SUS-17-005

$HeadURL:svn+ssh://svn.cern.ch/reps/tdr2/papers/SUS-17-005/trunk/SUS-17-005.tex$
$Id:SUS-17-005.tex4741272018-09-0614:55:40Zbargassa$

SUS-17-005

# Search for top squarks decaying via four-body or chargino-mediated modes in
single-lepton final states in proton-proton collisions at $\sqrt{s}=13\TeV$

###### Abstract

A search for the pair production of the lightest supersymmetric partner of the
top quark ($\PSQt_{1}$) is presented. The search focuses on a compressed
scenario where the mass difference between the top squark and the lightest
supersymmetric particle, often considered to be the lightest neutralino (), is
smaller than the mass of the boson. The proton-proton collision data were
recorded by the CMS experiment at a centre-of-mass energy of 13, and
correspond to an integrated luminosity of 35.9. In this search, two decay
modes of the top squark are considered: a four-body decay into a bottom quark,
two additional fermions, and a ; and a decay via an intermediate chargino.
Events are selected using the presence of a high-momentum jet, significant
missing transverse momentum, and a low transverse momentum electron or muon.
Two analysis techniques are used, targeting different decay modes of the
$\PSQt_{1}$: a sequential selection and a multivariate technique. No evidence
for the production of top squarks is found, and mass limits at 95% confidence
level are set that reach up to 560, depending on the
$m(\PSQt_{1})-m(\PSGczDo)$ mass difference and the decay mode.

##  0.1 Introduction

Searches for new phenomena, in particular supersymmetry (SUSY) [1, 2, 3, 4, 5,
6], are among the main objectives of the physics programme at the CERN LHC.
Supersymmetry, which is one of the most promising extensions of the standard
model (SM), predicts superpartners of SM particles, where the spin of each new
particle differs by one-half unit with respect to its SM counterpart. If
$R$-parity [7], a new quantum number, is conserved, supersymmetric particles
would be pair-produced and their decay chains would end with the lightest
supersymmetric particle (LSP). Supersymmetric models can offer solutions to
several shortcomings of the SM, in particular those related to the explanation
of the mass hierarchy of elementary particles [8, 9] and to the presence of
dark matter in the universe. The search for SUSY has special interest in view
of the recent discovery of the Higgs boson [10, 11, 12] as it naturally solves
the problem of quadratically divergent loop corrections to the mass of the
Higgs boson by associating with each SM particle a supersymmetric partner
having the same gauge quantum numbers. In many models of SUSY, the lightest
neutralino is the LSP and, being neutral and weakly interacting, would match
the characteristics required for a dark matter particle.

Supersymmetry predicts a scalar partner for each SM left- and right-handed
fermion. When SUSY is broken, the scalar partners acquire masses different
from those of their SM counterparts, and the mass splitting between the two
squark mass eigenstates is proportional to the mass of their SM partner. Given
the large mass of the top quark, this splitting can be the largest among all
squarks. Therefore the lightest supersymmetric partner of the top quark, the
$\PSQt_{1}$, is often the lightest squark. Furthermore, if SUSY is a symmetry
of nature, cosmological observations may suggest the lightest top squark to be
almost degenerate with the LSP [13]. This motivates the search for a four-body
$\PSQt_{1}$ decay:
$\PSQt_{1}\to\cPqb\mathrm{f}\overline{\mathrm{f}}^{\,\prime}\PSGczDo$, where
the fermions f and $\overline{\mathrm{f}}^{\,\prime}$ can be either quarks or
leptons. Here, due to the small mass difference between the $\PSQt_{1}$ and
the , two-body ($\PSQt_{1}\to\cPqt\PSGczDo$,
$\PSQt_{1}\to\cPqb\tilde{\chi}_{1}^{+}$) and three-body
($\PSQt_{1}\to\cPqb\PWp\PSGczDo$) decays of the lightest top squark are
kinematically forbidden, and the two-body ($\PSQt_{1}\to\cPqc\PSGczDo$) decay
can be suppressed depending on the details of the model. Alternatively, the
decay
$\PSQt_{1}\to\cPqb\tilde{\chi}_{1}^{+}\to\cPqb\mathrm{f}\overline{\mathrm{f}}^{\,\prime}\PSGczDo$
is possible if the mass of the lightest chargino is lower than the top squark
mass. Figure 1 represents the production of a pair of $\PSQt_{1}$ followed by
a four-body or chargino-mediated decay in simplified models [14].

Figure 1: Top squark pair production at the LHC with four-body (left) or
chargino-mediated (right) decays.

In this paper, we describe a search for pair production of the $\PSQt_{1}$ in
proton-proton () collisions at the LHC at $\sqrt{s}=13\TeV$, where each top
squark can decay either directly, or via a chargino, into the
$\cPqb\mathrm{f}\overline{\mathrm{f}}^{\,\prime}\PSGczDo$ final state. A 100%
branching fraction for each decay is assumed when interpreting the results
[14]. The final states considered contain jets, missing transverse momentum
(), and exactly one lepton, which can be either an electron or a muon,
originating from the decay of the top squark or the chargino, depending on the
considered decay scenario. The lepton can be efficiently reconstructed and
identified with transverse momentum () as low as 5.0 and 3.5for electrons and
muons, respectively. In this search, we expand the result of a previous CMS
search in collisions at $\sqrt{s}=8\TeV$ [15] by including the single-electron
final state and lowering the thresholds for leptons. Moreover, two different
approaches are used in this analysis. A signal selection based on sequentially
applied requirements on several discriminating variables (CC) has been
designed to provide good sensitivity over a wide range of kinematic signatures
corresponding to different $({m}(\PSQt_{1}),{m}(\PSGczDo))$ mass hypotheses
and different $\PSQt_{1}$ decay modes. The CC approach is applied to the four-
body and chargino-mediated $\PSQt_{1}$ decay scenarios. In addition, a
multivariate analysis (MVA) followed by a counting experiment approach is used
for the signal selection. Applied to the four-body scenario, this approach
exploits the correlations between discriminating variables and is adapted for
different $\Delta{m}=m(\PSQt_{1})-m(\PSGczDo)$ kinematic regions, thus
optimizing the search across the $({m}(\PSQt_{1}),{m}(\PSGczDo))$ space and
improving upon the sensitivity of the CC approach for this scenario. Both
approaches are based on a nearly identical preselection.

Other results in the single-lepton final state and for both the four-body and
chargino-mediated $\PSQt_{1}$ decays were reported by ATLAS at
$\sqrt{s}=8\TeV$ [16] and $\sqrt{s}=13\TeV$ [17]. Other final states at
$\sqrt{s}=13\TeV$ were investigated by ATLAS and CMS for all-hadronic events
[18, 19] and for final states with two isolated leptons [20, 21],
respectively.

##  0.2 Detector and object definition

The central feature of the CMS apparatus is a superconducting solenoid of 6m
internal diameter, providing a magnetic field of 3.8T. Within the solenoid
volume are a silicon pixel and strip tracker, a lead tungstate crystal
electromagnetic calorimeter (ECAL), and a brass and scintillator hadron
calorimeter (HCAL), each composed of a barrel and two endcap sections. Forward
calorimeters extend the pseudorapidity coverage provided by the barrel and
endcap detectors. The silicon tracker measures charged particles within the
pseudorapidity range $\abs{\eta}<2.5$. Muons are detected in gas-ionization
chambers embedded in the steel flux-return yoke outside the solenoid. The
detector is nearly hermetic, allowing for momentum balance measurements in the
plane transverse to the beam axis. Events are selected for further analysis by
a two-tier trigger system that uses custom hardware processors to make a fast
initial selection, followed by a more detailed selection executed on a
dedicated processor farm. A more detailed description of the CMS detector can
be found in Ref. [22].

This analysis utilizes the CMS particle-flow (PF) algorithm [23] to
reconstruct and identify PF candidates such as leptons (electrons and muons),
photons, and charged and neutral hadrons. The reconstructed vertex with the
largest value of summed physics-object $\pt^{2}$ is taken to be the primary
interaction vertex (PV). The physics objects are the jets, clustered using a
jet finding algorithm [24, 25] with the tracks assigned to the vertex as
inputs, and the associated missing transverse momentum, taken as the negative
vector sum of the of those jets.

The electron candidates are reconstructed from energy depositions in the ECAL
and from tracks in the inner tracker obtained using the Gaussian-sum filter
[26]. The misidentification of electrons is reduced by requiring additional
constraints on the shape of the electromagnetic shower in the ECAL, the
quality of the match between the trajectory of the track and the ECAL energy
deposit, and the relative HCAL deposition in the electron direction. For
reconstructing muons the tracks in both the silicon tracker and the muon
system are used [27]. The number of measurements in the tracker and muon
system and the quality of the track fit are used to reduce the
misidentification rate of muons. In order to select leptons ($\ell=\Pe$ or
$\mu$) from the primary interaction, the point of closest approach to the PV
of tracks associated with the lepton is required to have transverse component
$\abs{d_{xy}}<0.02\unit{cm}$ and longitudinal component
$\abs{d_{z}}<0.1\unit{cm}$ with respect to the PV. In order to suppress the
selection of nonprompt leptons, which may arise from jets produced in
association with the invisible decay of a boson, multijet production, or
$\PW$+jets and events with a lost lepton, selected leptons are required to be
isolated from jet activity by using a combination of absolute and relative
isolation variables. The absolute isolation ($I_{\text{abs}}$) of the lepton
is defined as the scalar sum of the of PF candidates within a cone size of
$R\equiv\sqrt{\smash[b]{(\Delta\phi)^{2}+(\Delta\eta)^{2}}}=0.3$. The leptons
and charged PF candidates not associated with the PV are not included in the
sum. The contribution of the neutral particles from simultaneous collisions
(pileup) is estimated according to the method described in Ref. [26], and
subtracted from $I_{\text{abs}}$. The relative isolation ($I_{\text{rel}}$) of
a lepton is defined as the ratio of lepton $I_{\text{abs}}$ to the lepton .
The electrons and muons are then required to satisfy $I_{\text{abs}}<5\GeV$
for $\pt(\ell)<25\GeV$ and $I_{\text{rel}}<0.2$ for $\pt(\ell)>25\GeV$. This
combined isolation criterion allows for a more uniform selection efficiency of
leptons as a function of lepton . Finally, the selected electrons and muons
are also required to have above $5.0\GeV$ and $3.5\GeV$ and $\abs{\eta}<2.5$
and $2.4$, respectively. Tau leptons with a hadronic decay are reconstructed
from the PF candidates using the “hadrons-plus-strips” algorithm [28], which
achieves an efficiency of 50–60%. The tau candidates are required to satisfy
$\abs{\eta}<2.4$.

The jets used in this analysis are reconstructed by clustering PF candidates
using the anti-algorithm [24] with a distance parameter of 0.4. The missing
transverse momentum vector, , in the event is defined as the negative of the
vectorial sum of the transverse momenta of all the PF candidates in the event
with its magnitude denoted as . The pileup contribution to the jet momenta is
partially taken into account by subtracting the energy of charged hadrons
originating from a vertex other than the PV. The jet momenta are further
calibrated to account for contributions from neutral pileup and any
inhomogeneities of detector response [29]. The jets have a threshold of
$\pt>30\GeV$ and are required to have $\abs{\eta}<2.4$.

Jets originating from bottom () quarks are identified (“tagged”) as “ jets”
using the combined secondary vertex algorithm [30, 31], which takes advantage
of MVA techniques. The medium working point of this algorithm is used in the
CC search, which has a probability of about 1% to misidentify a light quark
jet as a jet while correctly identifying a jet with an efficiency of about
65%. The same figures for the loose working point, which is used in the MVA
search, are 10% and 80%, respectively.

##  0.3 Samples and preselection

###  0.3.1 Data and simulated samples

The searches described in this paper are performed using data from collisions
recorded in 2016 by the CMS experiment at the LHC at a centre-of-mass energy
of 13corresponding to an integrated luminosity of 35.9. Events in the search
are collected based on triggers with thresholds ranging between 90 and 120.
Additional control samples used for estimating backgrounds are selected by
single-lepton triggers with thresholds of 24 and 27for muons and electrons,
respectively.

In this analysis, Monte Carlo (MC) simulation samples of SM processes are used
to relate background yields in control and signal regions, to validate the
background estimation methods based on data, and to predict contributions from
rare processes. Simulated samples are produced using multiple generators. The
main background samples, namely $\PW$+jets, , and $\cPZ/\gamma^{*}$ are
generated at leading order (LO) by 2.3.3 [32]. Next-to-leading order (NLO)
simulations with the v2.0 [33] and v1.0 [34] generators are used for single
top quark production and the associated production, respectively. Diboson
events are simulated at NLO with 2.3.3 and v2.0. The LO and NLO NNPDF3.0 [35]
parton distribution functions (PDFs) are used consistently with the order of
the matrix element calculation in the generated events. Hadronization and
showering of events in all generated samples have been simulated using 8.212
[36, 37] with the CUETP8M1 [38] tune for the underlying event. The response of
the CMS detector is modelled using the [39] program. Simulation and data
events are reconstructed with the same algorithms. The effect of pileup is
simulated in the MC samples in order to reproduce the observed pileup
conditions in data.

The signal samples for the pair production of top squarks
($\PSQt_{1}{\PASQt}_{1}$) are simulated for $250\leq m(\PSQt_{1})\leq 800$in
steps of 25, and $10\leq$ $\Delta{m}$ $\leq 80$in 10steps. The cross section
for $\PSQt_{1}{\PASQt}_{1}$ production at NLO and including next-to-leading
logarithmic (NLL) corrections, as calculated by v.2 [40, 41, 42, 43, 44, 45,
46], varies approximately between 20 and 0.1 pb for the mass range considered.
The pair production of squarks with up to two additional jets from initial-
state radiation (ISR) is generated with 2.3.3 and is then interfaced with
8.212 for the decay, hadronization, and showering. For the chargino-mediated
decay of the scalar top, $m(\PSGcpmDo)$ is taken to be the average of
$m(\PSQt_{1})$ and $m(\PSGczDo)$. The decay is generated to proceed via an
off-shell boson, and the $\PSQt_{1}$ decay length is set to zero. The
modelling of the detector response is performed with the CMS fast simulation
program [47].

Simulated background and signal samples are corrected for differences with
respect to the values measured in data control samples in the selection
efficiencies for leptons and jets, and for the misidentification probability
for light-quark and gluon jets as jets. These corrections are applied as
functions of the and $\eta$ of the objects. For the signal samples, additional
corrections are applied to take into account any potential differences between
the and fast simulations in regards to tagging efficiencies of jets, leptons,
and modelling of .

###  0.3.2 Preselection

The preselection requirements used in this paper are designed by considering
the general characteristics of the signal, and are based on the methods
presented in Ref. [15]. The CC and MVA approaches share similar preselection
requirements with a few minor differences that are noted below due to studies
showing that the MVA leads to better performance with slightly different
selection than the CC search. In order to match the trigger requirement,
events with $\ptmiss>200$ $(280)\GeV$ are selected for the CC (MVA) approach.
This requirement favours the signal, which tends to have larger missing
transverse momentum than SM processes due to two ’s escaping detection. The
efficiency of signal triggers is measured to be higher than 90 (98)% for
$\ptmiss>200$ $(280)\GeV$, and the simulated samples are reweighted as a
function of to account for the inefficiency.

Further suppression of SM processes such as $\PW$+jets is achieved by imposing
the additional requirement of $\HT>300\GeV$, where is defined as the scalar
sum of all jets. For the MVA search, this requirement is relaxed to
$\HT>200\GeV$. In order to improve the separation of signal and SM background,
we take advantage of events in which the $\PSQt_{1}$ pair system recoils
against an ISR jet. In this case the LSP becomes Lorentz boosted, which
increases the in the event, while jets and leptons remain relatively soft. The
ISR jet candidate in the event is selected as the leading jet with
$\abs{\eta}<2.4$, which is required to satisfy $\pt>100$ $(110)\GeV$ for the
CC (MVA) search. To reduce the contribution from production, events are
required to have at most two jets with $\pt>60\GeV$ in the CC search. In
events with two jets, the azimuthal angle between the leading and subleading
(in ) jets is required to be less than 2.5 radians in order to suppress the SM
multijet background.

Finally, the soft single-lepton topology is selected by requiring at least one
muon or electron in the event, while vetoing events with a $\tau$ lepton, or a
second electron or muon with $\pt>20\GeV$. At this stage of the selection, the
$\PW$+jets and processes represent approximately 70% and 20% of the total
expected background, respectively. The $\cPZ(\to\nu\nu)+\text{jets}$ process
contributes with jets, genuine , and a jet misidentified as a lepton. Diboson,
single top and Drell–Yan (DY) processes also contribute, with a lower expected
yield due to a low cross section or a low acceptance (or both).

##  0.4 The CC approach

###  0.4.1 Signal selection

After the preselection detailed in the previous section, $\PW$+jets is the
dominant background process, followed by a smaller contribution coming from
production. A kinematic variable with good discrimination against these
background processes is the transverse mass
$M_{\mathrm{T}}\equiv\sqrt{\smash[b]{2\ptmiss\pt(\ell)(1-\cos\Delta\phi)}}$,
where $\pt(\ell)$ is the transverse momentum of the selected lepton and
$\Delta\phi$ is the angular difference between the lepton $\ptvec(\ell)$ and .
The distributions of lepton and $M_{\mathrm{T}}$ are shown in Fig. 2 for the
observed data and simulated background and signal, where we observe good
agreement in the shapes of the distributions between data and background
simulation. The normalization of the simulation is corrected by the results of
a background estimation technique based partially on data, as described in
Section 0.4.2.

Figure 2: Distributions of lepton (left) and $M_{\mathrm{T}}$ (right) at the
preselection level in data and simulation. The lower panels show the ratio of
data to the sum of the SM backgrounds where the dark shaded bands indicate the
statistical uncertainties of the simulation. The distributions of two signal
points of the four-body decay are also represented, while not being added to
the background: $({m}(\PSQt_{1}),{m}(\PSGczDo))$ = (500,490) and (500,420).
The background distributions are obtained directly from simulation, and are
normalized to an integrated luminosity of 35.9. The last bin in each plot
includes events beyond 200.

The signal regions (SRs) in the CC analysis are defined to maximize the
sensitivity of the search by exploiting the differences between the kinematic
properties of the final-state particles in the signal and background
processes. The leptons originating from the decay of the $\PSQt_{1}$ squark
are expected to be much softer than those from SM processes. Therefore, all
SRs are required to satisfy $\pt(\ell)<30\GeV$. In order to retain sensitivity
to different $\Delta{m}$ mass gaps, two signal regions SR1 and SR2 are
designed targeting small and large mass differences, respectively. Moreover,
due to the strong dependence of the and $M_{\mathrm{T}}$ distributions for the
signal on $\Delta{m}$, these SRs are further subdivided into a total of 44
mutually exclusive regions, which are detailed below and summarized in Table
0.4.1. In this search, the correlations between , , and the transverse
momentum of the ISR jet candidate ($\pt(\mathrm{ISR})$) are taken into account
by defining SRs in terms of the variables $C_{\mathrm{T1}}$ and
$C_{\mathrm{T2}}$: $C_{\mathrm{T1}}\equiv\min(\ptmiss,\HT-100\GeV)$, and
$C_{\mathrm{T2}}\equiv\min(\ptmiss,\pt(\mathrm{ISR})-25\GeV)$, where the
numerical values of 100 and 25are determined by maximizing the ratio of signal
to the square root of background in the signal regions.

In SR1, events with a jet satisfying $\pt>30\GeV$ are rejected since the jets
in signal events with a small mass gap are expected to have typical values
smaller than this threshold. This -tag veto significantly reduces the
contribution of events. In this region, the and requirements of the
preselection are simultaneously tightened by requiring
$C_{\mathrm{T1}}>300\GeV$. Since the $\PW$+jets process is the dominant
background process for lower $M_{\mathrm{T}}$ values, we take advantage of the
charge asymmetry in the production of W bosons at LHC and require the lepton
to have a negative charge in SR1 regions with $M_{\mathrm{T}}<95\GeV$.
Moreover, the acceptance of the lepton is tightened by requiring
$\abs{\eta(\ell)}<1.5$, because leptons from decays of the W boson at the LHC
tend to be produced in the forward direction.

In SR2, we require at least one jet with $\pt<60\GeV$, but reject events with
any jet having $\pt>60\GeV$. These requirements increase the efficiency of
signal points with larger $\Delta{m}$ while keeping the background under
control. In this region we also require $C_{\mathrm{T2}}>300\GeV$, which is
more effective in reducing the background compared to the $C_{\mathrm{T1}}$
requirement.

The SR1 (SR2) region is further divided in bins of $M_{\mathrm{T}}$, lepton ,
and $C_{\mathrm{T1}}$ ($C_{\mathrm{T2}}$). The $M_{\mathrm{T}}$ binning is
done below and above the peak around the W boson mass in the $M_{\mathrm{T}}$
distribution, with the regions $M_{\mathrm{T}}<60\GeV$,
$60<M_{\mathrm{T}}<95$, and $M_{\mathrm{T}}>95\GeV$ labelled as a, b, and c,
respectively. It can be seen from Fig. 2 that the lower (higher)
$M_{\mathrm{T}}$ region is more sensitive to signals with smaller (larger)
mass gaps. In order to take advantage of the shape differences in lepton
distributions between various signal points and SM processes, each SR is
further divided into lepton regions $5$–$12$, $12$–$20$, and $20$–$30\GeV$,
referred to as L, M, and H, respectively. An additional region of
$3.5$–$5.0$is added only for muons and only for $M_{\mathrm{T}}<95$, and is
labelled VL. In addition, SR1 (SR2) is further separated into two regions in
$C_{\mathrm{T1}}$ ($C_{\mathrm{T2}}$) defined by
$300<C_{\mathrm{T1}}(C_{\mathrm{T2}})<400\GeV$ and
$C_{\mathrm{T1}}(C_{\mathrm{T2}})>400\GeV$ which are labelled X and Y,
respectively.

The CC search: definition of SRs. The subregions of SRs are denoted by tags in
parentheses, as described in the text: VL, L, M, and H refer to the four bins
in lepton , and X and Y to the $C_{\mathrm{T}}$ ranges specified in the table.
The corresponding control regions (CR) use the same selection with the
exception of the lepton as shown in the table. For jets, the attributes “soft”
and “hard” refer to the ranges $30$–$60\GeV$ and $>$60, respectively.
Variable Common to all SRs Number of hard jets $\leq$2 $\Delta\phi$(hard jets)
(rad) $<$2.5 () $>$300 Lepton rejection no $\tau$, or additional $\ell$ with
$\pt>20\GeV$ SR1 SR2 () $>$400 $>$300 $\pt$(ISR jet) () $>$100 $>$325 Number
of jets $0$ $\geq$1 soft, $0$ hard $|\eta(\ell)|$ $<$1.5 $<$2.4 SR1a SR1b SR1c
SR2a SR2b SR2c $M_{\mathrm{T}}$() $<$60 $60$–$95$ $>$95 $<$60 $60$–$95$ $>$95
$Q(\ell)$ $-1$ $-1$ any any any any $\pt(\mu)$ () $3.5$–$5$ (VL) $3.5$–$5$
(VL) — $3.5$–$5$ (VL) $3.5$–$5$ (VL) — $\pt(\Pe,\mu)$ () $5$–$12$ (L) $5$–$12$
(L) $5$–$12$ (L) $5$–$12$ (L) $5$–$12$ (L) $5$–$12$ (L) $12$–$20$ (M)
$12$–$20$ (M) $12$–$20$ (M) $12$–$20$ (M) $12$–$20$ (M) $12$–$20$ (M)
$20$–$30$ (H) $20$–$30$ (H) $20$–$30$ (H) $20$–$30$ (H) $20$–$30$ (H)
$20$–$30$ (H) $>$30 (CR) $>$30 (CR) $>$30 (CR) $>$30 (CR) $>$30 (CR) $>$30
(CR) $C_{\mathrm{T}}$ () $300<C_{\mathrm{T1}}<400$ (X)
$300<C_{\mathrm{T2}}<400$ (X) $C_{\mathrm{T1}}>400$ (Y) $C_{\mathrm{T2}}>400$
(Y)

###  0.4.2 Background prediction

The dominant backgrounds in most of the CC signal regions are $\PW$+jets and
production with a prompt lepton in the final state. The nonprompt sources of
leptons become more important in regions with large $M_{\mathrm{T}}$ or very
low lepton . In this section, the methods used to estimate the prompt and
nonprompt backgrounds from data are described. Simulation is used to estimate
other rare backgrounds with a prompt lepton, namely $\cPZ/\gamma^{*}$,
diboson, single top quark production, and production with an additional , , or
$\gamma$.

The nonprompt background due to misidentified leptons associated with a jet
becomes comparable to the prompt contribution in regions where $\PW$+jets and
production are suppressed, namely in regions of high-$M_{\mathrm{T}}$ and very
low lepton . This background is estimated fully from data using the “tight-to-
loose” method, where a “loose” set of identification and isolation criteria
are defined to select lepton candidates that are more likely to be nonprompt.
The loose selection is defined by relaxing the requirement on the lepton
isolation to $I_{\text{abs}}<20\GeV$ for $\pt(\ell)<25\GeV$ and
$I_{\text{rel}}<0.8$ for $\pt(\ell)>25\GeV$, as well as relaxing the impact
parameter conditions to $\abs{d_{xy}}<0.1\unit{cm}$ and
$\abs{d_{z}}<0.5\unit{cm}$. The “tight” criteria correspond to the final
lepton selection of the analysis, described in Section 0.2. The probability
that a loose lepton also passes the tight criteria, the tight-to-loose
fraction $\epsilon_{\mathrm{TL}}$, is measured as a function of lepton and
$\abs{\eta}$ in an orthogonal “measurement region” largely dominated by
multijet events, which is enriched in nonprompt leptons. The fraction
$\epsilon_{\mathrm{TL}}$ is measured from data, after the subtraction of the
simulated prompt lepton contribution. The final estimate of nonprompt leptons
in a SR or control region (CR) is based on the observed yield in an
”application region”. The latter is defined in the same way as the
corresponding SR or CR, with the exception that the lepton has to pass the
loose lepton criteria but not the tight ones. The final estimate is obtained
by scaling the data yield in the application region by
$\epsilon_{\mathrm{TL}}/(1-\epsilon_{\mathrm{TL}})$, after subtracting the
simulated prompt lepton contribution.

The absolute normalization of the prompt background simulation in each SR is
obtained from a CR with identical requirements as in the SR except for the
lepton selection. The CR is defined by replacing the lepton requirement of the
SR with $\pt(\ell)>30\GeV$; therefore, SRs that are only distinguished by
different selections in $\pt(\ell)$ share the same CR. The impact of potential
signal contamination is taken into account when deriving the results as
described in Section 0.6. In each CR, a scale factor for the prompt simulation
is obtained by normalizing the simulation to data, after subtracting nonprompt
and rare background sources from the observed number of events in the CR. The
nonprompt contribution used in the subtraction is estimated separately from
data. The composition of the CRs in terms of background processes, as well as
the total simulated and observed yields, are shown in Table 0.4.2. The scale
factors, ranging from 0.86 to 1.25, are then applied to the simulation in the
corresponding SRs. In order to verify the extrapolation of the scale factors
from CR to SR, we perform the same background estimation procedure in
validation regions (VRs), which are orthogonal to all SRs and CRs. Each
validation region is obtained by one of the following changes: (a) lowering
the $C_{\mathrm{T1}}$ (in SR1 and CR1) and $C_{\mathrm{T2}}$ (in SR2 and CR2)
requirements to $200<C_{\mathrm{T}}<300\GeV$, (b) replacing the conditions on
jets by requiring at least one jet with $\pt>60\GeV$. The predictions in the
validation regions are compatible with the observations within the
uncertainties.

The CC search: observed yields and simulated background contributions to CRs
normalized to an integrated luminosity of 35.9. The nonprompt contributions
are estimated from data. The last column shows the scale factors used for the
normalization of the $\PW$+jets and samples. Only statistical uncertainties
are reported.  Region $\PW$+jets Nonprompt Rare Total SM Data Scale factors
CR1aX $2133\pm 20$ $226.6\pm 3.5$ $44.5\pm 6.4$ $293.2\pm 5.9$ $2698\pm 22$
2945 $1.10\pm 0.03$ CR1aY $878.3\pm 8.6$ $65.8\pm 1.9$ $13.3\pm 3.6$ $139.4\pm
4.1$ $1097\pm 10$ 1197 $1.11\pm 0.04$ CR1bX $1107\pm 15$ $134.5\pm 2.7$
$7.8\pm 2.7$ $112.1\pm 4.1$ $1361\pm 16$ 1462 $1.08\pm 0.03$ CR1bY $438.2\pm
6.4$ $35.1\pm 1.4$ $1.6\pm 1.6$ $51.9\pm 2.9$ $526.8\pm 7.3$ 502 $0.95\pm
0.05$ CR1cX $642\pm 11$ $103.8\pm 2.3$ $12.7\pm 3.0$ $174.3\pm 5.5$ $932\pm
13$ 1051 $1.16\pm 0.05$ CR1cY $278.3\pm 8.3$ $25.5\pm 1.2$ $6.2\pm 2.2$
$102.2\pm 4.3$ $412.2\pm 9.6$ 432 $1.07\pm 0.08$ CR2aX $171.7\pm 2.5$
$195.6\pm 3.3$ $1.9\pm 1.9$ $64.2\pm 1.9$ $433.4\pm 4.9$ 451 $1.05\pm 0.06$
CR2aY $74.5\pm 1.0$ $58.4\pm 1.7$ $0.8\pm 0.8$ $25.6\pm 1.1$ $159.3\pm 2.4$
145 $0.89\pm 0.09$ CR2bX $104.9\pm 2.0$ $110.8\pm 2.5$ $1.2\pm 1.2$ $39.2\pm
1.6$ $256.1\pm 3.8$ 226 $0.86\pm 0.07$ CR2bY $42.6\pm 0.8$ $30.8\pm 1.3$
$0.3\pm 0.3$ $15.0\pm 0.9$ $88.6\pm 1.8$ 79 $0.87\pm 0.12$ CR2cX $17.3\pm 0.8$
$53.8\pm 1.7$ $1.7\pm 1.2$ $15.7\pm 1.0$ $88.4\pm 2.4$ 106 $1.25\pm 0.15$
CR2cY $7.5\pm 0.8$ $12.8\pm 0.8$ $0.6\pm 0.6$ $6.6\pm 0.7$ $27.5\pm 1.5$ 29
$1.07\pm 0.28$

###  0.4.3 Systematic uncertainties

Processes for which the absolute yield is predicted by simulation are subject
to systematic uncertainties in the determination of the integrated luminosity
(2.5%) [48]. All simulated samples are subject to experimental uncertainties
on the jet energy scale (JES) and jet energy resolution (JER). The
uncertainties due to miscalibration of the JES are estimated by varying the
jet energy corrections up and down by one standard deviation and propagating
the effect to the calculation of . Moreover, differences of the JER between
data and simulation are accounted for by smearing the momenta of jets in
simulation. The uncertainties corresponding to -tagging efficiencies and
misidentification rates for tagging light-flavoured or gluon jets as jets have
been evaluated for all simulated samples. The uncertainties corresponding to
the correction of simulated samples for trigger and lepton efficiencies are
taken as systematic uncertainties. The uncertainty due to the simulation of
pileup for simulated background processes is taken into account by varying the
expected cross section of inelastic collisions by 5% [49]. An uncertainty of
50% is assigned to the cross sections of all nonleading backgrounds. An
overview of all systematic uncertainties related to the background prediction
is presented in Table 0.4.3.

The nonprompt background estimation method of this search, as described in the
previous section, depends on the tight-to-loose fraction
$\epsilon_{\mathrm{TL}}$ which is sensitive to the flavour content of jets.
The systematic uncertainty due to possible differences in the flavour content
of jets between the measurement and application regions is assessed by varying
the -tagging requirements of the measurement region. The resulting uncertainty
ranges from 20 to 50% from low to high lepton , respectively. The consistency
of the method is tested by applying the same procedure to simulated data. To
account for any residual deviation found in the test, an additional
uncertainty of 20 to 200% is assigned in some regions, with the highest
uncertainties applying to regions that are dominated by prompt background.

The prompt background prediction procedure of this search, as described in the
previous section, relies on the simulation of $\PW$+jets and production and is
sensitive to theoretical uncertainties on ISR. The modelling of ISR for these
processes is checked in control samples in data that are highly enriched in or
$\PW$+jets events. The simulation of events is tested by comparing the jet
multiplicity observed in a control sample with the simulation. Simulated
events are reweighted based on this comparison, and half of the correction is
assigned as the systematic uncertainty [50]. This systematic uncertainty
affecting also affects the signal samples. Similarly, the simulation of
$\PW$+jets events is corrected based on the distribution of $\pt(\PW)$ in a
control sample, and the difference between the uncorrected and the corrected
simulation is assigned as a systematic uncertainty [51]. These two sources of
uncertainties lead to relative changes of the total background estimation in
the SRs that range from 2 to 10% for the $\PW$+jets process, and are less than
1% for the process. The estimate of the prompt background depends only weakly
on the background composition, since the distributions of $\pt(\ell)$ in
$\PW$+jets and processes are similar. The corresponding systematic uncertainty
is derived from a 20% variation in the relative yields of $\PW$+jets and
backgrounds.

The dominant source of systematic uncertainty for the signal is caused by the
modelling of ISR. It is minimized by reweighting the jet multiplicity in the
signal sample according to the corrections obtained in the sample.
Uncertainties due to unknown higher-order effects are estimated by variations
of the renormalization and factorization scales by factors of 0.5 and 2 [52].
Moreover, possible differences between the fast and the full -based modellings
of are taken into account and the corresponding uncertainties are assigned to
the signal yields as shown in Table 0.4.3. The statistical uncertainty of the
signal simulation ranges from 8 to 15%.

The CC search: typical ranges for relative systematic uncertainties (in %) on
the total background prediction and signal prediction in the main SRs. The “—”
means that a certain source of uncertainty is not applicable.  Systematic
Background Signal uncertainty SR1 SR2 Renormalization & factorization scales —
— 2–3 Pileup 0.1–1.8 0.1–2.0 1 JES 1.2–2.1 0.1–1.4 3–4 JER 0.1–0.5 0.1–1.1 0–1
-tagging 0.1 0.1–1.0 1–3 Trigger 0.0–0.1 0.0–0.1 1 Lepton efficiency 1.0–1.8
1.0–1.5 3 ISR (and signal) 0.1–0.5 0.1–0.8 5–7 ISR ($\PW$+jets) 4.5–10.2
1.9–4.4 — modelling (FastSim) — — 2–3 Relative yields $\PW$+jets/ 0.1–1.6
0.1–2.2 — Nonprompt 1.0–4.6 1.0–9.5 —

##  0.5 The MVA approach

###  0.5.1 Signal selection

For the selection of the signal events corresponding to four-body decays of
the $\PSQt_{1}$, we use a boosted decision tree (BDT) [53, 54] to take
advantage of the correlations among variables that discriminate between signal
and background.

Compared to the approach of Ref. [15], we use new variables and search for the
most reduced set of best-performing variables to be used as input to the BDT.
To find the most discriminating variables we test different sets maximizing
the figure of merit (FOM) [55]:

$\mathrm{FOM}=\sqrt{2\left((S+B)\ln\left[\frac{(S+B)\
(B+\sigma_{B}^{2})}{B^{2}+(S+B)\
\sigma_{B}^{2}}\right]-\left(\frac{B^{2}}{\sigma_{B}^{2}}\right)\ln\left[1+\frac{\sigma_{B}^{2}\
S}{B\ (B+\sigma_{B}^{2})}\right]\right)},$ (1)

where S and B stand for the expected signal and background yields. The term
$\sigma_{B}=(f\,B)$ represents the expected systematic uncertainty on the
background with $f$ being an estimate of the relative uncertainty of the
background yield, taken to be $f=20\%$ (see Section 0.5.3). A new variable is
incorporated into the set of input variables only if it significantly
increases the FOM. The full list of the final input variables is:

  * •

, $\pt(\ell)$, and $M_{\mathrm{T}}$: The correlation between and $\pt(\ell)$
differs between signal, where the is due to three missing objects (two and a
$\nu$), and and $\PW$+jets backgrounds where the is due to a single missing
object ($\nu$). The $M_{\mathrm{T}}$ distribution peaks at $\approx$80for SM
processes where a W boson is produced, while being a rather broad distribution
for signal.

  * •

$\eta(\ell)$ and $Q(\ell)$: The pseudorapidity of the lepton $\eta(\ell)$ is
considered because the decay products of the signal are more centrally
produced than those of the $\PW$+jets background. The charge of the lepton
$Q(\ell)$ is also considered, as and are produced unequally at the LHC, while
the signal events contain equal numbers of positive and negative leptons.

  * •

$\pt(\mathrm{ISR})$, $\pt(\cPqb)$, $N_{\text{jets}}$, and : The of the leading
jet, $\pt(\mathrm{ISR})$, captures the hard ISR jet in signal events, and
$\pt(\cPqb)$ is the of the jet with the highest tagging discriminant value.
Both are sensitive to the different phase space available for signal and
background events: $m(\cPqt)-m(\PW)$ for , and $m(\PSQt_{1})-m(\PSGczDo)$ for
signal. The multiplicity of selected jets $N_{\text{jets}}$ is included,
reflecting the mass of the mother particle $\PSQt_{1}$.

  * •

$N(\cPqb^{\text{loose}})$, $\Delta R(\ell,\cPqb)$, and $D(\cPqb)$: The number
of loose jets $N(\cPqb^{\text{loose}})$, the distance between the lepton and
the jet with the highest tagging discriminant $\Delta R(\ell,\cPqb)$, and the
highest tagging discriminant per event $D(\cPqb)$ are also included among the
input variables.

Because the discrimination power of each input variable varies as a function
of $\Delta{m}$, as shown in Fig. 3, the $({m}(\PSQt_{1}),{m}(\PSGczDo))$ plane
is partitioned into eight $\Delta{m}$ regions (from 10 to 80, in 10steps) and
a separate BDT is trained for each partition. The $\PW$+jets and processes,
which represent a large fraction of the total background after preselection,
are included in the training of the BDT. The $\cPZ(\to\nu\nu)+\text{jets}$
process, which represents a nonnegligible fraction of the remaining total
background at the final selection level, is also included. The training is
done with simulated events for both signal and background processes. The
background samples are normalized to their respective cross section to
realistically represent the SM background in the training. We take advantage
of the similar distribution of the input variables for different
$({m}(\PSQt_{1}),{m}(\PSGczDo))$ signal points with the same $\Delta{m}$, and
regroup all signal points for a given $\Delta{m}$ together when feeding signal
to the BDT training. This increases the number of signal events for each
training. Due to the large variation of the spectrum of the $\pt(\ell)$
variable across the $({m}(\PSQt_{1}),{m}(\PSGczDo))$ plane, we require
$\pt(\ell)<30$for signal points with $\Delta{m}\leq 60$before training
different BDTs, while there is no restriction on $\pt(\ell)$ for signal points
with higher $\Delta{m}$.

Figure 3: Simulated distributions of $\pt(\ell)$ (left) and $M_{\mathrm{T}}$
(right) at the preselection level for signal samples with different
$\Delta{m}$, and $\PW$+jets and background events. The area of each signal
distribution, and the total background contribution, are normalized to unit
area.

Figures 4 and 5 show the output distribution of the BDT in data and for the
total SM background as taken from simulation. In each case a representative
$({m}(\PSQt_{1}),{m}(\PSGczDo))$ signal point is also shown, chosen at the
limit of the expected sensitivity of the CC search (see Section 0.6) and
belonging to the $\Delta{m}$ for which the training has been done. We observe
that the responses of the BDT, henceforth called BDT outputs, are not the
same. This is due to the changing mix between signal and background as well as
varying differences of correlations across the
$({m}(\PSQt_{1}),{m}(\PSGczDo))$ plane, resulting in different BDT outputs for
different $\Delta{m}$ values. We observe good agreement between data and
simulation over the entire range dominated by the background (BDT output
smaller than 0.3) for the eight different trainings. The BDT output is also
checked in data to be well reproduced by the simulation in two validation
regions, across the entire range of the BDT output. These regions are
kinematically orthogonal to the preselection while using the same online
selection, and are defined as follows:

  * •

Preselection with $200<\ptmiss<280\GeV$,

  * •

Preselection with $\pt(\ell)>30$.

They are also used to evaluate the precision of the method for predicting
background, as described in Section 0.5.2. A SR is defined by applying a
threshold to each BDT output. The thresholds on the BDT output are reported in
Table 0.6. On average the BDT selection suppresses the SM background by a
factor ${\approx}3\times 10^{3}$ while reducing the signal by a factor
$\approx$25\. The total efficiency for signal points at the limit of the
sensitivity of the CC search, and across all selections, is of the order of
$1.3\times 10^{-4}$.

Figure 4: Distributions of the BDT output at the preselection level in data
and simulation in 10steps of $\Delta{m}$ from 10 (top-left) to 40(bottom-
right). For each case, a representative $({m}(\PSQt_{1}),{m}(\PSGczDo))$
signal point is also shown, but is not added to the SM background. The shaded
area on the Data/MC ratio represents the statistical uncertainty of the
simulated background.

Figure 5: Distributions of the BDT output at the preselection level in data
and simulation in 10steps of $\Delta{m}$ from 50 (top-left) to 80(bottom-
right). For each case, a representative $({m}(\PSQt_{1}),{m}(\PSGczDo))$
signal point is also shown, but is not added to the SM background. The shaded
area on the Data/MC ratio represents the statistical uncertainty of the
simulated background.

###  0.5.2 Background predictions

The predicted numbers of $\PW$+jets and events are obtained from data control
regions (CRs) based on the BDT output. The number of estimated prompt
background events in the SR, $Y^{\text{SR}}_{\text{prompt}}$, is derived as
follows:

$\displaystyle Y^{\text{SR}}_{\text{prompt}}(X)$ $\displaystyle=$
$\displaystyle N^{\text{SR}}_{\text{prompt}}(X)\
\left[\frac{N^{\text{CR}}(\text{Data})-N^{\text{CR}}_{\text{prompt}}(\text{Rare})-N^{\text{CR}}_{\text{nonprompt}}}{N^{\text{CR}}_{\text{prompt}}(X)}\right].$
(2)

Here, $X$ refers to background processes to be estimated, $\PW$+jets or . The
superscripts SR and CR, respectively, refer to the signal and control regions.
The term “prompt” refers to processes where a prompt lepton is produced. The
term “nonprompt” refers to processes where there is a jet misreconstructed as
a lepton. The numbers $N^{\text{SR,CR}}_{\text{prompt}}(X)$ are predicted from
simulated background. The number $N^{\text{CR}}_{\text{prompt}}(\text{Rare})$
refers to simulated background processes other than those being estimated, and
includes single top, DY, and diboson production. The number
$N^{\text{CR}}_{\text{nonprompt}}$ refers to the estimate of the backgrounds
with a nonprompt lepton from data (as explained in Section 0.4.2). Within the
region defined by the preselection, the CRs are obtained by requiring
$\mathrm{BDT}<0.2$ to get a data sample enriched in background. They are
further enriched in events by requiring them to have at least one tight jet,
and in $\PW$+jets events by requiring the number of loose jets to be zero. The
level of potential signal contamination in the CRs is well below 5% and is not
expected to impact the final result.

The systematic uncertainties associated with these predictions are based on
differences between the predicted number of events (obtained from Eq. (2)) and
the observed number of data events, both in validation regions as defined in
the previous section.

The number $Y^{\text{SR}}_{\text{nonprompt}}$ of background events with
nonprompt leptons is estimated from data in all signal regions with the method
described in Section 0.4.2. The yield of other SM processes such as diboson,
single top, and DY production are estimated from simulation.

###  0.5.3 Systematic uncertainties

All processes that are modelled by simulation are subject to the same
systematic uncertainties as described in Section 0.4.3. The statistical
uncertainty of the signal simulation ranges between 3 and 11%. The systematic
uncertainty affecting the prediction of the $\PW$+jets and backgrounds has
been described in Section 0.5.2, where the statistical uncertainty from the
number of events in CRs is included. The uncertainties are evaluated from both
validation regions, and the larger value is conservatively chosen.
Furthermore, uncertainties on the shape of the BDT output, which can affect
the background prediction, have been assessed. They are smaller than the
aforementioned systematic uncertainties. The systematic uncertainties
affecting the prediction of the nonprompt lepton background are the same as in
Section 0.4.2. As we perform a separate analysis for each $\Delta{m}$ region,
the uncertainties are evaluated separately and can therefore vary across
different values of $\Delta{m}$. The relative systematic uncertainties on the
predictions of the $\PW$+jets, , and nonprompt lepton on the total background
are provided in Table 0.5.3.

The MVA search: relative systematic uncertainties (in %) on the total
background and signal prediction. The “—” means that a certain source of
uncertainty is not applicable. In the case of the background, the
uncertainties are on the total background. Systematic uncertainties on the
data-driven prediction of the $\PW$+jets, , and nonprompt lepton backgrounds
are reported.  Systematic uncertainty Background Signal Renormalization &
factorization scales 0–1 1.5–3.0 Pileup 1 1 JES 0–2 6–13 JER 0–1 1–6 -tagging
0–1 0–7 Trigger 1 1 Lepton efficiency 0–1 4 ISR (and signal) 0–1 0–6 ISR
($\PW$+jets) 0–10 — modelling (FastSim) — 5 Prediction of $\PW$+jets 7.1–32.0
— Prediction of  4.1–16.0 — Prediction of nonprompt 6.7–15.6 —

##  0.6 Results

After performing the two searches, we find no evidence for direct top squark
production, as can be seen in Table 0.6 and in Fig. 6 for the MVA and CC
searches, respectively. Both sets of results include the prediction of the
$\PW$+jets and processes, the prediction of the background with a nonprompt
lepton, the prediction of other background processes from simulation, the
total expected background, and the observed number of data events. Systematic
uncertainties are included in the predictions. For the MVA search (Table 0.6),
the overlap between the SRs defined for different $\Delta{m}$ is generally
below 50% for adjacent regions, and ranges from 0 to 30% for nonadjacent
regions. Taking into account these results, the expected signal yield for each
$({m}(\PSQt_{1}),{m}(\PSGczDo))$ mass point, and the corresponding systematic
uncertainties, we interpret the absence of a clear excess in terms of a 95%
confidence level () exclusion of top squark pair production in the
$({m}(\PSQt_{1}),{m}(\PSGczDo))$ plane. The limits are calculated according to
the modified frequentist criterion [56, 57, 58]. A test statistic, defined to
be the likelihood ratio between the background-only and signal-plus-background
hypotheses, is used to set exclusion limits on top squark pair production. For
the CC search, which features a larger number of signal regions, an asymptotic
approximation [55] is used, while in the MVA search the distributions of these
test statistics are constructed using simulated experiments. Statistical
uncertainties are modelled as Poisson distributions. All systematic
uncertainties are modelled with a log-normal distribution. In the CC search,
the effect of signal contamination in the CRs is taken into account by
including the control regions, with the estimate of corresponding signal
yields, in the likelihood fit. When interpreting the results, we assume
branching fractions of 100% for the two considered decay scenarios.

Figure 7 represents the exclusion contour as a function of $m(\PSQt_{1})$ and
$\Delta{m}$ for both searches in the case of the four-body decay scenario. For
this decay mode, the CC search reaches its highest mass exclusion of 500for
$\Delta{m}\approx 30\GeV$. At $\Delta{m}=80\GeV$, the analysis probes masses
up to 390. For the MVA search, the maximum sensitivity is reached for the
highest $\Delta{m}$ of 80, where top squark masses up to 560are excluded. At
$\Delta{m}=10\GeV$, the corresponding value is 420. For both analyses, the
reduced sensitivity at the lowest mass differences is due to the decrease in
the transverse momenta of the visible decay products, as shown in Fig. 3, and
the corresponding loss in acceptance. For intermediate values of $\Delta{m}$,
the two approaches obtain similar limits. At the highest mass differences, the
MVA selection has higher acceptance than the CC approach as it also includes
events with lepton $\pt>30\GeV$ while keeping the level of background under
control.

Figure 8 represents the interpretation of the CC search for the chargino-
mediated scenario. For this model, with a chargino mass equal to the average
of the top squark and neutralino masses, the sensitivity is very similar to
the case of four-body decays, with the maximum exclusion being reached at
slightly higher values of $\Delta{m}$. Top squark masses of up to 540are
excluded for $\Delta{m}\approx 40$.

In order to constrain top squark pair production in both decay modes using the
information from several final states, a statistical combination of the CC
search with the all-hadronic search [19] for both decay scenarios of the top
squark is performed. The common systematic uncertainties of the two searches
are treated as fully correlated, and the possible correlations arising from
events passing the selection criteria of both searches are found to have
negligible impact on the final results. The combined limits, shown in Fig. 9,
include all SRs and CRs of the all-hadronic and the single-lepton CC searches.
The combination of the two searches extends the exclusion limits on the top
squark mass up to 590 and 670for the four-body and chargino-mediated
scenarios, respectively.

The MVA search: prediction of the $\PW$+jets, , nonprompt lepton, and other
backgrounds in the eight SRs defined by the threshold on the BDT output
reported in the second column. The prediction of the first three processes is
based on data, while that of $N^{SR}(\text{Rare})$, rare backgrounds, is based
on simulation. The uncertainties are the quadrature sum of the statistical
uncertainties, the systematic uncertainties of Table 0.5.3, and for the
backgrounds predicted from simulation, the cross section uncertainties. The
number of total expected background ($N^{SR}\text{(B)}$) and observed data
($N^{SR}\text{(D)}$) events in each SR are also reported.  BDT$>$
$Y^{\text{SR}}_{\text{prompt}}$ $Y^{\text{SR}}_{\text{prompt}}$
$Y^{\text{SR}}_{\text{nonprompt}}$ $N^{\text{SR}}$ $N^{\text{SR}}\text{(B)}$
$N^{\text{SR}}\text{(D)}$ ($\PW$+jets) () (Rare) $\Delta{m}$ = 10 0.31 18.4
$\pm$ 3.6 1.8 $\pm$ 4.8 8.0 $\pm$ 2.9 2.3 $\pm$ 1.4 30.3 $\pm$ 6.7 39
$\Delta{m}$ = 20 0.39 9.0 $\pm$ 2.0 1.3 $\pm$ 1.7 11.2 $\pm$ 3.2 3.1 $\pm$ 1.9
24.7 $\pm$ 4.5 20 $\Delta{m}$ = 30 0.47 4.0 $\pm$ 2.5 1.2 $\pm$ 0.6 8.8 $\pm$
2.5 1.7 $\pm$ 1.2 15.7 $\pm$ 3.7 22 $\Delta{m}$ = 40 0.48 4.1 $\pm$ 1.3 1.8
$\pm$ 0.7 7.6 $\pm$ 2.3 1.2 $\pm$ 0.9 14.8 $\pm$ 2.8 16 $\Delta{m}$ = 50 0.45
7.3 $\pm$ 2.1 4.7 $\pm$ 2.8 7.1 $\pm$ 2.0 5.5 $\pm$ 3.1 24.5 $\pm$ 4.8 36
$\Delta{m}$ = 60 0.50 2.0 $\pm$ 0.6 2.4 $\pm$ 1.2 3.1 $\pm$ 1.1 1.1 $\pm$ 0.9
8.7 $\pm$ 1.8 12 $\Delta{m}$ = 70 0.46 4.9 $\pm$ 1.6 3.4 $\pm$ 1.1 5.4 $\pm$
1.6 3.2 $\pm$ 1.9 16.8 $\pm$ 2.9 20 $\Delta{m}$ = 80 0.44 7.1 $\pm$ 1.6 5.1
$\pm$ 0.9 5.3 $\pm$ 1.6 5.2 $\pm$ 3.0 22.8 $\pm$ 3.3 26

Figure 6: The CC approach: summary of observed and expected background yields
in all SRs as defined in Table 0.4.1. The vertical bars and the shaded areas
represent the statistical uncertainty of the data and the total uncertainty in
the prediction, respectively. The lower panel shows the ratio of data to
prediction.

Figure 7: Exclusion limit at 95% for the four-body decay of the top squark as
a function of $m(\PSQt_{1})$ and $\Delta{m}$ for the CC (upper) and MVA
(lower) approaches. The colour shading corresponds to the observed limit on
the cross section. The solid black (dashed red) lines represent the observed
(expected) limits, derived using the expected top squark pair production cross
section. The thick lines represent the central values and the thin lines the
variations due to the theoretical or experimental uncertainties.  Figure 8:
Exclusion limit at 95% for the chargino-mediated decay of the top squark as a
function of $m(\PSQt_{1})$ and $\Delta{m}$ for the CC search. The colour
shading corresponds to the observed limit on the cross section. The solid
black (dashed red) lines represent the observed (expected) limits, derived
using the expected top squark pair production cross section. The thick lines
represent the central values and the thin lines the variations due to the
theoretical (experimental) uncertainties.

Figure 9: Combined limits at 95% between the CC single-lepton ($1\ell$) and
all-hadronic ($0\ell$) [19] searches for the four-body decay (upper) and the
chargino-mediated decay (lower) of the top squark in the
$m(\PSQt_{1})$–$\Delta m(\PSQt_{1},\PSGczDo)$ plane. The correlations between
the two searches have been taken into account. The colour shading corresponds
to the observed limit on the cross section. The solid black (dashed red) lines
show the observed (expected) mass limits, derived using the expected top
squark pair production cross section. The thick lines represent the central
values and the thin lines the variations due to the theoretical (experimental)
uncertainties. The dot-dashed blue and dotted green lines show the individual
expected mass limits for the $1\ell$ and $0\ell$ searches, respectively.

##  0.7 Summary

A search for direct top squark pair production is performed in a compressed
scenario where the mass difference $\Delta{m}$ between the lightest top squark
and the lightest supersymmetric particle (LSP), taken to be the lightest
neutralino , does not exceed the boson mass. Two decay modes of the top squark
are targeted: the four-body prompt decay to
$\cPqb\mathrm{f}\overline{\mathrm{f}}^{\,\prime}\PSGczDo$, and the chargino-
mediated decay to $\cPqb\PSGcpDo$ with a subsequent decay
$\PSGcpDo\to\mathrm{f}\overline{\mathrm{f}}^{\,\prime}\PSGczDo$. Results are
based on proton-proton collision data at $\sqrt{s}=13\TeV$, recorded with the
CMS detector in 2016 and corresponding to an integrated luminosity of 35.9.
Selected events are required to have a single lepton (electron or muon), and
significant missing transverse momentum (). Because of the small mass
difference between the top squark and the LSP, the decay products of the top
squark are expected to have low . Events where the presence of a jet from
initial-state radiation leads to a boost of the top squark pair and sizeable
are selected.

Two search strategies are pursued. In the sequential selection approach (CC),
signal regions are defined based on discriminating variables, particularly the
transverse mass of the lepton-system and the lepton momentum. In another
approach, a multivariate analysis (MVA) is employed that uses both kinematic
and topological variables and is specifically trained for different
$\Delta{m}$ regions of the four-body decay mode. In both approaches, the
dominant contributions to the signal regions from standard model processes
($\PW$+jets, , and events with misidentified leptons) are estimated from
control regions in data.

Data are found to be compatible with the predicted standard model backgrounds.
The results are used to set limits at 95% confidence level on the production
cross section as a function of the $\PSQt_{1}$ and masses, within the context
of simplified models. Assuming 100% branching fraction in the decay channel
under consideration and the top squark pair production cross section computed
at NLO+NLL precision [40, 41, 42, 43, 44, 45, 46], these limits are converted
into mass limits.

Both search strategies are applied to the four-body decay mode. For this decay
mode, the MVA search excludes top squark masses up to 420 and 560at
$\Delta{m}$ = 10 and 80, respectively. The CC approach covers the chargino-
mediated decays, where the chargino mass is taken as the average of the top
squark and the neutralino masses, probing $\PSQt_{1}$ masses up to 540for
$\Delta{m}\approx 40$. The results of the CC search have been combined with a
search for top squark pair production in the fully hadronic channel [19]. The
combined mass limits reach up to 590 and 670for four-body and chargino-
mediated decays, respectively. The reach of the $\Delta{m}$ dependent MVA
search in the four-body decay mode is noteworthy, as the exclusion limit goes
beyond that of the combined result at high $\Delta{m}$.

The results summarized in this paper represent the most stringent limits to
date on the top squark pair production cross section for mass differences
between the top squark and the lightest neutralino below the boson mass, and
for decays proceeding through the four-body or the chargino-mediated modes.

###### 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 centres 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: BMWFW and FWF (Austria); FNRS and FWO (Belgium); CNPq,
CAPES, FAPERJ, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST, and NSFC
(China); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RPF (Cyprus);
SENESCYT (Ecuador); MoER, ERC IUT, and ERDF (Estonia); Academy of Finland,
MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF
(Germany); GSRT (Greece); NKFIA (Hungary); DAE and DST (India); IPM (Iran);
SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); LAS
(Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and
UASLP-FAI (Mexico); MBIE (New Zealand); PAEC (Pakistan); MSHE and NSC
(Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom, RAS and RFBR (Russia);
MESTD (Serbia); SEIDI, CPAN, PCTI and FEDER (Spain); Swiss Funding Agencies
(Switzerland); MST (Taipei); ThEPCenter, IPST, STAR, and NSTDA (Thailand);
TUBITAK and TAEK (Turkey); NASU and SFFR (Ukraine); STFC (United Kingdom); DOE
and NSF (USA). Individuals have received support from the Marie-Curie
programme and the European Research Council and Horizon 2020 Grant, contract
No. 675440 (European Union); the Leventis Foundation; the A. P. Sloan
Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science
Policy Office; the Fonds pour la Formation à la Recherche dans l’Industrie et
dans l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door
Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium)
under the “Excellence of Science - EOS” - be.h project n. 30820817; the
Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the
Lendület (“Momentum”) Programme and the János Bolyai Research Scholarship of
the Hungarian Academy of Sciences, the New National Excellence Program ÚNKP,
the NKFIA research grants 123842, 123959, 124845, 124850 and 125105 (Hungary);
the Council of Science and Industrial Research, India; the HOMING PLUS
programme of the Foundation for Polish Science, cofinanced from European
Union, Regional Development Fund, the Mobility Plus programme of the Ministry
of Science and Higher Education, the National Science Center (Poland),
contracts Harmonia 2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543,
2014/15/B/ST2/03998, and 2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406;
the National Priorities Research Program by Qatar National Research Fund; the
Programa Estatal de Fomento de la Investigación Científica y Técnica de
Excelencia María de Maeztu, grant MDM-2015-0509 and the Programa Severo Ochoa
del Principado de Asturias; the Thalis and Aristeia programmes 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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##  .8 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan, A. Tumasyan Institut für Hochenergiephysik, Wien, Austria
W. Adam, F. Ambrogi, E. Asilar, T. Bergauer, J. Brandstetter, E. Brondolin, M.
Dragicevic, J. Erö, A. Escalante Del Valle, M. Flechl, R. Frühwirth1, V.M.
Ghete, J. Hrubec, M. Jeitler1, N. Krammer, I. Krätschmer, D. Liko, T.
Madlener, I. Mikulec, N. Rad, H. Rohringer, J. Schieck1, R. Schöfbeck, M.
Spanring, D. Spitzbart, A. Taurok, W. Waltenberger, J. Wittmann, C.-E. Wulz1,
M. Zarucki Institute for Nuclear Problems, Minsk, Belarus
V. Chekhovsky, V. Mossolov, J. Suarez Gonzalez Universiteit Antwerpen,
Antwerpen, Belgium
E.A. De Wolf, D. Di Croce, X. Janssen, J. Lauwers, M. Pieters, M. Van De
Klundert, H. Van Haevermaet, P. Van Mechelen, N. Van Remortel Vrije
Universiteit Brussel, Brussel, Belgium
S. Abu Zeid, F. Blekman, J. D’Hondt, I. De Bruyn, J. De Clercq, K. Deroover,
G. Flouris, D. Lontkovskyi, S. Lowette, I. Marchesini, S. Moortgat, L.
Moreels, Q. Python, K. Skovpen, S. Tavernier, W. Van Doninck, P. Van Mulders,
I. Van Parijs Université Libre de Bruxelles, Bruxelles, Belgium
D. Beghin, B. Bilin, H. Brun, B. Clerbaux, G. De Lentdecker, H. Delannoy, B.
Dorney, G. Fasanella, L. Favart, R. Goldouzian, A. Grebenyuk, A.K. Kalsi, T.
Lenzi, J. Luetic, N. Postiau, E. Starling, L. Thomas, C. Vander Velde, P.
Vanlaer, D. Vannerom, Q. Wang Ghent University, Ghent, Belgium
T. Cornelis, D. Dobur, A. Fagot, M. Gul, I. Khvastunov2, D. Poyraz, C. Roskas,
D. Trocino, M. Tytgat, W. Verbeke, B. Vermassen, M. Vit, N. Zaganidis
Université Catholique de Louvain, Louvain-la-Neuve, Belgium
H. Bakhshiansohi, O. Bondu, S. Brochet, G. Bruno, C. Caputo, P. David, C.
Delaere, M. Delcourt, B. Francois, A. Giammanco, G. Krintiras, V. Lemaitre, A.
Magitteri, A. Mertens, M. Musich, K. Piotrzkowski, A. Saggio, M. Vidal Marono,
S. Wertz, J. Zobec Centro Brasileiro de Pesquisas Fisicas, Rio de Janeiro,
Brazil
F.L. Alves, G.A. Alves, L. Brito, G. Correia Silva, C. Hensel, A. Moraes, M.E.
Pol, P. Rebello Teles Universidade do Estado do Rio de Janeiro, Rio de
Janeiro, Brazil
E. Belchior Batista Das Chagas, W. Carvalho, J. Chinellato3, E. Coelho, E.M.
Da Costa, G.G. Da Silveira4, D. De Jesus Damiao, C. De Oliveira Martins, S.
Fonseca De Souza, H. Malbouisson, D. Matos Figueiredo, M. Melo De Almeida, C.
Mora Herrera, L. Mundim, H. Nogima, W.L. Prado Da Silva, L.J. Sanchez Rosas,
A. Santoro, A. Sznajder, M. Thiel, E.J. Tonelli Manganote3, F. Torres Da Silva
De Araujo, A. Vilela Pereira Universidade Estadual Paulista a, Universidade
Federal do ABC b, São Paulo, Brazil
S. Ahujaa, C.A. Bernardesa, L. Calligarisa, T.R. Fernandez Perez Tomeia, E.M.
Gregoresb, P.G. Mercadanteb, S.F. Novaesa, SandraS. Padulaa, D. Romero Abadb
Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy of
Sciences, Sofia, Bulgaria
A. Aleksandrov, R. Hadjiiska, P. Iaydjiev, A. Marinov, M. Misheva, M. Rodozov,
M. Shopova, G. Sultanov University of Sofia, Sofia, Bulgaria
A. Dimitrov, L. Litov, B. Pavlov, P. Petkov Beihang University, Beijing, China
W. Fang5, X. Gao5, L. Yuan Institute of High Energy Physics, Beijing, China
M. Ahmad, J.G. Bian, G.M. Chen, H.S. Chen, M. Chen, Y. Chen, C.H. Jiang, D.
Leggat, H. Liao, Z. Liu, F. Romeo, S.M. Shaheen, A. Spiezia, J. Tao, C. Wang,
Z. Wang, E. Yazgan, H. Zhang, J. Zhao State Key Laboratory of Nuclear Physics
and Technology, Peking University, Beijing, China
Y. Ban, G. Chen, J. Li, L. Li, Q. Li, Y. Mao, S.J. Qian, D. Wang, Z. Xu
Tsinghua University, Beijing, China
Y. Wang Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, C.A. Carrillo Montoya, L.F. Chaparro Sierra, C. Florez,
C.F. González Hernández, M.A. Segura Delgado University of Split, Faculty of
Electrical Engineering, Mechanical Engineering and Naval Architecture, Split,
Croatia
B. Courbon, N. Godinovic, D. Lelas, I. Puljak, T. Sculac University of Split,
Faculty of Science, Split, Croatia
Z. Antunovic, M. Kovac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, D. Ferencek, K. Kadija, B. Mesic, A. Starodumov6, T. Susa
University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos,
P.A. Razis, H. Rykaczewski Charles University, Prague, Czech Republic
M. Finger7, M. Finger Jr.7 Escuela Politecnica Nacional, Quito, Ecuador
E. Ayala Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab
Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
S. Elgammal8, S. Khalil9, A. Mahrous10 National Institute of Chemical Physics
and Biophysics, Tallinn, Estonia
S. Bhowmik, A. Carvalho Antunes De Oliveira, R.K. Dewanjee, K. Ehataht, M.
Kadastik, M. Raidal, C. Veelken Department of Physics, University of Helsinki,
Helsinki, Finland
P. Eerola, H. Kirschenmann, J. Pekkanen, M. Voutilainen Helsinki Institute of
Physics, Helsinki, Finland
J. Havukainen, J.K. Heikkilä, T. Järvinen, V. Karimäki, R. Kinnunen, T.
Lampén, K. Lassila-Perini, S. Laurila, S. Lehti, T. Lindén, P. Luukka, T.
Mäenpää, H. Siikonen, E. Tuominen, J. Tuominiemi Lappeenranta University of
Technology, Lappeenranta, Finland
T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
M. Besancon, F. Couderc, M. Dejardin, D. Denegri, J.L. Faure, F. Ferri, S.
Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry, C. Leloup,
E. Locci, J. Malcles, G. Negro, J. Rander, A. Rosowsky, M.Ö. Sahin, M. Titov
Laboratoire Leprince-Ringuet, Ecole polytechnique, CNRS/IN2P3, Université
Paris-Saclay, Palaiseau, France
A. Abdulsalam11, C. Amendola, I. Antropov, F. Beaudette, P. Busson, C.
Charlot, R. Granier de Cassagnac, I. Kucher, S. Lisniak, A. Lobanov, J. Martin
Blanco, M. Nguyen, C. Ochando, G. Ortona, P. Pigard, R. Salerno, J.B. Sauvan,
Y. Sirois, A.G. Stahl Leiton, A. Zabi, A. Zghiche Université de Strasbourg,
CNRS, IPHC UMR 7178, F-67000 Strasbourg, France
J.-L. Agram12, J. Andrea, D. Bloch, J.-M. Brom, E.C. Chabert, V. Cherepanov,
C. Collard, E. Conte12, J.-C. Fontaine12, D. Gelé, U. Goerlach, M. Jansová,
A.-C. Le Bihan, N. Tonon, P. Van Hove Centre de Calcul de l’Institut National
de Physique Nucleaire et de Physique des Particules, CNRS/IN2P3, Villeurbanne,
France
S. Gadrat Université de Lyon, Université Claude Bernard Lyon 1, CNRS-IN2P3,
Institut de Physique Nucléaire de Lyon, Villeurbanne, France
S. Beauceron, C. Bernet, G. Boudoul, N. Chanon, R. Chierici, D. Contardo, P.
Depasse, H. El Mamouni, J. Fay, L. Finco, S. Gascon, M. Gouzevitch, G.
Grenier, B. Ille, F. Lagarde, I.B. Laktineh, H. Lattaud, M. Lethuillier, L.
Mirabito, A.L. Pequegnot, S. Perries, A. Popov13, V. Sordini, M. Vander
Donckt, S. Viret, S. Zhang Georgian Technical University, Tbilisi, Georgia
A. Khvedelidze7 Tbilisi State University, Tbilisi, Georgia
Z. Tsamalaidze7 RWTH Aachen University, I. Physikalisches Institut, Aachen,
Germany
C. Autermann, L. Feld, M.K. Kiesel, K. Klein, M. Lipinski, M. Preuten, M.P.
Rauch, C. Schomakers, J. Schulz, M. Teroerde, B. Wittmer, V. Zhukov13 RWTH
Aachen University, III. Physikalisches Institut A, Aachen, Germany
A. Albert, D. Duchardt, M. Endres, M. Erdmann, T. Esch, R. Fischer, S. Ghosh,
A. Güth, T. Hebbeker, C. Heidemann, K. Hoepfner, H. Keller, S. Knutzen, L.
Mastrolorenzo, M. Merschmeyer, A. Meyer, P. Millet, S. Mukherjee, T. Pook, M.
Radziej, H. Reithler, M. Rieger, F. Scheuch, A. Schmidt, D. Teyssier RWTH
Aachen University, III. Physikalisches Institut B, Aachen, Germany
G. Flügge, O. Hlushchenko, B. Kargoll, T. Kress, A. Künsken, T. Müller, A.
Nehrkorn, A. Nowack, C. Pistone, O. Pooth, H. Sert, A. Stahl14 Deutsches
Elektronen-Synchrotron, Hamburg, Germany
M. Aldaya Martin, T. Arndt, C. Asawatangtrakuldee, I. Babounikau, K.
Beernaert, O. Behnke, U. Behrens, A. Bermúdez Martínez, D. Bertsche, A.A. Bin
Anuar, K. Borras15, V. Botta, A. Campbell, P. Connor, C. Contreras-Campana, F.
Costanza, V. Danilov, A. De Wit, M.M. Defranchis, C. Diez Pardos, D. Domínguez
Damiani, G. Eckerlin, T. Eichhorn, A. Elwood, E. Eren, E. Gallo16, A. Geiser,
J.M. Grados Luyando, A. Grohsjean, P. Gunnellini, M. Guthoff, A. Harb, J.
Hauk, H. Jung, M. Kasemann, J. Keaveney, C. Kleinwort, J. Knolle, D. Krücker,
W. Lange, A. Lelek, T. Lenz, K. Lipka, W. Lohmann17, R. Mankel, I.-A. Melzer-
Pellmann, A.B. Meyer, M. Meyer, M. Missiroli, G. Mittag, J. Mnich, V.
Myronenko, S.K. Pflitsch, D. Pitzl, A. Raspereza, M. Savitskyi, P. Saxena, P.
Schütze, C. Schwanenberger, R. Shevchenko, A. Singh, N. Stefaniuk, H. Tholen,
A. Vagnerini, G.P. Van Onsem, R. Walsh, Y. Wen, K. Wichmann, C. Wissing, O.
Zenaiev University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, A. Benecke, V. Blobel, M. Centis Vignali, T. Dreyer, E.
Garutti, D. Gonzalez, J. Haller, A. Hinzmann, M. Hoffmann, A. Karavdina, G.
Kasieczka, R. Klanner, R. Kogler, N. Kovalchuk, S. Kurz, V. Kutzner, J. Lange,
D. Marconi, J. Multhaup, M. Niedziela, D. Nowatschin, A. Perieanu, A. Reimers,
O. Rieger, C. Scharf, P. Schleper, S. Schumann, J. Schwandt, J. Sonneveld, H.
Stadie, G. Steinbrück, F.M. Stober, M. Stöver, D. Troendle, E. Usai, A.
Vanhoefer, B. Vormwald Institut für Experimentelle Teilchenphysik, Karlsruhe,
Germany
M. Akbiyik, C. Barth, M. Baselga, S. Baur, E. Butz, R. Caspart, T. Chwalek, F.
Colombo, W. De Boer, A. Dierlamm, N. Faltermann, B. Freund, M. Giffels, M.A.
Harrendorf, F. Hartmann14, S.M. Heindl, U. Husemann, F. Kassel14, I. Katkov13,
S. Kudella, H. Mildner, S. Mitra, M.U. Mozer, Th. Müller, M. Plagge, G. Quast,
K. Rabbertz, M. Schröder, I. Shvetsov, G. Sieber, H.J. Simonis, R. Ulrich, S.
Wayand, M. Weber, T. Weiler, S. Williamson, C. Wöhrmann, R. Wolf Institute of
Nuclear and Particle Physics (INPP), NCSR Demokritos, Aghia Paraskevi, Greece
G. Anagnostou, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas, G.
Paspalaki, I. Topsis-Giotis National and Kapodistrian University of Athens,
Athens, Greece
G. Karathanasis, S. Kesisoglou, P. Kontaxakis, A. Panagiotou, N. Saoulidou, E.
Tziaferi, K. Vellidis National Technical University of Athens, Athens, Greece
K. Kousouris, I. Papakrivopoulos, G. Tsipolitis University of Ioánnina,
Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, S. Mallios, N.
Manthos, I. Papadopoulos, E. Paradas, J. Strologas, F.A. Triantis, D.
Tsitsonis MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
M. Csanad, N. Filipovic, P. Major, M.I. Nagy, G. Pasztor, O. Surányi, G.I.
Veres Wigner Research Centre for Physics, Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath18, Á. Hunyadi, F. Sikler, T.Á. Vámi, V.
Veszpremi, G. Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear
Research ATOMKI, Debrecen, Hungary
N. Beni, S. Czellar, J. Karancsi20, A. Makovec, J. Molnar, Z. Szillasi
Institute of Physics, University of Debrecen, Debrecen, Hungary
M. Bartók19, P. Raics, Z.L. Trocsanyi, B. Ujvari Indian Institute of Science
(IISc), Bangalore, India
S. Choudhury, J.R. Komaragiri National Institute of Science Education and
Research, HBNI, Bhubaneswar, India
S. Bahinipati21, P. Mal, K. Mandal, A. Nayak22, D.K. Sahoo21, S.K. Swain
Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, S. Chauhan, R. Chawla, N. Dhingra, R.
Gupta, A. Kaur, A. Kaur, M. Kaur, S. Kaur, R. Kumar, P. Kumari, M. Lohan, A.
Mehta, K. Sandeep, S. Sharma, J.B. Singh, G. Walia University of Delhi, Delhi,
India
A. Bhardwaj, B.C. Choudhary, R.B. Garg, M. Gola, S. Keshri, Ashok Kumar, S.
Malhotra, M. Naimuddin, P. Priyanka, K. Ranjan, Aashaq Shah, R. Sharma Saha
Institute of Nuclear Physics, HBNI, Kolkata, India
R. Bhardwaj23, M. Bharti, R. Bhattacharya, S. Bhattacharya, U. Bhawandeep23,
D. Bhowmik, S. Dey, S. Dutt23, S. Dutta, S. Ghosh, K. Mondal, S. Nandan, A.
Purohit, P.K. Rout, A. Roy, S. Roy Chowdhury, S. Sarkar, M. Sharan, B. Singh,
S. Thakur23 Indian Institute of Technology Madras, Madras, India
P.K. Behera Bhabha Atomic Research Centre, Mumbai, India
R. Chudasama, D. Dutta, V. Jha, V. Kumar, P.K. Netrakanti, L.M. Pant, P.
Shukla Tata Institute of Fundamental Research-A, Mumbai, India
T. Aziz, M.A. Bhat, S. Dugad, B. Mahakud, G.B. Mohanty, N. Sur, B. Sutar,
RavindraKumar Verma Tata Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, Sa. Jain, S.
Kumar, M. Maity24, G. Majumder, K. Mazumdar, N. Sahoo, T. Sarkar24 Indian
Institute of Science Education and Research (IISER), Pune, India
S. Chauhan, S. Dube, V. Hegde, A. Kapoor, K. Kothekar, S. Pandey, A. Rane, S.
Sharma Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
S. Chenarani25, E. Eskandari Tadavani, S.M. Etesami25, M. Khakzad, M.
Mohammadi Najafabadi, M. Naseri, F. Rezaei Hosseinabadi, B. Safarzadeh26, M.
Zeinali University College Dublin, Dublin, Ireland
M. Felcini, M. Grunewald INFN Sezione di Bari a, Università di Bari b,
Politecnico di Bari c, Bari, Italy
M. Abbresciaa,b, C. Calabriaa,b, A. Colaleoa, D. Creanzaa,c, L. Cristellaa,b,
N. De Filippisa,c, M. De Palmaa,b, A. Di Florioa,b, F. Erricoa,b, L. Fiorea,
A. Gelmia,b, G. Iasellia,c, S. Lezkia,b, G. Maggia,c, M. Maggia, G.
Minielloa,b, S. Mya,b, S. Nuzzoa,b, A. Pompilia,b, G. Pugliesea,c, R.
Radognaa, A. Ranieria, G. Selvaggia,b, A. Sharmaa, L. Silvestrisa,14, R.
Vendittia, P. Verwilligena, G. Zitoa INFN Sezione di Bologna a, Università di
Bologna b, Bologna, Italy
G. Abbiendia, C. Battilanaa,b, D. Bonacorsia,b, L. Borgonovia,b, S. Braibant-
Giacomellia,b, L. Brigliadoria,b, R. Campaninia,b, P. Capiluppia,b, A.
Castroa,b, F.R. Cavalloa, S.S. Chhibraa,b, G. Codispotia,b, M. Cuffiania,b,
G.M. Dallavallea, F. Fabbria, A. Fanfania,b, P. Giacomellia, C. Grandia, L.
Guiduccia,b, S. Marcellinia, G. Masettia, A. Montanaria, F.L. Navarriaa,b, A.
Perrottaa, A.M. Rossia,b, T. Rovellia,b, G.P. Sirolia,b, N. Tosia INFN Sezione
di Catania a, Università di Catania b, Catania, Italy
S. Albergoa,b, A. Di Mattiaa, R. Potenzaa,b, A. Tricomia,b, C. Tuvea,b INFN
Sezione di Firenze a, Università di Firenze b, Firenze, Italy
G. Barbaglia, K. Chatterjeea,b, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, E. Focardia,b, G. Latino, P. Lenzia,b, M. Meschinia, S.
Paolettia, L. Russoa,27, G. Sguazzonia, D. Stroma, L. Viliania INFN Laboratori
Nazionali di Frascati, Frascati, Italy
L. Benussi, S. Bianco, F. Fabbri, D. Piccolo, F. Primavera14 INFN Sezione di
Genova a, Università di Genova b, Genova, Italy
F. Ferroa, F. Raveraa,b, E. Robuttia, S. Tosia,b INFN Sezione di Milano-
Bicocca a, Università di Milano-Bicocca b, Milano, Italy
A. Benagliaa, A. Beschib, L. Brianzaa,b, F. Brivioa,b, V. Cirioloa,b,14, S. Di
Guidaa,d,14, M.E. Dinardoa,b, S. Fiorendia,b, S. Gennaia, A. Ghezzia,b, P.
Govonia,b, M. Malbertia,b, S. Malvezzia, R.A. Manzonia,b, A. Massironia,b, D.
Menascea, L. Moronia, M. Paganonia,b, D. Pedrinia, S. Ragazzia,b, T. Tabarelli
de Fatisa,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. Di Crescenzoa,b, F. Fabozzia,c, F. Fiengaa,b,
G. Galatia,b, A.O.M. Iorioa,b, W.A. Khana, L. Listaa, S. Meolaa,d,14, P.
Paoluccia,14, 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, L. Benatoa,b, D. Biselloa,b, A. Bolettia,b, A.
Bragagnolo, R. Carlina,b, P. Checchiaa, M. Dall’Ossoa,b, P. De Castro
Manzanoa, T. Dorigoa, U. Gasparinia,b, A. Gozzelinoa, S. Lacapraraa, P. Lujan,
M. Margonia,b, A.T. Meneguzzoa,b, F. Montecassianoa, N. Pozzobona,b, P.
Ronchesea,b, R. Rossina,b, F. Simonettoa,b, A. Tiko, E. Torassaa, M.
Zanettia,b, P. Zottoa,b, G. Zumerlea,b INFN Sezione di Pavia a, Università di
Pavia b, Pavia, Italy
A. Braghieria, A. Magnania, P. Montagnaa,b, S.P. Rattia,b, V. Rea, M.
Ressegottia,b, C. Riccardia,b, P. Salvinia, I. Vaia,b, P. Vituloa,b INFN
Sezione di Perugia a, Università di Perugia b, Perugia, Italy
L. Alunni Solestizia,b, M. Biasinia,b, G.M. Bileia, C. Cecchia,b, D.
Ciangottinia,b, L. Fanòa,b, P. Laricciaa,b, E. Manonia, G. Mantovania,b, V.
Mariania,b, M. Menichellia, A. Rossia,b, A. Santocchiaa,b, D. Spigaa INFN
Sezione di Pisa a, Università di Pisa b, Scuola Normale Superiore di Pisa c,
Pisa, Italy
K. Androsova, P. Azzurria, G. Bagliesia, L. Bianchinia, T. Boccalia, L.
Borrello, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, G. Fedia, L.
Gianninia,c, A. Giassia, M.T. Grippoa, F. Ligabuea,c, E. Mancaa,c, G.
Mandorlia,c, A. Messineoa,b, F. Pallaa, A. Rizzia,b, P. Spagnoloa, R.
Tenchinia, G. Tonellia,b, A. Venturia, P.G. Verdinia INFN Sezione di Roma a,
Sapienza Università di Roma b, Rome, Italy
L. Baronea,b, F. Cavallaria, M. Cipriania,b, N. Dacia, D. Del Rea,b, E. Di
Marcoa,b, M. Diemoza, S. Gellia,b, E. Longoa,b, B. Marzocchia,b, P.
Meridiania, G. Organtinia,b, F. Pandolfia, R. Paramattia,b, F. Preiatoa,b, S.
Rahatloua,b, C. Rovellia, F. Santanastasioa,b INFN Sezione di Torino a,
Università di Torino b, Torino, Italy, Università del Piemonte Orientale c,
Novara, Italy
N. Amapanea,b, R. Arcidiaconoa,c, S. Argiroa,b, M. Arneodoa,c, N. Bartosika,
R. Bellana,b, C. Biinoa, N. Cartigliaa, F. Cennaa,b, M. Costaa,b, R.
Covarellia,b, N. Demariaa, B. Kiania,b, C. Mariottia, S. Masellia, E.
Migliorea,b, V. Monacoa,b, E. Monteila,b, M. Montenoa, M.M. Obertinoa,b, L.
Pachera,b, N. Pastronea, M. Pelliccionia, G.L. Pinna Angionia,b, A. Romeroa,b,
M. Ruspaa,c, R. Sacchia,b, K. Shchelinaa,b, V. Solaa, A. Solanoa,b, A.
Staianoa INFN Sezione di Trieste a, Università di Trieste b, Trieste, Italy
S. Belfortea, V. Candelisea,b, M. Casarsaa, F. Cossuttia, G. Della Riccaa,b,
F. Vazzolera,b, A. Zanettia Kyungpook National University
D.H. Kim, G.N. Kim, M.S. Kim, J. Lee, S. Lee, S.W. Lee, C.S. Moon, Y.D. Oh, 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
J. Goh, T.J. Kim Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, D. Gyun, S. Ha, B. Hong, Y. Jo, K. Lee, K.S. Lee, S.
Lee, J. Lim, S.K. Park, Y. Roh Sejong University, Seoul, Korea
H.S. Kim Seoul National University, Seoul, Korea
J. Almond, J. Kim, J.S. Kim, H. Lee, K. Lee, K. Nam, S.B. Oh, B.C. Radburn-
Smith, S.h. Seo, U.K. Yang, H.D. Yoo, G.B. Yu University of Seoul, Seoul,
Korea
H. Kim, J.H. Kim, J.S.H. Lee, I.C. Park Sungkyunkwan University, Suwon, Korea
Y. Choi, C. Hwang, J. Lee, I. Yu Vilnius University, Vilnius, Lithuania
V. Dudenas, A. Juodagalvis, J. Vaitkus National Centre for Particle Physics,
Universiti Malaya, Kuala Lumpur, Malaysia
I. Ahmed, Z.A. Ibrahim, M.A.B. Md Ali28, F. Mohamad Idris29, W.A.T. Wan
Abdullah, M.N. Yusli, Z. Zolkapli Centro de Investigacion y de Estudios
Avanzados del IPN, Mexico City, Mexico
M.C. Duran-Osuna, H. Castilla-Valdez, E. De La Cruz-Burelo, G. Ramirez-
Sanchez, I. Heredia-De La Cruz30, R.I. Rabadan-Trejo, R. Lopez-Fernandez, J.
Mejia Guisao, R Reyes-Almanza, A. Sanchez-Hernandez Universidad
Iberoamericana, Mexico City, Mexico
S. Carrillo Moreno, C. Oropeza Barrera, F. Vazquez Valencia Benemerita
Universidad Autonoma de Puebla, Puebla, Mexico
J. Eysermans, I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada Universidad
Autónoma de San Luis Potosí, San Luis Potosí, Mexico
A. Morelos Pineda University of Auckland, Auckland, New Zealand
D. Krofcheck University of Canterbury, Christchurch, New Zealand
S. Bheesette, P.H. Butler National Centre for Physics, Quaid-I-Azam
University, Islamabad, Pakistan
A. Ahmad, M. Ahmad, M.I. Asghar, Q. Hassan, H.R. Hoorani, A. Saddique, M.A.
Shah, M. Shoaib, M. Waqas National Centre for Nuclear Research, Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, T. Frueboes, M. Górski, M. Kazana, K.
Nawrocki, M. Szleper, P. Traczyk, P. Zalewski Institute of Experimental
Physics, Faculty of Physics, University of Warsaw, Warsaw, Poland
K. Bunkowski, A. Byszuk31, K. Doroba, A. Kalinowski, M. Konecki, J.
Krolikowski, M. Misiura, M. Olszewski, A. Pyskir, M. Walczak Laboratório de
Instrumentação e Física Experimental de Partículas, Lisboa, Portugal
P. Bargassa, C. Beirão Da Cruz E Silva, A. Di Francesco, P. Faccioli, B.
Galinhas, M. Gallinaro, J. Hollar, N. Leonardo, L. Lloret Iglesias, M.V.
Nemallapudi, J. Seixas, G. Strong, O. Toldaiev, D. Vadruccio, J. Varela Joint
Institute for Nuclear Research, Dubna, Russia
S. Afanasiev, V. Alexakhin, P. Bunin, M. Gavrilenko, A. Golunov, I. Golutvin,
N. Gorbounov, V. Karjavin, A. Lanev, A. Malakhov, V. Matveev32,33, P. Moisenz,
V. Palichik, V. Perelygin, M. Savina, S. Shmatov, V. Smirnov, N. Voytishin, A.
Zarubin Petersburg Nuclear Physics Institute, Gatchina (St. Petersburg),
Russia
V. Golovtsov, Y. Ivanov, V. Kim34, E. Kuznetsova35, P. Levchenko, V. Murzin,
V. Oreshkin, I. Smirnov, D. Sosnov, V. Sulimov, L. Uvarov, S. Vavilov, A.
Vorobyev Institute for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov,
N. Krasnikov, A. Pashenkov, D. Tlisov, A. Toropin Institute for Theoretical
and Experimental Physics, Moscow, Russia
V. Epshteyn, V. Gavrilov, N. Lychkovskaya, V. Popov, I. Pozdnyakov, G.
Safronov, A. Spiridonov, A. Stepennov, V. Stolin, M. Toms, E. Vlasov, A.
Zhokin Moscow Institute of Physics and Technology, Moscow, Russia
T. Aushev, A. Bylinkin33 National Research Nuclear University ’Moscow
Engineering Physics Institute’ (MEPhI), Moscow, Russia
R. Chistov36, P. Parygin, D. Philippov, S. Polikarpov36, E. Tarkovskii P.N.
Lebedev Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin33, I. Dremin33, M. Kirakosyan33, S.V. Rusakov, A.
Terkulov Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State
University, Moscow, Russia
A. Baskakov, A. Belyaev, E. Boos, V. Bunichev, M. Dubinin37, L. Dudko, A.
Ershov, A. Gribushin, V. Klyukhin, O. Kodolova, I. Lokhtin, I. Miagkov, S.
Obraztsov, S. Petrushanko, V. Savrin Novosibirsk State University (NSU),
Novosibirsk, Russia
V. Blinov38, T. Dimova38, L. Kardapoltsev38, D. Shtol38, Y. Skovpen38 State
Research Center of Russian Federation, Institute for High Energy Physics of
NRC ’Kurchatov Institute’, Protvino, Russia
I. Azhgirey, I. Bayshev, S. Bitioukov, D. Elumakhov, A. Godizov, V. Kachanov,
A. Kalinin, D. Konstantinov, P. Mandrik, V. Petrov, R. Ryutin, S.
Slabospitskii, A. Sobol, S. Troshin, N. Tyurin, A. Uzunian, A. Volkov National
Research Tomsk Polytechnic University, Tomsk, Russia
A. Babaev University of Belgrade, Faculty of Physics and Vinca Institute of
Nuclear Sciences, Belgrade, Serbia
P. Adzic39, P. Cirkovic, D. Devetak, M. Dordevic, J. Milosevic Centro de
Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT), Madrid,
Spain
J. Alcaraz Maestre, A. Álvarez Fernández, I. Bachiller, M. Barrio Luna, J.A.
Brochero Cifuentes, M. Cerrada, N. Colino, B. De La Cruz, A. Delgado Peris, C.
Fernandez Bedoya, J.P. Fernández Ramos, J. Flix, M.C. Fouz, O. Gonzalez Lopez,
S. Goy Lopez, J.M. Hernandez, M.I. Josa, D. Moran, A. Pérez-Calero Yzquierdo,
J. Puerta Pelayo, I. Redondo, L. Romero, M.S. Soares, A. Triossi Universidad
Autónoma de Madrid, Madrid, Spain
C. Albajar, J.F. de Trocóniz Universidad de Oviedo, Oviedo, Spain
J. Cuevas, C. Erice, J. Fernandez Menendez, S. Folgueras, I. Gonzalez
Caballero, J.R. González Fernández, E. Palencia Cortezon, V. Rodríguez Bouza,
S. Sanchez Cruz, P. Vischia, J.M. Vizan Garcia Instituto de Física de
Cantabria (IFCA), CSIC-Universidad de Cantabria, Santander, Spain
I.J. Cabrillo, A. Calderon, B. Chazin Quero, J. Duarte Campderros, M.
Fernandez, P.J. Fernández Manteca, A. García Alonso, J. Garcia-Ferrero, G.
Gomez, A. Lopez Virto, J. Marco, C. Martinez Rivero, P. Martinez Ruiz del
Arbol, F. Matorras, J. Piedra Gomez, C. Prieels, T. Rodrigo, A. Ruiz-Jimeno,
L. Scodellaro, N. Trevisani, I. Vila, R. Vilar Cortabitarte CERN, European
Organization for Nuclear Research, Geneva, Switzerland
D. Abbaneo, B. Akgun, E. Auffray, P. Baillon, A.H. Ball, D. Barney, J.
Bendavid, M. Bianco, A. Bocci, C. Botta, T. Camporesi, M. Cepeda, G.
Cerminara, E. Chapon, Y. Chen, G. Cucciati, D. d’Enterria, A. Dabrowski, V.
Daponte, A. David, A. De Roeck, N. Deelen, M. Dobson, T. du Pree, M. Dünser,
N. Dupont, A. Elliott-Peisert, P. Everaerts, F. Fallavollita40, D. Fasanella,
G. Franzoni, J. Fulcher, W. Funk, D. Gigi, A. Gilbert, K. Gill, F. Glege, D.
Gulhan, J. Hegeman, V. Innocente, A. Jafari, P. Janot, O. Karacheban17, J.
Kieseler, V. Knünz, A. Kornmayer, M. Krammer1, C. Lange, P. Lecoq, C.
Lourenço, L. Malgeri, M. Mannelli, F. Meijers, J.A. Merlin, S. Mersi, E.
Meschi, P. Milenovic41, F. Moortgat, M. Mulders, H. Neugebauer, J. Ngadiuba,
S. Orfanelli, L. Orsini, F. Pantaleo14, L. Pape, E. Perez, M. Peruzzi, A.
Petrilli, G. Petrucciani, A. Pfeiffer, M. Pierini, F.M. Pitters, D. Rabady, A.
Racz, T. Reis, G. Rolandi42, M. Rovere, H. Sakulin, C. Schäfer, C. Schwick, M.
Seidel, M. Selvaggi, A. Sharma, P. Silva, P. Sphicas43, A. Stakia, J.
Steggemann, M. Tosi, D. Treille, A. Tsirou, V. Veckalns44, M. Verweij, W.D.
Zeuner Paul Scherrer Institut, Villigen, Switzerland
W. Bertl${}^{\textrm{\textdagger}}$, L. Caminada45, K. Deiters, W. Erdmann, R.
Horisberger, Q. Ingram, H.C. Kaestli, D. Kotlinski, U. Langenegger, T. Rohe,
S.A. Wiederkehr ETH Zurich - Institute for Particle Physics and Astrophysics
(IPA), Zurich, Switzerland
M. Backhaus, L. Bäni, P. Berger, N. Chernyavskaya, G. Dissertori, M. Dittmar,
M. Donegà, C. Dorfer, C. Grab, C. Heidegger, D. Hits, J. Hoss, T. Klijnsma, W.
Lustermann, M. Marionneau, M.T. Meinhard, D. Meister, F. Micheli, P. Musella,
F. Nessi-Tedaldi, J. Pata, F. Pauss, G. Perrin, L. Perrozzi, S. Pigazzini, M.
Quittnat, M. Reichmann, D. Ruini, D.A. Sanz Becerra, M. Schönenberger, L.
Shchutska, V.R. Tavolaro, K. Theofilatos, M.L. Vesterbacka Olsson, R. Wallny,
D.H. Zhu Universität Zürich, Zurich, Switzerland
T.K. Aarrestad, C. Amsler46, D. Brzhechko, M.F. Canelli, A. De Cosa, R. Del
Burgo, S. Donato, C. Galloni, T. Hreus, B. Kilminster, I. Neutelings, D.
Pinna, G. Rauco, P. Robmann, D. Salerno, K. Schweiger, C. Seitz, Y. Takahashi,
A. Zucchetta National Central University, Chung-Li, Taiwan
Y.H. Chang, K.y. Cheng, T.H. Doan, Sh. Jain, R. Khurana, C.M. Kuo, W. Lin, A.
Pozdnyakov, S.S. Yu National Taiwan University (NTU), Taipei, Taiwan
P. Chang, Y. Chao, K.F. Chen, P.H. Chen, W.-S. Hou, Arun Kumar, Y.y. Li, R.-S.
Lu, E. Paganis, A. Psallidas, A. Steen, J.f. Tsai Chulalongkorn University,
Faculty of Science, Department of Physics, Bangkok, Thailand
B. Asavapibhop, N. Srimanobhas, N. Suwonjandee Çukurova University, Physics
Department, Science and Art Faculty, Adana, Turkey
A. Bat, F. Boran, S. Cerci47, S. Damarseckin, Z.S. Demiroglu, C. Dozen, I.
Dumanoglu, S. Girgis, G. Gokbulut, Y. Guler, E. Gurpinar, I. Hos48, E.E.
Kangal49, O. Kara, A. Kayis Topaksu, U. Kiminsu, M. Oglakci, G. Onengut, K.
Ozdemir50, S. Ozturk51, D. Sunar Cerci47, B. Tali47, U.G. Tok, S. Turkcapar,
I.S. Zorbakir, C. Zorbilmez Middle East Technical University, Physics
Department, Ankara, Turkey
B. Isildak52, G. Karapinar53, M. Yalvac, M. Zeyrek Bogazici University,
Istanbul, Turkey
I.O. Atakisi, E. Gülmez, M. Kaya54, O. Kaya55, S. Tekten, E.A. Yetkin56
Istanbul Technical University, Istanbul, Turkey
M.N. Agaras, S. Atay, A. Cakir, K. Cankocak, Y. Komurcu, S. Sen57 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
T. Alexander, F. Ball, L. Beck, J.J. Brooke, D. Burns, E. Clement, D. Cussans,
O. Davignon, H. Flacher, J. Goldstein, G.P. Heath, H.F. Heath, L. Kreczko,
D.M. Newbold58, S. Paramesvaran, B. Penning, T. Sakuma, D. Smith, V.J. Smith,
J. Taylor Rutherford Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev59, C. Brew, R.M. Brown, D. Cieri, D.J.A. Cockerill, J.A.
Coughlan, K. Harder, S. Harper, J. Linacre, E. Olaiya, D. Petyt, C.H.
Shepherd-Themistocleous, A. Thea, I.R. Tomalin, T. Williams, W.J. Womersley
Imperial College, London, United Kingdom
G. Auzinger, R. Bainbridge, P. Bloch, J. Borg, S. Breeze, O. Buchmuller, A.
Bundock, S. Casasso, D. Colling, L. Corpe, P. Dauncey, G. Davies, M. Della
Negra, R. Di Maria, Y. Haddad, G. Hall, G. Iles, T. James, M. Komm, C. Laner,
L. Lyons, A.-M. Magnan, S. Malik, A. Martelli, J. Nash60, A. Nikitenko6, V.
Palladino, M. Pesaresi, A. Richards, A. Rose, E. Scott, C. Seez, A.
Shtipliyski, G. Singh, M. Stoye, T. Strebler, S. Summers, A. Tapper, K.
Uchida, T. Virdee14, N. Wardle, D. Winterbottom, J. Wright, S.C. Zenz Brunel
University, Uxbridge, United Kingdom
J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, C.K. Mackay, A. Morton, I.D. Reid,
L. Teodorescu, S. Zahid Baylor University, Waco, USA
K. Call, J. Dittmann, K. Hatakeyama, H. Liu, C. Madrid, B. Mcmaster, N.
Pastika, C. Smith Catholic University of America, Washington DC, USA
R. Bartek, A. Dominguez The University of Alabama, Tuscaloosa, USA
A. Buccilli, S.I. Cooper, C. Henderson, P. Rumerio, C. West Boston University,
Boston, USA
D. Arcaro, T. Bose, D. Gastler, D. Rankin, C. Richardson, J. Rohlf, L. Sulak,
D. Zou Brown University, Providence, USA
G. Benelli, X. Coubez, D. Cutts, M. Hadley, J. Hakala, U. Heintz, J.M.
Hogan61, K.H.M. Kwok, E. Laird, G. Landsberg, J. Lee, Z. Mao, M. Narain, J.
Pazzini, S. Piperov, S. Sagir62, R. Syarif, D. Yu University of California,
Davis, Davis, USA
R. Band, C. Brainerd, R. Breedon, D. Burns, M. Calderon De La Barca Sanchez,
M. Chertok, J. Conway, R. Conway, P.T. Cox, R. Erbacher, C. Flores, G. Funk,
W. Ko, O. Kukral, R. Lander, C. Mclean, M. Mulhearn, D. Pellett, J. Pilot, S.
Shalhout, M. Shi, D. Stolp, D. Taylor, K. Tos, M. Tripathi, Z. Wang, F. Zhang
University of California, Los Angeles, USA
M. Bachtis, C. Bravo, R. Cousins, A. Dasgupta, A. Florent, J. Hauser, M.
Ignatenko, N. Mccoll, S. Regnard, D. Saltzberg, C. Schnaible, V. Valuev
University of California, Riverside, Riverside, USA
E. Bouvier, K. Burt, R. Clare, J.W. Gary, S.M.A. Ghiasi Shirazi, G. Hanson, G.
Karapostoli, E. Kennedy, F. Lacroix, O.R. Long, M. Olmedo Negrete, M.I.
Paneva, W. Si, L. Wang, H. Wei, S. Wimpenny, B.R. Yates University of
California, San Diego, La Jolla, USA
J.G. Branson, S. Cittolin, M. Derdzinski, R. Gerosa, D. Gilbert, B. Hashemi,
A. Holzner, D. Klein, G. Kole, V. Krutelyov, J. Letts, M. Masciovecchio, D.
Olivito, S. Padhi, M. Pieri, M. Sani, V. Sharma, S. Simon, M. Tadel, A.
Vartak, S. Wasserbaech63, J. Wood, F. Würthwein, A. Yagil, G. Zevi Della Porta
University of California, Santa Barbara - Department of Physics, Santa
Barbara, USA
N. Amin, R. Bhandari, J. Bradmiller-Feld, C. Campagnari, M. Citron, A. Dishaw,
V. Dutta, M. Franco Sevilla, L. Gouskos, R. Heller, J. Incandela, A.
Ovcharova, H. Qu, J. Richman, D. Stuart, I. Suarez, S. Wang, J. Yoo California
Institute of Technology, Pasadena, USA
D. Anderson, A. Bornheim, J. Bunn, J.M. Lawhorn, H.B. Newman, T.Q. Nguyen, M.
Spiropulu, J.R. Vlimant, R. Wilkinson, S. Xie, Z. Zhang, R.Y. Zhu Carnegie
Mellon University, Pittsburgh, USA
M.B. Andrews, T. Ferguson, T. Mudholkar, M. Paulini, M. Sun, I. Vorobiev, M.
Weinberg University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, F. Jensen, A. Johnson, M. Krohn, S. Leontsinis, E.
MacDonald, T. Mulholland, K. Stenson, K.A. Ulmer, S.R. Wagner Cornell
University, Ithaca, USA
J. Alexander, J. Chaves, Y. Cheng, J. Chu, A. Datta, K. Mcdermott, N. Mirman,
J.R. Patterson, D. Quach, A. Rinkevicius, A. Ryd, L. Skinnari, L. Soffi, S.M.
Tan, Z. Tao, J. Thom, J. Tucker, P. Wittich, M. Zientek Fermi National
Accelerator Laboratory, Batavia, USA
S. Abdullin, M. Albrow, M. Alyari, G. Apollinari, A. Apresyan, A. Apyan, S.
Banerjee, L.A.T. Bauerdick, A. Beretvas, J. Berryhill, P.C. Bhat, G.
Bolla${}^{\textrm{\textdagger}}$, K. Burkett, J.N. Butler, A. Canepa, G.B.
Cerati, H.W.K. Cheung, F. Chlebana, M. Cremonesi, J. Duarte, V.D. Elvira, J.
Freeman, Z. Gecse, E. Gottschalk, L. Gray, D. Green, S. Grünendahl, O.
Gutsche, J. Hanlon, R.M. Harris, S. Hasegawa, J. Hirschauer, Z. Hu, B.
Jayatilaka, S. Jindariani, M. Johnson, U. Joshi, B. Klima, M.J. Kortelainen,
B. Kreis, S. Lammel, D. Lincoln, R. Lipton, M. Liu, T. Liu, J. Lykken, K.
Maeshima, J.M. Marraffino, D. Mason, P. McBride, P. Merkel, S. Mrenna, S.
Nahn, V. O’Dell, K. Pedro, C. Pena, O. Prokofyev, G. Rakness, L. Ristori, A.
Savoy-Navarro64, B. Schneider, E. Sexton-Kennedy, A. Soha, W.J. Spalding, L.
Spiegel, S. Stoynev, J. Strait, N. Strobbe, L. Taylor, S. Tkaczyk, N.V. Tran,
L. Uplegger, E.W. Vaandering, C. Vernieri, M. Verzocchi, R. Vidal, M. Wang,
H.A. Weber, A. Whitbeck University of Florida, Gainesville, USA
D. Acosta, P. Avery, P. Bortignon, D. Bourilkov, A. Brinkerhoff, L. Cadamuro,
A. Carnes, M. Carver, D. Curry, R.D. Field, S.V. Gleyzer, B.M. Joshi, J.
Konigsberg, A. Korytov, P. Ma, K. Matchev, H. Mei, G. Mitselmakher, K. Shi, D.
Sperka, J. Wang, S. Wang Florida International University, Miami, USA
Y.R. Joshi, S. Linn Florida State University, Tallahassee, USA
A. Ackert, T. Adams, A. Askew, S. Hagopian, V. Hagopian, K.F. Johnson, T.
Kolberg, G. Martinez, T. Perry, H. Prosper, A. Saha, A. Santra, V. Sharma, R.
Yohay Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, V. Bhopatkar, S. Colafranceschi, M. Hohlmann, D. Noonan, M.
Rahmani, T. Roy, F. Yumiceva University of Illinois at Chicago (UIC), Chicago,
USA
M.R. Adams, L. Apanasevich, D. Berry, R.R. Betts, R. Cavanaugh, X. Chen, S.
Dittmer, O. Evdokimov, C.E. Gerber, D.A. Hangal, D.J. Hofman, K. Jung, J.
Kamin, C. Mills, I.D. Sandoval Gonzalez, M.B. Tonjes, N. Varelas, H. Wang, Z.
Wu, J. Zhang The University of Iowa, Iowa City, USA
M. Alhusseini, B. Bilki65, W. Clarida, K. Dilsiz66, S. Durgut, R.P.
Gandrajula, M. Haytmyradov, V. Khristenko, J.-P. Merlo, A. Mestvirishvili, A.
Moeller, J. Nachtman, H. Ogul67, Y. Onel, F. Ozok68, A. Penzo, C. Snyder, E.
Tiras, J. Wetzel Johns Hopkins University, Baltimore, USA
B. Blumenfeld, A. Cocoros, N. Eminizer, D. Fehling, L. Feng, A.V. Gritsan,
W.T. Hung, P. Maksimovic, J. Roskes, U. Sarica, M. Swartz, M. Xiao, C. You The
University of Kansas, Lawrence, USA
A. Al-bataineh, P. Baringer, A. Bean, S. Boren, J. Bowen, J. Castle, S.
Khalil, A. Kropivnitskaya, D. Majumder, W. Mcbrayer, M. Murray, C. Rogan, S.
Sanders, E. Schmitz, J.D. Tapia Takaki, Q. Wang Kansas State University,
Manhattan, USA
A. Ivanov, K. Kaadze, D. Kim, Y. Maravin, D.R. Mendis, T. Mitchell, A. Modak,
A. Mohammadi, L.K. Saini, N. Skhirtladze Lawrence Livermore National
Laboratory, Livermore, USA
F. Rebassoo, D. Wright University of Maryland, College Park, USA
A. Baden, O. Baron, A. Belloni, S.C. Eno, Y. Feng, C. Ferraioli, N.J. Hadley,
S. Jabeen, G.Y. Jeng, R.G. Kellogg, J. Kunkle, A.C. Mignerey, F. Ricci-Tam,
Y.H. Shin, A. Skuja, S.C. Tonwar, K. Wong Massachusetts Institute of
Technology, Cambridge, USA
D. Abercrombie, B. Allen, V. Azzolini, R. Barbieri, A. Baty, G. Bauer, R. Bi,
S. Brandt, W. Busza, I.A. Cali, M. D’Alfonso, Z. Demiragli, G. Gomez Ceballos,
M. Goncharov, P. Harris, D. Hsu, M. Hu, Y. Iiyama, G.M. Innocenti, M. Klute,
D. Kovalskyi, Y.-J. Lee, A. Levin, P.D. Luckey, B. Maier, A.C. Marini, C.
Mcginn, C. Mironov, S. Narayanan, X. Niu, C. Paus, C. Roland, G. Roland,
G.S.F. Stephans, K. Sumorok, K. Tatar, D. Velicanu, J. Wang, T.W. Wang, B.
Wyslouch, S. Zhaozhong University of Minnesota, Minneapolis, USA
A.C. Benvenuti, R.M. Chatterjee, A. Evans, P. Hansen, S. Kalafut, Y. Kubota,
Z. Lesko, J. Mans, S. Nourbakhsh, N. Ruckstuhl, R. Rusack, J. Turkewitz, M.A.
Wadud University of Mississippi, Oxford, USA
J.G. Acosta, S. Oliveros University of Nebraska-Lincoln, Lincoln, USA
E. Avdeeva, K. Bloom, D.R. Claes, C. Fangmeier, F. Golf, R. Gonzalez Suarez,
R. Kamalieddin, I. Kravchenko, J. Monroy, J.E. Siado, G.R. Snow, B. Stieger
State University of New York at Buffalo, Buffalo, USA
A. Godshalk, C. Harrington, I. Iashvili, A. Kharchilava, D. Nguyen, A. Parker,
S. Rappoccio, B. Roozbahani Northeastern University, Boston, USA
E. Barberis, C. Freer, A. Hortiangtham, D.M. Morse, T. Orimoto, R. Teixeira De
Lima, T. Wamorkar, B. Wang, A. Wisecarver, D. Wood Northwestern University,
Evanston, USA
S. Bhattacharya, O. Charaf, K.A. Hahn, N. Mucia, N. Odell, M.H. Schmitt, K.
Sung, M. Trovato, M. Velasco University of Notre Dame, Notre Dame, USA
R. Bucci, N. Dev, M. Hildreth, K. Hurtado Anampa, C. Jessop, D.J. Karmgard, N.
Kellams, K. Lannon, W. Li, N. Loukas, N. Marinelli, F. Meng, C. Mueller, Y.
Musienko32, M. Planer, A. Reinsvold, R. Ruchti, P. Siddireddy, G. Smith, S.
Taroni, M. Wayne, A. Wightman, M. Wolf, A. Woodard The Ohio State University,
Columbus, USA
J. Alimena, L. Antonelli, B. Bylsma, L.S. Durkin, S. Flowers, B. Francis, A.
Hart, C. Hill, W. Ji, T.Y. Ling, W. Luo, B.L. Winer, H.W. Wulsin Princeton
University, Princeton, USA
S. Cooperstein, P. Elmer, J. Hardenbrook, P. Hebda, S. Higginbotham, A.
Kalogeropoulos, D. Lange, M.T. Lucchini, J. Luo, D. Marlow, K. Mei, I. Ojalvo,
J. Olsen, C. Palmer, P. Piroué, J. Salfeld-Nebgen, D. Stickland, C. Tully
University of Puerto Rico, Mayaguez, USA
S. Malik, S. Norberg Purdue University, West Lafayette, USA
A. Barker, V.E. Barnes, S. Das, L. Gutay, M. Jones, A.W. Jung, A. Khatiwada,
D.H. Miller, N. Neumeister, C.C. Peng, H. Qiu, J.F. Schulte, J. Sun, F. Wang,
R. Xiao, W. Xie Purdue University Northwest, Hammond, USA
T. Cheng, J. Dolen, N. Parashar Rice University, Houston, USA
Z. Chen, K.M. Ecklund, S. Freed, F.J.M. Geurts, M. Guilbaud, M. Kilpatrick, W.
Li, B. Michlin, B.P. Padley, J. Roberts, J. Rorie, W. Shi, Z. Tu, J. Zabel, A.
Zhang University of Rochester, Rochester, USA
A. Bodek, P. de Barbaro, R. Demina, Y.t. Duh, J.L. Dulemba, C. Fallon, T.
Ferbel, M. Galanti, A. Garcia-Bellido, J. Han, O. Hindrichs, A.
Khukhunaishvili, K.H. Lo, P. Tan, R. Taus, M. Verzetti Rutgers, The State
University of New Jersey, Piscataway, USA
A. Agapitos, J.P. Chou, Y. Gershtein, T.A. Gómez Espinosa, E. Halkiadakis, M.
Heindl, E. Hughes, S. Kaplan, R. Kunnawalkam Elayavalli, S. Kyriacou, A. Lath,
R. Montalvo, K. Nash, M. Osherson, H. Saka, S. Salur, S. Schnetzer, D.
Sheffield, S. Somalwar, R. Stone, S. Thomas, P. Thomassen, M. Walker
University of Tennessee, Knoxville, USA
A.G. Delannoy, J. Heideman, G. Riley, K. Rose, S. Spanier, K. Thapa Texas A&M
University, College Station, USA
O. Bouhali69, A. Castaneda Hernandez69, A. Celik, M. Dalchenko, M. De Mattia,
A. Delgado, S. Dildick, R. Eusebi, J. Gilmore, T. Huang, T. Kamon70, S. Luo,
R. Mueller, Y. Pakhotin, R. Patel, A. Perloff, L. Perniè, D. Rathjens, A.
Safonov, A. Tatarinov Texas Tech University, Lubbock, USA
N. Akchurin, J. Damgov, F. De Guio, P.R. Dudero, S. Kunori, K. Lamichhane,
S.W. Lee, T. Mengke, S. Muthumuni, T. Peltola, S. Undleeb, I. Volobouev, Z.
Wang Vanderbilt University, Nashville, USA
S. Greene, A. Gurrola, R. Janjam, W. Johns, C. Maguire, A. Melo, H. Ni, K.
Padeken, J.D. Ruiz Alvarez, P. Sheldon, S. Tuo, J. Velkovska, Q. Xu University
of Virginia, Charlottesville, USA
M.W. Arenton, P. Barria, B. Cox, R. Hirosky, M. Joyce, A. Ledovskoy, H. Li, C.
Neu, T. Sinthuprasith, Y. Wang, E. Wolfe, F. Xia Wayne State University,
Detroit, USA
R. Harr, P.E. Karchin, N. Poudyal, J. Sturdy, P. Thapa, S. Zaleski University
of Wisconsin - Madison, Madison, WI, USA
M. Brodski, J. Buchanan, C. Caillol, D. Carlsmith, S. Dasu, L. Dodd, S. Duric,
B. Gomber, M. Grothe, M. Herndon, A. Hervé, U. Hussain, P. Klabbers, A.
Lanaro, A. Levine, K. Long, R. Loveless, T. Ruggles, A. Savin, N. Smith, W.H.
Smith, N. Woods †: Deceased
1: Also at Vienna University of Technology, Vienna, Austria
2: Also at IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
3: Also at Universidade Estadual de Campinas, Campinas, Brazil
4: Also at Federal University of Rio Grande do Sul, Porto Alegre, Brazil
5: Also at Université Libre de Bruxelles, Bruxelles, Belgium
6: Also at Institute for Theoretical and Experimental Physics, Moscow, Russia
7: Also at Joint Institute for Nuclear Research, Dubna, Russia
8: Now at British University in Egypt, Cairo, Egypt
9: Also at Zewail City of Science and Technology, Zewail, Egypt
10: Now at Helwan University, Cairo, Egypt
11: Also at Department of Physics, King Abdulaziz University, Jeddah, Saudi
Arabia
12: Also at Université de Haute Alsace, Mulhouse, France
13: Also at Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State
University, Moscow, Russia
14: Also at CERN, European Organization for Nuclear Research, Geneva,
Switzerland
15: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
16: Also at University of Hamburg, Hamburg, Germany
17: Also at Brandenburg University of Technology, Cottbus, Germany
18: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary
19: Also at MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
20: Also at Institute of Physics, University of Debrecen, Debrecen, Hungary
21: Also at Indian Institute of Technology Bhubaneswar, Bhubaneswar, India
22: Also at Institute of Physics, Bhubaneswar, India
23: Also at Shoolini University, Solan, India
24: Also at University of Visva-Bharati, Santiniketan, India
25: Also at Isfahan University of Technology, Isfahan, Iran
26: Also at Plasma Physics Research Center, Science and Research Branch,
Islamic Azad University, Tehran, Iran
27: Also at Università degli Studi di Siena, Siena, Italy
28: Also at International Islamic University of Malaysia, Kuala Lumpur,
Malaysia
29: Also at Malaysian Nuclear Agency, MOSTI, Kajang, Malaysia
30: Also at Consejo Nacional de Ciencia y Tecnología, Mexico city, Mexico
31: Also at Warsaw University of Technology, Institute of Electronic Systems,
Warsaw, Poland
32: Also at Institute for Nuclear Research, Moscow, Russia
33: Now at National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
34: Also at St. Petersburg State Polytechnical University, St. Petersburg,
Russia
35: Also at University of Florida, Gainesville, USA
36: Also at P.N. Lebedev Physical Institute, Moscow, Russia
37: Also at California Institute of Technology, Pasadena, USA
38: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia
39: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia
40: Also at INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy
41: Also at University of Belgrade, Faculty of Physics and Vinca Institute of
Nuclear Sciences, Belgrade, Serbia
42: Also at Scuola Normale e Sezione dell’INFN, Pisa, Italy
43: Also at National and Kapodistrian University of Athens, Athens, Greece
44: Also at Riga Technical University, Riga, Latvia
45: Also at Universität Zürich, Zurich, Switzerland
46: Also at Stefan Meyer Institute for Subatomic Physics (SMI), Vienna,
Austria
47: Also at Adiyaman University, Adiyaman, Turkey
48: Also at Istanbul Aydin University, Istanbul, Turkey
49: Also at Mersin University, Mersin, Turkey
50: Also at Piri Reis University, Istanbul, Turkey
51: Also at Gaziosmanpasa University, Tokat, Turkey
52: Also at Ozyegin University, Istanbul, Turkey
53: Also at Izmir Institute of Technology, Izmir, Turkey
54: Also at Marmara University, Istanbul, Turkey
55: Also at Kafkas University, Kars, Turkey
56: Also at Istanbul Bilgi University, Istanbul, Turkey
57: Also at Hacettepe University, Ankara, Turkey
58: Also at Rutherford Appleton Laboratory, Didcot, United Kingdom
59: Also at School of Physics and Astronomy, University of Southampton,
Southampton, United Kingdom
60: Also at Monash University, Faculty of Science, Clayton, Australia
61: Also at Bethel University, St. Paul, USA
62: Also at Karamanoğlu Mehmetbey University, Karaman, Turkey
63: Also at Utah Valley University, Orem, USA
64: Also at Purdue University, West Lafayette, USA
65: Also at Beykent University, Istanbul, Turkey
66: Also at Bingol University, Bingol, Turkey
67: Also at Sinop University, Sinop, Turkey
68: Also at Mimar Sinan University, Istanbul, Istanbul, Turkey
69: Also at Texas A&M University at Qatar, Doha, Qatar
70: Also at Kyungpook National University, Daegu, Korea