B2G-17-013

$HeadURL:svn+ssh://svn.cern.ch/reps/tdr2/papers/B2G-17-013/trunk/B2G-17-013.tex$
$Id:B2G-17-013.tex4758232018-09-2214:00:46Zjpazzini$

B2G-17-013

# Search for a heavy resonance decaying into a boson and a or boson in $2\ell
2\PQq$ final states at $\sqrt{s}=13\TeV$

###### Abstract

A search has been performed for heavy resonances decaying to $\PZ\PZ$ or
$\PZ\PW$ in $2\ell 2\PQq$ final states, with two charged leptons
($\ell=\Pe,\Pgm$) produced by the decay of a boson, and two quarks produced by
the decay of a or boson. The analysis is sensitive to resonances with masses
in the range from 400 to 4500. Two categories are defined based on the merged
or resolved reconstruction of the hadronically decaying vector boson,
optimized for high- and low-mass resonances, respectively. The search is based
on data collected during 2016 by the CMS experiment at the LHC in proton-
proton collisions with a center-of-mass energy of $\sqrt{s}=13\TeV$,
corresponding to an integrated luminosity of 35.9. No excess is observed in
the data above the standard model background expectation. Upper limits on the
production cross section of heavy, narrow spin-1 and spin-2 resonances are
derived as a function of the resonance mass, and exclusion limits on the
production of bosons and bulk graviton particles are calculated in the
framework of the heavy vector triplet model and warped extra dimensions,
respectively.

##  0.1 Introduction

The validity of the standard model (SM) of particle physics is corroborated by
a wide set of precise experimental results with an impressive level of
accuracy. Nonetheless, there are several open points where the SM fails to
provide an explanation, either for experimental observations, as in the case
of the presence of dark matter in the universe, or for theoretical questions,
such as the omission of gravity from the SM, and the hierarchy problem.

Several SM extensions addressing the open questions of the SM predict the
presence of new heavy particles with an enhanced branching fraction for decays
into pairs of vector bosons. The existence of heavy spin-2 gravitons () is
predicted in the Randall–Sundrum model with warped extra spatial dimensions
(WED) [1, 2, 3]. In the bulk scenario [4, 5], the main free parameters are the
mass of the first Kaluza–Klein graviton excitation (the bulk graviton mass),
and the ratio $\widetilde{\kappa}\equiv\kappa/\overline{M}_{\text{Pl}}$, where
$\kappa$ is a curvature parameter of the WED metric and
$\overline{M}_{\text{Pl}}\equiv\Mpl/\sqrt{8\pi}$ is the reduced Planck mass.
The introduction of a spin-1 triplet of and bosons is described in the heavy
vector triplet (HVT) model [6], which generalizes a large number of explicit
models in terms of a small set of parameters: $c_{\mathrm{H}}$, controlling
the interactions of the triplet with the SM vector and Higgs bosons;
$c_{\mathrm{F}}$, which describes the direct interaction with fermions; and
$g_{\mathrm{V}}$, which represents the overall strength of the new vector
boson triplet interactions.

A variety of searches for heavy resonances decaying to two vector bosons have
been carried out in the past. The most recent results from the CERN LHC [7, 8,
9, 10, 11], with no evidence of signal, have provided stringent upper limits
on signal cross sections in these models.

This paper reports on the results of a search for heavy, narrow resonances
(collectively indicated as $\cmsSymbolFace{X}$) decaying into $2\ell 2\cPq$
final states, with two charged leptons ($\ell=\Pe,\Pgm$) produced by the
leptonic decay of a boson and a pair of quarks produced from the hadronic
decay of a vector boson ($\cmsSymbolFace{V}$ = or ). In the narrow-width
assumption, the width of the heavy resonance is taken to be small in
comparison to the experimental resolution. Two complementary search strategies
are defined to span the mass range $400<m_{\cmsSymbolFace{X}}<4500\GeV$, where
$m_{\cmsSymbolFace{X}}$ is the mass of the heavy resonance. The first
strategy, referred to as the “high-mass analysis”, is optimized for the range
$850<m_{\cmsSymbolFace{X}}<4500\GeV$ by selecting events where the vector
bosons have a large Lorentz boost, resulting in the collimation of their decay
products. The high-mass analysis uses dedicated leptonic reconstruction and
identification techniques to reconstruct leptons in close proximity to each
other in order to retain high signal efficiency, as well as jet substructure
techniques to identify the hadronic decay of the or boson into a pair of
quarks contained in a single merged reconstructed jet. For lower resonance
masses, the quarks produced by the hadronic decay of the $\cmsSymbolFace{V}$
boson may be sufficiently separated to be reconstructed as two single narrow
jets (dijet). A second strategy, referred to as the “low-mass analysis”, is
therefore defined in this regime, exploiting dijet reconstruction in addition
to the reconstruction of merged jets to retain signal efficiency in the range
$400<m_{\cmsSymbolFace{X}}<850\GeV$ for those events in which no merged
$\cmsSymbolFace{V}$ candidate is found. To increase the signal sensitivity, in
the low-mass analysis a categorization based on the flavor of the jets is
used, to exploit the relatively large decay branching fraction of the boson to
pairs of b quarks.

This paper is organized as follows: in Section 0.2, a description of the data
and simulated samples used in the analysis is provided; Section 0.3 briefly
describes the CMS detector; Section 0.4 provides a description of the event
reconstruction; in Section 0.5, the event selection is discussed; Section 0.6
contains the description of the signal and describes the estimation of the SM
background; the systematic uncertainties affecting the analysis are presented
in Section 0.7; and the results of the search for heavy spin-1 and spin-2
resonances are presented in Section 0.8. Finally, results are summarized in
Section 0.9.

##  0.2 Data and simulated samples

This analysis uses data collected by the CMS detector during proton-proton ()
collisions at the LHC at $\sqrt{s}=13\TeV$, corresponding to an integrated
luminosity of 35.9. The events were selected online by criteria that require
the presence of at least one electron or muon; these criteria are described in
Sec. 0.5.

Simulated signal samples are used in the analysis to optimize the search for
the potential production of heavy spin-1 or spin-2 resonances. For this
purpose, signal samples are generated according to the HVT and WED scenarios,
respectively. For both scenarios, the samples are generated at leading order
(LO) in QCD with the 2.2.2 generator [12]. Two HVT models are considered as
benchmarks, “model A” and “model B”, with different values of the three
defining parameters described earlier: for “model A”, $g_{\mathrm{V}}=1$,
$c_{\mathrm{H}}=-0.556$, and $c_{\mathrm{F}}=-1.316$, while for “model B”,
$g_{\mathrm{V}}=3$, $c_{\mathrm{H}}=-0.976$, and $c_{\mathrm{F}}=1.024$.

Different resonance mass hypotheses are considered in the range from 400 to
4500. The resonance width is predicted to be between 0.4 and 2.3for a
candidate in HVT model A, and between 14 and 64for HVT model B, depending on
the mass hypothesis [6]; in the WED model with $\widetilde{\kappa}=0.1$, the
bulk graviton signal width is predicted to range from 3.6 to 54 [13]. Since
the resonance width is small in comparison with the experimental resolution,
for simplicity, the width is taken to be 1in the simulation. In the case of
the spin-1 , the resonance is forced to decay into one and one boson;
additionally, the boson is then forced to decay to a pair of electrons, muons,
or tau leptons, while the boson is forced to decay into a pair of quarks. The
generated spin-2 bulk graviton is instead forced to decay into two bosons, one
decaying leptonically into any pair of charged leptons, and the other boson
decaying hadronically into a pair of quarks.

Several SM processes yielding final states with charged leptons and jets are
sources of background events for the analysis, and corresponding Monte Carlo
(MC) simulated samples have been generated and used in the analysis.

The SM production of a boson in association with quarks or gluons in the final
state ($\PZ+\text{jets}$) represents the dominant background process for the
analysis, having topological similarities to the signal because of the
presence of a pair of charged leptons and jets. However, since the quark- and
gluon-induced jets are not associated with the decay of a vector boson, the
jet mass spectrum is characterized by a smooth distribution and the
distribution of the $2\ell+\text{jet}$ system invariant mass falls
exponentially, in contrast with the peaking distribution expected from the
signal in both the jet and $2\ell+\text{jet}$ mass spectra. The
$\PZ+\text{jets}$ MC samples are produced with at next-to-leading order (NLO),
using the FxFx merging scheme [14] between the jets from matrix element
calculations and parton showers, and normalized to the next-to-NLO cross
section computed using v3.1 [15].

Another important source of SM background arises from processes leading to top
quark production. Simulated samples describing the production of top quark
pairs are generated with at LO, with the MLM matching scheme [16]. Single top
quark production is also considered; $s$\- and $t$-channel single top quark
samples are produced in the four-flavor scheme using and v2 [17, 18, 19, 20],
respectively, while $\PQt\PW$ production is simulated at NLO with in the five-
flavor scheme [21]. Additional top quark background processes, such as the
associated production of a or boson with pair-produced top quarks, and the
production of $\PQt\PQq\PZ$, are also considered in the analysis and produced
at NLO with .

The SM diboson production of $\cmsSymbolFace{V}\cmsSymbolFace{V}$ is an
irreducible source of background for the analysis, since the jet mass spectrum
will contain a peak from the hadronic decay of and bosons, like the expected
jet mass spectrum for the signal; however, this process produces a smoothly
falling $2\ell+\text{jet}$ invariant mass distribution. The SM production of
pairs of vector bosons ($\PW\PW$, $\PW\PZ$, and $\PZ\PZ$) is simulated at NLO
with .

For all the simulated samples used in the analysis, the simulation of parton
showering and hadronization is described by interfacing the event generators
with 8.212 [22] with the CUETP8M1 [23] tune, while the parton distribution
functions (PDFs) of the colliding protons are given by the NNPDF 3.0 [24] PDF
set. Additional $\Pp\Pp$ interactions occurring in the same or nearby bunch
crossings (pileup) are added to the event simulation, with a frequency
distribution adjusted to match that observed in data. All samples are
processed through a simulation of the CMS detector using [25], and
reconstructed using the same algorithms as those for the data collected.

##  0.3 The CMS detector

The central feature of the CMS apparatus is a superconducting solenoid of 6m
internal diameter, providing a magnetic field of 3.8T. Within the solenoid
volume are a silicon pixel and strip tracker, a lead tungstate crystal
electromagnetic calorimeter (ECAL), and a brass and scintillator hadron
calorimeter (HCAL), each composed of a barrel and two endcap sections. The
silicon tracker covers the pseudorapidity range $\abs{\eta}<2.5$, while the
ECAL and HCAL cover the range $\abs{\eta}<3.0$. Forward calorimeters extend
the coverage provided by the barrel and endcap detectors to $\abs{\eta}<5.2$.
Muons are detected in gas-ionization chambers embedded in the steel flux-
return yoke outside the solenoid, with detection planes made using three
technologies: drift tubes, cathode strip chambers, and resistive-plate
chambers.

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. [26].

##  0.4 Event reconstruction

The event reconstruction is performed globally using a particle-flow (PF)
algorithm [27], which reconstructs and identifies each individual particle
with an optimized combination of information from the various elements of the
CMS detector.

The reconstructed vertex with the largest value of summed physics-object
$\pt^{2}$ is taken to be the primary $\Pp\Pp$ interaction vertex. The physics
objects chosen are those that have been defined using information from the
tracking detector. These objects include jets, the associated missing
transverse momentum, which was taken as the negative vector sum of the
transverse momentum () of those jets, and charged leptons.

In the silicon tracker, isolated charged particles with $\pt=100\GeV$ and
$\abs{\eta}<1.4$ have track resolutions of 2.8% in and 10 (30)in the
transverse (longitudinal) impact parameter [28]. The energy of charged hadrons
is determined from a combination of their momenta measured in the tracker and
the matching ECAL and HCAL energy deposits, corrected for zero-suppression
effects and for the response function of the calorimeters to hadronic showers.
The energy of neutral hadrons is obtained from the corresponding corrected
ECAL and HCAL energies.

Electrons are required to be within the range $\abs{\eta}<2.5$ covered by the
silicon tracker, and are reconstructed from a combination of the deposited
energy of the ECAL clusters associated with the track reconstructed from the
measurements determined by the inner tracker, and the energy sum of all
photons spatially compatible with being bremsstrahlung from the electron
track. The identification of electrons is based on selection criteria relying
on the direction and momentum of the track in the inner tracker, its
compatibility with the primary vertex of the event [27], and on observables
sensitive to the shape of energy deposits along the electron trajectory. The
momentum resolution for electrons with $\pt\approx 45\GeV$ from $\PZ\to\Pe\Pe$
decays ranges from 1.7% to 4.5% [29]. It is generally better in the barrel
region than in the endcaps, and also depends on the amount of bremsstrahlung
emitted by the electron as it traverses the material in front of the ECAL.

Muons are reconstructed in the entire CMS muon system acceptance region of
$\abs{\eta}<2.4$ by combining in a global fit the information provided by the
measurements in the silicon tracker and the muon spectrometer. Candidate muons
are selected using criteria based on the degree of compatibility of the inner
track, which is reconstructed using the silicon tracker only, and the track
reconstructed using the combination of the hits in both the tracker and
spectrometer. Further reconstruction requirements include the compatibility of
the trajectory with the primary vertex of the event, and the number of hits
observed in the tracker and muon systems. The relative resolution achieved is
1.3–2.0% for muons with $20<\pt<100\GeV$ in the barrel and better than 6% in
the endcaps. The resolution in the barrel is better than 10% for muons with up
to 1 [30].

Both electrons and muons are required to be isolated from hadronic activity
and other leptons in the event. An isolation variable is defined as the scalar
sum of the of charged hadrons originating from the primary vertex, plus the
scalar sums of the transverse momenta for neutral hadrons and photons, in a
cone of $\Delta
R=\sqrt{\smash[b]{(\Delta\eta)^{2}+(\Delta\phi)^{2}}}<0.3\,(0.4)$ around the
electron (muon) direction corrected to account for the contribution from
neutral candidates originating from pileup, where $\phi$ is the azimuthal
angle in radians. In the high-mass analysis, a specific muon isolation
requirement is implemented to retain signal efficiency for high resonance
masses, where the large boson boost may result in extremely close pairs of
muons. For this reason, muon candidates in the high-mass analysis are retained
if they pass an isolation requirement based on the sum of reconstructed of all
tracks within $\Delta R<0.3$ from the muon trajectory, ignoring tracks
associated with other reconstructed muons.

Hadron jets are clustered from particles reconstructed by the PF algorithm
using the infrared- and collinear-safe anti-algorithm [31, 32] with distance
parameters of 0.4 (AK4 jets) and 0.8 (AK8 jets). The jet momentum is
determined as the vectorial sum of all constituent particle momenta.
Contamination from pileup is suppressed using charged hadron subtraction (CHS)
which removes from the list of PF candidates all charged particles originating
from vertices other than the primary interaction vertex of the event. The
residual contribution from neutral and charged particles originating from
pileup vertices is removed by means of an event-by-event jet-area-based
correction to the jet four-momentum. Identification requirements, based on the
estimation of the energy fraction carried by the different types of PF
candidates clustered into a jet, along with the multiplicity of the PF
candidates, are used to remove jets originating from calorimetric noise.
Corrections to the jet energy are derived from the simulation, and are
confirmed with in situ measurements with the energy balance of dijet,
multijet, $\text{photon}+\text{jet}$, and leptonically decaying
$\PZ+\text{jet}$ events [33].

A jet grooming technique is used for AK8 jets in this analysis to help
identify and discriminate between jets from boosted hadronic
$\cmsSymbolFace{V}$ decays, which we refer to as “merged jets”, and jets from
quarks and gluons. The AK8 jets are groomed by means of the modified mass drop
tagger algorithm [34], also known as the soft drop algorithm, with angular
exponent $\beta=0$, soft cutoff threshold $z_{\text{cut}}<0.1$, and
characteristic radius $R_{0}=0.8$ [35]. The soft drop algorithm does not fully
reject contributions from the underlying event and pileup. The mass of the AK8
jet ($m_{\mathrm{j}}$) is therefore defined as the invariant mass associated
to the four-momentum of the soft drop jet, after the application of the pileup
mitigation corrections provided by the pileup per particle identification
(PUPPI) algorithm [36].

Discrimination between AK8 jets originating from vector boson decays and those
originating from gluons and quarks is also achieved by the $N$-subjettiness
jet substructure variable [37]. This observable exploits the distribution of
the jet constituents found in the proximity of the subjet axes to determine if
the jet can be effectively subdivided into a number $N$ of subjets. The
generic $N$-subjettiness variable $\tau_{N}$ is defined as the -weighted sum
of the angular distance of all the $k$ jet constituents from the closest
subjet:

$\tau_{N}=\frac{1}{d_{0}}\sum_{k}p_{\mathrm{T},k}\min(\Delta R_{1,k},\Delta
R_{2,k},\dots,\Delta R_{N,k}).$ (1)

The normalization factor $d_{0}$ is defined as
$d_{0}=\sum_{k}p_{\mathrm{T},k}R_{0}$, with $R_{0}$ the clustering parameter
of the original jet. In this analysis, which aims to select
$\cmsSymbolFace{V}\to\cPq\cPq^{(\prime)}$ decays, the variable that best
discriminates V boson jets from those from quarks and gluons is the ratio of
the 2-subjettiness to the 1-subjettiness: $\tau_{21}=\tau_{2}/\tau_{1}$. The
$\tau_{21}$ observable is calculated for the jet before the grooming
procedure, and includes the PUPPI algorithm corrections for pileup mitigation.

For the identification of jets originating from the hadronization of bottom
quarks, the combined secondary vertex (CSVv2) algorithm [38, 39] is used,
either directly on the AK4 jets or on the AK8 soft drop subjets with CHS
pileup mitigation applied.

Only AK4 and AK8 jets reconstructed centrally in the detector acceptance,
within $\abs{\eta}<2.4$, are considered in the analysis.

##  0.5 Event selection

Events are selected online by requiring the reconstruction at trigger [40]
level of at least one charged lepton. For the high-mass analysis, thresholds
of 115 (50)are used for electrons (muons). No isolation requirements are
applied at trigger level, to retain efficiency for high-mass signals, where
the large boost expected for the leptonically decaying boson will cause the
two charged leptons to be collimated in the detector. For the low-mass
analysis a larger separation between the leptons is expected because of the
lower of the boson, and isolation requirements are included in the trigger
selection, allowing the use of lower lepton thresholds. The online selection
for the low-mass analysis requires at least one electron with $\pt>25\GeV$ and
$\abs{\eta}<2.1$ passing tight identification and isolation requirements, or
at least one muon with $\pt>24\GeV$ and $\abs{\eta}<2.4$, subject to loose
identification and isolation requirements, using the variables described in
Ref. [40].

To reconstruct the boson candidate, at least two well-identified leptons with
opposite charge and the same flavor are required to be present in the event.
The leading lepton in the event is required to pass more stringent selection
requirements than the online thresholds to avoid inefficiencies induced by the
trigger selections. In the high-mass analysis, the leading (subleading) lepton
is required to have $\pt>135\,(35)\GeV$ for electrons, and $\pt>55\,(20)\GeV$
for muons. Loose isolation and identification requirements are applied to the
leptons to retain high signal efficiency. For electrons, we use a set of
requirements that have been observed to have an efficiency of about 90% for
both low and high mass points. For muons, as the CMS standard requirements
[41] only have an efficiency of about 65% for close muons, we instead use a
dedicated selection where one of the two muons is allowed to be identified
only in the tracker. The isolation variable is calculated removing the
contribution of the other muon if it falls within the isolation cone,
therefore recovering a signal efficiency of about 90% for high mass
resonances. For the low-mass analysis, the leading (subleading) lepton is
required to have larger than $40\,(30)\GeV$ and to fall in the range
$\abs{\eta}<2.1\,(2.4)$.

The selection of the boson candidate relies on the invariant mass of the
dilepton pair, $m_{\ell\ell}$. This is required to satisfy
$70<m_{\ell\ell}<110\GeV$, except for the low-mass analysis in the resolved
category (discussed below) where the requirement is $76<m_{\ell\ell}<106\GeV$
to enhance the sensitivity to the signal by reducing the nonresonant
contribution in the sample with tagged jets.

Different strategies are used in the low- and high-mass analyses to identify
and reconstruct the hadronically decaying $\cmsSymbolFace{V}$ boson, as
described below, to cope with the different $\cmsSymbolFace{V}$ boson boost
regimes expected for low- and high-mass signal candidates.

In the high-mass analysis a merged jet is required in the event, and its mass
$m_{\mathrm{j}}$ is used to select the hadronically decaying or . The signal
is expected to be almost fully contained in the mass range
$65<m_{\mathrm{j}}<105\GeV$, which is thus defined as the signal region (SR).
In order to select candidate signal events, where a heavy massive particle
decays into a pair of boosted vector bosons, both the dilepton pair and the
leading jet selected in an event are required to have $\pt>200\GeV$; this is
motivated by the spectrum of the $\cmsSymbolFace{V}$ bosons observed in
simulation. Events are divided into categories depending on the flavor of the
charged leptons ( or ) and the value of the jet $\tau_{21}$ variable. As the
signal is expected to have lower values of $\tau_{21}$, two different purity
categories are defined: events with $\tau_{21}<0.35$ are defined as the high-
purity (HP) category, while events with $0.35<\tau_{21}<0.75$ fall into a low-
purity (LP) category, used to retain some sensitivity to signal although a
larger amount of background is expected with respect to the HP category. The
$\tau_{21}>0.75$ region is expected to be dominated by the background, and is
therefore not used in the high-mass analysis. In total, four exclusive
categories (from the two purity and two lepton flavor categories) are defined
for the high-mass analysis.

In the low-mass analysis, events are divided into two categories depending on
whether the two quarks from the hadronic $\cmsSymbolFace{V}$ decay merge into
a single reconstructed jet or can be resolved as two distinct jets. In the
merged category, merged jets with $\pt>200\GeV$ and $\tau_{21}<0.40$ are
selected. The choice of a looser $\tau_{21}$ selection with respect to the
cutoff applied in the HP category of the high-mass analysis is driven by the
higher expected signal efficiency for merged events, which are selected in the
low-mass analysis using only one $\tau_{21}$ category. As in the high-mass
analysis, the jet mass is required to be in the range
$65<m_{\mathrm{j}}<105\GeV$ for the jet to be considered a candidate or boson,
which is also defined as the SR for the merged low-mass analysis. The resolved
category contains events that do not contain a merged $\cmsSymbolFace{V}$
candidate, but instead two AK4 jets, both with $\pt>30\GeV$ that form a dijet
candidate with invariant mass $m_{\mathrm{jj}}>30\GeV$ and $\pt>100\GeV$. In
both the merged and resolved cases, the selection is determined by comparing
the spectrum of simulated signal events with the expected background. Both the
merged and resolved categories are further split into two tag categories.
Events in the merged tagged category are required to have at least one subjet
satisfying a tagging requirement corresponding to $\approx$65% efficiency for
b quark identification and $\approx$1% light-flavor jet mistag rate; events
not passing this requirement are placed in the merged untagged category. For
the resolved tagged category, events are required to have at least one jet
satisfying the same tagging requirement used in the merged category; a looser
tag selection is instead required for the other jet, with $\approx$80%
efficiency and $\approx$10% light-flavor jet mistag rate. Events failing these
requirements fall in the so-called resolved untagged category. An arbitration
procedure is used to select the dijet candidate in case of events containing
more than two selected narrow jets: first, if a dijet passing the tagging
requirements is selected in the event, the candidate in the tag category is
chosen; then the dijet candidate closest in mass to the boson mass is selected
as the candidate $\cmsSymbolFace{V}$ boson. The signal region for the low-mass
resolved category accepts events in the dijet mass range
$65<m_{\mathrm{jj}}<110\GeV$. Eight categories are defined in the low-mass
analysis, based on the lepton flavor, the -tag category, and the merged or
resolved reconstruction of the hadronically decaying $\cmsSymbolFace{V}$
candidate.

The $\tau_{21}$ and merged jet distributions of the $\cmsSymbolFace{V}$
candidate for events selected in the merged category of the low-mass analysis
are shown in Fig. 1, where the $m_{\mathrm{j}}$ and $m_{\mathrm{jj}}$
distributions for events with a $\cmsSymbolFace{V}$ candidate are also shown
for the merged and resolved low-mass analysis categories, respectively.

Figure 1:  Upper row: distribution of the merged $\cmsSymbolFace{V}$ candidate
$\tau_{21}$ (left), where the $\tau_{21}<0.4$ requirement has been removed,
and the jet (right) in data and simulation for events in the signal region of
the low-mass analysis. Lower row: $\cmsSymbolFace{V}$ candidate
$m_{\mathrm{j}}$ (left) and $m_{\mathrm{jj}}$ (right) in data and simulation
for events in the signal regions of the low-mass search. The points show the
data while the filled histograms show the background contributions. The gray
band shows the statistical and systematic uncertainties in the background,
while the dashed vertical region (“Higgs”) shows the expected SM Higgs boson
mass range, which is excluded from this analysis. A 600bulk graviton signal
prediction is represented by the black dashed histogram; for visibility, the
signal cross-section is increased by a factor of 5 in the merged category and
50 in the resolved category. With the exception of the jet , which typically
peaks at approximately half of the resonance mass, the quantities shown have
minimal dependence on the mass of the resonance. The background normalization
is derived from the final fit to the $m_{\cmsSymbolFace{V}\PZ}$ observable in
data.

##  0.6 Background estimation

###  0.6.1 High-mass analysis

The main source of background events in the final state of the analysis arises
from the production of a leptonically decaying boson in association with quark
and gluon jets. A second background source relevant for the analysis is SM
diboson production, mainly $\PZ\PZ$ and $\PZ\PW$, with a leptonically decaying
boson together with a or boson decaying hadronically. These diboson events are
an irreducible background for the analysis, as the mass distribution of the SM
$\cmsSymbolFace{V}$ jet peaks in the same region as the signal. Finally, top
quark production is considered as a source of background in the analysis,
despite having a much smaller contribution with respect to other SM
backgrounds in the region probed by this analysis, mostly because of the boson
invariant mass selection and the large boost required in the event.

All SM background processes are characterized by a smoothly falling
distribution of the invariant mass of the dilepton pair and the jet selected
($m_{\cmsSymbolFace{V}\PZ}$), whereas the signal is instead expected to appear
as a narrow peak at a value of $m_{\cmsSymbolFace{V}\PZ}$ close to the actual
value of the mass of the resonance $m_{\cmsSymbolFace{X}}$.

To minimize the dependency on the accuracy of the simulation, the contribution
of the dominant background, $\PZ+\text{jets}$ SM production, is estimated
using data. Two signal-depleted regions are defined by selecting events with
jet mass outside the $m_{\mathrm{j}}$ signal mass window defined in Section
0.5; these are the sideband (SB) regions. A lower sideband (LSB) region is
defined for events with $30<m_{\mathrm{j}}<65\GeV$, close to the SR of the
analysis, while a higher sideband (HSB) region contains events with
$135<m_{\mathrm{j}}<300\GeV$. The region $105<m_{\mathrm{j}}<135\GeV$ is not
used in the analysis, to exclude events containing the hadronic decays of a SM
Higgs boson, which are targeted in other CMS analyses, such as that described
in [42].

The $\PZ+\text{jets}$ background $m_{\cmsSymbolFace{V}\PZ}$ shape and
normalization are obtained by extrapolation from fits to data in the SB
regions.

The $m_{\mathrm{j}}$ distribution for the SM background sources considered in
the analysis is modeled by means of analytic functions describing the spectrum
of each background in the mass region $30<m_{\mathrm{j}}<300\GeV$. In the LP
category, the $m_{\mathrm{j}}$ spectrum in $\PZ+\text{jets}$ events is
described by a smoothly falling exponential distribution, while a broad
structure centered around the mass of the boson present in the HP category is
modeled with an error function convolved with an exponential distribution,
which is of particular importance for describing the behavior at large values
of $m_{\mathrm{j}}$. The peaking structure of the diboson background,
originating from the presence of a jet from a genuine or boson in the event,
is described in both the LP and HP categories with a Gaussian distribution.
The remaining component of the distribution, consisting of tails extending far
from the SR, is modeled in the LP category with an exponential function,
similarly to the $\PZ+\text{jets}$ case. In the HP category, the
$\cmsSymbolFace{V}\cmsSymbolFace{V}$ events are mostly contained in the SR,
and the small fraction of events present in the Higgs boson and LSB regions is
described with an additional broad Gaussian contribution. The top quark
background (, single top quark, $\PQt\PZ\PQq$, and $\ttbar\cmsSymbolFace{V}$
production) is mostly similar in shape to the $\PZ+\text{jets}$ background; in
the LP category, in addition to the exponentially falling component, a
Gaussian is included to model the top quark peak appearing in the HSB for
$m_{\mathrm{j}}\approx 170\GeV$.

The expected yield of the $\PZ+\text{jets}$ background in the SR is extracted
by a fit of the $m_{\mathrm{j}}$ distribution in the SBs taking into account
all background contributions. The parameters describing the $m_{\mathrm{j}}$
shape and normalization of the subdominant background processes are fixed to
those extracted from the simulation. All the parameters used to describe the
$\PZ+\text{jets}$ contribution are left free to float in the fit to the data
SBs. Alternative functions modeling the $m_{\mathrm{j}}$ shape of the main
$\PZ+\text{jets}$ background are used to evaluate the impact of the function
choice on the signal normalization.

The $m_{\mathrm{j}}$ distribution for expected and observed events is shown in
Fig. 2.

Figure 2: The $m_{\mathrm{j}}$ distributions of the events in data, compared
to the expected background shape, for the high-mass analysis in the electron
(upper) and muon (lower) channels, and for the high-purity (left) and low-
purity (right) categories. The expected background shape is extracted from a
fit to the data sidebands ($\PZ+\text{jets}$) or derived from simulation (“top
quark” and “$\cmsSymbolFace{V}\cmsSymbolFace{V}$”). The dashed region around
the background sum represents the uncertainty in the $\PZ+\text{jets}$
distribution, while the dashed vertical region (“Higgs”) shows the expected SM
Higgs boson mass range, excluded from the analysis. The bottom panels show the
pull distribution between data and SM background expectation from the fit,
where $\sigma_{\text{data}}$ is the Poisson uncertainty in the data.

To describe the shape of the $m_{\cmsSymbolFace{V}\PZ}$ variable for the
$\PZ+\text{jets}$ background in the SR, the following transfer function is
defined from simulation:

$\alpha(m_{\cmsSymbolFace{V}\PZ})=\frac{f_{\text{SR}}^{\text{MC},\PZ+\text{jets}}(m_{\cmsSymbolFace{V}\PZ})}{f_{\text{SB}}^{\text{MC},\PZ+\text{jets}}(m_{\cmsSymbolFace{V}\PZ})},$
(2)

where $f_{\text{SR}}^{\text{MC},\PZ+\text{jets}}(m_{\cmsSymbolFace{V}\PZ})$
and $f_{\text{SB}}^{\text{MC},\PZ+\text{jets}}(m_{\cmsSymbolFace{V}\PZ})$ are
the probability density functions describing the $m_{\cmsSymbolFace{V}\PZ}$
spectrum in the SR and SBs, respectively, of the simulated $\PZ+\text{jets}$
sample.

The shape of the $\PZ+\text{jets}$ background in the SR is then extracted from
a simultaneous fit to data in the SBs, and to simulation in both the SR and
SBs, to correct the functional form obtained from data using the
$\alpha(m_{\cmsSymbolFace{V}\PZ})$ ratio. The $m_{\cmsSymbolFace{V}\PZ}$ shape
is described by two-parameter exponential functions for both data and
simulation. The final estimate of the background $m_{\cmsSymbolFace{V}\PZ}$
shape predicted in the SR is then given by the following relation:

$N_{\text{SR}}^{\text{pred}}(m_{\cmsSymbolFace{V}\PZ})=N_{\text{SR}}^{\PZ+\text{jets}}f_{\text{SB}}^{\text{obs},\PZ+\text{jets}}(m_{\cmsSymbolFace{V}\PZ})\alpha(m_{\cmsSymbolFace{V}\PZ})+N_{\text{SR}}^{\text{MC},\PQt}f_{\text{SR}}^{\text{MC},\PQt}(m_{\cmsSymbolFace{V}\PZ})+N_{\text{SR}}^{\text{MC},\cmsSymbolFace{V}\cmsSymbolFace{V}}f_{\text{SR}}^{\text{MC},\cmsSymbolFace{V}\cmsSymbolFace{V}}(m_{\cmsSymbolFace{V}\PZ}),$
(3)

where $N_{\text{SR}}^{\text{pred}}(m_{\cmsSymbolFace{V}\PZ})$ is the predicted
background in the SR and
$f_{\text{SB}}^{\text{obs},\PZ+\text{jets}}(m_{\cmsSymbolFace{V}\PZ})$ is the
probability distribution function describing the $\PZ+\text{jets}$ background
in the SBs. This is obtained from a fit of the overall background components
to data in the SBs, after subtracting the subdominant top quark and
$\cmsSymbolFace{V}\cmsSymbolFace{V}$ components, which are derived from
simulation. The functions
$f_{\text{SR}}^{\text{MC},\PQt}(m_{\cmsSymbolFace{V}\PZ})$ and
$f_{\text{SR}}^{\text{MC},\cmsSymbolFace{V}\cmsSymbolFace{V}}(m_{\cmsSymbolFace{V}\PZ})$
are the probability distributions of the top quark and diboson components,
respectively, also in this case fixed to the shapes derived from the simulated
samples in the SR. The normalization of the $\PZ+\text{jets}$ background in
the SR, $N_{\text{SR}}^{\PZ+\text{jets}}$, is provided by the result of the
fit on the $m_{\mathrm{j}}$ data sidebands described above, while the
normalization of the top quark and $\cmsSymbolFace{V}\cmsSymbolFace{V}$
backgrounds, $N_{\text{SR}}^{\text{MC},\PQt}$ and
$N_{\text{SR}}^{\text{MC},\cmsSymbolFace{V}\cmsSymbolFace{V}}$, are fixed to
the expected yields from simulation.

The $\alpha(m_{\cmsSymbolFace{V}\PZ})$ function accounts for differences and
correlations in the transfer process from the SB regions to the SR, and is
largely unaffected by uncertainties in the overall $\PZ+\text{jets}$ cross
section and distribution shapes.

The final $m_{\cmsSymbolFace{V}\PZ}$ spectra in the SR are shown in Fig. 3,
compared to the expected estimated background.

The validity and robustness of the background estimation method is
demonstrated by the agreement observed between the shape and normalization for
events selected in an intermediate $m_{\mathrm{j}}$ mass region
($50<m_{\mathrm{j}}<65\GeV$), corresponding to the part of the LSB shown in
Fig. 2 above 50, and the prediction made using the events in the remaining
part of the LSB and the full HSB regions.

Figure 3: Expected and observed distributions of the resonance candidate mass
$m_{\cmsSymbolFace{V}\PZ}$ in the high-mass analysis, in the electron (upper)
and muon (lower) channels, and separately for the high-purity (left) and low-
purity (right) categories. The shaded area represents the post-fit uncertainty
in the background. The bottom panels show the pull distribution between data
and post-fit SM background fit, where $\sigma_{\text{data}}$ is the Poisson
uncertainty in the data. The expected contribution from signal candidates with
mass $m_{\cmsSymbolFace{X}}=2000\GeV$, normalized to a cross section of 100fb,
is also shown.

The description of the signal $m_{\cmsSymbolFace{V}\PZ}$ shape is extracted
from simulated signal samples. Several signal samples generated with resonance
mass ranging from 400 to 4500in the narrow width approximation are modeled
independently for each channel with a Crystal Ball (CB) function [43]. The
power-law component of the CB function improves the description of the
$m_{\cmsSymbolFace{V}\PZ}$ signal distribution by accounting for the small
contribution from lower $m_{\cmsSymbolFace{V}\PZ}$ tails appearing for high
signal masses. The resolution of the reconstructed $m_{\cmsSymbolFace{V}\PZ}$
can be extracted from the Gaussian core width of the CB function, and is
estimated to be 2–3.5% in the electron channel and 3–4% in the muon channel,
depending on the mass of the resonance.

###  0.6.2 Low-mass analysis

For the low-mass analysis, the $\PZ+\text{jets}$ background is characterized
using simulated $\text{Drell--Yan}+\text{jets}$ events. Because of the limited
number of simulated events, the $m_{\cmsSymbolFace{V}\PZ}$ distributions in
the -tagged categories are susceptible to sizable statistical fluctuations,
which affect the quality of the background modeling. It has been observed,
however, that within simulation uncertainties, the $\PZ+\text{jets}$ mass
shape is the same for events with and without -tagged jets. Therefore, the
$\PZ+\text{jets}$ shape in the -tagged category is described using the
$m_{\cmsSymbolFace{V}\PZ}$ shape obtained from the simulation without making
any tag requirements.

Sideband regions are defined depending on the mass of the hadronic
$\cmsSymbolFace{V}$ boson candidate. The mass ranges
$30<m_{\mathrm{j}}<65\GeV$ and $135<m_{\mathrm{j}}<180\GeV$ are used for the
merged category, whereas for the resolved event selection the upper mass
threshold is raised to 300to take advantage of the increased number of events
in that region.

In the final fit to the data, the $\PZ+\text{jets}$ background normalization
in the SR is constrained by the observed yield in the SBs; this procedure is
applied independently to each category. The shape predictions from the NLO
$\PZ+\text{jets}$ simulation are taken as a baseline
$m_{\cmsSymbolFace{V}\PZ}$ shape in the SR of every category; additionally, a
family of linear correction functions:

$\text{Corr}(m_{\cmsSymbolFace{X}},s)=1+s(m_{\cmsSymbolFace{X}}-500\GeV)/(500\GeV),$
(4)

with individual members of the family defined by the slope parameter $s$, is
considered. Figure 4 shows fits to the SB $m_{\cmsSymbolFace{V}\PZ}$
distributions where the slope parameter $s$, allowed to float freely, is
constrained by the observed shapes in data. The two-standard-deviation
uncertainties in the fitted linear correction functions, which are in the
range from $2\ten{-4}$ to $6\ten{-4}\GeV^{-1}$, depending on the category, are
observed to cover the residual shape differences in the SBs. In the signal
region fit of each category, the SB-constrained slope parameter $s$ is treated
as a $\PZ+\text{jets}$ shape systematic effect. In this way the background
shape can be corrected to that observed in data. Statistical uncertainties
associated with the simulated $\PZ+\text{jets}$ distributions are also taken
into account in the fit. The fits in the merged $\cmsSymbolFace{V}$ categories
include the peaking region of the background; Fig. 4 shows that the SB data in
this particular region are described well by the fit.

Dilepton backgrounds that do not contain a leptonic boson decay are estimated
from data using $\Pe\Pgm$ events passing the analysis selection. This approach
accounts for production, $\PW\PW+\text{jets}$, $\PZ\to\tau\tau+\text{jets}$,
single top quark, and hadrons misidentified as leptons, which we collectively
refer to as $\cPqt+\text{X}$. The relative yield of $\Pe\Pe$ and $\Pgm\Pgm$
events with respect to $\Pe\Pgm$ events has been estimated on a top
quark–enriched control sample and shown to be consistent with expectations.
Also, the $\Pe\Pgm$ $m_{\cmsSymbolFace{V}\PZ}$ distribution was compared with
the prediction from simulated background events with symmetric lepton flavor,
and found to be in agreement. The contribution of this $\cPqt+\text{X}$
background is 2% and 20% of the total background in the untagged and tagged
categories of the resolved analysis, respectively. The merged analysis has a
$\cPqt+\text{X}$ contribution of 0.5% and 1% in the untagged and tagged
categories, respectively.

The diboson background ($\PZ\PZ$ and $\PZ\PW$, with $\PZ\to\ell\ell$) is
estimated directly from simulation. The contribution from these events
represents 4% and 5% of the total background in the untagged and tagged
categories of the resolved analysis, respectively, while in the merged
analysis it is about 14% and 16% in the untagged and tagged categories,
respectively.

The $m_{\cmsSymbolFace{V}\PZ}$ distributions for the signal region for the
merged and resolved categories are depicted in Fig. 5.

Figure 4:  Sideband $m_{\cmsSymbolFace{V}\PZ}$ distributions for the low-mass
search in the merged $\cmsSymbolFace{V}$ (upper), resolved $\cmsSymbolFace{V}$
(lower), untagged (left), and tagged (right) categories, after fitting the
sideband data alone. The points show the data while the filled histograms show
the background contributions. Electron and muon categories are combined. The
gray band indicates the statistical and post-fit systematic uncertainties in
the normalization and shape of the background. Larger bin widths are used at
higher values of $m_{\cmsSymbolFace{V}\PZ}$; the bin widths are indicated by
the horizontal error bars.

Figure 5: The signal region $m_{\cmsSymbolFace{V}\PZ}$ distributions for the
low-mass search, in the merged $\cmsSymbolFace{V}$ (upper), resolved
$\cmsSymbolFace{V}$ (lower), untagged (left), and tagged (right) categories,
after fitting the signal and sideband regions. Electron and muon categories
are combined. A 600bulk graviton signal prediction is represented by the black
dashed histogram. The gray band indicates the statistical and post-fit
systematic uncertainties in the normalization and shape of the background.
Larger bin widths are used at higher values of $m_{\cmsSymbolFace{V}\PZ}$; the
bin widths are indicated by the horizontal error bars.

##  0.7 Systematic uncertainties

Several sources of systematic uncertainties influence both the normalization
and shape of the backgrounds and signal distributions in the analysis.

In the high-mass analysis, where the $\PZ+\text{jets}$ background component is
estimated with data, the main systematic uncertainties in the predicted
normalization for the $\PZ+\text{jets}$ background arise from the statistical
uncertainties in the fit of the $m_{\mathrm{j}}$ sidebands in data. Another
uncertainty affecting the normalization of the main background is evaluated by
taking the difference between the expected $\PZ+\text{jets}$ contribution in
the SR obtained by the main function used to describe the $m_{\mathrm{j}}$
spectrum, and an alternative function choice. An additional normalization
uncertainty is related to the choice of the function used to describe the
$m_{\mathrm{j}}$ spectrum for the subdominant top quark and
$\cmsSymbolFace{V}\cmsSymbolFace{V}$ backgrounds, evaluated from simulation,
and propagated to the $\PZ+\text{jets}$ normalization prediction in the SR.
Overall, the $\PZ+\text{jets}$ normalization uncertainties contribute from 9
to 15%, depending on the category. The main shape uncertainties in the
$\PZ+\text{jets}$ background are extracted from the covariance matrix of the
fit to the $m_{\cmsSymbolFace{V}\PZ}$ data SB spectrum, convolved with the
uncertainties provided by the $\alpha(m_{\cmsSymbolFace{V}\PZ})$ ratio, via
the simultaneous fit procedure described in Section 0.6.1.

In the low-mass analysis, to account for background shape systematic effects
not explicitly evaluated, data and simulation are compared in the sideband
region, and the residual shape difference is treated as an additional
uncertainty, resulting in the dominant background shape systematic uncertainty
of the low-mass analysis.

The top quark and $\cmsSymbolFace{V}\cmsSymbolFace{V}$ background components
have a systematic uncertainty in the normalization arising from the degree of
knowledge of the respective process production cross sections. The value of
the $\cmsSymbolFace{V}\cmsSymbolFace{V}$ production cross section, taken from
a recent measurement by the CMS Collaboration [44, 45], is assigned an
uncertainty of 12%. The top quark background uncertainties are estimated
differently in the low- and high-mass analyses: in the low-mass analysis,
where a dedicated $\Pe\Pgm$ control region is exploited to measure the
$\cPqt+\text{X}$ background normalization, a 4% uncertainty is estimated by
comparing the yield of $\Pe\Pgm$ events with $\Pe\Pe+\Pgm\Pgm$ data; in the
high-mass analysis, where the top quark production is taken from simulation, a
5% uncertainty in the cross section is used, which is extracted from the
recent CMS measurement of top quark pair production in dilepton events [46].

Uncertainties associated with the description in simulation of the trigger
efficiencies, as well as the uncertainties in the efficiency for electron and
muon reconstruction, identification, and isolation, are extracted from
dedicated studies of events with leptonic decays, and amount to 1.5–3%,
depending on the lepton flavor. The uncertainties in the lepton momentum and
energy scales are taken into account, and propagated to the signal shapes and
normalization, with a typical impact on the normalization of about 0.5–2%,
depending on the lepton flavor.

Uncertainties in the jet energy scale and resolution [47] affect both the
normalization and the shape of the background and signal samples. The momenta
of the reconstructed jets are varied according to the uncertainties in the jet
energy scale, and the selection efficiencies and $m_{\cmsSymbolFace{V}\PZ}$
signal shapes are reevaluated using these modified samples, resulting in a
change of 0.1 to 1.8%, depending on the jet selection. The impact of the jet
energy resolution is also propagated, and a smaller impact is observed
compared with that due to the uncertainty in the energy scale.

The dominant uncertainty in the signal selection efficiency is the uncertainty
in the $\cmsSymbolFace{V}$ boson identification efficiency, corresponding to
11% (23%) for the HP (LP) category in the high-mass analysis, and 6% for the
merged category of the low-mass analysis [48]. The V boson identification
efficiency, the groomed mass resolution of $\cmsSymbolFace{V}$ jets, and the
related systematic uncertainty are measured in data and simulation in an
almost pure selection of semileptonic events where boosted bosons produced in
the top quark decays are separated from the combinatorial background by means
of a simultaneous fit to the soft drop mass. The uncertainties in the soft
drop mass scale and resolution are propagated to the groomed jet mass, and the
impact on the expected selection efficiency of signal and
$\cmsSymbolFace{V}\cmsSymbolFace{V}$ background is taken into account. An
additional uncertainty affecting the signal normalization is included to
account for the extrapolation of the uncertainties extracted from a sample at
typical jet of 200to higher regimes, estimated from the differences between 8
and ++ [49] showering models, yielding an uncertainty from 2.5 to 20%
depending on the category. For the high-mass analysis, the uncertainties in
the V boson identification efficiency and the extrapolation are treated as
anticorrelated between the low- and high-purity categories.

For the low-mass analysis, one of the largest signal selection uncertainties
is the uncertainty in the tagging efficiency for the tagged categories of the
analysis. The tagging efficiencies and their corresponding systematic
uncertainties are measured in data using samples enriched in b quark content,
and their propagation to the signal region of the low-mass analysis produces
an uncertainty of up to 4.3%. The uncertainties in the mistag efficiency are
also considered; the uncertainties in the tagging and mistag efficiencies are
treated as anticorrelated between the tagged and untagged categories.

The impact of the uncertainties in the factorization and renormalization
scales is propagated both to the normalization and the
$m_{\cmsSymbolFace{V}\PZ}$ shapes for signal, and for the high-mass analysis
to top quark and $\cmsSymbolFace{V}\cmsSymbolFace{V}$ backgrounds. The
corresponding scales are varied by a factor of 2 to measure the effect,
resulting in an uncertainty of 2% for the diboson background normalization and
15% for top quarks. The impact on the signal acceptance is evaluated to be
0.1–3%, depending on the resonance mass and analysis category.

A systematic uncertainty associated with the choice of the set of PDFs used to
generate the simulated samples is evaluated by varying the NNPDF 3.0 PDF set
within its uncertainties, and its effect is propagated to both the signal and
background $m_{\cmsSymbolFace{V}\PZ}$ shapes and normalization, resulting in a
measured uncertainty of approximately 1%.

Additional systematic uncertainties affecting the normalization of backgrounds
and signal from the contributions of pileup events and the integrated
luminosity [50] are also considered and are reported in Table 0.7, together
with the complete list of uncertainties considered in the analysis. In the
high-mass analysis, the typical total uncertainty in the background
normalization is in the range 10–60%, depending on the signal mass, and it is
1–5%, depending on the category, in the low-mass analysis.

Summary of systematic uncertainties, quoted in percent, affecting the
normalization of background and signal samples. Where a systematic uncertainty
depends on the resonance mass (for signal) or on the category (for
background), the smallest and largest values are reported in the table. In the
case of a systematic uncertainty applying only to a specific background
source, the source is indicated in parentheses. Systematic uncertainties too
small to be considered are written as “$<$0.1”, while a dash () represents
uncertainties not applicable in the specific analysis category.  High-mass
Low-mass Low-mass Merged Merged Resolved Source Background Signal Background
Signal Background Signal Electron trigger and ID 2.0–3.0 2.0 2.0 Muon trigger
and ID 1.5–3.0 1.5 1.5 Electron energy scale $<$0.1 1.0 0.8 0.1–0.5 1.3
1.2–2.5 Muon momentum scale $<$0.1 0.5–2.0 0.6 0.1–0.4 1.4 0.2–2.0 Jet energy
scale 0.1–0.5 0.1 1.0 0.3–0.6 1.3 0.6–1.8 Jet energy resolution $<$0.1 $<$0.1
0.6 0.1 0.2 0.1–0.2 tag SF untagged 0.2 0.3–0.4 0.1 0.6 tag SF tagged 2.0
2.0–2.3 3.8 4.1–4.3 Mistag SF untagged 0.5 0.5–0.6 0.4 0.2–0.4 Mistag SF
tagged 1.5 0.4–0.6 4.3 0.5–1.4 SM $\cmsSymbolFace{V}\PZ$ production 12 12 12
SM t quark production 5 4 ($\Pe\Pgm$) 4 ($\Pe\Pgm$) $\cmsSymbolFace{V}$
identification ($\tau_{21}$) 11–23 6 ($\cmsSymbolFace{V}\PZ$) 6
$\cmsSymbolFace{V}$ identification (extrapolation) 2.5–20 2.6–6.0
$\cmsSymbolFace{V}$ mass scale 0.5–2.5 1.0–2.0 0.2 ($\cmsSymbolFace{V}\PZ$)
0.5–1.1 $\cmsSymbolFace{V}$ mass resolution 5.5 5–6 5.6
($\cmsSymbolFace{V}\PZ$) 5.7–6.0 $\PZ+\text{jets}$ normalization 9–15 Pileup
0.5–4.0 0.4 0.5 0.1–0.3 0.1 0.3–0.5 PDFs 0.3–1.5 0.5 1.5–1.6 0.3–1.1
Renorm./fact. scales 2 ($\cmsSymbolFace{V}\PZ$), 15 (Top) 1.0–3.0 0.1–0.3
0.2–0.3 Integrated luminosity 2.5 2.5 2.5

##  0.8 Results and interpretation

Results are extracted separately for the high- and low-mass analyses from a
combined maximum likelihood fit of signal and background to the
$m_{\cmsSymbolFace{V}\PZ}$ distribution, simultaneously in all the categories
used in the respective analysis. An unbinned fit is performed in the high-mass
analysis, while a binned fit is performed in the low-mass one; this choice is
determined by the fact that in the high-mass analysis, the signal and
background shapes are described with analytical functions, while in the low-
mass analysis, the background shapes are described by binned histograms. The
systematic uncertainties discussed in Section 0.7 are included as nuisance
parameters in the maximum likelihood fit, and the background-only hypothesis
is tested against the combined background and signal hypothesis [51, 52].

The largest excess of events with respect to the background-only hypothesis,
with a local significance of 2.5 standard deviations, is observed in the
vicinity of $m_{\cmsSymbolFace{X}}\approx 1.2\TeV$, and arises predominantly
from a localized excess of events in the dimuon HP category of the high-mass
analysis.

The limit at 95% confidence level () on the signal cross section for the
production of a heavy spin-1 or spin-2 resonance is set using the asymptotic
modified frequentist method ($\text{CL}_{\mathrm{s}}$) [51, 52, 53, 54].

The results of the low- and high-mass analyses should agree for the
intermediate mass range 800–900, which is accessible to both strategies with
similar expected efficiencies for signal candidates. The results of the
analysis are therefore presented based on the low-mass strategy up to
resonance masses $m_{\cmsSymbolFace{X}}\leq 850\GeV$, and based on the high-
mass analysis for $m_{\cmsSymbolFace{X}}\geq 850\GeV$. At the intermediate
mass point $m_{\cmsSymbolFace{X}}=850\GeV$, the results of both strategies are
presented, and the expected limits at 95% of the low- and high-mass analyses
on the signal cross sections are found to be in agreement within 3 and 6% for
the and bulk graviton signal model, respectively.

The observed upper limits on the resonance cross section, multiplied by the
branching fraction for the decay into one boson and a or boson,
$\sigma_{\PWpr}\mathcal{B}(\PWpr\to\PZ\PW)$ or
$\sigma_{\cPG}\mathcal{B}(\cPG\to\PZ\PZ)$, are reported as a function of the
resonance mass in Fig. 6 assuming a or produced in the narrow-width
approximation, and the local $p$-value [55] is shown in Fig. 7.

Based on the observed (expected) upper limits on the signal cross section, a
signal is excluded up to 2270 (2390)in the framework of HVT model A
($g_{\mathrm{V}}=1$), and up to 2330 (2630)for HVT model B
($g_{\mathrm{V}}=3$); a WED bulk graviton is excluded up to masses of 925
(960)for $\widetilde{\kappa}=0.5$.

Figure 6: Observed and expected 95% upper limit on
$\sigma_{\PWpr}\mathcal{B}(\PWpr\to\PZ\PW)$ (left) and
$\sigma_{\cPG}\mathcal{B}(\cPG\to\PZ\PZ)$ (right) as a function of the
resonance mass, taking into account all statistical and systematic
uncertainties. The electron and muon channels and the various categories used
in the analysis are combined together. The green (inner) and yellow (outer)
bands represent the 68% and 95% coverage of the expected limit in the
background-only hypothesis. The dashed vertical line represents the transition
from the low-mass to the high-mass analysis strategy. Theoretical predictions
for the signal production cross section are also shown: (left) produced in the
framework of HVT model A with $g_{\mathrm{v}}=1$ and model B with
$g_{\mathrm{v}}=3$; (right) produced in the WED bulk graviton model with
$\widetilde{\kappa}=0.5$.

Figure 7: Observed local $p$-values for (left) and (right) narrow resonances
as a function of the resonance mass. The dashed vertical line represents the
transition from the low-mass to the high-mass analysis strategy.

##  0.9 Summary

A search for a heavy resonance decaying into a boson and a or a boson in
$2\ell 2\cPq$ final states has been presented. The data analyzed were
collected by the CMS experiment in proton-proton collisions at
$\sqrt{s}=13\TeV$ during 2016 operations at the LHC, corresponding to an
integrated luminosity of 35.9. The final state of interest consists of a boson
decaying leptonically into an electron or muon pair, and the decay of an
additional or boson into a pair of quarks. Two analysis strategies, dedicated
to the low- and high-mass regimes (below and above 850, respectively), have
been used to set limits in the range of resonance mass from 400 to 4500.
Depending on the resonance mass, expected upper limits of 3–3000 and 1.5–400fb
have been set on the product of the cross section of a spin-1 and the $\PZ\PW$
branching fraction, and on the product of the cross section of a spin-2
graviton and the $\PZ\PZ$ branching fraction, respectively.

###### Acknowledgements.

We congratulate our colleagues in the CERN accelerator departments for the
excellent performance of the LHC and thank the technical and administrative
staffs at CERN and at other CMS institutes for their contributions to the
success of the CMS effort. In addition, we gratefully acknowledge the
computing centers and personnel of the Worldwide LHC Computing Grid for
delivering so effectively the computing infrastructure essential to our
analyses. Finally, we acknowledge the enduring support for the construction
and operation of the LHC and the CMS detector provided by the following
funding agencies: BMWFW and FWF (Austria); FNRS and FWO (Belgium); CNPq,
CAPES, FAPERJ, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST, and NSFC
(China); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RPF (Cyprus);
SENESCYT (Ecuador); MoER, ERC IUT, and ERDF (Estonia); Academy of Finland,
MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF
(Germany); GSRT (Greece); NKFIA (Hungary); DAE and DST (India); IPM (Iran);
SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); LAS
(Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and
UASLP-FAI (Mexico); MBIE (New Zealand); PAEC (Pakistan); MSHE and NSC
(Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom, RAS and RFBR (Russia);
MESTD (Serbia); SEIDI, CPAN, PCTI and FEDER (Spain); Swiss Funding Agencies
(Switzerland); MST (Taipei); ThEPCenter, IPST, STAR, and NSTDA (Thailand);
TUBITAK and TAEK (Turkey); NASU and SFFR (Ukraine); STFC (United Kingdom); DOE
and NSF (USA). Individuals have received support from the Marie-Curie program
and the European Research Council and Horizon 2020 Grant, contract No. 675440
(European Union); the Leventis Foundation; the A. P. Sloan Foundation; the
Alexander von Humboldt Foundation; the Belgian Federal Science Policy Office;
the Fonds pour la Formation à la Recherche dans l’Industrie et dans
l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en
Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium) under the
“Excellence of Science - EOS” - be.h project n. 30820817; the Ministry of
Education, Youth and Sports (MEYS) of the Czech Republic; the Lendület
(“Momentum”) Program and the János Bolyai Research Scholarship of the
Hungarian Academy of Sciences, the New National Excellence Program ÚNKP, the
NKFIA research grants 123842, 123959, 124845, 124850 and 125105 (Hungary); the
Council of Science and Industrial Research, India; the HOMING PLUS program of
the Foundation for Polish Science, cofinanced from European Union, Regional
Development Fund, the Mobility Plus program of the Ministry of Science and
Higher Education, the National Science Center (Poland), contracts Harmonia
2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543, 2014/15/B/ST2/03998, and
2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406; the National Priorities
Research Program by Qatar National Research Fund; the Programa Estatal de
Fomento de la Investigación Científica y Técnica de Excelencia María de
Maeztu, grant MDM-2015-0509 and the Programa Severo Ochoa del Principado de
Asturias; the Thalis and Aristeia programs cofinanced by EU-ESF and the Greek
NSRF; the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn
University and the Chulalongkorn Academic into Its 2nd Century Project
Advancement Project (Thailand); the Welch Foundation, contract C-1845; and the
Weston Havens Foundation (USA).

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##  .10 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan, A. Tumasyan Institut für Hochenergiephysik, Wien, Austria
W. Adam, F. Ambrogi, E. Asilar, T. Bergauer, J. Brandstetter, E. Brondolin, M.
Dragicevic, J. Erö, A. Escalante Del Valle, M. Flechl, M. Friedl, R.
Frühwirth1, V.M. Ghete, J. Grossmann, J. Hrubec, M. Jeitler1, A. König, N.
Krammer, I. Krätschmer, D. Liko, T. Madlener, I. Mikulec, E. Pree, N. Rad, H.
Rohringer, J. Schieck1, R. Schöfbeck, M. Spanring, D. Spitzbart, A. Taurok, W.
Waltenberger, J. Wittmann, C.-E. Wulz1, M. Zarucki Institute for Nuclear
Problems, Minsk, Belarus
V. Chekhovsky, V. Mossolov, J. Suarez Gonzalez Universiteit Antwerpen,
Antwerpen, Belgium
E.A. De Wolf, D. Di Croce, X. Janssen, J. Lauwers, M. Pieters, M. Van De
Klundert, H. Van Haevermaet, P. Van Mechelen, N. Van Remortel Vrije
Universiteit Brussel, Brussel, Belgium
S. Abu Zeid, F. Blekman, J. D’Hondt, I. De Bruyn, J. De Clercq, K. Deroover,
G. Flouris, D. Lontkovskyi, S. Lowette, I. Marchesini, S. Moortgat, L.
Moreels, Q. Python, K. Skovpen, S. Tavernier, W. Van Doninck, P. Van Mulders,
I. Van Parijs Université Libre de Bruxelles, Bruxelles, Belgium
D. Beghin, B. Bilin, H. Brun, B. Clerbaux, G. De Lentdecker, H. Delannoy, B.
Dorney, G. Fasanella, L. Favart, R. Goldouzian, A. Grebenyuk, A.K. Kalsi, T.
Lenzi, J. Luetic, T. Seva, E. Starling, C. Vander Velde, P. Vanlaer, D.
Vannerom, R. Yonamine Ghent University, Ghent, Belgium
T. Cornelis, D. Dobur, A. Fagot, M. Gul, I. Khvastunov2, D. Poyraz, C. Roskas,
D. Trocino, M. Tytgat, W. Verbeke, B. Vermassen, M. Vit, N. Zaganidis
Université Catholique de Louvain, Louvain-la-Neuve, Belgium
H. Bakhshiansohi, O. Bondu, S. Brochet, G. Bruno, C. Caputo, A. Caudron, P.
David, S. De Visscher, C. Delaere, M. Delcourt, B. Francois, A. Giammanco, G.
Krintiras, V. Lemaitre, A. Magitteri, A. Mertens, M. Musich, K. Piotrzkowski,
L. Quertenmont, A. Saggio, M. Vidal Marono, S. Wertz, J. Zobec Centro
Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil
W.L. Aldá Júnior, F.L. Alves, G.A. Alves, L. Brito, G. Correia Silva, C.
Hensel, A. Moraes, M.E. Pol, P. Rebello Teles Universidade do Estado do Rio de
Janeiro, Rio de Janeiro, Brazil
E. Belchior Batista Das Chagas, W. Carvalho, J. Chinellato3, E. Coelho, E.M.
Da Costa, G.G. Da Silveira4, D. De Jesus Damiao, S. Fonseca De Souza, H.
Malbouisson, M. Medina Jaime5, M. Melo De Almeida, C. Mora Herrera, L. Mundim,
H. Nogima, L.J. Sanchez Rosas, A. Santoro, A. Sznajder, M. Thiel, E.J. Tonelli
Manganote3, F. Torres Da Silva De Araujo, A. Vilela Pereira Universidade
Estadual Paulista a, Universidade Federal do ABC b, São Paulo, Brazil
S. Ahujaa, C.A. Bernardesa, L. Calligarisa, T.R. Fernandez Perez Tomeia, E.M.
Gregoresb, P.G. Mercadanteb, S.F. Novaesa, Sandra S. Padulaa, D. Romero Abadb,
J.C. Ruiz Vargasa Institute for Nuclear Research and Nuclear Energy, Bulgarian
Academy of Sciences, Sofia, Bulgaria
A. Aleksandrov, R. Hadjiiska, P. Iaydjiev, A. Marinov, M. Misheva, M. Rodozov,
M. Shopova, G. Sultanov University of Sofia, Sofia, Bulgaria
A. Dimitrov, L. Litov, B. Pavlov, P. Petkov Beihang University, Beijing, China
W. Fang6, X. Gao6, L. Yuan Institute of High Energy Physics, Beijing, China
M. Ahmad, J.G. Bian, G.M. Chen, H.S. Chen, M. Chen, Y. Chen, C.H. Jiang, D.
Leggat, H. Liao, Z. Liu, F. Romeo, S.M. Shaheen, A. Spiezia, J. Tao, C. Wang,
Z. Wang, E. Yazgan, H. Zhang, J. Zhao State Key Laboratory of Nuclear Physics
and Technology, Peking University, Beijing, China
Y. Ban, G. Chen, J. Li, Q. Li, S. Liu, Y. Mao, S.J. Qian, D. Wang, Z. Xu
Tsinghua University, Beijing, China
Y. Wang Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, C.A. Carrillo Montoya, L.F. Chaparro Sierra, C. Florez,
C.F. González Hernández, M.A. Segura Delgado University of Split, Faculty of
Electrical Engineering, Mechanical Engineering and Naval Architecture, Split,
Croatia
B. Courbon, N. Godinovic, D. Lelas, I. Puljak, P.M. Ribeiro Cipriano, T.
Sculac University of Split, Faculty of Science, Split, Croatia
Z. Antunovic, M. Kovac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, D. Ferencek, K. Kadija, B. Mesic, A. Starodumov7, T. Susa
University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos,
P.A. Razis, H. Rykaczewski Charles University, Prague, Czech Republic
M. Finger8, M. Finger Jr.8 Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab
Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
A. Ellithi Kamel9, Y. Mohammed10, E. Salama11,12 National Institute of
Chemical Physics and Biophysics, Tallinn, Estonia
S. Bhowmik, R.K. Dewanjee, M. Kadastik, L. Perrini, M. Raidal, C. Veelken
Department of Physics, University of Helsinki, Helsinki, Finland
P. Eerola, H. Kirschenmann, J. Pekkanen, M. Voutilainen Helsinki Institute of
Physics, Helsinki, Finland
J. Havukainen, J.K. Heikkilä, T. Järvinen, V. Karimäki, R. Kinnunen, T.
Lampén, K. Lassila-Perini, S. Laurila, S. Lehti, T. Lindén, P. Luukka, T.
Mäenpää, H. Siikonen, E. Tuominen, J. Tuominiemi Lappeenranta University of
Technology, Lappeenranta, Finland
T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
M. Besancon, F. Couderc, M. Dejardin, D. Denegri, J.L. Faure, F. Ferri, S.
Ganjour, S. Ghosh, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry,
C. Leloup, E. Locci, M. Machet, J. Malcles, G. Negro, J. Rander, A. Rosowsky,
M.Ö. Sahin, M. Titov Laboratoire Leprince-Ringuet, Ecole polytechnique,
CNRS/IN2P3, Université Paris-Saclay, Palaiseau, France
A. Abdulsalam13, C. Amendola, I. Antropov, S. Baffioni, F. Beaudette, P.
Busson, L. Cadamuro, C. Charlot, R. Granier de Cassagnac, M. Jo, I. Kucher, S.
Lisniak, A. Lobanov, J. Martin Blanco, M. Nguyen, C. Ochando, G. Ortona, P.
Paganini, P. Pigard, R. Salerno, J.B. Sauvan, Y. Sirois, A.G. Stahl Leiton, Y.
Yilmaz, A. Zabi, A. Zghiche Université de Strasbourg, CNRS, IPHC UMR 7178,
F-67000 Strasbourg, France
J.-L. Agram14, J. Andrea, D. Bloch, J.-M. Brom, E.C. Chabert, C. Collard, E.
Conte14, X. Coubez, F. Drouhin14, J.-C. Fontaine14, D. Gelé, U. Goerlach, M.
Jansová, P. Juillot, A.-C. Le Bihan, N. Tonon, P. Van Hove Centre de Calcul de
l’Institut National de Physique Nucleaire et de Physique des Particules,
CNRS/IN2P3, Villeurbanne, France
S. Gadrat Université de Lyon, Université Claude Bernard Lyon 1, CNRS-IN2P3,
Institut de Physique Nucléaire de Lyon, Villeurbanne, France
S. Beauceron, C. Bernet, G. Boudoul, N. Chanon, R. Chierici, D. Contardo, P.
Depasse, H. El Mamouni, J. Fay, L. Finco, S. Gascon, M. Gouzevitch, G.
Grenier, B. Ille, F. Lagarde, I.B. Laktineh, H. Lattaud, M. Lethuillier, L.
Mirabito, A.L. Pequegnot, S. Perries, A. Popov15, V. Sordini, M. Vander
Donckt, S. Viret, S. Zhang Georgian Technical University, Tbilisi, Georgia
T. Toriashvili16 Tbilisi State University, Tbilisi, Georgia
I. Bagaturia17 RWTH Aachen University, I. Physikalisches Institut, Aachen,
Germany
C. Autermann, L. Feld, M.K. Kiesel, K. Klein, M. Lipinski, M. Preuten, M.P.
Rauch, C. Schomakers, J. Schulz, M. Teroerde, B. Wittmer, V. Zhukov15 RWTH
Aachen University, III. Physikalisches Institut A, Aachen, Germany
A. Albert, D. Duchardt, M. Endres, M. Erdmann, S. Erdweg, T. Esch, R. Fischer,
A. Güth, T. Hebbeker, C. Heidemann, K. Hoepfner, S. Knutzen, M. Merschmeyer,
A. Meyer, P. Millet, S. Mukherjee, T. Pook, M. Radziej, H. Reithler, M.
Rieger, F. Scheuch, D. Teyssier, S. Thüer RWTH Aachen University, III.
Physikalisches Institut B, Aachen, Germany
G. Flügge, B. Kargoll, T. Kress, A. Künsken, T. Müller, A. Nehrkorn, A.
Nowack, C. Pistone, O. Pooth, A. Stahl18 Deutsches Elektronen-Synchrotron,
Hamburg, Germany
M. Aldaya Martin, T. Arndt, C. Asawatangtrakuldee, K. Beernaert, O. Behnke, U.
Behrens, A. Bermúdez Martínez, A.A. Bin Anuar, K. Borras19, V. Botta, A.
Campbell, P. Connor, C. Contreras-Campana, F. Costanza, V. Danilov, A. De Wit,
C. Diez Pardos, D. Domínguez Damiani, G. Eckerlin, D. Eckstein, T. Eichhorn,
A. Elwood, E. Eren, E. Gallo20, J. Garay Garcia, A. Geiser, J.M. Grados
Luyando, A. Grohsjean, P. Gunnellini, M. Guthoff, A. Harb, J. Hauk, M.
Hempel21, H. Jung, M. Kasemann, J. Keaveney, C. Kleinwort, J. Knolle, I.
Korol, D. Krücker, W. Lange, A. Lelek, T. Lenz, K. Lipka, W. Lohmann21, R.
Mankel, I.-A. Melzer-Pellmann, A.B. Meyer, M. Meyer, M. Missiroli, G. Mittag,
J. Mnich, A. Mussgiller, D. Pitzl, A. Raspereza, M. Savitskyi, P. Saxena, R.
Shevchenko, N. Stefaniuk, H. Tholen, G.P. Van Onsem, R. Walsh, Y. Wen, K.
Wichmann, C. Wissing, O. Zenaiev University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, V. Blobel, M. Centis Vignali, T. Dreyer, E. Garutti, D.
Gonzalez, J. Haller, A. Hinzmann, M. Hoffmann, A. Karavdina, G. Kasieczka, R.
Klanner, R. Kogler, N. Kovalchuk, S. Kurz, V. Kutzner, J. Lange, D. Marconi,
J. Multhaup, M. Niedziela, D. Nowatschin, T. Peiffer, A. Perieanu, A. Reimers,
C. Scharf, P. Schleper, A. Schmidt, S. Schumann, J. Schwandt, J. Sonneveld, H.
Stadie, G. Steinbrück, F.M. Stober, M. Stöver, D. Troendle, E. Usai, A.
Vanhoefer, B. Vormwald Institut für Experimentelle Teilchenphysik, Karlsruhe,
Germany
M. Akbiyik, C. Barth, M. Baselga, S. Baur, E. Butz, R. Caspart, T. Chwalek, F.
Colombo, W. De Boer, A. Dierlamm, N. Faltermann, B. Freund, R. Friese, M.
Giffels, M.A. Harrendorf, F. Hartmann18, S.M. Heindl, U. Husemann, F.
Kassel18, S. Kudella, H. Mildner, M.U. Mozer, Th. Müller, M. Plagge, G. Quast,
K. Rabbertz, M. Schröder, I. Shvetsov, G. Sieber, H.J. Simonis, R. Ulrich, S.
Wayand, M. Weber, T. Weiler, S. Williamson, C. Wöhrmann, R. Wolf Institute of
Nuclear and Particle Physics (INPP), NCSR Demokritos, Aghia Paraskevi, Greece
G. Anagnostou, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas, I. Topsis-
Giotis National and Kapodistrian University of Athens, Athens, Greece
G. Karathanasis, S. Kesisoglou, A. Panagiotou, N. Saoulidou, E. Tziaferi
National Technical University of Athens, Athens, Greece
K. Kousouris, I. Papakrivopoulos University of Ioánnina, Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, S. Mallios, N.
Manthos, I. Papadopoulos, E. Paradas, J. Strologas, F.A. Triantis, D.
Tsitsonis MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
M. Csanad, N. Filipovic, G. Pasztor, O. Surányi, G.I. Veres22 Wigner Research
Centre for Physics, Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath23, Á. Hunyadi, F. Sikler, T.Á. Vámi, V.
Veszpremi, G. Vesztergombi22 Institute of Nuclear Research ATOMKI, Debrecen,
Hungary
N. Beni, S. Czellar, J. Karancsi24, A. Makovec, J. Molnar, Z. Szillasi
Institute of Physics, University of Debrecen, Debrecen, Hungary
M. Bartók22, P. Raics, Z.L. Trocsanyi, B. Ujvari Indian Institute of Science
(IISc), Bangalore, India
S. Choudhury, J.R. Komaragiri National Institute of Science Education and
Research, Bhubaneswar, India
S. Bahinipati25, P. Mal, K. Mandal, A. Nayak26, D.K. Sahoo25, S.K. Swain
Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, S. Chauhan, R. Chawla, N. Dhingra, R.
Gupta, A. Kaur, M. Kaur, S. Kaur, R. Kumar, P. Kumari, M. Lohan, A. Mehta, S.
Sharma, J.B. Singh, G. Walia University of Delhi, Delhi, India
A. Bhardwaj, B.C. Choudhary, R.B. Garg, S. Keshri, A. Kumar, Ashok Kumar, S.
Malhotra, M. Naimuddin, K. Ranjan, Aashaq Shah, R. Sharma Saha Institute of
Nuclear Physics, HBNI, Kolkata, India
R. Bhardwaj27, R. Bhattacharya, S. Bhattacharya, U. Bhawandeep27, D. Bhowmik,
S. Dey, S. Dutt27, S. Dutta, S. Ghosh, N. Majumdar, K. Mondal, S.
Mukhopadhyay, S. Nandan, A. Purohit, P.K. Rout, A. Roy, S. Roy Chowdhury, S.
Sarkar, M. Sharan, B. Singh, S. Thakur27 Indian Institute of Technology
Madras, Madras, India
P.K. Behera Bhabha Atomic Research Centre, Mumbai, India
R. Chudasama, D. Dutta, V. Jha, V. Kumar, A.K. Mohanty18, P.K. Netrakanti,
L.M. Pant, P. Shukla, A. Topkar Tata Institute of Fundamental Research-A,
Mumbai, India
T. Aziz, S. Dugad, B. Mahakud, S. Mitra, G.B. Mohanty, N. Sur, B. Sutar Tata
Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, Sa. Jain, S.
Kumar, M. Maity28, G. Majumder, K. Mazumdar, N. Sahoo, T. Sarkar28, N.
Wickramage29 Indian Institute of Science Education and Research (IISER), Pune,
India
S. Chauhan, S. Dube, V. Hegde, A. Kapoor, K. Kothekar, S. Pandey, A. Rane, S.
Sharma Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
S. Chenarani30, E. Eskandari Tadavani, S.M. Etesami30, M. Khakzad, M.
Mohammadi Najafabadi, M. Naseri, S. Paktinat Mehdiabadi31, F. Rezaei
Hosseinabadi, B. Safarzadeh32, M. Zeinali University College Dublin, Dublin,
Ireland
M. Felcini, M. Grunewald INFN Sezione di Bari a, Università di Bari b,
Politecnico di Bari c, Bari, Italy
M. Abbresciaa,b, C. Calabriaa,b, A. Colaleoa, D. Creanzaa,c, L. Cristellaa,b,
N. De Filippisa,c, M. De Palmaa,b, A. Di Florioa,b, F. Erricoa,b, L. Fiorea,
A. Gelmia,b, G. Iasellia,c, S. Lezkia,b, G. Maggia,c, M. Maggia, B.
Marangellia,b, G. Minielloa,b, S. Mya,b, S. Nuzzoa,b, A. Pompilia,b, G.
Pugliesea,c, R. Radognaa, A. Ranieria, G. Selvaggia,b, A. Sharmaa, L.
Silvestrisa,18, R. Vendittia, P. Verwilligena, G. Zitoa INFN Sezione di
Bologna a, Università di Bologna b, Bologna, Italy
G. Abbiendia, C. Battilanaa,b, D. Bonacorsia,b, L. Borgonovia,b, S. Braibant-
Giacomellia,b, R. Campaninia,b, P. Capiluppia,b, A. Castroa,b, F.R. Cavalloa,
S.S. Chhibraa,b, G. Codispotia,b, M. Cuffiania,b, G.M. Dallavallea, F.
Fabbria, A. Fanfania,b, D. Fasanellaa,b, P. Giacomellia, C. Grandia, L.
Guiduccia,b, S. Marcellinia, G. Masettia, A. Montanaria, F.L. Navarriaa,b, F.
Odoricia, A. Perrottaa, A.M. Rossia,b, T. Rovellia,b, G.P. Sirolia,b, N. Tosia
INFN Sezione di Catania a, Università di Catania b, Catania, Italy
S. Albergoa,b, S. Costaa,b, A. Di Mattiaa, F. Giordanoa,b, R. Potenzaa,b, A.
Tricomia,b, C. Tuvea,b INFN Sezione di Firenze a, Università di Firenze b,
Firenze, Italy
G. Barbaglia, K. Chatterjeea,b, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, E. Focardia,b, G. Latino, P. Lenzia,b, M. Meschinia, S.
Paolettia, L. Russoa,33, G. Sguazzonia, D. Stroma, L. Viliania INFN Laboratori
Nazionali di Frascati, Frascati, Italy
L. Benussi, S. Bianco, F. Fabbri, D. Piccolo, F. Primavera18 INFN Sezione di
Genova a, Università di Genova b, Genova, Italy
V. Calvellia,b, F. Ferroa, F. Raveraa,b, E. Robuttia, S. Tosia,b INFN Sezione
di Milano-Bicocca a, Università di Milano-Bicocca b, Milano, Italy
A. Benagliaa, A. Beschib, L. Brianzaa,b, F. Brivioa,b, V. Cirioloa,b,18, M.E.
Dinardoa,b, S. Fiorendia,b, S. Gennaia, A. Ghezzia,b, P. Govonia,b, M.
Malbertia,b, S. Malvezzia, R.A. Manzonia,b, D. Menascea, L. Moronia, M.
Paganonia,b, K. Pauwelsa,b, D. Pedrinia, S. Pigazzinia,b,34, S. Ragazzia,b, T.
Tabarelli de Fatisa,b INFN Sezione di Napoli a, Università di Napoli ’Federico
II’ b, Napoli, Italy, Università della Basilicata c, Potenza, Italy,
Università G. Marconi d, Roma, Italy
S. Buontempoa, N. Cavalloa,c, S. Di Guidaa,d,18, F. Fabozzia,c, F. Fiengaa,b,
G. Galatia,b, A.O.M. Iorioa,b, W.A. Khana, L. Listaa, S. Meolaa,d,18, P.
Paoluccia,18, C. Sciaccaa,b, F. Thyssena, E. Voevodinaa,b INFN Sezione di
Padova a, Università di Padova b, Padova, Italy, Università di Trento c,
Trento, Italy
P. Azzia, N. Bacchettaa, L. Benatoa,b, D. Biselloa,b, A. Bolettia,b, R.
Carlina,b, A. Carvalho Antunes De Oliveiraa,b, P. Checchiaa, M. Dall’Ossoa,b,
P. De Castro Manzanoa, T. Dorigoa, U. Dossellia, F. Gasparinia,b, U.
Gasparinia,b, A. Gozzelinoa, S. Lacapraraa, P. Lujan, M. Margonia,b, A.T.
Meneguzzoa,b, N. Pozzobona,b, P. Ronchesea,b, R. Rossina,b, F. Simonettoa,b,
A. Tiko, E. Torassaa, M. Zanettia,b, P. Zottoa,b INFN Sezione di Pavia a,
Università di Pavia b, Pavia, Italy
A. Braghieria, A. Magnania, P. Montagnaa,b, S.P. Rattia,b, V. Rea, M.
Ressegottia,b, C. Riccardia,b, P. Salvinia, I. Vaia,b, P. Vituloa,b INFN
Sezione di Perugia a, Università di Perugia b, Perugia, Italy
L. Alunni Solestizia,b, M. Biasinia,b, G.M. Bileia, C. Cecchia,b, D.
Ciangottinia,b, L. Fanòa,b, P. Laricciaa,b, R. Leonardia,b, E. Manonia, G.
Mantovania,b, V. Mariania,b, M. Menichellia, A. Rossia,b, A. Santocchiaa,b, D.
Spigaa INFN Sezione di Pisa a, Università di Pisa b, Scuola Normale Superiore
di Pisa c, Pisa, Italy
K. Androsova, P. Azzurria,18, G. Bagliesia, L. Bianchinia, T. Boccalia, L.
Borrello, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, G. Fedia, L.
Gianninia,c, A. Giassia, M.T. Grippoa,33, F. Ligabuea,c, T. Lomtadzea, E.
Mancaa,c, G. Mandorlia,c, A. Messineoa,b, F. Pallaa, A. Rizzia,b, P.
Spagnoloa, R. Tenchinia, G. Tonellia,b, A. Venturia, P.G. Verdinia INFN
Sezione di Roma a, Sapienza Università di Roma b, Rome, Italy
L. Baronea,b, F. Cavallaria, M. Cipriania,b, N. Dacia, D. Del Rea,b, E. Di
Marcoa,b, M. Diemoza, S. Gellia,b, E. Longoa,b, B. Marzocchia,b, P.
Meridiania, G. Organtinia,b, F. Pandolfia, R. Paramattia,b, F. Preiatoa,b, S.
Rahatloua,b, C. Rovellia, F. Santanastasioa,b INFN Sezione di Torino a,
Università di Torino b, Torino, Italy, Università del Piemonte Orientale c,
Novara, Italy
N. Amapanea,b, R. Arcidiaconoa,c, S. Argiroa,b, M. Arneodoa,c, N. Bartosika,
R. Bellana,b, C. Biinoa, N. Cartigliaa, R. Castelloa,b, F. Cennaa,b, M.
Costaa,b, R. Covarellia,b, A. Deganoa,b, N. Demariaa, B. Kiania,b, C.
Mariottia, S. Masellia, E. Migliorea,b, V. Monacoa,b, E. Monteila,b, M.
Montenoa, M.M. Obertinoa,b, L. Pachera,b, N. Pastronea, M. Pelliccionia, G.L.
Pinna Angionia,b, A. Romeroa,b, M. Ruspaa,c, R. Sacchia,b, K. Shchelinaa,b, V.
Solaa, A. Solanoa,b, A. Staianoa INFN Sezione di Trieste a, Università di
Trieste b, Trieste, Italy
S. Belfortea, M. Casarsaa, F. Cossuttia, G. Della Riccaa,b, A. Zanettia
Kyungpook National University
D.H. Kim, G.N. Kim, M.S. Kim, J. Lee, S. Lee, S.W. Lee, C.S. Moon, Y.D. Oh, S.
Sekmen, D.C. Son, Y.C. Yang Chonnam National University, Institute for
Universe and Elementary Particles, Kwangju, Korea
H. Kim, D.H. Moon, G. Oh Hanyang University, Seoul, Korea
J.A. Brochero Cifuentes, J. Goh, T.J. Kim Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, D. Gyun, S. Ha, B. Hong, Y. Jo, Y. Kim, K. Lee, K.S.
Lee, S. Lee, J. Lim, S.K. Park, Y. Roh Seoul National University, Seoul, Korea
J. Almond, J. Kim, J.S. Kim, H. Lee, K. Lee, K. Nam, S.B. Oh, B.C. Radburn-
Smith, S.h. Seo, U.K. Yang, H.D. Yoo, G.B. Yu University of Seoul, Seoul,
Korea
H. Kim, J.H. Kim, J.S.H. Lee, I.C. Park Sungkyunkwan University, Suwon, Korea
Y. Choi, C. Hwang, J. Lee, I. Yu Vilnius University, Vilnius, Lithuania
V. Dudenas, A. Juodagalvis, J. Vaitkus National Centre for Particle Physics,
Universiti Malaya, Kuala Lumpur, Malaysia
I. Ahmed, Z.A. Ibrahim, M.A.B. Md Ali35, F. Mohamad Idris36, W.A.T. Wan
Abdullah, M.N. Yusli, Z. Zolkapli Centro de Investigacion y de Estudios
Avanzados del IPN, Mexico City, Mexico
Duran-Osuna, M. C., H. Castilla-Valdez, E. De La Cruz-Burelo, Ramirez-Sanchez,
G., I. Heredia-De La Cruz37, Rabadan-Trejo, R. I., R. Lopez-Fernandez, J.
Mejia Guisao, Reyes-Almanza, R, A. Sanchez-Hernandez Universidad
Iberoamericana, Mexico City, Mexico
S. Carrillo Moreno, C. Oropeza Barrera, F. Vazquez Valencia Benemerita
Universidad Autonoma de Puebla, Puebla, Mexico
J. Eysermans, I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada Universidad
Autónoma de San Luis Potosí, San Luis Potosí, Mexico
A. Morelos Pineda University of Auckland, Auckland, New Zealand
D. Krofcheck University of Canterbury, Christchurch, New Zealand
S. Bheesette, P.H. Butler National Centre for Physics, Quaid-I-Azam
University, Islamabad, Pakistan
A. Ahmad, M. Ahmad, Q. Hassan, H.R. Hoorani, A. Saddique, M.A. Shah, M.
Shoaib, M. Waqas National Centre for Nuclear Research, Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, T. Frueboes, M. Górski, M. Kazana, K.
Nawrocki, M. Szleper, P. Traczyk, P. Zalewski Institute of Experimental
Physics, Faculty of Physics, University of Warsaw, Warsaw, Poland
K. Bunkowski, A. Byszuk38, K. Doroba, A. Kalinowski, M. Konecki, J.
Krolikowski, M. Misiura, M. Olszewski, A. Pyskir, M. Walczak Laboratório de
Instrumentação e Física Experimental de Partículas, Lisboa, Portugal
P. Bargassa, C. Beirão Da Cruz E Silva, A. Di Francesco, P. Faccioli, B.
Galinhas, M. Gallinaro, J. Hollar, N. Leonardo, L. Lloret Iglesias, M.V.
Nemallapudi, J. Seixas, G. Strong, O. Toldaiev, D. Vadruccio, J. Varela Joint
Institute for Nuclear Research, Dubna, Russia
S. Afanasiev, P. Bunin, M. Gavrilenko, I. Golutvin, I. Gorbunov, A. Kamenev,
V. Karjavin, A. Lanev, A. Malakhov, V. Matveev39,40, P. Moisenz, V. Palichik,
V. Perelygin, S. Shmatov, S. Shulha, N. Skatchkov, V. Smirnov, N. Voytishin,
A. Zarubin Petersburg Nuclear Physics Institute, Gatchina (St. Petersburg),
Russia
Y. Ivanov, V. Kim41, E. Kuznetsova42, P. Levchenko, V. Murzin, V. Oreshkin, I.
Smirnov, D. Sosnov, V. Sulimov, L. Uvarov, S. Vavilov, A. Vorobyev Institute
for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov,
N. Krasnikov, A. Pashenkov, D. Tlisov, A. Toropin Institute for Theoretical
and Experimental Physics, Moscow, Russia
V. Epshteyn, V. Gavrilov, N. Lychkovskaya, V. Popov, I. Pozdnyakov, G.
Safronov, A. Spiridonov, A. Stepennov, V. Stolin, M. Toms, E. Vlasov, A.
Zhokin Moscow Institute of Physics and Technology, Moscow, Russia
T. Aushev, A. Bylinkin40 National Research Nuclear University ’Moscow
Engineering Physics Institute’ (MEPhI), Moscow, Russia
M. Chadeeva43, P. Parygin, D. Philippov, S. Polikarpov, E. Popova, V. Rusinov
P.N. Lebedev Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin40, I. Dremin40, M. Kirakosyan40, S.V. Rusakov, A.
Terkulov Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State
University, Moscow, Russia
A. Baskakov, A. Belyaev, E. Boos, V. Bunichev, M. Dubinin44, L. Dudko, A.
Ershov, A. Gribushin, V. Klyukhin, O. Kodolova, I. Lokhtin, I. Miagkov, S.
Obraztsov, V. Savrin, A. Snigirev Novosibirsk State University (NSU),
Novosibirsk, Russia
V. Blinov45, D. Shtol45, Y. Skovpen45 State Research Center of Russian
Federation, Institute for High Energy Physics of NRC &quot, Kurchatov
Institute&quot, , Protvino, Russia
I. Azhgirey, I. Bayshev, S. Bitioukov, D. Elumakhov, A. Godizov, V. Kachanov,
A. Kalinin, D. Konstantinov, P. Mandrik, V. Petrov, R. Ryutin, A. Sobol, S.
Troshin, N. Tyurin, A. Uzunian, A. Volkov National Research Tomsk Polytechnic
University, Tomsk, Russia
A. Babaev University of Belgrade, Faculty of Physics and Vinca Institute of
Nuclear Sciences, Belgrade, Serbia
P. Adzic46, P. Cirkovic, D. Devetak, M. Dordevic, J. Milosevic Centro de
Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT), Madrid,
Spain
J. Alcaraz Maestre, A. Álvarez Fernández, I. Bachiller, M. Barrio Luna, M.
Cerrada, N. Colino, B. De La Cruz, A. Delgado Peris, C. Fernandez Bedoya, J.P.
Fernández Ramos, J. Flix, M.C. Fouz, O. Gonzalez Lopez, S. Goy Lopez, J.M.
Hernandez, M.I. Josa, D. Moran, A. Pérez-Calero Yzquierdo, J. Puerta Pelayo,
I. Redondo, L. Romero, M.S. Soares, A. Triossi Universidad Autónoma de Madrid,
Madrid, Spain
C. Albajar, J.F. de Trocóniz Universidad de Oviedo, Oviedo, Spain
J. Cuevas, C. Erice, J. Fernandez Menendez, S. Folgueras, I. Gonzalez
Caballero, J.R. González Fernández, E. Palencia Cortezon, S. Sanchez Cruz, P.
Vischia, J.M. Vizan Garcia Instituto de Física de Cantabria (IFCA), CSIC-
Universidad de Cantabria, Santander, Spain
I.J. Cabrillo, A. Calderon, B. Chazin Quero, J. Duarte Campderros, M.
Fernandez, P.J. Fernández Manteca, A. García Alonso, J. Garcia-Ferrero, G.
Gomez, A. Lopez Virto, J. Marco, C. Martinez Rivero, P. Martinez Ruiz del
Arbol, F. Matorras, J. Piedra Gomez, C. Prieels, T. Rodrigo, A. Ruiz-Jimeno,
L. Scodellaro, N. Trevisani, I. Vila, R. Vilar Cortabitarte CERN, European
Organization for Nuclear Research, Geneva, Switzerland
D. Abbaneo, B. Akgun, E. Auffray, P. Baillon, A.H. Ball, D. Barney, J.
Bendavid, M. Bianco, A. Bocci, C. Botta, T. Camporesi, M. Cepeda, G.
Cerminara, E. Chapon, Y. Chen, D. d’Enterria, A. Dabrowski, V. Daponte, A.
David, M. De Gruttola, A. De Roeck, N. Deelen, M. Dobson, T. du Pree, M.
Dünser, N. Dupont, A. Elliott-Peisert, P. Everaerts, F. Fallavollita47, G.
Franzoni, J. Fulcher, W. Funk, D. Gigi, A. Gilbert, K. Gill, F. Glege, D.
Gulhan, J. Hegeman, V. Innocente, A. Jafari, P. Janot, O. Karacheban21, J.
Kieseler, V. Knünz, A. Kornmayer, M. Krammer1, C. Lange, P. Lecoq, C.
Lourenço, M.T. Lucchini, L. Malgeri, M. Mannelli, A. Martelli, F. Meijers,
J.A. Merlin, S. Mersi, E. Meschi, P. Milenovic48, F. Moortgat, M. Mulders, H.
Neugebauer, J. Ngadiuba, S. Orfanelli, L. Orsini, F. Pantaleo18, L. Pape, E.
Perez, M. Peruzzi, A. Petrilli, G. Petrucciani, A. Pfeiffer, M. Pierini, F.M.
Pitters, D. Rabady, A. Racz, T. Reis, G. Rolandi49, M. Rovere, H. Sakulin, C.
Schäfer, C. Schwick, M. Seidel, M. Selvaggi, A. Sharma, P. Silva, P.
Sphicas50, A. Stakia, J. Steggemann, M. Stoye, M. Tosi, D. Treille, A. Tsirou,
V. Veckalns51, M. Verweij, W.D. Zeuner Paul Scherrer Institut, Villigen,
Switzerland
W. Bertl${}^{\textrm{\textdagger}}$, L. Caminada52, K. Deiters, W. Erdmann, R.
Horisberger, Q. Ingram, H.C. Kaestli, D. Kotlinski, U. Langenegger, T. Rohe,
S.A. Wiederkehr ETH Zurich - Institute for Particle Physics and Astrophysics
(IPA), Zurich, Switzerland
M. Backhaus, L. Bäni, P. Berger, B. Casal, N. Chernyavskaya, G. Dissertori, M.
Dittmar, M. Donegà, C. Dorfer, C. Grab, C. Heidegger, D. Hits, J. Hoss, T.
Klijnsma, W. Lustermann, M. Marionneau, M.T. Meinhard, D. Meister, F. Micheli,
P. Musella, F. Nessi-Tedaldi, J. Pata, F. Pauss, G. Perrin, L. Perrozzi, M.
Quittnat, M. Reichmann, D. Ruini, D.A. Sanz Becerra, M. Schönenberger, L.
Shchutska, V.R. Tavolaro, K. Theofilatos, M.L. Vesterbacka Olsson, R. Wallny,
D.H. Zhu Universität Zürich, Zurich, Switzerland
T.K. Aarrestad, C. Amsler53, D. Brzhechko, M.F. Canelli, A. De Cosa, R. Del
Burgo, S. Donato, C. Galloni, T. Hreus, B. Kilminster, I. Neutelings, D.
Pinna, G. Rauco, P. Robmann, D. Salerno, K. Schweiger, C. Seitz, Y. Takahashi,
A. Zucchetta National Central University, Chung-Li, Taiwan
V. Candelise, Y.H. Chang, K.y. Cheng, T.H. Doan, Sh. Jain, R. Khurana, C.M.
Kuo, W. Lin, A. Pozdnyakov, S.S. Yu National Taiwan University (NTU), Taipei,
Taiwan
P. Chang, Y. Chao, K.F. Chen, P.H. Chen, F. Fiori, W.-S. Hou, Y. Hsiung, Arun
Kumar, Y.F. Liu, R.-S. Lu, E. Paganis, A. Psallidas, A. Steen, J.f. Tsai
Chulalongkorn University, Faculty of Science, Department of Physics, Bangkok,
Thailand
B. Asavapibhop, K. Kovitanggoon, G. Singh, N. Srimanobhas Çukurova University,
Physics Department, Science and Art Faculty, Adana, Turkey
M.N. Bakirci54, A. Bat, F. Boran, S. Cerci55, S. Damarseckin, Z.S. Demiroglu,
C. Dozen, I. Dumanoglu, S. Girgis, G. Gokbulut, Y. Guler, I. Hos56, E.E.
Kangal57, O. Kara, A. Kayis Topaksu, U. Kiminsu, M. Oglakci, G. Onengut, K.
Ozdemir58, B. Tali55, U.G. Tok, S. Turkcapar, I.S. Zorbakir, C. Zorbilmez
Middle East Technical University, Physics Department, Ankara, Turkey
G. Karapinar59, K. Ocalan60, M. Yalvac, M. Zeyrek Bogazici University,
Istanbul, Turkey
I.O. Atakisi, E. Gülmez, M. Kaya61, O. Kaya62, S. Tekten, E.A. Yetkin63
Istanbul Technical University, Istanbul, Turkey
M.N. Agaras, S. Atay, A. Cakir, K. Cankocak, Y. Komurcu Institute for
Scintillation Materials of National Academy of Science of Ukraine, Kharkov,
Ukraine
B. Grynyov National Scientific Center, Kharkov Institute of Physics and
Technology, Kharkov, Ukraine
L. Levchuk University of Bristol, Bristol, United Kingdom
F. Ball, L. Beck, J.J. Brooke, D. Burns, E. Clement, D. Cussans, O. Davignon,
H. Flacher, J. Goldstein, G.P. Heath, H.F. Heath, L. Kreczko, D.M. Newbold64,
S. Paramesvaran, T. Sakuma, S. Seif El Nasr-storey, D. Smith, V.J. Smith
Rutherford Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev65, C. Brew, R.M. Brown, D. Cieri, D.J.A. Cockerill, J.A.
Coughlan, K. Harder, S. Harper, J. Linacre, E. Olaiya, D. Petyt, C.H.
Shepherd-Themistocleous, A. Thea, I.R. Tomalin, T. Williams, W.J. Womersley
Imperial College, London, United Kingdom
G. Auzinger, R. Bainbridge, P. Bloch, J. Borg, S. Breeze, O. Buchmuller, A.
Bundock, S. Casasso, D. Colling, L. Corpe, P. Dauncey, G. Davies, M. Della
Negra, R. Di Maria, Y. Haddad, G. Hall, G. Iles, T. James, M. Komm, R. Lane,
C. Laner, L. Lyons, A.-M. Magnan, S. Malik, L. Mastrolorenzo, T. Matsushita,
J. Nash66, A. Nikitenko7, V. Palladino, M. Pesaresi, A. Richards, A. Rose, E.
Scott, C. Seez, A. Shtipliyski, T. Strebler, S. Summers, A. Tapper, K. Uchida,
M. Vazquez Acosta67, T. Virdee18, N. Wardle, D. Winterbottom, J. Wright, S.C.
Zenz Brunel University, Uxbridge, United Kingdom
J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, A. Morton, I.D. Reid, L.
Teodorescu, S. Zahid Baylor University, Waco, USA
A. Borzou, K. Call, J. Dittmann, K. Hatakeyama, H. Liu, N. Pastika, C. Smith
Catholic University of America, Washington DC, USA
R. Bartek, A. Dominguez The University of Alabama, Tuscaloosa, USA
A. Buccilli, S.I. Cooper, C. Henderson, P. Rumerio, C. West Boston University,
Boston, USA
D. Arcaro, A. Avetisyan, T. Bose, D. Gastler, D. Rankin, C. Richardson, J.
Rohlf, L. Sulak, D. Zou Brown University, Providence, USA
G. Benelli, D. Cutts, M. Hadley, J. Hakala, U. Heintz, J.M. Hogan68, K.H.M.
Kwok, E. Laird, G. Landsberg, J. Lee, Z. Mao, M. Narain, J. Pazzini, S.
Piperov, S. Sagir, R. Syarif, D. Yu University of California, Davis, Davis,
USA
R. Band, C. Brainerd, R. Breedon, D. Burns, M. Calderon De La Barca Sanchez,
M. Chertok, J. Conway, R. Conway, P.T. Cox, R. Erbacher, C. Flores, G. Funk,
W. Ko, R. Lander, C. Mclean, M. Mulhearn, D. Pellett, J. Pilot, S. Shalhout,
M. Shi, J. Smith, D. Stolp, D. Taylor, K. Tos, M. Tripathi, Z. Wang, F. Zhang
University of California, Los Angeles, USA
M. Bachtis, C. Bravo, R. Cousins, A. Dasgupta, A. Florent, J. Hauser, M.
Ignatenko, N. Mccoll, S. Regnard, D. Saltzberg, C. Schnaible, V. Valuev
University of California, Riverside, Riverside, USA
E. Bouvier, K. Burt, R. Clare, J. Ellison, J.W. Gary, S.M.A. Ghiasi Shirazi,
G. Hanson, G. Karapostoli, E. Kennedy, F. Lacroix, O.R. Long, M. Olmedo
Negrete, M.I. Paneva, W. Si, L. Wang, H. Wei, S. Wimpenny, B. R. Yates
University of California, San Diego, La Jolla, USA
J.G. Branson, S. Cittolin, M. Derdzinski, R. Gerosa, D. Gilbert, B. Hashemi,
A. Holzner, D. Klein, G. Kole, V. Krutelyov, J. Letts, M. Masciovecchio, D.
Olivito, S. Padhi, M. Pieri, M. Sani, V. Sharma, S. Simon, M. Tadel, A.
Vartak, S. Wasserbaech69, J. Wood, F. Würthwein, A. Yagil, G. Zevi Della Porta
University of California, Santa Barbara - Department of Physics, Santa
Barbara, USA
N. Amin, R. Bhandari, J. Bradmiller-Feld, C. Campagnari, M. Citron, A. Dishaw,
V. Dutta, M. Franco Sevilla, L. Gouskos, R. Heller, J. Incandela, A.
Ovcharova, H. Qu, J. Richman, D. Stuart, I. Suarez, J. Yoo California
Institute of Technology, Pasadena, USA
D. Anderson, A. Bornheim, J. Bunn, J.M. Lawhorn, H.B. Newman, T. Q. Nguyen, C.
Pena, M. Spiropulu, J.R. Vlimant, R. Wilkinson, S. Xie, Z. Zhang, R.Y. Zhu
Carnegie Mellon University, Pittsburgh, USA
M.B. Andrews, T. Ferguson, T. Mudholkar, M. Paulini, J. Russ, M. Sun, H.
Vogel, I. Vorobiev, M. Weinberg University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, F. Jensen, A. Johnson, M. Krohn, S. Leontsinis, E.
MacDonald, T. Mulholland, K. Stenson, K.A. Ulmer, S.R. Wagner Cornell
University, Ithaca, USA
J. Alexander, J. Chaves, Y. Cheng, J. Chu, A. Datta, K. Mcdermott, N. Mirman,
J.R. Patterson, D. Quach, A. Rinkevicius, A. Ryd, L. Skinnari, L. Soffi, S.M.
Tan, Z. Tao, J. Thom, J. Tucker, P. Wittich, M. Zientek Fermi National
Accelerator Laboratory, Batavia, USA
S. Abdullin, M. Albrow, M. Alyari, G. Apollinari, A. Apresyan, A. Apyan, S.
Banerjee, L.A.T. Bauerdick, A. Beretvas, J. Berryhill, P.C. Bhat, G.
Bolla${}^{\textrm{\textdagger}}$, K. Burkett, J.N. Butler, A. Canepa, G.B.
Cerati, H.W.K. Cheung, F. Chlebana, M. Cremonesi, J. Duarte, V.D. Elvira, J.
Freeman, Z. Gecse, E. Gottschalk, L. Gray, D. Green, S. Grünendahl, O.
Gutsche, J. Hanlon, R.M. Harris, S. Hasegawa, J. Hirschauer, Z. Hu, B.
Jayatilaka, S. Jindariani, M. Johnson, U. Joshi, B. Klima, M.J. Kortelainen,
B. Kreis, S. Lammel, D. Lincoln, R. Lipton, M. Liu, T. Liu, R. Lopes De Sá, J.
Lykken, K. Maeshima, N. Magini, J.M. Marraffino, D. Mason, P. McBride, P.
Merkel, S. Mrenna, S. Nahn, V. O’Dell, K. Pedro, O. Prokofyev, G. Rakness, L.
Ristori, A. Savoy-Navarro70, B. Schneider, E. Sexton-Kennedy, A. Soha, W.J.
Spalding, L. Spiegel, S. Stoynev, J. Strait, N. Strobbe, L. Taylor, S.
Tkaczyk, N.V. Tran, L. Uplegger, E.W. Vaandering, C. Vernieri, M. Verzocchi,
R. Vidal, M. Wang, H.A. Weber, A. Whitbeck, W. Wu University of Florida,
Gainesville, USA
D. Acosta, P. Avery, P. Bortignon, D. Bourilkov, A. Brinkerhoff, A. Carnes, M.
Carver, D. Curry, R.D. Field, I.K. Furic, S.V. Gleyzer, B.M. Joshi, J.
Konigsberg, A. Korytov, K. Kotov, P. Ma, K. Matchev, H. Mei, G. Mitselmakher,
K. Shi, D. Sperka, N. Terentyev, L. Thomas, J. Wang, S. Wang, J. Yelton
Florida International University, Miami, USA
Y.R. Joshi, S. Linn, P. Markowitz, J.L. Rodriguez Florida State University,
Tallahassee, USA
A. Ackert, T. Adams, A. Askew, S. Hagopian, V. Hagopian, K.F. Johnson, T.
Kolberg, G. Martinez, T. Perry, H. Prosper, A. Saha, A. Santra, V. Sharma, R.
Yohay Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, V. Bhopatkar, S. Colafranceschi, M. Hohlmann, D. Noonan, T.
Roy, F. Yumiceva University of Illinois at Chicago (UIC), Chicago, USA
M.R. Adams, L. Apanasevich, D. Berry, R.R. Betts, R. Cavanaugh, X. Chen, S.
Dittmer, O. Evdokimov, C.E. Gerber, D.A. Hangal, D.J. Hofman, K. Jung, J.
Kamin, I.D. Sandoval Gonzalez, M.B. Tonjes, N. Varelas, H. Wang, Z. Wu, J.
Zhang The University of Iowa, Iowa City, USA
B. Bilki71, W. Clarida, K. Dilsiz72, S. Durgut, R.P. Gandrajula, M.
Haytmyradov, V. Khristenko, J.-P. Merlo, H. Mermerkaya73, A. Mestvirishvili,
A. Moeller, J. Nachtman, H. Ogul74, Y. Onel, F. Ozok75, A. Penzo, C. Snyder,
E. Tiras, J. Wetzel, K. Yi Johns Hopkins University, Baltimore, USA
B. Blumenfeld, A. Cocoros, N. Eminizer, D. Fehling, L. Feng, A.V. Gritsan,
W.T. Hung, P. Maksimovic, J. Roskes, U. Sarica, M. Swartz, M. Xiao, C. You The
University of Kansas, Lawrence, USA
A. Al-bataineh, P. Baringer, A. Bean, S. Boren, J. Bowen, J. Castle, S.
Khalil, A. Kropivnitskaya, D. Majumder, W. Mcbrayer, M. Murray, C. Rogan, C.
Royon, S. Sanders, E. Schmitz, J.D. Tapia Takaki, Q. Wang Kansas State
University, Manhattan, USA
A. Ivanov, K. Kaadze, Y. Maravin, A. Modak, A. Mohammadi, L.K. Saini, N.
Skhirtladze Lawrence Livermore National Laboratory, Livermore, USA
F. Rebassoo, D. Wright University of Maryland, College Park, USA
A. Baden, O. Baron, A. Belloni, S.C. Eno, Y. Feng, C. Ferraioli, N.J. Hadley,
S. Jabeen, G.Y. Jeng, R.G. Kellogg, J. Kunkle, A.C. Mignerey, F. Ricci-Tam,
Y.H. Shin, A. Skuja, S.C. Tonwar Massachusetts Institute of Technology,
Cambridge, USA
D. Abercrombie, B. Allen, V. Azzolini, R. Barbieri, A. Baty, G. Bauer, R. Bi,
S. Brandt, W. Busza, I.A. Cali, M. D’Alfonso, Z. Demiragli, G. Gomez Ceballos,
M. Goncharov, P. Harris, D. Hsu, M. Hu, Y. Iiyama, G.M. Innocenti, M. Klute,
D. Kovalskyi, Y.-J. Lee, A. Levin, P.D. Luckey, B. Maier, A.C. Marini, C.
Mcginn, C. Mironov, S. Narayanan, X. Niu, C. Paus, C. Roland, G. Roland,
G.S.F. Stephans, K. Sumorok, K. Tatar, D. Velicanu, J. Wang, T.W. Wang, B.
Wyslouch, S. Zhaozhong University of Minnesota, Minneapolis, USA
A.C. Benvenuti, R.M. Chatterjee, A. Evans, P. Hansen, S. Kalafut, Y. Kubota,
Z. Lesko, J. Mans, S. Nourbakhsh, N. Ruckstuhl, R. Rusack, J. Turkewitz, M.A.
Wadud University of Mississippi, Oxford, USA
J.G. Acosta, S. Oliveros University of Nebraska-Lincoln, Lincoln, USA
E. Avdeeva, K. Bloom, D.R. Claes, C. Fangmeier, F. Golf, R. Gonzalez Suarez,
R. Kamalieddin, I. Kravchenko, J. Monroy, J.E. Siado, G.R. Snow, B. Stieger
State University of New York at Buffalo, Buffalo, USA
A. Godshalk, C. Harrington, I. Iashvili, D. Nguyen, A. Parker, S. Rappoccio,
B. Roozbahani Northeastern University, Boston, USA
G. Alverson, E. Barberis, C. Freer, A. Hortiangtham, A. Massironi, D.M. Morse,
T. Orimoto, R. Teixeira De Lima, T. Wamorkar, B. Wang, A. Wisecarver, D. Wood
Northwestern University, Evanston, USA
S. Bhattacharya, O. Charaf, K.A. Hahn, N. Mucia, N. Odell, M.H. Schmitt, K.
Sung, M. Trovato, M. Velasco University of Notre Dame, Notre Dame, USA
R. Bucci, N. Dev, M. Hildreth, K. Hurtado Anampa, C. Jessop, D.J. Karmgard, N.
Kellams, K. Lannon, W. Li, N. Loukas, N. Marinelli, F. Meng, C. Mueller, Y.
Musienko39, M. Planer, A. Reinsvold, R. Ruchti, P. Siddireddy, G. Smith, S.
Taroni, M. Wayne, A. Wightman, M. Wolf, A. Woodard The Ohio State University,
Columbus, USA
J. Alimena, L. Antonelli, B. Bylsma, L.S. Durkin, S. Flowers, B. Francis, A.
Hart, C. Hill, W. Ji, T.Y. Ling, W. Luo, B.L. Winer, H.W. Wulsin Princeton
University, Princeton, USA
S. Cooperstein, O. Driga, P. Elmer, J. Hardenbrook, P. Hebda, S. Higginbotham,
A. Kalogeropoulos, D. Lange, J. Luo, D. Marlow, K. Mei, I. Ojalvo, J. Olsen,
C. Palmer, P. Piroué, J. Salfeld-Nebgen, D. Stickland, C. Tully University of
Puerto Rico, Mayaguez, USA
S. Malik, S. Norberg Purdue University, West Lafayette, USA
A. Barker, V.E. Barnes, S. Das, L. Gutay, M. Jones, A.W. Jung, A. Khatiwada,
D.H. Miller, N. Neumeister, C.C. Peng, H. Qiu, J.F. Schulte, J. Sun, F. Wang,
R. Xiao, W. Xie Purdue University Northwest, Hammond, USA
T. Cheng, J. Dolen, N. Parashar Rice University, Houston, USA
Z. Chen, K.M. Ecklund, S. Freed, F.J.M. Geurts, M. Guilbaud, M. Kilpatrick, W.
Li, B. Michlin, B.P. Padley, J. Roberts, J. Rorie, W. Shi, Z. Tu, J. Zabel, A.
Zhang University of Rochester, Rochester, USA
A. Bodek, P. de Barbaro, R. Demina, Y.t. Duh, T. Ferbel, M. Galanti, A.
Garcia-Bellido, J. Han, O. Hindrichs, A. Khukhunaishvili, K.H. Lo, P. Tan, M.
Verzetti The Rockefeller University, New York, USA
R. Ciesielski, K. Goulianos, C. Mesropian Rutgers, The State University of New
Jersey, Piscataway, USA
A. Agapitos, J.P. Chou, Y. Gershtein, T.A. Gómez Espinosa, E. Halkiadakis, M.
Heindl, E. Hughes, S. Kaplan, R. Kunnawalkam Elayavalli, S. Kyriacou, A. Lath,
R. Montalvo, K. Nash, M. Osherson, H. Saka, S. Salur, S. Schnetzer, D.
Sheffield, S. Somalwar, R. Stone, S. Thomas, P. Thomassen, M. Walker
University of Tennessee, Knoxville, USA
A.G. Delannoy, J. Heideman, G. Riley, K. Rose, S. Spanier, K. Thapa Texas A&M
University, College Station, USA
O. Bouhali76, A. Castaneda Hernandez76, A. Celik, M. Dalchenko, M. De Mattia,
A. Delgado, S. Dildick, R. Eusebi, J. Gilmore, T. Huang, T. Kamon77, R.
Mueller, Y. Pakhotin, R. Patel, A. Perloff, L. Perniè, D. Rathjens, A.
Safonov, A. Tatarinov Texas Tech University, Lubbock, USA
N. Akchurin, J. Damgov, F. De Guio, P.R. Dudero, J. Faulkner, E. Gurpinar, S.
Kunori, K. Lamichhane, S.W. Lee, T. Mengke, S. Muthumuni, T. Peltola, S.
Undleeb, I. Volobouev, Z. Wang Vanderbilt University, Nashville, USA
S. Greene, A. Gurrola, R. Janjam, W. Johns, C. Maguire, A. Melo, H. Ni, K.
Padeken, J.D. Ruiz Alvarez, P. Sheldon, S. Tuo, J. Velkovska, Q. Xu University
of Virginia, Charlottesville, USA
M.W. Arenton, P. Barria, B. Cox, R. Hirosky, M. Joyce, A. Ledovskoy, H. Li, C.
Neu, T. Sinthuprasith, Y. Wang, E. Wolfe, F. Xia Wayne State University,
Detroit, USA
R. Harr, P.E. Karchin, N. Poudyal, J. Sturdy, P. Thapa, S. Zaleski University
of Wisconsin - Madison, Madison, WI, USA
M. Brodski, J. Buchanan, C. Caillol, D. Carlsmith, S. Dasu, L. Dodd, S. Duric,
B. Gomber, M. Grothe, M. Herndon, A. Hervé, U. Hussain, P. Klabbers, A.
Lanaro, A. Levine, K. Long, R. Loveless, V. Rekovic, T. Ruggles, A. Savin, N.
Smith, W.H. Smith, N. Woods †: Deceased
1: Also at Vienna University of Technology, Vienna, Austria
2: Also at IRFU; CEA; Université Paris-Saclay, Gif-sur-Yvette, France
3: Also at Universidade Estadual de Campinas, Campinas, Brazil
4: Also at Federal University of Rio Grande do Sul, Porto Alegre, Brazil
5: Also at Universidade Federal de Pelotas, Pelotas, Brazil
6: Also at Université Libre de Bruxelles, Bruxelles, Belgium
7: Also at Institute for Theoretical and Experimental Physics, Moscow, Russia
8: Also at Joint Institute for Nuclear Research, Dubna, Russia
9: Now at Cairo University, Cairo, Egypt
10: Now at Fayoum University, El-Fayoum, Egypt
11: Also at British University in Egypt, Cairo, Egypt
12: Now at Ain Shams University, Cairo, Egypt
13: Also at Department of Physics; King Abdulaziz University, Jeddah, Saudi
Arabia
14: Also at Université de Haute Alsace, Mulhouse, France
15: Also at Skobeltsyn Institute of Nuclear Physics; Lomonosov Moscow State
University, Moscow, Russia
16: Also at Tbilisi State University, Tbilisi, Georgia
17: Also at Ilia State University, Tbilisi, Georgia
18: Also at CERN; European Organization for Nuclear Research, Geneva,
Switzerland
19: Also at RWTH Aachen University; III. Physikalisches Institut A, Aachen,
Germany
20: Also at University of Hamburg, Hamburg, Germany
21: Also at Brandenburg University of Technology, Cottbus, Germany
22: Also at MTA-ELTE Lendület CMS Particle and Nuclear Physics Group; Eötvös
Loránd University, Budapest, Hungary
23: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary
24: Also at Institute of Physics; University of Debrecen, Debrecen, Hungary
25: Also at Indian Institute of Technology Bhubaneswar, Bhubaneswar, India
26: Also at Institute of Physics, Bhubaneswar, India
27: Also at Shoolini University, Solan, India
28: Also at University of Visva-Bharati, Santiniketan, India
29: Also at University of Ruhuna, Matara, Sri Lanka
30: Also at Isfahan University of Technology, Isfahan, Iran
31: Also at Yazd University, Yazd, Iran
32: Also at Plasma Physics Research Center; Science and Research Branch;
Islamic Azad University, Tehran, Iran
33: Also at Università degli Studi di Siena, Siena, Italy
34: Also at INFN Sezione di Milano-Bicocca; Università di Milano-Bicocca,
Milano, Italy
35: Also at International Islamic University of Malaysia, Kuala Lumpur,
Malaysia
36: Also at Malaysian Nuclear Agency; MOSTI, Kajang, Malaysia
37: Also at Consejo Nacional de Ciencia y Tecnología, Mexico city, Mexico
38: Also at Warsaw University of Technology; Institute of Electronic Systems,
Warsaw, Poland
39: Also at Institute for Nuclear Research, Moscow, Russia
40: Now at National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
41: Also at St. Petersburg State Polytechnical University, St. Petersburg,
Russia
42: Also at University of Florida, Gainesville, USA
43: Also at P.N. Lebedev Physical Institute, Moscow, Russia
44: Also at California Institute of Technology, Pasadena, USA
45: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia
46: Also at Faculty of Physics; University of Belgrade, Belgrade, Serbia
47: Also at INFN Sezione di Pavia; Università di Pavia, Pavia, Italy
48: Also at University of Belgrade; Faculty of Physics and Vinca Institute of
Nuclear Sciences, Belgrade, Serbia
49: Also at Scuola Normale e Sezione dell’INFN, Pisa, Italy
50: Also at National and Kapodistrian University of Athens, Athens, Greece
51: Also at Riga Technical University, Riga, Latvia
52: Also at Universität Zürich, Zurich, Switzerland
53: Also at Stefan Meyer Institute for Subatomic Physics (SMI), Vienna,
Austria
54: Also at Gaziosmanpasa University, Tokat, Turkey
55: Also at Adiyaman University, Adiyaman, Turkey
56: Also at Istanbul Aydin University, Istanbul, Turkey
57: Also at Mersin University, Mersin, Turkey
58: Also at Piri Reis University, Istanbul, Turkey
59: Also at Izmir Institute of Technology, Izmir, Turkey
60: Also at Necmettin Erbakan University, Konya, Turkey
61: Also at Marmara University, Istanbul, Turkey
62: Also at Kafkas University, Kars, Turkey
63: Also at Istanbul Bilgi University, Istanbul, Turkey
64: Also at Rutherford Appleton Laboratory, Didcot, United Kingdom
65: Also at School of Physics and Astronomy; University of Southampton,
Southampton, United Kingdom
66: Also at Monash University; Faculty of Science, Clayton, Australia
67: Also at Instituto de Astrofísica de Canarias, La Laguna, Spain
68: Also at Bethel University, ST. PAUL, USA
69: Also at Utah Valley University, Orem, USA
70: Also at Purdue University, West Lafayette, USA
71: Also at Beykent University, Istanbul, Turkey
72: Also at Bingol University, Bingol, Turkey
73: Also at Erzincan University, Erzincan, Turkey
74: Also at Sinop University, Sinop, Turkey
75: Also at Mimar Sinan University; Istanbul, Istanbul, Turkey
76: Also at Texas A&M University at Qatar, Doha, Qatar
77: Also at Kyungpook National University, Daegu, Korea