HIG-20-009

HIG-20-009

[inst1]The CMS Collaboration

# Search for lepton-flavor violating decays of the Higgs boson in the
$\PGm\PGt$ and $\Pe\PGt$ final states in proton-proton collisions at
$\sqrt{s}=13\TeV$

###### Abstract

A search is presented for lepton-flavor violating decays of the Higgs boson to
$\PGm\PGt$ and $\Pe\PGt$. The data set corresponds to an integrated luminosity
of 137collected at the LHC in proton-proton collisions at a center-of-mass
energy of 13. No significant excess has been found, and the results are
interpreted in terms of upper limits on lepton-flavor violating branching
fractions of the Higgs boson. The observed (expected) upper limits on the
branching fractions are, respectively,
$\mathcal{B}(\PH\to\PGm\PGt)<0.15\,(0.15)\%$ and
$\mathcal{B}(\PH\to\Pe\PGt)<0.22\,(0.16)\%$ at 95% confidence level.

##  0.1 Introduction

One of the main goals of the LHC program is to search for processes beyond the
standard model (BSM). The properties and decays of the Higgs boson () are thus
far consistent with expectations of the standard model (SM) [1, 2, 3, 4, 5,
6]. However, there is considerable motivation to search for BSM decays of the
Higgs boson. The lepton-flavor violating (LFV) decays of the Higgs boson [7,
8, 9] can provide possible signatures of such processes. A previous
investigation of the combined results from the CMS experiment constrained the
branching fraction for $\mathcal{B}(\PH\to\mathrm{BSM})$ to $<$0.36 at the 95%
confidence level (), leaving the possibility for a large contribution for
these decays [10].

The LFV decays $\PH\to\Pe\PGm$, $\PH\to\Pe\PGt$, or $\PH\to\PGm\PGt$ are
forbidden in the SM, but take place through the LFV Yukawa couplings
$Y_{\Pe\PGm}$, $Y_{\Pe\PGt}$, or $Y_{\PGm\PGt}$, respectively [11]. The LFV
decays arise in models with more than one Higgs boson doublet [12], certain
supersymmetric models [13, 14, 15], composite Higgs models [16, 17], models
with flavor symmetries [18], the Randall–Sundrum model of extra spatial
dimensions [19, 20, 21, 22, 23], and other models [24, 25, 26, 27, 28, 29].

Here we report a search for LFV decays of the Higgs boson in the $\PGm\PGt$
and $\Pe\PGt$ channels performed using data collected by the CMS experiment in
proton-proton () collisions at a center-of-mass energy of 13during the
2016–2018 data-taking period, corresponding to an integrated luminosity of
137. The CMS experiment set upper limits of 0.25% and 0.61% [30] and the ATLAS
experiment set upper limits of 0.28% and 0.47% [31] on
$\mathcal{B}(\PH\to\PGm\PGt)$ and $\mathcal{B}(\PH\to\Pe\PGt)$ at 95% ,
respectively, based on the 2016 data set, corresponding to an integrated
luminosity of 36.

The presence of an LFV Higgs boson coupling leads to processes such as
$\PGm\to\Pe$, $\PGt\to\PGm$, and $\PGt\to\Pe$ to proceed via a virtual Higgs
boson [32, 33]. The experimental limits on these decays yield indirect
constraints on $\mathcal{B}(\PH\to\Pe\PGm)$, $\mathcal{B}(\PH\to\PGm\PGt)$,
and $\mathcal{B}(\PH\to\Pe\PGt)$ [11, 34]. The null result for
$\PGm\to\Pe\Pgg$ [35] strongly constrains $\mathcal{B}(\PH\to\Pe\PGm)$ to
$<10^{-8}$. Searches for rare lepton decays [36], such as $\PGt\to\Pe\Pgg$ and
$\PGt\to\PGm\Pgg$, and the measurement of the electron and muon magnetic
moments, have set constraints on $\mathcal{B}(\PH\to\Pe\PGt)$ and
$\mathcal{B}(\PH\to\PGm\PGt)$ of ${\approx}10\%$, which are much less
stringent than those from the direct searches.

Our search is performed in the $\PGm\tauh$, $\PGm\PGt_{\Pe}$, $\Pe\tauh$, and
$\Pe\PGt_{\PGm}$ channels, where , $\PGt_{\Pe}$, and $\PGt_{\PGm}$ correspond
to the $\PGt\to\text{hadrons}$, electron, and muon decay channels of leptons,
respectively, each accompanied by its corresponding neutrinos. The
$\Pe\PGt_{\Pe}$ and $\PGm\PGt_{\PGm}$ decays are not considered because of the
large background contribution from /$\gamma^{*}$ decays.

Our search significantly improves the sensitivity relative to similar previous
studies [37, 31, 30]. The search makes use of boosted decision tree (BDT)
discriminants to distinguish signal from background in the distributions which
are then used for performing the statistical analysis. Constraints on the
branching fractions are extracted under the assumption that only one of the
LFV decays contributes additionally to the SM Higgs boson total width. The
constraints on the branching fractions are correspondingly translated into
limits on the $Y_{\Pe\PGt}$ and $Y_{\PGm\PGt}$ LFV Yukawa couplings.

This paper is organized as follows: a description of the CMS detector is given
in Section 0.2, collision data and simulated events are discussed in Section
0.3, event reconstruction is described in Section 0.4, and event selection is
described separately for the four decay channels in Section 0.5. Background
estimation and systematic uncertainties are described in Sections 0.6 and 0.7,
respectively. Results are presented in Section 0.8 and the paper is summarized
in Section 0.9.

##  0.2 The CMS detector

The CMS detector consists of a silicon pixel and strip tracker, a lead
tungstate crystal electromagnetic calorimeter (ECAL), a brass and scintillator
hadron calorimeter (HCAL), and a muon system composed of gaseous detectors.
Each subdetector consists of a barrel and two endcap sections. The central
feature of the CMS detector is a superconducting solenoid of 6m internal
diameter, providing a magnetic field of 3.8T. The tracking systems and the
calorimeters are contained within the solenoid volume; the muon chambers are
embedded in the steel flux-return yoke outside the solenoid. Forward
calorimeters extend the pseudorapidity ($\eta$) coverage provided by the
barrel and endcap detectors.

Events of interest are selected using a two-tiered trigger system. The first
level, composed of custom hardware processors, uses information from the
calorimeters and muon detectors to select events at a rate of
${\approx}100\unit{kHz}$ within a fixed latency of ${\approx}4\mus$ [38]. The
second level, the high-level trigger, consists of a farm of processors running
a version of the full event reconstruction software optimized for fast
processing that reduces the event rate to ${\approx}1\unit{kHz}$ before data
storage [39]. A more detailed description of the CMS detector, together with a
definition of the coordinate system and kinematic variables, can be found in
Ref. [40].

##  0.3 Collision data and simulated events

The search presented makes use of collisions collected at the CMS experiment
at a center-of-mass energy of 13in 2016–2018. The total integrated luminosity
amounted to 35.9in 2016, 41.5in 2017, and 59.7in 2018. Single-muon triggers
with isolation criteria are used to collect the data in the $\PGm\tauh$
channel. Electron-muon triggers are used to collect data in the
$\PGm\PGt_{\Pe}$ and $\Pe\PGt_{\PGm}$ channels. Triggers requiring a single
isolated electron, or a combination of an electron and , are used in the
$\Pe\tauh$ channel. The trigger thresholds are mentioned in Section 0.5.

Simulated events are used to model signal and background events using several
event generators. In all cases parton showering, hadronization, and underlying
event properties are modeled using [41] version 8.212. The parameters
affecting the description of the underlying event are set to the CUETP8M1 tune
in 2016 [42], except for the events that use the CP5 tune which is used for
all the events in 2017 and 2018 [43]. The NNPDF3.0 parton distribution
functions (PDFs) for all 2016 events and the NNPDF3.1 PDFs for the 2017 and
2018 events [44].

The simulation of interactions in the CMS detector is based on [45], using the
same reconstruction algorithms as used for data. The Higgs bosons are
generated in collisions predominantly through gluon fusion () [46], but also
via vector boson fusion (VBF) [47], and in association with a vector boson (or
) [48]. Such events are generated at next-to-leading order (NLO) in
perturbative quantum chromodynamics (QCD) with the v2.0 generator [49, 50, 51,
52, 53, 54], using the implementation of Refs. [55, 56]. For the LFV signal,
we consider just the Higgs bosons via the and VBF mechanisms as the
contribution from associated vector boson production is found to be
negligible.

The $\PZ\to\PGt\PGt$ background events are estimated in a data-driven manner
using the embedding technique because it provides a better description of
jets, pileup, as well as detector noise and resolution effects compared to
simulation. These events are obtained from data with well identified
$\PZ\to\PGm\PGm$ decays from which muons are removed, and simulated leptons
are embedded with the same kinematic variables as the replaced muons. The
generator [57] (version 2.2.2 in 2016, version 2.4.2 in 2017 and 2018) is used
to simulate the $\PZ\to\Pe\Pe{+}\text{jets}$ and
$\PZ\to\PGm\PGm{+}\text{jets}$ processes, the $\PW{+}\text{jets}$ background
process, and the electroweak (EW) /events. They are simulated at leading order
with the MLM jet matching and merging schemes [58].

Diboson production is simulated at NLO using the generator with the FxFx jet-
matching and merging scheme [59]. Top quark-antiquark pair and single top
quark production are generated at NLO using .

The effect of pileup, where events of interest have multiple interactions in
the same bunch crossing, is taken into account in simulated events by
generating concurrent minimum bias events. All simulated events are weighted
to match the pileup distribution observed in the data.

##  0.4 Event reconstruction

The particle flow (PF) algorithm [60] reconstructs and identifies each
particle in an event through an optimized combination of information from the
various subdetectors of the CMS detector. In this process, identifying the PF
candidate type (photons, electrons, muons, charged, and neutral hadrons) plays
an important role in determining particle direction and energy. The candidate
vertex with the largest value of summed physics object $\pt^{2}$, where is the
transverse momentum, is taken to be the primary interaction vertex (PV). The
physics objects are returned by a jet finding algorithm [61, 62] applied to
all charged tracks associated with the vertex, plus the corresponding
associated missing transverse momentum ().

An electron is identified as a track from the PV combined with one or more
ECAL energy clusters. These clusters correspond to the electron and possible
bremsstrahlung photons emitted when passing through the tracker. Electrons are
accepted in the range $\abs{\eta}<2.5$, except for the region
$1.44<\abs{\eta}<1.57$ where the detector’s service infrastructure is located.
They are identified with an efficiency of 80% using a multivariate
discriminator that combines observables sensitive to the amount of
bremsstrahlung energy deposited along the electron trajectory, the geometric
and momentum matching between the electron trajectory and associated clusters,
and the distribution in shower energy in the calorimeters [63]. Electrons from
photon conversions are removed. The electron momentum is estimated by
combining the energy measurement in the ECAL with the momentum measurement in
the tracker. The momentum resolution for electrons with $\pt\approx 45\GeV$
from $\PZ\to\Pe\Pe$ decays ranges from 1.7 to 4.5% depending on the
$\abs{\eta}$. It is generally better in the barrel region than in the endcaps
[64].

Muons are measured in the $\abs{\eta}<2.4$ range using the drift tube, cathode
strip chamber, and resistive plate chamber technologies. The efficiency to
reconstruct and identify muons is greater than 96%. Matching muons to tracks
measured in the silicon tracker results in a relative resolution for muons
with up to 100of 1% in the barrel and 3% in the endcaps [65].

The muon or electron isolations are measured relative to its $\pt^{\ell}$,
where $\ell$ is either or , values by summing over the scalar of PF particles
in a cone of $\Delta R=0.4$ or 0.3 around the lepton:

$I^{\ell}_{\text{rel}}=\left(\sum\pt^{\text{PV\,charged}}+\text{max}\left[0,\sum\pt^{\text{neutral}}+\sum\pt^{\Pgg}-\pt^{\mathrm{PU}}(\ell)\right]\right)\biggm{/}\pt^{\ell},$

where $\pt^{\text{PV\,charged}}$, $\pt^{\text{neutral}}$, and $\pt^{\Pgg}$
indicate the of a charged hadron, a neutral hadron, and a photon within the
cone, respectively. The neutral particle contribution to isolation from
pileup, $\pt^{\mathrm{PU}}(\ell)$, is estimated from the area of jet and its
median energy density in the event [66] for the electron. For the muon, half
of the sum of charged hadrons within the isolation cone, not originating from
the PV, is used instead. The charged-particle contribution to isolation from
the pileup is rejected by requiring the tracks to originate from the PV.

The reconstruction of is performed using the hadrons-plus-strips algorithm,
which combines the signature for charged hadrons composed of tracks left in
the tracker and energy depositions in the calorimeters with the signature for
electrons or photons from neutral pion decays that are reconstructed as
electromagnetic “strips” in $\eta$-$\phi$ space [67], where $\phi$ is the
azimuth in radians. The combination of these signatures provides the four-
vector for the parent . Based on the overall neutral versus charged contents
of the reconstruction, a decay mode is assigned as $\mathrm{h}^{\pm}$,
$\mathrm{h}^{\pm}\pi^{0}$, $\mathrm{h}^{\pm}\mathrm{h}^{\mp}\mathrm{h}^{\pm}$,
or $\mathrm{h}^{\pm}\mathrm{h}^{\mp}\mathrm{h}^{\pm}\pi^{0}$, where
$\mathrm{h}^{\pm}$ denotes a charged hadron. It has a reconstruction
efficiency of ${\approx}80\%$.

The reconstructed using the hadrons-plus-strips algorithm must be well
identified to reject jets, muons, and electrons misidentified as . A deep
neural network (DNN) discriminator is used to further improve identification
[68]. The input variables to the DNN include lifetime, isolation, and
information of PF candidates reconstructed within the lepton signal or
isolation cones. A dependent threshold on the output of the DNN is used to
distinguish from jets. The chosen working point (WP) has a identification
efficiency of 70% with a misidentification probability of 1%. The DNN can
reject electrons and muons misidentified as using dedicated criteria based on
the consistency between the tracker, calorimeter, and muon detector
measurements. In the $\PGm\tauh$ or $\Pe\tauh$ channel, we use a WP that has
an efficiency of 97.5% or 87.5% with a misidentification probability of 1–2%
or 0.2–0.3% to discriminate against electrons, and we use a WP that has an
efficiency of 99.6% or 99.8% with a misidentification probability of 0.04% or
0.06% to discriminate against muons, respectively.

Charged hadrons are defined as PF tracks from the PV not reconstructed as
electrons, muons, or leptons. Neutral hadrons are identified as HCAL energy
clusters not assigned to any charged hadron or as excesses in ECAL or HCAL
energies relative to the small charged-hadron energy deposit. All the PF
hadron candidates are clustered into jets using the infrared- and collinear-
safe anti-algorithm [61] with a distance parameter of 0.4. Jet momentum is
determined as the vectorial sum of all particle momenta in the jet. It is
found from simulation to be, on average, within 5–10% of the true momentum
over the entire spectrum and detector acceptance [69]. Jets that contain
quarks are tagged using a DNN-based algorithm, using a WP with efficiency of
70% for a misidentification probability for light-flavor jets of 1% [70].

The interactions from pileup add more tracks and calorimetric energy
depositions, thereby increasing the apparent jet momenta. To mitigate this
effect, tracks identified as originating from pileup vertices are discarded,
and an offset correction is applied to correct the remaining contributions
[71]. Jet energy corrections are obtained from simulation studies so that the
average measured energy of jets matches that of particle level jets. In-situ
measurements of the momentum balance in photon+jet, $\PZ{+}\text{jets}$, and
multijet events are used to determine any residual differences between the jet
energy scale in data and simulation, and appropriate corrections are applied
[72]. Additional selection criteria are applied to each jet to remove jets
potentially dominated by instrumental effects or reconstruction failures. When
combining information from the entire detector, the jet energy resolution
typically amounts to 15% at 10, 8% at 100, and 4% at 1. The variable $\Delta
R=\sqrt{\smash[b]{(\Delta\eta)^{2}+(\Delta\phi)^{2}}}$ is used to measure the
separation between reconstructed objects in the detector. Any jet within
$\Delta R=0.5$ of identified leptons is removed. The reconstructed jets must
have a $\pt>30\GeV$ and $\abs{\eta}<4.7$. Data collected in the high
$\abs{\eta}$ region of the ECAL endcaps were affected by noise during the 2017
data taking. This is mitigated by discarding events containing jets with
$\pt<50\GeV$ and $2.65<\abs{\eta}<3.14$ in the 2017 data.

The vector is computed as the negative of the vector sum of all the PF
candidates in an event, and its magnitude is denoted as [73]. The is modified
to account for corrections to the reconstructed jets’ energy scale in the
event. Anomalous high-events can originate from various reconstruction
failures, detector malfunctions, or backgrounds not from beam-beam sources.
Such events are rejected using event filters designed to identify more than
85–90% of the spurious high-events with a mistag rate of less than 0.1% [73].
In addition to the event-filtering algorithms, we require the jets to have a
neutral hadron energy fraction smaller than 0.9, which rejects more than 99%
of jets due to detector noise, independent of jet , with a negligible mistag
rate. Corrections applied to the reduce the mismodeling of in simulated , ,
and Higgs boson events. The corrections are applied to simulated events based
on the vectorial difference in the measured and total of neutrinos originating
from the decay of the , , or Higgs bosons. Their average effect is the
reduction of the magnitude of the obtained from the simulation by a few .

##  0.5 Event selection

The signal topology consists of a muon or an electron and an oppositely
charged lepton. The events in the $\PGm\PGt$ and $\Pe\PGt$ channels are
further divided into leptonic and hadronic channels based on the lepton decay
mode ($\PGt_{\PGm}$, $\PGt_{\Pe}$, or ). Jets misidentified as electrons or
muons are suppressed by imposing isolation requirements described above. A set
of loose selection criteria, known as the ’preselection’, is first defined in
each channel’s respective signature. Events with more than two jets are not
considered in the search. Each channel’s events are then divided into
categories based on the number of jets in the event (0-, 1-, or 2-jet) to
enhance different Higgs boson production mechanisms. The dominant production
mechanism contributing to the signal yield in the 0-jet category is ggH, while
in the 1-jet category, it is ggH with initial-state radiation. The 2-jet
category is further split into two based on the invariant mass of the two jets
($m_{\mathrm{jj}}$). The optimization resulted in a threshold of $550\GeV$ and
$500\GeV$ on $m_{\mathrm{jj}}$ for the $\PGm\PGt$ and $\Pe\PGt$ channels,
respectively, for the sensitivity optimization. The dominant production
mechanism is ggH with initial-state radiation for events with
$m_{\mathrm{jj}}<550\GeV$ and $<500\GeV$, while it is VBF for events with
$m_{\mathrm{jj}}>550\GeV$ and $>500\GeV$ for the $\PGm\PGt$ and $\Pe\PGt$
channels, respectively.

A variable providing an estimate of $m_{\PH}$ using the observed decay
products of the Higgs boson, the collinear mass, is defined as
$m_{\text{col}}=m_{\text{vis}}/\sqrt{\smash[b]{x_{\PGt}^{\text{vis}}}}$, where
$m_{\text{vis}}$ is the visible invariant mass of the -or -system and
$x_{\PGt}^{\text{vis}}$ is the fraction of the lepton carried by the visible
decay products of the lepton ($\vec{\PGt}^{\text{vis}}$). The definition is
based on the “collinear approximation” with the observation that, since
$m_{\PH}\gg m_{\PGt}$, the lepton decay products are Lorentz-boosted in the
direction of the lepton [74]. The momentum of neutrino(s) from the lepton
decay can be approximated to have the same direction as the
$\vec{\PGt}^{\text{vis}}$. The component of the in the direction of the
$\vec{\PGt}^{\text{vis}}$ is used to estimate the transverse component of the
neutrino momentum ($\pt^{\vec{\nu},\text{est}}$). This information is combined
to estimate the $x_{\PGt}^{\text{vis}}$ which is defined as
$\smash[b]{x_{\PGt}^{\text{vis}}=\pt^{\vec{\PGt}^{\text{vis}}}/(\pt^{\vec{\PGt}^{\text{vis}}}+\pt^{\vec{\nu},\text{est}})}$.
The collinear mass distributions of simulated signal, data, and backgrounds in
each channel are shown in Fig. 1.

Figure 1: Collinear mass distributions for the data and background processes.
A $\mathcal{B}(\PH\to\PGm\PGt)=20\%$ and $\mathcal{B}(\PH\to\Pe\PGt)=20\%$ are
assumed for the two signal processes. The channels are $\PH\to\PGm\tauh$
(upper row left), $\PH\to\PGm\PGt_{\Pe}$ (upper row right), $\PH\to\Pe\tauh$
(lower row left), and $\PH\to\Pe\PGt_{\PGm}$ (lower row right). The lower
panel in each plot shows the ratio of data and estimated background. The
uncertainty band corresponds to the background uncertainty in which the
statistical and systematic uncertainties are added in quadrature.

The transverse mass $\mT(\ell)$ is a variable constructed from the lepton and
the vectors:
$\mT(\ell)=\sqrt{\smash[b]{2\abs{\ptvec^{\ell}}\abs{\ptvecmiss}(1-\text{cos}\Delta\phi_{\ell,\ptvecmiss})}}$,
where $\Delta\phi_{\ell,\ptvecmiss}$ is the angle in the transverse plane
between the lepton and the , used to discriminate the Higgs boson signal from
the $\PW{+}\text{jets}$ background. The $\mT(\ell)$ distribution for the
signal defined using visible decay products of the lepton peaks at lower
values, while it peaks at higher values for the $\PW{+}\text{jets}$
background.

To improve discrimination between signal and background events, a BDT is
trained using the tmva toolkit of the Root analysis package [75]. A BDT is
trained in each channel using a mixture of simulated signal events comprising
the ggH and VBF processes, weighted according to their expected yield from SM
production cross sections. In hadronic channels, the dominant sources of
background come from the $\PZ\to\PGt\PGt$ process and events with
misidentified leptons. The background used for training a BDT in the hadronic
channels is obtained from data containing misidentified lepton events of the
same electric charge for both the leptons and $\PZ\to\ell\ell$
($\ell=\Pe,\PGm,\PGt$) simulated events with their applied signal selections.
In leptonic channels, the dominant sources of background come from the
$\PZ\to\PGt\PGt$ process, the process, and events with misidentified leptons.
The background used for training a BDT in the leptonic channels is obtained
from and $\PZ\to\ell\ell$ simulated events mixed and weighted according to
their expected yield from SM production cross sections. Additional background
for training comes from events with misidentified leptons in a control region
(CR) in data, where the isolation requirements are inverted with the same
electric charge for both the leptons. A detailed description of the different
background processes and their estimation is given in Section 0.6.

The input variables to the BDT are mentioned separately for each channel
below. The input variables are chosen based on their separation power as
observed during training the BDT. The trained BDT is validated in a dedicated
background enriched validation region (VR) for each channel and is detailed in
Section 0.6. In all the channels, events containing additional electrons,
muons, or candidates are vetoed. Also, events with at least one -tagged jet
are rejected to suppress the background. After applying the selections, a
maximum likelihood fit is performed to the BDT discriminant distributions in
each channel. The various systematic uncertainties are incorporated as
nuisance parameters in the fit. The BDT discriminant distributions in all the
channels are shown after determining the best fit values of the nuisance
parameters from the fit to the signal-plus-background hypothesis, as discussed
later in Section 0.7.

###  0.5.1 $\PH\to\mu\tauh$

In this channel, the preselection requires a muon and of opposite electric
charge with a separation of $\Delta R>0.5$. The trigger requires the presence
of an isolated muon with a threshold of 24. In 2017, this trigger is
“prescaled”, which means that only a fraction of events selected will pass the
trigger. Hence, it is used in conjunction with another trigger based on the
presence of an isolated muon with a threshold of 27. The muon is required to
have $\pt>26\GeV$, $\abs{\eta}<2.1$, and $I^{\PGm}_{\text{rel}}<0.15$. The is
required to have $\pt>30\GeV$ and $\abs{\eta}<2.3$. The selections for the
$\PGm\tauh$ channel are summarized in Table 0.5.2.

The input variables to the BDT are $\pt^{\PGm}$, $\pt^{\tauh}$,
$m_{\text{col}}$, , $\mT(\Pgt,\ptvecmiss)$, $\Delta\eta(\PGm,\tauh)$,
$\Delta\phi(\PGm,\tauh)$, and $\Delta\phi(\tauh,\ptvecmiss)$. The neutrino is
assumed to be collinear with , which motivates using the
$\Delta\phi(\tauh,\ptvecmiss)$ variable. The two leptons are usually produced
in opposite directions of the azimuthal plane, which motivates using the
$\Delta\phi(\PGm,\tauh)$ variable. The post-fit distributions of simulated
signal, data, and backgrounds in each category of the $\PGm\tauh$ channel are
shown in Fig. 2.

###  0.5.2 $\PH\to\mu\PGt_{\Pe}$

In this channel, the preselection requires a muon and electron of opposite
electric charge with a separation of $\Delta R>0.3$. The triggers require both
a muon and an electron, where the muon has above 23, and the electron has
above 12. The muon is required to have $\pt>24\GeV$, $\abs{\eta}<2.4$, and
$I^{\PGm}_{\text{rel}}<0.15$. The electron is required to have $\pt>13\GeV$,
$\abs{\eta}<2.5$, and $I^{\Pe}_{\text{rel}}<0.1$. The selections for the
$\PGm\PGt_{\Pe}$ channel are summarized in Table 0.5.2.

The input variables to the BDT are $\pt^{\PGm}$, $\pt^{\Pe}$,
$m_{\text{col}}$, $\mT(\Pgm,\ptvecmiss)$, $\mT(\Pe,\ptvecmiss)$,
$\Delta\phi(\Pe,\PGm)$, $\Delta\phi(\PGm,\ptvecmiss)$, and
$\Delta\phi(\Pe,\ptvecmiss)$. The neutrinos are assumed to be collinear with
the electron, which motivates using the $\Delta\phi(\Pe,\ptvecmiss)$ variable.
The two leptons are usually produced in opposite directions of the azimuthal
plane, which motivates using the $\Delta\phi(\Pe,\PGm)$ variable. The post-fit
distributions of simulated signal, data, and backgrounds in each category of
the $\PGm\PGt_{\Pe}$ channel are shown in Fig. 3.

Event selection criteria for the $\PH\to\PGm\PGt$ channels.

ccccc Variable & $\PGm\tauh$ $\PGm\PGt_{\Pe}$

$\pt^{\Pe}$ $>$13

$\pt^{\PGm}$ $>$26 $>$24

$\pt^{\tauh}$ $>$30

$\abs{\eta}^{\Pe}$ $<$2.5

$\abs{\eta}^{\PGm}$ $<$2.1 $<$2.4

$\abs{\eta}^{\tauh}$ $<$2.3

$I^{\Pe}_{\text{rel}}$ $<$0.1

$I^{\PGm}_{\text{rel}}$ $<$0.15 $<$0.15

Trigger requirement

$\pt^{\PGm}>24\GeV$ (all years)  $\pt^{\Pe}>12\GeV$ $\pt^{\PGm}>23\GeV$

$\pt^{\PGm}>27\GeV$ (2017)

Figure 2: BDT discriminant distributions for the data and background processes
in the $\PH\to\PGm\tauh$ channel. A $\mathcal{B}(\PH\to\PGm\PGt)=20\%$ is
assumed for the signal. The channel categories are 0 jets (upper row left), 1
jet (upper row right), 2 jets (lower row left), and 2 jets VBF (lower row
right). The lower panel in each plot shows the ratio of data and estimated
background. The uncertainty band corresponds to the background uncertainty in
which the post-fit statistical and systematic uncertainties are added in
quadrature.

Figure 3: BDT discriminant distributions for the data and background processes
in the $\PH\to\PGm\PGt_{\Pe}$ channel. A $\mathcal{B}(\PH\to\PGm\PGt)=20\%$ is
assumed for the signal. The channel categories are 0 jets (upper row left), 1
jet (upper row right), 2 jets (lower row left), and 2 jets VBF (lower row
right). The lower panel in each plot shows the ratio of data and estimated
background. The uncertainty band corresponds to the background uncertainty in
which the post-fit statistical and systematic uncertainties are added in
quadrature.

###  0.5.3 $\PH\to\Pe\tauh$

In this channel, the preselection requires an electron and of opposite
electric charge with a separation of $\Delta R>0.5$. The triggers require the
presence of an isolated electron with a threshold of 25(2016), 27(2017), or
32(2018). In 2017 and 2018, the signal acceptance is increased by selecting
events where the electron has above 24and the has above 30. The electron is
required to have $\pt>27\GeV$, $\abs{\eta}<2.1$, and
$I^{\Pe}_{\text{rel}}<0.15$. The is required to have $\pt>30\GeV$ and
$\abs{\eta}<2.3$. The selections for the $\Pe\tauh$ channel are summarized in
Table 0.5.4.

The input variables to the BDT are $\pt^{\Pe}$, $\pt^{\tauh}$,
$m_{\text{col}}$, $m_{\text{vis}}$, $\mT(\Pgt,\ptvecmiss)$,
$\Delta\eta(\Pe,\tauh)$, $\Delta\phi(\Pe,\tauh)$, and
$\Delta\phi(\tauh,\ptvecmiss)$. As can be seen, the input variables are
similar to $\PGm\tauh$ channel except for the addition of the variable
$m_{\text{vis}}$ and removing . The variable $m_{\text{vis}}$ has better
separation power as the $\Pe\tauh$ channel has more
$\PZ\to\Pe\Pe{+}\text{jets}$ background than the
$\PZ\to\PGm\PGm{+}\text{jets}$ background in the $\PGm\tauh$ channel. The
post-fit distributions of simulated signal, data, and backgrounds in each
category of the $\Pe\tauh$ channel are shown in Fig. 4.

###  0.5.4 $\PH\to\Pe\PGt_{\PGm}$

In this channel, the preselection requires an electron and muon of opposite
electric charge with a separation of $\Delta R>0.4$. The triggers require both
an electron and a muon, where the electron has above 23, and the muon has
above 8. The electron is required to have $\pt>24\GeV$, $\abs{\eta}<2.5$, and
$I^{\Pe}_{\text{rel}}<0.1$. The muon is required to have $\pt>10\GeV$,
$\abs{\eta}<2.4$, and $I^{\PGm}_{\text{rel}}<0.15$. The selections for the
$\Pe\PGt_{\PGm}$ channel are summarized in Table 0.5.4.

The input variables to the BDT are $\pt^{\PGm}$, $\pt^{\Pe}$,
$m_{\text{col}}$, $m_{\text{vis}}$, $\mT(\Pgm,\ptvecmiss)$,
$\Delta\phi(\Pe,\PGm)$, $\Delta\phi(\PGm,\ptvecmiss)$, and
$\Delta\phi(\Pe,\ptvecmiss)$. As can be seen, the input variables are similar
to $\PGm\PGt_{\Pe}$ channel except for the addition of the variable
$m_{\text{vis}}$ and removing $\mT(\Pe,\ptvecmiss)$. The post-fit
distributions of simulated signal, data, and backgrounds in each category of
the $\Pe\PGt_{\PGm}$ channel are shown in Fig. 5.

Event selection criteria for the $\PH\to\Pe\PGt$ channels.

ccccc Variable $\Pe\tauh$ $\Pe\PGt_{\PGm}$

$\pt^{\Pe}$ $>$27 $>$24

$\pt^{\PGm}$ $>$10

$\pt^{\tauh}$ $>$30

$\abs{\eta}^{\Pe}$ $<$2.1 $<$2.5

$\abs{\eta}^{\PGm}$ $<$2.4

$\abs{\eta}^{\tauh}$ $<$2.3

$I^{\Pe}_{\text{rel}}$ $<$0.15 $<$0.1

$I^{\PGm}_{\text{rel}}$ $<$0.15

Trigger requirement

$\pt^{\Pe}>25\GeV$ (2016)  $\pt^{\Pe}>23\GeV$ $\pt^{\PGm}>8\GeV$

$\pt^{\Pe}>27\GeV$ (2017)

$\pt^{\Pe}>32\GeV$ (2018)

$\pt^{\Pe}>24\GeV$ and $\pt^{\tauh}>30\GeV$ (2017, 2018)

Figure 4: BDT discriminant distributions for the data and background processes
in the $\PH\to\Pe\tauh$ channel. A $\mathcal{B}(\PH\to\Pe\PGt)=20\%$ is
assumed for the signal. The channel categories are 0 jets (upper row left), 1
jet (upper row right), 2 jets (lower row left), and 2 jets VBF (lower row
right). The lower panel in each plot shows the ratio of data and estimated
background. The uncertainty band corresponds to the background uncertainty in
which the post-fit statistical and systematic uncertainties are added in
quadrature.

Figure 5: BDT discriminant distributions for the data and background processes
in the $\PH\to\Pe\PGt_{\PGm}$ channel. A $\mathcal{B}(\PH\to\Pe\PGt)=20\%$ is
assumed for the signal. The channel categories are 0 jets (upper row left), 1
jet (upper row right), 2 jets (lower row left), and 2 jets VBF (lower row
right). The lower panel in each plot shows the ratio of data and estimated
background. The uncertainty band corresponds to the background uncertainty in
which the post-fit statistical and systematic uncertainties are added in
quadrature.

##  0.6 Background estimation

One of the major background contributions comes from the $\PZ\to\PGt\PGt$
process, in which the muon or electron arises from a lepton decay. The other
major background contributions arise from the $\PW{+}\text{jets}$ process and
from multijets events produced through the strong interaction (referred to as
QCD multijet events hereafter), where one or more of the jets are
misidentified as leptons. These backgrounds are estimated from data either
fully or with the aid of simulation. The and single top quark background
contributes substantially in leptonic channels and is estimated using
simulated events along with the other backgrounds. The background estimates
are validated in different orthogonal VRs constructed to have enhanced
contributions from specific backgrounds.

###  0.6.1 $\PZ\to\PGt\PGt$ background

The $\PZ\to\PGt\PGt$ background is estimated from data using an embedding
technique [76]. This technique allows for an estimation of the genuine SM
backgrounds from data with reduced simulation input. This minimizes the
uncertainties that arise from using simulation. Events with a pair of
oppositely charged muons are selected in data so that $\PZ\to\PGm\PGm$ events
largely dominate. These data events are selected independently of the event
selection criteria described in Section 0.5. The muons are removed from the
selected events and replaced with simulated leptons with the same kinematic
properties as those of the replaced muon. In that way, a set of hybrid events
is obtained that relies on simulation only for the decay of the leptons. The
description of the underlying event or the production of associated jets is
taken entirely from data. This technique results in a more accurate
description of the and jet-related variables than simulation and an overall
reduction in the systematic uncertainties. Embedded events cover all
backgrounds with two genuine leptons, and this includes a small fraction of ,
diboson, and EW /events. The simulated events from the , diboson, and EW
/where both candidates match to leptons at the generator level are removed to
avoid any double counting.

###  0.6.2 Misidentified lepton background

The misidentified lepton background corresponds to events where jets are
misidentified as leptons. They mostly arise from two sources:
$\PW{+}\text{jets}$ and QCD multijet events. In $\PW{+}\text{jets}$ background
events, one of the leptons is from the boson decay while the other is a jet
misidentified as a lepton. In QCD multijet events, both the leptons are
misidentified jets. In the $\PGm\tauh$ and $\Pe\tauh$ channels, the
contributions from misidentified lepton backgrounds have been estimated using
a “misidentification rate” approach. In the $\PGm\PGt_{\Pe}$ and
$\Pe\PGt_{\PGm}$ channels, an “extrapolation factor” approach is adopted,
which is consistent with the “misidentification rate” approach, and is used
because of limited statistical precision in the leptonic channels.

#### Misidentification rate approach

The misidentified lepton background in the signal region (SR) is estimated
using misidentification rates from $\PZ{+}\text{jets}$ CR measurement region
and applied to a background-enriched region from collision data. The
misidentification rates are evaluated using events with a boson and at least
one jet that can be misidentified as a lepton. The probabilities with which
jets are misidentified as an electron, muon, or are labeled as
$f_{\Pe},f_{\PGm}$, and $f_{\tauh}$, respectively. The boson is formed using
two muons with $\pt>26\GeV$, $\abs{\eta}<2.4$, and
$I^{\PGm}_{\text{rel}}<0.15$ for measuring the jet $\to\tauh,\PGm,\Pe$
misidentification rate. The muons are required to be oppositely charged and
have invariant mass between 70 and 110. The contribution from diboson events,
where there is a genuine lepton, is subtracted using simulation.

The jet is required to pass the same lepton identification criteria as used in
the SR. A “signal-like” and “background-like” regions are defined. The
isolation for the electron and muon is required to have
$I^{\ell}_{\text{rel}}<0.15$ and the discriminated against jets at a WP that
has an identification efficiency of about 70% for the “signal-like” region.
For the “background-like” region, lepton isolation is required to be
$0.15<I^{\PGm}_{\text{rel}}<0.25$ and $0.15<I^{\Pe}_{\text{rel}}<0.50$, and
the is discriminated against jets at a WP that has an identification
efficiency of about 80% and not pass the WP that has an identification
efficiency of about 70%. After the “signal-like” and “background-like” regions
are defined, the misidentification rates are computed as functions of the
lepton . The misidentification rates $f_{\Pe},f_{\PGm},$ and $f_{\tauh}$ are
estimated as:

$f_{i}=\frac{S_{i}}{B_{i}+S_{i}}$

where $S_{i}$ is the number of events in the “signal-like” region, while
$B_{i}$ is the number of events in the “background-like” region. The
misidentification rate shows a dependence that depends on the decay mode and
$\abs{\eta}$ and is therefore evaluated as a function of $\pt^{\PGt}$ for the
different decay modes and two $\eta$ regions ($\abs{\eta}<1.5$ or
$\abs{\eta}>1.5$).

In the $\Pe\tauh$ channel, the misidentification rate is evaluated using
events with a boson formed using two electrons with $\pt>27\GeV$,
$\abs{\eta}<2.5$, and $I^{\Pe}_{\text{rel}}<0.15$. The electrons must be
oppositely charged and have an invariant mass between 70 and 110. The reason
for using $\PZ\to\Pe\Pe$ events for evaluating the misidentification rate in
$\Pe\tauh$ channel is that the DNN WPs used for discriminating against
electrons and muons are different in this channel compared to the $\PGm\tauh$
channel as described in Section 0.4. The misidentification rates evaluated
using this CR measurement region are compatible with the misidentification
rates measured in $\PZ\to\PGm\PGm$ events.

The computed misidentification rates $f_{i}$ depend on the lepton for
electrons and muons or , $\eta$, and decay mode for the candidates. The
misidentification rates for electrons and muons are ${\approx}0.4$ and
${\approx}0.6$, respectively, at $\pt=30.0\GeV$. The misidentification rates
for candidates are in the range 0.02–0.24 at $\pt=30.0\GeV$. They are used to
estimate the background yields and obtain the distributions of the
misidentified lepton background. This is accomplished through the following
procedure. Each event in the “background-like” region, defined using the
collision data with the same selection as the SR, but loosening the isolation
requirements on one of the leptons, is weighted by a factor $f_{i}/(1-f_{i})$.
Events with the possibility of double counting because of two misidentified
leptons are subtracted. For example, events with both a misidentified muon or
electron and a misidentified are accounted once in the “background-like”
region for muon or electron with a weight $f_{\ell}/(1-f_{\ell})$ and another
time in the “background-like” region for with a weight $f_{\PGt}/(1-f_{\PGt})$
and are hence double counted. This is mitigated by subtracting their
contribution once using a weight,
$f_{\PGt}f_{\ell}/[(1-f_{\PGt})(1-f_{\ell})]$, where
$\ell=\PGm\,\text{or}\,\Pe$.

The background estimate is validated in a VR by requiring the two leptons to
have the same electric charge, enhancing the misidentified lepton background.
Figure 6 (left) shows the comparison of data with background estimates in this
VR for the $\PGm\tauh$ channel. The background estimate is also validated in a
boson enriched VR, as shown in Fig. 6 (middle). This VR is obtained by
applying the preselection, $\mT(\ell,\ptvecmiss)>60\GeV$ ($\ell=\Pe$ or ), and
$\mT(\tauh,\ptvecmiss)>80\GeV$. As detailed in Section 0.7, uncertainty in the
misidentified lepton background is taken from the boson enriched VR, which is
defined orthogonally to the SR.

#### Extrapolation factor approach

In the $\Pe\PGt_{\PGm}$ and $\PGm\PGt_{\Pe}$ channels, the QCD multijet
background is estimated from the data using events with an electron and a muon
with the same electric charge [77]. Contributions from other processes are
estimated from simulation and subtracted from the data. Extrapolation factors
from the CR application region requiring the two leptons to have the same
electric charge to the SR are measured in data as a function of the jet
multiplicity and the $\Delta R$ separation between the electron and muon.

The extrapolation factors are estimated using events with a muon failing the
isolation requirement and an isolated electron. The contribution from events
to the QCD multijet background gives rise to the $\Delta R$ dependence and is
parameterized with a linear function. The extrapolation factors are higher for
events with low $\Delta R$ separation between the electron and muon,
decreasing as the $\Delta R$ separation increases. The extrapolation factors
also depend on the electron and muon . This dependence comes from the leptons
arising from the semi-leptonic quark decay. These leptons tend to be softer in
and less isolated, resulting in a reduction in the number of such events
passing the and isolation requirements.

As the extrapolation factors are from CR measurement region where the muon
fails the isolation requirement, an additional correction is applied to cover
a potential mismodeling. This correction is calculated by measuring the
extrapolation factors in two different CR measurement regions. The first CR
measurement region has events where the muon is isolated, and the electron
fails the isolation requirement. The second CR measurement region has events
where both the electron and muon fail the isolation requirement. The ratio of
the extrapolation factors from these two CR measurement regions is taken as
the correction to account for the potential mismodeling induced by requiring
the muon to fail the isolation requirement.

###  0.6.3 Other backgrounds

Other background contributions come from processes in which a lepton pair is
produced from the weak decays of quarks and vector bosons. These include , , ,
and events. There are nonnegligible contributions from processes such as
$\PW\Pgg^{(\ast)}{+}\text{jets}$, single top quark production, and
$\PZ\to\ell\ell$ $(\ell=\Pe,\PGm)$. Figure 6 (right) shows the comparison of
data with background estimates in the VR for the $\PGm\PGt_{\Pe}$ channel.
This VR is defined by requiring the presence of at least one -tagged jet in
the event in addition to the preselection. The SM Higgs boson production
contribution mainly comes from $\PH\to\PGt\PGt$ and $\PH\to\PW\PW$ decays.

Figure 6: The $m_{\text{col}}$ distribution in VR with same electric charge
for both leptons (left), $\PW{+}\text{jets}$ VR (middle), and VR (right). In
each distribution, the VR’s dominant background is shown, and all the other
backgrounds are grouped into “Other bkg.”. A
$\mathcal{B}(\PH\to\PGm\PGt)=20\%$ is assumed for the signal. The lower panel
in each plot shows the ratio of data and estimated background. The uncertainty
band corresponds to the background uncertainty in which the post-fit
statistical and systematic uncertainties are added in quadrature.

##  0.7 Systematic uncertainties

Several sources of experimental and theoretical systematic uncertainties are
taken into account in the statistical analysis. These uncertainties affect
both the normalization and distribution of the different processes. The
different systematic uncertainties are incorporated in the likelihood as
nuisance parameters for which log-normal a priori distributions are assumed,
and distribution variations are taken into account via continuous morphing
[78]. The maximum likelihood and profile likelihood with asymptotic
approximation are then computed using the defined likelihood to obtain the
best fit branching fraction and upper limits on the branching fraction for the
LFV Higgs boson decays. As the search is categorized into different final
states, partial and complete correlations between the uncertainties in
different categories are taken into account and are summarized in Table 0.7.

The uncertainties to reconstruct a and estimate its identification efficiency
for different ranges are measured using a tag-and-probe method [79] and found
to be in the range of 2–3%. The uncertainties for different ranges of are
treated as uncorrelated. These uncertainties are also considered for the
embedded background, where they are treated as 50% correlated with the
simulation uncertainties. For the embedded events, triggering on muons before
being replaced by leptons leads to an uncertainty in the trigger efficiency of
about 4%, which is treated as uncorrelated between the three years due to
different triggering criteria. There are two effects that need to be
considered for the embedded events. The embedded events have higher track
reconstruction efficiency because of reconstruction in an empty detector
environment. The energy deposits of the replaced muons can cause event
migration for decay modes with a $\pi^{0}$. Data to simulation scale factors
cover these effects with corresponding systematic uncertainties.

Uncertainties arising from an electron or a muon misidentified as correspond
to between 7–40% or 10–70%, respectively, for different bins of , $\eta$, and
decay modes. The uncertainty in the energy scale is treated as uncorrelated
for different decay modes and 50% correlated between embedded and simulated
backgrounds and ranges from 0.7–1.2%. The uncertainty in the electron energy
scale and the muon momentum scale for misidentified leptons is independent of
the energy scale and amounts to 7% and 1%, respectively. The effect of lepton
energy resolution is found to be negligible.

The jet energy scale is affected by several sources, and its uncertainty is
evaluated as a function of and $\eta$. The jet energy scale’s effect is
propagated to the BDT discriminant and varies from 3–20% [72]. The
uncertainties in jet energy resolution are also taken into account and mostly
impact the $m_{\mathrm{jj}}$-defined categories. The jets with $\pt<10\GeV$
fall under unclustered energy. The unclustered energy scale is considered
independently for charged particles, neutral hadrons, photons, and very
forward particles, that affect both the distributions and the total yields and
are treated as uncorrelated. The efficiency to classify a jet as -tagged is
different in data and simulation, and scale factors that depend on jet are
used to correct the simulation. The uncertainties in the measured values of
these scale factors are taken as sources of systematic uncertainties.

The uncertainties in the reconstruction of electrons and muons, along with
their isolation criteria, are measured using the tag-and-probe method in data
in $\PZ\to\Pe\Pe$ and $\PZ\to\PGm\PGm$ events and sum up to about 2% [80, 64,
81]. The uncertainty in the measurement of the muon momentum scale is in the
range 0.4–2.7% for different $\abs{\eta}$ ranges, while for the electron
momentum scale, it is less than 1%. The selection of events using electron-
and muon-based triggers results in an additional 2% uncertainty in the yield
of simulated processes. In the $\Pe\tauh$ channel, an additional 5%
uncertainty is associated with using the trigger requiring the presence of
both an electron and in 2017 and 2018. The uncertainties related to the lepton
identification, momentum scale, and triggering are treated as correlated
between the three years.

The misidentification rates in the $\Pe\tauh$ and $\PGm\tauh$ final states are
parameterized using a linear function dependent on , where two uncertainties
are ascribed per fit function. The normalization uncertainties in the
estimates of the misidentified lepton backgrounds
($\text{jet}\to\tauh,\PGm,\Pe$) from data are taken from the VR, as described
in Section 0.6. Additional uncertainty is estimated for the misidentified
lepton background in the boson enriched VR. It is parameterized as a function
of $\Delta\phi(\PGm,\ptvecmiss)$ for the $\PGm\tauh$ channel and as a function
of $\Delta\phi(\Pe,\ptvecmiss)$ for the $\Pe\tauh$ channel. Discriminants with
different signal-to-background ratios are used to differentiate against
electrons and muons, which entails an additional 3% uncertainty for the
$\Pe\tauh$ channel.

The misidentified lepton background in the $\Pe\PGt_{\PGm}$ and
$\PGm\PGt_{\Pe}$ final states is affected by different uncertainties. The
statistical uncertainties arising from both fits of the extrapolation factors
as a function of the lepton and the spatial separation between electron and
muon are taken into account. The uncertainty in extrapolation factors
resulting from inverting the muon isolation is taken into account. These
uncertainties have a combined effect of about 20% on the normalization. The
dominant source of uncertainty in the simulated background processes,
$\PZ\to\Pe\Pe$, $\PZ\to\PGm\PGm$, $\PZ\to\PGt\PGt$, , , $\PW\Pgg$, , and
single top quark production is the measurement of the cross section for these
processes and is treated as correlated between the three years.

The theoretical uncertainties affecting the measurement of Higgs boson
production cross section are the QCD scales (renormalization and factorization
scales), the choice of PDFs, and the strong coupling constant () evaluated at
the boson mass. These uncertainties affect the signal’s normalization and are
treated as correlated between the three years [82]. The changes made in QCD
scales provide 3.9, 0.5, 0.9, and 0.8% uncertainties in the , VBF, , cross
sections, respectively, while changes in the PDFs and result in 3.2, 2.1, 1.3,
and 1.9% uncertainties, respectively. The acceptance is taken into account
when changes are made in QCD scales and the PDFs and .

The normalization of the event yield for $\PH\to\PGt\PGt$ is taken from
simulation. The uncertainty in the $\mathcal{B}(\PH\to\PGt\PGt)$ includes a
1.70% due to missing higher-order corrections, a 0.99% in the quark masses,
and a 0.62% on . The normalization of the event yield for $\PH\to\PW\PW$ is
taken from simulation. The uncertainty in the $\mathcal{B}(\PH\to\PW\PW)$
includes a 0.99% due to missing higher-order corrections, a 0.99% in the quark
masses, and a 0.66% in .

The bin-by-bin uncertainties account for the statistical uncertainties in each
bin of the distributions of every process. The Barlow–Beeston Lite [83]
approach is used, assigning a single parameter to scale the sum of the process
yields in each bin, constrained by the total uncertainty, instead of requiring
separate parameters, one per process. This is useful to reduce the number of
parameters required in the statistical analysis. They are treated as
uncorrelated between bins, categories, and channels.

The integrated luminosities of the 2016, 2017, and 2018 data taking periods
are individually known to have uncertainties in the 2.3–2.5% range [84, 85,
86], while the total integrated luminosity has an uncertainty of 1.8%, the
improvement in precision reflecting the uncorrelated time evolution of some
systematic effects. The uncertainty in the integrated luminosity affects all
processes, with the normalization taken directly from the simulation.
Uncertainty related to pileup is evaluated through changes made in the weights
applied to the simulation and is treated as correlated between the three
years. The dependence on weight is obtained through a 5% change in the total
inelastic cross section used to estimate the number of pileup events in data.
Other minimum-bias event modeling and initial- and final-state radiation
uncertainties are estimated to be much smaller than those on the rate and are
therefore neglected.

During the 2016 and 2017 data taking periods, a gradual shift in the timing of
the inputs from the ECAL first-level trigger in the region of $\abs{\eta}>2.0$
caused a specific trigger inefficiency. For events containing an electron or a
jet with respective $>50\GeV$ or $>100\GeV$, in the region
$2.5<\abs{\eta}<3.0$ the efficiency loss is 10–20%, depending on , $\eta$, and
time. Correction factors are computed from data and applied to the acceptance
evaluated through simulation. Uncertainty due to this correction factor is
${\approx}1\%$ and is treated as correlated between the two years.

Systematic uncertainties in the expected event yields. All uncertainties are
treated as correlated among categories, except those with two values separated
by the $\oplus$ sign. In this case, the first value is the correlated
uncertainty and the second value is the uncorrelated uncertainty for each
category.  l*4c Systematic uncertainty $\PGm\tauh$ $\PGm\PGt_{\Pe}$ $\Pe\tauh$
$\Pe\PGt_{\PGm}$
Muon ident. and iso. 2% 2% 2%
Electron ident. and iso. 2% 2% 2%
Trigger 2% 2% 2% 2%
ident. dep. (2–3%) dep. (2–15%)
$\PGm\to\tauh$ misid. 10–70%
$\Pe\to\tauh$ misid. 40%
tagging efficiency $<$6.5% $<$6.5% $<$6.5% $<$6.5%
Embedded bkg. 4% 4% 4% 4%
$\PZ\to\PGm\PGm$, bkg. 4% $\oplus$ 5% 4% $\oplus$ 5% 4% $\oplus$ 5% 4%
$\oplus$ 5%
EW bkg. 4% $\oplus$ 5% 4% $\oplus$ 5% 4% $\oplus$ 5% 4% $\oplus$ 5%
$\PW{+}\text{jets}$ bkg. 10% 10%
Diboson bkg. 5% $\oplus$ 5% 5% $\oplus$ 5% 5% $\oplus$ 5% 5% $\oplus$ 5%
bkg. 6% $\oplus$ 5% 6% $\oplus$ 5% 6% $\oplus$ 5% 6% $\oplus$ 5%
Single top quark bkg. 5% $\oplus$ 5% 5% $\oplus$ 5% 5% $\oplus$ 5% 5% $\oplus$
5%
$\text{Jet}\to\tauh$ bkg. 30% $\oplus$ 10% 30% $\oplus$ 10%
Jet energy scale 3–20% 3–20% 3–20% 3–20%
energy scale 0.7–1.2% 0.7–1.2%
$\Pe\to\tauh$ energy scale 1–7% 1–7%
$\PGm\to\tauh$ energy scale 1% 1%
Electron energy scale 1–2.5% 1–2.5% 1–2.5%
Muon energy scale 0.4–2.7% 0.4–2.7% 0.4–2.7%
Trigger timing inefficiency 0.2–1.3% 0.2–1.3% 0.2–1.3% 0.2–1.3%
Integrated luminosity 1.8% 1.8% 1.8% 1.8%
QCD scales () 3.9%
QCD scales (VBF) 0.5%
PDF + () 3.2%
PDF + (VBF) 2.1%
QCD acceptance () $-$10.3 to +5.9%
QCD acceptance (VBF) $-$2.7 to +2.3%
PDF + acceptance () $-$0.8 to +2.8%
PDF + acceptance (VBF) $-$1.7 to +2.3%

##  0.8 Results

No significant excess has been found for the LFV Higgs boson decays in both
channels, and upper limits have been placed. Upper limits on the branching
fraction of Higgs boson decay are computed using the modified frequentist
approach for , taking the profile likelihood as a test statistic [87, 88, 89]
in the asymptotic approximation. The observed (expected) upper limits on the
Higgs boson branching fractions are 0.15 (0.15)% for $\PH\to\PGm\PGt$ and 0.22
(0.16)% for $\PH\to\Pe\PGt$, respectively, at the 95% . The results have a
dominant contribution from systematic uncertainties. The bin-by-bin
uncertainties and the uncertainties related to the distribution of the
misidentified lepton background have a significant impact followed by the
lepton energy scale uncertainties.

The upper limits and the best fit branching fractions for
$\mathcal{B}(\PH\to\PGm\PGt)$ and $\mathcal{B}(\PH\to\Pe\PGt)$ are reported in
Tables 0.8 and 0.8. The limits are also summarized in Table 0.8 and
graphically shown in Fig. 7. The limits are improved from previous results
[30]. The improvement relies on the larger data set, the updated background
estimation techniques, and BDT classification. The results are cross-checked
with an additional investigation following the strategy in Ref. [30] and are
found to be consistent.

The upper limits on $\mathcal{B}(\PH\to\PGm\PGt)$ and
$\mathcal{B}(\PH\to\Pe\PGt)$ are subsequently used to put constraints on LFV
Yukawa couplings [11]. The LFV decays and arise at tree level from the assumed
flavor violating Yukawa interactions, $Y_{\ell^{\alpha}\ell^{\beta}}$, where
$\ell^{\alpha},\ell^{\beta}$ are the leptons of different flavors
($\ell^{\alpha}\neq\ell^{\beta}$). The decay widths
$\Gamma(\PH\to\ell^{\alpha}\ell^{\beta})$ in terms of the Yukawa couplings are
given by:

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

and the branching fractions are given by:

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

The SM Higgs boson decay width is assumed to be $\Gamma_{\mathrm{SM}}=4.1\MeV$
[90] for $m_{\PH}=125\GeV$. The 95% upper limit on the Yukawa couplings
obtained from the expression for the branching fraction above is shown in
Table 0.8. The limits on the Yukawa couplings are
$\sqrt{\smash[b]{\abs{Y_{\PGm\PGt}}^{2}+\abs{Y_{\PGt\PGm}}^{2}}}<1.11{\times}10^{-3}$
and
$\sqrt{\smash[b]{\abs{Y_{\Pe\PGt}}^{2}+\abs{Y_{\PGt\Pe}}^{2}}}<1.35{\times}10^{-3}$
and are shown in Fig. 8. Tabulated results are available in the HepData
database [91].

Figure 7: Observed (expected) 95% upper limits on the
$\mathcal{B}(\PH\to\PGm\PGt)$ (left) and $\mathcal{B}(\PH\to\Pe\PGt)$ (right)
for each individual category and combined. The categories from top to bottom
row are $\PGm\tauh$ 0Jets, $\PGm\tauh$ 1Jet, $\PGm\tauh$ 2 Jets, $\PGm\tauh$
VBF, $\PGm\PGt_{\Pe}$ 0Jets, $\PGm\PGt_{\Pe}$ 1Jet, $\PGm\PGt_{\Pe}$ 2 Jets,
$\PGm\PGt_{\Pe}$ VBF, and $\PGm\PGt$ combined (left) and $\Pe\tauh$ 0Jets,
$\Pe\tauh$ 1Jet, $\Pe\tauh$ 2 Jets, $\Pe\tauh$ VBF, $\Pe\PGt_{\PGm}$ 0Jets,
$\Pe\PGt_{\PGm}$ 1Jet, $\Pe\PGt_{\PGm}$ 2 Jets, $\Pe\PGt_{\PGm}$ VBF, and
$\Pe\PGt$ combined (right).

Figure 8: Expected (red line) and observed (black solid line) 95% upper limits
on the LFV Yukawa couplings, $\abs{Y_{\PGm\PGt}}$ vs. $\abs{Y_{\PGt\PGm}}$
(left) and $\abs{Y_{\Pe\PGt}}$ vs. $\abs{Y_{\PGt\Pe}}$ (right). The
$\abs{Y_{\PGm\PGt}}$ or $\abs{Y_{\Pe\PGt}}$ couplings correspond to left
chiral muon or electron and right chiral lepton, while $\abs{Y_{\PGt\PGm}}$ or
$\abs{Y_{\PGt\Pe}}$ couplings correspond to left chiral lepton and right
chiral muon or electron. In the left plot, the expected limit is covered by
the observed limit as they have similar values. The flavor diagonal Yukawa
couplings are approximated by their SM values. The green and yellow bands
indicate the range that is expected to contain 68% and 95% of all observed
limit variations from the expected limit. The shaded regions are constraints
obtained from null searches for $\PGt\to 3\PGm$ or $\PGt\to 3\Pe$ (dark blue)
[92] and $\PGt\to\PGm\Pgg$ or $\PGt\to\Pe\Pgg$ (purple) [93]. The blue
diagonal line is the theoretical naturalness limit
$\abs{Y_{ij}Y_{ji}}={m_{i}}m_{j}/v^{2}$ [11].

Observed and expected upper limits at 95% and best fit branching fractions for
each individual jet category, and their combinations, in the $\PH\to\PGm\PGt$
channel.

cccccc Expected limits (%)

0-jet 1-jet 2-jets VBF Combined

$\PGm\PGt_{\Pe}$ $<$0.34 $<$0.57 $<$1.13 $<$0.83 $<$0.27

$\PGm\tauh$ $<$0.33 $<$0.43 $<$0.49 $<$0.30 $<$0.18

$\PGm\PGt$ $<$0.15

Observed limits (%)

0-jet 1-jet 2-jets VBF Combined

$\PGm\PGt_{\Pe}$ $<$0.31 $<$0.36 $<$0.77 $<$0.58 $<$0.19

$\PGm\tauh$ $<$0.37 $<$0.40 $<$0.50 $<$0.39 $<$0.24

$\PGm\PGt$ $<$0.15

Best fit branching fractions (%)

0-jet 1-jet 2-jets VBF Combined

$\PGm\PGt_{\Pe}$ $-0.03\pm 0.17$ $-0.40\pm 0.28$ $-0.66\pm 0.56$ $-0.41\pm
0.39$ $-0.14\pm 0.13$

$\PGm\tauh$ $+0.05\pm 0.17$ $-0.05\pm 0.22$ $+0.02\pm 0.25$ $+0.10\pm 0.16$
$+0.07\pm 0.09$

$\PGm\PGt$ $+0.00\pm 0.07$

Observed and expected upper limits at 95% and best fit branching fractions for
each individual jet category, and their combinations, in the $\PH\to\Pe\PGt$
channel.

cccccc Expected limits (%)

0-jet 1-jet 2-jets VBF Combined

$\Pe\PGt_{\PGm}$ $<$0.34 $<$0.53 $<$1.08 $<$0.86 $<$0.26

$\Pe\tauh$ $<$0.39 $<$0.44 $<$0.55 $<$0.35 $<$0.20

$\Pe\PGt$ $<$0.16

Observed limits (%)

0-jet 1-jet 2-jets VBF Combined

$\Pe\PGt_{\PGm}$ $<$0.42 $<$0.56 $<$1.35 $<$0.42 $<$0.22

$\Pe\tauh$ $<$0.44 $<$0.68 $<$0.78 $<$0.57 $<$0.37

$\Pe\PGt$ $<$0.22

Best fit branching fractions (%)

0-jet 1-jet 2-jets VBF Combined

$\Pe\PGt_{\PGm}$ $+0.11\pm 0.17$ $+0.04\pm 0.27$ $+0.35\pm 0.55$ $-1.04\pm
0.44$ $-0.07\pm 0.13$

$\Pe\tauh$ $+0.07\pm 0.20$ $+0.29\pm 0.23$ $+0.27\pm 0.29$ $+0.27\pm 0.17$
$+0.20\pm 0.10$

$\Pe\PGt$ $+0.08\pm 0.08$

Summary of observed and expected upper limits at 95% , best fit branching
fractions and corresponding constraints on Yukawa couplings for the
$\PH\to\PGm\PGt$ and $\PH\to\Pe\PGt$ channels.

lccc Observed (expected) Best fit branching Yukawa coupling

upper limits (%) fractions (%) constraints

$\PH\to\PGm\PGt$ $<$0.15 (0.15) $0.00\pm 0.07$ $<1.11\,(1.10){\times}10^{-3}$

$\PH\to\Pe\PGt$ $<$0.22 (0.16) $0.08\pm 0.08$ $<1.35\,(1.14){\times}10^{-3}$

##  0.9 Summary

A search for lepton-flavor violation has been performed in the and final
states of the Higgs boson in data collected by the CMS experiment. The data
correspond to an integrated luminosity of 137of proton-proton collisions at a
center-of-mass energy of 13. The results are extracted through a maximum
likelihood fit to a boosted decision tree output, trained to distinguish the
expected signal from backgrounds. The observed (expected) upper limits on the
branching fraction of the Higgs boson to are 0.15 (0.15)% and to are 0.22
(0.16)%, respectively, at 95% confidence level. Upper limits on the off-
diagonal and couplings are derived from these constraints,
$\sqrt{\smash[b]{\abs{Y_{\PGm\PGt}}^{2}+\abs{Y_{\PGt\PGm}}^{2}}}<1.11{\times}10^{-3}$
and
$\sqrt{\smash[b]{\abs{Y_{\Pe\PGt}}^{2}+\abs{Y_{\PGt\Pe}}^{2}}}<1.35{\times}10^{-3}$.
These results constitute an improvement over the previous limits from CMS and
ATLAS experiments.

###### Acknowledgements.

We congratulate our colleagues in the CERN accelerator departments for the
excellent performance of the LHC and thank the technical and administrative
staffs at CERN and at other CMS institutes for their contributions to the
success of the CMS effort. In addition, we gratefully acknowledge the
computing centers and personnel of the Worldwide LHC Computing Grid and other
centers 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, the CMS detector, and the supporting
computing infrastructure 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);
MINCIENCIAS (Colombia); MSES and CSF (Croatia); RIF (Cyprus); SENESCYT
(Ecuador); MoER, ERC 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 program and the European Research Council and Horizon 2020
Grant, contract Nos. 675440, 724704, 752730, 765710 and 824093 (European
Union); the Leventis Foundation; the Alfred 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. Z191100007219010; 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, and
under project number 400140256 - GRK2497; the Lendület (“Momentum”) Program
and the János Bolyai Research Scholarship of the Hungarian Academy of
Sciences, the New National Excellence Program ÚNKP, the NKFIA research grants
123842, 123959, 124845, 124850, 125105, 128713, 128786, and 129058 (Hungary);
the Council of Science and Industrial Research, India; the Ministry of Science
and Higher Education and the National Science Center, contracts Opus
2014/15/B/ST2/03998 and 2015/19/B/ST2/02861 (Poland); the National Priorities
Research Program by Qatar National Research Fund; the Ministry of Science and
Higher Education, project no. 0723-2020-0041 (Russia); the Programa Estatal de
Fomento de la Investigación Científica y Técnica de Excelencia María de
Maeztu, grant MDM-2015-0509 and the Programa Severo Ochoa del Principado de
Asturias; the Thalis and Aristeia programs cofinanced by EU-ESF and the Greek
NSRF; the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn
University and the Chulalongkorn Academic into Its 2nd Century Project
Advancement Project (Thailand); the Kavli Foundation; the Nvidia Corporation;
the SuperMicro 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, J.W. Andrejkovic, T. Bergauer, S. Chatterjee, M. Dragicevic, A.
Escalante Del Valle, R. Frühwirth1, M. Jeitler1, N. Krammer, L. Lechner, D.
Liko, I. Mikulec, P. Paulitsch, F.M. Pitters, J. Schieck1, R. Schöfbeck, M.
Spanring, S. Templ, W. Waltenberger, C.-E. Wulz1 Institute for Nuclear
Problems, Minsk, Belarus
V. Chekhovsky, A. Litomin, V. Makarenko Universiteit Antwerpen, Antwerpen,
Belgium
M.R. Darwish2, E.A. De Wolf, X. Janssen, T. Kello3, A. Lelek, H. Rejeb Sfar,
P. Van Mechelen, S. Van Putte, N. Van Remortel Vrije Universiteit Brussel,
Brussel, Belgium
F. Blekman, E.S. Bols, J. D’Hondt, J. De Clercq, M. Delcourt, H. El Faham, S.
Lowette, S. Moortgat, A. Morton, D. Müller, A.R. Sahasransu, S. Tavernier, W.
Van Doninck, P. Van Mulders Université Libre de Bruxelles, Bruxelles, Belgium
D. Beghin, B. Bilin, B. Clerbaux, G. De Lentdecker, L. Favart, A. Grebenyuk,
A.K. Kalsi, K. Lee, M. Mahdavikhorrami, I. Makarenko, L. Moureaux, L. Pétré,
A. Popov, N. Postiau, E. Starling, L. Thomas, M. Vanden Bemden, C. Vander
Velde, P. Vanlaer, D. Vannerom, L. Wezenbeek Ghent University, Ghent, Belgium
T. Cornelis, D. Dobur, J. Knolle, L. Lambrecht, G. Mestdach, M. Niedziela, C.
Roskas, A. Samalan, K. Skovpen, T.T. Tran, M. Tytgat, W. Verbeke, B.
Vermassen, M. Vit Université Catholique de Louvain, Louvain-la-Neuve, Belgium
A. Bethani, G. Bruno, F. Bury, C. Caputo, P. David, C. Delaere, I.S. Donertas,
A. Giammanco, K. Jaffel, V. Lemaitre, K. Mondal, J. Prisciandaro, A.
Taliercio, M. Teklishyn, P. Vischia, S. Wertz, S. Wuyckens Centro Brasileiro
de Pesquisas Fisicas, Rio de Janeiro, Brazil
G.A. Alves, C. Hensel, A. Moraes Universidade do Estado do Rio de Janeiro, Rio
de Janeiro, Brazil
W.L. Aldá Júnior, M. Alves Gallo Pereira, M. Barroso Ferreira Filho, H.
BRANDAO MALBOUISSON, W. Carvalho, J. Chinellato4, E.M. Da Costa, G.G. Da
Silveira5, D. De Jesus Damiao, S. Fonseca De Souza, D. Matos Figueiredo, C.
Mora Herrera, K. Mota Amarilo, L. Mundim, H. Nogima, P. Rebello Teles, A.
Santoro, S.M. Silva Do Amaral, A. Sznajder, M. Thiel, F. Torres Da Silva De
Araujo, A. Vilela Pereira Universidade Estadual Paulista a, Universidade
Federal do ABC b, São Paulo, Brazil
C.A. Bernardesa,a, L. Calligarisa, T.R. Fernandez Perez Tomeia, E.M.
Gregoresa,b, D.S. Lemosa, P.G. Mercadantea,b, S.F. Novaesa, Sandra S. Padulaa
Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy of
Sciences, Sofia, Bulgaria
A. Aleksandrov, G. Antchev, R. Hadjiiska, P. Iaydjiev, M. Misheva, M. Rodozov,
M. Shopova, G. Sultanov University of Sofia, Sofia, Bulgaria
A. Dimitrov, T. Ivanov, L. Litov, B. Pavlov, P. Petkov, A. Petrov Beihang
University, Beijing, China
T. Cheng, W. Fang3, Q. Guo, T. Javaid6, M. Mittal, H. Wang, L. Yuan Department
of Physics, Tsinghua University, Beijing, China
M. Ahmad, G. Bauer, C. Dozen7, Z. Hu, J. Martins8, Y. Wang, K. Yi9,10
Institute of High Energy Physics, Beijing, China
E. Chapon, G.M. Chen6, H.S. Chen6, M. Chen, F. Iemmi, A. Kapoor, D. Leggat, H.
Liao, Z.-A. LIU6, V. Milosevic, F. Monti, R. Sharma, J. Tao, J. Thomas-
wilsker, J. Wang, H. Zhang, S. Zhang6, J. Zhao State Key Laboratory of Nuclear
Physics and Technology, Peking University, Beijing, China
A. Agapitos, Y. Ban, C. Chen, Q. Huang, A. Levin, Q. Li, X. Lyu, Y. Mao, S.J.
Qian, D. Wang, Q. Wang, J. Xiao Sun Yat-Sen University, Guangzhou, China
M. Lu, Z. You Institute of Modern Physics and Key Laboratory of Nuclear
Physics and Ion-beam Application (MOE) - Fudan University, Shanghai, China
X. Gao3, H. Okawa Zhejiang University, Hangzhou, China
Z. Lin, M. Xiao Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, C. Florez, J. Fraga, A. Sarkar, M.A. Segura Delgado
Universidad de Antioquia, Medellin, Colombia
J. Mejia Guisao, F. Ramirez, J.D. Ruiz Alvarez, C.A. Salazar González
University of Split, Faculty of Electrical Engineering, Mechanical Engineering
and Naval Architecture, Split, Croatia
D. Giljanovic, N. Godinovic, D. Lelas, I. Puljak University of Split, Faculty
of Science, Split, Croatia
Z. Antunovic, M. Kovac, T. Sculac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, D. Ferencek, D. Majumder, M. Roguljic, A. Starodumov11, T. Susa
University of Cyprus, Nicosia, Cyprus
A. Attikis, E. Erodotou, A. Ioannou, G. Kole, M. Kolosova, S. Konstantinou, J.
Mousa, C. Nicolaou, F. Ptochos, P.A. Razis, H. Rykaczewski, H. Saka Charles
University, Prague, Czech Republic
M. Finger12, M. Finger Jr.12, A. Kveton 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. Abdalla13, A. Ellithi Kamel13 Center for High Energy Physics (CHEP-FU),
Fayoum University, El-Fayoum, Egypt
A. Lotfy, M.A. Mahmoud National Institute of Chemical Physics and Biophysics,
Tallinn, Estonia
S. Bhowmik, A. Carvalho Antunes De Oliveira, R.K. Dewanjee, K. Ehataht, M.
Kadastik, C. Nielsen, J. Pata, M. Raidal, L. Tani, 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
S. Bharthuar, E. Brücken, F. Garcia, J. Havukainen, M.S. Kim, R. Kinnunen, T.
Lampén, K. Lassila-Perini, S. Lehti, T. Lindén, M. Lotti, L. Martikainen, J.
Ott, H. Siikonen, E. Tuominen, J. Tuominiemi Lappeenranta University of
Technology, Lappeenranta, Finland
P. Luukka, H. Petrow, T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-
Yvette, France
C. Amendola, M. Besancon, F. Couderc, M. Dejardin, D. Denegri, J.L. Faure, F.
Ferri, S. Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry,
B. Lenzi, E. Locci, J. Malcles, J. Rander, A. Rosowsky, M.Ö. Sahin, A. Savoy-
Navarro14, M. Titov, G.B. Yu Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole
Polytechnique, Institut Polytechnique de Paris, Palaiseau, France
S. Ahuja, F. Beaudette, M. Bonanomi, A. Buchot Perraguin, P. Busson, A.
Cappati, C. Charlot, O. Davignon, B. Diab, G. Falmagne, S. Ghosh, R. Granier
de Cassagnac, A. Hakimi, I. Kucher, 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. Apparu, D. Bloch, G. Bourgatte, J.-M. Brom, E.C.
Chabert, C. Collard, D. Darej, J.-C. Fontaine15, U. Goerlach, C. Grimault,
A.-C. Le Bihan, E. Nibigira, P. Van Hove Institut de Physique des 2 Infinis de
Lyon (IP2I ), Villeurbanne, France
E. Asilar, S. Beauceron, C. Bernet, G. Boudoul, C. Camen, A. Carle, N. Chanon,
D. Contardo, P. Depasse, H. El Mamouni, J. Fay, S. Gascon, M. Gouzevitch, B.
Ille, Sa. Jain, I.B. Laktineh, H. Lattaud, A. Lesauvage, M. Lethuillier, L.
Mirabito, S. Perries, K. Shchablo, V. Sordini, L. Torterotot, G. Touquet, M.
Vander Donckt, S. Viret Georgian Technical University, Tbilisi, Georgia
A. Khvedelidze12, I. Lomidze, Z. Tsamalaidze12 RWTH Aachen University, I.
Physikalisches Institut, Aachen, Germany
L. Feld, K. Klein, M. Lipinski, D. Meuser, A. Pauls, M.P. Rauch, N. Röwert, J.
Schulz, M. Teroerde RWTH Aachen University, III. Physikalisches Institut A,
Aachen, Germany
D. Eliseev, M. Erdmann, P. Fackeldey, B. Fischer, S. Ghosh, T. Hebbeker, K.
Hoepfner, F. Ivone, H. Keller, L. Mastrolorenzo, M. Merschmeyer, A. Meyer, G.
Mocellin, S. Mondal, S. Mukherjee, D. Noll, A. Novak, T. Pook, A. Pozdnyakov,
Y. Rath, H. Reithler, J. Roemer, A. Schmidt, S.C. Schuler, A. Sharma, S.
Wiedenbeck, S. Zaleski RWTH Aachen University, III. Physikalisches Institut B,
Aachen, Germany
C. Dziwok, G. Flügge, W. Haj Ahmad16, O. Hlushchenko, T. Kress, A. Nowack, C.
Pistone, O. Pooth, D. Roy, H. Sert, A. Stahl17, T. Ziemons Deutsches
Elektronen-Synchrotron, Hamburg, Germany
H. Aarup Petersen, M. Aldaya Martin, P. Asmuss, I. Babounikau, S. Baxter, O.
Behnke, A. Bermúdez Martínez, S. Bhattacharya, A.A. Bin Anuar, K. Borras18, V.
Botta, D. Brunner, A. Campbell, A. Cardini, C. Cheng, F. Colombina, S.
Consuegra Rodríguez, G. Correia Silva, V. Danilov, L. Didukh, G. Eckerlin, D.
Eckstein, L.I. Estevez Banos, O. Filatov, E. Gallo19, A. Geiser, A. Giraldi,
A. Grohsjean, M. Guthoff, A. Jafari20, N.Z. Jomhari, H. Jung, A. Kasem18, M.
Kasemann, H. Kaveh, C. Kleinwort, D. Krücker, W. Lange, J. Lidrych, K. Lipka,
W. Lohmann21, R. Mankel, I.-A. Melzer-Pellmann, J. Metwally, A.B. Meyer, M.
Meyer, J. Mnich, A. Mussgiller, Y. Otarid, D. Pérez Adán, D. Pitzl, A.
Raspereza, B. Ribeiro Lopes, J. Rübenach, A. Saggio, A. Saibel, M. Savitskyi,
M. Scham, V. Scheurer, C. Schwanenberger19, A. Singh, R.E. Sosa Ricardo, D.
Stafford, N. Tonon, O. Turkot, M. Van De Klundert, R. Walsh, D. Walter, Y.
Wen, K. Wichmann, L. Wiens, C. Wissing, S. Wuchterl University of Hamburg,
Hamburg, Germany
R. Aggleton, S. Bein, L. Benato, A. Benecke, P. Connor, K. De Leo, M. Eich, F.
Feindt, A. Fröhlich, C. Garbers, E. Garutti, P. Gunnellini, J. Haller, A.
Hinzmann, G. Kasieczka, R. Klanner, R. Kogler, T. Kramer, V. Kutzner, J.
Lange, T. Lange, A. Lobanov, A. Malara, A. Nigamova, K.J. Pena Rodriguez, O.
Rieger, P. Schleper, M. Schröder, J. Schwandt, D. Schwarz, J. Sonneveld, H.
Stadie, G. Steinbrück, A. Tews, B. Vormwald, I. Zoi Karlsruher Institut fuer
Technologie, Karlsruhe, Germany
J. Bechtel, T. Berger, E. Butz, R. Caspart, T. Chwalek, W. De
Boer${}^{\textrm{\textdagger}}$, A. Dierlamm, A. Droll, K. El Morabit, N.
Faltermann, M. Giffels, J.o. Gosewisch, A. Gottmann, F. Hartmann17, C.
Heidecker, U. Husemann, I. Katkov22, P. Keicher, R. Koppenhöfer, S. Maier, M.
Metzler, S. Mitra, Th. Müller, M. Neukum, A. Nürnberg, G. Quast, K. Rabbertz,
J. Rauser, D. Savoiu, M. Schnepf, D. Seith, I. Shvetsov, H.J. Simonis, R.
Ulrich, J. Van Der Linden, R.F. Von Cube, M. Wassmer, M. Weber, S. Wieland, R.
Wolf, S. Wozniewski, S. Wunsch Institute of Nuclear and Particle Physics
(INPP), NCSR Demokritos, Aghia Paraskevi, Greece
G. Anagnostou, P. Asenov, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas,
A. Stakia National and Kapodistrian University of Athens, Athens, Greece
M. Diamantopoulou, D. Karasavvas, G. Karathanasis, P. Kontaxakis, C.K. Koraka,
A. Manousakis-katsikakis, A. Panagiotou, I. Papavergou, N. Saoulidou, K.
Theofilatos, E. Tziaferi, K. Vellidis, E. Vourliotis National Technical
University of Athens, Athens, Greece
G. Bakas, K. Kousouris, I. Papakrivopoulos, G. Tsipolitis, A. Zacharopoulou
University of Ioánnina, Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, N. Manthos, I.
Papadopoulos, J. Strologas MTA-ELTE Lendület CMS Particle and Nuclear Physics
Group, Eötvös Loránd University, Budapest, Hungary
M. Csanad, K. Farkas, M.M.A. Gadallah23, S. Lökös24, P. Major, K. Mandal, A.
Mehta, G. Pasztor, A.J. Rádl, O. Surányi, G.I. Veres Wigner Research Centre
for Physics, Budapest, Hungary
M. Bartók25, G. Bencze, C. Hajdu, D. Horvath26, F. Sikler, V. Veszpremi, G.
Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear Research ATOMKI,
Debrecen, Hungary
S. Czellar, J. Karancsi25, J. Molnar, Z. Szillasi, D. Teyssier Institute of
Physics, University of Debrecen, Debrecen, Hungary
P. Raics, Z.L. Trocsanyi27, B. Ujvari Eszterhazy Karoly University, Karoly
Robert Campus, Gyongyos, Hungary
T. Csorgo28, F. Nemes28, T. Novak Indian Institute of Science (IISc),
Bangalore, India
J.R. Komaragiri, D. Kumar, L. Panwar, P.C. Tiwari National Institute of
Science Education and Research, HBNI, Bhubaneswar, India
S. Bahinipati29, D. Dash, C. Kar, P. Mal, T. Mishra, V.K. Muraleedharan Nair
Bindhu30, A. Nayak30, P. Saha, N. Sur, S.K. Swain, D. Vats30 Panjab
University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, G. Chaudhary, S. Chauhan, N. Dhingra31, R.
Gupta, A. Kaur, M. Kaur, S. Kaur, P. Kumari, M. Meena, K. Sandeep, J.B. Singh,
A.K. Virdi University of Delhi, Delhi, India
A. Ahmed, A. Bhardwaj, B.C. Choudhary, M. Gola, S. Keshri, A. Kumar, M.
Naimuddin, P. Priyanka, K. Ranjan, A. Shah Saha Institute of Nuclear Physics,
HBNI, Kolkata, India
M. Bharti32, R. Bhattacharya, S. Bhattacharya, D. Bhowmik, S. Dutta, S. Dutta,
B. Gomber33, M. Maity34, S. Nandan, P. Palit, P.K. Rout, G. Saha, B. Sahu, S.
Sarkar, M. Sharan, B. Singh32, S. Thakur32 Indian Institute of Technology
Madras, Madras, India
P.K. Behera, S.C. Behera, P. Kalbhor, A. Muhammad, R. Pradhan, P.R. Pujahari,
A. Sharma, A.K. Sikdar Bhabha Atomic Research Centre, Mumbai, India
D. Dutta, V. Jha, V. Kumar, D.K. Mishra, K. Naskar35, P.K. Netrakanti, L.M.
Pant, P. Shukla Tata Institute of Fundamental Research-A, Mumbai, India
T. Aziz, S. Dugad, M. Kumar, U. Sarkar Tata Institute of Fundamental
Research-B, Mumbai, India
S. Banerjee, R. Chudasama, M. Guchait, S. Karmakar, S. Kumar, G. Majumder, K.
Mazumdar, S. Mukherjee Indian Institute of Science Education and Research
(IISER), Pune, India
K. Alpana, S. Dube, B. Kansal, S. Pandey, A. Rane, A. Rastogi, S. Sharma
Department of Physics, Isfahan University of Technology, Isfahan, Iran
H. Bakhshiansohi36, M. Zeinali37 Institute for Research in Fundamental
Sciences (IPM), Tehran, Iran
S. Chenarani38, S.M. Etesami, M. Khakzad, M. Mohammadi Najafabadi University
College Dublin, Dublin, Ireland
M. Grunewald INFN Sezione di Bari a, Università di Bari b, Politecnico di Bari
c, Bari, Italy
M. Abbresciaa,b, R. Alya,b,39, C. Arutaa,b, A. Colaleoa, D. Creanzaa,c, N. De
Filippisa,c, M. De Palmaa,b, A. Di Florioa,b, A. Di Pilatoa,b, W.
Elmetenaweea,b, L. Fiorea, A. Gelmia,b, M. Gula, G. Iasellia,c, M. Incea,b, S.
Lezkia,b, G. Maggia,c, M. Maggia, I. Margjekaa,b, V. Mastrapasquaa,b, J.A.
Merlina, S. Mya,b, S. Nuzzoa,b, A. Pellecchiaa,b, A. Pompilia,b, G.
Pugliesea,c, A. Ranieria, G. Selvaggia,b, L. Silvestrisa, F.M. Simonea,b, 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, L.
Brigliadoria, R. Campaninia,b, P. Capiluppia,b, A. Castroa,b, F.R. Cavalloa,
M. Cuffiania,b, G.M. Dallavallea, T. Diotalevia,b, F. Fabbria, A. Fanfania,b,
P. Giacomellia, L. Giommia,b, C. Grandia, L. Guiduccia,b, S. Lo Meoa,40, L.
Lunertia,b, S. Marcellinia, G. Masettia, F.L. Navarriaa,b, A. Perrottaa, F.
Primaveraa,b, A.M. Rossia,b, T. Rovellia,b, G.P. Sirolia,b INFN Sezione di
Catania a, Università di Catania b, Catania, Italy
S. Albergoa,b,41, S. Costaa,b,41, A. Di Mattiaa, R. Potenzaa,b, A.
Tricomia,b,41, C. Tuvea,b INFN Sezione di Firenze a, Università di Firenze b,
Firenze, Italy
G. Barbaglia, A. Cassesea, R. Ceccarellia,b, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, E. Focardia,b, G. Latinoa,b, P. Lenzia,b, M. Lizzoa,b, M.
Meschinia, S. Paolettia, R. Seiditaa,b, G. Sguazzonia, 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, F. Brivioa,b, F. Cetorellia,b, V. Cirioloa,b,17, F. De Guioa,b,
M.E. Dinardoa,b, P. Dinia, S. Gennaia, A. Ghezzia,b, P. Govonia,b, L.
Guzzia,b, M. Malbertia, S. Malvezzia, A. Massironia, D. Menascea, L. Moronia,
M. Paganonia,b, D. Pedrinia, S. Ragazzia,b, N. Redaellia, T. Tabarelli de
Fatisa,b, D. Valsecchia,b,17, 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, F. Carnevalia,b, N. Cavalloa,c, A. De Iorioa,b, F. Fabozzia,c,
A.O.M. Iorioa,b, L. Listaa,b, S. Meolaa,d,17, P. Paoluccia,17, B. Rossia, C.
Sciaccaa,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, P. Bortignona, A. Bragagnoloa,b, R.
Carlina,b, P. Checchiaa, T. Dorigoa, U. Dossellia, F. Gasparinia,b, U.
Gasparinia,b, S.Y. Hoha,b, L. Layera,42, M. Margonia,b, A.T. Meneguzzoa,b, J.
Pazzinia,b, M. Presillaa,b, P. Ronchesea,b, R. Rossina,b, F. Simonettoa,b, G.
Stronga, M. Tosia,b, H. YARARa,b, M. Zanettia,b, P. Zottoa,b, A. Zucchettaa,b,
G. Zumerlea,b INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy
C. Aime‘a,b, A. Braghieria, S. Calzaferria,b, D. Fiorinaa,b, P. Montagnaa,b,
S.P. Rattia,b, V. Rea, C. Riccardia,b, P. Salvinia, I. Vaia, P. Vituloa,b INFN
Sezione di Perugia a, Università di Perugia b, Perugia, Italy
G.M. Bileia, D. Ciangottinia,b, L. Fanòa,b, P. Laricciaa,b, M. Magherinib, G.
Mantovania,b, V. Mariania,b, M. Menichellia, F. Moscatellia, A. Piccinellia,b,
A. Rossia,b, A. Santocchiaa,b, D. Spigaa, T. Tedeschia,b INFN Sezione di Pisa
a, Università di Pisa b, Scuola Normale Superiore di Pisa c, Pisa Italy,
Università di Siena d, Siena, Italy
P. Azzurria, G. Bagliesia, V. Bertacchia,c, L. Bianchinia, T. Boccalia, E.
Bossinia,b, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, M.R. Di Domenicoa,d,
S. Donatoa, A. Giassia, M.T. Grippoa, F. Ligabuea,c, E. Mancaa,c, G.
Mandorlia,c, A. Messineoa,b, F. Pallaa, S. Paroliaa,b, G. Ramirez-Sancheza,c,
A. Rizzia,b, G. Rolandia,c, S. Roy Chowdhurya,c, A. Scribanoa, N. Shafieia,b,
P. Spagnoloa, R. Tenchinia, G. Tonellia,b, N. Turinia,d, A. Venturia, P.G.
Verdinia INFN Sezione di Roma a, Sapienza Università di Roma b, Rome, Italy
M. Campanaa,b, F. Cavallaria, M. Cipriania,b, D. Del Rea,b, E. Di Marcoa, M.
Diemoza, E. Longoa,b, P. Meridiania, G. Organtinia,b, F. Pandolfia, R.
Paramattia,b, C. Quarantaa,b, S. Rahatloua,b, C. Rovellia, F.
Santanastasioa,b, L. Soffia, R. Tramontanoa,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, A. Belloraa,b, J. Berenguer Antequeraa,b, C. Biinoa, N.
Cartigliaa, S. Comettia, M. Costaa,b, R. Covarellia,b, N. Demariaa, B.
Kiania,b, F. Leggera, C. Mariottia, S. Masellia, E. Migliorea,b, E.
Monteila,b, M. Montenoa, M.M. Obertinoa,b, G. Ortonaa, L. Pachera,b, N.
Pastronea, M. Pelliccionia, G.L. Pinna Angionia,b, M. Ruspaa,c, R.
Salvaticoa,b, K. Shchelinaa,b, F. Sivieroa,b, V. Solaa, A. Solanoa,b, D.
Soldia,b, A. Staianoa, M. Tornagoa,b, D. Trocinoa,b, A. Vagnerini 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, G. Sorrentinoa,b, F. Vazzolera,b Kyungpook National
University, Daegu, Korea
S. Dogra, C. Huh, B. Kim, D.H. Kim, G.N. Kim, J. Kim, J. Lee, S.W. Lee, C.S.
Moon, Y.D. Oh, S.I. Pak, B.C. Radburn-Smith, S. Sekmen, Y.C. Yang Chonnam
National University, Institute for Universe and Elementary Particles, Kwangju,
Korea
H. Kim, D.H. Moon Hanyang University, Seoul, Korea
B. Francois, T.J. Kim, J. Park Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, B. Hong, K. Lee, K.S. Lee, J. Lim, J. Park, S.K. Park,
J. Yoo Kyung Hee University, Department of Physics, Seoul, Republic of Korea
J. Goh, A. Gurtu Sejong University, Seoul, Korea
H.S. Kim, Y. Kim Seoul National University, Seoul, Korea
J. Almond, J.H. Bhyun, J. Choi, S. Jeon, J. Kim, J.S. Kim, S. Ko, H. Kwon, H.
Lee, S. Lee, B.H. Oh, M. Oh, S.B. Oh, H. Seo, U.K. Yang, I. Yoon University of
Seoul, Seoul, Korea
W. Jang, D. Jeon, D.Y. Kang, Y. Kang, J.H. Kim, S. Kim, B. Ko, J.S.H. Lee, Y.
Lee, I.C. Park, Y. Roh, M.S. Ryu, D. Song, I.J. Watson, S. Yang Yonsei
University, Department of Physics, Seoul, Korea
S. Ha, H.D. Yoo Sungkyunkwan University, Suwon, Korea
Y. Jeong, H. Lee, Y. Lee, I. Yu College of Engineering and Technology,
American University of the Middle East (AUM), Egaila, Kuwait
T. Beyrouthy, Y. Maghrbi Riga Technical University, Riga, Latvia
V. Veckalns43 Vilnius University, Vilnius, Lithuania
M. Ambrozas, A. Juodagalvis, A. Rinkevicius, G. Tamulaitis, A. Vaitkevicius
National Centre for Particle Physics, Universiti Malaya, Kuala Lumpur,
Malaysia
N. Bin Norjoharuddeen, W.A.T. Wan Abdullah, M.N. Yusli, Z. Zolkapli
Universidad de Sonora (UNISON), Hermosillo, Mexico
J.F. Benitez, A. Castaneda Hernandez, M. León Coello, J.A. Murillo Quijada, A.
Sehrawat, L. Valencia Palomo Centro de Investigacion y de Estudios Avanzados
del IPN, Mexico City, Mexico
G. Ayala, H. Castilla-Valdez, I. Heredia-De La Cruz44, R. Lopez-Fernandez,
C.A. Mondragon Herrera, D.A. Perez Navarro, 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
I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada University of Montenegro,
Podgorica, Montenegro
J. Mijuskovic45, 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.I. Asghar, A. Awais, M.I.M. Awan, 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, K. Doroba, A. Kalinowski, M. Konecki, J. Krolikowski, M. Walczak
Laboratório de Instrumentação e Física Experimental de Partículas, Lisboa,
Portugal
M. Araujo, P. Bargassa, D. Bastos, A. Boletti, P. Faccioli, M. Gallinaro, J.
Hollar, N. Leonardo, T. Niknejad, M. Pisano, J. Seixas, O. Toldaiev, J. Varela
Joint Institute for Nuclear Research, Dubna, Russia
S. Afanasiev, D. Budkouski, I. Golutvin, I. Gorbunov, V. Karjavine, V.
Korenkov, A. Lanev, A. Malakhov, V. Matveev46,47, V. Palichik, V. Perelygin,
M. Savina, D. Seitova, V. Shalaev, S. Shmatov, S. Shulha, V. Smirnov, O.
Teryaev, N. Voytishin, B.S. Yuldashev48, A. Zarubin, I. Zhizhin Petersburg
Nuclear Physics Institute, Gatchina (St. Petersburg), Russia
G. Gavrilov, V. Golovtcov, Y. Ivanov, V. Kim49, E. Kuznetsova50, V. Murzin, V.
Oreshkin, I. Smirnov, D. Sosnov, V. Sulimov, L. Uvarov, S. Volkov, A. Vorobyev
Institute for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, D.
Kirpichnikov, M. Kirsanov, N. Krasnikov, A. Pashenkov, G. Pivovarov, D.
Tlisov${}^{\textrm{\textdagger}}$, 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. Nikitenko51, V. Popov, A.
Spiridonov, A. Stepennov, M. Toms, E. Vlasov, A. Zhokin Moscow Institute of
Physics and Technology, Moscow, Russia
T. Aushev National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
O. Bychkova, R. Chistov52, M. Danilov53, P. Parygin, S. Polikarpov52 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. Belyaev, E. Boos, V. Bunichev, M. Dubinin54, L. Dudko, A. Ershov, A.
Gribushin, V. Klyukhin, O. Kodolova, I. Lokhtin, S. Obraztsov, M. Perfilov, V.
Savrin Novosibirsk State University (NSU), Novosibirsk, Russia
V. Blinov55, T. Dimova55, L. Kardapoltsev55, A. Kozyrev55, I. Ovtin55, Y.
Skovpen55 Institute for High Energy Physics of National Research Centre
‘Kurchatov Institute’, Protvino, Russia
I. Azhgirey, I. Bayshev, D. Elumakhov, 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, 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, Belgrade, Serbia
P. Adzic56, M. Dordevic, P. Milenovic, J. Milosevic Centro de Investigaciones
Energéticas Medioambientales y Tecnológicas (CIEMAT), Madrid, Spain
M. Aguilar-Benitez, J. Alcaraz Maestre, A. Álvarez Fernández, I. Bachiller, M.
Barrio Luna, Cristina F. Bedoya, C.A. Carrillo Montoya, M. Cepeda, M. Cerrada,
N. Colino, B. De La Cruz, A. Delgado Peris, J.P. Fernández Ramos, J. Flix,
M.C. Fouz, O. Gonzalez Lopez, S. Goy Lopez, J.M. Hernandez, M.I. Josa, J. León
Holgado, D. Moran, Á. Navarro Tobar, A. Pérez-Calero Yzquierdo, J. Puerta
Pelayo, I. Redondo, L. Romero, S. Sánchez Navas, L. Urda Gómez, C. Willmott
Universidad Autónoma de Madrid, Madrid, Spain
J.F. de Trocóniz, R. Reyes-Almanza 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, E. Palencia Cortezon, C. Ramón Álvarez, J. Ripoll Sau,
V. Rodríguez Bouza, A. Trapote, N. Trevisani Instituto de Física de Cantabria
(IFCA), CSIC-Universidad de Cantabria, Santander, Spain
J.A. Brochero Cifuentes, I.J. Cabrillo, A. Calderon, J. Duarte Campderros, M.
Fernandez, C. Fernandez Madrazo, P.J. Fernández Manteca, A. García Alonso, G.
Gomez, C. Martinez Rivero, P. Martinez Ruiz del Arbol, F. Matorras, P.
Matorras Cuevas, J. Piedra Gomez, C. Prieels, T. Rodrigo, A. Ruiz-Jimeno, L.
Scodellaro, I. Vila, J.M. Vizan Garcia University of Colombo, Colombo, Sri
Lanka
MK Jayananda, B. Kailasapathy57, D.U.J. Sonnadara, DDC Wickramarathna
University of Ruhuna, Department of Physics, Matara, Sri Lanka
W.G.D. Dharmaratna, K. Liyanage, N. Perera, N. Wickramage CERN, European
Organization for Nuclear Research, Geneva, Switzerland
T.K. Aarrestad, D. Abbaneo, J. Alimena, E. Auffray, G. Auzinger, J. Baechler,
P. Baillon${}^{\textrm{\textdagger}}$, D. Barney, J. Bendavid, M. Bianco, A.
Bocci, T. Camporesi, M. Capeans Garrido, G. Cerminara, S.S. Chhibra, L.
Cristella, D. d’Enterria, A. Dabrowski, N. Daci, A. David, A. De Roeck, M.M.
Defranchis, M. Deile, M. Dobson, M. Dünser, N. Dupont, A. Elliott-Peisert, N.
Emriskova, F. Fallavollita58, D. Fasanella, S. Fiorendi, A. Florent, G.
Franzoni, W. Funk, S. Giani, D. Gigi, K. Gill, F. Glege, L. Gouskos, M.
Haranko, J. Hegeman, Y. Iiyama, V. Innocente, T. James, P. Janot, J. Kaspar,
J. Kieseler, M. Komm, N. Kratochwil, C. Lange, S. Laurila, P. Lecoq, K. Long,
C. Lourenço, L. Malgeri, S. Mallios, M. Mannelli, A.C. Marini, F. Meijers, S.
Mersi, E. Meschi, F. Moortgat, M. Mulders, S. Orfanelli, L. Orsini, F.
Pantaleo, L. Pape, E. Perez, M. Peruzzi, A. Petrilli, G. Petrucciani, A.
Pfeiffer, M. Pierini, D. Piparo, M. Pitt, H. Qu, T. Quast, D. Rabady, A. Racz,
G. Reales Gutiérrez, M. Rieger, M. Rovere, H. Sakulin, J. Salfeld-Nebgen, S.
Scarfi, C. Schäfer, C. Schwick, M. Selvaggi, A. Sharma, P. Silva, W. Snoeys,
P. Sphicas59, S. Summers, V.R. Tavolaro, D. Treille, A. Tsirou, G.P. Van
Onsem, M. Verzetti, J. Wanczyk60, K.A. Wozniak, W.D. Zeuner Paul Scherrer
Institut, Villigen, Switzerland
L. Caminada61, A. Ebrahimi, W. Erdmann, R. Horisberger, Q. Ingram, H.C.
Kaestli, D. Kotlinski, U. Langenegger, M. Missiroli, T. Rohe ETH Zurich -
Institute for Particle Physics and Astrophysics (IPA), Zurich, Switzerland
K. Androsov60, M. Backhaus, P. Berger, A. Calandri, N. Chernyavskaya, A. De
Cosa, G. Dissertori, M. Dittmar, M. Donegà, C. Dorfer, F. Eble, T.A. Gómez
Espinosa, C. Grab, D. Hits, W. Lustermann, A.-M. Lyon, R.A. Manzoni, C. Martin
Perez, M.T. Meinhard, F. Micheli, F. Nessi-Tedaldi, J. Niedziela, F. Pauss, V.
Perovic, G. Perrin, S. Pigazzini, M.G. Ratti, M. Reichmann, C. Reissel, T.
Reitenspiess, B. Ristic, D. Ruini, D.A. Sanz Becerra, M. Schönenberger, V.
Stampf, J. Steggemann60, R. Wallny, D.H. Zhu Universität Zürich, Zurich,
Switzerland
C. Amsler62, P. Bärtschi, C. Botta, D. Brzhechko, M.F. Canelli, K. Cormier, A.
De Wit, R. Del Burgo, J.K. Heikkilä, M. Huwiler, A. Jofrehei, B. Kilminster,
S. Leontsinis, A. Macchiolo, P. Meiring, V.M. Mikuni, U. Molinatti, I.
Neutelings, A. Reimers, P. Robmann, S. Sanchez Cruz, K. Schweiger, Y.
Takahashi National Central University, Chung-Li, Taiwan
C. Adloff63, C.M. Kuo, W. Lin, A. Roy, T. Sarkar34, S.S. Yu National Taiwan
University (NTU), Taipei, Taiwan
L. Ceard, Y. Chao, K.F. Chen, P.H. Chen, W.-S. Hou, Y.y. Li, R.-S. Lu, E.
Paganis, A. Psallidas, A. Steen, H.y. Wu, E. Yazgan, P.r. Yu Chulalongkorn
University, Faculty of Science, Department of Physics, Bangkok, Thailand
B. Asavapibhop, C. Asawatangtrakuldee, N. Srimanobhas Çukurova University,
Physics Department, Science and Art Faculty, Adana, Turkey
F. Boran, S. Damarseckin64, Z.S. Demiroglu, F. Dolek, I. Dumanoglu65, E.
Eskut, Y. Guler, E. Gurpinar Guler66, I. Hos67, C. Isik, O. Kara, A. Kayis
Topaksu, U. Kiminsu, G. Onengut, K. Ozdemir68, A. Polatoz, A.E. Simsek, B.
Tali69, U.G. Tok, S. Turkcapar, I.S. Zorbakir, C. Zorbilmez Middle East
Technical University, Physics Department, Ankara, Turkey
B. Isildak70, G. Karapinar71, K. Ocalan72, M. Yalvac73 Bogazici University,
Istanbul, Turkey
B. Akgun, I.O. Atakisi, E. Gülmez, M. Kaya74, O. Kaya75, Ö. Özçelik, S.
Tekten76, E.A. Yetkin77 Istanbul Technical University, Istanbul, Turkey
A. Cakir, K. Cankocak65, Y. Komurcu, S. Sen78 Istanbul University, Istanbul,
Turkey
S. Cerci69, B. Kaynak, S. Ozkorucuklu, D. Sunar Cerci69 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
D. Anthony, E. Bhal, S. Bologna, J.J. Brooke, A. Bundock, E. Clement, D.
Cussans, H. Flacher, J. Goldstein, G.P. Heath, H.F. Heath, L. Kreczko, B.
Krikler, S. Paramesvaran, S. Seif El Nasr-Storey, V.J. Smith, N. Stylianou79,
R. White Rutherford Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev80, C. Brew, R.M. Brown, D.J.A. Cockerill, K.V. Ellis, K.
Harder, S. Harper, J. Linacre, K. Manolopoulos, D.M. Newbold, E. Olaiya, D.
Petyt, T. Reis, T. Schuh, C.H. Shepherd-Themistocleous, I.R. Tomalin, T.
Williams Imperial College, London, United Kingdom
R. Bainbridge, P. Bloch, S. Bonomally, J. Borg, S. Breeze, O. Buchmuller, V.
Cepaitis, G.S. Chahal81, D. Colling, P. Dauncey, G. Davies, M. Della Negra, S.
Fayer, G. Fedi, G. Hall, M.H. Hassanshahi, G. Iles, J. Langford, L. Lyons,
A.-M. Magnan, S. Malik, A. Martelli, J. Nash82, M. Pesaresi, D.M. Raymond, A.
Richards, A. Rose, E. Scott, C. Seez, A. Shtipliyski, A. Tapper, K. Uchida, T.
Virdee17, N. Wardle, S.N. Webb, D. Winterbottom, A.G. Zecchinelli Brunel
University, Uxbridge, United Kingdom
K. Coldham, J.E. Cole, A. Khan, P. Kyberd, I.D. Reid, L. Teodorescu, S. Zahid
Baylor University, Waco, USA
S. Abdullin, A. Brinkerhoff, B. Caraway, J. Dittmann, K. Hatakeyama, A.R.
Kanuganti, B. McMaster, N. Pastika, S. Sawant, C. Sutantawibul, J. Wilson
Catholic University of America, Washington, DC, USA
R. Bartek, A. Dominguez, R. Uniyal, A.M. Vargas Hernandez The University of
Alabama, Tuscaloosa, USA
A. Buccilli, S.I. Cooper, D. Di Croce, S.V. Gleyzer, C. Henderson, C.U. Perez,
P. Rumerio83, C. West Boston University, Boston, USA
A. Akpinar, A. Albert, D. Arcaro, C. Cosby, Z. Demiragli, E. Fontanesi, D.
Gastler, J. Rohlf, K. Salyer, D. Sperka, D. Spitzbart, I. Suarez, A. Tsatsos,
S. Yuan, D. Zou Brown University, Providence, USA
G. Benelli, B. Burkle, X. Coubez18, D. Cutts, M. Hadley, U. Heintz, J.M.
Hogan84, G. Landsberg, K.T. Lau, M. Lukasik, J. Luo, M. Narain, S. Sagir85, E.
Usai, W.Y. Wong, X. Yan, D. Yu, W. Zhang University of California, Davis,
Davis, USA
J. Bonilla, C. Brainerd, R. Breedon, M. Calderon De La Barca Sanchez, M.
Chertok, J. Conway, P.T. Cox, R. Erbacher, G. Haza, F. Jensen, O. Kukral, R.
Lander, M. Mulhearn, D. Pellett, B. Regnery, D. Taylor, Y. Yao, F. Zhang
University of California, Los Angeles, USA
M. Bachtis, R. Cousins, A. Datta, D. Hamilton, J. Hauser, M. Ignatenko, M.A.
Iqbal, T. Lam, N. Mccoll, W.A. Nash, S. Regnard, D. Saltzberg, B. Stone, V.
Valuev University of California, Riverside, Riverside, USA
K. Burt, Y. Chen, R. Clare, J.W. Gary, M. Gordon, G. Hanson, G. Karapostoli,
O.R. Long, N. Manganelli, M. Olmedo Negrete, W. Si, S. Wimpenny, Y. Zhang
University of California, San Diego, La Jolla, USA
J.G. Branson, P. Chang, S. Cittolin, S. Cooperstein, N. Deelen, J. Duarte, R.
Gerosa, L. Giannini, D. Gilbert, J. Guiang, R. Kansal, V. Krutelyov, R. Lee,
J. Letts, M. Masciovecchio, S. May, M. Pieri, B.V. Sathia Narayanan, V.
Sharma, M. Tadel, A. Vartak, F. Würthwein, Y. Xiang, A. Yagil University of
California, Santa Barbara - Department of Physics, Santa Barbara, USA
N. Amin, C. Campagnari, M. Citron, A. Dorsett, V. Dutta, J. Incandela, M.
Kilpatrick, J. Kim, B. Marsh, H. Mei, M. Oshiro, M. Quinnan, J. Richman, U.
Sarica, D. Stuart, S. Wang California Institute of Technology, Pasadena, USA
A. Bornheim, O. Cerri, I. Dutta, J.M. Lawhorn, N. Lu, J. Mao, H.B. Newman, J.
Ngadiuba, T.Q. Nguyen, M. Spiropulu, J.R. Vlimant, C. Wang, S. Xie, Z. Zhang,
R.Y. Zhu Carnegie Mellon University, Pittsburgh, USA
J. Alison, S. An, M.B. Andrews, P. Bryant, T. Ferguson, A. Harilal, C. Liu, T.
Mudholkar, M. Paulini, A. Sanchez University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, A. Hassani, E. MacDonald, R. Patel, A. Perloff, C.
Savard, K. Stenson, K.A. Ulmer, S.R. Wagner Cornell University, Ithaca, USA
J. Alexander, Y. Cheng, D.J. Cranshaw, S. Hogan, J. Monroy, J.R. Patterson, D.
Quach, J. Reichert, A. Ryd, W. Sun, J. Thom, P. Wittich, R. Zou Fermi National
Accelerator Laboratory, Batavia, USA
M. Albrow, M. Alyari, G. Apollinari, A. Apresyan, A. Apyan, S. Banerjee,
L.A.T. Bauerdick, D. Berry, J. Berryhill, P.C. Bhat, K. Burkett, J.N. Butler,
A. Canepa, G.B. Cerati, H.W.K. Cheung, F. Chlebana, M. Cremonesi, K.F. Di
Petrillo, V.D. Elvira, Y. Feng, J. Freeman, Z. Gecse, L. Gray, D. Green, S.
Grünendahl, O. Gutsche, R.M. Harris, R. Heller, T.C. Herwig, J. Hirschauer, B.
Jayatilaka, S. Jindariani, M. Johnson, U. Joshi, T. Klijnsma, B. Klima, K.H.M.
Kwok, S. Lammel, D. Lincoln, R. Lipton, T. Liu, C. Madrid, K. Maeshima, C.
Mantilla, D. Mason, P. McBride, P. Merkel, S. Mrenna, S. Nahn, V. O’Dell, V.
Papadimitriou, K. Pedro, C. Pena54, O. Prokofyev, F. Ravera, A. Reinsvold
Hall, L. Ristori, B. Schneider, E. Sexton-Kennedy, N. Smith, A. Soha, W.J.
Spalding, L. Spiegel, S. Stoynev, J. Strait, L. Taylor, S. Tkaczyk, N.V. Tran,
L. Uplegger, E.W. Vaandering, H.A. Weber University of Florida, Gainesville,
USA
D. Acosta, P. Avery, D. Bourilkov, L. Cadamuro, V. Cherepanov, F. Errico, R.D.
Field, D. Guerrero, B.M. Joshi, M. Kim, E. Koenig, J. Konigsberg, A. Korytov,
K.H. Lo, K. Matchev, N. Menendez, G. Mitselmakher, A. Muthirakalayil Madhu, N.
Rawal, D. Rosenzweig, S. Rosenzweig, K. Shi, J. Sturdy, J. Wang, E. Yigitbasi,
X. Zuo Florida State University, Tallahassee, USA
T. Adams, A. Askew, D. Diaz, R. Habibullah, V. Hagopian, K.F. Johnson, R.
Khurana, T. Kolberg, G. Martinez, H. Prosper, C. Schiber, R. Yohay, J. Zhang
Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, S. Butalla, T. Elkafrawy86, M. Hohlmann, R. Kumar Verma, D.
Noonan, M. Rahmani, M. Saunders, F. Yumiceva University of Illinois at Chicago
(UIC), Chicago, USA
M.R. Adams, H. Becerril Gonzalez, R. Cavanaugh, X. Chen, S. Dittmer, O.
Evdokimov, C.E. Gerber, D.A. Hangal, D.J. Hofman, A.H. Merrit, C. Mills, G.
Oh, T. Roy, S. Rudrabhatla, M.B. Tonjes, N. Varelas, J. Viinikainen, X. Wang,
Z. Wu, Z. Ye The University of Iowa, Iowa City, USA
M. Alhusseini, K. Dilsiz87, R.P. Gandrajula, O.K. Köseyan, J.-P. Merlo, A.
Mestvirishvili88, J. Nachtman, H. Ogul89, Y. Onel, A. Penzo, C. Snyder, E.
Tiras90 Johns Hopkins University, Baltimore, USA
O. Amram, B. Blumenfeld, L. Corcodilos, J. Davis, M. Eminizer, A.V. Gritsan,
S. Kyriacou, P. Maksimovic, J. Roskes, M. Swartz, T.Á. Vámi The University of
Kansas, Lawrence, USA
J. Anguiano, C. Baldenegro Barrera, P. Baringer, A. Bean, A. Bylinkin, T.
Isidori, S. Khalil, J. King, G. Krintiras, A. Kropivnitskaya, C. Lindsey, N.
Minafra, M. Murray, C. Rogan, C. Royon, S. Sanders, E. Schmitz, C. Smith, 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, T. Mitchell, A. Modak, K.
Nam Lawrence Livermore National Laboratory, Livermore, USA
F. Rebassoo, D. Wright University of Maryland, College Park, USA
E. Adams, A. Baden, O. Baron, A. Belloni, S.C. Eno, N.J. Hadley, S. Jabeen,
R.G. Kellogg, T. Koeth, A.C. Mignerey, S. Nabili, M. Seidel, A. Skuja, L.
Wang, K. Wong Massachusetts Institute of Technology, Cambridge, USA
D. Abercrombie, G. Andreassi, R. Bi, S. Brandt, W. Busza, I.A. Cali, Y. Chen,
M. D’Alfonso, J. Eysermans, G. Gomez Ceballos, M. Goncharov, P. Harris, M. Hu,
M. Klute, D. Kovalskyi, J. Krupa, Y.-J. Lee, B. Maier, C. Mironov, C. Paus, D.
Rankin, C. Roland, G. Roland, Z. Shi, G.S.F. Stephans, K. Tatar, J. Wang, Z.
Wang, B. Wyslouch University of Minnesota, Minneapolis, USA
R.M. Chatterjee, A. Evans, P. Hansen, J. Hiltbrand, Sh. Jain, M. Krohn, Y.
Kubota, J. Mans, M. Revering, R. Rusack, R. Saradhy, N. Schroeder, N. Strobbe,
M.A. Wadud University of Nebraska-Lincoln, Lincoln, USA
K. Bloom, M. Bryson, S. Chauhan, D.R. Claes, C. Fangmeier, L. Finco, F. Golf,
J.R. González Fernández, C. Joo, I. Kravchenko, M. Musich, I. Reed, J.E.
Siado, G.R. Snow${}^{\textrm{\textdagger}}$, W. Tabb, F. Yan State University
of New York at Buffalo, Buffalo, USA
G. Agarwal, H. Bandyopadhyay, L. Hay, I. Iashvili, A. Kharchilava, C. McLean,
D. Nguyen, J. Pekkanen, S. Rappoccio, A. Williams Northeastern University,
Boston, USA
G. Alverson, E. Barberis, C. Freer, Y. Haddad, A. Hortiangtham, J. Li, G.
Madigan, B. Marzocchi, D.M. Morse, V. Nguyen, T. Orimoto, A. Parker, L.
Skinnari, A. Tishelman-Charny, T. Wamorkar, B. Wang, A. Wisecarver, D. Wood
Northwestern University, Evanston, USA
S. Bhattacharya, J. Bueghly, Z. Chen, A. Gilbert, T. Gunter, K.A. Hahn, N.
Odell, M.H. Schmitt, M. Velasco University of Notre Dame, Notre Dame, USA
R. Band, R. Bucci, A. Das, N. Dev, R. Goldouzian, M. Hildreth, K.W. Ho, K.
Hurtado Anampa, C. Jessop, K. Lannon, N. Loukas, N. Marinelli, I. Mcalister,
T. McCauley, F. Meng, K. Mohrman, Y. Musienko46, R. Ruchti, P. Siddireddy, S.
Taroni, M. Wayne, A. Wightman, M. Wolf, M. Zarucki, L. Zygala The Ohio State
University, Columbus, USA
B. Bylsma, B. Cardwell, L.S. Durkin, B. Francis, C. Hill, M. Nunez Ornelas, K.
Wei, B.L. Winer, B.R. Yates Princeton University, Princeton, USA
F.M. Addesa, B. Bonham, P. Das, G. Dezoort, P. Elmer, A. Frankenthal, B.
Greenberg, N. Haubrich, S. Higginbotham, A. Kalogeropoulos, G. Kopp, S. Kwan,
D. Lange, M.T. Lucchini, D. Marlow, K. Mei, I. Ojalvo, J. Olsen, C. Palmer, D.
Stickland, C. Tully University of Puerto Rico, Mayaguez, USA
S. Malik, S. Norberg Purdue University, West Lafayette, USA
A.S. Bakshi, V.E. Barnes, R. Chawla, S. Das, L. Gutay, M. Jones, A.W. Jung, S.
Karmarkar, M. Liu, G. Negro, N. Neumeister, G. Paspalaki, C.C. Peng, S.
Piperov, A. Purohit, J.F. Schulte, M. Stojanovic14, J. Thieman, F. Wang, R.
Xiao, W. Xie Purdue University Northwest, Hammond, USA
J. Dolen, N. Parashar Rice University, Houston, USA
A. Baty, M. Decaro, S. Dildick, K.M. Ecklund, S. Freed, P. Gardner, F.J.M.
Geurts, A. Kumar, W. Li, B.P. Padley, R. Redjimi, W. Shi, A.G. Stahl Leiton,
S. Yang, L. Zhang, Y. 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, O. Hindrichs, A. Khukhunaishvili, E. Ranken, 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, O. Karacheban21, I. Laflotte, A. Lath, R.
Montalvo, K. Nash, M. Osherson, S. Salur, S. Schnetzer, S. Somalwar, R. Stone,
S.A. Thayil, S. Thomas, H. Wang University of Tennessee, Knoxville, USA
H. Acharya, A.G. Delannoy, S. Spanier Texas A&M University, College Station,
USA
O. Bouhali91, M. Dalchenko, A. Delgado, R. Eusebi, J. Gilmore, T. Huang, T.
Kamon92, H. Kim, S. Luo, S. Malhotra, R. Mueller, D. Overton, D. Rathjens, A.
Safonov Texas Tech University, Lubbock, USA
N. Akchurin, J. Damgov, V. Hegde, S. Kunori, K. Lamichhane, S.W. Lee, T.
Mengke, S. Muthumuni, T. Peltola, I. Volobouev, Z. Wang, A. Whitbeck
Vanderbilt University, Nashville, USA
E. Appelt, S. Greene, A. Gurrola, W. Johns, A. Melo, H. Ni, K. Padeken, F.
Romeo, P. Sheldon, S. Tuo, J. Velkovska University of Virginia,
Charlottesville, USA
M.W. Arenton, B. Cox, G. Cummings, J. Hakala, R. Hirosky, M. Joyce, A.
Ledovskoy, A. Li, C. Neu, B. Tannenwald, S. White, E. Wolfe Wayne State
University, Detroit, USA
N. Poudyal University of Wisconsin - Madison, Madison, WI, USA
K. Black, T. Bose, J. Buchanan, C. Caillol, S. Dasu, I. De Bruyn, P.
Everaerts, F. Fienga, C. Galloni, H. He, M. Herndon, A. Hervé, U. Hussain, A.
Lanaro, A. Loeliger, R. Loveless, J. Madhusudanan Sreekala, A. Mallampalli, A.
Mohammadi, D. Pinna, A. Savin, V. Shang, V. Sharma, W.H. Smith, D. Teague, S.
Trembath-reichert, W. Vetens †: Deceased
1: Also at TU Wien, Wien, Austria
2: Also at Institute of Basic and Applied Sciences, Faculty of Engineering,
Arab Academy for Science, Technology and Maritime Transport, Alexandria,
Egypt, Alexandria, Egypt
3: Also at Université Libre de Bruxelles, Bruxelles, Belgium
4: Also at Universidade Estadual de Campinas, Campinas, Brazil
5: Also at Federal University of Rio Grande do Sul, Porto Alegre, Brazil
6: Also at University of Chinese Academy of Sciences, Beijing, China
7: Also at Department of Physics, Tsinghua University, Beijing, China,
Beijing, China
8: Also at UFMS, Nova Andradina, Brazil
9: Also at Nanjing Normal University Department of Physics, Nanjing, China
10: Now at The University of Iowa, Iowa City, USA
11: Also at Institute for Theoretical and Experimental Physics named by A.I.
Alikhanov of NRC ‘Kurchatov Institute’, Moscow, Russia
12: Also at Joint Institute for Nuclear Research, Dubna, Russia
13: Also at Cairo 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 Department of Physics, Isfahan University of Technology, Isfahan,
Iran, Isfahan, Iran
21: Also at Brandenburg University of Technology, Cottbus, Germany
22: Also at Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State
University, Moscow, Russia
23: Also at Physics Department, Faculty of Science, Assiut University, Assiut,
Egypt
24: Also at Eszterhazy Karoly University, Karoly Robert Campus, Gyongyos,
Hungary
25: Also at Institute of Physics, University of Debrecen, Debrecen, Hungary,
Debrecen, Hungary
26: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary
27: Also at MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary, Budapest, Hungary
28: Also at Wigner Research Centre for Physics, Budapest, Hungary
29: Also at IIT Bhubaneswar, Bhubaneswar, India, Bhubaneswar, India
30: Also at Institute of Physics, Bhubaneswar, India
31: Also at G.H.G. Khalsa College, Punjab, India
32: Also at Shoolini University, Solan, India
33: Also at University of Hyderabad, Hyderabad, India
34: Also at University of Visva-Bharati, Santiniketan, India
35: Also at Indian Institute of Technology (IIT), Mumbai, India
36: Also at Deutsches Elektronen-Synchrotron, Hamburg, Germany