B2G-20-008

B2G-20-008

[cern]The CMS Collaboration

# Search for heavy resonances decaying to
$\PZ(\PGn\PAGn)\PV(\PQq\PAQq^{\prime})$ in proton-proton collisions at
$\sqrt{s}=13\TeV$

###### Abstract

A search is presented for heavy bosons decaying to ()($\PQq\PAQq^{\prime}$),
where can be a or a boson. A sample of proton-proton collision data at
$\sqrt{s}=13\TeV$ was collected by the CMS experiment during 2016–2018. The
data correspond to an integrated luminosity of 137. The event categorization
is based on the presence of high-momentum jets in the forward region to
identify production through weak vector boson fusion. Additional
categorization uses jet substructure techniques and the presence of large
missing transverse momentum to identify and bosons decaying to quarks and
neutrinos, respectively. The dominant standard model backgrounds are estimated
using data taken from control regions. The results are interpreted in terms of
radion, boson, and graviton models, under the assumption that these bosons are
produced via gluon-gluon fusion, Drell–Yan, or weak vector boson fusion
processes. No evidence is found for physics beyond the standard model. Upper
limits are set at 95% confidence level on various types of hypothetical new
bosons. Observed (expected) exclusion limits on the masses of these bosons
range from 1.2 to 4.0 (1.1 to 3.7).

##  0.1 Introduction

The search for physics beyond the standard model (SM) using proton-proton
($\Pp\Pp$) collisions produced by the CERN LHC is a key goal of the CMS
physics program. The apparent large hierarchy between the electroweak scale
and the Planck scale, the nature of dark matter, and the possibility of a
unification of the gauge couplings at high energies, are among the outstanding
problems in particle physics not addressed within the SM. New bosons are
predicted in many beyond-the-SM theories, which attempt to answer some of
these questions. New spin-0 and spin-2 particles are predicted in the
Randall–Sundrum (RS) models of warped extra dimensions [1, 2], which arise
from radion and graviton fields. Spin-1 and bosons can also arise in these
models [3, 4, 5], as well as in left-right symmetric theories [6], and little
Higgs models [7, 8, 9]. When the interaction strength between a new boson and
the SM boson field is large, such as in the bulk RS models [10, 11], searches
for diboson resonances are motivated, either via weak vector boson fusion
(VBF) or strong production processes.

This paper presents a search for new bosons () decaying to a pair of bosons or
to a and a boson as shown in Fig. 1. In addition to VBF production, we
consider the gluon-gluon fusion (ggF) production of spin-0 and spin-2
particles and the Drell–Yan (DY) production of spin-1 particles. The targeted
final state is one in which one boson decays to a neutrino pair, while the
other vector boson decays hadronically. Events are categorized by the presence
or absence of forward jets. Events that include forward jets are typical of
VBF production. Events without forward jets more commonly occur in ggF or DY
processes. For sufficiently heavy new bosonic states, the decaying vector
bosons will have high momenta and, when decaying hadronically, will appear in
the detector as single jets. As a result, we employ jet substructure
techniques to identify these objects. Events are selected that have a high-
mass jet and a significant amount of missing transverse momentum, , which is
the signature of the $\PZ\to\PGn{}\PAGn$ decay. Contributions from the
dominant backgrounds are determined using control samples in data. Proton-
proton collisions at $\sqrt{s}=13\TeV$ recorded during 2016–2018 with the CMS
detector are analyzed. The data set corresponds to an integrated luminosity of
137. Searches for similar signatures have been presented by the ATLAS [12] and
CMS [13] Collaborations. As compared with earlier CMS publications, this paper
includes additional data and extends the analysis to new search regions by
including the forward jets, thus providing improved sensitivity to VBF-
produced resonances.

Figure 1: Representative Feynman diagrams for various production modes of a
heavy resonance . These modes are: a ggF-produced spin-0 or spin-2 resonance
decaying to $\PZ\PZ\to\qqbar\PGn\PAGn$ (left), a DY-produced spin-1 resonance
decaying to $\PW\PZ\to\qqbar^{\prime}\PGn\PAGn$ (center), and a VBF-produced
spin-1 resonance decaying to $\PW\PZ\to\qqbar^{\prime}\PGn\PAGn$ (right).

This paper is organized as follows. Section 0.2 gives a brief description of
the CMS detector and its components relevant to this work. Section 0.3
summarizes the simulated data sets used in this analysis. Algorithms for the
reconstruction of physics objects and the selection criteria applied to these
objects are described in Sections 0.4 and 0.5, respectively. Section 0.6
discusses methods used for estimating the SM backgrounds. The systematic
uncertainties relevant to this analysis are described in Section 0.7. The
final results are presented in Section 0.8, along with their statistical
interpretations. A summary of the work is presented in Section 0.9. Tabulated
results are provided in the HEPData record [14] for this analysis.

##  0.2 The CMS detector

The central feature of the CMS detector 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, and a brass and scintillator hadron calorimeter.
Each of these systems is composed of a barrel and two endcap sections. The
tracking detectors cover the pseudorapidity range $\abs{\eta}<2.5$. For the
electromagnetic and hadronic calorimeters, the barrel and endcap detectors
together cover the range $\abs{\eta}<3.0$. Forward calorimeters extend the
coverage to $\abs{\eta}<5.0$. Muons are measured and identified in both barrel
and endcap systems, which together cover the pseudorapidity range
$\abs{\eta}<2.4$. The detection planes are based on three technologies: drift
tubes, cathode strip chambers, and resistive-plate chambers, which are
embedded in the steel flux-return yoke outside the solenoid. The detector is
nearly hermetic, permitting accurate measurements of . Events of interest are
selected using a two-tiered trigger system [15, 16]. The first level, composed
of custom hardware processors, uses information from the calorimeters and muon
detectors to select events at a rate of around 100kHz within a fixed time
interval of less than 4. The second level, known as the high-level trigger,
consists of a farm of processors running a version of the full event
reconstruction software optimized for fast processing, and reduces the event
rate to around 1kHz before data storage. 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. [17].

##  0.3 Simulated event samples

This search uses Monte Carlo (MC) simulated data sets to study and guide the
estimations of SM background processes. Simulated data sets include: +jets,
where refers to or bosons, +jets, single top quark samples, diboson and
triboson samples ($\PW\PZ$, $\PW\PZ\PZ$, etc.), and +samples. Events in these
samples for 2016 (2017 and 2018) are generated using v2.2.2 (v2.4.2) [18, 19,
20, 21]. The +jets (+jets) samples are generated with leading order (LO)
precision and contain up to four (three) additional partons. The $t$-channel
and $\cPqt\PW$ single top quark events, and $\PW\PW$ events for 2016 (2017 and
2018) are generated using v1.0 (v2.0) [22, 23, 24, 25, 26]. All other SM
background samples are generated using at next-to-LO (NLO) precision. The
signal samples used for interpretation of the results are also generated using
with LO precision.

The parton showering and hadronization step in all simulations is performed
with two versions of [27]. The 2016 (2017 and 2018) simulation uses the
CUETP8M1 [28] (CP5 [29]) underlying event tune with PYTHIA v8.212 (v8.230).
The parton distribution functions (PDFs) used in the 2016 (2017 and 2018)
simulated data samples are NNPDF3.0 LO or NLO [30] (NNPDF3.1 NNLO [31]). The
simulation of the particle interactions and the CMS detector is performed with
[32]. The effects due to additional $\Pp\Pp$ interactions in the same or
adjacent bunch crossings (pileup) are also simulated and the events are
weighted to match the pileup distribution in data.

The cross sections used to normalize different SM processes correspond to NLO
or next-to-NLO (NNLO) accuracy [25, 26, 18, 33, 34, 35, 36, 37, 38, 39, 40,
41]. The +jets samples are weighted based on the of the and bosons. Weights
are derived from comparisons of LO simulations and simulations at NNLO
precision in quantum chromodynamics (QCD) interactions and NLO precision in
electroweak interactions [42].

Figure 2: Simulated distributions are shown for the cosine of the decay angle
of SM vector bosons in the rest frame of a parent particle with a mass
($m_{\PX}$) of 2. Solid lines represent VBF scenarios. Dashed lines represent
ggF/DY scenarios.

When diboson signatures are produced through VBF, correlations between the
spin of initial- and final-state bosons cause event kinematic distributions to
depend on the hypothesized resonance spin. These correlations are manifested
in the decay angle, $\theta^{*}$, of the vector bosons, defined in Ref. [43].
Figure 2 shows the distribution of the cosine of the decay angles in the rest
frame of for each spin configuration and production mechanism we consider,
given a parent particle mass of 2. Scalar resonances have no correlation
between initial- and final-state spins, which translates to a flat
$\cos\theta^{*}$ distribution. Spin-1 and spin-2 particles can have strong
correlations, especially when produced via VBF, where vector bosons are
preferentially produced in the forward direction ($\cos\theta^{*}=\pm 1$).
Such correlations are caused by the production of longitudinally or
transversely polarized vector bosons [44]. The decay angle is correlated with
the transverse momentum () of the resonance’s decay products. Thus, the
spectral features explored here are different from typical diboson searches
because the final state is only partially reconstructed. We explore the
sensitivity of the analysis to the assumed spin by considering three different
production models: radions (spin 0), bosons (spin 1), and gravitons (spin 2).
In each case, we consider both VBF and ggF or DY production, and masses
between 1.0and 4.5in a step of 200 (500)for masses less (greater) than 2.0.

To interpret the data in terms of spin-0 and spin-2 signals, radions and
gravitons arising from models of warped extra dimensions [1, 2], and
specifically from bulk scenarios [10, 11], are used as representative models.
The warped extra dimension model has several free parameters, including the
mass of the lowest excited state of the graviton, the radion mass, the
parameter $kr_{c}\pi$, where $k$ and $r_{c}$ are the curvature and the
compactification scale of the extra dimension respectively, the dimensionless
coupling $\widetilde{k}=k/\overline{M_{\mathrm{Pl}}}$, where
$\overline{M_{\mathrm{Pl}}}$ is the reduced Planck mass, and the energy scale
associated with radion interactions ($\Lambda_{\mathrm{R}}$). These parameters
and the corresponding cross sections are described in Ref. [44]. We assume
$\widetilde{k}=0.5$, $kr_{c}\pi=35$, and $\Lambda_{\mathrm{R}}=3\TeV$, which
results in decay widths for gravitons and radions smaller than 10% of their
mass. The QCD predicted theoretical cross section is accurate to NLO (LO) for
radions and gravitons produced via ggF (VBF).

To interpret data in terms of spin-1 signals, bosons within the heavy vector
triplet (HVT) framework [6] are used. The interactions between bosons and SM
particles are parameterized in terms of $c_{\mathrm{H}}$, $g_{\mathrm{V}}$,
and $c_{\mathrm{F}}$ couplings and the mass of the HVT boson. Together with
the SM $\mathrm{SU}(2)_{\mathrm{L}}$ gauge coupling, $g$, these parameters
determine the couplings between the HVT bosons and the SM Higgs boson, SM
vector bosons, and SM fermions, respectively, according to Ref. [6]. We
consider two specific cases of these model parameters: those corresponding to
model B ($c_{\mathrm{H}}=-0.98$, $g_{\mathrm{V}}=3$, and
$c_{\mathrm{F}}=1.02$) in Ref. [6] and model C
($c_{\mathrm{H}}=g_{\mathrm{V}}=1$ and $c_{\mathrm{F}}=0$). Model B suppresses
the fermion couplings, enhancing decays to SM vector bosons. Model C removes
direct couplings to fermions, ensuring that only VBF production is possible.
The QCD prediced theoretical cross section is accurate to LO for both DY and
VBF produced bosons.

##  0.4 Triggers and event reconstruction

Samples of collision data are selected using several triggers whose thresholds
vary between 90 and 140, depending on the data-taking period. The trigger
efficiencies were studied using samples of events selected with single lepton
triggers. The lepton triggers isolate events with neutrinos produced via boson
decays, which serve as a good proxy for both the signal and the dominant
backgrounds. The single lepton triggers required electrons with
$\pt>27\text{--}32\GeV$, depending on the data-taking period, or muons with
$\pt>24\GeV$. The efficiencies of the triggers for selecting events with large
($>$200) were measured as functions of . The differences between the
efficiencies determined using electron events and muon events were found to be
less than 1.4% and are assigned as systematic uncertainties. For all data-
taking periods, the trigger efficiency is found to reach 95% for $\ptmiss\geq
250\GeV$ and plateaus around 98% for $\ptmiss\geq 300\GeV$. At
$\ptmiss=200\GeV$, the efficiency is found to be greater than 75%. The
measured trigger efficiencies are applied to the simulation data as event
weights.

The event reconstruction proceeds from particles identified by the particle-
flow (PF) algorithm [45], which uses information from the silicon tracking
system, calorimeters, and muon systems to reconstruct PF candidates as
electrons, muons, charged or neutral hadrons, or photons. Charged-particle
tracks are required to originate from the event primary $\Pp\Pp$ interaction
vertex, defined as the candidate vertex with the largest value of summed
physics-object $\pt^{2}$. The physics objects used for calculating $\pt^{2}$
are jets, clustered using the anti-jet finding algorithm [46, 47] with the
tracks assigned to candidate vertices as inputs, and the associated , taken as
the negative vector sum of those jets.

Electrons are reconstructed by associating a charged-particle track with an
electromagnetic calorimeter supercluster [48]. The resulting electron
candidates are required to have $\pt>10\GeV$ and $\abs{\eta}<2.5$, and to
satisfy identification criteria designed to reject light-parton jets, photon
conversions, and electrons produced in the decays of heavy-flavor hadrons.
Muons are reconstructed by associating tracks in the muon system with those
found in the silicon tracker [49]. Muon candidates are required to satisfy
$\pt>10\GeV$ and $\abs{\eta}<2.4$.

Leptons (, ) are required to be isolated from other PF candidates to select
preferentially those originating from and boson decays and suppress
backgrounds in which leptons are produced in the decays of hadrons containing
heavy quarks. Isolation is quantified using an optimized version of the mini-
isolation variable originally introduced in Ref. [50]. The isolation variable,
$I_{\text{mini}}$, is calculated by summing the $\pt$ of the charged and
neutral hadrons, and photons with $\Delta
R\equiv\sqrt{\smash[b]{(\Delta\phi)^{2}+(\Delta\eta)^{2}}}<R_{0}$ of the
lepton momentum vector, $\vec{p}^{\kern 1.5pt\Pell}$, where $\phi$ is the
azimuthal angle and $R_{0}$ depends on the lepton . The three values of
$R_{0}$ used are: 0.2 for $\pt^{\Pell}\leq 50\GeV$; $(10\GeV)/\pt^{\Pell}$ for
$50<\pt^{\Pell}<200\GeV$; and 0.05 for $\pt^{\Pell}\geq 200\GeV$. Electrons
(muons) are then required to satisfy $I_{\text{mini}}/\pt^{\Pell}<0.1\,(0.2)$.
In order to mitigate the effects from pileup in an event, charged hadron
candidates are required to originate from the primary vertex of the given
event.

Events are vetoed in which muon or electron candidates are identified that
satisfy isolated track requirements, $\abs{\eta}<2.4$, and $\pt>5\GeV$), but
do not satisfy the kinematic requirements just described for isolated leptons.
Hadronically decaying leptons can produce isolated hadron tracks. Events in
which an isolated hadron track is identified that satisfies $\abs{\eta}<2.4$
and $\pt>10\GeV$ are also vetoed.

Events with one or more photons are vetoed. Photon candidates are required to
have $\pt>100\GeV$, $\abs{\eta}<2.4$, and be isolated from neutral hadrons,
charged hadrons, and electromagnetic particles, excluding the photon candidate
itself. The isolation criterion is calculated using a cone with $\Delta R<0.2$
around the photon [51].

Jets are reconstructed by clustering charged and neutral PF candidates using
the anti-algorithm [46]. Two collections of jets are considered clustered with
distance parameters $R=0.4$ and 0.8, as implemented in the package [47].
Depending on the distance parameter used, these jets are referred to as AK4
and AK8 jets, respectively. Jet energies are corrected using a - and
$\eta$-dependent jet energy calibration [52]. To mitigate contributions to jet
reconstruction from pileup, we utilize the charged-hadron subtraction method
for AK4 jets and the pileup-per-particle identification (PUPPI) method [53,
54] for AK8 jets.

Jets are required to satisfy $\pt>30\,(200)\GeV$, $\abs{\eta}<5.0\,(2.4)$, and
jet quality criteria [55, 56] for AK4 (AK8) jets. The mass of AK8 jets and
information about the pattern of energy deposited in various detectors are
used to reconstruct massive vector bosons. To further reduce the dependence on
pileup and to help reduce the effect of wide-angle soft radiation, the soft-
drop algorithm [57, 58] is used to remove constituents from the AK8 jets.
Corrections are applied to AK8 jets to ensure that the reconstructed jet mass
reproduces the pole masses of the SM bosons. Corrections are derived using
bosons from production to account for known differences between measured and
simulated jet mass scale and jet mass resolution [59]. Finally, jets having PF
constituents matched to an isolated lepton are removed from the jet
collection.

The $N$-subjettiness [60] technique is used to distinguish between AK8 jets
originating from the hadronic decay of or bosons and those originating from
QCD. In hadronic boson decays, the resulting jet is likely to have two
substructure components, which results in a smaller $N$-subjettiness ratio,
$\tau_{21}$. For QCD jets, $\tau_{21}$ tends to be higher. We require AK8 jets
to satisfy $\tau_{21}<0.75$. There is a loss of about 36% in background events
due to this requirement. Negligible loss of efficiency due to this requirement
in a signal sample of VBF-produced gravitons (VBFG) with a mass of 1is
observed. The efficiency for tagging bosons in simulated data sets is
validated against collision data using events [59]. Scale factors are applied
to simulated data sets to account for observed differences.

The AK4 jets are tagged as originating from the hadronization of quarks using
the deep combined secondary vertex algorithm [61]. For the medium working
point chosen here, the signal efficiency for identifying jets with
$\pt>30\GeV$ in $\ttbar$ events is about 68%. The probability of
misidentifying jets in $\ttbar$ events arising from quarks is approximately
12%, while the probability of misidentifying jets associated with light-flavor
quarks or gluons as jets is approximately 1%.

The vector is defined as the negative vector sum of all PF candidates and is
calibrated taking into account the jet energy corrections. Its magnitude is
denoted by . Dedicated event filters designed to reject instrumental noise are
applied to improve the correspondence between the reconstructed and the
genuine [62]. To suppress SM backgrounds, we use the quantity , defined as the
transverse mass of the system consisting of the highest AK8 jet
($\pt^{\mathrm{J}}$) and . The value of is computed as

$\mT=\sqrt{{2\pt^{\mathrm{J}}\ptmiss[1-\cos\Delta\Phi]}},$ (1)

where $\Delta\Phi$ is the difference between the azimuthal angle of the AK8
jet’s momentum and . The variable in Eq. 1 assumes that the new state decays
into much lighter daughter particles.

##  0.5 Event selection

The reconstructed objects (defined in Section 0.4) are used to define signal
regions (SRs) and control regions (CRs). These are all subsets of a baseline
phase space where at least one AK8 jet and $\ptmiss>200\GeV$ are required.
Events are rejected if they contain a reconstructed electron, muon, photon,
isolated track, or an AK4 jet which has been identified as a jet. Events are
vetoed if any jet passes the and $\eta$ requirements, but fails the jet
quality criteria. Events are also vetoed if is aligned with the transverse
projection of any of the four highest AK4 jets, defined as
$\Delta\phi(j_{i},\ptmiss)<0.5$.

The baseline phase space is split into two sets of regions, VBF and ggF/DY.
The VBF regions require at least two AK4 jets, each required to be separated
by $\Delta R>0.8$ from the AK8 jet. The two highest-AK4 jets are also required
to be reconstructed in opposite hemispheres ($\eta_{1}\eta_{2}<0$), have a
large pseudorapidity separation ($\Delta\eta>4.0$), and form a large invariant
mass ($m_{jj}>500\GeV$). These requirements are indicative of VBF production.
If any of these conditions is failed, the event is put into the ggF/DY
category.

Both the VBF and ggF/DY categories are further separated into high-purity (HP)
and low-purity (LP) categories, depending on the highest AK8 jet’s value of
$\tau_{21}$. Events in which the highest AK8 jet satisfies $\tau_{21}<0.35$
($0.35<\tau_{21}<0.75$) are referred to as HP (LP). The signal (VBFG 1)
selection efficiency with the high (low) purity cut is 69 (31)%. The
background selection efficiency with the high (low) purity cut is 32 (68)%.

Each of these four event categories is divided into a number of bins. Starting
from $\mT=400\GeV$, each bin has width 100. In the ggF/DY (VBF) HP category,
the bin width is constant until 2200 (1600), after which, bin boundaries
correspond to $\mT=$ 2200, 2350, 2550, 2750, and 3000 (1600, 1750, and 2075).
In the ggF/DY (VBF) LP category, the bin width is constant until 2900 (2300),
after which bin widths are 200until $\mT=3500$ (2700). The last bin in each of
the categories includes all events with above the final quoted bin boundary.

In all bins, an SR and a CR is defined based on the mass of the highest AK8
jet, $m_{\mathrm{J}}$. The SRs require $65<m_{\mathrm{J}}<105\GeV$, a range
which is chosen to accept both and bosons, but reject most hadronically
decaying Higgs bosons. The CRs require either $30<m_{\mathrm{J}}<65\GeV$ or
$135<m_{\mathrm{J}}<300\GeV$, which excludes the SR mass requirement and a
window around the Higgs boson mass. The event selections used in the analysis
are summarized in Table 0.5.

Summary of the event selections. ll Variable & Selection
$>$200
Veto electrons, muons, tau leptons, photons, jets
AK4 jet $>$30
$\Delta\phi(\ptvec^{\text{jets}},\ptvecmiss)$ $>$0.5
Leading AK8 jet and $\eta$ $>$200and $\abs{\eta}<2.4$
Leading AK8 jet mass SR: 65–105, CR: 30–65or 135–300
$\tau_{21}$ HP: $<$0.35, LP: 0.35–0.75
Forward jets $(\eta_{1}\eta_{2})<0$, $\Delta\eta>4.0$, $m_{\text{jj}}>500\GeV$

The dominant backgrounds are events originating from +jets and +jets
production, followed by the $\PW\to\Pell\PGn$ and $\PZ\to\PGn\PAGn$ decays.
These occur when the charged leptons do not pass the reconstruction
requirements described in Section 0.4, while the neutrinos manifest themselves
as . There are no massive, hadronically decaying vector bosons in these
processes; the massive reconstructed jet typically arises from the tail of the
jet mass distribution for single quarks and gluons and is typically a smoothly
falling distribution. We refer to these and other processes with similar
kinematic properties as nonresonant backgrounds. The shape and normalization
of nonresonant backgrounds are constrained using data, as described in Section
0.6.

Subdominant SM contributions come from , single top quark, and diboson
processes. These processes typically have a hadronically decaying vector
boson. There is a small contribution from the diboson events produced via the
vector boson scattering process. These events are expected to contribute less
than 10% of the total diboson event yield in the VBF SRs. The contributions
from subdominant backgrounds, referred to as resonant backgrounds, are
estimated using predictions from simulation.

The reconstructed distribution for the signal depends on the spin, mass, and
production mechanism. Using events from all signal regions and control regions
combined, Fig. 3 shows the distributions for the various signal hypotheses
considered. These distributions for VBF-produced and graviton resonances are a
reflection of the prevalence for vector bosons production at large $\eta$ due
to correlations between $\eta$ and $\cos\theta^{*}$. When vector bosons have
large $\eta$, the AK8 jet and the are lower, and in turn, the reconstructed is
lower. For models with multimodal $\cos{}\theta^{*}$ distributions, there is a
corresponding distortion in the distribution, which tends to produce higher
yields at lower values of .

Figure 3: Distributions of for ggF/DY- (left) and VBF-produced (right)
resonances of mass 4.5. Events used are from all SR and CR combined.

##  0.6 Background estimation method

The nonresonant backgrounds populate both the CRs and the SRs, while signal
events mainly populate the SRs. The yields observed in the CRs are weighted by
a transfer factor, $\alpha$, to account for known differences between the SR
and CR kinematic properties. The transfer factor is derived from simulated
data sets. Algebraically, the predictions of the nonresonant backgrounds are
given by

$\begin{gathered}N_{\text{pred}}^{\text{non-
res}}=\alpha(N_{\text{CR}}^{\text{obs}}-N_{\text{CR}}^{\text{res}}),\\\
\alpha=\frac{N_{\text{SR}}^{\text{non-res}}}{N_{\text{CR}}^{\text{non-
res}}},\end{gathered}$ (2)

where $N_{\text{CR}}^{\text{obs}}$ refers to the observed CR yields, and
$N^{\text{non-res}}$ ($N^{\text{res}}$) refers to the nonresonant (resonant)
background yields in simulated data sets. In this formula,
$N_{\text{*}}^{\text{res}}$ serves the role of removing the expected
contributions from resonant backgrounds, which have a systematically different
transfer factor and whose predictions are handled separately. All event yields
are derived separately for each bin and each event category. Figure 4 shows
$\alpha$ as a function of in each of the event categories.

Figure 4: The distributions of the transfer factors ($\alpha$) versus in the
various event categories are shown. The last bin corresponds to the value
obtained by integrating events above the penultimate bin.

The resonant backgrounds are directly estimated using predictions from
simulation, with systematic uncertainties evaluated to account for potential
data mismodeling. The resonant background yields are used to account for
contamination in the CRs before predicting the nonresonant backgrounds and
their contribution to the SRs themselves.

The procedures used to predict SM backgrounds have been validated using data
from a subset of each CR, which we refer to as the validation SR (vSR). The
vSRs have the same selections as the SRs except that the jet mass must satisfy
$55<m_{\mathrm{J}}<65\GeV$. The CRs for validation tests are redefined by
removing the events in the vSR. The vSR and analogous CRs are used to compute
the analog of $\alpha$, and the full background prediction is evaluated on
data. The predicted yields in the vSR are then compared to the observed event
yields. This comparison is performed in the corresponding vSR of each of the
event categories. The resulting distributions are shown in Fig. 5.

Figure 5: Comparison of background estimations and observations in the high-
purity ggF/DY (upper left), high-purity VBF (upper right), low-purity ggF/DY
(lower left), and low-purity VBF (lower right) validation signal regions. The
lower panel shows the ratio of the estimated and the observed event yields.
The hashed band in the ratio represents the total uncertainty in the
corresponding SR. The red line (lower left) is a fit to the ratio of
prediction to the data in the LP ggF/DY vSR.

The vSR tests show an over-prediction of data in each of the event categories.
The over-prediction is as large as 10% in the lowest bin. To account for any
potential mismodeling of our prediction, we derive a shape uncertainty. The
uncertainty is based on a linear fit to the ratio of the prediction and the
observation versus in the LP ggF/DY vSR. Based on the fit, an uncertainty is
assessed that corresponds to 7% in the lowest bin, and decreases linearly to
-40% in the highest bin. This shape uncertainty is applied to each of the four
signal regions assuming no correlation between the regions.

##  0.7 Systematic uncertainties

Because the nonresonant backgrounds are estimated from data, several
simulation-related systematic uncertainties have little to no effect on the
estimation of these backgrounds. However, PDF uncertainties, renormalization
($\mu_{\mathrm{R}}$) and factorization ($\mu_{\mathrm{F}}$) scale
uncertainties, and jet energy scale (JES) and resolution (JER) uncertainties
have nonnegligible effects on $\alpha$.

For PDF uncertainties, the distribution of $\alpha$ is evaluated for each
recommended PDF variation [30, 31]. An envelope of these variations is used to
assess an $\alpha$ shape uncertainty. The size of the uncertainty in a given
bin due to PDFs is as large as 3 (1.5)% for the VBF (ggF/DY) categories. The
effects of the scale uncertainties are evaluated by varying the values of
$\mu_{\mathrm{R}}$ and $\mu_{\mathrm{F}}$ simultaneously up and down by a
factor of two. Based on scale variations, an $\alpha$ shape uncertainty is
determined, which is less than 2% in any single bin. The JES and JER
uncertainties are propagated to $\alpha$, and are not larger than 3%. A
summary of all systematic uncertainties related to the nonresonant background
prediction is shown in the second and third columns of Table 0.7.

In addition to those listed in Table 0.7, we account for uncertainties due to
the limited size of simulated data sets. The Poisson uncertainties associated
with the observed CR yields are propagated to the nonresonant background
predictions in each bin. The uncertainties due to the limited size of
simulated data sets are treated as uncorrelated across each analysis bin.

The predicted resonant background and signal yield uncertainties are evaluated
from a larger list of potential sources. These sources include the integrated
luminosity, $\tau_{21}$ scale factors, pileup modeling, jet veto efficiency,
effects related to inefficiencies due to instrumental effects of the
electromagnetic calorimeter trigger (prefiring), modeling of unclustered
energy in the calculation of , jet mass scale (JMS) and resolution (JMR), JES
and JER, trigger efficiency modeling, PDF uncertainties, and scale
uncertainties. A summary of the impacts these uncertainties have on resonant
backgrounds and signal is shown in the fourth through seventh columns of Table
0.7 and in Table 0.7. A description of each uncertainty source is provided
below.

The measured integrated luminosity uncertainty is propagated to the prediction
of the resonant background and signal yields. This uncertainty amounts to 1.6%
on the entire data set [63, 64, 65] and is correlated across all signal
regions. The effects of the luminosity uncertainty on signal and nonresonant
backgrounds are treated as fully correlated.

Uncertainties associated with the determination of $\tau_{21}$ efficiency
scale factors that correct for systematic differences in simulated and
collision data sets are propagated to the predicted signal and resonant
background yields. These uncertainties have two components: one that affects
the normalization of predictions ($\tau_{21}$ SF) and another that accounts
for -dependent differences ($\tau_{21}$ extrap.) in the tagging efficiency,
which affects the shape of our predicted yields.

Jet energy and jet mass scales are varied within their uncertainties. Effects
of JMS uncertainties are treated as anticorrelated between SR and CR regions
and correlated across all categories. The magnitudes of JMS uncertainties are
assumed to be independent of . They are treated as fully correlated across all
bins and all regions.

Jet masses are smeared to broaden their distribution within measured JMR
uncertainties. Jet mass smearing is done for both SR and CR but for simulated
data sets only. The effect of jet smearing was at most 10%. The effects of JMR
uncertainties are correlated across bins and all categories, and between
signals and resonant backgrounds.

Similarly to the nonresonant background, the effect of JES and JER
uncertainties are propagated to the predicted event yields. These are found to
have minimal impact on the shape ($<$1%) within a given category but can cause
events to migrate from the ggF/DY to the VBF categories.

Other systematic uncertainties affect the normalization of resonant
backgrounds and signals. These include pileup uncertainties, tagging scale
factor uncertainties, prefiring corrections, unclustered energy scale
uncertainties, and trigger uncertainties. These effects are assumed to be
correlated across various categories, and between signal and resonant
backgrounds.

Finally, the statistical uncertainties due to the limited size of simulated
data sets are propagated to all predicted signal and resonant background
yields. In this analysis, all the uncertainties quoted are pre-fit values.

Summary of systematic uncertainties (in %) related to the SM background
predictions in various regions. Columns two and three tabulate the
representative size of effects on $\alpha$ in the VBF and ggF/DY events
categories, respectively. Columns four through seven tabulate the typical size
of effects on the prediction of resonant background yields in the VBF SR, VBF
CR, ggF/DY SR, and ggF/DY CR, respectively. All of these numbers are the pre-
fit values. For some systematic uncertainties, the variation in different bins
are shown as a range. Values of LP that are different from those of HP are
shown in parentheses. lcccccc Source $\alpha_{\text{VBF}}$
$\alpha_{\text{ggF/DY}}$ VBF SR VBF CR ggF/DY SR ggF/DY CR
Luminosity 1.6 1.6 1.6 1.6
$\tau_{21}$ SF 7.3 (17.0) 7.3 (17.0) 7.3 (17.0) 7.3 (17.0)
$\tau_{21}$ extrap. 2–18 (1–10) 2–18 (1–10) 2–18 (1–10) 2–18 (1–10)
Pileup 3.9 (3.9) 2.0 (3.7) 1.0 (0.8) 1.7 (0.9)
jet veto 2.4 (2.9) 3.5 (3.2) 1.8 (2.2) 2.8 (2.6)
Prefiring 0.7 (0.6) 0.8 (0.7) 0.3 (0.2) 0.4 (0.3)
Unclust. energy 2.4 (1.6) 1.8 (1.4) 1.8 (1.3) 1.6 (1.5)
JMS 0.5 (0.4) 1.8 (0.5) 0.3 (0.3) 1.6 (0.4)
JMR 1.2 (1.6) 5.9 (0.7) 1.7 (1.01) 7.1 (0.96)
Trigger 1.4 1.4 1.4 1.4
JES 3.0 1–2 40–13 40–13 4.0 4.0
JER 3.0 1.5 35–13 35–13 2.0 2.0
PDF norm. 5.0 5.0 2.0 2.0
PDF shape 3.0 1.5 0.5–4 0.5–4 0.5–4 0.5–4
$\mu_{\mathrm{R}},\ \mu_{\mathrm{F}}$ norm. 15 15 11 12
$\mu_{\mathrm{R}},\ \mu_{\mathrm{F}}$ shape 1–2 1–2 1–10 2–4 3–8 3–4
Nonclosure 7–40 7–40

Summary of the typical size of systematic uncertainties (in %) in the
predicted signal yields in various regions. All of these numbers are the pre-
fit values. A range is given for the shape systematic uncertainties. Values of
LP that are different from those of HP are shown in parentheses. lcccc Source
VBF SR VBF CR ggF/DY SR ggF/DY CR
Luminosity 1.6 1.6 1.6 1.6
$\tau_{21}$ SF 7.3 (17.0) 7.3 (17.0) 7.3 (17.0) 7.3 (17.0)
$\tau_{21}$ extrap. 2–18 (1–10) 2–18 (1–10) 2–18 (1–10) 2–18 (1–10)
Pileup 0.2 (0.5) 0.7 (1.0) 1.1 (0.9) 1.1 (0.6)
jet veto 1.3 (1.4) 1.4 (1.6) 1.3 (1.4) 1.3 (1.5)
Prefiring 1.0 (0.9) 1.0 (1.0) 0.6 (0.5) 0.6 (0.5)
Unclust. energy 0.1 0.1 0.1 0.1
JMS 0.9 (0.3) 2.6 (1.9) 0.7 (0.3) 2.4 (1.5)
JMR 3.2 (2.8) 6.9 (4.0) 3.3 (2.4) 7.5 (3.4)
Trigger 1.4 (1.4) 1.4 (1.5) 1.4 (1.6) 1.4 (1.5)
JES 5–11 4–14 1–7 1–5
JER 2–3 6–7 1–7 1–5

##  0.8 Results and interpretations

The final predicted event yields are computed using a simultaneous maximum
likelihood fit of event yields in all bins, and all SRs and CRs. Each bin is
modeled as a marked Poisson model [66] with mean value corresponding to the
sum of expected yields for resonant and nonresonant backgrounds, and signal.
An unconstrained nuisance parameter is implemented to allow for the fit to
independently adjust the nonresonant background in each bin of each category.
For each bin of each event category, this constraint is fully correlated
between the SR and CR. Systematic uncertainties are implemented using log-
normal priors. The likelihood is parameterized in terms of the signal strength
$\mu$, which is the ratio of the measured signal cross section and the
theoretical cross section.

The predicted event yields for all backgrounds and a graviton
($m_{\PXXG}=1\TeV$) are shown in Fig. 6 (7) for the CR (SR). The data are
compared to post-fit predictions, where fit refers to constraints on
predictions and their uncertainties based on a maximum likelihood fit to data
in which the signal strength is fixed to $\mu=0$. The post-fit predictions and
observations are consistent within the uncertainties, suggesting no evidence
of new sources of diboson production.

Figure 6: Distributions of for high-purity ggF/DY (upper left) and VBF (upper
right), and low-purity ggF/DY (lower left) and VBF (lower right) CR events
after performing background-only fits. The last bin in the upper left, upper
right, lower left, and lower right plot corresponds to the yields integrated
above 3, 2.3, 3.5, and 2.7, respectively. The top panel of each plot shows the
post-fit prediction, represented by filled histograms, compared to observed
yields, represented by black points. Both the ggF and VBF-produced 1graviton
signals are shown in each plot, represented by the open purple and red
histograms, respectively. The signal is normalized to 10fb. The blue hashed
area represents the total uncertainty from the post-fit predicted event yield
as a function of . The middle panel of each plot shows the ratio of data and
post-fit predictions in blue. The bottom panel of each plot shows the
difference between the observed event yields and the post-fit predictions
normalized by the quadratic sum of the statistical uncertainty of the observed
yield and the total uncertainty from the post-fit prediction in each bin.

Figure 7: Distribution of the predicted and observed event yields versus for
high-purity ggF/DY (upper left) and VBF (upper right), and low-purity ggF/DY
(lower left) and VBF (lower right) SR events. The last bin in each plot
corresponds to the yields integrated above the penultimate bin. The top panel
of each plot shows the prediction based on a background-only fit to data,
represented by filled histograms, compared to observed yields, represented by
black points. Both the ggF and VBF-produced 1graviton signals are shown in
each plot, represented by the open purple and red histograms, respectively.
The signal is normalized to 10fb. The middle panel of each plot shows the
ratio of data and post-fit predictions in blue. The blue hashed area
represents the total uncertainty from the post-fit predicted event yield as a
function of . The bottom panel of each plot shows the difference between the
observed event yields and the post-fit predictions normalized by the quadratic
sum of the statistical uncertainty of the observed yield and the total
uncertainty from the post-fit prediction in each bin.

We derive both expected and observed 95% confidence level () upper limits on
the $\PX\to\PV(\PQq\PAQq)\PZ(\PGn\PAGn)$ production cross section. A test
statistic is used in conjunction with the criterion [67] to set upper limits.
The test statistic is defined as
$q_{\mu}=-2\ln(\mathcal{L}_{\mu}/\mathcal{L}_{\text{max}})$, where $\
\mathcal{L}_{\text{max}}$ refers to the maximum value of the likelihood when
all parameters are varied and $\mathcal{L}_{\mu}$ refers to the likelihood
obtained by varying $\mu$ while profiling all the other parameters conditioned
on its value. Upper limits are computed using the asymptotic approximation
[68]. Expected limits are computed by evaluating the test statistic using the
post-fit predicted numbers of background events and their uncertainties.

Figure 8: Expected and observed 95% upper limits on the product of the radion
(R) production cross section and the ${\HepParticle{R}{}{}}\to\PZ\PZ$
branching fraction versus the radion mass are shown as dashed and solid black
lines, respectively. Green and yellow bands, respectively, represent the 68%
and 95% confidence intervals of the expected limit. The red curves show the
product of the theoretical radion production cross sections and their
branching fractions to . The hashed red areas represent the theoretical cross
section uncertainty due to limited knowledge of PDFs and scale choices. Limits
and theory cross sections for ggF-produced radions are shown in the left
figure, while the right figure shows the same for VBF-produced radions.

Figure 9: Expected and observed 95% upper limits on the product of the
production cross section and the $\PWpr\to\PW\PZ$ branching fraction versus
the mass are shown as dashed and solid black lines, respectively. Green and
yellow bands, respectively, represent the 68% and 95% confidence intervals of
the expected limit. The red curves show the product of the theoretical boson
production cross sections and their branching fractions to . The hashed red
areas represent the theoretical cross section uncertainty due to limited
knowledge of PDFs and scale choices. Limits and theory cross sections for DY-
produced bosons are shown in the left figure, while the right figure shows the
same for VBF-produced bosons. The grey curves in the left plot show the
previous CMS results with 36of data.

Figure 10: Expected and observed 95% upper limits on the product of the
graviton () production cross section and the $\PXXG\to\PZ\PZ$ branching
fraction versus the graviton mass are shown as dashed and solid black lines,
respectively. Green and yellow bands, respectively, represent 68% and 95%
confidence intervals of the expected limit. The red curves show the product of
the theoretical graviton production cross sections and their branching
fractions to . The hashed red areas represent the theoretical cross section
uncertainty due to limited knowledge of PDFs and scale choices. Limits and
theory cross sections for ggF-produced gravitons are shown in the left figure,
while the right figure shows the same for VBF-produced gravitons. The grey
curves in the left plot show the previous CMS results with 36of data.

Upper limits on the product of radion production cross sections and their
branching fraction to versus the radion mass are shown in Fig. 8. Limits are
computed assuming radions are produced entirely either through the ggF process
or the VBF process. The expected (observed) radion mass exclusion limits are
2.5 (3.0)for ggF-produced states. Figure 9 shows the expected and observed
upper limits on the product of boson production cross sections and their
branching fraction to , assuming exclusive production through the DY (model B)
or VBF process (model C). The expected and observed mass exclusion limits for
DY-produced resonances are found to be 3.7 and 4.0, respectively. This is an
improvement of 0.6 (0.4)in the observed (expected) mass exclusion limit from
the previous CMS result. Finally, the upper limits on the product of graviton
production cross sections and their branching fraction to are shown in Fig.
10. Limits are computed assuming gravitons are produced entirely either
through the ggF process or the VBF process. The expected (observed) graviton
mass exclusion limit is found to be 1.1 (1.2), assuming gravitons are produced
exclusively through the ggF mechanism. This is the first mass exclusion limits
set by CMS on a ggF-produced gravitons in this final state. For the VBF-
produced models considered here, no masses are excluded. The observed upper
limits on $\sigma{\mathcal{B}(\PX\to\PV\PZ)}$ vary between 0.2 and 9fb for
radions, 0.5 and 20fb for resonances, and 0.3 and 10fb for gravitons.

##  0.9 Summary

A search has been presented for new bosonic states decaying either to a pair
of bosons or to a boson and a boson. The analyzed final states require large
missing transverse momentum and one high-momentum, large-radius jet. Large-
radius jets are required to have a mass consistent with either a or boson.
Events are categorized based on the presence of large-radius jets passing
high-purity and low-purity substructure requirements. Events are also
categorized based on the presence or absence of high-momentum jets in the
forward region of the detector. Forward jets distinguish weak vector boson
fusion (VBF) from other production mechanisms. Contributions from the dominant
SM backgrounds are estimated from data control regions using an extrapolation
method. No deviation between SM expectation and data is found, and 95%
confidence level upper limits are set on the product of the production cross
section and branching fraction for several signal models. A lower observed
(expected) limit of 3.0 (2.5)is set on the mass of gluon-gluon fusion produced
radions. The observed (expected) mass exclusion limit for Drell–Yan produced
bosons is found to be 4.0 (3.7). The observed (expected) mass exclusion limit
for gluon-gluon fusion produced gravitons is found to be 1.2 (1.1). At 95%
confidence level, upper observed (expected) limits on the product of the VBF
production cross section and $\PX\to\PZ+\PW/\PZ$ branching fraction range
between 0.2 and 20 (0.3 and 30)fb.

###### Acknowledgements.

We congratulate our colleagues in the CERN accelerator departments for the
excellent performance of the LHC and thank the technical and administrative
staffs at CERN and at other CMS institutes for their contributions to the
success of the CMS effort. In addition, we gratefully acknowledge the
computing centers and personnel of the Worldwide LHC Computing Grid and other
centers for delivering so effectively the computing infrastructure essential
to our analyses. Finally, we acknowledge the enduring support for the
construction and operation of the LHC, the CMS detector, and the supporting
computing infrastructure provided by the following funding agencies: BMBWF and
FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and
FAPESP (Brazil); MES and BNSF (Bulgaria); CERN; CAS, MoST, and NSFC (China);
MINCIENCIAS (Colombia); MSES and CSF (Croatia); RIF (Cyprus); SENESCYT
(Ecuador); MoER, ERC PUT and ERDF (Estonia); Academy of Finland, MEC, and HIP
(Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRI
(Greece); NKFIA (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland);
INFN (Italy); MSIP and NRF (Republic of Korea); MES (Latvia); LAS (Lithuania);
MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI
(Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MSHE and NSC
(Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom, RAS, RFBR, and NRC KI
(Russia); MESTD (Serbia); SEIDI, CPAN, PCTI, and FEDER (Spain); MOSTR (Sri
Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); ThEPCenter, IPST,
STAR, and NSTDA (Thailand); TUBITAK and TAEK (Turkey); NASU (Ukraine); STFC
(United Kingdom); DOE and NSF (USA). Individuals have received support from
the Marie-Curie program and the European Research Council and Horizon 2020
Grant, contract Nos. 675440, 724704, 752730, 758316, 765710, 824093, 884104,
and COST Action CA16108 (European Union); the Leventis Foundation; the Alfred
P. Sloan Foundation; the Alexander von Humboldt Foundation; the Belgian
Federal Science Policy Office; the Fonds pour la Formation à la Recherche dans
l’Industrie et dans l’Agriculture (FRIA-Belgium); the Agentschap voor
Innovatie door Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and
FWO (Belgium) under the “Excellence of Science – EOS” – be.h project n.
30820817; the Beijing Municipal Science & Technology Commission, No.
Z191100007219010; the Ministry of Education, Youth and Sports (MEYS) of the
Czech Republic; the Deutsche Forschungsgemeinschaft (DFG), under Germany’s
Excellence Strategy – EXC 2121 “Quantum Universe” – 390833306, and under
project number 400140256 - GRK2497; the Lendület (“Momentum”) Program and the
János Bolyai Research Scholarship of the Hungarian Academy of Sciences, the
New National Excellence Program ÚNKP, the NKFIA research grants 123842,
123959, 124845, 124850, 125105, 128713, 128786, and 129058 (Hungary); the
Council of Science and Industrial Research, India; the Latvian Council of
Science; the Ministry of Science and Higher Education and the National Science
Center, contracts Opus 2014/15/B/ST2/03998 and 2015/19/B/ST2/02861 (Poland);
the Fundação para a Ciência e a Tecnologia, grant CEECIND/01334/2018
(Portugal); the National Priorities Research Program by Qatar National
Research Fund; the Ministry of Science and Higher Education, project no.
14.W03.31.0026 (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 Stavros Niarchos
Foundation (Greece); the Rachadapisek Sompot Fund for Postdoctoral Fellowship,
Chulalongkorn University and the Chulalongkorn Academic into Its 2nd Century
Project Advancement Project (Thailand); the Kavli Foundation; the Nvidia
Corporation; the SuperMicro Corporation; the Welch Foundation, contract
C-1845; and the Weston Havens Foundation (USA).

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##  .10 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A. Tumasyan Institut für Hochenergiephysik, Vienna, Austria
W. Adam, J.W. Andrejkovic, T. Bergauer, S. Chatterjee, M. Dragicevic, A.
Escalante Del Valle, R. Frühwirth1, M. Jeitler1, N. Krammer, L. Lechner, D.
Liko, I. Mikulec, P. Paulitsch, F.M. Pitters, J. Schieck1, R. Schöfbeck, M.
Spanring, S. Templ, W. Waltenberger, C.-E. Wulz1 Institute for Nuclear
Problems, Minsk, Belarus
V. Chekhovsky, A. Litomin, V. Makarenko Universiteit Antwerpen, Antwerpen,
Belgium
M.R. Darwish2, E.A. De Wolf, X. Janssen, T. Kello3, A. Lelek, H. Rejeb Sfar,
P. Van Mechelen, S. Van Putte, N. Van Remortel Vrije Universiteit Brussel,
Brussel, Belgium
F. Blekman, E.S. Bols, J. D’Hondt, J. De Clercq, M. Delcourt, H. El Faham, S.
Lowette, S. Moortgat, A. Morton, D. Müller, A.R. Sahasransu, S. Tavernier, W.
Van Doninck, P. Van Mulders Université Libre de Bruxelles, Bruxelles, Belgium
D. Beghin, B. Bilin, B. Clerbaux, G. De Lentdecker, L. Favart, A. Grebenyuk,
A.K. Kalsi, K. Lee, M. Mahdavikhorrami, I. Makarenko, L. Moureaux, L. Pétré,
A. Popov, N. Postiau, E. Starling, L. Thomas, M. Vanden Bemden, C. Vander
Velde, P. Vanlaer, D. Vannerom, L. Wezenbeek Ghent University, Ghent, Belgium
T. Cornelis, D. Dobur, J. Knolle, L. Lambrecht, G. Mestdach, M. Niedziela, C.
Roskas, A. Samalan, K. Skovpen, M. Tytgat, W. Verbeke, B. Vermassen, M. Vit
Université Catholique de Louvain, Louvain-la-Neuve, Belgium
A. Bethani, G. Bruno, F. Bury, C. Caputo, P. David, C. Delaere, I.S. Donertas,
A. Giammanco, K. Jaffel, Sa. Jain, V. Lemaitre, K. Mondal, J. Prisciandaro, A.
Taliercio, M. Teklishyn, T.T. Tran, P. Vischia, S. Wertz Centro Brasileiro de
Pesquisas Fisicas, Rio de Janeiro, Brazil
G.A. Alves, C. Hensel, A. Moraes Universidade do Estado do Rio de Janeiro, Rio
de Janeiro, Brazil
W.L. Aldá Júnior, M. Alves Gallo Pereira, M. Barroso Ferreira Filho, H.
BRANDAO MALBOUISSON, W. Carvalho, J. Chinellato4, E.M. Da Costa, G.G. Da
Silveira5, D. De Jesus Damiao, S. Fonseca De Souza, D. Matos Figueiredo, C.
Mora Herrera, K. Mota Amarilo, L. Mundim, H. Nogima, P. Rebello Teles, A.
Santoro, S.M. Silva Do Amaral, A. Sznajder, M. Thiel, F. Torres Da Silva De
Araujo, A. Vilela Pereira Universidade Estadual Paulista a, Universidade
Federal do ABC b, São Paulo, Brazil
C.A. Bernardesa,a,5, L. Calligarisa, T.R. Fernandez Perez Tomeia, E.M.
Gregoresa,b, D.S. Lemosa, P.G. Mercadantea,b, S.F. Novaesa, Sandra S. Padulaa
Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy of
Sciences, Sofia, Bulgaria
A. Aleksandrov, G. Antchev, R. Hadjiiska, P. Iaydjiev, M. Misheva, M. Rodozov,
M. Shopova, G. Sultanov University of Sofia, Sofia, Bulgaria
A. Dimitrov, T. Ivanov, L. Litov, B. Pavlov, P. Petkov, A. Petrov Beihang
University, Beijing, China
T. Cheng, Q. Guo, T. Javaid6, M. Mittal, H. Wang, L. Yuan Department of
Physics, Tsinghua University
M. Ahmad, G. Bauer, C. Dozen7, Z. Hu, J. Martins8, Y. Wang, K. Yi9,10
Institute of High Energy Physics, Beijing, China
E. Chapon, G.M. Chen6, H.S. Chen6, M. Chen, F. Iemmi, A. Kapoor, D. Leggat, H.
Liao, Z.-A. LIU6, V. Milosevic, F. Monti, R. Sharma, J. Tao, J. Thomas-
wilsker, J. Wang, H. Zhang, S. Zhang6, J. Zhao State Key Laboratory of Nuclear
Physics and Technology, Peking University, Beijing, China
A. Agapitos, Y. An, Y. Ban, C. Chen, A. Levin, Q. Li, X. Lyu, Y. Mao, S.J.
Qian, D. Wang, Q. Wang, J. Xiao Sun Yat-Sen University, Guangzhou, China
M. Lu, Z. You Institute of Modern Physics and Key Laboratory of Nuclear
Physics and Ion-beam Application (MOE) - Fudan University, Shanghai, China
X. Gao3, H. Okawa Zhejiang University, Hangzhou, China
Z. Lin, M. Xiao Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, C. Florez, J. Fraga, A. Sarkar, M.A. Segura Delgado
Universidad de Antioquia, Medellin, Colombia
J. Mejia Guisao, F. Ramirez, J.D. Ruiz Alvarez, C.A. Salazar González
University of Split, Faculty of Electrical Engineering, Mechanical Engineering
and Naval Architecture, Split, Croatia
D. Giljanovic, N. Godinovic, D. Lelas, I. Puljak University of Split, Faculty
of Science, Split, Croatia
Z. Antunovic, M. Kovac, T. Sculac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, D. Ferencek, D. Majumder, M. Roguljic, A. Starodumov11, T. Susa
University of Cyprus, Nicosia, Cyprus
A. Attikis, K. Christoforou, E. Erodotou, A. Ioannou, G. Kole, M. Kolosova, S.
Konstantinou, J. Mousa, C. Nicolaou, F. Ptochos, P.A. Razis, H. Rykaczewski,
H. Saka Charles University, Prague, Czech Republic
M. Finger12, M. Finger Jr.12, A. Kveton Escuela Politecnica Nacional, Quito,
Ecuador
E. Ayala Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab
Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
H. Abdalla13, A. Ellithi Kamel13 Center for High Energy Physics (CHEP-FU),
Fayoum University, El-Fayoum, Egypt
M.A. Mahmoud, Y. Mohammed National Institute of Chemical Physics and
Biophysics, Tallinn, Estonia
S. Bhowmik, R.K. Dewanjee, K. Ehataht, M. Kadastik, S. Nandan, C. Nielsen, J.
Pata, M. Raidal, L. Tani, C. Veelken Department of Physics, University of
Helsinki, Helsinki, Finland
P. Eerola, L. Forthomme, H. Kirschenmann, K. Osterberg, M. Voutilainen
Helsinki Institute of Physics, Helsinki, Finland
S. Bharthuar, E. Brücken, F. Garcia, J. Havukainen, M.S. Kim, R. Kinnunen, T.
Lampén, K. Lassila-Perini, S. Lehti, T. Lindén, M. Lotti, L. Martikainen, M.
Myllymäki, J. Ott, H. Siikonen, E. Tuominen, J. Tuominiemi Lappeenranta
University of Technology, Lappeenranta, Finland
P. Luukka, H. Petrow, T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-
Yvette, France
C. Amendola, M. Besancon, F. Couderc, M. Dejardin, D. Denegri, J.L. Faure, F.
Ferri, S. Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry,
B. Lenzi, E. Locci, J. Malcles, J. Rander, A. Rosowsky, M.Ö. Sahin, A. Savoy-
Navarro14, M. Titov, G.B. Yu Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole
Polytechnique, Institut Polytechnique de Paris, Palaiseau, France
S. Ahuja, F. Beaudette, M. Bonanomi, A. Buchot Perraguin, P. Busson, A.
Cappati, C. Charlot, O. Davignon, B. Diab, G. Falmagne, S. Ghosh, R. Granier
de Cassagnac, A. Hakimi, I. Kucher, J. Motta, M. Nguyen, C. Ochando, P.
Paganini, J. Rembser, R. Salerno, J.B. Sauvan, Y. Sirois, A. Tarabini, A.
Zabi, A. Zghiche Université de Strasbourg, CNRS, IPHC UMR 7178, Strasbourg,
France
J.-L. Agram15, J. Andrea, D. Apparu, D. Bloch, G. Bourgatte, J.-M. Brom, E.C.
Chabert, C. Collard, D. Darej, J.-C. Fontaine15, U. Goerlach, C. Grimault,
A.-C. Le Bihan, E. Nibigira, P. Van Hove Institut de Physique des 2 Infinis de
Lyon (IP2I ), Villeurbanne, France
E. Asilar, S. Beauceron, C. Bernet, G. Boudoul, C. Camen, A. Carle, N. Chanon,
D. Contardo, P. Depasse, H. El Mamouni, J. Fay, S. Gascon, M. Gouzevitch, B.
Ille, I.B. Laktineh, H. Lattaud, A. Lesauvage, M. Lethuillier, L. Mirabito, S.
Perries, K. Shchablo, V. Sordini, L. Torterotot, G. Touquet, M. Vander Donckt,
S. Viret Georgian Technical University, Tbilisi, Georgia
G. Adamov, I. Lomidze, Z. Tsamalaidze12 RWTH Aachen University, I.
Physikalisches Institut, Aachen, Germany
L. Feld, K. Klein, M. Lipinski, D. Meuser, A. Pauls, M.P. Rauch, N. Röwert, J.
Schulz, M. Teroerde RWTH Aachen University, III. Physikalisches Institut A,
Aachen, Germany
A. Dodonova, D. Eliseev, M. Erdmann, P. Fackeldey, B. Fischer, S. Ghosh, T.
Hebbeker, K. Hoepfner, F. Ivone, H. Keller, L. Mastrolorenzo, M. Merschmeyer,
A. Meyer, G. Mocellin, S. Mondal, S. Mukherjee, D. Noll, A. Novak, T. Pook, A.
Pozdnyakov, Y. Rath, H. Reithler, J. Roemer, A. Schmidt, S.C. Schuler, A.
Sharma, L. Vigilante, S. Wiedenbeck, S. Zaleski RWTH Aachen University, III.
Physikalisches Institut B, Aachen, Germany
C. Dziwok, G. Flügge, W. Haj Ahmad16, O. Hlushchenko, T. Kress, A. Nowack, C.
Pistone, O. Pooth, D. Roy, H. Sert, A. Stahl17, T. Ziemons Deutsches
Elektronen-Synchrotron, Hamburg, Germany
H. Aarup Petersen, M. Aldaya Martin, P. Asmuss, I. Babounikau, S. Baxter, O.
Behnke, A. Bermúdez Martínez, S. Bhattacharya, A.A. Bin Anuar, K. Borras18, V.
Botta, D. Brunner, A. Campbell, A. Cardini, C. Cheng, F. Colombina, S.
Consuegra Rodríguez, G. Correia Silva, V. Danilov, L. Didukh, G. Eckerlin, D.
Eckstein, L.I. Estevez Banos, O. Filatov, E. Gallo19, A. Geiser, A. Giraldi,
A. Grohsjean, M. Guthoff, A. Jafari20, N.Z. Jomhari, H. Jung, A. Kasem18, M.
Kasemann, H. Kaveh, C. Kleinwort, D. Krücker, W. Lange, J. Lidrych, K. Lipka,
W. Lohmann21, R. Mankel, I.-A. Melzer-Pellmann, M. Mendizabal Morentin, J.
Metwally, A.B. Meyer, M. Meyer, J. Mnich, A. Mussgiller, Y. Otarid, D. Pérez
Adán, D. Pitzl, A. Raspereza, B. Ribeiro Lopes, J. Rübenach, A. Saggio, A.
Saibel, M. Savitskyi, M. Scham, V. Scheurer, P. Schütze, C. Schwanenberger19,
A. Singh, R.E. Sosa Ricardo, D. Stafford, N. Tonon, O. Turkot, M. Van De
Klundert, R. Walsh, D. Walter, Y. Wen, K. Wichmann, L. Wiens, C. Wissing, S.
Wuchterl University of Hamburg, Hamburg, Germany
R. Aggleton, S. Albrecht, S. Bein, L. Benato, A. Benecke, P. Connor, K. De
Leo, M. Eich, F. Feindt, A. Fröhlich, C. Garbers, E. Garutti, P. Gunnellini,
J. Haller, A. Hinzmann, G. Kasieczka, R. Klanner, R. Kogler, T. Kramer, V.
Kutzner, J. Lange, T. Lange, A. Lobanov, A. Malara, A. Nigamova, K.J. Pena
Rodriguez, O. Rieger, P. Schleper, M. Schröder, J. Schwandt, D. Schwarz, J.
Sonneveld, H. Stadie, G. Steinbrück, A. Tews, B. Vormwald, I. Zoi Karlsruher
Institut fuer Technologie, Karlsruhe, Germany
J. Bechtel, T. Berger, E. Butz, R. Caspart, T. Chwalek, W. De
Boer${}^{\textrm{\textdagger}}$, A. Dierlamm, A. Droll, K. El Morabit, N.
Faltermann, M. Giffels, J.o. Gosewisch, A. Gottmann, F. Hartmann17, C.
Heidecker, U. Husemann, I. Katkov22, P. Keicher, R. Koppenhöfer, S. Maier, M.
Metzler, S. Mitra, Th. Müller, M. Neukum, A. Nürnberg, G. Quast, K. Rabbertz,
J. Rauser, D. Savoiu, M. Schnepf, D. Seith, I. Shvetsov, H.J. Simonis, R.
Ulrich, J. Van Der Linden, R.F. Von Cube, M. Wassmer, M. Weber, S. Wieland, R.
Wolf, S. Wozniewski, S. Wunsch Institute of Nuclear and Particle Physics
(INPP), NCSR Demokritos, Aghia Paraskevi, Greece
G. Anagnostou, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas, A. Stakia
National and Kapodistrian University of Athens, Athens, Greece
M. Diamantopoulou, D. Karasavvas, G. Karathanasis, P. Kontaxakis, C.K. Koraka,
A. Manousakis-katsikakis, A. Panagiotou, I. Papavergou, N. Saoulidou, K.
Theofilatos, E. Tziaferi, K. Vellidis, E. Vourliotis National Technical
University of Athens, Athens, Greece
G. Bakas, K. Kousouris, I. Papakrivopoulos, G. Tsipolitis, A. Zacharopoulou
University of Ioánnina, Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, N. Manthos, I.
Papadopoulos, J. Strologas MTA-ELTE Lendület CMS Particle and Nuclear Physics
Group, Eötvös Loránd University
M. Csanad, K. Farkas, M.M.A. Gadallah23, S. Lökös24, P. Major, K. Mandal, A.
Mehta, G. Pasztor, A.J. Rádl, O. Surányi, G.I. Veres Wigner Research Centre
for Physics, Budapest, Hungary
M. Bartók25, G. Bencze, C. Hajdu, D. Horvath26, F. Sikler, V. Veszpremi, G.
Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear Research ATOMKI,
Debrecen, Hungary
S. Czellar, J. Karancsi25, J. Molnar, Z. Szillasi, D. Teyssier Institute of
Physics, University of Debrecen
P. Raics, Z.L. Trocsanyi27, B. Ujvari Karoly Robert Campus, MATE Institute of
Technology
T. Csorgo28, F. Nemes28, T. Novak Indian Institute of Science (IISc),
Bangalore, India
J.R. Komaragiri, D. Kumar, L. Panwar, P.C. Tiwari National Institute of
Science Education and Research, HBNI, Bhubaneswar, India
S. Bahinipati29, C. Kar, P. Mal, T. Mishra, V.K. Muraleedharan Nair Bindhu30,
A. Nayak30, P. Saha, N. Sur, S.K. Swain, D. Vats30 Panjab University,
Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, G. Chaudhary, S. Chauhan, N. Dhingra31, R.
Gupta, A. Kaur, M. Kaur, S. Kaur, P. Kumari, M. Meena, K. Sandeep, J.B. Singh,
A.K. Virdi University of Delhi, Delhi, India
A. Ahmed, A. Bhardwaj, B.C. Choudhary, M. Gola, S. Keshri, A. Kumar, M.
Naimuddin, P. Priyanka, K. Ranjan, A. Shah Saha Institute of Nuclear Physics,
HBNI, Kolkata, India
M. Bharti32, R. Bhattacharya, S. Bhattacharya, D. Bhowmik, S. Dutta, S. Dutta,
B. Gomber33, M. Maity34, P. Palit, P.K. Rout, G. Saha, B. Sahu, S. Sarkar, M.
Sharan, B. Singh32, S. Thakur32 Indian Institute of Technology Madras, Madras,
India
P.K. Behera, S.C. Behera, P. Kalbhor, A. Muhammad, R. Pradhan, P.R. Pujahari,
A. Sharma, A.K. Sikdar Bhabha Atomic Research Centre, Mumbai, India
D. Dutta, V. Jha, V. Kumar, D.K. Mishra, K. Naskar35, P.K. Netrakanti, L.M.
Pant, P. Shukla Tata Institute of Fundamental Research-A, Mumbai, India
T. Aziz, S. Dugad, M. Kumar, U. Sarkar Tata Institute of Fundamental
Research-B, Mumbai, India
S. Banerjee, R. Chudasama, M. Guchait, S. Karmakar, S. Kumar, G. Majumder, K.
Mazumdar, S. Mukherjee Indian Institute of Science Education and Research
(IISER), Pune, India
K. Alpana, S. Dube, B. Kansal, A. Laha, S. Pandey, A. Rane, A. Rastogi, S.
Sharma Isfahan University of Technology, Isfahan, Iran
H. Bakhshiansohi36, M. Zeinali37 Institute for Research in Fundamental
Sciences (IPM), Tehran, Iran
S. Chenarani38, S.M. Etesami, M. Khakzad, M. Mohammadi Najafabadi University
College Dublin, Dublin, Ireland
M. Grunewald INFN Sezione di Bari a, Università di Bari b, Politecnico di Bari
c, Bari, Italy
M. Abbresciaa,b, R. Alya,b,39, C. Arutaa,b, A. Colaleoa, D. Creanzaa,c, N. De
Filippisa,c, M. De Palmaa,b, A. Di Florioa,b, A. Di Pilatoa,b, W.
Elmetenaweea,b, L. Fiorea, A. Gelmia,b, M. Gula, G. Iasellia,c, M. Incea,b, S.
Lezkia,b, G. Maggia,c, M. Maggia, I. Margjekaa,b, V. Mastrapasquaa,b, J.A.
Merlina, S. Mya,b, S. Nuzzoa,b, A. Pellecchiaa,b, A. Pompilia,b, G.
Pugliesea,c, A. Ranieria, G. Selvaggia,b, L. Silvestrisa, F.M. Simonea,b, R.
Vendittia, P. Verwilligena INFN Sezione di Bologna a, Università di Bologna b,
Bologna, Italy
G. Abbiendia, C. Battilanaa,b, D. Bonacorsia,b, L. Borgonovia, L.
Brigliadoria, R. Campaninia,b, P. Capiluppia,b, A. Castroa,b, F.R. Cavalloa,
M. Cuffiania,b, G.M. Dallavallea, T. Diotalevia,b, F. Fabbria, A. Fanfania,b,
P. Giacomellia, L. Giommia,b, C. Grandia, L. Guiduccia,b, S. Lo Meoa,40, L.
Lunertia,b, S. Marcellinia, G. Masettia, F.L. Navarriaa,b, A. Perrottaa, F.
Primaveraa,b, A.M. Rossia,b, T. Rovellia,b, G.P. Sirolia,b INFN Sezione di
Catania a, Università di Catania b, Catania, Italy
S. Albergoa,b,41, S. Costaa,b,41, A. Di Mattiaa, R. Potenzaa,b, A.
Tricomia,b,41, C. Tuvea,b INFN Sezione di Firenze a, Università di Firenze b,
Firenze, Italy
G. Barbaglia, A. Cassesea, R. Ceccarellia,b, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, E. Focardia,b, G. Latinoa,b, P. Lenzia,b, M. Lizzoa,b, M.
Meschinia, S. Paolettia, R. Seiditaa,b, G. Sguazzonia, L. Viliania INFN
Laboratori Nazionali di Frascati, Frascati, Italy
L. Benussi, S. Bianco, D. Piccolo INFN Sezione di Genova a, Università di
Genova b, Genova, Italy
M. Bozzoa,b, F. Ferroa, R. Mulargiaa,b, E. Robuttia, S. Tosia,b INFN Sezione
di Milano-Bicocca a, Università di Milano-Bicocca b, Milano, Italy
A. Benagliaa, F. Brivioa,b, F. Cetorellia,b, V. Cirioloa,b,17, F. De Guioa,b,
M.E. Dinardoa,b, P. Dinia, S. Gennaia, A. Ghezzia,b, P. Govonia,b, L.
Guzzia,b, M. Malbertia, S. Malvezzia, A. Massironia, D. Menascea, L. Moronia,
M. Paganonia,b, D. Pedrinia, S. Ragazzia,b, N. Redaellia, T. Tabarelli de
Fatisa,b, D. Valsecchia,b,17, D. Zuoloa,b INFN Sezione di Napoli a, Università
di Napoli ’Federico II’ b, Napoli, Italy, Università della Basilicata c,
Potenza, Italy, Università G. Marconi d, Roma, Italy
S. Buontempoa, F. Carnevalia,b, N. Cavalloa,c, A. De Iorioa,b, F. Fabozzia,c,
A.O.M. Iorioa,b, L. Listaa,b, S. Meolaa,d,17, P. Paoluccia,17, B. Rossia, C.
Sciaccaa,b INFN Sezione di Padova a, Università di Padova b, Padova, Italy,
Università di Trento c, Trento, Italy
P. Azzia, N. Bacchettaa, D. Biselloa,b, P. Bortignona, A. Bragagnoloa,b, R.
Carlina,b, P. Checchiaa, T. Dorigoa, U. Dossellia, F. Gasparinia,b, U.
Gasparinia,b, S.Y. Hoha,b, L. Layera,42, M. Margonia,b, A.T. Meneguzzoa,b, J.
Pazzinia,b, M. Presillaa,b, P. Ronchesea,b, R. Rossina,b, F. Simonettoa,b, G.
Stronga, M. Tosia,b, H. YARARa,b, M. Zanettia,b, P. Zottoa,b, A. Zucchettaa,b,
G. Zumerlea,b INFN Sezione di Pavia a, Università di Pavia b
C. Aime‘a,b, A. Braghieria, S. Calzaferria,b, D. Fiorinaa,b, P. Montagnaa,b,
S.P. Rattia,b, V. Rea, C. Riccardia,b, P. Salvinia, I. Vaia, P. Vituloa,b INFN
Sezione di Perugia a, Università di Perugia b, Perugia, Italy
P. Asenova,43, G.M. Bileia, D. Ciangottinia,b, L. Fanòa,b, P. Laricciaa,b, M.
Magherinib, G. Mantovania,b, V. Mariania,b, M. Menichellia, F. Moscatellia,43,
A. Piccinellia,b, A. Rossia,b, A. Santocchiaa,b, D. Spigaa, T. Tedeschia,b
INFN Sezione di Pisa a, Università di Pisa b, Scuola Normale Superiore di Pisa
c, Pisa Italy, Università di Siena d, Siena, Italy
P. Azzurria, G. Bagliesia, V. Bertacchia,c, L. Bianchinia, T. Boccalia, E.
Bossinia,b, R. Castaldia, M.A. Cioccia,b, V. D’Amantea,d, R. Dell’Orsoa, M.R.
Di Domenicoa,d, S. Donatoa, A. Giassia, F. Ligabuea,c, E. Mancaa,c, G.
Mandorlia,c, A. Messineoa,b, F. Pallaa, S. Paroliaa,b, G. Ramirez-Sancheza,c,
A. Rizzia,b, G. Rolandia,c, S. Roy Chowdhurya,c, A. Scribanoa, N. Shafieia,b,
P. Spagnoloa, R. Tenchinia, G. Tonellia,b, N. Turinia,d, A. Venturia, P.G.
Verdinia INFN Sezione di Roma a, Sapienza Università di Roma b, Rome, Italy
M. Campanaa,b, F. Cavallaria, D. Del Rea,b, E. Di Marcoa, M. Diemoza, E.
Longoa,b, P. Meridiania, G. Organtinia,b, F. Pandolfia, R. Paramattia,b, C.
Quarantaa,b, S. Rahatloua,b, C. Rovellia, F. Santanastasioa,b, L. Soffia, R.
Tramontanoa,b INFN Sezione di Torino a, Università di Torino b, Torino, Italy,
Università del Piemonte Orientale c, Novara, Italy
N. Amapanea,b, R. Arcidiaconoa,c, S. Argiroa,b, M. Arneodoa,c, N. Bartosika,
R. Bellana,b, A. Belloraa,b, J. Berenguer Antequeraa,b, C. Biinoa, N.
Cartigliaa, S. Comettia, M. Costaa,b, R. Covarellia,b, N. Demariaa, B.
Kiania,b, F. Leggera, C. Mariottia, S. Masellia, E. Migliorea,b, E.
Monteila,b, M. Montenoa, M.M. Obertinoa,b, G. Ortonaa, L. Pachera,b, N.
Pastronea, M. Pelliccionia, G.L. Pinna Angionia,b, M. Ruspaa,c, K.
Shchelinaa,b, F. Sivieroa,b, V. Solaa, A. Solanoa,b, D. Soldia,b, A. Staianoa,
M. Tornagoa,b, D. Trocinoa,b, A. Vagnerini INFN Sezione di Trieste a,
Università di Trieste b, Trieste, Italy
S. Belfortea, V. Candelisea,b, M. Casarsaa, F. Cossuttia, A. Da Rolda,b, G.
Della Riccaa,b, G. Sorrentinoa,b, F. Vazzolera,b Kyungpook National
University, Daegu, Korea
S. Dogra, C. Huh, B. Kim, D.H. Kim, G.N. Kim, J. Kim, J. Lee, S.W. Lee, C.S.
Moon, Y.D. Oh, S.I. Pak, B.C. Radburn-Smith, S. Sekmen, Y.C. Yang Chonnam
National University, Institute for Universe and Elementary Particles, Kwangju,
Korea
H. Kim, D.H. Moon Hanyang University, Seoul, Korea
B. Francois, T.J. Kim, J. Park Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, B. Hong, K. Lee, K.S. Lee, J. Lim, J. Park, S.K. Park,
J. Yoo Kyung Hee University, Department of Physics, Seoul, Republic of Korea
J. Goh, A. Gurtu Sejong University, Seoul, Korea
H.S. Kim, Y. Kim Seoul National University, Seoul, Korea
J. Almond, J.H. Bhyun, J. Choi, S. Jeon, J. Kim, J.S. Kim, S. Ko, H. Kwon, H.
Lee, S. Lee, B.H. Oh, M. Oh, S.B. Oh, H. Seo, U.K. Yang, I. Yoon University of
Seoul, Seoul, Korea
W. Jang, D. Jeon, D.Y. Kang, Y. Kang, J.H. Kim, S. Kim, B. Ko, J.S.H. Lee, Y.
Lee, I.C. Park, Y. Roh, M.S. Ryu, D. Song, I.J. Watson, S. Yang Yonsei
University, Department of Physics, Seoul, Korea
S. Ha, H.D. Yoo Sungkyunkwan University, Suwon, Korea
M. Choi, Y. Jeong, H. Lee, Y. Lee, I. Yu College of Engineering and
Technology, American University of the Middle East (AUM), Egaila, Kuwait
T. Beyrouthy, Y. Maghrbi Riga Technical University
T. Torims, V. Veckalns44 Vilnius University, Vilnius, Lithuania
M. Ambrozas, A. Carvalho Antunes De Oliveira, A. Juodagalvis, A. Rinkevicius,
G. Tamulaitis National Centre for Particle Physics, Universiti Malaya, Kuala
Lumpur, Malaysia
N. Bin Norjoharuddeen, W.A.T. Wan Abdullah, M.N. Yusli, Z. Zolkapli
Universidad de Sonora (UNISON), Hermosillo, Mexico
J.F. Benitez, A. Castaneda Hernandez, M. León Coello, J.A. Murillo Quijada, A.
Sehrawat, L. Valencia Palomo Centro de Investigacion y de Estudios Avanzados
del IPN, Mexico City, Mexico
G. Ayala, H. Castilla-Valdez, E. De La Cruz-Burelo, I. Heredia-De La Cruz45,
R. Lopez-Fernandez, C.A. Mondragon Herrera, D.A. Perez Navarro, A. Sanchez-
Hernandez Universidad Iberoamericana, Mexico City, Mexico
S. Carrillo Moreno, C. Oropeza Barrera, M. Ramirez-Garcia, F. Vazquez Valencia
Benemerita Universidad Autonoma de Puebla, Puebla, Mexico
I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada University of Montenegro,
Podgorica, Montenegro
J. Mijuskovic46, N. Raicevic University of Auckland, Auckland, New Zealand
D. Krofcheck University of Canterbury, Christchurch, New Zealand
S. Bheesette, P.H. Butler National Centre for Physics, Quaid-I-Azam
University, Islamabad, Pakistan
A. Ahmad, M.I. Asghar, A. Awais, M.I.M. Awan, H.R. Hoorani, W.A. Khan, M.A.
Shah, M. Shoaib, M. Waqas AGH University of Science and Technology Faculty of
Computer Science, Electronics and Telecommunications, Krakow, Poland
V. Avati, L. Grzanka, M. Malawski National Centre for Nuclear Research,
Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, M. Górski, M. Kazana, M. Szleper, P.
Zalewski Institute of Experimental Physics, Faculty of Physics, University of
Warsaw, Warsaw, Poland
K. Bunkowski, K. Doroba, A. Kalinowski, M. Konecki, J. Krolikowski, M. Walczak
Laboratório de Instrumentação e Física Experimental de Partículas, Lisboa,
Portugal
M. Araujo, P. Bargassa, D. Bastos, A. Boletti, P. Faccioli, M. Gallinaro, J.
Hollar, N. Leonardo, T. Niknejad, M. Pisano, J. Seixas, O. Toldaiev, J. Varela
Joint Institute for Nuclear Research, Dubna, Russia
S. Afanasiev, D. Budkouski, I. Golutvin, I. Gorbunov, V. Karjavine, V.
Korenkov, A. Lanev, A. Malakhov, V. Matveev47,48, V. Palichik, V. Perelygin,
M. Savina, D. Seitova, V. Shalaev, S. Shmatov, S. Shulha, V. Smirnov, O.
Teryaev, N. Voytishin, B.S. Yuldashev49, A. Zarubin, I. Zhizhin Petersburg
Nuclear Physics Institute, Gatchina (St. Petersburg), Russia
G. Gavrilov, V. Golovtcov, Y. Ivanov, V. Kim50, E. Kuznetsova51, V. Murzin, V.
Oreshkin, I. Smirnov, D. Sosnov, V. Sulimov, L. Uvarov, S. Volkov, A. Vorobyev
Institute for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, D.
Kirpichnikov, M. Kirsanov, N. Krasnikov, A. Pashenkov, G. Pivovarov, D.
Tlisov${}^{\textrm{\textdagger}}$, A. Toropin Institute for Theoretical and
Experimental Physics named by A.I. Alikhanov of NRC ‘Kurchatov Institute’,
Moscow, Russia
V. Epshteyn, V. Gavrilov, N. Lychkovskaya, A. Nikitenko52, V. Popov, A.
Spiridonov, A. Stepennov, M. Toms, E. Vlasov, A. Zhokin Moscow Institute of
Physics and Technology, Moscow, Russia
T. Aushev National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
O. Bychkova, M. Chadeeva53, P. Parygin, 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. Dubinin54, L. Dudko, A. Ershov, V.
Klyukhin, O. Kodolova, I. Lokhtin, S. Obraztsov, M. Perfilov, S. Petrushanko,
V. Savrin Novosibirsk State University (NSU), Novosibirsk, Russia
V. Blinov55, T. Dimova55, L. Kardapoltsev55, A. Kozyrev55, I. Ovtin55, Y.
Skovpen55 Institute for High Energy Physics of National Research Centre
‘Kurchatov Institute’, Protvino, Russia
I. Azhgirey, I. Bayshev, D. Elumakhov, V. Kachanov, D. Konstantinov, P.
Mandrik, V. Petrov, R. Ryutin, S. Slabospitskii, A. Sobol, S. Troshin, N.
Tyurin, A. Uzunian, A. Volkov National Research Tomsk Polytechnic University,
Tomsk, Russia
A. Babaev, V. Okhotnikov Tomsk State University, Tomsk, Russia
V. Borshch, V. Ivanchenko, E. Tcherniaev University of Belgrade: Faculty of
Physics and VINCA Institute of Nuclear Sciences, Belgrade, Serbia
P. Adzic56, M. Dordevic, P. Milenovic, J. Milosevic Centro de Investigaciones
Energéticas Medioambientales y Tecnológicas (CIEMAT), Madrid, Spain
M. Aguilar-Benitez, J. Alcaraz Maestre, A. Álvarez Fernández, I. Bachiller, M.
Barrio Luna, Cristina F. Bedoya, C.A. Carrillo Montoya, M. Cepeda, M. Cerrada,
N. Colino, B. De La Cruz, A. Delgado Peris, J.P. Fernández Ramos, J. Flix,
M.C. Fouz, O. Gonzalez Lopez, S. Goy Lopez, J.M. Hernandez, M.I. Josa, J. León
Holgado, D. Moran, Á. Navarro Tobar, C. Perez Dengra, A. Pérez-Calero
Yzquierdo, J. Puerta Pelayo, I. Redondo, L. Romero, S. Sánchez Navas, L. Urda
Gómez, C. Willmott Universidad Autónoma de Madrid, Madrid, Spain
J.F. de Trocóniz, R. Reyes-Almanza Universidad de Oviedo, Instituto
Universitario de Ciencias y Tecnologías Espaciales de Asturias (ICTEA),
Oviedo, Spain
B. Alvarez Gonzalez, J. Cuevas, C. Erice, J. Fernandez Menendez, S. Folgueras,
I. Gonzalez Caballero, J.R. González Fernández, E. Palencia Cortezon, C. Ramón
Álvarez, J. Ripoll Sau, V. Rodríguez Bouza, A. Trapote, N. Trevisani Instituto
de Física de Cantabria (IFCA), CSIC-Universidad de Cantabria, Santander, Spain
J.A. Brochero Cifuentes, I.J. Cabrillo, A. Calderon, J. Duarte Campderros, M.
Fernandez, C. Fernandez Madrazo, P.J. Fernández Manteca, A. García Alonso, G.
Gomez, C. Martinez Rivero, P. Martinez Ruiz del Arbol, F. Matorras, P.
Matorras Cuevas, J. Piedra Gomez, C. Prieels, T. Rodrigo, A. Ruiz-Jimeno, L.
Scodellaro, I. Vila, J.M. Vizan Garcia University of Colombo, Colombo, Sri
Lanka
MK Jayananda, B. Kailasapathy57, D.U.J. Sonnadara, DDC Wickramarathna
University of Ruhuna, Department of Physics, Matara, Sri Lanka
W.G.D. Dharmaratna, K. Liyanage, N. Perera, N. Wickramage CERN, European
Organization for Nuclear Research, Geneva, Switzerland
T.K. Aarrestad, D. Abbaneo, J. Alimena, E. Auffray, G. Auzinger, J. Baechler,
P. Baillon${}^{\textrm{\textdagger}}$, D. Barney, J. Bendavid, M. Bianco, A.
Bocci, T. Camporesi, M. Capeans Garrido, G. Cerminara, S.S. Chhibra, M.
Cipriani, L. Cristella, D. d’Enterria, A. Dabrowski, N. Daci, A. David, A. De
Roeck, M.M. Defranchis, M. Deile, M. Dobson, M. Dünser, N. Dupont, A. Elliott-
Peisert, N. Emriskova, F. Fallavollita58, D. Fasanella, A. Florent, G.
Franzoni, W. Funk, S. Giani, D. Gigi, K. Gill, F. Glege, L. Gouskos, M.
Haranko, J. Hegeman, Y. Iiyama, V. Innocente, T. James, P. Janot, J. Kaspar,
J. Kieseler, M. Komm, N. Kratochwil, C. Lange, S. Laurila, P. Lecoq, K. Long,
C. Lourenço, L. Malgeri, S. Mallios, M. Mannelli, A.C. Marini, F. Meijers, S.
Mersi, E. Meschi, F. Moortgat, M. Mulders, S. Orfanelli, L. Orsini, F.
Pantaleo, L. Pape, E. Perez, M. Peruzzi, A. Petrilli, G. Petrucciani, A.
Pfeiffer, M. Pierini, D. Piparo, M. Pitt, H. Qu, T. Quast, D. Rabady, A. Racz,
G. Reales Gutiérrez, M. Rieger, M. Rovere, H. Sakulin, J. Salfeld-Nebgen, S.
Scarfi, C. Schäfer, C. Schwick, M. Selvaggi, A. Sharma, P. Silva, W. Snoeys,
P. Sphicas59, S. Summers, K. Tatar, V.R. Tavolaro, D. Treille, A. Tsirou, G.P.
Van Onsem, M. Verzetti, J. Wanczyk60, K.A. Wozniak, W.D. Zeuner Paul Scherrer
Institut, Villigen, Switzerland
L. Caminada61, A. Ebrahimi, W. Erdmann, R. Horisberger, Q. Ingram, H.C.
Kaestli, D. Kotlinski, U. Langenegger, M. Missiroli, T. Rohe ETH Zurich -
Institute for Particle Physics and Astrophysics (IPA), Zurich, Switzerland
K. Androsov60, M. Backhaus, P. Berger, A. Calandri, N. Chernyavskaya, A. De
Cosa, G. Dissertori, M. Dittmar, M. Donegà, C. Dorfer, F. Eble, K. Gedia, F.
Glessgen, T.A. Gómez Espinosa, C. Grab, D. Hits, W. Lustermann, A.-M. Lyon,
R.A. Manzoni, C. Martin Perez, M.T. Meinhard, F. Nessi-Tedaldi, J. Niedziela,
F. Pauss, V. Perovic, S. Pigazzini, M.G. Ratti, M. Reichmann, C. Reissel, T.
Reitenspiess, B. Ristic, D. Ruini, D.A. Sanz Becerra, M. Schönenberger, V.
Stampf, J. Steggemann60, R. Wallny, D.H. Zhu Universität Zürich, Zurich,
Switzerland
C. Amsler62, P. Bärtschi, C. Botta, D. Brzhechko, M.F. Canelli, K. Cormier, A.
De Wit, R. Del Burgo, J.K. Heikkilä, M. Huwiler, W. Jin, A. Jofrehei, B.
Kilminster, S. Leontsinis, S.P. Liechti, A. Macchiolo, P. Meiring, V.M.
Mikuni, U. Molinatti, I. Neutelings, A. Reimers, P. Robmann, S. Sanchez Cruz,
K. Schweiger, Y. Takahashi National Central University, Chung-Li, Taiwan
C. Adloff63, C.M. Kuo, W. Lin, A. Roy, T. Sarkar34, S.S. Yu National Taiwan
University (NTU), Taipei, Taiwan
L. Ceard, Y. Chao, K.F. Chen, P.H. Chen, W.-S. Hou, Y.y. Li, R.-S. Lu, E.
Paganis, A. Psallidas, A. Steen, H.y. Wu, E. Yazgan, P.r. Yu Chulalongkorn
University, Faculty of Science, Department of Physics, Bangkok, Thailand
B. Asavapibhop, C. Asawatangtrakuldee, N. Srimanobhas Çukurova University,
Physics Department, Science and Art Faculty, Adana, Turkey
F. Boran, S. Damarseckin64, Z.S. Demiroglu, F. Dolek, I. Dumanoglu65, E.
Eskut, Y. Guler, E. Gurpinar Guler66, I. Hos67, C. Isik, O. Kara, A. Kayis
Topaksu, U. Kiminsu, G. Onengut, K. Ozdemir68, A. Polatoz, A.E. Simsek, B.
Tali69, U.G. Tok, S. Turkcapar, I.S. Zorbakir, C. Zorbilmez Middle East
Technical University, Physics Department, Ankara, Turkey
B. Isildak70, G. Karapinar71, K. Ocalan72, M. Yalvac73 Bogazici University,
Istanbul, Turkey
B. Akgun, I.O. Atakisi, E. Gülmez, M. Kaya74, O. Kaya75, Ö. Özçelik, S.
Tekten76, E.A. Yetkin77 Istanbul Technical University, Istanbul, Turkey
A. Cakir, K. Cankocak65, Y. Komurcu, S. Sen78 Istanbul University, Istanbul,
Turkey
S. Cerci69, B. Kaynak, S. Ozkorucuklu, D. Sunar Cerci69 Institute for
Scintillation Materials of National Academy of Science of Ukraine, Kharkov,
Ukraine
B. Grynyov National Scientific Center, Kharkov Institute of Physics and
Technology, Kharkov, Ukraine
L. Levchuk University of Bristol, Bristol, United Kingdom
D. Anthony, E. Bhal, S. Bologna, J.J. Brooke, A. Bundock, E. Clement, D.
Cussans, H. Flacher, J. Goldstein, G.P. Heath, H.F. Heath, M.l. Holmberg79, L.
Kreczko, B. Krikler, S. Paramesvaran, S. Seif El Nasr-Storey, V.J. Smith, N.
Stylianou80, K. Walkingshaw Pass, R. White Rutherford Appleton Laboratory,
Didcot, United Kingdom
K.W. Bell, A. Belyaev81, C. Brew, R.M. Brown, D.J.A. Cockerill, C. Cooke, K.V.
Ellis, K. Harder, S. Harper, J. Linacre, K. Manolopoulos, D.M. Newbold, E.
Olaiya, D. Petyt, T. Reis, T. Schuh, C.H. Shepherd-Themistocleous, I.R.
Tomalin, T. Williams Imperial College, London, United Kingdom
R. Bainbridge, P. Bloch, S. Bonomally, J. Borg, S. Breeze, O. Buchmuller, V.
Cepaitis, G.S. Chahal82, D. Colling, P. Dauncey, G. Davies, M. Della Negra, S.
Fayer, G. Fedi, G. Hall, M.H. Hassanshahi, G. Iles, J. Langford, L. Lyons,
A.-M. Magnan, S. Malik, A. Martelli, D.G. Monk, J. Nash83, M. Pesaresi, D.M.
Raymond, A. Richards, A. Rose, E. Scott, C. Seez, A. Shtipliyski, A. Tapper,
K. Uchida, T. Virdee17, M. Vojinovic, N. Wardle, S.N. Webb, D. Winterbottom,
A.G. Zecchinelli Brunel University, Uxbridge, United Kingdom
K. Coldham, J.E. Cole, A. Khan, P. Kyberd, I.D. Reid, L. Teodorescu, S. Zahid
Baylor University, Waco, USA
S. Abdullin, A. Brinkerhoff, B. Caraway, J. Dittmann, K. Hatakeyama, A.R.
Kanuganti, B. McMaster, N. Pastika, M. Saunders, S. Sawant, C. Sutantawibul,
J. Wilson Catholic University of America, Washington, DC, USA
R. Bartek, A. Dominguez, R. Uniyal, A.M. Vargas Hernandez The University of
Alabama, Tuscaloosa, USA
A. Buccilli, S.I. Cooper, D. Di Croce, S.V. Gleyzer, C. Henderson, C.U. Perez,
P. Rumerio84, C. West Boston University, Boston, USA
A. Akpinar, A. Albert, D. Arcaro, C. Cosby, Z. Demiragli, E. Fontanesi, D.
Gastler, J. Rohlf, K. Salyer, D. Sperka, D. Spitzbart, I. Suarez, A. Tsatsos,
S. Yuan, D. Zou Brown University, Providence, USA
G. Benelli, B. Burkle, X. Coubez18, D. Cutts, M. Hadley, U. Heintz, J.M.
Hogan85, G. Landsberg, K.T. Lau, M. Lukasik, J. Luo, M. Narain, S. Sagir86, E.
Usai, W.Y. Wong, X. Yan, D. Yu, W. Zhang University of California, Davis,
Davis, USA
J. Bonilla, C. Brainerd, R. Breedon, M. Calderon De La Barca Sanchez, M.
Chertok, J. Conway, P.T. Cox, R. Erbacher, G. Haza, F. Jensen, O. Kukral, R.
Lander, M. Mulhearn, D. Pellett, B. Regnery, D. Taylor, Y. Yao, F. Zhang
University of California, Los Angeles, USA
M. Bachtis, R. Cousins, A. Datta, D. Hamilton, J. Hauser, M. Ignatenko, M.A.
Iqbal, T. Lam, W.A. Nash, S. Regnard, D. Saltzberg, B. Stone, V. Valuev
University of California, Riverside, Riverside, USA
K. Burt, Y. Chen, R. Clare, J.W. Gary, M. Gordon, G. Hanson, G. Karapostoli,
O.R. Long, N. Manganelli, M. Olmedo Negrete, W. Si, S. Wimpenny, Y. Zhang
University of California, San Diego, La Jolla, USA
J.G. Branson, P. Chang, S. Cittolin, S. Cooperstein, N. Deelen, D. Diaz, J.
Duarte, R. Gerosa, L. Giannini, D. Gilbert, J. Guiang, R. Kansal, V.
Krutelyov, R. Lee, J. Letts, M. Masciovecchio, S. May, M. Pieri, B.V. Sathia
Narayanan, V. Sharma, M. Tadel, A. Vartak, F. Würthwein, Y. Xiang, A. Yagil
University of California, Santa Barbara - Department of Physics, Santa
Barbara, USA
N. Amin, C. Campagnari, M. Citron, A. Dorsett, V. Dutta, J. Incandela, M.
Kilpatrick, J. Kim, B. Marsh, H. Mei, M. Oshiro, M. Quinnan, J. Richman, U.
Sarica, J. Sheplock, D. Stuart, S. Wang California Institute of Technology,
Pasadena, USA
A. Bornheim, O. Cerri, I. Dutta, J.M. Lawhorn, N. Lu, J. Mao, H.B. Newman,
T.Q. Nguyen, M. Spiropulu, J.R. Vlimant, C. Wang, S. Xie, Z. Zhang, R.Y. Zhu
Carnegie Mellon University, Pittsburgh, USA
J. Alison, S. An, M.B. Andrews, P. Bryant, T. Ferguson, A. Harilal, C. Liu, T.
Mudholkar, M. Paulini, A. Sanchez, W. Terrill University of Colorado Boulder,
Boulder, USA
J.P. Cumalat, W.T. Ford, A. Hassani, E. MacDonald, R. Patel, A. Perloff, C.
Savard, K. Stenson, K.A. Ulmer, S.R. Wagner Cornell University, Ithaca, USA
J. Alexander, S. Bright-thonney, Y. Cheng, D.J. Cranshaw, S. Hogan, J. Monroy,
J.R. Patterson, D. Quach, J. Reichert, M. Reid, A. Ryd, W. Sun, J. Thom, P.
Wittich, R. Zou Fermi National Accelerator Laboratory, Batavia, USA
M. Albrow, M. Alyari, G. Apollinari, A. Apresyan, A. Apyan, S. Banerjee,
L.A.T. Bauerdick, D. Berry, J. Berryhill, P.C. Bhat, K. Burkett, J.N. Butler,
A. Canepa, G.B. Cerati, H.W.K. Cheung, F. Chlebana, M. Cremonesi, K.F. Di
Petrillo, V.D. Elvira, Y. Feng, J. Freeman, Z. Gecse, L. Gray, D. Green, S.
Grünendahl, O. Gutsche, R.M. Harris, R. Heller, T.C. Herwig, J. Hirschauer, B.
Jayatilaka, S. Jindariani, M. Johnson, U. Joshi, T. Klijnsma, B. Klima, K.H.M.
Kwok, S. Lammel, D. Lincoln, R. Lipton, T. Liu, C. Madrid, K. Maeshima, C.
Mantilla, D. Mason, P. McBride, P. Merkel, S. Mrenna, S. Nahn, J. Ngadiuba, V.
O’Dell, V. Papadimitriou, K. Pedro, C. Pena54, O. Prokofyev, F. Ravera, A.
Reinsvold Hall, L. Ristori, B. Schneider, E. Sexton-Kennedy, N. Smith, A.
Soha, W.J. Spalding, L. Spiegel, S. Stoynev, J. Strait, L. Taylor, S. Tkaczyk,
N.V. Tran, L. Uplegger, E.W. Vaandering, H.A. Weber University of Florida,
Gainesville, USA
D. Acosta, P. Avery, D. Bourilkov, L. Cadamuro, V. Cherepanov, F. Errico, R.D.
Field, D. Guerrero, B.M. Joshi, M. Kim, E. Koenig, J. Konigsberg, A. Korytov,
K.H. Lo, K. Matchev, N. Menendez, G. Mitselmakher, A. Muthirakalayil Madhu, N.
Rawal, D. Rosenzweig, S. Rosenzweig, K. Shi, J. Sturdy, J. Wang, E. Yigitbasi,
X. Zuo Florida State University, Tallahassee, USA
T. Adams, A. Askew, R. Habibullah, V. Hagopian, K.F. Johnson, R. Khurana, T.
Kolberg, G. Martinez, H. Prosper, C. Schiber, O. Viazlo, R. Yohay, J. Zhang
Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, S. Butalla, T. Elkafrawy87, M. Hohlmann, R. Kumar Verma, D.
Noonan, M. Rahmani, F. Yumiceva University of Illinois at Chicago (UIC),
Chicago, USA
M.R. Adams, H. Becerril Gonzalez, R. Cavanaugh, X. Chen, S. Dittmer, O.
Evdokimov, C.E. Gerber, D.A. Hangal, D.J. Hofman, A.H. Merrit, C. Mills, G.
Oh, T. Roy, S. Rudrabhatla, M.B. Tonjes, N. Varelas, J. Viinikainen, X. Wang,
Z. Wu, Z. Ye The University of Iowa, Iowa City, USA
M. Alhusseini, K. Dilsiz88, R.P. Gandrajula, O.K. Köseyan, J.-P. Merlo, A.
Mestvirishvili89, J. Nachtman, H. Ogul90, Y. Onel, A. Penzo, C. Snyder, E.
Tiras91 Johns Hopkins University, Baltimore, USA
O. Amram, B. Blumenfeld, L. Corcodilos, J. Davis, M. Eminizer, A.V. Gritsan,
S. Kyriacou, P. Maksimovic, J. Roskes, M. Swartz, T.Á. Vámi The University of
Kansas, Lawrence, USA
A. Abreu, J. Anguiano, C. Baldenegro Barrera, P. Baringer, A. Bean, A.
Bylinkin, Z. Flowers, T. Isidori, S. Khalil, J. King, G. Krintiras, A.
Kropivnitskaya, M. Lazarovits, C. Lindsey, J. Marquez, N. Minafra, M. Murray,
M. Nickel, C. Rogan, C. Royon, R. Salvatico, S. Sanders, E. Schmitz, C. Smith,
J.D. Tapia Takaki, Q. Wang, Z. Warner, J. Williams, G. Wilson Kansas State
University, Manhattan, USA
S. Duric, A. Ivanov, K. Kaadze, D. Kim, Y. Maravin, T. Mitchell, A. Modak, K.
Nam Lawrence Livermore National Laboratory, Livermore, USA
F. Rebassoo, D. Wright University of Maryland, College Park, USA
E. Adams, A. Baden, O. Baron, A. Belloni, S.C. Eno, N.J. Hadley, S. Jabeen,
R.G. Kellogg, T. Koeth, A.C. Mignerey, S. Nabili, C. Palmer, M. Seidel, A.
Skuja, L. Wang, K. Wong Massachusetts Institute of Technology, Cambridge, USA
D. Abercrombie, G. Andreassi, R. Bi, S. Brandt, W. Busza, I.A. Cali, Y. Chen,
M. D’Alfonso, J. Eysermans, C. Freer, G. Gomez Ceballos, M. Goncharov, P.
Harris, M. Hu, M. Klute, D. Kovalskyi, J. Krupa, Y.-J. Lee, B. Maier, C.
Mironov, C. Paus, D. Rankin, C. Roland, G. Roland, Z. Shi, G.S.F. Stephans, J.
Wang, Z. Wang, B. Wyslouch University of Minnesota, Minneapolis, USA
R.M. Chatterjee, A. Evans, P. Hansen, J. Hiltbrand, Sh. Jain, M. Krohn, Y.
Kubota, J. Mans, M. Revering, R. Rusack, R. Saradhy, N. Schroeder, N. Strobbe,
M.A. Wadud University of Nebraska-Lincoln, Lincoln, USA
K. Bloom, M. Bryson, S. Chauhan, D.R. Claes, C. Fangmeier, L. Finco, F. Golf,
C. Joo, I. Kravchenko, M. Musich, I. Reed, J.E. Siado, G.R.
Snow${}^{\textrm{\textdagger}}$, W. Tabb, F. Yan State University of New York
at Buffalo, Buffalo, USA
G. Agarwal, H. Bandyopadhyay, L. Hay, I. Iashvili, A. Kharchilava, C. McLean,
D. Nguyen, J. Pekkanen, S. Rappoccio, A. Williams Northeastern University,
Boston, USA
G. Alverson, E. Barberis, Y. Haddad, A. Hortiangtham, J. Li, G. Madigan, B.
Marzocchi, D.M. Morse, V. Nguyen, T. Orimoto, A. Parker, L. Skinnari, A.
Tishelman-Charny, T. Wamorkar, B. Wang, A. Wisecarver, D. Wood Northwestern
University, Evanston, USA
S. Bhattacharya, J. Bueghly, Z. Chen, A. Gilbert, T. Gunter, K.A. Hahn, Y.
Liu, N. Odell, M.H. Schmitt, M. Velasco University of Notre Dame, Notre Dame,
USA
R. Band, R. Bucci, A. Das, N. Dev, R. Goldouzian, M. Hildreth, K. Hurtado
Anampa, C. Jessop, K. Lannon, J. Lawrence, N. Loukas, D. Lutton, N. Marinelli,
I. Mcalister, T. McCauley, C. Mcgrady, F. Meng, K. Mohrman, Y. Musienko47, R.
Ruchti, P. Siddireddy, A. Townsend, M. Wayne, A. Wightman, M. Wolf, M.
Zarucki, L. Zygala The Ohio State University, Columbus, USA
B. Bylsma, B. Cardwell, L.S. Durkin, B. Francis, C. Hill, M. Nunez Ornelas, K.
Wei, B.L. Winer, B.R. Yates Princeton University, Princeton, USA
F.M. Addesa, B. Bonham, P. Das, G. Dezoort, P. Elmer, A. Frankenthal, B.
Greenberg, N. Haubrich, S. Higginbotham, A. Kalogeropoulos, G. Kopp, S. Kwan,
D. Lange, M.T. Lucchini, D. Marlow, K. Mei, I. Ojalvo, J. Olsen, D. Stickland,
C. Tully University of Puerto Rico, Mayaguez, USA
S. Malik, S. Norberg Purdue University, West Lafayette, USA
A.S. Bakshi, V.E. Barnes, R. Chawla, S. Das, L. Gutay, M. Jones, A.W. Jung, S.
Karmarkar, M. Liu, G. Negro, N. Neumeister, G. Paspalaki, C.C. Peng, S.
Piperov, A. Purohit, J.F. Schulte, M. Stojanovic14, J. Thieman, F. Wang, R.
Xiao, W. Xie Purdue University Northwest, Hammond, USA
J. Dolen, N. Parashar Rice University, Houston, USA
A. Baty, M. Decaro, S. Dildick, K.M. Ecklund, S. Freed, P. Gardner, F.J.M.
Geurts, A. Kumar, W. Li, B.P. Padley, R. Redjimi, W. Shi, A.G. Stahl Leiton,
S. Yang, L. Zhang, Y. Zhang University of Rochester, Rochester, USA
A. Bodek, P. de Barbaro, R. Demina, J.L. Dulemba, C. Fallon, T. Ferbel, M.
Galanti, A. Garcia-Bellido, O. Hindrichs, A. Khukhunaishvili, E. Ranken, R.
Taus Rutgers, The State University of New Jersey, Piscataway, USA
B. Chiarito, J.P. Chou, A. Gandrakota, Y. Gershtein, E. Halkiadakis, A. Hart,
M. Heindl, O. Karacheban21, I. Laflotte, A. Lath, R. Montalvo, K. Nash, M.
Osherson, S. Salur, S. Schnetzer, S. Somalwar, R. Stone, S.A. Thayil, S.
Thomas, H. Wang University of Tennessee, Knoxville, USA
H. Acharya, A.G. Delannoy, S. Fiorendi, S. Spanier Texas A&M University,
College Station, USA
O. Bouhali92, M. Dalchenko, A. Delgado, R. Eusebi, J. Gilmore, T. Huang, T.
Kamon93, H. Kim, S. Luo, S. Malhotra, R. Mueller, D. Overton, D. Rathjens, A.
Safonov Texas Tech University, Lubbock, USA
N. Akchurin, J. Damgov, V. Hegde, S. Kunori, K. Lamichhane, S.W. Lee, T.
Mengke, S. Muthumuni, T. Peltola, I. Volobouev, Z. Wang, A. Whitbeck
Vanderbilt University, Nashville, USA
E. Appelt, S. Greene, A. Gurrola, W. Johns, A. Melo, H. Ni, K. Padeken, F.
Romeo, P. Sheldon, S. Tuo, J. Velkovska University of Virginia,
Charlottesville, USA
M.W. Arenton, B. Cox, G. Cummings, J. Hakala, R. Hirosky, M. Joyce, A.
Ledovskoy, A. Li, C. Neu, B. Tannenwald, S. White, E. Wolfe Wayne State
University, Detroit, USA
N. Poudyal University of Wisconsin - Madison, Madison, WI, USA
K. Black, T. Bose, J. Buchanan, C. Caillol, S. Dasu, I. De Bruyn, P.
Everaerts, F. Fienga, C. Galloni, H. He, M. Herndon, A. Hervé, U. Hussain, A.
Lanaro, A. Loeliger, R. Loveless, J. Madhusudanan Sreekala, A. Mallampalli, A.
Mohammadi, D. Pinna, A. Savin, V. Shang, V. Sharma, W.H. Smith, D. Teague, S.
Trembath-reichert, W. Vetens †: Deceased
1: Also at TU Wien, Wien, Austria
2: Also at Institute of Basic and Applied Sciences, Faculty of Engineering,
Arab Academy for Science, Technology and Maritime Transport, Alexandria, Egypt
3: Also at Université Libre de Bruxelles, Bruxelles, Belgium
4: Also at Universidade Estadual de Campinas, Campinas, Brazil
5: Also at Federal University of Rio Grande do Sul, Porto Alegre, Brazil
6: Also at University of Chinese Academy of Sciences, Beijing, China
7: Also at Department of Physics, Tsinghua University, Beijing, China
8: Also at UFMS, Nova Andradina, Brazil
9: Also at Nanjing Normal University Department of Physics, Nanjing, China
10: Now at The University of Iowa, Iowa City, USA
11: Also at Institute for Theoretical and Experimental Physics named by A.I.
Alikhanov of NRC ‘Kurchatov Institute’, Moscow, Russia
12: Also at Joint Institute for Nuclear Research, Dubna, Russia
13: Also at Cairo University, Cairo, Egypt
14: Also at Purdue University, West Lafayette, USA
15: Also at Université de Haute Alsace, Mulhouse, France
16: Also at Erzincan Binali Yildirim University, Erzincan, Turkey
17: Also at CERN, European Organization for Nuclear Research, Geneva,
Switzerland
18: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
19: Also at University of Hamburg, Hamburg, Germany
20: Also at Isfahan University of Technology, Isfahan, Iran, Isfahan, Iran
21: Also at Brandenburg University of Technology, Cottbus, Germany
22: Also at Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State
University, Moscow, Russia
23: Also at Physics Department, Faculty of Science, Assiut University, Assiut,
Egypt
24: Also at Karoly Robert Campus, MATE Institute of Technology, Gyongyos,
Hungary
25: Also at Institute of Physics, University of Debrecen, Debrecen, Hungary
26: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary
27: Also at MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
28: Also at Wigner Research Centre for Physics, Budapest, Hungary
29: Also at IIT Bhubaneswar, Bhubaneswar, India
30: Also at Institute of Physics, Bhubaneswar, India
31: Also at G.H.G. Khalsa College, Punjab, India
32: Also at Shoolini University, Solan, India
33: Also at University of Hyderabad, Hyderabad, India
34: Also at University of Visva-Bharati, Santiniketan, India
35: Also at Indian Institute of Technology (IIT), Mumbai, India
36: Also at Deutsches Elektronen-Synchrotron, Hamburg, Germany
37: Also at Sharif University of Technology, Tehran, Iran
38: Also at Department of Physics, University of Science and Technology of
Mazandaran, Behshahr, Iran
39: Now at INFN Sezione di Bari a, Università di Bari b, Politecnico di Bari
c, Bari, Italy
40: Also at Italian National Agency for New Technologies, Energy and
Sustainable Economic Development, Bologna, Italy
41: Also at Centro Siciliano di Fisica Nucleare e di Struttura Della Materia,
Catania, Italy
42: Also at Università di Napoli ’Federico II’, Napoli, Italy
43: Also at Consiglio Nazionale delle Ricerche - Istituto Officina dei
Materiali, PERUGIA, Italy
44: Also at Riga Technical University, Riga, Latvia
45: Also at Consejo Nacional de Ciencia y Tecnología, Mexico City, Mexico
46: Also at IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
47: Also at Institute for Nuclear Research, Moscow, Russia
48: Now at National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
49: Also at Institute of Nuclear Physics of the Uzbekistan Academy of
Sciences, Tashkent, Uzbekistan
50: Also at St. Petersburg State Polytechnical University, St. Petersburg,
Russia
51: Also at University of Florida, Gainesville, USA
52: Also at Imperial College, London, United Kingdom
53: Also at P.N. Lebedev Physical Institute, Moscow, Russia
54: Also at California Institute of Technology, Pasadena, USA
55: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia
56: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia
57: Also at Trincomalee Campus, Eastern University, Sri Lanka, Nilaveli, Sri
Lanka
58: Also at INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy
59: Also at National and Kapodistrian University of Athens, Athens, Greece
60: Also at Ecole Polytechnique Fédérale Lausanne, Lausanne, Switzerland
61: Also at Universität Zürich, Zurich, Switzerland
62: Also at Stefan Meyer Institute for Subatomic Physics, Vienna, Austria
63: Also at Laboratoire d’Annecy-le-Vieux de Physique des Particules,
IN2P3-CNRS, Annecy-le-Vieux, France
64: Also at Şırnak University, Sirnak, Turkey
65: Also at Near East University, Research Center of Experimental Health
Science, Nicosia, Turkey
66: Also at Konya Technical University, Konya, Turkey
67: Also at Istanbul University - Cerrahpasa, Faculty of Engineering,
Istanbul, Turkey
68: Also at Piri Reis University, Istanbul, Turkey
69: Also at Adiyaman University, Adiyaman, Turkey
70: Also at Ozyegin University, Istanbul, Turkey
71: Also at Izmir Institute of Technology, Izmir, Turkey
72: Also at Necmettin Erbakan University, Konya, Turkey
73: Also at Bozok Universitetesi Rektörlügü, Yozgat, Turkey
74: Also at Marmara University, Istanbul, Turkey
75: Also at Milli Savunma University, Istanbul, Turkey
76: Also at Kafkas University, Kars, Turkey
77: Also at Istanbul Bilgi University, Istanbul, Turkey
78: Also at Hacettepe University, Ankara, Turkey
79: Also at Rutherford Appleton Laboratory, Didcot, United Kingdom
80: Also at Vrije Universiteit Brussel, Brussel, Belgium
81: Also at School of Physics and Astronomy, University of Southampton,
Southampton, United Kingdom
82: Also at IPPP Durham University, Durham, United Kingdom
83: Also at Monash University, Faculty of Science, Clayton, Australia
84: Also at Università di Torino, TORINO, Italy
85: Also at Bethel University, St. Paul, Minneapolis, USA, St. Paul, USA
86: Also at Karamanoğlu Mehmetbey University, Karaman, Turkey
87: Also at Ain Shams University, Cairo, Egypt
88: Also at Bingol University, Bingol, Turkey
89: Also at Georgian Technical University, Tbilisi, Georgia
90: Also at Sinop University, Sinop, Turkey
91: Also at Erciyes University, KAYSERI, Turkey
92: Also at Texas A&M University at Qatar, Doha, Qatar
93: Also at Kyungpook National University, Daegu, Korea, Daegu, Korea