TOP-18-002

$HeadURL$ $Id$

TOP-18-002

# Measurement of the cross section for $\ttbar$ production with additional
jets and jets in $\Pp\Pp$ collisions at $\sqrt{s}=13\TeV$

The CMS Collaboration

###### Abstract

Measurements of the cross section for the production of top quark pairs in
association with a pair of jets from bottom quarks
($\sigma_{\cPqt\cPaqt\cPqb\cPaqb}$) and in association with a pair of jets
from quarks of any flavor or gluons ($\sigma_{\cPqt\cPaqt\cmsSymbolFace{jj}}$)
and their ratio are presented. The data were collected in proton-proton
collisions at a center-of-mass energy of 13by the CMS experiment at the LHC in
2016 and correspond to an integrated luminosity of $35.9\fbinv$. The
measurements are performed in a fiducial phase space and extrapolated to the
full phase space, separately for the dilepton and lepton+jets channels, where
lepton corresponds to either an electron or a muon. The results of the
measurements in the fiducial phase space for the dilepton and lepton+jets
channels, respectively, are $\sigma_{\cPqt\cPaqt\cmsSymbolFace{jj}}=2.36\pm
0.02\stat\pm 0.20\syst\unit{pb}$ and $31.0\pm 0.2\stat\pm 2.9\syst\unit{pb}$,
and for the cross section ratio $0.017\pm 0.001\stat\pm 0.001\syst$ and
$0.020\pm 0.001\stat\pm 0.001\syst$. The values of
$\sigma_{\cPqt\cPaqt\cPqb\cPaqb}$ are determined from the product of the
$\sigma_{\cPqt\cPaqt\cmsSymbolFace{jj}}$ and the cross section ratio,
obtaining, respectively, $0.040\pm 0.002\stat\pm 0.005\syst\unit{pb}$ and
$0.62\pm 0.03\stat\pm 0.07\syst\unit{pb}$. These measurements are the most
precise to date and are consistent, within the uncertainties, with the
standard model expectations obtained using a matrix element calculation at
next-to-leading order in quantum chromodynamics matched to a parton shower.

##  0.1 Introduction

The production of top quark-antiquark pairs ($\ttbar$) in association with two
inclusive jets, $\cPqt\cPaqt\cmsSymbolFace{jj}$, where j denotes jets produced
from the fragmentation of quarks of any flavor (, , , , ) or from gluons, and
the special case of $\ttbar$ production in association with a $\cPqb\cPaqb$
pair ($\cPqt\cPaqt\cPqb\cPaqb$) are interesting from theoretical and
experimental points of view. Even though calculations at next-to-leading order
(NLO) in quantum chromodynamics (QCD) are available for both processes [1, 2,
3], they suffer from large uncertainties in the choice of factorization and
renormalization scales [4], because of the presence of two very different
scales, the top quark mass ($m_{\cPqt}$) and the jet transverse momentum (),
that both play a role in these processes. The experimental measurements of the
proton-proton ($\Pp\Pp$) production cross sections for
$\Pp\Pp\to\cPqt\cPaqt\cmsSymbolFace{jj}~{}(\sigma_{\cPqt\cPaqt\cmsSymbolFace{jj}})$
and $\Pp\Pp\to\cPqt\cPaqt\cPqb\cPaqb~{}(\sigma_{\cPqt\cPaqt\cPqb\cPaqb})$
provide a useful test of NLO QCD calculations.

The $\cPqt\cPaqt\cPqb\cPaqb$ process is also a dominant background for
different measurements of the Higgs boson and the top quark properties [5, 6,
7, 8, 9]. The Yukawa coupling of the Higgs boson to the top quark is one of
the relevant properties; its measurement probes the consistency of the
standard model (SM) Higgs sector. The top quark Yukawa coupling can be probed
directly in the associated production of the Higgs boson with $\ttbar$
($\cPqt\cPaqt\PH$). This process was recently observed by the ATLAS and CMS
Collaborations [10, 11]. Both experiments also reported searches for
$\cPqt\cPaqt\PH$ production in the $\cPqb\cPaqb$ decay channel of the Higgs
boson [12, 13]. A challenge in this decay channel is the irreducible
nonresonant background from $\cPqt\cPaqt\cPqb\cPaqb$ production.

In this paper, we present measurements of $\sigma_{\cPqt\cPaqt\cPqb\cPaqb}$,
$\sigma_{\cPqt\cPaqt\cmsSymbolFace{jj}}$, and their ratio
($R_{\cPqt\cPaqt\cPqb\cPaqb/\cPqt\cPaqt\cmsSymbolFace{jj}}$) in $\Pp\Pp$
collisions at a center-of-mass energy of 13, using data corresponding to an
integrated luminosity of $35.9\fbinv$ collected in 2016 with the CMS detector
at the LHC. The $\sigma_{\cPqt\cPaqt\cmsSymbolFace{jj}}$ cross section and
$R_{\cPqt\cPaqt\cPqb\cPaqb/\cPqt\cPaqt\cmsSymbolFace{jj}}$ ratio measurements
are separately performed in the dilepton ($\Pepm\Pemp$, $\PGmpm\PGmmp$ and
$\Pepm\PGmmp$) and lepton+jets ($\Pepm$+jets and $\PGmpm$+jets) channels, by
means of binned maximum likelihood fits to the measured tagging discriminant
distribution of additional jets in events with $\ttbar$ candidates. The
$\sigma_{\cPqt\cPaqt\cPqb\cPaqb}$ is determined by multiplying the obtained
$\sigma_{\cPqt\cPaqt\cmsSymbolFace{jj}}$ by the
$R_{\cPqt\cPaqt\cPqb\cPaqb/\cPqt\cPaqt\cmsSymbolFace{jj}}$. The cross section
ratio has a smaller relative systematic uncertainty than the absolute cross
section measurements, due to the partial cancellation of uncertainties.

As described in Ref. [1], the kinematic range of the theoretical predictions
for $\ttbar$ production in association with jets plays a fundamental role in
the reliability of the fixed-order estimation of the
$R_{\cPqt\cPaqt\cPqb\cPaqb/\cPqt\cPaqt\cmsSymbolFace{jj}}$ ratio. At jet $\pt$
values higher than $\approx$40, the stability of the perturbative expansion of
$\sigma_{\cPqt\cPaqt\cmsSymbolFace{jj}}$, and
$\sigma_{\cPqt\cPaqt\cPqb\cPaqb}$ can be lost and resummation of higher-order
effects must be considered. For this reason, the analysis is performed in two
phase space regions (defined in Section 0.4) with different jet $\pt$
requirements. These two measurements can improve the modeling of the
$\cPqt\cPaqt\cPqb\cPaqb$ process as well as the understanding of the related
theoretical uncertainties.

Previous measurements of the $\cPqt\cPaqt\cPqb\cPaqb$ to
$\cPqt\cPaqt\cmsSymbolFace{jj}$ cross section ratio at center-of-mass energies
of 8 and 13have been performed by ATLAS [14, 15] and CMS [16, 17, 18]. The
8results by ATLAS and CMS are compatible with the SM prediction within the
experimental uncertainties of 33 and 26%, respectively. The ATLAS
Collaboration measures at 13the production of $\ttbar$ pairs as a function of
the jet multiplicity in the dilepton (lepton+jets) channel with an uncertainty
of 13 (17)%. These measurements are compatible with theoretical predictions
within the uncertainties. The CMS measurement at 13in the dilepton channel
reports a value for this ratio that is 1.8 times larger than the NLO SM
prediction. However, the total uncertainty in this measurement is 32% and the
statistical significance of this deviation is therefore only around two
standard deviations. The latest measurement of the inclusive
$\cPqt\cPaqt\cPqb\cPaqb$ cross section performed by CMS [19] in the fully
hadronic final state reports a cross section slightly higher but consistent
with the prediction with a total uncertainty of 29%.

This paper presents the most precise measurements of the
$\cPqt\cPaqt\cPqb\cPaqb$ to $\cPqt\cPaqt\cmsSymbolFace{jj}$ cross section
ratio and the inclusive $\cPqt\cPaqt\cmsSymbolFace{jj}$ cross section to date
by means of an improved fit method and a data set about 15 times larger than
the previous CMS measurement [18] performed in the dilepton channel.
Additionally, this is the first measurement of the cross sections for $\ttbar$
events with additional jets and jets in the lepton+jets channel performed
using the CMS data at 13. This decay channel has a much higher branching
fraction than the dilepton channel, increasing the available number of events
to be analyzed.

The paper is organized as follows. A brief description of the CMS detector is
given in Section 0.2. Details of the event simulation of the $\ttbar$ and
other SM processes, together with the theoretical calculation of the $\ttbar$
cross section, are given in Section 0.3. Section 0.4 contains the definitions
of the fiducial and full phase space regions and the
$\cPqt\cPaqt\cmsSymbolFace{jj}$ event categorization. The event selection and
the methods used to measure the $\cPqt\cPaqt\cmsSymbolFace{jj}$ cross sections
and the $\cPqt\cPaqt\cPqb\cPaqb$ to $\cPqt\cPaqt\cmsSymbolFace{jj}$ cross
section ratios are discussed in Sections 0.5 and 0.6, respectively. A complete
description of the systematic uncertainties is given in Section 0.7. Finally,
the cross section measurements are reported in Section 0.8 and a summary is
provided in Section 0.9.

##  0.2 The CMS detector

The central feature of the CMS apparatus is a superconducting solenoid of 6m
internal diameter, providing a magnetic field of 3.8T. Within the solenoid
volume are a silicon pixel and strip tracker, a lead tungstate crystal
electromagnetic calorimeter (ECAL), and a brass and scintillator hadron
calorimeter, each composed of a barrel and two endcap sections. Forward
calorimeters extend the pseudorapidity ($\eta$) coverage provided by the
barrel and endcap detectors. The electron momentum is estimated by combining
the energy measurement in the ECAL with the momentum measurement in the
tracker. The momentum resolution for electrons is generally better in the
barrel region than in the endcaps, and depends on the bremsstrahlung energy
emitted by the electron as it traverses the material in front of the ECAL
[20]. Muons are detected in gas-ionization chambers embedded in the steel
flux-return yoke outside the solenoid. 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. The efficiency to
reconstruct and identify muons is greater than 96%. Matching muons to tracks
measured in the silicon tracker results in a relative transverse momentum
resolution, for muons with up to 100, of 1% in the barrel and 3% in the
endcaps. The resolution in the barrel is better than 7% for muons with up to 1
[21].

The global event reconstruction (also called particle-flow (PF) event
reconstruction [22]) aims to reconstruct and identify each individual particle
in an event, with an optimized combination of all subdetector information. In
this process, the identification of the particle type (photon, electron, muon,
charged or neutral hadron) plays an important role in the determination of the
particle direction and energy. For each event, hadronic jets are clustered
from the reconstructed particles using the infrared and collinear-safe anti-
algorithm [23, 24] with a distance parameter of 0.4. Jet momentum is
determined as the vectorial sum of all particle momenta in the jet, and is
found from simulation to be within 5 to 10% of the true momentum over the
whole spectrum and detector acceptance. The missing transverse momentum vector
is computed as the negative vector sum of the transverse momenta of all the PF
candidates in an event, and its magnitude is denoted as [25]. The is modified
to account for corrections to the energy scale of the reconstructed jets in
the event. The candidate 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 and the associated $\ptmiss$.

Events of interest are selected using a two-tiered trigger system [26]. The
first level, composed of custom hardware processors, uses information from the
calorimeters and muon detectors to select events at a rate of around 100kHz
within a time interval of less than 4. The second level, known as the high-
level trigger, consists of a farm of processors running a version of the full
event reconstruction software optimized for fast processing, and reduces the
event rate to around 1kHz before data storage.

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. [22].

##  0.3 Signal and background simulation

The signal and background processes are simulated using Monte Carlo (MC)
techniques. The signal sample is generated by (v2) [27, 28, 29, 30] at NLO and
combined with the parton shower (PS) and underlying event (UE) simulation from
8 (v8.219) [31] using the CUETP8M2T4 [32] tune. A second sample employs ++
[33] (v2.7.1) using the tune EE5C [34]. In these samples additional () jets
beyond real emission at NLO are generated by the parton shower. The proton
structure is described by the parton distribution function (PDF) set NNPDF3.0
[35]. To compare with an alternative theoretical prediction, (v2.2.2) (MG_a)
is used to generate events in the five-flavor scheme (5FS) [36, 37]. The
madspin [38] package is used to incorporate the correct treatment of the decay
particles preserving spin correlation effects. In addition to the pair, up to
two additional partons are simulated at NLO and matched using the FXFX
algorithm [39], and the PS simulation is performed by 8, denoted as MG_a+ 8
5FS [FXFX]. Detailed explanations of the different parameters in the $\ttbar$
simulations can be found in [32]. The $\ttbar$ simulations are normalized to
their inclusive cross section, $832~{}^{+20}_{-29}\,(\text{scale})\pm{35}$
(PDF and strong coupling $\alpS)\unit{pb}$, calculated with the top++ v2.0
program [40] to next-to-next-to-leading order (NNLO) in perturbative QCD,
including soft-gluon resummation at next-to-next-to-leading-logarithmic order
[41, 42, 43, 44, 45, 46].

The production of single top quarks in the $\cPqt\PW$ channel is simulated at
NLO with the generator in the 5FS, and normalized to the cross sections
calculated at NNLO [47]. The “diagram removal” (DR) scheme [36] is used to
account for the interference with ttbar production. The $t$-channel single top
quark production is simulated at NLO in the four-flavor scheme (4FS). The
$s$-channel single top quark production is generated at NLO in the 4FS with
MG_a, which is also used for the PS simulation. The MG_agenerator with the MLM
merging scheme [48] is used for the simulation at LO of +jets and +jets
production, and the samples are normalized to the inclusive cross sections
calculated at NNLO [49]. The background contribution from $\ttbar$ production
in association with a Higgs boson ($\cPqt\cPaqt\PH$) is generated with , while
$\ttbar$ production in association with a and a bosons (referred to as
$\cPqt\cPaqt\PV$) is generated at NLO using MG_a. These samples are normalized
to the cross sections at NLO [50, 51]. Both generators used for the $\ttbar$
production in association with bosons are interfaced with 8. All MC
simulations with top quark production assume a top quark mass of 172.5 [52].

Diboson production ($\PW\PW$, $\PW\PZ$, and $\PZ\PZ$, referred to as $\PV\PV$)
is simulated at leading order using 8, and normalized to the cross sections
calculated at NNLO for the $\PW\PW$ sample [53] and NLO for the $\PW\PZ$ and
$\PZ\PZ$ samples [54]. Triboson production (referred to as $\PV\PV\PV$) is
generated at NLO using MG_a, and 8 is used for simulation. The 8 generator is
also used to generate the QCD multijet background events. The CMS detector
response is simulated using (v.9.4) [55]. The simulations include multiple
$\Pp\Pp$ interactions per bunch crossing (pileup). The simulated events are
weighted, depending on the number of pileup interactions, to reproduce the
observed pileup distribution in data (on average, 23 collisions per bunch
crossing).

##  0.4 Definitions of the $\cPqt\cPaqt\cmsSymbolFace{jj}$ categories and
regions of phase space

Events with a $\ttbar$ pair and at least two additional jets in simulation are
categorized further using the flavor of the particle-level jets, found from
the MC generator information. The particle-level jets are obtained by
clustering final-state particles with a mean lifetime greater than 30ps
(except neutrinos) using the anti-algorithm with a distance parameter of 0.4.
The flavor of the particle-level jet is identified by the ghost-matching
technique [56]. The ghost-matched clusters are formed with final-state
particles supplemented by hadrons containing bottom and charm quarks (called
bottom and charm hadrons) that do not have further bottom or charm hadrons as
daughter particles. The momentum of each bottom and charm hadron is
artificially reduced to zero in order to avoid affecting the observable
particle-level jet momentum. If a particle-level jet contains bottom or charm
hadrons, it is assigned to the corresponding flavor and called a or jet.
Otherwise, it is considered as a particle-level light-flavor jet (from a
gluon, or , , or quark). Particle-level jets are identified as products of the
$\ttbar$ decay if the bottom hadron used for the flavor assignment belongs to
the simulation history of any of the top quarks.

The different $\cPqt\cPaqt\cmsSymbolFace{jj}$ categories are based on the
flavor of the particle-level jets that accompany the $\ttbar$ system:
$\cPqt\cPaqt\cPqb\cPaqb$ with at least two additional particle-level jets;
$\cPqt\cPaqt\cPqb\cmsSymbolFace{j}$ with one additional particle-level jet and
at least one additional particle-level or light-flavor jet;
$\cPqt\cPaqt\cPqc\cPaqc$ with at least two additional particle-level jets; and
$\cPqt\cPaqt\cmsSymbolFace{LF}$ with at least two additional particle-level
light-flavor jets or one particle-level light-flavor jet and one jet. The
$\cPqt\cPaqt\cPqb\cmsSymbolFace{j}$ final state mainly originates from the
merging of two particle-level jets or from one of the particle-level jets
failing the following acceptance requirements. The particle-level jets have to
satisfy $\abs{\eta}<2.5$ and $\pt>$ 30 (20)in the dilepton (lepton+jets)
channel. The different $\pt$ cut for the dilepton and lepton+jet channels
allows the definition of different phase space regions, providing additional
information for testing the theoretical predictions. The category
$\cPqt\cPaqt\cmsSymbolFace{jj}$ comprises all categories defined above, all
events with at least two additional particle-level jets, regardless of their
flavor. The $\ttbar$ events that do not belong to any of the
$\cPqt\cPaqt\cmsSymbolFace{jj}$ categories, , events with only one additional
particle-level jet, are treated as background. The particle-level leptons
originating from the decays of the top quarks, are defined using the anti-
clustering algorithm with a distance parameter of 0.1 to account for final-
state radiated photons, and are required to fulfill $\abs{\eta}<2.4$ and $>$
20 (30)for the dilepton (lepton+jets) channel.

Measurements of the inclusive cross sections,
$\sigma_{\cPqt\cPaqt\cPqb\cPaqb}$, $\sigma_{\cPqt\cPaqt\cmsSymbolFace{jj}}$,
and their ratio are reported both in the fiducial (“visible”) phase space
(VPS) and the full phase space (FPS). Simulated events are defined as signal
events in the VPS if they contain particle-level leptons and particle-level
jets satisfying the above acceptance requirements, and fulfill the following
separate criteria. In the dilepton channel, $\cPqt\cPaqt\cPqb\cPaqb$
($\cPqt\cPaqt\cmsSymbolFace{jj}$) events are considered in the VPS if they
have two particle-level leptons and at least four particle-level jets,
including at least four (two) particle-level jets. In a similar way, for the
lepton+jets channel, $\cPqt\cPaqt\cPqb\cPaqb$
($\cPqt\cPaqt\cmsSymbolFace{jj}$) events are considered in the VPS if they
have only one particle-level lepton and at least six particle-level jets,
including at least four (two) particle-level jets. The FPS is defined as the
$\cPqt\cPaqt\cPqb\cPaqb$ ($\cPqt\cPaqt\cmsSymbolFace{jj}$) final state with
all decay channels and at least two additional particle-level (any) jets that
do not originate from top quark decays. The selection criteria are summarized
in Table 0.4. The measurements performed in the FPS facilitate comparisons to
QCD calculations at NLO.

Summary of the requirements for a simulated event to be in the fiducial (VPS)
and full (FPS) phase space regions for the $\cPqt\cPaqt\cPqb\cPaqb$ and
$\cPqt\cPaqt\cmsSymbolFace{jj}$ categories in the dilepton and lepton+jets
channels. Details of the particle-level definitions are described in the text.
The symbol $\ell$ denotes a lepton (or ).  Channel Jet $\pt$ Phase space
$\cPqt\cPaqt\cPqb\cPaqb$ $\cPqt\cPaqt\cmsSymbolFace{jj}$ Dilepton $>$30 VPS
$\ell\ell$ \+ $\geq$ 4 jets (4 jets) $\ell\ell$ \+ $\geq$ 4 jets (2 jets) FPS
$\ttbar$ \+ $\geq$ 2 jets (not from $\ttbar$) $\ttbar$ \+ $\geq$ 2 jets (not
from $\ttbar$) Lepton+jets $>$20 VPS $\ell$ \+ $\geq$ 6 jets (4 jets) $\ell$
\+ $\geq$ 6 jets (2 jets) FPS $\ttbar$ \+ $\geq$ 2 jets (not from $\ttbar$)
$\ttbar$ \+ $\geq$ 2 jets (not from $\ttbar$)

##  0.5 Event selection

The measurements are performed independently in the dilepton and lepton+jets
final states of the ${\ttbar}$ decay. A combination of single-lepton and
dilepton triggers with specific transverse momentum requirements is applied to
filter events in the dilepton channel. In the $\Pepm\Pemp$ and $\PGmpm\PGmmp$
channels, the charged lepton with the highest is required to pass the trigger
with $\pt>23$ (17)for the electron (muon), and the charged lepton with the
lowest must have $\pt>12$ (8). Events in the $\Pepm\PGmmp$ channel are
selected requiring a trigger with either one electron with $\pt>12\GeV$ and
one muon with $\pt>23\GeV$, or one electron with $\pt>23\GeV$ and one muon
with $\pt>8\GeV$. Additionally, events selected by a set of single-lepton
triggers with one electron (muon) with $\pt>27$ (20)are assigned to one of the
three dilepton combinations. Events for the lepton+jets channel are required
to pass a single-electron (-muon) trigger with $\pt>32$ (24). The higher-$\pt$
thresholds for the single-lepton triggers used in the lepton+jets channel
reduce the contribution of processes with leptons from bottom/charm hadron
decays, electrons from misidentified jets, etc. Any overlap between the
dilepton and lepton+jets channels is avoided by requiring a different number
of leptons in each decay channel.

Leptons originating from the boson in top quark decays are expected to be
isolated. Therefore, isolation criteria are applied to the reconstructed and
selected electrons [20] and muons [21]. A relative isolation variable, the sum
of charged and neutral hadrons, and photons in a cone of $\Delta
R=\sqrt{\smash[b]{(\Delta\phi)^{2}+(\Delta\eta)^{2}}}$ around the direction of
the lepton divided by the lepton , is used, where $\Delta\phi$ and
$\Delta\eta$ are the azimuthal angle and pseudorapidity differences,
respectively, between the directions of the lepton and the other particle. The
relative isolation parameter defined in a cone of $\Delta R<0.3$ (0.4) is
required to be lower than 0.06 (0.15) for electrons (muons).

The energy of the jets reconstructed using the anti-jet clustering algorithm
[24, 23] is corrected for the pileup contributions using the average energy
density deposited by neutral particles in the event. The charged hadron
subtraction mitigates event by event the effect of tracks coming from pileup
on the transverse energy of the jet. Jet energy corrections are also applied
as functions of the jet and $\eta$ [57]. The jets are identified using the
combined secondary vertex (CSVv2) tagging algorithm [58]. Two different
operating points for the CSVv2 algorithm are chosen, based on the expected
background composition of each decay channel. The operating point selected for
the dilepton (lepton+jets) channel corresponds to an efficiency for correctly
identifying jets of about 70 (50)% and a probability of about 1 (0.1)% to
misidentify a light-flavor jet as a jet [58]. A tighter operating point in the
lepton+jets channel is chosen to increase the rejection of background
processes with multijet final states. Since the MC simulation does not
reproduce exactly the tagging discriminant distribution observed in data,
scale factors are applied to the simulated events in order to correct its
shape. The scale factors, derived using a tag-and-probe technique in enriched
regions for both and light-flavor jets, are applied as a function of the jet
tagging discriminant, $\pt$, and $\eta$ [58].

In the dilepton channel, events are selected to have exactly two oppositely
charged leptons with $\abs{\eta}<2.4$ and $\pt>25\GeV$ for the
higher-(leading) lepton and $\pt>20\GeV$ for the subleading one. Additional
requirements include: the invariant mass of the dilepton system
$m_{\ell^{+}\ell^{-}}\,>20\GeV$, four or more jets with $\pt>30\GeV$ and
$\abs{\eta}<2.4$, and at least two jets to be identified as a jet. Further
selection criteria are applied for the same-flavor lepton channels
($\Pepm\Pemp$ and $\PGmpm\PGmmp$) to reject events from +jets as follows:
$\ptmiss>40\GeV$ and $\abs{m_{\ell^{+}\ell^{-}}\,-m_{\PZ}}>15\GeV$, where
$m_{\PZ}$ is the $\PZ$ boson mass of 91 [59]. With these requirements, events
with two jets from the $\ttbar$ process and at least two additional jets are
selected.

Events in the lepton+jets channel are required to have exactly one isolated
electron (or muon) with $>$ 35 (30)and $\abs{\eta}<2.1$ (2.4), thus avoiding
events selected for the dilepton channel, and at least six reconstructed jets
with $\pt>30\GeV$ and $\abs{\eta}<2.4$, where at least two of them are
identified as jets. This requirement reflects the presence of four jets from
the $\ttbar$ process, plus at least two additional jets. A higher
reconstructed-jet $\pt$ requirement in comparison with the $\pt$ criterion for
particle-level jets is applied so that all events passing the reconstruction
selection criteria will be in the VPS. Besides the differences in the trigger
requirements for the dilepton and lepton+jets channels, a higher-$\pt$
requirement for the selected leptons reduces the contribution from processes
such as the QCD multijet background.

Table 0.5 lists the expected final numbers of data events in the dilepton and
lepton+jets channels. The numbers are given for the different $\ttbar$(+jets)
categories, assuming $\sigma_{\ttbar}=832\unit{pb}$ from an NNLO calculation
and the individual sources of background (from MC simulation), normalized to
an integrated luminosity of $35.9\fbinv$. The category “$\ttbar$ others”
corresponds to the background contribution from $\ttbar$ events that pass the
event selection but do not belong to any of the
$\cPqt\cPaqt\cmsSymbolFace{jj}$ categories. The background contributions from
+jets and QCD multijets in the dilepton channel and $\PV\PV\PV$ in the
lepton+jets channel are negligible in the corresponding final state. The
expected sample composition contains more than 75 (84)% of
$\cPqt\cPaqt\cmsSymbolFace{jj}$ signal events in the dilepton (lepton+jets)
channel. The small background contributions from non-$\ttbar$ processes are
estimated from simulation. The number of observed events for each channel is
also given in Table 0.5, and is consistent with the expected number within the
uncertainties.

Expected and observed numbers of events in the dilepton and lepton+jets
channels after applying the event selection. The results are given for the
different $\ttbar$(+jets) categories, the individual sources of background
(from MC simulation), normalized to an integrated luminosity of $35.9\fbinv$,
and the observed number from data. The uncertainties quoted for each MC
contribution include all the systematic uncertainties described in Section
0.7.  Source (MC simulation) Dilepton Lepton+jets $\cPqt\cPaqt\cPqb\cPaqb$ 327
$\pm$ 23 2470 $\pm$ 180 $\cPqt\cPaqt\cPqb\cmsSymbolFace{j}$ 1103 $\pm$ 62 3820
$\pm$ 170 $\cPqt\cPaqt\cPqc\cPaqc$ 353 $\pm$ 23 1627 $\pm$ 74
$\cPqt\cPaqt\cmsSymbolFace{LF}$ 10 860 $\pm$ 580 26 900 $\pm$ 1300 Total
$\cPqt\cPaqt\cmsSymbolFace{jj}$ 12 640 $\pm$ 590 34 800 $\pm$ 1400 others 3740
$\pm$ 180 4180 $\pm$ 190 Single top quark 500 $\pm$ 100 1460 $\pm$ 160 +jets
250 $\pm$ 35 +jets 50 $\pm$ 18 78 $\pm$ 23 $\PV\PV$ 3 $\pm$ 2 10 $\pm$ 5
$\PV\PV\PV$ 1 $\pm$ 1 QCD multijet 220 $\pm$ 130 $\cPqt\cPaqt\PH$ 54 $\pm$ 22
230 $\pm$ 130 $\cPqt\cPaqt\PV$ 86 $\pm$ 16 381 $\pm$ 55 Total signal and
background 17 100 $\pm$ 620 41 600 $\pm$ 1400 Data 16 167 39 819

##  0.6 Cross section measurement

The selected events described in Section 0.5 contain at least four (six) jets
in the dilepton (lepton+jets) channel where at least two of them must be
identified as jets. In order to identify the origin of the selected jets
(either from a top quark decay or not), two different approaches are followed,
based on the kinematic properties of each final state. In the dilepton
channel, the two jets with the largest values of the tagging discriminant
provided by the CSVv2 algorithm originate from a top quark decay in 85 (23)%
of selected $\cPqt\cPaqt\cmsSymbolFace{jj}$ ($\cPqt\cPaqt\cPqb\cPaqb$) events
[18], as determined from simulation. Therefore, the jets with the third- and
fourth-largest tagging discriminant values are considered as additional jets.
The identification of the origin of the jets in the lepton+jets channel is
more complex because the relatively large number of jets (at least six) leads
to ambiguities in the jet assignment. Therefore, a jet assignment based only
on the tagging discriminant is insufficient and a kinematic fit [60] is
applied.

The event inputs for the kinematic fit algorithm are the four-momenta of the
selected lepton and jets, whether a jet is identified as a jet, and $\ptmiss$.
The kinematic fit algorithm constrains the momentum of the aforementioned
objects to the hypothesis that two top quarks of the same mass are produced,
each one decaying to a bottom quark and a boson. The boson decay products are
also constrained to an invariant mass of $80.4\GeV$ [59]. The last constraint
in the kinematic fit requires that the jets associated with quarks from top
quark decays must be identified as jets as well. The algorithm assigns a
$\chi^{2}$ value [61] to each solution according to the goodness of the fit of
each jet permutation. The solution selected is the one with the lowest
$\chi^{2}$ value. This guarantees that the selected jet permutation is the
most compatible with the $\ttbar$ process. All jets that are not included in
the selected solution (i.e., not coming from the top quark or boson decay) are
considered as additional jets for the lepton+jets channel. This assumption
leads to a correct identification of at least one additional jet in 70 (50)%
of the cases for the $\cPqt\cPaqt\cmsSymbolFace{jj}$
($\cPqt\cPaqt\cPqb\cPaqb$) category. An efficiency of 40 (12)% is reached for
the correct identification of both additional () jets.

After identifying the origin of the jets of the $\ttbar$ system, the
additional jets in the event are arranged in decreasing order of the tagging
discriminant value. Only the first two additional jets (those with the highest
tagging discriminant value) are kept for further analysis. Figure 1 shows the
tagging discriminant distribution from data for the first (left) and second
(right) additional jets in the dilepton (upper) and lepton+jets (lower)
channels, along with the predictions from the MC simulations. The values of
the tagging discriminant for the first two additional jets allows one to
distinguish between the different $\cPqt\cPaqt\cmsSymbolFace{jj}$ categories.

Figure 1: The tagging discriminant distribution from data (points) for the
first (left) and second (right) additional jet for the dilepton (upper) and
lepton+jets (lower) channels in decreasing order of the tagging discriminant
value after event selection, and the predicted distributions for the signal
and background from simulation (shaded histograms). The contributions of
single top quark, $\cPqt\cPaqt\PV$, $\cPqt\cPaqt\PH$, and the non-top quark
processes are merged in the background (blue) entry. The
$\cPqt\cPaqt\cPqb\cPaqb$ process is located in the bottom part of the stack
due to its low contribution in comparison with the other entries. The lower
panels display the ratio of the data to the expectations. The grey bands
display the combination of the statistical and systematic uncertainties.

Exploiting the separation power of the tagging discriminant, the
$\cPqt\cPaqt\cPqb\cPaqb$ to $\cPqt\cPaqt\cmsSymbolFace{jj}$ cross section
ratio and the absolute $\cPqt\cPaqt\cmsSymbolFace{jj}$ cross section are
simultaneously extracted using a binned maximum likelihood fit. Figure 2 (3)
shows the two-dimensional (2D) distributions of the tagging discriminant from
simulation separately for the $\cPqt\cPaqt\cPqb\cPaqb$,
$\cPqt\cPaqt\cPqb\cmsSymbolFace{j}$, $\cPqt\cPaqt\cPqc\cPaqc$, and
$\cPqt\cPaqt\cmsSymbolFace{LF}$ events in the dilepton (lepton+jets) channel.
The dilepton and lepton+jets decay channels are fitted as two independent
measurements because of the different phase space definitions and different
background compositions. Then, within the dilepton and the lepton+jets
channels, the different final states are fitted simultaneously.

Figure 2: Two-dimensional distributions of the tagging discriminant for the
first and second additional jets in the dilepton channel shown separately for
different flavors of the additional jets: $\cPqt\cPaqt\cPqb\cPaqb$ (upper
left), $\cPqt\cPaqt\cPqb\cmsSymbolFace{j}$ (upper right),
$\cPqt\cPaqt\cPqc\cPaqc$ (lower left) and $\cPqt\cPaqt\cmsSymbolFace{LF}$
(lower right). The number of entries is normalized to unity. The histograms
are obtained from the MC simulation.

Figure 3: Two-dimensional distributions of the tagging discriminant for the
first and second additional jets in the lepton+jets channel shown separately
for different flavors of the additional jets: $\cPqt\cPaqt\cPqb\cPaqb$ (upper
left), $\cPqt\cPaqt\cPqb\cmsSymbolFace{j}$ (upper right),
$\cPqt\cPaqt\cPqc\cPaqc$ (lower left) and $\cPqt\cPaqt\cmsSymbolFace{LF}$
(lower right). The number of entries is normalized to unity. The histograms
are obtained from the MC simulation.

The following procedure is applied to each final state for both the dilepton
and the lepton+jets channels. A 2D joint distribution is formed from the
tagging discriminant values of both additional jets. The distribution is
binned into $10{\times}10$ ($20{\times}20$) equidistant intervals for the
dilepton (lepton+jets) channel.

The likelihood depends on the two parameters of interest (POI) in the VPS, the
$\cPqt\cPaqt\cmsSymbolFace{jj}$ cross section
($\sigma_{\cPqt\cPaqt\cmsSymbolFace{jj}}^{\cmsSymbolFace}{VPS}$) and the
$\cPqt\cPaqt\cPqb\cPaqb$ to $\cPqt\cPaqt\cmsSymbolFace{jj}$ cross section
ratio
($R_{\cPqt\cPaqt\cPqb\cPaqb/\cPqt\cPaqt\cmsSymbolFace{jj}}^{\cmsSymbolFace}{VPS}$),
and on nuisance parameters affecting the kinematic distributions to be
considered as part of the systematic uncertainties described in Section 0.7.
It is constructed as the product over all bins of the 2D joint distribution of
a Poisson probability with a mean defined in each bin by:

$\mathcal{M}{\scriptstyle\left(\sigma_{\cPqt\cPaqt\cmsSymbolFace{jj}}^{\cmsSymbolFace{VPS}},~{}R_{\cPqt\cPaqt\cPqb\cPaqb/\cPqt\cPaqt\cmsSymbolFace{jj}}^{\cmsSymbolFace{VPS}}\right)}=\left(\sigma_{\cPqt\cPaqt\cmsSymbolFace{jj}}^{\cmsSymbolFace{VPS}}\lumi\mathcal{E}_{\cPqt\cPaqt\cmsSymbolFace{jj}}\right)\mathrm{pdf}_{\cPqt\cPaqt\cmsSymbolFace{jj}}^{\mathrm{\;norm}}+\left(\frac{\sigma_{\cPqt\cPaqt\cmsSymbolFace{jj}}^{\cmsSymbolFace{VPS}}}{\sigma_{\cPqt\cPaqt\cmsSymbolFace{jj}}^{\mathrm{MC}}}\right)\mathrm{pdf_{\ttbar\,others}}+\mathrm{pdf_{bkg}},$
(1)

where $\mathrm{pdf_{bkg}}$ is the probability density function (pdf) for each
bin of the full set of backgrounds presented in Table 0.5, including
$\cPqt\cPaqt\PW$, $\cPqt\cPaqt\PZ$, and $\cPqt\cPaqt\PH$. Those contributions
are fixed in the fit to their estimated yields from the MC simulation.
Potential deviations of
$\sigma_{\cPqt\cPaqt\cmsSymbolFace{jj}}^{\cmsSymbolFace}{VPS}$ with respect to
the expectation from simulation
($\sigma_{\cPqt\cPaqt\cmsSymbolFace{jj}}^{\mathrm{MC}}$) are propagated to the
“$\ttbar$ others” component. Therefore, $\mathrm{pdf_{\ttbar\,others}}$ enters
the likelihood function corrected by the
$\sigma_{\cPqt\cPaqt\cmsSymbolFace{jj}}^{\cmsSymbolFace{VPS}}/\sigma_{\cPqt\cPaqt\cmsSymbolFace{jj}}^{\mathrm{MC}}$
term, where the constant parameter
$\sigma_{\cPqt\cPaqt\cmsSymbolFace{jj}}^{\mathrm{MC}}=163$ (290)pb for the
dilepton (lepton+jets) channel is taken from MC simulation. The remaining term
in Eq. (1) contains the total integrated luminosity, $\lumi$, the efficiency
for selecting $\cPqt\cPaqt\cmsSymbolFace{jj}$ events,
$\mathcal{E}_{\cPqt\cPaqt\cmsSymbolFace{jj}}$, and the pdf normalized to unity
for each bin of the $\cPqt\cPaqt\cmsSymbolFace{jj}$ category,
$\mathrm{pdf}_{\cPqt\cPaqt\cmsSymbolFace{jj}}^{\mathrm{norm}}$. The latter
parameter is defined as:

$\mathrm{pdf}_{\cPqt\cPaqt\cmsSymbolFace{jj}}^{\mathrm{\;norm}}=\left[R_{\cPqt\cPaqt\cPqb\cPaqb/\cPqt\cPaqt\cmsSymbolFace{jj}}^{\cmsSymbolFace{VPS}}\left(\frac{\mathcal{E}_{\cPqt\cPaqt\cPqb\cPaqb}}{\mathcal{E}_{\cPqt\cPaqt\cmsSymbolFace{jj}}}\right)\mathrm{pdf}_{\cPqt\cPaqt\cPqb\cPaqb}^{\mathrm{\;norm}}+R_{\cPqt\cPaqt\cPqb\cmsSymbolFace{j}/\cPqt\cPaqt\cmsSymbolFace{jj}}\,\mathrm{pdf}_{\cPqt\cPaqt\cPqb\cmsSymbolFace{j}}^{\mathrm{\;norm}}+R_{\cPqt\cPaqt\cPqc\cPaqc\cmsSymbolFace{LF}/\cPqt\cPaqt\cmsSymbolFace{jj}}\,\mathrm{pdf}_{\cPqt\cPaqt\cPqc\cPaqc\cmsSymbolFace{LF}}^{\mathrm{\;norm}}\right],$
(2)

where $\mathrm{pdf}_{\cPqt\cPaqt\cPqb\cPaqb}^{\mathrm{norm}}$,
$\mathrm{pdf}_{\cPqt\cPaqt\cPqb\cmsSymbolFace{j}}^{\mathrm{norm}}$, and
$\mathrm{pdf}_{\cPqt\cPaqt\cPqc\cPaqc\cmsSymbolFace{LF}}^{\mathrm{norm}}$ are
the normalized pdfs of the $\cPqt\cPaqt\cPqb\cPaqb$,
$\cPqt\cPaqt\cPqb\cmsSymbolFace{j}$, and combined $\cPqt\cPaqt\cPqc\cPaqc$ and
$\cPqt\cPaqt\cmsSymbolFace{LF}$ ($\cPqt\cPaqt\cPqc\cPaqc\cmsSymbolFace{LF}$)
categories. The parameter
$R_{\cPqt\cPaqt\cPqb\cmsSymbolFace{j}/\cPqt\cPaqt\cmsSymbolFace{jj}}$ is the
expected ratio of the number of $\cPqt\cPaqt\cPqb\cmsSymbolFace{j}$ events to
the number of $\cPqt\cPaqt\cmsSymbolFace{jj}$ events. This ratio is expressed
as a function of the POI
$R_{\cPqt\cPaqt\cPqb\cPaqb/\cPqt\cPaqt\cmsSymbolFace{jj}}^{\cmsSymbolFace{VPS}}$:

$R_{\cPqt\cPaqt\cPqb\cmsSymbolFace{j}/\cPqt\cPaqt\cmsSymbolFace{jj}}=R_{\cPqt\cPaqt\cPqb\cPaqb/\cPqt\cPaqt\cmsSymbolFace{jj}}^{\cmsSymbolFace{VPS}}\left(\frac{\mathcal{E}_{\cPqt\cPaqt\cPqb\cPaqb}}{\mathcal{E}_{\cPqt\cPaqt\cmsSymbolFace{jj}}}\right)R_{\cPqt\cPaqt\cPqb\cmsSymbolFace{j}/\cPqt\cPaqt\cPqb\cPaqb}^{\mathrm{MC}}.$
(3)

The parameter
$R_{\cPqt\cPaqt\cPqb\cmsSymbolFace{j}/\cPqt\cPaqt\cPqb\cPaqb}^{\mathrm{MC}}$
is the expected cross section ratio from MC simulation of the
$\cPqt\cPaqt\cPqb\cmsSymbolFace{j}$ and $\cPqt\cPaqt\cPqb\cPaqb$ processes.
This value is taken to be constant
($R_{\cPqt\cPaqt\cPqb\cmsSymbolFace{j}/\cPqt\cPaqt\cPqb\cPaqb}^{\mathrm{MC}}=1.5$),
considering the correlation between the definition of those two $\ttbar$
categories. The parameter $\mathcal{E}_{\cPqt\cPaqt\cPqb\cPaqb}$ is the
efficiency for selecting $\cPqt\cPaqt\cPqb\cPaqb$ events. The event selection
efficiencies, defined as the number of events after the full event selection
divided by the number of events in the VPS, are 25 (18)% for
$\cPqt\cPaqt\cPqb\cPaqb$ and 10 (5)% for $\cPqt\cPaqt\cmsSymbolFace{jj}$ in
the dilepton (lepton+jets) channel. The quantity
$R_{\cPqt\cPaqt\cPqc\cPaqc\cmsSymbolFace{LF}/\cPqt\cPaqt\cmsSymbolFace{jj}}$
is the ratio of the number of $\cPqt\cPaqt\cPqc\cPaqc\cmsSymbolFace{LF}$
events to the number of $\cPqt\cPaqt\cmsSymbolFace{jj}$ events, which
satisfies the unitarity requirement of
$R_{\cPqt\cPaqt\cPqc\cPaqc\cmsSymbolFace{LF}/\cPqt\cPaqt\cmsSymbolFace{jj}}+R_{\cPqt\cPaqt\cPqb\cPaqb/\cPqt\cPaqt\cmsSymbolFace{jj}}+R_{\cPqt\cPaqt\cPqb\cmsSymbolFace{j}/\cPqt\cPaqt\cmsSymbolFace{jj}}=1$.
The binned maximum likelihood fit includes the effect of the systematic
uncertainties, described in Section 0.7, as nuisance parameters.

The $\cPqt\cPaqt\cPqb\cPaqb$ to $\cPqt\cPaqt\cmsSymbolFace{jj}$ cross section
ratio and the absolute $\cPqt\cPaqt\cmsSymbolFace{jj}$ cross section in the
FPS are extrapolated from the results in the VPS by:

$R_{\cPqt\cPaqt\cPqb\cPaqb/\cPqt\cPaqt\cmsSymbolFace{jj}}^{\cmsSymbolFace{FPS}}=\frac{\mathcal{A}_{\cPqt\cPaqt\cmsSymbolFace{jj}}}{\mathcal{A}_{\cPqt\cPaqt\cPqb\cPaqb}}R_{\cPqt\cPaqt\cPqb\cPaqb/\cPqt\cPaqt\cmsSymbolFace{jj}}^{\cmsSymbolFace{VPS}};\qquad\sigma_{\cPqt\cPaqt\cmsSymbolFace{jj}}^{\cmsSymbolFace{FPS}}=\frac{\sigma_{\cPqt\cPaqt\cmsSymbolFace{jj}}^{\cmsSymbolFace{VPS}}}{\mathcal{A}_{\cPqt\cPaqt\cmsSymbolFace{jj}}\;\mathcal{B}}.$
(4)

Here, $\mathcal{A}_{\cPqt\cPaqt\cPqb\cPaqb}$ and
$\mathcal{A}_{\cPqt\cPaqt\cmsSymbolFace{jj}}$ are the acceptances for the
$\cPqt\cPaqt\cPqb\cPaqb$ and $\cPqt\cPaqt\cmsSymbolFace{jj}$ processes,
respectively, taken from the nominal MC simulation, and $\mathcal{B}$ is the
branching fraction of the $\ttbar$ pair to each decay channel considered. The
acceptance values, defined as the ratio between the number of events in the
visible and full phase space (including decays into $\tau$ leptons in the full
phase space) are 14 (31)% for $\cPqt\cPaqt\cPqb\cPaqb$ and 15 (25)% for
$\cPqt\cPaqt\cmsSymbolFace{jj}$ in the dilepton (lepton+jets) channel. The
total branching fraction for $\ttbar$ decays in the dilepton (lepton+jets)
channel is 0.104 (0.436) [59], including decays into the three lepton flavors.

Finally, the absolute $\cPqt\cPaqt\cPqb\cPaqb$ cross section
$\sigma_{\cPqt\cPaqt\cPqb\cPaqb}$ is calculated by multiplying the POI
$R_{\cPqt\cPaqt\cPqb\cPaqb/\cPqt\cPaqt\cmsSymbolFace{jj}}$ and
$\sigma_{\cPqt\cPaqt\cmsSymbolFace{jj}}$ in each phase space region. The total
uncertainty in $\sigma_{\cPqt\cPaqt\cPqb\cPaqb}$ is estimated by considering
the uncertainties in each POI and their correlation after the fit.

##  0.7 Systematic uncertainties

The systematic uncertainties are incorporated as nuisance parameters in the
maximum likelihood fit used to extract both POI: the cross section ratio of
$\cPqt\cPaqt\cPqb\cPaqb$ to $\cPqt\cPaqt\cmsSymbolFace{jj}$ production and the
absolute $\cPqt\cPaqt\cmsSymbolFace{jj}$ production cross section. The
relevance of extracting the cross section ratio is that several systematic
uncertainties cancel, specifically those related to the common normalization
in both processes, such as the integrated luminosity, trigger efficiencies,
lepton identification, and energy scale, as well as the jet energy scale (JES)
and resolution (JER).

There are two main categories of uncertainties: those from the detector
performance and signal efficiency, and those from the modeling of the signal.
All the uncertainties coming from the detector performance are included in the
maximum likelihood fit as shape-changing nuisance parameters. The theoretical
uncertainties from modeling are treated as rate-changing nuisance parameters.
This consideration is based on studies that show shape-changing nuisance
parameters in distributions with large statistical fluctuations (as the
$\ttbar$ MC samples for modeling uncertainties) produce incorrect constraints
in the post-fit uncertainties. The studies also show that, under these
conditions, the rate-changing nuisance parameters cover any expected shape
variation.

The trigger uncertainties are about 1% for all the lepton flavor combinations
in both channels. The systematic uncertainty in the lepton identification
efficiency is calculated by varying the correction factor for the efficiency
within its uncertainty, as derived from boson candidates as a function of the
lepton $\eta$ and , and also taking into account the different phase space
between boson and events. Lepton energy scale variations are also considered.

The effect of JES and JER variations is estimated as follows. The uncertainty
produced by the JER is obtained by smearing the jet energy in simulation by a
value dependent on $\pt^{\text{jet}}$ and $\eta_{\text{jet}}$. In the case of
the JES, the energies of all jets in the event are scaled up and down
according to the 25 independent variations described in Ref. [57]. The effect
of the energy scale on the is considered by propagating the variations of jet
momenta and the jet energy smearing to its value. The JES uncertainties are
also propagated to the tagging discriminant efficiencies.

The systematic uncertainties associated with the tagging efficiency for jets
and light-flavor jets are studied separately by varying their values within
their uncertainties [58]. The variations are divided into different categories
that include two statistical effects and three jet flavor effects. The
statistical uncertainties account for fluctuations in data and simulation,
assuming a linear dependence for the effect on the slope and a quadratic
dependence for the overall shift of the tagging discriminant distribution. The
uncertainty in the contamination by light- and heavy-flavor jets,
respectively, in the control regions used to determine the heavy- and light-
flavor jet efficiencies is estimated by varying the contamination fractions by
20%. The jet scale factors are assumed to be unity with an uncertainty twice
as large as the jet tagging scale factors.

The uncertainties in the amount of initial- and final-state radiation, ISR and
FSR, respectively, are considered by varying the corresponding scale
parameters by factors of 2 and $\sqrt{2}$ [62]. The matrix element (ME) to PS
matching uncertainty is evaluated by varying the model parameter by
$h_{\text{damp}}=(1.58~{}^{+0.66}_{-0.59}$) $m_{\cPqt}$, where $m_{\cPqt}$ is
the top quark mass, fixed at $172.5\GeV$ [32]. This parameter is used in the
simulation to control the matching of the jets from the ME calculations to
those from the 8 PS within its uncertainty.

The effect of the tune used by 8 to simulate the underlying event is evaluated
by varying the tune parameters according to their uncertainties [62].

The $\cPqt\cPaqt\cPqb\cPaqb$ cross section strongly depends on the choice of
the factorization ($\mu_{\mathrm{F}}$) and renormalization
($\mu_{\mathrm{R}}$) scales in the ME calculations, which are estimated by
making use of a weighting scheme implemented in to vary the scales by a factor
of two up and down with respect to their reference values
$\mu_{\mathrm{F}}=\mu_{\mathrm{R}}=\sqrt{\smash[b]{m^{2}_{\cPqt}+\pt^{2}}}$,
and $\pt$ is the transverse momentum of the top quark. The $\mu_{\mathrm{F}}$
and $\mu_{\mathrm{R}}$ scales are assumed uncorrelated.

Previous measurements of the differential $\ttbar$ cross section by CMS [61]
have shown a mismodeling of the distribution of the top quark. The current + 8
simulation has a harder top quark distribution than the one obtained from
data. The uncertainty (labeled top-) is estimated by reweighting the
distribution in the simulation to match the data.

The uncertainty associated with the
$\cPqt\cPaqt\cPqb\cmsSymbolFace{j}/\cPqt\cPaqt\cPqb\cPaqb$ ratio (parameter
$R_{\cPqt\cPaqt\cPqb\cmsSymbolFace{j}/\cPqt\cPaqt\cPqb\cPaqb}^{\mathrm{MC}}$
in Eq.(2)) is estimated by varying the central value obtained from the MC
prediction by $\pm 10\%$. This variation covers the differences obtained by
comparing the predictions from the different $\ttbar$ MC simulations.

The uncertainties affecting the acceptances for $\cPqt\cPaqt\cPqb\cPaqb$ and
$\cPqt\cPaqt\cmsSymbolFace{jj}$ events, used in the extrapolation to the
$\cmsSymbolFace{FPS}$, are not constrained in order to avoid any prior in the
theoretical model. The theoretical uncertainties described above are
evaluated, in addition to the effects of the PDF and the color reconnection
(CR). The PDF uncertainty is estimated using the 100 individual uncertainties
and $\alpS$ in the NNPDF3.0 set [63], following the prescription of PDF4LHC
[64]. The nominal CR model uses a scheme based on multiple parton interactions
with early resonance decays (ERD) turned off. The uncertainty from CR is
estimated by employing three alternative schemes: with the ERD switched on, a
QCD-inspired procedure [65], and a gluon-move scheme [66]. The uncertainty in
the $\ttbar$ branching fraction is not included in the extrapolation to the
FPS since its effect is negligible.

The number of pileup interactions in data is estimated from the measured
bunch-by-bunch luminosity multiplied by the total inelastic $\Pp\Pp$ cross
section. The uncertainty assigned to the pileup simulation is obtained by
varying the inelastic cross section of $69.2\unit{mb}$ by $\pm 4.6\%$ [67].

To estimate the background uncertainties, several rate-changing nuisance
parameters have been included, even if they do not affect the measurement
significantly. Different uncertainties depending on the experimental precision
reached in the measurement of the background cross sections are applied. For
the dilepton channel, the background uncertainties are assessed conservatively
by varying each contribution (+jets, $\PV\PV$, $\PV\PV\PV$, $\cPqt\cPaqt\PV$,
single top quark) by 30% to cover the uncertainty in the production cross
sections, and detector performance uncertainties, of these background
processes. In the lepton+jets channel, a 15% systematic uncertainty in the
single top quark background normalization is included, in addition to the
other detector performance uncertainties, and 10% for the +jets background
normalization. For the QCD multijet background, a variation of 50% beyond the
statistical uncertainty is assumed to cover the systematic variations in this
process. Finally, for the smaller backgrounds $\PV\PV$, $\cPqt\cPaqt\PV$, and
$\cPqt\cPaqt\PH$, uncertainties of 10, 20, and 60% are used, respectively.

Since the POI are sensitive to the finite number of available simulated events
in the $\cPqt\cPaqt\cPqb\cPaqb$ process (referred to as “simulated sample
size” in Table 0.7), the effective number of unweighted events in each
histogram bin is added as a nuisance parameter with a Poisson constraint on
the likelihood function. Finally, the uncertainty in the integrated luminosity
for the 2016 data-taking period is considered, corresponding to 2.5% [67].

All the systematic uncertainties are considered as fully correlated between
the different final states of each channel, except the trigger uncertainties,
which are treated as uncorrelated.

To show the effect of each nuisance parameter, we evaluate the impact of their
contributions to the uncertainty in the POI. The impact of a nuisance
parameter on a fit parameter is defined as the shift of the fit parameter from
its post-fit value while fixing the nuisance parameter to $\pm$1 standard
deviation from the post-fit value, with all other parameters profiled as
normal. The systematic uncertainties in
$R_{\cPqt\cPaqt\cPqb\cPaqb/\cPqt\cPaqt\cmsSymbolFace{jj}}$ and
$\sigma_{\cPqt\cPaqt\cmsSymbolFace{jj}}$ for the VPS are shown in Table 0.7.
The total systematic uncertainty in the cross sections ratio
($\cPqt\cPaqt\cmsSymbolFace{jj}$ cross section) for the VPS is 8.0 (8.8)% for
the dilepton channel and 5.5 (10)% for the lepton+jets channel. The JER, some
variations of the JES, and the tagging uncertainties for the -flavor jets are
some of the most constrained uncertainties in the fit.

Summary of the individual contributions to the systematic uncertainty in the
$R_{\cPqt\cPaqt\cPqb\cPaqb/\cPqt\cPaqt\cmsSymbolFace{jj}}$ and
$\sigma_{\cPqt\cPaqt\cmsSymbolFace{jj}}$ measurements for the VPS. The
uncertainties are given as relative uncertainties. Some sources include a
linear (lin.) or quadratic (quad.) dependency on the fluctuations in data and
simulation. The statistical uncertainty in the result is given for comparison.
Source
$R_{\cPqt\cPaqt\cPqb\cPaqb/\cPqt\cPaqt\cmsSymbolFace{jj}}^{\cmsSymbolFace{VPS}}$
[%] $\sigma_{\cPqt\cPaqt\cmsSymbolFace{jj}}^{\cmsSymbolFace{VPS}}$ [%]
Dilepton Lepton+jets Dilepton Lepton+jets Lepton uncertainties Trigger $<$0.1
0.2 1.0 0.5 Lepton identification 0.6 0.2 1.1 1.3 Lepton energy scale $<$0.1
0.1 Jet uncertainties Jet energy resolution (JER) 0.4 0.3 0.3 0.7 Jet energy
scale (JES) 1.5 1.2 2.9 3.6 b tagging uncertainties -flavor tag (lin.) 2.2 2.0
1.0 0.3 -flavor tag (quad.) 0.7 1.2 0.3 0.2 Heavy-flavor tag 4.0 0.1 0.5 0.9
Heavy-flavor tag (lin.) 0.9 0.4 1.5 0.5 Heavy-flavor tag (quad.) 2.0 0.3 1.5
0.8 Light-flavor tag 4.9 0.9 5.5 4.9 Light-flavor tag (lin.) 0.1 0.2 0.3 1.1
Light-flavor tag (quad.) 0.7 0.7 0.1 1.4 Theoretical uncertainties Initial-
state radiation (ISR) 1.0 2.2 2.5 1.2 Final-state radiation (FSR) 0.8 0.7 2.5
5.9 ME-PS matching 0.5 $<$0.1 1.8 1.9 Underlying event tune (UE) 1.5 1.5 0.4
1.4 $\mu_{\mathrm{F}}/\mu_{\mathrm{R}}$ scales (ME) 0.1 0.4 0.1 1.4 top- 0.2
0.4 1.6 0.3 Ratio
$R_{\cPqt\cPaqt\cPqb\cmsSymbolFace{j}/\cPqt\cPaqt\cPqb\cPaqb}^{\mathrm{MC}}$
1.4 0.2 1.3 0.7 Other uncertainties Pileup 0.7 0.2 1.3 0.1 Backgrounds 0.3 2.0
0.7 1.2 Simulated sample size 1.5 2.8 0.1 2.2 Luminosity 0.2 0.5 2.6 3.1 Total
systematic 8.0 5.5 8.8 10.0 Statistical 5.8 5.6 0.9 0.6

##  0.8 Results

The simultaneous fit for the $\cPqt\cPaqt\cPqb\cPaqb$ to
$\cPqt\cPaqt\cmsSymbolFace{jj}$ cross section ratio and the inclusive
$\cPqt\cPaqt\cmsSymbolFace{jj}$ cross section in the VPS for the dilepton
(with a jet $\pt>30\GeV$) and lepton+jets (with a jet $\pt>20\GeV$) channels,
along with its 68 and 95% confidence level () contours are shown in Fig. 4.
The measurements of
$R_{\cPqt\cPaqt\cPqb\cPaqb/\cPqt\cPaqt\cmsSymbolFace{jj}}$,
$\sigma_{\cPqt\cPaqt\cmsSymbolFace{jj}}$ and $\sigma_{\cPqt\cPaqt\cPqb\cPaqb}$
in the VPS and FPS for both decay channels are given in Table 0.8. The
$\cPqt\cPaqt\cPqb\cPaqb$ cross section is obtained by multiplying the
$\cPqt\cPaqt\cmsSymbolFace{jj}$ cross section by the
$R_{\cPqt\cPaqt\cPqb\cPaqb/\cPqt\cPaqt\cmsSymbolFace{jj}}$ cross section
ratio. Table 0.8 also includes the expected values from MC simulation and
their uncertainties neglecting possible higher-order ME effects.

The uncertainty in the $\cPqt\cPaqt\cmsSymbolFace{jj}$ cross section is
largely dominated by the theoretical uncertainties, such as from the FSR and
ME-PS matching, because the final state has a high jet multiplicity. The fit
sensitivity to high values of the tagging discriminant also leads to an
important sensitivity to the uncertainties in the tagging efficiency, and
specifically the light-flavor jet mistagging probability. Like the
$\cPqt\cPaqt\cmsSymbolFace{jj}$ cross section, the cross section ratio is also
sensitive to the tagging efficiency and the variations in the ISR.
Additionally, both measurements are sensitive to the size of the simulated
samples, especially in the lepton+jets channel. The two POI,
$\sigma_{\cPqt\cPaqt\cmsSymbolFace{jj}}$ and
$R_{\cPqt\cPaqt\cPqb\cPaqb/\cPqt\cPaqt\cmsSymbolFace{jj}}$, show a positive
correlation of 48 (12)% in the dilepton (lepton+jets) channel. The total
uncertainty in the $\cPqt\cPaqt\cPqb\cPaqb$ cross section for both phase space
regions is calculated taking into account these correlations.

Figure 4: Results of the simultaneous fit for
$R_{\cPqt\cPaqt\cPqb\cPaqb/\cPqt\cPaqt\cmsSymbolFace{jj}}$ and
$\sigma_{\cPqt\cPaqt\cmsSymbolFace{jj}}$ (denoted by the cross) in the visible
phase space, along with its 68 and 95% contours, are shown for the (left)
dilepton and (right) lepton+jets channels. The solid circle shows the
prediction by + 8. The uncertainties in the MC prediction are a combination of
statistical, $\mu_{\mathrm{F}}/\mu_{\mathrm{R}}$ scale, and PDF components;
they are assumed to be uncorrelated between
$R_{\cPqt\cPaqt\cPqb\cPaqb/\cPqt\cPaqt\cmsSymbolFace{jj}}$ and
$\sigma_{\cPqt\cPaqt\cmsSymbolFace{jj}}$.

Besides the simulation, the measurements of the
$\cPqt\cPaqt\cmsSymbolFace{jj}$ and $\cPqt\cPaqt\cPqb\cPaqb$ cross sections
and their ratio are compared with other MC predictions in Table 0.8. The
predictions for the inclusive $\cPqt\cPaqt\cPqb\cPaqb$ and
$\cPqt\cPaqt\cmsSymbolFace{jj}$ cross sections in the VPS are in agreement,
within the uncertainties, with the measured cross sections in both decay
channels. The cross section ratio measured in the VPS is larger than the
reference prediction by a factor of 1.3 (1.2), with a significance of three
(two) standard deviations, in the dilepton (lepton+jets) channel. The previous
CMS measurement in the dilepton channel at 13 [18] also reported a larger
cross section ratio with respect to the prediction, a factor of 1.8, with a
significance of two standard deviations.

The measurements of the cross sections and their ratio in the FPS is shown in
Fig. 5. Those results are obtained by applying the acceptance correction
described in Section 0.6 to the values measured in the VPS. The measured
inclusive $\cPqt\cPaqt\cmsSymbolFace{jj}$ and $\cPqt\cPaqt\cPqb\cPaqb$ cross
sections and their ratio for the FPS agree with the MC predictions from and
MG_ainterfaced with 8, within the uncertainties, which are larger in the FPS
compared to the VPS. Predictions from + ++ for the
$\cPqt\cPaqt\cmsSymbolFace{jj}/\cPqt\cPaqt\cPqb\cPaqb$ cross section ratio,
and in consequence for the inclusive $\cPqt\cPaqt\cPqb\cPaqb$ cross section,
are slightly lower than the measured values. The total relative uncertainties
in the $\cPqt\cPaqt\cPqb\cPaqb$ cross section for the VPS (FPS) are 14 (18)%
for the dilepton channel, and 11 (14)% for the lepton+jets channel. These are
the most precise measurements of the inclusive $\cPqt\cPaqt\cPqb\cPaqb$ and
$\cPqt\cPaqt\cmsSymbolFace{jj}$ cross sections, and their ratio, to date.

The measured cross sections $\sigma_{\cPqt\cPaqt\cPqb\cPaqb}$ and
$\sigma_{\cPqt\cPaqt\cmsSymbolFace{jj}}$, and their ratio, for the VPS and
FPS, with the results in the latter corrected for the acceptance and branching
fractions. In both the VPS and FPS definitions, the dilepton and lepton+jets
channels require particle-level jets with $\pt>30\GeV$ and 20, respectively.
The predictions from several MC simulations are also shown. The uncertainties
in the measurements are split into their statistical (first) and systematic
(second) components, while the uncertainties in the MC predictions are a
combination of the statistical, $\mu_{\mathrm{F}}/\mu_{\mathrm{R}}$ scale, and
PDF components.  $R_{\cPqt\cPaqt\cPqb\cPaqb/\cPqt\cPaqt\cmsSymbolFace{jj}}$
$\sigma_{\cPqt\cPaqt\cmsSymbolFace{jj}}$ [pb]
$\sigma_{\cPqt\cPaqt\cPqb\cPaqb}$ [pb] Dilepton channel (VPS) \+ 8 $0.013\pm
0.002$ $2.41\pm 0.21$ $0.032\pm 0.004$ Measurement $0.017\pm 0.001\pm 0.001$
$2.36\pm 0.02\pm 0.20$ $0.040\pm 0.002\pm 0.005$ Dilepton channel (FPS) \+ 8
$0.014\pm 0.003$ $163\pm 21$ $2.3\pm 0.4$ MG_a + 8 $0.015\pm 0.003$ $159\pm
25$ $2.4\pm 0.4$ 5FS [FxFx] \+ ++ $0.011\pm 0.002$ $170\pm 25$ $1.9\pm 0.3$
Measurement $0.018\pm 0.001\pm 0.002$ $159\pm 1\pm 15$ $2.9\pm 0.1\pm 0.5$
Lepton+jets channel (VPS) \+ 8 $0.017\pm 0.002$ $30.5\pm 3.0$ $0.52\pm 0.06$
Measurement $0.020\pm 0.001\pm 0.001$ $31.0\pm 0.2\pm 2.9$ $0.62\pm 0.03\pm
0.07$ Lepton+jets channel (FPS) \+ 8 $0.013\pm 0.002$ $290\pm 29$ $3.9\pm 0.4$
MG_a+ 8 $0.014\pm 0.003$ $280\pm 40$ $4.1\pm 0.4$ 5FS [FxFx] \+ ++ $0.011\pm
0.002$ $321\pm 36$ $3.4\pm 0.5$ Measurement $0.016\pm 0.001\pm 0.001$ $292\pm
1\pm 29$ $4.7\pm 0.2\pm 0.6$

Figure 5: Measured values (vertical lines) of the $\cPqt\cPaqt\cPqb\cPaqb$ and
$\cPqt\cPaqt\cmsSymbolFace{jj}$ cross sections and their ratio, along with
their statistical and total uncertainties (dark and light bands) in the
dilepton (upper) and lepton+jets (lower) channels in the FPS. Also shown are
the theoretical predictions obtained from and MG_a(5FS) interfaced with 8, and
interfaced with . The theoretical predictions for the
$\cPqt\cPaqt\cmsSymbolFace{jj}$ and $\cPqt\cPaqt\cPqb\cPaqb$ cross sections
are normalized to $\sigma_{\ttbar}^{\text{\tiny NNLO}}=832\unit{pb}$. The
previous measurement performed by the CMS Collaboration [18] is also shown
with a rhombus marker in the lower plot. The uncertainties in the MC
predictions are a combination of the statistical, $\mu_{F}/\mu_{R}$ scale, and
PDF components.

##  0.9 Summary

Measurements of the $\cPqt\cPaqt\cPqb\cPaqb$ and
$\cPqt\cPaqt\cmsSymbolFace{jj}$ cross sections and their ratio are performed
independently in the dilepton and lepton+jets final states using a data sample
of proton-proton collisions collected at $\sqrt{s}=13\TeV$ by the CMS
experiment at the LHC in 2016, and corresponding to an integrated luminosity
of $35.9\fbinv$. Leptons and particle-level jets must be in the experimentally
accessible kinematic region. The inclusive $\cPqt\cPaqt\cmsSymbolFace{jj}$
cross section and the $\cPqt\cPaqt\cPqb\cPaqb$ to
$\cPqt\cPaqt\cmsSymbolFace{jj}$ cross section ratio in the fiducial phase
space are measured by means of a binned maximum likelihood fit to the tagging
discriminant distribution of the additional jets, from which the inclusive
$\cPqt\cPaqt\cPqb\cPaqb$ cross section measurement is inferred. The cross
section ratio and the inclusive $\cPqt\cPaqt\cmsSymbolFace{jj}$ cross section
in the fiducial phase space are extrapolated to the full phase space after
correcting for the detector acceptance.

The measured inclusive cross sections in the fiducial phase space for the
dilepton and lepton+jets channels, respectively, are
$\sigma_{\cPqt\cPaqt\cPqb\cPaqb}=0.040\pm 0.002\stat\pm 0.005\syst\unit{pb}$
and $0.62\pm 0.03\stat\pm 0.07\syst\unit{pb}$, performed by multiplying
$\sigma_{\cPqt\cPaqt\cmsSymbolFace{jj}}$ with the ratio of
$\sigma_{\cPqt\cPaqt\cPqb\cPaqb}$ to $\sigma_{\cPqt\cPaqt\cmsSymbolFace{jj}}$,
where $\sigma_{\cPqt\cPaqt\cmsSymbolFace{jj}}=2.36\pm 0.02\stat\pm
0.20\syst\unit{pb}$ and $31.0\pm 0.2\stat\pm 2.9\syst\unit{pb}$ and the ratios
are $0.017\pm 0.001\stat\pm 0.001\syst$ and $0.020\pm 0.001\stat\pm
0.001\syst$. The treatment of the systematic uncertainties as nuisance
parameters in the fit leads to an improvement in the precision compared to
previous measurements. The inclusive $\cPqt\cPaqt\cPqb\cPaqb$ cross sections
and the cross section ratios for both decay channels measured in the full
phase space have values higher than, but consistent with, the predictions from
several different Monte Carlo generators. A measured $\cPqt\cPaqt\cPqb\cPaqb$
cross section higher than Monte Carlo predictions is also reported in a recent
measurement performed by the CMS Collaboration in the fully hadronic final
state [19].

###### Acknowledgements.

We congratulate our colleagues in the CERN accelerator departments for the
excellent performance of the LHC and thank the technical and administrative
staffs at CERN and at other CMS institutes for their contributions to the
success of the CMS effort. In addition, we gratefully acknowledge the
computing centers and personnel of the Worldwide LHC Computing Grid for
delivering so effectively the computing infrastructure essential to our
analyses. Finally, we acknowledge the enduring support for the construction
and operation of the LHC and the CMS detector provided by the following
funding agencies: BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq,
CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST,
and NSFC (China); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RPF
(Cyprus); SENESCYT (Ecuador); MoER, ERC IUT, PUT and ERDF (Estonia); Academy
of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG,
and HGF (Germany); GSRT (Greece); NKFIA (Hungary); DAE and DST (India); IPM
(Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); MES
(Latvia); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT,
LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC
(Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom,
RAS, RFBR, and NRC KI (Russia); MESTD (Serbia); SEIDI, CPAN, PCTI, and FEDER
(Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST
(Taipei); ThEPCenter, IPST, STAR, and NSTDA (Thailand); TUBITAK and TAEK
(Turkey); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (USA).
Individuals have received support from the Marie-Curie program and the
European Research Council and Horizon 2020 Grant, contract Nos. 675440,
752730, and 765710 (European Union); the Leventis Foundation; the A.P. Sloan
Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science
Policy Office; the Fonds pour la Formation à la Recherche dans l’Industrie et
dans l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door
Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium)
under the “Excellence of Science – EOS” – be.h project n. 30820817; the
Beijing Municipal Science & Technology Commission, No. Z191100007219010; the
Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the
Deutsche Forschungsgemeinschaft (DFG) under Germany’s Excellence Strategy –
EXC 2121 “Quantum Universe” – 390833306; the Lendület (“Momentum”) Program and
the János Bolyai Research Scholarship of the Hungarian Academy of Sciences,
the New National Excellence Program ÚNKP, the NKFIA research grants 123842,
123959, 124845, 124850, 125105, 128713, 128786, and 129058 (Hungary); the
Council of Science and Industrial Research, India; the HOMING PLUS program of
the Foundation for Polish Science, cofinanced from European Union, Regional
Development Fund, the Mobility Plus program of the Ministry of Science and
Higher Education, the National Science Center (Poland), contracts Harmonia
2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543, 2014/15/B/ST2/03998, and
2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406; the National Priorities
Research Program by Qatar National Research Fund; the Ministry of Science and
Education, grant no. 14.W03.31.0026 (Russia); the Tomsk Polytechnic University
Competitiveness Enhancement Program and “Nauka” Project FSWW-2020-0008
(Russia); the Programa Estatal de Fomento de la Investigación Científica y
Técnica de Excelencia María de Maeztu, grant MDM-2015-0509 and the Programa
Severo Ochoa del Principado de Asturias; the Thalis and Aristeia programs
cofinanced by EU-ESF and the Greek NSRF; the Rachadapisek Sompot Fund for
Postdoctoral Fellowship, Chulalongkorn University and the Chulalongkorn
Academic into Its 2nd Century Project Advancement Project (Thailand); the
Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation; the
Welch Foundation, contract C-1845; and the Weston Havens Foundation (USA).

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##  .10 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan${}^{\textrm{\textdagger}}$, A. Tumasyan Institut für
Hochenergiephysik, Wien, Austria
W. Adam, F. Ambrogi, T. Bergauer, M. Dragicevic, J. Erö, A. Escalante Del
Valle, M. Flechl, R. Frühwirth1, M. Jeitler1, N. Krammer, I. Krätschmer, D.
Liko, T. Madlener, I. Mikulec, N. Rad, J. Schieck1, R. Schöfbeck, M. Spanring,
D. Spitzbart, W. Waltenberger, C.-E. Wulz1, M. Zarucki Institute for Nuclear
Problems, Minsk, Belarus
V. Drugakov, V. Mossolov, J. Suarez Gonzalez Universiteit Antwerpen,
Antwerpen, Belgium
M.R. Darwish, E.A. De Wolf, D. Di Croce, X. Janssen, A. Lelek, M. Pieters, H.
Rejeb Sfar, H. Van Haevermaet, P. Van Mechelen, S. Van Putte, N. Van Remortel
Vrije Universiteit Brussel, Brussel, Belgium
F. Blekman, E.S. Bols, S.S. Chhibra, J. D’Hondt, J. De Clercq, D. Lontkovskyi,
S. Lowette, I. Marchesini, S. Moortgat, Q. Python, K. Skovpen, S. Tavernier,
W. Van Doninck, P. Van Mulders Université Libre de Bruxelles, Bruxelles,
Belgium
D. Beghin, B. Bilin, B. Clerbaux, G. De Lentdecker, H. Delannoy, B. Dorney, L.
Favart, A. Grebenyuk, A.K. Kalsi, A. Popov, N. Postiau, E. Starling, L.
Thomas, C. Vander Velde, P. Vanlaer, D. Vannerom Ghent University, Ghent,
Belgium
T. Cornelis, D. Dobur, I. Khvastunov2, M. Niedziela, C. Roskas, M. Tytgat, W.
Verbeke, B. Vermassen, M. Vit Université Catholique de Louvain, Louvain-la-
Neuve, Belgium
O. Bondu, G. Bruno, C. Caputo, P. David, C. Delaere, M. Delcourt, A.
Giammanco, V. Lemaitre, J. Prisciandaro, A. Saggio, M. Vidal Marono, P.
Vischia, J. Zobec Centro Brasileiro de Pesquisas Fisicas, Rio de Janeiro,
Brazil
F.L. Alves, G.A. Alves, G. Correia Silva, C. Hensel, A. Moraes, P. Rebello
Teles Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
E. Belchior Batista Das Chagas, W. Carvalho, J. Chinellato3, E. Coelho, E.M.
Da Costa, G.G. Da Silveira4, D. De Jesus Damiao, C. De Oliveira Martins, S.
Fonseca De Souza, L.M. Huertas Guativa, H. Malbouisson, J. Martins5, D. Matos
Figueiredo, M. Medina Jaime6, M. Melo De Almeida, C. Mora Herrera, L. Mundim,
H. Nogima, W.L. Prado Da Silva, L.J. Sanchez Rosas, A. Santoro, A. Sznajder,
M. Thiel, E.J. Tonelli Manganote3, F. Torres Da Silva De Araujo, A. Vilela
Pereira Universidade Estadual Paulista a, Universidade Federal do ABC b, São
Paulo, Brazil
C.A. Bernardesa, L. Calligarisa, T.R. Fernandez Perez Tomeia, E.M. Gregoresb,
D.S. Lemos, P.G. Mercadanteb, S.F. Novaesa, SandraS. Padulaa Institute for
Nuclear Research and Nuclear Energy, Bulgarian Academy of Sciences, Sofia,
Bulgaria
A. Aleksandrov, G. Antchev, R. Hadjiiska, P. Iaydjiev, M. Misheva, M. Rodozov,
M. Shopova, G. Sultanov University of Sofia, Sofia, Bulgaria
M. Bonchev, A. Dimitrov, T. Ivanov, L. Litov, B. Pavlov, P. Petkov Beihang
University, Beijing, China
W. Fang7, X. Gao7, L. Yuan Department of Physics, Tsinghua University,
Beijing, China
M. Ahmad, Z. Hu, Y. Wang Institute of High Energy Physics, Beijing, China
G.M. Chen, H.S. Chen, M. Chen, C.H. Jiang, D. Leggat, H. Liao, Z. Liu, A.
Spiezia, J. Tao, E. Yazgan, H. Zhang, S. Zhang8, J. Zhao State Key Laboratory
of Nuclear Physics and Technology, Peking University, Beijing, China
A. Agapitos, Y. Ban, G. Chen, A. Levin, J. Li, L. Li, Q. Li, Y. Mao, S.J.
Qian, D. Wang, Q. Wang Zhejiang University, Hangzhou, China
M. Xiao Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, C. Florez, C.F. González Hernández, M.A. Segura Delgado
Universidad de Antioquia, Medellin, Colombia
J. Mejia Guisao, J.D. Ruiz Alvarez, C.A. Salazar González, N. Vanegas Arbelaez
University of Split, Faculty of Electrical Engineering, Mechanical Engineering
and Naval Architecture, Split, Croatia
D. Giljanović, N. Godinovic, D. Lelas, I. Puljak, T. Sculac University of
Split, Faculty of Science, Split, Croatia
Z. Antunovic, M. Kovac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, D. Ferencek, K. Kadija, B. Mesic, M. Roguljic, A. Starodumov9,
T. Susa University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, E. Erodotou, A. Ioannou, M. Kolosova, S. Konstantinou,
G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos, P.A. Razis, H.
Rykaczewski, D. Tsiakkouri Charles University, Prague, Czech Republic
M. Finger10, M. Finger Jr.10, A. Kveton, J. Tomsa Escuela Politecnica
Nacional, Quito, Ecuador
E. Ayala Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab
Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
A.A. Abdelalim11,12, S. Abu Zeid13 National Institute of Chemical Physics and
Biophysics, Tallinn, Estonia
S. Bhowmik, A. Carvalho Antunes De Oliveira, R.K. Dewanjee, K. Ehataht, M.
Kadastik, M. Raidal, C. Veelken Department of Physics, University of Helsinki,
Helsinki, Finland
P. Eerola, L. Forthomme, H. Kirschenmann, K. Osterberg, M. Voutilainen
Helsinki Institute of Physics, Helsinki, Finland
F. Garcia, J. Havukainen, J.K. Heikkilä, V. Karimäki, M.S. Kim, R. Kinnunen,
T. Lampén, K. Lassila-Perini, S. Laurila, S. Lehti, T. Lindén, P. Luukka, T.
Mäenpää, H. Siikonen, E. Tuominen, J. Tuominiemi Lappeenranta University of
Technology, Lappeenranta, Finland
T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
M. Besancon, F. Couderc, M. Dejardin, D. Denegri, B. Fabbro, J.L. Faure, F.
Ferri, S. Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry,
C. Leloup, B. Lenzi, E. Locci, J. Malcles, J. Rander, A. Rosowsky, M.Ö. Sahin,
A. Savoy-Navarro14, M. Titov, G.B. Yu Laboratoire Leprince-Ringuet,
CNRS/IN2P3, Ecole Polytechnique, Institut Polytechnique de Paris
S. Ahuja, C. Amendola, F. Beaudette, P. Busson, C. Charlot, B. Diab, G.
Falmagne, R. Granier de Cassagnac, I. Kucher, A. Lobanov, C. Martin Perez, M.
Nguyen, C. Ochando, P. Paganini, J. Rembser, R. Salerno, J.B. Sauvan, Y.
Sirois, A. Zabi, A. Zghiche Université de Strasbourg, CNRS, IPHC UMR 7178,
Strasbourg, France
J.-L. Agram15, J. Andrea, D. Bloch, G. Bourgatte, J.-M. Brom, E.C. Chabert, C.
Collard, E. Conte15, J.-C. Fontaine15, D. Gelé, U. Goerlach, M. Jansová, A.-C.
Le Bihan, N. Tonon, P. Van Hove Centre de Calcul de l’Institut National de
Physique Nucleaire et de Physique des Particules, CNRS/IN2P3, Villeurbanne,
France
S. Gadrat Université de Lyon, Université Claude Bernard Lyon 1, CNRS-IN2P3,
Institut de Physique Nucléaire de Lyon, Villeurbanne, France
S. Beauceron, C. Bernet, G. Boudoul, C. Camen, A. Carle, N. Chanon, R.
Chierici, D. Contardo, P. Depasse, H. El Mamouni, J. Fay, S. Gascon, M.
Gouzevitch, B. Ille, Sa. Jain, F. Lagarde, I.B. Laktineh, H. Lattaud, A.
Lesauvage, M. Lethuillier, L. Mirabito, S. Perries, V. Sordini, L. Torterotot,
G. Touquet, M. Vander Donckt, S. Viret Georgian Technical University, Tbilisi,
Georgia
T. Toriashvili16 Tbilisi State University, Tbilisi, Georgia
Z. Tsamalaidze10 RWTH Aachen University, I. Physikalisches Institut, Aachen,
Germany
C. Autermann, L. Feld, K. Klein, M. Lipinski, D. Meuser, A. Pauls, M. Preuten,
M.P. Rauch, J. Schulz, M. Teroerde, B. Wittmer RWTH Aachen University, III.
Physikalisches Institut A, Aachen, Germany
M. Erdmann, B. Fischer, S. Ghosh, T. Hebbeker, K. Hoepfner, H. Keller, L.
Mastrolorenzo, M. Merschmeyer, A. Meyer, P. Millet, G. Mocellin, S. Mondal, S.
Mukherjee, D. Noll, A. Novak, T. Pook, A. Pozdnyakov, T. Quast, M. Radziej, Y.
Rath, H. Reithler, J. Roemer, A. Schmidt, S.C. Schuler, A. Sharma, S.
Wiedenbeck, S. Zaleski RWTH Aachen University, III. Physikalisches Institut B,
Aachen, Germany
G. Flügge, W. Haj Ahmad17, O. Hlushchenko, T. Kress, T. Müller, A. Nowack, C.
Pistone, O. Pooth, D. Roy, H. Sert, A. Stahl18 Deutsches Elektronen-
Synchrotron, Hamburg, Germany
M. Aldaya Martin, P. Asmuss, I. Babounikau, H. Bakhshiansohi, K. Beernaert, O.
Behnke, A. Bermúdez Martínez, D. Bertsche, A.A. Bin Anuar, K. Borras19, V.
Botta, A. Campbell, A. Cardini, P. Connor, S. Consuegra Rodríguez, C.
Contreras-Campana, V. Danilov, A. De Wit, M.M. Defranchis, C. Diez Pardos, D.
Domínguez Damiani, G. Eckerlin, D. Eckstein, T. Eichhorn, A. Elwood, E. Eren,
E. Gallo20, A. Geiser, A. Grohsjean, M. Guthoff, M. Haranko, A. Harb, A.
Jafari, N.Z. Jomhari, H. Jung, A. Kasem19, M. Kasemann, H. Kaveh, J. Keaveney,
C. Kleinwort, J. Knolle, D. Krücker, W. Lange, T. Lenz, J. Lidrych, K. Lipka,
W. Lohmann21, R. Mankel, I.-A. Melzer-Pellmann, A.B. Meyer, M. Meyer, M.
Missiroli, J. Mnich, A. Mussgiller, V. Myronenko, D. Pérez Adán, S.K.
Pflitsch, D. Pitzl, A. Raspereza, A. Saibel, M. Savitskyi, V. Scheurer, P.
Schütze, C. Schwanenberger, R. Shevchenko, A. Singh, H. Tholen, O. Turkot, A.
Vagnerini, M. Van De Klundert, R. Walsh, Y. Wen, K. Wichmann, C. Wissing, O.
Zenaiev, R. Zlebcik University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, L. Benato, A. Benecke, V. Blobel, T. Dreyer, A.
Ebrahimi, F. Feindt, A. Fröhlich, C. Garbers, E. Garutti, D. Gonzalez, P.
Gunnellini, J. Haller, A. Hinzmann, A. Karavdina, G. Kasieczka, R. Klanner, R.
Kogler, N. Kovalchuk, S. Kurz, V. Kutzner, J. Lange, T. Lange, A. Malara, J.
Multhaup, C.E.N. Niemeyer, A. Perieanu, A. Reimers, O. Rieger, C. Scharf, P.
Schleper, S. Schumann, J. Schwandt, J. Sonneveld, H. Stadie, G. Steinbrück,
F.M. Stober, B. Vormwald, I. Zoi Karlsruher Institut fuer Technologie,
Karlsruhe, Germany
M. Akbiyik, C. Barth, M. Baselga, S. Baur, T. Berger, E. Butz, R. Caspart, T.
Chwalek, W. De Boer, A. Dierlamm, K. El Morabit, N. Faltermann, M. Giffels, P.
Goldenzweig, A. Gottmann, M.A. Harrendorf, F. Hartmann18, U. Husemann, S.
Kudella, S. Mitra, M.U. Mozer, D. Müller, Th. Müller, M. Musich, A. Nürnberg,
G. Quast, K. Rabbertz, M. Schröder, I. Shvetsov, H.J. Simonis, R. Ulrich, M.
Wassmer, M. Weber, C. Wöhrmann, R. Wolf Institute of Nuclear and Particle
Physics (INPP), NCSR Demokritos, Aghia Paraskevi, Greece
G. Anagnostou, P. Asenov, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas,
G. Paspalaki National and Kapodistrian University of Athens, Athens, Greece
M. Diamantopoulou, G. Karathanasis, P. Kontaxakis, A. Manousakis-katsikakis,
A. Panagiotou, I. Papavergou, N. Saoulidou, A. Stakia, K. Theofilatos, K.
Vellidis, E. Vourliotis National Technical University of Athens, Athens,
Greece
G. Bakas, K. Kousouris, I. Papakrivopoulos, G. Tsipolitis University of
Ioánnina, Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, S. Mallios, K.
Manitara, N. Manthos, I. Papadopoulos, J. Strologas, F.A. Triantis, D.
Tsitsonis MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
M. Bartók22, R. Chudasama, M. Csanad, P. Major, K. Mandal, A. Mehta, M.I.
Nagy, G. Pasztor, O. Surányi, G.I. Veres Wigner Research Centre for Physics,
Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath23, F. Sikler, T.Á. Vámi, V. Veszpremi, G.
Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear Research ATOMKI,
Debrecen, Hungary
N. Beni, S. Czellar, J. Karancsi22, J. Molnar, Z. Szillasi Institute of
Physics, University of Debrecen, Debrecen, Hungary
P. Raics, D. Teyssier, Z.L. Trocsanyi, B. Ujvari Eszterhazy Karoly University,
Karoly Robert Campus, Gyongyos, Hungary
T. Csorgo, W.J. Metzger, F. Nemes, T. Novak Indian Institute of Science
(IISc), Bangalore, India
S. Choudhury, J.R. Komaragiri, P.C. Tiwari National Institute of Science
Education and Research, HBNI, Bhubaneswar, India
S. Bahinipati25, C. Kar, G. Kole, P. Mal, V.K. Muraleedharan Nair Bindhu, A.
Nayak26, D.K. Sahoo25, S.K. Swain Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, S. Chauhan, R. Chawla, N. Dhingra, R.
Gupta, A. Kaur, M. Kaur, S. Kaur, P. Kumari, M. Lohan, M. Meena, K. Sandeep,
S. Sharma, J.B. Singh, A.K. Virdi University of Delhi, Delhi, India
A. Bhardwaj, B.C. Choudhary, R.B. Garg, M. Gola, S. Keshri, Ashok Kumar, M.
Naimuddin, P. Priyanka, K. Ranjan, Aashaq Shah, R. Sharma Saha Institute of
Nuclear Physics, HBNI, Kolkata, India
R. Bhardwaj27, M. Bharti27, R. Bhattacharya, S. Bhattacharya, U. Bhawandeep27,
D. Bhowmik, S. Dutta, S. Ghosh, B. Gomber28, M. Maity29, K. Mondal, S. Nandan,
A. Purohit, P.K. Rout, G. Saha, S. Sarkar, T. Sarkar29, M. Sharan, B. Singh27,
S. Thakur27 Indian Institute of Technology Madras, Madras, India
P.K. Behera, P. Kalbhor, A. Muhammad, P.R. Pujahari, A. Sharma, A.K. Sikdar
Bhabha Atomic Research Centre, Mumbai, India
D. Dutta, V. Jha, V. Kumar, D.K. Mishra, P.K. Netrakanti, L.M. Pant, P. Shukla
Tata Institute of Fundamental Research-A, Mumbai, India
T. Aziz, M.A. Bhat, S. Dugad, G.B. Mohanty, N. Sur, RavindraKumar Verma Tata
Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, S. Karmakar,
S. Kumar, G. Majumder, K. Mazumdar, N. Sahoo, S. Sawant Indian Institute of
Science Education and Research (IISER), Pune, India
S. Dube, B. Kansal, A. Kapoor, K. Kothekar, S. Pandey, A. Rane, A. Rastogi, S.
Sharma Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
S. Chenarani30, E. Eskandari Tadavani, S.M. Etesami30, M. Khakzad, M.
Mohammadi Najafabadi, M. Naseri, F. Rezaei Hosseinabadi University College
Dublin, Dublin, Ireland
M. Felcini, M. Grunewald INFN Sezione di Bari a, Università di Bari b,
Politecnico di Bari c, Bari, Italy
M. Abbresciaa,b, R. Alya,b,31, C. Calabriaa,b, A. Colaleoa, D. Creanzaa,c, L.
Cristellaa,b, N. De Filippisa,c, M. De Palmaa,b, A. Di Florioa,b, W.
Elmetenaweea,b, L. Fiorea, A. Gelmia,b, G. Iasellia,c, M. Incea,b, S.
Lezkia,b, G. Maggia,c, M. Maggia, J.A. Merlin, G. Minielloa,b, S. Mya,b, S.
Nuzzoa,b, A. Pompilia,b, G. Pugliesea,c, R. Radognaa, A. Ranieria, G.
Selvaggia,b, L. Silvestrisa, F.M. Simonea,b, R. Vendittia, P. Verwilligena
INFN Sezione di Bologna a, Università di Bologna b, Bologna, Italy
G. Abbiendia, C. Battilanaa,b, D. Bonacorsia,b, L. Borgonovia,b, S. Braibant-
Giacomellia,b, R. Campaninia,b, P. Capiluppia,b, A. Castroa,b, F.R. Cavalloa,
C. Cioccaa, G. Codispotia,b, M. Cuffiania,b, G.M. Dallavallea, F. Fabbria, A.
Fanfania,b, E. Fontanesia,b, P. Giacomellia, C. Grandia, L. Guiduccia,b, F.
Iemmia,b, S. Lo Meoa,32, S. Marcellinia, G. Masettia, F.L. Navarriaa,b, A.
Perrottaa, F. Primaveraa,b, A.M. Rossia,b, T. Rovellia,b, G.P. Sirolia,b, N.
Tosia INFN Sezione di Catania a, Università di Catania b, Catania, Italy
S. Albergoa,b,33, S. Costaa,b, A. Di Mattiaa, R. Potenzaa,b, A. Tricomia,b,33,
C. Tuvea,b INFN Sezione di Firenze a, Università di Firenze b, Firenze, Italy
G. Barbaglia, A. Cassesea, R. Ceccarellia,b, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, F. Fioria,c, E. Focardia,b, G. Latinoa,b, P. Lenzia,b, M.
Meschinia, S. Paolettia, G. Sguazzonia, L. Viliania INFN Laboratori Nazionali
di Frascati, Frascati, Italy
L. Benussi, S. Bianco, D. Piccolo INFN Sezione di Genova a, Università di
Genova b, Genova, Italy
M. Bozzoa,b, F. Ferroa, R. Mulargiaa,b, E. Robuttia, S. Tosia,b INFN Sezione
di Milano-Bicocca a, Università di Milano-Bicocca b, Milano, Italy
A. Benagliaa, A. Beschia,b, F. Brivioa,b, V. Cirioloa,b,18, M.E. Dinardoa,b,
P. Dinia, S. Gennaia, A. Ghezzia,b, P. Govonia,b, L. Guzzia,b, M. Malbertia,
S. Malvezzia, D. Menascea, F. Montia,b, L. Moronia, M. Paganonia,b, D.
Pedrinia, S. Ragazzia,b, T. Tabarelli de Fatisa,b, D. Zuoloa,b INFN Sezione di
Napoli a, Università di Napoli ’Federico II’ b, Napoli, Italy, Università
della Basilicata c, Potenza, Italy, Università G. Marconi d, Roma, Italy
S. Buontempoa, N. Cavalloa,c, A. De Iorioa,b, A. Di Crescenzoa,b, F.
Fabozzia,c, F. Fiengaa, G. Galatia, A.O.M. Iorioa,b, L. Listaa,b, S.
Meolaa,d,18, P. Paoluccia,18, B. Rossia, C. Sciaccaa,b, E. Voevodinaa,b INFN
Sezione di Padova a, Università di Padova b, Padova, Italy, Università di
Trento c, Trento, Italy
P. Azzia, N. Bacchettaa, D. Biselloa,b, A. Bolettia,b, A. Bragagnoloa,b, R.
Carlina,b, P. Checchiaa, P. De Castro Manzanoa, T. Dorigoa, U. Dossellia, F.
Gasparinia,b, U. Gasparinia,b, A. Gozzelinoa, S.Y. Hoha,b, P. Lujana, M.
Margonia,b, A.T. Meneguzzoa,b, J. Pazzinia,b, M. Presillab, P. Ronchesea,b, R.
Rossina,b, F. Simonettoa,b, A. Tikoa, M. Tosia,b, M. Zanettia,b, P. Zottoa,b,
G. Zumerlea,b INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy
A. Braghieria, D. Fiorinaa,b, P. Montagnaa,b, S.P. Rattia,b, V. Rea, M.
Ressegottia,b, C. Riccardia,b, P. Salvinia, I. Vaia, P. Vituloa,b INFN Sezione
di Perugia a, Università di Perugia b, Perugia, Italy
M. Biasinia,b, G.M. Bileia, D. Ciangottinia,b, L. Fanòa,b, P. Laricciaa,b, R.
Leonardia,b, E. Manonia, G. Mantovania,b, V. Mariania,b, M. Menichellia, A.
Rossia,b, A. Santocchiaa,b, D. Spigaa INFN Sezione di Pisa a, Università di
Pisa b, Scuola Normale Superiore di Pisa c, Pisa, Italy
K. Androsova, P. Azzurria, G. Bagliesia, V. Bertacchia,c, L. Bianchinia, T.
Boccalia, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, S. Donatoa, G. Fedia,
L. Gianninia,c, A. Giassia, M.T. Grippoa, F. Ligabuea,c, E. Mancaa,c, G.
Mandorlia,c, A. Messineoa,b, F. Pallaa, A. Rizzia,b, G. Rolandi34, S. Roy
Chowdhury, A. Scribanoa, P. Spagnoloa, R. Tenchinia, G. Tonellia,b, N.
Turinia, A. Venturia, P.G. Verdinia INFN Sezione di Roma a, Sapienza
Università di Roma b, Rome, Italy
F. Cavallaria, M. Cipriania,b, D. Del Rea,b, E. Di Marcoa, M. Diemoza, E.
Longoa,b, P. Meridiania, G. Organtinia,b, F. Pandolfia, R. Paramattia,b, C.
Quarantaa,b, S. Rahatloua,b, C. Rovellia, F. Santanastasioa,b, L. Soffia,b
INFN Sezione di Torino a, Università di Torino b, Torino, Italy, Università
del Piemonte Orientale c, Novara, Italy
N. Amapanea,b, R. Arcidiaconoa,c, S. Argiroa,b, M. Arneodoa,c, N. Bartosika,
R. Bellana,b, A. Bellora, C. Biinoa, A. Cappatia,b, N. Cartigliaa, S.
Comettia, M. Costaa,b, R. Covarellia,b, N. Demariaa, B. Kiania,b, F. Legger,
C. Mariottia, S. Masellia, E. Migliorea,b, V. Monacoa,b, E. Monteila,b, M.
Montenoa, M.M. Obertinoa,b, G. Ortonaa,b, L. Pachera,b, N. Pastronea, M.
Pelliccionia, G.L. Pinna Angionia,b, A. Romeroa,b, M. Ruspaa,c, R.
Salvaticoa,b, V. Solaa, A. Solanoa,b, D. Soldia,b, A. Staianoa, D. Trocinoa,b
INFN Sezione di Trieste a, Università di Trieste b, Trieste, Italy
S. Belfortea, V. Candelisea,b, M. Casarsaa, F. Cossuttia, A. Da Rolda,b, G.
Della Riccaa,b, F. Vazzolera,b, A. Zanettia Kyungpook National University,
Daegu, Korea
B. Kim, D.H. Kim, G.N. Kim, J. Lee, S.W. Lee, C.S. Moon, Y.D. Oh, S.I. Pak, S.
Sekmen, D.C. Son, Y.C. Yang Chonnam National University, Institute for
Universe and Elementary Particles, Kwangju, Korea
H. Kim, D.H. Moon, G. Oh Hanyang University, Seoul, Korea
B. Francois, T.J. Kim, J. Park Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, S. Ha, B. Hong, K. Lee, K.S. Lee, J. Lim, J. Park,
S.K. Park, Y. Roh, J. Yoo Kyung Hee University, Department of Physics
J. Goh Sejong University, Seoul, Korea
H.S. Kim Seoul National University, Seoul, Korea
J. Almond, J.H. Bhyun, J. Choi, S. Jeon, J. Kim, J.S. Kim, H. Lee, K. Lee, S.
Lee, K. Nam, M. Oh, S.B. Oh, B.C. Radburn-Smith, U.K. Yang, H.D. Yoo, I. Yoon
University of Seoul, Seoul, Korea
D. Jeon, J.H. Kim, J.S.H. Lee, I.C. Park, I.J Watson Sungkyunkwan University,
Suwon, Korea
Y. Choi, C. Hwang, Y. Jeong, J. Lee, Y. Lee, I. Yu Riga Technical University,
Riga, Latvia
V. Veckalns35 Vilnius University, Vilnius, Lithuania
V. Dudenas, A. Juodagalvis, A. Rinkevicius, G. Tamulaitis, J. Vaitkus National
Centre for Particle Physics, Universiti Malaya, Kuala Lumpur, Malaysia
Z.A. Ibrahim, F. Mohamad Idris36, W.A.T. Wan Abdullah, M.N. Yusli, Z. Zolkapli
Universidad de Sonora (UNISON), Hermosillo, Mexico
J.F. Benitez, A. Castaneda Hernandez, J.A. Murillo Quijada, L. Valencia Palomo
Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico
H. Castilla-Valdez, E. De La Cruz-Burelo, I. Heredia-De La Cruz37, R. Lopez-
Fernandez, A. Sanchez-Hernandez Universidad Iberoamericana, Mexico City,
Mexico
S. Carrillo Moreno, C. Oropeza Barrera, M. Ramirez-Garcia, F. Vazquez Valencia
Benemerita Universidad Autonoma de Puebla, Puebla, Mexico
J. Eysermans, I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada Universidad
Autónoma de San Luis Potosí, San Luis Potosí, Mexico
A. Morelos Pineda University of Montenegro, Podgorica, Montenegro
J. Mijuskovic2, N. Raicevic University of Auckland, Auckland, New Zealand
D. Krofcheck University of Canterbury, Christchurch, New Zealand
S. Bheesette, P.H. Butler National Centre for Physics, Quaid-I-Azam
University, Islamabad, Pakistan
A. Ahmad, M. Ahmad, Q. Hassan, H.R. Hoorani, W.A. Khan, M.A. Shah, M. Shoaib,
M. Waqas AGH University of Science and Technology Faculty of Computer Science,
Electronics and Telecommunications, Krakow, Poland
V. Avati, L. Grzanka, M. Malawski National Centre for Nuclear Research,
Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, M. Górski, M. Kazana, M. Szleper, P.
Zalewski Institute of Experimental Physics, Faculty of Physics, University of
Warsaw, Warsaw, Poland
K. Bunkowski, A. Byszuk38, K. Doroba, A. Kalinowski, M. Konecki, J.
Krolikowski, M. Olszewski, M. Walczak Laboratório de Instrumentação e Física
Experimental de Partículas, Lisboa, Portugal
M. Araujo, P. Bargassa, D. Bastos, A. Di Francesco, P. Faccioli, B. Galinhas,
M. Gallinaro, J. Hollar, N. Leonardo, T. Niknejad, J. Seixas, K. Shchelina, G.
Strong, O. Toldaiev, J. Varela Joint Institute for Nuclear Research, Dubna,
Russia
S. Afanasiev, V. Alexakhin, M. Gavrilenko, I. Golutvin, I. Gorbunov, A.
Kamenev, V. Karjavine, V. Korenkov, A. Lanev, A. Malakhov, V. Matveev39,40, P.
Moisenz, V. Palichik, V. Perelygin, M. Savina, S. Shmatov, S. Shulha, N.
Voytishin, B.S. Yuldashev41, A. Zarubin Petersburg Nuclear Physics Institute,
Gatchina (St. Petersburg), Russia
L. Chtchipounov, V. Golovtcov, Y. Ivanov, V. Kim42, E. Kuznetsova43, P.
Levchenko, V. Murzin, V. Oreshkin, I. Smirnov, D. Sosnov, V. Sulimov, L.
Uvarov, A. Vorobyev Institute for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov,
N. Krasnikov, A. Pashenkov, D. Tlisov, A. Toropin Institute for Theoretical
and Experimental Physics named by A.I. Alikhanov of NRC ‘Kurchatov Institute’,
Moscow, Russia
V. Epshteyn, V. Gavrilov, N. Lychkovskaya, A. Nikitenko44, V. Popov, I.
Pozdnyakov, G. Safronov, A. Spiridonov, A. Stepennov, M. Toms, E. Vlasov, A.
Zhokin Moscow Institute of Physics and Technology, Moscow, Russia
T. Aushev National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
O. Bychkova, R. Chistov45, M. Danilov45, S. Polikarpov45, E. Tarkovskii P.N.
Lebedev Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin, I. Dremin, M. Kirakosyan, A. Terkulov Skobeltsyn
Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow,
Russia
A. Baskakov, A. Belyaev, E. Boos, V. Bunichev, M. Dubinin46, L. Dudko, V.
Klyukhin, N. Korneeva, I. Lokhtin, S. Obraztsov, M. Perfilov, V. Savrin, P.
Volkov Novosibirsk State University (NSU), Novosibirsk, Russia
A. Barnyakov47, V. Blinov47, T. Dimova47, L. Kardapoltsev47, Y. Skovpen47
Institute for High Energy Physics of National Research Centre ‘Kurchatov
Institute’, Protvino, Russia
I. Azhgirey, I. Bayshev, S. Bitioukov, V. Kachanov, D. Konstantinov, P.
Mandrik, V. Petrov, R. Ryutin, S. Slabospitskii, A. Sobol, S. Troshin, N.
Tyurin, A. Uzunian, A. Volkov National Research Tomsk Polytechnic University,
Tomsk, Russia
A. Babaev, A. Iuzhakov, V. Okhotnikov Tomsk State University, Tomsk, Russia
V. Borchsh, V. Ivanchenko, E. Tcherniaev University of Belgrade: Faculty of
Physics and VINCA Institute of Nuclear Sciences
P. Adzic48, P. Cirkovic, M. Dordevic, P. Milenovic, J. Milosevic, M.
Stojanovic Centro de Investigaciones Energéticas Medioambientales y
Tecnológicas (CIEMAT), Madrid, Spain
M. Aguilar-Benitez, J. Alcaraz Maestre, A. Álvarez Fernández, I. Bachiller, M.
Barrio Luna, CristinaF. Bedoya, J.A. Brochero Cifuentes, C.A. Carrillo
Montoya, M. Cepeda, M. Cerrada, N. Colino, B. De La Cruz, A. Delgado Peris,
J.P. Fernández Ramos, J. Flix, M.C. Fouz, O. Gonzalez Lopez, S. Goy Lopez,
J.M. Hernandez, M.I. Josa, D. Moran, Á. Navarro Tobar, A. Pérez-Calero
Yzquierdo, J. Puerta Pelayo, I. Redondo, L. Romero, S. Sánchez Navas, M.S.
Soares, A. Triossi, C. Willmott Universidad Autónoma de Madrid, Madrid, Spain
C. Albajar, J.F. de Trocóniz, R. Reyes-Almanza Universidad de Oviedo,
Instituto Universitario de Ciencias y Tecnologías Espaciales de Asturias
(ICTEA), Oviedo, Spain
B. Alvarez Gonzalez, J. Cuevas, C. Erice, J. Fernandez Menendez, S. Folgueras,
I. Gonzalez Caballero, J.R. González Fernández, E. Palencia Cortezon, V.
Rodríguez Bouza, S. Sanchez Cruz Instituto de Física de Cantabria (IFCA),
CSIC-Universidad de Cantabria, Santander, Spain
I.J. Cabrillo, A. Calderon, B. Chazin Quero, J. Duarte Campderros, M.
Fernandez, P.J. Fernández Manteca, A. García Alonso, G. Gomez, C. Martinez
Rivero, P. Martinez Ruiz del Arbol, F. Matorras, J. Piedra Gomez, C. Prieels,
T. Rodrigo, A. Ruiz-Jimeno, L. Russo49, L. Scodellaro, I. Vila, J.M. Vizan
Garcia University of Colombo, Colombo, Sri Lanka
D.U.J. Sonnadara University of Ruhuna, Department of Physics, Matara, Sri
Lanka
W.G.D. Dharmaratna, N. Wickramage CERN, European Organization for Nuclear
Research, Geneva, Switzerland
D. Abbaneo, B. Akgun, E. Auffray, G. Auzinger, J. Baechler, P. Baillon, A.H.
Ball, D. Barney, J. Bendavid, M. Bianco, A. Bocci, P. Bortignon, E. Bossini,
C. Botta, E. Brondolin, T. Camporesi, A. Caratelli, G. Cerminara, E. Chapon,
G. Cucciati, D. d’Enterria, A. Dabrowski, N. Daci, V. Daponte, A. David, O.
Davignon, A. De Roeck, M. Deile, M. Dobson, M. Dünser, N. Dupont, A. Elliott-
Peisert, N. Emriskova, F. Fallavollita50, D. Fasanella, S. Fiorendi, G.
Franzoni, J. Fulcher, W. Funk, S. Giani, D. Gigi, A. Gilbert, K. Gill, F.
Glege, L. Gouskos, M. Gruchala, M. Guilbaud, D. Gulhan, J. Hegeman, C.
Heidegger, Y. Iiyama, V. Innocente, T. James, P. Janot, O. Karacheban21, J.
Kaspar, J. Kieseler, M. Krammer1, N. Kratochwil, C. Lange, P. Lecoq, C.
Lourenço, L. Malgeri, M. Mannelli, A. Massironi, F. Meijers, S. Mersi, E.
Meschi, F. Moortgat, M. Mulders, J. Ngadiuba, J. Niedziela, S. Nourbakhsh, S.
Orfanelli, L. Orsini, F. Pantaleo18, L. Pape, E. Perez, M. Peruzzi, A.
Petrilli, G. Petrucciani, A. Pfeiffer, M. Pierini, F.M. Pitters, D. Rabady, A.
Racz, M. Rieger, M. Rovere, H. Sakulin, J. Salfeld-Nebgen, C. Schäfer, C.
Schwick, M. Selvaggi, A. Sharma, P. Silva, W. Snoeys, P. Sphicas51, J.
Steggemann, S. Summers, V.R. Tavolaro, D. Treille, A. Tsirou, G.P. Van Onsem,
A. Vartak, M. Verzetti, W.D. Zeuner Paul Scherrer Institut, Villigen,
Switzerland
L. Caminada52, K. Deiters, W. Erdmann, R. Horisberger, Q. Ingram, H.C.
Kaestli, D. Kotlinski, U. Langenegger, T. Rohe, S.A. Wiederkehr ETH Zurich -
Institute for Particle Physics and Astrophysics (IPA), Zurich, Switzerland
M. Backhaus, P. Berger, N. Chernyavskaya, G. Dissertori, M. Dittmar, M.
Donegà, C. Dorfer, T.A. Gómez Espinosa, C. Grab, D. Hits, W. Lustermann, R.A.
Manzoni, M.T. Meinhard, F. Micheli, P. Musella, F. Nessi-Tedaldi, F. Pauss, G.
Perrin, L. Perrozzi, S. Pigazzini, M.G. Ratti, M. Reichmann, C. Reissel, T.
Reitenspiess, B. Ristic, D. Ruini, D.A. Sanz Becerra, M. Schönenberger, L.
Shchutska, M.L. Vesterbacka Olsson, R. Wallny, D.H. Zhu Universität Zürich,
Zurich, Switzerland
T.K. Aarrestad, C. Amsler53, D. Brzhechko, M.F. Canelli, A. De Cosa, R. Del
Burgo, B. Kilminster, S. Leontsinis, V.M. Mikuni, I. Neutelings, G. Rauco, P.
Robmann, K. Schweiger, C. Seitz, Y. Takahashi, S. Wertz, A. Zucchetta National
Central University, Chung-Li, Taiwan
T.H. Doan, C.M. Kuo, W. Lin, A. Roy, S.S. Yu National Taiwan University (NTU),
Taipei, Taiwan
P. Chang, Y. Chao, K.F. Chen, P.H. Chen, W.-S. Hou, Y.y. Li, R.-S. Lu, E.
Paganis, A. Psallidas, A. Steen Chulalongkorn University, Faculty of Science,
Department of Physics, Bangkok, Thailand
B. Asavapibhop, C. Asawatangtrakuldee, N. Srimanobhas, N. Suwonjandee Çukurova
University, Physics Department, Science and Art Faculty, Adana, Turkey
A. Bat, F. Boran, A. Celik54, S. Cerci55, S. Damarseckin56, Z.S. Demiroglu, F.
Dolek, C. Dozen57, I. Dumanoglu, G. Gokbulut, EmineGurpinar Guler58, Y. Guler,
I. Hos59, C. Isik, E.E. Kangal60, O. Kara, A. Kayis Topaksu, U. Kiminsu, G.
Onengut, K. Ozdemir61, S. Ozturk62, A.E. Simsek, D. Sunar Cerci55, U.G. Tok,
S. Turkcapar, I.S. Zorbakir, C. Zorbilmez Middle East Technical University,
Physics Department, Ankara, Turkey
B. Isildak63, G. Karapinar64, M. Yalvac Bogazici University, Istanbul, Turkey
I.O. Atakisi, E. Gülmez, M. Kaya65, O. Kaya66, Ö. Özçelik, S. Tekten, E.A.
Yetkin67 Istanbul Technical University, Istanbul, Turkey
A. Cakir, K. Cankocak, Y. Komurcu, S. Sen68 Istanbul University, Istanbul,
Turkey
B. Kaynak, S. Ozkorucuklu Institute for Scintillation Materials of National
Academy of Science of Ukraine, Kharkov, Ukraine
B. Grynyov National Scientific Center, Kharkov Institute of Physics and
Technology, Kharkov, Ukraine
L. Levchuk University of Bristol, Bristol, United Kingdom
E. Bhal, S. Bologna, J.J. Brooke, D. Burns69, E. Clement, D. Cussans, H.
Flacher, J. Goldstein, G.P. Heath, H.F. Heath, L. Kreczko, B. Krikler, S.
Paramesvaran, B. Penning, T. Sakuma, S. Seif El Nasr-Storey, V.J. Smith, J.
Taylor, A. Titterton Rutherford Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev70, C. Brew, R.M. Brown, D.J.A. Cockerill, J.A. Coughlan,
K. Harder, S. Harper, J. Linacre, K. Manolopoulos, D.M. Newbold, E. Olaiya, D.
Petyt, T. Reis, T. Schuh, C.H. Shepherd-Themistocleous, A. Thea, I.R. Tomalin,
T. Williams, W.J. Womersley Imperial College, London, United Kingdom
R. Bainbridge, P. Bloch, J. Borg, S. Breeze, O. Buchmuller, A. Bundock,
GurpreetSingh CHAHAL71, D. Colling, P. Dauncey, G. Davies, M. Della Negra, R.
Di Maria, P. Everaerts, G. Hall, G. Iles, M. Komm, L. Lyons, A.-M. Magnan, S.
Malik, A. Martelli, V. Milosevic, A. Morton, J. Nash72, V. Palladino, M.
Pesaresi, D.M. Raymond, A. Richards, A. Rose, E. Scott, C. Seez, A.
Shtipliyski, M. Stoye, T. Strebler, A. Tapper, K. Uchida, T. Virdee18, N.
Wardle, D. Winterbottom, A.G. Zecchinelli, S.C. Zenz Brunel University,
Uxbridge, United Kingdom
J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, C.K. Mackay, I.D. Reid, L.
Teodorescu, S. Zahid Baylor University, Waco, USA
K. Call, B. Caraway, J. Dittmann, K. Hatakeyama, C. Madrid, B. McMaster, N.
Pastika, C. Smith Catholic University of America, Washington, DC, USA
R. Bartek, A. Dominguez, R. Uniyal, A.M. Vargas Hernandez The University of
Alabama, Tuscaloosa, USA
A. Buccilli, S.I. Cooper, C. Henderson, P. Rumerio, C. West Boston University,
Boston, USA
A. Albert, D. Arcaro, Z. Demiragli, D. Gastler, C. Richardson, J. Rohlf, D.
Sperka, I. Suarez, L. Sulak, D. Zou Brown University, Providence, USA
G. Benelli, B. Burkle, X. Coubez19, D. Cutts, Y.t. Duh, M. Hadley, U. Heintz,
J.M. Hogan73, K.H.M. Kwok, E. Laird, G. Landsberg, K.T. Lau, J. Lee, M.
Narain, S. Sagir74, R. Syarif, E. Usai, W.Y. Wong, D. Yu, W. Zhang University
of California, Davis, Davis, USA
R. Band, C. Brainerd, R. Breedon, M. Calderon De La Barca Sanchez, M. Chertok,
J. Conway, R. Conway, P.T. Cox, R. Erbacher, C. Flores, G. Funk, F. Jensen, W.
Ko, O. Kukral, R. Lander, M. Mulhearn, D. Pellett, J. Pilot, M. Shi, D.
Taylor, K. Tos, M. Tripathi, Z. Wang, F. Zhang University of California, Los
Angeles, USA
M. Bachtis, C. Bravo, R. Cousins, A. Dasgupta, A. Florent, J. Hauser, M.
Ignatenko, N. Mccoll, W.A. Nash, S. Regnard, D. Saltzberg, C. Schnaible, B.
Stone, V. Valuev University of California, Riverside, Riverside, USA
K. Burt, Y. Chen, R. Clare, J.W. Gary, S.M.A. Ghiasi Shirazi, G. Hanson, G.
Karapostoli, O.R. Long, M. Olmedo Negrete, M.I. Paneva, W. Si, L. Wang, S.
Wimpenny, B.R. Yates, Y. Zhang University of California, San Diego, La Jolla,
USA
J.G. Branson, P. Chang, S. Cittolin, S. Cooperstein, N. Deelen, M. Derdzinski,
R. Gerosa, D. Gilbert, B. Hashemi, D. Klein, V. Krutelyov, J. Letts, M.
Masciovecchio, S. May, S. Padhi, M. Pieri, V. Sharma, M. Tadel, F. Würthwein,
A. Yagil, G. Zevi Della Porta University of California, Santa Barbara -
Department of Physics, Santa Barbara, USA
N. Amin, R. Bhandari, C. Campagnari, M. Citron, V. Dutta, M. Franco Sevilla,
J. Incandela, B. Marsh, H. Mei, A. Ovcharova, H. Qu, J. Richman, U. Sarica, D.
Stuart, S. Wang California Institute of Technology, Pasadena, USA
D. Anderson, A. Bornheim, O. Cerri, I. Dutta, J.M. Lawhorn, N. Lu, J. Mao,
H.B. Newman, T.Q. Nguyen, J. Pata, M. Spiropulu, J.R. Vlimant, S. Xie, Z.
Zhang, R.Y. Zhu Carnegie Mellon University, Pittsburgh, USA
M.B. Andrews, T. Ferguson, T. Mudholkar, M. Paulini, M. Sun, I. Vorobiev, M.
Weinberg University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, E. MacDonald, T. Mulholland, R. Patel, A. Perloff, K.
Stenson, K.A. Ulmer, S.R. Wagner Cornell University, Ithaca, USA
J. Alexander, Y. Cheng, J. Chu, A. Datta, A. Frankenthal, K. Mcdermott, J.R.
Patterson, D. Quach, A. Ryd, S.M. Tan, Z. Tao, J. Thom, P. Wittich, M. Zientek
Fermi National Accelerator Laboratory, Batavia, USA
S. Abdullin, M. Albrow, M. Alyari, G. Apollinari, A. Apresyan, A. Apyan, S.
Banerjee, L.A.T. Bauerdick, A. Beretvas, D. Berry, J. Berryhill, P.C. Bhat, K.
Burkett, J.N. Butler, A. Canepa, G.B. Cerati, H.W.K. Cheung, F. Chlebana, M.
Cremonesi, J. Duarte, V.D. Elvira, J. Freeman, Z. Gecse, E. Gottschalk, L.
Gray, D. Green, S. Grünendahl, O. Gutsche, AllisonReinsvold Hall, J. Hanlon,
R.M. Harris, S. Hasegawa, R. Heller, J. Hirschauer, B. Jayatilaka, S.
Jindariani, M. Johnson, U. Joshi, T. Klijnsma, B. Klima, M.J. Kortelainen, B.
Kreis, S. Lammel, J. Lewis, D. Lincoln, R. Lipton, M. Liu, T. Liu, J. Lykken,
K. Maeshima, J.M. Marraffino, D. Mason, P. McBride, P. Merkel, S. Mrenna, S.
Nahn, V. O’Dell, V. Papadimitriou, K. Pedro, C. Pena, G. Rakness, F. Ravera,
L. Ristori, B. Schneider, E. Sexton-Kennedy, N. Smith, A. Soha, W.J. Spalding,
L. Spiegel, S. Stoynev, J. Strait, N. Strobbe, L. Taylor, S. Tkaczyk, N.V.
Tran, L. Uplegger, E.W. Vaandering, C. Vernieri, R. Vidal, M. Wang, H.A. Weber
University of Florida, Gainesville, USA
D. Acosta, P. Avery, D. Bourilkov, A. Brinkerhoff, L. Cadamuro, V. Cherepanov,
F. Errico, R.D. Field, S.V. Gleyzer, D. Guerrero, B.M. Joshi, M. Kim, J.
Konigsberg, A. Korytov, K.H. Lo, K. Matchev, N. Menendez, G. Mitselmakher, D.
Rosenzweig, K. Shi, J. Wang, S. Wang, X. Zuo Florida International University,
Miami, USA
Y.R. Joshi Florida State University, Tallahassee, USA
T. Adams, A. Askew, S. Hagopian, V. Hagopian, K.F. Johnson, R. Khurana, T.
Kolberg, G. Martinez, T. Perry, H. Prosper, C. Schiber, R. Yohay, J. Zhang
Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, M. Hohlmann, D. Noonan, M. Rahmani, M. Saunders, F. Yumiceva
University of Illinois at Chicago (UIC), Chicago, USA
M.R. Adams, L. Apanasevich, R.R. Betts, R. Cavanaugh, X. Chen, S. Dittmer, O.
Evdokimov, C.E. Gerber, D.A. Hangal, D.J. Hofman, C. Mills, T. Roy, M.B.
Tonjes, N. Varelas, J. Viinikainen, H. Wang, X. Wang, Z. Wu The University of
Iowa, Iowa City, USA
M. Alhusseini, B. Bilki58, K. Dilsiz75, S. Durgut, R.P. Gandrajula, M.
Haytmyradov, V. Khristenko, O.K. Köseyan, J.-P. Merlo, A. Mestvirishvili76, A.
Moeller, J. Nachtman, H. Ogul77, Y. Onel, F. Ozok78, A. Penzo, C. Snyder, E.
Tiras, J. Wetzel Johns Hopkins University, Baltimore, USA
B. Blumenfeld, A. Cocoros, N. Eminizer, A.V. Gritsan, W.T. Hung, S. Kyriacou,
P. Maksimovic, J. Roskes, M. Swartz The University of Kansas, Lawrence, USA
C. Baldenegro Barrera, P. Baringer, A. Bean, S. Boren, J. Bowen, A. Bylinkin,
T. Isidori, S. Khalil, J. King, G. Krintiras, A. Kropivnitskaya, C. Lindsey,
D. Majumder, W. Mcbrayer, N. Minafra, M. Murray, C. Rogan, C. Royon, S.
Sanders, E. Schmitz, J.D. Tapia Takaki, Q. Wang, J. Williams, G. Wilson Kansas
State University, Manhattan, USA
S. Duric, A. Ivanov, K. Kaadze, D. Kim, Y. Maravin, D.R. Mendis, T. Mitchell,
A. Modak, A. Mohammadi Lawrence Livermore National Laboratory, Livermore, USA
F. Rebassoo, D. Wright University of Maryland, College Park, USA
A. Baden, O. Baron, A. Belloni, S.C. Eno, Y. Feng, N.J. Hadley, S. Jabeen,
G.Y. Jeng, R.G. Kellogg, A.C. Mignerey, S. Nabili, F. Ricci-Tam, M. Seidel,
Y.H. Shin, A. Skuja, S.C. Tonwar, K. Wong Massachusetts Institute of
Technology, Cambridge, USA
D. Abercrombie, B. Allen, A. Baty, R. Bi, S. Brandt, W. Busza, I.A. Cali, M.
D’Alfonso, G. Gomez Ceballos, M. Goncharov, P. Harris, D. Hsu, M. Hu, M.
Klute, D. Kovalskyi, Y.-J. Lee, P.D. Luckey, B. Maier, A.C. Marini, C. Mcginn,
C. Mironov, S. Narayanan, X. Niu, C. Paus, D. Rankin, C. Roland, G. Roland, Z.
Shi, G.S.F. Stephans, K. Sumorok, K. Tatar, D. Velicanu, J. Wang, T.W. Wang,
B. Wyslouch University of Minnesota, Minneapolis, USA
R.M. Chatterjee, A. Evans, S. Guts${}^{\textrm{\textdagger}}$, P. Hansen, J.
Hiltbrand, Sh. Jain, Y. Kubota, Z. Lesko, J. Mans, M. Revering, R. Rusack, R.
Saradhy, N. Schroeder, M.A. Wadud University of Mississippi, Oxford, USA
J.G. Acosta, S. Oliveros University of Nebraska-Lincoln, Lincoln, USA
K. Bloom, S. Chauhan, D.R. Claes, C. Fangmeier, L. Finco, F. Golf, R.
Kamalieddin, I. Kravchenko, J.E. Siado, G.R. Snow${}^{\textrm{\textdagger}}$,
B. Stieger, W. Tabb State University of New York at Buffalo, Buffalo, USA
G. Agarwal, C. Harrington, I. Iashvili, A. Kharchilava, C. McLean, D. Nguyen,
A. Parker, J. Pekkanen, S. Rappoccio, B. Roozbahani Northeastern University,
Boston, USA
G. Alverson, E. Barberis, C. Freer, Y. Haddad, A. Hortiangtham, G. Madigan, B.
Marzocchi, D.M. Morse, T. Orimoto, L. Skinnari, A. Tishelman-Charny, T.
Wamorkar, B. Wang, A. Wisecarver, D. Wood Northwestern University, Evanston,
USA
S. Bhattacharya, J. Bueghly, T. Gunter, K.A. Hahn, N. Odell, M.H. Schmitt, K.
Sung, M. Trovato, M. Velasco University of Notre Dame, Notre Dame, USA
R. Bucci, N. Dev, R. Goldouzian, M. Hildreth, K. Hurtado Anampa, C. Jessop,
D.J. Karmgard, K. Lannon, W. Li, N. Loukas, N. Marinelli, I. Mcalister, F.
Meng, Y. Musienko39, R. Ruchti, P. Siddireddy, G. Smith, S. Taroni, M. Wayne,
A. Wightman, M. Wolf, A. Woodard The Ohio State University, Columbus, USA
J. Alimena, B. Bylsma, L.S. Durkin, B. Francis, C. Hill, W. Ji, A. Lefeld,
T.Y. Ling, B.L. Winer Princeton University, Princeton, USA
G. Dezoort, P. Elmer, J. Hardenbrook, N. Haubrich, S. Higginbotham, A.
Kalogeropoulos, S. Kwan, D. Lange, M.T. Lucchini, J. Luo, D. Marlow, K. Mei,
I. Ojalvo, J. Olsen, C. Palmer, P. Piroué, D. Stickland, C. Tully University
of Puerto Rico, Mayaguez, USA
S. Malik, S. Norberg Purdue University, West Lafayette, USA
A. Barker, V.E. Barnes, S. Das, L. Gutay, M. Jones, A.W. Jung, A. Khatiwada,
B. Mahakud, D.H. Miller, G. Negro, N. Neumeister, C.C. Peng, S. Piperov, H.
Qiu, J.F. Schulte, N. Trevisani, F. Wang, R. Xiao, W. Xie Purdue University
Northwest, Hammond, USA
T. Cheng, J. Dolen, N. Parashar Rice University, Houston, USA
U. Behrens, K.M. Ecklund, S. Freed, F.J.M. Geurts, M. Kilpatrick, Arun Kumar,
W. Li, B.P. Padley, R. Redjimi, J. Roberts, J. Rorie, W. Shi, A.G. Stahl
Leiton, Z. Tu, A. Zhang University of Rochester, Rochester, USA
A. Bodek, P. de Barbaro, R. Demina, J.L. Dulemba, C. Fallon, T. Ferbel, M.
Galanti, A. Garcia-Bellido, O. Hindrichs, A. Khukhunaishvili, E. Ranken, R.
Taus Rutgers, The State University of New Jersey, Piscataway, USA
B. Chiarito, J.P. Chou, A. Gandrakota, Y. Gershtein, E. Halkiadakis, A. Hart,
M. Heindl, E. Hughes, S. Kaplan, I. Laflotte, A. Lath, R. Montalvo, K. Nash,
M. Osherson, H. Saka, S. Salur, S. Schnetzer, S. Somalwar, R. Stone, S. Thomas
University of Tennessee, Knoxville, USA
H. Acharya, A.G. Delannoy, S. Spanier Texas A&M University, College Station,
USA
O. Bouhali79, M. Dalchenko, M. De Mattia, A. Delgado, S. Dildick, R. Eusebi,
J. Gilmore, T. Huang, T. Kamon80, H. Kim, S. Luo, S. Malhotra, D. Marley, R.
Mueller, D. Overton, L. Perniè, D. Rathjens, A. Safonov Texas Tech University,
Lubbock, USA
N. Akchurin, J. Damgov, F. De Guio, V. Hegde, S. Kunori, K. Lamichhane, S.W.
Lee, T. Mengke, S. Muthumuni, T. Peltola, S. Undleeb, I. Volobouev, Z. Wang,
A. Whitbeck Vanderbilt University, Nashville, USA
S. Greene, A. Gurrola, R. Janjam, W. Johns, C. Maguire, A. Melo, H. Ni, K.
Padeken, F. Romeo, P. Sheldon, S. Tuo, J. Velkovska, M. Verweij University of
Virginia, Charlottesville, USA
M.W. Arenton, P. Barria, B. Cox, G. Cummings, J. Hakala, R. Hirosky, M. Joyce,
A. Ledovskoy, C. Neu, B. Tannenwald, Y. Wang, E. Wolfe, F. Xia Wayne State
University, Detroit, USA
R. Harr, P.E. Karchin, N. Poudyal, J. Sturdy, P. Thapa University of Wisconsin
- Madison, Madison, WI, USA
T. Bose, J. Buchanan, C. Caillol, D. Carlsmith, S. Dasu, I. De Bruyn, L. Dodd,
C. Galloni, H. He, M. Herndon, A. Hervé, U. Hussain, A. Lanaro, A. Loeliger,
K. Long, R. Loveless, J. Madhusudanan Sreekala, D. Pinna, T. Ruggles, A.
Savin, V. Sharma, W.H. Smith, D. Teague, S. Trembath-reichert †: Deceased
1: Also at Vienna University of Technology, Vienna, Austria
2: Also at IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
3: Also at Universidade Estadual de Campinas, Campinas, Brazil
4: Also at Federal University of Rio Grande do Sul, Porto Alegre, Brazil
5: Also at UFMS, Nova Andradina, Brazil
6: Also at Universidade Federal de Pelotas, Pelotas, Brazil
7: Also at Université Libre de Bruxelles, Bruxelles, Belgium
8: Also at University of Chinese Academy of Sciences, Beijing, China
9: Also at Institute for Theoretical and Experimental Physics named by A.I.
Alikhanov of NRC ‘Kurchatov Institute’, Moscow, Russia
10: Also at Joint Institute for Nuclear Research, Dubna, Russia
11: Also at Helwan University, Cairo, Egypt
12: Now at Zewail City of Science and Technology, Zewail, Egypt
13: Also at Ain Shams University, Cairo, Egypt
14: Also at Purdue University, West Lafayette, USA
15: Also at Université de Haute Alsace, Mulhouse, France
16: Also at Tbilisi State University, Tbilisi, Georgia
17: Also at Erzincan Binali Yildirim University, Erzincan, Turkey
18: Also at CERN, European Organization for Nuclear Research, Geneva,
Switzerland
19: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
20: Also at University of Hamburg, Hamburg, Germany
21: Also at Brandenburg University of Technology, Cottbus, Germany
22: Also at Institute of Physics, University of Debrecen, Debrecen, Hungary,
Debrecen, Hungary
23: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary
24: Also at MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary, Budapest, Hungary
25: Also at IIT Bhubaneswar, Bhubaneswar, India, Bhubaneswar, India
26: Also at Institute of Physics, Bhubaneswar, India
27: Also at Shoolini University, Solan, India
28: Also at University of Hyderabad, Hyderabad, India
29: Also at University of Visva-Bharati, Santiniketan, India
30: Also at Isfahan University of Technology, Isfahan, Iran
31: Now at INFN Sezione di Bari a, Università di Bari b, Politecnico di Bari
c, Bari, Italy
32: Also at Italian National Agency for New Technologies, Energy and
Sustainable Economic Development, Bologna, Italy
33: Also at Centro Siciliano di Fisica Nucleare e di Struttura Della Materia,
Catania, Italy
34: Also at Scuola Normale e Sezione dell’INFN, Pisa, Italy
35: Also at Riga Technical University, Riga, Latvia, Riga, Latvia
36: Also at Malaysian Nuclear Agency, MOSTI, Kajang, Malaysia
37: Also at Consejo Nacional de Ciencia y Tecnología, Mexico City, Mexico
38: Also at Warsaw University of Technology, Institute of Electronic Systems,
Warsaw, Poland
39: Also at Institute for Nuclear Research, Moscow, Russia
40: Now at National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
41: Also at Institute of Nuclear Physics of the Uzbekistan Academy of
Sciences, Tashkent, Uzbekistan
42: Also at St. Petersburg State Polytechnical University, St. Petersburg,
Russia
43: Also at University of Florida, Gainesville, USA
44: Also at Imperial College, London, United Kingdom
45: Also at P.N. Lebedev Physical Institute, Moscow, Russia
46: Also at California Institute of Technology, Pasadena, USA
47: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia
48: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia
49: Also at Università degli Studi di Siena, Siena, Italy
50: Also at INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy,
Pavia, Italy
51: Also at National and Kapodistrian University of Athens, Athens, Greece
52: Also at Universität Zürich, Zurich, Switzerland
53: Also at Stefan Meyer Institute for Subatomic Physics, Vienna, Austria,
Vienna, Austria
54: Also at Burdur Mehmet Akif Ersoy University, BURDUR, Turkey
55: Also at Adiyaman University, Adiyaman, Turkey
56: Also at Şırnak University, Sirnak, Turkey
57: Also at Department of Physics, Tsinghua University, Beijing, China,
Beijing, China
58: Also at Beykent University, Istanbul, Turkey, Istanbul, Turkey
59: Also at Istanbul Aydin University, Application and Research Center for
Advanced Studies (App. & Res. Cent. for Advanced Studies), Istanbul, Turkey
60: Also at Mersin University, Mersin, Turkey
61: Also at Piri Reis University, Istanbul, Turkey
62: Also at Gaziosmanpasa University, Tokat, Turkey
63: Also at Ozyegin University, Istanbul, Turkey
64: Also at Izmir Institute of Technology, Izmir, Turkey
65: Also at Marmara University, Istanbul, Turkey
66: Also at Kafkas University, Kars, Turkey
67: Also at Istanbul Bilgi University, Istanbul, Turkey
68: Also at Hacettepe University, Ankara, Turkey
69: Also at Vrije Universiteit Brussel, Brussel, Belgium
70: Also at School of Physics and Astronomy, University of Southampton,
Southampton, United Kingdom
71: Also at IPPP Durham University, Durham, United Kingdom
72: Also at Monash University, Faculty of Science, Clayton, Australia
73: Also at Bethel University, St. Paul, Minneapolis, USA, St. Paul, USA
74: Also at Karamanoğlu Mehmetbey University, Karaman, Turkey
75: Also at Bingol University, Bingol, Turkey
76: Also at Georgian Technical University, Tbilisi, Georgia
77: Also at Sinop University, Sinop, Turkey
78: Also at Mimar Sinan University, Istanbul, Istanbul, Turkey
79: Also at Texas A&M University at Qatar, Doha, Qatar
80: Also at Kyungpook National University, Daegu, Korea, Daegu, Korea