SUS-19-004

SUS-19-004

# Search for top squarks in final states with two top quarks and several
light-flavor jets in proton-proton collisions at $\sqrt{s}=13\TeV$

###### Abstract

Many new physics models, including versions of supersymmetry characterized by
$R$-parity violation (RPV), compressed mass spectra, long decay chains, or
additional hidden sectors, predict the production of events with top quarks,
low missing transverse momentum, and many additional quarks or gluons. The
results of a search for new physics in events with two top quarks and
additional jets are reported. The search is performed using events with at
least seven jets and exactly one electron or muon. No requirement on missing
transverse momentum is imposed. The study is based on a sample of proton-
proton collisions at $\sqrt{s}=13\TeV$ corresponding to 137of integrated
luminosity collected with the CMS detector at the LHC in 2016–2018. The data
are used to determine best fit values and upper limits on the cross section
for pair production of top squarks in scenarios of RPV and stealth
supersymmetry. Top squark masses up to 670 (870)are excluded at 95% confidence
level for the RPV (stealth) scenario, and the maximum observed local signal
significance is 2.8 standard deviations for the RPV scenario with top squark
mass of 400.

##  0.1 Introduction

Supersymmetry [1, 2] (SUSY) is an extension of the standard model (SM) that
may provide a solution to the gauge hierarchy problem [3]. In the SUSY
framework, quadratically divergent radiative corrections to the Higgs boson
mass parameter, dominated by loops involving the top quark, are canceled by
loops with bosonic top quark superpartners (top squark, ). To avoid fine
tuning, the lightest and the superpartners of the Higgs bosons (higgsinos)
must have masses near the weak scale [4, 3, 5, 6, 7, 8], and could therefore
have nonnegligible production cross sections at the CERN Large Hadron Collider
(LHC).

Most searches for the look for an excess of events with large missing
transverse momentum originating from the undetected lightest SUSY particle
(LSP) produced in decays. It is typical in these searches to assume that the
LSP is the lightest neutralino , which is stable if $R$-parity [9] is
conserved. However, it has been shown [10, 11, 12] that this search strategy
is not sensitive to well-motivated SUSY models that predict signatures with
low in models with gauge mediated SUSY breaking [13], compressed mass spectra
[14, 15], hidden valleys [16], or other mechanisms. As searches performed at
the LHC using events with high set ever more stringent lower bounds on the
mass [17, 18, 19, 20, 21, 22], searches for low-alternatives become
increasingly important.

Models of $R$-parity violating (RPV) SUSY produce low-signatures by providing
a mechanism for the LSP, in this case , to decay. Among other couplings, RPV
SUSY includes a trilinear Yukawa coupling between quarks and squarks that
allows the to decay into three quarks via an off-shell squark [9]. These
couplings are typically referred to as $\Lampp_{ijk}$ where $i,j,$ and $k$
specify the generations of the participating (s)quarks. The benchmark RPV
model used in this analysis is illustrated in Fig. 1. The decays in the
typical way into a top quark and a , and the undergoes an RPV decay via
nonzero $\Lampp_{112}$ into three light-flavor quarks,
$\PSGczDo\to\PQu\PQd\PQs$. However, since this analysis does not distinguish
between jets originating from quarks of the first and second generation, our
results are more broadly applicable to any RPV model with coupling
$\Lampp_{abc}$ with $a,b,c\in\\{1,2\\}$.

Stealth SUSY models [12, 23, 24] introduce a new hidden “stealth” sector of
light particles with small or absent couplings to the SUSY breaking sector and
finite couplings to the visible sector. Because of the weak connection to the
SUSY breaking sector, SUSY is approximately conserved in the stealth sector,
resulting in stealth particles that are nearly mass-degenerate with their
superpartners. Production and decay of stealth particles via interactions with
visible particles can be achieved through a variety of “portals” including
mediation by the Higgs boson or new particles at a higher mass scale. The
benchmark stealth SUSY model used in the interpretation of the results of this
search (stealth
$\HepParticle{S}{}{}\HepParticle{Y}{}{}\HepAntiParticle{Y}{}{}$) [24] assumes
a minimal stealth sector containing only one scalar particle S with even
$R$-parity and its superpartner $\widetilde{\mathrm{S}}$, both of which are
singlets under all SM interactions, and a portal mediated by loop interactions
involving a new vector-like messenger field (Y), the gluon (), , S, and
$\widetilde{\mathrm{S}}$. Decays of the in the stealth
$\HepParticle{S}{}{}\HepParticle{Y}{}{}\HepAntiParticle{Y}{}{}$ model are
illustrated in Fig. 1. Each decays to a gluon, top quark, and
$\widetilde{\mathrm{S}}$, with subsequent decays of $\widetilde{\mathrm{S}}$
to S and a gravitino and S to jets via $\HepParticle{S}{}{}\to\Pg\Pg$. Because
of the small mass splitting between the S and $\widetilde{\mathrm{S}}$, as
well as the small mass, the undetected carries away very little momentum.
Thus, the stealth
$\HepParticle{S}{}{}\HepParticle{Y}{}{}\HepAntiParticle{Y}{}{}$ model shares
the general feature of all stealth SUSY models in that it naturally produces a
low-signature without $R$-parity violation or a special tuning of sparticle
masses.

The RPV and stealth
$\HepParticle{S}{}{}\HepParticle{Y}{}{}\HepAntiParticle{Y}{}{}$ models are
characterized by the masses of the particles and branching fractions in the
decay chain. In the benchmark RPV model, we take the mass to be 100. For the
benchmark stealth
$\HepParticle{S}{}{}\HepParticle{Y}{}{}\HepAntiParticle{Y}{}{}$ model, the
critical small $\widetilde{\mathrm{S}}$-S mass splitting is held constant at
10, and we assume a $\widetilde{\mathrm{S}}$ mass of 100and a mass of 1. For
both models, a range of masses ($m_{\PSQt}$) are considered from 300 to 1400,
and all decays described above are assumed to be prompt with unity branching
fractions.

Figure 1:  Diagrams of top squark pair production with decays to top quarks
and additional light-flavor quarks for the RPV SUSY model (left) and with
decays to top quarks and gluons for the stealth
$\HepParticle{S}{}{}\HepParticle{Y}{}{}\HepAntiParticle{Y}{}{}$ model (right).

In this paper, we describe a search for pair production followed by the decay
of each into a top quark and three light-flavor jets via the benchmark RPV and
stealth $\HepParticle{S}{}{}\HepParticle{Y}{}{}\HepAntiParticle{Y}{}{}$ models
described above. This is the first search of its kind at the LHC. Previous
searches for RPV decays focused on final states with dijet resonances [25,
26], lepton-jet resonances [27, 28], intermediate leptonic chargino decays
[29], or final states with many quarks [30]. Previous searches for stealth
SUSY targeted superpartners of light-flavor quarks with decays into gauge
bosons and jets [31, 32]. Measurements of the differential production cross
section have been reinterpreted in the context of RPV and stealth SUSY [33,
24] and were found to yield weak constraints for the models considered in this
paper.

Before describing each step in more detail in subsequent sections, we provide
an overview of the analysis strategy here. The main distinguishing feature of
the signals in this analysis, in addition to the presence of two top quarks,
is high jet multiplicity ($N_{\text{jets}}$). The SM backgrounds arise through
processes including top quark pair production (), multijet production from
quantum chromodynamics (QCD), production of in association with SM weak gauge
bosons or additional top quarks (+X), production of weak gauge bosons, and
single top quark production (other). These SM processes all include additional
jets from initial- and final-state radiation (ISR and FSR). The QCD background
is primarily suppressed by requiring the presence of exactly one charged
lepton (or ) arising from the leptonic decay of a top quark. Backgrounds that
do not produce any top quarks are suppressed by requiring the presence of at
least one jet identified as arising from the fragmentation of a bottom quark
(-tagged jet), and additionally that the invariant mass of the lepton and a
-tagged jet be consistent with the presence of a top quark.

The signal is distinguished from the dominant and irreducible background by
means of a neural network (NN) trained to recognize differences in the spatial
distribution of jets and decay kinematic distributions between signal and
background events. Events are divided into 24 categories based on their NN
score ($S_{\mathrm{NN}}$) and $N_{\text{jets}}$; categories with higher
(lower) $S_{\mathrm{NN}}$ and $N_{\text{jets}}$ tend to be signal enriched
(depleted). We perform a simultaneous fit to the number of events in data in
$S_{\mathrm{NN}}$ and $N_{\text{jets}}$ categories to estimate the total
numbers of and potential signal events present in the data, as well as the
distribution of events in $S_{\mathrm{NN}}$ and $N_{\text{jets}}$ categories.
The NN output is designed to have no dependence on $N_{\text{jets}}$, so that
the $N_{\text{jets}}$ distribution of events can be constrained in the fit to
be the same for all $S_{\mathrm{NN}}$ categories. This requirement for
$N_{\text{jets}}$ shape invariance is important for the analysis and will be
discussed throughout the paper.

This paper is organized as follows. We introduce the CMS detector and methods
for event reconstruction and selection in Section 0.2. Samples of simulated
events are described in Section 0.3. The estimation and modeling of SM
backgrounds are explained in Section 0.4, and the description of the treatment
of systematic uncertainties is in Section 0.5. Finally, the results and their
interpretation are in Section 0.6, followed by the summary in Section 0.7.

##  0.2 Experimental techniques

The search is performed using a data sample of proton-proton ($\Pp\Pp$)
collisions at $\sqrt{s}=13\TeV$, corresponding to an integrated luminosity of
137, collected in 2016–2018 with the CMS detector at the LHC. Data and
simulation samples from four periods (2016, 2017, 2018A, 2018B) are treated
separately in order to address variations in detector and LHC conditions. Data
from 2018 are divided into two samples (2018A and 2018B), with 2018B
corresponding to the period when a detector malfunction prevented readout from
3% of the hadron calorimeter. In this section, we define reconstructed physics
objects and describe the selection criteria for events in the signal region
(SR) and the control region (CR) of the analysis.

The central feature of the CMS apparatus is a superconducting solenoid of 6m
internal diameter, providing a magnetic field of 3.8T. Within the solenoid
volume are a silicon pixel and strip tracker, a lead tungstate crystal
electromagnetic calorimeter, and a brass and scintillator hadron calorimeter,
each composed of a barrel and two endcap sections. Forward calorimeters extend
the pseudorapidity coverage provided by the barrel and endcap detectors. Muons
are detected in gas-ionization chambers embedded in the steel flux-return yoke
outside the solenoid. A more detailed description of the CMS detector,
together with a definition of the coordinate system used and the relevant
kinematic variables, can be found in Ref. [34].

The CMS trigger system is described in Ref. [35]. Events are selected using
triggers that require the presence of at least one electron or one muon. The
minimum transverse momentum threshold is 27 (35)for electrons and 24 (24)for
muons in 2016 (2017–2018). The triggers at these thresholds require the lepton
to be isolated from tracks and calorimeter deposits in the detector. Events
may also be selected from single-lepton triggers with higher thresholds,
115for electrons and 50for muons, with no isolation requirements. The combined
trigger efficiency varies from 80% for leptons with close to the lower
thresholds to greater than 95% for leptons with $\pt>120\GeV$.

Events are reconstructed using the particle-flow (PF) algorithm [36], which
reconstructs particles in an event using an optimized combination of
information from the various elements of the CMS detector and identifies each
as a photon, electron, muon, charged hadron, or neutral hadron. These
particles are further clustered into jets as described below.

The reconstructed vertex with the largest value of summed physics-object
$p_{\mathrm{T}}^{2}$ is taken to be the primary $\Pp\Pp$ interaction vertex,
where the physics objects are the jets, clustered using the anti-algorithm
[37, 38] with the charged-particle tracks assigned to the vertex as inputs,
and the associated missing transverse momentum, taken as the negative vector
sum of the of those jets [39]. Charged-particle tracks associated with
vertices from other $\Pp\Pp$ interactions (pileup) are removed from further
consideration. The primary vertex is required to lie within 24cm of the
interaction point along the beam axis, and within 2cm in the plane transverse
to the beam axis.

Electrons and muons must satisfy $\pt>30\GeV$ and $\abs{\eta}<2.4$. For the
analysis of the 2017 and 2018 data, the electron threshold is increased to
37to account for the higher trigger threshold. The lepton identification
requirements are the “tight” criteria for electrons [40] and the “medium”
criteria for muons [41]. Leptons must be isolated within a cone of radius
$R=\sqrt{\smash[b]{(\Delta\phi)^{2}+(\Delta\eta)^{2}}}$ that scales as $1/\pt$
between a maximum of 0.2 for leptons with $\pt<50\GeV$ and a minimum of 0.05
for lepton $\pt>200\GeV$ [42].

Jets are clustered from the reconstructed PF particles using the anti-
algorithm with a distance parameter of 0.4. Criteria are applied to remove
events with jets arising from instrumental effects or reconstruction failures
[43, 44]. The reconstructed jet energies are corrected for the nonlinear
response of the detector [45, 46] and for contributions from neutral hadrons
from pileup [47]. Jets are required to have $\pt>30\GeV$ and $\abs{\eta}<2.4$.
Jets overlapping with a selected lepton within a cone of radius $R=0.4$ are
removed. A neural network-based algorithm [48] is used to identify quark jets;
for jets with $\pt$ around $30\GeV$, the algorithm has an efficiency of 65%
and a misidentification rate for light-flavor jets (including gluon jets) of
1%.

In addition to the trigger and vertex criteria above, events in the SR must
contain exactly one isolated electron or muon and at least seven jets, at
least one of which should be tagged. Samples with seven and eight jets include
a small number of expected signal events, but are included in the SR to
constrain the background. To further reject the QCD background, we require the
scalar sum of jet () to exceed 300. To suppress non-backgrounds, we require
the invariant mass of the system formed by the -tagged jet and the lepton to
be between 50 and 250. If there is more than one -tagged jet in the event, the
invariant mass of each -tagged jet and the lepton is considered, and at least
one combination is required to meet the above criterion. No requirement is
made on the event .

In addition to the SR, a signal-depleted control region (CR) dominated by QCD
background is defined with the dual purpose of determining the QCD
contribution to the SR and verifying the important assumption of
$N_{\text{jets}}$ shape invariance with $S_{\mathrm{NN}}$. Despite being
dominated by QCD background, the CR is useful for confirming $N_{\text{jets}}$
shape invariance because many of the jets used as inputs to the NN arise from
QCD radiation, which is common to the and QCD backgrounds; this claim is
verified in Section 0.5. The CR is defined similarly to the SR with the
differences being that the lepton is required to be a muon; the muon is
required to fail the SR isolation requirement; there is no requirement for a
-tagged jet, nor on the invariant mass of the lepton and -tagged jet; the only
trigger used is the high-threshold muon trigger without an isolation
requirement; and the muon threshold is 55.

##  0.3 Simulated event samples

Simulated event samples are used in the estimation of the expected number of
SM background and signal events passing the SR selection. Top quark pair and
single top quark events produced in the $t$ channel are generated with the
next-to-leading-order (NLO) v2.0 [49, 50, 51, 52, 53] generator, while single
top quark events in the t channel are generated with v1.0 [52]. Single top
quark production in the $s$ channel, as well as rare SM processes such as
$\ttbar\cPZ$ and $\ttbar\PW$ are generated at NLO accuracy with the v2.2.2
program. The v2.2.2 generator [54, 55] is used in the leading-order (LO) mode
to simulate QCD and ${\PW}+$jets events.

For the signal, top squark pair production events are generated using in LO
mode, including up to two additional partons in the matrix element
calculation. The top squarks are decayed using v8.212 (2016) or 8.226
(2017–2018) [56] according to the signal models described in Section 0.1. The
signal production cross section ($\sigma_{\PSQt\PASQt}$) is calculated as a
function of $m_{\PSQt}$ using approximate next-to-NLO (NNLO) plus next-to-
next-to-leading-logarithm (NNLL) calculations [57, 58].

The generation of these processes is based on either LO or NLO parton
distribution functions (PDFs) using NNPDF3.0 [59] for the simulated samples
corresponding to 2016 detector conditions, and using the NNLO PDF sets from
NNPDF3.1 [60] for the 2017 and 2018 simulated samples. Parton showering and
hadronization are simulated with using underlying event tune CUETP8M1 [61] for
2016 samples, except for production which used tune CUETP8M2T4 [62], or with
tune CP5 (CP2) [63] for all 2017 and 2018 background (signal) samples. To
model the effects of pileup, simulated events are generated with a nominal
distribution of pp interactions per bunch crossing and then reweighted to
match the corresponding distribution in data. The CMS detector response is
simulated using a -based model [64], and event reconstruction is performed in
the same manner as for collision data. The most precise cross section
calculations available are used to normalize the SM simulated samples,
corresponding to NLO or NNLO accuracy in most cases [54, 65, 66, 67, 68, 69,
70, 71].

The simulation is corrected to eliminate small discrepancies between data and
simulation in the trigger efficiency, lepton selection efficiency, and $\PQb$
tagging efficiency. Analysis-specific corrections for the distribution in
simulation, parameterized as functions of $N_{\text{jets}}$ and , are obtained
in a signal-depleted sample identical to the SR, except for the requirement
$5\leq N_{\text{jets}}\leq 7$. Events with $N_{\text{jets}}=7$ are common to
the SR, but as mentioned above, this sample has low signal contamination. The
correction is parameterized with an exponential function in with parameters
depending linearly on $N_{\text{jets}}$ in order to extend the correction into
the $N_{\text{jets}}>7$ SR. The correction is small at low and 20–40% at
$\HT=1500\GeV$, depending on $N_{\text{jets}}$.

##  0.4 Background estimation

Simulated background events passing the SR selection requirements
predominantly originate from production, with contributions of less than 10%
from QCD, and a few percent from the remaining minor backgrounds including
production in association with a vector boson, single top quark production,
and ${\PW}+$jets.

As introduced in Section 0.1, the crux of the analysis is to estimate the
dominant background in four bins of $S_{\mathrm{NN}}$ and six
$N_{\text{jets}}$ bins using a simultaneous binned maximum-likelihood fit
constraining the $N_{\text{jets}}$ shape to be the same in all
$S_{\mathrm{NN}}$ categories. Event yields, as well as the $N_{\text{jets}}$
and $S_{\mathrm{NN}}$ distributions, are fixed at values determined from a
signal-depleted data control sample for the QCD background and from simulation
for the minor backgrounds, as described later in this section. The yield and
$N_{\text{jets}}$ shape of the background, along with the signal strength, are
determined in the fit; signal strength is defined as the ratio of the fit
signal event yield to the one predicted by SUSY.

The NN is trained to discriminate between signal and background by exploiting
differences in the event shape and distributions of the kinematic variables.
The gradient reversal technique [72] is used to minimize dependence of the NN
output on $N_{\text{jets}}$, as required by the primary assumption that the
$N_{\text{jets}}$ shape is the same in all $S_{\mathrm{NN}}$ categories. All
NN input variables are computed in an approximate center-of-mass frame defined
by all jets in the event with $\pt>30\GeV$ and $\abs{\eta}<5$. The NN input
variables include the four-vector components for the seven jets in the event
with the highest momentum in the center-of-mass frame, the four-vector
components of the lepton in the event, the second through fifth Fox–Wolfram
moments [73] normalized by the first moment, and the three eigenvalues of the
sphericity tensor [74] normalized by the sum of the eigenvalues. The
Fox–Wolfram moments and sphericity tensor eigenvalues, which are computed from
the same seven highest momentum jets, quantify the distribution of jet energy
in the event, which tends to be more spherical for signal pair production than
for the background.

For the NN training, simulated events are used for the background sample, and
a mixture of RPV and stealth
$\HepParticle{S}{}{}\HepParticle{Y}{}{}\HepAntiParticle{Y}{}{}$ simulated
events with $m_{\PSQt}$ from 350–850is used as the signal sample. In this way,
the NN can identify common features among all signal samples ensuring a search
with broad sensitivity. Reflecting differences in simulation between the data
taking periods, as described in Section 0.3, a single training is used for
2017, 2018A, and 2018B, with a separate training used for 2016. The
$S_{\mathrm{NN}}$ distributions for the simulated background, several signal
models, and the 2016 and 2017+2018 data are shown in Fig. 2.

Figure 2:  The $S_{\mathrm{NN}}$ distributions for the 2016 training (left)
and 2017+2018 training (right) show the corresponding data in the SR (black
points); simulated background normalized to the number of data events (filled
histograms); RPV signal model with $m_{\PSQt}$ of 450(red short dashed); and
stealth $\HepParticle{S}{}{}\HepParticle{Y}{}{}\HepAntiParticle{Y}{}{}$ signal
model with $m_{\PSQt}$ of 850(cyan long dashed). All events shown pass the SR
event selection. The band on the total background histogram denotes the
dominant systematic uncertainties related to the modeling of , jet mass, and
jet in the simulation, as well as the statistical uncertainty for the non-
components. The lower panel shows the ratio of the number of data events to
the number of normalized simulated events with the band representing the
difference between the nominal ratio and the ratio obtained when varying the
total background by its uncertainty.

For each of the six $N_{\text{jets}}$ bins, events are divided into four
$S_{\mathrm{NN}}$ bins: $S_{\mathrm{NN,1}}$ (lowest $S_{\mathrm{NN}}$), …,
$S_{\mathrm{NN,4}}$ (highest $S_{\mathrm{NN}}$). The $S_{\mathrm{NN}}$ bin
boundaries are chosen separately for each $N_{\text{jets}}$ bin such that the
expected significance for the 550RPV signal model, which has expected
significance close to 5 standard deviations ($\sigma$), is maximized, under
the constraint that the fraction of simulated events in each $S_{\mathrm{NN}}$
bin is the same for all $N_{\text{jets}}$ bins. For example, the fraction of
all events in each $N_{\text{jets}}$ bin falling into the $S_{\mathrm{NN,1}}$
bin is constrained to be approximately $56\%$, while the fraction of events
falling into the $S_{\mathrm{NN,4}}$ bin is constrained to be approximately
2.4%. This constraint removes the small dependence of $S_{\mathrm{NN}}$ on
$N_{\text{jets}}$ that remains after NN training with gradient reversal.

In the maximum-likelihood fit, the $N_{\text{jets}}$ distribution is
parameterized with a function inspired by QCD jet scaling patterns [75] in
which the ratio $R(i)=M_{i+1}/M_{i}$, where $M_{i}$ is the number of events
with $N_{\text{jets}}=i$, can be described by a falling “Poisson” component at
low $N_{\text{jets}}$ and a constant “staircase” component at high
$N_{\text{jets}}$. This ratio is well modeled by the function

$f(i)=a_{2}+\left[\frac{(a_{1}-a_{2})^{i-7}}{(a_{0}-a_{2})^{i-9}}\right]^{1/2}.$

Notice that $a_{0}=f(7)$, $a_{1}=f(9)$, and $a_{2}$ is the asymptotic value
for large $i$. This particular parameterization was chosen to avoid large
correlations between the fit parameters. The $N_{\text{jets}}$ distribution
for each $S_{\mathrm{NN}}$ bin $j$ (see Fig. 4) is modeled using a recursive
expression given by $M^{j}_{i}=Y^{j}_{7}\,\Pi_{k=7}^{i-1}f(k)$ where
$Y^{j}_{7}$ are normalization parameters that are floating in the fit. The
last $N_{\text{jets}}$ bin considered is an inclusive $N_{\text{jets}}\geq 12$
bin, such that $i\in[7,12]$. In the maximum-likelihood fit, the free
parameters consist of the three shape parameters $a_{0}$, $a_{1}$, and
$a_{2}$; the four normalization parameters $Y^{j}_{7}$; the signal strength;
and all nuisance parameters related to systematic uncertainties described in
Section 0.5.

The QCD background yield parameters are fixed in the fit at the values
determined from the CR. More specifically, the QCD background prediction for
each $N_{\text{jets}}$-$S_{\mathrm{NN}}$ bin in the SR is given by the yield
for the same bin in the CR in data, after subtraction of the non-QCD
backgrounds as predicted from simulation, multiplied by the ratio of SR to CR
yields in simulation ($R_{\text{QCD}}$). This procedure is verified with a
closure test in the simulation. The yield parameters from the minor
backgrounds are also kept fixed in the fit at the values predicted by
simulation. While the yield parameters are fixed in the fit, the ultimate
contributions from QCD and minor backgrounds vary according to the constrained
nuisance parameters related to systematic uncertainties in those fit
components.

##  0.5 Systematic uncertainties and fit validation

As described in Section 0.4, an unbiased estimate of the dominant background
is obtained from the fit to data as long as the $N_{\text{jets}}$ shape is the
same for all four $S_{\mathrm{NN}}$ bins. By construction, $N_{\text{jets}}$
shape invariance is achieved in the simulation with an
$N_{\text{jets}}$-specific $S_{\mathrm{NN}}$ binning as described in the
previous section. Thus, systematic uncertainties on the background are
important to the degree that they violate the assumption that the
$S_{\mathrm{NN}}$ binning determined in simulation also applies to the data.
We quantify how each source of uncertainty causes deviations from the assumed
$N_{\text{jets}}$ shape invariance by comparing the nominal $N_{\text{jets}}$
shape to the $N_{\text{jets}}$ shapes in all $S_{\mathrm{NN}}$ bins after
performing the relevant systematic variation to the simulation. Each
systematic variation is associated with a constrained nuisance parameter in
the fit. The deviation in shape for each $N_{\text{jets}}$ distribution,
derived from the ratio of the post-variation shape divided by the nominal
shape, changes linearly with the associated nuisance parameter for the
systematic variation, while preserving the normalization of the distribution.

Sources of shape uncertainty include uncertainty in aspects of event
generation including PDFs, choice of renormalization and factorization scales
($\mu_{\mathrm{R}}$, $\mu_{\mathrm{F}}$ scales), and parton shower modeling,
which is itself composed of aspects related to modeling of ISR, FSR, color
reconnection in the parton shower, matrix element-parton shower matching scale
(ME-PS), underlying event (UE tune), and pileup modeling. The uncertainty due
to the choice in ($\mu_{\mathrm{R}}$, $\mu_{\mathrm{F}}$) scales is determined
by independently varying both by factors of 2.0 and 0.5 excluding the
variations (2.0, 0.5) and (0.5, 2.0) [55, 76, 77]. The ISR and FSR
uncertainties originate from variations of the renormalization scale for the
parton shower by factors 0.5 and 2.0, effectively varying the value of
$\alpha_{\text{S}}$. The color reconnection uncertainty is calculated by
allowing resonant decays to occur before the merging of multi-parton systems.
The ME-PS uncertainty is obtained by varying the parameter that governs ME-PS
matching about its nominal value according to
$h_{\text{damp}}=1.379^{+0.926}_{-0.505}$ times the top quark mass [63]. The
UE tune uncertainty comes from variation of the parameters that control the
modeling of the underlying event as described in Ref. [63]. The total
inelastic $\Pp\Pp$ cross section is changed by 5% to estimate the uncertainty
related to pileup [78].

Sources of shape uncertainty related mostly to aspects of detector simulation
include determination of jet energy scale (JES) and resolution (JER), modeling
of the tagging efficiency, modeling of the efficiency for lepton triggers,
identification, and isolation (lepton efficiencies); residual mismodeling of ,
jet , and jet mass; and use of the CR for measuring deviations from the
assumption of $N_{\text{jets}}$ shape invariance.

The uncertainty in the modeling of in the simulation is composed of four
separate components. The first uncertainty (primary) is taken as the full
difference in the background shape with and without the correction. The second
uncertainty (validation) is taken as the difference between the simulation
with nominal correction (described in Section 0.3) and the observed
distribution in the signal-depleted SR sample with $N_{\text{jets}}=8$. The
third and fourth uncertainties address the choices of parameterization of the
correction as functions of and $N_{\text{jets}}$. For these, we take the
uncertainty as the difference between the nominal correction and two alternate
corrections that use the $\HT=2000\GeV$ correction for all events with
$\HT>2000\GeV$ (-parameterization) and the $N_{\text{jets}}=7$ correction for
all values of $N_{\text{jets}}$ ($N_{\text{jets}}$-parameterization).

Comparisons of data and simulation in the CR show that the simulation predicts
distributions with higher values of jet and mass than observed. The observed
discrepancy at jet (mass) of 400 (50)depends on jet rank and is small for the
highest jet in each event growing to approximately 50% for the jet with sixth-
highest in each event. Similar trends are observed in the signal-depleted,
-dominated SR with $N_{\text{jets}}=7$. In the CR, the discrepancy in the
falling tail of each distribution is minimized when the (mass) of each jet is
scaled by the value 0.95, 0.95, 0.95, 0.95 (0.95, 1.01, 0.98, 0.98) for 2016,
2017, 2018A, and 2018B, respectively. Thus, the related shape uncertainty is
taken to be the resulting difference between scaled and nominal simulated
distributions. The dependence on jet rank indicates that the discrepancy
arises predominantly in the event generation; however, we choose to estimate
the associated systematic uncertainty separately for each data taking period
to include potential effects of detector response simulation. The correction
is omitted from the determination of these jet and mass uncertainties to avoid
double counting of mismodeling effects. In addition, because the estimation of
jet and mass uncertainties relies on variable scaling (rather than event
reweighting), the uncertainties include effects of changes in the
$S_{\mathrm{NN}}$ for each event, which is not included in the uncertainty.

As mentioned above, the use of $N_{\text{jets}}$-dependent $S_{\mathrm{NN}}$
binning ensures that the $N_{\text{jets}}$ shape is the same in all four
$S_{\mathrm{NN}}$ bins in simulation, and the use of the same binning in the
data assumes that the $N_{\text{jets}}$-$S_{\mathrm{NN}}$ dependence is well
modeled in the simulation. This assumption is confirmed and a related
systematic uncertainty is determined by comparing the $N_{\text{jets}}$ shapes
(in five uniform $S_{\mathrm{NN}}$ bins) for data and simulation in the CR.
For each of the six $N_{\text{jets}}$ bins, we compute the ratio
$R_{M}=(1/\mu_{i})\,(M_{\text{all}}/M_{i})$ as a function of
$S_{\mathrm{NN}}$, where $M_{\text{all}}$ is the total number of events in all
$N_{\text{jets}}$ bins, $M_{i}$ is the total number of events in the
$N_{\text{jets}}=i$ bin, and $\mu_{i}$ is the uncertainty-weighted average of
$M_{\text{all}}/M_{i}$ in the $N_{\text{jets}}=i$ bin used to facilitate
comparison of the $R_{M}$ shapes between samples and $N_{\text{jets}}$ bins
with different normalizations. Figure 3 shows a comparison of $R_{M}$ (from
$N_{\text{jets}}=7$ and 11 in the 2016 analysis) for simulation of the QCD
background in the CR ($\mathrm{QCD_{CR}^{MC}}$), simulation of in the SR
($\mathrm{\ttbar_{SR}^{MC}}$), and the data in the QCD background-dominated CR
($\mathrm{Data_{CR}}$). Agreement between $\mathrm{QCD_{CR}^{MC}}$ and
$\mathrm{\ttbar_{SR}^{MC}}$ demonstrates that QCD background-dominated data in
the CR are a good proxy for -dominated data in the SR, and agreement between
$\mathrm{QCD_{CR}^{MC}}$ and $\mathrm{Data_{CR}}$ verifies that the dependence
of the $N_{\text{jets}}$ shape on $S_{\mathrm{NN}}$ is well modeled in the
simulation. Similar agreement is found for the $R_{M}$ distributions for the
other $N_{\text{jets}}$ bins and data periods. The uncertainty related to the
combination of both effects is taken as the difference between
$\mathrm{\ttbar_{SR}^{MC}}$ and $\mathrm{Data_{CR}}$.

Figure 3: Distribution in $S_{\mathrm{NN}}$ of the ratio $R_{M}$, as defined
in the text, for $N_{\text{jets}}=7$ (left) and 11 (right), for the QCD CR
simulation (red circles), the SR simulation (green squares), and data in the
CR (blue crosses) for the 2016 data period. The error bars indicate the
statistical uncertainty in the value of $R_{M}$.

For the QCD background, the shape is obtained from data in the CR, and the
normalization is set with $R_{\text{QCD}}$. Because the systematic
uncertainties in the simulation largely cancel in the $R_{\text{QCD}}$ ratio,
the uncertainty in $R_{\text{QCD}}$ is dominated by the statistical
uncertainty of simulated samples and ranges from 15–25% depending on data
collection period.

Sources of systematic uncertainty in the predictions for signals and the minor
backgrounds include PDFs, JES, JER, tagging efficiency, lepton efficiency,
trigger efficiency, ($\mu_{\mathrm{R}}$, $\mu_{\mathrm{F}}$) scales, cross
sections for the minor backgrounds, and a 2.3–2.5% uncertainty in the
integrated luminosity [79, 80, 81]. Since the signal and minor backgrounds are
estimated directly from simulation, related uncertainties are included as the
full effect of the systematic variation on the yields in each
$N_{\text{jets}}$ and $S_{\mathrm{NN}}$ bin, thereby taking into account
normalization effects as well as shape changes.

Uncertainties derived from comparisons of data and simulation separately in
each data taking period (related to pileup, JES, JER, tagging efficiency,
lepton efficiencies, corrections, $N_{\text{jets}}$ shape invariance, and
integrated luminosity) are treated as uncorrelated among all data samples.
Uncertainties related to parton shower modeling are treated as fully
correlated for 2017, 2018A, and 2018B, while the corresponding uncertainties
for 2016 are uncorrelated with those from the other data taking periods;
uncertainties related to ($\mu_{\mathrm{R}}$, $\mu_{\mathrm{F}}$) scales and
cross sections for the minor backgrounds are treated as correlated between all
four periods.

Table 0.5 shows the impact of the systematic uncertainties on the expected
event yields for the background, minor backgrounds, and the RPV signal model
with $m_{\PSQt}=550\GeV$. For sources of uncertainty for which the size of the
impact depends on $N_{\text{jets}}$ and $S_{\mathrm{NN}}$, a representative
range of values is listed along with the maximum value from all bins.

Summary of the impact of systematic uncertainties in the expected event yields
for the background, minor backgrounds (both +X and other), and the RPV signal
model with $m_{\PSQt}=550\GeV$. Abbreviated names for each source of
uncertainty are explained in the text. For sources of uncertainty for which
the size of the impact depends on $N_{\text{jets}}$ and $S_{\mathrm{NN}}$, a
representative range of values showing the 16th and 84th percentile of all the
corrections is listed with the maximum value from all bins shown in
parentheses. All values are in units of percent. l r r r & & Minor RPV
Source of uncertainty background background signal
PDFs 0–1 (2) 0–1 (8) 0–2 (7)
($\mu_{\mathrm{R}}$, $\mu_{\mathrm{F}}$) scales 0–2 (5) 1–8 (18) 0–3 (4)
ISR 0–4 (15) — —
FSR 0–8 (27) — —
Color reconnection 0–10 (44) — —
ME-PS 0–14 (82) — —
UE tune 0–7 (100) — —
Pileup 0–2 (7) 0–7 (28) 0–2 (4)

JES 0–4 (18) 5–21 (100) 1–11 (31)
JER 0–2 (10) 1–15 (100) 0–6 (14)
tagging 0–1 (3) 0–2 (12) 0–2 (2)
Lepton efficiencies 0–1 (1) 3–5 (5) 3–4 (4)

primary 0–5 (17) — —
validation 0–1 (4) 0–6 (10) —
-parameterization 0–2 (9) — —
$N_{\text{jets}}$-parameterization 0–7 (27) — —
Jet 0–4 (15) — —
Jet mass 0–4 (15) — —
$N_{\text{jets}}$ shape invariance 0–12 (37) — —

Integrated luminosity — 2.3–2.5 2.3–2.5
Theoretical cross section — 30 —

##  0.6 Results and interpretation

Figure 4:  Fitted background prediction and observed data counts for 2016,
2017, 2018A, and 2018B (from upper to lower rows) as functions of
$N_{\text{jets}}$ in each of the four bins in $S_{\mathrm{NN}}$. The signal
distributions normalized to the predicted cross section for the RPV model with
$m_{\PSQt}=450\GeV$ and the stealth
$\HepParticle{S}{}{}\HepParticle{Y}{}{}\HepAntiParticle{Y}{}{}$ model with
$m_{\PSQt}=850\GeV$ are shown for comparison. The lower panel of each plot
displays the difference between the number of observed events and the number
of events determined by the fit divided by the statistical uncertainty
associated with the observed number of events ($\delta$) as black points with
error bars denoting $\delta$. The blue band shows the total systematic
uncertainty in the fit from all nuisance parameters.

The results of the fit to 2016, 2017, 2018A, and 2018B data sets with the
signal strength fixed to zero (background-only fit) are shown along with the
observed number of events in Fig. 4; each column (row) in the plot array
corresponds to a specific $S_{\mathrm{NN}}$ bin (data set). The expected
distributions for top squark pair production in the specific RPV
($m_{\PSQt}=450\GeV$) and stealth
$\HepParticle{S}{}{}\HepParticle{Y}{}{}\HepAntiParticle{Y}{}{}$ models
($m_{\PSQt}=850\GeV$) described in Section 0.1 are overlaid for illustration
purposes. The lower panel of each plot displays the difference between the
observed number of events and the total number of expected events determined
by the fit divided by the statistical uncertainty associated with the observed
number of events ($\delta$) as black points with error bars denoting $\delta$.
The blue band shows the total uncertainty in the fit determined from the full
fit covariance matrix in order to account for the correlations among fit
parameters. Figure 5 shows the results of the same background-only fit summed
over $S_{\mathrm{NN}}$ bins and data periods with separate contributions from
each background.

Figure 5:  Background prediction from the background-only fit and observed
data counts as a function of $N_{\text{jets}}$ summed over data periods and
$S_{\mathrm{NN}}$ bins. Overlaid are expected distributions for the RPV and
stealth $\HepParticle{S}{}{}\HepParticle{Y}{}{}\HepAntiParticle{Y}{}{}$ models
with $m_{\PSQt}=450$ and 850, respectively, normalized according to the top
squark pair production cross section. For visualization purposes, the hatched
band in the lower panel shows the quadrature sum of all of the uncertainties
on the background prediction.

The data are also used to determine the 95% confidence level (CL) upper limits
on $\sigma_{\PSQt\PASQt}$ and the signal strength $p$-values [82] for both the
RPV and stealth
$\HepParticle{S}{}{}\HepParticle{Y}{}{}\HepAntiParticle{Y}{}{}$ models
obtained using the approach [83, 84, 85] with asymptotic formulae [86] and the
profile likelihood ratio as the test statistic. Figure 6 shows the expected
and observed cross section limits as a function of $m_{\PSQt}$ for the
benchmark RPV and stealth
$\HepParticle{S}{}{}\HepParticle{Y}{}{}\HepAntiParticle{Y}{}{}$ signal models.
Comparing to the predicted cross section, these limits correspond to the
exclusion of top squark masses in the range 300–670 and 300–870for the
benchmark RPV and stealth
$\HepParticle{S}{}{}\HepParticle{Y}{}{}\HepAntiParticle{Y}{}{}$ models,
respectively. Figure 7 shows the local $p$-value [82] of the signal strength,
as a function of $m_{\PSQt}$, obtained from fits to the data with each signal
strength as a free parameter for both the RPV and stealth
$\HepParticle{S}{}{}\HepParticle{Y}{}{}\HepAntiParticle{Y}{}{}$ models. The
$p$-value quantifies the probability for the background to produce an upward
fluctuation at least as large as that observed. Fits are performed and
$p$-values obtained separately for each data set, as well as in a simultaneous
fit to all data sets. We observe the most extreme $p$-value to be 0.003, which
corresponds to a local significance of 2.8$\sigma$ and a best fit signal
strength of $0.21\pm 0.07$ for the RPV model with $m_{\PSQt}=400\GeV$ assuming
unity branching fractions for the decays described in Section 0.1.

Figure 6:  Expected and observed 95% CL upper limit on the top squark pair
production cross section as a function of the top squark mass for the RPV
(left) and stealth
$\HepParticle{S}{}{}\HepParticle{Y}{}{}\HepAntiParticle{Y}{}{}$ (right) SUSY
models. Particle masses and branching fractions assumed for each model are
included on each plot. The expected cross section computed at NNLO+NNLL
accuracy is shown in the red curve.

Figure 7: Local $p$-value as a function of top squark mass for the RPV (left)
and stealth $\HepParticle{S}{}{}\HepParticle{Y}{}{}\HepAntiParticle{Y}{}{}$
models (right). The colored lines show the $p$-values for separate fits of the
2016 (red dash dotted), 2017 (blue dotted), 2018A (green short dashed), and
2018B (orange long dashed) data sets; the black line shows the $p$-value for
the simultaneous fit of data sets. The lower panels show the best fit signal
strength ($\sigma_{\text{meas.}}/\sigma_{\text{pred.}}$) as a function of top
squark mass with uncertainty denoted by the green band.

The 2.8$\sigma$ local significance for the RPV model with $m_{\PSQt}=400\GeV$
is understood to arise from a combination of two effects. First, although the
level of agreement between the observed data and the background expectation
shown in Fig. 4 is reasonable, the agreement improves when the signal is
included in the fit, contributing approximately 1.1$\sigma$ to the
significance. Second, the constrained nuisance parameters (NP) are pulled less
from their initial values when the signal is included in the fit, contributing
approximately 1.7$\sigma$ to the significance. This second effect is
illustrated in Fig. 8 which shows for each of a selection of NP : the fit
value ($\theta$) and uncertainty ($\delta_{\theta}$) from both the background-
only fit (b) and the signal+background fit (s+b), as well as the
$\Delta\chi^{2}\equiv\chi^{2}(\mathrm{s}+\mathrm{b})-\chi^{2}(\mathrm{b})$
difference of $\chi^{2}\equiv(\theta/\delta_{\theta})^{2}$ from the two fits.
A $\theta$ value of one indicates that the fit value of the nuisance parameter
is one standard deviation from its nominal value, and a $\delta_{\theta}$
value less than one shows that the uncertainty is reduced in the fit relative
to its initial value. All NPs have $\theta$ values below one for the
background-only fit, and several NPs related to modeling are constrained with
$\delta_{\theta}$ in the range of 0.25–0.40. Figure 8 also shows the
cumulative and total sums of $\Delta\chi^{2}$ for the NPs, with the sum for
all NP of $\sum\Delta\chi^{2}=-3.0$ corresponding to an approximate
contribution to the signal significance of
$\sqrt{|\sum\Delta\chi^{2}|}=1.7\,\sigma$.

Figure 8:  The upper panel shows the fit values ($\theta$) and uncertainties
($\delta_{\theta}$) for a selection of nuisance parameters (NP) from both the
background-only fit (purple) and signal+background fit (blue) for the RPV
model with $m_{\PSQt}=400\GeV$. The x-axis labels refer to the NP sources
described in Section 0.5, the data period (16, 17, etc.), and the direction of
variation ($+$,$-$). The lower panel shows the
$\Delta\chi^{2}\equiv\chi^{2}(\mathrm{s}+\mathrm{b})-\chi^{2}(\mathrm{b})$
difference of $\chi^{2}\equiv(\theta/\delta_{\theta})^{2}$ from the
signal+background (s+b) and background-only (b) fits as a red point for each
NP and the cumulative sum of $\Delta\chi^{2}$ from left to right as a blue
shaded histogram. The fourteen selected NP are those with
$|\Delta\chi^{2}|>0.3$, and the NP are ordered from left to right by
decreasing $|\Delta\chi^{2}|$. The rightmost bin, separated by a vertical
solid line, shows the sum of $\Delta\chi^{2}$ for all NP not displayed in the
figure (red point) and the sum of $\Delta\chi^{2}$ for all NP (blue shaded
histogram).

##  0.7 Summary

A first of its kind search for top squark pair production with subsequent
decay characterized by two top quarks, additional gluons or light-flavor
quarks, and low missing transverse momentum () is described. Events containing
exactly one electron or muon and at least seven jets, of which at least one
should be tagged, are selected from a sample of proton-proton collisions at
$\sqrt{s}=13\TeV$ corresponding to an integrated luminosity of 137collected
with the CMS detector in 2016–2018. No requirement is made on . The dominant
background is predicted from data using a simultaneous fit of the jet
multiplicity distribution across four bins of a neural network score.

The results are interpreted in terms of top squark pair production in the
context of $R$-parity violating (RPV) and stealth supersymmetry models. Top
squark masses ($m_{\PSQt}$) up to 670are excluded at 95% confidence level for
the RPV model in which the top squark decays to a top quark and the lightest
neutralino, which subsequently decays to three light-flavor quarks via an off-
shell squark through a trilinear coupling . Top squark masses up to 870are
excluded for the stealth supersymmetry model in which the top squark decays to
a top quark, three gluons, and a gravitino via intermediate hidden sector
particles. The maximum observed local significance is 2.8 standard deviations
corresponding to a best fit signal strength of $0.21\pm 0.07$ for the RPV
model with $m_{\PSQt}=400\GeV$.

###### Acknowledgements.

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

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##  .8 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan${}^{\textrm{\textdagger}}$, A. Tumasyan Institut für
Hochenergiephysik, Wien, Austria
W. Adam, J.W. Andrejkovic, T. Bergauer, S. Chatterjee, M. Dragicevic, A.
Escalante Del Valle, R. Frühwirth1, M. Jeitler1, N. Krammer, L. Lechner, D.
Liko, I. Mikulec, F.M. Pitters, J. Schieck1, R. Schöfbeck, M. Spanring, S.
Templ, W. Waltenberger, C.-E. Wulz1 Institute for Nuclear Problems, Minsk,
Belarus
V. Chekhovsky, A. Litomin, V. Makarenko Universiteit Antwerpen, Antwerpen,
Belgium
M.R. Darwish2, E.A. De Wolf, X. Janssen, T. Kello3, A. Lelek, H. Rejeb Sfar,
P. Van Mechelen, S. Van Putte, N. Van Remortel Vrije Universiteit Brussel,
Brussel, Belgium
F. Blekman, E.S. Bols, J. D’Hondt, J. De Clercq, M. Delcourt, S. Lowette, S.
Moortgat, A. Morton, D. Müller, A.R. Sahasransu, S. Tavernier, W. Van Doninck,
P. Van Mulders Université Libre de Bruxelles, Bruxelles, Belgium
D. Beghin, B. Bilin, B. Clerbaux, G. De Lentdecker, L. Favart, A. Grebenyuk,
A.K. Kalsi, K. Lee, M. Mahdavikhorrami, I. Makarenko, L. Moureaux, L. Pétré,
A. Popov, N. Postiau, E. Starling, L. Thomas, M. Vanden Bemden, C. Vander
Velde, P. Vanlaer, D. Vannerom, L. Wezenbeek Ghent University, Ghent, Belgium
T. Cornelis, D. Dobur, M. Gruchala, G. Mestdach, M. Niedziela, C. Roskas, K.
Skovpen, M. Tytgat, W. Verbeke, B. Vermassen, M. Vit Université Catholique de
Louvain, Louvain-la-Neuve, Belgium
A. Bethani, G. Bruno, F. Bury, C. Caputo, P. David, C. Delaere, I.S. Donertas,
A. Giammanco, V. Lemaitre, K. Mondal, J. Prisciandaro, A. Taliercio, M.
Teklishyn, P. Vischia, S. Wertz, S. Wuyckens Centro Brasileiro de Pesquisas
Fisicas, Rio de Janeiro, Brazil
G.A. Alves, C. Hensel, A. Moraes Universidade do Estado do Rio de Janeiro, Rio
de Janeiro, Brazil
W.L. Aldá Júnior, M. Barroso Ferreira Filho, H. Brandao Malbouisson, W.
Carvalho, J. Chinellato4, E.M. Da Costa, G.G. Da Silveira5, D. De Jesus
Damiao, S. Fonseca De Souza, D. Matos Figueiredo, C. Mora Herrera, K. Mota
Amarilo, L. Mundim, H. Nogima, P. Rebello Teles, L.J. Sanchez Rosas, A.
Santoro, S.M. Silva Do Amaral, A. Sznajder, M. Thiel, F. Torres Da Silva De
Araujo, A. Vilela Pereira Universidade Estadual Paulista a, Universidade
Federal do ABC b, São Paulo, Brazil
C.A. Bernardesa,a, L. Calligarisa, T.R. Fernandez Perez Tomeia, E.M.
Gregoresa,b, D.S. Lemosa, P.G. Mercadantea,b, S.F. Novaesa, Sandra S. Padulaa
Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy of
Sciences, Sofia, Bulgaria
A. Aleksandrov, G. Antchev, I. Atanasov, R. Hadjiiska, P. Iaydjiev, M.
Misheva, M. Rodozov, M. Shopova, G. Sultanov University of Sofia, Sofia,
Bulgaria
A. Dimitrov, T. Ivanov, L. Litov, B. Pavlov, P. Petkov, A. Petrov Beihang
University, Beijing, China
T. Cheng, W. Fang3, Q. Guo, T. Javaid6, M. Mittal, H. Wang, L. Yuan Department
of Physics, Tsinghua University, Beijing, China
M. Ahmad, G. Bauer, C. Dozen7, Z. Hu, J. Martins8, Y. Wang, K. Yi9,10
Institute of High Energy Physics, Beijing, China
E. Chapon, G.M. Chen6, H.S. Chen6, M. Chen, A. Kapoor, D. Leggat, H. Liao,
Z.-A. LIU6, R. Sharma, A. Spiezia, J. Tao, J. Thomas-wilsker, J. Wang, H.
Zhang, S. Zhang6, J. Zhao State Key Laboratory of Nuclear Physics and
Technology, Peking University, Beijing, China
A. Agapitos, Y. Ban, C. Chen, Q. Huang, A. Levin, Q. Li, M. Lu, X. Lyu, Y.
Mao, S.J. Qian, D. Wang, Q. Wang, J. Xiao Sun Yat-Sen University, Guangzhou,
China
Z. You Institute of Modern Physics and Key Laboratory of Nuclear Physics and
Ion-beam Application (MOE) - Fudan University, Shanghai, China
X. Gao3, H. Okawa Zhejiang University, Hangzhou, China
M. Xiao Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, C. Florez, J. Fraga, A. Sarkar, M.A. Segura Delgado
Universidad de Antioquia, Medellin, Colombia
J. Jaramillo, J. Mejia Guisao, F. Ramirez, J.D. Ruiz Alvarez, C.A. Salazar
González, N. Vanegas Arbelaez University of Split, Faculty of Electrical
Engineering, Mechanical Engineering and Naval Architecture, Split, Croatia
D. Giljanovic, N. Godinovic, D. Lelas, I. Puljak University of Split, Faculty
of Science, Split, Croatia
Z. Antunovic, M. Kovac, T. Sculac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, D. Ferencek, D. Majumder, M. Roguljic, A. Starodumov11, T. Susa
University of Cyprus, Nicosia, Cyprus
A. Attikis, E. Erodotou, A. Ioannou, G. Kole, M. Kolosova, S. Konstantinou, J.
Mousa, C. Nicolaou, F. Ptochos, P.A. Razis, H. Rykaczewski, H. Saka Charles
University, Prague, Czech Republic
M. Finger12, M. Finger Jr.12, A. Kveton Escuela Politecnica Nacional, Quito,
Ecuador
E. Ayala Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab
Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
S. Abu Zeid13, S. Khalil14, E. Salama15,13 Center for High Energy Physics
(CHEP-FU), Fayoum University, El-Fayoum, Egypt
A. Lotfy, Y. Mohammed National Institute of Chemical Physics and Biophysics,
Tallinn, Estonia
S. Bhowmik, A. Carvalho Antunes De Oliveira, R.K. Dewanjee, K. Ehataht, M.
Kadastik, J. Pata, M. Raidal, C. Veelken Department of Physics, University of
Helsinki, Helsinki, Finland
P. Eerola, L. Forthomme, H. Kirschenmann, K. Osterberg, M. Voutilainen
Helsinki Institute of Physics, Helsinki, Finland
E. Brücken, F. Garcia, J. Havukainen, V. Karimäki, M.S. Kim, R. Kinnunen, T.
Lampén, K. Lassila-Perini, S. Lehti, T. Lindén, H. Siikonen, E. Tuominen, J.
Tuominiemi Lappeenranta University of Technology, Lappeenranta, Finland
P. Luukka, H. Petrow, T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-
Yvette, France
C. Amendola, M. Besancon, F. Couderc, M. Dejardin, D. Denegri, J.L. Faure, F.
Ferri, S. Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry,
B. Lenzi, E. Locci, J. Malcles, J. Rander, A. Rosowsky, M.Ö. Sahin, A. Savoy-
Navarro16, M. Titov, G.B. Yu Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole
Polytechnique, Institut Polytechnique de Paris, Palaiseau, France
S. Ahuja, F. Beaudette, M. Bonanomi, A. Buchot Perraguin, P. Busson, C.
Charlot, O. Davignon, B. Diab, G. Falmagne, S. Ghosh, R. Granier de Cassagnac,
A. Hakimi, I. Kucher, A. Lobanov, M. Nguyen, C. Ochando, P. Paganini, J.
Rembser, R. Salerno, J.B. Sauvan, Y. Sirois, A. Zabi, A. Zghiche Université de
Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, France
J.-L. Agram17, J. Andrea, D. Apparu, D. Bloch, G. Bourgatte, J.-M. Brom, E.C.
Chabert, C. Collard, D. Darej, J.-C. Fontaine17, U. Goerlach, C. Grimault,
A.-C. Le Bihan, P. Van Hove Institut de Physique des 2 Infinis de Lyon (IP2I
), Villeurbanne, France
E. Asilar, S. Beauceron, C. Bernet, G. Boudoul, C. Camen, A. Carle, N. Chanon,
D. Contardo, P. Depasse, H. El Mamouni, J. Fay, S. Gascon, M. Gouzevitch, B.
Ille, Sa. Jain, I.B. Laktineh, H. Lattaud, A. Lesauvage, M. Lethuillier, L.
Mirabito, K. Shchablo, L. Torterotot, G. Touquet, M. Vander Donckt, S. Viret
Georgian Technical University, Tbilisi, Georgia
G. Adamov, Z. Tsamalaidze12 RWTH Aachen University, I. Physikalisches
Institut, Aachen, Germany
L. Feld, K. Klein, M. Lipinski, D. Meuser, A. Pauls, M.P. Rauch, J. Schulz, M.
Teroerde RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
D. Eliseev, M. Erdmann, P. Fackeldey, B. Fischer, S. Ghosh, T. Hebbeker, K.
Hoepfner, H. Keller, L. Mastrolorenzo, M. Merschmeyer, A. Meyer, G. Mocellin,
S. Mondal, S. Mukherjee, D. Noll, A. Novak, T. Pook, A. Pozdnyakov, Y. Rath,
H. Reithler, J. Roemer, A. Schmidt, S.C. Schuler, A. Sharma, S. Wiedenbeck, S.
Zaleski RWTH Aachen University, III. Physikalisches Institut B, Aachen,
Germany
C. Dziwok, G. Flügge, W. Haj Ahmad18, O. Hlushchenko, T. Kress, A. Nowack, C.
Pistone, O. Pooth, D. Roy, H. Sert, A. Stahl19, T. Ziemons Deutsches
Elektronen-Synchrotron, Hamburg, Germany
H. Aarup Petersen, M. Aldaya Martin, P. Asmuss, I. Babounikau, S. Baxter, O.
Behnke, A. Bermúdez Martínez, A.A. Bin Anuar, K. Borras20, V. Botta, D.
Brunner, A. Campbell, A. Cardini, P. Connor, S. Consuegra Rodríguez, V.
Danilov, M.M. Defranchis, L. Didukh, D. Domínguez Damiani, G. Eckerlin, D.
Eckstein, L.I. Estevez Banos, E. Gallo21, A. Geiser, A. Giraldi, A. Grohsjean,
M. Guthoff, A. Harb, A. Jafari22, N.Z. Jomhari, H. Jung, A. Kasem20, M.
Kasemann, H. Kaveh, C. Kleinwort, J. Knolle, D. Krücker, W. Lange, T. Lenz, J.
Lidrych, K. Lipka, W. Lohmann23, T. Madlener, R. Mankel, I.-A. Melzer-
Pellmann, J. Metwally, A.B. Meyer, M. Meyer, J. Mnich, A. Mussgiller, V.
Myronenko, Y. Otarid, D. Pérez Adán, S.K. Pflitsch, D. Pitzl, A. Raspereza, A.
Saggio, A. Saibel, M. Savitskyi, V. Scheurer, C. Schwanenberger, A. Singh,
R.E. Sosa Ricardo, N. Tonon, O. Turkot, A. Vagnerini, M. Van De Klundert, R.
Walsh, D. Walter, Y. Wen, K. Wichmann, C. Wissing, S. Wuchterl, O. Zenaiev, R.
Zlebcik University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, L. Benato, A. Benecke, K. De Leo, T. Dreyer, M. Eich, F.
Feindt, A. Fröhlich, C. Garbers, E. Garutti, P. Gunnellini, J. Haller, A.
Hinzmann, A. Karavdina, G. Kasieczka, R. Klanner, R. Kogler, V. Kutzner, J.
Lange, T. Lange, A. Malara, A. Nigamova, K.J. Pena Rodriguez, O. Rieger, P.
Schleper, M. Schröder, J. Schwandt, D. Schwarz, J. Sonneveld, H. Stadie, G.
Steinbrück, A. Tews, B. Vormwald, I. Zoi Karlsruher Institut fuer Technologie,
Karlsruhe, Germany
J. Bechtel, T. Berger, E. Butz, R. Caspart, T. Chwalek, W. De Boer, A.
Dierlamm, A. Droll, K. El Morabit, N. Faltermann, K. Flöh, M. Giffels, J.o.
Gosewisch, A. Gottmann, F. Hartmann19, C. Heidecker, U. Husemann, I. Katkov24,
P. Keicher, R. Koppenhöfer, S. Maier, M. Metzler, S. Mitra, Th. Müller, M.
Musich, M. Neukum, G. Quast, K. Rabbertz, J. Rauser, D. Savoiu, D. Schäfer, M.
Schnepf, D. Seith, I. Shvetsov, H.J. Simonis, R. Ulrich, J. Van Der Linden,
R.F. Von Cube, M. Wassmer, M. Weber, S. Wieland, R. Wolf, S. Wozniewski, S.
Wunsch Institute of Nuclear and Particle Physics (INPP), NCSR Demokritos,
Aghia Paraskevi, Greece
G. Anagnostou, P. Asenov, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas,
A. Stakia National and Kapodistrian University of Athens, Athens, Greece
M. Diamantopoulou, D. Karasavvas, G. Karathanasis, P. Kontaxakis, C.K. Koraka,
A. Manousakis-katsikakis, A. Panagiotou, I. Papavergou, N. Saoulidou, K.
Theofilatos, E. Tziaferi, K. Vellidis, E. Vourliotis National Technical
University of Athens, Athens, Greece
G. Bakas, K. Kousouris, I. Papakrivopoulos, G. Tsipolitis, A. Zacharopoulou
University of Ioánnina, Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, N. Manthos, I.
Papadopoulos, J. Strologas MTA-ELTE Lendület CMS Particle and Nuclear Physics
Group, Eötvös Loránd University, Budapest, Hungary
M. Csanad, M.M.A. Gadallah25, S. Lökös26, P. Major, K. Mandal, A. Mehta, G.
Pasztor, A.J. Rádl, O. Surányi, G.I. Veres Wigner Research Centre for Physics,
Budapest, Hungary
M. Bartók27, G. Bencze, C. Hajdu, D. Horvath28, F. Sikler, V. Veszpremi, G.
Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear Research ATOMKI,
Debrecen, Hungary
S. Czellar, J. Karancsi27, J. Molnar, Z. Szillasi, D. Teyssier Institute of
Physics, University of Debrecen, Debrecen, Hungary
P. Raics, Z.L. Trocsanyi29, B. Ujvari Eszterhazy Karoly University, Karoly
Robert Campus, Gyongyos, Hungary
T. Csorgo30, F. Nemes30, T. Novak Indian Institute of Science (IISc),
Bangalore, India
S. Choudhury, J.R. Komaragiri, D. Kumar, L. Panwar, P.C. Tiwari National
Institute of Science Education and Research, HBNI, Bhubaneswar, India
S. Bahinipati31, D. Dash, C. Kar, P. Mal, T. Mishra, V.K. Muraleedharan Nair
Bindhu32, A. Nayak32, P. Saha, N. Sur, S.K. Swain Panjab University,
Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, G. Chaudhary, S. Chauhan, N. Dhingra33, R.
Gupta, A. Kaur, S. Kaur, P. Kumari, M. Meena, K. Sandeep, J.B. Singh, A.K.
Virdi University of Delhi, Delhi, India
A. Ahmed, A. Bhardwaj, B.C. Choudhary, R.B. Garg, M. Gola, S. Keshri, A.
Kumar, M. Naimuddin, P. Priyanka, K. Ranjan, A. Shah Saha Institute of Nuclear
Physics, HBNI, Kolkata, India
M. Bharti34, R. Bhattacharya, S. Bhattacharya, D. Bhowmik, S. Dutta, B.
Gomber35, M. Maity36, S. Nandan, P. Palit, P.K. Rout, G. Saha, B. Sahu, S.
Sarkar, M. Sharan, B. Singh34, S. Thakur34 Indian Institute of Technology
Madras, Madras, India
P.K. Behera, S.C. Behera, P. Kalbhor, A. Muhammad, R. Pradhan, P.R. Pujahari,
A. Sharma, A.K. Sikdar Bhabha Atomic Research Centre, Mumbai, India
D. Dutta, V. Jha, V. Kumar, D.K. Mishra, K. Naskar37, P.K. Netrakanti, L.M.
Pant, P. Shukla Tata Institute of Fundamental Research-A, Mumbai, India
T. Aziz, S. Dugad, G.B. Mohanty, U. Sarkar Tata Institute of Fundamental
Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, R. Chudasama, M. Guchait, S. Karmakar, S. Kumar,
G. Majumder, K. Mazumdar, S. Mukherjee, D. Roy Indian Institute of Science
Education and Research (IISER), Pune, India
S. Dube, B. Kansal, S. Pandey, A. Rane, A. Rastogi, S. Sharma Department of
Physics, Isfahan University of Technology, Isfahan, Iran
H. Bakhshiansohi38, M. Zeinali39 Institute for Research in Fundamental
Sciences (IPM), Tehran, Iran
S. Chenarani40, S.M. Etesami, M. Khakzad, M. Mohammadi Najafabadi University
College Dublin, Dublin, Ireland
M. Felcini, M. Grunewald INFN Sezione di Bari a, Università di Bari b,
Politecnico di Bari c, Bari, Italy
M. Abbresciaa,b, R. Alya,b,41, C. Arutaa,b, A. Colaleoa, D. Creanzaa,c, N. De
Filippisa,c, M. De Palmaa,b, A. Di Florioa,b, A. Di Pilatoa,b, W.
Elmetenaweea,b, L. Fiorea, A. Gelmia,b, M. Gula, G. Iasellia,c, M. Incea,b, S.
Lezkia,b, G. Maggia,c, M. Maggia, I. Margjekaa,b, V. Mastrapasquaa,b, J.A.
Merlina, S. Mya,b, S. Nuzzoa,b, A. Pompilia,b, G. Pugliesea,c, A. Ranieria, G.
Selvaggia,b, L. Silvestrisa, F.M. Simonea,b, R. Vendittia, P. Verwilligena
INFN Sezione di Bologna a, Università di Bologna b, Bologna, Italy
G. Abbiendia, C. Battilanaa,b, D. Bonacorsia,b, L. Borgonovia, S. Braibant-
Giacomellia,b, L. Brigliadoria, R. Campaninia,b, P. Capiluppia,b, A.
Castroa,b, F.R. Cavalloa, C. Cioccaa, M. Cuffiania,b, G.M. Dallavallea, T.
Diotalevia,b, F. Fabbria, A. Fanfania,b, E. Fontanesia,b, P. Giacomellia, L.
Giommia,b, C. Grandia, L. Guiduccia,b, F. Iemmia,b, S. Lo Meoa,42, 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,43, S. Costaa,b,43, A. Di Mattiaa, R. Potenzaa,b, A.
Tricomia,b,43, C. Tuvea,b INFN Sezione di Firenze a, Università di Firenze b,
Firenze, Italy
G. Barbaglia, A. Cassesea, R. Ceccarellia,b, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, F. Fioria,b, E. Focardia,b, G. Latinoa,b, P. Lenzia,b, M.
Lizzoa,b, M. Meschinia, S. Paolettia, R. Seiditaa,b, G. Sguazzonia, L.
Viliania INFN Laboratori Nazionali di Frascati, Frascati, Italy
L. Benussi, S. Bianco, D. Piccolo INFN Sezione di Genova a, Università di
Genova b, Genova, Italy
M. Bozzoa,b, F. Ferroa, R. Mulargiaa,b, E. Robuttia, S. Tosia,b INFN Sezione
di Milano-Bicocca a, Università di Milano-Bicocca b, Milano, Italy
A. Benagliaa, F. Brivioa,b, F. Cetorellia,b, V. Cirioloa,b,19, F. De Guioa,b,
M.E. Dinardoa,b, P. Dinia, S. Gennaia, A. Ghezzia,b, P. Govonia,b, L.
Guzzia,b, M. Malbertia, S. Malvezzia, A. Massironia, D. Menascea, F. Montia,b,
L. Moronia, M. Paganonia,b, D. Pedrinia, S. Ragazzia,b, T. Tabarelli de
Fatisa,b, D. Valsecchia,b,19, D. Zuoloa,b INFN Sezione di Napoli a, Università
di Napoli ’Federico II’ b, Napoli, Italy, Università della Basilicata c,
Potenza, Italy, Università G. Marconi d, Roma, Italy
S. Buontempoa, F. Carnevalia,b, N. Cavalloa,c, A. De Iorioa,b, F. Fabozzia,c,
A.O.M. Iorioa,b, L. Listaa,b, S. Meolaa,d,19, P. Paoluccia,19, B. Rossia, C.
Sciaccaa,b INFN Sezione di Padova a, Università di Padova b, Padova, Italy,
Università di Trento c, Trento, Italy
P. Azzia, N. Bacchettaa, D. Biselloa,b, P. Bortignona, A. Bragagnoloa,b, R.
Carlina,b, P. Checchiaa, P. De Castro Manzanoa, T. Dorigoa, F. Gasparinia,b,
U. Gasparinia,b, S.Y. Hoha,b, L. Layera,44, M. Margonia,b, A.T. Meneguzzoa,b,
M. Presillaa,b, P. Ronchesea,b, R. Rossina,b, F. Simonettoa,b, G. Stronga, M.
Tosia,b, H. YARARa,b, M. Zanettia,b, P. Zottoa,b, A. Zucchettaa,b, G.
Zumerlea,b INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy
C. Aime‘a,b, A. Braghieria, S. Calzaferria,b, D. Fiorinaa,b, P. Montagnaa,b,
S.P. Rattia,b, V. Rea, M. Ressegottia,b, C. Riccardia,b, P. Salvinia, I. Vaia,
P. Vituloa,b INFN Sezione di Perugia a, Università di Perugia b, Perugia,
Italy
G.M. Bileia, D. Ciangottinia,b, L. Fanòa,b, P. Laricciaa,b, G. Mantovania,b,
V. Mariania,b, M. Menichellia, F. Moscatellia, A. Piccinellia,b, A. Rossia,b,
A. Santocchiaa,b, D. Spigaa, T. Tedeschia,b INFN Sezione di Pisa a, Università
di Pisa b, Scuola Normale Superiore di Pisa c, Pisa Italy, Università di Siena
d, Siena, Italy
P. Azzurria, G. Bagliesia, V. Bertacchia,c, L. Bianchinia, T. Boccalia, E.
Bossini, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, M.R. Di Domenicoa,d, S.
Donatoa, A. Giassia, M.T. Grippoa, F. Ligabuea,c, E. Mancaa,c, G. Mandorlia,c,
A. Messineoa,b, F. Pallaa, G. Ramirez-Sancheza,c, A. Rizzia,b, G. Rolandia,c,
S. Roy Chowdhurya,c, A. Scribanoa, N. Shafieia,b, P. Spagnoloa, R. Tenchinia,
G. Tonellia,b, N. Turinia,d, A. Venturia, P.G. Verdinia INFN Sezione di Roma
a, Sapienza Università di Roma b, Rome, Italy
F. Cavallaria, M. Cipriania,b, D. Del Rea,b, E. Di Marcoa, M. Diemoza, E.
Longoa,b, P. Meridiania, G. Organtinia,b, F. Pandolfia, R. Paramattia,b, C.
Quarantaa,b, S. Rahatloua,b, C. Rovellia, F. Santanastasioa,b, L. Soffia,b, R.
Tramontanoa,b INFN Sezione di Torino a, Università di Torino b, Torino, Italy,
Università del Piemonte Orientale c, Novara, Italy
N. Amapanea,b, R. Arcidiaconoa,c, S. Argiroa,b, M. Arneodoa,c, N. Bartosika,
R. Bellana,b, A. Belloraa,b, J. Berenguer Antequeraa,b, C. Biinoa, A.
Cappatia,b, N. Cartigliaa, S. Comettia, M. Costaa,b, R. Covarellia,b, N.
Demariaa, B. Kiania,b, F. Leggera, C. Mariottia, S. Masellia, E. Migliorea,b,
V. Monacoa,b, E. Monteila,b, M. Montenoa, M.M. Obertinoa,b, G. Ortonaa, L.
Pachera,b, N. Pastronea, M. Pelliccionia, G.L. Pinna Angionia,b, M. Ruspaa,c,
R. Salvaticoa,b, K. Shchelinaa,b, F. Sivieroa,b, V. Solaa, A. Solanoa,b, D.
Soldia,b, A. Staianoa, M. Tornagoa,b, D. Trocinoa,b INFN Sezione di Trieste a,
Università di Trieste b, Trieste, Italy
S. Belfortea, V. Candelisea,b, M. Casarsaa, F. Cossuttia, A. Da Rolda,b, G.
Della Riccaa,b, F. Vazzolera,b Kyungpook National University, Daegu, Korea
S. Dogra, C. Huh, B. Kim, D.H. Kim, G.N. Kim, J. Lee, S.W. Lee, C.S. Moon,
Y.D. Oh, S.I. Pak, B.C. Radburn-Smith, S. Sekmen, Y.C. Yang Chonnam National
University, Institute for Universe and Elementary Particles, Kwangju, Korea
H. Kim, D.H. Moon Hanyang University, Seoul, Korea
T.J. Kim, J. Park Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, B. Hong, K. Lee, K.S. Lee, J. Lim, J. Park, S.K. Park,
J. Yoo Kyung Hee University, Department of Physics, Seoul, Republic of Korea
J. Goh, A. Gurtu Sejong University, Seoul, Korea
H.S. Kim, Y. Kim Seoul National University, Seoul, Korea
J. Almond, J.H. Bhyun, J. Choi, S. Jeon, J. Kim, J.S. Kim, S. Ko, H. Kwon, H.
Lee, S. Lee, B.H. Oh, M. Oh, S.B. Oh, H. Seo, U.K. Yang, I. Yoon University of
Seoul, Seoul, Korea
D. Jeon, J.H. Kim, B. Ko, J.S.H. Lee, I.C. Park, Y. Roh, D. Song, I.J. Watson
Yonsei University, Department of Physics, Seoul, Korea
S. Ha, H.D. Yoo Sungkyunkwan University, Suwon, Korea
Y. Choi, Y. Jeong, H. Lee, Y. Lee, I. Yu College of Engineering and
Technology, American University of the Middle East (AUM), Egaila, Kuwait
T. Beyrouthy, Y. Maghrbi Riga Technical University, Riga, Latvia
V. Veckalns45 Vilnius University, Vilnius, Lithuania
M. Ambrozas, A. Juodagalvis, A. Rinkevicius, G. Tamulaitis, A. Vaitkevicius
National Centre for Particle Physics, Universiti Malaya, Kuala Lumpur,
Malaysia
W.A.T. Wan Abdullah, M.N. Yusli, Z. Zolkapli Universidad de Sonora (UNISON),
Hermosillo, Mexico
J.F. Benitez, A. Castaneda Hernandez, J.A. Murillo Quijada, L. Valencia Palomo
Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico
G. Ayala, H. Castilla-Valdez, E. De La Cruz-Burelo, I. Heredia-De La Cruz46,
R. Lopez-Fernandez, C.A. Mondragon Herrera, D.A. Perez Navarro, A. Sanchez-
Hernandez Universidad Iberoamericana, Mexico City, Mexico
S. Carrillo Moreno, C. Oropeza Barrera, M. Ramirez-Garcia, F. Vazquez Valencia
Benemerita Universidad Autonoma de Puebla, Puebla, Mexico
I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada University of Montenegro,
Podgorica, Montenegro
J. Mijuskovic47, N. Raicevic University of Auckland, Auckland, New Zealand
D. Krofcheck University of Canterbury, Christchurch, New Zealand
S. Bheesette, P.H. Butler National Centre for Physics, Quaid-I-Azam
University, Islamabad, Pakistan
A. Ahmad, M.I. Asghar, A. Awais, M.I.M. Awan, H.R. Hoorani, W.A. Khan, M.A.
Shah, M. Shoaib, M. Waqas AGH University of Science and Technology Faculty of
Computer Science, Electronics and Telecommunications, Krakow, Poland
V. Avati, L. Grzanka, M. Malawski National Centre for Nuclear Research,
Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, T. Frueboes, M. Górski, M. Kazana, M.
Szleper, P. Traczyk, P. Zalewski Institute of Experimental Physics, Faculty of
Physics, University of Warsaw, Warsaw, Poland
K. Bunkowski, K. Doroba, A. Kalinowski, M. Konecki, J. Krolikowski, M. Walczak
Laboratório de Instrumentação e Física Experimental de Partículas, Lisboa,
Portugal
M. Araujo, P. Bargassa, D. Bastos, A. Boletti, P. Faccioli, M. Gallinaro, J.
Hollar, N. Leonardo, T. Niknejad, J. Seixas, O. Toldaiev, J. Varela Joint
Institute for Nuclear Research, Dubna, Russia
S. Afanasiev, D. Budkouski, P. Bunin, M. Gavrilenko, I. Golutvin, I. Gorbunov,
A. Kamenev, V. Karjavine, A. Lanev, A. Malakhov, V. Matveev48,49, V. Palichik,
V. Perelygin, M. Savina, D. Seitova, V. Shalaev, S. Shmatov, S. Shulha, V.
Smirnov, O. Teryaev, N. Voytishin, A. Zarubin, I. Zhizhin Petersburg Nuclear
Physics Institute, Gatchina (St. Petersburg), Russia
G. Gavrilov, V. Golovtcov, Y. Ivanov, V. Kim50, E. Kuznetsova51, V. Murzin, V.
Oreshkin, I. Smirnov, D. Sosnov, V. Sulimov, L. Uvarov, S. Volkov, A. Vorobyev
Institute for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov,
N. Krasnikov, A. Pashenkov, G. Pivovarov, D.
Tlisov${}^{\textrm{\textdagger}}$, A. Toropin Institute for Theoretical and
Experimental Physics named by A.I. Alikhanov of NRC ‘Kurchatov Institute’,
Moscow, Russia
V. Epshteyn, V. Gavrilov, N. Lychkovskaya, A. Nikitenko52, V. Popov, G.
Safronov, A. Spiridonov, A. Stepennov, M. Toms, E. Vlasov, A. Zhokin Moscow
Institute of Physics and Technology, Moscow, Russia
T. Aushev National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
R. Chistov53, M. Danilov54, A. Oskin, P. Parygin, S. Polikarpov53 P.N. Lebedev
Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin, I. Dremin, M. Kirakosyan, A. Terkulov Skobeltsyn
Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow,
Russia
A. Belyaev, E. Boos, M. Dubinin55, L. Dudko, A. Ershov, A. Gribushin, V.
Klyukhin, O. Kodolova, I. Lokhtin, S. Obraztsov, S. Petrushanko, V. Savrin, A.
Snigirev Novosibirsk State University (NSU), Novosibirsk, Russia
V. Blinov56, T. Dimova56, L. Kardapoltsev56, I. Ovtin56, Y. Skovpen56
Institute for High Energy Physics of National Research Centre ‘Kurchatov
Institute’, Protvino, Russia
I. Azhgirey, I. Bayshev, V. Kachanov, A. Kalinin, D. Konstantinov, V. Petrov,
R. Ryutin, A. Sobol, S. Troshin, N. Tyurin, A. Uzunian, A. Volkov National
Research Tomsk Polytechnic University, Tomsk, Russia
A. Babaev, V. Okhotnikov, L. Sukhikh Tomsk State University, Tomsk, Russia
V. Borchsh, V. Ivanchenko, E. Tcherniaev University of Belgrade: Faculty of
Physics and VINCA Institute of Nuclear Sciences, Belgrade, Serbia
P. Adzic57, M. Dordevic, P. Milenovic, J. Milosevic, V. Milosevic Centro de
Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT), Madrid,
Spain
M. Aguilar-Benitez, J. Alcaraz Maestre, A. Álvarez Fernández, I. Bachiller, M.
Barrio Luna, Cristina F. Bedoya, C.A. Carrillo Montoya, M. Cepeda, M. Cerrada,
N. Colino, B. De La Cruz, A. Delgado Peris, J.P. Fernández Ramos, J. Flix,
M.C. Fouz, O. Gonzalez Lopez, S. Goy Lopez, J.M. Hernandez, M.I. Josa, J. León
Holgado, D. Moran, Á. Navarro Tobar, A. Pérez-Calero Yzquierdo, J. Puerta
Pelayo, I. Redondo, L. Romero, S. Sánchez Navas, M.S. Soares, L. Urda Gómez,
C. Willmott Universidad Autónoma de Madrid, Madrid, Spain
J.F. de Trocóniz, R. Reyes-Almanza Universidad de Oviedo, Instituto
Universitario de Ciencias y Tecnologías Espaciales de Asturias (ICTEA),
Oviedo, Spain
B. Alvarez Gonzalez, J. Cuevas, C. Erice, J. Fernandez Menendez, S. Folgueras,
I. Gonzalez Caballero, E. Palencia Cortezon, C. Ramón Álvarez, J. Ripoll Sau,
V. Rodríguez Bouza, A. Trapote Instituto de Física de Cantabria (IFCA), CSIC-
Universidad de Cantabria, Santander, Spain
J.A. Brochero Cifuentes, I.J. Cabrillo, A. Calderon, B. Chazin Quero, J.
Duarte Campderros, M. Fernandez, C. Fernandez Madrazo, P.J. Fernández Manteca,
A. García Alonso, G. Gomez, C. Martinez Rivero, P. Martinez Ruiz del Arbol, F.
Matorras, J. Piedra Gomez, C. Prieels, F. Ricci-Tam, T. Rodrigo, A. Ruiz-
Jimeno, L. Scodellaro, N. Trevisani, I. Vila, J.M. Vizan Garcia University of
Colombo, Colombo, Sri Lanka
MK Jayananda, B. Kailasapathy58, D.U.J. Sonnadara, DDC Wickramarathna
University of Ruhuna, Department of Physics, Matara, Sri Lanka
W.G.D. Dharmaratna, K. Liyanage, N. Perera, N. Wickramage CERN, European
Organization for Nuclear Research, Geneva, Switzerland
T.K. Aarrestad, D. Abbaneo, J. Alimena, E. Auffray, G. Auzinger, J. Baechler,
P. Baillon${}^{\textrm{\textdagger}}$, A.H. Ball, D. Barney, J. Bendavid, N.
Beni, M. Bianco, A. Bocci, E. Brondolin, T. Camporesi, M. Capeans Garrido, G.
Cerminara, S.S. Chhibra, L. Cristella, D. d’Enterria, A. Dabrowski, N. Daci,
A. David, A. De Roeck, M. Deile, R. Di Maria, M. Dobson, M. Dünser, N. Dupont,
A. Elliott-Peisert, N. Emriskova, F. Fallavollita59, D. Fasanella, S.
Fiorendi, A. Florent, G. Franzoni, J. Fulcher, W. Funk, S. Giani, D. Gigi, K.
Gill, F. Glege, L. Gouskos, M. Haranko, J. Hegeman, Y. Iiyama, V. Innocente,
T. James, P. Janot, J. Kaspar, J. Kieseler, M. Komm, N. Kratochwil, C. Lange,
S. Laurila, P. Lecoq, K. Long, C. Lourenço, L. Malgeri, S. Mallios, M.
Mannelli, F. Meijers, S. Mersi, E. Meschi, F. Moortgat, M. Mulders, S.
Orfanelli, L. Orsini, F. Pantaleo, L. Pape, E. Perez, M. Peruzzi, A. Petrilli,
G. Petrucciani, A. Pfeiffer, M. Pierini, M. Pitt, H. Qu, T. Quast, D. Rabady,
A. Racz, M. Rieger, M. Rovere, H. Sakulin, J. Salfeld-Nebgen, S. Scarfi, C.
Schäfer, C. Schwick, M. Selvaggi, A. Sharma, P. Silva, W. Snoeys, P.
Sphicas60, S. Summers, V.R. Tavolaro, D. Treille, A. Tsirou, G.P. Van Onsem,
M. Verzetti, K.A. Wozniak, W.D. Zeuner Paul Scherrer Institut, Villigen,
Switzerland
L. Caminada61, A. Ebrahimi, W. Erdmann, R. Horisberger, Q. Ingram, H.C.
Kaestli, D. Kotlinski, U. Langenegger, M. Missiroli, T. Rohe ETH Zurich -
Institute for Particle Physics and Astrophysics (IPA), Zurich, Switzerland
K. Androsov62, M. Backhaus, P. Berger, A. Calandri, N. Chernyavskaya, A. De
Cosa, G. Dissertori, M. Dittmar, M. Donegà, C. Dorfer, T. Gadek, T.A. Gómez
Espinosa, C. Grab, D. Hits, W. Lustermann, A.-M. Lyon, R.A. Manzoni, C. Martin
Perez, M.T. Meinhard, F. Micheli, F. Nessi-Tedaldi, J. Niedziela, F. Pauss, V.
Perovic, G. Perrin, S. Pigazzini, M.G. Ratti, M. Reichmann, C. Reissel, T.
Reitenspiess, B. Ristic, D. Ruini, D.A. Sanz Becerra, M. Schönenberger, V.
Stampf, J. Steggemann62, R. Wallny, D.H. Zhu Universität Zürich, Zurich,
Switzerland
C. Amsler63, C. Botta, D. Brzhechko, M.F. Canelli, A. De Wit, R. Del Burgo,
J.K. Heikkilä, M. Huwiler, A. Jofrehei, B. Kilminster, S. Leontsinis, A.
Macchiolo, P. Meiring, V.M. Mikuni, U. Molinatti, I. Neutelings, G. Rauco, A.
Reimers, P. Robmann, S. Sanchez Cruz, K. Schweiger, Y. Takahashi National
Central University, Chung-Li, Taiwan
C. Adloff64, C.M. Kuo, W. Lin, A. Roy, T. Sarkar36, S.S. Yu National Taiwan
University (NTU), Taipei, Taiwan
L. Ceard, P. Chang, Y. Chao, K.F. Chen, P.H. Chen, W.-S. Hou, Y.y. Li, R.-S.
Lu, E. Paganis, A. Psallidas, A. Steen, E. Yazgan, P.r. Yu Chulalongkorn
University, Faculty of Science, Department of Physics, Bangkok, Thailand
B. Asavapibhop, C. Asawatangtrakuldee, N. Srimanobhas Çukurova University,
Physics Department, Science and Art Faculty, Adana, Turkey
M.N. Bakirci65, F. Boran, S. Damarseckin66, Z.S. Demiroglu, F. Dolek, I.
Dumanoglu67, G. Gokbulut, Y. Guler, E. Gurpinar Guler68, I. Hos69, C. Isik,
E.E. Kangal70, O. Kara, A. Kayis Topaksu, U. Kiminsu, G. Onengut, K.
Ozdemir71, A.E. Simsek, B. Tali72, U.G. Tok, H. Topakli73, S. Turkcapar, I.S.
Zorbakir, C. Zorbilmez Middle East Technical University, Physics Department,
Ankara, Turkey
B. Isildak74, G. Karapinar75, K. Ocalan76, M. Yalvac77 Bogazici University,
Istanbul, Turkey
B. Akgun, I.O. Atakisi, E. Gülmez, M. Kaya78, O. Kaya79, Ö. Özçelik, S.
Tekten80, E.A. Yetkin81 Istanbul Technical University, Istanbul, Turkey
A. Cakir, K. Cankocak67, Y. Komurcu, S. Sen82 Istanbul University, Istanbul,
Turkey
F. Aydogmus Sen, S. Cerci72, B. Kaynak, S. Ozkorucuklu, D. Sunar Cerci72
Institute for Scintillation Materials of National Academy of Science of
Ukraine, Kharkov, Ukraine
B. Grynyov National Scientific Center, Kharkov Institute of Physics and
Technology, Kharkov, Ukraine
L. Levchuk University of Bristol, Bristol, United Kingdom
E. Bhal, S. Bologna, J.J. Brooke, A. Bundock, E. Clement, D. Cussans, H.
Flacher, J. Goldstein, G.P. Heath, H.F. Heath, L. Kreczko, B. Krikler, S.
Paramesvaran, T. Sakuma, S. Seif El Nasr-Storey, V.J. Smith, N. Stylianou83,
J. Taylor, A. Titterton Rutherford Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev84, C. Brew, R.M. Brown, D.J.A. Cockerill, K.V. Ellis, K.
Harder, S. Harper, J. Linacre, K. Manolopoulos, D.M. Newbold, E. Olaiya, D.
Petyt, T. Reis, T. Schuh, C.H. Shepherd-Themistocleous, A. Thea, I.R. Tomalin,
T. Williams Imperial College, London, United Kingdom
R. Bainbridge, P. Bloch, S. Bonomally, J. Borg, S. Breeze, O. Buchmuller, V.
Cepaitis, G.S. Chahal85, D. Colling, P. Dauncey, G. Davies, M. Della Negra, S.
Fayer, G. Fedi, G. Hall, M.H. Hassanshahi, G. Iles, J. Langford, L. Lyons,
A.-M. Magnan, S. Malik, A. Martelli, J. Nash86, V. Palladino, M. Pesaresi,
D.M. Raymond, A. Richards, A. Rose, E. Scott, C. Seez, A. Shtipliyski, A.
Tapper, K. Uchida, T. Virdee19, N. Wardle, S.N. Webb, D. Winterbottom, A.G.
Zecchinelli Brunel University, Uxbridge, United Kingdom
J.E. Cole, A. Khan, P. Kyberd, C.K. Mackay, I.D. Reid, L. Teodorescu, S. Zahid
Baylor University, Waco, USA
S. Abdullin, A. Brinkerhoff, B. Caraway, J. Dittmann, K. Hatakeyama, A.R.
Kanuganti, B. McMaster, N. Pastika, S. Sawant, C. Smith, C. Sutantawibul, J.
Wilson Catholic University of America, Washington, DC, USA
R. Bartek, A. Dominguez, R. Uniyal, A.M. Vargas Hernandez The University of
Alabama, Tuscaloosa, USA
A. Buccilli, O. Charaf, S.I. Cooper, D. Di Croce, S.V. Gleyzer, C. Henderson,
C.U. Perez, P. Rumerio, C. West Boston University, Boston, USA
A. Akpinar, A. Albert, D. Arcaro, C. Cosby, Z. Demiragli, D. Gastler, J.
Rohlf, K. Salyer, D. Sperka, D. Spitzbart, I. Suarez, S. Yuan, D. Zou Brown
University, Providence, USA
G. Benelli, B. Burkle, X. Coubez20, D. Cutts, Y.t. Duh, M. Hadley, U. Heintz,
J.M. Hogan87, K.H.M. Kwok, E. Laird, G. Landsberg, K.T. Lau, J. Lee, J. Luo,
M. Narain, S. Sagir88, E. Usai, W.Y. Wong, X. Yan, D. Yu, W. Zhang University
of California, Davis, Davis, USA
C. Brainerd, R. Breedon, M. Calderon De La Barca Sanchez, M. Chertok, J.
Conway, P.T. Cox, R. Erbacher, F. Jensen, O. Kukral, R. Lander, M. Mulhearn,
D. Pellett, D. Taylor, M. Tripathi, Y. Yao, F. Zhang University of California,
Los Angeles, USA
M. Bachtis, R. Cousins, A. Dasgupta, A. Datta, D. Hamilton, J. Hauser, M.
Ignatenko, M.A. Iqbal, T. Lam, N. Mccoll, W.A. Nash, S. Regnard, D. Saltzberg,
C. Schnaible, B. Stone, V. Valuev University of California, Riverside,
Riverside, USA
K. Burt, Y. Chen, R. Clare, J.W. Gary, G. Hanson, G. Karapostoli, O.R. Long,
N. Manganelli, M. Olmedo Negrete, W. Si, S. Wimpenny, Y. Zhang University of
California, San Diego, La Jolla, USA
J.G. Branson, P. Chang, S. Cittolin, S. Cooperstein, N. Deelen, J. Duarte, R.
Gerosa, L. Giannini, D. Gilbert, J. Guiang, R. Kansal, V. Krutelyov, R. Lee,
J. Letts, M. Masciovecchio, S. May, S. Padhi, M. Pieri, B.V. Sathia Narayanan,
V. Sharma, M. Tadel, A. Vartak, F. Würthwein, Y. Xiang, A. Yagil University of
California, Santa Barbara - Department of Physics, Santa Barbara, USA
N. Amin, C. Campagnari, M. Citron, A. Dorsett, V. Dutta, J. Incandela, M.
Kilpatrick, B. Marsh, H. Mei, A. Ovcharova, M. Quinnan, J. Richman, U. Sarica,
D. Stuart, S. Wang California Institute of Technology, Pasadena, USA
A. Bornheim, O. Cerri, I. Dutta, J.M. Lawhorn, N. Lu, J. Mao, H.B. Newman, J.
Ngadiuba, T.Q. Nguyen, M. Spiropulu, J.R. Vlimant, C. Wang, S. Xie, Z. Zhang,
R.Y. Zhu Carnegie Mellon University, Pittsburgh, USA
J. Alison, M.B. Andrews, T. Ferguson, T. Mudholkar, M. Paulini, I. Vorobiev
University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, E. MacDonald, R. Patel, A. Perloff, K. Stenson, K.A.
Ulmer, S.R. Wagner Cornell University, Ithaca, USA
J. Alexander, Y. Cheng, J. Chu, D.J. Cranshaw, K. Mcdermott, J. Monroy, J.R.
Patterson, D. Quach, A. Ryd, W. Sun, S.M. Tan, Z. Tao, J. Thom, P. Wittich, M.
Zientek Fermi National Accelerator Laboratory, Batavia, USA
M. Albrow, M. Alyari, G. Apollinari, A. Apresyan, A. Apyan, S. Banerjee,
L.A.T. Bauerdick, A. Beretvas, D. Berry, J. Berryhill, P.C. Bhat, K. Burkett,
J.N. Butler, A. Canepa, G.B. Cerati, H.W.K. Cheung, F. Chlebana, M. Cremonesi,
K.F. Di Petrillo, V.D. Elvira, J. Freeman, Z. Gecse, L. Gray, D. Green, S.
Grünendahl, O. Gutsche, R.M. Harris, R. Heller, T.C. Herwig, J. Hirschauer, B.
Jayatilaka, S. Jindariani, M. Johnson, U. Joshi, P. Klabbers, T. Klijnsma, B.
Klima, M.J. Kortelainen, S. Lammel, D. Lincoln, R. Lipton, T. Liu, J. Lykken,
C. Madrid, K. Maeshima, C. Mantilla, D. Mason, P. McBride, P. Merkel, S.
Mrenna, S. Nahn, V. O’Dell, V. Papadimitriou, K. Pedro, C. Pena55, O.
Prokofyev, F. Ravera, A. Reinsvold Hall, L. Ristori, B. Schneider, E. Sexton-
Kennedy, N. Smith, A. Soha, L. Spiegel, S. Stoynev, J. Strait, L. Taylor, S.
Tkaczyk, N.V. Tran, L. Uplegger, E.W. Vaandering, H.A. Weber, A. Woodard
University of Florida, Gainesville, USA
D. Acosta, P. Avery, D. Bourilkov, L. Cadamuro, V. Cherepanov, F. Errico, R.D.
Field, D. Guerrero, B.M. Joshi, M. Kim, J. Konigsberg, A. Korytov, K.H. Lo, K.
Matchev, N. Menendez, G. Mitselmakher, D. Rosenzweig, K. Shi, J. Sturdy, J.
Wang, E. Yigitbasi, X. Zuo Florida State University, Tallahassee, USA
T. Adams, A. Askew, D. Diaz, R. Habibullah, S. Hagopian, V. Hagopian, K.F.
Johnson, R. Khurana, T. Kolberg, G. Martinez, H. Prosper, C. Schiber, R.
Yohay, J. Zhang Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, S. Butalla, T. Elkafrawy13, M. Hohlmann, R. Kumar Verma, D.
Noonan, M. Rahmani, M. Saunders, F. Yumiceva University of Illinois at Chicago
(UIC), Chicago, USA
M.R. Adams, L. Apanasevich, H. Becerril Gonzalez, R. Cavanaugh, X. Chen, S.
Dittmer, O. Evdokimov, C.E. Gerber, D.A. Hangal, D.J. Hofman, C. Mills, G. Oh,
T. Roy, M.B. Tonjes, N. Varelas, J. Viinikainen, X. Wang, Z. Wu, Z. Ye The
University of Iowa, Iowa City, USA
M. Alhusseini, K. Dilsiz89, S. Durgut, R.P. Gandrajula, M. Haytmyradov, V.
Khristenko, O.K. Köseyan, J.-P. Merlo, A. Mestvirishvili90, A. Moeller, J.
Nachtman, H. Ogul91, Y. Onel, F. Ozok92, A. Penzo, C. Snyder, E. Tiras93, J.
Wetzel Johns Hopkins University, Baltimore, USA
O. Amram, B. Blumenfeld, L. Corcodilos, M. Eminizer, A.V. Gritsan, S.
Kyriacou, P. Maksimovic, J. Roskes, M. Swartz, T.Á. Vámi The University of
Kansas, Lawrence, USA
C. Baldenegro Barrera, P. Baringer, A. Bean, A. Bylinkin, T. Isidori, S.
Khalil, J. King, G. Krintiras, A. Kropivnitskaya, C. Lindsey, N. Minafra, M.
Murray, C. Rogan, C. Royon, S. Sanders, E. Schmitz, J.D. Tapia Takaki, Q.
Wang, J. Williams, G. Wilson Kansas State University, Manhattan, USA
S. Duric, A. Ivanov, K. Kaadze, D. Kim, Y. Maravin, T. Mitchell, A. Modak, K.
Nam Lawrence Livermore National Laboratory, Livermore, USA
F. Rebassoo, D. Wright University of Maryland, College Park, USA
E. Adams, A. Baden, O. Baron, A. Belloni, S.C. Eno, Y. Feng, N.J. Hadley, S.
Jabeen, R.G. Kellogg, T. Koeth, A.C. Mignerey, S. Nabili, M. Seidel, A. Skuja,
S.C. Tonwar, L. Wang, K. Wong Massachusetts Institute of Technology,
Cambridge, USA
D. Abercrombie, G. Andreassi, R. Bi, S. Brandt, W. Busza, I.A. Cali, Y. Chen,
M. D’Alfonso, G. Gomez Ceballos, M. Goncharov, P. Harris, M. Hu, M. Klute, D.
Kovalskyi, J. Krupa, Y.-J. Lee, B. Maier, A.C. Marini, C. Mironov, C. Paus, D.
Rankin, C. Roland, G. Roland, Z. Shi, G.S.F. Stephans, K. Tatar, J. Wang, Z.
Wang, B. Wyslouch University of Minnesota, Minneapolis, USA
R.M. Chatterjee, A. Evans, P. Hansen, J. Hiltbrand, Sh. Jain, M. Krohn, Y.
Kubota, Z. Lesko, J. Mans, M. Revering, R. Rusack, R. Saradhy, N. Schroeder,
N. Strobbe, M.A. Wadud University of Mississippi, Oxford, USA
J.G. Acosta, S. Oliveros University of Nebraska-Lincoln, Lincoln, USA
K. Bloom, M. Bryson, S. Chauhan, D.R. Claes, C. Fangmeier, L. Finco, F. Golf,
J.R. González Fernández, C. Joo, I. Kravchenko, J.E. Siado, G.R.
Snow${}^{\textrm{\textdagger}}$, W. Tabb, F. Yan State University of New York
at Buffalo, Buffalo, USA
G. Agarwal, H. Bandyopadhyay, L. Hay, I. Iashvili, A. Kharchilava, C. McLean,
D. Nguyen, J. Pekkanen, S. Rappoccio, A. Williams Northeastern University,
Boston, USA
G. Alverson, E. Barberis, C. Freer, Y. Haddad, A. Hortiangtham, J. Li, G.
Madigan, B. Marzocchi, D.M. Morse, V. Nguyen, T. Orimoto, A. Parker, L.
Skinnari, A. Tishelman-Charny, T. Wamorkar, B. Wang, A. Wisecarver, D. Wood
Northwestern University, Evanston, USA
S. Bhattacharya, J. Bueghly, Z. Chen, A. Gilbert, T. Gunter, K.A. Hahn, N.
Odell, M.H. Schmitt, K. Sung, M. Velasco University of Notre Dame, Notre Dame,
USA
R. Band, R. Bucci, N. Dev, R. Goldouzian, M. Hildreth, K. Hurtado Anampa, C.
Jessop, K. Lannon, N. Loukas, N. Marinelli, I. Mcalister, F. Meng, K. Mohrman,
Y. Musienko48, R. Ruchti, P. Siddireddy, M. Wayne, A. Wightman, M. Wolf, M.
Zarucki, L. Zygala The Ohio State University, Columbus, USA
B. Bylsma, B. Cardwell, L.S. Durkin, B. Francis, C. Hill, A. Lefeld, B.L.
Winer, B.R. Yates Princeton University, Princeton, USA
F.M. Addesa, B. Bonham, P. Das, G. Dezoort, P. Elmer, A. Frankenthal, B.
Greenberg, N. Haubrich, S. Higginbotham, A. Kalogeropoulos, G. Kopp, S. Kwan,
D. Lange, M.T. Lucchini, D. Marlow, K. Mei, I. Ojalvo, J. Olsen, C. Palmer, D.
Stickland, C. Tully University of Puerto Rico, Mayaguez, USA
S. Malik, S. Norberg Purdue University, West Lafayette, USA
A.S. Bakshi, V.E. Barnes, R. Chawla, S. Das, L. Gutay, M. Jones, A.W. Jung, S.
Karmarkar, M. Liu, G. Negro, N. Neumeister, G. Paspalaki, C.C. Peng, S.
Piperov, A. Purohit, J.F. Schulte, M. Stojanovic16, J. Thieman, F. Wang, R.
Xiao, W. Xie Purdue University Northwest, Hammond, USA
J. Dolen, N. Parashar Rice University, Houston, USA
A. Baty, S. Dildick, K.M. Ecklund, S. Freed, F.J.M. Geurts, A. Kumar, W. Li,
B.P. Padley, R. Redjimi, J. Roberts${}^{\textrm{\textdagger}}$, W. Shi, A.G.
Stahl Leiton University of Rochester, Rochester, USA
A. Bodek, P. de Barbaro, R. Demina, J.L. Dulemba, C. Fallon, T. Ferbel, M.
Galanti, A. Garcia-Bellido, O. Hindrichs, A. Khukhunaishvili, E. Ranken, R.
Taus Rutgers, The State University of New Jersey, Piscataway, USA
B. Chiarito, J.P. Chou, A. Gandrakota, Y. Gershtein, E. Halkiadakis, A. Hart,
M. Heindl, E. Hughes, S. Kaplan, O. Karacheban23, I. Laflotte, A. Lath, R.
Montalvo, K. Nash, M. Osherson, S. Salur, S. Schnetzer, S. Somalwar, R. Stone,
S.A. Thayil, S. Thomas, H. Wang University of Tennessee, Knoxville, USA
H. Acharya, A.G. Delannoy, S. Spanier Texas A&M University, College Station,
USA
O. Bouhali94, M. Dalchenko, A. Delgado, R. Eusebi, J. Gilmore, T. Huang, T.
Kamon95, H. Kim, S. Luo, S. Malhotra, R. Mueller, D. Overton, D. Rathjens, A.
Safonov Texas Tech University, Lubbock, USA
N. Akchurin, J. Damgov, V. Hegde, S. Kunori, K. Lamichhane, S.W. Lee, T.
Mengke, S. Muthumuni, T. Peltola, S. Undleeb, I. Volobouev, Z. Wang, A.
Whitbeck Vanderbilt University, Nashville, USA
E. Appelt, S. Greene, A. Gurrola, W. Johns, C. Maguire, A. Melo, H. Ni, K.
Padeken, F. Romeo, P. Sheldon, S. Tuo, J. Velkovska University of Virginia,
Charlottesville, USA
M.W. Arenton, B. Cox, G. Cummings, J. Hakala, R. Hirosky, M. Joyce, A.
Ledovskoy, A. Li, C. Neu, B. Tannenwald, E. Wolfe Wayne State University,
Detroit, USA
P.E. Karchin, N. Poudyal, P. Thapa University of Wisconsin - Madison, Madison,
WI, USA
K. Black, T. Bose, J. Buchanan, C. Caillol, S. Dasu, I. De Bruyn, P.
Everaerts, F. Fienga, C. Galloni, H. He, M. Herndon, A. Hervé, U. Hussain, A.
Lanaro, A. Loeliger, R. Loveless, J. Madhusudanan Sreekala, A. Mallampalli, A.
Mohammadi, D. Pinna, A. Savin, V. Shang, V. Sharma, W.H. Smith, D. Teague, S.
Trembath-reichert, W. Vetens †: Deceased
1: Also at Vienna University of Technology, Vienna, Austria
2: Also at Institute of Basic and Applied Sciences, Faculty of Engineering,
Arab Academy for Science, Technology and Maritime Transport, Alexandria,
Egypt, Alexandria, Egypt
3: Also at Université Libre de Bruxelles, Bruxelles, Belgium
4: Also at Universidade Estadual de Campinas, Campinas, Brazil
5: Also at Federal University of Rio Grande do Sul, Porto Alegre, Brazil
6: Also at University of Chinese Academy of Sciences, Beijing, China
7: Also at Department of Physics, Tsinghua University, Beijing, China,
Beijing, China
8: Also at UFMS, Nova Andradina, Brazil
9: Also at Nanjing Normal University Department of Physics, Nanjing, China
10: Now at The University of Iowa, Iowa City, USA
11: Also at Institute for Theoretical and Experimental Physics named by A.I.
Alikhanov of NRC ‘Kurchatov Institute’, Moscow, Russia
12: Also at Joint Institute for Nuclear Research, Dubna, Russia
13: Also at Ain Shams University, Cairo, Egypt
14: Also at Zewail City of Science and Technology, Zewail, Egypt
15: Also at British University in Egypt, Cairo, Egypt
16: Also at Purdue University, West Lafayette, USA
17: Also at Université de Haute Alsace, Mulhouse, France
18: Also at Erzincan Binali Yildirim University, Erzincan, Turkey
19: Also at CERN, European Organization for Nuclear Research, Geneva,
Switzerland
20: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
21: Also at University of Hamburg, Hamburg, Germany
22: Also at Department of Physics, Isfahan University of Technology, Isfahan,
Iran, Isfahan, Iran
23: Also at Brandenburg University of Technology, Cottbus, Germany
24: Also at Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State
University, Moscow, Russia
25: Also at Physics Department, Faculty of Science, Assiut University, Assiut,
Egypt
26: Also at Eszterhazy Karoly University, Karoly Robert Campus, Gyongyos,
Hungary
27: Also at Institute of Physics, University of Debrecen, Debrecen, Hungary,
Debrecen, Hungary
28: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary
29: Also at MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary, Budapest, Hungary
30: Also at Wigner Research Centre for Physics, Budapest, Hungary
31: Also at IIT Bhubaneswar, Bhubaneswar, India, Bhubaneswar, India
32: Also at Institute of Physics, Bhubaneswar, India
33: Also at G.H.G. Khalsa College, Punjab, India
34: Also at Shoolini University, Solan, India
35: Also at University of Hyderabad, Hyderabad, India
36: Also at University of Visva-Bharati, Santiniketan, India
37: Also at Indian Institute of Technology (IIT), Mumbai, India
38: Also at Deutsches Elektronen-Synchrotron, Hamburg, Germany
39: Also at Sharif University of Technology, Tehran, Iran
40: Also at Department of Physics, University of Science and Technology of
Mazandaran, Behshahr, Iran
41: Now at INFN Sezione di Bari a, Università di Bari b, Politecnico di Bari
c, Bari, Italy
42: Also at Italian National Agency for New Technologies, Energy and
Sustainable Economic Development, Bologna, Italy
43: Also at Centro Siciliano di Fisica Nucleare e di Struttura Della Materia,
Catania, Italy
44: Also at Università di Napoli ’Federico II’, NAPOLI, Italy
45: Also at Riga Technical University, Riga, Latvia, Riga, Latvia
46: Also at Consejo Nacional de Ciencia y Tecnología, Mexico City, Mexico
47: Also at IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
48: Also at Institute for Nuclear Research, Moscow, Russia
49: Now at National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
50: Also at St. Petersburg State Polytechnical University, St. Petersburg,
Russia
51: Also at University of Florida, Gainesville, USA
52: Also at Imperial College, London, United Kingdom
53: Also at P.N. Lebedev Physical Institute, Moscow, Russia
54: Also at Moscow Institute of Physics and Technology, Moscow, Russia,
Moscow, Russia
55: Also at California Institute of Technology, Pasadena, USA
56: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia
57: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia
58: Also at Trincomalee Campus, Eastern University, Sri Lanka, Nilaveli, Sri
Lanka
59: Also at INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy,
Pavia, Italy
60: Also at National and Kapodistrian University of Athens, Athens, Greece
61: Also at Universität Zürich, Zurich, Switzerland
62: Also at Ecole Polytechnique Fédérale Lausanne, Lausanne, Switzerland
63: Also at Stefan Meyer Institute for Subatomic Physics, Vienna, Austria,
Vienna, Austria
64: Also at Laboratoire d’Annecy-le-Vieux de Physique des Particules,
IN2P3-CNRS, Annecy-le-Vieux, France
65: Also at Gaziosmanpasa University, Tokat, Turkey
66: Also at Şırnak University, Sirnak, Turkey
67: Also at Near East University, Research Center of Experimental Health
Science, Nicosia, Turkey
68: Also at Konya Technical University, Konya, Turkey
69: Also at Istanbul University - Cerraphasa, Faculty of Engineering,
Istanbul, Turkey
70: Also at Mersin University, Mersin, Turkey
71: Also at Piri Reis University, Istanbul, Turkey
72: Also at Adiyaman University, Adiyaman, Turkey
73: Also at Tarsus University, MERSIN, Turkey
74: Also at Ozyegin University, Istanbul, Turkey
75: Also at Izmir Institute of Technology, Izmir, Turkey
76: Also at Necmettin Erbakan University, Konya, Turkey
77: Also at Bozok Universitetesi Rektörlügü, Yozgat, Turkey, Yozgat, Turkey
78: Also at Marmara University, Istanbul, Turkey
79: Also at Milli Savunma University, Istanbul, Turkey
80: Also at Kafkas University, Kars, Turkey
81: Also at Istanbul Bilgi University, Istanbul, Turkey
82: Also at Hacettepe University, Ankara, Turkey
83: Also at Vrije Universiteit Brussel, Brussel, Belgium
84: Also at School of Physics and Astronomy, University of Southampton,
Southampton, United Kingdom
85: Also at IPPP Durham University, Durham, United Kingdom
86: Also at Monash University, Faculty of Science, Clayton, Australia
87: Also at Bethel University, St. Paul, Minneapolis, USA, St. Paul, USA
88: Also at Karamanoğlu Mehmetbey University, Karaman, Turkey
89: Also at Bingol University, Bingol, Turkey
90: Also at Georgian Technical University, Tbilisi, Georgia
91: Also at Sinop University, Sinop, Turkey
92: Also at Mimar Sinan University, Istanbul, Istanbul, Turkey
93: Also at Erciyes University, KAYSERI, Turkey
94: Also at Texas A&M University at Qatar, Doha, Qatar
95: Also at Kyungpook National University, Daegu, Korea, Daegu, Korea