B2G-18-001

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$HeadURL:svn+ssh://svn.cern.ch/reps/tdr2/papers/B2G-18-001/trunk/B2G-18-001.tex$
$Id:B2G-18-001.tex4886132019-02-0819:11:34Zknash$

B2G-18-001

# Search for a boson decaying to a vector-like quark and a top or bottom quark
in the all-jets final state

###### Abstract

A search for a heavy $\PWpr$ resonance decaying to one $\mathrm{B}$ or
$\mathrm{T}$ vector-like quark and a top or bottom quark, respectively, is
presented. The search uses proton-proton collision data collected in 2016 with
the CMS detector at the LHC, corresponding to an integrated luminosity of
35.9at $\sqrt{s}=13\TeV$. Both decay channels result in a final state with a
top quark, a Higgs boson, and a $\cPqb$ quark, each produced with significant
energy. The all-hadronic decays of both the Higgs boson and the top quark are
considered. The final-state jets, some of which correspond to merged decay
products of a boosted top quark and a Higgs boson, are selected using jet
substructure techniques, which help to suppress standard model backgrounds. A
$\PWpr$ boson signal would appear as a narrow peak in the invariant mass
distribution of these jets. No significant deviation in data with respect to
the standard model background predictions is observed. Cross section upper
limits on $\PWpr$ boson production in the top quark, Higgs boson, and $\cPqb$
quark decay mode are set as a function of the $\PWpr$ mass, for several
vector-like quark mass hypotheses. These are the first limits for $\PWpr$
boson production in this decay channel, and cover a range of 0.01 to
0.43$\unit{pb}$ in the $\PWpr$ mass range between 1.5 and 4.0.

##  0.1 Introduction

Many extensions of the standard model (SM) predict new massive charged gauge
bosons [1, 2, 3]. The $\PWpr$ boson is a hypothetical heavy partner of the SM
$\PW$ gauge boson that could be produced in proton-proton (pp) collisions at
the CERN LHC. Searches for $\PWpr$ bosons have been most recently performed at
a center-of-mass energy of $13\TeV$ by the CMS and ATLAS Collaborations in the
lepton-neutrino [4, 5], diboson [6, 7], and diquark [8, 9] final states.
Vector-like quarks (VLQs) are hypothetical heavy partners of SM quarks for
which the left- and right-handed chiralities transform the same way under SM
gauge groups. Searches for VLQs have been performed by the CMS and ATLAS
Collaborations in both the single [10, 11, 12, 13] and pair production [14,
15, 16] channels. The decay of the $\PWpr$ boson to a heavy $\mathrm{B}$ or
$\mathrm{T}$ VLQ and a top or $\cPqb$ quark, respectively, is predicted, , in
composite Higgs boson models with custodial symmetry protection [17, 18, 19].
These models stabilize the quantum corrections to the Higgs mass and preserve
naturalness. The $\PWpr$ branching fraction to a quark and a VLQ depends on
the VLQ mass, with a maximum of 50% in the high VLQ mass range at the
threshold of custodian production (see Ref. [20]).

A search for a $\PWpr$ boson in this decay mode is presented for the first
time. The analysis considers the decay channel where the $\mathrm{B}$ or
$\mathrm{T}$ VLQ decays into a Higgs boson and a $\cPqb$ or top quark,
respectively, in the all-jets final state. Both the $\mathrm{B}$ and
$\mathrm{T}$ VLQ-mediated decays result in the same signature, as can be seen
in Fig.1. Because of the high $\PWpr$ and VLQ masses considered in this
analysis, the decay products are highly Lorentz boosted. These boosted decay
products are reconstructed as single jets with distinct substructure, which is
used in the analysis to distinguish them from SM multijet production. An
inclusive search for a $\PWpr$ boson decaying to a top quark, a Higgs boson,
and a $\cPqb$ quark is performed. The SM background is dominated by events
comprised of jets produced via the strong interaction, referred to as quantum
chromodynamics (QCD) multijet events, and top quark pair production ($\ttbar$)
events. These backgrounds are modeled by a combination of Monte Carlo (MC)
simulation and control regions in data. The invariant mass distribution of the
three-jet system, $m_{\mathrm{\cPqt\PH\cPqb}}$, is used to set the first
limits on the $\PWpr$ boson production cross section in the decay channel to a
$\mathrm{B}$ or $\mathrm{T}$ VLQ. The data sample used in the analysis
corresponds to an integrated luminosity of 35.9$\fbinv$ [21] of pp collision
data at $\sqrt{s}=13\TeV$, recorded in 2016.

Figure 1:  The $\PWpr$ boson production and decays considered in the analysis.
The analysis assumes equal branching fractions for $\PWpr$ boson to
$\cPqt\mathrm{B}$ and $\cPqb\mathrm{T}$ and 50% for each VLQ to q.

The theoretical framework followed in the analysis is described in Ref. [20].
In this model the top and $\PWpr$ are superpositions of elementary and
composite modes, with the top degree of compositeness given by
$s_{\mathrm{L}}$, and the mixing angle of the elementary and composite $\PWpr$
states given by $\theta_{2}$. The $\PWpr$ boson production cross section is
inversely proportional to $\mathrm{cot^{2}}(\theta_{2})$, but low
$\cot(\theta_{2})$ values tend to be dominated by the leptonic $\PWpr$ boson
decay mode. High values of the $s_{\mathrm{L}}$ parameter increase the
relative phase space for the decay into two VLQs, whereas low $s_{\mathrm{L}}$
values enhance the $\PWpr$ diboson decays. The analysis assumes this
theoretical framework as evaluated at $s_{\mathrm{L}}=0.5$ and
$\cot(\theta_{2})=3$, which is chosen for the purposes of sensitivity in the
$\PWpr$ decay channel to a single VLQ. The expected signal cross sections in
the analysis are evaluated at 13using the framework of Ref. [20] for $\PWpr$
masses in the range 1.5 to 4.0with the assumptions that the $\PWpr\to$VLQ
branching fraction is equally distributed between the t$\mathrm{B}$ and
b$\mathrm{T}$ final states. As a benchmark for the analysis, the VLQ branching
fractions for each of the decays $\mathrm{B}\to$and $\mathrm{T}\to$are assumed
to be 50%, consistent with the benchmark used in other recent searches.

##  0.2 The CMS detector

The central feature of the CMS apparatus is a superconducting solenoid of 6m
internal diameter, providing a magnetic field of 3.8T. Within the solenoid
volume are a silicon pixel and strip tracker, a lead tungstate crystal
electromagnetic calorimeter (ECAL), and a brass and scintillator hadron
calorimeter (HCAL), each composed of a barrel and two endcap sections. Forward
calorimeters extend the pseudorapidity coverage provided by the barrel and
endcap detectors. Muons are detected in gas-ionization chambers embedded in
the steel flux-return yoke outside the solenoid. A more detailed description
of the CMS detector, together with a definition of the coordinate system used
and the relevant kinematic variables, can be found in Ref. [22].

The particle-flow algorithm [23] aims to reconstruct and identify each
individual particle with an optimized combination of information from the
various elements of the CMS detector. The energy of each photon is obtained
from the ECAL measurement. The energy of each electron is determined from a
combination of the electron momentum at the primary interaction vertex as
determined by the tracker, the energy of the corresponding ECAL cluster, and
the energy sum of all bremsstrahlung photons spatially compatible with
originating from the electron track. The energy of each muon is obtained from
the momentum, which is measured by the curvature of the corresponding track.
The energy of each charged hadron is determined from a combination of their
momentum measured in the tracker and the matching ECAL and HCAL energy
deposits, corrected for zero-suppression effects and for the response function
of the calorimeters to hadronic showers. Finally, the energy of each neutral
hadron is obtained from the corresponding corrected ECAL and HCAL energies
that are not associated with a charged hadron track.

Jets are clustered with the anti-$\kt$ [24] algorithm in the $\FASTJET$ 3.0
[25] software package. Jet momentum is determined as the vectorial sum of all
particle momenta in the jet, and is found from simulation to be within 5 to
10% of the true momentum over the whole spectrum and detector acceptance.
Additional pp interactions within the same or nearby bunch crossings (pileup)
can contribute additional tracks and calorimetric energy depositions to the
jet momentum. To mitigate this effect, charged particles originating from sub-
leading pp collision vertices within the same or adjacent bunch crossings are
discarded in the jet clustering procedure, where the primary collision vertex
is defined as the vertex largest quadrature-summed $\pt$ of all reconstructed
particles. To account for the neutral pileup component, the pileup per
particle identification (PUPPI) algorithm [26] is used, which applies weights
that rescale the jet transverse momentum based on the per-particle probability
of originating from the primary vertex prior to jet clustering. Jet energy
corrections are derived from simulation studies so that the average measured
response of jets becomes identical to that of particle level jets. In situ
measurements of the momentum balance in dijet, photon+jet, Z+jet, and multijet
events are used to determine any residual differences between the jet energy
scale in data and in simulation, and appropriate corrections are made [27].
Additional selection criteria are applied to each jet to remove jets
potentially dominated by instrumental effects or reconstruction failures. The
jet energy resolution amounts typically to 15% at 10, 8% at 100, and 4% at 1,
to be compared to about 40, 12, and 5% obtained when the calorimeters alone
are used for jet clustering.

Events of interest are selected using a two-tiered trigger system [28]. The
first level (L1), composed of custom hardware processors, uses information
from the calorimeters and muon detectors to select events at a rate of around
100kHz within a time interval of less than 4. The second level, known as the
high-level trigger (HLT), consists of a farm of processors running a version
of the full event reconstruction software optimized for fast processing, and
reduces the event rate to around 1kHz before data storage.

##  0.3 Simulated samples

The $\ttbar$ production background is estimated from simulation, and is
generated with 2.0 [29, 30, 31, 32]. The signal samples are generated at
leading order using 2.3.3 [33, 34] with the NNPDF3.0 leading order parton
distribution function (PDF) set, in the mass range from 1.5 to 4.0in
0.5increments. The analysis uses a QCD multijet sample as a cross check for
the background estimate, which is also generated at LO with . Parton showering
and hadronization are simulated with $\PYTHIA$8.212 [35] using either the
CUETP8M2T4 [36] or CUETP8M1 [37] underlying event tunes. For each $\PWpr$
boson mass point, three VLQ mass points are generated with the VLQ mass range
from 0.8 to 3.0. The generated VLQ masses are scaled to the $\PWpr$ boson mass
($m_{\PWpr}$) such that there is a low ($\approx$1/2 $m_{\PWpr}$), medium
($\approx$2/3 $m_{\PWpr}$), and high ($\approx$3/4 $m_{\PWpr}$) mass sample
for each $\PWpr$ boson mass point in order to explore the sensitive phase
space of the boosted $\PWpr$ boson decay products. The generated $\PWpr$ boson
and VLQ widths are narrow as compared with the detector and reconstruction
resolutions which is in accord with theoretical predictions for most of the
analyzed phase space. The simulation of the CMS detector uses $\GEANTfour$
[38]. All MC samples include pileup simulation and are weighted such that the
distribution of the number of interactions per bunch crossing agrees with that
observed in data.

##  0.4 Event reconstruction

The $\PWpr\to\mathrm{T}/\mathrm{B}\to\cPqt\PH\cPqb$ channel is characterized
by three high-$\pt$ jets. The jets from the top quark (top jet) and Higgs
boson (Higgs jet) decays tend to be wide and massive, whereas the jet from the
$\cPqb$ quark ($\cPqb$ jet) will tend to be narrow and have a lower mass.
Therefore, one jet clustered with the anti-$\kt$ algorithm with a distance
parameter of 0.8 (AK8 jet) with $\pt>300\GeV$ is required for the Higgs boson
candidate jet. One AK8 jet with $\pt>400\GeV$ is required for the top quark
candidate jet. One anti-jet with a distance parameter of 0.4 (AK4 jet) with
$\pt>200\GeV$ is required for the $\cPqb$ candidate jet. The separation
$\Delta$R ($\sqrt{\smash[b]{(\Delta\phi)^{2}+(\Delta\eta)^{2}}}$) between the
two AK8 jets is required to be at least 1.8 in order to reduce the correlation
of jet shapes arising from the abutting of jet boundaries, which can bias the
background estimate. The AK8 jets are then selected as being consistent with a
top quark or a Higgs boson decay using the tagging procedures defined below.
The collection of jets considered for the $\cPqb$ quark candidate is then
populated by AK4 jets with $\Delta$R of at least 1.2 from the tagged AK8 jets.
In the case of multiple jets with the same tag, the tagged candidate is chosen
randomly. Jet identification criteria are used for these three jets in order
to reduce the impact of spurious jets from detector noise [39]. All jets in
the analysis are required to be within $\abs{\eta}<2.4$.

Because the signal of interest is a high mass resonance decaying to multiple
high-$\pt$ jets, data events are triggered by $\HT>$800 or 900$\GeV$, where
$\HT$ is defined as the sum of all AK4 jet $\pt$ in the event, or a AK8 jet
$\pt>450\GeV$. The signal of interest usually fulfills the high $\HT$ trigger
requirement, and the AK8 jet $\pt$ trigger is included to overcome an issue in
the trigger $\HT$ calculation that impacts about 24% of the analyzed data.

The efficiency of the trigger selection is studied using a sample of events
that have at least one muon of $\pt>24\GeV$. The fraction of these events that
pass the full trigger selection is defined as the trigger efficiency and is
shown in Fig. 2 as a function of $\HT$. The offline event selection requires
that $\HT$ be larger than 1which ensures that the trigger efficiency is larger
than 93% near the threshold and is nearly 100% over most of the signal region.
Although there is little inefficiency due to the trigger, this is taken into
account as an event weight when processing MC samples.

Figure 2:  Trigger efficiency as a function of $\HT$. Events are required to
have $\HT>1\TeV$ as is indicated by the red dashed line. The $\HT$
distributions of two $\PWpr$ signal hypotheses are shown for comparison,
normalized to unit area.

###  0.4.1 Top jet tagging

For top quarks with $\pt>400\GeV$, the decay products, one $\cPqb$ quark and
two light quarks, can merge into a single AK8 jet. Top quark jets are
identified using a set of three quantities defined below.

The N-subjettiness [40] algorithm defines the $\tau_{N}$ variable, which
quantifies how consistent the jet energy pattern is with $N$ or fewer hard
partons, with the low $\tau_{N}$ values being more consistent with $N$ or
fewer partons. In the case of a top quark hadronic decay, the ratio of
$\tau_{3}$ to $\tau_{2}$ is used.

The merged top jet can also be discriminated from background by using the
large top quark mass. The modified mass drop tagger algorithm [41], also known
as the “soft drop” algorithm [42] with $\beta=0$ and $z=0.1$ is used to
calculate this mass variable, $m_{\mathrm{SD}}^{\cPqt}$. This algorithm
declusters the jet, and removes soft radiations, thus allowing a clearer
separation between the merged top jet and background.

Finally, as the top jet contains a $\cPqb$ quark, additional discrimination
power can be achieved by using subjet $\cPqb$ tagging with the combined
secondary vertex version 2 (CSVv2) $\cPqb$ tagging algorithm
($SJ_{\mathrm{csvmax}}$) [43]. We use a $\cPqb$ tagging operating point
defined by a 10% misidentification probability with approximately an 80%
efficiency.

The MC to data correction (scale factor) for the top tagging operating point
in Table 0.4.4 is measured to be $1.07^{+0.11}_{-0.04}$ in a sample enriched
in semileptonic $\ttbar$ production, using the same procedure as outlined in
Ref. [39].

###  0.4.2 Higgs jet tagging

In the case of a highly boosted Higgs boson in the $\bbbar$ decay mode, the
decay products tend to merge into a jet that has a mass consistent with a
Higgs boson and that contains two $\cPqb$ hadrons clustered into the jet. Once
again, the soft drop algorithm is used to provide the variable
$m_{\mathrm{SD}}^{\PH}$ as a measure of the Higgs boson jet mass, but in this
case the jet is scaled using a simulation-derived correction suitable for
resonances below the top jet tagging mass window [44], which is $\pt$ and
$\eta$ dependent but results in a 5-10% mass amplification in both data and
MC. Scale factors are used for the jet mass scale and resolution, which are
derived from a fit to the distribution of the $\PW$ boson jet
$m_{\mathrm{SD}}^{\PH}$ spectrum in a sample enriched in semileptonic $\ttbar$
production using the technique outlined in Ref. [39].

To identify the two $\cPqb$ quarks clustered into the merged Higgs jet, a
dedicated double-$\cPqb$ tagging algorithm (Dbtag) is used at an operating
point with a misidentification probability of approximately 3% and an
efficiency of 50%. Data samples enriched in QCD produced $\bbbar$ and $\ttbar$
events are used to establish scale factors for this tagger for the cases of
signal and mistagged top quarks, respectively [43].

Figure 3 shows the variable distributions that are used for top and Higgs
candidate jet tagging in $\ttbar$, QCD, and signal MC simulation. The
selections for these distributions includes all other top and Higgs candidate
jet selections in order to preserve variable correlations.

In the rare occurrence that a jet passes both the Higgs and top jet tags, the
ambiguity is resolved by giving the Higgs jet tag priority.

###  0.4.3 $\cPqb$ jet tagging

The $\cPqb$ quark from the VLQ or $\PWpr$ decay is reconstructed as an AK4 jet
that is required to pass the CSVv2 $\cPqb$ tagging algorithm [43] at the same
operating point as is used for the subjets of the merged top jet. A MC to data
scale factor for the $\cPqb$ tagging requirement is used in order to improve
the agreement of data and simulation.

###  0.4.4 Event selection

Event selection details can be found in Table 0.4.4. The signal region used
for setting cross section upper limits is required to contain a top, a Higgs
boson, and a $\cPqb$ tagged jet.

The sensitivity of the selections used in the analysis have been studied both
in the context of the expected limit and the $\PWpr$ discovery potential.
After identifying the top, Higgs, and $\cPqb$ candidate jets, the $\PWpr$
candidate mass is analyzed as the invariant mass of the three jets. Table
0.4.4 shows the signal efficiency for all samples considered in the analysis.

Selection regions used in the analysis. Tagging discriminator selections and
regions described in the text are explicitly defined here. The signal region
(SR) is used to set cross section upper limits, the control regions (CRN) are
used to estimate the QCD background, and the validation region (VR) is used to
validate the background estimation procedure.  Label Discriminator selections
$\PH_{\text{tag}}$ $\text{Dbtag}>0.8$ and $105<m_{\mathrm{SD}}^{\PH}<135\GeV$
$\cPqt_{\text{tag}}$ $SJ_{\text{csvmax}}>0.5426$ and $\tau_{3}/\tau_{2}<0.8$
and $105<m_{\mathrm{SD}}^{\cPqt}<210\GeV$ $\cPqb_{\text{tag}}$
$\text{CSVv2}>0.5426$ $\PH_{\text{antitag}}$ $m_{\mathrm{SD}}^{\PH}<30\GeV$
$\cPqt_{\text{antitag}}$ $SJ_{\text{csvmax}}>0.5426$ and
$\tau_{3}/\tau_{2}>0.65$ and $30<m_{\mathrm{SD}}^{\cPqt}<105\GeV$
$\cPqb_{\text{antitag}}$ $\text{CSVv2}<0.5426$
Signal region Region Top jet Higgs jet $\cPqb$ jet SR $\cPqt_{\text{tag}}$
$\PH_{\text{tag}}$ $\cPqb_{\text{tag}}$ Background estimation Region Top jet
Higgs jet $\cPqb$ jet CR1 $\cPqt_{\text{antitag}}$ $\PH_{\text{antitag}}$
$\cPqb_{\text{tag}}$ CR2 $\cPqt_{\text{antitag}}$ $\PH_{\text{tag}}$
$\cPqb_{\text{tag}}$ CR3 $\cPqt_{\text{tag}}$ $\PH_{\text{antitag}}$
$\cPqb_{\text{tag}}$ Validation region Region Top jet Higgs jet $\cPqb$ jet VR
$\cPqt_{\text{tag}}$ $\PH_{\text{tag}}$ $\cPqb_{\text{antitag}}$ Validation
background estimation Region Top jet Higgs jet $\cPqb$ jet CR4
$\cPqt_{\text{antitag}}$ $\PH_{\text{antitag}}$ $\cPqb_{\text{antitag}}$ CR5
$\cPqt_{\text{antitag}}$ $\PH_{\text{tag}}$ $\cPqb_{\text{antitag}}$ CR6
$\cPqt_{\text{tag}}$ $\PH_{\text{antitag}}$ $\cPqb_{\text{antitag}}$

Figure 3:  Normalized distributions of the discriminating variables in
$\ttbar$, QCD, and signal MC simulation. The distributions shown, from upper
left to lower right, are of the variables: the maximum subjet $\cPqb$ tag,
$\tau_{3}$/$\tau_{2}$, and $m_{\mathrm{SD}}^{\cPqt}$, all used for top quark
discrimination, and the double-$\cPqb$ tag discriminant and
$m_{\mathrm{SD}}^{\PH}$ used for tagging candidate Higgs boson jets. The QCD
distributions are extracted from events with the generator-level $\HT>1\TeV$.
Each variable distribution in this set of figures requires an event that
passes the selection on all other variables in order to preserve possible
correlations.

The selection efficiency (%) for each signal mass point in the analysis.
$m_{\mathrm{\PWpr}}(\GeVns{})$ $m_{\mathrm{VLQ}}(\GeVns{})$ 1500 2000 2500
3000 3500 4000 800 0.70 $\pm$ 0.13 1000 0.91 $\pm$ 0.18 2.3 $\pm$ 0.4 1300
0.48 $\pm$ 0.09 2.6 $\pm$ 0.4 3.7 $\pm$ 0.6 1500 2.1 $\pm$ 0.4 3.7 $\pm$ 0.6
4.2 $\pm$ 0.7 1800 3.2 $\pm$ 0.5 4.1 $\pm$ 0.7 4.4 $\pm$ 0.7 2100 3.7 $\pm$
0.6 4.2 $\pm$ 0.7 4.4 $\pm$ 0.7 2500 3.8 $\pm$ 0.6 4.0 $\pm$ 0.7 3000 3.4
$\pm$ 0.6

##  0.5 Background estimation

The primary background in this analysis is QCD multijet production, the
contribution of which is derived from data using control regions that are
selected with kinematic criteria that are similar to those used for the signal
region but with a reduced signal efficiency. This is achieved by inverting top
substructure selections and extracting the Higgs jet pass to fail ratio for
QCD jets. This ratio is then used as an event weight for events that pass the
top jet selection but fail the Higgs boson jet selection. The resulting
distribution is used as the background estimate for the signal region. The
primary assumption for the background estimate method is that the top jet
substructure selection can be inverted without largely biasing the Higgs jet
substructure selection.

A set of control regions are defined by requiring the Higgs jet candidate
$m_{\mathrm{SD}}^{\PH}$ to be less than 30$\GeV$ with no double-$\cPqb$
tagging selection. Table 0.4.4 defines various selection regions used in the
analysis. A transfer function $F(\pt,\eta)$ is extracted from data by
inverting the top jet candidate $m_{\mathrm{SD}}^{\cPqt}$ selection to be
between 30 and 105$\GeV$ and $\tau_{3}$/$\tau_{2}>0.65$. In this region,
$F(\pt,\eta)$ is defined as the ratio of the jet $\pt$ spectrum of the tagged
Higgs candidate in two $\eta$ regions (central, $\abs{\eta}<1.0$, and forward,
$\abs{\eta}>1.0$) for the full Higgs jet selection (CR2) to the inverted
selection (CR1) and is shown in Fig. 4. The $F(\pt,\eta)$ distribution is used
to transform the normalization and shape of distributions from the
$\PH_{\text{antitag}}$ region to the $\PH_{\text{tag}}$ selection region, and
is measured with low signal contamination.

The $F(\pt,\eta)$ function is then used to predict the background in the
signal region. This is accomplished by defining a control region in data with
identical top and $\cPqb$ jet candidate selections as in the signal region,
but with the inverted Higgs jet selection (CR3). In this region, the
$m_{\mathrm{\cPqt\PH\cPqb}}$ template is created using $F(\pt,\eta)$ as an
event weight in a given Higgs candidate jet $\pt$, $\eta$ bin. This weighted
template is used as the QCD background estimate in the signal region.

In the $F(\pt,\eta)$ extraction procedure, the $\ttbar$ production component
is subtracted from data in all distributions used for creating $F(\pt,\eta)$
in order to ensure that $F(\pt,\eta)$ refers only to the QCD component. The
fraction of $\ttbar$ simulation subtracted from the numerator and denominator
is low, 7.3 and 0.4% of the total distribution, respectively. Additionally,
the $\ttbar$ contamination of the QCD background estimate in the signal region
must to be subtracted. This is performed by applying the QCD background
estimation procedure to simulated $\ttbar$ events using the same $F(\pt,\eta)$
as is used when extracting the QCD estimate from data. The estimated
contribution accounts for 2.6% of the total QCD estimate in the signal region,
which is then subtracted when forming the background estimate. The $\ttbar$
contamination has only a small effect on the QCD background estimation, so the
systematic uncertainty due to the $\ttbar$ subtraction procedure is
conservatively taken as the difference between the QCD background estimate
extracted with and without the full $\ttbar$ subtraction procedure.

In order to test the applicability and versatility of the background estimate
in data, a validation region, VR as defined in Table 0.4.4, is defined based
on inverting the $\cPqb$ tagging criterion on the $\cPqb$ candidate jet, with
the corresponding control regions for background estimation (CR4–CR6). The
transfer function in this validation region $F_{\mathrm{v}}(\pt,\eta)$ is
estimated from the ratio of CR5 to CR4 using the same parameterization as
$F(\pt,\eta)$. The $m_{\mathrm{\cPqt\PH\cPqb}}$ background validation test in
this region can be seen in Fig. 5. This region validates the background
estimate analog with a $\chi^{2}$/ndf of 0.3 with systematic uncertainties
taken into account, where ndf is the number of degrees of freedom. The
$\ttbar$ component in this validation region is removed using the same
procedure that is used in the signal region background estimate. The agreement
in the $m_{\mathrm{\cPqt\PH\cPqb}}$ distribution background validation test
demonstrates that the top jet selection can be inverted without biasing the
Higgs jet selection. The Higgs jet candidate 4-vector mass for the SR
background estimate is set to the mean of the distribution extracted from the
VR in order to correct the small kinematic bias from the mass selection when
forming the $m_{\mathrm{\cPqt\PH\cPqb}}$ invariant mass. This correction has
only a small effect on the resulting distribution because of the fact that the
jet $\pt$ is large compared to the mass, and a systematic uncertainty is
evaluated as the root mean square of the distribution in the VR.

Additionally, the background validation can be studied with simulated QCD
events. Figure 6 shows the level of background agreement where the SR
selection and QCD background are evaluated using only simulated QCD events
with the same method as was previously described for data. A $\chi^{2}$/ndf of
1.4 is observed, and an additional systematic uncertainty is included when
evaluating the QCD background estimate in collision data. This correction is
extracted from the ratio of the SR to QCD background in the QCD MC validation
test, and is applied using an interpolation of the ratio in order to decrease
the effect of statistical fluctuations but to still keep the increased
uncertainty at low $m_{\mathrm{\cPqt\PH\cPqb}}$.

The $\ttbar$ component is estimated by using simulation with an additional
event weight to correct the generator top jet $\pt$ distribution [45]. This
generator correction is used in order to improve the agreement of MC with data
with respect to a known generator level mismodelling and is cross checked in
the VR region.

Figure 4:  Transfer function $F(\pt,\eta)$ used for estimation of the QCD
background in the signal region, shown in the central (left) and forward
(right) $\eta$ regions. The error bars represent the statistical uncertainty
in $F(\pt,\eta)$ only.  Figure 5:  Reconstructed $\PWpr$ mass distributions
($m_{\mathrm{\cPqt\PH\cPqb}}$) in the $\cPqb$ candidate inverted validation
region (VR) shown for data and background contributions. Several signal
hypotheses are shown to demonstrate the low signal contamination. The
background uncertainty includes all systematic and statistical uncertainties.
Figure 6:  Reconstructed $\PWpr$ mass distributions
($m_{\mathrm{\cPqt\PH\cPqb}}$) for the simulated QCD events in the signal
region for the purposes of validation. The agreement given the systematic
uncertainties is at the 1 standard deviation level. The background uncertainty
takes into account all systematic and statistical uncertainties.

##  0.6 Systematic uncertainties

This analysis considers a wide range of systematic uncertainties that are
organized into those that impact only the event yields, which are assumed to
follow a log-normal distribution [46], and those that affect the
$m_{\mathrm{\cPqt\PH\cPqb}}$ distribution shape as well. All of the systematic
uncertainties considered in the analysis are summarized in Table 0.6.2.

###  0.6.1 Normalization uncertainties

The uncertainty in the integrated luminosity is taken as 2.5% for the data set
used in the analysis [21].

The uncertainty in the correction to the efficiency of top jet tagging
algorithm is between $-$4 and $+$10% of the nominal value.

The theoretical uncertainty in the $\ttbar$ production cross section is taken
into account as an asymmetric uncertainty between $-$5.5 and $+$4.8% that is
calculated as the quadrature sum of the scale and PDF uncertainties on the
overall cross section.

###  0.6.2 Shape uncertainties

The uncertainty in the jet energy scale is taken into account by scaling the
four-vectors used in reconstructing the $m_{\mathrm{\cPqt\PH\cPqb}}$
distribution by the $\pm$1$\sigma$ jet energy scale uncertainty, which is
approximately 2% for jets in the analysis. The jet energy scale variation
impacts the $m_{\mathrm{\cPqt\PH\cPqb}}$ distribution shape through a
horizontal shift but can also cause a normalization difference in the case
that the jet falls above or below the kinematic threshold. The uncertainty in
the jet energy resolution is also taken into account by the $\pm$1$\sigma$
uncertainty in the jet energy resolution correction used for simulated
samples. This uncertainty is applied to all simulated samples used in the
analysis, and has only a small impact.

The uncertainty in the jet mass scale and resolution is measured in a highly
enriched sample of $\ttbar$ containing one final state lepton. In this sample,
a fit is performed to the $\PW$ boson jet mass peak in the corresponding AK8
jet PUPPI $m_{\mathrm{SD}}^{\PH}$ distribution, in which the mean and width of
the PUPPI $m_{\mathrm{SD}}^{\PH}$ spectrum is extracted. The mass scale
uncertainty is estimated from the shift of the $\PW$ mass peak to be 0.94%.
The uncertainty in the mass resolution is estimated from the $\PW$ boson mass
peak width to be 20%. These uncertainties are applied to the signal estimate
used in the analysis, and result in approximately 4 and 6% variations in the
overall yield for the scale and resolution uncertainties, respectively. The
differences in the $\PW$ and Higgs boson tagging efficiencies are estimated
from a comparison of parton showering methods and are found to be between
4–5%, so an additional 5% uncertainty is included for the signal simulated
samples used in the analysis.

The uncertainty used for the $\cPqb$ tagging requirement on the AK4 jet is
evaluated by varying the $\cPqb$ tagging and $\cPqb$ mistagging scale factor
within their $\pm$1$\sigma$ uncertainty and are considered uncorrelated with
each other. Given the kinematic selection on the AK4 jet, this uncertainty is
evaluated in four $\pt$ regions from 200–1000$\GeV$. For jets with a $\pt$
outside of this region, the uncertainty is evaluated as twice the uncertainty
at 1000$\GeV$. This uncertainty is applied to all simulated samples used in
the analysis, and results in approximately a 2% effect.

The double-$\cPqb$ tagging uncertainty used for the Higgs jet tagging [43]
selection is evaluated by varying the double-$\cPqb$ tagging scale factor by
the $\pm$1$\sigma$ uncertainty. The scale factor is parameterized using three
regions in . Similar to the AK4 $\cPqb$ tagging uncertainty, outside of the
kinematic range of the scale factor, the uncertainty is evaluated at twice the
maximum range. The double-$\cPqb$ tagging scale factor uncertainty results in
approximately a 5% effect. Also evaluated is the mistag scale factor in the
case of a Higgs boson mistagged as a top quark, as explained in Section 0.4.
The uncertainties in both the Higgs jet tagging efficiency and the mistag rate
are applied to all simulated samples used in the analysis, and are treated as
uncorrelated with each other during limit setting.

The events used by the analysis are largely collected where the trigger
efficiency is near 100%, however the small inefficiency is evaluated using the
trigger efficiency extracted from data as parameterized in $\HT$ (see Fig. 2),
and the uncertainty is evaluated as half of this inefficiency. This
uncertainty is small ($<$1%), and is applied to all simulated samples used in
the analysis.

As mentioned in Section 0.3, the simulated pileup distribution is reweighted
to match data using an effective total inelastic cross section of
69.2$\unit{mb}$. The uncertainty in this procedure is evaluated by varying the
total inelastic cross section by $\pm$4.6% [47]. This uncertainty is applied
to all simulated samples used in the analysis, and has only a small impact.

The $m_{\mathrm{\cPqt\PH\cPqb}}$ distribution from the $\ttbar$ simulation is
reweighted to correct for known differences in the generator $\pt$ spectrum
[45]. The $\pm$1$\sigma$ shape uncertainty in this procedure is estimated from
the difference with the unweighted distribution. This uncertainty is applied
to the $\ttbar$ simulated sample used in the analysis, and results in
approximately a 21% effect.

The PDF uncertainty is evaluated using the NNPDF3.0 set [48]. The NNPDF set
uses MC replicas, from which the uncertainty is evaluated as the RMS of the
distribution of the associated weights, and is then added in quadrature with
the $\alpha_{s}$ uncertainty. In the case of signal, the shapes are then
normalized to the nominal distribution, as to only preserve the shape of the
PDF uncertainty. The normalization component of the PDF uncertainty is
considered an uncertainty in the signal cross section.

The renormalization and factorization ($\mu_{\mathrm{R}}$ and
$\mu_{\mathrm{F}}$) scale uncertainty is evaluated using event weights
provided for varying the $\mu_{\mathrm{R}}$ or $\mu_{\mathrm{F}}$ scales up
and down by a factor of two. There are six total weights that represent the
independent and simultaneous variation of $\mu_{\mathrm{R}}$ and
$\mu_{\mathrm{F}}$. Per event, all weights are considered and the envelope is
then used as the $\pm$1$\sigma$ uncertainty band. This uncertainty is applied
to the $\ttbar$ MC sample used in the analysis, and results in an
approximately 30% effect. Similar to the PDF uncertainty, the normalization
component of this uncertainty is taken as the signal cross section theoretical
uncertainty, and the shape component alone is used for limit setting.

The analysis considers five sources of uncertainty in the shape of the QCD
background estimate derived from data. The statistical uncertainty in
$F(\pt,\eta)$ is propagated to the $m_{\mathrm{\cPqt\PH\cPqb}}$ spectrum by
evaluating the $F(\pt,\eta)$ weight at $\pm$1$\sigma$ in a given ($\pt$,
$\eta$) bin. The uncertainty from each $F(\pt,\eta)$ bin is added in
quadrature to form the full uncertainty in the $m_{\mathrm{\cPqt\PH\cPqb}}$
template. The up and down uncertainty variation in the $\ttbar$ subtraction
procedure is taken as the unsubtracted $m_{\mathrm{\cPqt\PH\cPqb}}$
distribution and the resulting $m_{\mathrm{\cPqt\PH\cPqb}}$ distribution given
twice the subtraction. The uncertainty in the four vector Higgs jet candidate
mass modification is taken as $\pm$30$\GeV$. The “nonclosure” uncertainty in
the QCD background estimate is evaluated as the difference between the full
selection and background prediction from the QCD MC closure test using the
interpolated ratio, and is the leading source of uncertainty in the QCD
background estimate of approximately 20%.

The MC statistical uncertainty is taken into account using the “Barlow–Beeston
lite” method [49] during limit setting.

Sources of systematic uncertainty affecting the $m_{\mathrm{\cPqt\PH\cPqb}}$
distribution. Sources that list the systematic variation as $\pm 1\sigma$
depend on the distribution of the variable given in the parentheses, while
those that list the variation in percent are rate uncertainties.  Source
Variation Process Integrated luminosity $\pm$2.5% signal, $\ttbar$ Top jet
tagging $+10.0$%, $-4$% signal, $\ttbar$ $\ttbar$ cross section $+4.8$%,
$-5.5$% $\ttbar$ Top quark $\pt$ reweighting $+1\sigma(\pt(\mathrm{gen}))$
$\ttbar$ Matrix element $\mu_{\mathrm{R}}/\mu_{\mathrm{F}}$ scales $\pm
1\sigma(\mu_{\mathrm{R}}/\mu_{\mathrm{F}})$ signal, $\ttbar$ Jet energy scale
$\pm 1\sigma(\pt,\eta)$ signal, $\ttbar$ Jet energy resolution $\pm
1\sigma(\pt,\eta)$ signal, $\ttbar$ Jet mass scale $\pm
1\sigma(m_{\mathrm{SD}}^{\PH})$ signal, $\ttbar$ Jet mass resolution $\pm
1\sigma(m_{\mathrm{SD}}^{\PH})$ signal, $\ttbar$ $\cPqb$ tagging $\pm
1\sigma(\pt)$ signal, $\ttbar$ $\cPqb$ mistagging $\pm 1\sigma(\pt)$ signal,
$\ttbar$ Double-$\cPqb$ tagging $\pm 1\sigma(\pt)$ signal, $\ttbar$
Double-$\cPqb$ mistagging $\pm 1\sigma(\pt)$ signal, $\ttbar$ Higgs jet
tagging $\pm$5% signal Pileup $\pm 1\sigma$ ($\sigma_{\mathrm{mb}}$) signal,
$\ttbar$ PDF $\pm 1\sigma(Q^{2},x)$ signal, $\ttbar$ $\HT$ trigger $\pm
1\sigma(\HT)$ signal, $\ttbar$ $\ttbar$ contamination $\pm 1\sigma(\pt)$ QCD
$F(\pt,\eta)$ $\pm 1\sigma(\pt,\eta)$ QCD Higgs jet mass modification $\pm
1\sigma(m_{\mathrm{\PH}})$ QCD Nonclosure $\pm
1\sigma(m_{\mathrm{\cPqt\PH\cPqb}})$ QCD

##  0.7 Results

The final $m_{\mathrm{\cPqt\PH\cPqb}}$ distribution is shown in Fig. 7, with a
$\chi^{2}$/ndf of 1.3 for the agreement of data and background. Table 0.7
shows the yield for data, QCD and $\ttbar$ backgrounds, for various selection
regions including the full selection.

Figure 7:  Reconstructed $\PWpr$ mass distributions
($m_{\mathrm{\cPqt\PH\cPqb}}$) in the signal region, compared with the
distributions of estimated backgrounds, and several benchmarks models. The
signal distributions include the contributions from $\PWpr$ decays to both the
$\mathrm{T}$ and $\mathrm{B}$ assuming equal branching fractions. The
uncertainties shown in the hatched region contain both statistical and
systematic uncertainties of all background components.

Event yield table after various selections. The definition of each region is
given in Table 0.4.4. The uncertainties shown here for the validation region
and the signal region are pre fit; the posteriori uncertainties for $\ttbar$
and QCD are constrained down by 40 and 14%, respectively.  Region Data QCD
$\ttbar$ CR1 79 104 $\NA$ 332 CR2 398 $\NA$ 25 CR3 45 646 $\NA$ 1365 CR4 288
926 $\NA$ 543 CR5 1 330 $\NA$ 76 CR6 154 608 $\NA$ 1991 VR $844\pm 30$ $659\pm
150$ $236\pm 83$ SR $284\pm 17$ $208\pm 49$ $71\pm 28$

Using a Bayesian approach with a flat prior for the signal cross section,
upper limits are obtained on the product of the $\PWpr$ boson production cross
section in the $s_{\mathrm{L}}=0.5$ and $\cot(\theta_{2})=3$ hypothesis, and
the benchmark $\PWpr\to\mathrm{T}/\mathrm{B}\to\cPqt\PH\cPqb$ branching
fraction. A binned likelihood is used to calculate 95% confidence level (CL)
upper limits, in a process where all systematic uncertainties listed in
Section 6 that affect the shape of the $m_{\mathrm{\cPqt\PH\cPqb}}$
distribution are included as nuisance parameters that modify the shape using
template interpolation, and those that affect the normalization are included
as nuisance parameters with lognormal priors. For the signal template, the sum
of reconstructed $m_{\mathrm{\cPqt\PH\cPqb}}$ distribution from the
$\mathrm{B}$ and $\mathrm{T}$ decay channels is used.

Pseudo-experiments are used to derive the ${\pm}1\sigma$ deviations in the
expected limit. The systematic uncertainties described above are accounted for
as nuisance parameters and the posterior probability is refitted for each
pseudo-experiment. Cross section upper limits are shown in Fig. 8. The highest
signal significance is at $M_{\PWpr}=2\TeV$ from the high VLQ mass hypothesis
at a value of 0.85 standard deviations. Although no signal mass points are
excluded by solely analyzing the all hadronic
$\PWpr\to\mathrm{T}/\mathrm{B}\to\cPqt\PH\cPqb$ decay in the democratic
$\cPqb\mathrm{T}$ and $\cPqt\mathrm{B}$ decay hypothesis, a $\PWpr$ with a
mass below 1.6is excluded at 95% CL in the case of a 100% $\cPqb\mathrm{T}$
branching fraction hypothesis.

Figure 8:  The $\PWpr$ boson 95% CL production cross section limits. The
expected limits (dashed) and observed limits (solid), as well as the $\PWpr$
boson theoretical cross section and the PDF and scale normalization
uncertainties are shown. The bands around the expected limit represent the
$\pm$1 and ${\pm}2\sigma_{exp}$ uncertainties in the expected limit. The
limits for low- (upper left), medium- (upper right), and high- (lower) mass
VLQ mass ranges, defined in Table 0.4.4, are shown.

##  0.8 Summary

A search for a heavy $\PWpr$ boson decaying to a $\mathrm{B}$ or $\mathrm{T}$
vector-like quark and a top or $\cPqb$ quark, respectively, has been
presented. The data correspond to an integrated luminosity of 35.9collected in
2016 with the CMS detector at the LHC. The signature considered for both decay
modes is a top quark and a Higgs boson, both decaying hadronically, and a
$\cPqb$ quark jet. Boosted heavy-resonance identification techniques are used
to exploit the event signature of three energetic jets and to suppress
standard model backgrounds. No significant deviation from the standard model
background prediction has been observed. Cross section upper limits on $\PWpr$
boson production in the top quark, Higgs boson, and $\cPqb$ quark decay mode
are set as a function of the $\PWpr$ mass, for several vector-like quark mass
hypotheses. These are the first limits for $\PWpr$ boson production in this
decay channel, and cover a range of 0.01 to 0.43$\unit{pb}$ in the $\PWpr$
mass range between 1.5 and 4.0.

###### 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”) Programme and the János Bolyai Research Scholarship of the
Hungarian Academy of Sciences, the New National Excellence Program ÚNKP, the
NKFIA research grants 123842, 123959, 124845, 124850 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 program cofinanced by EU-ESF and the Greek
NSRF; the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn
University and the Chulalongkorn Academic into Its 2nd Century Project
Advancement Project (Thailand); the Welch Foundation, contract C-1845; and the
Weston Havens Foundation (USA).

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##  .9 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan, 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, 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,
K. Piotrzkowski, A. Saggio, M. Vidal Marono, P. Vischia, 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, 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
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
M.A. Mahmoud9,10, Y. Mohammed9, E. Salama10,11 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. Abdulsalam12, 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. Paganini, 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. Agram13, J. Andrea, D. Bloch, J.-M. Brom, E.C. Chabert, V. Cherepanov,
C. Collard, E. Conte13, J.-C. Fontaine13, 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. Popov14, V. Sordini, G. Touquet, M. Vander Donckt, S.
Viret Georgian Technical University, Tbilisi, Georgia
T. Toriashvili15 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, 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, J.M. Grados
Luyando, A. Grohsjean, M. Guthoff, M. Haranko, A. Harb, 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, J. Mnich, V. Myronenko, S.K. Pflitsch, D.
Pitzl, A. Raspereza, 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, I.
Katkov14, S. Kudella, S. Mitra, M.U. Mozer, Th. Müller, M. Musich, M. Plagge,
G. Quast, K. Rabbertz, M. Schröder, I. Shvetsov, H.J. Simonis, R. Ulrich, S.
Wayand, M. Weber, T. Weiler, 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
National and Kapodistrian University of Athens, Athens, Greece
A. Agapitos, G. Karathanasis, 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. Karancsi20, 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, 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, D.K. Mishra, P.K. Netrakanti, L.M.
Pant, P. Shukla Tata Institute of Fundamental Research-A, Mumbai, India
T. Aziz, M.A. Bhat, S. Dugad, G.B. Mohanty, N. Sur, 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, A.
Rastogi, S. Sharma Institute for Research in Fundamental Sciences (IPM),
Tehran, Iran
S. Chenarani27, E. Eskandari Tadavani, S.M. Etesami27, M. Khakzad, M.
Mohammadi Najafabadi, M. Naseri, F. Rezaei Hosseinabadi, 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, F. Iemmia,b, S. Lo Meoa, S. Marcellinia, G. Masettia,
A. Montanaria, F.L. Navarriaa,b, A. Perrottaa, F. Primaveraa,b,16, 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, R. Mulargiaa,b, 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, 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, D. Biselloa,b, 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, M.
Presillab, P. Ronchesea,b, R. Rossina,b, F. Simonettoa,b, A. Tiko, E.
Torassaa, M. Tosia,b, M. Zanettia,b, P. Zottoa,b, G. Zumerlea,b INFN Sezione
di Pavia a, Università di Pavia b, Pavia, Italy
A. Braghieria, 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, G. Rolandi30, 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, A. Cappatia,b, 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, R. Salvaticoa,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. Goh31, 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 Ali32, F. Mohamad Idris33, W.A.T. Wan
Abdullah, M.N. Yusli, Z. Zolkapli Universidad de Sonora (UNISON), Hermosillo,
Mexico
J.F. Benitez, A. Castaneda Hernandez, J.A. Murillo Quijada 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
Cruz34, 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. Byszuk35, K. Doroba, A. Kalinowski, M. Konecki, J.
Krolikowski, M. Misiura, M. Olszewski, A. Pyskir, M. Walczak Laboratório de
Instrumentação e Física Experimental de Partículas, Lisboa, Portugal
M. Araujo, P. Bargassa, C. Beirão Da Cruz E Silva, A. Di Francesco, P.
Faccioli, B. Galinhas, M. Gallinaro, J. Hollar, N. Leonardo, J. Seixas, G.
Strong, O. Toldaiev, 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. Matveev36,37, 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. Kim38, E. Kuznetsova39, 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. Chistov40, M. Danilov40, P. Parygin, D. Philippov, S. Polikarpov40, E.
Tarkovskii P.N. Lebedev Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin, I. Dremin37, M. Kirakosyan, A. Terkulov Skobeltsyn
Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow,
Russia
A. Baskakov, A. Belyaev, E. Boos, V. Bunichev, M. Dubinin41, L. Dudko, A.
Ershov, V. Klyukhin, O. Kodolova, I. Lokhtin, I. Miagkov, S. Obraztsov, M.
Perfilov, S. Petrushanko, V. Savrin Novosibirsk State University (NSU),
Novosibirsk, Russia
A. Barnyakov42, V. Blinov42, T. Dimova42, L. Kardapoltsev42, Y. Skovpen42
Institute for High Energy Physics of National Research Centre ’Kurchatov
Institute’, Protvino, Russia
I. Azhgirey, I. Bayshev, S. Bitioukov, 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. Adzic43, 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, 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.
Fallavollita44, D. Fasanella, G. Franzoni, J. Fulcher, W. Funk, D. Gigi, A.
Gilbert, K. Gill, F. Glege, M. Gruchala, 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, A. Massironi, F. Meijers, J.A. Merlin, S. Mersi, E. Meschi, P.
Milenovic45, 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, M. Rovere, H. Sakulin, C. Schäfer, C. Schwick, M. Selvaggi, A.
Sharma, P. Silva, P. Sphicas46, A. Stakia, J. Steggemann, 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
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
M.N. Bakirci50, A. Bat, F. Boran, S. Cerci51, S. Damarseckin, Z.S. Demiroglu,
F. Dolek, C. Dozen, I. Dumanoglu, E. Eskut, S. Girgis, G. Gokbulut, Y. Guler,
E. Gurpinar, I. Hos52, C. Isik, E.E. Kangal53, O. Kara, U. Kiminsu, M.
Oglakci, G. Onengut, K. Ozdemir54, A. Polatoz, D. Sunar Cerci51, U.G. Tok, 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, 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, K. Manolopoulos, 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, 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, C. Campagnari, M. Citron, V. Dutta, M. Franco Sevilla,
L. Gouskos, R. Heller, J. Incandela, H. Mei, 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, N. Lu, 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, 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, D. Curry, R.D. Field, S.V. Gleyzer, B.M. Joshi, J. Konigsberg, A.
Korytov, K.H. Lo, P. Ma, K. Matchev, 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, M.B. Tonjes, 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 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, M. Seidel, 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, Z. Shi, G.S.F. Stephans, K.
Sumorok, K. Tatar, D. Velicanu, J. Wang, T.W. Wang, B. Wyslouch University of
Minnesota, Minneapolis, USA
A.C. Benvenuti${}^{\textrm{\textdagger}}$, R.M. Chatterjee, A. Evans, P.
Hansen, J. Hiltbrand, Sh. Jain, S. Kalafut, M. Krohn, 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, Y. Haddad, A. Hortiangtham, D.M. Morse, T.
Orimoto, T. Wamorkar, B. Wang, A. Wisecarver, D. Wood Northwestern University,
Evanston, USA
S. Bhattacharya, J. Bueghly, O. Charaf, T. Gunter, 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.
Musienko36, 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, 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, Z. Wang
University of Puerto Rico, Mayaguez, USA
S. Malik, S. Norberg Purdue University, West Lafayette, USA
A. Barker, V.E. Barnes, S. Das, L. Gutay, M. Jones, A.W. Jung, A. Khatiwada,
B. Mahakud, D.H. Miller, 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, 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, E. Ranken, P. Tan, R. Taus Rutgers, The State University of
New Jersey, Piscataway, USA
J.P. Chou, Y. Gershtein, E. Halkiadakis, A. Hart, M. Heindl, E. Hughes, S.
Kaplan, R. Kunnawalkam Elayavalli, S. Kyriacou, I. Laflotte, A. Lath, R.
Montalvo, K. Nash, M. Osherson, H. Saka, S. Salur, S. Schnetzer, D. Sheffield,
S. Somalwar, R. Stone, S. Thomas, P. Thomassen, 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, D. Marley, 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, F. Romeo, 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
J. Buchanan, C. Caillol, D. Carlsmith, S. Dasu, I. De Bruyn, 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: Also at Fayoum University, El-Fayoum, Egypt
10: Now at British University in Egypt, Cairo, Egypt
11: Now at Ain Shams University, Cairo, Egypt
12: Also at Department of Physics, King Abdulaziz University, Jeddah, Saudi
Arabia
13: Also at Université de Haute Alsace, Mulhouse, France
14: Also at Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State
University, Moscow, Russia
15: Also at Tbilisi State University, Tbilisi, Georgia
16: Also at CERN, European Organization for Nuclear Research, Geneva,
Switzerland
17: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
18: Also at University of Hamburg, Hamburg, Germany
19: Also at Brandenburg University of Technology, Cottbus, Germany
20: Also at Institute of Physics, University of Debrecen, Debrecen, Hungary
21: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary
22: Also at MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
23: Also at Indian Institute of Technology Bhubaneswar, Bhubaneswar, India
24: Also at Institute of Physics, Bhubaneswar, India
25: Also at Shoolini University, Solan, India
26: Also at University of Visva-Bharati, Santiniketan, India
27: Also at Isfahan University of Technology, Isfahan, Iran
28: Also at Plasma Physics Research Center, Science and Research Branch,
Islamic Azad University, Tehran, Iran
29: Also at Università degli Studi di Siena, Siena, Italy
30: Also at Scuola Normale e Sezione dell’INFN, Pisa, Italy
31: Also at Kyunghee University, Seoul, Korea
32: Also at International Islamic University of Malaysia, Kuala Lumpur,
Malaysia
33: Also at Malaysian Nuclear Agency, MOSTI, Kajang, Malaysia
34: Also at Consejo Nacional de Ciencia y Tecnología, Mexico City, Mexico
35: Also at Warsaw University of Technology, Institute of Electronic Systems,
Warsaw, Poland
36: Also at Institute for Nuclear Research, Moscow, Russia
37: Now at National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
38: Also at St. Petersburg State Polytechnical University, St. Petersburg,
Russia
39: Also at University of Florida, Gainesville, USA
40: Also at P.N. Lebedev Physical Institute, Moscow, Russia
41: Also at California Institute of Technology, Pasadena, USA
42: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia
43: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia
44: Also at INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy
45: Also at University of Belgrade, Faculty of Physics and Vinca Institute of
Nuclear Sciences, Belgrade, Serbia
46: Also at National and Kapodistrian University of Athens, Athens, Greece
47: Also at Riga Technical University, Riga, Latvia
48: Also at Universität Zürich, Zurich, Switzerland
49: Also at Stefan Meyer Institute for Subatomic Physics (SMI), Vienna,
Austria
50: Also at Gaziosmanpasa University, Tokat, Turkey
51: Also at Adiyaman University, Adiyaman, Turkey
52: Also at Istanbul Aydin University, Istanbul, Turkey
53: Also at Mersin University, Mersin, Turkey
54: Also at Piri Reis University, Istanbul, Turkey
55: Also at Ozyegin University, Istanbul, Turkey
56: Also at Izmir Institute of Technology, Izmir, Turkey
57: Also at Marmara University, Istanbul, Turkey
58: Also at Kafkas University, Kars, Turkey
59: Also at Istanbul University, Faculty of Science, Istanbul, Turkey
60: Also at Istanbul Bilgi University, Istanbul, Turkey
61: Also at Hacettepe University, Ankara, Turkey
62: Also at Rutherford Appleton Laboratory, Didcot, United Kingdom
63: Also at School of Physics and Astronomy, University of Southampton,
Southampton, United Kingdom
64: Also at Monash University, Faculty of Science, Clayton, Australia
65: Also at Bethel University, St. Paul, USA
66: Also at Karamanoğlu Mehmetbey University, Karaman, Turkey
67: Also at Utah Valley University, Orem, USA
68: Also at Purdue University, West Lafayette, USA
69: Also at Beykent University, Istanbul, Turkey
70: Also at Bingol University, Bingol, Turkey
71: Also at Sinop University, Sinop, Turkey
72: Also at Mimar Sinan University, Istanbul, Istanbul, Turkey
73: Also at Texas A&M University at Qatar, Doha, Qatar
74: Also at Kyungpook National University, Daegu, Korea