TOP-16-020

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$HeadURL:svn+ssh://svn.cern.ch/reps/tdr2/papers/TOP-16-020/trunk/TOP-16-020.tex$
$Id:TOP-16-020.tex4492182018-03-0515:58:35Zmara$

TOP-16-020

# Measurement of the associated production of a single top quark and a Z boson
in pp collisions at $\sqrt{s}=13$

###### Abstract

A measurement is presented of the associated production of a single top quark
and a Z boson. The study uses data from proton-proton collisions at
$\sqrt{s}=13$recorded by the CMS experiment, corresponding to an integrated
luminosity of 35.9. Using final states with three leptons (electrons or
muons), the tZq production cross section is measured to be
$\sigma(\Pp\Pp\to\PQt\PZ\PQq\to\PW\PQb\ell^{+}\ell^{-}\PQq)=123^{+33}_{-31}\stat^{+29}_{-23}\syst\unit{fb}$,
where $\ell$ stands for electrons, muons, or $\tau$ leptons, with observed and
expected significances of 3.7 and 3.1 standard deviations, respectively.

##  0.1 Introduction

At the CERN LHC, single top quark production proceeds through three
electroweak interaction processes: $t$-channel, $s$-channel, and associated
$\PQt\PW$ production. Cross sections for single top quark production have been
reported by the CDF and D0 Collaborations [1, 2], as well as by the ATLAS [3,
4, 5, 6, 7] and CMS [8, 9, 10, 11] Collaborations.

The high centre-of-mass proton-proton () collision energy of 13at the LHC,
together with large integrated luminosities, allows the study of processes
with very small cross sections that were not accessible at lower energies. One
example of such a process is the rare associated production of a single top
quark with a boson. This production mechanism, leading to a final state with a
top quark, a boson, and an additional quark, can probe the standard model (SM)
in a unique way. The main leading-order (LO) diagrams that contribute to this
final state are shown in Fig. 1. Although generically denoted in this Letter
by $\PQt\PZ\PQq$, this process also includes a small contribution from non-
resonant lepton pairs, as shown in the lower right-hand diagram in Fig. 1. The
process is sensitive to top quark couplings to the boson, as illustrated in
the middle right-hand diagram in Fig. 1, and also to the triple gauge-boson
coupling $\PW\PW\PZ$, as illustrated in the lower left-hand diagram in Fig. 1.

Figure 1: Leading-order $\PQt\PZ\PQq$ production diagrams. The lower right-
hand diagram represents the non-resonant contribution to the $\PQt\PZ\PQq$
process.

The top quark couplings to the boson and the triple gauge-boson couplings are
sensitive to new physical phenomena. In particular, measurements of
$\PQt\PZ\PQq$ production are sensitive to processes beyond the SM that have
similar experimental signatures, such as flavour-changing neutral currents
(FCNC) involving the direct coupling of the top quark to a boson and an up or
charm quark, at the top quark production or decay [12, 13]. Within the SM,
FCNC processes are forbidden at LO and suppressed at higher orders [14].
Deviations from the expected SM $\PQt\PZ\PQq$ production could therefore be
indicative of beyond-SM FCNC processes.

The next-to-leading-order (NLO) cross section for
$\PQt\PZ\PQq\to\PW\PQb\ell^{+}\ell^{-}\PQq$, considering only the leptonic
decays of bosons (to electrons, muons, or $\tau$ leptons, generically denoted
by $\ell$), is calculated for collisions at a centre-of-mass energy of 13,
using the Monte Carlo (MC) generator 2.2.2 [15]. The calculation, which
includes lepton pairs from off-shell bosons with invariant mass
$m_{\ell^{+}\ell^{-}}>30$, uses the NNPDF 3.0 set of parton distribution
functions (PDFs) [16] in the five-flavour scheme. The result is
$\sigma^{\mathrm{SM}}(\PQt\ell^{+}\ell^{-}\PQq)=94.2^{+1.9}_{-1.8}\,\text{(scale)}\pm
2.5\,\text{(PDF)}\unit{fb}$, with the “scale” and “PDF” uncertainties
estimated, respectively, by changing the quantum chromodynamics (QCD)
renormalization and factorization scales by factors of 0.5 and 2, and by using
the 68% confidence level (CL) uncertainty on the NNPDF3.0 PDF set. This cross
section is used as the reference in this analysis. Another calculation,
including all boson decays, gives a compatible cross section when the
branching fraction to charged leptons is taken into account [17]. Previous
searches for $\PQt\PZ\PQq$ production at 8by the CMS Collaboration [18]
reported a signal with a significance of 2.4 standard deviations. The ATLAS
Collaboration recently reported a measurement of the $\PQt\PZ\PQq$ production
cross section at 13 [19] with a significance of 4.2 standard deviations.

This Letter presents a search for $\PQt\PZ\PQq$ production in $\Pp\Pp$
collisions at $\sqrt{s}=13$, using data collected in 2016 by CMS,
corresponding to an integrated luminosity of 35.9. The signature for
$\PQt\PZ\PQq$ production consists of a single top quark produced in the $t$
channel, a boson, and an additional (“recoiling”) jet emitted at
pseudorapidity $\abs{\eta}<4.5$. The analysis uses events where the boson
decays to $\Pep\Pem$ or $\Pgmp\Pgmm$, while the boson, produced in the decay
of the top quark, decays to a neutrino and an electron or a muon, resulting in
four possible final-state leptonic combinations: $\Pe\Pe\Pe$, $\Pe\Pe\Pgm$,
$\Pe\Pgm\Pgm$, and $\Pgm\Pgm\Pgm$. There will also be a small contribution
from $\tau$ leptons decaying into electrons or muons. The final result
reflects an extrapolation to include all decay modes involving $\tau$ leptons.
The measurement is based on a multivariate analysis, where boosted decision
trees (BDTs) [20] are used to enhance the signal-to-background separation.
Several control regions are defined to better constrain the backgrounds, each
containing different contributions from signal and background processes.

##  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 measured in gas-ionization detectors embedded in
the steel flux-return yoke outside the solenoid. The electron momentum is
evaluated by combining the energy measurement in the ECAL with the momentum
measurement in the tracker. The momentum resolution for electrons with
transverse momentum, $\pt$, around $45\GeV$ from $\PZ\to\Pe\Pe$ decays ranges
from 1.7% for nonshowering electrons in the barrel region to 4.5% for
showering electrons in the endcaps [21]. Muons are measured in the range
$\abs{\eta}<2.4$, 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 relative transverse
momentum resolution for muons with $20<\pt<100\GeV$ of 1.3–2.0% in the barrel
and better than 6% in the endcaps [22]. 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. [23].

Events of interest are selected using a two-tiered trigger system [24]. 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 optimised for fast processing, and reduces the
event rate to less than 1kHz before data storage.

##  0.3 Online selection, reconstruction, and identification

The data are selected online using triggers that rely on the presence of
either one, two, or three high-leptons. The lowest thresholds of the three-
lepton triggers are 16, 12, and 8for electrons, and 12, 10, and 5for muons;
the corresponding values for the dilepton triggers are 23 and 12for electrons,
and 17 and 8for muons. Triggers requiring the presence of at least one
electron and at least one muon are also used. For the baseline offline
selection, a trigger efficiency of nearly 100% is achieved by including
single-lepton triggers with thresholds of 32 and 24for electrons and muons,
respectively, in addition to the two- and three-lepton triggers.

The events are reconstructed using the particle-flow (PF) algorithm [25],
which reconstructs and identifies each individual particle with an optimised
combination of information from the various elements of the CMS detector. The
energy of the photons is directly obtained from the ECAL measurement,
corrected for zero-suppression effects, while that of the electrons is
determined from a combination of the electron momentum at the primary
interaction vertex as determined by the tracker, the energy of the
corresponding ECAL cluster, and the total energy of all bremsstrahlung photons
spatially compatible with originating from the electron track. The energy of
the muons is obtained from the curvature of the corresponding track. The
energy of charged hadrons is determined from a combination of their momentum,
measured in the tracker, and the matching ECAL and HCAL energy deposits,
corrected for zero-suppression effects and for the response function of the
calorimeters to hadronic showers. Finally, the energy of neutral hadrons is
obtained from the corresponding ECAL and HCAL corrected energy deposits. For
each event, jets are clustered from the PF candidates using the anti-algorithm
[26, 27], with a distance parameter of 0.4. The reconstructed vertex with the
largest value of summed physics-object $\pt^{2}$ is taken to be the primary
$\Pp\Pp$ interaction vertex. The physics objects are the jets, clustered 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.
All charged particles considered in this analysis are required to be
compatible with originating from the primary interaction vertex.

The event selection relies on the concept of relative lepton isolation,
reflected in the variable $I_{\text{rel}}$, computed as the scalar sum of the
of all particles in a cone of radius $\Delta
R=\sqrt{\smash[b]{(\Delta\eta)^{2}+(\Delta\phi)^{2}}}$ around the lepton
(where $\phi$ is the azimuth), excluding the lepton, and divided by the lepton
. The sum is then corrected for the neutral particles produced in extra
$\Pp\Pp$ interactions within the same or neighbouring LHC bunch crossings,
referred to as pileup (PU) collisions. For electrons, $\Delta R$ is set to
0.3, and the expected PU within the isolation cone is estimated from the
median energy density per area of PU contamination. Muon $I_{\text{rel}}$ uses
$\Delta R=0.4$, and is corrected for the average neutral PU energy inside the
isolation cone, which has been measured in multijet events to be one half of
the energy coming from charged hadrons not associated with the primary vertex.
Electrons and muons are considered isolated if $I_{\text{rel}}$ is smaller
than 0.06 and 0.15, respectively.

The data with prompt leptons are contaminated by genuine leptons from hadron
decays (usually referred to as “nonprompt leptons”) and by hadrons or jets
misidentified as leptons (usually referred to as “fake leptons”). In addition,
nonprompt isolated electrons can arise from the conversion of photons. For
simplicity of notation, and given that these background sources are evaluated
with similar methods, based on control samples in data, all such sources are
referred to as “not-prompt” leptons, or simply “NPL”, in this Letter. Data
samples for evaluating the NPL background are built using objects
reconstructed similarly to the prompt leptons, with two important differences.
First, while the prompt and not-prompt leptons are identified using the same
variables [21, 22], less stringent criteria are applied to the NPL sample.
Second, leptons are considered not-prompt only if they are not isolated,
requiring not-prompt electrons or muons to have $I_{\text{rel}}>0.17$ or
$>0.25$, respectively. In addition, not-prompt electrons are required to have
$I_{\text{rel}}<1$, removing a large fraction of photons with
$I_{\text{rel}}\approx 1$ and $\PZ+$jets events containing a low-$\pt$ jet
misidentified as a high $I_{\text{rel}}$ electron. Tight criteria to reject
photon conversions [21] are required for both prompt and not-prompt electrons.

The jet momentum is determined from the vectorial sum of all particle momenta
in the jet, and is found in simulation studies to be within 5 to 10% of the
true momentum over the whole spectrum and detector acceptance. Jet energy
corrections are obtained from simulation studies and confirmed with in situ
measurements through the balance in dijet, multijet, photon+jet, and leptonic
+jet events [28]. In the central region, the jet energy resolution is
approximately 15% at 10, 8% at 100, and 4% at 1. Jets reconstructed at angular
distances $\Delta R<0.4$ from the selected leptons are not considered for
further analysis. As the region $2.7<\abs{\eta}<3.0$ is particularly affected
by noise, events with jets of $\pt<50\GeV$ in that region are rejected.

Jets that originate from the hadronization of a quark are identified (tagged)
using the combined secondary vertex (CSVv2) algorithm [29, 30], which combines
various track-based variables with secondary-vertex variables to construct a
discriminating observable in the region $\abs{\eta}<2.4$. At the chosen
operating point, the CSVv2 algorithm has an efficiency of about 83% to
correctly tag jets and a probability of 10% for mistagging gluons and light
quarks, as estimated from simulation studies of multijet events.

The missing transverse momentum vector is defined as the projection onto the
plane perpendicular to the beam axis of the negative vector sum of the momenta
of all reconstructed PF objects in an event. Its magnitude is denoted by . The
transverse mass of the W boson is defined as
$m_{\mathrm{T}}^{\PW}=\sqrt{\smash[b]{2\pt\ptmiss[1-\cos(\Delta\phi)]}}$,
where is the transverse momentum of the lepton produced in the boson decay,
and $\Delta\phi$ is the difference in azimuth between the direction of the
lepton and the direction of .

##  0.4 Simulated events

Monte Carlo simulated events are used extensively in this measurement to
evaluate the detector resolution, the efficiencies and acceptance, and to
estimate the contributions from background processes that have topologies
similar to the trilepton $\PQt\PZ\PQq$ final state.

The $\PQt\PZ\PQq$ signal samples are generated at NLO precision using the
2.2.2 package [15]. The two main background processes, $\PW\PZ$+jets and top
quark pair production in association with vector bosons ($\PQt\PAQt\PZ$ and
$\PQt\PAQt\PW$), are also simulated with the same event generator, with up to
one additional hadronic jet at NLO. Other minor backgrounds are $\PZ\PZ$ and
$\PQt\PAQt\PH$ production, for which we use the NLO generators and v2.0 [31,
32, 33, 34, 35, 36], respectively, and $\PQt\PW\PZ$ production, generated at
LO accuracy using . The PDF set NNPDF 3.0 is used in all generators. The
simulated samples are interfaced to 8.205 [37] with the CUETP8M1 tune [38] for
the parton shower and hadronization. The detector response is simulated using
the package [39].

The events are simulated in final states that include decays to electrons,
muons, and $\tau$ leptons. A top quark mass of 172.5is assumed. Multiple
minimum-bias events generated with are added to each simulated event to mimic
the presence of PU, with weights that reproduce the measured distribution of
the number of PU vertices.

The event samples are normalized to their expected cross sections, obtained
from NLO calculations for all processes, except for $\PQt\PW\PZ$, which is
estimated at LO accuracy.

Correction factors that depend on the $\pt$ and $\eta$ of the jets and leptons
are applied to the samples, so that the resolutions, energy scales, and
efficiencies measured in data are well reproduced by the simulation. The
corrections include an extra smearing of the jet energy, which has a better
resolution in the simulation than found in data, and scale factors that
account for different efficiencies in lepton identification and
reconstruction. The shape of the distribution in the CSVv2 discriminant is one
of the variables used in the multivariate analysis to extract the signal. The
simulated shape has been corrected [30, 29] to assure that the b tagging
efficiency and purity variables reproduce those found in data.

One of the most abundant background sources in the three-lepton final state
arises from events with at least one NPL. Unlike all other backgrounds, which
are modelled by MC simulation, the samples used to estimate the NPL background
contribution are obtained from the data, as described in Section 0.6.2.

##  0.5 Event selection: signal and background control regions

The event selection makes use of $\PQt\PZ\PQq$ event candidates where
$\PQt\to\PW\PQb$, $\PW\to l\nu$, and $\PZ\to l^{\prime+}l^{\prime-}$,

$\PQt\PZ\PQq\to(\PQt\to\PQb l\nu)\ (\PZ\to l^{\prime+}l^{\prime-})\PQq,$

where $l$ and $l^{\prime}$ are either electrons or muons, coming from the or
boson decay, respectively, as opposed to generic leptons (including $\tau$
leptons), which have been denoted by $\ell$. As stated in Section 1, $l$
includes a small contribution from $\PW\to\tau\nu$ decays, with the subsequent
decay of the $\tau$ into electrons or muons. The final result will be given
for all decay modes to a generic lepton, $\ell$. In single top quark
production, the associated recoil jet usually follows the direction of the
incoming proton, so it is detected in the very forward regions of the
detector. For this reason, we select jets in the extended pseudorapidity range
$\abs{\eta}<4.5$. Given the tracker acceptance, b-tagged jets are confined to
the $\abs{\eta}<2.4$ range. All jets, both tagged and untagged, are required
to have $\pt>30$.

The baseline selection for the analysis consists in exactly three leptons, two
of which have the same flavour, are oppositely charged, and have an invariant
mass compatible with the boson mass within 15 . Electrons and muons are
required to have $\pt>25$, and to be measured within $\abs{\eta}<2.5$ and 2.4,
respectively. To reduce backgrounds from four or more leptons in the final
state, from $\PZ\PZ$, $\PQt\PAQt\PZ$, and $\PQt\PAQt\PH$, events containing
additional leptons with $\pt>10$and passing looser identification criteria are
removed from the analysis.

Several other SM processes, some of which have much larger cross sections than
expected for $\PQt\PZ\PQq$, contain three reconstructed leptons in the final
state. Out of these, the most important are the $\PW\PZ$+jets, the
$\PQt\PAQt\PZ$, and those contributing to the NPL background. For the first
two, the three-lepton topology is identical to $\PQt\PZ\PQq$: two oppositely
charged leptons of same flavour decaying from the boson, and a third high-,
isolated lepton. The $\PQt\PAQt\PZ$ production for the four-lepton final state
has a smaller cross section than that for the three-lepton final state, and is
also suppressed by the already mentioned veto on events with four or more
leptons. Although the misidentification rate per lepton, especially for muons,
is small, the cross sections of the processes producing the NPL background
(dominated by Drell–Yan production in association with jets, DY+jets, and
production) are orders of magnitude larger than the expected $\PQt\PZ\PQq$
cross section, making NPL one of the most important backgrounds to the three-
lepton final state.

For the $\PQt\PZ\PQq$ final state, two jets are expected, one of which arises
from a b quark. In the $\PQt\PAQt\PZ$ three-lepton final state, two b jets are
expected. However, given the inefficiencies of the b tagging algorithm, one of
the two b jets may be untagged, leading to a final state identical to the
signal. Likewise, one of the b jets produced by gluon splitting in the
$\PW\PZ$+jets final states may be tagged, or, most frequently, light-flavour
jets from $\PW\PZ$+jets production can be mistagged as b jets, again resulting
in a topology identical to the signal.

To reduce the impact of the background-related uncertainties on the
measurement of the $\PQt\PZ\PQq$ yield, we proceed as follows. The baseline
three-lepton selection is subdivided into three regions of interest, one
enriched in $\PQt\PZ\PQq$ events, another selected to contain mostly
$\PQt\PAQt\PZ$ events, and a third containing mostly $\PW\PZ$+jets and NPL
background events. The final analysis performs a simultaneous fit to these
three regions, so that the signal cross section is determined and the
normalizations of the main backgrounds are better constrained.

The three regions are defined according to their jet and b-tagged jet
multiplicities, as follows:

  1. 1.

1bjet (signal region): defined to select events from $\PQt\PZ\PQq$ production
with one b jet and one recoiling jet. Events with a third jet are also
included, to cover cases where an additional jet is produced by radiation.

  2. 2.

2bjets control region ($\PQt\PAQt\PZ$ enriched): defined by requiring at least
two jets, with at least two of them b tagged, enhancing thereby the yield in
$\PQt\PAQt\PZ$ events.

  3. 3.

0bjet control region ($\PW\PZ$+jets enriched): defined by at least one jet,
but no b-tagged jets, selected as most likely originating from a process.
Since the majority of DY+jets events also do not contain b jets, this region
is also rich in NPL background events.

##  0.6 Shape-based analysis

The $\PQt\PZ\PQq$ cross section is extracted from a binned maximum-likelihood
fit to the distributions in the BDT discriminators (to be defined later) in
the 2bjets and 1bjet regions, and to the $m_{\mathrm{T}}^{\PW}$ distribution
in the 0bjet region. Normalized distributions (templates) are constructed
using these variables in their respective regions, for each of the four final
states ($\Pe\Pe\Pe$, $\Pe\Pe\Pgm$, $\Pe\Pgm\Pgm$, and $\Pgm\Pgm\Pgm$), adding
up to 12 distributions that are simultaneously fitted.

###  0.6.1 Input normalization of the SM predictions

The input (pre-fit) normalizations of the simulated backgrounds reflect their
corresponding theoretical cross sections. The contributions from +, +, and
+light-flavour jets in the +jets MC events are separated using generator-level
information, and considered as independent backgrounds in all steps of the
analysis. This provides a better modelling of the heavy-flavour content of the
+jets sample, and avoids relying on the flavour content of the MC simulation.

###  0.6.2 The NPL background

The templates for the NPL background are based on data. The origin of not-
prompt leptons depends on the lepton flavour. For muons, the dominant source
is the semileptonic decay of heavy-flavour hadrons. In the case of electrons,
the dominant sources are photon conversions and light hadrons that are
misreconstructed as electrons. The not-prompt electrons and muons are
therefore treated as separate background sources.

The background events containing not-prompt leptons originate from, in order
of importance, DY+jets processes, events containing two leptons, and $\PW\PW$
and $\PQt\PW$ processes. Each of these background sources contain two prompt
and one not-prompt leptons. Given the low probability that an NPL is
identified as a prompt lepton, the contribution from events with more than one
NPL is negligible. Not-prompt electron (muon) templates are obtained from
events containing exactly one not-prompt electron (muon), identified as
described in Section 3, and two prompt leptons (either electrons or muons). In
the NPL sample, the not-prompt leptons can be associated either with the top
quark or with the Z boson candidates.

The samples used to obtain the NPL background templates are quite copious,
typically having two orders of magnitude more events than the signal sample
obtained with the baseline selection. While the shapes of the distributions
used in the multivariate analysis are provided by templates, their
normalizations are determined through a two-step procedure. In the first step,
the $m_{\mathrm{T}}^{\PW}$ distribution in the 0bjet control region provides
the normalization of all NPL components, independently in the four channels.
This fixes the relative NPL normalization of the templates in the four
channels. In a subsequent step, the not-prompt electron and muon yields are
treated as free and independent parameters, in a simultaneous fit of the
0bjet/1bjet/2bjets regions. This second step represents the final fit used to
provide the results reported in this Letter.

The use of the 0bjet region to provide the relative NPL yields in the four
channels is justified by the dominance of the DY process as source of NPL
background events in all three b tagging regions. To check the validity of the
procedure, an independent analysis is performed where the weight of the DY
background relative to production is suppressed by means of mild requirements
on and $m_{\mathrm{T}}^{\PW}$. In this cross-check analysis, the relative
normalizations of the not-prompt electron and muon backgrounds are left free
in the four channels, and the results are obtained in a single common fit.
This alternative procedure gives similar final results.

###  0.6.3 Multivariate analysis

The signal extraction relies on a simultaneous fit to the data in the three
regions defined in Section 0.5, to better constrain the backgrounds in the
signal region.

Two multivariate discriminators, based on observables from the 1bjet and
2bjets regions, are used to enhance the separation between signal and
background processes. The discriminators are based on the BDT algorithm [20]
implemented in the toolkit for multivariate analysis TMVA [40]. The BDT is
trained using the simulated samples described in Section 0.4.

Several observables serve as input variables for the BDT. These include the
reconstructed top quark mass and distributions of variables reflecting the
kinematics and the angles of the recoiling jet, of the top quark, and of the
boson, as well as those of their decay products. Once the two oppositely
charged leptons of same flavour are identified as boson decay products, the
additional lepton is assumed to arise from the decay $\PW\to l\nu$. The
longitudinal component of the neutrino momentum is calculated using the mass
constraint for the $l+\nu$ system, and assuming the event to be equal to the
transverse momentum of the neutrino. The reconstructed boson candidate is then
associated to a b-jet candidate for the $\PQt\to\PW\PQb$ hypothesis. The b-jet
candidate is the tagged jet. If two solutions are found for the longitudinal
component of the neutrino momentum, or if more than one jet is tagged (in the
2bjets region), the solution giving the $\PW\PQb$ candidate invariant mass
closest to that of the top quark is taken. The remaining jet with the largest
is taken as the recoiling jet. The information related to b tagging is also
used through the distributions of the CSVv2 discriminant [30, 29] and the
b-tagged jet multiplicity.

Variables computed using the matrix element method (MEM) [41] are also
included in the multivariate analysis. A weight $w_{i,\alpha}$ is computed for
each event $i$ and hypothesis $\alpha$ (where $\alpha$ is either signal,
$\PQt\PAQt\PZ$, or +jets) as

$w_{i,\alpha}(\Phi^{\prime})=\frac{1}{\sigma_{\alpha}}\int\rd\Phi_{\alpha}\,\delta^{4}\Big{(}p_{1}^{\mu}+p_{2}^{\mu}-\sum_{k>1}p_{k}^{\mu}\Big{)}\,\frac{f(x_{1},\mu_{\mathrm{F}})f(x_{2},\mu_{\mathrm{F}})}{x_{1}x_{2}s}\,\Big{|}\mathcal{M}_{\alpha}(p_{k}^{\mu})\Big{|}^{2}\,W(\Phi^{\prime}|\Phi_{\alpha}),$

where: $\sigma_{\alpha}$ is the cross section; $\Phi^{\prime}$ are the
4-momenta of the reconstructed particles; $\rd\Phi_{\alpha}$ is the element of
phase space corresponding to parton-level variables with momentum conservation
enforced [42]; $f(x,\mu_{\mathrm{F}})$ are the PDFs, where $\mu_{\mathrm{F}}$
is the QCD factorization scale, computed using the NNPDF2.3LO set [43];
$\abs{\mathcal{M}_{\alpha}}^{2}$ is the squared matrix element, computed with
standalone [15] at LO accuracy, in a narrow-width approximation for the top
quarks; and $W$ are the transfer functions for jet energy and , relating
parton-level variables to reconstructed quantities, evaluated from simulation
studies and normalized to unity.

For all three processes, the mass of the $\PW$ boson arising from the top
quark decay follows a Breit–Wigner distribution, as specified in the matrix
element. The virtual boson in the $\PQt\PAQt\PZ$ hypothesis also follows a
Breit–Wigner form, and interference with $\Pgg^{*}$ is included in the matrix
element. The matrix element provided at LO in does not contain additional jets
that are present in the data. To evaluate the matrix element at LO, the
momentum of the $\PQt\PZ\PQq$ system must have a null transverse component.
The $\PQt\PZ\PQq$ momentum is computed as the sum of the momenta of all
particles from the $\PQt\PZ\PQq$ decay. An inverse boost corresponding to the
opposite of the $\PQt\PZ\PQq$ is applied to all final state particles,
correcting thereby any recoiling jets not present in the LO matrix element.

In computing the MEM weights, jets with the highest CSVv2 discriminant values
are assigned to the b quarks from top decays. Among the remaining jets, up to
two jets with the highest $\abs{\eta}$ (signal hypothesis), with invariant
mass closest to the $\PW$ boson mass (for the $\PQt\PAQt\PZ$ hypothesis), or
with the highest (for the +jets hypothesis), are assigned to the quarks at
parton level. Jets in the 1bjet region may not be matched to all parton-level
quarks needed in the $\PQt\PAQt\PZ$ hypothesis (two b-quarks and two not-b
quarks). In such cases, the $\PQt\PAQt\PZ$ weight can still be computed by
leaving the phase space of the missing jets unconstrained in the integral.

The final weight for each hypothesis $\alpha$ is taken as the average of the
weights computed for each lepton and jet permutation. The MEM weights are
combined in likelihood ratios of signal to the combination of $\PQt\PAQt\PZ$
and +jets in the 1bjet region and signal to $\PQt\PAQt\PZ$ in the 2bjets
region. These ratios are included as input variables to the BDT. In addition,
the maximum value of the function being integrated is also included,
corresponding to the MEM score associated to the most probable kinematic
configuration. Eight variables were tested and five were retained for the
training; the other three were excluded because they were highly correlated
with other variables or had a negligible discriminant power. The normalized
BDT discriminators for signal and backgrounds in the 1bjet and 2bjets regions
are shown in Fig. 2 for BDT trainings with and without MEM variables.
Including the MEM variables improves the expected significance by about 20%.

Figure 2: Normalized distributions of the BDT output for signal (thick lines)
and backgrounds (thin lines) from simulation for the 1bjet (left) and 2bjets
(right) regions. The discriminators including and excluding MEM variables in
the BDT training are shown, respectively, as solid and dashed lines.
Contributions from the four considered channels are included in the signals
and backgrounds.

The predictions for some of the most discriminating variables in the BDT for
the 1bjet and 2bjets regions are compared to data in Fig. 3. These variables
are the largest CSVv2 discriminant value among all selected jets, the
logarithm of the MEM score associated to the most probable $\PQt\PZ\PQq$
kinematic configuration, and the $\Delta R$ separation between the jet
identified as a b quark and the recoiling jet. Figure 4 shows, for events in
the 0bjet region, the $\eta$ and distributions of the recoiling jet,
$\eta({j^{\prime}})$ and $\pt({j^{\prime}})$, and the asymmetry of the top
quark decay lepton, defined as the product of its charge and pseudorapidity,
${\PQq}_{l}|\eta(l)|$. The distributions in Figs. 3 and 4 are shown combined
for the four channels: eee, ee$\mu$, e$\mu\mu$, and $\mu\mu\mu$. The quadratic
sum of the systematic and statistical uncertainties on the predictions is
shown as a hatched band. The pulls of the distributions, defined in each bin
as the difference between data and prediction, divided by the quadratic sum of
total uncertainties in the predictions (systematic and statistical) and the
data (statistical), are shown at the bottom of the plots.

Figure 3: Data-to-prediction comparisons in the 1bjet region (signal-enriched,
upper row) and in the 2bjets region (lower row) for the largest CSVv2
discriminant value among all selected jets (left), the logarithm of the MEM
score associated to the most probable $\PQt\PZ\PQq$ kinematic configuration
(centre), and the $\Delta R$ separation between the b quark and the recoiling
jet (right). The distributions include events from all final states.
Underflows and overflows are shown in the first and last bins, respectively.
The predictions correspond to the normalizations obtained after the fit
described in Section 0.8. The hatched bands include the total uncertainty on
the background and signal contributions. The pulls in the distributions are
shown in the bottom panels.  Figure 4: Data-to-prediction comparisons in the
0bjet region for the $\eta$ (left) and (centre) distributions of the recoiling
jet, and for the asymmetry of the top quark decay lepton (right). More details
are given in the caption of Fig. 3.

The complete list of variables used in the two BDTs is given in Appendix .10.

##  0.7 Systematic uncertainties

Different sources of systematic uncertainty can affect the number of events
passing the selections, or the shape of the distributions used in the
multivariate analysis.

The sources of systematic uncertainty considered correspond to:

  * •

Luminosity: An uncertainty of 2.5% on the sample integrated luminosity [44] is
propagated as a normalization-only uncertainty for the total predicted yields.

  * •

Correction factors applied to the signal and simulated backgrounds:

    * –

Pileup: The number of simulated pileup events is corrected to match the
measured number of events in data. The uncertainty on the total inelastic
cross section is taken as 4.6%, and considered only in the shapes of the
distributions.

    * –

Trigger: The trigger efficiency is estimated to be near 100% both in data and
in simulation. Variations in normalization of $\pm$1% ($\pm$2%) are applied to
the predicted yields in the $\Pgm\Pgm\Pgm$ and $\Pe\Pe\Pgm$ ($\Pe\Pgm\Pgm$ and
$\Pe\Pe\Pe$) channels to account for residual differences in trigger
efficiency between data and simulation.

    * –

Lepton selection: The factors used to correct the simulated distributions for
lepton isolation and identification efficiencies are varied by their
uncertainty, affecting both the shapes and the normalizations.

    * –

Jet energy scale and resolution: The jet energy scale and resolution
corrections are both varied by their uncertainty. The observed change is
propagated to all related kinematic quantities, in particular . These
uncertainties affect both the shape and the normalization of the simulated
distributions.

    * –

b tagging: The scale factors related to b tagging and mistagging efficiencies
are varied by one standard deviation. Eight independent changes are
considered, including two types of statistical uncertainties on the -, -, and
light-flavour components of the MC event samples, light-flavour contamination
of the b tagging scale factors, and b quark contamination in the mistag scale
factors. There is one “nuisance” parameter for each variation. Both shape and
normalization are affected.

  * •

The normalization of the simulated backgrounds: The input normalizations of
all simulated background distributions are assumed to have a relative
uncertainty of 30%. This reflects the theoretical uncertainties on the
corresponding cross sections, scaled up by a factor of two or more, to account
for possible limitations in the simulations in the phase space of the
analysis.

  * •

The NPL background estimation: The shape-related uncertainties on the
backgrounds involving not-prompt leptons, determined with control samples in
data, are estimated by varying the isolation criteria used to determine the
NPL sample. The shape variations of not-prompt muons and electrons involve
different nuisance parameters.

  * •

The scale and PDF uncertainties for simulated signal ($\PQt\PZ\PQq$) and
background processes: These uncertainties affect the shape of the signal as
well as the shape and normalization of the simulated background distributions,
except for $\PQt\PW\PZ$ events, for which only normalization uncertainties
from scales and PDF are considered.

    * –

The renormalization and factorization scales, at the matrix element level, are
set to an identical value, which depends on the event generator and on the
simulated processes. In particular, the scales for the simulated signal are
set to $\sum\sqrt{(m^{2}+\pt^{2})}/2$, where the sum runs over all particles
in the final state. The scales are varied up and down by a factor of 2.

    * –

The renormalization and factorization scales at the parton shower level,
identical to the matrix element scales, are also varied by factors of 0.5 and
2; this uncertainty is only evaluated for the signal sample.

    * –

The PDF uncertainties are evaluated by the root-mean-square of the results
from 100 variations of the NNPDF set.

##  0.8 Results

The tool used for this statistical analysis [45] is based on the RooStats
framework [46]. The analysis is performed beginning with a binned likelihood
function

${\mathcal{L}}(\text{data}|\mu,\theta)=\sum_{i}\frac{\left[\mu
s_{i}(\theta)+b_{i}(\theta)+\alpha_{\Pe}B_{i}^{\Pe}(\theta)+\alpha_{\mu}B_{i}^{\mu}(\theta)\right]^{N_{i}}}{N_{i}!}\re^{-\mu
s_{i}(\theta)-b_{i}(\theta)-\alpha_{\Pe}B_{i}^{\Pe}(\theta)-\alpha_{\mu}B_{i}^{\mu}(\theta)},$

where $N_{i}$ is the observed number of events in each bin, and
$s_{i}(\theta)$ and $b_{i}(\theta)$ are the expected signal and background
yields in each bin, respectively, normalized as discussed in the previous
sections and taking into account all systematic uncertainties, represented by
$\theta$, as nuisance parameters associated with log-normal priors. The
$B_{i}^{{\Pe},\mu}(\theta)$ are the yields of NPL backgrounds, and the
parameters $\alpha_{{\Pe},\mu}$, which determine the normalization of the NPL
backgrounds, are left free in the fit. The simultaneous fit to the data
templates (BDT discriminators or $m_{\mathrm{T}}^{\PW}$, depending on the
region) in the four channels maximizes
${\mathcal{L}}(\text{data}|\mu,\theta)$, from which the measured cross section
$\sigma(\PQt\ell^{+}\ell^{-}\PQq)$ is extracted according to its relation to
the signal strength

$\mu=\frac{\sigma(\PQt\ell^{+}\ell^{-}\PQq)}{\sigma^{\mathrm{SM}}(\PQt\ell^{+}\ell^{-}\PQq)},$

where the cross section is defined for any decay of the top quark, and any
decay of the boson to charged leptons. The reference cross section is
$\sigma^{\mathrm{SM}}(\PQt\ell^{+}\ell^{-}\PQq)=94.2$fb, for
$m_{\ell^{+}\ell^{-}}>30$. The measurement implies an extrapolation from the
considered phase space (Section 0.5), defined as containing three leptons in
the final state ($l^{\prime+}l^{\prime-}l$), and an additional constraint for
$m_{\ell^{+}\ell^{-}}$ to be within 15of the Z boson mass. The acceptance,
defined as the fraction of $\PQt\ell^{+}\ell^{-}\PQq$ events fulfilling the
event selection criteria, is estimated from the simulated $\PQt\PZ\PQq$ sample
as 1.81%, combining the 1bjet, 2bjets, and 0bjet regions. All nuisance
parameters are constrained in the fit.

The distributions resulting from the fit (post-fit) of the three variables
used as templates in the measurement are shown in Fig. 5. Although the fit is
performed for each channel, the figure displays the results combining the four
channels.

Table 0.8 shows the results for the post-fit yields, separately for each
channel, in the 1bjet region. The last two rows show the total number of
predicted (“Total”) and observed (“Data”) events. The last column displays the
ratio of the post-fit to pre-fit predictions, $N^{\text{post-
fit}}/N^{\text{pre-fit}}$, accounting for the systematic uncertainties. The
post-fit background normalizations are close to the pre-fit values for most of
the background processes. The event yields for the $\PW\PZ$+light-flavour jets
background preferred by the fit is significantly lower than the SM prediction.
This feature, which might reflect the somewhat worse agreement between
simulation and data for some bins of jet multiplicity [47], does not affect
the measurement, as verified by the following checks. First, the predicted
shapes of the kinematic variables relevant to the analysis are verified to
describe the data in the +light-flavour enriched region. The analysis is then
repeated with the +light-flavour normalization relative uncertainty increased
to 50%, leaving the results unchanged within about half a percent. Finally,
the +light-flavour yield is fitted simultaneously with the NPL background
yields using only the 0bjet region, and the resulting $N^{\text{post-
fit}}/N^{\text{pre-fit}}$ scale factor is found to be $0.73\pm 0.11$, in good
agreement with the results of Table 0.8. The post-fit number of $\PQt\PZ\PQq$
events in the 1bjet region is 32.3. The 0bjet and 2bjets control regions (not
shown) also contain $\PQt\PZ\PQq$ events, with post-fit yields of $\approx$23
and 19 events, respectively.

Observed and post-fit expected yields for each production process in the 1bjet
region. The yields of columns 2–5 correspond to each channel, and column 6
displays the total for all channels. The last column displays the ratio
between post-fit and pre-fit yields.  Process $\Pe\Pe\Pe$ $\Pe\Pe\Pgm$
$\Pe\Pgm\Pgm$ $\Pgm\Pgm\Pgm$ All channels $N^{\text{post-fit}}/N^{\text{pre-
fit}}$ $\PQt\PZ\PQq$ $5.0\,\pm\,1.5$ $6.6\,\pm\,1.9$ $8.5\,\pm\,2.5$
$12.3\,\pm\,3.6$ $32.3\,\pm\,5.0$ — $\PQt\PAQt\PZ$ $3.7\,\pm\,0.7$
$4.7\,\pm\,0.9$ $6.1\,\pm\,1.2$ $8.0\,\pm\,1.5$ $22.4\,\pm\,2.2$
$0.9\,\pm\,0.2$ $\PQt\PAQt\PW$ $0.3\,\pm\,0.1$ $0.3\,\pm\,0.1$ $0.7\,\pm\,0.2$
$0.6\,\pm\,0.2$ $1.9\,\pm\,0.3$ $1.0\,\pm\,0.2$  $4.8\,\pm\,1.3$
$3.2\,\pm\,0.9$ $9.0\,\pm\,2.5$ $7.8\,\pm\,2.2$ $24.7\,\pm\,3.6$
$1.3\,\pm\,0.3$ +b $3.0\,\pm\,0.9$ $3.4\,\pm\,1.1$ $4.6\,\pm\,1.4$
$5.5\,\pm\,1.7$ $16.6\,\pm\,2.6$ $1.0\,\pm\,0.2$ +c $9.0\,\pm\,2.4$
$13.7\,\pm\,3.7$ $18.0\,\pm\,4.9$ $24.2\,\pm\,6.5$ $64.8\,\pm\,9.3$
$1.0\,\pm\,0.2$ +light $12.2\,\pm\,1.6$ $16.6\,\pm\,2.0$ $22.4\,\pm\,2.8$
$29.1\,\pm\,3.4$ $80.3\,\pm\,5.1$ $0.7\,\pm\,0.1$ $\PQt\PAQt\PH$
$0.6\,\pm\,0.2$ $0.9\,\pm\,0.3$ $1.0\,\pm\,0.3$ $1.5\,\pm\,0.4$
$4.0\,\pm\,0.6$ $1.0\,\pm\,0.2$ tWZ $1.0\,\pm\,0.3$ $1.3\,\pm\,0.4$
$1.7\,\pm\,0.5$ $2.4\,\pm\,0.7$ $6.5\,\pm\,1.0$ $1.0\,\pm\,0.2$ NPL: electrons
$19.2\,\pm\,3.1$ $0.6\,\pm\,0.1$ $17.9\,\pm\,2.8$ — $37.7\,\pm\,4.2$ — NPL:
muons — $7.2\,\pm\,2.3$ $31.1\,\pm\,9.9$ $15.3\,\pm\,4.9$ $53.6\,\pm\,11.3$ —
Total $58.8\,\pm\,4.8$ $58.4\,\pm\,5.5$ $121\,\pm\,12$ $107\,\pm\,10$
$345\,\pm\,18$ Data $56$ $58$ $104$ $125$ $343$

Figure 5: Template distributions used for signal extraction. Left: BDT
discriminator in the 1bjet region; centre: BDT output in the 2bjets control
region; right: $m_{\mathrm{T}}^{\PW}$ in the 0bjet control region. More
details are given in the caption of Fig. 3.

The observed $\PQt\PZ\PQq$ signal strength is

$\mu=1.31^{+0.35}_{-0.33}\stat^{+0.31}_{-0.25}\syst,$

from which, using the reference NLO cross section, the measured cross section
is found to be

$\sigma(\PQt\ell^{+}\ell^{-}\PQq)~{}=123^{+33}_{-31}\stat^{+29}_{-23}\syst\unit{fb},$

for $m_{\ell^{+}\ell^{-}}>30$, where $\ell$ stands for electrons, muons, and
$\tau$ leptons. The best-fit signal strength and cross section, as well as an
approximate 68% CL interval, are extracted following the profile likelihood
scan procedure described in Ref. [48]. The fit is redone without including the
systematic uncertainties, to evaluate the statistical uncertainty of the
result. The quoted systematic uncertainty is then calculated as the difference
in quadrature between the 68% CL intervals obtained in the nominal fit and in
the fit without systematic uncertainties. The precision of the measurement is
limited by the statistical uncertainty. Among the systematic uncertainties,
the dominating ones arise from the normalization of the NPL background (left
free in the fit), the scale dependence at the parton shower level, the b
tagging efficiency, and the normalization of the $\PQt\PAQt\PZ$ background.
The corresponding observed (expected) significance against the background-only
hypothesis is 3.7 (3.1) standard deviations, with an observed statistical
$p$-value of 0.0001. The expected significance is estimated from an Asimov toy
dataset [49]. The 68% CL interval of the expected significance is 1.4–5.9.

Potential biases from the background yields used as input have been searched
for. First, the analysis was repeated to measure simultaneously the
$\PQt\PZ\PQq$ and $\PQt\PAQt\PZ$ cross sections, in addition to determining
the NPL background normalization. The $\PQt\PZ\PQq$ signal strength increases
by less than 1%, whereas the observed and expected significances decrease by
about 1%. Then, the not-prompt muon and electron normalizations are set to
their input values, described as the first step in Section 0.6.2, and allowed
to vary in the fit as Gaussian constraints of 100% uncertainty. In this case,
both the $\PQt\PZ\PQq$ signal strength and the significances increase by about
10%, while the uncertainties on the signal strength increase by about 5%. In
addition, the measurement is repeated in each channel, and the measured signal
strengths are found to be $1.32^{+1.14}_{-0.99},\ 0.66^{+0.78}_{-0.63},\
0.01^{+0.97}_{-0.01}$, and $1.22^{+0.75}_{-0.63}$ for the $\Pe\Pe\Pe$,
$\Pe\Pe\Pgm$, $\Pe\Pgm\Pgm$, and $\Pgm\Pgm\Pgm$ channels, respectively. The
highest observed (expected) significance is 2.1 (1.9) standard deviations in
the $\Pgm\Pgm\Pgm$ channel. Finally, the results were verified in a counting
analysis, using the yields observed in control regions selected using similar
criteria to those of the 1bjet, 2bjets, and 0bjet regions. The simulated
backgrounds are normalized according to their SM predictions, while the
normalization of the NPL contributions follows the procedure described in
Section 0.6.2 as the first step of the NPL normalization in the shape
analysis. The results from the counting and shape analyses are in agreement.

##  0.9 Summary

The associated production cross section of a single top quark and a boson was
measured using data from $\Pp\Pp$ collisions at 13collected by the CMS
experiment, corresponding to an integrated luminosity of 35.9. The measurement
uses events containing three charged leptons in the final state. Evidence for
$\PQt\PZ\PQq$ production is found with an observed (expected) significance of
3.7 (3.1) standard deviations. The cross section is measured to be
$\sigma(\PQt\ell^{+}\ell^{-}\PQq)=123^{+33}_{-31}\stat^{+29}_{-23}\syst\unit{fb}$,
for $m_{\ell^{+}\ell^{-}}>30$, where $\ell$ stands for electrons, muons, or
$\tau$ leptons. This value is compatible with the next-to-leading-order
standard model prediction of $94.2\pm 3.1$fb.

###### Acknowledgements.

We congratulate our colleagues in the CERN accelerator departments for the
excellent performance of the LHC and thank the technical and administrative
staffs at CERN and at other CMS institutes for their contributions to the
success of the CMS effort. In addition, we gratefully acknowledge the
computing centres and personnel of the Worldwide LHC Computing Grid for
delivering so effectively the computing infrastructure essential to our
analyses. Finally, we acknowledge the enduring support for the construction
and operation of the LHC and the CMS detector provided by the following
funding agencies: BMWFW and FWF (Austria); FNRS and FWO (Belgium); CNPq,
CAPES, FAPERJ, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST, and NSFC
(China); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RPF (Cyprus);
SENESCYT (Ecuador); MoER, ERC IUT, and ERDF (Estonia); Academy of Finland,
MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF
(Germany); GSRT (Greece); OTKA and NIH (Hungary); DAE and DST (India); IPM
(Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); LAS
(Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and
UASLP-FAI (Mexico); MBIE (New Zealand); PAEC (Pakistan); MSHE and NSC
(Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom, RAS, RFBR and RAEP
(Russia); MESTD (Serbia); SEIDI, CPAN, PCTI and FEDER (Spain); Swiss Funding
Agencies (Switzerland); MST (Taipei); ThEPCenter, IPST, STAR, and NSTDA
(Thailand); TUBITAK and TAEK (Turkey); NASU and SFFR (Ukraine); STFC (United
Kingdom); DOE and NSF (USA). Individuals have received support from the Marie-
Curie programme and the European Research Council and Horizon 2020 Grant,
contract No. 675440 (European Union); the Leventis Foundation; the A. P. Sloan
Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science
Policy Office; the Fonds pour la Formation à la Recherche dans l’Industrie et
dans l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door
Wetenschap en Technologie (IWT-Belgium); the Ministry of Education, Youth and
Sports (MEYS) of the Czech Republic; the Council of Science and Industrial
Research, India; the HOMING PLUS programme of the Foundation for Polish
Science, cofinanced from European Union, Regional Development Fund, the
Mobility Plus programme of the Ministry of Science and Higher Education, the
National Science Center (Poland), contracts Harmonia 2014/14/M/ST2/00428, Opus
2014/13/B/ST2/02543, 2014/15/B/ST2/03998, and 2015/19/B/ST2/02861, Sonata-bis
2012/07/E/ST2/01406; the National Priorities Research Program by Qatar
National Research Fund; the Programa Severo Ochoa del Principado de Asturias;
the Thalis and Aristeia programmes cofinanced by EU-ESF and the Greek NSRF;
the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn
University and the Chulalongkorn Academic into Its 2nd Century Project
Advancement Project (Thailand); the Welch Foundation, contract C-1845; and the
Weston Havens Foundation (USA).

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##  .10 Variables used in the shape analysis

A short description of the variables used in the Boosted Decision Trees in the
1bjet and 2bjets regions is given in Table .10. The BDTs in the 1bjet region
and 2bjets region used 16 and 15 variables each, respectively, 10 of which
common to the two regions (variables 1–9 and 17 in Table .10). From the total
of 21 variables, 16 are related to the kinematic quantities associated to the
final state objects (1–16 in Table .10), while five of them are related to the
MEM (17–21 in Table .10).

Description of the variables used in the BDTs. The symbol Y (N) in the third
and fourth columns indicates that the variable was (was not) used in the 1bjet
and 2bjets BDTs.  Variable description 1bjet 2bjets 1 CSVv2 algorithm
discriminant Y Y 2 $\Delta R$ separation between the jet identified as a b
quark and the recoiling jet Y Y 3 $\eta$ of the recoiling jet Y Y 4 of the
recoiling jet Y Y 5 $\eta$ of the boson Y Y 6 Top quark mass Y Y 7 $\Delta R$
separation between the top quark decay lepton and the jet closest to it Y Y 8
Top quark decay lepton asymmetry Y Y 9 Azimuth angle separation between the
top quark decay lepton and the boson Y Y 10 Azimuth angle separation between
the top quark decay lepton and the b quark Y N 11 $\eta$ of the top quark
decay lepton Y N 12 $\eta$ of the jet with highest  Y N 13 $\Delta R$
separation between the top quark decay lepton and the recoil jet N Y 14
$\Delta R$ separation between the boson and the top quark N Y 15 of the boson
N Y 16 Number of b tagged jets N Y 17 Logarithm of the MEM score associated to
the most probable $\PQt\PZ\PQq$ kinematic configuration Y Y 18 Logarithm of
the MEM score associated to the most probable $\PQt\PAQt\PZ$ kinematic
configuration N Y 19 Log-likelihood ratio of the $\PQt\PZ\PQq$ hypothesis
against the $\PQt\PAQt\PZ$ hypothesis Y N 20 Log-likelihood ratio of the
$\PQt\PZ\PQq$ hypothesis against the $\PQt\PAQt\PZ$ hypothesis Y N with
$\PQt\PAQt\PZ$ and $\PQt\PZ\PQq$ weights rescaled such that their mean values
are similar 21 Log-likelihood ratio of the MEM weights for $\PQt\PAQt\PZ$
against $\PQt\PAQt\PZ$ \+ hypothesis Y N

##  .11 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan, A. Tumasyan Institut für Hochenergiephysik, Wien, Austria
W. Adam, F. Ambrogi, E. Asilar, T. Bergauer, J. Brandstetter, E. Brondolin, M.
Dragicevic, J. Erö, A. Escalante Del Valle, M. Flechl, M. Friedl, R.
Frühwirth1, V.M. Ghete, J. Grossmann, J. Hrubec, M. Jeitler1, A. König, N.
Krammer, I. Krätschmer, D. Liko, T. Madlener, I. Mikulec, E. Pree, N. Rad, H.
Rohringer, J. Schieck1, R. Schöfbeck, M. Spanring, D. Spitzbart, A. Taurok, W.
Waltenberger, J. Wittmann, C.-E. Wulz1, M. Zarucki Institute for Nuclear
Problems, Minsk, Belarus
V. Chekhovsky, V. Mossolov, J. Suarez Gonzalez Universiteit Antwerpen,
Antwerpen, Belgium
E.A. De Wolf, D. Di Croce, X. Janssen, J. Lauwers, M. Van De Klundert, H. Van
Haevermaet, P. Van Mechelen, N. Van Remortel Vrije Universiteit Brussel,
Brussel, Belgium
S. Abu Zeid, F. Blekman, J. D’Hondt, I. De Bruyn, J. De Clercq, K. Deroover,
G. Flouris, D. Lontkovskyi, S. Lowette, I. Marchesini, S. Moortgat, L.
Moreels, Q. Python, K. Skovpen, S. Tavernier, W. Van Doninck, P. Van Mulders,
I. Van Parijs Université Libre de Bruxelles, Bruxelles, Belgium
D. Beghin, B. Bilin, H. Brun, B. Clerbaux, G. De Lentdecker, H. Delannoy, B.
Dorney, G. Fasanella, L. Favart, R. Goldouzian, A. Grebenyuk, A.K. Kalsi, T.
Lenzi, J. Luetic, T. Maerschalk, A. Marinov, T. Seva, E. Starling, C. Vander
Velde, P. Vanlaer, D. Vannerom, R. Yonamine, F. Zenoni Ghent University,
Ghent, Belgium
T. Cornelis, D. Dobur, A. Fagot, M. Gul, I. Khvastunov2, D. Poyraz, C. Roskas,
S. Salva, D. Trocino, M. Tytgat, W. Verbeke, M. Vit, N. Zaganidis Université
Catholique de Louvain, Louvain-la-Neuve, Belgium
H. Bakhshiansohi, O. Bondu, S. Brochet, G. Bruno, C. Caputo, A. Caudron, P.
David, S. De Visscher, C. Delaere, M. Delcourt, B. Francois, A. Giammanco, M.
Komm, G. Krintiras, V. Lemaitre, A. Magitteri, A. Mertens, M. Musich, K.
Piotrzkowski, L. Quertenmont, A. Saggio, M. Vidal Marono, S. Wertz, J. Zobec
Centro Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil
W.L. Aldá Júnior, F.L. Alves, G.A. Alves, L. Brito, G. Correia Silva, C.
Hensel, A. Moraes, M.E. Pol, P. Rebello Teles Universidade do Estado do Rio de
Janeiro, Rio de Janeiro, Brazil
E. Belchior Batista Das Chagas, W. Carvalho, J. Chinellato3, E. Coelho, E.M.
Da Costa, G.G. Da Silveira4, D. De Jesus Damiao, S. Fonseca De Souza, L.M.
Huertas Guativa, H. Malbouisson, M. Melo De Almeida, C. Mora Herrera, L.
Mundim, H. Nogima, L.J. Sanchez Rosas, A. Santoro, A. Sznajder, M. Thiel, E.J.
Tonelli Manganote3, F. Torres Da Silva De Araujo, A. Vilela Pereira
Universidade Estadual Paulista a, Universidade Federal do ABC b, São Paulo,
Brazil
S. Ahujaa, C.A. Bernardesa, T.R. Fernandez Perez Tomeia, E.M. Gregoresb, P.G.
Mercadanteb, S.F. Novaesa, Sandra S. Padulaa, D. Romero Abadb, J.C. Ruiz
Vargasa Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy
of Sciences, Sofia, Bulgaria
A. Aleksandrov, R. Hadjiiska, P. Iaydjiev, M. Misheva, M. Rodozov, M. Shopova,
G. Sultanov University of Sofia, Sofia, Bulgaria
A. Dimitrov, L. Litov, B. Pavlov, P. Petkov Beihang University, Beijing, China
W. Fang5, X. Gao5, L. Yuan Institute of High Energy Physics, Beijing, China
M. Ahmad, J.G. Bian, G.M. Chen, H.S. Chen, M. Chen, Y. Chen, C.H. Jiang, D.
Leggat, H. Liao, Z. Liu, F. Romeo, S.M. Shaheen, A. Spiezia, J. Tao, C. Wang,
Z. Wang, E. Yazgan, H. Zhang, J. Zhao State Key Laboratory of Nuclear Physics
and Technology, Peking University, Beijing, China
Y. Ban, G. Chen, J. Li, Q. Li, S. Liu, Y. Mao, S.J. Qian, D. Wang, Z. Xu, F.
Zhang5 Tsinghua University, Beijing, China
Y. Wang Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, C.A. Carrillo Montoya, L.F. Chaparro Sierra, C. Florez,
C.F. González Hernández, J.D. Ruiz Alvarez, M.A. Segura Delgado University of
Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval
Architecture, Split, Croatia
B. Courbon, N. Godinovic, D. Lelas, I. Puljak, P.M. Ribeiro Cipriano, T.
Sculac University of Split, Faculty of Science, Split, Croatia
Z. Antunovic, M. Kovac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, D. Ferencek, K. Kadija, B. Mesic, A. Starodumov6, T. Susa
University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos,
P.A. Razis, H. Rykaczewski Charles University, Prague, Czech Republic
M. Finger7, M. Finger Jr.7 Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab
Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
Y. Assran8,9, M.A. Mahmoud10,9, A. Mahrous11 National Institute of Chemical
Physics and Biophysics, Tallinn, Estonia
S. Bhowmik, R.K. Dewanjee, M. Kadastik, L. Perrini, M. Raidal, C. Veelken
Department of Physics, University of Helsinki, Helsinki, Finland
P. Eerola, H. Kirschenmann, J. Pekkanen, M. Voutilainen Helsinki Institute of
Physics, Helsinki, Finland
J. Havukainen, J.K. Heikkilä, T. Järvinen, V. Karimäki, R. Kinnunen, T.
Lampén, K. Lassila-Perini, S. Laurila, S. Lehti, T. Lindén, P. Luukka, T.
Mäenpää, H. Siikonen, E. Tuominen, J. Tuominiemi Lappeenranta University of
Technology, Lappeenranta, Finland
T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
M. Besancon, F. Couderc, M. Dejardin, D. Denegri, J.L. Faure, F. Ferri, S.
Ganjour, S. Ghosh, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry,
C. Leloup, E. Locci, M. Machet, J. Malcles, G. Negro, J. Rander, A. Rosowsky,
M.Ö. Sahin, M. Titov Laboratoire Leprince-Ringuet, Ecole polytechnique,
CNRS/IN2P3, Université Paris-Saclay, Palaiseau, France
A. Abdulsalam12, C. Amendola, I. Antropov, S. Baffioni, F. Beaudette, P.
Busson, L. Cadamuro, C. Charlot, R. Granier de Cassagnac, M. Jo, I. Kucher, S.
Lisniak, A. Lobanov, J. Martin Blanco, M. Nguyen, C. Ochando, G. Ortona, P.
Paganini, P. Pigard, R. Salerno, J.B. Sauvan, Y. Sirois, A.G. Stahl Leiton, T.
Strebler, Y. Yilmaz, A. Zabi, A. Zghiche Université de Strasbourg, CNRS, IPHC
UMR 7178, F-67000 Strasbourg, France
J.-L. Agram13, J. Andrea, D. Bloch, J.-M. Brom, M. Buttignol, E.C. Chabert, N.
Chanon, C. Collard, E. Conte13, X. Coubez, F. Drouhin13, J.-C. Fontaine13, D.
Gelé, U. Goerlach, M. Jansová, P. Juillot, A.-C. Le Bihan, N. Tonon, P. Van
Hove Centre de Calcul de l’Institut National de Physique Nucleaire et de
Physique des Particules, CNRS/IN2P3, Villeurbanne, France
S. Gadrat Université de Lyon, Université Claude Bernard Lyon 1, CNRS-IN2P3,
Institut de Physique Nucléaire de Lyon, Villeurbanne, France
S. Beauceron, C. Bernet, G. Boudoul, R. Chierici, D. Contardo, P. Depasse, H.
El Mamouni, J. Fay, L. Finco, S. Gascon, M. Gouzevitch, G. Grenier, B. Ille,
F. Lagarde, I.B. Laktineh, M. Lethuillier, L. Mirabito, A.L. Pequegnot, S.
Perries, A. Popov14, V. Sordini, M. Vander Donckt, S. Viret, S. Zhang Georgian
Technical University, Tbilisi, Georgia
T. Toriashvili15 Tbilisi State University, Tbilisi, Georgia
Z. Tsamalaidze7 RWTH Aachen University, I. Physikalisches Institut, Aachen,
Germany
C. Autermann, L. Feld, M.K. Kiesel, K. Klein, M. Lipinski, M. Preuten, C.
Schomakers, J. Schulz, M. Teroerde, B. Wittmer, V. Zhukov14 RWTH Aachen
University, III. Physikalisches Institut A, Aachen, Germany
A. Albert, D. Duchardt, M. Endres, M. Erdmann, S. Erdweg, T. Esch, R. Fischer,
A. Güth, T. Hebbeker, C. Heidemann, K. Hoepfner, S. Knutzen, M. Merschmeyer,
A. Meyer, P. Millet, S. Mukherjee, T. Pook, M. Radziej, H. Reithler, M.
Rieger, F. Scheuch, D. Teyssier, S. Thüer RWTH Aachen University, III.
Physikalisches Institut B, Aachen, Germany
G. Flügge, B. Kargoll, T. Kress, A. Künsken, T. Müller, A. Nehrkorn, A.
Nowack, C. Pistone, O. Pooth, A. Stahl16 Deutsches Elektronen-Synchrotron,
Hamburg, Germany
M. Aldaya Martin, T. Arndt, C. Asawatangtrakuldee, K. Beernaert, O. Behnke, U.
Behrens, A. Bermúdez Martínez, A.A. Bin Anuar, K. Borras17, V. Botta, A.
Campbell, P. Connor, C. Contreras-Campana, F. Costanza, C. Diez Pardos, G.
Eckerlin, D. Eckstein, T. Eichhorn, E. Eren, E. Gallo18, J. Garay Garcia, A.
Geiser, J.M. Grados Luyando, A. Grohsjean, P. Gunnellini, M. Guthoff, A. Harb,
J. Hauk, M. Hempel19, H. Jung, M. Kasemann, J. Keaveney, C. Kleinwort, I.
Korol, D. Krücker, W. Lange, A. Lelek, T. Lenz, K. Lipka, W. Lohmann19, R.
Mankel, I.-A. Melzer-Pellmann, A.B. Meyer, M. Missiroli, G. Mittag, J. Mnich,
A. Mussgiller, E. Ntomari, D. Pitzl, A. Raspereza, M. Savitskyi, P. Saxena, R.
Shevchenko, N. Stefaniuk, G.P. Van Onsem, R. Walsh, Y. Wen, K. Wichmann, C.
Wissing, O. Zenaiev University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, V. Blobel, M. Centis Vignali, T. Dreyer, E. Garutti, D.
Gonzalez, J. Haller, A. Hinzmann, M. Hoffmann, A. Karavdina, R. Klanner, R.
Kogler, N. Kovalchuk, S. Kurz, D. Marconi, M. Meyer, M. Niedziela, D.
Nowatschin, F. Pantaleo16, T. Peiffer, A. Perieanu, C. Scharf, P. Schleper, A.
Schmidt, S. Schumann, J. Schwandt, J. Sonneveld, H. Stadie, G. Steinbrück,
F.M. Stober, M. Stöver, H. Tholen, D. Troendle, E. Usai, A. Vanhoefer, B.
Vormwald Institut für Experimentelle Kernphysik, Karlsruhe, Germany
M. Akbiyik, C. Barth, M. Baselga, S. Baur, E. Butz, R. Caspart, T. Chwalek, F.
Colombo, W. De Boer, A. Dierlamm, N. Faltermann, B. Freund, R. Friese, M.
Giffels, M.A. Harrendorf, F. Hartmann16, S.M. Heindl, U. Husemann, F.
Kassel16, S. Kudella, H. Mildner, M.U. Mozer, Th. Müller, M. Plagge, G. Quast,
K. Rabbertz, M. Schröder, I. Shvetsov, G. Sieber, H.J. Simonis, R. Ulrich, S.
Wayand, M. Weber, T. Weiler, S. Williamson, C. Wöhrmann, R. Wolf Institute of
Nuclear and Particle Physics (INPP), NCSR Demokritos, Aghia Paraskevi, Greece
G. Anagnostou, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas, I. Topsis-
Giotis National and Kapodistrian University of Athens, Athens, Greece
G. Karathanasis, S. Kesisoglou, A. Panagiotou, N. Saoulidou, E. Tziaferi
National Technical University of Athens, Athens, Greece
K. Kousouris University of Ioánnina, Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, S. Mallios, N.
Manthos, I. Papadopoulos, E. Paradas, J. Strologas, F.A. Triantis, D.
Tsitsonis MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
M. Csanad, N. Filipovic, G. Pasztor, O. Surányi, G.I. Veres20 Wigner Research
Centre for Physics, Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath21, Á. Hunyadi, F. Sikler, V. Veszpremi, G.
Vesztergombi20 Institute of Nuclear Research ATOMKI, Debrecen, Hungary
N. Beni, S. Czellar, J. Karancsi22, A. Makovec, J. Molnar, Z. Szillasi
Institute of Physics, University of Debrecen, Debrecen, Hungary
M. Bartók20, P. Raics, Z.L. Trocsanyi, B. Ujvari Indian Institute of Science
(IISc), Bangalore, India
S. Choudhury, J.R. Komaragiri National Institute of Science Education and
Research, Bhubaneswar, India
S. Bahinipati23, P. Mal, K. Mandal, A. Nayak24, D.K. Sahoo23, N. Sahoo, S.K.
Swain Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, R. Chawla, N. Dhingra, A. Kaur, M. Kaur,
S. Kaur, R. Kumar, P. Kumari, A. Mehta, J.B. Singh, G. Walia University of
Delhi, Delhi, India
Ashok Kumar, Aashaq Shah, A. Bhardwaj, S. Chauhan, B.C. Choudhary, R.B. Garg,
S. Keshri, A. Kumar, S. Malhotra, M. Naimuddin, K. Ranjan, R. Sharma Saha
Institute of Nuclear Physics, HBNI, Kolkata, India
R. Bhardwaj25, R. Bhattacharya, S. Bhattacharya, U. Bhawandeep25, D. Bhowmik,
S. Dey, S. Dutt25, S. Dutta, S. Ghosh, N. Majumdar, A. Modak, K. Mondal, S.
Mukhopadhyay, S. Nandan, A. Purohit, P.K. Rout, A. Roy, S. Roy Chowdhury, S.
Sarkar, M. Sharan, B. Singh, S. Thakur25 Indian Institute of Technology
Madras, Madras, India
P.K. Behera Bhabha Atomic Research Centre, Mumbai, India
R. Chudasama, D. Dutta, V. Jha, V. Kumar, A.K. Mohanty16, P.K. Netrakanti,
L.M. Pant, P. Shukla, A. Topkar Tata Institute of Fundamental Research-A,
Mumbai, India
T. Aziz, S. Dugad, B. Mahakud, S. Mitra, G.B. Mohanty, N. Sur, B. Sutar Tata
Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, Sa. Jain, S.
Kumar, M. Maity26, G. Majumder, K. Mazumdar, T. Sarkar26, N. Wickramage27
Indian Institute of Science Education and Research (IISER), Pune, India
S. Chauhan, S. Dube, V. Hegde, A. Kapoor, K. Kothekar, S. Pandey, A. Rane, S.
Sharma Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
S. Chenarani28, E. Eskandari Tadavani, S.M. Etesami28, M. Khakzad, M.
Mohammadi Najafabadi, M. Naseri, S. Paktinat Mehdiabadi29, F. Rezaei
Hosseinabadi, B. Safarzadeh30, M. Zeinali University College Dublin, Dublin,
Ireland
M. Felcini, M. Grunewald INFN Sezione di Bari a, Università di Bari b,
Politecnico di Bari c, Bari, Italy
M. Abbresciaa,b, C. Calabriaa,b, A. Colaleoa, D. Creanzaa,c, L. Cristellaa,b,
N. De Filippisa,c, M. De Palmaa,b, F. Erricoa,b, L. Fiorea, G. Iasellia,c, S.
Lezkia,b, G. Maggia,c, M. Maggia, G. Minielloa,b, S. Mya,b, S. Nuzzoa,b, A.
Pompilia,b, G. Pugliesea,c, R. Radognaa, A. Ranieria, G. Selvaggia,b, A.
Sharmaa, L. Silvestrisa,16, R. Vendittia, P. Verwilligena INFN Sezione di
Bologna a, Università di Bologna b, Bologna, Italy
G. Abbiendia, C. Battilanaa,b, D. Bonacorsia,b, L. Borgonovia,b, S. Braibant-
Giacomellia,b, R. Campaninia,b, P. Capiluppia,b, A. Castroa,b, F.R. Cavalloa,
S.S. Chhibraa,b, G. Codispotia,b, M. Cuffiania,b, G.M. Dallavallea, F.
Fabbria, A. Fanfania,b, D. Fasanellaa,b, P. Giacomellia, C. Grandia, L.
Guiduccia,b, F. Iemmi, S. Marcellinia, G. Masettia, A. Montanaria, F.L.
Navarriaa,b, A. Perrottaa, A.M. Rossia,b, T. Rovellia,b, G.P. Sirolia,b, N.
Tosia INFN Sezione di Catania a, Università di Catania b, Catania, Italy
S. Albergoa,b, S. Costaa,b, A. Di Mattiaa, F. Giordanoa,b, R. Potenzaa,b, A.
Tricomia,b, C. Tuvea,b INFN Sezione di Firenze a, Università di Firenze b,
Firenze, Italy
G. Barbaglia, K. Chatterjeea,b, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, E. Focardia,b, P. Lenzia,b, M. Meschinia, S. Paolettia, L.
Russoa,31, G. Sguazzonia, D. Stroma, L. Viliania INFN Laboratori Nazionali di
Frascati, Frascati, Italy
L. Benussi, S. Bianco, F. Fabbri, D. Piccolo, F. Primavera16 INFN Sezione di
Genova a, Università di Genova b, Genova, Italy
V. Calvellia,b, F. Ferroa, F. Raveraa,b, E. Robuttia, S. Tosia,b INFN Sezione
di Milano-Bicocca a, Università di Milano-Bicocca b, Milano, Italy
A. Benagliaa, A. Beschib, L. Brianzaa,b, F. Brivioa,b, V. Cirioloa,b,16, M.E.
Dinardoa,b, S. Fiorendia,b, S. Gennaia, A. Ghezzia,b, P. Govonia,b, M.
Malbertia,b, S. Malvezzia, R.A. Manzonia,b, D. Menascea, L. Moronia, M.
Paganonia,b, K. Pauwelsa,b, D. Pedrinia, S. Pigazzinia,b,32, S. Ragazzia,b, T.
Tabarelli de Fatisa,b INFN Sezione di Napoli a, Università di Napoli ’Federico
II’ b, Napoli, Italy, Università della Basilicata c, Potenza, Italy,
Università G. Marconi d, Roma, Italy
S. Buontempoa, N. Cavalloa,c, S. Di Guidaa,d,16, F. Fabozzia,c, F. Fiengaa,b,
A.O.M. Iorioa,b, W.A. Khana, L. Listaa, S. Meolaa,d,16, P. Paoluccia,16, C.
Sciaccaa,b, F. Thyssena INFN Sezione di Padova a, Università di Padova b,
Padova, Italy, Università di Trento c, Trento, Italy
P. Azzia, L. Benatoa,b, D. Biselloa,b, A. Bolettia,b, R. Carlina,b, A.
Carvalho Antunes De Oliveiraa,b, P. Checchiaa, M. Dall’Ossoa,b, P. De Castro
Manzanoa, T. Dorigoa, U. Dossellia, F. Gasparinia,b, U. Gasparinia,b, A.
Gozzelinoa, S. Lacapraraa, P. Lujan, M. Margonia,b, A.T. Meneguzzoa,b, N.
Pozzobona,b, P. Ronchesea,b, R. Rossina,b, F. Simonettoa,b, A. Tiko, E.
Torassaa, S. Venturaa, P. Zottoa,b, G. Zumerlea,b INFN Sezione di Pavia a,
Università di Pavia b, Pavia, Italy
A. Braghieria, A. Magnania, P. Montagnaa,b, S.P. Rattia,b, V. Rea, M.
Ressegottia,b, C. Riccardia,b, P. Salvinia, I. Vaia,b, P. Vituloa,b INFN
Sezione di Perugia a, Università di Perugia b, Perugia, Italy
L. Alunni Solestizia,b, M. Biasinia,b, G.M. Bileia, C. Cecchia,b, D.
Ciangottinia,b, L. Fanòa,b, P. Laricciaa,b, R. Leonardia,b, E. Manonia, G.
Mantovania,b, V. Mariania,b, M. Menichellia, A. Rossia,b, A. Santocchiaa,b, D.
Spigaa INFN Sezione di Pisa a, Università di Pisa b, Scuola Normale Superiore
di Pisa c, Pisa, Italy
K. Androsova, P. Azzurria,16, G. Bagliesia, L. Bianchinia, T. Boccalia, L.
Borrello, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, G. Fedia, L.
Gianninia,c, A. Giassia, M.T. Grippoa,31, F. Ligabuea,c, T. Lomtadzea, E.
Mancaa,c, G. Mandorlia,c, A. Messineoa,b, F. Pallaa, A. Rizzia,b, A. Savoy-
Navarroa,33, P. Spagnoloa, R. Tenchinia, G. Tonellia,b, A. Venturia, P.G.
Verdinia INFN Sezione di Roma a, Sapienza Università di Roma b, Rome, Italy
L. Baronea,b, F. Cavallaria, M. Cipriania,b, N. Dacia, D. Del Rea,b, E. Di
Marcoa,b, M. Diemoza, S. Gellia,b, E. Longoa,b, F. Margarolia,b, B.
Marzocchia,b, P. Meridiania, G. Organtinia,b, R. Paramattia,b, F. Preiatoa,b,
S. Rahatloua,b, C. Rovellia, F. Santanastasioa,b INFN Sezione di Torino a,
Università di Torino b, Torino, Italy, Università del Piemonte Orientale c,
Novara, Italy
N. Amapanea,b, R. Arcidiaconoa,c, S. Argiroa,b, M. Arneodoa,c, N. Bartosika,
R. Bellana,b, C. Biinoa, N. Cartigliaa, F. Cennaa,b, M. Costaa,b, R.
Covarellia,b, A. Deganoa,b, N. Demariaa, B. Kiania,b, C. Mariottia, S.
Masellia, E. Migliorea,b, V. Monacoa,b, E. Monteila,b, M. Montenoa, M.M.
Obertinoa,b, L. Pachera,b, N. Pastronea, M. Pelliccionia, G.L. Pinna
Angionia,b, A. Romeroa,b, M. Ruspaa,c, R. Sacchia,b, K. Shchelinaa,b, V.
Solaa, A. Solanoa,b, A. Staianoa, P. Traczyka,b INFN Sezione di Trieste a,
Università di Trieste b, Trieste, Italy
S. Belfortea, M. Casarsaa, F. Cossuttia, G. Della Riccaa,b, A. Zanettia
Kyungpook National University, Daegu, Korea
D.H. Kim, G.N. Kim, M.S. Kim, J. Lee, S. Lee, S.W. Lee, C.S. Moon, Y.D. Oh, S.
Sekmen, D.C. Son, Y.C. Yang Chonnam National University, Institute for
Universe and Elementary Particles, Kwangju, Korea
H. Kim, D.H. Moon, G. Oh Hanyang University, Seoul, Korea
J.A. Brochero Cifuentes, J. Goh, T.J. Kim Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, D. Gyun, S. Ha, B. Hong, Y. Jo, Y. Kim, K. Lee, K.S.
Lee, S. Lee, J. Lim, S.K. Park, Y. Roh Seoul National University, Seoul, Korea
J. Almond, J. Kim, J.S. Kim, H. Lee, K. Lee, K. Nam, S.B. Oh, B.C. Radburn-
Smith, S.h. Seo, U.K. Yang, H.D. Yoo, G.B. Yu University of Seoul, Seoul,
Korea
H. Kim, J.H. Kim, J.S.H. Lee, I.C. Park Sungkyunkwan University, Suwon, Korea
Y. Choi, C. Hwang, J. Lee, I. Yu Vilnius University, Vilnius, Lithuania
V. Dudenas, A. Juodagalvis, J. Vaitkus National Centre for Particle Physics,
Universiti Malaya, Kuala Lumpur, Malaysia
I. Ahmed, Z.A. Ibrahim, M.A.B. Md Ali34, F. Mohamad Idris35, W.A.T. Wan
Abdullah, M.N. Yusli, Z. Zolkapli Centro de Investigacion y de Estudios
Avanzados del IPN, Mexico City, Mexico
Reyes-Almanza, R, Ramirez-Sanchez, G., Duran-Osuna, M. C., H. Castilla-Valdez,
E. De La Cruz-Burelo, I. Heredia-De La Cruz36, Rabadan-Trejo, R. I., R. Lopez-
Fernandez, J. Mejia Guisao, A. Sanchez-Hernandez Universidad Iberoamericana,
Mexico City, Mexico
S. Carrillo Moreno, C. Oropeza Barrera, F. Vazquez Valencia Benemerita
Universidad Autonoma de Puebla, Puebla, Mexico
J. Eysermans, I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada Universidad
Autónoma de San Luis Potosí, San Luis Potosí, Mexico
A. Morelos Pineda University of Auckland, Auckland, New Zealand
D. Krofcheck University of Canterbury, Christchurch, New Zealand
P.H. Butler National Centre for Physics, Quaid-I-Azam University, Islamabad,
Pakistan
A. Ahmad, M. Ahmad, Q. Hassan, H.R. Hoorani, A. Saddique, M.A. Shah, M.
Shoaib, M. Waqas National Centre for Nuclear Research, Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, T. Frueboes, M. Górski, M. Kazana, K.
Nawrocki, M. Szleper, P. Zalewski Institute of Experimental Physics, Faculty
of Physics, University of Warsaw, Warsaw, Poland
K. Bunkowski, A. Byszuk37, K. Doroba, A. Kalinowski, M. Konecki, J.
Krolikowski, M. Misiura, M. Olszewski, A. Pyskir, M. Walczak Laboratório de
Instrumentação e Física Experimental de Partículas, Lisboa, Portugal
P. Bargassa, C. Beirão Da Cruz E Silva, A. Di Francesco, P. Faccioli, B.
Galinhas, M. Gallinaro, J. Hollar, N. Leonardo, L. Lloret Iglesias, M.V.
Nemallapudi, J. Seixas, G. Strong, O. Toldaiev, D. Vadruccio, J. Varela Joint
Institute for Nuclear Research, Dubna, Russia
S. Afanasiev, P. Bunin, M. Gavrilenko, I. Golutvin, I. Gorbunov, A. Kamenev,
V. Karjavin, A. Lanev, A. Malakhov, V. Matveev38,39, P. Moisenz, V. Palichik,
V. Perelygin, S. Shmatov, S. Shulha, N. Skatchkov, V. Smirnov, N. Voytishin,
A. Zarubin Petersburg Nuclear Physics Institute, Gatchina (St. Petersburg),
Russia
Y. Ivanov, V. Kim40, E. Kuznetsova41, P. Levchenko, V. Murzin, V. Oreshkin, I.
Smirnov, D. Sosnov, V. Sulimov, L. Uvarov, S. Vavilov, A. Vorobyev Institute
for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov,
N. Krasnikov, A. Pashenkov, D. Tlisov, A. Toropin Institute for Theoretical
and Experimental Physics, Moscow, Russia
V. Epshteyn, V. Gavrilov, N. Lychkovskaya, V. Popov, I. Pozdnyakov, G.
Safronov, A. Spiridonov, A. Stepennov, V. Stolin, M. Toms, E. Vlasov, A.
Zhokin Moscow Institute of Physics and Technology, Moscow, Russia
T. Aushev, A. Bylinkin39 National Research Nuclear University ’Moscow
Engineering Physics Institute’ (MEPhI), Moscow, Russia
M. Chadeeva42, P. Parygin, D. Philippov, S. Polikarpov, E. Popova, V. Rusinov
P.N. Lebedev Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin39, I. Dremin39, M. Kirakosyan39, S.V. Rusakov, A.
Terkulov Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State
University, Moscow, Russia
A. Baskakov, A. Belyaev, E. Boos, V. Bunichev, M. Dubinin43, L. Dudko, A.
Gribushin, V. Klyukhin, N. Korneeva, I. Lokhtin, I. Miagkov, S. Obraztsov, M.
Perfilov, V. Savrin, P. Volkov Novosibirsk State University (NSU),
Novosibirsk, Russia
V. Blinov44, D. Shtol44, Y. Skovpen44 State Research Center of Russian
Federation, Institute for High Energy Physics of NRC "Kurchatov
Institute", Protvino, Russia
I. Azhgirey, I. Bayshev, S. Bitioukov, D. Elumakhov, A. Godizov, V. Kachanov,
A. Kalinin, D. Konstantinov, P. Mandrik, V. Petrov, R. Ryutin, A. Sobol, S.
Troshin, N. Tyurin, A. Uzunian, A. Volkov University of Belgrade, Faculty of
Physics and Vinca Institute of Nuclear Sciences, Belgrade, Serbia
P. Adzic45, P. Cirkovic, D. Devetak, M. Dordevic, J. Milosevic Centro de
Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT), Madrid,
Spain
J. Alcaraz Maestre, I. Bachiller, M. Barrio Luna, M. Cerrada, N. Colino, B. De
La Cruz, A. Delgado Peris, C. Fernandez Bedoya, J.P. Fernández Ramos, J. Flix,
M.C. Fouz, O. Gonzalez Lopez, S. Goy Lopez, J.M. Hernandez, M.I. Josa, D.
Moran, A. Pérez-Calero Yzquierdo, J. Puerta Pelayo, I. Redondo, L. Romero,
M.S. Soares, A. Triossi, A. Álvarez Fernández Universidad Autónoma de Madrid,
Madrid, Spain
C. Albajar, J.F. de Trocóniz Universidad de Oviedo, Oviedo, Spain
J. Cuevas, C. Erice, J. Fernandez Menendez, I. Gonzalez Caballero, J.R.
González Fernández, E. Palencia Cortezon, S. Sanchez Cruz, P. Vischia, J.M.
Vizan Garcia Instituto de Física de Cantabria (IFCA), CSIC-Universidad de
Cantabria, Santander, Spain
I.J. Cabrillo, A. Calderon, B. Chazin Quero, E. Curras, J. Duarte Campderros,
M. Fernandez, P.J. Fernández Manteca, J. Garcia-Ferrero, A. García Alonso, G.
Gomez, A. Lopez Virto, J. Marco, C. Martinez Rivero, P. Martinez Ruiz del
Arbol, F. Matorras, J. Piedra Gomez, C. Prieels, T. Rodrigo, A. Ruiz-Jimeno,
L. Scodellaro, N. Trevisani, I. Vila, R. Vilar Cortabitarte CERN, European
Organization for Nuclear Research, Geneva, Switzerland
D. Abbaneo, B. Akgun, E. Auffray, P. Baillon, A.H. Ball, D. Barney, J.
Bendavid, M. Bianco, A. Bocci, C. Botta, T. Camporesi, R. Castello, M. Cepeda,
G. Cerminara, E. Chapon, Y. Chen, D. d’Enterria, A. Dabrowski, V. Daponte, A.
David, M. De Gruttola, A. De Roeck, N. Deelen, M. Dobson, T. du Pree, M.
Dünser, N. Dupont, A. Elliott-Peisert, P. Everaerts, F. Fallavollita, G.
Franzoni, J. Fulcher, W. Funk, D. Gigi, A. Gilbert, K. Gill, F. Glege, D.
Gulhan, J. Hegeman, V. Innocente, A. Jafari, P. Janot, O. Karacheban19, J.
Kieseler, V. Knünz, A. Kornmayer, M.J. Kortelainen, M. Krammer1, C. Lange, P.
Lecoq, C. Lourenço, M.T. Lucchini, L. Malgeri, M. Mannelli, A. Martelli, F.
Meijers, J.A. Merlin, S. Mersi, E. Meschi, P. Milenovic46, F. Moortgat, M.
Mulders, H. Neugebauer, J. Ngadiuba, S. Orfanelli, L. Orsini, L. Pape, E.
Perez, M. Peruzzi, A. Petrilli, G. Petrucciani, A. Pfeiffer, M. Pierini, F.M.
Pitters, D. Rabady, A. Racz, T. Reis, G. Rolandi47, M. Rovere, H. Sakulin, C.
Schäfer, C. Schwick, M. Seidel, M. Selvaggi, A. Sharma, P. Silva, P.
Sphicas48, A. Stakia, J. Steggemann, M. Stoye, M. Tosi, D. Treille, A. Tsirou,
V. Veckalns49, M. Verweij, W.D. Zeuner Paul Scherrer Institut, Villigen,
Switzerland
W. Bertl${}^{\textrm{\textdagger}}$, L. Caminada50, K. Deiters, W. Erdmann, R.
Horisberger, Q. Ingram, H.C. Kaestli, D. Kotlinski, U. Langenegger, T. Rohe,
S.A. Wiederkehr ETH Zurich - Institute for Particle Physics and Astrophysics
(IPA), Zurich, Switzerland
M. Backhaus, L. Bäni, P. Berger, B. Casal, G. Dissertori, M. Dittmar, M.
Donegà, C. Dorfer, C. Grab, C. Heidegger, D. Hits, J. Hoss, G. Kasieczka, T.
Klijnsma, W. Lustermann, B. Mangano, M. Marionneau, M.T. Meinhard, D. Meister,
F. Micheli, P. Musella, F. Nessi-Tedaldi, F. Pandolfi, J. Pata, F. Pauss, G.
Perrin, L. Perrozzi, M. Quittnat, M. Reichmann, D.A. Sanz Becerra, M.
Schönenberger, L. Shchutska, V.R. Tavolaro, K. Theofilatos, M.L. Vesterbacka
Olsson, R. Wallny, D.H. Zhu Universität Zürich, Zurich, Switzerland
T.K. Aarrestad, C. Amsler51, M.F. Canelli, A. De Cosa, R. Del Burgo, S.
Donato, C. Galloni, T. Hreus, B. Kilminster, D. Pinna, G. Rauco, P. Robmann,
D. Salerno, K. Schweiger, C. Seitz, Y. Takahashi, A. Zucchetta National
Central University, Chung-Li, Taiwan
V. Candelise, Y.H. Chang, K.y. Cheng, T.H. Doan, Sh. Jain, R. Khurana, C.M.
Kuo, W. Lin, A. Pozdnyakov, S.S. Yu National Taiwan University (NTU), Taipei,
Taiwan
Arun Kumar, P. Chang, Y. Chao, K.F. Chen, P.H. Chen, F. Fiori, W.-S. Hou, Y.
Hsiung, Y.F. Liu, R.-S. Lu, E. Paganis, A. Psallidas, A. Steen, J.f. Tsai
Chulalongkorn University, Faculty of Science, Department of Physics, Bangkok,
Thailand
B. Asavapibhop, K. Kovitanggoon, G. Singh, N. Srimanobhas Çukurova University,
Physics Department, Science and Art Faculty, Adana, Turkey
A. Bat, F. Boran, S. Cerci52, S. Damarseckin, Z.S. Demiroglu, C. Dozen, I.
Dumanoglu, S. Girgis, G. Gokbulut, Y. Guler, I. Hos53, E.E. Kangal54, O. Kara,
A. Kayis Topaksu, U. Kiminsu, M. Oglakci, G. Onengut, K. Ozdemir55, D. Sunar
Cerci52, B. Tali52, U.G. Tok, S. Turkcapar, I.S. Zorbakir, C. Zorbilmez Middle
East Technical University, Physics Department, Ankara, Turkey
G. Karapinar56, K. Ocalan57, M. Yalvac, M. Zeyrek Bogazici University,
Istanbul, Turkey
E. Gülmez, M. Kaya58, O. Kaya59, S. Tekten, E.A. Yetkin60 Istanbul Technical
University, Istanbul, Turkey
M.N. Agaras, S. Atay, A. Cakir, K. Cankocak, Y. Komurcu Institute for
Scintillation Materials of National Academy of Science of Ukraine, Kharkov,
Ukraine
B. Grynyov National Scientific Center, Kharkov Institute of Physics and
Technology, Kharkov, Ukraine
L. Levchuk University of Bristol, Bristol, United Kingdom
F. Ball, L. Beck, J.J. Brooke, D. Burns, E. Clement, D. Cussans, O. Davignon,
H. Flacher, J. Goldstein, G.P. Heath, H.F. Heath, L. Kreczko, D.M. Newbold61,
S. Paramesvaran, T. Sakuma, S. Seif El Nasr-storey, D. Smith, V.J. Smith
Rutherford Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev62, C. Brew, R.M. Brown, L. Calligaris, D. Cieri, D.J.A.
Cockerill, J.A. Coughlan, K. Harder, S. Harper, J. Linacre, E. Olaiya, D.
Petyt, C.H. Shepherd-Themistocleous, A. Thea, I.R. Tomalin, T. Williams, W.J.
Womersley Imperial College, London, United Kingdom
G. Auzinger, R. Bainbridge, P. Bloch, J. Borg, S. Breeze, O. Buchmuller, A.
Bundock, S. Casasso, M. Citron, D. Colling, L. Corpe, P. Dauncey, G. Davies,
M. Della Negra, R. Di Maria, Y. Haddad, G. Hall, G. Iles, T. James, R. Lane,
C. Laner, L. Lyons, A.-M. Magnan, S. Malik, L. Mastrolorenzo, T. Matsushita,
J. Nash63, A. Nikitenko6, V. Palladino, M. Pesaresi, D.M. Raymond, A.
Richards, A. Rose, E. Scott, C. Seez, A. Shtipliyski, S. Summers, A. Tapper,
K. Uchida, M. Vazquez Acosta64, T. Virdee16, N. Wardle, D. Winterbottom, J.
Wright, S.C. Zenz Brunel University, Uxbridge, United Kingdom
J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, A. Morton, I.D. Reid, L.
Teodorescu, S. Zahid Baylor University, Waco, USA
A. Borzou, K. Call, J. Dittmann, K. Hatakeyama, H. Liu, N. Pastika, C. Smith
Catholic University of America, Washington DC, USA
R. Bartek, A. Dominguez The University of Alabama, Tuscaloosa, USA
A. Buccilli, S.I. Cooper, C. Henderson, P. Rumerio, C. West Boston University,
Boston, USA
D. Arcaro, A. Avetisyan, T. Bose, D. Gastler, D. Rankin, C. Richardson, J.
Rohlf, L. Sulak, D. Zou Brown University, Providence, USA
G. Benelli, D. Cutts, M. Hadley, J. Hakala, U. Heintz, J.M. Hogan, K.H.M.
Kwok, E. Laird, G. Landsberg, J. Lee, Z. Mao, M. Narain, J. Pazzini, S.
Piperov, S. Sagir, R. Syarif, D. Yu University of California, Davis, Davis,
USA
R. Band, C. Brainerd, R. Breedon, D. Burns, M. Calderon De La Barca Sanchez,
M. Chertok, J. Conway, R. Conway, P.T. Cox, R. Erbacher, C. Flores, G. Funk,
W. Ko, R. Lander, C. Mclean, M. Mulhearn, D. Pellett, J. Pilot, S. Shalhout,
M. Shi, J. Smith, D. Stolp, D. Taylor, K. Tos, M. Tripathi, Z. Wang University
of California, Los Angeles, USA
M. Bachtis, C. Bravo, R. Cousins, A. Dasgupta, A. Florent, J. Hauser, M.
Ignatenko, N. Mccoll, S. Regnard, D. Saltzberg, C. Schnaible, V. Valuev
University of California, Riverside, Riverside, USA
E. Bouvier, K. Burt, R. Clare, J. Ellison, J.W. Gary, S.M.A. Ghiasi Shirazi,
G. Hanson, J. Heilman, G. Karapostoli, E. Kennedy, F. Lacroix, O.R. Long, M.
Olmedo Negrete, M.I. Paneva, W. Si, L. Wang, H. Wei, S. Wimpenny, B. R. Yates
University of California, San Diego, La Jolla, USA
J.G. Branson, S. Cittolin, M. Derdzinski, R. Gerosa, D. Gilbert, B. Hashemi,
A. Holzner, D. Klein, G. Kole, V. Krutelyov, J. Letts, M. Masciovecchio, D.
Olivito, S. Padhi, M. Pieri, M. Sani, V. Sharma, S. Simon, M. Tadel, A.
Vartak, S. Wasserbaech65, J. Wood, F. Würthwein, A. Yagil, G. Zevi Della Porta
University of California, Santa Barbara - Department of Physics, Santa
Barbara, USA
N. Amin, R. Bhandari, J. Bradmiller-Feld, C. Campagnari, A. Dishaw, V. Dutta,
M. Franco Sevilla, L. Gouskos, R. Heller, J. Incandela, A. Ovcharova, H. Qu,
J. Richman, D. Stuart, I. Suarez, J. Yoo California Institute of Technology,
Pasadena, USA
D. Anderson, A. Bornheim, J. Bunn, I. Dutta, J.M. Lawhorn, H.B. Newman, T. Q.
Nguyen, C. Pena, M. Spiropulu, J.R. Vlimant, R. Wilkinson, S. Xie, Z. Zhang,
R.Y. Zhu Carnegie Mellon University, Pittsburgh, USA
M.B. Andrews, T. Ferguson, T. Mudholkar, M. Paulini, J. Russ, M. Sun, H.
Vogel, I. Vorobiev, M. Weinberg University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, F. Jensen, A. Johnson, M. Krohn, S. Leontsinis, E.
Macdonald, T. Mulholland, K. Stenson, S.R. Wagner Cornell University, Ithaca,
USA
J. Alexander, J. Chaves, Y. Cheng, J. Chu, S. Dittmer, K. Mcdermott, N.
Mirman, J.R. Patterson, D. Quach, A. Rinkevicius, A. Ryd, L. Skinnari, L.
Soffi, S.M. Tan, Z. Tao, J. Thom, J. Tucker, P. Wittich, M. Zientek Fermi
National Accelerator Laboratory, Batavia, USA
S. Abdullin, M. Albrow, M. Alyari, G. Apollinari, A. Apresyan, A. Apyan, S.
Banerjee, L.A.T. Bauerdick, A. Beretvas, J. Berryhill, P.C. Bhat, G.
Bolla${}^{\textrm{\textdagger}}$, K. Burkett, J.N. Butler, A. Canepa, G.B.
Cerati, H.W.K. Cheung, F. Chlebana, M. Cremonesi, J. Duarte, V.D. Elvira, J.
Freeman, Z. Gecse, E. Gottschalk, L. Gray, D. Green, S. Grünendahl, O.
Gutsche, J. Hanlon, R.M. Harris, S. Hasegawa, J. Hirschauer, Z. Hu, B.
Jayatilaka, S. Jindariani, M. Johnson, U. Joshi, B. Klima, B. Kreis, S.
Lammel, D. Lincoln, R. Lipton, M. Liu, T. Liu, R. Lopes De Sá, J. Lykken, K.
Maeshima, N. Magini, J.M. Marraffino, D. Mason, P. McBride, P. Merkel, S.
Mrenna, S. Nahn, V. O’Dell, K. Pedro, O. Prokofyev, G. Rakness, L. Ristori, B.
Schneider, E. Sexton-Kennedy, A. Soha, W.J. Spalding, L. Spiegel, S. Stoynev,
J. Strait, N. Strobbe, L. Taylor, S. Tkaczyk, N.V. Tran, L. Uplegger, E.W.
Vaandering, C. Vernieri, M. Verzocchi, R. Vidal, M. Wang, H.A. Weber, A.
Whitbeck, W. Wu University of Florida, Gainesville, USA
D. Acosta, P. Avery, P. Bortignon, D. Bourilkov, A. Brinkerhoff, A. Carnes, M.
Carver, D. Curry, R.D. Field, I.K. Furic, S.V. Gleyzer, B.M. Joshi, J.
Konigsberg, A. Korytov, K. Kotov, P. Ma, K. Matchev, H. Mei, G. Mitselmakher,
K. Shi, D. Sperka, N. Terentyev, L. Thomas, J. Wang, S. Wang, J. Yelton
Florida International University, Miami, USA
Y.R. Joshi, S. Linn, P. Markowitz, J.L. Rodriguez Florida State University,
Tallahassee, USA
A. Ackert, T. Adams, A. Askew, S. Hagopian, V. Hagopian, K.F. Johnson, T.
Kolberg, G. Martinez, T. Perry, H. Prosper, A. Saha, A. Santra, V. Sharma, R.
Yohay Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, V. Bhopatkar, S. Colafranceschi, M. Hohlmann, D. Noonan, T.
Roy, F. Yumiceva University of Illinois at Chicago (UIC), Chicago, USA
M.R. Adams, L. Apanasevich, D. Berry, R.R. Betts, R. Cavanaugh, X. Chen, O.
Evdokimov, C.E. Gerber, D.A. Hangal, D.J. Hofman, K. Jung, J. Kamin, I.D.
Sandoval Gonzalez, M.B. Tonjes, H. Trauger, N. Varelas, H. Wang, Z. Wu, J.
Zhang The University of Iowa, Iowa City, USA
B. Bilki66, W. Clarida, K. Dilsiz67, S. Durgut, R.P. Gandrajula, M.
Haytmyradov, V. Khristenko, J.-P. Merlo, H. Mermerkaya68, A. Mestvirishvili,
A. Moeller, J. Nachtman, H. Ogul69, Y. Onel, F. Ozok70, A. Penzo, C. Snyder,
E. Tiras, J. Wetzel, K. Yi Johns Hopkins University, Baltimore, USA
B. Blumenfeld, A. Cocoros, N. Eminizer, D. Fehling, L. Feng, A.V. Gritsan, P.
Maksimovic, J. Roskes, U. Sarica, M. Swartz, M. Xiao, C. You The University of
Kansas, Lawrence, USA
A. Al-bataineh, P. Baringer, A. Bean, S. Boren, J. Bowen, J. Castle, S.
Khalil, A. Kropivnitskaya, D. Majumder, W. Mcbrayer, M. Murray, C. Rogan, C.
Royon, S. Sanders, E. Schmitz, J.D. Tapia Takaki, Q. Wang Kansas State
University, Manhattan, USA
A. Ivanov, K. Kaadze, Y. Maravin, A. Mohammadi, L.K. Saini, N. Skhirtladze
Lawrence Livermore National Laboratory, Livermore, USA
F. Rebassoo, D. Wright University of Maryland, College Park, USA
A. Baden, O. Baron, A. Belloni, S.C. Eno, Y. Feng, C. Ferraioli, N.J. Hadley,
S. Jabeen, G.Y. Jeng, R.G. Kellogg, J. Kunkle, A.C. Mignerey, F. Ricci-Tam,
Y.H. Shin, A. Skuja, S.C. Tonwar Massachusetts Institute of Technology,
Cambridge, USA
D. Abercrombie, B. Allen, V. Azzolini, R. Barbieri, A. Baty, G. Bauer, R. Bi,
S. Brandt, W. Busza, I.A. Cali, M. D’Alfonso, Z. Demiragli, G. Gomez Ceballos,
M. Goncharov, P. Harris, D. Hsu, M. Hu, Y. Iiyama, G.M. Innocenti, M. Klute,
D. Kovalskyi, Y.-J. Lee, A. Levin, P.D. Luckey, B. Maier, A.C. Marini, C.
Mcginn, C. Mironov, S. Narayanan, X. Niu, C. Paus, C. Roland, G. Roland, J.
Salfeld-Nebgen, G.S.F. Stephans, K. Sumorok, K. Tatar, D. Velicanu, J. Wang,
T.W. Wang, B. Wyslouch University of Minnesota, Minneapolis, USA
A.C. Benvenuti, R.M. Chatterjee, A. Evans, P. Hansen, J. Hiltbrand, S.
Kalafut, Y. Kubota, Z. Lesko, J. Mans, S. Nourbakhsh, N. Ruckstuhl, R. Rusack,
J. Turkewitz, M.A. Wadud University of Mississippi, Oxford, USA
J.G. Acosta, S. Oliveros University of Nebraska-Lincoln, Lincoln, USA
E. Avdeeva, K. Bloom, D.R. Claes, C. Fangmeier, F. Golf, R. Gonzalez Suarez,
R. Kamalieddin, I. Kravchenko, J. Monroy, J.E. Siado, G.R. Snow, B. Stieger
State University of New York at Buffalo, Buffalo, USA
J. Dolen, A. Godshalk, C. Harrington, I. Iashvili, D. Nguyen, A. Parker, S.
Rappoccio, B. Roozbahani Northeastern University, Boston, USA
G. Alverson, E. Barberis, C. Freer, A. Hortiangtham, A. Massironi, D.M. Morse,
T. Orimoto, R. Teixeira De Lima, T. Wamorkar, B. Wang, A. Wisecarver, D. Wood
Northwestern University, Evanston, USA
S. Bhattacharya, O. Charaf, K.A. Hahn, N. Mucia, N. Odell, M.H. Schmitt, K.
Sung, M. Trovato, M. Velasco University of Notre Dame, Notre Dame, USA
R. Bucci, N. Dev, M. Hildreth, K. Hurtado Anampa, C. Jessop, D.J. Karmgard, N.
Kellams, K. Lannon, W. Li, N. Loukas, N. Marinelli, F. Meng, C. Mueller, Y.
Musienko38, M. Planer, A. Reinsvold, R. Ruchti, P. Siddireddy, G. Smith, S.
Taroni, M. Wayne, A. Wightman, M. Wolf, A. Woodard The Ohio State University,
Columbus, USA
J. Alimena, L. Antonelli, B. Bylsma, L.S. Durkin, S. Flowers, B. Francis, A.
Hart, C. Hill, W. Ji, T.Y. Ling, B. Liu, W. Luo, B.L. Winer, H.W. Wulsin
Princeton University, Princeton, USA
S. Cooperstein, O. Driga, P. Elmer, J. Hardenbrook, P. Hebda, S. Higginbotham,
A. Kalogeropoulos, D. Lange, J. Luo, D. Marlow, K. Mei, I. Ojalvo, J. Olsen,
C. Palmer, P. Piroué, D. Stickland, C. Tully University of Puerto Rico,
Mayaguez, USA
S. Malik, S. Norberg Purdue University, West Lafayette, USA
A. Barker, V.E. Barnes, S. Das, S. Folgueras, L. Gutay, M. Jones, A.W. Jung,
A. Khatiwada, D.H. Miller, N. Neumeister, C.C. Peng, H. Qiu, J.F. Schulte, J.
Sun, F. Wang, R. Xiao, W. Xie Purdue University Northwest, Hammond, USA
T. Cheng, N. Parashar, J. Stupak Rice University, Houston, USA
Z. Chen, K.M. Ecklund, S. Freed, F.J.M. Geurts, M. Guilbaud, M. Kilpatrick, W.
Li, B. Michlin, B.P. Padley, J. Roberts, J. Rorie, W. Shi, Z. Tu, J. Zabel, A.
Zhang University of Rochester, Rochester, USA
A. Bodek, P. de Barbaro, R. Demina, Y.t. Duh, T. Ferbel, M. Galanti, A.
Garcia-Bellido, J. Han, O. Hindrichs, A. Khukhunaishvili, K.H. Lo, P. Tan, M.
Verzetti The Rockefeller University, New York, USA
R. Ciesielski, K. Goulianos, C. Mesropian Rutgers, The State University of New
Jersey, Piscataway, USA
A. Agapitos, J.P. Chou, Y. Gershtein, T.A. Gómez Espinosa, E. Halkiadakis, M.
Heindl, E. Hughes, S. Kaplan, R. Kunnawalkam Elayavalli, S. Kyriacou, A. Lath,
R. Montalvo, K. Nash, M. Osherson, H. Saka, S. Salur, S. Schnetzer, D.
Sheffield, S. Somalwar, R. Stone, S. Thomas, P. Thomassen, M. Walker
University of Tennessee, Knoxville, USA
A.G. Delannoy, J. Heideman, G. Riley, K. Rose, S. Spanier, K. Thapa Texas A&M
University, College Station, USA
O. Bouhali71, A. Castaneda Hernandez71, A. Celik, M. Dalchenko, M. De Mattia,
A. Delgado, S. Dildick, R. Eusebi, J. Gilmore, T. Huang, T. Kamon72, R.
Mueller, Y. Pakhotin, R. Patel, A. Perloff, L. Perniè, D. Rathjens, A.
Safonov, A. Tatarinov, K.A. Ulmer Texas Tech University, Lubbock, USA
N. Akchurin, J. Damgov, F. De Guio, P.R. Dudero, J. Faulkner, E. Gurpinar, S.
Kunori, K. Lamichhane, S.W. Lee, T. Mengke, S. Muthumuni, T. Peltola, S.
Undleeb, I. Volobouev, Z. Wang Vanderbilt University, Nashville, USA
S. Greene, A. Gurrola, R. Janjam, W. Johns, C. Maguire, A. Melo, H. Ni, K.
Padeken, P. Sheldon, S. Tuo, J. Velkovska, Q. Xu University of Virginia,
Charlottesville, USA
M.W. Arenton, P. Barria, B. Cox, R. Hirosky, M. Joyce, A. Ledovskoy, H. Li, C.
Neu, T. Sinthuprasith, Y. Wang, E. Wolfe, F. Xia Wayne State University,
Detroit, USA
R. Harr, P.E. Karchin, N. Poudyal, J. Sturdy, P. Thapa, S. Zaleski University
of Wisconsin - Madison, Madison, WI, USA
M. Brodski, J. Buchanan, C. Caillol, D. Carlsmith, S. Dasu, L. Dodd, S. Duric,
B. Gomber, M. Grothe, M. Herndon, A. Hervé, U. Hussain, P. Klabbers, A.
Lanaro, A. Levine, K. Long, R. Loveless, V. Rekovic, T. Ruggles, A. Savin, N.
Smith, W.H. Smith, N. Woods †: Deceased
1: Also at Vienna University of Technology, Vienna, Austria
2: Also at IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
3: Also at Universidade Estadual de Campinas, Campinas, Brazil
4: Also at Federal University of Rio Grande do Sul, Porto Alegre, Brazil
5: Also at Université Libre de Bruxelles, Bruxelles, Belgium
6: Also at Institute for Theoretical and Experimental Physics, Moscow, Russia
7: Also at Joint Institute for Nuclear Research, Dubna, Russia
8: Also at Suez University, Suez, Egypt
9: Now at British University in Egypt, Cairo, Egypt
10: Also at Fayoum University, El-Fayoum, Egypt
11: Now at Helwan University, Cairo, Egypt
12: Also at Department of Physics, King Abdulaziz University, Jeddah, Saudi
Arabia
13: Also at Université de Haute Alsace, Mulhouse, France
14: Also at Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State
University, Moscow, Russia
15: Also at Tbilisi State University, Tbilisi, Georgia
16: Also at CERN, European Organization for Nuclear Research, Geneva,
Switzerland
17: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
18: Also at University of Hamburg, Hamburg, Germany
19: Also at Brandenburg University of Technology, Cottbus, Germany
20: Also at MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
21: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary
22: Also at Institute of Physics, University of Debrecen, Debrecen, Hungary
23: Also at Indian Institute of Technology Bhubaneswar, Bhubaneswar, India
24: Also at Institute of Physics, Bhubaneswar, India
25: Also at Shoolini University, Solan, India
26: Also at University of Visva-Bharati, Santiniketan, India
27: Also at University of Ruhuna, Matara, Sri Lanka
28: Also at Isfahan University of Technology, Isfahan, Iran
29: Also at Yazd University, Yazd, Iran
30: Also at Plasma Physics Research Center, Science and Research Branch,
Islamic Azad University, Tehran, Iran
31: Also at Università degli Studi di Siena, Siena, Italy
32: Also at INFN Sezione di Milano-Bicocca; Università di Milano-Bicocca,
Milano, Italy
33: Also at Purdue University, West Lafayette, USA
34: Also at International Islamic University of Malaysia, Kuala Lumpur,
Malaysia
35: Also at Malaysian Nuclear Agency, MOSTI, Kajang, Malaysia
36: Also at Consejo Nacional de Ciencia y Tecnología, Mexico city, Mexico
37: Also at Warsaw University of Technology, Institute of Electronic Systems,
Warsaw, Poland
38: Also at Institute for Nuclear Research, Moscow, Russia
39: Now at National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
40: Also at St. Petersburg State Polytechnical University, St. Petersburg,
Russia
41: Also at University of Florida, Gainesville, USA
42: Also at P.N. Lebedev Physical Institute, Moscow, Russia
43: Also at California Institute of Technology, Pasadena, USA
44: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia
45: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia
46: Also at University of Belgrade, Faculty of Physics and Vinca Institute of
Nuclear Sciences, Belgrade, Serbia
47: Also at Scuola Normale e Sezione dell’INFN, Pisa, Italy
48: Also at National and Kapodistrian University of Athens, Athens, Greece
49: Also at Riga Technical University, Riga, Latvia
50: Also at Universität Zürich, Zurich, Switzerland
51: Also at Stefan Meyer Institute for Subatomic Physics (SMI), Vienna,
Austria
52: Also at Adiyaman University, Adiyaman, Turkey
53: Also at Istanbul Aydin University, Istanbul, Turkey
54: Also at Mersin University, Mersin, Turkey
55: Also at Piri Reis University, Istanbul, Turkey
56: Also at Izmir Institute of Technology, Izmir, Turkey
57: Also at Necmettin Erbakan University, Konya, Turkey
58: Also at Marmara University, Istanbul, Turkey
59: Also at Kafkas University, Kars, Turkey
60: Also at Istanbul Bilgi University, Istanbul, Turkey
61: Also at Rutherford Appleton Laboratory, Didcot, United Kingdom
62: Also at School of Physics and Astronomy, University of Southampton,
Southampton, United Kingdom
63: Also at Monash University, Faculty of Science, Clayton, Australia
64: Also at Instituto de Astrofísica de Canarias, La Laguna, Spain
65: Also at Utah Valley University, Orem, USA
66: Also at Beykent University, Istanbul, Turkey
67: Also at Bingol University, Bingol, Turkey
68: Also at Erzincan University, Erzincan, Turkey
69: Also at Sinop University, Sinop, Turkey
70: Also at Mimar Sinan University, Istanbul, Istanbul, Turkey
71: Also at Texas A&M University at Qatar, Doha, Qatar
72: Also at Kyungpook National University, Daegu, Korea