SUS-16-040

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$Id:SUS-16-040.tex4657512018-06-2217:52:59Zjaehyeok$

SUS-16-040

# Search for $R$-parity violating supersymmetry in pp collisions at
$\sqrt{s}=13\TeV$ using jets in a final state with a single lepton, many jets,
and high sum of large-radius jet masses

###### Abstract

Results are reported from a search for physics beyond the standard model in
proton-proton collisions at a center-of-mass energy of $\sqrt{s}=13\TeV$. The
search uses a signature of a single lepton, large jet and bottom quark jet
multiplicities, and high sum of large-radius jet masses, without any
requirement on the missing transverse momentum in an event. The data sample
corresponds to an integrated luminosity of 35.9recorded by the CMS experiment
at the LHC. No significant excess beyond the prediction from standard model
processes is observed. The results are interpreted in terms of upper limits on
the production cross section for $R$-parity violating supersymmetric
extensions of the standard model using a benchmark model of gluino pair
production, in which each gluino decays promptly via $\PSg\to\PQt\PQb\PQs$.
Gluinos with a mass below 1610are excluded at 95% confidence level.

##  0.1 Introduction

Searches for physics beyond the standard model (SM) are motivated by several
considerations, including theoretical problems associated with explaining the
observed mass of the Higgs boson in the presence of quantum corrections (the
hierarchy problem) [1], and astrophysical evidence for dark matter [2]. While
the SM has been successful in describing a vast range of phenomena, its
inability to address these theoretical and experimental issues makes it an
incomplete description of fundamental particles and their interactions.

Supersymmetry (SUSY), a proposed extension of the SM, provides possible
solutions to these problems [3, 4, 5, 6, 7, 8, 9, 10, 11, 12]. The hierarchy
problem can be addressed by SUSY models with a sufficiently low-mass top
squark and gluino, and the lightest supersymmetric particle (LSP), if stable,
is a potential dark matter candidate [1, 13, 14, 15, 16]. That stability is
assured in $R$-parity conserving (RPC) SUSY models, where the $R$-parity of a
particle is defined as $(-1)^{2s+3(B-L)}$ with $s$, $B$, and $L$ denoting the
spin, baryon number, and lepton number of the particle, respectively [17].

Recent searches at the CERN LHC have set stringent limits on RPC SUSY
production, as mass limits for the models studied are reaching ${\sim}1\TeV$
for the top squark [18, 19, 20] and ${\sim}2\TeV$ [21, 22, 23, 24, 25, 26] for
the gluino. Due to these limits, there is mounting tension in the ability of
RPC SUSY models to explain the hierarchy problem with little fine tuning.
These RPC SUSY searches, however, typically require signatures with
significant missing transverse momentum () resulting from the undetected LSPs,
while in $R$-parity violating (RPV) SUSY, the LSP is not stable and decays to
SM particles, which removes the large signature. Though this disfavors the LSP
as a dark matter candidate, it allows RPV SUSY models to evade constraints
from typical RPC SUSY searches.

Given that there is no fundamental theoretical reason for $R$-parity
conservation, RPV SUSY yields an important class of models that can ease the
tension between natural solutions to the hierarchy problem and current
experimental limits. In addition, the absence of a requirement can allow RPV
SUSY searches to be sensitive to a parameter space of RPC SUSY where only a
small amount of is expected, such as in models where the mass splitting
between the supersymmetric particles is small. Therefore, RPV SUSY searches
help to complete the coverage of SUSY parameter space.

The additional $R$-parity violating terms in the superpotential are

$W=\frac{1}{2}\lambda^{ijk}L_{i}L_{j}\overline{\mathrm{e}}_{k}+\lambda^{{}^{\prime}ijk}L_{i}Q_{j}\cPaqd_{k}+\frac{1}{2}\lambda^{{}^{\prime\prime}ijk}\cPaqu_{i}\cPaqd_{j}\cPaqd_{k}+\mu^{{}^{\prime}i}L_{i}H_{u}.$
(1)

Here $L_{i}$, $Q_{j}$, and $H_{u}$ are SU(2) doublets corresponding to
leptons, quarks, and the Higgs boson, respectively. The fields
$\overline{\mathrm{e}}_{k}$, $\cPaqu_{i}$, and $\cPaqd_{j}$ are the charged
lepton, up-type quark, and down-type quark SU(2) singlets, while the various
$\lambda$ and $\mu$ factors denote the coupling strengths for their
corresponding interaction. Color indices are suppressed and letters $i$, $j$,
$k$ denote generation indices. More details on RPV SUSY can be found in Ref.
[27].

This search is motivated by a particular model of $R$-parity violation,
minimal flavor violating (MFV) SUSY [28], in which the $R$-parity violating
couplings arise from the SM Yukawa couplings. This makes the third generation
RPV couplings large and those of the first two generations small, which is
consistent with the strong experimental constraints from proton decay searches
on baryon and lepton number violation involving the lightest two generations
[27]. The coupling $\lambda^{\prime\prime ijk}$ must be antisymmetric in the
last two indices because of gauge invariance, which requires
$\lambda^{\prime\prime\cPqt\cPqb\cPqb}$ to be 0. Therefore, the largest
allowed MFV coupling is $\lambda^{\prime\prime\cPqt\cPqb\cPqs}$.

Due to the high $\PSg\PSg$ cross section and large value of
$\lambda^{\prime\prime\cPqt\cPqb\cPqs}$, a search for the pair production of
gluinos that decay via $\PSg\to\cPqt\PASQt\to\cPqt\cPqb\cPqs$ is well
motivated. The simplified model [29, 30] that is used in the interpretation
makes several assumptions about the SUSY mass spectrum. It is assumed that
squarks other than the top squark are much heavier than the gluino, so they do
not affect the gluino decay, and the branching ratio of
$\PSg\to\cPqt\PASQt\to\cPqt\cPqb\cPqs$ is $100\%$. The top squark is assumed
to be virtual in its decay. This results in a three-body decay, so searches
for dijet resonances, , $\PASQt\to\cPqb\cPqs$, are not applicable in this
scenario. It is further assumed that the gluinos decay promptly. An example
diagram for this simplified model is shown in Fig. 1. Although this benchmark
is used for interpreting results, the search is structured to be generically
sensitive to high-mass signatures with large jet and bottom quark jet
multiplicities and either little or no , which are potential features of other
models of physics beyond the SM. Previous limits on such MFV models were
obtained by the ATLAS and CMS Collaborations at $\sqrt{s}=8\TeV$ [31, 32, 33]
and by the ATLAS Collaboration at $\sqrt{s}=13\TeV$ [34], excluding gluino
masses below ${\sim}1\TeV$ and $1.6\TeV$, respectively.

This analysis searches in a single-lepton (electron or muon) final state for
an excess of events with a large number of identified bottom quark (-tagged)
jets in regions determined as a function of the jet multiplicity and the sum
of masses of large-radius jets, $M_{\text{J}}$. Signal events are expected to
contribute to this final state through the leptonic decay of one of the top
quarks while populating the high jet multiplicity and high $M_{\text{J}}$
kinematic regions due to the hadronic decay of the second top quark and the
additional bottom and strange quark jets. The four quarks, two from the top
quark decays and two from the top squark decays, provide a high -tagged jet
multiplicity signature. The quantity $M_{\text{J}}$ was proposed in
phenomenological studies [35, 36, 37] and was first used for RPC SUSY searches
by the ATLAS Collaboration in all-hadronic final states [38, 39] and by the
CMS Collaboration in single-lepton events [40, 26].

Figure 1: Example diagram for the simplified model used as the benchmark
signal in this analysis.

##  0.2 The CMS detector, samples, and event selection

This search uses a sample of proton-proton collision data at a center-of-mass
energy of $\sqrt{s}=13\TeV$ corresponding to an integrated luminosity of 35.9,
which was collected by the CMS experiment during 2016. The central feature of
the CMS detector is a superconducting solenoid of 6m internal diameter,
providing a magnetic field of 3.8T. Within the solenoid volume are the charged
particle tracking systems, composed of silicon-pixel and silicon-strip
detectors, and the calorimeter systems, consisting of a lead tungstate crystal
electromagnetic calorimeter (ECAL), and a brass and scintillator hadron
calorimeter. Muons are identified and measured by gas-ionization detectors
embedded in the magnetic flux-return yoke outside the solenoid. A more
detailed description of the CMS detector, together with a definition of the
coordinate system used and the relevant kinematic variables, is given in Ref.
[41].

The background predictions use Monte Carlo (MC) simulation samples with
corrections to the normalization and shape of distributions measured in data
control samples. 2.2.2 is used in leading-order mode [42, 43] to generate the
, $\PW\text{+jets}$, quantum chromodynamics multijet (QCD), and Drell–Yan
background processes with extra partons. Comparison to a 2.0 [44, 45, 46]
sample generated at next-to-leading order (NLO) shows that the NLO effects do
not have a significant impact. The $\ttbar$W, $\ttbar$Z, $\ttbar\ttbar$, and
$t$-channel single top quark production backgrounds are generated with 2.2.2
in NLO mode [47], while the $\cPqt\PW$, $\cPaqt\PW$, and $s$-channel single
top quark processes are generated with 2.0. The , $\PW\text{+jets}$, and QCD
samples are generated with up to 2, 4, 2 extra partons, respectively. All
samples are generated using a top quark mass of 172.5 and with the NNPDF3.0
set of parton distribution functions (PDF) [48]. For the fragmentation and
showering of partons, the generated samples are interfaced with PYTHIA 8.205
[49] and use the CUETP8M1 tune to describe the underlying event [50]. All
samples use the highest precision cross sections available [51, 52, 53, 54,
55, 56, 57]. The detector response is simulated with [58]. Simulated samples
are processed through the same reconstruction algorithms as the data.

The signal samples are generated with up to two extra partons in leading-order
mode and dynamic factorization and renormalization scales by 2.2.2. The same
fragmentation, parton showering, simulation, and event reconstruction
procedure as for the background samples is used. The samples are normalized to
NLO + next-to-leading logarithmic cross sections [59].

The reconstruction of objects in an event proceeds from the candidate
particles identified by the particle-flow (PF) algorithm [60], which uses
information from the tracker, calorimeters, and muon systems to identify the
candidates as charged or neutral hadrons, photons, electrons, or muons.
Charged-particle tracks are required to originate from the event primary
vertex (PV), which is the reconstructed vertex with the largest value of
summed physics-object squared transverse momentum ($\pt$). The physics objects
used for the PV reconstruction are those returned by a jet finding algorithm
[61, 62] with the tracks assigned to the vertex as inputs, and the associated
missing transverse momentum, taken as the negative vector sum of the of those
objects.

Electrons are reconstructed by pairing a charged-particle track with an ECAL
supercluster [63]. The resulting electron candidates are required to have
$\pt>20\GeV$ and $|\eta|<2.5$, and to satisfy identification criteria designed
to remove hadrons misidentified as electrons, photon conversions, and
electrons from heavy-flavor hadron decays. Muons are reconstructed by
associating tracks in the muon system with those found in the silicon tracker
[64]. Muon candidates are required to satisfy $\pt>20\GeV$, $|\eta|<2.4$, and
identification criteria designed to select a high-purity muon sample.

To preferentially select leptons that originate in the decay of $\PW$ and
$\PZ$ bosons, leptons are required to be isolated from other PF candidates.
The relative isolation of a particle $I^{\rm rel}$ is quantified using an
optimized version of the mini-isolation variable $I_{\rm mini}$. Mini-
isolation is computed as the scalar sum of the of charged hadrons from the PV,
neutral hadrons, and photons that are within a cone of radius $R^{\text{mini-
iso}}$ surrounding the lepton momentum vector $\vec{p}^{\,\ell}$ in
$\eta$-$\phi$ space [65]. The cone radius $R^{\text{mini-iso}}$ varies with
$1/\pt^{\,\ell}$ according to

$\displaystyle R^{\text{mini-iso}}$
$\displaystyle=\begin{cases}0.2,&\pt^{\,\ell}\leq 50\GeV\\\
{10\GeV}/{\pt^{\,\ell}},&50<\pt^{\,\ell}\leq 200\GeV\\\
0.05,&\pt^{\,\ell}>200\GeV.\end{cases}$ (2)

The -dependent cone size reduces the rate of accidental overlaps between the
lepton and jets in high-multiplicity or highly Lorentz-boosted events,
particularly overlaps between bottom quark jets and leptons originating from a
boosted top quark. Relative isolation is computed as $I^{\rm rel}=I_{\rm
mini}/\pt^{\,\ell}$ after subtraction of the average contribution from
additional proton-proton collisions in the same bunch-crossing (pileup). To be
considered isolated, electrons and muons must satisfy $I^{\rm rel}<0.1$ and
$0.2$, respectively, where the different thresholds account for purity
differences between electrons and muons.

The combined efficiency for the electron reconstruction, identification, and
isolation requirements is about 50% at $\pt^{\,\ell}$ of $20\GeV$, increasing
to 65% at $50\GeV$, and reaching a plateau of 80% above $200\GeV$. The
corresponding efficiency for muons is about 70% at $\pt^{\,\ell}$ of $20\GeV$,
increasing to 80% at $50\GeV$, and reaching a plateau of 95% for
$\pt^{\ell}>200\GeV$. Data-to-simulation corrections (scale factors) are
applied for both electrons and muons to correct the simulated lepton selection
efficiency to match that observed in data.

The charged PF candidates associated with the PV and the neutral PF candidates
are clustered into jets using the anti-$\kt$ algorithm [61] with distance
parameter $R=0.4$, as implemented in the fastjet package [62]. The estimated
contribution to the jet $\pt$ from neutral PF candidates produced by pileup is
removed with a correction based on the area of the jet and the average energy
density of the event [66]. The jet energy is calibrated using $\pt$\- and
$\eta$-dependent corrections; the resulting calibrated jets are selected if
they satisfy $\pt>30\GeV$ and $|\eta|\leq 2.4$. Each jet must also meet loose
identification requirements [67] to suppress, for example, calorimeter noise.
Finally, jets that have PF constituents matched to the selected lepton are
removed from the jet collection. These resulting jets are considered to be
“small-$R$” jets.

The combined secondary vertex algorithm [68, 69] is applied to each small-$R$
jet to create a subset of -tagged jets. The tagging efficiency for jets in the
range $\pt=30$ to $50\GeV$ is 60–67% (51–57%) in the barrel (endcap) and
increases with . Above $\pt\approx 150\GeV$ the efficiency decreases to
${\approx}50$%. The probability to misidentify jets arising from $\cPqc$
quarks is 13–15% (11–13%) in the barrel (endcap), while the misidentification
probability for light-flavor quarks or gluons is 1–2%. Data-derived scale
factors for the tag efficiency and mistag rate are applied to simulation such
that the simulated tagging performance matches that observed in data.

“Large-$R$” ($R=1.2$) jets are created by clustering small-$R$ jets and the
selected lepton using the anti-$\kt$ algorithm. Leptons are included to
encompass the full kinematics of the event. Clustering small-$R$ jets instead
of PF candidates incorporates the jet pileup corrections, thereby reducing the
dependence of the large-$R$ jet mass on pileup. This technique of clustering
small-$R$ jets into large-$R$ jets has been used previously, e.g. Refs. [70,
40, 18]. The variable $M_{\text{J}}$ is defined as the sum of all large-$R$
jet masses, where $m(J)$ is the mass of a single large-$R$ jet:

$\displaystyle M_{\text{J}}\,\,=\\!\\!\sum_{J_{i}\,\in\,{\text{large-}R\text{
jets}}}m(J_{i}).$ (3)

The quantity $M_{\text{J}}$ is used as a measure of the mass-scale of an
event. Signal events tend to have large $M_{\text{J}}$ as the large-$R$ jets
capture the kinematic information of the high-mass gluinos. Comparatively, SM
background processes tend to have smaller values of $M_{\text{J}}$ due to
their lower mass-scales. SM events, however, can have large values of
$M_{\text{J}}$ in the presence of significant initial-state-radiation (ISR).
For example, in events, ISR jets can either overlap with daughter jets or
boost the system such that the system is collimated, both of which result in
high-mass large-$R$ jets and, correspondingly, high $M_{\text{J}}$. The
$M_{\text{J}}$ distributions for and signal are shown in Fig. 2, which uses
events with $N_{\text{jet}}\geq 8$ to ensure similar $N_{\text{jet}}$
distributions for both and signal.

Events are selected with triggers [71] that require either at least one jet
with $\pt>450\GeV$ or the scalar sum of the of all small-$R$ jets () above
$900\GeV$. Trigger efficiencies are over $99\%$ for signal events passing the
analysis selection defined below.

These events are further selected with a baseline requirement of exactly one
electron or muon, $M_{\text{J}}>500\GeV$, $\HT>1200\GeV$, that the number of
small-$R$ jets ($N_{\text{jet}}$) be at least 4, and that the number of those
jets that are tagged as bottom quark jets ($N_{\PQb}$) be at least 1.

Figure 2: Distributions of $M_{\text{J}}$, normalized to the same area, for
events and signal events with two different gluino masses in a selection of
$\HT>1200\GeV$, $N_{\text{lep}}=1$, $N_{\text{jet}}\geq 8$,
$M_{\text{J}}>500\GeV$, and $N_{\PQb}\geq 1$.

##  0.3 Background prediction

After the baseline selection, the dominant background contribution is from the
$\ttbar$ process, with small contributions from $\PW\text{+jets}$ and QCD
events with a misidentified lepton. Rare background contributions, classified
below as “Other”, come from single top quark, $\ttbar\PW$, $\ttbar\cPZ$,
$\ttbar\PH$, $\ttbar\ttbar$, and Drell–Yan production.

To search for signal events arising from new high-mass particles decaying with
large jet and -jet multiplicities, the $N_{\PQb}$ distribution is examined in
different kinematic regions based on $N_{\text{jet}}$ and $M_{\text{J}}$. The
$N_{\text{jet}}$ bins are defined to be 4–5, 6–7, and $\geq 8$. The
$M_{\text{J}}$ bins are $500<M_{\text{J}}\leq 800$, $800<M_{\text{J}}\leq
1000$, and $M_{\text{J}}>1000$, with the two highest $M_{\text{J}}$ bins
merged for the $4\leq N_{\text{jet}}\leq 5$ case due to the limited data
sample size in the $M_{\text{J}}>1000$region. The low-$N_{\text{jet}}$,
low-$M_{\text{J}}$ bins are expected to be background-dominated and are used
as control regions to constrain systematic uncertainties, while the
high-$N_{\text{jet}}$, high-$M_{\text{J}}$ bins are used as signal regions. A
diagram representing this binning is shown in Figure 3. The $N_{\PQb}$
distribution is separated into $N_{\PQb}=1$, 2, 3, and ${\geq}4$ bins for each
region. The two highest $N_{\PQb}$ bins are the most sensitive to signal due
to larger signal-to-background ratios, while the lower $N_{\PQb}$ bins provide
constraints on the background normalizations and systematic uncertainties. The
signal efficiency for the bin requiring $N_{\text{jet}}\geq 8$ and
$M_{\text{J}}>1000$is $2\%$ and $8\%$ for $m_{\PSg}=1000\GeV$ and 1600,
respectively.

A global maximum-likelihood fit is performed to obtain predictions for the SM
background processes. This fit is carried out both for a background-only
hypothesis and for signal-plus-background hypotheses, in which an additional
signal contribution is extracted. The model is constructed using the poisson
probabilities of the bin contents of the $N_{\PQb}$ distribution for all
$N_{\text{jet}}$, $M_{\text{J}}$ regions, while systematic uncertainties are
applied as nuisance parameters. The $N_{\PQb}$ shape for each process is taken
from simulation, but varied to assess the impact of mismodeling of relevant
parameters, including the rate of gluon splitting to and tagging efficiencies
for heavy- and light-flavor jets [68, 69]. The appropriate ranges for these
parameters are determined based on measurements in dedicated control samples
and then constrained by a simultaneous fit across all bins of $N_{\text{jet}}$
and $M_{\text{J}}$ in a correlated manner. Various studies with simulated
pseudo-experiments were conducted to validate the likelihood model and to
confirm that signal contamination effects are negligible.

Because the kinematic tails of the $N_{\text{jet}}$ and $M_{\text{J}}$
variables are difficult to model reliably, the and QCD normalizations are
individually allowed to freely vary in each ($N_{\text{jet}}$, $M_{\text{J}}$)
bin. The normalizations are constrained in each bin by the background-
dominated $N_{\PQb}\leq 2$ bins, while the QCD normalizations are constrained
by control regions with no identified leptons ($N_{\text{lep}}=0$). These
$N_{\text{lep}}=0$ control regions follow the same kinematic binning as the
$N_{\text{lep}}=1$ bins, but are integrated in $N_{\PQb}$ for $N_{\PQb}\geq 1$
and use offset $N_{\text{jet}}$ bins of 6–7, 8–9, and ${\geq}10$ to account
for differences in the $N_{\text{jet}}$ distributions between the
$N_{\text{lep}}=1$ and $N_{\text{lep}}=0$ samples. The QCD contribution in a
particular $N_{\text{lep}}=1$ bin is then constrained by the corresponding
$N_{\text{lep}}=0$ bin. To avoid biasing the normalization measurement, the
small contribution of background to the $N_{\text{lep}}=0$ control regions is
included using the normalization from the corresponding $N_{\text{lep}}=1$
bins, while contributions from other processes are taken from simulation.

The $N_{\text{jet}}$ shape of the $\PW\text{+jets}$ background is taken from
simulation and allowed to vary based on the data-to-simulation agreement in a
kinematically similar $\PZ\text{+jets}$ sample selected with
$N_{\text{lep}}=2$ ($\Pe\Pe$ or $\PGm\PGm$), $\HT>1200\GeV$,
$M_{\text{J}}>500\GeV$, $N_{\PQb}=1$, and $80<m_{\ell\ell}<100\GeV$, where
$m_{\ell\ell}$ is the invariant mass of the two leptons. The $N_{\text{jet}}$
distribution and data/simulation yields ratio for this sample are shown in
Fig. 4. The $\PW\text{+jets}$ background is then determined in the fit with
one global normalization parameter and two parameters to adjust the bin-to-bin
normalization based on the difference between the ratios in adjacent
$N_{\text{jet}}$ bins – 17% between $4\leq N_{\text{jet}}\leq 5$ and $6\leq
N_{\text{jet}}\leq 7$ and 62% between $6\leq N_{\text{jet}}\leq 7$ and
$N_{\text{jet}}\geq 8$. After correcting the $N_{\text{jet}}$ spectrum, the
residual $M_{\text{J}}$ mismodeling is expected to be small, so no further
correction is applied.

The “Other” component is estimated from simulation. Its contribution is less
than 20% of the total backgrounds in all kinematic regions considered.

$M_{\text{J}}$ [] | $N_{\text{jet}}$
---|---
4–5 | 6–7 |  $\geq$8
500–800 | CR | CR | SR
800–1000 | CR | SR | SR
$>$1000 | SR | SR
Figure 3:  Illustration depicting the ($N_{\text{jet}}$, $M_{\text{J}}$)
binning after the baseline selection, with control and signal region bins
denoted by “CR” and “SR”, respectively. Figure 4: Jet multiplicity
distribution for data and MC simulation in a $\PZ\text{+jets}$ control sample
selected by requiring $N_{\text{lep}}=2$, $\HT>1200\GeV$,
$M_{\text{J}}>500\GeV$, $N_{\PQb}=1$, and $80<m_{\ell\ell}<100\GeV$. The total
yield from simulation is normalized to the number of events in data. The
uncertainty in the ratio of data to simulation yields (lower panel) is
statistical only.

##  0.4 Systematic uncertainties

###  0.4.1 Background systematic uncertainties

The nominal simulated shape of the $N_{\PQb}$ distribution is allowed to vary
by the inclusion of systematic uncertainties. Each uncertainty is incorporated
in the fit with template $N_{\PQb}$ histograms to account for the effects of
the systematic variation and a nuisance parameter $\theta$ to control the
variation amplitude. The nuisance parameters are subject to Gaussian
constraints, normalized so that $\theta=0$ corresponds to the nominal
$N_{\PQb}$ shape and $\theta=\pm 1$ corresponds to ${\pm}1$ standard deviation
(s.d.) variation of the systematic uncertainty. These uncertainties affect
only the $N_{\PQb}$ shape for , QCD, and W+jets backgrounds, because their
normalizations are determined from data, while for the other (subleading)
backgrounds the uncertainties affect both the $N_{\PQb}$ shape and
normalization.

The primary source of systematic uncertainty is the modeling of the gluon
splitting rate, which can produce additional quarks in events and may not be
properly simulated. To account for this, a nuisance parameter controlling the
gluon splitting rate is included in the likelihood. The size of the ${\pm}1$
s.d. variation for this nuisance parameter is estimated using a fit to the
$\Delta R_{\bbbar}$ distribution in a control sample, where $\Delta
R_{\bbbar}$ is defined as the $\Delta R$ between two -tagged jets in the
event. This control sample is selected by requiring $N_{\text{lep}}=0$,
$\HT>1500\GeV$, $N_{\PQb}=2$, $N_{\text{jet}}\geq 4$, and
$M_{\text{J}}>500\GeV$, as the gluon splitting signal in a $N_{\text{lep}}=1$
control region is contaminated by quarks from the decay of top quarks. To
ensure that these measurements in the QCD-dominated $N_{\text{lep}}=0$ region
are applicable to the ttbar-dominated $N_{\text{lep}}=1$ region, both
processes are simulated with the same procedure and settings. Furthermore, the
$N_{\text{lep}}=0$ control sample is formed from a subset of the data that is
selected to be most stable in the tagging algorithm performance, since the
precision of the $\Delta R_{\bbbar}$ fit is not limited by the data sample
size. This choice isolates the physical effects of gluon splitting from the
potential time dependence of the tagging performance due to variations in
experimental conditions, which are separately incorporated by the -tag scale
factor uncertainties. The nuisance parameter obtained from this control sample
is allowed to vary in the full likelihood fit and further constrained by the
observed data in the $N_{\text{lep}}=1$ regions.

Events where both of the -tagged jets originate from one gluon splitting
populate the low-$\Delta R_{\bbbar}$ region, while events without a gluon
splitting or where the splitting yields one or no -tagged jets populate both
the low- and high-$\Delta R_{\bbbar}$ regions roughly equally. Gluon
splittings can sometimes be reconstructed with fewer than two -tagged jets
either because the quarks are collimated into a single jet, one of the jets is
not tagged, or because one of the quarks is not within the kinematic
acceptance.

A fit to the $\Delta R_{\bbbar}$ distribution is used to extract the relative
contributions of events with and without gluon splitting and is performed in
four equal bins in the range $0\leq\Delta R_{\bbbar}<4.8$. This binning is
chosen to avoid relying on the fine details of the simulated $\Delta
R_{\bbbar}$ shape. The instances of gluon splitting in simulation are
identified by requiring a gluon with $\pt>30\GeV$ that decays to quarks. Three
categories are then defined: events with gluon splitting resulting in two
-tagged jets (denoted GSbb), with gluon splitting resulting in one or fewer
-tagged jets (GSb), and without any gluon splitting (no GS). In the fit, the
GSbb and GSb contributions are varied together with a single normalization
parameter.

The $\Delta R_{\bbbar}$ fit extracts a weight of $0.77\pm 0.09$ for gluon
splitting events and a weight of $1.21\pm 0.08$ for non-gluon splitting
events. The post-fit distributions are shown in Figure 5. The GSbb and GSb
categories are plotted separately to demonstrate the difference in shapes. The
discrepancy in the last bin does not significantly impact the fit because the
higher yield bins at lower values of $\Delta R_{\bbbar}$ constrain the fit.
The deviations of these weights from unity, summed in quadrature with their
post-fit uncertainty, are used to form the ${\pm}1$ s.d. variations of the
gluon splitting rate nuisance parameter by applying weights of $1\pm 0.25$ to
gluon splitting events and $1\mp 0.22$ to non-gluon splitting events in an
anti-correlated manner. The fit results are used as a measure of the
uncertainty on modelling of the GS rate as opposed to a correction to the
central value, since the $\Delta R_{\bbbar}$ variable may not be a perfect
proxy for the GS rate.

Various tests are conducted to assess the stability of the fit results. To
test the dependence of the gluon splitting weights across kinematic regions,
the fit is repeated both with a higher $M_{\text{J}}$ threshold and with
different $N_{\text{jet}}$ bins. Additionally the fit is conducted with finer
binning to test the dependence of the results on the binning of the $\Delta
R_{\bbbar}$ distribution. The resulting weights are all consistent with those
of the nominal fit.

Figure 5: Post-fit $\Delta R_{\bbbar}$ distributions in a selection with
$N_{\text{lep}}=0$, $\HT>1500\GeV$, $N_{\PQb}=2$, $N_{\text{jet}}\geq 4$, and
$M_{\text{J}}>500\GeV$ with the post-fit uncertainty represented by a hatched
band. The ratio of data to simulation yields is shown in the lower panel.

Another significant systematic uncertainty is the uncertainty in the data-to-
simulation scale factors (SF) for tagging efficiency and mistag rates. These
scale factors are derived from data in various QCD and control samples and are
binned in jet and jet flavor (light + , , and ) [72]. The ${\pm}1$ s.d.
$N_{\PQb}$ templates for these scale factors are assessed by varying them
according to the uncertainties in their measurements.

Other experimental uncertainties are small and include lepton selection
efficiency, lepton misidentification rate, jet energy scale, jet energy
resolution, and integrated luminosity. The uncertainty associated with lepton
selection efficiency is determined by varying the efficiency to select a
lepton within its uncertainty determined from data. The $N_{\text{lep}}$
distribution for QCD events may not be simulated well because it relies on
modeling the tail of the fragmentation function and various detector effects.
To account for this, an uncertainty of 20% is assigned to the relative
normalization of QCD events in the 0- and 1-lepton bins, which is motivated by
data-to-simulation studies of lepton isolation distributions. Jet energy scale
uncertainties [67, 73] are assessed by varying the of small-$R$ jets as a
function of and $\eta$. The uncertainty arising from jet energy resolution
[67, 73] is determined by applying an $\abs{\eta}$-dependent factor to the jet
to match the jet energy resolution observed in data. The integrated luminosity
is varied according to its uncertainty of 2.5% [74], affecting only the
backgrounds estimated from simulation. No uncertainty is applied for the
amount of pileup as studies have shown its effect to be negligible in this
high-selection. The uncertainties due to the limited size of simulation
samples are incorporated as uncorrelated nuisance parameters in the fit.

Theoretical systematic uncertainties are applied and include independent and
correlated variations of the renormalization and factorization scales.
Additionally, uncertainties on the PDF are incorporated by considering
variations in the NNPDF 3.0 scheme [48]. The size of these uncertainties is
typically small as the effect of these variations is largely to modify the
cross section of processes, which for the main backgrounds are constrained by
data.

The background systematic uncertainties that affect the $N_{\PQb}$ shape are
shown in Fig. 6 (left) for the most sensitive search bin.

Figure 6: Background (left) and $m_{\PSg}=1600\GeV$ signal (right) systematic
uncertainties affecting the $N_{\PQb}$ shape (in percent) in the
$N_{\text{jet}}\geq 8$ and $M_{\text{J}}\geq 1000\GeV$ bin. The bottom row
shows the total uncertainty for a given $N_{\PQb}$ bin by summing in
quadrature all uncertainties. These values are similar for other
($N_{\text{jet}}$, $M_{\text{J}}$) bins.

###  0.4.2 Signal systematic uncertainties

Several of the systematic uncertainties affecting the signal yield are
evaluated in the same way as the background yield. These are the uncertainties
due to gluon splitting, lepton selection efficiency, jet energy scale, jet
energy resolution, tagging scale factors, simulation sample size, integrated
luminosity, and theoretical uncertainties. All systematic variations affect
both the $N_{\PQb}$ shape and normalization, except for the gluon splitting
uncertainty, which is taken to affect only the $N_{\PQb}$ shape.

The number of jets from ISR produced in the signal simulation is reweighted
based on comparisons between data and simulated samples. The reweighting
factors vary between 0.92 and 0.51 for the number of ISR jets between 1 and
${\geq}6$. One half of the deviation from unity is taken as the systematic
uncertainty in these reweighting factors.

The systematic uncertainties affecting the signal $N_{\PQb}$ shape are shown
in Fig. 6 (right) for the most sensitive bin in a model with
$m_{\PSg}=1600\GeV$. The dominant signal systematic uncertainties arise from
the limited simulation sample size, the tagging efficiency scale factors, and
the ISR modeling. There is no systematic uncertainty taken for pileup
reweighting, as the signal efficiency is found to be insensitive to the number
of pileup interactions.

##  0.5 Results

The results of a background-only fit of the observed $N_{\PQb}$ distributions
are shown in Figs. 7 and 8. These figures separately show the
$N_{\text{lep}}=1$ control and signal regions, although the fit includes all
bins simultaneously. The $N_{\PQb}$ distributions in data are well described
by the fit, and examination of the nuisance parameters shows that none of them
are significantly changed by the fit. The post-fit yields are presented in
Table 0.5.

Figure 7: Data and the background-only post-fit $N_{\PQb}$ distribution for
bins with low expected signal contribution: $500<M_{\text{J}}\leq 800\GeV$,
$4\leq N_{\text{jet}}\leq 5$ (upper-left), $M_{\text{J}}>800\GeV$, $4\leq
N_{\text{jet}}\leq 5$ (upper-right), $500<M_{\text{J}}\leq 800\GeV$, $6\leq
N_{\text{jet}}\leq 7$ (lower-left), and $500<M_{\text{J}}\leq 800\GeV$,
$N_{\text{jet}}\geq 8$ (lower-right). The expected signal distribution is also
shown for a gluino mass of 1600. The ratio of data to post-fit yields is shown
in the lower panel. The post-fit uncertainty is depicted as a hatched band.

Figure 8: Data and the background-only post-fit $N_{\PQb}$ distribution for
bins with large expected signal contribution: $800<M_{\text{J}}\leq 1000\GeV$,
$6\leq N_{\text{jet}}\leq 7$ (upper-left), $800<M_{\text{J}}\leq 1000\GeV$,
$N_{\text{jet}}\geq 8$ (upper-right), $M_{\text{J}}>1000\GeV$, $6\leq
N_{\text{jet}}\leq 7$ (lower-left), and $M_{\text{J}}>1000\GeV$,
$N_{\text{jet}}\geq 8$ (lower-right). The expected signal distribution is also
shown for a gluino mass of 1600. The ratio of data to post-fit yields is shown
in the lower panel. The post-fit uncertainty is depicted as a hatched band.

Post-fit yields for the background-only fit, observed data, and expected
yields for $m_{\PSg}=1600\GeV$ in each search bin.  $N_{\PQb}$ QCD
$\cPqt\cPaqt$ +jets Other All bkg. Data Expected $m_{\PSg}=1600\GeV$ $4\leq
N_{\text{jet}}\leq 5,~{}500<M_{\text{J}}\leq 800\GeV$ $1$ $148$ $340$ $196$
$91$ $775\pm 43\phantom{0}$ $777$ $0.50\pm 0.13$ $2$ $29$ $175$ $30$ $31$
$264\pm 17\phantom{0}$ $264$ $0.39\pm 0.11$ $3$ $4.3$ $24.8$ $2.5$ $4.4$
$36\pm 4\phantom{0}$ $34$ $0.18\pm 0.08$ ${\geq}4$ $0.0$ $2.2$ $0.3$ $0.2$
$2.7\pm 0.4$ $3$ $0.04\pm 0.04$ $4\leq N_{\text{jet}}\leq
5,~{}M_{\text{J}}>800\GeV$ $1$ $16.5$ $26.3$ $22.5$ $11.0$ $76\pm
6\phantom{0}$ $77$ $0.32\pm 0.11$ $2$ $1.1$ $10.6$ $3.4$ $3.8$ $19\pm
2\phantom{0}$ $18$ $0.40\pm 0.12$ $3$ $0.7$ $1.3$ $0.3$ $0.3$ $2.7\pm 0.5$ $3$
$0.13\pm 0.06$ ${\geq}4$ $0.00$ $0.09$ $0.03$ $0.01$ $0.13\pm 0.03$ $0$
$0.03\pm 0.03$ $6\leq N_{\text{jet}}\leq 7,~{}500<M_{\text{J}}\leq 800\GeV$
$1$ $197$ $620$ $169$ $120$ $1106\pm 48\phantom{0}\phantom{0}$ $1105$ $2.5\pm
0.3$ $2$ $49$ $440$ $36$ $66$ $591\pm 21\phantom{0}$ $588$ $3.1\pm 0.3$ $3$
$6.4$ $89.2$ $4.6$ $13.4$ $114\pm 8\phantom{0}\phantom{0}$ $112$ $1.4\pm 0.2$
${\geq}4$ $1.9$ $11.4$ $0.6$ $2.1$ $16\pm 2\phantom{0}$ $21$ $0.25\pm 0.09$
$N_{\text{jet}}\geq 8,~{}500<M_{\text{J}}\leq 800\GeV$ $1$ $130$ $574$ $53$
$68$ $825\pm 38\phantom{0}$ $821$ $3.5\pm 0.3$ $2$ $45$ $478$ $14$ $49$
$586\pm 20\phantom{0}$ $603$ $5.4\pm 0.4$ $3$ $6.3$ $138.1$ $2.5$ $16.7$
$164\pm 9\phantom{0}\phantom{0}$ $148$ $3.0\pm 0.3$ ${\geq}4$ $2.8$ $29.8$
$0.4$ $4.8$ $38\pm 4\phantom{0}$ $40$ $1.4\pm 0.2$ $6\leq N_{\text{jet}}\leq
7,~{}800<M_{\text{J}}\leq 1000\GeV$ $1$ $17.3$ $48.4$ $19.2$ $12.3$ $97\pm
8\phantom{0}$ $105$ $1.2\pm 0.2$ $2$ $6.6$ $30.1$ $4.3$ $7.3$ $48\pm
4\phantom{0}$ $37$ $2.0\pm 0.3$ $3$ $0.8$ $6.6$ $0.5$ $1.3$ $9.3\pm 1.0$ $12$
$1.0\pm 0.2$ ${\geq}4$ $0.0$ $0.9$ $0.1$ $0.2$ $1.1\pm 0.2$ $2$ $0.31\pm 0.09$
$N_{\text{jet}}\geq 8,~{}800<M_{\text{J}}\leq 1000\GeV$ $1$ $17.0$ $58.7$
$10.3$ $10.2$ $96\pm 8\phantom{0}$ $90$ $4.2\pm 0.4$ $2$ $5.8$ $47.5$ $2.5$
$6.8$ $63\pm 5\phantom{0}$ $65$ $5.3\pm 0.4$ $3$ $1.1$ $15.0$ $0.4$ $2.0$
$19\pm 2\phantom{0}$ $22$ $2.6\pm 0.3$ ${\geq}4$ $0.2$ $3.4$ $0.1$ $0.9$
$4.6\pm 0.6$ $5$ $1.3\pm 0.2$ $6\leq N_{\text{jet}}\leq
7,~{}M_{\text{J}}>1000\GeV$ $1$ $4.4$ $8.7$ $6.0$ $4.1$ $23\pm 2\phantom{0}$
$21$ $2.0\pm 0.3$ $2$ $0.7$ $5.0$ $1.4$ $1.6$ $8.8\pm 1.2$ $11$ $2.3\pm 0.3$
$3$ $0.1$ $1.2$ $0.2$ $0.5$ $1.9\pm 0.3$ $2$ $1.0\pm 0.2$ ${\geq}4$ $0.00$
$0.13$ $0.01$ $0.05$ $0.19\pm 0.04$ $0$ $0.23\pm 0.08$ $N_{\text{jet}}\geq
8,~{}M_{\text{J}}>1000\GeV$ $1$ $6.4$ $16.7$ $3.5$ $4.1$ $31\pm 3\phantom{0}$
$28$ $5.4\pm 0.4$ $2$ $1.6$ $13.1$ $1.1$ $2.1$ $18\pm 2\phantom{0}$ $21$
$8.2\pm 0.5$ $3$ $0.6$ $4.2$ $0.2$ $1.0$ $6.0\pm 0.8$ $5$ $5.7\pm 0.4$
${\geq}4$ $0.0$ $1.2$ $0.0$ $0.2$ $1.4\pm 0.3$ $2$ $3.2\pm 0.3$

A signal-plus-background fit is performed for gluino masses ranging from 1000
to 2000. For all masses, the post-fit $N_{\PQb}$ distribution describes the
data well, and the fit extracts at most a small and insignificant signal
contribution. For example, with a 1600gluino, the extracted signal yield
relative to the model prediction is $r=0.18^{+0.41}_{-0.18}$. The change of
nuisance parameters by the fit is small and consistent with those of the
background-only fit. Limits on the signal production cross section are
calculated at 95% confidence level (CL) using the asymptotic approximation of
the $\textrm{CL}_{\textrm{s}}$ criterion [75, 76, 77, 78] and shown in Fig. 9.
Comparing the observed limit to the gluino pair production cross section [59],
gluino masses below 1610are excluded in the benchmark $\PSg\to\cPqt\cPqb\cPqs$
model.

Figure 9: Cross section upper limits at 95% CL for a model of gluino pair
production with $\PSg\to\cPqt\cPqb\cPqs$ compared to the gluino pair
production cross section. The theoretical uncertainties in the cross section
are shown as a band around the red line [59]. The expected limits (dashed
line) and their ${\pm}1$ s.d. and ${\pm}2$ s.d. variations are shown as green
and yellow bands, respectively. The observed limit is shown by the solid line
with dots.

##  0.6 Summary

Results are presented from a search for new phenomena in events with a single
lepton, large jet and bottom quark jet multiplicities, and high sum of large-
radius jet masses, without a missing transverse momentum requirement. The
background is predicted using a simultaneous fit in bins of the number of
jets, number of -tagged jets, and the sum of masses of large radius jets,
using Monte Carlo simulated predictions with corrections measured in data
control samples for the normalizations of the dominant backgrounds and
nuisance parameters for theoretical and experimental uncertainties.
Statistical uncertainties dominate in the signal regions, while the most
important systematic uncertainties arise from the modeling of gluon splitting
and the quark tagging efficiency and mistag rate. The observed data are
consistent with the background-only hypothesis. An upper limit of
approximately 10 fb is determined for the gluino-gluino production cross
section using a benchmark $R$-parity violating supersymmetry model of gluino
pair production with a prompt three-body decay to $\cPqt\cPqb\cPqs$ quarks, as
predicted in minimal flavor violating models. For this model, gluinos are
observed (expected) to be excluded up to $1610\ (1640)\GeV$ at a 95%
confidence level, which improves upon previous searches at $\sqrt{s}=8\TeV$
[31, 32, 33] and is comparable to recent results at $13\TeV$ [34].

###### 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); OTKA and NIH (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, RFBR and RAEP
(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 Science and Industrial
Research, India; the HOMING PLUS program of the Foundation for Polish Science,
cofinanced from European Union, Regional Development Fund, the Mobility Plus
program of the Ministry of Science and Higher Education, the National Science
Center (Poland), contracts Harmonia 2014/14/M/ST2/00428, Opus
2014/13/B/ST2/02543, 2014/15/B/ST2/03998, and 2015/19/B/ST2/02861, Sonata-bis
2012/07/E/ST2/01406; the National Priorities Research Program by Qatar
National Research Fund; the Programa Clarín-COFUND 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).

## References

  * [1] R. Barbieri and G. F. Giudice, “Upper bounds on supersymmetric particle masses”,  Nucl. Phys. B 306 (1988) 63, 10.1016/0550-3213(88)90171-X.
  * [2] G. Bertone, D. Hooper, and J. Silk, “Particle dark matter: evidence, candidates and constraints”,  Phys. Rept. 405 (2005) 279, 10.1016/j.physrep.2004.08.031, arXiv:hep-ph/0404175.
  * [3] E. Witten, “Dynamical breaking of supersymmetry”,  Nucl. Phys. B 188 (1981) 513, 10.1016/0550-3213(81)90006-7.
  * [4] S. Dimopoulos and H. Georgi, “Softly broken supersymmetry and SU(5)”,  Nucl. Phys. B 193 (1981) 150, 10.1016/0550-3213(81)90522-8.
  * [5] P. Ramond, “Dual theory for free fermions”,  Phys. Rev. D 3 (1971) 2415, 10.1103/PhysRevD.3.2415.
  * [6] Y. A. Gol’fand and E. P. Likhtman, “Extension of the algebra of Poincaré group generators and violation of P invariance”, JETP Lett. 13 (1971) 323\.
  * [7] A. Neveu and J. H. Schwarz, “Factorizable dual model of pions”,  Nucl. Phys. B 31 (1971) 86, 10.1016/0550-3213(71)90448-2.
  * [8] D. V. Volkov and V. P. Akulov, “Possible universal neutrino interaction”, JETP Lett. 16 (1972) 438\.
  * [9] J. Wess and B. Zumino, “A lagrangian model invariant under supergauge transformations”,  Phys. Lett. B 49 (1974) 52, 10.1016/0370-2693(74)90578-4.
  * [10] J. Wess and B. Zumino, “Supergauge transformations in four dimensions”,  Nucl. Phys. B 70 (1974) 39, 10.1016/0550-3213(74)90355-1.
  * [11] P. Fayet, “Supergauge invariant extension of the Higgs mechanism and a model for the electron and its neutrino”,  Nucl. Phys. B 90 (1975) 104, 10.1016/0550-3213(75)90636-7.
  * [12] H. P. Nilles, “Supersymmetry, supergravity and particle physics”,  Phys. Rep. 110 (1984) 1, 10.1016/0370-1573(84)90008-5.
  * [13] S. Dimopoulos and G. F. Giudice, “Naturalness constraints in supersymmetric theories with nonuniversal soft terms”,  Phys. Lett. B 357 (1995) 573, 10.1016/0370-2693(95)00961-J, arXiv:hep-ph/9507282.
  * [14] R. Barbieri and D. Pappadopulo, “S-particles at their naturalness limits”,  JHEP 10 (2009) 061, 10.1088/1126-6708/2009/10/061, arXiv:0906.4546.
  * [15] M. Papucci, J. T. Ruderman, and A. Weiler, “Natural SUSY Endures”,  JHEP 09 (2012) 035, 10.1007/JHEP09(2012)035, arXiv:1110.6926.
  * [16] J. L. Feng, “Naturalness and the status of supersymmetry”,  Ann. Rev. Nucl. Part. Sci. 63 (2013) 351, 10.1146/annurev-nucl-102010-130447, arXiv:1302.6587.
  * [17] G. R. Farrar and P. Fayet, “Phenomenology of the production, decay, and detection of new hadronic states associated with supersymmetry”,  Phys. Lett. B 76 (1978) 575, 10.1016/0370-2693(78)90858-4.
  * [18] ATLAS Collaboration, “Search for top-squark pair production in final states with one lepton, jets, and missing transverse momentum using 36 fb-1 of $\sqrt{s}=13$ TeV pp collision data with the ATLAS detector”, (2017). arXiv:1711.11520. Submitted to JHEP.
  * [19] CMS Collaboration, “Search for top squark pair production in pp collisions at $\sqrt{s}=13\TeV$ using single lepton events”,  JHEP 10 (2017) 013, 10.1007/JHEP10(2017)019, arXiv:1706.04402.
  * [20] ATLAS Collaboration, “Search for a scalar partner of the top quark in the jets plus missing transverse momentum final state at $\sqrt{s}$=13 TeV with the ATLAS detector”,  JHEP 12 (2017) 085, 10.1007/JHEP12(2017)085, arXiv:1709.04183.
  * [21] ATLAS Collaboration, “Search for squarks and gluinos in events with an isolated lepton, jets, and missing transverse momentum at $\sqrt{s}=13$ TeV with the ATLAS detector”,  Phys. Rev. D 96 (2017), no. 11, 112010, 10.1103/PhysRevD.96.112010, arXiv:1708.08232.
  * [22] ATLAS Collaboration, “Search for squarks and gluinos in final states with jets and missing transverse momentum using 36 fb-1 of $\sqrt{s}=13$ TeV pp collision data with the ATLAS detector”,  Phys. Rev. D 97 (2018), no. 11, 112001, 10.1103/PhysRevD.97.112001, arXiv:1712.02332.
  * [23] ATLAS Collaboration, “Search for new phenomena with large jet multiplicities and missing transverse momentum using large-radius jets and flavour-tagging at ATLAS in 13 TeV $pp$ collisions”,  JHEP 12 (2017) 034, 10.1007/JHEP12(2017)034, arXiv:1708.02794.
  * [24] CMS Collaboration, “Search for new phenomena with the $M_{\mathrm{T2}}$ variable in the all-hadronic final state produced in proton-proton collisions at $\sqrt{s}=13$”,  Eur. Phys. J. C 77 (2017) 710, 10.1140/epjc/s10052-017-5267-x, arXiv:1705.04650.
  * [25] CMS Collaboration, “Search for supersymmetry in multijet events with missing transverse momentum in proton-proton collisions at 13”,  Phys. Rev. D 96 (2017) 032003, 10.1103/PhysRevD.96.032003, arXiv:1704.07781.
  * [26] CMS Collaboration, “Search for supersymmetry in pp collisions at $\sqrt{s}=13\TeV$ in the single-lepton final state using the sum of masses of large-radius jets”,  Phys. Rev. Lett. 119 (2017) 151802, 10.1103/PhysRevLett.119.151802, arXiv:1705.04673.
  * [27] R. Barbier et al., “$R$-parity violating supersymmetry”,  Phys. Rept. 420 (2005) 1, 10.1016/j.physrep.2005.08.006, arXiv:hep-ph/0406039.
  * [28] C. Csáki, Y. Grossman, and B. Heidenreich, “MFV SUSY: a natural theory for $R$-parity violation”,  Phys. Rev. D 85 (2012) 095009, 10.1103/PhysRevD.85.095009, arXiv:1111.1239.
  * [29] J. Alwall, P. Schuster, and N. Toro, “Simplified models for a first characterization of new physics at the LHC”,  Phys. Rev. D 79 (2009) 075020, 10.1103/PhysRevD.79.075020, arXiv:0810.3921.
  * [30] LHC New Physics Working Group, D. Alves et al., “Simplified models for LHC new physics searches”,  J. Phys. G 39 (2012) 105005, 10.1088/0954-3899/39/10/105005, arXiv:1105.2838.
  * [31] ATLAS Collaboration, “A search for top squarks with $R$-parity-violating decays to all-hadronic final states with the ATLAS detector in $\sqrt{s}=8\TeV$ proton-proton collisions”,  JHEP 06 (2016) 067, 10.1007/JHEP06(2016)067, arXiv:1601.07453.
  * [32] CMS Collaboration, “Searches for $R$-parity-violating supersymmetry in pp collisions at $\sqrt{s}=8\TeV$ in final states with 0-4 leptons”,  Phys. Rev. D 94 (2016) 112009, 10.1103/PhysRevD.94.112009, arXiv:1606.08076.
  * [33] CMS Collaboration, “Search for $R$-parity violating supersymmetry with displaced vertices in proton-proton collisions at $\sqrt{s}=8\TeV$”,  Phys. Rev. D 95 (2017) 012009, 10.1103/PhysRevD.95.012009, arXiv:1610.05133.
  * [34] ATLAS Collaboration, “Search for new phenomena in a lepton plus high jet multiplicity final state with the ATLAS experiment using $\sqrt{s}=13\TeV$ proton-proton collision data”,  JHEP 09 (2017) 088, 10.1007/JHEP09(2017)088, arXiv:1704.08493.
  * [35] A. Hook, E. Izaguirre, M. Lisanti, and J. G. Wacker, “High multiplicity searches at the LHC using jet masses”,  Phys. Rev. D 85 (2012) 055029, 10.1103/PhysRevD.85.055029, arXiv:1202.0558.
  * [36] T. Cohen, E. Izaguirre, M. Lisanti, and H. K. Lou, “Jet substructure by accident”,  JHEP 03 (2013) 161, 10.1007/JHEP03(2013)161, arXiv:1212.1456.
  * [37] S. El Hedri, A. Hook, M. Jankowiak, and J. G. Wacker, “Learning how to count: a high multiplicity search for the LHC”,  JHEP 08 (2013) 136, 10.1007/JHEP08(2013)136, arXiv:1302.1870.
  * [38] ATLAS Collaboration, “Search for massive supersymmetric particles decaying to many jets using the ATLAS detector in pp collisions at $\sqrt{s}=8\TeV$”,  Phys. Rev. D 91 (2015) 112016, 10.1103/PhysRevD.91.112016, arXiv:1502.05686.
  * [39] ATLAS Collaboration, “Search for new phenomena in final states with large jet multiplicities and missing transverse momentum at $\sqrt{s}=8\TeV$ proton-proton collisions using the ATLAS experiment”,  JHEP 10 (2013) 130, 10.1007/JHEP10(2013)130, arXiv:1308.1841. [Erratum: 10.1007/JHEP01(2014)109].
  * [40] CMS Collaboration, “Search for supersymmetry in pp collisions at $\sqrt{s}=13\TeV$ in the single-lepton final state using the sum of masses of large-radius jets”,  JHEP 08 (2016) 122, 10.1007/JHEP08(2016)122, arXiv:1605.04608.
  * [41] CMS Collaboration, “The CMS experiment at the CERN LHC”,  JINST 3 (2008) S08004, 10.1088/1748-0221/3/08/S08004.
  * [42] J. Alwall et al., “The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations”,  JHEP 07 (2014) 079, 10.1007/JHEP07(2014)079, arXiv:1405.0301.
  * [43] J. Alwall et al., “Comparative study of various algorithms for the merging of parton showers and matrix elements in hadronic collisions”,  Eur. Phys. J. C 53 (2008) 473, 10.1140/epjc/s10052-007-0490-5, arXiv:0706.2569.
  * [44] P. Nason, “A new method for combining NLO QCD with shower Monte Carlo algorithms”,  JHEP 11 (2004) 040, 10.1088/1126-6708/2004/11/040, arXiv:hep-ph/0409146.
  * [45] S. Frixione, P. Nason, and C. Oleari, “Matching NLO QCD computations with parton shower simulations: the POWHEG method”,  JHEP 11 (2007) 070, 10.1088/1126-6708/2007/11/070, arXiv:0709.2092.
  * [46] S. Alioli, P. Nason, C. Oleari, and E. Re, “A general framework for implementing NLO calculations in shower Monte Carlo programs: the POWHEG BOX”,  JHEP 06 (2010) 043, 10.1007/JHEP06(2010)043, arXiv:1002.2581.
  * [47] R. Frederix and S. Frixione, “Merging meets matching in MC@NLO”,  JHEP 12 (2012) 061, 10.1007/JHEP12(2012)061, arXiv:1209.6215.
  * [48] NNPDF Collaboration, “Parton distributions for the LHC Run II”,  JHEP 04 (2015) 040, 10.1007/JHEP04(2015)040, arXiv:1410.8849.
  * [49] T. Sjöstrand et al., “An introduction to PYTHIA 8.2”,  Comput. Phys. Commun. 191 (2015) 159, 10.1016/j.cpc.2015.01.024.
  * [50] P. Skands, S. Carrazza, and J. Rojo, “Tuning PYTHIA 8.1: the Monash 2013 tune”,  Eur. Phys. J. C 74 (2014) 3024, 10.1140/epjc/s10052-014-3024-y.
  * [51] M. Czakon, P. Fiedler, and A. Mitov, “Total top-quark pair-production cross section at hadron colliders through $\mathcal{O}({\alpha}_{S}^{4})$”,  Phys. Rev. Lett. 110 (Jun, 2013) 252004, 10.1103/PhysRevLett.110.252004.
  * [52] R. Gavin, Y. Li, F. Petriello, and S. Quackenbush, “W Physics at the LHC with FEWZ 2.1”,  Comput. Phys. Commun. 184 (2013) 208, 10.1016/j.cpc.2012.09.005, arXiv:1201.5896.
  * [53] S. Alioli, P. Nason, C. Oleari, and E. Re, “NLO single-top production matched with shower in POWHEG: $s$\- and $t$-channel contributions”,  JHEP 09 (2009) 111, 10.1088/1126-6708/2009/09/111, arXiv:0907.4076. [Erratum: 10.1007/JHEP02(2010)011].
  * [54] E. Re, “Single-top Wt-channel production matched with parton showers using the POWHEG method”,  Eur. Phys. J. C 71 (2011) 1547, 10.1140/epjc/s10052-011-1547-z, arXiv:1009.2450.
  * [55] S. Frixione et al., “Electroweak and QCD corrections to top-pair hadroproduction in association with heavy bosons”,  JHEP 06 (2015) 184, 10.1007/JHEP06(2015)184, arXiv:1504.03446.
  * [56] G. Bevilacqua and M. Worek, “Constraining BSM Physics at the LHC: Four top final states with NLO accuracy in perturbative QCD”,  JHEP 07 (2012) 111, 10.1007/JHEP07(2012)111, arXiv:1206.3064.
  * [57] Z. Nagy, “Three jet cross-sections in hadron hadron collisions at next-to-leading order”,  Phys. Rev. Lett. 88 (2002) 122003, 10.1103/PhysRevLett.88.122003, arXiv:hep-ph/0110315.
  * [58] GEANT4 Collaboration, “GEANT4—a simulation toolkit”,  Nucl. Instrum. Meth. A 506 (2003) 250, 10.1016/S0168-9002(03)01368-8.
  * [59] C. Borschensky et al., “Squark and gluino production cross sections in pp collisions at $\sqrt{s}=$ 13, 14, 33, and 100”,  Eur. Phys. J. C 74 (2014) 3174, 10.1140/epjc/s10052-014-3174-y, arXiv:1407.5066.
  * [60] CMS Collaboration, “Particle-flow reconstruction and global event description with the CMS detector”,  JINST 12 (2017) P10003, 10.1088/1748-0221/12/10/P10003, arXiv:1706.04965.
  * [61] M. Cacciari, G. P. Salam, and G. Soyez, “The anti-$k_{t}$ jet clustering algorithm”,  JHEP 04 (2008) 063, 10.1088/1126-6708/2008/04/063, arXiv:0802.1189.
  * [62] M. Cacciari, G. P. Salam, and G. Soyez, “FastJet user manual”,  Eur. Phys. J. C 72 (2012) 1896, 10.1140/epjc/s10052-012-1896-2, arXiv:1111.6097.
  * [63] CMS Collaboration, “Performance of electron reconstruction and selection with the CMS detector in proton-proton collisions at $\sqrt{s}=8\TeV$”,  JINST 10 (2015) P06005, 10.1088/1748-0221/10/06/P06005, arXiv:1502.02701.
  * [64] CMS Collaboration, “Performance of CMS muon reconstruction in pp collision events at $\sqrt{s}=7\TeV$”,  JINST 7 (2012) P10002, 10.1088/1748-0221/7/10/P10002, arXiv:1206.4071.
  * [65] K. Rehermann and B. Tweedie, “Efficient identification of boosted semileptonic top quarks at the LHC”,  JHEP 03 (2011) 059, 10.1007/JHEP03(2011)059, arXiv:1007.2221.
  * [66] M. Cacciari and G. P. Salam, “Pileup subtraction using jet areas”,  Phys. Lett. B 659 (2008) 119, 10.1016/j.physletb.2007.09.077, arXiv:0707.1378.
  * [67] CMS Collaboration, “Determination of jet energy calibration and transverse momentum resolution in CMS”,  JINST 6 (2011) P11002, 10.1088/1748-0221/6/11/P11002, arXiv:1107.4277.
  * [68] CMS Collaboration, “Identification of b-quark jets with the CMS experiment”,  JINST 8 (2013) P04013, 10.1088/1748-0221/8/04/P04013, arXiv:1211.4462.
  * [69] CMS Collaboration, “Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV”,  JINST 13 (2018), no. 05, P05011, 10.1088/1748-0221/13/05/P05011, arXiv:1712.07158.
  * [70] ATLAS Collaboration, “Search for direct pair production of the top squark in all-hadronic final states in proton-proton collisions at $\sqrt{s}=8\TeV$ with the ATLAS detector”,  JHEP 09 (2014) 015, 10.1007/JHEP09(2014)015, arXiv:1406.1122.
  * [71] CMS Collaboration, “The CMS trigger system”,  JINST 12 (2017) P01020, 10.1088/1748-0221/12/01/P01020, arXiv:1609.02366.
  * [72] CMS Collaboration, “Performance of heavy flavour identification algorithms in proton-proton collisions at 13at the CMS experiment”, CMS Detector Performance Summary CMS-DP-2017-012, 2017.
  * [73] CMS Collaboration, “Jet energy scale and resolution in the CMS experiment in pp collisions at 8”,  JINST 12 (2017), no. 02, P02014, 10.1088/1748-0221/12/02/P02014, arXiv:1607.03663.
  * [74] CMS Collaboration, “CMS luminosity measurements for the 2016 data taking period”, CMS Physics Analysis Summary CMS-PAS-LUM-17-001, 2017.
  * [75] A. L. Read, “Presentation of search results: the $\rm CL_{s}$ technique”,  J. Phys. G 28 (2002) 2693, 10.1088/0954-3899/28/10/313.
  * [76] ATLAS and CMS Collaborations, LHC Higgs Combination Group, “Procedure for the LHC Higgs boson search combination in Summer 2011”, Technical Report CMS-NOTE-2011-005, ATL-PHYS-PUB-2011-11, 2011.
  * [77] G. Cowan, K. Cranmer, E. Gross, and O. Vitells, “Asymptotic formulae for likelihood-based tests of new physics”,  Eur. Phys. J. C 71 (2011) 1554, 10.1140/epjc/s10052-011-1554-0. [Erratum: 10.1140/epjc/s10052-013-2501-z].
  * [78] T. Junk, “Confidence level computation for combining searches with small statistics”,  Nucl. Instrum. Meth. A 434 (1999) 435, 10.1016/S0168-9002(99)00498-2, arXiv:hep-ex/9902006.

##  .7 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan, A. Tumasyan Institut für Hochenergiephysik, Wien, Austria
W. Adam, F. Ambrogi, E. Asilar, T. Bergauer, J. Brandstetter, E. Brondolin, M.
Dragicevic, J. Erö, A. Escalante Del Valle, M. Flechl, M. Friedl, R.
Frühwirth1, V.M. Ghete, J. Grossmann, J. Hrubec, M. Jeitler1, A. König, N.
Krammer, I. Krätschmer, D. Liko, T. Madlener, I. Mikulec, E. Pree, 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. 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, T. Maerschalk, A. Marinov, T. Seva, E. Starling, C. Vander
Velde, P. Vanlaer, D. Vannerom, R. Yonamine, F. Zenoni Ghent University,
Ghent, Belgium
T. Cornelis, D. Dobur, A. Fagot, M. Gul, I. Khvastunov2, D. Poyraz, C. Roskas,
S. Salva, D. Trocino, M. Tytgat, W. Verbeke, M. Vit, N. Zaganidis Université
Catholique de Louvain, Louvain-la-Neuve, Belgium
H. Bakhshiansohi, O. Bondu, S. Brochet, G. Bruno, C. Caputo, A. Caudron, P.
David, S. De Visscher, C. Delaere, M. Delcourt, B. Francois, A. Giammanco, M.
Komm, G. Krintiras, V. Lemaitre, A. Magitteri, A. Mertens, M. Musich, K.
Piotrzkowski, L. Quertenmont, A. Saggio, M. Vidal Marono, S. Wertz, J. Zobec
Centro Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil
W.L. Aldá Júnior, F.L. Alves, G.A. Alves, L. Brito, G. Correia Silva, C.
Hensel, A. Moraes, M.E. Pol, P. Rebello Teles Universidade do Estado do Rio de
Janeiro, Rio de Janeiro, Brazil
E. Belchior Batista Das Chagas, W. Carvalho, J. Chinellato3, E. Coelho, E.M.
Da Costa, G.G. Da Silveira4, D. De Jesus Damiao, S. Fonseca De Souza, L.M.
Huertas Guativa, H. Malbouisson, M. Melo De Almeida, C. Mora Herrera, L.
Mundim, H. Nogima, 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, T.R. Fernandez Perez Tomeia, E.M. Gregoresb, P.G.
Mercadanteb, S.F. Novaesa, Sandra S. Padulaa, D. Romero Abadb, J.C. Ruiz
Vargasa Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy
of Sciences, Sofia, Bulgaria
A. Aleksandrov, R. Hadjiiska, P. Iaydjiev, M. Misheva, M. Rodozov, M. Shopova,
G. Sultanov University of Sofia, Sofia, Bulgaria
A. Dimitrov, L. Litov, B. Pavlov, P. Petkov Beihang University, Beijing, China
W. Fang5, X. Gao5, L. Yuan Institute of High Energy Physics, Beijing, China
M. Ahmad, J.G. Bian, G.M. Chen, H.S. Chen, M. Chen, Y. Chen, C.H. Jiang, D.
Leggat, H. Liao, Z. Liu, F. Romeo, S.M. Shaheen, A. Spiezia, J. Tao, C. Wang,
Z. Wang, E. Yazgan, H. Zhang, J. Zhao State Key Laboratory of Nuclear Physics
and Technology, Peking University, Beijing, China
Y. Ban, G. Chen, J. Li, Q. Li, S. Liu, Y. Mao, S.J. Qian, D. Wang, Z. Xu, F.
Zhang5 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, J.D. Ruiz Alvarez, 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, P.M. Ribeiro Cipriano, T.
Sculac University of Split, Faculty of Science, Split, Croatia
Z. Antunovic, M. Kovac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, D. Ferencek, K. Kadija, B. Mesic, A. Starodumov6, T. Susa
University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos,
P.A. Razis, H. Rykaczewski Charles University, Prague, Czech Republic
M. Finger7, M. Finger Jr.7 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
A.A. Abdelalim8,9, S. Elgammal10, S. Khalil9 National Institute of Chemical
Physics and Biophysics, Tallinn, Estonia
S. Bhowmik, R.K. Dewanjee, M. Kadastik, L. Perrini, 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, S. Ghosh, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry,
C. Leloup, E. Locci, M. Machet, J. Malcles, G. Negro, J. Rander, A. Rosowsky,
M.Ö. Sahin, M. Titov Laboratoire Leprince-Ringuet, Ecole polytechnique,
CNRS/IN2P3, Université Paris-Saclay, Palaiseau, France
A. Abdulsalam11, C. Amendola, I. Antropov, S. Baffioni, F. Beaudette, P.
Busson, L. Cadamuro, C. Charlot, R. Granier de Cassagnac, M. Jo, I. Kucher, S.
Lisniak, 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, T.
Strebler, Y. Yilmaz, A. Zabi, A. Zghiche Université de Strasbourg, CNRS, IPHC
UMR 7178, F-67000 Strasbourg, France
J.-L. Agram12, J. Andrea, D. Bloch, J.-M. Brom, M. Buttignol, E.C. Chabert, N.
Chanon, C. Collard, E. Conte12, X. Coubez, F. Drouhin12, J.-C. Fontaine12, D.
Gelé, U. Goerlach, M. Jansová, P. Juillot, 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, 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, M. Lethuillier, L. Mirabito, A.L. Pequegnot, S.
Perries, A. Popov13, V. Sordini, M. Vander Donckt, S. Viret, S. Zhang Georgian
Technical University, Tbilisi, Georgia
A. Khvedelidze7 Tbilisi State University, Tbilisi, Georgia
L. Rurua RWTH Aachen University, I. Physikalisches Institut, Aachen, Germany
C. Autermann, L. Feld, M.K. Kiesel, K. Klein, M. Lipinski, M. Preuten, C.
Schomakers, J. Schulz, M. Teroerde, B. Wittmer, V. Zhukov13 RWTH Aachen
University, III. Physikalisches Institut A, Aachen, Germany
A. Albert, D. Duchardt, M. Endres, M. Erdmann, S. Erdweg, T. Esch, R. Fischer,
A. Güth, T. Hebbeker, C. Heidemann, K. Hoepfner, S. Knutzen, M. Merschmeyer,
A. Meyer, P. Millet, S. Mukherjee, T. Pook, M. Radziej, H. Reithler, M.
Rieger, F. Scheuch, D. Teyssier, S. Thüer RWTH Aachen University, III.
Physikalisches Institut B, Aachen, Germany
G. Flügge, B. Kargoll, T. Kress, A. Künsken, T. Müller, A. Nehrkorn, A.
Nowack, C. Pistone, O. Pooth, A. Stahl14 Deutsches Elektronen-Synchrotron,
Hamburg, Germany
M. Aldaya Martin, T. Arndt, C. Asawatangtrakuldee, K. Beernaert, O. Behnke, U.
Behrens, A. Bermúdez Martínez, A.A. Bin Anuar, K. Borras15, V. Botta, A.
Campbell, P. Connor, C. Contreras-Campana, F. Costanza, C. Diez Pardos, G.
Eckerlin, D. Eckstein, T. Eichhorn, E. Eren, E. Gallo16, J. Garay Garcia, A.
Geiser, J.M. Grados Luyando, A. Grohsjean, P. Gunnellini, M. Guthoff, A. Harb,
J. Hauk, M. Hempel17, H. Jung, M. Kasemann, J. Keaveney, C. Kleinwort, I.
Korol, D. Krücker, W. Lange, A. Lelek, T. Lenz, K. Lipka, W. Lohmann17, R.
Mankel, I.-A. Melzer-Pellmann, A.B. Meyer, M. Missiroli, G. Mittag, J. Mnich,
A. Mussgiller, E. Ntomari, D. Pitzl, A. Raspereza, M. Savitskyi, P. Saxena, R.
Shevchenko, N. Stefaniuk, G.P. Van Onsem, R. Walsh, Y. Wen, K. Wichmann, C.
Wissing, O. Zenaiev University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, V. Blobel, M. Centis Vignali, T. Dreyer, E. Garutti, D.
Gonzalez, J. Haller, A. Hinzmann, M. Hoffmann, A. Karavdina, R. Klanner, R.
Kogler, N. Kovalchuk, S. Kurz, D. Marconi, M. Meyer, M. Niedziela, D.
Nowatschin, F. Pantaleo14, T. Peiffer, A. Perieanu, C. Scharf, P. Schleper, A.
Schmidt, S. Schumann, J. Schwandt, J. Sonneveld, H. Stadie, G. Steinbrück,
F.M. Stober, M. Stöver, H. Tholen, D. Troendle, E. Usai, A. Vanhoefer, B.
Vormwald Institut für Experimentelle Kernphysik, Karlsruhe, Germany
M. Akbiyik, C. Barth, M. Baselga, S. Baur, E. Butz, R. Caspart, T. Chwalek, F.
Colombo, W. De Boer, A. Dierlamm, N. Faltermann, B. Freund, R. Friese, M.
Giffels, M.A. Harrendorf, F. Hartmann14, S.M. Heindl, U. Husemann, F.
Kassel14, S. Kudella, H. Mildner, 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, I. Topsis-
Giotis National and Kapodistrian University of Athens, Athens, Greece
G. Karathanasis, S. Kesisoglou, A. Panagiotou, N. Saoulidou, E. Tziaferi
National Technical University of Athens, Athens, Greece
K. Kousouris University of Ioánnina, Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, S. Mallios, N.
Manthos, I. Papadopoulos, E. Paradas, J. Strologas, F.A. Triantis, D.
Tsitsonis MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
M. Csanad, N. Filipovic, G. Pasztor, O. Surányi, G.I. Veres18 Wigner Research
Centre for Physics, Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath19, Á. Hunyadi, F. Sikler, V. Veszpremi, G.
Vesztergombi18 Institute of Nuclear Research ATOMKI, Debrecen, Hungary
N. Beni, S. Czellar, J. Karancsi20, A. Makovec, J. Molnar, Z. Szillasi
Institute of Physics, University of Debrecen, Debrecen, Hungary
M. Bartók18, P. Raics, Z.L. Trocsanyi, B. Ujvari Indian Institute of Science
(IISc), Bangalore, India
S. Choudhury, J.R. Komaragiri National Institute of Science Education and
Research, Bhubaneswar, India
S. Bahinipati21, P. Mal, K. Mandal, A. Nayak22, D.K. Sahoo21, N. Sahoo, S.K.
Swain Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, R. Chawla, N. Dhingra, A. Kaur, M. Kaur,
S. Kaur, R. Kumar, P. Kumari, A. Mehta, J.B. Singh, G. Walia University of
Delhi, Delhi, India
A. Bhardwaj, S. Chauhan, B.C. Choudhary, R.B. Garg, S. Keshri, A. Kumar, Ashok
Kumar, S. Malhotra, M. Naimuddin, K. Ranjan, Aashaq Shah, R. Sharma Saha
Institute of Nuclear Physics, HBNI, Kolkata, India
R. Bhardwaj23, R. Bhattacharya, S. Bhattacharya, U. Bhawandeep23, D. Bhowmik,
S. Dey, S. Dutt23, S. Dutta, S. Ghosh, N. Majumdar, A. Modak, K. Mondal, S.
Mukhopadhyay, S. Nandan, A. Purohit, P.K. Rout, A. Roy, S. Roy Chowdhury, S.
Sarkar, M. Sharan, B. Singh, S. Thakur23 Indian Institute of Technology
Madras, Madras, India
P.K. Behera Bhabha Atomic Research Centre, Mumbai, India
R. Chudasama, D. Dutta, V. Jha, V. Kumar, A.K. Mohanty14, P.K. Netrakanti,
L.M. Pant, P. Shukla, A. Topkar Tata Institute of Fundamental Research-A,
Mumbai, India
T. Aziz, S. Dugad, B. Mahakud, S. Mitra, G.B. Mohanty, N. Sur, B. Sutar Tata
Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, Sa. Jain, S.
Kumar, M. Maity24, G. Majumder, K. Mazumdar, T. Sarkar24, N. Wickramage25
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. Chenarani26, E. Eskandari Tadavani, S.M. Etesami26, M. Khakzad, M.
Mohammadi Najafabadi, M. Naseri, S. Paktinat Mehdiabadi27, 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, F. Erricoa,b, L. Fiorea, G. Iasellia,c, S.
Lezkia,b, G. Maggia,c, M. Maggia, G. Minielloa,b, S. Mya,b, S. Nuzzoa,b, A.
Pompilia,b, G. Pugliesea,c, R. Radognaa, A. Ranieria, G. Selvaggia,b, A.
Sharmaa, L. Silvestrisa,14, R. Vendittia, P. Verwilligena 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, G. Codispotia,b, M. Cuffiania,b, G.M. Dallavallea, F.
Fabbria, A. Fanfania,b, D. Fasanellaa,b, P. Giacomellia, C. Grandia, L.
Guiduccia,b, F. Iemmi, S. Marcellinia, G. Masettia, A. Montanaria, F.L.
Navarriaa,b, A. Perrottaa, A.M. Rossia,b, T. Rovellia,b, G.P. Sirolia,b, N.
Tosia INFN Sezione di Catania a, Università di Catania b, Catania, Italy
S. Albergoa,b, S. Costaa,b, A. Di Mattiaa, F. Giordanoa,b, 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, 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, F. Primavera14 INFN Sezione di
Genova a, Università di Genova b, Genova, Italy
V. Calvellia,b, F. Ferroa, F. Raveraa,b, E. Robuttia, S. Tosia,b INFN Sezione
di Milano-Bicocca a, Università di Milano-Bicocca b, Milano, Italy
A. Benagliaa, A. Beschib, L. Brianzaa,b, F. Brivioa,b, V. Cirioloa,b,14, M.E.
Dinardoa,b, S. Fiorendia,b, S. Gennaia, A. Ghezzia,b, P. Govonia,b, M.
Malbertia,b, S. Malvezzia, R.A. Manzonia,b, D. Menascea, L. Moronia, M.
Paganonia,b, K. Pauwelsa,b, D. Pedrinia, S. Pigazzinia,b,30, S. Ragazzia,b, T.
Tabarelli de Fatisa,b INFN Sezione di Napoli a, Università di Napoli ’Federico
II’ b, Napoli, Italy, Università della Basilicata c, Potenza, Italy,
Università G. Marconi d, Roma, Italy
S. Buontempoa, N. Cavalloa,c, S. Di Guidaa,d,14, F. Fabozzia,c, F. Fiengaa,b,
A.O.M. Iorioa,b, W.A. Khana, L. Listaa, S. Meolaa,d,14, P. Paoluccia,14, C.
Sciaccaa,b, F. Thyssena INFN Sezione di Padova a, Università di Padova b,
Padova, Italy, Università di Trento c, Trento, Italy
P. Azzia, N. Bacchettaa, S. Badoera, L. Benatoa,b, D. Biselloa,b, A.
Bolettia,b, R. Carlina,b, A. Carvalho Antunes De Oliveiraa,b, P. Checchiaa, M.
Dall’Ossoa,b, P. De Castro Manzanoa, T. Dorigoa, U. Dossellia, U.
Gasparinia,b, S. Lacapraraa, P. Lujan, M. Margonia,b, A.T. Meneguzzoa,b, N.
Pozzobona,b, P. Ronchesea,b, R. Rossina,b, F. Simonettoa,b, A. Tiko, E.
Torassaa, M. Zanettia,b, P. Zottoa,b, G. Zumerlea,b INFN Sezione di Pavia a,
Università di Pavia b, Pavia, Italy
A. Braghieria, A. Magnania, P. Montagnaa,b, S.P. Rattia,b, V. Rea, M.
Ressegottia,b, C. Riccardia,b, P. Salvinia, I. Vaia,b, P. Vituloa,b INFN
Sezione di Perugia a, Università di Perugia b, Perugia, Italy
L. Alunni Solestizia,b, M. Biasinia,b, G.M. Bileia, C. Cecchia,b, D.
Ciangottinia,b, L. Fanòa,b, P. Laricciaa,b, 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,14, G. Bagliesia, L. Bianchinia, T. Boccalia, L.
Borrello, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, G. Fedia, L.
Gianninia,c, A. Giassia, M.T. Grippoa,29, F. Ligabuea,c, T. Lomtadzea, E.
Mancaa,c, G. Mandorlia,c, A. Messineoa,b, F. Pallaa, A. Rizzia,b, A. Savoy-
Navarroa,31, 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, F. Margarolia,b, B.
Marzocchia,b, P. Meridiania, G. Organtinia,b, R. Paramattia,b, F. Preiatoa,b,
S. Rahatloua,b, C. Rovellia, F. Santanastasioa,b INFN Sezione di Torino a,
Università di Torino b, Torino, Italy, Università del Piemonte Orientale c,
Novara, Italy
N. Amapanea,b, R. Arcidiaconoa,c, S. Argiroa,b, M. Arneodoa,c, N. Bartosika,
R. Bellana,b, C. Biinoa, N. Cartigliaa, F. Cennaa,b, M. Costaa,b, R.
Covarellia,b, A. Deganoa,b, N. Demariaa, B. Kiania,b, C. Mariottia, S.
Masellia, E. Migliorea,b, V. Monacoa,b, E. Monteila,b, M. Montenoa, M.M.
Obertinoa,b, L. Pachera,b, N. Pastronea, M. Pelliccionia, G.L. Pinna
Angionia,b, A. Romeroa,b, M. Ruspaa,c, R. Sacchia,b, K. Shchelinaa,b, V.
Solaa, A. Solanoa,b, A. Staianoa, P. Traczyka,b INFN Sezione di Trieste a,
Università di Trieste b, Trieste, Italy
S. Belfortea, M. Casarsaa, F. Cossuttia, G. Della Riccaa,b, A. Zanettia
Kyungpook National University, Daegu, Korea
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.A. Brochero Cifuentes, J. Goh, T.J. Kim Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, D. Gyun, S. Ha, B. Hong, Y. Jo, Y. Kim, K. Lee, K.S.
Lee, S. Lee, J. Lim, S.K. Park, Y. Roh Seoul National University, Seoul, Korea
J. Almond, J. Kim, J.S. Kim, H. Lee, K. Lee, K. Nam, S.B. Oh, B.C. Radburn-
Smith, S.h. Seo, U.K. Yang, H.D. Yoo, G.B. Yu University of Seoul, Seoul,
Korea
H. Kim, J.H. Kim, J.S.H. Lee, I.C. Park Sungkyunkwan University, Suwon, Korea
Y. Choi, C. Hwang, J. Lee, I. Yu Vilnius University, Vilnius, Lithuania
V. Dudenas, A. Juodagalvis, J. Vaitkus National Centre for Particle Physics,
Universiti Malaya, Kuala Lumpur, Malaysia
I. Ahmed, Z.A. Ibrahim, M.A.B. Md Ali32, F. Mohamad Idris33, W.A.T. Wan
Abdullah, M.N. Yusli, Z. Zolkapli Centro de Investigacion y de Estudios
Avanzados del IPN, Mexico City, Mexico
Duran-Osuna, M. C., H. Castilla-Valdez, E. De La Cruz-Burelo, Ramirez-Sanchez,
G., I. Heredia-De La Cruz34, Rabadan-Trejo, R. I., R. Lopez-Fernandez, J.
Mejia Guisao, Reyes-Almanza, R, 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
P.H. Butler National Centre for Physics, Quaid-I-Azam University, Islamabad,
Pakistan
A. Ahmad, M. Ahmad, 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. Zalewski Institute of Experimental Physics, Faculty
of Physics, University of Warsaw, Warsaw, Poland
K. Bunkowski, A. Byszuk35, K. Doroba, A. Kalinowski, M. Konecki, J.
Krolikowski, M. Misiura, M. Olszewski, A. Pyskir, M. Walczak Laboratório de
Instrumentação e Física Experimental de Partículas, Lisboa, Portugal
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
V. Alexakhin, P. Bunin, M. Gavrilenko, A. Golunov, I. Golutvin, N. Gorbounov,
I. Gorbunov, V. Karjavin, A. Lanev, A. Malakhov, V. Matveev36,37, P. Moisenz,
V. Palichik, V. Perelygin, M. Savina, S. Shmatov, S. Shulha, V. Smirnov, A.
Zarubin Petersburg Nuclear Physics Institute, Gatchina (St. Petersburg),
Russia
Y. Ivanov, V. Kim38, E. Kuznetsova39, P. Levchenko, V. Murzin, V. Oreshkin, I.
Smirnov, D. Sosnov, V. Sulimov, L. Uvarov, S. Vavilov, A. Vorobyev Institute
for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov,
N. Krasnikov, A. Pashenkov, D. Tlisov, A. Toropin Institute for Theoretical
and Experimental Physics, Moscow, Russia
V. Epshteyn, V. Gavrilov, N. Lychkovskaya, V. Popov, I. Pozdnyakov, G.
Safronov, A. Spiridonov, A. Stepennov, V. Stolin, M. Toms, E. Vlasov, A.
Zhokin Moscow Institute of Physics and Technology, Moscow, Russia
T. Aushev, A. Bylinkin37 National Research Nuclear University ’Moscow
Engineering Physics Institute’ (MEPhI), Moscow, Russia
R. Chistov40, M. Danilov40, P. Parygin, D. Philippov, S. Polikarpov, E.
Tarkovskii P.N. Lebedev Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin37, I. Dremin37, M. Kirakosyan37, S.V. Rusakov, A.
Terkulov Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State
University, Moscow, Russia
A. Baskakov, A. Belyaev, E. Boos, M. Dubinin41, 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. Blinov42, D. Shtol42, Y. Skovpen42 State Research Center of Russian
Federation, Institute for High Energy Physics of NRC &quot, Kurchatov
Institute&quot, , Protvino, Russia
I. Azhgirey, I. Bayshev, S. Bitioukov, D. Elumakhov, A. Godizov, V. Kachanov,
A. Kalinin, D. Konstantinov, P. Mandrik, V. Petrov, R. Ryutin, A. Sobol, S.
Troshin, N. Tyurin, A. Uzunian, A. Volkov University of Belgrade, Faculty of
Physics and Vinca Institute of Nuclear Sciences, Belgrade, Serbia
P. Adzic43, 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, 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, I. Gonzalez Caballero, J.R.
González Fernández, E. Palencia Cortezon, 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, E. Curras, J. Duarte Campderros,
M. Fernandez, P.J. Fernández Manteca, A. García Alonso, J. Garcia-Ferrero, G.
Gomez, A. Lopez Virto, J. Marco, C. Martinez Rivero, P. Martinez Ruiz del
Arbol, F. Matorras, J. Piedra Gomez, C. Prieels, T. Rodrigo, A. Ruiz-Jimeno,
L. Scodellaro, N. Trevisani, I. Vila, R. Vilar Cortabitarte CERN, European
Organization for Nuclear Research, Geneva, Switzerland
D. Abbaneo, B. Akgun, E. Auffray, P. Baillon, A.H. Ball, D. Barney, J.
Bendavid, M. Bianco, A. Bocci, C. Botta, T. Camporesi, R. Castello, M. Cepeda,
G. Cerminara, E. Chapon, Y. Chen, D. d’Enterria, A. Dabrowski, V. Daponte, A.
David, M. De Gruttola, A. De Roeck, N. Deelen, M. Dobson, T. du Pree, M.
Dünser, N. Dupont, A. Elliott-Peisert, P. Everaerts, F. Fallavollita, G.
Franzoni, J. Fulcher, W. Funk, D. Gigi, A. Gilbert, K. Gill, F. Glege, D.
Gulhan, J. Hegeman, V. Innocente, A. Jafari, P. Janot, O. Karacheban17, J.
Kieseler, V. Knünz, A. Kornmayer, M.J. Kortelainen, M. Krammer1, C. Lange, P.
Lecoq, C. Lourenço, M.T. Lucchini, L. Malgeri, M. Mannelli, A. Martelli, F.
Meijers, J.A. Merlin, S. Mersi, E. Meschi, P. Milenovic44, F. Moortgat, M.
Mulders, H. Neugebauer, J. Ngadiuba, S. Orfanelli, L. Orsini, 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. Stoye, M. Tosi, D. Treille, A. Tsirou,
V. Veckalns47, M. Verweij, W.D. Zeuner Paul Scherrer Institut, Villigen,
Switzerland
W. Bertl${}^{\textrm{\textdagger}}$, 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, B. Casal, G. Dissertori, M. Dittmar, M.
Donegà, C. Dorfer, C. Grab, C. Heidegger, D. Hits, J. Hoss, G. Kasieczka, T.
Klijnsma, W. Lustermann, B. Mangano, M. Marionneau, M.T. Meinhard, D. Meister,
F. Micheli, P. Musella, F. Nessi-Tedaldi, F. Pandolfi, J. Pata, F. Pauss, G.
Perrin, L. Perrozzi, M. Quittnat, M. Reichmann, 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, M.F. Canelli, A. De Cosa, R. Del Burgo, S.
Donato, C. Galloni, T. Hreus, B. Kilminster, D. Pinna, G. Rauco, P. Robmann,
D. Salerno, K. Schweiger, C. Seitz, Y. Takahashi, A. Zucchetta National
Central University, Chung-Li, Taiwan
V. Candelise, 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, F. Fiori, W.-S. Hou, Y. Hsiung, Arun
Kumar, 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, K. Kovitanggoon, G. Singh, N. Srimanobhas Çukurova University,
Physics Department, Science and Art Faculty, Adana, Turkey
A. Bat, F. Boran, S. Cerci50, S. Damarseckin, Z.S. Demiroglu, C. Dozen, I.
Dumanoglu, S. Girgis, G. Gokbulut, Y. Guler, I. Hos51, E.E. Kangal52, O. Kara,
A. Kayis Topaksu, U. Kiminsu, M. Oglakci, G. Onengut, K. Ozdemir53, D. Sunar
Cerci50, B. Tali50, U.G. Tok, S. Turkcapar, I.S. Zorbakir, C. Zorbilmez Middle
East Technical University, Physics Department, Ankara, Turkey
G. Karapinar54, K. Ocalan55, M. Yalvac, M. Zeyrek Bogazici University,
Istanbul, Turkey
E. Gülmez, M. Kaya56, O. Kaya57, S. Tekten, E.A. Yetkin58 Istanbul Technical
University, Istanbul, Turkey
M.N. Agaras, S. Atay, A. Cakir, K. Cankocak, Y. Komurcu 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. Newbold59,
S. Paramesvaran, T. Sakuma, S. Seif El Nasr-storey, D. Smith, V.J. Smith
Rutherford Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev60, C. Brew, R.M. Brown, L. Calligaris, 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, M. Citron, D. Colling, L. Corpe, P. Dauncey, G. Davies,
M. Della Negra, R. Di Maria, Y. Haddad, G. Hall, G. Iles, T. James, R. Lane,
C. Laner, L. Lyons, A.-M. Magnan, S. Malik, L. Mastrolorenzo, T. Matsushita,
J. Nash61, A. Nikitenko6, V. Palladino, M. Pesaresi, D.M. Raymond, A.
Richards, A. Rose, E. Scott, C. Seez, A. Shtipliyski, S. Summers, A. Tapper,
K. Uchida, M. Vazquez Acosta62, T. Virdee14, N. Wardle, D. Winterbottom, J.
Wright, S.C. Zenz Brunel University, Uxbridge, United Kingdom
J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, A. Morton, I.D. Reid, L.
Teodorescu, S. Zahid Baylor University, Waco, USA
A. Borzou, K. Call, J. Dittmann, K. Hatakeyama, H. Liu, 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, A. Avetisyan, T. Bose, D. Gastler, D. Rankin, C. Richardson, J.
Rohlf, L. Sulak, D. Zou Brown University, Providence, USA
G. Benelli, D. Cutts, M. Hadley, J. Hakala, U. Heintz, J.M. Hogan, K.H.M.
Kwok, E. Laird, G. Landsberg, J. Lee, Z. Mao, M. Narain, J. Pazzini, S.
Piperov, S. Sagir, R. Syarif, D. Yu University of California, Davis, Davis,
USA
R. Band, C. Brainerd, R. Breedon, D. Burns, M. Calderon De La Barca Sanchez,
M. Chertok, J. Conway, R. Conway, P.T. Cox, R. Erbacher, C. Flores, G. Funk,
W. Ko, R. Lander, C. Mclean, M. Mulhearn, D. Pellett, J. Pilot, S. Shalhout,
M. Shi, J. Smith, D. Stolp, D. Taylor, K. Tos, M. Tripathi, Z. Wang 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. Ellison, J.W. Gary, S.M.A. Ghiasi Shirazi,
G. Hanson, G. Karapostoli, E. Kennedy, F. Lacroix, O.R. Long, M. Olmedo
Negrete, M.I. Paneva, W. Si, L. Wang, H. Wei, S. Wimpenny, B. R. Yates
University of California, San Diego, La Jolla, USA
J.G. Branson, S. Cittolin, M. Derdzinski, R. Gerosa, D. Gilbert, B. Hashemi,
A. Holzner, D. Klein, G. Kole, V. Krutelyov, J. Letts, M. Masciovecchio, D.
Olivito, S. Padhi, M. Pieri, M. Sani, V. Sharma, S. Simon, M. Tadel, A.
Vartak, S. Wasserbaech63, J. Wood, F. Würthwein, A. Yagil, G. Zevi Della Porta
University of California, Santa Barbara - Department of Physics, Santa
Barbara, USA
N. Amin, R. Bhandari, J. Bradmiller-Feld, C. Campagnari, A. Dishaw, V. Dutta,
M. Franco Sevilla, L. Gouskos, R. Heller, J. Incandela, A. Ovcharova, H. Qu,
J. Richman, D. Stuart, I. Suarez, J. Yoo California Institute of Technology,
Pasadena, USA
D. Anderson, A. Bornheim, J. Bunn, I. Dutta, J.M. Lawhorn, H.B. Newman, T. Q.
Nguyen, C. Pena, 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, J. Russ, M. Sun, H.
Vogel, 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, S.R. Wagner Cornell University, Ithaca,
USA
J. Alexander, J. Chaves, Y. Cheng, J. Chu, S. Dittmer, 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, B. Kreis, S.
Lammel, D. Lincoln, R. Lipton, M. Liu, T. Liu, R. Lopes De Sá, J. Lykken, K.
Maeshima, N. Magini, J.M. Marraffino, D. Mason, P. McBride, P. Merkel, S.
Mrenna, S. Nahn, V. O’Dell, K. Pedro, O. Prokofyev, G. Rakness, L. Ristori, 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, W. Wu University of Florida, Gainesville, USA
D. Acosta, P. Avery, P. Bortignon, D. Bourilkov, A. Brinkerhoff, A. Carnes, M.
Carver, D. Curry, R.D. Field, I.K. Furic, S.V. Gleyzer, B.M. Joshi, J.
Konigsberg, A. Korytov, K. Kotov, P. Ma, K. Matchev, H. Mei, G. Mitselmakher,
K. Shi, D. Sperka, N. Terentyev, L. Thomas, J. Wang, S. Wang, J. Yelton
Florida International University, Miami, USA
Y.R. Joshi, S. Linn, P. Markowitz, J.L. Rodriguez Florida State University,
Tallahassee, USA
A. Ackert, T. Adams, A. Askew, S. Hagopian, V. Hagopian, K.F. Johnson, T.
Kolberg, G. Martinez, T. Perry, H. Prosper, A. Saha, A. Santra, V. Sharma, R.
Yohay Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, V. Bhopatkar, S. Colafranceschi, M. Hohlmann, D. Noonan, 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, O.
Evdokimov, C.E. Gerber, D.A. Hangal, D.J. Hofman, K. Jung, J. Kamin, I.D.
Sandoval Gonzalez, M.B. Tonjes, H. Trauger, N. Varelas, H. Wang, Z. Wu, J.
Zhang The University of Iowa, Iowa City, USA
B. Bilki64, W. Clarida, K. Dilsiz65, S. Durgut, R.P. Gandrajula, M.
Haytmyradov, V. Khristenko, J.-P. Merlo, H. Mermerkaya66, A. Mestvirishvili,
A. Moeller, J. Nachtman, H. Ogul67, Y. Onel, F. Ozok68, A. Penzo, C. Snyder,
E. Tiras, J. Wetzel, K. Yi Johns Hopkins University, Baltimore, USA
B. Blumenfeld, A. Cocoros, N. Eminizer, D. Fehling, L. Feng, A.V. Gritsan, P.
Maksimovic, J. Roskes, U. Sarica, M. Swartz, M. Xiao, C. You The University of
Kansas, Lawrence, USA
A. Al-bataineh, P. Baringer, A. Bean, S. Boren, J. Bowen, J. Castle, S.
Khalil, A. Kropivnitskaya, D. Majumder, W. Mcbrayer, M. Murray, C. Rogan, C.
Royon, S. Sanders, E. Schmitz, J.D. Tapia Takaki, Q. Wang Kansas State
University, Manhattan, USA
A. Ivanov, K. Kaadze, Y. Maravin, 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 Massachusetts Institute of Technology,
Cambridge, USA
D. Abercrombie, B. Allen, V. Azzolini, R. Barbieri, A. Baty, G. Bauer, R. Bi,
S. Brandt, W. Busza, I.A. Cali, M. D’Alfonso, Z. Demiragli, G. Gomez Ceballos,
M. Goncharov, P. Harris, D. Hsu, M. Hu, Y. Iiyama, G.M. Innocenti, M. Klute,
D. Kovalskyi, Y.-J. Lee, A. Levin, P.D. Luckey, B. Maier, A.C. Marini, C.
Mcginn, C. Mironov, S. Narayanan, X. Niu, C. Paus, C. Roland, G. Roland, J.
Salfeld-Nebgen, G.S.F. Stephans, K. Sumorok, K. Tatar, D. Velicanu, J. Wang,
T.W. Wang, B. Wyslouch University of Minnesota, Minneapolis, USA
A.C. Benvenuti, R.M. Chatterjee, A. Evans, P. Hansen, J. Hiltbrand, 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
J. Dolen, A. Godshalk, C. Harrington, I. Iashvili, D. Nguyen, A. Parker, S.
Rappoccio, B. Roozbahani Northeastern University, Boston, USA
G. Alverson, E. Barberis, C. Freer, A. Hortiangtham, A. Massironi, 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.
Musienko36, 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, B. Liu, W. Luo, B.L. Winer, H.W. Wulsin
Princeton University, Princeton, USA
S. Cooperstein, O. Driga, P. Elmer, J. Hardenbrook, P. Hebda, S. Higginbotham,
A. Kalogeropoulos, D. Lange, J. Luo, D. Marlow, K. Mei, I. Ojalvo, J. Olsen,
C. Palmer, P. Piroué, 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, S. Folgueras, L. Gutay, M. Jones, A.W. Jung,
A. Khatiwada, D.H. Miller, N. Neumeister, C.C. Peng, H. Qiu, J.F. Schulte, J.
Sun, F. Wang, R. Xiao, W. Xie Purdue University Northwest, Hammond, USA
T. Cheng, N. Parashar, J. Stupak Rice University, Houston, USA
Z. Chen, K.M. Ecklund, S. Freed, F.J.M. Geurts, M. Guilbaud, M. Kilpatrick, W.
Li, B. Michlin, B.P. Padley, J. Roberts, J. Rorie, W. Shi, Z. Tu, J. Zabel, A.
Zhang University of Rochester, Rochester, USA
A. Bodek, P. de Barbaro, R. Demina, Y.t. Duh, T. Ferbel, M. Galanti, A.
Garcia-Bellido, J. Han, O. Hindrichs, A. Khukhunaishvili, K.H. Lo, P. Tan, M.
Verzetti The Rockefeller University, New York, USA
R. Ciesielski, K. Goulianos, C. Mesropian Rutgers, The State University of New
Jersey, Piscataway, USA
A. Agapitos, J.P. Chou, Y. Gershtein, T.A. Gómez Espinosa, E. Halkiadakis, M.
Heindl, E. Hughes, S. Kaplan, R. Kunnawalkam Elayavalli, S. Kyriacou, A. Lath,
R. Montalvo, K. Nash, M. Osherson, H. Saka, S. Salur, S. Schnetzer, D.
Sheffield, S. Somalwar, R. Stone, S. Thomas, P. Thomassen, M. Walker
University of Tennessee, Knoxville, USA
A.G. Delannoy, J. Heideman, G. Riley, K. Rose, S. Spanier, K. Thapa Texas A&M
University, College Station, USA
O. Bouhali69, A. Castaneda Hernandez69, A. Celik, M. Dalchenko, M. De Mattia,
A. Delgado, S. Dildick, R. Eusebi, J. Gilmore, T. Huang, T. Kamon70, R.
Mueller, Y. Pakhotin, R. Patel, A. Perloff, L. Perniè, D. Rathjens, A.
Safonov, A. Tatarinov, K.A. Ulmer Texas Tech University, Lubbock, USA
N. Akchurin, J. Damgov, F. De Guio, P.R. Dudero, J. Faulkner, E. Gurpinar, 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, P. Sheldon, S. Tuo, J. Velkovska, Q. Xu University of Virginia,
Charlottesville, USA
M.W. Arenton, P. Barria, B. Cox, R. Hirosky, M. Joyce, A. Ledovskoy, H. Li, C.
Neu, T. Sinthuprasith, Y. Wang, E. Wolfe, F. Xia Wayne State University,
Detroit, USA
R. Harr, P.E. Karchin, N. Poudyal, J. Sturdy, P. Thapa, S. Zaleski University
of Wisconsin - Madison, Madison, WI, USA
M. Brodski, J. Buchanan, C. Caillol, D. Carlsmith, S. Dasu, L. Dodd, S. Duric,
B. Gomber, M. Grothe, M. Herndon, A. Hervé, U. Hussain, P. Klabbers, A.
Lanaro, A. Levine, K. Long, R. Loveless, V. Rekovic, T. Ruggles, A. Savin, N.
Smith, W.H. Smith, N. Woods †: Deceased
1: Also at Vienna University of Technology, Vienna, Austria
2: Also at IRFU; CEA; Université Paris-Saclay, Gif-sur-Yvette, France
3: Also at Universidade Estadual de Campinas, Campinas, Brazil
4: Also at Federal University of Rio Grande do Sul, Porto Alegre, Brazil
5: Also at Université Libre de Bruxelles, Bruxelles, Belgium
6: Also at Institute for Theoretical and Experimental Physics, Moscow, Russia
7: Also at Joint Institute for Nuclear Research, Dubna, Russia
8: Also at Helwan University, Cairo, Egypt
9: Now at Zewail City of Science and Technology, Zewail, Egypt
10: Now at British University in Egypt, Cairo, Egypt
11: Also at Department of Physics; King Abdulaziz University, Jeddah, Saudi
Arabia
12: Also at Université de Haute Alsace, Mulhouse, France
13: Also at Skobeltsyn Institute of Nuclear Physics; Lomonosov Moscow State
University, Moscow, Russia
14: Also at CERN; European Organization for Nuclear Research, Geneva,
Switzerland
15: Also at RWTH Aachen University; III. Physikalisches Institut A, Aachen,
Germany
16: Also at University of Hamburg, Hamburg, Germany
17: Also at Brandenburg University of Technology, Cottbus, Germany
18: Also at MTA-ELTE Lendület CMS Particle and Nuclear Physics Group; Eötvös
Loránd University, Budapest, Hungary
19: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary
20: Also at Institute of Physics; University of Debrecen, Debrecen, Hungary
21: Also at Indian Institute of Technology Bhubaneswar, Bhubaneswar, India
22: Also at Institute of Physics, Bhubaneswar, India
23: Also at Shoolini University, Solan, India
24: Also at University of Visva-Bharati, Santiniketan, India
25: Also at University of Ruhuna, Matara, Sri Lanka
26: Also at Isfahan University of Technology, Isfahan, Iran
27: Also at Yazd University, Yazd, 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 INFN Sezione di Milano-Bicocca; Università di Milano-Bicocca,
Milano, Italy
31: Also at Purdue University, West Lafayette, USA
32: Also at International Islamic University of Malaysia, Kuala Lumpur,
Malaysia
33: Also at Malaysian Nuclear Agency; MOSTI, Kajang, Malaysia
34: Also at Consejo Nacional de Ciencia y Tecnología, Mexico city, Mexico
35: Also at Warsaw University of Technology; Institute of Electronic Systems,
Warsaw, Poland
36: Also at Institute for Nuclear Research, Moscow, Russia
37: Now at National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
38: Also at St. Petersburg State Polytechnical University, St. Petersburg,
Russia
39: Also at University of Florida, Gainesville, USA
40: Also at P.N. Lebedev Physical Institute, Moscow, Russia
41: Also at California Institute of Technology, Pasadena, USA
42: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia
43: Also at Faculty of Physics; University of Belgrade, Belgrade, Serbia
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 Izmir Institute of Technology, Izmir, Turkey
55: Also at Necmettin Erbakan University, Konya, Turkey
56: Also at Marmara University, Istanbul, Turkey
57: Also at Kafkas University, Kars, Turkey
58: Also at Istanbul Bilgi University, Istanbul, Turkey
59: Also at Rutherford Appleton Laboratory, Didcot, United Kingdom
60: Also at School of Physics and Astronomy; University of Southampton,
Southampton, United Kingdom
61: Also at Monash University; Faculty of Science, Clayton, Australia
62: Also at Instituto de Astrofísica de Canarias, La Laguna, Spain
63: Also at Utah Valley University, Orem, USA
64: Also at Beykent University, Istanbul, Turkey
65: Also at Bingol University, Bingol, Turkey
66: Also at Erzincan University, Erzincan, Turkey
67: Also at Sinop University, Sinop, Turkey
68: Also at Mimar Sinan University; Istanbul, Istanbul, Turkey
69: Also at Texas A&M University at Qatar, Doha, Qatar
70: Also at Kyungpook National University, Daegu, Korea