SUS-17-010

$HeadURL:svn+ssh://svn.cern.ch/reps/tdr2/papers/SUS-17-010/trunk/SUS-17-010.tex$
$Id:SUS-17-010.tex4819522018-11-2112:00:49Zbchazinq$

SUS-17-010

# Searches for pair production of charginos and top squarks in final states
with two oppositely charged leptons in proton-proton collisions at
$\sqrt{s}=13\TeV$

###### Abstract

A search for pair production of supersymmetric particles in events with two
oppositely charged leptons (electrons or muons) and missing transverse
momentum is reported. The data sample corresponds to an integrated luminosity
of 35.9of proton-proton collisions at $\sqrt{s}={13}\TeV$ collected with the
CMS detector during the 2016 data taking period at the LHC. No significant
deviation is observed from the predicted standard model background. The
results are interpreted in terms of several simplified models for chargino and
top squark pair production, assuming $R$-parity conservation and with the
neutralino as the lightest supersymmetric particle. When the chargino is
assumed to undergo a cascade decay through sleptons, with a slepton mass equal
to the average of the chargino and neutralino masses, exclusion limits at 95%
confidence level are set on the masses of the chargino and neutralino up to
800 and 320, respectively. For top squark pair production, the search focuses
on models with a small mass difference between the top squark and the lightest
neutralino. When the top squark decays into an off-shell top quark and a
neutralino, the limits extend up to 420 and 360for the top squark and
neutralino masses, respectively.

##  0.1 Introduction

The standard model (SM) of particle physics has so far been able to describe a
wide variety of phenomena with outstanding precision. However, the SM does not
address the hierarchy problem between the Higgs boson mass and the Planck
scale [1, 2], and does not contain a dark matter candidate to explain
cosmological observations [3, 4, 5]. Supersymmetry [6, 7, 8, 9, 10, 11, 12,
13, 14] is an extension of the SM that assigns a fermion (boson) superpartner
to every SM boson (fermion). This theory can solve the hierarchy problem since
the large quantum loop corrections to the Higgs boson mass, due mainly to the
top quark, can be largely canceled by the analogous corrections from the top
quark superpartner [15, 16, 17]. Moreover, if _R_ -parity [18] is conserved,
the lightest supersymmetric particle (LSP) is stable and, if massive, provides
a good candidate for dark matter.

This paper presents a search for supersymmetric particle production in final
states with two oppositely charged (OC) leptons ($\ell$) and missing
transverse momentum stemming from the two LSPs. Only electrons () and muons ()
are considered. The search targets two specific signal scenarios with chargino
() and top squark () pair production, using data from proton-proton ($\Pp\Pp$)
collisions at $\sqrt{s}={13}\TeV$ collected by the CMS experiment [19] at the
CERN LHC in 2016, and corresponding to an integrated luminosity of 35.9.

The results are interpreted in terms of simplified supersymmetric model
spectra (SMS) [20, 21, 22] scenarios. The search for chargino pair production
considers, as a reference, a model (Fig. 1, left) where the charginos decay
into a lepton, a neutrino (), and the lightest neutralino () via an
intermediate charged slepton
($\PSGcpmDo\to\Pgn\widetilde{\ell}\to\Pgn\ell\PSGczDo$) or sneutrino
($\PSGcpmDo\to\ell\sNu\to\ell\Pgn\PSGczDo$). The three generations of sleptons
are assumed to be degenerate, with a mass equal to the average of the chargino
and neutralino masses. The branching fractions ($\mathcal{B}$’s) of the
chargino decays into charged sleptons or sneutrinos are assumed to be equal.
Results are also interpreted in terms of a second model (Fig. 1, right), where
each chargino decays into the lightest neutralino and a boson. Searches for
chargino pair production have been previously published by the CMS
Collaboration in the context of the former scenario using 8collision data [23]
and by the ATLAS Collaboration in the context of both scenarios using 8 [24,
25, 26] and 13 [27, 28, 29] collision data.

Figure 1: Simplified-model diagrams of chargino pair production with two
benchmark decay modes: the left plot shows decays through intermediate
sleptons or sneutrinos, while the right one displays prompt decays into a
boson and the lightest neutralino.

The search for top squark pair production focuses on an SMS in which the top
squark decays into a top quark and the lightest neutralino as shown in Fig. 2
(left). The analysis strategy is optimized for a compressed spectrum scenario
where the mass difference ($\Delta m$) between the top squark and the lightest
neutralino lies between the top quark and boson masses $m_{\PW}<\Delta
m\lesssim m_{\PQt}$. In this regime, the top quarks are produced off-shell,
giving rise to final states with low-momentum bottom quarks which often fail
to be identified. Further interpretations of the results are given in terms of
an additional model, where each of the pair-produced top squarks decays into a
bottom quark and a chargino, which in turn decays into a boson and the
lightest neutralino, as shown in Fig. 2 (right). In this model, the mass of
the chargino is assumed to be equal to the average of the top squark and
neutralino masses. This work is complementary to another OC dilepton search
published by the CMS Collaboration [30], aimed at testing models where $\Delta
m>m_{\PQt}$, which result in signatures with on-shell top quarks and higher
momentum particles. With respect to that analysis, this search gains
sensitivity in the compressed mass region by loosening the requirements on the
jets from bottom quark hadronization and optimizing the signal event selection
for the lower momentum carried by the neutralino LSPs. The CMS Collaboration
has also published other searches targeting the same signal models in the
final states with exactly one lepton [31] and with no leptons [32], with the
latter also covering the four-body-decay of the top squark in the region
$\Delta m<80\GeV$. The ATLAS Collaboration published several searches
addressing these signal models using all three final states [33, 34, 35].

Figure 2: Simplified-model diagrams of top squark pair production with two
benchmark decay modes of the top squark: the left plot shows decays into a top
quark and the lightest neutralino, while the right one displays prompt decays
into a bottom quark and a chargino, further decaying into a neutralino and a
boson.

The paper is organized as follows: Section 0.2 introduces the experimental
apparatus; Sections 0.3 and 0.4 describe the data and simulated event samples
used in this search and the details on the reconstruction of the physics
objects, respectively; Section 0.5 presents the general strategy of the
analysis; Section 0.6 discusses the estimates of the contributions from SM
processes to the selected events; Section 0.7 details the sources of
systematic uncertainties for signal and background processes; Section 0.8
reports the results and their interpretation in terms of the considered SMS;
and finally Section 0.9 summarizes the results of the search.

##  0.2 The CMS detector

The central feature of the CMS apparatus is a superconducting solenoid of 6m
internal diameter, providing a magnetic field of 3.8T. In the inner part of
the solenoid volume is a silicon pixel and strip tracker, which reconstructs
the trajectories of the charged particles up to a pseudorapidity
$\abs{\eta}<2.5$. Outside the 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, measure
the energy of the particles in the region $\abs{\eta}<3$. Forward calorimeters
extend coverage provided by the barrel and endcap detectors up to
$\abs{\eta}<5$. The information from the tracker and calorimeter systems is
merged to reconstruct electrons and hadronic jets. Muons are detected in gas-
ionization chambers embedded in the steel flux-return yoke outside the
solenoid, covering the region $\abs{\eta}<2.4$. The detector is nearly
hermetic, allowing for momentum balance measurements in the plane transverse
to the beam direction. A more detailed description of the CMS detector,
together with a definition of the coordinate system used and the relevant
kinematic variables, can be found in Ref. [19].

##  0.3 Data and simulated samples

Events of interest are selected using triggers [36] which require the presence
of two leptons ($\Pe\Pe$, $\Pgm\Pgm$, $\Pe\Pgm$). The threshold on the
transverse momentum () of the leading lepton is 23for the $\Pe\Pe$ and
$\Pe\Pgm$ triggers, and 17for the $\Pgm\Pgm$ triggers. The threshold for the
trailing lepton is 8 (12)for muons (electrons). To increase the efficiency of
the trigger selection, events are also accepted by triggers requiring at least
one electron (muon) with $\pt>25$ (24), passing tighter identification
criteria than the ones applied in the double-lepton triggers. The trigger
performances are measured with leptons from $\PZ\to\ell^{+}\ell^{-}$ decays.
The combined efficiency of the dilepton and single-lepton triggers for signal
events is found to range between 90 and 99%, depending on the and $\eta$ of
the leptons.

Samples of Monte Carlo (MC) simulated events are used to study the
contribution of SM processes to the selected data set and the expected
acceptance for the different signal models. Events from top quark-antiquark
pair () production are generated with v2 [37, 38, 39] and normalized to the
expected cross section calculated at next-to-next-to-leading order (NNLO) in
perturbative quantum chromodynamics (QCD), including resummation of next-to-
next-to-leading logarithmic (NNLL) soft gluon terms [40]. Events with a single
top quark produced in association with a boson ($\PQt\PW$) are generated with
v1 [41] and normalized to an approximate NNLO cross section calculation [42].
Diboson production ($\PW\PW$, $\PW\PZ$, and $\PZ\PZ$) via quark-antiquark
annihilation is simulated at next-to-leading order (NLO) using v2 [43, 44].
The yields of events from $\PW\PW$ production are scaled to the NNLO cross
section [45]. Events from $\Pq\Paq\to\PZ\PZ$ production are reweighted via
NNLO/NLO _K_ factors, as functions of the generated $\PZ\PZ$ system mass [46].
Two additional sets of _K_ factors, as functions of the generated $\PZ\PZ$
system and of the azimuthal separation ($\Delta\phi$) between the bosons, are
used to evaluate the uncertainty in the kinematic properties of $\PZ\PZ$
production. Diboson production via gluon fusion is simulated using v7 [47],
and LO cross sections obtained from the generator are corrected with the
NNLO/LO _K_ factors [48, 46]. Drell–Yan events are generated with v2.2.2 [49]
at LO, and event yields are scaled to the NNLO cross section [50]. Events from
$\ttbar\PW$, $\ttbar\PZ$, triboson, and $\PH\to\PW\PW$ production are
generated at NLO [51, 52] with the generator.

Chargino pair production and top squark pair production events are generated
using at LO with up to two extra partons in the matrix element calculations,
and are normalized to the respective cross sections computed at NLO plus next-
to-leading logarithmic (NLL) precision [53, 54, 55, 56, 57, 58, 59, 60, 61],
with all the other sparticles assumed to be heavy and decoupled. In the case
of chargino pair production, calculations are performed in a limit of mass-
degenerate wino and , and light bino .

All processes are generated using the NNPDF3.0 [62] parton distribution
function (PDF) set. The parton showering, hadronization, and the underlying
event are modeled using 8.212 [63] with the CUETP8M1 [64] underlying event
tune for all the processes, except in the generation of events, where the
first emission is done at the matrix element level with v2 and the
CUETP8M2T4[65] tune is used. Weights for the estimation of theoretical
systematic uncertainties, including those related to the choice of PDFs, and
renormalization and factorization scales, are included in simulated events
[66].

The detector response to the generated events is simulated using a realistic
model of the CMS detector based on [67] for SM processes, while for signal
events a fast simulation (FastSim) [68] of the detector based on a
parametrization of the average response to particles is used. Simulated events
are subsequently reconstructed using the same algorithms as applied to data.

In order to model the effect of multiple interactions per bunch crossing
(pileup), simulated events are mixed with minimum-bias events simulated with ,
and are reweighted in order to match the observed rate of multiple
interactions.

The modeling and normalization of the main background processes are studied in
data, as discussed in Section 0.6. The modeling of , $\PQt\PW$, and $\PW\PW$
production is studied in data control regions (CRs), and their normalization
is determined via a maximum likelihood (ML) fit to data. The normalization of
the yields of events from $\ttbar\PZ$, $\PW\PZ$, $\PZ\PZ$, and Drell–Yan
production is taken from simulation and corrected by the event rates measured
in dedicated CRs.

To improve the modeling of jets from initial-state radiation (ISR) in
simulated signal events, reweighting factors are applied, which make the
distributions of observables for related SM processes in simulation agree with
control samples in data. For chargino pair production, mediated by the
electroweak interaction, the reweighting procedure is based on studies of
balance in inclusive boson production events [69]. Events are then reweighted
according to the total transverse momentum ($\pt^{\mathrm{ISR}}$) of the
system of supersymmetric particles. The reweighting factors range between 1.18
at $\pt^{\mathrm{ISR}}\approx 125\GeV$ and 0.78 for
$\pt^{\mathrm{ISR}}>600\GeV$. A global reweighting is further applied in order
not to alter the signal production cross section. As top squark pair
production occurs via strong interactions, a different set of reweighting
factors is derived as a function of the multiplicity of ISR jets
($N_{\text{jet}}^{\mathrm{ISR}}$) in a sample of events selected by requiring
an OC electron-muon pair and two jets identified as coming from bottom quark
hadronization. The measured reweighting factors vary between 0.92 and 0.51 for
$N_{\text{jet}}^{\mathrm{ISR}}$ between 1 and 6, with an additional scale
factor applied to keep the total event yields invariant.

##  0.4 Event reconstruction

The particle-flow algorithm [70] aims to reconstruct and identify each
individual particle in an event, with an optimized combination of information
from the various elements of the CMS detector. The energy of photons is
directly obtained from the ECAL measurement, corrected for zero-suppression
effects. The energy of electrons is determined from a combination of the
electron momentum at the primary interaction vertex as determined by the
tracker, the energy of the corresponding ECAL cluster, and the energy sum of
all bremsstrahlung photons spatially compatible with originating from the
electron track. The momentum of muons is obtained from the curvature of the
corresponding track. The energy of charged hadrons is determined from a
combination of their momentum measured in the tracker and the matching ECAL
and HCAL energy deposits, corrected for zero-suppression effects and for the
response function of the calorimeters to hadronic showers. Finally, the energy
of neutral hadrons is obtained from the corresponding corrected ECAL and HCAL
energy.

The reconstructed vertex with the largest value of summed physics-object
$\pt^{2}$ is taken to be the primary $\Pp\Pp$ interaction vertex. The physics
objects are the jets, clustered using a jet finding algorithm [71, 72] with
the tracks assigned to the vertex as inputs, and the associated momentum
imbalance in the transverse plane, taken as the negative vector sum of those
jets.

The identification of the muons used in the analysis is based on the number of
reconstructed energy deposits in the tracker and in the muon system, and on
the fit quality of the muon track [73]. Electron identification relies on
quality criteria of the electron track, matching between the electron
trajectory and the associated cluster in the calorimeter, and shape
observables of the electromagnetic shower observed in the ECAL [74]. The
efficiency for the reconstruction and selection of the muons (electrons) is
found to be 70–95 (30–75)% depending on their and $\eta$.

The lepton selection is further optimized to select leptons from the decays of
or bosons. The leptons are required to be isolated by measuring their relative
isolation ($I_{\text{rel}}$), as the ratio of the scalar sum of the photons
and of the neutral and charged hadrons within a cone of radius
$R=\sqrt{\smash[b]{(\Delta\phi)^{2}+(\Delta\eta)^{2}}}=0.3$ around the
candidate lepton, and the of the lepton itself. The contribution of particles
produced in pileup interactions is reduced by considering only charged hadrons
consistent with originating from the primary vertex of the event, and
correcting for the expected contribution of neutral hadrons from the pileup
[74, 73]. Leptons are considered to be isolated if their relative isolation
$I_{\text{rel}}$ is found to be smaller than 0.12. A looser requirement of
$I_{\text{rel}}<0.4$ is used to define a veto lepton selection. Candidate
lepton trajectories are further required to be compatible with the primary
interaction vertex by imposing constraints on their transverse
($d_{\mathrm{0}}$) and longitudinal ($d_{z}$) impact parameters, and on the
three-dimensional impact parameter significance
($S_{\mathrm{3D}}^{\mathrm{d}}$), computed as the ratio of the three-
dimensional impact parameter and its uncertainty. Both electrons and muons are
required to satisfy the conditions $\abs{d_{\mathrm{0}}}<0.05\cm$,
$\abs{d_{z}}<0.10\cm$, and $S_{\mathrm{3D}}^{\mathrm{d}}<4$. Finally,
electrons originating from photon conversions are rejected by requiring that
the electron track not have missing hits in the innermost layers of the
tracker, and not form a conversion vertex with any other candidate electron in
the event [74].

For each event, hadronic jets are clustered from the PF reconstructed
particles using the infrared and collinear-safe anti-algorithm [71, 72] with a
distance parameter of 0.4. The jet momentum is determined as the vectorial sum
of all particle momenta in the jet, and is found in the simulation to be
within 5 to 10% of its true value over the whole spectrum and detector
acceptance. Jet energy corrections are derived from simulation to bring the
measured response of jets to that of particle level jets on average. In situ
measurements of the momentum balance in the dijet, multijet, photon+jet, and
leptonically decaying +jet events are used to account for any residual
difference in jet energy scale in data and simulation [75]. Additional quality
criteria are applied to reject spurious jets from detector noise. Finally, the
jets overlapping with any selected lepton within a cone of radius $R<0.4$ are
removed.

Jets originating from the hadronization of bottom quarks ( jets) are
identified by the combined secondary vertex v2 -tagging algorithm, using the
medium operating point [76]. This requirement provides an efficiency for
identifying jets that increases from 50 to 70% for jets with from 20 to 100.
The misidentification rate for jets originating from light quarks and gluons
is about 1% in the same range.

The momentum imbalance of the event in the transverse plane is referred to as
missing transverse momentum () and it is defined as the negative vectorial sum
of all PF candidates in the event, taking into account the energy corrections
applied to the jets [77]. The magnitude of is denoted as .

Differences have been observed in the modeling of the resolution in events
simulated with FastSim and with the full detector simulation. To account for
this effect, the acceptance for signal events is computed both using the at
the generator level and after the event reconstruction. The average value of
the two acceptances in each analysis bin is taken as the central value for the
acceptance.

Simulated events are reweighted to account for differences with respect to
data in the efficiencies of the lepton reconstruction, identification, and
isolation requirements, and in the performance of -jet identification. The
values of the data-to-simulation scale factors differ from unity by less than
10% with typical efficiency corrections of 2–3 (5)% for the identification of
leptons ( jets) with $\pt>20\GeV$ and $\abs{\eta}<2.4$.

##  0.5 Search strategy

The search strategy is developed for two signal hypotheses: the chargino pair
and top squark pair productions. The first signal hypothesis is studied along
the whole ($m_{\PSGcpmDo}$, $m_{\PSGczDo}$) mass plane, while for the second
one the analysis is optimized on the compressed scenario, where the mass
difference of the top squark and the lightest neutralino is in between the top
quark and boson masses. The searches involve the same techniques for the
background estimation and the signal extraction, while they differ slightly in
the signal region (SR) selection in order to improve their respective
sensitivity.

The signal models are characterized by a common final state with two OC
leptons and two lightest neutralinos contributing to large . Based on this, a
general high-acceptance baseline selection is defined, requiring two OC
isolated leptons with $\abs{\eta}<2.4$ and $\pt\geq 25$ (20)for the leading
(trailing) lepton. Events with leptons decaying into electrons or muons that
satisfy the selection requirements are taken into account. To reduce the
contributions from low-mass resonances, $\PZ\to\PGt\PGt$ production, and
nonprompt leptons from hadronic jets, the invariant mass $m_{\ell\ell}$ of the
lepton pair is required to be greater than 20, and if both leptons have the
same flavor (SF), $m_{\ell\ell}$ is further required to satisfy
$\abs{m_{\ell\ell}-m_{\PZ}}>15\GeV$, where $m_{\PZ}$ is the boson mass. High
(${\geq}140\GeV$) is required. Events are further rejected if they contain a
third lepton with $\pt>15\GeV$, $\abs{\eta}<2.4$, and satisfying the veto
lepton selection (as detailed in Section 0.4). A summary of the baseline
selection is found in Table 0.5.

[center]Definition of the baseline selection used in the searches for chargino
and top squark pair production.  Variable Selection Lepton flavor , ,  Leading
lepton $\pt\geq 25\GeV$, $\abs{\eta}<2.4$ Trailing lepton $\pt\geq 20\GeV$,
$\abs{\eta}<2.4$ Third lepton veto $\pt\geq 15\GeV$, $\abs{\eta}<2.4$
$m_{\ell\ell}$ ${\geq}20\GeV$ $\abs{m_{\ell\ell}-m_{\PZ}}$ ${>}15\GeV$ only
for $\Pe\Pe$ and $\Pgm\Pgm$ events ${\geq}140\GeV$

The SM processes that contribute most after the baseline selection are ,
$\PQt\PW$, and $\PW\PW$ production. For all these backgrounds, the lepton pair
and come from a boson pair. Consequently, the variable $m_{\mathrm{T2}}$ [78]
is constructed to generalize the transverse mass ($m_{\mathrm{T}}$) for a
system with two invisible particles, by using the two leptons as the two
visible systems,

$m_{\mathrm{T2}}(\ell\ell)=\min_{{\vec{p}}^{\,\text{miss1}}_{\mathrm{T}}+{\vec{p}}^{\,\text{miss2}}_{\mathrm{T}}=\ptvecmiss}\left(\max\left[m_{\mathrm{T}}({\vec{p}}^{\,\text{lep1}}_{\mathrm{T}},{\vec{p}}^{\,\text{miss1}}_{\mathrm{T}}),m_{\mathrm{T}}({\vec{p}}^{\,\text{lep2}}_{\mathrm{T}},{\vec{p}}^{\,\text{miss2}}_{\mathrm{T}})\right]\right).$
(1)

This observable reaches a kinematic endpoint at the $m_{\PW}$ for the
considered backgrounds. Signal events, instead, present
$m_{\mathrm{T2}}(\ell\ell)$ spectra without such an endpoint because of the
additional contribution to the given by the neutralinos. The sensitivity of
the analysis is further enhanced by dividing the SR in bins of . This allows
the analysis not only to exploit the larger tails in the distribution of the
signal events, but also to optimize the sensitivity to signals with different
mass separation between the produced supersymmetric particle and the LSP. Each
bin is in turn divided into events with SF and different flavor (DF) leptons
to exploit the smaller contamination from $\PW\PZ$, $\PZ\PZ$, and Drell–Yan
production of the latter.

The SRs are further subdivided based on the specific characteristics of each
signal model. A veto on -tagged jets is applied to reject , $\PQt\PW$, and
$\ttbar\PZ$ events in the chargino search. Selected events in the bins below
300are then split into two different subregions, depending on the presence of
a jet with $\pt>20\GeV$ and $\abs{\eta}<2.4$. This allows for a better
discrimination between signal events and top quark background, which still
contaminates the SRs after applying the -tagged jet veto. Events with -tagged
jets are kept as a CR for the normalization of the background from and
$\PQt\PW$ production (discussed in Section 0.6).

The final states produced in the top squark decays are characterized by the
presence of two bottom quarks. When the difference in the mass of the top
squark and the neutralino is close to the edge of the compressed region,
$\Delta m\gtrsim m_{\PW}$, the bottom quarks are soft and give rise to jets
with relatively low momentum that have a lower probability to be tagged. In
this case, the top squark final states are similar to those from chargino pair
production, and requiring a veto on -tagged jets is again an effective
strategy to define SRs with reduced contamination from , $\PQt\PW$, and
$\ttbar\PZ$ backgrounds. For signal scenarios with larger $\Delta m$, instead,
the jets have higher momentum and the final states are more -like.
Consequently, sensitivity to top squark production is enhanced by requiring a
-tagged jet to reduce the background from diboson and Drell–Yan events.

Another useful means to discriminate top squark production from SM processes
is given by the presence of high-jets from ISR in the events. The invisible
particles (neutrinos and neutralinos) produced in the decay chain of the top
squark in the compressed scenario are expected to be soft; events with harder
neutralinos, however, can arise when the top squark pair system recoils
against a high-ISR jet. In this hard ISR regime, background is still
constrained by the kinematic $m_{\PW}$ endpoint in
$m_{\mathrm{T2}}(\ell\ell)$, and can be effectively separated from the signal.
Hard ISR events are selected by requiring that the leading jet satisfies
$\pt>150\GeV$ and is not tagged. In order to favor the topology in which the
jet recoils against the rest of the system, the $\Delta\phi$ between the jet
and the is required to be larger than 2.5 rad. This requirement is found to be
effective in discriminating top squark production from background events at
high , and is therefore applied only for events with $\ptmiss>300\GeV$.

A summary of the SRs for the chargino and top squark searches is given in
Tables 0.5 and 0.5, respectively, indicating the range, the selection on the
multiplicity of jets ($N_{\text{jets}}$) and jets ($N_{\text{\cPqb\ jets}}$)
in the event, and the ISR jet requirement. The observed distributions of some
observables used to define the SRs are compared to SM expectations in Fig. 3.

[center]Definition of the SRs for the chargino search as a function of the
value, the -jet multiplicity and jet multiplicity. Also shown are the CRs with
-tagged jets used for the normalization of the and $\PQt\PW$ backgrounds. Each
of the regions is further divided in seven $m_{\mathrm{T2}}(\ell\ell)$ bins as
described in the last row.  $\text{SR1}_{\text{0tag}}^{\text{0jet}}$
$\text{SR1}_{\text{0tag}}^{\text{jets}}$ $\text{CR1}_{\text{tags}}^{\text{}}$
$\text{SR2}_{\text{0tag}}^{\text{0jet}}$
$\text{SR2}_{\text{0tag}}^{\text{jets}}$ $\text{CR2}_{\text{tags}}^{\text{}}$
$\text{SR3}_{\text{0tag}}^{\text{}}$ $\text{CR3}_{\text{tags}}^{\text{}}$ []
140–200 140–200 140–200 200–300 200–300 200–300 ${\geq}300$ ${\geq}300$
$N_{\text{\cPqb\ jets}}$ 0 0 ${\geq}1$ 0 0 ${\geq}1$ 0 ${\geq}1$
$N_{\text{jets}}$ 0 ${\geq}1$ ${\geq}1$ 0 ${\geq}1$ ${\geq}1$ ${\geq}0$
${\geq}1$ Channels SF, DF SF, DF SF, DF SF, DF SF, DF SF, DF SF, DF SF, DF
$m_{\mathrm{T2}}(\ell\ell)$ 0–20, 20–40, 40–60, 60–80, 80–100, 100–120,
${\geq}120\GeV$

[center]Definition of the SRs for top squark production search as a function
of the value, the -jet multiplicity and the ISR jet requirement. Each of the
regions is further divided in seven $m_{\mathrm{T2}}(\ell\ell)$ bins as
described in the last row.  $\text{SR1}_{\text{0tag}}^{\text{}}$
$\text{SR1}_{\text{tags}}^{\text{}}$ $\text{SR2}_{\text{0tag}}^{\text{}}$
$\text{SR2}_{\text{tags}}^{\text{}}$ $\text{SR3}_{\text{0tag}}^{\text{ISR}}$
$\text{SR3}_{\text{tag}}^{\text{ISR}}$ [] 140–200 140–200 200–300 200–300
${\geq}300$ ${\geq}300$ $N_{\text{\cPqb\ jets}}$ 0 ${\geq}1$ 0 ${\geq}1$ 0
${\geq}1$ $N_{\text{jets}}$ ${\geq}0$ ${\geq}1$ ${\geq}0$ ${\geq}1$ ${\geq}1$
${\geq}2$ ISR jets ${\geq}0$ ${\geq}0$ ${\geq}0$ ${\geq}0$ ${\geq}1$ ${\geq}1$
Channels SF, DF SF, DF SF, DF SF, DF SF, DF SF, DF $m_{\mathrm{T2}}(\ell\ell)$
0–20, 20–40, 40–60, 60–80, 80–100, 100–120, ${\geq}120\GeV$

Figure 3: Observed and SM expected distributions of some observables used to
define the SRs for events with two OC isolated leptons and $\ptmiss\geq
140\GeV$ . Clockwise from top left: , $m_{\mathrm{T2}}(\ell\ell)$,
$\Delta\phi$ between the and the leading jet (required not to be -tagged and
with $\pt>150\GeV$, events missing this requirements are shown in the first
bin), and multiplicity of -tagged jets in the event. The last bin includes the
overflow entries. The contributions of minor backgrounds such as $\ttbar\PW$,
$\PH\to\PW\PW$, and triboson production are grouped together. In the bottom
panel, the ratio of observed and expected yields is shown. The hatched band
represents the total uncertainty in the background expectation, as described
in Section 0.7.

Each of the SRs defined in Tables 0.5 and 0.5 is further divided into seven
$m_{\mathrm{T2}}(\ell\ell)$ bins of 20width, starting from 0and with the last
bin collecting all events with $m_{\mathrm{T2}}(\ell\ell)>120$. A simultaneous
ML fit to the $m_{\mathrm{T2}}(\ell\ell)$ distribution in all the SRs is then
performed to extract the signal (as described in Section 0.8). Since the first
$m_{\mathrm{T2}}(\ell\ell)$ bins have a low signal contribution, we exploit
them to constrain the contributions of the dominant backgrounds in the SRs
with one -tagged jet (dominated by and $\PQt\PW$ production) and without
-tagged jets (where $\PW\PW$ production becomes relevant) through the fit.

##  0.6 Background estimation

The main contributions from SM processes to the SRs comes from , $\PQt\PW$,
and $\PW\PW$ production. The normalization of these backgrounds is determined
by the ML fit, as mentioned in Section 0.5. Their $m_{\mathrm{T2}}(\ell\ell)$
shape has a natural endpoint at the $m_{\PW}$, and events enter into the
relevant region for signal extraction ($m_{\mathrm{T2}}(\ell\ell)>80\GeV$)
mainly due to detector resolution effects, whose contributions are not easy to
model. For this reason, we study the modeling of the
$m_{\mathrm{T2}}(\ell\ell)$ distribution for these processes in dedicated CRs
in data described in Section 0.6.1. The contributions of the subleading
$\ttbar\PZ$, $\PW\PZ$, $\PZ\PZ$, and Drell–Yan backgrounds are also tested in
CRs, where correction factors for their normalizations are extracted, as
discussed in Section 0.6.2. Remaining minor backgrounds from $\ttbar\PW$,
$\PH\to\PW\PW$, and triboson production give small contributions in the SRs,
and the estimates for these processes are taken directly from simulation.
Background contributions from rest of the SM processes are found to be
negligible. The contribution of signal to any of the CRs used is found to be
negligible compared to SM processes.

###  0.6.1 Modeling of $m_{\mathrm{T2}}(\ell\ell)$ in , $\PQt\PW$, and
$\PW\PW$ events

The simulated $m_{\mathrm{T2}}(\ell\ell)$ distributions for , $\PQt\PW$, and
$\PW\PW$ backgrounds are validated in two CRs. To construct the first one, the
baseline selection is modified by requiring $100<\ptmiss<140\GeV$. The events
in this CR are further separated according to their -jet multiplicity to
define two sub-regions with different content in top quark (and $\PQt\PW$) and
$\PW\PW$ backgrounds. In order to reject events from Drell–Yan production,
only DF events are considered. The second CR aims at validating the modeling
of the $m_{\mathrm{T2}}(\ell\ell)$ distributions in events with
$\ptmiss>140\GeV$. For this purpose, we select events from $\PW\PZ\to 3\ell
1\nu$ production and emulate the $m_{\mathrm{T2}}(\ell\ell)$ shape of $\PW\PW$
and top quark events. We take the lepton from the boson with the same charge
as the lepton from the boson, and we add its vectorially to , effectively
treating it like a neutrino. These events are selected by requiring three
leptons and vetoing the presence of a fourth lepton passing the veto lepton
requirements. A veto is applied to events with -tagged jets to remove residual
events. Among the three leptons, a pair of OC SF leptons with an invariant
mass within 10of the boson mass is required to identify the boson. The
simulation is found to describe the data well in the CRs. Based on the
statistical precision of these CRs, a conservative uncertainty of 5, 10, 20,
and 30% is taken for the bins $60\leq m_{\mathrm{T2}}(\ell\ell)<80\GeV$,
$80\leq m_{\mathrm{T2}}(\ell\ell)<100\GeV$, $100\leq
m_{\mathrm{T2}}(\ell\ell)<120\GeV$, and $m_{\mathrm{T2}}(\ell\ell)\geq
120\GeV$, respectively. These uncertainties are applied to top quark and
$\PW\PW$ production, and treated as uncorrelated between the two types of
backgrounds.

Another potential source of mismodeling in the tails of the
$m_{\mathrm{T2}}(\ell\ell)$ distributions arises from nonprompt leptons
originating, for instance, from semileptonic decays of hadrons in jets or from
hadronic jets accidentally passing the lepton selection. The value of
$m_{\mathrm{T2}}(\ell\ell)$ in , $\PQt\PW$, and $\PW\PW$ events with one
nonprompt lepton replacing a prompt one failing the selection requirements
will not be bound by the $m_{\PW}$ endpoint. The contribution of these events
is found to be less than 1% of the expected background across the different
SRs. It becomes more relevant only at large values of
$m_{\mathrm{T2}}(\ell\ell)$ and , where it constitutes up to 20% of the
background. We study the modeling of the rate of nonprompt leptons in
simulation by selecting events with two leptons with the same charge and at
least one -tagged jet. The dominant contribution to this sample comes from
events with a nonprompt lepton. Based on the observed agreement with data, a
correction factor of $1.08\pm 0.21$ is derived for the nonprompt lepton rate
in simulation.

###  0.6.2 Normalization of $\ttbar\PZ$, $\PW\PZ$, $\PZ\PZ$, and Drell–Yan
backgrounds

The production of $\ttbar\PZ$ events where the two bosons decay leptonically
and the boson decays into neutrinos leads to final states with the same
experimental signature as the signal events and with no natural endpoint for
the reconstructed $m_{\mathrm{T2}}(\ell\ell)$ distribution, due to the
additional contribution of the neutrinos from the boson decay to the . The
normalization of this background is validated in events with three leptons,
$\ptmiss>140\GeV$, and at least two jets with $\pt>20\GeV$, of which at least
one is tagged as jet. At least one pair of OC SF leptons with an invariant
mass not further than 10from the boson mass is also required. A normalization
scale factor of $1.44\pm 0.36$ for $\ttbar\PZ$ production is measured
comparing the observed and predicted numbers of events.

Events from $\PW\PZ$ production enter the signal event selection when both
bosons decay leptonically and one of the three decay leptons fails the veto
lepton requirements. We test the modeling of this source of background in a CR
with three leptons, $\ptmiss>140\GeV$, and no -tagged jets, and derive a
normalization scale factor of $0.97\pm 0.09$ for the simulated $\PW\PZ$
background.

The $\PZ\PZ$ background is dominated by events with one boson decaying into
charged leptons and the other one decaying into neutrinos. This contribution
is studied by mimicking the $\PZ\PZ\to 2\ell 2\nu$ production via $\PZ\PZ\to
4\ell$ events, where the of one of the reconstructed bosons (randomly chosen
between the ones satisfying the $\abs{m_{\ell\ell}-m_{\PZ}}<15$condition) is
added to the . Events are selected by requiring four leptons, with one lepton
allowed to pass the looser veto lepton requirement in order to increase the
acceptance for $\PZ\PZ$ production. The events are retained if the four
leptons can be arranged into two pairs of OC SF leptons, both with an
invariant mass within 30of the boson mass, and at least one within 15. A scale
factor for the $\PZ\PZ$ background normalization is derived in events with
$\ptmiss>140\GeV$ and with no -tagged jets. Since the chargino search uses
separate SRs for events with or without jets, two corresponding scale factors
are also measured, which suggest a higher jet multiplicity in data than in
$\PZ\PZ$ simulated events.

A summary of the scale factors derived in this section is given in Table
0.6.2. For all the quoted scale factors, uncertainties include the statistical
uncertainties on data and simulated events, and the systematic uncertainties
on the number of expected events from the residual processes in the CRs.

Drell–Yan events can pass the baseline selection because of mismeasurements in
. We study the modeling of this background in events with two OC SF leptons
with $\abs{m_{\ell\ell}-m_{\PZ}}<15$, no additional leptons, and no -tagged
jets (Z boson events). The events with $100<\ptmiss<140$are dominated by
Drell–Yan production, and are used to derive a $m_{\mathrm{T2}}(\ell\ell)$
shape correction, which is subsequently tested in boson events with
$\ptmiss>140$. The correction ranges from a few percent at low
$m_{\mathrm{T2}}(\ell\ell)$ to about 50% for
$m_{\mathrm{T2}}(\ell\ell)>100\GeV$. An overall normalization uncertainty of
32% is also established by the observed disagreement between data and
simulated events with $100<\ptmiss<140$. Finally, the predictions for
Drell–Yan events with no jets are tested in boson events with no jets and
$\ptmiss>140\GeV$: a conservative uncertainty of 100% in this contribution is
applied. The Drell–Yan production is a subdominant background in the SRs with
no jets and this uncertainty has a negligible impact on the expected
sensitivity for signal production.

[center]Summary of the normalization scale factors for $\ttbar\PZ$, $\PW\PZ$,
and $\PZ\PZ$ backgrounds in the SRs used for the chargino (a) and top squark
(b) searches. Uncertainties include the statistical uncertainties of data and
simulated event samples, and the systematic uncertainties on the number of
expected events from the residual processes in the CRs.  Process Scale factors
$N_{\text{jets}}=0$ (a) $N_{\text{jets}}>0$ (a) $N_{\text{jets}}\geq 0$ (b)
$\ttbar\PZ$ $1.44\pm 0.36$ $1.44\pm 0.36$ $1.44\pm 0.36$ $\PW\PZ$ $0.97\pm
0.09$ $0.97\pm 0.09$ $0.97\pm 0.09$ $\PZ\PZ$ $0.74\pm 0.19$ $1.21\pm 0.17$
$1.05\pm 0.12$

##  0.7 Systematic uncertainties

Several sources of systematic uncertainty that affect both the normalizations
and the $m_{\mathrm{T2}}(\ell\ell)$ shapes of the background and signal events
are considered in the analysis.

  * •

The overall uncertainty in the integrated luminosity is estimated to be 2.5%
[79].

  * •

The uncertainty on the measured trigger efficiency is 2%.

  * •

Lepton identification and isolation efficiencies are corrected by data-to-
simulation scale factors measured in $\PZ\to\ell\ell$ events. The
corresponding uncertainties are typically smaller than 3% per lepton.

  * •

The jet energy scale is varied by its uncertainty [75], and the changes are
propagated to all the related observables in the event.

  * •

The energy scale of the low-particles that are not clustered in jets is varied
by its uncertainty, and the changes are propagated to the .

  * •

The efficiencies and misidentification rates of the -jet identification
algorithms are also corrected by data-to-simulation scale factors measured in
inclusive jet and events [76]. The respective uncertainties range between 1
and 6%, depending on the and $\eta$ of the jets.

  * •

The effect of the simulated data sample sizes on the modeling of the
$m_{\mathrm{T2}}(\ell\ell)$ distributions is taken into account by treating
the statistical uncertainty in each bin for each process as an additional
uncorrelated uncertainty.

  * •

Uncertainties in the renormalization and factorization scales, and PDFs are
propagated by taking the largest changes in the acceptance when independently
doubling and halving the renormalization and factorization scales, and when
varying the choice of PDFs between the NNPDF3.0 replicas. The PDF
uncertainties are not considered for signal models as they are found to be
redundant, once the uncertainty in the ISR modeling is included.

The estimates of the SM backgrounds are also affected by specific
uncertainties in the modeling of the different processes.

  * •

A background normalization uncertainty is applied for each background
separately. The normalizations of the , $\PQt\PW$, and $\PW\PW$ processes are
determined by the ML fit, as described in Section 0.8. We assign a common
normalization parameter for and $\PQt\PW$ events and another for $\PW\PW$
production. No explicit normalization uncertainty is defined for and $\PW\PW$
events, while a 10% uncertainty is set for the $\PQt\PW$ process to take into
account its relative normalization with respect to the production as well as
any interference effect between them. The uncertainties applied to $\ttbar\PZ$
(25%), $\PW\PZ$ (9%), and $\PZ\PZ$ (26% in the SRs with 0 jets, 14% in the SRs
with at least 1 jet, and 11% in the rest of the SRs) correspond to the scale
factor uncertainties obtained in Section 0.6.2. Minor backgrounds
($\ttbar\PW$, $\PH\to\PW\PW$, triboson production) are assigned a conservative
uncertainty of 50%. Finally, Drell–Yan events have a 100% normalization
uncertainty in the SR with no jets and 32% in all other SRs.

  * •

The modeling of the yields of events with no jets has been explicitly studied
in Section 0.6.2 for $\PZ\PZ$ and Drell–Yan production. For the other SM
processes, we introduce a related uncertainty by adding two free parameters in
the ML fit, scaling respectively the rate of events with no jets for diboson
and b-enriched (, $\PQt\PW$, $\ttbar\PZ$, and $\ttbar\PW$) backgrounds. The
total number of expected events without -tagged jets is constrained to remain
invariant, so that only a migration of events between the SRs with and without
jets is allowed.

  * •

The modeling of the $m_{\mathrm{T2}}(\ell\ell)$ shapes in events with an
endpoint at the $m_{\PW}$ (, $\PQt\PW$, and $\PW\PW$) has been studied in
Section 0.6.1: an uncertainty of 5, 10, 20, and 30% is assigned for the last
four $m_{\mathrm{T2}}(\ell\ell)$ bins.

  * •

The choice of the set of NNLO/NLO _K_ factors applied to the
$\Pq\Paq\to\PZ\PZ$ events affects the modeling of the
$m_{\mathrm{T2}}(\ell\ell)$ shape for the $\PZ\PZ$ background (as described in
Section 0.3). Relative variations range from 16% for
$m_{\mathrm{T2}}(\ell\ell)<20\GeV$ to about 2% for
$m_{\mathrm{T2}}(\ell\ell)>120\GeV$ and are taken as the uncertainties.

  * •

The $m_{\mathrm{T2}}(\ell\ell)$ distribution in Drell–Yan events has been
corrected by scale factors derived in bins of $m_{\mathrm{T2}}(\ell\ell)$ in
the validation region $100<\ptmiss<140$, as discussed in Section 0.6.2. The
full size of the correction in each bin is taken as an uncertainty.

  * •

The weight of events with nonprompt leptons in simulated samples is varied by
the ${\pm}19\%$ uncertainty in the correction factor derived in events with
two same-charge leptons, as described in Section 0.6.1.

  * •

The spectrum of top quark in events has been observed to be softer in data
than in simulated events [80, 81, 82]. An uncertainty is derived from the
observed variations when reweighting the events to the distribution observed
in data.

Finally, additional uncertainties in the modeling of signal events are taken
into account, mostly related to the performance of the event reconstruction in
FastSim.

  * •

The uncertainty in the lepton identification efficiency in events simulated
with FastSim, relative to the full detector simulation, is estimated to be 2%.

  * •

The analogous uncertainty in the -tagging efficiency in FastSim samples ranges
between 0.2–0.5%.

  * •

The modeling in FastSim events is studied by comparing the acceptances
computed using the at the generator level and after the event reconstruction.
Since the average of the two is taken as central value for the acceptance,
half of their difference is taken as an uncertainty, fully correlated among
bins.

  * •

An uncertainty in the modeling of pileup events in FastSim signal samples is
derived by studying the dependence of the acceptance on the multiplicity of
primary vertices reconstructed in the event. This uncertainty varies from 0 to
10% across the SRs and $m_{\mathrm{T2}}(\ell\ell)$ bins.

  * •

Simulated signal events are reweighted to improve the modeling of the ISR, as
described in Section 0.3. Uncertainties on the reweighting procedure are
derived from closure tests. For chargino models, the deviation from unity is
taken as the systematic uncertainty in the $\pt^{\mathrm{ISR}}$ reweighting
factors. For top squark models, half of the deviation from unity in the
$N_{\text{jet}}^{\mathrm{ISR}}$ factors is taken.

Tables 0.7 and 0.7 summarize the systematic uncertainties in the predicted
yields for SM processes and for two reference signals, respectively.

Sizes of systematic uncertainties in the predicted yields for SM processes.
The first column shows the range of the uncertainties in the global background
normalization across the different SRs. The second column quantifies the
effect on the $m_{\mathrm{T2}}(\ell\ell)$ shape. This is computed by taking
the maximum variation across the $m_{\mathrm{T2}}(\ell\ell)$ bins (after
renormalizing for the global change of all the distribution) in each SR. The
range of this variation across the SRs is given.  Source of uncertainty SM
processes Change in yields Change in $m_{\mathrm{T2}}(\ell\ell)$ shape
Integrated luminosity 2.5% Trigger 2% Lepton ident./isolation 4–5% ${<}1\%$
Jet energy scale 1–6% 3–15% Unclustered energy 1–2% 2–16% tagging ${<}3\%$
${<}2\%$ Renorm./fact. scales 1–10% 1–6% PDFs 1–5% 2–8% $\ttbar\PZ$
normalization ${<}1\%$ ${<}9\%$ $\PW\PZ$ normalization ${<}1\%$ ${<}1\%$
$\PZ\PZ$ normalization ${<}1\%$ ${<}5\%$ Drell–Yan normalization ${<}4\%$
1–11% $m_{\mathrm{T2}}(\ell\ell)$ shape (top quark) 4–18%
$m_{\mathrm{T2}}(\ell\ell)$ shape ($\PW\PW$) 1–15% $\PZ\PZ$ _K_ factors
${<}3\%$ $m_{\mathrm{T2}}(\ell\ell)$ shape (Drell–Yan) 1–13% Nonprompt leptons
${<}1\%$ ${<}4\%$ reweighting 1–4% 1–8%

Same as in Table 0.7 for two representative signal points, one for chargino
pair production and one for top squark pair production.  Source of uncertainty
$\PSGcpmDo\to\widetilde{\ell}\nu\text{(}\ell\sNu\text{)}\to\ell\nu\PSGczDo$
$\stone\to\cPqt\PSGczDo$ ($m_{\PSGcpmDo}=500\GeV$, $m_{\PSGczDo}=200\GeV$)
($m_{\stone}=350\GeV$, $m_{\PSGczDo}=225\GeV$) Yields
$m_{\mathrm{T2}}(\ell\ell)$ shape Yields $m_{\mathrm{T2}}(\ell\ell)$ shape
Integrated luminosity 2.5% 2.5% Trigger 2% 2% Lepton ident./isolation 4–5%
${<}1\%$ 4–5% ${<}1\%$ Jet energy scale 1–3% 3–11% 1–4% 2–14% Unclustered
energy 1–2% 8–13% 1–2% 2–7% tagging ${<}1\%$ ${<}1\%$ $1$–$3\%$ ${<}1\%$
Renorm./fact. scales 1–3% 1–3% 1–3% 1–3% Lept. id./iso. (FastSim) 4% ${<}1\%$
4% ${<}1\%$ tagging (FastSim) ${<}1\%$ ${<}1\%$ ${<}1\%$ ${<}1\%$ (FastSim)
1–4% 7–28% 1–6% 6–20% Pileup (FastSim) 1–6% 4–9% 2–4% 2–14% ISR reweighting
1–2% 1–6% 2–8% 1–6%

##  0.8 Results and interpretation

A simultaneous binned ML fit to the $m_{\mathrm{T2}}(\ell\ell)$ distribution
in all the SRs is performed. Uncertainties due to signal and background
normalizations are included through nuisance parameters with log-normal prior
distributions, while uncertainties in the shape of the
$m_{\mathrm{T2}}(\ell\ell)$ distributions are included with Gaussian prior
distributions. As explained in Section 0.6, the normalizations of the main
backgrounds from top quark and $\PW\PW$ production are left to be determined
in the fit via the constraint provided by the low $m_{\mathrm{T2}}(\ell\ell)$
region with and without -tagged jets. The results of the fit in the SRs for
the chargino search are shown in Figs. 4 and 5 for DF and SF events,
respectively. The results for the top squark search are shown in Figs. 6 and
7. Each figure compares the number of observed events in the SRs with the
expected yields from SM processes after a background-only fit. As a
comparison, the expected yields for a representative signal point are given.
The total expected SM contributions before the fit and after a
background+signal fit are also shown. Detailed information on the observed and
expected yields after the background-only fit are given in Tables 0.8–0.8 for
all dilepton final states and all SRs. No excess over SM prediction is
observed in data. The asymptotic approximation of the criterion [83, 84, 85]
is used to set upper limits at 95% confidence level () on the production cross
sections for the different signal models considered.

Figure 4: Distributions of $m_{\mathrm{T2}}(\ell\ell)$ after the fit to data
in the chargino SRs with $140<\ptmiss<200\GeV$ (upper plots),
$200<\ptmiss<300\GeV$ (middle), and $\ptmiss>300\GeV$ (lower), for DF events
without -tagged jets and at least one jet (left plots) and no jets (right
plots). The lower plot for the SR with $\ptmiss>300\GeV$ shows all the events
without -tagged jets regardless of their jet multiplicity. The solid magenta
histogram shows the expected $m_{\mathrm{T2}}(\ell\ell)$ distribution for
chargino pair production with $m_{\PSGcpmDo}=500\GeV$ and
$m_{\PSGczDo}=200\GeV$. Expected total SM contributions before the fit (dark
blue dashed line) and after a background+signal fit (dark red dotted line) are
also shown. The last bin includes the overflow entries. In the bottom panel,
the ratio of data and SM expectations is shown for the expected total SM
contribution after the fit using the background-only hypothesis (black dots)
and before any fit (dark blue dashed line). The hatched band represents the
total uncertainty after the fit.

Figure 5: The same distributions of $m_{\mathrm{T2}}(\ell\ell)$ as Fig. 4, but
for SF events.

Figure 6: Distributions of $m_{\mathrm{T2}}(\ell\ell)$ after the fit to data
in the top squark SRs with $140<\ptmiss<200\GeV$ (upper plots),
$200<\ptmiss<300\GeV$ (middle), or $\ptmiss>300\GeV$ (lower), for DF events
with -tagged jets (left plots) and without -tagged jets (right plots). The
solid magenta histogram shows the expected $m_{\mathrm{T2}}(\ell\ell)$
distribution for top squark pair production with $m_{\stone}=350\GeV$ and
$m_{\PSGczDo}=225\GeV$. Expected total SM contributions before the fit (dark
blue dashed line) and after a background+signal fit (dark red dotted line) are
also shown. The last bin includes the overflow entries. In the bottom panel,
the ratio of data and SM expectations is shown for the expected total SM
contribution after the fit using the background-only hypothesis (black dots)
and before any fit (dark blue dashed line). The hatched band represents the
total uncertainty after the fit.

Figure 7: The same distributions of $m_{\mathrm{T2}}(\ell\ell)$ as Fig. 6, but
for SF events.

Observed and expected yields of DF (the upper half of Table) and SF (the lower
half) events in the SRs for the chargino search. The quoted uncertainties in
the background predictions include statistical and systematic contributions.
$m_{\mathrm{T2}}(\ell\ell)$ [] 0–20 20–40 40–60 60–80 80–100 100–120
${\geq}120$ DF events $\text{SR1}_{\text{0tag}}^{\text{jets}}$ Predicted
$1493\pm 32$ $558\pm 12$ $719\pm 16$ $730\pm 16$ $316\pm 10$ $45.1\pm 3.1$
$13.7\pm 2.8$ Observed 1484 532 732 725 298 47 13
$\text{SR1}_{\text{0tag}}^{\text{0jet}}$ Predicted $41.9\pm 5$ $27.4\pm 3.8$
$34.1\pm 4.8$ $42\pm 5.5$ $21.1\pm 3.4$ $6\pm 1.3$ $7.9\pm 2.1$ Observed 39 24
33 44 13 6 9 $\text{SR2}_{\text{0tag}}^{\text{jets}}$ Predicted $534\pm 15$
$158.6\pm 5.9$ $167.9\pm 6.1$ $157.9\pm 6.5$ $42.4\pm 2.9$ $5.9\pm 1$ $9\pm
1.7$ Observed 511 162 156 176 43 5 9 $\text{SR2}_{\text{0tag}}^{\text{0jet}}$
Predicted $10.3\pm 1.7$ $7\pm 1.5$ $6.5\pm 1.3$ $6.9\pm 1.3$ $2.19\pm 0.69$
$1.59\pm 0.7$ $7.8\pm 1.8$ Observed 10 4 4 6 2 2 7
$\text{SR3}_{\text{0tag}}^{\text{}}$ Predicted $127.9\pm 7.2$ $28.3\pm 2$
$30.2\pm 2.4$ $23.1\pm 2$ $4.96\pm 0.73$ $1.12\pm 0.38$ $4.5\pm 1.2$ Observed
116 35 29 21 3 1 5 SF events $\text{SR1}_{\text{0tag}}^{\text{jets}}$
Predicted $1310\pm 29$ $499\pm 12$ $623\pm 14$ $634\pm 15$ $271.7\pm 8.9$
$51.6\pm 3.5$ $48.6\pm 5.5$ Observed 1324 499 609 659 284 57 47
$\text{SR1}_{\text{0tag}}^{\text{0jet}}$ Predicted $44.1\pm 7.5$ $28.5\pm 4.1$
$33.5\pm 4.4$ $33.5\pm 4.5$ $18.6\pm 2.6$ $7.7\pm 1.6$ $12.5\pm 2.5$ Observed
43 40 39 33 17 6 12 $\text{SR2}_{\text{0tag}}^{\text{jets}}$ Predicted $474\pm
14$ $134.8\pm 5.1$ $155.1\pm 5.5$ $128.5\pm 5.5$ $37.1\pm 2.5$ $7.29\pm 0.91$
$23.9\pm 2.4$ Observed 493 123 166 118 33 7 25
$\text{SR2}_{\text{0tag}}^{\text{0jet}}$ Predicted $10.9\pm 1.9$ $7.8\pm 1.8$
$7.3\pm 1.4$ $7.9\pm 1.3$ $1.9\pm 0.52$ $1.28\pm 0.58$ $7.1\pm 1.4$ Observed 8
12 11 10 3 2 7 $\text{SR3}_{\text{0tag}}^{\text{}}$ Predicted $112.8\pm 6.3$
$27.9\pm 2.2$ $24.2\pm 1.8$ $22.5\pm 1.8$ $5.2\pm 1$ $1.36\pm 0.36$ $10.6\pm
1.2$ Observed 110 35 26 26 2 1 14

Observed and expected yields of DF (the upper half of Table) and SF (the lower
half) events in the SRs for the top squark search. The quoted uncertainties in
the background predictions include statistical and systematic contributions.
$m_{\mathrm{T2}}(\ell\ell)$ [] 0–20 20–40 40–60 60–80 80–100 100–120
${\geq}120$ DF events $\text{SR1}_{\text{tags}}^{\text{}}$ Predicted $3525\pm
80$ $1505\pm 31$ $1958\pm 42$ $2049\pm 46$ $897\pm 22$ $108.4\pm 7.3$ $13.4\pm
2.2$ Observed 3534 1494 1938 2068 879 111 15
$\text{SR1}_{\text{0tag}}^{\text{}}$ Predicted $1542\pm 33$ $588\pm 13$
$756\pm 15$ $771\pm 19$ $338.3\pm 9.3$ $50.6\pm 3.8$ $21\pm 3.8$ Observed 1523
556 765 769 311 53 22 $\text{SR2}_{\text{tags}}^{\text{}}$ Predicted $1036\pm
37$ $363\pm 13$ $415\pm 14$ $377\pm 14$ $105.1\pm 6.5$ $12.3\pm 2$ $5.02\pm
0.82$ Observed 1045 357 412 389 111 11 1 $\text{SR2}_{\text{0tag}}^{\text{}}$
Predicted $545\pm 18$ $164.3\pm 7.3$ $173.2\pm 6.2$ $165.1\pm 6.8$ $44.8\pm
3.1$ $7.1\pm 1.4$ $15.5\pm 3$ Observed 521 166 160 182 45 7 16
$\text{SR3}_{\text{tags}}^{\text{ISR}}$ Predicted $152.1\pm 9.9$ $35.5\pm 2.7$
$32.3\pm 2.3$ $25\pm 2.2$ $4.67\pm 0.77$ $0.41\pm 0.38$ $0.41\pm 0.26$
Observed 133 44 36 26 2 1 0 $\text{SR3}_{\text{0tag}}^{\text{ISR}}$ Predicted
$103.9\pm 6.8$ $21.3\pm 1.9$ $22.2\pm 2.1$ $15.4\pm 1.6$ $3.51\pm 0.6$
$0.53\pm 0.21$ $0.53\pm 0.34$ Observed 100 27 22 12 3 0 1 SF events
$\text{SR1}_{\text{tags}}^{\text{}}$ Predicted $2979\pm 68$ $1277\pm 30$
$1644\pm 35$ $1712\pm 37$ $762\pm 19$ $91.9\pm 6.1$ $18.1\pm 2.1$ Observed
3003 1266 1674 1671 798 85 16 $\text{SR1}_{\text{0tag}}^{\text{}}$ Predicted
$1350\pm 33$ $526\pm 13$ $656\pm 15$ $670\pm 17$ $289.2\pm 7.6$ $57.9\pm 4.2$
$61.8\pm 5.8$ Observed 1367 539 648 692 301 63 59
$\text{SR2}_{\text{tags}}^{\text{}}$ Predicted $888\pm 30$ $319\pm 12$ $363\pm
14$ $323\pm 13$ $90.5\pm 5.5$ $10.8\pm 1.5$ $7.43\pm 0.98$ Observed 900 315
343 325 86 13 11 $\text{SR2}_{\text{0tag}}^{\text{}}$ Predicted $487\pm 16$
$140.7\pm 5.5$ $161.9\pm 5.9$ $134.5\pm 6.2$ $39.6\pm 2.7$ $8.1\pm 1.1$
$30.6\pm 3$ Observed 501 135 177 128 36 9 32
$\text{SR3}_{\text{tags}}^{\text{ISR}}$ Predicted $129.6\pm 8.9$ $29.6\pm 2.1$
$27.8\pm 2.1$ $22.2\pm 1.9$ $3.71\pm 0.57$ $0.47\pm 0.42$ $0.71\pm 0.38$
Observed 123 27 28 38 4 1 1 $\text{SR3}_{\text{0tag}}^{\text{ISR}}$ Predicted
$91.5\pm 6.1$ $20.1\pm 1.8$ $16.5\pm 1.4$ $13.7\pm 1.4$ $3.14\pm 0.58$
$0.78\pm 0.36$ $1.63\pm 0.42$ Observed 92 26 17 12 1 1 2

The 95% upper limits on chargino pair production cross sections with the
chargino decaying into sleptons are shown in Fig. 8 (left). The
$\PSGcpmDo\to\Pgn\widetilde{\ell}\to\Pgn\ell\PSGczDo$ and
$\PSGcpmDo\to\ell\sNu\to\ell\Pgn\PSGczDo$ decay chains are given a
$\mathcal{B}$ of 50% each, and the sleptons are assumed to be degenerate, with
a mass equal to the average of the chargino and neutralino masses. By
comparing the upper limits with $\Pp\Pp\to\PSGcpDo\PSGcmDo$ production cross
sections, observed and expected exclusion regions in the ($m_{\PSGcpmDo}$,
$m_{\PSGczDo}$) plane are also determined. Masses are excluded up to values of
about 800 and 320for the chargino and the neutralino, respectively. Limited
sensitivity is found when the chargino is assumed to decay into a boson and
the lightest neutralino, due to the relatively small $\mathcal{B}$ for the
leptonic decay of the boson. For this scenario, we derive upper limits on
chargino pair production cross section assuming a lightest neutralino mass of
1. Observed and expected upper limits as a function of the chargino mass are
compared to theoretical cross sections in Fig. 8 (right).

Figure 8: Left: upper limits at 95% on chargino pair production cross section
as a function of the chargino and neutralino masses, when the chargino
undergoes a cascade decay
$\PSGcpmDo\to\widetilde{\ell}\nu\text{(}\ell\sNu\text{)}\to\ell\nu\PSGczDo$.
Exclusion regions in the plane ($m_{\PSGcpmDo}$, $m_{\PSGczDo}$) are
determined by comparing the upper limits with the NLO+NLL production cross
sections. The thick dashed red line shows the expected exclusion region. The
thin dashed red lines show the variation of the exclusion regions due to the
experimental uncertainties. The thick black line shows the observed exclusion
region, while the thin black lines show the variation of the exclusion regions
due to the theoretical uncertainties in the production cross section. Right:
observed and expected upper limits at 95% as a function of the chargino mass
for a neutralino mass of 1, assuming chargino decays into a neutralino and a
boson ($\PSGcpmDo\to\PW\PSGczDo$).

Figure 9 shows the observed and expected 95% upper limits on top squark
production cross section for the two SMS considered. While the search strategy
has been optimized for a compressed scenario, the results are presented on the
whole ($m_{\stone}$, $m_{\PSGczDo}$) plane for completeness. Also shown are
the expected and observed exclusion regions when assuming NLO+NLL top squark
pair production cross sections. When assuming the top squark to decay into a
top quark and a neutralino, top squark (neutralino) masses are excluded up to
about 420 (360)in the compressed mass region where $\Delta m$ lies between the
top quark and boson masses. For the
$\,\stone\to\cPqb\PSGcpmDo\to\cPqb\PW\PSGczDo$ decay mode, a lower bound
$\Delta m\approx 2\,m_{\PW}$ is set by the assumption that
$m_{\PSGcpmDo}=(m_{\stone}+m_{\PSGczDo})/2$. For $\Delta m\approx 2\,m_{\PW}$,
top squark masses are excluded in the range 225–325. The uncovered region
around a top squark mass of 200in Fig. 9 (right) corresponds to a signal phase
space similar to that of events, with little contribution from the neutralinos
to . In this situation, the uncertainty in the modeling of in FastSim events
becomes too large to provide any signal sensitivity.

Figure 9: Upper limits at 95% on top squark production cross section as a
function of the top squark and neutralino masses. The plot on the left shows
the results when top squark decays into a top quark and a neutralino are
assumed. The two diagonal gray dashed lines enclose the compressed region
where $m_{\PW}<m_{\stone}-m_{\PSGczDo}\lesssim m_{\PQt}$. The plot on the
right gives the limits for top squarks decaying into a bottom quark and a
chargino, with the latter successively decaying into a boson and a neutralino.
The mass of the chargino is assumed to be equal to the average of the top
squark and neutralino masses. Exclusion regions in the plane ($m_{\stone}$,
$m_{\PSGczDo}$) are determined by comparing the upper limits with the NLO+NLL
production cross sections. The thick dashed red line shows the expected
exclusion region. The thin dashed red lines show the variation of the
exclusion regions due to the experimental uncertainties. The thick black line
shows the observed exclusion region, while the thin black lines show the
variation of the exclusion regions due to the theoretical uncertainties in the
production cross section.

##  0.9 Summary

A search has been presented for pair production of supersymmetric particles in
events with two oppositely charged isolated leptons and missing transverse
momentum. The data used consist of a sample of proton-proton collisions
collected with the CMS detector during the 2016 LHC run at a center-of-mass
energy of 13, corresponding to an integrated luminosity of 35.9. No evidence
for a deviation with respect to standard model predictions was observed in
data. The results have been interpreted as upper limits on the cross sections
of supersymmetric particle production for several simplified model spectra.

Chargino pair production has been investigated in two possible decay modes. If
the chargino is assumed to undergo a cascade decay through sleptons, an
exclusion region in the ($m_{\PSGcpmDo}$, $m_{\PSGczDo}$) plane can be
derived, extending to chargino masses of 800and neutralino masses of 320.
These are the most stringent limits on this model to date. For chargino decays
into a neutralino and a boson, limits on the production cross section have
been derived assuming a neutralino mass of 1, and chargino masses in the range
170–200have been excluded.

Top squark pair production was also tested, with a focus on compressed decay
modes. A model with the top squark decaying into a top quark and a neutralino
was considered. In the region where $m_{\PW}<m_{\stone}-m_{\PSGczDo}\lesssim
m_{\PQt}$, limits extend up to 420 and 360for the top squark and neutralino
masses, respectively. An alternative model has also been considered, where the
top squark decays into a chargino and a bottom quark, with the chargino
subsequently decaying into a boson and the lightest neutralino. The mass of
the chargino is assumed to be average between the top squark and neutralino
masses, which gives a lower bound to the mass difference ($\Delta m$) between
the top squark and the neutralino of $\Delta m\approx 2\,m_{\PW}$. This search
reduces by about 50the minimum $\Delta m$ excluded in the previous result with
two leptons in the final state [30] from the CMS Collaboration, excluding top
squark masses in the range 225–325for $\Delta m\approx 2\,m_{\PW}$.

In summary, by exploiting the full data set collected by the CMS experiment in
2016, this search extends the existing exclusion limits on the pair production
of charginos decaying via sleptons [29], improving by about 70the limit on the
chargino mass for a massless neutralino. Exclusion limits on top squark pair
production extend the results obtained by the CMS Collaboration in final
states with two oppositely charged leptons [30] to the compressed region,
where they are competitive with the results obtained by the ATLAS
Collaboration in the same decay channel [35].

###### Acknowledgements.

We congratulate our colleagues in the CERN accelerator departments for the
excellent performance of the LHC and thank the technical and administrative
staffs at CERN and at other CMS institutes for their contributions to the
success of the CMS effort. In addition, we gratefully acknowledge the
computing centers and personnel of the Worldwide LHC Computing Grid for
delivering so effectively the computing infrastructure essential to our
analyses. Finally, we acknowledge the enduring support for the construction
and operation of the LHC and the CMS detector provided by the following
funding agencies: 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 program
and the European Research Council and Horizon 2020 Grant, contract No. 675440
(European Union); the Leventis Foundation; the A. P. Sloan Foundation; the
Alexander von Humboldt Foundation; the Belgian Federal Science Policy Office;
the Fonds pour la Formation à la Recherche dans l’Industrie et dans
l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en
Technologie (IWT-Belgium); the Ministry of Education, Youth and Sports (MEYS)
of the Czech Republic; the Council of Scientific and Industrial Research,
India; the HOMING PLUS program of the Foundation for Polish Science,
cofinanced from European Union, Regional Development Fund, the Mobility Plus
program of the Ministry of Science and Higher Education, the National Science
Center (Poland), contracts Harmonia 2014/14/M/ST2/00428, Opus
2014/13/B/ST2/02543, 2014/15/B/ST2/03998, and 2015/19/B/ST2/02861, Sonata-bis
2012/07/E/ST2/01406; the National Priorities Research Program by Qatar
National Research Fund; the Programa Severo Ochoa del Principado de Asturias;
the Thalis and Aristeia programs cofinanced by EU-ESF and the Greek NSRF; the
Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University
and the Chulalongkorn Academic into Its 2nd Century Project Advancement
Project (Thailand); the Welch Foundation, contract C-1845; and the Weston
Havens Foundation (USA).

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##  .10 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan, A. Tumasyan Institut für Hochenergiephysik, Wien, Austria
W. Adam, F. Ambrogi, E. Asilar, T. Bergauer, J. Brandstetter, M. Dragicevic,
J. Erö, A. Escalante Del Valle, M. Flechl, R. Frühwirth1, V.M. Ghete, J.
Hrubec, M. Jeitler1, N. Krammer, I. Krätschmer, D. Liko, T. Madlener, I.
Mikulec, N. Rad, H. Rohringer, J. Schieck1, R. Schöfbeck, M. Spanring, D.
Spitzbart, A. Taurok, W. Waltenberger, J. Wittmann, C.-E. Wulz1, M. Zarucki
Institute for Nuclear Problems, Minsk, Belarus
V. Chekhovsky, V. Mossolov, J. Suarez Gonzalez Universiteit Antwerpen,
Antwerpen, Belgium
E.A. De Wolf, D. Di Croce, X. Janssen, J. Lauwers, M. Pieters, 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, M. Correa Martins Junior, G. Correia Silva,
C. Hensel, A. Moraes, M.E. Pol, P. Rebello Teles Universidade do Estado do Rio
de Janeiro, Rio de Janeiro, Brazil
E. Belchior Batista Das Chagas, W. Carvalho, J. Chinellato3, E. Coelho, E.M.
Da Costa, G.G. Da Silveira4, D. De Jesus Damiao, C. De Oliveira Martins, S.
Fonseca De Souza, H. Malbouisson, D. Matos Figueiredo, M. Melo De Almeida, C.
Mora Herrera, L. Mundim, H. Nogima, W.L. Prado Da Silva, L.J. Sanchez Rosas,
A. Santoro, A. Sznajder, M. Thiel, E.J. Tonelli Manganote3, F. Torres Da Silva
De Araujo, A. Vilela Pereira Universidade Estadual Paulista a, Universidade
Federal do ABC b, São Paulo, Brazil
S. Ahujaa, C.A. Bernardesa, L. Calligarisa, T.R. Fernandez Perez Tomeia, E.M.
Gregoresb, P.G. Mercadanteb, S.F. Novaesa, SandraS. Padulaa, 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. Shaheen6, 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, A. Levin, 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. Starodumov7, T. Susa
University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, M. Kolosova, G. Mavromanolakis, J. Mousa, C. Nicolaou,
F. Ptochos, P.A. Razis, H. Rykaczewski Charles University, Prague, Czech
Republic
M. Finger8, M. Finger Jr.8 Escuela Politecnica Nacional, Quito, Ecuador
E. Ayala Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab
Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
Y. Assran9,10, S. Khalil11, A. Mahrous12 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. Abdulsalam13, C. Amendola, I. Antropov, F. Beaudette, P. Busson, C.
Charlot, R. Granier de Cassagnac, I. Kucher, A. Lobanov, J. Martin Blanco, M.
Nguyen, C. Ochando, G. Ortona, P. Paganini, P. Pigard, R. Salerno, J.B.
Sauvan, Y. Sirois, A.G. Stahl Leiton, A. Zabi, A. Zghiche Université de
Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, France
J.-L. Agram14, J. Andrea, D. Bloch, J.-M. Brom, E.C. Chabert, V. Cherepanov,
C. Collard, E. Conte14, J.-C. Fontaine14, D. Gelé, U. Goerlach, M. Jansová,
A.-C. Le Bihan, N. Tonon, P. Van Hove Centre de Calcul de l’Institut National
de Physique Nucleaire et de Physique des Particules, CNRS/IN2P3, Villeurbanne,
France
S. Gadrat Université de Lyon, Université Claude Bernard Lyon 1, CNRS-IN2P3,
Institut de Physique Nucléaire de Lyon, Villeurbanne, France
S. Beauceron, C. Bernet, G. Boudoul, 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. Popov15, V. Sordini, M. Vander
Donckt, S. Viret, S. Zhang Georgian Technical University, Tbilisi, Georgia
A. Khvedelidze8 Tbilisi State University, Tbilisi, Georgia
Z. Tsamalaidze8 RWTH Aachen University, I. Physikalisches Institut, Aachen,
Germany
C. Autermann, L. Feld, M.K. Kiesel, K. Klein, M. Lipinski, M. Preuten, M.P.
Rauch, C. Schomakers, J. Schulz, M. Teroerde, B. Wittmer, V. Zhukov15 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. Stahl16 Deutsches
Elektronen-Synchrotron, Hamburg, Germany
M. Aldaya Martin, T. Arndt, C. Asawatangtrakuldee, I. Babounikau, K.
Beernaert, O. Behnke, U. Behrens, A. Bermúdez Martínez, D. Bertsche, A.A. Bin
Anuar, K. Borras17, V. Botta, A. Campbell, P. Connor, C. Contreras-Campana, 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. Gallo18, A. Geiser,
J.M. Grados Luyando, A. Grohsjean, P. Gunnellini, M. Guthoff, M. Haranko, 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. Lohmann19, 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,
O. Turkot, A. Vagnerini, G.P. Van Onsem, R. Walsh, Y. Wen, K. Wichmann, C.
Wissing, O. Zenaiev University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, L. Benato, A. Benecke, V. Blobel, M. Centis Vignali, T.
Dreyer, E. Garutti, D. Gonzalez, J. Haller, A. Hinzmann, A. Karavdina, G.
Kasieczka, R. Klanner, R. Kogler, N. Kovalchuk, S. Kurz, V. Kutzner, J. Lange,
D. Marconi, J. Multhaup, M. Niedziela, 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, A. Vanhoefer, B.
Vormwald Karlsruher Institut fuer Technology
M. Akbiyik, C. Barth, M. Baselga, S. Baur, E. Butz, R. Caspart, T. Chwalek, F.
Colombo, W. De Boer, A. Dierlamm, K. El Morabit, N. Faltermann, B. Freund, M.
Giffels, M.A. Harrendorf, F. Hartmann16, S.M. Heindl, U. Husemann, F.
Kassel16, I. Katkov15, 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. Bartók20, M. Csanad, N. Filipovic, P. Major, M.I. Nagy, G. Pasztor, O.
Surányi, G.I. Veres Wigner Research Centre for Physics, Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath21, Á. Hunyadi, F. Sikler, T.Á. Vámi, V.
Veszpremi, G. Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear
Research ATOMKI, Debrecen, Hungary
N. Beni, S. Czellar, J. Karancsi22, A. Makovec, J. Molnar, Z. Szillasi
Institute of Physics, University of Debrecen, Debrecen, Hungary
P. Raics, Z.L. Trocsanyi, B. Ujvari Indian Institute of Science (IISc),
Bangalore, India
S. Choudhury, J.R. Komaragiri, P.C. Tiwari National Institute of Science
Education and Research, HBNI, Bhubaneswar, India
S. Bahinipati23, C. Kar, P. Mal, K. Mandal, A. Nayak24, D.K. Sahoo23, S.K.
Swain Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, S. Chauhan, R. Chawla, N. Dhingra, R.
Gupta, A. Kaur, 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. Bhardwaj25, M. Bharti, R. Bhattacharya, S. Bhattacharya, U. Bhawandeep25,
D. Bhowmik, S. Dey, S. Dutt25, S. Dutta, S. Ghosh, K. Mondal, S. Nandan, A.
Purohit, P.K. Rout, A. Roy, S. Roy Chowdhury, S. Sarkar, M. Sharan, B. Singh,
S. Thakur25 Indian Institute of Technology Madras, Madras, India
P.K. Behera Bhabha Atomic Research Centre, Mumbai, India
R. Chudasama, D. Dutta, V. Jha, V. Kumar, P.K. Netrakanti, L.M. Pant, P.
Shukla Tata Institute of Fundamental Research-A, Mumbai, India
T. Aziz, M.A. Bhat, S. Dugad, G.B. Mohanty, N. Sur, B. Sutar, RavindraKumar
Verma Tata Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, Sa. Jain, S.
Karmakar, S. Kumar, M. Maity26, G. Majumder, K. Mazumdar, N. Sahoo, T.
Sarkar26 Indian Institute of Science Education and Research (IISER), Pune,
India
S. Chauhan, S. Dube, V. Hegde, A. Kapoor, K. Kothekar, S. Pandey, A. Rane, S.
Sharma Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
S. Chenarani27, E. Eskandari Tadavani, S.M. Etesami27, M. Khakzad, M.
Mohammadi Najafabadi, M. Naseri, F. Rezaei Hosseinabadi, B. Safarzadeh28, M.
Zeinali University College Dublin, Dublin, Ireland
M. Felcini, M. Grunewald INFN Sezione di Bari a, Università di Bari b,
Politecnico di Bari c, Bari, Italy
M. Abbresciaa,b, C. Calabriaa,b, A. Colaleoa, D. Creanzaa,c, L. Cristellaa,b,
N. De Filippisa,c, M. De Palmaa,b, A. Di Florioa,b, F. Erricoa,b, L. Fiorea,
A. Gelmia,b, G. Iasellia,c, M. Incea,b, S. Lezkia,b, G. Maggia,c, M. Maggia,
G. Minielloa,b, S. Mya,b, S. Nuzzoa,b, A. Pompilia,b, G. Pugliesea,c, R.
Radognaa, A. Ranieria, G. Selvaggia,b, A. Sharmaa, L. Silvestrisa, R.
Vendittia, P. Verwilligena, G. Zitoa INFN Sezione di Bologna a, Università di
Bologna b, Bologna, Italy
G. Abbiendia, C. Battilanaa,b, D. Bonacorsia,b, L. Borgonovia,b, S. Braibant-
Giacomellia,b, R. Campaninia,b, P. Capiluppia,b, A. Castroa,b, F.R. Cavalloa,
S.S. Chhibraa,b, C. Cioccaa, G. Codispotia,b, M. Cuffiania,b, G.M.
Dallavallea, F. Fabbria, A. Fanfania,b, P. Giacomellia, C. Grandia, L.
Guiduccia,b, F. Iemmia,b, S. Marcellinia, G. Masettia, A. Montanaria, F.L.
Navarriaa,b, A. Perrottaa, F. Primaveraa,b,16, 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,29, G. Sguazzonia, D. Stroma, L. Viliania INFN Laboratori
Nazionali di Frascati, Frascati, Italy
L. Benussi, S. Bianco, F. Fabbri, D. Piccolo INFN Sezione di Genova a,
Università di Genova b, Genova, Italy
F. Ferroa, 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,16, S. Di
Guidaa,d,16, M.E. Dinardoa,b, S. Fiorendia,b, S. Gennaia, A. Ghezzia,b, P.
Govonia,b, M. Malbertia,b, S. Malvezzia, A. Massironia,b, D. Menascea, L.
Moronia, M. Paganonia,b, D. Pedrinia, S. Ragazzia,b, T. Tabarelli de Fatisa,b,
D. Zuolo 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,
G. Galatia, A.O.M. Iorioa,b, W.A. Khana, L. Listaa, S. Meolaa,d,16, P.
Paoluccia,16, C. Sciaccaa,b, E. Voevodinaa,b INFN Sezione di Padova a,
Università di Padova b, Padova, Italy, Università di Trento c, Trento, Italy
P. Azzia, N. Bacchettaa, D. Biselloa,b, A. Bolettia,b, A. Bragagnolo, R.
Carlina,b, P. Checchiaa, M. Dall’Ossoa,b, P. De Castro Manzanoa, T. Dorigoa,
U. Dossellia, F. Gasparinia,b, U. Gasparinia,b, A. Gozzelinoa, S. Lacapraraa,
P. Lujan, M. Margonia,b, A.T. Meneguzzoa,b, J. Pazzinia,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, R. Leonardia,b, E. Manonia, G.
Mantovania,b, V. Mariania,b, M. Menichellia, A. Rossia,b, A. Santocchiaa,b, D.
Spigaa INFN Sezione di Pisa a, Università di Pisa b, Scuola Normale Superiore
di Pisa c, Pisa, Italy
K. Androsova, P. Azzurria, G. Bagliesia, L. Bianchinia, T. Boccalia, L.
Borrello, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, G. Fedia, F. Fioria,c,
L. Gianninia,c, A. Giassia, M.T. Grippoa, F. Ligabuea,c, E. Mancaa,c, G.
Mandorlia,c, A. Messineoa,b, F. Pallaa, A. Rizzia,b, 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, S. Cometti, 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, D. Soldi,
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. Goh30, T.J. Kim Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, D. Gyun, S. Ha, B. Hong, Y. Jo, K. Lee, K.S. Lee, S.
Lee, J. Lim, S.K. Park, Y. Roh Sejong University, Seoul, Korea
H.S. Kim Seoul National University, Seoul, Korea
J. Almond, J. Kim, J.S. Kim, H. Lee, K. Lee, K. Nam, S.B. Oh, B.C. Radburn-
Smith, S.h. Seo, U.K. Yang, H.D. Yoo, G.B. Yu University of Seoul, Seoul,
Korea
D. Jeon, H. Kim, J.H. Kim, J.S.H. Lee, I.C. Park Sungkyunkwan University,
Suwon, Korea
Y. Choi, C. Hwang, J. Lee, I. Yu Vilnius University, Vilnius, Lithuania
V. Dudenas, A. Juodagalvis, J. Vaitkus National Centre for Particle Physics,
Universiti Malaya, Kuala Lumpur, Malaysia
I. Ahmed, Z.A. Ibrahim, M.A.B. Md Ali31, F. Mohamad Idris32, W.A.T. Wan
Abdullah, M.N. Yusli, Z. Zolkapli Universidad de Sonora (UNISON), Hermosillo,
Mexico
A. Castaneda Hernandez, J.A. Murillo Quijada Centro de Investigacion y de
Estudios Avanzados del IPN, Mexico City, Mexico
H. Castilla-Valdez, E. De La Cruz-Burelo, M.C. Duran-Osuna, I. Heredia-De La
Cruz33, R. Lopez-Fernandez, J. Mejia Guisao, R.I. Rabadan-Trejo, M. Ramirez-
Garcia, G. Ramirez-Sanchez, R Reyes-Almanza, A. Sanchez-Hernandez Universidad
Iberoamericana, Mexico City, Mexico
S. Carrillo Moreno, C. Oropeza Barrera, F. Vazquez Valencia Benemerita
Universidad Autonoma de Puebla, Puebla, Mexico
J. Eysermans, I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada Universidad
Autónoma de San Luis Potosí, San Luis Potosí, Mexico
A. Morelos Pineda University of Auckland, Auckland, New Zealand
D. Krofcheck University of Canterbury, Christchurch, New Zealand
S. Bheesette, P.H. Butler National Centre for Physics, Quaid-I-Azam
University, Islamabad, Pakistan
A. Ahmad, M. Ahmad, M.I. Asghar, Q. Hassan, H.R. Hoorani, A. Saddique, M.A.
Shah, M. Shoaib, M. Waqas National Centre for Nuclear Research, Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, T. Frueboes, M. Górski, M. Kazana, K.
Nawrocki, M. Szleper, P. Traczyk, P. Zalewski Institute of Experimental
Physics, Faculty of Physics, University of Warsaw, Warsaw, Poland
K. Bunkowski, A. Byszuk34, 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. Matveev35,36, 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. Kim37, E. Kuznetsova38, P. Levchenko, V. Murzin,
V. Oreshkin, I. Smirnov, D. Sosnov, V. Sulimov, L. Uvarov, S. Vavilov, A.
Vorobyev Institute for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov,
N. Krasnikov, A. Pashenkov, D. Tlisov, A. Toropin Institute for Theoretical
and Experimental Physics, Moscow, Russia
V. Epshteyn, V. Gavrilov, N. Lychkovskaya, V. Popov, I. Pozdnyakov, G.
Safronov, A. Spiridonov, A. Stepennov, V. Stolin, M. Toms, E. Vlasov, A.
Zhokin Moscow Institute of Physics and Technology, Moscow, Russia
T. Aushev National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
M. Chadeeva39, P. Parygin, D. Philippov, S. Polikarpov39, E. Popova, V.
Rusinov P.N. Lebedev Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin36, I. Dremin36, M. Kirakosyan36, S.V. Rusakov, A.
Terkulov Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State
University, Moscow, Russia
A. Baskakov, A. Belyaev, E. Boos, M. Dubinin40, L. Dudko, A. Ershov, A.
Gribushin, V. Klyukhin, O. Kodolova, I. Lokhtin, I. Miagkov, S. Obraztsov, S.
Petrushanko, V. Savrin, A. Snigirev Novosibirsk State University (NSU),
Novosibirsk, Russia
V. Blinov41, T. Dimova41, L. Kardapoltsev41, D. Shtol41, Y. Skovpen41 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, S. Baidali, V. Okhotnikov University of Belgrade, Faculty of
Physics and Vinca Institute of Nuclear Sciences, Belgrade, Serbia
P. Adzic42, 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, E. Brondolin, 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, M. Dünser,
N. Dupont, A. Elliott-Peisert, P. Everaerts, F. Fallavollita43, D. Fasanella,
G. Franzoni, J. Fulcher, W. Funk, D. Gigi, A. Gilbert, K. Gill, F. Glege, M.
Guilbaud, D. Gulhan, J. Hegeman, V. Innocente, A. Jafari, P. Janot, O.
Karacheban19, J. Kieseler, A. Kornmayer, M. Krammer1, C. Lange, P. Lecoq, C.
Lourenço, L. Malgeri, M. Mannelli, F. Meijers, J.A. Merlin, S. Mersi, E.
Meschi, P. Milenovic44, F. Moortgat, M. Mulders, J. Ngadiuba, S. Orfanelli, L.
Orsini, F. Pantaleo16, L. Pape, E. Perez, M. Peruzzi, A. Petrilli, G.
Petrucciani, A. Pfeiffer, M. Pierini, F.M. Pitters, D. Rabady, A. Racz, T.
Reis, G. Rolandi45, M. Rovere, H. Sakulin, C. Schäfer, C. Schwick, M. Seidel,
M. Selvaggi, A. Sharma, P. Silva, P. Sphicas46, A. Stakia, J. Steggemann, M.
Tosi, D. Treille, A. Tsirou, V. Veckalns47, W.D. Zeuner Paul Scherrer
Institut, Villigen, Switzerland
L. Caminada48, K. Deiters, W. Erdmann, R. Horisberger, Q. Ingram, H.C.
Kaestli, D. Kotlinski, U. Langenegger, T. Rohe, S.A. Wiederkehr ETH Zurich -
Institute for Particle Physics and Astrophysics (IPA), Zurich, Switzerland
M. Backhaus, L. Bäni, P. Berger, N. Chernyavskaya, G. Dissertori, M. Dittmar,
M. Donegà, C. Dorfer, C. Grab, C. Heidegger, D. Hits, J. Hoss, T. Klijnsma, W.
Lustermann, R.A. Manzoni, M. Marionneau, M.T. Meinhard, F. Micheli, P.
Musella, F. Nessi-Tedaldi, J. Pata, F. Pauss, G. Perrin, L. Perrozzi, S.
Pigazzini, M. Quittnat, D. Ruini, D.A. Sanz Becerra, M. Schönenberger, L.
Shchutska, V.R. Tavolaro, K. Theofilatos, M.L. Vesterbacka Olsson, R. Wallny,
D.H. Zhu Universität Zürich, Zurich, Switzerland
T.K. Aarrestad, C. Amsler49, D. Brzhechko, M.F. Canelli, A. De Cosa, R. Del
Burgo, S. Donato, C. Galloni, T. Hreus, B. Kilminster, 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, Y.F.
Liu, 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. Cerci50, S. Damarseckin, Z.S. Demiroglu, F. Dolek, C.
Dozen, I. Dumanoglu, S. Girgis, G. Gokbulut, Y. Guler, E. Gurpinar, I. Hos51,
C. Isik, E.E. Kangal52, O. Kara, A. Kayis Topaksu, U. Kiminsu, M. Oglakci, G.
Onengut, K. Ozdemir53, S. Ozturk54, B. Tali50, U.G. Tok, H. Topakli54, S.
Turkcapar, I.S. Zorbakir, C. Zorbilmez Middle East Technical University,
Physics Department, Ankara, Turkey
B. Isildak55, G. Karapinar56, M. Yalvac, M. Zeyrek Bogazici University,
Istanbul, Turkey
I.O. Atakisi, E. Gülmez, M. Kaya57, O. Kaya58, S. Ozkorucuklu59, S. Tekten,
E.A. Yetkin60 Istanbul Technical University, Istanbul, Turkey
M.N. Agaras, S. Atay, A. Cakir, K. Cankocak, Y. Komurcu, S. Sen61 Institute
for Scintillation Materials of National Academy of Science of Ukraine,
Kharkov, Ukraine
B. Grynyov National Scientific Center, Kharkov Institute of Physics and
Technology, Kharkov, Ukraine
L. Levchuk University of Bristol, Bristol, United Kingdom
F. Ball, 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. Newbold62,
S. Paramesvaran, B. Penning, T. Sakuma, D. Smith, V.J. Smith, J. Taylor, A.
Titterton Rutherford Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev63, C. Brew, R.M. Brown, D. Cieri, D.J.A. Cockerill, J.A.
Coughlan, K. Harder, S. Harper, J. Linacre, 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. Nash64, A. Nikitenko7, 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. Virdee16, N. Wardle, D. Winterbottom, J. Wright, S.C. Zenz Brunel
University, Uxbridge, United Kingdom
J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, C.K. Mackay, A. Morton, I.D. Reid,
L. Teodorescu, S. Zahid Baylor University, Waco, USA
K. Call, J. Dittmann, K. Hatakeyama, H. Liu, C. Madrid, B. Mcmaster, N.
Pastika, C. Smith Catholic University of America, Washington DC, USA
R. Bartek, A. Dominguez The University of Alabama, Tuscaloosa, USA
A. Buccilli, S.I. Cooper, C. Henderson, P. Rumerio, C. West Boston University,
Boston, USA
D. Arcaro, T. Bose, D. Gastler, D. Rankin, C. Richardson, J. Rohlf, L. Sulak,
D. Zou Brown University, Providence, USA
G. Benelli, X. Coubez, D. Cutts, M. Hadley, J. Hakala, U. Heintz, J.M.
Hogan65, K.H.M. Kwok, E. Laird, G. Landsberg, J. Lee, Z. Mao, M. Narain, S.
Piperov, S. Sagir66, R. Syarif, E. Usai, D. Yu University of California,
Davis, Davis, USA
R. Band, C. Brainerd, R. Breedon, D. Burns, M. Calderon De La Barca Sanchez,
M. Chertok, J. Conway, R. Conway, P.T. Cox, R. Erbacher, C. Flores, G. Funk,
W. Ko, O. Kukral, R. Lander, 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. Wasserbaech67, J. Wood, F. Würthwein, A. Yagil, G. Zevi Della Porta
University of California, Santa Barbara - Department of Physics, Santa
Barbara, USA
N. Amin, R. Bhandari, 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.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-Navarro68, B. Schneider, E. Sexton-Kennedy, A. Soha, W.J. Spalding, L.
Spiegel, S. Stoynev, J. Strait, N. Strobbe, L. Taylor, S. Tkaczyk, N.V. Tran,
L. Uplegger, E.W. Vaandering, C. Vernieri, M. Verzocchi, R. Vidal, M. Wang,
H.A. Weber, A. Whitbeck University of Florida, Gainesville, USA
D. Acosta, P. Avery, P. Bortignon, D. Bourilkov, A. Brinkerhoff, L. Cadamuro,
A. Carnes, 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, 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, X.
Wang, Z. Wu, J. Zhang The University of Iowa, Iowa City, USA
M. Alhusseini, B. Bilki69, W. Clarida, K. Dilsiz70, S. Durgut, R.P.
Gandrajula, M. Haytmyradov, V. Khristenko, J.-P. Merlo, A. Mestvirishvili, A.
Moeller, J. Nachtman, H. Ogul71, Y. Onel, F. Ozok72, A. Penzo, C. Snyder, E.
Tiras, J. Wetzel Johns Hopkins University, Baltimore, USA
B. Blumenfeld, A. Cocoros, N. Eminizer, D. Fehling, L. Feng, A.V. Gritsan,
W.T. Hung, P. Maksimovic, J. Roskes, U. Sarica, M. Swartz, M. Xiao, C. You The
University of Kansas, Lawrence, USA
A. Al-bataineh, P. Baringer, A. Bean, S. Boren, J. Bowen, A. Bylinkin, J.
Castle, S. Khalil, A. Kropivnitskaya, D. Majumder, W. Mcbrayer, M. Murray, C.
Rogan, S. Sanders, E. Schmitz, J.D. Tapia Takaki, Q. Wang Kansas State
University, Manhattan, USA
S. Duric, A. Ivanov, K. Kaadze, D. Kim, Y. Maravin, D.R. Mendis, T. Mitchell,
A. Modak, A. Mohammadi, 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, A. Baty, G. Bauer, R. Bi, S. Brandt, W.
Busza, I.A. Cali, M. D’Alfonso, Z. Demiragli, G. Gomez Ceballos, M. Goncharov,
P. Harris, D. Hsu, M. Hu, Y. Iiyama, G.M. Innocenti, M. Klute, D. Kovalskyi,
Y.-J. Lee, P.D. Luckey, B. Maier, A.C. Marini, C. Mcginn, C. Mironov, S.
Narayanan, X. Niu, C. Paus, C. Roland, G. Roland, 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
G. Alverson, 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.
Musienko35, 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,
B. Mahakud, 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. 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, S. Spanier, K. Thapa Texas A&M
University, College Station, USA
O. Bouhali73, A. Celik, M. Dalchenko, M. De Mattia, A. Delgado, S. Dildick, R.
Eusebi, J. Gilmore, T. Huang, T. Kamon74, S. Luo, R. Mueller, R. Patel, A.
Perloff, L. Perniè, D. Rathjens, A. Safonov Texas Tech University, Lubbock,
USA
N. Akchurin, J. Damgov, F. De Guio, P.R. Dudero, S. Kunori, K. Lamichhane,
S.W. Lee, T. Mengke, S. Muthumuni, T. Peltola, S. Undleeb, I. Volobouev, Z.
Wang Vanderbilt University, Nashville, USA
S. Greene, A. Gurrola, R. Janjam, W. Johns, C. Maguire, A. Melo, H. Ni, K.
Padeken, J.D. Ruiz Alvarez, P. Sheldon, S. Tuo, J. Velkovska, M. Verweij, Q.
Xu University of Virginia, Charlottesville, USA
M.W. Arenton, P. Barria, B. Cox, R. Hirosky, M. Joyce, A. Ledovskoy, H. Li, C.
Neu, T. Sinthuprasith, Y. Wang, E. Wolfe, F. Xia Wayne State University,
Detroit, USA
R. Harr, P.E. Karchin, N. Poudyal, J. Sturdy, P. Thapa, S. Zaleski University
of Wisconsin - Madison, Madison, WI, USA
M. Brodski, J. Buchanan, C. Caillol, D. Carlsmith, S. Dasu, L. Dodd, 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 University of Chinese Academy of Sciences, Beijing, China
7: Also at Institute for Theoretical and Experimental Physics, Moscow, Russia
8: Also at Joint Institute for Nuclear Research, Dubna, Russia
9: Also at Suez University, Suez, Egypt
10: Now at British University in Egypt, Cairo, Egypt
11: Also at Zewail City of Science and Technology, Zewail, Egypt
12: Now at Helwan University, Cairo, Egypt
13: Also at Department of Physics, King Abdulaziz University, Jeddah, Saudi
Arabia
14: Also at Université de Haute Alsace, Mulhouse, France
15: Also at Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State
University, Moscow, Russia
16: Also at CERN, European Organization for Nuclear Research, Geneva,
Switzerland
17: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
18: Also at University of Hamburg, Hamburg, Germany
19: Also at Brandenburg University of Technology, Cottbus, Germany
20: Also at MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
21: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary
22: Also at Institute of Physics, University of Debrecen, Debrecen, Hungary
23: Also at Indian Institute of Technology Bhubaneswar, Bhubaneswar, India
24: Also at Institute of Physics, Bhubaneswar, India
25: Also at Shoolini University, Solan, India
26: Also at University of Visva-Bharati, Santiniketan, India
27: Also at Isfahan University of Technology, Isfahan, Iran
28: Also at Plasma Physics Research Center, Science and Research Branch,
Islamic Azad University, Tehran, Iran
29: Also at Università degli Studi di Siena, Siena, Italy
30: Also at Kyunghee University, Seoul, Korea
31: Also at International Islamic University of Malaysia, Kuala Lumpur,
Malaysia
32: Also at Malaysian Nuclear Agency, MOSTI, Kajang, Malaysia
33: Also at Consejo Nacional de Ciencia y Tecnología, Mexico city, Mexico
34: Also at Warsaw University of Technology, Institute of Electronic Systems,
Warsaw, Poland
35: Also at Institute for Nuclear Research, Moscow, Russia
36: Now at National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
37: Also at St. Petersburg State Polytechnical University, St. Petersburg,
Russia
38: Also at University of Florida, Gainesville, USA
39: Also at P.N. Lebedev Physical Institute, Moscow, Russia
40: Also at California Institute of Technology, Pasadena, USA
41: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia
42: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia
43: Also at INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy
44: Also at University of Belgrade, Faculty of Physics and Vinca Institute of
Nuclear Sciences, Belgrade, Serbia
45: Also at Scuola Normale e Sezione dell’INFN, Pisa, Italy
46: Also at National and Kapodistrian University of Athens, Athens, Greece
47: Also at Riga Technical University, Riga, Latvia
48: Also at Universität Zürich, Zurich, Switzerland
49: Also at Stefan Meyer Institute for Subatomic Physics (SMI), Vienna,
Austria
50: Also at Adiyaman University, Adiyaman, Turkey
51: Also at Istanbul Aydin University, Istanbul, Turkey
52: Also at Mersin University, Mersin, Turkey
53: Also at Piri Reis University, Istanbul, Turkey
54: Also at Gaziosmanpasa University, Tokat, Turkey
55: Also at Ozyegin University, Istanbul, Turkey
56: Also at Izmir Institute of Technology, Izmir, Turkey
57: Also at Marmara University, Istanbul, Turkey
58: Also at Kafkas University, Kars, Turkey
59: Also at Istanbul University, Faculty of Science, Istanbul, Turkey
60: Also at Istanbul Bilgi University, Istanbul, Turkey
61: Also at Hacettepe University, Ankara, Turkey
62: Also at Rutherford Appleton Laboratory, Didcot, United Kingdom
63: Also at School of Physics and Astronomy, University of Southampton,
Southampton, United Kingdom
64: Also at Monash University, Faculty of Science, Clayton, Australia
65: Also at Bethel University, St. Paul, USA
66: Also at Karamanoğlu Mehmetbey University, Karaman, Turkey