HIG-18-023

HIG-18-023

# Search for a heavy pseudoscalar Higgs boson decaying into a 125Higgs boson
and a boson in final states with two tau and two light leptons at
$\sqrt{s}=13\TeV$

###### Abstract

A search is performed for a pseudoscalar Higgs boson, A, decaying into a
125Higgs boson and a boson. The boson is specifically targeted in its decay
into a pair of tau leptons, while the boson decays into a pair of electrons or
muons. A data sample of proton-proton collisions collected by the CMS
experiment at the LHC at $\sqrt{s}=13\TeV$ is used, corresponding to an
integrated luminosity of 35.9. No excess above the standard model background
expectations is observed in data. A model-independent upper limit is set on
the product of the gluon fusion production cross section for the A boson and
the branching fraction to $\PZ\Ph\to\ell\ell\PGt\PGt$. The observed upper
limit at 95% confidence level ranges from 27 to 5fb for A boson masses from
220 to 400, respectively. The results are used to constrain the extended Higgs
sector parameters for two benchmark scenarios of the minimal supersymmetric
standard model.

##  0.1 Introduction

In the standard model (SM) [1, 2, 3], the Brout–Englert–Higgs mechanism [4, 5,
6, 7, 8, 9] is responsible for the electroweak symmetry breaking, and it
predicts the existence of the Higgs boson. A Higgs boson with a mass around
125was discovered by the ATLAS and CMS Collaborations in 2012 [10, 11, 12].
The best measurement of the Higgs boson mass to date, $125.26\pm 0.21\GeV$,
comes from a partial Run 2 data set analysis by the CMS Collaboration [13];
the result is consistent with the earlier Run 1 combined measurement by the
ATLAS and CMS Collaborations [14] and the recent results by the ATLAS
Collaboration [15]. The couplings of the observed boson have been studied
extensively, and are found to be compatible with the SM expectation [16, 17].

The observation of the Higgs boson has not only given closure to the search
for particles described by the SM, but also constrains the beyond-the-SM
theories proposed to explain some of the open questions in particle physics. A
class of simple extensions of the SM, two-Higgs-doublet models (2HDMs),
predicts the existence of five Higgs bosons [18, 19]. Two of these five
particles are CP-even Higgs bosons (and ), and thus either of them could
correspond to the observed particle. The properties of the observed state can
be used to exclude regions of the parameter space of 2HDMs. Further
constraints can be placed by performing searches for the four additional Higgs
bosons, namely the scalar , the CP-odd Higgs boson A, and two charged Higgs
bosons $\PH^{\pm}$. Moreover, 2HDMs are a prerequisite for the minimal
supersymmetric standard model (MSSM) where the extended Higgs sector at tree-
level is fully defined by two parameters, conventionally chosen to be the
ratio of the vacuum expectation values of the two Higgs doublets () and the
mass of the pseudoscalar A ($m_{\text{A}}$).

In the MSSM, given that the mass of the boson is as large as 125, the scale of
the soft supersymmetry breaking masses can be larger than 1. This is a
reasonable assumption based on the nonobservation of supersymmetric particles
at the CERN LHC thus far. In many of the MSSM benchmark scenarios typically
studied, the predicted mass of the Higgs boson is lower than 125in the low
$\tanb$ region [20]. We study two MSSM benchmark scenarios that can
accommodate these constraints in most of the $m_{\text{A}}$–$\tanb$ plane:
$\mathrm{M^{125}_{\Ph,EFT}}$ [20] and hMSSM [21, 22, 23, 24]. The Higgs sector
predictions of the $\mathrm{M^{125}_{\Ph,EFT}}$ scenario are derived from a
2HDM effective field theory framework, with a supersymmetric mass scale that
can reach up to $10^{16}\GeV$, in order for the Higgs boson mass to be
compatible with 125in the low $\tanb$ region. In the hMSSM scenario, by
requiring $m_{\Ph}=125\GeV$, the dominant radiative corrections to the Higgs
boson mass become fixed, which are then used to determine the masses and
couplings of the other Higgs bosons.

The parameter spaces of these benchmark scenarios can be explored by studying
processes producing an experimentally accessible signature with a 125Higgs
boson. One such process is the decay of the A boson into a 125Higgs boson and
a boson. In the parameter space region with low values, this decay has a
substantial branching fraction. For $\tanb\lesssim 5$ the A boson is produced
mainly in gluon fusion ($\Pg\Pg\to\text{{A}}$), but for higher values the
associated production with $\cPqb$ quarks ($\bbbar\text{{A}}$) becomes
dominant. The Feynman diagrams for both production processes are shown in Fig.
1.

Figure 1: Feynman diagrams for two dominant production processes for the
pseudoscalar A boson: gluon fusion (left) and associated production with
$\cPqb$ quarks (right). In both cases the A boson decays into a 125Higgs boson
and a boson.

This paper reports on a search for the pseudoscalar A boson decaying into a
125Higgs boson and a boson in proton-proton ($\Pp\Pp$) collisions at
$\sqrt{s}=13\TeV$. The search is based on a data set collected in 2016 by the
CMS experiment, corresponding to an integrated luminosity of 35.9. The
analysis is primarily sensitive to the assumed gluon fusion production of the
A boson, but the associated production with $\cPqb$ quarks is included in the
interpretation of the results. The studied signal mass range begins at
220because the A boson must be massive enough to decay into the considered
$\PZ\Ph$ state. The mass range extends up to 400, slightly above where the
mass of the A boson exceeds twice the top quark mass. In this region the
$\text{{A}}\to\ttbar$ decay channel is expected to dominate.

Previous searches for the $\text{{A}}\to\PZ\Ph$ process, performed by the
ATLAS and CMS Collaborations, considered a final state with two tau leptons
from the boson decay, and used data sets collected in $\Pp\Pp$ collisions at
$\sqrt{s}=8$ [25, 26]. The ATLAS and CMS Collaborations have also searched for
the pseudoscalar A decaying into the same intermediate $\PZ\Ph$ state but with
the Higgs boson decaying into a pair of bottom quarks in $\Pp\Pp$ collisions
at $\sqrt{s}=13\TeV$ [27, 28]. These analyses set both model-independent and
model-dependent limits in the context of 2HDMs.

In this search, the Higgs boson is sought in its decay into a pair of tau
leptons. Four possible $\PGt\PGt$ decay channels of the Higgs boson are
considered: $\Pe\tauh$, $\Pgm\tauh$, $\tauh\tauh$, and $\Pe\Pgm$, where
denotes a tau lepton decaying hadronically. Throughout the paper, neutrinos
are omitted from the notation of the final states. These four decay channels
are combined with the boson decays into two light leptons, ,
$\PZ\to\ell^{+}\ell^{-}$ ($\ell=\Pe,\Pgm$), resulting in eight distinct final
states of the A boson decay. To account for the missing transverse momentum
that results from the neutrinos in the final states, we use the svfit
algorithm [29] to reconstruct the four-vector of the Higgs boson while
constraining its mass to 125. Compared to the previous result presented by the
CMS Collaboration [26], this novel approach significantly increases the
sensitivity of the search.

##  0.2 The CMS detector

The central feature of the CMS apparatus is a superconducting solenoid of 6m
internal diameter, providing a magnetic field of 3.8T. Within the solenoid
volume are a silicon pixel and strip tracker, a lead tungstate crystal
electromagnetic calorimeter (ECAL), and a brass and scintillator hadron
calorimeter, each composed of a barrel and two endcap sections. Forward
calorimeters extend the pseudorapidity ($\eta$) coverage provided by the
barrel and endcap detectors. Muons are detected in gas-ionization chambers
embedded in the steel flux-return yoke outside the solenoid. Events of
interest are selected using a two-tiered trigger system [30]. The first level
(L1), composed of custom hardware processors, uses information from the
calorimeters and muon detectors to select events at a rate of around 100kHz
within a time interval of less than $4\mus$. The second level, known as the
high-level trigger (HLT), consists of a farm of processors running a version
of the full event reconstruction software optimized for fast processing, and
reduces the event rate to around 1kHz before data storage. A more detailed
description of the CMS detector, together with a definition of the coordinate
system used and the relevant kinematic variables, can be found in Ref. [31].

##  0.3 Simulated samples and models

Simulated signal events with a pseudoscalar Higgs boson A produced in gluon
fusion ($\Pg\Pg\to\text{{A}}$), decaying into a 125Higgs boson and a boson and
finally into two tau and two leptons (electrons or muons) are generated at
leading order (LO) using v2.4.2 [32]. The considered A boson mass points are
within 220–400, as in this mass range the $\text{{A}}\to\PZ\Ph$ decay becomes
predominant. The samples are based on the $m_{\Ph}^{\mathrm{mod+}}$ model
[33], assuming a low value of $\tanb$ ($\sim$2). The generated width of the A
boson is small compared to the instrumental resolution for all masses.
Additional signal events are simulated for a 300A boson produced in
association with $\cPqb$ quarks ($\bbbar\text{{A}}$) and are used only to
study the selection efficiency, necessary for setting model-dependent limits,
as explained in Section 0.8.

The background processes consist of all SM processes with nonnegligible yield
in the studied final states, including the Higgs boson production through
processes predicted in the SM (e.g. $\PZ\Ph$, $\PW\Ph$, $\ttbar\Ph$). The
background processes with a Higgs boson decaying into two tau leptons,
produced in association with a or boson ($\PW\Ph$ or $\PZ\Ph$), are generated
at next-to-LO (NLO) in perturbative quantum chromodynamics (QCD) with the
POWHEG 2.0 [34, 35, 36, 37, 38] generator extended with the MiNLO procedure
[39]. The contribution from Higgs boson events produced via gluon fusion or
vector boson fusion and decaying into two tau leptons is negligible. The
transverse momentum ($\pt$) distribution of the Higgs boson in the POWHEG
simulations is tuned to match closely the next-to-NLO (NNLO) plus next-to-
next-to-leading-logarithmic prediction in the HRes 2.3 generator [40, 41]. The
production cross sections and branching fractions for the SM Higgs boson
production and their corresponding uncertainties are taken from Refs. [42, 43,
44].

The background samples for $\ttbar\Ph$, $\ttbar$, $\PW\PZ$, and
$\cPq\cPq\to\PZ\PZ$, as well as $\PW\Ph\to\PW\PW\PW$, $\PW\Ph\to\PW\PZ\PZ$,
$\PZ\Ph\to\PZ\PW\PW$, $\PZ\Ph\to\PZ\PZ\PZ$, and $\Pg\Pg\to\Ph\to\PZ\PZ$
processes, are generated at NLO with POWHEG 2.0. The $\Pg\Pg\to\PZ\PZ$ process
is generated at LO with mcfm [45]. Samples for the $\cPq\cPq\to\PZ\PZ$ and
$\Pg\Pg\to\PZ\PZ$ processes include all SM events with two bosons in the final
states except the ones from the $\Pg\Pg\to\Ph\to\PZ\PZ$ process. The 2.2.2 or
2.3.3 generator is used for triboson, $\PZ+\text{jets}$, $\ttbar\PW$, and
$\ttbar\PZ$ production, with the jet matching and merging scheme applied
either at NLO with the FxFx algorithm [46] or at LO with the MLM algorithm
[47]. The generators are interfaced with 8.212 [48] to model the parton
showering and fragmentation, as well as the decay of the tau leptons. The
parameters affecting the description of the underlying event are set to the
CUETP8M1 tune [49]. The set of parton distribution functions (PDFs) used in
the simulation is NNPDF3.0 [50].

The generated events are processed through a simulation of the CMS detector
based on [51], and are reconstructed with the same algorithms that are used
for data. The simulated samples include additional $\Pp\Pp$ interactions per
bunch crossing, referred to as in-time pileup. The effect of inelastic
collisions happening in the preceding and subsequent bunch crossings (out-of-
time pileup) is also considered. The effect of pileup is taken into account by
generating concurrent minimum bias collision events. The simulated events are
weighted such that the distribution of the number of pileup interactions
matches with that observed in data. The pileup distribution is estimated from
the measured instantaneous luminosity for each bunch crossing, resulting in an
average of approximately 23 interactions per bunch crossing.

To produce model-dependent interpretations of the results described in Section
0.8, we utilize production cross section and branching fraction calculations
for the pseudoscalar A in the $\mathrm{M^{125}_{\Ph,EFT}}$ and hMSSM
scenarios. In the $\mathrm{M^{125}_{\Ph,EFT}}$ scenario, Higgs boson masses
and mixing parameters (and effective Yukawa couplings) were calculated with a
yet unpublished version of FeynHiggs based on version 2.14.3 [52, 53, 54, 55,
56, 20].

For the gluon-gluon fusion process in the $\mathrm{M^{125}_{\Ph,EFT}}$ (hMSSM)
scenario, inclusive cross sections are obtained with SusHi 1.7.0 (1.4.1) [57,
58], which includes supersymmetric NLO QCD corrections [59, 60, 61, 62, 63,
64], NNLO QCD corrections for the top quark contribution in an effective
theory of a heavy top quark [65, 66, 67, 68, 69] and electroweak effects from
light quarks [70, 71].

Inclusive $\bbbar\text{{A}}$ production cross sections at NNLO QCD accuracy in
the five-flavor scheme are calculated with SusHi, based on bbh@nlo [72]. The
results are combined with the $\bbbar\text{{A}}$ cross section calculation at
NLO in QCD in the four-flavor scheme [73, 74] using the Santander matching
scheme [75] for the hMSSM scenario, and matched predictions [76, 77, 78, 79]
for the $\mathrm{M^{125}_{\Ph,EFT}}$ scenario.

In the hMSSM scenario, branching fractions are solely computed with HDECAY
6.40 [80, 81, 82], whereas the $\mathrm{M^{125}_{\Ph,EFT}}$ scenario relies on
a yet unpublished version of FeynHiggs based on version 2.14.3 [52, 53, 54,
55, 56, 20].

##  0.4 Event reconstruction

Both observed and simulated events are reconstructed using the particle-flow
(PF) algorithm [83]. The particle-flow algorithm aims to reconstruct and
identify each individual particle in an event, with an optimized combination
of information from the various elements of the CMS detector. In this process
the reconstructed PF objects include photons, electrons, muons, neutral
hadrons, and charged hadrons.

Higher-level objects are reconstructed from combinations of the PF objects.
For example, jets are reconstructed with an anti-clustering algorithm
implemented in the library [84, 85]. The reconstruction is based on the
clustering of PF objects with a distance parameter of 0.4. Charged PF objects
are required to be associated with the primary vertex of the interaction. The
reconstructed vertex with the largest value of summed physics-object $\pt^{2}$
is taken to be the primary $\Pp\Pp$ interaction vertex. The physics objects
are the jets, clustered using the jet finding algorithm [86, 84] with the
tracks assigned to the vertex as inputs, and the associated missing transverse
momentum, taken as the negative vector sum of the of those jets. Jet energy
corrections are derived from simulation studies so that the average measured
response of jets becomes identical to that of particle level jets. In situ
measurements of the momentum balance in dijet, photon$+$jet, $\PZ+\text{jet}$,
and multijet events are used to determine any residual differences between the
jet energy scale in data and in simulation, and appropriate corrections are
applied [87].

While neutrinos cannot be detected directly, they contribute to the missing
transverse momentum. The missing transverse momentum vector $\ptvecmiss$ is
computed as the negative vector sum of the transverse momenta of all the PF
objects in an event [88]. The is modified to account for corrections to the
energy scale of the reconstructed jets in the event.

Electrons are identified by a multivariate analysis (MVA) discriminant that
requires as input several quantities describing the track quality, the shapes
of the energy deposits in the ECAL, and the compatibility of the measurements
from the tracker and the ECAL [89]. Muon identification relies on the number
of hits in the inner tracker and the muon systems, and on the quality of the
reconstructed tracks [90]. Electrons and muons selected in this analysis are
required to be consistent with originating from the primary vertex.

A lepton isolation discriminant $I^{\ell}$ is defined to reject nonprompt or
misidentified leptons ($\ell=\Pe,\Pgm$):

$I^{\ell}\equiv\frac{\sum_{\text{charged}}\PT+\max\left(0,\sum_{\text{neutral}}\PT-\frac{1}{2}\sum_{\text{charged,
PU}}\PT\right)}{\PT^{\ell}},$ (1)

where $\PT^{\ell}$ stands for the $\pt$ of the lepton. The variable
$\sum_{\text{charged}}\PT$ is the scalar sum of the transverse momenta of the
charged particles originating from the primary vertex and located in a cone of
size $\Delta R=\sqrt{\smash[b]{(\Delta\eta)^{2}+(\Delta\phi)^{2}}}=0.3$ (0.4)
centered on the electron (muon) direction, where $\phi$ is the azimuthal angle
in radians. The sum $\sum_{\text{neutral}}\PT$ represents a similar quantity
for neutral particles. The scalar sum of the transverse momenta of charged
hadrons originating from pileup vertices in the cone, $\sum_{\text{charged,
PU}}\PT$, is used to estimate the contribution of photons and neutral hadrons
originating from pileup vertices. The factor of $1/2$ corresponds
approximately to the ratio of neutral- to charged-hadron production in the
hadronization process of inelastic $\Pp\Pp$ collisions. The isolation
requirements based on $I^{\ell}$ are described in the following section.

The combined secondary vertex algorithm [91] is used to identify jets that are
likely to have originated from a bottom quark (“$\cPqb$-tagged jets”). In this
algorithm, the secondary vertices associated with the jet and the track-based
lifetime information are given as inputs to an MVA discriminant designed for
$\cPqb$ jet identification. Differences in the $\cPqb$ tagging efficiency
between data and simulation are taken into account by applying a set of
$\pt$-dependent correction factors to the simulated events [91]. The
identification efficiency for genuine $\cPqb$ jets in this analysis is
approximately 63%, whereas the misidentification probability for $\cPqc$
(light-flavor or gluon) jets is approximately 12 (0.9)% for jet $\pt>20\GeV$.

Anti-jets seed the hadron-plus-strips algorithm [92, 93] which is used to
reconstruct candidates. A hadronic decay of a tau lepton can result in one or
more charged hadrons, and additional $\PGpz$ particles. These $\PGpz$s are
reconstructed by clustering electromagnetic deposits in the ECAL into “strips“
in the $\eta$-$\phi$ plane. The strips are elongated in the $\phi$ direction
to contain the calorimeter signatures of converted photons from neutral pion
decays. The algorithm reconstructs candidates based on the number of tracks
and the number of strips representing the number of charged hadrons (“prongs”)
and the number of $\PGpz$s present in the decay. The candidates used in this
analysis are reconstructed in three decay modes: 1-prong,
1-prong+$\PGpz\mathrm{s}$, and 3-prong.

To suppress objects (jets and leptons) misidentified as candidates, an MVA
discriminant [93] including calorimetric information, isolation sums, and
lifetime information is used. A misidentification rate for quark- and gluon-
initiated jets of less than 1% is achieved within a range typical of a
candidate originating from an boson. At the same time, an efficiency for
selecting candidates of $\approx$70% can be achieved for candidates passing
the decay mode reconstruction discussed above. To further suppress electrons
and muons misidentified as candidates, dedicated criteria based on the
consistency between the measurements in the tracker, the calorimeters, and the
muon detectors are applied [92, 93]. The energy scale is measured from
$\cPZ\to\PGt\PGt$ events and the correction is propagated to the simulation
for each decay mode. A “tag-and-probe” measurement [93] in $\PZ\to\ell\ell$
events, where one of the $\ell$ is misidentified as a candidate, is used to
correct the energy scale of electrons and muons misidentified as candidates in
simulation.

The reconstructed mass of the A boson candidate can be used to discriminate
between signal- and background-like events. Multiple reconstruction methods
are considered and described below. The resulting mass distributions for the
signal process ($m_{\text{A}}=300\GeV$) are shown in Fig. 2. The shapes of the
background distributions do not depend on the mass reconstruction method as
strongly as the shape of the signal distribution. The simplest reconstructed
mass, $m^{\text{vis}}_{\ell\ell\PGt\PGt}$, uses only the visible decay
products and combines the reconstructed $\PZ\to\ell\ell$ four-vector with the
$\Ph\to\PGt\PGt$ four-vector based only on visible $\PGt$ decay products. The
resulting mass resolution for $m^{\text{vis}}_{\ell\ell\PGt\PGt}$ is
approximately 15% for an A boson with a mass of 300in all final states.

The mass resolution of the reconstructed A boson candidate can be
significantly improved by accounting for the neutrinos associated with the
leptonic and hadronic tau decays. We use the svfit algorithm [29] to estimate
the mass of the Higgs boson, denoted as $m_{\PGt\PGt}^{\mathrm{fit}}$. The
svfit algorithm combines the with the four-vectors of both $\PGt$ candidates
(electrons, muons, or ), resulting in an improved estimate of the four-vector
of boson that is then used to obtain a more accurate estimate of the A boson
candidate mass $m_{\ell\ell\PGt\PGt}^{\mathrm{fit}}$. The mass resolution of
$m_{\ell\ell\PGt\PGt}^{\mathrm{fit}}$ is 10% for an A boson with a mass of
300.

To further improve the mass resolution, the measured mass of the Higgs boson
(125) can be given as an input to the svfit algorithm. This yields a
constrained estimate of the four-vector of the boson, which results in an even
more precise estimate of the A boson candidate mass, denoted as
$m_{\ell\ell\PGt\PGt}^{\mathrm{c}}$. The resulting mass resolution of
$m_{\ell\ell\PGt\PGt}^{\mathrm{c}}$ is as good as 3% at 300, which improves
the expected 95% confidence level (CL) model-independent limits by
approximately 40% compared to using the visible mass of the A boson
$m^{\text{vis}}_{\ell\ell\PGt\PGt}$ as the discriminating variable. Thus, we
use $m_{\ell\ell\PGt\PGt}^{\mathrm{c}}$ as the discriminating variable between
the signal and the background processes for the final results.

Figure 2: The distribution of the A boson mass for the three studied mass
reconstruction methods at 300: using only the visible decay products
($m^{\text{vis}}_{\ell\ell\PGt\PGt}$, orange), using the svfit algorithm
($m_{\ell\ell\PGt\PGt}^{\mathrm{fit}}$, green), and using the svfit algorithm
with a mass constraint of 125for the Higgs boson
($m_{\ell\ell\PGt\PGt}^{\mathrm{c}}$, blue). The eight final states of the A
boson decay are combined for visualization purposes.

##  0.5 Event selection

Events are selected online using dilepton or single-lepton triggers targeting
leptonic decays of the bosons. The trigger and offline selection requirements
for the boson decay modes are presented in Table 0.5. Each lepton selected by
the trigger is required to be geometrically matched to a corresponding lepton
selected in the analysis. The light leptons in an event are required to be
separated from each other by $\Delta R>0.3$, while the candidates must be
separated from each other and from any other lepton by $\Delta R>0.5$. The
resulting event samples are made mutually exclusive by discarding events that
have additional identified and isolated electrons or muons. Small differences
in trigger selection efficiencies are observed between data and simulation,
and are accounted for by applying corrections to the simulated events.

The nontriggering electrons and muons are required to have $\pt>10\GeV$,
whereas candidates are required to have $\pt>20\GeV$. The $\abs\eta$
constraints from detector geometry are $\abs{\eta^{\Pe}}<2.5$,
$\abs{\eta^{\Pgm}}<2.4$, and $\abs{\eta^{\tauh}}<2.3$ for electrons, muons,
and candidates, respectively. The $\abs\eta$ boundaries are the same for both
triggering and nontriggering electrons and muons.

Trigger and offline selection requirements for the different boson decay
modes. The events are selected using either dilepton triggers with lower-$\pt$
thresholds or single-lepton triggers with higher-$\pt$ thresholds. The
subscripts 1 and 2 indicate the higher- and lower-$\pt$ leptons associated
with the boson, respectively.  Decay channel $\PZ\to\ell\ell$ trigger
selection $\PZ\to\ell\ell$ offline selection $\PZ\to\Pe\Pe$
$\left[\pt^{\Pe_{1}}>23\GeV~{}\&\,\pt^{\Pe_{2}}>12\GeV\right]$
$\left[\pt^{\Pe_{1}}>24\GeV~{}\&\,\pt^{\Pe_{2}}>13\GeV\right]$ or
$\pt^{\Pe_{1}}>27\GeV$ or
$\left[\pt^{\Pe_{1}}>28\GeV~{}\&\,\pt^{\Pe_{2}}>10\GeV\right]$
$\PZ\to\Pgm\Pgm$
$\left[\pt^{\Pgm_{1}}>17\GeV~{}\&\,\pt^{\Pgm_{2}}>8\GeV\right]$
$\left[\pt^{\Pgm_{1}}>18\GeV~{}\&\,\pt^{\Pgm_{2}}>10\GeV\right]$ or
$\pt^{\Pgm_{1}}>24\GeV$ or
$\left[\pt^{\Pgm_{1}}>25\GeV~{}\&\,\pt^{\Pgm_{2}}>10\GeV\right]$

The boson is reconstructed from a pair of opposite-charge, same-flavor light
leptons that fulfills $60<m_{\ell\ell}<120\GeV$. In case of multiple boson
candidates, we choose the one with the mass closest to the boson mass. Loose
identification and isolation selection criteria are applied to the leptons
associated to the boson to maintain a high signal acceptance. The leptons
forming the boson candidate are required to pass the lepton identification,
which has an efficiency of 90 ($>$99)% for electrons (muons). The muons must
pass an isolation requirement of $I^{\Pgm}<0.25$, while a loose isolation
requirement is already included in the electron identification selection.

The leptons associated with the boson decay are required to have opposite
charge. In case of the $\Pe\tauh$, $\Pgm\tauh$, and $\Pe\Pgm$ decay channels,
tighter selection criteria are applied to the light leptons to decrease the
background contributions from $\PZ+\text{jets}$ and other reducible
backgrounds. The specific signal selections detailed in Table 0.5, including
those chosen for the candidates, were optimized to obtain the best signal
sensitivity. The isolation requirements are $I^{\Pe(\Pgm)}<0.15$ for electrons
(muons) associated to a tau lepton decay. Electrons from tau lepton decays
need to pass the electron identification which has an efficiency of 80%. The
candidates associated with the Higgs boson must satisfy the identification and
isolation requirements which have an efficiency of 70%.

The large boson mass leads to relatively high-$\pt$ decay products compared to
the lower of jets misidentified as leptons from the $\PZ+\text{jets}$
background process. This background process is suppressed by selecting events
based on the scalar sum of the visible decay products of the Higgs boson,
$L_{\text{{T}}}^{\text{{h}}}$. In the $\ell\ell+\tauh\tauh$ final states,
which have a larger relative ratio of reducible to irreducible backgrounds,
events with $L_{\text{{T}}}^{\text{{h}}}>60\GeV$ are selected.

The signal events contain no $\cPqb$ jets ($\Pg\Pg\to\text{{A}}$), or only
$\cPqb$ jets with a relatively soft distribution ($\bbbar\text{{A}}$). We
suppress the contributions from background processes, especially $\ttbar$ and
$\ttbar\PZ$, by discarding all events with one or more $\cPqb$-tagged jets
with $\pt>20\GeV$ (“$\cPqb$ jet veto”) without significantly reducing the
signal selection efficiency. The total acceptance for the
$\Pg\Pg\to\text{{A}}$ ($\bbbar\text{{A}}$) signal events with
$m_{\text{A}}=300\GeV$ is 3.9 (3.0)%. The fraction of $\Pg\Pg\to\text{{A}}$
signal events lost due to the $\cPqb$ jet veto is negligible, while for the
$\bbbar\text{{A}}$ process approximately 17% of events are removed with this
selection.

The sensitivity of the analysis is improved by reducing the number of
background events using additional information regarding the Higgs boson
candidate. The constrained Higgs boson candidate four-vector, as estimated
with the svfit algorithm, is used to reconstruct the A boson mass, as
described in Section 0.4. By removing the mass constraint from the svfit
algorithm, the most likely mass of the Higgs boson candidate
$m_{\PGt\PGt}^{\mathrm{fit}}$ provides significant discrimination between
reducible backgrounds, which have a broad distribution due to their
nonresonant nature, and the signal processes, which have a resonance present
at 125. Moreover, the dominant irreducible background from $\PZ\PZ\to 4\ell$
($\cPq\cPq\to\PZ\PZ$ and $\Pg\Pg\to\PZ\PZ$) is suppressed, because for this
background the $m_{\PGt\PGt}^{\mathrm{fit}}$ distribution is concentrated near
the boson mass in contrast to the signal. The signal sensitivity is increased
by an additional $20\%$ by requiring $m_{\PGt\PGt}^{\mathrm{fit}}$ to be
within 90–180.

Kinematic selection requirements for each A boson decay channel, applied on
top of the looser selections and $\cPqb$ jet veto described in the text. The
efficiency of the identification (and isolation) requirement for a given
lepton type is labeled $\epsilon_{\mathrm{id.}}^{\ell}$. The leptons assigned
to the Higgs boson are required to have opposite charge. To increase the
sensitivity, we require $m_{\PGt\PGt}^{\mathrm{fit}}$ to be within 90–180. In
the $\ell\ell+\tauh\tauh$ channel, we additionally require
$L_{\text{{T}}}^{\text{{h}}}>60\GeV$, where $L_{\text{{T}}}^{\text{{h}}}$ is
the scalar sum of the visible decay products of the Higgs boson.  Channel
boson selection boson selection $\ell\ell+\Pe\tauh$
$\left.\vphantom{\begin{tabular}[]{c}4\\\ 4\\\ 4\\\ 4\end{tabular}}\right\\{$
$\left.\vphantom{\begin{tabular}[]{c}4\\\ 4\\\ 4\\\ 4\end{tabular}}\right\\}$
$\epsilon_{\mathrm{id.}}^{\Pe}=80\%$, $I^{\Pe}<0.15$,
$\epsilon_{\mathrm{id.+iso.}}^{\tauh}=70\%$ $\ell\ell+\Pgm\tauh$ Opposite-
charge, same-flavor light leptons $\epsilon_{\mathrm{id.}}^{\Pgm}>99\%$,
$I^{\Pgm}<0.15$ , $\epsilon_{\mathrm{id.+iso.}}^{\tauh}=70\%$
$\ell\ell+\tauh\tauh$ $60<m_{\ell\ell}<120\GeV$
$\epsilon_{\mathrm{id.+iso.}}^{\tauh}=70\%$,
$L_{\text{{T}}}^{\text{{h}}}>60\GeV$ $\ell\ell+\Pe\Pgm$
$\epsilon_{\mathrm{id.}}^{\Pe}=80\%$, $I^{\Pe}<0.15$,
$\epsilon_{\mathrm{id.}}^{\Pgm}>99\%$, $I^{\Pgm}<0.15$

##  0.6 Background estimation

The irreducible backgrounds ($\PZ\PZ\to 4\ell$, $\ttbar\PZ$, $\PW\PW\PZ$,
$\PW\PZ\PZ$, $\PZ\PZ\PZ$) and the production of the 125Higgs boson via the
processes predicted by the SM are estimated from simulation. They are scaled
by their theoretical cross sections calculated at the highest order available,
and the processes producing the 125Higgs boson are also scaled by their most
accurate branching fractions [42].

The reducible backgrounds, which have at least one jet misidentified as an
electron, muon, or candidate, are estimated from data. In this analysis the
dominant reducible contributions come from the $\ttbar$, $\PZ+\text{jets}$,
and $\PW\PZ+\text{jets}$ processes which produce jets misidentified as $\Pgt$
candidates. The estimation of the reducible background contribution is
performed with a so-called “fake rate method” which is based on measuring the
misidentification rates, , probabilities to misidentify a jet as a lepton.
Events with $\Pgt$ candidates failing the signal region identification and
isolation criteria are used along with the misidentification rates to estimate
the contribution from the reducible background in the signal region.

In total three different event samples are used to estimate the contribution
from the reducible background processes. First, the misidentification rates
are estimated in event samples independent from the signal region. This region
is called a “measurement region”. To understand to which extent the measured
misidentification rates describe the jets misidentified as leptons in the
signal region, closure tests comparing the observed and the estimated
reducible background yields are performed in yet another region (“validation
region”). The validation region is required to be independent from the signal
and the measurement regions. The closure tests are used to derive systematic
uncertainties to account for possible differences between the true and the
estimated reducible background yields in the signal region. Finally, the
misidentification rates are applied in an “application region”, formed by
events that fail the identification and isolation criteria required in the
signal region.

In this analysis we use a sample of $\PZ+\text{jet}$ events to estimate the
misidentification rates. The estimation of misidentification rates relies on
reconstructing an opposite-charge, same-flavor lepton pair compatible with a
boson, and requiring one additional loosely defined lepton (electron, muon, or
candidate). The requirements on the leptons associated with the boson are the
same as defined in Section 0.5, but they must fulfill a more stringent
dilepton mass requirement, $81.2<m_{\ell\ell}<120\GeV$. After reconstructing
the $\PZ\to\ell\ell$ decay, the jet-to-lepton misidentification rate is
estimated by applying the lepton identification algorithm to the additional
loosely defined lepton in the event. The misidentification rates are measured
in different bins of lepton , and are further split between reconstructed
decay modes for the candidate, and for muons and electrons in bins of lepton
$\eta$, based on the barrel and endcap regions. The events where the $\Pgt$
candidates arise from genuine tau leptons, electrons, or muons and not jets,
primarily from the $\PW\PZ$ process, are estimated from simulation and
subtracted from data so that the misidentification rates are measured for
genuine hadronic jets only. The obtained misidentification rates for electrons
(muons) are $<$5 (10)% in barrel and endcap regions for lepton $\pt>10\GeV$,
whereas for candidates the misidentification rates vary between $15$–$30\%$
for candidate $\pt>20\GeV$ depending on the decay mode.

The measured misidentification rates are validated in another region that
consists of events with a boson candidate and two additional loosely defined
leptons. To ensure that the validation region is not contaminated with signal
events or irreducible background contributions, the two additional leptons are
required to have the same charge. Modest differences in observed versus
predicted reducible background yields are observed. These differences are
accounted for by assigning a systematic uncertainty in the yield, taken to be
40% which is conservative enough to cover the observed nonclosure. This
uncertainty is uncorrelated between the Higgs boson decay channels resulting
in four uncertainties tied to $\ell\ell+\Pe\tauh$, $\ell\ell+\Pgm\tauh$,
$\ell\ell+\tauh\tauh$, and $\ell\ell+\Pe\Pgm$ channels. Further studies
confirmed that the final results of this analysis are not sensitive to the
exact magnitude of this systematic uncertainty.

To estimate the reducible background contribution in the signal region, we
apply a weight on data events where either one or both of the $\Pgt$
candidates associated to the Higgs boson fail the identification and isolation
criteria. These data events form the application region.

Events with exactly one object failing the identification and isolation
criteria receive a weight $f/(1-f)$, where $f$ is the misidentification rate
for the particular type of lepton. As such, this weight includes the
contribution from the $\PW\PZ+\text{jets}$ process, where we expect one
genuine lepton and one jet misidentified as a lepton in addition to the boson
candidate. Also $\ttbar$ and $\PZ+\text{jets}$ processes are accounted for by
the weight as either of the two jets can pass the identification and isolation
criteria even if neither of them is a genuine lepton. As a result, the weight
introduces double counting of events from $\ttbar$ and $\PZ+\text{jets}$
processes.

To remove the double-counted events from $\ttbar$ and $\PZ+\text{jets}$
processes, we define a weight with a negative sign that is given for events
with both objects failing the identification and isolation criteria, namely
$-f_{1}f_{2}/[(1-f_{1})(1-f_{2})]$. This subtraction, however, introduces
increased statistical uncertainties on the estimated yield of the reducible
background.

The statistical uncertainties can be controlled by taking the shape of the
$m_{\ell\ell\PGt\PGt}^{\mathrm{c}}$ distribution of the reducible background
contribution from data in another region with negligible signal and
irreducible background contributions. This region is defined similarly to the
signal region but with same-sign $\Pgt$ candidates passing relaxed
identification and isolation criteria, yielding a higher number of events
available for the shape estimation. This results in a smoother shape of the
$m_{\ell\ell\PGt\PGt}^{\mathrm{c}}$ distribution, which is normalized to the
estimated yield of the reducible background contribution in the signal region.

An alternative approach to estimate the reducible background contribution was
studied to reduce the statistical uncertainties and to cross check the results
obtained using the nominal method. Instead of using the same-sign data events
for the shape of the $m_{\ell\ell\PGt\PGt}^{\mathrm{c}}$ distribution, the
statistical uncertainties can be reduced considerably by giving a suitable
nonzero weight only for events with both candidates failing the selection
criteria, , by estimating only the contribution from the $\ttbar$ and
$\PZ+\text{jets}$ processes by using the misidentification rate method. The
contribution from events with a single object failing the identification and
isolation criteria is predicted from simulation, removing the double counting
present in the nominal method. As a result, this alternative approach requires
using a weight with a positive sign ($f_{1}f_{2}/[(1-f_{1})(1-f_{2})]$). Since
the statistical uncertainties are smaller, the shape of the
$m_{\ell\ell\PGt\PGt}^{\mathrm{c}}$ distribution is taken from the same events
that provide the estimated yield of the reducible background. The results of
the cross-check show that the two methods yield consistent expected 95% CL
model-independent limits.

To cross check the measured misidentification rates, we performed an
additional measurement using a sample of $\PZ+2\ \text{jets}$ events. In this
cross-check, the measurement region partially overlaps with the aforementioned
validation region, as in both cases the two lepton candidates are required to
have the same charge. The amount of overlap between the measurement and
validation regions depends on the lepton type and the decay channel of the
Higgs boson. The rates are measured in bins of lepton , and are separated by
the reconstructed decay mode of the candidates. Unlike above, the
misidentification rates are not split in bins of lepton $\eta$ for electrons
and muons. The measured misidentification rates result in a reducible
background yield and shape that are compatible with the reducible background
estimation obtained with the nominal misidentification rate measurement used
in this analysis.

##  0.7 Systematic uncertainties

All systematic uncertainties considered in the analysis are summarized in
Table 0.7. Different uncertainties are treated as uncorrelated, and each
uncertainty is assumed correlated between different processes and final
states, unless otherwise mentioned below.

The overall uncertainty in the identification and isolation efficiency for
genuine leptons is 5% [93], which has been measured with a tag-and-probe
method in $\PZ\to\PGt\PGt$ events. An uncertainty of 1.2% in the visible
energy scale of genuine candidates affects both the distributions and yields
of the signals and backgrounds. It is uncorrelated across the 1-prong,
1-prong+$\PGpz\mathrm{s}$, and 3-prong decay modes.

The uncertainties in the electron and muon identification and isolation
efficiencies lead to a normalization uncertainty of 2% for either electrons or
muons. The uncertainty in the trigger efficiency results in a normalization
uncertainty of 2% for both electron and muon triggers. In all channels, the
effect of the uncertainty in the electron and muon energy scales is
negligible.

The normalization uncertainty related to vetoing events with a $\cPqb$-tagged
jet is 4.5% for the background processes with heavy-flavor jets (from charm or
bottom quarks), , $\ttbar$, $\ttbar\PZ$, and $\ttbar\PW$. All other processes,
including the signal process, are dominated by light-flavor or gluon jets and
their normalization uncertainty is 0.15%.

The normalization uncertainties related to the choice of PDFs, and the
renormalization and factorization (RF) scales, affecting the acceptance of the
dominant background processes, are estimated from simulation separately for
each process. The uncertainty from the RF scales is determined by varying one
scale at a time by factors of 0.5 and 2.0, and calculating the change in
process acceptance. Combining the RF scale uncertainties with the PDF set
uncertainty [94] for the $\cPq\cPq\to\PZ\PZ$ process leads to an uncertainty
of 4.8%. The inclusive uncertainty for $\PZ\Ph$ production related to the PDFs
amounts to 1.6%, whereas the uncertainty for the variation of the RF scales is
3.8% [42]. For the subleading boson processes $\PW\Ph$,
$\Pg\Pg\to\Ph\to\PZ\PZ$, and $\ttbar\Ph$ the inclusive uncertainties related
to the PDFs amount to 1.9, 3.2, and 3.6% and the uncertainties for the
variation of the RF scales are 0.7, 3.9, and 7.5%, respectively [42].

For the $\Pg\Pg\to\PZ\PZ$ process, there is a 10% uncertainty in the NNLO
cross section estimate used in the analysis, which covers the PDF, RF scale
uncertainties, and the uncertainty on the strong coupling constant. An
additional 10% uncertainty is included to account for the assumptions used to
estimate the NNLO cross section [95]. The uncertainties in the cross section
of the rare $\ttbar\PZ$, $\ttbar\PW$, and triboson processes amount to 25%
[96].

The last theoretical uncertainty applied in this analysis is the uncertainty
in the theoretical calculations of the SM $\Ph\to\Pgt\Pgt$ branching fraction.
This uncertainty of 2% [42] is applied to both the $\Pg\Pg\to\text{{A}}$ and
$\bbbar\text{{A}}$ signal samples as well as all backgrounds that include the
$\Ph\to\Pgt\Pgt$ process.

Normalization uncertainties in the misidentification rates arising from the
subtraction of prompt lepton contribution estimated from simulation are taken
into account and propagated to the yield of the reducible background mass
distributions. The shape of the $m_{\ell\ell\PGt\PGt}^{\mathrm{c}}$
distribution of the reducible background is estimated from data in a region
where the $\Pgt$ candidates have the same charge and pass relaxed isolation
conditions. Therefore, the statistical uncertainties in the misidentification
rates do not have an impact on the shape of the
$m_{\ell\ell\PGt\PGt}^{\mathrm{c}}$ distribution. As discussed in Section 0.6,
an additional uncertainty is applied based on the results of the closure tests
comparing the differences between the observed and the estimated reducible
background yields. The uncertainty in the yield is taken to be 40%, and is
considered uncorrelated across the $\ell\ell+\Pe\tauh$, $\ell\ell+\Pgm\tauh$,
$\ell\ell+\tauh\tauh$, and $\ell\ell+\Pe\Pgm$ channels.

The $\ptvecmiss$ scale uncertainties [88], which are computed event-by-event,
affect the normalization of various processes as well as their distributions
through the propagation of these uncertainties to the di-tau masses
$m_{\PGt\PGt}^{\mathrm{fit}}$ and $m_{\PGt\PGt}^{\mathrm{c}}$. The
$\ptvecmiss$ scale uncertainties arising from unclustered energy deposits in
the detector come from four independent sources related to the tracker, ECAL,
hadron calorimeter, and forward calorimeters. The $\ptvecmiss$ scale
uncertainties related to the uncertainties in the jet energy scale
measurement, which affect the $\ptvecmiss$ calculation, are taken into account
as a separate uncertainty.

Uncertainties related to the finite number of simulated events are taken into
account using the Barlow-Beeston-lite method [97]. They are considered for all
bins of the background distributions used to extract the results. They are
uncorrelated across different samples, and across bins of a single
distribution. Finally, the uncertainty in the integrated luminosity amounts to
2.5% [98].

Sources of systematic uncertainty. The sign $\dagger$ marks the uncertainties
that affect both the shape and normalization of the final
$m_{\ell\ell\PGt\PGt}^{\mathrm{c}}$ distributions. Uncertainties that only
affect the normalizations have no marker. For the shape and normalization
uncertainties, the magnitude column lists an approximation of the associated
change in the normalization of the affected processes.  Source of uncertainty
Process Magnitude id. & isolation All simulated processes 5% energy scale†
(1.2% energy shift) All simulated processes $<$2% $\Pe$ id. & isolation All
simulated processes 2% $\Pe$ trigger All simulated processes 2% $\Pgm$ id. &
isolation All simulated processes 2% $\Pgm$ trigger All simulated processes 2%
$\cPqb$ jet veto All simulated processes 4.5% heavy flavor, 0.15% light flavor
or gluon $\cPq\cPq\to\PZ\PZ$ theoretical uncertainty $\cPq\cPq\to\PZ\PZ$ 4.8%
PDF set uncertainty $\PZ\Ph$, $\PW\Ph$, $\Pg\Pg\to\Ph\to\PZ\PZ$, and
$\ttbar\Ph$ Varies from 1.6 to 3.6% (see text) RF scale uncertainty $\PZ\Ph$,
$\PW\Ph$, $\Pg\Pg\to\Ph\to\PZ\PZ$, and $\ttbar\Ph$ Varies from 0.7 to 7.5%
(see text) $\Pg\Pg\to\PZ\PZ$ theoretical uncertainty $\Pg\Pg\to\PZ\PZ$ 10%
$\Pg\Pg\to\PZ\PZ$ NNLO cross section estimation assumptions $\Pg\Pg\to\PZ\PZ$
10% $\ttbar\PZ$ theoretical uncertainty $\ttbar\PZ$ 25% $\ttbar\PW$
theoretical uncertainty $\ttbar\PW$ 25% Triboson theoretical uncertainty
Triboson 25% Theoretical uncertainty on $\mathcal{B}(\Ph\to\PGt\PGt)$ Signal,
$\PZ\Ph$, and $\PW\Ph$ $<$2% Reducible background uncertainties: Reducible
background $\Pe$ prompt lepton subtraction $<$12% in $\ell\ell+\Pe\Pgm$, $<$1%
in $\ell\ell+\Pe\tauh$ $\Pgm$ prompt lepton subtraction $<$16% in
$\ell\ell+\Pe\Pgm$, $<$1.5% in $\ell\ell+\Pgm\tauh$ $\tau$ prompt lepton
subtraction $<$3.5% in $\ell\ell+\Pe\tauh$ and $\ell\ell+\Pgm\tauh$, $<$1% in
$\ell\ell+\tauh\tauh$ Normalization 40% in $\ell\ell+\Pe\tauh$,
$\ell\ell+\Pgm\tauh$, $\ell\ell+\tauh\tauh$, and $\ell\ell+\Pe\Pgm$
$\ptvecmiss$ energy scale† All simulated processes $<$2% Limited number of
events All background processes Statistical uncertainty in individual bins
Integrated luminosity All simulated processes 2.5%

##  0.8 Results

We use the reconstructed pseudoscalar Higgs boson mass,
$m_{\ell\ell\PGt\PGt}^{\mathrm{c}}$, as the discriminating variable between
the signal and the background processes. The results are based on a
simultaneous binned likelihood fit of the reconstructed mass distributions in
the eight final states. The eight final states are each fit as separate
distributions in the simultaneous fit. They are combined together for
visualization purposes only. Nuisance parameters, representing the systematic
uncertainties, are profiled in the fit. Even though the studied signal mass
range is 220–400, the distribution of the reconstructed mass
$m_{\ell\ell\PGt\PGt}^{\mathrm{c}}$ covers the mass range 200–600, as the
additional information on the background distributions is used to constrain
the corresponding parameters in the simultaneous fit. When displaying the
results, background processes are grouped as follows: “$\Ph(125\GeV)$”
includes all processes with the SM Higgs boson (including
$\Pg\Pg\to\Ph\to\PZ\PZ\to 4\ell$); “$\PZ\PZ\to 4\ell$” includes events from
$\cPq\cPq\to\PZ\PZ$ and $\Pg\Pg\to\PZ\PZ$ processes; “Other” includes events
from triboson, $\ttbar\PZ$, and $\ttbar\PW$ production; and “Reducible”
includes the reducible background contribution.

The $m_{\ell\ell\PGt\PGt}^{\mathrm{c}}$ distributions are shown in Fig. 3 for
each of the four boson decay channels, adding the $\PZ\to\ell\ell$ channels
together, and in Fig. 4 for all eight final states together. The distributions
are shown after a background-only fit to data and include both statistical and
systematic uncertainties. No excess above the standard model background
expectations is observed in data. The predicted signal and background yields,
as well as the number of observed events, are given in Table 0.8 for each of
the four $\PZ\Ph$ channels.

Figure 3: The reconstructed mass $m_{\ell\ell\PGt\PGt}^{\mathrm{c}}$
distributions and uncertainties after a background-only fit for the
$\ell\ell+\Pe\tauh$ (upper left), $\ell\ell+\Pgm\tauh$ (upper right),
$\ell\ell+\tauh\tauh$ (lower left), and $\ell\ell+\Pe\Pgm$ (lower right)
channels. In all cases the two decay channels of the boson are included as
separate distributions in the simultaneous fit; combining them together is for
visualization purposes only. The uncertainties include both statistical and
systematic components. The expected contribution from the
$\text{{A}}\to\PZ\Ph$ signal process is shown for a pseudoscalar Higgs boson
with $m_{\text{A}}=300\GeV$ with the product of the cross section and
branching fraction of 20fb and is for illustration only.  Figure 4: The
reconstructed mass $m_{\ell\ell\PGt\PGt}^{\mathrm{c}}$ distribution and
uncertainties after a background-only fit in all eight final states. The final
states are included as separate distributions in the simultaneous fit;
combining them together is for visualization purposes only. The uncertainties
include both statistical and systematic components. The expected contribution
from the $\text{{A}}\to\PZ\Ph$ signal process is shown for a pseudoscalar
Higgs boson with $m_{\text{A}}=300\GeV$ with the product of the cross section
and branching fraction of 20fb and is for illustration only.

Background and signal expectations together with the numbers of observed
events, for the signal region distributions after a background-only fit. The
expected contribution from the $\text{{A}}\to\PZ\Ph$ signal process is given
for a pseudoscalar Higgs boson with $m_{\text{A}}=300\GeV$ with the product of
the cross section and branching fraction of 20fb. The background uncertainty
accounts for all sources of background uncertainty, systematic as well as
statistical, after the simultaneous fit.  Process $\ell\ell+\Pe\tauh$
$\ell\ell+\Pgm\tauh$ $\ell\ell+\tauh\tauh$ $\ell\ell+\Pe\Pgm$ (125)
$0.77\,\pm\,0.02$ $1.39\,\pm\,0.03$ $1.28\,\pm\,0.04$ $0.45\,\pm\,0.01$
$\PZ\PZ\to 4\ell$ $6.48\,\pm\,0.13$ $11.38\,\pm\,0.25$ $7.59\,\pm\,0.20$
$4.57\,\pm\,0.09$ Other $0.10\,\pm\,0.01$ $0.24\,\pm\,0.02$ $0.04\,\pm\,0.01$
$0.69\,\pm\,0.04$ Reducible $5.52\,\pm\,0.42$ $9.12\,\pm\,0.93$
$6.68\,\pm\,0.65$ $2.04\,\pm\,0.24$ Total background $12.88\,\pm\,0.45$
$22.13\,\pm\,0.94$ $15.58\,\pm\,0.68$ $7.74\,\pm\,0.28$ $\text{{A}}\to\PZ\Ph$,
$m_{\text{A}}=300\GeV$, $\sigma\mathcal{B}=20\unit{fb}$ $4.13\,\pm\,0.18$
$7.32\,\pm\,0.30$ $7.01\,\pm\,0.40$ $2.26\,\pm\,0.10$ Observed 13 22 14 12

Upper limits at 95% CL [99, 100] are set in multiple scenarios. An asymptotic
approximation of the modified frequentist method [99, 100, 101, 102] is used
when calculating the 95% CL upper limits. Model-independent limits are set on
the product of the cross section and branching fraction,
$\sigma(\Pg\Pg\to\text{{A}})\mathcal{B}(\text{{A}}\to\PZ\Ph\to\ell\ell\PGt\PGt)$,
for the $\Pg\Pg\to\text{{A}}\to\PZ\Ph$ process. The model-independent 95% CL
limits are shown in Fig. 5 and are consistent with the observed lack of
signal.

Model-dependent interpretation of the results is performed in two MSSM
scenarios, $\mathrm{M^{125}_{\Ph,EFT}}$ and hMSSM, setting 95% CL limits in
the $m_{\text{A}}$–$\tanb$ plane. For both MSSM scenarios, limits are set
based on the $\Pg\Pg\to\text{{A}}$ and $\bbbar\text{{A}}$ production
processes. The signal samples used in the analysis are generated with the
$\Pg\Pg\to\text{{A}}$ process. To account for the $\bbbar\text{{A}}$
production, at each point in the $m_{\text{A}}$–$\tanb$ plane, the yield of
the signal process resulting from $\Pg\Pg\to\text{{A}}$ is scaled as follows:

$\textrm{Total signal yield}=\Pg\Pg\to\text{{A}}\
\text{yield}\left(1+\epsilon_{\bbbar\text{{A}}/\Pg\Pg\to\text{{A}}}\frac{\sigma_{\bbbar\text{{A}}}}{\sigma_{\Pg\Pg\to\text{{A}}}}\right).$
(2)

The scaling takes the estimated $\Pg\Pg\to\text{{A}}\ \text{yield}$ at each
$m_{\text{A}}$–$\tanb$ point and adds a contribution associated to
$\bbbar\text{{A}}$ according to the estimated selection efficiency ratio in
the signal region, $\epsilon_{\bbbar\text{{A}}/\Pg\Pg\to\text{{A}}}=0.76$, and
the ratio $\sigma_{\bbbar\text{{A}}}/\sigma_{\Pg\Pg\to\text{{A}}}$, which
depends on $m_{\text{A}}$ and $\tanb$. The signal region selection efficiency
ratio was estimated for a single mass point ($m_{\text{A}}=300\GeV$), and
additional studies were performed to confirm that for the studied mass range
(220–400) the efficiency ratio is nearly flat. The signal yield scaling allows
the estimated $\bbbar\text{{A}}$ contribution to be included which is
necessary when setting model-dependent limits in the parameter space region
where the $\bbbar\text{{A}}$ cross section becomes nonnegligible compared to
the $\Pg\Pg\to\text{{A}}$ cross section. For reference, at
$m_{\text{A}}=300\GeV$ and $\tanb=4$, in the hMSSM scenario,
$\sigma_{\bbbar\text{{A}}}/\sigma_{\Pg\Pg\to\text{{A}}}=0.22$, which is a
nonnegligible contribution.

The results in the $\mathrm{M^{125}_{\Ph,EFT}}$ scenario and the hMSSM
scenario are shown in Fig. 6. The observed limits exclude slightly higher
values in the $\mathrm{M^{125}_{\Ph,EFT}}$ scenario compared to the hMSSM
scenario: for example at $m_{\text{A}}=300\GeV$, values below 4.0 and 3.7 are
excluded at 95% confidence level in the $\mathrm{M^{125}_{\Ph,EFT}}$ and hMSSM
scenarios, respectively.

In the hMSSM scenario, this search constrains the parameter space region with
low $\tanb$ values when $220<m_{\text{A}}<350\GeV$, and supports the results
of previous indirect and direct searches. The combined measurements of the
standard model Higgs boson couplings result in indirect constrains on the
hMSSM scenario, that indicate that $m_{\text{A}}$ values below 600are
disfavored by the observed data [16, 17]. Out of the direct searches targeting
the $m_{\text{A}}$ values below 400, this analysis has a similar sensitivity
as the searches using the $\text{{A}}\to\PZ\Ph(\Ph\to\bbbar)$ decay, performed
by the ATLAS and CMS Collaborations [27, 28]. Moreover, together with the
results presented in Refs. [27, 28], this analysis complements the constraints
placed by analyses which target the decay of the boson into a pair of or
bosons [103, 104].

Figure 5:  The expected and observed 95% CL model-independent upper limits on
the product of the cross section and branching fraction
$\sigma(\Pg\Pg\to\text{{A}})\mathcal{B}(\text{{A}}\to\PZ\Ph\to\ell\ell\PGt\PGt)$
are shown. The green (yellow) band corresponds to the 68 (95)% confidence
intervals for the expected limit.

Figure 6:  The expected and observed 95% CL exclusion limits in the
$m_{\text{A}}$–$\tanb$ plane are shown for two MSSM scenarios:
$\mathrm{M^{125}_{\Ph,EFT}}$ (left) and hMSSM (right). The area under the
solid black curve is excluded. The dashed black curve corresponds to the
median expected limit, surrounded by the 68 (95)% confidence intervals in blue
(red). The limits are overlaid on a background showing the
$\sigma(\Pg\Pg\to\text{{A}}+\bbbar\text{{A}})\mathcal{B}(\text{{A}}\to\PZ\Ph\to\ell\ell\PGt\PGt)$
as predicted by each model at each grid point.

##  0.9 Summary

A search is presented for a pseudoscalar Higgs boson decaying into a 125Higgs
boson, which further decays into tau leptons, and a boson that decays into a
pair of electrons or muons. A data sample of proton-proton collisions
collected at $\sqrt{s}=13\TeV$ by the CMS experiment at the LHC is used,
corresponding to an integrated luminosity of 35.9. The sensitivity of the
study is increased by using the information on the Higgs boson mass [13] when
reconstructing the mass of the pseudoscalar Higgs boson. The signal extraction
is further optimized with kinematic selections based on the mass of the Higgs
boson. The data agree with the background predictions from the standard model.
The observed model-independent limits at 95% confidence level on the product
$\sigma(\Pg\Pg\to\text{{A}})\mathcal{B}(\text{{A}}\to\PZ\Ph\to\ell\ell\PGt\PGt)$
range from 27 to $5\unit{fb}$ for A boson mass 220 to 400, respectively. The
model-independent limits are interpreted in terms of
$\sigma(\Pg\Pg\to\text{{A}}+\bbbar\text{{A}})\mathcal{B}(\text{{A}}\to\PZ\Ph\to\ell\ell\PGt\PGt)$
for calculation of the model-dependent limits in two minimal supersymmetric
standard model scenarios, $\mathrm{M^{125}_{\Ph,EFT}}$ and hMSSM. In the
$\mathrm{M^{125}_{\Ph,EFT}}$ (hMSSM) scenario, the observed limits exclude
$\tanb$ values below 1.8 (1.6) at $m_{\text{A}}=220\GeV$ and 4.0 (3.7) at
$m_{\text{A}}=300\GeV$ at 95% confidence level.

###### Acknowledgements.

We congratulate our colleagues in the CERN accelerator departments for the
excellent performance of the LHC and thank the technical and administrative
staffs at CERN and at other CMS institutes for their contributions to the
success of the CMS effort. In addition, we gratefully acknowledge the
computing centers and personnel of the Worldwide LHC Computing Grid for
delivering so effectively the computing infrastructure essential to our
analyses. Finally, we acknowledge the enduring support for the construction
and operation of the LHC and the CMS detector provided by the following
funding agencies: BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq,
CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST,
and NSFC (China); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RPF
(Cyprus); SENESCYT (Ecuador); MoER, ERC IUT, 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,
752730, and 765710 (European Union); the Leventis Foundation; the A.P. Sloan
Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science
Policy Office; the Fonds pour la Formation à la Recherche dans l’Industrie et
dans l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door
Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium)
under the “Excellence of Science – EOS” – be.h project n. 30820817; the
Beijing Municipal Science & Technology Commission, No. Z181100004218003; the
Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the
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 HOMING PLUS program of the Foundation for Polish Science, cofinanced from
European Union, Regional Development Fund, the Mobility Plus program of the
Ministry of Science and Higher Education, the National Science Center
(Poland), contracts Harmonia 2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543,
2014/15/B/ST2/03998, and 2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406;
the National Priorities Research Program by Qatar National Research Fund; the
Ministry of Science and Education, grant no. 3.2989.2017 (Russia); the
Programa Estatal de Fomento de la Investigación Científica y Técnica de
Excelencia María de Maeztu, grant MDM-2015-0509 and the Programa Severo Ochoa
del Principado de Asturias; the Thalis and Aristeia 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 Nvidia Corporation;
the Welch Foundation, contract C-1845; and the Weston Havens Foundation (USA).

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##  .10 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan${}^{\textrm{\textdagger}}$, A. Tumasyan Institut für
Hochenergiephysik, Wien, Austria
W. Adam, F. Ambrogi, T. Bergauer, J. Brandstetter, M. Dragicevic, J. Erö, A.
Escalante Del Valle, M. Flechl, R. Frühwirth1, M. Jeitler1, N. Krammer, I.
Krätschmer, D. Liko, T. Madlener, I. Mikulec, N. Rad, J. Schieck1, R.
Schöfbeck, M. Spanring, D. Spitzbart, W. Waltenberger, C.-E. Wulz1, M. Zarucki
Institute for Nuclear Problems, Minsk, Belarus
V. Drugakov, V. Mossolov, J. Suarez Gonzalez Universiteit Antwerpen,
Antwerpen, Belgium
M.R. Darwish, E.A. De Wolf, D. Di Croce, X. Janssen, J. Lauwers, A. Lelek, M.
Pieters, H. Rejeb Sfar, H. Van Haevermaet, P. Van Mechelen, S. Van Putte, N.
Van Remortel Vrije Universiteit Brussel, Brussel, Belgium
F. Blekman, E.S. Bols, S.S. Chhibra, J. D’Hondt, J. De Clercq, D. Lontkovskyi,
S. Lowette, I. Marchesini, S. Moortgat, L. Moreels, Q. Python, K. Skovpen, S.
Tavernier, W. Van Doninck, P. Van Mulders, I. Van Parijs Université Libre de
Bruxelles, Bruxelles, Belgium
D. Beghin, B. Bilin, H. Brun, B. Clerbaux, G. De Lentdecker, H. Delannoy, B.
Dorney, L. Favart, A. Grebenyuk, A.K. Kalsi, J. Luetic, A. Popov, N. Postiau,
E. Starling, L. Thomas, C. Vander Velde, P. Vanlaer, D. Vannerom, Q. Wang
Ghent University, Ghent, Belgium
T. Cornelis, D. Dobur, I. Khvastunov2, C. Roskas, D. Trocino, M. Tytgat, W.
Verbeke, B. Vermassen, M. Vit, N. Zaganidis Université Catholique de Louvain,
Louvain-la-Neuve, Belgium
O. Bondu, G. Bruno, C. Caputo, P. David, C. Delaere, M. Delcourt, A.
Giammanco, V. Lemaitre, A. Magitteri, J. Prisciandaro, A. Saggio, M. Vidal
Marono, P. Vischia, J. Zobec Centro Brasileiro de Pesquisas Fisicas, Rio de
Janeiro, Brazil
F.L. Alves, G.A. Alves, G. Correia Silva, C. Hensel, A. Moraes, P. Rebello
Teles Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
E. Belchior Batista Das Chagas, W. Carvalho, J. Chinellato3, E. Coelho, E.M.
Da Costa, G.G. Da Silveira4, D. De Jesus Damiao, C. De Oliveira Martins, S.
Fonseca De Souza, L.M. Huertas Guativa, H. Malbouisson, J. Martins5, D. Matos
Figueiredo, M. Medina Jaime6, M. Melo De Almeida, C. Mora Herrera, L. Mundim,
H. Nogima, W.L. Prado Da Silva, L.J. Sanchez Rosas, A. Santoro, A. Sznajder,
M. Thiel, E.J. Tonelli Manganote3, F. Torres Da Silva De Araujo, A. Vilela
Pereira Universidade Estadual Paulista a, Universidade Federal do ABC b, São
Paulo, Brazil
S. Ahujaa, C.A. Bernardesa, L. Calligarisa, T.R. Fernandez Perez Tomeia, E.M.
Gregoresb, D.S. Lemos, P.G. Mercadanteb, S.F. Novaesa, SandraS. Padulaa
Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy of
Sciences, Sofia, Bulgaria
A. Aleksandrov, G. Antchev, R. Hadjiiska, P. Iaydjiev, A. Marinov, M. Misheva,
M. Rodozov, M. Shopova, G. Sultanov University of Sofia, Sofia, Bulgaria
M. Bonchev, A. Dimitrov, T. Ivanov, L. Litov, B. Pavlov, P. Petkov Beihang
University, Beijing, China
W. Fang7, X. Gao7, L. Yuan Institute of High Energy Physics, Beijing, China
M. Ahmad, G.M. Chen, H.S. Chen, M. Chen, C.H. Jiang, D. Leggat, H. Liao, Z.
Liu, S.M. Shaheen8, A. Spiezia, J. Tao, E. Yazgan, H. Zhang, S. Zhang8, J.
Zhao State Key Laboratory of Nuclear Physics and Technology, Peking
University, Beijing, China
A. Agapitos, Y. Ban, G. Chen, A. Levin, J. Li, L. Li, Q. Li, Y. Mao, S.J.
Qian, D. Wang Tsinghua University, Beijing, China
Z. Hu, Y. Wang Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, L.F. Chaparro Sierra, C. Florez, C.F. González
Hernández, M.A. Segura Delgado Universidad de Antioquia, Medellin, Colombia
J. Mejia Guisao, J.D. Ruiz Alvarez, C.A. Salazar González, N. Vanegas Arbelaez
University of Split, Faculty of Electrical Engineering, Mechanical Engineering
and Naval Architecture, Split, Croatia
D. Giljanović, N. Godinovic, D. Lelas, I. Puljak, T. Sculac University of
Split, Faculty of Science, Split, Croatia
Z. Antunovic, M. Kovac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, S. Ceci, D. Ferencek, K. Kadija, B. Mesic, M. Roguljic, A.
Starodumov9, T. Susa University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, E. Erodotou, A. Ioannou, M. Kolosova, S. Konstantinou,
G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos, P.A. Razis, H.
Rykaczewski, D. Tsiakkouri Charles University, Prague, Czech Republic
M. Finger10, M. Finger Jr.10, A. Kveton, J. Tomsa Escuela Politecnica
Nacional, Quito, Ecuador
E. Ayala Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab
Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
H. Abdalla11, A. Mohamed12 National Institute of Chemical Physics and
Biophysics, Tallinn, Estonia
S. Bhowmik, A. Carvalho Antunes De Oliveira, R.K. Dewanjee, K. Ehataht, M.
Kadastik, M. Raidal, C. Veelken Department of Physics, University of Helsinki,
Helsinki, Finland
P. Eerola, L. Forthomme, H. Kirschenmann, K. Osterberg, M. Voutilainen
Helsinki Institute of Physics, Helsinki, Finland
F. Garcia, J. Havukainen, J.K. Heikkilä, T. Järvinen, V. Karimäki, R.
Kinnunen, T. Lampén, K. Lassila-Perini, S. Laurila, S. Lehti, T. Lindén, P.
Luukka, T. Mäenpää, H. Siikonen, E. Tuominen, J. Tuominiemi Lappeenranta
University of Technology, Lappeenranta, Finland
T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
M. Besancon, F. Couderc, M. Dejardin, D. Denegri, B. Fabbro, J.L. Faure, F.
Ferri, S. Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry,
C. Leloup, E. Locci, J. Malcles, J. Rander, A. Rosowsky, M.Ö. Sahin, A. Savoy-
Navarro13, M. Titov Laboratoire Leprince-Ringuet, Ecole polytechnique,
CNRS/IN2P3, Université Paris-Saclay, Palaiseau, France
C. Amendola, F. Beaudette, P. Busson, C. Charlot, B. Diab, G. Falmagne, R.
Granier de Cassagnac, I. Kucher, A. Lobanov, C. Martin Perez, M. Nguyen, C.
Ochando, P. Paganini, J. Rembser, R. Salerno, J.B. Sauvan, Y. Sirois, A. Zabi,
A. Zghiche Université de Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, France
J.-L. Agram14, J. Andrea, D. Bloch, G. Bourgatte, J.-M. Brom, E.C. Chabert, C.
Collard, E. Conte14, J.-C. Fontaine14, D. Gelé, U. Goerlach, M. Jansová, A.-C.
Le Bihan, N. Tonon, P. Van Hove Centre de Calcul de l’Institut National de
Physique Nucleaire et de Physique des Particules, CNRS/IN2P3, Villeurbanne,
France
S. Gadrat Université de Lyon, Université Claude Bernard Lyon 1, CNRS-IN2P3,
Institut de Physique Nucléaire de Lyon, Villeurbanne, France
S. Beauceron, C. Bernet, G. Boudoul, C. Camen, N. Chanon, R. Chierici, D.
Contardo, P. Depasse, H. El Mamouni, J. Fay, S. Gascon, M. Gouzevitch, B.
Ille, Sa. Jain, F. Lagarde, I.B. Laktineh, H. Lattaud, M. Lethuillier, L.
Mirabito, S. Perries, V. Sordini, G. Touquet, M. Vander Donckt, S. Viret
Georgian Technical University, Tbilisi, Georgia
A. Khvedelidze10 Tbilisi State University, Tbilisi, Georgia
Z. Tsamalaidze10 RWTH Aachen University, I. Physikalisches Institut, Aachen,
Germany
C. Autermann, L. Feld, M.K. Kiesel, K. Klein, M. Lipinski, D. Meuser, A.
Pauls, M. Preuten, M.P. Rauch, C. Schomakers, J. Schulz, M. Teroerde, B.
Wittmer RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
A. Albert, M. Erdmann, S. Erdweg, T. Esch, B. Fischer, R. Fischer, S. Ghosh,
T. Hebbeker, K. Hoepfner, H. Keller, L. Mastrolorenzo, M. Merschmeyer, A.
Meyer, P. Millet, G. Mocellin, S. Mondal, S. Mukherjee, D. Noll, A. Novak, T.
Pook, A. Pozdnyakov, T. Quast, M. Radziej, Y. Rath, H. Reithler, M. Rieger, J.
Roemer, A. Schmidt, S.C. Schuler, A. Sharma, S. Thüer, S. Wiedenbeck RWTH
Aachen University, III. Physikalisches Institut B, Aachen, Germany
G. Flügge, W. Haj Ahmad15, O. Hlushchenko, T. Kress, T. Müller, A. Nehrkorn,
A. Nowack, C. Pistone, O. Pooth, D. Roy, H. Sert, A. Stahl16 Deutsches
Elektronen-Synchrotron, Hamburg, Germany
M. Aldaya Martin, P. Asmuss, I. Babounikau, H. Bakhshiansohi, K. Beernaert, O.
Behnke, U. Behrens, A. Bermúdez Martínez, D. Bertsche, A.A. Bin Anuar, K.
Borras17, V. Botta, A. Campbell, A. Cardini, P. Connor, S. Consuegra
Rodríguez, C. Contreras-Campana, V. Danilov, A. De Wit, M.M. Defranchis, C.
Diez Pardos, D. Domínguez Damiani, G. Eckerlin, D. Eckstein, T. Eichhorn, A.
Elwood, E. Eren, E. Gallo18, A. Geiser, J.M. Grados Luyando, A. Grohsjean, M.
Guthoff, M. Haranko, A. Harb, A. Jafari, N.Z. Jomhari, H. Jung, A. Kasem17, M.
Kasemann, H. Kaveh, J. Keaveney, C. Kleinwort, J. Knolle, D. Krücker, W.
Lange, T. Lenz, J. Leonard, J. Lidrych, K. Lipka, W. Lohmann19, R. Mankel,
I.-A. Melzer-Pellmann, A.B. Meyer, M. Meyer, M. Missiroli, G. Mittag, J.
Mnich, A. Mussgiller, V. Myronenko, D. Pérez Adán, S.K. Pflitsch, D. Pitzl, A.
Raspereza, A. Saibel, M. Savitskyi, V. Scheurer, P. Schütze, C.
Schwanenberger, R. Shevchenko, A. Singh, H. Tholen, O. Turkot, A. Vagnerini,
M. Van De Klundert, G.P. Van Onsem, R. Walsh, Y. Wen, K. Wichmann, C. Wissing,
O. Zenaiev, R. Zlebcik University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, L. Benato, A. Benecke, V. Blobel, T. Dreyer, A.
Ebrahimi, A. Fröhlich, C. Garbers, E. Garutti, D. Gonzalez, P. Gunnellini, J.
Haller, A. Hinzmann, A. Karavdina, G. Kasieczka, R. Klanner, R. Kogler, N.
Kovalchuk, S. Kurz, V. Kutzner, J. Lange, T. Lange, A. Malara, D. Marconi, J.
Multhaup, M. Niedziela, C.E.N. Niemeyer, D. Nowatschin, A. Perieanu, A.
Reimers, O. Rieger, C. Scharf, P. Schleper, S. Schumann, J. Schwandt, J.
Sonneveld, H. Stadie, G. Steinbrück, F.M. Stober, M. Stöver, B. Vormwald, I.
Zoi Karlsruher Institut fuer Technologie, Karlsruhe, Germany
M. Akbiyik, C. Barth, M. Baselga, S. Baur, T. Berger, E. Butz, R. Caspart, T.
Chwalek, W. De Boer, A. Dierlamm, K. El Morabit, N. Faltermann, M. Giffels, P.
Goldenzweig, A. Gottmann, M.A. Harrendorf, F. Hartmann16, U. Husemann, S.
Kudella, S. Mitra, M.U. Mozer, Th. Müller, M. Musich, A. Nürnberg, G. Quast,
K. Rabbertz, M. Schröder, I. Shvetsov, H.J. Simonis, R. Ulrich, M. Weber, C.
Wöhrmann, R. Wolf Institute of Nuclear and Particle Physics (INPP), NCSR
Demokritos, Aghia Paraskevi, Greece
G. Anagnostou, P. Asenov, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas,
G. Paspalaki National and Kapodistrian University of Athens, Athens, Greece
M. Diamantopoulou, G. Karathanasis, P. Kontaxakis, A. Panagiotou, I.
Papavergou, N. Saoulidou, A. Stakia, K. Theofilatos, K. Vellidis National
Technical University of Athens, Athens, Greece
G. Bakas, K. Kousouris, I. Papakrivopoulos, G. Tsipolitis University of
Ioánnina, Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, S. Mallios, K.
Manitara, N. Manthos, I. Papadopoulos, J. Strologas, F.A. Triantis, D.
Tsitsonis MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
M. Bartók20, M. Csanad, P. Major, K. Mandal, A. Mehta, M.I. Nagy, G. Pasztor,
O. Surányi, G.I. Veres Wigner Research Centre for Physics, Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath21, F. Sikler, T.Á. Vámi, V. Veszpremi, G.
Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear Research ATOMKI,
Debrecen, Hungary
N. Beni, S. Czellar, J. Karancsi20, A. Makovec, J. Molnar, Z. Szillasi
Institute of Physics, University of Debrecen, Debrecen, Hungary
P. Raics, D. Teyssier, Z.L. Trocsanyi, B. Ujvari Eszterhazy Karoly University,
Karoly Robert Campus, Gyongyos, Hungary
T. Csorgo, W.J. Metzger, F. Nemes, T. Novak Indian Institute of Science
(IISc), Bangalore, India
S. Choudhury, J.R. Komaragiri, P.C. Tiwari National Institute of Science
Education and Research, HBNI, Bhubaneswar, India
S. Bahinipati23, C. Kar, G. Kole, P. Mal, V.K. Muraleedharan Nair Bindhu, A.
Nayak24, D.K. Sahoo23, S.K. Swain Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, S. Chauhan, R. Chawla, N. Dhingra, R.
Gupta, A. Kaur, M. Kaur, S. Kaur, P. Kumari, M. Lohan, M. Meena, K. Sandeep,
S. Sharma, J.B. Singh, A.K. Virdi, G. Walia University of Delhi, Delhi, India
A. Bhardwaj, B.C. Choudhary, R.B. Garg, M. Gola, S. Keshri, Ashok Kumar, S.
Malhotra, M. Naimuddin, P. Priyanka, K. Ranjan, Aashaq Shah, R. Sharma Saha
Institute of Nuclear Physics, HBNI, Kolkata, India
R. Bhardwaj25, M. Bharti25, R. Bhattacharya, S. Bhattacharya, U. Bhawandeep25,
D. Bhowmik, S. Dey, S. Dutta, S. Ghosh, M. Maity26, K. Mondal, S. Nandan, A.
Purohit, P.K. Rout, G. Saha, S. Sarkar, T. Sarkar26, M. Sharan, B. Singh25, S.
Thakur25 Indian Institute of Technology Madras, Madras, India
P.K. Behera, P. Kalbhor, A. Muhammad, P.R. Pujahari, A. Sharma, A.K. Sikdar
Bhabha Atomic Research Centre, Mumbai, India
R. Chudasama, D. Dutta, V. Jha, V. Kumar, D.K. Mishra, P.K. Netrakanti, L.M.
Pant, P. Shukla Tata Institute of Fundamental Research-A, Mumbai, India
T. Aziz, M.A. Bhat, S. Dugad, G.B. Mohanty, N. Sur, RavindraKumar Verma Tata
Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, S. Karmakar,
S. Kumar, G. Majumder, K. Mazumdar, N. Sahoo, S. Sawant Indian Institute of
Science Education and Research (IISER), Pune, India
S. Chauhan, S. Dube, V. Hegde, A. Kapoor, K. Kothekar, S. Pandey, A. Rane, A.
Rastogi, S. Sharma Institute for Research in Fundamental Sciences (IPM),
Tehran, Iran
S. Chenarani27, E. Eskandari Tadavani, S.M. Etesami27, M. Khakzad, M.
Mohammadi Najafabadi, M. Naseri, F. Rezaei Hosseinabadi University College
Dublin, Dublin, Ireland
M. Felcini, M. Grunewald INFN Sezione di Bari a, Università di Bari b,
Politecnico di Bari c, Bari, Italy
M. Abbresciaa,b, C. Calabriaa,b, A. Colaleoa, D. Creanzaa,c, L. Cristellaa,b,
N. De Filippisa,c, M. De Palmaa,b, A. Di Florioa,b, L. Fiorea, A. Gelmia,b, G.
Iasellia,c, M. Incea,b, S. Lezkia,b, G. Maggia,c, M. Maggia, G. Minielloa,b,
S. Mya,b, S. Nuzzoa,b, A. Pompilia,b, G. Pugliesea,c, R. Radognaa, A.
Ranieria, G. Selvaggia,b, L. Silvestrisa, R. Vendittia, P. Verwilligena INFN
Sezione di Bologna a, Università di Bologna b, Bologna, Italy
G. Abbiendia, C. Battilanaa,b, D. Bonacorsia,b, L. Borgonovia,b, S. Braibant-
Giacomellia,b, R. Campaninia,b, P. Capiluppia,b, A. Castroa,b, F.R. Cavalloa,
C. Cioccaa, G. Codispotia,b, M. Cuffiania,b, G.M. Dallavallea, F. Fabbria, A.
Fanfania,b, E. Fontanesi, P. Giacomellia, C. Grandia, L. Guiduccia,b, F.
Iemmia,b, S. Lo Meoa,28, S. Marcellinia, G. Masettia, F.L. Navarriaa,b, A.
Perrottaa, F. Primaveraa,b, A.M. Rossia,b, T. Rovellia,b, G.P. Sirolia,b, N.
Tosia INFN Sezione di Catania a, Università di Catania b, Catania, Italy
S. Albergoa,b,29, S. Costaa,b, A. Di Mattiaa, R. Potenzaa,b, A. Tricomia,b,29,
C. Tuvea,b INFN Sezione di Firenze a, Università di Firenze b, Firenze, Italy
G. Barbaglia, R. Ceccarelli, K. Chatterjeea,b, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, E. Focardia,b, G. Latino, P. Lenzia,b, M. Meschinia, S.
Paolettia, G. Sguazzonia, D. Stroma, L. Viliania INFN Laboratori Nazionali di
Frascati, Frascati, Italy
L. Benussi, S. Bianco, D. Piccolo INFN Sezione di Genova a, Università di
Genova b, Genova, Italy
M. Bozzoa,b, F. Ferroa, R. Mulargiaa,b, E. Robuttia, S. Tosia,b INFN Sezione
di Milano-Bicocca a, Università di Milano-Bicocca b, Milano, Italy
A. Benagliaa, A. Beschia,b, F. Brivioa,b, V. Cirioloa,b,16, S. Di Guidaa,b,16,
M.E. Dinardoa,b, P. Dinia, S. Fiorendia,b, S. Gennaia, A. Ghezzia,b, P.
Govonia,b, L. Guzzia,b, M. Malbertia, S. Malvezzia, D. Menascea, F. Montia,b,
L. Moronia, G. Ortonaa,b, M. Paganonia,b, D. Pedrinia, S. Ragazzia,b, T.
Tabarelli de Fatisa,b, D. Zuoloa,b INFN Sezione di Napoli a, Università di
Napoli ’Federico II’ b, Napoli, Italy, Università della Basilicata c, Potenza,
Italy, Università G. Marconi d, Roma, Italy
S. Buontempoa, N. Cavalloa,c, A. De Iorioa,b, A. Di Crescenzoa,b, F.
Fabozzia,c, F. Fiengaa, G. Galatia, A.O.M. Iorioa,b, L. Listaa,b, S.
Meolaa,d,16, P. Paoluccia,16, B. Rossia, C. Sciaccaa,b, E. Voevodinaa,b INFN
Sezione di Padova a, Università di Padova b, Padova, Italy, Università di
Trento c, Trento, Italy
P. Azzia, N. Bacchettaa, D. Biselloa,b, A. Bolettia,b, A. Bragagnolo, R.
Carlina,b, P. Checchiaa, P. De Castro Manzanoa, T. Dorigoa, U. Dossellia, F.
Gasparinia,b, U. Gasparinia,b, A. Gozzelinoa, S.Y. Hoh, P. Lujan, M.
Margonia,b, A.T. Meneguzzoa,b, J. Pazzinia,b, M. Presillab, P. Ronchesea,b, R.
Rossina,b, F. Simonettoa,b, A. Tiko, M. Tosia,b, M. Zanettia,b, P. Zottoa,b,
G. Zumerlea,b INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy
A. Braghieria, P. Montagnaa,b, S.P. Rattia,b, V. Rea, M. Ressegottia,b, C.
Riccardia,b, P. Salvinia, I. Vaia,b, P. Vituloa,b INFN Sezione di Perugia a,
Università di Perugia b, Perugia, Italy
M. Biasinia,b, G.M. Bileia, C. Cecchia,b, D. Ciangottinia,b, L. Fanòa,b, P.
Laricciaa,b, R. Leonardia,b, E. Manonia, G. Mantovania,b, V. Mariania,b, M.
Menichellia, A. Rossia,b, A. Santocchiaa,b, D. Spigaa INFN Sezione di Pisa a,
Università di Pisa b, Scuola Normale Superiore di Pisa c, Pisa, Italy
K. Androsova, P. Azzurria, G. Bagliesia, V. Bertacchia,c, L. Bianchinia, T.
Boccalia, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, G. Fedia, L.
Gianninia,c, A. Giassia, M.T. Grippoa, F. Ligabuea,c, E. Mancaa,c, G.
Mandorlia,c, A. Messineoa,b, F. Pallaa, A. Rizzia,b, G. Rolandi30, S. Roy
Chowdhury, A. Scribanoa, P. Spagnoloa, R. Tenchinia, G. Tonellia,b, N. Turini,
A. Venturia, P.G. Verdinia INFN Sezione di Roma a, Sapienza Università di Roma
b, Rome, Italy
F. Cavallaria, M. Cipriania,b, D. Del Rea,b, E. Di Marcoa,b, M. Diemoza, E.
Longoa,b, B. Marzocchia,b, P. Meridiania, G. Organtinia,b, F. Pandolfia, R.
Paramattia,b, C. Quarantaa,b, S. Rahatloua,b, C. Rovellia, F.
Santanastasioa,b, L. Soffia,b INFN Sezione di Torino a, Università di Torino
b, Torino, Italy, Università del Piemonte Orientale c, Novara, Italy
N. Amapanea,b, R. Arcidiaconoa,c, S. Argiroa,b, M. Arneodoa,c, N. Bartosika,
R. Bellana,b, C. Biinoa, A. Cappatia,b, N. Cartigliaa, S. Comettia, M.
Costaa,b, R. Covarellia,b, N. Demariaa, B. Kiania,b, C. Mariottia, S.
Masellia, E. Migliorea,b, V. Monacoa,b, E. Monteila,b, M. Montenoa, M.M.
Obertinoa,b, L. Pachera,b, N. Pastronea, M. Pelliccionia, G.L. Pinna
Angionia,b, A. Romeroa,b, M. Ruspaa,c, R. Sacchia,b, R. Salvaticoa,b, V.
Solaa, A. Solanoa,b, D. Soldia,b, A. Staianoa INFN Sezione di Trieste a,
Università di Trieste b, Trieste, Italy
S. Belfortea, V. Candelisea,b, M. Casarsaa, F. Cossuttia, A. Da Rolda,b, G.
Della Riccaa,b, F. Vazzolera,b, A. Zanettia Kyungpook National University,
Daegu, Korea
B. Kim, D.H. Kim, G.N. Kim, M.S. Kim, J. Lee, S.W. Lee, C.S. Moon, Y.D. Oh,
S.I. Pak, S. Sekmen, D.C. Son, Y.C. Yang Chonnam National University,
Institute for Universe and Elementary Particles, Kwangju, Korea
H. Kim, D.H. Moon, G. Oh Hanyang University, Seoul, Korea
B. Francois, T.J. Kim, J. Park Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, D. Gyun, S. Ha, B. Hong, K. Lee, K.S. Lee, J. Lim, J.
Park, S.K. Park, Y. Roh Kyung Hee University, Department of Physics
J. Goh Sejong University, Seoul, Korea
H.S. Kim Seoul National University, Seoul, Korea
J. Almond, J.H. Bhyun, J. Choi, S. Jeon, J. Kim, J.S. Kim, H. Lee, K. Lee, S.
Lee, K. Nam, M. Oh, S.B. Oh, B.C. Radburn-Smith, U.K. Yang, H.D. Yoo, I. Yoon,
G.B. Yu University of Seoul, Seoul, Korea
D. Jeon, H. Kim, J.H. Kim, J.S.H. Lee, I.C. Park, I. Watson Sungkyunkwan
University, Suwon, Korea
Y. Choi, C. Hwang, Y. Jeong, J. Lee, Y. Lee, I. Yu Riga Technical University,
Riga, Latvia
V. Veckalns31 Vilnius University, Vilnius, Lithuania
V. Dudenas, A. Juodagalvis, G. Tamulaitis, J. Vaitkus National Centre for
Particle Physics, Universiti Malaya, Kuala Lumpur, Malaysia
Z.A. Ibrahim, F. Mohamad Idris32, W.A.T. Wan Abdullah, M.N. Yusli, Z. Zolkapli
Universidad de Sonora (UNISON), Hermosillo, Mexico
J.F. Benitez, A. Castaneda Hernandez, J.A. Murillo Quijada, L. Valencia Palomo
Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico
H. Castilla-Valdez, E. De La Cruz-Burelo, I. Heredia-De La Cruz33, R. Lopez-
Fernandez, A. Sanchez-Hernandez Universidad Iberoamericana, Mexico City,
Mexico
S. Carrillo Moreno, C. Oropeza Barrera, M. Ramirez-Garcia, F. Vazquez Valencia
Benemerita Universidad Autonoma de Puebla, Puebla, Mexico
J. Eysermans, I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada Universidad
Autónoma de San Luis Potosí, San Luis Potosí, Mexico
A. Morelos Pineda University of Montenegro, Podgorica, Montenegro
N. Raicevic University of Auckland, Auckland, New Zealand
D. Krofcheck University of Canterbury, Christchurch, New Zealand
S. Bheesette, P.H. Butler National Centre for Physics, Quaid-I-Azam
University, Islamabad, Pakistan
A. Ahmad, M. Ahmad, Q. Hassan, H.R. Hoorani, W.A. Khan, M.A. Shah, M. Shoaib,
M. Waqas AGH University of Science and Technology Faculty of Computer Science,
Electronics and Telecommunications, Krakow, Poland
V. Avati, L. Grzanka, M. Malawski National Centre for Nuclear Research,
Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, M. Górski, M. Kazana, M. Szleper, P.
Zalewski Institute of Experimental Physics, Faculty of Physics, University of
Warsaw, Warsaw, Poland
K. Bunkowski, A. Byszuk34, K. Doroba, A. Kalinowski, M. Konecki, J.
Krolikowski, M. Misiura, M. Olszewski, A. Pyskir, M. Walczak Laboratório de
Instrumentação e Física Experimental de Partículas, Lisboa, Portugal
M. Araujo, P. Bargassa, D. Bastos, A. Di Francesco, P. Faccioli, B. Galinhas,
M. Gallinaro, J. Hollar, N. Leonardo, J. Seixas, K. Shchelina, G. Strong, O.
Toldaiev, J. Varela Joint Institute for Nuclear Research, Dubna, Russia
P. Bunin, M. Gavrilenko, A. Golunov, I. Golutvin, N. Gorbounov, I. Gorbunov,
A. Kamenev, V. Karjavine, V. Korenkov, A. Lanev, A. Malakhov, V. Matveev35,36,
P. Moisenz, V. Palichik, V. Perelygin, M. Savina, S. Shmatov, S. Shulha, V.
Trofimov, A. Zarubin Petersburg Nuclear Physics Institute, Gatchina (St.
Petersburg), Russia
L. Chtchipounov, V. Golovtsov, Y. Ivanov, V. Kim37, E. Kuznetsova38, P.
Levchenko, V. Murzin, V. Oreshkin, I. Smirnov, D. Sosnov, V. Sulimov, L.
Uvarov, A. Vorobyev Institute for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov,
N. Krasnikov, A. Pashenkov, D. Tlisov, A. Toropin Institute for Theoretical
and Experimental Physics named by A.I. Alikhanov of NRC ‘Kurchatov Institute’,
Moscow, Russia
V. Epshteyn, V. Gavrilov, N. Lychkovskaya, A. Nikitenko39, V. Popov, I.
Pozdnyakov, G. Safronov, A. Spiridonov, A. Stepennov, M. Toms, E. Vlasov, A.
Zhokin Moscow Institute of Physics and Technology, Moscow, Russia
T. Aushev National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
M. Chadeeva40, P. Parygin, D. Philippov, E. Popova, V. Rusinov P.N. Lebedev
Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin, I. Dremin, M. Kirakosyan, A. Terkulov Skobeltsyn
Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow,
Russia
A. Belyaev, E. Boos, V. Bunichev, M. Dubinin41, L. Dudko, A. Gribushin, V.
Klyukhin, O. Kodolova, I. Lokhtin, S. Obraztsov, M. Perfilov, S. Petrushanko,
V. Savrin Novosibirsk State University (NSU), Novosibirsk, Russia
A. Barnyakov42, V. Blinov42, T. Dimova42, L. Kardapoltsev42, Y. Skovpen42
Institute for High Energy Physics of National Research Centre ‘Kurchatov
Institute’, Protvino, Russia
I. Azhgirey, I. Bayshev, S. Bitioukov, V. Kachanov, D. Konstantinov, P.
Mandrik, V. Petrov, R. Ryutin, S. Slabospitskii, A. Sobol, S. Troshin, N.
Tyurin, A. Uzunian, A. Volkov National Research Tomsk Polytechnic University,
Tomsk, Russia
A. Babaev, A. Iuzhakov, V. Okhotnikov Tomsk State University, Tomsk, Russia
V. Borchsh, V. Ivanchenko, E. Tcherniaev University of Belgrade: Faculty of
Physics and VINCA Institute of Nuclear Sciences
P. Adzic43, P. Cirkovic, D. Devetak, M. Dordevic, P. Milenovic, J. Milosevic,
M. Stojanovic Centro de Investigaciones Energéticas Medioambientales y
Tecnológicas (CIEMAT), Madrid, Spain
M. Aguilar-Benitez, J. Alcaraz Maestre, A. Álvarez Fernández, I. Bachiller, M.
Barrio Luna, J.A. Brochero Cifuentes, C.A. Carrillo Montoya, M. Cepeda, M.
Cerrada, N. Colino, B. De La Cruz, A. Delgado Peris, C. Fernandez Bedoya, J.P.
Fernández Ramos, J. Flix, M.C. Fouz, O. Gonzalez Lopez, S. Goy Lopez, J.M.
Hernandez, M.I. Josa, D. Moran, Á. Navarro Tobar, A. Pérez-Calero Yzquierdo,
J. Puerta Pelayo, I. Redondo, L. Romero, S. Sánchez Navas, M.S. Soares, A.
Triossi, C. Willmott Universidad Autónoma de Madrid, Madrid, Spain
C. Albajar, J.F. de Trocóniz Universidad de Oviedo, Instituto Universitario de
Ciencias y Tecnologías Espaciales de Asturias (ICTEA), Oviedo, Spain
B. Alvarez Gonzalez, J. Cuevas, C. Erice, J. Fernandez Menendez, S. Folgueras,
I. Gonzalez Caballero, J.R. González Fernández, E. Palencia Cortezon, V.
Rodríguez Bouza, S. Sanchez Cruz Instituto de Física de Cantabria (IFCA),
CSIC-Universidad de Cantabria, Santander, Spain
I.J. Cabrillo, A. Calderon, B. Chazin Quero, J. Duarte Campderros, M.
Fernandez, P.J. Fernández Manteca, A. García Alonso, G. Gomez, C. Martinez
Rivero, P. Martinez Ruiz del Arbol, F. Matorras, J. Piedra Gomez, C. Prieels,
T. Rodrigo, A. Ruiz-Jimeno, L. Russo44, L. Scodellaro, N. Trevisani, I. Vila,
J.M. Vizan Garcia University of Colombo, Colombo, Sri Lanka
K. Malagalage University of Ruhuna, Department of Physics, Matara, Sri Lanka
W.G.D. Dharmaratna, N. Wickramage CERN, European Organization for Nuclear
Research, Geneva, Switzerland
D. Abbaneo, B. Akgun, E. Auffray, G. Auzinger, J. Baechler, P. Baillon, A.H.
Ball, D. Barney, J. Bendavid, M. Bianco, A. Bocci, E. Bossini, C. Botta, E.
Brondolin, T. Camporesi, A. Caratelli, G. Cerminara, E. Chapon, G. Cucciati,
D. d’Enterria, A. Dabrowski, N. Daci, V. Daponte, A. David, O. Davignon, A. De
Roeck, N. Deelen, M. Deile, M. Dobson, M. Dünser, N. Dupont, A. Elliott-
Peisert, F. Fallavollita45, D. Fasanella, G. Franzoni, J. Fulcher, W. Funk, S.
Giani, D. Gigi, A. Gilbert, K. Gill, F. Glege, M. Gruchala, M. Guilbaud, D.
Gulhan, J. Hegeman, C. Heidegger, Y. Iiyama, V. Innocente, P. Janot, O.
Karacheban19, J. Kaspar, J. Kieseler, M. Krammer1, C. Lange, P. Lecoq, C.
Lourenço, L. Malgeri, M. Mannelli, A. Massironi, F. Meijers, J.A. Merlin, S.
Mersi, E. Meschi, F. Moortgat, M. Mulders, J. Ngadiuba, S. Nourbakhsh, S.
Orfanelli, L. Orsini, F. Pantaleo16, L. Pape, E. Perez, M. Peruzzi, A.
Petrilli, G. Petrucciani, A. Pfeiffer, M. Pierini, F.M. Pitters, D. Rabady, A.
Racz, M. Rovere, H. Sakulin, C. Schäfer, C. Schwick, M. Selvaggi, A. Sharma,
P. Silva, W. Snoeys, P. Sphicas46, J. Steggemann, V.R. Tavolaro, D. Treille,
A. Tsirou, A. Vartak, M. Verzetti, W.D. Zeuner Paul Scherrer Institut,
Villigen, Switzerland
L. Caminada47, K. Deiters, W. Erdmann, R. Horisberger, Q. Ingram, H.C.
Kaestli, D. Kotlinski, U. Langenegger, T. Rohe, S.A. Wiederkehr ETH Zurich -
Institute for Particle Physics and Astrophysics (IPA), Zurich, Switzerland
M. Backhaus, P. Berger, N. Chernyavskaya, G. Dissertori, M. Dittmar, M.
Donegà, C. Dorfer, T.A. Gómez Espinosa, C. Grab, D. Hits, T. Klijnsma, W.
Lustermann, R.A. Manzoni, M. Marionneau, M.T. Meinhard, F. Micheli, P.
Musella, F. Nessi-Tedaldi, F. Pauss, G. Perrin, L. Perrozzi, S. Pigazzini, M.
Reichmann, C. Reissel, T. Reitenspiess, D. Ruini, D.A. Sanz Becerra, M.
Schönenberger, L. Shchutska, M.L. Vesterbacka Olsson, R. Wallny, D.H. Zhu
Universität Zürich, Zurich, Switzerland
T.K. Aarrestad, C. Amsler48, D. Brzhechko, M.F. Canelli, A. De Cosa, R. Del
Burgo, S. Donato, B. Kilminster, S. Leontsinis, V.M. Mikuni, I. Neutelings, G.
Rauco, P. Robmann, D. Salerno, K. Schweiger, C. Seitz, Y. Takahashi, S. Wertz,
A. Zucchetta National Central University, Chung-Li, Taiwan
T.H. Doan, C.M. Kuo, W. Lin, A. Roy, S.S. Yu National Taiwan University (NTU),
Taipei, Taiwan
P. Chang, Y. Chao, K.F. Chen, P.H. Chen, W.-S. Hou, Y.y. Li, R.-S. Lu, E.
Paganis, A. Psallidas, A. Steen Chulalongkorn University, Faculty of Science,
Department of Physics, Bangkok, Thailand
B. Asavapibhop, C. Asawatangtrakuldee, N. Srimanobhas, N. Suwonjandee Çukurova
University, Physics Department, Science and Art Faculty, Adana, Turkey
A. Bat, F. Boran, S. Cerci49, S. Damarseckin50, Z.S. Demiroglu, F. Dolek, C.
Dozen, I. Dumanoglu, G. Gokbulut, EmineGurpinar Guler51, Y. Guler, I. Hos52,
C. Isik, E.E. Kangal53, O. Kara, A. Kayis Topaksu, U. Kiminsu, M. Oglakci, G.
Onengut, K. Ozdemir54, S. Ozturk55, A.E. Simsek, D. Sunar Cerci49, U.G. Tok,
S. Turkcapar, I.S. Zorbakir, C. Zorbilmez Middle East Technical University,
Physics Department, Ankara, Turkey
B. Isildak56, G. Karapinar57, M. Yalvac Bogazici University, Istanbul, Turkey
I.O. Atakisi, E. Gülmez, M. Kaya58, O. Kaya59, B. Kaynak, Ö. Özçelik, S.
Tekten, E.A. Yetkin60 Istanbul Technical University, Istanbul, Turkey
A. Cakir, K. Cankocak, Y. Komurcu, S. Sen61 Istanbul University, Istanbul,
Turkey
S. Ozkorucuklu Institute for Scintillation Materials of National Academy of
Science of Ukraine, Kharkov, Ukraine
B. Grynyov National Scientific Center, Kharkov Institute of Physics and
Technology, Kharkov, Ukraine
L. Levchuk University of Bristol, Bristol, United Kingdom
F. Ball, E. Bhal, S. Bologna, J.J. Brooke, D. Burns, E. Clement, D. Cussans,
H. Flacher, J. Goldstein, G.P. Heath, H.F. Heath, L. Kreczko, S. Paramesvaran,
B. Penning, T. Sakuma, S. Seif El Nasr-Storey, D. Smith, V.J. Smith, J.
Taylor, A. Titterton Rutherford Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev62, C. Brew, R.M. Brown, D. Cieri, D.J.A. Cockerill, J.A.
Coughlan, K. Harder, S. Harper, J. Linacre, K. Manolopoulos, D.M. Newbold, E.
Olaiya, D. Petyt, T. Reis, T. Schuh, C.H. Shepherd-Themistocleous, A. Thea,
I.R. Tomalin, T. Williams, W.J. Womersley Imperial College, London, United
Kingdom
R. Bainbridge, P. Bloch, J. Borg, S. Breeze, O. Buchmuller, A. Bundock,
GurpreetSingh CHAHAL63, D. Colling, P. Dauncey, G. Davies, M. Della Negra, R.
Di Maria, P. Everaerts, G. Hall, G. Iles, T. James, M. Komm, C. Laner, L.
Lyons, A.-M. Magnan, S. Malik, A. Martelli, V. Milosevic, J. Nash64, V.
Palladino, M. Pesaresi, D.M. Raymond, A. Richards, A. Rose, E. Scott, C. Seez,
A. Shtipliyski, M. Stoye, T. Strebler, S. Summers, A. Tapper, K. Uchida, T.
Virdee16, N. Wardle, D. Winterbottom, J. Wright, A.G. Zecchinelli, S.C. Zenz
Brunel University, Uxbridge, United Kingdom
J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, C.K. Mackay, A. Morton, I.D. Reid,
L. Teodorescu, S. Zahid Baylor University, Waco, USA
K. Call, J. Dittmann, K. Hatakeyama, C. Madrid, B. McMaster, N. Pastika, C.
Smith Catholic University of America, Washington, DC, USA
R. Bartek, A. Dominguez, R. Uniyal The University of Alabama, Tuscaloosa, USA
A. Buccilli, S.I. Cooper, C. Henderson, P. Rumerio, C. West Boston University,
Boston, USA
D. Arcaro, T. Bose, Z. Demiragli, D. Gastler, S. Girgis, D. Pinna, C.
Richardson, J. Rohlf, D. Sperka, I. Suarez, L. Sulak, D. Zou Brown University,
Providence, USA
G. Benelli, B. Burkle, X. Coubez, D. Cutts, Y.t. Duh, M. Hadley, J. Hakala, U.
Heintz, J.M. Hogan65, K.H.M. Kwok, E. Laird, G. Landsberg, J. Lee, Z. Mao, M.
Narain, S. Sagir66, R. Syarif, E. Usai, D. Yu University of California, Davis,
Davis, USA
R. Band, C. Brainerd, R. Breedon, M. Calderon De La Barca Sanchez, M. Chertok,
J. Conway, R. Conway, P.T. Cox, R. Erbacher, C. Flores, G. Funk, F. Jensen, W.
Ko, O. Kukral, R. Lander, M. Mulhearn, D. Pellett, J. Pilot, M. Shi, D. Stolp,
D. Taylor, K. Tos, M. Tripathi, Z. Wang, F. Zhang University of California,
Los Angeles, USA
M. Bachtis, C. Bravo, R. Cousins, A. Dasgupta, A. Florent, J. Hauser, M.
Ignatenko, N. Mccoll, W.A. Nash, S. Regnard, D. Saltzberg, C. Schnaible, B.
Stone, V. Valuev University of California, Riverside, Riverside, USA
K. Burt, R. Clare, J.W. Gary, S.M.A. Ghiasi Shirazi, G. Hanson, G.
Karapostoli, E. Kennedy, O.R. Long, M. Olmedo Negrete, M.I. Paneva, W. Si, L.
Wang, H. Wei, S. Wimpenny, B.R. Yates, Y. Zhang University of California, San
Diego, La Jolla, USA
J.G. Branson, P. Chang, S. Cittolin, M. Derdzinski, R. Gerosa, D. Gilbert, B.
Hashemi, D. Klein, V. Krutelyov, J. Letts, M. Masciovecchio, S. May, S. Padhi,
M. Pieri, V. Sharma, M. Tadel, F. Würthwein, A. Yagil, G. Zevi Della Porta
University of California, Santa Barbara - Department of Physics, Santa
Barbara, USA
N. Amin, R. Bhandari, C. Campagnari, M. Citron, V. Dutta, M. Franco Sevilla,
L. Gouskos, J. Incandela, B. Marsh, H. Mei, A. Ovcharova, H. Qu, J. Richman,
U. Sarica, D. Stuart, S. Wang, J. Yoo California Institute of Technology,
Pasadena, USA
D. Anderson, A. Bornheim, O. Cerri, I. Dutta, J.M. Lawhorn, N. Lu, J. Mao,
H.B. Newman, T.Q. Nguyen, J. Pata, M. Spiropulu, J.R. Vlimant, S. Xie, Z.
Zhang, R.Y. Zhu Carnegie Mellon University, Pittsburgh, USA
M.B. Andrews, T. Ferguson, T. Mudholkar, M. Paulini, M. Sun, I. Vorobiev, M.
Weinberg University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, A. Johnson, E. MacDonald, T. Mulholland, R. Patel, A.
Perloff, K. Stenson, K.A. Ulmer, S.R. Wagner Cornell University, Ithaca, USA
J. Alexander, J. Chaves, Y. Cheng, J. Chu, A. Datta, A. Frankenthal, K.
Mcdermott, N. Mirman, J.R. Patterson, D. Quach, A. Rinkevicius67, A. Ryd, S.M.
Tan, Z. Tao, J. Thom, P. Wittich, M. Zientek Fermi National Accelerator
Laboratory, Batavia, USA
S. Abdullin, M. Albrow, M. Alyari, G. Apollinari, A. Apresyan, A. Apyan, S.
Banerjee, L.A.T. Bauerdick, A. Beretvas, J. Berryhill, P.C. Bhat, K. Burkett,
J.N. Butler, A. Canepa, G.B. Cerati, H.W.K. Cheung, F. Chlebana, M. Cremonesi,
J. Duarte, V.D. Elvira, J. Freeman, Z. Gecse, E. Gottschalk, L. Gray, D.
Green, S. Grünendahl, O. Gutsche, AllisonReinsvold Hall, J. Hanlon, R.M.
Harris, S. Hasegawa, R. Heller, J. Hirschauer, B. Jayatilaka, S. Jindariani,
M. Johnson, U. Joshi, B. Klima, M.J. Kortelainen, B. Kreis, S. Lammel, J.
Lewis, D. Lincoln, R. Lipton, M. Liu, T. Liu, J. Lykken, K. Maeshima, J.M.
Marraffino, D. Mason, P. McBride, P. Merkel, S. Mrenna, S. Nahn, V. O’Dell, V.
Papadimitriou, K. Pedro, C. Pena, G. Rakness, F. Ravera, L. Ristori, B.
Schneider, E. Sexton-Kennedy, N. Smith, A. Soha, W.J. Spalding, L. Spiegel, S.
Stoynev, J. Strait, N. Strobbe, L. Taylor, S. Tkaczyk, N.V. Tran, L. Uplegger,
E.W. Vaandering, C. Vernieri, M. Verzocchi, R. Vidal, M. Wang, H.A. Weber
University of Florida, Gainesville, USA
D. Acosta, P. Avery, P. Bortignon, D. Bourilkov, A. Brinkerhoff, L. Cadamuro,
A. Carnes, V. Cherepanov, D. Curry, F. Errico, R.D. Field, S.V. Gleyzer, B.M.
Joshi, M. Kim, J. Konigsberg, A. Korytov, K.H. Lo, P. Ma, K. Matchev, N.
Menendez, G. Mitselmakher, D. Rosenzweig, K. Shi, J. Wang, S. Wang, X. Zuo
Florida International University, Miami, USA
Y.R. Joshi Florida State University, Tallahassee, USA
T. Adams, A. Askew, S. Hagopian, V. Hagopian, K.F. Johnson, R. Khurana, T.
Kolberg, G. Martinez, T. Perry, H. Prosper, C. Schiber, R. Yohay, J. Zhang
Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, V. Bhopatkar, M. Hohlmann, D. Noonan, M. Rahmani, M. Saunders,
F. Yumiceva University of Illinois at Chicago (UIC), Chicago, USA
M.R. Adams, L. Apanasevich, D. Berry, R.R. Betts, R. Cavanaugh, X. Chen, S.
Dittmer, O. Evdokimov, C.E. Gerber, D.A. Hangal, D.J. Hofman, K. Jung, C.
Mills, T. Roy, M.B. Tonjes, N. Varelas, H. Wang, X. Wang, Z. Wu The University
of Iowa, Iowa City, USA
M. Alhusseini, B. Bilki51, W. Clarida, K. Dilsiz68, S. Durgut, R.P.
Gandrajula, M. Haytmyradov, V. Khristenko, O.K. Köseyan, J.-P. Merlo, A.
Mestvirishvili69, A. Moeller, J. Nachtman, H. Ogul70, Y. Onel, F. Ozok71, A.
Penzo, C. Snyder, E. Tiras, J. Wetzel Johns Hopkins University, Baltimore, USA
B. Blumenfeld, A. Cocoros, N. Eminizer, D. Fehling, L. Feng, A.V. Gritsan,
W.T. Hung, P. Maksimovic, J. Roskes, M. Swartz, M. Xiao The University of
Kansas, Lawrence, USA
C. Baldenegro Barrera, P. Baringer, A. Bean, S. Boren, J. Bowen, A. Bylinkin,
T. Isidori, S. Khalil, J. King, G. Krintiras, A. Kropivnitskaya, C. Lindsey,
D. Majumder, W. Mcbrayer, N. Minafra, M. Murray, C. Rogan, C. Royon, S.
Sanders, E. Schmitz, J.D. Tapia Takaki, Q. Wang, J. Williams, G. Wilson Kansas
State University, Manhattan, USA
S. Duric, A. Ivanov, K. Kaadze, D. Kim, Y. Maravin, D.R. Mendis, T. Mitchell,
A. Modak, A. Mohammadi Lawrence Livermore National Laboratory, Livermore, USA
F. Rebassoo, D. Wright University of Maryland, College Park, USA
A. Baden, O. Baron, A. Belloni, S.C. Eno, Y. Feng, N.J. Hadley, S. Jabeen,
G.Y. Jeng, R.G. Kellogg, J. Kunkle, A.C. Mignerey, S. Nabili, F. Ricci-Tam, M.
Seidel, Y.H. Shin, A. Skuja, S.C. Tonwar, K. Wong Massachusetts Institute of
Technology, Cambridge, USA
D. Abercrombie, B. Allen, A. Baty, R. Bi, S. Brandt, W. Busza, I.A. Cali, M.
D’Alfonso, G. Gomez Ceballos, M. Goncharov, P. Harris, D. Hsu, M. Hu, M.
Klute, D. Kovalskyi, Y.-J. Lee, P.D. Luckey, B. Maier, A.C. Marini, C. Mcginn,
C. Mironov, S. Narayanan, X. Niu, C. Paus, D. Rankin, C. Roland, G. Roland, Z.
Shi, G.S.F. Stephans, K. Sumorok, K. Tatar, D. Velicanu, J. Wang, T.W. Wang,
B. Wyslouch University of Minnesota, Minneapolis, USA
A.C. Benvenuti${}^{\textrm{\textdagger}}$, R.M. Chatterjee, A. Evans, S. Guts,
P. Hansen, J. Hiltbrand, Sh. Jain, S. Kalafut, Y. Kubota, Z. Lesko, J. Mans,
R. Rusack, M.A. Wadud University of Mississippi, Oxford, USA
J.G. Acosta, S. Oliveros University of Nebraska-Lincoln, Lincoln, USA
K. Bloom, D.R. Claes, C. Fangmeier, L. Finco, F. Golf, R. Gonzalez Suarez, R.
Kamalieddin, I. Kravchenko, J.E. Siado, G.R. Snow, B. Stieger State University
of New York at Buffalo, Buffalo, USA
G. Agarwal, C. Harrington, I. Iashvili, A. Kharchilava, C. Mclean, D. Nguyen,
A. Parker, J. Pekkanen, S. Rappoccio, B. Roozbahani Northeastern University,
Boston, USA
G. Alverson, E. Barberis, C. Freer, Y. Haddad, A. Hortiangtham, G. Madigan,
D.M. Morse, T. Orimoto, L. Skinnari, A. Tishelman-Charny, T. Wamorkar, B.
Wang, A. Wisecarver, D. Wood Northwestern University, Evanston, USA
S. Bhattacharya, J. Bueghly, T. Gunter, K.A. Hahn, N. Odell, M.H. Schmitt, K.
Sung, M. Trovato, M. Velasco University of Notre Dame, Notre Dame, USA
R. Bucci, N. Dev, R. Goldouzian, M. Hildreth, K. Hurtado Anampa, C. Jessop,
D.J. Karmgard, K. Lannon, W. Li, N. Loukas, N. Marinelli, I. Mcalister, F.
Meng, C. Mueller, Y. Musienko35, M. Planer, R. Ruchti, P. Siddireddy, G.
Smith, S. Taroni, M. Wayne, A. Wightman, M. Wolf, A. Woodard The Ohio State
University, Columbus, USA
J. Alimena, B. Bylsma, L.S. Durkin, S. Flowers, B. Francis, C. Hill, W. Ji, A.
Lefeld, T.Y. Ling, B.L. Winer Princeton University, Princeton, USA
S. Cooperstein, G. Dezoort, P. Elmer, J. Hardenbrook, N. Haubrich, S.
Higginbotham, A. Kalogeropoulos, S. Kwan, D. Lange, M.T. Lucchini, J. Luo, D.
Marlow, K. Mei, I. Ojalvo, J. Olsen, C. Palmer, P. Piroué, J. Salfeld-Nebgen,
D. Stickland, C. Tully, Z. Wang University of Puerto Rico, Mayaguez, USA
S. Malik, S. Norberg Purdue University, West Lafayette, USA
A. Barker, V.E. Barnes, S. Das, L. Gutay, M. Jones, A.W. Jung, A. Khatiwada,
B. Mahakud, D.H. Miller, G. Negro, N. Neumeister, C.C. Peng, S. Piperov, H.
Qiu, J.F. Schulte, J. Sun, F. Wang, R. Xiao, W. Xie Purdue University
Northwest, Hammond, USA
T. Cheng, J. Dolen, N. Parashar Rice University, Houston, USA
K.M. Ecklund, S. Freed, F.J.M. Geurts, M. Kilpatrick, Arun Kumar, W. Li, B.P.
Padley, R. Redjimi, J. Roberts, J. Rorie, W. Shi, A.G. Stahl Leiton, Z. Tu, A.
Zhang University of Rochester, Rochester, USA
A. Bodek, P. de Barbaro, R. Demina, J.L. Dulemba, C. Fallon, T. Ferbel, M.
Galanti, A. Garcia-Bellido, J. Han, O. Hindrichs, A. Khukhunaishvili, E.
Ranken, P. Tan, R. Taus Rutgers, The State University of New Jersey,
Piscataway, USA
B. Chiarito, J.P. Chou, A. Gandrakota, Y. Gershtein, E. Halkiadakis, A. Hart,
M. Heindl, E. Hughes, S. Kaplan, S. Kyriacou, I. Laflotte, A. Lath, R.
Montalvo, K. Nash, M. Osherson, H. Saka, S. Salur, S. Schnetzer, D. Sheffield,
S. Somalwar, R. Stone, S. Thomas, P. Thomassen University of Tennessee,
Knoxville, USA
H. Acharya, A.G. Delannoy, J. Heideman, G. Riley, S. Spanier Texas A&M
University, College Station, USA
O. Bouhali72, A. Celik, M. Dalchenko, M. De Mattia, A. Delgado, S. Dildick, R.
Eusebi, J. Gilmore, T. Huang, T. Kamon73, S. Luo, D. Marley, R. Mueller, D.
Overton, L. Perniè, D. Rathjens, A. Safonov Texas Tech University, Lubbock,
USA
N. Akchurin, J. Damgov, F. De Guio, S. Kunori, K. Lamichhane, S.W. Lee, T.
Mengke, S. Muthumuni, T. Peltola, S. Undleeb, I. Volobouev, Z. Wang, A.
Whitbeck Vanderbilt University, Nashville, USA
S. Greene, A. Gurrola, R. Janjam, W. Johns, C. Maguire, A. Melo, H. Ni, K.
Padeken, F. Romeo, P. Sheldon, S. Tuo, J. Velkovska, M. Verweij University of
Virginia, Charlottesville, USA
M.W. Arenton, P. Barria, B. Cox, G. Cummings, R. Hirosky, M. Joyce, A.
Ledovskoy, C. Neu, B. Tannenwald, Y. Wang, E. Wolfe, F. Xia Wayne State
University, Detroit, USA
R. Harr, P.E. Karchin, N. Poudyal, J. Sturdy, P. Thapa, S. Zaleski University
of Wisconsin - Madison, Madison, WI, USA
J. Buchanan, C. Caillol, D. Carlsmith, S. Dasu, I. De Bruyn, L. Dodd, F.
Fiori, C. Galloni, B. Gomber74, M. Herndon, A. Hervé, U. Hussain, P. Klabbers,
A. Lanaro, A. Loeliger, K. Long, R. Loveless, J. Madhusudanan Sreekala, T.
Ruggles, A. Savin, V. Sharma, W.H. Smith, D. Teague, S. Trembath-reichert, N.
Woods †: Deceased
1: Also at Vienna University of Technology, Vienna, Austria
2: Also at IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
3: Also at Universidade Estadual de Campinas, Campinas, Brazil
4: Also at Federal University of Rio Grande do Sul, Porto Alegre, Brazil
5: Also at UFMS, Nova Andradina, Brazil
6: Also at Universidade Federal de Pelotas, Pelotas, Brazil
7: Also at Université Libre de Bruxelles, Bruxelles, Belgium
8: Also at University of Chinese Academy of Sciences, Beijing, China
9: Also at Institute for Theoretical and Experimental Physics named by A.I.
Alikhanov of NRC ‘Kurchatov Institute’, Moscow, Russia
10: Also at Joint Institute for Nuclear Research, Dubna, Russia
11: Also at Cairo University, Cairo, Egypt
12: Also at Zewail City of Science and Technology, Zewail, Egypt
13: Also at Purdue University, West Lafayette, USA
14: Also at Université de Haute Alsace, Mulhouse, France
15: Also at Erzincan Binali Yildirim University, Erzincan, Turkey
16: Also at CERN, European Organization for Nuclear Research, Geneva,
Switzerland
17: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
18: Also at University of Hamburg, Hamburg, Germany
19: Also at Brandenburg University of Technology, Cottbus, Germany
20: Also at Institute of Physics, University of Debrecen, Debrecen, Hungary,
Debrecen, Hungary
21: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary
22: Also at MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary, Budapest, Hungary
23: Also at IIT Bhubaneswar, Bhubaneswar, India, Bhubaneswar, India
24: Also at Institute of Physics, Bhubaneswar, India
25: Also at Shoolini University, Solan, India
26: Also at University of Visva-Bharati, Santiniketan, India
27: Also at Isfahan University of Technology, Isfahan, Iran
28: Also at Italian National Agency for New Technologies, Energy and
Sustainable Economic Development, Bologna, Italy
29: Also at Centro Siciliano di Fisica Nucleare e di Struttura Della Materia,
Catania, Italy
30: Also at Scuola Normale e Sezione dell’INFN, Pisa, Italy
31: Also at Riga Technical University, Riga, Latvia, Riga, Latvia
32: Also at Malaysian Nuclear Agency, MOSTI, Kajang, Malaysia
33: Also at Consejo Nacional de Ciencia y Tecnología, Mexico City, Mexico
34: Also at Warsaw University of Technology, Institute of Electronic Systems,
Warsaw, Poland
35: Also at Institute for Nuclear Research, Moscow, Russia
36: Now at National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
37: Also at St. Petersburg State Polytechnical University, St. Petersburg,
Russia
38: Also at University of Florida, Gainesville, USA
39: Also at Imperial College, London, United Kingdom
40: Also at P.N. Lebedev Physical Institute, Moscow, Russia
41: Also at California Institute of Technology, Pasadena, USA
42: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia
43: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia
44: Also at Università degli Studi di Siena, Siena, Italy
45: Also at INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy,
Pavia, Italy
46: Also at National and Kapodistrian University of Athens, Athens, Greece
47: Also at Universität Zürich, Zurich, Switzerland
48: Also at Stefan Meyer Institute for Subatomic Physics, Vienna, Austria,
Vienna, Austria
49: Also at Adiyaman University, Adiyaman, Turkey
50: Also at Şırnak University, Sirnak, Turkey
51: Also at Beykent University, Istanbul, Turkey, Istanbul, Turkey
52: Also at Istanbul Aydin University, Istanbul, Turkey
53: Also at Mersin University, Mersin, Turkey
54: Also at Piri Reis University, Istanbul, Turkey
55: Also at Gaziosmanpasa University, Tokat, Turkey
56: Also at Ozyegin University, Istanbul, Turkey
57: Also at Izmir Institute of Technology, Izmir, Turkey
58: Also at Marmara University, Istanbul, Turkey
59: Also at Kafkas University, Kars, Turkey
60: Also at Istanbul Bilgi University, Istanbul, Turkey
61: Also at Hacettepe University, Ankara, Turkey
62: Also at School of Physics and Astronomy, University of Southampton,
Southampton, United Kingdom
63: Also at IPPP Durham University, Durham, United Kingdom
64: Also at Monash University, Faculty of Science, Clayton, Australia