TOP-20-003

TOP-20-003

[inst1]The CMS Collaboration

# First measurement of the cross section for top quark pair production with
additional charm jets using dileptonic final states in $\Pp\Pp$ collisions at
$\sqrt{s}=13\TeV$

###### Abstract

The first measurement of the inclusive cross section for top quark pairs
($\ttbar$) produced in association with two additional charm jets is
presented. The analysis uses the dileptonic final states of $\ttbar$ events
produced in proton-proton collisions at a centre-of-mass energy of 13. The
data correspond to an integrated luminosity of 41.5, recorded by the CMS
experiment at the LHC. A new charm jet identification algorithm provides input
to a neural network that is trained to distinguish among $\ttbar$ events with
two additional charm (${\ttbar\PQc\PAQc}$), bottom (${\ttbar\PQb\PAQb}$), and
light-flavour or gluon (${\ttbar\text{LL}}$) jets. By means of a template
fitting procedure, the inclusive ${\ttbar\PQc\PAQc}$, ${\ttbar\PQb\PAQb}$, and
${\ttbar\text{LL}}$ cross sections are simultaneously measured, together with
their ratios to the inclusive $\ttbar$ \+ two jets cross section. This
provides measurements of the ${\ttbar\PQc\PAQc}$ and ${\ttbar\PQb\PAQb}$ cross
sections of $8.0\pm 1.1\stat\pm 1.3\syst\unit{pb}$ and $4.09\pm 0.34\stat\pm
0.55\syst\unit{pb}$, respectively, in the full phase space. The results are
consistent with expectations from the standard model.

##  0.1 Introduction

The modelling of top quark pair ($\ttbar$) production in association with jets
from the hadronization of bottom () or charm () quarks, referred to as jets
and jets, respectively, in proton-proton ($\Pp\Pp$) collisions at the CERN LHC
is challenging. Calculations of the production cross section for top quark
pairs with additional pairs of jets (${\ttbar\PQb\PAQb}$) are available at
next-to-leading order (NLO) in quantum chromodynamics (QCD) [1, 2, 3, 4, 5],
but suffer from large uncertainties due to the choice of factorization
($\mu_{\text{F}}$) and renormalization ($\mu_{\text{R}}$) scales. The
uncertainties arise from the different energy (or mass) scales in
${\ttbar\PQb\PAQb}$ production, which range from the large scales associated
with the top quark mass ($m_{\PQt}$), to the relatively small scales
associated with additional jets resulting mostly from gluon splitting into
$\PQb\PAQb$ pairs. To better understand the ${\ttbar\PQb\PAQb}$ process, the
ATLAS and CMS experiments have conducted several measurements in $\Pp\Pp$
collisions at centre-of-mass energies of 7, 8, and $13\TeV$ [6, 7, 8, 9, 10,
11, 12, 13].

The production of a $\ttbar$ pair with an additional pair of jets
(${\ttbar\PQc\PAQc}$) has received far less attention, both theoretically and
experimentally. It is much harder to experimentally distinguish than jets from
light-flavour (LF) or gluon jets, explaining the challenge of simultaneously
separating ${\ttbar\PQc\PAQc}$ and ${\ttbar\PQb\PAQb}$ events from a large
background of $\ttbar$ events with additional LF or gluon jets
(${\ttbar\text{LL}}$). With the development of a charm jet identification
algorithm (” tagger”) [14, 15], these signatures can now be disentangled. We
present the first measurement of the inclusive ${\ttbar\PQc\PAQc}$ cross
section and its ratio to the inclusive $\ttbar$ \+ two jets
(${\ttbar\text{jj}}$) cross section. A fully consistent treatment of the
different additional jet flavours is ensured using a technique that
simultaneously extracts the ${\ttbar\PQc\PAQc}$, ${\ttbar\PQb\PAQb}$, and
${\ttbar\text{LL}}$ cross sections. The measurement is performed in the
dileptonic decay channel of the $\ttbar$ events using a data sample of
$\Pp\Pp$ collisions at a centre-of-mass energy of $13\TeV$, collected with the
CMS detector in 2017, corresponding to an integrated luminosity of 41.5 [16].
This data set benefits from the upgrade of the pixel tracking detector [17],
which was installed in winter 2016–2017 and which has been shown to
significantly improve the performance of heavy-flavour (HF) jet identification
[18].

Although the additional jets in ${\ttbar\PQb\PAQb}$ events are predominantly
produced via gluon splitting into $\PQb\PAQb$ pairs, they can also originate
from the decay of a Higgs boson (). Previous measurements of Higgs boson
production in association with a top quark pair, where the Higgs boson decays
into a pair of quarks ($\ttbar\PH$, $\PH\to\PQb\PAQb$) [19, 20, 21, 22],
suffer from a non-resonant background of gluon-induced ${\ttbar\PQb\PAQb}$
events. Similarly, ${\ttbar\PQc\PAQc}$ production constitutes a non-negligible
background due to misidentification of jets as jets. The ${\ttbar\PQc\PAQc}$
background contribution has previously been estimated from simulations,
entailing uncertainties at the level of 50% [20, 22]. The measurement
presented here provides a basis for an estimate of the ${\ttbar\PQc\PAQc}$
background from data that can be used to significantly reduce this uncertainty
in future $\ttbar\PH$ analyses.

##  0.2 The CMS detector

The central feature of the CMS apparatus is a superconducting solenoid of 6m
internal diameter, providing a magnetic field of 3.8T. Within the solenoid
volume are a silicon pixel and strip tracker, a lead tungstate crystal
electromagnetic calorimeter (ECAL), and a brass and scintillator hadron
calorimeter (HCAL), each composed of a barrel and two endcap sections. The
silicon tracker measures charged particles within the pseudorapidity range
$\abs{\eta}<2.5$. During the LHC running period when the data used for this
analysis were recorded, the silicon tracker consisted of 1856 silicon pixel
and 15 148 silicon strip detector modules. For nonisolated particles of
$1<\pt<10\GeV$ and $\abs{\eta}<2.5$, the track resolutions are typically 1.5%
in and 20–75in the transverse impact parameter [23]. Forward calorimeters
extend the $\eta$ coverage provided by the barrel and endcap detectors. Muons
are detected in gas-ionization chambers embedded in the steel flux-return yoke
outside the solenoid. A more detailed description of the CMS detector,
together with a definition of the coordinate system used and the relevant
kinematic variables, can be found in Ref. [24].

Events of interest are selected using a two-tiered trigger system [25]. 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 fixed time interval of about 4. The second level, known as the high-
level trigger, consists of a farm of processors running a version of the full
event reconstruction software optimized for fast processing, and reduces the
event rate to around 1kHz before data storage.

##  0.3 Event simulation

Samples of signal and background events are simulated using Monte Carlo (MC)
event generators based on a fixed-order perturbative QCD calculation with up
to four noncollinear high-$\pt$ partons, supplemented with parton showering
(PS) and multiparton interactions. The matrix element (ME) generation of
$\ttbar$ events is performed with (v2) [26, 27, 28, 29, 30] at NLO in QCD,
followed by a simulation of the PS using $\PYTHIA 8.230$ [31] (referred to as
$\PYTHIA 8$ in the following), using the CP5 underlying event tune [32] and
the NNPDF3.1 [33] parton distribution functions (PDFs). A value of
$m_{\PQt}=172.5\GeV$ is used in the event generation. The $\ttbar$ cross
section is scaled to a theoretical prediction at next-to-next-to-leading order
in QCD including resummation of next-to-next-to-leading logarithmic soft-gluon
terms, which yields $\sigma_{\ttbar}=832^{\,+39.9}_{\,-45.8}$pb [34]. The
results are also compared to those from $\ttbar$ production simulated with the
(v2.4.2) [35] ME generator at NLO accuracy and with FxFx jet matching [36].

The background processes for this analysis consist mainly of Drell–Yan (DY)
and single top quark events, with additional minor contributions from
$+\,$jets, diboson, triboson, $\ttbar\PZ$, $\ttbar\PW$, and $\ttbar\PH$ events
(collectively referred to as rare backgrounds). For all these samples, the PS
is simulated using $\PYTHIA 8$. The ME generation of the DY, $+\,$jets,
triboson, $\ttbar\PZ$, and $\ttbar\PW$ events is handled using at leading
order in QCD, with MLM jet matching [37]. The $\ttbar\PH$ events are generated
using . Single top quark production in the $t$ and $s$ channels are simulated
at NLO in the four-flavour scheme using and , respectively, while the
$\PQt\PW$ channel is simulated at NLO with in the five-flavour scheme. The
diboson samples are simulated at leading order in QCD using $\PYTHIA 8$ for
both the ME calculations and PS description.

The interactions between particles and the material in the CMS detector are
simulated using (v10.02) [38]. The effect of additional $\Pp\Pp$ interactions
in the same or nearby beam crossings as the hard-scattering process (pileup)
is modelled by adding simulated minimum bias collisions generated in $\PYTHIA
8$.

##  0.4 Signal definition

A typical Feynman diagram describing the dileptonic ${\ttbar\PQc\PAQc}$
process is shown in Fig. 1. To provide an unambiguous interpretation of the
results, a generator-level definition of the event categories is needed, based
on the flavours of the additional jets. The HF jets are identified at the
generator level using the procedure of ghost matching [39], where in addition
to the reconstructed final-state particles, the generated and hadrons are
clustered into the jets. However, the modulus of the hadron four-momentum is
set to a small number to prevent these generated hadrons from affecting the
reconstructed jet momentum and to ensure that only their directional
information is retained. Jets that contain both and hadrons are labelled as
jets, since the hadrons most likely originate from a decay of a hadron. The
results of this measurement are reported in terms of the fiducial and full
phase spaces.

Figure 1:  Example of a Feynman diagram at the lowest order in QCD, describing
the dileptonic decay channel of a top quark pair with an additional
$\PQc\PAQc$ pair produced via gluon splitting.

Fiducial phase space: In the definition of the fiducial phase space, all of
the final-state particles (except for the neutrinos) resulting from the decay
chain:
$\Pp\Pp\to\ttbar\text{jj}\to\ell^{+}\nu_{\ell}\PQb\ell^{-}\overline{\nu}_{\ell}\PAQb\text{jj}$
are required to be within the region of the detector in which these objects
can be properly reconstructed. The fiducial phase space is therefore defined
by the presence of two oppositely charged electrons, muons, or $\tau$ leptons
($\ell$) at the generator level with $\pt>25\GeV$ and $\abs{\eta}<2.4$. Each
lepton is required to originate from a decay of a boson, which in turn results
from a top quark decay. Particle-level jets are defined by clustering
generated final-state particles with a mean lifetime greater than 30 ps,
excluding neutrinos, using the anti-algorithm [40, 41] with a distance
parameter of 0.4. We demand two particle-level jets from the top quark decays
with $\pt>20\GeV$ and $\abs{\eta}<2.4$. Besides these two jets, two additional
particle-level jets must be present, with the same kinematic requirements
imposed. Jets that lie within
$\Delta\text{R}=\sqrt{\smash[b]{(\Delta\eta)^{2}+(\Delta\phi)^{2}}}<0.4$ of
either one of the leptons from the boson decays, where $\phi$ denotes the
azimuthal angle, are excluded. The above requirements define the inclusive
$\ttbar\text{jj}$ signal, which is then subdivided based on the flavour
content of the additional particle-level jets (not from the top quark decays)
into the following categories:

${\ttbar\PQb\PAQb}$:

At least two additional jets are present, each containing at least one hadron.

${\ttbar\PQb\text{L}}$:

Only one additional jet is present, containing at least one hadron. In
addition, at least one additional LF or jet is present. This category results
from ${\ttbar\PQb\PAQb}$ events in which one of the two additional jets is
outside the acceptance, or two jets are merged into one.

${\ttbar\PQc\PAQc}$:

No additional jets are present, but at least two additional jets are found,
each containing at least one hadron.

${\ttbar\PQc\text{L}}$:

No additional jets are present, and only one additional jet is found,
containing at least one hadron. In addition, at least one additional LF jet is
present. This category results from ${\ttbar\PQc\PAQc}$ events in which one of
the two additional jets is outside the acceptance, or two jets are merged into
one.

${\ttbar\text{LL}}$:

No additional or jets are present, but at least two additional LF jets are
within the acceptance.

All other $\ttbar$ events that do not fit in any of the above categories,
because they do not fulfill the acceptance requirements described in the
definition of the fiducial phase space, are labelled as “$\ttbar$+other”.
These could for example be events with semileptonic or fully hadronic $\ttbar$
decays that pass the event selection criteria outlined in Section 0.6, or
events for which the leptons or the particle-level jets as described above are
not within the fiducial detector volume.

Full phase space: The definition of the full phase space comprises dileptonic,
semileptonic, and fully hadronic $\ttbar$ decays that contain in addition at
least two particle-level jets with $\pt>20\GeV$ and $\abs{\eta}<2.4$. These
jets must not originate from the decays of the top quarks or bosons. There are
no requirements imposed on the generator-level leptons or on the particle-
level jets that result from the top quark and boson decays. The measurement in
the fiducial phase space is extrapolated to the full phase space by applying
an acceptance factor, estimated from simulations, to each signal category
based on the additional jet flavours.

The definition of the fiducial phase space is much closer to the reconstructed
phase space in which the measurement is performed, and therefore expected to
suffers less from theoretical uncertainties that affect the extrapolation from
the fiducial to the full phase space. However, the full phase space definition
is better suited for comparison with theoretical calculations.

##  0.5 Object reconstruction

The global event reconstruction is based on the particle-flow algorithm [42],
which aims to reconstruct and identify each individual particle in an event by
combining information from the various elements of the CMS detector. The
energy of photons is obtained from the ECAL measurement. The energy of
electrons is determined from a combination of the electron momentum at the
primary interaction vertex as determined by the tracker, the energy of the
corresponding ECAL cluster, and the energy sum of all bremsstrahlung photons
spatially compatible with originating from the electron track. The energy of
muons is obtained from the curvature of the corresponding track. The energy of
charged hadrons is determined from a combination of their momentum measured in
the tracker and the matching ECAL and HCAL energy deposits, corrected for
zero-suppression effects and for the response function of the calorimeters to
hadronic showers. Finally, the energy of neutral hadrons is obtained from the
corresponding corrected ECAL and HCAL energies.

For each event, hadronic jets are clustered from these reconstructed particles
using the infrared and collinear safe anti-algorithm with a distance parameter
of 0.4. Jet momentum is determined as the vectorial sum of all particle
momenta in the jet, and is found from simulations to be, on average, within 5
to 10% of the true momentum over the whole $\pt$ spectrum and detector
acceptance. Pileup interactions can contribute additional tracks and
calorimetric energy depositions, increasing the apparent jet momentum. To
mitigate this effect, tracks identified as originating from pileup vertices
are discarded and an offset correction is applied to correct for remaining
contributions. Jet energy corrections are derived from simulation studies so
that the average measured response of jets becomes identical to that of
particle-level jets. In situ measurements of the momentum balance in dijet,
photon+jets, $\,+\,$jets, and multijet events are used to determine any
residual differences between the jet energy scale in data and simulations
[43], and appropriate corrections are made. Additional selection criteria are
applied to each jet to remove jets potentially dominated by instrumental
effects or reconstruction failures.

The missing transverse momentum vector is computed as the negative of the
vector sum of the of all the particle-flow candidates in an event, and its
magnitude is denoted as [44]. The is modified to account for corrections to
the energy scale of the reconstructed jets in the event.

The candidate vertex with the largest value of summed physics-object $\pt^{2}$
is taken to be the primary $\Pp\Pp$ interaction vertex. The physics objects
are the jets and the associated .

Jets originating from the hadronization of and quarks are identified using the
deep combined secondary vertex (DeepCSV) algorithm [15], which uses
information on the decay vertices of long-lived mesons and the impact
parameters of the charged particle tracks as input to a deep neural network
(NN) classifier. For the identification of jets, a medium working point is
chosen, corresponding to a ${\approx}70\%$ efficiency for correctly selecting
jets and a misidentification probability for LF (c) jets of
${\approx}1\,(12)\%$. The same algorithm also provides discriminators to
distinguish jets from LF and jets, which are collectively referred to as
tagging discriminators. The information from the distributions of these
observables plays a key role in this analysis, and the calibration of the
tagger is further discussed in Section 0.8.

##  0.6 Event selection

An event selection has been employed to select a subset of events that
consists almost exclusively (more than 95% as evaluated from simulations) of
dileptonic $\ttbar$ events with at least two additional jets. Exactly two
reconstructed, oppositely charged leptons (either electrons or muons) are
required to be present. This procedure also selects $\PGt$ leptons that decay
into an electron or a muon. The electrons and muons are required to have
$\pt>25\GeV$ and $\abs{\eta}<2.4$, and should be isolated from other objects
in the event. At least four jets are required in the event, all of which must
be spatially separated from the isolated leptons by imposing
$\Delta\text{R}(\ell,\text{jet})>0.5$. Only jets with $\pt>30\GeV$ and
$\abs{\eta}<2.4$ are considered. As described in Section 0.7, an assignment of
the jets to the expected partons is made to identify the jets from top quark
decays and jets originating from additional radiation. The two jets assigned
to the quarks from the top quark decays are required to be tagged. Neutrinos
from leptonically decaying bosons are not detected, but instead contribute to
the , which is required to exceed $30\GeV$ in events with two electrons
($\Pe\Pe$) or two muons ($\PGm\PGm$), in order to reduce contributions from DY
events. In events with one electron and one muon ($\Pe\PGm$), no requirement
is imposed on . In order to further reduce the contribution from DY production
in $\Pe\Pe$ and $\PGm\PGm$ events, the invariant mass of the two leptons
($m_{\ell\ell}$) is required to be outside of the boson mass window,
$m_{\ell\ell}\not\in[m_{\PZ}-15,m_{\PZ}+15]\GeV$, with $m_{\PZ}=91.2\GeV$
[45]. For all events, it is required that $m_{\ell\ell}>12\GeV$ in order to
minimize contributions from low-mass resonances.

##  0.7 Matching jets to partons

The distinction among the ${\ttbar\PQc\PAQc}$, ${\ttbar\PQb\PAQb}$, and
${\ttbar\text{LL}}$ categories relies on the correct identification of the
additional jets not coming from the decay of the top quarks. Assuming a 100%
branching fraction for the decay $\PQt\to\PQb\PW$, and focusing on the
dileptonic decay channel, two jets are expected from the top quark decays and
at least two additional jets are required through the other event selection
criteria. In practice, not all jets from top quark decays will be
reconstructed within the acceptance of the detector and additional jets will
also not necessarily pass the reconstruction and selection criteria. In this
section, a matching procedure is described to achieve the most accurate
correspondence between the final-state jets and the expected partons. This is
done by considering all possible permutations of four jets in the collection
of jets passing the selection criteria described in Section 0.6 and training
an NN to identify the correct jet-parton assignment.

Whether a given permutation corresponds to a correct jet-parton assignment can
be inferred from different quantities, such as the jet kinematic variables,
and tagging discriminators, and angular separations and invariant masses
between pairs of jets (and between jets and leptons). The NN processes for
each event all possible jet-parton assignments and is trained on the
aforementioned observables to give the highest possible score to the correct
permutation. For the ${\ttbar\text{LL}}$, ${\ttbar\PQc\PAQc}$, and
${\ttbar\PQc\text{L}}$ categories, the and tagging discriminators dominate the
decision made by the NN. For the ${\ttbar\PQb\PAQb}$ category all four partons
have the same flavour, so the correct assignment of the four jets relies
mainly on the angular separations and invariant masses between jets or
leptons. The best assignment for the ${\ttbar\PQb\text{L}}$ category benefits
from a combination of all these observables.

The main objective is to identify additional HF jets in the event. In the
assignment of jets from the $\ttbar$ decays, it does not matter which jet is
matched to the decay of the top quark or antiquark. Any permutation for which
these jets are reversed can still be considered appropriate for this
measurement. If at least one additional or jet is present, a correct
permutation has to prioritize these over other additional LF jets in the
event. The NN is also trained to choose a permutation in which the first
additional jet has a larger tagging discriminator value than the second
additional jet. With these considerations in mind, three output classes are
defined. One of the NN outputs denotes the probability for a given permutation
to correspond to the correct jet-parton assignment ($P^{+}$). Another output
class refers to those permutations for which the additional jets are correctly
matched, but the jets from the decays are reversed as explained above
($P^{\times}$). The third output represents all permutations for which the
matching is wrong ($P^{-}$), meaning that either at least one of the jets from
the decays is not correctly matched, or an additional HF jet is found but is
not identified as one of the two additional jets. The best jet-parton
assignment is then identified by selecting the permutation with the highest
value of:

$\max\left(\frac{P^{+}}{P^{+}+P^{-}},\frac{P^{\times}}{P^{\times}+P^{-}}\right).$
(1)

The NN is trained using the Keras deep learning library [46], interfaced to
TensorFlow [47] as a back end. Its architecture is composed of two fully
connected hidden layers with 50 neurons each, and with a rectified linear unit
activation function. The training is performed using an independent data set
of simulated $\ttbar$ events that pass all the event selections outlined in
Section 0.6, except for the tagging requirement. Only events for which the two
generator-level quarks from top quark decays lie within $\Delta\text{R}<$ 0.3
of a reconstructed jet are used for the training. These constitute
$\approx$76% of the simulated $\ttbar$ events with at least two additional
jets. Of those, the NN correctly identifies the two additional () jets in 50
(30)% of the cases for ${\ttbar\PQc\PAQc}$ (${\ttbar\PQb\PAQb}$) events. For
${\ttbar\PQb\PAQb}$ events, the matching is more challenging because the HF
tagging information cannot help in separating additional jets from those
originating from top quark decays. A comparison between data and simulations
of the NN score for the best permutation in each event is shown in Fig. 2,
indicating good agreement between the two.

Figure 2:  Comparison between data (points) and simulated predictions
(histograms) for the distribution of the NN score for the best permutations of
jet-parton assignments found in each event. Underflow is included in the first
bin. The lower panel shows the ratio of the yields in data to those predicted
in simulations. The vertical bars represent the statistical uncertainties in
data, while the hatched band show the statistical uncertainty in the simulated
predictions.

##  0.8 Charm jet identification and calibration

The DeepCSV HF tagging algorithm has a multiclass output structure that
predicts the probabilities for each jet to contain a single hadron
($P(\PQb)$), two hadrons ($P(\PQb\PQb)$), one or more hadrons ($P(\PQc)$), or
no or hadrons ($P\text{({\PQu}{\PQd}{\PQs}{\Pg})}$). The tagger used
throughout this analysis is constructed from the sum $P(\PQb)+P(\PQb\PQb)$.
However, different combinations of output values provide different types of
discrimination. Since the displacements of tracks and secondary vertices for
jets are on average smaller than those for jets and larger than those for LF
jets, a charm jet identification algorithm uses a combination of two
discriminators. The first is used to distinguish jets from LF jets (CvsL) and
the second separates jets from jets (CvsB). The CvsL and CvsB discriminators
are defined from the multiclass output structure of the DeepCSV algorithm as:

CvsL
$\displaystyle=\frac{P(\PQc)}{P(\PQc)+P\text{({\PQu}{\PQd}{\PQs}{\Pg})}},$ (2)
CvsB $\displaystyle=\frac{P(\PQc)}{P(\PQc)+P(\PQb)+P(\PQb\PQb)}.$

The tagging discriminators require calibration with the data, given that these
algorithms are trained on simulated events and are therefore prone to
mismodelling effects in the input variables. This is illustrated in the upper
row of plots in Fig. 3, especially the lower panels, where the ratio of the
data to the simulated predictions shows that the CvsL (left) and CvsB (right)
discriminator distributions for the first additional jet are not well
described by the simulations. These discrepancies can be as large as 40%. In
order to use these tagger discriminators for the fit of the template
distributions (as discussed in Section 0.9), the shape of the two-dimensional
(CvsL, CvsB) distribution is corrected to reproduce the distribution observed
in data. To this end, a novel calibration technique is employed that uses
three control regions selecting for $\ttbar$, $+\,$, and DY$\,+\,$jets events,
which are enriched in , , and LF jets, respectively [48]. By means of an
iterative fit in these three control regions, a set of scale factors for each
jet flavour is derived, as a function of both the CvsL and CvsB discriminator
values of a given jet. The effectiveness of this calibration for different jet
flavours has been validated in the three corresponding control regions
outlined above, showing that the calibrated distributions in simulations
indeed match those in data within the associated uncertainties. Additionally,
the method shows no bias when applied to pseudo-data constructed from
simulated events that have artificial scale factors applied to them.

After applying this calibration to the simulated events, the distributions of
the CvsL and CvsB discriminators provide a better match to those observed in
data, as seen in the lower plots of Fig. 3. Uncertainties related to this
calibration are found to be dominated by the choice of $\mu_{\text{R}}$ and
$\mu_{\text{F}}$ for and LF jets and by PS uncertainties for jets, together
with a significant contribution from statistical uncertainties for all jet
flavours.

Figure 3:  Comparison between data (points) and simulated predictions
(histograms) for the CvsL (left column) and CvsB (right column) tagging
discriminator distributions of the first additional jet, before (upper row)
and after (lower row) applying the tagging calibration. The lower panels show
the ratio of the yields in data to those predicted in simulations. The
vertical bars represent the statistical uncertainties in data, while the
hatched band show the statistical uncertainty in the simulated predictions.

##  0.9 Fit to an event-based neural network discriminator

The extraction of the ${\ttbar\PQc\PAQc}$, ${\ttbar\PQb\PAQb}$, and
${\ttbar\text{LL}}$ cross sections proceeds by means of a template fit to an
observable that can distinguish among the different flavour categories. Since
the differentiation relies mainly on the additional-jet flavour, the tagging
discriminators of the first and second additional jets provide a natural
choice for separating the different signals. The CvsL discriminator
distinguishes the ${\ttbar\PQc\PAQc}$ and ${\ttbar\PQb\PAQb}$ from the
${\ttbar\text{LL}}$ events, whereas the CvsB discriminator provides additional
information that can be used to distinguish between the ${\ttbar\PQc\PAQc}$
and ${\ttbar\PQb\PAQb}$ events. The flavour tagging information for the second
additional jet allows the ${\ttbar\PQc\text{L}}$ and ${\ttbar\PQb\text{L}}$
processes to be identified. Additional information is extracted from two
kinematic variables. The first is the angular separation $\Delta$R between the
two additional jets. In the ${\ttbar\PQc\PAQc}$ and ${\ttbar\PQb\PAQb}$
processes, the additional jets arise predominantly from gluon splitting into
$\PQc\PAQc$ and $\PQb\PAQb$ pairs, respectively, and are therefore expected to
be more collimated. The second is the NN score for the best permutation (shown
in Fig. 2), which is expected to be larger on average for ${\ttbar\text{LL}}$
events because the additional jets are well distinguished from the jets from
top quark decays. This observable indirectly incorporates information on the
event kinematic features through its input variables.

Using the six aforementioned observables, an NN is trained. Given the
relatively small number of inputs, an architecture for the NN is chosen with
one hidden layer that comprises 30 neurons with a rectified linear unit
activation function. This NN predicts output probabilities for five output
classes: $P$(${\ttbar\PQc\PAQc}$), $P$(${\ttbar\PQc\text{L}}$),
$P$(${\ttbar\PQb\PAQb}$), $P$(${\ttbar\PQb\text{L}}$), and
$P$(${\ttbar\text{LL}}$). To obtain the distributions that are used in the
fit, these probabilities are projected onto a two-dimensional phase space
spanned by two derived discriminators:

$\displaystyle\Delta_{\PQb}^{\PQc}$
$\displaystyle=\frac{P({\ttbar\PQc\PAQc})}{P({\ttbar\PQc\PAQc})+P({\ttbar\PQb\PAQb})},$
(3) $\displaystyle\Delta_{\text{L}}^{\PQc}$
$\displaystyle=\frac{P({\ttbar\PQc\PAQc})}{P({\ttbar\PQc\PAQc})+P({\ttbar\text{LL}})}.$

These discriminators can be interpreted as topology-specific tagger
discriminators that augment the information on the jet flavour of the two
additional jets with additional event kinematic features to optimally
distinguish different signal categories. The two-dimensional distributions of
these discriminators, normalized to unit area, are shown in Fig. 4 for
simulated dileptonic $\ttbar$ events. The different signal categories occupy
different parts of this phase space, demonstrating that a fit of templates
derived from these distributions to the data can be used to extract the
${\ttbar\PQc\PAQc}$, ${\ttbar\PQb\PAQb}$, and ${\ttbar\text{LL}}$ cross
sections. It can be seen that the lower right corner of this phase space is
almost exclusively populated with ${\ttbar\PQb\PAQb}$ events, whereas the
upper right corner is dominated by ${\ttbar\PQc\PAQc}$ events.

Figure 4:  Normalized two-dimensional distributions of $\Delta_{\PQb}^{\PQc}$
vs. $\Delta_{\text{L}}^{\PQc}$ in simulated dileptonic $\ttbar$ events, for
each of the event categories outlined in Section 0.4. The colour scale on the
right shows the normalized event yields.

Templates are constructed separately for each dilepton channel (ee, $\mu\mu$,
and e$\mu$) and are fitted simultaneously to data by means of a binned maximum
likelihood fit assuming Poisson statistics. A first fit is performed to
extract the absolute cross section ($\sigma$) of the ${\ttbar\PQc\PAQc}$,
${\ttbar\PQb\PAQb}$, and ${\ttbar\text{LL}}$ events in the fiducial phase
space, where the expected yield in each bin is parametrized using the
function:

$\displaystyle
F_{i}\left(\sigma_{{\ttbar\PQc\PAQc}},\,\sigma_{{\ttbar\PQb\PAQb}},\,\sigma_{{\ttbar\text{LL}}}\right)=$
(4) $\displaystyle\Lumi^{\text{int}}\left\\{\
\sigma_{{\ttbar\PQc\PAQc}}\,\epsilon_{{\ttbar\PQc\PAQc}}\left(f^{\text{norm}}_{{\ttbar\PQc\PAQc},i}+\frac{N^{\text{MC}}_{{\ttbar\PQc\text{L}}}}{N^{\text{MC}}_{{\ttbar\PQc\PAQc}}}\,f^{\text{norm}}_{{\ttbar\PQc\text{L}},i}\right)+\sigma_{{\ttbar\PQb\PAQb}}\,\epsilon_{{\ttbar\PQb\PAQb}}\left(f^{\text{norm}}_{{\ttbar\PQb\PAQb},i}+\frac{N^{\text{MC}}_{{\ttbar\PQb\text{L}}}}{N^{\text{MC}}_{{\ttbar\PQb\PAQb}}}\,f^{\text{norm}}_{{\ttbar\PQb\text{L}},i}\right)\right.$
$\displaystyle\qquad+\left.\,\sigma_{{\ttbar\text{LL}}}\,\epsilon_{{\ttbar\text{LL}}}\left(f^{\text{norm}}_{{\ttbar\text{LL}},i}+\frac{N^{\text{MC}}_{\ttbar+\text{other}}}{N^{\text{MC}}_{{\ttbar\text{LL}}}}\,f^{\text{norm}}_{\ttbar+\text{other},i}\right)+\,\sigma_{\text{bkg}}\,f^{\text{norm}}_{\text{bkg},i}\frac{}{}\right\\}.$

Here, $\Lumi^{\text{int}}$ denotes the total integrated luminosity and
$f^{\text{norm}}_{i}$ represents a given bin (with index $i$) of the simulated
two-dimensional template, normalized to unit area. The measurements in the
reconstructed phase space are corrected to the fiducial phase space through an
efficiency ($\epsilon$). To extract the result in the full phase space, an
additional acceptance factor, $\mathcal{A}$, is applied to the extracted
fiducial cross section to account for the difference in acceptance between the
fiducial and full phase spaces. The efficiency and acceptance factors are
summarized in Table 0.9, and are calculated from simulations. The largest
contribution to the acceptance can be attributed to the extrapolation from the
dileptonic to the fully inclusive decays of the $\ttbar$ pairs. The remaining
contributions are due to changes in kinematic requirements on the generator-
level objects. As discussed in Section 0.4, the ${\ttbar\PQc\text{L}}$ and
${\ttbar\PQb\text{L}}$ categories result from ${\ttbar\PQc\PAQc}$ and
${\ttbar\PQb\PAQb}$ events, respectively, where one of the additional HF jets
is outside the acceptance or both are merged into one jet. Therefore, these
components are scaled with the same factors as the ${\ttbar\PQc\PAQc}$ and
${\ttbar\PQb\PAQb}$ templates, respectively, such that the relative yield of
${\ttbar\PQc\text{L}}$ (${\ttbar\PQb\text{L}}$) with respect to
${\ttbar\PQc\PAQc}$ (${\ttbar\PQb\PAQb}$) events is fixed to that predicted in
the simulations. The predicted yields from the simulations are denoted by
$N^{\text{MC}}_{k}$, where $k$ denotes the signal process. The $\ttbar$+other
component is scaled with the same factor as the ${\ttbar\text{LL}}$ component,
motivated by their similar LF content. Uncertainties in the ratios of
simulated yields are taken into account in the fit. The background processes
are summed together into one template and their yield is fixed to the
predictions from simulations, with uncertainties taken into account as
discussed in Section 0.10. The sum of the cross sections for the background
processes (obtained from simulations) is denoted as $\sigma_{\text{bkg}}$.

Selection efficiencies and acceptance factors for events in different signal
categories. The values are obtained from simulated $\ttbar$ events.  Event
category ${\ttbar\PQb\PAQb}$ ${\ttbar\PQb\text{L}}$ ${\ttbar\PQc\PAQc}$
${\ttbar\PQc\text{L}}$ ${\ttbar\text{LL}}$ Efficiency $\epsilon$ (%) 12.0 8.5
6.8 5.6 4.5 Acceptance $\mathcal{A}$ (%) 2.9 2.5 2.0 2.0 2.3

A second binned maximum likelihood fit is performed assuming Poisson
statistics to extract the ratios of the ${\ttbar\PQc\PAQc}$ and
${\ttbar\PQb\PAQb}$ cross sections to the overall inclusive
${\ttbar\text{jj}}$ cross section (denoted $R_{\PQc}$ and $R_{\PQb}$,
respectively) in the fiducial phase space, with the expected yield in bin $i$
calculated using the function:

$\displaystyle
F_{i}\left(\sigma_{{\ttbar\text{jj}}},R_{\PQc},R_{\PQb}\right)=$ (5)
$\displaystyle\Lumi^{\text{int}}\sigma_{{\ttbar\text{jj}}}\left\\{\
R_{\PQc}\,\epsilon_{{\ttbar\PQc\PAQc}}\left(f^{\text{norm}}_{{\ttbar\PQc\PAQc},i}+\frac{N^{\text{MC}}_{{\ttbar\PQc\text{L}}}}{N^{\text{MC}}_{{\ttbar\PQc\PAQc}}}\,f^{\text{norm}}_{{\ttbar\PQc\text{L}},i}\right)+R_{\PQb}\,\epsilon_{{\ttbar\PQb\PAQb}}\left(f^{\text{norm}}_{{\ttbar\PQb\PAQb},i}+\frac{N^{\text{MC}}_{{\ttbar\PQb\text{L}}}}{N^{\text{MC}}_{{\ttbar\PQb\PAQb}}}\,f^{\text{norm}}_{{\ttbar\PQb\text{L}},i}\right)\right.$
$\displaystyle\qquad\qquad+\left.\left(1-R_{\PQc}-R_{\PQc\text{L}}-R_{\PQb}-R_{\PQb\text{L}}\right)\epsilon_{{\ttbar\text{LL}}}\left(f^{\text{norm}}_{{\ttbar\text{LL}},i}+\frac{N^{\text{MC}}_{\ttbar+\text{other}}}{N^{\text{MC}}_{{\ttbar\text{LL}}}}\,f^{\text{norm}}_{\ttbar+\text{other},i}\right)\right\\}$
$\displaystyle+\mathcal{L}^{\text{int}}\,\sigma_{\text{bkg}}\,f^{\text{norm}}_{\text{bkg},i},$

with

$\begin{cases}R_{\PQc\text{L}}=\,R_{\PQc}\left(\frac{N^{\text{MC}}_{{\ttbar\PQc\text{L}}}\,\epsilon_{{\ttbar\PQc\PAQc}}}{N^{\text{MC}}_{{\ttbar\PQc\PAQc}}\,\epsilon_{{\ttbar\PQc\text{L}}}}\right),\\\
R_{\PQb\text{L}}=\,R_{\PQb}\left(\frac{N^{\text{MC}}_{{\ttbar\PQb\text{L}}}\,\epsilon_{{\ttbar\PQb\PAQb}}}{N^{\text{MC}}_{{\ttbar\PQb\PAQb}}\,\epsilon_{{\ttbar\PQb\text{L}}}}\right).\end{cases}$
(6)

The parameters $R_{\PQc\text{L}}$ and $R_{\PQb\text{L}}$ are used to denote,
respectively, the ratios of the ${\ttbar\PQc\text{L}}$ and
${\ttbar\PQb\text{L}}$ cross sections to the inclusive ${\ttbar\text{jj}}$
cross section, and are defined as a function of $R_{\PQc}$ and $R_{\PQb}$ in
Eq. (6).

##  0.10 Systematic uncertainties

This section summarizes the systematic uncertainties related to the extraction
of the ${\ttbar\PQc\PAQc}$, ${\ttbar\PQb\PAQb}$, and ${\ttbar\text{LL}}$ cross
sections (and the ratios $R_{\PQc}$ and $R_{\PQb}$), as well as corrections
applied to the simulated events to account for differences with respect to
data. Systematic uncertainties are treated as nuisance parameters in the
template fit to data that can affect both the shapes of the templates and the
yields of the signal and background processes. A smoothing procedure [49] is
applied to the templates that describe the uncertainty variations affecting
the template shape. The sources of systematic uncertainties are subdivided
into experimental and theoretical components and are discussed below. The
individual impacts from each source of uncertainty in the cross sections (and
ratios) for the fiducial phase space are summarized in Table 0.10. The
dominant systematic uncertainties are related to the tagging calibration,
followed by jet energy scale uncertainties, as well as uncertainties related
to the matching between ME and PS, and the choice of $\mu_{\text{R}}$ and
$\mu_{\text{F}}$ scales in the ME calculation.

Sources of systematic uncertainties in the measured parameters and their
individual impact in percent for the fiducial phase space. The upper (lower)
rows of the table list uncertainties related to the experimental conditions
(theoretical modelling). The last row gives the overall systematic uncertainty
in each quantity, which results from the nuisance parameter variations in the
fit and is not the quadrature sum of the individual components.

Sources | Systematic uncertainty (%)
---|---
$\Delta\sigma_{{\ttbar\PQc\PAQc}}$ | $\Delta\sigma_{{\ttbar\PQb\PAQb}}$ | $\Delta\sigma_{{\ttbar\text{LL}}}$ |  $\Delta$$R_{\PQc}$ |  $\Delta$$R_{\PQb}$
Jet energy scale | 7.9 | 4.0 | 5.4 | 3.5 | 4.6
Jet energy resolution | 2.1 | 0.8 | 1.1 | 2.7 | 1.4
tagging calibration | 8.1 | 3.9 | 1.8 | 8.0 | 4.4
Lepton identification and isolation | 1.2 | 1.2 | 1.1 | 0.3 | 0.4
Trigger | 2.0 | 2.0 | 2.0 |  $<$0.1 |  $<$0.1
Pileup | 0.7 | 0.7 | 0.4 | 1.0 | 0.4
Total integrated luminosity | 2.3 | 2.3 | 2.3 |  $<$0.1 |  $<$0.1
$\mu_{\text{R}}$ and $\mu_{\text{F}}$ scales in ME | 6.2 | 7.4 | 1.5 | 5.7 | 7.5
PS scale | 0.8 | 0.9 | 0.2 | 0.5 | 0.6
PDF | 0.5 | 0.1 | 0.1 | 0.4 | 0.1
ME-PS matching | 7.5 | 5.7 | 4.0 | 2.7 | 1.4
Underlying event | 1.9 | 2.0 | 0.9 | 0.8 | 1.0
fragmentation | 0.4 | 3.6 | 0.3 | 0.4 | 3.6
${\ttbar\PQb\text{L}}(\PQc\text{L})$/${\ttbar\PQb\PAQb}(\ccbar)$ and $\ttbar$+other/${\ttbar\text{LL}}$ | 2.4 | 1.7 | 1.2 | 1.9 | 1.4
Efficiency (theoretical) | 2.0 | 2.0 | 2.0 |  $<$0.1 |  $<$0.1
Simulated sample size | 4.4 | 3.0 | 1.3 | 4.3 | 3.1
Background normalization | 0.6 |  $<$0.1 | 0.4 | 0.3 | 0.4
Total | 15.1 | 12.6 | 8.3 | 12.0 | 10.3

Experimental uncertainties: These uncertainties affect both the shape and
normalization of the templates. The jet energy resolution is known to be worse
in data than in simulations, and a corresponding additional smearing is
applied to the simulated jet energies [43]. Systematic uncertainties are
estimated by varying the smearing of the jet energy within its uncertainties
in the calculation of the cross sections. Similarly, we take into account
corrections and uncertainties from observed differences in the jet energy
scale. These corrections are evaluated and applied in different regions of jet
$\pt$ and $\abs{\eta}$. Observed differences in electron and muon
identification, isolation, reconstruction, and trigger efficiencies between
data and simulations are taken into account through $\pt$\- and
$\eta$-dependent scale factors, with the corresponding uncertainties accounted
for. The distribution of the number of pileup collisions in simulated events
is reweighted to match the distribution observed in data, using an inelastic
$\Pp\Pp$ cross section of 69.2mb [50]. An uncertainty related to this
correction is applied by varying this inelastic cross section by $\pm$ 4.6%.
An uncertainty of 2.3% [16] in the total integrated luminosity is also taken
into account. The scale factors extracted from the tagging calibration are
applied to the simulated events, and corresponding uncertainties are
considered. Most of the theoretical and experimental sources of uncertainty
are in common between the control regions in which the tagging calibration is
derived and the $\ttbar$ dileptonic signal region considered in this analysis.
In such cases, the common uncertainties are considered fully correlated and
evaluated simultaneously.

Theoretical uncertainties: In the ME calculation, the choice of
$\mu_{\text{R}}$ and $\mu_{\text{F}}$ can have an impact on the kinematic
distributions of the final-state objects. Uncertainties in these scales are
taken into account by rescaling $\mu_{\text{F}}$ and $\mu_{\text{R}}$ up or
down by a factor of two at the ME level [51, 52]. In the PS, the uncertainty
in the value of the strong coupling constant () evaluated at $m_{\PZ}$ is
taken into account by varying the renormalization scale of QCD emissions in
the initial- and final-state radiation up and down by a factor of two.
Similarly, uncertainties in the momentum transfer from quarks to hadrons (
fragmentation) in the PS have been included. Uncertainties associated with the
PDF, as well as with the value of in the PDF of the proton are considered. For
all of the aforementioned theoretical sources of uncertainty, the effects of
these variations on the shape of the fitted templates are taken into account
in the fit, whereas their impact on the signal yields is considered as an
uncertainty in the theoretical prediction to which the measurement is compared
in Section 0.11. The residual theoretical uncertainty that enters the measured
cross sections through the $\epsilon_{i}$ terms in Eqs. (4) and (5) is
accounted for by a separate theoretical uncertainty in the efficiency. The
experimental uncertainties in the efficiency are already taken into account
through the uncertainties in the normalization of the templates. The matching
between the ME and PS is governed by a parameter called $h_{\text{damp}}$. The
value of this parameter is varied according to
$h_{\text{damp}}=\left(1.379^{\,+0.926}_{\,-0.505}\right)m_{\PQt}$ [32] and
included as a systematic uncertainty in the yield. The remnants of the
$\Pp\Pp$ collisions that do not take part in the hard scattering are referred
to as the underlying event. Their kinematic distributions are tuned in the
generators to match those observed in the data [32]. The uncertainties arising
from the changes in these tunes are propagated to the measured cross sections.
The effects of the theoretical uncertainties listed above on the fixed ratios
of ${\ttbar\PQb\text{L}}$ to ${\ttbar\PQb\PAQb}$, ${\ttbar\PQc\text{L}}$ to
${\ttbar\PQc\PAQc}$, and $\ttbar$+other to ${\ttbar\text{LL}}$ yields, taken
from simulations in Eqs. (4) and (5), are also included in the fit. An
uncertainty of 25% is assigned to the total cross section of all background
processes, based on the precision of recent measurements of the dominant
background processes [53, 54]. The statistical uncertainty due to the finite
size of the simulated samples is taken into account in the fit. In
extrapolating the results from the fiducial to the full phase space, some of
these theoretical uncertainties also affect the acceptance for the different
signal categories. The individual and combined impacts of different sources of
uncertainty in the acceptance are summarized in Table 0.10.

Sources of theoretical uncertainties in the acceptance, used to extrapolate
the results from the fiducial to the full phase space, for different signal
categories, together with their individual impact in percent. The last row of
the table quotes the total relative uncertainty in the acceptance, calculated
by adding in quadrature the effects from individual sources.  Sources
Uncertainty in the acceptance (%) ${\ttbar\PQc\PAQc}$ ${\ttbar\PQc\text{L}}$
${\ttbar\PQb\PAQb}$ ${\ttbar\PQb\text{L}}$ ${\ttbar\text{LL}}$
$\mu_{\text{R}}$ and $\mu_{\text{F}}$ 1.0 0.8 0.4 0.6 0.5 PS scale 1.6 1.6 1.2
1.4 1.5 PDF 0.6 0.8 1.2 1.2 0.9 Underlying event 1.0 1.2 1.8 1.1 0.3 ME-PS
matching 2.1 2.3 1.0 2.5 1.7 fragmentation 2.2 2.0 4.2 2.6 2.3 Total 3.8 3.8
5.0 4.2 3.4

##  0.11 Results

The binning of the two-dimensional $\Delta_{\text{L}}^{\PQc}$ vs.
$\Delta_{\PQb}^{\PQc}$ distribution is chosen to be:

$\Delta_{\text{L}}^{\PQc}\otimes\Delta_{\PQb}^{\PQc}:[0,0.45,0.6,0.9,1.0]\otimes[0,0.3,0.45,0.5,1.0].$
(7)

This gives a total of 16 bins with varying compositions of signal categories.
From these 16 bins, a one-dimensional histogram is created in which the first
four bins correspond to the first bin in $\Delta_{\text{L}}^{\PQc}$, with
increasing values of $\Delta_{\PQb}^{\PQc}$, and then analogously for the
remainder of the bins. This histogram is shown in Fig. 5 after normalizing the
simulated templates according to the fitted cross sections. The factors by
which the templates of the ${\ttbar\PQc\PAQc}$, ${\ttbar\PQb\PAQb}$, and
${\ttbar\text{LL}}$ processes (using the ME generator) are scaled to match the
data are denoted $\mu_{{\ttbar\PQc\PAQc}}$, $\mu_{{\ttbar\PQb\PAQb}}$, and
$\mu_{{\ttbar\text{LL}}}$, respectively. Their measured values from the fit
are displayed in the top panel of Fig. 5, together with their combined
statistical and systematic uncertainties.

Figure 5:  A one-dimensional representation of the two-dimensional
$\Delta_{\text{L}}^{\PQc}$ vs. $\Delta_{\PQb}^{\PQc}$ distributions, in the
simulations (histograms) and in data (points), after normalizing the simulated
templates according to the fitted cross sections. The lower panel shows the
ratio of the yields in data to those predicted in the simulations. The brown
and grey uncertainty bands denote, respectively, the statistical and total
uncertainties from the fit. The factors ($\mu$) by which the templates of the
different processes (using the ME generator) are scaled, are also displayed,
together with their combined statistical and systematic uncertainties.

The measured cross sections in the fiducial and full phase spaces, together
with their statistical and systematic uncertainties, are summarized in Table
0.11 and compared to predictions using the and ME generators. Uncertainties in
the measured values of the cross sections and ratios are determined from the
points where the decrease in the logarithm of the profiled likelihood from the
best-fit value intersects with 0.5. The inclusive ${\ttbar\PQc\PAQc}$ cross
section and the ratio $R_{\PQc}$ are measured here for the first time. They
are in agreement with both theoretical predictions, within the uncertainties.

Measured parameter values in the fiducial (upper rows) and full (lower rows)
phase spaces with their statistical and systematic uncertainties listed in
that order. The last two columns display the expectations from the simulated
$\ttbar$ samples using the or ME generators. The uncertainties quoted for
these predictions include the contributions from the theoretical uncertainties
listed in the lower rows of Table 0.10, as well as the uncertainty in the
$\ttbar$ cross section.  Result Fiducial phase space
$\sigma_{{\ttbar\PQc\PAQc}}$ [pb] $0.165\,\pm\,0.023\pm 0.025$
$0.187\,\pm\,0.038$ $0.189\,\pm\,0.032$ $\sigma_{{\ttbar\PQb\PAQb}}$ [pb]
$0.119\,\pm\,0.010\pm 0.015$ $0.097\,\pm\,0.021$ $0.101\,\pm\,0.023$
$\sigma_{{\ttbar\text{LL}}}$ [pb] $5.40\,\pm\,0.11\pm 0.45$ $5.95\,\pm\,1.02$
$6.32\,\pm\,0.94$ $R_{\PQc}$ [%] $2.42\,\pm\,0.32\pm 0.29$ $2.53\,\pm\,0.18$
$2.43\,\pm\,0.17$ $R_{\PQb}$ [%] $1.75\,\pm\,0.14\pm 0.18$ $1.31\,\pm\,0.12$
$1.30\,\pm\,0.16$ Full phase space $\sigma_{{\ttbar\PQc\PAQc}}$ [pb]
$8.0\,\pm\,1.1\pm 1.3$ $9.1\,\pm\,1.8$ $8.9\,\pm\,1.5$
$\sigma_{{\ttbar\PQb\PAQb}}$ [pb] $4.09\,\pm\,0.34\pm 0.55$ $3.34\,\pm\,0.72$
$3.39\,\pm\,0.66$ $\sigma_{{\ttbar\text{LL}}}$ [pb] $231\,\pm\,5\pm 21$
$255\,\pm\,43$ $261\,\pm\,37$ $R_{\PQc}$ [%] $2.69\,\pm\,0.36\pm 0.32$
$2.81\,\pm\,0.20$ $2.72\,\pm\,0.19$ $R_{\PQb}$ [%] $1.37\,\pm\,0.11\pm 0.14$
$1.03\,\pm\,0.08$ $1.03\,\pm\,0.09$

In addition, two-dimensional likelihood scans are performed over different
combinations of cross sections or ratios. These are shown in Fig. 6 for the
measurements in the fiducial phase space. The 68 and 95% confidence level
contours are shown, along with the predictions from the and ME generators. The
measured values and theoretical predictions agree at the level of one to two
standard deviations. Results for $\sigma_{{\ttbar\PQb\PAQb}}$ and $R_{\PQb}$
are consistent with previous measurements targeting specifically this
signature [6, 7, 8, 9, 10, 11, 12, 13] and show the same tendency to be
slightly above the predictions from simulations.

Figure 6:  Results of the two-dimensional likelihood scans for several
combinations of the parameters of interest in the fiducial phase space. The
best-fit value (black cross) with the corresponding 68% (full) and 95%
(dashed) confidence level () contours are shown, compared to the theoretical
predictions using either the (blue star) or (red diamond) ME generators.
Uncertainties in the theoretical predictions are displayed by the horizontal
and vertical bars on the markers.

##  0.12 Summary

The production of a top quark pair ($\ttbar$) in association with additional
bottom or charm jets at the LHC provides challenges both in the theoretical
modelling and experimental measurement of this process. Whereas $\ttbar$
production with two additional bottom jets (${\ttbar\PQb\PAQb}$) has been
measured by the ATLAS and CMS Collaborations at different centre-of-mass
energies [6, 7, 8, 9, 10, 11, 12, 13], this analysis presents the first
measurement of the cross section for $\ttbar$ production with two additional
charm jets (${\ttbar\PQc\PAQc}$). The analysis is conducted using data from
proton-proton collisions recorded by the CMS experiment at a centre-of-mass
energy of $13\TeV$, corresponding to an integrated luminosity of 41.5. The
measurement is performed in the dileptonic channel of the $\ttbar$ decays and
relies on the use of recently developed charm jet identification algorithms (
tagging). A template fitting method is used, based on the outputs of a neural
network classifier trained to identify the signal categories defined by the
flavour of the additional jets. This allows the simultaneous extraction of the
cross section for the ${\ttbar\PQc\PAQc}$, ${\ttbar\PQb\PAQb}$, and $\ttbar$
with two additional light-flavour or gluon jets (${\ttbar\text{LL}}$)
processes. A novel multidimensional calibration of the shape of the tagging
discriminator distributions is employed, such that this information can be
reliably used in the neural network classifier.

The ${\ttbar\PQc\PAQc}$ cross section is measured for the first time to be
$0.165\pm 0.023\stat\pm 0.025\syst\unit{pb}$ in the fiducial phase space
(matching closely the sensitive region of the detector) and $8.0\pm
1.1\stat\pm 1.3\syst\unit{pb}$ in the full phase space. The ratio of the
${\ttbar\PQc\PAQc}$ to the inclusive $\ttbar$ \+ two jets cross section is
found to be $(2.42\pm 0.32\stat\pm 0.29\syst)\%$ in the fiducial phase space
and $(2.69\pm 0.36\stat\pm 0.32\syst)\%$ in the full phase space. Agreement is
observed at the level of one to two standard deviations between the measured
values and theoretical predictions for the ${\ttbar\PQc\PAQc}$,
${\ttbar\PQb\PAQb}$, and ${\ttbar\text{LL}}$ processes.

###### Acknowledgements.

We congratulate our colleagues in the CERN accelerator departments for the
excellent performance of the LHC and thank the technical and administrative
staffs at CERN and at other CMS institutes for their contributions to the
success of the CMS effort. In addition, we gratefully acknowledge the
computing centres and personnel of the Worldwide LHC Computing Grid for
delivering so effectively the computing infrastructure essential to our
analyses. Finally, we acknowledge the enduring support for the construction
and operation of the LHC and the CMS detector provided by the following
funding agencies: BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq,
CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST,
and NSFC (China); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RIF
(Cyprus); SENESCYT (Ecuador); MoER, ERC PUT and ERDF (Estonia); Academy of
Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and
HGF (Germany); GSRT (Greece); NKFIA (Hungary); DAE and DST (India); IPM
(Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); MES
(Latvia); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT,
LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC
(Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom,
RAS, RFBR, and NRC KI (Russia); MESTD (Serbia); SEIDI, CPAN, PCTI, and FEDER
(Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST
(Taipei); ThEPCenter, IPST, STAR, and NSTDA (Thailand); TUBITAK and TAEK
(Turkey); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (USA).
Individuals have received support from the Marie-Curie programme and the
European Research Council and Horizon 2020 Grant, contract Nos. 675440,
724704, 752730, and 765710 (European Union); the Leventis Foundation; the A.P.
Sloan Foundation; the Alexander von Humboldt Foundation; the Belgian Federal
Science Policy Office; the Fonds pour la Formation à la Recherche dans
l’Industrie et dans l’Agriculture (FRIA-Belgium); the Agentschap voor
Innovatie door Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and
FWO (Belgium) under the “Excellence of Science – EOS” – be.h project n.
30820817; the Beijing Municipal Science & Technology Commission, No.
Z191100007219010; the Ministry of Education, Youth and Sports (MEYS) of the
Czech Republic; the Deutsche Forschungsgemeinschaft (DFG) under Germany’s
Excellence Strategy – EXC 2121 “Quantum Universe” – 390833306; the Lendület
(“Momentum”) Programme and the János Bolyai Research Scholarship of the
Hungarian Academy of Sciences, the New National Excellence Program ÚNKP, the
NKFIA research grants 123842, 123959, 124845, 124850, 125105, 128713, 128786,
and 129058 (Hungary); the Council of Science and Industrial Research, India;
the HOMING PLUS programme of the Foundation for Polish Science, cofinanced
from European Union, Regional Development Fund, the Mobility Plus programme of
the Ministry of Science and Higher Education, the National Science Center
(Poland), contracts Harmonia 2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543,
2014/15/B/ST2/03998, and 2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406;
the National Priorities Research Program by Qatar National Research Fund; the
Ministry of Science and Higher Education, project no. 0723-2020-0041 (Russia);
the Tomsk Polytechnic University Competitiveness Enhancement Program; the
Programa Estatal de Fomento de la Investigación Científica y Técnica de
Excelencia María de Maeztu, grant MDM-2015-0509 and the Programa Severo Ochoa
del Principado de Asturias; the Thalis and Aristeia programmes cofinanced by
EU-ESF and the Greek NSRF; the Rachadapisek Sompot Fund for Postdoctoral
Fellowship, Chulalongkorn University and the Chulalongkorn Academic into Its
2nd Century Project Advancement Project (Thailand); the Kavli Foundation; the
Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation, contract
C-1845; and the Weston Havens Foundation (USA).

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##  .13 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan${}^{\textrm{\textdagger}}$, A. Tumasyan Institut für
Hochenergiephysik, Wien, Austria
W. Adam, T. Bergauer, M. Dragicevic, A. Escalante Del Valle, R. Frühwirth1, M.
Jeitler1, N. Krammer, L. Lechner, D. Liko, I. Mikulec, F.M. Pitters, J.
Schieck1, R. Schöfbeck, M. Spanring, S. Templ, W. Waltenberger, C.-E. Wulz1,
M. Zarucki Institute for Nuclear Problems, Minsk, Belarus
V. Chekhovsky, A. Litomin, V. Makarenko Universiteit Antwerpen, Antwerpen,
Belgium
M.R. Darwish2, E.A. De Wolf, X. Janssen, T. Kello3, A. Lelek, M. Pieters, H.
Rejeb Sfar, P. Van Mechelen, S. Van Putte, N. Van Remortel Vrije Universiteit
Brussel, Brussel, Belgium
F. Blekman, E.S. Bols, J. D’Hondt, J. De Clercq, D. Lontkovskyi, S. Lowette,
I. Marchesini, S. Moortgat, A. Morton, D. Müller, A.R. Sahasransu, S.
Tavernier, W. Van Doninck, P. Van Mulders Université Libre de Bruxelles,
Bruxelles, Belgium
D. Beghin, B. Bilin, B. Clerbaux, G. De Lentdecker, B. Dorney, L. Favart, A.
Grebenyuk, A.K. Kalsi, K. Lee, I. Makarenko, L. Moureaux, L. Pétré, A. Popov,
N. Postiau, E. Starling, L. Thomas, C. Vander Velde, P. Vanlaer, D. Vannerom,
L. Wezenbeek Ghent University, Ghent, Belgium
T. Cornelis, D. Dobur, M. Gruchala, I. Khvastunov4, G. Mestdach, M. Niedziela,
C. Roskas, K. Skovpen, M. Tytgat, W. Verbeke, B. Vermassen, M. Vit Université
Catholique de Louvain, Louvain-la-Neuve, Belgium
A. Bethani, G. Bruno, F. Bury, C. Caputo, P. David, C. Delaere, M. Delcourt,
I.S. Donertas, A. Giammanco, V. Lemaitre, K. Mondal, J. Prisciandaro, A.
Taliercio, M. Teklishyn, P. Vischia, S. Wertz, S. Wuyckens Centro Brasileiro
de Pesquisas Fisicas, Rio de Janeiro, Brazil
G.A. Alves, C. Hensel, A. Moraes Universidade do Estado do Rio de Janeiro, Rio
de Janeiro, Brazil
W.L. Aldá Júnior, E. Belchior Batista Das Chagas, H. BRANDAO MALBOUISSON, W.
Carvalho, J. Chinellato5, E. Coelho, E.M. Da Costa, G.G. Da Silveira6, D. De
Jesus Damiao, S. Fonseca De Souza, J. Martins7, D. Matos Figueiredo, C. Mora
Herrera, L. Mundim, H. Nogima, P. Rebello Teles, L.J. Sanchez Rosas, A.
Santoro, S.M. Silva Do Amaral, A. Sznajder, M. Thiel, F. Torres Da Silva De
Araujo, A. Vilela Pereira Universidade Estadual Paulista a, Universidade
Federal do ABC b, São Paulo, Brazil
C.A. Bernardesa,a, L. Calligarisa, T.R. Fernandez Perez Tomeia, E.M.
Gregoresa,b, D.S. Lemosa, P.G. Mercadantea,b, S.F. Novaesa, Sandra S. Padulaa
Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy of
Sciences, Sofia, Bulgaria
A. Aleksandrov, G. Antchev, I. Atanasov, R. Hadjiiska, P. Iaydjiev, M.
Misheva, M. Rodozov, M. Shopova, G. Sultanov University of Sofia, Sofia,
Bulgaria
A. Dimitrov, T. Ivanov, L. Litov, B. Pavlov, P. Petkov, A. Petrov Beihang
University, Beijing, China
T. Cheng, W. Fang3, Q. Guo, M. Mittal, H. Wang, L. Yuan Department of Physics,
Tsinghua University, Beijing, China
M. Ahmad, G. Bauer, Z. Hu, Y. Wang, K. Yi8,9 Institute of High Energy Physics,
Beijing, China
E. Chapon, G.M. Chen10, H.S. Chen10, M. Chen, T. Javaid10, A. Kapoor, D.
Leggat, H. Liao, Z.-A. LIU10, R. Sharma, A. Spiezia, J. Tao, J. Thomas-
wilsker, J. Wang, H. Zhang, S. Zhang10, J. Zhao State Key Laboratory of
Nuclear Physics and Technology, Peking University, Beijing, China
A. Agapitos, Y. Ban, C. Chen, Q. Huang, A. Levin, Q. Li, M. Lu, X. Lyu, Y.
Mao, S.J. Qian, D. Wang, Q. Wang, J. Xiao Sun Yat-Sen University, Guangzhou,
China
Z. You Institute of Modern Physics and Key Laboratory of Nuclear Physics and
Ion-beam Application (MOE) - Fudan University, Shanghai, China
X. Gao3, H. Okawa Zhejiang University, Hangzhou, China
M. Xiao Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, C. Florez, J. Fraga, A. Sarkar, M.A. Segura Delgado
Universidad de Antioquia, Medellin, Colombia
J. Jaramillo, J. Mejia Guisao, F. Ramirez, J.D. Ruiz Alvarez, C.A. Salazar
González, N. Vanegas Arbelaez University of Split, Faculty of Electrical
Engineering, Mechanical Engineering and Naval Architecture, Split, Croatia
D. Giljanovic, N. Godinovic, D. Lelas, I. Puljak University of Split, Faculty
of Science, Split, Croatia
Z. Antunovic, M. Kovac, T. Sculac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, D. Ferencek, D. Majumder, M. Roguljic, A. Starodumov11, T. Susa
University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, E. Erodotou, A. Ioannou, G. Kole, M. Kolosova, S.
Konstantinou, J. Mousa, C. Nicolaou, F. Ptochos, P.A. Razis, H. Rykaczewski,
H. Saka, D. Tsiakkouri Charles University, Prague, Czech Republic
M. Finger12, M. Finger Jr.12, A. Kveton, J. Tomsa Escuela Politecnica
Nacional, Quito, Ecuador
E. Ayala Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab
Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
A.A. Abdelalim13,14, S. Abu Zeid15, S. Elgammal16 Center for High Energy
Physics (CHEP-FU), Fayoum University, El-Fayoum, Egypt
M.A. Mahmoud, Y. Mohammed National Institute of Chemical Physics and
Biophysics, Tallinn, Estonia
S. Bhowmik, A. Carvalho Antunes De Oliveira, R.K. Dewanjee, K. Ehataht, M.
Kadastik, J. Pata, M. Raidal, C. Veelken Department of Physics, University of
Helsinki, Helsinki, Finland
P. Eerola, L. Forthomme, H. Kirschenmann, K. Osterberg, M. Voutilainen
Helsinki Institute of Physics, Helsinki, Finland
E. Brücken, F. Garcia, J. Havukainen, V. Karimäki, M.S. Kim, R. Kinnunen, T.
Lampén, K. Lassila-Perini, S. Lehti, T. Lindén, H. Siikonen, E. Tuominen, J.
Tuominiemi Lappeenranta University of Technology, Lappeenranta, Finland
P. Luukka, T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
C. Amendola, M. Besancon, F. Couderc, M. Dejardin, D. Denegri, J.L. Faure, F.
Ferri, S. Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry,
B. Lenzi, E. Locci, J. Malcles, J. Rander, A. Rosowsky, M.Ö. Sahin, A. Savoy-
Navarro17, M. Titov, G.B. Yu Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole
Polytechnique, Institut Polytechnique de Paris, Palaiseau, France
S. Ahuja, F. Beaudette, M. Bonanomi, A. Buchot Perraguin, P. Busson, C.
Charlot, O. Davignon, B. Diab, G. Falmagne, R. Granier de Cassagnac, A.
Hakimi, I. Kucher, A. Lobanov, C. Martin Perez, M. Nguyen, C. Ochando, P.
Paganini, J. Rembser, R. Salerno, J.B. Sauvan, Y. Sirois, A. Zabi, A. Zghiche
Université de Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, France
J.-L. Agram18, J. Andrea, D. Bloch, G. Bourgatte, J.-M. Brom, E.C. Chabert, C.
Collard, J.-C. Fontaine18, U. Goerlach, C. Grimault, A.-C. Le Bihan, P. Van
Hove Université de Lyon, Université Claude Bernard Lyon 1, CNRS-IN2P3,
Institut de Physique Nucléaire de Lyon, Villeurbanne, France
E. Asilar, S. Beauceron, C. Bernet, G. Boudoul, C. Camen, A. Carle, N. Chanon,
D. Contardo, P. Depasse, H. El Mamouni, J. Fay, S. Gascon, M. Gouzevitch, B.
Ille, Sa. Jain, I.B. Laktineh, H. Lattaud, A. Lesauvage, M. Lethuillier, L.
Mirabito, K. Shchablo, L. Torterotot, G. Touquet, M. Vander Donckt, S. Viret
Georgian Technical University, Tbilisi, Georgia
A. Khvedelidze12, Z. Tsamalaidze12 RWTH Aachen University, I. Physikalisches
Institut, Aachen, Germany
L. Feld, K. Klein, M. Lipinski, D. Meuser, A. Pauls, M.P. Rauch, J. Schulz, M.
Teroerde RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
D. Eliseev, M. Erdmann, P. Fackeldey, B. Fischer, S. Ghosh, T. Hebbeker, K.
Hoepfner, H. Keller, L. Mastrolorenzo, M. Merschmeyer, A. Meyer, G. Mocellin,
S. Mondal, S. Mukherjee, D. Noll, A. Novak, T. Pook, A. Pozdnyakov, Y. Rath,
H. Reithler, J. Roemer, A. Schmidt, S.C. Schuler, A. Sharma, S. Wiedenbeck, S.
Zaleski RWTH Aachen University, III. Physikalisches Institut B, Aachen,
Germany
C. Dziwok, G. Flügge, W. Haj Ahmad19, O. Hlushchenko, T. Kress, A. Nowack, C.
Pistone, O. Pooth, D. Roy, H. Sert, A. Stahl20, T. Ziemons Deutsches
Elektronen-Synchrotron, Hamburg, Germany
H. Aarup Petersen, M. Aldaya Martin, P. Asmuss, I. Babounikau, S. Baxter, O.
Behnke, A. Bermúdez Martínez, A.A. Bin Anuar, K. Borras21, V. Botta, D.
Brunner, A. Campbell, A. Cardini, P. Connor, S. Consuegra Rodríguez, V.
Danilov, A. De Wit, M.M. Defranchis, L. Didukh, D. Domínguez Damiani, G.
Eckerlin, D. Eckstein, L.I. Estevez Banos, E. Gallo22, A. Geiser, A. Giraldi,
A. Grohsjean, M. Guthoff, A. Harb, A. Jafari23, N.Z. Jomhari, H. Jung, A.
Kasem21, M. Kasemann, H. Kaveh, C. Kleinwort, J. Knolle, D. Krücker, W. Lange,
T. Lenz, J. Lidrych, K. Lipka, W. Lohmann24, T. Madlener, R. Mankel, I.-A.
Melzer-Pellmann, J. Metwally, A.B. Meyer, M. Meyer, J. Mnich, A. Mussgiller,
V. Myronenko, Y. Otarid, D. Pérez Adán, S.K. Pflitsch, D. Pitzl, A. Raspereza,
A. Saggio, A. Saibel, M. Savitskyi, V. Scheurer, C. Schwanenberger, A. Singh,
R.E. Sosa Ricardo, N. Tonon, O. Turkot, A. Vagnerini, M. Van De Klundert, R.
Walsh, D. Walter, Y. Wen, K. Wichmann, C. Wissing, S. Wuchterl, O. Zenaiev, R.
Zlebcik University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, L. Benato, A. Benecke, K. De Leo, T. Dreyer, M. Eich, F.
Feindt, A. Fröhlich, C. Garbers, E. Garutti, P. Gunnellini, J. Haller, A.
Hinzmann, A. Karavdina, G. Kasieczka, R. Klanner, R. Kogler, V. Kutzner, J.
Lange, T. Lange, A. Malara, C.E.N. Niemeyer, A. Nigamova, K.J. Pena Rodriguez,
O. Rieger, P. Schleper, M. Schröder, S. Schumann, J. Schwandt, D. Schwarz, J.
Sonneveld, H. Stadie, G. Steinbrück, A. Tews, B. Vormwald, I. Zoi Karlsruher
Institut fuer Technologie, Karlsruhe, Germany
J. Bechtel, T. Berger, E. Butz, R. Caspart, T. Chwalek, W. De Boer, A.
Dierlamm, A. Droll, K. El Morabit, N. Faltermann, K. Flöh, M. Giffels, J.o.
Gosewisch, A. Gottmann, F. Hartmann20, C. Heidecker, U. Husemann, I. Katkov25,
P. Keicher, R. Koppenhöfer, S. Maier, M. Metzler, S. Mitra, Th. Müller, M.
Musich, M. Neukum, G. Quast, K. Rabbertz, J. Rauser, D. Savoiu, D. Schäfer, M.
Schnepf, D. Seith, I. Shvetsov, H.J. Simonis, R. Ulrich, J. Van Der Linden,
R.F. Von Cube, M. Wassmer, M. Weber, S. Wieland, R. Wolf, S. Wozniewski, S.
Wunsch Institute of Nuclear and Particle Physics (INPP), NCSR Demokritos,
Aghia Paraskevi, Greece
G. Anagnostou, P. Asenov, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas,
G. Paspalaki, A. Stakia National and Kapodistrian University of Athens,
Athens, Greece
M. Diamantopoulou, D. Karasavvas, G. Karathanasis, P. Kontaxakis, C.K. Koraka,
A. Manousakis-katsikakis, A. Panagiotou, I. Papavergou, N. Saoulidou, K.
Theofilatos, E. Tziaferi, K. Vellidis, E. Vourliotis National Technical
University of Athens, Athens, Greece
G. Bakas, K. Kousouris, I. Papakrivopoulos, G. Tsipolitis, A. Zacharopoulou
University of Ioánnina, Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, K. Manitara,
N. Manthos, I. Papadopoulos, J. Strologas MTA-ELTE Lendület CMS Particle and
Nuclear Physics Group, Eötvös Loránd University, Budapest, Hungary
M. Csanad, M.M.A. Gadallah26, S. Lökös27, P. Major, K. Mandal, A. Mehta, G.
Pasztor, O. Surányi, G.I. Veres Wigner Research Centre for Physics, Budapest,
Hungary
M. Bartók28, G. Bencze, C. Hajdu, D. Horvath29, F. Sikler, V. Veszpremi, G.
Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear Research ATOMKI,
Debrecen, Hungary
S. Czellar, J. Karancsi28, J. Molnar, Z. Szillasi, D. Teyssier Institute of
Physics, University of Debrecen, Debrecen, Hungary
P. Raics, Z.L. Trocsanyi30, B. Ujvari Eszterhazy Karoly University, Karoly
Robert Campus, Gyongyos, Hungary
T. Csorgo31, F. Nemes31, T. Novak Indian Institute of Science (IISc),
Bangalore, India
S. Choudhury, J.R. Komaragiri, D. Kumar, L. Panwar, P.C. Tiwari National
Institute of Science Education and Research, HBNI, Bhubaneswar, India
S. Bahinipati32, D. Dash, C. Kar, P. Mal, T. Mishra, V.K. Muraleedharan Nair
Bindhu33, A. Nayak33, N. Sur, S.K. Swain Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, G. Chaudhary, S. Chauhan, N. Dhingra34, R.
Gupta, A. Kaur, S. Kaur, P. Kumari, M. Meena, K. Sandeep, J.B. Singh, A.K.
Virdi University of Delhi, Delhi, India
A. Ahmed, A. Bhardwaj, B.C. Choudhary, R.B. Garg, M. Gola, S. Keshri, A.
Kumar, M. Naimuddin, P. Priyanka, K. Ranjan, A. Shah Saha Institute of Nuclear
Physics, HBNI, Kolkata, India
M. Bharti35, R. Bhattacharya, S. Bhattacharya, D. Bhowmik, S. Dutta, S. Ghosh,
B. Gomber36, M. Maity37, S. Nandan, P. Palit, P.K. Rout, G. Saha, B. Sahu, S.
Sarkar, M. Sharan, B. Singh35, S. Thakur35 Indian Institute of Technology
Madras, Madras, India
P.K. Behera, S.C. Behera, P. Kalbhor, A. Muhammad, R. Pradhan, P.R. Pujahari,
A. Sharma, A.K. Sikdar Bhabha Atomic Research Centre, Mumbai, India
D. Dutta, V. Jha, V. Kumar, D.K. Mishra, K. Naskar38, P.K. Netrakanti, L.M.
Pant, P. Shukla Tata Institute of Fundamental Research-A, Mumbai, India
T. Aziz, S. Dugad, G.B. Mohanty, U. Sarkar Tata Institute of Fundamental
Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, R. Chudasama, M. Guchait, S.
Karmakar, S. Kumar, G. Majumder, K. Mazumdar, S. Mukherjee, D. Roy Indian
Institute of Science Education and Research (IISER), Pune, India
S. Dube, B. Kansal, S. Pandey, A. Rane, A. Rastogi, S. Sharma Department of
Physics, Isfahan University of Technology, Isfahan, Iran
H. Bakhshiansohi39, M. Zeinali40 Institute for Research in Fundamental
Sciences (IPM), Tehran, Iran
S. Chenarani41, S.M. Etesami, M. Khakzad, M. Mohammadi Najafabadi University
College Dublin, Dublin, Ireland
M. Felcini, M. Grunewald INFN Sezione di Bari a, Università di Bari b,
Politecnico di Bari c, Bari, Italy
M. Abbresciaa,b, R. Alya,b,42, C. Arutaa,b, A. Colaleoa, D. Creanzaa,c, N. De
Filippisa,c, M. De Palmaa,b, A. Di Florioa,b, A. Di Pilatoa,b, W.
Elmetenaweea,b, L. Fiorea, A. Gelmia,b, M. Gula, G. Iasellia,c, M. Incea,b, S.
Lezkia,b, G. Maggia,c, M. Maggia, I. Margjekaa,b, V. Mastrapasquaa,b, J.A.
Merlina, S. Mya,b, S. Nuzzoa,b, A. Pompilia,b, G. Pugliesea,c, A. Ranieria, G.
Selvaggia,b, L. Silvestrisa, F.M. Simonea,b, R. Vendittia, P. Verwilligena
INFN Sezione di Bologna a, Università di Bologna b, Bologna, Italy
G. Abbiendia, C. Battilanaa,b, D. Bonacorsia,b, L. Borgonovia, S. Braibant-
Giacomellia,b, R. Campaninia,b, P. Capiluppia,b, A. Castroa,b, F.R. Cavalloa,
C. Cioccaa, M. Cuffiania,b, G.M. Dallavallea, T. Diotalevia,b, F. Fabbria, A.
Fanfania,b, E. Fontanesia,b, P. Giacomellia, L. Giommia,b, C. Grandia, L.
Guiduccia,b, F. Iemmia,b, S. Lo Meoa,43, S. Marcellinia, G. Masettia, F.L.
Navarriaa,b, A. Perrottaa, F. Primaveraa,b, A.M. Rossia,b, T. Rovellia,b, G.P.
Sirolia,b, N. Tosia INFN Sezione di Catania a, Università di Catania b,
Catania, Italy
S. Albergoa,b,44, S. Costaa,b, A. Di Mattiaa, R. Potenzaa,b, A. Tricomia,b,44,
C. Tuvea,b INFN Sezione di Firenze a, Università di Firenze b, Firenze, Italy
G. Barbaglia, A. Cassesea, R. Ceccarellia,b, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, F. Fioria, E. Focardia,b, G. Latinoa,b, P. Lenzia,b, M.
Lizzoa,b, M. Meschinia, S. Paolettia, R. Seiditaa,b, G. Sguazzonia, L.
Viliania INFN Laboratori Nazionali di Frascati, Frascati, Italy
L. Benussi, S. Bianco, D. Piccolo INFN Sezione di Genova a, Università di
Genova b, Genova, Italy
M. Bozzoa,b, F. Ferroa, R. Mulargiaa,b, E. Robuttia, S. Tosia,b INFN Sezione
di Milano-Bicocca a, Università di Milano-Bicocca b, Milano, Italy
A. Benagliaa, A. Beschia,b, F. Brivioa,b, F. Cetorellia,b, V. Cirioloa,b,20,
F. De Guioa,b, M.E. Dinardoa,b, P. Dinia, S. Gennaia, A. Ghezzia,b, P.
Govonia,b, L. Guzzia,b, M. Malbertia, S. Malvezzia, A. Massironia, D.
Menascea, F. Montia,b, L. Moronia, M. Paganonia,b, D. Pedrinia, S. Ragazzia,b,
T. Tabarelli de Fatisa,b, D. Valsecchia,b,20, D. Zuoloa,b INFN Sezione di
Napoli a, Università di Napoli ’Federico II’ b, Napoli, Italy, Università
della Basilicata c, Potenza, Italy, Università G. Marconi d, Roma, Italy
S. Buontempoa, N. Cavalloa,c, A. De Iorioa,b, F. Fabozzia,c, F. Fiengaa,
A.O.M. Iorioa,b, L. Listaa,b, S. Meolaa,d,20, P. Paoluccia,20, B. Rossia, C.
Sciaccaa,b INFN Sezione di Padova a, Università di Padova b, Padova, Italy,
Università di Trento c, Trento, Italy
P. Azzia, N. Bacchettaa, D. Biselloa,b, P. Bortignona, A. Bragagnoloa,b, R.
Carlina,b, P. Checchiaa, P. De Castro Manzanoa, T. Dorigoa, F. Gasparinia,b,
U. Gasparinia,b, S.Y. Hoha,b, L. Layera,45, M. Margonia,b, A.T. Meneguzzoa,b,
M. Presillaa,b, P. Ronchesea,b, R. Rossina,b, F. Simonettoa,b, G. Stronga, M.
Tosia,b, H. YARARa,b, M. Zanettia,b, P. Zottoa,b, A. Zucchettaa,b, G.
Zumerlea,b INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy
C. Aime‘a,b, A. Braghieria, S. Calzaferria,b, D. Fiorinaa,b, P. Montagnaa,b,
S.P. Rattia,b, V. Rea, M. Ressegottia,b, C. Riccardia,b, P. Salvinia, I. Vaia,
P. Vituloa,b INFN Sezione di Perugia a, Università di Perugia b, Perugia,
Italy
G.M. Bileia, D. Ciangottinia,b, L. Fanòa,b, P. Laricciaa,b, G. Mantovania,b,
V. Mariania,b, M. Menichellia, F. Moscatellia, A. Piccinellia,b, A. Rossia,b,
A. Santocchiaa,b, D. Spigaa, T. Tedeschia,b INFN Sezione di Pisa a, Università
di Pisa b, Scuola Normale Superiore di Pisa c, Pisa Italy, Università di Siena
d, Siena, Italy
K. Androsova, P. Azzurria, G. Bagliesia, V. Bertacchia,c, L. Bianchinia, T.
Boccalia, E. Bossini, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, M.R. Di
Domenicoa,d, S. Donatoa, A. Giassia, M.T. Grippoa, F. Ligabuea,c, E. Mancaa,c,
G. Mandorlia,c, A. Messineoa,b, F. Pallaa, G. Ramirez-Sancheza,c, A. Rizzia,b,
G. Rolandia,c, S. Roy Chowdhurya,c, A. Scribanoa, N. Shafieia,b, P. Spagnoloa,
R. Tenchinia, G. Tonellia,b, N. Turinia,d, A. Venturia, P.G. Verdinia INFN
Sezione di Roma a, Sapienza Università di Roma b, Rome, Italy
F. Cavallaria, M. Cipriania,b, D. Del Rea,b, E. Di Marcoa, M. Diemoza, E.
Longoa,b, P. Meridiania, G. Organtinia,b, F. Pandolfia, R. Paramattia,b, C.
Quarantaa,b, S. Rahatloua,b, C. Rovellia, F. Santanastasioa,b, L. Soffia,b, R.
Tramontanoa,b INFN Sezione di Torino a, Università di Torino b, Torino, Italy,
Università del Piemonte Orientale c, Novara, Italy
N. Amapanea,b, R. Arcidiaconoa,c, S. Argiroa,b, M. Arneodoa,c, N. Bartosika,
R. Bellana,b, A. Belloraa,b, J. Berenguer Antequeraa,b, C. Biinoa, A.
Cappatia,b, N. Cartigliaa, S. Comettia, M. Costaa,b, R. Covarellia,b, N.
Demariaa, B. Kiania,b, F. Leggera, C. Mariottia, S. Masellia, E. Migliorea,b,
V. Monacoa,b, E. Monteila,b, M. Montenoa, M.M. Obertinoa,b, G. Ortonaa, L.
Pachera,b, N. Pastronea, M. Pelliccionia, G.L. Pinna Angionia,b, M. Ruspaa,c,
R. Salvaticoa,b, F. Sivieroa,b, V. Solaa, A. Solanoa,b, D. Soldia,b, A.
Staianoa, M. Tornagoa,b, D. Trocinoa,b INFN Sezione di Trieste a, Università
di Trieste b, Trieste, Italy
S. Belfortea, V. Candelisea,b, M. Casarsaa, F. Cossuttia, A. Da Rolda,b, G.
Della Riccaa,b, F. Vazzolera,b Kyungpook National University, Daegu, Korea
S. Dogra, C. Huh, B. Kim, D.H. Kim, G.N. Kim, J. Lee, S.W. Lee, C.S. Moon,
Y.D. Oh, S.I. Pak, B.C. Radburn-Smith, S. Sekmen, Y.C. Yang Chonnam National
University, Institute for Universe and Elementary Particles, Kwangju, Korea
H. Kim, D.H. Moon Hanyang University, Seoul, Korea
B. Francois, T.J. Kim, J. Park Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, B. Hong, K. Lee, K.S. Lee, J. Lim, J. Park, S.K. Park,
J. Yoo Kyung Hee University, Department of Physics, Seoul, Republic of Korea
J. Goh, A. Gurtu Sejong University, Seoul, Korea
H.S. Kim, Y. Kim Seoul National University, Seoul, Korea
J. Almond, J.H. Bhyun, J. Choi, S. Jeon, J. Kim, J.S. Kim, S. Ko, H. Kwon, H.
Lee, S. Lee, K. Nam, B.H. Oh, M. Oh, S.B. Oh, H. Seo, U.K. Yang, I. Yoon
University of Seoul, Seoul, Korea
D. Jeon, J.H. Kim, B. Ko, J.S.H. Lee, I.C. Park, Y. Roh, D. Song, I.J. Watson
Yonsei University, Department of Physics, Seoul, Korea
S. Ha, H.D. Yoo Sungkyunkwan University, Suwon, Korea
Y. Choi, C. Hwang, Y. Jeong, H. Lee, Y. Lee, I. Yu College of Engineering and
Technology, American University of the Middle East (AUM), Dasman, Kuwait
Y. Maghrbi Riga Technical University, Riga, Latvia
V. Veckalns46 Vilnius University, Vilnius, Lithuania
M. Ambrozas, A. Juodagalvis, A. Rinkevicius, G. Tamulaitis, A. Vaitkevicius
National Centre for Particle Physics, Universiti Malaya, Kuala Lumpur,
Malaysia
W.A.T. Wan Abdullah, M.N. Yusli, Z. Zolkapli Universidad de Sonora (UNISON),
Hermosillo, Mexico
J.F. Benitez, A. Castaneda Hernandez, J.A. Murillo Quijada, L. Valencia Palomo
Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico
G. Ayala, H. Castilla-Valdez, E. De La Cruz-Burelo, I. Heredia-De La Cruz47,
R. Lopez-Fernandez, C.A. Mondragon Herrera, D.A. Perez Navarro, A. Sanchez-
Hernandez Universidad Iberoamericana, Mexico City, Mexico
S. Carrillo Moreno, C. Oropeza Barrera, M. Ramirez-Garcia, F. Vazquez Valencia
Benemerita Universidad Autonoma de Puebla, Puebla, Mexico
I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada University of Montenegro,
Podgorica, Montenegro
J. Mijuskovic4, N. Raicevic University of Auckland, Auckland, New Zealand
D. Krofcheck University of Canterbury, Christchurch, New Zealand
S. Bheesette, P.H. Butler National Centre for Physics, Quaid-I-Azam
University, Islamabad, Pakistan
A. Ahmad, M.I. Asghar, A. Awais, M.I.M. Awan, H.R. Hoorani, W.A. Khan, M.A.
Shah, M. Shoaib, M. Waqas AGH University of Science and Technology Faculty of
Computer Science, Electronics and Telecommunications, Krakow, Poland
V. Avati, L. Grzanka, M. Malawski National Centre for Nuclear Research,
Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, T. Frueboes, M. Górski, M. Kazana, M.
Szleper, P. Traczyk, P. Zalewski Institute of Experimental Physics, Faculty of
Physics, University of Warsaw, Warsaw, Poland
K. Bunkowski, K. Doroba, A. Kalinowski, M. Konecki, J. Krolikowski, M. Walczak
Laboratório de Instrumentação e Física Experimental de Partículas, Lisboa,
Portugal
M. Araujo, P. Bargassa, D. Bastos, A. Boletti, P. Faccioli, M. Gallinaro, J.
Hollar, N. Leonardo, T. Niknejad, J. Seixas, K. Shchelina, O. Toldaiev, J.
Varela Joint Institute for Nuclear Research, Dubna, Russia
S. Afanasiev, D. Budkouski, P. Bunin, M. Gavrilenko, I. Golutvin, I. Gorbunov,
A. Kamenev, V. Karjavine, A. Lanev, A. Malakhov, V. Matveev48,49, V. Palichik,
V. Perelygin, M. Savina, D. Seitova, V. Shalaev, S. Shmatov, S. Shulha, V.
Smirnov, O. Teryaev, N. Voytishin, A. Zarubin, I. Zhizhin Petersburg Nuclear
Physics Institute, Gatchina (St. Petersburg), Russia
G. Gavrilov, V. Golovtcov, Y. Ivanov, V. Kim50, E. Kuznetsova51, V. Murzin, V.
Oreshkin, I. Smirnov, D. Sosnov, V. Sulimov, L. Uvarov, S. Volkov, A. Vorobyev
Institute for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov,
N. Krasnikov, A. Pashenkov, G. Pivovarov, D.
Tlisov${}^{\textrm{\textdagger}}$, A. Toropin Institute for Theoretical and
Experimental Physics named by A.I. Alikhanov of NRC ‘Kurchatov Institute’,
Moscow, Russia
V. Epshteyn, V. Gavrilov, N. Lychkovskaya, A. Nikitenko52, V. Popov, G.
Safronov, A. Spiridonov, A. Stepennov, M. Toms, E. Vlasov, A. Zhokin Moscow
Institute of Physics and Technology, Moscow, Russia
T. Aushev National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
R. Chistov53, M. Danilov54, A. Oskin, P. Parygin, S. Polikarpov53 P.N. Lebedev
Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin, I. Dremin, M. Kirakosyan, A. Terkulov Skobeltsyn
Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow,
Russia
A. Belyaev, E. Boos, V. Bunichev, M. Dubinin55, L. Dudko, A. Gribushin, V.
Klyukhin, N. Korneeva, I. Lokhtin, S. Obraztsov, M. Perfilov, V. Savrin, P.
Volkov Novosibirsk State University (NSU), Novosibirsk, Russia
V. Blinov56, T. Dimova56, L. Kardapoltsev56, I. Ovtin56, Y. Skovpen56
Institute for High Energy Physics of National Research Centre ‘Kurchatov
Institute’, Protvino, Russia
I. Azhgirey, I. Bayshev, V. Kachanov, A. Kalinin, D. Konstantinov, V. Petrov,
R. Ryutin, A. Sobol, S. Troshin, N. Tyurin, A. Uzunian, A. Volkov National
Research Tomsk Polytechnic University, Tomsk, Russia
A. Babaev, A. Iuzhakov, V. Okhotnikov, L. Sukhikh Tomsk State University,
Tomsk, Russia
V. Borchsh, V. Ivanchenko, E. Tcherniaev University of Belgrade: Faculty of
Physics and VINCA Institute of Nuclear Sciences, Belgrade, Serbia
P. Adzic57, M. Dordevic, P. Milenovic, J. Milosevic Centro de Investigaciones
Energéticas Medioambientales y Tecnológicas (CIEMAT), Madrid, Spain
M. Aguilar-Benitez, J. Alcaraz Maestre, A. Álvarez Fernández, I. Bachiller, M.
Barrio Luna, Cristina F. Bedoya, C.A. Carrillo Montoya, M. Cepeda, M. Cerrada,
N. Colino, B. De La Cruz, A. Delgado Peris, J.P. Fernández Ramos, J. Flix,
M.C. Fouz, O. Gonzalez Lopez, S. Goy Lopez, J.M. Hernandez, M.I. Josa, J. León
Holgado, D. Moran, Á. Navarro Tobar, A. Pérez-Calero Yzquierdo, J. Puerta
Pelayo, I. Redondo, L. Romero, S. Sánchez Navas, M.S. Soares, L. Urda Gómez,
C. Willmott Universidad Autónoma de Madrid, Madrid, Spain
C. Albajar, J.F. de Trocóniz, R. Reyes-Almanza Universidad de Oviedo,
Instituto Universitario de Ciencias y Tecnologías Espaciales de Asturias
(ICTEA), Oviedo, Spain
B. Alvarez Gonzalez, J. Cuevas, C. Erice, J. Fernandez Menendez, S. Folgueras,
I. Gonzalez Caballero, E. Palencia Cortezon, C. Ramón Álvarez, J. Ripoll Sau,
V. Rodríguez Bouza, S. Sanchez Cruz, A. Trapote Instituto de Física de
Cantabria (IFCA), CSIC-Universidad de Cantabria, Santander, Spain
J.A. Brochero Cifuentes, I.J. Cabrillo, A. Calderon, B. Chazin Quero, J.
Duarte Campderros, M. Fernandez, C. Fernandez Madrazo, P.J. Fernández Manteca,
A. García Alonso, G. Gomez, C. Martinez Rivero, P. Martinez Ruiz del Arbol, F.
Matorras, J. Piedra Gomez, C. Prieels, F. Ricci-Tam, T. Rodrigo, A. Ruiz-
Jimeno, L. Scodellaro, N. Trevisani, I. Vila, J.M. Vizan Garcia University of
Colombo, Colombo, Sri Lanka
MK Jayananda, B. Kailasapathy58, D.U.J. Sonnadara, DDC Wickramarathna
University of Ruhuna, Department of Physics, Matara, Sri Lanka
W.G.D. Dharmaratna, K. Liyanage, N. Perera, N. Wickramage CERN, European
Organization for Nuclear Research, Geneva, Switzerland
T.K. Aarrestad, D. Abbaneo, E. Auffray, G. Auzinger, J. Baechler, P. Baillon,
A.H. Ball, D. Barney, J. Bendavid, N. Beni, M. Bianco, A. Bocci, E. Brondolin,
T. Camporesi, M. Capeans Garrido, G. Cerminara, S.S. Chhibra, L. Cristella, D.
d’Enterria, A. Dabrowski, N. Daci, A. David, A. De Roeck, M. Deile, R. Di
Maria, M. Dobson, M. Dünser, N. Dupont, A. Elliott-Peisert, N. Emriskova, F.
Fallavollita59, D. Fasanella, S. Fiorendi, A. Florent, G. Franzoni, J.
Fulcher, W. Funk, S. Giani, D. Gigi, K. Gill, F. Glege, L. Gouskos, M.
Guilbaud, M. Haranko, J. Hegeman, Y. Iiyama, V. Innocente, T. James, P. Janot,
J. Kaspar, J. Kieseler, M. Komm, N. Kratochwil, C. Lange, S. Laurila, P.
Lecoq, K. Long, C. Lourenço, L. Malgeri, S. Mallios, M. Mannelli, F. Meijers,
S. Mersi, E. Meschi, F. Moortgat, M. Mulders, S. Orfanelli, L. Orsini, F.
Pantaleo20, L. Pape, E. Perez, M. Peruzzi, A. Petrilli, G. Petrucciani, A.
Pfeiffer, M. Pierini, T. Quast, D. Rabady, A. Racz, M. Rieger, M. Rovere, H.
Sakulin, J. Salfeld-Nebgen, S. Scarfi, C. Schäfer, C. Schwick, M. Selvaggi, A.
Sharma, P. Silva, W. Snoeys, P. Sphicas60, S. Summers, V.R. Tavolaro, D.
Treille, A. Tsirou, G.P. Van Onsem, M. Verzetti, K.A. Wozniak, W.D. Zeuner
Paul Scherrer Institut, Villigen, Switzerland
L. Caminada61, A. Ebrahimi, W. Erdmann, R. Horisberger, Q. Ingram, H.C.
Kaestli, D. Kotlinski, U. Langenegger, M. Missiroli, T. Rohe ETH Zurich -
Institute for Particle Physics and Astrophysics (IPA), Zurich, Switzerland
M. Backhaus, P. Berger, A. Calandri, N. Chernyavskaya, A. De Cosa, G.
Dissertori, M. Dittmar, M. Donegà, C. Dorfer, T. Gadek, T.A. Gómez Espinosa,
C. Grab, D. Hits, W. Lustermann, A.-M. Lyon, R.A. Manzoni, M.T. Meinhard, F.
Micheli, F. Nessi-Tedaldi, J. Niedziela, F. Pauss, V. Perovic, G. Perrin, S.
Pigazzini, M.G. Ratti, M. Reichmann, C. Reissel, T. Reitenspiess, B. Ristic,
D. Ruini, D.A. Sanz Becerra, M. Schönenberger, V. Stampf, J. Steggemann62, R.
Wallny, D.H. Zhu Universität Zürich, Zurich, Switzerland
C. Amsler63, C. Botta, D. Brzhechko, M.F. Canelli, R. Del Burgo, J.K.
Heikkilä, M. Huwiler, A. Jofrehei, B. Kilminster, S. Leontsinis, A. Macchiolo,
P. Meiring, V.M. Mikuni, U. Molinatti, I. Neutelings, G. Rauco, A. Reimers, P.
Robmann, K. Schweiger, Y. Takahashi National Central University, Chung-Li,
Taiwan
C. Adloff64, C.M. Kuo, W. Lin, A. Roy, T. Sarkar37, S.S. Yu National Taiwan
University (NTU), Taipei, Taiwan
L. Ceard, P. Chang, Y. Chao, K.F. Chen, P.H. Chen, W.-S. Hou, Y.y. Li, R.-S.
Lu, E. Paganis, A. Psallidas, A. Steen, E. Yazgan Chulalongkorn University,
Faculty of Science, Department of Physics, Bangkok, Thailand
B. Asavapibhop, C. Asawatangtrakuldee, N. Srimanobhas Çukurova University,
Physics Department, Science and Art Faculty, Adana, Turkey
F. Boran, S. Damarseckin65, Z.S. Demiroglu, F. Dolek, C. Dozen66, I.
Dumanoglu67, E. Eskut, G. Gokbulut, Y. Guler, E. Gurpinar Guler68, I. Hos69,
C. Isik, E.E. Kangal70, O. Kara, A. Kayis Topaksu, U. Kiminsu, G. Onengut, K.
Ozdemir71, A. Polatoz, A.E. Simsek, B. Tali72, U.G. Tok, S. Turkcapar, I.S.
Zorbakir, C. Zorbilmez Middle East Technical University, Physics Department,
Ankara, Turkey
B. Isildak73, G. Karapinar74, K. Ocalan75, M. Yalvac76 Bogazici University,
Istanbul, Turkey
B. Akgun, I.O. Atakisi, E. Gülmez, M. Kaya77, O. Kaya78, Ö. Özçelik, S.
Tekten79, E.A. Yetkin80 Istanbul Technical University, Istanbul, Turkey
A. Cakir, K. Cankocak67, Y. Komurcu, S. Sen81 Istanbul University, Istanbul,
Turkey
F. Aydogmus Sen, S. Cerci72, B. Kaynak, S. Ozkorucuklu, D. Sunar Cerci72
Institute for Scintillation Materials of National Academy of Science of
Ukraine, Kharkov, Ukraine
B. Grynyov National Scientific Center, Kharkov Institute of Physics and
Technology, Kharkov, Ukraine
L. Levchuk University of Bristol, Bristol, United Kingdom
E. Bhal, S. Bologna, J.J. Brooke, A. Bundock, E. Clement, D. Cussans, H.
Flacher, J. Goldstein, G.P. Heath, H.F. Heath, L. Kreczko, B. Krikler, S.
Paramesvaran, T. Sakuma, S. Seif El Nasr-Storey, V.J. Smith, N. Stylianou82,
J. Taylor, A. Titterton Rutherford Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev83, C. Brew, R.M. Brown, D.J.A. Cockerill, K.V. Ellis, K.
Harder, S. Harper, J. Linacre, K. Manolopoulos, D.M. Newbold, E. Olaiya, D.
Petyt, T. Reis, T. Schuh, C.H. Shepherd-Themistocleous, A. Thea, I.R. Tomalin,
T. Williams Imperial College, London, United Kingdom
R. Bainbridge, P. Bloch, S. Bonomally, J. Borg, S. Breeze, O. Buchmuller, V.
Cepaitis, G.S. Chahal84, D. Colling, P. Dauncey, G. Davies, M. Della Negra, G.
Fedi, G. Hall, G. Iles, J. Langford, L. Lyons, A.-M. Magnan, S. Malik, A.
Martelli, V. Milosevic, J. Nash85, V. Palladino, M. Pesaresi, D.M. Raymond, A.
Richards, A. Rose, E. Scott, C. Seez, A. Shtipliyski, A. Tapper, K. Uchida, T.
Virdee20, N. Wardle, S.N. Webb, D. Winterbottom, A.G. Zecchinelli Brunel
University, Uxbridge, United Kingdom
J.E. Cole, A. Khan, P. Kyberd, C.K. Mackay, I.D. Reid, L. Teodorescu, S. Zahid
Baylor University, Waco, USA
S. Abdullin, A. Brinkerhoff, K. Call, B. Caraway, J. Dittmann, K. Hatakeyama,
A.R. Kanuganti, C. Madrid, B. McMaster, N. Pastika, S. Sawant, C. Smith, C.
Sutantawibul, J. Wilson Catholic University of America, Washington, DC, USA
R. Bartek, A. Dominguez, R. Uniyal, A.M. Vargas Hernandez The University of
Alabama, Tuscaloosa, USA
A. Buccilli, O. Charaf, S.I. Cooper, D. Di Croce, S.V. Gleyzer, C. Henderson,
C.U. Perez, P. Rumerio, C. West Boston University, Boston, USA
A. Akpinar, A. Albert, D. Arcaro, C. Cosby, Z. Demiragli, D. Gastler, J.
Rohlf, K. Salyer, D. Sperka, D. Spitzbart, I. Suarez, S. Yuan, D. Zou Brown
University, Providence, USA
G. Benelli, B. Burkle, X. Coubez21, D. Cutts, Y.t. Duh, M. Hadley, U. Heintz,
J.M. Hogan86, K.H.M. Kwok, E. Laird, G. Landsberg, K.T. Lau, J. Lee, J. Luo,
M. Narain, S. Sagir87, E. Usai, W.Y. Wong, X. Yan, D. Yu, W. Zhang University
of California, Davis, Davis, USA
R. Band, C. Brainerd, R. Breedon, M. Calderon De La Barca Sanchez, M. Chertok,
J. Conway, R. Conway, P.T. Cox, R. Erbacher, C. Flores, F. Jensen, O. Kukral,
R. Lander, M. Mulhearn, D. Pellett, M. Shi, D. Taylor, M. Tripathi, Y. Yao, F.
Zhang University of California, Los Angeles, USA
M. Bachtis, R. Cousins, A. Dasgupta, A. Datta, D. Hamilton, J. Hauser, M.
Ignatenko, M.A. Iqbal, T. Lam, N. Mccoll, W.A. Nash, S. Regnard, D. Saltzberg,
C. Schnaible, B. Stone, V. Valuev University of California, Riverside,
Riverside, USA
K. Burt, Y. Chen, R. Clare, J.W. Gary, G. Hanson, G. Karapostoli, O.R. Long,
N. Manganelli, M. Olmedo Negrete, W. Si, S. Wimpenny, Y. Zhang University of
California, San Diego, La Jolla, USA
J.G. Branson, P. Chang, S. Cittolin, S. Cooperstein, N. Deelen, J. Duarte, R.
Gerosa, L. Giannini, D. Gilbert, V. Krutelyov, J. Letts, M. Masciovecchio, S.
May, S. Padhi, M. Pieri, V. Sharma, M. Tadel, A. Vartak, F. Würthwein, A.
Yagil University of California, Santa Barbara - Department of Physics, Santa
Barbara, USA
N. Amin, C. Campagnari, M. Citron, A. Dorsett, V. Dutta, J. Incandela, M.
Kilpatrick, B. Marsh, H. Mei, A. Ovcharova, H. Qu, M. Quinnan, J. Richman, U.
Sarica, D. Stuart, S. Wang California Institute of Technology, Pasadena, USA
A. Bornheim, O. Cerri, I. Dutta, J.M. Lawhorn, N. Lu, J. Mao, H.B. Newman, J.
Ngadiuba, T.Q. Nguyen, M. Spiropulu, J.R. Vlimant, C. Wang, S. Xie, Z. Zhang,
R.Y. Zhu Carnegie Mellon University, Pittsburgh, USA
J. Alison, M.B. Andrews, T. Ferguson, T. Mudholkar, M. Paulini, I. Vorobiev
University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, E. MacDonald, R. Patel, A. Perloff, K. Stenson, K.A.
Ulmer, S.R. Wagner Cornell University, Ithaca, USA
J. Alexander, Y. Cheng, J. Chu, D.J. Cranshaw, K. Mcdermott, J. Monroy, J.R.
Patterson, D. Quach, A. Ryd, W. Sun, S.M. Tan, Z. Tao, J. Thom, P. Wittich, M.
Zientek Fermi National Accelerator Laboratory, Batavia, USA
M. Albrow, M. Alyari, G. Apollinari, A. Apresyan, A. Apyan, S. Banerjee,
L.A.T. Bauerdick, A. Beretvas, D. Berry, J. Berryhill, P.C. Bhat, K. Burkett,
J.N. Butler, A. Canepa, G.B. Cerati, H.W.K. Cheung, F. Chlebana, M. Cremonesi,
K.F. Di Petrillo, V.D. Elvira, J. Freeman, Z. Gecse, L. Gray, D. Green, S.
Grünendahl, O. Gutsche, R.M. Harris, R. Heller, T.C. Herwig, J. Hirschauer, B.
Jayatilaka, S. Jindariani, M. Johnson, U. Joshi, P. Klabbers, T. Klijnsma, B.
Klima, M.J. Kortelainen, S. Lammel, D. Lincoln, R. Lipton, T. Liu, J. Lykken,
K. Maeshima, D. Mason, P. McBride, P. Merkel, S. Mrenna, S. Nahn, V. O’Dell,
V. Papadimitriou, K. Pedro, C. Pena55, O. Prokofyev, F. Ravera, A. Reinsvold
Hall, L. Ristori, B. Schneider, E. Sexton-Kennedy, N. Smith, A. Soha, L.
Spiegel, S. Stoynev, J. Strait, L. Taylor, S. Tkaczyk, N.V. Tran, L. Uplegger,
E.W. Vaandering, H.A. Weber University of Florida, Gainesville, USA
D. Acosta, P. Avery, D. Bourilkov, L. Cadamuro, V. Cherepanov, F. Errico, R.D.
Field, D. Guerrero, B.M. Joshi, M. Kim, J. Konigsberg, A. Korytov, K.H. Lo, K.
Matchev, N. Menendez, G. Mitselmakher, D. Rosenzweig, K. Shi, J. Sturdy, J.
Wang, X. Zuo Florida State University, Tallahassee, USA
T. Adams, A. Askew, D. Diaz, R. Habibullah, S. Hagopian, V. Hagopian, K.F.
Johnson, R. Khurana, T. Kolberg, G. Martinez, H. Prosper, C. Schiber, R.
Yohay, J. Zhang Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, S. Butalla, T. Elkafrawy15, M. Hohlmann, R. Kumar Verma, D.
Noonan, M. Rahmani, M. Saunders, F. Yumiceva University of Illinois at Chicago
(UIC), Chicago, USA
M.R. Adams, L. Apanasevich, H. Becerril Gonzalez, R. Cavanaugh, X. Chen, S.
Dittmer, O. Evdokimov, C.E. Gerber, D.A. Hangal, D.J. Hofman, C. Mills, G. Oh,
T. Roy, M.B. Tonjes, N. Varelas, J. Viinikainen, X. Wang, Z. Wu, Z. Ye The
University of Iowa, Iowa City, USA
M. Alhusseini, K. Dilsiz88, S. Durgut, R.P. Gandrajula, M. Haytmyradov, V.
Khristenko, O.K. Köseyan, J.-P. Merlo, A. Mestvirishvili89, A. Moeller, J.
Nachtman, H. Ogul90, Y. Onel, F. Ozok91, A. Penzo, C. Snyder, E. Tiras92, J.
Wetzel Johns Hopkins University, Baltimore, USA
O. Amram, B. Blumenfeld, L. Corcodilos, M. Eminizer, A.V. Gritsan, S.
Kyriacou, P. Maksimovic, C. Mantilla, J. Roskes, M. Swartz, T.Á. Vámi The
University of Kansas, Lawrence, USA
C. Baldenegro Barrera, P. Baringer, A. Bean, A. Bylinkin, T. Isidori, S.
Khalil, J. King, G. Krintiras, A. Kropivnitskaya, C. Lindsey, N. Minafra, M.
Murray, C. Rogan, C. Royon, S. Sanders, E. Schmitz, J.D. Tapia Takaki, Q.
Wang, J. Williams, G. Wilson Kansas State University, Manhattan, USA
S. Duric, A. Ivanov, K. Kaadze, D. Kim, Y. Maravin, T. Mitchell, A. Modak
Lawrence Livermore National Laboratory, Livermore, USA
F. Rebassoo, D. Wright University of Maryland, College Park, USA
E. Adams, A. Baden, O. Baron, A. Belloni, S.C. Eno, Y. Feng, N.J. Hadley, S.
Jabeen, R.G. Kellogg, T. Koeth, A.C. Mignerey, S. Nabili, M. Seidel, A. Skuja,
S.C. Tonwar, L. Wang, K. Wong Massachusetts Institute of Technology,
Cambridge, USA
D. Abercrombie, R. Bi, S. Brandt, W. Busza, I.A. Cali, Y. Chen, M. D’Alfonso,
G. Gomez Ceballos, M. Goncharov, P. Harris, M. Hu, M. Klute, D. Kovalskyi, J.
Krupa, Y.-J. Lee, P.D. Luckey, B. Maier, A.C. Marini, C. Mironov, X. Niu, C.
Paus, D. Rankin, C. Roland, G. Roland, Z. Shi, G.S.F. Stephans, K. Tatar, D.
Velicanu, J. Wang, T.W. Wang, Z. Wang, B. Wyslouch University of Minnesota,
Minneapolis, USA
R.M. Chatterjee, A. Evans, P. Hansen, J. Hiltbrand, Sh. Jain, M. Krohn, Y.
Kubota, Z. Lesko, J. Mans, M. Revering, R. Rusack, R. Saradhy, N. Schroeder,
N. Strobbe, M.A. Wadud University of Mississippi, Oxford, USA
J.G. Acosta, S. Oliveros University of Nebraska-Lincoln, Lincoln, USA
K. Bloom, M. Bryson, S. Chauhan, D.R. Claes, C. Fangmeier, L. Finco, F. Golf,
J.R. González Fernández, C. Joo, I. Kravchenko, J.E. Siado, G.R.
Snow${}^{\textrm{\textdagger}}$, W. Tabb, F. Yan State University of New York
at Buffalo, Buffalo, USA
G. Agarwal, H. Bandyopadhyay, L. Hay, I. Iashvili, A. Kharchilava, C. McLean,
D. Nguyen, J. Pekkanen, S. Rappoccio Northeastern University, Boston, USA
G. Alverson, E. Barberis, C. Freer, Y. Haddad, A. Hortiangtham, J. Li, G.
Madigan, B. Marzocchi, D.M. Morse, V. Nguyen, T. Orimoto, A. Parker, L.
Skinnari, A. Tishelman-Charny, T. Wamorkar, B. Wang, A. Wisecarver, D. Wood
Northwestern University, Evanston, USA
S. Bhattacharya, J. Bueghly, Z. Chen, A. Gilbert, T. Gunter, K.A. Hahn, N.
Odell, M.H. Schmitt, K. Sung, M. Velasco University of Notre Dame, Notre Dame,
USA
R. Bucci, N. Dev, R. Goldouzian, M. Hildreth, K. Hurtado Anampa, C. Jessop, K.
Lannon, N. Loukas, N. Marinelli, I. Mcalister, F. Meng, K. Mohrman, Y.
Musienko48, R. Ruchti, P. Siddireddy, M. Wayne, A. Wightman, M. Wolf, L.
Zygala The Ohio State University, Columbus, USA
J. Alimena, B. Bylsma, B. Cardwell, L.S. Durkin, B. Francis, C. Hill, A.
Lefeld, B.L. Winer, B.R. Yates Princeton University, Princeton, USA
F.M. Addesa, B. Bonham, P. Das, G. Dezoort, P. Elmer, A. Frankenthal, B.
Greenberg, N. Haubrich, S. Higginbotham, A. Kalogeropoulos, G. Kopp, S. Kwan,
D. Lange, M.T. Lucchini, D. Marlow, K. Mei, I. Ojalvo, J. Olsen, C. Palmer, D.
Stickland, C. Tully University of Puerto Rico, Mayaguez, USA
S. Malik, S. Norberg Purdue University, West Lafayette, USA
A.S. Bakshi, V.E. Barnes, R. Chawla, S. Das, L. Gutay, M. Jones, A.W. Jung, S.
Karmarkar, M. Liu, G. Negro, N. Neumeister, C.C. Peng, S. Piperov, A. Purohit,
J.F. Schulte, M. Stojanovic17, J. Thieman, F. Wang, R. Xiao, W. Xie Purdue
University Northwest, Hammond, USA
J. Dolen, N. Parashar Rice University, Houston, USA
A. Baty, S. Dildick, K.M. Ecklund, S. Freed, F.J.M. Geurts, A. Kumar, W. Li,
B.P. Padley, R. Redjimi, J. Roberts${}^{\textrm{\textdagger}}$, W. Shi, A.G.
Stahl Leiton University of Rochester, Rochester, USA
A. Bodek, P. de Barbaro, R. Demina, J.L. Dulemba, C. Fallon, T. Ferbel, M.
Galanti, A. Garcia-Bellido, O. Hindrichs, A. Khukhunaishvili, E. Ranken, R.
Taus Rutgers, The State University of New Jersey, Piscataway, USA
B. Chiarito, J.P. Chou, A. Gandrakota, Y. Gershtein, E. Halkiadakis, A. Hart,
M. Heindl, E. Hughes, S. Kaplan, O. Karacheban24, I. Laflotte, A. Lath, R.
Montalvo, K. Nash, M. Osherson, S. Salur, S. Schnetzer, S. Somalwar, R. Stone,
S.A. Thayil, S. Thomas, H. Wang University of Tennessee, Knoxville, USA
H. Acharya, A.G. Delannoy, S. Spanier Texas A&M University, College Station,
USA
O. Bouhali93, M. Dalchenko, A. Delgado, R. Eusebi, J. Gilmore, T. Huang, T.
Kamon94, H. Kim, S. Luo, S. Malhotra, R. Mueller, D. Overton, D. Rathjens, A.
Safonov Texas Tech University, Lubbock, USA
N. Akchurin, J. Damgov, V. Hegde, S. Kunori, K. Lamichhane, S.W. Lee, T.
Mengke, S. Muthumuni, T. Peltola, S. Undleeb, I. Volobouev, Z. Wang, A.
Whitbeck Vanderbilt University, Nashville, USA
E. Appelt, S. Greene, A. Gurrola, W. Johns, C. Maguire, A. Melo, H. Ni, K.
Padeken, F. Romeo, P. Sheldon, S. Tuo, J. Velkovska University of Virginia,
Charlottesville, USA
M.W. Arenton, B. Cox, G. Cummings, J. Hakala, R. Hirosky, M. Joyce, A.
Ledovskoy, A. Li, C. Neu, B. Tannenwald, E. Wolfe Wayne State University,
Detroit, USA
P.E. Karchin, N. Poudyal, P. Thapa University of Wisconsin - Madison, Madison,
WI, USA
K. Black, T. Bose, J. Buchanan, C. Caillol, S. Dasu, I. De Bruyn, P.
Everaerts, C. Galloni, H. He, M. Herndon, A. Hervé, U. Hussain, A. Lanaro, A.
Loeliger, R. Loveless, J. Madhusudanan Sreekala, A. Mallampalli, A. Mohammadi,
D. Pinna, A. Savin, V. Shang, V. Sharma, W.H. Smith, D. Teague, S. Trembath-
reichert, W. Vetens †: Deceased
1: Also at Vienna University of Technology, Vienna, Austria
2: Also at Institute of Basic and Applied Sciences, Faculty of Engineering,
Arab Academy for Science, Technology and Maritime Transport, Alexandria,
Egypt, Alexandria, Egypt
3: Also at Université Libre de Bruxelles, Bruxelles, Belgium
4: Also at IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
5: Also at Universidade Estadual de Campinas, Campinas, Brazil
6: Also at Federal University of Rio Grande do Sul, Porto Alegre, Brazil
7: Also at UFMS, Nova Andradina, Brazil
8: Also at Nanjing Normal University Department of Physics, Nanjing, China
9: Now at The University of Iowa, Iowa City, USA
10: Also at University of Chinese Academy of Sciences, Beijing, China
11: Also at Institute for Theoretical and Experimental Physics named by A.I.
Alikhanov of NRC ‘Kurchatov Institute’, Moscow, Russia
12: Also at Joint Institute for Nuclear Research, Dubna, Russia
13: Also at Helwan University, Cairo, Egypt
14: Now at Zewail City of Science and Technology, Zewail, Egypt
15: Also at Ain Shams University, Cairo, Egypt
16: Now at British University in Egypt, Cairo, Egypt
17: Also at Purdue University, West Lafayette, USA
18: Also at Université de Haute Alsace, Mulhouse, France
19: Also at Erzincan Binali Yildirim University, Erzincan, Turkey
20: Also at CERN, European Organization for Nuclear Research, Geneva,
Switzerland
21: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
22: Also at University of Hamburg, Hamburg, Germany
23: Also at Department of Physics, Isfahan University of Technology, Isfahan,
Iran, Isfahan, Iran
24: Also at Brandenburg University of Technology, Cottbus, Germany
25: Also at Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State
University, Moscow, Russia
26: Also at Physics Department, Faculty of Science, Assiut University, Assiut,
Egypt
27: Also at Eszterhazy Karoly University, Karoly Robert Campus, Gyongyos,
Hungary
28: Also at Institute of Physics, University of Debrecen, Debrecen, Hungary,
Debrecen, Hungary
29: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary
30: Also at MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary, Budapest, Hungary
31: Also at Wigner Research Centre for Physics, Budapest, Hungary
32: Also at IIT Bhubaneswar, Bhubaneswar, India, Bhubaneswar, India
33: Also at Institute of Physics, Bhubaneswar, India
34: Also at G.H.G. Khalsa College, Punjab, India
35: Also at Shoolini University, Solan, India
36: Also at University of Hyderabad, Hyderabad, India
37: Also at University of Visva-Bharati, Santiniketan, India
38: Also at Indian Institute of Technology (IIT), Mumbai, India
39: Also at Deutsches Elektronen-Synchrotron, Hamburg, Germany
40: Also at Sharif University of Technology, Tehran, Iran
41: Also at Department of Physics, University of Science and Technology of
Mazandaran, Behshahr, Iran
42: Now at INFN Sezione di Bari a, Università di Bari b, Politecnico di Bari
c, Bari, Italy
43: Also at Italian National Agency for New Technologies, Energy and
Sustainable Economic Development, Bologna, Italy
44: Also at Centro Siciliano di Fisica Nucleare e di Struttura Della Materia,
Catania, Italy
45: Also at Università di Napoli ’Federico II’, NAPOLI, Italy
46: Also at Riga Technical University, Riga, Latvia, Riga, Latvia
47: Also at Consejo Nacional de Ciencia y Tecnología, Mexico City, Mexico
48: Also at Institute for Nuclear Research, Moscow, Russia
49: Now at National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
50: Also at St. Petersburg State Polytechnical University, St. Petersburg,
Russia
51: Also at University of Florida, Gainesville, USA
52: Also at Imperial College, London, United Kingdom
53: Also at P.N. Lebedev Physical Institute, Moscow, Russia
54: Also at Moscow Institute of Physics and Technology, Moscow, Russia,
Moscow, Russia
55: Also at California Institute of Technology, Pasadena, USA
56: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia
57: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia
58: Also at Trincomalee Campus, Eastern University, Sri Lanka, Nilaveli, Sri
Lanka
59: Also at INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy,
Pavia, Italy
60: Also at National and Kapodistrian University of Athens, Athens, Greece
61: Also at Universität Zürich, Zurich, Switzerland
62: Also at Ecole Polytechnique Fédérale Lausanne, Lausanne, Switzerland
63: Also at Stefan Meyer Institute for Subatomic Physics, Vienna, Austria,
Vienna, Austria
64: Also at Laboratoire d’Annecy-le-Vieux de Physique des Particules,
IN2P3-CNRS, Annecy-le-Vieux, France
65: Also at Şırnak University, Sirnak, Turkey
66: Also at Department of Physics, Tsinghua University, Beijing, China,
Beijing, China
67: Also at Near East University, Research Center of Experimental Health
Science, Nicosia, Turkey
68: Also at Beykent University, Istanbul, Turkey, Istanbul, Turkey
69: Also at Istanbul Aydin University, Application and Research Center for
Advanced Studies (App. & Res. Cent. for Advanced Studies), Istanbul, Turkey
70: Also at Mersin University, Mersin, Turkey
71: Also at Piri Reis University, Istanbul, Turkey
72: Also at Adiyaman University, Adiyaman, Turkey
73: Also at Ozyegin University, Istanbul, Turkey
74: Also at Izmir Institute of Technology, Izmir, Turkey
75: Also at Necmettin Erbakan University, Konya, Turkey
76: Also at Bozok Universitetesi Rektörlügü, Yozgat, Turkey, Yozgat, Turkey
77: Also at Marmara University, Istanbul, Turkey
78: Also at Milli Savunma University, Istanbul, Turkey
79: Also at Kafkas University, Kars, Turkey
80: Also at Istanbul Bilgi University, Istanbul, Turkey
81: Also at Hacettepe University, Ankara, Turkey
82: Also at Vrije Universiteit Brussel, Brussel, Belgium
83: Also at School of Physics and Astronomy, University of Southampton,
Southampton, United Kingdom
84: Also at IPPP Durham University, Durham, United Kingdom
85: Also at Monash University, Faculty of Science, Clayton, Australia
86: Also at Bethel University, St. Paul, Minneapolis, USA, St. Paul, USA
87: Also at Karamanoğlu Mehmetbey University, Karaman, Turkey
88: Also at Bingol University, Bingol, Turkey
89: Also at Georgian Technical University, Tbilisi, Georgia
90: Also at Sinop University, Sinop, Turkey
91: Also at Mimar Sinan University, Istanbul, Istanbul, Turkey
92: Also at Erciyes University, KAYSERI, Turkey
93: Also at Texas A&M University at Qatar, Doha, Qatar
94: Also at Kyungpook National University, Daegu, Korea, Daegu, Korea