SMP-19-004

SMP-19-004

# Measurement of the associated production of a boson with charm or bottom
quark jets in proton-proton collisions at $\sqrt{s}=13$

###### Abstract

Ratios of cross sections,
$\sigma(\PZ+\cPqc\text{~{}jets})/\sigma(\PZ+\text{jets})$,
$\sigma(\PZ+\cPqb\text{~{}jets})/\sigma(\PZ+\text{jets})$, and
$\sigma(\PZ+\cPqc\text{~{}jets})/\sigma(\PZ+\cPqb\text{~{}jets})$ in the
associated production of a boson with at least one charm or bottom quark jet
are measured in proton-proton collisions at $\sqrt{s}=13$. The data sample,
collected by the CMS experiment at the CERN LHC, corresponds to an integrated
luminosity of 35.9, with a fiducial volume of $\pt>30\GeV$ and $|\eta|<2.4$
for the jets, where and $\eta$ represent transverse momentum and
pseudorapidity, respectively. The boson candidates come from leptonic decays
into electrons or muons with $\pt>25\GeV$ and $\abs{\eta}<2.4$, and the
dilepton mass satisfies $71<m_{\PZ}<111\GeV$. The measured values are
$\sigma(\PZ+\cPqc\text{~{}jets})/\sigma(\PZ+\text{jets})=0.102\pm 0.002\pm
0.009$, $\sigma(\PZ+\cPqb\text{~{}jets})/\sigma(\PZ+\text{jets})=0.0633\pm
0.0004\pm 0.0015$, and
$\sigma(\PZ+\cPqc\text{~{}jets})/\sigma(\PZ+\cPqb\text{~{}jets})=1.62\pm
0.03\pm 0.15$. Results on the inclusive and differential cross section ratios
as functions of jet and boson transverse momentum are compared with
predictions from leading and next-to-leading order perturbative quantum
chromodynamics calculations. These are the first measurements of the cross
section ratios at 13.

##  0.1 Introduction

Studies of boson production in association with heavy-flavor (HF) jets from
the hadronization of heavy (or ) quarks provide important tests of
perturbative quantum chromodynamics (pQCD) calculations. A good description of
these processes is also important since they form a major background for a
variety of physics processes including Higgs boson production in association
with a boson, $\PZ\PH$ ($\PH\to\cPqc\cPaqc$ or $\PH\to\cPqb\cPaqb$), and
searches for new physics signatures in final states with leptons and HF jets.
Two different approaches are currently available for calculating the
$\PZ+\text{HF~{}jets}$ production: the five-flavor scheme (5FS) [1] and the
four-flavor scheme (4FS) [2]. Both approaches yield consistent results within
theoretical uncertainties [3].

Several $\PZ+\text{HF~{}jets}$ measurements have been performed by the CDF and
D0 Collaborations at the FNAL Tevatron [4, 5, 6] and by the ATLAS, CMS, and
LHCb Collaborations at the CERN LHC [7, 8, 9, 10]. The D0 Collaboration
reported on the first
$\sigma(\PZ+\cPqc\text{~{}jets})/\sigma(\PZ+\cPqb\text{~{}jets})$ cross
section ratio measurement [5] and observed a significantly higher value
compared to next-to-leading order (NLO) pQCD calculations. A measurement of
the $\sigma(\PZ+\cPqc\text{~{}jets})/\sigma(\PZ+\cPqb\text{~{}jets})$ cross
section ratio in 8proton-proton ($\Pp\Pp$) collisions at the LHC has been
recently reported by the CMS Collaboration [11] and is in agreement with
predictions from leading order (LO) and NLO calculations obtained with the
[12] and [13] programs, respectively.

The current paper reports on simultaneous measurements of the and quark jet
contents in a sample containing a boson (in the following, is used as a
shorthand for /$\gamma^{*}$) produced in association with at least one jet.
These measurements provide the first results for proton-proton collisions at
$\sqrt{s}=13$. The experimental precision is improved with respect to previous
LHC results because of the increased size of the data sample and advanced
heavy-flavor tagging techniques. The bosons are identified through
reconstructed dielectrons or dimuons, where the individual leptons are subject
to requirements on transverse momentum ($\pt>25\GeV$) and pseudorapidity
($\abs{\eta}<2.4$). The dilepton invariant mass must be within a boson window
of 71–111, and jets are required to have $\pt>30\GeV$ and $\abs{\eta}<2.4$.

The following cross section ratios are measured:
$\sigma(\PZ+\cPqc\text{~{}jets})/\sigma(\PZ+\text{jets})$,
$\sigma(\PZ+\cPqb\text{~{}jets})/\sigma(\PZ+\text{jets})$, and
$\sigma(\PZ+\cPqc\text{~{}jets})/\sigma(\PZ+\cPqb\text{~{}jets})$. These cross
section ratios are measured inclusively and differentially as functions of the
transverse momentum of the jet and the boson, and are unfolded to the particle
level taking into account detector effects. The measurements of the cross
section ratios benefit from cancellations of several systematic uncertainties
related to the jet, lepton, and luminosity measurements. A number of theory-
related uncertainties are reduced as well, including those linked to the
details of parton showering and hadronization. Therefore, by measuring cross
section ratios one can more precisely compare data with theoretical
calculations.

The paper is organized as follows. The CMS experiment and data together with
simulated samples used in the analysis are described in Sections 0.2 and 0.3.
Details of the measurements are described in Sections 0.4, 0.5, and 0.6, while
Sections 0.7 and 0.8 present the systematic uncertainties and the measurement
results, respectively, followed by a summary in Section 0.9.

##  0.2 The CMS detector

The central feature of the CMS apparatus is a superconducting solenoid of 6m
internal diameter, providing a magnetic field of 3.8T. Within the solenoid
volume are a silicon pixel and strip tracker, covering a pseudorapidity region
of $\abs{\eta}<2.5$, a lead tungstate crystal electromagnetic calorimeter
(ECAL), and a brass and scintillator hadron calorimeter, each composed of a
barrel and two endcap sections. Forward calorimeters, made of steel and quartz
fibers, extend the pseudorapidity coverage provided by the barrel and endcap
detectors to $\abs{\eta}<5$. Muons are detected in gas-ionization chambers
embedded in the steel flux-return yoke outside the solenoid and covering
$\abs{\eta}<2.4$.

Events of interest are selected using a two-tiered trigger system [14]. The
first level, composed of custom hardware processors, uses information from the
calorimeters and muon detectors to select events at a rate of around 100kHz
within a time interval of less than 4. The second level, known as the high-
level trigger, consists of a farm of processors running a version of the full
event reconstruction software optimized for fast processing, and reduces the
event rate to around 1kHz before data storage.

A more detailed description of the CMS detector, together with a definition of
the coordinate system used and the relevant kinematic variables, can be found
in Ref. [15].

##  0.3 Data and simulated samples

The cross section ratio measurements are based on proton-proton ($\Pp\Pp$)
collision data at $\sqrt{s}=13\TeV$ collected with the CMS detector in 2016
and corresponding to an integrated luminosity of 35.9 [16]. Recorded events
have an average 23 additional $\Pp\Pp$ interactions per bunch crossing
(pileup) together with the hard process.

Various Monte Carlo (MC) event generators are used to simulate the
$\PZ+\text{jets}$ signal and background processes. The full detector
simulation is based on the package [17]. The simulation includes the pileup
effects from the same or nearby bunch crossings by overlapping the hard
process of interest with the pileup events. The simulated events are
reconstructed with the same algorithms as used for the data.

The $\PZ+\text{jets}$ events are generated by v2.2.2 [13] (using 5FS; denoted
as mg5_aMC in the following) at NLO in pQCD with up to two additional partons
at the matrix element level, generated for each parton multiplicity and then
merged. The mg5_aMC matrix element generator is interfaced with v8.212 [18],
which simulates the parton shower and hadronization processes, through the
FxFx merging scheme [19] at a matching scale of 19. The predicted numbers of
events from $\PZ+\text{jets}$ production are estimated using a cross section
at next-to-next-to-leading order (NNLO) accuracy obtained from v3.1 [20].

The background events originate from top quark and diboson processes. Top
quark-antiquark () production, which forms the dominant background, is
generated at NLO by v2.0 [21, 22, 23, 24] and normalized to a cross section
calculated by using top++ v2.0 [25] at NNLO accuracy including soft-gluon
resummation. The diboson ($\PW\PW$, $\PW\PZ$, $\PZ\PZ$) backgrounds are
generated by while and NLO mg5_aMC are used to simulate the single top quark
processes ($s$-channel, $t$-channel, and $\cPqt\PW$). The generator is also
interfaced with for parton showering and hadronization. The diboson and single
top quark predictions are normalized to NNLO [26, 27] cross sections.

The NNPDF 3.0 NLO and LO parton distribution functions (PDF) [28] are used for
the NLO and LO calculations, respectively. uses the NNPDF 2.3 LO PDF set and
the CUETP8M1 [29] or CUETP8M2T4 [30] (sample) for the underlying event tune.

##  0.4 Object reconstruction and event selection

The particle-flow (PF) algorithm [31] reconstructs and identifies each
individual particle in an event, with an optimized combination of information
from the various elements of the CMS detector. The neutral particle energy
deposits are determined in the calorimeters, whereas charged tracks are
measured in the central tracking and muon systems.

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, clustered using the jet finding algorithm [32, 33] with the
tracks assigned to candidate vertices as inputs, and leptons. More details are
given in Ref. [34].

Electrons are reconstructed using momentum measurements in the tracker
combined with the energy deposits in the ECAL [35]. The identification
requirements are based on the ECAL shower shape, matching between the electron
track and the energy clusters in the ECAL, and observables characterizing the
bremsstrahlung along the electron trajectory. Electrons are required to
originate from the primary vertex. The electron momentum is estimated by
combining the energy measurement in the ECAL with the momentum measurement in
the tracker. The momentum resolution for electrons with $\pt\approx 45\GeV$
from $\PZ\to\Pep\Pem$ decays ranges from 1.7 to 4.5%. The resolution tends to
be better in the barrel region than in the endcaps, and it also depends on the
bremsstrahlung energy emitted by the electron as it traverses the material in
front of the ECAL. The dielectron mass resolution for $\PZ\to\Pep\Pem$ decays
when both electrons are in the ECAL barrel is 1.9%, and is 2.9% when both
electrons are in the endcaps [35].

Muon candidates are built by combining signals from the tracker and the muon
subsystems. The identification criteria are based on the number of
measurements in the detectors, the fit quality of the track, and requirements
on its association with the primary vertex. Matching muons to tracks measured
in the tracker results in a relative transverse momentum resolution, for muons
with up to 100, of 1% in the barrel and 3% in the endcaps [36].

To reduce the misidentification rate, electrons and muons are required to be
isolated. Activity near an electron (muon) is quantified as the sum of
transverse momenta of PF candidates within the isolation cone radius of
$\Delta R=\sqrt{\smash[b]{(\Delta\eta)^{2}+(\Delta\phi)^{2}}}=0.3$ (0.4)
around the electron (muon) track, where $\phi$ is the azimuthal angle. After
compensating for the energy contribution from pileup in the isolation cone,
the resultant sum is required to be less than 25% of the lepton transverse
momentum. The lepton isolation, along with other requirements to select
$\PZ+\text{jets}$ events, strongly suppresses background events with
misidentified dileptons such as $\PW+\text{jets}$ and QCD multijets.

Based on the PF candidates, jets are reconstructed using the anti-algorithm
with a distance parameter of 0.4. Jet momentum is determined as the vector sum
of all particle momenta in the jet; based on simulation this is, on average,
within 5 to 10% of the true jet momentum over the entire spectrum and detector
acceptance. Pileup interactions can result in more tracks and calorimetric
energy depositions, increasing the apparent jet momentum. To mitigate this
effect, tracks originating from pileup vertices are discarded and an offset
correction is applied to account for remaining contributions [37, 38]. 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+jet, $\PZ$+jet, and
multijet events are used to determine any residual differences between the jet
energy scale (JES) in data and in simulation, and appropriate corrections are
applied [39]. The jet energy resolution (JER) typically amounts to 16% at
30and 8% at 100. Additional selection criteria are applied to remove jets
potentially dominated by instrumental effects or reconstruction failures [40].

For this analysis, the selection for $\PZ+\text{jets}$ events starts with the
trigger requirements based on two electron (muon) objects identified by the
trigger system that pass thresholds of 23 and 13(17 and 8). The
$\PZ+\text{jets}$ events are further selected by requiring two reconstructed
electrons or muons with $\pt>25\GeV$ and within $\abs{\eta}<2.4$. The
requirement is chosen to obtain high trigger efficiency for selecting the
signal events. Events containing two selected electrons (muons) are
categorized in the electron (muon) channel. The lepton candidates are subject
to requirements on their transverse impact parameter,
$\abs{d_{xy}}<0.05\unit{cm}$, and their longitudinal impact parameter,
$\abs{d_{z}}<0.2\unit{cm}$, both with respect to the primary vertex. The boson
candidate is reconstructed from a pair of oppositely charged same-flavor
leptons with invariant mass between 71 and 111. An event must contain at least
one associated jet with $\pt>30\GeV$ and $\abs{\eta}<2.4$.

Missing transverse momentum is used in this analysis to reduce background
contributions from and single top quark production processes. In contrast to
the $\PZ+\text{jets}$, these processes have a significant amount of missing
energy because of undetected neutrinos in top quark decays. The missing
transverse momentum vector, , is computed as the negative vector sum of the
transverse momenta of all PF candidates in an event [41] and is further
modified to account for corrections to the energy scale of the reconstructed
jets. Its magnitude, , is required to be less than 40, which results in a
signal efficiency of ${\approx}80\%$ with the rejection factor of
${\approx}4.8$.

A $\PZ+\text{jets}$ sample with enriched and quark jet content is selected by
applying an HF tagging requirement to jets in the $\PZ+\text{jets}$ sample
described above. The discrimination of HF jets from light-flavor quark and
gluon jets, referred to as light jets in the following, is achieved by
constructing a discriminator variable from tracks and secondary vertex (SV)
characteristics. Artificial neural network algorithms are used to combine
specific properties of the HF quarks, long lifetime and substantial mass, to
build the discriminator. The algorithm used in the analysis, the combined
secondary vertex (Version 2), is described in Ref. [42]. Some of the important
input variables are the number of secondary vertices and the number of tracks
associated with each of them, the mass and 2D decay distance significance of
the SV with the smallest decay distance uncertainty, and the signed 3D impact
parameter significance of the tracks. Here the significance is defined as the
ratio between a measured quantity and its uncertainty. Although the combined
secondary vertex (Version 2) is trained to distinguish jets, it does
occasionally tag a jet. Therefore, at a proper operating point, the algorithm
can retain a sufficient amount of jets while heavily suppressing light jets.
The analysis uses a “medium” operating point, which corresponds to
approximately 10 (60)% tagging efficiencies for () quark jets and a
misidentification probability of 1% for a light jet. The
$\PZ+\text{HF~{}jets}$ sample must contain at least one tagged jet. The
tagging efficiencies are determined using MC samples and corrected for the
difference between data and simulation. The corresponding correction factors
are derived from the data versus simulation efficiency comparisons in
dedicated control samples containing and multijet events [42].

In simulation, the classification of reconstructed $\PZ+\text{jets}$ events
into $\PZ+\cPqc\text{~{}jets}$, $\PZ+\cPqb\text{~{}jets}$, and
$\PZ+\text{light~{}jets}$ categories is based on the flavors of reconstructed
jets with $\pt>30\GeV$ and $\abs{\eta}<2.4$. They are classified as or jets if
they are matched to MC generated or hadrons [42]. In the case where both and
hadrons are matched, the jet is considered a jet. Based on reconstructed jets
with defined flavors, events are classified as $\PZ+\cPqb\text{~{}jets}$ if
they contain at least one jet. Of the remaining events, those that contain at
least one hadron are considered as $\PZ+\cPqc\text{~{}jets}$ and those that
contain neither nor hadrons are classified as $\PZ+\text{light~{}jets}$.

Table 0.4 lists the number of events estimated in simulation and found in data
that satisfy the $\PZ+\text{jets}$ and $\PZ+\text{HF~{}jets}$ selection
criteria for both the electron and muon channels. The background, mostly from
top quark and diboson processes, is approximately 5% in the
$\PZ+\text{HF~{}jets}$ sample, and the diboson background is dominated by the
$\PW\PZ$ events.

Numbers of events that satisfy the $\PZ+\text{jets}$ and
$\PZ+\text{HF~{}jets}$ selection criteria in the electron and muon channels.
The uncertainties are statistical only. lcccc & $\PZ+\text{jets}$ sample
$\PZ+\text{HF~{}jets}$ sample
Electron Muon Electron Muon
$\PZ+\cPqc\text{~{}jets}$ 171 970 $\pm$ 530 287 090 $\pm$ 720 18 870 $\pm$ 170
32 310 $\pm$ 230
$\PZ+\cPqb\text{~{}jets}$ 95 910 $\pm$ 410 159 500 $\pm$ 560 60 100 $\pm$ 310
100 630 $\pm$ 420
$\PZ+\text{light~{}jets}$ 1 531 900 $\pm$ 1 600 2 612 100 $\pm$ 2 200 6 170
$\pm$ 100 10 810 $\pm$ 140
5 850 $\pm$ 50 9 440 $\pm$ 60 3 850 $\pm$ 40 6 180 $\pm$ 50
Diboson 10 040 $\pm$ 60 16 310 $\pm$ 80 780 $\pm$ 20 1 320 $\pm$ 20
Single 580 $\pm$ 10 950 $\pm$ 10 303 $\pm$ 7 500 $\pm$ 10
Total, simulation 1 816 200 $\pm$ 1 700 3 085 400 $\pm$ 2 400 90 070 $\pm$ 370
151 740 $\pm$ 510
Data 1 759 047 2 959 629 79 015 130 775
Data/simulation 0.969 $\pm$ 0.001 0.959 $\pm$ 0.001 0.877 $\pm$ 0.005 0.862
$\pm$ 0.004

##  0.5 Cross section ratio measurements

###  0.5.1 Analysis strategy

The goal of the analysis is to precisely measure the fraction of jets with
heavy flavors in $\PZ+\text{jets}$ events. For this purpose, the SV invariant
mass, $M_{\text{SV}}$, of the tagged jet with highest in the
$\PZ+\text{HF~{}jets}$ events is used. The SV is reconstructed using an
adaptive vertex reconstruction algorithm [43] from selected tracks within a
cone of $\Delta R<0.3$ around the jet axis. The distance between the track and
the jet axis measured at their point of closest approach must be less than
0.2. Details of track selections and SV reconstructions can be found in Refs.
[44, 42]. The $M_{\text{SV}}$ is calculated using the momenta of charged-
particle tracks associated with the SV. The corresponding particles are
assumed to have the pion mass for the purpose of calculating the SV mass. The
$M_{\text{SV}}$ distributions possess specific features depending on the jet
flavor, and can be used as templates in a fit to the $M_{\text{SV}}$
distribution in data to extract the fractions of and jets, as discussed in
Section 0.5.3.

The template fit is performed in the $\PZ+\text{jets}$ sample enriched with HF
jets, , in the $\PZ+\text{HF~{}jets}$ sample, and the observed number of
$\PZ+\cPqc\text{~{}jets}$ ($N_{\PQc}$) and $\PZ+\cPqb\text{~{}jets}$
($N_{\PQb}$) events are derived. They are corrected for the efficiencies of
tagging events, $\epsilon^{\PQc}_{\text{tag}}$ and
$\epsilon^{\PQb}_{\text{tag}}$ for $N_{\PQc}$ and $N_{\PQb}$, respectively, to
obtain the numbers of $\PZ+\cPqc\text{~{}jets}$ and $\PZ+\cPqb\text{~{}jets}$
events in the $\PZ+\text{jets}$ sample. The cross section ratios are then
calculated as

$\text{R}(\cPqc/\text{j})=\frac{\sigma(\PZ+\cPqc\text{~{}jets})}{\sigma(\PZ+\text{jets})}=\frac{N_{\PQc}}{N_{\text{jet}}\epsilon^{\PQc}_{\text{tag}}},$
(1)
$\text{R}(\cPqb/\text{j})=\frac{\sigma(\PZ+\cPqb\text{~{}jets})}{\sigma(\PZ+\text{jets})}=\frac{N_{\PQb}}{N_{\text{jet}}\epsilon^{\PQb}_{\text{tag}}},$
(2)
$\text{R}(\cPqc/\cPqb)=\frac{\sigma(\PZ+\cPqc\text{~{}jets})}{\sigma(\PZ+\cPqb\text{~{}jets})}=\frac{N_{\PQc}\epsilon^{\PQb}_{\text{tag}}}{N_{\PQb}\epsilon^{\PQc}_{\text{tag}}},$
(3)

where $N_{\text{jet}}$ is the number of selected $\PZ+\text{jets}$ events
remaining after subtracting background contributions (, diboson, and single
top) from data. These backgrounds are estimated using simulation. In the above
formulas, the integrated luminosity as well as the efficiencies that are
related to lepton and requirements in the $\PZ+\text{jets}$ event selection
cancel.

For the differential measurements, the same procedure described here is
applied in each jet or boson interval. Dedicated $M_{\text{SV}}$ templates are
derived for each interval to take into account the dependence of the
$M_{\text{SV}}$ shape on jet kinematic variables. Finally, the cross section
ratios are unfolded for various experimental effects, most notably the
detector resolution and efficiencies.

###  0.5.2 +HF jets event tagging efficiency

The efficiencies of tagging $\PZ+\text{HF~{}jets}$ events,
$\epsilon^{\PQc}_{\text{tag}}$ and $\epsilon^{\PQb}_{\text{tag}}$, are
calculated as the ratio between numbers of selected $\PZ+\cPqc\text{~{}jets}$
and $\PZ+\cPqb\text{~{}jets}$ events, respectively, in the
$\PZ+\text{HF~{}jets}$ and the $\PZ+\text{jets}$ samples. They are estimated
using simulations, which are corrected with data. In the jet range between 30
and 200the efficiencies vary only slightly and range from 8.3 to 11.3% for
$\PZ+\cPqc\text{~{}jets}$ and from 45.9 to 60.7% for $\PZ+\cPqb\text{~{}jets}$
events. The $\PZ+\text{light~{}jets}$ mistagging rate increases from 0.3 to
1.0% in the same range.

###  0.5.3 Estimation of the event yields

A binned maximum likelihood template fit, based on $M_{\text{SV}}$
distributions of the leading HF-tagged jets, is used to obtain the numbers of
$\PZ+\cPqc\text{~{}jets}$ and $\PZ+\cPqb\text{~{}jets}$ events in the
$\PZ+\text{HF~{}jets}$ sample. The parameters of interest are the scale
factors, $\mathrm{SF}_{\PQc}$ and $\mathrm{SF}_{\PQb}$, that adjust the MC
rates to fit the data, while their uncertainties are treated as nuisance
parameters. The $M_{\text{SV}}$ distributions of the simulated
$\PZ+\cPqc\text{~{}jets}$, $\PZ+\cPqb\text{~{}jets}$, and
$\PZ+\text{light~{}jets}$ categories are normalized to the integrated
luminosity of the data sample using an NNLO cross section for the total
$\PZ+\text{jets}$ rate. The top quark and diboson backgrounds, which
contribute about 5% of the events in the $\PZ+\text{HF~{}jets}$ sample, are
estimated from simulation. The predicted yields of all these processes are
shown in Table 0.4.

For each $M_{\text{SV}}$ bin a Poisson distribution is constructed from the
number of observed events, with its mean taken from MC predictions of signal
($\PZ+\cPqc\text{~{}jets}$ and $\PZ+\cPqb\text{~{}jets}$) and background
($\PZ+\text{light~{}jets}$, top quark, and diboson) yields. The likelihood is
the product of the Poisson distributions and Gaussian (or log-normal)
distributions, where the latter are used to constrain the nuisance parameters.
The choice of Gaussian or log-normal constraints depends on whether the
corresponding systematic uncertainty affects the shape or normalization of the
templates, respectively. To obtain a combined result, the electron and muon
channel data are fitted simultaneously using a common set of scale factors.
After the fit, the numbers of $\PZ+\cPqc\text{~{}jets}$ and
$\PZ+\cPqb\text{~{}jets}$ events, $N_{\PQc}$ and $N_{\PQb}$, are obtained from
the MC predictions scaled by the $\mathrm{SF}_{\PQc}$ and $\mathrm{SF}_{\PQb}$
factors.

The $M_{\text{SV}}$ template of jets in the $\PZ+\cPqc\text{~{}jets}$ events
is obtained from simulation. The jet $M_{\text{SV}}$ shape is validated with a
-enriched data sample where only one of the bosons decays to leptons. The
other boson decays hadronically with a branching fraction of 33% for a charm
quark in the final state. The event selection requires a well-identified and
isolated muon having $\pt>25\GeV$ and $\abs{\eta}<2.4$ together with at least
four jets, each with $\pt>30\GeV$ and $\abs{\eta}<2.4$. The and jet
identification is performed with the following procedure. To reduce the
combinatorics the best pair (triplet) of jets is chosen by minimizing the
reconstructed and nominal mass of the boson (top quark). From this
optimization, the and jet candidates from top quark and boson decays are
identified. The event is kept if these candidates pass the jet HF tagging
requirement described in Section 0.4. In the resulting sample of the jet
candidates about half have correct flavor assignment whereas the other half
constitute mostly jets that are misidentified as jets. The jet $M_{\text{SV}}$
distribution in data is found by subtracting the backgrounds containing jets
and light jets in the sample.

The jet $M_{\text{SV}}$ template from simulation is compared with that
observed in the validation sample and agreement is found within the
statistical uncertainties as shown in Fig. 1 (left). The pronounced
enhancement seen in the jets $M_{\text{SV}}$ distribution near 1.8is due to
charm meson decays.

Figure 1: Comparisons of jet (left) and jet (right) $M_{\text{SV}}$
distributions for data and simulation. A shape correction is applied to the
data-driven distribution in the right panel to account for the difference
between the jets in and $\PZ+\cPqb\text{~{}jets}$ processes.

The $M_{\text{SV}}$ template for jets is derived from a high-purity data
sample of events decaying to final states of $\Pe\PGm$ $+\geq$ 2 jets with at
least one b-tagged jet. Leptons must pass similar requirements as those used
in the selection of $\PZ+\text{jets}$ events except for a tighter isolation
criterion (other activity with less than 15% of the lepton transverse
momentum, instead of 25%) to strongly suppress multijet and $\PW+\text{jets}$
backgrounds. The $M_{\text{SV}}$ shape depends on the kinematic distributions
of the corresponding jets, therefore the jet $M_{\text{SV}}$ templates
obtained with the data are corrected to account for the difference between the
jet spectra in and $\PZ+\cPqb\text{~{}jets}$ events. This correction is
derived from simulation by comparing the jet $M_{\text{SV}}$ shapes in those
two samples of events. It is parameterized as a second-order polynomial
function of $M_{\text{SV}}$ and varies between 3 and 20% across jet ranges. A
comparison between the simulated and data-driven jet $M_{\text{SV}}$
distributions are presented in Fig. 1 (right). This correction procedure is
applied in both the inclusive and differential measurements.

The $M_{\text{SV}}$ modeling of light jets in simulation is checked in the
validation sample containing $\PW+\text{jets}$ events selected by requiring a
well-identified and isolated muon together with at least one jet.
Discrimination criteria of jet versus light jets are applied, resulting in a
sample with light jet purity of ${\approx}40\%$. The light jet $M_{\text{SV}}$
templates in data are derived from the validation sample after subtracting
nonlight jet components, which mainly consist of the $\PW+\cPqc\text{~{}jets}$
events. Good agreement between the data-driven template shape and the
simulation is observed.

The scale factors obtained from the combined fit in the inclusive
$\PZ+\text{HF~{}jets}$ data sample are $\text{SF}_{\text{c}}=0.849\pm
0.013\text{ (stat)}\pm 0.064\text{ (syst)}$ and $\text{SF}_{\text{b}}=0.873\pm
0.005\text{ (stat)}\pm 0.013\text{ (syst)}$. Tables 0.5.3 and 0.5.3 list the
scale factors estimated in the jet and bins. Details on the evaluations of
systematic uncertainties in the scale factors are discussed in Section 0.7.
The two channels pass a $\chi^{2}$ based compatibility check except for the
$\mathrm{SF}_{\PQc}$ fluctuation in one jet bin of 50–110with a $p$-value of
${\approx}0.3\%$. The post-fit $M_{\text{SV}}$ distributions are shown in Fig.
2 for the measurements using the inclusive $\PZ+\text{HF~{}jets}$ samples.
Examples of the post-fit $M_{\text{SV}}$ distributions in the muon channel for
exclusive jet bins are given in Appendix .10, Fig. 6.

The $\mathrm{SF}_{\PQc}$ and $\mathrm{SF}_{\PQb}$ scale factor fit results for
electron, muon, and combined channels in jet bins. The first and second
uncertainty values correspond to the statistical and systematic contributions,
respectively. The fractions of the observed number of
$\PZ+\cPqc\text{~{}jets}$ and $\PZ+\cPqb\text{~{}jets}$ in the total number of
$\PZ+\text{jets}$ events selected in the $\PZ+\text{HF~{}jets}$ sample are
shown in the parentheses and are determined by applying the scale factors to
the corresponding MC events.  ccccccc  Jet bins () $\mathrm{SF}_{\PQc}$
$\mathrm{SF}_{\PQb}$
($\PZ+\cPqc\text{~{}jet}$ fraction) ($\PZ+\cPqb\text{~{}jet}$ fraction)
Electron Muon Combined Electron Muon Combined
30–350.91$\pm$0.05$\pm$0.07 0.88$\pm$0.03$\pm$0.07 0.89$\pm$0.03$\pm$0.06
0.83$\pm$0.02$\pm$0.03 0.83$\pm$0.01$\pm$0.03 0.83$\pm$0.01$\pm$0.03
(25.4$\pm$1.3$\pm$2.1)% (25.8$\pm$1.0$\pm$2.1)% (25.6$\pm$0.8$\pm$1.7)%
(64.8$\pm$1.4$\pm$2.4)% (64.2$\pm$1.1$\pm$2.4)% (64.5$\pm$0.9$\pm$1.9)%
35–400.78$\pm$0.05$\pm$0.08 0.79$\pm$0.04$\pm$0.07 0.79$\pm$0.03$\pm$0.06
0.91$\pm$0.02$\pm$0.03 0.87$\pm$0.01$\pm$0.03 0.89$\pm$0.01$\pm$0.03
(21.1$\pm$1.4$\pm$2.0)% (22.3$\pm$1.1$\pm$2.0)% (21.7$\pm$0.9$\pm$1.7)%
(69.7$\pm$1.5$\pm$2.6)% (68.7$\pm$1.2$\pm$2.4)% (69.2$\pm$0.9$\pm$2.1)%
40–500.66$\pm$0.04$\pm$0.06 0.67$\pm$0.03$\pm$0.07 0.67$\pm$0.03$\pm$0.06
0.83$\pm$0.01$\pm$0.02 0.83$\pm$0.01$\pm$0.02 0.83$\pm$0.01$\pm$0.02
(18.2$\pm$1.2$\pm$1.8)% (18.4$\pm$0.9$\pm$1.8)% (18.3$\pm$0.7$\pm$1.7)%
(73.9$\pm$1.3$\pm$1.9)% (73.1$\pm$1.1$\pm$1.8)% (73.4$\pm$0.8$\pm$1.6)%
50–1100.89$\pm$0.04$\pm$0.06 0.73$\pm$0.03$\pm$0.05 0.79$\pm$0.02$\pm$0.05
0.89$\pm$0.01$\pm$0.02 0.92$\pm$0.01$\pm$0.03 0.91$\pm$0.01$\pm$0.03
(20.3$\pm$0.8$\pm$1.3)% (17.5$\pm$0.6$\pm$1.2)% (18.6$\pm$0.5$\pm$1.1)%
(72.9$\pm$1.0$\pm$1.8)% (75.1$\pm$0.7$\pm$2.5)% (74.1$\pm$0.6$\pm$2.3)%
110–2000.70$\pm$0.09$\pm$0.06 0.85$\pm$0.07$\pm$0.07 0.79$\pm$0.06$\pm$0.05
0.92$\pm$0.04$\pm$0.04 0.81$\pm$0.03$\pm$0.04 0.84$\pm$0.02$\pm$0.04
(18.0$\pm$2.3$\pm$1.6)% (21.4$\pm$1.8$\pm$1.7)% (20.2$\pm$1.4$\pm$1.4)%
(71.5$\pm$2.7$\pm$3.1)% (67.1$\pm$2.2$\pm$3.2)% (68.7$\pm$1.7$\pm$3.0)%

The $\mathrm{SF}_{\PQc}$ and $\mathrm{SF}_{\PQb}$ scale factor fit results for
electron, muon, and combined channels in bins. The first and second
uncertainty values correspond to the statistical and systematic contributions,
respectively. The fraction of the observed number of $\PZ+\cPqc\text{~{}jets}$
and $\PZ+\cPqb\text{~{}jets}$ in the total number of $\PZ+\text{jets}$ events
selected in the $\PZ+\text{HF~{}jets}$ sample are shown in the parentheses and
are derived by applying the scale factors to the corresponding MC events.
ccccccc  bins () $\mathrm{SF}_{\PQc}$ $\mathrm{SF}_{\PQb}$
($\PZ+\cPqc\text{~{}jet}$ fraction) ($\PZ+\cPqb\text{~{}jet}$ fraction)
Electron Muon Combined Electron Muon Combined
0–300.83$\pm$0.04$\pm$0.12 0.78$\pm$0.03$\pm$0.10 0.80$\pm$0.03$\pm$0.11
0.93$\pm$0.02$\pm$0.02 0.91$\pm$0.01$\pm$0.02 0.92$\pm$0.01$\pm$0.02
(22.9$\pm$1.1$\pm$3.3)% (21.9$\pm$0.9$\pm$2.9)% (22.4$\pm$0.7$\pm$3.1)%
(67.9$\pm$1.2$\pm$1.7)% (68.1$\pm$1.0$\pm$1.7)% (67.9$\pm$0.7$\pm$1.6)%
30–500.79$\pm$0.04$\pm$0.07 0.72$\pm$0.03$\pm$0.06 0.75$\pm$0.02$\pm$0.06
0.84$\pm$0.01$\pm$0.02 0.84$\pm$0.01$\pm$0.02 0.84$\pm$0.01$\pm$0.02
(21.6$\pm$1.0$\pm$1.9)% (20.5$\pm$0.8$\pm$1.8)% (20.9$\pm$0.6$\pm$1.8)%
(71.0$\pm$1.1$\pm$1.4)% (71.6$\pm$0.9$\pm$1.4)% (71.4$\pm$0.7$\pm$1.4)%
50–900.92$\pm$0.04$\pm$0.06 0.77$\pm$0.03$\pm$0.05 0.82$\pm$0.03$\pm$0.05
0.85$\pm$0.01$\pm$0.01 0.88$\pm$0.01$\pm$0.01 0.87$\pm$0.01$\pm$0.01
(21.0$\pm$1.0$\pm$1.3)% (18.5$\pm$0.7$\pm$1.2)% (19.5$\pm$0.6$\pm$1.2)%
(72.0$\pm$1.1$\pm$1.1)% (74.0$\pm$0.8$\pm$1.1)% (73.2$\pm$0.7$\pm$1.0)%
90–2000.76$\pm$0.06$\pm$0.05 0.90$\pm$0.05$\pm$0.05 0.84$\pm$0.04$\pm$0.04
0.97$\pm$0.02$\pm$0.02 0.90$\pm$0.02$\pm$0.02 0.92$\pm$0.01$\pm$0.02
(17.2$\pm$1.4$\pm$1.0)% (20.6$\pm$1.1$\pm$1.2)% (19.2$\pm$0.9$\pm$1.0)%
(73.4$\pm$1.6$\pm$1.3)% (69.9$\pm$1.2$\pm$1.3)% (71.3$\pm$1.0$\pm$1.2)%

Figure 2: Secondary vertex invariant mass distributions for the electron
(left) and muon (right) channels derived from fits using the inclusive
$\PZ+\text{HF~{}jets}$ data sample. The post-fit uncertainty bands indicate
the total uncertainties, added in quadrature, of the best-fit values of signal
and background process rates.

##  0.6 Unfolding

The unfolding procedure corrects the measured cross section ratios for effects
related to the detector response and the event reconstruction procedures,
which can lead to migrations between bins and therefore alter the true
distributions. The bin-by-bin migrations are corrected by the response
matrices, which quantify the migration probability between the measured and
true values of a given observable (jet or ). These matrices are derived in
simulation by comparing the final-state objects (jets and leptons) at the pre-
reconstruction (“MC-particle”) and reconstruction levels.

At the MC-particle level (denoted as “particle level”), leptons are stable
particles from boson decays, dressed by adding the momenta of all photons
within $\Delta R<0.1$ around the lepton directions. The particle-level jets
are formed from stable particles (c$\tau>1\unit{cm}$), except neutrinos, and
overlapping leptons from boson decays, using the same anti-jet algorithm used
for reconstructed jets.

The boson mass and at the particle level are calculated using the two leptons
originating from this boson. The fiducial volume is defined by the particle-
level leptons and jets with the same kinematic requirements (, $\eta$, and
dilepton invariant mass) used in the measurement.

The response matrix is constructed using MC $\PZ+\text{jets}$ samples. The
reconstructed jets and a pair of electrons or muons are spatially matched to
the corresponding particle-level objects by requiring that they are within
$\Delta R<0.2$. In addition, the flavor of the reconstructed jets and the
matched particle-level jets must be the same. Events that have reconstructed
objects without matched particle-level objects are included in the background
category and are subtracted from the sample. The acceptance and efficiency
corrections account for other events that have particle-level objects in the
fiducial volume but no matching reconstructed objects.

For the inclusive measurement, the acceptance corrections are derived from
simulation and defined as the ratios between the number of selected events at
the reconstruction level and the number of generated events within the
fiducial volume. These acceptance correction factors, which depend on the jet
flavor, are applied to the measured cross section ratios.

To unfold the differential distributions, the TUnfold package [45], which is
based on a least-square fit, is used. The unfolding procedure, which solves
for a well-conditioned unfolding problem in this case, is performed without
regularization to avoid potential biases toward MC spectra. The data
distributions of $\PZ+\cPqc\text{~{}jets}$, $\PZ+\cPqb\text{~{}jets}$, and
$\PZ+\text{jets}$ are unfolded simultaneously to include the correlations
between the denominator and numerator when deriving the unfolded ratios. The
numbers of bins in the unfolded distributions are about half of those used in
data to maintain the stability of the unfolding procedure. The combined
response matrix used in the simultaneous unfolding is constructed from
individual jet category matrices. The TUnfold package provides unfolded
distributions together with a covariance matrix, which is used to estimate the
uncertainties in the unfolded cross section ratios. The unfolding procedure is
checked with closure tests and bias studies using MC samples. In the closure
test, response matrices are derived using one-half of the sample and the
unfolding is performed on the other half. Within statistical uncertainties,
the unfolded and MC truth distributions agree with each other. In the bias
studies, a pull distribution is constructed by performing the unfolding on a
set of ${\approx}100$ MC sub-samples. The unfolding procedure converges and
shows no bias.

##  0.7 Systematic uncertainties

The systematic uncertainties include experimental sources that affect the
shape or normalization of templates in the scale factor fits, and the heavy-
flavor tagging efficiencies. The unfolded results contain additional
uncertainties related to the unfolding procedure. The following systematic
uncertainties are considered in the analysis:

Jet energy scale and resolution correction: The reconstructed jet energy is
corrected using a factorized model to compensate for the nonlinear and
nonuniform response in the calorimeters as detailed in Ref. [39]. Since the
JER is different in data and simulation, the jet energy in simulation is
spread to match the resolution observed in data. Both the JES and JER
corrections affect the shape of $M_{\text{SV}}$ distributions used in the
scale factor fits. Therefore, they contribute to the uncertainties in the
$\PZ+\cPqc$ and $\PZ+\cPqb\text{~{}jets}$ event yields.

Pileup weighting: The distribution of the number of pileup events in
simulation is weighted to match that in data. The corresponding uncertainty is
estimated by varying the total $\Pp\Pp$ inelastic cross section by 4.6% based
on the measurement described in Ref. [46]. Since the shapes of $M_{\text{SV}}$
templates are affected by the pileup weighting, this uncertainty source
contributes to the $\PZ+\cPqc\text{~{}jets}$ and $\PZ+\cPqb\text{~{}jets}$
event yields as well.

Gluon splitting: Particles from a pair of collimated or quarks may end up in
the same reconstructed jet, which can affect the shape of $M_{\text{SV}}$
template. To quantify the corresponding uncertainties in the scale factor fit,
the fraction of MC events with gluon splitting is varied by 50%, which is
about three times the experimental uncertainty in the gluon splitting rate
measured at LEP [47, 48]. The resulting variations in MC $M_{\text{SV}}$ shape
is propagated to the scale factor fit.

Background rates: The , single top quark, and diboson backgrounds are
estimated in simulation using NNLO and NLO cross sections to normalize the
event rates. The uncertainties in the and diboson background contributions are
obtained by varying their production cross sections by 5.5 and 3.2%,
respectively. The uncertainty in the single top backgrounds is ignored because
these backgrounds represent a very small fraction (${<}1\%$) of the total
event sample.

Statistical uncertainties of $M_{\text{SV}}$ templates: A systematic
uncertainty is associated to the limited number of events in the MC samples
used to define the template shapes. To estimate the corresponding uncertainty,
an ensemble of the $M_{\text{SV}}$ templates has been created where the bin
contents have been modified by additional statistical fluctuations.

Correction of the jet $M_{\text{SV}}$ template: This systematic uncertainty is
related to the ad hoc shape correction function used to derive the jet
$M_{\text{SV}}$ template from control samples in data. This correction,
parameterized as a second order polynomial, accounts for the difference in
shape of $M_{\text{SV}}$ distributions in and $\PZ+\text{jets}$ events. The
uncertainty of the shape correction is estimated by changing the polynomial
functional forms.

Heavy-flavor tagging efficiency: The HF tagging efficiencies for and jets are
estimated in simulation and corrected by the efficiency scale factors as
described in Section 0.5.2. The systematic uncertainties of the efficiency
scale factors of jets and jets with $30<\pt<100\GeV$ and $\abs{\eta}<2.4$ are
${\approx}3.5\%$ and ${\approx}1.4\%$, respectively [42].

Missing transverse momentum selection efficiency: This uncertainty source
accounts for possible differences in the selection ($\ptmiss<40\GeV$)
efficiencies for $\PZ+\text{jets}$ and $\PZ+\text{HF~{}jets}$ events. The
effect comes from contributions of semileptonic decays of HF hadrons in
$\PZ+\text{HF~{}jets}$ events, which results in large values. Therefore, the
efficiencies tend to be lower for $\PZ+\text{HF~{}jets}$ events by
${\approx}1\%$ at high jet and boson regions compared to those of
$\PZ+\text{jets}$ events. An uncertainty of 1.5% is included in the R(c/j) and
R(b/j) differential results for jet (boson) bins where $\pt>60$ (90).

PDF and $\mu_{\text{R}}$, $\mu_{\text{F}}$ scale uncertainties: These
uncertainty sources affect the unfolding correction described in Section 0.6,
which is based on the $\PZ+\text{jets}$ MC samples. The unfolding is performed
with different PDF replicas and alternative choices of the renormalization and
factorization scales. The uncertainties are obtained from variations of the
unfolded results and they are less than 2.5%, 2.8%, and 2.9% in all jet and
bins for R(c/j), R(b/j), and R(c/b), respectively.

Parton shower and hadronization model: The unfolding procedure is based on
response matrices constructed from the $\PZ+\text{jets}$ simulation sample
described in Section 0.3. This sample uses to simulate the parton shower and
hadronization. An alternative model is provided by the generator [49]. The
uncertainties in parton shower and hadronization modeling are estimated by
comparing the unfolded results using response matrices from those two models.
They are less than 3% for all differential cross section ratios.

Table 0.7 summarizes the effects of systematic uncertainty sources on the
$\mathrm{SF}_{\PQc}$ and $\mathrm{SF}_{\PQb}$ shown in Tables 0.5.3 and 0.5.3.
They are quantified as the differences in quadrature between scale factor
uncertainties obtained in two fits: the nominal one where all parameters are
allowed to float, and an alternative fit where the nuisance parameter
corresponding to the uncertainty source of interest is fixed. The
uncertainties from the scale factors and HF tagging efficiency together with
the statistical uncertainties of the cross section ratios are listed in Table
0.7.

In the unfolded differential results, the uncertainties of the measurements
described here are included in the data covariance matrix, which is used to
build a least squares fit of the unfolding. An error covariance matrix for the
unfolded distributions is estimated. This includes the uncertainties from the
data, response matrix, and the unfolding procedure.

Systematic uncertainties in the scale factor measurements. The uncertainty
ranges correspond to variations across jet and bins. ccc $\mathrm{SF}_{\PQc}$
$\mathrm{SF}_{\PQb}$
JES, JER 1.7–7.4% 0.3–2.1%
Template statistics 2.4–6.1% 0.6–2.7%
Gluon splitting 2.2–3.9% 0.5–2.0%
Pileup weighting 1.6–2.8% 0.3–1.0%
Background uncertainty 0.3–1.0% 0.4–1.2%
jet $M_{\text{SV}}$ correction 0.2–1.6% 0.2–0.8%
Total systematic uncertainty 4.8–12.5% 1.1–4.9%

The systematic uncertainties in the cross section ratio measurements. The
uncertainty ranges correspond to variations across jet and bins. cccc R(c/j)
R(b/j) R(c/b)
Scale factor measurement 5.4–13.8% 1.4–4.4% 5.6–12.6%
HF tagging 3.8–4.6% 1.1–1.5% 4.9–6.1%
Statistical uncertainty 1.6–7.5% 0.6–3.0% 1.8–8.6%

##  0.8 Results

The observed and corrected (for the acceptance and efficiency) cross section
ratios for the inclusive measurements are summarized in Tables 0.8 and 0.8,
respectively. The measured differential cross section ratios are presented in
Appendix .11, Tables .11 and .11.

Cross section ratios measured in the electron and muon channels, along with
the combined results. The first and second uncertainty values correspond to
the statistical and systematic contributions, respectively.  cccc Electron
Muon Combined
R(c/j) 0.098 $\pm$ 0.002 $\pm$ 0.009 0.094 $\pm$ 0.002 $\pm$ 0.008 0.095 $\pm$
0.002 $\pm$ 0.008
R(b/j) 0.0546 $\pm$ 0.0005 $\pm$ 0.0010 0.0538 $\pm$ 0.0004 $\pm$ 0.0010
0.0541 $\pm$ 0.0003 $\pm$ 0.0011
R(c/b) 1.80 $\pm$ 0.05 $\pm$ 0.17 1.75 $\pm$ 0.04 $\pm$ 0.16 1.76 $\pm$ 0.03
$\pm$ 0.16

Unfolded cross section ratios in the electron and muon channels, along with
the combined results. The first and second uncertainty values correspond to
the statistical and systematic contributions, respectively. cccc Electron Muon
Combined
R(c/j) 0.105 $\pm$ 0.003 $\pm$ 0.009 0.101 $\pm$ 0.002 $\pm$ 0.009 0.102 $\pm$
0.002 $\pm$ 0.009
R(b/j) 0.0639 $\pm$ 0.0006 $\pm$ 0.0015 0.0629 $\pm$ 0.0005 $\pm$ 0.0014
0.0633 $\pm$ 0.0004 $\pm$ 0.0015
R(c/b) 1.65 $\pm$ 0.04 $\pm$ 0.15 1.61 $\pm$ 0.04 $\pm$ 0.15 1.62 $\pm$ 0.03
$\pm$ 0.15

The unfolded differential cross section ratios, R(c/j), R(b/j), and R(c/b),
versus the jet and boson are shown in Figs. 3, 4, and 5, respectively. The
results are compared with predictions from the mg5_aMC and programs [50, 51,
52], both at LO and NLO. The renormalization and factorization scales in the
matrix element and the PDF uncertainties are included in these predictions.
For the former, the scales are varied between 0.5 and 2 times their nominal
value such that the $\mu_{\text{R}}$/$\mu_{\text{F}}$ ratio is kept between
0.5 and 2. This conventional choice is implemented in the CMS-default settings
for generating samples to estimate the theoretical mg5_aMC LO and NLO cross
sections. The uncertainty due to the scales is taken to be the envelope of
these predictions. In addition, for the calculation, the constraint on the
$\mu_{\text{R}}$/$\mu_{\text{F}}$ ratio is dropped, , the scales are varied
independently. This more conservative choice is motivated by the fact that the
$\PZ+\text{HF~{}jets}$ cross sections as functions of the renormalization and
factorization scales have opposite trends [1]. The error bands in Figs. 3–5
correspond to this choice of scale variation. The uncertainties due to the
scales in the cross section ratios are obtained by adding uncertainties in the
numerator and denominator in quadrature, , they are assumed to be
uncorrelated. The PDF uncertainty is evaluated by changing the replicas of the
PDF set.

The LO cross sections are computed using mg5_aMC interfaced with through the
-MLM matching scheme [53, 54]. The LO matrix element calculations include
processes with up to 4 outgoing partons. The NNPDF 3.0 LO PDF set is used and
the matching scale together with the strong coupling constant at the boson
mass are set at 19and 0.130, respectively. The multileg mg5_aMC generator
interfaced with using the FxFx matching scheme evaluates the cross section
ratios at NLO precision. The choice of parameters is described in Section 0.3.

The generator is used to perform calculations of the cross sections and cross
section ratios at the parton level in the 5FS. The $\PZ+\text{jets}$ cross
sections are evaluated by a simple cone algorithm with a radius of 0.4 (,
partons are merged if the distances, $\Delta R$, between them are less than
0.4). The central values for the cross sections are evaluated at
$\mu_{\text{R}}$ and $\mu_{\text{F}}$ set to the mass of the boson. In
addition, the NLO results are shown for two PDFs, NNPDF 3.0 and MMHT14 [55],
along with the LO cross section ratios. The values of are taken from those
PDFs. Table 0.8 shows the predicted inclusive cross section ratios from
mg5_aMC and .

Predicted cross section ratios from mg5_aMC and at LO and NLO accuracy. The
first and second sets of uncertainties correspond to PDF and scale variations,
respectively. The scale uncertainties for with
$\mu_{\text{R}}$/$\mu_{\text{F}}$ ratio kept between 0.5 and 2 are in the
parentheses.  ccccc mg5_aMC MG5_aMC (NLO, FxFx) (NLO) mg5_aMC (LO, MLM) (LO)
R(c/j) 0.111 $\pm$ 0.003 ${}^{+0.010}_{-0.011}$ 0.090 $\pm$ 0.003
${}^{+0.010}_{-0.012}$ (${}^{+0.008}_{-0.007}$) 0.103 $\pm$ 0.003
${}^{+0.028}_{-0.026}$ 0.087 $\pm$ 0.003 ${}^{+0.025}_{-0.022}$
R(b/j) 0.067 $\pm$ 0.002 $\pm$ 0.006 0.068 $\pm$ 0.002 ${}^{+0.008}_{-0.011}$
($\pm$0.006) 0.062 $\pm$ 0.002 ${}^{+0.018}_{-0.015}$ 0.071 $\pm$ 0.002
${}^{+0.023}_{-0.021}$
R(c/b) 1.64 $\pm$ 0.05 ${}^{+0.15}_{-0.16}$ 1.33 $\pm$ 0.04
${}^{+0.16}_{-0.21}$ (${}^{+0.10}_{-0.12}$) 1.67 $\pm$ 0.06
${}^{+0.54}_{-0.40}$ 1.20 $\pm$ 0.04 ${}^{+0.42}_{-0.38}$

Figure 3: Unfolded, particle-level mg5_aMC, and parton-level R(c/j) cross
section ratios versus jet (left) and boson (right) transverse momentum. The
vertical error bars for the data points are statistical while the hatched band
represents the total uncertainties. The predictions are slightly shifted along
the $x$-axis for readability in the upper plots, and their total PDF and scale
uncertainties are shown as error bands in the ratio plots.

A few comments are in order when comparing data with various predictions. The
mg5_aMC predictions for the cross section ratios are higher in most of the
bins, although still compatible with the data given the large uncertainties,
except for the R(c/j) versus jet , where the deviations are more pronounced.
The data are better described with mg5_aMC at LO compared to mg5_aMC at NLO.
These observations are similar to those reported in previous measurements at 8
[56, 11]. The predictions for R(c/j) and R(b/j) disagree with data at high jet
and , except for R(c/j) versus jet , where in general there is good agreement
with LO or NLO calculations, and for both PDFs considered. For R(c/b),
however, all theoretical predictions are consistent with the measured ratios,
except for the prediction for the highest boson bin. The difference between
the parton- and particle-level jets may affect the predictions, although the
corresponding effects are significantly reduced or vanish in the cross section
ratios. Alternatively, higher order pQCD calculations might be needed to
describe the data.

Figure 4: Unfolded, particle-level mg5_aMC, and parton-level R(b/j) cross
section ratio versus jet (left) and boson (right) transverse momentum. The
vertical error bars for the data points are statistical while the hatched band
presents the total uncertainties. The predictions are slightly shifted along
the $x$-axis for readability in the upper plots, and their total PDF and scale
uncertainties are shown as error bands in the ratio plots.

Figure 5: Unfolded, particle-level mg5_aMC, and parton-level R(c/b) cross
section ratio versus jet (left) and boson (right) transverse momentum. The
vertical error bars for the data points are statistical while the hatched band
represents the total uncertainties. The predictions are slightly shifted along
the $x$-axis for readability in the upper plots, and their total PDF and scale
uncertainties are shown as error bands in the ratio plots.

##  0.9 Summary

Ratios of cross sections,
$\sigma(\PZ+\cPqc\text{~{}jets})/\sigma(\PZ+\text{jets})$,
$\sigma(\PZ+\cPqb\text{~{}jets})/\sigma(\PZ+\text{jets})$, and
$\sigma(\PZ+\cPqc\text{~{}jets})/\sigma(\PZ+\cPqb\text{~{}jets})$ in the
associated production of a boson with at least one charm or bottom quark jet
have been measured in proton-proton collisions at $\sqrt{s}=13\TeV$ using
35.9of data collected by the CMS experiment at the LHC. The fiducial volume of
the measurement is defined by $\pt>30\GeV$ and $|\eta|<2.4$ for the jets,
where and $\eta$ represent transverse momentum and pseudorapidity,
respectively. The bosons are selected within the mass range of 71 and
111requiring leptons (electrons or muons) with $\pt>25\GeV$ and
$\abs{\eta}<2.4$. The measured values are
$\sigma(\PZ+\cPqc\text{~{}jets})/\sigma(\PZ+\text{jets})=0.102\pm 0.002\pm
0.009$, $\sigma(\PZ+\cPqb\text{~{}jets})/\sigma(\PZ+\text{jets})=0.0633\pm
0.0004\pm 0.0015$, and
$\sigma(\PZ+\cPqc\text{~{}jets})/\sigma(\PZ+\cPqb\text{~{}jets})=1.62\pm
0.03\pm 0.15$. Results for the inclusive and differential cross section ratios
as functions of jet and boson transverse momentum are compared with
predictions from leading and next-to-leading order perturbative quantum
chromodynamics calculations. These are the first results of this kind at 13.

###### Acknowledgements.

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

## References

  * [1] J. M. Campbell, R. K. Ellis, F. Maltoni, and S. Willenbrock, “Associated production of a Z boson and a single heavy quark jet”,  Phys. Rev. D 69 (2004) 074021, 10.1103/PhysRevD.69.074021, arXiv:hep-ph/0312024.
  * [2] F. Febres Cordero, L. Reina, and D. Wackeroth, “W and Z boson production with a massive bottom-quark pair at the Large Hadron Collider”,  Phys. Rev. D 80 (2009) 034015, 10.1103/PhysRevD.80.034015, arXiv:0906.1923.
  * [3] F. Maltoni, G. Ridolfi, and M. Ubiali, “b-initiated processes at the LHC: a reappraisal”,  JHEP 07 (2012) 022, 10.1007/JHEP07(2012)022, arXiv:1203.6393.
  * [4] CDF Collaboration, “Measurement of cross sections for $\mathrm{b}$ jet production in events with a Z boson in $\mathrm{p}\bar{\mathrm{p}}$ collisions at $\sqrt{s}$ = 1.96 TeV”,  Phys. Rev. D 79 (2009) 052008, 10.1103/PhysRevD.79.052008, arXiv:0812.4458.
  * [5] D0 Collaboration, “Measurement of associated production of Z bosons with charm quark jets in $\mathrm{p}\bar{\mathrm{p}}$ collisions at $\sqrt{s}=1.96$ TeV”,  Phys. Rev. Lett. 112 (2014) 042001, 10.1103/PhysRevLett.112.042001, arXiv:1308.4384.
  * [6] D0 Collaboration, “Measurement of the ratio of inclusive cross sections $\sigma(\mathrm{p}\bar{\mathrm{p}}\rightarrow\mathrm{Z}~{}+~{}2~{}\mathrm{b}~{}\text{jets})/\sigma(\mathrm{p}\bar{\mathrm{p}}\rightarrow\mathrm{Z}~{}+~{}\text{2 jets})$ in $\mathrm{p}\bar{\mathrm{p}}$ collisions at $\sqrt{s}=1.96$ TeV”,  Phys. Rev. D 91 (2015) 052010, 10.1103/PhysRevD.91.052010, arXiv:1501.05325.
  * [7] ATLAS Collaboration, “Measurement of differential production cross-sections for a Z boson in association with b jets in 7 TeV proton-proton collisions with the ATLAS detector”,  JHEP 10 (2014) 141, 10.1007/JHEP10(2014)141, arXiv:1407.3643.
  * [8] CMS Collaboration, “Measurement of the production cross sections for a Z boson and one or more b jets in pp collisions at $\sqrt{s}$ = 7 TeV”,  JHEP 06 (2014) 120, 10.1007/JHEP06(2014)120, arXiv:1402.1521.
  * [9] LHCb Collaboration, “Measurement of the Z+b-jet cross-section in pp collisions at $\sqrt{s}$ = 7 TeV in the forward region”,  JHEP 01 (2015) 064, 10.1007/JHEP01(2015)064, arXiv:1411.1264.
  * [10] LHCb Collaboration, “Observation of associated production of a Z boson with a D meson in the forward region”,  JHEP 04 (2014) 091, 10.1007/JHEP04(2014)091, arXiv:1401.3245.
  * [11] CMS Collaboration, “Measurement of associated + charm production in proton-proton collisions at $\sqrt{s}=$ 8 ”,  Eur. Phys. J. C 78 (2018) 287, 10.1140/epjc/s10052-018-5752-x, arXiv:1711.02143.
  * [12] J. Alwall et al., “Madgraph 5: going beyond”,  JHEP 06 (2011) 128, 10.1007/JHEP06(2011)128, arXiv:1106.0522.
  * [13] J. Alwall et al., “The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations”,  JHEP 07 (2014) 079, 10.1007/JHEP07(2014)079, arXiv:1405.0301.
  * [14] CMS Collaboration, “The CMS trigger system”,  JINST 12 (2017) P01020, 10.1088/1748-0221/12/01/P01020, arXiv:1609.02366.
  * [15] CMS Collaboration, “The CMS experiment at the CERN LHC”,  JINST 3 (2008) S08004, 10.1088/1748-0221/3/08/S08004.
  * [16] CMS Collaboration, “CMS luminosity measurements for the 2016 data taking period”, CMS Physics Analysis Summary CMS-PAS-LUM-17-001, 2017.
  * [17] GEANT4 Collaboration, “—a simulation toolkit”,  Nucl. Instrum. Meth. A 506 (2003) 250, 10.1016/S0168-9002(03)01368-8.
  * [18] T. Sjöstrand et al., “An introduction to Pythia 8.2”,  Comput. Phys. Commun. 191 (2015) 159, 10.1016/j.cpc.2015.01.024, arXiv:1410.3012.
  * [19] R. Frederix and S. Frixione, “Merging meets matching in MC@NLO”,  JHEP 12 (2012) 061, 10.1007/JHEP12(2012)061, arXiv:1209.6215.
  * [20] K. Melnikov and F. Petriello, “Electroweak gauge boson production at hadron colliders through $\mathcal{O}(\alpha_{S}^{2})$”,  Phys. Rev. D 74 (2006) 114017, 10.1103/PhysRevD.74.114017, arXiv:hep-ph/0609070.
  * [21] P. Nason, “A new method for combining NLO QCD with shower Monte Carlo algorithms”,  JHEP 11 (2004) 040, 10.1088/1126-6708/2004/11/040, arXiv:hep-ph/0409146.
  * [22] S. Frixione, P. Nason, and C. Oleari, “Matching NLO QCD computations with parton shower simulations: the POWHEG method”,  JHEP 11 (2007) 070, 10.1088/1126-6708/2007/11/070, arXiv:0709.2092.
  * [23] S. Alioli, P. Nason, C. Oleari, and E. Re, “A general framework for implementing NLO calculations in shower Monte Carlo programs: the POWHEG BOX”,  JHEP 06 (2010) 043, 10.1007/JHEP06(2010)043, arXiv:1002.2581.
  * [24] S. Frixione, P. Nason, and G. Ridolfi, “A positive-weight next-to-leading-order Monte Carlo for heavy flavour hadroproduction”,  JHEP 09 (2007) 126, 10.1088/1126-6708/2007/09/126, arXiv:0707.3088.
  * [25] M. Czakon and A. Mitov, “Top++: a program for the calculation of the top-pair cross-section at hadron colliders”,  Comput. Phys. Commun. 185 (2014) 2930, 10.1016/j.cpc.2014.06.021, arXiv:1112.5675.
  * [26] M. Grazzini, S. Kallweit, and M. Wiesemann, “Fully differential NNLO computations with MATRIX”,  Eur. Phys. J. C 78 (2018) 537, 10.1140/epjc/s10052-018-5771-7, arXiv:1711.06631.
  * [27] N. Kidonakis, “Top quark production”, in  Proceedings, Helmholtz International Summer School on Physics of Heavy Quarks and Hadrons (HQ 2013): JINR, Dubna, Russia, July 15-28, 2013 (Desy: Verlag Deutsches Elektronen-Synchrotron, Hamburg, 2014).  arXiv:1311.0283.  10.3204/DESY-PROC-2013-03/Kidonakis.
  * [28] NNPDF Collaboration, “Parton distributions for the LHC run II”,  JHEP 04 (2015) 040, 10.1007/JHEP04(2015)040, arXiv:1410.8849.
  * [29] CMS Collaboration, “Event generator tunes obtained from underlying event and multiparton scattering measurements”,  Eur. Phys. J. C 76 (2016) 155, 10.1140/epjc/s10052-016-3988-x, arXiv:1512.00815.
  * [30] CMS Collaboration, “Investigations of the impact of the parton shower tuning in Pythia8 in the modelling of at $\sqrt{s}=$ 8 and 13 TeV”, CMS Physics Analysis Summary CMS-PAS-TOP-16-021, 2016.
  * [31] CMS Collaboration, “Particle-flow reconstruction and global event description with the CMS detector”,  JINST 12 (2017) P10003, 10.1088/1748-0221/12/10/P10003, arXiv:1706.04965.
  * [32] M. Cacciari, G. P. Salam, and G. Soyez, “The anti-jet clustering algorithm”,  JHEP 04 (2008) 063, 10.1088/1126-6708/2008/04/063, arXiv:0802.1189.
  * [33] M. Cacciari, G. P. Salam, and G. Soyez, “FastJet user manual”,  Eur. Phys. J. C 72 (2012) 1896, 10.1140/epjc/s10052-012-1896-2, arXiv:1111.6097.
  * [34] CMS Collaboration, “Technical proposal for the Phase-II upgrade of the Compact Muon Solenoid”, CMS Technical proposal CERN-LHCC-2015-010, CMS-TDR-15-02, CERN, 2015.
  * [35] CMS Collaboration, “Performance of electron reconstruction and selection with the CMS detector in proton-proton collisions at $\sqrt{s}=8$”,  JINST 10 (2015) P06005, 10.1088/1748-0221/10/06/P06005, arXiv:1502.02701.
  * [36] CMS Collaboration, “Performance of the CMS muon detector and muon reconstruction with proton-proton collisions at $\sqrt{s}=13$”,  JINST 13 (2018) P06015, 10.1088/1748-0221/13/06/P06015, arXiv:1804.04528.
  * [37] M. Cacciari and G. P. Salam, “Pileup subtraction using jet areas”,  Phys. Lett. B 659 (2008) 119, 10.1016/j.physletb.2007.09.077, arXiv:0707.1378.
  * [38] CMS Collaboration, “Determination of jet energy calibration and transverse momentum resolution in CMS”,  JINST 6 (2011) P11002, 10.1088/1748-0221/6/11/P11002, arXiv:1107.4277.
  * [39] CMS Collaboration, “Jet energy scale and resolution in the CMS experiment in pp collisions at 8 TeV”,  JINST 12 (2017) P02014, 10.1088/1748-0221/12/02/P02014, arXiv:1607.03663.
  * [40] CMS Collaboration, “Jet algorithms performance in 13 TeV data”, CMS Physics Analysis Summary CMS-PAS-JME-16-003, 2017.
  * [41] CMS Collaboration, “Performance of missing transverse momentum in proton-proton collisions at $\sqrt{s}=13\TeV$ using the CMS detector”,  JINST 14 (2019) P07004, 10.1088/1748-0221/14/07/P07004, arXiv:1903.06078.
  * [42] CMS Collaboration, “Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV”,  JINST 13 (2018) P05011, 10.1088/1748-0221/13/05/P05011, arXiv:1712.07158.
  * [43] W. Waltenberger, “Adaptive vertex reconstruction”, Technical Report CMS-NOTE-2008-033, 2008.
  * [44] CMS Collaboration, “Identification of b-quark jets with the CMS experiment”,  JINST 8 (2013) P04013, 10.1088/1748-0221/8/04/P04013, arXiv:1211.4462.
  * [45] S. Schmitt, “TUnfold: an algorithm for correcting migration effects in high energy physics”,  JINST 7 (2012) T10003, 10.1088/1748-0221/7/10/T10003, arXiv:1205.6201.
  * [46] CMS Collaboration, “Measurement of the inelastic proton-proton cross section at $\sqrt{s}=13$”,  JHEP 07 (2018) 161, 10.1007/JHEP07(2018)161, arXiv:1802.02613.
  * [47] ALEPH Collaboration, “A measurement of the gluon splitting rate into pairs in hadronic decays”,  Phys. Lett. B 561 (2003) 213, 10.1016/S0370-2693(03)00495-7, arXiv:hep-ex/0302003.
  * [48] ALEPH Collaboration, “A measurement of the gluon splitting rate into pairs in hadronic decays”,  Phys. Lett. B 434 (1998) 437, 10.1016/S0370-2693(98)00850-8.
  * [49] M. Bähr et al., “Herwig++ physics and manual”,  Eur. Phys. J. C 58 (2008) 639, 10.1140/epjc/s10052-008-0798-9, arXiv:0803.0883.
  * [50] J. M. Campbell and R. K. Ellis, “Update on vector boson pair production at hadron colliders”,  Phys. Rev. D 60 (1999) 113006, 10.1103/PhysRevD.60.113006, arXiv:hep-ph/9905386.
  * [51] J. M. Campbell, R. K. Ellis, and C. Williams, “Vector boson pair production at the LHC”,  JHEP 07 (2011) 018, 10.1007/JHEP07(2011)018, arXiv:1105.0020.
  * [52] J. M. Campbell, R. K. Ellis, and W. Giele, “A multi-threaded version of MCFM”,  Eur. Phys. J. C 75 (2015) 246, 10.1140/epjc/s10052-015-3461-2, arXiv:1503.06182.
  * [53] J. Alwall et al., “Comparative study of various algorithms for the merging of parton showers and matrix elements in hadronic collisions”,  Eur. Phys. J. C 53 (2008) 473, 10.1140/epjc/s10052-007-0490-5, arXiv:0706.2569.
  * [54] J. Alwall, S. de Visscher, and F. Maltoni, “QCD radiation in the production of heavy colored particles at the LHC”,  JHEP 02 (2009) 017, 10.1088/1126-6708/2009/02/017, arXiv:0810.5350.
  * [55] L. A. Harland-Lang, A. D. Martin, P. Motylinski, and R. S. Thorne, “Parton distributions in the LHC era: MMHT 2014 PDFs”,  Eur. Phys. J. C 75 (2015) 204, 10.1140/epjc/s10052-015-3397-6, arXiv:1412.3989.
  * [56] CMS Collaboration, “Measurements of the associated production of a boson and b jets in pp collisions at $\sqrt{s}=8$”,  Eur. Phys. J. C 77 (2017) 751, 10.1140/epjc/s10052-017-5140-y, arXiv:1611.06507.

##  .10 Post-fit $M_{\text{SV}}$ distributions in the exclusive jet bins

Figure 6: Secondary vertex invariant mass distributions for jet bins 30–35,
50–110, and 110-200in the muon channel derived from fits using the
corresponding jet binned $\PZ+\text{HF~{}jets}$ data samples. The post-fit
uncertainty bands indicate the total uncertainties, added in quadrature, of
the best-fit values of signal and background process rates.

##  .11 Measured differential cross section ratios

The cross section ratios for the electron, muon, and combined channels in jet
bins.  Jet  Electron Muon Combined R(c/j) R(b/j) R(c/b) R(c/j) R(b/j) R(c/b)
R(c/j) R(b/j) R(c/b) 30–35 0.105$\pm$0.006$\pm$0.010
0.0487$\pm$0.0011$\pm$0.0020 2.15$\pm$0.13$\pm$0.28 0.103$\pm$0.004$\pm$0.010
0.0468$\pm$0.0009$\pm$0.0019 2.21$\pm$0.10$\pm$0.28 0.104$\pm$0.003$\pm$0.008
0.0475$\pm$0.0007$\pm$0.0016 2.19$\pm$0.09$\pm$0.23 35–40
0.091$\pm$0.006$\pm$0.010 0.0568$\pm$0.0014$\pm$0.0023 1.59$\pm$0.12$\pm$0.22
0.093$\pm$0.005$\pm$0.010 0.0545$\pm$0.0011$\pm$0.0021 1.71$\pm$0.10$\pm$0.22
0.092$\pm$0.004$\pm$0.008 0.0556$\pm$0.0009$\pm$0.0019 1.65$\pm$0.08$\pm$0.18
40–50 0.079$\pm$0.005$\pm$0.008 0.0554$\pm$0.0010$\pm$0.0016
1.43$\pm$0.10$\pm$0.18 0.080$\pm$0.004$\pm$0.009 0.0546$\pm$0.0008$\pm$0.0016
1.46$\pm$0.08$\pm$0.18 0.079$\pm$0.003$\pm$0.008 0.0549$\pm$0.0007$\pm$0.0014
1.44$\pm$0.06$\pm$0.16 50–60 0.102$\pm$0.004$\pm$0.008
0.0596$\pm$0.0009$\pm$0.0017 1.71$\pm$0.08$\pm$0.14 0.084$\pm$0.003$\pm$0.007
0.0611$\pm$0.0008$\pm$0.0022 1.38$\pm$0.06$\pm$0.12 0.092$\pm$0.003$\pm$0.007
0.0606$\pm$0.0007$\pm$0.0020 1.51$\pm$0.05$\pm$0.12 60–70
0.093$\pm$0.004$\pm$0.007 0.0591$\pm$0.0009$\pm$0.0019 1.57$\pm$0.07$\pm$0.13
0.084$\pm$0.003$\pm$0.007 0.0600$\pm$0.0007$\pm$0.0024 1.39$\pm$0.06$\pm$0.12
0.088$\pm$0.003$\pm$0.006 0.0597$\pm$0.0006$\pm$0.0022 1.47$\pm$0.05$\pm$0.11
70–90 0.091$\pm$0.004$\pm$0.007 0.0580$\pm$0.0009$\pm$0.0018
1.56$\pm$0.08$\pm$0.13 0.078$\pm$0.003$\pm$0.006 0.0592$\pm$0.0008$\pm$0.0023
1.32$\pm$0.06$\pm$0.11 0.083$\pm$0.003$\pm$0.006 0.0587$\pm$0.0007$\pm$0.0021
1.42$\pm$0.05$\pm$0.11 90–110 0.093$\pm$0.004$\pm$0.007
0.0541$\pm$0.0009$\pm$0.0017 1.71$\pm$0.08$\pm$0.14 0.075$\pm$0.003$\pm$0.006
0.0550$\pm$0.0007$\pm$0.0021 1.36$\pm$0.06$\pm$0.12 0.082$\pm$0.003$\pm$0.006
0.0546$\pm$0.0007$\pm$0.0020 1.50$\pm$0.06$\pm$0.11 110–130
0.070$\pm$0.009$\pm$0.007 0.0529$\pm$0.0022$\pm$0.0025 1.32$\pm$0.18$\pm$0.19
0.084$\pm$0.007$\pm$0.008 0.0487$\pm$0.0017$\pm$0.0025 1.73$\pm$0.17$\pm$0.21
0.078$\pm$0.006$\pm$0.006 0.0499$\pm$0.0014$\pm$0.0024 1.57$\pm$0.13$\pm$0.16
130–150 0.073$\pm$0.009$\pm$0.007 0.0510$\pm$0.0022$\pm$0.0024
1.43$\pm$0.20$\pm$0.20 0.081$\pm$0.007$\pm$0.007 0.0458$\pm$0.0017$\pm$0.0023
1.76$\pm$0.18$\pm$0.22 0.078$\pm$0.006$\pm$0.006 0.0474$\pm$0.0014$\pm$0.0023
1.64$\pm$0.14$\pm$0.17 150–200 0.064$\pm$0.008$\pm$0.006
0.0447$\pm$0.0019$\pm$0.0021 1.43$\pm$0.20$\pm$0.20 0.081$\pm$0.007$\pm$0.007
0.0454$\pm$0.0017$\pm$0.0023 1.78$\pm$0.18$\pm$0.22 0.074$\pm$0.006$\pm$0.006
0.0448$\pm$0.0014$\pm$0.0021 1.65$\pm$0.14$\pm$0.17

The cross section ratios in the electron, muon and combined channels in the
boson bins.  boson  Electron Muon Combined R(c/j) R(b/j) R(c/b) R(c/j) R(b/j)
R(c/b) R(c/j) R(b/j) R(c/b) 0–20 0.066$\pm$0.003$\pm$0.010
0.0371$\pm$0.0008$\pm$0.0011 1.77$\pm$0.10$\pm$0.26 0.061$\pm$0.003$\pm$0.009
0.0348$\pm$0.0006$\pm$0.0010 1.77$\pm$0.09$\pm$0.24 0.064$\pm$0.002$\pm$0.009
0.0357$\pm$0.0005$\pm$0.0010 1.78$\pm$0.07$\pm$0.25 20–30
0.095$\pm$0.005$\pm$0.014 0.0505$\pm$0.0010$\pm$0.0015 1.89$\pm$0.11$\pm$0.28
0.088$\pm$0.004$\pm$0.012 0.0509$\pm$0.0008$\pm$0.0015 1.73$\pm$0.08$\pm$0.23
0.091$\pm$0.003$\pm$0.013 0.0509$\pm$0.0007$\pm$0.0014 1.80$\pm$0.07$\pm$0.25
30–40 0.089$\pm$0.004$\pm$0.009 0.0502$\pm$0.0009$\pm$0.0012
1.78$\pm$0.09$\pm$0.19 0.083$\pm$0.003$\pm$0.008 0.0500$\pm$0.0007$\pm$0.0012
1.67$\pm$0.07$\pm$0.17 0.085$\pm$0.003$\pm$0.008 0.0501$\pm$0.0006$\pm$0.0012
1.71$\pm$0.06$\pm$0.17 40–50 0.086$\pm$0.004$\pm$0.008
0.0537$\pm$0.0010$\pm$0.0013 1.60$\pm$0.09$\pm$0.17 0.084$\pm$0.003$\pm$0.008
0.0547$\pm$0.0008$\pm$0.0013 1.53$\pm$0.07$\pm$0.15 0.084$\pm$0.003$\pm$0.008
0.0543$\pm$0.0007$\pm$0.0013 1.55$\pm$0.06$\pm$0.15 50–60
0.102$\pm$0.005$\pm$0.008 0.0593$\pm$0.0010$\pm$0.0012 1.71$\pm$0.09$\pm$0.15
0.090$\pm$0.004$\pm$0.007 0.0598$\pm$0.0008$\pm$0.0012 1.50$\pm$0.07$\pm$0.13
0.095$\pm$0.003$\pm$0.007 0.0596$\pm$0.0007$\pm$0.0011 1.59$\pm$0.06$\pm$0.13
60–70 0.103$\pm$0.005$\pm$0.008 0.0631$\pm$0.0011$\pm$0.0013
1.64$\pm$0.08$\pm$0.14 0.091$\pm$0.004$\pm$0.007 0.0648$\pm$0.0009$\pm$0.0013
1.41$\pm$0.06$\pm$0.12 0.096$\pm$0.003$\pm$0.007 0.0641$\pm$0.0007$\pm$0.0012
1.50$\pm$0.05$\pm$0.12 70–90 0.112$\pm$0.005$\pm$0.008
0.0627$\pm$0.0011$\pm$0.0012 1.79$\pm$0.09$\pm$0.15 0.098$\pm$0.004$\pm$0.008
0.0658$\pm$0.0009$\pm$0.0013 1.49$\pm$0.07$\pm$0.13 0.104$\pm$0.003$\pm$0.008
0.0646$\pm$0.0008$\pm$0.0012 1.60$\pm$0.06$\pm$0.13 90–120
0.096$\pm$0.008$\pm$0.007 0.0724$\pm$0.0017$\pm$0.0019 1.32$\pm$0.12$\pm$0.13
0.115$\pm$0.006$\pm$0.008 0.0690$\pm$0.0014$\pm$0.0019 1.67$\pm$0.10$\pm$0.14
0.107$\pm$0.005$\pm$0.007 0.0704$\pm$0.0012$\pm$0.0018 1.52$\pm$0.08$\pm$0.12
120–150 0.099$\pm$0.008$\pm$0.007 0.0755$\pm$0.0018$\pm$0.0020
1.31$\pm$0.12$\pm$0.13 0.116$\pm$0.007$\pm$0.008 0.0685$\pm$0.0015$\pm$0.0019
1.69$\pm$0.11$\pm$0.15 0.109$\pm$0.005$\pm$0.007 0.0712$\pm$0.0013$\pm$0.0018
1.53$\pm$0.08$\pm$0.12 150–200 0.114$\pm$0.009$\pm$0.009
0.0710$\pm$0.0017$\pm$0.0019 1.61$\pm$0.14$\pm$0.15 0.136$\pm$0.008$\pm$0.010
0.0708$\pm$0.0015$\pm$0.0019 1.93$\pm$0.12$\pm$0.17 0.127$\pm$0.006$\pm$0.009
0.0709$\pm$0.0013$\pm$0.0018 1.79$\pm$0.10$\pm$0.14

##  .12 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan${}^{\textrm{\textdagger}}$, A. Tumasyan Institut für
Hochenergiephysik, Wien, Austria
W. Adam, F. Ambrogi, T. Bergauer, J. Brandstetter, M. Dragicevic, J. Erö, A.
Escalante Del Valle, M. Flechl, R. Frühwirth1, M. Jeitler1, N. Krammer, I.
Krätschmer, D. Liko, T. Madlener, I. Mikulec, N. Rad, J. Schieck1, R.
Schöfbeck, M. Spanring, D. Spitzbart, W. Waltenberger, C.-E. Wulz1, M. Zarucki
Institute for Nuclear Problems, Minsk, Belarus
V. Drugakov, V. Mossolov, J. Suarez Gonzalez Universiteit Antwerpen,
Antwerpen, Belgium
M.R. Darwish, E.A. De Wolf, D. Di Croce, X. Janssen, A. Lelek, M. Pieters, H.
Rejeb Sfar, H. Van Haevermaet, P. Van Mechelen, S. Van Putte, N. Van Remortel
Vrije Universiteit Brussel, Brussel, Belgium
F. Blekman, E.S. Bols, S.S. Chhibra, J. D’Hondt, J. De Clercq, D. Lontkovskyi,
S. Lowette, I. Marchesini, S. Moortgat, Q. Python, K. Skovpen, S. Tavernier,
W. Van Doninck, P. Van Mulders Université Libre de Bruxelles, Bruxelles,
Belgium
D. Beghin, B. Bilin, H. Brun, B. Clerbaux, G. De Lentdecker, H. Delannoy, B.
Dorney, L. Favart, A. Grebenyuk, A.K. Kalsi, A. Popov, N. Postiau, E.
Starling, L. Thomas, C. Vander Velde, P. Vanlaer, D. Vannerom Ghent
University, Ghent, Belgium
T. Cornelis, D. Dobur, I. Khvastunov2, M. Niedziela, C. Roskas, M. Tytgat, W.
Verbeke, B. Vermassen, M. Vit Université Catholique de Louvain, Louvain-la-
Neuve, Belgium
O. Bondu, G. Bruno, C. Caputo, P. David, C. Delaere, M. Delcourt, A.
Giammanco, V. Lemaitre, J. Prisciandaro, A. Saggio, M. Vidal Marono, P.
Vischia, J. Zobec Centro Brasileiro de Pesquisas Fisicas, Rio de Janeiro,
Brazil
F.L. Alves, G.A. Alves, G. Correia Silva, C. Hensel, A. Moraes, P. Rebello
Teles Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
E. Belchior Batista Das Chagas, W. Carvalho, J. Chinellato3, E. Coelho, E.M.
Da Costa, G.G. Da Silveira4, D. De Jesus Damiao, C. De Oliveira Martins, S.
Fonseca De Souza, L.M. Huertas Guativa, H. Malbouisson, J. Martins5, D. Matos
Figueiredo, M. Medina Jaime6, M. Melo De Almeida, C. Mora Herrera, L. Mundim,
H. Nogima, W.L. Prado Da Silva, L.J. Sanchez Rosas, A. Santoro, A. Sznajder,
M. Thiel, E.J. Tonelli Manganote3, F. Torres Da Silva De Araujo, A. Vilela
Pereira Universidade Estadual Paulista a, Universidade Federal do ABC b, São
Paulo, Brazil
C.A. Bernardesa, L. Calligarisa, T.R. Fernandez Perez Tomeia, E.M. Gregoresb,
D.S. Lemos, P.G. Mercadanteb, S.F. Novaesa, SandraS. Padulaa Institute for
Nuclear Research and Nuclear Energy, Bulgarian Academy of Sciences, Sofia,
Bulgaria
A. Aleksandrov, G. Antchev, R. Hadjiiska, P. Iaydjiev, M. Misheva, M. Rodozov,
M. Shopova, G. Sultanov University of Sofia, Sofia, Bulgaria
M. Bonchev, A. Dimitrov, T. Ivanov, L. Litov, B. Pavlov, P. Petkov Beihang
University, Beijing, China
W. Fang7, X. Gao7, L. Yuan Department of Physics, Tsinghua University,
Beijing, China
M. Ahmad, Z. Hu, Y. Wang Institute of High Energy Physics, Beijing, China
G.M. Chen, H.S. Chen, M. Chen, C.H. Jiang, D. Leggat, H. Liao, Z. Liu, A.
Spiezia, J. Tao, E. Yazgan, H. Zhang, S. Zhang8, J. Zhao State Key Laboratory
of Nuclear Physics and Technology, Peking University, Beijing, China
A. Agapitos, Y. Ban, G. Chen, A. Levin, J. Li, L. Li, Q. Li, Y. Mao, S.J.
Qian, D. Wang, Q. Wang Zhejiang University, Hangzhou, China
M. Xiao Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, C. Florez, C.F. González Hernández, M.A. Segura Delgado
Universidad de Antioquia, Medellin, Colombia
J. Mejia Guisao, J.D. Ruiz Alvarez, C.A. Salazar González, N. Vanegas Arbelaez
University of Split, Faculty of Electrical Engineering, Mechanical Engineering
and Naval Architecture, Split, Croatia
D. Giljanović, N. Godinovic, D. Lelas, I. Puljak, T. Sculac University of
Split, Faculty of Science, Split, Croatia
Z. Antunovic, M. Kovac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, D. Ferencek, K. Kadija, B. Mesic, M. Roguljic, A. Starodumov9,
T. Susa University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, E. Erodotou, A. Ioannou, M. Kolosova, S. Konstantinou,
G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos, P.A. Razis, H.
Rykaczewski, D. Tsiakkouri Charles University, Prague, Czech Republic
M. Finger10, M. Finger Jr.10, A. Kveton, J. Tomsa Escuela Politecnica
Nacional, Quito, Ecuador
E. Ayala Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab
Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
S. Elgammal11, E. Salama11,12 National Institute of Chemical Physics and
Biophysics, Tallinn, Estonia
S. Bhowmik, A. Carvalho Antunes De Oliveira, R.K. Dewanjee, K. Ehataht, M.
Kadastik, M. Raidal, C. Veelken Department of Physics, University of Helsinki,
Helsinki, Finland
P. Eerola, L. Forthomme, H. Kirschenmann, K. Osterberg, M. Voutilainen
Helsinki Institute of Physics, Helsinki, Finland
F. Garcia, J. Havukainen, J.K. Heikkilä, V. Karimäki, M.S. Kim, R. Kinnunen,
T. Lampén, K. Lassila-Perini, S. Laurila, S. Lehti, T. Lindén, P. Luukka, T.
Mäenpää, H. Siikonen, E. Tuominen, J. Tuominiemi Lappeenranta University of
Technology, Lappeenranta, Finland
T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
M. Besancon, F. Couderc, M. Dejardin, D. Denegri, B. Fabbro, J.L. Faure, F.
Ferri, S. Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry,
C. Leloup, B. Lenzi, E. Locci, J. Malcles, J. Rander, A. Rosowsky, M.Ö. Sahin,
A. Savoy-Navarro13, M. Titov, G.B. Yu Laboratoire Leprince-Ringuet,
CNRS/IN2P3, Ecole Polytechnique, Institut Polytechnique de Paris
S. Ahuja, C. Amendola, F. Beaudette, P. Busson, C. Charlot, B. Diab, G.
Falmagne, R. Granier de Cassagnac, I. Kucher, A. Lobanov, C. Martin Perez, M.
Nguyen, C. Ochando, P. Paganini, J. Rembser, R. Salerno, J.B. Sauvan, Y.
Sirois, A. Zabi, A. Zghiche Université de Strasbourg, CNRS, IPHC UMR 7178,
Strasbourg, France
J.-L. Agram14, J. Andrea, D. Bloch, G. Bourgatte, J.-M. Brom, E.C. Chabert, C.
Collard, E. Conte14, J.-C. Fontaine14, D. Gelé, U. Goerlach, M. Jansová, A.-C.
Le Bihan, N. Tonon, P. Van Hove Centre de Calcul de l’Institut National de
Physique Nucleaire et de Physique des Particules, CNRS/IN2P3, Villeurbanne,
France
S. Gadrat Université de Lyon, Université Claude Bernard Lyon 1, CNRS-IN2P3,
Institut de Physique Nucléaire de Lyon, Villeurbanne, France
S. Beauceron, C. Bernet, G. Boudoul, C. Camen, A. Carle, N. Chanon, R.
Chierici, D. Contardo, P. Depasse, H. El Mamouni, J. Fay, S. Gascon, M.
Gouzevitch, B. Ille, Sa. Jain, F. Lagarde, I.B. Laktineh, H. Lattaud, A.
Lesauvage, M. Lethuillier, L. Mirabito, S. Perries, V. Sordini, L. Torterotot,
G. Touquet, M. Vander Donckt, S. Viret Georgian Technical University, Tbilisi,
Georgia
T. Toriashvili15 Tbilisi State University, Tbilisi, Georgia
Z. Tsamalaidze10 RWTH Aachen University, I. Physikalisches Institut, Aachen,
Germany
C. Autermann, L. Feld, M.K. Kiesel, K. Klein, M. Lipinski, D. Meuser, A.
Pauls, M. Preuten, M.P. Rauch, J. Schulz, M. Teroerde, B. Wittmer RWTH Aachen
University, III. Physikalisches Institut A, Aachen, Germany
M. Erdmann, B. Fischer, S. Ghosh, T. Hebbeker, K. Hoepfner, H. Keller, L.
Mastrolorenzo, M. Merschmeyer, A. Meyer, P. Millet, G. Mocellin, S. Mondal, S.
Mukherjee, D. Noll, A. Novak, T. Pook, A. Pozdnyakov, T. Quast, M. Radziej, Y.
Rath, H. Reithler, J. Roemer, A. Schmidt, S.C. Schuler, A. Sharma, S.
Wiedenbeck, S. Zaleski RWTH Aachen University, III. Physikalisches Institut B,
Aachen, Germany
G. Flügge, W. Haj Ahmad16, O. Hlushchenko, T. Kress, T. Müller, A. Nowack, C.
Pistone, O. Pooth, D. Roy, H. Sert, A. Stahl17 Deutsches Elektronen-
Synchrotron, Hamburg, Germany
M. Aldaya Martin, P. Asmuss, I. Babounikau, H. Bakhshiansohi, K. Beernaert, O.
Behnke, A. Bermúdez Martínez, D. Bertsche, A.A. Bin Anuar, K. Borras18, V.
Botta, A. Campbell, A. Cardini, P. Connor, S. Consuegra Rodríguez, C.
Contreras-Campana, V. Danilov, A. De Wit, M.M. Defranchis, C. Diez Pardos, D.
Domínguez Damiani, G. Eckerlin, D. Eckstein, T. Eichhorn, A. Elwood, E. Eren,
E. Gallo19, A. Geiser, A. Grohsjean, M. Guthoff, M. Haranko, A. Harb, A.
Jafari, N.Z. Jomhari, H. Jung, A. Kasem18, M. Kasemann, H. Kaveh, J. Keaveney,
C. Kleinwort, J. Knolle, D. Krücker, W. Lange, T. Lenz, J. Lidrych, K. Lipka,
W. Lohmann20, R. Mankel, I.-A. Melzer-Pellmann, A.B. Meyer, M. Meyer, M.
Missiroli, G. Mittag, J. Mnich, A. Mussgiller, V. Myronenko, D. Pérez Adán,
S.K. Pflitsch, D. Pitzl, A. Raspereza, A. Saibel, M. Savitskyi, V. Scheurer,
P. Schütze, C. Schwanenberger, R. Shevchenko, A. Singh, H. Tholen, O. Turkot,
A. Vagnerini, M. Van De Klundert, R. Walsh, Y. Wen, K. Wichmann, C. Wissing,
O. Zenaiev, R. Zlebcik University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, L. Benato, A. Benecke, V. Blobel, T. Dreyer, A.
Ebrahimi, F. Feindt, A. Fröhlich, C. Garbers, E. Garutti, D. Gonzalez, P.
Gunnellini, J. Haller, A. Hinzmann, A. Karavdina, G. Kasieczka, R. Klanner, R.
Kogler, N. Kovalchuk, S. Kurz, V. Kutzner, J. Lange, T. Lange, A. Malara, J.
Multhaup, C.E.N. Niemeyer, A. Perieanu, A. Reimers, O. Rieger, C. Scharf, P.
Schleper, S. Schumann, J. Schwandt, J. Sonneveld, H. Stadie, G. Steinbrück,
F.M. Stober, B. Vormwald, I. Zoi Karlsruher Institut fuer Technologie,
Karlsruhe, Germany
M. Akbiyik, C. Barth, M. Baselga, S. Baur, T. Berger, E. Butz, R. Caspart, T.
Chwalek, W. De Boer, A. Dierlamm, K. El Morabit, N. Faltermann, M. Giffels, P.
Goldenzweig, A. Gottmann, M.A. Harrendorf, F. Hartmann17, U. Husemann, S.
Kudella, S. Mitra, M.U. Mozer, D. Müller, Th. Müller, M. Musich, A. Nürnberg,
G. Quast, K. Rabbertz, M. Schröder, I. Shvetsov, H.J. Simonis, R. Ulrich, M.
Wassmer, M. Weber, C. Wöhrmann, R. Wolf Institute of Nuclear and Particle
Physics (INPP), NCSR Demokritos, Aghia Paraskevi, Greece
G. Anagnostou, P. Asenov, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas,
G. Paspalaki National and Kapodistrian University of Athens, Athens, Greece
M. Diamantopoulou, G. Karathanasis, P. Kontaxakis, A. Manousakis-katsikakis,
A. Panagiotou, I. Papavergou, N. Saoulidou, A. Stakia, K. Theofilatos, K.
Vellidis, E. Vourliotis National Technical University of Athens, Athens,
Greece
G. Bakas, K. Kousouris, I. Papakrivopoulos, G. Tsipolitis University of
Ioánnina, Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, S. Mallios, K.
Manitara, N. Manthos, I. Papadopoulos, J. Strologas, F.A. Triantis, D.
Tsitsonis MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
M. Bartók21, R. Chudasama, M. Csanad, P. Major, K. Mandal, A. Mehta, M.I.
Nagy, G. Pasztor, O. Surányi, G.I. Veres Wigner Research Centre for Physics,
Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath22, F. Sikler, T.Á. Vámi, V. Veszpremi, G.
Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear Research ATOMKI,
Debrecen, Hungary
N. Beni, S. Czellar, J. Karancsi21, A. Makovec, J. Molnar, Z. Szillasi
Institute of Physics, University of Debrecen, Debrecen, Hungary
P. Raics, D. Teyssier, Z.L. Trocsanyi, B. Ujvari Eszterhazy Karoly University,
Karoly Robert Campus, Gyongyos, Hungary
T. Csorgo, W.J. Metzger, F. Nemes, T. Novak Indian Institute of Science
(IISc), Bangalore, India
S. Choudhury, J.R. Komaragiri, P.C. Tiwari National Institute of Science
Education and Research, HBNI, Bhubaneswar, India
S. Bahinipati24, C. Kar, G. Kole, P. Mal, V.K. Muraleedharan Nair Bindhu, A.
Nayak25, D.K. Sahoo24, S.K. Swain Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, S. Chauhan, R. Chawla, N. Dhingra, R.
Gupta, A. Kaur, M. Kaur, S. Kaur, P. Kumari, M. Lohan, M. Meena, K. Sandeep,
S. Sharma, J.B. Singh, A.K. Virdi University of Delhi, Delhi, India
A. Bhardwaj, B.C. Choudhary, R.B. Garg, M. Gola, S. Keshri, Ashok Kumar, M.
Naimuddin, P. Priyanka, K. Ranjan, Aashaq Shah, R. Sharma Saha Institute of
Nuclear Physics, HBNI, Kolkata, India
R. Bhardwaj26, M. Bharti26, R. Bhattacharya, S. Bhattacharya, U. Bhawandeep26,
D. Bhowmik, S. Dutta, S. Ghosh, B. Gomber27, M. Maity28, K. Mondal, S. Nandan,
A. Purohit, P.K. Rout, G. Saha, S. Sarkar, T. Sarkar28, M. Sharan, B. Singh26,
S. Thakur26 Indian Institute of Technology Madras, Madras, India
P.K. Behera, P. Kalbhor, A. Muhammad, P.R. Pujahari, A. Sharma, A.K. Sikdar
Bhabha Atomic Research Centre, Mumbai, India
D. Dutta, V. Jha, V. Kumar, D.K. Mishra, P.K. Netrakanti, L.M. Pant, P. Shukla
Tata Institute of Fundamental Research-A, Mumbai, India
T. Aziz, M.A. Bhat, S. Dugad, G.B. Mohanty, N. Sur, RavindraKumar Verma Tata
Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, S. Karmakar,
S. Kumar, G. Majumder, K. Mazumdar, N. Sahoo, S. Sawant Indian Institute of
Science Education and Research (IISER), Pune, India
S. Dube, B. Kansal, A. Kapoor, K. Kothekar, S. Pandey, A. Rane, A. Rastogi, S.
Sharma Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
S. Chenarani29, E. Eskandari Tadavani, S.M. Etesami29, M. Khakzad, M.
Mohammadi Najafabadi, M. Naseri, F. Rezaei Hosseinabadi University College
Dublin, Dublin, Ireland
M. Felcini, M. Grunewald INFN Sezione di Bari a, Università di Bari b,
Politecnico di Bari c, Bari, Italy
M. Abbresciaa,b, R. Alya,b,30, C. Calabriaa,b, A. Colaleoa, D. Creanzaa,c, L.
Cristellaa,b, N. De Filippisa,c, M. De Palmaa,b, A. Di Florioa,b, W.
Elmetenaweea,b, L. Fiorea, A. Gelmia,b, G. Iasellia,c, M. Incea,b, S.
Lezkia,b, G. Maggia,c, M. Maggia, J.A. Merlin, G. Minielloa,b, S. Mya,b, S.
Nuzzoa,b, A. Pompilia,b, G. Pugliesea,c, R. Radognaa, A. Ranieria, G.
Selvaggia,b, L. Silvestrisa, F.M. Simonea,b, R. Vendittia, P. Verwilligena
INFN Sezione di Bologna a, Università di Bologna b, Bologna, Italy
G. Abbiendia, C. Battilanaa,b, D. Bonacorsia,b, L. Borgonovia,b, S. Braibant-
Giacomellia,b, R. Campaninia,b, P. Capiluppia,b, A. Castroa,b, F.R. Cavalloa,
C. Cioccaa, G. Codispotia,b, M. Cuffiania,b, G.M. Dallavallea, F. Fabbria, A.
Fanfania,b, E. Fontanesia,b, P. Giacomellia, C. Grandia, L. Guiduccia,b, F.
Iemmia,b, S. Lo Meoa,31, 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,32, S. Costaa,b, A. Di Mattiaa, R. Potenzaa,b, A. Tricomia,b,32,
C. Tuvea,b INFN Sezione di Firenze a, Università di Firenze b, Firenze, Italy
G. Barbaglia, A. Cassese, R. Ceccarelli, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, F. Fioria,c, E. Focardia,b, G. Latinoa,b, P. Lenzia,b, M.
Meschinia, S. Paolettia, G. Sguazzonia, L. Viliania INFN Laboratori Nazionali
di Frascati, Frascati, Italy
L. Benussi, S. Bianco, D. Piccolo INFN Sezione di Genova a, Università di
Genova b, Genova, Italy
M. Bozzoa,b, F. Ferroa, R. Mulargiaa,b, E. Robuttia, S. Tosia,b INFN Sezione
di Milano-Bicocca a, Università di Milano-Bicocca b, Milano, Italy
A. Benagliaa, A. Beschia,b, F. Brivioa,b, V. Cirioloa,b,17, S. Di Guidaa,b,17,
M.E. Dinardoa,b, P. Dinia, S. Gennaia, A. Ghezzia,b, P. Govonia,b, L.
Guzzia,b, M. Malbertia, S. Malvezzia, D. Menascea, F. Montia,b, L. Moronia, M.
Paganonia,b, D. Pedrinia, S. Ragazzia,b, T. Tabarelli de Fatisa,b, D. Zuoloa,b
INFN Sezione di Napoli a, Università di Napoli ’Federico II’ b, Napoli, Italy,
Università della Basilicata c, Potenza, Italy, Università G. Marconi d, Roma,
Italy
S. Buontempoa, N. Cavalloa,c, A. De Iorioa,b, A. Di Crescenzoa,b, F.
Fabozzia,c, F. Fiengaa, G. Galatia, A.O.M. Iorioa,b, L. Listaa,b, S.
Meolaa,d,17, P. Paoluccia,17, B. Rossia, C. Sciaccaa,b, E. Voevodinaa,b INFN
Sezione di Padova a, Università di Padova b, Padova, Italy, Università di
Trento c, Trento, Italy
P. Azzia, N. Bacchettaa, D. Biselloa,b, A. Bolettia,b, A. Bragagnoloa,b, R.
Carlina,b, P. Checchiaa, P. De Castro Manzanoa, T. Dorigoa, U. Dossellia, F.
Gasparinia,b, U. Gasparinia,b, A. Gozzelinoa, S.Y. Hoha,b, P. Lujana, M.
Margonia,b, A.T. Meneguzzoa,b, J. Pazzinia,b, M. Presillab, P. Ronchesea,b, R.
Rossina,b, F. Simonettoa,b, A. Tikoa, M. Tosia,b, M. Zanettia,b, P. Zottoa,b,
G. Zumerlea,b INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy
A. Braghieria, D. Fiorinaa,b, P. Montagnaa,b, S.P. Rattia,b, V. Rea, M.
Ressegottia,b, C. Riccardia,b, P. Salvinia, I. Vaia, P. Vituloa,b INFN Sezione
di Perugia a, Università di Perugia b, Perugia, Italy
M. Biasinia,b, G.M. Bileia, D. Ciangottinia,b, L. Fanòa,b, P. Laricciaa,b, R.
Leonardia,b, E. Manonia, G. Mantovania,b, V. Mariania,b, M. Menichellia, A.
Rossia,b, A. Santocchiaa,b, D. Spigaa INFN Sezione di Pisa a, Università di
Pisa b, Scuola Normale Superiore di Pisa c, Pisa, Italy
K. Androsova, P. Azzurria, G. Bagliesia, V. Bertacchia,c, L. Bianchinia, T.
Boccalia, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, S. Donatoa, G. Fedia,
L. Gianninia,c, A. Giassia, M.T. Grippoa, F. Ligabuea,c, E. Mancaa,c, G.
Mandorlia,c, A. Messineoa,b, F. Pallaa, A. Rizzia,b, G. Rolandi33, S. Roy
Chowdhury, A. Scribanoa, P. Spagnoloa, R. Tenchinia, G. Tonellia,b, N. Turini,
A. Venturia, P.G. Verdinia INFN Sezione di Roma a, Sapienza Università di Roma
b, Rome, Italy
F. Cavallaria, M. Cipriania,b, D. Del Rea,b, E. Di Marcoa, M. Diemoza, E.
Longoa,b, P. Meridiania, G. Organtinia,b, F. Pandolfia, R. Paramattia,b, C.
Quarantaa,b, S. Rahatloua,b, C. Rovellia, F. Santanastasioa,b, L. Soffia,b
INFN Sezione di Torino a, Università di Torino b, Torino, Italy, Università
del Piemonte Orientale c, Novara, Italy
N. Amapanea,b, R. Arcidiaconoa,c, S. Argiroa,b, M. Arneodoa,c, N. Bartosika,
R. Bellana,b, A. Bellora, C. Biinoa, A. Cappatia,b, N. Cartigliaa, S.
Comettia, M. Costaa,b, R. Covarellia,b, N. Demariaa, B. Kiania,b, F. Legger,
C. Mariottia, S. Masellia, E. Migliorea,b, V. Monacoa,b, E. Monteila,b, M.
Montenoa, M.M. Obertinoa,b, G. Ortonaa,b, L. Pachera,b, N. Pastronea, M.
Pelliccionia, G.L. Pinna Angionia,b, A. Romeroa,b, M. Ruspaa,c, R.
Salvaticoa,b, V. Solaa, A. Solanoa,b, D. Soldia,b, A. Staianoa, D. Trocinoa,b
INFN Sezione di Trieste a, Università di Trieste b, Trieste, Italy
S. Belfortea, V. Candelisea,b, M. Casarsaa, F. Cossuttia, A. Da Rolda,b, G.
Della Riccaa,b, F. Vazzolera,b, A. Zanettia Kyungpook National University,
Daegu, Korea
B. Kim, D.H. Kim, G.N. Kim, J. Lee, S.W. Lee, C.S. Moon, Y.D. Oh, S.I. Pak, S.
Sekmen, D.C. Son, Y.C. Yang Chonnam National University, Institute for
Universe and Elementary Particles, Kwangju, Korea
H. Kim, D.H. Moon, G. Oh Hanyang University, Seoul, Korea
B. Francois, T.J. Kim, J. Park Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, S. Ha, B. Hong, K. Lee, K.S. Lee, J. Lim, J. Park,
S.K. Park, Y. Roh, J. Yoo Kyung Hee University, Department of Physics
J. Goh Sejong University, Seoul, Korea
H.S. Kim Seoul National University, Seoul, Korea
J. Almond, J.H. Bhyun, J. Choi, S. Jeon, J. Kim, J.S. Kim, H. Lee, K. Lee, S.
Lee, K. Nam, M. Oh, S.B. Oh, B.C. Radburn-Smith, U.K. Yang, H.D. Yoo, I. Yoon
University of Seoul, Seoul, Korea
D. Jeon, J.H. Kim, J.S.H. Lee, I.C. Park, I.J Watson Sungkyunkwan University,
Suwon, Korea
Y. Choi, C. Hwang, Y. Jeong, J. Lee, Y. Lee, I. Yu Riga Technical University,
Riga, Latvia
V. Veckalns34 Vilnius University, Vilnius, Lithuania
V. Dudenas, A. Juodagalvis, A. Rinkevicius, G. Tamulaitis, J. Vaitkus National
Centre for Particle Physics, Universiti Malaya, Kuala Lumpur, Malaysia
Z.A. Ibrahim, F. Mohamad Idris35, W.A.T. Wan Abdullah, M.N. Yusli, Z. Zolkapli
Universidad de Sonora (UNISON), Hermosillo, Mexico
J.F. Benitez, A. Castaneda Hernandez, J.A. Murillo Quijada, L. Valencia Palomo
Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico
H. Castilla-Valdez, E. De La Cruz-Burelo, I. Heredia-De La Cruz36, R. Lopez-
Fernandez, A. Sanchez-Hernandez Universidad Iberoamericana, Mexico City,
Mexico
S. Carrillo Moreno, C. Oropeza Barrera, M. Ramirez-Garcia, F. Vazquez Valencia
Benemerita Universidad Autonoma de Puebla, Puebla, Mexico
J. Eysermans, I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada Universidad
Autónoma de San Luis Potosí, San Luis Potosí, Mexico
A. Morelos Pineda University of Montenegro, Podgorica, Montenegro
J. Mijuskovic2, N. Raicevic University of Auckland, Auckland, New Zealand
D. Krofcheck University of Canterbury, Christchurch, New Zealand
S. Bheesette, P.H. Butler National Centre for Physics, Quaid-I-Azam
University, Islamabad, Pakistan
A. Ahmad, M. Ahmad, Q. Hassan, H.R. Hoorani, W.A. Khan, M.A. Shah, M. Shoaib,
M. Waqas AGH University of Science and Technology Faculty of Computer Science,
Electronics and Telecommunications, Krakow, Poland
V. Avati, L. Grzanka, M. Malawski National Centre for Nuclear Research,
Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, M. Górski, M. Kazana, M. Szleper, P.
Zalewski Institute of Experimental Physics, Faculty of Physics, University of
Warsaw, Warsaw, Poland
K. Bunkowski, A. Byszuk37, K. Doroba, A. Kalinowski, M. Konecki, J.
Krolikowski, M. Olszewski, M. Walczak Laboratório de Instrumentação e Física
Experimental de Partículas, Lisboa, Portugal
M. Araujo, P. Bargassa, D. Bastos, A. Di Francesco, P. Faccioli, B. Galinhas,
M. Gallinaro, J. Hollar, N. Leonardo, T. Niknejad, J. Seixas, K. Shchelina, G.
Strong, O. Toldaiev, J. Varela Joint Institute for Nuclear Research, Dubna,
Russia
S. Afanasiev, P. Bunin, M. Gavrilenko, I. Golutvin, I. Gorbunov, A. Kamenev,
V. Karjavine, A. Lanev, A. Malakhov, V. Matveev38,39, P. Moisenz, V. Palichik,
V. Perelygin, M. Savina, S. Shmatov, S. Shulha, N. Skatchkov, V. Smirnov, N.
Voytishin, A. Zarubin Petersburg Nuclear Physics Institute, Gatchina (St.
Petersburg), Russia
L. Chtchipounov, V. Golovtcov, Y. Ivanov, V. Kim40, E. Kuznetsova41, P.
Levchenko, V. Murzin, V. Oreshkin, I. Smirnov, D. Sosnov, V. Sulimov, L.
Uvarov, A. Vorobyev Institute for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov,
N. Krasnikov, A. Pashenkov, D. Tlisov, A. Toropin Institute for Theoretical
and Experimental Physics named by A.I. Alikhanov of NRC ‘Kurchatov Institute’,
Moscow, Russia
V. Epshteyn, V. Gavrilov, N. Lychkovskaya, A. Nikitenko42, V. Popov, I.
Pozdnyakov, G. Safronov, A. Spiridonov, A. Stepennov, M. Toms, E. Vlasov, A.
Zhokin Moscow Institute of Physics and Technology, Moscow, Russia
T. Aushev National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
M. Chadeeva43, P. Parygin, D. Philippov, E. Popova, V. Rusinov 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. Dubinin44, L. Dudko, V. Klyukhin, O.
Kodolova, I. Lokhtin, S. Obraztsov, M. Perfilov, S. Petrushanko, V. Savrin, A.
Snigirev Novosibirsk State University (NSU), Novosibirsk, Russia
A. Barnyakov45, V. Blinov45, T. Dimova45, L. Kardapoltsev45, Y. Skovpen45
Institute for High Energy Physics of National Research Centre ‘Kurchatov
Institute’, Protvino, Russia
I. Azhgirey, I. Bayshev, S. Bitioukov, V. Kachanov, D. Konstantinov, P.
Mandrik, V. Petrov, R. Ryutin, S. Slabospitskii, A. Sobol, S. Troshin, N.
Tyurin, A. Uzunian, A. Volkov National Research Tomsk Polytechnic University,
Tomsk, Russia
A. Babaev, A. Iuzhakov, V. Okhotnikov Tomsk State University, Tomsk, Russia
V. Borchsh, V. Ivanchenko, E. Tcherniaev University of Belgrade: Faculty of
Physics and VINCA Institute of Nuclear Sciences
P. Adzic46, P. Cirkovic, M. Dordevic, P. Milenovic, J. Milosevic, M.
Stojanovic Centro de Investigaciones Energéticas Medioambientales y
Tecnológicas (CIEMAT), Madrid, Spain
M. Aguilar-Benitez, J. Alcaraz Maestre, A. Álvarez Fernández, I. Bachiller, M.
Barrio Luna, CristinaF. Bedoya, J.A. Brochero Cifuentes, C.A. Carrillo
Montoya, M. Cepeda, M. Cerrada, N. Colino, B. De La Cruz, A. Delgado Peris,
J.P. Fernández Ramos, J. Flix, M.C. Fouz, O. Gonzalez Lopez, S. Goy Lopez,
J.M. Hernandez, M.I. Josa, D. Moran, Á. Navarro Tobar, A. Pérez-Calero
Yzquierdo, J. Puerta Pelayo, I. Redondo, L. Romero, S. Sánchez Navas, M.S.
Soares, A. Triossi, C. Willmott Universidad Autónoma de Madrid, Madrid, Spain
C. Albajar, J.F. de Trocóniz, R. Reyes-Almanza Universidad de Oviedo,
Instituto Universitario de Ciencias y Tecnologías Espaciales de Asturias
(ICTEA), Oviedo, Spain
B. Alvarez Gonzalez, J. Cuevas, C. Erice, J. Fernandez Menendez, S. Folgueras,
I. Gonzalez Caballero, J.R. González Fernández, E. Palencia Cortezon, V.
Rodríguez Bouza, S. Sanchez Cruz Instituto de Física de Cantabria (IFCA),
CSIC-Universidad de Cantabria, Santander, Spain
I.J. Cabrillo, A. Calderon, B. Chazin Quero, J. Duarte Campderros, M.
Fernandez, P.J. Fernández Manteca, A. García Alonso, G. Gomez, C. Martinez
Rivero, P. Martinez Ruiz del Arbol, F. Matorras, J. Piedra Gomez, C. Prieels,
T. Rodrigo, A. Ruiz-Jimeno, L. Russo47, L. Scodellaro, I. Vila, J.M. Vizan
Garcia University of Colombo, Colombo, Sri Lanka
K. Malagalage University of Ruhuna, Department of Physics, Matara, Sri Lanka
W.G.D. Dharmaratna, N. Wickramage CERN, European Organization for Nuclear
Research, Geneva, Switzerland
D. Abbaneo, B. Akgun, E. Auffray, G. Auzinger, J. Baechler, P. Baillon, A.H.
Ball, D. Barney, J. Bendavid, M. Bianco, A. Bocci, P. Bortignon, E. Bossini,
C. Botta, E. Brondolin, T. Camporesi, A. Caratelli, G. Cerminara, E. Chapon,
G. Cucciati, D. d’Enterria, A. Dabrowski, N. Daci, V. Daponte, A. David, O.
Davignon, A. De Roeck, M. Deile, M. Dobson, M. Dünser, N. Dupont, A. Elliott-
Peisert, N. Emriskova, F. Fallavollita48, D. Fasanella, S. Fiorendi, G.
Franzoni, J. Fulcher, W. Funk, S. Giani, D. Gigi, A. Gilbert, K. Gill, F.
Glege, L. Gouskos, M. Gruchala, M. Guilbaud, D. Gulhan, J. Hegeman, C.
Heidegger, Y. Iiyama, V. Innocente, T. James, P. Janot, O. Karacheban20, J.
Kaspar, J. Kieseler, M. Krammer1, N. Kratochwil, C. Lange, P. Lecoq, C.
Lourenço, L. Malgeri, M. Mannelli, A. Massironi, F. Meijers, S. Mersi, E.
Meschi, F. Moortgat, M. Mulders, J. Ngadiuba, J. Niedziela, S. Nourbakhsh, S.
Orfanelli, L. Orsini, F. Pantaleo17, L. Pape, E. Perez, M. Peruzzi, A.
Petrilli, G. Petrucciani, A. Pfeiffer, M. Pierini, F.M. Pitters, D. Rabady, A.
Racz, M. Rieger, M. Rovere, H. Sakulin, J. Salfeld-Nebgen, C. Schäfer, C.
Schwick, M. Selvaggi, A. Sharma, P. Silva, W. Snoeys, P. Sphicas49, J.
Steggemann, S. Summers, V.R. Tavolaro, D. Treille, A. Tsirou, G.P. Van Onsem,
A. Vartak, M. Verzetti, W.D. Zeuner Paul Scherrer Institut, Villigen,
Switzerland
L. Caminada50, K. Deiters, W. Erdmann, R. Horisberger, Q. Ingram, H.C.
Kaestli, D. Kotlinski, U. Langenegger, T. Rohe, S.A. Wiederkehr ETH Zurich -
Institute for Particle Physics and Astrophysics (IPA), Zurich, Switzerland
M. Backhaus, P. Berger, N. Chernyavskaya, G. Dissertori, M. Dittmar, M.
Donegà, C. Dorfer, T.A. Gómez Espinosa, C. Grab, D. Hits, W. Lustermann, R.A.
Manzoni, M.T. Meinhard, F. Micheli, P. Musella, F. Nessi-Tedaldi, F. Pauss, G.
Perrin, L. Perrozzi, S. Pigazzini, M.G. Ratti, M. Reichmann, C. Reissel, T.
Reitenspiess, B. Ristic, D. Ruini, D.A. Sanz Becerra, M. Schönenberger, L.
Shchutska, M.L. Vesterbacka Olsson, R. Wallny, D.H. Zhu Universität Zürich,
Zurich, Switzerland
T.K. Aarrestad, C. Amsler51, D. Brzhechko, M.F. Canelli, A. De Cosa, R. Del
Burgo, B. Kilminster, S. Leontsinis, V.M. Mikuni, I. Neutelings, G. Rauco, P.
Robmann, K. Schweiger, C. Seitz, Y. Takahashi, S. Wertz, A. Zucchetta National
Central University, Chung-Li, Taiwan
T.H. Doan, C.M. Kuo, W. Lin, A. Roy, S.S. Yu National Taiwan University (NTU),
Taipei, Taiwan
P. Chang, Y. Chao, K.F. Chen, P.H. Chen, W.-S. Hou, Y.y. Li, R.-S. Lu, E.
Paganis, A. Psallidas, A. Steen Chulalongkorn University, Faculty of Science,
Department of Physics, Bangkok, Thailand
B. Asavapibhop, C. Asawatangtrakuldee, N. Srimanobhas, N. Suwonjandee Çukurova
University, Physics Department, Science and Art Faculty, Adana, Turkey
A. Bat, F. Boran, A. Celik52, S. Cerci53, S. Damarseckin54, Z.S. Demiroglu, F.
Dolek, C. Dozen55, I. Dumanoglu, G. Gokbulut, EmineGurpinar Guler56, Y. Guler,
I. Hos57, C. Isik, E.E. Kangal58, O. Kara, A. Kayis Topaksu, U. Kiminsu, G.
Onengut, K. Ozdemir59, S. Ozturk60, A.E. Simsek, D. Sunar Cerci53, U.G. Tok,
S. Turkcapar, I.S. Zorbakir, C. Zorbilmez Middle East Technical University,
Physics Department, Ankara, Turkey
B. Isildak61, G. Karapinar62, M. Yalvac Bogazici University, Istanbul, Turkey
I.O. Atakisi, E. Gülmez, M. Kaya63, O. Kaya64, Ö. Özçelik, S. Tekten, E.A.
Yetkin65 Istanbul Technical University, Istanbul, Turkey
A. Cakir, K. Cankocak, Y. Komurcu, S. Sen66 Istanbul University, Istanbul,
Turkey
B. Kaynak, S. Ozkorucuklu Institute for Scintillation Materials of National
Academy of Science of Ukraine, Kharkov, Ukraine
B. Grynyov National Scientific Center, Kharkov Institute of Physics and
Technology, Kharkov, Ukraine
L. Levchuk University of Bristol, Bristol, United Kingdom
E. Bhal, S. Bologna, J.J. Brooke, D. Burns67, E. Clement, D. Cussans, H.
Flacher, J. Goldstein, G.P. Heath, H.F. Heath, L. Kreczko, B. Krikler, S.
Paramesvaran, B. Penning, T. Sakuma, S. Seif El Nasr-Storey, V.J. Smith, J.
Taylor, A. Titterton Rutherford Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev68, C. Brew, R.M. Brown, D.J.A. Cockerill, J.A. Coughlan,
K. Harder, S. Harper, J. Linacre, K. Manolopoulos, D.M. Newbold, E. Olaiya, D.
Petyt, T. Reis, T. Schuh, C.H. Shepherd-Themistocleous, A. Thea, I.R. Tomalin,
T. Williams, W.J. Womersley Imperial College, London, United Kingdom
R. Bainbridge, P. Bloch, J. Borg, S. Breeze, O. Buchmuller, A. Bundock,
GurpreetSingh CHAHAL69, D. Colling, P. Dauncey, G. Davies, M. Della Negra, R.
Di Maria, P. Everaerts, G. Hall, G. Iles, M. Komm, C. Laner, L. Lyons, A.-M.
Magnan, S. Malik, A. Martelli, V. Milosevic, A. Morton, J. Nash70, V.
Palladino, M. Pesaresi, D.M. Raymond, A. Richards, A. Rose, E. Scott, C. Seez,
A. Shtipliyski, M. Stoye, T. Strebler, A. Tapper, K. Uchida, T. Virdee17, N.
Wardle, D. Winterbottom, J. Wright, A.G. Zecchinelli, S.C. Zenz Brunel
University, Uxbridge, United Kingdom
J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, C.K. Mackay, I.D. Reid, L.
Teodorescu, S. Zahid Baylor University, Waco, USA
K. Call, B. Caraway, J. Dittmann, K. Hatakeyama, C. Madrid, B. McMaster, N.
Pastika, C. Smith Catholic University of America, Washington, DC, USA
R. Bartek, A. Dominguez, R. Uniyal, A.M. Vargas Hernandez The University of
Alabama, Tuscaloosa, USA
A. Buccilli, S.I. Cooper, C. Henderson, P. Rumerio, C. West Boston University,
Boston, USA
A. Albert, D. Arcaro, Z. Demiragli, D. Gastler, C. Richardson, J. Rohlf, D.
Sperka, I. Suarez, L. Sulak, D. Zou Brown University, Providence, USA
G. Benelli, B. Burkle, X. Coubez18, D. Cutts, Y.t. Duh, M. Hadley, U. Heintz,
J.M. Hogan71, K.H.M. Kwok, E. Laird, G. Landsberg, K.T. Lau, J. Lee, Z. Mao,
M. Narain, S. Sagir72, R. Syarif, E. Usai, W.Y. Wong, D. Yu, W. Zhang
University of California, Davis, Davis, USA
R. Band, C. Brainerd, R. Breedon, M. Calderon De La Barca Sanchez, M. Chertok,
J. Conway, R. Conway, P.T. Cox, R. Erbacher, C. Flores, G. Funk, F. Jensen, W.
Ko, O. Kukral, R. Lander, M. Mulhearn, D. Pellett, J. Pilot, M. Shi, D.
Taylor, K. Tos, M. Tripathi, Z. Wang, F. Zhang University of California, Los
Angeles, USA
M. Bachtis, C. Bravo, R. Cousins, A. Dasgupta, A. Florent, J. Hauser, M.
Ignatenko, N. Mccoll, W.A. Nash, S. Regnard, D. Saltzberg, C. Schnaible, B.
Stone, V. Valuev University of California, Riverside, Riverside, USA
K. Burt, Y. Chen, R. Clare, J.W. Gary, S.M.A. Ghiasi Shirazi, G. Hanson, G.
Karapostoli, E. Kennedy, O.R. Long, M. Olmedo Negrete, M.I. Paneva, W. Si, L.
Wang, S. Wimpenny, B.R. Yates, Y. Zhang University of California, San Diego,
La Jolla, USA
J.G. Branson, P. Chang, S. Cittolin, S. Cooperstein, N. Deelen, M. Derdzinski,
R. Gerosa, D. Gilbert, B. Hashemi, D. Klein, V. Krutelyov, J. Letts, M.
Masciovecchio, S. May, S. Padhi, M. Pieri, V. Sharma, M. Tadel, F. Würthwein,
A. Yagil, G. Zevi Della Porta University of California, Santa Barbara -
Department of Physics, Santa Barbara, USA
N. Amin, R. Bhandari, C. Campagnari, M. Citron, V. Dutta, M. Franco Sevilla,
J. Incandela, B. Marsh, H. Mei, A. Ovcharova, H. Qu, J. Richman, U. Sarica, D.
Stuart, S. Wang California Institute of Technology, Pasadena, USA
D. Anderson, A. Bornheim, O. Cerri, I. Dutta, J.M. Lawhorn, N. Lu, J. Mao,
H.B. Newman, T.Q. Nguyen, J. Pata, M. Spiropulu, J.R. Vlimant, S. Xie, Z.
Zhang, R.Y. Zhu Carnegie Mellon University, Pittsburgh, USA
M.B. Andrews, T. Ferguson, T. Mudholkar, M. Paulini, M. Sun, I. Vorobiev, M.
Weinberg University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, E. MacDonald, T. Mulholland, R. Patel, A. Perloff, K.
Stenson, K.A. Ulmer, S.R. Wagner Cornell University, Ithaca, USA
J. Alexander, Y. Cheng, J. Chu, A. Datta, A. Frankenthal, K. Mcdermott, J.R.
Patterson, D. Quach, A. Ryd, S.M. Tan, Z. Tao, J. Thom, P. Wittich, M. Zientek
Fermi National Accelerator Laboratory, Batavia, USA
S. Abdullin, M. Albrow, M. Alyari, G. Apollinari, A. Apresyan, A. Apyan, S.
Banerjee, L.A.T. Bauerdick, A. Beretvas, D. Berry, J. Berryhill, P.C. Bhat, K.
Burkett, J.N. Butler, A. Canepa, G.B. Cerati, H.W.K. Cheung, F. Chlebana, M.
Cremonesi, J. Duarte, V.D. Elvira, J. Freeman, Z. Gecse, E. Gottschalk, L.
Gray, D. Green, S. Grünendahl, O. Gutsche, AllisonReinsvold Hall, J. Hanlon,
R.M. Harris, S. Hasegawa, R. Heller, J. Hirschauer, B. Jayatilaka, S.
Jindariani, M. Johnson, U. Joshi, T. Klijnsma, B. Klima, M.J. Kortelainen, B.
Kreis, S. Lammel, J. Lewis, D. Lincoln, R. Lipton, M. Liu, T. Liu, J. Lykken,
K. Maeshima, J.M. Marraffino, D. Mason, P. McBride, P. Merkel, S. Mrenna, S.
Nahn, V. O’Dell, V. Papadimitriou, K. Pedro, C. Pena, G. Rakness, F. Ravera,
L. Ristori, B. Schneider, E. Sexton-Kennedy, N. Smith, A. Soha, W.J. Spalding,
L. Spiegel, S. Stoynev, J. Strait, N. Strobbe, L. Taylor, S. Tkaczyk, N.V.
Tran, L. Uplegger, E.W. Vaandering, C. Vernieri, R. Vidal, M. Wang, H.A. Weber
University of Florida, Gainesville, USA
D. Acosta, P. Avery, D. Bourilkov, A. Brinkerhoff, L. Cadamuro, A. Carnes, V.
Cherepanov, F. Errico, R.D. Field, S.V. Gleyzer, D. Guerrero, B.M. Joshi, M.
Kim, J. Konigsberg, A. Korytov, K.H. Lo, P. Ma, K. Matchev, N. Menendez, G.
Mitselmakher, D. Rosenzweig, K. Shi, J. Wang, S. Wang, X. Zuo Florida
International University, Miami, USA
Y.R. Joshi Florida State University, Tallahassee, USA
T. Adams, A. Askew, S. Hagopian, V. Hagopian, K.F. Johnson, R. Khurana, T.
Kolberg, G. Martinez, T. Perry, H. Prosper, C. Schiber, R. Yohay, J. Zhang
Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, M. Hohlmann, D. Noonan, M. Rahmani, M. Saunders, F. Yumiceva
University of Illinois at Chicago (UIC), Chicago, USA
M.R. Adams, L. Apanasevich, R.R. Betts, R. Cavanaugh, X. Chen, S. Dittmer, O.
Evdokimov, C.E. Gerber, D.A. Hangal, D.J. Hofman, K. Jung, C. Mills, T. Roy,
M.B. Tonjes, N. Varelas, J. Viinikainen, H. Wang, X. Wang, Z. Wu The
University of Iowa, Iowa City, USA
M. Alhusseini, B. Bilki56, W. Clarida, K. Dilsiz73, S. Durgut, R.P.
Gandrajula, M. Haytmyradov, V. Khristenko, O.K. Köseyan, J.-P. Merlo, A.
Mestvirishvili74, A. Moeller, J. Nachtman, H. Ogul75, Y. Onel, F. Ozok76, A.
Penzo, C. Snyder, E. Tiras, J. Wetzel Johns Hopkins University, Baltimore, USA
B. Blumenfeld, A. Cocoros, N. Eminizer, A.V. Gritsan, W.T. Hung, S. Kyriacou,
P. Maksimovic, J. Roskes, M. Swartz The University of Kansas, Lawrence, USA
C. Baldenegro Barrera, P. Baringer, A. Bean, S. Boren, J. Bowen, A. Bylinkin,
T. Isidori, S. Khalil, J. King, G. Krintiras, A. Kropivnitskaya, C. Lindsey,
D. Majumder, W. Mcbrayer, N. Minafra, M. Murray, C. Rogan, C. Royon, S.
Sanders, E. Schmitz, J.D. Tapia Takaki, Q. Wang, J. Williams, G. Wilson Kansas
State University, Manhattan, USA
S. Duric, A. Ivanov, K. Kaadze, D. Kim, Y. Maravin, D.R. Mendis, T. Mitchell,
A. Modak, A. Mohammadi Lawrence Livermore National Laboratory, Livermore, USA
F. Rebassoo, D. Wright University of Maryland, College Park, USA
A. Baden, O. Baron, A. Belloni, S.C. Eno, Y. Feng, N.J. Hadley, S. Jabeen,
G.Y. Jeng, R.G. Kellogg, J. Kunkle, A.C. Mignerey, S. Nabili, F. Ricci-Tam, M.
Seidel, Y.H. Shin, A. Skuja, S.C. Tonwar, K. Wong Massachusetts Institute of
Technology, Cambridge, USA
D. Abercrombie, B. Allen, A. Baty, R. Bi, S. Brandt, W. Busza, I.A. Cali, M.
D’Alfonso, G. Gomez Ceballos, M. Goncharov, P. Harris, D. Hsu, M. Hu, M.
Klute, D. Kovalskyi, Y.-J. Lee, P.D. Luckey, B. Maier, A.C. Marini, C. Mcginn,
C. Mironov, S. Narayanan, X. Niu, C. Paus, D. Rankin, C. Roland, G. Roland, Z.
Shi, G.S.F. Stephans, K. Sumorok, K. Tatar, D. Velicanu, J. Wang, T.W. Wang,
B. Wyslouch University of Minnesota, Minneapolis, USA
R.M. Chatterjee, A. Evans, S. Guts${}^{\textrm{\textdagger}}$, P. Hansen, J.
Hiltbrand, Sh. Jain, Y. Kubota, Z. Lesko, J. Mans, M. Revering, R. Rusack, R.
Saradhy, N. Schroeder, M.A. Wadud University of Mississippi, Oxford, USA
J.G. Acosta, S. Oliveros University of Nebraska-Lincoln, Lincoln, USA
K. Bloom, S. Chauhan, D.R. Claes, C. Fangmeier, L. Finco, F. Golf, R.
Kamalieddin, I. Kravchenko, J.E. Siado, G.R. Snow${}^{\textrm{\textdagger}}$,
B. Stieger, W. Tabb State University of New York at Buffalo, Buffalo, USA
G. Agarwal, A. Godshalk, S. Gozpinar, C. Harrington, I. Iashvili, A.
Kharchilava, C. McLean, D. Nguyen, A. Parker, J. Pekkanen, S. Rappoccio, B.
Roozbahani Northeastern University, Boston, USA
G. Alverson, E. Barberis, C. Freer, Y. Haddad, A. Hortiangtham, G. Madigan, B.
Marzocchi, D.M. Morse, T. Orimoto, L. Skinnari, A. Tishelman-Charny, T.
Wamorkar, B. Wang, A. Wisecarver, D. Wood Northwestern University, Evanston,
USA
S. Bhattacharya, J. Bueghly, T. Gunter, K.A. Hahn, N. Odell, M.H. Schmitt, K.
Sung, M. Trovato, M. Velasco University of Notre Dame, Notre Dame, USA
R. Bucci, N. Dev, R. Goldouzian, M. Hildreth, K. Hurtado Anampa, C. Jessop,
D.J. Karmgard, K. Lannon, W. Li, N. Loukas, N. Marinelli, I. Mcalister, F.
Meng, C. Mueller, Y. Musienko38, M. Planer, R. Ruchti, P. Siddireddy, G.
Smith, S. Taroni, M. Wayne, A. Wightman, M. Wolf, A. Woodard The Ohio State
University, Columbus, USA
J. Alimena, B. Bylsma, L.S. Durkin, B. Francis, C. Hill, W. Ji, A. Lefeld,
T.Y. Ling, B.L. Winer Princeton University, Princeton, USA
G. Dezoort, P. Elmer, J. Hardenbrook, N. Haubrich, S. Higginbotham, A.
Kalogeropoulos, S. Kwan, D. Lange, M.T. Lucchini, J. Luo, D. Marlow, K. Mei,
I. Ojalvo, J. Olsen, C. Palmer, P. Piroué, D. Stickland, C. Tully, Z. Wang
University of Puerto Rico, Mayaguez, USA
S. Malik, S. Norberg Purdue University, West Lafayette, USA
A. Barker, V.E. Barnes, S. Das, L. Gutay, M. Jones, A.W. Jung, A. Khatiwada,
B. Mahakud, D.H. Miller, G. Negro, N. Neumeister, C.C. Peng, S. Piperov, H.
Qiu, J.F. Schulte, N. Trevisani, F. Wang, R. Xiao, W. Xie Purdue University
Northwest, Hammond, USA
T. Cheng, J. Dolen, N. Parashar Rice University, Houston, USA
U. Behrens, K.M. Ecklund, S. Freed, F.J.M. Geurts, M. Kilpatrick, Arun Kumar,
W. Li, B.P. Padley, R. Redjimi, J. Roberts, J. Rorie, W. Shi, A.G. Stahl
Leiton, Z. Tu, A. Zhang University of Rochester, Rochester, USA
A. Bodek, P. de Barbaro, R. Demina, J.L. Dulemba, C. Fallon, T. Ferbel, M.
Galanti, A. Garcia-Bellido, O. Hindrichs, A. Khukhunaishvili, E. Ranken, R.
Taus Rutgers, The State University of New Jersey, Piscataway, USA
B. Chiarito, J.P. Chou, A. Gandrakota, Y. Gershtein, E. Halkiadakis, A. Hart,
M. Heindl, E. Hughes, S. Kaplan, I. Laflotte, A. Lath, R. Montalvo, K. Nash,
M. Osherson, H. Saka, S. Salur, S. Schnetzer, S. Somalwar, R. Stone, S. Thomas
University of Tennessee, Knoxville, USA
H. Acharya, A.G. Delannoy, S. Spanier Texas A&M University, College Station,
USA
O. Bouhali77, M. Dalchenko, M. De Mattia, A. Delgado, S. Dildick, R. Eusebi,
J. Gilmore, T. Huang, T. Kamon78, H. Kim, S. Luo, S. Malhotra, D. Marley, R.
Mueller, D. Overton, L. Perniè, D. Rathjens, A. Safonov Texas Tech University,
Lubbock, USA
N. Akchurin, J. Damgov, F. De Guio, V. Hegde, S. Kunori, K. Lamichhane, S.W.
Lee, T. Mengke, S. Muthumuni, T. Peltola, S. Undleeb, I. Volobouev, Z. Wang,
A. Whitbeck Vanderbilt University, Nashville, USA
S. Greene, A. Gurrola, R. Janjam, W. Johns, C. Maguire, A. Melo, H. Ni, K.
Padeken, F. Romeo, P. Sheldon, S. Tuo, J. Velkovska, M. Verweij University of
Virginia, Charlottesville, USA
M.W. Arenton, P. Barria, B. Cox, G. Cummings, J. Hakala, R. Hirosky, M. Joyce,
A. Ledovskoy, C. Neu, B. Tannenwald, Y. Wang, E. Wolfe, F. Xia Wayne State
University, Detroit, USA
R. Harr, P.E. Karchin, N. Poudyal, J. Sturdy, P. Thapa University of Wisconsin
- Madison, Madison, WI, USA
T. Bose, J. Buchanan, C. Caillol, D. Carlsmith, S. Dasu, I. De Bruyn, L. Dodd,
C. Galloni, H. He, M. Herndon, A. Hervé, U. Hussain, P. Klabbers, A. Lanaro,
A. Loeliger, K. Long, R. Loveless, J. Madhusudanan Sreekala, D. Pinna, T.
Ruggles, A. Savin, V. Sharma, W.H. Smith, D. Teague, S. Trembath-reichert, N.
Woods †: Deceased
1: Also at Vienna University of Technology, Vienna, Austria
2: Also at IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
3: Also at Universidade Estadual de Campinas, Campinas, Brazil
4: Also at Federal University of Rio Grande do Sul, Porto Alegre, Brazil
5: Also at UFMS, Nova Andradina, Brazil
6: Also at Universidade Federal de Pelotas, Pelotas, Brazil
7: Also at Université Libre de Bruxelles, Bruxelles, Belgium
8: Also at University of Chinese Academy of Sciences, Beijing, China
9: Also at Institute for Theoretical and Experimental Physics named by A.I.
Alikhanov of NRC ‘Kurchatov Institute’, Moscow, Russia
10: Also at Joint Institute for Nuclear Research, Dubna, Russia
11: Now at British University in Egypt, Cairo, Egypt
12: Now at Ain Shams University, Cairo, Egypt
13: Also at Purdue University, West Lafayette, USA
14: Also at Université de Haute Alsace, Mulhouse, France
15: Also at Tbilisi State University, Tbilisi, Georgia
16: Also at Erzincan Binali Yildirim University, Erzincan, Turkey
17: Also at CERN, European Organization for Nuclear Research, Geneva,
Switzerland
18: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
19: Also at University of Hamburg, Hamburg, Germany
20: Also at Brandenburg University of Technology, Cottbus, Germany
21: Also at Institute of Physics, University of Debrecen, Debrecen, Hungary,
Debrecen, Hungary
22: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary
23: Also at MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary, Budapest, Hungary
24: Also at IIT Bhubaneswar, Bhubaneswar, India, Bhubaneswar, India
25: Also at Institute of Physics, Bhubaneswar, India
26: Also at Shoolini University, Solan, India
27: Also at University of Hyderabad, Hyderabad, India
28: Also at University of Visva-Bharati, Santiniketan, India
29: Also at Isfahan University of Technology, Isfahan, Iran
30: Now at INFN Sezione di Bari a, Università di Bari b, Politecnico di Bari
c, Bari, Italy
31: Also at Italian National Agency for New Technologies, Energy and
Sustainable Economic Development, Bologna, Italy
32: Also at Centro Siciliano di Fisica Nucleare e di Struttura Della Materia,
Catania, Italy
33: Also at Scuola Normale e Sezione dell’INFN, Pisa, Italy
34: Also at Riga Technical University, Riga, Latvia, Riga, Latvia
35: Also at Malaysian Nuclear Agency, MOSTI, Kajang, Malaysia
36: Also at Consejo Nacional de Ciencia y Tecnología, Mexico City, Mexico
37: Also at Warsaw University of Technology, Institute of Electronic Systems,
Warsaw, Poland
38: Also at Institute for Nuclear Research, Moscow, Russia
39: Now at National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
40: Also at St. Petersburg State Polytechnical University, St. Petersburg,
Russia
41: Also at University of Florida, Gainesville, USA
42: Also at Imperial College, London, United Kingdom
43: Also at P.N. Lebedev Physical Institute, Moscow, Russia
44: Also at California Institute of Technology, Pasadena, USA
45: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia
46: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia
47: Also at Università degli Studi di Siena, Siena, Italy
48: Also at INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy,
Pavia, Italy
49: Also at National and Kapodistrian University of Athens, Athens, Greece
50: Also at Universität Zürich, Zurich, Switzerland
51: Also at Stefan Meyer Institute for Subatomic Physics, Vienna, Austria,
Vienna, Austria
52: Also at Burdur Mehmet Akif Ersoy University, BURDUR, Turkey
53: Also at Adiyaman University, Adiyaman, Turkey
54: Also at Şırnak University, Sirnak, Turkey
55: Also at Department of Physics, Tsinghua University, Beijing, China,
Beijing, China
56: Also at Beykent University, Istanbul, Turkey, Istanbul, Turkey
57: Also at Istanbul Aydin University, Application and Research Center for
Advanced Studies (App. & Res. Cent. for Advanced Studies), Istanbul, Turkey
58: Also at Mersin University, Mersin, Turkey
59: Also at Piri Reis University, Istanbul, Turkey
60: Also at Gaziosmanpasa University, Tokat, Turkey
61: Also at Ozyegin University, Istanbul, Turkey
62: Also at Izmir Institute of Technology, Izmir, Turkey
63: Also at Marmara University, Istanbul, Turkey
64: Also at Kafkas University, Kars, Turkey
65: Also at Istanbul Bilgi University, Istanbul, Turkey
66: Also at Hacettepe University, Ankara, Turkey
67: Also at Vrije Universiteit Brussel, Brussel, Belgium
68: Also at School of Physics and Astronomy, University of Southampton,
Southampton, United Kingdom
69: Also at IPPP Durham University, Durham, United Kingdom
70: Also at Monash University, Faculty of Science, Clayton, Australia
71: Also at Bethel University, St. Paul, Minneapolis, USA, St. Paul, USA
72: Also at Karamanoğlu Mehmetbey University, Karaman, Turkey
73: Also at Bingol University, Bingol, Turkey
74: Also at Georgian Technical University, Tbilisi, Georgia
75: Also at Sinop University, Sinop, Turkey
76: Also at Mimar Sinan University, Istanbul, Istanbul, Turkey
77: Also at Texas A&M University at Qatar, Doha, Qatar
78: Also at Kyungpook National University, Daegu, Korea, Daegu, Korea