SUS-19-003

SUS-19-003

# Search for top squark pair production in a final state with two tau leptons
in proton-proton collisions at $\sqrt{s}=13\TeV$

###### Abstract

A search for pair production of the supersymmetric partner of the top quark,
the top squark, in proton-proton collision events at $\sqrt{s}=13\TeV$ is
presented in a final state containing hadronically decaying tau leptons and
large missing transverse momentum. This final state is highly sensitive to
high-or higgsino-like scenarios in which decays of electroweak gauginos to tau
leptons are dominant. The search uses a data set corresponding to an
integrated luminosity of 77.2, which was recorded with the CMS detector during
2016 and 2017. No significant excess is observed with respect to the
background prediction. Exclusion limits at 95% confidence level are presented
in the top squark and lightest neutralino mass plane within the framework of
simplified models, in which top squark masses up to 1100are excluded for a
nearly massless neutralino.

##  0.1 Introduction

Supersymmetry (SUSY) [1, 2, 3, 4, 5, 6, 7, 8, 9] is one of the most widely
studied theories of physics beyond the standard model (SM), providing
solutions to various shortcomings of the SM. In SUSY models there is a bosonic
superpartner for each fermion (and vice-versa), the superpartner having the
same quantum numbers, other than spin, as its SM partner. The superpartners of
the SM gauge and Higgs bosons (gauginos and higgsinos, respectively) mix to
produce charginos and neutralinos. The weakly interacting lightest neutralino
can be a dark matter candidate in $R$-parity conserving SUSY models [10]. The
SUSY partners of left- and right-handed top quarks are the top squarks, and .
These particles can mix with each other, resulting in physical states and ,
with defined to be the lighter of the two. The top squarks play an important
role in stabilizing the Higgs boson mass by canceling the dominant top quark
loop correction. Therefore, there is a strong motivation to perform searches
for top squark production.

In this study, we focus on the signal of top squark pair production in a final
state with two tau leptons. This probes the part of the parameter space of the
minimal supersymmetric standard model (MSSM) in which the lightest charginos
() and neutralino preferentially couple to third-generation fermions, such as
tau leptons. The interaction of the charginos and neutralinos with fermion-
sfermion pairs involves both gauge and Yukawa terms [9], so if charginos and
neutralinos are predominantly higgsino-like, they will preferentially couple
to third-generation fermion-sfermion pairs through the large Yukawa coupling.
Moreover, the Yukawa coupling to the tau lepton-slepton pairs can be large for
a high value of even if the higgsino component is relatively small.
Additionally, a large value of can make the lighter state of the superpartner
of the tau lepton ($\PSGtDo$) much lighter than the superpartners of the first
and second generation leptons. Consequently, the chargino decays predominantly
as $\PSGcpDo\to\HepSusyParticle{\PGt}{1}{+}\Xspace\PGnGt$ or $\PGtp\PSGnGt$
(charge conjugation is assumed throughout in this paper), and the decay rates
in the electron and muon channels are greatly reduced [11, 12]. Therefore,
searches for SUSY signals in electron and muon channels are less sensitive to
this scenario.

We focus on the top squark decays
$\PSQtDo\to\PQb\PSGcpDo\to\PQb\HepSusyParticle{\PGt}{1}{+}\Xspace\PGnGt\to\PQb\PGtp\PSGczDo\PGnGt$
and $\PSQtDo\to\PQb\PSGcpDo\to\PQb\PGtp\PSGnGt\to\PQb\PGtp\PSGczDo\PGnGt$. The
is assumed to be the lightest SUSY particle (LSP). Being neutral and weakly
interacting, it leaves no signature in the detector, resulting in an imbalance
in transverse momentum . The neutrinos produced in the decay chains also
contribute to the imbalance. Hence, the events of interest contain two tau
leptons, two quarks, and a imbalance. The decay chains are depicted by the
four diagrams in Fig. 1 within the simplified model spectra (SMS) framework
[13, 14]. It is assumed that the $\PSGcpDo$ decays to 1+or with equal
probability.

Figure 1: Top squark pair production in proton-proton collisions at the LHC,
producing pairs of quarks and taus accompanied by neutrinos and LSPs in the
final state.

This search is performed using proton-proton collision events at a center-of-
mass energy of 13, recorded by the CMS experiment at the CERN LHC. The data
sample corresponds to integrated luminosities of 35.9 and 41.3collected during
the 2016 and 2017 operating periods of the LHC, respectively. Signal-like
events are characterized by the presence of hadronically decaying tau leptons
(), jets identified as likely to have originated from the fragmentation of
quarks, and large missing . Contributions from SM processes with the same
final state are estimated using a combination of Monte Carlo (MC) simulated
samples and control samples in data.

Searches for top squark pair production in leptonic and hadronic final states
have been performed by the CMS [15, 16, 17, 18, 19, 20, 21, 22] and ATLAS [23,
24, 25, 26, 27] Collaborations, establishing limits on top squark masses in
the framework of SMS models. The ATLAS Collaboration performed a search [28]
based on 2015 and 2016 data probing the same final state as that used here,
but optimized for a gauge-mediated SUSY breaking scenario with an almost
massless gravitino as a source of missing momentum. Therefore, final states
containing hadronically decaying tau leptons have not been extensively
explored in the context of top squark searches motivated by high-and higgsino-
like scenarios.

The paper is organized as follows. A brief description of the CMS detector is
presented in Section 0.2, followed by descriptions of the event simulation in
Section 0.3, and reconstruction in Section 0.4. The event selection and search
strategy are detailed in Section 0.5. We explain the various methods used for
background estimation in Section 0.6, the systematic uncertainties are
discussed in Section 0.7, and the results are provided in Section 0.8.
Finally, the analysis is summarized in Section 0.9.

##  0.2 The CMS detector

The central feature of the CMS apparatus is a superconducting solenoid of 6m
internal diameter, providing a magnetic field of 3.8T. Within the solenoid
volume are a silicon pixel and strip tracker, a lead tungstate crystal
electromagnetic calorimeter (ECAL), and a brass and scintillator hadron
calorimeter (HCAL), each composed of a barrel and two endcap sections. Forward
calorimeters extend the pseudorapidity coverage provided by the barrel and
endcap detectors. Muons are detected in gas-ionization chambers embedded in
the steel flux-return yoke outside the solenoid. A more detailed description
of the CMS detector, together with a definition of the coordinate system used
and the relevant kinematic variables, can be found in Ref. [29].

Events of interest are selected using a two-tiered trigger system [30]. The
first level, composed of custom hardware processors, uses information from the
calorimeters and muon detectors to select events at a rate of around 100kHz
within a time interval of less than 4. The second level, known as the high-
level trigger, consists of a farm of processors running a version of the full
event reconstruction software optimized for fast processing, and reduces the
event rate to around 1kHz before data storage.

##  0.3 Monte Carlo simulation

Simulation is used to estimate several of the SM backgrounds. The predictions
for signal event rates are also estimated using simulation, based on
simplified SUSY signal models. The simulation is corrected for small
discrepancies observed with respect to collision data using a number of scale
factors (SFs). These will be discussed in later sections.

The pair production of top quarks () is generated at next-to-leading order
(NLO) in using v2 [31, 32, 33, 34, 35]. The same generator has been used for
the single top quark $t$-channel process, whereas v1 has been used for the tW
process [36]. The v2.2.2 (v2.4.2 for 2017) [37] generator is used at leading
order (LO) for modeling the Drell–Yan+jets (DY+jets) and W+jets backgrounds,
which are normalized to the next-to-next-to-leading order (NNLO) cross
sections. The generator is also used at NLO for simulating the diboson,
$\PV\PH$, and $\ttbar\PV$ (= or ) backgrounds. For the 2016 analysis, the
parton shower and hadronization are simulated with v8.212 [38] using the
underlying event tunes CUETP8M2T4 [39] (for only) or CUETP8M1 [40]. For the
2017 analysis, v8.230 with the tune CP5 [41] is used. The CMS detector
response is modeled using [42], and the simulated events are then
reconstructed in the same way as collision data.

Signal processes for top squark pair production shown in Fig. 1 are generated
at LO using v2.2.2. The tunes CUETP8M1 and CP2 [41] are used for the 2016 and
2017 analyses, respectively. The signal cross sections are evaluated using
NNLO plus next-to-leading logarithmic (NLL) calculations [43, 44, 45, 46, 47].
Detector response for the signal events is simulated using the fast CMS
detector simulation (FastSim) [48].

We assume a branching fraction of 50% for each of the two decay modes of the
chargino, $\PSGcpDo\to\HepSusyParticle{\PGt}{1}{+}\Xspace\PGnGt$ and
$\PSGcpDo\to\PGtp\PSGnGt$. Each of the four diagrams in Fig. 1 therefore
contributes 25% of the generated signal events. The masses of SUSY particles
appearing in the decay chain are determined by the parameterization

$\displaystyle m_{\PSGcmDo}-m_{\PSGczDo}$ $\displaystyle=0.5\
(m_{\stone}-m_{\PSGczDo}),$ (1) $\displaystyle m_{\PSGt_{1}}-m_{\PSGczDo}$
$\displaystyle=x\ (m_{\PSGcmDo}-m_{\PSGczDo}),$ $\displaystyle x$
$\displaystyle\in[0.25,\ 0.5,\ 0.75],$ $\displaystyle m_{\PSGnGt}$
$\displaystyle=m_{\PSGt_{1}}.$

In this parameterization, the chargino mass is fixed to be the mean of the top
squark and $\PSGczDo$ masses. The masses of the leptonic superpartners are set
by the value of $x$ for a given pair of top squark and $\PSGczDo$ masses. The
kinematic properties of the final state particles in each of the decay chains
depicted in Fig. 1 therefore depend on the choice of $x$.

  * •

$x=0.25$: the mass of the lepton superpartner is closer to that of the
$\PSGczDo$ than to that of the $\PSGcmDo$. Hence, the upper left diagram in
Fig. 1 produces lower energy tau leptons than the upper right. The lower two
diagrams both typically produce two tau leptons with a large difference in
energy.

  * •

$x=0.75$: the masses of the 1±and the are relatively close, so the upper left
diagram in Fig. 1 produces more energetic tau leptons than the upper right.
The lower two diagrams produce the same energy asymmetry as in the case of
$x=0.25$.

  * •

$x=0.5$: the tau leptons in all four diagrams have similar energies.

In fact, when all four diagrams are taken into account the distributions of
the kinematic properties are found to be very similar for the three different
values of $x$, for a given set of chargino and LSP masses.

It is important to note, however, that the choice of chargino mass does affect
the overall sensitivity. For instance, if the chargino is very close in mass
to the top squark, then the momenta of the jets are reduced and those of the
remaining decay products are increased. This results in an increase in the
overall sensitivity, provided the jet values are within the acceptance. On the
other hand if the chargino is very close in mass to the LSP, then an overall
loss of sensitivity is expected. Such scenarios are not explored in this
paper, where the default chargino mass given in Eq. (1) is taken throughout.
The polarizations of the tau leptons originating from SUSY cascade decays,
which have been found to be useful for studying SUSY signals [12], have not
been exploited here.

##  0.4 Event reconstruction

The particle-flow (PF) algorithm [49] aims at reconstructing each individual
particle in an event, with an optimized combination of information from the
various components of the CMS detector. The energy of photons is obtained from
the ECAL measurement, whereas the momentum of electrons is determined from a
combination of the measurement of momentum by the tracker, the energy of
matching ECAL deposits, and the energy of all bremsstrahlung photons
consistent with originating from the track. The momentum of muons is obtained
from the curvature of the corresponding track. The energy of charged hadrons
is determined from a combination of the momentum measured in the tracker and
the matching ECAL and HCAL energy deposits, corrected for zero-suppression
effects and for the response function of the calorimeters to hadronic showers.
Finally, the energy of neutral hadrons is obtained from the corresponding
corrected ECAL and HCAL energies.

Reconstruction of jets is performed by clustering PF objects using the anti-
algorithm [50, 51] with a distance parameter of $R=0.4$. Jet momentum is
determined as the vectorial sum of all particle momenta in the jet, and is
found in simulation to be, on average, within 5–10% of the generated momentum
over the whole spectrum and detector acceptance. Additional proton-proton
interactions within the same or nearby bunch crossings (pileup) can contribute
spurious tracks and calorimetric energy deposits, increasing the apparent jet
momentum. In order to mitigate this effect, tracks identified as originating
from pileup vertices are discarded, and an offset is applied to correct for
the remaining contributions [52]. Jets are calibrated using both simulation
and data studies [52]. Additional selection criteria are applied to each jet
to remove those potentially dominated by instrumental effects or
reconstruction failures [53]. Jets with $\pt>20\GeV$ and $\abs{\eta}<2.4$ are
used in this analysis.

Vertices reconstructed in an event are required to be within 24cm of the
center of the detector in the $z$ direction, and to have a transverse
displacement from the beam line of less than 2cm. The vertex with the largest
value of summed physics-object $\pt^{2}$ is taken to be the primary $\Pp\Pp$
interaction vertex. The physics objects used for this purpose are jets,
clustered using the aforementioned jet finding algorithm with the tracks
assigned to the vertex as inputs, and the associated missing transverse
momentum, taken as the negative vector sum of the of those jets.

Jets originating from the fragmentation of quarks are identified as -tagged
jets by using the combined secondary vertex (CSVv2) algorithm [54], which
utilizes information from displaced tracks and reconstructed secondary
vertices. An operating point is chosen corresponding to a signal efficiency of
70% with a mistagging probability of about 1% for light jets (from up, down
and strange quarks, and gluons) and 15% for jets originating from charm
quarks.

The momentum resolution for electrons with $\pt\approx 45\GeV$ from
$\PZ\rightarrow\Pe\Pe$ decays ranges from 1.7 to 4.5%. It is generally better
in the barrel region than in the endcaps, and also depends on the
bremsstrahlung energy emitted by the electron as it traverses the material in
front of the ECAL [55]. Electrons with $\pt>20\GeV$ and $\abs{\eta}<2.4$ are
used for this analysis.

Muons are measured with detection planes made using three technologies: drift
tubes, cathode strip chambers, and resistive plate chambers. Matching muons to
tracks measured in the silicon tracker results in a resolution of 1% in the
barrel and 3% in the endcaps, for muons with a of up to 100. The resolution in
the barrel is better than 7% for muons with a of up to 1 [56]. This search
uses muons with $\pt>20$and $\abs{\eta}<2.4$.

Isolation criteria are imposed on the lepton (electron and muon) candidates to
reject leptons originating from hadronic decays. The isolation variable used
for this purpose is defined as the scalar sum of the of reconstructed charged
and neutral particles within a cone of radius $\Delta
R=\sqrt{\smash[b]{(\Delta\eta)^{2}+(\Delta\phi)^{2}}}$ = 0.3 (0.4) around the
electron (muon) candidate track, excluding the lepton candidate, divided by
the of the lepton candidate. Charged particles not originating from the
primary vertex are excluded from this sum and a correction is applied to
account for the neutral components originating from pileup, following the
procedure described in Ref. [55]. This relative isolation is required to be
less than 15 (20)% for electron (muons). The electron and muon candidates
passing the aforementioned criteria are used to identify a control region (CR)
that is used for the estimation of the background from top quark pair
production, as explained in Section 0.6.1.

The missing transverse momentum vector is computed as the negative vector sum
of the of all the PF candidates in an event, and its magnitude is denoted as .
The is modified to account for the energy calibration of the reconstructed
jets in the event. The energy calibration of the PF candidates that have not
been clustered into jets is also taken into account. Anomalous high-events may
appear because of a variety of reconstruction failures, detector malfunctions,
or backgrounds not originating from collisions (e.g., particles in the beam
halo). Such events are rejected by filters that are designed to identify more
than 85–90% of the spurious high-events with a misidentification rate of less
than 0.1% [57]. In order to minimize the effect of extra noise in the ECAL
endcap in 2017, forward jets with uncalibrated $\pt<50$ GeV and
$2.65<\abs{\eta}<3.14$ are removed from the calculation of in both data and
simulation. This improves the agreement between simulation and data at the
cost of degrading the resolution by only a few percent.

The hadrons-plus-strips algorithm [58] is used to reconstruct candidates: one
charged hadron and up to two neutral pions, or three charged hadrons,
consistent with originating from the decay of a tau lepton. The probability of
an electron or muon being misidentified as a candidate is greatly reduced by
combining information from the tracker, calorimeters, and muon detector. The
isolation of the candidate is determined from the presence of reconstructed
particles within a radius of $\Delta R=0.3$ around the axis that are not
compatible with the decay, and is a useful quantity to distinguish between
jets and decays. In order to distinguish between jets originating from quarks
or gluons, and genuine hadronic tau lepton decays, a multivariate discriminant
is calculated from information including the isolation and measured lifetime.
The candidates are selected with $\pt>40\GeV$, $\abs{\eta}<2.1$, and the
“tight” working point of the above discriminant. This working point has an
efficiency of ${\approx}$50% with a misidentification probability of
${\approx}$0.03%. The “loose” working point, which has an efficiency of
${\approx}$65% and a misidentification probability of ${\approx}$0.07%, is
used for estimating the background from misidentified candidates.

##  0.5 Event selection

The sources of in the signal events are the neutrinos and the weakly
interacting neutralinos, which are correlated with the visible objects (in
particular the $\tauh$ decays). In contrast, in the SM background processes is
primarily due to neutrinos. This difference can be exploited by first
constructing the transverse mass , defined as follows:

$\displaystyle\mT^{2}(\vec{\pt}^{\text{vis}},\vec{\pt}^{\text{inv}})$
$\displaystyle=m^{2}_{\text{vis}}+m^{2}_{\text{inv}}+2(\et^{\text{vis}}\et^{\text{inv}}-\vec{\pt}^{\text{vis}}\cdot\vec{\pt}^{\text{inv}}),$
(2) $\displaystyle\text{where}\qquad\et^{2}$ $\displaystyle=m^{2}+\pt^{2}.$

Here the masses of the visible (vis) and invisible (inv) particles are denoted
by $m_{\text{vis}}$ and $m_{\text{inv}}$, respectively. The value of has a
maximum at the mass of the parent of the visible and the invisible particles.
To account for multiple sources of missing momentum in the signal process, the
“stransverse mass” [59, 60] is defined as:

$\displaystyle\mTii^{2}(\text{vis1},\text{vis2},\ptmiss)$
$\displaystyle=\min_{\vec{\pt}^{\text{\text{inv1}}}+\vec{\pt}^{\text{\text{inv2}}}=\ptvecmiss}[\max\\{\mT^{2}(\vec{\pt}^{\text{\text{vis1}}},\vec{\pt}^{\text{\text{inv1}}}),\mT^{2}(\vec{\pt}^{\text{\text{vis2}}},\vec{\pt}^{\text{\text{inv2}}})\\}].$
(3)

Since the momenta of the individual invisible particles in Eq. (3) are
unknown, is divided into two components ($\vec{\pt}^{\text{\text{inv1}}}$ and
$\vec{\pt}^{\text{\text{inv2}}}$) in such a way that the value of is
minimized. If is computed using the two $\tauh$ candidates as the visible
objects, vis1 and vis2, then its upper limit in the signal will be at the
chargino mass. This is different from the SM background processes. For example
in events, the upper limit is at the boson mass. For this analysis, is
calculated with the masses of the invisible particles in Eq. (2) set to zero
[61].

The signal and background processes can be further separated by utilizing the
total visible momentum of the system. This is characterized using the quantity
, which is defined as the scalar sum of the of all jets and the candidates in
the event. Jets lying within a cone of $\Delta R=0.3$ around either of the two
selected $\tauh$ candidates are excluded from this sum to avoid double
counting. Being a measure of the total energy of the system, is sensitive to
the mass of the top squark.

Signal events are selected using triggers, where both candidates are required
to have $\abs{\eta}<2.1$, and $\pt>35$ or 40 depending on the trigger path.
The trigger has an efficiency of ${\approx}$95% for candidates that pass the
offline selection. The trigger efficiencies in simulation are corrected to
match the efficiencies measured in data. For the offline selection, signal
events are required to have $\ptmiss>50\GeV$, $\HT>100\GeV$, at least two
oppositely charged candidates with $\pt>40\GeV$ and $\abs{\eta}<2.1$, and at
least one -tagged jet with $\pt>20\GeV$ and $\abs{\eta}<2.4$. The requirements
on and the number of -tagged jets ($n_{\Pb}$) help to reduce the contributions
from DY+jets and SM events comprised uniquely of jets produced through the
strong interaction, referred to as multijet events. Distributions of the
variables , , and after this selection are shown in Fig. 2 for data and the
predicted background, along with representative signal distributions. The
background prediction includes , DY+jets, events with misidentified , and
other rare SM processes. Detailed descriptions of the background estimation
methods are presented in Section 0.6.

Figure 2:  Distributions of the search variables , , and after event
selection, for data and the predicted background. The histograms for the
background processes are stacked, and the distributions for a few
representative signal points corresponding to $x=0.5$ and [$m_{\PSQtDo}$,
$m_{\PSGczDo}$] = [300, 100], [500, 350], and [800, 300]are overlaid. The
lower panel indicates the ratio of the observed data to the background
prediction. The shaded bands indicate the statistical and systematic
uncertainties on the background, added in quadrature.

Signal events with different top squark and LSP masses populate different
regions of the phase space. For example, regions with low , , and are
sensitive to signals with low top squark masses. On the other hand, events
with high , , and are sensitive to models with high top squark and low LSP
masses. In order to obtain the highest sensitivity over the entire phase
space, the signal region (SR) is divided into 15 bins as a function of the
measured , , and , which are illustrated in Fig. 3.

Figure 3: The 15 search regions defined in bins of , , and .

##  0.6 Background estimation

The most significant background is production, either with two genuine decays
or because of jets being misidentified as candidates. Because of theoretical
uncertainties in the background modeling in the SR (which contains events that
populate the tails of the kinematic distributions), we estimate the
contribution to events with two genuine decays using CRs in data, as discussed
below. The background contribution from DY events is typically minor in the
most sensitive bins, and has been estimated using simulation. To account for
residual discrepancies between data and the LO DY sample, correction factors
for simulated events are derived from DY-enriched dimuon CRs in data and
simulation as functions of the dimuon invariant mass and . The contribution
from multijet events is negligible because of the selections $\ptmiss>50\GeV$
and $n_{\Pb}\geq 1$. Other less significant backgrounds, such as +jets,
$\PV\PV$, $\PV\PH$, and $\ttbar\PV$ are also estimated from simulation. The
overall SM contribution from jets being misidentified as candidates is
estimated using CRs in data. In the following sections we detail the
estimation of those backgrounds that are obtained from CRs in data.

###  0.6.1 Tau lepton pairs from top production

The estimation of the background from events in which there are two genuine
decays is based on the method described in Ref. [62]. The predicted yields in
each SR bin from simulation are multiplied by correction factors derived in a
-enriched CR. The -enriched CR is identified by selecting events with an
oppositely charged pair. These events are selected with triggers, and are
required to satisfy the same offline requirements as the SR with the and
replacing the two candidates. The triggers are ${\approx}$95% efficient for
lepton candidates. In addition, in order to reduce possible DY contamination
(from the tail of the invariant mass distribution in the process
$\PZ/\gamma^{*}\to\tau\tau\to{\Pe}\PGm$) in this CR, events are vetoed if the
invariant mass of the system lies in the range $60<m_{{\Pe}\PGm}<120\GeV$.
This selection on the dilepton invariant mass is more effective in the CR to
be discussed later, but is also applied here in order to be consistent. Other
objects, such as jets and -tagged jets, are selected using the same kinematic
requirements and working points as in the SR. The definitions of the search
variables for this CR are the same as those in the SR, except that the pair is
used in place of the pair for evaluating the search variables. The purity of
in the CR (i.e., the fraction of events in each bin) is measured in simulation
as ${\gtrsim}$85% , as shown in Fig. 4 (upper panels).

Residual differences between data and simulation are quantified by SFs. For a
given SR region ($i$) we define

$\displaystyle SF_{i}$ $\displaystyle=\frac{N^{{\Pe}\PGm\ \text{CR}}_{i,\
\text{data}}}{N^{{\Pe}\PGm\ \text{CR}}_{i,\ \text{MC}}},$ (4)

where the numerator and the denominator represent the yields in the CR in data
and simulation, respectively. The corrected yield in simulation in each region
of the SR is then obtained as:

$\displaystyle N^{{\tauh}\tauh\ \text{SR}}_{i,\ \text{corr }\ttbar}$
$\displaystyle=N^{{\tauh}\tauh\ \text{SR}}_{i,\ \ttbar\ \text{MC}}SF_{i}$
$\displaystyle=\frac{N^{{\Pe}\PGm\ \text{CR}}_{i,\ \text{data}}\
N^{{\tauh}\tauh\ \text{SR}}_{i,\ \ttbar\ \text{MC}}}{N^{{\Pe}\PGm\
\text{CR}}_{i,\ \text{MC}}},$ (5)

where $N^{{\tauh}\tauh\ \text{SR}}_{i,\ \ttbar\ \text{MC}}$ is the prediction
from simulated events in the SR. An alternative way of interpreting this
method is that we take the spectrum from a -enriched CR in data
($N^{{\Pe}\PGm\ \text{CR}}_{i,\ \text{data}}$) and extrapolate it to the
${\tauh}\tauh$ SR by accounting for the differences between the properties of
${\tauh}\tauh$ and final states with the ratio $N^{{\tauh}\tauh\
\text{SR}}_{i,\ \ttbar\ \text{MC}}/N^{{\Pe}\PGm\ \text{CR}}_{i,\ \text{MC}}$
taken from simulation. The SFs in the different bins, shown in Fig. 4 (middle
row) for both 2016 and 2017 data, are mostly found to be within ${\approx}$10%
of unity. Note that separate SFs for bins 14 and 15 are shown for information,
but these are merged and a single SF is used in subsequent calculations to
reduce the statistical uncertainty.

In order to cross-check the validity of this method, the same technique is
applied to an independent -enriched CR with an oppositely charged pair in the
final state. These events are selected with single muon triggers that reach
${\approx}$95% efficiency. The event selection for the CR is similar to that
for the ${\Pe}\PGm$ CR. This cross-check evaluates the effect of possible
contamination from DY events (the branching fraction of
$\PZ/\gamma^{*}\to\Pgm\Pgm$ being much higher than that of
$\PZ/\gamma^{*}\to\tau\tau\to{\Pe}\PGm$), and is also useful for checking any
dependence of the SFs on lepton reconstruction. The differences between the
SFs calculated in the main and cross-check CRs, shown in Fig. 4 (bottom row),
are small (within ${\approx}$10% in most cases), and are taken as an
uncertainty in the SFs. These are added in quadrature to the statistical
uncertainty in the SFs, and propagated as a contribution to the uncertainty in
the final prediction.

Figure 4: The purities (upper row), scale factors (middle row), and
$\text{SF}^{\Pe\mu}-\text{SF}^{\mu\mu}$ (bottom row) in the different bins (as
defined in Fig. 3) of the CR for 2016 (left) and 2017 (right) data. The scale
factor in bin 15 of the CR in 2016 is off the visible scale. Note that bins 14
and 15 are merged to provide a single SF for subsequent calculations.

###  0.6.2 Misidentified hadronically decaying tau lepton candidates

The next largest component of the total background originates from quark or
gluon jets that are misidentified as a candidate. The largest sources of such
events in the SR are semileptonic and hadronic decays. We estimate this
contribution to the SR following a strategy [63] that uses the yields in CRs,
defined by inverting the requirements on the working point of the
identification.

For a genuine passing the loose identification requirements, we define $g$ as
the probability that it also passes the tight identification requirements. We
define $f$ as the corresponding probability for a misidentified candidate. We
then define $N_{\text{gf}}$ as the number of events where the candidate with
the highest is genuine and that with the second-highest is misidentified, with
other terms ($N_{\text{fg}}$, $N_{\text{gg}}$, and $N_{\text{ff}}$) defined
similarly. We also define $N_{\text{TL}}$ as the number of events where the
candidate with the highest passes the tight identification criteria and that
with the second-highest fails, but passes the loose criteria, with other terms
($N_{\text{LT}}$, $N_{\text{LL}}$, and $N_{\text{TT}}$) defined similarly. If
$N$ is the total number of events, the following set of equations can be
constructed:

$\displaystyle N$
$\displaystyle=N_{\text{gg}}+N_{\text{fg}}+N_{\text{gf}}+N_{\text{ff}}=N_{\text{TT}}+N_{\text{LT}}+N_{\text{TL}}+N_{\text{LL}},$
(6) $\displaystyle N_{\text{LL}}$
$\displaystyle=(1-g_{1})(1-g_{2})N_{\text{gg}}+(1-f_{1})(1-g_{2})N_{\text{fg}}+(1-g_{1})(1-f_{2})N_{\text{gf}}+(1-f_{1})(1-f_{2})N_{\text{ff}},$
$\displaystyle N_{\text{LT}}$
$\displaystyle=(1-g_{1})g_{2}N_{\text{gg}}+(1-f_{1})g_{2}N_{\text{fg}}+(1-g_{1})f_{2}N_{\text{gf}}+(1-f_{1})f_{2}N_{\text{ff}},$
$\displaystyle N_{\text{TL}}$
$\displaystyle=g_{1}(1-g_{2})N_{\text{gg}}+f_{1}(1-g_{2})N_{\text{fg}}+g_{1}(1-f_{2})N_{\text{gf}}+f_{1}(1-f_{2})N_{\text{ff}},$
$\displaystyle N_{\text{TT}}$
$\displaystyle=g_{1}g_{2}N_{\text{gg}}+f_{1}g_{2}N_{\text{fg}}+g_{1}f_{2}N_{\text{gf}}+f_{1}f_{2}N_{\text{ff}},$

where the subscripts 1 and 2 on $g$ and $f$ refer to the candidates with the
highest and second-highest , respectively.

The above equations can be inverted to give the numbers of genuine and
misidentified candidate events in the SR:

$N_{\text{TT}}=N^{\text{gen}}_{\text{TT}}+N^{\text{misid}}_{\text{TT}},$ (7)

where

$\displaystyle N^{\text{gen}}_{\text{TT}}$
$\displaystyle=g_{1}g_{2}N_{\text{gg}},$ $\displaystyle
N^{\text{misid}}_{\text{TT}}$
$\displaystyle=f_{1}g_{2}N_{\text{fg}}+g_{1}f_{2}N_{\text{gf}}+f_{1}f_{2}N_{\text{ff}}.$

Here $N^{\text{gen}}_{\text{TT}}$ represents the number of events in the SR
with two genuine candidates in the final state, and
$N^{\text{misid}}_{\text{TT}}$ the number of events in the SR with one or two
misidentified candidates.

The probability $g$ is determined using simulation, with the candidate being
matched to a generated hadronically decaying tau within a cone of radius
$\Delta R=0.3$. The value of $g$ is calculated as the ratio between the number
of genuine jets passing the tight identification criteria and the number
passing the loose criteria. It is evaluated as a function of the decay modes
and and is observed to be about 80% with very little dependence on the of the
. The dependence on the decay mode is observed to be at the 10% level.

The misidentification rate $f$ is estimated using a multijet-enriched CR in
data. This CR is defined by requiring a same-charge pair satisfying the
selection criteria, and by requiring $\ptmiss<50\GeV$. The misidentification
rate for a single candidate is estimated from this CR using the following two
definitions:

$\displaystyle f(\text{LL}\rightarrow\text{TL})$
$\displaystyle=\frac{\tauh^{1}(\text{T})\
\tauh^{2}(\text{L})}{\tauh^{1}(\text{L})\ \tauh^{2}(\text{L})},$ (8)
$\displaystyle f(\text{TL}\rightarrow\text{TT})$
$\displaystyle=\frac{\tauh^{1}(\text{T})\
\tauh^{2}(\text{T})}{\tauh^{1}(\text{T})\ \tauh^{2}(\text{L})}.$

Here, the term $\tauh^{i}(\text{X})$ denotes the number of events where the
candidate with the highest ($i=1$) or second-highest ($i=2$) passes the tight
(X=T) or loose (X=L) identification criteria. In each of the two definitions
above, the working point of one of the candidates in the numerator is changed
with respect to the denominator, so they could be expected to yield the same
result. However, if the probability of one candidate passing the tight
criteria is correlated with the probability of the other to pass, differences
may occur. In practice, differences of up to ${\approx}$10%, depending on the
and the decay mode of the , are observed between the two definitions. These
differences are used to estimate the uncertainty in this method.

The misidentification rate is measured as a function of the decay modes and .
It is found to be around 35% with a mild dependence on the of the candidate.
The variations with decay mode are up to the 20% level. It has been found in
simulation studies [63] that the misidentification rate also depends on the
flavor of the parton corresponding to the jet that is misidentified as a .
Since the jet flavor cannot be reliably determined in data, an additional 15%
uncertainty in $f$ is included. This uncertainty is evaluated as the relative
difference between the average and the maximum (or minimum) of the
misidentification rates corresponding to the different jet flavors (up, down,
strange, and bottom quarks, and gluons), estimated using simulated +jets
events.

##  0.7 Systematic uncertainties

There are several sources of systematic uncertainty that are propagated to the
prediction of the final signal and background yields. The most significant is
the uncertainty in the modeling of the identification and isolation
requirements (ID-iso) [58] of the candidates, estimated to be approximately
10% for all processes in 2016, and 20% in 2017. The other sources of
uncertainty affecting all processes include the jet energy scale (JES) and jet
energy resolution (JER), the energy scale, the effect of unclustered
components in calculating , pileup reweighting, and the tagging efficiency.
The simulation is reweighted to make its pileup distribution identical to that
in data. The pileup in data depends on the measured total inelastic cross
section [64], which is varied by $\pm$2.5% to obtain the uncertainty in this
correction.

Since the contribution in the SR is obtained by multiplying the simulated
yield by a SF, defined as the ratio between the number of events in data and
simulation, several uncertainties cancel to first order. As mentioned earlier,
the difference between the SFs obtained in the and CRs, added in quadrature
with the statistical uncertainty, is taken as the uncertainty in this method.
The difference between the two definitions of the misidentification rate, as
defined in Eq. (8), is taken to be the uncertainty in the misidentification
rate, while the flavor dependence of the rate is accounted for by adding an
additional 15% uncertainty.

The factorization ($\mu_{\text{F}}$) and renormalization ($\mu_{\text{R}}$)
scales used in the simulation are varied up and down by a factor of two,
avoiding the cases in which one is doubled and the other is halved. The
SysCalc package [65] has been used for this purpose. The resulting uncertainty
is estimated to be less than 6% for both signal and background processes
estimated from simulation. A 2.5% uncertainty in the measured integrated
luminosity is used for 2016 [66], reducing to 2.3% for 2017 [67]. The
uncertainty in the boson correction applied to DY+jets events is taken to be
equal to the deviation of the correction factor from unity. A normalization
uncertainty of 15% is assigned to the production cross sections of the
background processes that are evaluated directly from simulation [68, 69, 70,
71, 72, 73, 74].

Since the simulation of the detector for signal events is performed using
FastSim, the signal yields are corrected to account for the differences in the
identification efficiency with respect to the simulation used for the
backgrounds. The statistical uncertainty in this correction is propagated as
its uncertainty. The FastSim package has a worse resolution than the full
simulation, resulting in a potential artificial enhancement of the signal
yields. To account for this, the signal yields are corrected, and the
uncertainty in the resulting correction to the yield is estimated to be 5–10%
.

The uncertainties in the signal and background from all sources are presented
in Table 0.7. Upper and lower numbers correspond to the relative uncertainties
due to the upward and downward variations of the respective source. These
values are the weighted averages of the relative uncertainties in the
different search bins with the weights being the yields in the respective
bins. The tabulated sources of systematic uncertainties are modeled by log-
normal distributions [75] in the likelihood function used for the statistical
interpretation of the results, which is discussed in Section 0.8. These
uncertainties are considered not to be correlated with each other, but
correlated across the 15 search bins. In addition, the statistical
uncertainties are also taken into account and are considered to be
uncorrelated across the bins.

Relative systematic uncertainties from different sources in signal and
background yields in the 2016 and 2017 analyses combined. These values are the
weighted (by the yields in the respective bins) averages of the relative
uncertainties in the different search regions. For the asymmetric
uncertainties, the upper (lower) entry is the uncertainty due to the upward
(downward) variation, which can be in the same direction as a result of taking
the weighted average. The numbers in square brackets in the heading indicate
the top squark and LSP masses in , respectively.

Uncertainty source |  $x=0.5$ $[300,100]$ |  $x=0.5$ $[500,350]$ |  $x=0.5$ $[800,300]$ | $\ttbar$ | DY+jets | Other SM | Misid.
---|---|---|---|---|---|---|---
Signal cross section | $\pm 6.7\%$ | $\pm 7.5\%$ | $\pm 9.5\%$ |  |  |  |
FastSim $\ptmiss$ resolution | $\pm 7.4\%$ | $\pm 10\%$ | $\pm 5.1\%$ |  |  |  |
$\tauh$ FastSim/  |  $+4.4\%$ $-4.3\%$ |  $+3.7\%$ $-3.6\%$ |  $+7.9\%$ $-7.5\%$ |  |  |  |
JER |  $-0.27\%$ $<$0.1% |  $-0.81\%$ $<$0.1% |  $<$0.1% $<$0.1% |  |  $+0.47\%$ $+0.27\%$ |  $-0.95\%$ $-0.29\%$ |
JES |  $+0.18\%$ $-0.57\%$ |  $<$0.1% $-0.81\%$ |  $+0.1\%$ $<$0.1% |  |  $+1.1\%$ $-1.7\%$ |  $+0.2\%$ $-1.6\%$ |
$\mu_{\text{R}}$ and $\mu_{\text{F}}$ scales |  $+0.6\%$ $-0.7\%$ |  $+1.9\%$ $-2.1\%$ |  $+0.31\%$ $-0.35\%$ |  |  $+4.6\%$ $-4.4\%$ |  $+3.2\%$ $-2.6\%$ |
$\tauh$ ID-iso |  $+16\%$ $-14\%$ |  $+16\%$ $-14\%$ |  $+17\%$ $-15\%$ |  $+16\%$ $-15\%$ |  $+16\%$ $-14\%$ |  $+16\%$ $-14\%$ |
Pileup |  $<$0.1% $<$0.1% |  $+0.25\%$ $-0.18\%$ |  $-0.69\%$ $+0.68\%$ |  |  $-0.23\%$ $+0.24\%$ |  $-0.88\%$ $+0.88\%$ |
unclustered energy |  $-0.16\%$ $-0.78\%$ |  $+1\%$ $-2\%$ |  $<$0.1% $<$0.1% |  |  $+7\%$ $-2\%$ |  $+1.3\%$ $-1.6\%$ |
Background normalization |  |  |  |  | $\pm 15\%$ | $\pm 15\%$ |
Luminosity | $\pm 2.4\%$ | $\pm 2.4\%$ | $\pm 2.4\%$ |  | $\pm 2.4\%$ | $\pm 2.4\%$ |
tagging |  $+1.1\%$ $-1.2\%$ |  $+0.65\%$ $-0.67\%$ |  $+0.73\%$ $-0.75\%$ |  |  $+4.7\%$ $-4.7\%$ |  $+2.4\%$ $-2.4\%$ |
$\tauh$ energy scale |  $+2\%$ $-3\%$ |  $+2.3\%$ $-3.3\%$ |  $+1.1\%$ $-0.9\%$ |  $+1.5\%$ $-1.8\%$ |  $+1.2\%$ $-1.6\%$ |  $+0.7\%$ $-1.3\%$ |
$\ttbar$ SF |  |  |  | $\pm 2.5\%$ |  |  |
$\pt$ reweighting |  |  |  |  |  $+9.1\%$ $-9.1\%$ |  |
$\tauh$ misid. rate (parton flavour) |  |  |  |  |  |  |  $+16\%$ $-17\%$
$\tauh$ misid. rate ($\text{LL}\to\text{TL}$ vs. $\text{TL}\to\text{TT}$) |  |  |  |  |  |  |  $+2.7\%$ $-2.5\%$

##  0.8 Results

We present the observed and expected yields in all 15 search bins in Table 0.8
along with their uncertainties. Figure 5 shows the observed data in all of the
search bins, compared to the signal and background predictions.

As expected, the dominant contributions in the sensitive signal bins are from
and misidentified backgrounds. In cases where the background prediction of a
process in a given bin is negligible, the statistical uncertainty is modeled
by a gamma distribution [75] in the likelihood function used for the
statistical interpretation, and the Poissonian upper limit at 68% confidence
level () is shown as a positive uncertainty in the table. The number of events
observed in data is found to be consistent with the SM background prediction.

The test statistic used for the interpretation of the result is the profile
likelihood ratio
$q_{\mu}=-2\ln{(\mathcal{L}_{\mu}/\mathcal{L}_{\text{max}})}$, where
$\mathcal{L}_{\mu}$ is the maximum likelihood for a fixed signal strength
$\mu$, and $\mathcal{L}_{\text{max}}$ is the global maximum of the likelihood
[75]. We set upper limits on signal production at 95% using a modified
frequentist approach and the criterion [76, 77], implemented through an
asymptotic approximation of the test statistic [78]. In this calculation all
the background and signal uncertainties are modeled as nuisance parameters and
profiled in the maximum likelihood fit.

Final results are obtained by combining the yields from 2016 and 2017 data
sets. The systematic uncertainties due to JES, factorization and
renormalization scales, misidentification rate measurement, and FastSim
correction are taken as correlated, and the rest of the uncertainties are
treated as uncorrelated between the two data sets. The results are presented
as observed and expected exclusion limits in the top squark and LSP mass plane
in Fig. 6. Top squark masses up to $1100\GeV$ are excluded for a nearly
massless LSP, and LSP masses up to $450\GeV$ are excluded for a top squark
mass of $900\GeV$. The exclusion limits are not very sensitive to the choice
of $x$ because of the complementary nature of the signal diagrams, as
discussed in Section 0.3.

The most sensitive search bins for the higher top squark masses are 14 and 15.
The observed data in these two bins are lower than the total background
prediction, resulting in the observed limit being higher than the expected
one. Hence, even though there are more events in data than prediction overall,
the observed mass limit is stronger than expected. The excesses are primarily
in bins 2, 5, 7, and 12 which are more significant for low top squark masses,
hence the observed limit is slightly worse than expected in that region. The
limits become weaker with decreasing $\Delta m=m_{\PSQtDo}-m_{\PSGczDo}$,
corresponding to a parameter space with final-state particles having lower
momentum and hence less sensitivity.

Event yields along with statistical and systematic uncertainties in the 2016
and 2017 analyses combined, for different background sources and the total
background in the 15 search bins, as defined in Fig. 3. The uncertainties that
are smaller than 0.05 are listed as 0.0. The number of events observed in data
is also shown. The notation used is
$\text{yield}^{+\text{stat}+\text{syst}}_{-\text{stat}-\text{syst}}$.

SR | $\ttbar$ | DY+jets | Other SM | Misid.  | Total bkg. | Data
---|---|---|---|---|---|---
1 | $170^{+8+21}_{-8-19}$ | $98^{+10+22}_{-10-20}$ | $23^{+2+4}_{-2-4}$ | $150^{+21+6}_{-21-19}$ | $441^{+25+42}_{-25-43}$ | $417$
2 | $200^{+8+22}_{-8-21}$ | $154^{+11+35}_{-11-33}$ | $29^{+3+6}_{-3-5}$ | $94^{+19+22}_{-19-20}$ | $477^{+23+59}_{-23-55}$ | $559$
3 | $20^{+3+2}_{-3-2}$ | $20^{+3+6}_{-3-5}$ | $5.1^{+0.8+1.1}_{-0.8-1.6}$ | $12^{+6+4}_{-6-3}$ | $57^{+7+9}_{-7-8}$ | $49$
4 | $19^{+3+2}_{-3-2}$ | $4.1^{+1.5+1.0}_{-1.5-1.0}$ | $3.8^{+0.8+0.9}_{-0.8-0.9}$ | $3.2^{+3.5+1.2}_{-3.5-0.9}$ | $30^{+5+4}_{-5-4}$ | $28$
5 | $8.9^{+1.6+1.0}_{-1.6-1.0}$ | $0.5^{+4.8+0.1}_{-0.5-0.1}$ | $1.6^{+0.4+0.4}_{-0.4-0.3}$ | $3.8^{+3.2+1.2}_{-3.2-0.8}$ | $15^{+6+2}_{-4-1}$ | $22$
6 | $51^{+4+6}_{-4-5}$ | $36^{+6+9}_{-6-10}$ | $7.8^{+1.3+1.6}_{-1.3-1.8}$ | $78^{+14+12}_{-14-13}$ | $173^{+15+19}_{-15-20}$ | $169$
7 | $46^{+4+5}_{-4-5}$ | $14^{+4+6}_{-4-4}$ | $5.2^{+0.8+1.0}_{-0.8-1.1}$ | $48^{+11+12}_{-11-10}$ | $113^{+12+15}_{-12-13}$ | $133$
8 | $4.4^{+1.3+0.6}_{-1.3-0.5}$ | $4.3^{+1.5+2.0}_{-1.5-1.1}$ | $1.4^{+0.4+0.3}_{-0.4-0.3}$ | $9.0^{+3.2+3.9}_{-3.2-2.7}$ | $19^{+4+5}_{-4-3}$ | $23$
9 | $3.7^{+1.1+0.5}_{-1.1-0.4}$ | $0.0^{+3.5+0.0}_{-0.0-0.0}$ | $0.7^{+0.3+0.2}_{-0.3-0.2}$ | $4.6^{+1.7+2.0}_{-1.7-1.4}$ | $9.0^{+4.0+2.1}_{-2.0-1.5}$ | $4$
10 | $1.0^{+0.6+0.2}_{-0.6-0.1}$ | $0.0^{+3.5+0.0}_{-0.0-0.0}$ | $0.5^{+0.2+0.1}_{-0.2-0.1}$ | $3.2^{+1.4+1.3}_{-1.4-1.0}$ | $4.7^{+3.8+1.3}_{-1.6-1.0}$ | $1$
11 | $6.8^{+1.6+1.2}_{-1.6-0.9}$ | $2.4^{+1.5+1.3}_{-1.5-0.7}$ | $1.4^{+0.4+0.7}_{-0.4-0.3}$ | $16^{+6+4}_{-6-3}$ | $27^{+6+5}_{-6-4}$ | $30$
12 | $2.9^{+1.0+0.4}_{-1.0-0.3}$ | $8.3^{+2.5+2.8}_{-2.5-1.8}$ | $2.1^{+0.3+0.4}_{-0.3-0.4}$ | $11^{+6+2}_{-6-2}$ | $24^{+7+4}_{-7-3}$ | $41$
13 | $0.7^{+0.9+0.2}_{-0.5-0.4}$ | $2.2^{+0.9+0.6}_{-0.9-0.9}$ | $0.7^{+0.2+0.2}_{-0.2-0.2}$ | $3.5^{+2.1+1.3}_{-2.1-0.9}$ | $7.1^{+2.4+1.5}_{-2.3-1.4}$ | $6$
14 | $0.5^{+0.9+0.1}_{-0.5-0.1}$ | $0.0^{+3.5+0.0}_{-0.0-0.0}$ | $0.6^{+0.2+0.1}_{-0.2-0.1}$ | $0.0^{+1.8+0.0}_{-0.0-0.0}$ | $1.1^{+4.1+0.2}_{-0.6-0.2}$ | $1$
15 | $0.3^{+0.8+0.1}_{-0.3-0.1}$ | $0.4^{+2.2+0.1}_{-0.4-0.1}$ | $0.1^{+0.0+0.0}_{-0.0-0.0}$ | $1.0^{+1.8+0.4}_{-0.0-0.3}$ | $1.8^{+3.0+0.5}_{-0.5-0.3}$ | $0$
Total | $535^{+14+63}_{-14-58}$ | $344^{+19+78}_{-17-73}$ | $83^{+4+16}_{-4-16}$ | $437^{+35+73}_{-35-78}$ | $1400^{+43+125}_{-42-123}$ | $1483$

Figure 5:  Event yields in the 15 search bins as defined in Fig. 3. The yields
for the background processes are stacked, and those for a few representative
signal points corresponding to $x=0.5$ and
$[m_{\PSQtDo},m_{\PSGczDo}]=[300,100]$, [500, 350], and [800, 300]are
overlaid. The lower panel indicates the ratio of the observed data to the
background prediction in each bin. The shaded bands indicate the statistical
and systematic uncertainties in the background, added in quadrature.

Figure 6:  Exclusion limits at 95% for the pair production of top squarks
decaying to a final state, displayed in the ${m_{\PSQtDo}}$-$m_{\PSGczDo}$
plane for $x=0.25$ (upper left), 0.5 (upper right) and 0.75 (lower), as
described in Eq. (1). The color axis represents the observed limit in the
cross section, while the black (red) lines represent the observed (expected)
mass limits. The signal cross sections are evaluated using NNLO plus next-to-
leading logarithmic (NLL) calculations. The solid lines represent the central
values. The dashed red lines indicate the region containing 68% of the
distribution of limits expected under the background-only hypothesis. The
dashed black lines show the change in the observed limit due to variation of
the signal cross sections within their theoretical uncertainties.

##  0.9 Summary

The signature of top squark pair production in final states with two tau
leptons has been explored in data collected with the CMS detector during 2016
and 2017, corresponding to integrated luminosities of 35.9 and 41.3,
respectively. The search was performed in the final state containing an
oppositely charged hadronic tau lepton pair, at least one jet identified as
likely to originate from the fragmentation of a quark, and missing transverse
momentum. The dominant standard model backgrounds were found to originate from
top quark pair production and processes where jets were misidentified as
hadronic tau lepton decays. Control samples in data were used to estimate
these backgrounds, while other backgrounds were estimated using simulation.

No significant excess was observed, and exclusion limits on the top squark
mass in terms of the mass of the lightest neutralino were set at 95%
confidence level within the framework of simplified models where the top
squark decays via a chargino to final states including tau leptons. In such
models, top squark masses are excluded up to 1100for an almost massless
neutralino, and LSP masses up to 450are excluded for a top squark mass of 900.
These results probe a region of the supersymmetric parameter space
corresponding to high-and higgsino-like scenarios.

###### Acknowledgements.

We congratulate our colleagues in the CERN accelerator departments for the
excellent performance of the LHC and thank the technical and administrative
staffs at CERN and at other CMS institutes for their contributions to the
success of the CMS effort. In addition, we gratefully acknowledge the
computing centers and personnel of the Worldwide LHC Computing Grid for
delivering so effectively the computing infrastructure essential to our
analyses. Finally, we acknowledge the enduring support for the construction
and operation of the LHC and the CMS detector provided by the following
funding agencies: BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq,
CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST,
and NSFC (China); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RPF
(Cyprus); SENESCYT (Ecuador); MoER, ERC IUT, PUT and ERDF (Estonia); Academy
of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG,
and HGF (Germany); GSRT (Greece); NKFIA (Hungary); DAE and DST (India); IPM
(Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); MES
(Latvia); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT,
LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC
(Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom,
RAS, RFBR, and NRC KI (Russia); MESTD (Serbia); SEIDI, CPAN, PCTI, and FEDER
(Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST
(Taipei); ThEPCenter, IPST, STAR, and NSTDA (Thailand); TUBITAK and TAEK
(Turkey); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (USA).
Individuals have received support from the Marie-Curie program and the
European Research Council and Horizon 2020 Grant, contract Nos. 675440,
752730, and 765710 (European Union); the Leventis Foundation; the A.P. Sloan
Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science
Policy Office; the Fonds pour la Formation à la Recherche dans l’Industrie et
dans l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door
Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium)
under the “Excellence of Science – EOS” – be.h project n. 30820817; the
Beijing Municipal Science & Technology Commission, No. Z181100004218003; the
Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the
Lendület (“Momentum”) Program and the János Bolyai Research Scholarship of the
Hungarian Academy of Sciences, the New National Excellence Program ÚNKP, the
NKFIA research grants 123842, 123959, 124845, 124850, 125105, 128713, 128786,
and 129058 (Hungary); the Council of Science and Industrial Research, India;
the HOMING PLUS program of the Foundation for Polish Science, cofinanced from
European Union, Regional Development Fund, the Mobility Plus program of the
Ministry of Science and Higher Education, the National Science Center
(Poland), contracts Harmonia 2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543,
2014/15/B/ST2/03998, and 2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406;
the National Priorities Research Program by Qatar National Research Fund; the
Ministry of Science and Education, grant no. 3.2989.2017 (Russia); the
Programa Estatal de Fomento de la Investigación Científica y Técnica de
Excelencia María de Maeztu, grant MDM-2015-0509 and the Programa Severo Ochoa
del Principado de Asturias; the Thalis and Aristeia programs cofinanced by EU-
ESF and the Greek NSRF; the Rachadapisek Sompot Fund for Postdoctoral
Fellowship, Chulalongkorn University and the Chulalongkorn Academic into Its
2nd Century Project Advancement Project (Thailand); the Nvidia Corporation;
the Welch Foundation, contract C-1845; and the Weston Havens Foundation (USA).

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##  .10 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan${}^{\textrm{\textdagger}}$, A. Tumasyan Institut für
Hochenergiephysik, Wien, Austria
W. Adam, F. Ambrogi, T. Bergauer, J. Brandstetter, M. Dragicevic, J. Erö, A.
Escalante Del Valle, M. Flechl, R. Frühwirth1, M. Jeitler1, N. Krammer, I.
Krätschmer, D. Liko, T. Madlener, I. Mikulec, N. Rad, J. Schieck1, R.
Schöfbeck, M. Spanring, D. Spitzbart, W. Waltenberger, C.-E. Wulz1, M. Zarucki
Institute for Nuclear Problems, Minsk, Belarus
V. Drugakov, V. Mossolov, J. Suarez Gonzalez Universiteit Antwerpen,
Antwerpen, Belgium
M.R. Darwish, E.A. De Wolf, D. Di Croce, X. Janssen, A. Lelek, M. Pieters, H.
Rejeb Sfar, H. Van Haevermaet, P. Van Mechelen, S. Van Putte, N. Van Remortel
Vrije Universiteit Brussel, Brussel, Belgium
F. Blekman, E.S. Bols, S.S. Chhibra, J. D’Hondt, J. De Clercq, D. Lontkovskyi,
S. Lowette, I. Marchesini, S. Moortgat, Q. Python, K. Skovpen, S. Tavernier,
W. Van Doninck, P. Van Mulders Université Libre de Bruxelles, Bruxelles,
Belgium
D. Beghin, B. Bilin, H. Brun, B. Clerbaux, G. De Lentdecker, H. Delannoy, B.
Dorney, L. Favart, A. Grebenyuk, A.K. Kalsi, A. Popov, N. Postiau, E.
Starling, L. Thomas, C. Vander Velde, P. Vanlaer, D. Vannerom Ghent
University, Ghent, Belgium
T. Cornelis, D. Dobur, I. Khvastunov2, M. Niedziela, C. Roskas, D. Trocino, M.
Tytgat, W. Verbeke, B. Vermassen, M. Vit, N. Zaganidis Université Catholique
de Louvain, Louvain-la-Neuve, Belgium
O. Bondu, G. Bruno, C. Caputo, P. David, C. Delaere, M. Delcourt, A.
Giammanco, V. Lemaitre, A. Magitteri, J. Prisciandaro, A. Saggio, M. Vidal
Marono, P. Vischia, J. Zobec Centro Brasileiro de Pesquisas Fisicas, Rio de
Janeiro, Brazil
F.L. Alves, G.A. Alves, G. Correia Silva, C. Hensel, A. Moraes, P. Rebello
Teles Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
E. Belchior Batista Das Chagas, W. Carvalho, J. Chinellato3, E. Coelho, E.M.
Da Costa, G.G. Da Silveira4, D. De Jesus Damiao, C. De Oliveira Martins, S.
Fonseca De Souza, L.M. Huertas Guativa, H. Malbouisson, J. Martins5, D. Matos
Figueiredo, M. Medina Jaime6, M. Melo De Almeida, C. Mora Herrera, L. Mundim,
H. Nogima, W.L. Prado Da Silva, L.J. Sanchez Rosas, A. Santoro, A. Sznajder,
M. Thiel, E.J. Tonelli Manganote3, F. Torres Da Silva De Araujo, A. Vilela
Pereira Universidade Estadual Paulista a, Universidade Federal do ABC b, São
Paulo, Brazil
C.A. Bernardesa, L. Calligarisa, T.R. Fernandez Perez Tomeia, E.M. Gregoresb,
D.S. Lemos, P.G. Mercadanteb, S.F. Novaesa, SandraS. Padulaa Institute for
Nuclear Research and Nuclear Energy, Bulgarian Academy of Sciences, Sofia,
Bulgaria
A. Aleksandrov, G. Antchev, R. Hadjiiska, P. Iaydjiev, M. Misheva, M. Rodozov,
M. Shopova, G. Sultanov University of Sofia, Sofia, Bulgaria
M. Bonchev, A. Dimitrov, T. Ivanov, L. Litov, B. Pavlov, P. Petkov Beihang
University, Beijing, China
W. Fang7, X. Gao7, L. Yuan Institute of High Energy Physics, Beijing, China
G.M. Chen, H.S. Chen, M. Chen, C.H. Jiang, D. Leggat, H. Liao, Z. Liu, A.
Spiezia, J. Tao, E. Yazgan, H. Zhang, S. Zhang8, J. Zhao State Key Laboratory
of Nuclear Physics and Technology, Peking University, Beijing, China
A. Agapitos, Y. Ban, G. Chen, A. Levin, J. Li, L. Li, Q. Li, Y. Mao, S.J.
Qian, D. Wang, Q. Wang Tsinghua University, Beijing, China
M. Ahmad, Z. Hu, Y. Wang Zhejiang University, Hangzhou, China
M. Xiao Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, C. Florez, C.F. González Hernández, M.A. Segura Delgado
Universidad de Antioquia, Medellin, Colombia
J. Mejia Guisao, J.D. Ruiz Alvarez, C.A. Salazar González, N. Vanegas Arbelaez
University of Split, Faculty of Electrical Engineering, Mechanical Engineering
and Naval Architecture, Split, Croatia
D. Giljanović, N. Godinovic, D. Lelas, I. Puljak, T. Sculac University of
Split, Faculty of Science, Split, Croatia
Z. Antunovic, M. Kovac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, S. Ceci, D. Ferencek, K. Kadija, B. Mesic, M. Roguljic, A.
Starodumov9, T. Susa University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, E. Erodotou, A. Ioannou, M. Kolosova, S. Konstantinou,
G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos, P.A. Razis, H.
Rykaczewski, D. Tsiakkouri Charles University, Prague, Czech Republic
M. Finger10, M. Finger Jr.10, A. Kveton, J. Tomsa Escuela Politecnica
Nacional, Quito, Ecuador
E. Ayala Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab
Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
S. Abu Zeid11, S. Khalil12 National Institute of Chemical Physics and
Biophysics, Tallinn, Estonia
S. Bhowmik, A. Carvalho Antunes De Oliveira, R.K. Dewanjee, K. Ehataht, M.
Kadastik, M. Raidal, C. Veelken Department of Physics, University of Helsinki,
Helsinki, Finland
P. Eerola, L. Forthomme, H. Kirschenmann, K. Osterberg, M. Voutilainen
Helsinki Institute of Physics, Helsinki, Finland
F. Garcia, J. Havukainen, J.K. Heikkilä, T. Järvinen, V. Karimäki, M.S. Kim,
R. Kinnunen, T. Lampén, K. Lassila-Perini, S. Laurila, S. Lehti, T. Lindén, P.
Luukka, T. Mäenpää, H. Siikonen, E. Tuominen, J. Tuominiemi Lappeenranta
University of Technology, Lappeenranta, Finland
T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
M. Besancon, F. Couderc, M. Dejardin, D. Denegri, B. Fabbro, J.L. Faure, F.
Ferri, S. Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry,
C. Leloup, E. Locci, J. Malcles, J. Rander, A. Rosowsky, M.Ö. Sahin, A. Savoy-
Navarro13, M. Titov Laboratoire Leprince-Ringuet, Ecole polytechnique,
CNRS/IN2P3, Université Paris-Saclay, Palaiseau, France
S. Ahuja, C. Amendola, F. Beaudette, P. Busson, C. Charlot, B. Diab, G.
Falmagne, R. Granier de Cassagnac, I. Kucher, A. Lobanov, C. Martin Perez, M.
Nguyen, C. Ochando, P. Paganini, J. Rembser, R. Salerno, J.B. Sauvan, Y.
Sirois, A. Zabi, A. Zghiche Université de Strasbourg, CNRS, IPHC UMR 7178,
Strasbourg, France
J.-L. Agram14, J. Andrea, D. Bloch, G. Bourgatte, J.-M. Brom, E.C. Chabert, C.
Collard, E. Conte14, J.-C. Fontaine14, D. Gelé, U. Goerlach, M. Jansová, A.-C.
Le Bihan, N. Tonon, P. Van Hove Centre de Calcul de l’Institut National de
Physique Nucleaire et de Physique des Particules, CNRS/IN2P3, Villeurbanne,
France
S. Gadrat Université de Lyon, Université Claude Bernard Lyon 1, CNRS-IN2P3,
Institut de Physique Nucléaire de Lyon, Villeurbanne, France
S. Beauceron, C. Bernet, G. Boudoul, C. Camen, A. Carle, N. Chanon, R.
Chierici, D. Contardo, P. Depasse, H. El Mamouni, J. Fay, S. Gascon, M.
Gouzevitch, B. Ille, Sa. Jain, F. Lagarde, I.B. Laktineh, H. Lattaud, A.
Lesauvage, M. Lethuillier, L. Mirabito, S. Perries, V. Sordini, L. Torterotot,
G. Touquet, M. Vander Donckt, S. Viret Georgian Technical University, Tbilisi,
Georgia
G. Adamov Tbilisi State University, Tbilisi, Georgia
Z. Tsamalaidze10 RWTH Aachen University, I. Physikalisches Institut, Aachen,
Germany
C. Autermann, L. Feld, M.K. Kiesel, K. Klein, M. Lipinski, D. Meuser, A.
Pauls, M. Preuten, M.P. Rauch, J. Schulz, M. Teroerde, B. Wittmer RWTH Aachen
University, III. Physikalisches Institut A, Aachen, Germany
A. Albert, M. Erdmann, B. Fischer, S. Ghosh, T. Hebbeker, K. Hoepfner, H.
Keller, L. Mastrolorenzo, M. Merschmeyer, A. Meyer, P. Millet, G. Mocellin, S.
Mondal, S. Mukherjee, D. Noll, A. Novak, T. Pook, A. Pozdnyakov, T. Quast, M.
Radziej, Y. Rath, H. Reithler, J. Roemer, A. Schmidt, S.C. Schuler, A. Sharma,
S. Wiedenbeck, S. Zaleski RWTH Aachen University, III. Physikalisches Institut
B, Aachen, Germany
G. Flügge, W. Haj Ahmad15, O. Hlushchenko, T. Kress, T. Müller, A. Nehrkorn,
A. Nowack, C. Pistone, O. Pooth, D. Roy, H. Sert, A. Stahl16 Deutsches
Elektronen-Synchrotron, Hamburg, Germany
M. Aldaya Martin, P. Asmuss, I. Babounikau, H. Bakhshiansohi, K. Beernaert, O.
Behnke, A. Bermúdez Martínez, D. Bertsche, A.A. Bin Anuar, K. Borras17, V.
Botta, A. Campbell, A. Cardini, P. Connor, S. Consuegra Rodríguez, C.
Contreras-Campana, V. Danilov, A. De Wit, M.M. Defranchis, C. Diez Pardos, D.
Domínguez Damiani, G. Eckerlin, D. Eckstein, T. Eichhorn, A. Elwood, E. Eren,
E. Gallo18, A. Geiser, A. Grohsjean, M. Guthoff, M. Haranko, A. Harb, A.
Jafari, N.Z. Jomhari, H. Jung, A. Kasem17, M. Kasemann, H. Kaveh, J. Keaveney,
C. Kleinwort, J. Knolle, D. Krücker, W. Lange, T. Lenz, J. Lidrych, K. Lipka,
W. Lohmann19, R. Mankel, I.-A. Melzer-Pellmann, A.B. Meyer, M. Meyer, M.
Missiroli, G. Mittag, J. Mnich, A. Mussgiller, V. Myronenko, D. Pérez Adán,
S.K. Pflitsch, D. Pitzl, A. Raspereza, A. Saibel, M. Savitskyi, V. Scheurer,
P. Schütze, C. Schwanenberger, R. Shevchenko, A. Singh, H. Tholen, O. Turkot,
A. Vagnerini, M. Van De Klundert, R. Walsh, Y. Wen, K. Wichmann, C. Wissing,
O. Zenaiev, R. Zlebcik University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, L. Benato, A. Benecke, V. Blobel, T. Dreyer, A.
Ebrahimi, F. Feindt, A. Fröhlich, C. Garbers, E. Garutti, D. Gonzalez, P.
Gunnellini, J. Haller, A. Hinzmann, A. Karavdina, G. Kasieczka, R. Klanner, R.
Kogler, N. Kovalchuk, S. Kurz, V. Kutzner, J. Lange, T. Lange, A. Malara, J.
Multhaup, C.E.N. Niemeyer, A. Perieanu, A. Reimers, O. Rieger, C. Scharf, P.
Schleper, S. Schumann, J. Schwandt, J. Sonneveld, H. Stadie, G. Steinbrück,
F.M. Stober, B. Vormwald, I. Zoi Karlsruher Institut fuer Technologie,
Karlsruhe, Germany
M. Akbiyik, C. Barth, M. Baselga, S. Baur, T. Berger, E. Butz, R. Caspart, T.
Chwalek, W. De Boer, A. Dierlamm, K. El Morabit, N. Faltermann, M. Giffels, P.
Goldenzweig, A. Gottmann, M.A. Harrendorf, F. Hartmann16, U. Husemann, S.
Kudella, S. Mitra, M.U. Mozer, D. Müller, Th. Müller, M. Musich, A. Nürnberg,
G. Quast, K. Rabbertz, M. Schröder, I. Shvetsov, H.J. Simonis, R. Ulrich, M.
Wassmer, M. Weber, C. Wöhrmann, R. Wolf Institute of Nuclear and Particle
Physics (INPP), NCSR Demokritos, Aghia Paraskevi, Greece
G. Anagnostou, P. Asenov, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas,
G. Paspalaki National and Kapodistrian University of Athens, Athens, Greece
M. Diamantopoulou, G. Karathanasis, P. Kontaxakis, A. Manousakis-katsikakis,
A. Panagiotou, I. Papavergou, N. Saoulidou, A. Stakia, K. Theofilatos, K.
Vellidis, E. Vourliotis National Technical University of Athens, Athens,
Greece
G. Bakas, K. Kousouris, I. Papakrivopoulos, G. Tsipolitis University of
Ioánnina, Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, S. Mallios, K.
Manitara, N. Manthos, I. Papadopoulos, J. Strologas, F.A. Triantis, D.
Tsitsonis MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
M. Bartók20, R. Chudasama, M. Csanad, P. Major, K. Mandal, A. Mehta, M.I.
Nagy, G. Pasztor, O. Surányi, G.I. Veres Wigner Research Centre for Physics,
Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath21, F. Sikler, T.Á. Vámi, V. Veszpremi, G.
Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear Research ATOMKI,
Debrecen, Hungary
N. Beni, S. Czellar, J. Karancsi20, A. Makovec, J. Molnar, Z. Szillasi
Institute of Physics, University of Debrecen, Debrecen, Hungary
P. Raics, D. Teyssier, Z.L. Trocsanyi, B. Ujvari Eszterhazy Karoly University,
Karoly Robert Campus, Gyongyos, Hungary
T. Csorgo, W.J. Metzger, F. Nemes, T. Novak Indian Institute of Science
(IISc), Bangalore, India
S. Choudhury, J.R. Komaragiri, P.C. Tiwari National Institute of Science
Education and Research, HBNI, Bhubaneswar, India
S. Bahinipati23, C. Kar, G. Kole, P. Mal, V.K. Muraleedharan Nair Bindhu, A.
Nayak24, D.K. Sahoo23, S.K. Swain Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, S. Chauhan, R. Chawla, N. Dhingra, R.
Gupta, A. Kaur, M. Kaur, S. Kaur, P. Kumari, M. Lohan, M. Meena, K. Sandeep,
S. Sharma, J.B. Singh, A.K. Virdi, G. Walia University of Delhi, Delhi, India
A. Bhardwaj, B.C. Choudhary, R.B. Garg, M. Gola, S. Keshri, Ashok Kumar, M.
Naimuddin, P. Priyanka, K. Ranjan, Aashaq Shah, R. Sharma Saha Institute of
Nuclear Physics, HBNI, Kolkata, India
R. Bhardwaj25, M. Bharti25, R. Bhattacharya, S. Bhattacharya, U. Bhawandeep25,
D. Bhowmik, S. Dutta, S. Ghosh, M. Maity26, K. Mondal, S. Nandan, A. Purohit,
P.K. Rout, G. Saha, S. Sarkar, T. Sarkar26, M. Sharan, B. Singh25, S. Thakur25
Indian Institute of Technology Madras, Madras, India
P.K. Behera, P. Kalbhor, A. Muhammad, P.R. Pujahari, A. Sharma, A.K. Sikdar
Bhabha Atomic Research Centre, Mumbai, India
D. Dutta, V. Jha, V. Kumar, D.K. Mishra, P.K. Netrakanti, L.M. Pant, P. Shukla
Tata Institute of Fundamental Research-A, Mumbai, India
T. Aziz, M.A. Bhat, S. Dugad, G.B. Mohanty, N. Sur, RavindraKumar Verma Tata
Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, S. Karmakar,
S. Kumar, G. Majumder, K. Mazumdar, N. Sahoo, S. Sawant Indian Institute of
Science Education and Research (IISER), Pune, India
S. Chauhan, S. Dube, V. Hegde, B. Kansal, A. Kapoor, K. Kothekar, S. Pandey,
A. Rane, A. Rastogi, S. Sharma Institute for Research in Fundamental Sciences
(IPM), Tehran, Iran
S. Chenarani27, E. Eskandari Tadavani, S.M. Etesami27, M. Khakzad, M.
Mohammadi Najafabadi, M. Naseri, F. Rezaei Hosseinabadi University College
Dublin, Dublin, Ireland
M. Felcini, M. Grunewald INFN Sezione di Bari a, Università di Bari b,
Politecnico di Bari c, Bari, Italy
M. Abbresciaa,b, R. Alya,b,28, C. Calabriaa,b, A. Colaleoa, D. Creanzaa,c, L.
Cristellaa,b, N. De Filippisa,c, M. De Palmaa,b, A. Di Florioa,b, W.
Elmetenaweea,b, L. Fiorea, A. Gelmia,b, G. Iasellia,c, M. Incea,b, S.
Lezkia,b, G. Maggia,c, M. Maggia, G. Minielloa,b, S. Mya,b, S. Nuzzoa,b, A.
Pompilia,b, G. Pugliesea,c, R. Radognaa, A. Ranieria, G. Selvaggia,b, L.
Silvestrisa, F.M. Simonea, R. Vendittia, P. Verwilligena INFN Sezione di
Bologna a, Università di Bologna b, Bologna, Italy
G. Abbiendia, C. Battilanaa,b, D. Bonacorsia,b, L. Borgonovia,b, S. Braibant-
Giacomellia,b, R. Campaninia,b, P. Capiluppia,b, A. Castroa,b, F.R. Cavalloa,
C. Cioccaa, G. Codispotia,b, M. Cuffiania,b, G.M. Dallavallea, F. Fabbria, A.
Fanfania,b, E. Fontanesia,b, P. Giacomellia, C. Grandia, L. Guiduccia,b, F.
Iemmia,b, S. Lo Meoa,29, S. Marcellinia, G. Masettia, F.L. Navarriaa,b, A.
Perrottaa, F. Primaveraa,b, A.M. Rossia,b, T. Rovellia,b, G.P. Sirolia,b, N.
Tosia INFN Sezione di Catania a, Università di Catania b, Catania, Italy
S. Albergoa,b,30, S. Costaa,b, A. Di Mattiaa, R. Potenzaa,b, A. Tricomia,b,30,
C. Tuvea,b INFN Sezione di Firenze a, Università di Firenze b, Firenze, Italy
G. Barbaglia, A. Cassese, R. Ceccarelli, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, E. Focardia,b, G. Latinoa,b, P. Lenzia,b, M. Meschinia, S.
Paolettia, G. Sguazzonia, L. Viliania INFN Laboratori Nazionali di Frascati,
Frascati, Italy
L. Benussi, S. Bianco, D. Piccolo INFN Sezione di Genova a, Università di
Genova b, Genova, Italy
M. Bozzoa,b, F. Ferroa, R. Mulargiaa,b, E. Robuttia, S. Tosia,b INFN Sezione
di Milano-Bicocca a, Università di Milano-Bicocca b, Milano, Italy
A. Benagliaa, A. Beschia,b, F. Brivioa,b, V. Cirioloa,b,16, S. Di Guidaa,b,16,
M.E. Dinardoa,b, P. Dinia, S. Gennaia, A. Ghezzia,b, P. Govonia,b, L.
Guzzia,b, M. Malbertia, S. Malvezzia, D. Menascea, F. Montia,b, L. Moronia, M.
Paganonia,b, D. Pedrinia, S. Ragazzia,b, T. Tabarelli de Fatisa,b, D. Zuoloa,b
INFN Sezione di Napoli a, Università di Napoli ’Federico II’ b, Napoli, Italy,
Università della Basilicata c, Potenza, Italy, Università G. Marconi d, Roma,
Italy
S. Buontempoa, N. Cavalloa,c, A. De Iorioa,b, A. Di Crescenzoa,b, F.
Fabozzia,c, F. Fiengaa, G. Galatia, A.O.M. Iorioa,b, L. Listaa,b, S.
Meolaa,d,16, P. Paoluccia,16, B. Rossia, C. Sciaccaa,b, E. Voevodinaa,b INFN
Sezione di Padova a, Università di Padova b, Padova, Italy, Università di
Trento c, Trento, Italy
P. Azzia, N. Bacchettaa, D. Biselloa,b, A. Bolettia,b, A. Bragagnoloa,b, R.
Carlina,b, P. Checchiaa, P. De Castro Manzanoa, T. Dorigoa, U. Dossellia, F.
Gasparinia,b, U. Gasparinia,b, A. Gozzelinoa, S.Y. Hoha,b, P. Lujana, M.
Margonia,b, A.T. Meneguzzoa,b, J. Pazzinia,b, M. Presillab, P. Ronchesea,b, R.
Rossina,b, F. Simonettoa,b, A. Tikoa, M. Tosia,b, M. Zanettia,b, P. Zottoa,b,
G. Zumerlea,b INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy
A. Braghieria, D. Fiorinaa,b, P. Montagnaa,b, S.P. Rattia,b, V. Rea, M.
Ressegottia,b, C. Riccardia,b, P. Salvinia, I. Vaia, P. Vituloa,b INFN Sezione
di Perugia a, Università di Perugia b, Perugia, Italy
M. Biasinia,b, G.M. Bileia, D. Ciangottinia,b, L. Fanòa,b, P. Laricciaa,b, R.
Leonardia,b, E. Manonia, G. Mantovania,b, V. Mariania,b, M. Menichellia, A.
Rossia,b, A. Santocchiaa,b, D. Spigaa INFN Sezione di Pisa a, Università di
Pisa b, Scuola Normale Superiore di Pisa c, Pisa, Italy
K. Androsova, P. Azzurria, G. Bagliesia, V. Bertacchia,c, L. Bianchinia, T.
Boccalia, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, G. Fedia, L.
Gianninia,c, A. Giassia, M.T. Grippoa, F. Ligabuea,c, E. Mancaa,c, G.
Mandorlia,c, A. Messineoa,b, F. Pallaa, A. Rizzia,b, G. Rolandi31, S. Roy
Chowdhury, A. Scribanoa, P. Spagnoloa, R. Tenchinia, G. Tonellia,b, N. Turini,
A. Venturia, P.G. Verdinia INFN Sezione di Roma a, Sapienza Università di Roma
b, Rome, Italy
F. Cavallaria, M. Cipriania,b, D. Del Rea,b, E. Di Marcoa,b, M. Diemoza, E.
Longoa,b, P. Meridiania, G. Organtinia,b, F. Pandolfia, R. Paramattia,b, C.
Quarantaa,b, S. Rahatloua,b, C. Rovellia, F. Santanastasioa,b, L. Soffia,b
INFN Sezione di Torino a, Università di Torino b, Torino, Italy, Università
del Piemonte Orientale c, Novara, Italy
N. Amapanea,b, R. Arcidiaconoa,c, S. Argiroa,b, M. Arneodoa,c, N. Bartosika,
R. Bellana,b, A. Bellora, C. Biinoa, A. Cappatia,b, N. Cartigliaa, S.
Comettia, M. Costaa,b, R. Covarellia,b, N. Demariaa, B. Kiania,b, C.
Mariottia, S. Masellia, E. Migliorea,b, V. Monacoa,b, E. Monteila,b, M.
Montenoa, M.M. Obertinoa,b, G. Ortonaa,b, L. Pachera,b, N. Pastronea, M.
Pelliccionia, G.L. Pinna Angionia,b, A. Romeroa,b, M. Ruspaa,c, R.
Salvaticoa,b, V. Solaa, A. Solanoa,b, D. Soldia,b, A. Staianoa INFN Sezione di
Trieste a, Università di Trieste b, Trieste, Italy
S. Belfortea, V. Candelisea,b, M. Casarsaa, F. Cossuttia, A. Da Rolda,b, G.
Della Riccaa,b, F. Vazzolera,b, A. Zanettia Kyungpook National University,
Daegu, Korea
B. Kim, D.H. Kim, G.N. Kim, J. Lee, S.W. Lee, C.S. Moon, Y.D. Oh, S.I. Pak, S.
Sekmen, D.C. Son, Y.C. Yang Chonnam National University, Institute for
Universe and Elementary Particles, Kwangju, Korea
H. Kim, D.H. Moon, G. Oh Hanyang University, Seoul, Korea
B. Francois, T.J. Kim, J. Park Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, D. Gyun, S. Ha, B. Hong, K. Lee, K.S. Lee, J. Lim, J.
Park, S.K. Park, Y. Roh, J. Yoo Kyung Hee University, Department of Physics
J. Goh Sejong University, Seoul, Korea
H.S. Kim Seoul National University, Seoul, Korea
J. Almond, J.H. Bhyun, J. Choi, S. Jeon, J. Kim, J.S. Kim, H. Lee, K. Lee, S.
Lee, K. Nam, M. Oh, S.B. Oh, B.C. Radburn-Smith, U.K. Yang, H.D. Yoo, I. Yoon,
G.B. Yu University of Seoul, Seoul, Korea
D. Jeon, H. Kim, J.H. Kim, J.S.H. Lee, I.C. Park, I.J Watson Sungkyunkwan
University, Suwon, Korea
Y. Choi, C. Hwang, Y. Jeong, J. Lee, Y. Lee, I. Yu Riga Technical University,
Riga, Latvia
V. Veckalns32 Vilnius University, Vilnius, Lithuania
V. Dudenas, A. Juodagalvis, G. Tamulaitis, J. Vaitkus National Centre for
Particle Physics, Universiti Malaya, Kuala Lumpur, Malaysia
Z.A. Ibrahim, F. Mohamad Idris33, W.A.T. Wan Abdullah, M.N. Yusli, Z. Zolkapli
Universidad de Sonora (UNISON), Hermosillo, Mexico
J.F. Benitez, A. Castaneda Hernandez, J.A. Murillo Quijada, L. Valencia Palomo
Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico
H. Castilla-Valdez, E. De La Cruz-Burelo, I. Heredia-De La Cruz34, R. Lopez-
Fernandez, A. Sanchez-Hernandez Universidad Iberoamericana, Mexico City,
Mexico
S. Carrillo Moreno, C. Oropeza Barrera, M. Ramirez-Garcia, F. Vazquez Valencia
Benemerita Universidad Autonoma de Puebla, Puebla, Mexico
J. Eysermans, I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada Universidad
Autónoma de San Luis Potosí, San Luis Potosí, Mexico
A. Morelos Pineda University of Montenegro, Podgorica, Montenegro
J. Mijuskovic, N. Raicevic University of Auckland, Auckland, New Zealand
D. Krofcheck University of Canterbury, Christchurch, New Zealand
S. Bheesette, P.H. Butler National Centre for Physics, Quaid-I-Azam
University, Islamabad, Pakistan
A. Ahmad, M. Ahmad, Q. Hassan, H.R. Hoorani, W.A. Khan, M.A. Shah, M. Shoaib,
M. Waqas AGH University of Science and Technology Faculty of Computer Science,
Electronics and Telecommunications, Krakow, Poland
V. Avati, L. Grzanka, M. Malawski National Centre for Nuclear Research,
Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, M. Górski, M. Kazana, M. Szleper, P.
Zalewski Institute of Experimental Physics, Faculty of Physics, University of
Warsaw, Warsaw, Poland
K. Bunkowski, A. Byszuk35, K. Doroba, A. Kalinowski, M. Konecki, J.
Krolikowski, M. Misiura, M. Olszewski, M. Walczak Laboratório de
Instrumentação e Física Experimental de Partículas, Lisboa, Portugal
M. Araujo, P. Bargassa, D. Bastos, A. Di Francesco, P. Faccioli, B. Galinhas,
M. Gallinaro, J. Hollar, N. Leonardo, T.S. Niknejad, J. Seixas, K. Shchelina,
G. Strong, O. Toldaiev, J. Varela Joint Institute for Nuclear Research, Dubna,
Russia
S. Afanasiev, P. Bunin, M. Gavrilenko, I. Golutvin, I. Gorbunov, A. Kamenev,
V. Karjavine, A. Lanev, A. Malakhov, V. Matveev36,37, P. Moisenz, V. Palichik,
V. Perelygin, M. Savina, S. Shmatov, S. Shulha, N. Skatchkov, V. Smirnov, N.
Voytishin, A. Zarubin Petersburg Nuclear Physics Institute, Gatchina (St.
Petersburg), Russia
L. Chtchipounov, V. Golovtcov, Y. Ivanov, V. Kim38, E. Kuznetsova39, P.
Levchenko, V. Murzin, V. Oreshkin, I. Smirnov, D. Sosnov, V. Sulimov, L.
Uvarov, A. Vorobyev Institute for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov,
N. Krasnikov, A. Pashenkov, D. Tlisov, A. Toropin Institute for Theoretical
and Experimental Physics named by A.I. Alikhanov of NRC ‘Kurchatov Institute’,
Moscow, Russia
V. Epshteyn, V. Gavrilov, N. Lychkovskaya, A. Nikitenko40, V. Popov, I.
Pozdnyakov, G. Safronov, A. Spiridonov, A. Stepennov, M. Toms, E. Vlasov, A.
Zhokin Moscow Institute of Physics and Technology, Moscow, Russia
T. Aushev National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
M. Chadeeva41, P. Parygin, D. Philippov, V. Rusinov, E. Zhemchugov P.N.
Lebedev Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin, I. Dremin, M. Kirakosyan, A. Terkulov Skobeltsyn
Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow,
Russia
A. Baskakov, A. Belyaev, E. Boos, V. Bunichev, M. Dubinin42, L. Dudko, A.
Ershov, V. Klyukhin, O. Kodolova, I. Lokhtin, S. Obraztsov, S. Petrushanko, V.
Savrin Novosibirsk State University (NSU), Novosibirsk, Russia
A. Barnyakov43, V. Blinov43, T. Dimova43, L. Kardapoltsev43, Y. Skovpen43
Institute for High Energy Physics of National Research Centre ‘Kurchatov
Institute’, Protvino, Russia
I. Azhgirey, I. Bayshev, S. Bitioukov, V. Kachanov, D. Konstantinov, P.
Mandrik, V. Petrov, R. Ryutin, S. Slabospitskii, A. Sobol, S. Troshin, N.
Tyurin, A. Uzunian, A. Volkov National Research Tomsk Polytechnic University,
Tomsk, Russia
A. Babaev, A. Iuzhakov, V. Okhotnikov Tomsk State University, Tomsk, Russia
V. Borchsh, V. Ivanchenko, E. Tcherniaev University of Belgrade: Faculty of
Physics and VINCA Institute of Nuclear Sciences
P. Adzic44, P. Cirkovic, D. Devetak, M. Dordevic, P. Milenovic, J. Milosevic,
M. Stojanovic Centro de Investigaciones Energéticas Medioambientales y
Tecnológicas (CIEMAT), Madrid, Spain
M. Aguilar-Benitez, J. Alcaraz Maestre, A. Álvarez Fernández, I. Bachiller, M.
Barrio Luna, J.A. Brochero Cifuentes, C.A. Carrillo Montoya, M. Cepeda, 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, Á. Navarro Tobar, A. Pérez-Calero Yzquierdo,
J. Puerta Pelayo, I. Redondo, L. Romero, S. Sánchez Navas, M.S. Soares, A.
Triossi, C. Willmott Universidad Autónoma de Madrid, Madrid, Spain
C. Albajar, J.F. de Trocóniz, R. Reyes-Almanza Universidad de Oviedo,
Instituto Universitario de Ciencias y Tecnologías Espaciales de Asturias
(ICTEA), Oviedo, Spain
B. Alvarez Gonzalez, J. Cuevas, C. Erice, J. Fernandez Menendez, S. Folgueras,
I. Gonzalez Caballero, J.R. González Fernández, E. Palencia Cortezon, V.
Rodríguez Bouza, S. Sanchez Cruz Instituto de Física de Cantabria (IFCA),
CSIC-Universidad de Cantabria, Santander, Spain
I.J. Cabrillo, A. Calderon, B. Chazin Quero, J. Duarte Campderros, M.
Fernandez, P.J. Fernández Manteca, A. García Alonso, G. Gomez, C. Martinez
Rivero, P. Martinez Ruiz del Arbol, F. Matorras, J. Piedra Gomez, C. Prieels,
T. Rodrigo, A. Ruiz-Jimeno, L. Russo45, L. Scodellaro, N. Trevisani, I. Vila,
J.M. Vizan Garcia University of Colombo, Colombo, Sri Lanka
K. Malagalage University of Ruhuna, Department of Physics, Matara, Sri Lanka
W.G.D. Dharmaratna, N. Wickramage CERN, European Organization for Nuclear
Research, Geneva, Switzerland
D. Abbaneo, B. Akgun, E. Auffray, G. Auzinger, J. Baechler, P. Baillon, A.H.
Ball, D. Barney, J. Bendavid, M. Bianco, A. Bocci, P. Bortignon, E. Bossini,
C. Botta, E. Brondolin, T. Camporesi, A. Caratelli, G. Cerminara, E. Chapon,
G. Cucciati, D. d’Enterria, A. Dabrowski, N. Daci, V. Daponte, A. David, O.
Davignon, A. De Roeck, M. Deile, M. Dobson, M. Dünser, N. Dupont, A. Elliott-
Peisert, N. Emriskova, F. Fallavollita46, D. Fasanella, S. Fiorendi, G.
Franzoni, J. Fulcher, W. Funk, S. Giani, D. Gigi, A. Gilbert, K. Gill, F.
Glege, M. Gruchala, M. Guilbaud, D. Gulhan, J. Hegeman, C. Heidegger, Y.
Iiyama, V. Innocente, P. Janot, O. Karacheban19, J. Kaspar, J. Kieseler, M.
Krammer1, N. Kratochwil, C. Lange, P. Lecoq, C. Lourenço, L. Malgeri, M.
Mannelli, A. Massironi, F. Meijers, J.A. Merlin, S. Mersi, E. Meschi, F.
Moortgat, M. Mulders, J. Ngadiuba, J. Niedziela, S. Nourbakhsh, S. Orfanelli,
L. Orsini, F. Pantaleo16, L. Pape, E. Perez, M. Peruzzi, A. Petrilli, G.
Petrucciani, A. Pfeiffer, M. Pierini, F.M. Pitters, D. Rabady, A. Racz, M.
Rieger, M. Rovere, H. Sakulin, C. Schäfer, C. Schwick, M. Selvaggi, A. Sharma,
P. Silva, W. Snoeys, P. Sphicas47, J. Steggemann, S. Summers, V.R. Tavolaro,
D. Treille, A. Tsirou, G.P. Van Onsem, A. Vartak, M. Verzetti, W.D. Zeuner
Paul Scherrer Institut, Villigen, Switzerland
L. 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, P. Berger, N. Chernyavskaya, G. Dissertori, M. Dittmar, M.
Donegà, C. Dorfer, T.A. Gómez Espinosa, C. Grab, D. Hits, T. Klijnsma, W.
Lustermann, R.A. Manzoni, M. Marionneau, M.T. Meinhard, F. Micheli, P.
Musella, F. Nessi-Tedaldi, F. Pauss, G. Perrin, L. Perrozzi, S. Pigazzini,
M.G. Ratti, M. Reichmann, C. Reissel, T. Reitenspiess, D. Ruini, D.A. Sanz
Becerra, M. Schönenberger, L. Shchutska, M.L. Vesterbacka Olsson, R. Wallny,
D.H. Zhu Universität Zürich, Zurich, Switzerland
T.K. Aarrestad, C. Amsler49, D. Brzhechko, M.F. Canelli, A. De Cosa, R. Del
Burgo, S. Donato, B. Kilminster, S. Leontsinis, V.M. Mikuni, I. Neutelings, G.
Rauco, P. Robmann, D. Salerno, K. Schweiger, C. Seitz, Y. Takahashi, S. Wertz,
A. Zucchetta National Central University, Chung-Li, Taiwan
T.H. Doan, C.M. Kuo, W. Lin, A. Roy, S.S. Yu National Taiwan University (NTU),
Taipei, Taiwan
P. Chang, Y. Chao, K.F. Chen, P.H. Chen, W.-S. Hou, Y.y. Li, R.-S. Lu, E.
Paganis, A. Psallidas, A. Steen Chulalongkorn University, Faculty of Science,
Department of Physics, Bangkok, Thailand
B. Asavapibhop, C. Asawatangtrakuldee, N. Srimanobhas, N. Suwonjandee Çukurova
University, Physics Department, Science and Art Faculty, Adana, Turkey
A. Bat, F. Boran, A. Celik50, S. Cerci51, S. Damarseckin52, Z.S. Demiroglu, F.
Dolek, C. Dozen53, I. Dumanoglu, G. Gokbulut, EmineGurpinar Guler54, Y. Guler,
I. Hos55, C. Isik, E.E. Kangal56, O. Kara, A. Kayis Topaksu, U. Kiminsu, G.
Onengut, K. Ozdemir57, S. Ozturk58, A.E. Simsek, D. Sunar Cerci51, U.G. Tok,
S. Turkcapar, I.S. Zorbakir, C. Zorbilmez Middle East Technical University,
Physics Department, Ankara, Turkey
B. Isildak59, G. Karapinar60, M. Yalvac Bogazici University, Istanbul, Turkey
I.O. Atakisi, E. Gülmez, M. Kaya61, O. Kaya62, Ö. Özçelik, S. Tekten, E.A.
Yetkin63 Istanbul Technical University, Istanbul, Turkey
A. Cakir, K. Cankocak, Y. Komurcu, S. Sen64 Istanbul University, Istanbul,
Turkey
B. Kaynak, S. Ozkorucuklu Institute for Scintillation Materials of National
Academy of Science of Ukraine, Kharkov, Ukraine
B. Grynyov National Scientific Center, Kharkov Institute of Physics and
Technology, Kharkov, Ukraine
L. Levchuk University of Bristol, Bristol, United Kingdom
E. Bhal, S. Bologna, J.J. Brooke, D. Burns65, E. Clement, D. Cussans, H.
Flacher, J. Goldstein, G.P. Heath, H.F. Heath, L. Kreczko, S. Paramesvaran, B.
Penning, T. Sakuma, S. Seif El Nasr-Storey, V.J. Smith, J. Taylor, A.
Titterton Rutherford Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev66, C. Brew, R.M. Brown, D. Cieri, D.J.A. Cockerill, J.A.
Coughlan, K. Harder, S. Harper, J. Linacre, K. Manolopoulos, D.M. Newbold, E.
Olaiya, D. Petyt, T. Reis, T. Schuh, C.H. Shepherd-Themistocleous, A. Thea,
I.R. Tomalin, T. Williams, W.J. Womersley Imperial College, London, United
Kingdom
R. Bainbridge, P. Bloch, J. Borg, S. Breeze, O. Buchmuller, A. Bundock,
GurpreetSingh CHAHAL67, D. Colling, P. Dauncey, G. Davies, M. Della Negra, R.
Di Maria, P. Everaerts, G. Hall, G. Iles, T. James, M. Komm, C. Laner, L.
Lyons, A.-M. Magnan, S. Malik, A. Martelli, V. Milosevic, J. Nash68, V.
Palladino, M. Pesaresi, D.M. Raymond, A. Richards, A. Rose, E. Scott, C. Seez,
A. Shtipliyski, M. Stoye, T. Strebler, A. Tapper, K. Uchida, T. Virdee16, N.
Wardle, D. Winterbottom, J. Wright, A.G. Zecchinelli, S.C. Zenz Brunel
University, Uxbridge, United Kingdom
J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, C.K. Mackay, A. Morton, I.D. Reid,
L. Teodorescu, S. Zahid Baylor University, Waco, USA
K. Call, B. Caraway, J. Dittmann, K. Hatakeyama, C. Madrid, B. McMaster, N.
Pastika, C. Smith Catholic University of America, Washington, DC, USA
R. Bartek, A. Dominguez, R. Uniyal, A.M. Vargas Hernandez The University of
Alabama, Tuscaloosa, USA
A. Buccilli, S.I. Cooper, C. Henderson, P. Rumerio, C. West Boston University,
Boston, USA
D. Arcaro, Z. Demiragli, D. Gastler, C. Richardson, J. Rohlf, D. Sperka, I.
Suarez, L. Sulak, D. Zou Brown University, Providence, USA
G. Benelli, B. Burkle, X. Coubez17, D. Cutts, Y.t. Duh, M. Hadley, J. Hakala,
U. Heintz, J.M. Hogan69, K.H.M. Kwok, E. Laird, G. Landsberg, J. Lee, Z. Mao,
M. Narain, S. Sagir70, R. Syarif, E. Usai, D. Yu, W. Zhang University of
California, Davis, Davis, USA
R. Band, C. Brainerd, R. Breedon, M. Calderon De La Barca Sanchez, M. Chertok,
J. Conway, R. Conway, P.T. Cox, R. Erbacher, C. Flores, G. Funk, F. Jensen, W.
Ko, O. Kukral, R. Lander, M. Mulhearn, D. Pellett, J. Pilot, M. Shi, D.
Taylor, K. Tos, M. Tripathi, Z. Wang, F. Zhang University of California, Los
Angeles, USA
M. Bachtis, C. Bravo, R. Cousins, A. Dasgupta, A. Florent, J. Hauser, M.
Ignatenko, N. Mccoll, W.A. Nash, S. Regnard, D. Saltzberg, C. Schnaible, B.
Stone, V. Valuev University of California, Riverside, Riverside, USA
K. Burt, Y. Chen, R. Clare, J.W. Gary, S.M.A. Ghiasi Shirazi, G. Hanson, G.
Karapostoli, E. Kennedy, O.R. Long, M. Olmedo Negrete, M.I. Paneva, W. Si, L.
Wang, S. Wimpenny, B.R. Yates, Y. Zhang University of California, San Diego,
La Jolla, USA
J.G. Branson, P. Chang, S. Cittolin, S. Cooperstein, N. Deelen, M. Derdzinski,
R. Gerosa, D. Gilbert, B. Hashemi, D. Klein, V. Krutelyov, J. Letts, M.
Masciovecchio, S. May, S. Padhi, M. Pieri, V. Sharma, M. Tadel, F. Würthwein,
A. Yagil, G. Zevi Della Porta University of California, Santa Barbara -
Department of Physics, Santa Barbara, USA
N. Amin, R. Bhandari, C. Campagnari, M. Citron, V. Dutta, M. Franco Sevilla,
L. Gouskos, J. Incandela, B. Marsh, H. Mei, A. Ovcharova, H. Qu, J. Richman,
U. Sarica, D. Stuart, S. Wang California Institute of Technology, Pasadena,
USA
D. Anderson, A. Bornheim, O. Cerri, I. Dutta, J.M. Lawhorn, N. Lu, J. Mao,
H.B. Newman, T.Q. Nguyen, J. Pata, M. Spiropulu, J.R. Vlimant, S. Xie, Z.
Zhang, R.Y. Zhu Carnegie Mellon University, Pittsburgh, USA
M.B. Andrews, T. Ferguson, T. Mudholkar, M. Paulini, M. Sun, I. Vorobiev, M.
Weinberg University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, A. Johnson, E. MacDonald, T. Mulholland, R. Patel, A.
Perloff, K. Stenson, K.A. Ulmer, S.R. Wagner Cornell University, Ithaca, USA
J. Alexander, J. Chaves, Y. Cheng, J. Chu, A. Datta, A. Frankenthal, K.
Mcdermott, J.R. Patterson, D. Quach, A. Rinkevicius71, A. Ryd, S.M. Tan, Z.
Tao, J. Thom, P. Wittich, M. Zientek Fermi National Accelerator Laboratory,
Batavia, USA
S. Abdullin, M. Albrow, M. Alyari, G. Apollinari, A. Apresyan, A. Apyan, S.
Banerjee, L.A.T. Bauerdick, A. Beretvas, D. Berry, J. Berryhill, P.C. Bhat, K.
Burkett, J.N. Butler, A. Canepa, G.B. Cerati, H.W.K. Cheung, F. Chlebana, M.
Cremonesi, J. Duarte, V.D. Elvira, J. Freeman, Z. Gecse, E. Gottschalk, L.
Gray, D. Green, S. Grünendahl, O. Gutsche, AllisonReinsvold Hall, J. Hanlon,
R.M. Harris, S. Hasegawa, R. Heller, J. Hirschauer, B. Jayatilaka, S.
Jindariani, M. Johnson, U. Joshi, B. Klima, M.J. Kortelainen, B. Kreis, S.
Lammel, J. Lewis, D. Lincoln, R. Lipton, M. Liu, T. Liu, J. Lykken, K.
Maeshima, J.M. Marraffino, D. Mason, P. McBride, P. Merkel, S. Mrenna, S.
Nahn, V. O’Dell, V. Papadimitriou, K. Pedro, C. Pena, G. Rakness, F. Ravera,
L. Ristori, B. Schneider, E. Sexton-Kennedy, N. Smith, A. Soha, W.J. Spalding,
L. Spiegel, S. Stoynev, J. Strait, N. Strobbe, L. Taylor, S. Tkaczyk, N.V.
Tran, L. Uplegger, E.W. Vaandering, C. Vernieri, R. Vidal, M. Wang, H.A. Weber
University of Florida, Gainesville, USA
D. Acosta, P. Avery, D. Bourilkov, A. Brinkerhoff, L. Cadamuro, A. Carnes, V.
Cherepanov, F. Errico, R.D. Field, S.V. Gleyzer, B.M. Joshi, M. Kim, J.
Konigsberg, A. Korytov, K.H. Lo, P. Ma, K. Matchev, N. Menendez, G.
Mitselmakher, D. Rosenzweig, K. Shi, J. Wang, S. Wang, X. Zuo Florida
International University, Miami, USA
Y.R. Joshi Florida State University, Tallahassee, USA
T. Adams, A. Askew, S. Hagopian, V. Hagopian, K.F. Johnson, R. Khurana, T.
Kolberg, G. Martinez, T. Perry, H. Prosper, C. Schiber, R. Yohay, J. Zhang
Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, M. Hohlmann, D. Noonan, M. Rahmani, M. Saunders, F. Yumiceva
University of Illinois at Chicago (UIC), Chicago, USA
M.R. Adams, L. Apanasevich, R.R. Betts, R. Cavanaugh, X. Chen, S. Dittmer, O.
Evdokimov, C.E. Gerber, D.A. Hangal, D.J. Hofman, K. Jung, C. Mills, T. Roy,
M.B. Tonjes, N. Varelas, J. Viinikainen, H. Wang, X. Wang, Z. Wu The
University of Iowa, Iowa City, USA
M. Alhusseini, B. Bilki54, W. Clarida, K. Dilsiz72, S. Durgut, R.P.
Gandrajula, M. Haytmyradov, V. Khristenko, O.K. Köseyan, J.-P. Merlo, A.
Mestvirishvili73, A. Moeller, J. Nachtman, H. Ogul74, Y. Onel, F. Ozok75, A.
Penzo, C. Snyder, E. Tiras, J. Wetzel Johns Hopkins University, Baltimore, USA
B. Blumenfeld, A. Cocoros, N. Eminizer, A.V. Gritsan, W.T. Hung, S. Kyriacou,
P. Maksimovic, J. Roskes, M. Swartz The University of Kansas, Lawrence, USA
C. Baldenegro Barrera, P. Baringer, A. Bean, S. Boren, J. Bowen, A. Bylinkin,
T. Isidori, S. Khalil, J. King, G. Krintiras, A. Kropivnitskaya, C. Lindsey,
D. Majumder, W. Mcbrayer, N. Minafra, M. Murray, C. Rogan, C. Royon, S.
Sanders, E. Schmitz, J.D. Tapia Takaki, Q. Wang, J. Williams, G. Wilson Kansas
State University, Manhattan, USA
S. Duric, A. Ivanov, K. Kaadze, D. Kim, Y. Maravin, D.R. Mendis, T. Mitchell,
A. Modak, A. Mohammadi Lawrence Livermore National Laboratory, Livermore, USA
F. Rebassoo, D. Wright University of Maryland, College Park, USA
A. Baden, O. Baron, A. Belloni, S.C. Eno, Y. Feng, N.J. Hadley, S. Jabeen,
G.Y. Jeng, R.G. Kellogg, J. Kunkle, A.C. Mignerey, S. Nabili, F. Ricci-Tam, M.
Seidel, Y.H. Shin, A. Skuja, S.C. Tonwar, K. Wong Massachusetts Institute of
Technology, Cambridge, USA
D. Abercrombie, B. Allen, A. Baty, R. Bi, S. Brandt, W. Busza, I.A. Cali, M.
D’Alfonso, G. Gomez Ceballos, M. Goncharov, P. Harris, D. Hsu, M. Hu, M.
Klute, D. Kovalskyi, Y.-J. Lee, P.D. Luckey, B. Maier, A.C. Marini, C. Mcginn,
C. Mironov, S. Narayanan, X. Niu, C. Paus, D. Rankin, C. Roland, G. Roland, Z.
Shi, G.S.F. Stephans, K. Sumorok, K. Tatar, D. Velicanu, J. Wang, T.W. Wang,
B. Wyslouch University of Minnesota, Minneapolis, USA
R.M. Chatterjee, A. Evans, S. Guts, P. Hansen, J. Hiltbrand, Sh. Jain, Y.
Kubota, Z. Lesko, J. Mans, R. Rusack, M.A. Wadud University of Mississippi,
Oxford, USA
J.G. Acosta, S. Oliveros University of Nebraska-Lincoln, Lincoln, USA
K. Bloom, D.R. Claes, C. Fangmeier, L. Finco, F. Golf, R. Kamalieddin, I.
Kravchenko, J.E. Siado, G.R. Snow${}^{\textrm{\textdagger}}$, B. Stieger, W.
Tabb State University of New York at Buffalo, Buffalo, USA
G. Agarwal, C. Harrington, I. Iashvili, A. Kharchilava, C. McLean, D. Nguyen,
A. Parker, J. Pekkanen, S. Rappoccio, B. Roozbahani Northeastern University,
Boston, USA
G. Alverson, E. Barberis, C. Freer, Y. Haddad, A. Hortiangtham, G. Madigan, B.
Marzocchi, D.M. Morse, T. Orimoto, L. Skinnari, A. Tishelman-Charny, T.
Wamorkar, B. Wang, A. Wisecarver, D. Wood Northwestern University, Evanston,
USA
S. Bhattacharya, J. Bueghly, T. Gunter, K.A. Hahn, N. Odell, M.H. Schmitt, K.
Sung, M. Trovato, M. Velasco University of Notre Dame, Notre Dame, USA
R. Bucci, N. Dev, R. Goldouzian, M. Hildreth, K. Hurtado Anampa, C. Jessop,
D.J. Karmgard, K. Lannon, W. Li, N. Loukas, N. Marinelli, I. Mcalister, F.
Meng, C. Mueller, Y. Musienko36, M. Planer, R. Ruchti, P. Siddireddy, G.
Smith, S. Taroni, M. Wayne, A. Wightman, M. Wolf, A. Woodard The Ohio State
University, Columbus, USA
J. Alimena, B. Bylsma, L.S. Durkin, S. Flowers, B. Francis, C. Hill, W. Ji, A.
Lefeld, T.Y. Ling, B.L. Winer Princeton University, Princeton, USA
G. Dezoort, P. Elmer, J. Hardenbrook, N. Haubrich, S. Higginbotham, A.
Kalogeropoulos, S. Kwan, D. Lange, M.T. Lucchini, J. Luo, D. Marlow, K. Mei,
I. Ojalvo, J. Olsen, C. Palmer, P. Piroué, J. Salfeld-Nebgen, D. Stickland, C.
Tully, Z. Wang University of Puerto Rico, Mayaguez, USA
S. Malik, S. Norberg Purdue University, West Lafayette, USA
A. Barker, V.E. Barnes, S. Das, L. Gutay, M. Jones, A.W. Jung, A. Khatiwada,
B. Mahakud, D.H. Miller, G. Negro, N. Neumeister, C.C. Peng, S. Piperov, H.
Qiu, J.F. Schulte, J. Sun, F. Wang, R. Xiao, W. Xie Purdue University
Northwest, Hammond, USA
T. Cheng, J. Dolen, N. Parashar Rice University, Houston, USA
U. Behrens, K.M. Ecklund, S. Freed, F.J.M. Geurts, M. Kilpatrick, Arun Kumar,
W. Li, B.P. Padley, R. Redjimi, J. Roberts, J. Rorie, W. Shi, A.G. Stahl
Leiton, Z. Tu, A. Zhang University of Rochester, Rochester, USA
A. Bodek, P. de Barbaro, R. Demina, J.L. Dulemba, C. Fallon, T. Ferbel, M.
Galanti, A. Garcia-Bellido, O. Hindrichs, A. Khukhunaishvili, E. Ranken, R.
Taus Rutgers, The State University of New Jersey, Piscataway, USA
B. Chiarito, J.P. Chou, A. Gandrakota, Y. Gershtein, E. Halkiadakis, A. Hart,
M. Heindl, E. Hughes, S. Kaplan, I. Laflotte, A. Lath, R. Montalvo, K. Nash,
M. Osherson, H. Saka, S. Salur, S. Schnetzer, S. Somalwar, R. Stone, S. Thomas
University of Tennessee, Knoxville, USA
H. Acharya, A.G. Delannoy, G. Riley, S. Spanier Texas A&M University, College
Station, USA
O. Bouhali76, M. Dalchenko, M. De Mattia, A. Delgado, S. Dildick, R. Eusebi,
J. Gilmore, T. Huang, T. Kamon77, S. Luo, S. Malhotra, D. Marley, R. Mueller,
D. Overton, L. Perniè, D. Rathjens, A. Safonov Texas Tech University, Lubbock,
USA
N. Akchurin, J. Damgov, F. De Guio, S. Kunori, K. Lamichhane, S.W. Lee, T.
Mengke, S. Muthumuni, T. Peltola, S. Undleeb, I. Volobouev, Z. Wang, A.
Whitbeck Vanderbilt University, Nashville, USA
S. Greene, A. Gurrola, R. Janjam, W. Johns, C. Maguire, A. Melo, H. Ni, K.
Padeken, F. Romeo, P. Sheldon, S. Tuo, J. Velkovska, M. Verweij University of
Virginia, Charlottesville, USA
M.W. Arenton, P. Barria, B. Cox, G. Cummings, R. Hirosky, M. Joyce, A.
Ledovskoy, C. Neu, B. Tannenwald, Y. Wang, E. Wolfe, F. Xia Wayne State
University, Detroit, USA
R. Harr, P.E. Karchin, N. Poudyal, J. Sturdy, P. Thapa University of Wisconsin
- Madison, Madison, WI, USA
T. Bose, J. Buchanan, C. Caillol, D. Carlsmith, S. Dasu, I. De Bruyn, L. Dodd,
F. Fiori, C. Galloni, B. Gomber78, H. He, M. Herndon, A. Hervé, U. Hussain, P.
Klabbers, A. Lanaro, A. Loeliger, K. Long, R. Loveless, J. Madhusudanan
Sreekala, D. Pinna, T. Ruggles, A. Savin, V. Sharma, W.H. Smith, D. Teague, S.
Trembath-reichert, N. Woods †: Deceased
1: Also at Vienna University of Technology, Vienna, Austria
2: Also at IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
3: Also at Universidade Estadual de Campinas, Campinas, Brazil
4: Also at Federal University of Rio Grande do Sul, Porto Alegre, Brazil
5: Also at UFMS, Nova Andradina, Brazil
6: Also at Universidade Federal de Pelotas, Pelotas, Brazil
7: Also at Université Libre de Bruxelles, Bruxelles, Belgium
8: Also at University of Chinese Academy of Sciences, Beijing, China
9: Also at Institute for Theoretical and Experimental Physics named by A.I.
Alikhanov of NRC ‘Kurchatov Institute’, Moscow, Russia
10: Also at Joint Institute for Nuclear Research, Dubna, Russia
11: Also at Ain Shams University, Cairo, Egypt
12: Also at Zewail City of Science and Technology, Zewail, Egypt
13: Also at Purdue University, West Lafayette, USA
14: Also at Université de Haute Alsace, Mulhouse, France
15: Also at Erzincan Binali Yildirim University, Erzincan, Turkey
16: Also at CERN, European Organization for Nuclear Research, Geneva,
Switzerland
17: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
18: Also at University of Hamburg, Hamburg, Germany
19: Also at Brandenburg University of Technology, Cottbus, Germany
20: Also at Institute of Physics, University of Debrecen, Debrecen, Hungary,
Debrecen, Hungary
21: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary
22: Also at MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary, Budapest, Hungary
23: Also at IIT Bhubaneswar, Bhubaneswar, India, Bhubaneswar, India
24: Also at Institute of Physics, Bhubaneswar, India
25: Also at Shoolini University, Solan, India
26: Also at University of Visva-Bharati, Santiniketan, India
27: Also at Isfahan University of Technology, Isfahan, Iran
28: Now at INFN Sezione di Bari a, Università di Bari b, Politecnico di Bari
c, Bari, Italy
29: Also at Italian National Agency for New Technologies, Energy and
Sustainable Economic Development, Bologna, Italy
30: Also at Centro Siciliano di Fisica Nucleare e di Struttura Della Materia,
Catania, Italy
31: Also at Scuola Normale e Sezione dell’INFN, Pisa, Italy
32: Also at Riga Technical University, Riga, Latvia, Riga, Latvia
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 Imperial College, London, United Kingdom
41: Also at P.N. Lebedev Physical Institute, Moscow, Russia
42: Also at California Institute of Technology, Pasadena, USA
43: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia
44: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia
45: Also at Università degli Studi di Siena, Siena, Italy
46: Also at INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy,
Pavia, Italy
47: Also at National and Kapodistrian University of Athens, Athens, Greece
48: Also at Universität Zürich, Zurich, Switzerland
49: Also at Stefan Meyer Institute for Subatomic Physics, Vienna, Austria,
Vienna, Austria
50: Also at Burdur Mehmet Akif Ersoy University, BURDUR, Turkey
51: Also at Adiyaman University, Adiyaman, Turkey
52: Also at Şırnak University, Sirnak, Turkey
53: Also at Tsinghua University, Beijing, China
54: Also at Beykent University, Istanbul, Turkey, Istanbul, Turkey
55: Also at Istanbul Aydin University, Istanbul, Turkey
56: Also at Mersin University, Mersin, Turkey
57: Also at Piri Reis University, Istanbul, Turkey
58: Also at Gaziosmanpasa University, Tokat, Turkey
59: Also at Ozyegin University, Istanbul, Turkey
60: Also at Izmir Institute of Technology, Izmir, Turkey
61: Also at Marmara University, Istanbul, Turkey
62: Also at Kafkas University, Kars, Turkey
63: Also at Istanbul Bilgi University, Istanbul, Turkey
64: Also at Hacettepe University, Ankara, Turkey
65: Also at Vrije Universiteit Brussel, Brussel, Belgium
66: Also at School of Physics and Astronomy, University of Southampton,
Southampton, United Kingdom
67: Also at IPPP Durham University, Durham, United Kingdom
68: Also at Monash University, Faculty of Science, Clayton, Australia
69: Also at Bethel University, St. Paul, Minneapolis, USA, St. Paul, USA
70: Also at Karamanoğlu Mehmetbey University, Karaman, Turkey
71: Also at Vilnius University, Vilnius, Lithuania
72: Also at Bingol University, Bingol, Turkey
73: Also at Georgian Technical University, Tbilisi, Georgia
74: Also at Sinop University, Sinop, Turkey
75: Also at Mimar Sinan University, Istanbul, Istanbul, Turkey
76: Also at Texas A&M University at Qatar, Doha, Qatar
77: Also at Kyungpook National University, Daegu, Korea, Daegu, Korea
78: Also at University of Hyderabad, Hyderabad, India