B2G-18-002

$HeadURL$ $Id$

B2G-18-002

# A multi-dimensional search for new heavy resonances decaying to boosted , ,
or boson pairs in the dijet final state at 13

###### Abstract

A search in an all-jet final state for new massive resonances decaying to , ,
or boson pairs using a novel analysis method is presented. The analysis is
performed on data corresponding to an integrated luminosity of 77.3recorded
with the CMS experiment at the LHC at a centre-of-mass energy of 13. The
search is focussed on potential narrow-width resonances with masses above 1.2,
where the decay products of each or boson are expected to be collimated into a
single, large-radius jet. The signal is extracted using a three-dimensional
maximum likelihood fit of the two jet masses and the dijet invariant mass,
yielding an improvement in sensitivity of up to 30% relative to previous
search methods. No excess is observed above the estimated standard model
background. In a heavy vector triplet model, spin-1 and resonances with masses
below 3.5 and 3.8, respectively, are excluded at 95% confidence level. In a
bulk graviton model, upper limits on cross sections are set between 27 and
0.2fb for resonance masses between 1.2 and 5.2, respectively. The limits
presented in this paper are the best to date in the dijet final state.

##  0.1 Introduction

The standard model (SM) of particle physics has been exceptionally successful
in accommodating a multitude of experimental measurements and observations,
yet it falls short in a variety of aspects. These shortcomings motivate
theoretical extensions of the SM that typically introduce new particles, which
could be created in proton-proton () collisions at the CERN LHC. In this
analysis, we test theoretical models that predict new heavy resonances that
decay to pairs of vector bosons ( and bosons, collectively referred to as
bosons). These models usually aim to clarify open questions in the SM such as
the large difference between the electroweak and the Planck scales. We
consider the bulk scenario of the Randall–Sundrum (RS) model with warped extra
dimensions [1, 2, 3, 4, 5], where the spin-2 bulk graviton has an enhanced
branching fraction to massive particles, and the heavy vector triplet (HVT)
framework [6], which serves as a template that reproduces a large class of
explicit models predicting spin-1 resonances.

No significant deviations from the SM background expectation have been
observed in previous searches by the CMS Collaboration for such particles in
the [7, 8, 9, 10, 11] and [12, 13, 14, 15, 16, 17] channels, where denotes the
Higgs boson. Similar results were obtained independently by the ATLAS
Collaboration in [18, 19, 20, 21, 22, 23] and [24, 25, 26] resonance searches.
In addition, statistical combinations of diboson and leptonic decay channels
of the 2016 data set [27, 28] have been performed, which extend the exclusion
regions of the individual analyses. Lower limits on the masses of these
resonances have been set at the scale. The search presented here focusses on
resonances with masses above 1.2, in the decays of which the vector bosons are
produced at high Lorentz boost. Because of the large boost of the vector
bosons, their decay products are merged into single, large-radius jets,
leading to dijet final states. These jets are identified through dedicated jet
substructure algorithms. Compared to previous analyses in this final state [7,
10, 29, 26, 22, 23], an improved background estimation and signal extraction
procedure based on a three-dimensional (3D) maximum likelihood fit is
employed, increasing the sensitivity of the analysis. The method can be
applied to any search with final states expected to cause resonant behaviour
in three observables, whereas previous methods used solely the invariant mass
of the final decay products as the search variable. The improved sensitivity
and scope has motivated a reanalysis of the collision data collected by the
CMS experiment during the 2016 data taking period, as well as a first analysis
of the 2017 data set, corresponding to a total integrated luminosity of 77.3at
a centre-of-mass energy of 13.

##  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, and a brass and scintillator hadron calorimeter,
each composed of a barrel and two endcap sections. Forward calorimeters extend
the pseudorapidity ($\eta$) coverage provided by the barrel and endcap
detectors. Muons are detected in gas-ionization chambers embedded in the steel
flux-return yoke outside the solenoid. 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. [30].

Events of interest are selected using a two-tiered trigger system [31]. The
first level, composed of custom hardware processors, uses information from the
calorimeters and muon detectors to select events at a rate of around 100kHz
within a time interval of less than 4. The second level, known as the high-
level trigger, consists of a farm of processors running a version of the full
event reconstruction software optimized for fast processing, and reduces the
event rate to around 1kHz before data storage.

##  0.3 Simulated events

The resonances associated with the considered phenomenologies are the bulk
gravitons ($\PXXG_{\text{bulk}}$) generated for the bulk scenario [3, 4, 5] of
the RS model of warped extra dimensions [1, 2], and the heavy new bosons (and
) that can be part of an heavy vector triplet [6] or can be mass degenerate as
a vector singlet [32, 33].

The bulk graviton model is characterized by two free parameters: the mass of
the first Kaluza–Klein (KK) excitation of a spin-2 boson (the KK bulk
graviton), and the ratio $\tilde{\kappa}=\kappa\sqrt{8\pi}/\Mpl$, with
$\kappa$ being the unknown curvature scale of the extra dimension and the
Planck mass. A scenario with $\tilde{\kappa}=0.5$ is considered in this
analysis, as motivated in Ref. [34].

The HVT framework generically represents a large number of models predicting
additional gauge bosons, such as the composite Higgs [35, 36, 37, 38, 39] and
little Higgs [40, 41] models. The benchmark points are formulated in terms of
a few parameters: two coefficients $c_{\mathrm{F}}$ and $c_{\PH{}}$, that
scale the couplings of the additional gauge bosons to fermions; to the Higgs
boson and longitudinally polarized SM vector bosons, respectively, and
$g_{\mathrm{V}}$, representing the typical strength of the new vector boson
interaction. For the analysis presented here, samples were simulated in the
HVT model B, corresponding to $g_{\mathrm{V}}=3$, $c_{\PH{}}=-0.98$, and
$c_{\mathrm{F}}=1.02$ [6]. For these parameters, the new resonances are narrow
and have large branching fractions to vector boson pairs, while the fermionic
couplings are suppressed.

All signals considered in the analysis satisfy the narrow-width approximation.
The quoted results are therefore valid independent of the exact theoretical
signal widths as long as the resonance widths remain smaller than the detector
resolution. This makes our modelling of the detector effects on the signal
shape independent of the actual model used for generating the events. All
simulated samples are produced with a relative resonance width of 0.1%, in
order to be within the validity of the narrow-width approximation. Monte Carlo
(MC) simulated events of the bulk graviton and HVT signals are generated at
leading-order (LO) in quantum chromodynamics (QCD) with versions 2.2.2 and
2.4.3 [42] and hadronization showering is simulated with versions 8.205 and
8.230 [43], for 2016 and 2017 detector conditions, respectively. The NNPDF 3.0
[44] LO parton distribution functions (PDFs) are used together with the
CUETP8M1 [45] and CP5 [46] underlying event tunes in for 2016 and 2017
conditions, respectively.

Simulated samples of the SM background processes are used to optimize the
analysis and create background templates, as described in Section 0.5. The QCD
multijet production is simulated with four generator configurations: only, the
LO mode of [47] matched and showered with , [48, 49, 50, 51] matched and
showered with , and ++ 2.7.1 [52] with the CUETHS1 tune [45]. Top quark pair
production is modelled at next-to-LO (NLO) with [53], showered with . To
calculate systematic uncertainties related to the vector boson tagging
efficiency, two additional simulated samples of top quark production at LO are
used: one generated with and interfaced with , and the second one generated
and showered with . The production of +jets and +jets (+jets) is simulated at
LO with matched and showered with . The same underlying event tune as for the
signal samples is used for those of the background. Two corrections dependent
on the transverse momentum () [54, 55] are applied to the +jets backgrounds to
correct the -distribution of the vector bosons computed at LO in QCD to the
one predicted at NLO in QCD, and to account for electroweak effects at high .
The NNPDF 3.1 [56] next-to-NLO (NNLO) PDFs are employed for simulated +jets
events with the 2017 data taking conditions for both the 2016 and 2017 data
analyses.

All samples are processed through a -based [57] simulation of the CMS
detector. To simulate the effect of additional pp collisions within the same
or adjacent bunch crossings (pileup), additional inelastic events are
generated using and superimposed on the hard-scattering events. The MC
simulated events are weighted to reproduce the distribution of the number of
reconstructed pileup vertices observed in the 2016 and 2017 data separately.

##  0.4 Reconstruction and selection of events

###  0.4.1 Jet reconstruction

Event reconstruction is based on the particle flow (PF) algorithm [58], which
reconstructs and identifies individual particles with information from the
various elements of the CMS detector. Jets are reconstructed from these
particles, using the anti- jet clustering algorithm [59] with a distance
parameter of $R=0.8$ (AK8 jets) as implemented in the package [60]. In order
to mitigate the effect of pileup, two different algorithms are used: for 2016
data and simulation, charged particles identified as originating from pileup
vertices are excluded before jet clustering begins. For 2017, we take
advantage of the pileup per particle identification (PUPPI) [61] algorithm.
This method uses local shape information of charged pileup, event pileup
properties, and tracking information in order to rescale the four-momentum of
each neutral and charged PF candidate with a weight that describes the
likelihood that each particle originates from a pileup interaction. All jets
are further required to pass tight jet identification requirements [62]. Jets
are corrected for nonlinearities in and $\eta$ using jet energy corrections as
described in Ref. [63]. Additionally, residual contributions from pileup are
corrected using the approach outlined in Ref. [64].

Two variables are used to tag jets as potentially originating from vector
boson decays to quarks for further event selection: the “groomed” mass of the
jet obtained using a modified mass drop algorithm [65, 66] known as soft drop
[67], and the $N$-subjettiness ratio $\tau_{21}=\tau_{2}/\tau_{1}$ obtained
with the $N$-subjettiness algorithm [68]. For both 2016 and 2017 data these
observables are reconstructed from AK8 jets with PUPPI pileup mitigation
applied, decreasing their dependence on pileup as shown in Ref. [62], while
the overall jet four-momenta are calculated using the pileup mitigation
algorithms as described above.

The groomed jet mass is calculated using the soft drop algorithm, with angular
exponent $\beta=1$, soft cutoff threshold $z_{\mathrm{cut}}<0.1$, and
characteristic radius $R_{0}=0.8$ [67], which is applied to remove soft, wide-
angle radiation from the jet. This is a generalization of the “modified mass”
drop tagger algorithm [65, 66], and the two are identical when the angular
exponent $\beta=0$. This algorithm is based on reclustering the constituents
of the AK8 jets using the Cambridge–Aachen algorithm [69, 70]. The soft drop
jet mass $m_{\text{jet}}$, calculated as the invariant mass of the four-
momenta sum of the final remaining jet constituents, weighted according to the
PUPPI algorithm, is utilized in the offline analysis and will be denoted as
jet mass in the following. The mass is corrected for - and $\eta$-dependent
nonuniformities due to detector effects, following the procedure described in
Ref. [62].

This algorithm is used for the offline analysis, while the jet-trimming
algorithm [71] is used at trigger level, see Section 0.4.2. The jet-trimming
algorithm reclusters each AK8 jet starting from all its original constituents
using the algorithm [72] to create subjets with a size parameter
$R_{\text{subjet}}$ set to 0.2, discarding any subjet with
$\PT^{\mathrm{subjet}}/\PT^{\mathrm{jet}}<0.03$.

The $N$-subjettiness variable, $\tau_{N}$, is defined as

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

where the index $k$ runs over the PF constituents of the jet, and the
distances $\Delta R_{n,k}$ are calculated relative to the axis of the $n$-th
subjet. The normalization factor $r_{0}$ is calculated as
$r_{0}=R_{0}\sum_{k}p_{\mathrm{T},k}$, setting $R_{0}$ to the distance
parameter used in the clustering of the original jet. The variable $\tau_{N}$
quantifies the compatibility of the jet clustering with the hypothesis that
exactly $N$ subjets are present, with small values of $\tau_{N}$ indicating
greater compatibility. The ratio between 2- and 1-subjettiness,
$\tau_{21}=\tau_{2}/\tau_{1}$, is found to be a powerful discriminant between
jets originating from decays into quarks (V boson jets) and jets developed
from prompt quarks and gluons (quark/gluon jets). Jets from or decays in
signal events are characterized by lower values of $\tau_{21}$ relative to SM
backgrounds. However, the $\tau_{21}$ variable shows a dependence on the jet
-scale as well as the jet mass. This particularly affects the monotonically
falling behaviour of the nonresonant background distributions. Since this
search probes a large range of jet masses and dijet invariant masses
($m_{\mathrm{jj}}$), we decorrelate $\tau_{21}$ from the jet -scale and jet
mass following the “designed decorrelated taggers (DDT)” methodology presented
in Ref. [73]. We thereby reduce the $\tau_{21}$ profile dependence on
$\rho^{\prime}=\ln(m_{\text{jet}}^{2}/(\mathrm{p}_{T}\mu))$, where
$\mu=1\GeV$. This leads to the following definition of
$\tau_{21}^{\text{DDT}}$:

$\tau_{21}^{\text{DDT}}=\tau_{21}-M\,\rho^{\prime},$ (2)

where $M$ is the extracted slope from a fit to the $\tau_{21}$ profile versus
$\rho^{\prime}$ in QCD multijet events simulated with after applying the full
analysis selections. It is evaluated to be $M=-0.080$. The $\tau_{21}$ (left)
and $\tau_{21}^{\text{DDT}}$ (right) profile dependencies on $\rho^{\prime}$
are shown in Fig. 1. We observe a small residual difference between intervals
of , but this has a negligible impact on the analysis.

Figure 1: The $\tau_{21}$ (left) and $\tau_{21}^{\text{DDT}}$ (right) profile
dependencies on $\rho^{\prime}=\ln(m_{\text{jet}}^{2}/(\PT\mu))$ examined in
QCD multijet events simulated with . A fit to the linear part of the spectrum
for $\PT>200\GeV$ yields the slope $M=-0.080$, which is used to define the
mass- and -decorrelated variable
$\tau_{21}^{\text{DDT}}=\tau_{21}-M\,\rho^{\prime}$.

We observe a significant gain in analysis sensitivity when using
$\tau_{21}^{\text{DDT}}$. Since this variable is a function of both
$N$-subjettiness and the ratio of jet mass and transverse momentum, it leads
to a larger separation between signal and background as shown in the
comparison of $\tau_{21}^{\text{DDT}}$ and $\tau_{21}$ in Fig. 2 (left).
Furthermore, using the $\tau_{21}^{\text{DDT}}$ variable reduces the
dependency of $m_{\text{jet}}$ on $m_{\mathrm{jj}}$, leading to smoothly
falling distributions in the jet mass as shown in Fig. 2 (right).

Figure 2: Distribution of the $N$-subjettiness discriminants ($\tau_{21}$ and
$\tau_{21}^{\text{DDT}}$) for -jets and quark/gluon jets from QCD multijet
events (left). Distributions in the jet mass of QCD multijet events for four
$m_{\mathrm{jj}}$ bins in the range 1126–5500after a cut on $\tau_{21}$ and
$\tau_{21}^{\text{DDT}}$ corresponding to the same mistag rate of about 2%
(right). For both discriminants, darker colours correspond to higher
$m_{\mathrm{jj}}$ ranges. The distributions are arbitrarily scaled for better
readability. The analysis selections applied to derive these distributions are
specified in the plots. For this analysis the working point of
$\tau_{21}^{\text{DDT}}$ ${\leq}0.43$ is chosen.

###  0.4.2 Trigger and preliminary offline selection

Events are selected online with a variety of different jet triggers based on
the highest jet or the sum of all jets in the event (). For some of these
triggers additional requirements on the trimmed mass are applied in order to
be able to lower the and thresholds. For example, for 2017 data taking,
requiring the trimmed jet mass of the leading-jet to be above 30allows the
lowering of the threshold from 500 to 360while maintaining a similar rate. In
the case of the -triggers, the threshold can be lowered from 1050 to
750–800when requiring a trimmed jet mass greater 50. The -triggers utilize a
standard jet collection of anti-jets with a distance parameter $R=0.4$, while
the triggers based on the trimmed jet mass operate on AK8 jets. The triggers
used for the 2017 data set are conceptually similar to those used in 2016, and
correspond to those used in Ref. [29]. The 2017 trigger thresholds were
slightly greater than those in 2016 in order to maintain the same trigger rate
despite a higher instantaneous luminosity.

The trigger efficiency as a function of the dijet invariant mass is measured
in an orthogonal single muon data set, shown in Fig. 3, using a combination of
all triggers (left), and as a function of the jet mass for the triggers
exploiting the trimmed jet mass (right). For the trimmed jet mass triggers,
the efficiency plateau as a function of the jet mass does not reach 100% for
the full 2017 data set (full yellow circles), since these triggers were not
used for the first 4.8of data recorded. The trigger efficiency excluding this
period is shown with open yellow circles. The combination of all triggers is
$>99$% efficient above dijet invariant masses of 990 and 1126for the full 2016
and 2017 data sets, respectively. For simplicity, the subsequent analysis
requires the dijet invariant mass to be above 1126for both data sets. Given
the $m_{\mathrm{jj}}$resolution of about 10%, the lowest resonance mass that
is accepted with high efficiency by the analysis is 1.2.

Figure 3: The trigger efficiency as a function of the dijet invariant mass for
a combination of all triggers used in this analysis (left) and the event
efficiency for either of the selected jets to pass triggers requiring an
online trimmed mass of at least 30as a function of the jet mass (right). The
solid yellow circles correspond to the trigger efficiencies for the full 2017
data set and do not reach 100% efficiency because the jet mass based triggers
were unavailable for a period at the beginning of data taking (corresponding
to 4.8). The open yellow circles are the corresponding efficiencies excluding
this period. The uncertainties shown are statistical only.

All events are required to have at least one primary vertex reconstructed
within a 24window along the beam axis, with a transverse distance from the
average interaction region of less than 2 [74]. The reconstructed vertex with
the largest value of summed physics-object $\PT^{2}$ is taken to be the
primary interaction vertex. The physics objects are the jets, clustered using
the jet finding algorithm [59, 60] with the tracks assigned to the vertex as
inputs, and the associated missing transverse momentum, taken as the negative
vector sum of the of those jets.

###  0.4.3 Event selection

Figure 4: Jet mass (upper left) and $\tau_{21}^{\text{DDT}}$ (upper right)
distributions for selected jets (one random jet per event), and dijet
invariant mass distribution (lower), for events with a jet mass between 55 and
215. For the QCD multijet simulation, several alternative predictions are
shown, scaled to the data minus the other background processes, which are
scaled to their SM expectation as described in the text. The different signal
distributions are scaled to be visible. No selection on
$\tau_{21}^{\text{DDT}}$ is applied. The ratio plots show the fraction of data
over QCD multijet simulation for 8 (black markers), ++ (dotted line), and +8
(dashed line).

Events are selected by requiring at least two jets with $\PT>200\GeV$ and
$\abs{\eta}<2.5$. The two jets with the highest in the event are selected as
potential vector boson candidates and are required to have a separation of
$\abs{\Delta\eta}<1.3$ in order to reduce the QCD multijet background. In
addition to the requirement that the two jets invariant mass
$m_{\mathrm{jj}}>1126\GeV$, based on the trigger selection discussed above, it
is further required that $m_{\mathrm{jj}}<5500\GeV$. The upper cut on mjj is
well above the highest dijet mass event observed in data. To simplify the
modelling of the 3D shapes in the
$m_{\mathrm{jj}}$-$m_{\text{jet1}}$-$m_{\text{jet2}}$ space, the two jets are
labelled at random so that the mass distributions of the first and second
selected jet, $m_{\text{jet1}}$ and $m_{\text{jet2}}$, have the same shape.
Jets originating from the misreconstruction of a high momentum lepton are
rejected by requiring an angular separation $\Delta R>0.8$ to muons
(electrons) with $\PT$ greater than 20 (35)and satisfying identification
criteria optimized for high-momentum leptons [75, 76]. To reduce the QCD
multijet background, we require the jet mass to be between 55 and 215. The
selected events are further grouped into two categories according to their
likelihood to originate from a boson decay into quarks, as quantified by
$\tau_{21}^{\text{DDT}}$. The jet mass, $\tau_{21}^{\text{DDT}}$, and dijet
invariant mass distributions for data and simulation are shown in Fig. 4.

In the high-purity (HPHP) category, both jets are required to have
$0<\tau_{21}^{\text{DDT}}\leq 0.43$, while in the low-purity (HPLP) category
only one of the jets needs to fulfill this requirement, and the other must
satisfy $0.43<\tau_{21}^{\text{DDT}}\leq 0.79$. These conditions yield the
highest expected signal significance over the whole mass range, while at the
same time selecting at least 95% of the signal. The addition of the HPLP
category improves the expected cross section upper limit by around 20% at high
dijet invariant mass where the background is low. Finally, a loose requirement
of $\rho=\ln(m_{\text{jet}}^{2}/\PT^{2})<-1.8$ is applied in order to veto
events in which the jet mass is high, but the jet is low. In these cases the
cone size of $\Delta R=0.8$ is too small to contain the full jet, affecting
both the jet mass resolution and the $\tau_{21}^{\text{DDT}}$ tagging
efficiency, which is not well modelled in simulation. This selection has a
negligible effect on the signal, which typically has jets with masses around
the or boson mass and high .

###  0.4.4 Substructure variable corrections and validation

Figure 4 shows a notable deviation in the shape of the
$\tau_{21}^{\text{DDT}}$ distribution between data and simulation. Such
mismodelling introduces a bias in the jet tagging efficiency for the signal,
and as a consequence in the measured signal rate. We therefore compute scale
factors to correct the signal efficiency accordingly. For the background jets,
this mismodelling requires no further correction, because of the data-driven
approach adopted in this analysis, where the background shape and
normalization are fitted to data with large pre-fit uncertainties as described
in the following sections.

The boson tagging scale factors and jet mass scale and resolution
uncertainties are estimated from data by isolating a control sample of merged
bosons in a high- sample. This is done by performing a simultaneous fit to the
jet mass distributions for the two ranges of $\tau_{21}^{\text{DDT}}$, as
detailed in Ref. [62]. To extract the efficiency from a clean sample of merged
W bosons, the sample is split into two components, depending on whether the
quarks from the W boson decay at truth level are within $\delta R=0.8$ or not,
on whether the hadronic boson decay is merged into a single jet or not. Only
the merged component is considered in the efficiency calculation and the mass
scale and resolution extraction.

The efficiencies and scale factors obtained are listed in Table 0.4.4 for 2016
and 2017 data, with the corresponding fits shown in Fig. 5. The boson tagging
efficiency in the selected events of around 7% is relatively low compared to
the efficiency in signal events, since these events are dominated by boson
jets with a of around 200, just at the threshold where the decay products of
the boson merge into a single jet. The signal jets, however, mostly have a
above 600, and a tagging efficiency around 35%. The signal efficiency for
$\tau_{21}^{\text{DDT}}$ increases with the jet , whereas the background
efficiency is constant, as shown in Ref. [62]. Two systematic uncertainties in
the scale factors are added: one due to differences in MC generation and
modelling of the parton shower and one due to NNLO corrections. The former is
evaluated by comparing the resulting scale factors when using simulation
produced with different generators. The latter is evaluated by comparing the
extracted efficiencies with and without reweighting according to the top quark
, where the reweighting is derived from data in order to better describe the
observed distribution in data [77]. The jet mass scale and resolution are
estimated in the same fits and also listed in Table 0.4.4. The difference in
jet mass scale between data and simulation is around 2%, and the jet mass
resolution difference is roughly 8%. These are used to scale and smear the jet
mass in simulation, and their uncertainties are additionally inserted as
systematic uncertainties in the final fit.

---
Figure 5: The jet mass distribution for events that pass (left) and fail
(right) the $\tau_{21}^{\text{DDT}}\leq 0.43$ selection in the control sample.
The results of the separate fits to data and to simulation are shown by the
dash-dotted blue and solid red lines, respectively. The background components
of the fits are shown as dashed and dash-dotted lines. The fit to 2016 data is
shown in the upper panels and the fit to 2017 data in the lower panels. The
associated uncertainties are shown in Table 0.4.4 and discussed further in
Section 0.4.4.

The jet mass peak position (m) and resolution ($\sigma$), and the -tagging
efficiencies, as extracted from top quark enriched data and from simulation,
together with the corresponding data-to-simulation scale factors. The
uncertainties in the scale factors include systematic uncertainties estimated
as described in Ref. [62].  m [] $\sigma$ [] -tagging efficiency 2016
$\tau_{21}^{\text{DDT}}<0.43$ Data $82.0\pm 0.5\stat$ $7.1\pm 0.5\stat$
$0.080\pm 0.008\stat$ Simulation $80.9\pm 0.2\stat$ $6.6\pm 0.2\stat$
$0.085\pm 0.003\stat$ Data/simulation $1.014\pm 0.007$ (stat+syst) $1.09\pm
0.09$ (stat+syst) $0.94\pm 0.10$ (stat+syst)
$0.43<\tau_{21}^{\text{DDT}}<0.79$ Data $0.920\pm 0.008\stat$ Simulation
$0.915\pm 0.003\stat$ Data/simulation $1.006\pm 0.009$ (stat+syst) 2017
$\tau_{21}^{\text{DDT}}<0.43$ Data $80.8\pm 0.4\stat$ $7.7\pm 0.4\stat$
$0.065\pm 0.006\stat$ Simulation $82.2\pm 0.3\stat$ $7.1\pm 0.3\stat$
$0.068\pm 0.005\stat$ Data/simulation $0.983\pm 0.007$ (stat+syst) $1.08\pm
0.08$ (stat+syst) $0.96\pm 0.12$ (stat+syst)
$0.43<\tau_{21}^{\text{DDT}}<0.79$ Data $0.935\pm 0.006\stat$ Simulation
$0.932\pm 0.005\stat$ Data/simulation $1.003\pm 0.008$ (stat+syst)

##  0.5 The multi-dimensional fit

The background estimation technique used in previous versions of this analysis
[7, 10, 29] relied on a one-dimensional (1D) fit of the dijet invariant mass
after a tight jet mass selection (65–105) has been applied. In the analysis
presented here, we take advantage of the fact that the signal peaks in three
observables (the jet masses $m_{\text{jet1}}$ and $m_{\text{jet2}}$, and the
dijet invariant mass $m_{\mathrm{jj}}$), and attempt to extract the signal by
searching for peaks in the multi-dimensional
$m_{\mathrm{jj}}$-$m_{\text{jet1}}$-$m_{\text{jet2}}$ space. This method
permits searches for generic resonances, decaying to two SM or non-SM bosons,
anywhere in the jet mass and dijet invariant mass spectra in the future.
Additionally, tight jet mass cuts as used in previous diboson resonance
searches are no longer needed, as we fit the full jet mass line shape to
extract the signal. Since such a cut around the vector boson mass leads to
about 20% inefficiency for the and boson signals, including all the events
that would fall outside the mass window reduces the statistical uncertainties
in the fitting procedure. Furthermore, the background $m_{\mathrm{jj}}$ shape
is better constrained at high dijet invariant masses than it is in the
previous method.

Fitting the jet mass and resonance mass together also allows us to add
nuisance parameters that simultaneously affect the jet masses and the
resonance mass, accounting for their correlation. We build a three dimensional
background model starting from simulation. As the number of simulated events
is small, a forward-folding kernel approach is used to ensure a full and
smooth model, as described in Section 0.5.2. Further, to account for
discrepancies in the QCD multijet background simulation and data, we allow the
background model to adapt to the data using physically motivated shape
variations.

The random jet labelling adapted in the analysis results in essentially the
same jet mass distributions for jet-1 and jet-2 in the modelling and removes
any correlations between the two jet masses. Thus only the distribution for
one of the jet masses are shown in the following Figures.

###  0.5.1 Signal modelling

For each mass point $m_{\mathrm{X}}$ and each purity category, the signal
yield per pb of cross section is calculated as the integral of the histogram
produced from the parameterization. The total signal yield for events passing
all analysis selections divided by the number of generated events as a
function of $m_{\mathrm{X}}$ is shown in Fig. 6.

Figure 6: Total signal efficiency as a function of $m_{\mathrm{X}}$ after all
selections are applied, for signal models with a decaying to ,
$\PXXG_{\text{bulk}}$ decaying to , $\PWpr$ decaying to , and
$\PXXG_{\text{bulk}}$ decaying to . The denominator is the number of generated
events. The solid and dashed lines show the signal efficiencies for the HPHP
and HPLP categories, respectively. The decrease in efficiency between 5.0 and
5.5is due to the requirement $m_{\mathrm{jj}}<5500$.

The signal shape in three dimensions is defined as a product of the shape of
the resonance mass and the jet masses:

$P_{\mathrm{sig}}(m_{\mathrm{jj}},m_{\text{jet1}},m_{\text{jet2}}|\overline{\theta}^{\mathrm{s}}(m_{\mathrm{X}}))=P_{\PV{}\PV{}}(m_{\mathrm{jj}}|\overline{\theta}_{1}^{\mathrm{s}}(m_{\mathrm{X}}))\,P_{\mathrm{j1}}(m_{\text{jet1}}|\overline{\theta}_{2}^{\mathrm{s}}(m_{\mathrm{X}}))\,P_{\mathrm{j2}}(m_{\text{jet2}}|\overline{\theta}_{3}^{\mathrm{s}}(m_{\mathrm{X}})).$

The shapes for $m_{\mathrm{jj}}$, $m_{\text{jet1}}$, and $m_{\text{jet2}}$ are
parameterized independently as a function of the hypothesized mass
($m_{\mathrm{X}}$) of a new particle and a set of general probability density
function (pdf) parameters
$\overline{\theta}^{\mathrm{s}}=(\overline{\theta}_{1}^{\mathrm{s}},\overline{\theta}_{2}^{\mathrm{s}},\overline{\theta}_{3}^{\mathrm{s}})$
that depend on $m_{\mathrm{X}}$. The parameters
$\overline{\theta}^{\mathrm{s}}$ denote for instance the mean and width of the
analytic function chosen to model the signal distributions. The
$m_{\text{jet}}$ and $m_{\mathrm{jj}}$ distributions can be treated as
uncorrelated since correlations are found to be negligible for the signal. The
signal is parameterized by fitting the simulated resonance mass and jet mass
line shapes for each $m_{\mathrm{X}}$, extracting the quantities, and then
interpolating these to intermediate values of the resonance mass.
$P_{\mathrm{j1}}$ and $P_{\mathrm{j2}}$ are fitted and parameterized
separately from each other using different sets of
$\overline{\theta}^{\mathrm{s}}$, although they are effectively identical
because of the random jet labelling. For the parameterization of the resonance
mass $m_{\mathrm{jj}}$ and the $m_{\text{jet}}$ masses, double-sided Crystal
Ball (dCB) functions [78] are used for each $m_{\mathrm{X}}$. Each parameter
of the dCB is interpolated between different resonance masses using
polynomials of a degree sufficient to ensure a smooth shape interpolation for
all resonance masses. The resulting signal shapes for all signal models are
shown in Fig. 7 for the dijet invariant mass (left) and the mass of jet-1 in
the HPHP category (right). Because of the random jet labelling the
distribution for jet-2 is effectively identical to that shown for jet-1. The
jet mass scale and resolution as a function of the dijet invariant mass are
extracted from the mean and width of the dCB function. The mass scale and
resolution are shown in Fig. 8 after the full HPHP (HPLP) analysis selections
have been applied. The jet mass resolution increases about 3% from the lowest
to the highest resonance mass, while its scale is found to be stable. The mean
of the dijet invariant mass distributions is consistent with the mass of the
resonance $m_{\mathrm{X}}$, as seen in Figure 7 left.

Figure 7: The final $m_{\mathrm{jj}}$ (left) and $m_{\text{jet1}}$ (right)
signal shapes extracted from the parameterization of the dCB function. The
same $m_{\mathrm{jj}}$ shapes are used for both purity categories. The jet
mass distributions are shown for a range of resonance masses between 1.2 and
5.2for one of the two jets in the events in the HPHP category. Because the
jets are labelled randomly, the jet mass distributions for the second jet are
essentially the same as the one shown. The distributions for a
$\PXXG_{\text{bulk}}$ decaying to have the same shapes as those for the signal
and are therefore not visible.

Figure 8: The mass scale (left) and resolution (right) of the jet as a
function of $m_{\mathrm{X}}$, obtained from the mean and width of the dCB
function used to fit the jet mass spectrum. The HPHP (solid lines) and HPLP
(dotted lines) categories are shown for different signal models. The
distributions are only shown for one of the two jets in the event, since the
distributions for the second jet are essentially the same.

###  0.5.2 Background modelling

#### Nonresonant background

As mentioned above, previous versions of this analysis estimate the QCD
multijet background by a parametric fit to the data in the $m_{\mathrm{jj}}$
signal region. The fit is well-constrained by highly populated bins with small
statistical uncertainties at low $m_{\mathrm{jj}}$, but is less constrained
for high values of $m_{\mathrm{jj}}$. This method allows the incorporation of
additional information in the fit by modelling the correlations between the
jet mass and the dijet invariant mass for SM background processes, which were
not explicitly studied in the past. It is important to note that the
correlations between $m_{\text{jet}}$ and $m_{\mathrm{jj}}$ have to be
modelled for the QCD multijet background, as opposed to the signals negligible
correlations due to its localization in the three-dimensional space. In this
analysis, we build a three dimensional background model starting from
simulation. Since the size of the simulated samples is limited, we start from
particle-level information and use a “forward-folding” kernel approach that is
similar to the technique presented in Ref. [79] and used in Ref. [11].
Finally, we incorporate sufficient nuisance parameters into the fit to account
for any discrepancies between data and simulation. In order to model the QCD
multijet background in the 3D
$m_{\mathrm{jj}}$-$m_{\text{jet1}}$-$m_{\text{jet2}}$ hyperplane, we use the
following conditional product:

$P_{\mathrm{QCD}}(m_{\mathrm{jj}},m_{\text{jet1}},m_{\text{jet2}}|\overline{\theta})=P_{\PV{}\PV{}}(m_{\mathrm{jj}}|\overline{\theta}_{1}^{\mathrm{QCD}})\,P_{\mathrm{cond,1}}(m_{\text{jet1}}|m_{\mathrm{jj}},\overline{\theta}_{2}^{\mathrm{QCD}})\,P_{\mathrm{cond,2}}(m_{\text{jet2}}|m_{\mathrm{jj}},\overline{\theta}_{3}^{\mathrm{QCD}}).$

Since the jet mass is correlated with the jet for the QCD multijet background,
its shape is required to be modelled conditionally as a function of
$m_{\mathrm{jj}}$ for both jets. Two two-dimensional (2D) templates (denoted
as $P_{\mathrm{cond,1}}$ and $P_{\mathrm{cond,2}}$) are modelled for the two
jet masses separately, containing different jet mass shapes in bins of
$m_{\mathrm{jj}}$. The $m_{\mathrm{jj}}$ distribution is computed as a 1D pdf.
The parameter sets denoted by $\overline{\theta}^{\mathrm{QCD}}$ represent the
nuisance parameters in each pdf.

To build the 2D conditional templates, $P_{\mathrm{cond,1}}$ and
$P_{\mathrm{cond,2}}$, each available particle-level event is smoothed with a
2D Gaussian kernel, where each 2D kernel links the particle-level event
quantities to the reconstruction level. Thus each simulated event contributes
a smoothed Gaussian shape to the total conditional pdf. The Gaussian kernel
depends on the dijet invariant mass scale and resolution, as well as the jet
mass scale and resolution. The $m_{\text{jet}}$ and $m_{\mathrm{jj}}$ scale
and resolution are extracted from a Gaussian fit to either
$m_{\text{jet}}(\mathrm{reco})/m_{\text{jet}}(\mathrm{gen})$ or
$m_{\mathrm{jj}}(\mathrm{reco})/m_{\mathrm{jj}}(\mathrm{gen})$, in bins of
particle-level jet .

The mass scale and resolution are then used to populate the conditional 2D
histogram. Each generated event $i$ is smeared with a 2D Gaussian kernel,

$k(m_{\text{jet}},m_{\mathrm{jj}})=\frac{w_{i}}{2\pi
r_{m_{\mathrm{jj}},i}r_{m_{\text{jet}},i}}\exp\left[-\frac{1}{2}\left(\frac{m_{\mathrm{jj}}-s_{m_{\mathrm{jj}},i}}{r_{m_{\mathrm{jj}},i}}\right)^{2}-\frac{1}{2}\left(\frac{m_{\text{jet}}-s_{m_{\text{jet}},i}}{r_{m_{\text{jet}},i}}\right)^{2}\right],$

where $s_{i}$ and $r_{i}$ are the scale and the resolution derived in the
previous step, and $w_{i}$ is a product of event weights accounting for the
normalization effects such as the individual sample production cross sections.
In this way, the jet mass in generated events is scaled and smeared according
to the evaluated scale and resolution, and a 2D histogram is filled with
smooth Gaussian shapes. According to this procedure the jet mass
($m_{\mathrm{jj}}$) resolution is about 7–10% (3–7%) of the mass of the
generated jet, depending on its . This procedure is performed separately for
$m_{\text{jet1}}$ and $m_{\text{jet2}}$ however the two resulting templates
$P_{\mathrm{cond,1}}$ and $P_{\mathrm{cond,2}}$ are essentially the same
because of the random jet labels. Finally, we interpolate the 2D histogram in
order to have valid values of the pdf in all $m_{\mathrm{jj}}$ bins. Starting
from the histogram, coarsely binned in $m_{\mathrm{jj}}$, for each
$m_{\text{jet}}$ bin a spline is fitted over all $m_{\mathrm{jj}}$ bins. The
spline is then used to interpolate values of the histogram for all final
$m_{\mathrm{jj}}$ bins, resulting in a 2D histogram with the desired binning.

To build the 1D template for the dijet invariant mass, $P_{\PV{}\PV{}}$, a 1D
Gaussian kernel is constructed starting from particle-level quantities where,
for each MC event, a Gaussian probability distribution, rather than a single
point representing the mean, contributes to the total 1D pdf using the same
procedure as for the 2D templates.

Because of the low number of events in the HPHP category, the 3D kernel
derived in the HPLP category, which has a similar shape, is used to model the
HPHP background. This is done by adapting the templates derived in the HPLP
category to the HPHP category in the QCD multijet simulation through a fit,
and then by using the corresponding post-fit distribution as the nominal
template for the HPHP category. The free parameters in the fit are the
alternate shapes proportional to $m_{\mathrm{jj}}$, $m_{\text{jet}}$,
1/$m_{\mathrm{jj}}$, and 1/$m_{\text{jet}}$, as listed in Section 0.6. The
projections on the three different axes of the final 3D pdf, in bins of
$m_{\mathrm{jj}}$ or $m_{\text{jet}}$, are shown in Fig. 9, compared to the
spectra obtained using bare QCD multijet simulation events. Good agreement is
observed, and any residual discrepancies are covered by the systematic shape
uncertainties described in Section 0.6 and also shown in Fig. 9. Repeating the
template building process and performing fits to a control region in data
where both jets fail the high-purity requirement confirms the validity of this
approach. In addition, the adaptability of the method was further confirmed by
fitting a QCD multijet background generated at NLO with .

#### Resonant background

The resonant background is defined as all SM processes containing at least one
jet originating from a genuine or boson decay. It is dominated by +jets
events, with a minor contribution from production and an inconsequential
contribution from SM $\PV{}\PV{}$ production, that is absorbed into the +jets
modelling. As the labelling of each jet is arbitrary, each jet mass
distribution contains two contributions: a resonant part consisting of genuine
vector-boson jets, peaking around the or boson mass; and a nonresonant part,
composed of mistagged jets originating from a prompt quark or a gluon. These
two contributions are modelled separately for each jet mass dimension. A 3D
pdf for the resonant backgrounds, $P_{\mathrm{\PV{}+jets}}$, is built as a
product of three 1D pdfs as follows:

$\displaystyle\begin{split}P_{\mathrm{\PV{}+jets}}\left(m_{\text{jet1}},m_{\text{jet2}},m_{\mathrm{jj}}|\overline{\theta}\right)&=0.5\,\left(P_{\PV{}\PV{}}(m_{\mathrm{jj}}|\overline{\theta}_{1})\,P_{\text{res}}(m_{\text{jet1}}|\overline{\theta}_{2})\,P_{\text{nonres}}(m_{\text{jet2}}|\overline{\theta}_{3})\right)\\\
&+0.5\,\left(P_{\PV{}\PV{}}(m_{\mathrm{jj}}|\overline{\theta}_{1})\,P_{\text{res}}(m_{\text{jet2}}|\overline{\theta}_{2})\,P_{\text{nonres}}(m_{\text{jet1}}|\overline{\theta}_{3})\right).\end{split}$

The resonant mass shape $P_{\text{res}}$ is derived by fitting a dCB function
to the simulated jet mass spectrum, performed separately for $m_{\text{jet1}}$
and $m_{\text{jet2}}$. The resonant events are separated from the nonresonant
ones when building the pdfs by requiring that there is a generated boson in a
cone of $\Delta R=0.8$ around the reconstructed merged jet. The nonresonant
component of the jet mass shape is fitted separately with a Gaussian function.
The contributions of +jets and production are considered as one combined
background shape, because both have a resonant peak around the -boson mass,
while the +jets background contribution is considered separately. The top mass
peak does not need to be modelled since the overall contribution of events is
less than 2%. The nonresonant dijet invariant mass shape of the +jets
backgrounds, $P_{\PV{}\PV{}}$, is modelled with a one dimensional kernel, in
the same way as the dijet invariant mass shape of the QCD multijet background.

##  0.6 Systematic uncertainties

###  0.6.1 Systematic uncertainties in the background estimation

Uncertainties in the QCD multijet background shape are included in the fit
using alternative pdfs derived with the template-building method described in
Section 0.5.2. We define five nuisance parameters that vary the shape, each of
the parameters corresponding to an upward and a downward variation of
alternative shapes that simultaneously affect all three dimensions. The first
nuisance parameter accounts for a variation of the underlying spectrum, and
the two corresponding mirrored templates are obtained by applying up and down
variations of the expected yields to each bin along the two jet masses and
$m_{\mathrm{jj}}$ by a quantity proportional to $m_{\text{jet}}$ and
$m_{\mathrm{jj}}$. The second nuisance parameter is a variation of the mass
scale, and is taken into account through two mirrored alternative shapes
obtained by applying up and down variations of each bin content along the two
jet masses and $m_{\mathrm{jj}}$ by a quantity proportional to
1/$m_{\text{jet}}$ and 1/$m_{\mathrm{jj}}$. Two additional alternative shapes
that simultaneously affect resonance mass and jet groomed mass are also added
in order to take into account differences in MC generation and modelling of
the parton shower. These alternative templates are derived using the ++ and +8
QCD multijet simulation. This allows us to include all known background
variations into the fit. For events with a large $m_{\text{jet}}$ ($>$175) and
low $m_{\mathrm{jj}}$ ($<$1200), there is an expected turn-on due to the
trigger thresholds. Therefore, an additional shape uncertainty parameterizing
any discrepancy between the 3D template and the QCD multijet simulation is
added to the fit. Note that this shape uncertainty only affects this
particular region, which is far from where a diboson signal, as relevant for
this analysis, is expected. The nuisance parameters associated with these
alternative shapes are constrained using Gaussian pdfs in the fit, with the
pre-fit values chosen in order to cover any differences between data and
simulation observed in the control regions. The alternative shapes described
above are shown in Fig. 9.

A similar procedure is used for the +jets background, adding two alternative
shapes to the $m_{\mathrm{jj}}$ templates derived by a variation proportional
to $m_{\mathrm{jj}}$ and $1/m_{\mathrm{jj}}$. The resonant jet mass shapes for
this background are subject to the same uncertainties as the signal. The
normalizations of the +jets and QCD background are obtained directly from
simulation and are allowed to vary within 20 and 50%, respectively. The same
nuisance parameters are used for the fit to 2016 and 2017 data, which reduces
the fit complexity while not impacting the result of the fit.

###  0.6.2 Systematic uncertainties in the signal prediction

The dominant uncertainty in the signal selection efficiency arises from
uncertainties in the boson tagging efficiency. The effect of this uncertainty
is evaluated per jet and assumed to be fully correlated between both jets in
the event. The boson tagging efficiency scale factor is fully anticorrelated
between the HPHP and HPLP categories (3–10%), and fully correlated between
signal and +jets backgrounds. The -dependence uncertainty in the scale factor
arises from the extrapolation to higher ’s of the boson tagging efficiency
scale factors, which are measured in events where the jet has a around 200.
This uncertainty is estimated in signal simulation, and is based on the
difference in tagging efficiency between the samples matched and showered
either with or with ++, as a function of , relative to the difference at 200.
This is considered as correlated between the $\tau_{21}^{\text{DDT}}$
categories, and is given as $6\,(7)\%\ln(\PT/300\,(\GeVns{}))$ for the HPHP
(HPLP) categories. The shape uncertainties in the jet masses are considered
fully correlated between signal and +jets backgrounds, allowing the data to
constrain these parameters. These affect the mean and the width of the signal
and +jets pdfs. All uncertainties entering the fit are listed in Table 0.6.2.

Summary of the systematic uncertainties and their impact the affected
quantities. Numbers in parentheses correspond to uncertainties for the 2016
analysis if these differ from those for 2017. Dashes indicate shape variations
that cannot be described by a single parameter, and are discussed in the text.
Source Relevant quantity HPHP unc. (%) HPLP unc. (%) PDFs Signal yield 3 boson
tagging efficiency Signal + +jets yield 25 (21) 13 (11) boson tagging
dependence Signal + +jets yield 8–23 9–25 Integrated luminosity Signal + +jets
yield 2.3 (2.6) QCD normalization Background yield 50 +jets normalization
Background yield 20 +jets normalization Migration 20 PDFs Signal
$m_{\mathrm{jj}}$/$m_{\text{jet}}$ mean and width $<$1 Jet energy scale Signal
$m_{\mathrm{jj}}$ mean 2 Jet energy resolution Signal $m_{\mathrm{jj}}$ width
5 Jet mass scale Signal + +jets $m_{\text{jet}}$ mean 2 Jet mass resolution
Signal + +jets $m_{\text{jet}}$ width 8 QCD ++ QCD shape — QCD +8 QCD shape —
variations QCD shape — Scale variations QCD shape — High-$m_{\text{jet}}$
turn-on QCD shape — variations +jets $m_{\mathrm{jj}}$ shape —

Figure 9: Nominal QCD multijet simulation using 8 (data points) and three-
dimensional pdfs derived using a forward-folding kernel approach (black solid
line), shown together with the five alternate shapes that are added to the
multi-dimensional fit as shape nuisance parameters. The shapes for the high-
purity (left) and low-purity (right) categories obtained with the 2017
simulation are shown for the projection on $m_{\text{jet1}}$ (upper) and
$m_{\mathrm{jj}}$ (lower). The projection on $m_{\text{jet2}}$ is omitted
since it is equivalent to the $m_{\text{jet1}}$ projection except for
statistical fluctuations. The pdfs and the simulations shown are normalized to
unity. The normalization uncertainty of 50% is not shown. The distributions
for 2016 simulations are similar.

##  0.7 Statistical interpretation

To test for the presence of narrow resonances decaying to two vector bosons we
follow the prescription, evaluated using asymptotic expressions described in
Refs. [80, 81, 82]. The limits are computed using a shape analysis of the
three dimensional $m_{\mathrm{jj}}$-$m_{\text{jet1}}$-$m_{\text{jet2}}$
spectrum, where the 3D signal and background pdfs obtained above are fitted
simultaneously to the data for each signal mass hypothesis and category. The
signal and background yields are determined simultaneously in this fit.
Systematic uncertainties are treated as nuisance parameters and profiled in
the statistical interpretation using log-normal constraints, while Gaussian
constraints are used for shape uncertainties.

Figures 10 and 11 show the $m_{\text{jet}}$ and $m_{\mathrm{jj}}$ spectra in
data for the high- and low-purity categories, respectively. The solid gray
curves represent the results of the maximum likelihood fit to the data,
allowing the signal yields to assume their best fit value, while the lower
panels show the corresponding pull distributions, quantifying the agreement
between the hypothesis of signal plus background and the data. The resonant
background components are shown separately. A signal is superimposed onto all
three projections corresponding to a signal yield as expected from the
theoretical prediction and the analysis selection efficiency, and scaled by a
factor of 5. The background yields in the signal region extracted from a
background-only fit, together with their post-fit uncertainties, are
summarised in Table 0.7 and compared with observations, separately for the two
categories. The extracted +jets cross sections are found to be compatible with
the SM expectations within one standard deviation of the post-fit
uncertainties.

Figure 10: For the HPHP category: comparison between the signal+background fit
and the data distributions of $m_{\text{jet1}}$ (upper left),
$m_{\text{jet2}}$ (upper right), and $m_{\mathrm{jj}}$ (lower). The background
shape uncertainty is shown as a gray shaded band, and the statistical
uncertainties of the data are shown as vertical bars. An example of a signal
distribution is overlaid, where the number of expected events is scaled by a
factor of 5. Shown below each mass plot is the corresponding pull distribution
(Data-fit)/$\sigma$, where
$\sigma=\sqrt{\sigma_{\mathrm{data}}^{2}-\sigma_{\mathrm{fit}}^{2}}$ for each
bin to ensure a Gaussian pull-distribution, as defined in Ref. [83].

Figure 11: For the HPLP category: comparison between the signal+background fit
and the data distributions of $m_{\text{jet1}}$ (upper left),
$m_{\text{jet2}}$ (upper right), and $m_{\mathrm{jj}}$ (lower). The background
shape uncertainty is shown as a gray shaded band, and the statistical
uncertainties of the data are shown as vertical bars. An example of a signal
distribution is overlaid, where the number of expected events is scaled by a
factor of 5. Shown below each mass plot is the corresponding pull distribution
(Data-fit)/$\sigma$, where
$\sigma=\sqrt{\sigma_{\text{data}}^{2}-\sigma_{\text{fit}}^{2}}$ for each bin
to ensure a Gaussian pull-distribution, as defined in Ref. [83].

Observed yield and background yields extracted from the background-only fit
together with post-fit uncertainties, in the two purity categories.  Category
HPHP HPLP +jets $100\,\pm\,11$ $4600\,\pm\,200$ +jets $33\,\pm\,4$
$1580\,\pm\,160$ QCD multijets $650\,\pm\,4$ $51100\,\pm\,300$ Predicted total
background $783\,\pm\,12$ $57200\,\pm\,400$ Observed yield $780\,\pm\,30$
$57230\,\pm\,240$

No significant excess over the background estimation is observed. Upper limits
on the production cross section at 95% confidence level () are set. Limits are
set in the context of the bulk graviton model and the HVT model B scenario,
using the narrow-width approximation. Figure 12 shows the resulting limits as
a function of the resonance mass compared to theoretical predictions. The
theoretical cross sections shown in Figure 12 are calculated to LO in QCD as
detailed in Ref [34, 6]. For the HVT model B, we exclude at 95% and spin-1
resonances with masses below 3.8 and 3.5, respectively. In the narrow-width
bulk graviton model, upper limits on the production cross sections for
$\PXXG_{\text{bulk}}\to\PW{}\PW(\PZ{}\PZ{})$ are set in the range from 20
(27)fb for a resonance mass of 1.2, down to 0.2fb for a resonance mass of 5.2.

Figure 12: Observed and expected 95% upper limits on the product of the
production cross section ($\sigma$) and the branching fraction, obtained after
combining categories of all purities with 77.3of 13data, for
$\PXXG_{\text{bulk}}\to\PW\PW$ (upper left), $\PXXG_{\text{bulk}}\to\PZ\PZ$
(upper right), $\PWpr\to\PW\PZ$ (lower left), and $\PZpr\to\PW\PW$ (lower
right) signals. For each signal scenario the theoretical prediction (red line)
and its uncertainty associated with the choice of PDF set (red hashed band) is
shown. The theory cross sections (red line) are calculated at LO in QCD [34,
6].

The expected upper limits obtained using the multi-dimensional fit method
introduced here are compared to those obtained in a previous search [29] using
the same data set in order to estimate the sensitivity gain by using the new
method. Figure 13 shows the expected limits for one signal model based on an
analyses of the data collected in 2016, using either the fit method presented
here, or previous methods. We obtain a 20–30% improvement in sensitivity when
using the multi-dimensional fit method, and about a 35–40% improvement when
combining the data sets recorded in 2016 and 2017 relative to the individual
results. The same conclusion holds for the other signal models investigated in
this paper. The results obtained with the multi-dimensional fit are the best
to date in the channel and reach a similar sensitivity at high masses
($m_{\mathrm{X}}$) as the combination of diboson and leptonic decay channels
for the 36recorded in 2016 [27, 28].

Figure 13: Expected 95% upper limits on the product of the production cross
section ($\sigma$) and the branching fraction for a
$\PXXG_{\text{bulk}}\to\PW\PW$ signal using 35.9of data collected in 2016
obtained using the multi-dimensional fit method presented here (red solid
line), compared to the result obtained with previous methods (black dash-
dotted line) [29]. The final limit obtained when combining data collected in
2016 and 2017 is also shown (blue dashed line).

##  0.8 Summary

A search is presented for resonances with masses above 1.2that decay to , , or
boson pairs. Each of the two bosons decays into one large-radius jet, yielding
dijet final states. The search is conducted using a novel approach based on a
three-dimensional maximum likelihood fit in the dijet invariant mass as well
as the two jet masses, thus taking advantage of the fact that the expected
signal is resonant in all three mass dimensions. This method yields an
improvement in sensitivity of up to 30% relative to previous search methods.
The new method places additional constraints on systematic uncertainties
affecting the signal by measuring the standard model background from or
production with associated jets, accounting for 5% of the overall improvement
in sensitivity. Decays of $\PW$ and $\PZ$ bosons are identified using jet
substructure observables that reduce the background from quantum
chromodynamics multijet production. No evidence is found for a signal, and
upper limits on the resonance production cross section are set as a function
of the resonance mass. The limits presented in this paper are the best to date
in the dijet final state, and have a similar sensitivity as the combinations
of diboson and leptonic decay channels using the 2016 data set. The results
are interpreted within bulk graviton models, and as limits on the production
of the and bosons within the heavy vector triplet framework. For the heavy
vector triplet model B, we exclude at 95% confidence level and spin-1
resonances with masses below 3.8 and 3.5, respectively. In the narrow-width
bulk graviton model, upper limits on the production cross sections for
$\PXXG_{\text{bulk}}\to\PW\PW~{}(\PZ{}\PZ{})$ are set in the range of 20
(27)fb to 0.2fb for resonance masses between 1.2 and 5.2.

###### 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 centres and personnel of the Worldwide LHC Computing Grid for
delivering so effectively the computing infrastructure essential to our
analyses. Finally, we acknowledge the enduring support for the construction
and operation of the LHC and the CMS detector provided by the following
funding agencies: BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq,
CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST,
and NSFC (China); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RPF
(Cyprus); SENESCYT (Ecuador); MoER, ERC IUT, PUT and ERDF (Estonia); Academy
of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG,
and HGF (Germany); GSRT (Greece); NKFIA (Hungary); DAE and DST (India); IPM
(Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); MES
(Latvia); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT,
LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC
(Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom,
RAS, RFBR, and NRC KI (Russia); MESTD (Serbia); SEIDI, CPAN, PCTI, and FEDER
(Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST
(Taipei); ThEPCenter, IPST, STAR, and NSTDA (Thailand); TUBITAK and TAEK
(Turkey); NASU and SFFR (Ukraine); STFC (United Kingdom); DOE and NSF (USA).
Individuals have received support from the Marie-Curie programme and the
European Research Council and Horizon 2020 Grant, contract Nos. 675440,
752730, and 765710 (European Union); the Leventis Foundation; the A.P. Sloan
Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science
Policy Office; the Fonds pour la Formation à la Recherche dans l’Industrie et
dans l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door
Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium)
under the “Excellence of Science – EOS” – be.h project n. 30820817; the
Beijing Municipal Science & Technology Commission, No. Z181100004218003; the
Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the
Lendület (“Momentum”) Programme and the János Bolyai Research Scholarship of
the Hungarian Academy of Sciences, the New National Excellence Program ÚNKP,
the NKFIA research grants 123842, 123959, 124845, 124850, 125105, 128713,
128786, and 129058 (Hungary); the Council of Science and Industrial Research,
India; the HOMING PLUS programme of the Foundation for Polish Science,
cofinanced from European Union, Regional Development Fund, the Mobility Plus
programme of the Ministry of Science and Higher Education, the National
Science Center (Poland), contracts Harmonia 2014/14/M/ST2/00428, Opus
2014/13/B/ST2/02543, 2014/15/B/ST2/03998, and 2015/19/B/ST2/02861, Sonata-bis
2012/07/E/ST2/01406; the National Priorities Research Program by Qatar
National Research Fund; the Ministry of Science and Education, grant no.
3.2989.2017 (Russia); the Programa Estatal de Fomento de la Investigación
Científica y Técnica de Excelencia María de Maeztu, grant MDM-2015-0509 and
the Programa Severo Ochoa del Principado de Asturias; the Thalis and Aristeia
programmes cofinanced by EU-ESF and the Greek NSRF; the Rachadapisek Sompot
Fund for Postdoctoral Fellowship, Chulalongkorn University and the
Chulalongkorn Academic into Its 2nd Century Project Advancement Project
(Thailand); the Welch Foundation, contract C-1845; and the Weston Havens
Foundation (USA).

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##  .9 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan${}^{\textrm{\textdagger}}$, A. Tumasyan Institut für
Hochenergiephysik, Wien, Austria
W. Adam, F. Ambrogi, T. Bergauer, J. Brandstetter, M. Dragicevic, J. Erö, A.
Escalante Del Valle, M. Flechl, R. Frühwirth1, M. Jeitler1, N. Krammer, I.
Krätschmer, D. Liko, T. Madlener, I. Mikulec, N. Rad, J. Schieck1, R.
Schöfbeck, M. Spanring, D. Spitzbart, W. Waltenberger, C.-E. Wulz1, M. Zarucki
Institute for Nuclear Problems, Minsk, Belarus
V. Drugakov, V. Mossolov, J. Suarez Gonzalez Universiteit Antwerpen,
Antwerpen, Belgium
M.R. Darwish, E.A. De Wolf, D. Di Croce, X. Janssen, J. Lauwers, A. Lelek, M.
Pieters, H. Rejeb Sfar, H. Van Haevermaet, P. Van Mechelen, S. Van Putte, N.
Van Remortel Vrije Universiteit Brussel, Brussel, Belgium
F. Blekman, E.S. Bols, S.S. Chhibra, J. D’Hondt, J. De Clercq, D. Lontkovskyi,
S. Lowette, I. Marchesini, S. Moortgat, L. Moreels, Q. Python, K. Skovpen, S.
Tavernier, W. Van Doninck, P. Van Mulders, I. Van Parijs Université Libre de
Bruxelles, Bruxelles, Belgium
D. Beghin, B. Bilin, H. Brun, B. Clerbaux, G. De Lentdecker, H. Delannoy, B.
Dorney, L. Favart, A. Grebenyuk, A.K. Kalsi, J. Luetic, A. Popov, N. Postiau,
E. Starling, L. Thomas, C. Vander Velde, P. Vanlaer, D. Vannerom Ghent
University, Ghent, Belgium
T. Cornelis, D. Dobur, I. Khvastunov2, M. Niedziela, C. Roskas, D. Trocino, M.
Tytgat, W. Verbeke, B. Vermassen, M. Vit, N. Zaganidis Université Catholique
de Louvain, Louvain-la-Neuve, Belgium
O. Bondu, G. Bruno, C. Caputo, P. David, C. Delaere, M. Delcourt, A.
Giammanco, V. Lemaitre, A. Magitteri, J. Prisciandaro, A. Saggio, M. Vidal
Marono, P. Vischia, J. Zobec Centro Brasileiro de Pesquisas Fisicas, Rio de
Janeiro, Brazil
F.L. Alves, G.A. Alves, G. Correia Silva, C. Hensel, A. Moraes, P. Rebello
Teles Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
E. Belchior Batista Das Chagas, W. Carvalho, J. Chinellato3, E. Coelho, E.M.
Da Costa, G.G. Da Silveira4, D. De Jesus Damiao, C. De Oliveira Martins, S.
Fonseca De Souza, L.M. Huertas Guativa, H. Malbouisson, J. Martins5, D. Matos
Figueiredo, M. Medina Jaime6, M. Melo De Almeida, C. Mora Herrera, L. Mundim,
H. Nogima, W.L. Prado Da Silva, L.J. Sanchez Rosas, A. Santoro, A. Sznajder,
M. Thiel, E.J. Tonelli Manganote3, F. Torres Da Silva De Araujo, A. Vilela
Pereira Universidade Estadual Paulista a, Universidade Federal do ABC b, São
Paulo, Brazil
S. Ahujaa, C.A. Bernardesa, L. Calligarisa, T.R. Fernandez Perez Tomeia, E.M.
Gregoresb, D.S. Lemos, P.G. Mercadanteb, S.F. Novaesa, SandraS. Padulaa
Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy of
Sciences, Sofia, Bulgaria
A. Aleksandrov, G. Antchev, R. Hadjiiska, P. Iaydjiev, A. Marinov, M. Misheva,
M. Rodozov, M. Shopova, G. Sultanov University of Sofia, Sofia, Bulgaria
M. Bonchev, A. Dimitrov, T. Ivanov, L. Litov, B. Pavlov, P. Petkov Beihang
University, Beijing, China
W. Fang7, X. Gao7, L. Yuan Department of Physics, Tsinghua University,
Beijing, China
Z. Hu, Y. Wang Institute of High Energy Physics, Beijing, China
M. Ahmad, G.M. Chen8, H.S. Chen8, M. Chen, C.H. Jiang, D. Leggat, H. Liao, Z.
Liu, S.M. Shaheen8, A. Spiezia, J. Tao, E. Yazgan, H. Zhang, S. Zhang8, J.
Zhao State Key Laboratory of Nuclear Physics and Technology, Peking
University, Beijing, China
A. Agapitos, Y. Ban, G. Chen, A. Levin, J. Li, L. Li, Q. Li, Y. Mao, S.J.
Qian, D. Wang, Q. Wang Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, L.F. Chaparro Sierra, C. Florez, C.F. González
Hernández, M.A. Segura Delgado Universidad de Antioquia, Medellin, Colombia
J. Mejia Guisao, J.D. Ruiz Alvarez, C.A. Salazar González, N. Vanegas Arbelaez
University of Split, Faculty of Electrical Engineering, Mechanical Engineering
and Naval Architecture, Split, Croatia
D. Giljanović, N. Godinovic, D. Lelas, I. Puljak, T. Sculac University of
Split, Faculty of Science, Split, Croatia
Z. Antunovic, M. Kovac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, S. Ceci, D. Ferencek, K. Kadija, B. Mesic, M. Roguljic, A.
Starodumov9, T. Susa University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, E. Erodotou, A. Ioannou, M. Kolosova, S. Konstantinou,
G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos, P.A. Razis, H.
Rykaczewski, D. Tsiakkouri Charles University, Prague, Czech Republic
M. Finger10, M. Finger Jr.10, A. Kveton, J. Tomsa Escuela Politecnica
Nacional, Quito, Ecuador
E. Ayala Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab
Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
S. Abu Zeid11, S. Khalil12 National Institute of Chemical Physics and
Biophysics, Tallinn, Estonia
S. Bhowmik, A. Carvalho Antunes De Oliveira, R.K. Dewanjee, K. Ehataht, M.
Kadastik, M. Raidal, C. Veelken Department of Physics, University of Helsinki,
Helsinki, Finland
P. Eerola, L. Forthomme, H. Kirschenmann, K. Osterberg, M. Voutilainen
Helsinki Institute of Physics, Helsinki, Finland
F. Garcia, J. Havukainen, J.K. Heikkilä, T. Järvinen, V. Karimäki, R.
Kinnunen, T. Lampén, K. Lassila-Perini, S. Laurila, S. Lehti, T. Lindén, P.
Luukka, T. Mäenpää, H. Siikonen, E. Tuominen, J. Tuominiemi Lappeenranta
University of Technology, Lappeenranta, Finland
T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
M. Besancon, F. Couderc, M. Dejardin, D. Denegri, B. Fabbro, J.L. Faure, F.
Ferri, S. Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry,
C. Leloup, E. Locci, J. Malcles, J. Rander, A. Rosowsky, M.Ö. Sahin, A. Savoy-
Navarro13, M. Titov Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole
Polytechnique, Institut Polytechnique de Paris
C. Amendola, F. Beaudette, P. Busson, C. Charlot, B. Diab, G. Falmagne, R.
Granier de Cassagnac, I. Kucher, A. Lobanov, C. Martin Perez, M. Nguyen, C.
Ochando, P. Paganini, J. Rembser, R. Salerno, J.B. Sauvan, Y. Sirois, A. Zabi,
A. Zghiche Université de Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, France
J.-L. Agram14, J. Andrea, D. Bloch, G. Bourgatte, J.-M. Brom, E.C. Chabert, C.
Collard, E. Conte14, J.-C. Fontaine14, D. Gelé, U. Goerlach, M. Jansová, A.-C.
Le Bihan, N. Tonon, P. Van Hove Centre de Calcul de l’Institut National de
Physique Nucleaire et de Physique des Particules, CNRS/IN2P3, Villeurbanne,
France
S. Gadrat Université de Lyon, Université Claude Bernard Lyon 1, CNRS-IN2P3,
Institut de Physique Nucléaire de Lyon, Villeurbanne, France
S. Beauceron, C. Bernet, G. Boudoul, C. Camen, N. Chanon, R. Chierici, D.
Contardo, P. Depasse, H. El Mamouni, J. Fay, S. Gascon, M. Gouzevitch, B.
Ille, Sa. Jain, F. Lagarde, I.B. Laktineh, H. Lattaud, M. Lethuillier, L.
Mirabito, S. Perries, V. Sordini, G. Touquet, M. Vander Donckt, S. Viret
Georgian Technical University, Tbilisi, Georgia
G. Adamov Tbilisi State University, Tbilisi, Georgia
Z. Tsamalaidze10 RWTH Aachen University, I. Physikalisches Institut, Aachen,
Germany
C. Autermann, L. Feld, M.K. Kiesel, K. Klein, M. Lipinski, D. Meuser, A.
Pauls, M. Preuten, M.P. Rauch, C. Schomakers, J. Schulz, M. Teroerde, B.
Wittmer RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
A. Albert, M. Erdmann, S. Erdweg, T. Esch, B. Fischer, R. Fischer, S. Ghosh,
T. Hebbeker, K. Hoepfner, H. Keller, L. Mastrolorenzo, M. Merschmeyer, A.
Meyer, P. Millet, G. Mocellin, S. Mondal, S. Mukherjee, D. Noll, A. Novak, T.
Pook, A. Pozdnyakov, T. Quast, M. Radziej, Y. Rath, H. Reithler, M. Rieger, J.
Roemer, A. Schmidt, S.C. Schuler, A. Sharma, S. Thüer, S. Wiedenbeck RWTH
Aachen University, III. Physikalisches Institut B, Aachen, Germany
G. Flügge, W. Haj Ahmad15, O. Hlushchenko, T. Kress, T. Müller, A. Nehrkorn,
A. Nowack, C. Pistone, O. Pooth, D. Roy, H. Sert, A. Stahl16 Deutsches
Elektronen-Synchrotron, Hamburg, Germany
M. Aldaya Martin, P. Asmuss, I. Babounikau, H. Bakhshiansohi, K. Beernaert, O.
Behnke, U. Behrens, A. Bermúdez Martínez, D. Bertsche, A.A. Bin Anuar, K.
Borras17, V. Botta, A. Campbell, A. Cardini, P. Connor, S. Consuegra
Rodríguez, C. Contreras-Campana, V. Danilov, A. De Wit, M.M. Defranchis, C.
Diez Pardos, D. Domínguez Damiani, G. Eckerlin, D. Eckstein, T. Eichhorn, A.
Elwood, E. Eren, E. Gallo18, A. Geiser, J.M. Grados Luyando, A. Grohsjean, M.
Guthoff, M. Haranko, A. Harb, A. Jafari, N.Z. Jomhari, H. Jung, A. Kasem17, M.
Kasemann, H. Kaveh, J. Keaveney, C. Kleinwort, J. Knolle, D. Krücker, W.
Lange, T. Lenz, J. Leonard, J. Lidrych, K. Lipka, W. Lohmann19, R. Mankel,
I.-A. Melzer-Pellmann, A.B. Meyer, M. Meyer, M. Missiroli, G. Mittag, J.
Mnich, A. Mussgiller, V. Myronenko, D. Pérez Adán, S.K. Pflitsch, D. Pitzl, A.
Raspereza, A. Saibel, M. Savitskyi, V. Scheurer, P. Schütze, C.
Schwanenberger, R. Shevchenko, A. Singh, H. Tholen, O. Turkot, A. Vagnerini,
M. Van De Klundert, G.P. Van Onsem, R. Walsh, Y. Wen, K. Wichmann, C. Wissing,
O. Zenaiev, R. Zlebcik University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, L. Benato, A. Benecke, V. Blobel, T. Dreyer, A.
Ebrahimi, A. Fröhlich, C. Garbers, E. Garutti, D. Gonzalez, P. Gunnellini, J.
Haller, A. Hinzmann, A. Karavdina, G. Kasieczka, R. Klanner, R. Kogler, N.
Kovalchuk, S. Kurz, V. Kutzner, J. Lange, T. Lange, A. Malara, D. Marconi, J.
Multhaup, C.E.N. Niemeyer, D. Nowatschin, A. Perieanu, A. Reimers, O. Rieger,
C. Scharf, P. Schleper, S. Schumann, J. Schwandt, J. Sonneveld, H. Stadie, G.
Steinbrück, F.M. Stober, M. Stöver, B. Vormwald, I. Zoi Karlsruher Institut
fuer Technologie, Karlsruhe, Germany
C. Barth, M. Baselga, S. Baur, T. Berger, E. Butz, R. Caspart, T. Chwalek, W.
De Boer, A. Dierlamm, K. El Morabit, N. Faltermann, M. Giffels, P.
Goldenzweig, A. Gottmann, M.A. Harrendorf, F. Hartmann16, U. Husemann, S.
Kudella, S. Mitra, M.U. Mozer, Th. Müller, M. Musich, A. Nürnberg, G. Quast,
K. Rabbertz, D. Schäfer, M. Schröder, I. Shvetsov, H.J. Simonis, R. Ulrich, M.
Weber, C. Wöhrmann, R. Wolf Institute of Nuclear and Particle Physics (INPP),
NCSR Demokritos, Aghia Paraskevi, Greece
G. Anagnostou, P. Asenov, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas,
G. Paspalaki National and Kapodistrian University of Athens, Athens, Greece
M. Diamantopoulou, G. Karathanasis, P. Kontaxakis, A. Panagiotou, I.
Papavergou, N. Saoulidou, A. Stakia, K. Theofilatos, K. Vellidis National
Technical University of Athens, Athens, Greece
G. Bakas, K. Kousouris, I. Papakrivopoulos, G. Tsipolitis University of
Ioánnina, Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, S. Mallios, K.
Manitara, N. Manthos, I. Papadopoulos, J. Strologas, F.A. Triantis, D.
Tsitsonis MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
M. Bartók20, M. Csanad, P. Major, K. Mandal, A. Mehta, M.I. Nagy, G. Pasztor,
O. Surányi, G.I. Veres Wigner Research Centre for Physics, Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath21, F. Sikler, T.Á. Vámi, V. Veszpremi, G.
Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear Research ATOMKI,
Debrecen, Hungary
N. Beni, S. Czellar, J. Karancsi20, A. Makovec, J. Molnar, Z. Szillasi
Institute of Physics, University of Debrecen, Debrecen, Hungary
P. Raics, D. Teyssier, Z.L. Trocsanyi, B. Ujvari Eszterhazy Karoly University,
Karoly Robert Campus, Gyongyos, Hungary
T. Csorgo, W.J. Metzger, F. Nemes, T. Novak Indian Institute of Science
(IISc), Bangalore, India
S. Choudhury, J.R. Komaragiri, P.C. Tiwari National Institute of Science
Education and Research, HBNI, Bhubaneswar, India
S. Bahinipati23, C. Kar, G. Kole, P. Mal, V.K. Muraleedharan Nair Bindhu, A.
Nayak24, D.K. Sahoo23, S.K. Swain Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, S. Chauhan, R. Chawla, N. Dhingra, R.
Gupta, A. Kaur, M. Kaur, S. Kaur, P. Kumari, M. Lohan, M. Meena, K. Sandeep,
S. Sharma, J.B. Singh, A.K. Virdi, G. Walia University of Delhi, Delhi, India
A. Bhardwaj, B.C. Choudhary, R.B. Garg, M. Gola, S. Keshri, Ashok Kumar, S.
Malhotra, M. Naimuddin, P. Priyanka, K. Ranjan, Aashaq Shah, R. Sharma Saha
Institute of Nuclear Physics, HBNI, Kolkata, India
R. Bhardwaj25, M. Bharti25, R. Bhattacharya, S. Bhattacharya, U. Bhawandeep25,
D. Bhowmik, S. Dey, S. Dutta, S. Ghosh, M. Maity26, K. Mondal, S. Nandan, A.
Purohit, P.K. Rout, G. Saha, S. Sarkar, T. Sarkar26, M. Sharan, B. Singh25, S.
Thakur25 Indian Institute of Technology Madras, Madras, India
P.K. Behera, P. Kalbhor, A. Muhammad, P.R. Pujahari, A. Sharma, A.K. Sikdar
Bhabha Atomic Research Centre, Mumbai, India
R. Chudasama, D. Dutta, V. Jha, V. Kumar, D.K. Mishra, P.K. Netrakanti, L.M.
Pant, P. Shukla Tata Institute of Fundamental Research-A, Mumbai, India
T. Aziz, M.A. Bhat, S. Dugad, G.B. Mohanty, N. Sur, RavindraKumar Verma Tata
Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, S. Karmakar,
S. Kumar, G. Majumder, K. Mazumdar, N. Sahoo, S. Sawant Indian Institute of
Science Education and Research (IISER), Pune, India
S. Chauhan, S. Dube, V. Hegde, A. Kapoor, K. Kothekar, S. Pandey, A. Rane, A.
Rastogi, S. Sharma Institute for Research in Fundamental Sciences (IPM),
Tehran, Iran
S. Chenarani27, E. Eskandari Tadavani, S.M. Etesami27, M. Khakzad, M.
Mohammadi Najafabadi, M. Naseri, F. Rezaei Hosseinabadi University College
Dublin, Dublin, Ireland
M. Felcini, M. Grunewald INFN Sezione di Bari a, Università di Bari b,
Politecnico di Bari c, Bari, Italy
M. Abbresciaa,b, R. Alya,b,28, C. Calabriaa,b, A. Colaleoa, D. Creanzaa,c, L.
Cristellaa,b, N. De Filippisa,c, M. De Palmaa,b, A. Di Florioa,b, L. Fiorea,
A. Gelmia,b, G. Iasellia,c, M. Incea,b, S. Lezkia,b, G. Maggia,c, M. Maggia,
G. Minielloa,b, S. Mya,b, S. Nuzzoa,b, A. Pompilia,b, G. Pugliesea,c, R.
Radognaa, A. Ranieria, G. Selvaggia,b, L. Silvestrisa, R. Vendittia, P.
Verwilligena INFN Sezione di Bologna a, Università di Bologna b, Bologna,
Italy
G. Abbiendia, C. Battilanaa,b, D. Bonacorsia,b, L. Borgonovia,b, S. Braibant-
Giacomellia,b, R. Campaninia,b, P. Capiluppia,b, A. Castroa,b, F.R. Cavalloa,
C. Cioccaa, G. Codispotia,b, M. Cuffiania,b, G.M. Dallavallea, F. Fabbria, A.
Fanfania,b, E. Fontanesi, P. Giacomellia, C. Grandia, L. Guiduccia,b, F.
Iemmia,b, S. Lo Meoa,29, 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,30, S. Costaa,b, A. Di Mattiaa, R. Potenzaa,b, A. Tricomia,b,30,
C. Tuvea,b INFN Sezione di Firenze a, Università di Firenze b, Firenze, Italy
G. Barbaglia, R. Ceccarelli, K. Chatterjeea,b, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, E. Focardia,b, G. Latino, P. Lenzia,b, M. Meschinia, S.
Paolettia, G. Sguazzonia, D. Stroma, L. Viliania INFN Laboratori Nazionali di
Frascati, Frascati, Italy
L. Benussi, S. Bianco, D. Piccolo INFN Sezione di Genova a, Università di
Genova b, Genova, Italy
M. Bozzoa,b, F. Ferroa, R. Mulargiaa,b, E. Robuttia, S. Tosia,b INFN Sezione
di Milano-Bicocca a, Università di Milano-Bicocca b, Milano, Italy
A. Benagliaa, A. Beschia,b, F. Brivioa,b, V. Cirioloa,b,16, S. Di Guidaa,b,16,
M.E. Dinardoa,b, P. Dinia, S. Fiorendia,b, S. Gennaia, A. Ghezzia,b, P.
Govonia,b, L. Guzzia,b, M. Malbertia, S. Malvezzia, D. Menascea, F. Montia,b,
L. Moronia, G. Ortonaa,b, M. Paganonia,b, D. Pedrinia, S. Ragazzia,b, T.
Tabarelli de Fatisa,b, D. Zuoloa,b INFN Sezione di Napoli a, Università di
Napoli ’Federico II’ b, Napoli, Italy, Università della Basilicata c, Potenza,
Italy, Università G. Marconi d, Roma, Italy
S. Buontempoa, N. Cavalloa,c, A. De Iorioa,b, A. Di Crescenzoa,b, F.
Fabozzia,c, F. Fiengaa, G. Galatia, A.O.M. Iorioa,b, L. Listaa,b, S.
Meolaa,d,16, P. Paoluccia,16, B. Rossia, C. Sciaccaa,b, E. Voevodinaa,b INFN
Sezione di Padova a, Università di Padova b, Padova, Italy, Università di
Trento c, Trento, Italy
P. Azzia, N. Bacchettaa, D. Biselloa,b, A. Bolettia,b, A. Bragagnolo, R.
Carlina,b, P. Checchiaa, P. De Castro Manzanoa, T. Dorigoa, U. Dossellia, F.
Gasparinia,b, U. Gasparinia,b, A. Gozzelinoa, S.Y. Hoh, P. Lujan, M.
Margonia,b, A.T. Meneguzzoa,b, J. Pazzinia,b, M. Presillab, P. Ronchesea,b, R.
Rossina,b, F. Simonettoa,b, A. Tiko, M. Tosia,b, M. Zanettia,b, P. Zottoa,b,
G. Zumerlea,b INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy
A. Braghieria, P. Montagnaa,b, S.P. Rattia,b, V. Rea, M. Ressegottia,b, C.
Riccardia,b, P. Salvinia, I. Vaia,b, P. Vituloa,b INFN Sezione di Perugia a,
Università di Perugia b, Perugia, Italy
M. Biasinia,b, G.M. Bileia, C. Cecchia,b, D. Ciangottinia,b, L. Fanòa,b, P.
Laricciaa,b, R. Leonardia,b, E. Manonia, G. Mantovania,b, V. Mariania,b, M.
Menichellia, A. Rossia,b, A. Santocchiaa,b, D. Spigaa INFN Sezione di Pisa a,
Università di Pisa b, Scuola Normale Superiore di Pisa c, Pisa, Italy
K. Androsova, P. Azzurria, G. Bagliesia, V. Bertacchia,c, L. Bianchinia, T.
Boccalia, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, G. Fedia, L.
Gianninia,c, A. Giassia, M.T. Grippoa, F. Ligabuea,c, E. Mancaa,c, G.
Mandorlia,c, A. Messineoa,b, F. Pallaa, A. Rizzia,b, G. Rolandi31, S. Roy
Chowdhury, A. Scribanoa, P. Spagnoloa, R. Tenchinia, G. Tonellia,b, N. Turini,
A. Venturia, P.G. Verdinia INFN Sezione di Roma a, Sapienza Università di Roma
b, Rome, Italy
F. Cavallaria, M. Cipriania,b, D. Del Rea,b, E. Di Marcoa,b, M. Diemoza, E.
Longoa,b, B. Marzocchia,b, P. Meridiania, G. Organtinia,b, F. Pandolfia, R.
Paramattia,b, C. Quarantaa,b, S. Rahatloua,b, C. Rovellia, F.
Santanastasioa,b, L. Soffia,b INFN Sezione di Torino a, Università di Torino
b, Torino, Italy, Università del Piemonte Orientale c, Novara, Italy
N. Amapanea,b, R. Arcidiaconoa,c, S. Argiroa,b, M. Arneodoa,c, N. Bartosika,
R. Bellana,b, C. Biinoa, A. Cappatia,b, N. Cartigliaa, S. Comettia, M.
Costaa,b, R. Covarellia,b, N. Demariaa, B. Kiania,b, C. Mariottia, S.
Masellia, E. Migliorea,b, V. Monacoa,b, E. Monteila,b, M. Montenoa, M.M.
Obertinoa,b, L. Pachera,b, N. Pastronea, M. Pelliccionia, G.L. Pinna
Angionia,b, A. Romeroa,b, M. Ruspaa,c, R. Sacchia,b, R. Salvaticoa,b, V.
Solaa, A. Solanoa,b, D. Soldia,b, A. Staianoa INFN Sezione di Trieste a,
Università di Trieste b, Trieste, Italy
S. Belfortea, V. Candelisea,b, M. Casarsaa, F. Cossuttia, A. Da Rolda,b, G.
Della Riccaa,b, F. Vazzolera,b, A. Zanettia Kyungpook National University,
Daegu, Korea
B. Kim, D.H. Kim, G.N. Kim, M.S. Kim, J. Lee, S.W. Lee, C.S. Moon, Y.D. Oh,
S.I. Pak, S. Sekmen, D.C. Son, Y.C. Yang Chonnam National University,
Institute for Universe and Elementary Particles, Kwangju, Korea
H. Kim, D.H. Moon, G. Oh Hanyang University, Seoul, Korea
B. Francois, T.J. Kim, J. Park Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, D. Gyun, S. Ha, B. Hong, K. Lee, K.S. Lee, J. Lim, J.
Park, S.K. Park, Y. Roh Kyung Hee University, Department of Physics
J. Goh Sejong University, Seoul, Korea
H.S. Kim Seoul National University, Seoul, Korea
J. Almond, J.H. Bhyun, J. Choi, S. Jeon, J. Kim, J.S. Kim, H. Lee, K. Lee, S.
Lee, K. Nam, M. Oh, S.B. Oh, B.C. Radburn-Smith, U.K. Yang, H.D. Yoo, I. Yoon,
G.B. Yu University of Seoul, Seoul, Korea
D. Jeon, H. Kim, J.H. Kim, J.S.H. Lee, I.C. Park, I. Watson Sungkyunkwan
University, Suwon, Korea
Y. Choi, C. Hwang, Y. Jeong, J. Lee, Y. Lee, I. Yu Riga Technical University,
Riga, Latvia
V. Veckalns32 Vilnius University, Vilnius, Lithuania
V. Dudenas, A. Juodagalvis, G. Tamulaitis, J. Vaitkus National Centre for
Particle Physics, Universiti Malaya, Kuala Lumpur, Malaysia
Z.A. Ibrahim, F. Mohamad Idris33, W.A.T. Wan Abdullah, M.N. Yusli, Z. Zolkapli
Universidad de Sonora (UNISON), Hermosillo, Mexico
J.F. Benitez, A. Castaneda Hernandez, J.A. Murillo Quijada, L. Valencia Palomo
Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico
H. Castilla-Valdez, E. De La Cruz-Burelo, I. Heredia-De La Cruz34, R. Lopez-
Fernandez, A. Sanchez-Hernandez Universidad Iberoamericana, Mexico City,
Mexico
S. Carrillo Moreno, C. Oropeza Barrera, M. Ramirez-Garcia, F. Vazquez Valencia
Benemerita Universidad Autonoma de Puebla, Puebla, Mexico
J. Eysermans, I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada Universidad
Autónoma de San Luis Potosí, San Luis Potosí, Mexico
A. Morelos Pineda University of Montenegro, Podgorica, Montenegro
N. Raicevic University of Auckland, Auckland, New Zealand
D. Krofcheck University of Canterbury, Christchurch, New Zealand
S. Bheesette, P.H. Butler National Centre for Physics, Quaid-I-Azam
University, Islamabad, Pakistan
A. Ahmad, M. Ahmad, Q. Hassan, H.R. Hoorani, W.A. Khan, M.A. Shah, M. Shoaib,
M. Waqas AGH University of Science and Technology Faculty of Computer Science,
Electronics and Telecommunications, Krakow, Poland
V. Avati, L. Grzanka, M. Malawski National Centre for Nuclear Research,
Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, M. Górski, M. Kazana, M. Szleper, P.
Zalewski Institute of Experimental Physics, Faculty of Physics, University of
Warsaw, Warsaw, Poland
K. Bunkowski, A. Byszuk35, K. Doroba, A. Kalinowski, M. Konecki, J.
Krolikowski, M. Misiura, M. Olszewski, A. Pyskir, M. Walczak Laboratório de
Instrumentação e Física Experimental de Partículas, Lisboa, Portugal
M. Araujo, P. Bargassa, D. Bastos, A. Di Francesco, P. Faccioli, B. Galinhas,
M. Gallinaro, J. Hollar, N. Leonardo, J. Seixas, K. Shchelina, G. Strong, O.
Toldaiev, J. Varela Joint Institute for Nuclear Research, Dubna, Russia
P. Bunin, I. Golutvin, I. Gorbunov, A. Kamenev, V. Karjavine, A. Lanev, A.
Malakhov, V. Matveev36,37, P. Moisenz, V. Palichik, V. Perelygin, M. Savina,
S. Shmatov, S. Shulha, A. Zarubin Petersburg Nuclear Physics Institute,
Gatchina (St. Petersburg), Russia
L. Chtchipounov, V. Golovtsov, Y. Ivanov, V. Kim38, E. Kuznetsova39, P.
Levchenko, V. Murzin, V. Oreshkin, I. Smirnov, D. Sosnov, V. Sulimov, L.
Uvarov, A. Vorobyev Institute for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov,
N. Krasnikov, A. Pashenkov, D. Tlisov, A. Toropin Institute for Theoretical
and Experimental Physics named by A.I. Alikhanov of NRC ‘Kurchatov Institute’,
Moscow, Russia
V. Epshteyn, V. Gavrilov, N. Lychkovskaya, A. Nikitenko40, V. Popov, I.
Pozdnyakov, G. Safronov, A. Spiridonov, A. Stepennov, M. Toms, E. Vlasov, A.
Zhokin Moscow Institute of Physics and Technology, Moscow, Russia
T. Aushev National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
M. Chadeeva41, P. Parygin, D. Philippov, E. Popova, V. Rusinov P.N. Lebedev
Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin, I. Dremin, M. Kirakosyan, A. Terkulov Skobeltsyn
Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow,
Russia
A. Belyaev, E. Boos, V. Bunichev, M. Dubinin42, L. Dudko, A. Ershov, A.
Gribushin, V. Klyukhin, O. Kodolova, I. Lokhtin, S. Obraztsov, M. Perfilov, V.
Savrin Novosibirsk State University (NSU), Novosibirsk, Russia
A. Barnyakov43, V. Blinov43, T. Dimova43, L. Kardapoltsev43, Y. Skovpen43
Institute for High Energy Physics of National Research Centre ‘Kurchatov
Institute’, Protvino, Russia
I. Azhgirey, I. Bayshev, S. Bitioukov, V. Kachanov, D. Konstantinov, P.
Mandrik, V. Petrov, R. Ryutin, S. Slabospitskii, A. Sobol, S. Troshin, N.
Tyurin, A. Uzunian, A. Volkov National Research Tomsk Polytechnic University,
Tomsk, Russia
A. Babaev, A. Iuzhakov, V. Okhotnikov Tomsk State University, Tomsk, Russia
V. Borchsh, V. Ivanchenko, E. Tcherniaev University of Belgrade: Faculty of
Physics and VINCA Institute of Nuclear Sciences
P. Adzic44, P. Cirkovic, D. Devetak, M. Dordevic, P. Milenovic, J. Milosevic,
M. Stojanovic Centro de Investigaciones Energéticas Medioambientales y
Tecnológicas (CIEMAT), Madrid, Spain
M. Aguilar-Benitez, J. Alcaraz Maestre, A. Álvarez Fernández, I. Bachiller, M.
Barrio Luna, J.A. Brochero Cifuentes, C.A. Carrillo Montoya, M. Cepeda, M.
Cerrada, N. Colino, B. De La Cruz, A. Delgado Peris, C. Fernandez Bedoya, J.P.
Fernández Ramos, J. Flix, M.C. Fouz, O. Gonzalez Lopez, S. Goy Lopez, J.M.
Hernandez, M.I. Josa, D. Moran, Á. Navarro Tobar, A. Pérez-Calero Yzquierdo,
J. Puerta Pelayo, I. Redondo, L. Romero, S. Sánchez Navas, M.S. Soares, A.
Triossi, C. Willmott Universidad Autónoma de Madrid, Madrid, Spain
C. Albajar, J.F. de Trocóniz Universidad de Oviedo, Instituto Universitario de
Ciencias y Tecnologías Espaciales de Asturias (ICTEA), Oviedo, Spain
B. Alvarez Gonzalez, J. Cuevas, C. Erice, J. Fernandez Menendez, S. Folgueras,
I. Gonzalez Caballero, J.R. González Fernández, E. Palencia Cortezon, V.
Rodríguez Bouza, S. Sanchez Cruz Instituto de Física de Cantabria (IFCA),
CSIC-Universidad de Cantabria, Santander, Spain
I.J. Cabrillo, A. Calderon, B. Chazin Quero, J. Duarte Campderros, M.
Fernandez, P.J. Fernández Manteca, A. García Alonso, G. Gomez, C. Martinez
Rivero, P. Martinez Ruiz del Arbol, F. Matorras, J. Piedra Gomez, C. Prieels,
T. Rodrigo, A. Ruiz-Jimeno, L. Russo45, L. Scodellaro, N. Trevisani, I. Vila,
J.M. Vizan Garcia University of Colombo, Colombo, Sri Lanka
K. Malagalage University of Ruhuna, Department of Physics, Matara, Sri Lanka
W.G.D. Dharmaratna, N. Wickramage CERN, European Organization for Nuclear
Research, Geneva, Switzerland
D. Abbaneo, B. Akgun, E. Auffray, G. Auzinger, J. Baechler, P. Baillon, A.H.
Ball, D. Barney, J. Bendavid, M. Bianco, A. Bocci, E. Bossini, C. Botta, E.
Brondolin, T. Camporesi, A. Caratelli, G. Cerminara, E. Chapon, G. Cucciati,
D. d’Enterria, A. Dabrowski, N. Daci, V. Daponte, A. David, O. Davignon, A. De
Roeck, N. Deelen, M. Deile, M. Dobson, M. Dünser, N. Dupont, A. Elliott-
Peisert, F. Fallavollita46, D. Fasanella, G. Franzoni, J. Fulcher, W. Funk, S.
Giani, D. Gigi, A. Gilbert, K. Gill, F. Glege, M. Gruchala, M. Guilbaud, D.
Gulhan, J. Hegeman, C. Heidegger, Y. Iiyama, V. Innocente, P. Janot, O.
Karacheban19, J. Kaspar, J. Kieseler, M. Krammer1, C. Lange, P. Lecoq, C.
Lourenço, L. Malgeri, M. Mannelli, A. Massironi, F. Meijers, J.A. Merlin, S.
Mersi, E. Meschi, F. Moortgat, M. Mulders, J. Ngadiuba, S. Nourbakhsh, S.
Orfanelli, L. Orsini, F. Pantaleo16, L. Pape, E. Perez, M. Peruzzi, A.
Petrilli, G. Petrucciani, A. Pfeiffer, M. Pierini, F.M. Pitters, D. Rabady, A.
Racz, M. Rovere, H. Sakulin, C. Schäfer, C. Schwick, M. Selvaggi, A. Sharma,
P. Silva, W. Snoeys, P. Sphicas47, J. Steggemann, V.R. Tavolaro, D. Treille,
A. Tsirou, A. Vartak, M. Verzetti, W.D. Zeuner Paul Scherrer Institut,
Villigen, Switzerland
L. Caminada48, K. Deiters, W. Erdmann, R. Horisberger, Q. Ingram, H.C.
Kaestli, D. Kotlinski, U. Langenegger, T. Rohe, S.A. Wiederkehr ETH Zurich -
Institute for Particle Physics and Astrophysics (IPA), Zurich, Switzerland
M. Backhaus, P. Berger, N. Chernyavskaya, G. Dissertori, M. Dittmar, M.
Donegà, C. Dorfer, T.A. Gómez Espinosa, C. Grab, D. Hits, T. Klijnsma, W.
Lustermann, R.A. Manzoni, M. Marionneau, M.T. Meinhard, F. Micheli, P.
Musella, F. Nessi-Tedaldi, F. Pauss, G. Perrin, L. Perrozzi, S. Pigazzini, M.
Reichmann, C. Reissel, T. Reitenspiess, D. Ruini, D.A. Sanz Becerra, M.
Schönenberger, L. Shchutska, M.L. Vesterbacka Olsson, R. Wallny, D.H. Zhu
Universität Zürich, Zurich, Switzerland
T.K. Aarrestad, C. Amsler49, D. Brzhechko, M.F. Canelli, A. De Cosa, R. Del
Burgo, S. Donato, B. Kilminster, S. Leontsinis, V.M. Mikuni, I. Neutelings, G.
Rauco, P. Robmann, D. Salerno, K. Schweiger, C. Seitz, Y. Takahashi, S. Wertz,
A. Zucchetta National Central University, Chung-Li, Taiwan
T.H. Doan, C.M. Kuo, W. Lin, A. Roy, S.S. Yu National Taiwan University (NTU),
Taipei, Taiwan
P. Chang, Y. Chao, K.F. Chen, P.H. Chen, W.-S. Hou, Y.y. Li, R.-S. Lu, E.
Paganis, A. Psallidas, A. Steen Chulalongkorn University, Faculty of Science,
Department of Physics, Bangkok, Thailand
B. Asavapibhop, C. Asawatangtrakuldee, N. Srimanobhas, N. Suwonjandee Çukurova
University, Physics Department, Science and Art Faculty, Adana, Turkey
A. Bat, F. Boran, S. Cerci50, S. Damarseckin51, Z.S. Demiroglu, F. Dolek, C.
Dozen, I. Dumanoglu, G. Gokbulut, EmineGurpinar Guler52, Y. Guler, I. Hos53,
C. Isik, E.E. Kangal54, O. Kara, A. Kayis Topaksu, U. Kiminsu, M. Oglakci, G.
Onengut, K. Ozdemir55, S. Ozturk56, A.E. Simsek, D. Sunar Cerci50, U.G. Tok,
S. Turkcapar, I.S. Zorbakir, C. Zorbilmez Middle East Technical University,
Physics Department, Ankara, Turkey
B. Isildak57, G. Karapinar58, M. Yalvac Bogazici University, Istanbul, Turkey
I.O. Atakisi, E. Gülmez, M. Kaya59, O. Kaya60, B. Kaynak, Ö. Özçelik, S.
Tekten, E.A. Yetkin61 Istanbul Technical University, Istanbul, Turkey
A. Cakir, K. Cankocak, Y. Komurcu, S. Sen62 Istanbul University, Istanbul,
Turkey
S. Ozkorucuklu Institute for Scintillation Materials of National Academy of
Science of Ukraine, Kharkov, Ukraine
B. Grynyov National Scientific Center, Kharkov Institute of Physics and
Technology, Kharkov, Ukraine
L. Levchuk University of Bristol, Bristol, United Kingdom
F. Ball, E. Bhal, S. Bologna, J.J. Brooke, D. Burns, E. Clement, D. Cussans,
H. Flacher, J. Goldstein, G.P. Heath, H.F. Heath, L. Kreczko, S. Paramesvaran,
B. Penning, T. Sakuma, S. Seif El Nasr-Storey, D. Smith, V.J. Smith, J.
Taylor, A. Titterton Rutherford Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev63, C. Brew, R.M. Brown, D. Cieri, D.J.A. Cockerill, J.A.
Coughlan, K. Harder, S. Harper, J. Linacre, K. Manolopoulos, D.M. Newbold, E.
Olaiya, D. Petyt, T. Reis, T. Schuh, C.H. Shepherd-Themistocleous, A. Thea,
I.R. Tomalin, T. Williams, W.J. Womersley Imperial College, London, United
Kingdom
R. Bainbridge, P. Bloch, J. Borg, S. Breeze, O. Buchmuller, A. Bundock,
GurpreetSingh CHAHAL64, D. Colling, P. Dauncey, G. Davies, M. Della Negra, R.
Di Maria, P. Everaerts, G. Hall, G. Iles, T. James, M. Komm, C. Laner, L.
Lyons, A.-M. Magnan, S. Malik, A. Martelli, V. Milosevic, J. Nash65, V.
Palladino, M. Pesaresi, D.M. Raymond, A. Richards, A. Rose, E. Scott, C. Seez,
A. Shtipliyski, M. Stoye, T. Strebler, S. Summers, A. Tapper, K. Uchida, T.
Virdee16, N. Wardle, D. Winterbottom, J. Wright, A.G. Zecchinelli, S.C. Zenz
Brunel University, Uxbridge, United Kingdom
J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, C.K. Mackay, A. Morton, I.D. Reid,
L. Teodorescu, S. Zahid Baylor University, Waco, USA
K. Call, J. Dittmann, K. Hatakeyama, C. Madrid, B. McMaster, N. Pastika, C.
Smith Catholic University of America, Washington, DC, USA
R. Bartek, A. Dominguez, R. Uniyal The University of Alabama, Tuscaloosa, USA
A. Buccilli, S.I. Cooper, C. Henderson, P. Rumerio, C. West Boston University,
Boston, USA
D. Arcaro, T. Bose, Z. Demiragli, D. Gastler, S. Girgis, D. Pinna, C.
Richardson, J. Rohlf, D. Sperka, I. Suarez, L. Sulak, D. Zou Brown University,
Providence, USA
G. Benelli, B. Burkle, X. Coubez, D. Cutts, Y.t. Duh, M. Hadley, J. Hakala, U.
Heintz, J.M. Hogan66, K.H.M. Kwok, E. Laird, G. Landsberg, J. Lee, Z. Mao, M.
Narain, S. Sagir67, R. Syarif, E. Usai, D. Yu University of California, Davis,
Davis, USA
R. Band, C. Brainerd, R. Breedon, M. Calderon De La Barca Sanchez, M. Chertok,
J. Conway, R. Conway, P.T. Cox, R. Erbacher, C. Flores, G. Funk, F. Jensen, W.
Ko, O. Kukral, R. Lander, M. Mulhearn, D. Pellett, J. Pilot, M. Shi, D. Stolp,
D. Taylor, K. Tos, M. Tripathi, Z. Wang, F. Zhang University of California,
Los Angeles, USA
M. Bachtis, C. Bravo, R. Cousins, A. Dasgupta, A. Florent, J. Hauser, M.
Ignatenko, N. Mccoll, W.A. Nash, S. Regnard, D. Saltzberg, C. Schnaible, B.
Stone, V. Valuev University of California, Riverside, Riverside, USA
K. Burt, R. Clare, J.W. Gary, S.M.A. Ghiasi Shirazi, G. Hanson, G.
Karapostoli, E. Kennedy, O.R. Long, M. Olmedo Negrete, M.I. Paneva, W. Si, L.
Wang, H. Wei, S. Wimpenny, B.R. Yates, Y. Zhang University of California, San
Diego, La Jolla, USA
J.G. Branson, P. Chang, S. Cittolin, M. Derdzinski, R. Gerosa, D. Gilbert, B.
Hashemi, D. Klein, V. Krutelyov, J. Letts, M. Masciovecchio, S. May, S. Padhi,
M. Pieri, V. Sharma, M. Tadel, F. Würthwein, A. Yagil, G. Zevi Della Porta
University of California, Santa Barbara - Department of Physics, Santa
Barbara, USA
N. Amin, R. Bhandari, C. Campagnari, M. Citron, V. Dutta, M. Franco Sevilla,
L. Gouskos, J. Incandela, B. Marsh, H. Mei, A. Ovcharova, H. Qu, J. Richman,
U. Sarica, D. Stuart, S. Wang, J. Yoo California Institute of Technology,
Pasadena, USA
D. Anderson, A. Bornheim, O. Cerri, I. Dutta, J.M. Lawhorn, N. Lu, J. Mao,
H.B. Newman, T.Q. Nguyen, J. Pata, M. Spiropulu, J.R. Vlimant, S. Xie, Z.
Zhang, R.Y. Zhu Carnegie Mellon University, Pittsburgh, USA
M.B. Andrews, T. Ferguson, T. Mudholkar, M. Paulini, M. Sun, I. Vorobiev, M.
Weinberg University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, A. Johnson, E. MacDonald, T. Mulholland, R. Patel, A.
Perloff, K. Stenson, K.A. Ulmer, S.R. Wagner Cornell University, Ithaca, USA
J. Alexander, J. Chaves, Y. Cheng, J. Chu, A. Datta, A. Frankenthal, K.
Mcdermott, N. Mirman, J.R. Patterson, D. Quach, A. Rinkevicius68, A. Ryd, S.M.
Tan, Z. Tao, J. Thom, P. Wittich, M. Zientek Fermi National Accelerator
Laboratory, Batavia, USA
S. Abdullin, M. Albrow, M. Alyari, G. Apollinari, A. Apresyan, A. Apyan, S.
Banerjee, L.A.T. Bauerdick, A. Beretvas, J. Berryhill, P.C. Bhat, K. Burkett,
J.N. Butler, A. Canepa, G.B. Cerati, H.W.K. Cheung, F. Chlebana, M. Cremonesi,
J. Duarte, V.D. Elvira, J. Freeman, Z. Gecse, E. Gottschalk, L. Gray, D.
Green, S. Grünendahl, O. Gutsche, AllisonReinsvold Hall, J. Hanlon, R.M.
Harris, S. Hasegawa, R. Heller, J. Hirschauer, B. Jayatilaka, S. Jindariani,
M. Johnson, U. Joshi, B. Klima, M.J. Kortelainen, B. Kreis, S. Lammel, J.
Lewis, D. Lincoln, R. Lipton, M. Liu, T. Liu, J. Lykken, K. Maeshima, J.M.
Marraffino, D. Mason, P. McBride, P. Merkel, S. Mrenna, S. Nahn, V. O’Dell, V.
Papadimitriou, K. Pedro, C. Pena, G. Rakness, F. Ravera, L. Ristori, B.
Schneider, E. Sexton-Kennedy, N. Smith, A. Soha, W.J. Spalding, L. Spiegel, S.
Stoynev, J. Strait, N. Strobbe, L. Taylor, S. Tkaczyk, N.V. Tran, L. Uplegger,
E.W. Vaandering, C. Vernieri, M. Verzocchi, R. Vidal, M. Wang, H.A. Weber
University of Florida, Gainesville, USA
D. Acosta, P. Avery, P. Bortignon, D. Bourilkov, A. Brinkerhoff, L. Cadamuro,
A. Carnes, V. Cherepanov, D. Curry, F. Errico, R.D. Field, S.V. Gleyzer, B.M.
Joshi, M. Kim, J. Konigsberg, A. Korytov, K.H. Lo, P. Ma, K. Matchev, N.
Menendez, G. Mitselmakher, D. Rosenzweig, K. Shi, J. Wang, S. Wang, X. Zuo
Florida International University, Miami, USA
Y.R. Joshi Florida State University, Tallahassee, USA
T. Adams, A. Askew, S. Hagopian, V. Hagopian, K.F. Johnson, R. Khurana, T.
Kolberg, G. Martinez, T. Perry, H. Prosper, C. Schiber, R. Yohay, J. Zhang
Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, V. Bhopatkar, M. Hohlmann, D. Noonan, M. Rahmani, M. Saunders,
F. Yumiceva University of Illinois at Chicago (UIC), Chicago, USA
M.R. Adams, L. Apanasevich, D. Berry, R.R. Betts, R. Cavanaugh, X. Chen, S.
Dittmer, O. Evdokimov, C.E. Gerber, D.A. Hangal, D.J. Hofman, K. Jung, C.
Mills, T. Roy, M.B. Tonjes, N. Varelas, H. Wang, X. Wang, Z. Wu The University
of Iowa, Iowa City, USA
M. Alhusseini, B. Bilki52, W. Clarida, K. Dilsiz69, S. Durgut, R.P.
Gandrajula, M. Haytmyradov, V. Khristenko, O.K. Köseyan, J.-P. Merlo, A.
Mestvirishvili70, A. Moeller, J. Nachtman, H. Ogul71, Y. Onel, F. Ozok72, A.
Penzo, C. Snyder, E. Tiras, J. Wetzel Johns Hopkins University, Baltimore, USA
B. Blumenfeld, A. Cocoros, N. Eminizer, D. Fehling, L. Feng, A.V. Gritsan,
W.T. Hung, P. Maksimovic, J. Roskes, M. Swartz, M. Xiao The University of
Kansas, Lawrence, USA
C. Baldenegro Barrera, P. Baringer, A. Bean, S. Boren, J. Bowen, A. Bylinkin,
T. Isidori, S. Khalil, J. King, G. Krintiras, A. Kropivnitskaya, C. Lindsey,
D. Majumder, W. Mcbrayer, N. Minafra, M. Murray, C. Rogan, C. Royon, S.
Sanders, E. Schmitz, J.D. Tapia Takaki, Q. Wang, J. Williams, G. Wilson Kansas
State University, Manhattan, USA
S. Duric, A. Ivanov, K. Kaadze, D. Kim, Y. Maravin, D.R. Mendis, T. Mitchell,
A. Modak, A. Mohammadi Lawrence Livermore National Laboratory, Livermore, USA
F. Rebassoo, D. Wright University of Maryland, College Park, USA
A. Baden, O. Baron, A. Belloni, S.C. Eno, Y. Feng, N.J. Hadley, S. Jabeen,
G.Y. Jeng, R.G. Kellogg, J. Kunkle, A.C. Mignerey, S. Nabili, F. Ricci-Tam, M.
Seidel, Y.H. Shin, A. Skuja, S.C. Tonwar, K. Wong Massachusetts Institute of
Technology, Cambridge, USA
D. Abercrombie, B. Allen, A. Baty, R. Bi, S. Brandt, W. Busza, I.A. Cali, M.
D’Alfonso, G. Gomez Ceballos, M. Goncharov, P. Harris, D. Hsu, M. Hu, M.
Klute, D. Kovalskyi, Y.-J. Lee, P.D. Luckey, B. Maier, A.C. Marini, C. Mcginn,
C. Mironov, S. Narayanan, X. Niu, C. Paus, D. Rankin, C. Roland, G. Roland, Z.
Shi, G.S.F. Stephans, K. Sumorok, K. Tatar, D. Velicanu, J. Wang, T.W. Wang,
B. Wyslouch University of Minnesota, Minneapolis, USA
A.C. Benvenuti${}^{\textrm{\textdagger}}$, R.M. Chatterjee, A. Evans, S. Guts,
P. Hansen, J. Hiltbrand, Sh. Jain, S. Kalafut, Y. Kubota, Z. Lesko, J. Mans,
R. Rusack, M.A. Wadud University of Mississippi, Oxford, USA
J.G. Acosta, S. Oliveros University of Nebraska-Lincoln, Lincoln, USA
K. Bloom, D.R. Claes, C. Fangmeier, L. Finco, F. Golf, R. Gonzalez Suarez, R.
Kamalieddin, I. Kravchenko, J.E. Siado, G.R. Snow, B. Stieger State University
of New York at Buffalo, Buffalo, USA
G. Agarwal, C. Harrington, I. Iashvili, A. Kharchilava, C. Mclean, D. Nguyen,
A. Parker, J. Pekkanen, S. Rappoccio, B. Roozbahani Northeastern University,
Boston, USA
G. Alverson, E. Barberis, C. Freer, Y. Haddad, A. Hortiangtham, G. Madigan,
D.M. Morse, T. Orimoto, L. Skinnari, A. Tishelman-Charny, T. Wamorkar, B.
Wang, A. Wisecarver, D. Wood Northwestern University, Evanston, USA
S. Bhattacharya, J. Bueghly, T. Gunter, K.A. Hahn, N. Odell, M.H. Schmitt, K.
Sung, M. Trovato, M. Velasco University of Notre Dame, Notre Dame, USA
R. Bucci, N. Dev, R. Goldouzian, M. Hildreth, K. Hurtado Anampa, C. Jessop,
D.J. Karmgard, K. Lannon, W. Li, N. Loukas, N. Marinelli, I. Mcalister, F.
Meng, C. Mueller, Y. Musienko36, M. Planer, R. Ruchti, P. Siddireddy, G.
Smith, S. Taroni, M. Wayne, A. Wightman, M. Wolf, A. Woodard The Ohio State
University, Columbus, USA
J. Alimena, B. Bylsma, L.S. Durkin, S. Flowers, B. Francis, C. Hill, W. Ji, A.
Lefeld, T.Y. Ling, B.L. Winer Princeton University, Princeton, USA
S. Cooperstein, G. Dezoort, P. Elmer, J. Hardenbrook, N. Haubrich, S.
Higginbotham, A. Kalogeropoulos, S. Kwan, D. Lange, M.T. Lucchini, J. Luo, D.
Marlow, K. Mei, I. Ojalvo, J. Olsen, C. Palmer, P. Piroué, J. Salfeld-Nebgen,
D. Stickland, C. Tully, Z. Wang University of Puerto Rico, Mayaguez, USA
S. Malik, S. Norberg Purdue University, West Lafayette, USA
A. Barker, V.E. Barnes, S. Das, L. Gutay, M. Jones, A.W. Jung, A. Khatiwada,
B. Mahakud, D.H. Miller, G. Negro, N. Neumeister, C.C. Peng, S. Piperov, H.
Qiu, J.F. Schulte, J. Sun, F. Wang, R. Xiao, W. Xie Purdue University
Northwest, Hammond, USA
T. Cheng, J. Dolen, N. Parashar Rice University, Houston, USA
K.M. Ecklund, S. Freed, F.J.M. Geurts, M. Kilpatrick, Arun Kumar, W. Li, B.P.
Padley, R. Redjimi, J. Roberts, J. Rorie, W. Shi, A.G. Stahl Leiton, Z. Tu, A.
Zhang University of Rochester, Rochester, USA
A. Bodek, P. de Barbaro, R. Demina, J.L. Dulemba, C. Fallon, T. Ferbel, M.
Galanti, A. Garcia-Bellido, J. Han, O. Hindrichs, A. Khukhunaishvili, E.
Ranken, P. Tan, R. Taus Rutgers, The State University of New Jersey,
Piscataway, USA
B. Chiarito, J.P. Chou, A. Gandrakota, Y. Gershtein, E. Halkiadakis, A. Hart,
M. Heindl, E. Hughes, S. Kaplan, S. Kyriacou, I. Laflotte, A. Lath, R.
Montalvo, K. Nash, M. Osherson, H. Saka, S. Salur, S. Schnetzer, D. Sheffield,
S. Somalwar, R. Stone, S. Thomas, P. Thomassen University of Tennessee,
Knoxville, USA
H. Acharya, A.G. Delannoy, J. Heideman, G. Riley, S. Spanier Texas A&M
University, College Station, USA
O. Bouhali73, A. Celik, M. Dalchenko, M. De Mattia, A. Delgado, S. Dildick, R.
Eusebi, J. Gilmore, T. Huang, T. Kamon74, S. Luo, D. Marley, R. Mueller, D.
Overton, L. Perniè, D. Rathjens, A. Safonov Texas Tech University, Lubbock,
USA
N. Akchurin, J. Damgov, F. De Guio, S. Kunori, K. Lamichhane, S.W. Lee, T.
Mengke, S. Muthumuni, T. Peltola, S. Undleeb, I. Volobouev, Z. Wang, A.
Whitbeck Vanderbilt University, Nashville, USA
S. Greene, A. Gurrola, R. Janjam, W. Johns, C. Maguire, A. Melo, H. Ni, K.
Padeken, F. Romeo, P. Sheldon, S. Tuo, J. Velkovska, M. Verweij University of
Virginia, Charlottesville, USA
M.W. Arenton, P. Barria, B. Cox, G. Cummings, R. Hirosky, M. Joyce, A.
Ledovskoy, C. Neu, B. Tannenwald, Y. Wang, E. Wolfe, F. Xia Wayne State
University, Detroit, USA
R. Harr, P.E. Karchin, N. Poudyal, J. Sturdy, P. Thapa, S. Zaleski University
of Wisconsin - Madison, Madison, WI, USA
J. Buchanan, C. Caillol, D. Carlsmith, S. Dasu, I. De Bruyn, L. Dodd, F.
Fiori, C. Galloni, B. Gomber75, M. Herndon, A. Hervé, U. Hussain, P. Klabbers,
A. Lanaro, A. Loeliger, K. Long, R. Loveless, J. Madhusudanan Sreekala, T.
Ruggles, A. Savin, V. Sharma, W.H. Smith, D. Teague, S. Trembath-reichert, N.
Woods †: Deceased
1: Also at Vienna University of Technology, Vienna, Austria
2: Also at IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
3: Also at Universidade Estadual de Campinas, Campinas, Brazil
4: Also at Federal University of Rio Grande do Sul, Porto Alegre, Brazil
5: Also at UFMS, Nova Andradina, Brazil
6: Also at Universidade Federal de Pelotas, Pelotas, Brazil
7: Also at Université Libre de Bruxelles, Bruxelles, Belgium
8: Also at University of Chinese Academy of Sciences, Beijing, China
9: Also at Institute for Theoretical and Experimental Physics named by A.I.
Alikhanov of NRC ‘Kurchatov Institute’, Moscow, Russia
10: Also at Joint Institute for Nuclear Research, Dubna, Russia
11: Also at Ain Shams University, Cairo, Egypt
12: Also at Zewail City of Science and Technology, Zewail, Egypt
13: Also at Purdue University, West Lafayette, USA
14: Also at Université de Haute Alsace, Mulhouse, France
15: Also at Erzincan Binali Yildirim University, Erzincan, Turkey
16: Also at CERN, European Organization for Nuclear Research, Geneva,
Switzerland
17: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
18: Also at University of Hamburg, Hamburg, Germany
19: Also at Brandenburg University of Technology, Cottbus, Germany
20: Also at Institute of Physics, University of Debrecen, Debrecen, Hungary,
Debrecen, Hungary
21: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary
22: Also at MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary, Budapest, Hungary
23: Also at IIT Bhubaneswar, Bhubaneswar, India, Bhubaneswar, India
24: Also at Institute of Physics, Bhubaneswar, India
25: Also at Shoolini University, Solan, India
26: Also at University of Visva-Bharati, Santiniketan, India
27: Also at Isfahan University of Technology, Isfahan, Iran
28: Now at INFN Sezione di Bari a, Università di Bari b, Politecnico di Bari
c, Bari, Italy
29: Also at Italian National Agency for New Technologies, Energy and
Sustainable Economic Development, Bologna, Italy
30: Also at Centro Siciliano di Fisica Nucleare e di Struttura Della Materia,
Catania, Italy
31: Also at Scuola Normale e Sezione dell’INFN, Pisa, Italy
32: Also at Riga Technical University, Riga, Latvia, Riga, Latvia
33: Also at Malaysian Nuclear Agency, MOSTI, Kajang, Malaysia
34: Also at Consejo Nacional de Ciencia y Tecnología, Mexico City, Mexico
35: Also at Warsaw University of Technology, Institute of Electronic Systems,
Warsaw, Poland
36: Also at Institute for Nuclear Research, Moscow, Russia
37: Now at National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
38: Also at St. Petersburg State Polytechnical University, St. Petersburg,
Russia
39: Also at University of Florida, Gainesville, USA
40: Also at Imperial College, London, United Kingdom
41: Also at P.N. Lebedev Physical Institute, Moscow, Russia
42: Also at California Institute of Technology, Pasadena, USA
43: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia
44: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia
45: Also at Università degli Studi di Siena, Siena, Italy
46: Also at INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy,
Pavia, Italy
47: Also at National and Kapodistrian University of Athens, Athens, Greece
48: Also at Universität Zürich, Zurich, Switzerland
49: Also at Stefan Meyer Institute for Subatomic Physics, Vienna, Austria,
Vienna, Austria
50: Also at Adiyaman University, Adiyaman, Turkey
51: Also at Şırnak University, Sirnak, Turkey
52: Also at Beykent University, Istanbul, Turkey, Istanbul, Turkey
53: Also at Istanbul Aydin University, Application and Research Center for
Advanced Studies (App. & Res. Cent. for Advanced Studies), Istanbul, Turkey
54: Also at Mersin University, Mersin, Turkey
55: Also at Piri Reis University, Istanbul, Turkey
56: Also at Gaziosmanpasa University, Tokat, Turkey
57: Also at Ozyegin University, Istanbul, Turkey
58: Also at Izmir Institute of Technology, Izmir, Turkey
59: Also at Marmara University, Istanbul, Turkey
60: Also at Kafkas University, Kars, Turkey
61: Also at Istanbul Bilgi University, Istanbul, Turkey
62: Also at Hacettepe University, Ankara, Turkey
63: Also at School of Physics and Astronomy, University of Southampton,
Southampton, United Kingdom
64: Also at IPPP Durham University, Durham, United Kingdom
65: Also at Monash University, Faculty of Science, Clayton, Australia
66: Also at Bethel University, St. Paul, Minneapolis, USA, St. Paul, USA
67: Also at Karamanoğlu Mehmetbey University, Karaman, Turkey
68: Also at Vilnius University, Vilnius, Lithuania
69: Also at Bingol University, Bingol, Turkey
70: Also at Georgian Technical University, Tbilisi, Georgia
71: Also at Sinop University, Sinop, Turkey
72: Also at Mimar Sinan University, Istanbul, Istanbul, Turkey
73: Also at Texas A&M University at Qatar, Doha, Qatar
74: Also at Kyungpook National University, Daegu, Korea, Daegu, Korea
75: Also at University of Hyderabad, Hyderabad, India