B2G-17-017

$Revision:491530$
$HeadURL:svn+ssh://svn.cern.ch/reps/tdr2/papers/B2G-17-017/trunk/B2G-17-017.tex$
$Id:B2G-17-017.tex4915302019-03-1116:05:54Zdrberry$

B2G-17-017

# Search for resonant production in proton-proton collisions at
$\sqrt{s}=13\TeV$

###### Abstract

A search for a heavy resonance decaying into a top quark and antiquark () pair
is performed using proton-proton collisions at $\sqrt{s}=13\TeV$. The search
uses the data set collected with the CMS detector in 2016, which corresponds
to an integrated luminosity of 35.9. The analysis considers three exclusive
final states and uses reconstruction techniques that are optimized for top
quarks with high Lorentz boosts, which requires the use of nonisolated leptons
and jet substructure techniques. No significant excess of events relative to
the expected yield from standard model processes is observed. Upper limits on
the production cross section of heavy resonances decaying to a pair are
calculated. Limits are derived for a leptophobic topcolor resonance with
widths of 1, 10, and 30%, relative to the mass of the resonance, and exclude
masses up to 3.80, 5.25, and 6.65, respectively. Kaluza–Klein excitations of
the gluon in the Randall–Sundrum model are excluded up to 4.55. To date, these
are the most stringent limits on resonances.

##  0.1 Introduction

The top quark () is the most massive known fundamental particle [1, 2] in the
standard model. It has a Yukawa coupling to the Higgs field that is near
unity. It is also closely connected to the hierarchy problem, where the
largest corrections to the Higgs mass arise from top quark loops. Furthermore,
studies of the top quark may provide insight into the mechanism of electroweak
(EW) symmetry breaking.

Many theories beyond the standard model (SM) predict heavy resonances at the
TeV scale, which would decay to top quark and antiquark () pairs. These
resonances can present themselves as peaks on top of the falling invariant
mass spectrum or as a distortion of the spectrum if the resonance has a large
width and a mass above the center-of-mass energy of the colliding partons.
Resonances decaying to pairs can be found in models that contain TeV scale
color singlet bosons [3, 4, 5], a pseudoscalar Higgs boson that may couple
strongly to pairs [6], axigluons [7, 8, 9], or colorons [10, 11, 12, 13], and
especially models that contain a leptophobic topcolor [14]. Additionally,
extensions of the Randall–Sundrum model [15, 16] with extra dimensions predict
Kaluza–Klein (KK) excitations of the gluons $\Pg_{\text{KK}}$ [17] or
gravitons $\text{G}_{\text{KK}}$ [18], which can have large branching
fractions to pairs. This analysis searches for spin-1 resonances that do not
interfere with SM production. Previous searches at the Fermilab Tevatron have
excluded a leptophobic boson up to 900 [19, 20, 21, 22, 23, 24] at 95%
confidence level (). Experiments at the CERN LHC have excluded various and
$\Pg_{\text{KK}}$ models at 95% in the 1–4mass range [25, 26, 27, 28, 29, 30,
31, 32]. The results presented here represent a significant improvement on the
previous searches for resonances.

This paper presents a model-independent search for resonances. Since no excess
is seen, limits are calculated on several spin-1 resonance models of varying
widths. The system, and all its daughter particles, decay as described by the
SM. The top quark predominately decays to a boson and a bottom quark (). Each
of the two bosons in the event can decay to either a lepton and its
corresponding neutrino or to hadrons. The analysis considers three subanalyses
based on the decay modes of the two bosons: dilepton, single-lepton, and fully
hadronic decay modes of the system. In the fully hadronic channel, both bosons
decay to hadrons. In the single-lepton channel, one boson decays to an
electron () or muon () and its neutrino ($\nu$) counterpart, while the other
boson decays to hadrons. In the dilepton channel, both bosons decay to an or
and a $\nu$. The leptonic selections are not optimized to identify electrons
or muons originating from leptonically decaying tau leptons; however, such
particles are not excluded by the event selections. The search is based on
$\sqrt{s}=13\TeV$ proton-proton () collision data collected in 2016 by the CMS
experiment at the LHC, corresponding to an integrated luminosity of 35.9.

The dilepton final state consists of two leptons (, , or ), two jets
originating from bottom quarks (jets) with high transverse momentum (), and
missing transverse momentum (). The large mass of the resonance causes the
resulting top quarks to have a significant Lorentz boost, which leads to a
collimated system consisting of a lepton and a jet. To account for the overlap
between the lepton and the jet, special reconstruction and selection criteria
are used to increase lepton selection efficiency and reduce the SM background.
The dominant irreducible SM background arises from nonresonant production.
Smaller contributions are due to a boson produced in association with jets
($\PZ$+jets), single top quark, and diboson processes. Events that have a
large separation between the lepton and jet are allocated to control regions
(CR), which are used to validate the modeling of the SM backgrounds.

The single-lepton final state consists of one lepton ( or ), at least two
high-jets, and . In this channel also, the final state particles from the
decay of the pairs have a large Lorentz boost because of the mass of the
resonance. Leptons from the decay of the boson are found in near proximity to
the jet from the top quark decay. The same lepton reconstruction and selection
criteria used in the dilepton channel are used in the single-lepton channel.
In addition to those techniques, a special triggering technique is used to
select events with a single nonisolated lepton and an additional jet. A
$\cPqt$ tagging algorithm is used to identify top quarks where the daughter
boson decays hadronically ($\PQt\to\PW\ \PQb\to\cPq\cPaq^{\prime}\PQb$).
Events with a jet that passes the $\cPqt$ tagging criteria are classified into
a category with higher sensitivity. The largest irreducible background is the
continuum production, while the largest reducible background is from bosons
produced in association with jets ($\PW$+jets). The latter background is
separated from the signal using a multivariate analysis technique.

The fully hadronic channel contains events with a dijet topology, where both
large radius jets are required to pass t tagging criteria that select Lorentz-
boosted hadronically decaying top quarks. Because of the dijet topology of the
search region, the largest reducible background arises from dijet events
produced from quantum chromodynamic (QCD) interactions between the colliding
protons. This background, referred to as QCD multijet production, can be
reduced considerably by requiring one of the subjets in each of the two large
radius jets, which are selected by the $\cPqt$ tagging algorithm, to be
consistent with the fragmentation of a bottom quark [33]. A subjet is defined
as a smaller radius jet reconstructed within a larger radius jet. The use of
subjet $\cPqb$ tagging for categorization nearly eliminates the QCD multijet
background leaving only the continuum in the highest sensitivity category.

Except for the QCD multijet background in the fully hadronic channel, the
shapes of all SM backgrounds are estimated from simulation. The total
normalization of each simulated sample is obtained from a simultaneous binned
maximum likelihood fit to the reconstructed invariant mass ($m_{\ttbar}$)
distribution for the single-lepton and fully hadronic analyses and
$S_{\text{T}}$ for the dilepton analysis, where $S_{\text{T}}$ is defined as

$S_{\text{T}}=\sum_{i=1}^{N_{\text{jet}}}p_{\text{T}_{i}}^{\text{jet}}+\sum_{i=1}^{2}p_{\text{T}_{i}}^{\ell}+\ptmiss.$
(1)

The variable $S_{\text{T}}$ is used because it has a greater sensitivity to
signal than $m_{\ttbar}$, in the dilepton final state. A limit on the
production cross section of heavy resonances is extracted by performing a
template-based statistical evaluation of the $m_{\ttbar}$ (single-lepton and
fully hadronic) and $S_{\text{T}}$ (dilepton) distributions simultaneously in
all of the channels.

This paper is organized the following way. Section 0.2 provides a description
of the CMS detector. The reconstruction and identification of electrons,
muons, and jets are described in Section 0.3. Section 0.3 also gives an
overview of the $\cPqt$ tagging algorithms used. The data sets and triggering
techniques are described in Section 0.4. The simulated Monte Carlo (MC)
samples used in the analysis are discussed in Section 0.5. Section 0.6
describes the event selection for the three different channels. Section 0.7
describes the evaluation of the SM background processes. Systematic
uncertainties affecting the signal and background shapes and normalization are
discussed in Section 0.8. The statistical analysis and the results are given
in Sections 0.9 and 0.10, respectively, and a summary is presented in Section
0.11.

##  0.2 The CMS detector

The central feature of the CMS detector is a superconducting solenoid of 6m
internal diameter, providing a magnetic field of 3.8T. Within the solenoid
volume are a silicon pixel and strip tracker, a lead tungstate crystal
electromagnetic calorimeter (ECAL), and a brass and scintillator hadron
calorimeter (HCAL), each composed of a barrel and two endcap sections. In
addition to the barrel and endcap detectors, CMS has extensive forward
calorimetry. Muons are detected by four layers of gas-ionization detectors
embedded in the steel flux-return yoke of the magnet. The inner tracker
measures charged-particle trajectories within the pseudorapidity range
$\abs{\eta}<2.5$, and provides an impact parameter resolution of approximately
15. A two-stage trigger system [34] selects collision events of interest for
use in physics analyses. 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. [35].

##  0.3 Event reconstruction

The CMS event reconstruction uses a particle-flow (PF) technique that
aggregates input from all subdetectors for event reconstruction [36]. Typical
examples of PF inputs are charged-particle tracks from the tracking system and
energy deposits from the ECAL and HCAL. The PF approach enables the global
event description to take advantage of the excellent granularity of the CMS
detector. Clusters of tracks and energy deposits are iteratively classified as
muons, electrons, photons, charged hadrons, and neutral hadrons. Vertices are
reconstructed from tracks using a deterministic annealing filter algorithm
[37]. The reconstructed vertex with the largest value of summed physics-object
$\pt^{2}$ is taken as the primary interaction vertex (PV). For the PV
reconstruction, the physics objects are jets, clustered with the jet finding
algorithm [38, 39] using only tracking information, with the tracks assigned
to the PV as inputs. The reconstructed leptons and photons in the event are
included as inputs to the jet clustering algorithm.

The is defined as the projection onto the plane perpendicular to the beam axis
of the negative vector sum of the momenta of all reconstructed PF candidates
in an event [40]. Its magnitude is referred to as . Corrections to the jet
energy scale and jet energy resolution are propagated to the measurement of .

Muons are reconstructed in the pseudorapidity range $\abs{\eta}<2.4$ using the
information from the tracker and muon chambers [37]. Tracks associated with
muon candidates must be consistent with a muon originating from the PV, and
tracks must satisfy fit quality requirements.

Electrons are detected and measured in the pseudorapidity range
$\abs{\eta}<2.5$, by combining tracking information with energy deposits in
the ECAL [41, 42]. Candidate electrons are required to originate from the PV.
The track quality, electromagnetic shower shape, displacement between the
track and electromagnetic shower, and ratio of energy between the HCAL and
ECAL are used to identify electrons. Reconstructed electrons that originate
from photon conversions are rejected.

No isolation requirements are placed on the leptons at the trigger or analysis
level. This is because the lepton, bottom quark, and neutrino from the top
quark decay are highly collimated, and the lepton is not well separated from
the products of fragmentation of the bottom quark. Additionally, jets that
contain an electron are reclustered and corrected with the track and
calorimeter deposit of the electron removed. Kinematic restrictions are placed
on the electron and on the overall event to reduce the contribution from
electrons not originating from t decays. Details on these requirements can be
found in Section 0.6.

The PF candidates are clustered into jets using the software package [39].
Charged hadrons that are not associated with the PV in the event are excluded
from the jet clustering procedure via charged hadron subtraction (CHS) [36].
All jets are required to have $\abs{\eta}<2.4$. Jets are clustered using the
anti-jet clustering algorithm [38] with a distance parameter of 0.4 (AK4
jets). If a lepton is found with ${\Delta R<0.4}$ of an AK4 jet, its four-
momentum is subtracted from that of the jet. The single-lepton and fully
hadronic analyses also use anti-clustered jets with a distance parameter of
0.8 (AK8 jets). These larger-radius jets are used to tag the hadronic decay of
top quarks. A high-mass resonance decay creates daughter particles with
significant Lorentz boost. The three jets from the top quark decay merge into
a single-larger AK8 jet. Jets in all three channels are contaminated with
neutral particles that are generated from additional collisions within the
same or a neighboring bunch crossing (pileup). The extra energy in each jet is
corrected based on the average expectation of the pileup within the jet
footprint [43]. The expected energy offset due to pileup is modeled as a
function of the number of primary vertices in the event [40]. Jets that are
produced from the decay of charm and bottom quarks are identified using the
combined secondary vertex algorithm (CSV) [44]. Loose, medium, and tight
operating points are used in this analysis. They have a probability of 10, 1,
and 0.1%, respectively, of misidentifying a light-parton jet as heavy flavor,
where the light-flavor jet has $\pt>30\GeV$ and is determined from a simulated
multijet sample with a center-of-mass energy between 80 and 120 [33]. They
correspond to a $\cPqb$ tagging efficiency of 81, 63, and 41%, respectively,
for jets ($\pt>20\GeV$) in simulated events. All jets are required to pass a
minimal set of criteria to separate them from calorimeter noise and other
sources of jets that do not originate from the PV [45]. Events are also
required to pass a set of selections that remove spurious that is generated
from calorimeter noise [46].

The $\cPqt$ tagging algorithm [47, 48], which is based on the algorithm
described in Ref. [49], is applied to AK8 jets that use pileup per particle
identification (PUPPI) corrections [50], referred to as PUPPI jets, in order
to separate hadronically decaying top quarks from light quark or gluon jets.
While CHS only removes charged particles originating from pileup, PUPPI
corrects for both charged and neutral pileup particles. PUPPI jets, as opposed
to CHS jets, are therefore used for $\cPqt$ tagging because of their better
performance as a function of pileup. The CMS $\cPqt$ tagging algorithm only
considers jets with $\pt>400\GeV$, as lower-momentum top quarks frequently
decay into resolved jets. The algorithm iteratively reverses the jet
clustering procedure in order to remove soft radiation. First, it reclusters
the AK8 PUPPI jet with the Cambridge-Aachen jet clustering algorithm [51]. It
then separates the jet ($j$) into two subjets, $j_{1}$ and $j_{2}$, which must
satisfy the “soft drop” (SD) criterion

$\frac{\min(p_{\text{T}1},p_{\text{T}2})}{p_{\text{T}1}+p_{\text{T}2}}>z_{\text{cut}}\left(\frac{\Delta
R_{12}}{R_{0}}\right)^{\beta},$ (2)

where $p_{\text{T}1}$ and $p_{\text{T}2}$ are the transverse momenta of the
two subjets and $\Delta R_{12}$ is the distance between them. The
implementation of the SD algorithm used in this analysis has an angular
exponent $\beta=0$, making it equivalent to the “modified mass drop tagger”
algorithm [52]. Additionally, a soft cutoff threshold of $z_{\text{cut}}=0.1$
and a characteristic radius $R_{0}=0.8$ [53] are used. If the SD criterion is
met, the procedure ends with $j$ as the resulting jet. If not, the lower-$\pt$
subjet is discarded and the declustering procedure continues with the
higher-$\pt$ subjet. The SD mass ($m_{\text{SD}}$) of the jet pair is required
to be near the mass of the top quark ($105<m_{\text{SD}}<210\GeV$). The CMS
$\cPqt$ tagging algorithm also requires that the $N$-subjettiness [54, 55]
ratio ($\tau_{32}\equiv\tau_{3}/\tau_{2}$) must be less than 0.65. The
$N$-subjettiness ($\tau_{N}$) is a measure of the consistency of an AK8 PUPPI
jet with $N$ or fewer subjets, and is defined as

$\tau_{N}=\frac{1}{d_{0}}\sum_{i}p_{\text{T},i}\min\left[\Delta R_{1,i},\Delta
R_{2,i},\cdots,\Delta R_{N,i}\right],$ (3)

where $i$ is a summation over all jet constituents, $d_{0}$ is a normalization
constant, and $\Delta R$ is the distance between a given jet constituent $i$
and a candidate subjet axis $N$.

##  0.4 Triggers and data set

The events in the dilepton channel are triggered by single-lepton and dilepton
triggers without isolation requirements. The triggers for and events require
one muon with $\pt>50\GeV$ and with $\abs{\eta}<2.4$ that is seeded by hits in
either the muon chambers or the inner tracker. The events are selected using a
dielectron trigger that requires the presence of two electrons with
$\pt>33\GeV$ and $\abs{\eta}<2.5$.

Events used in the single-lepton channel pass either a single electron or a
single muon trigger. The single-lepton muon channel uses the same triggers as
the dilepton and channels. The triggers for the electron channel require one
electron with $\pt>115\GeV$ or an electron with $\pt>55\GeV$ and a PF jet with
$\pt>165\GeV$. Both triggers require electrons within $\abs{\eta}<2.5$, and
the electron-jet combination trigger requires the jet to be within
$\abs{\eta}<2.4$. In the combination trigger, if the electron lies within the
jet footprint, the four-vector of the electron is subtracted from the
uncorrected four-vector of the jet, and then the jet energy corrections are
reapplied. Neither the muon or electron triggers have isolation requirements.

The fully hadronic analysis uses events that are selected by a logical ‘OR’ of
five different triggers. The first trigger requires a single AK8 jet with
$\pt>450\GeV$, a second trigger requires an AK4 jet with $\pt>360\GeV$ and
mass $(m_{\text{jet}})>30\GeV$. A third trigger requires $\HT>800\GeV$, where
the is the scalar sum of the of every AK4 PF jet above 30in the event. A
fourth trigger requires $\HT>900\GeV$, and remains un-prescaled during the
acquisition of data. The final trigger requires that the $\HT>700\GeV$, but
also requires a jet with $m_{\text{jet}}>50\GeV$.

Small differences in trigger efficiency between data and simulation in the
dilepton and single-lepton channels are accounted for with corrections
determined from events selected by triggers with different conditions.

##  0.5 Simulated events

The $\PZpr\to\ttbar$ process is simulated using the v5.2.2.2 [56] event
generator, which produces a resonance with the same spin and left- and right-
handed couplings to fermions as the SM boson. Matrix element calculations are
done at tree level and include up to three additional partons for the
$\Pg_{\text{KK}}$ and most models, bosons above 5are simulated with only up to
two additional partons in their final state. The $\PZpr\to\ttbar$ process is
simulated at masses between 500and 7for resonances with a relative decay width
($\Gamma/m$) of 1% (narrow), 10% (wide), and 30% (extra-wide). Matching
between the hard matrix element interactions and the lower energy parton
showers is done using the MLM algorithm [57]. The KK gluon excitation is
simulated using 8.212 [58] with the couplings described in Ref. [59]. The
$\Gamma/m$ of the $\Pg_{\text{KK}}$ resonance lies between the wide and extra-
wide resonances, depending on its coupling to the top quark. The expected
production cross section is calculated at NLO accuracy, and the
$\Pg_{\text{KK}}$ production cross section is calculated at LO. A
multiplicative factor of 1.3 is applied to the $\Pg_{\text{KK}}$ cross section
as an NLO $K$ factor [60]. Both the and $\Pg_{\text{KK}}$ processes are
simulated without interference from SM production.

The invariant mass distribution of the system at the parton level for
resonances with three different widths and a $\Pg_{\text{KK}}$ resonance can
be seen in Fig. 1. The plots are normalized such that the total integral of
each signal model is 1. A resonant structure is manifest at 3, but at 5the
off-shell component of the signal is strongly enhanced by the available parton
luminosity at lower masses. This effect is not noticeable for the narrow
signal, but becomes more apparent for the wider resonances. Such behavior is
expected for resonant production in general.

The pair production background is simulated at next-to-leading order (NLO)
with the v2 generator [61, 62, 63, 64]. The generator is also used to simulate
single top quark production via EW interactions at NLO [65, 66]. The
$\PW$+jets background is simulated with the generator with the FxFx matching
prescription between matrix element calculations and parton shower simulations
[67]. The Drell–Yan (DY) process with an invariant mass between 10 and 50is
simulated at NLO with the same generator, while for an invariant mass above
50, leading order (LO) precision is used. Diboson and QCD multijet production
are simulated at LO with . It should be noted that simulated multijet events
are only used for the background estimate when QCD multijet production is a
secondary background. In the case of the fully hadronic analysis, the multijet
background is estimated from a CR in data, as described in Section 0.7.3. For
all simulated events, with the CUETP8M1 tune [68] is used to describe the
fragmentation and hadronization. All the samples are generated with the NNPDF
3.0 parton distribution functions (PDFs) [69]. All sample cross sections are
normalized to the latest theoretical calculations, usually at next-to-NLO
precision [70, 71, 72, 73].

All samples are processed through a -based simulation [74], which models the
propagation of the particles through the CMS apparatus and the corresponding
detector response. For all samples, the pileup distributions are weighted to
have an average of 23 pileup interactions per event, as measured in data. The
same event reconstruction software is used for data and simulated events.
Differences of a few percent in the resolution and reconstruction efficiency
are corrected to match those measured in data using dedicated samples from
data [75].

Figure 1: The invariant mass distributions for four signal models with
resonance masses of 3(left) and 5(right). The $\Pg_{\text{KK}}$ resonance has
a relative width $\Gamma/m\approx\text{15--20}\%$, which is between those of
the wide and extra-wide boson signal models.

##  0.6 Reconstruction and categorization of events

###  0.6.1 Dilepton channel

Events in the dilepton channel are selected by requiring oppositely charged
high-lepton pairs: or . Leptons with $\pt>53$ and 25 (45 and 36)in the (ee)
channel are selected. In the channel, muons are required to have $\pt>53\GeV$
and electrons are required to have $\pt>25\GeV$. Muons (electrons) are
required to be within $\abs{\eta}<2.4$ (2.5). To remove contributions from
low-mass resonances and $\PZ/\gamma(\to\ell\ell)$+jets production in events
with same-flavor lepton pairs, the dilepton invariant mass is required to be
above 20and outside of the boson mass window 76 to 106\. Contamination from
QCD multijet background is reduced by applying a two-dimensional (2D)
selection for both leptons: $\Delta R_{\min}(\ell,j)>0.4$ or
$p_{\text{T},\text{rel}}(\ell,j)>15\GeV$, where $\Delta R_{\min}(\ell,j)$ is
the minimum $\Delta R$-distance between the lepton candidate and any AK4 jet
with $\pt>15\GeV$ and $\abs{\eta}<3$ and $p_{\text{T},\text{rel}}(\ell,j)$ is
the of the lepton with respect to the axis of the $\Delta R$-nearest AK4 jet.
The 2D selection reduces the QCD multijet background by a factor of
${\approx}100$. Events are further required to contain at least two AK4 jets
with $\abs{\eta}<2.4$ and $\pt>100$ and 50for the leading and subleading jets,
respectively. It is required that at least one of the two leading jets must be
tagged as determined by the loose CSV tagger operating point. Finally, is
required to be larger than 30. The resulting sample is dominated by the
irreducible background, which amounts to $>$90% of the total background.

Figure 2 shows the distributions of $\Delta R_{\text{sum}}=\Delta
R(\ell_{1},j)+\Delta R(\ell_{2},j)$ in , , and subchannels, where $\Delta
R(\ell_{1},j)$ and $\Delta R(\ell_{2},j)$ are the $\Delta\text{R}$ variables
between the leading and subleading lepton and the nearest jet. The lepton-jet
pairs from boson decays are expected to be collimated and populate the
low-$\Delta R_{\text{sum}}$ region. The $\Delta R_{\text{sum}}$ variable is
used to separate events into signal- and background-enriched samples: $\Delta
R_{\text{sum}}<1$ and $1<\Delta R_{\text{sum}}<2$ defines the boosted and
nonboosted signal regions (SRs), respectively, whereas $\Delta
R_{\text{sum}}>2$ defines the background-enriched region. The shape and
normalization are in agreement between data and simulation at low $\Delta
R_{\text{sum}}$, which is the region of interest for separating boosted and
resolved events.

Figure 2: Distributions of $\Delta R_{\text{sum}}$ in (upper left), (upper
right), and (lower) events. The contribution expected from a 4 boson, with a
relative width of 1%, is shown normalized to a cross section of 25pb. The
hatched band on the simulated distribution represents the statistical and
systematic uncertainties. The lower panels in each plot show the ratio of data
to the SM background prediction and the light (dark) gray band represents
statistical (systematic) uncertainty. The error bars on the data points
indicate the Poisson statistical uncertainties.

###  0.6.2 Single-lepton channel

The selection for events used in the single-lepton analysis requires the
presence of a muon with ${\pt>55\GeV}$ and ${\abs{\eta}<2.4}$ or an electron
with ${\pt>80\GeV}$ and ${\abs{\eta}<2.5}$. Neither lepton has an isolation
requirement other than passing the lepton 2D selection, which requires the
$\Delta R_{\min}(\ell,j)>0.4$ or the $p_{\text{T},\text{rel}}(\ell,j)>25\GeV$,
where both quantities are calculated with respect to all AK4 jets with
$\pt>15\GeV$. Events with a second lepton are removed from the sample to avoid
any overlap with the dilepton channel. Events are also required to contain at
least two AK4 jets with $\abs{\eta}<2.4$ and a minimum of 150 (185)for the
leading jet in the muon (electron) channel, and 50for the subleading jet. To
reduce the contributions to the sample from QCD multijet events, additional
requirements are imposed. In the muon channel, and $\HT^{\ell}$ are required
to be greater than 50 and 150, respectively, where
$\HT^{\ell}\equiv\ptmiss+\pt^{\ell}$. In the electron channel, it is required
that $\ptmiss>120\GeV$. The electron channel has a higher requirement because
of the larger QCD multijet background. As a result of this requirement, an
additional selection on $\HT^{\ell}$ would not increase performance. In order
to suppress the contamination from events originating from $\PW$+jets events,
a boosted decision tree [76] ($\PW$+jets BDT) was trained using the tmva
software package [77] on the jet-related variables listed below, in order of
importance.

  1. 1.

$\Delta R_{\text{min}}(\ell,j)$, , the separation between the lepton and its
closest jet.

  2. 2.

The CSV score of the subleading and leading AK4 jets.

  3. 3.

The number of jets.

  4. 4.

$p_{\text{T},\text{rel}}(\ell,j)$, , the relative momentum between the jet and
nearby lepton.

  5. 5.

The reconstructed mass of the leading AK4 jet.

  6. 6.

$\Delta R_{\text{min}}(\ell,j)\,\pt(j)$, , the $\Delta R$ separation between
the jet and nearby lepton scaled by the of the jet.

  7. 7.

The reconstructed mass of the subleading AK4 jet.

  8. 8.

The shape variable $S^{33}$ of the sphericity tensor
$S^{\alpha\beta}=(\sum_{i}p^{\alpha}_{i}p^{\beta}_{i})/(\sum_{i}\abs{p_{i}}^{2})$,
where $\alpha,\beta$ correspond to the $x$, $y$, and $z$ components of the
momentum vectors of the jets [78, 79].

  9. 9.

\+ $\HT^{\ell}$, , the summation of the hadronic, leptonic, and in the event.

Figure 3 shows the $\PW$+jets BDT distribution in the muon and electron
channels. The requirement $\PW\text{+jets BDT}\geq 0.5$ is applied to the
events in the SR, which is further separated in two regions, depending on the
presence of a -tagged AK8 jet with $\pt>400\GeV$ and rapidity $\abs{y}<2.4$.
Events with no -tagged AK8 jet and $\PW\text{+jets BDT}<-0.75$ or
$0<\PW\text{+jets BDT}<0.5$ are dominated by $\PW$+jets and events,
respectively, and constitute the background enriched CRs.

---
Figure 3: $\PW$+jets BDT distributions in the muon (left) and electron (right)
single-lepton channel. The SR is defined as events with $\PW\text{+jets
BDT}\geq 0.5$. The contribution expected from a 4 boson, with a relative width
of 1%, is shown normalized to a cross section of 10pb. The hatched band on the
simulation represents the statistical and systematic uncertainties. The lower
panels in each plot shows the ratio of data to the SM background prediction
and the light (dark) gray band represents statistical (systematic)
uncertainty. The error bars on the data points indicate Poisson statistical
uncertainty.

The system is reconstructed by assigning the four-vectors of the reconstructed
final-state objects (charged lepton, , and jets) to the leptonic or hadronic
legs of the decay. For events without an AK8 jet, several hypotheses are built
based on possible assignments of each AK4 jet to either the leptonic decay,
the hadronic decay, or neither. For events with an AK8 jet, that jet is
associated with the hadronic decay, and the leptonic decay hypotheses only
consider AK4 jets that are separated from the AK8 jet by $\Delta R>1.2$. In
both cases, the combination chosen is the one that minimizes the $\chi^{2}$
discriminator, where

$\chi^{2}=\chi^{2}_{\text{lep}}+\chi^{2}_{\text{had}}=\left[\frac{m_{\text{lep}}-\overline{m}_{\text{lep}}}{\sigma_{m_{\text{lep}}}}\right]^{2}+\left[\frac{m_{\text{had}}-\overline{m}_{\text{had}}}{\sigma_{m_{\text{had}}}}\right]^{2}.$
(4)

In this equation, $m_{\text{lep}}$ and $m_{\text{had}}$ are the invariant
masses of the reconstructed leptonically and hadronically decaying top quarks,
respectively. The parameters $\overline{m}_{\text{lep}}$,
$\sigma_{m_{\text{lep}}}$, $\overline{m}_{\text{had}}$, and
$\sigma_{m_{\text{had}}}$ in the $\chi^{2}$ discriminator are determined from
simulation by matching reconstructed final-state objects of the hypothesis to
the corresponding generator-level particles from the decay. Events in signal-
and background-enriched regions are all required to have $\chi^{2}<30$. Events
with two -tagged AK8 jets are removed from the sample in order to avoid any
overlap with the fully hadronic channel.

###  0.6.3 Fully hadronic channel

All events used in the fully hadronic analysis are required to fulfill the
following kinematic and $\cPqt$ tagging criteria. In order to reach a trigger
efficiency of ${\approx}100$%, each event must have $\HT>950\GeV$. Events are
reconstructed using the two -leading AK8 jets, both of which are required to
have $\pt>400\GeV$ and $\abs{y}<2.4$. In order to ensure a back-to-back
topology, the two jets must have an azimuthal separation
$\abs{\Delta\phi}>2.1$. These kinematic requirements are later referred to as
the fully hadronic preselection. Both AK8 jets are required to be tagged for
events to enter the SR. These events are then separated into six SRs based on
two criteria: the rapidity difference between the two jets ($\abs{\Delta
y}<1.0$ or $\abs{\Delta y}>1.0$) and the number of jets with a -tagged subjet
(0, 1, or 2).

The categories with a greater number of jets with a -tagged subjet are
expected to provide higher sensitivity, while those with fewer -tagged subjets
are included to provide better constraints on the backgrounds and additional
sensitivity to the analysis. The low-$\abs{\Delta y}$ region is expected to be
more sensitive than the high-$\abs{\Delta y}$ region. At high values of
$m_{\ttbar}$, QCD multijet events will have jets with greater $y$ separation,
as compared to those from a massive particle decay, in order to achieve such
high invariant masses. This is illustrated in Fig. 4, which shows the dijet
rapidity difference for events passing the fully hadronic event selection. The
plot on the left is inclusive in $m_{\ttbar}$, while the plot on the right
shows events with $m_{\ttbar}>2\TeV$.

---
Figure 4: Dijet rapidity difference $(\Delta y)$ for events passing the fully
hadronic event selection for all $m_{\ttbar}$ (left) and for events with an
$m_{\ttbar}>2\TeV$ (right). The contribution expected from a 4 boson, with a
relative width of 1%, is shown normalized to a cross section of 10pb. The
hatched band around the simulated distribution represents the statistical and
systematic uncertainties. The lower panels in each plot show the ratio of data
to the SM background prediction and the light (dark) gray band represents
statistical (systematic) uncertainty.

##  0.7 Estimation of the background

###  0.7.1 Dilepton channel

The dominant irreducible background in the dilepton channel is production.
Other secondary backgrounds arise from $\PZ$+jets, single top quark, and
diboson processes. Simulated events are used to model the shape of the
kinematic distributions for the background processes, including modeling the
$S_{\text{T}}$ variable used in the statistical interpretation of the
observations. The overall normalization of the background processes is based
on the corresponding theoretical cross sections. The distributions are allowed
to vary within prior bounds of rate and shape uncertainties during the
statistical treatment, which employs six signal- and three background-enriched
regions, defined in Section 0.6.1. Modeling of the background is separately
checked in the background-enriched CR obtained with the requirement $\Delta
R_{\text{sum}}>2$. Figure 5 shows the distributions of $S_{\text{T}}$ in the
CR for , , and channels. The background simulation is in agreement with data
within the statistical and systematical uncertainties. The quantity ‘pull’,
shown in Fig. 5 and subsequent figures, is computed according to the following
procedure. First, the total uncertainty per bin is determined by adding the
statistical and all systematic uncertainties together in quadrature. Based on
the expected number of events and the total uncertainty in each bin, pseudo-
experiments are performed by sampling from a Gaussian distribution with the
mean equal to the expected number of events and the standard deviation equal
to the total uncertainty. For each pseudo-experiment, a distribution of the
number of expected events is populated using Poisson statistics convolved with
the Gaussian distribution describing the variation in the expected number of
events in the bin. Finally, the number of events observed in data is used in
conjunction with the distribution of pseudo-experiments to calculate a
p-value, and the corresponding z-score is taken to be the pull.

Figure 5: Distributions of $S_{\text{T}}$ in the background-enriched CR for
(upper left), (upper right), and (lower) subchannels. The contribution
expected from a 4 boson, with a relative width of 1%, is shown normalized to a
cross section of 1pb. The hatched band on the simulation represents the
uncertainty in the background prediction. The lower panel shows the pull of
each histogram bin from the SM prediction. The light (dark) gray band
represents a pull of one (two) standard deviations (s.d.) from the predicted
value.

###  0.7.2 Single-lepton channel

Standard model production is the main irreducible background in the single-
lepton channel. Other background processes include $\PW$+jets, single top
quark, $\PZ$+jets, and diboson production. The QCD multijet background is a
minor contribution in the single muon channel (${\approx}3$%), and is
suppressed to a negligible level in the single-electron channel because of
higher and requirements. All background processes in the single-lepton channel
are modeled from simulated events, and the normalization of each background is
based on its theoretical cross section. The rate and shape of the backgrounds
are allowed to vary in the statistical analysis as described in Section 0.9.
Events that pass the requirements in Section 0.6.2 are separated in two
signal- and two background-enriched regions, defined as follows.

  1. 1.

Signal Region (SR1T): $\chi^{2}<30$, $\PW\text{+jets BDT}\geq 0.5$, 1 -tagged
AK8 jet.

  2. 2.

Signal Region (SR0T): $\chi^{2}<30$, $\PW\text{+jets BDT}\geq 0.5$, no -tagged
AK8 jet.

  3. 3.

Control Region (CR1): $\chi^{2}<30$, $\PW\text{+jets BDT}<-0.75$.

  4. 4.

Control Region (CR2): $\chi^{2}<30$, $0.0<\PW\text{+jets BDT}<0.5.$

The first control region (CR1) is dominated by +jet events, while CR2 is
dominated by events. For all regions, events are separated based on the lepton
flavor (, ), which results in eight exclusive categories used in the binned
maximum likelihood fit. The rate at which light-flavor quarks and gluons are
misidentified as originating from top quarks ($\cPqt$ mistag) is measured in
data and simulation using a $\PW$+jets mistag CR with
$\chi^{2}_{\text{lep}}>30$ and $\PW\text{+jets BDT}<-0.5$. The and
$m_{\text{SD}}$ distributions in the $\PW$+jets background can be seen in Fig.
6.

---
Figure 6: Distributions of (upper) and $m_{\text{SD}}$ (lower) for the
$\PW$+jets background in the muon (left) and electron (right) channels using
the $\PW$+jets mistag CR. The jet information is taken from the CHS jets,
while the $m_{\text{SD}}$ is take from the PUPPI jets. The hatched band on the
simulation represents the uncertainty in the background prediction. The lower
panels in each plot show the ratio of data to the SM background prediction and
the light (dark) gray band represents statistical (systematic) uncertainty.

###  0.7.3 Fully hadronic channel

The two main sources of background in the fully hadronic channel are QCD
multijet and production. For the latter background, simulated events are used
to model the shape of the $m_{\ttbar}$ distribution. This distribution is
initially normalized to the theoretical cross section, but it is allowed to
vary within the bounds of rate and shape uncertainties during the statistical
treatment. The final normalization and shape are determined by fitting the
distributions in the six SRs, defined in Section 0.6.3.

The QCD multijet background is estimated from data, using a method similar to
the techniques described in Ref. [30]. The preselection described in Section
0.6.3 is enforced in order to select a back-to-back dijet event topology. In
the first step of the background estimate, the $\cPqt$ mistag rate in QCD
multijet events is measured. A QCD multijet enriched region is selected by
requiring one of the two jets to be “anti-tagged,” meaning it has a PUPPI soft
drop mass in the -tag mass window $105<m_{\text{SD}}<210\GeV$, but the
N-subjettiness requirement is inverted to $\tau_{32}>0.65$. The opposite
“probe” jet is used to determine the $\cPqt$ mistag rate. This rate is
parametrized as a function of probe jet momentum ($p$) and is measured for
each of the three subjet -tag categories (Fig. 7). This “anti-tag and probe”
procedure is repeated for the simulation, indicating that there is a small
(${\approx}2$%) contribution from SM events. The observed contamination is
then subtracted from the anti-tag and probe data selection.

Figure 7: The $\cPqt$ mistag rate as measured with an anti-tag and probe
procedure separately for each -tag category.

After the $\cPqt$ mistag rate has been measured in the QCD multijet CR, it is
used to estimate the $m_{\ttbar}$ QCD multijet distribution in the SR. First,
a “single-tagged” region is selected, in which at least one of the two jets is
required to be tagged, meaning it has a PUPPI $m_{\text{SD}}$ in the -tag mass
window $105<m_{\text{SD}}<210\GeV$ and an N-subjettiness requirement of
$\tau_{32}<0.65$. One of the two top quark jet candidates is randomly
selected, in order to avoid bias. If the selected jet is tagged, the event is
included in the QCD multijet estimate. The event is weighted by the previously
measured $\cPqt$ mistag rate, based on the momentum of the opposite jet and
the number of subjet tags in the event. Again, the procedure is repeated for
the simulation, and the contamination is subtracted from the QCD multijet
background estimate. This eliminates double counting between the and QCD
multijet distributions.

Finally, a “mass-modified” procedure is employed in order to ensure that the
jets used in the QCD multijet estimate mimic the relevant kinematics of the
jets in the SR. If the mass of the second QCD multijet jet is not in the top
quark mass window, it is assigned a random value within that window. This
modified mass is randomly selected from the distribution of simulated light-
flavor jets, with masses within the -tag window, $105<m_{\text{SD}}<210\GeV$.
A check of the entire background estimation method using simulated QCD
multijet events is self-consistent.

##  0.8 Systematic uncertainties

Several sources of uncertainty that impact the final results of this search
are considered. In all cases, the uncertainties in reconstruction efficiency
and event interpretation are propagated to the distribution used for signal
extraction. These uncertainties can be broadly grouped into two categories:
those uncertainties that affect only the overall normalization of expected
background events and those uncertainties that can result in a different
reconstruction of the system, and therefore change the shape of the
$m_{\ttbar}$ distribution. Each source of systematic uncertainty is accounted
for through unique nuisance parameters applied to the likelihood described in
Section 0.10. For contributions that apply to multiple analysis channels, the
nuisance parameters are fully correlated, allowing better constraints to be
placed on sources of systematic uncertainties. The individual sources of
uncertainty are described in detail below, and are summarized in Table 0.8.

Sources of systematic uncertainty that affect the $m_{\ttbar}$ and
$S_{\text{T}}$ distributions in each analysis channel. For uncertainty sources
that apply to multiple channels, the corresponding nuisance parameter is fully
correlated across these channels if the symbol $\checkmark$ appears in the
same row. For normalization uncertainties, the size of the effect on the prior
distribution is indicated. Shape uncertainties have priors of $\pm 1$ s.d.,
and the dependence on the kinematic quantities is shown. lcccc Uncertainty
Channel
Source Prior Dilepton Single-Lepton Hadronic
$\cPqb$ tagging efficiency $\pm 1\text{ s.d.}(\pt,\eta)$ $\checkmark$
$\checkmark$
mistag rate $\pm 1\text{ s.d.}(\pt,\eta)$ $\checkmark$ $\checkmark$
Parton distribution functions $\pm 1\text{ s.d.}$ $\checkmark$ $\checkmark$
$\checkmark$
CSV discriminator shape $\pm 1\text{ s.d.}$ $\checkmark$
Diboson cross section 50% $\checkmark$
Electron trigger $\pm 1\text{ s.d.}(\pt,\eta)$ $\checkmark$ $\checkmark$
Electron identification $\pm 1\text{ s.d.}(\pt,\eta)$ $\checkmark$
$\checkmark$
Jet energy scale $\pm 1\text{ s.d.}(\pt,\eta)$ $\checkmark$ $\checkmark$
$\checkmark$
Jet energy resolution $\pm 1\text{ s.d.}(\eta)$ $\checkmark$ $\checkmark$
$\checkmark$
Integrated luminosity measurement 2.5% $\checkmark$ $\checkmark$ $\checkmark$
QCD multijet modified mass procedure $\pm 1\text{ s.d.}$ $\checkmark$
QCD multijet estimate closure test $\pm 1\text{ s.d.}$ $\checkmark$
Muon trigger $\pm 1\text{ s.d.}(\pt,\eta)$ $\checkmark$ $\checkmark$
Muon identification $\pm 1\text{ s.d.}(\eta)$ $\checkmark$ $\checkmark$
Pileup reweighting $\pm 1\text{ s.d.}$ $\checkmark$ $\checkmark$ $\checkmark$
Renorm/fact. scales (production) $\pm 1\text{ s.d.}$ $\checkmark$ $\checkmark$
$\checkmark$
Single top quark cross section 50% $\checkmark$
$\cPqt$ tagging efficiency unconstrained $\checkmark$ $\checkmark$
$\cPqt$ mistag rate (fully hadronic) $\pm 1\text{ s.d.}(p)$ $\checkmark$
$\cPqt$ mistag rate (single-lepton) $\pm 1\text{ s.d.}$ $\checkmark$
Top quark pair cross section 20% $\checkmark$ $\checkmark$ $\checkmark$
Top quark reweighting $\pm 1\text{ s.d.}$ $\checkmark$ $\checkmark$
$\checkmark$
$\PW$+jets cross section 25% $\checkmark$ $\checkmark$
$\PZ$+jets cross section 50% $\checkmark$ $\checkmark$

Including all the systematic uncertainties degrades the final cross section
limits by 10% for resonance masses above 2.5. Lower mass hypotheses are more
sensitive to the systematic effects, thus the limit on the cross section
degrades by up to 60% for the lowest mass resonance considered (500). The
uncertainties in the jet energy corrections, pileup distribution, and cross
section are the most significant. They result in a reduction of the excluded
mass by 1.1, 1.0, and 1.0%, respectively. All other systematic uncertainties
have less than a 1% effect. Per channel, the most significant systematic
uncertainties are the b tagging scale factor, the renormalization and
factorization scales, and the standard model cross section for the dilepton,
single-lepton, and all hadronic channels, respectively. The most constrained
nuisance parameters are those associated with the renormalization and
factorization scales as well as the top tagging efficiency, which are
constrained to 8.5 and 9.2% of their prior uncertainty. The average nuisance
parameter has a post-fit uncertainty that is 75% lower than its prior
estimate.

  1. 1.

Standard model cross sections: Uncertainties in the cross sections used to
normalize simulated background processes are obtained using the fitting
procedure described in Section 0.1. For the , $\PW$+jets, and $\PZ$+jets
backgrounds, a priori uncertainties of 20, 25, and 50% are assigned,
respectively. A cross section uncertainty of 50% is used for the subdominant
diboson and single top quark backgrounds. The values chosen reflect the
relatively large uncertainties associated with modeling these backgrounds in
the Lorentz-boosted phase space where the analysis is performed.

  2. 2.

Integrated luminosity: The uncertainty in the measurement of the integrated
luminosity is 2.5% [80], and is applied to all simulated signal and background
samples.

  3. 3.

Pileup reweighting: All simulated samples used in the analysis are reweighted
to ensure that the distribution of the number of pileup interactions per event
matches the corresponding distribution in data. This pileup distribution is
obtained using a total inelastic cross section value of 69.2mb [81, 82]. A
systematic uncertainty in the distribution is obtained by varying the value by
$\pm 4.6$%, which is calculated using the method described in [82] using the
cross sections from [81]. The resulting uncertainty has both a normalization
and shape component.

  4. 4.

Lepton reconstruction and triggers: Simulated events are corrected by scale
factors to account for differences between data and simulation in the
efficiencies in the identification criteria for muons and electrons. By
applying the scale factors shifted up or down by their uncertainties, new
templates are obtained that correspond to these uncertainties. These templates
can be used as the nuisance parameters, which are correlated between channels
as identical identification criteria are used. The scale factors are
parametrized as functions of lepton and $\eta$ to account for different
detector response. In the same way, uncertainties in the trigger efficiency
are also accounted for, in the muon and electron trigger selections for this
analysis.

  5. 5.

Jet energy scale and resolution: Uncertainties in the energy corrections
applied to jets are propagated to the final discriminating distributions by
reconstructing events with the jet level corrections shifted within their
corresponding uncertainties, which depend on the jet and $\eta$.

  6. 6.

Jet $\cPqb$ tagging: Simulated events are corrected with scale factors to
account for differences in the efficiency for identifying a jet between data
and simulation. There are two components to this process, each with an
independent, uncorrelated nuisance parameter: one that accounts for the scale
factor applied to the rate of identifying -tagged jets (efficiency) and one
that accounts for the scale factor applied to the rate of mistakenly
identifying light-flavor jets as jets ( mistag rate). In each case, the
uncertainty is obtained by shifting these -dependent scale factors within
their uncertainties. The $\cPqb$ tagging uncertainties are fully correlated
between the dilepton and fully hadronic analyses, as they use the same $\cPqb$
tagging criteria.

  7. 7.

CSV discriminant shape: The CSV tagger provides a continuous variable that can
be used to identify jets. This continuous variable is used as an input to the
$\PW$+jets BDT described above. The $\PW$+jets BDT is only used in the single-
lepton analysis, therefore the CSV shape systematic uncertainty only applies
to that analysis. Several sources of systematic uncertainties are evaluated,
including jet energy scale, flavor effects, and statistical effects. Each of
these effects contributes an additional uncertainty in the CSV value that is
propagated to the final signal discrimination process.

  8. 8.

Jet $\cPqt$ tagging: It is not possible to define a CR that is capable of
measuring the $\cPqt$ tagging scale factor without overlapping the SR. The
$\cPqt$ tagging efficiency scale factor is determined during the statistical
analysis. This is done by including a nuisance parameter with a flat prior
distribution that is unconstrained and correlated between the fully hadronic
and single-lepton channels. Sources of misidentified -tagged jets are
different in the single-lepton channel, where they originate from $\PW$+jets
processes, and in the fully hadronic channel, where they originate from QCD
multijet processes. Therefore, the nuisance parameters corresponding to the
uncertainty in the $\cPqt$ mistag rate are treated as uncorrelated between the
channels, and are also uncorrelated with the nuisance parameter assigned to
the $\cPqt$ tagging efficiency.

  9. 9.

Parton distribution functions: For the simulated sample, the PDFs from the
NNPDF3.0 set [69] are used to evaluate the systematic uncertainty in the
choice of PDF, according to the procedure described in Ref. [83].

  10. 10.

Scale uncertainties: For the sample, the matrix element renormalization and
factorization scales were varied up and down independently by a factor of 2 to
account for uncertainties in the choice of $Q^{2}$ used to generate the
simulated sample.

  11. 11.

Top quark reweighting: The simulated SM process was corrected at parton-level
using a function derived from the ratio of top quark measured in data and
next-to-NLO predictions from and [84]. The uncertainty in this process is
estimated by taking the difference between the unweighted and weighted results
applied symmetrically to the nominal value as a function of . The top quark
reweighting does not significantly impact the $m_{\ttbar}$ and $S_{\text{T}}$
distributions, and would not obscure a resonance signal.

  12. 12.

QCD multijet background estimation: The ‘mass-modified’ procedure described
above to predict the shape of the background in the fully hadronic channel
includes an uncertainty in the resulting distribution, equivalent to half of
the difference between the uncorrected and ‘mass-modified’ background shapes.
This difference affects both the shape and normalization of the final
distributions, and the corresponding nuisance parameter is independent from
all other effects. The uncertainties in the $\cPqt$ mistag rates are
propagated to the final distributions, and the corresponding uncertainty is
handled via the $\cPqt$ mistag rate nuisance parameter described above. A
closure test is performed with simulated QCD multijet events to test the
accuracy of the method. An additional systematic uncertainty is included,
equal to the magnitude of the discrepancy observed from the closure tests
results, evaluated and applied on a bin-by-bin basis to the fully hadronic
signal categories. This systematic most greatly affects the two -tag,
high-$\abs{\Delta y}$ category, for which the method only closes within 20%.
For the other categories, the method closes within ${\approx}4$%.

##  0.9 Statistical analysis

Before extracting the final results of the analysis, a background-only binned
maximum likelihood fit is performed on the signal and control regions to
determine the preferred values of the background process normalizations and
shapes, using constraints from the sources of systematic uncertainty described
above. Each source of systematic uncertainty is included through a unique
nuisance parameter that is allowed to vary within the rate and shape
constraints described above, using a log-normal prior distribution. The post-
fit values of the nuisance parameters are used to correct the normalization
and shape of each background process. The $m_{\ttbar}$ and $S_{\text{T}}$
distributions after the fitting procedure are shown in Figs. 8, 9–10, and 11,
for the dilepton, single-lepton, and fully hadronic channels, respectively.
The mild deficits at low $m_{\ttbar}$ in the two plots on the left in Fig. 10
do not significantly impact the limit, because this region is used to evaluate
the and $\PW$+jets cross sections and is not sensitive to the resonance
signal. The $\cPqt$ tagging efficiency is measured simultaneously in signal
and control regions during the maximum likelihood fit, as it is not possible
to select a CR that might not be contaminated by the potential signal. The
$\cPqt$ tagging efficiency scale factor is modeled as a free nuisance
parameter, with an unconstrained prior, in the binned likelihood fit. The
$\cPqt$ tagging efficiency scale factor measured by the fit is $1.001\pm
0.012$.

Data are found to be in agreement with expectations in each of the categories
considered in this analysis. Limits on the product of the production cross
section and branching fraction are calculated, $\sigma(\Pp\Pp\to
X)\,\mathcal{B}(X\to\ttbar)$, for heavy resonances decaying to a pair of top
quarks. A shape-based analysis is performed using both the signal and control
regions from the three exclusive analysis channels. The Theta software package
[85] simultaneously fits the $m_{\ttbar}$ distributions from the single-lepton
and fully hadronic channels and the $S_{\text{T}}$ distributions from the
dilepton channel. For the limit calculation, a Bayesian likelihood-based
method is used [86, 87] with each bin of the distributions combined
statistically, along with the implementation of unique nuisance parameters
that correspond to the systematic uncertainties described in Section 0.8. The
signal normalization is allowed to vary with a distinct unconstrained nuisance
parameter having a uniform prior, while the other nuisance parameters have
log-normal prior distributions. Finally, to account for the limited number of
simulated events, an additional statistical uncertainty is included for each
process relying on simulated events through the “Barlow–Beeston lite” method
[88]. Prior to the statistical analysis, the $m_{\ttbar}$ distributions are
rebinned. For the fully hadronic and dilepton channels, the total statistical
uncertainty in the background is required to be below 30% in any given bin. In
the single-lepton channel, the total statistical uncertainty in the background
expectation for the sum of small backgrounds (single top quark, multijet,
$\PZ$+jets, $\PW+\PQb$, or jets) is required to be below 10% in each bin. The
tighter statistical uncertainty requirement is needed for these backgrounds
because the events are rejected at a high rate, resulting in significantly
fewer simulated events that pass the final selection.

Figure 8: Distributions of $S_{\text{T}}$ for the (upper), (middle), and
(lower) SRs in the boosted (left) and nonboosted (right) regions, as defined
in Section 0.6.1. The contribution expected from a 4 boson, with a relative
width of 1%, is shown normalized to a cross section of 1pb. The hatched band
on the simulation represents the uncertainty in the background prediction. The
lower panel in each plot shows the pull of each histogram bin from the SM
prediction. The light (dark) gray band represents a pull of one (two) s.d.
from the predicted value.

Figure 9: Distributions of $m_{\ttbar}$ for the single-lepton channel SRs for
the muon (left) and electron (right) categories with (upper) and without
(lower) $\cPqt$ tagging. The contribution expected from a 4 boson, with a
relative width of 1%, is shown normalized to a cross section of 1pb. The
hatched band on the simulation represents the uncertainty in the background
prediction. The lower panel in each plot shows the pull of each histogram bin
from the SM prediction. The light (dark) gray band represents a pull of one
(two) s.d. from the predicted value.

Figure 10: Distributions of $m_{\ttbar}$ for the single-lepton channel CR1
(upper) and CR2 (lower) for the muon (left) and electron (right) categories.
The contribution expected from a 4 boson, with a relative width of 1%, is
shown normalized to a cross section of 1pb. The hatched band on the simulation
represents the uncertainty in the background prediction. The lower panel in
each plot shows the pull of each histogram bin from the SM prediction. The
light (dark) gray band represents a pull of one (two) s.d. from the predicted
value.

Figure 11: Distributions of $m_{\ttbar}$ for the fully hadronic channel SR
categories, used to extract the final results. The contribution expected from
a 4 boson, with a relative width of 1%, is shown normalized to a cross section
of 1pb. The hatched band on the simulation represents the uncertainty in the
background prediction. The lower panel in each plot shows the pull of each
histogram bin from the SM prediction. The light (dark) gray band represents a
pull of one (two) s.d. from the predicted value.

Figure 12 shows a comparison of the expected sensitivities in each of the
three analysis channels in terms of the expected limits for the
$\Pg_{\text{KK}}$ signal model. The contributions from the single-lepton and
fully hadronic channels dominate the sensitivity over most of the mass range,
apart from the region of lowest masses, where the dilepton channel makes a
significant contribution.

Figure 12: Comparison of the sensitivities for each analysis channel
contributing to the combination. The expected limits at 95% are shown for each
channel with the narrow colored lines, while the combination result is shown
with thick the black line. These results are shown specifically for the
$\Pg_{\text{KK}}$ signal hypothesis, as this model has characteristics that
are common to many resonance searches. The multiplicative factor of 1.3 for
the $\Pg_{\text{KK}}$ is the NLO $K$ factor.

##  0.10 Results

The statistical analysis is performed for each of the signal models considered
in this analysis: three variations of a boson having a width-to-mass ratio of
1, 10, and 30%, as well as a $\Pg_{\text{KK}}$. In each case, a 95% limit is
obtained on the product of the resonance production cross section and
branching fraction. The observed and expected limits and 1 and 2 s.d. bands
are calculated for resonance masses ranging from 0.5 to 5.0and are listed in
Tables 0.10–0.10.

Limits at 95% on the product of the resonance production cross section and
branching fraction for the narrow ($\Gamma/m=1\%$) boson resonance hypothesis.
ccccc Mass [TeV] & Obs. [pb] Median exp. [pb] 68% Exp. [pb] 95% Exp. [pb]
0.50 29 28 [13, 49] [7.5, 78]
0.75 1.1 2.4 [1.5, 3.7] [1.0, 5.6]
1.00 0.37 0.54 [0.37, 0.77] [0.26, 1.1]
1.25 0.31 0.16 [0.11, 0.24] [0.080, 0.35]
1.50 0.091 0.076 [0.051, 0.12] [0.036, 0.17]
2.00 0.023 0.027 [0.018, 0.041] [0.012, 0.061]
2.50 0.018 0.012 [0.0083, 0.019] [0.0056, 0.029]
3.00 0.0042 0.0075 [0.0051, 0.011] [0.0035, 0.017]
3.50 0.0046 0.0052 [0.0035, 0.0081] [0.0025, 0.012]
4.00 0.0041 0.0042 [0.0028, 0.0065] [0.0020, 0.010]
4.50 0.0030 0.0035 [0.0023, 0.0054] [0.0016, 0.0082]
5.00 0.0023 0.0032 [0.0021, 0.0049] [0.0014, 0.0079]
6.00 0.0013 0.0027 [0.0017, 0.0042] [0.0011, 0.0069]
6.50 0.0012 0.0026 [0.0016, 0.0040] [0.0011, 0.0065]
7.00 0.0012 0.0024 [0.0016, 0.0038] [0.0011, 0.0063]

Limits at 95% on the product of the resonance production cross section and
branching fraction for the wide ($\Gamma/m=10\%$) boson resonance hypothesis.
ccccc Mass [TeV] Obs. [pb] Median exp. [pb] 68% Exp. [pb] 95% Exp. [pb]
0.50 31 22 [9.8, 43] [5.4, 70]
0.75 2.9 3.6 [2.2, 6.1] [1.3, 9.5]
1.00 0.93 0.72 [0.48, 1.1] [0.34, 1.5]
1.25 0.55 0.24 [0.16, 0.37] [0.11, 0.54]
1.50 0.17 0.12 [0.073, 0.18] [0.050, 0.29]
2.00 0.041 0.040 [0.027, 0.063] [0.018, 0.096]
2.50 0.027 0.020 [0.013, 0.030] [0.0088, 0.046]
3.00 0.0084 0.013 [0.0088, 0.020] [0.0061, 0.031]
3.50 0.0091 0.011 [0.0073, 0.017] [0.0051, 0.025]
4.00 0.0092 0.010 [0.0064, 0.015] [0.0044, 0.023]
4.50 0.0087 0.010 [0.0067, 0.016] [0.0046, 0.024]
5.00 0.0097 0.012 [0.0078, 0.019] [0.0056, 0.029]
6.00 0.015 0.021 [0.014, 0.034] [0.0095, 0.053]
6.50 0.016 0.025 [0.017, 0.040] [0.011, 0.062]
7.00 0.022 0.032 [0.021, 0.050] [0.014, 0.081]

Limits at 95% on the product of the resonance production cross section and
branching fraction for the extra-wide ($\Gamma/m=30\%$) boson resonance
hypothesis. ccccc Mass [TeV] Obs. [pb] Median exp. [pb] 68% Exp. [pb] 95% Exp.
[pb]
1.0 2.0 1.1 [0.63, 1.8] [0.41, 2.7]
2.0 0.078 0.066 [0.041, 0.11] [0.027, 0.18]
3.0 0.019 0.026 [0.017, 0.040] [0.012, 0.061]
4.0 0.019 0.023 [0.015, 0.035] [0.011, 0.053]
5.0 0.022 0.025 [0.016, 0.039] [0.011, 0.062]
6.0 0.029 0.035 [0.023, 0.055] [0.015, 0.086]
6.5 0.030 0.040 [0.026, 0.061] [0.018, 0.097]
7.0 0.035 0.044 [0.029, 0.070] [0.019, 0.11]

Limits at 95% on the product of the resonance production cross section and
branching fraction for the $\Pg_{\text{KK}}$ gluon resonance hypothesis. ccccc
Mass [TeV] Obs. [pb] Median exp. [pb] 68% Exp. [pb] 95% Exp. [pb]
0.50 9.5 30 [13, 55] [6.1, 82]
0.75 4.6 5.0 [2.6, 8.3] [1.5, 13]
1.00 0.71 0.99 [0.64, 1.5] [0.44, 2.3]
1.25 0.77 0.42 [0.26, 0.67] [0.18, 1.0]
1.50 0.30 0.19 [0.12, 0.32] [0.081, 0.56]
2.00 0.090 0.065 [0.042, 0.10] [0.028, 0.17]
2.50 0.045 0.035 [0.022, 0.055] [0.015, 0.089]
3.00 0.021 0.025 [0.017, 0.039] [0.012, 0.061]
3.50 0.016 0.022 [0.014, 0.033] [0.0098, 0.051]
4.00 0.020 0.021 [0.014, 0.032] [0.0096, 0.050]
4.50 0.019 0.024 [0.016, 0.038] [0.011, 0.059]
5.00 0.025 0.030 [0.020, 0.047] [0.014, 0.074]

New exclusion limits on the mass of resonances decaying to are set by
comparing the observed limit to the theoretical cross section, where the
branching fraction $\mathcal{B}(X\to\ttbar)$ is assumed to be 1. As shown in
Fig. 13, the analysis excludes narrow bosons with masses up to
3.80(3.75expected), wide bosons with masses up to 5.25(5.10expected), and
extra-wide bosons with masses up to 6.65(6.40expected). For the
$\Pg_{\text{KK}}$ resonance hypothesis, the analysis excludes masses up to
4.55(4.45expected). These results represent a significant improvement on the
previous results in this channel from the 2015 data taking period, not only
because of the increase in integrated luminosity, but also the reduction in
the uncertainty in the multijet background estimate in the fully hadronic
channel, the improved $\PW$+jets rejection via the $\PW$+jets BDT in the
single-lepton channel, and the inclusion of dilepton event categories in the
combination. The absolute cross section limits are 10–40% better, for
$m_{\ttbar}$ above 2, than the previous result released by CMS [31] scaled to
an integrated luminosity of 35.9. These results are the most stringent
exclusion limits on a resonance to date.

Figure 13: Observed and expected limits at 95% for each of the four signal
hypotheses considered in this analysis.

##  0.11 Summary

A search for a generic massive top quark and antiquark () resonance has been
presented. The analysis was performed using data collected by the CMS
experiment in 2016 at the LHC at $\sqrt{s}=13\TeV$, corresponding to an
integrated luminosity of 35.9. The analysis is focused on searching for a
resonance above 2, where the decay products of the top quark become collimated
because of its large Lorentz boost. The analysis performed a simultaneous
measurement of the backgrounds and the $\cPqt$ tagging efficiency from data.
The data are consistent with the background-only hypothesis, and no evidence
for a massive resonance has been found. Limits at 95% confidence level are
calculated for the production cross section for a spin-1 resonance decaying to
pairs with a variety of decay widths.

Limits were calculated for two benchmark signal processes that decay to pairs.
A topcolor boson with relative widths of 1, 10, or 30% is excluded in the mass
ranges 0.50–3.80, 0.50–5.25, and 0.50–6.65, respectively. The first
Kaluza–Klein excitation of the gluon in the Randall–Sundrum scenario
($\Pg_{\text{KK}}$) is excluded in the range 0.50–4.55. This is the first
search by any experiment at $\sqrt{s}=13\TeV$ for resonances that combines all
three decay topologies of the system: dilepton, single-lepton, and fully
hadronic.

The sensitivity of the analysis exceeds previous searches at $\sqrt{s}=8$ and
13, particularly at high invariant mass. Previous measurements have excluded a
topcolor up to 3.0, 3.9, and 4.0, for relative widths of 1, 10, and 30%, and
$\Pg_{\text{KK}}$ from 3.3 to 3.8, depending on model [32, 31]. The presented
analysis improves upon those limits, extending the exclusions to 3.80, 5.25,
and 6.65and the $\Pg_{\text{KK}}$ exclusion to 4.55. These are the most
stringent limits on the topcolor and $\Pg_{\text{KK}}$ models to date.

###### Acknowledgements.

We congratulate our colleagues in the CERN accelerator departments for the
excellent performance of the LHC and thank the technical and administrative
staffs at CERN and at other CMS institutes for their contributions to the
success of the CMS effort. In addition, we gratefully acknowledge the
computing centers and personnel of the Worldwide LHC Computing Grid for
delivering so effectively the computing infrastructure essential to our
analyses. Finally, we acknowledge the enduring support for the construction
and operation of the LHC and the CMS detector provided by the following
funding agencies: BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq,
CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST,
and NSFC (China); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RPF
(Cyprus); SENESCYT (Ecuador); MoER, ERC IUT, 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 program and the
European Research Council and Horizon 2020 Grant, contract No. 675440
(European Union); the Leventis Foundation; the A. P. Sloan Foundation; the
Alexander von Humboldt Foundation; the Belgian Federal Science Policy Office;
the Fonds pour la Formation à la Recherche dans l’Industrie et dans
l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en
Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium) under the
“Excellence of Science - EOS” - be.h project n. 30820817; the Ministry of
Education, Youth and Sports (MEYS) of the Czech Republic; the Lendület
(“Momentum”) Program and the János Bolyai Research Scholarship of the
Hungarian Academy of Sciences, the New National Excellence Program ÚNKP, the
NKFIA research grants 123842, 123959, 124845, 124850 and 125105 (Hungary); the
Council of Science and Industrial Research, India; the HOMING PLUS program of
the Foundation for Polish Science, cofinanced from European Union, Regional
Development Fund, the Mobility Plus program of the Ministry of Science and
Higher Education, the National Science Center (Poland), contracts Harmonia
2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543, 2014/15/B/ST2/03998, and
2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406; the National Priorities
Research Program by Qatar National Research Fund; the Programa Estatal de
Fomento de la Investigación Científica y Técnica de Excelencia María de
Maeztu, grant MDM-2015-0509 and the Programa Severo Ochoa del Principado de
Asturias; the Thalis and Aristeia programs cofinanced by EU-ESF and the Greek
NSRF; the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn
University and the Chulalongkorn Academic into Its 2nd Century Project
Advancement Project (Thailand); the Welch Foundation, contract C-1845; and the
Weston Havens Foundation (USA).

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##  .12 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan, A. Tumasyan Institut für Hochenergiephysik, Wien, Austria
W. Adam, F. Ambrogi, E. Asilar, T. Bergauer, J. Brandstetter, M. Dragicevic,
J. Erö, A. Escalante Del Valle, M. Flechl, R. Frühwirth1, V.M. Ghete, J.
Hrubec, M. Jeitler1, N. Krammer, I. Krätschmer, D. Liko, T. Madlener, I.
Mikulec, N. Rad, H. Rohringer, J. Schieck1, R. Schöfbeck, M. Spanring, D.
Spitzbart, A. Taurok, W. Waltenberger, J. Wittmann, C.-E. Wulz1, M. Zarucki
Institute for Nuclear Problems, Minsk, Belarus
V. Chekhovsky, V. Mossolov, J. Suarez Gonzalez Universiteit Antwerpen,
Antwerpen, Belgium
E.A. De Wolf, D. Di Croce, X. Janssen, J. Lauwers, M. Pieters, H. Van
Haevermaet, P. Van Mechelen, N. Van Remortel Vrije Universiteit Brussel,
Brussel, Belgium
S. Abu Zeid, F. Blekman, J. D’Hondt, I. De Bruyn, J. De Clercq, K. Deroover,
G. Flouris, D. Lontkovskyi, S. Lowette, I. Marchesini, S. Moortgat, L.
Moreels, Q. Python, K. Skovpen, S. Tavernier, W. Van Doninck, P. Van Mulders,
I. Van Parijs Université Libre de Bruxelles, Bruxelles, Belgium
D. Beghin, B. Bilin, H. Brun, B. Clerbaux, G. De Lentdecker, H. Delannoy, B.
Dorney, G. Fasanella, L. Favart, R. Goldouzian, A. Grebenyuk, A.K. Kalsi, T.
Lenzi, J. Luetic, N. Postiau, E. Starling, L. Thomas, C. Vander Velde, P.
Vanlaer, D. Vannerom, Q. Wang Ghent University, Ghent, Belgium
T. Cornelis, D. Dobur, A. Fagot, M. Gul, I. Khvastunov2, D. Poyraz, C. Roskas,
D. Trocino, M. Tytgat, W. Verbeke, B. Vermassen, M. Vit, N. Zaganidis
Université Catholique de Louvain, Louvain-la-Neuve, Belgium
H. Bakhshiansohi, O. Bondu, S. Brochet, G. Bruno, C. Caputo, P. David, C.
Delaere, M. Delcourt, A. Giammanco, G. Krintiras, V. Lemaitre, A. Magitteri,
A. Mertens, M. Musich, K. Piotrzkowski, A. Saggio, M. Vidal Marono, S. Wertz,
J. Zobec Centro Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil
F.L. Alves, G.A. Alves, M. Correa Martins Junior, G. Correia Silva, C. Hensel,
A. Moraes, M.E. Pol, P. Rebello Teles Universidade do Estado do Rio de
Janeiro, Rio de Janeiro, Brazil
E. Belchior Batista Das Chagas, W. Carvalho, J. Chinellato3, E. Coelho, E.M.
Da Costa, G.G. Da Silveira4, D. De Jesus Damiao, C. De Oliveira Martins, S.
Fonseca De Souza, H. Malbouisson, D. Matos Figueiredo, 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, P.G. Mercadanteb, S.F. Novaesa, SandraS. Padulaa Institute for
Nuclear Research and Nuclear Energy, Bulgarian Academy of Sciences, Sofia,
Bulgaria
A. Aleksandrov, R. Hadjiiska, P. Iaydjiev, A. Marinov, M. Misheva, M. Rodozov,
M. Shopova, G. Sultanov University of Sofia, Sofia, Bulgaria
A. Dimitrov, L. Litov, B. Pavlov, P. Petkov Beihang University, Beijing, China
W. Fang5, X. Gao5, L. Yuan Institute of High Energy Physics, Beijing, China
M. Ahmad, J.G. Bian, G.M. Chen, H.S. Chen, M. Chen, Y. Chen, C.H. Jiang, D.
Leggat, H. Liao, Z. Liu, F. Romeo, S.M. Shaheen6, A. Spiezia, J. Tao, Z. Wang,
E. Yazgan, H. Zhang, S. Zhang6, J. Zhao State Key Laboratory of Nuclear
Physics and Technology, Peking University, Beijing, China
Y. Ban, G. Chen, A. Levin, J. Li, L. Li, Q. Li, Y. Mao, S.J. Qian, D. Wang, Z.
Xu Tsinghua University, Beijing, China
Y. Wang Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, C.A. Carrillo Montoya, L.F. Chaparro Sierra, C. Florez,
C.F. González Hernández, M.A. Segura Delgado University of Split, Faculty of
Electrical Engineering, Mechanical Engineering and Naval Architecture, Split,
Croatia
B. Courbon, N. Godinovic, D. Lelas, I. Puljak, T. Sculac University of Split,
Faculty of Science, Split, Croatia
Z. Antunovic, M. Kovac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, D. Ferencek, K. Kadija, B. Mesic, A. Starodumov7, T. Susa
University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, M. Kolosova, G. Mavromanolakis, J. Mousa, C. Nicolaou,
F. Ptochos, P.A. Razis, H. Rykaczewski Charles University, Prague, Czech
Republic
M. Finger8, M. Finger Jr.8 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
A. Mahrous9, A. Mohamed10, E. Salama11,12 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, H. Kirschenmann, J. Pekkanen, M. Voutilainen Helsinki Institute of
Physics, Helsinki, Finland
J. Havukainen, J.K. Heikkilä, T. Järvinen, V. Karimäki, R. Kinnunen, T.
Lampén, K. Lassila-Perini, S. Laurila, S. Lehti, T. Lindén, P. Luukka, T.
Mäenpää, H. Siikonen, E. Tuominen, J. Tuominiemi Lappeenranta University of
Technology, Lappeenranta, Finland
T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
M. Besancon, F. Couderc, M. Dejardin, D. Denegri, J.L. Faure, F. Ferri, S.
Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry, C. Leloup,
E. Locci, J. Malcles, G. Negro, J. Rander, A. Rosowsky, M.Ö. Sahin, M. Titov
Laboratoire Leprince-Ringuet, Ecole polytechnique, CNRS/IN2P3, Université
Paris-Saclay, Palaiseau, France
A. Abdulsalam13, C. Amendola, I. Antropov, F. Beaudette, P. Busson, C.
Charlot, R. Granier de Cassagnac, I. Kucher, A. Lobanov, J. Martin Blanco, C.
Martin Perez, M. Nguyen, C. Ochando, G. Ortona, P. Pigard, J. Rembser, R.
Salerno, J.B. Sauvan, Y. Sirois, A.G. Stahl Leiton, A. Zabi, A. Zghiche
Université de Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, France
J.-L. Agram14, J. Andrea, D. Bloch, J.-M. Brom, E.C. Chabert, V. Cherepanov,
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, N. Chanon, R. Chierici, D. Contardo, P.
Depasse, H. El Mamouni, J. Fay, L. Finco, S. Gascon, M. Gouzevitch, G.
Grenier, B. Ille, F. Lagarde, I.B. Laktineh, H. Lattaud, M. Lethuillier, L.
Mirabito, S. Perries, A. Popov15, V. Sordini, G. Touquet, M. Vander Donckt, S.
Viret Georgian Technical University, Tbilisi, Georgia
A. Khvedelidze8 Tbilisi State University, Tbilisi, Georgia
Z. Tsamalaidze8 RWTH Aachen University, I. Physikalisches Institut, Aachen,
Germany
C. Autermann, L. Feld, M.K. Kiesel, K. Klein, M. Lipinski, M. Preuten, M.P.
Rauch, C. Schomakers, J. Schulz, M. Teroerde, B. Wittmer RWTH Aachen
University, III. Physikalisches Institut A, Aachen, Germany
A. Albert, D. Duchardt, M. Erdmann, S. Erdweg, T. Esch, R. Fischer, S. Ghosh,
A. Güth, T. Hebbeker, C. Heidemann, K. Hoepfner, H. Keller, L. Mastrolorenzo,
M. Merschmeyer, A. Meyer, P. Millet, S. Mukherjee, T. Pook, M. Radziej, H.
Reithler, M. Rieger, A. Schmidt, D. Teyssier, S. Thüer RWTH Aachen University,
III. Physikalisches Institut B, Aachen, Germany
G. Flügge, O. Hlushchenko, T. Kress, A. Künsken, T. Müller, A. Nehrkorn, A.
Nowack, C. Pistone, O. Pooth, D. Roy, H. Sert, A. Stahl16 Deutsches
Elektronen-Synchrotron, Hamburg, Germany
M. Aldaya Martin, T. Arndt, C. Asawatangtrakuldee, I. Babounikau, K.
Beernaert, O. Behnke, U. Behrens, A. Bermúdez Martínez, D. Bertsche, A.A. Bin
Anuar, K. Borras17, V. Botta, A. Campbell, P. Connor, C. Contreras-Campana, V.
Danilov, A. De Wit, M.M. Defranchis, C. Diez Pardos, D. Domínguez Damiani, G.
Eckerlin, T. Eichhorn, A. Elwood, E. Eren, E. Gallo18, A. Geiser, A.
Grohsjean, M. Guthoff, M. Haranko, A. Harb, J. Hauk, H. Jung, M. Kasemann, J.
Keaveney, C. Kleinwort, J. Knolle, D. Krücker, W. Lange, A. Lelek, T. Lenz, J.
Leonard, K. Lipka, W. Lohmann19, R. Mankel, I.-A. Melzer-Pellmann, A.B. Meyer,
M. Meyer, M. Missiroli, G. Mittag, J. Mnich, V. Myronenko, S.K. Pflitsch, D.
Pitzl, A. Raspereza, M. Savitskyi, P. Saxena, P. Schütze, C. Schwanenberger,
R. Shevchenko, A. Singh, H. Tholen, O. Turkot, A. Vagnerini, G.P. Van Onsem,
R. Walsh, Y. Wen, K. Wichmann, C. Wissing, O. Zenaiev University of Hamburg,
Hamburg, Germany
R. Aggleton, S. Bein, L. Benato, A. Benecke, V. Blobel, T. Dreyer, A.
Ebrahimi, 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, D. Marconi, J. Multhaup, M. Niedziela, C.E.N. Niemeyer, D.
Nowatschin, A. Perieanu, A. Reimers, O. Rieger, C. Scharf, P. Schleper, S.
Schumann, J. Schwandt, J. Sonneveld, H. Stadie, G. Steinbrück, F.M. Stober, M.
Stöver, A. Vanhoefer, B. Vormwald, I. Zoi Karlsruher Institut fuer
Technologie, Karlsruhe, Germany
M. Akbiyik, C. Barth, M. Baselga, S. Baur, E. Butz, R. Caspart, T. Chwalek, F.
Colombo, W. De Boer, A. Dierlamm, K. El Morabit, N. Faltermann, B. Freund, M.
Giffels, M.A. Harrendorf, F. Hartmann16, S.M. Heindl, U. Husemann, F.
Kassel16, I. Katkov15, S. Kudella, S. Mitra, M.U. Mozer, Th. Müller, M.
Plagge, G. Quast, K. Rabbertz, M. Schröder, I. Shvetsov, G. Sieber, H.J.
Simonis, R. Ulrich, S. Wayand, M. Weber, T. Weiler, S. Williamson, C.
Wöhrmann, R. Wolf Institute of Nuclear and Particle Physics (INPP), NCSR
Demokritos, Aghia Paraskevi, Greece
G. Anagnostou, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas, G.
Paspalaki, I. Topsis-Giotis National and Kapodistrian University of Athens,
Athens, Greece
G. Karathanasis, S. Kesisoglou, P. Kontaxakis, A. Panagiotou, I. Papavergou,
N. Saoulidou, E. Tziaferi, K. Vellidis National Technical University of
Athens, Athens, Greece
K. Kousouris, I. Papakrivopoulos, G. Tsipolitis University of Ioánnina,
Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, S. Mallios, N.
Manthos, I. Papadopoulos, E. Paradas, J. Strologas, F.A. Triantis, D.
Tsitsonis MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
M. Bartók20, M. Csanad, N. Filipovic, P. Major, M.I. Nagy, G. Pasztor, O.
Surányi, G.I. Veres Wigner Research Centre for Physics, Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath21, Á. Hunyadi, F. Sikler, T.Á. Vámi, V.
Veszpremi, G. Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear
Research ATOMKI, Debrecen, Hungary
N. Beni, S. Czellar, J. Karancsi22, A. Makovec, J. Molnar, Z. Szillasi
Institute of Physics, University of Debrecen, Debrecen, Hungary
P. Raics, Z.L. Trocsanyi, B. Ujvari 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, P. Mal, K. Mandal, 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, R. Kumar, P. Kumari, M. Lohan, A. Mehta, 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. Dutt25, S. Dutta, S. Ghosh, K. Mondal, S. Nandan, A.
Purohit, P.K. Rout, A. Roy, S. Roy Chowdhury, G. Saha, S. Sarkar, M. Sharan,
B. Singh25, S. Thakur25 Indian Institute of Technology Madras, Madras, India
P.K. Behera Bhabha Atomic Research Centre, Mumbai, India
R. Chudasama, D. Dutta, V. Jha, V. Kumar, 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, B. Sutar, RavindraKumar
Verma Tata Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, Sa. Jain, S.
Karmakar, S. Kumar, M. Maity26, G. Majumder, K. Mazumdar, N. Sahoo, T.
Sarkar26 Indian Institute of Science Education and Research (IISER), Pune,
India
S. Chauhan, S. Dube, V. Hegde, A. Kapoor, K. Kothekar, S. Pandey, A. Rane, S.
Sharma Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
S. Chenarani27, E. Eskandari Tadavani, S.M. Etesami27, M. Khakzad, M.
Mohammadi Najafabadi, M. Naseri, F. Rezaei Hosseinabadi, B. Safarzadeh28, M.
Zeinali University College Dublin, Dublin, Ireland
M. Felcini, M. Grunewald INFN Sezione di Bari a, Università di Bari b,
Politecnico di Bari c, Bari, Italy
M. Abbresciaa,b, C. Calabriaa,b, A. Colaleoa, D. Creanzaa,c, L. Cristellaa,b,
N. De Filippisa,c, M. De Palmaa,b, A. Di Florioa,b, F. Erricoa,b, L. Fiorea,
A. Gelmia,b, G. Iasellia,c, 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, A. Sharmaa, L. Silvestrisa, R.
Vendittia, P. Verwilligena, G. Zitoa INFN Sezione di Bologna a, Università di
Bologna b, Bologna, Italy
G. Abbiendia, C. Battilanaa,b, D. Bonacorsia,b, L. Borgonovia,b, S. Braibant-
Giacomellia,b, R. Campaninia,b, P. Capiluppia,b, A. Castroa,b, F.R. Cavalloa,
S.S. Chhibraa,b, 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, S. Lo Meoa, S. Marcellinia, G. Masettia, A.
Montanaria, F.L. Navarriaa,b, A. Perrottaa, F. Primaveraa,b,16, 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, A. Di Mattiaa, R. Potenzaa,b, A. Tricomia,b, C. Tuvea,b INFN
Sezione di Firenze a, Università di Firenze b, Firenze, Italy
G. Barbaglia, K. Chatterjeea,b, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, E. Focardia,b, G. Latino, P. Lenzia,b, M. Meschinia, S.
Paolettia, L. Russoa,29, G. Sguazzonia, D. Stroma, L. Viliania INFN Laboratori
Nazionali di Frascati, Frascati, Italy
L. Benussi, S. Bianco, F. Fabbri, D. Piccolo INFN Sezione di Genova a,
Università di Genova b, Genova, Italy
F. Ferroa, F. Raveraa,b, E. Robuttia, S. Tosia,b INFN Sezione di Milano-
Bicocca a, Università di Milano-Bicocca b, Milano, Italy
A. Benagliaa, A. Beschib, F. Brivioa,b, V. Cirioloa,b,16, S. Di Guidaa,d,16,
M.E. Dinardoa,b, S. Fiorendia,b, S. Gennaia, A. Ghezzia,b, P. Govonia,b, M.
Malbertia,b, S. Malvezzia, A. Massironia,b, D. Menascea, F. Monti, L. Moronia,
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, W.A. Khana, L. Listaa, S.
Meolaa,d,16, P. Paoluccia,16, 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, A. Bolettia,b, A. Bragagnolo, R. Carlina,b, P.
Checchiaa, M. Dall’Ossoa,b, P. De Castro Manzanoa, T. Dorigoa, U. Dossellia,
F. Gasparinia,b, U. Gasparinia,b, A. Gozzelinoa, S.Y. Hoh, S. Lacapraraa, P.
Lujan, M. Margonia,b, A.T. Meneguzzoa,b, J. Pazzinia,b, N. Pozzobona,b, P.
Ronchesea,b, R. Rossina,b, F. Simonettoa,b, A. Tiko, E. Torassaa, S. Venturaa,
M. Zanettia,b, P. Zottoa,b INFN Sezione di Pavia a, Università di Pavia b,
Pavia, Italy
A. Braghieria, A. Magnania, P. Montagnaa,b, S.P. Rattia,b, V. Rea, M.
Ressegottia,b, C. Riccardia,b, P. Salvinia, I. Vaia,b, P. Vituloa,b INFN
Sezione di Perugia a, Università di Perugia b, Perugia, Italy
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, L. Bianchinia, T. Boccalia, L.
Borrello, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, G. Fedia, F. Fioria,c,
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, P. Spagnoloa, R.
Tenchinia, G. Tonellia,b, A. Venturia, P.G. Verdinia INFN Sezione di Roma a,
Sapienza Università di Roma b, Rome, Italy
L. Baronea,b, F. Cavallaria, M. Cipriania,b, D. Del Rea,b, E. Di Marcoa,b, M.
Diemoza, S. Gellia,b, E. Longoa,b, B. Marzocchia,b, P. Meridiania, G.
Organtinia,b, F. Pandolfia, R. Paramattia,b, F. Preiatoa,b, S. Rahatloua,b, C.
Rovellia, F. Santanastasioa,b INFN Sezione di Torino a, Università di Torino
b, Torino, Italy, Università del Piemonte Orientale c, Novara, Italy
N. Amapanea,b, R. Arcidiaconoa,c, S. Argiroa,b, M. Arneodoa,c, N. Bartosika,
R. Bellana,b, C. Biinoa, N. Cartigliaa, F. Cennaa,b, 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, K. Shchelinaa,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
D.H. Kim, G.N. Kim, M.S. Kim, J. Lee, S. 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, J. Goh30, T.J. Kim Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, D. Gyun, S. Ha, B. Hong, Y. Jo, K. Lee, K.S. Lee, S.
Lee, J. Lim, S.K. Park, Y. Roh Sejong University, Seoul, Korea
H.S. Kim Seoul National University, Seoul, Korea
J. Almond, J. Kim, J.S. Kim, H. Lee, K. Lee, K. Nam, S.B. Oh, B.C. Radburn-
Smith, S.h. Seo, U.K. Yang, H.D. Yoo, G.B. Yu University of Seoul, Seoul,
Korea
D. Jeon, H. Kim, J.H. Kim, J.S.H. Lee, I.C. Park Sungkyunkwan University,
Suwon, Korea
Y. Choi, C. Hwang, J. Lee, I. Yu Vilnius University, Vilnius, Lithuania
V. Dudenas, A. Juodagalvis, J. Vaitkus National Centre for Particle Physics,
Universiti Malaya, Kuala Lumpur, Malaysia
I. Ahmed, Z.A. Ibrahim, M.A.B. Md Ali31, F. Mohamad Idris32, W.A.T. Wan
Abdullah, M.N. Yusli, Z. Zolkapli Universidad de Sonora (UNISON), Hermosillo,
Mexico
J.F. Benitez, A. Castaneda Hernandez, J.A. Murillo Quijada Centro de
Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico
H. Castilla-Valdez, E. De La Cruz-Burelo, M.C. Duran-Osuna, I. Heredia-De La
Cruz33, R. Lopez-Fernandez, J. Mejia Guisao, R.I. Rabadan-Trejo, M. Ramirez-
Garcia, G. Ramirez-Sanchez, R Reyes-Almanza, A. Sanchez-Hernandez Universidad
Iberoamericana, Mexico City, Mexico
S. Carrillo Moreno, C. Oropeza Barrera, F. Vazquez Valencia Benemerita
Universidad Autonoma de Puebla, Puebla, Mexico
J. Eysermans, I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada Universidad
Autónoma de San Luis Potosí, San Luis Potosí, Mexico
A. Morelos Pineda University of Auckland, Auckland, New Zealand
D. Krofcheck University of Canterbury, Christchurch, New Zealand
S. Bheesette, P.H. Butler National Centre for Physics, Quaid-I-Azam
University, Islamabad, Pakistan
A. Ahmad, M. Ahmad, M.I. Asghar, Q. Hassan, H.R. Hoorani, A. Saddique, M.A.
Shah, M. Shoaib, M. Waqas National Centre for Nuclear Research, Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, T. Frueboes, M. Górski, M. Kazana, M.
Szleper, P. Traczyk, P. Zalewski Institute of Experimental Physics, Faculty of
Physics, University of Warsaw, Warsaw, Poland
K. Bunkowski, A. Byszuk34, K. Doroba, A. Kalinowski, M. Konecki, J.
Krolikowski, M. Misiura, M. Olszewski, A. Pyskir, M. Walczak Laboratório de
Instrumentação e Física Experimental de Partículas, Lisboa, Portugal
M. Araujo, P. Bargassa, C. Beirão Da Cruz E Silva, A. Di Francesco, P.
Faccioli, B. Galinhas, M. Gallinaro, J. Hollar, N. Leonardo, M.V. Nemallapudi,
J. Seixas, G. Strong, O. Toldaiev, D. Vadruccio, J. Varela Joint Institute for
Nuclear Research, Dubna, Russia
S. Afanasiev, P. Bunin, M. Gavrilenko, I. Golutvin, I. Gorbunov, A. Kamenev,
V. Karjavine, A. Lanev, A. Malakhov, V. Matveev35,36, P. Moisenz, V. Palichik,
V. Perelygin, S. Shmatov, S. Shulha, N. Skatchkov, V. Smirnov, N. Voytishin,
A. Zarubin Petersburg Nuclear Physics Institute, Gatchina (St. Petersburg),
Russia
V. Golovtsov, Y. Ivanov, V. Kim37, E. Kuznetsova38, P. Levchenko, V. Murzin,
V. Oreshkin, I. Smirnov, D. Sosnov, V. Sulimov, L. Uvarov, S. Vavilov, A.
Vorobyev Institute for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov,
N. Krasnikov, A. Pashenkov, D. Tlisov, A. Toropin Institute for Theoretical
and Experimental Physics, Moscow, Russia
V. Epshteyn, V. Gavrilov, N. Lychkovskaya, V. Popov, I. Pozdnyakov, G.
Safronov, A. Spiridonov, A. Stepennov, V. Stolin, M. Toms, E. Vlasov, A.
Zhokin Moscow Institute of Physics and Technology, Moscow, Russia
T. Aushev National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
R. Chistov39, M. Danilov39, P. Parygin, D. Philippov, S. Polikarpov39, E.
Tarkovskii P.N. Lebedev Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin, I. Dremin36, M. Kirakosyan, S.V. Rusakov, A. Terkulov
Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University,
Moscow, Russia
A. Baskakov, A. Belyaev, E. Boos, M. Dubinin40, L. Dudko, A. Ershov, A.
Gribushin, V. Klyukhin, O. Kodolova, I. Lokhtin, I. Miagkov, S. Obraztsov, S.
Petrushanko, V. Savrin, A. Snigirev Novosibirsk State University (NSU),
Novosibirsk, Russia
A. Barnyakov41, V. Blinov41, T. Dimova41, L. Kardapoltsev41, Y. Skovpen41
Institute for High Energy Physics of National Research Centre ’Kurchatov
Institute’, Protvino, Russia
I. Azhgirey, I. Bayshev, S. Bitioukov, D. Elumakhov, A. Godizov, V. Kachanov,
A. Kalinin, 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, S. Baidali, V. Okhotnikov University of Belgrade, Faculty of
Physics and Vinca Institute of Nuclear Sciences, Belgrade, Serbia
P. Adzic42, P. Cirkovic, D. Devetak, M. Dordevic, J. Milosevic Centro de
Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT), Madrid,
Spain
J. Alcaraz Maestre, A. Álvarez Fernández, I. Bachiller, M. Barrio Luna, J.A.
Brochero Cifuentes, M. Cerrada, N. Colino, B. De La Cruz, A. Delgado Peris, C.
Fernandez Bedoya, J.P. Fernández Ramos, J. Flix, M.C. Fouz, O. Gonzalez Lopez,
S. Goy Lopez, J.M. Hernandez, M.I. Josa, D. Moran, A. Pérez-Calero Yzquierdo,
J. Puerta Pelayo, I. Redondo, L. Romero, M.S. Soares, A. Triossi Universidad
Autónoma de Madrid, Madrid, Spain
C. Albajar, J.F. de Trocóniz Universidad de Oviedo, Oviedo, Spain
J. Cuevas, C. Erice, J. Fernandez Menendez, S. Folgueras, I. Gonzalez
Caballero, J.R. González Fernández, E. Palencia Cortezon, V. Rodríguez Bouza,
S. Sanchez Cruz, P. Vischia, J.M. Vizan Garcia Instituto de Física de
Cantabria (IFCA), CSIC-Universidad de Cantabria, Santander, Spain
I.J. Cabrillo, A. Calderon, B. Chazin Quero, J. Duarte Campderros, M.
Fernandez, P.J. Fernández Manteca, A. García Alonso, J. Garcia-Ferrero, G.
Gomez, A. Lopez Virto, J. Marco, C. Martinez Rivero, P. Martinez Ruiz del
Arbol, F. Matorras, J. Piedra Gomez, C. Prieels, T. Rodrigo, A. Ruiz-Jimeno,
L. Scodellaro, N. Trevisani, I. Vila, R. Vilar Cortabitarte University of
Ruhuna, Department of Physics, Matara, Sri Lanka
N. Wickramage CERN, European Organization for Nuclear Research, Geneva,
Switzerland
D. Abbaneo, B. Akgun, E. Auffray, G. Auzinger, P. Baillon, A.H. Ball, D.
Barney, J. Bendavid, M. Bianco, A. Bocci, C. Botta, E. Brondolin, T.
Camporesi, M. Cepeda, G. Cerminara, E. Chapon, Y. Chen, G. Cucciati, D.
d’Enterria, A. Dabrowski, N. Daci, V. Daponte, A. David, A. De Roeck, N.
Deelen, M. Dobson, M. Dünser, N. Dupont, A. Elliott-Peisert, P. Everaerts, F.
Fallavollita43, D. Fasanella, G. Franzoni, J. Fulcher, W. Funk, D. Gigi, A.
Gilbert, K. Gill, F. Glege, M. Guilbaud, D. Gulhan, J. Hegeman, C. Heidegger,
V. Innocente, A. Jafari, P. Janot, O. Karacheban19, J. Kieseler, A. Kornmayer,
M. Krammer1, C. Lange, P. Lecoq, C. Lourenço, L. Malgeri, M. Mannelli, F.
Meijers, J.A. Merlin, S. Mersi, E. Meschi, P. Milenovic44, 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, T. Reis, G. Rolandi45, M. Rovere,
H. Sakulin, C. Schäfer, C. Schwick, M. Seidel, M. Selvaggi, A. Sharma, P.
Silva, P. Sphicas46, A. Stakia, J. Steggemann, M. Tosi, D. Treille, A. Tsirou,
V. Veckalns47, 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, L. Bäni, 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, J. Pata, F. Pauss, G. Perrin, L. Perrozzi, S.
Pigazzini, M. Quittnat, C. Reissel, D. Ruini, D.A. Sanz Becerra, M.
Schönenberger, L. Shchutska, V.R. Tavolaro, K. Theofilatos, M.L. Vesterbacka
Olsson, R. Wallny, D.H. Zhu Universität Zürich, Zurich, Switzerland
T.K. Aarrestad, C. Amsler49, D. Brzhechko, M.F. Canelli, A. De Cosa, R. Del
Burgo, S. Donato, C. Galloni, T. Hreus, B. Kilminster, S. Leontsinis, I.
Neutelings, G. Rauco, P. Robmann, D. Salerno, K. Schweiger, C. Seitz, Y.
Takahashi, A. Zucchetta National Central University, Chung-Li, Taiwan
Y.H. Chang, K.y. Cheng, T.H. Doan, R. Khurana, C.M. Kuo, W. Lin, A.
Pozdnyakov, S.S. Yu National Taiwan University (NTU), Taipei, Taiwan
P. Chang, Y. Chao, K.F. Chen, P.H. Chen, W.-S. Hou, Arun Kumar, Y.F. Liu,
R.-S. Lu, E. Paganis, A. Psallidas, A. Steen Chulalongkorn University, Faculty
of Science, Department of Physics, Bangkok, Thailand
B. Asavapibhop, N. Srimanobhas, N. Suwonjandee Çukurova University, Physics
Department, Science and Art Faculty, Adana, Turkey
A. Bat, F. Boran, S. Damarseckin, Z.S. Demiroglu, F. Dolek, C. Dozen, I.
Dumanoglu, E. Eskut, S. Girgis, G. Gokbulut, Y. Guler, E. Gurpinar, I. Hos50,
C. Isik, E.E. Kangal51, O. Kara, A. Kayis Topaksu, U. Kiminsu, M. Oglakci, G.
Onengut, K. Ozdemir52, D. Sunar Cerci53, B. Tali53, U.G. Tok, H. Topakli54, S.
Turkcapar, I.S. Zorbakir, C. Zorbilmez Middle East Technical University,
Physics Department, Ankara, Turkey
B. Isildak55, G. Karapinar56, M. Yalvac, M. Zeyrek Bogazici University,
Istanbul, Turkey
I.O. Atakisi, E. Gülmez, M. Kaya57, O. Kaya58, S. Ozkorucuklu59, S. Tekten,
E.A. Yetkin60 Istanbul Technical University, Istanbul, Turkey
M.N. Agaras, A. Cakir, K. Cankocak, Y. Komurcu, S. Sen61 Institute for
Scintillation Materials of National Academy of Science of Ukraine, Kharkov,
Ukraine
B. Grynyov National Scientific Center, Kharkov Institute of Physics and
Technology, Kharkov, Ukraine
L. Levchuk University of Bristol, Bristol, United Kingdom
F. Ball, L. Beck, J.J. Brooke, D. Burns, E. Clement, D. Cussans, O. Davignon,
H. Flacher, J. Goldstein, G.P. Heath, H.F. Heath, L. Kreczko, D.M. Newbold62,
S. Paramesvaran, B. Penning, T. Sakuma, 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, E. Olaiya, D. Petyt, 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, D.
Colling, P. Dauncey, G. Davies, M. Della Negra, R. Di Maria, Y. Haddad, G.
Hall, G. Iles, T. James, M. Komm, C. Laner, L. Lyons, A.-M. Magnan, S. Malik,
A. Martelli, J. Nash64, A. Nikitenko7, V. Palladino, M. Pesaresi, D.M.
Raymond, A. Richards, A. Rose, E. Scott, C. Seez, A. Shtipliyski, G. Singh, M.
Stoye, T. Strebler, S. Summers, A. Tapper, K. Uchida, T. Virdee16, N. Wardle,
D. Winterbottom, J. Wright, 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, H. Liu, C. Madrid, B. Mcmaster, N.
Pastika, C. Smith Catholic University of America, Washington DC, USA
R. Bartek, A. Dominguez The University of Alabama, Tuscaloosa, USA
A. Buccilli, S.I. Cooper, C. Henderson, P. Rumerio, C. West Boston University,
Boston, USA
D. Arcaro, T. Bose, D. Gastler, D. Pinna, D. Rankin, C. Richardson, J. Rohlf,
L. Sulak, D. Zou Brown University, Providence, USA
G. Benelli, X. Coubez, D. Cutts, M. Hadley, J. Hakala, U. Heintz, J.M.
Hogan65, K.H.M. Kwok, E. Laird, G. Landsberg, J. Lee, Z. Mao, M. Narain, S.
Sagir66, R. Syarif, E. Usai, D. Yu University of California, Davis, Davis, USA
R. Band, C. Brainerd, R. Breedon, D. Burns, M. Calderon De La Barca Sanchez,
M. Chertok, J. Conway, R. Conway, P.T. Cox, R. Erbacher, C. Flores, G. Funk,
W. Ko, O. Kukral, R. Lander, M. Mulhearn, D. Pellett, J. Pilot, S. Shalhout,
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, S. Regnard, D. Saltzberg, C. Schnaible, V. Valuev
University of California, Riverside, Riverside, USA
E. Bouvier, K. Burt, R. Clare, J.W. Gary, S.M.A. Ghiasi Shirazi, G. Hanson, G.
Karapostoli, E. Kennedy, F. Lacroix, O.R. Long, M. Olmedo Negrete, M.I.
Paneva, W. Si, L. Wang, H. Wei, S. Wimpenny, B.R. Yates University of
California, San Diego, La Jolla, USA
J.G. Branson, P. Chang, S. Cittolin, M. Derdzinski, R. Gerosa, D. Gilbert, B.
Hashemi, A. Holzner, D. Klein, G. Kole, V. Krutelyov, J. Letts, M.
Masciovecchio, D. Olivito, S. Padhi, M. Pieri, M. Sani, V. Sharma, S. Simon,
M. Tadel, A. Vartak, S. Wasserbaech67, J. Wood, F. Würthwein, A. Yagil, G.
Zevi Della Porta University of California, Santa Barbara - Department of
Physics, Santa Barbara, USA
N. Amin, R. Bhandari, J. Bradmiller-Feld, C. Campagnari, M. Citron, A. Dishaw,
V. Dutta, M. Franco Sevilla, L. Gouskos, R. Heller, J. Incandela, A.
Ovcharova, H. Qu, J. Richman, D. Stuart, I. Suarez, S. Wang, J. Yoo California
Institute of Technology, Pasadena, USA
D. Anderson, A. Bornheim, J.M. Lawhorn, H.B. Newman, T.Q. Nguyen, M.
Spiropulu, J.R. Vlimant, R. Wilkinson, S. Xie, Z. Zhang, R.Y. Zhu Carnegie
Mellon University, Pittsburgh, USA
M.B. Andrews, T. Ferguson, T. Mudholkar, M. Paulini, M. Sun, I. Vorobiev, M.
Weinberg University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, F. Jensen, A. Johnson, M. Krohn, 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, K. Mcdermott, N. Mirman,
J.R. Patterson, D. Quach, A. Rinkevicius, A. Ryd, L. Skinnari, L. Soffi, S.M.
Tan, Z. Tao, J. Thom, J. Tucker, P. Wittich, M. Zientek Fermi National
Accelerator Laboratory, Batavia, USA
S. Abdullin, M. Albrow, M. Alyari, G. Apollinari, A. Apresyan, A. Apyan, S.
Banerjee, L.A.T. Bauerdick, A. Beretvas, J. Berryhill, P.C. Bhat, K. Burkett,
J.N. Butler, A. Canepa, G.B. Cerati, H.W.K. Cheung, F. Chlebana, M. Cremonesi,
J. Duarte, V.D. Elvira, J. Freeman, Z. Gecse, E. Gottschalk, L. Gray, D.
Green, S. Grünendahl, O. Gutsche, J. Hanlon, R.M. Harris, S. Hasegawa, J.
Hirschauer, Z. Hu, B. Jayatilaka, S. Jindariani, M. Johnson, U. Joshi, B.
Klima, M.J. Kortelainen, B. Kreis, S. Lammel, D. Lincoln, R. Lipton, M. Liu,
T. Liu, J. Lykken, K. Maeshima, J.M. Marraffino, D. Mason, P. McBride, P.
Merkel, S. Mrenna, S. Nahn, V. O’Dell, K. Pedro, C. Pena, O. Prokofyev, G.
Rakness, L. Ristori, A. Savoy-Navarro68, B. Schneider, E. Sexton-Kennedy, A.
Soha, W.J. Spalding, L. Spiegel, S. Stoynev, J. Strait, N. Strobbe, L. Taylor,
S. Tkaczyk, N.V. Tran, L. Uplegger, E.W. Vaandering, C. Vernieri, M.
Verzocchi, R. Vidal, M. Wang, H.A. Weber, A. Whitbeck University of Florida,
Gainesville, USA
D. Acosta, P. Avery, P. Bortignon, D. Bourilkov, A. Brinkerhoff, L. Cadamuro,
A. Carnes, M. Carver, D. Curry, R.D. Field, S.V. Gleyzer, B.M. Joshi, J.
Konigsberg, A. Korytov, K.H. Lo, P. Ma, K. Matchev, H. Mei, G. Mitselmakher,
D. Rosenzweig, K. Shi, D. Sperka, J. Wang, S. Wang, X. Zuo Florida
International University, Miami, USA
Y.R. Joshi, S. Linn Florida State University, Tallahassee, USA
A. Ackert, T. Adams, A. Askew, S. Hagopian, V. Hagopian, K.F. Johnson, T.
Kolberg, G. Martinez, T. Perry, H. Prosper, A. Saha, C. Schiber, R. Yohay
Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, V. Bhopatkar, S. Colafranceschi, M. Hohlmann, D. Noonan, M.
Rahmani, T. Roy, F. Yumiceva University of Illinois at Chicago (UIC), Chicago,
USA
M.R. Adams, L. Apanasevich, D. Berry, R.R. Betts, R. Cavanaugh, X. Chen, S.
Dittmer, O. Evdokimov, C.E. Gerber, D.A. Hangal, D.J. Hofman, K. Jung, J.
Kamin, C. Mills, I.D. Sandoval Gonzalez, M.B. Tonjes, H. Trauger, N. Varelas,
H. Wang, X. Wang, Z. Wu, J. Zhang The University of Iowa, Iowa City, USA
M. Alhusseini, B. Bilki69, W. Clarida, K. Dilsiz70, S. Durgut, R.P.
Gandrajula, M. Haytmyradov, V. Khristenko, J.-P. Merlo, A. Mestvirishvili, 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, U. Sarica, M. Swartz, M. Xiao, C. You The
University of Kansas, Lawrence, USA
A. Al-bataineh, P. Baringer, A. Bean, S. Boren, J. Bowen, A. Bylinkin, J.
Castle, S. Khalil, A. Kropivnitskaya, D. Majumder, W. Mcbrayer, M. Murray, C.
Rogan, S. Sanders, E. Schmitz, J.D. Tapia Takaki, Q. Wang Kansas State
University, Manhattan, USA
S. Duric, A. Ivanov, K. Kaadze, D. Kim, Y. Maravin, D.R. Mendis, T. Mitchell,
A. Modak, A. Mohammadi, L.K. Saini, N. Skhirtladze Lawrence Livermore National
Laboratory, Livermore, USA
F. Rebassoo, D. Wright University of Maryland, College Park, USA
A. Baden, O. Baron, A. Belloni, S.C. Eno, Y. Feng, C. Ferraioli, N.J. Hadley,
S. Jabeen, G.Y. Jeng, R.G. Kellogg, J. Kunkle, A.C. Mignerey, S. Nabili, F.
Ricci-Tam, Y.H. Shin, A. Skuja, S.C. Tonwar, K. Wong Massachusetts Institute
of Technology, Cambridge, USA
D. Abercrombie, B. Allen, V. Azzolini, A. Baty, G. Bauer, R. Bi, S. Brandt, W.
Busza, I.A. Cali, M. D’Alfonso, Z. Demiragli, G. Gomez Ceballos, M. Goncharov,
P. Harris, D. Hsu, M. Hu, Y. Iiyama, G.M. Innocenti, M. Klute, D. Kovalskyi,
Y.-J. Lee, P.D. Luckey, B. Maier, A.C. Marini, C. Mcginn, C. Mironov, S.
Narayanan, X. Niu, C. Paus, C. Roland, G. Roland, G.S.F. Stephans, K. Sumorok,
K. Tatar, D. Velicanu, J. Wang, T.W. Wang, B. Wyslouch, S. Zhaozhong
University of Minnesota, Minneapolis, USA
A.C. Benvenuti${}^{\textrm{\textdagger}}$, R.M. Chatterjee, A. Evans, P.
Hansen, J. Hiltbrand, Sh. Jain, S. Kalafut, Y. Kubota, Z. Lesko, J. Mans, N.
Ruckstuhl, R. Rusack, M.A. Wadud University of Mississippi, Oxford, USA
J.G. Acosta, S. Oliveros University of Nebraska-Lincoln, Lincoln, USA
E. Avdeeva, K. Bloom, D.R. Claes, C. Fangmeier, F. Golf, R. Gonzalez Suarez,
R. Kamalieddin, I. Kravchenko, J. Monroy, J.E. Siado, G.R. Snow, B. Stieger
State University of New York at Buffalo, Buffalo, USA
A. Godshalk, C. Harrington, I. Iashvili, A. Kharchilava, C. Mclean, D. Nguyen,
A. Parker, S. Rappoccio, B. Roozbahani Northeastern University, Boston, USA
G. Alverson, E. Barberis, C. Freer, A. Hortiangtham, D.M. Morse, T. Orimoto,
R. Teixeira De Lima, T. Wamorkar, B. Wang, A. Wisecarver, D. Wood Northwestern
University, Evanston, USA
S. Bhattacharya, O. Charaf, K.A. Hahn, N. Mucia, N. Odell, M.H. Schmitt, K.
Sung, M. Trovato, M. Velasco University of Notre Dame, Notre Dame, USA
R. Bucci, N. Dev, M. Hildreth, K. Hurtado Anampa, C. Jessop, D.J. Karmgard, N.
Kellams, K. Lannon, W. Li, N. Loukas, N. Marinelli, F. Meng, C. Mueller, Y.
Musienko35, M. Planer, A. Reinsvold, R. Ruchti, P. Siddireddy, G. Smith, S.
Taroni, M. Wayne, A. Wightman, M. Wolf, A. Woodard The Ohio State University,
Columbus, USA
J. Alimena, L. Antonelli, B. Bylsma, L.S. Durkin, S. Flowers, B. Francis, A.
Hart, C. Hill, W. Ji, T.Y. Ling, W. Luo, B.L. Winer Princeton University,
Princeton, USA
S. Cooperstein, P. Elmer, J. Hardenbrook, S. Higginbotham, A. Kalogeropoulos,
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 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, 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
Z. Chen, K.M. Ecklund, S. Freed, F.J.M. Geurts, M. Kilpatrick, W. Li, B.P.
Padley, R. Redjimi, J. Roberts, J. Rorie, W. Shi, Z. Tu, J. Zabel, A. Zhang
University of Rochester, Rochester, USA
A. Bodek, P. de Barbaro, R. Demina, Y.t. Duh, J.L. Dulemba, C. Fallon, T.
Ferbel, M. Galanti, A. Garcia-Bellido, J. Han, O. Hindrichs, A.
Khukhunaishvili, P. Tan, R. Taus Rutgers, The State University of New Jersey,
Piscataway, USA
A. Agapitos, J.P. Chou, Y. Gershtein, E. Halkiadakis, M. Heindl, E. Hughes, S.
Kaplan, R. Kunnawalkam Elayavalli, S. Kyriacou, A. Lath, R. Montalvo, K. Nash,
M. Osherson, H. Saka, S. Salur, S. Schnetzer, D. Sheffield, S. Somalwar, R.
Stone, S. Thomas, P. Thomassen, M. Walker University of Tennessee, Knoxville,
USA
A.G. Delannoy, J. Heideman, G. Riley, 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, R. Mueller, D. Overton, L.
Perniè, D. Rathjens, A. Safonov Texas Tech University, Lubbock, USA
N. Akchurin, J. Damgov, F. De Guio, P.R. Dudero, S. Kunori, K. Lamichhane,
S.W. Lee, T. Mengke, S. Muthumuni, T. Peltola, S. Undleeb, I. Volobouev, Z.
Wang Vanderbilt University, Nashville, USA
S. Greene, A. Gurrola, R. Janjam, W. Johns, C. Maguire, A. Melo, H. Ni, K.
Padeken, J.D. Ruiz Alvarez, P. Sheldon, S. Tuo, J. Velkovska, M. Verweij, Q.
Xu University of Virginia, Charlottesville, USA
M.W. Arenton, P. Barria, B. Cox, R. Hirosky, M. Joyce, A. Ledovskoy, H. Li, C.
Neu, T. Sinthuprasith, Y. Wang, E. Wolfe, F. Xia Wayne State University,
Detroit, USA
R. Harr, P.E. Karchin, N. Poudyal, J. Sturdy, P. Thapa, S. Zaleski University
of Wisconsin - Madison, Madison, WI, USA
M. Brodski, J. Buchanan, C. Caillol, D. Carlsmith, S. Dasu, L. Dodd, B.
Gomber, M. Grothe, M. Herndon, A. Hervé, U. Hussain, P. Klabbers, A. Lanaro,
K. Long, R. Loveless, T. Ruggles, A. Savin, V. Sharma, N. Smith, W.H. Smith,
N. Woods †: Deceased
1: Also at Vienna University of Technology, Vienna, Austria
2: Also at IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
3: Also at Universidade Estadual de Campinas, Campinas, Brazil
4: Also at Federal University of Rio Grande do Sul, Porto Alegre, Brazil
5: Also at Université Libre de Bruxelles, Bruxelles, Belgium
6: Also at University of Chinese Academy of Sciences, Beijing, China
7: Also at Institute for Theoretical and Experimental Physics, Moscow, Russia
8: Also at Joint Institute for Nuclear Research, Dubna, Russia
9: Now at Helwan University, Cairo, Egypt
10: Also at Zewail City of Science and Technology, Zewail, Egypt