EXO-20-001

EXO-20-001

[inst1]The CMS Collaboration

# Search for $\PW\PGg$ resonances in proton-proton collisions at
$\sqrt{s}=13\TeV$ using hadronic decays of Lorentz-boosted $\PW$ bosons

###### Abstract

A search for $\PW\PGg$ resonances in the mass range between 0.7 and 6.0is
presented. The boson is reconstructed via its hadronic decays, with the final-
state products forming a single large-radius jet, owing to a high Lorentz
boost of the boson. The search is based on proton-proton collision data at
$\sqrt{s}=13\TeV$, corresponding to an integrated luminosity of 137, collected
with the CMS detector at the LHC in 2016–2018. The $\PW\PGg$ mass spectrum is
parameterized with a smoothly falling background function and examined for the
presence of resonance-like signals. No significant excess above the predicted
background is observed. Model-specific upper limits at 95% confidence level on
the product of the cross section and branching fraction to the $\PW\PGg$
channel are set. Limits for narrow resonances and for resonances with an
intrinsic width equal to 5% of their mass, for spin-0 and spin-1 hypotheses,
range between 0.17fb at 6.0and 55fb at 0.7. These are the most restrictive
limits to date on the existence of such resonances. In specific narrow-
resonance benchmark models, heavy scalar (vector) triplet resonances with
masses between 0.75 (1.15) and 1.40 (1.36)are excluded for a range of model
parameters. Model-independent limits on the product of the cross section,
signal acceptance, and branching fraction to the $\PW\PGg$ channel are set for
minimum $\PW\PGg$ mass thresholds between 1.5 and 8.0.

##  0.1 Introduction

Searches for new resonances predicted in theories beyond the standard model
(SM) are among the key components of the physics program at the CERN LHC. Many
of these searches have been carried out in the past decade, since the start of
the LHC operation, and have helped to reshape the landscape of allowed beyond-
the-SM physics. Experimental searches for the production of new resonances
decaying into a pair of SM particles are a notable aspect of this program. In
the case of a new resonance with significant couplings to quarks and gluons,
it would be produced in quark-antiquark, quark-gluon, or gluon-gluon
interactions in proton-proton ($\Pp\Pp$) collisions at the LHC and
subsequently decay into a pair of jets. On the other hand, if the couplings to
quarks and gluons were suppressed, other decay channels, including decays into
pairs of vector bosons (diboson decays), would become dominant.

Diboson decays involving photons, , $\PW\PGg$, $\PZ\Pgg$, $\PH\PGg$, and
$\PGg\PGg$ channels, are important parts of this search program, owing to the
excellent detection efficiency and photon energy resolution of the ATLAS and
CMS detectors. For example, searches in the $\PGg\PGg$ channel contributed
significantly to the discovery of the Higgs boson by the ATLAS and CMS
Collaborations in 2012 [1, 2, 3]. Nevertheless, the above-mentioned decay
modes involving photons, in particular the $\PW\PGg$ decay mode, are generally
less studied over a large range of masses than other diboson signatures. There
are multiple beyond-the-SM theories that predict $\PW\PGg$ resonances,
including new particles in models with an extended Higgs sector [4], such as
charged Higgs bosons in generic two Higgs doublet models [5, 6], as well as
particles predicted in technicolor [7, 8, 9, 10], heavy vector triplet [11],
and electroweak singlet [12] models, or scalar “quirks” in folded
supersymmetry [13]. A number of nonresonant $\PW\PGg$ analyses that mainly
probe anomalous $\PW\PW\PGg$ couplings have been conducted at the CERN
S$\Pp\PAp$S, LEP, and LHC, as well as at the Fermilab Tevatron. However,
searches for $\PW\PGg$ resonances at high mass have been conducted only by the
ATLAS experiment, in the leptonic decay channel of the boson at the center-of-
mass energies of 7 [14] and 8 [15], and in the hadronic decay channel at 13
[16]. The best 95% confidence level () upper limits on the product of the
cross section and branching fraction to $\PW\PGg$ for narrow spin-1 resonances
in the leptonic channel are 0.5–6.0fb in the 0.2–1.6mass range [15], while the
analogous limits in the hadronic channel [16] are 0.14–10fb in the 1.0–6.8mass
range.

In this Letter, we describe a search for $\PW\PGg$ resonances in the hadronic
decay channel of the boson using $\Pp\Pp$ collision data at $\sqrt{s}=13\TeV$
delivered by the LHC in 2016–2018. The results of the search are interpreted
in terms of limits on narrow and broad, spin-0 and spin-1 resonances in a mass
range between 0.7 and 6.0. Narrow resonances are taken to be those with widths
$\Gamma_{\PX}$ that are negligible compared to the experimental resolution,
while for broad resonances we consider the representative case for which
$\Gamma_{\PX}/m_{\PX}=5\%$, where $m_{\PX}$ is the resonance mass. Given the
large mass of the resonances probed in this analysis, the boson is produced
with a high Lorentz boost and is reconstructed as a single large-radius jet.
The two-prong structure and mass of this jet are established using jet
substructure techniques, allowing for the reduction of the dominant background
from direct photon production, where a jet recoiling against a photon
originates from quantum chromodynamics (QCD) radiation.

Digitized versions of tables and plots from this paper can be found in the
HEPData database [17].

##  0.2 The CMS detector

The central feature of the CMS apparatus is a superconducting solenoid of 6m
internal diameter, providing a magnetic field of 3.8T. Within the solenoid
volume are a silicon pixel and strip tracker, a lead tungstate crystal
electromagnetic calorimeter (ECAL), and a brass and scintillator hadron
calorimeter (HCAL), each composed of a barrel and two endcap sections. Forward
calorimeters extend the pseudorapidity ($\eta$) coverage provided by the
barrel and endcap detectors. Muons are detected in gas-ionization chambers
embedded in the steel flux-return yoke outside the solenoid.

In the region $\abs{\eta}<1.74$, the HCAL cells have widths of 0.087 in $\eta$
and 0.087 in azimuth ($\phi$). In the $\eta$-$\phi$ plane, and for
$\abs{\eta}<1.48$, the HCAL cells map on to $5{\times}5$ arrays of ECAL
crystals to form calorimeter towers projecting radially outwards from close to
the nominal interaction point. For $\abs{\eta}>1.74$, the coverage of the
towers increases progressively to a maximum of 0.174 in $\Delta\eta$ and
$\Delta\phi$. Within each tower, the energy deposits in ECAL and HCAL cells
are summed to define the calorimeter tower energies, and subsequently used to
provide the energies and directions of hadronic jets.

Events of interest are selected using a two-tiered trigger system [18]. The
first level (L1), composed of custom hardware processors, uses information
from the calorimeters and muon detectors to select events at a rate of around
100kHz within a fixed latency of about 4 [19]. The second level, known as the
high-level trigger, consists of a farm of processors running a version of the
full event reconstruction software optimized for fast processing, and reduces
the event rate to around 1kHz before data storage [18].

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. [20].

##  0.3 Data sets and event selection

The data used in this search correspond to an total integrated luminosity of
137and were recorded by the CMS experiment at $\sqrt{s}=13$in 2016–2018 (36,
41, and 60in 2016, 2017, and 2018, respectively) [21, 22, 23]. The high
instantaneous luminosity delivered by the LHC results in additional
interactions in the same or neighboring bunch crossings as the hard scattering
interactions (pileup). The average number of pileup interactions in the 2016
(2017 and 2018) data set is around 23 (32).

The data are selected via single-photon triggers that require the photon
candidate to be within $\abs{\eta}<2.5$, and to have transverse momentum
$\pt>165$ or 175in 2016 and $\pt>200$in 2017–2018. We determine the selection
efficiencies for these triggers using unbiased data samples collected with
single-muon triggers. The single-photon triggers are found to be 98–100%
efficient with respect to the offline selection described below, for the
entire mass range used in the analysis. The small residual inefficiency is
taken into account when calculating the signal acceptance.

Simulated Monte Carlo (MC) signal samples are produced at leading order (LO)
in perturbative QCD. They are used to optimize the analysis selection and to
calculate the signal efficiency. Simulated signal events of spin-0 resonances
decaying to $\PW\PGg$ are generated using electroweak triplet pseudo-Goldstone
bosons $\PGp_{3}$ [24] and a heavy (pseudo-)scalar $SU(2)_{\mathrm{L}}$
triplet [25], while for spin-1 resonances, a heavy vector $SU(2)_{\mathrm{L}}$
triplet [25] is used. Several signal samples are generated with masses ranging
from 0.7 to 6.0. Two resonance width assumptions are used in the simulation:
one, termed “narrow”, has a width which is significantly smaller than the
detector resolution, and the second, referred to as “broad”, has
$\Gamma_{\PX}/m_{\PX}=5\%$. The latter choice is representative of broad
resonances, for which the impact of the off-shell production on the signal
efficiency becomes sizable.

Simulated background events do not enter the analyses directly, as the
background is obtained from a fit to data. They are only used to assess the
accuracy of the background model and to optimize the event selection. The
dominant background from $\Pgg$+jet production as well as the QCD multijet
background from SM events composed uniquely of jets produced through the
strong interaction which have a jet misidentified as a photon are generated at
LO using . Smaller backgrounds from $\PW$+jets and $\PW$+production, as well
as top quark backgrounds, are not simulated, as their contribution is far less
than that of the dominant backgrounds and does not affect the search
optimization procedure.

Both the signal and background samples are generated using 2.2.2 (2.4.2) [26]
with NNPDF3.0 NLO [27] (NNPDF3.1 NNLO [28]) parton distribution functions
(PDFs) for 2016 (2017 and 2018) conditions. Fragmentation and hadronization
are simulated with 8.205 (8.230) [29] with the CUETP8M1 [30, 31] (CP5 [32])
underlying event tune for 2016 (2017 and 2018) samples. All simulated samples
are processed with the full CMS detector model based on [33] and reconstructed
with the same suite of programs as used for collision data. Pileup effects are
taken into account by superimposing simulated minimum bias events on the hard
scattering interaction, with the multiplicity distribution matching that
observed in data.

A particle-flow (PF) event algorithm [34] is used, which aims to reconstruct
and identify each individual particle in an event, with an optimized
combination of information from the various elements of the CMS detector. The
energy of photons is obtained from the ECAL measurement. The energy of
electrons is determined from a combination of the electron momentum at the
primary interaction vertex as determined by the tracker, the energy of the
corresponding ECAL cluster, and the energy sum of all bremsstrahlung photons
spatially compatible with originating from the electron track. The energy of
muons is obtained from the curvature of the corresponding track. The energy of
charged hadrons is determined from a combination of their momentum measured in
the tracker and the matching ECAL and HCAL energy deposits, corrected for the
response function of the calorimeters to hadronic showers. Finally, the energy
of neutral hadrons is obtained from the corresponding corrected ECAL and HCAL
energies.

The events must contain at least one reconstructed primary vertex with at
least four associated tracks, with transverse (longitudinal) coordinates
required to be within 2 (24)cm of the nominal collision point. The candidate
vertex with the largest value of summed physics-object $\pt^{2}$ is taken to
be the primary $\Pp\Pp$ interaction vertex. The physics objects are the jets,
clustered using the jet finding algorithm [35, 36] with the tracks assigned to
candidate vertices as inputs, and the associated missing transverse momentum,
taken as the negative vector sum of the of those jets.

Since the dominant background in the analysis is from direct photon production
($\Pgg$+jets), rather than from sources with a misidentified photon, we chose
a “loose” photon identification working point of a standard CMS sequential-
selection algorithm, which maximizes the photon efficiency at the cost of a
slightly higher misidentification rate compared to other available working
points [37]. The identification is based on photon shower shape and isolation
variables. The latter are computed from various types of PF candidates in a
cone of radius $\Delta
R=\sqrt{\smash[b]{(\Delta\eta)^{2}+(\Delta\phi)^{2}}}=0.3$ around the photon
candidate, corrected for the pileup effects. In addition, a conversion-safe
electron veto [37] is applied. The loose working point gives an efficiency of
approximately 90% that does not depend on the photon up to the highest values
explored in the analysis, while reducing the background from misidentified
photons by approximately a factor of 7. The photon candidates are required to
have $\pt>225$and to be within the barrel fiducial region of the ECAL
($\abs{\eta}<1.44$). Since events with a photon reconstructed in the endcap
region suffer from high background from $\Pgg$+jets, which peaks in the
forward direction, they do not add to the sensitivity of the analysis and
therefore are not included.

Large-radius jets ($\mathrm{J}$) are used to reconstruct hadronically
decaying, highly Lorentz-boosted $\PW$ boson candidates. These jets are
reconstructed from PF candidates clustered using the anti-algorithm [35, 36]
with a distance parameter of 0.8. Charged hadrons not originating from the
primary vertex are not considered in the jet clustering. The pileup per
particle identification algorithm (PUPPI) [38, 39] is used to mitigate the
effect of pileup at the reconstructed particle level, making use of local
shape information, event pileup properties, and tracking information. Charged
particles identified to be originating from pileup vertices are discarded. The
momenta of the neutral particles are rescaled according to their probability
to originate from the primary interaction vertex deduced from the local shape
variable, superseding the need for jet-based pileup corrections [38]. Jet
energy corrections are derived from simulation studies so that the average
measured energy of jets becomes identical to that of particle-level jets. In
situ measurements of the momentum balance in dijet, $\Pgg$+jet, $\PZ$+jet, and
multijet events are used to determine any residual differences between the jet
energy scale in data and in simulation, and appropriate corrections are
applied [40]. Additional quality criteria [41] are used to remove jets due to
rare spurious noise patterns in the calorimeters, and also to suppress leptons
misidentified as jets. The jet energy resolution typically amounts to 15% at
10, 8% at 100, and 4% at 1. In each event, the jet selected to be the hadronic
candidate must have $\pt>225$, to balance the $\pt$ of the selected photon,
and is required to be separated from the photon by a distance of $\Delta
R>1.1$ to reduces the contamination of the photon isolation cone with the jet
constituents.

Since the signal jets are merged products of the boson decay, we require the
jet mass to be within a certain range of the boson mass to reduce the very
large background from QCD jets, which have steeply falling jet mass
distribution. To improve the signal and background separation, a jet grooming
algorithm known as “soft drop” (SD) [42], with parameters $\beta=0$ and
$z_{\text{cut}}=0.1$, is applied to recursively remove soft, wide-angle
radiation from anti-jets. The groomed jet mass ($m_{\mathrm{J}}^{\text{SD}}$)
is then computed from the four-momentum sum of the remaining jet constituents,
whose energies are corrected with the same factor that has already been used
in the generic jet reconstruction described above. The typical mass resolution
for a boson jet is 10% [43]. Finally, in order to avoid the region of rapidly
varying efficiency near threshold, we require the invariant mass of the
selected jet and photon ($m_{\mathrm{J}\PGg}$) to exceed 0.6.

##  0.4 Analysis optimization

The analysis is optimized using a sequential selection on a number of
kinematic variables. These variables fall into two classes: those related to
the resonance decay kinematics and those related to the properties of a large-
radius jet. The former are: pseudorapidities of the photon $\eta_{\PGg}$ and
the jet $\eta_{\mathrm{J}}$, the cosine of the polar decay angle in the
center-of-mass frame of the $\mathrm{J}\PGg$ system with respect to the beam
axis $\cos\theta^{*}_{\PGg}$, and the ratio of the photon transverse momentum
$\pt^{\PGg}$ to $m_{\mathrm{J}\PGg}$. The last two variables mentioned are
highly correlated and are both used to separate the mostly central $s$-channel
signal events from the mostly forward $t$-channel direct photon background.
For the purpose of tagging jets originating from the boson decay (jet
tagging), selections are also applied on two variables related to the
properties of a large-radius jet, which are the jet mass
$m_{\mathrm{J}}^{\text{SD}}$ and the jet substructure variable $\tau_{21}$.
$m_{\mathrm{J}}^{\text{SD}}$ peaks at the mass for the signal and falls
rapidly for the QCD background. The variable $\tau_{21}$ is defined as the
ratio of $\tau_{2}$ to $\tau_{1}$, where $\tau_{N}$ is a set of
$N$-subjettiness [44] variables. Such variables are measures of how likely it
is that a large-radius jet has a substructure of $N$ subjets. For a jet with
exactly $N$ subjets, $\tau_{N}$ tends to small values, while $\tau_{M}$ values
for $M\neq N$ are shifted to larger values. Thus, $\tau_{21}$ is expected to
be generally smaller for a signal, which contains a jet produced by an overlap
of the quark jets from a two-prong boson decay, than for the background, which
mostly consists of structureless QCD jets.

The optimization aims at maximizing the expected signal significance for a
large range of tested masses, where the background yield ($B$) is typically
much larger than that of the signal we probe ($S$), and is large enough to use
the Gaussian approximation, , maximizing $S/\sqrt{B}$. This figure of merit
(FOM) has the advantage that its maximum is independent of the signal cross
section. The optimization is based on the events in the control region (CR) in
data, defined as the lower sideband of the boson jet mass
$40<m_{\mathrm{J}}^{\text{SD}}<65$ GeV, which has negligible signal
contamination for the range of the signal cross sections probed in this
analysis, as well as events in the signal region (SR) in signal simulation
samples, which is defined as $68<m_{\mathrm{J}}^{\text{SD}}<94\GeV$, as
determined via the optimization using the FOM described above. The CR and SR
are illustrated in Fig. 1, which also shows the expected distributions in
$m_{\mathrm{J}}^{\text{SD}}$ for the benchmark narrow spin-0 signals. It was
demonstrated that the differences in this distribution between different
signal spin and width hypotheses are negligible.

Figure 1: Definitions of the signal and control regions in data, based on the
jet mass $m_{\mathrm{J}}^{\text{SD}}$. The stacked filled histograms represent
dominant backgrounds from simulation, normalized to the $\pt^{\PGg}$ spectrum
in the signal region. The red circles (black squares) correspond to data in
the signal (control) region. Benchmark narrow spin-0 signal distributions,
normalized to a cross section of 2pb for two masses, 1.0 and 3.5 TeV, are
shown by the solid orange and dashed magenta lines, respectively. The lower
panel shows the data-to-simulation ratio in the control and signal regions.
The gray hatched band shows the statistical uncertainty in the background
estimation.

The optimization of the FOM uses simulated signal events and two different
estimates of the background in the signal region. The first background
estimate uses data events in the CR that have been scaled by a factor of
approximately 0.86 to match the yield of events in the SR. This procedure was
justified by comparing the kinematic distributions of simulated background
events in the SR and CR and finding that they are quite similar. The second
background sample uses simulated background events that have been rescaled to
account for the missing higher-order effects, as well as for an imperfect
description of the misidentification of jets as photons in the QCD multijet
simulation. This is done by fitting the $\pt^{\PGg}$ spectrum in data with the
sum of these two backgrounds, with the normalizations allowed to float in the
fit. Figure 2 shows that before the optimization an adequate description of
the SR data is achieved in the main kinematic variables used for the analysis
optimization by using either MC simulation or CR data, with the exception of
the $\tau_{21}$ variable, which has a different shape in the data CR due to a
strong correlation of the $\tau_{21}$ variable with
$m_{\mathrm{J}}^{\text{SD}}$. Consequently, the optimization of the
$\tau_{21}$ selection is performed only using simulated backgrounds. The
residual discrepancies seen in other distributions are due to missing minor
backgrounds and the limitations of the MC simulation modeling of the data.
They are typically present near the kinematic limits of the corresponding
variables, far from the region of optimal selections, and consequently do not
bias the optimization procedure. Since neither the simulated backgrounds nor
data CR events are used in the final analysis, beyond the optimization step,
we find that the level of agreement between the data and simulated backgrounds
is adequate. Figure 2 also shows several benchmark signal points and, given
the similarity of signal shapes for various spin and width combinations, the
spin-0, narrow-width hypothesis is used, unless indicated otherwise in the
legend.

Figure 2: Distributions of some of the kinematic variables used in the
analysis. Upper row: $m_{\mathrm{J}\PGg}$ (left), $\tau_{21}$ (right); middle
row: $\eta_{\PGg}$ (left), $\eta_{\mathrm{J}}$ (right); lower row:
$\pt^{\PGg}/m_{\mathrm{J}\PGg}$ (left), $\cos\theta^{*}_{\PGg}$ (right),
except that the yield in the control region is normalized to that in the
signal region. Several benchmark signals are also shown, as indicated by the
legend. By default, the spin-0, narrow width hypothesis is used unless
indicated otherwise. Signals are normalized to a cross section of 5fb, except
for the $\tau_{21}$ distribution, for which the normalization is 2pb.
Optimized selections are indicated with the black arrows. The two lower panels
show the data-to-simulation ratio in the control and signal regions,
respectively. The gray hatched band shows the statistical uncertainty in the
background estimation.

For most of the variables, and the signal masses, widths, and spins probed,
the maxima of the FOM distributions are fairly broad, which justifies using a
single set of selections for all signal masses and spin/width hypotheses,
without compromising the search performance. We have tested whether combining
the input variables into a multivariate discriminant using a boosted decision
tree with adaptive boosting, instead of selecting on individual variables,
results in a gain in performance. However, the best gain we were able to
achieve was only a 5% increase in the FOM value, which required separate
discriminants constructed for each signal mass point. Consequently, a single
set of selections on the individual variables was chosen, which simplifies the
analysis without introducing a noticeable performance loss. The best
selections chosen as a result of the above procedure are:
$\abs{\eta_{\PGg}}<1.44$ (, the photon in the ECAL barrel),
$\abs{\eta_{\mathrm{J}}}<2.0$, $\tau_{21}<0.35$,
$68<m_{\mathrm{J}}^{\text{SD}}<94\GeV$, $\pt^{\PGg}/m_{\mathrm{J}\PGg}>0.37$,
and $\cos\theta^{*}_{\PGg}<0.6$. The use of these optimal selections combined
improves the FOM by up to 90% for signals with masses ranging from 0.7to 3.5.
The requirement on the $\tau_{21}$ variable alone improves the FOM by 40%,
making it the most discriminating variable.

##  0.5 Signal and background modeling

We describe the shape of the $m_{\mathrm{J}\PGg}$ distributions for the
signals probed by fitting simulated signal samples with analytic functions.
For narrow resonances, we use a sum of Crystal Ball (CB) [45] and Gaussian
functions with different means. For broad resonances, we use a sum of CB and
two Gaussian functions, where the two Gaussian functions have a common mean
that may be different from that of the CB function. In order to obtain the
signal shapes for the mass points where no simulated samples were generated,
we use linear morphing between the adjacent simulated signal points [46]. The
simulated signal samples for each of the three years of data taking reflect
changes in MC tunes, pileup and trigger conditions, selection criteria, and
detector performance along with time. We verify that the signal shape in
simulation is consistent for the three years and use samples produced with the
2017 conditions as the signal model for all three data-taking years
Consequently, we also combine the $m_{\mathrm{J}\PGg}$ spectra for the three
data-taking periods and search for the presence of signal in this combined
data set.

The overall signal acceptance $\mathcal{A}$, and the product of the acceptance
and signal efficiency $\mathcal{A}\varepsilon$ for the optimal selection for
spin-0 and spin-1 resonances, are shown as functions of the resonance mass in
Fig. 3 (upper row). The latter ranges between 6.1 (10.0)% and 12.3 (16.4)% for
spin-0 (spin-1) signals over the mass range probed. It was verified that for
both $\mathcal{A}$ and $\mathcal{A}\varepsilon$ the differences between three
years of data taking are small; thus the values for the 2017 data taking are
used as the nominal ones. The efficiencies for narrow resonances are generally
1–2% higher than for the broad ones, mainly because of the long low-mass tail
in the $m_{\mathrm{J}\PGg}$ distribution for broad resonances, caused by the
quickly falling PDFs convoluted with the Breit–Wigner resonant shape. In order
to be less sensitive to the exact description of this tail, which depends on
both the PDF choice and the parameterization of the signal resonance line-
shape, we use a window of $\pm 25\%$ of the resonance mass, centered on the
mass. The size of the window corresponds to roughly $\pm 5$ effective widths
of a broad resonance. The window requirement is included in the definition of
the signal acceptance. The jet tagging efficiency
($\epsilon_{\PW\text{-tag}}$, which is a part of the overall efficiency) is
shown in Fig. 3 (lower), which illustrate that a slight decrease in the
overall product of the acceptance and efficiency at high masses is due to a
less effective tagging for very energetic jets.

Figure 3: Signal acceptance $\mathcal{A}$ (upper left), the product of the
signal acceptance and selection efficiency $\mathcal{A}\varepsilon$ (upper
right), and the tagging efficiency (lower) for spin-0 (solid lines) and spin-1
(dashed lines) resonances, for the narrow (pink) and broad (blue) hypotheses.
The curves are obtained by fitting fourth-order polynomials to the set of
discrete mass points, for which simulated signal samples are available. For
the tagging efficiency, the average value obtained for the different spin and
width hypotheses and the spread of the individual efficiencies about the
average are shown with the solid line and the shaded band, respectively.

After the final selection, the background shape of the $m_{\mathrm{J}\PGg}$
spectrum in the SR is modeled by a background-only fit with a smooth,
monotonically falling function. A variety of functional forms are considered
for the background fit, and for each function, a goodness-of-fit (GOF) test
based on the Kolmogorov–Smirnov statistic is performed in the SR. The nominal
background fit function is chosen as the one with the best GOF achieved with
the minimal number of parameters:

$\frac{\rd N}{\rd
m}=p_{0}(m/\sqrt{s})^{p_{1}+p_{2}\log(m/\sqrt{s})+p_{3}\log^{2}(m/\sqrt{s})}$
(1)

where $p_{i}$ (with $i=$ 0–3) are the free parameters of the fit. The best fit
to data with the background-only hypothesis is shown in Fig. 4. This smooth
background function provides an adequate description of the data in the entire
mass range probed.

Figure 4: Background-only fit to data (black points) with the chosen
background function. The green (inner) and yellow (outer) bands show,
respectively, the 68 and 95% confidence level statistical uncertainties in the
fit. The lower panel contains the pull distribution, defined as the difference
between the data yield and the background prediction, divided by their
combined uncertainty. Expected signal shapes are also shown in the lower panel
for three different resonance mass hypotheses, 1.0(red), 2.6(magenta), and
4.0(green), and for both the narrow (solid) and broad (dashed) cases. Signal
normalizations are set to 15, 1.0, and 0.30fb, respectively, for illustrative
purposes.

##  0.6 Systematic uncertainties

In order to prove that no systematic bias arises from the choice of the
background fit function, an alternative fit function that performed relatively
well in the GOF test is used to generate a large number of
$m_{\mathrm{J}\PGg}$ spectra, with or without signal injection. The spectra
are then fit to the sum of the chosen background function and a signal
template with the mass and normalization allowed to float. The signal
significance is extracted from each fit and the distributions of the pull of
the signal yield are constructed, where the pull is defined as the difference
between the injected and extracted signal normalizations, divided by the
statistical uncertainty in the extracted signal normalization from the fit. We
observe that the distributions of the pulls are consistent with a Gaussian
function with a zero mean and a standard deviation of unity, and thus conclude
that any systematic bias from the background fitting procedure is negligible
compared to the statistical uncertainties in the fit. We therefore use the
latter as the only uncertainties associated with the background estimate.

The systematic uncertainty associated with the background shape is evaluated
via likelihood profiling. This procedure refits for the optimal values of the
background parameters for each signal mass hypothesis with the parameters of
the background function allowed to vary freely in the fit, thus accounting for
the uncertainty in the background prediction.

There are several sources of uncertainties in the description of the signal.
Most of the uncertainties only affect the signal yield and do not affect the
shape of the signal mass distribution. These include the integrated
luminosity, trigger plateau efficiency, photon identification efficiency,
pileup description, choice of PDFs, and the efficiency of tagging the boson
jet, including the efficiencies of the SR selection on the
$m_{\mathrm{J}}^{\text{SD}}$ and of the $\tau_{21}$ requirement. Uncertainties
affect both the yield and the shape of the signal distributions include the
jet and photon energy scales and resolutions.

The uncertainties in the integrated luminosity are 2.5% [21], 2.3% [22], and
2.5% [23] in 2016, 2017, and 2018, respectively. The efficiency of the trigger
is obtained using an independent suite of triggers. A systematic uncertainty
of 1.0 (2.3)% is assigned to the trigger efficiency in 2016 and 2018 (2017)
data taking, based on the difference between the observed plateau value and
unity. The uncertainty due to the photon identification efficiency is obtained
by comparing the efficiency in simulation with that in $\PZ\to\Pe\Pe$ events,
where electrons are reconstructed as photons. It amounts to a 3–6% uncertainty
in the signal yield.

The uncertainty due to the description of pileup in the simulated samples is
estimated by changing the value of the total inelastic cross section by $\pm
4.6\%$ [47] and recalculating the signal efficiency after the corresponding
change in the pileup distribution. The resulting uncertainty is 1.0–1.5
(1.0–2.0)% for narrow (broad) resonances. The PDF uncertainty is determined
using the PDF4LHC prescription [48]. Only the effect on the signal acceptance
is included as a source of the experimental uncertainty, and amounts to 2%.
The uncertainty in the jet tagging efficiency mainly originates from the
$\tau_{21}$ selection efficiency. It amounts to an uncertainty of 3.2–11% in
the signal yield.

The uncertainty in the jet energy scale is obtained by varying the energy
scale of the jet corresponding to the candidate by a $\pt$\- and
$\eta$-dependent correction [40]. The uncertainty due to the jet energy
resolution is obtained by smearing the momentum of the jet in simulation to
match that in data. The combined effect on the signal yield is 1.3%, which is
the major source of the uncertainty in the signal shapes. The effects of the
photon energy scale and resolution are accounted for in a similar fashion;
they are significantly less important than those stemming from the jets,
because of a much higher precision of the photon energy reconstruction [37].

Given that the signal is extracted from the $m_{\mathrm{J}\PGg}$ spectrum
combined over the three data-taking periods, we take into account correlations
between the uncertainties across the three years, and use the luminosity-
weighted linear (quadratic) average for correlated (uncorrelated)
uncertainties. The integrated luminosity uncertainty has both correlated and
uncorrelated components, resulting in the overall uncertainty of 1.8% when
applied to the full data set. The trigger and tagging efficiencies, as well as
the jet energy scale and resolution uncertainties, are treated as uncorrelated
across the three years, while the rest are treated as fully correlated. A
summary of the systematic uncertainties, as well as the effect of the year-to-
year correlations, is given in Table 0.6.

Systematic uncertainties affecting the signal description. Uncertainties
marked with “$\dagger$” affect both the yield and the shape of the signal
distribution, while the rest only affect the signal yield. In cases where the
uncertainty is different for various data-taking periods, the three numbers
given in the second column correspond to the 2016/2017/2018 data taking, while
the third column shows the combined uncertainties across the three years,
taking into account the year-to-year correlations. The effect on the signal
yield is the same for all the signal hypotheses studied.  Source Effect on the
signal yield (%) Combined (%) Integrated luminosity 2.5/2.3/2.5 1.8 Trigger
efficiency 1.0/2.3/1.0 0.9 Photon ident. efficiency 4.7/6.0/3.0 4.4 Pileup
1.0/2.0/1.0 1.3 PDF 2.0 2.0 tagging efficiency 11/7.4/3.2 3.9 Jet energy scale
and resolution† 1.3 0.8 Photon energy scale and resolution† 0.5/1.0/1.0 0.9
Total 12.6/10.6/5.8 6.7

##  0.7 Results

We set model-specific upper limits on the product of the cross section and
branching fraction for both narrow and broad, spin-0 and spin-1 resonances
using the modified frequentist criterion [49, 50, 51], with a likelihood ratio
in the asymptotic approximation [52] used as a test statistic. The yield
(shape) uncertainties are incorporated as nuisance parameters with log-normal
(Gaussian) priors. These limits, at 95% , are shown in Fig. 5, separately for
the spin-0 and spin-1 hypotheses, as well as for narrow and broad resonances,
and are the most restrictive limits to date on the existence of $\PW\PGg$
resonances over the majority of the masses probed. The $p$-values for the
background-only fit are shown in Fig. 6 for the narrow (left) and broad
(right) resonances for both spin hypotheses. The largest excess seen in the
limit plots has a mass around 1.58, with a local significance of 2.8 (3.1)
standard deviations for narrow (broad) signals for both spin hypotheses. After
taking into account the look-elsewhere effect [53], the global significance of
the excess is estimated to be 1.1 (1.7) standard deviations, favoring its
interpretation as a statistical fluctuation in data.

In the case of narrow resonances, we compare these limits with the predictions
of two of the models described in Ref. [25], which we use as benchmarks. In
the spin-0 case, a scalar or pseudoscalar $SU(2)_{\mathrm{L}}$ triplet
$\phi^{\alpha}$ that couples to the SM vector boson fields via anomaly-induced
interactions $\phi^{a}W_{\mu\nu}^{a}\widetilde{B}^{\mu\nu}/\Lambda$ and to the
SM fermionic fields with an effective coupling $y_{m}/\Lambda$, is considered.
Here, $\Lambda$ is the ultraviolet cutoff of the model, chosen to be 2, 4, or
5 times the resonance mass, while $y_{m}$ is set to 0.10 or 0.15 to suppress
fermionic decays. In the spin-1 case, a vector $SU(2)_{\mathrm{L}}$ triplet
$V^{a}_{\mu}$ that couples to the SM vector boson fields via a higher-
dimensional operator
$c_{W}V^{a\nu}_{\mu}W^{a\alpha}_{\nu}B^{\mu}_{\alpha}/\Lambda^{2}$ and to the
SM fermionic fields directly with the coupling $g_{m}$ is considered. Similar
to the scalar triplet case, we set $\Lambda$ to 4 or 5 times the resonance
mass, $c_{W}$ to 1, and $g_{m}$ to 0.10 or 0.15. Additionally, we set the
coupling of the vector triplet to the Higgs boson $c_{h}$ to zero. These
choices of parameters assure narrow resonances in the mass range of interest,
with sizable branching fractions to $\PW\PGg$. As a result of this search,
benchmark heavy scalar (vector) triplet bosons with masses between 0.75 (1.15)
and 1.40 (1.36)are excluded at 95% , as shown in Fig. 5. Spin-0 $\pi_{3}$
states of Ref. [24] are beyond the sensitivity of this search, as they decay
predominantly into hadronic final states and the branching fraction into the
$\PW\PGg$ channel is suppressed.

Figure 5: Expected and observed 95% confidence level limits on
$\sigma\mathcal{B}(\PX\to\PW\PGg)$ for the spin-0 (upper row) and spin-1
(lower row) resonances for the narrow (left column) and broad (right column)
resonance cases. Also shown, for spin-0 (spin-1) narrow-resonance case,
theoretical cross sections for heavy scalar (vector) triplet resonance
production in the benchmark model of Ref. [25], which can be probed by this
search.

Figure 6: Observed local $p$-values for spin-0 (left) and spin-1 (right)
resonance hypotheses. The largest excess observed at 1.58corresponds to a
local significance of 2.8 (3.1) standard deviations (s.d.) for narrow (broad)
signals, for both spin hypotheses.

Figure 7: Expected and observed 95% confidence level model-independent limits
on $\sigma\mathcal{B}(\PX\to\PW\PGg)\mathcal{A}$ (left) and
$\sigma\mathcal{B}(\PX\to\mathrm{J}\PGg)\mathcal{A}\epsilon_{\PW\text{-tag}}$
(right), as a function of the minimum invariant mass requirement on the
$\mathrm{J}\PGg$ system.

Model-independent upper limits on the product of the cross section, branching
fraction, and signal acceptance are set in the context of a simple counting
experiment that considers the number of events observed and expected above a
variable $\mathrm{J}\PGg$ invariant mass threshold. These limits allow the
interpretation of our results in nonresonant and other resonant models by
estimating their acceptance for the selections used in this analysis. Since
the exact description of the signal shape is no longer important in the region
where little background is expected, these limits are continued up to the mass
threshold of 8.0 TeV, thus extending the mass range of phenomena that can be
investigated. To make this interpretation possible for a broad set of models,
we treat the tagging efficiency as a part of either the experimental
efficiency or the signal acceptance. The former case, shown in Fig. 7 (left),
would apply to models yielding signatures with a hadronically decaying
Lorentz-boosted boson, as the tagging efficiency in this case should be very
similar to that in the model-specific analysis. The latter case, shown in Fig.
7 (right), would apply to an even broader set of models, , the ones predicting
${\PZ}{\PGg}$ signatures, by accounting for the efficiency of tagging a boson
using our tagging requirements as a part of the signal acceptance.

##  0.8 Summary

A search for $\PW\PGg$ resonances in the mass range between 0.7 and 6.0has
been presented. The boson is reconstructed from its hadronic decay, in which
the final-state products form a single large-radius jet owing to the large
Lorentz boost of the boson. The search is based on proton-proton collision
data collected at $\sqrt{s}=13\TeV$ with the CMS detector at the LHC in
2016–2018, corresponding to an integrated luminosity of 137. No significant
excess above the smoothly falling background is observed. Limits at 95%
confidence level on the product of the cross section and branching fraction
for $\PW\PGg$ resonances are set, ranging from 37 (55) to 0.21 (0.30)fb for
the narrow (broad) spin-0 hypothesis, and from 29 (51) to 0.17 (0.19)fb for
the for the narrow (broad) spin-1 hypothesis. The results reported are the
most restrictive limits to date on the existence of such resonances. In
specific narrow-resonance benchmark models, heavy scalar (vector) triplet
resonances with masses between 0.75 (1.15) and 1.40 (1.35)are excluded for a
range of model parameters probed. In addition, model-independent limits are
set on the product of the cross section, branching fraction, and signal
acceptance, as functions of the minimum invariant mass of the jet-photon
system, making possible the interpretation of these results in the context of
a broader class of models predicting similar signatures.

###### Acknowledgements.

We congratulate our colleagues in the CERN accelerator departments for the
excellent performance of the LHC and thank the technical and administrative
staffs at CERN and at other CMS institutes for their contributions to the
success of the CMS effort. In addition, we gratefully acknowledge the
computing centers and personnel of the Worldwide LHC Computing Grid and other
centers for delivering so effectively the computing infrastructure essential
to our analyses. Finally, we acknowledge the enduring support for the
construction and operation of the LHC, the CMS detector, and the supporting
computing infrastructure provided by the following funding agencies: BMBWF and
FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and
FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST, and NSFC (China);
MINCIENCIAS (Colombia); MSES and CSF (Croatia); RIF (Cyprus); SENESCYT
(Ecuador); MoER, ERC PUT and ERDF (Estonia); Academy of Finland, MEC, and HIP
(Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRT
(Greece); NKFIA (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland);
INFN (Italy); MSIP and NRF (Republic of Korea); MES (Latvia); LAS (Lithuania);
MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI
(Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MSHE and NSC
(Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom, RAS, RFBR, and NRC KI
(Russia); MESTD (Serbia); SEIDI, CPAN, PCTI, and FEDER (Spain); MOSTR (Sri
Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); ThEPCenter, IPST,
STAR, and NSTDA (Thailand); TUBITAK and TAEK (Turkey); NASU (Ukraine); STFC
(United Kingdom); DOE and NSF (USA). Individuals have received support from
the Marie-Curie program and the European Research Council and Horizon 2020
Grant, contract Nos. 675440, 724704, 752730, 765710 and 824093 (European
Union); the Leventis Foundation; the Alfred P. Sloan Foundation; the Alexander
von Humboldt Foundation; the Belgian Federal Science Policy Office; the Fonds
pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture (FRIA-
Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-
Belgium); the F.R.S.-FNRS and FWO (Belgium) under the “Excellence of Science –
EOS” – be.h project n. 30820817; the Beijing Municipal Science & Technology
Commission, No. Z191100007219010; the Ministry of Education, Youth and Sports
(MEYS) of the Czech Republic; the Deutsche Forschungsgemeinschaft (DFG), under
Germany’s Excellence Strategy – EXC 2121 “Quantum Universe” – 390833306, and
under project number 400140256 - GRK2497; the Lendület (“Momentum”) Program
and the János Bolyai Research Scholarship of the Hungarian Academy of
Sciences, the New National Excellence Program ÚNKP, the NKFIA research grants
123842, 123959, 124845, 124850, 125105, 128713, 128786, and 129058 (Hungary);
the Council of Science and Industrial Research, India; the Latvian Council of
Science; the Ministry of Science and Higher Education and the National Science
Center, contracts Opus 2014/15/B/ST2/03998 and 2015/19/B/ST2/02861 (Poland);
the National Priorities Research Program by Qatar National Research Fund; the
Ministry of Science and Higher Education, project no. 0723-2020-0041 (Russia);
the Programa Estatal de Fomento de la Investigación Científica y Técnica de
Excelencia María de Maeztu, grant MDM-2015-0509 and the Programa Severo Ochoa
del Principado de Asturias; the Thalis and Aristeia programs cofinanced by EU-
ESF and the Greek NSRF; the Rachadapisek Sompot Fund for Postdoctoral
Fellowship, Chulalongkorn University and the Chulalongkorn Academic into Its
2nd Century Project Advancement Project (Thailand); the Kavli Foundation; the
Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation, contract
C-1845; and the Weston Havens Foundation (USA).

## References

  * [1] ATLAS Collaboration, “Observation of a new particle in the search for the standard model Higgs boson with the ATLAS detector at the LHC”,  Phys. Lett. B 716 (2012) 1, 10.1016/j.physletb.2012.08.020, arXiv:1207.7214.
  * [2] CMS Collaboration, “Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC”,  Phys. Lett. B 716 (2012) 30, 10.1016/j.physletb.2012.08.021, arXiv:1207.7235.
  * [3] CMS Collaboration, “Observation of a new boson with mass near 125 GeV in pp collisions at $\sqrt{s}=7$ and 8 TeV”,  JHEP 06 (2013) 081, 10.1007/JHEP06(2013)081, arXiv:1303.4571.
  * [4] J. F. Gunion, G. L. Kane, and J. Wudka, “Search techniques for charged and neutral intermediate mass Higgs bosons”,  Nucl. Phys. B 299 (1988) 231, 10.1016/0550-3213(88)90284-2.
  * [5] J. F. Gunion, H. E. Haber, G. L. Kane, and S. Dawson, “The Higgs hunter’s guide”, volume 80 of  Frontiers in Physics.  Westview Press, Boulder, Colorado, 2000.  ISBN 073820305X,
  * [6] M. Capdequi Peyranere, H. E. Haber, and P. Irulegui, “$H^{\pm}\to W^{\pm}\gamma$ and $H^{\pm}\to W^{\pm}Z$ in two Higgs doublet models. 1. The large fermion mass limit”,  Phys. Rev. D 44 (1991) 191, 10.1103/PhysRevD.44.191.
  * [7] S. Weinberg, “Implications of dynamical symmetry breaking”,  Phys. Rev. D 13 (1976) 974, 10.1103/PhysRevD.13.974. [Addendum: 10.1103/PhysRevD.19.1277].
  * [8] L. Susskind, “Dynamics of spontaneous symmetry breaking in the Weinberg–Salam theory”,  Phys. Rev. D 20 (1979) 2619, 10.1103/PhysRevD.20.2619.
  * [9] K. D. Lane and E. Eichten, “Two scale technicolor”,  Phys. Lett. B 222 (1989) 274, 10.1016/0370-2693(89)91265-3.
  * [10] E. Eichten and K. Lane, “Low-scale technicolor at the Tevatron and LHC”,  Phys. Lett. B 669 (2008) 235, 10.1016/j.physletb.2008.09.047, arXiv:0706.2339.
  * [11] D. Pappadopulo, A. Thamm, R. Torre, and A. Wulzer, “Heavy vector triplets: Bridging theory and data”,  JHEP 09 (2014) 060, 10.1007/JHEP09(2014)060, arXiv:1402.4431.
  * [12] K. Howe, S. Knapen, and D. J. Robinson, “Diphotons from electroweak triplet-singlet mixing”,  Phys. Rev. D 94 (2016) 035021, 10.1103/PhysRevD.94.035021, arXiv:1603.08932.
  * [13] G. Burdman et al., “The quirky collider signals of folded supersymmetry”,  Phys. Rev. D 78 (2008) 075028, 10.1103/PhysRevD.78.075028, arXiv:0805.4667.
  * [14] ATLAS Collaboration, “Measurements of $W\gamma$ and $Z\gamma$ production in $pp$ collisions at $\sqrt{s}$=7 TeV with the ATLAS detector at the LHC”,  Phys. Rev. D 87 (2013) 112003, 10.1103/PhysRevD.87.112003, arXiv:1302.1283. [Erratum: 10.1103/PhysRevD.91.119901].
  * [15] ATLAS Collaboration, “Search for new resonances in $W\gamma$ and $Z\gamma$ final states in $pp$ collisions at $\sqrt{s}=8$ TeV with the ATLAS detector”,  Phys. Lett. B 738 (2014) 428, 10.1016/j.physletb.2014.10.002, arXiv:1407.8150.
  * [16] ATLAS Collaboration, “Search for heavy resonances decaying to a photon and a hadronically decaying $Z/W/H$ boson in $pp$ collisions at $\sqrt{s}=13$ $\mathrm{TeV}$ with the ATLAS detector”,  Phys. Rev. D 98 (2018) 032015, 10.1103/PhysRevD.98.032015, arXiv:1805.01908.
  * [17] “HEPData record for this analysis”, 2021.  10.17182/hepdata.106162.
  * [18] CMS Collaboration, “The CMS trigger system”,  JINST 12 (2017) P01020, 10.1088/1748-0221/12/01/P01020, arXiv:1609.02366.
  * [19] CMS Collaboration, “Performance of the CMS Level-1 trigger in proton-proton collisions at $\sqrt{s}=13$ TeV”,  JINST 15 (2020) P10017, 10.1088/1748-0221/15/10/P10017, arXiv:2006.10165.
  * [20] CMS Collaboration, “The CMS experiment at the CERN LHC”,  JINST 3 (2008) S08004, 10.1088/1748-0221/3/08/S08004.
  * [21] CMS Collaboration, “CMS luminosity measurements for the 2016 data-taking period”, CMS Physics Analysis Summary CMS-PAS-LUM-17-001, 2017.
  * [22] CMS Collaboration, “CMS luminosity measurements for the 2017 data-taking period at $\sqrt{s}=13\TeV$”, CMS Physics Analysis Summary CMS-PAS-LUM-17-004, 2018.
  * [23] CMS Collaboration, “CMS luminosity measurements for the 2018 data-taking period at $\sqrt{s}=13\TeV$”, CMS Physics Analysis Summary CMS-PAS-LUM-18-001, 2018.
  * [24] N. Arkani-Hamed, R. T. D’Agnolo, M. Low, and D. Pinner, “Unification and new particles at the LHC”,  JHEP 11 (2016) 082, 10.1007/JHEP11(2016)082, arXiv:1608.01675.
  * [25] R. M. Capdevilla, R. Harnik, and A. Martin, “The radiation valley and exotic resonances in $W\gamma$ production at the LHC”,  JHEP 03 (2020) 117, 10.1007/JHEP03(2020)117, arXiv:1912.08234.
  * [26] J. Alwall et al., “The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations”,  JHEP 07 (2014) 079, 10.1007/JHEP07(2014)079, arXiv:1405.0301.
  * [27] NNPDF Collaboration, “Parton distributions for the LHC Run II”,  JHEP 04 (2015) 040, 10.1007/JHEP04(2015)040, arXiv:1410.8849.
  * [28] NNPDF Collaboration, “Parton distributions from high-precision collider data”,  Eur. Phys. J. C 77 (2017) 663, 10.1140/epjc/s10052-017-5199-5, arXiv:1706.00428.
  * [29] T. Sjöstrand et al., “An introduction to PYTHIA 8.2”,  Comput. Phys. Commun. 191 (2015) 159, 10.1016/j.cpc.2015.01.024, arXiv:1410.3012.
  * [30] P. Skands, S. Carrazza, and J. Rojo, “Tuning PYTHIA 8.1: the Monash 2013 tune”,  Eur. Phys. J. C 74 (2014) 3024, 10.1140/epjc/s10052-014-3024-y, arXiv:1404.5630.
  * [31] CMS Collaboration, “Event generator tunes obtained from underlying event and multiparton scattering measurements”,  Eur. Phys. J. C 76 (2016) 155, 10.1140/epjc/s10052-016-3988-x, arXiv:1512.00815.
  * [32] CMS Collaboration, “Extraction and validation of a new set of CMS PYTHIA8 tunes from underlying-event measurements”,  Eur. Phys. J. C 80 (2020) 4, 10.1140/epjc/s10052-019-7499-4, arXiv:1903.12179.
  * [33] GEANT4 Collaboration, “ — a simulation toolkit”,  Nucl. Instrum. Meth. A 506 (2003) 250, 10.1016/S0168-9002(03)01368-8.
  * [34] CMS Collaboration, “Particle-flow reconstruction and global event description with the CMS detector”,  JINST 12 (2017) P10003, 10.1088/1748-0221/12/10/P10003, arXiv:1706.04965.
  * [35] M. Cacciari, G. P. Salam, and G. Soyez, “The anti-$k_{\text{t}}$ jet clustering algorithm”,  JHEP 04 (2008) 063, 10.1088/1126-6708/2008/04/063, arXiv:0802.1189.
  * [36] M. Cacciari, G. P. Salam, and G. Soyez, “FastJet user manual”,  Eur. Phys. J. C 72 (2012) 1896, 10.1140/epjc/s10052-012-1896-2, arXiv:1111.6097.
  * [37] CMS Collaboration, “Performance of photon reconstruction and identification with the CMS detector in proton-proton collisions at $\sqrt{s}=8$ TeV”,  JINST 10 (2015) P08010, 10.1088/1748-0221/10/08/P08010, arXiv:1502.02702.
  * [38] CMS Collaboration, “Pileup mitigation at CMS in 13 TeV data”,  JINST 15 (2020) P09018, 10.1088/1748-0221/15/09/p09018, arXiv:2003.00503.
  * [39] D. Bertolini, P. Harris, M. Low, and N. Tran, “Pileup per particle identification”,  JHEP 10 (2014) 059, 10.1007/JHEP10(2014)059, arXiv:1407.6013.
  * [40] CMS Collaboration, “Jet energy scale and resolution in the CMS experiment in pp collisions at 8 TeV”,  JINST 12 (2017) P02014, 10.1088/1748-0221/12/02/P02014, arXiv:1607.03663.
  * [41] CMS Collaboration, “Jet algorithms performance in 13 TeV data”, CMS Physics Analysis Summary CMS-PAS-JME-16-003, 2017.
  * [42] A. J. Larkoski, S. Marzani, G. Soyez, and J. Thaler, “Soft drop”,  JHEP 05 (2014) 146, 10.1007/JHEP05(2014)146, arXiv:1402.2657.
  * [43] CMS Collaboration, “Search for a massive resonance decaying to a pair of Higgs bosons in the four b quark final state in proton-proton collisions at $\sqrt{s}=$ 13 TeV”,  Phys. Lett. B 781 (2018) 244, 10.1016/j.physletb.2018.03.084, arXiv:1710.04960.
  * [44] J. Thaler and K. Van Tilburg, “Identifying boosted objects with $N$-subjettiness”,  JHEP 03 (2011) 015, 10.1007/JHEP03(2011)015, arXiv:1011.2268.
  * [45] M. J. Oreglia, “A study of the reactions $\psi^{\prime}\to\gamma\gamma\psi$”.  PhD thesis, Stanford University, 1980.  SLAC Report SLAC-R-236.
  * [46] A. L. Read, “Linear interpolation of histograms”,  Nucl. Instrum. Meth. A 425 (1999) 357, 10.1016/S0168-9002(98)01347-3.
  * [47] CMS Collaboration, “Measurement of the inelastic proton-proton cross section at $\sqrt{s}=13$ TeV”,  JHEP 07 (2018) 161, 10.1007/JHEP07(2018)161, arXiv:1802.02613.
  * [48] J. Butterworth et al., “PDF4LHC recommendations for LHC Run II”,  J. Phys. G 43 (2016) 023001, 10.1088/0954-3899/43/2/023001, arXiv:1510.03865.
  * [49] T. Junk, “Confidence level computation for combining searches with small statistics”,  Nucl. Instrum. Meth. A 434 (1999) 435, 10.1016/S0168-9002(99)00498-2, arXiv:hep-ex/9902006.
  * [50] A. L. Read, “Presentation of search results: the $\mathrm{CL_{s}}$ technique”,  J. Phys. G 28 (2002) 2693, 10.1088/0954-3899/28/10/313.
  * [51] ATLAS and CMS Collaborations, “Procedure for the LHC Higgs boson search combination in Summer 2011”, ATL-PHYS-PUB-2011-011, CMS NOTE-2011/005, 2011.
  * [52] G. Cowan, K. Cranmer, E. Gross, and O. Vitells, “Asymptotic formulae for likelihood-based tests of new physics”,  Eur. Phys. J. C 71 (2011) 1554, 10.1140/epjc/s10052-011-1554-0, arXiv:1007.1727. [Erratum: 10.1140/epjc/s10052-013-2501-z].
  * [53] E. Gross and O. Vitells, “Trial factors for the look elsewhere effect in high energy physics”,  Eur. Phys. J. C 70 (2010) 525, 10.1140/epjc/s10052-010-1470-8, arXiv:1005.1891.

##  .9 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A. Tumasyan Institut für Hochenergiephysik, Wien, Austria
W. Adam, J.W. Andrejkovic, T. Bergauer, S. Chatterjee, M. Dragicevic, A.
Escalante Del Valle, R. Frühwirth1, M. Jeitler1, N. Krammer, L. Lechner, D.
Liko, I. Mikulec, F.M. Pitters, J. Schieck1, R. Schöfbeck, M. Spanring, S.
Templ, W. Waltenberger, C.-E. Wulz1 Institute for Nuclear Problems, Minsk,
Belarus
V. Chekhovsky, A. Litomin, V. Makarenko Universiteit Antwerpen, Antwerpen,
Belgium
M.R. Darwish2, E.A. De Wolf, X. Janssen, T. Kello3, A. Lelek, H. Rejeb Sfar,
P. Van Mechelen, S. Van Putte, N. Van Remortel Vrije Universiteit Brussel,
Brussel, Belgium
F. Blekman, E.S. Bols, J. D’Hondt, J. De Clercq, M. Delcourt, S. Lowette, S.
Moortgat, A. Morton, D. Müller, A.R. Sahasransu, S. Tavernier, W. Van Doninck,
P. Van Mulders Université Libre de Bruxelles, Bruxelles, Belgium
D. Beghin, B. Bilin, B. Clerbaux, G. De Lentdecker, L. Favart, A. Grebenyuk,
A.K. Kalsi, K. Lee, M. Mahdavikhorrami, I. Makarenko, L. Moureaux, L. Pétré,
A. Popov, N. Postiau, E. Starling, L. Thomas, M. Vanden Bemden, C. Vander
Velde, P. Vanlaer, D. Vannerom, L. Wezenbeek Ghent University, Ghent, Belgium
T. Cornelis, D. Dobur, M. Gruchala, G. Mestdach, M. Niedziela, C. Roskas, K.
Skovpen, M. Tytgat, W. Verbeke, B. Vermassen, M. Vit Université Catholique de
Louvain, Louvain-la-Neuve, Belgium
A. Bethani, G. Bruno, F. Bury, C. Caputo, P. David, C. Delaere, I.S. Donertas,
A. Giammanco, V. Lemaitre, K. Mondal, J. Prisciandaro, A. Taliercio, M.
Teklishyn, P. Vischia, S. Wertz, S. Wuyckens Centro Brasileiro de Pesquisas
Fisicas, Rio de Janeiro, Brazil
G.A. Alves, C. Hensel, A. Moraes Universidade do Estado do Rio de Janeiro, Rio
de Janeiro, Brazil
W.L. Aldá Júnior, M. Barroso Ferreira Filho, H. BRANDAO MALBOUISSON, W.
Carvalho, J. Chinellato4, E.M. Da Costa, G.G. Da Silveira5, D. De Jesus
Damiao, S. Fonseca De Souza, D. Matos Figueiredo, C. Mora Herrera, K. Mota
Amarilo, L. Mundim, H. Nogima, P. Rebello Teles, L.J. Sanchez Rosas, A.
Santoro, S.M. Silva Do Amaral, A. Sznajder, M. Thiel, F. Torres Da Silva De
Araujo, A. Vilela Pereira Universidade Estadual Paulista a, Universidade
Federal do ABC b, São Paulo, Brazil
C.A. Bernardesa,a, L. Calligarisa, T.R. Fernandez Perez Tomeia, E.M.
Gregoresa,b, D.S. Lemosa, P.G. Mercadantea,b, S.F. Novaesa, Sandra S. Padulaa
Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy of
Sciences, Sofia, Bulgaria
A. Aleksandrov, G. Antchev, I. Atanasov, R. Hadjiiska, P. Iaydjiev, M.
Misheva, M. Rodozov, M. Shopova, G. Sultanov University of Sofia, Sofia,
Bulgaria
A. Dimitrov, T. Ivanov, L. Litov, B. Pavlov, P. Petkov, A. Petrov Beihang
University, Beijing, China
T. Cheng, W. Fang3, Q. Guo, T. Javaid6, M. Mittal, H. Wang, L. Yuan Department
of Physics, Tsinghua University, Beijing, China
M. Ahmad, G. Bauer, C. Dozen7, Z. Hu, J. Martins8, Y. Wang, K. Yi9,10
Institute of High Energy Physics, Beijing, China
E. Chapon, G.M. Chen6, H.S. Chen6, M. Chen, A. Kapoor, D. Leggat, H. Liao,
Z.-A. LIU6, R. Sharma, A. Spiezia, J. Tao, J. Thomas-wilsker, J. Wang, H.
Zhang, S. Zhang6, J. Zhao State Key Laboratory of Nuclear Physics and
Technology, Peking University, Beijing, China
A. Agapitos, Y. Ban, C. Chen, Q. Huang, A. Levin, Q. Li, M. Lu, X. Lyu, Y.
Mao, S.J. Qian, D. Wang, Q. Wang, J. Xiao Sun Yat-Sen University, Guangzhou,
China
Z. You Institute of Modern Physics and Key Laboratory of Nuclear Physics and
Ion-beam Application (MOE) - Fudan University, Shanghai, China
X. Gao3, H. Okawa Zhejiang University, Hangzhou, China
M. Xiao Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, C. Florez, J. Fraga, A. Sarkar, M.A. Segura Delgado
Universidad de Antioquia, Medellin, Colombia
J. Jaramillo, J. Mejia Guisao, F. Ramirez, J.D. Ruiz Alvarez, C.A. Salazar
González, N. Vanegas Arbelaez University of Split, Faculty of Electrical
Engineering, Mechanical Engineering and Naval Architecture, Split, Croatia
D. Giljanovic, N. Godinovic, D. Lelas, I. Puljak University of Split, Faculty
of Science, Split, Croatia
Z. Antunovic, M. Kovac, T. Sculac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, D. Ferencek, D. Majumder, M. Roguljic, A. Starodumov11, T. Susa
University of Cyprus, Nicosia, Cyprus
A. Attikis, E. Erodotou, A. Ioannou, G. Kole, M. Kolosova, S. Konstantinou, J.
Mousa, C. Nicolaou, F. Ptochos, P.A. Razis, H. Rykaczewski, H. Saka Charles
University, Prague, Czech Republic
M. Finger12, M. Finger Jr.12, A. Kveton Escuela Politecnica Nacional, Quito,
Ecuador
E. Ayala Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab
Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
S. Abu Zeid13, S. Elgammal14, E. Salama14,13 Center for High Energy Physics
(CHEP-FU), Fayoum University, El-Fayoum, Egypt
M.A. Mahmoud, Y. Mohammed National Institute of Chemical Physics and
Biophysics, Tallinn, Estonia
S. Bhowmik, A. Carvalho Antunes De Oliveira, R.K. Dewanjee, K. Ehataht, M.
Kadastik, J. Pata, M. Raidal, C. Veelken Department of Physics, University of
Helsinki, Helsinki, Finland
P. Eerola, L. Forthomme, H. Kirschenmann, K. Osterberg, M. Voutilainen
Helsinki Institute of Physics, Helsinki, Finland
E. Brücken, F. Garcia, J. Havukainen, V. Karimäki, M.S. Kim, R. Kinnunen, T.
Lampén, K. Lassila-Perini, S. Lehti, T. Lindén, M. Lotti, H. Siikonen, E.
Tuominen, J. Tuominiemi Lappeenranta University of Technology, Lappeenranta,
Finland
P. Luukka, H. Petrow, T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-
Yvette, France
C. Amendola, M. Besancon, F. Couderc, M. Dejardin, D. Denegri, J.L. Faure, F.
Ferri, S. Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry,
B. Lenzi, E. Locci, J. Malcles, J. Rander, A. Rosowsky, M.Ö. Sahin, A. Savoy-
Navarro15, M. Titov, G.B. Yu Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole
Polytechnique, Institut Polytechnique de Paris, Palaiseau, France
S. Ahuja, F. Beaudette, M. Bonanomi, A. Buchot Perraguin, P. Busson, C.
Charlot, O. Davignon, B. Diab, G. Falmagne, S. Ghosh, R. Granier de Cassagnac,
A. Hakimi, I. Kucher, A. Lobanov, M. Nguyen, C. Ochando, P. Paganini, J.
Rembser, R. Salerno, J.B. Sauvan, Y. Sirois, A. Zabi, A. Zghiche Université de
Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, France
J.-L. Agram16, J. Andrea, D. Apparu, D. Bloch, G. Bourgatte, J.-M. Brom, E.C.
Chabert, C. Collard, D. Darej, J.-C. Fontaine16, U. Goerlach, C. Grimault,
A.-C. Le Bihan, P. Van Hove Institut de Physique des 2 Infinis de Lyon (IP2I
), Villeurbanne, France
E. Asilar, S. Beauceron, C. Bernet, G. Boudoul, C. Camen, A. Carle, N. Chanon,
D. Contardo, P. Depasse, H. El Mamouni, J. Fay, S. Gascon, M. Gouzevitch, B.
Ille, Sa. Jain, I.B. Laktineh, H. Lattaud, A. Lesauvage, M. Lethuillier, L.
Mirabito, K. Shchablo, L. Torterotot, G. Touquet, M. Vander Donckt, S. Viret
Georgian Technical University, Tbilisi, Georgia
A. Khvedelidze12, Z. Tsamalaidze12 RWTH Aachen University, I. Physikalisches
Institut, Aachen, Germany
L. Feld, K. Klein, M. Lipinski, D. Meuser, A. Pauls, M.P. Rauch, M. Teroerde
RWTH Aachen University, III. Physikalisches Institut A, Aachen, Germany
D. Eliseev, M. Erdmann, P. Fackeldey, B. Fischer, S. Ghosh, T. Hebbeker, K.
Hoepfner, F. Ivone, H. Keller, L. Mastrolorenzo, M. Merschmeyer, A. Meyer, G.
Mocellin, S. Mondal, S. Mukherjee, D. Noll, A. Novak, T. Pook, A. Pozdnyakov,
Y. Rath, H. Reithler, J. Roemer, A. Schmidt, S.C. Schuler, A. Sharma, S.
Wiedenbeck, S. Zaleski RWTH Aachen University, III. Physikalisches Institut B,
Aachen, Germany
C. Dziwok, G. Flügge, W. Haj Ahmad17, O. Hlushchenko, T. Kress, A. Nowack, C.
Pistone, O. Pooth, D. Roy, H. Sert, A. Stahl18, T. Ziemons Deutsches
Elektronen-Synchrotron, Hamburg, Germany
H. Aarup Petersen, M. Aldaya Martin, P. Asmuss, I. Babounikau, S. Baxter, O.
Behnke, A. Bermúdez Martínez, A.A. Bin Anuar, K. Borras19, V. Botta, D.
Brunner, A. Campbell, A. Cardini, P. Connor, S. Consuegra Rodríguez, V.
Danilov, M.M. Defranchis, L. Didukh, G. Eckerlin, D. Eckstein, L.I. Estevez
Banos, E. Gallo20, A. Geiser, A. Giraldi, A. Grohsjean, M. Guthoff, A. Harb,
A. Jafari21, N.Z. Jomhari, H. Jung, A. Kasem19, M. Kasemann, H. Kaveh, C.
Kleinwort, J. Knolle, D. Krücker, W. Lange, T. Lenz, J. Lidrych, K. Lipka, W.
Lohmann22, T. Madlener, R. Mankel, I.-A. Melzer-Pellmann, J. Metwally, A.B.
Meyer, M. Meyer, J. Mnich, A. Mussgiller, V. Myronenko, Y. Otarid, D. Pérez
Adán, D. Pitzl, A. Raspereza, J. Rübenach, A. Saggio, A. Saibel, M. Savitskyi,
V. Scheurer, C. Schwanenberger20, A. Singh, R.E. Sosa Ricardo, N. Tonon, O.
Turkot, A. Vagnerini, M. Van De Klundert, R. Walsh, D. Walter, Y. Wen, K.
Wichmann, C. Wissing, S. Wuchterl, R. Zlebcik University of Hamburg, Hamburg,
Germany
R. Aggleton, S. Bein, L. Benato, A. Benecke, K. De Leo, T. Dreyer, M. Eich, F.
Feindt, A. Fröhlich, C. Garbers, E. Garutti, P. Gunnellini, J. Haller, A.
Hinzmann, A. Karavdina, G. Kasieczka, R. Klanner, R. Kogler, V. Kutzner, J.
Lange, T. Lange, A. Malara, A. Nigamova, K.J. Pena Rodriguez, O. Rieger, P.
Schleper, M. Schröder, J. Schwandt, D. Schwarz, J. Sonneveld, H. Stadie, G.
Steinbrück, A. Tews, B. Vormwald, I. Zoi Karlsruher Institut fuer Technologie,
Karlsruhe, Germany
J. Bechtel, T. Berger, E. Butz, R. Caspart, T. Chwalek, W. De
Boer${}^{\textrm{\textdagger}}$, A. Dierlamm, A. Droll, K. El Morabit, N.
Faltermann, K. Flöh, M. Giffels, J.o. Gosewisch, A. Gottmann, F. Hartmann18,
C. Heidecker, U. Husemann, I. Katkov23, P. Keicher, R. Koppenhöfer, S. Maier,
M. Metzler, S. Mitra, Th. Müller, M. Neukum, G. Quast, K. Rabbertz, J. Rauser,
D. Savoiu, D. Schäfer, M. Schnepf, D. Seith, I. Shvetsov, H.J. Simonis, R.
Ulrich, J. Van Der Linden, R.F. Von Cube, M. Wassmer, M. Weber, S. Wieland, R.
Wolf, S. Wozniewski, S. Wunsch Institute of Nuclear and Particle Physics
(INPP), NCSR Demokritos, Aghia Paraskevi, Greece
G. Anagnostou, P. Asenov, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas,
A. Stakia National and Kapodistrian University of Athens, Athens, Greece
M. Diamantopoulou, D. Karasavvas, G. Karathanasis, P. Kontaxakis, C.K. Koraka,
A. Manousakis-katsikakis, A. Panagiotou, I. Papavergou, N. Saoulidou, K.
Theofilatos, E. Tziaferi, K. Vellidis, E. Vourliotis National Technical
University of Athens, Athens, Greece
G. Bakas, K. Kousouris, I. Papakrivopoulos, G. Tsipolitis, A. Zacharopoulou
University of Ioánnina, Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, N. Manthos, I.
Papadopoulos, J. Strologas MTA-ELTE Lendület CMS Particle and Nuclear Physics
Group, Eötvös Loránd University, Budapest, Hungary
M. Csanad, K. Farkas, M.M.A. Gadallah24, S. Lökös25, P. Major, K. Mandal, A.
Mehta, G. Pasztor, A.J. Rádl, O. Surányi, G.I. Veres Wigner Research Centre
for Physics, Budapest, Hungary
M. Bartók26, G. Bencze, C. Hajdu, D. Horvath27, F. Sikler, V. Veszpremi, G.
Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear Research ATOMKI,
Debrecen, Hungary
S. Czellar, J. Karancsi26, J. Molnar, Z. Szillasi, D. Teyssier Institute of
Physics, University of Debrecen, Debrecen, Hungary
P. Raics, Z.L. Trocsanyi28, B. Ujvari Karoly Robert Campus, MATE Institute of
Technology
T. Csorgo29, F. Nemes29, T. Novak Indian Institute of Science (IISc),
Bangalore, India
S. Choudhury, J.R. Komaragiri, D. Kumar, L. Panwar, P.C. Tiwari National
Institute of Science Education and Research, HBNI, Bhubaneswar, India
S. Bahinipati30, D. Dash, C. Kar, P. Mal, T. Mishra, V.K. Muraleedharan Nair
Bindhu31, A. Nayak31, P. Saha, N. Sur, S.K. Swain Panjab University,
Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, G. Chaudhary, S. Chauhan, N. Dhingra32, R.
Gupta, A. Kaur, S. Kaur, P. Kumari, M. Meena, K. Sandeep, J.B. Singh, A.K.
Virdi University of Delhi, Delhi, India
A. Ahmed, A. Bhardwaj, B.C. Choudhary, R.B. Garg, M. Gola, S. Keshri, A.
Kumar, M. Naimuddin, P. Priyanka, K. Ranjan, A. Shah Saha Institute of Nuclear
Physics, HBNI, Kolkata, India
M. Bharti33, R. Bhattacharya, S. Bhattacharya, D. Bhowmik, S. Dutta, B.
Gomber34, M. Maity35, S. Nandan, P. Palit, P.K. Rout, G. Saha, B. Sahu, S.
Sarkar, M. Sharan, B. Singh33, S. Thakur33 Indian Institute of Technology
Madras, Madras, India
P.K. Behera, S.C. Behera, P. Kalbhor, A. Muhammad, R. Pradhan, P.R. Pujahari,
A. Sharma, A.K. Sikdar Bhabha Atomic Research Centre, Mumbai, India
D. Dutta, V. Jha, V. Kumar, D.K. Mishra, K. Naskar36, P.K. Netrakanti, L.M.
Pant, P. Shukla Tata Institute of Fundamental Research-A, Mumbai, India
T. Aziz, S. Dugad, G.B. Mohanty, U. Sarkar Tata Institute of Fundamental
Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, R. Chudasama, M. Guchait, S. Karmakar, S. Kumar,
G. Majumder, K. Mazumdar, S. Mukherjee, D. Roy Indian Institute of Science
Education and Research (IISER), Pune, India
S. Dube, B. Kansal, S. Pandey, A. Rane, A. Rastogi, S. Sharma Department of
Physics, Isfahan University of Technology, Isfahan, Iran
H. Bakhshiansohi37, M. Zeinali38 Institute for Research in Fundamental
Sciences (IPM), Tehran, Iran
S. Chenarani39, S.M. Etesami, M. Khakzad, M. Mohammadi Najafabadi University
College Dublin, Dublin, Ireland
M. Felcini, M. Grunewald INFN Sezione di Bari a, Università di Bari b,
Politecnico di Bari c, Bari, Italy
M. Abbresciaa,b, R. Alya,b,40, C. Arutaa,b, A. Colaleoa, D. Creanzaa,c, N. De
Filippisa,c, M. De Palmaa,b, A. Di Florioa,b, A. Di Pilatoa,b, W.
Elmetenaweea,b, L. Fiorea, A. Gelmia,b, M. Gula, G. Iasellia,c, M. Incea,b, S.
Lezkia,b, G. Maggia,c, M. Maggia, I. Margjekaa,b, V. Mastrapasquaa,b, J.A.
Merlina, S. Mya,b, S. Nuzzoa,b, A. Pellecchiaa,b, A. Pompilia,b, G.
Pugliesea,c, A. Ranieria, G. Selvaggia,b, L. Silvestrisa, F.M. Simonea,b, R.
Vendittia, P. Verwilligena INFN Sezione di Bologna a, Università di Bologna b,
Bologna, Italy
G. Abbiendia, C. Battilanaa,b, D. Bonacorsia,b, L. Borgonovia, S. Braibant-
Giacomellia,b, L. Brigliadoria, R. Campaninia,b, P. Capiluppia,b, A.
Castroa,b, F.R. Cavalloa, C. Cioccaa, M. Cuffiania,b, G.M. Dallavallea, T.
Diotalevia,b, F. Fabbria, A. Fanfania,b, E. Fontanesia,b, P. Giacomellia, L.
Giommia,b, C. Grandia, L. Guiduccia,b, F. Iemmia,b, S. Lo Meoa,41, S.
Marcellinia, G. Masettia, F.L. Navarriaa,b, A. Perrottaa, F. Primaveraa,b,
A.M. Rossia,b, T. Rovellia,b, G.P. Sirolia,b, N. Tosia INFN Sezione di Catania
a, Università di Catania b, Catania, Italy
S. Albergoa,b,42, S. Costaa,b,42, A. Di Mattiaa, R. Potenzaa,b, A.
Tricomia,b,42, C. Tuvea,b INFN Sezione di Firenze a, Università di Firenze b,
Firenze, Italy
G. Barbaglia, A. Cassesea, R. Ceccarellia,b, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, F. Fioria,b, E. Focardia,b, G. Latinoa,b, P. Lenzia,b, M.
Lizzoa,b, M. Meschinia, S. Paolettia, R. Seiditaa,b, G. Sguazzonia, L.
Viliania INFN Laboratori Nazionali di Frascati, Frascati, Italy
L. Benussi, S. Bianco, D. Piccolo INFN Sezione di Genova a, Università di
Genova b, Genova, Italy
M. Bozzoa,b, F. Ferroa, R. Mulargiaa,b, E. Robuttia, S. Tosia,b INFN Sezione
di Milano-Bicocca a, Università di Milano-Bicocca b, Milano, Italy
A. Benagliaa, F. Brivioa,b, F. Cetorellia,b, V. Cirioloa,b,18, F. De Guioa,b,
M.E. Dinardoa,b, P. Dinia, S. Gennaia, A. Ghezzia,b, P. Govonia,b, L.
Guzzia,b, M. Malbertia, S. Malvezzia, A. Massironia, D. Menascea, F. Montia,b,
L. Moronia, M. Paganonia,b, D. Pedrinia, S. Ragazzia,b, T. Tabarelli de
Fatisa,b, D. Valsecchia,b,18, D. Zuoloa,b INFN Sezione di Napoli a, Università
di Napoli ’Federico II’ b, Napoli, Italy, Università della Basilicata c,
Potenza, Italy, Università G. Marconi d, Roma, Italy
S. Buontempoa, F. Carnevalia,b, N. Cavalloa,c, A. De Iorioa,b, F. Fabozzia,c,
A.O.M. Iorioa,b, L. Listaa,b, S. Meolaa,d,18, P. Paoluccia,18, B. Rossia, C.
Sciaccaa,b INFN Sezione di Padova a, Università di Padova b, Padova, Italy,
Università di Trento c, Trento, Italy
P. Azzia, N. Bacchettaa, D. Biselloa,b, P. Bortignona, A. Bragagnoloa,b, R.
Carlina,b, P. Checchiaa, P. De Castro Manzanoa, T. Dorigoa, F. Gasparinia,b,
U. Gasparinia,b, S.Y. Hoha,b, L. Layera,43, M. Margonia,b, A.T. Meneguzzoa,b,
M. Presillaa,b, P. Ronchesea,b, R. Rossina,b, F. Simonettoa,b, G. Stronga, M.
Tosia,b, H. YARARa,b, M. Zanettia,b, P. Zottoa,b, A. Zucchettaa,b, G.
Zumerlea,b INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy
C. Aime‘a,b, A. Braghieria, S. Calzaferria,b, D. Fiorinaa,b, P. Montagnaa,b,
S.P. Rattia,b, V. Rea, M. Ressegottia,b, C. Riccardia,b, P. Salvinia, I. Vaia,
P. Vituloa,b INFN Sezione di Perugia a, Università di Perugia b, Perugia,
Italy
G.M. Bileia, D. Ciangottinia,b, L. Fanòa,b, P. Laricciaa,b, G. Mantovania,b,
V. Mariania,b, M. Menichellia, F. Moscatellia, A. Piccinellia,b, A. Rossia,b,
A. Santocchiaa,b, D. Spigaa, T. Tedeschia,b INFN Sezione di Pisa a, Università
di Pisa b, Scuola Normale Superiore di Pisa c, Pisa Italy, Università di Siena
d, Siena, Italy
P. Azzurria, G. Bagliesia, V. Bertacchia,c, L. Bianchinia, T. Boccalia, E.
Bossini, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, M.R. Di Domenicoa,d, S.
Donatoa, A. Giassia, M.T. Grippoa, F. Ligabuea,c, E. Mancaa,c, G. Mandorlia,c,
A. Messineoa,b, F. Pallaa, S. Paroliaa,b, G. Ramirez-Sancheza,c, A. Rizzia,b,
G. Rolandia,c, S. Roy Chowdhurya,c, A. Scribanoa, N. Shafieia,b, P. Spagnoloa,
R. Tenchinia, G. Tonellia,b, N. Turinia,d, A. Venturia, P.G. Verdinia INFN
Sezione di Roma a, Sapienza Università di Roma b, Rome, Italy
M. Campanaa,b, F. Cavallaria, M. Cipriania,b, D. Del Rea,b, E. Di Marcoa, M.
Diemoza, E. Longoa,b, P. Meridiania, G. Organtinia,b, F. Pandolfia, R.
Paramattia,b, C. Quarantaa,b, S. Rahatloua,b, C. Rovellia, F.
Santanastasioa,b, L. Soffia, R. Tramontanoa,b INFN Sezione di Torino a,
Università di Torino b, Torino, Italy, Università del Piemonte Orientale c,
Novara, Italy
N. Amapanea,b, R. Arcidiaconoa,c, S. Argiroa,b, M. Arneodoa,c, N. Bartosika,
R. Bellana,b, A. Belloraa,b, J. Berenguer Antequeraa,b, C. Biinoa, A.
Cappatia,b, N. Cartigliaa, S. Comettia, M. Costaa,b, R. Covarellia,b, N.
Demariaa, B. Kiania,b, F. Leggera, C. Mariottia, S. Masellia, E. Migliorea,b,
E. Monteila,b, M. Montenoa, M.M. Obertinoa,b, G. Ortonaa, L. Pachera,b, N.
Pastronea, M. Pelliccionia, G.L. Pinna Angionia,b, M. Ruspaa,c, R.
Salvaticoa,b, K. Shchelinaa,b, F. Sivieroa,b, V. Solaa, A. Solanoa,b, D.
Soldia,b, A. Staianoa, M. Tornagoa,b, D. Trocinoa,b INFN Sezione di Trieste a,
Università di Trieste b, Trieste, Italy
S. Belfortea, V. Candelisea,b, M. Casarsaa, F. Cossuttia, A. Da Rolda,b, G.
Della Riccaa,b, G. Sorrentinoa,b, F. Vazzolera,b Kyungpook National
University, Daegu, Korea
S. Dogra, C. Huh, B. Kim, D.H. Kim, G.N. Kim, J. Lee, S.W. Lee, C.S. Moon,
Y.D. Oh, S.I. Pak, B.C. Radburn-Smith, S. Sekmen, Y.C. Yang Chonnam National
University, Institute for Universe and Elementary Particles, Kwangju, Korea
H. Kim, D.H. Moon Hanyang University, Seoul, Korea
T.J. Kim, J. Park Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, B. Hong, K. Lee, K.S. Lee, J. Lim, J. Park, S.K. Park,
J. Yoo Kyung Hee University, Department of Physics, Seoul, Republic of Korea
J. Goh, A. Gurtu Sejong University, Seoul, Korea
H.S. Kim, Y. Kim Seoul National University, Seoul, Korea
J. Almond, J.H. Bhyun, J. Choi, S. Jeon, J. Kim, J.S. Kim, S. Ko, H. Kwon, H.
Lee, S. Lee, B.H. Oh, M. Oh, S.B. Oh, H. Seo, U.K. Yang, I. Yoon University of
Seoul, Seoul, Korea
D. Jeon, J.H. Kim, B. Ko, J.S.H. Lee, I.C. Park, Y. Roh, D. Song, I.J. Watson
Yonsei University, Department of Physics, Seoul, Korea
S. Ha, H.D. Yoo Sungkyunkwan University, Suwon, Korea
Y. Choi, Y. Jeong, H. Lee, Y. Lee, I. Yu College of Engineering and
Technology, American University of the Middle East (AUM), Egaila, Kuwait
T. Beyrouthy, Y. Maghrbi Riga Technical University, Riga, Latvia
V. Veckalns44 Vilnius University, Vilnius, Lithuania
M. Ambrozas, A. Juodagalvis, A. Rinkevicius, G. Tamulaitis, A. Vaitkevicius
National Centre for Particle Physics, Universiti Malaya, Kuala Lumpur,
Malaysia
N. Bin Norjoharuddeen, W.A.T. Wan Abdullah, M.N. Yusli, Z. Zolkapli
Universidad de Sonora (UNISON), Hermosillo, Mexico
J.F. Benitez, A. Castaneda Hernandez, J.A. Murillo Quijada, L. Valencia Palomo
Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico
G. Ayala, H. Castilla-Valdez, E. De La Cruz-Burelo, I. Heredia-De La Cruz45,
R. Lopez-Fernandez, C.A. Mondragon Herrera, D.A. Perez Navarro, A. Sanchez-
Hernandez Universidad Iberoamericana, Mexico City, Mexico
S. Carrillo Moreno, C. Oropeza Barrera, M. Ramirez-Garcia, F. Vazquez Valencia
Benemerita Universidad Autonoma de Puebla, Puebla, Mexico
I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada University of Montenegro,
Podgorica, Montenegro
J. Mijuskovic46, N. Raicevic University of Auckland, Auckland, New Zealand
D. Krofcheck University of Canterbury, Christchurch, New Zealand
S. Bheesette, P.H. Butler National Centre for Physics, Quaid-I-Azam
University, Islamabad, Pakistan
A. Ahmad, M.I. Asghar, A. Awais, M.I.M. Awan, H.R. Hoorani, W.A. Khan, M.A.
Shah, M. Shoaib, M. Waqas AGH University of Science and Technology Faculty of
Computer Science, Electronics and Telecommunications, Krakow, Poland
V. Avati, L. Grzanka, M. Malawski National Centre for Nuclear Research,
Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, T. Frueboes, M. Górski, M. Kazana, M.
Szleper, P. Traczyk, P. Zalewski Institute of Experimental Physics, Faculty of
Physics, University of Warsaw, Warsaw, Poland
K. Bunkowski, K. Doroba, A. Kalinowski, M. Konecki, J. Krolikowski, M. Walczak
Laboratório de Instrumentação e Física Experimental de Partículas, Lisboa,
Portugal
M. Araujo, P. Bargassa, D. Bastos, A. Boletti, P. Faccioli, M. Gallinaro, J.
Hollar, N. Leonardo, T. Niknejad, J. Seixas, O. Toldaiev, J. Varela Joint
Institute for Nuclear Research, Dubna, Russia
S. Afanasiev, D. Budkouski, P. Bunin, M. Gavrilenko, I. Golutvin, I. Gorbunov,
A. Kamenev, V. Karjavine, A. Lanev, A. Malakhov, V. Matveev47,48, V. Palichik,
V. Perelygin, M. Savina, D. Seitova, V. Shalaev, S. Shmatov, S. Shulha, V.
Smirnov, O. Teryaev, N. Voytishin, A. Zarubin, I. Zhizhin Petersburg Nuclear
Physics Institute, Gatchina (St. Petersburg), Russia
G. Gavrilov, V. Golovtcov, Y. Ivanov, V. Kim49, E. Kuznetsova50, V. Murzin, V.
Oreshkin, I. Smirnov, D. Sosnov, V. Sulimov, L. Uvarov, S. Volkov, A. Vorobyev
Institute for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov,
N. Krasnikov, A. Pashenkov, G. Pivovarov, D.
Tlisov${}^{\textrm{\textdagger}}$, A. Toropin Institute for Theoretical and
Experimental Physics named by A.I. Alikhanov of NRC ‘Kurchatov Institute’,
Moscow, Russia
V. Epshteyn, V. Gavrilov, N. Lychkovskaya, A. Nikitenko51, V. Popov, G.
Safronov, A. Spiridonov, A. Stepennov, M. Toms, E. Vlasov, A. Zhokin Moscow
Institute of Physics and Technology, Moscow, Russia
T. Aushev National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
O. Bychkova, M. Chadeeva52, P. Parygin, E. Popova, V. Rusinov P.N. Lebedev
Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin, I. Dremin, M. Kirakosyan, A. Terkulov Skobeltsyn
Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow,
Russia
A. Belyaev, E. Boos, M. Dubinin53, L. Dudko, A. Ershov, A. Gribushin, V.
Klyukhin, O. Kodolova, I. Lokhtin, S. Obraztsov, S. Petrushanko, V. Savrin, A.
Snigirev Novosibirsk State University (NSU), Novosibirsk, Russia
V. Blinov54, T. Dimova54, L. Kardapoltsev54, I. Ovtin54, Y. Skovpen54
Institute for High Energy Physics of National Research Centre ‘Kurchatov
Institute’, Protvino, Russia
I. Azhgirey, I. Bayshev, V. Kachanov, A. Kalinin, D. Konstantinov, V. Petrov,
R. Ryutin, A. Sobol, S. Troshin, N. Tyurin, A. Uzunian, A. Volkov National
Research Tomsk Polytechnic University, Tomsk, Russia
A. Babaev, V. Okhotnikov, L. Sukhikh Tomsk State University, Tomsk, Russia
V. Borchsh, V. Ivanchenko, E. Tcherniaev University of Belgrade: Faculty of
Physics and VINCA Institute of Nuclear Sciences, Belgrade, Serbia
P. Adzic55, M. Dordevic, P. Milenovic, J. Milosevic, V. Milosevic Centro de
Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT), Madrid,
Spain
M. Aguilar-Benitez, J. Alcaraz Maestre, A. Álvarez Fernández, I. Bachiller, M.
Barrio Luna, Cristina F. Bedoya, C.A. Carrillo Montoya, M. Cepeda, M. Cerrada,
N. Colino, B. De La Cruz, A. Delgado Peris, J.P. Fernández Ramos, J. Flix,
M.C. Fouz, O. Gonzalez Lopez, S. Goy Lopez, J.M. Hernandez, M.I. Josa, J. León
Holgado, D. Moran, Á. Navarro Tobar, A. Pérez-Calero Yzquierdo, J. Puerta
Pelayo, I. Redondo, L. Romero, S. Sánchez Navas, M.S. Soares, L. Urda Gómez,
C. Willmott Universidad Autónoma de Madrid, Madrid, Spain
J.F. de Trocóniz, R. Reyes-Almanza Universidad de Oviedo, Instituto
Universitario de Ciencias y Tecnologías Espaciales de Asturias (ICTEA),
Oviedo, Spain
B. Alvarez Gonzalez, J. Cuevas, C. Erice, J. Fernandez Menendez, S. Folgueras,
I. Gonzalez Caballero, E. Palencia Cortezon, C. Ramón Álvarez, J. Ripoll Sau,
V. Rodríguez Bouza, A. Trapote Instituto de Física de Cantabria (IFCA), CSIC-
Universidad de Cantabria, Santander, Spain
J.A. Brochero Cifuentes, I.J. Cabrillo, A. Calderon, B. Chazin Quero, J.
Duarte Campderros, M. Fernandez, C. Fernandez Madrazo, P.J. Fernández Manteca,
A. García Alonso, G. Gomez, C. Martinez Rivero, P. Martinez Ruiz del Arbol, F.
Matorras, J. Piedra Gomez, C. Prieels, F. Ricci-Tam, T. Rodrigo, A. Ruiz-
Jimeno, L. Scodellaro, N. Trevisani, I. Vila, J.M. Vizan Garcia University of
Colombo, Colombo, Sri Lanka
MK Jayananda, B. Kailasapathy56, D.U.J. Sonnadara, DDC Wickramarathna
University of Ruhuna, Department of Physics, Matara, Sri Lanka
W.G.D. Dharmaratna, K. Liyanage, N. Perera, N. Wickramage CERN, European
Organization for Nuclear Research, Geneva, Switzerland
T.K. Aarrestad, D. Abbaneo, J. Alimena, E. Auffray, G. Auzinger, J. Baechler,
P. Baillon${}^{\textrm{\textdagger}}$, A.H. Ball, D. Barney, J. Bendavid, N.
Beni, M. Bianco, A. Bocci, E. Brondolin, T. Camporesi, M. Capeans Garrido, G.
Cerminara, S.S. Chhibra, L. Cristella, D. d’Enterria, A. Dabrowski, N. Daci,
A. David, A. De Roeck, M. Deile, R. Di Maria, M. Dobson, M. Dünser, N. Dupont,
A. Elliott-Peisert, N. Emriskova, F. Fallavollita57, D. Fasanella, S.
Fiorendi, A. Florent, G. Franzoni, J. Fulcher, W. Funk, S. Giani, D. Gigi, K.
Gill, F. Glege, L. Gouskos, M. Haranko, J. Hegeman, Y. Iiyama, V. Innocente,
T. James, P. Janot, J. Kaspar, J. Kieseler, M. Komm, N. Kratochwil, C. Lange,
S. Laurila, P. Lecoq, K. Long, C. Lourenço, L. Malgeri, S. Mallios, M.
Mannelli, F. Meijers, S. Mersi, E. Meschi, F. Moortgat, M. Mulders, S.
Orfanelli, L. Orsini, F. Pantaleo, L. Pape, E. Perez, M. Peruzzi, A. Petrilli,
G. Petrucciani, A. Pfeiffer, M. Pierini, M. Pitt, H. Qu, T. Quast, D. Rabady,
A. Racz, M. Rieger, M. Rovere, H. Sakulin, J. Salfeld-Nebgen, S. Scarfi, C.
Schäfer, C. Schwick, M. Selvaggi, A. Sharma, P. Silva, W. Snoeys, P.
Sphicas58, S. Summers, V.R. Tavolaro, D. Treille, A. Tsirou, G.P. Van Onsem,
M. Verzetti, J. Wanczyk59, K.A. Wozniak, W.D. Zeuner Paul Scherrer Institut,
Villigen, Switzerland
L. Caminada60, A. Ebrahimi, W. Erdmann, R. Horisberger, Q. Ingram, H.C.
Kaestli, D. Kotlinski, U. Langenegger, M. Missiroli, T. Rohe ETH Zurich -
Institute for Particle Physics and Astrophysics (IPA), Zurich, Switzerland
K. Androsov59, M. Backhaus, P. Berger, A. Calandri, N. Chernyavskaya, A. De
Cosa, G. Dissertori, M. Dittmar, M. Donegà, C. Dorfer, F. Eble, T. Gadek, T.A.
Gómez Espinosa, C. Grab, D. Hits, W. Lustermann, A.-M. Lyon, R.A. Manzoni, C.
Martin Perez, M.T. Meinhard, F. Micheli, F. Nessi-Tedaldi, J. Niedziela, F.
Pauss, V. Perovic, G. Perrin, S. Pigazzini, M.G. Ratti, M. Reichmann, C.
Reissel, T. Reitenspiess, B. Ristic, D. Ruini, D.A. Sanz Becerra, M.
Schönenberger, V. Stampf, J. Steggemann59, R. Wallny, D.H. Zhu Universität
Zürich, Zurich, Switzerland
C. Amsler61, P. Bärtschi, C. Botta, D. Brzhechko, M.F. Canelli, A. De Wit, R.
Del Burgo, J.K. Heikkilä, M. Huwiler, A. Jofrehei, B. Kilminster, S.
Leontsinis, A. Macchiolo, P. Meiring, V.M. Mikuni, U. Molinatti, I.
Neutelings, G. Rauco, A. Reimers, P. Robmann, S. Sanchez Cruz, K. Schweiger,
Y. Takahashi National Central University, Chung-Li, Taiwan
C. Adloff62, C.M. Kuo, W. Lin, A. Roy, T. Sarkar35, S.S. Yu National Taiwan
University (NTU), Taipei, Taiwan
L. Ceard, P. Chang, Y. Chao, K.F. Chen, P.H. Chen, W.-S. Hou, Y.y. Li, R.-S.
Lu, E. Paganis, A. Psallidas, A. Steen, E. Yazgan, P.r. Yu Chulalongkorn
University, Faculty of Science, Department of Physics, Bangkok, Thailand
B. Asavapibhop, C. Asawatangtrakuldee, N. Srimanobhas Çukurova University,
Physics Department, Science and Art Faculty, Adana, Turkey
F. Boran, S. Damarseckin63, Z.S. Demiroglu, F. Dolek, I. Dumanoglu64, E.
Eskut, G. Gokbulut, Y. Guler, E. Gurpinar Guler65, I. Hos66, C. Isik, E.E.
Kangal67, O. Kara, A. Kayis Topaksu, U. Kiminsu, G. Onengut, K. Ozdemir68, A.
Polatoz, A.E. Simsek, B. Tali69, U.G. Tok, S. Turkcapar, I.S. Zorbakir, C.
Zorbilmez Middle East Technical University, Physics Department, Ankara, Turkey
B. Isildak70, G. Karapinar71, K. Ocalan72, M. Yalvac73 Bogazici University,
Istanbul, Turkey
B. Akgun, I.O. Atakisi, E. Gülmez, M. Kaya74, O. Kaya75, Ö. Özçelik, S.
Tekten76, E.A. Yetkin77 Istanbul Technical University, Istanbul, Turkey
A. Cakir, K. Cankocak64, Y. Komurcu, S. Sen78 Istanbul University, Istanbul,
Turkey
F. Aydogmus Sen, S. Cerci69, B. Kaynak, S. Ozkorucuklu, D. Sunar Cerci69
Institute for Scintillation Materials of National Academy of Science of
Ukraine, Kharkov, Ukraine
B. Grynyov National Scientific Center, Kharkov Institute of Physics and
Technology, Kharkov, Ukraine
L. Levchuk University of Bristol, Bristol, United Kingdom
E. Bhal, S. Bologna, J.J. Brooke, A. Bundock, E. Clement, D. Cussans, H.
Flacher, J. Goldstein, G.P. Heath, H.F. Heath, L. Kreczko, B. Krikler, S.
Paramesvaran, T. Sakuma, S. Seif El Nasr-Storey, V.J. Smith, N. Stylianou79,
J. Taylor, A. Titterton Rutherford Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev80, C. Brew, R.M. Brown, D.J.A. Cockerill, K.V. Ellis, K.
Harder, S. Harper, J. Linacre, K. Manolopoulos, D.M. Newbold, E. Olaiya, D.
Petyt, T. Reis, T. Schuh, C.H. Shepherd-Themistocleous, A. Thea, I.R. Tomalin,
T. Williams Imperial College, London, United Kingdom
R. Bainbridge, P. Bloch, S. Bonomally, J. Borg, S. Breeze, O. Buchmuller, V.
Cepaitis, G.S. Chahal81, D. Colling, P. Dauncey, G. Davies, M. Della Negra, S.
Fayer, G. Fedi, G. Hall, M.H. Hassanshahi, G. Iles, J. Langford, L. Lyons,
A.-M. Magnan, S. Malik, A. Martelli, J. Nash82, V. Palladino, M. Pesaresi,
D.M. Raymond, A. Richards, A. Rose, E. Scott, C. Seez, A. Shtipliyski, A.
Tapper, K. Uchida, T. Virdee18, N. Wardle, S.N. Webb, D. Winterbottom, A.G.
Zecchinelli Brunel University, Uxbridge, United Kingdom
J.E. Cole, A. Khan, P. Kyberd, C.K. Mackay, I.D. Reid, L. Teodorescu, S. Zahid
Baylor University, Waco, USA
S. Abdullin, A. Brinkerhoff, B. Caraway, J. Dittmann, K. Hatakeyama, A.R.
Kanuganti, B. McMaster, N. Pastika, S. Sawant, C. Smith, C. Sutantawibul, J.
Wilson Catholic University of America, Washington, DC, USA
R. Bartek, A. Dominguez, R. Uniyal, A.M. Vargas Hernandez The University of
Alabama, Tuscaloosa, USA
A. Buccilli, O. Charaf, S.I. Cooper, D. Di Croce, S.V. Gleyzer, C. Henderson,
C.U. Perez, P. Rumerio83, C. West Boston University, Boston, USA
A. Akpinar, A. Albert, D. Arcaro, C. Cosby, Z. Demiragli, D. Gastler, J.
Rohlf, K. Salyer, D. Sperka, D. Spitzbart, I. Suarez, A. Tsatsos, S. Yuan, D.
Zou Brown University, Providence, USA
G. Benelli, B. Burkle, X. Coubez19, D. Cutts, Y.t. Duh, M. Hadley, U. Heintz,
J.M. Hogan84, E. Laird, G. Landsberg, K.T. Lau, J. Lee, J. Luo, M. Narain, S.
Sagir85, E. Usai, W.Y. Wong, X. Yan, D. Yu, W. Zhang University of California,
Davis, Davis, USA
C. Brainerd, R. Breedon, M. Calderon De La Barca Sanchez, M. Chertok, J.
Conway, P.T. Cox, R. Erbacher, F. Jensen, O. Kukral, R. Lander, M. Mulhearn,
D. Pellett, B. Regnery, D. Taylor, M. Tripathi, Y. Yao, F. Zhang University of
California, Los Angeles, USA
M. Bachtis, R. Cousins, A. Dasgupta, A. Datta, D. Hamilton, J. Hauser, M.
Ignatenko, M.A. Iqbal, T. Lam, N. Mccoll, W.A. Nash, S. Regnard, D. Saltzberg,
C. Schnaible, B. Stone, V. Valuev University of California, Riverside,
Riverside, USA
K. Burt, Y. Chen, R. Clare, J.W. Gary, G. Hanson, G. Karapostoli, O.R. Long,
N. Manganelli, M. Olmedo Negrete, W. Si, S. Wimpenny, Y. Zhang University of
California, San Diego, La Jolla, USA
J.G. Branson, P. Chang, S. Cittolin, S. Cooperstein, N. Deelen, J. Duarte, R.
Gerosa, L. Giannini, D. Gilbert, J. Guiang, R. Kansal, V. Krutelyov, R. Lee,
J. Letts, M. Masciovecchio, S. May, S. Padhi, M. Pieri, B.V. Sathia Narayanan,
V. Sharma, M. Tadel, A. Vartak, F. Würthwein, Y. Xiang, A. Yagil University of
California, Santa Barbara - Department of Physics, Santa Barbara, USA
N. Amin, C. Campagnari, M. Citron, A. Dorsett, V. Dutta, J. Incandela, M.
Kilpatrick, J. Kim, B. Marsh, H. Mei, M. Oshiro, A. Ovcharova, M. Quinnan, J.
Richman, U. Sarica, D. Stuart, S. Wang California Institute of Technology,
Pasadena, USA
A. Bornheim, O. Cerri, I. Dutta, J.M. Lawhorn, N. Lu, J. Mao, H.B. Newman, J.
Ngadiuba, T.Q. Nguyen, M. Spiropulu, J.R. Vlimant, C. Wang, S. Xie, Z. Zhang,
R.Y. Zhu Carnegie Mellon University, Pittsburgh, USA
J. Alison, M.B. Andrews, T. Ferguson, T. Mudholkar, M. Paulini, I. Vorobiev
University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, E. MacDonald, R. Patel, A. Perloff, K. Stenson, K.A.
Ulmer, S.R. Wagner Cornell University, Ithaca, USA
J. Alexander, Y. Cheng, J. Chu, D.J. Cranshaw, K. Mcdermott, J. Monroy, J.R.
Patterson, D. Quach, J. Reichert, A. Ryd, W. Sun, S.M. Tan, Z. Tao, J. Thom,
P. Wittich, M. Zientek Fermi National Accelerator Laboratory, Batavia, USA
M. Albrow, M. Alyari, G. Apollinari, A. Apresyan, A. Apyan, S. Banerjee,
L.A.T. Bauerdick, A. Beretvas, D. Berry, J. Berryhill, P.C. Bhat, K. Burkett,
J.N. Butler, A. Canepa, G.B. Cerati, H.W.K. Cheung, F. Chlebana, M. Cremonesi,
K.F. Di Petrillo, V.D. Elvira, J. Freeman, Z. Gecse, L. Gray, D. Green, S.
Grünendahl, O. Gutsche, R.M. Harris, R. Heller, T.C. Herwig, J. Hirschauer, B.
Jayatilaka, S. Jindariani, M. Johnson, U. Joshi, P. Klabbers, T. Klijnsma, B.
Klima, M.J. Kortelainen, K.H.M. Kwok, S. Lammel, D. Lincoln, R. Lipton, T.
Liu, J. Lykken, C. Madrid, K. Maeshima, C. Mantilla, D. Mason, P. McBride, P.
Merkel, S. Mrenna, S. Nahn, V. O’Dell, V. Papadimitriou, K. Pedro, C. Pena53,
O. Prokofyev, F. Ravera, A. Reinsvold Hall, L. Ristori, B. Schneider, E.
Sexton-Kennedy, N. Smith, A. Soha, L. Spiegel, S. Stoynev, J. Strait, L.
Taylor, S. Tkaczyk, N.V. Tran, L. Uplegger, E.W. Vaandering, H.A. Weber, A.
Woodard University of Florida, Gainesville, USA
D. Acosta, P. Avery, D. Bourilkov, L. Cadamuro, V. Cherepanov, F. Errico, R.D.
Field, D. Guerrero, B.M. Joshi, M. Kim, J. Konigsberg, A. Korytov, K.H. Lo, K.
Matchev, N. Menendez, G. Mitselmakher, D. Rosenzweig, K. Shi, J. Sturdy, J.
Wang, E. Yigitbasi, X. Zuo Florida State University, Tallahassee, USA
T. Adams, A. Askew, D. Diaz, R. Habibullah, S. Hagopian, V. Hagopian, K.F.
Johnson, R. Khurana, T. Kolberg, G. Martinez, H. Prosper, C. Schiber, R.
Yohay, J. Zhang Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, S. Butalla, T. Elkafrawy13, M. Hohlmann, R. Kumar Verma, D.
Noonan, M. Rahmani, M. Saunders, F. Yumiceva University of Illinois at Chicago
(UIC), Chicago, USA
M.R. Adams, L. Apanasevich, H. Becerril Gonzalez, R. Cavanaugh, X. Chen, S.
Dittmer, O. Evdokimov, C.E. Gerber, D.A. Hangal, D.J. Hofman, C. Mills, G. Oh,
T. Roy, M.B. Tonjes, N. Varelas, J. Viinikainen, X. Wang, Z. Wu, Z. Ye The
University of Iowa, Iowa City, USA
M. Alhusseini, K. Dilsiz86, S. Durgut, R.P. Gandrajula, M. Haytmyradov, V.
Khristenko, O.K. Köseyan, J.-P. Merlo, A. Mestvirishvili87, A. Moeller, J.
Nachtman, H. Ogul88, Y. Onel, F. Ozok89, A. Penzo, C. Snyder, E. Tiras90, J.
Wetzel Johns Hopkins University, Baltimore, USA
O. Amram, B. Blumenfeld, L. Corcodilos, J. Davis, M. Eminizer, A.V. Gritsan,
S. Kyriacou, P. Maksimovic, J. Roskes, M. Swartz, T.Á. Vámi The University of
Kansas, Lawrence, USA
C. Baldenegro Barrera, P. Baringer, A. Bean, A. Bylinkin, T. Isidori, S.
Khalil, J. King, G. Krintiras, A. Kropivnitskaya, C. Lindsey, N. Minafra, M.
Murray, C. Rogan, C. Royon, S. Sanders, E. Schmitz, J.D. Tapia Takaki, Q.
Wang, J. Williams, G. Wilson Kansas State University, Manhattan, USA
S. Duric, A. Ivanov, K. Kaadze, D. Kim, Y. Maravin, T. Mitchell, A. Modak, K.
Nam Lawrence Livermore National Laboratory, Livermore, USA
F. Rebassoo, D. Wright University of Maryland, College Park, USA
E. Adams, A. Baden, O. Baron, A. Belloni, S.C. Eno, Y. Feng, N.J. Hadley, S.
Jabeen, R.G. Kellogg, T. Koeth, A.C. Mignerey, S. Nabili, M. Seidel, A. Skuja,
S.C. Tonwar, L. Wang, K. Wong Massachusetts Institute of Technology,
Cambridge, USA
D. Abercrombie, G. Andreassi, R. Bi, S. Brandt, W. Busza, I.A. Cali, Y. Chen,
M. D’Alfonso, G. Gomez Ceballos, M. Goncharov, P. Harris, M. Hu, M. Klute, D.
Kovalskyi, J. Krupa, Y.-J. Lee, B. Maier, A.C. Marini, C. Mironov, C. Paus, D.
Rankin, C. Roland, G. Roland, Z. Shi, G.S.F. Stephans, K. Tatar, J. Wang, Z.
Wang, B. Wyslouch University of Minnesota, Minneapolis, USA
R.M. Chatterjee, A. Evans, P. Hansen, J. Hiltbrand, Sh. Jain, M. Krohn, Y.
Kubota, Z. Lesko, J. Mans, M. Revering, R. Rusack, R. Saradhy, N. Schroeder,
N. Strobbe, M.A. Wadud University of Mississippi, Oxford, USA
J.G. Acosta, S. Oliveros University of Nebraska-Lincoln, Lincoln, USA
K. Bloom, M. Bryson, S. Chauhan, D.R. Claes, C. Fangmeier, L. Finco, F. Golf,
J.R. González Fernández, C. Joo, I. Kravchenko, M. Musich, J.E. Siado, G.R.
Snow${}^{\textrm{\textdagger}}$, W. Tabb, F. Yan State University of New York
at Buffalo, Buffalo, USA
G. Agarwal, H. Bandyopadhyay, L. Hay, I. Iashvili, A. Kharchilava, C. McLean,
D. Nguyen, J. Pekkanen, S. Rappoccio, A. Williams Northeastern University,
Boston, USA
G. Alverson, E. Barberis, C. Freer, Y. Haddad, A. Hortiangtham, J. Li, G.
Madigan, B. Marzocchi, D.M. Morse, V. Nguyen, T. Orimoto, A. Parker, L.
Skinnari, A. Tishelman-Charny, T. Wamorkar, B. Wang, A. Wisecarver, D. Wood
Northwestern University, Evanston, USA
S. Bhattacharya, J. Bueghly, Z. Chen, A. Gilbert, T. Gunter, K.A. Hahn, N.
Odell, M.H. Schmitt, K. Sung, M. Velasco University of Notre Dame, Notre Dame,
USA
R. Band, R. Bucci, N. Dev, R. Goldouzian, M. Hildreth, K. Hurtado Anampa, C.
Jessop, K. Lannon, N. Loukas, N. Marinelli, I. Mcalister, F. Meng, K. Mohrman,
Y. Musienko47, R. Ruchti, P. Siddireddy, M. Wayne, A. Wightman, M. Wolf, M.
Zarucki, L. Zygala The Ohio State University, Columbus, USA
B. Bylsma, B. Cardwell, L.S. Durkin, B. Francis, C. Hill, A. Lefeld, B.L.
Winer, B.R. Yates Princeton University, Princeton, USA
F.M. Addesa, B. Bonham, P. Das, G. Dezoort, P. Elmer, A. Frankenthal, B.
Greenberg, N. Haubrich, S. Higginbotham, A. Kalogeropoulos, G. Kopp, S. Kwan,
D. Lange, M.T. Lucchini, D. Marlow, K. Mei, I. Ojalvo, J. Olsen, C. Palmer, D.
Stickland, C. Tully University of Puerto Rico, Mayaguez, USA
S. Malik, S. Norberg Purdue University, West Lafayette, USA
A.S. Bakshi, V.E. Barnes, R. Chawla, S. Das, L. Gutay, M. Jones, A.W. Jung, S.
Karmarkar, M. Liu, G. Negro, N. Neumeister, G. Paspalaki, C.C. Peng, S.
Piperov, A. Purohit, J.F. Schulte, M. Stojanovic15, J. Thieman, F. Wang, R.
Xiao, W. Xie Purdue University Northwest, Hammond, USA
J. Dolen, N. Parashar Rice University, Houston, USA
A. Baty, S. Dildick, K.M. Ecklund, S. Freed, F.J.M. Geurts, A. Kumar, W. Li,
B.P. Padley, R. Redjimi, J. Roberts${}^{\textrm{\textdagger}}$, W. Shi, A.G.
Stahl Leiton University of Rochester, Rochester, USA
A. Bodek, P. de Barbaro, R. Demina, J.L. Dulemba, C. Fallon, T. Ferbel, M.
Galanti, A. Garcia-Bellido, O. Hindrichs, A. Khukhunaishvili, E. Ranken, R.
Taus Rutgers, The State University of New Jersey, Piscataway, USA
B. Chiarito, J.P. Chou, A. Gandrakota, Y. Gershtein, E. Halkiadakis, A. Hart,
M. Heindl, E. Hughes, S. Kaplan, O. Karacheban22, I. Laflotte, A. Lath, R.
Montalvo, K. Nash, M. Osherson, S. Salur, S. Schnetzer, S. Somalwar, R. Stone,
S.A. Thayil, S. Thomas, H. Wang University of Tennessee, Knoxville, USA
H. Acharya, A.G. Delannoy, S. Spanier Texas A&M University, College Station,
USA
O. Bouhali91, M. Dalchenko, A. Delgado, R. Eusebi, J. Gilmore, T. Huang, T.
Kamon92, H. Kim, S. Luo, S. Malhotra, R. Mueller, D. Overton, D. Rathjens, A.
Safonov Texas Tech University, Lubbock, USA
N. Akchurin, J. Damgov, V. Hegde, S. Kunori, K. Lamichhane, S.W. Lee, T.
Mengke, S. Muthumuni, T. Peltola, S. Undleeb, I. Volobouev, Z. Wang, A.
Whitbeck Vanderbilt University, Nashville, USA
E. Appelt, S. Greene, A. Gurrola, W. Johns, C. Maguire, A. Melo, H. Ni, K.
Padeken, F. Romeo, P. Sheldon, S. Tuo, J. Velkovska University of Virginia,
Charlottesville, USA
M.W. Arenton, B. Cox, G. Cummings, J. Hakala, R. Hirosky, M. Joyce, A.
Ledovskoy, A. Li, C. Neu, B. Tannenwald, E. Wolfe Wayne State University,
Detroit, USA
P.E. Karchin, N. Poudyal, P. Thapa University of Wisconsin - Madison, Madison,
WI, USA
K. Black, T. Bose, J. Buchanan, C. Caillol, S. Dasu, I. De Bruyn, P.
Everaerts, F. Fienga, C. Galloni, H. He, M. Herndon, A. Hervé, U. Hussain, A.
Lanaro, A. Loeliger, R. Loveless, J. Madhusudanan Sreekala, A. Mallampalli, A.
Mohammadi, D. Pinna, A. Savin, V. Shang, V. Sharma, W.H. Smith, D. Teague, S.
Trembath-reichert, W. Vetens †: Deceased
1: Also at TU Wien, Wien, Austria
2: Also at Institute of Basic and Applied Sciences, Faculty of Engineering,
Arab Academy for Science, Technology and Maritime Transport, Alexandria,
Egypt, Alexandria, Egypt
3: Also at Université Libre de Bruxelles, Bruxelles, Belgium
4: Also at Universidade Estadual de Campinas, Campinas, Brazil
5: Also at Federal University of Rio Grande do Sul, Porto Alegre, Brazil
6: Also at University of Chinese Academy of Sciences, Beijing, China
7: Also at Department of Physics, Tsinghua University, Beijing, China,
Beijing, China
8: Also at UFMS, Nova Andradina, Brazil
9: Also at Nanjing Normal University Department of Physics, Nanjing, China
10: Now at The University of Iowa, Iowa City, USA
11: Also at Institute for Theoretical and Experimental Physics named by A.I.
Alikhanov of NRC ‘Kurchatov Institute’, Moscow, Russia
12: Also at Joint Institute for Nuclear Research, Dubna, Russia
13: Also at Ain Shams University, Cairo, Egypt
14: Now at British University in Egypt, Cairo, Egypt
15: Also at Purdue University, West Lafayette, USA
16: Also at Université de Haute Alsace, Mulhouse, France
17: Also at Erzincan Binali Yildirim University, Erzincan, Turkey
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 Department of Physics, Isfahan University of Technology, Isfahan,
Iran, Isfahan, Iran
22: Also at Brandenburg University of Technology, Cottbus, Germany
23: Also at Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State
University, Moscow, Russia
24: Also at Physics Department, Faculty of Science, Assiut University, Assiut,
Egypt
25: Also at Karoly Robert Campus, MATE Institute of Technology, Gyongyos,
Hungary
26: Also at Institute of Physics, University of Debrecen, Debrecen, Hungary,
Debrecen, Hungary
27: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary
28: Also at MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary, Budapest, Hungary
29: Also at Wigner Research Centre for Physics, Budapest, Hungary
30: Also at IIT Bhubaneswar, Bhubaneswar, India, Bhubaneswar, India
31: Also at Institute of Physics, Bhubaneswar, India
32: Also at G.H.G. Khalsa College, Punjab, India
33: Also at Shoolini University, Solan, India
34: Also at University of Hyderabad, Hyderabad, India
35: Also at University of Visva-Bharati, Santiniketan, India
36: Also at Indian Institute of Technology (IIT), Mumbai, India
37: Also at Deutsches Elektronen-Synchrotron, Hamburg, Germany
38: Also at Sharif University of Technology, Tehran, Iran
39: Also at Department of Physics, University of Science and Technology of
Mazandaran, Behshahr, Iran
40: Now at INFN Sezione di Bari a, Università di Bari b, Politecnico di Bari
c, Bari, Italy
41: Also at Italian National Agency for New Technologies, Energy and
Sustainable Economic Development, Bologna, Italy
42: Also at Centro Siciliano di Fisica Nucleare e di Struttura Della Materia,
Catania, Italy
43: Also at Università di Napoli ’Federico II’, NAPOLI, Italy
44: Also at Riga Technical University, Riga, Latvia, Riga, Latvia
45: Also at Consejo Nacional de Ciencia y Tecnología, Mexico City, Mexico
46: Also at IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
47: Also at Institute for Nuclear Research, Moscow, Russia
48: Now at National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
49: Also at St. Petersburg State Polytechnical University, St. Petersburg,
Russia
50: Also at University of Florida, Gainesville, USA
51: Also at Imperial College, London, United Kingdom
52: Also at P.N. Lebedev Physical Institute, Moscow, Russia
53: Also at California Institute of Technology, Pasadena, USA
54: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia
55: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia
56: Also at Trincomalee Campus, Eastern University, Sri Lanka, Nilaveli, Sri
Lanka
57: Also at INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy,
Pavia, Italy
58: Also at National and Kapodistrian University of Athens, Athens, Greece
59: Also at Ecole Polytechnique Fédérale Lausanne, Lausanne, Switzerland
60: Also at Universität Zürich, Zurich, Switzerland
61: Also at Stefan Meyer Institute for Subatomic Physics, Vienna, Austria,
Vienna, Austria
62: Also at Laboratoire d’Annecy-le-Vieux de Physique des Particules,
IN2P3-CNRS, Annecy-le-Vieux, France
63: Also at Şırnak University, Sirnak, Turkey
64: Also at Near East University, Research Center of Experimental Health
Science, Nicosia, Turkey
65: Also at Konya Technical University, Konya, Turkey
66: Also at Istanbul University - Cerrahpasa, Faculty of Engineering,
Istanbul, Turkey
67: Also at Mersin University, Mersin, Turkey
68: Also at Piri Reis University, Istanbul, Turkey
69: Also at Adiyaman University, Adiyaman, Turkey
70: Also at Ozyegin University, Istanbul, Turkey
71: Also at Izmir Institute of Technology, Izmir, Turkey
72: Also at Necmettin Erbakan University, Konya, Turkey
73: Also at Bozok Universitetesi Rektörlügü, Yozgat, Turkey, Yozgat, Turkey
74: Also at Marmara University, Istanbul, Turkey
75: Also at Milli Savunma University, Istanbul, Turkey
76: Also at Kafkas University, Kars, Turkey
77: Also at Istanbul Bilgi University, Istanbul, Turkey
78: Also at Hacettepe University, Ankara, Turkey
79: Also at Vrije Universiteit Brussel, Brussel, Belgium
80: Also at School of Physics and Astronomy, University of Southampton,
Southampton, United Kingdom
81: Also at IPPP Durham University, Durham, United Kingdom
82: Also at Monash University, Faculty of Science, Clayton, Australia
83: Also at Università di Torino, TORINO, Italy
84: Also at Bethel University, St. Paul, Minneapolis, USA, St. Paul, USA
85: Also at Karamanoğlu Mehmetbey University, Karaman, Turkey
86: Also at Bingol University, Bingol, Turkey
87: Also at Georgian Technical University, Tbilisi, Georgia
88: Also at Sinop University, Sinop, Turkey
89: Also at Mimar Sinan University, Istanbul, Istanbul, Turkey
90: Also at Erciyes University, KAYSERI, Turkey
91: Also at Texas A&M University at Qatar, Doha, Qatar
92: Also at Kyungpook National University, Daegu, Korea, Daegu, Korea