B2G-18-005

$HeadURL$ $Id$

B2G-18-005

# Search for pair production of vector-like quarks in the fully hadronic final
state

###### Abstract

The results of two searches for pair production of vector-like or quarks in
fully hadronic final states are presented, using data from the CMS experiment
at a center-of-mass energy of 13. The data were collected at the LHC during
2016 and correspond to an integrated luminosity of 35.9. A cut-based analysis
specifically targets the decay mode of the quark and allows for the
reconstruction of the quark candidates. In a second analysis, a
multiclassification algorithm, the “boosted event shape tagger,” is deployed
to label candidate jets as originating from top quarks, and , , and .
Candidate events are categorized according to the multiplicities of identified
jets, and the scalar sum of all observed jet momenta is used to discriminate
signal events from the quantum chromodynamics multijet background. Both
analyses probe all possible branching fraction combinations of the and quarks
and set limits at $95\%{}$ confidence level on their masses, ranging from 740
to 1370. These results represent a significant improvement relative to
existing searches in the fully hadronic final state.

##  0.1 Introduction

With the discovery of a light Higgs boson () by the ATLAS and CMS
Collaborations in 2012 [1, 2, 3], the standard model (SM) is complete as a
low-energy effective theory describing all known fundamental particles and
their interactions. However, several questions still remain with the theory,
for example, why the mass of the observed Higgs boson is 125, whereas quantum
loop corrections would be expected to drive the mass up towards the Planck
scale. Many models of new physics beyond the SM predict additional particles
that can affect the quantum corrections to the Higgs boson mass and resolve
this so-called “hierarchy problem”. New states proposed include new particles
such as supersymmetric partners of SM particles, or fourth-generation quarks.

Chiral fourth-generation quarks, or , with identical properties to the SM
third-generation and quarks, but with larger masses, are effectively excluded
because of their impact on the Higgs boson production cross section. However,
many models of new physics, such as those predicting a composite Higgs boson
[4, 5, 6, 7, 8], or “little-Higgs” models [9, 10], include fourth-generation
particles of a new type, called vector-like quarks (VLQs), labeled and B,
having electric charges of $+2e/3$ and $-1e/3$, respectively. These VLQs do
not obtain their mass via the Higgs boson Yukawa coupling, and will not affect
the values of the Higgs boson production cross section or decay width.
Therefore, these are viable search candidates for the LHC experiments, and are
predicted to have masses at the scale [11], allowing the hierarchy problem to
be resolved. We do not search for the related X and Y particles.

The VLQs are called “vector-like” because their left-handed and right-handed
chiralities transform under the same $SU(2)\otimes U(1)$ symmetry group of the
SM electroweak gauge bosons. This leads to several decay modes of the VLQs,
through charged- and neutral-current interactions. Although decays to light
first- and second-generation quarks are possible, the dominant decay modes of
the VLQs are to third-generation SM quarks[12]. The possible decay modes of
the VLQs to the third-generation quarks are as follows (charge-conjugate modes
implied):

$\displaystyle\PQT$ $\displaystyle\to\cPqb{}\PW{},$ $\displaystyle\PQB$
$\displaystyle\to\cPqt{}\PW{},$ (1) $\displaystyle\PQT$
$\displaystyle\to\cPqt{}\PZ{},$ $\displaystyle\PQB$
$\displaystyle\to\cPqb{}\PZ{},$ $\displaystyle\PQT$
$\displaystyle\to\cPqt{}\PH{},$ $\displaystyle\PQB$
$\displaystyle\to\cPqb{}\PH{}.$

Specific model assumptions can influence the proportions of these VLQ decay
modes. Both single and pair production of VLQs are possible, with single
production dominating at larger VLQ masses ($\approx$2), while single and pair
production rates are comparable for VLQ masses $\approx$1\. This analysis
considers only the pair production of VLQs.

Both the ATLAS and CMS Collaborations have recently presented searches for
pair production of VLQs. The CMS Collaboration has searched for and quarks in
the dilepton final state, targeting VLQ decays to bosons [13], and excluding
() quark masses up to 1280 (1130). An analysis from CMS including single-
lepton, dilepton, and multilepton final states [14] probes all decay modes of
the VLQs, and excludes quark masses in the range 1140–1300and quark masses in
910–1240, depending on the combination of the VLQ branching fractions.
Finally, a CMS result optimized for the channel, using single-lepton final
states, excludes quark masses up to 1295[15]. The ATLAS Collaboration has
recently presented a search for VLQ pair production in the fully hadronic
channel, with sensitivity to all possible decay modes of the VLQs [16]. This
analysis most strongly excludes and quarks when they decay to Higgs bosons,
with mass exclusion limits of 1010. The ATLAS Collaboration has also performed
a combination of searches utilizing various final states, resulting in mass
exclusion limits of up to 1370[17].

In this paper, we describe two independent analyses targeting pair production
of vector-like quarks in fully hadronic final states. We first present an
analysis that employs a traditional strategy, utilizing boson tagging and
quark tagging algorithms. This analysis specifically targets the decay mode of
the quark, but is used to evaluate sensitivity to all possible decays of the
or quark, and is referred to as the “cut-based analysis”. The second analysis
uses a novel machine learning technique to identify and classify different
varieties of Lorentz-boosted particles that originate from VLQ decays. This
strategy allows the analysis to target all the decay modes of the or quark. We
refer to this analysis as the “NN (neural network) analysis”.

The cut-based analysis uses dedicated algorithms to identify efficiently jets
consistent with bosons and the hadronization of quarks. These algorithms allow
the reconstruction of each VLQ quark present in the event, providing a
mechanism to reduce further the contribution of background processes. At least
four jets are required to be present, and events are classified according to
the number of jets that are identified as being consistent with a boson, to
obtain signal regions of varying signal purities. The $\HT$ distribution,
defined as the scalar sum of jet transverse momenta (), is used for signal
discrimination in each category. The NN analysis uses a neural network
algorithm with a multiple-class output to identify jets as consistent with one
of six distinct decay topologies from highly boosted particles: top quark,
boson, boson, Higgs boson, quark, and light /// quark or gluon (denoted “light
jets”). Events with exactly four jets are considered for the analysis, which
is the expected final state for fully hadronic decays of VLQ pairs, as seen in
Eq. 1. The multiplicities of jets falling into each of the six categories are
used to define 126 independent signal regions, in which the value of $\HT$ is
used to discriminate signal from the expected background processes.

The main background contribution in these fully hadronic final states
comprises multijet events from quantum chromodynamics (QCD) processes.
Techniques based on control samples in data are used to predict the expected
QCD multijet background yield and $\HT$ shape. In the cut-based analysis,
control regions are used to measure QCD multijet background yields and shapes,
which are then extrapolated to the signal regions. In the NN analysis,
misidentification rates for each of the six categories of jets considered in
the multiclassification algorithm are used to predict the level of
contribution of multijet events in the signal regions. Each method is
validated using samples of observed and simulated events.

The paper has the following structure. Section 0.2 provides a description of
the CMS detector and trigger system. The event reconstruction, including jet
reconstruction, jet substructure, and the multiclassification algorithm used
in the NN analysis, is described in Section 0.3. The data sets and simulated
samples used are presented in Section 0.4. Information about the definition of
the signal and control regions is included in Section 0.5. The methods
employed to predict the QCD multijet background from data for each analysis
are explained in Section 0.6, and details of the systematic uncertainties
affecting the analyses are itemized in Section 0.7. Signal region yields and
distributions are given in Section 0.8, and the statistical analysis used to
extract the results is described in Section 0.9. Finally, the results of the
two analyses are presented in Section 0.10, and a summary is given in the last
section.

##  0.2 The CMS detector

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

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

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. [19].

##  0.3 Event reconstruction

To reconstruct and identify each individual particle in an event, a “particle-
flow algorithm” [20] that uses an optimized combination of information from
the various elements of the CMS detector is employed. The energy of photons is
obtained from the ECAL measurement. The energy of electrons is determined from
a combination of the electron momentum at the primary interaction vertex as
determined by the tracker, the energy of the corresponding ECAL cluster, and
the energy sum of all bremsstrahlung photons spatially compatible with
originating from the electron track. The energy of muons is obtained from the
curvature of the corresponding track. The energy of charged hadrons is
determined from a combination of their momentum measured in the tracker and
the matching ECAL and HCAL energy deposits, corrected for zero-suppression
effects and for the response function of the calorimeters to hadronic showers.
Finally, the energy of neutral hadrons is obtained from the corresponding
corrected ECAL and HCAL energy.

The reconstructed vertex with the largest value of summed physics-object
$\pt^{2}$ is taken to be the primary proton-proton ($\Pp\Pp$) interaction
vertex. Here the physics objects are the jets, clustered using the jet finding
algorithm [21, 22] with the tracks assigned to the vertex as inputs, and the
associated missing transverse momentum, taken as the negative vector sum of
those jets.

The output of the particle-flow algorithm provides a list of particles that
are used as inputs to the jet finding algorithm. Charged hadrons that are not
associated with the primary interaction vertex are removed before jet finding
to mitigate the effects of additional $\Pp\Pp$ (“pileup”) interactions
occurring in the same or neighboring bunch crossings as the interaction of
interest. The anti-clustering algorithm [21] is used, as implemented in the
FastJet software package [22], to produce two collections of jets, the first
obtained with a distance parameter of $R=0.4$ (AK4 jets), and the second
obtained with $R=0.8$ (AK8 jets), where $R$ is the radius of the jet in the
$\eta,\phi$ plane (where $\phi{}$ is the azimuthal angle). The AK8 jets are
used to identify the hadronic decays of massive SM particles, including top
quarks, and , , and Higgs bosons, while the AK4 jets are used to identify
other hadronic activity in the event. The cut-based analysis uses both AK4 and
AK8 jets, while the NN analysis only uses the AK8 jets for analysis.

The 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. Pileup interactions
can contribute additional tracks and calorimetric energy deposits, increasing
the apparent jet momentum. To mitigate this effect, tracks identified to be
originating from pileup vertices are discarded and an offset correction is
applied to correct for remaining contributions. 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, +jet, and multijet events are used to
determine any residual differences between the observed and simulated jet
energy scale, and to derive appropriate corrections [23]. Additional selection
criteria are applied to each jet to remove jets potentially dominated by
instrumental effects or reconstruction failures. The jet energy resolution is
approximately 15% at 10, 8% at 100, and 4% at 1.

###  0.3.1 Jet substructure

To identify the hadronic decays of highly Lorentz-boosted objects, including
top quarks, and , , and , jet substructure information provides powerful
discrimination from massive jets originating from QCD multijet production.

The mass of the jet itself can discriminate QCD jets from boosted heavy
objects. A grooming algorithm is applied to jet constituents to better
estimate the mass of the originating particle of the jet. In the algorithm
used, the constituents of the AK8 jets are reclustered using the
Cambridge–Aachen algorithm [24, 25]. The “modified mass drop tagger” algorithm
[26], also known as the “soft-drop” (SD) algorithm, with angular exponent
$\beta=0$, soft cutoff threshold $z_{\mathrm{cut}}<0.1$, and characteristic
radius $R_{0}=0.8$ [27], is applied to remove soft, wide-angle radiation from
the jet. The SD mass ($m_{\mathrm{SD}}$) is used to determine the consistency
of a jet with a given boosted heavy object.

In addition to the $m_{\mathrm{SD}}$, information about the distribution of
particles within the jet can be used for further discrimination. A quantity
called “$N$-subjettiness” [28, 29] is used to determine the consistency of a
jet with $N$ or fewer subjets. The $N$-subjettiness values $\tau_{N}$ are
defined as

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

where the index $i$ refers to each jet constituent, and $\Delta
R\equiv\sqrt{\smash[b]{(\Delta\eta)^{2}+(\Delta\phi)^{2}}}$ is the angular
distance between a jet constituent and a candidate subjet axis. The quantity
$d_{0}$ is a normalization constant. To identify boosted top quarks, the
quantity $\tau_{32}\equiv\tau_{3}/\tau_{2}$ is used to target the expected
three-subjet signature, while for , , and Higgs bosons, the quantity
$\tau_{21}$ is used because of the expected two-subjet decay topology.

Jets originating from bottom quarks, which hadronize and subsequently decay,
are selected with an algorithm to identify and reconstruct displaced vertices,
along with their associated tracking information. Known as the combined
secondary vertex algorithm (CSVv2) [30], it provides several working points of
varying efficiencies and misidentification rates. In the cut-based analysis,
the CSVv2 algorithm is applied to AK4 jets using a working point corresponding
to a misidentification probability in simulated events of 0.01 for /// jets
and an efficiency for identifying genuine jets of approximately 0.63. In the
NN analysis, the CSVv2 algorithm is applied to the subjets of the AK8 jets to
increase the categorization efficiency for decays of top quarks, and Higgs
bosons, which can have one or more displaced vertices within the jet. A CSVv2
working point is not explicitly used in the NN analysis, however the output
value of the CSVv2 discriminator for each subjet is used as an input to the
multiclassification algorithm to categorize jets.

In the cut-based analysis, a working point for identifying merged decay
products of a highly-boosted boson in a single jet ( tagging) is chosen. To be
considered for tagging, an AK8 jet must have $\pt>200$. The jet must satisfy
$65<m_{\mathrm{SD}}<105$and $\tau_{21}<0.55$ to be tagged. This working point
corresponds to an efficiency of about 0.50 to identify genuine jets and a
misidentification probability of about 0.03 [31]. Because of an observed
dependence of $m_{\mathrm{SD}}$ on the jet momentum, an additional correction
is applied to ensure the tagged jet $m_{\mathrm{SD}}$ peak is stable and the
tagging efficiency remains roughly constant as a function of jet momentum.

###  0.3.2 Boosted event shape tagger (BEST) algorithm

The NN analysis does not focus on a single VLQ decay mode and thus the
expected signatures can contain various combinations of top and bottom quarks
along with , , and . Using standard cut-based working points for each type of
particle leads to complications with overlaps in selection criteria when
considering many different final states simultaneously. For this reason, a new
algorithm is used that simultaneously attempts to identify six categories of
jets: , , , , , and light jets. The algorithm is called the boosted event
shape tagger (BEST) algorithm, as first detailed in Ref. [32], and uses
hypothesized reference frames to determine the consistency of a jet with the
expected topology from top quark, , , decays, quark and light jets. The
algorithm uses a neural network to classify jets according to one of those six
possibilities. The NN analysis presented here is the first CMS result to use
the BEST algorithm.

The BEST algorithm relies on the fact that jets from very high energy (“highly
boosted”) heavy-particle decays will have a distinct topology in the rest
frame of the decaying object. For example, the decay of a highly boosted quark
produces three collimated particles in the laboratory frame, but in the rest
frame of the quark, the three distinct jet directions lie in a plane. By
Lorentz-boosting the particles or constituents in a jet back to the rest
frame, it can be seen whether the distribution of particles is consistent with
that expected from a top quark decay. This boost transformation is applied
four different times to obtain four sets of jet constituents. The boost
transformation is performed assuming the jet originates from a top quark, , ,
or , after forming the boost vector by using the jet four-vector with the mass
altered to be that of the particle under consideration, while keeping the jet
momentum constant.

The sets of jet constituents resulting from each boost transformation are used
to compute kinematic quantities, including Fox–Wolfram moments [33],
aplanarity, sphericity, and isotropy, based on the eigenvalues of the
sphericity tensor [34], and the jet thrust [35]. In each boosted reference
frame, jet constituents are reclustered to obtain a set of objects relative to
the transformed jet axis. These objects are used to compute the longitudinal
asymmetry, defined as the ratio of the longitudinal-component sum of the
momenta to the sum of this set of objects. This ratio gives another way to
compute the isotropy of constituents that is expected for a jet consistent
with one of the hypothesized particles. Additionally, the jet
$m_{\mathrm{SD}}$, jet $\eta$, charge, $\tau_{32}$, $\tau_{21}$, and subjet
CSVv2 scores from the original jet reference frame are used. In total, 59
kinematic quantities from the original and transformed sets of constituents
are used as inputs to a deep neural network to discriminate between the
different jet species. These kinematic quantities are validated by examining
distributions in data and simulated events, where good agreement in shape is
observed.

The BEST neural network is trained using samples of simulated AK8 jets that
originate from the decay of heavy resonances and that correspond to the final
state objects (, , , , , or light jets). The jets in the training sample are
matched to the object of interest using the generator-level information.
Samples with heavy resonance masses from 1 to 4are used to populate the jet
range from 0.4 to $\approx$2\. The neural network is trained using the Python-
based scikit-learn package, using the MLPClassifier module [36]. The network
architecture consists of 3 hidden layers with 40 nodes in each layer using a
rectified-linear activation function. There are six output nodes,
corresponding to the six particle species of interest. A sample of 500 000
jets is used to train the network, split evenly between the six training
samples. The six outputs from the network represent probabilities for the jet
to originate from the corresponding particle. The classification of an AK8 jet
is chosen according to the output node with the highest probability. Several
validation studies have been performed in different samples of data events
enriched in different types of processes: a muon+jets sample containing
boosted top quarks and boosted bosons, a sample containing events from QCD
processes enriched in gluon-initiated jets, and a sample of photon+jets events
enriched in quark-initiated jets. In each of these samples, we find good
agreement in the shape and rate of the BEST neural network inputs, as well as
the output probabilities [37].

##  0.4 Data set and simulated samples

Both the cut-based and NN analyses use the data set collected by the CMS
experiment at the CERN LHC in 2016, corresponding to an integrated luminosity
of $\Pp\Pp$ collisions of 35.9. Events in the cut-based analysis are selected
online using a trigger algorithm requiring an $\HT$ value of at least 800, or
700if a jet with mass above 50is present. Events are also selected by another
two triggers, which require a single jet with either $\pt\geq 450$ or 360with
a mass above 30. The above trigger selection is measured to be fully efficient
for the signal regions, with corrections applied for percent-level
inefficiencies in control regions. Events in the NN analysis are selected
online using the above trigger algorithms in combination with all other
algorithms requiring multijet topologies. The trigger requirements for the NN
analysis are fully efficient in the signal and control regions, because of the
higher jet momenta considered.

Methods utilizing data are employed to estimate the dominant background from
QCD multijet production, however, samples of simulated events are used to
validate the background estimation techniques described in Section 0.6. These
samples of QCD multijet events are generated at leading order with [38, 39].

Simulated events are used to model the subdominant background contributions.
The largest of these in both analyses comes from the SM pair production of top
quarks, generated at next-to-leading order (NLO) with v2 [40, 41] and showered
with 8.212, using the event tune CUETP8M2T4 [42]. The production of a or boson
in association with additional jets, where the / boson decays to quarks, is
generated at leading-order (LO) with 2.2.2 [43, 44] and also showered with
8.212. Diboson events (, , ) are generated at LO with , and rare top quark
production processes (, , ) are generated at NLO with and showered with .
Background contributions from Higgs boson production in the dominant gluon
fusion mode with decays to and are included via events generated with plus and
v2 + , respectively. Backgrounds other than using use the CUETP8M1 event tune
[45]. The cut-based analysis considers only the and +jets background
contributions. Other processes such as +jets were measured to contribute at
only the $1\%$ level to the total background expectation, and therefore were
not further investigated.

Event samples of pair-produced vector-like and quarks, with masses ranging
from 0.7 to 1.8in increments of 100, are generated at LO using [46] \+ . They
are inclusive with respect to the VLQ decay mode, and are generated with equal
branching fractions for /quark decays to each of the three modes (/, /, /).
Events are weighted to produce results for different combinations of branching
fractions, and are normalized to theoretical cross section expectations
calculated at the next-to-next-to-leading order (NNLO), including next-to-
leading-logarithmic order soft-gluon resummation, with Top++2.0 [47], as
listed in Table 0.4.

Theoretical cross sections for and production, calculated at NNLO with
Top++2.0.

cc /mass [] & Cross section [fb]

700 455

800 196

900 90.4

1000 44.0

1100 22.0

1200 11.8

1300 6.4

1400 3.5

1500 2.0

1600 1.15

1700 0.67

1800 0.39

##  0.5 Event selection

In this section, the event selection and reconstruction techniques applied to
the two analyses are described.

###  0.5.1 Cut-based analysis

The cut-based analysis, optimized for both quarks decaying to a quark and
boson, requires at least two AK8 jets with $\pt>200$and $\abs{\eta}<2.4$. The
AK8 jets serve as boosted boson candidates, and are evaluated with the boson
tagging algorithm described above. In addition, the analysis requires at least
two AK4 jets with $\pt>30$and $\abs{\eta}<2.4$, serving as jet candidates. At
least two of the selected AK4 jets must be distinct from the AK8 jets,
requiring an angular separation of $\Delta R>0.8$. If there are more than two
AK8 or AK4 jets, the two with the highest are used. The analysis requires the
scalar sum of AK4 jet energies, $\HT^{\mathrm{AK4}}$, to be larger than 1200.
For a signal mass of 1200, this selection is $95\%$ efficient. With the two
AK4 and two AK8 jets, there are two possible combinations of a jet and jet
candidate that can be formed. As signal events are expected to produce two
particles with equal mass, we can form the variable

$\Delta m=2\frac{m_{\PQT 1}-m_{\PQT 2}}{m_{\PQT 1}+m_{\PQT 2}},$ (3)

where $m_{\PQT 1}$ is the mass of the higher-quark candidate and $m_{\PQT 2}$
the mass of the lower-quark candidate, of two candidates each formed from one
AK8 jet and one AK4 jet. The assignment of AK4 and AK8 jets to quark
candidates is chosen to minimize the value of $\Delta m$. Events are required
to have $\Delta m<0.1$.

Events passing the $\HT^{\mathrm{AK4}}$ and $\Delta m$ requirements are
further divided into categories. Applying the boson tagging and quark tagging
working points described above, events are divided into categories based on
the multiplicity of and tags in the event. There are nine tagging
combinations, with possibilities of 0, 1, or $\geq$2 tags in combination with
0, 1, or $\geq$2 tags.

###  0.5.2 Neural network analysis

In the NN analysis, each jet in events with exactly four jets is classified
according to one of the six categorizations from the BEST algorithm: , , , , ,
or light. The number of jets with each BEST classification label is used to
divide events into exclusive categories of varying signal and background
contributions, with categories containing larger numbers of , , , , or
candidates being enriched in the VLQ signal, as it is expected to decay to
multiple highly boosted massive objects. In each category, the distribution of
$\HT^{\mathrm{AK8}}$, which is the scalar sum of the four selected AK8 jet
energies, is used to discriminate signal from the background processes.

The signal regions are defined as follows:

  * •

exactly 4 AK8 jets, each with $\pt{}>400$and $\abs{\eta{}}<2.4$;

  * •

a unique set of ($N_{\cPqt{}}$, $N_{\PH{}}$, $N_{\PW{}}$, $N_{\PZ{}}$,
$N_{\cPqb{}}$, $N_{\mathrm{j}}$), labeling each event by the combination of
jet tags.

The possible combinations of $N_{\mathrm{i}}$ satisfying the above conditions
give 126 independent signal region categories. In some categories, where there
is a lack of simulated events to model the subdominant background processes, a
single bin is used as a counting experiment instead of the full
$\HT^{\mathrm{AK8}}$ shape information. This occurs in 14 of the 126 total
categories, which are:  00013, 00022, 00210, 00300, 01003, 01021, 02101,
10120, 10210, 10300, 11020, 13000, 20020, 20200. No further selections are
applied on the jet kinematic variables or the BEST algorithm output
probabilities.

##  0.6 Background estimation methodology

After the requirements described above have been applied to select the
expected signal events, both the cut-based and NN analyses remain dominated by
background events from QCD multijet production processes. Since simulated QCD
multijet events do not reliably model the observed data, because of missing
higher-order contributions during event generation, both analyses incorporate
a method to estimate the background contribution from QCD multijet production
directly from observed data events. This section describes the methodology
employed by each analysis. The non-QCD background contributions are taken from
simulation.

###  0.6.1 Cut-based analysis

The cut-based analysis uses an “ABCD” matrix method based on observed
distributions of two uncorrelated event quantities to predict the shape and
rate for the expected QCD multijet background in the signal region. The two
quantities used to define the control regions are $\HT^{\mathrm{AK4}}$ and
$\Delta m$. The shape of the expected QCD multijet background is obtained by
selecting data events passing the $\HT^{\mathrm{AK4}}>1200$requirement, but
failing the $\Delta m<0.1$ selection. This control region is labeled region B.
The expected backgrounds from and +jets events, as estimated from simulation,
are subtracted from the observed distribution to obtain the expected
contribution solely from QCD multijet events. After obtaining the shape, the
rate can be estimated by defining another set of control regions, namely with
$\HT^{\mathrm{AK4}}<1200$. This sideband, with $\Delta m<0.1$, is labeled
region A. The ratio of the number of events in A to (again after subtracting
the and +jets component) results in an extrapolation scale factor. The control
region with $\HT^{\mathrm{AK4}}{}>1200$, $\Delta m>0.1$ is labeled D. The
scale factor is then applied to the shape obtained from region D to describe
the expected $\HT^{\mathrm{AK4}}$ distribution of QCD multijet events in the
signal region, labeled region C in this description.

The above procedure is only valid if the quantities $\HT^{\mathrm{AK4}}$ and
$\Delta m$ are uncorrelated. In simulated QCD multijet events, a small
correlation ($<$5%) is observed, therefore a residual correction is derived
from these events. Specifically, the ABCD procedure is performed in the
simulated sample, and the resulting prediction is compared with the observed
yield of simulated events in the signal region. A trend in the ratio of these
two $\HT^{\mathrm{AK4}}$ distributions is observed and fit using a linear
function. This function is used to scale the resulting $\HT^{\mathrm{AK4}}$
distribution in data. Three functions are derived, for simulated events with
0, 1, or 2 -tagged jets. The procedure is validated by applying it in observed
events with exactly 0 -tagged jets, and agreement is found within 2.5%.

###  0.6.2 The NN analysis

The NN analysis uses a method based on the classification fractions of the
BEST algorithm to estimate the shape and rate of the QCD multijet background
using data. In the inclusive sample of observed events with exactly three AK8
jets, independent from the four jet sample in which signal is extracted, the
classification fraction $\epsilon_{X}$ for a given jet category $X$ of the
BEST algorithm is computed according to the following definition:

$\epsilon_{X}(\pt)=\frac{N_{X}}{N},$ (4)

where $N_{X}$ represents the number of jets in BEST category $X$, and $N$
represents the total number of jets. The classification fraction is measured
as a function of jet using data events. There is negligible signal
contamination in this region, which is dominated by QCD multijet events. The
fractions for each BEST category are shown in Fig. 1. These fractions are used
to estimate the yield of events having any arbitrary combination of BEST
labels.

To obtain the QCD multijet yield as well as the $\HT^{\mathrm{AK8}}$ shape,
the inclusive sample of events with exactly four AK8 jets is used in data,
however, the BEST labels are not utilized. For each of the 126 signal regions
in the NN analysis, every event is evaluated as a candidate for the given
signal region. Each possible way of assigning jet labels to get the event
category is considered, with each assignment resulting in a jet weight
according to the classification fraction measured above, as a function of each
jet’s . The four jet weights are then multiplied to obtain the final event
weight. The $\HT^{\mathrm{AK8}}$ value of an event enters the binned
$\HT^{\mathrm{AK8}}$ distribution with the event-level weight applied. After
repeating this process for all possible assignments of the BEST labels, and
iterating over all events, the final $\HT^{\mathrm{AK8}}$ distribution for the
expected QCD multijet contribution is obtained:

$r=\sum_{\text{events}}\left\\{\sum_{\text{perms}}\left[\prod_{i=1}^{4}\epsilon_{X_{i}}(\pt{}(i))\right]\right\\},$
(5)

where $r$ represents the expected QCD multijet shape distribution and yield,
and the index $i$ corresponds to one of the four jets in the event.

The procedure above is validated in a sample of simulated QCD multijet events,
and agreement is obtained between predicted and observed events in both the
yield and shape of the $\HT^{\mathrm{AK8}}$ distribution, within the
uncertainties propagated from the measurement of the classification fractions,
for all of the 126 signal regions considered.

Figure 1: Classification fractions for the six categories of the BEST
algorithm, measured in data events with exactly three AK8 jets, as a function
of jet . Error bars shown indicate statistical uncertainties in the fractions
to be propagated to the estimate of the QCD multijet background contribution.
The rightmost bin includes jets with values above 3.

##  0.7 Systematic uncertainties

Several sources of systematic uncertainty are evaluated and included in the
final analysis results. Table 0.7 summarizes the different contributions, and
the analysis to which they contribute. They are described in detail below.

Sources of systematic uncertainties that affect the $\HT^{\mathrm{AK4}}$ or
$\HT^{\mathrm{AK8}}$ distribution in each analysis. Systematic sources with an
uncertainty of “$\pm 1\sigma$” affect the shape and rate, all others affect
the rate only. Sources of systematic error that affect “all simulation” impact
both the signal simulation and simulated backgrounds.

lc cc c Uncertainty Contribution to:
Source Uncertainty Cut-based NN Applies to samples:
Diboson cross section 50% ✓ VV only
Rare top quark process cross sections 50% ✓ V ,
Higgs boson cross section 50% ✓ H only
+jets cross section 15% ✓✓ +jets only
+jets cross section 15% ✓+jets only
Integrated luminosity measurement 2.5% ✓✓ All simulation
Pileup reweighting $\pm 1\sigma$ ✓✓ All simulation
Jet energy scale $\pm 1\sigma(\pt,\eta)$ ✓✓ All simulation
Jet energy resolution $\pm 1\sigma(\eta)$ ✓✓ All simulation
Parton distribution functions $\pm 1\sigma$ ✓ ✓ , VLQ
Renormalization and factorization scales $\pm 1\sigma$ ✓✓ , VLQ
CSVv2 discriminant reshaping $\delta$(wgt., unwgt.) ✓ All simulation
BEST classification fractions $\pm 1\sigma(\pt)$ ✓ QCD multijet
BEST classification scale factor 5% ✓ All simulation
BEST misclassification scale factor 5% ✓ All simulation
Trigger 2% ✓ All simulation
tag scale factor $\pm 1\sigma$ ✓ All simulation
Soft drop jet mass scale $\pm 1\sigma$ ✓ All simulation
Soft drop jet mass resolution $\pm 1\sigma$ ✓ All simulation
tag scale factor $\pm 1\sigma$ ✓ All simulation
Extrapolation fit $\pm 1\sigma$ ✓ Background from data
Normalization of $1$background prediction 1.9% ✓ Background from data
Normalization of $2$background prediction 1.1% ✓ Background from data

  * •

Process cross sections: Uncertainties in the cross sections used to normalize
simulated background processes are included. For the +jets and +jets
backgrounds, uncertainties of 15% are applied [48, 49]. For the subdominant
diboson, rare top quark process (V, ), and Higgs boson contributions, a
conservative uncertainty in the cross section value of 50% is applied. For
backgrounds, the uncertainty in the cross section value is included through
the scale uncertainties described below, which cover both shape and
normalization effects.

  * •

Integrated luminosity: The uncertainty in the measurement of the integrated
luminosity recorded during the 2016 data-taking period by CMS is 2.5%, and is
applied to all simulated signal and background samples [50].

  * •

Pileup reweighting: All simulated samples used in the analysis are reweighted
to ensure the distribution of the number of pileup interactions per event
matches the corresponding observed distribution for the 2016 run. This pileup
distribution is obtained using a proton-proton inelastic cross section value
of 69.2mb [51, 52]. A systematic uncertainty in the distribution is obtained
by varying the value by $\pm 4.6$%, resulting in an uncertainty with both a
normalization and shape component.

  * •

Jet energy scale and resolution: Uncertainties in the 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 $\eta$ [23].

  * •

Parton distribution functions: For the and VLQ signal simulated samples, we
use PDFs from the NNPDF3.0 set [53], and evaluate the systematic uncertainty
due to the choice of PDF according to the process described in Ref. [54]. For
the signal samples, changes in the shape and normalization are considered in
the NN-based analysis. In the cut-based analysis, we find the shape component
to be negligible, and consider only a normalization uncertainty.

  * •

Scale uncertainties: For the and VLQ signal simulated samples, we vary the
renormalization and factorization scales up and down independently by factors
of 2 to account for uncertainties in the choice of scales used to generate the
simulated sample. For the samples, the effect associated with this scale
variation is sufficiently large to cover the uncertainty in the cross section
as well. For the signal samples, changes in the shape and normalization are
considered in the NN-based analysis. In the cut-based analysis, we find the
shape component to be negligible, and consider only a normalization
uncertainty.

  * •

The CSVv2 discriminant reshaping (NN-based): When using the shape of the CSV
discriminant, as we do for inputs to the BEST algorithm, a reshaping event
weight is applied based on the CSVv2 scores of the AK8 jets [30]. We keep the
nominal analysis result without the addition of these CSVv2 reshaping weights,
but add an additional systematic uncertainty where the standard deviation
(s.d.) value is the difference between applying the weights and not applying
the weights.

  * •

The BEST classification scale factors (NN-based): Uncertainties in the
classification and misclassification scale factors are included through 11
independent nuisance parameters, one each for the classification and
misclassification efficiencies for the 5 heavy objects (, , , , ), and a final
nuisance for the QCD categorization efficiency . Weights are applied on a jet-
by-jet basis in each event to produce shape variations in each of the signal
regions. An uncertainty of 5% per BEST classification is used to compute event
weights, and shape templates are formed for each category of the BEST
algorithm, separately for correctly and incorrectly classified jets. This
uncertainty is allowed to float during the signal extraction, to measure a
value for each scale factor.

  * •

The BEST classification fractions for data-driven method (NN-based): We
propagate the uncertainty in the measurement of the classification fractions,
due to limited event counts in data control regions, to the background
estimate. The uncertainties from the six classification fractions
$\epsilon_{X}$ used are added in quadrature to obtain the total uncertainty
for a given event of the expected QCD multijet background distribution $r$, as
described in Section 0.6.2.

  * •

Trigger uncertainty: We measure the trigger efficiency to be > $99\%$ in our
signal region. A 2$\%$ uncertainty is applied to cover small observed trigger
inefficiencies for events with low $\HT^{\mathrm{AK4}}$ values. The impact of
the trigger inefficiency has been measured to be negligible for the NN
analysis because the signal regions are higher in jet momenta and therefore
further away from the trigger turn-on region. No additional systematic
uncertainty is applied to simulated events in the NN analysis.

  * •

tagging scale factor uncertainty (cut-based): We apply scale factors to
account for the difference in  jet tagging efficiency between simulation and
data. The factor is applied as a weight to simulated events based on the
number of  tags. The uncertainty in this factor is $14\%$, plus a small factor
due to extrapolating the tagging efficiency to higher . The uncertainty for
each tag is increased by $4.1\%\log{\pt}_{\PW{}}/200$, where ${\pt}_{\PW{}}$
is the transverse momentum of the tagged  jet.

  * •

Soft drop jet mass scale and resolution (cut-based): To account for the
uncertainty in the soft drop selection used in tagging, the jet
$m_{\mathrm{SD}}$ is varied in simulation according to an uncertainty in the
mass scale and the mass resolution. We consider only the impact on selection
efficiency from this variation. The mass is varied by $0.94\%$ to account for
the scale, and the resolution on the mass is varied by $20\%$. These scale
factor and mass uncertainties are derived in Ref. [31].

  * •

tagging scale factor uncertainty (cut-based): We apply scale factors to
account for the difference in the  jet tagging efficiency between simulation
and data [30]. This factor, as well as its uncertainty, depends on the ,
$\eta$, and hadron flavor of the jet. This affects the shape of the
$\HT^{\mathrm{AK4}}$ distribution, and is applied by varying the scale factor
of  and  jets simultaneously. Light-jet weights are varied separately,
resulting in two separate systematic uncertainties.

  * •

Extrapolation fit (cut-based): The function we use to correct the QCD multijet
background prediction from data carries some statistical uncertainty from the
fitting procedure. We assign a corresponding systematic uncertainty equal to
the combined uncertainty on the fit parameters. We generate templates by
shifting the fitted function by these uncertainties, and reevaluating the
background in each bin. There is one fit per  tag category, and therefore two
independent nuisance parameters. These are correlated across  tag categories
with equal  tags.

  * •

Normalization systematic (cut-based): Sideband regions with 0 -tagged jets and
1 or 2 -tagged jets are used to validate the cut-based analysis method. A
small normalization discrepancy is observed after applying the QCD multijet
background estimation technique. Two conservative, independent, log-normal
nuisance parameters are therefore included for the QCD multijet background
estimation, one applying to the 1 categories and one applying to the 2
categories, each with a value of 20%. We perform a maximum likelihood fit
using only the $0$ tag sideband categories, and extract scale factors and
associated uncertainties for these two parameters. The extracted scale factors
are then applied to the signal regions as listed in Table 0.7.

##  0.8 Signal discrimination

In this section, we present the distributions used to test for the presence of
a signal. For the cut-based analysis, there are 4 independent categories: 1
tag with either 1 or 2 -tagged jets, and 2  tags with either 1 or 2 -tagged
jets. In each category, the $\HT^{\mathrm{AK4}}$ distribution is used for
signal discrimination. Figure 2 shows the $\HT^{\mathrm{AK4}}$ distributions
for each of the 4 signal region categories. The amount of signal that falls
into these categories depends on the hypothesized mass and decay fraction; for
a  decay, the acceptance ranges from 6.1 to 7.5%. For a  decay, the range is
3.8 to 7.5%, and for  it is 3.6 to 6.9%.

Figure 2: The distributions of $\HT^{\mathrm{AK4}}$ for each of the four
signal region categories in the cut-based analysis. The upper row shows
channels with $2$ tags, and $2$ or $1$ tags, respectively. The lower row is
for $1$ tag. The shaded error band represents the statistical uncertainty in
the background. These distributions reflect the nuisance parameters evaluated
after a likelihood fit to a background plus signal hypothesis, where the
hypothesized signal is a quark with a mass of 1200and 100% branching fraction
to . The signal distributions show the expected yield of events assuming the
cross section values in Table 0.4. The vertical axis labels denote that bin
contents in these distributions have been scaled by their corresponding bin
widths, which are the widths used in the fit. The lower panel of each plot
shows the ratio of the observed number of events in a bin to the expected
number.

For the NN analysis, there are 126 independent signal region categories,
corresponding to all the possible combinations of BEST label multiplicities
for 4 AK8 jet events. Between $0.3\%{}$ and $15\%{}$ of signal events with a
decay pass the kinematic requirements to be placed into these signal regions,
depending on the VLQ mass. For a  decay, the range is 0.47% to 16%, and for
it is 0.33% to 21%. Figure 3 shows a visualization of the expected and
observed yields in each of the 126 categories. For further signal
discrimination, the analysis results use the $\HT^{\mathrm{AK8}}$ distribution
in each of the signal region categories. Figure 4 shows the
$\HT^{\mathrm{AK8}}$ distributions for combined categories including at least
one , , , , or  candidate, as well as the inclusive distribution summing all
126 signal regions. The individual distributions shown in Fig. 4 are not
independent, as a particular category may satisfy the criteria for several
distributions.

Figure 3: A summary of the 126 signal region categories used in the NN
analysis. This figure shows the expected yields in each category, while the
signal discrimination is performed with the $\HT^{\mathrm{AK8}}$ distributions
from each of the categories. The bottom panel shows the ratio of observed data
to total background in each category, with Poisson error bars where
applicable, along with the total background uncertainty shown for each
category by the gray band.

Figure 4: Distributions of $\HT^{\mathrm{AK8}}$ for all events entering the
126 signal regions of the NN analysis (upper left), as well as for only
categories containing at least one candidate of each of the particle types
identified by the BEST algorithm: $\geq$1 jet (upper right), $\geq$1 jet
(middle left), $\geq$1 jet (middle right), $\geq$1 jet (lower left), and
$\geq$1 jet (lower right). The plots shown here are not mutually exclusive, as
a particular signal region may satisfy several of the criteria for the
individual summary categories. The vertical axis labels denote that bin
contents in these distributions have been scaled by their corresponding bin
widths. The lower panel of each plot shows the ratio of the observed number of
events in a bin to the expected number.

##  0.9 Statistical analysis

Independent statistical procedures are performed for the cut-based and NN
analyses, using the same methodology. No explicit combination of the two
analyses is presented here, as they are performed on many of the same events.
The Theta software package [55] is used to perform a Bayesian shape-based
analysis using the distributions from the signal region categories. Each bin
of the distributions is combined statistically in a likelihood, where
contributions from systematic and statistical uncertainties are added through
nuisance parameters in the likelihood function. Each of the rate nuisance
parameters is implemented with a log-normal prior distribution, while the
shape-based nuisance parameters utilize Gaussian prior distributions.

In the cut-based analysis, all four signal regions are fit simultaneously.
Most systematic uncertainties are fit simultaneously across the four
categories, with the exception of the extrapolation fit and normalization
uncertainties. For these parameters, there are independent uncertainties for
the two tag multiplicities. The ratio of events in the control regions to
signal regions is fixed when calculating the multijet background component,
and is not a parameter considered in the fit.

For the NN analysis, nuisance parameters for the BEST classification
efficiency scale factors are allowed to fluctuate unconstrained, allowing a
simultaneous measurement of scale factor values. A uniform prior distribution
is assumed for the signal normalization. Additionally, due to the limited
numbers of simulated events, we follow the “Barlow–Beeston lite” method [56]
and assign an additional nuisance parameter to each bin of background
components relying on simulated events. Prior to the statistical analysis, the
discriminating distributions are rebinned from a width of 100, to reduce the
statistical uncertainty on the total background in the tails of the
distributions to below $30\%$, as they can suffer from the effects of having
limited events passing all signal criteria. The likelihood function is used to
extract Bayesian upper limits on the cross section for pair production of or
quarks at $95\%$ confidence level (CL). Additionally, samples of pseudodata
are formed by sampling the expected backgrounds after varying the
uncertainties within their prior distributions. For each pseudodata sample,
the statistical analysis is performed to extract a range of upper limit
outcomes. The median of these outcomes is the expected limit, and the range of
outcomes within one or two standard deviations of the median is also shown for
comparison.

##  0.10 Results

We observe no statistically significant excess over the expected background.
The expected and observed limits on the cross section for pair production of
and quarks are shown in the case of branching fractions of one for the
individual decay modes in Figs. 5 and 6, for the cut-based and NN analyses,
respectively. Because the cut-based analysis is optimized for the  decay mode
and includes selections targeting boosted jets, it lacks sensitivity to the
other decay modes. The NN analysis does not target a specific decay mode, but
shows the best sensitivity to quark decays to  and , or quark decays to . It
has lower sensitivity in the  channel due to lower efficiency for correctly
identifying  jets using AK8 reconstructed jets with the BEST algorithm.

A scan over all branching fractions considered is performed in increments of
0.2, with the results translated to limits on the VLQ mass. Figure 7 shows the
results for the quark graphically, with the values tabulated in Table 0.10.
Figure 8 and Table 0.10 show the corresponding results for the quark.

We exclude vector-like quark masses ranging from 740, up to 1370for the  decay
mode in the NN analysis. The cut-based analysis provides additional
sensitivity to the  decay mode, with a quark mass exclusion of 1040for decays
solely to . These results complement the existing results from other decay
channels, and in the hadronic channel extends the excluded quark mass from
705obtained in the previous 8analysis [57] to 1040. For vector-like quarks,
sensitivity is lost because of the additional  quarks present in the decays,
for which the BEST analysis has a larger misidentification rate. The cut-based
analysis is not currently optimized for quarks, however does provide some
complementary sensitivity to the  decay mode. These analyses exclude vector-
like quarks with masses up to 1230, for decays solely to . A mass exclusion of
1070is obtained in the cut-based analysis for the  decay mode scenario.

Figure 5: Limits at $95\%{}$ confidence level on the ratio of the cross
section to the theoretical cross section for pair production quarks (left) and
quarks (right) in the cut-based analysis, with decays solely to / (upper), /
(middle), and / (lower). The solid black line shows the observed limit, while
the dashed black line shows the median of the distribution of limits expected
under the background-only hypothesis. The inner (green) band and the outer
(yellow) band indicate the regions containing 68 and 95%, respectively, of the
distribution of limits expected under the background-only hypothesis.

Figure 6: Limits at $95\%{}$ confidence level on the ratio of the cross
section to the theoretical cross section for pair production quarks (left) and
quarks (right) in the NN analysis, with decays solely to / (upper), /
(middle), and / (lower). The solid black line shows the observed limit, while
the dashed black line shows the median of the distribution of limits expected
under the background-only hypothesis. The inner (green) band and the outer
(yellow) band indicate the regions containing 68 and 95%, respectively, of the
distribution of limits expected under the background-only hypothesis.

Exclusion limits at $95\%{}$ confidence level presented in terms of the quark
mass, for the different branching fraction scenarios considered, in each of
the two analyses.

ccc cccc Observed limits [] Expected limits []

$\mathcal{B}(\cPqt\PZ)$ $\mathcal{B}(\cPqb\PW)$ $\mathcal{B}(\cPqt\PH)$ Cut-
based NN Cut-based NN

0.0 0.0 1.0 840 1370 780 1170

0.0 0.2 0.8 900 1230 850 1040

0.0 0.4 0.6 920 1090 910 830

0.0 0.6 0.4 960 890 970 $<$700

0.0 0.8 0.2 990 830 1020 $<$700

0.0 1.0 0.0 1040 780 1070 $<$700

0.2 0.0 0.8 840 1280 790 1150

0.2 0.2 0.6 900 1230 850 1020

0.2 0.4 0.4 920 1090 920 850

0.2 0.6 0.2 960 950 980 $<$700

0.2 0.8 0.0 1000 810 1030 $<$700

0.4 0.0 0.6 760 1280 800 1130

0.4 0.2 0.4 880 1210 860 990

0.4 0.4 0.2 910 1070 930 830

0.4 0.6 0.0 950 930 1000 $<$700

0.6 0.0 0.4 780 1280 810 1130

0.6 0.2 0.2 850 1210 880 980

0.6 0.4 0.0 910 1040 940 $<$700

0.8 0.0 0.2 750 1300 810 1110

0.8 0.2 0.0 850 1210 890 970

1.0 0.0 0.0 $<$700 1260 920 1100

Figure 7: Observed (left) and expected (right) mass exclusion limits at
$95\%{}$ confidence level for each combination of quark branching fractions,
in the cut-based analysis (upper) and NN analysis (lower).

Exclusion limits at $95\%{}$ confidence level presented in terms of the quark
mass, for the different branching fraction scenarios considered, in each of
the two analyses.

ccc cccc Observed limits [] Expected limits []

$\mathcal{B}(\cPqb\PZ)$ $\mathcal{B}(\cPqt\PW)$ $\mathcal{B}(\cPqb\PH)$ Cut-
based NN Cut-based NN

0.0 0.0 1.0 980 $<$700 870 850

0.0 0.2 0.8 950 810 860 810

0.0 0.4 0.6 920 890 850 810

0.0 0.6 0.4 830 1100 830 800

0.0 0.8 0.2 $<$700 1140 $<$700 910

0.0 1.0 0.0 $<$700 1230 $<$700 950

0.2 0.0 0.8 1000 $<$700 950 820

0.2 0.2 0.6 950 830 930 730

0.2 0.4 0.4 940 900 920 740

0.2 0.6 0.2 890 940 910 820

0.2 0.8 0.0 860 1150 880 880

0.4 0.0 0.6 1020 740 1000 770

0.4 0.2 0.4 980 820 1000 $<$700

0.4 0.4 0.2 970 880 980 $<$700

0.4 0.6 0.0 880 1110 970 790

0.6 0.0 0.4 1030 740 1050 740

0.6 0.2 0.2 1020 810 1040 $<$700

0.6 0.4 0.0 1000 920 1040 $<$700

0.8 0.0 0.2 1050 760 1100 720

0.8 0.2 0.0 1030 820 1090 $<$700

1.0 0.0 0.0 1070 740 1130 720

Figure 8: Observed (left) and expected (right) mass exclusion limits at
$95\%{}$ confidence level for each combination of quark branching fractions,
in the cut-based analysis (upper) and NN analysis (lower).

##  0.11 Summary

Two independent searches for vector-like and quarks using the fully hadronic
final states have been presented. Both searches use data collected by the CMS
experiment in 2016 at a center-of-mass energy of 13, corresponding to an
integrated luminosity of 35.9. A cut-based analysis, using jet substructure
observables to identify hadronic decays of boosted  bosons, targets the  decay
mode of the quark, and improves sensitivity relative to results of such
searches conducted previously. The analysis uses a quantum chromodynamics
multijet background estimation method based on shape and rate extrapolations
from various control regions to the signal region. Improvements in  tagging
techniques, as well as the addition of signal regions requiring just a single
-tagged jet, enhance the performance of this analysis relative to previous
searches based on different strategies. This search extends the quark mass
exclusion to 1040, relative to the previous exclusion of 705obtained by a
similar analysis targeting the  decay mode using data collected at 8 [57].

A new strategy is presented and compared with the traditional cut-based
approach. The neural network analysis uses a multiclassification technique,
the boosted event shape tagger algorithm, to identify jets originating from
heavy objects such as  or  quarks, and , , or . This allows the analysis to be
sensitive to all decay modes of the and quarks. Using classification
fractions, the dominant multijet background is estimated using data. The
neural network analysis provides sensitivity for the  and  decay modes
competitive with that obtained by other searches utilizing lepton+jets or
multilepton topologies.

For each analysis, results are presented in terms of cross section limits for
the pair production of and quarks, along with exclusion limits in terms of the
and quark masses, for the different combinations of branching fractions
considered. The mass exclusion limits at $95\%{}$ confidence level for the
neural network analysis range from 740 to 1370, providing comparable
sensitivity to the CMS searches utilizing leptons, which exclude vector-like
quark masses in the range 910–1300 [14]. These results represent the most
stringent limits on pair produced vector-like quarks in the fully hadronic
channel 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, PUT and ERDF (Estonia); Academy
of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG,
and HGF (Germany); GSRT (Greece); NKFIA (Hungary); DAE and DST (India); IPM
(Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); MES
(Latvia); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT,
LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC
(Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom,
RAS, RFBR, and NRC KI (Russia); MESTD (Serbia); SEIDI, CPAN, PCTI, and FEDER
(Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST
(Taipei); ThEPCenter, IPST, STAR, and NSTDA (Thailand); TUBITAK and TAEK
(Turkey); NASU and SFFR (Ukraine); STFC (United Kingdom); DOE and NSF (USA).
Individuals have received support from the Marie-Curie program and the
European Research Council and Horizon 2020 Grant, contract Nos. 675440 and
765710 (European Union); the Leventis Foundation; the A.P. Sloan Foundation;
the Alexander von Humboldt Foundation; the Belgian Federal Science Policy
Office; the Fonds pour la Formation à la Recherche dans l’Industrie et dans
l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en
Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium) under the
“Excellence of Science – EOS” – be.h project n. 30820817; the Beijing
Municipal Science & Technology Commission, No. Z181100004218003; the Ministry
of Education, Youth and Sports (MEYS) of the Czech Republic; the Lendület
(“Momentum”) Programme and the János Bolyai Research Scholarship of the
Hungarian Academy of Sciences, the New National Excellence Program ÚNKP, the
NKFIA research grants 123842, 123959, 124845, 124850, 125105, 128713, 128786,
and 129058 (Hungary); the Council of Science and Industrial Research, India;
the HOMING PLUS 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${}^{\textrm{\textdagger}}$, A. Tumasyan Institut für
Hochenergiephysik, Wien, Austria
W. Adam, F. Ambrogi, T. Bergauer, J. Brandstetter, M. Dragicevic, J. Erö, A.
Escalante Del Valle, M. Flechl, R. Frühwirth1, M. Jeitler1, N. Krammer, I.
Krätschmer, D. Liko, T. Madlener, I. Mikulec, N. Rad, J. Schieck1, R.
Schöfbeck, M. Spanring, D. Spitzbart, W. Waltenberger, C.-E. Wulz1, M. Zarucki
Institute for Nuclear Problems, Minsk, Belarus
V. Drugakov, V. Mossolov, J. Suarez Gonzalez Universiteit Antwerpen,
Antwerpen, Belgium
M.R. Darwish, E.A. De Wolf, D. Di Croce, X. Janssen, J. Lauwers, A. Lelek, M.
Pieters, H. Rejeb Sfar, H. Van Haevermaet, P. Van Mechelen, S. Van Putte, N.
Van Remortel Vrije Universiteit Brussel, Brussel, Belgium
F. Blekman, E.S. Bols, S.S. Chhibra, J. D’Hondt, J. De Clercq, D. Lontkovskyi,
S. Lowette, I. Marchesini, S. Moortgat, L. Moreels, Q. Python, K. Skovpen, S.
Tavernier, W. Van Doninck, P. Van Mulders, I. Van Parijs Université Libre de
Bruxelles, Bruxelles, Belgium
D. Beghin, B. Bilin, H. Brun, B. Clerbaux, G. De Lentdecker, H. Delannoy, B.
Dorney, L. Favart, A. Grebenyuk, A.K. Kalsi, J. Luetic, A. Popov, N. Postiau,
E. Starling, L. Thomas, C. Vander Velde, P. Vanlaer, D. Vannerom, Q. Wang
Ghent University, Ghent, Belgium
T. Cornelis, D. Dobur, I. Khvastunov2, C. Roskas, D. Trocino, M. Tytgat, W.
Verbeke, B. Vermassen, M. Vit, N. Zaganidis Université Catholique de Louvain,
Louvain-la-Neuve, Belgium
O. Bondu, G. Bruno, C. Caputo, P. David, C. Delaere, M. Delcourt, A.
Giammanco, V. Lemaitre, A. Magitteri, J. Prisciandaro, A. Saggio, M. Vidal
Marono, P. Vischia, J. Zobec Centro Brasileiro de Pesquisas Fisicas, Rio de
Janeiro, Brazil
F.L. Alves, G.A. Alves, G. Correia Silva, C. Hensel, A. Moraes, P. Rebello
Teles Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
E. Belchior Batista Das Chagas, W. Carvalho, J. Chinellato3, E. Coelho, E.M.
Da Costa, G.G. Da Silveira4, D. De Jesus Damiao, C. De Oliveira Martins, S.
Fonseca De Souza, L.M. Huertas Guativa, H. Malbouisson, J. Martins5, D. Matos
Figueiredo, M. Medina Jaime6, M. Melo De Almeida, C. Mora Herrera, L. Mundim,
H. Nogima, W.L. Prado Da Silva, L.J. Sanchez Rosas, A. Santoro, A. Sznajder,
M. Thiel, E.J. Tonelli Manganote3, F. Torres Da Silva De Araujo, A. Vilela
Pereira Universidade Estadual Paulista a, Universidade Federal do ABC b, São
Paulo, Brazil
S. Ahujaa, C.A. Bernardesa, L. Calligarisa, T.R. Fernandez Perez Tomeia, E.M.
Gregoresb, D.S. Lemos, P.G. Mercadanteb, S.F. Novaesa, SandraS. Padulaa
Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy of
Sciences, Sofia, Bulgaria
A. Aleksandrov, G. Antchev, R. Hadjiiska, P. Iaydjiev, A. Marinov, M. Misheva,
M. Rodozov, M. Shopova, G. Sultanov University of Sofia, Sofia, Bulgaria
M. Bonchev, A. Dimitrov, T. Ivanov, L. Litov, B. Pavlov, P. Petkov Beihang
University, Beijing, China
W. Fang7, X. Gao7, L. Yuan Institute of High Energy Physics, Beijing, China
M. Ahmad, G.M. Chen, H.S. Chen, M. Chen, C.H. Jiang, D. Leggat, H. Liao, Z.
Liu, S.M. Shaheen8, A. Spiezia, J. Tao, E. Yazgan, H. Zhang, S. Zhang8, J.
Zhao State Key Laboratory of Nuclear Physics and Technology, Peking
University, Beijing, China
A. Agapitos, Y. Ban, G. Chen, A. Levin, J. Li, L. Li, Q. Li, Y. Mao, S.J.
Qian, D. Wang Tsinghua University, Beijing, China
Z. Hu, Y. Wang Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, L.F. Chaparro Sierra, C. Florez, C.F. González
Hernández, M.A. Segura Delgado Universidad de Antioquia, Medellin, Colombia
J. Mejia Guisao, J.D. Ruiz Alvarez, C.A. Salazar González, N. Vanegas Arbelaez
University of Split, Faculty of Electrical Engineering, Mechanical Engineering
and Naval Architecture, Split, Croatia
D. Giljanović, N. Godinovic, D. Lelas, I. Puljak, T. Sculac University of
Split, Faculty of Science, Split, Croatia
Z. Antunovic, M. Kovac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, S. Ceci, D. Ferencek, K. Kadija, B. Mesic, M. Roguljic, A.
Starodumov9, T. Susa University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, E. Erodotou, A. Ioannou, M. Kolosova, S. Konstantinou,
G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos, P.A. Razis, H.
Rykaczewski, D. Tsiakkouri Charles University, Prague, Czech Republic
M. Finger10, M. Finger Jr.10, A. Kveton, J. Tomsa Escuela Politecnica
Nacional, Quito, Ecuador
E. Ayala Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab
Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
S. Elgammal11, A. Ellithi Kamel12 National Institute of Chemical Physics and
Biophysics, Tallinn, Estonia
S. Bhowmik, A. Carvalho Antunes De Oliveira, R.K. Dewanjee, K. Ehataht, M.
Kadastik, M. Raidal, C. Veelken Department of Physics, University of Helsinki,
Helsinki, Finland
P. Eerola, L. Forthomme, H. Kirschenmann, K. Osterberg, M. Voutilainen
Helsinki Institute of Physics, Helsinki, Finland
F. Garcia, J. Havukainen, J.K. Heikkilä, T. Järvinen, V. Karimäki, R.
Kinnunen, T. Lampén, K. Lassila-Perini, S. Laurila, S. Lehti, T. Lindén, P.
Luukka, T. Mäenpää, H. Siikonen, E. Tuominen, J. Tuominiemi Lappeenranta
University of Technology, Lappeenranta, Finland
T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
M. Besancon, F. Couderc, M. Dejardin, D. Denegri, B. Fabbro, J.L. Faure, F.
Ferri, S. Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry,
C. Leloup, E. Locci, J. Malcles, J. Rander, A. Rosowsky, M.Ö. Sahin, A. Savoy-
Navarro13, M. Titov Laboratoire Leprince-Ringuet, Ecole polytechnique,
CNRS/IN2P3, Université Paris-Saclay, Palaiseau, France
C. Amendola, F. Beaudette, P. Busson, C. Charlot, B. Diab, G. Falmagne, R.
Granier de Cassagnac, I. Kucher, A. Lobanov, C. Martin Perez, M. Nguyen, C.
Ochando, P. Paganini, J. Rembser, R. Salerno, J.B. Sauvan, Y. Sirois, A. Zabi,
A. Zghiche Université de Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, France
J.-L. Agram14, J. Andrea, D. Bloch, G. Bourgatte, J.-M. Brom, E.C. Chabert, C.
Collard, E. Conte14, J.-C. Fontaine14, D. Gelé, U. Goerlach, M. Jansová, A.-C.
Le Bihan, N. Tonon, P. Van Hove Centre de Calcul de l’Institut National de
Physique Nucleaire et de Physique des Particules, CNRS/IN2P3, Villeurbanne,
France
S. Gadrat Université de Lyon, Université Claude Bernard Lyon 1, CNRS-IN2P3,
Institut de Physique Nucléaire de Lyon, Villeurbanne, France
S. Beauceron, C. Bernet, G. Boudoul, C. Camen, N. Chanon, R. Chierici, D.
Contardo, P. Depasse, H. El Mamouni, J. Fay, S. Gascon, M. Gouzevitch, B.
Ille, Sa. Jain, F. Lagarde, I.B. Laktineh, H. Lattaud, M. Lethuillier, L.
Mirabito, S. Perries, V. Sordini, G. Touquet, M. Vander Donckt, S. Viret
Georgian Technical University, Tbilisi, Georgia
T. Toriashvili15 Tbilisi State University, Tbilisi, Georgia
Z. Tsamalaidze10 RWTH Aachen University, I. Physikalisches Institut, Aachen,
Germany
C. Autermann, L. Feld, M.K. Kiesel, K. Klein, M. Lipinski, D. Meuser, A.
Pauls, M. Preuten, M.P. Rauch, C. Schomakers, J. Schulz, M. Teroerde, B.
Wittmer RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
A. Albert, M. Erdmann, S. Erdweg, T. Esch, B. Fischer, R. Fischer, S. Ghosh,
T. Hebbeker, K. Hoepfner, H. Keller, L. Mastrolorenzo, M. Merschmeyer, A.
Meyer, P. Millet, G. Mocellin, S. Mondal, S. Mukherjee, D. Noll, A. Novak, T.
Pook, A. Pozdnyakov, T. Quast, M. Radziej, Y. Rath, H. Reithler, M. Rieger, J.
Roemer, A. Schmidt, S.C. Schuler, A. Sharma, S. Thüer, S. Wiedenbeck RWTH
Aachen University, III. Physikalisches Institut B, Aachen, Germany
G. Flügge, W. Haj Ahmad16, O. Hlushchenko, T. Kress, T. Müller, A. Nehrkorn,
A. Nowack, C. Pistone, O. Pooth, D. Roy, H. Sert, A. Stahl17 Deutsches
Elektronen-Synchrotron, Hamburg, Germany
M. Aldaya Martin, P. Asmuss, I. Babounikau, H. Bakhshiansohi, K. Beernaert, O.
Behnke, U. Behrens, A. Bermúdez Martínez, D. Bertsche, A.A. Bin Anuar, K.
Borras18, V. Botta, A. Campbell, A. Cardini, P. Connor, S. Consuegra
Rodríguez, C. Contreras-Campana, V. Danilov, A. De Wit, M.M. Defranchis, C.
Diez Pardos, D. Domínguez Damiani, G. Eckerlin, D. Eckstein, T. Eichhorn, A.
Elwood, E. Eren, E. Gallo19, A. Geiser, J.M. Grados Luyando, A. Grohsjean, M.
Guthoff, M. Haranko, A. Harb, A. Jafari, N.Z. Jomhari, H. Jung, A. Kasem18, M.
Kasemann, H. Kaveh, J. Keaveney, C. Kleinwort, J. Knolle, D. Krücker, W.
Lange, T. Lenz, J. Leonard, J. Lidrych, K. Lipka, W. Lohmann20, R. Mankel,
I.-A. Melzer-Pellmann, A.B. Meyer, M. Meyer, M. Missiroli, G. Mittag, J.
Mnich, A. Mussgiller, V. Myronenko, D. Pérez Adán, S.K. Pflitsch, D. Pitzl, A.
Raspereza, A. Saibel, M. Savitskyi, V. Scheurer, P. Schütze, C.
Schwanenberger, R. Shevchenko, A. Singh, H. Tholen, O. Turkot, A. Vagnerini,
M. Van De Klundert, G.P. Van Onsem, R. Walsh, Y. Wen, K. Wichmann, C. Wissing,
O. Zenaiev, R. Zlebcik University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, L. Benato, A. Benecke, V. Blobel, T. Dreyer, A.
Ebrahimi, A. Fröhlich, C. Garbers, E. Garutti, D. Gonzalez, P. Gunnellini, J.
Haller, A. Hinzmann, A. Karavdina, G. Kasieczka, R. Klanner, R. Kogler, N.
Kovalchuk, S. Kurz, V. Kutzner, J. Lange, T. Lange, A. Malara, D. Marconi, J.
Multhaup, 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, B. Vormwald, I.
Zoi Karlsruher Institut fuer Technologie, Karlsruhe, Germany
M. Akbiyik, C. Barth, M. Baselga, S. Baur, T. Berger, E. Butz, R. Caspart, T.
Chwalek, W. De Boer, A. Dierlamm, K. El Morabit, N. Faltermann, M. Giffels, P.
Goldenzweig, A. Gottmann, M.A. Harrendorf, F. Hartmann17, U. Husemann, S.
Kudella, S. Mitra, M.U. Mozer, Th. Müller, M. Musich, A. Nürnberg, G. Quast,
K. Rabbertz, M. Schröder, I. Shvetsov, H.J. Simonis, R. Ulrich, M. Weber, C.
Wöhrmann, R. Wolf Institute of Nuclear and Particle Physics (INPP), NCSR
Demokritos, Aghia Paraskevi, Greece
G. Anagnostou, P. Asenov, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas,
G. Paspalaki National and Kapodistrian University of Athens, Athens, Greece
M. Diamantopoulou, G. Karathanasis, P. Kontaxakis, A. Panagiotou, I.
Papavergou, N. Saoulidou, A. Stakia, K. Theofilatos, K. Vellidis National
Technical University of Athens, Athens, Greece
G. Bakas, K. Kousouris, I. Papakrivopoulos, G. Tsipolitis University of
Ioánnina, Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, S. Mallios, K.
Manitara, N. Manthos, I. Papadopoulos, J. Strologas, F.A. Triantis, D.
Tsitsonis MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
M. Bartók21, M. Csanad, P. Major, K. Mandal, A. Mehta, M.I. Nagy, G. Pasztor,
O. Surányi, G.I. Veres Wigner Research Centre for Physics, Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath22, F. Sikler, T.Á. Vámi, V. Veszpremi, G.
Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear Research ATOMKI,
Debrecen, Hungary
N. Beni, S. Czellar, J. Karancsi21, A. Makovec, J. Molnar, Z. Szillasi
Institute of Physics, University of Debrecen, Debrecen, Hungary
P. Raics, D. Teyssier, Z.L. Trocsanyi, B. Ujvari Eszterhazy Karoly University,
Karoly Robert Campus, Gyongyos, Hungary
T. Csorgo, W.J. Metzger, F. Nemes, T. Novak Indian Institute of Science
(IISc), Bangalore, India
S. Choudhury, J.R. Komaragiri, P.C. Tiwari National Institute of Science
Education and Research, HBNI, Bhubaneswar, India
S. Bahinipati24, C. Kar, G. Kole, P. Mal, V.K. Muraleedharan Nair Bindhu, A.
Nayak25, D.K. Sahoo24, S.K. Swain Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, S. Chauhan, R. Chawla, N. Dhingra, R.
Gupta, A. Kaur, M. Kaur, S. Kaur, P. Kumari, M. Lohan, M. Meena, K. Sandeep,
S. Sharma, J.B. Singh, A.K. Virdi, G. Walia University of Delhi, Delhi, India
A. Bhardwaj, B.C. Choudhary, R.B. Garg, M. Gola, S. Keshri, Ashok Kumar, S.
Malhotra, M. Naimuddin, P. Priyanka, K. Ranjan, Aashaq Shah, R. Sharma Saha
Institute of Nuclear Physics, HBNI, Kolkata, India
R. Bhardwaj26, M. Bharti26, R. Bhattacharya, S. Bhattacharya, U. Bhawandeep26,
D. Bhowmik, S. Dey, S. Dutta, S. Ghosh, M. Maity27, K. Mondal, S. Nandan, A.
Purohit, P.K. Rout, G. Saha, S. Sarkar, T. Sarkar27, M. Sharan, B. Singh26, S.
Thakur26 Indian Institute of Technology Madras, Madras, India
P.K. Behera, P. Kalbhor, A. Muhammad, P.R. Pujahari, A. Sharma, A.K. Sikdar
Bhabha Atomic Research Centre, Mumbai, India
R. Chudasama, D. Dutta, V. Jha, V. Kumar, D.K. Mishra, P.K. Netrakanti, L.M.
Pant, P. Shukla Tata Institute of Fundamental Research-A, Mumbai, India
T. Aziz, M.A. Bhat, S. Dugad, G.B. Mohanty, N. Sur, RavindraKumar Verma Tata
Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, S. Karmakar,
S. Kumar, G. Majumder, K. Mazumdar, N. Sahoo, S. Sawant Indian Institute of
Science Education and Research (IISER), Pune, India
S. Chauhan, S. Dube, V. Hegde, A. Kapoor, K. Kothekar, S. Pandey, A. Rane, A.
Rastogi, S. Sharma Institute for Research in Fundamental Sciences (IPM),
Tehran, Iran
S. Chenarani28, E. Eskandari Tadavani, S.M. Etesami28, M. Khakzad, M.
Mohammadi Najafabadi, M. Naseri, F. Rezaei Hosseinabadi University College
Dublin, Dublin, Ireland
M. Felcini, M. Grunewald INFN Sezione di Bari a, Università di Bari b,
Politecnico di Bari c, Bari, Italy
M. Abbresciaa,b, C. Calabriaa,b, A. Colaleoa, D. Creanzaa,c, L. Cristellaa,b,
N. De Filippisa,c, M. De Palmaa,b, A. Di Florioa,b, L. Fiorea, A. Gelmia,b, G.
Iasellia,c, M. Incea,b, S. Lezkia,b, G. Maggia,c, M. Maggia, G. Minielloa,b,
S. Mya,b, S. Nuzzoa,b, A. Pompilia,b, G. Pugliesea,c, R. Radognaa, A.
Ranieria, G. Selvaggia,b, L. Silvestrisa, R. Vendittia, P. Verwilligena INFN
Sezione di Bologna a, Università di Bologna b, Bologna, Italy
G. Abbiendia, C. Battilanaa,b, D. Bonacorsia,b, L. Borgonovia,b, S. Braibant-
Giacomellia,b, R. Campaninia,b, P. Capiluppia,b, A. Castroa,b, F.R. Cavalloa,
C. Cioccaa, G. Codispotia,b, M. Cuffiania,b, G.M. Dallavallea, F. Fabbria, A.
Fanfania,b, E. Fontanesi, P. Giacomellia, C. Grandia, L. Guiduccia,b, F.
Iemmia,b, S. Lo Meoa,29, S. Marcellinia, G. Masettia, F.L. Navarriaa,b, A.
Perrottaa, F. Primaveraa,b, A.M. Rossia,b, T. Rovellia,b, G.P. Sirolia,b, N.
Tosia INFN Sezione di Catania a, Università di Catania b, Catania, Italy
S. Albergoa,b,30, S. Costaa,b, A. Di Mattiaa, R. Potenzaa,b, A. Tricomia,b,30,
C. Tuvea,b INFN Sezione di Firenze a, Università di Firenze b, Firenze, Italy
G. Barbaglia, R. Ceccarelli, K. Chatterjeea,b, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, E. Focardia,b, G. Latino, P. Lenzia,b, M. Meschinia, S.
Paolettia, G. Sguazzonia, D. Stroma, L. Viliania INFN Laboratori Nazionali di
Frascati, Frascati, Italy
L. Benussi, S. Bianco, D. Piccolo INFN Sezione di Genova a, Università di
Genova b, Genova, Italy
M. Bozzoa,b, F. Ferroa, R. Mulargiaa,b, E. Robuttia, S. Tosia,b INFN Sezione
di Milano-Bicocca a, Università di Milano-Bicocca b, Milano, Italy
A. Benagliaa, A. Beschia,b, F. Brivioa,b, V. Cirioloa,b,17, S. Di Guidaa,b,17,
M.E. Dinardoa,b, P. Dinia, S. Fiorendia,b, S. Gennaia, A. Ghezzia,b, P.
Govonia,b, L. Guzzia,b, M. Malbertia, S. Malvezzia, D. Menascea, F. Montia,b,
L. Moronia, G. Ortonaa,b, M. Paganonia,b, D. Pedrinia, S. Ragazzia,b, T.
Tabarelli de Fatisa,b, D. Zuoloa,b INFN Sezione di Napoli a, Università di
Napoli ’Federico II’ b, Napoli, Italy, Università della Basilicata c, Potenza,
Italy, Università G. Marconi d, Roma, Italy
S. Buontempoa, N. Cavalloa,c, A. De Iorioa,b, A. Di Crescenzoa,b, F.
Fabozzia,c, F. Fiengaa, G. Galatia, A.O.M. Iorioa,b, L. Listaa,b, S.
Meolaa,d,17, P. Paoluccia,17, B. Rossia, C. Sciaccaa,b, E. Voevodinaa,b INFN
Sezione di Padova a, Università di Padova b, Padova, Italy, Università di
Trento c, Trento, Italy
P. Azzia, N. Bacchettaa, D. Biselloa,b, A. Bolettia,b, A. Bragagnolo, R.
Carlina,b, P. Checchiaa, P. De Castro Manzanoa, T. Dorigoa, U. Dossellia, F.
Gasparinia,b, U. Gasparinia,b, A. Gozzelinoa, S.Y. Hoh, P. Lujan, M.
Margonia,b, A.T. Meneguzzoa,b, J. Pazzinia,b, M. Presillab, P. Ronchesea,b, R.
Rossina,b, F. Simonettoa,b, A. Tiko, M. Tosia,b, M. Zanettia,b, P. Zottoa,b,
G. Zumerlea,b INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy
A. Braghieria, P. Montagnaa,b, S.P. Rattia,b, V. Rea, M. Ressegottia,b, C.
Riccardia,b, P. Salvinia, I. Vaia,b, P. Vituloa,b INFN Sezione di Perugia a,
Università di Perugia b, Perugia, Italy
M. Biasinia,b, G.M. Bileia, C. Cecchia,b, D. Ciangottinia,b, L. Fanòa,b, P.
Laricciaa,b, R. Leonardia,b, E. Manonia, G. Mantovania,b, V. Mariania,b, M.
Menichellia, A. Rossia,b, A. Santocchiaa,b, D. Spigaa INFN Sezione di Pisa a,
Università di Pisa b, Scuola Normale Superiore di Pisa c, Pisa, Italy
K. Androsova, P. Azzurria, G. Bagliesia, V. Bertacchia,c, L. Bianchinia, T.
Boccalia, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, G. Fedia, L.
Gianninia,c, A. Giassia, M.T. Grippoa, F. Ligabuea,c, E. Mancaa,c, G.
Mandorlia,c, A. Messineoa,b, F. Pallaa, A. Rizzia,b, G. Rolandi31, S. Roy
Chowdhury, A. Scribanoa, P. Spagnoloa, R. Tenchinia, G. Tonellia,b, N. Turini,
A. Venturia, P.G. Verdinia INFN Sezione di Roma a, Sapienza Università di Roma
b, Rome, Italy
F. Cavallaria, M. Cipriania,b, D. Del Rea,b, E. Di Marcoa,b, M. Diemoza, E.
Longoa,b, B. Marzocchia,b, P. Meridiania, G. Organtinia,b, F. Pandolfia, R.
Paramattia,b, C. Quarantaa,b, S. Rahatloua,b, C. Rovellia, F.
Santanastasioa,b, L. Soffia,b INFN Sezione di Torino a, Università di Torino
b, Torino, Italy, Università del Piemonte Orientale c, Novara, Italy
N. Amapanea,b, R. Arcidiaconoa,c, S. Argiroa,b, M. Arneodoa,c, N. Bartosika,
R. Bellana,b, C. Biinoa, A. Cappatia,b, N. Cartigliaa, S. Comettia, M.
Costaa,b, R. Covarellia,b, N. Demariaa, B. Kiania,b, C. Mariottia, S.
Masellia, E. Migliorea,b, V. Monacoa,b, E. Monteila,b, M. Montenoa, M.M.
Obertinoa,b, L. Pachera,b, N. Pastronea, M. Pelliccionia, G.L. Pinna
Angionia,b, A. Romeroa,b, M. Ruspaa,c, R. Sacchia,b, R. Salvaticoa,b, V.
Solaa, A. Solanoa,b, D. Soldia,b, A. Staianoa INFN Sezione di Trieste a,
Università di Trieste b, Trieste, Italy
S. Belfortea, V. Candelisea,b, M. Casarsaa, F. Cossuttia, A. Da Rolda,b, G.
Della Riccaa,b, F. Vazzolera,b, A. Zanettia Kyungpook National University,
Daegu, Korea
B. Kim, D.H. Kim, G.N. Kim, M.S. Kim, J. Lee, S.W. Lee, C.S. Moon, Y.D. Oh,
S.I. Pak, S. Sekmen, D.C. Son, Y.C. Yang Chonnam National University,
Institute for Universe and Elementary Particles, Kwangju, Korea
H. Kim, D.H. Moon, G. Oh Hanyang University, Seoul, Korea
B. Francois, T.J. Kim, J. Park Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, D. Gyun, S. Ha, B. Hong, K. Lee, K.S. Lee, J. Lim, J.
Park, S.K. Park, Y. Roh Kyung Hee University, Department of Physics
J. Goh Sejong University, Seoul, Korea
H.S. Kim Seoul National University, Seoul, Korea
J. Almond, J.H. Bhyun, J. Choi, S. Jeon, J. Kim, J.S. Kim, H. Lee, K. Lee, S.
Lee, K. Nam, M. Oh, S.B. Oh, B.C. Radburn-Smith, U.K. Yang, H.D. Yoo, I. Yoon,
G.B. Yu University of Seoul, Seoul, Korea
D. Jeon, H. Kim, J.H. Kim, J.S.H. Lee, I.C. Park, I. Watson Sungkyunkwan
University, Suwon, Korea
Y. Choi, C. Hwang, Y. Jeong, J. Lee, Y. Lee, I. Yu Riga Technical University,
Riga, Latvia
V. Veckalns32 Vilnius University, Vilnius, Lithuania
V. Dudenas, A. Juodagalvis, J. Vaitkus National Centre for Particle Physics,
Universiti Malaya, Kuala Lumpur, Malaysia
Z.A. Ibrahim, F. Mohamad Idris33, W.A.T. Wan Abdullah, M.N. Yusli, Z. Zolkapli
Universidad de Sonora (UNISON), Hermosillo, Mexico
J.F. Benitez, A. Castaneda Hernandez, J.A. Murillo Quijada, L. Valencia Palomo
Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico
H. Castilla-Valdez, E. De La Cruz-Burelo, I. Heredia-De La Cruz34, R. Lopez-
Fernandez, A. Sanchez-Hernandez Universidad Iberoamericana, Mexico City,
Mexico
S. Carrillo Moreno, C. Oropeza Barrera, M. Ramirez-Garcia, F. Vazquez Valencia
Benemerita Universidad Autonoma de Puebla, Puebla, Mexico
J. Eysermans, I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada Universidad
Autónoma de San Luis Potosí, San Luis Potosí, Mexico
A. Morelos Pineda University of Montenegro, Podgorica, Montenegro
N. Raicevic University of Auckland, Auckland, New Zealand
D. Krofcheck University of Canterbury, Christchurch, New Zealand
S. Bheesette, P.H. Butler National Centre for Physics, Quaid-I-Azam
University, Islamabad, Pakistan
A. Ahmad, M. Ahmad, Q. Hassan, H.R. Hoorani, W.A. Khan, M.A. Shah, M. Shoaib,
M. Waqas AGH University of Science and Technology Faculty of Computer Science,
Electronics and Telecommunications, Krakow, Poland
V. Avati, L. Grzanka, M. Malawski National Centre for Nuclear Research,
Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, M. Górski, M. Kazana, M. Szleper, P.
Zalewski Institute of Experimental Physics, Faculty of Physics, University of
Warsaw, Warsaw, Poland
K. Bunkowski, A. Byszuk35, K. Doroba, A. Kalinowski, M. Konecki, J.
Krolikowski, M. Misiura, M. Olszewski, A. Pyskir, M. Walczak Laboratório de
Instrumentação e Física Experimental de Partículas, Lisboa, Portugal
M. Araujo, P. Bargassa, D. Bastos, A. Di Francesco, P. Faccioli, B. Galinhas,
M. Gallinaro, J. Hollar, N. Leonardo, J. Seixas, K. Shchelina, G. Strong, O.
Toldaiev, J. Varela Joint Institute for Nuclear Research, Dubna, Russia
P. Bunin, Y. Ershov, M. Gavrilenko, I. Golutvin, N. Gorbounov, I. Gorbunov, A.
Kamenev, V. Karjavine, V. Korenkov, A. Lanev, A. Malakhov, V. Matveev36,37, P.
Moisenz, V. Palichik, V. Perelygin, M. Savina, S. Shmatov, N. Voytishin, B.S.
Yuldashev38, A. Zarubin Petersburg Nuclear Physics Institute, Gatchina (St.
Petersburg), Russia
L. Chtchipounov, V. Golovtsov, Y. Ivanov, V. Kim39, E. Kuznetsova40, P.
Levchenko, V. Murzin, V. Oreshkin, I. Smirnov, D. Sosnov, V. Sulimov, L.
Uvarov, A. Vorobyev Institute for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov,
N. Krasnikov, A. Pashenkov, D. Tlisov, A. Toropin Institute for Theoretical
and Experimental Physics named by A.I. Alikhanov of NRC ‘Kurchatov Institute’,
Moscow, Russia
V. Epshteyn, V. Gavrilov, N. Lychkovskaya, A. Nikitenko41, V. Popov, I.
Pozdnyakov, G. Safronov, A. Spiridonov, A. Stepennov, M. Toms, E. Vlasov, A.
Zhokin Moscow Institute of Physics and Technology, Moscow, Russia
T. Aushev National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
M. Chadeeva42, P. Parygin, D. Philippov, E. Popova, E. Zhemchugov P.N. Lebedev
Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin, I. Dremin, M. Kirakosyan, A. Terkulov Skobeltsyn
Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow,
Russia
A. Baskakov, A. Belyaev, E. Boos, V. Bunichev, M. Dubinin43, L. Dudko, A.
Ershov, A. Gribushin, V. Klyukhin, O. Kodolova, I. Lokhtin, S. Obraztsov, V.
Savrin Novosibirsk State University (NSU), Novosibirsk, Russia
A. Barnyakov44, V. Blinov44, T. Dimova44, L. Kardapoltsev44, Y. Skovpen44
Institute for High Energy Physics of National Research Centre ‘Kurchatov
Institute’, Protvino, Russia
I. Azhgirey, I. Bayshev, S. Bitioukov, V. Kachanov, D. Konstantinov, P.
Mandrik, V. Petrov, R. Ryutin, S. Slabospitskii, A. Sobol, S. Troshin, N.
Tyurin, A. Uzunian, A. Volkov National Research Tomsk Polytechnic University,
Tomsk, Russia
A. Babaev, A. Iuzhakov, V. Okhotnikov Tomsk State University, Tomsk, Russia
V. Borchsh, V. Ivanchenko, E. Tcherniaev University of Belgrade: Faculty of
Physics and VINCA Institute of Nuclear Sciences
P. Adzic45, P. Cirkovic, D. Devetak, M. Dordevic, P. Milenovic, J. Milosevic,
M. Stojanovic Centro de Investigaciones Energéticas Medioambientales y
Tecnológicas (CIEMAT), Madrid, Spain
M. Aguilar-Benitez, J. Alcaraz Maestre, A. Álvarez Fernández, I. Bachiller, M.
Barrio Luna, J.A. Brochero Cifuentes, C.A. Carrillo Montoya, M. Cepeda, M.
Cerrada, N. Colino, B. De La Cruz, A. Delgado Peris, C. Fernandez Bedoya, J.P.
Fernández Ramos, J. Flix, M.C. Fouz, O. Gonzalez Lopez, S. Goy Lopez, J.M.
Hernandez, M.I. Josa, D. Moran, Á. Navarro Tobar, A. Pérez-Calero Yzquierdo,
J. Puerta Pelayo, I. Redondo, L. Romero, S. Sánchez Navas, M.S. Soares, A.
Triossi, C. Willmott Universidad Autónoma de Madrid, Madrid, Spain
C. Albajar, J.F. de Trocóniz Universidad de Oviedo, Instituto Universitario de
Ciencias y Tecnologías Espaciales de Asturias (ICTEA)
B. Alvarez Gonzalez, J. Cuevas, C. Erice, J. Fernandez Menendez, S. Folgueras,
I. Gonzalez Caballero, J.R. González Fernández, E. Palencia Cortezon, V.
Rodríguez Bouza, S. Sanchez Cruz Instituto de Física de Cantabria (IFCA),
CSIC-Universidad de Cantabria, Santander, Spain
I.J. Cabrillo, A. Calderon, B. Chazin Quero, J. Duarte Campderros, M.
Fernandez, P.J. Fernández Manteca, A. García Alonso, G. Gomez, C. Martinez
Rivero, P. Martinez Ruiz del Arbol, F. Matorras, J. Piedra Gomez, C. Prieels,
T. Rodrigo, A. Ruiz-Jimeno, L. Russo46, L. Scodellaro, N. Trevisani, I. Vila,
J.M. Vizan Garcia University of Colombo, Colombo, Sri Lanka
K. Malagalage University of Ruhuna, Department of Physics, Matara, Sri Lanka
W.G.D. Dharmaratna, N. Wickramage CERN, European Organization for Nuclear
Research, Geneva, Switzerland
D. Abbaneo, B. Akgun, E. Auffray, G. Auzinger, J. Baechler, P. Baillon, A.H.
Ball, D. Barney, J. Bendavid, M. Bianco, A. Bocci, E. Bossini, C. Botta, E.
Brondolin, T. Camporesi, A. Caratelli, G. Cerminara, E. Chapon, G. Cucciati,
D. d’Enterria, A. Dabrowski, N. Daci, V. Daponte, A. David, O. Davignon, A. De
Roeck, N. Deelen, M. Deile, M. Dobson, M. Dünser, N. Dupont, A. Elliott-
Peisert, F. Fallavollita47, D. Fasanella, G. Franzoni, J. Fulcher, W. Funk, S.
Giani, D. Gigi, A. Gilbert, K. Gill, F. Glege, M. Gruchala, M. Guilbaud, D.
Gulhan, J. Hegeman, C. Heidegger, Y. Iiyama, V. Innocente, P. Janot, O.
Karacheban20, J. Kaspar, J. Kieseler, M. Krammer1, C. Lange, P. Lecoq, C.
Lourenço, L. Malgeri, M. Mannelli, A. Massironi, F. Meijers, J.A. Merlin, S.
Mersi, E. Meschi, F. Moortgat, M. Mulders, J. Ngadiuba, S. Nourbakhsh, S.
Orfanelli, L. Orsini, F. Pantaleo17, L. Pape, E. Perez, M. Peruzzi, A.
Petrilli, G. Petrucciani, A. Pfeiffer, M. Pierini, F.M. Pitters, D. Rabady, A.
Racz, M. Rovere, H. Sakulin, C. Schäfer, C. Schwick, M. Selvaggi, A. Sharma,
P. Silva, W. Snoeys, P. Sphicas48, J. Steggemann, V.R. Tavolaro, D. Treille,
A. Tsirou, A. Vartak, M. Verzetti, W.D. Zeuner Paul Scherrer Institut,
Villigen, Switzerland
L. Caminada49, K. Deiters, W. Erdmann, R. Horisberger, Q. Ingram, H.C.
Kaestli, D. Kotlinski, U. Langenegger, T. Rohe, S.A. Wiederkehr ETH Zurich -
Institute for Particle Physics and Astrophysics (IPA), Zurich, Switzerland
M. Backhaus, P. Berger, N. Chernyavskaya, G. Dissertori, M. Dittmar, M.
Donegà, C. Dorfer, T.A. Gómez Espinosa, C. Grab, D. Hits, T. Klijnsma, W.
Lustermann, R.A. Manzoni, M. Marionneau, M.T. Meinhard, F. Micheli, P.
Musella, F. Nessi-Tedaldi, F. Pauss, G. Perrin, L. Perrozzi, S. Pigazzini, M.
Reichmann, C. Reissel, T. Reitenspiess, D. Ruini, D.A. Sanz Becerra, M.
Schönenberger, L. Shchutska, M.L. Vesterbacka Olsson, R. Wallny, D.H. Zhu
Universität Zürich, Zurich, Switzerland
T.K. Aarrestad, C. Amsler50, D. Brzhechko, M.F. Canelli, A. De Cosa, R. Del
Burgo, S. Donato, B. Kilminster, S. Leontsinis, V.M. Mikuni, I. Neutelings, G.
Rauco, P. Robmann, D. Salerno, K. Schweiger, C. Seitz, Y. Takahashi, S. Wertz,
A. Zucchetta National Central University, Chung-Li, Taiwan
T.H. Doan, C.M. Kuo, W. Lin, A. Roy, S.S. Yu National Taiwan University (NTU),
Taipei, Taiwan
P. Chang, Y. Chao, K.F. Chen, P.H. Chen, W.-S. Hou, Y.y. Li, R.-S. Lu, E.
Paganis, A. Psallidas, A. Steen Chulalongkorn University, Faculty of Science,
Department of Physics, Bangkok, Thailand
B. Asavapibhop, C. Asawatangtrakuldee, N. Srimanobhas, N. Suwonjandee Çukurova
University, Physics Department, Science and Art Faculty, Adana, Turkey
A. Bat, F. Boran, S. Cerci51, S. Damarseckin52, Z.S. Demiroglu, F. Dolek, C.
Dozen, I. Dumanoglu, G. Gokbulut, EmineGurpinar Guler53, Y. Guler, I. Hos54,
C. Isik, E.E. Kangal55, O. Kara, A. Kayis Topaksu, U. Kiminsu, M. Oglakci, G.
Onengut, K. Ozdemir56, S. Ozturk57, A.E. Simsek, D. Sunar Cerci51, U.G. Tok,
S. Turkcapar, I.S. Zorbakir, C. Zorbilmez Middle East Technical University,
Physics Department, Ankara, Turkey
B. Isildak58, G. Karapinar59, M. Yalvac Bogazici University, Istanbul, Turkey
I.O. Atakisi, E. Gülmez, M. Kaya60, O. Kaya61, B. Kaynak, Ö. Özçelik, S.
Tekten, E.A. Yetkin62 Istanbul Technical University, Istanbul, Turkey
A. Cakir, Y. Komurcu, S. Sen63 Istanbul University, Istanbul, Turkey
S. Ozkorucuklu Institute for Scintillation Materials of National Academy of
Science of Ukraine, Kharkov, Ukraine
B. Grynyov National Scientific Center, Kharkov Institute of Physics and
Technology, Kharkov, Ukraine
L. Levchuk University of Bristol, Bristol, United Kingdom
F. Ball, E. Bhal, S. Bologna, J.J. Brooke, D. Burns, E. Clement, D. Cussans,
H. Flacher, J. Goldstein, G.P. Heath, H.F. Heath, L. Kreczko, S. Paramesvaran,
B. Penning, T. Sakuma, S. Seif El Nasr-Storey, D. Smith, V.J. Smith, J.
Taylor, A. Titterton Rutherford Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev64, C. Brew, R.M. Brown, D. Cieri, D.J.A. Cockerill, J.A.
Coughlan, K. Harder, S. Harper, J. Linacre, K. Manolopoulos, D.M. Newbold, E.
Olaiya, D. Petyt, T. Reis, T. Schuh, C.H. Shepherd-Themistocleous, A. Thea,
I.R. Tomalin, T. Williams, W.J. Womersley Imperial College, London, United
Kingdom
R. Bainbridge, P. Bloch, J. Borg, S. Breeze, O. Buchmuller, A. Bundock,
GurpreetSingh CHAHAL65, D. Colling, P. Dauncey, G. Davies, M. Della Negra, R.
Di Maria, P. Everaerts, G. Hall, G. Iles, T. James, M. Komm, C. Laner, L.
Lyons, A.-M. Magnan, S. Malik, A. Martelli, V. Milosevic, J. Nash66, V.
Palladino, M. Pesaresi, D.M. Raymond, A. Richards, A. Rose, E. Scott, C. Seez,
A. Shtipliyski, M. Stoye, T. Strebler, S. Summers, A. Tapper, K. Uchida, T.
Virdee17, N. Wardle, D. Winterbottom, J. Wright, A.G. Zecchinelli, S.C. Zenz
Brunel University, Uxbridge, United Kingdom
J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, C.K. Mackay, A. Morton, I.D. Reid,
L. Teodorescu, S. Zahid Baylor University, Waco, USA
K. Call, J. Dittmann, K. Hatakeyama, C. Madrid, B. McMaster, N. Pastika, C.
Smith Catholic University of America, Washington, DC, USA
R. Bartek, A. Dominguez, R. Uniyal The University of Alabama, Tuscaloosa, USA
A. Buccilli, S.I. Cooper, C. Henderson, P. Rumerio, C. West Boston University,
Boston, USA
D. Arcaro, T. Bose, Z. Demiragli, D. Gastler, S. Girgis, D. Pinna, C.
Richardson, J. Rohlf, D. Sperka, I. Suarez, L. Sulak, D. Zou Brown University,
Providence, USA
G. Benelli, B. Burkle, X. Coubez, D. Cutts, Y.t. Duh, M. Hadley, J. Hakala, U.
Heintz, J.M. Hogan67, K.H.M. Kwok, E. Laird, G. Landsberg, J. Lee, Z. Mao, M.
Narain, S. Sagir68, R. Syarif, E. Usai, D. Yu University of California, Davis,
Davis, USA
R. Band, C. Brainerd, R. Breedon, M. Calderon De La Barca Sanchez, M. Chertok,
J. Conway, R. Conway, P.T. Cox, R. Erbacher, C. Flores, G. Funk, F. Jensen, W.
Ko, O. Kukral, R. Lander, M. Mulhearn, D. Pellett, J. Pilot, M. Shi, D. Stolp,
D. Taylor, K. Tos, M. Tripathi, Z. Wang, F. Zhang University of California,
Los Angeles, USA
M. Bachtis, C. Bravo, R. Cousins, A. Dasgupta, A. Florent, J. Hauser, M.
Ignatenko, N. Mccoll, W.A. Nash, S. Regnard, D. Saltzberg, C. Schnaible, B.
Stone, V. Valuev University of California, Riverside, Riverside, USA
K. Burt, R. Clare, J.W. Gary, S.M.A. Ghiasi Shirazi, G. Hanson, G.
Karapostoli, E. Kennedy, O.R. Long, M. Olmedo Negrete, M.I. Paneva, W. Si, L.
Wang, H. Wei, S. Wimpenny, B.R. Yates, Y. Zhang University of California, San
Diego, La Jolla, USA
J.G. Branson, P. Chang, S. Cittolin, M. Derdzinski, R. Gerosa, D. Gilbert, B.
Hashemi, D. Klein, V. Krutelyov, J. Letts, M. Masciovecchio, S. May, S. Padhi,
M. Pieri, V. Sharma, M. Tadel, F. Würthwein, A. Yagil, G. Zevi Della Porta
University of California, Santa Barbara - Department of Physics, Santa
Barbara, USA
N. Amin, R. Bhandari, C. Campagnari, M. Citron, V. Dutta, M. Franco Sevilla,
L. Gouskos, J. Incandela, B. Marsh, H. Mei, A. Ovcharova, H. Qu, J. Richman,
U. Sarica, D. Stuart, S. Wang, J. Yoo California Institute of Technology,
Pasadena, USA
D. Anderson, A. Bornheim, O. Cerri, I. Dutta, J.M. Lawhorn, N. Lu, J. Mao,
H.B. Newman, T.Q. Nguyen, J. Pata, M. Spiropulu, J.R. Vlimant, C. Wang, S.
Xie, Z. Zhang, R.Y. Zhu Carnegie Mellon University, Pittsburgh, USA
M.B. Andrews, T. Ferguson, T. Mudholkar, M. Paulini, M. Sun, I. Vorobiev, M.
Weinberg University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, A. Johnson, E. MacDonald, T. Mulholland, R. Patel, A.
Perloff, K. Stenson, K.A. Ulmer, S.R. Wagner Cornell University, Ithaca, USA
J. Alexander, J. Chaves, Y. Cheng, J. Chu, A. Datta, A. Frankenthal, K.
Mcdermott, N. Mirman, J.R. Patterson, D. Quach, A. Rinkevicius69, A. Ryd, S.M.
Tan, Z. Tao, J. Thom, P. Wittich, M. Zientek Fermi National Accelerator
Laboratory, Batavia, USA
S. Abdullin, M. Albrow, M. Alyari, G. Apollinari, A. Apresyan, A. Apyan, S.
Banerjee, L.A.T. Bauerdick, A. Beretvas, J. Berryhill, P.C. Bhat, K. Burkett,
J.N. Butler, A. Canepa, G.B. Cerati, H.W.K. Cheung, F. Chlebana, M. Cremonesi,
J. Duarte, V.D. Elvira, J. Freeman, Z. Gecse, E. Gottschalk, L. Gray, D.
Green, S. Grünendahl, O. Gutsche, AllisonReinsvold Hall, J. Hanlon, R.M.
Harris, S. Hasegawa, R. Heller, J. Hirschauer, B. Jayatilaka, S. Jindariani,
M. Johnson, U. Joshi, B. Klima, M.J. Kortelainen, B. Kreis, S. Lammel, J.
Lewis, D. Lincoln, R. Lipton, M. Liu, T. Liu, J. Lykken, K. Maeshima, J.M.
Marraffino, D. Mason, P. McBride, P. Merkel, S. Mrenna, S. Nahn, V. O’Dell, V.
Papadimitriou, K. Pedro, C. Pena, G. Rakness, F. Ravera, L. Ristori, B.
Schneider, E. Sexton-Kennedy, N. Smith, A. Soha, W.J. Spalding, L. Spiegel, S.
Stoynev, J. Strait, N. Strobbe, L. Taylor, S. Tkaczyk, N.V. Tran, L. Uplegger,
E.W. Vaandering, C. Vernieri, M. Verzocchi, R. Vidal, M. Wang, H.A. Weber
University of Florida, Gainesville, USA
D. Acosta, P. Avery, P. Bortignon, D. Bourilkov, A. Brinkerhoff, L. Cadamuro,
A. Carnes, V. Cherepanov, D. Curry, F. Errico, R.D. Field, S.V. Gleyzer, B.M.
Joshi, M. Kim, J. Konigsberg, A. Korytov, K.H. Lo, P. Ma, K. Matchev, N.
Menendez, G. Mitselmakher, D. Rosenzweig, K. Shi, J. Wang, S. Wang, X. Zuo
Florida International University, Miami, USA
Y.R. Joshi Florida State University, Tallahassee, USA
T. Adams, A. Askew, S. Hagopian, V. Hagopian, K.F. Johnson, R. Khurana, T.
Kolberg, G. Martinez, T. Perry, H. Prosper, C. Schiber, R. Yohay, J. Zhang
Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, V. Bhopatkar, M. Hohlmann, D. Noonan, M. Rahmani, M. Saunders,
F. Yumiceva University of Illinois at Chicago (UIC), Chicago, USA
M.R. Adams, L. Apanasevich, D. Berry, R.R. Betts, R. Cavanaugh, X. Chen, S.
Dittmer, O. Evdokimov, C.E. Gerber, D.A. Hangal, D.J. Hofman, K. Jung, C.
Mills, T. Roy, M.B. Tonjes, N. Varelas, H. Wang, X. Wang, Z. Wu The University
of Iowa, Iowa City, USA
M. Alhusseini, B. Bilki53, W. Clarida, K. Dilsiz70, S. Durgut, R.P.
Gandrajula, M. Haytmyradov, V. Khristenko, O.K. Köseyan, J.-P. Merlo, A.
Mestvirishvili71, A. Moeller, J. Nachtman, H. Ogul72, Y. Onel, F. Ozok73, A.
Penzo, C. Snyder, E. Tiras, J. Wetzel Johns Hopkins University, Baltimore, USA
B. Blumenfeld, A. Cocoros, N. Eminizer, D. Fehling, L. Feng, A.V. Gritsan,
W.T. Hung, P. Maksimovic, J. Roskes, M. Swartz, M. Xiao The University of
Kansas, Lawrence, USA
C. Baldenegro Barrera, P. Baringer, A. Bean, S. Boren, J. Bowen, A. Bylinkin,
T. Isidori, S. Khalil, J. King, G. Krintiras, A. Kropivnitskaya, C. Lindsey,
D. Majumder, W. Mcbrayer, N. Minafra, M. Murray, C. Rogan, C. Royon, S.
Sanders, E. Schmitz, J.D. Tapia Takaki, Q. Wang, J. Williams, G. Wilson Kansas
State University, Manhattan, USA
S. Duric, A. Ivanov, K. Kaadze, D. Kim, Y. Maravin, D.R. Mendis, T. Mitchell,
A. Modak, A. Mohammadi Lawrence Livermore National Laboratory, Livermore, USA
F. Rebassoo, D. Wright University of Maryland, College Park, USA
A. Baden, O. Baron, A. Belloni, S.C. Eno, Y. Feng, N.J. Hadley, S. Jabeen,
G.Y. Jeng, R.G. Kellogg, J. Kunkle, A.C. Mignerey, S. Nabili, F. Ricci-Tam, M.
Seidel, Y.H. Shin, A. Skuja, S.C. Tonwar, K. Wong Massachusetts Institute of
Technology, Cambridge, USA
D. Abercrombie, B. Allen, A. Baty, R. Bi, S. Brandt, W. Busza, I.A. Cali, M.
D’Alfonso, G. Gomez Ceballos, M. Goncharov, P. Harris, D. Hsu, M. Hu, M.
Klute, D. Kovalskyi, Y.-J. Lee, P.D. Luckey, B. Maier, A.C. Marini, C. Mcginn,
C. Mironov, S. Narayanan, X. Niu, C. Paus, D. Rankin, C. Roland, G. Roland, Z.
Shi, G.S.F. Stephans, K. Sumorok, K. Tatar, D. Velicanu, J. Wang, T.W. Wang,
B. Wyslouch University of Minnesota, Minneapolis, USA
A.C. Benvenuti${}^{\textrm{\textdagger}}$, R.M. Chatterjee, A. Evans, S. Guts,
P. Hansen, J. Hiltbrand, S. Kalafut, Y. Kubota, Z. Lesko, J. Mans, R. Rusack,
M.A. Wadud University of Mississippi, Oxford, USA
J.G. Acosta, S. Oliveros University of Nebraska-Lincoln, Lincoln, USA
K. Bloom, D.R. Claes, C. Fangmeier, L. Finco, F. Golf, R. Gonzalez Suarez, R.
Kamalieddin, I. Kravchenko, J.E. Siado, G.R. Snow, B. Stieger State University
of New York at Buffalo, Buffalo, USA
G. Agarwal, C. Harrington, I. Iashvili, A. Kharchilava, C. Mclean, D. Nguyen,
A. Parker, J. Pekkanen, S. Rappoccio, B. Roozbahani Northeastern University,
Boston, USA
G. Alverson, E. Barberis, C. Freer, Y. Haddad, A. Hortiangtham, G. Madigan,
D.M. Morse, T. Orimoto, L. Skinnari, A. Tishelman-Charny, T. Wamorkar, B.
Wang, A. Wisecarver, D. Wood Northwestern University, Evanston, USA
S. Bhattacharya, J. Bueghly, T. Gunter, K.A. Hahn, N. Odell, M.H. Schmitt, K.
Sung, M. Trovato, M. Velasco University of Notre Dame, Notre Dame, USA
R. Bucci, N. Dev, R. Goldouzian, M. Hildreth, K. Hurtado Anampa, C. Jessop,
D.J. Karmgard, K. Lannon, W. Li, N. Loukas, N. Marinelli, I. Mcalister, F.
Meng, C. Mueller, Y. Musienko36, M. Planer, R. Ruchti, P. Siddireddy, G.
Smith, S. Taroni, M. Wayne, A. Wightman, M. Wolf, A. Woodard The Ohio State
University, Columbus, USA
J. Alimena, B. Bylsma, L.S. Durkin, S. Flowers, B. Francis, C. Hill, W. Ji, A.
Lefeld, T.Y. Ling, B.L. Winer Princeton University, Princeton, USA
S. Cooperstein, G. Dezoort, P. Elmer, J. Hardenbrook, N. Haubrich, S.
Higginbotham, A. Kalogeropoulos, S. Kwan, D. Lange, M.T. Lucchini, J. Luo, D.
Marlow, K. Mei, I. Ojalvo, J. Olsen, C. Palmer, P. Piroué, J. Salfeld-Nebgen,
D. Stickland, C. Tully, Z. Wang University of Puerto Rico, Mayaguez, USA
S. Malik, S. Norberg Purdue University, West Lafayette, USA
A. Barker, V.E. Barnes, S. Das, L. Gutay, M. Jones, A.W. Jung, A. Khatiwada,
B. Mahakud, D.H. Miller, G. Negro, N. Neumeister, C.C. Peng, S. Piperov, H.
Qiu, J.F. Schulte, J. Sun, F. Wang, R. Xiao, W. Xie Purdue University
Northwest, Hammond, USA
T. Cheng, J. Dolen, N. Parashar Rice University, Houston, USA
K.M. Ecklund, S. Freed, F.J.M. Geurts, M. Kilpatrick, Arun Kumar, W. Li, B.P.
Padley, R. Redjimi, J. Roberts, J. Rorie, W. Shi, A.G. Stahl Leiton, Z. Tu, A.
Zhang University of Rochester, Rochester, USA
A. Bodek, P. de Barbaro, R. Demina, J.L. Dulemba, C. Fallon, T. Ferbel, M.
Galanti, A. Garcia-Bellido, J. Han, O. Hindrichs, A. Khukhunaishvili, E.
Ranken, P. Tan, R. Taus Rutgers, The State University of New Jersey,
Piscataway, USA
B. Chiarito, J.P. Chou, A. Gandrakota, Y. Gershtein, E. Halkiadakis, A. Hart,
M. Heindl, E. Hughes, S. Kaplan, S. Kyriacou, I. Laflotte, A. Lath, R.
Montalvo, K. Nash, M. Osherson, H. Saka, S. Salur, S. Schnetzer, D. Sheffield,
S. Somalwar, R. Stone, S. Thomas, P. Thomassen University of Tennessee,
Knoxville, USA
H. Acharya, A.G. Delannoy, J. Heideman, G. Riley, S. Spanier Texas A&M
University, College Station, USA
O. Bouhali74, A. Celik, M. Dalchenko, M. De Mattia, A. Delgado, S. Dildick, R.
Eusebi, J. Gilmore, T. Huang, T. Kamon75, S. Luo, D. Marley, R. Mueller, D.
Overton, L. Perniè, D. Rathjens, A. Safonov Texas Tech University, Lubbock,
USA
N. Akchurin, J. Damgov, F. De Guio, S. Kunori, K. Lamichhane, S.W. Lee, T.
Mengke, S. Muthumuni, T. Peltola, S. Undleeb, I. Volobouev, Z. Wang, A.
Whitbeck Vanderbilt University, Nashville, USA
S. Greene, A. Gurrola, R. Janjam, W. Johns, C. Maguire, A. Melo, H. Ni, K.
Padeken, F. Romeo, P. Sheldon, S. Tuo, J. Velkovska, M. Verweij University of
Virginia, Charlottesville, USA
M.W. Arenton, P. Barria, B. Cox, G. Cummings, R. Hirosky, M. Joyce, A.
Ledovskoy, C. Neu, B. Tannenwald, Y. Wang, E. Wolfe, F. Xia Wayne State
University, Detroit, USA
R. Harr, P.E. Karchin, N. Poudyal, J. Sturdy, P. Thapa, S. Zaleski University
of Wisconsin - Madison, Madison, WI, USA
J. Buchanan, C. Caillol, D. Carlsmith, S. Dasu, I. De Bruyn, L. Dodd, F.
Fiori, C. Galloni, B. Gomber76, M. Herndon, A. Hervé, U. Hussain, P. Klabbers,
A. Lanaro, A. Loeliger, K. Long, R. Loveless, J. Madhusudanan Sreekala, T.
Ruggles, A. Savin, V. Sharma, W.H. Smith, D. Teague, S. Trembath-reichert, N.
Woods †: Deceased
1: Also at Vienna University of Technology, Vienna, Austria
2: Also at IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
3: Also at Universidade Estadual de Campinas, Campinas, Brazil
4: Also at Federal University of Rio Grande do Sul, Porto Alegre, Brazil
5: Also at UFMS/CPNA — Federal University of Mato Grosso do Sul/Campus of Nova
Andradina, Nova Andradina, Brazil
6: Also at Universidade Federal de Pelotas, Pelotas, Brazil
7: Also at Université Libre de Bruxelles, Bruxelles, Belgium
8: Also at University of Chinese Academy of Sciences, Beijing, China
9: Also at Institute for Theoretical and Experimental Physics named by A.I.
Alikhanov of NRC ‘Kurchatov Institute’, Moscow, Russia
10: Also at Joint Institute for Nuclear Research, Dubna, Russia
11: Now at British University in Egypt, Cairo, Egypt
12: Now at Cairo University, Cairo, Egypt
13: Also at Purdue University, West Lafayette, USA
14: Also at Université de Haute Alsace, Mulhouse, France
15: Also at Tbilisi State University, Tbilisi, Georgia
16: Also at Erzincan Binali Yildirim University, Erzincan, Turkey
17: Also at CERN, European Organization for Nuclear Research, Geneva,
Switzerland
18: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
19: Also at University of Hamburg, Hamburg, Germany
20: Also at Brandenburg University of Technology, Cottbus, Germany
21: Also at Institute of Physics, University of Debrecen, Debrecen, Hungary
22: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary
23: Also at MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
24: Also at Indian Institute of Technology Bhubaneswar, Bhubaneswar, India
25: Also at Institute of Physics, Bhubaneswar, India
26: Also at Shoolini University, Solan, India
27: Also at University of Visva-Bharati, Santiniketan, India
28: Also at Isfahan University of Technology, Isfahan, Iran
29: Also at ITALIAN NATIONAL AGENCY FOR NEW TECHNOLOGIES, ENERGY AND
SUSTAINABLE ECONOMIC DEVELOPMENT, Bologna, Italy
30: Also at CENTRO SICILIANO DI FISICA NUCLEARE E DI STRUTTURA DELLA MATERIA,
Catania, Italy
31: Also at Scuola Normale e Sezione dell’INFN, Pisa, Italy
32: Also at Riga Technical University, Riga, Latvia
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 Institute of Nuclear Physics of the Uzbekistan Academy of
Sciences, Tashkent, Uzbekistan
39: Also at St. Petersburg State Polytechnical University, St. Petersburg,
Russia
40: Also at University of Florida, Gainesville, USA
41: Also at Imperial College, London, United Kingdom
42: Also at P.N. Lebedev Physical Institute, Moscow, Russia
43: Also at California Institute of Technology, Pasadena, USA
44: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia
45: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia
46: Also at Università degli Studi di Siena, Siena, Italy
47: Also at INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy
48: Also at National and Kapodistrian University of Athens, Athens, Greece
49: Also at Universität Zürich, Zurich, Switzerland
50: Also at Stefan Meyer Institute for Subatomic Physics (SMI), Vienna,
Austria
51: Also at Adiyaman University, Adiyaman, Turkey
52: Also at Sirnak University, SIRNAK, Turkey
53: Also at Beykent University, Istanbul, Turkey
54: Also at Istanbul Aydin University, Istanbul, Turkey
55: Also at Mersin University, Mersin, Turkey
56: Also at Piri Reis University, Istanbul, Turkey
57: Also at Gaziosmanpasa University, Tokat, Turkey
58: Also at Ozyegin University, Istanbul, Turkey
59: Also at Izmir Institute of Technology, Izmir, Turkey
60: Also at Marmara University, Istanbul, Turkey
61: Also at Kafkas University, Kars, Turkey
62: Also at Istanbul Bilgi University, Istanbul, Turkey
63: Also at Hacettepe University, Ankara, Turkey
64: Also at School of Physics and Astronomy, University of Southampton,
Southampton, United Kingdom
65: Also at Institute for Particle Physics Phenomenology Durham University,
Durham, United Kingdom
66: Also at Monash University, Faculty of Science, Clayton, Australia
67: Also at Bethel University, St. Paul, USA
68: Also at Karamanoğlu Mehmetbey University, Karaman, Turkey
69: Also at Vilnius University, Vilnius, Lithuania
70: Also at Bingol University, Bingol, Turkey
71: Also at Georgian Technical University, Tbilisi, Georgia
72: Also at Sinop University, Sinop, Turkey
73: Also at Mimar Sinan University, Istanbul, Istanbul, Turkey
74: Also at Texas A&M University at Qatar, Doha, Qatar
75: Also at Kyungpook National University, Daegu, Korea
76: Also at University of Hyderabad, Hyderabad, India