TOP-16-014

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$HeadURL:svn+ssh://svn.cern.ch/reps/tdr2/papers/TOP-16-014/trunk/TOP-16-014.tex$
$Id:TOP-16-014.tex4631302018-06-0409:19:50Zdoburns$

TOP-16-014

# Measurements of differential cross sections of top quark pair production as
a function of kinematic event variables in proton-proton collisions at
$\sqrt{s}=13\TeV$

###### Abstract

Measurements of differential production cross sections are presented in the
single-lepton decay channel, as a function of a number of kinematic event
variables. The measurements are performed with proton-proton collision data at
$\sqrt{s}=13\TeV$, collected by the CMS experiment at the LHC during 2016,
with an integrated luminosity of $35.9\fbinv$. The data are compared to a
variety of state-of-the-art leading-order and next-to-leading-order
simulations.

##  0.1 Introduction

In 2016 the CERN LHC collided protons at $\sqrt{s}=13\TeV$, resulting in a
data set recorded by the CMS experiment [1], with an integrated luminosity of
$35.9\fbinv$. Approximately 30 million top quark-antiquark pairs () are
present in this data set, which allows detailed studies of the production
properties of events to be performed.

Measurements of kinematic distributions in events are important for verifying
current theoretical models of production and decay. As production and top
quark decay can be a significant source of background events in many searches
for physics beyond the standard model, for example in searches for
supersymmetric models with top-quark-like signatures, it is important that
production be well understood and modeled. In addition to physics beyond the
standard model, a good understanding of production is necessary for
measurements of rare standard model processes, such as production in
association with a , , or Higgs boson.

In this paper, we present measurements of differential production cross
sections, as a function of kinematic event variables that do not require the
reconstruction of the system. Events are considered when the final state
includes exactly one isolated lepton ($\ell=\Pe$ or ) with large transverse
momentum and at least four jets, of which at least two are tagged as
originating from a bottom () quark. The kinematic event variables are the jet
multiplicity ($N_{\text{jets}}$), the scalar sum of the jet (), the scalar sum
of the of all particles ($S_{\text{T}}$), the transverse momentum imbalance
(), the magnitude of the of the leptonically decaying boson
($p_{\text{T}}^{\PW}$), and the magnitudes of the and pseudorapidity of the
lepton ($p_{\text{T}}^{\ell}$ and $\abs{\eta^{\ell}}$).

The measurements of the differential production cross sections are presented
at particle level, with respect to generated “stable” particles (with a mean
lifetime longer than $30\unit{ps}$), in a phase space that closely resembles
that accessible by the CMS detector (the visible phase space). This avoids the
influence of large theoretical uncertainties that would be introduced by
extrapolating the measurements to a larger phase space, or by presenting the
measurements at parton level.

Several measurements of the differential production cross sections as a
function of the properties of the system and of the jet activity in events
have been performed at the LHC, at 7 and 8 [2, 3, 4, 5, 6], and 13 [7, 8, 9,
10]. Measurements with respect to kinematic event variables in events have
been performed with the CMS detector at 7 and 8 [11].

##  0.2 The CMS detector

The central feature of the CMS apparatus is a superconducting solenoid of 6m
internal diameter, providing a magnetic field strength of 3.8T. Within the
solenoid volume are a silicon pixel and strip tracker, a lead tungstate
crystal electromagnetic calorimeter (ECAL), and a brass and scintillator
hadron calorimeter, each composed of a barrel and two endcap sections. Forward
calorimeters extend the $\eta$ coverage provided by the barrel and endcap
detectors. Muons are measured in gas-ionization detectors embedded in the
steel flux-return yoke outside the solenoid. 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. [1].

Events of interest are selected using a two-tiered trigger system [12]. 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 (HLT), consists of a farm of processors running a version of the
full event reconstruction software optimized for fast processing, and reduces
the event rate to around 1kHz before data storage.

##  0.3 Signal sample and background simulation

Two independent samples are simulated with the (v2) generator [13, 14, 15,
16], which utilizes next-to-leading-order (NLO) matrix-element calculations.
One sample uses (v8.212) [17, 18] with the CUETP8M2T4 tune [19] for the
simulation of the parton shower and hadronization. The second has parton
showering and hadronization performed by (2.7.1) [20] using the tune EE5C
[21].

Two additional independent simulated samples are produced with the mg5_amc@nlo
(v2.2.2) generator [22]. In the first, mg5_amc@nlo is used to generate events
at leading-order (LO) accuracy with up to three additional partons, and is
employed with the CUETP8M1 tune [23] for parton showering and hadronization.
The MLM jet-parton matching algorithm [24] is used in this sample, referred to
as mg5_amc@nlo-lo. In the second, mg5_amc@nlo simulates events to NLO accuracy
with up to two additional partons, where parton showering and hadronization
are performed using with the CUETP8M2T4 tune. The FxFx jet-parton matching
algorithm [25] is used, and this sample is referred to as mg5_amc@nlo-nlo. It
is important to compare multiple generators in order to find the current most
suitable description of top quark production and decay, and to identify any
discrepancies in the models.

In all simulated samples, the top quark mass is set to 172.5. The
NNPDF30_nlo_as_0118 parton distribution function (PDF) set is used for the NLO
samples while the NNPDF30_lo_as_0130 set is used for the LO samples [26]. When
comparing with reconstructed data, a cross section of
$832^{+20}_{-29}\,(\text{scale})\pm{35}\,(\mathrm{PDF}+\alpS)\unit{pb}$ is
used to normalize the samples, where is the strong coupling constant. This
cross section is calculated to next-to-next-to-leading-order (NNLO) accuracy
in quantum chromodynamics (QCD) including resummation of next-to-next-to-
leading logarithmic soft-gluon terms with Top++ (v2.0) [27, 28, 29, 30, 31,
32, 33]. The scale uncertainty in this cross section comes from the
independent variation of the factorization and renormalization scales.

The dominant background processes to production, the production of single top
quarks and the production of vector bosons in association with jets, are also
simulated. Single top quark processes are generated with interfaced with , and
are normalized to cross sections that are calculated to NLO precision [34,
35]. Separate samples are generated for $t$\- and $s$-channel production [36,
37]. The sample of single top quarks in association with a boson is produced
with (v1) [38]. In this sample, the diagram removal scheme [39] is used to
avoid double counting of Feynman diagrams in the production of single top
quarks in association with a boson at NLO and top quark pair production.
Samples of and boson production with leptonic final states, in association
with jets (V+jets), are generated with mg5_amc@nlo-lo. Separate samples are
generated with exactly one, two, three, and four additional jets to ensure a
large sample of events that are likely to mimic the signature of production.
These samples are normalized to their NNLO cross sections [40].

In addition, QCD multijet events are generated with for matrix-element
calculations, parton shower simulation, and hadronization. To obtain a large
sample of QCD multijet events that are likely to mimic the signature of
production in the single-lepton decay channel, only events with large
electromagnetic activity or containing a muon are generated. These samples are
normalized to their LO cross sections and are used to create transfer factors
from a control region to the signal region for a QCD background estimate based
on data in the control region. The CMS detector response for all simulated
samples is modeled using [41].

##  0.4 Event reconstruction and selection

Parallel selection paths are defined to target events that decay to final
states containing an electron (+jets) or a muon (+jets). The HLT in the +jets
channel requires at least one isolated electron candidate with $\pt>32\GeV$
and $\abs{\eta}<2.1$. The corresponding requirements in the +jets channel are
at least one isolated muon candidate with $\pt>24\ \GeV$ and $\abs{\eta}<2.4$.

Offline reconstruction and selection uses the particle-flow (PF) algorithm
[42] to reconstruct and identify each individual particle with an optimized
combination of information from the subdetectors of CMS. In the +jets channel,
electron candidates are required to satisfy $\pt>34\GeV$ and $\abs{\eta}<2.1$.
Electron candidates whose energy deposition in the ECAL is in the transition
region between the barrel and endcap regions of the ECAL are not considered
due to less efficient electron reconstruction. Electron candidates must also
satisfy several identification criteria [43] to suppress the rate of jets and
converted photons that are identified incorrectly as electron candidates. In
addition, electron candidates must be isolated. To calculate the isolation, a
cone of size $\Delta R=\sqrt{\smash[b]{(\Delta\eta)^{2}+(\Delta\phi)^{2}}}$
$=0.3$ is constructed around the electron direction, where $\phi$ is the
azimuthal angle. The sum of the of all PF candidates within this cone is
calculated, excluding the lepton candidate and is corrected for the effects of
additional proton-proton collisions within the same or nearby bunch crossings.
The relative isolation variable $I_{\text{rel}}$ is defined as the ratio of
this sum to the electron , and is required to be less than 6%.

In the +jets channel, muon candidates are required to satisfy $\pt>26\GeV$ and
$\abs{\eta}<2.4$. Similarly to the electron candidates, muon candidates must
satisfy additional identification criteria [44]. Muon candidates must be
isolated, satisfying $I_{\text{rel}}<15\%$ where $I_{\text{rel}}$ is defined
as for electrons, but with a cone of size $\DR=0.4$.

For both electron and muon candidates, the lepton must be associated with the
primary interaction vertex of the event. The primary interaction vertex is
defined as the reconstructed vertex associated with the largest sum of
$\pt^{2}$ from physics objects that have been defined using information from
the tracking detector, including jets, the associated missing transverse
momentum, which was taken as the negative vector sum of the of those jets, and
charged leptons.

The trigger, reconstruction and identification efficiencies for both electrons
and muons are measured in data, and corrected in simulation to match those
seen in data. The efficiencies are calculated using the tag-and-probe method
[45] from events containing a boson. The total lepton correction factors are
between 0.95 and 1.

Jets are clustered from PF candidates with the anti-algorithm [46] implemented
in the FastJet package [47], with a distance parameter of 0.4. The jet
momentum is determined as the vector sum of the of all PF candidates in the
jet. A correction is applied to jet energies to take into account the
contribution from additional proton-proton interactions using the charged
hadron subtraction method [48]. The measured energy of each jet is corrected
for known variations in the jet energy response as a function of the measured
jet $\eta$ and $\pt$. The jet energy resolution (JER) is corrected in
simulation to match that seen in data. Jets are required to satisfy
$\pt>30\GeV$ and $\abs{\eta}<2.4$. Jets closer than $\Delta R=0.4$ to
identified isolated leptons are removed, as they are likely to have originated
from the lepton itself.

The combined secondary vertex algorithm [49, 50] is used to identify jets
originating from a quark. The threshold of the algorithm is chosen such that
the identification efficiency (in simulation) of genuine quark jets is
$\approx$70%, and the probability to mistag a light quark or gluon jet is
$\approx$1%. The identification efficiency of quark jets in simulation is
corrected to match that seen in data.

The distribution of the number of additional proton-proton interactions in
simulation is corrected to match data. Events must contain exactly one high-,
isolated electron or muon. Events are vetoed if they contain an additional
isolated lepton candidate with $\pt>15\GeV$ and $\abs{\eta}<2.4$. Events must
also contain at least four jets, at least two of which are required to be
identified as originating from a quark.

##  0.5 Cross section measurement

As stated in Section 0.1, the differential production cross sections are
measured as a function of the kinematic event variables: $N_{\text{jets}}$, ,
$S_{\text{T}}$, , $p_{\text{T}}^{\PW}$, $p_{\text{T}}^{\ell}$ and
$\abs{\eta^{\ell}}$. The $N_{\text{jets}}$ variable is the total number of
jets in the event with $\pt>30\GeV$ and $\abs{\eta}<2.4$. The variable is the
scalar sum of the of these jets. The quantity is defined as the magnitude of ,
the transverse projection of the negative vector sum of the momenta of all
reconstructed PF candidates in an event. The $p_{\text{T}}^{\ell}$ and
$\abs{\eta^{\ell}}$ variables are magnitudes of the transverse momentum and
the pseudorapidity of the lepton in the event, respectively. The variable
$S_{\text{T}}$ is the sum of , , and $p_{\text{T}}^{\ell}$. The variable
$p_{\text{T}}^{\PW}$ is the magnitude of the transverse momentum of the
leptonically decaying boson, which is constructed from
${\vec{p}}_{\mathrm{T}}^{\ell}$ and .

The distributions of these variables measured in data are shown in Figs. 1 and
2, and are compared to the sum of signal and background events from
simulation. A total of 662 381 events are measured in data, of which 92.1% are
predicted from the + simulation to be events. Single top quark production and
V+jets production contribute 4.4% and 2.1% to the total number of events,
respectively, as estimated from simulation. The component of multijet QCD
events is estimated from control regions in the data, and comprises
approximately 1.4% of the total number of events. The control regions are
designed to obtain data samples that are enriched in QCD multijet events that
are kinematically similar to the signal region, but with little contamination
from , single top quark, and V+jets events. In the +jets channel, the control
region is obtained by inverting the isolation criterion on electron
candidates. In the +jets channel, the control region is obtained by requiring
muon candidates to satisfy $0.15<I_{\text{rel}}<0.30$. In the control regions
for both channels, the number of -tagged jets is also required to be exactly
zero. The contribution of , single top quark and V+jets events to the control
regions ($\approx$15–20%) is estimated from simulation with all corrections
and subtracted from the data. The ratio of the number of multijet QCD events
in the control region to that in the signal region (the transfer factor), both
predicted from simulation, is then used to scale the normalization of the data
control region to obtain the multijet QCD estimate in the signal region. Other
sources of background are negligible, and are not considered in this
measurement. The level of agreement between the total event count of data and
simulation, within 0.2% , indicates that the total cross section is compatible
to that stated in Section 0.3.

Previous measurements [2, 3, 4, 5, 6, 7, 8] report that the top quark spectrum
in data is softer than that predicted by NLO simulation. This effect can be
seen in some of the distributions in Figs. 1 and 2, where distributions
correlated with the top quark are also softer in data than those predicted by
the simulation.

Figure 1: The distributions of $N_{\text{jets}}$, and $S_{\text{T}}$ after
full event selection. The simulation is normalized to the NNLO prediction. The
ratio of the number of events in data to that in simulation is shown below
each of the distributions, with the statistical uncertainty in the data shown
by the vertical uncertainty bars. The statistical uncertainty in the number of
simulation events and the uncertainties in the modeling in simulation are
shown by the hatched band.

Figure 2: The distributions of , $p_{\text{T}}^{\PW}$, $p_{\text{T}}^{\ell}$
and $\abs{\eta^{\ell}}$ after full event selection. The simulation is
normalized to the NNLO prediction. The ratio of the number of events in data
to that in simulation is shown below each of the distributions, with the
statistical uncertainty in the data shown by the vertical uncertainty bars.
The statistical uncertainty in the number of simulation events and the
uncertainties in modeling in simulation are shown by the hatched band.

###  0.5.1 Particle level and visible phase space definitions

The results are presented at particle level, with respect to the stable
particles produced in simulation by the event generator, before detector
interactions are modeled. The generator-level definitions for the particles
and visible phase space are based on the RIVET framework [51], following the
prescriptions adopted in Ref. [52]. Generated electrons and muons not
originating from a hadron or a quark are used to define electrons and muons at
particle level. Photons that are near the lepton are assumed to have radiated
from it, and are clustered together with the anti-algorithm with a distance
parameter of 0.1.

Particle-level jets are constructed by clustering all stable particles,
excluding the lepton, with the anti-algorithm using a distance parameter of
0.4. To determine if a particle-level jet originated from a quark, hadrons are
included in the clustering of jets, but with the magnitude of the four-
momentum of the hadron scaled to a negligible value. The b hadrons can then be
clustered into jets without affecting the kinematic properties of the jet. A
jet with a hadron among its constituents is considered to have originated from
a quark. The particle-level is calculated from all stable visible particles.

The differential production cross sections are measured in a visible phase
space, which is chosen to be the same for both +jets and +jets channels, and
to closely resemble the criteria used to select events in data. Particle-level
objects are used to define the common visible phase space of events for both
+jets and +jets channels, all within $\abs{\eta}<2.4$, which requires exactly
one electron or muon with $\pt>26\GeV$, and no additional electrons or muons
with $\pt>15\GeV$. The event must also contain at least three particle-level
jets with $\pt>30\GeV$, and one jet with $\pt>20\GeV$. Two of these particle-
level jets must also be tagged as originating from a quark. The ,
$S_{\text{T}}$, and $N_{\text{jets}}$ variables are calculated at the particle
level with respect to all particle-level jets with $\pt>20\GeV$ and
$\abs{\eta}<2.4$. This choice of particle-level phase space is made to obtain
the largest possible data sample, and the uncertainty in the resulting
extrapolation makes only a small contribution to the uncertainty in the final
results.

The yield of events for each bin in data is obtained by subtracting the
contribution of each background process. The contribution of events that
satisfy the selection criteria, but do not enter the visible phase space at
particle level, is estimated from simulation and also subtracted from the
data. This amounts to approximately 7% of all events and are predominately
those in which one of the jets fails the particle-level jet selection, but
passes the reconstructed jet selection because of the resolution of the
detector. No selection is applied on the decay channel of the top quarks, so
the phase space does not exclusively contain semileptonic (electron or muon)
events. In particular, there are contributions from events where one top quark
decays to a tau lepton and subsequently to an electron or muon, or where both
top quarks decay leptonically but one lepton is not within the acceptance.

###  0.5.2 Unfolding and cross section calculation

For each kinematic event variable the yield of events in each bin is unfolded
to correct for the detector acceptance, efficiency, and bin-to-bin migrations
stemming from the detector resolution to obtain the yield of events in the
visible phase space at the particle level. The bin widths are chosen to give a
low level of bin-to-bin migration, and are always greater than the detector
resolution.

A response matrix, constructed using the + sample, relates the kinematic event
distributions at reconstruction level to those at particle level. The response
matrix also includes efficiency and acceptance corrections. Unfolding is
performed by inverting the response matrix, based on a least-squares fit with
Tikhonov regularization, implemented in the TUnfold software framework [53].
Regularization dampens nonphysical fluctuations in the unfolded yields, and
the regularization parameter is chosen by minimizing the average global
statistical correlation between the bins of each variable. The typical
regularization parameters are found to be of order $10^{-4}-10^{-3}$, and
significantly lower for the $\abs{\eta^{\ell}}$ variable.

The yields of events are unfolded separately in the +jets and +jets channels
and then combined after unfolding, giving the total number of events at
particle level in the visible phase space, $N_{\ttbar}$. The normalized
differential cross section with respect to each variable, $X$, can then be
calculated using

$\frac{1}{\sigma_{\ttbar}^{\text{vis}}}\frac{\rd\sigma_{\ttbar}^{i}}{\rd
X}=\frac{1}{\sum_{j}N_{\ttbar}^{j}}\frac{N_{\ttbar}^{i}}{\Delta X^{i}},$ (1)

where $\sigma_{\ttbar}^{\text{vis}}$ is the total production cross section in
the visible phase space, $\sigma_{\ttbar}^{i}$ is the production cross section
in bin $i$, $N_{\ttbar}^{i(j)}$ is the number of events in bin $i(j)$ after
unfolding, and $\Delta X^{i}$ is the width of bin $i$. The absolute
differential cross section can be calculated as

$\frac{\rd\sigma_{\ttbar}^{i}}{\rd X}=\frac{N_{\ttbar}^{i}}{\Lumi\,\Delta
X^{i}},$ (2)

where $\Lumi$ is the integrated luminosity of the data.

##  0.6 Systematic uncertainties

Sources of systematic uncertainties are evaluated and propagated to the final
result by recalculating the response matrix with a modified simulation and/or
by modifying the background predictions.

The uncertainty in the integrated luminosity of the data is estimated to be
$\pm$2.5% [54]. The uncertainty in the number of additional inelastic
interactions in the same or nearby bunch crossings is estimated by varying the
total proton-proton inelastic cross section by $\pm$4.6% [55]. This cross
section is used in determining the distribution of additional inelastic
interactions in data, which is used to correct the simulation.

The uncertainty in the efficiency of the quark jet identification and
mistagging rate in the simulation is taken as the uncertainty in the ,
$\abs{\eta}$, and flavor-dependent correction factors [50]. The uncertainties
in the lepton trigger, reconstruction, and identification correction factors
are similarly propagated to the final results.

The uncertainties in the jet energy scale (JES) and JER are estimated as
functions of jet and $\abs{\eta}$ [48]. The uncertainty in the JES is also
propagated into the calculation of . Additional uncertainties in the of
electrons, muons, tau leptons and other unclustered PF candidates, that are
used in the calculation of , are considered and found to be negligible.

The uncertainties in the normalization of the single top quark and V+jets
background sources are based on measurements performed in [56, 57, 58] and
take into account an extrapolation to the current analysis phase space. They
are estimated to be $\pm$30% and $\pm$50% respectively and typically result in
a normalization uncertainty that is negligible. The uncertainty in the
normalization and shape of the multijet QCD background is estimated by using
alternative control regions containing conversion electrons in the +jets
channel and muons with $I_{\text{rel}}>0.3$ in the +jets channel. This
effectively varies the total normalization of the multijet QCD background by
up to 60%, and also the shape of the contribution by up to $\pm$30% in any one
bin, but is found to result in a negligible uncertainty after unfolding,
except at large $\abs{\eta^{\ell}}$.

Uncertainties in the top quark mass are estimated by using simulated samples
where the top quark mass has been varied up and down by 1, which is comparable
to the uncertainty in the measured top quark mass [59].

The uncertainty from the PDF used in the simulation is estimated by
considering 100 independent replicas of NNPDF30_nlo_as_0118. The RMS of the
uncertainties originating from the variation of each replica is taken as the
PDF uncertainty. The uncertainty resulting from using the NNPDF30_nlo_as_0118
set derived with varied values of is combined in quadrature with the PDF
uncertainty.

The uncertainty arising from the mismodeling of the top quark spectrum is
estimated by reweighting the distribution in simulation to match that measured
by the previous measurements [7, 8]. The reweighting varies the yield of
simulated events in the bins of the measurement by up to 20%, and results in a
negligible uncertainty in the measured cross section.

Several sources of uncertainty for the modeling of the parton shower in the
simulated + sample are considered.

The uncertainty originating from the parton shower scale used when simulating
the initial-state radiation is estimated by varying the scale up and down by a
factor of two. Similarly the uncertainty originating from the scale for final-
state radiation, which is constrained by measurements made at the LEP collider
[60], is estimated by varying the scale up and down by a factor of $\sqrt{2}$.
The renormalization and factorization scales used in the matrix-element
calculations are also varied independently by factors of 0.5 and 2. An
additional variation is performed where both scales are varied simultaneously
by the same factors. The shower scale uncertainty is defined as the envelope
of the parton shower scale uncertainties and the matrix-element scale
uncertainties.

The systematic uncertainty in matching the matrix-element to the parton shower
is determined by varying the parameter $h_{\text{damp}}$, which regulates the
high-radiation by damping real emission generated in , within its
uncertainties. The parameter is set to $h_{\text{damp}}=1.58^{+0.66}_{-0.59}$
multiplied by the mass of the top quark in the CUETP8M2T4 tune [19]. The
parameters controlling the underlying event in the CUETP8M2T4 tune are also
varied to estimate the uncertainty in this source [19].

The uncertainty in the modeling of the momentum transfer from quarks to
hadrons is estimated by reweighting the tuned quantity
$x_{\mathrm{b}}=\pt(\PB)/\pt(\text{\cPqb jet})$ for each particle-level
-tagged jet within its uncertainties, where () is the transverse momentum of
the hadron, and ( jet) is the transverse momentum of the particle-level
-tagged jet. The difference when using an alternative model (the Peterson
model [61]) for the fragmentation of quarks is also included as an additional
uncertainty. The energy response of jets is sensitive to the single-lepton
branching fractions of hadrons, and the uncertainty originating from the
choice of branching fractions in the + simulation is estimated by reweighting
the branching fractions to those reported in Ref. pdg2016.

The effects of any mismodeling of the color reconnection in the simulation are
estimated by comparing the cross sections obtained with samples including and
excluding the effects of color reconnection on the decay products of the top
quarks (Early resonance decays). A comparison to two samples obtained with
alternative models of color reconnection are also included, one where QCD
color rules are considered in the simulation of the color reconnection (QCD-
based) [62], and another where gluons can be moved to different color strings
during the simulation of the color reconnection (Gluon move) [63].

The statistical uncertainty arising from the finite size of the + sample,
which is used to construct the nominal response matrix, is propagated to the
final measurement. This uncertainty is negligible.

Each source of systematic uncertainty is summarised for each variable in Table
0.6, where the minimum and maximum relative uncertainty in the normalized
differential cross section (over all bins) are shown. The minimum and maximum
of the total relative uncertainty over all bins are also shown. Sources of
uncertainties in the calculation of do not affect some distributions, and are
indicated in the table by —. The dominant uncertainty in the measurement of
the normalized cross sections comes from the uncertainty in the JES. Other
significant uncertainties come from the theoretical modeling of production in
simulation, in particular from the uncertainty in the shower scale for final-
state radiation. A similar table for the absolute differential cross section
uncertainties is shown in Appendix .11. The uncertainty in the JES is also
significant in the measurements of the absolute cross sections, however the
uncertainty in the final-state radiation scale becomes dominant. The total
uncertainty from all sources in the normalized cross section is typically
below 5% in each bin, and can be as large as 21%. For the measurements of the
absolute cross section, the total uncertainty is typically 10%, and can be as
large as 22%.

The upper and lower bounds, in %, from each source of systematic uncertainty
in the normalised differential cross section, over all bins of the measurement
for each variable. The bounds of the total relative uncertainty are also
shown.  Relative uncertainty source $(\%)$ $N_{\text{jets}}$ $S_{\text{T}}$
$p_{\text{T}}^{\PW}$ $p_{\text{T}}^{\ell}$ $\abs{\eta^{\ell}}$ quark tagging
efficiency 0.1 – 0.8 0.2 – 1.1 0.2 – 1.5 0.1 – 1.2 0.1 – 1.7 0.1 – 1.9 0.1 –
0.5 Electron efficiency 0.1 – 0.2 0.1 – 0.7 0.1 – 0.9 0.1 – 0.7 0.1 – 1.4 0.3
– 2.1 0.1 – 0.8 Muon efficiency 0.1 – 0.3 0.1 – 0.2 0.1 – 0.3 0.1 – 0.2 0.1 –
0.6 0.1 – 1.0 0.1 – 0.1 JER 0.1 – 0.6 0.1 – 0.7 0.2 – 1.8 0.6 – 5.8 0.2 – 2.1
0.1 – 0.2 $<$0.1 JES 0.1 – 5.5 2.1 – 13.6 2.1 – 15.9 2.1 – 7.1 0.5 – 4.9 0.1 –
2.0 0.1 – 0.2 Electron transverse momentum in  — — 0.1 – 0.3 0.1 – 0.9 0.1 –
0.6 — — Muon transverse momentum in  — — 0.1 – 0.9 0.1 – 3.5 0.1 – 0.9 — — Tau
transverse momentum in  — — 0.1 – 1.4 0.1 – 1.2 0.1 – 1.4 — — Unclustered
transverse momentum in  — — 0.1 – 1.7 0.2 – 1.9 0.1 – 1.0 — — QCD bkg cross
section 0.1 – 0.5 0.1 – 1.0 0.1 – 1.7 0.2 – 0.6 0.1 – 0.8 0.1 – 4.5 0.2 – 2.9
QCD bkg shape 0.1 – 0.1 0.1 – 0.7 0.1 – 1.0 0.1 – 0.1 0.1 – 1.5 0.1 – 4.7 0.1
– 1.5 Single top quark cross section 0.1 – 0.4 0.1 – 2.1 0.1 – 4.4 0.1 – 4.9
0.1 – 7.1 0.1 – 6.0 $<$0.1 V+jets cross section 0.1 – 0.3 0.1 – 2.5 0.1 – 3.7
0.1 – 2.0 0.1 – 3.6 0.1 – 5.4 0.1 – 1.5 PDF 0.1 – 0.3 0.1 – 0.3 0.1 – 0.6 0.1
– 0.3 0.1 – 0.4 0.1 – 0.4 $<$0.1 Color reconnection (Gluon move) 0.1 – 2.8 0.1
– 4.0 0.1 – 11.7 0.2 – 0.9 0.1 – 1.0 0.2 – 4.8 0.1 – 0.4 Color reconnection
(QCD-based) 0.1 – 2.0 0.1 – 4.2 0.1 – 6.6 0.4 – 4.2 0.1 – 3.5 0.1 – 7.6 0.1 –
1.2 Color reconnection (Early resonance decays) 0.2 – 3.9 0.1 – 7.1 0.1 – 4.1
0.1 – 1.6 0.1 – 3.8 0.1 – 5.0 0.1 – 1.0 Fragmentation 0.1 – 0.4 0.1 – 0.5 0.1
– 0.5 0.1 – 0.6 0.1 – 0.6 0.1 – 0.4 $<$0.1 $h_{\text{damp}}$ 0.3 – 3.8 0.1 –
3.1 0.2 – 2.9 0.1 – 2.3 0.1 – 2.7 0.1 – 2.8 0.2 – 1.2 Top quark mass 0.2 – 1.0
0.1 – 3.1 0.2 – 3.5 0.1 – 4.0 0.2 – 1.1 0.2 – 4.5 0.1 – 0.6 Peterson
fragmentation model 0.1 – 1.3 0.1 – 0.6 0.1 – 0.9 0.1 – 1.1 0.1 – 1.0 0.1 –
1.3 $<$0.1 Shower scales 0.4 – 4.3 0.5 – 4.5 0.5 – 4.9 0.2 – 2.4 0.3 – 3.5 0.1
– 4.5 0.1 – 0.7 hadron decay semileptonic branching fraction 0.1 – 0.1 0.1 –
0.1 0.1 – 0.1 $<$0.1 $<$0.1 $<$0.1 $<$0.1 Top quark $\pt$ 0.1 – 0.7 0.1 – 0.9
0.1 – 1.0 0.1 – 0.8 0.1 – 0.9 0.1 – 1.3 $<$0.1 Underlying event tune 0.1 – 2.7
0.1 – 5.5 0.2 – 4.4 0.1 – 5.4 0.2 – 2.6 0.2 – 6.1 0.1 – 0.9 Simulated sample
size 0.1 – 1.6 0.1 – 1.6 0.1 – 1.9 0.1 – 2.2 0.1 – 1.4 0.1 – 1.7 0.1 – 0.4
Additional interactions 0.1 – 0.4 0.1 – 1.0 0.1 – 1.7 0.1 – 1.5 0.1 – 0.9 0.1
– 1.0 $<$0.1 Integrated luminosity $<$0.1 $<$0.1 $<$0.1 $<$0.1 $<$0.1 $<$0.1
$<$0.1 Total 0.6 – 9.6 2.7 – 14.1 2.8 – 17.4 2.9 – 11.7 0.8 – 12.6 0.7 – 13.4
0.7 – 4.4

##  0.7 Cross section results

The normalized differential production cross section with respect to
$N_{\text{jets}}$ is shown in Fig. 3, with respect to and $S_{\text{T}}$ in
Fig. 4, with respect to and $p_{\text{T}}^{\PW}$ in Fig. 5 and with respect to
$p_{\text{T}}^{\ell}$ and $\abs{\eta^{\ell}}$ in Fig. 6. Tabulated results are
listed in Appendix .9. Measurements of the absolute differential production
cross sections are shown in Figs. 7, 8, 9 and 10, and tabulated in Appendix
.10. In each figure, the measured cross section is compared with the
predictions from several combinations of matrix-element and parton shower
generators, namely +, +, mg5_amc@nlo-nlo, and mg5_amc@nlo-lo. Each measured
cross section is also compared to the + generator after varying the shower
scales and the $h_{\text{damp}}$ parameter used in generating the sample
within their uncertainties, and also after reweighting the top quark as
described in Section 0.6.

The level of agreement between the measured and predicted differential cross
sections are determined through a $\chi^{2}$ test, where the full covariance
matrix, including the correlations between the statistical and systematic
uncertainties in each bin of the measurements, is taken into account. The
results, including the p-value of each test, are shown in Tables 0.7 and 0.7.

The predictions of the + model are consistent with data for the
$N_{\text{jets}}$, , $S_{\text{T}}$, and $p_{\text{T}}^{\ell}$ distributions.
In particular, the prediction of the $N_{\text{jets}}$ distribution has a
$\chi^{2}$ per degree of freedom of 2/5 for the normalized and 2.2/6 for the
absolute cross section measurement. The jet multiplicity from previous
8measurements was used in deriving the CUETP8M2T4 tune [19], and this confirms
that the tune continues to accurately describe the jet multiplicity on a
larger data set with a higher $\sqrt{s}$. On the other hand, tensions are
observed for the , $p_{\text{T}}^{\PW}$ and $\abs{\eta^{\ell}}$ variables. An
additional $\chi^{2}$ calculation between the + model and unfolded data is
performed, where the theoretical uncertainties within the generator, described
in Section 0.6, are included, as well as in the unfolded data. The
correlations between the uncertainties in the prediction of the generator and
the unfolded data are taken into account. The result of this test demonstrates
that the theoretical uncertainties in the + model cover the differences
between the + model and the unfolded data in the phase space analyzed.

The + and mg5_amc@nlo-nlo models are broadly consistent with the unfolded
data, even without including the theoretical uncertainties in the $\chi^{2}$
test, with the exception of $N_{\text{jets}}$ in + and $\abs{\eta^{\ell}}$ in
mg5_amc@nlo-nlo. Without these uncertainties, the mg5_amc@nlo-lo model is not
compatible with any kinematic event distribution in the unfolded data
presented here.

The effect of the regularization in the unfolding procedure is investigated by
unfolding without regularization, which typically results in a small change in
the $\chi^{2}$. When unfolding without regularization, the largest changes in
$\chi^{2}$ for the normalized cross sections are for the distribution with the
mg5_amc@nlo-nlo model, where the $\chi^{2}$ per degree of freedom increases
from 11/12 to 12/12, and for the distribution in the + model (including the
model theoretical uncertainties), where the $\chi^{2}$ per degree of freedom
decreases from 2.9/5 to 2.1/5. The effects on the $\chi^{2}$ for all other
variables and models are small. The $\chi^{2}$ does not change for the
$p_{\text{T}}^{\ell}$ and $\abs{\eta^{\ell}}$ distributions for any model when
unfolding without regularization.

Results of a goodness-of-fit test between the normalized cross sections in
data and several models, with values given as $\chi^{2}$/number of degrees of
freedom (ndf)  + With MC theoretical uncertainties $\chi^{2}$/ndf p-value
$\chi^{2}$/ndf p-value $N_{\text{jets}}$ 2 / 5 0.85 1.5 / 5 0.91 26 / 12 $<$
0.01 4.8 / 12 0.97 $S_{\text{T}}$ 22 / 12 0.04 4.2 / 12 0.98 11 / 5 0.06 2.9 /
5 0.72 $p_{\text{T}}^{\PW}$ 16 / 6 0.01 2.5 / 6 0.87 $p_{\text{T}}^{\ell}$ 24
/ 16 0.09 14 / 16 0.63 $\abs{\eta^{\ell}}$ 19 / 7 $<$ 0.01 15 / 7 0.04
+ mg5_amc@nlo-nlo+ mg5_amc@nlo-lo+ $\chi^{2}$/ndf p-value $\chi^{2}$/ndf p-value $\chi^{2}$/ndf p-value $N_{\text{jets}}$ 38 / 5 $<$ 0.01 9.5 / 5 0.09 78 / 5 $<$ 0.01 23 / 12 0.03 11 / 12 0.52 160 / 12 $<$ 0.01 $S_{\text{T}}$ 21 / 12 0.04 11 / 12 0.57 110 / 12 $<$ 0.01 1.3 / 5 0.93 5.9 / 5 0.31 23 / 5 $<$ 0.01 $p_{\text{T}}^{\PW}$ 0.81 / 6 0.99 8.9 / 6 0.18 30 / 6 $<$ 0.01 $p_{\text{T}}^{\ell}$ 11 / 16 0.82 16 / 16 0.44 37 / 16 $<$ 0.01 $\abs{\eta^{\ell}}$ 19 / 7 $<$ 0.01 24 / 7 $<$ 0.01 30 / 7 $<$ 0.01

Results of a goodness-of-fit test between the absolute cross sections in data
and several models, with values given as $\chi^{2}$/number of degrees of
freedom (ndf)  + With MC theoretical uncertainties $\chi^{2}$/ndf p-value
$\chi^{2}$/ndf p-value $N_{\text{jets}}$ 2.2 / 6 0.90 1.7 / 6 0.95 23 / 13
0.05 4.3 / 13 0.99 $S_{\text{T}}$ 19 / 13 0.11 4.7 / 13 0.98 13 / 6 0.05 3.1 /
6 0.80 $p_{\text{T}}^{\PW}$ 17 / 7 0.02 2.7 / 7 0.91 $p_{\text{T}}^{\ell}$ 20
/ 17 0.28 14 / 17 0.68 $\abs{\eta^{\ell}}$ 16 / 8 0.04 15 / 8 0.06
+ mg5_amc@nlo-nlo+ mg5_amc@nlo-lo+ $\chi^{2}$/ndf p-value $\chi^{2}$/ndf p-value $\chi^{2}$/ndf p-value $N_{\text{jets}}$ 39 / 6 $<$ 0.01 12 / 6 0.07 93 / 6 $<$ 0.01 21 / 13 0.07 10 / 13 0.66 150 / 13 $<$ 0.01 $S_{\text{T}}$ 18 / 13 0.17 9.3 / 13 0.75 110 / 13 $<$ 0.01 1.5 / 6 0.96 6.6 / 6 0.36 26 / 6 $<$ 0.01 $p_{\text{T}}^{\PW}$ 0.90 / 7 1.00 9.2 / 7 0.24 33 / 7 $<$ 0.01 $p_{\text{T}}^{\ell}$ 11 / 17 0.87 15 / 17 0.58 36 / 17 $<$ 0.01 $\abs{\eta^{\ell}}$ 17 / 8 0.04 23 / 8 $<$ 0.01 31 / 8 $<$ 0.01

Figure 3: Normalized $N_{\text{jets}}$ differential cross section, compared to
different simulations in the left plot, and compared to the + simulation after
varying the shower scales, and $h_{\text{damp}}$ parameter, within their
uncertainties, in the right plot. The vertical bars on the data represent the
statistical and systematic uncertainties added in quadrature. The bottom
panels show the ratio of the predictions to the data.

Figure 4: Normalized (upper) and $S_{\text{T}}$ (lower) differential cross
sections, compared to different simulations in the left plots, and compared to
the + simulation after varying the shower scales, and $h_{\text{damp}}$
parameter, within their uncertainties, in the right plots. The vertical bars
on the data represent the statistical and systematic uncertainties added in
quadrature. The bottom panels show the ratio of the predictions to the data.

Figure 5: Normalized (upper) and $p_{\text{T}}^{\PW}$ (lower) differential
cross sections, compared to different simulations in the left plots, and
compared to the + simulation after varying the shower scales, and
$h_{\text{damp}}$ parameter, within their uncertainties, in the right plots.
The vertical bars on the data represent the statistical and systematic
uncertainties added in quadrature. The bottom panels show the ratio of the
predictions to the data.

Figure 6: Normalized $p_{\text{T}}^{\ell}$ (upper) and $\abs{\eta^{\ell}}$
(lower) differential cross sections, compared to different simulations in the
left plots, and compared to the + simulation after varying the shower scales,
and $h_{\text{damp}}$ parameter, within their uncertainties, in the right
plots. The vertical bars on the data represent the statistical and systematic
uncertainties added in quadrature. The bottom panels show the ratio of the
predictions to the data.

Figure 7: Absolute $N_{\text{jets}}$ differential cross section, compared to
different simulations in the left plot, and compared to the + simulation after
varying the shower scales, and $h_{\text{damp}}$ parameter, within their
uncertainties, in the right plot. The vertical bars on the data represent the
statistical and systematic uncertainties added in quadrature. The bottom
panels show the ratio of the predictions to the data.

Figure 8: Absolute (upper) and $S_{\text{T}}$ (lower) differential cross
sections, compared to different simulations in the left plots, and compared to
the + simulation after varying the shower scales, and $h_{\text{damp}}$
parameter, within their uncertainties, in the right plots. The vertical bars
on the data represent the statistical and systematic uncertainties added in
quadrature. The bottom panels show the ratio of the predictions to the data.

Figure 9: Absolute (upper) and $p_{\text{T}}^{\PW}$ (lower) differential cross
sections, compared to different simulations in the left plots, and compared to
the + simulation after varying the shower scales, and $h_{\text{damp}}$
parameter, within their uncertainties, in the right plots. The vertical bars
on the data represent the statistical and systematic uncertainties added in
quadrature. The bottom panels show the ratio of the predictions to the data.

Figure 10: Absolute $p_{\text{T}}^{\ell}$ (upper) and $\abs{\eta^{\ell}}$
(lower) differential cross sections, compared to different simulations in the
left plots, and compared to the + simulation after varying the shower scales,
and $h_{\text{damp}}$ parameter, within their uncertainties, in the right
plots. The vertical bars on the data represent the statistical and systematic
uncertainties added in quadrature. The bottom panels show the ratio of the
predictions to the data.

##  0.8 Summary

Normalized and absolute differential production cross sections with respect to
several kinematic event variables are measured at the particle level in a
visible phase space region. The results are based on proton-proton collision
data at $\sqrt{s}=13\TeV$, collected by the CMS experiment with an integrated
luminosity of $35.9\fbinv$. The total cross section is observed to be
consistent with previous results and next-to-next-to-leading-order
calculations, and the differential measurements are compared to several
production models: +, +, mg5_amc@nlo-lo, and mg5_amc@nlo-nlo.

The + simulation is found to be generally consistent with the data, with
residual differences covered by theoretical uncertainties. The jet
multiplicity distribution is particularly well-modeled, having been tuned on
LHC 8data. The + and mg5_amc@nlo-nlo models are shown to be consistent with
data for most kinematic event variables, while the mg5_amc@nlo-lo model does
not provide an accurate description of any variable measured in the data.

It is expected that the results presented here will be useful for tuning
generators and models in the future. To facilitate this, the measurements
presented here have been implemented in the RIVET framework and will be
available to the wider community.

###### Acknowledgements.

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

##  .9 Tabulated normalized differential production cross sections

Results of the normalised differential cross sections with relative
uncertainties in the combined channel with respect to $N_{\text{jets}}$.
$N_{\text{jets}}$ $\frac{1}{\rd\sigma}\frac{\rd\sigma}{\rd N_{\text{jets}}}$
Stat. unc. Syst. unc. (%) (%) 3.5–4.5 0.405 0.27 2.6 4.5–5.5 0.318 0.27 0.65
5.5–6.5 0.168 0.47 3.0 6.5–7.5 7.19$\times 10^{-2}$ 1.0 5.3 7.5–8.5
2.60$\times 10^{-2}$ 3.0 9.6 8.5–10.5 5.89$\times 10^{-3}$ 3.9 7.8

Results of the normalised differential cross sections with relative
uncertainties in the combined channel with respect to .
$\frac{1}{\rd\sigma}\frac{\rd\sigma}{\rd\HT}$ Stat. unc. Syst. unc. ()
($\GeVns^{-1}$) (%) (%) 110–220 1.80$\times 10^{-3}$ 0.54 14 220–275
4.13$\times 10^{-3}$ 0.37 2.7 275–340 3.16$\times 10^{-3}$ 0.34 3.5 340–410
2.00$\times 10^{-3}$ 0.49 4.1 410–485 1.18$\times 10^{-3}$ 0.69 4.2 485–570
6.73$\times 10^{-4}$ 0.89 4.9 570–660 3.75$\times 10^{-4}$ 1.2 5.5 660–760
2.05$\times 10^{-4}$ 1.5 5.2 760–870 1.15$\times 10^{-4}$ 1.9 7.2 870–990
6.23$\times 10^{-5}$ 2.5 5.6 990–1115 3.28$\times 10^{-5}$ 3.6 13 1115–1250
1.79$\times 10^{-5}$ 4.5 12 1250–1925 4.78$\times 10^{-6}$ 3.3 12

Results of the normalised differential cross sections with relative
uncertainties in the combined channel with respect to $S_{\text{T}}$.
$S_{\text{T}}$ $\frac{1}{\rd\sigma}\frac{\rd\sigma}{\rd S_{\text{T}}}$ Stat.
unc. Syst. unc. () ($\GeVns^{-1}$) (%) (%) 136–315 9.99$\times 10^{-4}$ 0.66
17 315–390 3.49$\times 10^{-3}$ 0.37 2.8 390–475 2.57$\times 10^{-3}$ 0.32 3.7
475–565 1.54$\times 10^{-3}$ 0.49 4.6 565–665 8.52$\times 10^{-4}$ 0.71 5.1
665–770 4.68$\times 10^{-4}$ 1.0 5.5 770–885 2.54$\times 10^{-4}$ 1.3 5.4
885–1010 1.34$\times 10^{-4}$ 1.8 6.4 1010–1140 7.36$\times 10^{-5}$ 2.5 6.1
1140–1285 3.98$\times 10^{-5}$ 3.2 11 1285–1440 1.96$\times 10^{-5}$ 4.6 9.9
1440–1615 1.16$\times 10^{-5}$ 5.3 17 1615–2490 2.35$\times 10^{-6}$ 4.6 14

Results of the normalised differential cross sections with relative
uncertainties in the combined channel with respect to .
$\frac{1}{\rd\sigma}\frac{\rd\sigma}{\rd\ptmiss}$ Stat. unc. Syst. unc. ()
($\GeVns^{-1}$) (%) (%) 0–50 1.05$\times 10^{-2}$ 0.16 3.7 50–105 6.62$\times
10^{-3}$ 0.24 2.9 105–175 1.26$\times 10^{-3}$ 0.75 9.1 175–245 2.43$\times
10^{-4}$ 2.0 4.4 245–315 5.93$\times 10^{-5}$ 4.5 11 315–565 7.63$\times
10^{-6}$ 5.8 12

Results of the normalised differential cross sections with relative
uncertainties in the combined channel with respect to $p_{\text{T}}^{\PW}$.
$p_{\text{T}}^{\PW}$ $\frac{1}{\rd\sigma}\frac{\rd\sigma}{\rd
p_{\text{T}}^{\PW}}$ Stat. unc. Syst. unc. () ($\GeVns^{-1}$) (%) (%) 0–50
5.12$\times 10^{-3}$ 0.36 3.9 50–105 7.44$\times 10^{-3}$ 0.22 0.81 105–165
3.72$\times 10^{-3}$ 0.37 3.2 165–240 1.09$\times 10^{-3}$ 0.69 4.8 240–325
2.59$\times 10^{-4}$ 1.4 5.4 325–415 6.32$\times 10^{-5}$ 2.8 7.7 415–845
5.47$\times 10^{-6}$ 4.1 13

Results of the normalised differential cross sections with relative
uncertainties in the combined channel with respect to $p_{\text{T}}^{\ell}$.
$p_{\text{T}}^{\ell}$ $\frac{1}{\rd\sigma}\frac{\rd\sigma}{\rd
p_{\text{T}}^{\ell}}$ Stat. unc. Syst. unc. () ($\GeVns^{-1}$) (%) (%) 26–40
2.14$\times 10^{-2}$ 0.30 1.2 40–55 1.63$\times 10^{-2}$ 0.26 0.89 55–70
1.10$\times 10^{-2}$ 0.32 0.69 70–85 7.08$\times 10^{-3}$ 0.41 1.0 85–100
4.40$\times 10^{-3}$ 0.52 1.4 100–115 2.74$\times 10^{-3}$ 0.68 1.8 115–130
1.79$\times 10^{-3}$ 0.86 3.0 130–145 1.13$\times 10^{-3}$ 1.1 2.6 145–160
7.21$\times 10^{-4}$ 1.4 2.6 160–175 4.76$\times 10^{-4}$ 1.9 4.2 175–190
3.31$\times 10^{-4}$ 2.3 7.4 190–205 2.17$\times 10^{-4}$ 3.0 6.3 205–220
1.51$\times 10^{-4}$ 3.8 8.8 220–235 1.06$\times 10^{-4}$ 4.7 9.6 235–255
7.28$\times 10^{-5}$ 4.6 13 255–285 4.25$\times 10^{-5}$ 4.5 11 285–435
9.45$\times 10^{-6}$ 4.4 13

Results of the normalised differential cross sections with relative
uncertainties in the combined channel with respect to $\abs{\eta^{\ell}}$.
$\abs{\eta^{\ell}}$
$\frac{1}{\rd\sigma}\frac{\rd\sigma}{\rd\abs{\eta^{\ell}}}$ Stat. unc. Syst.
unc. (%) (%) 0.00–0.30 0.648 0.28 1.0 0.30–0.60 0.622 0.28 0.89 0.60–0.90
0.563 0.30 0.86 0.90–1.20 0.490 0.33 0.69 1.20–1.50 0.388 0.40 1.1 1.50–1.80
0.288 0.54 1.9 1.80–2.00 0.213 0.74 3.0 2.00–2.40 0.144 1.0 4.4

##  .10 Tabulated absolute differential production cross sections

Results of the absolute differential cross sections with relative
uncertainties in the combined channel with respect to $N_{\text{jets}}$.
$N_{\text{jets}}$ $\frac{\rd\sigma}{\rd N_{\text{jets}}}$ Stat. unc. Syst.
unc. (pb) (%) (%) 3.5–4.5 40.6 0.33 9.3 4.5–5.5 31.9 0.29 8.7 5.5–6.5 16.8
0.47 9.9 6.5–7.5 7.22 1.0 11 7.5–8.5 2.60 3.0 13 8.5–10.5 0.591 3.9 12

Results of the absolute differential cross sections with relative
uncertainties in the combined channel with respect to .
$\frac{\rd\sigma}{\rd\HT}$ Stat. unc. Syst. unc. () (pb $\GeVns^{-1}$) (%) (%)
110–220 0.184 0.62 15 220–275 0.424 0.38 9.7 275–340 0.324 0.35 10 340–410
0.205 0.50 10 410–485 0.121 0.70 10 485–570 6.91$\times 10^{-2}$ 0.89 11
570–660 3.85$\times 10^{-2}$ 1.2 11 660–760 2.10$\times 10^{-2}$ 1.5 10
760–870 1.18$\times 10^{-2}$ 2.0 12 870–990 6.39$\times 10^{-3}$ 2.5 11
990–1115 3.37$\times 10^{-3}$ 3.6 16 1115–1250 1.84$\times 10^{-3}$ 4.5 14
1250–1925 4.91$\times 10^{-4}$ 3.3 16

Results of the absolute differential cross sections with relative
uncertainties in the combined channel with respect to $S_{\text{T}}$.
$S_{\text{T}}$ $\frac{\rd\sigma}{\rd S_{\text{T}}}$ Stat. unc. Syst. unc. ()
(pb $\GeVns^{-1}$) (%) (%) 136–315 0.103 0.73 18 315–390 0.358 0.38 9.5
390–475 0.264 0.34 10 475–565 0.158 0.50 10 565–665 8.76$\times 10^{-2}$ 0.71
11 665–770 4.81$\times 10^{-2}$ 1.0 11 770–885 2.61$\times 10^{-2}$ 1.3 11
885–1010 1.38$\times 10^{-2}$ 1.8 11 1010–1140 7.56$\times 10^{-3}$ 2.5 11
1140–1285 4.09$\times 10^{-3}$ 3.2 15 1285–1440 2.02$\times 10^{-3}$ 4.6 13
1440–1615 1.20$\times 10^{-3}$ 5.3 20 1615–2490 2.42$\times 10^{-4}$ 4.6 18

Results of the absolute differential cross sections with relative
uncertainties in the combined channel with respect to .
$\frac{\rd\sigma}{\rd\ptmiss}$ Stat. unc. Syst. unc. () (pb $\GeVns^{-1}$) (%)
(%) 0–50 1.05 0.21 8.8 50–105 0.664 0.28 10 105–175 0.126 0.76 15 175–245
2.44$\times 10^{-2}$ 2.0 11 245–315 5.96$\times 10^{-3}$ 4.5 15 315–565
7.66$\times 10^{-4}$ 5.8 17

Results of the absolute differential cross sections with relative
uncertainties in the combined channel with respect to $p_{\text{T}}^{\PW}$.
$p_{\text{T}}^{\PW}$ $\frac{\rd\sigma}{\rd p_{\text{T}}^{\PW}}$ Stat. unc.
Syst. unc. () (pb $\GeVns^{-1}$) (%) (%) 0–50 0.518 0.41 8.3 50–105 0.752 0.25
8.7 105–165 0.377 0.39 11 165–240 0.111 0.70 11 240–325 2.62$\times 10^{-2}$
1.4 12 325–415 6.40$\times 10^{-3}$ 2.8 13 415–845 5.54$\times 10^{-4}$ 4.1 17

Results of the absolute differential cross sections with relative
uncertainties in the combined channel with respect to $p_{\text{T}}^{\ell}$.
$p_{\text{T}}^{\ell}$ $\frac{\rd\sigma}{\rd p_{\text{T}}^{\ell}}$ Stat. unc.
Syst. unc. () (pb $\GeVns^{-1}$) (%) (%) 26–40 2.16 0.39 9.5 40–55 1.64 0.28
9.2 55–70 1.11 0.33 9.1 70–85 0.715 0.41 8.8 85–100 0.443 0.53 8.9 100–115
0.277 0.68 8.7 115–130 0.181 0.86 9.0 130–145 0.114 1.1 8.6 145–160
7.28$\times 10^{-2}$ 1.4 8.8 160–175 4.80$\times 10^{-2}$ 1.9 9.0 175–190
3.34$\times 10^{-2}$ 2.3 11 190–205 2.19$\times 10^{-2}$ 3.0 9.8 205–220
1.52$\times 10^{-2}$ 3.8 12 220–235 1.07$\times 10^{-2}$ 4.7 13 235–255
7.34$\times 10^{-3}$ 4.6 16 255–285 4.29$\times 10^{-3}$ 4.5 14 285–435
9.53$\times 10^{-4}$ 4.4 16

Results of the absolute differential cross sections with relative
uncertainties in the combined channel with respect to $\abs{\eta^{\ell}}$.
$\abs{\eta^{\ell}}$ $\frac{\rd\sigma}{\rd\abs{\eta^{\ell}}}$ Stat. unc. Syst.
unc. (pb) (%) (%) 0.00–0.30 65.5 0.31 8.9 0.30–0.60 62.9 0.30 8.8 0.60–0.90
56.9 0.32 8.8 0.90–1.20 49.5 0.35 9.0 1.20–1.50 39.2 0.43 9.1 1.50–1.80 29.1
0.57 9.5 1.80–2.00 21.6 0.76 10 2.00–2.40 14.6 1.1 11

##  .11 Tabulated minimum and maximum relative uncertainties for absolute
cross sections

The upper and lower bounds, in %, from each source of systematic uncertainty
in the absolute differential cross section, over all bins of the measurement
for each variable. The bounds of the total relative uncertainty are also
shown.  Relative uncertainty source $(\%)$ $N_{\text{jets}}$ $S_{\text{T}}$
$p_{\text{T}}^{\PW}$ $p_{\text{T}}^{\ell}$ $\abs{\eta^{\ell}}$ tagging
efficiency 3.1 – 4.0 3.5 – 4.5 3.5 – 5.0 3.6 – 4.8 3.6 – 5.2 3.6 – 5.4 3.6 –
4.2 Electron efficiency 1.6 – 1.9 1.6 – 2.3 1.4 – 2.5 1.6 – 2.4 1.2 – 3.0 1.0
– 3.5 1.0 – 2.1 Muon efficiency 2.0 – 2.5 2.2 – 2.5 2.2 – 2.6 2.3 – 2.6 2.3 –
2.9 2.2 – 3.3 2.1 – 2.3 JER 0.1 – 0.8 0.1 – 1.1 0.2 – 2.3 0.4 – 5.6 0.4 – 1.6
0.1 – 0.3 0.3 – 0.4 JES 1.4 – 9.2 4.3 – 12.8 4.3 – 15.1 2.2 – 10.9 2.2 – 7.8
1.8 – 4.1 3.8 – 4.1 Electron transverse momentum in  — — 0.1 – 0.3 0.1 – 0.9
0.1 – 0.6 — — Muon transverse momentum in  — — 0.1 – 0.9 0.1 – 3.5 0.1 – 0.8 —
— Tau transverse momentum in  — — 0.1 – 1.4 0.1 – 1.2 0.1 – 1.4 — —
Unclustered transverse momentum in  — — 0.1 – 1.7 0.2 – 1.9 0.1 – 1.0 — — QCD
bkg cross section 0.1 – 0.9 0.1 – 1.6 0.1 – 2.3 0.1 – 0.8 0.1 – 1.3 0.1 – 5.0
0.1 – 3.6 QCD bkg shape 0.1 – 0.1 0.1 – 0.8 0.1 – 1.0 0.1 – 0.1 0.1 – 1.6 0.1
– 4.8 0.1 – 1.5 Single top quark cross section 1.1 – 1.7 1.1 – 3.5 1.1 – 5.8
1.3 – 6.3 1.1 – 8.4 1.3 – 7.4 1.4 – 1.5 V+jets cross section 0.7 – 1.1 0.6 –
3.4 0.5 – 4.6 0.7 – 2.9 0.7 – 4.5 0.6 – 6.3 0.6 – 2.5 PDF 0.1 – 0.3 0.1 – 0.4
0.1 – 0.6 0.1 – 0.3 0.1 – 0.4 0.1 – 0.5 $<$0.1 Color reconnection (Gluon move)
0.2 – 2.8 0.2 – 4.1 0.1 – 11.8 0.2 – 1.0 0.1 – 1.1 0.2 – 4.7 0.1 – 0.5 Color
reconnection (QCD-based) 0.2 – 2.1 0.1 – 4.3 0.1 – 6.7 0.3 – 4.6 0.3 – 3.8 0.1
– 7.9 0.1 – 1.7 Color reconnection (Early resonance decays) 0.1 – 3.9 0.1 –
7.1 0.1 – 4.1 0.1 – 1.4 0.1 – 3.8 0.1 – 5.0 0.1 – 1.1 Fragmentation 0.1 – 0.7
0.6 – 1.6 0.5 – 1.4 0.1 – 0.7 0.2 – 0.8 0.1 – 0.8 0.3 – 0.4 $h_{\text{damp}}$
0.1 – 3.5 0.2 – 3.1 0.1 – 3.0 0.6 – 2.1 0.2 – 3.2 0.4 – 3.4 0.5 – 2.0 Top
quark mass 0.7 – 2.0 0.3 – 3.4 0.3 – 3.7 0.3 – 5.0 0.4 – 2.0 0.3 – 3.5 0.9 –
1.7 Peterson fragmentation model 0.3 – 2.0 1.6 – 2.7 1.9 – 3.2 1.1 – 2.6 1.3 –
2.6 1.2 – 2.9 1.5 – 1.5 Shower scales 2.7 – 6.5 2.6 – 6.4 3.0 – 7.0 4.0 – 6.6
4.6 – 6.2 3.9 – 6.2 4.9 – 5.6 hadron decay semileptonic branching fraction 0.2
– 0.3 0.1 – 0.3 0.1 – 0.3 0.2 – 0.3 0.2 – 0.3 0.2 – 0.3 0.2 – 0.3 Top quark
$\pt$ 0.4 – 1.2 0.1 – 0.8 0.1 – 0.9 0.1 – 1.5 0.1 – 1.0 0.1 – 1.0 0.6 – 0.7
Underlying event tune 0.1 – 2.9 0.2 – 5.3 0.2 – 4.4 0.2 – 5.4 0.1 – 2.6 0.1 –
6.0 0.2 – 0.9 Simulated sample size 0.1 – 1.6 0.1 – 1.6 0.1 – 1.9 0.1 – 2.2
0.1 – 1.4 0.1 – 1.7 0.1 – 0.4 Additional interactions 0.1 – 0.3 0.1 – 0.7 0.1
– 1.3 0.3 – 1.3 0.1 – 0.6 0.1 – 0.8 0.1 – 0.3 Integrated luminosity 2.5 – 2.5
2.5 – 2.5 2.5 – 2.5 2.5 – 2.5 2.5 – 2.5 2.5 – 2.5 2.5 – 2.5 Total 8.7 – 13.4
9.7 – 15.9 9.5 – 20.0 8.8 – 17.1 8.3 – 17.5 8.6 – 16.1 8.8 – 10.6

## References

  * [1] CMS Collaboration, “The CMS experiment at the CERN LHC”,  JINST 3 (2008) S08004, 10.1088/1748-0221/3/08/S08004.
  * [2] CMS Collaboration, “Measurement of differential top-quark pair production cross sections in pp collisions at $\sqrt{s}=7$ TeV”,  Eur. Phys. J. C 73 (2013) 2339, 10.1140/epjc/s10052-013-2339-4, arXiv:1211.2220.
  * [3] CMS Collaboration, “Measurement of the differential cross section for top quark pair production in pp collisions at $\sqrt{s}=8\,\text{TeV}$”,  Eur. Phys. J. C 75 (2015) 542, 10.1140/epjc/s10052-015-3709-x, arXiv:1505.04480.
  * [4] CMS Collaboration, “Measurement of double-differential cross sections for top quark pair production in pp collisions at $\sqrt{s}=8$ TeV and impact on parton distribution functions”,  Eur. Phys. J. C 77 (2017) 459, 10.1140/epjc/s10052-017-4984-5, arXiv:1703.01630.
  * [5] CMS Collaboration, “Measurement of the integrated and differential $t\bar{t}$ production cross sections for high-$p_{T}$ top quarks in pp collisions at $\sqrt{s}=$ 8 TeV”,  Phys. Rev. D 94 (2016) 072002, 10.1103/PhysRevD.94.072002, arXiv:1605.00116.
  * [6] CMS Collaboration, “Measurement of the $\mathrm{t\bar{t}}$ production cross section in the all-jets final state in pp collisions at $\sqrt{s}=$ 8 TeV”,  Eur. Phys. J. C 76 (2015) 128, 10.1140/epjc/s10052-016-3956-5, arXiv:1509.06076.
  * [7] CMS Collaboration, “Measurement of differential cross sections for top quark pair production using the lepton+jets final state in proton-proton collisions at 13 TeV”,  Phys. Rev. D 95 (2017) 092001, 10.1103/PhysRevD.95.092001, arXiv:1610.04191.
  * [8] CMS Collaboration, “Measurement of normalized differential $\mathrm{t\bar{t}}$ cross sections in the dilepton channel from pp collisions at $\sqrt{s}=13~{}\mathrm{TeV}$”, (2017). arXiv:1708.07638. Submitted to JHEP.
  * [9] ATLAS Collaboration, “Measurements of top-quark pair differential cross-sections in the lepton+jets channel in $pp$ collisions at $\sqrt{s}=13$ TeV using the ATLAS detector”,  JHEP 11 (2017) 191, 10.1007/JHEP11(2017)191, arXiv:1708.00727.
  * [10] ATLAS Collaboration, “Measurements of top-quark pair differential cross-sections in the $e\mu$ channel in pp collisions at $\sqrt{s}=13$ TeV using the ATLAS detector”,  Eur. Phys. J. C 77 (2017) 292, 10.1140/epjc/s10052-017-4821-x, arXiv:1612.05220.
  * [11] CMS Collaboration, “Measurement of the differential cross sections for top quark pair production as a function of kinematic event variables in pp collisions at $\sqrt{s}=7$ and 8 TeV”,  Phys. Rev. D 94 (2016) 052006, 10.1103/PhysRevD.94.052006, arXiv:1607.00837.
  * [12] CMS Collaboration, “The CMS trigger system”,  JINST 12 (2017) P01020, 10.1088/1748-0221/12/01/P01020, arXiv:1609.02366.
  * [13] 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.
  * [14] 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.
  * [15] 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.
  * [16] 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.
  * [17] T. Sjöstrand, S. Mrenna, and P. Skands, “PYTHIA 6.4 physics and manual”,  JHEP 05 (2006) 026, 10.1088/1126-6708/2006/05/026, arXiv:hep-ph/0603175.
  * [18] T. Sjöstrand, S. Mrenna, and P. Skands, “A brief introduction to PYTHIA 8.1”,  Comput. Phys. Commun. 178 (2008) 852, 10.1016/j.cpc.2008.01.036, arXiv:0710.3820.
  * [19] CMS Collaboration, “Investigations of the impact of the parton shower tuning in Pythia 8 in the modelling of $\mathrm{t\overline{t}}$ at $\sqrt{s}=8$ and 13 TeV”, CMS Physics Analysis Summary CMS-PAS-TOP-16-021, 2016.
  * [20] M. Bahr et al., “Herwig++ physics and manual”,  Eur. Phys. J. C 58 (2008) 639, 10.1140/epjc/s10052-008-0798-9, arXiv:0803.0883.
  * [21] S. Gieseke, C. Rohr, and A. Siodmok, “Colour reconnections in Herwig++”,  Eur. Phys. J. C 72 (2012) 2225, 10.1140/epjc/s10052-012-2225-5, arXiv:1206.0041.
  * [22] 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.
  * [23] 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.
  * [24] 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.
  * [25] R. Frederix and S. Frixione, “Merging meets matching in MC@NLO”,  JHEP 12 (2012) 061, 10.1007/JHEP12(2012)061, arXiv:1209.6215.
  * [26] NNPDF Collaboration, “Parton distributions for the LHC Run II”,  JHEP 04 (2015) 040, 10.1007/JHEP04(2015)040, arXiv:1410.8849.
  * [27] M. Beneke, P. Falgari, S. Klein, and C. Schwinn, “Hadronic top-quark pair production with NNLL threshold resummation”,  Nucl. Phys. B 855 (2012) 695, 10.1016/j.nuclphysb.2011.10.021, arXiv:1109.1536.
  * [28] M. Cacciari et al., “Top-pair production at hadron colliders with next-to-next-to-leading logarithmic soft-gluon resummation”,  Phys. Lett. B 710 (2012) 612, 10.1016/j.physletb.2012.03.013, arXiv:1111.5869.
  * [29] P. Bärnreuther, M. Czakon, and A. Mitov, “Percent-level-precision physics at the Tevatron: Next-to-next-to-leading order QCD corrections to $\mathrm{q\bar{q}}\to\mathrm{t\bar{t}}+\mathrm{X}$”,  Phys. Rev. Lett. 109 (2012) 132001, 10.1103/PhysRevLett.109.132001, arXiv:1204.5201.
  * [30] M. Czakon and A. Mitov, “NNLO corrections to top-pair production at hadron colliders: the all-fermionic scattering channels”,  JHEP 12 (2012) 054, 10.1007/JHEP12(2012)054, arXiv:1207.0236.
  * [31] M. Czakon and A. Mitov, “NNLO corrections to top pair production at hadron colliders: the quark-gluon reaction”,  JHEP 01 (2013) 080, 10.1007/JHEP01(2013)080, arXiv:1210.6832.
  * [32] M. Czakon, P. Fiedler, and A. Mitov, “Total top-quark pair-production cross section at hadron colliders through O($\alpha^{4}_{S}$)”,  Phys. Rev. Lett. 110 (2013) 252004, 10.1103/PhysRevLett.110.252004, arXiv:1303.6254.
  * [33] 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.
  * [34] M. Aliev et al., “HATHOR: HAdronic Top and Heavy quarks crOss section calculatoR”,  Comput. Phys. Commun. 182 (2011) 1034, 10.1016/j.cpc.2010.12.040, arXiv:1007.1327.
  * [35] P. Kant et al., “HATHOR for single top-quark production: Updated predictions and uncertainty estimates for single top-quark production in hadronic collisions”,  Comput. Phys. Commun. 191 (2015) 74, 10.1016/j.cpc.2015.02.001, arXiv:1406.4403.
  * [36] R. Frederix, E. Re, and P. Torrielli, “Single-top $t$-channel hadroproduction in the four-flavour scheme with POWHEG and aMC@NLO”,  JHEP 09 (2012) 130, 10.1007/JHEP09(2012)130, arXiv:1207.5391.
  * [37] S. Alioli, P. Nason, C. Oleari, and E. Re, “NLO single-top production matched with shower in POWHEG: $s$\- and $t$-channel contributions”,  JHEP 09 (2009) 111, 10.1088/1126-6708/2009/09/111, arXiv:0907.4076. [Erratum: 10.1007/JHEP02(2010)011].
  * [38] E. Re, “Single-top Wt-channel production matched with parton showers using the POWHEG method”,  Eur. Phys. J. C 71 (2011) 1547, 10.1140/epjc/s10052-011-1547-z, arXiv:1009.2450.
  * [39] S. Frixione et al., “Single-top hadroproduction in association with a W boson”,  JHEP 07 (2008) 029, 10.1088/1126-6708/2008/07/029, arXiv:0805.3067.
  * [40] Y. Li and F. Petriello, “Combining QCD and electroweak corrections to dilepton production in FEWZ”,  Phys. Rev. D 86 (2012) 094034, 10.1103/PhysRevD.86.094034, arXiv:1208.5967.
  * [41] GEANT4 Collaboration, “—a simulation toolkit”,  Nucl. Instrum. Meth. A 506 (2003) 250, 10.1016/S0168-9002(03)01368-8.
  * [42] 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.
  * [43] CMS Collaboration, “Performance of electron reconstruction and selection with the CMS detector in proton-proton collisions at ${\sqrt{s}}=8\,\text{TeV}$”,  JINST 10 (2015) P06005, 10.1088/1748-0221/10/06/P06005, arXiv:1502.02701.
  * [44] CMS Collaboration, “Performance of CMS muon reconstruction in pp collision events at $\sqrt{s}=7$ TeV”,  JINST 7 (2012) P10002, 10.1088/1748-0221/7/10/P10002, arXiv:1206.4071.
  * [45] CMS Collaboration, “Measurement of the inclusive $W$ and $Z$ production cross sections in pp collisions at $\sqrt{s}=7$ TeV with the CMS experiment”,  JHEP 10 (2011) 132, 10.1007/JHEP10(2011)132, arXiv:1107.4789.
  * [46] 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.
  * [47] 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.
  * [48] 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.
  * [49] 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.
  * [50] CMS Collaboration, “Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV”, (2017). arXiv:1712.07158. Submitted to JINST.
  * [51] A. Buckley et al., “RIVET user manual”,  Comput. Phys. Commun. 184 (2013) 2803, 10.1016/j.cpc.2013.05.021, arXiv:1003.0694.
  * [52] CMS Collaboration, “Object definitions for top quark analyses at the particle level”, Technical Report CMS-NOTE-2017-004, 2017.
  * [53] S Schmitt, “TUnfold, an algorithm for correcting migration effects in high energy physics”,  JINST 7 (2012) 10.1088/1748-0221/7/10/T10003.
  * [54] CMS Collaboration, “CMS luminosity measurements for the 2016 data taking period”, CMS Physics Analysis Summary CMS-PAS-LUM-17-001, 2017.
  * [55] CMS Collaboration, “Measurement of the inelastic proton-proton cross section at $\sqrt{s}=13~{}\mathrm{TeV}$”, (2018). arXiv:1802.02613. Submitted to JHEP.
  * [56] CMS Collaboration, “Cross section measurement of t-channel single top quark production in pp collisions at $\sqrt{s}=$ 13 TeV”,  Phys. Lett. B 772 (2017) 752, 10.1016/j.physletb.2017.07.047, arXiv:1610.00678.
  * [57] CMS Collaboration, “Measurement of the production cross section of a W boson in association with two b jets in pp collisions at $\sqrt{s}=8{\,\mathrm{{TeV}}}$”,  Eur. Phys. J. C 77 (2017) 92, 10.1140/epjc/s10052-016-4573-z, arXiv:1608.07561.
  * [58] CMS Collaboration, “Measurements of the associated production of a Z boson and b jets in pp collisions at ${\sqrt{s}}=8\,\text{TeV}$”,  Eur. Phys. J. C 77 (2017) 751, 10.1140/epjc/s10052-017-5140-y, arXiv:1611.06507.
  * [59] Particle Data Group, C. Patrignani et al., “Review of particle physics”,  Chin. Phys. C 40 (2016) 100001, 10.1088/1674-1137/40/10/100001.
  * [60] P. Skands, S. Carrazza, and J. Rojo, “Tuning PYTHIA 8.1: the Monash 2013 Tune”,  Eur. Phys. J. C 74 (2014) 3024, 10.1140/epjc/s10052-014-3024-y, arXiv:1404.5630.
  * [61] C. Peterson, D. Schlatter, I. Schmitt, and P. M. Zerwas, “Scaling violations in inclusive $\mathrm{{e}^{+}{e}}^{-}$ annihilation spectra”,  Phys. Rev. D 27 (1983) 105, 10.1103/PhysRevD.27.105.
  * [62] J. R. Christiansen and P. Z. Skands, “String formation beyond leading colour”,  JHEP 08 (2015) 003, 10.1007/JHEP08(2015)003, arXiv:1505.01681.
  * [63] S. Argyropoulos and T. Sjöstrand, “Effects of color reconnection on $\mathrm{t\bar{t}}$ final states at the LHC”,  JHEP 11 (2014) 043, 10.1007/JHEP11(2014)043, arXiv:1407.6653.

##  .12 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan, A. Tumasyan Institut für Hochenergiephysik, Wien, Austria
W. Adam, F. Ambrogi, E. Asilar, T. Bergauer, J. Brandstetter, E. Brondolin, M.
Dragicevic, J. Erö, A. Escalante Del Valle, M. Flechl, M. Friedl, R.
Frühwirth1, V.M. Ghete, J. Grossmann, J. Hrubec, M. Jeitler1, A. König, N.
Krammer, I. Krätschmer, D. Liko, T. Madlener, I. Mikulec, E. Pree, N. Rad, H.
Rohringer, J. Schieck1, R. Schöfbeck, M. Spanring, D. Spitzbart, A. Taurok, W.
Waltenberger, J. Wittmann, C.-E. Wulz1, M. Zarucki Institute for Nuclear
Problems, Minsk, Belarus
V. Chekhovsky, V. Mossolov, J. Suarez Gonzalez Universiteit Antwerpen,
Antwerpen, Belgium
E.A. De Wolf, D. Di Croce, X. Janssen, J. Lauwers, M. Pieters, M. Van De
Klundert, H. Van Haevermaet, P. Van Mechelen, N. Van Remortel Vrije
Universiteit Brussel, Brussel, Belgium
S. Abu Zeid, F. Blekman, J. D’Hondt, I. De Bruyn, J. De Clercq, K. Deroover,
G. Flouris, D. Lontkovskyi, S. Lowette, I. Marchesini, S. Moortgat, L.
Moreels, Q. Python, K. Skovpen, S. Tavernier, W. Van Doninck, P. Van Mulders,
I. Van Parijs Université Libre de Bruxelles, Bruxelles, Belgium
D. Beghin, B. Bilin, H. Brun, B. Clerbaux, G. De Lentdecker, H. Delannoy, B.
Dorney, G. Fasanella, L. Favart, R. Goldouzian, A. Grebenyuk, A.K. Kalsi, T.
Lenzi, J. Luetic, T. Maerschalk, T. Seva, E. Starling, C. Vander Velde, P.
Vanlaer, D. Vannerom, R. Yonamine, F. Zenoni Ghent University, Ghent, Belgium
T. Cornelis, D. Dobur, A. Fagot, M. Gul, I. Khvastunov2, D. Poyraz, C. Roskas,
D. Trocino, M. Tytgat, W. Verbeke, M. Vit, N. Zaganidis Université Catholique
de Louvain, Louvain-la-Neuve, Belgium
H. Bakhshiansohi, O. Bondu, S. Brochet, G. Bruno, C. Caputo, A. Caudron, P.
David, S. De Visscher, C. Delaere, M. Delcourt, B. Francois, A. Giammanco, G.
Krintiras, V. Lemaitre, A. Magitteri, A. Mertens, M. Musich, K. Piotrzkowski,
L. Quertenmont, A. Saggio, M. Vidal Marono, S. Wertz, J. Zobec Centro
Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil
W.L. Aldá Júnior, F.L. Alves, G.A. Alves, L. Brito, G. Correia Silva, C.
Hensel, A. Moraes, M.E. Pol, P. Rebello Teles Universidade do Estado do Rio de
Janeiro, Rio de Janeiro, Brazil
E. Belchior Batista Das Chagas, W. Carvalho, J. Chinellato3, E. Coelho, E.M.
Da Costa, G.G. Da Silveira4, D. De Jesus Damiao, S. Fonseca De Souza, L.M.
Huertas Guativa, H. Malbouisson, M. Melo De Almeida, C. Mora Herrera, L.
Mundim, H. Nogima, L.J. Sanchez Rosas, A. Santoro, A. Sznajder, M. Thiel, E.J.
Tonelli Manganote3, F. Torres Da Silva De Araujo, A. Vilela Pereira
Universidade Estadual Paulista a, Universidade Federal do ABC b, São Paulo,
Brazil
S. Ahujaa, C.A. Bernardesa, T.R. Fernandez Perez Tomeia, E.M. Gregoresb, P.G.
Mercadanteb, S.F. Novaesa, Sandra S. Padulaa, D. Romero Abadb, J.C. Ruiz
Vargasa Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy
of Sciences, Sofia, Bulgaria
A. Aleksandrov, R. Hadjiiska, P. Iaydjiev, A. Marinov, M. Misheva, M. Rodozov,
M. Shopova, G. Sultanov University of Sofia, Sofia, Bulgaria
A. Dimitrov, L. Litov, B. Pavlov, P. Petkov Beihang University, Beijing, China
W. Fang5, X. Gao5, L. Yuan Institute of High Energy Physics, Beijing, China
M. Ahmad, J.G. Bian, G.M. Chen, H.S. Chen, M. Chen, Y. Chen, C.H. Jiang, D.
Leggat, H. Liao, Z. Liu, F. Romeo, S.M. Shaheen, A. Spiezia, J. Tao, C. Wang,
Z. Wang, E. Yazgan, H. Zhang, J. Zhao State Key Laboratory of Nuclear Physics
and Technology, Peking University, Beijing, China
Y. Ban, G. Chen, J. Li, Q. Li, S. Liu, Y. Mao, S.J. Qian, D. Wang, Z. Xu
Tsinghua University, Beijing, China
Y. Wang Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, C.A. Carrillo Montoya, L.F. Chaparro Sierra, C. Florez,
C.F. González Hernández, J.D. Ruiz Alvarez, M.A. Segura Delgado University of
Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval
Architecture, Split, Croatia
B. Courbon, N. Godinovic, D. Lelas, I. Puljak, P.M. Ribeiro Cipriano, T.
Sculac University of Split, Faculty of Science, Split, Croatia
Z. Antunovic, M. Kovac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, D. Ferencek, K. Kadija, B. Mesic, A. Starodumov6, T. Susa
University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos,
P.A. Razis, H. Rykaczewski Charles University, Prague, Czech Republic
M. Finger7, M. Finger Jr.7 Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab
Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
H. Abdalla8, A.A. Abdelalim9,10, S. Khalil10 National Institute of Chemical
Physics and Biophysics, Tallinn, Estonia
S. Bhowmik, R.K. Dewanjee, M. Kadastik, L. Perrini, M. Raidal, C. Veelken
Department of Physics, University of Helsinki, Helsinki, Finland
P. Eerola, H. Kirschenmann, J. Pekkanen, M. Voutilainen Helsinki Institute of
Physics, Helsinki, Finland
J. Havukainen, J.K. Heikkilä, T. Järvinen, V. Karimäki, R. Kinnunen, T.
Lampén, K. Lassila-Perini, S. Laurila, S. Lehti, T. Lindén, P. Luukka, T.
Mäenpää, H. Siikonen, E. Tuominen, J. Tuominiemi Lappeenranta University of
Technology, Lappeenranta, Finland
T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
M. Besancon, F. Couderc, M. Dejardin, D. Denegri, J.L. Faure, F. Ferri, S.
Ganjour, S. Ghosh, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry,
C. Leloup, E. Locci, M. Machet, J. Malcles, G. Negro, J. Rander, A. Rosowsky,
M.Ö. Sahin, M. Titov Laboratoire Leprince-Ringuet, Ecole polytechnique,
CNRS/IN2P3, Université Paris-Saclay, Palaiseau, France
A. Abdulsalam11, C. Amendola, I. Antropov, S. Baffioni, F. Beaudette, P.
Busson, L. Cadamuro, C. Charlot, R. Granier de Cassagnac, M. Jo, I. Kucher, S.
Lisniak, A. Lobanov, J. Martin Blanco, M. Nguyen, C. Ochando, G. Ortona, P.
Paganini, P. Pigard, R. Salerno, J.B. Sauvan, Y. Sirois, A.G. Stahl Leiton, Y.
Yilmaz, A. Zabi, A. Zghiche Université de Strasbourg, CNRS, IPHC UMR 7178,
F-67000 Strasbourg, France
J.-L. Agram12, J. Andrea, D. Bloch, J.-M. Brom, M. Buttignol, E.C. Chabert, C.
Collard, E. Conte12, X. Coubez, F. Drouhin12, J.-C. Fontaine12, D. Gelé, U.
Goerlach, M. Jansová, P. Juillot, A.-C. Le Bihan, N. Tonon, P. Van Hove Centre
de Calcul de l’Institut National de Physique Nucleaire et de Physique des
Particules, CNRS/IN2P3, Villeurbanne, France
S. Gadrat Université de Lyon, Université Claude Bernard Lyon 1, CNRS-IN2P3,
Institut de Physique Nucléaire de Lyon, Villeurbanne, France
S. Beauceron, C. Bernet, G. Boudoul, N. Chanon, R. Chierici, D. Contardo, P.
Depasse, H. El Mamouni, J. Fay, L. Finco, S. Gascon, M. Gouzevitch, G.
Grenier, B. Ille, F. Lagarde, I.B. Laktineh, H. Lattaud, M. Lethuillier, L.
Mirabito, A.L. Pequegnot, S. Perries, A. Popov13, V. Sordini, M. Vander
Donckt, S. Viret, S. Zhang Georgian Technical University, Tbilisi, Georgia
A. Khvedelidze7 Tbilisi State University, Tbilisi, Georgia
D. Lomidze RWTH Aachen University, I. Physikalisches Institut, Aachen, Germany
C. Autermann, L. Feld, M.K. Kiesel, K. Klein, M. Lipinski, M. Preuten, C.
Schomakers, J. Schulz, M. Teroerde, B. Wittmer, V. Zhukov13 RWTH Aachen
University, III. Physikalisches Institut A, Aachen, Germany
A. Albert, D. Duchardt, M. Endres, M. Erdmann, S. Erdweg, T. Esch, R. Fischer,
A. Güth, T. Hebbeker, C. Heidemann, K. Hoepfner, S. Knutzen, M. Merschmeyer,
A. Meyer, P. Millet, S. Mukherjee, T. Pook, M. Radziej, H. Reithler, M.
Rieger, F. Scheuch, D. Teyssier, S. Thüer RWTH Aachen University, III.
Physikalisches Institut B, Aachen, Germany
G. Flügge, B. Kargoll, T. Kress, A. Künsken, T. Müller, A. Nehrkorn, A.
Nowack, C. Pistone, O. Pooth, A. Stahl14 Deutsches Elektronen-Synchrotron,
Hamburg, Germany
M. Aldaya Martin, T. Arndt, C. Asawatangtrakuldee, K. Beernaert, O. Behnke, U.
Behrens, A. Bermúdez Martínez, A.A. Bin Anuar, K. Borras15, V. Botta, A.
Campbell, P. Connor, C. Contreras-Campana, F. Costanza, A. De Wit, C. Diez
Pardos, G. Eckerlin, D. Eckstein, T. Eichhorn, E. Eren, E. Gallo16, J. Garay
Garcia, A. Geiser, J.M. Grados Luyando, A. Grohsjean, P. Gunnellini, M.
Guthoff, A. Harb, J. Hauk, M. Hempel17, H. Jung, M. Kasemann, J. Keaveney, C.
Kleinwort, I. Korol, D. Krücker, W. Lange, A. Lelek, T. Lenz, K. Lipka, W.
Lohmann17, R. Mankel, I.-A. Melzer-Pellmann, A.B. Meyer, M. Meyer, M.
Missiroli, G. Mittag, J. Mnich, A. Mussgiller, D. Pitzl, A. Raspereza, M.
Savitskyi, P. Saxena, R. Shevchenko, N. Stefaniuk, H. Tholen, G.P. Van Onsem,
R. Walsh, Y. Wen, K. Wichmann, C. Wissing, O. Zenaiev University of Hamburg,
Hamburg, Germany
R. Aggleton, S. Bein, V. Blobel, M. Centis Vignali, T. Dreyer, E. Garutti, D.
Gonzalez, J. Haller, A. Hinzmann, M. Hoffmann, A. Karavdina, G. Kasieczka, R.
Klanner, R. Kogler, N. Kovalchuk, S. Kurz, D. Marconi, J. Multhaup, M.
Niedziela, D. Nowatschin, T. Peiffer, A. Perieanu, A. Reimers, C. Scharf, P.
Schleper, A. Schmidt, S. Schumann, J. Schwandt, J. Sonneveld, H. Stadie, G.
Steinbrück, F.M. Stober, M. Stöver, D. Troendle, E. Usai, A. Vanhoefer, B.
Vormwald Institut für Experimentelle Teilchenphysik, Karlsruhe, Germany
M. Akbiyik, C. Barth, M. Baselga, S. Baur, E. Butz, R. Caspart, T. Chwalek, F.
Colombo, W. De Boer, A. Dierlamm, N. Faltermann, B. Freund, R. Friese, M.
Giffels, M.A. Harrendorf, F. Hartmann14, S.M. Heindl, U. Husemann, F.
Kassel14, S. Kudella, H. Mildner, M.U. Mozer, Th. Müller, M. Plagge, G. Quast,
K. Rabbertz, M. Schröder, I. Shvetsov, G. Sieber, H.J. Simonis, R. Ulrich, S.
Wayand, M. Weber, T. Weiler, S. Williamson, C. Wöhrmann, R. Wolf Institute of
Nuclear and Particle Physics (INPP), NCSR Demokritos, Aghia Paraskevi, Greece
G. Anagnostou, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas, I. Topsis-
Giotis National and Kapodistrian University of Athens, Athens, Greece
G. Karathanasis, S. Kesisoglou, A. Panagiotou, N. Saoulidou, E. Tziaferi
National Technical University of Athens, Athens, Greece
K. Kousouris, I. Papakrivopoulos University of Ioánnina, Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, S. Mallios, N.
Manthos, I. Papadopoulos, E. Paradas, J. Strologas, F.A. Triantis, D.
Tsitsonis MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
M. Csanad, N. Filipovic, G. Pasztor, O. Surányi, G.I. Veres18 Wigner Research
Centre for Physics, Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath19, Á. Hunyadi, F. Sikler, V. Veszpremi, G.
Vesztergombi18, T.Á. Vámi Institute of Nuclear Research ATOMKI, Debrecen,
Hungary
N. Beni, S. Czellar, J. Karancsi20, A. Makovec, J. Molnar, Z. Szillasi
Institute of Physics, University of Debrecen, Debrecen, Hungary
M. Bartók18, P. Raics, Z.L. Trocsanyi, B. Ujvari Indian Institute of Science
(IISc), Bangalore, India
S. Choudhury, J.R. Komaragiri National Institute of Science Education and
Research, Bhubaneswar, India
S. Bahinipati21, P. Mal, K. Mandal, A. Nayak22, D.K. Sahoo21, N. Sahoo, S.K.
Swain Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, R. Chawla, N. Dhingra, R. Gupta, A. Kaur,
M. Kaur, S. Kaur, R. Kumar, P. Kumari, A. Mehta, S. Sharma, J.B. Singh, G.
Walia University of Delhi, Delhi, India
Ashok Kumar, Aashaq Shah, A. Bhardwaj, S. Chauhan, B.C. Choudhary, R.B. Garg,
S. Keshri, A. Kumar, S. Malhotra, M. Naimuddin, K. Ranjan, R. Sharma Saha
Institute of Nuclear Physics, HBNI, Kolkata, India
R. Bhardwaj23, R. Bhattacharya, S. Bhattacharya, U. Bhawandeep23, D. Bhowmik,
S. Dey, S. Dutt23, S. Dutta, S. Ghosh, N. Majumdar, A. Modak, K. Mondal, S.
Mukhopadhyay, S. Nandan, A. Purohit, P.K. Rout, A. Roy, S. Roy Chowdhury, S.
Sarkar, M. Sharan, B. Singh, S. Thakur23 Indian Institute of Technology
Madras, Madras, India
P.K. Behera Bhabha Atomic Research Centre, Mumbai, India
R. Chudasama, D. Dutta, V. Jha, V. Kumar, A.K. Mohanty14, P.K. Netrakanti,
L.M. Pant, P. Shukla, A. Topkar Tata Institute of Fundamental Research-A,
Mumbai, India
T. Aziz, S. Dugad, B. Mahakud, S. Mitra, G.B. Mohanty, N. Sur, B. Sutar Tata
Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, Sa. Jain, S.
Kumar, M. Maity24, G. Majumder, K. Mazumdar, T. Sarkar24, N. Wickramage25
Indian Institute of Science Education and Research (IISER), Pune, India
S. Chauhan, S. Dube, V. Hegde, A. Kapoor, K. Kothekar, S. Pandey, A. Rane, S.
Sharma Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
S. Chenarani26, E. Eskandari Tadavani, S.M. Etesami26, M. Khakzad, M.
Mohammadi Najafabadi, M. Naseri, S. Paktinat Mehdiabadi27, F. Rezaei
Hosseinabadi, B. Safarzadeh28, M. Zeinali University College Dublin, Dublin,
Ireland
M. Felcini, M. Grunewald INFN Sezione di Bari a, Università di Bari b,
Politecnico di Bari c, Bari, Italy
M. Abbresciaa,b, C. Calabriaa,b, A. Colaleoa, D. Creanzaa,c, L. Cristellaa,b,
N. De Filippisa,c, M. De Palmaa,b, A. Di Florioa,b, F. Erricoa,b, L. Fiorea,
G. Iasellia,c, S. Lezkia,b, G. Maggia,c, M. Maggia, B. Marangellia,b, G.
Minielloa,b, S. Mya,b, S. Nuzzoa,b, A. Pompilia,b, G. Pugliesea,c, R.
Radognaa, A. Ranieria, G. Selvaggia,b, A. Sharmaa, L. Silvestrisa,14, R.
Vendittia, P. Verwilligena, G. Zitoa 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, L. Brigliadoria,b, R. Campaninia,b, P. Capiluppia,b, A.
Castroa,b, F.R. Cavalloa, S.S. Chhibraa,b, G. Codispotia,b, M. Cuffiania,b,
G.M. Dallavallea, F. Fabbria, A. Fanfania,b, D. Fasanellaa,b, P. Giacomellia,
C. Grandia, L. Guiduccia,b, F. Iemmi, S. Marcellinia, G. Masettia, A.
Montanaria, F.L. Navarriaa,b, A. Perrottaa, T. Rovellia,b, G.P. Sirolia,b, N.
Tosia INFN Sezione di Catania a, Università di Catania b, Catania, Italy
S. Albergoa,b, S. Costaa,b, A. Di Mattiaa, F. Giordanoa,b, R. Potenzaa,b, A.
Tricomia,b, C. Tuvea,b INFN Sezione di Firenze a, Università di Firenze b,
Firenze, Italy
G. Barbaglia, K. Chatterjeea,b, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, E. Focardia,b, G. Latino, P. Lenzia,b, M. Meschinia, S.
Paolettia, L. Russoa,29, G. Sguazzonia, D. Stroma, L. Viliania INFN Laboratori
Nazionali di Frascati, Frascati, Italy
L. Benussi, S. Bianco, F. Fabbri, D. Piccolo, F. Primavera14 INFN Sezione di
Genova a, Università di Genova b, Genova, Italy
V. Calvellia,b, F. Ferroa, F. Raveraa,b, E. Robuttia, S. Tosia,b INFN Sezione
di Milano-Bicocca a, Università di Milano-Bicocca b, Milano, Italy
A. Benagliaa, A. Beschib, L. Brianzaa,b, F. Brivioa,b, V. Cirioloa,b,14, M.E.
Dinardoa,b, S. Fiorendia,b, S. Gennaia, A. Ghezzia,b, P. Govonia,b, M.
Malbertia,b, S. Malvezzia, R.A. Manzonia,b, D. Menascea, L. Moronia, M.
Paganonia,b, K. Pauwelsa,b, D. Pedrinia, S. Pigazzinia,b,30, S. Ragazzia,b, T.
Tabarelli de Fatisa,b INFN Sezione di Napoli a, Università di Napoli ’Federico
II’ b, Napoli, Italy, Università della Basilicata c, Potenza, Italy,
Università G. Marconi d, Roma, Italy
S. Buontempoa, N. Cavalloa,c, S. Di Guidaa,d,14, F. Fabozzia,c, F. Fiengaa,b,
A.O.M. Iorioa,b, W.A. Khana, L. Listaa, S. Meolaa,d,14, P. Paoluccia,14, C.
Sciaccaa,b, F. Thyssena INFN Sezione di Padova a, Università di Padova b,
Padova, Italy, Università di Trento c, Trento, Italy
P. Azzia, N. Bacchettaa, L. Benatoa,b, M. Biasottoa,31, D. Biselloa,b, A.
Bolettia,b, R. Carlina,b, A. Carvalho Antunes De Oliveiraa,b, P. Checchiaa, M.
Dall’Ossoa,b, P. De Castro Manzanoa, T. Dorigoa, U. Dossellia, F.
Gasparinia,b, A. Gozzelinoa, S. Lacapraraa, P. Lujan, M. Margonia,b, A.T.
Meneguzzoa,b, N. Pozzobona,b, P. Ronchesea,b, R. Rossina,b, F. Simonettoa,b,
A. Tiko, E. Torassaa, M. Zanettia,b, P. Zottoa,b INFN Sezione di Pavia a,
Università di Pavia b, Pavia, Italy
A. Braghieria, A. Magnania, P. Montagnaa,b, S.P. Rattia,b, V. Rea, M.
Ressegottia,b, C. Riccardia,b, P. Salvinia, I. Vaia,b, P. Vituloa,b INFN
Sezione di Perugia a, Università di Perugia b, Perugia, Italy
L. Alunni Solestizia,b, M. Biasinia,b, G.M. Bileia, C. Cecchia,b, D.
Ciangottinia,b, L. Fanòa,b, P. Laricciaa,b, R. Leonardia,b, E. Manonia, G.
Mantovania,b, V. Mariania,b, M. Menichellia, A. Rossia,b, A. Santocchiaa,b, D.
Spigaa INFN Sezione di Pisa a, Università di Pisa b, Scuola Normale Superiore
di Pisa c, Pisa, Italy
K. Androsova, P. Azzurria,14, G. Bagliesia, L. Bianchinia, T. Boccalia, L.
Borrello, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, G. Fedia, L.
Gianninia,c, A. Giassia, M.T. Grippoa,29, F. Ligabuea,c, T. Lomtadzea, E.
Mancaa,c, G. Mandorlia,c, A. Messineoa,b, F. Pallaa, A. Rizzia,b, P.
Spagnoloa, R. Tenchinia, G. Tonellia,b, A. Venturia, P.G. Verdinia INFN
Sezione di Roma a, Sapienza Università di Roma b, Rome, Italy
L. Baronea,b, F. Cavallaria, M. Cipriania,b, N. Dacia, D. Del Rea,b, E. Di
Marcoa,b, M. Diemoza, S. Gellia,b, E. Longoa,b, F. Margarolia,b, B.
Marzocchia,b, P. Meridiania, G. Organtinia,b, R. Paramattia,b, F. Preiatoa,b,
S. Rahatloua,b, C. Rovellia, F. Santanastasioa,b INFN Sezione di Torino a,
Università di Torino b, Torino, Italy, Università del Piemonte Orientale c,
Novara, Italy
N. Amapanea,b, R. Arcidiaconoa,c, S. Argiroa,b, M. Arneodoa,c, N. Bartosika,
R. Bellana,b, C. Biinoa, N. Cartigliaa, R. Castelloa,b, F. Cennaa,b, M.
Costaa,b, R. Covarellia,b, A. Deganoa,b, N. Demariaa, B. Kiania,b, C.
Mariottia, S. Masellia, E. Migliorea,b, V. Monacoa,b, E. Monteila,b, M.
Montenoa, M.M. Obertinoa,b, L. Pachera,b, N. Pastronea, M. Pelliccionia, G.L.
Pinna Angionia,b, A. Romeroa,b, M. Ruspaa,c, R. Sacchia,b, K. Shchelinaa,b, V.
Solaa, A. Solanoa,b, A. Staianoa, P. Traczyka,b INFN Sezione di Trieste a,
Università di Trieste b, Trieste, Italy
S. Belfortea, M. Casarsaa, F. Cossuttia, G. Della Riccaa,b, A. Zanettia
Kyungpook National University
D.H. Kim, G.N. Kim, M.S. Kim, J. Lee, S. Lee, S.W. Lee, C.S. Moon, Y.D. Oh, S.
Sekmen, D.C. Son, Y.C. Yang Chonnam National University, Institute for
Universe and Elementary Particles, Kwangju, Korea
H. Kim, D.H. Moon, G. Oh Hanyang University, Seoul, Korea
J.A. Brochero Cifuentes, J. Goh, T.J. Kim Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, D. Gyun, S. Ha, B. Hong, Y. Jo, Y. Kim, K. Lee, K.S.
Lee, S. Lee, J. Lim, S.K. Park, Y. Roh Seoul National University, Seoul, Korea
J. Almond, J. Kim, J.S. Kim, H. Lee, K. Lee, K. Nam, S.B. Oh, B.C. Radburn-
Smith, S.h. Seo, U.K. Yang, H.D. Yoo, G.B. Yu University of Seoul, Seoul,
Korea
H. Kim, J.H. Kim, J.S.H. Lee, I.C. Park Sungkyunkwan University, Suwon, Korea
Y. Choi, C. Hwang, J. Lee, I. Yu Vilnius University, Vilnius, Lithuania
V. Dudenas, A. Juodagalvis, J. Vaitkus National Centre for Particle Physics,
Universiti Malaya, Kuala Lumpur, Malaysia
I. Ahmed, Z.A. Ibrahim, M.A.B. Md Ali32, F. Mohamad Idris33, W.A.T. Wan
Abdullah, M.N. Yusli, Z. Zolkapli Centro de Investigacion y de Estudios
Avanzados del IPN, Mexico City, Mexico
Reyes-Almanza, R, Ramirez-Sanchez, G., Duran-Osuna, M. C., H. Castilla-Valdez,
E. De La Cruz-Burelo, I. Heredia-De La Cruz34, Rabadan-Trejo, R. I., R. Lopez-
Fernandez, J. Mejia Guisao, A. Sanchez-Hernandez Universidad Iberoamericana,
Mexico City, Mexico
S. Carrillo Moreno, C. Oropeza Barrera, F. Vazquez Valencia Benemerita
Universidad Autonoma de Puebla, Puebla, Mexico
J. Eysermans, I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada Universidad
Autónoma de San Luis Potosí, San Luis Potosí, Mexico
A. Morelos Pineda University of Auckland, Auckland, New Zealand
D. Krofcheck University of Canterbury, Christchurch, New Zealand
S. Bheesette, P.H. Butler National Centre for Physics, Quaid-I-Azam
University, Islamabad, Pakistan
A. Ahmad, M. Ahmad, Q. Hassan, H.R. Hoorani, A. Saddique, M.A. Shah, M.
Shoaib, M. Waqas National Centre for Nuclear Research, Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, T. Frueboes, M. Górski, M. Kazana, K.
Nawrocki, M. Szleper, P. Zalewski Institute of Experimental Physics, Faculty
of Physics, University of Warsaw, Warsaw, Poland
K. Bunkowski, A. Byszuk35, K. Doroba, A. Kalinowski, M. Konecki, J.
Krolikowski, M. Misiura, M. Olszewski, A. Pyskir, M. Walczak Laboratório de
Instrumentação e Física Experimental de Partículas, Lisboa, Portugal
P. Bargassa, C. Beirão Da Cruz E Silva, A. Di Francesco, P. Faccioli, B.
Galinhas, M. Gallinaro, J. Hollar, N. Leonardo, L. Lloret Iglesias, M.V.
Nemallapudi, J. Seixas, G. Strong, O. Toldaiev, D. Vadruccio, J. Varela Joint
Institute for Nuclear Research, Dubna, Russia
S. Afanasiev, P. Bunin, M. Gavrilenko, I. Golutvin, I. Gorbunov, A. Kamenev,
V. Karjavin, A. Lanev, A. Malakhov, V. Matveev36,37, P. Moisenz, V. Palichik,
V. Perelygin, S. Shmatov, S. Shulha, N. Skatchkov, V. Smirnov, N. Voytishin,
A. Zarubin Petersburg Nuclear Physics Institute, Gatchina (St. Petersburg),
Russia
Y. Ivanov, V. Kim38, E. Kuznetsova39, P. Levchenko, V. Murzin, V. Oreshkin, I.
Smirnov, D. Sosnov, V. Sulimov, L. Uvarov, S. Vavilov, A. Vorobyev Institute
for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov,
N. Krasnikov, A. Pashenkov, D. Tlisov, A. Toropin Institute for Theoretical
and Experimental Physics, Moscow, Russia
V. Epshteyn, V. Gavrilov, N. Lychkovskaya, V. Popov, I. Pozdnyakov, G.
Safronov, A. Spiridonov, A. Stepennov, V. Stolin, M. Toms, E. Vlasov, A.
Zhokin Moscow Institute of Physics and Technology, Moscow, Russia
T. Aushev, A. Bylinkin37 National Research Nuclear University ’Moscow
Engineering Physics Institute’ (MEPhI), Moscow, Russia
R. Chistov40, M. Danilov40, P. Parygin, D. Philippov, S. Polikarpov, E.
Tarkovskii P.N. Lebedev Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin37, I. Dremin37, M. Kirakosyan37, S.V. Rusakov, A.
Terkulov Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State
University, Moscow, Russia
A. Baskakov, A. Belyaev, E. Boos, V. Bunichev, M. Dubinin41, L. Dudko, A.
Gribushin, V. Klyukhin, N. Korneeva, I. Lokhtin, I. Miagkov, S. Obraztsov, M.
Perfilov, V. Savrin, P. Volkov Novosibirsk State University (NSU),
Novosibirsk, Russia
V. Blinov42, D. Shtol42, Y. Skovpen42 State Research Center of Russian
Federation, Institute for High Energy Physics of NRC "Kurchatov
Institute", Protvino, Russia
I. Azhgirey, I. Bayshev, S. Bitioukov, D. Elumakhov, A. Godizov, V. Kachanov,
A. Kalinin, D. Konstantinov, P. Mandrik, V. Petrov, R. Ryutin, A. Sobol, S.
Troshin, N. Tyurin, A. Uzunian, A. Volkov National Research Tomsk Polytechnic
University, Tomsk, Russia
A. Babaev University of Belgrade, Faculty of Physics and Vinca Institute of
Nuclear Sciences, Belgrade, Serbia
P. Adzic43, P. Cirkovic, D. Devetak, M. Dordevic, J. Milosevic Centro de
Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT), Madrid,
Spain
J. Alcaraz Maestre, I. Bachiller, M. Barrio Luna, M. Cerrada, N. Colino, B. De
La Cruz, A. Delgado Peris, C. Fernandez Bedoya, J.P. Fernández Ramos, J. Flix,
M.C. Fouz, O. Gonzalez Lopez, S. Goy Lopez, J.M. Hernandez, M.I. Josa, D.
Moran, A. Pérez-Calero Yzquierdo, J. Puerta Pelayo, I. Redondo, L. Romero,
M.S. Soares, A. Triossi, A. Álvarez Fernández Universidad Autónoma de Madrid,
Madrid, Spain
C. Albajar, J.F. de Trocóniz Universidad de Oviedo, Oviedo, Spain
J. Cuevas, C. Erice, J. Fernandez Menendez, S. Folgueras, I. Gonzalez
Caballero, J.R. González Fernández, E. Palencia Cortezon, S. Sanchez Cruz, P.
Vischia, J.M. Vizan Garcia Instituto de Física de Cantabria (IFCA), CSIC-
Universidad de Cantabria, Santander, Spain
I.J. Cabrillo, A. Calderon, B. Chazin Quero, J. Duarte Campderros, M.
Fernandez, P.J. Fernández Manteca, J. Garcia-Ferrero, A. García Alonso, G.
Gomez, A. Lopez Virto, J. Marco, C. Martinez Rivero, P. Martinez Ruiz del
Arbol, F. Matorras, J. Piedra Gomez, C. Prieels, T. Rodrigo, A. Ruiz-Jimeno,
L. Scodellaro, N. Trevisani, I. Vila, R. Vilar Cortabitarte CERN, European
Organization for Nuclear Research, Geneva, Switzerland
D. Abbaneo, B. Akgun, E. Auffray, P. Baillon, A.H. Ball, D. Barney, J.
Bendavid, M. Bianco, A. Bocci, C. Botta, T. Camporesi, M. Cepeda, G.
Cerminara, E. Chapon, Y. Chen, D. d’Enterria, A. Dabrowski, V. Daponte, A.
David, M. De Gruttola, A. De Roeck, N. Deelen, M. Dobson, T. du Pree, M.
Dünser, N. Dupont, A. Elliott-Peisert, P. Everaerts, F. Fallavollita44, G.
Franzoni, J. Fulcher, W. Funk, D. Gigi, A. Gilbert, K. Gill, F. Glege, D.
Gulhan, J. Hegeman, V. Innocente, A. Jafari, P. Janot, O. Karacheban17, J.
Kieseler, V. Knünz, A. Kornmayer, M.J. Kortelainen, M. Krammer1, C. Lange, P.
Lecoq, C. Lourenço, M.T. Lucchini, L. Malgeri, M. Mannelli, A. Martelli, F.
Meijers, J.A. Merlin, S. Mersi, E. Meschi, P. Milenovic45, F. Moortgat, M.
Mulders, H. Neugebauer, J. Ngadiuba, S. Orfanelli, L. Orsini, F. Pantaleo14,
L. Pape, E. Perez, M. Peruzzi, A. Petrilli, G. Petrucciani, A. Pfeiffer, M.
Pierini, F.M. Pitters, D. Rabady, A. Racz, T. Reis, G. Rolandi46, M. Rovere,
H. Sakulin, C. Schäfer, C. Schwick, M. Seidel, M. Selvaggi, A. Sharma, P.
Silva, P. Sphicas47, A. Stakia, J. Steggemann, M. Stoye, M. Tosi, D. Treille,
A. Tsirou, V. Veckalns48, M. Verweij, W.D. Zeuner Paul Scherrer Institut,
Villigen, Switzerland
W. Bertl${}^{\textrm{\textdagger}}$, L. Caminada49, K. Deiters, W. Erdmann, R.
Horisberger, Q. Ingram, H.C. Kaestli, D. Kotlinski, U. Langenegger, T. Rohe,
S.A. Wiederkehr ETH Zurich - Institute for Particle Physics and Astrophysics
(IPA), Zurich, Switzerland
M. Backhaus, L. Bäni, P. Berger, B. Casal, G. Dissertori, M. Dittmar, M.
Donegà, C. Dorfer, C. Grab, C. Heidegger, D. Hits, J. Hoss, T. Klijnsma, W.
Lustermann, B. Mangano, M. Marionneau, M.T. Meinhard, D. Meister, F. Micheli,
P. Musella, F. Nessi-Tedaldi, F. Pandolfi, J. Pata, F. Pauss, G. Perrin, L.
Perrozzi, M. Quittnat, M. Reichmann, D.A. Sanz Becerra, M. Schönenberger, L.
Shchutska, V.R. Tavolaro, K. Theofilatos, M.L. Vesterbacka Olsson, R. Wallny,
D.H. Zhu Universität Zürich, Zurich, Switzerland
T.K. Aarrestad, C. Amsler50, D. Brzhechko, M.F. Canelli, A. De Cosa, R. Del
Burgo, S. Donato, C. Galloni, T. Hreus, B. Kilminster, I. Neutelings, D.
Pinna, G. Rauco, P. Robmann, D. Salerno, K. Schweiger, C. Seitz, Y. Takahashi,
A. Zucchetta National Central University, Chung-Li, Taiwan
V. Candelise, Y.H. Chang, K.y. Cheng, T.H. Doan, Sh. Jain, R. Khurana, C.M.
Kuo, W. Lin, A. Pozdnyakov, S.S. Yu National Taiwan University (NTU), Taipei,
Taiwan
Arun Kumar, P. Chang, Y. Chao, K.F. Chen, P.H. Chen, F. Fiori, W.-S. Hou, Y.
Hsiung, Y.F. Liu, R.-S. Lu, E. Paganis, A. Psallidas, A. Steen, J.f. Tsai
Chulalongkorn University, Faculty of Science, Department of Physics, Bangkok,
Thailand
B. Asavapibhop, K. Kovitanggoon, G. Singh, N. Srimanobhas Çukurova University,
Physics Department, Science and Art Faculty, Adana, Turkey
A. Bat, F. Boran, S. Cerci51, S. Damarseckin, Z.S. Demiroglu, C. Dozen, I.
Dumanoglu, S. Girgis, G. Gokbulut, Y. Guler, I. Hos52, E.E. Kangal53, O. Kara,
A. Kayis Topaksu, U. Kiminsu, M. Oglakci, G. Onengut, K. Ozdemir54, D. Sunar
Cerci51, U.G. Tok, H. Topakli55, S. Turkcapar, I.S. Zorbakir, C. Zorbilmez
Middle East Technical University, Physics Department, Ankara, Turkey
G. Karapinar56, K. Ocalan57, M. Yalvac, M. Zeyrek Bogazici University,
Istanbul, Turkey
E. Gülmez, M. Kaya58, O. Kaya59, S. Tekten, E.A. Yetkin60 Istanbul Technical
University, Istanbul, Turkey
M.N. Agaras, S. Atay, A. Cakir, K. Cankocak, Y. Komurcu Institute for
Scintillation Materials of National Academy of Science of Ukraine, Kharkov,
Ukraine
B. Grynyov National Scientific Center, Kharkov Institute of Physics and
Technology, Kharkov, Ukraine
L. Levchuk University of Bristol, Bristol, United Kingdom
F. Ball, L. Beck, J.J. Brooke, D. Burns, E. Clement, D. Cussans, O. Davignon,
H. Flacher, J. Goldstein, G.P. Heath, H.F. Heath, L. Kreczko, D.M. Newbold61,
S. Paramesvaran, T. Sakuma, S. Seif El Nasr-storey, D. Smith, V.J. Smith
Rutherford Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev62, C. Brew, R.M. Brown, L. Calligaris, D. Cieri, D.J.A.
Cockerill, J.A. Coughlan, K. Harder, S. Harper, J. Linacre, E. Olaiya, D.
Petyt, C.H. Shepherd-Themistocleous, A. Thea, I.R. Tomalin, T. Williams, W.J.
Womersley Imperial College, London, United Kingdom
G. Auzinger, R. Bainbridge, P. Bloch, J. Borg, S. Breeze, O. Buchmuller, A.
Bundock, S. Casasso, D. Colling, L. Corpe, P. Dauncey, G. Davies, M. Della
Negra, R. Di Maria, Y. Haddad, G. Hall, G. Iles, T. James, M. Komm, R. Lane,
C. Laner, L. Lyons, A.-M. Magnan, S. Malik, L. Mastrolorenzo, T. Matsushita,
J. Nash63, A. Nikitenko6, V. Palladino, M. Pesaresi, A. Richards, A. Rose, E.
Scott, C. Seez, A. Shtipliyski, T. Strebler, S. Summers, A. Tapper, K. Uchida,
M. Vazquez Acosta64, T. Virdee14, N. Wardle, D. Winterbottom, J. Wright, S.C.
Zenz Brunel University, Uxbridge, United Kingdom
J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, A. Morton, I.D. Reid, L.
Teodorescu, S. Zahid Baylor University, Waco, USA
A. Borzou, K. Call, J. Dittmann, K. Hatakeyama, H. Liu, N. Pastika, C. Smith
Catholic University of America, Washington DC, USA
R. Bartek, A. Dominguez The University of Alabama, Tuscaloosa, USA
A. Buccilli, S.I. Cooper, C. Henderson, P. Rumerio, C. West Boston University,
Boston, USA
D. Arcaro, A. Avetisyan, T. Bose, D. Gastler, D. Rankin, C. Richardson, J.
Rohlf, L. Sulak, D. Zou Brown University, Providence, USA
G. Benelli, D. Cutts, M. Hadley, J. Hakala, U. Heintz, J.M. Hogan65, K.H.M.
Kwok, E. Laird, G. Landsberg, J. Lee, Z. Mao, M. Narain, J. Pazzini, S.
Piperov, S. Sagir, R. Syarif, D. Yu University of California, Davis, Davis,
USA
R. Band, C. Brainerd, R. Breedon, D. Burns, M. Calderon De La Barca Sanchez,
M. Chertok, J. Conway, R. Conway, P.T. Cox, R. Erbacher, C. Flores, G. Funk,
W. Ko, R. Lander, C. Mclean, M. Mulhearn, D. Pellett, J. Pilot, S. Shalhout,
M. Shi, J. Smith, D. Stolp, D. Taylor, K. Tos, M. Tripathi, Z. Wang, F. Zhang
University of California, Los Angeles, USA
M. Bachtis, C. Bravo, R. Cousins, A. Dasgupta, A. Florent, J. Hauser, M.
Ignatenko, N. Mccoll, S. Regnard, D. Saltzberg, C. Schnaible, V. Valuev
University of California, Riverside, Riverside, USA
E. Bouvier, K. Burt, R. Clare, J. Ellison, J.W. Gary, S.M.A. Ghiasi Shirazi,
G. Hanson, G. Karapostoli, E. Kennedy, F. Lacroix, O.R. Long, M. Olmedo
Negrete, M.I. Paneva, W. Si, L. Wang, H. Wei, S. Wimpenny, B. R. Yates
University of California, San Diego, La Jolla, USA
J.G. Branson, S. Cittolin, M. Derdzinski, R. Gerosa, D. Gilbert, B. Hashemi,
A. Holzner, D. Klein, G. Kole, V. Krutelyov, J. Letts, M. Masciovecchio, D.
Olivito, S. Padhi, M. Pieri, M. Sani, V. Sharma, S. Simon, M. Tadel, A.
Vartak, S. Wasserbaech66, J. Wood, F. Würthwein, A. Yagil, G. Zevi Della Porta
University of California, Santa Barbara - Department of Physics, Santa
Barbara, USA
N. Amin, R. Bhandari, J. Bradmiller-Feld, C. Campagnari, M. Citron, A. Dishaw,
V. Dutta, M. Franco Sevilla, L. Gouskos, R. Heller, J. Incandela, A.
Ovcharova, H. Qu, J. Richman, D. Stuart, I. Suarez, J. Yoo California
Institute of Technology, Pasadena, USA
D. Anderson, A. Bornheim, J. Bunn, J.M. Lawhorn, H.B. Newman, T. Q. Nguyen, C.
Pena, M. Spiropulu, J.R. Vlimant, R. Wilkinson, S. Xie, Z. Zhang, R.Y. Zhu
Carnegie Mellon University, Pittsburgh, USA
M.B. Andrews, T. Ferguson, T. Mudholkar, M. Paulini, J. Russ, M. Sun, H.
Vogel, I. Vorobiev, M. Weinberg University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, F. Jensen, A. Johnson, M. Krohn, S. Leontsinis, E.
Macdonald, T. Mulholland, K. Stenson, K.A. Ulmer, S.R. Wagner Cornell
University, Ithaca, USA
J. Alexander, J. Chaves, Y. Cheng, J. Chu, A. Datta, S. Dittmer, K. Mcdermott,
N. Mirman, J.R. Patterson, D. Quach, A. Rinkevicius, A. Ryd, L. Skinnari, L.
Soffi, S.M. Tan, Z. Tao, J. Thom, J. Tucker, P. Wittich, M. Zientek Fermi
National Accelerator Laboratory, Batavia, USA
S. Abdullin, M. Albrow, M. Alyari, G. Apollinari, A. Apresyan, A. Apyan, S.
Banerjee, L.A.T. Bauerdick, A. Beretvas, J. Berryhill, P.C. Bhat, G.
Bolla${}^{\textrm{\textdagger}}$, K. Burkett, J.N. Butler, A. Canepa, G.B.
Cerati, H.W.K. Cheung, F. Chlebana, M. Cremonesi, J. Duarte, V.D. Elvira, J.
Freeman, Z. Gecse, E. Gottschalk, L. Gray, D. Green, S. Grünendahl, O.
Gutsche, J. Hanlon, R.M. Harris, S. Hasegawa, J. Hirschauer, Z. Hu, B.
Jayatilaka, S. Jindariani, M. Johnson, U. Joshi, B. Klima, B. Kreis, S.
Lammel, D. Lincoln, R. Lipton, M. Liu, T. Liu, R. Lopes De Sá, J. Lykken, K.
Maeshima, N. Magini, J.M. Marraffino, D. Mason, P. McBride, P. Merkel, S.
Mrenna, S. Nahn, V. O’Dell, K. Pedro, O. Prokofyev, G. Rakness, L. Ristori, A.
Savoy-Navarro67, B. Schneider, E. Sexton-Kennedy, A. Soha, W.J. Spalding, L.
Spiegel, S. Stoynev, J. Strait, N. Strobbe, L. Taylor, S. Tkaczyk, N.V. Tran,
L. Uplegger, E.W. Vaandering, C. Vernieri, M. Verzocchi, R. Vidal, M. Wang,
H.A. Weber, A. Whitbeck, W. Wu University of Florida, Gainesville, USA
D. Acosta, P. Avery, P. Bortignon, D. Bourilkov, A. Brinkerhoff, A. Carnes, M.
Carver, D. Curry, R.D. Field, I.K. Furic, S.V. Gleyzer, B.M. Joshi, J.
Konigsberg, A. Korytov, K. Kotov, P. Ma, K. Matchev, H. Mei, G. Mitselmakher,
K. Shi, D. Sperka, N. Terentyev, L. Thomas, J. Wang, S. Wang, J. Yelton
Florida International University, Miami, USA
Y.R. Joshi, S. Linn, P. Markowitz, J.L. Rodriguez Florida State University,
Tallahassee, USA
A. Ackert, T. Adams, A. Askew, S. Hagopian, V. Hagopian, K.F. Johnson, T.
Kolberg, G. Martinez, T. Perry, H. Prosper, A. Saha, A. Santra, V. Sharma, R.
Yohay Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, V. Bhopatkar, S. Colafranceschi, M. Hohlmann, D. Noonan, T.
Roy, F. Yumiceva University of Illinois at Chicago (UIC), Chicago, USA
M.R. Adams, L. Apanasevich, D. Berry, R.R. Betts, R. Cavanaugh, X. Chen, O.
Evdokimov, C.E. Gerber, D.A. Hangal, D.J. Hofman, K. Jung, J. Kamin, I.D.
Sandoval Gonzalez, M.B. Tonjes, N. Varelas, H. Wang, Z. Wu, J. Zhang The
University of Iowa, Iowa City, USA
B. Bilki68, W. Clarida, K. Dilsiz69, S. Durgut, R.P. Gandrajula, M.
Haytmyradov, V. Khristenko, J.-P. Merlo, H. Mermerkaya70, A. Mestvirishvili,
A. Moeller, J. Nachtman, H. Ogul71, Y. Onel, F. Ozok72, A. Penzo, C. Snyder,
E. Tiras, J. Wetzel, K. Yi Johns Hopkins University, Baltimore, USA
B. Blumenfeld, A. Cocoros, N. Eminizer, D. Fehling, L. Feng, A.V. Gritsan, P.
Maksimovic, J. Roskes, U. Sarica, M. Swartz, M. Xiao, C. You The University of
Kansas, Lawrence, USA
A. Al-bataineh, P. Baringer, A. Bean, S. Boren, J. Bowen, J. Castle, S.
Khalil, A. Kropivnitskaya, D. Majumder, W. Mcbrayer, M. Murray, C. Rogan, C.
Royon, S. Sanders, E. Schmitz, J.D. Tapia Takaki, Q. Wang Kansas State
University, Manhattan, USA
A. Ivanov, K. Kaadze, Y. Maravin, A. Mohammadi, L.K. Saini, N. Skhirtladze
Lawrence Livermore National Laboratory, Livermore, USA
F. Rebassoo, D. Wright University of Maryland, College Park, USA
A. Baden, O. Baron, A. Belloni, S.C. Eno, Y. Feng, C. Ferraioli, N.J. Hadley,
S. Jabeen, G.Y. Jeng, R.G. Kellogg, J. Kunkle, A.C. Mignerey, F. Ricci-Tam,
Y.H. Shin, A. Skuja, S.C. Tonwar Massachusetts Institute of Technology,
Cambridge, USA
D. Abercrombie, B. Allen, V. Azzolini, R. Barbieri, A. Baty, G. Bauer, R. Bi,
S. Brandt, W. Busza, I.A. Cali, M. D’Alfonso, Z. Demiragli, G. Gomez Ceballos,
M. Goncharov, P. Harris, D. Hsu, M. Hu, Y. Iiyama, G.M. Innocenti, M. Klute,
D. Kovalskyi, Y.-J. Lee, A. Levin, P.D. Luckey, B. Maier, A.C. Marini, C.
Mcginn, C. Mironov, S. Narayanan, X. Niu, C. Paus, C. Roland, G. Roland, J.
Salfeld-Nebgen, G.S.F. Stephans, K. Sumorok, K. Tatar, D. Velicanu, J. Wang,
T.W. Wang, B. Wyslouch, S. Zhaozhong University of Minnesota, Minneapolis, USA
A.C. Benvenuti, R.M. Chatterjee, A. Evans, P. Hansen, S. Kalafut, Y. Kubota,
Z. Lesko, J. Mans, S. Nourbakhsh, N. Ruckstuhl, R. Rusack, J. Turkewitz, M.A.
Wadud University of Mississippi, Oxford, USA
J.G. Acosta, S. Oliveros University of Nebraska-Lincoln, Lincoln, USA
E. Avdeeva, K. Bloom, D.R. Claes, C. Fangmeier, F. Golf, R. Gonzalez Suarez,
R. Kamalieddin, I. Kravchenko, J. Monroy, J.E. Siado, G.R. Snow, B. Stieger
State University of New York at Buffalo, Buffalo, USA
J. Dolen, A. Godshalk, C. Harrington, I. Iashvili, D. Nguyen, A. Parker, S.
Rappoccio, B. Roozbahani Northeastern University, Boston, USA
G. Alverson, E. Barberis, C. Freer, A. Hortiangtham, A. Massironi, D.M. Morse,
T. Orimoto, R. Teixeira De Lima, T. Wamorkar, B. Wang, A. Wisecarver, D. Wood
Northwestern University, Evanston, USA
S. Bhattacharya, O. Charaf, K.A. Hahn, N. Mucia, N. Odell, M.H. Schmitt, K.
Sung, M. Trovato, M. Velasco University of Notre Dame, Notre Dame, USA
R. Bucci, N. Dev, M. Hildreth, K. Hurtado Anampa, C. Jessop, D.J. Karmgard, N.
Kellams, K. Lannon, W. Li, N. Loukas, N. Marinelli, F. Meng, C. Mueller, Y.
Musienko36, M. Planer, A. Reinsvold, R. Ruchti, P. Siddireddy, G. Smith, S.
Taroni, M. Wayne, A. Wightman, M. Wolf, A. Woodard The Ohio State University,
Columbus, USA
J. Alimena, L. Antonelli, B. Bylsma, L.S. Durkin, S. Flowers, B. Francis, A.
Hart, C. Hill, W. Ji, T.Y. Ling, W. Luo, B.L. Winer, H.W. Wulsin Princeton
University, Princeton, USA
S. Cooperstein, O. Driga, P. Elmer, J. Hardenbrook, P. Hebda, S. Higginbotham,
A. Kalogeropoulos, D. Lange, J. Luo, D. Marlow, K. Mei, I. Ojalvo, J. Olsen,
C. Palmer, P. Piroué, D. Stickland, C. Tully University of Puerto Rico,
Mayaguez, USA
S. Malik, S. Norberg Purdue University, West Lafayette, USA
A. Barker, V.E. Barnes, S. Das, L. Gutay, M. Jones, A.W. Jung, A. Khatiwada,
D.H. Miller, N. Neumeister, C.C. Peng, H. Qiu, J.F. Schulte, J. Sun, F. Wang,
R. Xiao, W. Xie Purdue University Northwest, Hammond, USA
T. Cheng, N. Parashar Rice University, Houston, USA
Z. Chen, K.M. Ecklund, S. Freed, F.J.M. Geurts, M. Guilbaud, M. Kilpatrick, W.
Li, B. Michlin, B.P. Padley, J. Roberts, J. Rorie, W. Shi, Z. Tu, J. Zabel, A.
Zhang University of Rochester, Rochester, USA
A. Bodek, P. de Barbaro, R. Demina, Y.t. Duh, T. Ferbel, M. Galanti, A.
Garcia-Bellido, J. Han, O. Hindrichs, A. Khukhunaishvili, K.H. Lo, P. Tan, M.
Verzetti The Rockefeller University, New York, USA
R. Ciesielski, K. Goulianos, C. Mesropian Rutgers, The State University of New
Jersey, Piscataway, USA
A. Agapitos, J.P. Chou, Y. Gershtein, T.A. Gómez Espinosa, E. Halkiadakis, M.
Heindl, E. Hughes, S. Kaplan, R. Kunnawalkam Elayavalli, S. Kyriacou, A. Lath,
R. Montalvo, K. Nash, M. Osherson, H. Saka, S. Salur, S. Schnetzer, D.
Sheffield, S. Somalwar, R. Stone, S. Thomas, P. Thomassen, M. Walker
University of Tennessee, Knoxville, USA
A.G. Delannoy, J. Heideman, G. Riley, K. Rose, S. Spanier, K. Thapa Texas A&M
University, College Station, USA
O. Bouhali73, A. Castaneda Hernandez73, A. Celik, M. Dalchenko, M. De Mattia,
A. Delgado, S. Dildick, R. Eusebi, J. Gilmore, T. Huang, T. Kamon74, R.
Mueller, Y. Pakhotin, R. Patel, A. Perloff, L. Perniè, D. Rathjens, A.
Safonov, A. Tatarinov Texas Tech University, Lubbock, USA
N. Akchurin, J. Damgov, F. De Guio, P.R. Dudero, J. Faulkner, E. Gurpinar, S.
Kunori, K. Lamichhane, S.W. Lee, T. Mengke, S. Muthumuni, T. Peltola, S.
Undleeb, I. Volobouev, Z. Wang Vanderbilt University, Nashville, USA
S. Greene, A. Gurrola, R. Janjam, W. Johns, C. Maguire, A. Melo, H. Ni, K.
Padeken, P. Sheldon, S. Tuo, J. Velkovska, Q. Xu University of Virginia,
Charlottesville, USA
M.W. Arenton, P. Barria, B. Cox, R. Hirosky, M. Joyce, A. Ledovskoy, H. Li, C.
Neu, T. Sinthuprasith, Y. Wang, E. Wolfe, F. Xia Wayne State University,
Detroit, USA
R. Harr, P.E. Karchin, N. Poudyal, J. Sturdy, P. Thapa, S. Zaleski University
of Wisconsin - Madison, Madison, WI, USA
M. Brodski, J. Buchanan, C. Caillol, D. Carlsmith, S. Dasu, L. Dodd, S. Duric,
B. Gomber, M. Grothe, M. Herndon, A. Hervé, U. Hussain, P. Klabbers, A.
Lanaro, A. Levine, K. Long, R. Loveless, V. Rekovic, T. Ruggles, A. Savin, N.
Smith, W.H. Smith, N. Woods †: Deceased
1: Also at Vienna University of Technology, Vienna, Austria
2: Also at IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
3: Also at Universidade Estadual de Campinas, Campinas, Brazil
4: Also at Federal University of Rio Grande do Sul, Porto Alegre, Brazil
5: Also at Université Libre de Bruxelles, Bruxelles, Belgium
6: Also at Institute for Theoretical and Experimental Physics, Moscow, Russia
7: Also at Joint Institute for Nuclear Research, Dubna, Russia
8: Also at Cairo University, Cairo, Egypt
9: Also at Helwan University, Cairo, Egypt
10: Now at Zewail City of Science and Technology, Zewail, Egypt
11: Also at Department of Physics, King Abdulaziz University, Jeddah, Saudi
Arabia
12: Also at Université de Haute Alsace, Mulhouse, France
13: Also at Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State
University, Moscow, Russia
14: Also at CERN, European Organization for Nuclear Research, Geneva,
Switzerland
15: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
16: Also at University of Hamburg, Hamburg, Germany
17: Also at Brandenburg University of Technology, Cottbus, Germany
18: Also at MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
19: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary
20: Also at Institute of Physics, University of Debrecen, Debrecen, Hungary
21: Also at Indian Institute of Technology Bhubaneswar, Bhubaneswar, India
22: Also at Institute of Physics, Bhubaneswar, India
23: Also at Shoolini University, Solan, India
24: Also at University of Visva-Bharati, Santiniketan, India
25: Also at University of Ruhuna, Matara, Sri Lanka
26: Also at Isfahan University of Technology, Isfahan, Iran
27: Also at Yazd University, Yazd, Iran
28: Also at Plasma Physics Research Center, Science and Research Branch,
Islamic Azad University, Tehran, Iran
29: Also at Università degli Studi di Siena, Siena, Italy
30: Also at INFN Sezione di Milano-Bicocca; Università di Milano-Bicocca,
Milano, Italy
31: Also at Laboratori Nazionali di Legnaro dell’INFN, Legnaro, Italy
32: Also at International Islamic University of Malaysia, Kuala Lumpur,
Malaysia
33: Also at Malaysian Nuclear Agency, MOSTI, Kajang, Malaysia
34: Also at Consejo Nacional de Ciencia y Tecnología, Mexico city, Mexico
35: Also at Warsaw University of Technology, Institute of Electronic Systems,
Warsaw, Poland
36: Also at Institute for Nuclear Research, Moscow, Russia
37: Now at National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
38: Also at St. Petersburg State Polytechnical University, St. Petersburg,
Russia
39: Also at University of Florida, Gainesville, USA
40: Also at P.N. Lebedev Physical Institute, Moscow, Russia
41: Also at California Institute of Technology, Pasadena, USA
42: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia
43: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia
44: Also at INFN Sezione di Pavia; Università di Pavia, Pavia, Italy
45: Also at University of Belgrade, Faculty of Physics and Vinca Institute of
Nuclear Sciences, Belgrade, Serbia
46: Also at Scuola Normale e Sezione dell’INFN, Pisa, Italy
47: Also at National and Kapodistrian University of Athens, Athens, Greece
48: Also at Riga Technical University, Riga, Latvia
49: Also at Universität Zürich, Zurich, Switzerland
50: Also at Stefan Meyer Institute for Subatomic Physics (SMI), Vienna,
Austria
51: Also at Adiyaman University, Adiyaman, Turkey
52: Also at Istanbul Aydin University, Istanbul, Turkey
53: Also at Mersin University, Mersin, Turkey
54: Also at Piri Reis University, Istanbul, Turkey
55: Also at Gaziosmanpasa University, Tokat, Turkey
56: Also at Izmir Institute of Technology, Izmir, Turkey
57: Also at Necmettin Erbakan University, Konya, Turkey
58: Also at Marmara University, Istanbul, Turkey
59: Also at Kafkas University, Kars, Turkey
60: Also at Istanbul Bilgi University, Istanbul, Turkey
61: Also at Rutherford Appleton Laboratory, Didcot, United Kingdom
62: Also at School of Physics and Astronomy, University of Southampton,
Southampton, United Kingdom
63: Also at Monash University, Faculty of Science, Clayton, Australia
64: Also at Instituto de Astrofísica de Canarias, La Laguna, Spain
65: Also at Bethel University, ST. PAUL, USA
66: Also at Utah Valley University, Orem, USA
67: Also at Purdue University, West Lafayette, USA
68: Also at Beykent University, Istanbul, Turkey
69: Also at Bingol University, Bingol, Turkey
70: Also at Erzincan University, Erzincan, Turkey
71: Also at Sinop University, Sinop, Turkey
72: Also at Mimar Sinan University, Istanbul, Istanbul, Turkey
73: Also at Texas A&M University at Qatar, Doha, Qatar
74: Also at Kyungpook National University, Daegu, Korea