SUS-19-009

SUS-19-009

# Search for direct top squark pair production in events with one lepton,
jets, and missing transverse momentum at 13with the CMS experiment

###### Abstract

A search for direct top squark pair production is presented. The search is
based on proton-proton collision data at a center-of-mass energy of 13recorded
by the CMS experiment at the LHC during 2016, 2017, and 2018, corresponding to
an integrated luminosity of $137\fbinv$. The search is carried out using
events with a single isolated electron or muon, multiple jets, and large
transverse momentum imbalance. The observed data are consistent with the
expectations from standard model processes. Exclusions are set in the context
of simplified top squark pair production models. Depending on the model,
exclusion limits at 95% confidence level for top squark masses up to 1.2are
set for a massless lightest supersymmetric particle, assumed to be the
neutralino. For models with top squark masses of 1, neutralino masses up to
600are excluded.

##  0.1 Introduction

Supersymmetry (SUSY) [1, 2, 3, 4, 5, 6, 7, 8] is an attractive extension of
the standard model (SM), characterized by the presence of SUSY partners for
every SM particle. These partner particles have the same quantum numbers as
their SM counterparts, except for the spin, which differs by one-half unit. In
models with $R$-parity conservation [9], the lightest supersymmetric particle
(LSP) is stable, and, if neutral, could be a dark matter candidate [10]. The
extended particle spectrum in SUSY scenarios allows for the cancellation of
quadratic divergences arising from quantum corrections to the Higgs boson mass
[11, 12, 13, 14, 15]. Scenarios realizing this cancellation often contain top
squarks ($\PSQt$), SUSY partners of the SM top quark ($\PQt$), and higgsinos,
SUSY partners of the SM Higgs boson, with masses near the electroweak scale.
The pair production cross section is expected to be large compared to the
electroweak production of higgsinos at CERN LHC for $\PSQt$ masses near the
electroweak scale.

In this paper, a search is presented for top squark pair production in final
states with events from $\Pp{}\Pp{}$ collisions at $\sqrt{s}=13\TeV$,
collected between 2016 and 2018 by the CMS experiment, corresponding to an
integrated luminosity of $137\fbinv$. Two top squark decay modes are
considered: the decay to a top quark and the lightest neutralino ($\PSGczDo$),
which is taken to be the LSP, or the decay to a bottom quark () and the
lightest chargino ($\PSGcpmDo$). In the latter scenario, it is assumed that
the $\PSGcpmDo$ decays to a $\PW$ boson and the $\PSGczDo$. The mass of the
chargino is chosen to be $(m_{\PSQt}+m_{\PSGczDo})/2$. The corresponding
diagrams are given in Fig. 1. The common experimental signature for pair
production with these decay modes is $\PW\PW^{(*)}+\PQb\PQb+\PSGczDo\PSGczDo$.
The analysis is based on events where one of the bosons decays leptonically
and the other hadronically. This results in the event selection of one
isolated lepton, at least 2 jets, and large missing transverse momentum
($\ptmiss$) from the two neutralinos and the neutrino.

Figure 1:  Diagrams for top squark pair production, with each decaying either
to $\PQt\PSGczDo$ or to $\PQb\PSGcpmDo$. For the latter decay, the decays
further into a $\PW$ boson and a .

Dedicated searches for top squark pair production in 13proton-proton
($\Pp{}\Pp{}$) collision events have been carried out by both the ATLAS [16,
17, 18, 19, 20, 21, 22, 23, 24, 25] and CMS [26, 27, 28, 29, 30, 31, 32, 33,
34, 35, 36, 37, 38] Collaborations. The search presented here improves the
previous one [29] by adding the data collected in 2017 and 2018, resulting in
approximately a factor of four increase in the size of the data sample. In
addition, new search regions have been added, which are sensitive to scenarios
where the mass of the top squark is close to the sum of the masses of either
the and the top quark, or the and the $\PW$ boson. These scenarios are
referred to as compressed mass scenarios hereafter. In addition, a method has
been implemented to identify top quarks that decay hadronically, and also the
background estimation techniques have been improved. The paper is organized as
follows: Section 0.2 and 0.3 describe the CMS detector and the simulated
samples used in this analysis. The object reconstruction and search strategy
are presented in Section 0.4. The background prediction methods are described
in Section 0.5, and the relevant systematic uncertainties are discussed in
Section 0.6. Results and interpretations are detailed in Section 0.7, and a
summary is presented in Section 0.8.

##  0.2 The CMS detector

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

Events of interest are selected using a two-tier trigger system. The first
level, composed of custom hardware processors, uses information from the
calorimeters and muon detectors to select events in a fixed time interval of
less than 4. The second level, called the high-level trigger, further
decreases the event rate from around 100kHz to less than 1kHz before data
storage. A more detailed description of the CMS detector, together with a
definition of the coordinate system used and the relevant kinematic variables,
can be found in Refs. [39, 40]. The pixel tracker was upgraded before the
start of the data taking period in 2017, providing one additional layer of
measurements compared to the older tracker [41].

##  0.3 Simulated samples

Monte Carlo (MC) simulation is used to design the search, to aid in the
estimation of SM backgrounds, and to evaluate the sensitivity of the analysis
to top squark pair production. Samples of events of SM $\ttbar$,
$\PW+\text{jets}$, $\PZ+\text{jets}$, and $\Pgg+\text{jets}$ processes and
simplified SUSY top squark pair production models are generated at leading-
order (LO) in quantum chromodynamics (QCD) using the 2 (2.2.2 or 2.4.2)
generator [42]. The at next-to-LO (NLO) in QCD is used to generate samples of
$\ttbar\PZ$, $\PW\PZ$, and $\ttbar\PW$ events, while single top quark events
are generated at NLO in QCD using the 2.0 [43, 44, 45, 46] program. Samples of
$\PW+\text{jets}$, , and SUSY events are generated with four, three, and two
additional partons included in the matrix element calculations, respectively.

Since the data used for this search were collected in three distinct periods
(2016, 2017, and 2018), different detector MC simulations are used to reflect
the running conditions. In addition, in some cases, the generator settings are
also different as described below.

The NNPDF3.0 [47, 48] parton distribution functions (PDFs) are used to
generate all 2016 MC samples, while NNPDF3.1 [49] is used for 2017 and 2018
samples. The parton shower and hadronization are modeled with 8.2 (8.205 or
8.230) [50]. The MLM [51] and FxFx [52] prescriptions are employed to match
partons from the matrix element calculation to those from the parton showers,
for the LO and NLO samples, respectively.

The 2016 MC samples are generated with the CUETP8M1 [53] tune. For the later
running periods, the CP5 [54] tune was used for SM samples, and the SUSY
samples use LO PDFs, combined with tune CP2, in order to avoid large negative
weights that arise from PDF interpolations at very large energies. The
differences in jet kinematic properties between the SUSY and SM samples are
due to different tunes and are within 5% of each other. The [55] package is
used to simulate the response of the CMS detector for all SM processes, while
the CMS fast simulation program [56, 57] is used for SUSY samples.

Cross section calculations performed at next-to-NLO (NNLO) in QCD are used to
normalize the MC samples of $\PW+\text{jets}$ [58] and single top quark [59,
60] events. The $\ttbar$ samples are normalized to a cross section determined
at NNLO in QCD that includes the resummation of the next-to-next-to-leading
logarithmic (NNLL) soft-gluon terms [61, 62, 63, 64, 65, 66, 67]. Monte Carlo
samples of other SM background processes are normalized to cross sections
obtained from the MC event generators at either LO or NLO in QCD. The SUSY
cross sections are computed at approximately NNLO plus NNLL precision with all
other SUSY particles assumed to be heavy and decoupled [68, 69, 70, 71, 72,
73, 74].

To improve the modeling of the multiplicity of additional jets either from
initial-state radiation (ISR) or final-state radiation (FSR), simulated SM and
SUSY events are reweighted so as to make the jet multiplicity agree with data.
The reweighting is applied to all SUSY samples but only to 2016 SM samples. No
reweighting is applied for 2017 and 2018 SM simulation because of the improved
tuning of the MC generators mentioned above. The procedure is based on a
comparison of the light-flavor jet multiplicity in dilepton $\ttbar$ events in
data and simulation. The comparison is performed after selecting events with
two leptons and two -tagged jets, which are jets identified as originating
from the fragmentation of bottom quarks. The reweighting factors obtained vary
from 0.92 to 0.51 for one to six additional jets. The uncertainties in the
reweighting factors are evaluated as half of the deviation from unity. These
uncertainties cover the data-simulation differences observed in $\ttbar$
enriched validation samples obtained by selecting events with an $\Pe\PGm$
pair and at least one -tagged jet.

The and its vector ($\ptvecmiss$), defined in Section 0.4, are key ingredients
of the analysis. The modeling of their resolution in the simulation is studied
in $\Pgg+\text{jets}$ samples for each data taking period. Based on these
studies, the simulated resolution is corrected with scale factors, the
magnitudes of which are around 10% for the 2018 data and up to 15% for the
latter subset of the 2017 data. The correction factors for the earlier subset
of the 2017 data, or the entire 2016 data are close to unity. The variations
seen in the resolution factors in the three data taking periods are mainly
caused by different pileup and detector conditions, which are addressed in the
next section.

##  0.4 Event reconstruction and search strategy

The overall strategy of the analysis follows that of the search presented in
Ref. [29]. Three categories of search regions are defined. The “standard
selection” is designed to be sensitive to the majority of the top squark
scenarios under consideration with $\Delta
m\left(\PSQt,\PSGczDo\right)>m_{\PQt}$. In this paper we use the symbol
$\Delta m(\mathrm{a},\mathrm{b})$ to indicate the mass difference between
particles $\mathrm{a}$ and $\mathrm{b}$, and $m_{\mathrm{a}}$ to denote the
mass of $\mathrm{a}$. Two additional sets of signal regions are used to target
decays of the top squark to a top quark and a neutralino with mass splittings
between these particles of either $\Delta m\left(\PSQt,\PSGczDo\right)\sim
m_{\PQt}$, or $\Delta m\left(\PSQt,\PSGczDo\right)\sim m_{\PW}$.

###  0.4.1 Event reconstruction

The events used in this analysis are selected using triggers that require
either large , or the presence of an isolated electron or muon. The
$\ptvecmiss$ is first computed from the negative vector sum of the $\pt$ of
all particle-flow candidates, described below. The trigger selects events with
$\ptmiss>120\GeV$. The minimum requirement on the lepton $\pt$ varied between
27 and 35for electrons, and between 24 and 27for muons, depending on the data
taking period. The combined trigger efficiency, measured with a data sample of
events with a large scalar sum of jet , is greater than 99% for events with
$\ptmiss>250\GeV$ and lepton $\pt>20\GeV$.

The CMS event reconstruction is based on a particle-flow (PF) algorithm [75].
The algorithm combines information from all CMS subdetectors to identify
charged and neutral hadrons, photons, electrons, and muons, collectively
referred to as PF candidates.

Each event must contain at least one reconstructed $\Pp{}\Pp{}$ interaction
vertex. The reconstructed vertex with the largest value of the summed
$\pt^{2}$ of physics objects is taken to be the primary vertex (PV). The
physics objects are the objects reconstructed by the anti-$\kt$ jet finding
algorithm [76, 77, 78] with the tracks assigned to the vertex as inputs, and
the associated missing transverse momentum (), taken as the magnitude of the
negative vector sum of the of those jets.

Events with possible contributions from beam halo interactions or anomalous
noise in the calorimeter are rejected using dedicated filters [79]. For the
2017 and 2018 data taking periods, the ratio of the scalar sums of jet within
$\abs{\eta}<5.0$ and of jet within $\abs{\eta}<2.4$ is required to be smaller
than 1.5 to reject events with significant arising from noise in the ECAL
endcap forward region. Additionally, during part of the 2018 data taking
period, two sectors of the HCAL endcap detector experienced a power loss. The
affected data sample size is about 39. As the identification of both electrons
and jets depends on correct energy fraction measurements, events from the
affected data taking periods containing an electron or a jet in the region
$-2.4<\eta<-1.4$ and azimuthal angle $-1.6<\phi<-0.8$ radians are rejected.
The effect is estimated to be an approximately 2% loss in signal and
background acceptance for the full dataset. The simulation is corrected to
take this loss into account.

Figure 2:  The distributions of (upper left) and $N_{\mathrm{j}}$ (upper
right) are shown after applying the preselection requirements of Table 0.4.1,
including the requirement on the variable shown, and the distributions of
$M_{\mathrm{T}}$ (lower left) and $\min\Delta\phi(j_{1,2},\ptvecmiss)$ (lower
right) are shown after applying the preselection requirements, excluding the
requirement on the variable shown with the green, dashed vertical line marking
the location of the requirement. The stacked histograms for the SM background
contributions (categorized as described in Section 0.5) are from the
simulation to illustrate the discriminating power of these variables. The gray
hashed region indicates the statistical uncertainty of the simulated samples.
The last bin in each distribution includes the overflow events. The
expectations for three signal hypotheses are overlaid, and the corresponding
numbers in parentheses in the legends refer to the masses of the top squark
and neutralino, respectively. For models with $\PSGcpmDo$ decays, the mass of
the chargino is chosen to be $(m_{\PSQt}+m_{\PSGczDo})/2$.

After these initial requirements, we apply an event preselection summarized in
Table 0.4.1 and described below. Selected events are required to have exactly
one electron [80] or muon [81] originating from the PV and isolated from other
activity in the event. Leptons are identified as isolated if the scalar sum of
the of all PF candidates in a cone around the lepton, excluding the lepton
itself, is less than 10% of the lepton . Typical lepton selection efficiencies
are approximately 85% for electrons and 95% for muons, depending on and
$\eta$.

Summary of the event preselection requirements. The magnitude of the negative
vector sum of the of all jets and leptons in the event is denoted by . The
symbols $\pt^{\ell}$ and $\eta^{\ell}$ correspond to the transverse momentum
and pseudorapidity of the lepton. The symbol $\pt^{\text{sum}}$ is the scalar
sum of the of all (charged) PF candidates in a cone around the lepton (track),
excluding the lepton (track) itself. Finally, $N_{\PQb,\text{ med}}$ and
$N_{\PQb,\text{ soft}}$ are the multiplicity of -tagged jets (medium working
point) and soft objects, respectively.  Trigger (2016) $\ptmiss>170\GeV$ or
$\ptmiss>120\GeV$ and $\mht>120\GeV$ or isolated $\PGm(\Pe)$ with
$\pt^{\ell}>24(25)\GeV$ Trigger (2017, 2018) $\ptmiss>120\GeV$ and
$\mht>120\GeV$ or isolated $\PGm(\Pe)$ with $\pt^{\ell}>27(35)\GeV$
$\pt^{\text{sum}}$ cone size for $\PGm$ or $\Pe$: $\Delta
R=\min[\max(0.05,10\GeV/\pt^{\ell}),0.2]$ for track: $\Delta R=0.3$ Lepton
$\PGm(\Pe)$ with $\pt^{\ell}>20\GeV$, $\abs{\eta^{\ell}}<2.4~{}(1.44)$
$\pt^{\text{sum}}<0.1\times\pt^{\ell}$ Veto lepton $\PGm$ or $\Pe$ with
$\pt^{\ell}>5\GeV$, $\abs{\eta^{\ell}}<2.4$
$\pt^{\text{sum}}<0.2\times\pt^{\ell}$ Veto track Charged PF candidate,
$\pt>10\GeV$, $\abs{\eta}<2.4$
$\pt^{\text{sum}}<\min\left(0.1\times\pt,\mathrm{6}\GeV\right)$ Jets
$\pt>30\GeV$, $\abs{\eta}<2.4$, $N_{\mathrm{j}}\geq 2$ tagging $N_{\PQb,\text{
med}}\geq 1$ for standard and $\Delta m\left(\PSQt,\PSGczDo\right)\sim
m_{\PQt}$ selection $N_{\PQb,\text{ soft}}\geq 1$ for $\Delta
m\left(\PSQt,\PSGczDo\right)\sim m_{\PW}$ selection $>250\GeV$
$M_{\mathrm{T}}$ $>150\GeV$ $\min\Delta\phi(j_{1,2},\ptvecmiss)$ $>0.8$
radians for standard search $>0.5$ radians for compressed scenarios

The PF candidates are clustered into jets using the anti-algorithm with a
distance parameter of 0.4. Jet energies are corrected for contributions from
multiple interactions in the same or adjacent beam crossing (pileup) [82, 83]
and to account for nonuniformity in the detector response. These jet energy
corrections are propagated to the calculation of $\ptvecmiss$ [84, 85].

Jets in the analysis are required to be within $\pt>30\GeV$ and
$\abs{\eta}<2.4$, and the number of these jets ($N_{\mathrm{j}}$) is required
to be at least two. Jets overlapping with the selected lepton within a cone
radius of $\Delta R=0.4$ are not counted. The distribution of the number of
jets after the preselection requirements is shown in Fig. 2 (upper right). The
jet multiplicity is used to define the signal region bins to optimize
sensitivity for a variety of signal models and SUSY particle masses, as shown
in this figure.

After these requirements, jets originating from a bottom quark fragmentation
are identified as -tagged jets by the combined secondary vertex algorithm
using a deep neural network (DeepCSV) [86]. The preselection requires at least
one -tagged jet with either a medium or tight working point. The threshold on
the discriminator value corresponding to the medium (tight) working point is
chosen so that the tagging rate for light-flavor jets is about 1% (0.1%),
corresponding to an efficiency to identify a jet originating from a bottom-
flavored hadron of 65–80 (40–65)%, for jet of 30–400.

To enhance sensitivity to signal scenarios with a compressed mass spectra, we
use a secondary vertex (SV), not associated to jets or leptons, to identify
soft hadrons [30] with $\pt>1\GeV$ and $\abs{\eta}<2.5$. The SV is
reconstructed by the inclusive vertex finding algorithm [87]. At least two
tracks must be associated to the SV and the sum of the transverse momenta of
all the associated tracks is required to be below 20. The distance between the
SV and the PV must be ${<}3\unit{cm}$ and the significance of this distance is
required to be $>$4\. The cosine of the pointing angle defined by the scalar
product between the distance vector, $\overrightarrow{(\text{PV,SV})}$, and
the $\vec{p}_{\text{SV}}$, where the $\vec{p}_{\text{SV}}$ is the total three-
momentum of the tracks associated with the SV, must be $>$0.98. These
requirements help suppress background from light-flavor hadrons and jets.
Events containing objects that pass these selections, are said to contain a
“soft object”. These requirements result in a 40–55 (2–5)% efficiency to
select a soft object originating from a soft bottom-flavor (light-flavor)
hadron. As listed in Table 0.4.1, the preselection requires the presence of at
least one soft object in the signal regions dedicated to the compressed mass
spectra.

The background processes relevant for this search are semileptonic or
dileptonic ($\ttbar\to 1\ell+\text{X}$ or $\ttbar\to 2\ell+\text{X}$), single
top quark production (mostly in the $\PQt\PW$ channel), $\PW+\text{jets}$, and
processes containing a boson decaying into a pair of neutrinos
($\PZ\to\nu\bar{\nu}$), such as $\ttbar\PZ$ or $\PW\Z$. Contributions to the
background from semileptonic $\ttbar$ and $\PW+\text{jets}$ are heavily
suppressed by requiring in the preselection that the transverse mass
($M_{\mathrm{T}}$) be greater than 150and the to be greater than 250, as shown
in Fig. 2 (upper left and lower left, respectively). The $M_{\mathrm{T}}$ is
defined as $\sqrt{\smash[b]{2\pt^{\ell}\ptmiss[1-\cos(\Delta\phi)]}}$ with
$\pt^{\ell}$ denoting the lepton , and $\Delta\phi$ the azimuthal separation
between the lepton direction and .

In addition, to suppress background from processes with two leptonically
decaying bosons, primarily $\ttbar$ and $\PQt\PW$, we also reject events
containing either an additional lepton passing a loose selection (denoted as
“veto lepton” in Table 0.4.1) or an isolated track. Further rejection is
achieved by requiring that the minimum angle in the transverse plane between
the and the directions of the two leading jets in the event (denoted as
$j_{1,2}$), $\min\Delta\phi(j_{1,2},\ptvecmiss)$, is greater than 0.8 or 0.5,
depending on the signal region. This can be seen from the distribution of
$\min\Delta\phi(j_{1,2},\ptvecmiss)$, after applying the rest of the
preselection requirements, shown in Fig. 2 (lower right).

In addition to the preselection requirements, we also use two deep neural
networks (DNNs) to categorize events based on the identification of
hadronically decaying top quarks.

One DNN, referred to as the resolved tagger, uses the DeepResolved algorithm
to identify hadronically decaying top quarks with a moderate Lorentz boost.
The decay products of these objects result in three separate jets (resolved
top quark decay). The DeepResolved algorithm identifies top quarks decaying
into three distinct jets passing the selection requirements. The three jets
($\pt>40$, 30, 20) of each candidate must have an invariant mass between 100
and 250, no more than one of the jets can be identified as a -tagged jet, and
the three jets must all lie within a cone of $\Delta R<3.14$ of the trijet
centroid.

A neural network is used to distinguish trijet combinations which match to a
top quark versus those which do not. The network uses high-level information
such as the invariant mass of the trijet system and of the individual dijet
pairs, as well as kinematic information from each jet. This includes its
Lorentz vector, DeepCSV heavy-flavor discriminator values, jet shape
variables, and detector level particle multiplicity and energy fraction
variables. The network is trained using both and QCD simulation, and data as
training inputs. The simulation is used to define the examples of signal and
background. The signal is defined as any trijet passing the preselection
requirements, where each jet is matched to a generator level daughter of a top
quark within a cone of $\Delta R<0.4$ and the overall trijet system is matched
to the generator level top quark within a cone of $\Delta R<0.6$. The
background category is defined as any trijet combination that is not
categorized as signal. This includes trijet combinations for which some, but
not all, of the jets match top decay products. The data is included in the
training to inhibit the network from learning features of the MC which are not
present in data. This is achieved through a technique called domain adaption
via gradient reversal [88]. With this method, an additional output is added to
the neural network to distinguishing between trijet candidates from QCD
simulation and a QCD-enriched data sample. The main network is then restricted
to minimize its ability to discriminate simulation from data. This yields a
network with good separation between signal and background while minimizing
over-fitting on features that exist only in simulation. Before the final
selection of trijets as top quarks can be made, any trijet candidates that may
share the jets with another candidate must be removed. This is achieved by
always favoring the candidate with a higher top discriminator value as
determined by the neural network. The reconstructed candidates are identified
as hadronic tops when the neural network discriminator is above the threshold
corresponding to an efficiency of 45$\%$ and the mistagging rate is 10$\%$ for
dileptonic events.

The second DNN, referred to as a merged tagger, uses the DeepAK8 [89]
algorithm to identify top quarks with large boost, where the decay products
are merged into a single jet (merged top quark decay). The identification of
this boosted top quark signature is based on anti-jets clustered with a
distance parameter of 0.8. The efficiency for lepton + hadronic-top events is
40% and the mistagging rate is 5% for dileptonic events.

###  0.4.2 Search strategy

The signal regions for the standard search are summarized in Table 0.4.2, and
are defined by categorizing events passing the preselection requirements based
on $N_{\mathrm{j}}$, the number of identified hadronic top quarks, , the
invariant mass ($M_{\ell{\PQb}}$) of the lepton and the closest -tagged jet in
$\Delta R$, and a modified version of the topness variable [90],
$t_{\text{mod}}$ [27], which is defined as:

$t_{\text{mod}}=\ln(\min S),\text{ with
}S=\frac{\left(m_{\PW}^{2}-(p_{\nu}+p_{\ell})^{2}\right)^{2}}{a_{\PW}^{4}}+\frac{\left(m_{\PQt}^{2}-(p_{\PQb}+p_{\PW})^{2}\right)^{2}}{a_{\PQt}^{4}},$

with resolution parameters $a_{\PW}=5\GeV$ and $a_{\PQt}=15\GeV$. The
$t_{\text{mod}}$ variable is a $\chi^{2}$-like variable that discriminates
signal from leptonically decaying $\ttbar$ events: an event with a small value
of $t_{\text{mod}}$ is likely to be a dilepton $\ttbar$ event, while signal
events tend to have larger $t_{\text{mod}}$ values. The first term in its
definition corresponds to the top quark decay containing the reconstructed
lepton, and the second term corresponds to the top quark decay containing the
missing lepton. The $p_{\PW}$ in the second term symbolizes the momentum of
the missing lepton and neutrino from the W decay. The minimization of the
variable $S$ is done with respect to all components of the three momentum
$\vec{p}_{\PW}$ and the component of the three momentum $\vec{p}_{\nu}$ along
the beam line with the constraints that
$\ptvecmiss=\vec{p}_{\mathrm{T},\PW}+\vec{p}_{\mathrm{T},\nu}$ and
$p^{2}_{\PW}=m^{2}_{\PW}$. The distribution of $t_{\text{mod}}$ for events
passing the preselection is shown in Fig. 3 (upper left). The $t_{\text{mod}}$
distribution is split into three bins, each sensitive to a different mass
splitting of the top squark and neutralino.

The 39 signal regions of the standard selection, with each neighboring pair of
values in the bins column defines a single signal region. At least one -tagged
jet selected using the medium (tight) working point is required for search
regions with $M_{\ell{\PQb}}$ lower (higher) than 175. For the top quark
tagging categories, we use the abbreviations U for untagged, M for merged, and
R for resolved.  Label $N_{\mathrm{j}}$ $t_{\text{mod}}$ $M_{\ell{\PQb}}$
tagging bins [] [] category A0 2–3 $>$10 $\leq$175 — [600, 750, $+\infty$] A1
U [350, 450, 600] A2 M [250, 600] B 2–3 $>$10 $>$175 — [250, 450, 700,
$+\infty$] C $\geq$4 $\leq$0 $\leq$175 — [350, 450, 550, 650, 800, $+\infty$]
D $\geq$4 $\leq$0 $>$175 — [250, 350, 450, 600, $+\infty$] E0 $\geq$4 0–10
$\leq$175 — [450, 600, $+\infty$] E1 U [250, 350, 450] E2 M [250, 350, 450] E3
R [250, 350, 450] F $\geq$4 0–10 $>$175 — [250, 350, 450, $+\infty$] G0
$\geq$4 $>$10 $\leq$175 — [450, 550, 750, $+\infty$] G1 U [250, 350, 450] G2 M
[250, 350, 450] G3 R [250, 350, 450] H $\geq$4 $>$10 $>$175 — [250, 500,
$+\infty$]

In events containing a leptonically decaying top quark, the invariant mass of
the lepton and the bottom quark jet from the same top quark decay is bound by

$M_{\ell{\PQb}}\leq m_{\PQt}\sqrt{1-\frac{m^{2}_{\PW}}{m^{2}_{\PQt}}}.$

This bound does not apply to either $\PW+\text{jets}$ events or signal events,
where the top squark decays to a bottom quark and a chargino. To maintain
acceptance to a broad range of signal scenarios, rather than requiring a
selection on $M_{\ell{\PQb}}$, events are placed into low- or
high-$M_{\ell{\PQb}}$ categories if the value of $M_{\ell{\PQb}}$ is less or
greater than 175, respectively. In signal regions with
$M_{\ell{\PQb}}>175\GeV$, at least one jet is required to satisfy the tight
tagging working point of the DeepCSV discriminator to suppress the background
from $\PW+\text{jets}$ events. The distribution of $M_{\ell{\PQb}}$ in the
signal regions is shown in Fig. 3 (upper right). As seen from this figure, the
low $M_{\ell{\PQb}}$ regions are more sensitive to $\PSGczDo$ and the
$M_{\ell{\PQb}}>175\GeV$ are more sensitive to .

Figure 3:  The distributions of $t_{\text{mod}}$ (upper left),
$M_{\ell{\PQb}}$ (upper right), the merged top quark tagging discriminant
(lower left), and the resolved top quark tagging discriminant (lower right)
are shown after the preselection requirements. The green, dashed vertical
lines mark the locations of the binning or tagging requirements. The stacked
histograms showing the SM background contributions (categorized as described
in Section 0.5) are from the simulation to illustrate the discriminating power
of these variables. The gray hashed region indicates the statistical
uncertainty of the simulated samples. Events outside the range of the
distributions shown are included in the first or last bins. The expectations
for three signal hypotheses are overlaid, and the corresponding numbers in
parentheses in the legends refer to the masses of the top squark and
neutralino, respectively. For models with $\PSGcpmDo$ decays, the mass of the
chargino is chosen to be $(m_{\PSQt}+m_{\PSGczDo})/2$.

Hadronic top quark taggers are used in signal regions sensitive to SUSY
scenarios with hadronically decaying top quarks when most of the expected SM
background does not contain such a top quark decay. Therefore, the hadronic
top taggers are deployed in the low $M_{\ell{\PQb}}$, $t_{\text{mod}}\geq 0$,
and relatively modest signal regions. Events containing two or three jets and
$\ptmiss\leq 600\GeV$, or at least four jets and $\ptmiss\leq 450\GeV$, are
categorized according to the presence of a merged top quark tag. The resolved
top quark tagger is used to further categorize events with four or more jets.
If an event contains both merged and resolved top quark tags, it is placed in
the merged top category, while events containing neither are categorized as
untagged. Distributions of the discriminant of the merged and resolved top
quark taggers in the signal regions are also shown in Fig. 3 (lower left and
lower right, respectively).

The small mass splitting in SUSY models with a compressed mass spectrum
results in soft decay products. High values of can only be caused by large
boost from ISR. As a result, in signal regions targeting these models the jet
with the highest is expected to be from ISR and therefore it is required to
not be identified as a bottom quark jet. We also impose an upper bound on the
lepton relative to the , since this requirement provides an additional handle
to reject SM $\PW+\text{jets}$ and $\ttbar$ backgrounds. Regions targeting
signal scenarios with $\Delta m\left(\PSQt,\PSGczDo\right)\sim m_{\PQt}$
require at least five jets and at least one -tagged jet based on the DeepCSV
medium working point. For signal scenarios with $\Delta
m\left(\PSQt,\PSGczDo\right)\sim m_{\PW}$, the bottom quarks are expected to
have low . Therefore, in these regions the $N_{\mathrm{j}}$ selection is
relaxed to $N_{\mathrm{j}}\geq 3$ and instead of requiring the presence of a
-tagged jet we require the presence of a soft object. Note that soft objects
are included in the jet count in these regions. The requirements for the two
sets of signal regions targeting compressed mass spectrum SUSY scenarios are
summarized in Table 0.4.2.

Definitions of the total 10 search regions targeting signal scenarios with a
compressed mass spectrum. Search regions for $\Delta
m\left(\PSQt,\PSGczDo\right)\sim m_{\PQt}$ and $\sim m_{\PW}$ scenarios are
labeled with the letter I and J, respectively. The symbol $\pt^{\ell}$ denotes
the transverse momentum of the lepton. Each neighboring pair of values in the
bins column defines a single signal region.  Compressed spectra with $\Delta
m\left(\PSQt,\PSGczDo\right)\sim m_{\PQt}$ Label I Selection criteria
$N_{\mathrm{j}}\geq 5$, leading-$\pt$ jet not -tagged, $N_{\PQb,\text{
med}}\geq 1$, $\pt^{\ell}<\max\left(50,\
250-100\times\Delta\phi(\ptvecmiss,\ptvec^{\ell})\right)\GeV$, bins [] [250,
350, 450, 550, 750, $+\infty$] Compressed spectra with $\Delta
m\left(\PSQt,\PSGczDo\right)\sim m_{\PW}$ Label J Selection criteria
$N_{\mathrm{j}}\geq 3$, leading-$\pt$ jet not -tagged, $N_{\PQb,\text{
soft}}\geq 1$, $\pt^{\ell}<\max\left(50,\
250-100\times\Delta\phi(\ptvecmiss,\ptvec^{\ell})\right)\GeV$, bins [] [250,
350, 450, 550, 750, $+\infty$]

##  0.5 Background estimation

Three categories of SM backgrounds remain after the selection requirements
described in Section 0.4.

  * •

The lost-lepton background consists of events with two $\PW$ bosons decaying
leptonically, where one of the leptons is either not reconstructed, or not
identified. This background arises primarily from $\ttbar$ events, with a
smaller contribution from single top quark processes. It is the dominant
background in regions with low values of $M_{\ell{\PQb}}$, no top quark tag,
or $N_{\mathrm{j}}\geq 5$. This background is estimated using a dilepton
control sample.

  * •

The one-lepton background consists of events with a single $\PW$ boson
decaying leptonically and without any additional source of genuine . The
requirements of $\ptmiss>250\GeV$ and $M_{\mathrm{T}}>150\GeV$ heavily
suppress this background. The one-lepton background is estimated from
simulation when it originates from top quark decays (mainly semi-leptonic ).
Background events not originating from top quark decays, instead mainly from
direct production, are estimated using a control sample of events with no
-tagged jets.

  * •

The $\PZ\to\nu\bar{\nu}$ background consists of events with a single
leptonically decaying $\PW$ boson and a $\PZ$ boson that decays to a pair of
neutrinos, , $\Pp{}\Pp{}\to\ttbar\PZ$ or $\PW\PZ$. This background is
estimated using simulation.

###  0.5.1 Lost-lepton background

The lost-lepton background in each of the signal regions is estimated from
corresponding dilepton control samples. Each dilepton control sample is
obtained with the signal selections except for the requirement of a second
isolated lepton with $\pt>10\GeV$ and the removal of the lepton, track, and
tau vetoes. The estimated background in each search region is obtained from
the yield of data events in the corresponding control sample and a transfer
factor obtained from simulation, $R^{\text{lost-}\ell/2\ell}_{\text{MC}}$. The
transfer factor is defined as the ratio of the expected lost-lepton yield in
the signal region and the yield of dilepton SM events in the control sample.
These transfer factors are validated by checking the modeling of lepton
reconstruction and selections as well as the kinematical properties of leptons
in simulation. Corrections obtained from studies of samples of
$\cPZ,\,\JPsi\to\ell\ell$ events are applied to the transfer factor to account
for differences in lepton reconstruction and selection efficiencies between
data and simulation. The kinematical properties of leptons are well modeled in
simulation and have a data to simulation agreement within 10$\%$ or better.
Simulation shows that the dilepton control sample have high purity (70–80%) of
the main processes (dileptonic and $\PQt\PW$) contributing to the lost-lepton
background. Small contamination from semileptonic and other process, where the
additional lepton is a fake or non-prompt lepton, are subtracted from the
control sample data yields.

When defining the in this control sample, the trailing lepton is added to to
enhanced data statistics and all related quantities are recalculated. The
distribution of for after this addition is shown in Fig. 4 (left) for an
inclusive selection.

Figure 4:  Distributions of kinematic variables in the inclusive control
samples used for the background estimation. The gray hashed region indicates
the statistical uncertainty of the simulated samples. The distributions for
data are shown as points with error bars corresponding to the statistical
uncertainty. The stacked histograms show the expected SM background
contributions from simulation, normalized to the number of events observed in
data. The last bin in each distribution also includes the overflow. Left:
Distribution of in the dilepton control sample. Right: Distribution of
$M_{\ell{\PQb}}$ in the 0 control sample.

Some control samples only contain a small number of events. These samples,
corresponding to multiple bins, are combined into a single control sample
until the expected yield in simulation is at least five events, as detailed in
Table 0.5.1. The number of data events in the combined control sample is used
to estimate the sum of expected background events in the corresponding signal
regions. This sum is then distributed across bins according to the expectation
from simulation using an extrapolation factor $k(\ptmiss)$. Additional
corrections to account for the shape mismodeling observed in simulation with
respect to data are derived in an orthogonal enriched dilepton sample and
applied to the simulation in these regions.

Dilepton control samples that are combined when estimating the lost-lepton
background.  Label Selection bins [GeV] A0 2–3 jets, $t_{\text{mod}}>10$,
$M_{\ell{\PQb}}\leq 175\GeV$ [600, 750, $+\infty$] B 2–3 jets,
$t_{\text{mod}}>10$, $M_{\ell{\PQb}}>175\GeV$ [450, 700, $+\infty$] C $\geq$4
jets, $t_{\text{mod}}\leq 0$, $M_{\ell{\PQb}}\leq 175\GeV$ [650, 800,
$+\infty$] E0 $\geq$4 jets, $0<t_{\text{mod}}\leq 10$, $M_{\ell{\PQb}}\leq
175\GeV$ [450, 600, $+\infty$] G0 $\geq$4 jets, $t_{\text{mod}}>10$,
$M_{\ell{\PQb}}\leq 175\GeV$ [550, 750, $+\infty$] H $\geq$4 jets,
$t_{\text{mod}}>10$, $M_{\ell{\PQb}}>175\GeV$ [250, 500, $+\infty$] I $\geq$5
jets, $N_{\PQb,\text{ med}}\geq 1$, $N_{\PQb,\text{ soft}}\geq 0$ [550, 750,
$+\infty$] J $\geq$3 jets, $N_{\PQb,\text{ med}}\geq 0$, $N_{\PQb,\text{
soft}}\geq 1$ [550, 750, $+\infty$]

The lost-lepton background in each signal region,
$N^{\text{SR}}_{\text{lost-}\ell}$, is obtained by scaling the number of
events in the control sample, $N^{\text{CR}}_{2\ell}$, using the transfer
factor $R^{\text{lost-}\ell/2\ell}_{\text{MC}}$ and the extrapolation factor
$k(\ptmiss)$ as follows:

$N^{\text{SR}}_{\text{lost-}\ell}=N^{\text{CR}}_{2\ell}\,R^{\text{lost-}\ell/2\ell}_{\text{MC}}\,k(\ptmiss).$
(1)

The dominant uncertainties in the transfer factors are the statistical
uncertainties in the simulated samples, the uncertainties in the lepton
efficiencies, and the uncertainties in the jet energy scale. These
uncertainties range between 3–68%, 2–20%, and 1–16%, respectively.
Uncertainties in the tagging efficiency and in the choices of the
renormalization and factorization scales are small. The total uncertainty in
the transfer factor is 6–100%, depending on the region. The uncertainty in the
transfer factor is typically comparable to the statistical uncertainty in the
control sample yield. Associated uncertainties in the $k(\ptmiss)$
extrapolation factor used in the regions shown in Table 0.5.1 were derived
from an orthogonal enriched dilepton sample. The leading uncertainty
associated with the extrapolation is the statistical uncertainty in the
simulated samples (5–60%).

###  0.5.2 One-lepton background

The one-lepton ($1\ell$) background is suppressed by the $\ptmiss>250\GeV$ and
$M_{\mathrm{T}}>150\GeV$ requirements. This suppression is more effective for
events with a $\PW$ boson originating from a top quark decay than for direct
$\PW$ boson production ($\PW+\text{jets}$). In the case of a top quark decay,
the mass of the top quark sets bound at the mass of the lepton-neutrino
system. As a result, the contribution of semileptonic $\ttbar$ events to the
tail of the $M_{\mathrm{T}}$ distribution is caused by resolution effects,
while in the case of $\PW+\text{jets}$ events the contribution from off-shell
$\PW$ bosons is dominant.

The semileptonic $\ttbar$ background is taken from simulation. Studies with
simulated samples indicate that the contribution to the total background from
semileptonic $\ttbar$ events is less than 10% in most search regions, except
in a few regions with $\geq$1 top quark tags, where the contribution becomes
as large as 30% [29]. An uncertainty of 100$\%$ is assigned to cover the
impact of the uncertainties in the resolution as measured in a photon data
sample.

The $\PW+\text{jets}$ background is estimated from a control sample with no
-tagged jets nor soft objects (0 sample) obtained by inverting the -tagging
requirement. Figure 4 (right) shows the $M_{\ell{\PQb}}$ distribution in the 0
control sample, where this quantity is computed from the jet with the highest
value of the DeepCSV discriminant. The modeling of this distribution in
simulation is validated by comparing simulation and data in a
$\PW+\text{jets}$ enriched control sample obtained by selecting events with
1–2 jets and $60<M_{\mathrm{T}}<120\GeV$.

The $\PW+\text{jets}$ background estimate in each search region is obtained
from the yield in the corresponding control samples and a transfer factor
determined from simulation. These control samples are shown to have high
purity (70–80%) of the $\PW+\text{jets}$ process in places where this
background is more significant in the corresponding ($M_{\ell{\PQb}}>175\GeV$)
search region. In other cases, the purity can go down to 50%. Contamination
from lost-lepton and other processes are subtracted from the control sample
data yields. The transfer factor, defined as the ratio of the expected one
lepton (not from ) yield in the signal region and the yield of events in the 0
control sample, accounts for the acceptance and the tagging efficiency. The
transfer factors are validated by checking the differences in performance of
the b tagging algorithm and the off-shell W production modeling between data
and simulation. Corrections are applied for differences in tagging
efficiencies between data and simulation. The $\PW+\text{jets}$ kinematic
properties in the 0 control sample show good agreement between data and
simulation as shown in Figure 4. As in the case of the lost-lepton background
estimate, multiple control samples are combined into a single control sample
until the expected yield in simulation is at least five events, as detailed in
Table 0.5.2.

The dominant uncertainties in the transfer factors are the statistical
uncertainties in the simulated samples, the uncertainties in the tagging
efficiencies, and the $\PW+\PQb(\cPaqb)$ cross section.

Search regions where the corresponding 0 control samples are combined when
estimating the $\PW+\text{jets}$ background.  Label Selection bins [] C
$\geq$4 jets, $t_{\text{mod}}\leq 0$, $M_{\ell{\PQb}}\leq 175\GeV$ [650, 800,
$+\infty$] E0 $\geq$4 jets, $0<t_{\text{mod}}\leq 10$, $M_{\ell{\PQb}}\leq
175\GeV$ [450, 600, $+\infty$] G0 $\geq$4 jets, $t_{\text{mod}}>10$,
$M_{\ell{\PQb}}\leq 175\GeV$ [550, 750, $+\infty$]

###  0.5.3 Background from events containing $\PZ\to\nu\bar{\nu}$

The third category arises from $\ttbar\PZ$, $\PW\PZ$, and other rare
multiboson processes. In all these processes, events from a leptonically
decaying $\PW$ boson, and one or more $\PZ$ bosons decaying to neutrinos,
enter the search regions. In most search regions, $\ttbar\PZ$ is the most
important process contributing to this category. These backgrounds are
estimated from simulation. The contribution from $\ttbar\PZ$ is normalized
using the measured value of the cross section [91]. This normalization results
in a rescaling of the theoretical cross section by $1.17^{+0.10}_{-0.09}$,
where the uncertainty is taken from the statistical uncertainty in the
measurement.

##  0.6 Systematic uncertainties

The contributions to the total uncertainty in the estimated backgrounds and
expected signal yields are summarized in Table 0.6. The total uncertainty is
generally larger at higher or when yields in the control samples become small.
Out of the uncertainties quoted, the theoretical uncertainties are correlated
across the different data-taking periods because they are independent of the
data-taking period. The uncertainties on lepton efficiency are also assumed to
be fully correlated, but other experimental uncertainties are taken as
uncorrelated between the different data-taking years.

Summary of major systematic uncertainties. The range of values reflect their
impact on the estimated backgrounds and signal yields in different signal
regions. A 100% uncertainty is assigned to the $1\ell$ (from ) background
estimated from simulation.  Source Signal Lost lepton $1\ell$ (not from )
$\PZ\to\nu\bar{\nu}$ Data statistical uncertainty — 5–50% 4–30% — Simulation
statistical uncertainty 6–36% 3–68% 5–70% 4–41% modeling — 3–50% — — Signal
modeling 1–25% — — — QCD scales 1–5% 0–3% 2–5% 1–40% Parton distribution —
0–4% 1–8% 1–12% Pileup 1–5% 1–8% 0–5% 0–7% Luminosity 2.3–2.5% — — 2.3–2.5%
$\PW+\PQb(\cPaqb)$ cross section — — 20–40% — $\ttbar\PZ$ cross section — — —
5–10% System recoil (ISR) 1–13% 0–3% — — Jet energy scale 2–24% 1–16% 1–34%
1–28% resolution — 1–10% 1–5% — Trigger 2–3% 1–3% — 2–3% Lepton efficiency
3–4% 2–12% — 1–2% Merged tagging efficiency 3–6% — — 5–10% Resolved tagging
efficiency 5–6% — — 3–5% tagging efficiency 0–2% 0–1% 1–7% 1–10% Soft tagging
efficiency 2–3% 0–1% 0–1% 0–5%

Theoretical uncertainties affect all quantities derived from simulation such
as the signal acceptance, the transfer factors used in the estimate of the
lost lepton and one-lepton backgrounds, and the estimate of the
$\PZ\to\nu\bar{\nu}$ background. The uncertainty resulting from missing
higher-order corrections is estimated by varying the renormalization and
factorization scales by a factor of two [92, 93] with the two scales taken to
be the same in each variation. The effect of the uncertainties in the parton
distribution functions is estimated using 100 variations provided with the
NNPDF sets, and the effect of the uncertainty in the value of the strong
coupling constant is estimated by varying the value $\alpS(m_{\PZ})=0.1180$ by
$\pm 0.0015$ [94]. All theory uncertainties are varied based on the NNPDF3.0
scheme.

The lineshape is corrected to account for mismodeling effects from resolution
and $N_{\mathrm{j}}^{\mathrm{ISR/FSR}}$. The uncertainty in these corrections
results in a 1–50% uncertainty in the estimated backgrounds, depending on
signal region. The uncertainty in the $N_{\mathrm{j}}^{\mathrm{ISR/FSR}}$
rescaling also affects the signal acceptance. The effect is small in most
search regions, but can be noticeable in signal scenarios with a compressed
mass spectrum.

The effect of the uncertainty in the jet energy scale is 1–34% in the
estimated backgrounds and up to 24% in the signal acceptance. Variations in
the efficiency of the jet and soft object identification typically affect the
estimated signal and background yields by 0.1% and 3%, with a full range up to
10%.

The uncertainty in the cross section of $\PW+\text{jets}$ events with jets
containing quarks is an important source of uncertainty in the estimation of
the $\PW+\text{jets}$ background. A comparison of the multiplicity of -tagged
jets between data and simulation is performed in a $\PW+\text{jets}$ enriched
control sample obtained with the same selection as for the $M_{\ell{\PQb}}$
validation test, with the additional requirement of $\ptmiss>250\GeV$. From
this study, we estimate a 50% uncertainty in the $\PW+\PQb(\cPaqb)$ cross
section resulting in a 20–40% uncertainty in the $\PW+\text{jets}$ background
estimate.

##  0.7 Results and interpretation

The event yields and the SM predictions in the search regions are summarized
in Tables 0.7 and 0.7. These results are also illustrated in Fig. 5. The
observed yields are consistent with the estimated SM backgrounds. Isolated
fluctuations are observed in a few signal region bins. The data events in
these signal region bins were inspected carefully to determine if any detector
or reconstruction effects were the source of the high . No such issues were
detected.

The observed and expected yields in the standard search regions. For the top
quark tagging categories, we use the abbreviations U for untagged, M for
merged, and R for resolved.  Label $N_{\mathrm{j}}$ $t_{\text{mod}}$
$M_{\ell{\PQb}}$ $\ptmiss$ Lost 1$\ell$ (not 1$\ell$ $\PZ\to\nu\bar{\nu}$
Total Total [] cat. [] lepton from $\PQt$) (from $\PQt$) expected observed A0
2–3 $>$10 $\leq$175 — 600–750 $1.6\pm 0.7$ $1.1\pm 0.5$ $0.09\pm 0.09$ $1.8\pm
0.4$ $4.5\pm 0.9$ 3 750–$+\infty$ $0.26\pm 0.19$ $0.37\pm 0.28$ — $0.59\pm
0.20$ $1.2\pm 0.4$ 4 A1 U 350–450 $46\pm 5$ $16\pm 5$ $0.5\pm 0.5$ $8.5\pm
1.2$ $71\pm 8$ 88 450–600 $9.4\pm 1.5$ $7.3\pm 2.4$ $0.12\pm 0.12$ $3.9\pm
0.7$ $20.7\pm 3.0$ 19 A2 M 250–600 $4.5\pm 1.1$ $1.2\pm 0.4$ $0.03\pm 0.03$
$1.6\pm 0.4$ $7.4\pm 1.3$ 7 B 2–3 $>$10 $>$175 — 250–450 $6.6\pm 1.5$ $21\pm
10$ $0.18\pm 0.18$ $4.1\pm 0.9$ $32\pm 11$ 31 450–700 $0.55\pm 0.26$ $7\pm 4$
— $1.7\pm 0.5$ $9\pm 4$ 10 700–$+\infty$ $0.07\pm 0.06$ $2.0\pm 1.1$ —
$0.36\pm 0.15$ $2.4\pm 1.1$ 2 C $\geq$4 $\leq$0 $\leq$175 — 350–450 $245\pm
23$ $9.8\pm 3.5$ $21\pm 21$ $12.1\pm 2.7$ $289\pm 32$ 293 450–550 $48\pm 7$
$1.8\pm 0.7$ $4\pm 4$ $4.2\pm 0.9$ $58\pm 8$ 70 550–650 $16\pm 4$ $1.8\pm 1.0$
$0.6\pm 0.6$ $1.04\pm 0.31$ $19\pm 4$ 13 650–800 $6.6\pm 2.5$ $0.9\pm 0.4$
$0.7\pm 0.7$ $0.47\pm 0.19$ $8.6\pm 2.6$ 12 800–$+\infty$ $0.6\pm 0.7$
$0.25\pm 0.13$ $0.08\pm 0.08$ $0.12\pm 0.08$ $1.0\pm 0.7$ 4 D $\geq$4 $\leq$0
$>$175 — 250–350 $144\pm 13$ $38\pm 13$ $32\pm 32$ $6.5\pm 1.5$ $221\pm 37$
186 350–450 $33\pm 5$ $8.3\pm 3.4$ $5\pm 5$ $2.5\pm 0.7$ $48\pm 8$ 45 450–600
$8.9\pm 2.5$ $4.5\pm 1.9$ $0.6\pm 0.6$ $1.05\pm 0.26$ $15.0\pm 3.2$ 17
600–$+\infty$ $3.2\pm 2.1$ $2.4\pm 0.9$ $0.35\pm 0.35$ $0.17\pm 0.16$ $6.2\pm
2.4$ 0 E0 $\geq$4 0–10 $\leq$175 — 450–600 $5.9\pm 1.5$ $1.4\pm 0.7$ — $3.0\pm
0.7$ $10.4\pm 1.8$ 9 600–$+\infty$ $0.45\pm 0.28$ $0.34\pm 0.18$ — $0.62\pm
0.24$ $1.4\pm 0.4$ 0 E1 U 250–350 $186\pm 17$ $18\pm 6$ $4\pm 4$ $21\pm 4$
$230\pm 19$ 245 350–450 $26\pm 4$ $5.4\pm 1.8$ $0.6\pm 0.6$ $7.8\pm 1.3$
$40\pm 4$ 53 E2 M 250–350 $1.7\pm 0.9$ $0.38\pm 0.16$ $2.7\pm 2.7$ $0.95\pm
0.27$ $5.7\pm 2.8$ 8 350–450 $2.4\pm 1.4$ $0.12\pm 0.12$ $0.5\pm 0.5$ $1.05\pm
0.29$ $4.1\pm 1.5$ 1 E3 R 250–350 $5.6\pm 1.8$ $0.7\pm 0.4$ $1.9\pm 1.9$
$6.8\pm 1.5$ $15.0\pm 3.0$ 12 350–450 $2.6\pm 1.4$ $0.48\pm 0.25$ $0.15\pm
0.15$ $2.0\pm 0.5$ $5.3\pm 1.5$ 6 F $\geq$4 0–10 $>$175 — 250–350 $10.4\pm
2.5$ $6.2\pm 3.2$ $1.0\pm 1.0$ $3.8\pm 0.8$ $21\pm 4$ 23 350–450 $1.2\pm 0.9$
$2.3\pm 1.2$ $0.12\pm 0.12$ $1.9\pm 0.8$ $5.6\pm 1.7$ 9 450–$+\infty$
$0.5^{+1.0}_{-0.5}$ $1.2\pm 0.7$ $0.08\pm 0.08$ $0.69\pm 0.25$ $2.5\pm 1.2$ 4
G0 $\geq$4 $>$10 $\leq$175 — 450–550 $6.5\pm 1.9$ $3.8\pm 1.7$ $0.5\pm 0.5$
$5.7\pm 1.0$ $16.6\pm 2.8$ 12 550–750 $2.7\pm 1.2$ $3.1\pm 1.2$ $0.1\pm 0.1$
$3.7\pm 0.8$ $9.5\pm 1.9$ 6 750–$+\infty$ $0.33\pm 0.18$ $0.83\pm 0.35$ —
$0.79\pm 0.16$ $1.9\pm 0.4$ 3 G1 U 250–350 $34\pm 5$ $2.8\pm 1.2$ $1.1\pm 1.1$
$7.9\pm 1.8$ $46\pm 6$ 46 350–450 $19\pm 4$ $3.8\pm 1.6$ $0.8\pm 0.8$ $6.3\pm
1.5$ $30\pm 4$ 22 G2 M 250–350 $0.37\pm 0.27$ $0.1\pm 0.06$ $0.6\pm 0.6$
$0.46\pm 0.15$ $1.5\pm 0.6$ 3 350–450 $0.8\pm 0.5$ $0.2\pm 0.1$ $0.3\pm 0.3$
$1.12\pm 0.23$ $2.4\pm 0.6$ 2 G3 R 250–350 $2.3\pm 1.0$ $0.06\pm 0.09$
$0.09\pm 0.09$ $2.4\pm 0.5$ $4.8\pm 1.2$ 3 350–450 $0.8\pm 0.5$ $0.12\pm 0.08$
$0.31\pm 0.31$ $2.4\pm 0.6$ $3.6\pm 0.8$ 6 H $\geq$4 $>$10 $>$175 — 250–500
$3.4\pm 1.4$ $4.2\pm 2.0$ $0.09\pm 0.09$ $1.7\pm 0.4$ $9.4\pm 2.5$ 8
500–$+\infty$ $1.1\pm 0.5$ $1.8\pm 1.0$ $0.3\pm 0.3$ $1.8\pm 0.6$ $5.0\pm 1.3$
4

The observed and expected yields for signal regions targeting scenarios of top
squark production with a compressed mass spectrum.  Label $N_{\mathrm{j}}$
$N_{\PQb,\text{ med}}$ $N_{\PQb,\text{ soft}}$ $\ptmiss$ Lost 1$\ell$ (not
1$\ell$ $\PZ\to\nu\bar{\nu}$ Total Total [] lepton from $\PQt$) (from $\PQt$)
expected observed I $\geq$5 $\geq$1 $\geq$0 250–350 $403\pm 40$ $21\pm 8$
$71\pm 71$ $17\pm 4$ $511\pm 81$ 513 350–450 $108\pm 15$ $6.8\pm 2.5$ $12\pm
12$ $7.8\pm 1.6$ $134\pm 19$ 140 450–550 $31\pm 8$ $2.5\pm 1.0$ $2.0\pm 2.0$
$2.9\pm 0.8$ $39\pm 8$ 37 550–750 $11\pm 5$ $1.4\pm 0.6$ $0.27\pm 0.27$
$1.8\pm 0.5$ $14\pm 5$ 10 750–$+\infty$ $1.8\pm 1.1$ $1.9^{+2.5}_{-1.9}$
$0.16\pm 0.16$ $0.28\pm 0.10$ $4.1\pm 2.5$ 6 J $\geq$3 $\geq$0 $\geq$1 250–350
$201\pm 21$ $37\pm 7$ $27\pm 27$ $10.4\pm 1.5$ $276\pm 35$ 268 350–450 $38\pm
7$ $11.6\pm 2.2$ $3.4\pm 3.4$ $4.3\pm 0.9$ $58\pm 8$ 60 450–550 $11.5\pm 3.5$
$3.3\pm 0.6$ $0.7\pm 0.7$ $1.7\pm 0.6$ $17\pm 4$ 16 550–750 $3.5\pm 2.3$
$2.1\pm 0.5$ — $1.1\pm 0.8$ $6.6\pm 2.5$ 6 750–$+\infty$ $0.4\pm 0.4$ $0.44\pm
0.16$ $0.02\pm 0.02$ $0.2\pm 0.4$ $1.0\pm 0.6$ 4

Figure 5:  The observed and expected yields in Tables 0.7 and 0.7 and their
ratios are shown as stacked histograms. The lost lepton and 1$\ell$ (not from
$\PQt$) are estimated from data-driven methods, while 1$\ell$ (from $\PQt$)
and $\PZ\to\nu\bar{\nu}$ backgrounds are taken from simulation. The
uncertainties consist of statistical and systematic components summed in
quadrature and are shown as shaded bands.

Results are interpreted in the context of top squark pair production models
described in Section 0.1. For a given model, 95% confidence level (CL) upper
limits on the production cross sections are derived as a function of the mass
of the SUSY particles. The search regions are combined using a modified
frequentist approach, employing the $\CLs$ criterion and an asymptotic
formulation [95, 96, 97, 98]. The likelihood function is constructed by
multiplying the probability density functions from each search region. These
probability density functions are products of Poisson functions for the
control region yields and log-normal constraint functions for the nuisance
parameters, with correlated parameters among the search regions being
accounted for. When computing the limit, the expected signal yields are
corrected for the possible contributions of signal events to the control
samples. These corrections are typically around 5–10%.

For the models in which both top squarks decay to a top quark and an , the
limits are derived from the $\Delta m\left(\PSQt,\PSGczDo\right)\sim m_{\PW}$
search regions when $100\leq\Delta m\left(\PSQt,\PSGczDo\right)\leq 150\GeV$,
and from the $\Delta m\left(\PSQt,\PSGczDo\right)\sim m_{\PQt}$ search regions
when $150\leq\Delta m\left(\PSQt,\PSGczDo\right)\leq 225\GeV$. For all other
models, the cross section limits are obtained from the standard search
regions.

In the case of $\Delta m\left(\PSQt,\PSGczDo\right)\sim m_{\PW}$, the
specially designed signal regions result in improvements of up to a factor of
five in cross section sensitivity with respect to the results that would have
been obtained based on the standard search regions. On the other hand, the
corresponding improvements from the signal regions designed for $\Delta
m\left(\PSQt,\PSGczDo\right)\sim m_{\PQt}$ are typically of the order of
10–20%. In the high mass region, this analysis is sensitive to an additional
$\sim$200in expected limit for top squark masses [29].

The 95% CL upper limits on cross sections for the
$\Pp\Pp\to\PSQt\PASQt\to\PQt\PAQt\PSGczDo\PSGczDo$ process, as a function of
sparticle masses and assuming that the top quarks are not polarized, are shown
in Fig. 6. In this figure we also show the excluded region of parameter space
based on the expected cross section for top squark pair production. We exclude
the existence of top squarks with masses up to 1.2for a massless neutralino,
and neutralinos with masses up to 600for $m_{\PSQt}=1\TeV$. The most sensitive
search regions for these processes are those with high $t_{\text{mod}}$ and
low $M_{\ell{\PQb}}$ values. Signal models with higher $\Delta
m\left(\PSQt,\PSGczDo\right)$ are more sensitive in the regions with higher .
The white band corresponds to the region
$\abs{m_{\PSQt}-m_{\mathrm{t}}-m_{\PSGczDo}}<25\GeV,\,m_{\PSQt}<275\GeV$,
where the selection acceptance for top squark pair production changes rapidly.
In this region the acceptance is very sensitive to the details of the
simulation, and therefore no interpretation is performed.

Figures 7 and 8 display the equivalent limits for the
$\Pp\Pp\to\PSQt\PASQt\to\PQb\PAQb\PSGcpmDo\PSGcpmDo\left(\PSGcpmDo\to\PW\PSGczDo\right)$
and
$\Pp\Pp\to\PSQt\PASQt\to\PQt\PQb\PSGcpmDo\PSGczDo\left(\PSGcpmDo\to\PW^{*}\PSGczDo\right)$
scenarios, respectively. The search regions with high $M_{\ell{\PQb}}$ are
most sensitive to these models. These models are characterized by three mass
parameters (for the top squark, the chargino, and the neutralino). In the
mixed decay scenario of Fig. 8, we have assumed a compressed mass spectrum for
the neutralino-chargino pair, which is theoretically favored if the
$\PSGcpmDo$ and the $\PSGczDo$ are higgsinos. The search has very poor
sensitivity for models with this mass spectrum when both top squarks decay to
charginos. Therefore in the case of Fig. 7, we have chosen a larger mass
splitting between the $\PSGcpmDo$ and the $\PSGczDo$.

Figure 6:  Exclusion limits at 95% CL for the
$\Pp\Pp\to\PSQt\PASQt\to\PQt\PAQt\PSGczDo\PSGczDo$ scenario. The colored map
illustrates the 95% CL upper limits on the product of the production cross
section and branching fraction. The area enclosed by the thick black curve
represents the observed exclusion region, and that enclosed by the thick,
dashed red curve represents the expected exclusion. The thin dotted (red)
curves indicate the region containing 68% of the distribution of limits
expected under the background-only hypothesis. The thin solid (black) curves
show the change in the observed limit by varying the signal cross sections
within their theoretical uncertainties. The white band excluded from the
limits corresponds to the region
$\abs{m_{\PSQt}-m_{\mathrm{t}}-m_{\PSGczDo}}<25\GeV,\,m_{\PSQt}<275\GeV$,
where the selection acceptance for top squark pair production changes rapidly
and is therefore very sensitive to the details of the simulation.  Figure 7:
Exclusion limits at 95% CL for the
$\Pp\Pp\to\PSQt\PASQt\to\PQb\PAQb\PSGcpmDo\PSGcpmDo\left(\PSGcpmDo\to\PW\PSGczDo\right)$
scenario. The mass of is chosen to be $(m_{\PSQt}+m_{\PSGczDo})/2$. The
colored map illustrates the 95% CL upper limits on the product of the
production cross section and branching fraction. The area enclosed by the
thick black curve represents the observed exclusion region, and that enclosed
by the thick, dashed red curve represents the expected exclusion. The thin
dotted (red) curves indicate the region containing 68% of the distribution of
limits expected under the background-only hypothesis. The thin solid (black)
curves show the change in the observed limit by varying the signal cross
sections within their theoretical uncertainties.  Figure 8:  Exclusion limits
at 95% CL for the
$\Pp\Pp\to\PSQt\PASQt\to\PQt{\PQb}\PSGcpmDo\PSGczDo\left(\PSGcpmDo\to\PW^{*}\PSGczDo\right)$
scenario. The mass difference between the and the is taken to be 5. The
colored map illustrates the 95% CL upper limits on the product of the
production cross section and branching fraction. The area enclosed by the
thick black curve represents the observed exclusion region, and that enclosed
by the thick, dashed red curve represents the expected exclusion. The thin
dotted (red) curves indicate the region containing 68% of the distribution of
limits expected under the background-only hypothesis. The thin solid (black)
curves show the change in the observed limit by varying the signal cross
sections within their theoretical uncertainties.

##  0.8 Summary

A search for direct top squark pair production is performed using events with
one lepton, jets, and significant missing transverse momentum. The search is
based on proton-proton collision data at a center-of-mass energy of 13recorded
by the CMS experiment at the LHC during 2016-2018 and corresponding to an
integrated luminosity of $137\fbinv$. The leading backgrounds in this
analysis, mainly dileptonic decays, where one of the leptons is not
reconstructed or identified, and $\PW+\text{jets}$ production are estimated
from data control regions. The semileptonic $\ttbar$ and $\PZ\to\nu\bar{\nu}$
backgrounds are taken from simulation. No significant deviations from the
standard model expectations are observed. Limits on pair-produced top squarks
are established in the context of supersymmetry models conserving $R$-parity.
Exclusion limits at 95% CL for top squark masses up to 1.2are set for a
massless neutralino. For models with a top squark mass of 1, neutralino masses
up to 600are excluded.

###### Acknowledgements.

We congratulate our colleagues in the CERN accelerator departments for the
excellent performance of the LHC and thank the technical and administrative
staffs at CERN and at other CMS institutes for their contributions to the
success of the CMS effort. In addition, we gratefully acknowledge the
computing centers and personnel of the Worldwide LHC Computing Grid for
delivering so effectively the computing infrastructure essential to our
analyses. Finally, we acknowledge the enduring support for the construction
and operation of the LHC and the CMS detector provided by the following
funding agencies: BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq,
CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST,
and NSFC (China); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RPF
(Cyprus); SENESCYT (Ecuador); MoER, ERC IUT, PUT and ERDF (Estonia); Academy
of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG,
and HGF (Germany); GSRT (Greece); NKFIA (Hungary); DAE and DST (India); IPM
(Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); MES
(Latvia); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT,
LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC
(Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom,
RAS, RFBR, and NRC KI (Russia); MESTD (Serbia); SEIDI, CPAN, PCTI, and FEDER
(Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST
(Taipei); ThEPCenter, IPST, STAR, and NSTDA (Thailand); TUBITAK and TAEK
(Turkey); NASU (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,
752730, and 765710 (European Union); the Leventis Foundation; the A.P. Sloan
Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science
Policy Office; the Fonds pour la Formation à la Recherche dans l’Industrie et
dans l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door
Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium)
under the “Excellence of Science – EOS” – be.h project n. 30820817; the
Beijing Municipal Science & Technology Commission, No. Z181100004218003; the
Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the
Lend’́ulet (“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 Education, grant no.
3.2989.2017 (Russia); the Programa Estatal de Fomento de la Investigación
Científica y Técnica de Excelencia María de Maeztu, grant MDM-2015-0509 and
the Programa Severo Ochoa del Principado de Asturias; the Thalis and Aristeia
programs cofinanced by EU-ESF and the Greek NSRF; the Rachadapisek Sompot Fund
for Postdoctoral Fellowship, Chulalongkorn University and the Chulalongkorn
Academic into Its 2nd Century Project Advancement Project (Thailand); the
Welch Foundation, contract C-1845; and the Weston Havens Foundation (USA).

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##  .9 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan${}^{\textrm{\textdagger}}$, A. Tumasyan Institut für
Hochenergiephysik, Wien, Austria
W. Adam, F. Ambrogi, T. Bergauer, J. Brandstetter, M. Dragicevic, J. Erö, A.
Escalante Del Valle, M. Flechl, R. Frühwirth1, M. Jeitler1, N. Krammer, I.
Krätschmer, D. Liko, T. Madlener, I. Mikulec, N. Rad, J. Schieck1, R.
Schöfbeck, M. Spanring, D. Spitzbart, W. Waltenberger, C.-E. Wulz1, M. Zarucki
Institute for Nuclear Problems, Minsk, Belarus
V. Drugakov, V. Mossolov, J. Suarez Gonzalez Universiteit Antwerpen,
Antwerpen, Belgium
M.R. Darwish, E.A. De Wolf, D. Di Croce, X. Janssen, A. Lelek, M. Pieters, H.
Rejeb Sfar, H. Van Haevermaet, P. Van Mechelen, S. Van Putte, N. Van Remortel
Vrije Universiteit Brussel, Brussel, Belgium
F. Blekman, E.S. Bols, S.S. Chhibra, J. D’Hondt, J. De Clercq, D. Lontkovskyi,
S. Lowette, I. Marchesini, S. Moortgat, Q. Python, K. Skovpen, S. Tavernier,
W. Van Doninck, P. Van Mulders Université Libre de Bruxelles, Bruxelles,
Belgium
D. Beghin, B. Bilin, H. Brun, B. Clerbaux, G. De Lentdecker, H. Delannoy, B.
Dorney, L. Favart, A. Grebenyuk, A.K. Kalsi, A. Popov, N. Postiau, E.
Starling, L. Thomas, C. Vander Velde, P. Vanlaer, D. Vannerom Ghent
University, Ghent, Belgium
T. Cornelis, D. Dobur, I. Khvastunov2, M. Niedziela, C. Roskas, M. Tytgat, W.
Verbeke, B. Vermassen, M. Vit Université Catholique de Louvain, Louvain-la-
Neuve, Belgium
O. Bondu, G. Bruno, C. Caputo, P. David, C. Delaere, M. Delcourt, A.
Giammanco, V. Lemaitre, J. Prisciandaro, A. Saggio, M. Vidal Marono, P.
Vischia, J. Zobec Centro Brasileiro de Pesquisas Fisicas, Rio de Janeiro,
Brazil
F.L. Alves, G.A. Alves, G. Correia Silva, C. Hensel, A. Moraes, P. Rebello
Teles Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
E. Belchior Batista Das Chagas, W. Carvalho, J. Chinellato3, E. Coelho, E.M.
Da Costa, G.G. Da Silveira4, D. De Jesus Damiao, C. De Oliveira Martins, S.
Fonseca De Souza, L.M. Huertas Guativa, H. Malbouisson, J. Martins5, D. Matos
Figueiredo, M. Medina Jaime6, M. Melo De Almeida, C. Mora Herrera, L. Mundim,
H. Nogima, W.L. Prado Da Silva, L.J. Sanchez Rosas, A. Santoro, A. Sznajder,
M. Thiel, E.J. Tonelli Manganote3, F. Torres Da Silva De Araujo, A. Vilela
Pereira Universidade Estadual Paulista a, Universidade Federal do ABC b, São
Paulo, Brazil
C.A. Bernardesa, L. Calligarisa, T.R. Fernandez Perez Tomeia, E.M. Gregoresb,
D.S. Lemos, P.G. Mercadanteb, S.F. Novaesa, SandraS. Padulaa Institute for
Nuclear Research and Nuclear Energy, Bulgarian Academy of Sciences, Sofia,
Bulgaria
A. Aleksandrov, G. Antchev, R. Hadjiiska, P. Iaydjiev, M. Misheva, M. Rodozov,
M. Shopova, G. Sultanov University of Sofia, Sofia, Bulgaria
M. Bonchev, A. Dimitrov, T. Ivanov, L. Litov, B. Pavlov, P. Petkov Beihang
University, Beijing, China
W. Fang7, X. Gao7, L. Yuan Institute of High Energy Physics, Beijing, China
G.M. Chen, H.S. Chen, M. Chen, C.H. Jiang, D. Leggat, H. Liao, Z. Liu, A.
Spiezia, J. Tao, E. Yazgan, H. Zhang, S. Zhang8, J. Zhao State Key Laboratory
of Nuclear Physics and Technology, Peking University, Beijing, China
A. Agapitos, Y. Ban, G. Chen, A. Levin, J. Li, L. Li, Q. Li, Y. Mao, S.J.
Qian, D. Wang, Q. Wang Tsinghua University, Beijing, China
M. Ahmad, Z. Hu, Y. Wang Zhejiang University, Hangzhou, China
M. Xiao Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, C. Florez, C.F. González Hernández, M.A. Segura Delgado
Universidad de Antioquia, Medellin, Colombia
J. Mejia Guisao, J.D. Ruiz Alvarez, C.A. Salazar González, N. Vanegas Arbelaez
University of Split, Faculty of Electrical Engineering, Mechanical Engineering
and Naval Architecture, Split, Croatia
D. Giljanović, N. Godinovic, D. Lelas, I. Puljak, T. Sculac University of
Split, Faculty of Science, Split, Croatia
Z. Antunovic, M. Kovac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, D. Ferencek, K. Kadija, B. Mesic, M. Roguljic, A. Starodumov9,
T. Susa University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, E. Erodotou, A. Ioannou, M. Kolosova, S. Konstantinou,
G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos, P.A. Razis, H.
Rykaczewski, D. Tsiakkouri Charles University, Prague, Czech Republic
M. Finger10, M. Finger Jr.10, A. Kveton, J. Tomsa Escuela Politecnica
Nacional, Quito, Ecuador
E. Ayala Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab
Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
H. Abdalla11, S. Khalil12 National Institute of Chemical Physics and
Biophysics, Tallinn, Estonia
S. Bhowmik, A. Carvalho Antunes De Oliveira, R.K. Dewanjee, K. Ehataht, M.
Kadastik, M. Raidal, C. Veelken Department of Physics, University of Helsinki,
Helsinki, Finland
P. Eerola, L. Forthomme, H. Kirschenmann, K. Osterberg, M. Voutilainen
Helsinki Institute of Physics, Helsinki, Finland
F. Garcia, J. Havukainen, J.K. Heikkilä, V. Karimäki, M.S. Kim, R. Kinnunen,
T. Lampén, K. Lassila-Perini, S. Laurila, S. Lehti, T. Lindén, P. Luukka, T.
Mäenpää, H. Siikonen, E. Tuominen, J. Tuominiemi Lappeenranta University of
Technology, Lappeenranta, Finland
T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
M. Besancon, F. Couderc, M. Dejardin, D. Denegri, B. Fabbro, J.L. Faure, F.
Ferri, S. Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry,
C. Leloup, B. Lenzi, E. Locci, J. Malcles, J. Rander, A. Rosowsky, M.Ö. Sahin,
A. Savoy-Navarro13, M. Titov, G.B. Yu Laboratoire Leprince-Ringuet,
CNRS/IN2P3, Ecole Polytechnique, Institut Polytechnique de Paris
S. Ahuja, C. Amendola, F. Beaudette, P. Busson, C. Charlot, B. Diab, G.
Falmagne, R. Granier de Cassagnac, I. Kucher, A. Lobanov, C. Martin Perez, M.
Nguyen, C. Ochando, P. Paganini, J. Rembser, R. Salerno, J.B. Sauvan, Y.
Sirois, A. Zabi, A. Zghiche Université de Strasbourg, CNRS, IPHC UMR 7178,
Strasbourg, France
J.-L. Agram14, J. Andrea, D. Bloch, G. Bourgatte, J.-M. Brom, E.C. Chabert, C.
Collard, E. Conte14, J.-C. Fontaine14, D. Gelé, U. Goerlach, M. Jansová, A.-C.
Le Bihan, N. Tonon, P. Van Hove Centre de Calcul de l’Institut National de
Physique Nucleaire et de Physique des Particules, CNRS/IN2P3, Villeurbanne,
France
S. Gadrat Université de Lyon, Université Claude Bernard Lyon 1, CNRS-IN2P3,
Institut de Physique Nucléaire de Lyon, Villeurbanne, France
S. Beauceron, C. Bernet, G. Boudoul, C. Camen, A. Carle, N. Chanon, R.
Chierici, D. Contardo, P. Depasse, H. El Mamouni, J. Fay, S. Gascon, M.
Gouzevitch, B. Ille, Sa. Jain, F. Lagarde, I.B. Laktineh, H. Lattaud, A.
Lesauvage, M. Lethuillier, L. Mirabito, S. Perries, V. Sordini, L. Torterotot,
G. Touquet, M. Vander Donckt, S. Viret Georgian Technical University, Tbilisi,
Georgia
G. Adamov Tbilisi State University, Tbilisi, Georgia
Z. Tsamalaidze10 RWTH Aachen University, I. Physikalisches Institut, Aachen,
Germany
C. Autermann, L. Feld, M.K. Kiesel, K. Klein, M. Lipinski, D. Meuser, A.
Pauls, M. Preuten, M.P. Rauch, J. Schulz, M. Teroerde, B. Wittmer RWTH Aachen
University, III. Physikalisches Institut A, Aachen, Germany
M. Erdmann, B. Fischer, S. Ghosh, T. Hebbeker, K. Hoepfner, H. Keller, L.
Mastrolorenzo, M. Merschmeyer, A. Meyer, P. Millet, G. Mocellin, S. Mondal, S.
Mukherjee, D. Noll, A. Novak, T. Pook, A. Pozdnyakov, T. Quast, M. Radziej, Y.
Rath, H. Reithler, J. Roemer, A. Schmidt, S.C. Schuler, A. Sharma, S.
Wiedenbeck, S. Zaleski RWTH Aachen University, III. Physikalisches Institut B,
Aachen, Germany
G. Flügge, W. Haj Ahmad15, O. Hlushchenko, T. Kress, T. Müller, A. Nowack, C.
Pistone, O. Pooth, D. Roy, H. Sert, A. Stahl16 Deutsches Elektronen-
Synchrotron, Hamburg, Germany
M. Aldaya Martin, P. Asmuss, I. Babounikau, H. Bakhshiansohi, K. Beernaert, O.
Behnke, A. Bermúdez Martínez, D. Bertsche, A.A. Bin Anuar, K. Borras17, V.
Botta, A. Campbell, A. Cardini, P. Connor, S. Consuegra Rodríguez, C.
Contreras-Campana, V. Danilov, A. De Wit, M.M. Defranchis, C. Diez Pardos, D.
Domínguez Damiani, G. Eckerlin, D. Eckstein, T. Eichhorn, A. Elwood, E. Eren,
E. Gallo18, A. Geiser, A. Grohsjean, M. Guthoff, M. Haranko, A. Harb, A.
Jafari, N.Z. Jomhari, H. Jung, A. Kasem17, M. Kasemann, H. Kaveh, J. Keaveney,
C. Kleinwort, J. Knolle, D. Krücker, W. Lange, T. Lenz, J. Lidrych, K. Lipka,
W. Lohmann19, R. Mankel, I.-A. Melzer-Pellmann, A.B. Meyer, M. Meyer, M.
Missiroli, G. Mittag, J. Mnich, A. Mussgiller, V. Myronenko, D. Pérez Adán,
S.K. Pflitsch, D. Pitzl, A. Raspereza, A. Saibel, M. Savitskyi, V. Scheurer,
P. Schütze, C. Schwanenberger, R. Shevchenko, A. Singh, H. Tholen, O. Turkot,
A. Vagnerini, M. Van De Klundert, R. Walsh, Y. Wen, K. Wichmann, C. Wissing,
O. Zenaiev, R. Zlebcik University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, L. Benato, A. Benecke, V. Blobel, T. Dreyer, A.
Ebrahimi, F. Feindt, A. Fröhlich, C. Garbers, E. Garutti, D. Gonzalez, P.
Gunnellini, J. Haller, A. Hinzmann, A. Karavdina, G. Kasieczka, R. Klanner, R.
Kogler, N. Kovalchuk, S. Kurz, V. Kutzner, J. Lange, T. Lange, A. Malara, J.
Multhaup, C.E.N. Niemeyer, A. Perieanu, A. Reimers, O. Rieger, C. Scharf, P.
Schleper, S. Schumann, J. Schwandt, J. Sonneveld, H. Stadie, G. Steinbrück,
F.M. Stober, B. Vormwald, I. Zoi Karlsruher Institut fuer Technologie,
Karlsruhe, Germany
M. Akbiyik, C. Barth, M. Baselga, S. Baur, T. Berger, E. Butz, R. Caspart, T.
Chwalek, W. De Boer, A. Dierlamm, K. El Morabit, N. Faltermann, M. Giffels, P.
Goldenzweig, A. Gottmann, M.A. Harrendorf, F. Hartmann16, U. Husemann, S.
Kudella, S. Mitra, M.U. Mozer, D. Müller, Th. Müller, M. Musich, A. Nürnberg,
G. Quast, K. Rabbertz, M. Schröder, I. Shvetsov, H.J. Simonis, R. Ulrich, M.
Wassmer, M. Weber, C. Wöhrmann, R. Wolf Institute of Nuclear and Particle
Physics (INPP), NCSR Demokritos, Aghia Paraskevi, Greece
G. Anagnostou, P. Asenov, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas,
G. Paspalaki National and Kapodistrian University of Athens, Athens, Greece
M. Diamantopoulou, G. Karathanasis, P. Kontaxakis, A. Manousakis-katsikakis,
A. Panagiotou, I. Papavergou, N. Saoulidou, A. Stakia, K. Theofilatos, K.
Vellidis, E. Vourliotis National Technical University of Athens, Athens,
Greece
G. Bakas, K. Kousouris, I. Papakrivopoulos, G. Tsipolitis University of
Ioánnina, Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, S. Mallios, K.
Manitara, N. Manthos, I. Papadopoulos, J. Strologas, F.A. Triantis, D.
Tsitsonis MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
M. Bartók20, R. Chudasama, M. Csanad, P. Major, K. Mandal, A. Mehta, M.I.
Nagy, G. Pasztor, O. Surányi, G.I. Veres Wigner Research Centre for Physics,
Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath21, F. Sikler, T.Á. Vámi, V. Veszpremi, G.
Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear Research ATOMKI,
Debrecen, Hungary
N. Beni, S. Czellar, J. Karancsi20, A. Makovec, J. Molnar, Z. Szillasi
Institute of Physics, University of Debrecen, Debrecen, Hungary
P. Raics, D. Teyssier, Z.L. Trocsanyi, B. Ujvari Eszterhazy Karoly University,
Karoly Robert Campus, Gyongyos, Hungary
T. Csorgo, W.J. Metzger, F. Nemes, T. Novak Indian Institute of Science
(IISc), Bangalore, India
S. Choudhury, J.R. Komaragiri, P.C. Tiwari National Institute of Science
Education and Research, HBNI, Bhubaneswar, India
S. Bahinipati23, C. Kar, G. Kole, P. Mal, V.K. Muraleedharan Nair Bindhu, A.
Nayak24, D.K. Sahoo23, S.K. Swain Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, S. Chauhan, R. Chawla, N. Dhingra, R.
Gupta, A. Kaur, M. Kaur, S. Kaur, P. Kumari, M. Lohan, M. Meena, K. Sandeep,
S. Sharma, J.B. Singh, A.K. Virdi University of Delhi, Delhi, India
A. Bhardwaj, B.C. Choudhary, R.B. Garg, M. Gola, S. Keshri, Ashok Kumar, M.
Naimuddin, P. Priyanka, K. Ranjan, Aashaq Shah, R. Sharma Saha Institute of
Nuclear Physics, HBNI, Kolkata, India
R. Bhardwaj25, M. Bharti25, R. Bhattacharya, S. Bhattacharya, U. Bhawandeep25,
D. Bhowmik, S. Dutta, S. Ghosh, B. Gomber26, M. Maity27, K. Mondal, S. Nandan,
A. Purohit, P.K. Rout, G. Saha, S. Sarkar, T. Sarkar27, M. Sharan, B. Singh25,
S. Thakur25 Indian Institute of Technology Madras, Madras, India
P.K. Behera, P. Kalbhor, A. Muhammad, P.R. Pujahari, A. Sharma, A.K. Sikdar
Bhabha Atomic Research Centre, Mumbai, India
D. Dutta, V. Jha, V. Kumar, D.K. Mishra, P.K. Netrakanti, L.M. Pant, P. Shukla
Tata Institute of Fundamental Research-A, Mumbai, India
T. Aziz, M.A. Bhat, S. Dugad, G.B. Mohanty, N. Sur, RavindraKumar Verma Tata
Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, S. Karmakar,
S. Kumar, G. Majumder, K. Mazumdar, N. Sahoo, S. Sawant Indian Institute of
Science Education and Research (IISER), Pune, India
S. Dube, V. Hegde, B. Kansal, A. Kapoor, K. Kothekar, S. Pandey, A. Rane, A.
Rastogi, S. Sharma Institute for Research in Fundamental Sciences (IPM),
Tehran, Iran
S. Chenarani28, E. Eskandari Tadavani, S.M. Etesami28, M. Khakzad, M.
Mohammadi Najafabadi, M. Naseri, F. Rezaei Hosseinabadi University College
Dublin, Dublin, Ireland
M. Felcini, M. Grunewald INFN Sezione di Bari a, Università di Bari b,
Politecnico di Bari c, Bari, Italy
M. Abbresciaa,b, R. Alya,b,29, C. Calabriaa,b, A. Colaleoa, D. Creanzaa,c, L.
Cristellaa,b, N. De Filippisa,c, M. De Palmaa,b, A. Di Florioa,b, W.
Elmetenaweea,b, L. Fiorea, A. Gelmia,b, G. Iasellia,c, M. Incea,b, S.
Lezkia,b, G. Maggia,c, M. Maggia, G. Minielloa,b, S. Mya,b, S. Nuzzoa,b, A.
Pompilia,b, G. Pugliesea,c, R. Radognaa, A. Ranieria, G. Selvaggia,b, L.
Silvestrisa, 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,b, S. Braibant-
Giacomellia,b, R. Campaninia,b, P. Capiluppia,b, A. Castroa,b, F.R. Cavalloa,
C. Cioccaa, G. Codispotia,b, M. Cuffiania,b, G.M. Dallavallea, F. Fabbria, A.
Fanfania,b, E. Fontanesia,b, P. Giacomellia, C. Grandia, L. Guiduccia,b, F.
Iemmia,b, S. Lo Meoa,30, 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,31, S. Costaa,b, A. Di Mattiaa, R. Potenzaa,b, A. Tricomia,b,31,
C. Tuvea,b INFN Sezione di Firenze a, Università di Firenze b, Firenze, Italy
G. Barbaglia, A. Cassese, R. Ceccarelli, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, E. Focardia,b, G. Latinoa,b, P. Lenzia,b, M. Meschinia, S.
Paolettia, 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, A. Beschia,b, F. Brivioa,b, V. Cirioloa,b,16, S. Di Guidaa,b,16,
M.E. Dinardoa,b, P. Dinia, S. Gennaia, A. Ghezzia,b, P. Govonia,b, L.
Guzzia,b, M. Malbertia, S. Malvezzia, D. Menascea, F. Montia,b, L. Moronia, M.
Paganonia,b, D. Pedrinia, S. Ragazzia,b, T. Tabarelli de Fatisa,b, D. Zuoloa,b
INFN Sezione di Napoli a, Università di Napoli ’Federico II’ b, Napoli, Italy,
Università della Basilicata c, Potenza, Italy, Università G. Marconi d, Roma,
Italy
S. Buontempoa, N. Cavalloa,c, A. De Iorioa,b, A. Di Crescenzoa,b, F.
Fabozzia,c, F. Fiengaa, G. Galatia, A.O.M. Iorioa,b, L. Listaa,b, S.
Meolaa,d,16, P. Paoluccia,16, B. Rossia, C. Sciaccaa,b, E. Voevodinaa,b INFN
Sezione di Padova a, Università di Padova b, Padova, Italy, Università di
Trento c, Trento, Italy
P. Azzia, N. Bacchettaa, D. Biselloa,b, A. Bolettia,b, A. Bragagnoloa,b, R.
Carlina,b, P. Checchiaa, P. De Castro Manzanoa, T. Dorigoa, U. Dossellia, F.
Gasparinia,b, U. Gasparinia,b, A. Gozzelinoa, S.Y. Hoha,b, P. Lujana, M.
Margonia,b, A.T. Meneguzzoa,b, J. Pazzinia,b, M. Presillab, P. Ronchesea,b, R.
Rossina,b, F. Simonettoa,b, A. Tikoa, M. Tosia,b, M. Zanettia,b, P. Zottoa,b,
G. Zumerlea,b INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy
A. Braghieria, 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
M. Biasinia,b, G.M. Bileia, D. Ciangottinia,b, L. Fanòa,b, P. Laricciaa,b, R.
Leonardia,b, E. Manonia, G. Mantovania,b, V. Mariania,b, M. Menichellia, A.
Rossia,b, A. Santocchiaa,b, D. Spigaa INFN Sezione di Pisa a, Università di
Pisa b, Scuola Normale Superiore di Pisa c, Pisa, Italy
K. Androsova, P. Azzurria, G. Bagliesia, V. Bertacchia,c, L. Bianchinia, T.
Boccalia, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, S. Donatoa, G. Fedia,
L. Gianninia,c, A. Giassia, M.T. Grippoa, F. Ligabuea,c, E. Mancaa,c, G.
Mandorlia,c, A. Messineoa,b, F. Pallaa, A. Rizzia,b, G. Rolandi32, S. Roy
Chowdhury, A. Scribanoa, P. Spagnoloa, R. Tenchinia, G. Tonellia,b, N. Turini,
A. Venturia, P.G. Verdinia INFN Sezione di Roma a, Sapienza Università di Roma
b, Rome, Italy
F. Cavallaria, M. Cipriania,b, D. Del Rea,b, E. Di Marcoa,b, M. Diemoza, E.
Longoa,b, P. Meridiania, G. Organtinia,b, F. Pandolfia, R. Paramattia,b, C.
Quarantaa,b, S. Rahatloua,b, C. Rovellia, F. Santanastasioa,b, L. Soffia,b
INFN Sezione di Torino a, Università di Torino b, Torino, Italy, Università
del Piemonte Orientale c, Novara, Italy
N. Amapanea,b, R. Arcidiaconoa,c, S. Argiroa,b, M. Arneodoa,c, N. Bartosika,
R. Bellana,b, A. Bellora, C. Biinoa, A. Cappatia,b, N. Cartigliaa, S.
Comettia, M. Costaa,b, R. Covarellia,b, N. Demariaa, B. Kiania,b, F. Legger,
C. Mariottia, S. Masellia, E. Migliorea,b, V. Monacoa,b, E. Monteila,b, M.
Montenoa, M.M. Obertinoa,b, G. Ortonaa,b, L. Pachera,b, N. Pastronea, M.
Pelliccionia, G.L. Pinna Angionia,b, A. Romeroa,b, M. Ruspaa,c, R.
Salvaticoa,b, V. Solaa, A. Solanoa,b, D. Soldia,b, A. Staianoa, 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, A. Zanettia Kyungpook National University,
Daegu, Korea
B. Kim, D.H. Kim, G.N. Kim, J. Lee, S.W. Lee, C.S. Moon, Y.D. Oh, S.I. Pak, S.
Sekmen, D.C. Son, Y.C. Yang Chonnam National University, Institute for
Universe and Elementary Particles, Kwangju, Korea
H. Kim, D.H. Moon, G. Oh Hanyang University, Seoul, Korea
B. Francois, T.J. Kim, J. Park Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, S. Ha, B. Hong, K. Lee, K.S. Lee, J. Lim, J. Park,
S.K. Park, Y. Roh, J. Yoo Kyung Hee University, Department of Physics
J. Goh Sejong University, Seoul, Korea
H.S. Kim Seoul National University, Seoul, Korea
J. Almond, J.H. Bhyun, J. Choi, S. Jeon, J. Kim, J.S. Kim, H. Lee, K. Lee, S.
Lee, K. Nam, M. Oh, S.B. Oh, B.C. Radburn-Smith, U.K. Yang, H.D. Yoo, I. Yoon
University of Seoul, Seoul, Korea
D. Jeon, H. Kim, J.H. Kim, J.S.H. Lee, I.C. Park, I.J Watson Sungkyunkwan
University, Suwon, Korea
Y. Choi, C. Hwang, Y. Jeong, J. Lee, Y. Lee, I. Yu Riga Technical University,
Riga, Latvia
V. Veckalns33 Vilnius University, Vilnius, Lithuania
V. Dudenas, A. Juodagalvis, A. Rinkevicius, G. Tamulaitis, J. Vaitkus National
Centre for Particle Physics, Universiti Malaya, Kuala Lumpur, Malaysia
Z.A. Ibrahim, F. Mohamad Idris34, W.A.T. Wan Abdullah, M.N. Yusli, Z. Zolkapli
Universidad de Sonora (UNISON), Hermosillo, Mexico
J.F. Benitez, A. Castaneda Hernandez, J.A. Murillo Quijada, L. Valencia Palomo
Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico
H. Castilla-Valdez, E. De La Cruz-Burelo, I. Heredia-De La Cruz35, R. Lopez-
Fernandez, A. Sanchez-Hernandez Universidad Iberoamericana, Mexico City,
Mexico
S. Carrillo Moreno, C. Oropeza Barrera, M. Ramirez-Garcia, F. Vazquez Valencia
Benemerita Universidad Autonoma de Puebla, Puebla, Mexico
J. Eysermans, I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada Universidad
Autónoma de San Luis Potosí, San Luis Potosí, Mexico
A. Morelos Pineda University of Montenegro, Podgorica, Montenegro
J. Mijuskovic2, N. Raicevic University of Auckland, Auckland, New Zealand
D. Krofcheck University of Canterbury, Christchurch, New Zealand
S. Bheesette, P.H. Butler National Centre for Physics, Quaid-I-Azam
University, Islamabad, Pakistan
A. Ahmad, M. Ahmad, Q. Hassan, H.R. Hoorani, W.A. Khan, M.A. Shah, M. Shoaib,
M. Waqas AGH University of Science and Technology Faculty of Computer Science,
Electronics and Telecommunications, Krakow, Poland
V. Avati, L. Grzanka, M. Malawski National Centre for Nuclear Research,
Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, M. Górski, M. Kazana, M. Szleper, P.
Zalewski Institute of Experimental Physics, Faculty of Physics, University of
Warsaw, Warsaw, Poland
K. Bunkowski, A. Byszuk36, K. Doroba, A. Kalinowski, M. Konecki, J.
Krolikowski, M. Misiura, M. Olszewski, M. Walczak Laboratório de
Instrumentação e Física Experimental de Partículas, Lisboa, Portugal
M. Araujo, P. Bargassa, D. Bastos, A. Di Francesco, P. Faccioli, B. Galinhas,
M. Gallinaro, J. Hollar, N. Leonardo, T. Niknejad, J. Seixas, K. Shchelina, G.
Strong, O. Toldaiev, J. Varela Joint Institute for Nuclear Research, Dubna,
Russia
S. Afanasiev, P. Bunin, M. Gavrilenko, I. Golutvin, I. Gorbunov, A. Kamenev,
V. Karjavine, A. Lanev, A. Malakhov, V. Matveev37,38, P. Moisenz, V. Palichik,
V. Perelygin, M. Savina, S. Shmatov, S. Shulha, N. Skatchkov, V. Smirnov, N.
Voytishin, A. Zarubin Petersburg Nuclear Physics Institute, Gatchina (St.
Petersburg), Russia
L. Chtchipounov, V. Golovtcov, Y. Ivanov, V. Kim39, E. Kuznetsova40, P.
Levchenko, V. Murzin, V. Oreshkin, I. Smirnov, D. Sosnov, V. Sulimov, L.
Uvarov, A. Vorobyev Institute for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov,
N. Krasnikov, A. Pashenkov, D. Tlisov, A. Toropin Institute for Theoretical
and Experimental Physics named by A.I. Alikhanov of NRC ‘Kurchatov Institute’,
Moscow, Russia
V. Epshteyn, V. Gavrilov, N. Lychkovskaya, A. Nikitenko41, V. Popov, I.
Pozdnyakov, G. Safronov, A. Spiridonov, A. Stepennov, M. Toms, E. Vlasov, A.
Zhokin Moscow Institute of Physics and Technology, Moscow, Russia
T. Aushev National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
M. Chadeeva42, P. Parygin, D. Philippov, E. Popova, V. Rusinov P.N. Lebedev
Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin, I. Dremin, M. Kirakosyan, A. Terkulov Skobeltsyn
Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow,
Russia
A. Baskakov, A. Belyaev, E. Boos, V. Bunichev, M. Dubinin43, L. Dudko, A.
Gribushin, V. Klyukhin, I. Lokhtin, S. Obraztsov, M. Perfilov, S. Petrushanko,
V. Savrin Novosibirsk State University (NSU), Novosibirsk, Russia
A. Barnyakov44, V. Blinov44, T. Dimova44, L. Kardapoltsev44, Y. Skovpen44
Institute for High Energy Physics of National Research Centre ‘Kurchatov
Institute’, Protvino, Russia
I. Azhgirey, I. Bayshev, S. Bitioukov, V. Kachanov, D. Konstantinov, P.
Mandrik, V. Petrov, R. Ryutin, S. Slabospitskii, A. Sobol, S. Troshin, N.
Tyurin, A. Uzunian, A. Volkov National Research Tomsk Polytechnic University,
Tomsk, Russia
A. Babaev, A. Iuzhakov, V. Okhotnikov Tomsk State University, Tomsk, Russia
V. Borchsh, V. Ivanchenko, E. Tcherniaev University of Belgrade: Faculty of
Physics and VINCA Institute of Nuclear Sciences
P. Adzic45, P. Cirkovic, M. Dordevic, P. Milenovic, J. Milosevic, M.
Stojanovic Centro de Investigaciones Energéticas Medioambientales y
Tecnológicas (CIEMAT), Madrid, Spain
M. Aguilar-Benitez, J. Alcaraz Maestre, A. Álvarez Fernández, I. Bachiller, M.
Barrio Luna, CristinaF. Bedoya, J.A. Brochero Cifuentes, 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, D. Moran, Á. Navarro Tobar, A. Pérez-Calero
Yzquierdo, J. Puerta Pelayo, I. Redondo, L. Romero, S. Sánchez Navas, M.S.
Soares, A. Triossi, C. Willmott Universidad Autónoma de Madrid, Madrid, Spain
C. Albajar, J.F. de Trocóniz, 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, J.R. González Fernández, E. Palencia Cortezon, V.
Rodríguez Bouza, S. Sanchez Cruz Instituto de Física de Cantabria (IFCA),
CSIC-Universidad de Cantabria, Santander, Spain
I.J. Cabrillo, A. Calderon, B. Chazin Quero, J. Duarte Campderros, M.
Fernandez, P.J. Fernández Manteca, A. García Alonso, G. Gomez, C. Martinez
Rivero, P. Martinez Ruiz del Arbol, F. Matorras, J. Piedra Gomez, C. Prieels,
T. Rodrigo, A. Ruiz-Jimeno, L. Russo46, L. Scodellaro, I. Vila, J.M. Vizan
Garcia University of Colombo, Colombo, Sri Lanka
K. Malagalage University of Ruhuna, Department of Physics, Matara, Sri Lanka
W.G.D. Dharmaratna, N. Wickramage CERN, European Organization for Nuclear
Research, Geneva, Switzerland
D. Abbaneo, B. Akgun, E. Auffray, G. Auzinger, J. Baechler, P. Baillon, A.H.
Ball, D. Barney, J. Bendavid, M. Bianco, A. Bocci, P. Bortignon, E. Bossini,
C. Botta, E. Brondolin, T. Camporesi, A. Caratelli, G. Cerminara, E. Chapon,
G. Cucciati, D. d’Enterria, A. Dabrowski, N. Daci, V. Daponte, A. David, O.
Davignon, A. De Roeck, M. Deile, M. Dobson, M. Dünser, N. Dupont, A. Elliott-
Peisert, N. Emriskova, F. Fallavollita47, D. Fasanella, S. Fiorendi, G.
Franzoni, J. Fulcher, W. Funk, S. Giani, D. Gigi, A. Gilbert, K. Gill, F.
Glege, L. Gouskos, M. Gruchala, M. Guilbaud, D. Gulhan, J. Hegeman, C.
Heidegger, Y. Iiyama, V. Innocente, T. James, P. Janot, O. Karacheban19, J.
Kaspar, J. Kieseler, M. Krammer1, N. Kratochwil, C. Lange, P. Lecoq, C.
Lourenço, L. Malgeri, M. Mannelli, A. Massironi, F. Meijers, J.A. Merlin, S.
Mersi, E. Meschi, F. Moortgat, M. Mulders, J. Ngadiuba, J. Niedziela, S.
Nourbakhsh, S. Orfanelli, L. Orsini, F. Pantaleo16, L. Pape, E. Perez, M.
Peruzzi, A. Petrilli, G. Petrucciani, A. Pfeiffer, M. Pierini, F.M. Pitters,
D. Rabady, A. Racz, M. Rieger, M. Rovere, H. Sakulin, C. Schäfer, C. Schwick,
M. Selvaggi, A. Sharma, P. Silva, W. Snoeys, P. Sphicas48, J. Steggemann, S.
Summers, V.R. Tavolaro, D. Treille, A. Tsirou, G.P. Van Onsem, A. Vartak, M.
Verzetti, W.D. Zeuner Paul Scherrer Institut, Villigen, Switzerland
L. Caminada49, K. Deiters, W. Erdmann, R. Horisberger, Q. Ingram, H.C.
Kaestli, D. Kotlinski, U. Langenegger, T. Rohe, S.A. Wiederkehr ETH Zurich -
Institute for Particle Physics and Astrophysics (IPA), Zurich, Switzerland
M. Backhaus, P. Berger, N. Chernyavskaya, G. Dissertori, M. Dittmar, M.
Donegà, C. Dorfer, T.A. Gómez Espinosa, C. Grab, D. Hits, W. Lustermann, R.A.
Manzoni, M.T. Meinhard, F. Micheli, P. Musella, F. Nessi-Tedaldi, F. Pauss, G.
Perrin, L. Perrozzi, S. Pigazzini, M.G. Ratti, M. Reichmann, C. Reissel, T.
Reitenspiess, B. Ristic, D. Ruini, D.A. Sanz Becerra, M. Schönenberger, L.
Shchutska, M.L. Vesterbacka Olsson, R. Wallny, D.H. Zhu Universität Zürich,
Zurich, Switzerland
T.K. Aarrestad, C. Amsler50, D. Brzhechko, M.F. Canelli, A. De Cosa, R. Del
Burgo, B. Kilminster, S. Leontsinis, V.M. Mikuni, I. Neutelings, G. Rauco, P.
Robmann, K. Schweiger, C. Seitz, Y. Takahashi, S. Wertz, A. Zucchetta National
Central University, Chung-Li, Taiwan
T.H. Doan, C.M. Kuo, W. Lin, A. Roy, S.S. Yu National Taiwan University (NTU),
Taipei, Taiwan
P. Chang, Y. Chao, K.F. Chen, P.H. Chen, W.-S. Hou, Y.y. Li, R.-S. Lu, E.
Paganis, A. Psallidas, A. Steen Chulalongkorn University, Faculty of Science,
Department of Physics, Bangkok, Thailand
B. Asavapibhop, C. Asawatangtrakuldee, N. Srimanobhas, N. Suwonjandee Çukurova
University, Physics Department, Science and Art Faculty, Adana, Turkey
A. Bat, F. Boran, A. Celik51, S. Cerci52, S. Damarseckin53, Z.S. Demiroglu, F.
Dolek, C. Dozen54, I. Dumanoglu, G. Gokbulut, EmineGurpinar Guler55, Y. Guler,
I. Hos56, C. Isik, E.E. Kangal57, O. Kara, A. Kayis Topaksu, U. Kiminsu, G.
Onengut, K. Ozdemir58, S. Ozturk59, A.E. Simsek, D. Sunar Cerci52, U.G. Tok,
S. Turkcapar, I.S. Zorbakir, C. Zorbilmez Middle East Technical University,
Physics Department, Ankara, Turkey
B. Isildak60, G. Karapinar61, M. Yalvac Bogazici University, Istanbul, Turkey
I.O. Atakisi, E. Gülmez, M. Kaya62, O. Kaya63, Ö. Özçelik, S. Tekten, E.A.
Yetkin64 Istanbul Technical University, Istanbul, Turkey
A. Cakir, K. Cankocak, Y. Komurcu, S. Sen65 Istanbul University, Istanbul,
Turkey
B. Kaynak, S. Ozkorucuklu Institute for Scintillation Materials of National
Academy of Science of Ukraine, Kharkov, Ukraine
B. Grynyov National Scientific Center, Kharkov Institute of Physics and
Technology, Kharkov, Ukraine
L. Levchuk University of Bristol, Bristol, United Kingdom
E. Bhal, S. Bologna, J.J. Brooke, D. Burns66, E. Clement, D. Cussans, H.
Flacher, J. Goldstein, G.P. Heath, H.F. Heath, L. Kreczko, B. Krikler, S.
Paramesvaran, B. Penning, T. Sakuma, S. Seif El Nasr-Storey, V.J. Smith, J.
Taylor, A. Titterton Rutherford Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev67, C. Brew, R.M. Brown, D.J.A. Cockerill, J.A. Coughlan,
K. Harder, S. Harper, J. Linacre, K. Manolopoulos, D.M. Newbold, E. Olaiya, D.
Petyt, T. Reis, T. Schuh, C.H. Shepherd-Themistocleous, A. Thea, I.R. Tomalin,
T. Williams, W.J. Womersley Imperial College, London, United Kingdom
R. Bainbridge, P. Bloch, J. Borg, S. Breeze, O. Buchmuller, A. Bundock,
GurpreetSingh CHAHAL68, D. Colling, P. Dauncey, G. Davies, M. Della Negra, R.
Di Maria, P. Everaerts, G. Hall, G. Iles, M. Komm, C. Laner, L. Lyons, A.-M.
Magnan, S. Malik, A. Martelli, V. Milosevic, A. Morton, J. Nash69, V.
Palladino, M. Pesaresi, D.M. Raymond, A. Richards, A. Rose, E. Scott, C. Seez,
A. Shtipliyski, M. Stoye, T. Strebler, A. Tapper, K. Uchida, T. Virdee16, N.
Wardle, D. Winterbottom, J. Wright, A.G. Zecchinelli, S.C. Zenz Brunel
University, Uxbridge, United Kingdom
J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, C.K. Mackay, I.D. Reid, L.
Teodorescu, S. Zahid Baylor University, Waco, USA
K. Call, B. Caraway, J. Dittmann, K. Hatakeyama, C. Madrid, B. McMaster, N.
Pastika, C. Smith Catholic University of America, Washington, DC, USA
R. Bartek, A. Dominguez, R. Uniyal, A.M. Vargas Hernandez The University of
Alabama, Tuscaloosa, USA
A. Buccilli, S.I. Cooper, C. Henderson, P. Rumerio, C. West Boston University,
Boston, USA
A. Albert, D. Arcaro, Z. Demiragli, D. Gastler, C. Richardson, J. Rohlf, D.
Sperka, I. Suarez, L. Sulak, D. Zou Brown University, Providence, USA
G. Benelli, B. Burkle, X. Coubez17, D. Cutts, Y.t. Duh, M. Hadley, U. Heintz,
J.M. Hogan70, K.H.M. Kwok, E. Laird, G. Landsberg, K.T. Lau, J. Lee, Z. Mao,
M. Narain, S. Sagir71, R. Syarif, E. Usai, D. Yu, W. Zhang University of
California, Davis, Davis, USA
R. Band, C. Brainerd, R. Breedon, M. Calderon De La Barca Sanchez, M. Chertok,
J. Conway, R. Conway, P.T. Cox, R. Erbacher, C. Flores, G. Funk, F. Jensen, W.
Ko, O. Kukral, R. Lander, M. Mulhearn, D. Pellett, J. Pilot, M. Shi, D.
Taylor, K. Tos, M. Tripathi, Z. Wang, F. Zhang University of California, Los
Angeles, USA
M. Bachtis, C. Bravo, R. Cousins, A. Dasgupta, A. Florent, J. Hauser, M.
Ignatenko, N. Mccoll, W.A. Nash, S. Regnard, D. Saltzberg, C. Schnaible, B.
Stone, V. Valuev University of California, Riverside, Riverside, USA
K. Burt, Y. Chen, R. Clare, J.W. Gary, S.M.A. Ghiasi Shirazi, G. Hanson, G.
Karapostoli, E. Kennedy, O.R. Long, M. Olmedo Negrete, M.I. Paneva, W. Si, L.
Wang, S. Wimpenny, B.R. Yates, Y. Zhang University of California, San Diego,
La Jolla, USA
J.G. Branson, P. Chang, S. Cittolin, S. Cooperstein, N. Deelen, M. Derdzinski,
R. Gerosa, D. Gilbert, B. Hashemi, D. Klein, V. Krutelyov, J. Letts, M.
Masciovecchio, S. May, S. Padhi, M. Pieri, V. Sharma, M. Tadel, F. Würthwein,
A. Yagil, G. Zevi Della Porta University of California, Santa Barbara -
Department of Physics, Santa Barbara, USA
N. Amin, R. Bhandari, C. Campagnari, M. Citron, V. Dutta, M. Franco Sevilla,
J. Incandela, B. Marsh, H. Mei, A. Ovcharova, H. Qu, J. Richman, U. Sarica, D.
Stuart, S. Wang California Institute of Technology, Pasadena, USA
D. Anderson, A. Bornheim, O. Cerri, I. Dutta, J.M. Lawhorn, N. Lu, J. Mao,
H.B. Newman, T.Q. Nguyen, J. Pata, M. Spiropulu, J.R. Vlimant, S. Xie, Z.
Zhang, R.Y. Zhu Carnegie Mellon University, Pittsburgh, USA
M.B. Andrews, T. Ferguson, T. Mudholkar, M. Paulini, M. Sun, I. Vorobiev, M.
Weinberg University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, E. MacDonald, T. Mulholland, R. Patel, A. Perloff, K.
Stenson, K.A. Ulmer, S.R. Wagner Cornell University, Ithaca, USA
J. Alexander, Y. Cheng, J. Chu, A. Datta, A. Frankenthal, K. Mcdermott, J.R.
Patterson, D. Quach, A. Ryd, S.M. Tan, Z. Tao, J. Thom, P. Wittich, M. Zientek
Fermi National Accelerator Laboratory, Batavia, USA
S. Abdullin, M. Albrow, M. Alyari, G. Apollinari, A. Apresyan, A. Apyan, S.
Banerjee, L.A.T. Bauerdick, A. Beretvas, 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, J. Duarte, V.D. Elvira, J. Freeman, Z. Gecse, E. Gottschalk, L.
Gray, D. Green, S. Grünendahl, O. Gutsche, AllisonReinsvold Hall, J. Hanlon,
R.M. Harris, S. Hasegawa, R. Heller, J. Hirschauer, B. Jayatilaka, S.
Jindariani, M. Johnson, U. Joshi, T. Klijnsma, B. Klima, M.J. Kortelainen, B.
Kreis, S. Lammel, J. Lewis, D. Lincoln, R. Lipton, M. Liu, T. Liu, J. Lykken,
K. Maeshima, J.M. Marraffino, D. Mason, P. McBride, P. Merkel, S. Mrenna, S.
Nahn, V. O’Dell, V. Papadimitriou, K. Pedro, C. Pena, G. Rakness, F. Ravera,
L. Ristori, B. Schneider, E. Sexton-Kennedy, N. Smith, A. Soha, W.J. Spalding,
L. Spiegel, S. Stoynev, J. Strait, N. Strobbe, L. Taylor, S. Tkaczyk, N.V.
Tran, L. Uplegger, E.W. Vaandering, C. Vernieri, R. Vidal, M. Wang, H.A. Weber
University of Florida, Gainesville, USA
D. Acosta, P. Avery, D. Bourilkov, A. Brinkerhoff, L. Cadamuro, A. Carnes, V.
Cherepanov, F. Errico, R.D. Field, S.V. Gleyzer, B.M. Joshi, M. Kim, J.
Konigsberg, A. Korytov, K.H. Lo, P. Ma, K. Matchev, N. Menendez, G.
Mitselmakher, D. Rosenzweig, K. Shi, J. Wang, S. Wang, X. Zuo Florida
International University, Miami, USA
Y.R. Joshi Florida State University, Tallahassee, USA
T. Adams, A. Askew, S. Hagopian, V. Hagopian, K.F. Johnson, R. Khurana, T.
Kolberg, G. Martinez, T. Perry, H. Prosper, C. Schiber, R. Yohay, J. Zhang
Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, M. Hohlmann, D. Noonan, M. Rahmani, M. Saunders, F. Yumiceva
University of Illinois at Chicago (UIC), Chicago, USA
M.R. Adams, L. Apanasevich, R.R. Betts, R. Cavanaugh, X. Chen, S. Dittmer, O.
Evdokimov, C.E. Gerber, D.A. Hangal, D.J. Hofman, K. Jung, C. Mills, T. Roy,
M.B. Tonjes, N. Varelas, J. Viinikainen, H. Wang, X. Wang, Z. Wu The
University of Iowa, Iowa City, USA
M. Alhusseini, B. Bilki55, W. Clarida, K. Dilsiz72, S. Durgut, R.P.
Gandrajula, M. Haytmyradov, V. Khristenko, O.K. Köseyan, J.-P. Merlo, A.
Mestvirishvili73, A. Moeller, J. Nachtman, H. Ogul74, Y. Onel, F. Ozok75, A.
Penzo, C. Snyder, E. Tiras, J. Wetzel Johns Hopkins University, Baltimore, USA
B. Blumenfeld, A. Cocoros, N. Eminizer, A.V. Gritsan, W.T. Hung, S. Kyriacou,
P. Maksimovic, J. Roskes, M. Swartz The University of Kansas, Lawrence, USA
C. Baldenegro Barrera, P. Baringer, A. Bean, S. Boren, J. Bowen, A. Bylinkin,
T. Isidori, S. Khalil, J. King, G. Krintiras, A. Kropivnitskaya, C. Lindsey,
D. Majumder, W. Mcbrayer, N. Minafra, M. Murray, C. Rogan, C. Royon, S.
Sanders, E. Schmitz, J.D. Tapia Takaki, Q. Wang, J. Williams, G. Wilson Kansas
State University, Manhattan, USA
S. Duric, A. Ivanov, K. Kaadze, D. Kim, Y. Maravin, D.R. Mendis, T. Mitchell,
A. Modak, A. Mohammadi Lawrence Livermore National Laboratory, Livermore, USA
F. Rebassoo, D. Wright University of Maryland, College Park, USA
A. Baden, O. Baron, A. Belloni, S.C. Eno, Y. Feng, N.J. Hadley, S. Jabeen,
G.Y. Jeng, R.G. Kellogg, J. Kunkle, A.C. Mignerey, S. Nabili, F. Ricci-Tam, M.
Seidel, Y.H. Shin, A. Skuja, S.C. Tonwar, K. Wong Massachusetts Institute of
Technology, Cambridge, USA
D. Abercrombie, B. Allen, A. Baty, R. Bi, S. Brandt, W. Busza, I.A. Cali, M.
D’Alfonso, G. Gomez Ceballos, M. Goncharov, P. Harris, D. Hsu, M. Hu, M.
Klute, D. Kovalskyi, Y.-J. Lee, P.D. Luckey, B. Maier, A.C. Marini, C. Mcginn,
C. Mironov, S. Narayanan, X. Niu, C. Paus, D. Rankin, C. Roland, G. Roland, Z.
Shi, G.S.F. Stephans, K. Sumorok, K. Tatar, D. Velicanu, J. Wang, T.W. Wang,
B. Wyslouch University of Minnesota, Minneapolis, USA
R.M. Chatterjee, A. Evans, S. Guts${}^{\textrm{\textdagger}}$, P. Hansen, J.
Hiltbrand, Sh. Jain, Y. Kubota, Z. Lesko, J. Mans, M. Revering, R. Rusack, R.
Saradhy, N. Schroeder, M.A. Wadud University of Mississippi, Oxford, USA
J.G. Acosta, S. Oliveros University of Nebraska-Lincoln, Lincoln, USA
K. Bloom, S. Chauhan, D.R. Claes, C. Fangmeier, L. Finco, F. Golf, R.
Kamalieddin, I. Kravchenko, J.E. Siado, G.R. Snow${}^{\textrm{\textdagger}}$,
B. Stieger, W. Tabb State University of New York at Buffalo, Buffalo, USA
G. Agarwal, C. Harrington, I. Iashvili, A. Kharchilava, C. McLean, D. Nguyen,
A. Parker, J. Pekkanen, S. Rappoccio, B. Roozbahani Northeastern University,
Boston, USA
G. Alverson, E. Barberis, C. Freer, Y. Haddad, A. Hortiangtham, G. Madigan, B.
Marzocchi, D.M. Morse, T. Orimoto, L. Skinnari, A. Tishelman-Charny, T.
Wamorkar, B. Wang, A. Wisecarver, D. Wood Northwestern University, Evanston,
USA
S. Bhattacharya, J. Bueghly, T. Gunter, K.A. Hahn, N. Odell, M.H. Schmitt, K.
Sung, M. Trovato, M. Velasco University of Notre Dame, Notre Dame, USA
R. Bucci, N. Dev, R. Goldouzian, M. Hildreth, K. Hurtado Anampa, C. Jessop,
D.J. Karmgard, K. Lannon, W. Li, N. Loukas, N. Marinelli, I. Mcalister, F.
Meng, C. Mueller, Y. Musienko37, M. Planer, R. Ruchti, P. Siddireddy, G.
Smith, S. Taroni, M. Wayne, A. Wightman, M. Wolf, A. Woodard The Ohio State
University, Columbus, USA
J. Alimena, B. Bylsma, L.S. Durkin, B. Francis, C. Hill, W. Ji, A. Lefeld,
T.Y. Ling, B.L. Winer Princeton University, Princeton, USA
G. Dezoort, P. Elmer, J. Hardenbrook, N. Haubrich, S. Higginbotham, A.
Kalogeropoulos, S. Kwan, D. Lange, M.T. Lucchini, J. Luo, D. Marlow, K. Mei,
I. Ojalvo, J. Olsen, C. Palmer, P. Piroué, J. Salfeld-Nebgen, D. Stickland, C.
Tully, Z. Wang University of Puerto Rico, Mayaguez, USA
S. Malik, S. Norberg Purdue University, West Lafayette, USA
A. Barker, V.E. Barnes, S. Das, L. Gutay, M. Jones, A.W. Jung, A. Khatiwada,
B. Mahakud, D.H. Miller, G. Negro, N. Neumeister, C.C. Peng, S. Piperov, H.
Qiu, J.F. Schulte, N. Trevisani, F. Wang, R. Xiao, W. Xie Purdue University
Northwest, Hammond, USA
T. Cheng, J. Dolen, N. Parashar Rice University, Houston, USA
U. Behrens, K.M. Ecklund, S. Freed, F.J.M. Geurts, M. Kilpatrick, Arun Kumar,
W. Li, B.P. Padley, R. Redjimi, J. Roberts, J. Rorie, W. Shi, A.G. Stahl
Leiton, Z. Tu, A. Zhang University of Rochester, Rochester, USA
A. Bodek, P. de Barbaro, R. Demina, J.L. Dulemba, C. Fallon, T. Ferbel, M.
Galanti, A. Garcia-Bellido, 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, I. Laflotte, A. Lath, R. Montalvo, K. Nash,
M. Osherson, H. Saka, S. Salur, S. Schnetzer, S. Somalwar, R. Stone, S. Thomas
University of Tennessee, Knoxville, USA
H. Acharya, A.G. Delannoy, S. Spanier Texas A&M University, College Station,
USA
O. Bouhali76, M. Dalchenko, M. De Mattia, A. Delgado, S. Dildick, R. Eusebi,
J. Gilmore, T. Huang, T. Kamon77, S. Luo, S. Malhotra, D. Marley, R. Mueller,
D. Overton, L. Perniè, D. Rathjens, A. Safonov Texas Tech University, Lubbock,
USA
N. Akchurin, J. Damgov, F. De Guio, S. Kunori, K. Lamichhane, S.W. Lee, T.
Mengke, S. Muthumuni, T. Peltola, S. Undleeb, I. Volobouev, Z. Wang, A.
Whitbeck Vanderbilt University, Nashville, USA
S. Greene, A. Gurrola, R. Janjam, W. Johns, C. Maguire, A. Melo, H. Ni, K.
Padeken, F. Romeo, P. Sheldon, S. Tuo, J. Velkovska, M. Verweij University of
Virginia, Charlottesville, USA
M.W. Arenton, P. Barria, B. Cox, G. Cummings, J. Hakala, R. Hirosky, M. Joyce,
A. Ledovskoy, C. Neu, B. Tannenwald, Y. Wang, E. Wolfe, F. Xia Wayne State
University, Detroit, USA
R. Harr, P.E. Karchin, N. Poudyal, J. Sturdy, P. Thapa University of Wisconsin
- Madison, Madison, WI, USA
T. Bose, J. Buchanan, C. Caillol, D. Carlsmith, S. Dasu, I. De Bruyn, L. Dodd,
F. Fiori, C. Galloni, H. He, M. Herndon, A. Hervé, U. Hussain, P. Klabbers, A.
Lanaro, A. Loeliger, K. Long, R. Loveless, J. Madhusudanan Sreekala, D. Pinna,
T. Ruggles, A. Savin, V. Sharma, W.H. Smith, D. Teague, S. Trembath-reichert,
N. Woods †: Deceased
1: Also at Vienna University of Technology, Vienna, Austria
2: Also at IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
3: Also at Universidade Estadual de Campinas, Campinas, Brazil
4: Also at Federal University of Rio Grande do Sul, Porto Alegre, Brazil
5: Also at UFMS, Nova Andradina, Brazil
6: Also at Universidade Federal de Pelotas, Pelotas, Brazil
7: Also at Université Libre de Bruxelles, Bruxelles, Belgium
8: Also at University of Chinese Academy of Sciences, Beijing, China
9: Also at Institute for Theoretical and Experimental Physics named by A.I.
Alikhanov of NRC ‘Kurchatov Institute’, Moscow, Russia
10: Also at Joint Institute for Nuclear Research, Dubna, Russia
11: Also at Cairo University, Cairo, Egypt
12: Also at Zewail City of Science and Technology, Zewail, Egypt
13: Also at Purdue University, West Lafayette, USA
14: Also at Université de Haute Alsace, Mulhouse, France
15: Also at Erzincan Binali Yildirim University, Erzincan, Turkey
16: Also at CERN, European Organization for Nuclear Research, Geneva,
Switzerland
17: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
18: Also at University of Hamburg, Hamburg, Germany
19: Also at Brandenburg University of Technology, Cottbus, Germany
20: Also at Institute of Physics, University of Debrecen, Debrecen, Hungary,
Debrecen, Hungary
21: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary
22: Also at MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary, Budapest, Hungary
23: Also at IIT Bhubaneswar, Bhubaneswar, India, Bhubaneswar, India
24: Also at Institute of Physics, Bhubaneswar, India
25: Also at Shoolini University, Solan, India
26: Also at University of Hyderabad, Hyderabad, India
27: Also at University of Visva-Bharati, Santiniketan, India
28: Also at Isfahan University of Technology, Isfahan, Iran
29: Now at INFN Sezione di Bari a, Università di Bari b, Politecnico di Bari
c, Bari, Italy
30: Also at Italian National Agency for New Technologies, Energy and
Sustainable Economic Development, Bologna, Italy