled to account for the differences between
the background composition in the like and opposite sign samples. The scaling
factors are extracted from QCD multijet enriched control samples, composed of
events where the lepton candidates satisfy inverted isolation requirements, as
explained in Ref. [45]. This background contribution accounts for a negligible
fraction of the total yield after selection in both $\mu\tau_{\Pe}$ and
$\Pe\tau_{\mu}$ channels.

##  0.7 Systematic uncertainties

Systematic uncertainties arise from both experimental and theoretical sources
and can affect the normalisation and the shape of the collinear mass
distribution. They are summarized in Table 0.7.

The systematic uncertainties for the four channels. All uncertainties are
treated as correlated between the categories, except those with more values
separated by the $\oplus$ symbol. In the case of two values, the first value
is the correlated uncertainty and the second value is the uncorrelated
uncertainty for each individual category. In the case of three values, the
first and second values correspond to the uncertainties arising from
factorisation and renormalisation scales and PDF variations and are correlated
between categories, while the third value is the uncorrelated uncertainty for
each individual category. Two values separated by the “–” sign represent the
range of the uncertainties from the different sources and/or in the different
jet categories.  Systematic uncertainty $\PH\to\PGm\tauh$
$\PH\to\PGm\PGt_{\Pe}$ $\PH\to\Pe\tauh$ $\PH\to\Pe\PGt_{\PGm}$ Muon
trigger/ID/isolation 2% 2% — 2% Electron trigger/ID/isolation — 2% 2% 2%
Hadronic $\tauh$ efficiency 5% — 5% — High $\pt$ $\tauh$ efficiency
${}^{+5}_{-35}\%{\times}\pt{\times}0.001$ —
${}^{+5}_{-35}\%{\times}\pt{\times}0.001$ — tagging veto 2.0–2.5% 2.0–2.5%
2.0–2.5% 2.0–2.5% $\PGm\to\tauh$ background 25% — — — $\Pe\to\tauh$ background
— — 12% — $\text{jet}\to\tauh$ background 30%$\oplus$10% — 30%$\oplus$10% —
QCD multijet background — 30% — 30% $\PZ\to\mu\mu/\Pe\Pe+\textrm{jets}$
background — 0.1%$\oplus$2%$\oplus$5% — 0.1%$\oplus$2%$\oplus$5%
$\PZ\to\tau\tau+\text{jets}$ background 0.1%$\oplus$2%$\oplus$5%
0.1%$\oplus$2%$\oplus$5% 0.1%$\oplus$2%$\oplus$5% 0.1%$\oplus$2%$\oplus$5%
$\PW\\!+\\!\text{jets}$ background — 0.8%$\oplus$3.8%$\oplus$5% —
0.8%$\oplus$3.8%$\oplus$5% $\PW\PW,\PZ\PZ,\PW\PZ$ background
3.5%$\oplus$5%$\oplus$5% 3.5%$\oplus$5%$\oplus$5% 3.5%$\oplus$5%$\oplus$5%
3.5%$\oplus$5%$\oplus$5% $\PW\\!+\\!\gamma$ background — 10%$\oplus$5% —
10%$\oplus$5% Single top quark background 3%$\oplus$5%$\oplus$5%
3%$\oplus$5%$\oplus$5% 3%$\oplus$5%$\oplus$5% 3%$\oplus$5%$\oplus$5% $\ttbar$
background 10%$\oplus$5% 10%$\oplus$5% 10%$\oplus$5% 10%$\oplus$5% SM Higgs
fact./renorm. scales 3.9 % 3.9 % 3.9 % 3.9 % SM Higgs PDF$+\alpS$ 3.2 % 3.2 %
3.2 % 3.2 % Jet energy scale 3–20% 3–20% 3–20% 3–20% $\tauh$ energy scale 1.2%
— 1.2% — $\PGm,\Pe\to\tauh$ energy scale 1.5% — 3% — $\PGm$ energy scale 0.2%
0.2% — 0.2% $\Pe$ energy scale — 0.1–0.5% 0.1–0.5% 0.1–0.5% Unclustered energy
scale $\pm 1\sigma$ $\pm 1\sigma$ $\pm 1\sigma$ $\pm 1\sigma$ IntegRated
luminosity 2.5% 2.5% 2.5% 2.5%

The uncertainties in the muon, electron and lepton selection including the
trigger, identification (ID), and isolation efficiencies are estimated from
collision data sets of $\PZ$ bosons decaying to
$\Pe\Pe,\PGm\PGm,\PGt_{\PGm}\tauh$ [68, 69, 73]. They result in changes of
normalisation, with the exception of the uncertainty on high lepton efficiency
that changes both yield and $M_{\text{col}}$ distribution shape. The tagging
efficiency is measured in collision data, and the simulation is adjusted
accordingly to match with it. The uncertainty in this measurement is taken as
the systematic error affecting the normalisation of the simulation [76].

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, as described in Section 0.6;
they affect both the normalisation and the shape of the $M_{\text{col}}$
distribution. The uncertainty in the QCD multijet background yield is 30%, and
corresponds to the uncertainty in the extrapolation factor from the same-sign
to the opposite-sign region, as determined in Ref. [45]. The uncertainties in
the background contributions from $\PZ$, $\PW\PW,\PZ\PZ$, $\PW\gamma$,
$\ttbar$ and single-top quark arise predominantly from those in the measured
cross sections of these processes and are applied as uncertainties in sample
normalisation.

The uncertainties in the Higgs boson production cross sections due to the
factorisation and the renormalisation scales, as well as the PDFs and the
strong coupling constant ($\alpS$), result in changes in normalisation. They
are taken from Ref. [79] and summarized in Table 0.7 for the SM Higgs boson
and Table 0.7 for heavy Higgs bosons. Only effects on the total rate are
considered. Effects on the acceptance have been neglected.

Theoretical uncertainties from [79] are applied to the Higgs boson production
cross sections for the different masses. In the reference, the PDF and $\alpS$
uncertainties are computed following the recommendation of the PDF4LHC working
group. The remaining Gaussian uncertainty accounts for additional intrinsic
sources of theory uncertainty described in detail in the reference.

$m_{\PH}$ () | Cross section (pb) | Theory, Gaussian (%) | PDF$+\alpS$ (%)
---|---|---|---
200 | 16.94 |  $\pm$1.8 |  $\pm$3.0
300 | 6.59 |  $\pm$1.8 |  $\pm$3.0
450 | 2.30 |  $\pm$2.0 |  $\pm$3.1
600 | 1 |  $\pm$2.1 |  $\pm$3.5
750 | 0.50 |  $\pm$2.1 |  $\pm$4.0
900 | 0.27 |  $\pm$2.2 |  $\pm$4.6

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 $\pm 1$ standard deviation to the fit
templates of each process. There are 27 independent sources of jet energy
scale uncertainty, fully correlated between categories and $\PGt$ lepton decay
channels.

The uncertainty in the $\tauh$ energy scale is treated equally for the two
independent channels: $\PH\to\PGm\PGt_{h}$ and $\PH\to\Pe\PGt_{h}$. It is
propagated to the collinear mass distributions. Also, the uncertainty in the
energy scale of electrons and muons misidentified as is propagated to the
$M_{\text{col}}$ distributions and produces changes in the distribution shape
and normalization. Systematic uncertainties in the electron energy scale and
resolution include the effects of electron selection efficiency,
pseudorapidity dependence and categorisation, summed in quadrature. They
impact both the normalization and shape of the $M_{\text{col}}$ distribution.
The systematic uncertainties in the energy resolution have negligible effect.
The uncertainty in muon energy scale and resolution is also treated in the
same manner. Scale uncertainties on the energy from jets with below 15and PF
candidates not clustered inside jets (unclustered energy scale uncertainty)
are also considered [77]. They are estimated independently for four particle
categories: charged particles, photons, neutral hadrons, and very forward
particles which are not contained in jets. The effect of shifting the energy
of each particle by its uncertainty is propagated to and leads to both changes
in shape of the distribution and in overall predicted yields. The different
systematic uncertainties from the four particle categories, for the
unclustered energy scale, are considered uncorrelated.

The bin-by-bin uncertainties [80] account for the statistical uncertainties in
every bin of the template distributions of every process. They are
uncorrelated between bins, processes, and categories.

Shape uncertainties related to the pileup have been considered by varying the
weights applied to simulation. This weight variation is obtained changing by
5% the total inelastic cross section used in the estimate of the pileup events
in data [81]. The new values are then applied, event by event, to produce
alternative collinear mass distributions used as shape uncertainties in the
fit. Other minimum bias event modelling and simulation uncertainties are
estimated to be much smaller and are therefore neglected. The uncertainty on
the integrated luminosity affects all processes with normalization taken
directly from simulation.

##  0.8 Results

After all selection criteria have been applied, a binned maximum likelihood
fit is performed on the $M_{\text{col}}$ distributions in the range 0–1400,
looking for a signal-like excess on top of the estimated background. No fit on
the control region is performed. The systematic uncertainties are represented
by nuisance parameters, assuming a log normal prior for normalisation
parameters, and Gaussian priors for $M_{\text{col}}$ shape uncertainties. The
uncertainties that affect the shape of the $M_{\text{col}}$ distribution,
mainly those corresponding to the energy scales, are represented by nuisance
parameters whose variation results in a modification of the distribution [82,
83]. A profile likelihood ratio is used as test statistic. The $95$% CL upper
limits on the production cross section times branching fraction to LFV lepton
channels, $\sigma(\Pg\Pg\to\PH)\mathcal{B}(\PH\to\PGm\PGt)$ and
$\sigma(\Pg\Pg\to\PH)\mathcal{B}(\PH\to\Pe\PGt)$, are set using the criterion
[84, 85] and the asymptotic approximation of the distributions of the LHC
test-statistic [86], in a combined fit to the $M_{\text{col}}$ distributions.
The limits are also computed per channel and category. The upper limits are
derived in the analysed mass range in steps of 50. Where simulated samples are
not available, shapes and yields are interpolated from the neighbouring
samples with a moment morphing algorithm that derive the $M_{\text{col}}$
distribution from the two closest simulated mass points.

###  0.8.1 $\PH\to\mu\tau$ results

The distributions of the collinear mass $M_{\text{col}}$ compared to the
signal and background contributions in the $\PH\to\PGm\PGt_{\PSh}$ and
$\PH\to\PGm\PGt_{\Pe}$ channels, in each jet category, are shown in Figs. 2
and 3. No excess over the background expectation is observed. The observed and
median expected 95% CL upper limits on
$\sigma(\Pg\Pg\to\PH)\mathcal{B}(\PH\to\PGm\PGt)$ range from 51.9 (57.4)fb to
1.6 (2.1)fb, and are given for each category in Table 0.8.1.The limits are
also summarized graphically in Fig. 4 for the individual categories, and in
Fig. 5 for the combination of the two $\PGt$ decay channels.

Figure 2: The $M_{\text{col}}$ distribution in the signal region, for the
$\PGm\tauh$ (upper) and $\PGm\PGt_{\Pe}$ (lower) channels for the Higgs boson
mass in the range 200–450for 0-jet (left) and 1-jet (right) categories. The
uncertainty bands include both statistical and systematic uncertainties. The
plotted values are number of events per bin using a variable bin size. The
background is normalised to the best fit values from a binned likelihood fit,
discussed in the text, to the background only hypothesis. For depicting the
signals a branching fraction of 1% and BSM cross sections from Ref. [79] are
assumed.

Figure 3: The $M_{\text{col}}$ distribution in the signal region, for the
$\PGm\tauh$ (upper) and $\PGm\PGt_{\Pe}$ (lower) channels for the Higgs boson
mass in the range 450–900for 0-jet (left) and 1-jet (right) categories. The
uncertainty bands include both statistical and systematic uncertainties. The
plotted values are number of events per bin using a variable bin size. The
background is normalised to the best fit values from a binned likelihood fit,
discussed in the text, to the background only hypothesis. For depicting the
signals a branching fraction of 1% and BSM cross sections from Ref. [79] are
assumed.

The observed and median expected 95% CL upper limits on
$\sigma(\Pg\Pg\to\PH)\mathcal{B}(\PH\to\PGm\PGt)$.  Observed 95% CL upper
limit on $\sigma(\Pg\Pg\to\PH)\mathcal{B}(\PH\to\PGm\PGt)$ (fb)
$\PGm\PGt_{\Pe}$ $\PGm\tauh$ $\PGm\PGt$ $m_{\PH}$ () 0 jet 1 jet comb 0 jet 1
jet comb 0 jet 1 jet comb 200 147.8 262.1 159.4 53.1 136.9 46.4 53.3 133.9
51.9 300 30.1 100.8 29.3 57.4 49.4 51.4 33.2 45.5 32.7 450 31.1 35.3 23.7 9.1
14.2 7.3 14.7 14.6 8.1 600 8.1 15.2 6.8 7.5 7.4 5.3 9.1 6.5 4.1 750 6.5 7.8
4.7 4.8 4.8 3.2 3.6 3.7 2.5 900 4.4 5.6 2.9 4.6 2.6 2.3 3.0 2.1 1.6
Median expected 95% CL upper limit on
$\sigma(\Pg\Pg\to\PH)\mathcal{B}(\PH\to\PGm\PGt)$ (fb) $\PGm\PGt_{\Pe}$
$\PGm\tauh$ $\PGm\PGt$ $m_{\PH}$ () 0 jet 1 jet comb 0 jet 1 jet comb 0 jet 1
jet comb 200 107.5 209.8 95.6 79.7 151.6 72.5 63.7 126.1 57.4 300 49.8 108.6
45.2 31.0 54.8 27.7 25.9 48.8 23.4 450 17.5 32.8 20.4 9.4 15.3 8.0 8.2 13.6
7.7 600 10.4 17.9 8.9 6.2 8.3 4.9 5.1 7.4 4.2 750 8.0 11.1 6.1 4.3 5.4 3.1 3.6
4.7 2.7 900 6.9 8.0 4.9 3.3 4.3 2.4 2.8 3.5 2.1

Figure 4: The observed and median expected 95% CL upper limits on
$\sigma(\Pg\Pg\to\PH)\mathcal{B}(\PH\to\PGm\PGt)$, for the $\PGm\tauh$ (upper)
and $\PGm\PGt_{\Pe}$ (lower) channels, for 0-jet (left) and 1-jet (right)
categories. The dashed line shows the transition between the two investigated
mass ranges.

Figure 5: The combined observed and median expected 95% CL upper limits on
$\sigma(\Pg\Pg\to\PH)\mathcal{B}(\PH\to\PGm\PGt)$, for $\PGm\tauh$ (upper
left) and $\PGm\PGt_{\Pe}$ (lower right) channels, and their combination
$\PGm\PGt$ (lower). The dashed line shows the transition between the two
investigated mass ranges.

###  0.8.2 $\PH\to\Pe\tau$ results

The distributions of the collinear mass $M_{\text{col}}$ compared to the
signal and background contributions in the $\PH\to\Pe\PGt_{\PSh}$ and
$\PH\to\Pe\PGt_{\PGm}$ channels, in each category, are shown in Figs. 6 and 7.
No excess over the background expectation is observed. The observed and median
expected 95% CL upper limits on
$\sigma(\Pg\Pg\to\PH)\mathcal{B}(\PH\to\Pe\PGt)$ range from 94.1 (91.6)fb to
2.3 (2.3)fb, and are given for each category in Table 0.8.2. The limits are
also summarized graphically in Fig. 8 for the individual categories, and in
Fig. 9 for the combination of both two $\PGt$ decay channels.

Figure 6: The $M_{\text{col}}$ distribution in the signal region, for the
$\Pe\tauh$ (upper) and $\Pe\PGt_{\PGm}$ (lower) channels for the Higgs boson
mass in the range 200–450for 0-jet (left) and 1-jet (right) categories. The
uncertainty bands include both statistical and systematic uncertainties. The
plotted values are number of events per bin using a variable bin size. The
background is normalised to the best fit values from a binned likelihood fit,
discussed in the text, to the background only hypothesis. For depicting the
signals a branching fraction of 1% and BSM cross sections from Ref. [79] are
assumed.

Figure 7: The $M_{\text{col}}$ distribution in the signal region, for the
$\Pe\tauh$ (upper) and $\Pe\PGt_{\PGm}$ (lower) channels for the Higgs boson
mass in the range 450–900for 0-jet (left) and 1-jet (right) categories. The
uncertainty bands include both statistical and systematic uncertainties. The
plotted values are number of events per bin using a variable bin size. The
background is normalised to the best fit values from a binned likelihood fit,
discussed in the text, to the background only hypothesis. For depicting the
signals a branching fraction of 1% and BSM cross sections from Ref. [79] are
assumed.

The observed and median expected 95% CL upper limits on
$\sigma(\Pg\Pg\to\PH)\mathcal{B}(\PH\to\Pe\PGt)$.

Observed 95% CL upper limit on
$\sigma(\Pg\Pg\to\PH)\mathcal{B}(\PH\to\Pe\PGt)$ (fb)
---
| $\Pe\PGt_{\PGm}$ |  | $\Pe\tauh$ |  | $\Pe\PGt$
$m_{\PH}$ () | 0 jet | 1 jet | comb |  | 0 jet | 1 jet | comb |  | 0 jet | 1 jet | comb
200 | 119.2 | 365.3 | 117.8 |  | 179.4 | 197.8 | 139.6 |  | 103.2 | 180.1 | 94.1
300 | 85.1 | 208.7 | 94.5 |  | 56.4 | 56.4 | 43.2 |  | 50.6 | 65.4 | 46.0
450 | 14.0 | 25.1 | 11.7 |  | 7.6 | 16.9 | 6.8 |  | 5.9 | 13.2 | 5.2
600 | 17.4 | 13.9 | 11.7 |  | 9.3 | 9.1 | 6.3 |  | 8.8 | 6.9 | 5.8
750 | 5.1 | 9.5 | 4.1 |  | 4.7 | 5.6 | 3.3 |  | 2.9 | 4.5 | 2.3
900 | 7.7 | 8.3 | 5.3 |  | 3.8 | 5.0 | 2.7 |  | 3.1 | 4.0 | 2.3

Median expected 95% CL upper limit on
$\sigma(\Pg\Pg\to\PH)\mathcal{B}(\PH\to\Pe\PGt)$ (fb)
---
| $\Pe\PGt_{\PGm}$ |  | $\Pe\tauh$ |  | $\Pe\PGt$
$m_{\PH}$ () | 0 jet | 1 jet | comb |  | 0 jet | 1 jet | comb |  | 0 jet | 1 jet | comb
200 | 158.2 | 366.6 | 142.3 |  | 135.7 | 238.9 | 120.1 |  | 102.9 | 200.5 | 91.6
300 | 57.9 | 123.0 | 52.3 |  | 42.9 | 70.3 | 37.5 |  | 34.5 | 62.0 | 30.2
450 | 20.4 | 32.6 | 17.2 |  | 10.1 | 18.0 | 8.7 |  | 9.0 | 15.4 | 7.8
600 | 14.7 | 22.1 | 11.9 |  | 8.6 | 11.6 | 6.8 |  | 7.5 | 9.9 | 5.9
750 | 8.6 | 10.5 | 6.2 |  | 4.9 | 6.5 | 3.7 |  | 4.1 | 5.3 | 3.0
900 | 8.5 | 9.0 | 5.7 |  | 4.0 | 4.7 | 2.6 |  | 3.3 | 4.0 | 2.3

Figure 8: The observed and median expected 95% CL upper limits on
$\sigma(\Pg\Pg\to\PH)\mathcal{B}(\PH\to\Pe\PGt)$, for the $\Pe\tauh$ (upper)
and $\Pe\PGt_{\PGm}$ (lower) channels, for 0-jet (left) and 1-jet (right)
categories. The dashed line shows the transition between the two investigated
mass ranges.

Figure 9: The combined observed and median expected 95% CL upper limits on
$\sigma(\Pg\Pg\to\PH)\mathcal{B}(\PH\to\Pe\PGt)$, for $\Pe\tauh$ (upper left)
and $\Pe\PGt_{\PGm}$ (upper right) channels, and their combination $\Pe\PGt$
(lower). The dashed line shows the transition between the two investigated
mass ranges.

##  0.9 Summary

The first direct search for lepton flavour violating decays of a neutral non-
standard-model Higgs boson (H) in the $\PGm\PGt$ and $\Pe\PGt$ channels is
presented in this paper. The analyzed data set corresponds to an integrated
luminosity of 35.9of proton-proton collision data recorded at $\sqrt{s}=13$.
The results are extracted from a fit to the collinear mass distributions. No
evidence is found for lepton flavour violating decays of $\PH$ in the
investigated mass range. The observed (expected) upper limits at 95%
confidence level on the product of production cross section with branching
fraction, for $\PH$ mass in the range 200–900, decaying to $\PGm\PGt$ and
$\Pe\PGt$ vary from 51.9 (57.4)fb to 1.6 (2.1)fb and from 94.1 (91.6)fb to 2.3
(2.3)fb, respectively.

###### 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: BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq,
CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST,
and NSFC (China); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RPF
(Cyprus); SENESCYT (Ecuador); MoER, ERC IUT, PUT and ERDF (Estonia); Academy
of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG,
and HGF (Germany); GSRT (Greece); NKFIA (Hungary); DAE and DST (India); IPM
(Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); MES
(Latvia); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT,
LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC
(Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom,
RAS, RFBR, and NRC KI (Russia); MESTD (Serbia); SEIDI, CPAN, PCTI, and FEDER
(Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST
(Taipei); ThEPCenter, IPST, STAR, and NSTDA (Thailand); TUBITAK and TAEK
(Turkey); NASU (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 Nos. 675440,
752730, and 765710 (European Union); the Leventis Foundation; the A.P. Sloan
Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science
Policy Office; the Fonds pour la Formation à la Recherche dans l’Industrie et
dans l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door
Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium)
under the “Excellence of Science – EOS” – be.h project n. 30820817; the
Beijing Municipal Science & Technology Commission, No. Z181100004218003; the
Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the
Deutsche Forschungsgemeinschaft (DFG) under Germany’s Excellence Strategy –
EXC 2121 “Quantum Universe” – 390833306; the Lendület (“Momentum”) Programme
and the János Bolyai Research Scholarship of the Hungarian Academy of
Sciences, the New National Excellence Program ÚNKP, the NKFIA research grants
123842, 123959, 124845, 124850, 125105, 128713, 128786, and 129058 (Hungary);
the Council of Science and Industrial Research, India; the HOMING PLUS
programme of the Foundation for Polish Science, cofinanced from European
Union, Regional Development Fund, the Mobility Plus programme of the Ministry
of Science and Higher Education, the National Science Center (Poland),
contracts Harmonia 2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543,
2014/15/B/ST2/03998, and 2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406;
the National Priorities Research Program by Qatar National Research Fund; the
Ministry of Science and Education, grant no. 3.2989.2017 (Russia); the
Programa Estatal de Fomento de la Investigación Científica y Técnica de
Excelencia María de Maeztu, grant MDM-2015-0509 and the Programa Severo Ochoa
del Principado de Asturias; the Thalis and Aristeia 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 Nvidia Corporation;
the Welch Foundation, contract C-1845; and the Weston Havens Foundation (USA).

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##  .10 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan${}^{\textrm{\textdagger}}$, A. Tumasyan Institut für
Hochenergiephysik, Wien, Austria
W. Adam, F. Ambrogi, T. Bergauer, J. Brandstetter, M. Dragicevic, J. Erö, A.
Escalante Del Valle, M. Flechl, R. Frühwirth1, M. Jeitler1, N. Krammer, I.
Krätschmer, D. Liko, T. Madlener, I. Mikulec, N. Rad, J. Schieck1, R.
Schöfbeck, M. Spanring, D. Spitzbart, W. Waltenberger, C.-E. Wulz1, M. Zarucki
Institute for Nuclear Problems, Minsk, Belarus
V. Drugakov, V. Mossolov, J. Suarez Gonzalez Universiteit Antwerpen,
Antwerpen, Belgium
M.R. Darwish, E.A. De Wolf, D. Di Croce, X. Janssen, J. Lauwers, A. Lelek, M.
Pieters, H. Rejeb Sfar, H. Van Haevermaet, P. Van Mechelen, S. Van Putte, N.
Van Remortel Vrije Universiteit Brussel, Brussel, Belgium
F. Blekman, E.S. Bols, S.S. Chhibra, J. D’Hondt, J. De Clercq, D. Lontkovskyi,
S. Lowette, I. Marchesini, S. Moortgat, L. Moreels, Q. Python, K. Skovpen, S.
Tavernier, W. Van Doninck, P. Van Mulders, I. Van Parijs Université Libre de
Bruxelles, Bruxelles, Belgium
D. Beghin, B. Bilin, H. Brun, B. Clerbaux, G. De Lentdecker, H. Delannoy, B.
Dorney, L. Favart, A. Grebenyuk, A.K. Kalsi, J. Luetic, A. Popov, N. Postiau,
E. Starling, L. Thomas, C. Vander Velde, P. Vanlaer, D. Vannerom Ghent
University, Ghent, Belgium
T. Cornelis, D. Dobur, I. Khvastunov2, M. Niedziela, C. Roskas, D. Trocino, M.
Tytgat, W. Verbeke, B. Vermassen, M. Vit, N. Zaganidis Université Catholique
de Louvain, Louvain-la-Neuve, Belgium
O. Bondu, G. Bruno, C. Caputo, P. David, C. Delaere, M. Delcourt, A.
Giammanco, V. Lemaitre, A. Magitteri, J. Prisciandaro, A. Saggio, M. Vidal
Marono, P. Vischia, J. Zobec Centro Brasileiro de Pesquisas Fisicas, Rio de
Janeiro, Brazil
F.L. Alves, G.A. Alves, G. Correia Silva, C. Hensel, A. Moraes, P. Rebello
Teles Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
E. Belchior Batista Das Chagas, W. Carvalho, J. Chinellato3, E. Coelho, E.M.
Da Costa, G.G. Da Silveira4, D. De Jesus Damiao, C. De Oliveira Martins, S.
Fonseca De Souza, L.M. Huertas Guativa, H. Malbouisson, J. Martins5, D. Matos
Figueiredo, M. Medina Jaime6, M. Melo De Almeida, C. Mora Herrera, L. Mundim,
H. Nogima, W.L. Prado Da Silva, L.J. Sanchez Rosas, A. Santoro, A. Sznajder,
M. Thiel, E.J. Tonelli Manganote3, F. Torres Da Silva De Araujo, A. Vilela
Pereira Universidade Estadual Paulista a, Universidade Federal do ABC b, São
Paulo, Brazil
S. Ahujaa, C.A. Bernardesa, L. Calligarisa, T.R. Fernandez Perez Tomeia, E.M.
Gregoresb, D.S. Lemos, P.G. Mercadanteb, S.F. Novaesa, SandraS. Padulaa
Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy of
Sciences, Sofia, Bulgaria
A. Aleksandrov, G. Antchev, R. Hadjiiska, P. Iaydjiev, A. Marinov, M. Misheva,
M. Rodozov, M. Shopova, G. Sultanov University of Sofia, Sofia, Bulgaria
M. Bonchev, A. Dimitrov, T. Ivanov, L. Litov, B. Pavlov, P. Petkov Beihang
University, Beijing, China
W. Fang7, X. Gao7, L. Yuan Department of Physics, Tsinghua University,
Beijing, China
Z. Hu, Y. Wang Institute of High Energy Physics, Beijing, China
M. Ahmad, G.M. Chen, H.S. Chen, M. Chen, C.H. Jiang, D. Leggat, H. Liao, Z.
Liu, S.M. Shaheen8, A. Spiezia, J. Tao, E. Yazgan, H. Zhang, S. Zhang8, J.
Zhao State Key Laboratory of Nuclear Physics and Technology, Peking
University, Beijing, China
A. Agapitos, Y. Ban, G. Chen, A. Levin, J. Li, L. Li, Q. Li, Y. Mao, S.J.
Qian, D. Wang, Q. Wang Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, L.F. Chaparro Sierra, C. Florez, C.F. González
Hernández, M.A. Segura Delgado Universidad de Antioquia, Medellin, Colombia
J. Mejia Guisao, J.D. Ruiz Alvarez, C.A. Salazar González, N. Vanegas Arbelaez
University of Split, Faculty of Electrical Engineering, Mechanical Engineering
and Naval Architecture, Split, Croatia
D. Giljanović, N. Godinovic, D. Lelas, I. Puljak, T. Sculac University of
Split, Faculty of Science, Split, Croatia
Z. Antunovic, M. Kovac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, S. Ceci, D. Ferencek, K. Kadija, B. Mesic, M. Roguljic, A.
Starodumov9, T. Susa University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, E. Erodotou, A. Ioannou, M. Kolosova, S. Konstantinou,
G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos, P.A. Razis, H.
Rykaczewski, D. Tsiakkouri Charles University, Prague, Czech Republic
M. Finger10, M. Finger Jr.10, A. Kveton, J. Tomsa Escuela Politecnica
Nacional, Quito, Ecuador
E. Ayala Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab
Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
H. Abdalla11, E. Salama12,13 National Institute of Chemical Physics and
Biophysics, Tallinn, Estonia
S. Bhowmik, A. Carvalho Antunes De Oliveira, R.K. Dewanjee, K. Ehataht, M.
Kadastik, M. Raidal, C. Veelken Department of Physics, University of Helsinki,
Helsinki, Finland
P. Eerola, L. Forthomme, H. Kirschenmann, K. Osterberg, M. Voutilainen
Helsinki Institute of Physics, Helsinki, Finland
F. Garcia, J. Havukainen, J.K. Heikkilä, T. Järvinen, V. Karimäki, R.
Kinnunen, T. Lampén, K. Lassila-Perini, S. Laurila, S. Lehti, T. Lindén, P.
Luukka, T. Mäenpää, H. Siikonen, E. Tuominen, J. Tuominiemi Lappeenranta
University of Technology, Lappeenranta, Finland
T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
M. Besancon, F. Couderc, M. Dejardin, D. Denegri, B. Fabbro, J.L. Faure, F.
Ferri, S. Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry,
C. Leloup, E. Locci, J. Malcles, J. Rander, A. Rosowsky, M.Ö. Sahin, A. Savoy-
Navarro14, M. Titov Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole
Polytechnique, Institut Polytechnique de Paris
C. Amendola, F. Beaudette, P. Busson, C. Charlot, B. Diab, G. Falmagne, R.
Granier de Cassagnac, I. Kucher, A. Lobanov, C. Martin Perez, M. Nguyen, C.
Ochando, P. Paganini, J. Rembser, R. Salerno, J.B. Sauvan, Y. Sirois, A. Zabi,
A. Zghiche Université de Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, France
J.-L. Agram15, J. Andrea, D. Bloch, G. Bourgatte, J.-M. Brom, E.C. Chabert, C.
Collard, E. Conte15, J.-C. Fontaine15, D. Gelé, U. Goerlach, M. Jansová, A.-C.
Le Bihan, N. Tonon, P. Van Hove Centre de Calcul de l’Institut National de
Physique Nucleaire et de Physique des Particules, CNRS/IN2P3, Villeurbanne,
France
S. Gadrat Université de Lyon, Université Claude Bernard Lyon 1, CNRS-IN2P3,
Institut de Physique Nucléaire de Lyon, Villeurbanne, France
S. Beauceron, C. Bernet, G. Boudoul, C. Camen, N. Chanon, R. Chierici, D.
Contardo, P. Depasse, H. El Mamouni, J. Fay, S. Gascon, M. Gouzevitch, B.
Ille, Sa. Jain, F. Lagarde, I.B. Laktineh, H. Lattaud, M. Lethuillier, L.
Mirabito, S. Perries, V. Sordini, G. Touquet, M. Vander Donckt, S. Viret
Georgian Technical University, Tbilisi, Georgia
A. Khvedelidze10 Tbilisi State University, Tbilisi, Georgia
Z. Tsamalaidze10 RWTH Aachen University, I. Physikalisches Institut, Aachen,
Germany
C. Autermann, L. Feld, M.K. Kiesel, K. Klein, M. Lipinski, D. Meuser, A.
Pauls, M. Preuten, M.P. Rauch, C. Schomakers, J. Schulz, M. Teroerde, B.
Wittmer RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
A. Albert, M. Erdmann, S. Erdweg, T. Esch, B. Fischer, R. Fischer, S. Ghosh,
T. Hebbeker, K. Hoepfner, H. Keller, L. Mastrolorenzo, M. Merschmeyer, A.
Meyer, P. Millet, G. Mocellin, S. Mondal, S. Mukherjee, D. Noll, A. Novak, T.
Pook, A. Pozdnyakov, T. Quast, M. Radziej, Y. Rath, H. Reithler, M. Rieger, J.
Roemer, A. Schmidt, S.C. Schuler, A. Sharma, S. Thüer, S. Wiedenbeck RWTH
Aachen University, III. Physikalisches Institut B, Aachen, Germany
G. Flügge, W. Haj Ahmad16, O. Hlushchenko, T. Kress, T. Müller, A. Nehrkorn,
A. Nowack, C. Pistone, O. Pooth, D. Roy, H. Sert, A. Stahl17 Deutsches
Elektronen-Synchrotron, Hamburg, Germany
M. Aldaya Martin, P. Asmuss, I. Babounikau, H. Bakhshiansohi, K. Beernaert, O.
Behnke, U. Behrens, A. Bermúdez Martínez, D. Bertsche, A.A. Bin Anuar, K.
Borras18, V. Botta, A. Campbell, A. Cardini, P. Connor, S. Consuegra
Rodríguez, C. Contreras-Campana, V. Danilov, A. De Wit, M.M. Defranchis, C.
Diez Pardos, D. Domínguez Damiani, G. Eckerlin, D. Eckstein, T. Eichhorn, A.
Elwood, E. Eren, E. Gallo19, A. Geiser, J.M. Grados Luyando, A. Grohsjean, M.
Guthoff, M. Haranko, A. Harb, A. Jafari, N.Z. Jomhari, H. Jung, A. Kasem18, M.
Kasemann, H. Kaveh, J. Keaveney, C. Kleinwort, J. Knolle, D. Krücker, W.
Lange, T. Lenz, J. Leonard, J. Lidrych, K. Lipka, W. Lohmann20, R. Mankel,
I.-A. Melzer-Pellmann, A.B. Meyer, M. Meyer, M. Missiroli, G. Mittag, J.
Mnich, A. Mussgiller, V. Myronenko, D. Pérez Adán, S.K. Pflitsch, D. Pitzl, A.
Raspereza, A. Saibel, M. Savitskyi, V. Scheurer, P. Schütze, C.
Schwanenberger, R. Shevchenko, A. Singh, H. Tholen, O. Turkot, A. Vagnerini,
M. Van De Klundert, G.P. Van Onsem, R. Walsh, Y. Wen, K. Wichmann, C. Wissing,
O. Zenaiev, R. Zlebcik University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, L. Benato, A. Benecke, V. Blobel, T. Dreyer, A.
Ebrahimi, A. Fröhlich, C. Garbers, E. Garutti, D. Gonzalez, P. Gunnellini, J.
Haller, A. Hinzmann, A. Karavdina, G. Kasieczka, R. Klanner, R. Kogler, N.
Kovalchuk, S. Kurz, V. Kutzner, J. Lange, T. Lange, A. Malara, D. Marconi, J.
Multhaup, C.E.N. Niemeyer, D. Nowatschin, A. Perieanu, A. Reimers, O. Rieger,
C. Scharf, P. Schleper, S. Schumann, J. Schwandt, J. Sonneveld, H. Stadie, G.
Steinbrück, F.M. Stober, M. Stöver, B. Vormwald, I. Zoi Karlsruher Institut
fuer Technologie, Karlsruhe, Germany
M. Akbiyik, C. Barth, M. Baselga, S. Baur, T. Berger, E. Butz, R. Caspart, T.
Chwalek, W. De Boer, A. Dierlamm, K. El Morabit, N. Faltermann, M. Giffels, P.
Goldenzweig, A. Gottmann, M.A. Harrendorf, F. Hartmann17, U. Husemann, S.
Kudella, S. Mitra, M.U. Mozer, Th. Müller, M. Musich, A. Nürnberg, G. Quast,
K. Rabbertz, M. Schröder, I. Shvetsov, H.J. Simonis, R. Ulrich, M. Weber, C.
Wöhrmann, R. Wolf Institute of Nuclear and Particle Physics (INPP), NCSR
Demokritos, Aghia Paraskevi, Greece
G. Anagnostou, P. Asenov, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas,
G. Paspalaki National and Kapodistrian University of Athens, Athens, Greece
M. Diamantopoulou, G. Karathanasis, P. Kontaxakis, A. Panagiotou, I.
Papavergou, N. Saoulidou, A. Stakia, K. Theofilatos, K. Vellidis National
Technical University of Athens, Athens, Greece
G. Bakas, K. Kousouris, I. Papakrivopoulos, G. Tsipolitis University of
Ioánnina, Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, S. Mallios, K.
Manitara, N. Manthos, I. Papadopoulos, J. Strologas, F.A. Triantis, D.
Tsitsonis MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
M. Bartók21, M. Csanad, P. Major, K. Mandal, A. Mehta, M.I. Nagy, G. Pasztor,
O. Surányi, G.I. Veres Wigner Research Centre for Physics, Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath22, F. Sikler, T.Á. Vámi, V. Veszpremi, G.
Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear Research ATOMKI,
Debrecen, Hungary
N. Beni, S. Czellar, J. Karancsi21, A. Makovec, J. Molnar, Z. Szillasi
Institute of Physics, University of Debrecen, Debrecen, Hungary
P. Raics, D. Teyssier, Z.L. Trocsanyi, B. Ujvari Eszterhazy Karoly University,
Karoly Robert Campus, Gyongyos, Hungary
T. Csorgo, W.J. Metzger, F. Nemes, T. Novak Indian Institute of Science
(IISc), Bangalore, India
S. Choudhury, J.R. Komaragiri, P.C. Tiwari National Institute of Science
Education and Research, HBNI, Bhubaneswar, India
S. Bahinipati24, C. Kar, G. Kole, P. Mal, V.K. Muraleedharan Nair Bindhu, A.
Nayak25, D.K. Sahoo24, S.K. Swain Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, S. Chauhan, R. Chawla, N. Dhingra, R.
Gupta, A. Kaur, M. Kaur, S. Kaur, P. Kumari, M. Lohan, M. Meena, K. Sandeep,
S. Sharma, J.B. Singh, A.K. Virdi, G. Walia University of Delhi, Delhi, India
A. Bhardwaj, B.C. Choudhary, R.B. Garg, M. Gola, S. Keshri, Ashok Kumar, S.
Malhotra, M. Naimuddin, P. Priyanka, K. Ranjan, Aashaq Shah, R. Sharma Saha
Institute of Nuclear Physics, HBNI, Kolkata, India
R. Bhardwaj26, M. Bharti26, R. Bhattacharya, S. Bhattacharya, U. Bhawandeep26,
D. Bhowmik, S. Dey, S. Dutta, S. Ghosh, M. Maity27, K. Mondal, S. Nandan, A.
Purohit, P.K. Rout, G. Saha, S. Sarkar, T. Sarkar27, M. Sharan, B. Singh26, S.
Thakur26 Indian Institute of Technology Madras, Madras, India
P.K. Behera, P. Kalbhor, A. Muhammad, P.R. Pujahari, A. Sharma, A.K. Sikdar
Bhabha Atomic Research Centre, Mumbai, India
R. Chudasama, D. Dutta, V. Jha, V. Kumar, D.K. Mishra, P.K. Netrakanti, L.M.
Pant, P. Shukla Tata Institute of Fundamental Research-A, Mumbai, India
T. Aziz, M.A. Bhat, S. Dugad, G.B. Mohanty, N. Sur, RavindraKumar Verma Tata
Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, S. Karmakar,
S. Kumar, G. Majumder, K. Mazumdar, N. Sahoo, S. Sawant Indian Institute of
Science Education and Research (IISER), Pune, India
S. Chauhan, S. Dube, V. Hegde, A. Kapoor, K. Kothekar, S. Pandey, A. Rane, A.
Rastogi, S. Sharma Institute for Research in Fundamental Sciences (IPM),
Tehran, Iran
S. Chenarani28, E. Eskandari Tadavani, S.M. Etesami28, M. Khakzad, M.
Mohammadi Najafabadi, M. Naseri, F. Rezaei Hosseinabadi University College
Dublin, Dublin, Ireland
M. Felcini, M. Grunewald INFN Sezione di Bari a, Università di Bari b,
Politecnico di Bari c, Bari, Italy
M. Abbresciaa,b, R. Alya,b,29, C. Calabriaa,b, A. Colaleoa, D. Creanzaa,c, L.
Cristellaa,b, N. De Filippisa,c, M. De Palmaa,b, A. Di Florioa,b, L. Fiorea,
A. Gelmia,b, G. Iasellia,c, M. Incea,b, S. Lezkia,b, G. Maggia,c, M. Maggia,
G. Minielloa,b, S. Mya,b, S. Nuzzoa,b, A. Pompilia,b, G. Pugliesea,c, R.
Radognaa, A. Ranieria, G. Selvaggia,b, L. Silvestrisa, R. Vendittia, P.
Verwilligena INFN Sezione di Bologna a, Università di Bologna b, Bologna,
Italy
G. Abbiendia, C. Battilanaa,b, D. Bonacorsia,b, L. Borgonovia,b, S. Braibant-
Giacomellia,b, R. Campaninia,b, P. Capiluppia,b, A. Castroa,b, F.R. Cavalloa,
C. Cioccaa, G. Codispotia,b, M. Cuffiania,b, G.M. Dallavallea, F. Fabbria, A.
Fanfania,b, E. Fontanesi, P. Giacomellia, C. Grandia, L. Guiduccia,b, F.
Iemmia,b, S. Lo Meoa,30, S. Marcellinia, G. Masettia, F.L. Navarriaa,b, A.
Perrottaa, F. Primaveraa,b, A.M. Rossia,b, T. Rovellia,b, G.P. Sirolia,b, N.
Tosia INFN Sezione di Catania a, Università di Catania b, Catania, Italy
S. Albergoa,b,31, S. Costaa,b, A. Di Mattiaa, R. Potenzaa,b, A. Tricomia,b,31,
C. Tuvea,b INFN Sezione di Firenze a, Università di Firenze b, Firenze, Italy
G. Barbaglia, R. Ceccarelli, K. Chatterjeea,b, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, E. Focardia,b, G. Latino, P. Lenzia,b, M. Meschinia, S.
Paolettia, G. Sguazzonia, D. Stroma, L. Viliania INFN Laboratori Nazionali di
Frascati, Frascati, Italy
L. Benussi, S. Bianco, D. Piccolo INFN Sezione di Genova a, Università di
Genova b, Genova, Italy
M. Bozzoa,b, F. Ferroa, R. Mulargiaa,b, E. Robuttia, S. Tosia,b INFN Sezione
di Milano-Bicocca a, Università di Milano-Bicocca b, Milano, Italy
A. Benagliaa, A. Beschia,b, F. Brivioa,b, V. Cirioloa,b,17, S. Di Guidaa,b,17,
M.E. Dinardoa,b, P. Dinia, S. Fiorendia,b, S. Gennaia, A. Ghezzia,b, P.
Govonia,b, L. Guzzia,b, M. Malbertia, S. Malvezzia, D. Menascea, F. Montia,b,
L. Moronia, G. Ortonaa,b, M. Paganonia,b, D. Pedrinia, S. Ragazzia,b, T.
Tabarelli de Fatisa,b, D. Zuoloa,b INFN Sezione di Napoli a, Università di
Napoli ’Federico II’ b, Napoli, Italy, Università della Basilicata c, Potenza,
Italy, Università G. Marconi d, Roma, Italy
S. Buontempoa, N. Cavalloa,c, A. De Iorioa,b, A. Di Crescenzoa,b, F.
Fabozzia,c, F. Fiengaa, G. Galatia, A.O.M. Iorioa,b, L. Listaa,b, S.
Meolaa,d,17, P. Paoluccia,17, B. Rossia, C. Sciaccaa,b, E. Voevodinaa,b INFN
Sezione di Padova a, Università di Padova b, Padova, Italy, Università di
Trento c, Trento, Italy
P. Azzia, N. Bacchettaa, D. Biselloa,b, A. Bolettia,b, A. Bragagnolo, R.
Carlina,b, P. Checchiaa, P. De Castro Manzanoa, T. Dorigoa, U. Dossellia, F.
Gasparinia,b, U. Gasparinia,b, A. Gozzelinoa, S.Y. Hoh, P. Lujan, M.
Margonia,b, A.T. Meneguzzoa,b, J. Pazzinia,b, M. Presillab, P. Ronchesea,b, R.
Rossina,b, F. Simonettoa,b, A. Tiko, M. Tosia,b, M. Zanettia,b, P. Zottoa,b,
G. Zumerlea,b INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy
A. Braghieria, P. Montagnaa,b, S.P. Rattia,b, V. Rea, M. Ressegottia,b, C.
Riccardia,b, P. Salvinia, I. Vaia,b, P. Vituloa,b INFN Sezione di Perugia a,
Università di Perugia b, Perugia, Italy
M. Biasinia,b, G.M. Bileia, C. Cecchia,b, D. Ciangottinia,b, L. Fanòa,b, P.
Laricciaa,b, R. Leonardia,b, E. Manonia, G. Mantovania,b, V. Mariania,b, M.
Menichellia, A. Rossia,b, A. Santocchiaa,b, D. Spigaa INFN Sezione di Pisa a,
Università di Pisa b, Scuola Normale Superiore di Pisa c, Pisa, Italy
K. Androsova, P. Azzurria, G. Bagliesia, V. Bertacchia,c, L. Bianchinia, T.
Boccalia, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, G. Fedia, L.
Gianninia,c, A. Giassia, M.T. Grippoa, F. Ligabuea,c, E. Mancaa,c, G.
Mandorlia,c, A. Messineoa,b, F. Pallaa, A. Rizzia,b, G. Rolandi32, S. Roy
Chowdhury, A. Scribanoa, P. Spagnoloa, R. Tenchinia, G. Tonellia,b, N. Turini,
A. Venturia, P.G. Verdinia INFN Sezione di Roma a, Sapienza Università di Roma
b, Rome, Italy
F. Cavallaria, M. Cipriania,b, D. Del Rea,b, E. Di Marcoa,b, M. Diemoza, E.
Longoa,b, B. Marzocchia,b, P. Meridiania, G. Organtinia,b, F. Pandolfia, R.
Paramattia,b, C. Quarantaa,b, S. Rahatloua,b, C. Rovellia, F.
Santanastasioa,b, L. Soffia,b INFN Sezione di Torino a, Università di Torino
b, Torino, Italy, Università del Piemonte Orientale c, Novara, Italy
N. Amapanea,b, R. Arcidiaconoa,c, S. Argiroa,b, M. Arneodoa,c, N. Bartosika,
R. Bellana,b, C. Biinoa, A. Cappatia,b, N. Cartigliaa, S. Comettia, M.
Costaa,b, R. Covarellia,b, N. Demariaa, B. Kiania,b, C. Mariottia, S.
Masellia, E. Migliorea,b, V. Monacoa,b, E. Monteila,b, M. Montenoa, M.M.
Obertinoa,b, L. Pachera,b, N. Pastronea, M. Pelliccionia, G.L. Pinna
Angionia,b, A. Romeroa,b, M. Ruspaa,c, R. Sacchia,b, R. Salvaticoa,b, V.
Solaa, A. Solanoa,b, D. Soldia,b, A. Staianoa INFN Sezione di Trieste a,
Università di Trieste b, Trieste, Italy
S. Belfortea, V. Candelisea,b, M. Casarsaa, F. Cossuttia, A. Da Rolda,b, G.
Della Riccaa,b, F. Vazzolera,b, A. Zanettia Kyungpook National University,
Daegu, Korea
B. Kim, D.H. Kim, G.N. Kim, M.S. Kim, J. Lee, S.W. Lee, C.S. Moon, Y.D. Oh,
S.I. Pak, S. Sekmen, D.C. Son, Y.C. Yang Chonnam National University,
Institute for Universe and Elementary Particles, Kwangju, Korea
H. Kim, D.H. Moon, G. Oh Hanyang University, Seoul, Korea
B. Francois, T.J. Kim, J. Park Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, D. Gyun, S. Ha, B. Hong, K. Lee, K.S. Lee, J. Lim, J.
Park, S.K. Park, Y. Roh Kyung Hee University, Department of Physics
J. Goh Sejong University, Seoul, Korea
H.S. Kim Seoul National University, Seoul, Korea
J. Almond, J.H. Bhyun, J. Choi, S. Jeon, J. Kim, J.S. Kim, H. Lee, K. Lee, S.
Lee, K. Nam, M. Oh, S.B. Oh, B.C. Radburn-Smith, U.K. Yang, H.D. Yoo, I. Yoon,
G.B. Yu University of Seoul, Seoul, Korea
D. Jeon, H. Kim, J.H. Kim, J.S.H. Lee, I.C. Park, I. Watson Sungkyunkwan
University, Suwon, Korea
Y. Choi, C. Hwang, Y. Jeong, J. Lee, Y. Lee, I. Yu Riga Technical University,
Riga, Latvia
V. Veckalns33 Vilnius University, Vilnius, Lithuania
V. Dudenas, A. Juodagalvis, G. Tamulaitis, J. Vaitkus National Centre for
Particle Physics, Universiti Malaya, Kuala Lumpur, Malaysia
Z.A. Ibrahim, F. Mohamad Idris34, W.A.T. Wan Abdullah, M.N. Yusli, Z. Zolkapli
Universidad de Sonora (UNISON), Hermosillo, Mexico
J.F. Benitez, A. Castaneda Hernandez, J.A. Murillo Quijada, L. Valencia Palomo
Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico
H. Castilla-Valdez, E. De La Cruz-Burelo, I. Heredia-De La Cruz35, R. Lopez-
Fernandez, A. Sanchez-Hernandez Universidad Iberoamericana, Mexico City,
Mexico
S. Carrillo Moreno, C. Oropeza Barrera, M. Ramirez-Garcia, F. Vazquez Valencia
Benemerita Universidad Autonoma de Puebla, Puebla, Mexico
J. Eysermans, I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada Universidad
Autónoma de San Luis Potosí, San Luis Potosí, Mexico
A. Morelos Pineda University of Montenegro, Podgorica, Montenegro
N. Raicevic University of Auckland, Auckland, New Zealand
D. Krofcheck University of Canterbury, Christchurch, New Zealand
S. Bheesette, P.H. Butler National Centre for Physics, Quaid-I-Azam
University, Islamabad, Pakistan
A. Ahmad, M. Ahmad, Q. Hassan, H.R. Hoorani, W.A. Khan, M.A. Shah, M. Shoaib,
M. Waqas AGH University of Science and Technology Faculty of Computer Science,
Electronics and Telecommunications, Krakow, Poland
V. Avati, L. Grzanka, M. Malawski National Centre for Nuclear Research,
Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, M. Górski, M. Kazana, M. Szleper, P.
Zalewski Institute of Experimental Physics, Faculty of Physics, University of
Warsaw, Warsaw, Poland
K. Bunkowski, A. Byszuk36, K. Doroba, A. Kalinowski, M. Konecki, J.
Krolikowski, M. Misiura, M. Olszewski, A. Pyskir, M. Walczak Laboratório de
Instrumentação e Física Experimental de Partículas, Lisboa, Portugal
M. Araujo, P. Bargassa, D. Bastos, A. Di Francesco, P. Faccioli, B. Galinhas,
M. Gallinaro, J. Hollar, N. Leonardo, J. Seixas, K. Shchelina, G. Strong, O.
Toldaiev, J. Varela Joint Institute for Nuclear Research, Dubna, Russia
A. Baginyan, Y. Ershov, M. Gavrilenko, A. Golunov, I. Golutvin, N. Gorbounov,
I. Gorbunov, V. Karjavine, A. Lanev, A. Malakhov, V. Matveev37,38, V.V.
Mitsyn, P. Moisenz, V. Palichik, V. Perelygin, M. Savina, S. Shmatov, S.
Shulha, N. Voytishin, A. Zarubin Petersburg Nuclear Physics Institute,
Gatchina (St. Petersburg), Russia
L. Chtchipounov, V. Golovtsov, Y. Ivanov, V. Kim39, E. Kuznetsova40, P.
Levchenko, V. Murzin, V. Oreshkin, I. Smirnov, D. Sosnov, V. Sulimov, L.
Uvarov, A. Vorobyev Institute for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov,
N. Krasnikov, A. Pashenkov, D. Tlisov, A. Toropin Institute for Theoretical
and Experimental Physics named by A.I. Alikhanov of NRC ‘Kurchatov Institute’,
Moscow, Russia
V. Epshteyn, V. Gavrilov, N. Lychkovskaya, A. Nikitenko41, V. Popov, I.
Pozdnyakov, G. Safronov, A. Spiridonov, A. Stepennov, M. Toms, E. Vlasov, A.
Zhokin Moscow Institute of Physics and Technology, Moscow, Russia
T. Aushev National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
M. Chadeeva42, P. Parygin, D. Philippov, E. Popova, V. Rusinov P.N. Lebedev
Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin, I. Dremin, M. Kirakosyan, A. Terkulov Skobeltsyn
Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow,
Russia
A. Baskakov, A. Belyaev, E. Boos, V. Bunichev, M. Dubinin43, L. Dudko, V.
Klyukhin, O. Kodolova, I. Lokhtin, S. Obraztsov, M. Perfilov, S. Petrushanko,
V. Savrin Novosibirsk State University (NSU), Novosibirsk, Russia
A. Barnyakov44, V. Blinov44, T. Dimova44, L. Kardapoltsev44, Y. Skovpen44
Institute for High Energy Physics of National Research Centre ‘Kurchatov
Institute’, Protvino, Russia
I. Azhgirey, I. Bayshev, S. Bitioukov, V. Kachanov, D. Konstantinov, P.
Mandrik, V. Petrov, R. Ryutin, S. Slabospitskii, A. Sobol, S. Troshin, N.
Tyurin, A. Uzunian, A. Volkov National Research Tomsk Polytechnic University,
Tomsk, Russia
A. Babaev, A. Iuzhakov, V. Okhotnikov Tomsk State University, Tomsk, Russia
V. Borchsh, V. Ivanchenko, E. Tcherniaev University of Belgrade: Faculty of
Physics and VINCA Institute of Nuclear Sciences
P. Adzic45, P. Cirkovic, D. Devetak, M. Dordevic, P. Milenovic, J. Milosevic,
M. Stojanovic Centro de Investigaciones Energéticas Medioambientales y
Tecnológicas (CIEMAT), Madrid, Spain
M. Aguilar-Benitez, J. Alcaraz Maestre, A. Álvarez Fernández, I. Bachiller, M.
Barrio Luna, J.A. Brochero Cifuentes, C.A. Carrillo Montoya, M. Cepeda, M.
Cerrada, N. Colino, B. De La Cruz, A. Delgado Peris, C. Fernandez Bedoya, J.P.
Fernández Ramos, J. Flix, M.C. Fouz, O. Gonzalez Lopez, S. Goy Lopez, J.M.
Hernandez, M.I. Josa, D. Moran, Á. Navarro Tobar, A. Pérez-Calero Yzquierdo,
J. Puerta Pelayo, I. Redondo, L. Romero, S. Sánchez Navas, M.S. Soares, A.
Triossi, C. Willmott Universidad Autónoma de Madrid, Madrid, Spain
C. Albajar, J.F. de Trocóniz Universidad de Oviedo, Instituto Universitario de
Ciencias y Tecnologías Espaciales de Asturias (ICTEA), Oviedo, Spain
B. Alvarez Gonzalez, J. Cuevas, C. Erice, J. Fernandez Menendez, S. Folgueras,
I. Gonzalez Caballero, J.R. González Fernández, E. Palencia Cortezon, V.
Rodríguez Bouza, S. Sanchez Cruz Instituto de Física de Cantabria (IFCA),
CSIC-Universidad de Cantabria, Santander, Spain
I.J. Cabrillo, A. Calderon, B. Chazin Quero, J. Duarte Campderros, M.
Fernandez, P.J. Fernández Manteca, A. García Alonso, G. Gomez, C. Martinez
Rivero, P. Martinez Ruiz del Arbol, F. Matorras, J. Piedra Gomez, C. Prieels,
T. Rodrigo, A. Ruiz-Jimeno, L. Russo46, L. Scodellaro, N. Trevisani, I. Vila,
J.M. Vizan Garcia University of Colombo, Colombo, Sri Lanka
K. Malagalage University of Ruhuna, Department of Physics, Matara, Sri Lanka
W.G.D. Dharmaratna, N. Wickramage CERN, European Organization for Nuclear
Research, Geneva, Switzerland
D. Abbaneo, B. Akgun, E. Auffray, G. Auzinger, J. Baechler, P. Baillon, A.H.
Ball, D. Barney, J. Bendavid, M. Bianco, A. Bocci, P. Bortignon, E. Bossini,
C. Botta, E. Brondolin, T. Camporesi, A. Caratelli, G. Cerminara, E. Chapon,
G. Cucciati, D. d’Enterria, A. Dabrowski, N. Daci, V. Daponte, A. David, O.
Davignon, A. De Roeck, N. Deelen, M. Deile, M. Dobson, M. Dünser, N. Dupont,
A. Elliott-Peisert, F. Fallavollita47, D. Fasanella, G. Franzoni, J. Fulcher,
W. Funk, S. Giani, D. Gigi, A. Gilbert, K. Gill, F. Glege, M. Gruchala, M.
Guilbaud, D. Gulhan, J. Hegeman, C. Heidegger, Y. Iiyama, V. Innocente, P.
Janot, O. Karacheban20, J. Kaspar, J. Kieseler, M. Krammer1, C. Lange, P.
Lecoq, C. Lourenço, L. Malgeri, M. Mannelli, A. Massironi, F. Meijers, J.A.
Merlin, S. Mersi, E. Meschi, F. Moortgat, M. Mulders, J. Ngadiuba, S.
Nourbakhsh, S. Orfanelli, L. Orsini, F. Pantaleo17, L. Pape, E. Perez, M.
Peruzzi, A. Petrilli, G. Petrucciani, A. Pfeiffer, M. Pierini, F.M. Pitters,
D. Rabady, A. Racz, M. Rovere, H. Sakulin, C. Schäfer, C. Schwick, M.
Selvaggi, A. Sharma, P. Silva, W. Snoeys, P. Sphicas48, J. Steggemann, V.R.
Tavolaro, D. Treille, A. Tsirou, A. Vartak, M. Verzetti, W.D. Zeuner Paul
Scherrer Institut, Villigen, Switzerland
L. Caminada49, K. Deiters, W. Erdmann, R. Horisberger, Q. Ingram, H.C.
Kaestli, D. Kotlinski, U. Langenegger, T. Rohe, S.A. Wiederkehr ETH Zurich -
Institute for Particle Physics and Astrophysics (IPA), Zurich, Switzerland
M. Backhaus, P. Berger, N. Chernyavskaya, G. Dissertori, M. Dittmar, M.
Donegà, C. Dorfer, T.A. Gómez Espinosa, C. Grab, D. Hits, T. Klijnsma, W.
Lustermann, R.A. Manzoni, M. Marionneau, M.T. Meinhard, F. Micheli, P.
Musella, F. Nessi-Tedaldi, F. Pauss, G. Perrin, L. Perrozzi, S. Pigazzini, M.
Reichmann, C. Reissel, T. Reitenspiess, D. Ruini, D.A. Sanz Becerra, M.
Schönenberger, L. Shchutska, M.L. Vesterbacka Olsson, R. Wallny, D.H. Zhu
Universität Zürich, Zurich, Switzerland
T.K. Aarrestad, C. Amsler50, D. Brzhechko, M.F. Canelli, A. De Cosa, R. Del
Burgo, S. Donato, B. Kilminster, S. Leontsinis, V.M. Mikuni, I. Neutelings, G.
Rauco, P. Robmann, D. Salerno, K. Schweiger, C. Seitz, Y. Takahashi, S. Wertz,
A. Zucchetta National Central University, Chung-Li, Taiwan
T.H. Doan, C.M. Kuo, W. Lin, A. Roy, S.S. Yu National Taiwan University (NTU),
Taipei, Taiwan
P. Chang, Y. Chao, K.F. Chen, P.H. Chen, W.-S. Hou, Y.y. Li, R.-S. Lu, E.
Paganis, A. Psallidas, A. Steen Chulalongkorn University, Faculty of Science,
Department of Physics, Bangkok, Thailand
B. Asavapibhop, C. Asawatangtrakuldee, N. Srimanobhas, N. Suwonjandee Çukurova
University, Physics Department, Science and Art Faculty, Adana, Turkey
A. Bat, F. Boran, S. Cerci51, S. Damarseckin52, Z.S. Demiroglu, F. Dolek, C.
Dozen, I. Dumanoglu, G. Gokbulut, EmineGurpinar Guler53, Y. Guler, I. Hos54,
C. Isik, E.E. Kangal55, O. Kara, A. Kayis Topaksu, U. Kiminsu, M. Oglakci, G.
Onengut, K. Ozdemir56, S. Ozturk57, A.E. Simsek, D. Sunar Cerci51, U.G. Tok,
S. Turkcapar, I.S. Zorbakir, C. Zorbilmez Middle East Technical University,
Physics Department, Ankara, Turkey
B. Isildak58, G. Karapinar59, M. Yalvac Bogazici University, Istanbul, Turkey
I.O. Atakisi, E. Gülmez, M. Kaya60, O. Kaya61, B. Kaynak, Ö. Özçelik, S.
Tekten, E.A. Yetkin62 Istanbul Technical University, Istanbul, Turkey
A. Cakir, K. Cankocak, Y. Komurcu, S. Sen63 Istanbul University, Istanbul,
Turkey
S. Ozkorucuklu Institute for Scintillation Materials of National Academy of
Science of Ukraine, Kharkov, Ukraine
B. Grynyov National Scientific Center, Kharkov Institute of Physics and
Technology, Kharkov, Ukraine
L. Levchuk University of Bristol, Bristol, United Kingdom
F. Ball, E. Bhal, S. Bologna, J.J. Brooke, D. Burns, E. Clement, D. Cussans,
H. Flacher, J. Goldstein, G.P. Heath, H.F. Heath, L. Kreczko, S. Paramesvaran,
B. Penning, T. Sakuma, S. Seif El Nasr-Storey, D. Smith, V.J. Smith, J.
Taylor, A. Titterton Rutherford Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev64, C. Brew, R.M. Brown, D. Cieri, D.J.A. Cockerill, J.A.
Coughlan, K. Harder, S. Harper, J. Linacre, K. Manolopoulos, D.M. Newbold, E.
Olaiya, D. Petyt, T. Reis, T. Schuh, C.H. Shepherd-Themistocleous, A. Thea,
I.R. Tomalin, T. Williams, W.J. Womersley Imperial College, London, United
Kingdom
R. Bainbridge, P. Bloch, J. Borg, S. Breeze, O. Buchmuller, A. Bundock,
GurpreetSingh CHAHAL65, D. Colling, P. Dauncey, G. Davies, M. Della Negra, R.
Di Maria, P. Everaerts, G. Hall, G. Iles, T. James, M. Komm, C. Laner, L.
Lyons, A.-M. Magnan, S. Malik, A. Martelli, V. Milosevic, J. Nash66, V.
Palladino, M. Pesaresi, D.M. Raymond, A. Richards, A. Rose, E. Scott, C. Seez,
A. Shtipliyski, M. Stoye, T. Strebler, S. Summers, A. Tapper, K. Uchida, T.
Virdee17, N. Wardle, D. Winterbottom, J. Wright, A.G. Zecchinelli, S.C. Zenz
Brunel University, Uxbridge, United Kingdom
J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, C.K. Mackay, A. Morton, I.D. Reid,
L. Teodorescu, S. Zahid Baylor University, Waco, USA
K. Call, J. Dittmann, K. Hatakeyama, C. Madrid, B. McMaster, N. Pastika, C.
Smith Catholic University of America, Washington, DC, USA
R. Bartek, A. Dominguez, R. Uniyal The University of Alabama, Tuscaloosa, USA
A. Buccilli, S.I. Cooper, C. Henderson, P. Rumerio, C. West Boston University,
Boston, USA
D. Arcaro, T. Bose, Z. Demiragli, D. Gastler, S. Girgis, D. Pinna, C.
Richardson, J. Rohlf, D. Sperka, I. Suarez, L. Sulak, D. Zou Brown University,
Providence, USA
G. Benelli, B. Burkle, X. Coubez, D. Cutts, Y.t. Duh, M. Hadley, J. Hakala, U.
Heintz, J.M. Hogan67, K.H.M. Kwok, E. Laird, G. Landsberg, J. Lee, Z. Mao, M.
Narain, S. Sagir68, R. Syarif, E. Usai, D. Yu University of California, Davis,
Davis, USA
R. Band, C. Brainerd, R. Breedon, M. Calderon De La Barca Sanchez, M. Chertok,
J. Conway, R. Conway, P.T. Cox, R. Erbacher, C. Flores, G. Funk, F. Jensen, W.
Ko, O. Kukral, R. Lander, M. Mulhearn, D. Pellett, J. Pilot, M. Shi, D. Stolp,
D. Taylor, K. Tos, M. Tripathi, Z. Wang, F. Zhang University of California,
Los Angeles, USA
M. Bachtis, C. Bravo, R. Cousins, A. Dasgupta, A. Florent, J. Hauser, M.
Ignatenko, N. Mccoll, W.A. Nash, S. Regnard, D. Saltzberg, C. Schnaible, B.
Stone, V. Valuev University of California, Riverside, Riverside, USA
K. Burt, R. Clare, J.W. Gary, S.M.A. Ghiasi Shirazi, G. Hanson, G.
Karapostoli, E. Kennedy, O.R. Long, M. Olmedo Negrete, M.I. Paneva, W. Si, L.
Wang, H. Wei, S. Wimpenny, B.R. Yates, Y. Zhang University of California, San
Diego, La Jolla, USA
J.G. Branson, P. Chang, S. Cittolin, M. Derdzinski, R. Gerosa, D. Gilbert, B.
Hashemi, D. Klein, V. Krutelyov, J. Letts, M. Masciovecchio, S. May, S. Padhi,
M. Pieri, V. Sharma, M. Tadel, F. Würthwein, A. Yagil, G. Zevi Della Porta
University of California, Santa Barbara - Department of Physics, Santa
Barbara, USA
N. Amin, R. Bhandari, C. Campagnari, M. Citron, V. Dutta, M. Franco Sevilla,
L. Gouskos, J. Incandela, B. Marsh, H. Mei, A. Ovcharova, H. Qu, J. Richman,
U. Sarica, D. Stuart, S. Wang, J. Yoo California Institute of Technology,
Pasadena, USA
D. Anderson, A. Bornheim, O. Cerri, I. Dutta, J.M. Lawhorn, N. Lu, J. Mao,
H.B. Newman, T.Q. Nguyen, J. Pata, M. Spiropulu, J.R. Vlimant, S. Xie, Z.
Zhang, R.Y. Zhu Carnegie Mellon University, Pittsburgh, USA
M.B. Andrews, T. Ferguson, T. Mudholkar, M. Paulini, M. Sun, I. Vorobiev, M.
Weinberg University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, A. Johnson, E. MacDonald, T. Mulholland, R. Patel, A.
Perloff, K. Stenson, K.A. Ulmer, S.R. Wagner Cornell University, Ithaca, USA
J. Alexander, J. Chaves, Y. Cheng, J. Chu, A. Datta, A. Frankenthal, K.
Mcdermott, N. Mirman, J.R. Patterson, D. Quach, A. Rinkevicius69, A. Ryd, S.M.
Tan, Z. Tao, J. Thom, P. Wittich, M. Zientek Fermi National Accelerator
Laboratory, Batavia, USA
S. Abdullin, M. Albrow, M. Alyari, G. Apollinari, A. Apresyan, A. Apyan, S.
Banerjee, L.A.T. Bauerdick, A. Beretvas, J. Berryhill, P.C. Bhat, K. Burkett,
J.N. Butler, A. Canepa, G.B. Cerati, H.W.K. Cheung, F. Chlebana, M. Cremonesi,
J. Duarte, V.D. Elvira, J. Freeman, Z. Gecse, E. Gottschalk, L. Gray, D.
Green, S. Grünendahl, O. Gutsche, AllisonReinsvold Hall, J. Hanlon, R.M.
Harris, S. Hasegawa, R. Heller, J. Hirschauer, B. Jayatilaka, S. Jindariani,
M. Johnson, U. Joshi, B. Klima, M.J. Kortelainen, B. Kreis, S. Lammel, J.
Lewis, D. Lincoln, R. Lipton, M. Liu, T. Liu, J. Lykken, K. Maeshima, J.M.
Marraffino, D. Mason, P. McBride, P. Merkel, S. Mrenna, S. Nahn, V. O’Dell, V.
Papadimitriou, K. Pedro, C. Pena, G. Rakness, F. Ravera, L. Ristori, B.
Schneider, E. Sexton-Kennedy, N. Smith, A. Soha, W.J. Spalding, L. Spiegel, S.
Stoynev, J. Strait, N. Strobbe, L. Taylor, S. Tkaczyk, N.V. Tran, L. Uplegger,
E.W. Vaandering, C. Vernieri, M. Verzocchi, R. Vidal, M. Wang, H.A. Weber
University of Florida, Gainesville, USA
D. Acosta, P. Avery, D. Bourilkov, A. Brinkerhoff, L. Cadamuro, A. Carnes, V.
Cherepanov, D. Curry, F. Errico, R.D. Field, S.V. Gleyzer, B.M. Joshi, M. Kim,
J. Konigsberg, A. Korytov, K.H. Lo, P. Ma, K. Matchev, N. Menendez, G.
Mitselmakher, D. Rosenzweig, K. Shi, J. Wang, S. Wang, X. Zuo Florida
International University, Miami, USA
Y.R. Joshi Florida State University, Tallahassee, USA
T. Adams, A. Askew, S. Hagopian, V. Hagopian, K.F. Johnson, R. Khurana, T.
Kolberg, G. Martinez, T. Perry, H. Prosper, C. Schiber, R. Yohay, J. Zhang
Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, V. Bhopatkar, M. Hohlmann, D. Noonan, M. Rahmani, M. Saunders,
F. Yumiceva University of Illinois at Chicago (UIC), Chicago, USA
M.R. Adams, L. Apanasevich, D. Berry, R.R. Betts, R. Cavanaugh, X. Chen, S.
Dittmer, O. Evdokimov, C.E. Gerber, D.A. Hangal, D.J. Hofman, K. Jung, C.
Mills, T. Roy, M.B. Tonjes, N. Varelas, H. Wang, X. Wang, Z. Wu The University
of Iowa, Iowa City, USA
M. Alhusseini, B. Bilki53, W. Clarida, K. Dilsiz70, S. Durgut, R.P.
Gandrajula, M. Haytmyradov, V. Khristenko, O.K. Köseyan, J.-P. Merlo, A.
Mestvirishvili71, A. Moeller, J. Nachtman, H. Ogul72, Y. Onel, F. Ozok73, A.
Penzo, C. Snyder, E. Tiras, J. Wetzel Johns Hopkins University, Baltimore, USA
B. Blumenfeld, A. Cocoros, N. Eminizer, D. Fehling, L. Feng, A.V. Gritsan,
W.T. Hung, P. Maksimovic, J. Roskes, M. Swartz, M. Xiao The University of
Kansas, Lawrence, USA
C. Baldenegro Barrera, P. Baringer, A. Bean, S. Boren, J. Bowen, A. Bylinkin,
T. Isidori, S. Khalil, J. King, G. Krintiras, A. Kropivnitskaya, C. Lindsey,
D. Majumder, W. Mcbrayer, N. Minafra, M. Murray, C. Rogan, C. Royon, S.
Sanders, E. Schmitz, J.D. Tapia Takaki, Q. Wang, J. Williams, G. Wilson Kansas
State University, Manhattan, USA
S. Duric, A. Ivanov, K. Kaadze, D. Kim, Y. Maravin, D.R. Mendis, T. Mitchell,
A. Modak, A. Mohammadi Lawrence Livermore National Laboratory, Livermore, USA
F. Rebassoo, D. Wright University of Maryland, College Park, USA
A. Baden, O. Baron, A. Belloni, S.C. Eno, Y. Feng, N.J. Hadley, S. Jabeen,
G.Y. Jeng, R.G. Kellogg, J. Kunkle, A.C. Mignerey, S. Nabili, F. Ricci-Tam, M.
Seidel, Y.H. Shin, A. Skuja, S.C. Tonwar, K. Wong Massachusetts Institute of
Technology, Cambridge, USA
D. Abercrombie, B. Allen, A. Baty, R. Bi, S. Brandt, W. Busza, I.A. Cali, M.
D’Alfonso, G. Gomez Ceballos, M. Goncharov, P. Harris, D. Hsu, M. Hu, M.
Klute, D. Kovalskyi, Y.-J. Lee, P.D. Luckey, B. Maier, A.C. Marini, C. Mcginn,
C. Mironov, S. Narayanan, X. Niu, C. Paus, D. Rankin, C. Roland, G. Roland, Z.
Shi, G.S.F. Stephans, K. Sumorok, K. Tatar, D. Velicanu, J. Wang, T.W. Wang,
B. Wyslouch University of Minnesota, Minneapolis, USA
A.C. Benvenuti${}^{\textrm{\textdagger}}$, R.M. Chatterjee, A. Evans, S. Guts,
P. Hansen, J. Hiltbrand, Sh. Jain, S. Kalafut, Y. Kubota, Z. Lesko, J. Mans,
R. Rusack, M.A. Wadud University of Mississippi, Oxford, USA
J.G. Acosta, S. Oliveros University of Nebraska-Lincoln, Lincoln, USA
K. Bloom, D.R. Claes, C. Fangmeier, L. Finco, F. Golf, R. Gonzalez Suarez, R.
Kamalieddin, I. Kravchenko, J.E. Siado, G.R. Snow, B. Stieger State University
of New York at Buffalo, Buffalo, USA
G. Agarwal, C. Harrington, I. Iashvili, A. Kharchilava, C. Mclean, D. Nguyen,
A. Parker, J. Pekkanen, S. Rappoccio, B. Roozbahani Northeastern University,
Boston, USA
G. Alverson, E. Barberis, C. Freer, Y. Haddad, A. Hortiangtham, G. Madigan,
D.M. Morse, T. Orimoto, L. Skinnari, A. Tishelman-Charny, T. Wamorkar, B.
Wang, A. Wisecarver, D. Wood Northwestern University, Evanston, USA
S. Bhattacharya, J. Bueghly, T. Gunter, K.A. Hahn, N. Odell, M.H. Schmitt, K.
Sung, M. Trovato, M. Velasco University of Notre Dame, Notre Dame, USA
R. Bucci, N. Dev, R. Goldouzian, M. Hildreth, K. Hurtado Anampa, C. Jessop,
D.J. Karmgard, K. Lannon, W. Li, N. Loukas, N. Marinelli, I. Mcalister, F.
Meng, C. Mueller, Y. Musienko37, M. Planer, R. Ruchti, P. Siddireddy, G.
Smith, S. Taroni, M. Wayne, A. Wightman, M. Wolf, A. Woodard The Ohio State
University, Columbus, USA
J. Alimena, B. Bylsma, L.S. Durkin, S. Flowers, B. Francis, C. Hill, W. Ji, A.
Lefeld, T.Y. Ling, B.L. Winer Princeton University, Princeton, USA
S. Cooperstein, G. Dezoort, P. Elmer, J. Hardenbrook, N. Haubrich, S.
Higginbotham, A. Kalogeropoulos, S. Kwan, D. Lange, M.T. Lucchini, J. Luo, D.
Marlow, K. Mei, I. Ojalvo, J. Olsen, C. Palmer, P. Piroué, J. Salfeld-Nebgen,
D. Stickland, C. Tully, Z. Wang University of Puerto Rico, Mayaguez, USA
S. Malik, S. Norberg Purdue University, West Lafayette, USA
A. Barker, V.E. Barnes, S. Das, L. Gutay, M. Jones, A.W. Jung, A. Khatiwada,
B. Mahakud, D.H. Miller, G. Negro, N. Neumeister, C.C. Peng, S. Piperov, H.
Qiu, J.F. Schulte, J. Sun, F. Wang, R. Xiao, W. Xie Purdue University
Northwest, Hammond, USA
T. Cheng, J. Dolen, N. Parashar Rice University, Houston, USA
K.M. Ecklund, S. Freed, F.J.M. Geurts, M. Kilpatrick, Arun Kumar, W. Li, B.P.
Padley, R. Redjimi, J. Roberts, J. Rorie, W. Shi, A.G. Stahl Leiton, Z. Tu, A.
Zhang University of Rochester, Rochester, USA
A. Bodek, P. de Barbaro, R. Demina, J.L. Dulemba, C. Fallon, T. Ferbel, M.
Galanti, A. Garcia-Bellido, J. Han, O. Hindrichs, A. Khukhunaishvili, E.
Ranken, P. Tan, R. Taus Rutgers, The State University of New Jersey,
Piscataway, USA
B. Chiarito, J.P. Chou, A. Gandrakota, Y. Gershtein, E. Halkiadakis, A. Hart,
M. Heindl, E. Hughes, S. Kaplan, S. Kyriacou, I. Laflotte, A. Lath, R.
Montalvo, K. Nash, M. Osherson, H. Saka, S. Salur, S. Schnetzer, D. Sheffield,
S. Somalwar, R. Stone, S. Thomas, P. Thomassen University of Tennessee,
Knoxville, USA
H. Acharya, A.G. Delannoy, J. Heideman, G. Riley, S. Spanier Texas A&M
University, College Station, USA
O. Bouhali74, A. Celik, M. Dalchenko, M. De Mattia, A. Delgado, S. Dildick, R.
Eusebi, J. Gilmore, T. Huang, T. Kamon75, S. Luo, D. Marley, R. Mueller, D.
Overton, L. Perniè, D. Rathjens, A. Safonov Texas Tech University, Lubbock,
USA
N. Akchurin, J. Damgov, F. De Guio, S. Kunori, K. Lamichhane, S.W. Lee, T.
Mengke, S. Muthumuni, T. Peltola, S. Undleeb, I. Volobouev, Z. Wang, A.
Whitbeck Vanderbilt University, Nashville, USA
S. Greene, A. Gurrola, R. Janjam, W. Johns, C. Maguire, A. Melo, H. Ni, K.
Padeken, F. Romeo, P. Sheldon, S. Tuo, J. Velkovska, M. Verweij University of
Virginia, Charlottesville, USA
M.W. Arenton, P. Barria, B. Cox, G. Cummings, R. Hirosky, M. Joyce, A.
Ledovskoy, C. Neu, B. Tannenwald, Y. Wang, E. Wolfe, F. Xia Wayne State
University, Detroit, USA
R. Harr, P.E. Karchin, N. Poudyal, J. Sturdy, P. Thapa, S. Zaleski University
of Wisconsin - Madison, Madison, WI, USA
J. Buchanan, C. Caillol, D. Carlsmith, S. Dasu, I. De Bruyn, L. Dodd, F.
Fiori, C. Galloni, B. Gomber76, H. He, M. Herndon, A. Hervé, U. Hussain, P.
Klabbers, A. Lanaro, A. Loeliger, K. Long, R. Loveless, J. Madhusudanan
Sreekala, T. Ruggles, A. Savin, V. Sharma, W.H. Smith, D. Teague, S. Trembath-
reichert, N. Woods †: Deceased
1: Also at Vienna University of Technology, Vienna, Austria
2: Also at IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
3: Also at Universidade Estadual de Campinas, Campinas, Brazil
4: Also at Federal University of Rio Grande do Sul, Porto Alegre, Brazil
5: Also at UFMS, Nova Andradina, Brazil
6: Also at Universidade Federal de Pelotas, Pelotas, Brazil
7: Also at Université Libre de Bruxelles, Bruxelles, Belgium
8: Also at University of Chinese Academy of Sciences, Beijing, China
9: Also at Institute for Theoretical and Experimental Physics named by A.I.
Alikhanov of NRC ‘Kurchatov Institute’, Moscow, Russia
10: Also at Joint Institute for Nuclear Research, Dubna, Russia
11: Also at Cairo University, Cairo, Egypt
12: Also at British University in Egypt, Cairo, Egypt
13: Now at Ain Shams University, Cairo, Egypt
14: Also at Purdue University, West Lafayette, USA
15: Also at Université de Haute Alsace, Mulhouse, France
16: Also at Erzincan Binali Yildirim University, Erzincan, Turkey
17: Also at CERN, European Organization for Nuclear Research, Geneva,
Switzerland
18: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
19: Also at University of Hamburg, Hamburg, Germany
20: Also at Brandenburg University of Technology, Cottbus, Germany
21: Also at Institute of Physics, University of Debrecen, Debrecen, Hungary,
Debrecen, Hungary
22: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary
23: Also at MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary, Budapest, Hungary
24: Also at IIT Bhubaneswar, Bhubaneswar, India, Bhubaneswar, India
25: Also at Institute of Physics, Bhubaneswar, India
26: Also at Shoolini University, Solan, India
27: Also at University of Visva-Bharati, Santiniketan, India
28: Also at Isfahan University of Technology, Isfahan, Iran
29: Now at INFN Sezione di Bari a, Università di Bari b, Politecnico di Bari
c, Bari, Italy
30: Also at Italian National Agency for New Technologies, Energy and
Sustainable Economic Development, Bologna, Italy
31: Also at Centro Siciliano di Fisica Nucleare e di Struttura Della Materia,
Catania, Italy
32: Also at Scuola Normale e Sezione dell’INFN, Pisa, Italy
33: Also at Riga Technical University, Riga, Latvia, Riga, Latvia
34: Also at Malaysian Nuclear Agency, MOSTI, Kajang, Malaysia
35: Also at Consejo Nacional de Ciencia y Tecnología, Mexico City, Mexico
36: Also at Warsaw University of Technology, Institute of Electronic Systems,
Warsaw, Poland
37: Also at Institute for Nuclear Research, Moscow, Russia
38: Now at National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
39: Also at St. Petersburg State Polytechnical University, St. Petersburg,
Russia
40: Also at University of Florida, Gainesville, USA
41: Also at Imperial College, London, United Kingdom
42: Also at P.N. Lebedev Physical Institute, Moscow, Russia
43: Also at California Institute of Technology, Pasadena, USA
44: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia
45: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia
46: Also at Università degli Studi di Siena, Siena, Italy
47: Also at INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy,
Pavia, Italy
48: Also at National and Kapodistrian University of Athens, Athens, Greece
49: Also at Universität Zürich, Zurich, Switzerland
50: Also at Stefan Meyer Institute for Subatomic Physics, Vienna, Austria,
Vienna, Austria
51: Also at Adiyaman University, Adiyaman, Turkey
52: Also at Şırnak University, Sirnak, Turkey
53: Also at Beykent University, Istanbul, Turkey, Istanbul, Turkey
54: Also at Istanbul Aydin University, Application and Research Center for
Advanced Studies (App. & Res. Cent. for Advanced Studies), Istanbul, Turkey
55: Also at Mersin University, Mersin, Turkey
56: Also at Piri Reis University, Istanbul, Turkey
57: Also at Gaziosmanpasa University, Tokat, Turkey
58: Also at Ozyegin University, Istanbul, Turkey
59: Also at Izmir Institute of Technology, Izmir, Turkey
60: Also at Marmara University, Istanbul, Turkey
61: Also at Kafkas University, Kars, Turkey
62: Also at Istanbul Bilgi University, Istanbul, Turkey
63: Also at Hacettepe University, Ankara, Turkey
64: Also at School of Physics and Astronomy, University of Southampton,
Southampton, United Kingdom
65: Also at IPPP Durham University, Durham, United Kingdom
66: Also at Monash University, Faculty of Science, Clayton, Australia
67: Also at Bethel University, St. Paul, Minneapolis, USA, St. Paul, USA
68: Also at Karamanoğlu Mehmetbey University, Karaman, Turkey
69: Also at Vilnius University, Vilnius, Lithuania
70: Also at Bingol University, Bingol, Turkey
71: Also at Georgian Technical University, Tbilisi, Georgia
72: Also at Sinop University, Sinop, Turkey
73: Also at Mimar Sinan University, Istanbul, Istanbul, Turkey
74: Also at Texas A&M University at Qatar, Doha, Qatar
75: Also at Kyungpook National University, Daegu, Korea, Daegu, Korea
76: Also at University of Hyderabad, Hyderabad, India