TOP-16-023

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$HeadURL:svn+ssh://svn.cern.ch/reps/tdr2/papers/TOP-16-023/trunk/TOP-16-023.tex$
$Id:TOP-16-023.tex4450162018-02-0822:33:46Zalverson$

TOP-16-023

# Measurement of the inclusive cross section in pp collisions at $\sqrt{s}$ =
5.02using final states with at least one charged lepton

###### Abstract

The top quark pair production cross section ($\sigma_{\ttbar}$) is measured
for the first time in pp collisions at a center-of-mass energy of 5.02. The
data were collected by the CMS experiment at the LHC and correspond to an
integrated luminosity of 27.4. The measurement is performed by analyzing
events with at least one charged lepton. The measured cross section is
$\sigma_{\ttbar}=69.5\pm 6.1\stat\pm 5.6\syst\pm 1.6\lum\unit{pb}$, with a
total relative uncertainty of 12%. The result is in agreement with the
expectation from the standard model. The impact of the presented measurement
on the determination of the gluon distribution function is investigated.

##  0.1 Introduction

The top quark, the heaviest elementary particle in the standard model (SM),
has been the subject of numerous detailed studies using hadron-hadron
collisions. The pair production () cross section ($\sigma_{\ttbar}$) as a
function of center-of-mass energy can be of interest for the extraction of the
top quark pole mass [1] and has been used to constrain the gluon distribution
function [2] at large fractions $x$ of the proton longitudinal momentum
carried by the gluon, where the gluon distribution is poorly known. Precise
measurements of $\sigma_{\ttbar}$ in proton-proton (pp) collisions have been
published at $\sqrt{s}$ values of 7 and 8 [3, 4, 5, 6] and 13 [7, 8, 9, 10] by
the ATLAS and CMS Collaborations at the LHC.

In November 2015, the LHC delivered pp collisions at $\sqrt{s}=5.02$. The
fraction of events initiated by gluon-gluon collisions grows monotonically
with $\sqrt{s}$. It is around 73% at 5.02, as calculated with (v2) [11, 12,
13] at next-to-leading order (NLO) using the NNPDF3.0 NLO [14] parton
distribution functions (PDFs), and increases to around 86% at 13, making this
new data set partially complementary to the higher-energy samples.
Measurements of production at various $\sqrt{s}$ probe different values of $x$
and thus can provide complementary information on the gluon distribution. In
addition, future measurements of $\sigma_{\ttbar}$ in nuclear collisions at
the same nucleon-nucleon center-of-mass energy [15, 16] would profit from the
availability of a reference measurement in pp collisions at
$\sqrt{s}=5.02\TeV$, without the need to extrapolate from measurements at
different $\sqrt{s}$. This has already been demonstrated with the first
observation of the process using proton-nucleus collisions at a higher
nucleon-nucleon center-of-mass energy [17].

In the SM, top quarks in $\Pp\Pp$ collisions are mostly produced as pairs.
Each top quark decays predominantly to a boson and a bottom (b) quark. The
events are categorized according to the decay of the two W bosons. In events
where one boson decays leptonically and the other hadronically ($\ell$+jets
channel), the final state presents a typical signature of one isolated lepton,
missing transverse momentum, two jets from the W boson hadronic decay, and two
jets coming from the hadronization of the b quarks (“ jets”). On the other
hand, in events where both bosons decay leptonically (dilepton channel), the
final state contains two leptons of opposite electric charge, missing
transverse momentum, and at least two b jets. The $\ell$+jets channel has a
large branching ratio with a moderate amount of background, while the dilepton
channel is characterized by a high purity.

This analysis represents the first measurement of $\sigma_{\ttbar}$ in pp
collisions at $\sqrt{s}=5.02$using candidate events with $\ell$+jets, where
leptons are either electrons ($\ell=\Pe$) or muons ($\ell=\mu$), and dilepton
($\Pe^{\pm}\Pgm^{\mp}$ or $\Pgm^{\pm}\Pgm^{\mp}$) final states. In the former
case, $\sigma_{\ttbar}$ is extracted by a fit to the distribution of a
kinematic variable for different categories of lepton flavor and jet
multiplicity, while in the latter an event counting approach is used. The two
results are then combined in the final measurement, which is used as input to
a quantum chromodynamics (QCD) analysis at next-to-next-to-leading order
(NNLO) to investigate the impact on the determination of the gluon
distribution in the less-explored kinematic range of $x\gtrsim 0.1$.

This paper is structured as follows. Section 0.2 describes the CMS detector.
Section 0.3 gives a summary of the data and simulated samples used. After the
discussion of the object reconstruction in Section 0.4, and of the trigger and
event selection in Section 0.5, Section 0.6 describes the determination of the
background sources. The systematic uncertainties are discussed in Section 0.7.
The extraction of $\sigma_{\ttbar}$ is presented in Section 0.8 and the impact
of the presented measurement on the determination of the proton PDFs is
discussed in Section 0.9. A summary of all the results is given in Section
0.10.

##  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 parallel to the beam
direction.

Within the solenoid volume are a silicon pixel and strip tracker, a lead
tungstate crystal electromagnetic calorimeter (ECAL), and a brass and
scintillator hadron calorimeter (HCAL), each composed of a barrel and two
endcap sections. A preshower detector, consisting of two planes of silicon
sensors interleaved with about 3 radiation lengths of lead, is located in
front of the endcap regions of ECAL. Hadron forward calorimeters using steel
as an absorber and quartz fibers as the sensitive material extend the
pseudorapidity coverage provided by the barrel and endcap detectors from
$\abs{\eta}=3.0$ to $5.2$.

Charged particle trajectories with $\abs{\eta}<2.5$ are measured by the
tracker system, while the energy deposits in ECAL and HCAL cells are summed to
define the calorimeter tower energies, subsequently used to calculate the
energies and directions of hadronic jets. Muons are detected in the
pseudorapidity window $\abs{\eta}<2.4$ in gas-ionization detectors embedded in
the steel flux-return yoke outside the solenoid. Photons and electrons are
reconstructed by their deposited energy in groups of ECAL crystals
(“clusters”). Events of interest are selected using a two-tiered trigger
system [18]. 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. [19].

##  0.3 Data, simulated samples and theoretical cross section

This analysis is based on an integrated luminosity of $27.4\pm 0.6\pbinv$
[20]. The presence of multiple proton collisions in the same or nearby bunch
crossings (“pileup”) results in an average number of overlapping interactions
estimated online to be 1.4, assuming a total inelastic cross section of 65mb.

Several Monte Carlo (MC) event generators are used to simulate signal and
background events. The NLO (v2) [11, 12, 13] generator is used for events,
assuming a value of 172.5for the top quark mass ($m_{\text{top}}$). These
events are passed to (v8.205) [21, 22] to simulate parton showering,
hadronization, and the underlying event, using the CUETP8M1 [23, 24] tune for
the default MC sample. The NNPDF3.0 NLO PDFs with strong coupling
$\alpha_{\mathrm{s}}(M_{\Z})=0.118$ at the Z boson mass scale $M_{\Z}$ are
utilized in the MC calculations.

The (v5_2.2.2) generator [25] is used to simulate W boson production with
additional jets (+jets), and high-mass ($>$50) Drell–Yan quark-antiquark
annihilation into lepton-antilepton pairs through Z boson or virtual-photon
exchange (referred to as “Z$/\gamma^{*}$”). The simulation includes up to two
extra partons at matrix element level, and the FxFx merging procedure [26] is
used to interface with . Low-mass Z/$\gamma^{*}$ events (20–50) are simulated
with . The normalization of the +jets and Z/$\gamma^{*}$ processes is either
derived from data (in the dilepton channel) or estimated based on the NNLO
cross sections (in the $\ell$+jets channel) from the program (v3.1.b2) [27].
Single top quark plus boson events ($\PQt\PW$) are simulated using (v1) [28,
29] interfaced with , and are normalized to the approximate NNLO cross
sections [30]. The contributions from and production (referred to as “WV”) are
simulated with , and are normalized to the NLO cross sections calculated with
the (v8.0) program [31]. All generated events undergo a full [32] simulation
of the detector response.

The expected signal yields are normalized to the value of the SM prediction
for the production cross section:

$\sigma^{\mathrm{NNLO}}=68.9\ ^{+1.9}_{-2.3}\,\text{(scale)}\pm
2.3\,\mathrm{(PDF)}\ ^{+1.4}_{-1.0}\ (\alpha_{\mathrm{s}})\unit{pb},$ (1)

as calculated with the Top++ program [33] at NNLO in perturbative QCD,
including soft-gluon resummation at next-to-next-to-leading-logarithmic order
[34], using the NNPDF 3.0 NNLO PDF set, with
$\alpha_{\mathrm{s}}(M_{\Z})=0.118$ and $m_{\text{top}}=172.5\GeV$. The
systematic uncertainties in the theoretical cross section are associated with
the choice of the renormalization ($\mu_{\mathrm{R}}$) and factorization
($\mu_{\mathrm{F}}$) scales–nominally set at
$\mu_{\mathrm{R}}=\mu_{\mathrm{F}}=\sqrt{\smash[b]{m_{\text{top}}^{2}+p_{\mathrm{T},\text{top}}^{2}}}$
with $p_{\mathrm{T},\text{top}}$ the top quark transverse momentum–as well as
with the PDF set and the $\alpha_{s}$ value. The uncertainty of 0.1% in the
LHC beam energy [35] translates into an additional uncertainty of 0.22pb in
the expected cross section, with negligible impact on the acceptance of any of
the channels included in this analysis.

##  0.4 Object reconstruction

The particle-flow (PF) algorithm [36] is used to reconstruct and identify
individual particles using an optimized combination of information from the
various elements of the CMS detector.

The electron momentum is calculated by combining the energy measurement in the
ECAL with the momentum measurement in the tracker, taking into account the
bremsstrahlung photons spatially compatible with originating from the electron
track. The momentum resolution for electrons with transverse momentum
$\pt\approx 45\GeV$ from $\Z\to\Pe\Pe$ decays ranges from 1.7% for
nonshowering electrons in the barrel region to 4.5% for showering electrons in
the endcaps [37]. Muon candidates are reconstructed from a combination of the
information collected by the muon spectrometer and the silicon tracker. This
results in a relative resolution of 1.3–2.0% in the barrel and better than 6%
in the endcaps, for muons with $20<\pt<100\GeV$ and within the range
$\abs{\eta}<2.4$ [38, 39]. The photon energy is directly obtained from the
ECAL measurement, corrected for zero-suppression effects. The charged hadron
energies are determined from a combination of their momenta measured in the
tracker and the matching ECAL and HCAL energy deposits, corrected for zero-
suppression effects and for the response function of the calorimeters to
hadronic showers. Finally, the neutral hadron energies are obtained from the
corresponding corrected ECAL and HCAL energies.

The missing transverse momentum vector is defined as the negative vector sum
of the momenta of all reconstructed PF candidates in an event, projected onto
the plane perpendicular to the direction of the proton beams. Its magnitude is
referred to as and the corrections to jet momenta are propagated to the
calculation [40].

The reconstructed vertex with the largest value of summed physics-object
$\pt^{2}$ is taken to be the primary $\Pp\Pp$ interaction vertex. The physics
objects are the jets, clustered using the jet finding algorithm [41, 42] with
the tracks assigned to the vertex as inputs, and the associated . The
isolation of electron and muon candidates from nearby jet activity is then
evaluated as follows. For electron and muon candidates, a cone of $\Delta
R=0.3$ and 0.4, respectively, is constructed around the direction of the
lepton track at the primary event vertex, where $\Delta R$ is defined as
$\sqrt{\smash[b]{(\Delta\eta)^{2}+(\Delta\phi)^{2}}}$, and $\Delta\eta$ and
$\Delta\phi$ are the differences in pseudorapidity and azimuthal angle between
the directions of the lepton and another particle. A relative isolation
discriminant, $I_{\text{rel}}$, is calculated by the ratio between the scalar
sum of all particle candidates inside the cone consistent with originating
from the primary vertex and the of the lepton candidate. In this sum, we
exclude the of the lepton candidate. The neutral particle contribution to
$I_{\text{rel}}$ is corrected for energy deposits from pileup interactions
using different techniques for electrons and muons. For muons, half of the
total of the charged hadron PF candidates not originating from the primary
vertex is subtracted. The factor of one half accounts for the different
fraction of charged and neutral particles in the cone. For electrons, the
FastJet technique [43] is used, in which the median of the energy-density
distribution of neutral particles (within the area of any jet in the event)
multiplied by the geometric area of the isolation cone–scaled by a factor that
accounts for the residual $\eta$-dependence of the average energy deposition
due to pileup–is subtracted.

The efficiency of the lepton selection is measured using a “tag-and-probe”
method in same-flavor dilepton events enriched in boson candidates, following
the method of Ref. [44]. The sample of $\Z\to\Pgmp\Pgmm$ events used for muon
efficiency extraction is selected by the same trigger requirement used by the
main analysis (Section 0.5). The $\Z\to\Pep\Pem$ sample for electron
efficiency extraction makes use of events that satisfy a diphoton trigger with
symmetric transverse energy, $\ET=\sum_{i}E_{i}\sin\theta_{i}$, thresholds of
$\ET=15\GeV$ covering the full tracker acceptance, where $E_{i}$ is the energy
seen by the calorimeters for the $i$th particle, $\theta_{i}$ is the polar
angle of particle $i$, and the sum is over all particles emitted into a fixed
solid angle in the event. Pairs of photon candidates above the threshold are
accepted only if their invariant mass is above 50. The trigger selection
requires a loose identification using cluster shower shapes and a selection
based on the ratio of the hadronic to the electromagnetic energy of the photon
candidates. Based on a comparison of the lepton selection efficiency in data
and simulation, the event yield in simulation is corrected using data-to-
simulation scale factors.

Jets are reconstructed from the PF candidates using the anti-clustering
algorithm [41] with a distance parameter of 0.4. Jets closer than $\Delta
R=0.3$ to the nearest muon or electron are discarded. Jet energy corrections
extracted from full detector simulation are also applied as a function of jet
$\pt$ and $\eta$ [45] to data and simulation. A residual correction to the
data is applied to account for the discrepancy between data and simulation in
the jet response.

##  0.5 Event selection

The event sample is selected by a loose online trigger and further filtered
offline to remove noncollision events, such as beam-gas interactions or cosmic
rays. Collision events containing one high-$\pt$ electron (muon) candidate are
selected online by requiring values of () greater than 40 (15)and of
$\abs{\eta}$ less than 3.1 (2.5). The measured trigger efficiency for each
decay channel, relative to the final selection, is higher than 90%.

In the $\ell$+jets analysis, electron candidates are selected if they have
$\pt>40\GeV$ and $\abs{\eta}<2.1$. Further identification and isolation
criteria are applied to the electron candidates. Electrons reconstructed in
the ECAL barrel (endcap) are required to have $I_{\text{rel}}<4$ ($5$)$\%$.
Electron candidates in the $1.44<\abs{\eta}<1.57$ region, i.e., in the
transition region between the barrel and endcap sections of the ECAL, are
excluded because the reconstruction of an electron object in this region is
less efficient. Muons are required to have $\pt>25\GeV$ and $\abs{\eta}<2.1$.
Additional identification criteria are applied and $I_{\text{rel}}$ is
required to be $<15\%$. Events are rejected if they contain extra electrons or
muons identified using a looser set of identification criteria and have
$\pt>10$ or $15\GeV$, respectively.

The distinct signature of two jets, expected in decays, is rare in background
events, and thus is exploited in the $\ell$+jets analysis. Backgrounds from
+jets, QCD multijet, and Z/$\gamma^{*}$ events are controlled by counting the
number of b jets in the selected events. In addition, two light-flavor jets
are expected to be produced in the decay of one of the bosons for signal
events. The correlation in phase space of these light jets carries a
distinctive hallmark with respect to the main backgrounds. To that end, jets
are selected if they have $\pt>30\GeV$ and $\abs{\eta}<2.4$. The flavor of the
jets is identified using a combined secondary vertex algorithm [46] with an
operating point that yields a b jet identification efficiency of about 70%,
and misidentification (mistag) probabilities of about 1% and 15% for light-
flavor (, , , and gluons) and jets, respectively. The event selection requires
at least two non--tagged jets to be identified as candidates from the W boson
hadronic decay. Additional jets passing the quark identification criteria are
counted and used to classify the selected events in none (0 ), exactly one (1
), or at least two ($\geq$2 ) tagged jet categories. The efficiency of the jet
identification algorithm is measured in situ, simultaneously with the signal
cross section.

Dilepton events are required to contain at least one muon candidate at trigger
level. No requirement on the presence of electron candidates is made at
trigger level owing to the relatively high-$\et$ threshold (40) of the
trigger. Electrons are selected if they have $\pt>20\GeV$, $\abs{\eta}<2.4$,
and $I_{\text{rel}}<9$ (or 12)% if in the barrel (or one of the endcaps). As
in the $\ell$+jets channel, electrons detected in the transition region
between the barrel and endcap sections of the ECAL are excluded. Muons are
required to have $\pt>18\GeV$, $\abs{\eta}<2.1$, and $I_{\text{rel}}<15\%$. At
least two jets satisfying the criteria $\pt>25\GeV$ and $\abs{\eta}<3$ are
required. Events are subsequently selected if they have a pair of leptons with
opposite charge ($\Pe^{\pm}\Pgm^{\mp}$ or $\Pgm^{\pm}\Pgm^{\mp}$) passing the
requirements listed above. In events with more than one pair of leptons
passing the above selection, the two leptons of opposite charge that yield the
highest scalar sum are selected.

Candidate events with dilepton invariant masses of $M_{\ell\ell}<20$are
removed to suppress events from decays of heavy-flavor resonances and low-mass
Z/$\gamma^{*}$ processes. Dilepton events with two muons in the final state
are still dominated by the Z/$\gamma^{*}$ background. In order to suppress
this contribution, events in the Z boson mass window of
$76<M_{\ell\ell}<106$are vetoed in this channel. To further suppress the
Z/$\gamma^{*}$ events, a requirement on of $>$35is imposed.

In both the $\ell$+jets and dilepton analyses, events with leptons are
considered as signal if they decay to electrons or muons that satisfy the
selection requirements, and are included in the simulation.

##  0.6 Background estimation

###  0.6.1 The $\ell$+jets final state

In the $\ell$+jets analysis, the contributions of all background processes are
estimated from simulation, with the exception of the QCD multijet background.
Due to its large cross section, there is a nonnegligible contribution from the
latter faking a event with $\ell$+jets in the final state. Both the
contribution from hard fragmentation of c and b quarks whose hadrons decay
semileptonically, and the contribution from misidentified leptons, such as
from either punch-through hadrons or collimated jets with a high
electromagnetic fraction, can yield $\ell$+jets-like topologies.

The estimation of the QCD multijet background is separately performed for the
events with 0, 1, or $\geq$2 jets using a control region where either the muon
candidate fails a looser isolation requirement ($I_{\text{rel}}<20\%$) or the
electron candidate fails the identification criteria. The choice of the QCD
multijet control region has been made in such a way as to minimize the
contamination due to the signal and +jets events, while retaining a large
number of events in the sample for the estimation of this type of background.
The initial normalization of the QCD multijet contribution in the signal
region is derived from events with $\ptmiss<20\GeV$ (“reduced-signal” region).
Events in both the reduced-signal and control regions fulfilling this
requirement are counted. After subtracting the expected contributions from
non-QCD processes, the ratio between the numbers of events observed in the
reduced-signal region and in the control region, is used as a transfer factor
to normalize the QCD multijet background estimate. In both the electron and
muon channels, a 30% uncertainty is assigned to the estimate of the expected
contribution from non-QCD processes, estimated after varying the QCD scales in
the +jets simulation. This uncertainty propagates as both a normalization and
a shape uncertainty in the predicted distributions for the QCD multijet
processes. The variations are applied independently in the reduced-signal and
control regions in order to determine an uncertainty envelope. A more accurate
normalization for this contribution is obtained by the fit performed to
extract the final cross section, described in Section 0.8.1.

###  0.6.2 The dilepton final state

Final states with two genuine leptons can originate from background processes,
primarily from Z/$\gamma^{*}\to\tau^{+}\tau^{-}$ (where the $\tau$ leptonic
decays can yield $\Pe^{\pm}\Pgm^{\mp}$ or $\Pgm^{\pm}\Pgm^{\mp}$ plus due to
the neutrinos), $\PQt\PW$, and $\PW\cmsSymbolFace{V}$ events. Other background
sources, such as +jets events or production in the $\ell$+jets final state,
can contaminate the signal sample if a jet is misidentified as a lepton, or if
an event contains a lepton from the decay of b or c hadrons. These are
included in the “non-/” category, since genuine leptons are defined as
originating from decays of W or Z bosons. The yields from $\PQt\PW$ and
$\PW\cmsSymbolFace{V}$ events are estimated from simulation, while the
contribution of the Z/$\gamma^{*}$ background is evaluated using control
samples in data. The rate of non-/ backgrounds is extracted from control
samples in data for the $\Pe^{\pm}\Pgm^{\mp}$ channel and is estimated from
simulation for the $\Pgm^{\pm}\Pgm^{\mp}$ channel.

A scale factor for the Z/$\gamma^{*}$ background normalization is estimated,
as in Ref. [47], from the number of events within the boson mass window in
data, which is extrapolated to the number of events outside the window. A
scale factor of $0.91\pm 0.14\stat$ is obtained in the $\Pe^{\pm}\Pgm^{\mp}$
channel, and $0.96\pm 0.78\stat$ in the $\Pgm^{\pm}\Pgm^{\mp}$ channel. The
estimation is performed using events with at least two jets, and the
dependence on different jet multiplicities is discussed in Section 0.7.

The non-/ background in the $\Pe^{\pm}\Pgm^{\mp}$ channel is estimated using
an extrapolation from a control region of same-sign (SS) dilepton events to
the signal region of opposite-sign (OS) dileptons. The SS control region is
defined using the same criteria as for the nominal signal region, except
requiring dilepton pairs of the same charge. The muon isolation requirement is
relaxed in order to enhance the number of events. The SS dilepton events
predominantly contain at least one misidentified lepton. Other SM processes
produce genuine SS or charge-misidentified dilepton events with significantly
smaller rates; these are estimated using simulation and subtracted from the
observed number of events in data. The scaling from the SS control region in
data to the signal region is performed using an extrapolation factor extracted
from MC simulation, given by the ratio of the number of OS events with
misidentified leptons to the number of SS events with misidentified leptons.
The resulting estimate for the non-W/Z background is $1.0\pm 0.9\stat$ events,
where the central value comes from the estimation using events with at least
two jets. No particular dependence of this scale factor is observed for
different jet multiplicities within the large statistical uncertainty.

##  0.7 Systematic uncertainties

The integrated luminosity has been estimated offline using a pixel cluster
counting method [20]. The estimation takes into account normalization
uncertainties and uncertainties related to the different conditions during
typical physics periods relative to the specially tailored beam-separation
scans, adding up to a total uncertainty of $\pm 2.3\%$.

The uncertainties in the electron trigger efficiency (1.5%) and the
identification and isolation efficiency (2.5%) are estimated by changing the
values of the data-to-simulation scale factors within their uncertainties, as
obtained from the “tag-and-probe” method. The uncertainty in the muon
identification and isolation efficiency, including the trigger efficiency, is
3% and covers one standard deviation of the scale factor from unity.

The impact of the uncertainty in the jet energy scale (JES) is estimated by
changing the \- and $\eta$-dependent JES corrections by a constant 2.8% [45,
48]. The uncertainty in jet energy resolution (JER) is estimated through
$\eta$-dependent changes in the JER corrections to the simulation [45, 48].
The uncertainty arising from the use of in the $\Pgm^{\pm}\Pgm^{\mp}$ channel
is dominated by the unclustered energy contribution to [40]. Finally, a 30%
uncertainty is conservatively assigned to the jet misidentification
probability in the $\ell$+jets analysis, as no dedicated measurement of this
quantity has been performed for the considered data set.

Theoretical uncertainties in the simulation of production cause a systematic
bias related to the missing higher-order diagrams in , which is estimated
through studies of the signal modeling by modifying the
$\mu_{\mathrm{R}},\mu_{\mathrm{F}}$ scales within a factor of two with respect
to their nominal value. In the $\ell$+jets analysis, the impact of the
$\mu_{\mathrm{R}},\mu_{\mathrm{F}}$ variations are examined independently,
while in the dilepton analysis they are varied simultaneously. In both
analyses, these variations are applied independently at the matrix element
(ME) and parton shower (PS) levels. The uncertainty arising from the
hadronization model mainly affects the JES and the fragmentation of jets. The
hadronization uncertainty is determined by comparing samples of events
generated with , where the hadronization is either modeled with or (v2.7.1)
[49]. This also accounts for differences in the PS model and the underlying
event. The uncertainty from the choice of PDF is determined by reweighting the
sample of simulated events according to the root-mean-square (RMS) variation
of the NNPDF3.0 replica set. Two extra variations of $\alpha_{\mathrm{s}}$ are
added in quadrature to determine the total PDF uncertainty.

In the $\ell$+jets analysis, the uncertainty in the choice of the
$\mu_{\mathrm{R}},\mu_{\mathrm{F}}$ scales in the +jets simulation is taken
into account by considering alternative shapes and yields after varying
independently the $\mu_{\mathrm{R}},\mu_{\mathrm{F}}$ scales, following a
similar procedure to that described above for the signal. Due to the finite
event count in the +jets simulated sample, an additional bin-by-bin
uncertainty is assigned by generating an alternative shape to fit (see Section
0.8.1), where the bin prediction is varied by $\pm$1 standard deviation, while
keeping all the other bins at their nominal expectation. The uncertainty
assigned to the QCD multijet background includes the statistical uncertainty
in the data, and the uncertainty from the non-QCD multijet contributions
subtracted from the control region, as described in Section 0.6.1, and an
additional 30%–100% normalization uncertainty. The latter depends on the event
category and stems from the measured difference with respect to an alternative
estimate of the QCD normalization based on the transverse mass,
$m_{\text{T}}$, of the lepton and system. The magnitude of $m_{\text{T}}$
equals $\sqrt{\smash[b]{2\pt\ptmiss(1-\cos\Delta\phi)}}$, where is the lepton
transverse momentum and $\Delta\phi$ is the azimuthal angle between the lepton
and the direction of . Finally, a 30% normalization uncertainty in the
theoretical tW, Z/$\gamma^{*}$, and WV background cross sections is assigned
[5], given the previously unexplored $\sqrt{s}$ value and that the final
states contain several jets.

In the dilepton channel, an uncertainty of 30% is assumed [5] for the cross
sections of the $\PQt\PW$ and $\PW\cmsSymbolFace{V}$ backgrounds to cover the
theoretical uncertainties and the effect of finite simulated samples. The
uncertainty in the Z/$\gamma^{*}$ estimation is calculated by combining in
quadrature the statistical uncertainty and an additional 30% from the
variation of the scale factor in the different levels of selection, resulting
in uncertainties of about 30 and 80% in the $\Pe^{\pm}\Pgm^{\mp}$ and
$\Pgm^{\pm}\Pgm^{\mp}$ channels, respectively. The systematic uncertainty in
the non-W/Z background is estimated to be 90% in the $\Pe^{\pm}\Pgm^{\mp}$
channel and is dominated by the statistical uncertainty in the method. Owing
to the limited sample size in the data, the method cannot be applied in the
$\Pgm^{\pm}\Pgm^{\mp}$ channel. The estimation is therefore based on MC
simulation, and an uncertainty of 100% is conservatively assigned.

##  0.8 Measurement of the cross section

###  0.8.1 The $\ell$+jets final state

In the $\ell$+jets analysis, the cross section is measured in a fiducial phase
space by means of a fit. Two variables were independently considered for the
fit, which are sensitive to the resonant behavior of the light jets produced
from the boson hadronic decay in a event. Given that these light jets, here
denoted by $j$ and $j^{\prime}$, are correlated during production, they are
also expected to be closer in phase space when compared to pairs of other jets
in the event. The angular distance $\Delta R$ can thus be used as a metric to
rank all pairs of non--tagged jets in the event, maximizing the probability of
selecting those from the boson hadronic decay in cases where more than two non
--tagged jets are found. From simulation we expect that the signal peaks at
low $\Delta R$, while the background is uniformly distributed up to $\Delta
R\approx 3$. Above that value, fewer events are expected and background
processes are predicted to dominate. The invariant mass $M(j,j^{\prime})$ of
jets $j$ and $j^{\prime}$ also has a distinctive peaking feature for the
signal in contrast with a smooth background continuum. From simulation we
expect that the minimum angular distance $\Delta R$ between all pairs of jets
$j$ and $j^{\prime}$, $\Delta R_{\text{min}}(j,j^{\prime})$, is robust against
signal modeling uncertainties such as the choice of the
$\mu_{\mathrm{R}},\mu_{\mathrm{F}}$ scales and jet energy scale and
resolution, while the $M(j,j^{\prime})$ variable tends to be more affected by
such uncertainties. Owing to its more robust systematic uncertainties and
signal-to-background discrimination power, the $\Delta
R_{\text{min}}(j,j^{\prime})$ variable is used to extract the cross section.

In order to maximize the sensitivity of the analysis, the $\Delta
R_{\text{min}}(j,j^{\prime})$ distributions are categorized according to the
number of jets–in addition to the ones assigned to the W boson hadronic
decay–passing the b quark identification criteria. In total, 6 categories are
used, corresponding to electron or muon events with 0, 1, or $\geq$2 jets. The
expected number of signal and background events in each category prior to the
fit and the observed yields are given in Table 0.8.1. Good agreement is
observed between data and expectations.

The number of expected background and signal events and the observed event
yields in the different b tag categories for the $\Pe$+jets and $\mu$+jets
analyses, prior to the fit. With the exception of the QCD multijet estimate,
for which the total uncertainty is reported, the uncertainties reflect the
statistical uncertainty in the simulated samples.  Source b tag category 0  1
$\geq$2  $\Pe$+jets $\mu$+jets $\Pe$+jets $\mu$+jets $\Pe$+jets $\mu$+jets
$\PQt\PW$ $3.03\,\pm\,0.02$ $5.6\,\pm\,0.03$ $2.49\,\pm\,0.02$
$4.5\,\pm\,0.03$ $0.39\,\pm\,0.01$ $0.67\,\pm\,0.01$ +jets $776\,\pm\,17$
$1704\,\pm\,26$ $13\,\pm\,2$ $26\,\pm\,3$ $0.2\,\pm\,0.3$ $0.8\,\pm\,0.6$
Z/$\gamma^{*}$ $136\,\pm\,4$ $162\,\pm\,5$ $1.7\,\pm\,0.5$ $2.8\,\pm\,0.6$
$0.1\,\pm\,0.1$ $0.1\,\pm\,0.1$ WV $0.52\,\pm\,0.01$ $1.01\,\pm\,0.02$ $<$0.01
$<$0.02 $<$0.01 $<$0.01 QCD multijet $440\,\pm\,130$ $490\,\pm\,150$
$3.6\,\pm\,1.1$ $28\,\pm\,8$ $2.5\,\pm\,0.8$ $2.0\,\pm\,0.8$ signal
$22.8\,\pm\,0.3$ $42.3\,\pm\,0.4$ $36.9\,\pm\,0.4$ $71.1\,\pm\,0.5$
$13.8\,\pm\,0.2$ $27.0\,\pm\,0.3$ Total $1380\,\pm\,130$ $2410\,\pm\,150$
$57.7\,\pm\,2.4$ $131\,\pm\,9$ $16.8\,\pm\,0.9$ $31\,\pm\,1$ Observed data
1375 2406 61 129 19 33

The $M(j,j^{\prime})$ and $\Delta R_{\text{min}}(j,j^{\prime})$ distributions
are shown in Fig. 1. The distributions have been combined for the $\Pe$+jets
and $\mu$+jets channels to maximize the statistical precision and are shown
for events with different -tagged jet multiplicities. Fair agreement is
observed between data and the pre-fit expectations.

Figure 1:  The predicted and observed distributions of the (upper row)
$M(j,j^{\prime})$ and (lower row) $\Delta R_{\text{min}}(j,j^{\prime})$
variable for $\ell$+jets events in the 0 (left), 1 (center), and $\geq$2
(right) tagged jet categories. The distributions from data are compared to the
sum of the expectations for the signal and backgrounds prior to any fit. The
QCD multijet background is estimated from data (see Section 5.1). The cross-
hatched band represents the statistical and the integrated luminosity
uncertainties in the expected signal and background yields added in
quadrature. The vertical bars on the data points represent the statistical
uncertainties.

A profile likelihood ratio (PLR) method, similar to the one employed in Ref.
[10], is used to perform the fit. In addition, a scale factor for the tagging
efficiency ($\mathrm{SF}_{\PQb}$) is included as a parameter of interest in
the fit. The PLR is written as:

$\lambda(\mu,\mathrm{SF}_{\PQb})=\frac{\mathcal{L}(\mu,\mathrm{SF}_{\PQb},\hat{\hat{\Theta}})}{\mathcal{L}(\hat{\mu},\hat{\mathrm{SF}}_{\PQb},\hat{\Theta})},$
(2)

where $\mu=\sigma/\sigma_{\text{theo}}$ is the signal strength (ratio of the
observed cross section to the expectation from theory) and $\Theta$ is a set
of nuisance parameters that encode the effect on the expectations due to
variations in the sources of the systematic uncertainties described in Section
0.7. The quantities $\hat{\hat{\Theta}}$ correspond to the values of the
nuisance parameters that maximize the likelihood for the specified signal
strength and tagging efficiency (conditional likelihood), and $\hat{\mu}$,
$\hat{\mathrm{SF}}_{\PQb}$, $\hat{\Theta}$ are, respectively, the values of
the signal strength, tagging efficiency, and nuisance parameters that maximize
the likelihood.

Figure 2 (left) shows the two-dimensional contours at the 68% confidence level
(CL) obtained from the scan of $-2\ln(\lambda)$, as functions of $\mu$ and
$\mathrm{SF}_{\PQb}$. The expected results, obtained using the Asimov data set
[50], are compared to the observed results and found to be in agreement well
within one standard deviation. The signal strength is obtained after profiling
$\mathrm{SF}_{\PQb}$ and the result is $\mu=1.00\ ^{+0.10}_{-0.09}\stat\
^{+0.09}_{-0.08}\syst$. As a cross-check, the signal strength is also
extracted by fitting only the total number of events observed in each of the
six categories. The observed value $\mu=1.03\ ^{+0.10}_{-0.10}\stat\
^{+0.21}_{-0.11}\syst$ is in agreement with the analysis using the $\Delta
R_{\text{min}}(j,j^{\prime})$ distributions. Figure 2 (right) summarizes the
results obtained for the signal strength fit in each channel separately from
the analysis of the distributions and from event counting. In both cases, a
large contribution to the uncertainty is systematic in nature, although the
statistical component is still significant. In the $\ell$+jets combination,
the $\mu$+jets channel is expected and observed to carry the largest weight.

Figure 2: Left: The 68% CL contour obtained from the scan of the likelihood in
$\ell$+jets analysis, as a function of $\mu$ and $\mathrm{SF}_{\PQb}$ in the
$\ell$+jets analysis. The solid (dashed) contour refers to the result from
data (expectation from simulation). The solid (hollow) diamond represents the
observed fit result (SM expectation). Right: Summary of the signal strengths
separately obtained in the $\Pe$+jets and $\mu$+jets channels, and after their
combination in the $\ell$+jets channel. The results of the analysis from the
distributions are compared to those from the cross-check analysis with event
counting (Count). The inner (outer) bars correspond to the statistical (total)
uncertainty in the signal strengths.

In order to estimate the impact of the experimental systematic uncertainties
in the measured signal strength, the fit is repeated after fixing one nuisance
parameter at a time at its post-fit uncertainty ($\pm$1 standard deviation)
values. The impact on the signal strength fit is then evaluated from the
difference induced in the final result from this procedure. By repeating the
fits, the effect of some nuisance parameters being fixed may be reabsorbed by
a variation of the ones being profiled, owing to correlations. As such, the
individual experimental uncertainties obtained and summarized in Table 0.8.1
can only be interpreted as the observed post-fit values, and not as an
absolute, orthogonalized breakdown of uncertainties. With respect to the event
counting, the analysis of the distributions is less prone to the uncertainties
in the QCD multijet background, jet energy resolution, and signal modeling. In
both cases, the signal modeling uncertainties and the tagging efficiency are
among the largest sources of uncertainty.

Estimated impact of each source of uncertainty in the value of $\mu$ extracted
from the analysis of distributions, and in the cross-check from event
counting. The “Other background” component includes the contributions from
Z/$\gamma^{*}$, $\PQt\PW$, and WV events. The total uncertainty is obtained by
adding in quadrature the statistical, experimental systematic, and theoretical
uncertainties. The individual experimental uncertainties are obtained by
repeating the fit after fixing one nuisance parameter at a time at its post-
fit uncertainty ($\pm$1 standard deviation) value. The values quoted have been
symmetrized.  Source $\Delta\mu/\mu$ Distr. Count Statistical uncertainty
0.095 0.100 Experimental systematic uncertainty 0.085 0.160 Individual
experimental uncertainties +jets background 0.035 0.025 QCD multijet
background 0.024 0.044 Other background 0.013 0.013 Jet energy scale 0.030
0.031 Jet energy resolution 0.006 0.023 tagging 0.034 0.045 Electron
efficiency 0.011 0.028 Muon efficiency 0.017 0.022 Theoretical uncertainties
Hadronization model of signal 0.028 0.069 $\mu_{\mathrm{R}},\mu_{\mathrm{F}}$
scales of signal (PS) 0.044 0.115 $\mu_{\mathrm{R}},\mu_{\mathrm{F}}$ scales
of signal (ME) $<$0.010 $<$0.010 Total uncertainty 0.127 0.189

The fiducial cross section is measured in events with one electron (muon) in
the range $\pt>35$ (25)and $\abs{\eta}<2.1$ (including the transition region
for electrons), and at least two jets with $\pt>25\GeV$ and $\abs{\eta}<2.4$.
After multiplying the signal strength by the theoretical expectations (Eq.
(1)), we find

$\sigma_{\text{fid}}=20.8\pm 2.0\stat\pm 1.8\syst\pm 0.5\lum\unit{pb}.$

The combined acceptance in the $\Pe$+jets and $\mu$+jets channels is estimated
using the NLO simulation to be $\mathcal{A}=0.301\pm 0.007$, with the
uncertainty being dominated by the variation of the
$\mu_{\mathrm{R}},\mu_{\mathrm{F}}$ scales at ME and PS levels and the
hadronization model used for the signal. The uncertainty due to the PDFs is
included but verified to be less important. Taking into account the acceptance
of the analysis and its uncertainty, the inclusive cross section is determined
to be

$\sigma_{\ttbar}=68.9\pm 6.5\stat\pm 6.1\syst\pm 1.6\lum\unit{pb},$

in agreement with the SM prediction and attaining a 13% total relative
uncertainty.

###  0.8.2 The dilepton final state

In the dilepton analysis, the cross section is extracted from an event
counting measurement. Figure 3 shows the distributions of the jet multiplicity
and the scalar sum of all jets ($H_{\mathrm{T}}$), for events passing the
dilepton criteria in the $\Pe^{\pm}\Pgm^{\mp}$ channel. In addition, it
displays the lepton-pair invariant mass and distributions, after requiring at
least two jets in the event in the $\Pe^{\pm}\Pgm^{\mp}$ channel. Figure 4
shows the and the lepton-pair invariant mass distributions in the
$\Pgm^{\pm}\Pgm^{\mp}$ channel for events passing the dilepton criteria, and
the Z boson veto with the $\ptmiss>35$requirement, in the second case. The
predicted distributions take into account the efficiency corrections described
in Section 0.5 and the background estimations discussed in Section 0.6.2. Good
agreement is observed between the data and predictions for both signal and
background.

Figure 3:  Predicted and observed distributions of the (upper row) jet
multiplicity and scalar sum of all jets ($H_{\mathrm{T}}$) for events passing
the dilepton criteria, and of the (lower row) invariant mass and of the lepton
pair after requiring at least two jets, in the $\Pe^{\pm}\Pgm^{\mp}$ channel.
The Z/$\gamma^{*}$ and non-/ backgrounds are determined from data (see Section
0.6.2). The cross-hatched band represents the statistical and integrated
luminosity uncertainties in the expected signal and background yields added in
quadrature. The vertical bars on the data points represent the statistical
uncertainties. The last bin of the distributions contains the overflow events.

Figure 4:  Predicted and observed distributions of the (left) in events
passing the dilepton criteria and Z boson veto, and of the (right) invariant
mass of the lepton pair after the $\ptmiss>35$requirement in the
$\Pgm^{\pm}\Pgm^{\mp}$ channel. The cross-hatched band represents the
statistical and integrated luminosity uncertainties in the expected signal and
background yields added in quadrature. The vertical bars on the data points
represent the statistical uncertainties. The last bin of the distributions
contains the overflow events.

The fiducial production cross section is measured by counting events in the
visible phase space (defined by the same $\pt$, $\abs{\eta}$, and multiplicity
requirements for leptons and jets as described in Section 0.5, but including
the transition region for electrons) and is denoted by $\sigma_{\text{fid}}$.
It is extrapolated to the full phase space in order to determine the inclusive
cross section using the expression

$\sigma_{\ttbar}=\frac{N-N_{\mathrm{B}}}{\varepsilon\,\mathcal{A}\,{\mathcal{L}}}=\frac{\sigma_{\text{fid}}}{\mathcal{A}},$
(3)

where $N$ is the total number of dilepton events observed in data,
$N_{\mathrm{B}}$ the number of estimated background events, $\varepsilon$ the
selection efficiency, $\mathcal{A}$ the acceptance, and $\mathcal{L}$ the
integrated luminosity. Table 0.8.2 gives the total number of events observed
in data, together with the total number of signal and background events
expected from simulation or estimated from data, after the full set of
selection criteria. The total detector, trigger, and reconstruction efficiency
is estimated from data to be $\varepsilon=0.55\pm 0.02$ ($0.57\pm 0.04$) in
the $\Pe^{\pm}\Pgm^{\mp}$ ($\Pgm^{\pm}\Pgm^{\mp}$) channel. Using the
definitions above, the yields from Table 0.8.2, and the systematic
uncertainties from Table 0.8.2, the measured fiducial cross section for
production is

$\sigma_{\text{fid}}=41\pm 10\stat\pm 2\syst\pm 1\lum\unit{pb}$

in the $\Pe^{\pm}\Pgm^{\mp}$ channel and

$\sigma_{\text{fid}}=22\pm 11\stat\pm 4\syst\pm 1\lum\unit{pb}$

in the $\Pgm^{\pm}\Pgm^{\mp}$ channel.

The predicted and observed numbers of dilepton events obtained after applying
the full selection. The values are given for the individual sources of
background, signal, and data. The uncertainties correspond to the statistical
component.  Source $\Pe^{\pm}\Pgm^{\mp}$ $\Pgm^{\pm}\Pgm^{\mp}$ $\PQt\PW$ 0.92
$\pm$ 0.02 0.29 $\pm$ 0.01 Non-W/Z leptons 1.0 $\pm$ 0.9 0.04 $\pm$ 0.01
Z/$\gamma^{*}$ 1.6 $\pm$ 0.2 1.1 $\pm$ 0.8 $\PW\cmsSymbolFace{V}$ 0.44 $\pm$
0.02 0.15 $\pm$ 0.01 signal 18.0 $\pm$ 0.3 6.4 $\pm$ 0.2 Total 22.0 $\pm$ 0.9
7.9 $\pm$ 0.8 Observed data 24 7

Summary of the individual contributions to the systematic uncertainty in the
$\sigma_{\ttbar}$ measurements for the dilepton channels. The relative
uncertainties $\Delta\sigma_{\ttbar}/\sigma_{\ttbar}$ (in %), as well as
absolute uncertainties in $\sigma_{\ttbar}$, $\Delta\sigma_{\ttbar}$ (in pb),
are presented. The statistical and total uncertainties are also given, where
the latter are the quadrature sum of the statistical and systematic
uncertainties.  $\Pe^{\pm}\Pgm^{\mp}$ $\Pgm^{\pm}\Pgm^{\mp}$ Source
$\Delta\sigma_{\ttbar}/\sigma_{\ttbar}$ (%) $\Delta\sigma_{\ttbar}$ (pb)
$\Delta\sigma_{\ttbar}/\sigma_{\ttbar}$ (%) $\Delta\sigma_{\ttbar}$ (pb)
Electron efficiency 1.4 1.0 — — Muon efficiency 3.0 2.3 6.1 3.6 Jet energy
scale 1.3 1.0 1.3 0.7 Jet energy resolution $<$0.1 $<$0.1 $<$0.1 $<$0.1
Missing transverse momentum — — 0.7 0.4 $\mu_{\mathrm{R}},\mu_{\mathrm{F}}$
scales of signal (PS) 1.2 0.9 1.7 1.0 $\mu_{\mathrm{R}},\mu_{\mathrm{F}}$
scales of signal (ME) 0.2 0.1 1.1 0.6 Hadronization model of signal 1.2 0.9
5.2 3.1 PDF 0.5 0.4 0.4 0.2 MC sample size 1.4 1.1 2.4 1.4 $\PQt\PW$
background 1.4 1.1 1.6 0.9 WV background 0.7 0.5 0.9 0.5 Z/$\gamma^{*}$
background 2.7 2.1 15 9.1 Non-W/Z background 2.5 1.9 0.7 0.4 Total systematic
uncertainty 5.8 4.4 18 11 (w/o integrated luminosity) Integrated luminosity
2.3 1.8 2.3 1.4 Statistical uncertainty 25 19 48 29 Total uncertainty 25 19 52
31

The acceptance, as estimated from MC simulation, is found to be
$\mathcal{A}=0.53\pm 0.01$ ($0.37\pm 0.01$) in the $\Pe^{\pm}\Pgm^{\mp}$
($\Pgm^{\pm}\Pgm^{\mp}$) channel. The statistical uncertainty (from MC
simulation) is included in the uncertainty in $\mathcal{A}$. By extrapolating
to the full phase space, the inclusive cross section is measured to be

$\sigma_{\ttbar}=77\pm 19\stat\pm 4\syst\pm 2\lum\unit{pb}$

in the $\Pe^{\pm}\Pgm^{\mp}$ channel and

$\sigma_{\ttbar}=59\pm 29\stat\pm 11\syst\pm 1\lum\unit{pb}$

in the $\Pgm^{\pm}\Pgm^{\mp}$ channel. Table 0.8.2 summarizes the relative and
absolute statistical and systematic uncertainties from different sources
contributing to $\sigma_{\ttbar}$. The separate total systematic uncertainty
without the uncertainty in the integrated luminosity, the part attributed to
the integrated luminosity, and the statistical contribution are added in
quadrature to obtain the total uncertainty. The cross sections, measured with
a relative uncertainty of 25 and 52%, are in agreement with the SM prediction
(Eq. (1)) within the uncertainties in the measurements.

###  0.8.3 Combination

The three individual $\sigma_{\ttbar}$ measurements are combined using the
BLUE method [51, 52] to determine an overall cross section. All systematic
uncertainties are considered as fully correlated across all channels, with the
following exceptions: the uncertainty associated with the finite event size of
the simulated samples is taken as uncorrelated; the electron identification is
not relevant for the $\mu\mu$ channel; and the tagging and QCD multijet
background uncertainties are only considered for the $\ell$+jets channel. In
the $\ell+$jets channel, the WV and $\cPZ/\gamma^{*}$ backgrounds are not
considered separately but as part of the “Other backgrounds” component, which
is dominated by tW events. The uncertainty associated with this category is
therefore treated as fully correlated with the tW uncertainty in the
dileptonic channels and uncorrelated with the WV and $\cPZ/\gamma^{*}$
uncertainties.

The combined inclusive cross section is measured to be:

$\sigma_{\ttbar}=69.5\pm 6.1\stat\pm 5.6\syst\pm 1.6\lum\unit{pb}=69.5\pm
8.4\,\text{(total)}\unit{pb},$

where the total uncertainty is the sum in quadrature of the individual
uncertainties. The weights of the individual measurements, to be understood in
the sense of Ref. [52], are 81.8% for $\ell+$jets, 13.5% for
$\Pe^{\pm}\Pgm^{\mp}$, and 4.7% for $\Pgm^{\pm}\Pgm^{\mp}$ channels.

The combined result is found to be robust by performing an iterative variant
of the BLUE method [53] and varying some assumptions on the correlations of
different combinations of systematic uncertainties. Also, the post-fit
correlations between the nuisance parameters in the $\ell$+jets channel have
been checked and found to have negligible impact.

Figure 5 presents a summary of CMS measurements [5, 6, 9, 10] of
$\sigma_{\ttbar}$ in $\Pp\Pp$ collisions at different $\sqrt{s}$ in the
$\ell$+jets and dilepton channels, compared to the NNLO+NNLL prediction using
the NNPDF3.0 PDF set with $\alpha_{\mathrm{s}}(M_{\Z})=0.118$ and
$m_{\text{top}}=172.5\GeV$. In the inset, the results from this analysis at
$\sqrt{s}=5.02$are also compared to the predictions from the MMHT14 [54], CT14
[55], and ABMP16 [56] PDF sets, with the latter using
$\alpha_{\mathrm{s}}(M_{\Z})=0.115$ and $m_{\text{top}}=170.4\GeV$.
Theoretical predictions using different PDF sets have comparable values and
uncertainties, once consistent values of $\alpha_{\mathrm{s}}$ and
$m_{\text{top}}$ are associated with the respective PDF set.

Figure 5:  Inclusive $\sigma_{\ttbar}$ in pp collisions as a function of the
center-of-mass energy; previous CMS measurements at $\sqrt{s}=7$, 8 [5, 6],
and 13 [9, 10]in the separate $\ell$+jets and dilepton channels are displayed,
along with the combined measurement at 5.02from this analysis. The NNLO+NNLL
theoretical prediction [34] using the NNPDF3.0 [14] PDF set with
$\alpha_{\mathrm{s}}(M_{\Z})=0.118$ and $m_{\text{top}}=172.5\GeV$ is shown in
the main plot. In the inset, additional predictions at $\sqrt{s}=5.02$using
the MMHT14 [54], CT14 [55], and ABMP16 [56] PDF sets, the latter with
$\alpha_{\mathrm{s}}(M_{\Z})=0.115$ and $m_{\text{top}}=170.4\GeV$, are
compared, along with the NNPDF3.0 prediction, to the individual and combined
results from this analysis. The vertical bars and bands represent the total
uncertainties in the data and in the predictions, respectively.

##  0.9 QCD analysis

To illustrate the impact of the $\sigma_{\ttbar}$ measurements at
$\sqrt{s}=5.02$on the knowledge of the proton PDFs, the results are used in a
QCD analysis at NNLO, together with the combined measurements of neutral- and
charged-current cross sections for deep inelastic electron- and positron-
proton scattering (DIS) at HERA [57], and the CMS measurement [58] of the muon
charge asymmetry in $\PW$ boson production at $\sqrt{s}=8$. The latter data
set is used in order to improve the constraint on the light-quark
distributions.

Version 2.0.0 of xFitter [59, 60], the open-source QCD-analysis framework for
PDF determination, is employed, with the partons evolved using the
Dokshitzer–Gribov–Lipatov–Altarelli–Parisi equations [61, 62, 63, 64, 65, 66]
at NNLO, as implemented in the qcdnum 17-01/13 program [67]. The treatment and
the choices for the central values and variations of the c and b quark masses,
the strong coupling, and the strange-quark content fraction of the proton
follow that of earlier CMS analyses, e.g., Ref. [58]. The
$\mu_{\mathrm{R}},\mu_{\mathrm{F}}$ scales are set to the four-momentum
transfer in the case of the DIS data, the $\PW$ boson mass for the muon charge
asymmetry results, and the top quark mass in the case of $\sigma_{\ttbar}$.

The systematic uncertainties in all three measurements of $\sigma_{\ttbar}$
and their correlations are treated the same way as in the combination
described in Section 0.8.3. The theoretical predictions for $\sigma_{\ttbar}$
are obtained at NNLO using the hathor calculation [68], assuming
$m_{\text{top}}=172.5\GeV$. The bin-to-bin correlations of the experimental
uncertainties in the muon charge asymmetry and DIS measurements are taken into
account. The theoretical predictions for the muon charge asymmetry are
obtained as described in Ref. [58].

The procedure for the determination of the PDFs follows the approach used in
the QCD analysis of Ref. [58] and results in a 14-parameter fit. The
parametrized PDFs are the gluon distribution, $x\Pg$, the valence quark
distributions, $x\PQu_{v}$, $x\PQd_{v}$, and the $\PQu$-type and $\PQd$-type
antiquark distributions, $x\overline{U}$, $x\overline{D}$. The relations
$x\overline{U}=x\cPaqu$ and $x\overline{D}=x\PAQd+x\PAQs$ are assumed at the
initial scale of the QCD evolution $Q_{0}^{2}=1.9\GeV^{2}$. At this scale, the
parametrizations are of the form:

$\displaystyle x\Pg(x)$
$\displaystyle=A_{\Pg}x^{B_{\Pg}}\,(1-x)^{C_{\Pg}}\,(1+D_{\Pg}x),$ (4)
$\displaystyle x\PQu_{v}(x)$
$\displaystyle=A_{\PQu_{v}}x^{B_{\PQu_{v}}}\,(1-x)^{C_{\PQu_{v}}}\,(1+D_{\PQu_{v}}x+E_{\PQu_{v}}x^{2}),$
(5) $\displaystyle x\PQd_{v}(x)$
$\displaystyle=A_{\PQd_{v}}x^{B_{\PQd_{v}}}\,(1-x)^{C_{\PQd_{v}}},$ (6)
$\displaystyle x\overline{U}(x)$
$\displaystyle=A_{\overline{U}}x^{B_{\overline{U}}}\,(1-x)^{C_{\overline{U}}}\,(1+E_{\overline{U}}x^{2}),$
(7) $\displaystyle x\overline{D}(x)$
$\displaystyle=A_{\overline{D}}x^{B_{\overline{D}}}\,(1-x)^{C_{\overline{D}}}.$
(8)

The normalization parameters $A_{\PQu_{v}}$, $A_{\PQd_{v}}$, and $A_{\cPg}$
are determined by the QCD sum rules, the $B$ parameters are responsible for
the small-$x$ behavior of the PDFs, and the $C$ parameters describe the shape
of the distribution as $x\to 1$. Additional constraints
$B_{\overline{U}}=B_{\overline{D}}$ and
$A_{\overline{U}}=A_{\overline{D}}(1-f_{\PQs})$ are imposed, with $f_{\PQs}$
being the strangeness fraction, $\PAQs/(\PAQd+\PAQs)$, which is set to
$0.31\pm 0.08$ as in Ref. [69], consistent with the value obtained using the
CMS measurements of $\PW$+c production [70]. Using the measured values for
$\sigma_{\ttbar}$ allows the addition of a new free parameter, $D_{\PQu_{v}}$,
in Eq. (5), as compared to the analysis in Ref. [58].

The predicted and measured cross sections for all the data sets, together with
their corresponding uncertainties, are used to build a global $\chi^{2}$,
minimized to determine the PDF parameters [59, 60]. The results of the fit are
given in Table 0.9. The quality of the overall fit can be judged based on the
global $\chi^{2}$ divided by the number of degrees of freedom,
$n_{\mathrm{dof}}$. For each data set included in the fit, the partial
$\chi^{2}$ divided by the number of the measurements (data points),
$n_{\mathrm{dp}}$, is also provided. The correlated part of $\chi^{2}$, also
given in Table 0.9, quantifies the influence of the correlated systematic
uncertainties in the fit. The global and partial $\chi^{2}$ values indicate a
general agreement among all the data sets. The somewhat high
$\chi^{2}/n_{\mathrm{dp}}$ values for the combined DIS data are very similar
to those observed in Ref. [57], where they are investigated in detail.

Partial $\chi^{2}$ per number of data points, $n_{\mathrm{dp}}$, and the
global $\chi^{2}$ per degrees of freedom, $n_{\text{dof}}$, as obtained in the
QCD analysis of DIS data, the CMS muon charge asymmetry measurements, and the
$\sigma_{\ttbar}$ results at $\sqrt{s}=5.02$from this analysis. For the HERA
measurements, the energy of the proton beam ($E_{\Pp}$) is listed for each
data set, with the electron/positron energy of 27.5. The correlated part of
the global $\chi^{2}$ value is also given.

Data sets | Partial $\chi^{2}/n_{\mathrm{dp}}$
---|---
HERA neutral current, $\Pep\Pp$, $E_{\Pp}=920\GeV$ | $449/377$
HERA neutral current, $\Pep\Pp$, $E_{\Pp}=820\GeV$ | $71/70$
HERA neutral current, $\Pep\Pp$, $E_{\Pp}=575\GeV$ | $224/254$
HERA neutral current, $\Pep\Pp$, $E_{\Pp}=460\GeV$ | $218/204$
HERA neutral current, $\Pem\Pp$, $E_{\Pp}=920\GeV$ | $218/159$
HERA charged current, $\Pep\Pp$, $E_{\Pp}=920\GeV$ | $43/39$
HERA charged current, $\Pem\Pp$, $E_{\Pp}=920\GeV$ | $53/42$
CMS $\PW^{\pm}$ muon charge asymmetry | $2.4/11$
CMS $\sigma_{\ttbar}$, $\Pe^{\pm}\Pgm^{\mp}$, 5.02  | $1.03/1$
CMS $\sigma_{\ttbar}$, $\Pgm^{\pm}\Pgm^{\mp}$, 5.02  | $0.01/1$
CMS $\sigma_{\ttbar}$, $\ell$+jets, 5.02  | $0.70/1$
Correlated $\chi^{2}$ | 100
Global $\chi^{2}/n_{\text{dof}}$ | $1387/1145$

The experimental uncertainties in the measurements are propagated to the
extracted QCD fit parameters using the MC method [71, 72]. In this method, 400
replicas of pseudo-data are generated, with measured values for
$\sigma_{\ttbar}$ allowed to vary within the statistical and systematic
uncertainties. For each of them, the PDF fit is performed and the uncertainty
is estimated as the RMS around the central value. In Fig. 6, the ratio and the
relative uncertainties in the gluon distributions, as obtained in the QCD
analyses with and without the measured values for $\sigma_{\ttbar}$, are
shown. A moderate reduction of the uncertainty in the gluon distribution at
$x\gtrsim 0.1$ is observed, once the measured values for $\sigma_{\ttbar}$ are
included in the fit. The uncertainties in the valence quark distributions
remain unaffected. All changes in the central values of the PDFs are well
within the fit uncertainties.

Possible effects from varying the model input parameters and the initial PDF
parametrization are investigated in the same way as in the similar analysis of
Ref. [58]. The two cases when the measured values for $\sigma_{\ttbar}$ are
included or excluded from the fit are considered, resulting in the same
associated model and parametrization uncertainties.

In conclusion, the $\sigma_{\ttbar}$ measurements at $\sqrt{s}=5.02$provide
improved uncertainties in the gluon PDF at high $x$, though the impact is
small, owing to the large experimental uncertainties.

Figure 6:  The relative uncertainties in the gluon distribution function of
the proton as a function of $x$ at $\mu^{2}_{\mathrm{F}}=10^{5}\GeV^{2}$ from
a QCD analysis using the HERA DIS and CMS muon charge asymmetry measurements
(hatched area), and also including the CMS $\sigma_{\ttbar}$ results at
$\sqrt{s}=5.02$(solid area). The relative uncertainties are found after the
two gluon distributions have been normalized to unity. The solid line shows
the ratio of the gluon distribution function found from the fit with the CMS
$\sigma_{\ttbar}$ measurements included to that found without.

##  0.10 Summary

The first measurement of the top quark pair () production cross section in pp
collisions at $\sqrt{s}=5.02$is presented for events with one or two leptons
and at least two jets, using a data sample collected by the CMS experiment,
corresponding to an integrated luminosity of $27.4\pm 0.6\pbinv$. The final
measurement is obtained from the combination of the measurements in the
individual channels. The result is $\sigma_{\ttbar}=69.5\pm 6.1\stat\pm
5.6\syst\pm 1.6\lum\unit{pb}$, with a total relative uncertainty of 12%, which
is consistent with the standard model prediction. The impact of the measured
cross section in the determination of the parton distribution functions of the
proton is studied in a quantum chromodynamics analysis at next-to-next-to-
leading order. A moderate decrease of the uncertainty in the gluon
distribution is observed at high values of $x$, the fractional momentum of the
proton carried by the gluon.

###### Acknowledgements.

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

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##  .11 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan, A. Tumasyan Institut für Hochenergiephysik, Wien, Austria
W. Adam, F. Ambrogi, E. Asilar, T. Bergauer, J. Brandstetter, E. Brondolin, M.
Dragicevic, J. Erö, M. Flechl, M. Friedl, R. Frühwirth1, V.M. Ghete, J.
Grossmann, J. Hrubec, M. Jeitler1, A. König, N. Krammer, I. Krätschmer, D.
Liko, T. Madlener, I. Mikulec, E. Pree, D. Rabady, N. Rad, H. Rohringer, J.
Schieck1, R. Schöfbeck, M. Spanring, D. Spitzbart, W. Waltenberger, J.
Wittmann, C.-E. Wulz1, M. Zarucki Institute for Nuclear Problems, Minsk,
Belarus
V. Chekhovsky, V. Mossolov, J. Suarez Gonzalez Universiteit Antwerpen,
Antwerpen, Belgium
E.A. De Wolf, D. Di Croce, X. Janssen, J. Lauwers, M. Van De Klundert, H. Van
Haevermaet, P. Van Mechelen, N. Van Remortel Vrije Universiteit Brussel,
Brussel, Belgium
S. Abu Zeid, F. Blekman, J. D’Hondt, I. De Bruyn, J. De Clercq, K. Deroover,
G. Flouris, D. Lontkovskyi, S. Lowette, S. Moortgat, L. Moreels, Q. Python, K.
Skovpen, S. Tavernier, W. Van Doninck, P. Van Mulders, I. Van Parijs
Université Libre de Bruxelles, Bruxelles, Belgium
H. Brun, B. Clerbaux, G. De Lentdecker, H. Delannoy, G. Fasanella, L. Favart,
R. Goldouzian, A. Grebenyuk, G. Karapostoli, T. Lenzi, J. Luetic, T.
Maerschalk, A. Marinov, A. Randle-conde, T. Seva, C. Vander Velde, P. Vanlaer,
D. Vannerom, R. Yonamine, F. Zenoni, F. Zhang2 Ghent University, Ghent,
Belgium
A. Cimmino, T. Cornelis, D. Dobur, A. Fagot, M. Gul, I. Khvastunov, D. Poyraz,
C. Roskas, S. Salva, M. Tytgat, W. Verbeke, N. Zaganidis Université Catholique
de Louvain, Louvain-la-Neuve, Belgium
H. Bakhshiansohi, O. Bondu, S. Brochet, G. Bruno, C. Caputo, A. Caudron, S. De
Visscher, C. Delaere, M. Delcourt, B. Francois, A. Giammanco, A. Jafari, M.
Komm, G. Krintiras, V. Lemaitre, A. Magitteri, A. Mertens, M. Musich, K.
Piotrzkowski, L. Quertenmont, M. Vidal Marono, S. Wertz Université de Mons,
Mons, Belgium
N. Beliy Centro Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil
W.L. Aldá Júnior, F.L. Alves, G.A. Alves, L. Brito, M. Correa Martins Junior,
C. Hensel, A. Moraes, M.E. Pol, P. Rebello Teles Universidade do Estado do Rio
de Janeiro, Rio de Janeiro, Brazil
E. Belchior Batista Das Chagas, W. Carvalho, J. Chinellato3, A. Custódio, E.M.
Da Costa, G.G. Da Silveira4, D. De Jesus Damiao, S. Fonseca De Souza, L.M.
Huertas Guativa, H. Malbouisson, M. Melo De Almeida, C. Mora Herrera, L.
Mundim, H. Nogima, A. Santoro, A. Sznajder, E.J. Tonelli Manganote3, F. Torres
Da Silva De Araujo, A. Vilela Pereira Universidade Estadual Paulista a,
Universidade Federal do ABC b, São Paulo, Brazil
S. Ahujaa, C.A. Bernardesa, T.R. Fernandez Perez Tomeia, E.M. Gregoresb, P.G.
Mercadanteb, S.F. Novaesa, Sandra S. Padulaa, D. Romero Abadb, J.C. Ruiz
Vargasa Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy
of Sciences, Sofia, Bulgaria
A. Aleksandrov, R. Hadjiiska, P. Iaydjiev, M. Misheva, M. Rodozov, M. Shopova,
S. Stoykova, G. Sultanov University of Sofia, Sofia, Bulgaria
A. Dimitrov, I. Glushkov, L. Litov, B. Pavlov, P. Petkov Beihang University,
Beijing, China
W. Fang5, X. Gao5 Institute of High Energy Physics, Beijing, China
M. Ahmad, J.G. Bian, G.M. Chen, H.S. Chen, M. Chen, Y. Chen, C.H. Jiang, D.
Leggat, H. Liao, Z. Liu, F. Romeo, S.M. Shaheen, A. Spiezia, J. Tao, C. Wang,
Z. Wang, E. Yazgan, H. Zhang, S. Zhang, J. Zhao State Key Laboratory of
Nuclear Physics and Technology, Peking University, Beijing, China
Y. Ban, G. Chen, Q. Li, S. Liu, Y. Mao, S.J. Qian, D. Wang, Z. Xu Universidad
de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, L.F. Chaparro Sierra, C. Florez, C.F. González
Hernández, J.D. Ruiz Alvarez University of Split, Faculty of Electrical
Engineering, Mechanical Engineering and Naval Architecture, Split, Croatia
B. Courbon, N. Godinovic, D. Lelas, I. Puljak, P.M. Ribeiro Cipriano, T.
Sculac University of Split, Faculty of Science, Split, Croatia
Z. Antunovic, M. Kovac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, D. Ferencek, K. Kadija, B. Mesic, A. Starodumov6, T. Susa
University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos,
P.A. Razis, H. Rykaczewski Charles University, Prague, Czech Republic
M. Finger7, M. Finger Jr.7 Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab
Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
Y. Assran8,9, M.A. Mahmoud10,9, A. Mahrous11 National Institute of Chemical
Physics and Biophysics, Tallinn, Estonia
R.K. Dewanjee, M. Kadastik, L. Perrini, M. Raidal, A. Tiko, C. Veelken
Department of Physics, University of Helsinki, Helsinki, Finland
P. Eerola, J. Pekkanen, M. Voutilainen Helsinki Institute of Physics,
Helsinki, Finland
J. Härkönen, T. Järvinen, V. Karimäki, R. Kinnunen, T. Lampén, K. Lassila-
Perini, S. Lehti, T. Lindén, P. Luukka, E. Tuominen, J. Tuominiemi, E.
Tuovinen Lappeenranta University of Technology, Lappeenranta, Finland
J. Talvitie, T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette,
France
M. Besancon, F. Couderc, M. Dejardin, D. Denegri, J.L. Faure, F. Ferri, S.
Ganjour, S. Ghosh, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry,
I. Kucher, E. Locci, M. Machet, J. Malcles, G. Negro, J. Rander, A. Rosowsky,
M.Ö. Sahin, M. Titov Laboratoire Leprince-Ringuet, Ecole polytechnique,
CNRS/IN2P3, Université Paris-Saclay, Palaiseau, France
A. Abdulsalam, I. Antropov, S. Baffioni, F. Beaudette, P. Busson, L. Cadamuro,
C. Charlot, R. Granier de Cassagnac, M. Jo, S. Lisniak, A. Lobanov, J. Martin
Blanco, M. Nguyen, C. Ochando, G. Ortona, P. Paganini, P. Pigard, S. Regnard,
R. Salerno, J.B. Sauvan, Y. Sirois, A.G. Stahl Leiton, T. Strebler, Y. Yilmaz,
A. Zabi, A. Zghiche Université de Strasbourg, CNRS, IPHC UMR 7178, F-67000
Strasbourg, France
J.-L. Agram12, J. Andrea, D. Bloch, J.-M. Brom, M. Buttignol, E.C. Chabert, N.
Chanon, C. Collard, E. Conte12, X. Coubez, J.-C. Fontaine12, 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, R. Chierici, D. Contardo, P. Depasse, H.
El Mamouni, J. Fay, L. Finco, S. Gascon, M. Gouzevitch, G. Grenier, B. Ille,
F. Lagarde, I.B. Laktineh, M. Lethuillier, L. Mirabito, A.L. Pequegnot, S.
Perries, A. Popov13, V. Sordini, M. Vander Donckt, S. Viret Georgian Technical
University, Tbilisi, Georgia
A. Khvedelidze7 Tbilisi State University, Tbilisi, Georgia
D. Lomidze RWTH Aachen University, I. Physikalisches Institut, Aachen, Germany
C. Autermann, S. Beranek, L. Feld, M.K. Kiesel, K. Klein, M. Lipinski, M.
Preuten, C. Schomakers, J. Schulz, T. Verlage, V. Zhukov13 RWTH Aachen
University, III. Physikalisches Institut A, Aachen, Germany
A. Albert, E. Dietz-Laursonn, D. Duchardt, M. Endres, M. Erdmann, S. Erdweg,
T. Esch, R. Fischer, A. Güth, M. Hamer, T. Hebbeker, C. Heidemann, K.
Hoepfner, S. Knutzen, M. Merschmeyer, A. Meyer, P. Millet, S. Mukherjee, M.
Olschewski, K. Padeken, T. Pook, M. Radziej, H. Reithler, M. Rieger, F.
Scheuch, D. Teyssier, S. Thüer RWTH Aachen University, III. Physikalisches
Institut B, Aachen, Germany
G. Flügge, B. Kargoll, T. Kress, A. Künsken, J. Lingemann, T. Müller, A.
Nehrkorn, A. Nowack, C. Pistone, O. Pooth, A. Stahl14 Deutsches Elektronen-
Synchrotron, Hamburg, Germany
M. Aldaya Martin, T. Arndt, C. Asawatangtrakuldee, K. Beernaert, O. Behnke, U.
Behrens, A. Bermúdez Martínez, A.A. Bin Anuar, K. Borras15, V. Botta, A.
Campbell, P. Connor, C. Contreras-Campana, F. Costanza, C. Diez Pardos, G.
Eckerlin, D. Eckstein, T. Eichhorn, E. Eren, E. Gallo16, J. Garay Garcia, A.
Geiser, A. Gizhko, J.M. Grados Luyando, A. Grohsjean, P. Gunnellini, M.
Guthoff, A. Harb, J. Hauk, M. Hempel17, H. Jung, A. Kalogeropoulos, M.
Kasemann, J. Keaveney, C. Kleinwort, I. Korol, D. Krücker, W. Lange, A. Lelek,
T. Lenz, J. Leonard, K. Lipka, W. Lohmann17, R. Mankel, I.-A. Melzer-Pellmann,
A.B. Meyer, G. Mittag, J. Mnich, A. Mussgiller, E. Ntomari, D. Pitzl, A.
Raspereza, B. Roland, M. Savitskyi, P. Saxena, R. Shevchenko, S. Spannagel, N.
Stefaniuk, G.P. Van Onsem, R. Walsh, Y. Wen, K. Wichmann, C. Wissing, O.
Zenaiev University of Hamburg, Hamburg, Germany
S. Bein, V. Blobel, M. Centis Vignali, T. Dreyer, E. Garutti, D. Gonzalez, J.
Haller, A. Hinzmann, M. Hoffmann, A. Karavdina, R. Klanner, R. Kogler, N.
Kovalchuk, S. Kurz, T. Lapsien, I. Marchesini, D. Marconi, M. Meyer, M.
Niedziela, D. Nowatschin, F. Pantaleo14, T. Peiffer, A. Perieanu, C. Scharf,
P. Schleper, A. Schmidt, S. Schumann, J. Schwandt, J. Sonneveld, H. Stadie, G.
Steinbrück, F.M. Stober, M. Stöver, H. Tholen, D. Troendle, E. Usai, L.
Vanelderen, A. Vanhoefer, B. Vormwald Institut für Experimentelle Kernphysik,
Karlsruhe, Germany
M. Akbiyik, C. Barth, S. Baur, E. Butz, R. Caspart, T. Chwalek, F. Colombo, W.
De Boer, A. Dierlamm, B. Freund, R. Friese, M. Giffels, A. Gilbert, D. Haitz,
F. Hartmann14, S.M. Heindl, U. Husemann, F. Kassel14, S. Kudella, H. Mildner,
M.U. Mozer, Th. Müller, M. Plagge, G. Quast, K. Rabbertz, M. Schröder, I.
Shvetsov, G. Sieber, H.J. Simonis, R. Ulrich, S. Wayand, M. Weber, T. Weiler,
S. Williamson, C. Wöhrmann, R. Wolf Institute of Nuclear and Particle Physics
(INPP), NCSR Demokritos, Aghia Paraskevi, Greece
G. Anagnostou, G. Daskalakis, T. Geralis, V.A. Giakoumopoulou, A. Kyriakis, D.
Loukas, I. Topsis-Giotis National and Kapodistrian University of Athens,
Athens, Greece
G. Karathanasis, S. Kesisoglou, A. Panagiotou, N. Saoulidou National Technical
University of Athens, Athens, Greece
K. Kousouris University of Ioánnina, Ioánnina, Greece
I. Evangelou, C. Foudas, P. Kokkas, S. Mallios, N. Manthos, I. Papadopoulos,
E. Paradas, J. Strologas, F.A. Triantis MTA-ELTE Lendület CMS Particle and
Nuclear Physics Group, Eötvös Loránd University, Budapest, Hungary
M. Csanad, N. Filipovic, G. Pasztor, G.I. Veres18 Wigner Research Centre for
Physics, Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath19, Á. Hunyadi, F. Sikler, V. Veszpremi, A.J.
Zsigmond Institute of Nuclear Research ATOMKI, Debrecen, Hungary
N. Beni, S. Czellar, J. Karancsi20, A. Makovec, J. Molnar, Z. Szillasi
Institute of Physics, University of Debrecen, Debrecen, Hungary
M. Bartók18, P. Raics, Z.L. Trocsanyi, B. Ujvari Indian Institute of Science
(IISc), Bangalore, India
S. Choudhury, J.R. Komaragiri National Institute of Science Education and
Research, Bhubaneswar, India
S. Bahinipati21, S. Bhowmik, P. Mal, K. Mandal, A. Nayak22, D.K. Sahoo21, N.
Sahoo, S.K. Swain Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, R. Chawla, N. Dhingra, A.K. Kalsi, A.
Kaur, M. Kaur, R. Kumar, P. Kumari, A. Mehta, J.B. Singh, G. Walia University
of Delhi, Delhi, India
Ashok Kumar, Aashaq Shah, A. Bhardwaj, S. Chauhan, B.C. Choudhary, R.B. Garg,
S. Keshri, A. Kumar, S. Malhotra, M. Naimuddin, K. Ranjan, R. Sharma Saha
Institute of Nuclear Physics, HBNI, Kolkata, India
R. Bhardwaj, R. Bhattacharya, S. Bhattacharya, U. Bhawandeep, S. Dey, S. Dutt,
S. Dutta, S. Ghosh, N. Majumdar, A. Modak, K. Mondal, S. Mukhopadhyay, S.
Nandan, A. Purohit, A. Roy, D. Roy, S. Roy Chowdhury, S. Sarkar, M. Sharan, S.
Thakur Indian Institute of Technology Madras, Madras, India
P.K. Behera Bhabha Atomic Research Centre, Mumbai, India
R. Chudasama, D. Dutta, V. Jha, V. Kumar, A.K. Mohanty14, P.K. Netrakanti,
L.M. Pant, P. Shukla, A. Topkar Tata Institute of Fundamental Research-A,
Mumbai, India
T. Aziz, S. Dugad, B. Mahakud, S. Mitra, G.B. Mohanty, N. Sur, B. Sutar Tata
Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, Sa. Jain, S.
Kumar, M. Maity23, G. Majumder, K. Mazumdar, T. Sarkar23, N. Wickramage24
Indian Institute of Science Education and Research (IISER), Pune, India
S. Chauhan, S. Dube, V. Hegde, A. Kapoor, K. Kothekar, S. Pandey, A. Rane, S.
Sharma Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
S. Chenarani25, E. Eskandari Tadavani, S.M. Etesami25, M. Khakzad, M.
Mohammadi Najafabadi, M. Naseri, S. Paktinat Mehdiabadi26, F. Rezaei
Hosseinabadi, B. Safarzadeh27, M. Zeinali University College Dublin, Dublin,
Ireland
M. Felcini, M. Grunewald INFN Sezione di Bari a, Università di Bari b,
Politecnico di Bari c, Bari, Italy
M. Abbresciaa,b, C. Calabriaa,b, A. Colaleoa, D. Creanzaa,c, L. Cristellaa,b,
N. De Filippisa,c, M. De Palmaa,b, F. Erricoa,b, L. Fiorea, G. Iasellia,c, S.
Lezkia,b, G. Maggia,c, M. Maggia, G. Minielloa,b, S. Mya,b, S. Nuzzoa,b, A.
Pompilia,b, G. Pugliesea,c, R. Radognaa, A. Ranieria, G. Selvaggia,b, A.
Sharmaa, L. Silvestrisa,14, R. Vendittia, P. Verwilligena INFN Sezione di
Bologna a, Università di Bologna b, Bologna, Italy
G. Abbiendia, C. Battilanaa,b, D. Bonacorsia,b, S. Braibant-Giacomellia,b, R.
Campaninia,b, P. Capiluppia,b, A. Castroa,b, F.R. Cavalloa, S.S. Chhibraa, G.
Codispotia,b, M. Cuffiania,b, G.M. Dallavallea, F. Fabbria, A. Fanfania,b, D.
Fasanellaa,b, P. Giacomellia, C. Grandia, L. Guiduccia,b, S. Marcellinia, G.
Masettia, A. Montanaria, F.L. Navarriaa,b, A. Perrottaa, A.M. Rossia,b, T.
Rovellia,b, G.P. Sirolia,b, N. Tosia INFN Sezione di Catania a, Università di
Catania b, Catania, Italy
S. Albergoa,b, S. Costaa,b, A. Di Mattiaa, F. Giordanoa,b, R. Potenzaa,b, A.
Tricomia,b, C. Tuvea,b INFN Sezione di Firenze a, Università di Firenze b,
Firenze, Italy
G. Barbaglia, K. Chatterjeea,b, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, E. Focardia,b, P. Lenzia,b, M. Meschinia, S. Paolettia, L.
Russoa,28, G. Sguazzonia, D. Stroma, L. Viliania,b,14 INFN Laboratori
Nazionali di Frascati, Frascati, Italy
L. Benussi, S. Bianco, F. Fabbri, D. Piccolo, F. Primavera14 INFN Sezione di
Genova a, Università di Genova b, Genova, Italy
V. Calvellia,b, F. Ferroa, E. Robuttia, S. Tosia,b INFN Sezione di Milano-
Bicocca a, Università di Milano-Bicocca b, Milano, Italy
A. Benagliaa, L. Brianzaa,b, F. Brivioa,b, V. Cirioloa,b, M.E. Dinardoa,b, S.
Fiorendia,b, S. Gennaia, A. Ghezzia,b, P. Govonia,b, M. Malbertia,b, S.
Malvezzia, R.A. Manzonia,b, D. Menascea, L. Moronia, M. Paganonia,b, K.
Pauwelsa,b, D. Pedrinia, S. Pigazzinia,b,29, S. Ragazzia,b, T. Tabarelli de
Fatisa,b INFN Sezione di Napoli a, Università di Napoli ’Federico II’ b,
Napoli, Italy, Università della Basilicata c, Potenza, Italy, Università G.
Marconi d, Roma, Italy
S. Buontempoa, N. Cavalloa,c, S. Di Guidaa,d,14, F. Fabozzia,c, F. Fiengaa,b,
A.O.M. Iorioa,b, W.A. Khana, L. Listaa, S. Meolaa,d,14, P. Paoluccia,14, C.
Sciaccaa,b, F. Thyssena INFN Sezione di Padova a, Università di Padova b,
Padova, Italy, Università di Trento c, Trento, Italy
P. Azzia,14, N. Bacchettaa, L. Benatoa,b, D. Biselloa,b, A. Bolettia,b, R.
Carlina,b, A. Carvalho Antunes De Oliveiraa,b, P. Checchiaa, M. Dall’Ossoa,b,
P. De Castro Manzanoa, T. Dorigoa, U. Dossellia, F. Gasparinia,b, A.
Gozzelinoa, S. Lacapraraa, P. Lujan, M. Margonia,b, A.T. Meneguzzoa,b, N.
Pozzobona,b, P. Ronchesea,b, R. Rossina,b, F. Simonettoa,b, S. Venturaa, M.
Zanettia,b, P. Zottoa,b, G. Zumerlea,b INFN Sezione di Pavia a, Università di
Pavia b, Pavia, Italy
A. Braghieria, A. Magnania,b, P. Montagnaa,b, S.P. Rattia,b, V. Rea, M.
Ressegotti, C. Riccardia,b, P. Salvinia, I. Vaia,b, P. Vituloa,b INFN Sezione
di Perugia a, Università di Perugia b, Perugia, Italy
L. Alunni Solestizia,b, M. Biasinia,b, G.M. Bileia, C. Cecchia,b, D.
Ciangottinia,b, L. Fanòa,b, P. Laricciaa,b, R. Leonardia,b, E. Manonia, G.
Mantovania,b, V. Mariania,b, M. Menichellia, A. Rossia,b, A. Santocchiaa,b, D.
Spigaa INFN Sezione di Pisa a, Università di Pisa b, Scuola Normale Superiore
di Pisa c, Pisa, Italy
K. Androsova, P. Azzurria,14, G. Bagliesia, T. Boccalia, L. Borrello, R.
Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, G. Fedia, L. Gianninia,c, A.
Giassia, M.T. Grippoa,28, F. Ligabuea,c, T. Lomtadzea, E. Mancaa,c, G.
Mandorlia,c, L. Martinia,b, A. Messineoa,b, F. Pallaa, A. Rizzia,b, A. Savoy-
Navarroa,30, P. Spagnoloa, R. Tenchinia, G. Tonellia,b, A. Venturia, P.G.
Verdinia INFN Sezione di Roma a, Sapienza Università di Roma b, Rome, Italy
L. Baronea,b, F. Cavallaria, M. Cipriania,b, N. Dacia, D. Del Rea,b,14, E. Di
Marcoa,b, M. Diemoza, S. Gellia,b, E. Longoa,b, F. Margarolia,b, B.
Marzocchia,b, P. Meridiania, G. Organtinia,b, R. Paramattia,b, F. Preiatoa,b,
S. Rahatloua,b, C. Rovellia, F. Santanastasioa,b INFN Sezione di Torino a,
Università di Torino b, Torino, Italy, Università del Piemonte Orientale c,
Novara, Italy
N. Amapanea,b, R. Arcidiaconoa,c, S. Argiroa,b, M. Arneodoa,c, N. Bartosika,
R. Bellana,b, C. Biinoa, N. Cartigliaa, F. Cennaa,b, M. Costaa,b, R.
Covarellia,b, A. Deganoa,b, N. Demariaa, B. Kiania,b, C. Mariottia, S.
Masellia, E. Migliorea,b, V. Monacoa,b, E. Monteila,b, M. Montenoa, M.M.
Obertinoa,b, L. Pachera,b, N. Pastronea, M. Pelliccionia, G.L. Pinna
Angionia,b, F. Raveraa,b, A. Romeroa,b, M. Ruspaa,c, R. Sacchia,b, K.
Shchelinaa,b, V. Solaa, A. Solanoa,b, A. Staianoa, P. Traczyka,b INFN Sezione
di Trieste a, Università di Trieste b, Trieste, Italy
S. Belfortea, M. Casarsaa, F. Cossuttia, G. Della Riccaa,b, A. Zanettia
Kyungpook National University, Daegu, Korea
D.H. Kim, G.N. Kim, M.S. Kim, J. Lee, S. Lee, S.W. Lee, C.S. Moon, Y.D. Oh, S.
Sekmen, D.C. Son, Y.C. Yang Chonbuk National University, Jeonju, Korea
A. Lee Chonnam National University, Institute for Universe and Elementary
Particles, Kwangju, Korea
H. Kim, D.H. Moon, G. Oh Hanyang University, Seoul, Korea
J.A. Brochero Cifuentes, J. Goh, T.J. Kim Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, D. Gyun, S. Ha, B. Hong, Y. Jo, Y. Kim, K. Lee, K.S.
Lee, S. Lee, J. Lim, S.K. Park, Y. Roh Seoul National University, Seoul, Korea
J. Almond, J. Kim, J.S. Kim, H. Lee, K. Lee, K. Nam, S.B. Oh, B.C. Radburn-
Smith, S.h. Seo, U.K. Yang, H.D. Yoo, G.B. Yu University of Seoul, Seoul,
Korea
M. Choi, H. Kim, J.H. Kim, J.S.H. Lee, I.C. Park Sungkyunkwan University,
Suwon, Korea
Y. Choi, C. Hwang, J. Lee, I. Yu Vilnius University, Vilnius, Lithuania
V. Dudenas, A. Juodagalvis, J. Vaitkus National Centre for Particle Physics,
Universiti Malaya, Kuala Lumpur, Malaysia
I. Ahmed, Z.A. Ibrahim, M.A.B. Md Ali31, F. Mohamad Idris32, W.A.T. Wan
Abdullah, M.N. Yusli, Z. Zolkapli Centro de Investigacion y de Estudios
Avanzados del IPN, Mexico City, Mexico
Reyes-Almanza, R, Ramirez-Sanchez, G., Duran-Osuna, M. C., H. Castilla-Valdez,
E. De La Cruz-Burelo, I. Heredia-De La Cruz33, Rabadan-Trejo, R. I., R. Lopez-
Fernandez, J. Mejia Guisao, A. Sanchez-Hernandez Universidad Iberoamericana,
Mexico City, Mexico
S. Carrillo Moreno, C. Oropeza Barrera, F. Vazquez Valencia Benemerita
Universidad Autonoma de Puebla, Puebla, Mexico
I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada Universidad Autónoma de
San Luis Potosí, San Luis Potosí, Mexico
A. Morelos Pineda University of Auckland, Auckland, New Zealand
D. Krofcheck University of Canterbury, Christchurch, New Zealand
P.H. Butler National Centre for Physics, Quaid-I-Azam University, Islamabad,
Pakistan
A. Ahmad, M. Ahmad, Q. Hassan, H.R. Hoorani, S. Qazi, A. Saddique, M.A. Shah,
M. Waqas National Centre for Nuclear Research, Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, T. Frueboes, M. Górski, M. Kazana, K.
Nawrocki, M. Szleper, P. Zalewski Institute of Experimental Physics, Faculty
of Physics, University of Warsaw, Warsaw, Poland
K. Bunkowski, A. Byszuk34, K. Doroba, A. Kalinowski, M. Konecki, J.
Krolikowski, M. Misiura, M. Olszewski, A. Pyskir, M. Walczak Laboratório de
Instrumentação e Física Experimental de Partículas, Lisboa, Portugal
P. Bargassa, C. Beirão Da Cruz E Silva, A. Di Francesco, P. Faccioli, B.
Galinhas, M. Gallinaro, J. Hollar, N. Leonardo, L. Lloret Iglesias, M.V.
Nemallapudi, J. Seixas, G. Strong, O. Toldaiev, D. Vadruccio, J. Varela Joint
Institute for Nuclear Research, Dubna, Russia
I. Golutvin, V. Karjavin, I. Kashunin, V. Korenkov, G. Kozlov, A. Lanev, A.
Malakhov, V. Matveev35,36, V.V. Mitsyn, V. Palichik, V. Perelygin, S. Shmatov,
V. Smirnov, V. Trofimov, N. Voytishin, B.S. Yuldashev37, A. Zarubin, V.
Zhiltsov Petersburg Nuclear Physics Institute, Gatchina (St. Petersburg),
Russia
Y. Ivanov, V. Kim38, E. Kuznetsova39, P. Levchenko, V. Murzin, V. Oreshkin, I.
Smirnov, V. Sulimov, L. Uvarov, S. Vavilov, A. Vorobyev Institute for Nuclear
Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov,
N. Krasnikov, A. Pashenkov, D. Tlisov, A. Toropin Institute for Theoretical
and Experimental Physics, Moscow, Russia
V. Epshteyn, V. Gavrilov, N. Lychkovskaya, V. Popov, I. Pozdnyakov, G.
Safronov, A. Spiridonov, A. Stepennov, M. Toms, E. Vlasov, A. Zhokin Moscow
Institute of Physics and Technology, Moscow, Russia
T. Aushev, A. Bylinkin36 National Research Nuclear University ’Moscow
Engineering Physics Institute’ (MEPhI), Moscow, Russia
M. Chadeeva40, P. Parygin, D. Philippov, S. Polikarpov, E. Popova, V. Rusinov
P.N. Lebedev Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin36, I. Dremin36, M. Kirakosyan36, A. Terkulov Skobeltsyn
Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow,
Russia
A. Baskakov, A. Belyaev, E. Boos, V. Bunichev, M. Dubinin41, L. Dudko, A.
Gribushin, V. Klyukhin, N. Korneeva, I. Lokhtin, I. Miagkov, S. Obraztsov, M.
Perfilov, V. Savrin, P. Volkov Novosibirsk State University (NSU),
Novosibirsk, Russia
V. Blinov42, Y.Skovpen42, D. Shtol42 State Research Center of Russian
Federation, Institute for High Energy Physics, Protvino, Russia
I. Azhgirey, I. Bayshev, S. Bitioukov, D. Elumakhov, V. Kachanov, A. Kalinin,
D. Konstantinov, V. Krychkine, V. Petrov, R. Ryutin, A. Sobol, S. Troshin, N.
Tyurin, A. Uzunian, A. Volkov University of Belgrade, Faculty of Physics and
Vinca Institute of Nuclear Sciences, Belgrade, Serbia
P. Adzic43, P. Cirkovic, D. Devetak, M. Dordevic, J. Milosevic, V. Rekovic
Centro de Investigaciones Energéticas Medioambientales y Tecnológicas
(CIEMAT), Madrid, Spain
J. Alcaraz Maestre, M. Barrio Luna, M. Cerrada, N. Colino, B. De La Cruz, A.
Delgado Peris, A. Escalante Del Valle, C. Fernandez Bedoya, J.P. Fernández
Ramos, J. Flix, M.C. Fouz, P. Garcia-Abia, O. Gonzalez Lopez, S. Goy Lopez,
J.M. Hernandez, M.I. Josa, A. Pérez-Calero Yzquierdo, J. Puerta Pelayo, A.
Quintario Olmeda, I. Redondo, L. Romero, M.S. Soares, A. Álvarez Fernández
Universidad Autónoma de Madrid, Madrid, Spain
C. Albajar, J.F. de Trocóniz, M. Missiroli, D. Moran Universidad de Oviedo,
Oviedo, Spain
J. Cuevas, C. Erice, J. Fernandez Menendez, I. Gonzalez Caballero, J.R.
González Fernández, E. Palencia Cortezon, S. Sanchez Cruz, P. Vischia, J.M.
Vizan Garcia Instituto de Física de Cantabria (IFCA), CSIC-Universidad de
Cantabria, Santander, Spain
I.J. Cabrillo, A. Calderon, B. Chazin Quero, E. Curras, J. Duarte Campderros,
M. Fernandez, J. Garcia-Ferrero, G. Gomez, A. Lopez Virto, J. Marco, C.
Martinez Rivero, P. Martinez Ruiz del Arbol, F. Matorras, J. Piedra Gomez, T.
Rodrigo, A. Ruiz-Jimeno, L. Scodellaro, N. Trevisani, I. Vila, R. Vilar
Cortabitarte CERN, European Organization for Nuclear Research, Geneva,
Switzerland
D. Abbaneo, E. Auffray, P. Baillon, A.H. Ball, D. Barney, M. Bianco, P. Bloch,
A. Bocci, C. Botta, T. Camporesi, R. Castello, M. Cepeda, G. Cerminara, E.
Chapon, Y. Chen, D. d’Enterria, A. Dabrowski, V. Daponte, A. David, M. De
Gruttola, A. De Roeck, M. Dobson, B. Dorney, T. du Pree, M. Dünser, N. Dupont,
A. Elliott-Peisert, P. Everaerts, F. Fallavollita, G. Franzoni, J. Fulcher, W.
Funk, D. Gigi, K. Gill, F. Glege, D. Gulhan, P. Harris, J. Hegeman, V.
Innocente, P. Janot, O. Karacheban17, J. Kieseler, H. Kirschenmann, V. Knünz,
A. Kornmayer14, M.J. Kortelainen, M. Krammer1, C. Lange, P. Lecoq, C.
Lourenço, M.T. Lucchini, L. Malgeri, M. Mannelli, A. Martelli, F. Meijers,
J.A. Merlin, S. Mersi, E. Meschi, P. Milenovic44, F. Moortgat, M. Mulders, H.
Neugebauer, S. Orfanelli, L. Orsini, L. Pape, E. Perez, M. Peruzzi, A.
Petrilli, G. Petrucciani, A. Pfeiffer, M. Pierini, A. Racz, T. Reis, G.
Rolandi45, M. Rovere, H. Sakulin, C. Schäfer, C. Schwick, M. Seidel, M.
Selvaggi, A. Sharma, P. Silva, P. Sphicas46, A. Stakia, J. Steggemann, M.
Stoye, M. Tosi, D. Treille, A. Triossi, A. Tsirou, V. Veckalns47, M. Verweij,
W.D. Zeuner Paul Scherrer Institut, Villigen, Switzerland
W. Bertl${}^{\textrm{\textdagger}}$, L. Caminada48, K. Deiters, W. Erdmann, R.
Horisberger, Q. Ingram, H.C. Kaestli, D. Kotlinski, U. Langenegger, T. Rohe,
S.A. Wiederkehr ETH Zurich - Institute for Particle Physics and Astrophysics
(IPA), Zurich, Switzerland
F. Bachmair, L. Bäni, P. Berger, L. Bianchini, B. Casal, G. Dissertori, M.
Dittmar, M. Donegà, C. Grab, C. Heidegger, D. Hits, J. Hoss, G. Kasieczka, T.
Klijnsma, W. Lustermann, B. Mangano, M. Marionneau, M.T. Meinhard, D. Meister,
F. Micheli, P. Musella, F. Nessi-Tedaldi, F. Pandolfi, J. Pata, F. Pauss, G.
Perrin, L. Perrozzi, M. Quittnat, M. Reichmann, M. Schönenberger, L.
Shchutska, V.R. Tavolaro, K. Theofilatos, M.L. Vesterbacka Olsson, R. Wallny,
D.H. Zhu Universität Zürich, Zurich, Switzerland
T.K. Aarrestad, C. Amsler49, M.F. Canelli, A. De Cosa, R. Del Burgo, S.
Donato, C. Galloni, T. Hreus, B. Kilminster, J. Ngadiuba, D. Pinna, G. Rauco,
P. Robmann, D. Salerno, C. Seitz, Y. Takahashi, A. Zucchetta National Central
University, Chung-Li, Taiwan
V. Candelise, T.H. Doan, Sh. Jain, R. Khurana, C.M. Kuo, W. Lin, A.
Pozdnyakov, S.S. Yu National Taiwan University (NTU), Taipei, Taiwan
Arun Kumar, P. Chang, Y. Chao, K.F. Chen, P.H. Chen, F. Fiori, W.-S. Hou, Y.
Hsiung, Y.F. Liu, R.-S. Lu, E. Paganis, A. Psallidas, A. Steen, J.f. Tsai
Chulalongkorn University, Faculty of Science, Department of Physics, Bangkok,
Thailand
B. Asavapibhop, K. Kovitanggoon, G. Singh, N. Srimanobhas Çukurova University,
Physics Department, Science and Art Faculty, Adana, Turkey
M.N. Bakirci50, F. Boran, S. Damarseckin, Z.S. Demiroglu, C. Dozen, S. Girgis,
G. Gokbulut, Y. Guler, I. Hos51, E.E. Kangal52, O. Kara, A. Kayis Topaksu, U.
Kiminsu, M. Oglakci, G. Onengut53, K. Ozdemir54, S. Ozturk50, A. Polatoz, H.
Topakli50, S. Turkcapar, I.S. Zorbakir, C. Zorbilmez Middle East Technical
University, Physics Department, Ankara, Turkey
B. Bilin, G. Karapinar55, K. Ocalan56, M. Yalvac, M. Zeyrek Bogazici
University, Istanbul, Turkey
E. Gülmez, M. Kaya57, O. Kaya58, S. Tekten, E.A. Yetkin59 Istanbul Technical
University, Istanbul, Turkey
M.N. Agaras, S. Atay, A. Cakir, K. Cankocak 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, P. Sorokin University of Bristol, Bristol, United Kingdom
R. Aggleton, F. Ball, L. Beck, J.J. Brooke, D. Burns, E. Clement, D. Cussans,
O. Davignon, H. Flacher, J. Goldstein, M. Grimes, G.P. Heath, H.F. Heath, J.
Jacob, L. Kreczko, C. Lucas, D.M. Newbold60, S. Paramesvaran, A. Poll, T.
Sakuma, S. Seif El Nasr-storey, D. Smith, V.J. Smith Rutherford Appleton
Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev61, C. Brew, R.M. Brown, L. Calligaris, D. Cieri, D.J.A.
Cockerill, J.A. Coughlan, K. Harder, S. Harper, E. Olaiya, D. Petyt, C.H.
Shepherd-Themistocleous, A. Thea, I.R. Tomalin, T. Williams Imperial College,
London, United Kingdom
G. Auzinger, R. Bainbridge, S. Breeze, O. Buchmuller, A. Bundock, S. Casasso,
M. Citron, D. Colling, L. Corpe, P. Dauncey, G. Davies, A. De Wit, M. Della
Negra, R. Di Maria, A. Elwood, Y. Haddad, G. Hall, G. Iles, T. James, R. Lane,
C. Laner, L. Lyons, A.-M. Magnan, S. Malik, L. Mastrolorenzo, T. Matsushita,
J. Nash, A. Nikitenko6, V. Palladino, M. Pesaresi, D.M. Raymond, A. Richards,
A. Rose, E. Scott, C. Seez, A. Shtipliyski, S. Summers, A. Tapper, K. Uchida,
M. Vazquez Acosta62, T. Virdee14, N. Wardle, D. Winterbottom, J. Wright, S.C.
Zenz Brunel University, Uxbridge, United Kingdom
J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, I.D. Reid, P. Symonds, L.
Teodorescu, M. Turner Baylor University, Waco, USA
A. Borzou, K. Call, J. Dittmann, K. Hatakeyama, H. Liu, N. Pastika, C. Smith
Catholic University of America, Washington DC, USA
R. Bartek, A. Dominguez The University of Alabama, Tuscaloosa, USA
A. Buccilli, S.I. Cooper, C. Henderson, P. Rumerio, C. West Boston University,
Boston, USA
D. Arcaro, A. Avetisyan, T. Bose, D. Gastler, D. Rankin, C. Richardson, J.
Rohlf, L. Sulak, D. Zou Brown University, Providence, USA
G. Benelli, D. Cutts, A. Garabedian, J. Hakala, U. Heintz, J.M. Hogan, K.H.M.
Kwok, E. Laird, G. Landsberg, Z. Mao, M. Narain, J. Pazzini, S. Piperov, S.
Sagir, R. Syarif, D. Yu University of California, Davis, Davis, USA
R. Band, C. Brainerd, R. Breedon, D. Burns, M. Calderon De La Barca Sanchez,
M. Chertok, J. Conway, R. Conway, P.T. Cox, R. Erbacher, C. Flores, G. Funk,
M. Gardner, W. Ko, R. Lander, C. Mclean, M. Mulhearn, D. Pellett, J. Pilot, S.
Shalhout, M. Shi, J. Smith, M. Squires, D. Stolp, K. Tos, M. Tripathi, Z. Wang
University of California, Los Angeles, USA
M. Bachtis, C. Bravo, R. Cousins, A. Dasgupta, A. Florent, J. Hauser, M.
Ignatenko, N. Mccoll, D. Saltzberg, C. Schnaible, V. Valuev University of
California, Riverside, Riverside, USA
E. Bouvier, K. Burt, R. Clare, J. Ellison, J.W. Gary, S.M.A. Ghiasi Shirazi,
G. Hanson, J. Heilman, P. Jandir, E. Kennedy, F. Lacroix, O.R. Long, M. Olmedo
Negrete, M.I. Paneva, A. Shrinivas, W. Si, L. Wang, H. Wei, S. Wimpenny, B. R.
Yates University of California, San Diego, La Jolla, USA
J.G. Branson, S. Cittolin, M. Derdzinski, R. Gerosa, B. Hashemi, A. Holzner,
D. Klein, G. Kole, V. Krutelyov, J. Letts, I. Macneill, M. Masciovecchio, D.
Olivito, S. Padhi, M. Pieri, M. Sani, V. Sharma, S. Simon, M. Tadel, A.
Vartak, S. Wasserbaech63, J. Wood, F. Würthwein, A. Yagil, G. Zevi Della Porta
University of California, Santa Barbara - Department of Physics, Santa
Barbara, USA
N. Amin, R. Bhandari, J. Bradmiller-Feld, C. Campagnari, A. Dishaw, V. Dutta,
M. Franco Sevilla, C. George, F. Golf, L. Gouskos, J. Gran, R. Heller, J.
Incandela, S.D. Mullin, A. Ovcharova, H. Qu, J. Richman, D. Stuart, I. Suarez,
J. Yoo California Institute of Technology, Pasadena, USA
D. Anderson, J. Bendavid, A. Bornheim, J.M. Lawhorn, H.B. Newman, T. Nguyen,
C. Pena, 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, J. Russ, M. Sun, H.
Vogel, I. Vorobiev, M. Weinberg University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, F. Jensen, A. Johnson, M. Krohn, S. Leontsinis, T.
Mulholland, K. Stenson, S.R. Wagner Cornell University, Ithaca, USA
J. Alexander, J. Chaves, J. Chu, S. Dittmer, K. Mcdermott, N. Mirman, J.R.
Patterson, A. Rinkevicius, A. Ryd, L. Skinnari, L. Soffi, S.M. Tan, Z. Tao, J.
Thom, J. Tucker, P. Wittich, M. Zientek Fermi National Accelerator Laboratory,
Batavia, USA
S. Abdullin, M. Albrow, G. Apollinari, A. Apresyan, A. Apyan, S. Banerjee,
L.A.T. Bauerdick, A. Beretvas, J. Berryhill, P.C. Bhat, G.
Bolla${}^{\textrm{\textdagger}}$, K. Burkett, J.N. Butler, A. Canepa, G.B.
Cerati, H.W.K. Cheung, F. Chlebana, M. Cremonesi, J. Duarte, V.D. Elvira, J.
Freeman, Z. Gecse, E. Gottschalk, L. Gray, D. Green, S. Grünendahl, O.
Gutsche, R.M. Harris, S. Hasegawa, J. Hirschauer, Z. Hu, B. Jayatilaka, S.
Jindariani, M. Johnson, U. Joshi, B. Klima, B. Kreis, S. Lammel, D. Lincoln,
R. Lipton, M. Liu, T. Liu, R. Lopes De Sá, J. Lykken, K. Maeshima, N. Magini,
J.M. Marraffino, S. Maruyama, D. Mason, P. McBride, P. Merkel, S. Mrenna, S.
Nahn, V. O’Dell, K. Pedro, O. Prokofyev, G. Rakness, L. Ristori, B. Schneider,
E. Sexton-Kennedy, A. Soha, W.J. Spalding, L. Spiegel, S. Stoynev, J. Strait,
N. Strobbe, L. Taylor, S. Tkaczyk, N.V. Tran, L. Uplegger, E.W. Vaandering, C.
Vernieri, M. Verzocchi, R. Vidal, M. Wang, H.A. Weber, A. Whitbeck University
of Florida, Gainesville, USA
D. Acosta, P. Avery, P. Bortignon, D. Bourilkov, A. Brinkerhoff, A. Carnes, M.
Carver, D. Curry, R.D. Field, I.K. Furic, J. Konigsberg, A. Korytov, K. Kotov,
P. Ma, K. Matchev, H. Mei, G. Mitselmakher, D. Rank, D. Sperka, N. Terentyev,
L. Thomas, J. Wang, S. Wang, J. Yelton Florida International University,
Miami, USA
Y.R. Joshi, S. Linn, P. Markowitz, J.L. Rodriguez Florida State University,
Tallahassee, USA
A. Ackert, T. Adams, A. Askew, S. Hagopian, V. Hagopian, K.F. Johnson, T.
Kolberg, G. Martinez, T. Perry, H. Prosper, A. Saha, A. Santra, V. Sharma, R.
Yohay Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, V. Bhopatkar, S. Colafranceschi, M. Hohlmann, D. Noonan, T.
Roy, F. Yumiceva University of Illinois at Chicago (UIC), Chicago, USA
M.R. Adams, L. Apanasevich, D. Berry, R.R. Betts, R. Cavanaugh, X. Chen, O.
Evdokimov, C.E. Gerber, D.A. Hangal, D.J. Hofman, K. Jung, J. Kamin, I.D.
Sandoval Gonzalez, M.B. Tonjes, H. Trauger, N. Varelas, H. Wang, Z. Wu, J.
Zhang The University of Iowa, Iowa City, USA
B. Bilki64, W. Clarida, K. Dilsiz65, S. Durgut, R.P. Gandrajula, M.
Haytmyradov, V. Khristenko, J.-P. Merlo, H. Mermerkaya66, A. Mestvirishvili,
A. Moeller, J. Nachtman, H. Ogul67, Y. Onel, F. Ozok68, A. Penzo, C. Snyder,
E. Tiras, J. Wetzel, K. Yi Johns Hopkins University, Baltimore, USA
B. Blumenfeld, A. Cocoros, N. Eminizer, D. Fehling, L. Feng, A.V. Gritsan, P.
Maksimovic, J. Roskes, U. Sarica, M. Swartz, M. Xiao, C. You The University of
Kansas, Lawrence, USA
A. Al-bataineh, P. Baringer, A. Bean, S. Boren, J. Bowen, J. Castle, S.
Khalil, A. Kropivnitskaya, D. Majumder, W. Mcbrayer, M. Murray, C. Royon, S.
Sanders, E. Schmitz, J.D. Tapia Takaki, Q. Wang Kansas State University,
Manhattan, USA
A. Ivanov, K. Kaadze, Y. Maravin, A. Mohammadi, L.K. Saini, N. Skhirtladze, S.
Toda Lawrence Livermore National Laboratory, Livermore, USA
F. Rebassoo, D. Wright University of Maryland, College Park, USA
C. Anelli, A. Baden, O. Baron, A. Belloni, B. Calvert, S.C. Eno, C. Ferraioli,
N.J. Hadley, S. Jabeen, G.Y. Jeng, R.G. Kellogg, J. Kunkle, A.C. Mignerey, F.
Ricci-Tam, Y.H. Shin, A. Skuja, S.C. Tonwar Massachusetts Institute of
Technology, Cambridge, USA
D. Abercrombie, B. Allen, V. Azzolini, R. Barbieri, A. Baty, R. Bi, S. Brandt,
W. Busza, I.A. Cali, M. D’Alfonso, Z. Demiragli, G. Gomez Ceballos, M.
Goncharov, D. Hsu, Y. Iiyama, G.M. Innocenti, M. Klute, D. Kovalskyi, Y.S.
Lai, Y.-J. Lee, A. Levin, P.D. Luckey, B. Maier, A.C. Marini, C. Mcginn, C.
Mironov, S. Narayanan, X. Niu, C. Paus, C. Roland, G. Roland, J. Salfeld-
Nebgen, G.S.F. Stephans, K. Tatar, D. Velicanu, J. Wang, T.W. Wang, B.
Wyslouch University of Minnesota, Minneapolis, USA
A.C. Benvenuti, R.M. Chatterjee, A. Evans, P. Hansen, S. Kalafut, Y. Kubota,
Z. Lesko, J. Mans, S. Nourbakhsh, N. Ruckstuhl, R. Rusack, J. Turkewitz
University of Mississippi, Oxford, USA
J.G. Acosta, S. Oliveros University of Nebraska-Lincoln, Lincoln, USA
E. Avdeeva, K. Bloom, D.R. Claes, C. Fangmeier, R. Gonzalez Suarez, R.
Kamalieddin, I. Kravchenko, J. Monroy, J.E. Siado, G.R. Snow, B. Stieger State
University of New York at Buffalo, Buffalo, USA
M. Alyari, J. Dolen, A. Godshalk, C. Harrington, I. Iashvili, D. Nguyen, A.
Parker, S. Rappoccio, B. Roozbahani Northeastern University, Boston, USA
G. Alverson, E. Barberis, A. Hortiangtham, A. Massironi, D.M. Morse, D. Nash,
T. Orimoto, R. Teixeira De Lima, D. Trocino, D. Wood Northwestern University,
Evanston, USA
S. Bhattacharya, O. Charaf, K.A. Hahn, N. Mucia, N. Odell, B. Pollack, M.H.
Schmitt, K. Sung, M. Trovato, M. Velasco University of Notre Dame, Notre Dame,
USA
N. Dev, M. Hildreth, K. Hurtado Anampa, C. Jessop, D.J. Karmgard, N. Kellams,
K. Lannon, N. Loukas, N. Marinelli, F. Meng, C. Mueller, Y. Musienko35, M.
Planer, A. Reinsvold, R. Ruchti, G. Smith, S. Taroni, M. Wayne, M. Wolf, A.
Woodard The Ohio State University, Columbus, USA
J. Alimena, L. Antonelli, B. Bylsma, L.S. Durkin, S. Flowers, B. Francis, A.
Hart, C. Hill, W. Ji, B. Liu, W. Luo, D. Puigh, B.L. Winer, H.W. Wulsin
Princeton University, Princeton, USA
S. Cooperstein, O. Driga, P. Elmer, J. Hardenbrook, P. Hebda, S. Higginbotham,
D. Lange, J. Luo, D. Marlow, K. Mei, I. Ojalvo, J. Olsen, C. Palmer, P.
Piroué, D. Stickland, C. Tully University of Puerto Rico, Mayaguez, USA
S. Malik, S. Norberg Purdue University, West Lafayette, USA
A. Barker, V.E. Barnes, S. Das, S. Folgueras, L. Gutay, M.K. Jha, M. Jones,
A.W. Jung, A. Khatiwada, D.H. Miller, N. Neumeister, C.C. Peng, J.F. Schulte,
J. Sun, F. Wang, W. Xie Purdue University Northwest, Hammond, USA
T. Cheng, N. Parashar, J. Stupak Rice University, Houston, USA
A. Adair, B. Akgun, Z. Chen, K.M. Ecklund, F.J.M. Geurts, M. Guilbaud, W. Li,
B. Michlin, M. Northup, B.P. Padley, J. Roberts, J. Rorie, Z. Tu, J. Zabel
University of Rochester, Rochester, USA
A. Bodek, P. de Barbaro, R. Demina, Y.t. Duh, T. Ferbel, M. Galanti, A.
Garcia-Bellido, J. Han, O. Hindrichs, A. Khukhunaishvili, K.H. Lo, P. Tan, M.
Verzetti The Rockefeller University, New York, USA
R. Ciesielski, K. Goulianos, C. Mesropian Rutgers, The State University of New
Jersey, Piscataway, USA
A. Agapitos, J.P. Chou, Y. Gershtein, T.A. Gómez Espinosa, E. Halkiadakis, M.
Heindl, E. Hughes, S. Kaplan, R. Kunnawalkam Elayavalli, S. Kyriacou, A. Lath,
R. Montalvo, K. Nash, M. Osherson, H. Saka, S. Salur, S. Schnetzer, D.
Sheffield, S. Somalwar, R. Stone, S. Thomas, P. Thomassen, M. Walker
University of Tennessee, Knoxville, USA
A.G. Delannoy, M. Foerster, J. Heideman, G. Riley, K. Rose, S. Spanier, K.
Thapa Texas A&M University, College Station, USA
O. Bouhali69, A. Castaneda Hernandez69, A. Celik, M. Dalchenko, M. De Mattia,
A. Delgado, S. Dildick, R. Eusebi, J. Gilmore, T. Huang, T. Kamon70, R.
Mueller, Y. Pakhotin, R. Patel, A. Perloff, L. Perniè, D. Rathjens, A.
Safonov, A. Tatarinov, K.A. Ulmer Texas Tech University, Lubbock, USA
N. Akchurin, J. Damgov, F. De Guio, P.R. Dudero, J. Faulkner, E. Gurpinar, S.
Kunori, K. Lamichhane, S.W. Lee, T. Libeiro, T. Peltola, S. Undleeb, I.
Volobouev, Z. Wang Vanderbilt University, Nashville, USA
S. Greene, A. Gurrola, R. Janjam, W. Johns, C. Maguire, A. Melo, H. Ni, P.
Sheldon, S. Tuo, J. Velkovska, Q. Xu University of Virginia, Charlottesville,
USA
M.W. Arenton, P. Barria, B. Cox, R. Hirosky, M. Joyce, A. Ledovskoy, H. Li, C.
Neu, T. Sinthuprasith, Y. Wang, E. Wolfe, F. Xia Wayne State University,
Detroit, USA
R. Harr, P.E. Karchin, J. Sturdy, S. Zaleski University of Wisconsin -
Madison, Madison, WI, USA
M. Brodski, J. Buchanan, C. Caillol, S. Dasu, L. Dodd, S. Duric, B. Gomber, M.
Grothe, M. Herndon, A. Hervé, U. Hussain, P. Klabbers, A. Lanaro, A. Levine,
K. Long, R. Loveless, G.A. Pierro, G. Polese, T. Ruggles, A. Savin, N. Smith,
W.H. Smith, D. Taylor, N. Woods †: Deceased
1: Also at Vienna University of Technology, Vienna, Austria
2: Also at State Key Laboratory of Nuclear Physics and Technology, Peking
University, Beijing, China
3: Also at Universidade Estadual de Campinas, Campinas, Brazil
4: Also at Universidade Federal de Pelotas, Pelotas, Brazil
5: Also at Université Libre de Bruxelles, Bruxelles, Belgium
6: Also at Institute for Theoretical and Experimental Physics, Moscow, Russia
7: Also at Joint Institute for Nuclear Research, Dubna, Russia
8: Also at Suez University, Suez, Egypt
9: Now at British University in Egypt, Cairo, Egypt
10: Also at Fayoum University, El-Fayoum, Egypt
11: Now at Helwan University, Cairo, Egypt
12: Also at Université de Haute Alsace, Mulhouse, France
13: Also at Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State
University, Moscow, Russia
14: Also at CERN, European Organization for Nuclear Research, Geneva,
Switzerland
15: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
16: Also at University of Hamburg, Hamburg, Germany
17: Also at Brandenburg University of Technology, Cottbus, Germany
18: Also at MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
19: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary
20: Also at Institute of Physics, University of Debrecen, Debrecen, Hungary
21: Also at Indian Institute of Technology Bhubaneswar, Bhubaneswar, India
22: Also at Institute of Physics, Bhubaneswar, India
23: Also at University of Visva-Bharati, Santiniketan, India
24: Also at University of Ruhuna, Matara, Sri Lanka
25: Also at Isfahan University of Technology, Isfahan, Iran
26: Also at Yazd University, Yazd, Iran
27: Also at Plasma Physics Research Center, Science and Research Branch,
Islamic Azad University, Tehran, Iran
28: Also at Università degli Studi di Siena, Siena, Italy
29: Also at INFN Sezione di Milano-Bicocca; Università di Milano-Bicocca,
Milano, Italy
30: Also at Purdue University, West Lafayette, USA
31: Also at International Islamic University of Malaysia, Kuala Lumpur,
Malaysia
32: Also at Malaysian Nuclear Agency, MOSTI, Kajang, Malaysia
33: Also at Consejo Nacional de Ciencia y Tecnología, Mexico city, Mexico
34: Also at Warsaw University of Technology, Institute of Electronic Systems,
Warsaw, Poland
35: Also at Institute for Nuclear Research, Moscow, Russia
36: Now at National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
37: Also at Institute of Nuclear Physics of the Uzbekistan Academy of
Sciences, Tashkent, Uzbekistan
38: Also at St. Petersburg State Polytechnical University, St. Petersburg,
Russia
39: Also at University of Florida, Gainesville, USA
40: Also at P.N. Lebedev Physical Institute, Moscow, Russia
41: Also at California Institute of Technology, Pasadena, USA
42: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia
43: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia
44: Also at University of Belgrade, Faculty of Physics and Vinca Institute of
Nuclear Sciences, Belgrade, Serbia
45: Also at Scuola Normale e Sezione dell’INFN, Pisa, Italy
46: Also at National and Kapodistrian University of Athens, Athens, Greece
47: Also at Riga Technical University, Riga, Latvia
48: Also at Universität Zürich, Zurich, Switzerland
49: Also at Stefan Meyer Institute for Subatomic Physics (SMI), Vienna,
Austria
50: Also at Gaziosmanpasa University, Tokat, Turkey
51: Also at Istanbul Aydin University, Istanbul, Turkey
52: Also at Mersin University, Mersin, Turkey
53: Also at Cag University, Mersin, Turkey
54: Also at Piri Reis University, Istanbul, Turkey
55: Also at Izmir Institute of Technology, Izmir, Turkey
56: Also at Necmettin Erbakan University, Konya, Turkey
57: Also at Marmara University, Istanbul, Turkey
58: Also at Kafkas University, Kars, Turkey
59: Also at Istanbul Bilgi University, Istanbul, Turkey
60: Also at Rutherford Appleton Laboratory, Didcot, United Kingdom
61: Also at School of Physics and Astronomy, University of Southampton,
Southampton, United Kingdom
62: Also at Instituto de Astrofísica de Canarias, La Laguna, Spain
63: Also at Utah Valley University, Orem, USA
64: Also at Beykent University, Istanbul, Turkey
65: Also at Bingol University, Bingol, Turkey
66: Also at Erzincan University, Erzincan, Turkey
67: Also at Sinop University, Sinop, Turkey
68: Also at Mimar Sinan University, Istanbul, Istanbul, Turkey
69: Also at Texas A&M University at Qatar, Doha, Qatar
70: Also at Kyungpook National University, Daegu, Korea